nn.Module:\\n self.device = device\\n return super().to(device)\\n \\n def forward(self, x, *args, **kwargs):\\n raise NotImplementedError\\n \\n def _infer(self, x: torch.Tensor):\\n \\\"\\\"\\\"\\n Inference interface for the model\\n Args:\\n x (torch.Tensor): input tensor of shape (b, c, h, w)\\n Returns:\\n torch.Tensor: output tensor of shape (b, 1, h, w)\\n \\\"\\\"\\\"\\n return self(x)['metric_depth']\\n \\n def _infer_with_pad_aug(self, x: torch.Tensor, pad_input: bool=True, fh: float=3, fw: float=3, upsampling_mode: str='bicubic', padding_mode=\\\"reflect\\\", **kwargs) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Inference interface for the model with padding augmentation\\n Padding augmentation fixes the boundary artifacts in the output depth map.\\n Boundary artifacts are sometimes caused by the fact that the model is trained on NYU raw dataset which has a black or white border around the image.\\n This augmentation pads the input image and crops the prediction back to the original size / view.\\n\\n Note: This augmentation is not required for the models trained with 'avoid_boundary'=True.\\n Args:\\n x (torch.Tensor): input tensor of shape (b, c, h, w)\\n pad_input (bool, optional): whether to pad the input or not. Defaults to True.\\n fh (float, optional): height padding factor. The padding is calculated as sqrt(h/2) * fh. Defaults to 3.\\n fw (float, optional): width padding factor. The padding is calculated as sqrt(w/2) * fw. Defaults to 3.\\n upsampling_mode (str, optional): upsampling mode. Defaults to 'bicubic'.\\n padding_mode (str, optional): padding mode. Defaults to \\\"reflect\\\".\\n Returns:\\n torch.Tensor: output tensor of shape (b, 1, h, w)\\n \\\"\\\"\\\"\\n # assert x is nchw and c = 3\\n assert x.dim() == 4, \\\"x must be 4 dimensional, got {}\\\".format(x.dim())\\n assert x.shape[1] == 3, \\\"x must have 3 channels, got {}\\\".format(x.shape[1])\\n\\n if pad_input:\\n assert fh > 0 or fw > 0, \\\"atlease one of fh and fw must be greater than 0\\\"\\n pad_h = int(np.sqrt(x.shape[2]/2) * fh)\\n pad_w = int(np.sqrt(x.shape[3]/2) * fw)\\n padding = [pad_w, pad_w]\\n if pad_h > 0:\\n padding += [pad_h, pad_h]\\n \\n x = F.pad(x, padding, mode=padding_mode, **kwargs)\\n out = self._infer(x)\\n if out.shape[-2:] != x.shape[-2:]:\\n out = F.interpolate(out, size=(x.shape[2], x.shape[3]), mode=upsampling_mode, align_corners=False)\\n if pad_input:\\n # crop to the original size, handling the case where pad_h and pad_w is 0\\n if pad_h > 0:\\n out = out[:, :, pad_h:-pad_h,:]\\n if pad_w > 0:\\n out = out[:, :, :, pad_w:-pad_w]\\n return out\\n \\n def infer_with_flip_aug(self, x, pad_input: bool=True, **kwargs) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Inference interface for the model with horizontal flip augmentation\\n Horizontal flip augmentation improves the accuracy of the model by averaging the output of the model with and without horizontal flip.\\n Args:\\n x (torch.Tensor): input tensor of shape (b, c, h, w)\\n pad_input (bool, optional): whether to use padding augmentation. Defaults to True.\\n Returns:\\n torch.Tensor: output tensor of shape (b, 1, h, w)\\n \\\"\\\"\\\"\\n # infer with horizontal flip and average\\n out = self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs)\\n out_flip = self._infer_with_pad_aug(torch.flip(x, dims=[3]), pad_input=pad_input, **kwargs)\\n out = (out + torch.flip(out_flip, dims=[3])) / 2\\n return out\\n \\n def infer(self, x, pad_input: bool=True, with_flip_aug: bool=True, **kwargs) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Inference interface for the model\\n Args:\\n x (torch.Tensor): input tensor of shape (b, c, h, w)\\n pad_input (bool, optional): whether to use padding augmentation. Defaults to True.\\n with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True.\\n Returns:\\n torch.Tensor: output tensor of shape (b, 1, h, w)\\n \\\"\\\"\\\"\\n if with_flip_aug:\\n return self.infer_with_flip_aug(x, pad_input=pad_input, **kwargs)\\n else:\\n return self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs)\\n \\n @torch.no_grad()\\n def infer_pil(self, pil_img, pad_input: bool=True, with_flip_aug: bool=True, output_type: str=\\\"numpy\\\", **kwargs) -> Union[np.ndarray, PIL.Image.Image, torch.Tensor]:\\n \\\"\\\"\\\"\\n Inference interface for the model for PIL image\\n Args:\\n pil_img (PIL.Image.Image): input PIL image\\n pad_input (bool, optional): whether to use padding augmentation. Defaults to True.\\n with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True.\\n output_type (str, optional): output type. Supported values are 'numpy', 'pil' and 'tensor'. Defaults to \\\"numpy\\\".\\n \\\"\\\"\\\"\\n x = transforms.ToTensor()(pil_img).unsqueeze(0).to(self.device)\\n out_tensor = self.infer(x, pad_input=pad_input, with_flip_aug=with_flip_aug, **kwargs)\\n if output_type == \\\"numpy\\\":\\n return out_tensor.squeeze().cpu().numpy()\\n elif output_type == \\\"pil\\\":\\n # uint16 is required for depth pil image\\n out_16bit_numpy = (out_tensor.squeeze().cpu().numpy()*256).astype(np.uint16)\\n return Image.fromarray(out_16bit_numpy)\\n elif output_type == \\\"tensor\\\":\\n return out_tensor.squeeze().cpu()\\n else:\\n raise ValueError(f\\\"output_type {output_type} not supported. Supported values are 'numpy', 'pil' and 'tensor'\\\")\"\n },\n {\n \"identifier\": \"MidasCore\",\n \"path\": \"zoedepth/models/base_models/midas.py\",\n \"snippet\": \"class MidasCore(nn.Module):\\n def __init__(self, midas, trainable=False, fetch_features=True, layer_names=('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1'), freeze_bn=False, keep_aspect_ratio=True,\\n img_size=384, **kwargs):\\n \\\"\\\"\\\"Midas Base model used for multi-scale feature extraction.\\n\\n Args:\\n midas (torch.nn.Module): Midas model.\\n trainable (bool, optional): Train midas model. Defaults to False.\\n fetch_features (bool, optional): Extract multi-scale features. Defaults to True.\\n layer_names (tuple, optional): Layers used for feature extraction. Order = (head output features, last layer features, ...decoder features). Defaults to ('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1').\\n freeze_bn (bool, optional): Freeze BatchNorm. Generally results in better finetuning performance. Defaults to False.\\n keep_aspect_ratio (bool, optional): Keep the aspect ratio of input images while resizing. Defaults to True.\\n img_size (int, tuple, optional): Input resolution. Defaults to 384.\\n \\\"\\\"\\\"\\n super().__init__()\\n self.core = midas\\n self.output_channels = None\\n self.core_out = {}\\n self.trainable = trainable\\n self.fetch_features = fetch_features\\n # midas.scratch.output_conv = nn.Identity()\\n self.handles = []\\n # self.layer_names = ['out_conv','l4_rn', 'r4', 'r3', 'r2', 'r1']\\n self.layer_names = layer_names\\n\\n self.set_trainable(trainable)\\n self.set_fetch_features(fetch_features)\\n\\n self.prep = PrepForMidas(keep_aspect_ratio=keep_aspect_ratio,\\n img_size=img_size, do_resize=kwargs.get('do_resize', True))\\n\\n if freeze_bn:\\n self.freeze_bn()\\n\\n def set_trainable(self, trainable):\\n self.trainable = trainable\\n if trainable:\\n self.unfreeze()\\n else:\\n self.freeze()\\n return self\\n\\n def set_fetch_features(self, fetch_features):\\n self.fetch_features = fetch_features\\n if fetch_features:\\n if len(self.handles) == 0:\\n self.attach_hooks(self.core)\\n else:\\n self.remove_hooks()\\n return self\\n\\n def freeze(self):\\n for p in self.parameters():\\n p.requires_grad = False\\n self.trainable = False\\n return self\\n\\n def unfreeze(self):\\n for p in self.parameters():\\n p.requires_grad = True\\n self.trainable = True\\n return self\\n\\n def freeze_bn(self):\\n for m in self.modules():\\n if isinstance(m, nn.BatchNorm2d):\\n m.eval()\\n return self\\n\\n def forward(self, x, denorm=False, return_rel_depth=False):\\n with torch.no_grad():\\n if denorm:\\n x = denormalize(x)\\n x = self.prep(x)\\n # print(\\\"Shape after prep: \\\", x.shape)\\n\\n with torch.set_grad_enabled(self.trainable):\\n\\n # print(\\\"Input size to Midascore\\\", x.shape)\\n rel_depth = self.core(x)\\n # print(\\\"Output from midas shape\\\", rel_depth.shape)\\n if not self.fetch_features:\\n return rel_depth\\n out = [self.core_out[k] for k in self.layer_names]\\n\\n if return_rel_depth:\\n return rel_depth, out\\n return out\\n\\n def get_rel_pos_params(self):\\n for name, p in self.core.pretrained.named_parameters():\\n if \\\"relative_position\\\" in name:\\n yield p\\n\\n def get_enc_params_except_rel_pos(self):\\n for name, p in self.core.pretrained.named_parameters():\\n if \\\"relative_position\\\" not in name:\\n yield p\\n\\n def freeze_encoder(self, freeze_rel_pos=False):\\n if freeze_rel_pos:\\n for p in self.core.pretrained.parameters():\\n p.requires_grad = False\\n else:\\n for p in self.get_enc_params_except_rel_pos():\\n p.requires_grad = False\\n return self\\n\\n def attach_hooks(self, midas):\\n if len(self.handles) > 0:\\n self.remove_hooks()\\n if \\\"out_conv\\\" in self.layer_names:\\n self.handles.append(list(midas.scratch.output_conv.children())[\\n 3].register_forward_hook(get_activation(\\\"out_conv\\\", self.core_out)))\\n if \\\"r4\\\" in self.layer_names:\\n self.handles.append(midas.scratch.refinenet4.register_forward_hook(\\n get_activation(\\\"r4\\\", self.core_out)))\\n if \\\"r3\\\" in self.layer_names:\\n self.handles.append(midas.scratch.refinenet3.register_forward_hook(\\n get_activation(\\\"r3\\\", self.core_out)))\\n if \\\"r2\\\" in self.layer_names:\\n self.handles.append(midas.scratch.refinenet2.register_forward_hook(\\n get_activation(\\\"r2\\\", self.core_out)))\\n if \\\"r1\\\" in self.layer_names:\\n self.handles.append(midas.scratch.refinenet1.register_forward_hook(\\n get_activation(\\\"r1\\\", self.core_out)))\\n if \\\"l4_rn\\\" in self.layer_names:\\n self.handles.append(midas.scratch.layer4_rn.register_forward_hook(\\n get_activation(\\\"l4_rn\\\", self.core_out)))\\n\\n return self\\n\\n def remove_hooks(self):\\n for h in self.handles:\\n h.remove()\\n return self\\n\\n def __del__(self):\\n self.remove_hooks()\\n\\n def set_output_channels(self, model_type):\\n self.output_channels = MIDAS_SETTINGS[model_type]\\n\\n @staticmethod\\n def build(midas_model_type=\\\"DPT_BEiT_L_384\\\", train_midas=False, use_pretrained_midas=True, fetch_features=False, freeze_bn=True, force_keep_ar=False, force_reload=False, **kwargs):\\n if midas_model_type not in MIDAS_SETTINGS:\\n raise ValueError(\\n f\\\"Invalid model type: {midas_model_type}. Must be one of {list(MIDAS_SETTINGS.keys())}\\\")\\n if \\\"img_size\\\" in kwargs:\\n kwargs = MidasCore.parse_img_size(kwargs)\\n img_size = kwargs.pop(\\\"img_size\\\", [384, 384])\\n print(\\\"img_size\\\", img_size)\\n midas = torch.hub.load(\\\"intel-isl/MiDaS\\\", midas_model_type,\\n pretrained=use_pretrained_midas, force_reload=force_reload)\\n kwargs.update({'keep_aspect_ratio': force_keep_ar})\\n midas_core = MidasCore(midas, trainable=train_midas, fetch_features=fetch_features,\\n freeze_bn=freeze_bn, img_size=img_size, **kwargs)\\n midas_core.set_output_channels(midas_model_type)\\n return midas_core\\n\\n @staticmethod\\n def build_from_config(config):\\n return MidasCore.build(**config)\\n\\n @staticmethod\\n def parse_img_size(config):\\n assert 'img_size' in config\\n if isinstance(config['img_size'], str):\\n assert \\\",\\\" in config['img_size'], \\\"img_size should be a string with comma separated img_size=H,W\\\"\\n config['img_size'] = list(map(int, config['img_size'].split(\\\",\\\")))\\n assert len(\\n config['img_size']) == 2, \\\"img_size should be a string with comma separated img_size=H,W\\\"\\n elif isinstance(config['img_size'], int):\\n config['img_size'] = [config['img_size'], config['img_size']]\\n else:\\n assert isinstance(config['img_size'], list) and len(\\n config['img_size']) == 2, \\\"img_size should be a list of H,W\\\"\\n return config\"\n },\n {\n \"identifier\": \"AttractorLayer\",\n \"path\": \"zoedepth/models/layers/attractor.py\",\n \"snippet\": \"class AttractorLayer(nn.Module):\\n def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10,\\n alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False):\\n \\\"\\\"\\\"\\n Attractor layer for bin centers. Bin centers are bounded on the interval (min_depth, max_depth)\\n \\\"\\\"\\\"\\n super().__init__()\\n\\n self.n_attractors = n_attractors\\n self.n_bins = n_bins\\n self.min_depth = min_depth\\n self.max_depth = max_depth\\n self.alpha = alpha\\n self.gamma = gamma\\n self.kind = kind\\n self.attractor_type = attractor_type\\n self.memory_efficient = memory_efficient\\n\\n self._net = nn.Sequential(\\n nn.Conv2d(in_features, mlp_dim, 1, 1, 0),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(mlp_dim, n_attractors*2, 1, 1, 0), # x2 for linear norm\\n nn.ReLU(inplace=True)\\n )\\n\\n def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False):\\n \\\"\\\"\\\"\\n Args:\\n x (torch.Tensor) : feature block; shape - n, c, h, w\\n b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w\\n \\n Returns:\\n tuple(torch.Tensor,torch.Tensor) : new bin centers normed and scaled; shape - n, nbins, h, w\\n \\\"\\\"\\\"\\n if prev_b_embedding is not None:\\n if interpolate:\\n prev_b_embedding = nn.functional.interpolate(\\n prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True)\\n x = x + prev_b_embedding\\n\\n A = self._net(x)\\n eps = 1e-3\\n A = A + eps\\n n, c, h, w = A.shape\\n A = A.view(n, self.n_attractors, 2, h, w)\\n A_normed = A / A.sum(dim=2, keepdim=True) # n, a, 2, h, w\\n A_normed = A[:, :, 0, ...] # n, na, h, w\\n\\n b_prev = nn.functional.interpolate(\\n b_prev, (h, w), mode='bilinear', align_corners=True)\\n b_centers = b_prev\\n\\n if self.attractor_type == 'exp':\\n dist = exp_attractor\\n else:\\n dist = inv_attractor\\n\\n if not self.memory_efficient:\\n func = {'mean': torch.mean, 'sum': torch.sum}[self.kind]\\n # .shape N, nbins, h, w\\n delta_c = func(dist(A_normed.unsqueeze(\\n 2) - b_centers.unsqueeze(1)), dim=1)\\n else:\\n delta_c = torch.zeros_like(b_centers, device=b_centers.device)\\n for i in range(self.n_attractors):\\n # .shape N, nbins, h, w\\n delta_c += dist(A_normed[:, i, ...].unsqueeze(1) - b_centers)\\n\\n if self.kind == 'mean':\\n delta_c = delta_c / self.n_attractors\\n\\n b_new_centers = b_centers + delta_c\\n B_centers = (self.max_depth - self.min_depth) * \\\\\\n b_new_centers + self.min_depth\\n B_centers, _ = torch.sort(B_centers, dim=1)\\n B_centers = torch.clip(B_centers, self.min_depth, self.max_depth)\\n return b_new_centers, B_centers\"\n },\n {\n \"identifier\": \"AttractorLayerUnnormed\",\n \"path\": \"zoedepth/models/layers/attractor.py\",\n \"snippet\": \"class AttractorLayerUnnormed(nn.Module):\\n def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10,\\n alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False):\\n \\\"\\\"\\\"\\n Attractor layer for bin centers. Bin centers are unbounded\\n \\\"\\\"\\\"\\n super().__init__()\\n\\n self.n_attractors = n_attractors\\n self.n_bins = n_bins\\n self.min_depth = min_depth\\n self.max_depth = max_depth\\n self.alpha = alpha\\n self.gamma = gamma\\n self.kind = kind\\n self.attractor_type = attractor_type\\n self.memory_efficient = memory_efficient\\n\\n self._net = nn.Sequential(\\n nn.Conv2d(in_features, mlp_dim, 1, 1, 0),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(mlp_dim, n_attractors, 1, 1, 0),\\n nn.Softplus()\\n )\\n\\n def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False):\\n \\\"\\\"\\\"\\n Args:\\n x (torch.Tensor) : feature block; shape - n, c, h, w\\n b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w\\n \\n Returns:\\n tuple(torch.Tensor,torch.Tensor) : new bin centers unbounded; shape - n, nbins, h, w. Two outputs just to keep the API consistent with the normed version\\n \\\"\\\"\\\"\\n if prev_b_embedding is not None:\\n if interpolate:\\n prev_b_embedding = nn.functional.interpolate(\\n prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True)\\n x = x + prev_b_embedding\\n\\n A = self._net(x)\\n n, c, h, w = A.shape\\n\\n b_prev = nn.functional.interpolate(\\n b_prev, (h, w), mode='bilinear', align_corners=True)\\n b_centers = b_prev\\n\\n if self.attractor_type == 'exp':\\n dist = exp_attractor\\n else:\\n dist = inv_attractor\\n\\n if not self.memory_efficient:\\n func = {'mean': torch.mean, 'sum': torch.sum}[self.kind]\\n # .shape N, nbins, h, w\\n delta_c = func(\\n dist(A.unsqueeze(2) - b_centers.unsqueeze(1)), dim=1)\\n else:\\n delta_c = torch.zeros_like(b_centers, device=b_centers.device)\\n for i in range(self.n_attractors):\\n delta_c += dist(A[:, i, ...].unsqueeze(1) -\\n b_centers) # .shape N, nbins, h, w\\n\\n if self.kind == 'mean':\\n delta_c = delta_c / self.n_attractors\\n\\n b_new_centers = b_centers + delta_c\\n B_centers = b_new_centers\\n\\n return b_new_centers, B_centers\"\n },\n {\n \"identifier\": \"ConditionalLogBinomial\",\n \"path\": \"zoedepth/models/layers/dist_layers.py\",\n \"snippet\": \"class ConditionalLogBinomial(nn.Module):\\n def __init__(self, in_features, condition_dim, n_classes=256, bottleneck_factor=2, p_eps=1e-4, max_temp=50, min_temp=1e-7, act=torch.softmax):\\n \\\"\\\"\\\"Conditional Log Binomial distribution\\n\\n Args:\\n in_features (int): number of input channels in main feature\\n condition_dim (int): number of input channels in condition feature\\n n_classes (int, optional): Number of classes. Defaults to 256.\\n bottleneck_factor (int, optional): Hidden dim factor. Defaults to 2.\\n p_eps (float, optional): small eps value. Defaults to 1e-4.\\n max_temp (float, optional): Maximum temperature of output distribution. Defaults to 50.\\n min_temp (float, optional): Minimum temperature of output distribution. Defaults to 1e-7.\\n \\\"\\\"\\\"\\n super().__init__()\\n self.p_eps = p_eps\\n self.max_temp = max_temp\\n self.min_temp = min_temp\\n self.log_binomial_transform = LogBinomial(n_classes, act=act)\\n bottleneck = (in_features + condition_dim) // bottleneck_factor\\n self.mlp = nn.Sequential(\\n nn.Conv2d(in_features + condition_dim, bottleneck,\\n kernel_size=1, stride=1, padding=0),\\n nn.GELU(),\\n # 2 for p linear norm, 2 for t linear norm\\n nn.Conv2d(bottleneck, 2+2, kernel_size=1, stride=1, padding=0),\\n nn.Softplus()\\n )\\n\\n def forward(self, x, cond):\\n \\\"\\\"\\\"Forward pass\\n\\n Args:\\n x (torch.Tensor - NCHW): Main feature\\n cond (torch.Tensor - NCHW): condition feature\\n\\n Returns:\\n torch.Tensor: Output log binomial distribution\\n \\\"\\\"\\\"\\n pt = self.mlp(torch.concat((x, cond), dim=1))\\n p, t = pt[:, :2, ...], pt[:, 2:, ...]\\n\\n p = p + self.p_eps\\n p = p[:, 0, ...] / (p[:, 0, ...] + p[:, 1, ...])\\n\\n t = t + self.p_eps\\n t = t[:, 0, ...] / (t[:, 0, ...] + t[:, 1, ...])\\n t = t.unsqueeze(1)\\n t = (self.max_temp - self.min_temp) * t + self.min_temp\\n\\n return self.log_binomial_transform(p, t)\"\n },\n {\n \"identifier\": \"ConditionalLogBinomialV2\",\n \"path\": \"zoedepth/models/layers/dist_layers.py\",\n \"snippet\": \"class ConditionalLogBinomialV2(nn.Module):\\n def __init__(self, in_features, condition_dim, n_classes=256, bottleneck_factor=2, p_eps=1e-4, max_temp=50, min_temp=1e-7, act=torch.softmax):\\n \\\"\\\"\\\"Conditional Log Binomial distribution\\n\\n Args:\\n in_features (int): number of input channels in main feature\\n condition_dim (int): number of input channels in condition feature\\n n_classes (int, optional): Number of classes. Defaults to 256.\\n bottleneck_factor (int, optional): Hidden dim factor. Defaults to 2.\\n p_eps (float, optional): small eps value. Defaults to 1e-4.\\n max_temp (float, optional): Maximum temperature of output distribution. Defaults to 50.\\n min_temp (float, optional): Minimum temperature of output distribution. Defaults to 1e-7.\\n \\\"\\\"\\\"\\n super().__init__()\\n self.p_eps = p_eps\\n self.max_temp = max_temp\\n self.min_temp = min_temp\\n self.log_binomial_transform = LogBinomial(n_classes, act=act)\\n bottleneck = (in_features + condition_dim) // bottleneck_factor\\n self.mlp = nn.Sequential(\\n nn.Conv2d(in_features + condition_dim, bottleneck,\\n kernel_size=1, stride=1, padding=0),\\n nn.GELU(),\\n # 2 for p linear norm, 2 for t linear norm\\n nn.Conv2d(bottleneck, n_classes*2, kernel_size=1, stride=1, padding=0),\\n nn.Sigmoid()\\n )\\n self.n_classes = n_classes\\n\\n\\n def forward(self, x, cond):\\n \\\"\\\"\\\"Forward pass\\n\\n Args:\\n x (torch.Tensor - NCHW): Main feature\\n cond (torch.Tensor - NCHW): condition feature\\n\\n Returns:\\n torch.Tensor: Output log binomial distribution\\n \\\"\\\"\\\"\\n pt = self.mlp(torch.concat((x, cond), dim=1))\\n prob, shift = pt[:, :self.n_classes, ...], pt[:, self.n_classes:, ...]\\n return prob, shift\"\n },\n {\n \"identifier\": \"Projector\",\n \"path\": \"zoedepth/models/layers/localbins_layers.py\",\n \"snippet\": \"class Projector(nn.Module):\\n def __init__(self, in_features, out_features, mlp_dim=128):\\n \\\"\\\"\\\"Projector MLP\\n\\n Args:\\n in_features (int): input channels\\n out_features (int): output channels\\n mlp_dim (int, optional): hidden dimension. Defaults to 128.\\n \\\"\\\"\\\"\\n super().__init__()\\n\\n self._net = nn.Sequential(\\n nn.Conv2d(in_features, mlp_dim, 1, 1, 0),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(mlp_dim, out_features, 1, 1, 0),\\n )\\n\\n def forward(self, x):\\n return self._net(x)\"\n },\n {\n \"identifier\": \"SeedBinRegressor\",\n \"path\": \"zoedepth/models/layers/localbins_layers.py\",\n \"snippet\": \"class SeedBinRegressor(nn.Module):\\n def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10):\\n \\\"\\\"\\\"Bin center regressor network. Bin centers are bounded on (min_depth, max_depth) interval.\\n\\n Args:\\n in_features (int): input channels\\n n_bins (int, optional): Number of bin centers. Defaults to 16.\\n mlp_dim (int, optional): Hidden dimension. Defaults to 256.\\n min_depth (float, optional): Min depth value. Defaults to 1e-3.\\n max_depth (float, optional): Max depth value. Defaults to 10.\\n \\\"\\\"\\\"\\n super().__init__()\\n self.version = \\\"1_1\\\"\\n self.min_depth = min_depth\\n self.max_depth = max_depth\\n\\n self._net = nn.Sequential(\\n nn.Conv2d(in_features, mlp_dim, 1, 1, 0),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(mlp_dim, n_bins, 1, 1, 0),\\n nn.ReLU(inplace=True)\\n )\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n Returns tensor of bin_width vectors (centers). One vector b for every pixel\\n \\\"\\\"\\\"\\n B = self._net(x)\\n eps = 1e-3\\n B = B + eps\\n B_widths_normed = B / B.sum(dim=1, keepdim=True)\\n B_widths = (self.max_depth - self.min_depth) * \\\\\\n B_widths_normed # .shape NCHW\\n # pad has the form (left, right, top, bottom, front, back)\\n B_widths = nn.functional.pad(\\n B_widths, (0, 0, 0, 0, 1, 0), mode='constant', value=self.min_depth)\\n B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW\\n\\n B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:, 1:, ...])\\n return B_widths_normed, B_centers\"\n },\n {\n \"identifier\": \"SeedBinRegressorUnnormed\",\n \"path\": \"zoedepth/models/layers/localbins_layers.py\",\n \"snippet\": \"class SeedBinRegressorUnnormed(nn.Module):\\n def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10):\\n \\\"\\\"\\\"Bin center regressor network. Bin centers are unbounded\\n\\n Args:\\n in_features (int): input channels\\n n_bins (int, optional): Number of bin centers. Defaults to 16.\\n mlp_dim (int, optional): Hidden dimension. Defaults to 256.\\n min_depth (float, optional): Not used. (for compatibility with SeedBinRegressor)\\n max_depth (float, optional): Not used. (for compatibility with SeedBinRegressor)\\n \\\"\\\"\\\"\\n super().__init__()\\n self.version = \\\"1_1\\\"\\n self._net = nn.Sequential(\\n nn.Conv2d(in_features, mlp_dim, 1, 1, 0),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(mlp_dim, n_bins, 1, 1, 0),\\n nn.Softplus()\\n )\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n Returns tensor of bin_width vectors (centers). One vector b for every pixel\\n \\\"\\\"\\\"\\n B_centers = self._net(x)\\n return B_centers, B_centers\"\n },\n {\n \"identifier\": \"load_state_from_resource\",\n \"path\": \"zoedepth/models/model_io.py\",\n \"snippet\": \"def load_state_from_resource(model, resource: str):\\n \\\"\\\"\\\"Loads weights to the model from a given resource. A resource can be of following types:\\n 1. URL. Prefixed with \\\"url::\\\"\\n e.g. url::http(s)://url.resource.com/ckpt.pt\\n\\n 2. Local path. Prefixed with \\\"local::\\\"\\n e.g. local::/path/to/ckpt.pt\\n\\n\\n Args:\\n model (torch.nn.Module): Model\\n resource (str): resource string\\n\\n Returns:\\n torch.nn.Module: Model with loaded weights\\n \\\"\\\"\\\"\\n print(f\\\"Using pretrained resource {resource}\\\")\\n\\n if resource.startswith('url::'):\\n url = resource.split('url::')[1]\\n return load_state_dict_from_url(model, url, progress=True)\\n\\n elif resource.startswith('local::'):\\n path = resource.split('local::')[1]\\n return load_wts(model, path)\\n \\n else:\\n raise ValueError(\\\"Invalid resource type, only url:: and local:: are supported\\\")\"\n },\n {\n \"identifier\": \"generatemask\",\n \"path\": \"zoedepth/utils/misc.py\",\n \"snippet\": \"def generatemask(size, k_size=-1, sigma=-1, h_factor=0.03, w_factor=0.02):\\n # Generates a Guassian mask\\n mask = np.zeros(size, dtype=np.float32)\\n if sigma == -1:\\n sigma = int(size[0]/16)\\n if k_size == -1:\\n k_size = int(2 * np.ceil(2 * int(size[0]/16)) + 1)\\n # mask[int(0.02*size[0]):size[0] - int(0.02*size[0]), int(0.015*size[1]): size[1] - int(0.015*size[1])] = 1\\n mask[int(h_factor*size[0]):size[0] - int(h_factor*size[0]), int(w_factor*size[1]): size[1] - int(w_factor*size[1])] = 1\\n mask = cv2.GaussianBlur(mask, (int(k_size), int(k_size)), sigma)\\n mask = (mask - mask.min()) / (mask.max() - mask.min())\\n mask = mask.astype(np.float32)\\n return mask\"\n },\n {\n \"identifier\": \"TransformerDecoderLayer\",\n \"path\": \"zoedepth/models/layers/transformer.py\",\n \"snippet\": \"class TransformerDecoderLayer(nn.Module):\\n\\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\\n activation=\\\"relu\\\", normalize_before=False):\\n super().__init__()\\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\\n # Implementation of Feedforward model\\n self.linear1 = nn.Linear(d_model, dim_feedforward)\\n self.dropout = nn.Dropout(dropout)\\n self.linear2 = nn.Linear(dim_feedforward, d_model)\\n\\n self.norm1 = nn.LayerNorm(d_model)\\n self.norm2 = nn.LayerNorm(d_model)\\n self.norm3 = nn.LayerNorm(d_model)\\n self.dropout1 = nn.Dropout(dropout)\\n self.dropout2 = nn.Dropout(dropout)\\n self.dropout3 = nn.Dropout(dropout)\\n\\n self.activation = _get_activation_fn(activation)\\n self.normalize_before = normalize_before\\n\\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\\n return tensor if pos is None else tensor + pos\\n\\n def forward_post(self, tgt, memory,\\n tgt_mask: Optional[Tensor] = None,\\n memory_mask: Optional[Tensor] = None,\\n tgt_key_padding_mask: Optional[Tensor] = None,\\n memory_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None,\\n query_pos: Optional[Tensor] = None):\\n q = k = self.with_pos_embed(tgt, query_pos)\\n tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\\n key_padding_mask=tgt_key_padding_mask)[0]\\n tgt = tgt + self.dropout1(tgt2)\\n tgt = self.norm1(tgt)\\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\\n key=self.with_pos_embed(memory, pos),\\n value=memory, attn_mask=memory_mask,\\n key_padding_mask=memory_key_padding_mask)[0]\\n tgt = tgt + self.dropout2(tgt2)\\n tgt = self.norm2(tgt)\\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\\n tgt = tgt + self.dropout3(tgt2)\\n tgt = self.norm3(tgt)\\n return tgt\\n\\n def forward_pre(self, tgt, memory,\\n tgt_mask: Optional[Tensor] = None,\\n memory_mask: Optional[Tensor] = None,\\n tgt_key_padding_mask: Optional[Tensor] = None,\\n memory_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None,\\n query_pos: Optional[Tensor] = None):\\n tgt2 = self.norm1(tgt)\\n q = k = self.with_pos_embed(tgt2, query_pos)\\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\\n key_padding_mask=tgt_key_padding_mask)[0]\\n tgt = tgt + self.dropout1(tgt2)\\n tgt2 = self.norm2(tgt)\\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\\n key=self.with_pos_embed(memory, pos),\\n value=memory, attn_mask=memory_mask,\\n key_padding_mask=memory_key_padding_mask)[0]\\n tgt = tgt + self.dropout2(tgt2)\\n tgt2 = self.norm3(tgt)\\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\\n tgt = tgt + self.dropout3(tgt2)\\n return tgt\\n\\n def forward(self, tgt, memory,\\n tgt_mask: Optional[Tensor] = None,\\n memory_mask: Optional[Tensor] = None,\\n tgt_key_padding_mask: Optional[Tensor] = None,\\n memory_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None,\\n query_pos: Optional[Tensor] = None):\\n if self.normalize_before:\\n return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\\n return self.forward_post(tgt, memory, tgt_mask, memory_mask,\\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\"\n },\n {\n \"identifier\": \"TransformerEncoderLayer\",\n \"path\": \"zoedepth/models/layers/transformer.py\",\n \"snippet\": \"class TransformerEncoderLayer(nn.Module):\\n\\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.0,\\n activation=\\\"gelu\\\", normalize_before=False):\\n super().__init__()\\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\\n # Implementation of Feedforward model\\n self.linear1 = nn.Linear(d_model, dim_feedforward)\\n self.dropout = nn.Dropout(dropout)\\n self.linear2 = nn.Linear(dim_feedforward, d_model)\\n\\n self.norm1 = nn.LayerNorm(d_model)\\n self.norm2 = nn.LayerNorm(d_model)\\n self.dropout1 = nn.Dropout(dropout)\\n self.dropout2 = nn.Dropout(dropout)\\n\\n self.activation = _get_activation_fn(activation)\\n self.normalize_before = normalize_before\\n\\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\\n return tensor if pos is None else tensor + pos\\n\\n def forward_post(self,\\n src,\\n src_mask: Optional[Tensor] = None,\\n src_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None):\\n q = k = self.with_pos_embed(src, pos)\\n src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,\\n key_padding_mask=src_key_padding_mask)[0]\\n src = src + self.dropout1(src2)\\n src = self.norm1(src)\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\\n src = src + self.dropout2(src2)\\n src = self.norm2(src)\\n return src\\n\\n def forward_pre(self, src,\\n src_mask: Optional[Tensor] = None,\\n src_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None):\\n src2 = self.norm1(src)\\n q = k = self.with_pos_embed(src2, pos)\\n src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,\\n key_padding_mask=src_key_padding_mask)[0]\\n src = src + self.dropout1(src2)\\n src2 = self.norm2(src)\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\\n src = src + self.dropout2(src2)\\n return src\\n\\n def forward(self, src,\\n src_mask: Optional[Tensor] = None,\\n src_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None):\\n if self.normalize_before:\\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\"\n },\n {\n \"identifier\": \"TransformerEncoder\",\n \"path\": \"zoedepth/models/layers/transformer.py\",\n \"snippet\": \"class TransformerEncoder(nn.Module):\\n\\n def __init__(self, encoder_layer, num_layers, norm=None, num_patches=None, ape=True, embed_dim=None, input_dim=None):\\n super().__init__()\\n self.layers = _get_clones(encoder_layer, num_layers)\\n self.num_layers = num_layers\\n self.norm = norm\\n\\n self.embed_dim = embed_dim\\n if input_dim != embed_dim:\\n self.proj_x = nn.Conv2d(input_dim, embed_dim, 3, padding=1)\\n else:\\n self.proj_x = None\\n\\n self.embed_proj = nn.Conv2d(1, embed_dim, 1, 1, 0) # learnable\\n self.ape = ape\\n if self.ape:\\n self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim), requires_grad=True)\\n trunc_normal_(self.absolute_pos_embed, std=.02)\\n\\n def forward(self, x,\\n mask: Optional[Tensor] = None,\\n src_key_padding_mask: Optional[Tensor] = None,\\n pos: Optional[Tensor] = None,\\n area_prior = None):\\n\\n if self.proj_x is not None:\\n x = self.proj_x(x)\\n \\n if area_prior is not None:\\n prior_embed = self.embed_proj(area_prior)\\n x = x + prior_embed\\n\\n Wh, Ww = x.size(2), x.size(3)\\n x = x.flatten(2).transpose(1, 2)\\n\\n if self.ape:\\n x = x + self.absolute_pos_embed # this line is later added\\n\\n for layer in self.layers:\\n x = layer(x, src_mask=None, src_key_padding_mask=None, pos=None)\\n\\n if self.norm is not None:\\n x = self.norm(x)\\n\\n x = x.view(-1, Wh, Ww, self.embed_dim).permute(0, 3, 1, 2).contiguous()\\n return x\"\n },\n {\n \"identifier\": \"colorize\",\n \"path\": \"zoedepth/utils/misc.py\",\n \"snippet\": \"def colorize(value, vmin=None, vmax=None, cmap='turbo_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):\\n \\\"\\\"\\\"Converts a depth map to a color image.\\n\\n Args:\\n value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed\\n vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.\\n vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.\\n cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.\\n invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.\\n invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.\\n background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).\\n gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.\\n value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.\\n\\n Returns:\\n numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)\\n \\\"\\\"\\\"\\n if isinstance(value, torch.Tensor):\\n value = value.detach().cpu().numpy()\\n\\n value = value.squeeze()\\n if invalid_mask is None:\\n invalid_mask = value == invalid_val\\n mask = np.logical_not(invalid_mask)\\n\\n # normalize\\n vmin = np.percentile(value[mask],2) if vmin is None else vmin\\n vmax = np.percentile(value[mask],85) if vmax is None else vmax\\n if vmin != vmax:\\n value = (value - vmin) / (vmax - vmin) # vmin..vmax\\n else:\\n # Avoid 0-division\\n value = value * 0.\\n\\n # squeeze last dim if it exists\\n # grey out the invalid values\\n\\n value[invalid_mask] = np.nan\\n cmapper = matplotlib.cm.get_cmap(cmap)\\n if value_transform:\\n value = value_transform(value)\\n # value = value / value.max()\\n value = cmapper(value, bytes=True) # (nxmx4)\\n\\n # img = value[:, :, :]\\n img = value[...]\\n img[invalid_mask] = background_color\\n\\n # return img.transpose((2, 0, 1))\\n if gamma_corrected:\\n # gamma correction\\n img = img / 255\\n img = np.power(img, 2.2)\\n img = img * 255\\n img = img.astype(np.uint8)\\n return img\"\n },\n {\n \"identifier\": \"colors\",\n \"path\": \"zoedepth/utils/misc.py\",\n \"snippet\": \"class colors:\\n '''Colors class:\\n Reset all colors with colors.reset\\n Two subclasses fg for foreground and bg for background.\\n Use as colors.subclass.colorname.\\n i.e. colors.fg.red or colors.bg.green\\n Also, the generic bold, disable, underline, reverse, strikethrough,\\n and invisible work with the main class\\n i.e. colors.bold\\n '''\\n reset = '\\\\033[0m'\\n bold = '\\\\033[01m'\\n disable = '\\\\033[02m'\\n underline = '\\\\033[04m'\\n reverse = '\\\\033[07m'\\n strikethrough = '\\\\033[09m'\\n invisible = '\\\\033[08m'\\n\\n class fg:\\n black = '\\\\033[30m'\\n red = '\\\\033[31m'\\n green = '\\\\033[32m'\\n orange = '\\\\033[33m'\\n blue = '\\\\033[34m'\\n purple = '\\\\033[35m'\\n cyan = '\\\\033[36m'\\n lightgrey = '\\\\033[37m'\\n darkgrey = '\\\\033[90m'\\n lightred = '\\\\033[91m'\\n lightgreen = '\\\\033[92m'\\n yellow = '\\\\033[93m'\\n lightblue = '\\\\033[94m'\\n pink = '\\\\033[95m'\\n lightcyan = '\\\\033[96m'\\n\\n class bg:\\n black = '\\\\033[40m'\\n red = '\\\\033[41m'\\n green = '\\\\033[42m'\\n orange = '\\\\033[43m'\\n blue = '\\\\033[44m'\\n purple = '\\\\033[45m'\\n cyan = '\\\\033[46m'\\n lightgrey = '\\\\033[47m'\"\n },\n {\n \"identifier\": \"UNetv1\",\n \"path\": \"zoedepth/models/layers/fusion_network.py\",\n \"snippet\": \"class UNetv1(nn.Module):\\n def __init__(self, n_channels, g2l, pos_embed=False, use_area_prior=True):\\n super(UNetv1, self).__init__()\\n self.n_channels = n_channels\\n\\n self.inc = DoubleConv(n_channels, 32)\\n self.down1 = Down(32, 256)\\n self.down2 = Down(256, 256)\\n self.down3 = Down(256, 256)\\n self.down4 = Down(256, 256)\\n self.down5 = Down(256, 256)\\n\\n self.up1 = Upv1(256+256+256, 256, 384)\\n self.up2 = Upv1(256+256+256, 256, 384)\\n self.up3 = Upv1(256+256+256, 256, 384)\\n self.up4 = Upv1(256+256+256, 256, 384)\\n self.up5 = Upv1(256+32+256, 32, 272)\\n\\n self.g2l = g2l\\n \\n if self.g2l:\\n self.g2l_att = nn.ModuleList()\\n win = 12\\n in_channels = [32, 256, 256, 256, 256, 256]\\n crf_dims = [32, 256, 256, 256, 256, 256]\\n\\n self.g2l5 = G2LFusion(input_dim=in_channels[5], embed_dim=crf_dims[5], window_size=win, num_heads=32, depth=4, num_patches=12*16)\\n self.g2l4 = G2LFusion(input_dim=in_channels[4], embed_dim=crf_dims[4], window_size=win, num_heads=32, depth=4, num_patches=24*32)\\n self.g2l3 = G2LFusion(input_dim=in_channels[3], embed_dim=crf_dims[3], window_size=win, num_heads=16, depth=3, num_patches=48*64)\\n self.g2l2 = G2LFusion(input_dim=in_channels[2], embed_dim=crf_dims[2], window_size=win, num_heads=16, depth=3, num_patches=96*128)\\n self.g2l1 = G2LFusion(input_dim=in_channels[1], embed_dim=crf_dims[1], window_size=win, num_heads=8, depth=2, num_patches=192*256)\\n self.g2l0 = G2LFusion(input_dim=in_channels[0], embed_dim=crf_dims[0], window_size=win, num_heads=8, depth=2, num_patches=384*512) \\n\\n self.conv5 = DoubleConvWOBN(in_channels[4] * 2, in_channels[4], in_channels[4])\\n self.conv4 = DoubleConvWOBN(in_channels[4] * 2, in_channels[4], in_channels[4])\\n self.conv3 = DoubleConvWOBN(in_channels[3] * 2, in_channels[3], in_channels[3])\\n self.conv2 = DoubleConvWOBN(in_channels[2] * 2, in_channels[2], in_channels[2])\\n self.conv1 = DoubleConvWOBN(in_channels[1] * 2, in_channels[1], in_channels[1])\\n self.conv0 = DoubleConvWOBN(in_channels[0] * 2, in_channels[0], in_channels[0])\\n \\n def forward(self, \\n input_tensor, \\n guide_plus, \\n guide_cat, \\n crop_area_resize=None, \\n bbox=None, \\n fine_feat_crop=None, \\n coarse_feat_whole=None, \\n coarse_feat_whole_hack=None, \\n coarse_feat_crop=None):\\n\\n # apply unscaled feat to swin\\n if coarse_feat_whole_hack is not None:\\n coarse_feat_whole = coarse_feat_whole_hack\\n\\n if crop_area_resize is None:\\n not_use_prior = True\\n else:\\n not_use_prior = False\\n \\n x1 = self.inc(input_tensor)\\n x2 = self.down1(x1)\\n x3 = self.down2(x2)\\n x4 = self.down3(x3) \\n x5 = self.down4(x4)\\n x6 = self.down5(x5)\\n if self.g2l:\\n g2l_feat5 = self.g2l5(coarse_feat_whole[0], crop_area_resize[0])\\n g2l_feat5 = torch_roi_align(g2l_feat5, bbox, (12, 16), 12/384, aligned=True)\\n x6 = self.conv5(torch.cat([x6, g2l_feat5], dim=1))\\n \\n x5 = self.up1(torch.cat([x6, guide_cat[0]], dim=1), x5)\\n if self.g2l:\\n g2l_feat4 = self.g2l4(coarse_feat_whole[1], crop_area_resize[1])\\n g2l_feat4 = torch_roi_align(g2l_feat4, bbox, (24, 32), 24/384, aligned=True)\\n x5 = self.conv4(torch.cat([x5, g2l_feat4], dim=1)) \\n\\n x4 = self.up2(torch.cat([x5, guide_cat[1]], dim=1), x4)\\n if self.g2l:\\n g2l_feat3 = self.g2l3(coarse_feat_whole[2], crop_area_resize[2])\\n g2l_feat3 = torch_roi_align(g2l_feat3, bbox, (48, 64), 48/384, aligned=True)\\n x4 = self.conv3(torch.cat([x4, g2l_feat3], dim=1))\\n\\n x3 = self.up3(torch.cat([x4, guide_cat[2]], dim=1), x3)\\n if self.g2l:\\n g2l_feat2 = self.g2l2(coarse_feat_whole[3], crop_area_resize[3])\\n g2l_feat2 = torch_roi_align(g2l_feat2, bbox, (96, 128), 96/384, aligned=True)\\n x3 = self.conv2(torch.cat([x3, g2l_feat2], dim=1))\\n\\n x2 = self.up4(torch.cat([x3, guide_cat[3]], dim=1), x2)\\n if self.g2l:\\n g2l_feat1 = self.g2l1(coarse_feat_whole[4], crop_area_resize[4])\\n g2l_feat1 = torch_roi_align(g2l_feat1, bbox, (192, 256), 192/384, aligned=True)\\n x2 = self.conv1(torch.cat([x2, g2l_feat1], dim=1))\\n\\n x1 = self.up5(torch.cat([x2, guide_cat[4]], dim=1), x1)\\n if self.g2l:\\n g2l_feat0 = self.g2l0(coarse_feat_whole[5], crop_area_resize[5])\\n g2l_feat0 = torch_roi_align(g2l_feat0, bbox, (384, 512), 384/384, aligned=True)\\n x1 = self.conv0(torch.cat([x1, g2l_feat0], dim=1))\\n\\n output = [x1, x2, x3, x4, x5, x6]\\n return output\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import itertools\nimport math\nimport copy\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.pyplot as plt\nimport os\nimport torch.distributed as dist\nimport torch.nn.functional as F\nfrom zoedepth.models.depth_model import DepthModel\nfrom zoedepth.models.base_models.midas import MidasCore\nfrom zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed\nfrom zoedepth.models.layers.dist_layers import ConditionalLogBinomial, ConditionalLogBinomialV2\nfrom zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor, SeedBinRegressorUnnormed)\nfrom zoedepth.models.model_io import load_state_from_resource\nfrom torchvision.transforms import Normalize\nfrom torchvision.ops import roi_align as torch_roi_align\nfrom zoedepth.utils.misc import generatemask\nfrom zoedepth.models.layers.transformer import TransformerDecoderLayer, TransformerEncoderLayer, TransformerEncoder\nfrom zoedepth.utils.misc import colorize, colors\nfrom zoedepth.models.layers.fusion_network import UNetv1\n from zoedepth.models.zoedepth_custom.zoedepth_custom import ZoeDepthCustom"},"token_num":{"kind":"number","value":14798,"string":"14,798"},"cropped_code":{"kind":"string","value":"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n# File author: Zhenyu Li\n\n\n\n\n\n\n\n\ndef check_keywords_in_name(name, keywords=()):\n isin = False\n for keyword in keywords:\n if keyword in name:\n isin = True\n return isin\n\ndef get_activation(name, bank):\n # input of forward_hook will be a function of model/inp/oup\n def hook(module, input, output):\n bank[name] = output\n return hook\n\ndef get_input(name, bank):\n # input of forward_hook will be a function of model/inp/oup\n def hook(module, input, output):\n bank[name] = input\n return hook\n \nclass AttributeDict(dict):\n def __getattr__(self, key):\n try:\n return self[key]\n except KeyError:\n raise AttributeError(key)\n\n def __setattr__(self, key, value):\n self[key] = value\n\n def __delattr__(self, key):\n try:\n del self[key]\n except KeyError:\n raise AttributeError(key)\n\nclass PatchFusion(DepthModel):\n def __init__(self, coarse_model, fine_model, n_bins=64, bin_centers_type=\"softplus\", bin_embedding_dim=128, min_depth=1e-3, max_depth=10,\n n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', min_temp=5, max_temp=50, train_midas=True,\n midas_lr_factor=10, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, sr_ratio=1,\n raw_depth_shape=(2160, 3840), transform_sample_gt_size=(2160, 3840), representation='',\n fetch_features=True, sample_feat_level=3, use_hr=False, deform=False, wo_bins=False, baseline=False,\n condition=True, freeze=False, g2l=False, use_fusion_network=False, use_area_prior=False,\n unet_version='v1', consistency_training=False, consistency_target='unet_feat', pos_embed=False, **kwargs):\n \"\"\"ZoeDepth model. This is the version of ZoeDepth that has a single metric head\n\n Args:\n core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of \"relative\" features\n n_bins (int, optional): Number of bin centers. Defaults to 64.\n 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.\n For \"softplus\", softplus activation is used and thus are unbounded. Defaults to \"softplus\".\n bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.\n min_depth (float, optional): Lower bound for normed bin centers. Defaults to 1e-3.\n max_depth (float, optional): Upper bound for normed bin centers. Defaults to 10.\n n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1].\n attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300.\n attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2.\n attractor_kind (str, optional): Attraction aggregation \"sum\" or \"mean\". Defaults to 'sum'.\n attractor_type (str, optional): Type of attractor to use; \"inv\" (Inverse attractor) or \"exp\" (Exponential attractor). Defaults to 'exp'.\n min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.\n max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.\n train_midas (bool, optional): Whether to train \"core\", the base midas model. Defaults to True.\n midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10.\n encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10.\n pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10.\n\n sr_ratio: sr ratio during infer\n raw_depth_shape: raw depth shape during infer. times sr_ratio will be the target resolution. Used to sample points during training\n transform_sample_gt_size: training depth shape # influenced by crop shape which is not included in this pipeline right now\n representation: I use it to test the \"bilap head\" and a discarded idea\n fetch_features: if fetch feats. Default=True\n \"\"\"\n super().__init__()\n\n self.coarse_model = coarse_model\n self.fine_model = fine_model\n self.max_depth = max_depth\n self.min_depth = min_depth\n self.min_temp = min_temp\n self.bin_centers_type = bin_centers_type\n\n self.midas_lr_factor = midas_lr_factor\n self.encoder_lr_factor = encoder_lr_factor\n self.pos_enc_lr_factor = pos_enc_lr_factor\n self.train_midas = train_midas\n self.inverse_midas = inverse_midas\n \n if bin_centers_type == \"normed\":\n SeedBinRegressorLayer = SeedBinRegressor\n Attractor = AttractorLayer\n elif bin_centers_type == \"softplus\": # default\n SeedBinRegressorLayer = SeedBinRegressorUnnormed\n Attractor = AttractorLayerUnnormed\n elif bin_centers_type == \"hybrid1\":\n SeedBinRegressorLayer = SeedBinRegressor\n Attractor = AttractorLayerUnnormed\n elif bin_centers_type == \"hybrid2\":\n SeedBinRegressorLayer = SeedBinRegressorUnnormed\n Attractor = AttractorLayer\n else:\n raise ValueError(\n \"bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'\")\n \n N_MIDAS_OUT = 32\n btlnck_features = self.fine_model.core.output_channels[0]\n num_out_features = self.fine_model.core.output_channels[1:] # all of them are the same\n\n self.seed_bin_regressor = SeedBinRegressorLayer(\n btlnck_features, n_bins=n_bins, min_depth=min_depth, max_depth=max_depth)\n"},"all_code":{"kind":"string","value":"# MIT License\n\n# Copyright (c) 2022 Intelligent Systems Lab Org\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n# File author: Zhenyu Li\n\n\n\n\n\n\n\n\ndef check_keywords_in_name(name, keywords=()):\n isin = False\n for keyword in keywords:\n if keyword in name:\n isin = True\n return isin\n\ndef get_activation(name, bank):\n # input of forward_hook will be a function of model/inp/oup\n def hook(module, input, output):\n bank[name] = output\n return hook\n\ndef get_input(name, bank):\n # input of forward_hook will be a function of model/inp/oup\n def hook(module, input, output):\n bank[name] = input\n return hook\n \nclass AttributeDict(dict):\n def __getattr__(self, key):\n try:\n return self[key]\n except KeyError:\n raise AttributeError(key)\n\n def __setattr__(self, key, value):\n self[key] = value\n\n def __delattr__(self, key):\n try:\n del self[key]\n except KeyError:\n raise AttributeError(key)\n\nclass PatchFusion(DepthModel):\n def __init__(self, coarse_model, fine_model, n_bins=64, bin_centers_type=\"softplus\", bin_embedding_dim=128, min_depth=1e-3, max_depth=10,\n n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', min_temp=5, max_temp=50, train_midas=True,\n midas_lr_factor=10, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, sr_ratio=1,\n raw_depth_shape=(2160, 3840), transform_sample_gt_size=(2160, 3840), representation='',\n fetch_features=True, sample_feat_level=3, use_hr=False, deform=False, wo_bins=False, baseline=False,\n condition=True, freeze=False, g2l=False, use_fusion_network=False, use_area_prior=False,\n unet_version='v1', consistency_training=False, consistency_target='unet_feat', pos_embed=False, **kwargs):\n \"\"\"ZoeDepth model. This is the version of ZoeDepth that has a single metric head\n\n Args:\n core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of \"relative\" features\n n_bins (int, optional): Number of bin centers. Defaults to 64.\n 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.\n For \"softplus\", softplus activation is used and thus are unbounded. Defaults to \"softplus\".\n bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.\n min_depth (float, optional): Lower bound for normed bin centers. Defaults to 1e-3.\n max_depth (float, optional): Upper bound for normed bin centers. Defaults to 10.\n n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1].\n attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300.\n attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2.\n attractor_kind (str, optional): Attraction aggregation \"sum\" or \"mean\". Defaults to 'sum'.\n attractor_type (str, optional): Type of attractor to use; \"inv\" (Inverse attractor) or \"exp\" (Exponential attractor). Defaults to 'exp'.\n min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.\n max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.\n train_midas (bool, optional): Whether to train \"core\", the base midas model. Defaults to True.\n midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10.\n encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10.\n pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10.\n\n sr_ratio: sr ratio during infer\n raw_depth_shape: raw depth shape during infer. times sr_ratio will be the target resolution. Used to sample points during training\n transform_sample_gt_size: training depth shape # influenced by crop shape which is not included in this pipeline right now\n representation: I use it to test the \"bilap head\" and a discarded idea\n fetch_features: if fetch feats. Default=True\n \"\"\"\n super().__init__()\n\n self.coarse_model = coarse_model\n self.fine_model = fine_model\n self.max_depth = max_depth\n self.min_depth = min_depth\n self.min_temp = min_temp\n self.bin_centers_type = bin_centers_type\n\n self.midas_lr_factor = midas_lr_factor\n self.encoder_lr_factor = encoder_lr_factor\n self.pos_enc_lr_factor = pos_enc_lr_factor\n self.train_midas = train_midas\n self.inverse_midas = inverse_midas\n \n if bin_centers_type == \"normed\":\n SeedBinRegressorLayer = SeedBinRegressor\n Attractor = AttractorLayer\n elif bin_centers_type == \"softplus\": # default\n SeedBinRegressorLayer = SeedBinRegressorUnnormed\n Attractor = AttractorLayerUnnormed\n elif bin_centers_type == \"hybrid1\":\n SeedBinRegressorLayer = SeedBinRegressor\n Attractor = AttractorLayerUnnormed\n elif bin_centers_type == \"hybrid2\":\n SeedBinRegressorLayer = SeedBinRegressorUnnormed\n Attractor = AttractorLayer\n else:\n raise ValueError(\n \"bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'\")\n \n N_MIDAS_OUT = 32\n btlnck_features = self.fine_model.core.output_channels[0]\n num_out_features = self.fine_model.core.output_channels[1:] # all of them are the same\n\n self.seed_bin_regressor = SeedBinRegressorLayer(\n btlnck_features, n_bins=n_bins, min_depth=min_depth, max_depth=max_depth)"},"next_line":{"kind":"string","value":" self.seed_projector = Projector(btlnck_features, bin_embedding_dim)"},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-12-04 08:43:15+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5369,"cells":{"repo_name":{"kind":"string","value":"baaivision/GeoDream"},"file_path":{"kind":"string","value":"threestudio/models/geometry/tetrahedra_sdf_grid.py"},"context":{"kind":"list like","value":[{"identifier":"BaseExplicitGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\n \"bbox\",\n torch.as_tensor(\n [\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\n ],\n dtype=torch.float32,\n ),\n )"},{"identifier":"BaseGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseGeometry(BaseModule):\n @dataclass\n class Config(BaseModule.Config):\n pass\n\n cfg: Config\n\n @staticmethod\n def create_from(\n other: \"BaseGeometry\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\n ) -> \"BaseGeometry\":\n raise TypeError(\n f\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\"\n )\n\n def export(self, *args, **kwargs) -> Dict[str, Any]:\n return {}"},{"identifier":"contract_to_unisphere","path":"threestudio/models/geometry/base.py","snippet":"def contract_to_unisphere(\n x: Float[Tensor, \"... 3\"], bbox: Float[Tensor, \"2 3\"], unbounded: bool = False\n) -> Float[Tensor, \"... 3\"]:\n if unbounded:\n x = scale_tensor(x, bbox, (0, 1))\n x = x * 2 - 1 # aabb is at [-1, 1]\n mag = x.norm(dim=-1, keepdim=True)\n mask = mag.squeeze(-1) > 1\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\n else:\n x = scale_tensor(x, bbox, (0, 1))\n return x"},{"identifier":"ImplicitSDF","path":"threestudio/models/geometry/implicit_sdf.py","snippet":"class ImplicitSDF(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: Union[\n float, str\n ] = 0.01 # in [float, \"progressive\"]\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n sdf_bias: Union[float, str] = 0.0\n sdf_bias_params: Optional[Any] = None\n\n # no need to removal outlier for SDF\n isosurface_remove_outliers: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.sdf_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n if self.cfg.isosurface_deformable_grid:\n assert (\n self.cfg.isosurface_method == \"mt\"\n ), \"isosurface_deformable_grid only works with mt\"\n self.deformation_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n self.finite_difference_normal_eps: Optional[float] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n if self.cfg.sdf_bias != 0.0:\n threestudio.warn(\n \"shape_init and sdf_bias are both specified, which may lead to unexpected results.\"\n )\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n import trimesh\n\n scene = trimesh.load(mesh_path)\n if isinstance(scene, trimesh.Trimesh):\n mesh = scene\n elif isinstance(scene, trimesh.scene.Scene):\n mesh = trimesh.Trimesh()\n for obj in scene.geometry.values():\n mesh = trimesh.util.concatenate([mesh, obj])\n else:\n raise ValueError(f\"Unknown mesh type at {mesh_path}.\")\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n from pysdf import SDF\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\n from tqdm import tqdm\n\n for _ in tqdm(\n range(1000),\n desc=f\"Initializing SDF to a(n) {self.cfg.shape_init}:\",\n disable=get_rank() != 0,\n ):\n points_rand = (\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\n )\n sdf_gt = get_gt_sdf(points_rand)\n sdf_pred = self.forward_sdf(points_rand)\n loss = F.mse_loss(sdf_pred, sdf_gt)\n optim.zero_grad()\n loss.backward()\n optim.step()\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def get_shifted_sdf(\n self, points: Float[Tensor, \"*N Di\"], sdf: Float[Tensor, \"*N 1\"]\n ) -> Float[Tensor, \"*N 1\"]:\n sdf_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.sdf_bias == \"ellipsoid\":\n assert (\n isinstance(self.cfg.sdf_bias_params, Sized)\n and len(self.cfg.sdf_bias_params) == 3\n )\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\n sdf_bias = ((points / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n elif self.cfg.sdf_bias == \"sphere\":\n assert isinstance(self.cfg.sdf_bias_params, float)\n radius = self.cfg.sdf_bias_params\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\n elif isinstance(self.cfg.sdf_bias, float):\n sdf_bias = self.cfg.sdf_bias\n else:\n raise ValueError(f\"Unknown sdf bias {self.cfg.sdf_bias}\")\n return sdf + sdf_bias\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n output = {\"sdf\": sdf}\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n assert self.finite_difference_normal_eps is not None\n eps: float = self.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 6 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (\n 0.5\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 3 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\n normal = F.normalize(sdf_grad, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n sdf_grad = normal\n elif self.cfg.normal_type == \"analytic\":\n sdf_grad = -torch.autograd.grad(\n sdf,\n points_unscaled,\n grad_outputs=torch.ones_like(sdf),\n create_graph=True,\n )[0]\n normal = F.normalize(sdf_grad, dim=-1)\n if not grad_enabled:\n sdf_grad = sdf_grad.detach()\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update(\n {\"normal\": normal, \"shading_normal\": normal, \"sdf_grad\": sdf_grad}\n )\n return output\n\n def forward_sdf(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n sdf = self.sdf_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n return sdf\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n deformation: Optional[Float[Tensor, \"*N 3\"]] = None\n if self.cfg.isosurface_deformable_grid:\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\n return sdf, deformation\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return field - threshold\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n if isinstance(self.cfg.finite_difference_normal_eps, float):\n self.finite_difference_normal_eps = (\n self.cfg.finite_difference_normal_eps\n )\n elif self.cfg.finite_difference_normal_eps == \"progressive\":\n # progressive finite difference eps from Neuralangelo\n # https://arxiv.org/abs/2306.03092\n hg_conf: Any = self.cfg.pos_encoding_config\n assert (\n hg_conf.otype == \"ProgressiveBandHashGrid\"\n ), \"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\"\n current_level = min(\n hg_conf.start_level\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\n hg_conf.n_levels,\n )\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\n current_level - 1\n )\n grid_size = 2 * self.cfg.radius / grid_res\n if grid_size != self.finite_difference_normal_eps:\n threestudio.info(\n f\"Update finite_difference_normal_eps to {grid_size}\"\n )\n self.finite_difference_normal_eps = grid_size\n else:\n raise ValueError(\n f\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\"\n )"},{"identifier":"ImplicitVolume","path":"threestudio/models/geometry/implicit_volume.py","snippet":"class ImplicitVolume(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n density_activation: Optional[str] = \"softplus\"\n density_bias: Union[float, str] = \"blob_magic3d\"\n density_blob_scale: float = 10.0\n density_blob_std: float = 0.5\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: float = 0.01\n\n # automatically determine the threshold\n isosurface_threshold: Union[float, str] = 25.0\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.density_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n def get_activated_density(\n self, points: Float[Tensor, \"*N Di\"], density: Float[Tensor, \"*N 1\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Float[Tensor, \"*N 1\"]]:\n density_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.density_bias == \"blob_dreamfusion\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * torch.exp(\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\n )[..., None]\n )\n elif self.cfg.density_bias == \"blob_magic3d\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * (\n 1\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\n )[..., None]\n )\n elif isinstance(self.cfg.density_bias, float):\n density_bias = self.cfg.density_bias\n else:\n raise ValueError(f\"Unknown density bias {self.cfg.density_bias}\")\n raw_density: Float[Tensor, \"*N 1\"] = density + density_bias\n density = get_activation(self.cfg.density_activation)(raw_density)\n return raw_density, density\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n density = self.density_network(enc).view(*points.shape[:-1], 1)\n raw_density, density = self.get_activated_density(points_unscaled, density)\n\n output = {\n \"density\": density,\n }\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n # TODO: use raw density\n eps = self.cfg.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 6 1\"] = self.forward_density(\n points_offset\n )\n normal = (\n -0.5\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 3 1\"] = self.forward_density(\n points_offset\n )\n normal = -(density_offset[..., 0::1, 0] - density) / eps\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"analytic\":\n normal = -torch.autograd.grad(\n density,\n points_unscaled,\n grad_outputs=torch.ones_like(density),\n create_graph=True,\n )[0]\n normal = F.normalize(normal, dim=-1)\n if not grad_enabled:\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update({\"normal\": normal, \"shading_normal\": normal})\n\n torch.set_grad_enabled(grad_enabled)\n return output\n\n def forward_density(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n density = self.density_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n\n _, density = self.get_activated_density(points_unscaled, density)\n return density\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n if self.cfg.isosurface_deformable_grid:\n threestudio.warn(\n f\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\"\n )\n density = self.forward_density(points)\n return density, None\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return -(field - threshold)\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"ImplicitVolume\":\n if isinstance(other, ImplicitVolume):\n instance = ImplicitVolume(cfg, **kwargs)\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.density_network.load_state_dict(other.density_network.state_dict())\n if copy_net:\n if (\n instance.cfg.n_feature_dims > 0\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\n ):\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n if (\n instance.cfg.normal_type == \"pred\"\n and other.cfg.normal_type == \"pred\"\n ):\n instance.normal_network.load_state_dict(\n other.normal_network.state_dict()\n )\n return instance\n else:\n raise TypeError(\n f\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\"\n )"},{"identifier":"MarchingTetrahedraHelper","path":"threestudio/models/isosurface.py","snippet":"class MarchingTetrahedraHelper(IsosurfaceHelper):\n def __init__(self, resolution: int, tets_path: str):\n super().__init__()\n self.resolution = resolution\n self.tets_path = tets_path\n\n self.triangle_table: Float[Tensor, \"...\"]\n self.register_buffer(\n \"triangle_table\",\n torch.as_tensor(\n [\n [-1, -1, -1, -1, -1, -1],\n [1, 0, 2, -1, -1, -1],\n [4, 0, 3, -1, -1, -1],\n [1, 4, 2, 1, 3, 4],\n [3, 1, 5, -1, -1, -1],\n [2, 3, 0, 2, 5, 3],\n [1, 4, 0, 1, 5, 4],\n [4, 2, 5, -1, -1, -1],\n [4, 5, 2, -1, -1, -1],\n [4, 1, 0, 4, 5, 1],\n [3, 2, 0, 3, 5, 2],\n [1, 3, 5, -1, -1, -1],\n [4, 1, 2, 4, 3, 1],\n [3, 0, 4, -1, -1, -1],\n [2, 0, 1, -1, -1, -1],\n [-1, -1, -1, -1, -1, -1],\n ],\n dtype=torch.long,\n ),\n persistent=False,\n )\n self.num_triangles_table: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"num_triangles_table\",\n torch.as_tensor(\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\n ),\n persistent=False,\n )\n self.base_tet_edges: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"base_tet_edges\",\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\n persistent=False,\n )\n\n tets = np.load(self.tets_path)\n self._grid_vertices: Float[Tensor, \"...\"]\n self.register_buffer(\n \"_grid_vertices\",\n torch.from_numpy(tets[\"vertices\"]).float(),\n persistent=False,\n )\n self.indices: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"indices\", torch.from_numpy(tets[\"indices\"]).long(), persistent=False\n )\n\n self._all_edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n\n def normalize_grid_deformation(\n self, grid_vertex_offsets: Float[Tensor, \"Nv 3\"]\n ) -> Float[Tensor, \"Nv 3\"]:\n return (\n (self.points_range[1] - self.points_range[0])\n / (self.resolution) # half tet size is approximately 1 / self.resolution\n * torch.tanh(grid_vertex_offsets)\n ) # FIXME: hard-coded activation\n\n @property\n def grid_vertices(self) -> Float[Tensor, \"Nv 3\"]:\n return self._grid_vertices\n\n @property\n def all_edges(self) -> Integer[Tensor, \"Ne 2\"]:\n if self._all_edges is None:\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\n edges = torch.tensor(\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\n dtype=torch.long,\n device=self.indices.device,\n )\n _all_edges = self.indices[:, edges].reshape(-1, 2)\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\n self._all_edges = _all_edges\n return self._all_edges\n\n def sort_edges(self, edges_ex2):\n with torch.no_grad():\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\n order = order.unsqueeze(dim=1)\n\n a = torch.gather(input=edges_ex2, index=order, dim=1)\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\n\n return torch.stack([a, b], -1)\n\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\n with torch.no_grad():\n occ_n = sdf_n > 0\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\n occ_sum = torch.sum(occ_fx4, -1)\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\n occ_sum = occ_sum[valid_tets]\n\n # find all vertices\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\n all_edges = self.sort_edges(all_edges)\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\n\n unique_edges = unique_edges.long()\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\n mapping = (\n torch.ones(\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\n )\n * -1\n )\n mapping[mask_edges] = torch.arange(\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\n )\n idx_map = mapping[idx_map] # map edges to verts\n\n interp_v = unique_edges[mask_edges]\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\n edges_to_interp_sdf[:, -1] *= -1\n\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\n\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\n\n idx_map = idx_map.reshape(-1, 6)\n\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\n num_triangles = self.num_triangles_table[tetindex]\n\n # Generate triangle indices\n faces = torch.cat(\n (\n torch.gather(\n input=idx_map[num_triangles == 1],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\n ).reshape(-1, 3),\n torch.gather(\n input=idx_map[num_triangles == 2],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\n ).reshape(-1, 3),\n ),\n dim=0,\n )\n\n return verts, faces\n\n def forward(\n self,\n level: Float[Tensor, \"N3 1\"],\n deformation: Optional[Float[Tensor, \"N3 3\"]] = None,\n ) -> Mesh:\n if deformation is not None:\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\n deformation\n )\n else:\n grid_vertices = self.grid_vertices\n\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\n\n mesh = Mesh(\n v_pos=v_pos,\n t_pos_idx=t_pos_idx,\n # extras\n grid_vertices=grid_vertices,\n tet_edges=self.all_edges,\n grid_level=level,\n grid_deformation=deformation,\n )\n\n return mesh"},{"identifier":"Mesh","path":"threestudio/models/mesh.py","snippet":"class Mesh:\n def __init__(\n self, v_pos: Float[Tensor, \"Nv 3\"], t_pos_idx: Integer[Tensor, \"Nf 3\"], **kwargs\n ) -> None:\n self.v_pos: Float[Tensor, \"Nv 3\"] = v_pos\n self.t_pos_idx: Integer[Tensor, \"Nf 3\"] = t_pos_idx\n self._v_nrm: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tng: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tex: Optional[Float[Tensor, \"Nt 3\"]] = None\n self._t_tex_idx: Optional[Float[Tensor, \"Nf 3\"]] = None\n self._v_rgb: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n self.extras: Dict[str, Any] = {}\n for k, v in kwargs.items():\n self.add_extra(k, v)\n\n def add_extra(self, k, v) -> None:\n self.extras[k] = v\n\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\n if self.requires_grad:\n threestudio.debug(\"Mesh is differentiable, not removing outliers\")\n return self\n\n # use trimesh to first split the mesh into connected components\n # then remove the components with less than n_face_threshold faces\n import trimesh\n\n # construct a trimesh object\n mesh = trimesh.Trimesh(\n vertices=self.v_pos.detach().cpu().numpy(),\n faces=self.t_pos_idx.detach().cpu().numpy(),\n )\n\n # split the mesh into connected components\n components = mesh.split(only_watertight=False)\n # log the number of faces in each component\n threestudio.debug(\n \"Mesh has {} components, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n\n n_faces_threshold: int\n if isinstance(outlier_n_faces_threshold, float):\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\n n_faces_threshold = int(\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\n )\n else:\n # set the threshold directly to outlier_n_faces_threshold\n n_faces_threshold = outlier_n_faces_threshold\n\n # log the threshold\n threestudio.debug(\n \"Removing components with less than {} faces\".format(n_faces_threshold)\n )\n\n # remove the components with less than n_face_threshold faces\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\n\n # log the number of faces in each component after removing outliers\n threestudio.debug(\n \"Mesh has {} components after removing outliers, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n # merge the components\n mesh = trimesh.util.concatenate(components)\n\n # convert back to our mesh format\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\n\n clean_mesh = Mesh(v_pos, t_pos_idx)\n # keep the extras unchanged\n\n if len(self.extras) > 0:\n clean_mesh.extras = self.extras\n threestudio.debug(\n f\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\"\n )\n return clean_mesh\n\n @property\n def requires_grad(self):\n return self.v_pos.requires_grad\n\n @property\n def v_nrm(self):\n if self._v_nrm is None:\n self._v_nrm = self._compute_vertex_normal()\n return self._v_nrm\n\n @property\n def v_tng(self):\n if self._v_tng is None:\n self._v_tng = self._compute_vertex_tangent()\n return self._v_tng\n\n @property\n def v_tex(self):\n if self._v_tex is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._v_tex\n\n @property\n def t_tex_idx(self):\n if self._t_tex_idx is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._t_tex_idx\n\n @property\n def v_rgb(self):\n return self._v_rgb\n\n @property\n def edges(self):\n if self._edges is None:\n self._edges = self._compute_edges()\n return self._edges\n\n def _compute_vertex_normal(self):\n i0 = self.t_pos_idx[:, 0]\n i1 = self.t_pos_idx[:, 1]\n i2 = self.t_pos_idx[:, 2]\n\n v0 = self.v_pos[i0, :]\n v1 = self.v_pos[i1, :]\n v2 = self.v_pos[i2, :]\n\n face_normals = torch.cross(v1 - v0, v2 - v0)\n\n # Splat face normals to vertices\n v_nrm = torch.zeros_like(self.v_pos)\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\n\n # Normalize, replace zero (degenerated) normals with some default value\n v_nrm = torch.where(\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\n )\n v_nrm = F.normalize(v_nrm, dim=1)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(v_nrm))\n\n return v_nrm\n\n def _compute_vertex_tangent(self):\n vn_idx = [None] * 3\n pos = [None] * 3\n tex = [None] * 3\n for i in range(0, 3):\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\n # t_nrm_idx is always the same as t_pos_idx\n vn_idx[i] = self.t_pos_idx[:, i]\n\n tangents = torch.zeros_like(self.v_nrm)\n tansum = torch.zeros_like(self.v_nrm)\n\n # Compute tangent space for each triangle\n uve1 = tex[1] - tex[0]\n uve2 = tex[2] - tex[0]\n pe1 = pos[1] - pos[0]\n pe2 = pos[2] - pos[0]\n\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\n\n # Avoid division by zero for degenerated texture coordinates\n tang = nom / torch.where(\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\n )\n\n # Update all 3 vertices\n for i in range(0, 3):\n idx = vn_idx[i][:, None].repeat(1, 3)\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\n tansum.scatter_add_(\n 0, idx, torch.ones_like(tang)\n ) # tansum[n_i] = tansum[n_i] + 1\n tangents = tangents / tansum\n\n # Normalize and make sure tangent is perpendicular to normal\n tangents = F.normalize(tangents, dim=1)\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(tangents))\n\n return tangents\n\n def _unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n threestudio.info(\"Using xatlas to perform UV unwrapping, may take a while ...\")\n\n import xatlas\n\n atlas = xatlas.Atlas()\n atlas.add_mesh(\n self.v_pos.detach().cpu().numpy(),\n self.t_pos_idx.cpu().numpy(),\n )\n co = xatlas.ChartOptions()\n po = xatlas.PackOptions()\n for k, v in xatlas_chart_options.items():\n setattr(co, k, v)\n for k, v in xatlas_pack_options.items():\n setattr(po, k, v)\n atlas.generate(co, po)\n vmapping, indices, uvs = atlas.get_mesh(0)\n vmapping = (\n torch.from_numpy(\n vmapping.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\n indices = (\n torch.from_numpy(\n indices.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n return uvs, indices\n\n def unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\n xatlas_chart_options, xatlas_pack_options\n )\n\n def set_vertex_color(self, v_rgb):\n assert v_rgb.shape[0] == self.v_pos.shape[0]\n self._v_rgb = v_rgb\n\n def _compute_edges(self):\n # Compute edges\n edges = torch.cat(\n [\n self.t_pos_idx[:, [0, 1]],\n self.t_pos_idx[:, [1, 2]],\n self.t_pos_idx[:, [2, 0]],\n ],\n dim=0,\n )\n edges = edges.sort()[0]\n edges = torch.unique(edges, dim=0)\n return edges\n\n def normal_consistency(self) -> Float[Tensor, \"\"]:\n edge_nrm: Float[Tensor, \"Ne 2 3\"] = self.v_nrm[self.edges]\n nc = (\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\n ).mean()\n return nc\n\n def _laplacian_uniform(self):\n # from stable-dreamfusion\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\n verts, faces = self.v_pos, self.t_pos_idx\n\n V = verts.shape[0]\n F = faces.shape[0]\n\n # Neighbor indices\n ii = faces[:, [1, 2, 0]].flatten()\n jj = faces[:, [2, 0, 1]].flatten()\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\n dim=1\n )\n adj_values = torch.ones(adj.shape[1]).to(verts)\n\n # Diagonal indices\n diag_idx = adj[0]\n\n # Build the sparse matrix\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\n values = torch.cat((-adj_values, adj_values))\n\n # The coalesce operation sums the duplicate indices, resulting in the\n # correct diagonal\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\n\n def laplacian(self) -> Float[Tensor, \"\"]:\n with torch.no_grad():\n L = self._laplacian_uniform()\n loss = L.mm(self.v_pos)\n loss = loss.norm(dim=1)\n loss = loss.mean()\n return loss"},{"identifier":"get_encoding","path":"threestudio/models/networks.py","snippet":"def get_encoding(n_input_dims: int, config) -> nn.Module:\n # input suppose to be range [0, 1]\n encoding: nn.Module\n if config.otype == \"ProgressiveBandFrequency\":\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\n elif config.otype == \"ProgressiveBandHashGrid\":\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\n else:\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\n encoding = CompositeEncoding(\n encoding,\n include_xyz=config.get(\"include_xyz\", False),\n xyz_scale=2.0,\n xyz_offset=-1.0,\n ) # FIXME: hard coded\n return encoding"},{"identifier":"get_mlp","path":"threestudio/models/networks.py","snippet":"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\n network: nn.Module\n if config.otype == \"VanillaMLP\":\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\n elif config.otype == \"SphereInitVanillaMLP\":\n network = SphereInitVanillaMLP(\n n_input_dims, n_output_dims, config_to_primitive(config)\n )\n else:\n assert (\n config.get(\"sphere_init\", False) is False\n ), \"sphere_init=True only supported by VanillaMLP\"\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\n return network"},{"identifier":"broadcast","path":"threestudio/utils/misc.py","snippet":"def broadcast(tensor, src=0):\n if not _distributed_available():\n return tensor\n else:\n torch.distributed.broadcast(tensor, src=src)\n return tensor"},{"identifier":"scale_tensor","path":"threestudio/utils/ops.py","snippet":"def scale_tensor(\n dat: Num[Tensor, \"... D\"], inp_scale: ValidScale, tgt_scale: ValidScale\n):\n if inp_scale is None:\n inp_scale = (0, 1)\n if tgt_scale is None:\n tgt_scale = (0, 1)\n if isinstance(tgt_scale, Tensor):\n assert dat.shape[-1] == tgt_scale.shape[-1]\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\n return dat"},{"identifier":"GeodreamGeometryVolume","path":"threestudio/models/geometry/geodream_geometry_volume.py","snippet":"class GeodreamGeometryVolume(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n density_activation: Optional[str] = \"softplus\"\n density_bias: Union[float, str] = \"blob_magic3d\"\n density_blob_scale: float = 10.0\n density_blob_std: float = 0.5\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: float = 0.01\n\n # automatically determine the threshold\n isosurface_threshold: Union[float, str] = 25.0\n init_volume_path: str = \"con_volume_lod0.pth\"\n one2345_weight: str = \"pretrain.pth\"\n sdf_network_grad: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n \n \n self.sdf_layers = SdfLayer()\n self.deviation_network = SingleVarianceNetwork(self.cfg.one2345_weight)\n\n # sdf_layers weight\n sdf_layers_weight = torch.load(self.cfg.one2345_weight)['sdf_network_lod0']\n selected_state_dict = {}\n prefix = 'sdf_layer'\n for key, value in sdf_layers_weight.items():\n if key.startswith(prefix):\n selected_state_dict[key[10:]] = value# key need remove sdf_layer prefix\n self.sdf_layers.load_state_dict(selected_state_dict)\n print(\"sdf_layers is loading weight at \" + self.cfg.one2345_weight)\n \n # sdf_layers freeze \n if self.cfg.sdf_network_grad:\n print(\"sdf_layers network is training\")\n else:\n for p in self.sdf_layers.parameters():\n p.requires_grad_(False)\n print(\"sdf_layers network is freezeing\")\n\n # volume weight\n volume_weight = torch.load(self.cfg.init_volume_path)\n\n self.volume = nn.Parameter(volume_weight, requires_grad=True)\n print(\"volume network is loading weight at \" + self.cfg.init_volume_path)\n\n def get_activated_density(\n self, points: Float[Tensor, \"*N Di\"], density: Float[Tensor, \"*N 1\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Float[Tensor, \"*N 1\"]]:\n density_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.density_bias == \"blob_dreamfusion\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * torch.exp(\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\n )[..., None]\n )\n elif self.cfg.density_bias == \"blob_magic3d\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * (\n 1\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\n )[..., None]\n )\n elif isinstance(self.cfg.density_bias, float):\n density_bias = self.cfg.density_bias\n else:\n raise ValueError(f\"Unknown density bias {self.cfg.density_bias}\")\n raw_density: Float[Tensor, \"*N 1\"] = density + density_bias\n density = get_activation(self.cfg.density_activation)(raw_density)\n return raw_density, density\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], viewdirs, dists, output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n \n sdf, feature_vector = self.sdf(points.view(-1, self.cfg.n_input_dims))\n\n output = {\n \"density\": sdf,\n }\n \n g = self.gradient(points.view(-1, self.cfg.n_input_dims))\n alphas = self.get_alpha(points.view(-1, self.cfg.n_input_dims), viewdirs, dists, feature_vector, sdf, g)\n output.update({\"ALPHA\": alphas})\n\n \n points_norm = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n \n enc = self.encoding(points_norm.view(-1, self.cfg.n_input_dims))\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n \n torch.set_grad_enabled(grad_enabled)\n return output\n\n def forward_density(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n density, _ = self.sdf(points.view(-1, self.cfg.n_input_dims))\n density = density.reshape(*points.shape[:-1], 1)\n return density\n \n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n sdf, _ = self.sdf(points.view(-1, self.cfg.n_input_dims))\n sdf = sdf.reshape(*points.shape[:-1], 1)\n deformation: Optional[Float[Tensor, \"*N 3\"]] = None\n return sdf, deformation\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return field - threshold\n \n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"GeodreamGeometryVolume\":\n if isinstance(other, GeodreamGeometryVolume):\n instance = GeodreamGeometryVolume(cfg, **kwargs)\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.density_network.load_state_dict(other.density_network.state_dict())\n if copy_net:\n if (\n instance.cfg.n_feature_dims > 0\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\n ):\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n if (\n instance.cfg.normal_type == \"pred\"\n and other.cfg.normal_type == \"pred\"\n ):\n instance.normal_network.load_state_dict(\n other.normal_network.state_dict()\n )\n return instance\n else:\n raise TypeError(\n f\"Cannot create {GeodreamGeometryVolume.__name__} from {other.__class__.__name__}\"\n )\n \n def forward_sdf(self, pts):\n sdf, _ = self.sdf(pts)\n return sdf\n \n def sdf(self, pts, lod=0):\n conditional_volume = self.volume\n num_pts = pts.shape[0]\n device = pts.device\n pts_ = pts.clone()\n pts = pts.view(1, 1, 1, num_pts, 3) # - should be in range (-1, 1)\n\n pts = torch.flip(pts, dims=[-1])\n sampled_feature = grid_sample_3d(conditional_volume, pts) # [1, c, 1, 1, num_pts]\n sampled_feature = sampled_feature.view(-1, num_pts).permute(1, 0).contiguous().to(device)\n\n sdf_pts = self.sdf_layers(pts_, sampled_feature)\n\n return sdf_pts[:, :1], sdf_pts[:, 1:]\n \n def get_alpha(self, ray_samples, rays_d, dists, feature_vector, sdf=None, gradients=None):\n \"\"\"compute alpha from sdf as in NeuS\"\"\"\n inv_variance = self.deviation_network(feature_vector)[:, :1].clip(1e-6, 1e6) # Single parameter\n\n \n #gradients = torch.ones_like(rays_d, requires_grad=False, device=rays_d.device)\n true_dot_val = (rays_d * gradients).sum(-1, keepdim=True) # * calculate\n alpha_inter_ratio = 0.0 \n iter_cos = -(F.relu(-true_dot_val * 0.5 + 0.5) * (1.0 - alpha_inter_ratio) + F.relu(\n -true_dot_val) * alpha_inter_ratio) # always non-positive\n\n true_estimate_sdf_half_next = sdf + iter_cos.clip(-10.0, 10.0) * dists.reshape(-1, 1) * 0.5\n true_estimate_sdf_half_prev = sdf - iter_cos.clip(-10.0, 10.0) * dists.reshape(-1, 1) * 0.5\n\n prev_cdf = torch.sigmoid(true_estimate_sdf_half_prev * inv_variance)\n next_cdf = torch.sigmoid(true_estimate_sdf_half_next * inv_variance)\n\n p = prev_cdf - next_cdf\n c = prev_cdf\n\n alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0)\n\n return alpha\n \n def gradient(self, x):\n \n x.requires_grad_(True)\n with torch.enable_grad():\n sdf, _ = self.sdf(x)\n y = sdf\n\n d_output = torch.ones_like(y, requires_grad=False, device=y.device)\n # ! Distributed Data Parallel doesn’t work with torch.autograd.grad()\n # ! (i.e. it will only work if gradients are to be accumulated in .grad attributes of parameters).\n gradients = torch.autograd.grad(\n outputs=y,\n inputs=x,\n grad_outputs=d_output,\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n return gradients"}],"string":"[\n {\n \"identifier\": \"BaseExplicitGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseExplicitGeometry(BaseGeometry):\\n @dataclass\\n class Config(BaseGeometry.Config):\\n radius: float = 1.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n self.bbox: Float[Tensor, \\\"2 3\\\"]\\n self.register_buffer(\\n \\\"bbox\\\",\\n torch.as_tensor(\\n [\\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\\n ],\\n dtype=torch.float32,\\n ),\\n )\"\n },\n {\n \"identifier\": \"BaseGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseGeometry(BaseModule):\\n @dataclass\\n class Config(BaseModule.Config):\\n pass\\n\\n cfg: Config\\n\\n @staticmethod\\n def create_from(\\n other: \\\"BaseGeometry\\\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\\n ) -> \\\"BaseGeometry\\\":\\n raise TypeError(\\n f\\\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\\\"\\n )\\n\\n def export(self, *args, **kwargs) -> Dict[str, Any]:\\n return {}\"\n },\n {\n \"identifier\": \"contract_to_unisphere\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"def contract_to_unisphere(\\n x: Float[Tensor, \\\"... 3\\\"], bbox: Float[Tensor, \\\"2 3\\\"], unbounded: bool = False\\n) -> Float[Tensor, \\\"... 3\\\"]:\\n if unbounded:\\n x = scale_tensor(x, bbox, (0, 1))\\n x = x * 2 - 1 # aabb is at [-1, 1]\\n mag = x.norm(dim=-1, keepdim=True)\\n mask = mag.squeeze(-1) > 1\\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\\n else:\\n x = scale_tensor(x, bbox, (0, 1))\\n return x\"\n },\n {\n \"identifier\": \"ImplicitSDF\",\n \"path\": \"threestudio/models/geometry/implicit_sdf.py\",\n \"snippet\": \"class ImplicitSDF(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: Union[\\n float, str\\n ] = 0.01 # in [float, \\\"progressive\\\"]\\n shape_init: Optional[str] = None\\n shape_init_params: Optional[Any] = None\\n shape_init_mesh_up: str = \\\"+z\\\"\\n shape_init_mesh_front: str = \\\"+x\\\"\\n force_shape_init: bool = False\\n sdf_bias: Union[float, str] = 0.0\\n sdf_bias_params: Optional[Any] = None\\n\\n # no need to removal outlier for SDF\\n isosurface_remove_outliers: bool = False\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.sdf_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n if self.cfg.isosurface_deformable_grid:\\n assert (\\n self.cfg.isosurface_method == \\\"mt\\\"\\n ), \\\"isosurface_deformable_grid only works with mt\\\"\\n self.deformation_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n self.finite_difference_normal_eps: Optional[float] = None\\n\\n def initialize_shape(self) -> None:\\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\\n return\\n\\n # do not initialize shape if weights are provided\\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\\n return\\n\\n if self.cfg.sdf_bias != 0.0:\\n threestudio.warn(\\n \\\"shape_init and sdf_bias are both specified, which may lead to unexpected results.\\\"\\n )\\n\\n get_gt_sdf: Callable[[Float[Tensor, \\\"N 3\\\"]], Float[Tensor, \\\"N 1\\\"]]\\n assert isinstance(self.cfg.shape_init, str)\\n if self.cfg.shape_init == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.shape_init_params, Sized)\\n and len(self.cfg.shape_init_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return ((points_rand / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init == \\\"sphere\\\":\\n assert isinstance(self.cfg.shape_init_params, float)\\n radius = self.cfg.shape_init_params\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init.startswith(\\\"mesh:\\\"):\\n assert isinstance(self.cfg.shape_init_params, float)\\n mesh_path = self.cfg.shape_init[5:]\\n if not os.path.exists(mesh_path):\\n raise ValueError(f\\\"Mesh file {mesh_path} does not exist.\\\")\\n\\n import trimesh\\n\\n scene = trimesh.load(mesh_path)\\n if isinstance(scene, trimesh.Trimesh):\\n mesh = scene\\n elif isinstance(scene, trimesh.scene.Scene):\\n mesh = trimesh.Trimesh()\\n for obj in scene.geometry.values():\\n mesh = trimesh.util.concatenate([mesh, obj])\\n else:\\n raise ValueError(f\\\"Unknown mesh type at {mesh_path}.\\\")\\n\\n # move to center\\n centroid = mesh.vertices.mean(0)\\n mesh.vertices = mesh.vertices - centroid\\n\\n # align to up-z and front-x\\n dirs = [\\\"+x\\\", \\\"+y\\\", \\\"+z\\\", \\\"-x\\\", \\\"-y\\\", \\\"-z\\\"]\\n dir2vec = {\\n \\\"+x\\\": np.array([1, 0, 0]),\\n \\\"+y\\\": np.array([0, 1, 0]),\\n \\\"+z\\\": np.array([0, 0, 1]),\\n \\\"-x\\\": np.array([-1, 0, 0]),\\n \\\"-y\\\": np.array([0, -1, 0]),\\n \\\"-z\\\": np.array([0, 0, -1]),\\n }\\n if (\\n self.cfg.shape_init_mesh_up not in dirs\\n or self.cfg.shape_init_mesh_front not in dirs\\n ):\\n raise ValueError(\\n f\\\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\\\"\\n )\\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\\n raise ValueError(\\n \\\"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\\\"\\n )\\n z_, x_ = (\\n dir2vec[self.cfg.shape_init_mesh_up],\\n dir2vec[self.cfg.shape_init_mesh_front],\\n )\\n y_ = np.cross(z_, x_)\\n std2mesh = np.stack([x_, y_, z_], axis=0).T\\n mesh2std = np.linalg.inv(std2mesh)\\n\\n # scaling\\n scale = np.abs(mesh.vertices).max()\\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\\n\\n from pysdf import SDF\\n\\n sdf = SDF(mesh.vertices, mesh.faces)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n # add a negative signed here\\n # as in pysdf the inside of the shape has positive signed distance\\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\\n points_rand\\n )[..., None]\\n\\n get_gt_sdf = func\\n\\n else:\\n raise ValueError(\\n f\\\"Unknown shape initialization type: {self.cfg.shape_init}\\\"\\n )\\n\\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\\n from tqdm import tqdm\\n\\n for _ in tqdm(\\n range(1000),\\n desc=f\\\"Initializing SDF to a(n) {self.cfg.shape_init}:\\\",\\n disable=get_rank() != 0,\\n ):\\n points_rand = (\\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\\n )\\n sdf_gt = get_gt_sdf(points_rand)\\n sdf_pred = self.forward_sdf(points_rand)\\n loss = F.mse_loss(sdf_pred, sdf_gt)\\n optim.zero_grad()\\n loss.backward()\\n optim.step()\\n\\n # explicit broadcast to ensure param consistency across ranks\\n for param in self.parameters():\\n broadcast(param, src=0)\\n\\n def get_shifted_sdf(\\n self, points: Float[Tensor, \\\"*N Di\\\"], sdf: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n sdf_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.sdf_bias == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.sdf_bias_params, Sized)\\n and len(self.cfg.sdf_bias_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\\n sdf_bias = ((points / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n elif self.cfg.sdf_bias == \\\"sphere\\\":\\n assert isinstance(self.cfg.sdf_bias_params, float)\\n radius = self.cfg.sdf_bias_params\\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n elif isinstance(self.cfg.sdf_bias, float):\\n sdf_bias = self.cfg.sdf_bias\\n else:\\n raise ValueError(f\\\"Unknown sdf bias {self.cfg.sdf_bias}\\\")\\n return sdf + sdf_bias\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n output = {\\\"sdf\\\": sdf}\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n assert self.finite_difference_normal_eps is not None\\n eps: float = self.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (\\n 0.5\\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\\n normal = F.normalize(sdf_grad, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n sdf_grad = normal\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n sdf_grad = -torch.autograd.grad(\\n sdf,\\n points_unscaled,\\n grad_outputs=torch.ones_like(sdf),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(sdf_grad, dim=-1)\\n if not grad_enabled:\\n sdf_grad = sdf_grad.detach()\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update(\\n {\\\"normal\\\": normal, \\\"shading_normal\\\": normal, \\\"sdf_grad\\\": sdf_grad}\\n )\\n return output\\n\\n def forward_sdf(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n sdf = self.sdf_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n return sdf\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n deformation: Optional[Float[Tensor, \\\"*N 3\\\"]] = None\\n if self.cfg.isosurface_deformable_grid:\\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\\n return sdf, deformation\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return field - threshold\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n if isinstance(self.cfg.finite_difference_normal_eps, float):\\n self.finite_difference_normal_eps = (\\n self.cfg.finite_difference_normal_eps\\n )\\n elif self.cfg.finite_difference_normal_eps == \\\"progressive\\\":\\n # progressive finite difference eps from Neuralangelo\\n # https://arxiv.org/abs/2306.03092\\n hg_conf: Any = self.cfg.pos_encoding_config\\n assert (\\n hg_conf.otype == \\\"ProgressiveBandHashGrid\\\"\\n ), \\\"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\\\"\\n current_level = min(\\n hg_conf.start_level\\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\\n hg_conf.n_levels,\\n )\\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\\n current_level - 1\\n )\\n grid_size = 2 * self.cfg.radius / grid_res\\n if grid_size != self.finite_difference_normal_eps:\\n threestudio.info(\\n f\\\"Update finite_difference_normal_eps to {grid_size}\\\"\\n )\\n self.finite_difference_normal_eps = grid_size\\n else:\\n raise ValueError(\\n f\\\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\\\"\\n )\"\n },\n {\n \"identifier\": \"ImplicitVolume\",\n \"path\": \"threestudio/models/geometry/implicit_volume.py\",\n \"snippet\": \"class ImplicitVolume(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n density_activation: Optional[str] = \\\"softplus\\\"\\n density_bias: Union[float, str] = \\\"blob_magic3d\\\"\\n density_blob_scale: float = 10.0\\n density_blob_std: float = 0.5\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: float = 0.01\\n\\n # automatically determine the threshold\\n isosurface_threshold: Union[float, str] = 25.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.density_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n def get_activated_density(\\n self, points: Float[Tensor, \\\"*N Di\\\"], density: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Float[Tensor, \\\"*N 1\\\"]]:\\n density_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.density_bias == \\\"blob_dreamfusion\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * torch.exp(\\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\\n )[..., None]\\n )\\n elif self.cfg.density_bias == \\\"blob_magic3d\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * (\\n 1\\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\\n )[..., None]\\n )\\n elif isinstance(self.cfg.density_bias, float):\\n density_bias = self.cfg.density_bias\\n else:\\n raise ValueError(f\\\"Unknown density bias {self.cfg.density_bias}\\\")\\n raw_density: Float[Tensor, \\\"*N 1\\\"] = density + density_bias\\n density = get_activation(self.cfg.density_activation)(raw_density)\\n return raw_density, density\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n density = self.density_network(enc).view(*points.shape[:-1], 1)\\n raw_density, density = self.get_activated_density(points_unscaled, density)\\n\\n output = {\\n \\\"density\\\": density,\\n }\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n # TODO: use raw density\\n eps = self.cfg.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = (\\n -0.5\\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = -(density_offset[..., 0::1, 0] - density) / eps\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n normal = -torch.autograd.grad(\\n density,\\n points_unscaled,\\n grad_outputs=torch.ones_like(density),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(normal, dim=-1)\\n if not grad_enabled:\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update({\\\"normal\\\": normal, \\\"shading_normal\\\": normal})\\n\\n torch.set_grad_enabled(grad_enabled)\\n return output\\n\\n def forward_density(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n density = self.density_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n\\n _, density = self.get_activated_density(points_unscaled, density)\\n return density\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n if self.cfg.isosurface_deformable_grid:\\n threestudio.warn(\\n f\\\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\\\"\\n )\\n density = self.forward_density(points)\\n return density, None\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return -(field - threshold)\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry,\\n cfg: Optional[Union[dict, DictConfig]] = None,\\n copy_net: bool = True,\\n **kwargs,\\n ) -> \\\"ImplicitVolume\\\":\\n if isinstance(other, ImplicitVolume):\\n instance = ImplicitVolume(cfg, **kwargs)\\n instance.encoding.load_state_dict(other.encoding.state_dict())\\n instance.density_network.load_state_dict(other.density_network.state_dict())\\n if copy_net:\\n if (\\n instance.cfg.n_feature_dims > 0\\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\\n ):\\n instance.feature_network.load_state_dict(\\n other.feature_network.state_dict()\\n )\\n if (\\n instance.cfg.normal_type == \\\"pred\\\"\\n and other.cfg.normal_type == \\\"pred\\\"\\n ):\\n instance.normal_network.load_state_dict(\\n other.normal_network.state_dict()\\n )\\n return instance\\n else:\\n raise TypeError(\\n f\\\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\\\"\\n )\"\n },\n {\n \"identifier\": \"MarchingTetrahedraHelper\",\n \"path\": \"threestudio/models/isosurface.py\",\n \"snippet\": \"class MarchingTetrahedraHelper(IsosurfaceHelper):\\n def __init__(self, resolution: int, tets_path: str):\\n super().__init__()\\n self.resolution = resolution\\n self.tets_path = tets_path\\n\\n self.triangle_table: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"triangle_table\\\",\\n torch.as_tensor(\\n [\\n [-1, -1, -1, -1, -1, -1],\\n [1, 0, 2, -1, -1, -1],\\n [4, 0, 3, -1, -1, -1],\\n [1, 4, 2, 1, 3, 4],\\n [3, 1, 5, -1, -1, -1],\\n [2, 3, 0, 2, 5, 3],\\n [1, 4, 0, 1, 5, 4],\\n [4, 2, 5, -1, -1, -1],\\n [4, 5, 2, -1, -1, -1],\\n [4, 1, 0, 4, 5, 1],\\n [3, 2, 0, 3, 5, 2],\\n [1, 3, 5, -1, -1, -1],\\n [4, 1, 2, 4, 3, 1],\\n [3, 0, 4, -1, -1, -1],\\n [2, 0, 1, -1, -1, -1],\\n [-1, -1, -1, -1, -1, -1],\\n ],\\n dtype=torch.long,\\n ),\\n persistent=False,\\n )\\n self.num_triangles_table: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"num_triangles_table\\\",\\n torch.as_tensor(\\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\\n ),\\n persistent=False,\\n )\\n self.base_tet_edges: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"base_tet_edges\\\",\\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\\n persistent=False,\\n )\\n\\n tets = np.load(self.tets_path)\\n self._grid_vertices: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"_grid_vertices\\\",\\n torch.from_numpy(tets[\\\"vertices\\\"]).float(),\\n persistent=False,\\n )\\n self.indices: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"indices\\\", torch.from_numpy(tets[\\\"indices\\\"]).long(), persistent=False\\n )\\n\\n self._all_edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n\\n def normalize_grid_deformation(\\n self, grid_vertex_offsets: Float[Tensor, \\\"Nv 3\\\"]\\n ) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return (\\n (self.points_range[1] - self.points_range[0])\\n / (self.resolution) # half tet size is approximately 1 / self.resolution\\n * torch.tanh(grid_vertex_offsets)\\n ) # FIXME: hard-coded activation\\n\\n @property\\n def grid_vertices(self) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return self._grid_vertices\\n\\n @property\\n def all_edges(self) -> Integer[Tensor, \\\"Ne 2\\\"]:\\n if self._all_edges is None:\\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\\n edges = torch.tensor(\\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\\n dtype=torch.long,\\n device=self.indices.device,\\n )\\n _all_edges = self.indices[:, edges].reshape(-1, 2)\\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\\n self._all_edges = _all_edges\\n return self._all_edges\\n\\n def sort_edges(self, edges_ex2):\\n with torch.no_grad():\\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\\n order = order.unsqueeze(dim=1)\\n\\n a = torch.gather(input=edges_ex2, index=order, dim=1)\\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\\n\\n return torch.stack([a, b], -1)\\n\\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\\n with torch.no_grad():\\n occ_n = sdf_n > 0\\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\\n occ_sum = torch.sum(occ_fx4, -1)\\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\\n occ_sum = occ_sum[valid_tets]\\n\\n # find all vertices\\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\\n all_edges = self.sort_edges(all_edges)\\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\\n\\n unique_edges = unique_edges.long()\\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\\n mapping = (\\n torch.ones(\\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\\n )\\n * -1\\n )\\n mapping[mask_edges] = torch.arange(\\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\\n )\\n idx_map = mapping[idx_map] # map edges to verts\\n\\n interp_v = unique_edges[mask_edges]\\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\\n edges_to_interp_sdf[:, -1] *= -1\\n\\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\\n\\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\\n\\n idx_map = idx_map.reshape(-1, 6)\\n\\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\\n num_triangles = self.num_triangles_table[tetindex]\\n\\n # Generate triangle indices\\n faces = torch.cat(\\n (\\n torch.gather(\\n input=idx_map[num_triangles == 1],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\\n ).reshape(-1, 3),\\n torch.gather(\\n input=idx_map[num_triangles == 2],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\\n ).reshape(-1, 3),\\n ),\\n dim=0,\\n )\\n\\n return verts, faces\\n\\n def forward(\\n self,\\n level: Float[Tensor, \\\"N3 1\\\"],\\n deformation: Optional[Float[Tensor, \\\"N3 3\\\"]] = None,\\n ) -> Mesh:\\n if deformation is not None:\\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\\n deformation\\n )\\n else:\\n grid_vertices = self.grid_vertices\\n\\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\\n\\n mesh = Mesh(\\n v_pos=v_pos,\\n t_pos_idx=t_pos_idx,\\n # extras\\n grid_vertices=grid_vertices,\\n tet_edges=self.all_edges,\\n grid_level=level,\\n grid_deformation=deformation,\\n )\\n\\n return mesh\"\n },\n {\n \"identifier\": \"Mesh\",\n \"path\": \"threestudio/models/mesh.py\",\n \"snippet\": \"class Mesh:\\n def __init__(\\n self, v_pos: Float[Tensor, \\\"Nv 3\\\"], t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"], **kwargs\\n ) -> None:\\n self.v_pos: Float[Tensor, \\\"Nv 3\\\"] = v_pos\\n self.t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"] = t_pos_idx\\n self._v_nrm: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tng: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tex: Optional[Float[Tensor, \\\"Nt 3\\\"]] = None\\n self._t_tex_idx: Optional[Float[Tensor, \\\"Nf 3\\\"]] = None\\n self._v_rgb: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n self.extras: Dict[str, Any] = {}\\n for k, v in kwargs.items():\\n self.add_extra(k, v)\\n\\n def add_extra(self, k, v) -> None:\\n self.extras[k] = v\\n\\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\\n if self.requires_grad:\\n threestudio.debug(\\\"Mesh is differentiable, not removing outliers\\\")\\n return self\\n\\n # use trimesh to first split the mesh into connected components\\n # then remove the components with less than n_face_threshold faces\\n import trimesh\\n\\n # construct a trimesh object\\n mesh = trimesh.Trimesh(\\n vertices=self.v_pos.detach().cpu().numpy(),\\n faces=self.t_pos_idx.detach().cpu().numpy(),\\n )\\n\\n # split the mesh into connected components\\n components = mesh.split(only_watertight=False)\\n # log the number of faces in each component\\n threestudio.debug(\\n \\\"Mesh has {} components, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n\\n n_faces_threshold: int\\n if isinstance(outlier_n_faces_threshold, float):\\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\\n n_faces_threshold = int(\\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\\n )\\n else:\\n # set the threshold directly to outlier_n_faces_threshold\\n n_faces_threshold = outlier_n_faces_threshold\\n\\n # log the threshold\\n threestudio.debug(\\n \\\"Removing components with less than {} faces\\\".format(n_faces_threshold)\\n )\\n\\n # remove the components with less than n_face_threshold faces\\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\\n\\n # log the number of faces in each component after removing outliers\\n threestudio.debug(\\n \\\"Mesh has {} components after removing outliers, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n # merge the components\\n mesh = trimesh.util.concatenate(components)\\n\\n # convert back to our mesh format\\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\\n\\n clean_mesh = Mesh(v_pos, t_pos_idx)\\n # keep the extras unchanged\\n\\n if len(self.extras) > 0:\\n clean_mesh.extras = self.extras\\n threestudio.debug(\\n f\\\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\\\"\\n )\\n return clean_mesh\\n\\n @property\\n def requires_grad(self):\\n return self.v_pos.requires_grad\\n\\n @property\\n def v_nrm(self):\\n if self._v_nrm is None:\\n self._v_nrm = self._compute_vertex_normal()\\n return self._v_nrm\\n\\n @property\\n def v_tng(self):\\n if self._v_tng is None:\\n self._v_tng = self._compute_vertex_tangent()\\n return self._v_tng\\n\\n @property\\n def v_tex(self):\\n if self._v_tex is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._v_tex\\n\\n @property\\n def t_tex_idx(self):\\n if self._t_tex_idx is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._t_tex_idx\\n\\n @property\\n def v_rgb(self):\\n return self._v_rgb\\n\\n @property\\n def edges(self):\\n if self._edges is None:\\n self._edges = self._compute_edges()\\n return self._edges\\n\\n def _compute_vertex_normal(self):\\n i0 = self.t_pos_idx[:, 0]\\n i1 = self.t_pos_idx[:, 1]\\n i2 = self.t_pos_idx[:, 2]\\n\\n v0 = self.v_pos[i0, :]\\n v1 = self.v_pos[i1, :]\\n v2 = self.v_pos[i2, :]\\n\\n face_normals = torch.cross(v1 - v0, v2 - v0)\\n\\n # Splat face normals to vertices\\n v_nrm = torch.zeros_like(self.v_pos)\\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\\n\\n # Normalize, replace zero (degenerated) normals with some default value\\n v_nrm = torch.where(\\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\\n )\\n v_nrm = F.normalize(v_nrm, dim=1)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(v_nrm))\\n\\n return v_nrm\\n\\n def _compute_vertex_tangent(self):\\n vn_idx = [None] * 3\\n pos = [None] * 3\\n tex = [None] * 3\\n for i in range(0, 3):\\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\\n # t_nrm_idx is always the same as t_pos_idx\\n vn_idx[i] = self.t_pos_idx[:, i]\\n\\n tangents = torch.zeros_like(self.v_nrm)\\n tansum = torch.zeros_like(self.v_nrm)\\n\\n # Compute tangent space for each triangle\\n uve1 = tex[1] - tex[0]\\n uve2 = tex[2] - tex[0]\\n pe1 = pos[1] - pos[0]\\n pe2 = pos[2] - pos[0]\\n\\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\\n\\n # Avoid division by zero for degenerated texture coordinates\\n tang = nom / torch.where(\\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\\n )\\n\\n # Update all 3 vertices\\n for i in range(0, 3):\\n idx = vn_idx[i][:, None].repeat(1, 3)\\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\\n tansum.scatter_add_(\\n 0, idx, torch.ones_like(tang)\\n ) # tansum[n_i] = tansum[n_i] + 1\\n tangents = tangents / tansum\\n\\n # Normalize and make sure tangent is perpendicular to normal\\n tangents = F.normalize(tangents, dim=1)\\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(tangents))\\n\\n return tangents\\n\\n def _unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n threestudio.info(\\\"Using xatlas to perform UV unwrapping, may take a while ...\\\")\\n\\n import xatlas\\n\\n atlas = xatlas.Atlas()\\n atlas.add_mesh(\\n self.v_pos.detach().cpu().numpy(),\\n self.t_pos_idx.cpu().numpy(),\\n )\\n co = xatlas.ChartOptions()\\n po = xatlas.PackOptions()\\n for k, v in xatlas_chart_options.items():\\n setattr(co, k, v)\\n for k, v in xatlas_pack_options.items():\\n setattr(po, k, v)\\n atlas.generate(co, po)\\n vmapping, indices, uvs = atlas.get_mesh(0)\\n vmapping = (\\n torch.from_numpy(\\n vmapping.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\\n indices = (\\n torch.from_numpy(\\n indices.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n return uvs, indices\\n\\n def unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\\n xatlas_chart_options, xatlas_pack_options\\n )\\n\\n def set_vertex_color(self, v_rgb):\\n assert v_rgb.shape[0] == self.v_pos.shape[0]\\n self._v_rgb = v_rgb\\n\\n def _compute_edges(self):\\n # Compute edges\\n edges = torch.cat(\\n [\\n self.t_pos_idx[:, [0, 1]],\\n self.t_pos_idx[:, [1, 2]],\\n self.t_pos_idx[:, [2, 0]],\\n ],\\n dim=0,\\n )\\n edges = edges.sort()[0]\\n edges = torch.unique(edges, dim=0)\\n return edges\\n\\n def normal_consistency(self) -> Float[Tensor, \\\"\\\"]:\\n edge_nrm: Float[Tensor, \\\"Ne 2 3\\\"] = self.v_nrm[self.edges]\\n nc = (\\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\\n ).mean()\\n return nc\\n\\n def _laplacian_uniform(self):\\n # from stable-dreamfusion\\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\\n verts, faces = self.v_pos, self.t_pos_idx\\n\\n V = verts.shape[0]\\n F = faces.shape[0]\\n\\n # Neighbor indices\\n ii = faces[:, [1, 2, 0]].flatten()\\n jj = faces[:, [2, 0, 1]].flatten()\\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\\n dim=1\\n )\\n adj_values = torch.ones(adj.shape[1]).to(verts)\\n\\n # Diagonal indices\\n diag_idx = adj[0]\\n\\n # Build the sparse matrix\\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\\n values = torch.cat((-adj_values, adj_values))\\n\\n # The coalesce operation sums the duplicate indices, resulting in the\\n # correct diagonal\\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\\n\\n def laplacian(self) -> Float[Tensor, \\\"\\\"]:\\n with torch.no_grad():\\n L = self._laplacian_uniform()\\n loss = L.mm(self.v_pos)\\n loss = loss.norm(dim=1)\\n loss = loss.mean()\\n return loss\"\n },\n {\n \"identifier\": \"get_encoding\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_encoding(n_input_dims: int, config) -> nn.Module:\\n # input suppose to be range [0, 1]\\n encoding: nn.Module\\n if config.otype == \\\"ProgressiveBandFrequency\\\":\\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\\n elif config.otype == \\\"ProgressiveBandHashGrid\\\":\\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\\n else:\\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\\n encoding = CompositeEncoding(\\n encoding,\\n include_xyz=config.get(\\\"include_xyz\\\", False),\\n xyz_scale=2.0,\\n xyz_offset=-1.0,\\n ) # FIXME: hard coded\\n return encoding\"\n },\n {\n \"identifier\": \"get_mlp\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\\n network: nn.Module\\n if config.otype == \\\"VanillaMLP\\\":\\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\\n elif config.otype == \\\"SphereInitVanillaMLP\\\":\\n network = SphereInitVanillaMLP(\\n n_input_dims, n_output_dims, config_to_primitive(config)\\n )\\n else:\\n assert (\\n config.get(\\\"sphere_init\\\", False) is False\\n ), \\\"sphere_init=True only supported by VanillaMLP\\\"\\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\\n return network\"\n },\n {\n \"identifier\": \"broadcast\",\n \"path\": \"threestudio/utils/misc.py\",\n \"snippet\": \"def broadcast(tensor, src=0):\\n if not _distributed_available():\\n return tensor\\n else:\\n torch.distributed.broadcast(tensor, src=src)\\n return tensor\"\n },\n {\n \"identifier\": \"scale_tensor\",\n \"path\": \"threestudio/utils/ops.py\",\n \"snippet\": \"def scale_tensor(\\n dat: Num[Tensor, \\\"... D\\\"], inp_scale: ValidScale, tgt_scale: ValidScale\\n):\\n if inp_scale is None:\\n inp_scale = (0, 1)\\n if tgt_scale is None:\\n tgt_scale = (0, 1)\\n if isinstance(tgt_scale, Tensor):\\n assert dat.shape[-1] == tgt_scale.shape[-1]\\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\\n return dat\"\n },\n {\n \"identifier\": \"GeodreamGeometryVolume\",\n \"path\": \"threestudio/models/geometry/geodream_geometry_volume.py\",\n \"snippet\": \"class GeodreamGeometryVolume(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n density_activation: Optional[str] = \\\"softplus\\\"\\n density_bias: Union[float, str] = \\\"blob_magic3d\\\"\\n density_blob_scale: float = 10.0\\n density_blob_std: float = 0.5\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: float = 0.01\\n\\n # automatically determine the threshold\\n isosurface_threshold: Union[float, str] = 25.0\\n init_volume_path: str = \\\"con_volume_lod0.pth\\\"\\n one2345_weight: str = \\\"pretrain.pth\\\"\\n sdf_network_grad: bool = False\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n \\n \\n self.sdf_layers = SdfLayer()\\n self.deviation_network = SingleVarianceNetwork(self.cfg.one2345_weight)\\n\\n # sdf_layers weight\\n sdf_layers_weight = torch.load(self.cfg.one2345_weight)['sdf_network_lod0']\\n selected_state_dict = {}\\n prefix = 'sdf_layer'\\n for key, value in sdf_layers_weight.items():\\n if key.startswith(prefix):\\n selected_state_dict[key[10:]] = value# key need remove sdf_layer prefix\\n self.sdf_layers.load_state_dict(selected_state_dict)\\n print(\\\"sdf_layers is loading weight at \\\" + self.cfg.one2345_weight)\\n \\n # sdf_layers freeze \\n if self.cfg.sdf_network_grad:\\n print(\\\"sdf_layers network is training\\\")\\n else:\\n for p in self.sdf_layers.parameters():\\n p.requires_grad_(False)\\n print(\\\"sdf_layers network is freezeing\\\")\\n\\n # volume weight\\n volume_weight = torch.load(self.cfg.init_volume_path)\\n\\n self.volume = nn.Parameter(volume_weight, requires_grad=True)\\n print(\\\"volume network is loading weight at \\\" + self.cfg.init_volume_path)\\n\\n def get_activated_density(\\n self, points: Float[Tensor, \\\"*N Di\\\"], density: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Float[Tensor, \\\"*N 1\\\"]]:\\n density_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.density_bias == \\\"blob_dreamfusion\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * torch.exp(\\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\\n )[..., None]\\n )\\n elif self.cfg.density_bias == \\\"blob_magic3d\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * (\\n 1\\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\\n )[..., None]\\n )\\n elif isinstance(self.cfg.density_bias, float):\\n density_bias = self.cfg.density_bias\\n else:\\n raise ValueError(f\\\"Unknown density bias {self.cfg.density_bias}\\\")\\n raw_density: Float[Tensor, \\\"*N 1\\\"] = density + density_bias\\n density = get_activation(self.cfg.density_activation)(raw_density)\\n return raw_density, density\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], viewdirs, dists, output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n \\n sdf, feature_vector = self.sdf(points.view(-1, self.cfg.n_input_dims))\\n\\n output = {\\n \\\"density\\\": sdf,\\n }\\n \\n g = self.gradient(points.view(-1, self.cfg.n_input_dims))\\n alphas = self.get_alpha(points.view(-1, self.cfg.n_input_dims), viewdirs, dists, feature_vector, sdf, g)\\n output.update({\\\"ALPHA\\\": alphas})\\n\\n \\n points_norm = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n \\n enc = self.encoding(points_norm.view(-1, self.cfg.n_input_dims))\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n \\n torch.set_grad_enabled(grad_enabled)\\n return output\\n\\n def forward_density(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n density, _ = self.sdf(points.view(-1, self.cfg.n_input_dims))\\n density = density.reshape(*points.shape[:-1], 1)\\n return density\\n \\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n sdf, _ = self.sdf(points.view(-1, self.cfg.n_input_dims))\\n sdf = sdf.reshape(*points.shape[:-1], 1)\\n deformation: Optional[Float[Tensor, \\\"*N 3\\\"]] = None\\n return sdf, deformation\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return field - threshold\\n \\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry,\\n cfg: Optional[Union[dict, DictConfig]] = None,\\n copy_net: bool = True,\\n **kwargs,\\n ) -> \\\"GeodreamGeometryVolume\\\":\\n if isinstance(other, GeodreamGeometryVolume):\\n instance = GeodreamGeometryVolume(cfg, **kwargs)\\n instance.encoding.load_state_dict(other.encoding.state_dict())\\n instance.density_network.load_state_dict(other.density_network.state_dict())\\n if copy_net:\\n if (\\n instance.cfg.n_feature_dims > 0\\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\\n ):\\n instance.feature_network.load_state_dict(\\n other.feature_network.state_dict()\\n )\\n if (\\n instance.cfg.normal_type == \\\"pred\\\"\\n and other.cfg.normal_type == \\\"pred\\\"\\n ):\\n instance.normal_network.load_state_dict(\\n other.normal_network.state_dict()\\n )\\n return instance\\n else:\\n raise TypeError(\\n f\\\"Cannot create {GeodreamGeometryVolume.__name__} from {other.__class__.__name__}\\\"\\n )\\n \\n def forward_sdf(self, pts):\\n sdf, _ = self.sdf(pts)\\n return sdf\\n \\n def sdf(self, pts, lod=0):\\n conditional_volume = self.volume\\n num_pts = pts.shape[0]\\n device = pts.device\\n pts_ = pts.clone()\\n pts = pts.view(1, 1, 1, num_pts, 3) # - should be in range (-1, 1)\\n\\n pts = torch.flip(pts, dims=[-1])\\n sampled_feature = grid_sample_3d(conditional_volume, pts) # [1, c, 1, 1, num_pts]\\n sampled_feature = sampled_feature.view(-1, num_pts).permute(1, 0).contiguous().to(device)\\n\\n sdf_pts = self.sdf_layers(pts_, sampled_feature)\\n\\n return sdf_pts[:, :1], sdf_pts[:, 1:]\\n \\n def get_alpha(self, ray_samples, rays_d, dists, feature_vector, sdf=None, gradients=None):\\n \\\"\\\"\\\"compute alpha from sdf as in NeuS\\\"\\\"\\\"\\n inv_variance = self.deviation_network(feature_vector)[:, :1].clip(1e-6, 1e6) # Single parameter\\n\\n \\n #gradients = torch.ones_like(rays_d, requires_grad=False, device=rays_d.device)\\n true_dot_val = (rays_d * gradients).sum(-1, keepdim=True) # * calculate\\n alpha_inter_ratio = 0.0 \\n iter_cos = -(F.relu(-true_dot_val * 0.5 + 0.5) * (1.0 - alpha_inter_ratio) + F.relu(\\n -true_dot_val) * alpha_inter_ratio) # always non-positive\\n\\n true_estimate_sdf_half_next = sdf + iter_cos.clip(-10.0, 10.0) * dists.reshape(-1, 1) * 0.5\\n true_estimate_sdf_half_prev = sdf - iter_cos.clip(-10.0, 10.0) * dists.reshape(-1, 1) * 0.5\\n\\n prev_cdf = torch.sigmoid(true_estimate_sdf_half_prev * inv_variance)\\n next_cdf = torch.sigmoid(true_estimate_sdf_half_next * inv_variance)\\n\\n p = prev_cdf - next_cdf\\n c = prev_cdf\\n\\n alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0)\\n\\n return alpha\\n \\n def gradient(self, x):\\n \\n x.requires_grad_(True)\\n with torch.enable_grad():\\n sdf, _ = self.sdf(x)\\n y = sdf\\n\\n d_output = torch.ones_like(y, requires_grad=False, device=y.device)\\n # ! Distributed Data Parallel doesn’t work with torch.autograd.grad()\\n # ! (i.e. it will only work if gradients are to be accumulated in .grad attributes of parameters).\\n gradients = torch.autograd.grad(\\n outputs=y,\\n inputs=x,\\n grad_outputs=d_output,\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True)[0]\\n return gradients\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport threestudio\n import trimesh\nfrom dataclasses import dataclass, field\nfrom threestudio.models.geometry.base import (\n BaseExplicitGeometry,\n BaseGeometry,\n contract_to_unisphere,\n)\nfrom threestudio.models.geometry.implicit_sdf import ImplicitSDF\nfrom threestudio.models.geometry.implicit_volume import ImplicitVolume\nfrom threestudio.models.isosurface import MarchingTetrahedraHelper\nfrom threestudio.models.mesh import Mesh\nfrom threestudio.models.networks import get_encoding, get_mlp\nfrom threestudio.utils.misc import broadcast\nfrom threestudio.utils.ops import scale_tensor\nfrom threestudio.models.geometry.geodream_geometry_volume import GeodreamGeometryVolume\nfrom threestudio.utils.typing import *\n from pysdf import SDF"},"token_num":{"kind":"number","value":18340,"string":"18,340"},"cropped_code":{"kind":"string","value":" mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n return {\"features\": features}\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"TetrahedraSDFGrid\":\n if isinstance(other, TetrahedraSDFGrid):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution\n instance.isosurface_bbox = other.isosurface_bbox.clone()\n instance.sdf.data = other.sdf.data.clone()\n if (\n instance.cfg.isosurface_deformable_grid\n and other.cfg.isosurface_deformable_grid\n ):\n assert (\n instance.deformation is not None and other.deformation is not None\n )\n instance.deformation.data = other.deformation.data.clone()\n if (\n not instance.cfg.geometry_only\n and not other.cfg.geometry_only\n and copy_net\n ):\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n elif isinstance(other, ImplicitVolume):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n if other.cfg.isosurface_method != \"mt\":\n other.cfg.isosurface_method = \"mt\"\n threestudio.warn(\n f\"Override isosurface_method of the source geometry to 'mt'\"\n )\n if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:\n other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution\n threestudio.warn(\n f\"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}\"\n )\n mesh = other.isosurface()\n instance.isosurface_bbox = mesh.extras[\"bbox\"]\n instance.sdf.data = (\n mesh.extras[\"grid_level\"].to(instance.sdf.data).clamp(-1, 1)\n )\n if not instance.cfg.geometry_only and copy_net:\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n"},"all_code":{"kind":"string","value":"\n\n\n\n@threestudio.register(\"tetrahedra-sdf-grid\")\nclass TetrahedraSDFGrid(BaseExplicitGeometry):\n @dataclass\n class Config(BaseExplicitGeometry.Config):\n isosurface_resolution: int = 128\n isosurface_deformable_grid: bool = True\n isosurface_remove_outliers: bool = False\n isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01\n\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n geometry_only: bool = False\n fix_geometry: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n\n # this should be saved to state_dict, register as buffer\n self.isosurface_bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\"isosurface_bbox\", self.bbox.clone())\n\n self.isosurface_helper = MarchingTetrahedraHelper(\n self.cfg.isosurface_resolution,\n f\"load/tets/{self.cfg.isosurface_resolution}_tets.npz\",\n )\n\n self.sdf: Float[Tensor, \"Nv 1\"]\n self.deformation: Optional[Float[Tensor, \"Nv 3\"]]\n\n if not self.cfg.fix_geometry:\n self.register_parameter(\n \"sdf\",\n nn.Parameter(\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n )\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_parameter(\n \"deformation\",\n nn.Parameter(\n torch.zeros_like(self.isosurface_helper.grid_vertices)\n ),\n )\n else:\n self.deformation = None\n else:\n self.register_buffer(\n \"sdf\",\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_buffer(\n \"deformation\",\n torch.zeros_like(self.isosurface_helper.grid_vertices),\n )\n else:\n self.deformation = None\n\n if not self.cfg.geometry_only:\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n self.mesh: Optional[Mesh] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n return {\"features\": features}\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"TetrahedraSDFGrid\":\n if isinstance(other, TetrahedraSDFGrid):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution\n instance.isosurface_bbox = other.isosurface_bbox.clone()\n instance.sdf.data = other.sdf.data.clone()\n if (\n instance.cfg.isosurface_deformable_grid\n and other.cfg.isosurface_deformable_grid\n ):\n assert (\n instance.deformation is not None and other.deformation is not None\n )\n instance.deformation.data = other.deformation.data.clone()\n if (\n not instance.cfg.geometry_only\n and not other.cfg.geometry_only\n and copy_net\n ):\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n elif isinstance(other, ImplicitVolume):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n if other.cfg.isosurface_method != \"mt\":\n other.cfg.isosurface_method = \"mt\"\n threestudio.warn(\n f\"Override isosurface_method of the source geometry to 'mt'\"\n )\n if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:\n other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution\n threestudio.warn(\n f\"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}\"\n )\n mesh = other.isosurface()\n instance.isosurface_bbox = mesh.extras[\"bbox\"]\n instance.sdf.data = (\n mesh.extras[\"grid_level\"].to(instance.sdf.data).clamp(-1, 1)\n )\n if not instance.cfg.geometry_only and copy_net:\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance"},"next_line":{"kind":"string","value":" elif isinstance(other, ImplicitSDF) or isinstance(other, GeodreamGeometryVolume):"},"gold_snippet_index":{"kind":"number","value":11,"string":"11"},"created_at":{"kind":"string","value":"2023-12-01 01:59:42+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5370,"cells":{"repo_name":{"kind":"string","value":"horseee/DeepCache"},"file_path":{"kind":"string","value":"DeepCache/sd/pipeline_text_to_video_zero.py"},"context":{"kind":"list like","value":[{"identifier":"UNet2DConditionModel","path":"DeepCache/sd/unet_2d_condition.py","snippet":"class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\n flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock2DCrossAttn\"`):\n Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or\n `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")`):\n The tuple of upsample blocks to use.\n only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):\n Whether to include self-attention in the basic transformer blocks, see\n [`~models.attention.BasicTransformerBlock`].\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n encoder_hid_dim (`int`, *optional*, defaults to None):\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n dimension to `cross_attention_dim`.\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n num_attention_heads (`int`, *optional*):\n The number of attention heads. If not defined, defaults to `attention_head_dim`\n resnet_time_scale_shift (`str`, *optional*, defaults to `\"default\"`): Time scale shift config\n for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n addition_embed_type (`str`, *optional*, defaults to `None`):\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n \"text\". \"text\" will use the `TextTimeEmbedding` layer.\n addition_time_embed_dim: (`int`, *optional*, defaults to `None`):\n Dimension for the timestep embeddings.\n num_class_embeds (`int`, *optional*, defaults to `None`):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n time_embedding_type (`str`, *optional*, defaults to `positional`):\n The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\n time_embedding_dim (`int`, *optional*, defaults to `None`):\n An optional override for the dimension of the projected time embedding.\n time_embedding_act_fn (`str`, *optional*, defaults to `None`):\n Optional activation function to use only once on the time embeddings before they are passed to the rest of\n the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\n timestep_post_act (`str`, *optional*, defaults to `None`):\n The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n The dimension of `cond_proj` layer in the timestep embedding.\n conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.\n conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.\n projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when\n `class_embed_type=\"projection\"`. Required when `class_embed_type=\"projection\"`.\n class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\n embeddings with the class embeddings.\n mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):\n Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If\n `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the\n `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`\n otherwise.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\",\n up_block_types: Tuple[str] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: Union[int, Tuple[int]] = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n dropout: float = 0.0,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: Union[int, Tuple[int]] = 1280,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n resnet_skip_time_act: bool = False,\n resnet_out_scale_factor: int = 1.0,\n time_embedding_type: str = \"positional\",\n time_embedding_dim: Optional[int] = None,\n time_embedding_act_fn: Optional[str] = None,\n timestep_post_act: Optional[str] = None,\n time_cond_proj_dim: Optional[int] = None,\n conv_in_kernel: int = 3,\n conv_out_kernel: int = 3,\n projection_class_embeddings_input_dim: Optional[int] = None,\n attention_type: str = \"default\",\n class_embeddings_concat: bool = False,\n mid_block_only_cross_attention: Optional[bool] = None,\n cross_attention_norm: Optional[str] = None,\n addition_embed_type_num_heads=64,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n if num_attention_heads is not None:\n raise ValueError(\n \"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.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(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(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n 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}.\"\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 if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n 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}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n 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}.\"\n )\n\n # input\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n if time_embedding_type == \"fourier\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 2\n if time_embed_dim % 2 != 0:\n raise ValueError(f\"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.\")\n self.time_proj = GaussianFourierProjection(\n time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos\n )\n timestep_input_dim = time_embed_dim\n elif time_embedding_type == \"positional\":\n time_embed_dim = time_embedding_dim or 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 else:\n raise ValueError(\n f\"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`.\"\n )\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n post_act_fn=timestep_post_act,\n cond_proj_dim=time_cond_proj_dim,\n )\n\n if encoder_hid_dim_type is None and encoder_hid_dim is not None:\n encoder_hid_dim_type = \"text_proj\"\n self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)\n logger.info(\"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.\")\n\n if encoder_hid_dim is None and encoder_hid_dim_type is not None:\n raise ValueError(\n f\"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}.\"\n )\n\n if encoder_hid_dim_type == \"text_proj\":\n self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)\n elif encoder_hid_dim_type == \"text_image_proj\":\n # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image_proj\"` (Kadinsky 2.1)`\n self.encoder_hid_proj = TextImageProjection(\n text_embed_dim=encoder_hid_dim,\n image_embed_dim=cross_attention_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2\n self.encoder_hid_proj = ImageProjection(\n image_embed_dim=encoder_hid_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type is not None:\n raise ValueError(\n f\"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'.\"\n )\n else:\n self.encoder_hid_proj = None\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, act_fn=act_fn)\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 elif class_embed_type == \"simple_projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n if addition_embed_type == \"text\":\n if encoder_hid_dim is not None:\n text_time_embedding_from_dim = encoder_hid_dim\n else:\n text_time_embedding_from_dim = cross_attention_dim\n\n self.add_embedding = TextTimeEmbedding(\n text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads\n )\n elif addition_embed_type == \"text_image\":\n # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image\"` (Kadinsky 2.1)`\n self.add_embedding = TextImageTimeEmbedding(\n text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim\n )\n elif addition_embed_type == \"text_time\":\n self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif addition_embed_type == \"image\":\n # Kandinsky 2.2\n self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 ControlNet\n self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type is not None:\n raise ValueError(f\"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.\")\n\n if time_embedding_act_fn is None:\n self.time_embed_act = None\n else:\n self.time_embed_act = get_activation(time_embedding_act_fn)\n\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = only_cross_attention\n\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = False\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * 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 if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n if class_embeddings_concat:\n # The time embeddings are concatenated with the class embeddings. The dimension of the\n # time embeddings passed to the down, middle, and up blocks is twice the dimension of the\n # regular time embeddings\n blocks_time_embed_dim = time_embed_dim * 2\n else:\n blocks_time_embed_dim = time_embed_dim\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=blocks_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[i],\n num_attention_heads=num_attention_heads[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 attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock2DCrossAttn\":\n self.mid_block = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block[-1],\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n dropout=dropout,\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[-1],\n num_attention_heads=num_attention_heads[-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 attention_type=attention_type,\n )\n elif mid_block_type == \"UNetMidBlock2DSimpleCrossAttn\":\n self.mid_block = UNetMidBlock2DSimpleCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n dropout=dropout,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim[-1],\n attention_head_dim=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n only_cross_attention=mid_block_only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n )\n elif mid_block_type is None:\n self.mid_block = None\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_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n only_cross_attention = list(reversed(only_cross_attention))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=blocks_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=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[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 attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n\n self.conv_act = get_activation(act_fn)\n\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n if attention_type in [\"gated\", \"gated-text-image\"]:\n positive_len = 768\n if isinstance(cross_attention_dim, int):\n positive_len = cross_attention_dim\n elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):\n positive_len = cross_attention_dim[0]\n\n feature_type = \"text-only\" if attention_type == \"gated\" else \"text-image\"\n self.position_net = PositionNet(\n positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type\n )\n\n @property\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n def set_attn_processor(\n self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False\n ):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor, _remove_lora=_remove_lora)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"), _remove_lora=_remove_lora)\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor, _remove_lora=True)\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 splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n quick_replicate: bool = False,\n replicate_prv_feature: Optional[List[torch.Tensor]] = None,\n cache_layer_id: Optional[int] = None,\n cache_block_id: Optional[int] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`UNet2DConditionModel`] forward method.\n\n Args:\n sample (`torch.FloatTensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.FloatTensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise\n a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\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 # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample:\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=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\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 # `Timesteps` does not contain any weights and will always return f32 tensors\n # there might be better ways to encapsulate this.\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\n\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n elif self.config.addition_embed_type == \"text_image\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n text_embs = added_cond_kwargs.get(\"text_embeds\", encoder_hidden_states)\n aug_emb = self.add_embedding(text_embs, image_embs)\n elif self.config.addition_embed_type == \"text_time\":\n # SDXL - style\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n elif self.config.addition_embed_type == \"image\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n aug_emb = self.add_embedding(image_embs)\n elif self.config.addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs or \"hint\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n hint = added_cond_kwargs.get(\"hint\")\n aug_emb, hint = self.add_embedding(image_embs, hint)\n sample = torch.cat([sample, hint], dim=1)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_proj\":\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_image_proj\":\n # Kadinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(image_embeds)\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 2.5 GLIGEN position net\n if cross_attention_kwargs is not None and cross_attention_kwargs.get(\"gligen\", None) is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n gligen_args = cross_attention_kwargs.pop(\"gligen\")\n cross_attention_kwargs[\"gligen\"] = {\"objs\": self.position_net(**gligen_args)}\n\n # 3. down\n lora_scale = cross_attention_kwargs.get(\"scale\", 1.0) if cross_attention_kwargs is not None else 1.0\n\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n if quick_replicate and replicate_prv_feature is not None:\n # Down\n for i, downsample_block in enumerate(self.down_blocks):\n if i > cache_layer_id:\n break\n\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlock2D\n additional_residuals = {}\n if is_adapter and len(down_block_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_block_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n exist_block_number=cache_block_id if i == cache_layer_id else None,\n **additional_residuals,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)\n\n if is_adapter and len(down_block_additional_residuals) > 0:\n sample += down_block_additional_residuals.pop(0)\n\n down_block_res_samples += res_samples\n\n # No Middle\n # Up\n #print(\"down_block_res_samples:\", [res_sample.shape for res_sample in down_block_res_samples])\n sample = replicate_prv_feature\n #down_block_res_samples = down_block_res_samples[:-1]\n if cache_block_id == len(self.down_blocks[cache_layer_id].attentions) :\n cache_block_id = 0\n cache_layer_id += 1\n else:\n cache_block_id += 1\n\n for i, upsample_block in enumerate(self.up_blocks):\n if i < len(self.up_blocks) - 1 - cache_layer_id:\n continue\n\n if i == len(self.up_blocks) - 1 - cache_layer_id:\n trunc_upsample_block = cache_block_id + 1\n else:\n trunc_upsample_block = len(upsample_block.resnets)\n\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-trunc_upsample_block:]\n down_block_res_samples = down_block_res_samples[: -trunc_upsample_block]\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 #print(sample.shape, [res_sample.shape for res_sample in res_samples])\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 cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n enter_block_number=cache_block_id if i == len(self.up_blocks) - 1 - cache_layer_id else None,\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 scale=lora_scale,\n )\n \n prv_f = replicate_prv_feature\n else:\n for i, downsample_block in enumerate(self.down_blocks):\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlock2D\n additional_residuals = {}\n if is_adapter and len(down_block_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_block_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n **additional_residuals,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)\n\n if is_adapter and len(down_block_additional_residuals) > 0:\n sample += down_block_additional_residuals.pop(0)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n # To support T2I-Adapter-XL\n if (\n is_adapter\n and len(down_block_additional_residuals) > 0\n and sample.shape == down_block_additional_residuals[0].shape\n ):\n sample += down_block_additional_residuals.pop(0)\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n if cache_block_id is not None:\n if cache_block_id == len(self.down_blocks[cache_layer_id].attentions) :\n cache_block_id = 0\n cache_layer_id += 1\n else:\n cache_block_id += 1\n #print(\"down_block_res_samples:\", [res_sample.shape for res_sample in down_block_res_samples])\n #print(cache_block_id, cache_layer_id)\n prv_f = None\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 #print(sample.shape, [res_sample.shape for res_sample in res_samples])\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, current_record_f = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_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 scale=lora_scale,\n )\n current_record_f = None\n\n #print(\"Append prv_feature with shape:\", sample.shape)\n if cache_layer_id is not None and current_record_f is not None and i == len(self.up_blocks) - cache_layer_id - 1:\n prv_f = current_record_f[-cache_block_id-1]\n \n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n if not return_dict:\n return (sample, prv_f,)\n \n return UNet2DConditionOutput(sample=sample)"},{"identifier":"StableDiffusionPipeline","path":"DeepCache/sd/pipeline_stable_diffusion.py","snippet":"EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\ndef sample_gaussian_centered(n=1000, sample_size=100, std_dev=100):\ndef sample_from_quad(total_numbers, n_samples, pow=1.2):\ndef sample_from_quad_center(total_numbers, n_samples, center, pow=1.2):\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n def enable_vae_slicing(self):\n def disable_vae_slicing(self):\n def enable_vae_tiling(self):\n def disable_vae_tiling(self):\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n ):\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n ):\n def run_safety_checker(self, image, device, dtype):\n def decode_latents(self, latents):\n def prepare_extra_step_kwargs(self, generator, eta):\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n cache_interval: int = 1,\n cache_layer_id: int = None,\n cache_block_id: int = None,\n uniform: bool = True,\n pow: float = None,\n center: int = None,\n output_all_sequence: bool = False,\n ):\nclass StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin):"}],"string":"[\n {\n \"identifier\": \"UNet2DConditionModel\",\n \"path\": \"DeepCache/sd/unet_2d_condition.py\",\n \"snippet\": \"class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\\n r\\\"\\\"\\\"\\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\\n shaped output.\\n\\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\\n for all models (such as downloading or saving).\\n\\n Parameters:\\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\\n Height and width of input/output sample.\\n in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\\n center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\\n flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\\n Whether to flip the sin to cos in the time embedding.\\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\\\"CrossAttnDownBlock2D\\\", \\\"CrossAttnDownBlock2D\\\", \\\"CrossAttnDownBlock2D\\\", \\\"DownBlock2D\\\")`):\\n The tuple of downsample blocks to use.\\n mid_block_type (`str`, *optional*, defaults to `\\\"UNetMidBlock2DCrossAttn\\\"`):\\n Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or\\n `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.\\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\\\"UpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\")`):\\n The tuple of upsample blocks to use.\\n only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):\\n Whether to include self-attention in the basic transformer blocks, see\\n [`~models.attention.BasicTransformerBlock`].\\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\\n The tuple of output channels for each block.\\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\\n act_fn (`str`, *optional*, defaults to `\\\"silu\\\"`): The activation function to use.\\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\\n If `None`, normalization and activation layers is skipped in post-processing.\\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\\n The dimension of the cross attention features.\\n transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):\\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\\n [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\\n encoder_hid_dim (`int`, *optional*, defaults to None):\\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\\n dimension to `cross_attention_dim`.\\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\\n num_attention_heads (`int`, *optional*):\\n The number of attention heads. If not defined, defaults to `attention_head_dim`\\n resnet_time_scale_shift (`str`, *optional*, defaults to `\\\"default\\\"`): Time scale shift config\\n for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\\n class_embed_type (`str`, *optional*, defaults to `None`):\\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\\n `\\\"timestep\\\"`, `\\\"identity\\\"`, `\\\"projection\\\"`, or `\\\"simple_projection\\\"`.\\n addition_embed_type (`str`, *optional*, defaults to `None`):\\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\\n \\\"text\\\". \\\"text\\\" will use the `TextTimeEmbedding` layer.\\n addition_time_embed_dim: (`int`, *optional*, defaults to `None`):\\n Dimension for the timestep embeddings.\\n num_class_embeds (`int`, *optional*, defaults to `None`):\\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\\n class conditioning with `class_embed_type` equal to `None`.\\n time_embedding_type (`str`, *optional*, defaults to `positional`):\\n The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\\n time_embedding_dim (`int`, *optional*, defaults to `None`):\\n An optional override for the dimension of the projected time embedding.\\n time_embedding_act_fn (`str`, *optional*, defaults to `None`):\\n Optional activation function to use only once on the time embeddings before they are passed to the rest of\\n the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\\n timestep_post_act (`str`, *optional*, defaults to `None`):\\n The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\\n The dimension of `cond_proj` layer in the timestep embedding.\\n conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.\\n conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.\\n projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when\\n `class_embed_type=\\\"projection\\\"`. Required when `class_embed_type=\\\"projection\\\"`.\\n class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\\n embeddings with the class embeddings.\\n mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):\\n Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If\\n `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the\\n `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`\\n otherwise.\\n \\\"\\\"\\\"\\n\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n center_input_sample: bool = False,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"DownBlock2D\\\",\\n ),\\n mid_block_type: Optional[str] = \\\"UNetMidBlock2DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\\"UpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\"),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: Union[int, Tuple[int]] = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n dropout: float = 0.0,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: Optional[int] = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: Union[int, Tuple[int]] = 1280,\\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\\n encoder_hid_dim: Optional[int] = None,\\n encoder_hid_dim_type: Optional[str] = None,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\\n dual_cross_attention: bool = False,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n addition_embed_type: Optional[str] = None,\\n addition_time_embed_dim: Optional[int] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n resnet_skip_time_act: bool = False,\\n resnet_out_scale_factor: int = 1.0,\\n time_embedding_type: str = \\\"positional\\\",\\n time_embedding_dim: Optional[int] = None,\\n time_embedding_act_fn: Optional[str] = None,\\n timestep_post_act: Optional[str] = None,\\n time_cond_proj_dim: Optional[int] = None,\\n conv_in_kernel: int = 3,\\n conv_out_kernel: int = 3,\\n projection_class_embeddings_input_dim: Optional[int] = None,\\n attention_type: str = \\\"default\\\",\\n class_embeddings_concat: bool = False,\\n mid_block_only_cross_attention: Optional[bool] = None,\\n cross_attention_norm: Optional[str] = None,\\n addition_embed_type_num_heads=64,\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n\\n if num_attention_heads is not None:\\n raise ValueError(\\n \\\"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.\\\"\\n )\\n\\n # If `num_attention_heads` is not defined (which is the case for most models)\\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\\n # which is why we correct for the naming here.\\n num_attention_heads = num_attention_heads or attention_head_dim\\n\\n # Check inputs\\n if len(down_block_types) != len(up_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\\\"\\n )\\n\\n if len(block_out_channels) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(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(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\\n raise ValueError(\\n 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}.\\\"\\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 if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\\n raise ValueError(\\n 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}.\\\"\\n )\\n\\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\\n raise ValueError(\\n 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}.\\\"\\n )\\n\\n # input\\n conv_in_padding = (conv_in_kernel - 1) // 2\\n self.conv_in = nn.Conv2d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n\\n # time\\n if time_embedding_type == \\\"fourier\\\":\\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 2\\n if time_embed_dim % 2 != 0:\\n raise ValueError(f\\\"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.\\\")\\n self.time_proj = GaussianFourierProjection(\\n time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos\\n )\\n timestep_input_dim = time_embed_dim\\n elif time_embedding_type == \\\"positional\\\":\\n time_embed_dim = time_embedding_dim or 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 else:\\n raise ValueError(\\n f\\\"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`.\\\"\\n )\\n\\n self.time_embedding = TimestepEmbedding(\\n timestep_input_dim,\\n time_embed_dim,\\n act_fn=act_fn,\\n post_act_fn=timestep_post_act,\\n cond_proj_dim=time_cond_proj_dim,\\n )\\n\\n if encoder_hid_dim_type is None and encoder_hid_dim is not None:\\n encoder_hid_dim_type = \\\"text_proj\\\"\\n self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)\\n logger.info(\\\"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.\\\")\\n\\n if encoder_hid_dim is None and encoder_hid_dim_type is not None:\\n raise ValueError(\\n f\\\"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}.\\\"\\n )\\n\\n if encoder_hid_dim_type == \\\"text_proj\\\":\\n self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)\\n elif encoder_hid_dim_type == \\\"text_image_proj\\\":\\n # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much\\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\\n # case when `addition_embed_type == \\\"text_image_proj\\\"` (Kadinsky 2.1)`\\n self.encoder_hid_proj = TextImageProjection(\\n text_embed_dim=encoder_hid_dim,\\n image_embed_dim=cross_attention_dim,\\n cross_attention_dim=cross_attention_dim,\\n )\\n elif encoder_hid_dim_type == \\\"image_proj\\\":\\n # Kandinsky 2.2\\n self.encoder_hid_proj = ImageProjection(\\n image_embed_dim=encoder_hid_dim,\\n cross_attention_dim=cross_attention_dim,\\n )\\n elif encoder_hid_dim_type is not None:\\n raise ValueError(\\n f\\\"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'.\\\"\\n )\\n else:\\n self.encoder_hid_proj = None\\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, act_fn=act_fn)\\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 elif class_embed_type == \\\"simple_projection\\\":\\n if projection_class_embeddings_input_dim is None:\\n raise ValueError(\\n \\\"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set\\\"\\n )\\n self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n if addition_embed_type == \\\"text\\\":\\n if encoder_hid_dim is not None:\\n text_time_embedding_from_dim = encoder_hid_dim\\n else:\\n text_time_embedding_from_dim = cross_attention_dim\\n\\n self.add_embedding = TextTimeEmbedding(\\n text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads\\n )\\n elif addition_embed_type == \\\"text_image\\\":\\n # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much\\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\\n # case when `addition_embed_type == \\\"text_image\\\"` (Kadinsky 2.1)`\\n self.add_embedding = TextImageTimeEmbedding(\\n text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim\\n )\\n elif addition_embed_type == \\\"text_time\\\":\\n self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)\\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\\n elif addition_embed_type == \\\"image\\\":\\n # Kandinsky 2.2\\n self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\\n elif addition_embed_type == \\\"image_hint\\\":\\n # Kandinsky 2.2 ControlNet\\n self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\\n elif addition_embed_type is not None:\\n raise ValueError(f\\\"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.\\\")\\n\\n if time_embedding_act_fn is None:\\n self.time_embed_act = None\\n else:\\n self.time_embed_act = get_activation(time_embedding_act_fn)\\n\\n self.down_blocks = nn.ModuleList([])\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n if mid_block_only_cross_attention is None:\\n mid_block_only_cross_attention = only_cross_attention\\n\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if mid_block_only_cross_attention is None:\\n mid_block_only_cross_attention = False\\n\\n if isinstance(num_attention_heads, int):\\n num_attention_heads = (num_attention_heads,) * 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 if isinstance(cross_attention_dim, int):\\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\\n\\n if isinstance(layers_per_block, int):\\n layers_per_block = [layers_per_block] * len(down_block_types)\\n\\n if isinstance(transformer_layers_per_block, int):\\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\\n\\n if class_embeddings_concat:\\n # The time embeddings are concatenated with the class embeddings. The dimension of the\\n # time embeddings passed to the down, middle, and up blocks is twice the dimension of the\\n # regular time embeddings\\n blocks_time_embed_dim = time_embed_dim * 2\\n else:\\n blocks_time_embed_dim = time_embed_dim\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block[i],\\n transformer_layers_per_block=transformer_layers_per_block[i],\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=blocks_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[i],\\n num_attention_heads=num_attention_heads[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 attention_type=attention_type,\\n resnet_skip_time_act=resnet_skip_time_act,\\n resnet_out_scale_factor=resnet_out_scale_factor,\\n cross_attention_norm=cross_attention_norm,\\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\\n dropout=dropout,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n if mid_block_type == \\\"UNetMidBlock2DCrossAttn\\\":\\n self.mid_block = UNetMidBlock2DCrossAttn(\\n transformer_layers_per_block=transformer_layers_per_block[-1],\\n in_channels=block_out_channels[-1],\\n temb_channels=blocks_time_embed_dim,\\n dropout=dropout,\\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[-1],\\n num_attention_heads=num_attention_heads[-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 attention_type=attention_type,\\n )\\n elif mid_block_type == \\\"UNetMidBlock2DSimpleCrossAttn\\\":\\n self.mid_block = UNetMidBlock2DSimpleCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=blocks_time_embed_dim,\\n dropout=dropout,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n cross_attention_dim=cross_attention_dim[-1],\\n attention_head_dim=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n skip_time_act=resnet_skip_time_act,\\n only_cross_attention=mid_block_only_cross_attention,\\n cross_attention_norm=cross_attention_norm,\\n )\\n elif mid_block_type is None:\\n self.mid_block = None\\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_num_attention_heads = list(reversed(num_attention_heads))\\n reversed_layers_per_block = list(reversed(layers_per_block))\\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\\n only_cross_attention = list(reversed(only_cross_attention))\\n\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=reversed_layers_per_block[i] + 1,\\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=blocks_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=reversed_cross_attention_dim[i],\\n num_attention_heads=reversed_num_attention_heads[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 attention_type=attention_type,\\n resnet_skip_time_act=resnet_skip_time_act,\\n resnet_out_scale_factor=resnet_out_scale_factor,\\n cross_attention_norm=cross_attention_norm,\\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\\n dropout=dropout,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n if norm_num_groups is not None:\\n self.conv_norm_out = nn.GroupNorm(\\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\\n )\\n\\n self.conv_act = get_activation(act_fn)\\n\\n else:\\n self.conv_norm_out = None\\n self.conv_act = None\\n\\n conv_out_padding = (conv_out_kernel - 1) // 2\\n self.conv_out = nn.Conv2d(\\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\\n )\\n\\n if attention_type in [\\\"gated\\\", \\\"gated-text-image\\\"]:\\n positive_len = 768\\n if isinstance(cross_attention_dim, int):\\n positive_len = cross_attention_dim\\n elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):\\n positive_len = cross_attention_dim[0]\\n\\n feature_type = \\\"text-only\\\" if attention_type == \\\"gated\\\" else \\\"text-image\\\"\\n self.position_net = PositionNet(\\n positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type\\n )\\n\\n @property\\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\\n r\\\"\\\"\\\"\\n Returns:\\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\\n indexed by its weight name.\\n \\\"\\\"\\\"\\n # set recursively\\n processors = {}\\n\\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\\n if hasattr(module, \\\"get_processor\\\"):\\n processors[f\\\"{name}.processor\\\"] = module.get_processor(return_deprecated_lora=True)\\n\\n for sub_name, child in module.named_children():\\n fn_recursive_add_processors(f\\\"{name}.{sub_name}\\\", child, processors)\\n\\n return processors\\n\\n for name, module in self.named_children():\\n fn_recursive_add_processors(name, module, processors)\\n\\n return processors\\n\\n def set_attn_processor(\\n self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False\\n ):\\n r\\\"\\\"\\\"\\n Sets the attention processor to use to compute attention.\\n\\n Parameters:\\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\\n for **all** `Attention` layers.\\n\\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\\n processor. This is strongly recommended when setting trainable attention processors.\\n\\n \\\"\\\"\\\"\\n count = len(self.attn_processors.keys())\\n\\n if isinstance(processor, dict) and len(processor) != count:\\n raise ValueError(\\n f\\\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\\\"\\n f\\\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\\\"\\n )\\n\\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\\n if hasattr(module, \\\"set_processor\\\"):\\n if not isinstance(processor, dict):\\n module.set_processor(processor, _remove_lora=_remove_lora)\\n else:\\n module.set_processor(processor.pop(f\\\"{name}.processor\\\"), _remove_lora=_remove_lora)\\n\\n for sub_name, child in module.named_children():\\n fn_recursive_attn_processor(f\\\"{name}.{sub_name}\\\", child, processor)\\n\\n for name, module in self.named_children():\\n fn_recursive_attn_processor(name, module, processor)\\n\\n def set_default_attn_processor(self):\\n \\\"\\\"\\\"\\n Disables custom attention processors and sets the default attention implementation.\\n \\\"\\\"\\\"\\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\\n processor = AttnAddedKVProcessor()\\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\\n processor = AttnProcessor()\\n else:\\n raise ValueError(\\n f\\\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\\\"\\n )\\n\\n self.set_attn_processor(processor, _remove_lora=True)\\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 splits the input tensor in slices to compute attention in\\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, input to the attention heads is halved, so attention is computed in two steps. If\\n `\\\"max\\\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_sliceable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_sliceable_dims(module)\\n\\n num_sliceable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_sliceable_layers * [1]\\n\\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if hasattr(module, \\\"gradient_checkpointing\\\"):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n timestep_cond: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n encoder_attention_mask: Optional[torch.Tensor] = None,\\n quick_replicate: bool = False,\\n replicate_prv_feature: Optional[List[torch.Tensor]] = None,\\n cache_layer_id: Optional[int] = None,\\n cache_block_id: Optional[int] = None,\\n return_dict: bool = True,\\n ) -> Union[UNet2DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n The [`UNet2DConditionModel`] forward method.\\n\\n Args:\\n sample (`torch.FloatTensor`):\\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\\n timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.\\n encoder_hidden_states (`torch.FloatTensor`):\\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\\n encoder_attention_mask (`torch.Tensor`):\\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\\n which adds large negative values to the attention scores corresponding to \\\"discard\\\" tokens.\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\\n tuple.\\n cross_attention_kwargs (`dict`, *optional*):\\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\\n added_cond_kwargs: (`dict`, *optional*):\\n A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that\\n are passed along to the UNet blocks.\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\\n If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise\\n a `tuple` is returned where the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\\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 # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\\n # expects mask of shape:\\n # [batch, key_tokens]\\n # adds singleton query_tokens dimension:\\n # [batch, 1, key_tokens]\\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\\n if attention_mask is not None:\\n # assume that mask is expressed as:\\n # (1 = keep, 0 = discard)\\n # convert mask into a bias that can be added to attention scores:\\n # (keep = +0, discard = -10000.0)\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\\n if encoder_attention_mask is not None:\\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\\n\\n # 0. center input if necessary\\n if self.config.center_input_sample:\\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=sample.dtype)\\n\\n emb = self.time_embedding(t_emb, timestep_cond)\\n aug_emb = None\\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 # `Timesteps` does not contain any weights and will always return f32 tensors\\n # there might be better ways to encapsulate this.\\n class_labels = class_labels.to(dtype=sample.dtype)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\\n\\n if self.config.class_embeddings_concat:\\n emb = torch.cat([emb, class_emb], dim=-1)\\n else:\\n emb = emb + class_emb\\n\\n if self.config.addition_embed_type == \\\"text\\\":\\n aug_emb = self.add_embedding(encoder_hidden_states)\\n elif self.config.addition_embed_type == \\\"text_image\\\":\\n # Kandinsky 2.1 - style\\n if \\\"image_embeds\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\\\"\\n )\\n\\n image_embs = added_cond_kwargs.get(\\\"image_embeds\\\")\\n text_embs = added_cond_kwargs.get(\\\"text_embeds\\\", encoder_hidden_states)\\n aug_emb = self.add_embedding(text_embs, image_embs)\\n elif self.config.addition_embed_type == \\\"text_time\\\":\\n # SDXL - style\\n if \\\"text_embeds\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\\\"\\n )\\n text_embeds = added_cond_kwargs.get(\\\"text_embeds\\\")\\n if \\\"time_ids\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\\\"\\n )\\n time_ids = added_cond_kwargs.get(\\\"time_ids\\\")\\n time_embeds = self.add_time_proj(time_ids.flatten())\\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\\n\\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\\n add_embeds = add_embeds.to(emb.dtype)\\n aug_emb = self.add_embedding(add_embeds)\\n elif self.config.addition_embed_type == \\\"image\\\":\\n # Kandinsky 2.2 - style\\n if \\\"image_embeds\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\\\"\\n )\\n image_embs = added_cond_kwargs.get(\\\"image_embeds\\\")\\n aug_emb = self.add_embedding(image_embs)\\n elif self.config.addition_embed_type == \\\"image_hint\\\":\\n # Kandinsky 2.2 - style\\n if \\\"image_embeds\\\" not in added_cond_kwargs or \\\"hint\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`\\\"\\n )\\n image_embs = added_cond_kwargs.get(\\\"image_embeds\\\")\\n hint = added_cond_kwargs.get(\\\"hint\\\")\\n aug_emb, hint = self.add_embedding(image_embs, hint)\\n sample = torch.cat([sample, hint], dim=1)\\n\\n emb = emb + aug_emb if aug_emb is not None else emb\\n\\n if self.time_embed_act is not None:\\n emb = self.time_embed_act(emb)\\n\\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \\\"text_proj\\\":\\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \\\"text_image_proj\\\":\\n # Kadinsky 2.1 - style\\n if \\\"image_embeds\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\\\"\\n )\\n\\n image_embeds = added_cond_kwargs.get(\\\"image_embeds\\\")\\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)\\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \\\"image_proj\\\":\\n # Kandinsky 2.2 - style\\n if \\\"image_embeds\\\" not in added_cond_kwargs:\\n raise ValueError(\\n f\\\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\\\"\\n )\\n image_embeds = added_cond_kwargs.get(\\\"image_embeds\\\")\\n encoder_hidden_states = self.encoder_hid_proj(image_embeds)\\n # 2. pre-process\\n sample = self.conv_in(sample)\\n\\n # 2.5 GLIGEN position net\\n if cross_attention_kwargs is not None and cross_attention_kwargs.get(\\\"gligen\\\", None) is not None:\\n cross_attention_kwargs = cross_attention_kwargs.copy()\\n gligen_args = cross_attention_kwargs.pop(\\\"gligen\\\")\\n cross_attention_kwargs[\\\"gligen\\\"] = {\\\"objs\\\": self.position_net(**gligen_args)}\\n\\n # 3. down\\n lora_scale = cross_attention_kwargs.get(\\\"scale\\\", 1.0) if cross_attention_kwargs is not None else 1.0\\n\\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\\n is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None\\n\\n down_block_res_samples = (sample,)\\n if quick_replicate and replicate_prv_feature is not None:\\n # Down\\n for i, downsample_block in enumerate(self.down_blocks):\\n if i > cache_layer_id:\\n break\\n\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n # For t2i-adapter CrossAttnDownBlock2D\\n additional_residuals = {}\\n if is_adapter and len(down_block_additional_residuals) > 0:\\n additional_residuals[\\\"additional_residuals\\\"] = down_block_additional_residuals.pop(0)\\n\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n cross_attention_kwargs=cross_attention_kwargs,\\n encoder_attention_mask=encoder_attention_mask,\\n exist_block_number=cache_block_id if i == cache_layer_id else None,\\n **additional_residuals,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)\\n\\n if is_adapter and len(down_block_additional_residuals) > 0:\\n sample += down_block_additional_residuals.pop(0)\\n\\n down_block_res_samples += res_samples\\n\\n # No Middle\\n # Up\\n #print(\\\"down_block_res_samples:\\\", [res_sample.shape for res_sample in down_block_res_samples])\\n sample = replicate_prv_feature\\n #down_block_res_samples = down_block_res_samples[:-1]\\n if cache_block_id == len(self.down_blocks[cache_layer_id].attentions) :\\n cache_block_id = 0\\n cache_layer_id += 1\\n else:\\n cache_block_id += 1\\n\\n for i, upsample_block in enumerate(self.up_blocks):\\n if i < len(self.up_blocks) - 1 - cache_layer_id:\\n continue\\n\\n if i == len(self.up_blocks) - 1 - cache_layer_id:\\n trunc_upsample_block = cache_block_id + 1\\n else:\\n trunc_upsample_block = len(upsample_block.resnets)\\n\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-trunc_upsample_block:]\\n down_block_res_samples = down_block_res_samples[: -trunc_upsample_block]\\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 #print(sample.shape, [res_sample.shape for res_sample in res_samples])\\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 cross_attention_kwargs=cross_attention_kwargs,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n enter_block_number=cache_block_id if i == len(self.up_blocks) - 1 - cache_layer_id else None,\\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 scale=lora_scale,\\n )\\n \\n prv_f = replicate_prv_feature\\n else:\\n for i, downsample_block in enumerate(self.down_blocks):\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n # For t2i-adapter CrossAttnDownBlock2D\\n additional_residuals = {}\\n if is_adapter and len(down_block_additional_residuals) > 0:\\n additional_residuals[\\\"additional_residuals\\\"] = down_block_additional_residuals.pop(0)\\n\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n cross_attention_kwargs=cross_attention_kwargs,\\n encoder_attention_mask=encoder_attention_mask,\\n **additional_residuals,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)\\n\\n if is_adapter and len(down_block_additional_residuals) > 0:\\n sample += down_block_additional_residuals.pop(0)\\n\\n down_block_res_samples += res_samples\\n\\n if is_controlnet:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # 4. mid\\n if self.mid_block is not None:\\n sample = self.mid_block(\\n sample,\\n emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n cross_attention_kwargs=cross_attention_kwargs,\\n encoder_attention_mask=encoder_attention_mask,\\n )\\n # To support T2I-Adapter-XL\\n if (\\n is_adapter\\n and len(down_block_additional_residuals) > 0\\n and sample.shape == down_block_additional_residuals[0].shape\\n ):\\n sample += down_block_additional_residuals.pop(0)\\n\\n if is_controlnet:\\n sample = sample + mid_block_additional_residual\\n\\n # 5. up\\n if cache_block_id is not None:\\n if cache_block_id == len(self.down_blocks[cache_layer_id].attentions) :\\n cache_block_id = 0\\n cache_layer_id += 1\\n else:\\n cache_block_id += 1\\n #print(\\\"down_block_res_samples:\\\", [res_sample.shape for res_sample in down_block_res_samples])\\n #print(cache_block_id, cache_layer_id)\\n prv_f = None\\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 #print(sample.shape, [res_sample.shape for res_sample in res_samples])\\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, current_record_f = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_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 scale=lora_scale,\\n )\\n current_record_f = None\\n\\n #print(\\\"Append prv_feature with shape:\\\", sample.shape)\\n if cache_layer_id is not None and current_record_f is not None and i == len(self.up_blocks) - cache_layer_id - 1:\\n prv_f = current_record_f[-cache_block_id-1]\\n \\n # 6. post-process\\n if self.conv_norm_out:\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n sample = self.conv_out(sample)\\n if not return_dict:\\n return (sample, prv_f,)\\n \\n return UNet2DConditionOutput(sample=sample)\"\n },\n {\n \"identifier\": \"StableDiffusionPipeline\",\n \"path\": \"DeepCache/sd/pipeline_stable_diffusion.py\",\n \"snippet\": \"EXAMPLE_DOC_STRING = \\\"\\\"\\\"\\n Examples:\\n ```py\\n >>> import torch\\n >>> from diffusers import StableDiffusionPipeline\\n\\n >>> pipe = StableDiffusionPipeline.from_pretrained(\\\"runwayml/stable-diffusion-v1-5\\\", torch_dtype=torch.float16)\\n >>> pipe = pipe.to(\\\"cuda\\\")\\n\\n >>> prompt = \\\"a photo of an astronaut riding a horse on mars\\\"\\n >>> image = pipe(prompt).images[0]\\n ```\\n\\\"\\\"\\\"\\ndef sample_gaussian_centered(n=1000, sample_size=100, std_dev=100):\\ndef sample_from_quad(total_numbers, n_samples, pow=1.2):\\ndef sample_from_quad_center(total_numbers, n_samples, center, pow=1.2):\\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\\n def __init__(\\n self,\\n vae: AutoencoderKL,\\n text_encoder: CLIPTextModel,\\n tokenizer: CLIPTokenizer,\\n unet: UNet2DConditionModel,\\n scheduler: KarrasDiffusionSchedulers,\\n safety_checker: StableDiffusionSafetyChecker,\\n feature_extractor: CLIPImageProcessor,\\n requires_safety_checker: bool = True,\\n ):\\n def enable_vae_slicing(self):\\n def disable_vae_slicing(self):\\n def enable_vae_tiling(self):\\n def disable_vae_tiling(self):\\n def _encode_prompt(\\n self,\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt=None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n lora_scale: Optional[float] = None,\\n ):\\n def encode_prompt(\\n self,\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt=None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n lora_scale: Optional[float] = None,\\n ):\\n def run_safety_checker(self, image, device, dtype):\\n def decode_latents(self, latents):\\n def prepare_extra_step_kwargs(self, generator, eta):\\n def check_inputs(\\n self,\\n prompt,\\n height,\\n width,\\n callback_steps,\\n negative_prompt=None,\\n prompt_embeds=None,\\n negative_prompt_embeds=None,\\n ):\\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n num_inference_steps: int = 50,\\n guidance_scale: float = 7.5,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n guidance_rescale: float = 0.0,\\n cache_interval: int = 1,\\n cache_layer_id: int = None,\\n cache_block_id: int = None,\\n uniform: bool = True,\\n pow: float = None,\\n center: int = None,\\n output_all_sequence: bool = False,\\n ):\\nclass StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin):\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import copy\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom dataclasses import dataclass\nfrom typing import Callable, List, Optional, Union\nfrom torch.nn.functional import grid_sample\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\nfrom diffusers.models import AutoencoderKL\nfrom .unet_2d_condition import UNet2DConditionModel\nfrom .pipeline_stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import BaseOutput\nfrom diffusers.utils.torch_utils import randn_tensor"},"token_num":{"kind":"number","value":16961,"string":"16,961"},"cropped_code":{"kind":"string","value":" value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n return hidden_states\n\n\n@dataclass\nclass TextToVideoPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for zero-shot text-to-video pipeline.\n\n Args:\n images (`[List[PIL.Image.Image]`, `np.ndarray`]):\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`[List[bool]]`):\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\ndef coords_grid(batch, ht, wd, device):\n # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py\n coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))\n coords = torch.stack(coords[::-1], dim=0).float()\n return coords[None].repeat(batch, 1, 1, 1)\n\n\ndef warp_single_latent(latent, reference_flow):\n \"\"\"\n Warp latent of a single frame with given flow\n\n Args:\n latent: latent code of a single frame\n reference_flow: flow which to warp the latent with\n\n Returns:\n warped: warped latent\n \"\"\"\n _, _, H, W = reference_flow.size()\n _, _, h, w = latent.size()\n coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)\n\n coords_t0 = coords0 + reference_flow\n coords_t0[:, 0] /= W\n coords_t0[:, 1] /= H\n\n coords_t0 = coords_t0 * 2.0 - 1.0\n coords_t0 = F.interpolate(coords_t0, size=(h, w), mode=\"bilinear\")\n coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))\n\n warped = grid_sample(latent, coords_t0, mode=\"nearest\", padding_mode=\"reflection\")\n return warped\n\n\ndef create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):\n \"\"\"\n Create translation motion field\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n device: device\n dtype: dtype\n\n Returns:\n\n \"\"\"\n seq_length = len(frame_ids)\n reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)\n for fr_idx in range(seq_length):\n reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])\n reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])\n return reference_flow\n\n\ndef create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):\n \"\"\"\n Creates translation motion and warps the latents accordingly\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n latents: latent codes of frames\n\n Returns:\n warped_latents: warped latents\n \"\"\"\n motion_field = create_motion_field(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n frame_ids=frame_ids,\n device=latents.device,\n dtype=latents.dtype,\n )\n warped_latents = latents.clone().detach()\n for i in range(len(warped_latents)):\n warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])\n return warped_latents\n\n\n"},"all_code":{"kind":"string","value":"\n\n\ndef sample_gaussian_centered(n=1000, sample_size=100, std_dev=100):\n samples = []\n \n while len(samples) < sample_size:\n # Sample from a Gaussian centered at n/2\n sample = int(np.random.normal(loc=n/2, scale=std_dev))\n \n # Check if the sample is in bounds\n if 1 <= sample < n and sample not in samples:\n samples.append(sample)\n \n return samples\n\ndef sample_from_quad(total_numbers, n_samples, pow=1.2):\n while pow > 1:\n # Generate linearly spaced values between 0 and a max value\n x_values = np.linspace(0, total_numbers**(1/pow), n_samples+1)\n\n # Raise these values to the power of 1.5 to get a non-linear distribution\n indices = np.unique(np.int32(x_values**pow))[:-1]\n if len(indices) == n_samples:\n break\n pow -=0.02\n if pow <= 1:\n raise ValueError(\"Cannot find suitable pow. Please adjust n_samples or decrease center.\")\n return indices, pow\n\ndef sample_from_quad_center(total_numbers, n_samples, center, pow=1.2):\n while pow > 1:\n # Generate linearly spaced values between 0 and a max value\n x_values = np.linspace((-center)**(1/pow), (total_numbers-center)**(1/pow), n_samples+1)\n indices = [0] + [x+center for x in np.unique(np.int32(x_values**pow))[1:-1]]\n if len(indices) == n_samples:\n break\n pow -=0.02\n if pow <= 1:\n raise ValueError(\"Cannot find suitable pow. Please adjust n_samples or decrease center.\")\n return indices, pow\n\ndef rearrange_0(tensor, f):\n F, C, H, W = tensor.size()\n tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))\n return tensor\n\n\ndef rearrange_1(tensor):\n B, C, F, H, W = tensor.size()\n return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))\n\n\ndef rearrange_3(tensor, f):\n F, D, C = tensor.size()\n return torch.reshape(tensor, (F // f, f, D, C))\n\n\ndef rearrange_4(tensor):\n B, F, D, C = tensor.size()\n return torch.reshape(tensor, (B * F, D, C))\n\n\nclass CrossFrameAttnProcessor:\n \"\"\"\n Cross frame attention processor. Each frame attends the first frame.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = key.size()[0] // self.batch_size\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass CrossFrameAttnProcessor2_0:\n \"\"\"\n Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n inner_dim = hidden_states.shape[-1]\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = max(1, key.size()[0] // self.batch_size)\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n return hidden_states\n\n\n@dataclass\nclass TextToVideoPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for zero-shot text-to-video pipeline.\n\n Args:\n images (`[List[PIL.Image.Image]`, `np.ndarray`]):\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`[List[bool]]`):\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\ndef coords_grid(batch, ht, wd, device):\n # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py\n coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))\n coords = torch.stack(coords[::-1], dim=0).float()\n return coords[None].repeat(batch, 1, 1, 1)\n\n\ndef warp_single_latent(latent, reference_flow):\n \"\"\"\n Warp latent of a single frame with given flow\n\n Args:\n latent: latent code of a single frame\n reference_flow: flow which to warp the latent with\n\n Returns:\n warped: warped latent\n \"\"\"\n _, _, H, W = reference_flow.size()\n _, _, h, w = latent.size()\n coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)\n\n coords_t0 = coords0 + reference_flow\n coords_t0[:, 0] /= W\n coords_t0[:, 1] /= H\n\n coords_t0 = coords_t0 * 2.0 - 1.0\n coords_t0 = F.interpolate(coords_t0, size=(h, w), mode=\"bilinear\")\n coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))\n\n warped = grid_sample(latent, coords_t0, mode=\"nearest\", padding_mode=\"reflection\")\n return warped\n\n\ndef create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):\n \"\"\"\n Create translation motion field\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n device: device\n dtype: dtype\n\n Returns:\n\n \"\"\"\n seq_length = len(frame_ids)\n reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)\n for fr_idx in range(seq_length):\n reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])\n reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])\n return reference_flow\n\n\ndef create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):\n \"\"\"\n Creates translation motion and warps the latents accordingly\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n latents: latent codes of frames\n\n Returns:\n warped_latents: warped latents\n \"\"\"\n motion_field = create_motion_field(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n frame_ids=frame_ids,\n device=latents.device,\n dtype=latents.dtype,\n )\n warped_latents = latents.clone().detach()\n for i in range(len(warped_latents)):\n warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])\n return warped_latents\n\n"},"next_line":{"kind":"string","value":"class TextToVideoZeroPipeline(StableDiffusionPipeline):"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-01 10:54:04+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5371,"cells":{"repo_name":{"kind":"string","value":"alvinliu0/HumanGaussian"},"file_path":{"kind":"string","value":"threestudio/models/geometry/tetrahedra_sdf_grid.py"},"context":{"kind":"list like","value":[{"identifier":"BaseExplicitGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\n \"bbox\",\n torch.as_tensor(\n [\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\n ],\n dtype=torch.float32,\n ),\n )"},{"identifier":"BaseGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseGeometry(BaseModule):\n @dataclass\n class Config(BaseModule.Config):\n pass\n\n cfg: Config\n\n @staticmethod\n def create_from(\n other: \"BaseGeometry\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\n ) -> \"BaseGeometry\":\n raise TypeError(\n f\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\"\n )\n\n def export(self, *args, **kwargs) -> Dict[str, Any]:\n return {}"},{"identifier":"contract_to_unisphere","path":"threestudio/models/geometry/base.py","snippet":"def contract_to_unisphere(\n x: Float[Tensor, \"... 3\"], bbox: Float[Tensor, \"2 3\"], unbounded: bool = False\n) -> Float[Tensor, \"... 3\"]:\n if unbounded:\n x = scale_tensor(x, bbox, (0, 1))\n x = x * 2 - 1 # aabb is at [-1, 1]\n mag = x.norm(dim=-1, keepdim=True)\n mask = mag.squeeze(-1) > 1\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\n else:\n x = scale_tensor(x, bbox, (0, 1))\n return x"},{"identifier":"ImplicitSDF","path":"threestudio/models/geometry/implicit_sdf.py","snippet":"class ImplicitSDF(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: Union[\n float, str\n ] = 0.01 # in [float, \"progressive\"]\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n sdf_bias: Union[float, str] = 0.0\n sdf_bias_params: Optional[Any] = None\n\n # no need to removal outlier for SDF\n isosurface_remove_outliers: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.sdf_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n if self.cfg.isosurface_deformable_grid:\n assert (\n self.cfg.isosurface_method == \"mt\"\n ), \"isosurface_deformable_grid only works with mt\"\n self.deformation_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n self.finite_difference_normal_eps: Optional[float] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n if self.cfg.sdf_bias != 0.0:\n threestudio.warn(\n \"shape_init and sdf_bias are both specified, which may lead to unexpected results.\"\n )\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n import trimesh\n\n scene = trimesh.load(mesh_path)\n if isinstance(scene, trimesh.Trimesh):\n mesh = scene\n elif isinstance(scene, trimesh.scene.Scene):\n mesh = trimesh.Trimesh()\n for obj in scene.geometry.values():\n mesh = trimesh.util.concatenate([mesh, obj])\n else:\n raise ValueError(f\"Unknown mesh type at {mesh_path}.\")\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n from pysdf import SDF\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\n from tqdm import tqdm\n\n for _ in tqdm(\n range(1000),\n desc=f\"Initializing SDF to a(n) {self.cfg.shape_init}:\",\n disable=get_rank() != 0,\n ):\n points_rand = (\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\n )\n sdf_gt = get_gt_sdf(points_rand)\n sdf_pred = self.forward_sdf(points_rand)\n loss = F.mse_loss(sdf_pred, sdf_gt)\n optim.zero_grad()\n loss.backward()\n optim.step()\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def get_shifted_sdf(\n self, points: Float[Tensor, \"*N Di\"], sdf: Float[Tensor, \"*N 1\"]\n ) -> Float[Tensor, \"*N 1\"]:\n sdf_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.sdf_bias == \"ellipsoid\":\n assert (\n isinstance(self.cfg.sdf_bias_params, Sized)\n and len(self.cfg.sdf_bias_params) == 3\n )\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\n sdf_bias = ((points / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n elif self.cfg.sdf_bias == \"sphere\":\n assert isinstance(self.cfg.sdf_bias_params, float)\n radius = self.cfg.sdf_bias_params\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\n elif isinstance(self.cfg.sdf_bias, float):\n sdf_bias = self.cfg.sdf_bias\n else:\n raise ValueError(f\"Unknown sdf bias {self.cfg.sdf_bias}\")\n return sdf + sdf_bias\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n output = {\"sdf\": sdf}\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n assert self.finite_difference_normal_eps is not None\n eps: float = self.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 6 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (\n 0.5\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 3 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\n normal = F.normalize(sdf_grad, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n sdf_grad = normal\n elif self.cfg.normal_type == \"analytic\":\n sdf_grad = -torch.autograd.grad(\n sdf,\n points_unscaled,\n grad_outputs=torch.ones_like(sdf),\n create_graph=True,\n )[0]\n normal = F.normalize(sdf_grad, dim=-1)\n if not grad_enabled:\n sdf_grad = sdf_grad.detach()\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update(\n {\"normal\": normal, \"shading_normal\": normal, \"sdf_grad\": sdf_grad}\n )\n return output\n\n def forward_sdf(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n sdf = self.sdf_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n return sdf\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n deformation: Optional[Float[Tensor, \"*N 3\"]] = None\n if self.cfg.isosurface_deformable_grid:\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\n return sdf, deformation\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return field - threshold\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n if isinstance(self.cfg.finite_difference_normal_eps, float):\n self.finite_difference_normal_eps = (\n self.cfg.finite_difference_normal_eps\n )\n elif self.cfg.finite_difference_normal_eps == \"progressive\":\n # progressive finite difference eps from Neuralangelo\n # https://arxiv.org/abs/2306.03092\n hg_conf: Any = self.cfg.pos_encoding_config\n assert (\n hg_conf.otype == \"ProgressiveBandHashGrid\"\n ), \"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\"\n current_level = min(\n hg_conf.start_level\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\n hg_conf.n_levels,\n )\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\n current_level - 1\n )\n grid_size = 2 * self.cfg.radius / grid_res\n if grid_size != self.finite_difference_normal_eps:\n threestudio.info(\n f\"Update finite_difference_normal_eps to {grid_size}\"\n )\n self.finite_difference_normal_eps = grid_size\n else:\n raise ValueError(\n f\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\"\n )"},{"identifier":"ImplicitVolume","path":"threestudio/models/geometry/implicit_volume.py","snippet":"class ImplicitVolume(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n density_activation: Optional[str] = \"softplus\"\n density_bias: Union[float, str] = \"blob_magic3d\"\n density_blob_scale: float = 10.0\n density_blob_std: float = 0.5\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: float = 0.01\n\n # automatically determine the threshold\n isosurface_threshold: Union[float, str] = 25.0\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.density_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n def get_activated_density(\n self, points: Float[Tensor, \"*N Di\"], density: Float[Tensor, \"*N 1\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Float[Tensor, \"*N 1\"]]:\n density_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.density_bias == \"blob_dreamfusion\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * torch.exp(\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\n )[..., None]\n )\n elif self.cfg.density_bias == \"blob_magic3d\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * (\n 1\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\n )[..., None]\n )\n elif isinstance(self.cfg.density_bias, float):\n density_bias = self.cfg.density_bias\n else:\n raise ValueError(f\"Unknown density bias {self.cfg.density_bias}\")\n raw_density: Float[Tensor, \"*N 1\"] = density + density_bias\n density = get_activation(self.cfg.density_activation)(raw_density)\n return raw_density, density\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n density = self.density_network(enc).view(*points.shape[:-1], 1)\n raw_density, density = self.get_activated_density(points_unscaled, density)\n\n output = {\n \"density\": density,\n }\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n # TODO: use raw density\n eps = self.cfg.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 6 1\"] = self.forward_density(\n points_offset\n )\n normal = (\n -0.5\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 3 1\"] = self.forward_density(\n points_offset\n )\n normal = -(density_offset[..., 0::1, 0] - density) / eps\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"analytic\":\n normal = -torch.autograd.grad(\n density,\n points_unscaled,\n grad_outputs=torch.ones_like(density),\n create_graph=True,\n )[0]\n normal = F.normalize(normal, dim=-1)\n if not grad_enabled:\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update({\"normal\": normal, \"shading_normal\": normal})\n\n torch.set_grad_enabled(grad_enabled)\n return output\n\n def forward_density(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n density = self.density_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n\n _, density = self.get_activated_density(points_unscaled, density)\n return density\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n if self.cfg.isosurface_deformable_grid:\n threestudio.warn(\n f\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\"\n )\n density = self.forward_density(points)\n return density, None\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return -(field - threshold)\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"ImplicitVolume\":\n if isinstance(other, ImplicitVolume):\n instance = ImplicitVolume(cfg, **kwargs)\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.density_network.load_state_dict(other.density_network.state_dict())\n if copy_net:\n if (\n instance.cfg.n_feature_dims > 0\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\n ):\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n if (\n instance.cfg.normal_type == \"pred\"\n and other.cfg.normal_type == \"pred\"\n ):\n instance.normal_network.load_state_dict(\n other.normal_network.state_dict()\n )\n return instance\n else:\n raise TypeError(\n f\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\"\n )"},{"identifier":"MarchingTetrahedraHelper","path":"threestudio/models/isosurface.py","snippet":"class MarchingTetrahedraHelper(IsosurfaceHelper):\n def __init__(self, resolution: int, tets_path: str):\n super().__init__()\n self.resolution = resolution\n self.tets_path = tets_path\n\n self.triangle_table: Float[Tensor, \"...\"]\n self.register_buffer(\n \"triangle_table\",\n torch.as_tensor(\n [\n [-1, -1, -1, -1, -1, -1],\n [1, 0, 2, -1, -1, -1],\n [4, 0, 3, -1, -1, -1],\n [1, 4, 2, 1, 3, 4],\n [3, 1, 5, -1, -1, -1],\n [2, 3, 0, 2, 5, 3],\n [1, 4, 0, 1, 5, 4],\n [4, 2, 5, -1, -1, -1],\n [4, 5, 2, -1, -1, -1],\n [4, 1, 0, 4, 5, 1],\n [3, 2, 0, 3, 5, 2],\n [1, 3, 5, -1, -1, -1],\n [4, 1, 2, 4, 3, 1],\n [3, 0, 4, -1, -1, -1],\n [2, 0, 1, -1, -1, -1],\n [-1, -1, -1, -1, -1, -1],\n ],\n dtype=torch.long,\n ),\n persistent=False,\n )\n self.num_triangles_table: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"num_triangles_table\",\n torch.as_tensor(\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\n ),\n persistent=False,\n )\n self.base_tet_edges: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"base_tet_edges\",\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\n persistent=False,\n )\n\n tets = np.load(self.tets_path)\n self._grid_vertices: Float[Tensor, \"...\"]\n self.register_buffer(\n \"_grid_vertices\",\n torch.from_numpy(tets[\"vertices\"]).float(),\n persistent=False,\n )\n self.indices: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"indices\", torch.from_numpy(tets[\"indices\"]).long(), persistent=False\n )\n\n self._all_edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n\n def normalize_grid_deformation(\n self, grid_vertex_offsets: Float[Tensor, \"Nv 3\"]\n ) -> Float[Tensor, \"Nv 3\"]:\n return (\n (self.points_range[1] - self.points_range[0])\n / (self.resolution) # half tet size is approximately 1 / self.resolution\n * torch.tanh(grid_vertex_offsets)\n ) # FIXME: hard-coded activation\n\n @property\n def grid_vertices(self) -> Float[Tensor, \"Nv 3\"]:\n return self._grid_vertices\n\n @property\n def all_edges(self) -> Integer[Tensor, \"Ne 2\"]:\n if self._all_edges is None:\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\n edges = torch.tensor(\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\n dtype=torch.long,\n device=self.indices.device,\n )\n _all_edges = self.indices[:, edges].reshape(-1, 2)\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\n self._all_edges = _all_edges\n return self._all_edges\n\n def sort_edges(self, edges_ex2):\n with torch.no_grad():\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\n order = order.unsqueeze(dim=1)\n\n a = torch.gather(input=edges_ex2, index=order, dim=1)\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\n\n return torch.stack([a, b], -1)\n\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\n with torch.no_grad():\n occ_n = sdf_n > 0\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\n occ_sum = torch.sum(occ_fx4, -1)\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\n occ_sum = occ_sum[valid_tets]\n\n # find all vertices\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\n all_edges = self.sort_edges(all_edges)\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\n\n unique_edges = unique_edges.long()\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\n mapping = (\n torch.ones(\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\n )\n * -1\n )\n mapping[mask_edges] = torch.arange(\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\n )\n idx_map = mapping[idx_map] # map edges to verts\n\n interp_v = unique_edges[mask_edges]\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\n edges_to_interp_sdf[:, -1] *= -1\n\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\n\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\n\n idx_map = idx_map.reshape(-1, 6)\n\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\n num_triangles = self.num_triangles_table[tetindex]\n\n # Generate triangle indices\n faces = torch.cat(\n (\n torch.gather(\n input=idx_map[num_triangles == 1],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\n ).reshape(-1, 3),\n torch.gather(\n input=idx_map[num_triangles == 2],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\n ).reshape(-1, 3),\n ),\n dim=0,\n )\n\n return verts, faces\n\n def forward(\n self,\n level: Float[Tensor, \"N3 1\"],\n deformation: Optional[Float[Tensor, \"N3 3\"]] = None,\n ) -> Mesh:\n if deformation is not None:\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\n deformation\n )\n else:\n grid_vertices = self.grid_vertices\n\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\n\n mesh = Mesh(\n v_pos=v_pos,\n t_pos_idx=t_pos_idx,\n # extras\n grid_vertices=grid_vertices,\n tet_edges=self.all_edges,\n grid_level=level,\n grid_deformation=deformation,\n )\n\n return mesh"},{"identifier":"Mesh","path":"threestudio/models/mesh.py","snippet":"class Mesh:\n def __init__(\n self, v_pos: Float[Tensor, \"Nv 3\"], t_pos_idx: Integer[Tensor, \"Nf 3\"], **kwargs\n ) -> None:\n self.v_pos: Float[Tensor, \"Nv 3\"] = v_pos\n self.t_pos_idx: Integer[Tensor, \"Nf 3\"] = t_pos_idx\n self._v_nrm: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tng: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tex: Optional[Float[Tensor, \"Nt 3\"]] = None\n self._t_tex_idx: Optional[Float[Tensor, \"Nf 3\"]] = None\n self._v_rgb: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n self.extras: Dict[str, Any] = {}\n for k, v in kwargs.items():\n self.add_extra(k, v)\n\n def add_extra(self, k, v) -> None:\n self.extras[k] = v\n\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\n if self.requires_grad:\n threestudio.debug(\"Mesh is differentiable, not removing outliers\")\n return self\n\n # use trimesh to first split the mesh into connected components\n # then remove the components with less than n_face_threshold faces\n import trimesh\n\n # construct a trimesh object\n mesh = trimesh.Trimesh(\n vertices=self.v_pos.detach().cpu().numpy(),\n faces=self.t_pos_idx.detach().cpu().numpy(),\n )\n\n # split the mesh into connected components\n components = mesh.split(only_watertight=False)\n # log the number of faces in each component\n threestudio.debug(\n \"Mesh has {} components, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n\n n_faces_threshold: int\n if isinstance(outlier_n_faces_threshold, float):\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\n n_faces_threshold = int(\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\n )\n else:\n # set the threshold directly to outlier_n_faces_threshold\n n_faces_threshold = outlier_n_faces_threshold\n\n # log the threshold\n threestudio.debug(\n \"Removing components with less than {} faces\".format(n_faces_threshold)\n )\n\n # remove the components with less than n_face_threshold faces\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\n\n # log the number of faces in each component after removing outliers\n threestudio.debug(\n \"Mesh has {} components after removing outliers, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n # merge the components\n mesh = trimesh.util.concatenate(components)\n\n # convert back to our mesh format\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\n\n clean_mesh = Mesh(v_pos, t_pos_idx)\n # keep the extras unchanged\n\n if len(self.extras) > 0:\n clean_mesh.extras = self.extras\n threestudio.debug(\n f\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\"\n )\n return clean_mesh\n\n @property\n def requires_grad(self):\n return self.v_pos.requires_grad\n\n @property\n def v_nrm(self):\n if self._v_nrm is None:\n self._v_nrm = self._compute_vertex_normal()\n return self._v_nrm\n\n @property\n def v_tng(self):\n if self._v_tng is None:\n self._v_tng = self._compute_vertex_tangent()\n return self._v_tng\n\n @property\n def v_tex(self):\n if self._v_tex is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._v_tex\n\n @property\n def t_tex_idx(self):\n if self._t_tex_idx is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._t_tex_idx\n\n @property\n def v_rgb(self):\n return self._v_rgb\n\n @property\n def edges(self):\n if self._edges is None:\n self._edges = self._compute_edges()\n return self._edges\n\n def _compute_vertex_normal(self):\n i0 = self.t_pos_idx[:, 0]\n i1 = self.t_pos_idx[:, 1]\n i2 = self.t_pos_idx[:, 2]\n\n v0 = self.v_pos[i0, :]\n v1 = self.v_pos[i1, :]\n v2 = self.v_pos[i2, :]\n\n face_normals = torch.cross(v1 - v0, v2 - v0)\n\n # Splat face normals to vertices\n v_nrm = torch.zeros_like(self.v_pos)\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\n\n # Normalize, replace zero (degenerated) normals with some default value\n v_nrm = torch.where(\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\n )\n v_nrm = F.normalize(v_nrm, dim=1)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(v_nrm))\n\n return v_nrm\n\n def _compute_vertex_tangent(self):\n vn_idx = [None] * 3\n pos = [None] * 3\n tex = [None] * 3\n for i in range(0, 3):\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\n # t_nrm_idx is always the same as t_pos_idx\n vn_idx[i] = self.t_pos_idx[:, i]\n\n tangents = torch.zeros_like(self.v_nrm)\n tansum = torch.zeros_like(self.v_nrm)\n\n # Compute tangent space for each triangle\n uve1 = tex[1] - tex[0]\n uve2 = tex[2] - tex[0]\n pe1 = pos[1] - pos[0]\n pe2 = pos[2] - pos[0]\n\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\n\n # Avoid division by zero for degenerated texture coordinates\n tang = nom / torch.where(\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\n )\n\n # Update all 3 vertices\n for i in range(0, 3):\n idx = vn_idx[i][:, None].repeat(1, 3)\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\n tansum.scatter_add_(\n 0, idx, torch.ones_like(tang)\n ) # tansum[n_i] = tansum[n_i] + 1\n tangents = tangents / tansum\n\n # Normalize and make sure tangent is perpendicular to normal\n tangents = F.normalize(tangents, dim=1)\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(tangents))\n\n return tangents\n\n def _unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n threestudio.info(\"Using xatlas to perform UV unwrapping, may take a while ...\")\n\n import xatlas\n\n atlas = xatlas.Atlas()\n atlas.add_mesh(\n self.v_pos.detach().cpu().numpy(),\n self.t_pos_idx.cpu().numpy(),\n )\n co = xatlas.ChartOptions()\n po = xatlas.PackOptions()\n for k, v in xatlas_chart_options.items():\n setattr(co, k, v)\n for k, v in xatlas_pack_options.items():\n setattr(po, k, v)\n atlas.generate(co, po)\n vmapping, indices, uvs = atlas.get_mesh(0)\n vmapping = (\n torch.from_numpy(\n vmapping.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\n indices = (\n torch.from_numpy(\n indices.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n return uvs, indices\n\n def unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\n xatlas_chart_options, xatlas_pack_options\n )\n\n def set_vertex_color(self, v_rgb):\n assert v_rgb.shape[0] == self.v_pos.shape[0]\n self._v_rgb = v_rgb\n\n def _compute_edges(self):\n # Compute edges\n edges = torch.cat(\n [\n self.t_pos_idx[:, [0, 1]],\n self.t_pos_idx[:, [1, 2]],\n self.t_pos_idx[:, [2, 0]],\n ],\n dim=0,\n )\n edges = edges.sort()[0]\n edges = torch.unique(edges, dim=0)\n return edges\n\n def normal_consistency(self) -> Float[Tensor, \"\"]:\n edge_nrm: Float[Tensor, \"Ne 2 3\"] = self.v_nrm[self.edges]\n nc = (\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\n ).mean()\n return nc\n\n def _laplacian_uniform(self):\n # from stable-dreamfusion\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\n verts, faces = self.v_pos, self.t_pos_idx\n\n V = verts.shape[0]\n F = faces.shape[0]\n\n # Neighbor indices\n ii = faces[:, [1, 2, 0]].flatten()\n jj = faces[:, [2, 0, 1]].flatten()\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\n dim=1\n )\n adj_values = torch.ones(adj.shape[1]).to(verts)\n\n # Diagonal indices\n diag_idx = adj[0]\n\n # Build the sparse matrix\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\n values = torch.cat((-adj_values, adj_values))\n\n # The coalesce operation sums the duplicate indices, resulting in the\n # correct diagonal\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\n\n def laplacian(self) -> Float[Tensor, \"\"]:\n with torch.no_grad():\n L = self._laplacian_uniform()\n loss = L.mm(self.v_pos)\n loss = loss.norm(dim=1)\n loss = loss.mean()\n return loss"},{"identifier":"get_encoding","path":"threestudio/models/networks.py","snippet":"def get_encoding(n_input_dims: int, config) -> nn.Module:\n # input suppose to be range [0, 1]\n encoding: nn.Module\n if config.otype == \"ProgressiveBandFrequency\":\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\n elif config.otype == \"ProgressiveBandHashGrid\":\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\n else:\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\n encoding = CompositeEncoding(\n encoding,\n include_xyz=config.get(\"include_xyz\", False),\n xyz_scale=2.0,\n xyz_offset=-1.0,\n ) # FIXME: hard coded\n return encoding"},{"identifier":"get_mlp","path":"threestudio/models/networks.py","snippet":"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\n network: nn.Module\n if config.otype == \"VanillaMLP\":\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\n elif config.otype == \"SphereInitVanillaMLP\":\n network = SphereInitVanillaMLP(\n n_input_dims, n_output_dims, config_to_primitive(config)\n )\n else:\n assert (\n config.get(\"sphere_init\", False) is False\n ), \"sphere_init=True only supported by VanillaMLP\"\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\n return network"},{"identifier":"broadcast","path":"threestudio/utils/misc.py","snippet":"def broadcast(tensor, src=0):\n if not _distributed_available():\n return tensor\n else:\n torch.distributed.broadcast(tensor, src=src)\n return tensor"},{"identifier":"scale_tensor","path":"threestudio/utils/ops.py","snippet":"def scale_tensor(\n dat: Num[Tensor, \"... D\"], inp_scale: ValidScale, tgt_scale: ValidScale\n):\n if inp_scale is None:\n inp_scale = (0, 1)\n if tgt_scale is None:\n tgt_scale = (0, 1)\n if isinstance(tgt_scale, Tensor):\n assert dat.shape[-1] == tgt_scale.shape[-1]\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\n return dat"}],"string":"[\n {\n \"identifier\": \"BaseExplicitGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseExplicitGeometry(BaseGeometry):\\n @dataclass\\n class Config(BaseGeometry.Config):\\n radius: float = 1.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n self.bbox: Float[Tensor, \\\"2 3\\\"]\\n self.register_buffer(\\n \\\"bbox\\\",\\n torch.as_tensor(\\n [\\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\\n ],\\n dtype=torch.float32,\\n ),\\n )\"\n },\n {\n \"identifier\": \"BaseGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseGeometry(BaseModule):\\n @dataclass\\n class Config(BaseModule.Config):\\n pass\\n\\n cfg: Config\\n\\n @staticmethod\\n def create_from(\\n other: \\\"BaseGeometry\\\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\\n ) -> \\\"BaseGeometry\\\":\\n raise TypeError(\\n f\\\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\\\"\\n )\\n\\n def export(self, *args, **kwargs) -> Dict[str, Any]:\\n return {}\"\n },\n {\n \"identifier\": \"contract_to_unisphere\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"def contract_to_unisphere(\\n x: Float[Tensor, \\\"... 3\\\"], bbox: Float[Tensor, \\\"2 3\\\"], unbounded: bool = False\\n) -> Float[Tensor, \\\"... 3\\\"]:\\n if unbounded:\\n x = scale_tensor(x, bbox, (0, 1))\\n x = x * 2 - 1 # aabb is at [-1, 1]\\n mag = x.norm(dim=-1, keepdim=True)\\n mask = mag.squeeze(-1) > 1\\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\\n else:\\n x = scale_tensor(x, bbox, (0, 1))\\n return x\"\n },\n {\n \"identifier\": \"ImplicitSDF\",\n \"path\": \"threestudio/models/geometry/implicit_sdf.py\",\n \"snippet\": \"class ImplicitSDF(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: Union[\\n float, str\\n ] = 0.01 # in [float, \\\"progressive\\\"]\\n shape_init: Optional[str] = None\\n shape_init_params: Optional[Any] = None\\n shape_init_mesh_up: str = \\\"+z\\\"\\n shape_init_mesh_front: str = \\\"+x\\\"\\n force_shape_init: bool = False\\n sdf_bias: Union[float, str] = 0.0\\n sdf_bias_params: Optional[Any] = None\\n\\n # no need to removal outlier for SDF\\n isosurface_remove_outliers: bool = False\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.sdf_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n if self.cfg.isosurface_deformable_grid:\\n assert (\\n self.cfg.isosurface_method == \\\"mt\\\"\\n ), \\\"isosurface_deformable_grid only works with mt\\\"\\n self.deformation_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n self.finite_difference_normal_eps: Optional[float] = None\\n\\n def initialize_shape(self) -> None:\\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\\n return\\n\\n # do not initialize shape if weights are provided\\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\\n return\\n\\n if self.cfg.sdf_bias != 0.0:\\n threestudio.warn(\\n \\\"shape_init and sdf_bias are both specified, which may lead to unexpected results.\\\"\\n )\\n\\n get_gt_sdf: Callable[[Float[Tensor, \\\"N 3\\\"]], Float[Tensor, \\\"N 1\\\"]]\\n assert isinstance(self.cfg.shape_init, str)\\n if self.cfg.shape_init == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.shape_init_params, Sized)\\n and len(self.cfg.shape_init_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return ((points_rand / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init == \\\"sphere\\\":\\n assert isinstance(self.cfg.shape_init_params, float)\\n radius = self.cfg.shape_init_params\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init.startswith(\\\"mesh:\\\"):\\n assert isinstance(self.cfg.shape_init_params, float)\\n mesh_path = self.cfg.shape_init[5:]\\n if not os.path.exists(mesh_path):\\n raise ValueError(f\\\"Mesh file {mesh_path} does not exist.\\\")\\n\\n import trimesh\\n\\n scene = trimesh.load(mesh_path)\\n if isinstance(scene, trimesh.Trimesh):\\n mesh = scene\\n elif isinstance(scene, trimesh.scene.Scene):\\n mesh = trimesh.Trimesh()\\n for obj in scene.geometry.values():\\n mesh = trimesh.util.concatenate([mesh, obj])\\n else:\\n raise ValueError(f\\\"Unknown mesh type at {mesh_path}.\\\")\\n\\n # move to center\\n centroid = mesh.vertices.mean(0)\\n mesh.vertices = mesh.vertices - centroid\\n\\n # align to up-z and front-x\\n dirs = [\\\"+x\\\", \\\"+y\\\", \\\"+z\\\", \\\"-x\\\", \\\"-y\\\", \\\"-z\\\"]\\n dir2vec = {\\n \\\"+x\\\": np.array([1, 0, 0]),\\n \\\"+y\\\": np.array([0, 1, 0]),\\n \\\"+z\\\": np.array([0, 0, 1]),\\n \\\"-x\\\": np.array([-1, 0, 0]),\\n \\\"-y\\\": np.array([0, -1, 0]),\\n \\\"-z\\\": np.array([0, 0, -1]),\\n }\\n if (\\n self.cfg.shape_init_mesh_up not in dirs\\n or self.cfg.shape_init_mesh_front not in dirs\\n ):\\n raise ValueError(\\n f\\\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\\\"\\n )\\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\\n raise ValueError(\\n \\\"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\\\"\\n )\\n z_, x_ = (\\n dir2vec[self.cfg.shape_init_mesh_up],\\n dir2vec[self.cfg.shape_init_mesh_front],\\n )\\n y_ = np.cross(z_, x_)\\n std2mesh = np.stack([x_, y_, z_], axis=0).T\\n mesh2std = np.linalg.inv(std2mesh)\\n\\n # scaling\\n scale = np.abs(mesh.vertices).max()\\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\\n\\n from pysdf import SDF\\n\\n sdf = SDF(mesh.vertices, mesh.faces)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n # add a negative signed here\\n # as in pysdf the inside of the shape has positive signed distance\\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\\n points_rand\\n )[..., None]\\n\\n get_gt_sdf = func\\n\\n else:\\n raise ValueError(\\n f\\\"Unknown shape initialization type: {self.cfg.shape_init}\\\"\\n )\\n\\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\\n from tqdm import tqdm\\n\\n for _ in tqdm(\\n range(1000),\\n desc=f\\\"Initializing SDF to a(n) {self.cfg.shape_init}:\\\",\\n disable=get_rank() != 0,\\n ):\\n points_rand = (\\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\\n )\\n sdf_gt = get_gt_sdf(points_rand)\\n sdf_pred = self.forward_sdf(points_rand)\\n loss = F.mse_loss(sdf_pred, sdf_gt)\\n optim.zero_grad()\\n loss.backward()\\n optim.step()\\n\\n # explicit broadcast to ensure param consistency across ranks\\n for param in self.parameters():\\n broadcast(param, src=0)\\n\\n def get_shifted_sdf(\\n self, points: Float[Tensor, \\\"*N Di\\\"], sdf: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n sdf_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.sdf_bias == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.sdf_bias_params, Sized)\\n and len(self.cfg.sdf_bias_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\\n sdf_bias = ((points / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n elif self.cfg.sdf_bias == \\\"sphere\\\":\\n assert isinstance(self.cfg.sdf_bias_params, float)\\n radius = self.cfg.sdf_bias_params\\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n elif isinstance(self.cfg.sdf_bias, float):\\n sdf_bias = self.cfg.sdf_bias\\n else:\\n raise ValueError(f\\\"Unknown sdf bias {self.cfg.sdf_bias}\\\")\\n return sdf + sdf_bias\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n output = {\\\"sdf\\\": sdf}\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n assert self.finite_difference_normal_eps is not None\\n eps: float = self.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (\\n 0.5\\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\\n normal = F.normalize(sdf_grad, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n sdf_grad = normal\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n sdf_grad = -torch.autograd.grad(\\n sdf,\\n points_unscaled,\\n grad_outputs=torch.ones_like(sdf),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(sdf_grad, dim=-1)\\n if not grad_enabled:\\n sdf_grad = sdf_grad.detach()\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update(\\n {\\\"normal\\\": normal, \\\"shading_normal\\\": normal, \\\"sdf_grad\\\": sdf_grad}\\n )\\n return output\\n\\n def forward_sdf(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n sdf = self.sdf_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n return sdf\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n deformation: Optional[Float[Tensor, \\\"*N 3\\\"]] = None\\n if self.cfg.isosurface_deformable_grid:\\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\\n return sdf, deformation\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return field - threshold\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n if isinstance(self.cfg.finite_difference_normal_eps, float):\\n self.finite_difference_normal_eps = (\\n self.cfg.finite_difference_normal_eps\\n )\\n elif self.cfg.finite_difference_normal_eps == \\\"progressive\\\":\\n # progressive finite difference eps from Neuralangelo\\n # https://arxiv.org/abs/2306.03092\\n hg_conf: Any = self.cfg.pos_encoding_config\\n assert (\\n hg_conf.otype == \\\"ProgressiveBandHashGrid\\\"\\n ), \\\"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\\\"\\n current_level = min(\\n hg_conf.start_level\\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\\n hg_conf.n_levels,\\n )\\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\\n current_level - 1\\n )\\n grid_size = 2 * self.cfg.radius / grid_res\\n if grid_size != self.finite_difference_normal_eps:\\n threestudio.info(\\n f\\\"Update finite_difference_normal_eps to {grid_size}\\\"\\n )\\n self.finite_difference_normal_eps = grid_size\\n else:\\n raise ValueError(\\n f\\\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\\\"\\n )\"\n },\n {\n \"identifier\": \"ImplicitVolume\",\n \"path\": \"threestudio/models/geometry/implicit_volume.py\",\n \"snippet\": \"class ImplicitVolume(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n density_activation: Optional[str] = \\\"softplus\\\"\\n density_bias: Union[float, str] = \\\"blob_magic3d\\\"\\n density_blob_scale: float = 10.0\\n density_blob_std: float = 0.5\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: float = 0.01\\n\\n # automatically determine the threshold\\n isosurface_threshold: Union[float, str] = 25.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.density_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n def get_activated_density(\\n self, points: Float[Tensor, \\\"*N Di\\\"], density: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Float[Tensor, \\\"*N 1\\\"]]:\\n density_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.density_bias == \\\"blob_dreamfusion\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * torch.exp(\\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\\n )[..., None]\\n )\\n elif self.cfg.density_bias == \\\"blob_magic3d\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * (\\n 1\\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\\n )[..., None]\\n )\\n elif isinstance(self.cfg.density_bias, float):\\n density_bias = self.cfg.density_bias\\n else:\\n raise ValueError(f\\\"Unknown density bias {self.cfg.density_bias}\\\")\\n raw_density: Float[Tensor, \\\"*N 1\\\"] = density + density_bias\\n density = get_activation(self.cfg.density_activation)(raw_density)\\n return raw_density, density\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n density = self.density_network(enc).view(*points.shape[:-1], 1)\\n raw_density, density = self.get_activated_density(points_unscaled, density)\\n\\n output = {\\n \\\"density\\\": density,\\n }\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n # TODO: use raw density\\n eps = self.cfg.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = (\\n -0.5\\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = -(density_offset[..., 0::1, 0] - density) / eps\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n normal = -torch.autograd.grad(\\n density,\\n points_unscaled,\\n grad_outputs=torch.ones_like(density),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(normal, dim=-1)\\n if not grad_enabled:\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update({\\\"normal\\\": normal, \\\"shading_normal\\\": normal})\\n\\n torch.set_grad_enabled(grad_enabled)\\n return output\\n\\n def forward_density(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n density = self.density_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n\\n _, density = self.get_activated_density(points_unscaled, density)\\n return density\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n if self.cfg.isosurface_deformable_grid:\\n threestudio.warn(\\n f\\\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\\\"\\n )\\n density = self.forward_density(points)\\n return density, None\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return -(field - threshold)\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry,\\n cfg: Optional[Union[dict, DictConfig]] = None,\\n copy_net: bool = True,\\n **kwargs,\\n ) -> \\\"ImplicitVolume\\\":\\n if isinstance(other, ImplicitVolume):\\n instance = ImplicitVolume(cfg, **kwargs)\\n instance.encoding.load_state_dict(other.encoding.state_dict())\\n instance.density_network.load_state_dict(other.density_network.state_dict())\\n if copy_net:\\n if (\\n instance.cfg.n_feature_dims > 0\\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\\n ):\\n instance.feature_network.load_state_dict(\\n other.feature_network.state_dict()\\n )\\n if (\\n instance.cfg.normal_type == \\\"pred\\\"\\n and other.cfg.normal_type == \\\"pred\\\"\\n ):\\n instance.normal_network.load_state_dict(\\n other.normal_network.state_dict()\\n )\\n return instance\\n else:\\n raise TypeError(\\n f\\\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\\\"\\n )\"\n },\n {\n \"identifier\": \"MarchingTetrahedraHelper\",\n \"path\": \"threestudio/models/isosurface.py\",\n \"snippet\": \"class MarchingTetrahedraHelper(IsosurfaceHelper):\\n def __init__(self, resolution: int, tets_path: str):\\n super().__init__()\\n self.resolution = resolution\\n self.tets_path = tets_path\\n\\n self.triangle_table: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"triangle_table\\\",\\n torch.as_tensor(\\n [\\n [-1, -1, -1, -1, -1, -1],\\n [1, 0, 2, -1, -1, -1],\\n [4, 0, 3, -1, -1, -1],\\n [1, 4, 2, 1, 3, 4],\\n [3, 1, 5, -1, -1, -1],\\n [2, 3, 0, 2, 5, 3],\\n [1, 4, 0, 1, 5, 4],\\n [4, 2, 5, -1, -1, -1],\\n [4, 5, 2, -1, -1, -1],\\n [4, 1, 0, 4, 5, 1],\\n [3, 2, 0, 3, 5, 2],\\n [1, 3, 5, -1, -1, -1],\\n [4, 1, 2, 4, 3, 1],\\n [3, 0, 4, -1, -1, -1],\\n [2, 0, 1, -1, -1, -1],\\n [-1, -1, -1, -1, -1, -1],\\n ],\\n dtype=torch.long,\\n ),\\n persistent=False,\\n )\\n self.num_triangles_table: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"num_triangles_table\\\",\\n torch.as_tensor(\\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\\n ),\\n persistent=False,\\n )\\n self.base_tet_edges: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"base_tet_edges\\\",\\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\\n persistent=False,\\n )\\n\\n tets = np.load(self.tets_path)\\n self._grid_vertices: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"_grid_vertices\\\",\\n torch.from_numpy(tets[\\\"vertices\\\"]).float(),\\n persistent=False,\\n )\\n self.indices: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"indices\\\", torch.from_numpy(tets[\\\"indices\\\"]).long(), persistent=False\\n )\\n\\n self._all_edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n\\n def normalize_grid_deformation(\\n self, grid_vertex_offsets: Float[Tensor, \\\"Nv 3\\\"]\\n ) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return (\\n (self.points_range[1] - self.points_range[0])\\n / (self.resolution) # half tet size is approximately 1 / self.resolution\\n * torch.tanh(grid_vertex_offsets)\\n ) # FIXME: hard-coded activation\\n\\n @property\\n def grid_vertices(self) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return self._grid_vertices\\n\\n @property\\n def all_edges(self) -> Integer[Tensor, \\\"Ne 2\\\"]:\\n if self._all_edges is None:\\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\\n edges = torch.tensor(\\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\\n dtype=torch.long,\\n device=self.indices.device,\\n )\\n _all_edges = self.indices[:, edges].reshape(-1, 2)\\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\\n self._all_edges = _all_edges\\n return self._all_edges\\n\\n def sort_edges(self, edges_ex2):\\n with torch.no_grad():\\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\\n order = order.unsqueeze(dim=1)\\n\\n a = torch.gather(input=edges_ex2, index=order, dim=1)\\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\\n\\n return torch.stack([a, b], -1)\\n\\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\\n with torch.no_grad():\\n occ_n = sdf_n > 0\\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\\n occ_sum = torch.sum(occ_fx4, -1)\\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\\n occ_sum = occ_sum[valid_tets]\\n\\n # find all vertices\\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\\n all_edges = self.sort_edges(all_edges)\\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\\n\\n unique_edges = unique_edges.long()\\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\\n mapping = (\\n torch.ones(\\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\\n )\\n * -1\\n )\\n mapping[mask_edges] = torch.arange(\\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\\n )\\n idx_map = mapping[idx_map] # map edges to verts\\n\\n interp_v = unique_edges[mask_edges]\\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\\n edges_to_interp_sdf[:, -1] *= -1\\n\\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\\n\\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\\n\\n idx_map = idx_map.reshape(-1, 6)\\n\\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\\n num_triangles = self.num_triangles_table[tetindex]\\n\\n # Generate triangle indices\\n faces = torch.cat(\\n (\\n torch.gather(\\n input=idx_map[num_triangles == 1],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\\n ).reshape(-1, 3),\\n torch.gather(\\n input=idx_map[num_triangles == 2],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\\n ).reshape(-1, 3),\\n ),\\n dim=0,\\n )\\n\\n return verts, faces\\n\\n def forward(\\n self,\\n level: Float[Tensor, \\\"N3 1\\\"],\\n deformation: Optional[Float[Tensor, \\\"N3 3\\\"]] = None,\\n ) -> Mesh:\\n if deformation is not None:\\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\\n deformation\\n )\\n else:\\n grid_vertices = self.grid_vertices\\n\\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\\n\\n mesh = Mesh(\\n v_pos=v_pos,\\n t_pos_idx=t_pos_idx,\\n # extras\\n grid_vertices=grid_vertices,\\n tet_edges=self.all_edges,\\n grid_level=level,\\n grid_deformation=deformation,\\n )\\n\\n return mesh\"\n },\n {\n \"identifier\": \"Mesh\",\n \"path\": \"threestudio/models/mesh.py\",\n \"snippet\": \"class Mesh:\\n def __init__(\\n self, v_pos: Float[Tensor, \\\"Nv 3\\\"], t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"], **kwargs\\n ) -> None:\\n self.v_pos: Float[Tensor, \\\"Nv 3\\\"] = v_pos\\n self.t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"] = t_pos_idx\\n self._v_nrm: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tng: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tex: Optional[Float[Tensor, \\\"Nt 3\\\"]] = None\\n self._t_tex_idx: Optional[Float[Tensor, \\\"Nf 3\\\"]] = None\\n self._v_rgb: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n self.extras: Dict[str, Any] = {}\\n for k, v in kwargs.items():\\n self.add_extra(k, v)\\n\\n def add_extra(self, k, v) -> None:\\n self.extras[k] = v\\n\\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\\n if self.requires_grad:\\n threestudio.debug(\\\"Mesh is differentiable, not removing outliers\\\")\\n return self\\n\\n # use trimesh to first split the mesh into connected components\\n # then remove the components with less than n_face_threshold faces\\n import trimesh\\n\\n # construct a trimesh object\\n mesh = trimesh.Trimesh(\\n vertices=self.v_pos.detach().cpu().numpy(),\\n faces=self.t_pos_idx.detach().cpu().numpy(),\\n )\\n\\n # split the mesh into connected components\\n components = mesh.split(only_watertight=False)\\n # log the number of faces in each component\\n threestudio.debug(\\n \\\"Mesh has {} components, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n\\n n_faces_threshold: int\\n if isinstance(outlier_n_faces_threshold, float):\\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\\n n_faces_threshold = int(\\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\\n )\\n else:\\n # set the threshold directly to outlier_n_faces_threshold\\n n_faces_threshold = outlier_n_faces_threshold\\n\\n # log the threshold\\n threestudio.debug(\\n \\\"Removing components with less than {} faces\\\".format(n_faces_threshold)\\n )\\n\\n # remove the components with less than n_face_threshold faces\\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\\n\\n # log the number of faces in each component after removing outliers\\n threestudio.debug(\\n \\\"Mesh has {} components after removing outliers, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n # merge the components\\n mesh = trimesh.util.concatenate(components)\\n\\n # convert back to our mesh format\\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\\n\\n clean_mesh = Mesh(v_pos, t_pos_idx)\\n # keep the extras unchanged\\n\\n if len(self.extras) > 0:\\n clean_mesh.extras = self.extras\\n threestudio.debug(\\n f\\\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\\\"\\n )\\n return clean_mesh\\n\\n @property\\n def requires_grad(self):\\n return self.v_pos.requires_grad\\n\\n @property\\n def v_nrm(self):\\n if self._v_nrm is None:\\n self._v_nrm = self._compute_vertex_normal()\\n return self._v_nrm\\n\\n @property\\n def v_tng(self):\\n if self._v_tng is None:\\n self._v_tng = self._compute_vertex_tangent()\\n return self._v_tng\\n\\n @property\\n def v_tex(self):\\n if self._v_tex is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._v_tex\\n\\n @property\\n def t_tex_idx(self):\\n if self._t_tex_idx is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._t_tex_idx\\n\\n @property\\n def v_rgb(self):\\n return self._v_rgb\\n\\n @property\\n def edges(self):\\n if self._edges is None:\\n self._edges = self._compute_edges()\\n return self._edges\\n\\n def _compute_vertex_normal(self):\\n i0 = self.t_pos_idx[:, 0]\\n i1 = self.t_pos_idx[:, 1]\\n i2 = self.t_pos_idx[:, 2]\\n\\n v0 = self.v_pos[i0, :]\\n v1 = self.v_pos[i1, :]\\n v2 = self.v_pos[i2, :]\\n\\n face_normals = torch.cross(v1 - v0, v2 - v0)\\n\\n # Splat face normals to vertices\\n v_nrm = torch.zeros_like(self.v_pos)\\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\\n\\n # Normalize, replace zero (degenerated) normals with some default value\\n v_nrm = torch.where(\\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\\n )\\n v_nrm = F.normalize(v_nrm, dim=1)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(v_nrm))\\n\\n return v_nrm\\n\\n def _compute_vertex_tangent(self):\\n vn_idx = [None] * 3\\n pos = [None] * 3\\n tex = [None] * 3\\n for i in range(0, 3):\\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\\n # t_nrm_idx is always the same as t_pos_idx\\n vn_idx[i] = self.t_pos_idx[:, i]\\n\\n tangents = torch.zeros_like(self.v_nrm)\\n tansum = torch.zeros_like(self.v_nrm)\\n\\n # Compute tangent space for each triangle\\n uve1 = tex[1] - tex[0]\\n uve2 = tex[2] - tex[0]\\n pe1 = pos[1] - pos[0]\\n pe2 = pos[2] - pos[0]\\n\\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\\n\\n # Avoid division by zero for degenerated texture coordinates\\n tang = nom / torch.where(\\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\\n )\\n\\n # Update all 3 vertices\\n for i in range(0, 3):\\n idx = vn_idx[i][:, None].repeat(1, 3)\\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\\n tansum.scatter_add_(\\n 0, idx, torch.ones_like(tang)\\n ) # tansum[n_i] = tansum[n_i] + 1\\n tangents = tangents / tansum\\n\\n # Normalize and make sure tangent is perpendicular to normal\\n tangents = F.normalize(tangents, dim=1)\\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(tangents))\\n\\n return tangents\\n\\n def _unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n threestudio.info(\\\"Using xatlas to perform UV unwrapping, may take a while ...\\\")\\n\\n import xatlas\\n\\n atlas = xatlas.Atlas()\\n atlas.add_mesh(\\n self.v_pos.detach().cpu().numpy(),\\n self.t_pos_idx.cpu().numpy(),\\n )\\n co = xatlas.ChartOptions()\\n po = xatlas.PackOptions()\\n for k, v in xatlas_chart_options.items():\\n setattr(co, k, v)\\n for k, v in xatlas_pack_options.items():\\n setattr(po, k, v)\\n atlas.generate(co, po)\\n vmapping, indices, uvs = atlas.get_mesh(0)\\n vmapping = (\\n torch.from_numpy(\\n vmapping.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\\n indices = (\\n torch.from_numpy(\\n indices.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n return uvs, indices\\n\\n def unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\\n xatlas_chart_options, xatlas_pack_options\\n )\\n\\n def set_vertex_color(self, v_rgb):\\n assert v_rgb.shape[0] == self.v_pos.shape[0]\\n self._v_rgb = v_rgb\\n\\n def _compute_edges(self):\\n # Compute edges\\n edges = torch.cat(\\n [\\n self.t_pos_idx[:, [0, 1]],\\n self.t_pos_idx[:, [1, 2]],\\n self.t_pos_idx[:, [2, 0]],\\n ],\\n dim=0,\\n )\\n edges = edges.sort()[0]\\n edges = torch.unique(edges, dim=0)\\n return edges\\n\\n def normal_consistency(self) -> Float[Tensor, \\\"\\\"]:\\n edge_nrm: Float[Tensor, \\\"Ne 2 3\\\"] = self.v_nrm[self.edges]\\n nc = (\\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\\n ).mean()\\n return nc\\n\\n def _laplacian_uniform(self):\\n # from stable-dreamfusion\\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\\n verts, faces = self.v_pos, self.t_pos_idx\\n\\n V = verts.shape[0]\\n F = faces.shape[0]\\n\\n # Neighbor indices\\n ii = faces[:, [1, 2, 0]].flatten()\\n jj = faces[:, [2, 0, 1]].flatten()\\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\\n dim=1\\n )\\n adj_values = torch.ones(adj.shape[1]).to(verts)\\n\\n # Diagonal indices\\n diag_idx = adj[0]\\n\\n # Build the sparse matrix\\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\\n values = torch.cat((-adj_values, adj_values))\\n\\n # The coalesce operation sums the duplicate indices, resulting in the\\n # correct diagonal\\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\\n\\n def laplacian(self) -> Float[Tensor, \\\"\\\"]:\\n with torch.no_grad():\\n L = self._laplacian_uniform()\\n loss = L.mm(self.v_pos)\\n loss = loss.norm(dim=1)\\n loss = loss.mean()\\n return loss\"\n },\n {\n \"identifier\": \"get_encoding\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_encoding(n_input_dims: int, config) -> nn.Module:\\n # input suppose to be range [0, 1]\\n encoding: nn.Module\\n if config.otype == \\\"ProgressiveBandFrequency\\\":\\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\\n elif config.otype == \\\"ProgressiveBandHashGrid\\\":\\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\\n else:\\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\\n encoding = CompositeEncoding(\\n encoding,\\n include_xyz=config.get(\\\"include_xyz\\\", False),\\n xyz_scale=2.0,\\n xyz_offset=-1.0,\\n ) # FIXME: hard coded\\n return encoding\"\n },\n {\n \"identifier\": \"get_mlp\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\\n network: nn.Module\\n if config.otype == \\\"VanillaMLP\\\":\\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\\n elif config.otype == \\\"SphereInitVanillaMLP\\\":\\n network = SphereInitVanillaMLP(\\n n_input_dims, n_output_dims, config_to_primitive(config)\\n )\\n else:\\n assert (\\n config.get(\\\"sphere_init\\\", False) is False\\n ), \\\"sphere_init=True only supported by VanillaMLP\\\"\\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\\n return network\"\n },\n {\n \"identifier\": \"broadcast\",\n \"path\": \"threestudio/utils/misc.py\",\n \"snippet\": \"def broadcast(tensor, src=0):\\n if not _distributed_available():\\n return tensor\\n else:\\n torch.distributed.broadcast(tensor, src=src)\\n return tensor\"\n },\n {\n \"identifier\": \"scale_tensor\",\n \"path\": \"threestudio/utils/ops.py\",\n \"snippet\": \"def scale_tensor(\\n dat: Num[Tensor, \\\"... D\\\"], inp_scale: ValidScale, tgt_scale: ValidScale\\n):\\n if inp_scale is None:\\n inp_scale = (0, 1)\\n if tgt_scale is None:\\n tgt_scale = (0, 1)\\n if isinstance(tgt_scale, Tensor):\\n assert dat.shape[-1] == tgt_scale.shape[-1]\\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\\n return dat\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport threestudio\n import trimesh\nfrom dataclasses import dataclass, field\nfrom threestudio.models.geometry.base import (\n BaseExplicitGeometry,\n BaseGeometry,\n contract_to_unisphere,\n)\nfrom threestudio.models.geometry.implicit_sdf import ImplicitSDF\nfrom threestudio.models.geometry.implicit_volume import ImplicitVolume\nfrom threestudio.models.isosurface import MarchingTetrahedraHelper\nfrom threestudio.models.mesh import Mesh\nfrom threestudio.models.networks import get_encoding, get_mlp\nfrom threestudio.utils.misc import broadcast\nfrom threestudio.utils.ops import scale_tensor\nfrom threestudio.utils.typing import *\n from pysdf import SDF"},"token_num":{"kind":"number","value":14932,"string":"14,932"},"cropped_code":{"kind":"string","value":" if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale\n"},"all_code":{"kind":"string","value":"\n\n\n\n@threestudio.register(\"tetrahedra-sdf-grid\")\nclass TetrahedraSDFGrid(BaseExplicitGeometry):\n @dataclass\n class Config(BaseExplicitGeometry.Config):\n isosurface_resolution: int = 128\n isosurface_deformable_grid: bool = True\n isosurface_remove_outliers: bool = False\n isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01\n\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n geometry_only: bool = False\n fix_geometry: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n\n # this should be saved to state_dict, register as buffer\n self.isosurface_bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\"isosurface_bbox\", self.bbox.clone())\n\n self.isosurface_helper = MarchingTetrahedraHelper(\n self.cfg.isosurface_resolution,\n f\"load/tets/{self.cfg.isosurface_resolution}_tets.npz\",\n )\n\n self.sdf: Float[Tensor, \"Nv 1\"]\n self.deformation: Optional[Float[Tensor, \"Nv 3\"]]\n\n if not self.cfg.fix_geometry:\n self.register_parameter(\n \"sdf\",\n nn.Parameter(\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n )\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_parameter(\n \"deformation\",\n nn.Parameter(\n torch.zeros_like(self.isosurface_helper.grid_vertices)\n ),\n )\n else:\n self.deformation = None\n else:\n self.register_buffer(\n \"sdf\",\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_buffer(\n \"deformation\",\n torch.zeros_like(self.isosurface_helper.grid_vertices),\n )\n else:\n self.deformation = None\n\n if not self.cfg.geometry_only:\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n self.mesh: Optional[Mesh] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale"},"next_line":{"kind":"string","value":" points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)"},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-11-27 02:39:39+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5372,"cells":{"repo_name":{"kind":"string","value":"EricGuo5513/momask-codes"},"file_path":{"kind":"string","value":"gen_t2m.py"},"context":{"kind":"list like","value":[{"identifier":"MaskTransformer","path":"models/mask_transformer/transformer.py","snippet":"class MaskTransformer(nn.Module):\n def __init__(self, code_dim, cond_mode, latent_dim=256, ff_size=1024, num_layers=8,\n num_heads=4, dropout=0.1, clip_dim=512, cond_drop_prob=0.1,\n clip_version=None, opt=None, **kargs):\n super(MaskTransformer, self).__init__()\n print(f'latent_dim: {latent_dim}, ff_size: {ff_size}, nlayers: {num_layers}, nheads: {num_heads}, dropout: {dropout}')\n\n self.code_dim = code_dim\n self.latent_dim = latent_dim\n self.clip_dim = clip_dim\n self.dropout = dropout\n self.opt = opt\n\n self.cond_mode = cond_mode\n self.cond_drop_prob = cond_drop_prob\n\n if self.cond_mode == 'action':\n assert 'num_actions' in kargs\n self.num_actions = kargs.get('num_actions', 1)\n\n '''\n Preparing Networks\n '''\n self.input_process = InputProcess(self.code_dim, self.latent_dim)\n self.position_enc = PositionalEncoding(self.latent_dim, self.dropout)\n\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\n nhead=num_heads,\n dim_feedforward=ff_size,\n dropout=dropout,\n activation='gelu')\n\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\n num_layers=num_layers)\n\n self.encode_action = partial(F.one_hot, num_classes=self.num_actions)\n\n # if self.cond_mode != 'no_cond':\n if self.cond_mode == 'text':\n self.cond_emb = nn.Linear(self.clip_dim, self.latent_dim)\n elif self.cond_mode == 'action':\n self.cond_emb = nn.Linear(self.num_actions, self.latent_dim)\n elif self.cond_mode == 'uncond':\n self.cond_emb = nn.Identity()\n else:\n raise KeyError(\"Unsupported condition mode!!!\")\n\n\n _num_tokens = opt.num_tokens + 2 # two dummy tokens, one for masking, one for padding\n self.mask_id = opt.num_tokens\n self.pad_id = opt.num_tokens + 1\n\n self.output_process = OutputProcess_Bert(out_feats=opt.num_tokens, latent_dim=latent_dim)\n\n self.token_emb = nn.Embedding(_num_tokens, self.code_dim)\n\n self.apply(self.__init_weights)\n\n '''\n Preparing frozen weights\n '''\n\n if self.cond_mode == 'text':\n print('Loading CLIP...')\n self.clip_version = clip_version\n self.clip_model = self.load_and_freeze_clip(clip_version)\n\n self.noise_schedule = cosine_schedule\n\n def load_and_freeze_token_emb(self, codebook):\n '''\n :param codebook: (c, d)\n :return:\n '''\n assert self.training, 'Only necessary in training mode'\n c, d = codebook.shape\n self.token_emb.weight = nn.Parameter(torch.cat([codebook, torch.zeros(size=(2, d), device=codebook.device)], dim=0)) #add two dummy tokens, 0 vectors\n self.token_emb.requires_grad_(False)\n # self.token_emb.weight.requires_grad = False\n # self.token_emb_ready = True\n print(\"Token embedding initialized!\")\n\n def __init_weights(self, module):\n if isinstance(module, (nn.Linear, nn.Embedding)):\n module.weight.data.normal_(mean=0.0, std=0.02)\n if isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n\n def parameters_wo_clip(self):\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\n\n def load_and_freeze_clip(self, clip_version):\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\n jit=False) # Must set jit=False for training\n # Cannot run on cpu\n clip.model.convert_weights(\n clip_model) # Actually this line is unnecessary since clip by default already on float16\n # Date 0707: It's necessary, only unecessary when load directly to gpu. Disable if need to run on cpu\n\n # Freeze CLIP weights\n clip_model.eval()\n for p in clip_model.parameters():\n p.requires_grad = False\n\n return clip_model\n\n def encode_text(self, raw_text):\n device = next(self.parameters()).device\n text = clip.tokenize(raw_text, truncate=True).to(device)\n feat_clip_text = self.clip_model.encode_text(text).float()\n return feat_clip_text\n\n def mask_cond(self, cond, force_mask=False):\n bs, d = cond.shape\n if force_mask:\n return torch.zeros_like(cond)\n elif self.training and self.cond_drop_prob > 0.:\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_drop_prob).view(bs, 1)\n return cond * (1. - mask)\n else:\n return cond\n\n def trans_forward(self, motion_ids, cond, padding_mask, force_mask=False):\n '''\n :param motion_ids: (b, seqlen)\n :padding_mask: (b, seqlen), all pad positions are TRUE else FALSE\n :param cond: (b, embed_dim) for text, (b, num_actions) for action\n :param force_mask: boolean\n :return:\n -logits: (b, num_token, seqlen)\n '''\n\n cond = self.mask_cond(cond, force_mask=force_mask)\n\n # print(motion_ids.shape)\n x = self.token_emb(motion_ids)\n # print(x.shape)\n # (b, seqlen, d) -> (seqlen, b, latent_dim)\n x = self.input_process(x)\n\n cond = self.cond_emb(cond).unsqueeze(0) #(1, b, latent_dim)\n\n x = self.position_enc(x)\n xseq = torch.cat([cond, x], dim=0) #(seqlen+1, b, latent_dim)\n\n padding_mask = torch.cat([torch.zeros_like(padding_mask[:, 0:1]), padding_mask], dim=1) #(b, seqlen+1)\n # print(xseq.shape, padding_mask.shape)\n\n # print(padding_mask.shape, xseq.shape)\n\n output = self.seqTransEncoder(xseq, src_key_padding_mask=padding_mask)[1:] #(seqlen, b, e)\n logits = self.output_process(output) #(seqlen, b, e) -> (b, ntoken, seqlen)\n return logits\n\n def forward(self, ids, y, m_lens):\n '''\n :param ids: (b, n)\n :param y: raw text for cond_mode=text, (b, ) for cond_mode=action\n :m_lens: (b,)\n :return:\n '''\n\n bs, ntokens = ids.shape\n device = ids.device\n\n # Positions that are PADDED are ALL FALSE\n non_pad_mask = lengths_to_mask(m_lens, ntokens) #(b, n)\n ids = torch.where(non_pad_mask, ids, self.pad_id)\n\n force_mask = False\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(y)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(y).to(device).float()\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(bs, self.latent_dim).float().to(device)\n force_mask = True\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n\n '''\n Prepare mask\n '''\n rand_time = uniform((bs,), device=device)\n rand_mask_probs = self.noise_schedule(rand_time)\n num_token_masked = (ntokens * rand_mask_probs).round().clamp(min=1)\n\n batch_randperm = torch.rand((bs, ntokens), device=device).argsort(dim=-1)\n # Positions to be MASKED are ALL TRUE\n mask = batch_randperm < num_token_masked.unsqueeze(-1)\n\n # Positions to be MASKED must also be NON-PADDED\n mask &= non_pad_mask\n\n # Note this is our training target, not input\n labels = torch.where(mask, ids, self.mask_id)\n\n x_ids = ids.clone()\n\n # Further Apply Bert Masking Scheme\n # Step 1: 10% replace with an incorrect token\n mask_rid = get_mask_subset_prob(mask, 0.1)\n rand_id = torch.randint_like(x_ids, high=self.opt.num_tokens)\n x_ids = torch.where(mask_rid, rand_id, x_ids)\n # Step 2: 90% x 10% replace with correct token, and 90% x 88% replace with mask token\n mask_mid = get_mask_subset_prob(mask & ~mask_rid, 0.88)\n\n # mask_mid = mask\n\n x_ids = torch.where(mask_mid, self.mask_id, x_ids)\n\n logits = self.trans_forward(x_ids, cond_vector, ~non_pad_mask, force_mask)\n ce_loss, pred_id, acc = cal_performance(logits, labels, ignore_index=self.mask_id)\n\n return ce_loss, pred_id, acc\n\n def forward_with_cond_scale(self,\n motion_ids,\n cond_vector,\n padding_mask,\n cond_scale=3,\n force_mask=False):\n # bs = motion_ids.shape[0]\n # if cond_scale == 1:\n if force_mask:\n return self.trans_forward(motion_ids, cond_vector, padding_mask, force_mask=True)\n\n logits = self.trans_forward(motion_ids, cond_vector, padding_mask)\n if cond_scale == 1:\n return logits\n\n aux_logits = self.trans_forward(motion_ids, cond_vector, padding_mask, force_mask=True)\n\n scaled_logits = aux_logits + (logits - aux_logits) * cond_scale\n return scaled_logits\n\n @torch.no_grad()\n @eval_decorator\n def generate(self,\n conds,\n m_lens,\n timesteps: int,\n cond_scale: int,\n temperature=1,\n topk_filter_thres=0.9,\n gsample=False,\n force_mask=False\n ):\n # print(self.opt.num_quantizers)\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\n\n device = next(self.parameters()).device\n seq_len = max(m_lens)\n batch_size = len(m_lens)\n\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(conds)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(conds).to(device)\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\n # print(padding_mask.shape, )\n\n # Start from all tokens being masked\n ids = torch.where(padding_mask, self.pad_id, self.mask_id)\n scores = torch.where(padding_mask, 1e5, 0.)\n starting_temperature = temperature\n\n for timestep, steps_until_x0 in zip(torch.linspace(0, 1, timesteps, device=device), reversed(range(timesteps))):\n # 0 < timestep < 1\n rand_mask_prob = self.noise_schedule(timestep) # Tensor\n\n '''\n Maskout, and cope with variable length\n '''\n # fix: the ratio regarding lengths, instead of seq_len\n num_token_masked = torch.round(rand_mask_prob * m_lens).clamp(min=1) # (b, )\n\n # select num_token_masked tokens with lowest scores to be masked\n sorted_indices = scores.argsort(\n dim=1) # (b, k), sorted_indices[i, j] = the index of j-th lowest element in scores on dim=1\n ranks = sorted_indices.argsort(dim=1) # (b, k), rank[i, j] = the rank (0: lowest) of scores[i, j] on dim=1\n is_mask = (ranks < num_token_masked.unsqueeze(-1))\n ids = torch.where(is_mask, self.mask_id, ids)\n\n '''\n Preparing input\n '''\n # (b, num_token, seqlen)\n logits = self.forward_with_cond_scale(ids, cond_vector=cond_vector,\n padding_mask=padding_mask,\n cond_scale=cond_scale,\n force_mask=force_mask)\n\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\n # print(logits.shape, self.opt.num_tokens)\n # clean low prob token\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\n\n '''\n Update ids\n '''\n # if force_mask:\n temperature = starting_temperature\n # else:\n # temperature = starting_temperature * (steps_until_x0 / timesteps)\n # temperature = max(temperature, 1e-4)\n # print(filtered_logits.shape)\n # temperature is annealed, gradually reducing temperature as well as randomness\n if gsample: # use gumbel_softmax sampling\n # print(\"1111\")\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\n else: # use multinomial sampling\n # print(\"2222\")\n probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\n # print(temperature, starting_temperature, steps_until_x0, timesteps)\n # print(probs / temperature)\n pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\n\n # print(pred_ids.max(), pred_ids.min())\n # if pred_ids.\n ids = torch.where(is_mask, pred_ids, ids)\n\n '''\n Updating scores\n '''\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\n scores = probs_without_temperature.gather(2, pred_ids.unsqueeze(dim=-1)) # (b, seqlen, 1)\n scores = scores.squeeze(-1) # (b, seqlen)\n\n # We do not want to re-mask the previously kept tokens, or pad tokens\n scores = scores.masked_fill(~is_mask, 1e5)\n\n ids = torch.where(padding_mask, -1, ids)\n # print(\"Final\", ids.max(), ids.min())\n return ids\n\n\n @torch.no_grad()\n @eval_decorator\n def edit(self,\n conds,\n tokens,\n m_lens,\n timesteps: int,\n cond_scale: int,\n temperature=1,\n topk_filter_thres=0.9,\n gsample=False,\n force_mask=False,\n edit_mask=None,\n padding_mask=None,\n ):\n\n assert edit_mask.shape == tokens.shape if edit_mask is not None else True\n device = next(self.parameters()).device\n seq_len = tokens.shape[1]\n\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(conds)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(conds).to(device)\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(1, self.latent_dim).float().to(device)\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n if padding_mask == None:\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\n\n # Start from all tokens being masked\n if edit_mask == None:\n mask_free = True\n ids = torch.where(padding_mask, self.pad_id, tokens)\n edit_mask = torch.ones_like(padding_mask)\n edit_mask = edit_mask & ~padding_mask\n edit_len = edit_mask.sum(dim=-1)\n scores = torch.where(edit_mask, 0., 1e5)\n else:\n mask_free = False\n edit_mask = edit_mask & ~padding_mask\n edit_len = edit_mask.sum(dim=-1)\n ids = torch.where(edit_mask, self.mask_id, tokens)\n scores = torch.where(edit_mask, 0., 1e5)\n starting_temperature = temperature\n\n for timestep, steps_until_x0 in zip(torch.linspace(0, 1, timesteps, device=device), reversed(range(timesteps))):\n # 0 < timestep < 1\n rand_mask_prob = 0.16 if mask_free else self.noise_schedule(timestep) # Tensor\n\n '''\n Maskout, and cope with variable length\n '''\n # fix: the ratio regarding lengths, instead of seq_len\n num_token_masked = torch.round(rand_mask_prob * edit_len).clamp(min=1) # (b, )\n\n # select num_token_masked tokens with lowest scores to be masked\n sorted_indices = scores.argsort(\n dim=1) # (b, k), sorted_indices[i, j] = the index of j-th lowest element in scores on dim=1\n ranks = sorted_indices.argsort(dim=1) # (b, k), rank[i, j] = the rank (0: lowest) of scores[i, j] on dim=1\n is_mask = (ranks < num_token_masked.unsqueeze(-1))\n # is_mask = (torch.rand_like(scores) < 0.8) * ~padding_mask if mask_free else is_mask\n ids = torch.where(is_mask, self.mask_id, ids)\n\n '''\n Preparing input\n '''\n # (b, num_token, seqlen)\n logits = self.forward_with_cond_scale(ids, cond_vector=cond_vector,\n padding_mask=padding_mask,\n cond_scale=cond_scale,\n force_mask=force_mask)\n\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\n # print(logits.shape, self.opt.num_tokens)\n # clean low prob token\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\n\n '''\n Update ids\n '''\n # if force_mask:\n temperature = starting_temperature\n # else:\n # temperature = starting_temperature * (steps_until_x0 / timesteps)\n # temperature = max(temperature, 1e-4)\n # print(filtered_logits.shape)\n # temperature is annealed, gradually reducing temperature as well as randomness\n if gsample: # use gumbel_softmax sampling\n # print(\"1111\")\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\n else: # use multinomial sampling\n # print(\"2222\")\n probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\n # print(temperature, starting_temperature, steps_until_x0, timesteps)\n # print(probs / temperature)\n pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\n\n # print(pred_ids.max(), pred_ids.min())\n # if pred_ids.\n ids = torch.where(is_mask, pred_ids, ids)\n\n '''\n Updating scores\n '''\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\n scores = probs_without_temperature.gather(2, pred_ids.unsqueeze(dim=-1)) # (b, seqlen, 1)\n scores = scores.squeeze(-1) # (b, seqlen)\n\n # We do not want to re-mask the previously kept tokens, or pad tokens\n scores = scores.masked_fill(~edit_mask, 1e5) if mask_free else scores.masked_fill(~is_mask, 1e5)\n\n ids = torch.where(padding_mask, -1, ids)\n # print(\"Final\", ids.max(), ids.min())\n return ids\n\n @torch.no_grad()\n @eval_decorator\n def edit_beta(self,\n conds,\n conds_og,\n tokens,\n m_lens,\n cond_scale: int,\n force_mask=False,\n ):\n\n device = next(self.parameters()).device\n seq_len = tokens.shape[1]\n\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(conds)\n if conds_og is not None:\n cond_vector_og = self.encode_text(conds_og)\n else:\n cond_vector_og = None\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(conds).to(device)\n if conds_og is not None:\n cond_vector_og = self.enc_action(conds_og).to(device)\n else:\n cond_vector_og = None\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\n\n # Start from all tokens being masked\n ids = torch.where(padding_mask, self.pad_id, tokens) # Do not mask anything\n\n '''\n Preparing input\n '''\n # (b, num_token, seqlen)\n logits = self.forward_with_cond_scale(ids,\n cond_vector=cond_vector,\n cond_vector_neg=cond_vector_og,\n padding_mask=padding_mask,\n cond_scale=cond_scale,\n force_mask=force_mask)\n\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\n\n '''\n Updating scores\n '''\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\n tokens[tokens == -1] = 0 # just to get through an error when index = -1 using gather\n og_tokens_scores = probs_without_temperature.gather(2, tokens.unsqueeze(dim=-1)) # (b, seqlen, 1)\n og_tokens_scores = og_tokens_scores.squeeze(-1) # (b, seqlen)\n\n return og_tokens_scores"},{"identifier":"ResidualTransformer","path":"models/mask_transformer/transformer.py","snippet":"class ResidualTransformer(nn.Module):\n def __init__(self, code_dim, cond_mode, latent_dim=256, ff_size=1024, num_layers=8, cond_drop_prob=0.1,\n num_heads=4, dropout=0.1, clip_dim=512, shared_codebook=False, share_weight=False,\n clip_version=None, opt=None, **kargs):\n super(ResidualTransformer, self).__init__()\n print(f'latent_dim: {latent_dim}, ff_size: {ff_size}, nlayers: {num_layers}, nheads: {num_heads}, dropout: {dropout}')\n\n # assert shared_codebook == True, \"Only support shared codebook right now!\"\n\n self.code_dim = code_dim\n self.latent_dim = latent_dim\n self.clip_dim = clip_dim\n self.dropout = dropout\n self.opt = opt\n\n self.cond_mode = cond_mode\n # self.cond_drop_prob = cond_drop_prob\n\n if self.cond_mode == 'action':\n assert 'num_actions' in kargs\n self.num_actions = kargs.get('num_actions', 1)\n self.cond_drop_prob = cond_drop_prob\n\n '''\n Preparing Networks\n '''\n self.input_process = InputProcess(self.code_dim, self.latent_dim)\n self.position_enc = PositionalEncoding(self.latent_dim, self.dropout)\n\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\n nhead=num_heads,\n dim_feedforward=ff_size,\n dropout=dropout,\n activation='gelu')\n\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\n num_layers=num_layers)\n\n self.encode_quant = partial(F.one_hot, num_classes=self.opt.num_quantizers)\n self.encode_action = partial(F.one_hot, num_classes=self.num_actions)\n\n self.quant_emb = nn.Linear(self.opt.num_quantizers, self.latent_dim)\n # if self.cond_mode != 'no_cond':\n if self.cond_mode == 'text':\n self.cond_emb = nn.Linear(self.clip_dim, self.latent_dim)\n elif self.cond_mode == 'action':\n self.cond_emb = nn.Linear(self.num_actions, self.latent_dim)\n else:\n raise KeyError(\"Unsupported condition mode!!!\")\n\n\n _num_tokens = opt.num_tokens + 1 # one dummy tokens for padding\n self.pad_id = opt.num_tokens\n\n # self.output_process = OutputProcess_Bert(out_feats=opt.num_tokens, latent_dim=latent_dim)\n self.output_process = OutputProcess(out_feats=code_dim, latent_dim=latent_dim)\n\n if shared_codebook:\n token_embed = nn.Parameter(torch.normal(mean=0, std=0.02, size=(_num_tokens, code_dim)))\n self.token_embed_weight = token_embed.expand(opt.num_quantizers-1, _num_tokens, code_dim)\n if share_weight:\n self.output_proj_weight = self.token_embed_weight\n self.output_proj_bias = None\n else:\n output_proj = nn.Parameter(torch.normal(mean=0, std=0.02, size=(_num_tokens, code_dim)))\n output_bias = nn.Parameter(torch.zeros(size=(_num_tokens,)))\n # self.output_proj_bias = 0\n self.output_proj_weight = output_proj.expand(opt.num_quantizers-1, _num_tokens, code_dim)\n self.output_proj_bias = output_bias.expand(opt.num_quantizers-1, _num_tokens)\n\n else:\n if share_weight:\n self.embed_proj_shared_weight = nn.Parameter(torch.normal(mean=0, std=0.02, size=(opt.num_quantizers - 2, _num_tokens, code_dim)))\n self.token_embed_weight_ = nn.Parameter(torch.normal(mean=0, std=0.02, size=(1, _num_tokens, code_dim)))\n self.output_proj_weight_ = nn.Parameter(torch.normal(mean=0, std=0.02, size=(1, _num_tokens, code_dim)))\n self.output_proj_bias = None\n self.registered = False\n else:\n output_proj_weight = torch.normal(mean=0, std=0.02,\n size=(opt.num_quantizers - 1, _num_tokens, code_dim))\n\n self.output_proj_weight = nn.Parameter(output_proj_weight)\n self.output_proj_bias = nn.Parameter(torch.zeros(size=(opt.num_quantizers, _num_tokens)))\n token_embed_weight = torch.normal(mean=0, std=0.02,\n size=(opt.num_quantizers - 1, _num_tokens, code_dim))\n self.token_embed_weight = nn.Parameter(token_embed_weight)\n\n self.apply(self.__init_weights)\n self.shared_codebook = shared_codebook\n self.share_weight = share_weight\n\n if self.cond_mode == 'text':\n print('Loading CLIP...')\n self.clip_version = clip_version\n self.clip_model = self.load_and_freeze_clip(clip_version)\n\n # def\n\n def mask_cond(self, cond, force_mask=False):\n bs, d = cond.shape\n if force_mask:\n return torch.zeros_like(cond)\n elif self.training and self.cond_drop_prob > 0.:\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_drop_prob).view(bs, 1)\n return cond * (1. - mask)\n else:\n return cond\n\n def __init_weights(self, module):\n if isinstance(module, (nn.Linear, nn.Embedding)):\n module.weight.data.normal_(mean=0.0, std=0.02)\n if isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n\n def parameters_wo_clip(self):\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\n\n def load_and_freeze_clip(self, clip_version):\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\n jit=False) # Must set jit=False for training\n # Cannot run on cpu\n clip.model.convert_weights(\n clip_model) # Actually this line is unnecessary since clip by default already on float16\n # Date 0707: It's necessary, only unecessary when load directly to gpu. Disable if need to run on cpu\n\n # Freeze CLIP weights\n clip_model.eval()\n for p in clip_model.parameters():\n p.requires_grad = False\n\n return clip_model\n\n def encode_text(self, raw_text):\n device = next(self.parameters()).device\n text = clip.tokenize(raw_text, truncate=True).to(device)\n feat_clip_text = self.clip_model.encode_text(text).float()\n return feat_clip_text\n\n\n def q_schedule(self, bs, low, high):\n noise = uniform((bs,), device=self.opt.device)\n schedule = 1 - cosine_schedule(noise)\n return torch.round(schedule * (high - low)) + low\n\n def process_embed_proj_weight(self):\n if self.share_weight and (not self.shared_codebook):\n # if not self.registered:\n self.output_proj_weight = torch.cat([self.embed_proj_shared_weight, self.output_proj_weight_], dim=0)\n self.token_embed_weight = torch.cat([self.token_embed_weight_, self.embed_proj_shared_weight], dim=0)\n # self.registered = True\n\n def output_project(self, logits, qids):\n '''\n :logits: (bs, code_dim, seqlen)\n :qids: (bs)\n\n :return:\n -logits (bs, ntoken, seqlen)\n '''\n # (num_qlayers-1, num_token, code_dim) -> (bs, ntoken, code_dim)\n output_proj_weight = self.output_proj_weight[qids]\n # (num_qlayers, ntoken) -> (bs, ntoken)\n output_proj_bias = None if self.output_proj_bias is None else self.output_proj_bias[qids]\n\n output = torch.einsum('bnc, bcs->bns', output_proj_weight, logits)\n if output_proj_bias is not None:\n output += output + output_proj_bias.unsqueeze(-1)\n return output\n\n\n\n def trans_forward(self, motion_codes, qids, cond, padding_mask, force_mask=False):\n '''\n :param motion_codes: (b, seqlen, d)\n :padding_mask: (b, seqlen), all pad positions are TRUE else FALSE\n :param qids: (b), quantizer layer ids\n :param cond: (b, embed_dim) for text, (b, num_actions) for action\n :return:\n -logits: (b, num_token, seqlen)\n '''\n cond = self.mask_cond(cond, force_mask=force_mask)\n\n # (b, seqlen, d) -> (seqlen, b, latent_dim)\n x = self.input_process(motion_codes)\n\n # (b, num_quantizer)\n q_onehot = self.encode_quant(qids).float().to(x.device)\n\n q_emb = self.quant_emb(q_onehot).unsqueeze(0) # (1, b, latent_dim)\n cond = self.cond_emb(cond).unsqueeze(0) # (1, b, latent_dim)\n\n x = self.position_enc(x)\n xseq = torch.cat([cond, q_emb, x], dim=0) # (seqlen+2, b, latent_dim)\n\n padding_mask = torch.cat([torch.zeros_like(padding_mask[:, 0:2]), padding_mask], dim=1) # (b, seqlen+2)\n output = self.seqTransEncoder(xseq, src_key_padding_mask=padding_mask)[2:] # (seqlen, b, e)\n logits = self.output_process(output)\n return logits\n\n def forward_with_cond_scale(self,\n motion_codes,\n q_id,\n cond_vector,\n padding_mask,\n cond_scale=3,\n force_mask=False):\n bs = motion_codes.shape[0]\n # if cond_scale == 1:\n qids = torch.full((bs,), q_id, dtype=torch.long, device=motion_codes.device)\n if force_mask:\n logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask, force_mask=True)\n logits = self.output_project(logits, qids-1)\n return logits\n\n logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask)\n logits = self.output_project(logits, qids-1)\n if cond_scale == 1:\n return logits\n\n aux_logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask, force_mask=True)\n aux_logits = self.output_project(aux_logits, qids-1)\n\n scaled_logits = aux_logits + (logits - aux_logits) * cond_scale\n return scaled_logits\n\n def forward(self, all_indices, y, m_lens):\n '''\n :param all_indices: (b, n, q)\n :param y: raw text for cond_mode=text, (b, ) for cond_mode=action\n :m_lens: (b,)\n :return:\n '''\n\n self.process_embed_proj_weight()\n\n bs, ntokens, num_quant_layers = all_indices.shape\n device = all_indices.device\n\n # Positions that are PADDED are ALL FALSE\n non_pad_mask = lengths_to_mask(m_lens, ntokens) # (b, n)\n\n q_non_pad_mask = repeat(non_pad_mask, 'b n -> b n q', q=num_quant_layers)\n all_indices = torch.where(q_non_pad_mask, all_indices, self.pad_id) #(b, n, q)\n\n # randomly sample quantization layers to work on, [1, num_q)\n active_q_layers = q_schedule(bs, low=1, high=num_quant_layers, device=device)\n\n # print(self.token_embed_weight.shape, all_indices.shape)\n token_embed = repeat(self.token_embed_weight, 'q c d-> b c d q', b=bs)\n gather_indices = repeat(all_indices[..., :-1], 'b n q -> b n d q', d=token_embed.shape[2])\n # print(token_embed.shape, gather_indices.shape)\n all_codes = token_embed.gather(1, gather_indices) # (b, n, d, q-1)\n\n cumsum_codes = torch.cumsum(all_codes, dim=-1) #(b, n, d, q-1)\n\n active_indices = all_indices[torch.arange(bs), :, active_q_layers] # (b, n)\n history_sum = cumsum_codes[torch.arange(bs), :, :, active_q_layers - 1]\n\n force_mask = False\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(y)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(y).to(device).float()\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(bs, self.latent_dim).float().to(device)\n force_mask = True\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n logits = self.trans_forward(history_sum, active_q_layers, cond_vector, ~non_pad_mask, force_mask)\n logits = self.output_project(logits, active_q_layers-1)\n ce_loss, pred_id, acc = cal_performance(logits, active_indices, ignore_index=self.pad_id)\n\n return ce_loss, pred_id, acc\n\n @torch.no_grad()\n @eval_decorator\n def generate(self,\n motion_ids,\n conds,\n m_lens,\n temperature=1,\n topk_filter_thres=0.9,\n cond_scale=2,\n num_res_layers=-1, # If it's -1, use all.\n ):\n\n # print(self.opt.num_quantizers)\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\n self.process_embed_proj_weight()\n\n device = next(self.parameters()).device\n seq_len = motion_ids.shape[1]\n batch_size = len(conds)\n\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(conds)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(conds).to(device)\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n # token_embed = repeat(self.token_embed_weight, 'c d -> b c d', b=batch_size)\n # gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\n # history_sum = token_embed.gather(1, gathered_ids)\n\n # print(pa, seq_len)\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\n # print(padding_mask.shape, motion_ids.shape)\n motion_ids = torch.where(padding_mask, self.pad_id, motion_ids)\n all_indices = [motion_ids]\n history_sum = 0\n num_quant_layers = self.opt.num_quantizers if num_res_layers==-1 else num_res_layers+1\n\n for i in range(1, num_quant_layers):\n # print(f\"--> Working on {i}-th quantizer\")\n # Start from all tokens being masked\n # qids = torch.full((batch_size,), i, dtype=torch.long, device=motion_ids.device)\n token_embed = self.token_embed_weight[i-1]\n token_embed = repeat(token_embed, 'c d -> b c d', b=batch_size)\n gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\n history_sum += token_embed.gather(1, gathered_ids)\n\n logits = self.forward_with_cond_scale(history_sum, i, cond_vector, padding_mask, cond_scale=cond_scale)\n # logits = self.trans_forward(history_sum, qids, cond_vector, padding_mask)\n\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\n # clean low prob token\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\n\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\n\n # probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\n # # print(temperature, starting_temperature, steps_until_x0, timesteps)\n # # print(probs / temperature)\n # pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\n\n ids = torch.where(padding_mask, self.pad_id, pred_ids)\n\n motion_ids = ids\n all_indices.append(ids)\n\n all_indices = torch.stack(all_indices, dim=-1)\n # padding_mask = repeat(padding_mask, 'b n -> b n q', q=all_indices.shape[-1])\n # all_indices = torch.where(padding_mask, -1, all_indices)\n all_indices = torch.where(all_indices==self.pad_id, -1, all_indices)\n # all_indices = all_indices.masked_fill()\n return all_indices\n\n @torch.no_grad()\n @eval_decorator\n def edit(self,\n motion_ids,\n conds,\n m_lens,\n temperature=1,\n topk_filter_thres=0.9,\n cond_scale=2\n ):\n\n # print(self.opt.num_quantizers)\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\n self.process_embed_proj_weight()\n\n device = next(self.parameters()).device\n seq_len = motion_ids.shape[1]\n batch_size = len(conds)\n\n if self.cond_mode == 'text':\n with torch.no_grad():\n cond_vector = self.encode_text(conds)\n elif self.cond_mode == 'action':\n cond_vector = self.enc_action(conds).to(device)\n elif self.cond_mode == 'uncond':\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\n else:\n raise NotImplementedError(\"Unsupported condition mode!!!\")\n\n # token_embed = repeat(self.token_embed_weight, 'c d -> b c d', b=batch_size)\n # gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\n # history_sum = token_embed.gather(1, gathered_ids)\n\n # print(pa, seq_len)\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\n # print(padding_mask.shape, motion_ids.shape)\n motion_ids = torch.where(padding_mask, self.pad_id, motion_ids)\n all_indices = [motion_ids]\n history_sum = 0\n\n for i in range(1, self.opt.num_quantizers):\n # print(f\"--> Working on {i}-th quantizer\")\n # Start from all tokens being masked\n # qids = torch.full((batch_size,), i, dtype=torch.long, device=motion_ids.device)\n token_embed = self.token_embed_weight[i-1]\n token_embed = repeat(token_embed, 'c d -> b c d', b=batch_size)\n gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\n history_sum += token_embed.gather(1, gathered_ids)\n\n logits = self.forward_with_cond_scale(history_sum, i, cond_vector, padding_mask, cond_scale=cond_scale)\n # logits = self.trans_forward(history_sum, qids, cond_vector, padding_mask)\n\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\n # clean low prob token\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\n\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\n\n # probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\n # # print(temperature, starting_temperature, steps_until_x0, timesteps)\n # # print(probs / temperature)\n # pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\n\n ids = torch.where(padding_mask, self.pad_id, pred_ids)\n\n motion_ids = ids\n all_indices.append(ids)\n\n all_indices = torch.stack(all_indices, dim=-1)\n # padding_mask = repeat(padding_mask, 'b n -> b n q', q=all_indices.shape[-1])\n # all_indices = torch.where(padding_mask, -1, all_indices)\n all_indices = torch.where(all_indices==self.pad_id, -1, all_indices)\n # all_indices = all_indices.masked_fill()\n return all_indices"},{"identifier":"RVQVAE","path":"models/vq/model.py","snippet":"class RVQVAE(nn.Module):\n def __init__(self,\n args,\n input_width=263,\n nb_code=1024,\n code_dim=512,\n output_emb_width=512,\n down_t=3,\n stride_t=2,\n width=512,\n depth=3,\n dilation_growth_rate=3,\n activation='relu',\n norm=None):\n\n super().__init__()\n assert output_emb_width == code_dim\n self.code_dim = code_dim\n self.num_code = nb_code\n # self.quant = args.quantizer\n self.encoder = Encoder(input_width, output_emb_width, down_t, stride_t, width, depth,\n dilation_growth_rate, activation=activation, norm=norm)\n self.decoder = Decoder(input_width, output_emb_width, down_t, stride_t, width, depth,\n dilation_growth_rate, activation=activation, norm=norm)\n rvqvae_config = {\n 'num_quantizers': args.num_quantizers,\n 'shared_codebook': args.shared_codebook,\n 'quantize_dropout_prob': args.quantize_dropout_prob,\n 'quantize_dropout_cutoff_index': 0,\n 'nb_code': nb_code,\n 'code_dim':code_dim, \n 'args': args,\n }\n self.quantizer = ResidualVQ(**rvqvae_config)\n\n def preprocess(self, x):\n # (bs, T, Jx3) -> (bs, Jx3, T)\n x = x.permute(0, 2, 1).float()\n return x\n\n def postprocess(self, x):\n # (bs, Jx3, T) -> (bs, T, Jx3)\n x = x.permute(0, 2, 1)\n return x\n\n def encode(self, x):\n N, T, _ = x.shape\n x_in = self.preprocess(x)\n x_encoder = self.encoder(x_in)\n # print(x_encoder.shape)\n code_idx, all_codes = self.quantizer.quantize(x_encoder, return_latent=True)\n # print(code_idx.shape)\n # code_idx = code_idx.view(N, -1)\n # (N, T, Q)\n # print()\n return code_idx, all_codes\n\n def forward(self, x):\n x_in = self.preprocess(x)\n # Encode\n x_encoder = self.encoder(x_in)\n\n ## quantization\n # x_quantized, code_idx, commit_loss, perplexity = self.quantizer(x_encoder, sample_codebook_temp=0.5,\n # force_dropout_index=0) #TODO hardcode\n x_quantized, code_idx, commit_loss, perplexity = self.quantizer(x_encoder, sample_codebook_temp=0.5)\n\n # print(code_idx[0, :, 1])\n ## decoder\n x_out = self.decoder(x_quantized)\n # x_out = self.postprocess(x_decoder)\n return x_out, commit_loss, perplexity\n\n def forward_decoder(self, x):\n x_d = self.quantizer.get_codes_from_indices(x)\n # x_d = x_d.view(1, -1, self.code_dim).permute(0, 2, 1).contiguous()\n x = x_d.sum(dim=0).permute(0, 2, 1)\n\n # decoder\n x_out = self.decoder(x)\n # x_out = self.postprocess(x_decoder)\n return x_out"},{"identifier":"LengthEstimator","path":"models/vq/model.py","snippet":"class LengthEstimator(nn.Module):\n def __init__(self, input_size, output_size):\n super(LengthEstimator, self).__init__()\n nd = 512\n self.output = nn.Sequential(\n nn.Linear(input_size, nd),\n nn.LayerNorm(nd),\n nn.LeakyReLU(0.2, inplace=True),\n\n nn.Dropout(0.2),\n nn.Linear(nd, nd // 2),\n nn.LayerNorm(nd // 2),\n nn.LeakyReLU(0.2, inplace=True),\n\n nn.Dropout(0.2),\n nn.Linear(nd // 2, nd // 4),\n nn.LayerNorm(nd // 4),\n nn.LeakyReLU(0.2, inplace=True),\n\n nn.Linear(nd // 4, output_size)\n )\n\n self.output.apply(self.__init_weights)\n\n def __init_weights(self, module):\n if isinstance(module, (nn.Linear, nn.Embedding)):\n module.weight.data.normal_(mean=0.0, std=0.02)\n if isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n\n def forward(self, text_emb):\n return self.output(text_emb)"},{"identifier":"EvalT2MOptions","path":"options/eval_option.py","snippet":"class EvalT2MOptions(BaseOptions):\n def initialize(self):\n BaseOptions.initialize(self)\n self.parser.add_argument('--which_epoch', type=str, default=\"latest\", help='Checkpoint you want to use, {latest, net_best_fid, etc}')\n self.parser.add_argument('--batch_size', type=int, default=32, help='Batch size')\n\n self.parser.add_argument('--ext', type=str, default='text2motion', help='Extension of the result file or folder')\n self.parser.add_argument(\"--num_batch\", default=2, type=int,\n help=\"Number of batch for generation\")\n self.parser.add_argument(\"--repeat_times\", default=1, type=int,\n help=\"Number of repetitions, per sample text prompt\")\n self.parser.add_argument(\"--cond_scale\", default=4, type=float,\n help=\"For classifier-free sampling - specifies the s parameter, as defined in the paper.\")\n self.parser.add_argument(\"--temperature\", default=1., type=float,\n help=\"Sampling Temperature.\")\n self.parser.add_argument(\"--topkr\", default=0.9, type=float,\n help=\"Filter out percentil low prop entries.\")\n self.parser.add_argument(\"--time_steps\", default=18, type=int,\n help=\"Mask Generate steps.\")\n self.parser.add_argument(\"--seed\", default=10107, type=int)\n\n self.parser.add_argument('--gumbel_sample', action=\"store_true\", help='True: gumbel sampling, False: categorical sampling.')\n self.parser.add_argument('--use_res_model', action=\"store_true\", help='Whether to use residual transformer.')\n # self.parser.add_argument('--est_length', action=\"store_true\", help='Training iterations')\n\n self.parser.add_argument('--res_name', type=str, default='tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw', help='Model name of residual transformer')\n self.parser.add_argument('--text_path', type=str, default=\"\", help='Text prompt file')\n\n\n self.parser.add_argument('-msec', '--mask_edit_section', nargs='*', type=str, help='Indicate sections for editing, use comma to separate the start and end of a section'\n 'type int will specify the token frame, type float will specify the ratio of seq_len')\n self.parser.add_argument('--text_prompt', default='', type=str, help=\"A text prompt to be generated. If empty, will take text prompts from dataset.\")\n self.parser.add_argument('--source_motion', default='example_data/000612.npy', type=str, help=\"Source motion path for editing. (new_joint_vecs format .npy file)\")\n self.parser.add_argument(\"--motion_length\", default=0, type=int,\n help=\"Motion length for generation, only applicable with single text prompt.\")\n self.is_train = False"},{"identifier":"get_opt","path":"utils/get_opt.py","snippet":"def get_opt(opt_path, device, **kwargs):\n opt = Namespace()\n opt_dict = vars(opt)\n\n skip = ('-------------- End ----------------',\n '------------ Options -------------',\n '\\n')\n print('Reading', opt_path)\n with open(opt_path, 'r') as f:\n for line in f:\n if line.strip() not in skip:\n # print(line.strip())\n key, value = line.strip('\\n').split(': ')\n if value in ('True', 'False'):\n opt_dict[key] = (value == 'True')\n # print(key, value)\n elif is_float(value):\n opt_dict[key] = float(value)\n elif is_number(value):\n opt_dict[key] = int(value)\n else:\n opt_dict[key] = str(value)\n\n # print(opt)\n opt_dict['which_epoch'] = 'finest'\n opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)\n opt.model_dir = pjoin(opt.save_root, 'model')\n opt.meta_dir = pjoin(opt.save_root, 'meta')\n\n if opt.dataset_name == 't2m':\n opt.data_root = './dataset/HumanML3D/'\n opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')\n opt.text_dir = pjoin(opt.data_root, 'texts')\n opt.joints_num = 22\n opt.dim_pose = 263\n opt.max_motion_length = 196\n opt.max_motion_frame = 196\n opt.max_motion_token = 55\n elif opt.dataset_name == 'kit':\n opt.data_root = './dataset/KIT-ML/'\n opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')\n opt.text_dir = pjoin(opt.data_root, 'texts')\n opt.joints_num = 21\n opt.dim_pose = 251\n opt.max_motion_length = 196\n opt.max_motion_frame = 196\n opt.max_motion_token = 55\n else:\n raise KeyError('Dataset not recognized')\n if not hasattr(opt, 'unit_length'):\n opt.unit_length = 4\n opt.dim_word = 300\n opt.num_classes = 200 // opt.unit_length\n opt.dim_pos_ohot = len(POS_enumerator)\n opt.is_train = False\n opt.is_continue = False\n opt.device = device\n\n opt_dict.update(kwargs) # Overwrite with kwargs params\n\n return opt"},{"identifier":"fixseed","path":"utils/fixseed.py","snippet":"def fixseed(seed):\n torch.backends.cudnn.benchmark = False\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)"},{"identifier":"Joint2BVHConvertor","path":"visualization/joints2bvh.py","snippet":"class Joint2BVHConvertor:\n def __init__(self):\n self.template = BVH.load('./visualization/data/template.bvh', need_quater=True)\n self.re_order = [0, 1, 4, 7, 10, 2, 5, 8, 11, 3, 6, 9, 12, 15, 13, 16, 18, 20, 14, 17, 19, 21]\n\n self.re_order_inv = [0, 1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12, 14, 18, 13, 15, 19, 16, 20, 17, 21]\n self.end_points = [4, 8, 13, 17, 21]\n\n self.template_offset = self.template.offsets.copy()\n self.parents = [-1, 0, 1, 2, 3, 0, 5, 6, 7, 0, 9, 10, 11, 12, 11, 14, 15, 16, 11, 18, 19, 20]\n\n def convert(self, positions, filename, iterations=10, foot_ik=True):\n '''\n Convert the SMPL joint positions to Mocap BVH\n :param positions: (N, 22, 3)\n :param filename: Save path for resulting BVH\n :param iterations: iterations for optimizing rotations, 10 is usually enough\n :param foot_ik: whether to enfore foot inverse kinematics, removing foot slide issue.\n :return:\n '''\n positions = positions[:, self.re_order]\n new_anim = self.template.copy()\n new_anim.rotations = Quaternions.id(positions.shape[:-1])\n new_anim.positions = new_anim.positions[0:1].repeat(positions.shape[0], axis=-0)\n new_anim.positions[:, 0] = positions[:, 0]\n\n if foot_ik:\n positions = remove_fs(positions, None, fid_l=(3, 4), fid_r=(7, 8), interp_length=5,\n force_on_floor=True)\n ik_solver = BasicInverseKinematics(new_anim, positions, iterations=iterations, silent=True)\n new_anim = ik_solver()\n\n # BVH.save(filename, new_anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\n glb = Animation.positions_global(new_anim)[:, self.re_order_inv]\n if filename is not None:\n BVH.save(filename, new_anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\n return new_anim, glb\n\n def convert_sgd(self, positions, filename, iterations=100, foot_ik=True):\n '''\n Convert the SMPL joint positions to Mocap BVH\n\n :param positions: (N, 22, 3)\n :param filename: Save path for resulting BVH\n :param iterations: iterations for optimizing rotations, 10 is usually enough\n :param foot_ik: whether to enfore foot inverse kinematics, removing foot slide issue.\n :return:\n '''\n\n ## Positional Foot locking ##\n glb = positions[:, self.re_order]\n\n if foot_ik:\n glb = remove_fs(glb, None, fid_l=(3, 4), fid_r=(7, 8), interp_length=2,\n force_on_floor=True)\n\n ## Fit BVH ##\n new_anim = self.template.copy()\n new_anim.rotations = Quaternions.id(glb.shape[:-1])\n new_anim.positions = new_anim.positions[0:1].repeat(glb.shape[0], axis=-0)\n new_anim.positions[:, 0] = glb[:, 0]\n anim = new_anim.copy()\n\n rot = torch.tensor(anim.rotations.qs, dtype=torch.float)\n pos = torch.tensor(anim.positions[:, 0, :], dtype=torch.float)\n offset = torch.tensor(anim.offsets, dtype=torch.float)\n\n glb = torch.tensor(glb, dtype=torch.float)\n ik_solver = InverseKinematics(rot, pos, offset, anim.parents, glb)\n print('Fixing foot contact using IK...')\n for i in tqdm(range(iterations)):\n mse = ik_solver.step()\n # print(i, mse)\n\n rotations = ik_solver.rotations.detach().cpu()\n norm = torch.norm(rotations, dim=-1, keepdim=True)\n rotations /= norm\n\n anim.rotations = Quaternions(rotations.numpy())\n anim.rotations[:, self.end_points] = Quaternions.id((anim.rotations.shape[0], len(self.end_points)))\n anim.positions[:, 0, :] = ik_solver.position.detach().cpu().numpy()\n if filename is not None:\n BVH.save(filename, anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\n # BVH.save(filename[:-3] + 'bvh', anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\n glb = Animation.positions_global(anim)[:, self.re_order_inv]\n return anim, glb"},{"identifier":"recover_from_ric","path":"utils/motion_process.py","snippet":"def recover_from_ric(data, joints_num):\n r_rot_quat, r_pos = recover_root_rot_pos(data)\n positions = data[..., 4:(joints_num - 1) * 3 + 4]\n positions = positions.view(positions.shape[:-1] + (-1, 3))\n\n '''Add Y-axis rotation to local joints'''\n positions = qrot(qinv(r_rot_quat[..., None, :]).expand(positions.shape[:-1] + (4,)), positions)\n\n '''Add root XZ to joints'''\n positions[..., 0] += r_pos[..., 0:1]\n positions[..., 2] += r_pos[..., 2:3]\n\n '''Concate root and joints'''\n positions = torch.cat([r_pos.unsqueeze(-2), positions], dim=-2)\n\n return positions"},{"identifier":"plot_3d_motion","path":"utils/plot_script.py","snippet":"def plot_3d_motion(save_path, kinematic_tree, joints, title, figsize=(10, 10), fps=120, radius=4):\n matplotlib.use('Agg')\n\n title_sp = title.split(' ')\n if len(title_sp) > 20:\n title = '\\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:20]), ' '.join(title_sp[20:])])\n elif len(title_sp) > 10:\n title = '\\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:])])\n\n def init():\n ax.set_xlim3d([-radius / 2, radius / 2])\n ax.set_ylim3d([0, radius])\n ax.set_zlim3d([0, radius])\n # print(title)\n fig.suptitle(title, fontsize=20)\n ax.grid(b=False)\n\n def plot_xzPlane(minx, maxx, miny, minz, maxz):\n ## Plot a plane XZ\n verts = [\n [minx, miny, minz],\n [minx, miny, maxz],\n [maxx, miny, maxz],\n [maxx, miny, minz]\n ]\n xz_plane = Poly3DCollection([verts])\n xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5))\n ax.add_collection3d(xz_plane)\n\n # return ax\n\n # (seq_len, joints_num, 3)\n data = joints.copy().reshape(len(joints), -1, 3)\n fig = plt.figure(figsize=figsize)\n ax = p3.Axes3D(fig)\n init()\n MINS = data.min(axis=0).min(axis=0)\n MAXS = data.max(axis=0).max(axis=0)\n colors = ['red', 'blue', 'black', 'red', 'blue',\n 'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue',\n 'darkred', 'darkred', 'darkred', 'darkred', 'darkred']\n frame_number = data.shape[0]\n # print(data.shape)\n\n height_offset = MINS[1]\n data[:, :, 1] -= height_offset\n trajec = data[:, 0, [0, 2]]\n\n data[..., 0] -= data[:, 0:1, 0]\n data[..., 2] -= data[:, 0:1, 2]\n\n # print(trajec.shape)\n\n def update(index):\n # print(index)\n ax.lines = []\n ax.collections = []\n ax.view_init(elev=120, azim=-90)\n ax.dist = 7.5\n # ax =\n plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1],\n MAXS[2] - trajec[index, 1])\n # ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3)\n\n if index > 1:\n ax.plot3D(trajec[:index, 0] - trajec[index, 0], np.zeros_like(trajec[:index, 0]),\n trajec[:index, 1] - trajec[index, 1], linewidth=1.0,\n color='blue')\n # ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2])\n\n for i, (chain, color) in enumerate(zip(kinematic_tree, colors)):\n # print(color)\n if i < 5:\n linewidth = 4.0\n else:\n linewidth = 2.0\n ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth,\n color=color)\n # print(trajec[:index, 0].shape)\n\n plt.axis('off')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.set_zticklabels([])\n\n ani = FuncAnimation(fig, update, frames=frame_number, interval=1000 / fps, repeat=False)\n\n # writer = FFMpegFileWriter(fps=fps)\n ani.save(save_path, fps=fps)\n plt.close()"},{"identifier":"t2m_kinematic_chain","path":"utils/paramUtil.py","snippet":""}],"string":"[\n {\n \"identifier\": \"MaskTransformer\",\n \"path\": \"models/mask_transformer/transformer.py\",\n \"snippet\": \"class MaskTransformer(nn.Module):\\n def __init__(self, code_dim, cond_mode, latent_dim=256, ff_size=1024, num_layers=8,\\n num_heads=4, dropout=0.1, clip_dim=512, cond_drop_prob=0.1,\\n clip_version=None, opt=None, **kargs):\\n super(MaskTransformer, self).__init__()\\n print(f'latent_dim: {latent_dim}, ff_size: {ff_size}, nlayers: {num_layers}, nheads: {num_heads}, dropout: {dropout}')\\n\\n self.code_dim = code_dim\\n self.latent_dim = latent_dim\\n self.clip_dim = clip_dim\\n self.dropout = dropout\\n self.opt = opt\\n\\n self.cond_mode = cond_mode\\n self.cond_drop_prob = cond_drop_prob\\n\\n if self.cond_mode == 'action':\\n assert 'num_actions' in kargs\\n self.num_actions = kargs.get('num_actions', 1)\\n\\n '''\\n Preparing Networks\\n '''\\n self.input_process = InputProcess(self.code_dim, self.latent_dim)\\n self.position_enc = PositionalEncoding(self.latent_dim, self.dropout)\\n\\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\\n nhead=num_heads,\\n dim_feedforward=ff_size,\\n dropout=dropout,\\n activation='gelu')\\n\\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\\n num_layers=num_layers)\\n\\n self.encode_action = partial(F.one_hot, num_classes=self.num_actions)\\n\\n # if self.cond_mode != 'no_cond':\\n if self.cond_mode == 'text':\\n self.cond_emb = nn.Linear(self.clip_dim, self.latent_dim)\\n elif self.cond_mode == 'action':\\n self.cond_emb = nn.Linear(self.num_actions, self.latent_dim)\\n elif self.cond_mode == 'uncond':\\n self.cond_emb = nn.Identity()\\n else:\\n raise KeyError(\\\"Unsupported condition mode!!!\\\")\\n\\n\\n _num_tokens = opt.num_tokens + 2 # two dummy tokens, one for masking, one for padding\\n self.mask_id = opt.num_tokens\\n self.pad_id = opt.num_tokens + 1\\n\\n self.output_process = OutputProcess_Bert(out_feats=opt.num_tokens, latent_dim=latent_dim)\\n\\n self.token_emb = nn.Embedding(_num_tokens, self.code_dim)\\n\\n self.apply(self.__init_weights)\\n\\n '''\\n Preparing frozen weights\\n '''\\n\\n if self.cond_mode == 'text':\\n print('Loading CLIP...')\\n self.clip_version = clip_version\\n self.clip_model = self.load_and_freeze_clip(clip_version)\\n\\n self.noise_schedule = cosine_schedule\\n\\n def load_and_freeze_token_emb(self, codebook):\\n '''\\n :param codebook: (c, d)\\n :return:\\n '''\\n assert self.training, 'Only necessary in training mode'\\n c, d = codebook.shape\\n self.token_emb.weight = nn.Parameter(torch.cat([codebook, torch.zeros(size=(2, d), device=codebook.device)], dim=0)) #add two dummy tokens, 0 vectors\\n self.token_emb.requires_grad_(False)\\n # self.token_emb.weight.requires_grad = False\\n # self.token_emb_ready = True\\n print(\\\"Token embedding initialized!\\\")\\n\\n def __init_weights(self, module):\\n if isinstance(module, (nn.Linear, nn.Embedding)):\\n module.weight.data.normal_(mean=0.0, std=0.02)\\n if isinstance(module, nn.Linear) and module.bias is not None:\\n module.bias.data.zero_()\\n elif isinstance(module, nn.LayerNorm):\\n module.bias.data.zero_()\\n module.weight.data.fill_(1.0)\\n\\n def parameters_wo_clip(self):\\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\\n\\n def load_and_freeze_clip(self, clip_version):\\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\\n jit=False) # Must set jit=False for training\\n # Cannot run on cpu\\n clip.model.convert_weights(\\n clip_model) # Actually this line is unnecessary since clip by default already on float16\\n # Date 0707: It's necessary, only unecessary when load directly to gpu. Disable if need to run on cpu\\n\\n # Freeze CLIP weights\\n clip_model.eval()\\n for p in clip_model.parameters():\\n p.requires_grad = False\\n\\n return clip_model\\n\\n def encode_text(self, raw_text):\\n device = next(self.parameters()).device\\n text = clip.tokenize(raw_text, truncate=True).to(device)\\n feat_clip_text = self.clip_model.encode_text(text).float()\\n return feat_clip_text\\n\\n def mask_cond(self, cond, force_mask=False):\\n bs, d = cond.shape\\n if force_mask:\\n return torch.zeros_like(cond)\\n elif self.training and self.cond_drop_prob > 0.:\\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_drop_prob).view(bs, 1)\\n return cond * (1. - mask)\\n else:\\n return cond\\n\\n def trans_forward(self, motion_ids, cond, padding_mask, force_mask=False):\\n '''\\n :param motion_ids: (b, seqlen)\\n :padding_mask: (b, seqlen), all pad positions are TRUE else FALSE\\n :param cond: (b, embed_dim) for text, (b, num_actions) for action\\n :param force_mask: boolean\\n :return:\\n -logits: (b, num_token, seqlen)\\n '''\\n\\n cond = self.mask_cond(cond, force_mask=force_mask)\\n\\n # print(motion_ids.shape)\\n x = self.token_emb(motion_ids)\\n # print(x.shape)\\n # (b, seqlen, d) -> (seqlen, b, latent_dim)\\n x = self.input_process(x)\\n\\n cond = self.cond_emb(cond).unsqueeze(0) #(1, b, latent_dim)\\n\\n x = self.position_enc(x)\\n xseq = torch.cat([cond, x], dim=0) #(seqlen+1, b, latent_dim)\\n\\n padding_mask = torch.cat([torch.zeros_like(padding_mask[:, 0:1]), padding_mask], dim=1) #(b, seqlen+1)\\n # print(xseq.shape, padding_mask.shape)\\n\\n # print(padding_mask.shape, xseq.shape)\\n\\n output = self.seqTransEncoder(xseq, src_key_padding_mask=padding_mask)[1:] #(seqlen, b, e)\\n logits = self.output_process(output) #(seqlen, b, e) -> (b, ntoken, seqlen)\\n return logits\\n\\n def forward(self, ids, y, m_lens):\\n '''\\n :param ids: (b, n)\\n :param y: raw text for cond_mode=text, (b, ) for cond_mode=action\\n :m_lens: (b,)\\n :return:\\n '''\\n\\n bs, ntokens = ids.shape\\n device = ids.device\\n\\n # Positions that are PADDED are ALL FALSE\\n non_pad_mask = lengths_to_mask(m_lens, ntokens) #(b, n)\\n ids = torch.where(non_pad_mask, ids, self.pad_id)\\n\\n force_mask = False\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(y)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(y).to(device).float()\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(bs, self.latent_dim).float().to(device)\\n force_mask = True\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n\\n '''\\n Prepare mask\\n '''\\n rand_time = uniform((bs,), device=device)\\n rand_mask_probs = self.noise_schedule(rand_time)\\n num_token_masked = (ntokens * rand_mask_probs).round().clamp(min=1)\\n\\n batch_randperm = torch.rand((bs, ntokens), device=device).argsort(dim=-1)\\n # Positions to be MASKED are ALL TRUE\\n mask = batch_randperm < num_token_masked.unsqueeze(-1)\\n\\n # Positions to be MASKED must also be NON-PADDED\\n mask &= non_pad_mask\\n\\n # Note this is our training target, not input\\n labels = torch.where(mask, ids, self.mask_id)\\n\\n x_ids = ids.clone()\\n\\n # Further Apply Bert Masking Scheme\\n # Step 1: 10% replace with an incorrect token\\n mask_rid = get_mask_subset_prob(mask, 0.1)\\n rand_id = torch.randint_like(x_ids, high=self.opt.num_tokens)\\n x_ids = torch.where(mask_rid, rand_id, x_ids)\\n # Step 2: 90% x 10% replace with correct token, and 90% x 88% replace with mask token\\n mask_mid = get_mask_subset_prob(mask & ~mask_rid, 0.88)\\n\\n # mask_mid = mask\\n\\n x_ids = torch.where(mask_mid, self.mask_id, x_ids)\\n\\n logits = self.trans_forward(x_ids, cond_vector, ~non_pad_mask, force_mask)\\n ce_loss, pred_id, acc = cal_performance(logits, labels, ignore_index=self.mask_id)\\n\\n return ce_loss, pred_id, acc\\n\\n def forward_with_cond_scale(self,\\n motion_ids,\\n cond_vector,\\n padding_mask,\\n cond_scale=3,\\n force_mask=False):\\n # bs = motion_ids.shape[0]\\n # if cond_scale == 1:\\n if force_mask:\\n return self.trans_forward(motion_ids, cond_vector, padding_mask, force_mask=True)\\n\\n logits = self.trans_forward(motion_ids, cond_vector, padding_mask)\\n if cond_scale == 1:\\n return logits\\n\\n aux_logits = self.trans_forward(motion_ids, cond_vector, padding_mask, force_mask=True)\\n\\n scaled_logits = aux_logits + (logits - aux_logits) * cond_scale\\n return scaled_logits\\n\\n @torch.no_grad()\\n @eval_decorator\\n def generate(self,\\n conds,\\n m_lens,\\n timesteps: int,\\n cond_scale: int,\\n temperature=1,\\n topk_filter_thres=0.9,\\n gsample=False,\\n force_mask=False\\n ):\\n # print(self.opt.num_quantizers)\\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\\n\\n device = next(self.parameters()).device\\n seq_len = max(m_lens)\\n batch_size = len(m_lens)\\n\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(conds)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(conds).to(device)\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\\n # print(padding_mask.shape, )\\n\\n # Start from all tokens being masked\\n ids = torch.where(padding_mask, self.pad_id, self.mask_id)\\n scores = torch.where(padding_mask, 1e5, 0.)\\n starting_temperature = temperature\\n\\n for timestep, steps_until_x0 in zip(torch.linspace(0, 1, timesteps, device=device), reversed(range(timesteps))):\\n # 0 < timestep < 1\\n rand_mask_prob = self.noise_schedule(timestep) # Tensor\\n\\n '''\\n Maskout, and cope with variable length\\n '''\\n # fix: the ratio regarding lengths, instead of seq_len\\n num_token_masked = torch.round(rand_mask_prob * m_lens).clamp(min=1) # (b, )\\n\\n # select num_token_masked tokens with lowest scores to be masked\\n sorted_indices = scores.argsort(\\n dim=1) # (b, k), sorted_indices[i, j] = the index of j-th lowest element in scores on dim=1\\n ranks = sorted_indices.argsort(dim=1) # (b, k), rank[i, j] = the rank (0: lowest) of scores[i, j] on dim=1\\n is_mask = (ranks < num_token_masked.unsqueeze(-1))\\n ids = torch.where(is_mask, self.mask_id, ids)\\n\\n '''\\n Preparing input\\n '''\\n # (b, num_token, seqlen)\\n logits = self.forward_with_cond_scale(ids, cond_vector=cond_vector,\\n padding_mask=padding_mask,\\n cond_scale=cond_scale,\\n force_mask=force_mask)\\n\\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\\n # print(logits.shape, self.opt.num_tokens)\\n # clean low prob token\\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\\n\\n '''\\n Update ids\\n '''\\n # if force_mask:\\n temperature = starting_temperature\\n # else:\\n # temperature = starting_temperature * (steps_until_x0 / timesteps)\\n # temperature = max(temperature, 1e-4)\\n # print(filtered_logits.shape)\\n # temperature is annealed, gradually reducing temperature as well as randomness\\n if gsample: # use gumbel_softmax sampling\\n # print(\\\"1111\\\")\\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\\n else: # use multinomial sampling\\n # print(\\\"2222\\\")\\n probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\\n # print(temperature, starting_temperature, steps_until_x0, timesteps)\\n # print(probs / temperature)\\n pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\\n\\n # print(pred_ids.max(), pred_ids.min())\\n # if pred_ids.\\n ids = torch.where(is_mask, pred_ids, ids)\\n\\n '''\\n Updating scores\\n '''\\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\\n scores = probs_without_temperature.gather(2, pred_ids.unsqueeze(dim=-1)) # (b, seqlen, 1)\\n scores = scores.squeeze(-1) # (b, seqlen)\\n\\n # We do not want to re-mask the previously kept tokens, or pad tokens\\n scores = scores.masked_fill(~is_mask, 1e5)\\n\\n ids = torch.where(padding_mask, -1, ids)\\n # print(\\\"Final\\\", ids.max(), ids.min())\\n return ids\\n\\n\\n @torch.no_grad()\\n @eval_decorator\\n def edit(self,\\n conds,\\n tokens,\\n m_lens,\\n timesteps: int,\\n cond_scale: int,\\n temperature=1,\\n topk_filter_thres=0.9,\\n gsample=False,\\n force_mask=False,\\n edit_mask=None,\\n padding_mask=None,\\n ):\\n\\n assert edit_mask.shape == tokens.shape if edit_mask is not None else True\\n device = next(self.parameters()).device\\n seq_len = tokens.shape[1]\\n\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(conds)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(conds).to(device)\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(1, self.latent_dim).float().to(device)\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n if padding_mask == None:\\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\\n\\n # Start from all tokens being masked\\n if edit_mask == None:\\n mask_free = True\\n ids = torch.where(padding_mask, self.pad_id, tokens)\\n edit_mask = torch.ones_like(padding_mask)\\n edit_mask = edit_mask & ~padding_mask\\n edit_len = edit_mask.sum(dim=-1)\\n scores = torch.where(edit_mask, 0., 1e5)\\n else:\\n mask_free = False\\n edit_mask = edit_mask & ~padding_mask\\n edit_len = edit_mask.sum(dim=-1)\\n ids = torch.where(edit_mask, self.mask_id, tokens)\\n scores = torch.where(edit_mask, 0., 1e5)\\n starting_temperature = temperature\\n\\n for timestep, steps_until_x0 in zip(torch.linspace(0, 1, timesteps, device=device), reversed(range(timesteps))):\\n # 0 < timestep < 1\\n rand_mask_prob = 0.16 if mask_free else self.noise_schedule(timestep) # Tensor\\n\\n '''\\n Maskout, and cope with variable length\\n '''\\n # fix: the ratio regarding lengths, instead of seq_len\\n num_token_masked = torch.round(rand_mask_prob * edit_len).clamp(min=1) # (b, )\\n\\n # select num_token_masked tokens with lowest scores to be masked\\n sorted_indices = scores.argsort(\\n dim=1) # (b, k), sorted_indices[i, j] = the index of j-th lowest element in scores on dim=1\\n ranks = sorted_indices.argsort(dim=1) # (b, k), rank[i, j] = the rank (0: lowest) of scores[i, j] on dim=1\\n is_mask = (ranks < num_token_masked.unsqueeze(-1))\\n # is_mask = (torch.rand_like(scores) < 0.8) * ~padding_mask if mask_free else is_mask\\n ids = torch.where(is_mask, self.mask_id, ids)\\n\\n '''\\n Preparing input\\n '''\\n # (b, num_token, seqlen)\\n logits = self.forward_with_cond_scale(ids, cond_vector=cond_vector,\\n padding_mask=padding_mask,\\n cond_scale=cond_scale,\\n force_mask=force_mask)\\n\\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\\n # print(logits.shape, self.opt.num_tokens)\\n # clean low prob token\\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\\n\\n '''\\n Update ids\\n '''\\n # if force_mask:\\n temperature = starting_temperature\\n # else:\\n # temperature = starting_temperature * (steps_until_x0 / timesteps)\\n # temperature = max(temperature, 1e-4)\\n # print(filtered_logits.shape)\\n # temperature is annealed, gradually reducing temperature as well as randomness\\n if gsample: # use gumbel_softmax sampling\\n # print(\\\"1111\\\")\\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\\n else: # use multinomial sampling\\n # print(\\\"2222\\\")\\n probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\\n # print(temperature, starting_temperature, steps_until_x0, timesteps)\\n # print(probs / temperature)\\n pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\\n\\n # print(pred_ids.max(), pred_ids.min())\\n # if pred_ids.\\n ids = torch.where(is_mask, pred_ids, ids)\\n\\n '''\\n Updating scores\\n '''\\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\\n scores = probs_without_temperature.gather(2, pred_ids.unsqueeze(dim=-1)) # (b, seqlen, 1)\\n scores = scores.squeeze(-1) # (b, seqlen)\\n\\n # We do not want to re-mask the previously kept tokens, or pad tokens\\n scores = scores.masked_fill(~edit_mask, 1e5) if mask_free else scores.masked_fill(~is_mask, 1e5)\\n\\n ids = torch.where(padding_mask, -1, ids)\\n # print(\\\"Final\\\", ids.max(), ids.min())\\n return ids\\n\\n @torch.no_grad()\\n @eval_decorator\\n def edit_beta(self,\\n conds,\\n conds_og,\\n tokens,\\n m_lens,\\n cond_scale: int,\\n force_mask=False,\\n ):\\n\\n device = next(self.parameters()).device\\n seq_len = tokens.shape[1]\\n\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(conds)\\n if conds_og is not None:\\n cond_vector_og = self.encode_text(conds_og)\\n else:\\n cond_vector_og = None\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(conds).to(device)\\n if conds_og is not None:\\n cond_vector_og = self.enc_action(conds_og).to(device)\\n else:\\n cond_vector_og = None\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\\n\\n # Start from all tokens being masked\\n ids = torch.where(padding_mask, self.pad_id, tokens) # Do not mask anything\\n\\n '''\\n Preparing input\\n '''\\n # (b, num_token, seqlen)\\n logits = self.forward_with_cond_scale(ids,\\n cond_vector=cond_vector,\\n cond_vector_neg=cond_vector_og,\\n padding_mask=padding_mask,\\n cond_scale=cond_scale,\\n force_mask=force_mask)\\n\\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\\n\\n '''\\n Updating scores\\n '''\\n probs_without_temperature = logits.softmax(dim=-1) # (b, seqlen, ntoken)\\n tokens[tokens == -1] = 0 # just to get through an error when index = -1 using gather\\n og_tokens_scores = probs_without_temperature.gather(2, tokens.unsqueeze(dim=-1)) # (b, seqlen, 1)\\n og_tokens_scores = og_tokens_scores.squeeze(-1) # (b, seqlen)\\n\\n return og_tokens_scores\"\n },\n {\n \"identifier\": \"ResidualTransformer\",\n \"path\": \"models/mask_transformer/transformer.py\",\n \"snippet\": \"class ResidualTransformer(nn.Module):\\n def __init__(self, code_dim, cond_mode, latent_dim=256, ff_size=1024, num_layers=8, cond_drop_prob=0.1,\\n num_heads=4, dropout=0.1, clip_dim=512, shared_codebook=False, share_weight=False,\\n clip_version=None, opt=None, **kargs):\\n super(ResidualTransformer, self).__init__()\\n print(f'latent_dim: {latent_dim}, ff_size: {ff_size}, nlayers: {num_layers}, nheads: {num_heads}, dropout: {dropout}')\\n\\n # assert shared_codebook == True, \\\"Only support shared codebook right now!\\\"\\n\\n self.code_dim = code_dim\\n self.latent_dim = latent_dim\\n self.clip_dim = clip_dim\\n self.dropout = dropout\\n self.opt = opt\\n\\n self.cond_mode = cond_mode\\n # self.cond_drop_prob = cond_drop_prob\\n\\n if self.cond_mode == 'action':\\n assert 'num_actions' in kargs\\n self.num_actions = kargs.get('num_actions', 1)\\n self.cond_drop_prob = cond_drop_prob\\n\\n '''\\n Preparing Networks\\n '''\\n self.input_process = InputProcess(self.code_dim, self.latent_dim)\\n self.position_enc = PositionalEncoding(self.latent_dim, self.dropout)\\n\\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\\n nhead=num_heads,\\n dim_feedforward=ff_size,\\n dropout=dropout,\\n activation='gelu')\\n\\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\\n num_layers=num_layers)\\n\\n self.encode_quant = partial(F.one_hot, num_classes=self.opt.num_quantizers)\\n self.encode_action = partial(F.one_hot, num_classes=self.num_actions)\\n\\n self.quant_emb = nn.Linear(self.opt.num_quantizers, self.latent_dim)\\n # if self.cond_mode != 'no_cond':\\n if self.cond_mode == 'text':\\n self.cond_emb = nn.Linear(self.clip_dim, self.latent_dim)\\n elif self.cond_mode == 'action':\\n self.cond_emb = nn.Linear(self.num_actions, self.latent_dim)\\n else:\\n raise KeyError(\\\"Unsupported condition mode!!!\\\")\\n\\n\\n _num_tokens = opt.num_tokens + 1 # one dummy tokens for padding\\n self.pad_id = opt.num_tokens\\n\\n # self.output_process = OutputProcess_Bert(out_feats=opt.num_tokens, latent_dim=latent_dim)\\n self.output_process = OutputProcess(out_feats=code_dim, latent_dim=latent_dim)\\n\\n if shared_codebook:\\n token_embed = nn.Parameter(torch.normal(mean=0, std=0.02, size=(_num_tokens, code_dim)))\\n self.token_embed_weight = token_embed.expand(opt.num_quantizers-1, _num_tokens, code_dim)\\n if share_weight:\\n self.output_proj_weight = self.token_embed_weight\\n self.output_proj_bias = None\\n else:\\n output_proj = nn.Parameter(torch.normal(mean=0, std=0.02, size=(_num_tokens, code_dim)))\\n output_bias = nn.Parameter(torch.zeros(size=(_num_tokens,)))\\n # self.output_proj_bias = 0\\n self.output_proj_weight = output_proj.expand(opt.num_quantizers-1, _num_tokens, code_dim)\\n self.output_proj_bias = output_bias.expand(opt.num_quantizers-1, _num_tokens)\\n\\n else:\\n if share_weight:\\n self.embed_proj_shared_weight = nn.Parameter(torch.normal(mean=0, std=0.02, size=(opt.num_quantizers - 2, _num_tokens, code_dim)))\\n self.token_embed_weight_ = nn.Parameter(torch.normal(mean=0, std=0.02, size=(1, _num_tokens, code_dim)))\\n self.output_proj_weight_ = nn.Parameter(torch.normal(mean=0, std=0.02, size=(1, _num_tokens, code_dim)))\\n self.output_proj_bias = None\\n self.registered = False\\n else:\\n output_proj_weight = torch.normal(mean=0, std=0.02,\\n size=(opt.num_quantizers - 1, _num_tokens, code_dim))\\n\\n self.output_proj_weight = nn.Parameter(output_proj_weight)\\n self.output_proj_bias = nn.Parameter(torch.zeros(size=(opt.num_quantizers, _num_tokens)))\\n token_embed_weight = torch.normal(mean=0, std=0.02,\\n size=(opt.num_quantizers - 1, _num_tokens, code_dim))\\n self.token_embed_weight = nn.Parameter(token_embed_weight)\\n\\n self.apply(self.__init_weights)\\n self.shared_codebook = shared_codebook\\n self.share_weight = share_weight\\n\\n if self.cond_mode == 'text':\\n print('Loading CLIP...')\\n self.clip_version = clip_version\\n self.clip_model = self.load_and_freeze_clip(clip_version)\\n\\n # def\\n\\n def mask_cond(self, cond, force_mask=False):\\n bs, d = cond.shape\\n if force_mask:\\n return torch.zeros_like(cond)\\n elif self.training and self.cond_drop_prob > 0.:\\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_drop_prob).view(bs, 1)\\n return cond * (1. - mask)\\n else:\\n return cond\\n\\n def __init_weights(self, module):\\n if isinstance(module, (nn.Linear, nn.Embedding)):\\n module.weight.data.normal_(mean=0.0, std=0.02)\\n if isinstance(module, nn.Linear) and module.bias is not None:\\n module.bias.data.zero_()\\n elif isinstance(module, nn.LayerNorm):\\n module.bias.data.zero_()\\n module.weight.data.fill_(1.0)\\n\\n def parameters_wo_clip(self):\\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\\n\\n def load_and_freeze_clip(self, clip_version):\\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\\n jit=False) # Must set jit=False for training\\n # Cannot run on cpu\\n clip.model.convert_weights(\\n clip_model) # Actually this line is unnecessary since clip by default already on float16\\n # Date 0707: It's necessary, only unecessary when load directly to gpu. Disable if need to run on cpu\\n\\n # Freeze CLIP weights\\n clip_model.eval()\\n for p in clip_model.parameters():\\n p.requires_grad = False\\n\\n return clip_model\\n\\n def encode_text(self, raw_text):\\n device = next(self.parameters()).device\\n text = clip.tokenize(raw_text, truncate=True).to(device)\\n feat_clip_text = self.clip_model.encode_text(text).float()\\n return feat_clip_text\\n\\n\\n def q_schedule(self, bs, low, high):\\n noise = uniform((bs,), device=self.opt.device)\\n schedule = 1 - cosine_schedule(noise)\\n return torch.round(schedule * (high - low)) + low\\n\\n def process_embed_proj_weight(self):\\n if self.share_weight and (not self.shared_codebook):\\n # if not self.registered:\\n self.output_proj_weight = torch.cat([self.embed_proj_shared_weight, self.output_proj_weight_], dim=0)\\n self.token_embed_weight = torch.cat([self.token_embed_weight_, self.embed_proj_shared_weight], dim=0)\\n # self.registered = True\\n\\n def output_project(self, logits, qids):\\n '''\\n :logits: (bs, code_dim, seqlen)\\n :qids: (bs)\\n\\n :return:\\n -logits (bs, ntoken, seqlen)\\n '''\\n # (num_qlayers-1, num_token, code_dim) -> (bs, ntoken, code_dim)\\n output_proj_weight = self.output_proj_weight[qids]\\n # (num_qlayers, ntoken) -> (bs, ntoken)\\n output_proj_bias = None if self.output_proj_bias is None else self.output_proj_bias[qids]\\n\\n output = torch.einsum('bnc, bcs->bns', output_proj_weight, logits)\\n if output_proj_bias is not None:\\n output += output + output_proj_bias.unsqueeze(-1)\\n return output\\n\\n\\n\\n def trans_forward(self, motion_codes, qids, cond, padding_mask, force_mask=False):\\n '''\\n :param motion_codes: (b, seqlen, d)\\n :padding_mask: (b, seqlen), all pad positions are TRUE else FALSE\\n :param qids: (b), quantizer layer ids\\n :param cond: (b, embed_dim) for text, (b, num_actions) for action\\n :return:\\n -logits: (b, num_token, seqlen)\\n '''\\n cond = self.mask_cond(cond, force_mask=force_mask)\\n\\n # (b, seqlen, d) -> (seqlen, b, latent_dim)\\n x = self.input_process(motion_codes)\\n\\n # (b, num_quantizer)\\n q_onehot = self.encode_quant(qids).float().to(x.device)\\n\\n q_emb = self.quant_emb(q_onehot).unsqueeze(0) # (1, b, latent_dim)\\n cond = self.cond_emb(cond).unsqueeze(0) # (1, b, latent_dim)\\n\\n x = self.position_enc(x)\\n xseq = torch.cat([cond, q_emb, x], dim=0) # (seqlen+2, b, latent_dim)\\n\\n padding_mask = torch.cat([torch.zeros_like(padding_mask[:, 0:2]), padding_mask], dim=1) # (b, seqlen+2)\\n output = self.seqTransEncoder(xseq, src_key_padding_mask=padding_mask)[2:] # (seqlen, b, e)\\n logits = self.output_process(output)\\n return logits\\n\\n def forward_with_cond_scale(self,\\n motion_codes,\\n q_id,\\n cond_vector,\\n padding_mask,\\n cond_scale=3,\\n force_mask=False):\\n bs = motion_codes.shape[0]\\n # if cond_scale == 1:\\n qids = torch.full((bs,), q_id, dtype=torch.long, device=motion_codes.device)\\n if force_mask:\\n logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask, force_mask=True)\\n logits = self.output_project(logits, qids-1)\\n return logits\\n\\n logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask)\\n logits = self.output_project(logits, qids-1)\\n if cond_scale == 1:\\n return logits\\n\\n aux_logits = self.trans_forward(motion_codes, qids, cond_vector, padding_mask, force_mask=True)\\n aux_logits = self.output_project(aux_logits, qids-1)\\n\\n scaled_logits = aux_logits + (logits - aux_logits) * cond_scale\\n return scaled_logits\\n\\n def forward(self, all_indices, y, m_lens):\\n '''\\n :param all_indices: (b, n, q)\\n :param y: raw text for cond_mode=text, (b, ) for cond_mode=action\\n :m_lens: (b,)\\n :return:\\n '''\\n\\n self.process_embed_proj_weight()\\n\\n bs, ntokens, num_quant_layers = all_indices.shape\\n device = all_indices.device\\n\\n # Positions that are PADDED are ALL FALSE\\n non_pad_mask = lengths_to_mask(m_lens, ntokens) # (b, n)\\n\\n q_non_pad_mask = repeat(non_pad_mask, 'b n -> b n q', q=num_quant_layers)\\n all_indices = torch.where(q_non_pad_mask, all_indices, self.pad_id) #(b, n, q)\\n\\n # randomly sample quantization layers to work on, [1, num_q)\\n active_q_layers = q_schedule(bs, low=1, high=num_quant_layers, device=device)\\n\\n # print(self.token_embed_weight.shape, all_indices.shape)\\n token_embed = repeat(self.token_embed_weight, 'q c d-> b c d q', b=bs)\\n gather_indices = repeat(all_indices[..., :-1], 'b n q -> b n d q', d=token_embed.shape[2])\\n # print(token_embed.shape, gather_indices.shape)\\n all_codes = token_embed.gather(1, gather_indices) # (b, n, d, q-1)\\n\\n cumsum_codes = torch.cumsum(all_codes, dim=-1) #(b, n, d, q-1)\\n\\n active_indices = all_indices[torch.arange(bs), :, active_q_layers] # (b, n)\\n history_sum = cumsum_codes[torch.arange(bs), :, :, active_q_layers - 1]\\n\\n force_mask = False\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(y)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(y).to(device).float()\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(bs, self.latent_dim).float().to(device)\\n force_mask = True\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n logits = self.trans_forward(history_sum, active_q_layers, cond_vector, ~non_pad_mask, force_mask)\\n logits = self.output_project(logits, active_q_layers-1)\\n ce_loss, pred_id, acc = cal_performance(logits, active_indices, ignore_index=self.pad_id)\\n\\n return ce_loss, pred_id, acc\\n\\n @torch.no_grad()\\n @eval_decorator\\n def generate(self,\\n motion_ids,\\n conds,\\n m_lens,\\n temperature=1,\\n topk_filter_thres=0.9,\\n cond_scale=2,\\n num_res_layers=-1, # If it's -1, use all.\\n ):\\n\\n # print(self.opt.num_quantizers)\\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\\n self.process_embed_proj_weight()\\n\\n device = next(self.parameters()).device\\n seq_len = motion_ids.shape[1]\\n batch_size = len(conds)\\n\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(conds)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(conds).to(device)\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n # token_embed = repeat(self.token_embed_weight, 'c d -> b c d', b=batch_size)\\n # gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\\n # history_sum = token_embed.gather(1, gathered_ids)\\n\\n # print(pa, seq_len)\\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\\n # print(padding_mask.shape, motion_ids.shape)\\n motion_ids = torch.where(padding_mask, self.pad_id, motion_ids)\\n all_indices = [motion_ids]\\n history_sum = 0\\n num_quant_layers = self.opt.num_quantizers if num_res_layers==-1 else num_res_layers+1\\n\\n for i in range(1, num_quant_layers):\\n # print(f\\\"--> Working on {i}-th quantizer\\\")\\n # Start from all tokens being masked\\n # qids = torch.full((batch_size,), i, dtype=torch.long, device=motion_ids.device)\\n token_embed = self.token_embed_weight[i-1]\\n token_embed = repeat(token_embed, 'c d -> b c d', b=batch_size)\\n gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\\n history_sum += token_embed.gather(1, gathered_ids)\\n\\n logits = self.forward_with_cond_scale(history_sum, i, cond_vector, padding_mask, cond_scale=cond_scale)\\n # logits = self.trans_forward(history_sum, qids, cond_vector, padding_mask)\\n\\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\\n # clean low prob token\\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\\n\\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\\n\\n # probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\\n # # print(temperature, starting_temperature, steps_until_x0, timesteps)\\n # # print(probs / temperature)\\n # pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\\n\\n ids = torch.where(padding_mask, self.pad_id, pred_ids)\\n\\n motion_ids = ids\\n all_indices.append(ids)\\n\\n all_indices = torch.stack(all_indices, dim=-1)\\n # padding_mask = repeat(padding_mask, 'b n -> b n q', q=all_indices.shape[-1])\\n # all_indices = torch.where(padding_mask, -1, all_indices)\\n all_indices = torch.where(all_indices==self.pad_id, -1, all_indices)\\n # all_indices = all_indices.masked_fill()\\n return all_indices\\n\\n @torch.no_grad()\\n @eval_decorator\\n def edit(self,\\n motion_ids,\\n conds,\\n m_lens,\\n temperature=1,\\n topk_filter_thres=0.9,\\n cond_scale=2\\n ):\\n\\n # print(self.opt.num_quantizers)\\n # assert len(timesteps) >= len(cond_scales) == self.opt.num_quantizers\\n self.process_embed_proj_weight()\\n\\n device = next(self.parameters()).device\\n seq_len = motion_ids.shape[1]\\n batch_size = len(conds)\\n\\n if self.cond_mode == 'text':\\n with torch.no_grad():\\n cond_vector = self.encode_text(conds)\\n elif self.cond_mode == 'action':\\n cond_vector = self.enc_action(conds).to(device)\\n elif self.cond_mode == 'uncond':\\n cond_vector = torch.zeros(batch_size, self.latent_dim).float().to(device)\\n else:\\n raise NotImplementedError(\\\"Unsupported condition mode!!!\\\")\\n\\n # token_embed = repeat(self.token_embed_weight, 'c d -> b c d', b=batch_size)\\n # gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\\n # history_sum = token_embed.gather(1, gathered_ids)\\n\\n # print(pa, seq_len)\\n padding_mask = ~lengths_to_mask(m_lens, seq_len)\\n # print(padding_mask.shape, motion_ids.shape)\\n motion_ids = torch.where(padding_mask, self.pad_id, motion_ids)\\n all_indices = [motion_ids]\\n history_sum = 0\\n\\n for i in range(1, self.opt.num_quantizers):\\n # print(f\\\"--> Working on {i}-th quantizer\\\")\\n # Start from all tokens being masked\\n # qids = torch.full((batch_size,), i, dtype=torch.long, device=motion_ids.device)\\n token_embed = self.token_embed_weight[i-1]\\n token_embed = repeat(token_embed, 'c d -> b c d', b=batch_size)\\n gathered_ids = repeat(motion_ids, 'b n -> b n d', d=token_embed.shape[-1])\\n history_sum += token_embed.gather(1, gathered_ids)\\n\\n logits = self.forward_with_cond_scale(history_sum, i, cond_vector, padding_mask, cond_scale=cond_scale)\\n # logits = self.trans_forward(history_sum, qids, cond_vector, padding_mask)\\n\\n logits = logits.permute(0, 2, 1) # (b, seqlen, ntoken)\\n # clean low prob token\\n filtered_logits = top_k(logits, topk_filter_thres, dim=-1)\\n\\n pred_ids = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) # (b, seqlen)\\n\\n # probs = F.softmax(filtered_logits, dim=-1) # (b, seqlen, ntoken)\\n # # print(temperature, starting_temperature, steps_until_x0, timesteps)\\n # # print(probs / temperature)\\n # pred_ids = Categorical(probs / temperature).sample() # (b, seqlen)\\n\\n ids = torch.where(padding_mask, self.pad_id, pred_ids)\\n\\n motion_ids = ids\\n all_indices.append(ids)\\n\\n all_indices = torch.stack(all_indices, dim=-1)\\n # padding_mask = repeat(padding_mask, 'b n -> b n q', q=all_indices.shape[-1])\\n # all_indices = torch.where(padding_mask, -1, all_indices)\\n all_indices = torch.where(all_indices==self.pad_id, -1, all_indices)\\n # all_indices = all_indices.masked_fill()\\n return all_indices\"\n },\n {\n \"identifier\": \"RVQVAE\",\n \"path\": \"models/vq/model.py\",\n \"snippet\": \"class RVQVAE(nn.Module):\\n def __init__(self,\\n args,\\n input_width=263,\\n nb_code=1024,\\n code_dim=512,\\n output_emb_width=512,\\n down_t=3,\\n stride_t=2,\\n width=512,\\n depth=3,\\n dilation_growth_rate=3,\\n activation='relu',\\n norm=None):\\n\\n super().__init__()\\n assert output_emb_width == code_dim\\n self.code_dim = code_dim\\n self.num_code = nb_code\\n # self.quant = args.quantizer\\n self.encoder = Encoder(input_width, output_emb_width, down_t, stride_t, width, depth,\\n dilation_growth_rate, activation=activation, norm=norm)\\n self.decoder = Decoder(input_width, output_emb_width, down_t, stride_t, width, depth,\\n dilation_growth_rate, activation=activation, norm=norm)\\n rvqvae_config = {\\n 'num_quantizers': args.num_quantizers,\\n 'shared_codebook': args.shared_codebook,\\n 'quantize_dropout_prob': args.quantize_dropout_prob,\\n 'quantize_dropout_cutoff_index': 0,\\n 'nb_code': nb_code,\\n 'code_dim':code_dim, \\n 'args': args,\\n }\\n self.quantizer = ResidualVQ(**rvqvae_config)\\n\\n def preprocess(self, x):\\n # (bs, T, Jx3) -> (bs, Jx3, T)\\n x = x.permute(0, 2, 1).float()\\n return x\\n\\n def postprocess(self, x):\\n # (bs, Jx3, T) -> (bs, T, Jx3)\\n x = x.permute(0, 2, 1)\\n return x\\n\\n def encode(self, x):\\n N, T, _ = x.shape\\n x_in = self.preprocess(x)\\n x_encoder = self.encoder(x_in)\\n # print(x_encoder.shape)\\n code_idx, all_codes = self.quantizer.quantize(x_encoder, return_latent=True)\\n # print(code_idx.shape)\\n # code_idx = code_idx.view(N, -1)\\n # (N, T, Q)\\n # print()\\n return code_idx, all_codes\\n\\n def forward(self, x):\\n x_in = self.preprocess(x)\\n # Encode\\n x_encoder = self.encoder(x_in)\\n\\n ## quantization\\n # x_quantized, code_idx, commit_loss, perplexity = self.quantizer(x_encoder, sample_codebook_temp=0.5,\\n # force_dropout_index=0) #TODO hardcode\\n x_quantized, code_idx, commit_loss, perplexity = self.quantizer(x_encoder, sample_codebook_temp=0.5)\\n\\n # print(code_idx[0, :, 1])\\n ## decoder\\n x_out = self.decoder(x_quantized)\\n # x_out = self.postprocess(x_decoder)\\n return x_out, commit_loss, perplexity\\n\\n def forward_decoder(self, x):\\n x_d = self.quantizer.get_codes_from_indices(x)\\n # x_d = x_d.view(1, -1, self.code_dim).permute(0, 2, 1).contiguous()\\n x = x_d.sum(dim=0).permute(0, 2, 1)\\n\\n # decoder\\n x_out = self.decoder(x)\\n # x_out = self.postprocess(x_decoder)\\n return x_out\"\n },\n {\n \"identifier\": \"LengthEstimator\",\n \"path\": \"models/vq/model.py\",\n \"snippet\": \"class LengthEstimator(nn.Module):\\n def __init__(self, input_size, output_size):\\n super(LengthEstimator, self).__init__()\\n nd = 512\\n self.output = nn.Sequential(\\n nn.Linear(input_size, nd),\\n nn.LayerNorm(nd),\\n nn.LeakyReLU(0.2, inplace=True),\\n\\n nn.Dropout(0.2),\\n nn.Linear(nd, nd // 2),\\n nn.LayerNorm(nd // 2),\\n nn.LeakyReLU(0.2, inplace=True),\\n\\n nn.Dropout(0.2),\\n nn.Linear(nd // 2, nd // 4),\\n nn.LayerNorm(nd // 4),\\n nn.LeakyReLU(0.2, inplace=True),\\n\\n nn.Linear(nd // 4, output_size)\\n )\\n\\n self.output.apply(self.__init_weights)\\n\\n def __init_weights(self, module):\\n if isinstance(module, (nn.Linear, nn.Embedding)):\\n module.weight.data.normal_(mean=0.0, std=0.02)\\n if isinstance(module, nn.Linear) and module.bias is not None:\\n module.bias.data.zero_()\\n elif isinstance(module, nn.LayerNorm):\\n module.bias.data.zero_()\\n module.weight.data.fill_(1.0)\\n\\n def forward(self, text_emb):\\n return self.output(text_emb)\"\n },\n {\n \"identifier\": \"EvalT2MOptions\",\n \"path\": \"options/eval_option.py\",\n \"snippet\": \"class EvalT2MOptions(BaseOptions):\\n def initialize(self):\\n BaseOptions.initialize(self)\\n self.parser.add_argument('--which_epoch', type=str, default=\\\"latest\\\", help='Checkpoint you want to use, {latest, net_best_fid, etc}')\\n self.parser.add_argument('--batch_size', type=int, default=32, help='Batch size')\\n\\n self.parser.add_argument('--ext', type=str, default='text2motion', help='Extension of the result file or folder')\\n self.parser.add_argument(\\\"--num_batch\\\", default=2, type=int,\\n help=\\\"Number of batch for generation\\\")\\n self.parser.add_argument(\\\"--repeat_times\\\", default=1, type=int,\\n help=\\\"Number of repetitions, per sample text prompt\\\")\\n self.parser.add_argument(\\\"--cond_scale\\\", default=4, type=float,\\n help=\\\"For classifier-free sampling - specifies the s parameter, as defined in the paper.\\\")\\n self.parser.add_argument(\\\"--temperature\\\", default=1., type=float,\\n help=\\\"Sampling Temperature.\\\")\\n self.parser.add_argument(\\\"--topkr\\\", default=0.9, type=float,\\n help=\\\"Filter out percentil low prop entries.\\\")\\n self.parser.add_argument(\\\"--time_steps\\\", default=18, type=int,\\n help=\\\"Mask Generate steps.\\\")\\n self.parser.add_argument(\\\"--seed\\\", default=10107, type=int)\\n\\n self.parser.add_argument('--gumbel_sample', action=\\\"store_true\\\", help='True: gumbel sampling, False: categorical sampling.')\\n self.parser.add_argument('--use_res_model', action=\\\"store_true\\\", help='Whether to use residual transformer.')\\n # self.parser.add_argument('--est_length', action=\\\"store_true\\\", help='Training iterations')\\n\\n self.parser.add_argument('--res_name', type=str, default='tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw', help='Model name of residual transformer')\\n self.parser.add_argument('--text_path', type=str, default=\\\"\\\", help='Text prompt file')\\n\\n\\n self.parser.add_argument('-msec', '--mask_edit_section', nargs='*', type=str, help='Indicate sections for editing, use comma to separate the start and end of a section'\\n 'type int will specify the token frame, type float will specify the ratio of seq_len')\\n self.parser.add_argument('--text_prompt', default='', type=str, help=\\\"A text prompt to be generated. If empty, will take text prompts from dataset.\\\")\\n self.parser.add_argument('--source_motion', default='example_data/000612.npy', type=str, help=\\\"Source motion path for editing. (new_joint_vecs format .npy file)\\\")\\n self.parser.add_argument(\\\"--motion_length\\\", default=0, type=int,\\n help=\\\"Motion length for generation, only applicable with single text prompt.\\\")\\n self.is_train = False\"\n },\n {\n \"identifier\": \"get_opt\",\n \"path\": \"utils/get_opt.py\",\n \"snippet\": \"def get_opt(opt_path, device, **kwargs):\\n opt = Namespace()\\n opt_dict = vars(opt)\\n\\n skip = ('-------------- End ----------------',\\n '------------ Options -------------',\\n '\\\\n')\\n print('Reading', opt_path)\\n with open(opt_path, 'r') as f:\\n for line in f:\\n if line.strip() not in skip:\\n # print(line.strip())\\n key, value = line.strip('\\\\n').split(': ')\\n if value in ('True', 'False'):\\n opt_dict[key] = (value == 'True')\\n # print(key, value)\\n elif is_float(value):\\n opt_dict[key] = float(value)\\n elif is_number(value):\\n opt_dict[key] = int(value)\\n else:\\n opt_dict[key] = str(value)\\n\\n # print(opt)\\n opt_dict['which_epoch'] = 'finest'\\n opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)\\n opt.model_dir = pjoin(opt.save_root, 'model')\\n opt.meta_dir = pjoin(opt.save_root, 'meta')\\n\\n if opt.dataset_name == 't2m':\\n opt.data_root = './dataset/HumanML3D/'\\n opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')\\n opt.text_dir = pjoin(opt.data_root, 'texts')\\n opt.joints_num = 22\\n opt.dim_pose = 263\\n opt.max_motion_length = 196\\n opt.max_motion_frame = 196\\n opt.max_motion_token = 55\\n elif opt.dataset_name == 'kit':\\n opt.data_root = './dataset/KIT-ML/'\\n opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')\\n opt.text_dir = pjoin(opt.data_root, 'texts')\\n opt.joints_num = 21\\n opt.dim_pose = 251\\n opt.max_motion_length = 196\\n opt.max_motion_frame = 196\\n opt.max_motion_token = 55\\n else:\\n raise KeyError('Dataset not recognized')\\n if not hasattr(opt, 'unit_length'):\\n opt.unit_length = 4\\n opt.dim_word = 300\\n opt.num_classes = 200 // opt.unit_length\\n opt.dim_pos_ohot = len(POS_enumerator)\\n opt.is_train = False\\n opt.is_continue = False\\n opt.device = device\\n\\n opt_dict.update(kwargs) # Overwrite with kwargs params\\n\\n return opt\"\n },\n {\n \"identifier\": \"fixseed\",\n \"path\": \"utils/fixseed.py\",\n \"snippet\": \"def fixseed(seed):\\n torch.backends.cudnn.benchmark = False\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\"\n },\n {\n \"identifier\": \"Joint2BVHConvertor\",\n \"path\": \"visualization/joints2bvh.py\",\n \"snippet\": \"class Joint2BVHConvertor:\\n def __init__(self):\\n self.template = BVH.load('./visualization/data/template.bvh', need_quater=True)\\n self.re_order = [0, 1, 4, 7, 10, 2, 5, 8, 11, 3, 6, 9, 12, 15, 13, 16, 18, 20, 14, 17, 19, 21]\\n\\n self.re_order_inv = [0, 1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12, 14, 18, 13, 15, 19, 16, 20, 17, 21]\\n self.end_points = [4, 8, 13, 17, 21]\\n\\n self.template_offset = self.template.offsets.copy()\\n self.parents = [-1, 0, 1, 2, 3, 0, 5, 6, 7, 0, 9, 10, 11, 12, 11, 14, 15, 16, 11, 18, 19, 20]\\n\\n def convert(self, positions, filename, iterations=10, foot_ik=True):\\n '''\\n Convert the SMPL joint positions to Mocap BVH\\n :param positions: (N, 22, 3)\\n :param filename: Save path for resulting BVH\\n :param iterations: iterations for optimizing rotations, 10 is usually enough\\n :param foot_ik: whether to enfore foot inverse kinematics, removing foot slide issue.\\n :return:\\n '''\\n positions = positions[:, self.re_order]\\n new_anim = self.template.copy()\\n new_anim.rotations = Quaternions.id(positions.shape[:-1])\\n new_anim.positions = new_anim.positions[0:1].repeat(positions.shape[0], axis=-0)\\n new_anim.positions[:, 0] = positions[:, 0]\\n\\n if foot_ik:\\n positions = remove_fs(positions, None, fid_l=(3, 4), fid_r=(7, 8), interp_length=5,\\n force_on_floor=True)\\n ik_solver = BasicInverseKinematics(new_anim, positions, iterations=iterations, silent=True)\\n new_anim = ik_solver()\\n\\n # BVH.save(filename, new_anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\\n glb = Animation.positions_global(new_anim)[:, self.re_order_inv]\\n if filename is not None:\\n BVH.save(filename, new_anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\\n return new_anim, glb\\n\\n def convert_sgd(self, positions, filename, iterations=100, foot_ik=True):\\n '''\\n Convert the SMPL joint positions to Mocap BVH\\n\\n :param positions: (N, 22, 3)\\n :param filename: Save path for resulting BVH\\n :param iterations: iterations for optimizing rotations, 10 is usually enough\\n :param foot_ik: whether to enfore foot inverse kinematics, removing foot slide issue.\\n :return:\\n '''\\n\\n ## Positional Foot locking ##\\n glb = positions[:, self.re_order]\\n\\n if foot_ik:\\n glb = remove_fs(glb, None, fid_l=(3, 4), fid_r=(7, 8), interp_length=2,\\n force_on_floor=True)\\n\\n ## Fit BVH ##\\n new_anim = self.template.copy()\\n new_anim.rotations = Quaternions.id(glb.shape[:-1])\\n new_anim.positions = new_anim.positions[0:1].repeat(glb.shape[0], axis=-0)\\n new_anim.positions[:, 0] = glb[:, 0]\\n anim = new_anim.copy()\\n\\n rot = torch.tensor(anim.rotations.qs, dtype=torch.float)\\n pos = torch.tensor(anim.positions[:, 0, :], dtype=torch.float)\\n offset = torch.tensor(anim.offsets, dtype=torch.float)\\n\\n glb = torch.tensor(glb, dtype=torch.float)\\n ik_solver = InverseKinematics(rot, pos, offset, anim.parents, glb)\\n print('Fixing foot contact using IK...')\\n for i in tqdm(range(iterations)):\\n mse = ik_solver.step()\\n # print(i, mse)\\n\\n rotations = ik_solver.rotations.detach().cpu()\\n norm = torch.norm(rotations, dim=-1, keepdim=True)\\n rotations /= norm\\n\\n anim.rotations = Quaternions(rotations.numpy())\\n anim.rotations[:, self.end_points] = Quaternions.id((anim.rotations.shape[0], len(self.end_points)))\\n anim.positions[:, 0, :] = ik_solver.position.detach().cpu().numpy()\\n if filename is not None:\\n BVH.save(filename, anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\\n # BVH.save(filename[:-3] + 'bvh', anim, names=new_anim.names, frametime=1 / 20, order='zyx', quater=True)\\n glb = Animation.positions_global(anim)[:, self.re_order_inv]\\n return anim, glb\"\n },\n {\n \"identifier\": \"recover_from_ric\",\n \"path\": \"utils/motion_process.py\",\n \"snippet\": \"def recover_from_ric(data, joints_num):\\n r_rot_quat, r_pos = recover_root_rot_pos(data)\\n positions = data[..., 4:(joints_num - 1) * 3 + 4]\\n positions = positions.view(positions.shape[:-1] + (-1, 3))\\n\\n '''Add Y-axis rotation to local joints'''\\n positions = qrot(qinv(r_rot_quat[..., None, :]).expand(positions.shape[:-1] + (4,)), positions)\\n\\n '''Add root XZ to joints'''\\n positions[..., 0] += r_pos[..., 0:1]\\n positions[..., 2] += r_pos[..., 2:3]\\n\\n '''Concate root and joints'''\\n positions = torch.cat([r_pos.unsqueeze(-2), positions], dim=-2)\\n\\n return positions\"\n },\n {\n \"identifier\": \"plot_3d_motion\",\n \"path\": \"utils/plot_script.py\",\n \"snippet\": \"def plot_3d_motion(save_path, kinematic_tree, joints, title, figsize=(10, 10), fps=120, radius=4):\\n matplotlib.use('Agg')\\n\\n title_sp = title.split(' ')\\n if len(title_sp) > 20:\\n title = '\\\\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:20]), ' '.join(title_sp[20:])])\\n elif len(title_sp) > 10:\\n title = '\\\\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:])])\\n\\n def init():\\n ax.set_xlim3d([-radius / 2, radius / 2])\\n ax.set_ylim3d([0, radius])\\n ax.set_zlim3d([0, radius])\\n # print(title)\\n fig.suptitle(title, fontsize=20)\\n ax.grid(b=False)\\n\\n def plot_xzPlane(minx, maxx, miny, minz, maxz):\\n ## Plot a plane XZ\\n verts = [\\n [minx, miny, minz],\\n [minx, miny, maxz],\\n [maxx, miny, maxz],\\n [maxx, miny, minz]\\n ]\\n xz_plane = Poly3DCollection([verts])\\n xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5))\\n ax.add_collection3d(xz_plane)\\n\\n # return ax\\n\\n # (seq_len, joints_num, 3)\\n data = joints.copy().reshape(len(joints), -1, 3)\\n fig = plt.figure(figsize=figsize)\\n ax = p3.Axes3D(fig)\\n init()\\n MINS = data.min(axis=0).min(axis=0)\\n MAXS = data.max(axis=0).max(axis=0)\\n colors = ['red', 'blue', 'black', 'red', 'blue',\\n 'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue',\\n 'darkred', 'darkred', 'darkred', 'darkred', 'darkred']\\n frame_number = data.shape[0]\\n # print(data.shape)\\n\\n height_offset = MINS[1]\\n data[:, :, 1] -= height_offset\\n trajec = data[:, 0, [0, 2]]\\n\\n data[..., 0] -= data[:, 0:1, 0]\\n data[..., 2] -= data[:, 0:1, 2]\\n\\n # print(trajec.shape)\\n\\n def update(index):\\n # print(index)\\n ax.lines = []\\n ax.collections = []\\n ax.view_init(elev=120, azim=-90)\\n ax.dist = 7.5\\n # ax =\\n plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1],\\n MAXS[2] - trajec[index, 1])\\n # ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3)\\n\\n if index > 1:\\n ax.plot3D(trajec[:index, 0] - trajec[index, 0], np.zeros_like(trajec[:index, 0]),\\n trajec[:index, 1] - trajec[index, 1], linewidth=1.0,\\n color='blue')\\n # ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2])\\n\\n for i, (chain, color) in enumerate(zip(kinematic_tree, colors)):\\n # print(color)\\n if i < 5:\\n linewidth = 4.0\\n else:\\n linewidth = 2.0\\n ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth,\\n color=color)\\n # print(trajec[:index, 0].shape)\\n\\n plt.axis('off')\\n ax.set_xticklabels([])\\n ax.set_yticklabels([])\\n ax.set_zticklabels([])\\n\\n ani = FuncAnimation(fig, update, frames=frame_number, interval=1000 / fps, repeat=False)\\n\\n # writer = FFMpegFileWriter(fps=fps)\\n ani.save(save_path, fps=fps)\\n plt.close()\"\n },\n {\n \"identifier\": \"t2m_kinematic_chain\",\n \"path\": \"utils/paramUtil.py\",\n \"snippet\": \"\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom os.path import join as pjoin\nfrom models.mask_transformer.transformer import MaskTransformer, ResidualTransformer\nfrom models.vq.model import RVQVAE, LengthEstimator\nfrom options.eval_option import EvalT2MOptions\nfrom utils.get_opt import get_opt\nfrom utils.fixseed import fixseed\nfrom visualization.joints2bvh import Joint2BVHConvertor\nfrom torch.distributions.categorical import Categorical\nfrom utils.motion_process import recover_from_ric\nfrom utils.plot_script import plot_3d_motion\nfrom utils.paramUtil import t2m_kinematic_chain"},"token_num":{"kind":"number","value":16497,"string":"16,497"},"cropped_code":{"kind":"string","value":"\n\n\n\n\n\n\n\nclip_version = 'ViT-B/32'\n\ndef load_vq_model(vq_opt):\n # opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')\n vq_model = RVQVAE(vq_opt,\n vq_opt.dim_pose,\n vq_opt.nb_code,\n vq_opt.code_dim,\n vq_opt.output_emb_width,\n vq_opt.down_t,\n vq_opt.stride_t,\n vq_opt.width,\n vq_opt.depth,\n vq_opt.dilation_growth_rate,\n vq_opt.vq_act,\n vq_opt.vq_norm)\n ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', 'net_best_fid.tar'),\n map_location='cpu')\n model_key = 'vq_model' if 'vq_model' in ckpt else 'net'\n vq_model.load_state_dict(ckpt[model_key])\n print(f'Loading VQ Model {vq_opt.name} Completed!')\n return vq_model, vq_opt\n\ndef load_trans_model(model_opt, opt, which_model):\n t2m_transformer = MaskTransformer(code_dim=model_opt.code_dim,\n cond_mode='text',\n latent_dim=model_opt.latent_dim,\n ff_size=model_opt.ff_size,\n num_layers=model_opt.n_layers,\n num_heads=model_opt.n_heads,\n dropout=model_opt.dropout,\n clip_dim=512,\n cond_drop_prob=model_opt.cond_drop_prob,\n clip_version=clip_version,\n opt=model_opt)\n ckpt = torch.load(pjoin(model_opt.checkpoints_dir, model_opt.dataset_name, model_opt.name, 'model', which_model),\n map_location='cpu')\n model_key = 't2m_transformer' if 't2m_transformer' in ckpt else 'trans'\n # print(ckpt.keys())\n missing_keys, unexpected_keys = t2m_transformer.load_state_dict(ckpt[model_key], strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n print(f'Loading Transformer {opt.name} from epoch {ckpt[\"ep\"]}!')\n return t2m_transformer\n\ndef load_res_model(res_opt, vq_opt, opt):\n res_opt.num_quantizers = vq_opt.num_quantizers\n res_opt.num_tokens = vq_opt.nb_code\n res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,\n cond_mode='text',\n latent_dim=res_opt.latent_dim,\n ff_size=res_opt.ff_size,\n num_layers=res_opt.n_layers,\n num_heads=res_opt.n_heads,\n dropout=res_opt.dropout,\n clip_dim=512,\n shared_codebook=vq_opt.shared_codebook,\n cond_drop_prob=res_opt.cond_drop_prob,\n # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,\n share_weight=res_opt.share_weight,\n clip_version=clip_version,\n opt=res_opt)\n\n ckpt = torch.load(pjoin(res_opt.checkpoints_dir, res_opt.dataset_name, res_opt.name, 'model', 'net_best_fid.tar'),\n map_location=opt.device)\n missing_keys, unexpected_keys = res_transformer.load_state_dict(ckpt['res_transformer'], strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n print(f'Loading Residual Transformer {res_opt.name} from epoch {ckpt[\"ep\"]}!')\n return res_transformer\n\ndef load_len_estimator(opt):\n model = LengthEstimator(512, 50)\n ckpt = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'length_estimator', 'model', 'finest.tar'),\n map_location=opt.device)\n model.load_state_dict(ckpt['estimator'])\n print(f'Loading Length Estimator from epoch {ckpt[\"epoch\"]}!')\n return model\n\n\nif __name__ == '__main__':\n parser = EvalT2MOptions()\n opt = parser.parse()\n"},"all_code":{"kind":"string","value":"\n\n\n\n\n\n\n\nclip_version = 'ViT-B/32'\n\ndef load_vq_model(vq_opt):\n # opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')\n vq_model = RVQVAE(vq_opt,\n vq_opt.dim_pose,\n vq_opt.nb_code,\n vq_opt.code_dim,\n vq_opt.output_emb_width,\n vq_opt.down_t,\n vq_opt.stride_t,\n vq_opt.width,\n vq_opt.depth,\n vq_opt.dilation_growth_rate,\n vq_opt.vq_act,\n vq_opt.vq_norm)\n ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', 'net_best_fid.tar'),\n map_location='cpu')\n model_key = 'vq_model' if 'vq_model' in ckpt else 'net'\n vq_model.load_state_dict(ckpt[model_key])\n print(f'Loading VQ Model {vq_opt.name} Completed!')\n return vq_model, vq_opt\n\ndef load_trans_model(model_opt, opt, which_model):\n t2m_transformer = MaskTransformer(code_dim=model_opt.code_dim,\n cond_mode='text',\n latent_dim=model_opt.latent_dim,\n ff_size=model_opt.ff_size,\n num_layers=model_opt.n_layers,\n num_heads=model_opt.n_heads,\n dropout=model_opt.dropout,\n clip_dim=512,\n cond_drop_prob=model_opt.cond_drop_prob,\n clip_version=clip_version,\n opt=model_opt)\n ckpt = torch.load(pjoin(model_opt.checkpoints_dir, model_opt.dataset_name, model_opt.name, 'model', which_model),\n map_location='cpu')\n model_key = 't2m_transformer' if 't2m_transformer' in ckpt else 'trans'\n # print(ckpt.keys())\n missing_keys, unexpected_keys = t2m_transformer.load_state_dict(ckpt[model_key], strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n print(f'Loading Transformer {opt.name} from epoch {ckpt[\"ep\"]}!')\n return t2m_transformer\n\ndef load_res_model(res_opt, vq_opt, opt):\n res_opt.num_quantizers = vq_opt.num_quantizers\n res_opt.num_tokens = vq_opt.nb_code\n res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,\n cond_mode='text',\n latent_dim=res_opt.latent_dim,\n ff_size=res_opt.ff_size,\n num_layers=res_opt.n_layers,\n num_heads=res_opt.n_heads,\n dropout=res_opt.dropout,\n clip_dim=512,\n shared_codebook=vq_opt.shared_codebook,\n cond_drop_prob=res_opt.cond_drop_prob,\n # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,\n share_weight=res_opt.share_weight,\n clip_version=clip_version,\n opt=res_opt)\n\n ckpt = torch.load(pjoin(res_opt.checkpoints_dir, res_opt.dataset_name, res_opt.name, 'model', 'net_best_fid.tar'),\n map_location=opt.device)\n missing_keys, unexpected_keys = res_transformer.load_state_dict(ckpt['res_transformer'], strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n print(f'Loading Residual Transformer {res_opt.name} from epoch {ckpt[\"ep\"]}!')\n return res_transformer\n\ndef load_len_estimator(opt):\n model = LengthEstimator(512, 50)\n ckpt = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'length_estimator', 'model', 'finest.tar'),\n map_location=opt.device)\n model.load_state_dict(ckpt['estimator'])\n print(f'Loading Length Estimator from epoch {ckpt[\"epoch\"]}!')\n return model\n\n\nif __name__ == '__main__':\n parser = EvalT2MOptions()\n opt = parser.parse()"},"next_line":{"kind":"string","value":" fixseed(opt.seed)"},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-11-29 19:21:27+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5373,"cells":{"repo_name":{"kind":"string","value":"dvlab-research/LLMGA"},"file_path":{"kind":"string","value":"llmga/diffusers/src/diffusers/models/transformer_temporal.py"},"context":{"kind":"list like","value":[{"identifier":"ConfigMixin","path":"llmga/diffusers/src/diffusers/configuration_utils.py","snippet":"class ConfigMixin:\n r\"\"\"\n Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also\n provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and\n saving classes that inherit from [`ConfigMixin`].\n\n Class attributes:\n - **config_name** (`str`) -- A filename under which the config should stored when calling\n [`~ConfigMixin.save_config`] (should be overridden by parent class).\n - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be\n overridden by subclass).\n - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).\n - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function\n should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by\n subclass).\n \"\"\"\n config_name = None\n ignore_for_config = []\n has_compatibles = False\n\n _deprecated_kwargs = []\n\n def register_to_config(self, **kwargs):\n if self.config_name is None:\n raise NotImplementedError(f\"Make sure that {self.__class__} has defined a class name `config_name`\")\n # Special case for `kwargs` used in deprecation warning added to schedulers\n # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,\n # or solve in a more general way.\n kwargs.pop(\"kwargs\", None)\n\n if not hasattr(self, \"_internal_dict\"):\n internal_dict = kwargs\n else:\n previous_dict = dict(self._internal_dict)\n internal_dict = {**self._internal_dict, **kwargs}\n logger.debug(f\"Updating config from {previous_dict} to {internal_dict}\")\n\n self._internal_dict = FrozenDict(internal_dict)\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129\n\n Tihs funtion is mostly copied from PyTorch's __getattr__ overwrite:\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n \"\"\"\n\n is_in_config = \"_internal_dict\" in self.__dict__ and hasattr(self.__dict__[\"_internal_dict\"], name)\n is_attribute = name in self.__dict__\n\n if is_in_config and not is_attribute:\n deprecation_message = f\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'scheduler.config.{name}'.\"\n deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n return self._internal_dict[name]\n\n raise AttributeError(f\"'{type(self).__name__}' object has no attribute '{name}'\")\n\n def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):\n \"\"\"\n Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the\n [`~ConfigMixin.from_config`] class method.\n\n Args:\n save_directory (`str` or `os.PathLike`):\n Directory where the configuration JSON file is saved (will be created if it does not exist).\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n if os.path.isfile(save_directory):\n raise AssertionError(f\"Provided path ({save_directory}) should be a directory, not a file\")\n\n os.makedirs(save_directory, exist_ok=True)\n\n # If we save using the predefined names, we can load using `from_config`\n output_config_file = os.path.join(save_directory, self.config_name)\n\n self.to_json_file(output_config_file)\n logger.info(f\"Configuration saved in {output_config_file}\")\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n @classmethod\n def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):\n r\"\"\"\n Instantiate a Python class from a config dictionary.\n\n Parameters:\n config (`Dict[str, Any]`):\n A config dictionary from which the Python class is instantiated. Make sure to only load configuration\n files of compatible classes.\n return_unused_kwargs (`bool`, *optional*, defaults to `False`):\n Whether kwargs that are not consumed by the Python class should be returned or not.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to update the configuration object (after it is loaded) and initiate the Python class.\n `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually\n overwrite the same named arguments in `config`.\n\n Returns:\n [`ModelMixin`] or [`SchedulerMixin`]:\n A model or scheduler object instantiated from a config dictionary.\n\n Examples:\n\n ```python\n >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler\n\n >>> # Download scheduler from huggingface.co and cache.\n >>> scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cifar10-32\")\n\n >>> # Instantiate DDIM scheduler class with same config as DDPM\n >>> scheduler = DDIMScheduler.from_config(scheduler.config)\n\n >>> # Instantiate PNDM scheduler class with same config as DDPM\n >>> scheduler = PNDMScheduler.from_config(scheduler.config)\n ```\n \"\"\"\n # <===== TO BE REMOVED WITH DEPRECATION\n # TODO(Patrick) - make sure to remove the following lines when config==\"model_path\" is deprecated\n if \"pretrained_model_name_or_path\" in kwargs:\n config = kwargs.pop(\"pretrained_model_name_or_path\")\n\n if config is None:\n raise ValueError(\"Please make sure to provide a config as the first positional argument.\")\n # ======>\n\n if not isinstance(config, dict):\n deprecation_message = \"It is deprecated to pass a pretrained model name or path to `from_config`.\"\n if \"Scheduler\" in cls.__name__:\n deprecation_message += (\n f\"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead.\"\n \" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will\"\n \" be removed in v1.0.0.\"\n )\n elif \"Model\" in cls.__name__:\n deprecation_message += (\n f\"If you were trying to load a model, please use {cls}.load_config(...) followed by\"\n f\" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary\"\n \" instead. This functionality will be removed in v1.0.0.\"\n )\n deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)\n\n init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)\n\n # Allow dtype to be specified on initialization\n if \"dtype\" in unused_kwargs:\n init_dict[\"dtype\"] = unused_kwargs.pop(\"dtype\")\n\n # add possible deprecated kwargs\n for deprecated_kwarg in cls._deprecated_kwargs:\n if deprecated_kwarg in unused_kwargs:\n init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)\n\n # Return model and optionally state and/or unused_kwargs\n model = cls(**init_dict)\n\n # make sure to also save config parameters that might be used for compatible classes\n model.register_to_config(**hidden_dict)\n\n # add hidden kwargs of compatible classes to unused_kwargs\n unused_kwargs = {**unused_kwargs, **hidden_dict}\n\n if return_unused_kwargs:\n return (model, unused_kwargs)\n else:\n return model\n\n @classmethod\n def get_config_dict(cls, *args, **kwargs):\n deprecation_message = (\n f\" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be\"\n \" removed in version v1.0.0\"\n )\n deprecate(\"get_config_dict\", \"1.0.0\", deprecation_message, standard_warn=False)\n return cls.load_config(*args, **kwargs)\n\n @classmethod\n def load_config(\n cls,\n pretrained_model_name_or_path: Union[str, os.PathLike],\n return_unused_kwargs=False,\n return_commit_hash=False,\n **kwargs,\n ) -> Tuple[Dict[str, Any], Dict[str, Any]]:\n r\"\"\"\n Load a model or scheduler configuration.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing model weights saved with\n [`~ConfigMixin.save_config`].\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download (`bool`, *optional*, defaults to `False`):\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\n incompletely downloaded files are deleted.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n use_auth_token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n return_unused_kwargs (`bool`, *optional*, defaults to `False):\n Whether unused keyword arguments of the config are returned.\n return_commit_hash (`bool`, *optional*, defaults to `False):\n Whether the `commit_hash` of the loaded configuration are returned.\n\n Returns:\n `dict`:\n A dictionary of all the parameters stored in a JSON configuration file.\n\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", DIFFUSERS_CACHE)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n use_auth_token = kwargs.pop(\"use_auth_token\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n _ = kwargs.pop(\"mirror\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n user_agent = kwargs.pop(\"user_agent\", {})\n\n user_agent = {**user_agent, \"file_type\": \"config\"}\n user_agent = http_user_agent(user_agent)\n\n pretrained_model_name_or_path = str(pretrained_model_name_or_path)\n\n if cls.config_name is None:\n raise ValueError(\n \"`self.config_name` is not defined. Note that one should not load a config from \"\n \"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`\"\n )\n\n if os.path.isfile(pretrained_model_name_or_path):\n config_file = pretrained_model_name_or_path\n elif os.path.isdir(pretrained_model_name_or_path):\n if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):\n # Load from a PyTorch checkpoint\n config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)\n elif subfolder is not None and os.path.isfile(\n os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\n ):\n config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\n else:\n raise EnvironmentError(\n f\"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}.\"\n )\n else:\n try:\n # Load from URL or cache if already cached\n config_file = hf_hub_download(\n pretrained_model_name_or_path,\n filename=cls.config_name,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n use_auth_token=use_auth_token,\n user_agent=user_agent,\n subfolder=subfolder,\n revision=revision,\n )\n except RepositoryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier\"\n \" listed on 'https://huggingface.co/models'\\nIf this is a private repository, make sure to pass a\"\n \" token having permission to this repo with `use_auth_token` or log in with `huggingface-cli\"\n \" login`.\"\n )\n except RevisionNotFoundError:\n raise EnvironmentError(\n f\"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for\"\n \" this model name. Check the model page at\"\n f\" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.\"\n )\n except EntryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}.\"\n )\n except HTTPError as err:\n raise EnvironmentError(\n \"There was a specific connection error when trying to load\"\n f\" {pretrained_model_name_or_path}:\\n{err}\"\n )\n except ValueError:\n raise EnvironmentError(\n f\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it\"\n f\" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a\"\n f\" directory containing a {cls.config_name} file.\\nCheckout your internet connection or see how to\"\n \" run the library in offline mode at\"\n \" 'https://huggingface.co/docs/diffusers/installation#offline-mode'.\"\n )\n except EnvironmentError:\n raise EnvironmentError(\n f\"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from \"\n \"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \"\n f\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \"\n f\"containing a {cls.config_name} file\"\n )\n\n try:\n # Load config dict\n config_dict = cls._dict_from_json_file(config_file)\n\n commit_hash = extract_commit_hash(config_file)\n except (json.JSONDecodeError, UnicodeDecodeError):\n raise EnvironmentError(f\"It looks like the config file at '{config_file}' is not a valid JSON file.\")\n\n if not (return_unused_kwargs or return_commit_hash):\n return config_dict\n\n outputs = (config_dict,)\n\n if return_unused_kwargs:\n outputs += (kwargs,)\n\n if return_commit_hash:\n outputs += (commit_hash,)\n\n return outputs\n\n @staticmethod\n def _get_init_keys(cls):\n return set(dict(inspect.signature(cls.__init__).parameters).keys())\n\n @classmethod\n def extract_init_dict(cls, config_dict, **kwargs):\n # Skip keys that were not present in the original config, so default __init__ values were used\n used_defaults = config_dict.get(\"_use_default_values\", [])\n config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != \"_use_default_values\"}\n\n # 0. Copy origin config dict\n original_dict = dict(config_dict.items())\n\n # 1. Retrieve expected config attributes from __init__ signature\n expected_keys = cls._get_init_keys(cls)\n expected_keys.remove(\"self\")\n # remove general kwargs if present in dict\n if \"kwargs\" in expected_keys:\n expected_keys.remove(\"kwargs\")\n # remove flax internal keys\n if hasattr(cls, \"_flax_internal_args\"):\n for arg in cls._flax_internal_args:\n expected_keys.remove(arg)\n\n # 2. Remove attributes that cannot be expected from expected config attributes\n # remove keys to be ignored\n if len(cls.ignore_for_config) > 0:\n expected_keys = expected_keys - set(cls.ignore_for_config)\n\n # load diffusers library to import compatible and original scheduler\n diffusers_library = importlib.import_module(__name__.split(\".\")[0])\n\n if cls.has_compatibles:\n compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]\n else:\n compatible_classes = []\n\n expected_keys_comp_cls = set()\n for c in compatible_classes:\n expected_keys_c = cls._get_init_keys(c)\n expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)\n expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)\n config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}\n\n # remove attributes from orig class that cannot be expected\n orig_cls_name = config_dict.pop(\"_class_name\", cls.__name__)\n if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name):\n orig_cls = getattr(diffusers_library, orig_cls_name)\n unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys\n config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}\n\n # remove private attributes\n config_dict = {k: v for k, v in config_dict.items() if not k.startswith(\"_\")}\n\n # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments\n init_dict = {}\n for key in expected_keys:\n # if config param is passed to kwarg and is present in config dict\n # it should overwrite existing config dict key\n if key in kwargs and key in config_dict:\n config_dict[key] = kwargs.pop(key)\n\n if key in kwargs:\n # overwrite key\n init_dict[key] = kwargs.pop(key)\n elif key in config_dict:\n # use value from config dict\n init_dict[key] = config_dict.pop(key)\n\n # 4. Give nice warning if unexpected values have been passed\n if len(config_dict) > 0:\n logger.warning(\n f\"The config attributes {config_dict} were passed to {cls.__name__}, \"\n \"but are not expected and will be ignored. Please verify your \"\n f\"{cls.config_name} configuration file.\"\n )\n\n # 5. Give nice info if config attributes are initiliazed to default because they have not been passed\n passed_keys = set(init_dict.keys())\n if len(expected_keys - passed_keys) > 0:\n logger.info(\n f\"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values.\"\n )\n\n # 6. Define unused keyword arguments\n unused_kwargs = {**config_dict, **kwargs}\n\n # 7. Define \"hidden\" config parameters that were saved for compatible classes\n hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}\n\n return init_dict, unused_kwargs, hidden_config_dict\n\n @classmethod\n def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):\n with open(json_file, \"r\", encoding=\"utf-8\") as reader:\n text = reader.read()\n return json.loads(text)\n\n def __repr__(self):\n return f\"{self.__class__.__name__} {self.to_json_string()}\"\n\n @property\n def config(self) -> Dict[str, Any]:\n \"\"\"\n Returns the config of the class as a frozen dictionary\n\n Returns:\n `Dict[str, Any]`: Config of the class.\n \"\"\"\n return self._internal_dict\n\n def to_json_string(self) -> str:\n \"\"\"\n Serializes the configuration instance to a JSON string.\n\n Returns:\n `str`:\n String containing all the attributes that make up the configuration instance in JSON format.\n \"\"\"\n config_dict = self._internal_dict if hasattr(self, \"_internal_dict\") else {}\n config_dict[\"_class_name\"] = self.__class__.__name__\n config_dict[\"_diffusers_version\"] = __version__\n\n def to_json_saveable(value):\n if isinstance(value, np.ndarray):\n value = value.tolist()\n elif isinstance(value, PosixPath):\n value = str(value)\n return value\n\n config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}\n # Don't save \"_ignore_files\" or \"_use_default_values\"\n config_dict.pop(\"_ignore_files\", None)\n config_dict.pop(\"_use_default_values\", None)\n\n return json.dumps(config_dict, indent=2, sort_keys=True) + \"\\n\"\n\n def to_json_file(self, json_file_path: Union[str, os.PathLike]):\n \"\"\"\n Save the configuration instance's parameters to a JSON file.\n\n Args:\n json_file_path (`str` or `os.PathLike`):\n Path to the JSON file to save a configuration instance's parameters.\n \"\"\"\n with open(json_file_path, \"w\", encoding=\"utf-8\") as writer:\n writer.write(self.to_json_string())"},{"identifier":"register_to_config","path":"llmga/diffusers/src/diffusers/configuration_utils.py","snippet":"def register_to_config(self, **kwargs):\n if self.config_name is None:\n raise NotImplementedError(f\"Make sure that {self.__class__} has defined a class name `config_name`\")\n # Special case for `kwargs` used in deprecation warning added to schedulers\n # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,\n # or solve in a more general way.\n kwargs.pop(\"kwargs\", None)\n\n if not hasattr(self, \"_internal_dict\"):\n internal_dict = kwargs\n else:\n previous_dict = dict(self._internal_dict)\n internal_dict = {**self._internal_dict, **kwargs}\n logger.debug(f\"Updating config from {previous_dict} to {internal_dict}\")\n\n self._internal_dict = FrozenDict(internal_dict)"},{"identifier":"BaseOutput","path":"llmga/diffusers/src/diffusers/utils/outputs.py","snippet":"class BaseOutput(OrderedDict):\n \"\"\"\n Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a\n tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular\n Python dictionary.\n\n <Tip warning={true}>\n\n You can't unpack a [`BaseOutput`] directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple\n first.\n\n </Tip>\n \"\"\"\n\n def __post_init__(self):\n class_fields = fields(self)\n\n # Safety and consistency checks\n if not len(class_fields):\n raise ValueError(f\"{self.__class__.__name__} has no fields.\")\n\n first_field = getattr(self, class_fields[0].name)\n other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])\n\n if other_fields_are_none and isinstance(first_field, dict):\n for key, value in first_field.items():\n self[key] = value\n else:\n for field in class_fields:\n v = getattr(self, field.name)\n if v is not None:\n self[field.name] = v\n\n def __delitem__(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.\")\n\n def setdefault(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.\")\n\n def pop(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``pop`` on a {self.__class__.__name__} instance.\")\n\n def update(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``update`` on a {self.__class__.__name__} instance.\")\n\n def __getitem__(self, k):\n if isinstance(k, str):\n inner_dict = dict(self.items())\n return inner_dict[k]\n else:\n return self.to_tuple()[k]\n\n def __setattr__(self, name, value):\n if name in self.keys() and value is not None:\n # Don't call self.__setitem__ to avoid recursion errors\n super().__setitem__(name, value)\n super().__setattr__(name, value)\n\n def __setitem__(self, key, value):\n # Will raise a KeyException if needed\n super().__setitem__(key, value)\n # Don't call self.__setattr__ to avoid recursion errors\n super().__setattr__(key, value)\n\n def __reduce__(self):\n if not is_dataclass(self):\n return super().__reduce__()\n callable, _args, *remaining = super().__reduce__()\n args = tuple(getattr(self, field.name) for field in fields(self))\n return callable, args, *remaining\n\n def to_tuple(self) -> Tuple[Any]:\n \"\"\"\n Convert self to a tuple containing all the attributes/keys that are not `None`.\n \"\"\"\n return tuple(self[k] for k in self.keys())"},{"identifier":"BasicTransformerBlock","path":"llmga/diffusers/src/diffusers/models/attention.py","snippet":"class BasicTransformerBlock(nn.Module):\n r\"\"\"\n A basic Transformer block.\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n num_embeds_ada_norm (:\n obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.\n attention_bias (:\n obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used.\n double_self_attention (`bool`, *optional*):\n Whether to use two self-attention layers. In this case no cross attention layers are used.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the attention computation to float32. This is useful for mixed precision training.\n norm_elementwise_affine (`bool`, *optional*, defaults to `True`):\n Whether to use learnable elementwise affine parameters for normalization.\n norm_type (`str`, *optional*, defaults to `\"layer_norm\"`):\n The normalization layer to use. Can be `\"layer_norm\"`, `\"ada_norm\"` or `\"ada_norm_zero\"`.\n final_dropout (`bool` *optional*, defaults to False):\n Whether to apply a final dropout after the last feed-forward layer.\n attention_type (`str`, *optional*, defaults to `\"default\"`):\n The type of attention to use. Can be `\"default\"` or `\"gated\"` or `\"gated-text-image\"`.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n double_self_attention: bool = False,\n upcast_attention: bool = False,\n norm_elementwise_affine: bool = True,\n norm_type: str = \"layer_norm\",\n final_dropout: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n\n self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm_zero\"\n self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm\"\n\n if norm_type in (\"ada_norm\", \"ada_norm_zero\") and num_embeds_ada_norm is None:\n raise ValueError(\n f\"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to\"\n f\" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.\"\n )\n\n # Define 3 blocks. Each block has its own normalization layer.\n # 1. Self-Attn\n if self.use_ada_layer_norm:\n self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)\n elif self.use_ada_layer_norm_zero:\n self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)\n else:\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim if only_cross_attention else None,\n upcast_attention=upcast_attention,\n )\n\n # 2. Cross-Attn\n if cross_attention_dim is not None or double_self_attention:\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\n # the second cross attention block.\n self.norm2 = (\n AdaLayerNorm(dim, num_embeds_ada_norm)\n if self.use_ada_layer_norm\n else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n )\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim if not double_self_attention else None,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n ) # is self-attn if encoder_hidden_states is none\n else:\n self.norm2 = None\n self.attn2 = None\n\n # 3. Feed-forward\n self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)\n\n # 4. Fuser\n if attention_type == \"gated\" or attention_type == \"gated-text-image\":\n self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)\n\n # let chunk size default to None\n self._chunk_size = None\n self._chunk_dim = 0\n\n def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):\n # Sets chunk feed-forward\n self._chunk_size = chunk_size\n self._chunk_dim = dim\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n ) -> torch.FloatTensor:\n # Notice that normalization is always applied before the real computation in the following blocks.\n # 0. Self-Attention\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Retrieve lora scale.\n lora_scale = cross_attention_kwargs.get(\"scale\", 1.0) if cross_attention_kwargs is not None else 1.0\n\n # 2. Prepare GLIGEN inputs\n cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}\n gligen_kwargs = cross_attention_kwargs.pop(\"gligen\", None)\n\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n # 2.5 GLIGEN Control\n if gligen_kwargs is not None:\n hidden_states = self.fuser(hidden_states, gligen_kwargs[\"objs\"])\n # 2.5 ends\n\n # 3. Cross-Attention\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 4. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n if self._chunk_size is not None:\n # \"feed_forward_chunk_size\" can be used to save memory\n if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:\n raise ValueError(\n f\"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.\"\n )\n\n num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size\n ff_output = torch.cat(\n [\n self.ff(hid_slice, scale=lora_scale)\n for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)\n ],\n dim=self._chunk_dim,\n )\n else:\n ff_output = self.ff(norm_hidden_states, scale=lora_scale)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states"},{"identifier":"ModelMixin","path":"llmga/diffusers/src/diffusers/models/modeling_utils.py","snippet":"class ModelMixin(torch.nn.Module, PushToHubMixin):\n r\"\"\"\n Base class for all models.\n\n [`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and\n saving models.\n\n - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].\n \"\"\"\n config_name = CONFIG_NAME\n _automatically_saved_args = [\"_diffusers_version\", \"_class_name\", \"_name_or_path\"]\n _supports_gradient_checkpointing = False\n _keys_to_ignore_on_load_unexpected = None\n _hf_peft_config_loaded = False\n\n def __init__(self):\n super().__init__()\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite\n __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n \"\"\"\n\n is_in_config = \"_internal_dict\" in self.__dict__ and hasattr(self.__dict__[\"_internal_dict\"], name)\n is_attribute = name in self.__dict__\n\n if is_in_config and not is_attribute:\n deprecation_message = f\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'.\"\n deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False, stacklevel=3)\n return self._internal_dict[name]\n\n # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n return super().__getattr__(name)\n\n @property\n def is_gradient_checkpointing(self) -> bool:\n \"\"\"\n Whether gradient checkpointing is activated for this model or not.\n \"\"\"\n return any(hasattr(m, \"gradient_checkpointing\") and m.gradient_checkpointing for m in self.modules())\n\n def enable_gradient_checkpointing(self):\n \"\"\"\n Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\n *checkpoint activations* in other frameworks).\n \"\"\"\n if not self._supports_gradient_checkpointing:\n raise ValueError(f\"{self.__class__.__name__} does not support gradient checkpointing.\")\n self.apply(partial(self._set_gradient_checkpointing, value=True))\n\n def disable_gradient_checkpointing(self):\n \"\"\"\n Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\n *checkpoint activations* in other frameworks).\n \"\"\"\n if self._supports_gradient_checkpointing:\n self.apply(partial(self._set_gradient_checkpointing, value=False))\n\n def set_use_memory_efficient_attention_xformers(\n self, valid: bool, attention_op: Optional[Callable] = None\n ) -> None:\n # Recursively walk through all the children.\n # Any children which exposes the set_use_memory_efficient_attention_xformers method\n # gets the message\n def fn_recursive_set_mem_eff(module: torch.nn.Module):\n if hasattr(module, \"set_use_memory_efficient_attention_xformers\"):\n module.set_use_memory_efficient_attention_xformers(valid, attention_op)\n\n for child in module.children():\n fn_recursive_set_mem_eff(child)\n\n for module in self.children():\n if isinstance(module, torch.nn.Module):\n fn_recursive_set_mem_eff(module)\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n r\"\"\"\n Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\n\n When this option is enabled, you should observe lower GPU memory usage and a potential speed up during\n inference. Speed up during training is not guaranteed.\n\n <Tip warning={true}>\n\n ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes\n precedent.\n\n </Tip>\n\n Parameters:\n attention_op (`Callable`, *optional*):\n Override the default `None` operator for use as `op` argument to the\n [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)\n function of xFormers.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import UNet2DConditionModel\n >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp\n\n >>> model = UNet2DConditionModel.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1\", subfolder=\"unet\", torch_dtype=torch.float16\n ... )\n >>> model = model.to(\"cuda\")\n >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)\n ```\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(True, attention_op)\n\n def disable_xformers_memory_efficient_attention(self):\n r\"\"\"\n Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(False)\n\n def add_adapter(self, adapter_config, adapter_name: str = \"default\") -> None:\n r\"\"\"\n Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned\n to the adapter to follow the convention of the PEFT library.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT\n [documentation](https://huggingface.co/docs/peft).\n\n Args:\n adapter_config (`[~peft.PeftConfig]`):\n The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt\n methods.\n adapter_name (`str`, *optional*, defaults to `\"default\"`):\n The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n from peft import PeftConfig, inject_adapter_in_model\n\n if not self._hf_peft_config_loaded:\n self._hf_peft_config_loaded = True\n elif adapter_name in self.peft_config:\n raise ValueError(f\"Adapter with name {adapter_name} already exists. Please use a different name.\")\n\n if not isinstance(adapter_config, PeftConfig):\n raise ValueError(\n f\"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.\"\n )\n\n # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is\n # handled by the `load_lora_layers` or `LoraLoaderMixin`. Therefore we set it to `None` here.\n adapter_config.base_model_name_or_path = None\n inject_adapter_in_model(adapter_config, self, adapter_name)\n self.set_adapter(adapter_name)\n\n def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:\n \"\"\"\n Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n official documentation: https://huggingface.co/docs/peft\n\n Args:\n adapter_name (Union[str, List[str]])):\n The list of adapters to set or the adapter name in case of single adapter.\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n if isinstance(adapter_name, str):\n adapter_name = [adapter_name]\n\n missing = set(adapter_name) - set(self.peft_config)\n if len(missing) > 0:\n raise ValueError(\n f\"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s).\"\n f\" current loaded adapters are: {list(self.peft_config.keys())}\"\n )\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n _adapters_has_been_set = False\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"set_adapter\"):\n module.set_adapter(adapter_name)\n # Previous versions of PEFT does not support multi-adapter inference\n elif not hasattr(module, \"set_adapter\") and len(adapter_name) != 1:\n raise ValueError(\n \"You are trying to set multiple adapters and you have a PEFT version that does not support multi-adapter inference. Please upgrade to the latest version of PEFT.\"\n \" `pip install -U peft` or `pip install -U git+https://github.com/huggingface/peft.git`\"\n )\n else:\n module.active_adapter = adapter_name\n _adapters_has_been_set = True\n\n if not _adapters_has_been_set:\n raise ValueError(\n \"Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters.\"\n )\n\n def disable_adapters(self) -> None:\n r\"\"\"\n Disable all adapters attached to the model and fallback to inference with the base model only.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n official documentation: https://huggingface.co/docs/peft\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"enable_adapters\"):\n module.enable_adapters(enabled=False)\n else:\n # support for older PEFT versions\n module.disable_adapters = True\n\n def enable_adapters(self) -> None:\n \"\"\"\n Enable adapters that are attached to the model. The model will use `self.active_adapters()` to retrieve the\n list of adapters to enable.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n official documentation: https://huggingface.co/docs/peft\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"enable_adapters\"):\n module.enable_adapters(enabled=True)\n else:\n # support for older PEFT versions\n module.disable_adapters = False\n\n def active_adapters(self) -> List[str]:\n \"\"\"\n Gets the current list of active adapters of the model.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n official documentation: https://huggingface.co/docs/peft\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n return module.active_adapter\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n is_main_process: bool = True,\n save_function: Callable = None,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n push_to_hub: bool = False,\n **kwargs,\n ):\n \"\"\"\n Save a model and its configuration file to a directory so that it can be reloaded using the\n [`~models.ModelMixin.from_pretrained`] class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save a model and its configuration file to. Will be created if it doesn't exist.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n variant (`str`, *optional*):\n If specified, weights are saved in the format `pytorch_model.<variant>.bin`.\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n os.makedirs(save_directory, exist_ok=True)\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n # Only save the model itself if we are using distributed training\n model_to_save = self\n\n # Attach architecture to the config\n # Save the config\n if is_main_process:\n model_to_save.save_config(save_directory)\n\n # Save the model\n state_dict = model_to_save.state_dict()\n\n weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME\n weights_name = _add_variant(weights_name, variant)\n\n # Save the model\n if safe_serialization:\n safetensors.torch.save_file(\n state_dict, os.path.join(save_directory, weights_name), metadata={\"format\": \"pt\"}\n )\n else:\n torch.save(state_dict, os.path.join(save_directory, weights_name))\n\n logger.info(f\"Model weights saved in {os.path.join(save_directory, weights_name)}\")\n\n if push_to_hub:\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n @classmethod\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a pretrained PyTorch model from a pretrained model configuration.\n\n The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To\n train the model, set it back in training mode with `model.train()`.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n with [`~ModelMixin.save_pretrained`].\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If `\"auto\"` is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download (`bool`, *optional*, defaults to `False`):\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\n incompletely downloaded files are deleted.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info (`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n use_auth_token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n from_flax (`bool`, *optional*, defaults to `False`):\n Load the model weights from a Flax checkpoint save file.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn't need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if `device_map` contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n variant (`str`, *optional*):\n Load weights from a specified `variant` filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the\n `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`\n weights. If set to `False`, `safetensors` weights are not loaded.\n\n <Tip>\n\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\n `huggingface-cli login`. You can also activate the special\n [\"offline-mode\"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a\n firewalled environment.\n\n </Tip>\n\n Example:\n\n ```py\n from diffusers import UNet2DConditionModel\n\n unet = UNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\")\n ```\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```bash\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", DIFFUSERS_CACHE)\n ignore_mismatched_sizes = kwargs.pop(\"ignore_mismatched_sizes\", False)\n force_download = kwargs.pop(\"force_download\", False)\n from_flax = kwargs.pop(\"from_flax\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n output_loading_info = kwargs.pop(\"output_loading_info\", False)\n local_files_only = kwargs.pop(\"local_files_only\", HF_HUB_OFFLINE)\n use_auth_token = kwargs.pop(\"use_auth_token\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n device_map = kwargs.pop(\"device_map\", None)\n max_memory = kwargs.pop(\"max_memory\", None)\n offload_folder = kwargs.pop(\"offload_folder\", None)\n offload_state_dict = kwargs.pop(\"offload_state_dict\", False)\n low_cpu_mem_usage = kwargs.pop(\"low_cpu_mem_usage\", _LOW_CPU_MEM_USAGE_DEFAULT)\n variant = kwargs.pop(\"variant\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n if low_cpu_mem_usage and not is_accelerate_available():\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if device_map is not None and not is_accelerate_available():\n raise NotImplementedError(\n \"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set\"\n \" `device_map=None`. You can install accelerate with `pip install accelerate`.\"\n )\n\n # Check if we can handle device_map and dispatching the weights\n if device_map is not None and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `device_map=None`.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n if low_cpu_mem_usage is False and device_map is not None:\n raise ValueError(\n f\"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and\"\n \" dispatching. Please make sure to set `low_cpu_mem_usage=True`.\"\n )\n\n # Load config if we don't provide a configuration\n config_path = pretrained_model_name_or_path\n\n user_agent = {\n \"diffusers\": __version__,\n \"file_type\": \"model\",\n \"framework\": \"pytorch\",\n }\n\n # load config\n config, unused_kwargs, commit_hash = cls.load_config(\n config_path,\n cache_dir=cache_dir,\n return_unused_kwargs=True,\n return_commit_hash=True,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n use_auth_token=use_auth_token,\n revision=revision,\n subfolder=subfolder,\n device_map=device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n user_agent=user_agent,\n **kwargs,\n )\n\n # load model\n model_file = None\n if from_flax:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=FLAX_WEIGHTS_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n use_auth_token=use_auth_token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n model = cls.from_config(config, **unused_kwargs)\n\n # Convert the weights\n from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model\n\n model = load_flax_checkpoint_in_pytorch_model(model, model_file)\n else:\n if use_safetensors:\n try:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n use_auth_token=use_auth_token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n except IOError as e:\n if not allow_pickle:\n raise e\n pass\n if model_file is None:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=_add_variant(WEIGHTS_NAME, variant),\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n use_auth_token=use_auth_token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n\n if low_cpu_mem_usage:\n # Instantiate model with empty weights\n with accelerate.init_empty_weights():\n model = cls.from_config(config, **unused_kwargs)\n\n # if device_map is None, load the state dict and move the params from meta device to the cpu\n if device_map is None:\n param_device = \"cpu\"\n state_dict = load_state_dict(model_file, variant=variant)\n model._convert_deprecated_attention_blocks(state_dict)\n # move the params from meta device to cpu\n missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())\n if len(missing_keys) > 0:\n raise ValueError(\n f\"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are\"\n f\" missing: \\n {', '.join(missing_keys)}. \\n Please make sure to pass\"\n \" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize\"\n \" those weights or else make sure your checkpoint file is correct.\"\n )\n\n unexpected_keys = load_model_dict_into_meta(\n model,\n state_dict,\n device=param_device,\n dtype=torch_dtype,\n model_name_or_path=pretrained_model_name_or_path,\n )\n\n if cls._keys_to_ignore_on_load_unexpected is not None:\n for pat in cls._keys_to_ignore_on_load_unexpected:\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\n\n if len(unexpected_keys) > 0:\n logger.warn(\n f\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\n {[', '.join(unexpected_keys)]}\"\n )\n\n else: # else let accelerate handle loading and dispatching.\n # Load weights and dispatch according to the device_map\n # by default the device_map is None and the weights are loaded on the CPU\n try:\n accelerate.load_checkpoint_and_dispatch(\n model,\n model_file,\n device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n dtype=torch_dtype,\n )\n except AttributeError as e:\n # When using accelerate loading, we do not have the ability to load the state\n # dict and rename the weight names manually. Additionally, accelerate skips\n # torch loading conventions and directly writes into `module.{_buffers, _parameters}`\n # (which look like they should be private variables?), so we can't use the standard hooks\n # to rename parameters on load. We need to mimic the original weight names so the correct\n # attributes are available. After we have loaded the weights, we convert the deprecated\n # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert\n # the weights so we don't have to do this again.\n\n if \"'Attention' object has no attribute\" in str(e):\n logger.warn(\n f\"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}\"\n \" was saved with deprecated attention block weight names. We will load it with the deprecated attention block\"\n \" names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,\"\n \" so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,\"\n \" please also re-upload it or open a PR on the original repository.\"\n )\n model._temp_convert_self_to_deprecated_attention_blocks()\n accelerate.load_checkpoint_and_dispatch(\n model,\n model_file,\n device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n dtype=torch_dtype,\n )\n model._undo_temp_convert_self_to_deprecated_attention_blocks()\n else:\n raise e\n\n loading_info = {\n \"missing_keys\": [],\n \"unexpected_keys\": [],\n \"mismatched_keys\": [],\n \"error_msgs\": [],\n }\n else:\n model = cls.from_config(config, **unused_kwargs)\n\n state_dict = load_state_dict(model_file, variant=variant)\n model._convert_deprecated_attention_blocks(state_dict)\n\n model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(\n model,\n state_dict,\n model_file,\n pretrained_model_name_or_path,\n ignore_mismatched_sizes=ignore_mismatched_sizes,\n )\n\n loading_info = {\n \"missing_keys\": missing_keys,\n \"unexpected_keys\": unexpected_keys,\n \"mismatched_keys\": mismatched_keys,\n \"error_msgs\": error_msgs,\n }\n\n if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):\n raise ValueError(\n f\"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}.\"\n )\n elif torch_dtype is not None:\n model = model.to(torch_dtype)\n\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n\n # Set model in evaluation mode to deactivate DropOut modules by default\n model.eval()\n if output_loading_info:\n return model, loading_info\n\n return model\n\n @classmethod\n def _load_pretrained_model(\n cls,\n model,\n state_dict,\n resolved_archive_file,\n pretrained_model_name_or_path,\n ignore_mismatched_sizes=False,\n ):\n # Retrieve missing & unexpected_keys\n model_state_dict = model.state_dict()\n loaded_keys = list(state_dict.keys())\n\n expected_keys = list(model_state_dict.keys())\n\n original_loaded_keys = loaded_keys\n\n missing_keys = list(set(expected_keys) - set(loaded_keys))\n unexpected_keys = list(set(loaded_keys) - set(expected_keys))\n\n # Make sure we are able to load base models as well as derived models (with heads)\n model_to_load = model\n\n def _find_mismatched_keys(\n state_dict,\n model_state_dict,\n loaded_keys,\n ignore_mismatched_sizes,\n ):\n mismatched_keys = []\n if ignore_mismatched_sizes:\n for checkpoint_key in loaded_keys:\n model_key = checkpoint_key\n\n if (\n model_key in model_state_dict\n and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape\n ):\n mismatched_keys.append(\n (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)\n )\n del state_dict[checkpoint_key]\n return mismatched_keys\n\n if state_dict is not None:\n # Whole checkpoint\n mismatched_keys = _find_mismatched_keys(\n state_dict,\n model_state_dict,\n original_loaded_keys,\n ignore_mismatched_sizes,\n )\n error_msgs = _load_state_dict_into_model(model_to_load, state_dict)\n\n if len(error_msgs) > 0:\n error_msg = \"\\n\\t\".join(error_msgs)\n if \"size mismatch\" in error_msg:\n error_msg += (\n \"\\n\\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.\"\n )\n raise RuntimeError(f\"Error(s) in loading state_dict for {model.__class__.__name__}:\\n\\t{error_msg}\")\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when\"\n f\" initializing {model.__class__.__name__}: {unexpected_keys}\\n- This IS expected if you are\"\n f\" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task\"\n \" or with another architecture (e.g. initializing a BertForSequenceClassification model from a\"\n \" BertForPreTraining model).\\n- This IS NOT expected if you are initializing\"\n f\" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly\"\n \" identical (initializing a BertForSequenceClassification model from a\"\n \" BertForSequenceClassification model).\"\n )\n else:\n logger.info(f\"All model checkpoint weights were used when initializing {model.__class__.__name__}.\\n\")\n if len(missing_keys) > 0:\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\\nYou should probably\"\n \" TRAIN this model on a down-stream task to be able to use it for predictions and inference.\"\n )\n elif len(mismatched_keys) == 0:\n logger.info(\n f\"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path}.\\nIf your task is similar to the task the model of the\"\n f\" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions\"\n \" without further training.\"\n )\n if len(mismatched_keys) > 0:\n mismatched_warning = \"\\n\".join(\n [\n f\"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated\"\n for key, shape1, shape2 in mismatched_keys\n ]\n )\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path} and are newly initialized because the shapes did not\"\n f\" match:\\n{mismatched_warning}\\nYou should probably TRAIN this model on a down-stream task to be\"\n \" able to use it for predictions and inference.\"\n )\n\n return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs\n\n @property\n def device(self) -> device:\n \"\"\"\n `torch.device`: The device on which the module is (assuming that all the module parameters are on the same\n device).\n \"\"\"\n return get_parameter_device(self)\n\n @property\n def dtype(self) -> torch.dtype:\n \"\"\"\n `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).\n \"\"\"\n return get_parameter_dtype(self)\n\n def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:\n \"\"\"\n Get number of (trainable or non-embedding) parameters in the module.\n\n Args:\n only_trainable (`bool`, *optional*, defaults to `False`):\n Whether or not to return only the number of trainable parameters.\n exclude_embeddings (`bool`, *optional*, defaults to `False`):\n Whether or not to return only the number of non-embedding parameters.\n\n Returns:\n `int`: The number of parameters.\n\n Example:\n\n ```py\n from diffusers import UNet2DConditionModel\n\n model_id = \"runwayml/stable-diffusion-v1-5\"\n unet = UNet2DConditionModel.from_pretrained(model_id, subfolder=\"unet\")\n unet.num_parameters(only_trainable=True)\n 859520964\n ```\n \"\"\"\n\n if exclude_embeddings:\n embedding_param_names = [\n f\"{name}.weight\"\n for name, module_type in self.named_modules()\n if isinstance(module_type, torch.nn.Embedding)\n ]\n non_embedding_parameters = [\n parameter for name, parameter in self.named_parameters() if name not in embedding_param_names\n ]\n return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)\n else:\n return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)\n\n def _convert_deprecated_attention_blocks(self, state_dict):\n deprecated_attention_block_paths = []\n\n def recursive_find_attn_block(name, module):\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_paths.append(name)\n\n for sub_name, sub_module in module.named_children():\n sub_name = sub_name if name == \"\" else f\"{name}.{sub_name}\"\n recursive_find_attn_block(sub_name, sub_module)\n\n recursive_find_attn_block(\"\", self)\n\n # NOTE: we have to check if the deprecated parameters are in the state dict\n # because it is possible we are loading from a state dict that was already\n # converted\n\n for path in deprecated_attention_block_paths:\n # group_norm path stays the same\n\n # query -> to_q\n if f\"{path}.query.weight\" in state_dict:\n state_dict[f\"{path}.to_q.weight\"] = state_dict.pop(f\"{path}.query.weight\")\n if f\"{path}.query.bias\" in state_dict:\n state_dict[f\"{path}.to_q.bias\"] = state_dict.pop(f\"{path}.query.bias\")\n\n # key -> to_k\n if f\"{path}.key.weight\" in state_dict:\n state_dict[f\"{path}.to_k.weight\"] = state_dict.pop(f\"{path}.key.weight\")\n if f\"{path}.key.bias\" in state_dict:\n state_dict[f\"{path}.to_k.bias\"] = state_dict.pop(f\"{path}.key.bias\")\n\n # value -> to_v\n if f\"{path}.value.weight\" in state_dict:\n state_dict[f\"{path}.to_v.weight\"] = state_dict.pop(f\"{path}.value.weight\")\n if f\"{path}.value.bias\" in state_dict:\n state_dict[f\"{path}.to_v.bias\"] = state_dict.pop(f\"{path}.value.bias\")\n\n # proj_attn -> to_out.0\n if f\"{path}.proj_attn.weight\" in state_dict:\n state_dict[f\"{path}.to_out.0.weight\"] = state_dict.pop(f\"{path}.proj_attn.weight\")\n if f\"{path}.proj_attn.bias\" in state_dict:\n state_dict[f\"{path}.to_out.0.bias\"] = state_dict.pop(f\"{path}.proj_attn.bias\")\n\n def _temp_convert_self_to_deprecated_attention_blocks(self):\n deprecated_attention_block_modules = []\n\n def recursive_find_attn_block(module):\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_modules.append(module)\n\n for sub_module in module.children():\n recursive_find_attn_block(sub_module)\n\n recursive_find_attn_block(self)\n\n for module in deprecated_attention_block_modules:\n module.query = module.to_q\n module.key = module.to_k\n module.value = module.to_v\n module.proj_attn = module.to_out[0]\n\n # We don't _have_ to delete the old attributes, but it's helpful to ensure\n # that _all_ the weights are loaded into the new attributes and we're not\n # making an incorrect assumption that this model should be converted when\n # it really shouldn't be.\n del module.to_q\n del module.to_k\n del module.to_v\n del module.to_out\n\n def _undo_temp_convert_self_to_deprecated_attention_blocks(self):\n deprecated_attention_block_modules = []\n\n def recursive_find_attn_block(module):\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_modules.append(module)\n\n for sub_module in module.children():\n recursive_find_attn_block(sub_module)\n\n recursive_find_attn_block(self)\n\n for module in deprecated_attention_block_modules:\n module.to_q = module.query\n module.to_k = module.key\n module.to_v = module.value\n module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])\n\n del module.query\n del module.key\n del module.value\n del module.proj_attn"}],"string":"[\n {\n \"identifier\": \"ConfigMixin\",\n \"path\": \"llmga/diffusers/src/diffusers/configuration_utils.py\",\n \"snippet\": \"class ConfigMixin:\\n r\\\"\\\"\\\"\\n Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also\\n provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and\\n saving classes that inherit from [`ConfigMixin`].\\n\\n Class attributes:\\n - **config_name** (`str`) -- A filename under which the config should stored when calling\\n [`~ConfigMixin.save_config`] (should be overridden by parent class).\\n - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be\\n overridden by subclass).\\n - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).\\n - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function\\n should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by\\n subclass).\\n \\\"\\\"\\\"\\n config_name = None\\n ignore_for_config = []\\n has_compatibles = False\\n\\n _deprecated_kwargs = []\\n\\n def register_to_config(self, **kwargs):\\n if self.config_name is None:\\n raise NotImplementedError(f\\\"Make sure that {self.__class__} has defined a class name `config_name`\\\")\\n # Special case for `kwargs` used in deprecation warning added to schedulers\\n # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,\\n # or solve in a more general way.\\n kwargs.pop(\\\"kwargs\\\", None)\\n\\n if not hasattr(self, \\\"_internal_dict\\\"):\\n internal_dict = kwargs\\n else:\\n previous_dict = dict(self._internal_dict)\\n internal_dict = {**self._internal_dict, **kwargs}\\n logger.debug(f\\\"Updating config from {previous_dict} to {internal_dict}\\\")\\n\\n self._internal_dict = FrozenDict(internal_dict)\\n\\n def __getattr__(self, name: str) -> Any:\\n \\\"\\\"\\\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129\\n\\n Tihs funtion is mostly copied from PyTorch's __getattr__ overwrite:\\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\\n \\\"\\\"\\\"\\n\\n is_in_config = \\\"_internal_dict\\\" in self.__dict__ and hasattr(self.__dict__[\\\"_internal_dict\\\"], name)\\n is_attribute = name in self.__dict__\\n\\n if is_in_config and not is_attribute:\\n deprecation_message = f\\\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'scheduler.config.{name}'.\\\"\\n deprecate(\\\"direct config name access\\\", \\\"1.0.0\\\", deprecation_message, standard_warn=False)\\n return self._internal_dict[name]\\n\\n raise AttributeError(f\\\"'{type(self).__name__}' object has no attribute '{name}'\\\")\\n\\n def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):\\n \\\"\\\"\\\"\\n Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the\\n [`~ConfigMixin.from_config`] class method.\\n\\n Args:\\n save_directory (`str` or `os.PathLike`):\\n Directory where the configuration JSON file is saved (will be created if it does not exist).\\n push_to_hub (`bool`, *optional*, defaults to `False`):\\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\\n namespace).\\n kwargs (`Dict[str, Any]`, *optional*):\\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\\n \\\"\\\"\\\"\\n if os.path.isfile(save_directory):\\n raise AssertionError(f\\\"Provided path ({save_directory}) should be a directory, not a file\\\")\\n\\n os.makedirs(save_directory, exist_ok=True)\\n\\n # If we save using the predefined names, we can load using `from_config`\\n output_config_file = os.path.join(save_directory, self.config_name)\\n\\n self.to_json_file(output_config_file)\\n logger.info(f\\\"Configuration saved in {output_config_file}\\\")\\n\\n if push_to_hub:\\n commit_message = kwargs.pop(\\\"commit_message\\\", None)\\n private = kwargs.pop(\\\"private\\\", False)\\n create_pr = kwargs.pop(\\\"create_pr\\\", False)\\n token = kwargs.pop(\\\"token\\\", None)\\n repo_id = kwargs.pop(\\\"repo_id\\\", save_directory.split(os.path.sep)[-1])\\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\\n\\n self._upload_folder(\\n save_directory,\\n repo_id,\\n token=token,\\n commit_message=commit_message,\\n create_pr=create_pr,\\n )\\n\\n @classmethod\\n def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):\\n r\\\"\\\"\\\"\\n Instantiate a Python class from a config dictionary.\\n\\n Parameters:\\n config (`Dict[str, Any]`):\\n A config dictionary from which the Python class is instantiated. Make sure to only load configuration\\n files of compatible classes.\\n return_unused_kwargs (`bool`, *optional*, defaults to `False`):\\n Whether kwargs that are not consumed by the Python class should be returned or not.\\n kwargs (remaining dictionary of keyword arguments, *optional*):\\n Can be used to update the configuration object (after it is loaded) and initiate the Python class.\\n `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually\\n overwrite the same named arguments in `config`.\\n\\n Returns:\\n [`ModelMixin`] or [`SchedulerMixin`]:\\n A model or scheduler object instantiated from a config dictionary.\\n\\n Examples:\\n\\n ```python\\n >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler\\n\\n >>> # Download scheduler from huggingface.co and cache.\\n >>> scheduler = DDPMScheduler.from_pretrained(\\\"google/ddpm-cifar10-32\\\")\\n\\n >>> # Instantiate DDIM scheduler class with same config as DDPM\\n >>> scheduler = DDIMScheduler.from_config(scheduler.config)\\n\\n >>> # Instantiate PNDM scheduler class with same config as DDPM\\n >>> scheduler = PNDMScheduler.from_config(scheduler.config)\\n ```\\n \\\"\\\"\\\"\\n # <===== TO BE REMOVED WITH DEPRECATION\\n # TODO(Patrick) - make sure to remove the following lines when config==\\\"model_path\\\" is deprecated\\n if \\\"pretrained_model_name_or_path\\\" in kwargs:\\n config = kwargs.pop(\\\"pretrained_model_name_or_path\\\")\\n\\n if config is None:\\n raise ValueError(\\\"Please make sure to provide a config as the first positional argument.\\\")\\n # ======>\\n\\n if not isinstance(config, dict):\\n deprecation_message = \\\"It is deprecated to pass a pretrained model name or path to `from_config`.\\\"\\n if \\\"Scheduler\\\" in cls.__name__:\\n deprecation_message += (\\n f\\\"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead.\\\"\\n \\\" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will\\\"\\n \\\" be removed in v1.0.0.\\\"\\n )\\n elif \\\"Model\\\" in cls.__name__:\\n deprecation_message += (\\n f\\\"If you were trying to load a model, please use {cls}.load_config(...) followed by\\\"\\n f\\\" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary\\\"\\n \\\" instead. This functionality will be removed in v1.0.0.\\\"\\n )\\n deprecate(\\\"config-passed-as-path\\\", \\\"1.0.0\\\", deprecation_message, standard_warn=False)\\n config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)\\n\\n init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)\\n\\n # Allow dtype to be specified on initialization\\n if \\\"dtype\\\" in unused_kwargs:\\n init_dict[\\\"dtype\\\"] = unused_kwargs.pop(\\\"dtype\\\")\\n\\n # add possible deprecated kwargs\\n for deprecated_kwarg in cls._deprecated_kwargs:\\n if deprecated_kwarg in unused_kwargs:\\n init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)\\n\\n # Return model and optionally state and/or unused_kwargs\\n model = cls(**init_dict)\\n\\n # make sure to also save config parameters that might be used for compatible classes\\n model.register_to_config(**hidden_dict)\\n\\n # add hidden kwargs of compatible classes to unused_kwargs\\n unused_kwargs = {**unused_kwargs, **hidden_dict}\\n\\n if return_unused_kwargs:\\n return (model, unused_kwargs)\\n else:\\n return model\\n\\n @classmethod\\n def get_config_dict(cls, *args, **kwargs):\\n deprecation_message = (\\n f\\\" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be\\\"\\n \\\" removed in version v1.0.0\\\"\\n )\\n deprecate(\\\"get_config_dict\\\", \\\"1.0.0\\\", deprecation_message, standard_warn=False)\\n return cls.load_config(*args, **kwargs)\\n\\n @classmethod\\n def load_config(\\n cls,\\n pretrained_model_name_or_path: Union[str, os.PathLike],\\n return_unused_kwargs=False,\\n return_commit_hash=False,\\n **kwargs,\\n ) -> Tuple[Dict[str, Any], Dict[str, Any]]:\\n r\\\"\\\"\\\"\\n Load a model or scheduler configuration.\\n\\n Parameters:\\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\\n Can be either:\\n\\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\\n the Hub.\\n - A path to a *directory* (for example `./my_model_directory`) containing model weights saved with\\n [`~ConfigMixin.save_config`].\\n\\n cache_dir (`Union[str, os.PathLike]`, *optional*):\\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\\n is not used.\\n force_download (`bool`, *optional*, defaults to `False`):\\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\\n cached versions if they exist.\\n resume_download (`bool`, *optional*, defaults to `False`):\\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\\n incompletely downloaded files are deleted.\\n proxies (`Dict[str, str]`, *optional*):\\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\\n output_loading_info(`bool`, *optional*, defaults to `False`):\\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\\n local_files_only (`bool`, *optional*, defaults to `False`):\\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\\n won't be downloaded from the Hub.\\n use_auth_token (`str` or *bool*, *optional*):\\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\\n revision (`str`, *optional*, defaults to `\\\"main\\\"`):\\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\\n allowed by Git.\\n subfolder (`str`, *optional*, defaults to `\\\"\\\"`):\\n The subfolder location of a model file within a larger model repository on the Hub or locally.\\n return_unused_kwargs (`bool`, *optional*, defaults to `False):\\n Whether unused keyword arguments of the config are returned.\\n return_commit_hash (`bool`, *optional*, defaults to `False):\\n Whether the `commit_hash` of the loaded configuration are returned.\\n\\n Returns:\\n `dict`:\\n A dictionary of all the parameters stored in a JSON configuration file.\\n\\n \\\"\\\"\\\"\\n cache_dir = kwargs.pop(\\\"cache_dir\\\", DIFFUSERS_CACHE)\\n force_download = kwargs.pop(\\\"force_download\\\", False)\\n resume_download = kwargs.pop(\\\"resume_download\\\", False)\\n proxies = kwargs.pop(\\\"proxies\\\", None)\\n use_auth_token = kwargs.pop(\\\"use_auth_token\\\", None)\\n local_files_only = kwargs.pop(\\\"local_files_only\\\", False)\\n revision = kwargs.pop(\\\"revision\\\", None)\\n _ = kwargs.pop(\\\"mirror\\\", None)\\n subfolder = kwargs.pop(\\\"subfolder\\\", None)\\n user_agent = kwargs.pop(\\\"user_agent\\\", {})\\n\\n user_agent = {**user_agent, \\\"file_type\\\": \\\"config\\\"}\\n user_agent = http_user_agent(user_agent)\\n\\n pretrained_model_name_or_path = str(pretrained_model_name_or_path)\\n\\n if cls.config_name is None:\\n raise ValueError(\\n \\\"`self.config_name` is not defined. Note that one should not load a config from \\\"\\n \\\"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`\\\"\\n )\\n\\n if os.path.isfile(pretrained_model_name_or_path):\\n config_file = pretrained_model_name_or_path\\n elif os.path.isdir(pretrained_model_name_or_path):\\n if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):\\n # Load from a PyTorch checkpoint\\n config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)\\n elif subfolder is not None and os.path.isfile(\\n os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\\n ):\\n config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\\n else:\\n raise EnvironmentError(\\n f\\\"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}.\\\"\\n )\\n else:\\n try:\\n # Load from URL or cache if already cached\\n config_file = hf_hub_download(\\n pretrained_model_name_or_path,\\n filename=cls.config_name,\\n cache_dir=cache_dir,\\n force_download=force_download,\\n proxies=proxies,\\n resume_download=resume_download,\\n local_files_only=local_files_only,\\n use_auth_token=use_auth_token,\\n user_agent=user_agent,\\n subfolder=subfolder,\\n revision=revision,\\n )\\n except RepositoryNotFoundError:\\n raise EnvironmentError(\\n f\\\"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier\\\"\\n \\\" listed on 'https://huggingface.co/models'\\\\nIf this is a private repository, make sure to pass a\\\"\\n \\\" token having permission to this repo with `use_auth_token` or log in with `huggingface-cli\\\"\\n \\\" login`.\\\"\\n )\\n except RevisionNotFoundError:\\n raise EnvironmentError(\\n f\\\"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for\\\"\\n \\\" this model name. Check the model page at\\\"\\n f\\\" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.\\\"\\n )\\n except EntryNotFoundError:\\n raise EnvironmentError(\\n f\\\"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}.\\\"\\n )\\n except HTTPError as err:\\n raise EnvironmentError(\\n \\\"There was a specific connection error when trying to load\\\"\\n f\\\" {pretrained_model_name_or_path}:\\\\n{err}\\\"\\n )\\n except ValueError:\\n raise EnvironmentError(\\n f\\\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it\\\"\\n f\\\" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a\\\"\\n f\\\" directory containing a {cls.config_name} file.\\\\nCheckout your internet connection or see how to\\\"\\n \\\" run the library in offline mode at\\\"\\n \\\" 'https://huggingface.co/docs/diffusers/installation#offline-mode'.\\\"\\n )\\n except EnvironmentError:\\n raise EnvironmentError(\\n f\\\"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from \\\"\\n \\\"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \\\"\\n f\\\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \\\"\\n f\\\"containing a {cls.config_name} file\\\"\\n )\\n\\n try:\\n # Load config dict\\n config_dict = cls._dict_from_json_file(config_file)\\n\\n commit_hash = extract_commit_hash(config_file)\\n except (json.JSONDecodeError, UnicodeDecodeError):\\n raise EnvironmentError(f\\\"It looks like the config file at '{config_file}' is not a valid JSON file.\\\")\\n\\n if not (return_unused_kwargs or return_commit_hash):\\n return config_dict\\n\\n outputs = (config_dict,)\\n\\n if return_unused_kwargs:\\n outputs += (kwargs,)\\n\\n if return_commit_hash:\\n outputs += (commit_hash,)\\n\\n return outputs\\n\\n @staticmethod\\n def _get_init_keys(cls):\\n return set(dict(inspect.signature(cls.__init__).parameters).keys())\\n\\n @classmethod\\n def extract_init_dict(cls, config_dict, **kwargs):\\n # Skip keys that were not present in the original config, so default __init__ values were used\\n used_defaults = config_dict.get(\\\"_use_default_values\\\", [])\\n config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != \\\"_use_default_values\\\"}\\n\\n # 0. Copy origin config dict\\n original_dict = dict(config_dict.items())\\n\\n # 1. Retrieve expected config attributes from __init__ signature\\n expected_keys = cls._get_init_keys(cls)\\n expected_keys.remove(\\\"self\\\")\\n # remove general kwargs if present in dict\\n if \\\"kwargs\\\" in expected_keys:\\n expected_keys.remove(\\\"kwargs\\\")\\n # remove flax internal keys\\n if hasattr(cls, \\\"_flax_internal_args\\\"):\\n for arg in cls._flax_internal_args:\\n expected_keys.remove(arg)\\n\\n # 2. Remove attributes that cannot be expected from expected config attributes\\n # remove keys to be ignored\\n if len(cls.ignore_for_config) > 0:\\n expected_keys = expected_keys - set(cls.ignore_for_config)\\n\\n # load diffusers library to import compatible and original scheduler\\n diffusers_library = importlib.import_module(__name__.split(\\\".\\\")[0])\\n\\n if cls.has_compatibles:\\n compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]\\n else:\\n compatible_classes = []\\n\\n expected_keys_comp_cls = set()\\n for c in compatible_classes:\\n expected_keys_c = cls._get_init_keys(c)\\n expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)\\n expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)\\n config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}\\n\\n # remove attributes from orig class that cannot be expected\\n orig_cls_name = config_dict.pop(\\\"_class_name\\\", cls.__name__)\\n if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name):\\n orig_cls = getattr(diffusers_library, orig_cls_name)\\n unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys\\n config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}\\n\\n # remove private attributes\\n config_dict = {k: v for k, v in config_dict.items() if not k.startswith(\\\"_\\\")}\\n\\n # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments\\n init_dict = {}\\n for key in expected_keys:\\n # if config param is passed to kwarg and is present in config dict\\n # it should overwrite existing config dict key\\n if key in kwargs and key in config_dict:\\n config_dict[key] = kwargs.pop(key)\\n\\n if key in kwargs:\\n # overwrite key\\n init_dict[key] = kwargs.pop(key)\\n elif key in config_dict:\\n # use value from config dict\\n init_dict[key] = config_dict.pop(key)\\n\\n # 4. Give nice warning if unexpected values have been passed\\n if len(config_dict) > 0:\\n logger.warning(\\n f\\\"The config attributes {config_dict} were passed to {cls.__name__}, \\\"\\n \\\"but are not expected and will be ignored. Please verify your \\\"\\n f\\\"{cls.config_name} configuration file.\\\"\\n )\\n\\n # 5. Give nice info if config attributes are initiliazed to default because they have not been passed\\n passed_keys = set(init_dict.keys())\\n if len(expected_keys - passed_keys) > 0:\\n logger.info(\\n f\\\"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values.\\\"\\n )\\n\\n # 6. Define unused keyword arguments\\n unused_kwargs = {**config_dict, **kwargs}\\n\\n # 7. Define \\\"hidden\\\" config parameters that were saved for compatible classes\\n hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}\\n\\n return init_dict, unused_kwargs, hidden_config_dict\\n\\n @classmethod\\n def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):\\n with open(json_file, \\\"r\\\", encoding=\\\"utf-8\\\") as reader:\\n text = reader.read()\\n return json.loads(text)\\n\\n def __repr__(self):\\n return f\\\"{self.__class__.__name__} {self.to_json_string()}\\\"\\n\\n @property\\n def config(self) -> Dict[str, Any]:\\n \\\"\\\"\\\"\\n Returns the config of the class as a frozen dictionary\\n\\n Returns:\\n `Dict[str, Any]`: Config of the class.\\n \\\"\\\"\\\"\\n return self._internal_dict\\n\\n def to_json_string(self) -> str:\\n \\\"\\\"\\\"\\n Serializes the configuration instance to a JSON string.\\n\\n Returns:\\n `str`:\\n String containing all the attributes that make up the configuration instance in JSON format.\\n \\\"\\\"\\\"\\n config_dict = self._internal_dict if hasattr(self, \\\"_internal_dict\\\") else {}\\n config_dict[\\\"_class_name\\\"] = self.__class__.__name__\\n config_dict[\\\"_diffusers_version\\\"] = __version__\\n\\n def to_json_saveable(value):\\n if isinstance(value, np.ndarray):\\n value = value.tolist()\\n elif isinstance(value, PosixPath):\\n value = str(value)\\n return value\\n\\n config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}\\n # Don't save \\\"_ignore_files\\\" or \\\"_use_default_values\\\"\\n config_dict.pop(\\\"_ignore_files\\\", None)\\n config_dict.pop(\\\"_use_default_values\\\", None)\\n\\n return json.dumps(config_dict, indent=2, sort_keys=True) + \\\"\\\\n\\\"\\n\\n def to_json_file(self, json_file_path: Union[str, os.PathLike]):\\n \\\"\\\"\\\"\\n Save the configuration instance's parameters to a JSON file.\\n\\n Args:\\n json_file_path (`str` or `os.PathLike`):\\n Path to the JSON file to save a configuration instance's parameters.\\n \\\"\\\"\\\"\\n with open(json_file_path, \\\"w\\\", encoding=\\\"utf-8\\\") as writer:\\n writer.write(self.to_json_string())\"\n },\n {\n \"identifier\": \"register_to_config\",\n \"path\": \"llmga/diffusers/src/diffusers/configuration_utils.py\",\n \"snippet\": \"def register_to_config(self, **kwargs):\\n if self.config_name is None:\\n raise NotImplementedError(f\\\"Make sure that {self.__class__} has defined a class name `config_name`\\\")\\n # Special case for `kwargs` used in deprecation warning added to schedulers\\n # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,\\n # or solve in a more general way.\\n kwargs.pop(\\\"kwargs\\\", None)\\n\\n if not hasattr(self, \\\"_internal_dict\\\"):\\n internal_dict = kwargs\\n else:\\n previous_dict = dict(self._internal_dict)\\n internal_dict = {**self._internal_dict, **kwargs}\\n logger.debug(f\\\"Updating config from {previous_dict} to {internal_dict}\\\")\\n\\n self._internal_dict = FrozenDict(internal_dict)\"\n },\n {\n \"identifier\": \"BaseOutput\",\n \"path\": \"llmga/diffusers/src/diffusers/utils/outputs.py\",\n \"snippet\": \"class BaseOutput(OrderedDict):\\n \\\"\\\"\\\"\\n Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a\\n tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular\\n Python dictionary.\\n\\n <Tip warning={true}>\\n\\n You can't unpack a [`BaseOutput`] directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple\\n first.\\n\\n </Tip>\\n \\\"\\\"\\\"\\n\\n def __post_init__(self):\\n class_fields = fields(self)\\n\\n # Safety and consistency checks\\n if not len(class_fields):\\n raise ValueError(f\\\"{self.__class__.__name__} has no fields.\\\")\\n\\n first_field = getattr(self, class_fields[0].name)\\n other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])\\n\\n if other_fields_are_none and isinstance(first_field, dict):\\n for key, value in first_field.items():\\n self[key] = value\\n else:\\n for field in class_fields:\\n v = getattr(self, field.name)\\n if v is not None:\\n self[field.name] = v\\n\\n def __delitem__(self, *args, **kwargs):\\n raise Exception(f\\\"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.\\\")\\n\\n def setdefault(self, *args, **kwargs):\\n raise Exception(f\\\"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.\\\")\\n\\n def pop(self, *args, **kwargs):\\n raise Exception(f\\\"You cannot use ``pop`` on a {self.__class__.__name__} instance.\\\")\\n\\n def update(self, *args, **kwargs):\\n raise Exception(f\\\"You cannot use ``update`` on a {self.__class__.__name__} instance.\\\")\\n\\n def __getitem__(self, k):\\n if isinstance(k, str):\\n inner_dict = dict(self.items())\\n return inner_dict[k]\\n else:\\n return self.to_tuple()[k]\\n\\n def __setattr__(self, name, value):\\n if name in self.keys() and value is not None:\\n # Don't call self.__setitem__ to avoid recursion errors\\n super().__setitem__(name, value)\\n super().__setattr__(name, value)\\n\\n def __setitem__(self, key, value):\\n # Will raise a KeyException if needed\\n super().__setitem__(key, value)\\n # Don't call self.__setattr__ to avoid recursion errors\\n super().__setattr__(key, value)\\n\\n def __reduce__(self):\\n if not is_dataclass(self):\\n return super().__reduce__()\\n callable, _args, *remaining = super().__reduce__()\\n args = tuple(getattr(self, field.name) for field in fields(self))\\n return callable, args, *remaining\\n\\n def to_tuple(self) -> Tuple[Any]:\\n \\\"\\\"\\\"\\n Convert self to a tuple containing all the attributes/keys that are not `None`.\\n \\\"\\\"\\\"\\n return tuple(self[k] for k in self.keys())\"\n },\n {\n \"identifier\": \"BasicTransformerBlock\",\n \"path\": \"llmga/diffusers/src/diffusers/models/attention.py\",\n \"snippet\": \"class BasicTransformerBlock(nn.Module):\\n r\\\"\\\"\\\"\\n A basic Transformer block.\\n\\n Parameters:\\n dim (`int`): The number of channels in the input and output.\\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\\n attention_head_dim (`int`): The number of channels in each head.\\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\\n activation_fn (`str`, *optional*, defaults to `\\\"geglu\\\"`): Activation function to be used in feed-forward.\\n num_embeds_ada_norm (:\\n obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.\\n attention_bias (:\\n obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.\\n only_cross_attention (`bool`, *optional*):\\n Whether to use only cross-attention layers. In this case two cross attention layers are used.\\n double_self_attention (`bool`, *optional*):\\n Whether to use two self-attention layers. In this case no cross attention layers are used.\\n upcast_attention (`bool`, *optional*):\\n Whether to upcast the attention computation to float32. This is useful for mixed precision training.\\n norm_elementwise_affine (`bool`, *optional*, defaults to `True`):\\n Whether to use learnable elementwise affine parameters for normalization.\\n norm_type (`str`, *optional*, defaults to `\\\"layer_norm\\\"`):\\n The normalization layer to use. Can be `\\\"layer_norm\\\"`, `\\\"ada_norm\\\"` or `\\\"ada_norm_zero\\\"`.\\n final_dropout (`bool` *optional*, defaults to False):\\n Whether to apply a final dropout after the last feed-forward layer.\\n attention_type (`str`, *optional*, defaults to `\\\"default\\\"`):\\n The type of attention to use. Can be `\\\"default\\\"` or `\\\"gated\\\"` or `\\\"gated-text-image\\\"`.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n dim: int,\\n num_attention_heads: int,\\n attention_head_dim: int,\\n dropout=0.0,\\n cross_attention_dim: Optional[int] = None,\\n activation_fn: str = \\\"geglu\\\",\\n num_embeds_ada_norm: Optional[int] = None,\\n attention_bias: bool = False,\\n only_cross_attention: bool = False,\\n double_self_attention: bool = False,\\n upcast_attention: bool = False,\\n norm_elementwise_affine: bool = True,\\n norm_type: str = \\\"layer_norm\\\",\\n final_dropout: bool = False,\\n attention_type: str = \\\"default\\\",\\n ):\\n super().__init__()\\n self.only_cross_attention = only_cross_attention\\n\\n self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == \\\"ada_norm_zero\\\"\\n self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == \\\"ada_norm\\\"\\n\\n if norm_type in (\\\"ada_norm\\\", \\\"ada_norm_zero\\\") and num_embeds_ada_norm is None:\\n raise ValueError(\\n f\\\"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to\\\"\\n f\\\" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.\\\"\\n )\\n\\n # Define 3 blocks. Each block has its own normalization layer.\\n # 1. Self-Attn\\n if self.use_ada_layer_norm:\\n self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)\\n elif self.use_ada_layer_norm_zero:\\n self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)\\n else:\\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\\n self.attn1 = Attention(\\n query_dim=dim,\\n heads=num_attention_heads,\\n dim_head=attention_head_dim,\\n dropout=dropout,\\n bias=attention_bias,\\n cross_attention_dim=cross_attention_dim if only_cross_attention else None,\\n upcast_attention=upcast_attention,\\n )\\n\\n # 2. Cross-Attn\\n if cross_attention_dim is not None or double_self_attention:\\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\\n # the second cross attention block.\\n self.norm2 = (\\n AdaLayerNorm(dim, num_embeds_ada_norm)\\n if self.use_ada_layer_norm\\n else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\\n )\\n self.attn2 = Attention(\\n query_dim=dim,\\n cross_attention_dim=cross_attention_dim if not double_self_attention else None,\\n heads=num_attention_heads,\\n dim_head=attention_head_dim,\\n dropout=dropout,\\n bias=attention_bias,\\n upcast_attention=upcast_attention,\\n ) # is self-attn if encoder_hidden_states is none\\n else:\\n self.norm2 = None\\n self.attn2 = None\\n\\n # 3. Feed-forward\\n self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\\n self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)\\n\\n # 4. Fuser\\n if attention_type == \\\"gated\\\" or attention_type == \\\"gated-text-image\\\":\\n self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)\\n\\n # let chunk size default to None\\n self._chunk_size = None\\n self._chunk_dim = 0\\n\\n def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):\\n # Sets chunk feed-forward\\n self._chunk_size = chunk_size\\n self._chunk_dim = dim\\n\\n def forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n timestep: Optional[torch.LongTensor] = None,\\n cross_attention_kwargs: Dict[str, Any] = None,\\n class_labels: Optional[torch.LongTensor] = None,\\n ) -> torch.FloatTensor:\\n # Notice that normalization is always applied before the real computation in the following blocks.\\n # 0. Self-Attention\\n if self.use_ada_layer_norm:\\n norm_hidden_states = self.norm1(hidden_states, timestep)\\n elif self.use_ada_layer_norm_zero:\\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\\n )\\n else:\\n norm_hidden_states = self.norm1(hidden_states)\\n\\n # 1. Retrieve lora scale.\\n lora_scale = cross_attention_kwargs.get(\\\"scale\\\", 1.0) if cross_attention_kwargs is not None else 1.0\\n\\n # 2. Prepare GLIGEN inputs\\n cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}\\n gligen_kwargs = cross_attention_kwargs.pop(\\\"gligen\\\", None)\\n\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n if self.use_ada_layer_norm_zero:\\n attn_output = gate_msa.unsqueeze(1) * attn_output\\n hidden_states = attn_output + hidden_states\\n\\n # 2.5 GLIGEN Control\\n if gligen_kwargs is not None:\\n hidden_states = self.fuser(hidden_states, gligen_kwargs[\\\"objs\\\"])\\n # 2.5 ends\\n\\n # 3. Cross-Attention\\n if self.attn2 is not None:\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n\\n attn_output = self.attn2(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=encoder_attention_mask,\\n **cross_attention_kwargs,\\n )\\n hidden_states = attn_output + hidden_states\\n\\n # 4. Feed-forward\\n norm_hidden_states = self.norm3(hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\\n\\n if self._chunk_size is not None:\\n # \\\"feed_forward_chunk_size\\\" can be used to save memory\\n if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:\\n raise ValueError(\\n f\\\"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.\\\"\\n )\\n\\n num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size\\n ff_output = torch.cat(\\n [\\n self.ff(hid_slice, scale=lora_scale)\\n for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)\\n ],\\n dim=self._chunk_dim,\\n )\\n else:\\n ff_output = self.ff(norm_hidden_states, scale=lora_scale)\\n\\n if self.use_ada_layer_norm_zero:\\n ff_output = gate_mlp.unsqueeze(1) * ff_output\\n\\n hidden_states = ff_output + hidden_states\\n\\n return hidden_states\"\n },\n {\n \"identifier\": \"ModelMixin\",\n \"path\": \"llmga/diffusers/src/diffusers/models/modeling_utils.py\",\n \"snippet\": \"class ModelMixin(torch.nn.Module, PushToHubMixin):\\n r\\\"\\\"\\\"\\n Base class for all models.\\n\\n [`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and\\n saving models.\\n\\n - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].\\n \\\"\\\"\\\"\\n config_name = CONFIG_NAME\\n _automatically_saved_args = [\\\"_diffusers_version\\\", \\\"_class_name\\\", \\\"_name_or_path\\\"]\\n _supports_gradient_checkpointing = False\\n _keys_to_ignore_on_load_unexpected = None\\n _hf_peft_config_loaded = False\\n\\n def __init__(self):\\n super().__init__()\\n\\n def __getattr__(self, name: str) -> Any:\\n \\\"\\\"\\\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite\\n __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':\\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\\n \\\"\\\"\\\"\\n\\n is_in_config = \\\"_internal_dict\\\" in self.__dict__ and hasattr(self.__dict__[\\\"_internal_dict\\\"], name)\\n is_attribute = name in self.__dict__\\n\\n if is_in_config and not is_attribute:\\n deprecation_message = f\\\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'.\\\"\\n deprecate(\\\"direct config name access\\\", \\\"1.0.0\\\", deprecation_message, standard_warn=False, stacklevel=3)\\n return self._internal_dict[name]\\n\\n # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\\n return super().__getattr__(name)\\n\\n @property\\n def is_gradient_checkpointing(self) -> bool:\\n \\\"\\\"\\\"\\n Whether gradient checkpointing is activated for this model or not.\\n \\\"\\\"\\\"\\n return any(hasattr(m, \\\"gradient_checkpointing\\\") and m.gradient_checkpointing for m in self.modules())\\n\\n def enable_gradient_checkpointing(self):\\n \\\"\\\"\\\"\\n Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\\n *checkpoint activations* in other frameworks).\\n \\\"\\\"\\\"\\n if not self._supports_gradient_checkpointing:\\n raise ValueError(f\\\"{self.__class__.__name__} does not support gradient checkpointing.\\\")\\n self.apply(partial(self._set_gradient_checkpointing, value=True))\\n\\n def disable_gradient_checkpointing(self):\\n \\\"\\\"\\\"\\n Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\\n *checkpoint activations* in other frameworks).\\n \\\"\\\"\\\"\\n if self._supports_gradient_checkpointing:\\n self.apply(partial(self._set_gradient_checkpointing, value=False))\\n\\n def set_use_memory_efficient_attention_xformers(\\n self, valid: bool, attention_op: Optional[Callable] = None\\n ) -> None:\\n # Recursively walk through all the children.\\n # Any children which exposes the set_use_memory_efficient_attention_xformers method\\n # gets the message\\n def fn_recursive_set_mem_eff(module: torch.nn.Module):\\n if hasattr(module, \\\"set_use_memory_efficient_attention_xformers\\\"):\\n module.set_use_memory_efficient_attention_xformers(valid, attention_op)\\n\\n for child in module.children():\\n fn_recursive_set_mem_eff(child)\\n\\n for module in self.children():\\n if isinstance(module, torch.nn.Module):\\n fn_recursive_set_mem_eff(module)\\n\\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\\n r\\\"\\\"\\\"\\n Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\\n\\n When this option is enabled, you should observe lower GPU memory usage and a potential speed up during\\n inference. Speed up during training is not guaranteed.\\n\\n <Tip warning={true}>\\n\\n ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes\\n precedent.\\n\\n </Tip>\\n\\n Parameters:\\n attention_op (`Callable`, *optional*):\\n Override the default `None` operator for use as `op` argument to the\\n [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)\\n function of xFormers.\\n\\n Examples:\\n\\n ```py\\n >>> import torch\\n >>> from diffusers import UNet2DConditionModel\\n >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp\\n\\n >>> model = UNet2DConditionModel.from_pretrained(\\n ... \\\"stabilityai/stable-diffusion-2-1\\\", subfolder=\\\"unet\\\", torch_dtype=torch.float16\\n ... )\\n >>> model = model.to(\\\"cuda\\\")\\n >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)\\n ```\\n \\\"\\\"\\\"\\n self.set_use_memory_efficient_attention_xformers(True, attention_op)\\n\\n def disable_xformers_memory_efficient_attention(self):\\n r\\\"\\\"\\\"\\n Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\\n \\\"\\\"\\\"\\n self.set_use_memory_efficient_attention_xformers(False)\\n\\n def add_adapter(self, adapter_config, adapter_name: str = \\\"default\\\") -> None:\\n r\\\"\\\"\\\"\\n Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned\\n to the adapter to follow the convention of the PEFT library.\\n\\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT\\n [documentation](https://huggingface.co/docs/peft).\\n\\n Args:\\n adapter_config (`[~peft.PeftConfig]`):\\n The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt\\n methods.\\n adapter_name (`str`, *optional*, defaults to `\\\"default\\\"`):\\n The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.\\n \\\"\\\"\\\"\\n check_peft_version(min_version=MIN_PEFT_VERSION)\\n\\n from peft import PeftConfig, inject_adapter_in_model\\n\\n if not self._hf_peft_config_loaded:\\n self._hf_peft_config_loaded = True\\n elif adapter_name in self.peft_config:\\n raise ValueError(f\\\"Adapter with name {adapter_name} already exists. Please use a different name.\\\")\\n\\n if not isinstance(adapter_config, PeftConfig):\\n raise ValueError(\\n f\\\"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.\\\"\\n )\\n\\n # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is\\n # handled by the `load_lora_layers` or `LoraLoaderMixin`. Therefore we set it to `None` here.\\n adapter_config.base_model_name_or_path = None\\n inject_adapter_in_model(adapter_config, self, adapter_name)\\n self.set_adapter(adapter_name)\\n\\n def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:\\n \\\"\\\"\\\"\\n Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.\\n\\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\\n official documentation: https://huggingface.co/docs/peft\\n\\n Args:\\n adapter_name (Union[str, List[str]])):\\n The list of adapters to set or the adapter name in case of single adapter.\\n \\\"\\\"\\\"\\n check_peft_version(min_version=MIN_PEFT_VERSION)\\n\\n if not self._hf_peft_config_loaded:\\n raise ValueError(\\\"No adapter loaded. Please load an adapter first.\\\")\\n\\n if isinstance(adapter_name, str):\\n adapter_name = [adapter_name]\\n\\n missing = set(adapter_name) - set(self.peft_config)\\n if len(missing) > 0:\\n raise ValueError(\\n f\\\"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s).\\\"\\n f\\\" current loaded adapters are: {list(self.peft_config.keys())}\\\"\\n )\\n\\n from peft.tuners.tuners_utils import BaseTunerLayer\\n\\n _adapters_has_been_set = False\\n\\n for _, module in self.named_modules():\\n if isinstance(module, BaseTunerLayer):\\n if hasattr(module, \\\"set_adapter\\\"):\\n module.set_adapter(adapter_name)\\n # Previous versions of PEFT does not support multi-adapter inference\\n elif not hasattr(module, \\\"set_adapter\\\") and len(adapter_name) != 1:\\n raise ValueError(\\n \\\"You are trying to set multiple adapters and you have a PEFT version that does not support multi-adapter inference. Please upgrade to the latest version of PEFT.\\\"\\n \\\" `pip install -U peft` or `pip install -U git+https://github.com/huggingface/peft.git`\\\"\\n )\\n else:\\n module.active_adapter = adapter_name\\n _adapters_has_been_set = True\\n\\n if not _adapters_has_been_set:\\n raise ValueError(\\n \\\"Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters.\\\"\\n )\\n\\n def disable_adapters(self) -> None:\\n r\\\"\\\"\\\"\\n Disable all adapters attached to the model and fallback to inference with the base model only.\\n\\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\\n official documentation: https://huggingface.co/docs/peft\\n \\\"\\\"\\\"\\n check_peft_version(min_version=MIN_PEFT_VERSION)\\n\\n if not self._hf_peft_config_loaded:\\n raise ValueError(\\\"No adapter loaded. Please load an adapter first.\\\")\\n\\n from peft.tuners.tuners_utils import BaseTunerLayer\\n\\n for _, module in self.named_modules():\\n if isinstance(module, BaseTunerLayer):\\n if hasattr(module, \\\"enable_adapters\\\"):\\n module.enable_adapters(enabled=False)\\n else:\\n # support for older PEFT versions\\n module.disable_adapters = True\\n\\n def enable_adapters(self) -> None:\\n \\\"\\\"\\\"\\n Enable adapters that are attached to the model. The model will use `self.active_adapters()` to retrieve the\\n list of adapters to enable.\\n\\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\\n official documentation: https://huggingface.co/docs/peft\\n \\\"\\\"\\\"\\n check_peft_version(min_version=MIN_PEFT_VERSION)\\n\\n if not self._hf_peft_config_loaded:\\n raise ValueError(\\\"No adapter loaded. Please load an adapter first.\\\")\\n\\n from peft.tuners.tuners_utils import BaseTunerLayer\\n\\n for _, module in self.named_modules():\\n if isinstance(module, BaseTunerLayer):\\n if hasattr(module, \\\"enable_adapters\\\"):\\n module.enable_adapters(enabled=True)\\n else:\\n # support for older PEFT versions\\n module.disable_adapters = False\\n\\n def active_adapters(self) -> List[str]:\\n \\\"\\\"\\\"\\n Gets the current list of active adapters of the model.\\n\\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\\n official documentation: https://huggingface.co/docs/peft\\n \\\"\\\"\\\"\\n check_peft_version(min_version=MIN_PEFT_VERSION)\\n\\n if not self._hf_peft_config_loaded:\\n raise ValueError(\\\"No adapter loaded. Please load an adapter first.\\\")\\n\\n from peft.tuners.tuners_utils import BaseTunerLayer\\n\\n for _, module in self.named_modules():\\n if isinstance(module, BaseTunerLayer):\\n return module.active_adapter\\n\\n def save_pretrained(\\n self,\\n save_directory: Union[str, os.PathLike],\\n is_main_process: bool = True,\\n save_function: Callable = None,\\n safe_serialization: bool = True,\\n variant: Optional[str] = None,\\n push_to_hub: bool = False,\\n **kwargs,\\n ):\\n \\\"\\\"\\\"\\n Save a model and its configuration file to a directory so that it can be reloaded using the\\n [`~models.ModelMixin.from_pretrained`] class method.\\n\\n Arguments:\\n save_directory (`str` or `os.PathLike`):\\n Directory to save a model and its configuration file to. Will be created if it doesn't exist.\\n is_main_process (`bool`, *optional*, defaults to `True`):\\n Whether the process calling this is the main process or not. Useful during distributed training and you\\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\\n process to avoid race conditions.\\n save_function (`Callable`):\\n The function to use to save the state dictionary. Useful during distributed training when you need to\\n replace `torch.save` with another method. Can be configured with the environment variable\\n `DIFFUSERS_SAVE_MODE`.\\n safe_serialization (`bool`, *optional*, defaults to `True`):\\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\\n variant (`str`, *optional*):\\n If specified, weights are saved in the format `pytorch_model.<variant>.bin`.\\n push_to_hub (`bool`, *optional*, defaults to `False`):\\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\\n namespace).\\n kwargs (`Dict[str, Any]`, *optional*):\\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\\n \\\"\\\"\\\"\\n if os.path.isfile(save_directory):\\n logger.error(f\\\"Provided path ({save_directory}) should be a directory, not a file\\\")\\n return\\n\\n os.makedirs(save_directory, exist_ok=True)\\n\\n if push_to_hub:\\n commit_message = kwargs.pop(\\\"commit_message\\\", None)\\n private = kwargs.pop(\\\"private\\\", False)\\n create_pr = kwargs.pop(\\\"create_pr\\\", False)\\n token = kwargs.pop(\\\"token\\\", None)\\n repo_id = kwargs.pop(\\\"repo_id\\\", save_directory.split(os.path.sep)[-1])\\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\\n\\n # Only save the model itself if we are using distributed training\\n model_to_save = self\\n\\n # Attach architecture to the config\\n # Save the config\\n if is_main_process:\\n model_to_save.save_config(save_directory)\\n\\n # Save the model\\n state_dict = model_to_save.state_dict()\\n\\n weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME\\n weights_name = _add_variant(weights_name, variant)\\n\\n # Save the model\\n if safe_serialization:\\n safetensors.torch.save_file(\\n state_dict, os.path.join(save_directory, weights_name), metadata={\\\"format\\\": \\\"pt\\\"}\\n )\\n else:\\n torch.save(state_dict, os.path.join(save_directory, weights_name))\\n\\n logger.info(f\\\"Model weights saved in {os.path.join(save_directory, weights_name)}\\\")\\n\\n if push_to_hub:\\n self._upload_folder(\\n save_directory,\\n repo_id,\\n token=token,\\n commit_message=commit_message,\\n create_pr=create_pr,\\n )\\n\\n @classmethod\\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\\n r\\\"\\\"\\\"\\n Instantiate a pretrained PyTorch model from a pretrained model configuration.\\n\\n The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To\\n train the model, set it back in training mode with `model.train()`.\\n\\n Parameters:\\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\\n Can be either:\\n\\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\\n the Hub.\\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\\n with [`~ModelMixin.save_pretrained`].\\n\\n cache_dir (`Union[str, os.PathLike]`, *optional*):\\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\\n is not used.\\n torch_dtype (`str` or `torch.dtype`, *optional*):\\n Override the default `torch.dtype` and load the model with another dtype. If `\\\"auto\\\"` is passed, the\\n dtype is automatically derived from the model's weights.\\n force_download (`bool`, *optional*, defaults to `False`):\\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\\n cached versions if they exist.\\n resume_download (`bool`, *optional*, defaults to `False`):\\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\\n incompletely downloaded files are deleted.\\n proxies (`Dict[str, str]`, *optional*):\\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\\n output_loading_info (`bool`, *optional*, defaults to `False`):\\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\\n local_files_only(`bool`, *optional*, defaults to `False`):\\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\\n won't be downloaded from the Hub.\\n use_auth_token (`str` or *bool*, *optional*):\\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\\n revision (`str`, *optional*, defaults to `\\\"main\\\"`):\\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\\n allowed by Git.\\n from_flax (`bool`, *optional*, defaults to `False`):\\n Load the model weights from a Flax checkpoint save file.\\n subfolder (`str`, *optional*, defaults to `\\\"\\\"`):\\n The subfolder location of a model file within a larger model repository on the Hub or locally.\\n mirror (`str`, *optional*):\\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\\n information.\\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\\n A map that specifies where each submodule should go. It doesn't need to be defined for each\\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\\n same device.\\n\\n Set `device_map=\\\"auto\\\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\\n more information about each option see [designing a device\\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\\n max_memory (`Dict`, *optional*):\\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\\n each GPU and the available CPU RAM if unset.\\n offload_folder (`str` or `os.PathLike`, *optional*):\\n The path to offload weights if `device_map` contains the value `\\\"disk\\\"`.\\n offload_state_dict (`bool`, *optional*):\\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\\n when there is some disk offload.\\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\\n argument to `True` will raise an error.\\n variant (`str`, *optional*):\\n Load weights from a specified `variant` filename such as `\\\"fp16\\\"` or `\\\"ema\\\"`. This is ignored when\\n loading `from_flax`.\\n use_safetensors (`bool`, *optional*, defaults to `None`):\\n If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the\\n `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`\\n weights. If set to `False`, `safetensors` weights are not loaded.\\n\\n <Tip>\\n\\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\\n `huggingface-cli login`. You can also activate the special\\n [\\\"offline-mode\\\"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a\\n firewalled environment.\\n\\n </Tip>\\n\\n Example:\\n\\n ```py\\n from diffusers import UNet2DConditionModel\\n\\n unet = UNet2DConditionModel.from_pretrained(\\\"runwayml/stable-diffusion-v1-5\\\", subfolder=\\\"unet\\\")\\n ```\\n\\n If you get the error message below, you need to finetune the weights for your downstream task:\\n\\n ```bash\\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n ```\\n \\\"\\\"\\\"\\n cache_dir = kwargs.pop(\\\"cache_dir\\\", DIFFUSERS_CACHE)\\n ignore_mismatched_sizes = kwargs.pop(\\\"ignore_mismatched_sizes\\\", False)\\n force_download = kwargs.pop(\\\"force_download\\\", False)\\n from_flax = kwargs.pop(\\\"from_flax\\\", False)\\n resume_download = kwargs.pop(\\\"resume_download\\\", False)\\n proxies = kwargs.pop(\\\"proxies\\\", None)\\n output_loading_info = kwargs.pop(\\\"output_loading_info\\\", False)\\n local_files_only = kwargs.pop(\\\"local_files_only\\\", HF_HUB_OFFLINE)\\n use_auth_token = kwargs.pop(\\\"use_auth_token\\\", None)\\n revision = kwargs.pop(\\\"revision\\\", None)\\n torch_dtype = kwargs.pop(\\\"torch_dtype\\\", None)\\n subfolder = kwargs.pop(\\\"subfolder\\\", None)\\n device_map = kwargs.pop(\\\"device_map\\\", None)\\n max_memory = kwargs.pop(\\\"max_memory\\\", None)\\n offload_folder = kwargs.pop(\\\"offload_folder\\\", None)\\n offload_state_dict = kwargs.pop(\\\"offload_state_dict\\\", False)\\n low_cpu_mem_usage = kwargs.pop(\\\"low_cpu_mem_usage\\\", _LOW_CPU_MEM_USAGE_DEFAULT)\\n variant = kwargs.pop(\\\"variant\\\", None)\\n use_safetensors = kwargs.pop(\\\"use_safetensors\\\", None)\\n\\n allow_pickle = False\\n if use_safetensors is None:\\n use_safetensors = True\\n allow_pickle = True\\n\\n if low_cpu_mem_usage and not is_accelerate_available():\\n low_cpu_mem_usage = False\\n logger.warning(\\n \\\"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\\\"\\n \\\" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\\\"\\n \\\" `accelerate` for faster and less memory-intense model loading. You can do so with: \\\\n```\\\\npip\\\"\\n \\\" install accelerate\\\\n```\\\\n.\\\"\\n )\\n\\n if device_map is not None and not is_accelerate_available():\\n raise NotImplementedError(\\n \\\"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set\\\"\\n \\\" `device_map=None`. You can install accelerate with `pip install accelerate`.\\\"\\n )\\n\\n # Check if we can handle device_map and dispatching the weights\\n if device_map is not None and not is_torch_version(\\\">=\\\", \\\"1.9.0\\\"):\\n raise NotImplementedError(\\n \\\"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set\\\"\\n \\\" `device_map=None`.\\\"\\n )\\n\\n if low_cpu_mem_usage is True and not is_torch_version(\\\">=\\\", \\\"1.9.0\\\"):\\n raise NotImplementedError(\\n \\\"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\\\"\\n \\\" `low_cpu_mem_usage=False`.\\\"\\n )\\n\\n if low_cpu_mem_usage is False and device_map is not None:\\n raise ValueError(\\n f\\\"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and\\\"\\n \\\" dispatching. Please make sure to set `low_cpu_mem_usage=True`.\\\"\\n )\\n\\n # Load config if we don't provide a configuration\\n config_path = pretrained_model_name_or_path\\n\\n user_agent = {\\n \\\"diffusers\\\": __version__,\\n \\\"file_type\\\": \\\"model\\\",\\n \\\"framework\\\": \\\"pytorch\\\",\\n }\\n\\n # load config\\n config, unused_kwargs, commit_hash = cls.load_config(\\n config_path,\\n cache_dir=cache_dir,\\n return_unused_kwargs=True,\\n return_commit_hash=True,\\n force_download=force_download,\\n resume_download=resume_download,\\n proxies=proxies,\\n local_files_only=local_files_only,\\n use_auth_token=use_auth_token,\\n revision=revision,\\n subfolder=subfolder,\\n device_map=device_map,\\n max_memory=max_memory,\\n offload_folder=offload_folder,\\n offload_state_dict=offload_state_dict,\\n user_agent=user_agent,\\n **kwargs,\\n )\\n\\n # load model\\n model_file = None\\n if from_flax:\\n model_file = _get_model_file(\\n pretrained_model_name_or_path,\\n weights_name=FLAX_WEIGHTS_NAME,\\n cache_dir=cache_dir,\\n force_download=force_download,\\n resume_download=resume_download,\\n proxies=proxies,\\n local_files_only=local_files_only,\\n use_auth_token=use_auth_token,\\n revision=revision,\\n subfolder=subfolder,\\n user_agent=user_agent,\\n commit_hash=commit_hash,\\n )\\n model = cls.from_config(config, **unused_kwargs)\\n\\n # Convert the weights\\n from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model\\n\\n model = load_flax_checkpoint_in_pytorch_model(model, model_file)\\n else:\\n if use_safetensors:\\n try:\\n model_file = _get_model_file(\\n pretrained_model_name_or_path,\\n weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),\\n cache_dir=cache_dir,\\n force_download=force_download,\\n resume_download=resume_download,\\n proxies=proxies,\\n local_files_only=local_files_only,\\n use_auth_token=use_auth_token,\\n revision=revision,\\n subfolder=subfolder,\\n user_agent=user_agent,\\n commit_hash=commit_hash,\\n )\\n except IOError as e:\\n if not allow_pickle:\\n raise e\\n pass\\n if model_file is None:\\n model_file = _get_model_file(\\n pretrained_model_name_or_path,\\n weights_name=_add_variant(WEIGHTS_NAME, variant),\\n cache_dir=cache_dir,\\n force_download=force_download,\\n resume_download=resume_download,\\n proxies=proxies,\\n local_files_only=local_files_only,\\n use_auth_token=use_auth_token,\\n revision=revision,\\n subfolder=subfolder,\\n user_agent=user_agent,\\n commit_hash=commit_hash,\\n )\\n\\n if low_cpu_mem_usage:\\n # Instantiate model with empty weights\\n with accelerate.init_empty_weights():\\n model = cls.from_config(config, **unused_kwargs)\\n\\n # if device_map is None, load the state dict and move the params from meta device to the cpu\\n if device_map is None:\\n param_device = \\\"cpu\\\"\\n state_dict = load_state_dict(model_file, variant=variant)\\n model._convert_deprecated_attention_blocks(state_dict)\\n # move the params from meta device to cpu\\n missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())\\n if len(missing_keys) > 0:\\n raise ValueError(\\n f\\\"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are\\\"\\n f\\\" missing: \\\\n {', '.join(missing_keys)}. \\\\n Please make sure to pass\\\"\\n \\\" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize\\\"\\n \\\" those weights or else make sure your checkpoint file is correct.\\\"\\n )\\n\\n unexpected_keys = load_model_dict_into_meta(\\n model,\\n state_dict,\\n device=param_device,\\n dtype=torch_dtype,\\n model_name_or_path=pretrained_model_name_or_path,\\n )\\n\\n if cls._keys_to_ignore_on_load_unexpected is not None:\\n for pat in cls._keys_to_ignore_on_load_unexpected:\\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\\n\\n if len(unexpected_keys) > 0:\\n logger.warn(\\n f\\\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\\\n {[', '.join(unexpected_keys)]}\\\"\\n )\\n\\n else: # else let accelerate handle loading and dispatching.\\n # Load weights and dispatch according to the device_map\\n # by default the device_map is None and the weights are loaded on the CPU\\n try:\\n accelerate.load_checkpoint_and_dispatch(\\n model,\\n model_file,\\n device_map,\\n max_memory=max_memory,\\n offload_folder=offload_folder,\\n offload_state_dict=offload_state_dict,\\n dtype=torch_dtype,\\n )\\n except AttributeError as e:\\n # When using accelerate loading, we do not have the ability to load the state\\n # dict and rename the weight names manually. Additionally, accelerate skips\\n # torch loading conventions and directly writes into `module.{_buffers, _parameters}`\\n # (which look like they should be private variables?), so we can't use the standard hooks\\n # to rename parameters on load. We need to mimic the original weight names so the correct\\n # attributes are available. After we have loaded the weights, we convert the deprecated\\n # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert\\n # the weights so we don't have to do this again.\\n\\n if \\\"'Attention' object has no attribute\\\" in str(e):\\n logger.warn(\\n f\\\"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}\\\"\\n \\\" was saved with deprecated attention block weight names. We will load it with the deprecated attention block\\\"\\n \\\" names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,\\\"\\n \\\" so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,\\\"\\n \\\" please also re-upload it or open a PR on the original repository.\\\"\\n )\\n model._temp_convert_self_to_deprecated_attention_blocks()\\n accelerate.load_checkpoint_and_dispatch(\\n model,\\n model_file,\\n device_map,\\n max_memory=max_memory,\\n offload_folder=offload_folder,\\n offload_state_dict=offload_state_dict,\\n dtype=torch_dtype,\\n )\\n model._undo_temp_convert_self_to_deprecated_attention_blocks()\\n else:\\n raise e\\n\\n loading_info = {\\n \\\"missing_keys\\\": [],\\n \\\"unexpected_keys\\\": [],\\n \\\"mismatched_keys\\\": [],\\n \\\"error_msgs\\\": [],\\n }\\n else:\\n model = cls.from_config(config, **unused_kwargs)\\n\\n state_dict = load_state_dict(model_file, variant=variant)\\n model._convert_deprecated_attention_blocks(state_dict)\\n\\n model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(\\n model,\\n state_dict,\\n model_file,\\n pretrained_model_name_or_path,\\n ignore_mismatched_sizes=ignore_mismatched_sizes,\\n )\\n\\n loading_info = {\\n \\\"missing_keys\\\": missing_keys,\\n \\\"unexpected_keys\\\": unexpected_keys,\\n \\\"mismatched_keys\\\": mismatched_keys,\\n \\\"error_msgs\\\": error_msgs,\\n }\\n\\n if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):\\n raise ValueError(\\n f\\\"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}.\\\"\\n )\\n elif torch_dtype is not None:\\n model = model.to(torch_dtype)\\n\\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\\n\\n # Set model in evaluation mode to deactivate DropOut modules by default\\n model.eval()\\n if output_loading_info:\\n return model, loading_info\\n\\n return model\\n\\n @classmethod\\n def _load_pretrained_model(\\n cls,\\n model,\\n state_dict,\\n resolved_archive_file,\\n pretrained_model_name_or_path,\\n ignore_mismatched_sizes=False,\\n ):\\n # Retrieve missing & unexpected_keys\\n model_state_dict = model.state_dict()\\n loaded_keys = list(state_dict.keys())\\n\\n expected_keys = list(model_state_dict.keys())\\n\\n original_loaded_keys = loaded_keys\\n\\n missing_keys = list(set(expected_keys) - set(loaded_keys))\\n unexpected_keys = list(set(loaded_keys) - set(expected_keys))\\n\\n # Make sure we are able to load base models as well as derived models (with heads)\\n model_to_load = model\\n\\n def _find_mismatched_keys(\\n state_dict,\\n model_state_dict,\\n loaded_keys,\\n ignore_mismatched_sizes,\\n ):\\n mismatched_keys = []\\n if ignore_mismatched_sizes:\\n for checkpoint_key in loaded_keys:\\n model_key = checkpoint_key\\n\\n if (\\n model_key in model_state_dict\\n and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape\\n ):\\n mismatched_keys.append(\\n (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)\\n )\\n del state_dict[checkpoint_key]\\n return mismatched_keys\\n\\n if state_dict is not None:\\n # Whole checkpoint\\n mismatched_keys = _find_mismatched_keys(\\n state_dict,\\n model_state_dict,\\n original_loaded_keys,\\n ignore_mismatched_sizes,\\n )\\n error_msgs = _load_state_dict_into_model(model_to_load, state_dict)\\n\\n if len(error_msgs) > 0:\\n error_msg = \\\"\\\\n\\\\t\\\".join(error_msgs)\\n if \\\"size mismatch\\\" in error_msg:\\n error_msg += (\\n \\\"\\\\n\\\\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.\\\"\\n )\\n raise RuntimeError(f\\\"Error(s) in loading state_dict for {model.__class__.__name__}:\\\\n\\\\t{error_msg}\\\")\\n\\n if len(unexpected_keys) > 0:\\n logger.warning(\\n f\\\"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when\\\"\\n f\\\" initializing {model.__class__.__name__}: {unexpected_keys}\\\\n- This IS expected if you are\\\"\\n f\\\" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task\\\"\\n \\\" or with another architecture (e.g. initializing a BertForSequenceClassification model from a\\\"\\n \\\" BertForPreTraining model).\\\\n- This IS NOT expected if you are initializing\\\"\\n f\\\" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly\\\"\\n \\\" identical (initializing a BertForSequenceClassification model from a\\\"\\n \\\" BertForSequenceClassification model).\\\"\\n )\\n else:\\n logger.info(f\\\"All model checkpoint weights were used when initializing {model.__class__.__name__}.\\\\n\\\")\\n if len(missing_keys) > 0:\\n logger.warning(\\n f\\\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\\\"\\n f\\\" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\\\\nYou should probably\\\"\\n \\\" TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\\"\\n )\\n elif len(mismatched_keys) == 0:\\n logger.info(\\n f\\\"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at\\\"\\n f\\\" {pretrained_model_name_or_path}.\\\\nIf your task is similar to the task the model of the\\\"\\n f\\\" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions\\\"\\n \\\" without further training.\\\"\\n )\\n if len(mismatched_keys) > 0:\\n mismatched_warning = \\\"\\\\n\\\".join(\\n [\\n f\\\"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated\\\"\\n for key, shape1, shape2 in mismatched_keys\\n ]\\n )\\n logger.warning(\\n f\\\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\\\"\\n f\\\" {pretrained_model_name_or_path} and are newly initialized because the shapes did not\\\"\\n f\\\" match:\\\\n{mismatched_warning}\\\\nYou should probably TRAIN this model on a down-stream task to be\\\"\\n \\\" able to use it for predictions and inference.\\\"\\n )\\n\\n return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs\\n\\n @property\\n def device(self) -> device:\\n \\\"\\\"\\\"\\n `torch.device`: The device on which the module is (assuming that all the module parameters are on the same\\n device).\\n \\\"\\\"\\\"\\n return get_parameter_device(self)\\n\\n @property\\n def dtype(self) -> torch.dtype:\\n \\\"\\\"\\\"\\n `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).\\n \\\"\\\"\\\"\\n return get_parameter_dtype(self)\\n\\n def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:\\n \\\"\\\"\\\"\\n Get number of (trainable or non-embedding) parameters in the module.\\n\\n Args:\\n only_trainable (`bool`, *optional*, defaults to `False`):\\n Whether or not to return only the number of trainable parameters.\\n exclude_embeddings (`bool`, *optional*, defaults to `False`):\\n Whether or not to return only the number of non-embedding parameters.\\n\\n Returns:\\n `int`: The number of parameters.\\n\\n Example:\\n\\n ```py\\n from diffusers import UNet2DConditionModel\\n\\n model_id = \\\"runwayml/stable-diffusion-v1-5\\\"\\n unet = UNet2DConditionModel.from_pretrained(model_id, subfolder=\\\"unet\\\")\\n unet.num_parameters(only_trainable=True)\\n 859520964\\n ```\\n \\\"\\\"\\\"\\n\\n if exclude_embeddings:\\n embedding_param_names = [\\n f\\\"{name}.weight\\\"\\n for name, module_type in self.named_modules()\\n if isinstance(module_type, torch.nn.Embedding)\\n ]\\n non_embedding_parameters = [\\n parameter for name, parameter in self.named_parameters() if name not in embedding_param_names\\n ]\\n return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)\\n else:\\n return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)\\n\\n def _convert_deprecated_attention_blocks(self, state_dict):\\n deprecated_attention_block_paths = []\\n\\n def recursive_find_attn_block(name, module):\\n if hasattr(module, \\\"_from_deprecated_attn_block\\\") and module._from_deprecated_attn_block:\\n deprecated_attention_block_paths.append(name)\\n\\n for sub_name, sub_module in module.named_children():\\n sub_name = sub_name if name == \\\"\\\" else f\\\"{name}.{sub_name}\\\"\\n recursive_find_attn_block(sub_name, sub_module)\\n\\n recursive_find_attn_block(\\\"\\\", self)\\n\\n # NOTE: we have to check if the deprecated parameters are in the state dict\\n # because it is possible we are loading from a state dict that was already\\n # converted\\n\\n for path in deprecated_attention_block_paths:\\n # group_norm path stays the same\\n\\n # query -> to_q\\n if f\\\"{path}.query.weight\\\" in state_dict:\\n state_dict[f\\\"{path}.to_q.weight\\\"] = state_dict.pop(f\\\"{path}.query.weight\\\")\\n if f\\\"{path}.query.bias\\\" in state_dict:\\n state_dict[f\\\"{path}.to_q.bias\\\"] = state_dict.pop(f\\\"{path}.query.bias\\\")\\n\\n # key -> to_k\\n if f\\\"{path}.key.weight\\\" in state_dict:\\n state_dict[f\\\"{path}.to_k.weight\\\"] = state_dict.pop(f\\\"{path}.key.weight\\\")\\n if f\\\"{path}.key.bias\\\" in state_dict:\\n state_dict[f\\\"{path}.to_k.bias\\\"] = state_dict.pop(f\\\"{path}.key.bias\\\")\\n\\n # value -> to_v\\n if f\\\"{path}.value.weight\\\" in state_dict:\\n state_dict[f\\\"{path}.to_v.weight\\\"] = state_dict.pop(f\\\"{path}.value.weight\\\")\\n if f\\\"{path}.value.bias\\\" in state_dict:\\n state_dict[f\\\"{path}.to_v.bias\\\"] = state_dict.pop(f\\\"{path}.value.bias\\\")\\n\\n # proj_attn -> to_out.0\\n if f\\\"{path}.proj_attn.weight\\\" in state_dict:\\n state_dict[f\\\"{path}.to_out.0.weight\\\"] = state_dict.pop(f\\\"{path}.proj_attn.weight\\\")\\n if f\\\"{path}.proj_attn.bias\\\" in state_dict:\\n state_dict[f\\\"{path}.to_out.0.bias\\\"] = state_dict.pop(f\\\"{path}.proj_attn.bias\\\")\\n\\n def _temp_convert_self_to_deprecated_attention_blocks(self):\\n deprecated_attention_block_modules = []\\n\\n def recursive_find_attn_block(module):\\n if hasattr(module, \\\"_from_deprecated_attn_block\\\") and module._from_deprecated_attn_block:\\n deprecated_attention_block_modules.append(module)\\n\\n for sub_module in module.children():\\n recursive_find_attn_block(sub_module)\\n\\n recursive_find_attn_block(self)\\n\\n for module in deprecated_attention_block_modules:\\n module.query = module.to_q\\n module.key = module.to_k\\n module.value = module.to_v\\n module.proj_attn = module.to_out[0]\\n\\n # We don't _have_ to delete the old attributes, but it's helpful to ensure\\n # that _all_ the weights are loaded into the new attributes and we're not\\n # making an incorrect assumption that this model should be converted when\\n # it really shouldn't be.\\n del module.to_q\\n del module.to_k\\n del module.to_v\\n del module.to_out\\n\\n def _undo_temp_convert_self_to_deprecated_attention_blocks(self):\\n deprecated_attention_block_modules = []\\n\\n def recursive_find_attn_block(module):\\n if hasattr(module, \\\"_from_deprecated_attn_block\\\") and module._from_deprecated_attn_block:\\n deprecated_attention_block_modules.append(module)\\n\\n for sub_module in module.children():\\n recursive_find_attn_block(sub_module)\\n\\n recursive_find_attn_block(self)\\n\\n for module in deprecated_attention_block_modules:\\n module.to_q = module.query\\n module.to_k = module.key\\n module.to_v = module.value\\n module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])\\n\\n del module.query\\n del module.key\\n del module.value\\n del module.proj_attn\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from dataclasses import dataclass\nfrom typing import Optional\nfrom torch import nn\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom .attention import BasicTransformerBlock\nfrom .modeling_utils import ModelMixin\nimport torch"},"token_num":{"kind":"number","value":20040,"string":"20,040"},"cropped_code":{"kind":"string","value":"# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\n@dataclass\nclass TransformerTemporalModelOutput(BaseOutput):\n \"\"\"\n The output of [`TransformerTemporalModel`].\n\n Args:\n sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input.\n \"\"\"\n\n sample: torch.FloatTensor\n\n\n"},"all_code":{"kind":"string","value":"# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\n@dataclass\nclass TransformerTemporalModelOutput(BaseOutput):\n \"\"\"\n The output of [`TransformerTemporalModel`].\n\n Args:\n sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input.\n \"\"\"\n\n sample: torch.FloatTensor\n\n"},"next_line":{"kind":"string","value":"class TransformerTemporalModel(ModelMixin, ConfigMixin):"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-11-27 18:46:55+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5374,"cells":{"repo_name":{"kind":"string","value":"JiahuiLei/GART"},"file_path":{"kind":"string","value":"solver.py"},"context":{"kind":"list like","value":[{"identifier":"prepare_real_seq","path":"lib_data/get_data.py","snippet":"def prepare_real_seq(\n seq_name,\n dataset_mode,\n split=\"train\",\n image_zoom_ratio=0.5,\n balance=False,\n ins_avt_wild_start_end_skip=None,\n):\n logging.info(\"Prepare real seq: {}\".format(seq_name))\n # * Get dataset\n if dataset_mode == \"ubcfashion\":\n dataset = UBCFasionDataset(\n data_root=\"./data/ubcfashion/\",\n video_list=[seq_name],\n image_zoom_ratio=image_zoom_ratio,\n start_end_skip=ins_avt_wild_start_end_skip,\n )\n elif dataset_mode == \"people_snapshot\":\n dataset = InstantAvatarDataset(\n noisy_flag=False,\n data_root=\"./data/people_snapshot/\",\n video_name=seq_name,\n split=split,\n image_zoom_ratio=image_zoom_ratio,\n )\n print(\"Load Instant Avatar processed PeopleSnapshot\")\n elif dataset_mode == \"zju\":\n dataset = ZJUDataset(\n data_root=\"./data/zju_mocap\",\n video_name=seq_name,\n split=split,\n image_zoom_ratio=image_zoom_ratio,\n )\n elif dataset_mode == \"instant_avatar_wild\":\n # assert image_zoom_ratio == 1.0, \"Check! in the wild data should use 1.0\"\n if image_zoom_ratio != 1.0:\n logging.warning(\n f\"Check! in the wild data should use 1.0, but got {image_zoom_ratio}\"\n )\n dataset = InstantAvatarWildDataset(\n data_root=\"./data/insav_wild\",\n video_name=seq_name,\n split=split,\n image_zoom_ratio=image_zoom_ratio,\n start_end_skip=ins_avt_wild_start_end_skip,\n )\n elif dataset_mode == \"dog_demo\":\n dataset = DogDemoDataset(data_root=\"./data/dog_data_official/\", video_name=seq_name)\n else:\n raise NotImplementedError(\"Unknown mode: {}\".format(dataset_mode))\n\n # prepare an optimizable data provider\n optimizable_data_provider = RealDataOptimizablePoseProviderPose(\n dataset,\n balance=balance,\n )\n return optimizable_data_provider, dataset"},{"identifier":"DatabasePoseProvider","path":"lib_data/data_provider.py","snippet":"class DatabasePoseProvider(nn.Module):\n def __init__(\n self,\n pose_dirs: list,\n da_pose_prob=0.1,\n da_range=[0.0, np.pi / 4],\n device=torch.device(\"cuda\"),\n ) -> None:\n super().__init__()\n self.device = device\n self.base_R = matrix_to_axis_angle(\n torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, \"sxyz\"))[None]\n )[0]\n self.base_R = self.base_R.float().to(self.device)\n\n self.da_pose_prob = da_pose_prob\n self.da_range = da_range\n\n self.data = []\n\n # cache the poses\n for d in pose_dirs:\n print(f\"Caching {d} ...\")\n for subject in tqdm(os.listdir(d)):\n sub_dir = os.path.join(d, subject)\n if not os.path.isdir(sub_dir):\n continue\n npz_files = [f for f in os.listdir(sub_dir) if f.endswith(\".npz\")]\n npz_files.sort()\n for fn in npz_files:\n try:\n npz_fn = os.path.join(sub_dir, fn)\n pose_data = np.load(npz_fn)\n amass_len = pose_data[\"poses\"].shape[0]\n smplx_to_smpl = list(range(66)) + [72, 73, 74, 117, 118, 119]\n poses = pose_data[\"poses\"][:, smplx_to_smpl].reshape(\n amass_len, 24, 3\n )\n self.data.append(poses.astype(np.float16))\n except:\n # print(f\"Error in {npz_fn}, skip!\")\n pass\n self.data = np.concatenate(self.data, axis=0)\n print(\n f\"Database has poses {len(self.data)} with DA-pose prob {self.da_pose_prob} and range {self.da_range}\"\n )\n return\n\n def forward(self, N: int):\n pose, trans = self.sample_pose(N)\n return pose, trans\n\n def sample_pose(self, N: int):\n # da pose\n pose_list = []\n for i in range(N):\n seed = np.random.rand()\n if seed > self.da_pose_prob:\n # from database\n idx = np.random.randint(len(self.data))\n pose = torch.from_numpy(self.data[idx]).float().to(self.device)\n else:\n # da pose\n pose = torch.zeros(24, 3).to(self.device)\n da_theta = float(np.random.uniform(*self.da_range))\n pose[1, -1] = da_theta\n pose[2, -1] = -da_theta\n pose[0] = self.base_R\n pose_list.append(pose)\n pose = torch.stack(pose_list, dim=0)\n trans = torch.zeros(N, 3).to(self.device)\n return pose, trans"},{"identifier":"get_template","path":"lib_gart/templates.py","snippet":"def get_template(\n mode, init_beta, cano_pose_type, voxel_deformer_res, template_model_path=None\n):\n if mode == \"human\":\n template = SMPLTemplate(\n smpl_model_path=template_model_path,\n init_beta=init_beta,\n cano_pose_type=cano_pose_type,\n voxel_deformer_res=voxel_deformer_res,\n )\n elif mode == \"dog\":\n template = SMALTemplate(\n init_beta=init_beta,\n cano_pose_type=cano_pose_type,\n voxel_deformer_res=voxel_deformer_res,\n )\n else:\n raise ValueError(f\"Unknown mode {mode}\")\n return template"},{"identifier":"GaussianTemplateModel","path":"lib_gart/model.py","snippet":"class GaussianTemplateModel(nn.Module):\n def __init__(\n self,\n template,\n add_bones: AdditionalBones,\n ##################################\n # attr config\n w_correction_flag=True,\n # w_rest_dim=0, # additional skinnign weight\n f_localcode_dim=0,\n max_sph_order=0,\n w_memory_type=\"point\",\n ##################################\n max_scale=0.1, # use sigmoid activation, can't be too large\n min_scale=0.0,\n # geo init\n init_mode=\"on_mesh\",\n opacity_init_value=0.9, # the init value of opacity\n # on mesh init params\n onmesh_init_subdivide_num=0,\n onmesh_init_scale_factor=1.0,\n onmesh_init_thickness_factor=0.5,\n # near mesh init params\n scale_init_value=0.01, # the init value of scale\n nearmesh_init_num=10000,\n nearmesh_init_std=0.1,\n ##################################\n ) -> None:\n super().__init__()\n\n self.template = template\n self.num_bones = template.voxel_deformer.num_bones\n self.add_bones = add_bones\n self.num_add_bones = add_bones.num_bones\n\n self.max_scale = max_scale\n self.min_scale = min_scale\n self._init_act(self.max_scale, self.min_scale)\n self.opacity_init_logit = self.o_inv_act(opacity_init_value)\n\n # * init geometry\n if init_mode == \"on_mesh\":\n x, q, s, o = get_on_mesh_init_geo_values(\n template,\n on_mesh_subdivide=onmesh_init_subdivide_num,\n scale_init_factor=onmesh_init_scale_factor,\n thickness_init_factor=onmesh_init_thickness_factor,\n max_scale=max_scale,\n min_scale=min_scale,\n s_inv_act=self.s_inv_act,\n opacity_init_logit=self.opacity_init_logit,\n )\n elif init_mode == \"near_mesh\":\n self.scale_init_logit = self.s_inv_act(scale_init_value)\n x, q, s, o = get_near_mesh_init_geo_values(\n template,\n scale_base_logit=self.scale_init_logit,\n opacity_base_logit=self.opacity_init_logit,\n random_init_num=nearmesh_init_num,\n random_init_std=nearmesh_init_std,\n )\n elif init_mode == \"in_mesh\":\n self.scale_init_logit = self.s_inv_act(scale_init_value)\n x, q, s, o = get_inside_mesh_init_geo_values(\n template,\n scale_base_logit=self.scale_init_logit,\n opacity_base_logit=self.opacity_init_logit,\n random_init_num=nearmesh_init_num,\n )\n else:\n raise NotImplementedError(f\"Unknown init_mode {init_mode}\")\n self._xyz = nn.Parameter(x)\n self._rotation = nn.Parameter(q)\n self._scaling = nn.Parameter(s)\n self._opacity = nn.Parameter(o)\n\n # * init attributes\n self.w_memory_type = w_memory_type\n assert self.w_memory_type in [\"point\", \"voxel\"], f\"Unknown {w_memory_type}\"\n\n self.max_sph_order = max_sph_order\n self.w_dc_dim = self.template.dim if w_correction_flag else 0\n self.w_rest_dim = self.add_bones.num_bones\n self.f_localcode_dim = f_localcode_dim\n\n sph_rest_dim = 3 * (sph_order2nfeat(self.max_sph_order) - 1)\n self._features_dc = nn.Parameter(torch.zeros_like(self._xyz))\n self._features_rest = nn.Parameter(torch.zeros(self.N, sph_rest_dim))\n\n # * Different implementation of smoothness\n if self.w_memory_type == \"point\":\n self._w_correction_dc = nn.Parameter(torch.zeros(self.N, self.w_dc_dim))\n self._w_correction_rest = nn.Parameter(\n torch.ones(self.N, self.w_rest_dim) * 1e-4\n )\n elif self.w_memory_type == \"voxel\":\n self._w_correction_dc = nn.Parameter(torch.zeros(self.N, 0))\n self._w_correction_rest = nn.Parameter(torch.zeros(self.N, 0))\n if self.w_dc_dim > 0:\n self.template.voxel_deformer.enable_voxel_correction()\n if self.w_rest_dim > 0:\n self.template.voxel_deformer.enable_additional_correction(\n self.w_rest_dim\n )\n elif self.w_memory_type == \"hash\":\n raise NotImplementedError(\"TODO\")\n else:\n raise NotImplementedError(f\"Unknown {w_memory_type}\")\n\n self._features_localcode = nn.Parameter(\n torch.zeros(self.N, self.f_localcode_dim)\n )\n\n assert self.f_localcode_dim == 0, \"TODO, add local mlp ablation\"\n\n # * States\n # warning, our code use N, instead of (N,1) as in GS code\n self.register_buffer(\"xyz_gradient_accum\", torch.zeros(self.N).float())\n self.register_buffer(\"xyz_gradient_denom\", torch.zeros(self.N).long())\n self.register_buffer(\"max_radii2D\", torch.zeros(self.N).float())\n\n self.op_update_exclude = [\"add_bones\"]\n if self.w_memory_type != \"point\":\n self.op_update_exclude.extend([\"w_dc_vox\", \"w_rest_vox\"])\n # self.summary()\n return\n\n def summary(self):\n # logging.info number of parameters per pytorch sub module\n msg = \"\"\n for name, param in self.named_parameters():\n if name.startswith(\"add_bones\"):\n continue # compact print\n msg = msg + f\"[{name}:{param.numel()/1e3:.1f}K] \" \n # logging.info(f\"{name}, {param.numel()/1e6:.3f}M\")\n logging.info(msg)\n return\n\n def _init_act(self, max_s_value, min_s_value):\n def s_act(x):\n if isinstance(x, float):\n x = torch.tensor(x).squeeze()\n return min_s_value + torch.sigmoid(x) * (max_s_value - min_s_value)\n\n def s_inv_act(x):\n if isinstance(x, float):\n x = torch.tensor(x).squeeze()\n y = (x - min_s_value) / (max_s_value - min_s_value) + 1e-5\n y = torch.logit(y)\n assert not torch.isnan(\n y\n ).any(), f\"{x.min()}, {x.max()}, {y.min()}, {y.max()}\"\n return y\n\n def o_act(x):\n if isinstance(x, float):\n x = torch.tensor(x).squeeze()\n return torch.sigmoid(x)\n\n def o_inv_act(x):\n if isinstance(x, float):\n x = torch.tensor(x).squeeze()\n return torch.logit(x)\n\n self.s_act = s_act\n self.s_inv_act = s_inv_act\n self.o_act = o_act\n self.o_inv_act = o_inv_act\n\n return\n\n @property\n def N(self):\n return len(self._xyz)\n\n @property\n def get_x(self):\n return self._xyz\n\n @property\n def get_R(self):\n return quaternion_to_matrix(self._rotation)\n\n @property\n def get_o(self):\n return self.o_act(self._opacity)\n\n @property\n def get_s(self):\n return self.s_act(self._scaling)\n\n @property\n def get_c(self):\n return torch.cat([self._features_dc, self._features_rest], dim=-1)\n\n def cache_for_fast(self):\n _cached_W, _ = self.template.forward(None, self._xyz)\n self._cached_W = _cached_W.detach().clone()\n return\n\n def forward(\n self, theta, trans, additional_dict={}, active_sph_order=None, fast=False\n ):\n # * fast will use the cached per point attr, no query anymore\n # TODO: the additional dict contain info to do flexible skinning: it can contain the As directly for optimization, or it can contain t index to query some buffers to provide As, or it can contain t along with the input theta to query some MLP;\n\n # TODO: if use vol memory, every forward update self.xxx, and remove them from parameters, pretend that the attributes are per point, but actually they are queried every forward\n\n # theta: B,24,3; trans: B,3\n B = len(theta)\n if active_sph_order is None:\n active_sph_order = self.max_sph_order\n else:\n assert (\n active_sph_order <= self.max_sph_order\n ), \"active_sph_order should be smaller\"\n sph_dim = 3 * sph_order2nfeat(active_sph_order)\n\n xyz = self.get_x\n mu_can = xyz\n frame_can = self.get_R\n s = self.get_s\n o = self.get_o\n sph = self.get_c[:, :sph_dim]\n\n mu_can = mu_can[None].expand(B, -1, -1)\n frame_can = frame_can[None].expand(B, -1, -1, -1)\n\n if fast:\n # only forward skeleton, no query voxel\n _, A = self.template.forward(theta, None)\n W = self._cached_W[None].expand(B, -1, -1)\n else:\n W, A = self.template.forward(theta, mu_can)\n if self._w_correction_dc.shape[-1] > 0:\n W = W + self._w_correction_dc[None]\n T = torch.einsum(\"bnj, bjrc -> bnrc\", W[..., : self.num_bones], A)\n\n # * additional correction here\n if \"pose\" not in additional_dict.keys():\n # maybe later we want to viz the different pose effect in cano\n additional_dict[\"pose\"] = theta.reshape(B, -1)[:, 3:]\n add_A = self.add_bones(**additional_dict)\n if add_A is not None:\n if theta.ndim == 2:\n global_axis_angle = theta[:, :3]\n else:\n global_axis_angle = theta[:, 0]\n global_orient_action = self.template.get_rot_action(global_axis_angle) # B,4,4\n add_A = torch.einsum(\"bij, bnjk -> bnik\", global_orient_action, add_A)\n\n if self.w_memory_type == \"point\":\n assert self._w_correction_rest.shape[-1] > 0\n add_W = self._w_correction_rest[None].expand(B, -1, -1)\n elif self.w_memory_type == \"voxel\":\n add_W = W[..., self.num_bones :]\n\n add_T = torch.einsum(\"bnj, bjrc -> bnrc\", add_W, add_A)\n T = T + add_T # Linear\n additional_dict[\"As\"] = add_A\n\n R, t = T[:, :, :3, :3], T[:, :, :3, 3] # B,N,3,3; B,N,3\n\n mu = torch.einsum(\"bnij,bnj->bni\", R, mu_can) + t # B,N,3\n frame = torch.einsum(\"bnij,bnjk->bnik\", R, frame_can) # B,N,3,3\n\n s = s[None].expand(B, -1, -1) # B,N,1\n o = o[None].expand(B, -1, -1) # B,N,1\n sph = sph[:, :sph_dim][None].expand(B, -1, -1) # B,N,C\n\n mu = mu + trans[:, None, :]\n\n return mu, frame, s, o, sph, additional_dict\n\n def compute_reg(self, K):\n # !can cancel the knn, but the w reg is critical\n if K > 0:\n xyz = self._xyz\n # todo: this can be cached and updated every several steps!!\n dist_sq, nn_ind, _ = knn_points(xyz[None], xyz[None], K=K, return_nn=False)\n nn_ind = nn_ind.squeeze(0)\n # reg the std inside knn\n q = self._rotation[nn_ind, :] # N,K,4\n s = self.get_s[nn_ind, :] # N,K,3\n o = self.get_o[nn_ind, :] # N,K,1\n q_std = q.std(dim=1).mean()\n s_std = s.std(dim=1).mean()\n o_std = o.std(dim=1).mean()\n\n cd = self._features_dc[nn_ind, :] # N,K,3\n ch = self._features_rest[nn_ind, :] # N,K,C\n cd_std = cd.std(dim=1).mean()\n ch_std = ch.std(dim=1).mean()\n if ch.shape[-1] == 0:\n ch_std = torch.zeros_like(ch_std)\n\n w = self._w_correction_dc[nn_ind, :] # N,K,3\n w_rest = self._w_correction_rest[nn_ind, :] # N,K,C\n f = self._features_localcode[nn_ind, :] # N,K,C\n w_std = w.std(dim=1).mean()\n w_rest_std = w_rest.std(dim=1).mean()\n f_std = f.std(dim=1).mean()\n if w.shape[-1] == 0:\n w_std = torch.zeros_like(cd_std)\n if w_rest.shape[-1] == 0:\n w_rest_std = torch.zeros_like(cd_std)\n if f.shape[-1] == 0:\n f_std = torch.zeros_like(cd_std)\n else:\n dummy = torch.zeros(1).to(self._xyz).squeeze()\n q_std, s_std, o_std = dummy, dummy, dummy\n cd_std, ch_std = dummy, dummy\n w_std, w_rest_std, f_std = dummy, dummy, dummy\n dist_sq = dummy\n\n w_norm = self._w_correction_dc.norm(dim=-1).mean() # N\n w_rest_norm = self._w_correction_rest.norm(dim=-1).mean() # N\n\n if self.w_memory_type == \"voxel\":\n # update the w related std and norm\n w_std = self.template.voxel_deformer.get_tv(\"dc\")\n w_rest_std = self.template.voxel_deformer.get_tv(\"rest\")\n w_norm = self.template.voxel_deformer.get_mag(\"dc\")\n w_rest_norm = self.template.voxel_deformer.get_mag(\"rest\")\n\n max_s_square = torch.mean((self.get_s.max(dim=1).values) ** 2)\n\n return (\n q_std,\n s_std,\n o_std,\n cd_std,\n ch_std,\n w_std,\n w_rest_std,\n f_std,\n w_norm,\n w_rest_norm,\n dist_sq.mean(),\n max_s_square,\n )\n\n def get_optimizable_list(\n self,\n lr_p=0.00016,\n lr_q=0.001,\n lr_s=0.005,\n lr_o=0.05,\n lr_sph=0.0025,\n lr_sph_rest=None,\n lr_w=0.001,\n lr_w_rest=0.001,\n lr_f=0.0001,\n ):\n lr_sph_rest = lr_sph / 20 if lr_sph_rest is None else lr_sph_rest\n l = [\n {\"params\": [self._xyz], \"lr\": lr_p, \"name\": \"xyz\"},\n {\"params\": [self._opacity], \"lr\": lr_o, \"name\": \"opacity\"},\n {\"params\": [self._scaling], \"lr\": lr_s, \"name\": \"scaling\"},\n {\"params\": [self._rotation], \"lr\": lr_q, \"name\": \"rotation\"},\n {\"params\": [self._features_dc], \"lr\": lr_sph, \"name\": \"f_dc\"},\n {\"params\": [self._features_rest], \"lr\": lr_sph_rest, \"name\": \"f_rest\"},\n {\"params\": [self._w_correction_dc], \"lr\": lr_w, \"name\": \"w_dc\"},\n {\"params\": [self._w_correction_rest], \"lr\": lr_w_rest, \"name\": \"w_rest\"},\n {\"params\": [self._features_localcode], \"lr\": lr_f, \"name\": \"f_localcode\"},\n ]\n if self.w_memory_type == \"voxel\":\n if self.w_dc_dim > 0:\n l.append(\n {\n \"params\": [self.template.voxel_deformer.voxel_w_correction],\n \"lr\": lr_w,\n \"name\": \"w_dc_vox\",\n }\n )\n if self.w_rest_dim > 0:\n l.append(\n {\n \"params\": [self.template.voxel_deformer.additional_correction],\n \"lr\": lr_w_rest,\n \"name\": \"w_rest_vox\",\n }\n )\n return l\n\n # * Gaussian Control\n def record_xyz_grad_radii(self, viewspace_point_tensor, radii, update_filter):\n # Record the gradient norm, invariant across different poses\n assert len(viewspace_point_tensor) == self.N\n self.xyz_gradient_accum[update_filter] += torch.norm(\n viewspace_point_tensor.grad[update_filter, :2], dim=-1, keepdim=False\n )\n self.xyz_gradient_denom[update_filter] += 1\n self.max_radii2D[update_filter] = torch.max(\n self.max_radii2D[update_filter], radii[update_filter]\n )\n return\n\n def _densification_postprocess(\n self,\n optimizer,\n new_xyz,\n new_r,\n new_s,\n new_o,\n new_sph_dc,\n new_sph_rest,\n new_w_dc,\n new_w_rest,\n new_localcode,\n ):\n d = {\n \"xyz\": new_xyz,\n \"f_dc\": new_sph_dc,\n \"f_rest\": new_sph_rest,\n \"opacity\": new_o,\n \"scaling\": new_s,\n \"rotation\": new_r,\n \"w_dc\": new_w_dc,\n \"w_rest\": new_w_rest,\n \"f_localcode\": new_localcode,\n }\n d = {k: v for k, v in d.items() if v is not None}\n\n # First cat to optimizer and then return to self\n optimizable_tensors = cat_tensors_to_optimizer(optimizer, d)\n\n self._xyz = optimizable_tensors[\"xyz\"]\n self._opacity = optimizable_tensors[\"opacity\"]\n self._scaling = optimizable_tensors[\"scaling\"]\n self._rotation = optimizable_tensors[\"rotation\"]\n self._features_dc = optimizable_tensors[\"f_dc\"]\n self._features_rest = optimizable_tensors[\"f_rest\"]\n self._w_correction_dc = optimizable_tensors[\"w_dc\"]\n self._w_correction_rest = optimizable_tensors[\"w_rest\"]\n self._features_localcode = optimizable_tensors[\"f_localcode\"]\n\n self.xyz_gradient_accum = torch.zeros(self._xyz.shape[0], device=\"cuda\")\n self.xyz_gradient_denom = torch.zeros(self._xyz.shape[0], device=\"cuda\")\n self.max_radii2D = torch.cat(\n [self.max_radii2D, torch.zeros_like(new_xyz[:, 0])], dim=0\n )\n return\n\n def _densify_and_clone(self, optimizer, grad_norm, grad_threshold, scale_th):\n # Extract points that satisfy the gradient condition\n # padding for enabling both call of clone and split\n padded_grad = torch.zeros((self.N), device=\"cuda\")\n padded_grad[: grad_norm.shape[0]] = grad_norm.squeeze()\n selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)\n selected_pts_mask = torch.logical_and(\n selected_pts_mask,\n torch.max(self.get_s, dim=1).values <= scale_th,\n )\n if selected_pts_mask.sum() == 0:\n return 0\n\n new_xyz = self._xyz[selected_pts_mask]\n new_rotation = self._rotation[selected_pts_mask]\n new_scaling = self._scaling[selected_pts_mask]\n new_opacities = self._opacity[selected_pts_mask]\n new_features_dc = self._features_dc[selected_pts_mask]\n new_features_rest = self._features_rest[selected_pts_mask]\n new_w_dc = self._w_correction_dc[selected_pts_mask]\n new_w_rest = self._w_correction_rest[selected_pts_mask]\n new_localcode = self._features_localcode[selected_pts_mask]\n\n self._densification_postprocess(\n optimizer,\n new_xyz=new_xyz,\n new_r=new_rotation,\n new_s=new_scaling,\n new_o=new_opacities,\n new_sph_dc=new_features_dc,\n new_sph_rest=new_features_rest,\n new_w_dc=new_w_dc,\n new_w_rest=new_w_rest,\n new_localcode=new_localcode,\n )\n\n return len(new_xyz)\n\n def _densify_and_split(\n self,\n optimizer,\n grad_norm,\n grad_threshold,\n scale_th,\n N=2,\n ):\n # Extract points that satisfy the gradient condition\n _scaling = self.get_s\n # padding for enabling both call of clone and split\n padded_grad = torch.zeros((self.N), device=\"cuda\")\n padded_grad[: grad_norm.shape[0]] = grad_norm.squeeze()\n selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)\n selected_pts_mask = torch.logical_and(\n selected_pts_mask,\n torch.max(_scaling, dim=1).values > scale_th,\n )\n if selected_pts_mask.sum() == 0:\n return 0\n\n stds = _scaling[selected_pts_mask].repeat(N, 1)\n means = torch.zeros((stds.size(0), 3), device=\"cuda\")\n samples = torch.normal(mean=means, std=stds)\n rots = quaternion_to_matrix(self._rotation[selected_pts_mask]).repeat(N, 1, 1)\n new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self._xyz[\n selected_pts_mask\n ].repeat(N, 1)\n new_scaling = _scaling[selected_pts_mask].repeat(N, 1) / (0.8 * N)\n new_scaling = torch.clamp(new_scaling, max=self.max_scale, min=self.min_scale)\n new_scaling = self.s_inv_act(new_scaling)\n new_rotation = self._rotation[selected_pts_mask].repeat(N, 1)\n new_features_dc = self._features_dc[selected_pts_mask].repeat(N, 1)\n new_features_rest = self._features_rest[selected_pts_mask].repeat(N, 1)\n new_opacities = self._opacity[selected_pts_mask].repeat(N, 1)\n new_w_dc = self._w_correction_dc[selected_pts_mask].repeat(N, 1)\n new_w_rest = self._w_correction_rest[selected_pts_mask].repeat(N, 1)\n new_localcode = self._features_localcode[selected_pts_mask].repeat(N, 1)\n\n self._densification_postprocess(\n optimizer,\n new_xyz=new_xyz,\n new_r=new_rotation,\n new_s=new_scaling,\n new_o=new_opacities,\n new_sph_dc=new_features_dc,\n new_sph_rest=new_features_rest,\n new_w_dc=new_w_dc,\n new_w_rest=new_w_rest,\n new_localcode=new_localcode,\n )\n\n prune_filter = torch.cat(\n (\n selected_pts_mask,\n torch.zeros(N * selected_pts_mask.sum(), device=\"cuda\", dtype=bool),\n )\n )\n self._prune_points(optimizer, prune_filter)\n return len(new_xyz)\n\n def densify(self, optimizer, max_grad, percent_dense, extent, verbose=True):\n grads = self.xyz_gradient_accum / self.xyz_gradient_denom\n grads[grads.isnan()] = 0.0\n\n # n_clone = self._densify_and_clone(optimizer, grads, max_grad)\n n_clone = self._densify_and_clone(\n optimizer, grads, max_grad, percent_dense * extent\n )\n n_split = self._densify_and_split(\n optimizer, grads, max_grad, percent_dense * extent, N=2\n )\n\n if verbose:\n logging.info(f\"Densify: Clone[+] {n_clone}, Split[+] {n_split}\")\n # logging.info(f\"Densify: Clone[+] {n_clone}\")\n # torch.cuda.empty_cache()\n return\n\n def random_grow(self, optimizer, num_factor=0.05, std=0.1, init_opa_value=0.1):\n # * New operation, randomly add largely disturbed points to the geometry\n ind = torch.randperm(self.N)[: int(self.N * num_factor)]\n selected_pts_mask = torch.zeros(self.N, dtype=bool, device=\"cuda\")\n selected_pts_mask[ind] = True\n\n new_xyz = self._xyz[selected_pts_mask]\n noise = torch.randn_like(new_xyz) * std\n new_xyz = new_xyz + noise\n new_features_dc = self._features_dc[selected_pts_mask]\n new_features_rest = self._features_rest[selected_pts_mask]\n\n new_opacities = torch.ones_like(self._opacity[selected_pts_mask])\n new_opacities = new_opacities * self.o_inv_act(init_opa_value)\n\n new_scaling = self._scaling[selected_pts_mask]\n new_rotation = self._rotation[selected_pts_mask]\n\n new_w_dc = self._w_correction_dc[selected_pts_mask]\n new_w_rest = self._w_correction_rest[selected_pts_mask]\n new_localcode = self._features_localcode[selected_pts_mask]\n\n self._densification_postprocess(\n optimizer,\n new_xyz=new_xyz,\n new_r=new_rotation,\n new_s=new_scaling,\n new_o=new_opacities,\n new_sph_dc=new_features_dc,\n new_sph_rest=new_features_rest,\n new_w_dc=new_w_dc,\n new_w_rest=new_w_rest,\n new_localcode=new_localcode,\n )\n logging.info(f\"Random grow: {len(new_xyz)}\")\n return len(new_xyz)\n\n def prune_points(self, optimizer, min_opacity, max_screen_size, verbose=True):\n opacity = self.o_act(self._opacity)\n prune_mask = (opacity < min_opacity).squeeze()\n if max_screen_size: # if a point is too large\n big_points_vs = self.max_radii2D > max_screen_size\n prune_mask = torch.logical_or(prune_mask, big_points_vs)\n # * reset the maxRadii\n self.max_radii2D = torch.zeros_like(self.max_radii2D)\n self._prune_points(optimizer, prune_mask)\n if verbose:\n logging.info(f\"Prune: {prune_mask.sum()}\")\n\n def _prune_points(self, optimizer, mask):\n valid_points_mask = ~mask\n optimizable_tensors = prune_optimizer(\n optimizer,\n valid_points_mask,\n exclude_names=self.op_update_exclude,\n )\n\n self._xyz = optimizable_tensors[\"xyz\"]\n if getattr(self, \"color_memory\", None) is None:\n self._features_dc = optimizable_tensors[\"f_dc\"]\n self._features_rest = optimizable_tensors[\"f_rest\"]\n self._opacity = optimizable_tensors[\"opacity\"]\n self._scaling = optimizable_tensors[\"scaling\"]\n self._rotation = optimizable_tensors[\"rotation\"]\n self._w_correction_dc = optimizable_tensors[\"w_dc\"]\n self._w_correction_rest = optimizable_tensors[\"w_rest\"]\n self._features_localcode = optimizable_tensors[\"f_localcode\"]\n\n self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]\n self.xyz_gradient_denom = self.xyz_gradient_denom[valid_points_mask]\n self.max_radii2D = self.max_radii2D[valid_points_mask]\n # torch.cuda.empty_cache()\n return\n\n @torch.no_grad()\n def regaussian(self, optimizer, max_scale=0.03):\n # raise NotImplementedError(\"TODO, like split\")\n # * New operation, manually split the large gaussians with smaller ones to approximate\n # * Now, try bi-split\n\n # Extract points that satisfy the gradient condition\n _scaling = self.get_s\n selected_pts_mask = torch.max(_scaling, dim=1).values > max_scale\n\n step = 0\n before_num = self.N\n while selected_pts_mask.any():\n # This can be done more than 3 times, becuase there may be huge gaussians, which should be devided several times\n fg_xyz = self._xyz[selected_pts_mask]\n fg_scale = _scaling[selected_pts_mask]\n fg_frame = quaternion_to_matrix(self._rotation[selected_pts_mask])\n # each column is the direction of axis in global frame\n axis_ind = torch.argmax(fg_scale, dim=1)\n axis_scale = fg_scale.max(dim=1).values\n # select column\n axis_dir = torch.gather(\n fg_frame, dim=2, index=axis_ind[:, None, None].expand(-1, 3, -1)\n ).squeeze(\n -1\n ) # N,3\n new_x1 = fg_xyz + axis_dir.squeeze() * axis_scale[:, None] / 2.0\n new_x2 = fg_xyz - axis_dir.squeeze() * axis_scale[:, None] / 2.0\n # Repeat will change [1,2,3...] to [1,2,3..., 1,2,3...]\n new_xyz = torch.cat([new_x1, new_x2], dim=0).reshape(-1, 3)\n new_scaling = _scaling[selected_pts_mask]\n new_scaling = torch.scatter(\n new_scaling,\n dim=1,\n index=axis_ind[:, None],\n src=axis_scale[:, None] / 2.0,\n ).repeat(2, 1)\n new_scaling = torch.clamp(\n new_scaling, max=self.max_scale, min=self.min_scale\n )\n new_scaling = self.s_inv_act(new_scaling)\n new_rotation = self._rotation[selected_pts_mask].repeat(2, 1)\n new_features_dc = self._features_dc[selected_pts_mask].repeat(2, 1)\n new_features_rest = self._features_rest[selected_pts_mask].repeat(2, 1)\n new_opacities = self._opacity[selected_pts_mask].repeat(2, 1)\n new_w_dc = self._w_correction_dc[selected_pts_mask].repeat(2, 1)\n new_w_rest = self._w_correction_rest[selected_pts_mask].repeat(2, 1)\n new_localcode = self._features_localcode[selected_pts_mask].repeat(2, 1)\n\n self._densification_postprocess(\n optimizer,\n new_xyz=new_xyz.float(),\n new_r=new_rotation.float(),\n new_s=new_scaling.float(),\n new_o=new_opacities.float(),\n new_sph_dc=new_features_dc.float(),\n new_sph_rest=new_features_rest.float(),\n new_w_dc=new_w_dc.float(),\n new_w_rest=new_w_rest.float(),\n new_localcode=new_localcode.float(),\n )\n\n prune_filter = torch.cat(\n (\n selected_pts_mask,\n torch.zeros(2 * selected_pts_mask.sum(), device=\"cuda\", dtype=bool),\n )\n )\n self._prune_points(optimizer, prune_filter)\n\n step += 1\n logging.info(\n f\"Regaussian-[{step}], {selected_pts_mask.sum()} ({selected_pts_mask.float().mean()*100}% pts-scale>{max_scale})\"\n )\n\n _scaling = self.get_s\n selected_pts_mask = torch.max(_scaling, dim=1).values > max_scale\n logging.info(f\"Re-gaussian: {before_num} -> {self.N}\")\n return\n\n def reset_opacity(self, optimizer, value=0.01, verbose=True):\n opacities_new = self.o_inv_act(\n torch.min(self.o_act(self._opacity), torch.ones_like(self._opacity) * value)\n )\n optimizable_tensors = replace_tensor_to_optimizer(\n optimizer, opacities_new, \"opacity\"\n )\n if verbose:\n logging.info(f\"Reset opacity to {value}\")\n self._opacity = optimizable_tensors[\"opacity\"]\n\n def load(self, ckpt):\n # because N changed, have to re-init the buffers\n self._xyz = nn.Parameter(torch.as_tensor(ckpt[\"_xyz\"], dtype=torch.float32))\n\n self._features_dc = nn.Parameter(\n torch.as_tensor(ckpt[\"_features_dc\"], dtype=torch.float32)\n )\n self._features_rest = nn.Parameter(\n torch.as_tensor(ckpt[\"_features_rest\"], dtype=torch.float32)\n )\n self._opacity = nn.Parameter(\n torch.as_tensor(ckpt[\"_opacity\"], dtype=torch.float32)\n )\n self._scaling = nn.Parameter(\n torch.as_tensor(ckpt[\"_scaling\"], dtype=torch.float32)\n )\n self._rotation = nn.Parameter(\n torch.as_tensor(ckpt[\"_rotation\"], dtype=torch.float32)\n )\n self._w_correction_dc = nn.Parameter(\n torch.as_tensor(ckpt[\"_w_correction_dc\"], dtype=torch.float32)\n )\n self._w_correction_rest = nn.Parameter(\n torch.as_tensor(ckpt[\"_w_correction_rest\"], dtype=torch.float32)\n )\n self._features_localcode = nn.Parameter(\n torch.as_tensor(ckpt[\"_features_localcode\"], dtype=torch.float32)\n )\n self.xyz_gradient_accum = torch.as_tensor(\n ckpt[\"xyz_gradient_accum\"], dtype=torch.float32\n )\n self.xyz_gradient_denom = torch.as_tensor(\n ckpt[\"xyz_gradient_denom\"], dtype=torch.int64\n )\n self.max_radii2D = torch.as_tensor(ckpt[\"max_radii2D\"], dtype=torch.float32)\n\n # * add bones may have different total_t\n if \"add_bones.dt_list\" in ckpt.keys():\n self.add_bones.total_t = ckpt[\"add_bones.dt_list\"].shape[0]\n self.add_bones.dt_list = nn.Parameter(\n torch.as_tensor(ckpt[\"add_bones.dt_list\"], dtype=torch.float32)\n )\n self.add_bones.dr_list = nn.Parameter(\n torch.as_tensor(ckpt[\"add_bones.dr_list\"], dtype=torch.float32)\n )\n # load others\n self.load_state_dict(ckpt, strict=True)\n # this is critical, reinit the funcs\n self._init_act(self.max_scale, self.min_scale)\n return"},{"identifier":"AdditionalBones","path":"lib_gart/model.py","snippet":"class AdditionalBones(nn.Module):\n def __init__(\n self, # additional bones\n num_bones: int = 0,\n total_t: int = 0, # any usage of time should use this!\n mode=\"pose-mlp\",\n # pose-mlp\n pose_dim=23 * 3,\n mlp_hidden_dims=[256, 256, 256, 256],\n mlp_act=nn.LeakyReLU,\n # pose+t-mlp\n ):\n super().__init__()\n self.num_bones = num_bones\n if self.num_bones == 0:\n return\n self.mode = mode\n assert self.mode in [\"pose-mlp\", \"pose+t-mlp\", \"delta-list\", \"list\"]\n self.total_t = total_t\n\n if self.mode == \"pose-mlp\":\n self.pose_dim = pose_dim\n self.mlp_layers = nn.ModuleList()\n c_in = self.pose_dim\n for c_out in mlp_hidden_dims:\n self.mlp_layers.append(nn.Sequential(nn.Linear(c_in, c_out), mlp_act()))\n c_in = c_out\n self.mlp_output_head = nn.Linear(c_in, 7 * self.num_bones, bias=False)\n with torch.no_grad():\n self.mlp_output_head.weight.data = (\n torch.randn_like(self.mlp_output_head.weight.data) * 1e-3\n )\n elif self.mode == \"delta-list\":\n self.dr_list = nn.Parameter(torch.zeros(self.total_t, num_bones, 3))\n self.dt_list = nn.Parameter(torch.zeros(self.total_t, num_bones, 3))\n else:\n raise NotImplementedError()\n\n return\n\n def forward(self, pose=None, t=None, As=None):\n if self.num_bones == 0:\n # * No additional bones\n return None\n if As is not None:\n # * Directly return if As already provided\n return As\n if self.mode == \"pose-mlp\":\n assert pose is not None\n assert pose.ndim == 2 and pose.shape[1] == self.pose_dim\n B = len(pose)\n x = pose\n for layer in self.mlp_layers:\n x = layer(x)\n x = self.mlp_output_head(x).reshape(B, -1, 7)\n q, t = x[:, :, :4], x[:, :, 4:]\n q[..., 0] = q[..., 0] + 1.0\n q = F.normalize(q, dim=-1)\n R = quaternion_to_matrix(q)\n Rt = torch.cat([R, t[:, :, :, None]], dim=-1)\n bottom = torch.zeros_like(Rt[:, :, 0:1])\n bottom[:, :, :, -1] = 1.0\n As = torch.cat([Rt, bottom], dim=2)\n return As\n elif self.mode == \"delta-list\":\n As = self._roll_out_continuous_T()\n if t is None:\n B = len(pose)\n # # ! If no time is set, now return eye(4)\n # ret = (\n # torch.eye(4)\n # .to(As.device)[None, None]\n # .repeat(B, self.num_bones, 1, 1)\n # )\n # ! If no time is set, now return first frame\n ret = As[0][None].repeat(B, 1, 1, 1)\n else:\n if isinstance(t, int):\n t = torch.tensor([t]).to(As.device)\n ret = As[t]\n return ret\n else:\n raise NotImplementedError()\n\n return # As in canonical frame\n\n def _roll_out_continuous_T(self):\n # ! this assumes continuous frames, single frame!\n R = axis_angle_to_matrix(self.dr_list)\n dT = (\n torch.eye(4).to(R.device)[None, None].repeat(self.total_t, R.shape[1], 1, 1)\n )\n dT[:, :, :3, :3] = dT[:, :, :3, :3] * 0 + R\n dT[:, :, :3, 3] = dT[:, :, :3, 3] * 0 + self.dt_list\n T = [dT[0]]\n for i in range(1, self.total_t):\n T.append(torch.einsum(\"nij, njk->nik\", T[-1], dT[i]))\n T = torch.stack(T, dim=0)\n return T"},{"identifier":"render_cam_pcl","path":"lib_render/gauspl_renderer.py","snippet":"def render_cam_pcl(\n xyz,\n frame,\n scale,\n opacity,\n color_feat,\n H,\n W,\n CAM_K,\n verbose=False,\n active_sph_order=0,\n bg_color=[1.0, 1.0, 1.0],\n):\n # ! Camera is at origin, every input is in camera coordinate space\n\n S = torch.zeros_like(frame)\n S[:, 0, 0] = scale[:, 0]\n S[:, 1, 1] = scale[:, 1]\n S[:, 2, 2] = scale[:, 2]\n actual_covariance = frame @ (S**2) @ frame.permute(0, 2, 1)\n\n # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means\n device = xyz.device\n screenspace_points = (\n torch.zeros_like(xyz, dtype=xyz.dtype, requires_grad=True, device=xyz.device) + 0\n )\n # screenspace_points.retain_grad()\n try:\n screenspace_points.retain_grad()\n except:\n pass\n\n # * Specially handle the non-centered camera, using first padding and finally crop\n if abs(H // 2 - CAM_K[1, 2]) > 1.0 or abs(W // 2 - CAM_K[0, 2]) > 1.0:\n center_handling_flag = True\n left_w, right_w = CAM_K[0, 2], W - CAM_K[0, 2]\n top_h, bottom_h = CAM_K[1, 2], H - CAM_K[1, 2]\n new_W = int(2 * max(left_w, right_w))\n new_H = int(2 * max(top_h, bottom_h))\n else:\n center_handling_flag = False\n new_W, new_H = W, H\n\n # Set up rasterization configuration\n FoVx = focal2fov(CAM_K[0, 0], new_W)\n FoVy = focal2fov(CAM_K[1, 1], new_H)\n tanfovx = math.tan(FoVx * 0.5)\n tanfovy = math.tan(FoVy * 0.5)\n\n # TODO: Check dynamic gaussian repos and original gaussian repo, they use projection matrix to handle non-centered K, not using this stupid padding like me\n viewmatrix = torch.from_numpy(getWorld2View2(np.eye(3), np.zeros(3)).transpose(0, 1)).to(device)\n projection_matrix = (\n getProjectionMatrix(znear=0.01, zfar=1.0, fovX=FoVx, fovY=FoVy).transpose(0, 1).to(device)\n )\n full_proj_transform = (viewmatrix.unsqueeze(0).bmm(projection_matrix.unsqueeze(0))).squeeze(0)\n camera_center = viewmatrix.inverse()[3, :3]\n\n raster_settings = GaussianRasterizationSettings(\n image_height=new_H,\n image_width=new_W,\n tanfovx=tanfovx,\n tanfovy=tanfovy,\n bg=torch.tensor(bg_color, dtype=torch.float32, device=device),\n scale_modifier=1.0,\n viewmatrix=viewmatrix,\n projmatrix=full_proj_transform,\n sh_degree=0, # ! use pre-compute color!\n campos=camera_center,\n prefiltered=False,\n debug=False,\n )\n rasterizer = GaussianRasterizer(raster_settings=raster_settings)\n\n means3D = xyz\n means2D = screenspace_points\n # opacity = torch.ones_like(means3D[:, 0]) * sigma\n\n # If precomputed 3d covariance is provided, use it. If not, then it will be computed from\n # scaling / rotation by the rasterizer.\n scales = None\n rotations = None\n # JH\n cov3D_precomp = strip_lowerdiag(actual_covariance)\n\n # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors\n # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.\n # xyz are in camera frame, so the dir in camera frame is just their normalized direction\n dir_cam = F.normalize(xyz, dim=-1)\n # P_w = Frame @ P_local\n dir_local = torch.einsum(\"nji,nj->ni\", frame, dir_cam) # note the transpose\n dir_local = F.normalize(\n dir_local, dim=-1\n ) # If frame is not SO(3) but Affinity, have to normalize\n N = len(color_feat)\n shs_view = color_feat.reshape(N, -1, 3) # N, Deg, Channels\n sh2rgb = eval_sh(active_sph_order, shs_view.permute(0, 2, 1), dir_local)\n colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)\n # colors_precomp = color_feat\n\n # Rasterize visible Gaussians to image, obtain their radii (on screen).\n\n start_time = time.time()\n ret = rasterizer(\n means3D=means3D.float(),\n means2D=means2D.float(),\n shs=None,\n colors_precomp=colors_precomp.float(),\n opacities=opacity.float(),\n scales=scales,\n rotations=rotations,\n cov3D_precomp=cov3D_precomp.float(),\n )\n if len(ret) == 2:\n rendered_image, radii = ret\n depth, alpha = None, None\n elif len(ret) == 4:\n rendered_image, radii, depth, alpha = ret\n else:\n raise ValueError(f\"Unexpected return value from rasterizer with len={len(ret)}\")\n if verbose:\n print(\n f\"render time: {(time.time() - start_time)*1000:.3f}ms\",\n )\n ret = {\n \"rgb\": rendered_image,\n \"dep\": depth,\n \"alpha\": alpha,\n \"viewspace_points\": screenspace_points,\n \"visibility_filter\": radii > 0,\n \"radii\": radii,\n }\n if center_handling_flag:\n for k in [\"rgb\", \"dep\", \"alpha\"]:\n if ret[k] is None:\n continue\n if left_w > right_w:\n ret[k] = ret[k][:, :, :W]\n else:\n ret[k] = ret[k][:, :, -W:]\n if top_h > bottom_h:\n ret[k] = ret[k][:, :H, :]\n else:\n ret[k] = ret[k][:, -H:, :]\n return ret"},{"identifier":"transform_mu_frame","path":"lib_gart/model_utils.py","snippet":"def transform_mu_frame(mu, frame, T):\n if len(mu) != len(T):\n assert len(mu) == 1 and len(frame) == 1\n mu = mu.expand(len(T), -1, -1)\n frame = frame.expand(len(T), -1, -1, -1)\n R, t = T[:, :3, :3], T[:, :3, 3]\n new_frame = torch.einsum(\"bij, bnjk->bnik\", R, frame)\n new_mu = torch.einsum(\"bij, bnj->bni\", R, mu) + t[:, None]\n return new_mu, new_frame"},{"identifier":"viz_render","path":"utils/viz.py","snippet":"def viz_render(gt_rgb, gt_mask, pred_pkg, save_path=None):\n pred_rgb = pred_pkg[\"rgb\"].permute(1, 2, 0)\n pred_mask = pred_pkg[\"alpha\"].squeeze(0)\n pred_depth = pred_pkg[\"dep\"].squeeze(0)\n fig = plt.figure(figsize=(20, 5))\n plt.subplot(1, 5, 1)\n plt.imshow(torch.clamp(gt_rgb, 0.0, 1.0).detach().cpu().numpy())\n plt.title(\"GT\"), plt.axis(\"off\")\n plt.subplot(1, 5, 2)\n plt.imshow(torch.clamp(pred_rgb, 0.0, 1.0).detach().cpu().numpy())\n plt.title(\"Pred view\"), plt.axis(\"off\")\n plt.subplot(1, 5, 3)\n error = torch.clamp(abs(pred_rgb - gt_rgb), 0.0, 1.0).detach().cpu().numpy().max(axis=-1)\n cmap = plt.imshow(error)\n plt.title(\"Render Error (max in rgb)\"), plt.axis(\"off\")\n plt.colorbar(cmap, shrink=0.8)\n\n plt.subplot(1, 5, 4)\n error = torch.clamp(pred_mask - gt_mask, -1.0, 1.0).detach().cpu().numpy()\n cmap = plt.imshow(error)\n plt.title(\"(Pr - GT) Mask Error\"), plt.axis(\"off\")\n plt.colorbar(cmap, shrink=0.8)\n \n plt.subplot(1, 5, 5)\n depth = pred_depth.detach().cpu().numpy()\n cmap = plt.imshow(depth)\n plt.title(\"Pred Depth\"), plt.axis(\"off\")\n plt.colorbar(cmap, shrink=0.8)\n\n plt.tight_layout()\n fig.canvas.draw()\n fig_np = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n fig_np = fig_np.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n if save_path is not None:\n plt.savefig(save_path)\n plt.close(fig)\n return fig_np"},{"identifier":"sample_camera","path":"lib_guidance/camera_sampling.py","snippet":"def sample_camera(\n global_step=1,\n n_view=4,\n real_batch_size=1,\n random_azimuth_range=[-180.0, 180.0],\n random_elevation_range=[0.0, 30.0],\n eval_elevation_deg=15,\n camera_distance_range=[0.8, 1.0], # relative\n fovy_range=[15, 60],\n zoom_range=[1.0, 1.0],\n progressive_until=0,\n relative_radius=True,\n):\n # camera_perturb = 0.0\n # center_perturb = 0.0\n # up_perturb: 0.0\n\n # ! from uncond.py\n # ThreeStudio has progressive increase of camera poses, from eval to random\n r = min(1.0, global_step / (progressive_until + 1))\n elevation_range = [\n (1 - r) * eval_elevation_deg + r * random_elevation_range[0],\n (1 - r) * eval_elevation_deg + r * random_elevation_range[1],\n ]\n azimuth_range = [\n (1 - r) * 0.0 + r * random_azimuth_range[0],\n (1 - r) * 0.0 + r * random_azimuth_range[1],\n ]\n\n # sample elevation angles\n if random.random() < 0.5:\n # sample elevation angles uniformly with a probability 0.5 (biased towards poles)\n elevation_deg = (\n torch.rand(real_batch_size) * (elevation_range[1] - elevation_range[0])\n + elevation_range[0]\n ).repeat_interleave(n_view, dim=0)\n elevation = elevation_deg * math.pi / 180\n else:\n # otherwise sample uniformly on sphere\n elevation_range_percent = [\n (elevation_range[0] + 90.0) / 180.0,\n (elevation_range[1] + 90.0) / 180.0,\n ]\n # inverse transform sampling\n elevation = torch.asin(\n 2\n * (\n torch.rand(real_batch_size)\n * (elevation_range_percent[1] - elevation_range_percent[0])\n + elevation_range_percent[0]\n )\n - 1.0\n ).repeat_interleave(n_view, dim=0)\n elevation_deg = elevation / math.pi * 180.0\n\n # sample azimuth angles from a uniform distribution bounded by azimuth_range\n # ensures sampled azimuth angles in a batch cover the whole range\n azimuth_deg = (\n torch.rand(real_batch_size).reshape(-1, 1) + torch.arange(n_view).reshape(1, -1)\n ).reshape(-1) / n_view * (azimuth_range[1] - azimuth_range[0]) + azimuth_range[0]\n azimuth = azimuth_deg * math.pi / 180\n\n ######## Different from original ########\n # sample fovs from a uniform distribution bounded by fov_range\n fovy_deg = (\n torch.rand(real_batch_size) * (fovy_range[1] - fovy_range[0]) + fovy_range[0]\n ).repeat_interleave(n_view, dim=0)\n fovy = fovy_deg * math.pi / 180\n\n # sample distances from a uniform distribution bounded by distance_range\n camera_distances = (\n torch.rand(real_batch_size) * (camera_distance_range[1] - camera_distance_range[0])\n + camera_distance_range[0]\n ).repeat_interleave(n_view, dim=0)\n if relative_radius:\n scale = 1 / torch.tan(0.5 * fovy)\n camera_distances = scale * camera_distances\n\n # zoom in by decreasing fov after camera distance is fixed\n zoom = (\n torch.rand(real_batch_size) * (zoom_range[1] - zoom_range[0]) + zoom_range[0]\n ).repeat_interleave(n_view, dim=0)\n fovy = fovy * zoom\n fovy_deg = fovy_deg * zoom\n ###########################################\n\n # convert spherical coordinates to cartesian coordinates\n # right hand coordinate system, x back, y right, z up\n # elevation in (-90, 90), azimuth from +x to +y in (-180, 180)\n camera_positions = torch.stack(\n [\n camera_distances * torch.cos(elevation) * torch.cos(azimuth),\n camera_distances * torch.cos(elevation) * torch.sin(azimuth),\n camera_distances * torch.sin(elevation),\n ],\n dim=-1,\n )\n\n azimuth, elevation\n # build opencv camera\n z = -torch.stack(\n [\n torch.cos(elevation) * torch.cos(azimuth),\n torch.cos(elevation) * torch.sin(azimuth),\n torch.sin(elevation),\n ],\n -1,\n ) # nview, 3\n # up is 0,0,1\n x = torch.cross(z, torch.tensor([0.0, 0.0, 1.0], device=z.device).repeat(n_view, 1), -1)\n y = torch.cross(z, x, -1)\n\n R_wc = torch.stack([x, y, z], dim=2) # nview, 3, 3, col is basis\n t_wc = camera_positions\n\n T_wc = torch.eye(4, device=R_wc.device).repeat(n_view, 1, 1)\n T_wc[:, :3, :3] = R_wc\n T_wc[:, :3, 3] = t_wc\n\n return T_wc, fovy_deg # B,4,4, B"},{"identifier":"fov2K","path":"lib_guidance/camera_sampling.py","snippet":"def fov2K(fov=90, H=256, W=256):\n if isinstance(fov, torch.Tensor):\n f = H / (2 * torch.tan(fov / 2 * np.pi / 180))\n K = torch.eye(3).repeat(fov.shape[0], 1, 1).to(fov)\n K[:, 0, 0], K[:, 0, 2] = f, W / 2.0\n K[:, 1, 1], K[:, 1, 2] = f, H / 2.0\n return K.clone()\n else:\n f = H / (2 * np.tan(fov / 2 * np.pi / 180))\n K = np.eye(3)\n K[0, 0], K[0, 2] = f, W / 2.0\n K[1, 1], K[1, 2] = f, H / 2.0\n return K.copy()"},{"identifier":"opencv2blender","path":"lib_guidance/camera_sampling.py","snippet":"def opencv2blender(T):\n ret = T.clone()\n # y,z are negative\n ret[:, :, 1] *= -1\n ret[:, :, 2] *= -1\n return ret"},{"identifier":"viz_spinning","path":"viz_utils.py","snippet":"@torch.no_grad()\ndef viz_spinning(\n model,\n pose,\n trans,\n H,\n W,\n K,\n save_path,\n time_index=None,\n n_spinning=10,\n model_mask=None,\n active_sph_order=0,\n bg_color=[1.0, 1.0, 1.0],\n):\n device = pose.device\n mu, fr, s, o, sph, additional_ret = model(\n pose, trans, {\"t\": time_index}, active_sph_order=active_sph_order\n )\n if model_mask is not None:\n assert len(model_mask) == mu.shape[1]\n mu = mu[:, model_mask.bool()]\n fr = fr[:, model_mask.bool()]\n s = s[:, model_mask.bool()]\n o = o[:, model_mask.bool()]\n sph = sph[:, model_mask.bool()]\n\n viz_frames = []\n for vid in range(n_spinning):\n spin_R = (\n torch.from_numpy(euler2mat(0, 2 * np.pi * vid / n_spinning, 0, \"sxyz\"))\n .to(device)\n .float()\n )\n spin_t = mu.mean(1)[0]\n spin_t = (torch.eye(3).to(device) - spin_R) @ spin_t[:, None]\n spin_T = torch.eye(4).to(device)\n spin_T[:3, :3] = spin_R\n spin_T[:3, 3] = spin_t.squeeze(-1)\n viz_mu, viz_fr = transform_mu_frame(mu, fr, spin_T[None])\n\n render_pkg = render_cam_pcl(\n viz_mu[0],\n viz_fr[0],\n s[0],\n o[0],\n sph[0],\n H,\n W,\n K,\n False,\n active_sph_order,\n bg_color=bg_color,\n )\n viz_frame = (\n torch.clamp(render_pkg[\"rgb\"], 0.0, 1.0)\n .permute(1, 2, 0)\n .detach()\n .cpu()\n .numpy()\n )\n viz_frame = (viz_frame * 255).astype(np.uint8)\n viz_frames.append(viz_frame)\n imageio.mimsave(save_path, viz_frames)\n return"},{"identifier":"viz_human_all","path":"viz_utils.py","snippet":"@torch.no_grad()\ndef viz_human_all(\n solver,\n data_provider: RealDataOptimizablePoseProviderPose = None,\n ckpt_dir=None,\n training_skip=1,\n n_spinning=40,\n novel_pose_dir=\"novel_poses\",\n novel_skip=2,\n model=None,\n model_mask=None,\n viz_name=\"\",\n export_mesh_flag=False, # remove this from release version\n):\n if model is None:\n model = solver.load_saved_model(ckpt_dir)\n model.eval()\n\n viz_dir = osp.join(solver.log_dir, f\"{viz_name}_human_viz\")\n os.makedirs(viz_dir, exist_ok=True)\n\n active_sph_order = int(model.max_sph_order)\n\n if data_provider is not None:\n # if ckpt_dir is None:\n # ckpt_dir = solver.log_dir\n # pose_path = osp.join(ckpt_dir, \"pose.pth\")\n pose_base_list = data_provider.pose_base_list\n pose_rest_list = data_provider.pose_rest_list\n global_trans_list = data_provider.global_trans_list\n pose_list = torch.cat([pose_base_list, pose_rest_list], 1)\n pose_list, global_trans_list = pose_list.to(\n solver.device\n ), global_trans_list.to(solver.device)\n rgb_list = data_provider.rgb_list\n mask_list = data_provider.mask_list\n K_list = data_provider.K_list\n H, W = rgb_list.shape[1:3]\n else:\n H, W = 512, 512\n K_list = [torch.from_numpy(fov2K(45, H, W)).float().to(solver.device)]\n global_trans_list = torch.zeros(1, 3).to(solver.device)\n global_trans_list[0, -1] = 3.0\n\n # viz training\n if data_provider is not None:\n print(\"Viz training...\")\n viz_frames = []\n for t in range(len(pose_list)):\n if t % training_skip != 0:\n continue\n pose = pose_list[t][None]\n K = K_list[t]\n trans = global_trans_list[t][None]\n time_index = torch.Tensor([t]).long().to(solver.device)\n mu, fr, s, o, sph, _ = model(\n pose,\n trans,\n {\"t\": time_index}, # use time_index from training set\n active_sph_order=active_sph_order,\n )\n if model_mask is not None:\n assert len(model_mask) == mu.shape[1]\n mu = mu[:, model_mask.bool()]\n fr = fr[:, model_mask.bool()]\n s = s[:, model_mask.bool()]\n o = o[:, model_mask.bool()]\n sph = sph[:, model_mask.bool()]\n render_pkg = render_cam_pcl(\n mu[0],\n fr[0],\n s[0],\n o[0],\n sph[0],\n H,\n W,\n K,\n False,\n active_sph_order,\n bg_color=getattr(solver, \"DEFAULT_BG\", [1.0, 1.0, 1.0]),\n )\n viz_frame = viz_render(rgb_list[t], mask_list[t], render_pkg)\n viz_frames.append(viz_frame)\n imageio.mimsave(f\"{viz_dir}/training.gif\", viz_frames)\n\n # viz static spinning\n print(\"Viz spinning...\")\n can_pose = model.template.canonical_pose.detach()\n viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, \"sxyz\"))\n viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()\n can_pose[0] = viz_base_R_opencv.to(can_pose.device)\n can_pose = matrix_to_axis_angle(can_pose)[None]\n dapose = torch.from_numpy(np.zeros((1, 24, 3))).float().to(solver.device)\n dapose[:, 1, -1] = np.pi / 4\n dapose[:, 2, -1] = -np.pi / 4\n dapose[:, 0] = matrix_to_axis_angle(solver.viz_base_R[None])[0]\n tpose = torch.from_numpy(np.zeros((1, 24, 3))).float().to(solver.device)\n tpose[:, 0] = matrix_to_axis_angle(solver.viz_base_R[None])[0]\n to_viz = {\"cano-pose\": can_pose, \"t-pose\": tpose, \"da-pose\": dapose}\n if data_provider is not None:\n to_viz[\"first-frame\"] = pose_list[0][None]\n\n for name, pose in to_viz.items():\n print(f\"Viz novel {name}...\")\n # if export_mesh_flag:\n # from lib_marchingcubes.gaumesh_utils import MeshExtractor\n # # also extract a mesh\n # mesh = solver.extract_mesh(model, pose)\n # mesh.export(f\"{viz_dir}/mc_{name}.obj\", \"obj\")\n\n # # for making figures, the rotation is in another way\n # viz_spinning_self_rotate(\n # model,\n # solver.viz_base_R.detach(),\n # pose,\n # global_trans_list[0][None],\n # H,\n # W,\n # K_list[0],\n # f\"{viz_dir}/{name}_selfrotate.gif\",\n # time_index=None, # if set to None and use t, the add_bone will hand this\n # n_spinning=n_spinning,\n # active_sph_order=model.max_sph_order,\n # )\n viz_spinning(\n model,\n pose,\n global_trans_list[0][None],\n H,\n W,\n K_list[0],\n f\"{viz_dir}/{name}.gif\",\n time_index=None, # if set to None and use t, the add_bone will hand this\n n_spinning=n_spinning,\n active_sph_order=model.max_sph_order,\n bg_color=getattr(solver, \"DEFAULT_BG\", [1.0, 1.0, 1.0]),\n )\n\n # viz novel pose dynamic spinning\n print(\"Viz novel seq...\")\n novel_pose_names = [\n f[:-4] for f in os.listdir(novel_pose_dir) if f.endswith(\".npy\")\n ]\n seq_viz_todo = {}\n for name in novel_pose_names:\n novel_pose_fn = osp.join(novel_pose_dir, f\"{name}.npy\")\n novel_poses = np.load(novel_pose_fn, allow_pickle=True)\n novel_poses = novel_poses[::novel_skip]\n N_frames = len(novel_poses)\n novel_poses = torch.from_numpy(novel_poses).float().to(solver.device)\n novel_poses = novel_poses.reshape(N_frames, 24, 3)\n\n seq_viz_todo[name] = (novel_poses, N_frames)\n if data_provider is not None:\n seq_viz_todo[\"training\"] = [pose_list, len(pose_list)]\n\n for name, (novel_poses, N_frames) in seq_viz_todo.items():\n base_R = solver.viz_base_R.detach().cpu().numpy()\n viz_frames = []\n K = K_list[0]\n for vid in range(N_frames):\n pose = novel_poses[vid][None]\n # pose = novel_poses[0][None] # debug\n rotation = euler2mat(2 * np.pi * vid / N_frames, 0.0, 0.0, \"syxz\")\n rotation = torch.from_numpy(rotation @ base_R).float().to(solver.device)\n pose[:, 0] = matrix_to_axis_angle(rotation[None])[0]\n trans = global_trans_list[0][None]\n mu, fr, s, o, sph, _ = model(\n pose,\n trans,\n # not pass in {}, so t is auto none\n additional_dict={},\n active_sph_order=active_sph_order,\n )\n if model_mask is not None:\n assert len(model_mask) == mu.shape[1]\n mu = mu[:, model_mask.bool()]\n fr = fr[:, model_mask.bool()]\n s = s[:, model_mask.bool()]\n o = o[:, model_mask.bool()]\n sph = sph[:, model_mask.bool()]\n render_pkg = render_cam_pcl(\n mu[0],\n fr[0],\n s[0],\n o[0],\n sph[0],\n H,\n W,\n K,\n False,\n active_sph_order,\n bg_color=getattr(solver, \"DEFAULT_BG\", [1.0, 1.0, 1.0]),\n # bg_color=[1.0, 1.0, 1.0], # ! use white bg for viz\n )\n viz_frame = (\n torch.clamp(render_pkg[\"rgb\"], 0.0, 1.0)\n .permute(1, 2, 0)\n .detach()\n .cpu()\n .numpy()\n )\n viz_frame = (viz_frame * 255).astype(np.uint8)\n viz_frames.append(viz_frame)\n imageio.mimsave(f\"{viz_dir}/novel_pose_{name}.gif\", viz_frames)\n return"},{"identifier":"viz_dog_all","path":"viz_utils.py","snippet":"@torch.no_grad()\ndef viz_dog_all(solver, data_provider, model=None, ckpt_dir=None, viz_name=\"\"):\n if model is None:\n model = solver.load_saved_model(ckpt_dir)\n model.eval()\n viz_dir = osp.join(solver.log_dir, f\"{viz_name}_dog_viz\")\n os.makedirs(viz_dir, exist_ok=True)\n\n viz_pose = (\n torch.cat([data_provider.pose_base_list, data_provider.pose_rest_list], 1)\n .detach()\n .clone()\n )\n viz_pose = torch.mean(viz_pose, dim=0, keepdim=True) # use mean pose for viz \n limb = viz_pose[:, -7:] \n pose = viz_pose[:, :-7].reshape(-1, 35, 3)\n pose[:, :-3] = 0 # exclude ears and mouth poses\n\n viz_pose = torch.concat([pose.reshape(1, -1), limb], dim=1)\n viz_trans = torch.tensor([[0.0, -0.3, 25.0]], device=\"cuda:0\")\n\n viz_dog_spin(\n model.to(\"cuda\"),\n viz_pose,\n viz_trans,\n data_provider.H,\n data_provider.W,\n data_provider.K_list[0],\n save_path=osp.join(viz_dir, \"spin.gif\"),\n n_spinning=42,\n )\n\n viz_dog_spin2(\n model.to(\"cuda\"),\n viz_pose,\n viz_trans,\n data_provider.H,\n data_provider.W,\n data_provider.K_list[0],\n save_path=osp.join(viz_dir, \"spin2.gif\"),\n n_spinning=20,\n )\n\n ######################################################################\n # Dataset pose seq\n viz_pose = (\n torch.cat([data_provider.pose_base_list, data_provider.pose_rest_list], 1)\n .detach()\n .clone()\n )\n viz_pose = torch.mean(viz_pose, dim=0, keepdim=True)\n pose = viz_pose[:, :-7].reshape(-1, 35, 3)\n limb = viz_pose[:, -7:]\n\n # Animation\n aroot = osp.join(osp.dirname(__file__), \"novel_poses/husky\")\n window = list(range(350, 440)) # Run\n trans = torch.tensor([[0.3, -0.3, 25.0]], device=\"cuda:0\")\n files = [f\"{aroot}/{i:04d}.npz\" for i in window]\n pose_list = [dict(np.load(file))[\"pred_pose\"] for file in files]\n pose_list = np.concatenate(pose_list)\n animation = matrix_to_axis_angle(torch.from_numpy(pose_list)).to(solver.device)\n animation[:, [32, 33, 34]] = pose[:, [32, 33, 34]] \n\n viz_dog_animation(\n model.to(\"cuda\"),\n animation,\n limb,\n trans,\n data_provider.H,\n data_provider.W,\n data_provider.K_list[0],\n save_path=osp.join(viz_dir, \"animation.gif\"),\n fps=12,\n )\n return"},{"identifier":"ssim","path":"utils/ssim.py","snippet":"def ssim(img1, img2, window_size=11, size_average=True):\n channel = img1.size(-3)\n window = create_window(window_size, channel)\n\n if img1.is_cuda:\n window = window.cuda(img1.get_device())\n window = window.type_as(img1)\n\n return _ssim(img1, img2, window, window_size, channel, size_average)"},{"identifier":"test","path":"test_utils/test_func.py","snippet":"def test(\n solver,\n seq_name: str,\n tto_flag=True,\n tto_step=300,\n tto_decay=60,\n tto_decay_factor=0.5,\n pose_base_lr=3e-3,\n pose_rest_lr=3e-3,\n trans_lr=3e-3,\n dataset_mode=\"people_snapshot\",\n training_optimized_seq=None,\n):\n device = solver.device\n model = solver.load_saved_model()\n\n assert dataset_mode in [\n \"people_snapshot\",\n \"zju\",\n \"instant_avatar_wild\",\n \"dog_demo\",\n ], f\"Unknown dataset mode {dataset_mode}\"\n\n if dataset_mode == \"people_snapshot\":\n eval_mode = \"avatar\"\n bg = [1.0, 1.0, 1.0]\n test_dataset = InstantAvatarDataset(\n noisy_flag=False,\n data_root=\"./data/people_snapshot/\",\n video_name=seq_name,\n split=\"test\",\n image_zoom_ratio=0.5,\n )\n elif dataset_mode == \"zju\":\n eval_mode = \"nvr\"\n test_dataset = ZJUDataset(\n data_root=\"./data/zju_mocap\",\n video_name=seq_name,\n split=\"test\",\n image_zoom_ratio=0.5,\n )\n bg = [0.0, 0.0, 0.0] # zju use black background\n elif dataset_mode == \"instant_avatar_wild\":\n eval_mode = \"avatar\"\n test_dataset = InstantAvatarWildDataset(\n data_root=\"./data/insav_wild\",\n video_name=seq_name,\n split=\"test\",\n image_zoom_ratio=1.0,\n # ! warning, here follow the `ubc_hard.yaml` in InstAVT setting, use slicing\n start_end_skip=[2, 1000000000, 4],\n )\n bg = [1.0, 1.0, 1.0]\n\n test_len = len(test_dataset)\n assert (training_optimized_seq.total_t == test_len) or (\n training_optimized_seq.total_t == 1 + test_len\n ), \"Now UBC can only support the same length of training and testing or + 1\"\n test_dataset.smpl_params[\"body_pose\"] = (\n training_optimized_seq.pose_rest_list.reshape(-1, 69)[:test_len]\n .detach()\n .cpu()\n .numpy()\n )\n test_dataset.smpl_params[\"global_orient\"] = (\n training_optimized_seq.pose_base_list.reshape(-1, 3)[:test_len]\n .detach()\n .cpu()\n .numpy()\n )\n test_dataset.smpl_params[\"transl\"] = (\n training_optimized_seq.global_trans_list.reshape(-1, 3)[:test_len]\n .detach()\n .cpu()\n .numpy()\n )\n elif dataset_mode == \"dog_demo\":\n eval_mode = \"avatar_brightness\"\n bg = [1.0, 1.0, 1.0]\n test_dataset = DogDemoDataset(\n data_root=\"./data/dog_data_official/\", video_name=seq_name, test=True\n )\n else:\n raise NotImplementedError()\n\n evaluator = get_evaluator(eval_mode, device)\n\n _save_eval_maps(\n solver.log_dir,\n \"test\",\n model,\n solver,\n test_dataset,\n dataset_mode=dataset_mode,\n device=device,\n bg=bg,\n tto_flag=tto_flag,\n tto_step=tto_step,\n tto_decay=tto_decay,\n tto_decay_factor=tto_decay_factor,\n tto_evaluator=evaluator,\n pose_base_lr=pose_base_lr,\n pose_rest_lr=pose_rest_lr,\n trans_lr=trans_lr,\n )\n\n if tto_flag:\n _evaluate_dir(evaluator, solver.log_dir, \"test_tto\")\n else:\n _evaluate_dir(evaluator, solver.log_dir, \"test\")\n\n return"}],"string":"[\n {\n \"identifier\": \"prepare_real_seq\",\n \"path\": \"lib_data/get_data.py\",\n \"snippet\": \"def prepare_real_seq(\\n seq_name,\\n dataset_mode,\\n split=\\\"train\\\",\\n image_zoom_ratio=0.5,\\n balance=False,\\n ins_avt_wild_start_end_skip=None,\\n):\\n logging.info(\\\"Prepare real seq: {}\\\".format(seq_name))\\n # * Get dataset\\n if dataset_mode == \\\"ubcfashion\\\":\\n dataset = UBCFasionDataset(\\n data_root=\\\"./data/ubcfashion/\\\",\\n video_list=[seq_name],\\n image_zoom_ratio=image_zoom_ratio,\\n start_end_skip=ins_avt_wild_start_end_skip,\\n )\\n elif dataset_mode == \\\"people_snapshot\\\":\\n dataset = InstantAvatarDataset(\\n noisy_flag=False,\\n data_root=\\\"./data/people_snapshot/\\\",\\n video_name=seq_name,\\n split=split,\\n image_zoom_ratio=image_zoom_ratio,\\n )\\n print(\\\"Load Instant Avatar processed PeopleSnapshot\\\")\\n elif dataset_mode == \\\"zju\\\":\\n dataset = ZJUDataset(\\n data_root=\\\"./data/zju_mocap\\\",\\n video_name=seq_name,\\n split=split,\\n image_zoom_ratio=image_zoom_ratio,\\n )\\n elif dataset_mode == \\\"instant_avatar_wild\\\":\\n # assert image_zoom_ratio == 1.0, \\\"Check! in the wild data should use 1.0\\\"\\n if image_zoom_ratio != 1.0:\\n logging.warning(\\n f\\\"Check! in the wild data should use 1.0, but got {image_zoom_ratio}\\\"\\n )\\n dataset = InstantAvatarWildDataset(\\n data_root=\\\"./data/insav_wild\\\",\\n video_name=seq_name,\\n split=split,\\n image_zoom_ratio=image_zoom_ratio,\\n start_end_skip=ins_avt_wild_start_end_skip,\\n )\\n elif dataset_mode == \\\"dog_demo\\\":\\n dataset = DogDemoDataset(data_root=\\\"./data/dog_data_official/\\\", video_name=seq_name)\\n else:\\n raise NotImplementedError(\\\"Unknown mode: {}\\\".format(dataset_mode))\\n\\n # prepare an optimizable data provider\\n optimizable_data_provider = RealDataOptimizablePoseProviderPose(\\n dataset,\\n balance=balance,\\n )\\n return optimizable_data_provider, dataset\"\n },\n {\n \"identifier\": \"DatabasePoseProvider\",\n \"path\": \"lib_data/data_provider.py\",\n \"snippet\": \"class DatabasePoseProvider(nn.Module):\\n def __init__(\\n self,\\n pose_dirs: list,\\n da_pose_prob=0.1,\\n da_range=[0.0, np.pi / 4],\\n device=torch.device(\\\"cuda\\\"),\\n ) -> None:\\n super().__init__()\\n self.device = device\\n self.base_R = matrix_to_axis_angle(\\n torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, \\\"sxyz\\\"))[None]\\n )[0]\\n self.base_R = self.base_R.float().to(self.device)\\n\\n self.da_pose_prob = da_pose_prob\\n self.da_range = da_range\\n\\n self.data = []\\n\\n # cache the poses\\n for d in pose_dirs:\\n print(f\\\"Caching {d} ...\\\")\\n for subject in tqdm(os.listdir(d)):\\n sub_dir = os.path.join(d, subject)\\n if not os.path.isdir(sub_dir):\\n continue\\n npz_files = [f for f in os.listdir(sub_dir) if f.endswith(\\\".npz\\\")]\\n npz_files.sort()\\n for fn in npz_files:\\n try:\\n npz_fn = os.path.join(sub_dir, fn)\\n pose_data = np.load(npz_fn)\\n amass_len = pose_data[\\\"poses\\\"].shape[0]\\n smplx_to_smpl = list(range(66)) + [72, 73, 74, 117, 118, 119]\\n poses = pose_data[\\\"poses\\\"][:, smplx_to_smpl].reshape(\\n amass_len, 24, 3\\n )\\n self.data.append(poses.astype(np.float16))\\n except:\\n # print(f\\\"Error in {npz_fn}, skip!\\\")\\n pass\\n self.data = np.concatenate(self.data, axis=0)\\n print(\\n f\\\"Database has poses {len(self.data)} with DA-pose prob {self.da_pose_prob} and range {self.da_range}\\\"\\n )\\n return\\n\\n def forward(self, N: int):\\n pose, trans = self.sample_pose(N)\\n return pose, trans\\n\\n def sample_pose(self, N: int):\\n # da pose\\n pose_list = []\\n for i in range(N):\\n seed = np.random.rand()\\n if seed > self.da_pose_prob:\\n # from database\\n idx = np.random.randint(len(self.data))\\n pose = torch.from_numpy(self.data[idx]).float().to(self.device)\\n else:\\n # da pose\\n pose = torch.zeros(24, 3).to(self.device)\\n da_theta = float(np.random.uniform(*self.da_range))\\n pose[1, -1] = da_theta\\n pose[2, -1] = -da_theta\\n pose[0] = self.base_R\\n pose_list.append(pose)\\n pose = torch.stack(pose_list, dim=0)\\n trans = torch.zeros(N, 3).to(self.device)\\n return pose, trans\"\n },\n {\n \"identifier\": \"get_template\",\n \"path\": \"lib_gart/templates.py\",\n \"snippet\": \"def get_template(\\n mode, init_beta, cano_pose_type, voxel_deformer_res, template_model_path=None\\n):\\n if mode == \\\"human\\\":\\n template = SMPLTemplate(\\n smpl_model_path=template_model_path,\\n init_beta=init_beta,\\n cano_pose_type=cano_pose_type,\\n voxel_deformer_res=voxel_deformer_res,\\n )\\n elif mode == \\\"dog\\\":\\n template = SMALTemplate(\\n init_beta=init_beta,\\n cano_pose_type=cano_pose_type,\\n voxel_deformer_res=voxel_deformer_res,\\n )\\n else:\\n raise ValueError(f\\\"Unknown mode {mode}\\\")\\n return template\"\n },\n {\n \"identifier\": \"GaussianTemplateModel\",\n \"path\": \"lib_gart/model.py\",\n \"snippet\": \"class GaussianTemplateModel(nn.Module):\\n def __init__(\\n self,\\n template,\\n add_bones: AdditionalBones,\\n ##################################\\n # attr config\\n w_correction_flag=True,\\n # w_rest_dim=0, # additional skinnign weight\\n f_localcode_dim=0,\\n max_sph_order=0,\\n w_memory_type=\\\"point\\\",\\n ##################################\\n max_scale=0.1, # use sigmoid activation, can't be too large\\n min_scale=0.0,\\n # geo init\\n init_mode=\\\"on_mesh\\\",\\n opacity_init_value=0.9, # the init value of opacity\\n # on mesh init params\\n onmesh_init_subdivide_num=0,\\n onmesh_init_scale_factor=1.0,\\n onmesh_init_thickness_factor=0.5,\\n # near mesh init params\\n scale_init_value=0.01, # the init value of scale\\n nearmesh_init_num=10000,\\n nearmesh_init_std=0.1,\\n ##################################\\n ) -> None:\\n super().__init__()\\n\\n self.template = template\\n self.num_bones = template.voxel_deformer.num_bones\\n self.add_bones = add_bones\\n self.num_add_bones = add_bones.num_bones\\n\\n self.max_scale = max_scale\\n self.min_scale = min_scale\\n self._init_act(self.max_scale, self.min_scale)\\n self.opacity_init_logit = self.o_inv_act(opacity_init_value)\\n\\n # * init geometry\\n if init_mode == \\\"on_mesh\\\":\\n x, q, s, o = get_on_mesh_init_geo_values(\\n template,\\n on_mesh_subdivide=onmesh_init_subdivide_num,\\n scale_init_factor=onmesh_init_scale_factor,\\n thickness_init_factor=onmesh_init_thickness_factor,\\n max_scale=max_scale,\\n min_scale=min_scale,\\n s_inv_act=self.s_inv_act,\\n opacity_init_logit=self.opacity_init_logit,\\n )\\n elif init_mode == \\\"near_mesh\\\":\\n self.scale_init_logit = self.s_inv_act(scale_init_value)\\n x, q, s, o = get_near_mesh_init_geo_values(\\n template,\\n scale_base_logit=self.scale_init_logit,\\n opacity_base_logit=self.opacity_init_logit,\\n random_init_num=nearmesh_init_num,\\n random_init_std=nearmesh_init_std,\\n )\\n elif init_mode == \\\"in_mesh\\\":\\n self.scale_init_logit = self.s_inv_act(scale_init_value)\\n x, q, s, o = get_inside_mesh_init_geo_values(\\n template,\\n scale_base_logit=self.scale_init_logit,\\n opacity_base_logit=self.opacity_init_logit,\\n random_init_num=nearmesh_init_num,\\n )\\n else:\\n raise NotImplementedError(f\\\"Unknown init_mode {init_mode}\\\")\\n self._xyz = nn.Parameter(x)\\n self._rotation = nn.Parameter(q)\\n self._scaling = nn.Parameter(s)\\n self._opacity = nn.Parameter(o)\\n\\n # * init attributes\\n self.w_memory_type = w_memory_type\\n assert self.w_memory_type in [\\\"point\\\", \\\"voxel\\\"], f\\\"Unknown {w_memory_type}\\\"\\n\\n self.max_sph_order = max_sph_order\\n self.w_dc_dim = self.template.dim if w_correction_flag else 0\\n self.w_rest_dim = self.add_bones.num_bones\\n self.f_localcode_dim = f_localcode_dim\\n\\n sph_rest_dim = 3 * (sph_order2nfeat(self.max_sph_order) - 1)\\n self._features_dc = nn.Parameter(torch.zeros_like(self._xyz))\\n self._features_rest = nn.Parameter(torch.zeros(self.N, sph_rest_dim))\\n\\n # * Different implementation of smoothness\\n if self.w_memory_type == \\\"point\\\":\\n self._w_correction_dc = nn.Parameter(torch.zeros(self.N, self.w_dc_dim))\\n self._w_correction_rest = nn.Parameter(\\n torch.ones(self.N, self.w_rest_dim) * 1e-4\\n )\\n elif self.w_memory_type == \\\"voxel\\\":\\n self._w_correction_dc = nn.Parameter(torch.zeros(self.N, 0))\\n self._w_correction_rest = nn.Parameter(torch.zeros(self.N, 0))\\n if self.w_dc_dim > 0:\\n self.template.voxel_deformer.enable_voxel_correction()\\n if self.w_rest_dim > 0:\\n self.template.voxel_deformer.enable_additional_correction(\\n self.w_rest_dim\\n )\\n elif self.w_memory_type == \\\"hash\\\":\\n raise NotImplementedError(\\\"TODO\\\")\\n else:\\n raise NotImplementedError(f\\\"Unknown {w_memory_type}\\\")\\n\\n self._features_localcode = nn.Parameter(\\n torch.zeros(self.N, self.f_localcode_dim)\\n )\\n\\n assert self.f_localcode_dim == 0, \\\"TODO, add local mlp ablation\\\"\\n\\n # * States\\n # warning, our code use N, instead of (N,1) as in GS code\\n self.register_buffer(\\\"xyz_gradient_accum\\\", torch.zeros(self.N).float())\\n self.register_buffer(\\\"xyz_gradient_denom\\\", torch.zeros(self.N).long())\\n self.register_buffer(\\\"max_radii2D\\\", torch.zeros(self.N).float())\\n\\n self.op_update_exclude = [\\\"add_bones\\\"]\\n if self.w_memory_type != \\\"point\\\":\\n self.op_update_exclude.extend([\\\"w_dc_vox\\\", \\\"w_rest_vox\\\"])\\n # self.summary()\\n return\\n\\n def summary(self):\\n # logging.info number of parameters per pytorch sub module\\n msg = \\\"\\\"\\n for name, param in self.named_parameters():\\n if name.startswith(\\\"add_bones\\\"):\\n continue # compact print\\n msg = msg + f\\\"[{name}:{param.numel()/1e3:.1f}K] \\\" \\n # logging.info(f\\\"{name}, {param.numel()/1e6:.3f}M\\\")\\n logging.info(msg)\\n return\\n\\n def _init_act(self, max_s_value, min_s_value):\\n def s_act(x):\\n if isinstance(x, float):\\n x = torch.tensor(x).squeeze()\\n return min_s_value + torch.sigmoid(x) * (max_s_value - min_s_value)\\n\\n def s_inv_act(x):\\n if isinstance(x, float):\\n x = torch.tensor(x).squeeze()\\n y = (x - min_s_value) / (max_s_value - min_s_value) + 1e-5\\n y = torch.logit(y)\\n assert not torch.isnan(\\n y\\n ).any(), f\\\"{x.min()}, {x.max()}, {y.min()}, {y.max()}\\\"\\n return y\\n\\n def o_act(x):\\n if isinstance(x, float):\\n x = torch.tensor(x).squeeze()\\n return torch.sigmoid(x)\\n\\n def o_inv_act(x):\\n if isinstance(x, float):\\n x = torch.tensor(x).squeeze()\\n return torch.logit(x)\\n\\n self.s_act = s_act\\n self.s_inv_act = s_inv_act\\n self.o_act = o_act\\n self.o_inv_act = o_inv_act\\n\\n return\\n\\n @property\\n def N(self):\\n return len(self._xyz)\\n\\n @property\\n def get_x(self):\\n return self._xyz\\n\\n @property\\n def get_R(self):\\n return quaternion_to_matrix(self._rotation)\\n\\n @property\\n def get_o(self):\\n return self.o_act(self._opacity)\\n\\n @property\\n def get_s(self):\\n return self.s_act(self._scaling)\\n\\n @property\\n def get_c(self):\\n return torch.cat([self._features_dc, self._features_rest], dim=-1)\\n\\n def cache_for_fast(self):\\n _cached_W, _ = self.template.forward(None, self._xyz)\\n self._cached_W = _cached_W.detach().clone()\\n return\\n\\n def forward(\\n self, theta, trans, additional_dict={}, active_sph_order=None, fast=False\\n ):\\n # * fast will use the cached per point attr, no query anymore\\n # TODO: the additional dict contain info to do flexible skinning: it can contain the As directly for optimization, or it can contain t index to query some buffers to provide As, or it can contain t along with the input theta to query some MLP;\\n\\n # TODO: if use vol memory, every forward update self.xxx, and remove them from parameters, pretend that the attributes are per point, but actually they are queried every forward\\n\\n # theta: B,24,3; trans: B,3\\n B = len(theta)\\n if active_sph_order is None:\\n active_sph_order = self.max_sph_order\\n else:\\n assert (\\n active_sph_order <= self.max_sph_order\\n ), \\\"active_sph_order should be smaller\\\"\\n sph_dim = 3 * sph_order2nfeat(active_sph_order)\\n\\n xyz = self.get_x\\n mu_can = xyz\\n frame_can = self.get_R\\n s = self.get_s\\n o = self.get_o\\n sph = self.get_c[:, :sph_dim]\\n\\n mu_can = mu_can[None].expand(B, -1, -1)\\n frame_can = frame_can[None].expand(B, -1, -1, -1)\\n\\n if fast:\\n # only forward skeleton, no query voxel\\n _, A = self.template.forward(theta, None)\\n W = self._cached_W[None].expand(B, -1, -1)\\n else:\\n W, A = self.template.forward(theta, mu_can)\\n if self._w_correction_dc.shape[-1] > 0:\\n W = W + self._w_correction_dc[None]\\n T = torch.einsum(\\\"bnj, bjrc -> bnrc\\\", W[..., : self.num_bones], A)\\n\\n # * additional correction here\\n if \\\"pose\\\" not in additional_dict.keys():\\n # maybe later we want to viz the different pose effect in cano\\n additional_dict[\\\"pose\\\"] = theta.reshape(B, -1)[:, 3:]\\n add_A = self.add_bones(**additional_dict)\\n if add_A is not None:\\n if theta.ndim == 2:\\n global_axis_angle = theta[:, :3]\\n else:\\n global_axis_angle = theta[:, 0]\\n global_orient_action = self.template.get_rot_action(global_axis_angle) # B,4,4\\n add_A = torch.einsum(\\\"bij, bnjk -> bnik\\\", global_orient_action, add_A)\\n\\n if self.w_memory_type == \\\"point\\\":\\n assert self._w_correction_rest.shape[-1] > 0\\n add_W = self._w_correction_rest[None].expand(B, -1, -1)\\n elif self.w_memory_type == \\\"voxel\\\":\\n add_W = W[..., self.num_bones :]\\n\\n add_T = torch.einsum(\\\"bnj, bjrc -> bnrc\\\", add_W, add_A)\\n T = T + add_T # Linear\\n additional_dict[\\\"As\\\"] = add_A\\n\\n R, t = T[:, :, :3, :3], T[:, :, :3, 3] # B,N,3,3; B,N,3\\n\\n mu = torch.einsum(\\\"bnij,bnj->bni\\\", R, mu_can) + t # B,N,3\\n frame = torch.einsum(\\\"bnij,bnjk->bnik\\\", R, frame_can) # B,N,3,3\\n\\n s = s[None].expand(B, -1, -1) # B,N,1\\n o = o[None].expand(B, -1, -1) # B,N,1\\n sph = sph[:, :sph_dim][None].expand(B, -1, -1) # B,N,C\\n\\n mu = mu + trans[:, None, :]\\n\\n return mu, frame, s, o, sph, additional_dict\\n\\n def compute_reg(self, K):\\n # !can cancel the knn, but the w reg is critical\\n if K > 0:\\n xyz = self._xyz\\n # todo: this can be cached and updated every several steps!!\\n dist_sq, nn_ind, _ = knn_points(xyz[None], xyz[None], K=K, return_nn=False)\\n nn_ind = nn_ind.squeeze(0)\\n # reg the std inside knn\\n q = self._rotation[nn_ind, :] # N,K,4\\n s = self.get_s[nn_ind, :] # N,K,3\\n o = self.get_o[nn_ind, :] # N,K,1\\n q_std = q.std(dim=1).mean()\\n s_std = s.std(dim=1).mean()\\n o_std = o.std(dim=1).mean()\\n\\n cd = self._features_dc[nn_ind, :] # N,K,3\\n ch = self._features_rest[nn_ind, :] # N,K,C\\n cd_std = cd.std(dim=1).mean()\\n ch_std = ch.std(dim=1).mean()\\n if ch.shape[-1] == 0:\\n ch_std = torch.zeros_like(ch_std)\\n\\n w = self._w_correction_dc[nn_ind, :] # N,K,3\\n w_rest = self._w_correction_rest[nn_ind, :] # N,K,C\\n f = self._features_localcode[nn_ind, :] # N,K,C\\n w_std = w.std(dim=1).mean()\\n w_rest_std = w_rest.std(dim=1).mean()\\n f_std = f.std(dim=1).mean()\\n if w.shape[-1] == 0:\\n w_std = torch.zeros_like(cd_std)\\n if w_rest.shape[-1] == 0:\\n w_rest_std = torch.zeros_like(cd_std)\\n if f.shape[-1] == 0:\\n f_std = torch.zeros_like(cd_std)\\n else:\\n dummy = torch.zeros(1).to(self._xyz).squeeze()\\n q_std, s_std, o_std = dummy, dummy, dummy\\n cd_std, ch_std = dummy, dummy\\n w_std, w_rest_std, f_std = dummy, dummy, dummy\\n dist_sq = dummy\\n\\n w_norm = self._w_correction_dc.norm(dim=-1).mean() # N\\n w_rest_norm = self._w_correction_rest.norm(dim=-1).mean() # N\\n\\n if self.w_memory_type == \\\"voxel\\\":\\n # update the w related std and norm\\n w_std = self.template.voxel_deformer.get_tv(\\\"dc\\\")\\n w_rest_std = self.template.voxel_deformer.get_tv(\\\"rest\\\")\\n w_norm = self.template.voxel_deformer.get_mag(\\\"dc\\\")\\n w_rest_norm = self.template.voxel_deformer.get_mag(\\\"rest\\\")\\n\\n max_s_square = torch.mean((self.get_s.max(dim=1).values) ** 2)\\n\\n return (\\n q_std,\\n s_std,\\n o_std,\\n cd_std,\\n ch_std,\\n w_std,\\n w_rest_std,\\n f_std,\\n w_norm,\\n w_rest_norm,\\n dist_sq.mean(),\\n max_s_square,\\n )\\n\\n def get_optimizable_list(\\n self,\\n lr_p=0.00016,\\n lr_q=0.001,\\n lr_s=0.005,\\n lr_o=0.05,\\n lr_sph=0.0025,\\n lr_sph_rest=None,\\n lr_w=0.001,\\n lr_w_rest=0.001,\\n lr_f=0.0001,\\n ):\\n lr_sph_rest = lr_sph / 20 if lr_sph_rest is None else lr_sph_rest\\n l = [\\n {\\\"params\\\": [self._xyz], \\\"lr\\\": lr_p, \\\"name\\\": \\\"xyz\\\"},\\n {\\\"params\\\": [self._opacity], \\\"lr\\\": lr_o, \\\"name\\\": \\\"opacity\\\"},\\n {\\\"params\\\": [self._scaling], \\\"lr\\\": lr_s, \\\"name\\\": \\\"scaling\\\"},\\n {\\\"params\\\": [self._rotation], \\\"lr\\\": lr_q, \\\"name\\\": \\\"rotation\\\"},\\n {\\\"params\\\": [self._features_dc], \\\"lr\\\": lr_sph, \\\"name\\\": \\\"f_dc\\\"},\\n {\\\"params\\\": [self._features_rest], \\\"lr\\\": lr_sph_rest, \\\"name\\\": \\\"f_rest\\\"},\\n {\\\"params\\\": [self._w_correction_dc], \\\"lr\\\": lr_w, \\\"name\\\": \\\"w_dc\\\"},\\n {\\\"params\\\": [self._w_correction_rest], \\\"lr\\\": lr_w_rest, \\\"name\\\": \\\"w_rest\\\"},\\n {\\\"params\\\": [self._features_localcode], \\\"lr\\\": lr_f, \\\"name\\\": \\\"f_localcode\\\"},\\n ]\\n if self.w_memory_type == \\\"voxel\\\":\\n if self.w_dc_dim > 0:\\n l.append(\\n {\\n \\\"params\\\": [self.template.voxel_deformer.voxel_w_correction],\\n \\\"lr\\\": lr_w,\\n \\\"name\\\": \\\"w_dc_vox\\\",\\n }\\n )\\n if self.w_rest_dim > 0:\\n l.append(\\n {\\n \\\"params\\\": [self.template.voxel_deformer.additional_correction],\\n \\\"lr\\\": lr_w_rest,\\n \\\"name\\\": \\\"w_rest_vox\\\",\\n }\\n )\\n return l\\n\\n # * Gaussian Control\\n def record_xyz_grad_radii(self, viewspace_point_tensor, radii, update_filter):\\n # Record the gradient norm, invariant across different poses\\n assert len(viewspace_point_tensor) == self.N\\n self.xyz_gradient_accum[update_filter] += torch.norm(\\n viewspace_point_tensor.grad[update_filter, :2], dim=-1, keepdim=False\\n )\\n self.xyz_gradient_denom[update_filter] += 1\\n self.max_radii2D[update_filter] = torch.max(\\n self.max_radii2D[update_filter], radii[update_filter]\\n )\\n return\\n\\n def _densification_postprocess(\\n self,\\n optimizer,\\n new_xyz,\\n new_r,\\n new_s,\\n new_o,\\n new_sph_dc,\\n new_sph_rest,\\n new_w_dc,\\n new_w_rest,\\n new_localcode,\\n ):\\n d = {\\n \\\"xyz\\\": new_xyz,\\n \\\"f_dc\\\": new_sph_dc,\\n \\\"f_rest\\\": new_sph_rest,\\n \\\"opacity\\\": new_o,\\n \\\"scaling\\\": new_s,\\n \\\"rotation\\\": new_r,\\n \\\"w_dc\\\": new_w_dc,\\n \\\"w_rest\\\": new_w_rest,\\n \\\"f_localcode\\\": new_localcode,\\n }\\n d = {k: v for k, v in d.items() if v is not None}\\n\\n # First cat to optimizer and then return to self\\n optimizable_tensors = cat_tensors_to_optimizer(optimizer, d)\\n\\n self._xyz = optimizable_tensors[\\\"xyz\\\"]\\n self._opacity = optimizable_tensors[\\\"opacity\\\"]\\n self._scaling = optimizable_tensors[\\\"scaling\\\"]\\n self._rotation = optimizable_tensors[\\\"rotation\\\"]\\n self._features_dc = optimizable_tensors[\\\"f_dc\\\"]\\n self._features_rest = optimizable_tensors[\\\"f_rest\\\"]\\n self._w_correction_dc = optimizable_tensors[\\\"w_dc\\\"]\\n self._w_correction_rest = optimizable_tensors[\\\"w_rest\\\"]\\n self._features_localcode = optimizable_tensors[\\\"f_localcode\\\"]\\n\\n self.xyz_gradient_accum = torch.zeros(self._xyz.shape[0], device=\\\"cuda\\\")\\n self.xyz_gradient_denom = torch.zeros(self._xyz.shape[0], device=\\\"cuda\\\")\\n self.max_radii2D = torch.cat(\\n [self.max_radii2D, torch.zeros_like(new_xyz[:, 0])], dim=0\\n )\\n return\\n\\n def _densify_and_clone(self, optimizer, grad_norm, grad_threshold, scale_th):\\n # Extract points that satisfy the gradient condition\\n # padding for enabling both call of clone and split\\n padded_grad = torch.zeros((self.N), device=\\\"cuda\\\")\\n padded_grad[: grad_norm.shape[0]] = grad_norm.squeeze()\\n selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)\\n selected_pts_mask = torch.logical_and(\\n selected_pts_mask,\\n torch.max(self.get_s, dim=1).values <= scale_th,\\n )\\n if selected_pts_mask.sum() == 0:\\n return 0\\n\\n new_xyz = self._xyz[selected_pts_mask]\\n new_rotation = self._rotation[selected_pts_mask]\\n new_scaling = self._scaling[selected_pts_mask]\\n new_opacities = self._opacity[selected_pts_mask]\\n new_features_dc = self._features_dc[selected_pts_mask]\\n new_features_rest = self._features_rest[selected_pts_mask]\\n new_w_dc = self._w_correction_dc[selected_pts_mask]\\n new_w_rest = self._w_correction_rest[selected_pts_mask]\\n new_localcode = self._features_localcode[selected_pts_mask]\\n\\n self._densification_postprocess(\\n optimizer,\\n new_xyz=new_xyz,\\n new_r=new_rotation,\\n new_s=new_scaling,\\n new_o=new_opacities,\\n new_sph_dc=new_features_dc,\\n new_sph_rest=new_features_rest,\\n new_w_dc=new_w_dc,\\n new_w_rest=new_w_rest,\\n new_localcode=new_localcode,\\n )\\n\\n return len(new_xyz)\\n\\n def _densify_and_split(\\n self,\\n optimizer,\\n grad_norm,\\n grad_threshold,\\n scale_th,\\n N=2,\\n ):\\n # Extract points that satisfy the gradient condition\\n _scaling = self.get_s\\n # padding for enabling both call of clone and split\\n padded_grad = torch.zeros((self.N), device=\\\"cuda\\\")\\n padded_grad[: grad_norm.shape[0]] = grad_norm.squeeze()\\n selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)\\n selected_pts_mask = torch.logical_and(\\n selected_pts_mask,\\n torch.max(_scaling, dim=1).values > scale_th,\\n )\\n if selected_pts_mask.sum() == 0:\\n return 0\\n\\n stds = _scaling[selected_pts_mask].repeat(N, 1)\\n means = torch.zeros((stds.size(0), 3), device=\\\"cuda\\\")\\n samples = torch.normal(mean=means, std=stds)\\n rots = quaternion_to_matrix(self._rotation[selected_pts_mask]).repeat(N, 1, 1)\\n new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self._xyz[\\n selected_pts_mask\\n ].repeat(N, 1)\\n new_scaling = _scaling[selected_pts_mask].repeat(N, 1) / (0.8 * N)\\n new_scaling = torch.clamp(new_scaling, max=self.max_scale, min=self.min_scale)\\n new_scaling = self.s_inv_act(new_scaling)\\n new_rotation = self._rotation[selected_pts_mask].repeat(N, 1)\\n new_features_dc = self._features_dc[selected_pts_mask].repeat(N, 1)\\n new_features_rest = self._features_rest[selected_pts_mask].repeat(N, 1)\\n new_opacities = self._opacity[selected_pts_mask].repeat(N, 1)\\n new_w_dc = self._w_correction_dc[selected_pts_mask].repeat(N, 1)\\n new_w_rest = self._w_correction_rest[selected_pts_mask].repeat(N, 1)\\n new_localcode = self._features_localcode[selected_pts_mask].repeat(N, 1)\\n\\n self._densification_postprocess(\\n optimizer,\\n new_xyz=new_xyz,\\n new_r=new_rotation,\\n new_s=new_scaling,\\n new_o=new_opacities,\\n new_sph_dc=new_features_dc,\\n new_sph_rest=new_features_rest,\\n new_w_dc=new_w_dc,\\n new_w_rest=new_w_rest,\\n new_localcode=new_localcode,\\n )\\n\\n prune_filter = torch.cat(\\n (\\n selected_pts_mask,\\n torch.zeros(N * selected_pts_mask.sum(), device=\\\"cuda\\\", dtype=bool),\\n )\\n )\\n self._prune_points(optimizer, prune_filter)\\n return len(new_xyz)\\n\\n def densify(self, optimizer, max_grad, percent_dense, extent, verbose=True):\\n grads = self.xyz_gradient_accum / self.xyz_gradient_denom\\n grads[grads.isnan()] = 0.0\\n\\n # n_clone = self._densify_and_clone(optimizer, grads, max_grad)\\n n_clone = self._densify_and_clone(\\n optimizer, grads, max_grad, percent_dense * extent\\n )\\n n_split = self._densify_and_split(\\n optimizer, grads, max_grad, percent_dense * extent, N=2\\n )\\n\\n if verbose:\\n logging.info(f\\\"Densify: Clone[+] {n_clone}, Split[+] {n_split}\\\")\\n # logging.info(f\\\"Densify: Clone[+] {n_clone}\\\")\\n # torch.cuda.empty_cache()\\n return\\n\\n def random_grow(self, optimizer, num_factor=0.05, std=0.1, init_opa_value=0.1):\\n # * New operation, randomly add largely disturbed points to the geometry\\n ind = torch.randperm(self.N)[: int(self.N * num_factor)]\\n selected_pts_mask = torch.zeros(self.N, dtype=bool, device=\\\"cuda\\\")\\n selected_pts_mask[ind] = True\\n\\n new_xyz = self._xyz[selected_pts_mask]\\n noise = torch.randn_like(new_xyz) * std\\n new_xyz = new_xyz + noise\\n new_features_dc = self._features_dc[selected_pts_mask]\\n new_features_rest = self._features_rest[selected_pts_mask]\\n\\n new_opacities = torch.ones_like(self._opacity[selected_pts_mask])\\n new_opacities = new_opacities * self.o_inv_act(init_opa_value)\\n\\n new_scaling = self._scaling[selected_pts_mask]\\n new_rotation = self._rotation[selected_pts_mask]\\n\\n new_w_dc = self._w_correction_dc[selected_pts_mask]\\n new_w_rest = self._w_correction_rest[selected_pts_mask]\\n new_localcode = self._features_localcode[selected_pts_mask]\\n\\n self._densification_postprocess(\\n optimizer,\\n new_xyz=new_xyz,\\n new_r=new_rotation,\\n new_s=new_scaling,\\n new_o=new_opacities,\\n new_sph_dc=new_features_dc,\\n new_sph_rest=new_features_rest,\\n new_w_dc=new_w_dc,\\n new_w_rest=new_w_rest,\\n new_localcode=new_localcode,\\n )\\n logging.info(f\\\"Random grow: {len(new_xyz)}\\\")\\n return len(new_xyz)\\n\\n def prune_points(self, optimizer, min_opacity, max_screen_size, verbose=True):\\n opacity = self.o_act(self._opacity)\\n prune_mask = (opacity < min_opacity).squeeze()\\n if max_screen_size: # if a point is too large\\n big_points_vs = self.max_radii2D > max_screen_size\\n prune_mask = torch.logical_or(prune_mask, big_points_vs)\\n # * reset the maxRadii\\n self.max_radii2D = torch.zeros_like(self.max_radii2D)\\n self._prune_points(optimizer, prune_mask)\\n if verbose:\\n logging.info(f\\\"Prune: {prune_mask.sum()}\\\")\\n\\n def _prune_points(self, optimizer, mask):\\n valid_points_mask = ~mask\\n optimizable_tensors = prune_optimizer(\\n optimizer,\\n valid_points_mask,\\n exclude_names=self.op_update_exclude,\\n )\\n\\n self._xyz = optimizable_tensors[\\\"xyz\\\"]\\n if getattr(self, \\\"color_memory\\\", None) is None:\\n self._features_dc = optimizable_tensors[\\\"f_dc\\\"]\\n self._features_rest = optimizable_tensors[\\\"f_rest\\\"]\\n self._opacity = optimizable_tensors[\\\"opacity\\\"]\\n self._scaling = optimizable_tensors[\\\"scaling\\\"]\\n self._rotation = optimizable_tensors[\\\"rotation\\\"]\\n self._w_correction_dc = optimizable_tensors[\\\"w_dc\\\"]\\n self._w_correction_rest = optimizable_tensors[\\\"w_rest\\\"]\\n self._features_localcode = optimizable_tensors[\\\"f_localcode\\\"]\\n\\n self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]\\n self.xyz_gradient_denom = self.xyz_gradient_denom[valid_points_mask]\\n self.max_radii2D = self.max_radii2D[valid_points_mask]\\n # torch.cuda.empty_cache()\\n return\\n\\n @torch.no_grad()\\n def regaussian(self, optimizer, max_scale=0.03):\\n # raise NotImplementedError(\\\"TODO, like split\\\")\\n # * New operation, manually split the large gaussians with smaller ones to approximate\\n # * Now, try bi-split\\n\\n # Extract points that satisfy the gradient condition\\n _scaling = self.get_s\\n selected_pts_mask = torch.max(_scaling, dim=1).values > max_scale\\n\\n step = 0\\n before_num = self.N\\n while selected_pts_mask.any():\\n # This can be done more than 3 times, becuase there may be huge gaussians, which should be devided several times\\n fg_xyz = self._xyz[selected_pts_mask]\\n fg_scale = _scaling[selected_pts_mask]\\n fg_frame = quaternion_to_matrix(self._rotation[selected_pts_mask])\\n # each column is the direction of axis in global frame\\n axis_ind = torch.argmax(fg_scale, dim=1)\\n axis_scale = fg_scale.max(dim=1).values\\n # select column\\n axis_dir = torch.gather(\\n fg_frame, dim=2, index=axis_ind[:, None, None].expand(-1, 3, -1)\\n ).squeeze(\\n -1\\n ) # N,3\\n new_x1 = fg_xyz + axis_dir.squeeze() * axis_scale[:, None] / 2.0\\n new_x2 = fg_xyz - axis_dir.squeeze() * axis_scale[:, None] / 2.0\\n # Repeat will change [1,2,3...] to [1,2,3..., 1,2,3...]\\n new_xyz = torch.cat([new_x1, new_x2], dim=0).reshape(-1, 3)\\n new_scaling = _scaling[selected_pts_mask]\\n new_scaling = torch.scatter(\\n new_scaling,\\n dim=1,\\n index=axis_ind[:, None],\\n src=axis_scale[:, None] / 2.0,\\n ).repeat(2, 1)\\n new_scaling = torch.clamp(\\n new_scaling, max=self.max_scale, min=self.min_scale\\n )\\n new_scaling = self.s_inv_act(new_scaling)\\n new_rotation = self._rotation[selected_pts_mask].repeat(2, 1)\\n new_features_dc = self._features_dc[selected_pts_mask].repeat(2, 1)\\n new_features_rest = self._features_rest[selected_pts_mask].repeat(2, 1)\\n new_opacities = self._opacity[selected_pts_mask].repeat(2, 1)\\n new_w_dc = self._w_correction_dc[selected_pts_mask].repeat(2, 1)\\n new_w_rest = self._w_correction_rest[selected_pts_mask].repeat(2, 1)\\n new_localcode = self._features_localcode[selected_pts_mask].repeat(2, 1)\\n\\n self._densification_postprocess(\\n optimizer,\\n new_xyz=new_xyz.float(),\\n new_r=new_rotation.float(),\\n new_s=new_scaling.float(),\\n new_o=new_opacities.float(),\\n new_sph_dc=new_features_dc.float(),\\n new_sph_rest=new_features_rest.float(),\\n new_w_dc=new_w_dc.float(),\\n new_w_rest=new_w_rest.float(),\\n new_localcode=new_localcode.float(),\\n )\\n\\n prune_filter = torch.cat(\\n (\\n selected_pts_mask,\\n torch.zeros(2 * selected_pts_mask.sum(), device=\\\"cuda\\\", dtype=bool),\\n )\\n )\\n self._prune_points(optimizer, prune_filter)\\n\\n step += 1\\n logging.info(\\n f\\\"Regaussian-[{step}], {selected_pts_mask.sum()} ({selected_pts_mask.float().mean()*100}% pts-scale>{max_scale})\\\"\\n )\\n\\n _scaling = self.get_s\\n selected_pts_mask = torch.max(_scaling, dim=1).values > max_scale\\n logging.info(f\\\"Re-gaussian: {before_num} -> {self.N}\\\")\\n return\\n\\n def reset_opacity(self, optimizer, value=0.01, verbose=True):\\n opacities_new = self.o_inv_act(\\n torch.min(self.o_act(self._opacity), torch.ones_like(self._opacity) * value)\\n )\\n optimizable_tensors = replace_tensor_to_optimizer(\\n optimizer, opacities_new, \\\"opacity\\\"\\n )\\n if verbose:\\n logging.info(f\\\"Reset opacity to {value}\\\")\\n self._opacity = optimizable_tensors[\\\"opacity\\\"]\\n\\n def load(self, ckpt):\\n # because N changed, have to re-init the buffers\\n self._xyz = nn.Parameter(torch.as_tensor(ckpt[\\\"_xyz\\\"], dtype=torch.float32))\\n\\n self._features_dc = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_features_dc\\\"], dtype=torch.float32)\\n )\\n self._features_rest = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_features_rest\\\"], dtype=torch.float32)\\n )\\n self._opacity = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_opacity\\\"], dtype=torch.float32)\\n )\\n self._scaling = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_scaling\\\"], dtype=torch.float32)\\n )\\n self._rotation = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_rotation\\\"], dtype=torch.float32)\\n )\\n self._w_correction_dc = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_w_correction_dc\\\"], dtype=torch.float32)\\n )\\n self._w_correction_rest = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_w_correction_rest\\\"], dtype=torch.float32)\\n )\\n self._features_localcode = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"_features_localcode\\\"], dtype=torch.float32)\\n )\\n self.xyz_gradient_accum = torch.as_tensor(\\n ckpt[\\\"xyz_gradient_accum\\\"], dtype=torch.float32\\n )\\n self.xyz_gradient_denom = torch.as_tensor(\\n ckpt[\\\"xyz_gradient_denom\\\"], dtype=torch.int64\\n )\\n self.max_radii2D = torch.as_tensor(ckpt[\\\"max_radii2D\\\"], dtype=torch.float32)\\n\\n # * add bones may have different total_t\\n if \\\"add_bones.dt_list\\\" in ckpt.keys():\\n self.add_bones.total_t = ckpt[\\\"add_bones.dt_list\\\"].shape[0]\\n self.add_bones.dt_list = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"add_bones.dt_list\\\"], dtype=torch.float32)\\n )\\n self.add_bones.dr_list = nn.Parameter(\\n torch.as_tensor(ckpt[\\\"add_bones.dr_list\\\"], dtype=torch.float32)\\n )\\n # load others\\n self.load_state_dict(ckpt, strict=True)\\n # this is critical, reinit the funcs\\n self._init_act(self.max_scale, self.min_scale)\\n return\"\n },\n {\n \"identifier\": \"AdditionalBones\",\n \"path\": \"lib_gart/model.py\",\n \"snippet\": \"class AdditionalBones(nn.Module):\\n def __init__(\\n self, # additional bones\\n num_bones: int = 0,\\n total_t: int = 0, # any usage of time should use this!\\n mode=\\\"pose-mlp\\\",\\n # pose-mlp\\n pose_dim=23 * 3,\\n mlp_hidden_dims=[256, 256, 256, 256],\\n mlp_act=nn.LeakyReLU,\\n # pose+t-mlp\\n ):\\n super().__init__()\\n self.num_bones = num_bones\\n if self.num_bones == 0:\\n return\\n self.mode = mode\\n assert self.mode in [\\\"pose-mlp\\\", \\\"pose+t-mlp\\\", \\\"delta-list\\\", \\\"list\\\"]\\n self.total_t = total_t\\n\\n if self.mode == \\\"pose-mlp\\\":\\n self.pose_dim = pose_dim\\n self.mlp_layers = nn.ModuleList()\\n c_in = self.pose_dim\\n for c_out in mlp_hidden_dims:\\n self.mlp_layers.append(nn.Sequential(nn.Linear(c_in, c_out), mlp_act()))\\n c_in = c_out\\n self.mlp_output_head = nn.Linear(c_in, 7 * self.num_bones, bias=False)\\n with torch.no_grad():\\n self.mlp_output_head.weight.data = (\\n torch.randn_like(self.mlp_output_head.weight.data) * 1e-3\\n )\\n elif self.mode == \\\"delta-list\\\":\\n self.dr_list = nn.Parameter(torch.zeros(self.total_t, num_bones, 3))\\n self.dt_list = nn.Parameter(torch.zeros(self.total_t, num_bones, 3))\\n else:\\n raise NotImplementedError()\\n\\n return\\n\\n def forward(self, pose=None, t=None, As=None):\\n if self.num_bones == 0:\\n # * No additional bones\\n return None\\n if As is not None:\\n # * Directly return if As already provided\\n return As\\n if self.mode == \\\"pose-mlp\\\":\\n assert pose is not None\\n assert pose.ndim == 2 and pose.shape[1] == self.pose_dim\\n B = len(pose)\\n x = pose\\n for layer in self.mlp_layers:\\n x = layer(x)\\n x = self.mlp_output_head(x).reshape(B, -1, 7)\\n q, t = x[:, :, :4], x[:, :, 4:]\\n q[..., 0] = q[..., 0] + 1.0\\n q = F.normalize(q, dim=-1)\\n R = quaternion_to_matrix(q)\\n Rt = torch.cat([R, t[:, :, :, None]], dim=-1)\\n bottom = torch.zeros_like(Rt[:, :, 0:1])\\n bottom[:, :, :, -1] = 1.0\\n As = torch.cat([Rt, bottom], dim=2)\\n return As\\n elif self.mode == \\\"delta-list\\\":\\n As = self._roll_out_continuous_T()\\n if t is None:\\n B = len(pose)\\n # # ! If no time is set, now return eye(4)\\n # ret = (\\n # torch.eye(4)\\n # .to(As.device)[None, None]\\n # .repeat(B, self.num_bones, 1, 1)\\n # )\\n # ! If no time is set, now return first frame\\n ret = As[0][None].repeat(B, 1, 1, 1)\\n else:\\n if isinstance(t, int):\\n t = torch.tensor([t]).to(As.device)\\n ret = As[t]\\n return ret\\n else:\\n raise NotImplementedError()\\n\\n return # As in canonical frame\\n\\n def _roll_out_continuous_T(self):\\n # ! this assumes continuous frames, single frame!\\n R = axis_angle_to_matrix(self.dr_list)\\n dT = (\\n torch.eye(4).to(R.device)[None, None].repeat(self.total_t, R.shape[1], 1, 1)\\n )\\n dT[:, :, :3, :3] = dT[:, :, :3, :3] * 0 + R\\n dT[:, :, :3, 3] = dT[:, :, :3, 3] * 0 + self.dt_list\\n T = [dT[0]]\\n for i in range(1, self.total_t):\\n T.append(torch.einsum(\\\"nij, njk->nik\\\", T[-1], dT[i]))\\n T = torch.stack(T, dim=0)\\n return T\"\n },\n {\n \"identifier\": \"render_cam_pcl\",\n \"path\": \"lib_render/gauspl_renderer.py\",\n \"snippet\": \"def render_cam_pcl(\\n xyz,\\n frame,\\n scale,\\n opacity,\\n color_feat,\\n H,\\n W,\\n CAM_K,\\n verbose=False,\\n active_sph_order=0,\\n bg_color=[1.0, 1.0, 1.0],\\n):\\n # ! Camera is at origin, every input is in camera coordinate space\\n\\n S = torch.zeros_like(frame)\\n S[:, 0, 0] = scale[:, 0]\\n S[:, 1, 1] = scale[:, 1]\\n S[:, 2, 2] = scale[:, 2]\\n actual_covariance = frame @ (S**2) @ frame.permute(0, 2, 1)\\n\\n # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means\\n device = xyz.device\\n screenspace_points = (\\n torch.zeros_like(xyz, dtype=xyz.dtype, requires_grad=True, device=xyz.device) + 0\\n )\\n # screenspace_points.retain_grad()\\n try:\\n screenspace_points.retain_grad()\\n except:\\n pass\\n\\n # * Specially handle the non-centered camera, using first padding and finally crop\\n if abs(H // 2 - CAM_K[1, 2]) > 1.0 or abs(W // 2 - CAM_K[0, 2]) > 1.0:\\n center_handling_flag = True\\n left_w, right_w = CAM_K[0, 2], W - CAM_K[0, 2]\\n top_h, bottom_h = CAM_K[1, 2], H - CAM_K[1, 2]\\n new_W = int(2 * max(left_w, right_w))\\n new_H = int(2 * max(top_h, bottom_h))\\n else:\\n center_handling_flag = False\\n new_W, new_H = W, H\\n\\n # Set up rasterization configuration\\n FoVx = focal2fov(CAM_K[0, 0], new_W)\\n FoVy = focal2fov(CAM_K[1, 1], new_H)\\n tanfovx = math.tan(FoVx * 0.5)\\n tanfovy = math.tan(FoVy * 0.5)\\n\\n # TODO: Check dynamic gaussian repos and original gaussian repo, they use projection matrix to handle non-centered K, not using this stupid padding like me\\n viewmatrix = torch.from_numpy(getWorld2View2(np.eye(3), np.zeros(3)).transpose(0, 1)).to(device)\\n projection_matrix = (\\n getProjectionMatrix(znear=0.01, zfar=1.0, fovX=FoVx, fovY=FoVy).transpose(0, 1).to(device)\\n )\\n full_proj_transform = (viewmatrix.unsqueeze(0).bmm(projection_matrix.unsqueeze(0))).squeeze(0)\\n camera_center = viewmatrix.inverse()[3, :3]\\n\\n raster_settings = GaussianRasterizationSettings(\\n image_height=new_H,\\n image_width=new_W,\\n tanfovx=tanfovx,\\n tanfovy=tanfovy,\\n bg=torch.tensor(bg_color, dtype=torch.float32, device=device),\\n scale_modifier=1.0,\\n viewmatrix=viewmatrix,\\n projmatrix=full_proj_transform,\\n sh_degree=0, # ! use pre-compute color!\\n campos=camera_center,\\n prefiltered=False,\\n debug=False,\\n )\\n rasterizer = GaussianRasterizer(raster_settings=raster_settings)\\n\\n means3D = xyz\\n means2D = screenspace_points\\n # opacity = torch.ones_like(means3D[:, 0]) * sigma\\n\\n # If precomputed 3d covariance is provided, use it. If not, then it will be computed from\\n # scaling / rotation by the rasterizer.\\n scales = None\\n rotations = None\\n # JH\\n cov3D_precomp = strip_lowerdiag(actual_covariance)\\n\\n # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors\\n # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.\\n # xyz are in camera frame, so the dir in camera frame is just their normalized direction\\n dir_cam = F.normalize(xyz, dim=-1)\\n # P_w = Frame @ P_local\\n dir_local = torch.einsum(\\\"nji,nj->ni\\\", frame, dir_cam) # note the transpose\\n dir_local = F.normalize(\\n dir_local, dim=-1\\n ) # If frame is not SO(3) but Affinity, have to normalize\\n N = len(color_feat)\\n shs_view = color_feat.reshape(N, -1, 3) # N, Deg, Channels\\n sh2rgb = eval_sh(active_sph_order, shs_view.permute(0, 2, 1), dir_local)\\n colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)\\n # colors_precomp = color_feat\\n\\n # Rasterize visible Gaussians to image, obtain their radii (on screen).\\n\\n start_time = time.time()\\n ret = rasterizer(\\n means3D=means3D.float(),\\n means2D=means2D.float(),\\n shs=None,\\n colors_precomp=colors_precomp.float(),\\n opacities=opacity.float(),\\n scales=scales,\\n rotations=rotations,\\n cov3D_precomp=cov3D_precomp.float(),\\n )\\n if len(ret) == 2:\\n rendered_image, radii = ret\\n depth, alpha = None, None\\n elif len(ret) == 4:\\n rendered_image, radii, depth, alpha = ret\\n else:\\n raise ValueError(f\\\"Unexpected return value from rasterizer with len={len(ret)}\\\")\\n if verbose:\\n print(\\n f\\\"render time: {(time.time() - start_time)*1000:.3f}ms\\\",\\n )\\n ret = {\\n \\\"rgb\\\": rendered_image,\\n \\\"dep\\\": depth,\\n \\\"alpha\\\": alpha,\\n \\\"viewspace_points\\\": screenspace_points,\\n \\\"visibility_filter\\\": radii > 0,\\n \\\"radii\\\": radii,\\n }\\n if center_handling_flag:\\n for k in [\\\"rgb\\\", \\\"dep\\\", \\\"alpha\\\"]:\\n if ret[k] is None:\\n continue\\n if left_w > right_w:\\n ret[k] = ret[k][:, :, :W]\\n else:\\n ret[k] = ret[k][:, :, -W:]\\n if top_h > bottom_h:\\n ret[k] = ret[k][:, :H, :]\\n else:\\n ret[k] = ret[k][:, -H:, :]\\n return ret\"\n },\n {\n \"identifier\": \"transform_mu_frame\",\n \"path\": \"lib_gart/model_utils.py\",\n \"snippet\": \"def transform_mu_frame(mu, frame, T):\\n if len(mu) != len(T):\\n assert len(mu) == 1 and len(frame) == 1\\n mu = mu.expand(len(T), -1, -1)\\n frame = frame.expand(len(T), -1, -1, -1)\\n R, t = T[:, :3, :3], T[:, :3, 3]\\n new_frame = torch.einsum(\\\"bij, bnjk->bnik\\\", R, frame)\\n new_mu = torch.einsum(\\\"bij, bnj->bni\\\", R, mu) + t[:, None]\\n return new_mu, new_frame\"\n },\n {\n \"identifier\": \"viz_render\",\n \"path\": \"utils/viz.py\",\n \"snippet\": \"def viz_render(gt_rgb, gt_mask, pred_pkg, save_path=None):\\n pred_rgb = pred_pkg[\\\"rgb\\\"].permute(1, 2, 0)\\n pred_mask = pred_pkg[\\\"alpha\\\"].squeeze(0)\\n pred_depth = pred_pkg[\\\"dep\\\"].squeeze(0)\\n fig = plt.figure(figsize=(20, 5))\\n plt.subplot(1, 5, 1)\\n plt.imshow(torch.clamp(gt_rgb, 0.0, 1.0).detach().cpu().numpy())\\n plt.title(\\\"GT\\\"), plt.axis(\\\"off\\\")\\n plt.subplot(1, 5, 2)\\n plt.imshow(torch.clamp(pred_rgb, 0.0, 1.0).detach().cpu().numpy())\\n plt.title(\\\"Pred view\\\"), plt.axis(\\\"off\\\")\\n plt.subplot(1, 5, 3)\\n error = torch.clamp(abs(pred_rgb - gt_rgb), 0.0, 1.0).detach().cpu().numpy().max(axis=-1)\\n cmap = plt.imshow(error)\\n plt.title(\\\"Render Error (max in rgb)\\\"), plt.axis(\\\"off\\\")\\n plt.colorbar(cmap, shrink=0.8)\\n\\n plt.subplot(1, 5, 4)\\n error = torch.clamp(pred_mask - gt_mask, -1.0, 1.0).detach().cpu().numpy()\\n cmap = plt.imshow(error)\\n plt.title(\\\"(Pr - GT) Mask Error\\\"), plt.axis(\\\"off\\\")\\n plt.colorbar(cmap, shrink=0.8)\\n \\n plt.subplot(1, 5, 5)\\n depth = pred_depth.detach().cpu().numpy()\\n cmap = plt.imshow(depth)\\n plt.title(\\\"Pred Depth\\\"), plt.axis(\\\"off\\\")\\n plt.colorbar(cmap, shrink=0.8)\\n\\n plt.tight_layout()\\n fig.canvas.draw()\\n fig_np = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\\n fig_np = fig_np.reshape(fig.canvas.get_width_height()[::-1] + (3,))\\n if save_path is not None:\\n plt.savefig(save_path)\\n plt.close(fig)\\n return fig_np\"\n },\n {\n \"identifier\": \"sample_camera\",\n \"path\": \"lib_guidance/camera_sampling.py\",\n \"snippet\": \"def sample_camera(\\n global_step=1,\\n n_view=4,\\n real_batch_size=1,\\n random_azimuth_range=[-180.0, 180.0],\\n random_elevation_range=[0.0, 30.0],\\n eval_elevation_deg=15,\\n camera_distance_range=[0.8, 1.0], # relative\\n fovy_range=[15, 60],\\n zoom_range=[1.0, 1.0],\\n progressive_until=0,\\n relative_radius=True,\\n):\\n # camera_perturb = 0.0\\n # center_perturb = 0.0\\n # up_perturb: 0.0\\n\\n # ! from uncond.py\\n # ThreeStudio has progressive increase of camera poses, from eval to random\\n r = min(1.0, global_step / (progressive_until + 1))\\n elevation_range = [\\n (1 - r) * eval_elevation_deg + r * random_elevation_range[0],\\n (1 - r) * eval_elevation_deg + r * random_elevation_range[1],\\n ]\\n azimuth_range = [\\n (1 - r) * 0.0 + r * random_azimuth_range[0],\\n (1 - r) * 0.0 + r * random_azimuth_range[1],\\n ]\\n\\n # sample elevation angles\\n if random.random() < 0.5:\\n # sample elevation angles uniformly with a probability 0.5 (biased towards poles)\\n elevation_deg = (\\n torch.rand(real_batch_size) * (elevation_range[1] - elevation_range[0])\\n + elevation_range[0]\\n ).repeat_interleave(n_view, dim=0)\\n elevation = elevation_deg * math.pi / 180\\n else:\\n # otherwise sample uniformly on sphere\\n elevation_range_percent = [\\n (elevation_range[0] + 90.0) / 180.0,\\n (elevation_range[1] + 90.0) / 180.0,\\n ]\\n # inverse transform sampling\\n elevation = torch.asin(\\n 2\\n * (\\n torch.rand(real_batch_size)\\n * (elevation_range_percent[1] - elevation_range_percent[0])\\n + elevation_range_percent[0]\\n )\\n - 1.0\\n ).repeat_interleave(n_view, dim=0)\\n elevation_deg = elevation / math.pi * 180.0\\n\\n # sample azimuth angles from a uniform distribution bounded by azimuth_range\\n # ensures sampled azimuth angles in a batch cover the whole range\\n azimuth_deg = (\\n torch.rand(real_batch_size).reshape(-1, 1) + torch.arange(n_view).reshape(1, -1)\\n ).reshape(-1) / n_view * (azimuth_range[1] - azimuth_range[0]) + azimuth_range[0]\\n azimuth = azimuth_deg * math.pi / 180\\n\\n ######## Different from original ########\\n # sample fovs from a uniform distribution bounded by fov_range\\n fovy_deg = (\\n torch.rand(real_batch_size) * (fovy_range[1] - fovy_range[0]) + fovy_range[0]\\n ).repeat_interleave(n_view, dim=0)\\n fovy = fovy_deg * math.pi / 180\\n\\n # sample distances from a uniform distribution bounded by distance_range\\n camera_distances = (\\n torch.rand(real_batch_size) * (camera_distance_range[1] - camera_distance_range[0])\\n + camera_distance_range[0]\\n ).repeat_interleave(n_view, dim=0)\\n if relative_radius:\\n scale = 1 / torch.tan(0.5 * fovy)\\n camera_distances = scale * camera_distances\\n\\n # zoom in by decreasing fov after camera distance is fixed\\n zoom = (\\n torch.rand(real_batch_size) * (zoom_range[1] - zoom_range[0]) + zoom_range[0]\\n ).repeat_interleave(n_view, dim=0)\\n fovy = fovy * zoom\\n fovy_deg = fovy_deg * zoom\\n ###########################################\\n\\n # convert spherical coordinates to cartesian coordinates\\n # right hand coordinate system, x back, y right, z up\\n # elevation in (-90, 90), azimuth from +x to +y in (-180, 180)\\n camera_positions = torch.stack(\\n [\\n camera_distances * torch.cos(elevation) * torch.cos(azimuth),\\n camera_distances * torch.cos(elevation) * torch.sin(azimuth),\\n camera_distances * torch.sin(elevation),\\n ],\\n dim=-1,\\n )\\n\\n azimuth, elevation\\n # build opencv camera\\n z = -torch.stack(\\n [\\n torch.cos(elevation) * torch.cos(azimuth),\\n torch.cos(elevation) * torch.sin(azimuth),\\n torch.sin(elevation),\\n ],\\n -1,\\n ) # nview, 3\\n # up is 0,0,1\\n x = torch.cross(z, torch.tensor([0.0, 0.0, 1.0], device=z.device).repeat(n_view, 1), -1)\\n y = torch.cross(z, x, -1)\\n\\n R_wc = torch.stack([x, y, z], dim=2) # nview, 3, 3, col is basis\\n t_wc = camera_positions\\n\\n T_wc = torch.eye(4, device=R_wc.device).repeat(n_view, 1, 1)\\n T_wc[:, :3, :3] = R_wc\\n T_wc[:, :3, 3] = t_wc\\n\\n return T_wc, fovy_deg # B,4,4, B\"\n },\n {\n \"identifier\": \"fov2K\",\n \"path\": \"lib_guidance/camera_sampling.py\",\n \"snippet\": \"def fov2K(fov=90, H=256, W=256):\\n if isinstance(fov, torch.Tensor):\\n f = H / (2 * torch.tan(fov / 2 * np.pi / 180))\\n K = torch.eye(3).repeat(fov.shape[0], 1, 1).to(fov)\\n K[:, 0, 0], K[:, 0, 2] = f, W / 2.0\\n K[:, 1, 1], K[:, 1, 2] = f, H / 2.0\\n return K.clone()\\n else:\\n f = H / (2 * np.tan(fov / 2 * np.pi / 180))\\n K = np.eye(3)\\n K[0, 0], K[0, 2] = f, W / 2.0\\n K[1, 1], K[1, 2] = f, H / 2.0\\n return K.copy()\"\n },\n {\n \"identifier\": \"opencv2blender\",\n \"path\": \"lib_guidance/camera_sampling.py\",\n \"snippet\": \"def opencv2blender(T):\\n ret = T.clone()\\n # y,z are negative\\n ret[:, :, 1] *= -1\\n ret[:, :, 2] *= -1\\n return ret\"\n },\n {\n \"identifier\": \"viz_spinning\",\n \"path\": \"viz_utils.py\",\n \"snippet\": \"@torch.no_grad()\\ndef viz_spinning(\\n model,\\n pose,\\n trans,\\n H,\\n W,\\n K,\\n save_path,\\n time_index=None,\\n n_spinning=10,\\n model_mask=None,\\n active_sph_order=0,\\n bg_color=[1.0, 1.0, 1.0],\\n):\\n device = pose.device\\n mu, fr, s, o, sph, additional_ret = model(\\n pose, trans, {\\\"t\\\": time_index}, active_sph_order=active_sph_order\\n )\\n if model_mask is not None:\\n assert len(model_mask) == mu.shape[1]\\n mu = mu[:, model_mask.bool()]\\n fr = fr[:, model_mask.bool()]\\n s = s[:, model_mask.bool()]\\n o = o[:, model_mask.bool()]\\n sph = sph[:, model_mask.bool()]\\n\\n viz_frames = []\\n for vid in range(n_spinning):\\n spin_R = (\\n torch.from_numpy(euler2mat(0, 2 * np.pi * vid / n_spinning, 0, \\\"sxyz\\\"))\\n .to(device)\\n .float()\\n )\\n spin_t = mu.mean(1)[0]\\n spin_t = (torch.eye(3).to(device) - spin_R) @ spin_t[:, None]\\n spin_T = torch.eye(4).to(device)\\n spin_T[:3, :3] = spin_R\\n spin_T[:3, 3] = spin_t.squeeze(-1)\\n viz_mu, viz_fr = transform_mu_frame(mu, fr, spin_T[None])\\n\\n render_pkg = render_cam_pcl(\\n viz_mu[0],\\n viz_fr[0],\\n s[0],\\n o[0],\\n sph[0],\\n H,\\n W,\\n K,\\n False,\\n active_sph_order,\\n bg_color=bg_color,\\n )\\n viz_frame = (\\n torch.clamp(render_pkg[\\\"rgb\\\"], 0.0, 1.0)\\n .permute(1, 2, 0)\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n viz_frame = (viz_frame * 255).astype(np.uint8)\\n viz_frames.append(viz_frame)\\n imageio.mimsave(save_path, viz_frames)\\n return\"\n },\n {\n \"identifier\": \"viz_human_all\",\n \"path\": \"viz_utils.py\",\n \"snippet\": \"@torch.no_grad()\\ndef viz_human_all(\\n solver,\\n data_provider: RealDataOptimizablePoseProviderPose = None,\\n ckpt_dir=None,\\n training_skip=1,\\n n_spinning=40,\\n novel_pose_dir=\\\"novel_poses\\\",\\n novel_skip=2,\\n model=None,\\n model_mask=None,\\n viz_name=\\\"\\\",\\n export_mesh_flag=False, # remove this from release version\\n):\\n if model is None:\\n model = solver.load_saved_model(ckpt_dir)\\n model.eval()\\n\\n viz_dir = osp.join(solver.log_dir, f\\\"{viz_name}_human_viz\\\")\\n os.makedirs(viz_dir, exist_ok=True)\\n\\n active_sph_order = int(model.max_sph_order)\\n\\n if data_provider is not None:\\n # if ckpt_dir is None:\\n # ckpt_dir = solver.log_dir\\n # pose_path = osp.join(ckpt_dir, \\\"pose.pth\\\")\\n pose_base_list = data_provider.pose_base_list\\n pose_rest_list = data_provider.pose_rest_list\\n global_trans_list = data_provider.global_trans_list\\n pose_list = torch.cat([pose_base_list, pose_rest_list], 1)\\n pose_list, global_trans_list = pose_list.to(\\n solver.device\\n ), global_trans_list.to(solver.device)\\n rgb_list = data_provider.rgb_list\\n mask_list = data_provider.mask_list\\n K_list = data_provider.K_list\\n H, W = rgb_list.shape[1:3]\\n else:\\n H, W = 512, 512\\n K_list = [torch.from_numpy(fov2K(45, H, W)).float().to(solver.device)]\\n global_trans_list = torch.zeros(1, 3).to(solver.device)\\n global_trans_list[0, -1] = 3.0\\n\\n # viz training\\n if data_provider is not None:\\n print(\\\"Viz training...\\\")\\n viz_frames = []\\n for t in range(len(pose_list)):\\n if t % training_skip != 0:\\n continue\\n pose = pose_list[t][None]\\n K = K_list[t]\\n trans = global_trans_list[t][None]\\n time_index = torch.Tensor([t]).long().to(solver.device)\\n mu, fr, s, o, sph, _ = model(\\n pose,\\n trans,\\n {\\\"t\\\": time_index}, # use time_index from training set\\n active_sph_order=active_sph_order,\\n )\\n if model_mask is not None:\\n assert len(model_mask) == mu.shape[1]\\n mu = mu[:, model_mask.bool()]\\n fr = fr[:, model_mask.bool()]\\n s = s[:, model_mask.bool()]\\n o = o[:, model_mask.bool()]\\n sph = sph[:, model_mask.bool()]\\n render_pkg = render_cam_pcl(\\n mu[0],\\n fr[0],\\n s[0],\\n o[0],\\n sph[0],\\n H,\\n W,\\n K,\\n False,\\n active_sph_order,\\n bg_color=getattr(solver, \\\"DEFAULT_BG\\\", [1.0, 1.0, 1.0]),\\n )\\n viz_frame = viz_render(rgb_list[t], mask_list[t], render_pkg)\\n viz_frames.append(viz_frame)\\n imageio.mimsave(f\\\"{viz_dir}/training.gif\\\", viz_frames)\\n\\n # viz static spinning\\n print(\\\"Viz spinning...\\\")\\n can_pose = model.template.canonical_pose.detach()\\n viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, \\\"sxyz\\\"))\\n viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()\\n can_pose[0] = viz_base_R_opencv.to(can_pose.device)\\n can_pose = matrix_to_axis_angle(can_pose)[None]\\n dapose = torch.from_numpy(np.zeros((1, 24, 3))).float().to(solver.device)\\n dapose[:, 1, -1] = np.pi / 4\\n dapose[:, 2, -1] = -np.pi / 4\\n dapose[:, 0] = matrix_to_axis_angle(solver.viz_base_R[None])[0]\\n tpose = torch.from_numpy(np.zeros((1, 24, 3))).float().to(solver.device)\\n tpose[:, 0] = matrix_to_axis_angle(solver.viz_base_R[None])[0]\\n to_viz = {\\\"cano-pose\\\": can_pose, \\\"t-pose\\\": tpose, \\\"da-pose\\\": dapose}\\n if data_provider is not None:\\n to_viz[\\\"first-frame\\\"] = pose_list[0][None]\\n\\n for name, pose in to_viz.items():\\n print(f\\\"Viz novel {name}...\\\")\\n # if export_mesh_flag:\\n # from lib_marchingcubes.gaumesh_utils import MeshExtractor\\n # # also extract a mesh\\n # mesh = solver.extract_mesh(model, pose)\\n # mesh.export(f\\\"{viz_dir}/mc_{name}.obj\\\", \\\"obj\\\")\\n\\n # # for making figures, the rotation is in another way\\n # viz_spinning_self_rotate(\\n # model,\\n # solver.viz_base_R.detach(),\\n # pose,\\n # global_trans_list[0][None],\\n # H,\\n # W,\\n # K_list[0],\\n # f\\\"{viz_dir}/{name}_selfrotate.gif\\\",\\n # time_index=None, # if set to None and use t, the add_bone will hand this\\n # n_spinning=n_spinning,\\n # active_sph_order=model.max_sph_order,\\n # )\\n viz_spinning(\\n model,\\n pose,\\n global_trans_list[0][None],\\n H,\\n W,\\n K_list[0],\\n f\\\"{viz_dir}/{name}.gif\\\",\\n time_index=None, # if set to None and use t, the add_bone will hand this\\n n_spinning=n_spinning,\\n active_sph_order=model.max_sph_order,\\n bg_color=getattr(solver, \\\"DEFAULT_BG\\\", [1.0, 1.0, 1.0]),\\n )\\n\\n # viz novel pose dynamic spinning\\n print(\\\"Viz novel seq...\\\")\\n novel_pose_names = [\\n f[:-4] for f in os.listdir(novel_pose_dir) if f.endswith(\\\".npy\\\")\\n ]\\n seq_viz_todo = {}\\n for name in novel_pose_names:\\n novel_pose_fn = osp.join(novel_pose_dir, f\\\"{name}.npy\\\")\\n novel_poses = np.load(novel_pose_fn, allow_pickle=True)\\n novel_poses = novel_poses[::novel_skip]\\n N_frames = len(novel_poses)\\n novel_poses = torch.from_numpy(novel_poses).float().to(solver.device)\\n novel_poses = novel_poses.reshape(N_frames, 24, 3)\\n\\n seq_viz_todo[name] = (novel_poses, N_frames)\\n if data_provider is not None:\\n seq_viz_todo[\\\"training\\\"] = [pose_list, len(pose_list)]\\n\\n for name, (novel_poses, N_frames) in seq_viz_todo.items():\\n base_R = solver.viz_base_R.detach().cpu().numpy()\\n viz_frames = []\\n K = K_list[0]\\n for vid in range(N_frames):\\n pose = novel_poses[vid][None]\\n # pose = novel_poses[0][None] # debug\\n rotation = euler2mat(2 * np.pi * vid / N_frames, 0.0, 0.0, \\\"syxz\\\")\\n rotation = torch.from_numpy(rotation @ base_R).float().to(solver.device)\\n pose[:, 0] = matrix_to_axis_angle(rotation[None])[0]\\n trans = global_trans_list[0][None]\\n mu, fr, s, o, sph, _ = model(\\n pose,\\n trans,\\n # not pass in {}, so t is auto none\\n additional_dict={},\\n active_sph_order=active_sph_order,\\n )\\n if model_mask is not None:\\n assert len(model_mask) == mu.shape[1]\\n mu = mu[:, model_mask.bool()]\\n fr = fr[:, model_mask.bool()]\\n s = s[:, model_mask.bool()]\\n o = o[:, model_mask.bool()]\\n sph = sph[:, model_mask.bool()]\\n render_pkg = render_cam_pcl(\\n mu[0],\\n fr[0],\\n s[0],\\n o[0],\\n sph[0],\\n H,\\n W,\\n K,\\n False,\\n active_sph_order,\\n bg_color=getattr(solver, \\\"DEFAULT_BG\\\", [1.0, 1.0, 1.0]),\\n # bg_color=[1.0, 1.0, 1.0], # ! use white bg for viz\\n )\\n viz_frame = (\\n torch.clamp(render_pkg[\\\"rgb\\\"], 0.0, 1.0)\\n .permute(1, 2, 0)\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n viz_frame = (viz_frame * 255).astype(np.uint8)\\n viz_frames.append(viz_frame)\\n imageio.mimsave(f\\\"{viz_dir}/novel_pose_{name}.gif\\\", viz_frames)\\n return\"\n },\n {\n \"identifier\": \"viz_dog_all\",\n \"path\": \"viz_utils.py\",\n \"snippet\": \"@torch.no_grad()\\ndef viz_dog_all(solver, data_provider, model=None, ckpt_dir=None, viz_name=\\\"\\\"):\\n if model is None:\\n model = solver.load_saved_model(ckpt_dir)\\n model.eval()\\n viz_dir = osp.join(solver.log_dir, f\\\"{viz_name}_dog_viz\\\")\\n os.makedirs(viz_dir, exist_ok=True)\\n\\n viz_pose = (\\n torch.cat([data_provider.pose_base_list, data_provider.pose_rest_list], 1)\\n .detach()\\n .clone()\\n )\\n viz_pose = torch.mean(viz_pose, dim=0, keepdim=True) # use mean pose for viz \\n limb = viz_pose[:, -7:] \\n pose = viz_pose[:, :-7].reshape(-1, 35, 3)\\n pose[:, :-3] = 0 # exclude ears and mouth poses\\n\\n viz_pose = torch.concat([pose.reshape(1, -1), limb], dim=1)\\n viz_trans = torch.tensor([[0.0, -0.3, 25.0]], device=\\\"cuda:0\\\")\\n\\n viz_dog_spin(\\n model.to(\\\"cuda\\\"),\\n viz_pose,\\n viz_trans,\\n data_provider.H,\\n data_provider.W,\\n data_provider.K_list[0],\\n save_path=osp.join(viz_dir, \\\"spin.gif\\\"),\\n n_spinning=42,\\n )\\n\\n viz_dog_spin2(\\n model.to(\\\"cuda\\\"),\\n viz_pose,\\n viz_trans,\\n data_provider.H,\\n data_provider.W,\\n data_provider.K_list[0],\\n save_path=osp.join(viz_dir, \\\"spin2.gif\\\"),\\n n_spinning=20,\\n )\\n\\n ######################################################################\\n # Dataset pose seq\\n viz_pose = (\\n torch.cat([data_provider.pose_base_list, data_provider.pose_rest_list], 1)\\n .detach()\\n .clone()\\n )\\n viz_pose = torch.mean(viz_pose, dim=0, keepdim=True)\\n pose = viz_pose[:, :-7].reshape(-1, 35, 3)\\n limb = viz_pose[:, -7:]\\n\\n # Animation\\n aroot = osp.join(osp.dirname(__file__), \\\"novel_poses/husky\\\")\\n window = list(range(350, 440)) # Run\\n trans = torch.tensor([[0.3, -0.3, 25.0]], device=\\\"cuda:0\\\")\\n files = [f\\\"{aroot}/{i:04d}.npz\\\" for i in window]\\n pose_list = [dict(np.load(file))[\\\"pred_pose\\\"] for file in files]\\n pose_list = np.concatenate(pose_list)\\n animation = matrix_to_axis_angle(torch.from_numpy(pose_list)).to(solver.device)\\n animation[:, [32, 33, 34]] = pose[:, [32, 33, 34]] \\n\\n viz_dog_animation(\\n model.to(\\\"cuda\\\"),\\n animation,\\n limb,\\n trans,\\n data_provider.H,\\n data_provider.W,\\n data_provider.K_list[0],\\n save_path=osp.join(viz_dir, \\\"animation.gif\\\"),\\n fps=12,\\n )\\n return\"\n },\n {\n \"identifier\": \"ssim\",\n \"path\": \"utils/ssim.py\",\n \"snippet\": \"def ssim(img1, img2, window_size=11, size_average=True):\\n channel = img1.size(-3)\\n window = create_window(window_size, channel)\\n\\n if img1.is_cuda:\\n window = window.cuda(img1.get_device())\\n window = window.type_as(img1)\\n\\n return _ssim(img1, img2, window, window_size, channel, size_average)\"\n },\n {\n \"identifier\": \"test\",\n \"path\": \"test_utils/test_func.py\",\n \"snippet\": \"def test(\\n solver,\\n seq_name: str,\\n tto_flag=True,\\n tto_step=300,\\n tto_decay=60,\\n tto_decay_factor=0.5,\\n pose_base_lr=3e-3,\\n pose_rest_lr=3e-3,\\n trans_lr=3e-3,\\n dataset_mode=\\\"people_snapshot\\\",\\n training_optimized_seq=None,\\n):\\n device = solver.device\\n model = solver.load_saved_model()\\n\\n assert dataset_mode in [\\n \\\"people_snapshot\\\",\\n \\\"zju\\\",\\n \\\"instant_avatar_wild\\\",\\n \\\"dog_demo\\\",\\n ], f\\\"Unknown dataset mode {dataset_mode}\\\"\\n\\n if dataset_mode == \\\"people_snapshot\\\":\\n eval_mode = \\\"avatar\\\"\\n bg = [1.0, 1.0, 1.0]\\n test_dataset = InstantAvatarDataset(\\n noisy_flag=False,\\n data_root=\\\"./data/people_snapshot/\\\",\\n video_name=seq_name,\\n split=\\\"test\\\",\\n image_zoom_ratio=0.5,\\n )\\n elif dataset_mode == \\\"zju\\\":\\n eval_mode = \\\"nvr\\\"\\n test_dataset = ZJUDataset(\\n data_root=\\\"./data/zju_mocap\\\",\\n video_name=seq_name,\\n split=\\\"test\\\",\\n image_zoom_ratio=0.5,\\n )\\n bg = [0.0, 0.0, 0.0] # zju use black background\\n elif dataset_mode == \\\"instant_avatar_wild\\\":\\n eval_mode = \\\"avatar\\\"\\n test_dataset = InstantAvatarWildDataset(\\n data_root=\\\"./data/insav_wild\\\",\\n video_name=seq_name,\\n split=\\\"test\\\",\\n image_zoom_ratio=1.0,\\n # ! warning, here follow the `ubc_hard.yaml` in InstAVT setting, use slicing\\n start_end_skip=[2, 1000000000, 4],\\n )\\n bg = [1.0, 1.0, 1.0]\\n\\n test_len = len(test_dataset)\\n assert (training_optimized_seq.total_t == test_len) or (\\n training_optimized_seq.total_t == 1 + test_len\\n ), \\\"Now UBC can only support the same length of training and testing or + 1\\\"\\n test_dataset.smpl_params[\\\"body_pose\\\"] = (\\n training_optimized_seq.pose_rest_list.reshape(-1, 69)[:test_len]\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n test_dataset.smpl_params[\\\"global_orient\\\"] = (\\n training_optimized_seq.pose_base_list.reshape(-1, 3)[:test_len]\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n test_dataset.smpl_params[\\\"transl\\\"] = (\\n training_optimized_seq.global_trans_list.reshape(-1, 3)[:test_len]\\n .detach()\\n .cpu()\\n .numpy()\\n )\\n elif dataset_mode == \\\"dog_demo\\\":\\n eval_mode = \\\"avatar_brightness\\\"\\n bg = [1.0, 1.0, 1.0]\\n test_dataset = DogDemoDataset(\\n data_root=\\\"./data/dog_data_official/\\\", video_name=seq_name, test=True\\n )\\n else:\\n raise NotImplementedError()\\n\\n evaluator = get_evaluator(eval_mode, device)\\n\\n _save_eval_maps(\\n solver.log_dir,\\n \\\"test\\\",\\n model,\\n solver,\\n test_dataset,\\n dataset_mode=dataset_mode,\\n device=device,\\n bg=bg,\\n tto_flag=tto_flag,\\n tto_step=tto_step,\\n tto_decay=tto_decay,\\n tto_decay_factor=tto_decay_factor,\\n tto_evaluator=evaluator,\\n pose_base_lr=pose_base_lr,\\n pose_rest_lr=pose_rest_lr,\\n trans_lr=trans_lr,\\n )\\n\\n if tto_flag:\\n _evaluate_dir(evaluator, solver.log_dir, \\\"test_tto\\\")\\n else:\\n _evaluate_dir(evaluator, solver.log_dir, \\\"test\\\")\\n\\n return\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from matplotlib import pyplot as plt\nfrom pytorch3d.transforms import matrix_to_axis_angle\nfrom tqdm import tqdm\nfrom transforms3d.euler import euler2mat\nfrom omegaconf import OmegaConf\nfrom lib_data.get_data import prepare_real_seq\nfrom lib_data.data_provider import DatabasePoseProvider\nfrom lib_gart.templates import get_template\nfrom lib_gart.model import GaussianTemplateModel, AdditionalBones\nfrom lib_gart.optim_utils import *\nfrom lib_render.gauspl_renderer import render_cam_pcl\nfrom lib_gart.model_utils import transform_mu_frame\nfrom utils.misc import *\nfrom utils.viz import viz_render\nfrom pytorch3d.transforms import axis_angle_to_matrix, matrix_to_axis_angle\nfrom pytorch3d.ops import knn_points\nfrom lib_guidance.camera_sampling import sample_camera, fov2K, opencv2blender\nfrom viz_utils import viz_spinning, viz_human_all, viz_dog_all\nfrom utils.ssim import ssim\nfrom datetime import datetime\nfrom test_utils import test\n from lib_guidance.mvdream.mvdream_guidance import MVDream\n from utils.lpips import LPIPS\nimport imageio\nimport torch\nimport numpy as np\nimport os, os.path as osp, shutil, sys\nimport time\nimport logging\nimport argparse"},"token_num":{"kind":"number","value":20848,"string":"20,848"},"cropped_code":{"kind":"string","value":" self.profile_fn = profile_fn\n try:\n shutil.copy(profile_fn, osp.join(self.log_dir, osp.basename(profile_fn)))\n except:\n pass\n\n self.mode = mode\n assert self.mode in [\"human\", \"dog\"], \"Only support human and dog for now\"\n\n self.template_model_path = template_model_path\n self.device = device\n\n # * auto set attr\n cfg = OmegaConf.load(profile_fn)\n # assign the cfg to self attribute\n for k, v in cfg.items():\n setattr(self, k, v)\n\n for k, v in kwargs.items():\n setattr(self, k, v)\n\n # * explicitly set flags\n self.FAST_TRAINING = getattr(self, \"FAST_TRAINING\", False)\n\n self.LAMBDA_SSIM = getattr(self, \"LAMBDA_SSIM\", 0.0)\n self.LAMBDA_LPIPS = getattr(self, \"LAMBDA_LPIPS\", 0.0)\n if self.LAMBDA_LPIPS > 0:\n\n self.lpips = LPIPS(net=\"vgg\").to(self.device)\n for param in self.lpips.parameters():\n param.requires_grad = False\n\n if isinstance(self.RESET_OPACITY_STEPS, int):\n self.RESET_OPACITY_STEPS = [\n i\n for i in range(1, self.TOTAL_steps)\n if i % self.RESET_OPACITY_STEPS == 0\n ]\n if isinstance(self.REGAUSSIAN_STEPS, int):\n self.REGAUSSIAN_STEPS = [\n i for i in range(1, self.TOTAL_steps) if i % self.REGAUSSIAN_STEPS == 0\n ]\n\n # prepare base R\n if self.mode == \"human\":\n viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, \"sxyz\"))\n else:\n viz_base_R_opencv = np.asarray(euler2mat(np.pi / 2.0, 0, np.pi, \"rxyz\"))\n viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()\n self.viz_base_R = viz_base_R_opencv.to(self.device)\n\n if self.mode == \"human\":\n self.reg_base_R_global = (\n matrix_to_axis_angle(\n torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, \"sxyz\"))[\n None\n ]\n )[0]\n .float()\n .to(self.device)\n )\n else:\n # TODO, for generation of dog\n pass\n\n self.writer = create_log(\n self.log_dir, name=osp.basename(self.profile_fn).split(\".\")[0], debug=False\n )\n return\n\n def prepare_fake_data(self, mode, *args, **kwargs):\n if mode == \"amass\":\n # todo: change to amass\n provider = DatabasePoseProvider(*args, **kwargs, device=torch.device(\"cpu\"))\n return provider\n return provider\n\n def prepare_real_seq(\n self,\n seq_name,\n dataset_mode,\n split,\n ins_avt_wild_start_end_skip=None,\n image_zoom_ratio=0.5,\n data_stay_gpu_flag=True,\n ):\n provider, dataset = prepare_real_seq(\n seq_name=seq_name,\n dataset_mode=dataset_mode,\n split=split,\n ins_avt_wild_start_end_skip=ins_avt_wild_start_end_skip,\n image_zoom_ratio=getattr(\n self, \"IMAGE_ZOOM_RATIO\", image_zoom_ratio\n ), # ! this overwrite the func arg\n balance=getattr(self, \"VIEW_BALANCE_FLAG\", False),\n )\n provider.to(self.device)\n if getattr(self, \"DATA_STAY_GPU_FLAG\", data_stay_gpu_flag):\n provider.move_images_to_device(self.device)\n provider.viz_selection_prob(\n osp.join(self.log_dir, f\"split_{split}_view_prob.png\")\n )\n return provider, dataset\n\n def load_saved_model(self, ckpt_path=None):\n if ckpt_path is None:\n ckpt_path = osp.join(self.log_dir, \"model.pth\")\n ret = self._get_model_optimizer(betas=None)\n model = ret[0]\n\n model.load(torch.load(ckpt_path))\n model.to(self.device)\n model.eval()\n logging.info(\"After loading:\")\n model.summary()\n return model\n\n def _get_model_optimizer(self, betas, add_bones_total_t=0):\n seed_everything(self.SEED)\n\n"},"all_code":{"kind":"string","value":"\n\n\n\n# from lib_marchingcubes.gaumesh_utils import MeshExtractor\n\n\n\n\n\n\ntry:\n # from lib_guidance.sd_utils import StableDiffusion\nexcept:\n logging.warning(\"No guidance module\")\n\n\nclass TGFitter:\n def __init__(\n self,\n log_dir,\n profile_fn,\n mode,\n template_model_path=\"data/smpl_model/SMPL_NEUTRAL.pkl\",\n device=torch.device(\"cuda:0\"),\n **kwargs,\n ) -> None:\n self.log_dir = log_dir\n os.makedirs(self.log_dir, exist_ok=True)\n\n self.profile_fn = profile_fn\n try:\n shutil.copy(profile_fn, osp.join(self.log_dir, osp.basename(profile_fn)))\n except:\n pass\n\n self.mode = mode\n assert self.mode in [\"human\", \"dog\"], \"Only support human and dog for now\"\n\n self.template_model_path = template_model_path\n self.device = device\n\n # * auto set attr\n cfg = OmegaConf.load(profile_fn)\n # assign the cfg to self attribute\n for k, v in cfg.items():\n setattr(self, k, v)\n\n for k, v in kwargs.items():\n setattr(self, k, v)\n\n # * explicitly set flags\n self.FAST_TRAINING = getattr(self, \"FAST_TRAINING\", False)\n\n self.LAMBDA_SSIM = getattr(self, \"LAMBDA_SSIM\", 0.0)\n self.LAMBDA_LPIPS = getattr(self, \"LAMBDA_LPIPS\", 0.0)\n if self.LAMBDA_LPIPS > 0:\n\n self.lpips = LPIPS(net=\"vgg\").to(self.device)\n for param in self.lpips.parameters():\n param.requires_grad = False\n\n if isinstance(self.RESET_OPACITY_STEPS, int):\n self.RESET_OPACITY_STEPS = [\n i\n for i in range(1, self.TOTAL_steps)\n if i % self.RESET_OPACITY_STEPS == 0\n ]\n if isinstance(self.REGAUSSIAN_STEPS, int):\n self.REGAUSSIAN_STEPS = [\n i for i in range(1, self.TOTAL_steps) if i % self.REGAUSSIAN_STEPS == 0\n ]\n\n # prepare base R\n if self.mode == \"human\":\n viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, \"sxyz\"))\n else:\n viz_base_R_opencv = np.asarray(euler2mat(np.pi / 2.0, 0, np.pi, \"rxyz\"))\n viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()\n self.viz_base_R = viz_base_R_opencv.to(self.device)\n\n if self.mode == \"human\":\n self.reg_base_R_global = (\n matrix_to_axis_angle(\n torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, \"sxyz\"))[\n None\n ]\n )[0]\n .float()\n .to(self.device)\n )\n else:\n # TODO, for generation of dog\n pass\n\n self.writer = create_log(\n self.log_dir, name=osp.basename(self.profile_fn).split(\".\")[0], debug=False\n )\n return\n\n def prepare_fake_data(self, mode, *args, **kwargs):\n if mode == \"amass\":\n # todo: change to amass\n provider = DatabasePoseProvider(*args, **kwargs, device=torch.device(\"cpu\"))\n return provider\n return provider\n\n def prepare_real_seq(\n self,\n seq_name,\n dataset_mode,\n split,\n ins_avt_wild_start_end_skip=None,\n image_zoom_ratio=0.5,\n data_stay_gpu_flag=True,\n ):\n provider, dataset = prepare_real_seq(\n seq_name=seq_name,\n dataset_mode=dataset_mode,\n split=split,\n ins_avt_wild_start_end_skip=ins_avt_wild_start_end_skip,\n image_zoom_ratio=getattr(\n self, \"IMAGE_ZOOM_RATIO\", image_zoom_ratio\n ), # ! this overwrite the func arg\n balance=getattr(self, \"VIEW_BALANCE_FLAG\", False),\n )\n provider.to(self.device)\n if getattr(self, \"DATA_STAY_GPU_FLAG\", data_stay_gpu_flag):\n provider.move_images_to_device(self.device)\n provider.viz_selection_prob(\n osp.join(self.log_dir, f\"split_{split}_view_prob.png\")\n )\n return provider, dataset\n\n def load_saved_model(self, ckpt_path=None):\n if ckpt_path is None:\n ckpt_path = osp.join(self.log_dir, \"model.pth\")\n ret = self._get_model_optimizer(betas=None)\n model = ret[0]\n\n model.load(torch.load(ckpt_path))\n model.to(self.device)\n model.eval()\n logging.info(\"After loading:\")\n model.summary()\n return model\n\n def _get_model_optimizer(self, betas, add_bones_total_t=0):\n seed_everything(self.SEED)\n"},"next_line":{"kind":"string","value":" template = get_template("},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-11-27 17:30:04+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5375,"cells":{"repo_name":{"kind":"string","value":"skhu101/GauHuman"},"file_path":{"kind":"string","value":"scene/dataset_readers.py"},"context":{"kind":"list like","value":[{"identifier":"read_extrinsics_text","path":"scene/colmap_loader.py","snippet":"def read_extrinsics_text(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\n \"\"\"\n images = {}\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n image_id = int(elems[0])\n qvec = np.array(tuple(map(float, elems[1:5])))\n tvec = np.array(tuple(map(float, elems[5:8])))\n camera_id = int(elems[8])\n image_name = elems[9]\n elems = fid.readline().split()\n xys = np.column_stack([tuple(map(float, elems[0::3])),\n tuple(map(float, elems[1::3]))])\n point3D_ids = np.array(tuple(map(int, elems[2::3])))\n images[image_id] = Image(\n id=image_id, qvec=qvec, tvec=tvec,\n camera_id=camera_id, name=image_name,\n xys=xys, point3D_ids=point3D_ids)\n return images"},{"identifier":"read_intrinsics_text","path":"scene/colmap_loader.py","snippet":"def read_intrinsics_text(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\n \"\"\"\n cameras = {}\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n camera_id = int(elems[0])\n model = elems[1]\n assert model == \"PINHOLE\", \"While the loader support other types, the rest of the code assumes PINHOLE\"\n width = int(elems[2])\n height = int(elems[3])\n params = np.array(tuple(map(float, elems[4:])))\n cameras[camera_id] = Camera(id=camera_id, model=model,\n width=width, height=height,\n params=params)\n return cameras"},{"identifier":"qvec2rotmat","path":"scene/colmap_loader.py","snippet":"def qvec2rotmat(qvec):\n return np.array([\n [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,\n 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],\n 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],\n [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],\n 1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,\n 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],\n [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],\n 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],\n 1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])"},{"identifier":"read_extrinsics_binary","path":"scene/colmap_loader.py","snippet":"def read_extrinsics_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadImagesBinary(const std::string& path)\n void Reconstruction::WriteImagesBinary(const std::string& path)\n \"\"\"\n images = {}\n with open(path_to_model_file, \"rb\") as fid:\n num_reg_images = read_next_bytes(fid, 8, \"Q\")[0]\n for _ in range(num_reg_images):\n binary_image_properties = read_next_bytes(\n fid, num_bytes=64, format_char_sequence=\"idddddddi\")\n image_id = binary_image_properties[0]\n qvec = np.array(binary_image_properties[1:5])\n tvec = np.array(binary_image_properties[5:8])\n camera_id = binary_image_properties[8]\n image_name = \"\"\n current_char = read_next_bytes(fid, 1, \"c\")[0]\n while current_char != b\"\\x00\": # look for the ASCII 0 entry\n image_name += current_char.decode(\"utf-8\")\n current_char = read_next_bytes(fid, 1, \"c\")[0]\n num_points2D = read_next_bytes(fid, num_bytes=8,\n format_char_sequence=\"Q\")[0]\n x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,\n format_char_sequence=\"ddq\"*num_points2D)\n xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),\n tuple(map(float, x_y_id_s[1::3]))])\n point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))\n images[image_id] = Image(\n id=image_id, qvec=qvec, tvec=tvec,\n camera_id=camera_id, name=image_name,\n xys=xys, point3D_ids=point3D_ids)\n return images"},{"identifier":"read_intrinsics_binary","path":"scene/colmap_loader.py","snippet":"def read_intrinsics_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::WriteCamerasBinary(const std::string& path)\n void Reconstruction::ReadCamerasBinary(const std::string& path)\n \"\"\"\n cameras = {}\n with open(path_to_model_file, \"rb\") as fid:\n num_cameras = read_next_bytes(fid, 8, \"Q\")[0]\n for _ in range(num_cameras):\n camera_properties = read_next_bytes(\n fid, num_bytes=24, format_char_sequence=\"iiQQ\")\n camera_id = camera_properties[0]\n model_id = camera_properties[1]\n model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name\n width = camera_properties[2]\n height = camera_properties[3]\n num_params = CAMERA_MODEL_IDS[model_id].num_params\n params = read_next_bytes(fid, num_bytes=8*num_params,\n format_char_sequence=\"d\"*num_params)\n cameras[camera_id] = Camera(id=camera_id,\n model=model_name,\n width=width,\n height=height,\n params=np.array(params))\n assert len(cameras) == num_cameras\n return cameras"},{"identifier":"read_points3D_binary","path":"scene/colmap_loader.py","snippet":"def read_points3D_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadPoints3DBinary(const std::string& path)\n void Reconstruction::WritePoints3DBinary(const std::string& path)\n \"\"\"\n\n\n with open(path_to_model_file, \"rb\") as fid:\n num_points = read_next_bytes(fid, 8, \"Q\")[0]\n\n xyzs = np.empty((num_points, 3))\n rgbs = np.empty((num_points, 3))\n errors = np.empty((num_points, 1))\n\n for p_id in range(num_points):\n binary_point_line_properties = read_next_bytes(\n fid, num_bytes=43, format_char_sequence=\"QdddBBBd\")\n xyz = np.array(binary_point_line_properties[1:4])\n rgb = np.array(binary_point_line_properties[4:7])\n error = np.array(binary_point_line_properties[7])\n track_length = read_next_bytes(\n fid, num_bytes=8, format_char_sequence=\"Q\")[0]\n track_elems = read_next_bytes(\n fid, num_bytes=8*track_length,\n format_char_sequence=\"ii\"*track_length)\n xyzs[p_id] = xyz\n rgbs[p_id] = rgb\n errors[p_id] = error\n return xyzs, rgbs, errors"},{"identifier":"read_points3D_text","path":"scene/colmap_loader.py","snippet":"def read_points3D_text(path):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadPoints3DText(const std::string& path)\n void Reconstruction::WritePoints3DText(const std::string& path)\n \"\"\"\n xyzs = None\n rgbs = None\n errors = None\n num_points = 0\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n num_points += 1\n\n\n xyzs = np.empty((num_points, 3))\n rgbs = np.empty((num_points, 3))\n errors = np.empty((num_points, 1))\n count = 0\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n xyz = np.array(tuple(map(float, elems[1:4])))\n rgb = np.array(tuple(map(int, elems[4:7])))\n error = np.array(float(elems[7]))\n xyzs[count] = xyz\n rgbs[count] = rgb\n errors[count] = error\n count += 1\n\n return xyzs, rgbs, errors"},{"identifier":"getWorld2View2","path":"utils/graphics_utils.py","snippet":"def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):\n Rt = np.zeros((4, 4))\n Rt[:3, :3] = R.transpose()\n Rt[:3, 3] = t\n Rt[3, 3] = 1.0\n\n C2W = np.linalg.inv(Rt)\n cam_center = C2W[:3, 3]\n cam_center = (cam_center + translate) * scale\n C2W[:3, 3] = cam_center\n Rt = np.linalg.inv(C2W)\n return np.float32(Rt)"},{"identifier":"focal2fov","path":"utils/graphics_utils.py","snippet":"def focal2fov(focal, pixels):\n return 2*math.atan(pixels/(2*focal))"},{"identifier":"fov2focal","path":"utils/graphics_utils.py","snippet":"def fov2focal(fov, pixels):\n return pixels / (2 * math.tan(fov / 2))"},{"identifier":"SH2RGB","path":"utils/sh_utils.py","snippet":"def SH2RGB(sh):\n return sh * C0 + 0.5"},{"identifier":"BasicPointCloud","path":"scene/gaussian_model.py","snippet":"class GaussianModel:\n def setup_functions(self):\n def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation, transform):\n def __init__(self, sh_degree : int, smpl_type : str, motion_offset_flag : bool, actor_gender: str):\n def capture(self):\n def restore(self, model_args, training_args):\n def get_scaling(self):\n def get_rotation(self):\n def get_xyz(self):\n def get_features(self):\n def get_opacity(self):\n def get_covariance(self, scaling_modifier = 1, transform=None):\n def oneupSHdegree(self):\n def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):\n def training_setup(self, training_args):\n def update_learning_rate(self, iteration):\n def construct_list_of_attributes(self):\n def save_ply(self, path):\n def reset_opacity(self):\n def load_ply(self, path):\n def replace_tensor_to_optimizer(self, tensor, name):\n def _prune_optimizer(self, mask):\n def prune_points(self, mask):\n def cat_tensors_to_optimizer(self, tensors_dict):\n def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):\n def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):\n def densify_and_clone(self, grads, grad_threshold, scene_extent):\n def kl_densify_and_clone(self, grads, grad_threshold, scene_extent, kl_threshold=0.4):\n def kl_densify_and_split(self, grads, grad_threshold, scene_extent, kl_threshold=0.4, N=2):\n def kl_merge(self, grads, grad_threshold, scene_extent, kl_threshold=0.1):\n def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, kl_threshold=0.4, t_vertices=None, iter=None):\n def kl_div(self, mu_0, rotation_0_q, scaling_0_diag, mu_1, rotation_1_q, scaling_1_diag):\n def add_densification_stats(self, viewspace_point_tensor, update_filter):\n def coarse_deform_c2source(self, query_pts, params, t_params, t_vertices, lbs_weights=None, correct_Rs=None, return_transl=False):\ndef read_pickle(pkl_path):\ndef SMPL_to_tensor(params, device):\ndef batch_rodrigues_torch(poses):\ndef get_rigid_transformation_torch(rot_mats, joints, parents):\ndef get_transform_params_torch(smpl, params, rot_mats=None, correct_Rs=None):\ndef batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n L = build_scaling_rotation(scaling_modifier * scaling, rotation)\n L_0 = rotation_0 @ scaling_0\n A = torch.matmul(bweights, A.reshape(bs, joints_num, -1))\n A = torch.reshape(A, (bs, -1, 4, 4))\n A = torch.matmul(bweights, self.s_A.reshape(bs, joints_num, -1))\n A = torch.reshape(A, (bs, -1, 4, 4))\n K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1)\n K = K.reshape([batch_size, 3, 3])\n A = get_rigid_transformation_torch(rot_mats, joints, parents)\n R = params['R'] \n K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device)\n K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \\\n .view((batch_size, 3, 3))"},{"identifier":"SMPL","path":"smpl/smpl_numpy.py","snippet":"class SMPL():\n def __init__(self, sex, model_dir):\n super(SMPL, self).__init__()\n\n model_paths = {\n 'male': os.path.join(model_dir, MALE_PATH),\n 'female': os.path.join(model_dir, FEMALE_PATH),\n # 'neutral': os.path.join(model_dir, NEUTRAL_PATH)\n 'neutral': os.path.join('assets/SMPL_NEUTRAL.pkl')\n }\n\n with open(model_paths[sex], 'rb') as f:\n smpl_model = pickle.load(f, encoding='latin1')\n self.J_regressor = np.array(smpl_model['J_regressor'].todense()) # (24, 6890)\n self.weights = smpl_model['weights'] # (6890, 24)\n self.posedirs = smpl_model['posedirs'] # (6890, 3, 207)\n self.v_template = smpl_model['v_template'] # (6890, 3)\n self.shapedirs = np.array(smpl_model['shapedirs']) # (6890, 3, 10)\n self.faces = smpl_model['f'].astype('int32') # (13776, 3)\n self.kintree_table = smpl_model['kintree_table'].astype('int64') # (2, 24)\n\n id_to_col = {self.kintree_table[1, i].item(): i for i in range(self.kintree_table.shape[1])}\n self.parent = np.array([id_to_col[self.kintree_table[0, it]] for it in range(1, self.kintree_table.shape[1])])\n\n self.pose_shape = [24, 3]\n self.beta_shape = [10]\n self.pose = np.zeros(self.pose_shape)\n self.beta = np.zeros(self.beta_shape)\n\n self.verts = None\n self.J = None\n self.R = None\n\n def __call__(self, pose, beta):\n\n v_template = self.v_template # (6890, 3)\n shapedirs = self.shapedirs.reshape(-1,10) # (6890*3, 10)\n beta = beta[:, None] # (10, 1)\n\n v_shaped = shapedirs.dot(beta).reshape(6890, 3) + v_template # (6890, 3)\n J = self.J_regressor.dot(v_shaped) # (24, 3)\n\n # input is a rotation matrix: (24,3,3)\n if pose.shape == (24, 3, 3):\n R = pose\n # input is a rotation axis-angle vector: (1, 72), (72, 1) or (72, )\n elif pose.shape == (1, 72) or pose.shape == (72, 1) or pose.shape == (72,):\n pose_vectors = pose.reshape(-1, 3) # (24, 3)\n R = np.array([rodrigues(pose_vectors[p_idx])[0] \n for p_idx in range(pose_vectors.shape[0])\n ], \n dtype='float32') # (24, 3, 3)\n else:\n raise ValueError(\"Unsupported Pose Inputs - the Pose Shape is {}\".format(pose.shape))\n\n Is = np.eye(3, dtype='float32')[None, :] # (1, 3, 3)\n lrotmin = (R[1:,:] - Is).reshape(-1, 1) # (23x3x3, 1)\n posedirs = self.posedirs.reshape(-1,207) # (6890x3, 207)\n v_posed = v_shaped + posedirs.dot(lrotmin).reshape(6890, 3) # (6890, 3)\n\n J_ = J.copy()\n J_[1:, :] = J[1:, :] - J[self.parent, :] # (24, 3)\n G_ = np.concatenate([R, J_[:, :, None]], axis=-1) # (24, 3, 4)\n pad_rows = np.array([[0, 0, 0, 1]], dtype='float32')\n pad_rows = np.repeat(pad_rows, 24, axis=0).reshape(-1, 1, 4)\n G_ = np.concatenate([G_, pad_rows], axis=1) # (24, 4, 4)\n\n G = [G_[0].copy()]\n for i in range(1, 24):\n G.append(G[self.parent[i-1]].dot(G_[i, :, :]))\n G = np.stack(G, axis=0) # (24, 4, 4)\n\n joints = G[:, :3, 3]\n rest_joints = np.concatenate([J, np.zeros((24, 1))], axis=-1)[:, :, None] # (24, 4, 1)\n zeros = np.zeros((24, 4, 3), dtype='float32') # (24, 4, 3)\n rest_joints_mtx = np.concatenate([zeros, rest_joints], axis=-1) # (24, 4, 4) \n # print(\"G1: \", G[0], \"rest_joints_mtx1: \", rest_joints_mtx[0])\n posed_joints_mtx = np.matmul(G, rest_joints_mtx)\n # print(\"rest_joints_mtx2: \", posed_joints_mtx[0])\n G = G - posed_joints_mtx\n # print(G[0]) \n rest_shape_h = np.concatenate([v_posed, np.ones(v_posed.shape[0])[:, None]], axis=-1) #(6890, 4)\n T = self.weights.dot(G.reshape(24, -1)).reshape(6890, 4, 4)\n v = np.matmul(T, rest_shape_h[:, :, None])[:, :3, 0]\n \n return v, joints"},{"identifier":"SMPLX","path":"smplx/body_models.py","snippet":"class SMPLX(SMPLH):\n '''\n SMPL-X (SMPL eXpressive) is a unified body model, with shape parameters\n trained jointly for the face, hands and body.\n SMPL-X uses standard vertex based linear blend skinning with learned\n corrective blend shapes, has N=10475 vertices and K=54 joints,\n which includes joints for the neck, jaw, eyeballs and fingers.\n '''\n\n NUM_BODY_JOINTS = SMPLH.NUM_BODY_JOINTS\n NUM_HAND_JOINTS = 15\n NUM_FACE_JOINTS = 3\n NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS + NUM_FACE_JOINTS\n EXPRESSION_SPACE_DIM = 100\n NECK_IDX = 12\n\n def __init__(\n self, model_path: str,\n kid_template_path: str = '',\n num_expression_coeffs: int = 10,\n create_expression: bool = True,\n expression: Optional[Tensor] = None,\n create_jaw_pose: bool = True,\n jaw_pose: Optional[Tensor] = None,\n create_leye_pose: bool = True,\n leye_pose: Optional[Tensor] = None,\n create_reye_pose=True,\n reye_pose: Optional[Tensor] = None,\n use_face_contour: bool = False,\n batch_size: int = 1,\n gender: str = 'neutral',\n age: str = 'adult',\n dtype=torch.float32,\n ext: str = 'npz',\n **kwargs\n ) -> None:\n ''' SMPLX model constructor\n\n Parameters\n ----------\n model_path: str\n The path to the folder or to the file where the model\n parameters are stored\n num_expression_coeffs: int, optional\n Number of expression components to use\n (default = 10).\n create_expression: bool, optional\n Flag for creating a member variable for the expression space\n (default = True).\n expression: torch.tensor, optional, Bx10\n The default value for the expression member variable.\n (default = None)\n create_jaw_pose: bool, optional\n Flag for creating a member variable for the jaw pose.\n (default = False)\n jaw_pose: torch.tensor, optional, Bx3\n The default value for the jaw pose variable.\n (default = None)\n create_leye_pose: bool, optional\n Flag for creating a member variable for the left eye pose.\n (default = False)\n leye_pose: torch.tensor, optional, Bx10\n The default value for the left eye pose variable.\n (default = None)\n create_reye_pose: bool, optional\n Flag for creating a member variable for the right eye pose.\n (default = False)\n reye_pose: torch.tensor, optional, Bx10\n The default value for the right eye pose variable.\n (default = None)\n use_face_contour: bool, optional\n Whether to compute the keypoints that form the facial contour\n batch_size: int, optional\n The batch size used for creating the member variables\n gender: str, optional\n Which gender to load\n dtype: torch.dtype\n The data type for the created variables\n '''\n\n # Load the model\n if osp.isdir(model_path):\n model_fn = 'SMPLX_{}.{ext}'.format(gender.upper(), ext=ext)\n smplx_path = os.path.join(model_path, model_fn)\n else:\n smplx_path = model_path\n assert osp.exists(smplx_path), 'Path {} does not exist!'.format(\n smplx_path)\n\n if ext == 'pkl':\n with open(smplx_path, 'rb') as smplx_file:\n model_data = pickle.load(smplx_file, encoding='latin1')\n elif ext == 'npz':\n model_data = np.load(smplx_path, allow_pickle=True)\n else:\n raise ValueError('Unknown extension: {}'.format(ext))\n\n data_struct = Struct(**model_data)\n\n super(SMPLX, self).__init__(\n model_path=model_path,\n kid_template_path=kid_template_path,\n data_struct=data_struct,\n dtype=dtype,\n batch_size=batch_size,\n vertex_ids=VERTEX_IDS['smplx'],\n gender=gender, age=age, ext=ext,\n **kwargs)\n\n lmk_faces_idx = data_struct.lmk_faces_idx\n self.register_buffer('lmk_faces_idx',\n torch.tensor(lmk_faces_idx, dtype=torch.long))\n lmk_bary_coords = data_struct.lmk_bary_coords\n self.register_buffer('lmk_bary_coords',\n torch.tensor(lmk_bary_coords, dtype=dtype))\n\n self.use_face_contour = use_face_contour\n if self.use_face_contour:\n dynamic_lmk_faces_idx = data_struct.dynamic_lmk_faces_idx\n dynamic_lmk_faces_idx = torch.tensor(\n dynamic_lmk_faces_idx,\n dtype=torch.long)\n self.register_buffer('dynamic_lmk_faces_idx',\n dynamic_lmk_faces_idx)\n\n dynamic_lmk_bary_coords = data_struct.dynamic_lmk_bary_coords\n dynamic_lmk_bary_coords = torch.tensor(\n dynamic_lmk_bary_coords, dtype=dtype)\n self.register_buffer('dynamic_lmk_bary_coords',\n dynamic_lmk_bary_coords)\n\n neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents)\n self.register_buffer(\n 'neck_kin_chain',\n torch.tensor(neck_kin_chain, dtype=torch.long))\n\n if create_jaw_pose:\n if jaw_pose is None:\n default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype)\n else:\n default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype)\n jaw_pose_param = nn.Parameter(default_jaw_pose,\n requires_grad=True)\n self.register_parameter('jaw_pose', jaw_pose_param)\n\n if create_leye_pose:\n if leye_pose is None:\n default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype)\n else:\n default_leye_pose = torch.tensor(leye_pose, dtype=dtype)\n leye_pose_param = nn.Parameter(default_leye_pose,\n requires_grad=True)\n self.register_parameter('leye_pose', leye_pose_param)\n\n if create_reye_pose:\n if reye_pose is None:\n default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype)\n else:\n default_reye_pose = torch.tensor(reye_pose, dtype=dtype)\n reye_pose_param = nn.Parameter(default_reye_pose,\n requires_grad=True)\n self.register_parameter('reye_pose', reye_pose_param)\n\n shapedirs = data_struct.shapedirs\n if len(shapedirs.shape) < 3:\n shapedirs = shapedirs[:, :, None]\n if (shapedirs.shape[-1] < self.SHAPE_SPACE_DIM +\n self.EXPRESSION_SPACE_DIM):\n print(f'WARNING: You are using a {self.name()} model, with only'\n ' 10 shape and 10 expression coefficients.')\n expr_start_idx = 10\n expr_end_idx = 20\n num_expression_coeffs = min(num_expression_coeffs, 10)\n else:\n expr_start_idx = self.SHAPE_SPACE_DIM\n expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs\n num_expression_coeffs = min(\n num_expression_coeffs, self.EXPRESSION_SPACE_DIM)\n\n self._num_expression_coeffs = num_expression_coeffs\n\n expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx]\n self.register_buffer(\n 'expr_dirs', to_tensor(to_np(expr_dirs), dtype=dtype))\n\n if create_expression:\n if expression is None:\n default_expression = torch.zeros(\n [batch_size, self.num_expression_coeffs], dtype=dtype)\n else:\n default_expression = torch.tensor(expression, dtype=dtype)\n expression_param = nn.Parameter(default_expression,\n requires_grad=True)\n self.register_parameter('expression', expression_param)\n\n def name(self) -> str:\n return 'SMPL-X'\n\n @property\n def num_expression_coeffs(self):\n return self._num_expression_coeffs\n\n def create_mean_pose(self, data_struct, flat_hand_mean=False):\n # Create the array for the mean pose. If flat_hand is false, then use\n # the mean that is given by the data, rather than the flat open hand\n global_orient_mean = torch.zeros([3], dtype=self.dtype)\n body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3],\n dtype=self.dtype)\n jaw_pose_mean = torch.zeros([3], dtype=self.dtype)\n leye_pose_mean = torch.zeros([3], dtype=self.dtype)\n reye_pose_mean = torch.zeros([3], dtype=self.dtype)\n # pose_mean = np.concatenate([global_orient_mean, body_pose_mean, jaw_pose_mean, leye_pose_mean, reye_pose_mean, self.left_hand_mean, self.right_hand_mean], axis=0)\n pose_mean = torch.cat([global_orient_mean, body_pose_mean, jaw_pose_mean, leye_pose_mean, reye_pose_mean, self.left_hand_mean, self.right_hand_mean], 0)\n\n return pose_mean\n\n def extra_repr(self):\n msg = super(SMPLX, self).extra_repr()\n msg = [\n msg,\n f'Number of Expression Coefficients: {self.num_expression_coeffs}'\n ]\n return '\\n'.join(msg)\n\n def forward(\n self,\n betas: Optional[Tensor] = None,\n global_orient: Optional[Tensor] = None,\n body_pose: Optional[Tensor] = None,\n left_hand_pose: Optional[Tensor] = None,\n right_hand_pose: Optional[Tensor] = None,\n transl: Optional[Tensor] = None,\n expression: Optional[Tensor] = None,\n jaw_pose: Optional[Tensor] = None,\n leye_pose: Optional[Tensor] = None,\n reye_pose: Optional[Tensor] = None,\n return_verts: bool = True,\n return_full_pose: bool = False,\n pose2rot: bool = True,\n return_shaped: bool = True,\n **kwargs\n ) -> TensorOutput:\n '''\n Forward pass for the SMPLX model\n\n Parameters\n ----------\n global_orient: torch.tensor, optional, shape Bx3\n If given, ignore the member variable and use it as the global\n rotation of the body. Useful if someone wishes to predicts this\n with an external model. (default=None)\n betas: torch.tensor, optional, shape BxN_b\n If given, ignore the member variable `betas` and use it\n instead. For example, it can used if shape parameters\n `betas` are predicted from some external model.\n (default=None)\n expression: torch.tensor, optional, shape BxN_e\n If given, ignore the member variable `expression` and use it\n instead. For example, it can used if expression parameters\n `expression` are predicted from some external model.\n body_pose: torch.tensor, optional, shape Bx(J*3)\n If given, ignore the member variable `body_pose` and use it\n instead. For example, it can used if someone predicts the\n pose of the body joints are predicted from some external model.\n It should be a tensor that contains joint rotations in\n axis-angle format. (default=None)\n left_hand_pose: torch.tensor, optional, shape BxP\n If given, ignore the member variable `left_hand_pose` and\n use this instead. It should either contain PCA coefficients or\n joint rotations in axis-angle format.\n right_hand_pose: torch.tensor, optional, shape BxP\n If given, ignore the member variable `right_hand_pose` and\n use this instead. It should either contain PCA coefficients or\n joint rotations in axis-angle format.\n jaw_pose: torch.tensor, optional, shape Bx3\n If given, ignore the member variable `jaw_pose` and\n use this instead. It should either joint rotations in\n axis-angle format.\n transl: torch.tensor, optional, shape Bx3\n If given, ignore the member variable `transl` and use it\n instead. For example, it can used if the translation\n `transl` is predicted from some external model.\n (default=None)\n return_verts: bool, optional\n Return the vertices. (default=True)\n return_full_pose: bool, optional\n Returns the full axis-angle pose vector (default=False)\n\n Returns\n -------\n output: ModelOutput\n A named tuple of type `ModelOutput`\n '''\n\n # If no shape and pose parameters are passed along, then use the\n # ones from the module\n global_orient = (global_orient if global_orient is not None else\n self.global_orient)\n body_pose = body_pose if body_pose is not None else self.body_pose\n betas = betas if betas is not None else self.betas\n\n left_hand_pose = (left_hand_pose if left_hand_pose is not None else\n self.left_hand_pose)\n right_hand_pose = (right_hand_pose if right_hand_pose is not None else\n self.right_hand_pose)\n jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose\n leye_pose = leye_pose if leye_pose is not None else self.leye_pose\n reye_pose = reye_pose if reye_pose is not None else self.reye_pose\n expression = expression if expression is not None else self.expression\n\n apply_trans = transl is not None or hasattr(self, 'transl')\n if transl is None:\n if hasattr(self, 'transl'):\n transl = self.transl\n\n if self.use_pca:\n left_hand_pose = torch.einsum(\n 'bi,ij->bj', [left_hand_pose, self.left_hand_components])\n right_hand_pose = torch.einsum(\n 'bi,ij->bj', [right_hand_pose, self.right_hand_components])\n\n full_pose = torch.cat([global_orient.reshape(-1, 1, 3),\n body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3),\n jaw_pose.reshape(-1, 1, 3),\n leye_pose.reshape(-1, 1, 3),\n reye_pose.reshape(-1, 1, 3),\n left_hand_pose.reshape(-1, 15, 3),\n right_hand_pose.reshape(-1, 15, 3)],\n dim=1).reshape(-1, 165).to(self.pose_mean.device)\n\n # Add the mean pose of the model. Does not affect the body, only the\n # hands when flat_hand_mean == False\n full_pose += self.pose_mean\n\n batch_size = max(betas.shape[0], global_orient.shape[0],\n body_pose.shape[0])\n # Concatenate the shape and expression coefficients\n scale = int(batch_size / betas.shape[0])\n if scale > 1:\n betas = betas.expand(scale, -1)\n shape_components = torch.cat([betas, expression], dim=-1).to(self.pose_mean.device)\n\n shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)\n\n vertices, joints, A, T = lbs(shape_components, full_pose, self.v_template,\n shapedirs, self.posedirs,\n self.J_regressor, self.parents,\n self.lbs_weights, pose2rot=pose2rot,\n )\n\n lmk_faces_idx = self.lmk_faces_idx.unsqueeze(\n dim=0).expand(batch_size, -1).contiguous()\n lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(\n self.batch_size, 1, 1)\n if self.use_face_contour:\n lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(\n vertices, full_pose, self.dynamic_lmk_faces_idx,\n self.dynamic_lmk_bary_coords,\n self.neck_kin_chain,\n pose2rot=True,\n )\n dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords\n\n lmk_faces_idx = torch.cat([lmk_faces_idx,\n dyn_lmk_faces_idx], 1)\n lmk_bary_coords = torch.cat(\n [lmk_bary_coords.expand(batch_size, -1, -1),\n dyn_lmk_bary_coords], 1)\n\n landmarks = vertices2landmarks(vertices, self.faces_tensor,\n lmk_faces_idx,\n lmk_bary_coords)\n\n # import matplotlib.pyplot as plt\n # import numpy as np\n # xs = joints[0,:,0]\n # ys = joints[0,:,1]\n # plt.scatter(xs, ys)\n\n # # zip joins x and y coordinates in pairs\n # count = 0\n # for x,y in zip(xs, ys):\n\n # label = \"{:.2f}\".format(count)\n\n # plt.annotate(label, # this is the text\n # (x,y), # these are the coordinates to position the label\n # textcoords=\"offset points\", # how to position the text\n # xytext=(0,10), # distance from text to points (x,y)\n # ha='center') # horizontal alignment can be left, right or center\n # count += 1\n # plt.savefig(\"joints.png\")\n # import pdb; pdb.set_trace()\n\n # Add any extra joints that might be needed\n joints = self.vertex_joint_selector(vertices, joints)\n # Add the landmarks to the joints\n joints = torch.cat([joints, landmarks], dim=1)\n # Map the joints to the current dataset\n\n if self.joint_mapper is not None:\n joints = self.joint_mapper(joints=joints, vertices=vertices)\n\n if apply_trans:\n joints += transl.unsqueeze(dim=1)\n vertices += transl.unsqueeze(dim=1)\n # clone because we are modifying them in-place\n A = A.clone()\n A[..., :3, 3] += transl.unsqueeze(dim=1)\n T = T.clone()\n T[..., :3, 3] += transl.unsqueeze(dim=1)\n\n v_shaped = None\n if return_shaped:\n v_shaped = self.v_template + blend_shapes(betas, self.shapedirs)\n else:\n v_shaped = Tensor(0)\n\n output = TensorOutput(vertices=vertices if return_verts else None,\n joints=joints,\n betas=betas,\n expression=expression,\n global_orient=global_orient,\n body_pose=body_pose,\n left_hand_pose=left_hand_pose,\n right_hand_pose=right_hand_pose,\n jaw_pose=jaw_pose,\n v_shaped=v_shaped,\n full_pose=full_pose if return_full_pose else None,\n A=A,\n T=T,\n f=self.faces)\n return output"},{"identifier":"SMCReader","path":"data/dna_rendering/dna_rendering_sample_code/SMCReader.py","snippet":"class SMCReader:\n\n def __init__(self, file_path):\n \"\"\"Read SenseMocapFile endswith \".smc\".\n\n Args:\n file_path (str):\n Path to an SMC file.\n body_model (nn.Module or dict):\n Only needed for SMPL transformation to device frame\n if nn.Module: a body_model instance\n if dict: a body_model config\n \"\"\"\n self.smc = h5py.File(file_path, 'r')\n self.__calibration_dict__ = None\n self.__kinect_calib_dict__ = None \n self.__available_keys__ = list(self.smc.keys())\n \n self.actor_info = None \n if hasattr(self.smc, 'attrs') and len(self.smc.attrs.keys()) > 0:\n self.actor_info = dict(\n id=self.smc.attrs['actor_id'],\n perf_id=self.smc.attrs['performance_id'],\n age=self.smc.attrs['age'],\n gender=self.smc.attrs['gender'],\n height=self.smc.attrs['height'],\n weight=self.smc.attrs['weight'],\n ethnicity=self.smc.attrs['ethnicity'],\n )\n\n self.Camera_5mp_info = None \n if 'Camera_5mp' in self.smc:\n self.Camera_5mp_info = dict(\n num_device=self.smc['Camera_5mp'].attrs['num_device'],\n num_frame=self.smc['Camera_5mp'].attrs['num_frame'],\n resolution=self.smc['Camera_5mp'].attrs['resolution'],\n )\n self.Camera_12mp_info = None \n if 'Camera_12mp' in self.smc:\n self.Camera_12mp_info = dict(\n num_device=self.smc['Camera_12mp'].attrs['num_device'],\n num_frame=self.smc['Camera_12mp'].attrs['num_frame'],\n resolution=self.smc['Camera_12mp'].attrs['resolution'],\n )\n self.Kinect_info = None\n if 'Kinect' in self.smc:\n self.Kinect_info=dict(\n num_device=self.smc['Kinect'].attrs['num_device'],\n num_frame=self.smc['Kinect'].attrs['num_frame'],\n resolution=self.smc['Kinect'].attrs['resolution'],\n )\n\n def get_available_keys(self):\n return self.__available_keys__ \n\n def get_actor_info(self):\n return self.actor_info\n \n def get_Camera_12mp_info(self):\n return self.Camera_12mp_info\n\n def get_Camera_5mp_info(self):\n return self.Camera_5mp_info\n \n def get_Kinect_info(self):\n return self.Kinect_info\n \n ### RGB Camera Calibration\n def get_Calibration_all(self):\n \"\"\"Get calibration matrix of all cameras and save it in self\n \n Args:\n None\n\n Returns:\n Dictionary of calibration matrixs of all matrixs.\n dict( \n Camera_Parameter: Camera_id : Matrix_type : value\n )\n Notice:\n Camera_id(str) in {'Camera_5mp': '0'~'47', 'Camera_12mp':'48'~'60'}\n Matrix_type in ['D', 'K', 'RT', 'Color_Calibration'] \n \"\"\" \n if not 'Camera_Parameter' in self.smc:\n print(\"=== no key: Camera_Parameter.\\nplease check available keys!\")\n return None \n\n if self.__calibration_dict__ is not None:\n return self.__calibration_dict__\n\n self.__calibration_dict__ = dict()\n for ci in self.smc['Camera_Parameter'].keys():\n self.__calibration_dict__.setdefault(ci,dict())\n for mt in ['D', 'K', 'RT', 'Color_Calibration'] :\n self.__calibration_dict__[ci][mt] = \\\n self.smc['Camera_Parameter'][ci][mt][()]\n return self.__calibration_dict__\n\n def get_Calibration(self, Camera_id):\n \"\"\"Get calibration matrixs of a certain camera by its type and id \n\n Args:\n Camera_id (int/str of a number):\n Camera_id(str) in {'Camera_5mp': '0'~'47', \n 'Camera_12mp':'48'~'60'}\n Returns:\n Dictionary of calibration matrixs.\n ['D', 'K', 'RT', 'Color_Calibration'] \n \"\"\"\n if not 'Camera_Parameter' in self.smc:\n print(\"=== no key: Camera_Parameter.\\nplease check available keys!\")\n return None \n\n rs = dict()\n for k in ['D', 'K', 'RT', 'Color_Calibration'] :\n rs[k] = self.smc['Camera_Parameter'][f'{int(Camera_id):02d}'][k][()]\n return rs\n\n ### Kinect Camera Calibration\n def get_Kinect_Calibration_all(self):\n \"\"\"Get calibration matrix of all kinect cameras and save it in self\n \n Args:\n None\n\n Returns:\n Dictionary of calibration matrixs of all matrixs.\n dict( \n Camera_group: Camera_id : Matrix_type : value\n )\n Notice:\n Camera_group(str) in ['Kinect']\n Camera_id(str) in {'Kinect': '0'~'7'}\n Matrix_type in ['D', 'K', 'RT'] \n \"\"\" \n if not 'Calibration' in self.smc:\n print(\"=== no key: Calibration.\\nplease check available keys!\")\n return None \n\n if self.__kinect_calib_dict__ is not None:\n return self.__kinect_calib_dict__\n\n self.__kinect_calib_dict__ = dict()\n for cg in ['Kinect']:\n self.__kinect_calib_dict__.setdefault(cg,dict())\n for ci in self.smc['Calibration'][cg].keys():\n self.__kinect_calib_dict__[cg].setdefault(ci,dict())\n for mt in ['D', 'K', 'RT'] :\n self.__kinect_calib_dict__[cg][ci][mt] = \\\n self.smc['Calibration'][cg][ci][mt][()]\n return self.__kinect_calib_dict__\n\n def get_kinect_Calibration(self, Camera_id):\n \"\"\"Get calibration matrixs of a certain kinect camera by its type and id \n\n Args:\n Camera_group (str):\n Camera_group in ['Kinect'].\n Camera_id (int/str of a number):\n CameraID(str) in {'Kinect': '0'~'7'}\n Returns:\n Dictionary of calibration matrixs.\n ['D', 'K', 'RT'] \n \"\"\" \n if not 'Calibration' in self.smc:\n print(\"=== no key: Calibration.\\nplease check available keys!\")\n return None \n\n Camera_id = f'{int(Camera_id):02d}'\n assert(Camera_id in self.smc['Calibration'][\"Kinect\"].keys())\n rs = dict()\n for k in ['D', 'K', 'RT']:\n rs[k] = self.smc['Calibration'][\"Kinect\"][Camera_id][k][()]\n return rs\n\n ### RGB image\n def __read_color_from_bytes__(self, color_array):\n \"\"\"Decode an RGB image from an encoded byte array.\"\"\"\n return cv2.imdecode(color_array, cv2.IMREAD_COLOR)\n\n def get_mask(self, Camera_id, Frame_id=None, disable_tqdm=True):\n \"\"\"Get mask from Camera_id, Frame_id\n\n Args:\n Camera_id (int/str of a number):\n Camera_id (str) in \n {'Camera_5mp': '0'~'47', \n 'Camera_12mp':'48'~'60',\n 'Kinect': '0'~'7'}\n Frame_id a.(int/str of a number): '0' ~ 'num_frame'\n b.list of numbers (int/str)\n c.None: get batch of all imgs in order of time sequence \n Returns:\n a single img :\n 'color': HWC in bgr (uint8)\n 'mask' : HW (uint8)\n 'depth': HW (uint16)\n \"\"\" \n if not 'Mask' in self.smc:\n print(\"=== no key: Mask.\\nplease check available keys!\")\n return None \n\n Camera_id = str(Camera_id)\n\n assert(isinstance(Frame_id,(list,int, str, type(None))))\n if isinstance(Frame_id, (str,int)):\n Frame_id = str(Frame_id)\n assert(Frame_id in self.smc['Mask'][Camera_id]['mask'].keys())\n img_byte = self.smc['Mask'][Camera_id]['mask'][Frame_id][()]\n img_color = self.__read_color_from_bytes__(img_byte)\n img_color = np.max(img_color,2)\n return img_color \n else:\n if Frame_id is None:\n Frame_id_list =sorted([int(l) for l in self.smc['Mask'][Camera_id]['mask'].keys()])\n elif isinstance(Frame_id, list):\n Frame_id_list = Frame_id\n rs = []\n for fi in tqdm.tqdm(Frame_id_list, disable=disable_tqdm):\n rs.append(self.get_mask(Camera_id,fi))\n return np.stack(rs,axis=0)\n\n def get_img(self, Camera_group, Camera_id, Image_type, Frame_id=None, disable_tqdm=True):\n \"\"\"Get image its Camera_group, Camera_id, Image_type and Frame_id\n\n Args:\n Camera_group (str):\n Camera_group in ['Camera_12mp', 'Camera_5mp','Kinect'].\n Camera_id (int/str of a number):\n CameraID (str) in \n {'Camera_5mp': '0'~'47', \n 'Camera_12mp':'48'~'60',\n 'Kinect': '0'~'7'}\n Image_type(str) in \n {'Camera_5mp': ['color'], \n 'Camera_12mp': ['color'],\n 'Kinect': ['depth', 'mask']}\n Frame_id a.(int/str of a number): '0' ~ 'num_frame'('149') \n b.list of numbers (int/str)\n c.None: get batch of all imgs in order of time sequence \n Returns:\n a single img :\n 'color': HWC in bgr (uint8)\n 'mask' : HW (uint8)\n 'depth': HW (uint16)\n \"\"\" \n if not Camera_group in self.smc:\n print(\"=== no key: %s.\\nplease check available keys!\" % Camera_group)\n return None\n\n assert(Camera_group in ['Camera_12mp', 'Camera_5mp','Kinect'])\n Camera_id = str(Camera_id)\n assert(Camera_id in self.smc[Camera_group].keys())\n assert(Image_type in self.smc[Camera_group][Camera_id].keys())\n assert(isinstance(Frame_id,(list,int, str, type(None))))\n if isinstance(Frame_id, (str,int)):\n Frame_id = str(Frame_id)\n assert(Frame_id in self.smc[Camera_group][Camera_id][Image_type].keys())\n if Image_type in ['color']:\n img_byte = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\n img_color = self.__read_color_from_bytes__(img_byte)\n if Image_type == 'mask':\n img_byte = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\n img_color = self.__read_color_from_bytes__(img_byte)\n img_color = np.max(img_color,2)\n if Image_type == 'depth':\n img_color = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\n return img_color \n else:\n if Frame_id is None:\n Frame_id_list =sorted([int(l) for l in self.smc[Camera_group][Camera_id][Image_type].keys()])\n elif isinstance(Frame_id, list):\n Frame_id_list = Frame_id\n rs = []\n for fi in tqdm(Frame_id_list, disable=disable_tqdm):\n rs.append(self.get_img(Camera_group, Camera_id, Image_type,fi))\n return np.stack(rs,axis=0)\n \n ###Keypoints2d\n def get_Keypoints2d(self, Camera_id, Frame_id=None):\n \"\"\"Get keypoint2D by its Camera_group, Camera_id and Frame_id\n\n Args:\n Camera_id (int/str of a number):\n CameraID (str) in \n {'Camera_5mp': '0'~'47', \n 'Camera_12mp':'48'~'60',}\n Frame_id a.(int/str of a number): '0' ~ 'num_frame-1'('149') \n b.list of numbers (int/str)\n c.None: get batch of all imgs in order of time sequence \n Returns:\n a single img :\n 'color': HWC in bgr (uint8)\n 'mask' : HW (uint8)\n 'depth': HW (uint16)\n \"\"\" \n if not 'Keypoints_2D' in self.smc:\n print(\"=== no key: Keypoints_2D.\\nplease check available keys!\")\n return None \n\n Camera_id = f'{int(Camera_id):02d}'\n assert(isinstance(Frame_id,(list,int, str, type(None))))\n if isinstance(Frame_id, (str,int)):\n Frame_id = int(Frame_id)\n return self.smc['Keypoints_2D'][Camera_id][()][Frame_id,:]\n else:\n if Frame_id is None:\n return self.smc['Keypoints_2D'][Camera_id][()]\n elif isinstance(Frame_id, list):\n Frame_id_list = Frame_id\n rs = []\n for fi in tqdm.tqdm(Frame_id_list):\n rs.append(self.get_Keypoints2d(Camera_id,fi))\n return np.stack(rs,axis=0)\n\n ###Keypoints3d\n def get_Keypoints3d(self, Frame_id=None):\n \"\"\"Get keypoint3D Frame_id, TODO coordinate\n\n Args:\n Frame_id a.(int/str of a number): '0' ~ 'num_frame-1'('149') \n b.list of numbers (int/str)\n c.None: get batch of all imgs in order of time sequence \n Returns:\n Keypoints3d tensor: np.ndarray of shape ([N], ,3)\n \"\"\" \n if not 'Keypoints_3D' in self.smc:\n print(\"=== no key: Keypoints_3D.\\nplease check available keys!\")\n return None \n\n if isinstance(Frame_id, (str,int)):\n Frame_id = int(Frame_id)\n return self.smc['Keypoints_3D'][\"keypoints3d\"][Frame_id,:]\n else:\n if Frame_id is None:\n return self.smc['Keypoints_3D'][\"keypoints3d\"]\n elif isinstance(Frame_id, list):\n Frame_id_list = Frame_id\n rs = []\n for fi in tqdm.tqdm(Frame_id_list):\n rs.append(self.get_Keypoints3d(fi))\n return np.stack(rs,axis=0)\n\n ###SMPLx\n def get_SMPLx(self, Frame_id=None):\n \"\"\"Get SMPL (world coordinate) computed by mocap processing pipeline.\n\n Args:\n Frame_id (int, list or None, optional):\n int: frame id of one selected frame\n list: a list of frame id\n None: all frames will be returned\n Defaults to None.\n\n Returns:\n dict:\n 'global_orient': np.ndarray of shape (N, 3)\n 'body_pose': np.ndarray of shape (N, 21, 3)\n 'transl': np.ndarray of shape (N, 3)\n 'betas': np.ndarray of shape (1, 10)\n \"\"\"\n if not 'SMPLx' in self.smc:\n print(\"=== no key: SMPLx.\\nplease check available keys!\")\n return None \n\n t_frame = self.smc['SMPLx']['betas'][()].shape[0]\n if Frame_id is None:\n frame_list = range(t_frame)\n elif isinstance(Frame_id, list):\n frame_list = [int(fi) for fi in Frame_id]\n elif isinstance(Frame_id, (int,str)):\n Frame_id = int(Frame_id)\n assert Frame_id < t_frame,\\\n f'Invalid frame_index {Frame_id}'\n frame_list = Frame_id\n else:\n raise TypeError('frame_id should be int, list or None.')\n\n smpl_dict = {}\n for key in ['betas', 'expression', 'fullpose', 'transl']:\n smpl_dict[key] = self.smc['SMPLx'][key][()][frame_list, ...]\n smpl_dict['scale'] = self.smc['SMPLx']['scale'][()]\n\n return smpl_dict\n\n def release(self):\n self.smc = None \n self.__calibration_dict__ = None\n self.__kinect_calib_dict__ = None\n self.__available_keys__ = None\n self.actor_info = None \n self.Camera_5mp_info = None\n self.Camera_12mp_info = None \n self.Kinect_info = None"}],"string":"[\n {\n \"identifier\": \"read_extrinsics_text\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_extrinsics_text(path):\\n \\\"\\\"\\\"\\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\\n \\\"\\\"\\\"\\n images = {}\\n with open(path, \\\"r\\\") as fid:\\n while True:\\n line = fid.readline()\\n if not line:\\n break\\n line = line.strip()\\n if len(line) > 0 and line[0] != \\\"#\\\":\\n elems = line.split()\\n image_id = int(elems[0])\\n qvec = np.array(tuple(map(float, elems[1:5])))\\n tvec = np.array(tuple(map(float, elems[5:8])))\\n camera_id = int(elems[8])\\n image_name = elems[9]\\n elems = fid.readline().split()\\n xys = np.column_stack([tuple(map(float, elems[0::3])),\\n tuple(map(float, elems[1::3]))])\\n point3D_ids = np.array(tuple(map(int, elems[2::3])))\\n images[image_id] = Image(\\n id=image_id, qvec=qvec, tvec=tvec,\\n camera_id=camera_id, name=image_name,\\n xys=xys, point3D_ids=point3D_ids)\\n return images\"\n },\n {\n \"identifier\": \"read_intrinsics_text\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_intrinsics_text(path):\\n \\\"\\\"\\\"\\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\\n \\\"\\\"\\\"\\n cameras = {}\\n with open(path, \\\"r\\\") as fid:\\n while True:\\n line = fid.readline()\\n if not line:\\n break\\n line = line.strip()\\n if len(line) > 0 and line[0] != \\\"#\\\":\\n elems = line.split()\\n camera_id = int(elems[0])\\n model = elems[1]\\n assert model == \\\"PINHOLE\\\", \\\"While the loader support other types, the rest of the code assumes PINHOLE\\\"\\n width = int(elems[2])\\n height = int(elems[3])\\n params = np.array(tuple(map(float, elems[4:])))\\n cameras[camera_id] = Camera(id=camera_id, model=model,\\n width=width, height=height,\\n params=params)\\n return cameras\"\n },\n {\n \"identifier\": \"qvec2rotmat\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def qvec2rotmat(qvec):\\n return np.array([\\n [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,\\n 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],\\n 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],\\n [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],\\n 1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,\\n 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],\\n [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],\\n 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],\\n 1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])\"\n },\n {\n \"identifier\": \"read_extrinsics_binary\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_extrinsics_binary(path_to_model_file):\\n \\\"\\\"\\\"\\n see: src/base/reconstruction.cc\\n void Reconstruction::ReadImagesBinary(const std::string& path)\\n void Reconstruction::WriteImagesBinary(const std::string& path)\\n \\\"\\\"\\\"\\n images = {}\\n with open(path_to_model_file, \\\"rb\\\") as fid:\\n num_reg_images = read_next_bytes(fid, 8, \\\"Q\\\")[0]\\n for _ in range(num_reg_images):\\n binary_image_properties = read_next_bytes(\\n fid, num_bytes=64, format_char_sequence=\\\"idddddddi\\\")\\n image_id = binary_image_properties[0]\\n qvec = np.array(binary_image_properties[1:5])\\n tvec = np.array(binary_image_properties[5:8])\\n camera_id = binary_image_properties[8]\\n image_name = \\\"\\\"\\n current_char = read_next_bytes(fid, 1, \\\"c\\\")[0]\\n while current_char != b\\\"\\\\x00\\\": # look for the ASCII 0 entry\\n image_name += current_char.decode(\\\"utf-8\\\")\\n current_char = read_next_bytes(fid, 1, \\\"c\\\")[0]\\n num_points2D = read_next_bytes(fid, num_bytes=8,\\n format_char_sequence=\\\"Q\\\")[0]\\n x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,\\n format_char_sequence=\\\"ddq\\\"*num_points2D)\\n xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),\\n tuple(map(float, x_y_id_s[1::3]))])\\n point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))\\n images[image_id] = Image(\\n id=image_id, qvec=qvec, tvec=tvec,\\n camera_id=camera_id, name=image_name,\\n xys=xys, point3D_ids=point3D_ids)\\n return images\"\n },\n {\n \"identifier\": \"read_intrinsics_binary\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_intrinsics_binary(path_to_model_file):\\n \\\"\\\"\\\"\\n see: src/base/reconstruction.cc\\n void Reconstruction::WriteCamerasBinary(const std::string& path)\\n void Reconstruction::ReadCamerasBinary(const std::string& path)\\n \\\"\\\"\\\"\\n cameras = {}\\n with open(path_to_model_file, \\\"rb\\\") as fid:\\n num_cameras = read_next_bytes(fid, 8, \\\"Q\\\")[0]\\n for _ in range(num_cameras):\\n camera_properties = read_next_bytes(\\n fid, num_bytes=24, format_char_sequence=\\\"iiQQ\\\")\\n camera_id = camera_properties[0]\\n model_id = camera_properties[1]\\n model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name\\n width = camera_properties[2]\\n height = camera_properties[3]\\n num_params = CAMERA_MODEL_IDS[model_id].num_params\\n params = read_next_bytes(fid, num_bytes=8*num_params,\\n format_char_sequence=\\\"d\\\"*num_params)\\n cameras[camera_id] = Camera(id=camera_id,\\n model=model_name,\\n width=width,\\n height=height,\\n params=np.array(params))\\n assert len(cameras) == num_cameras\\n return cameras\"\n },\n {\n \"identifier\": \"read_points3D_binary\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_points3D_binary(path_to_model_file):\\n \\\"\\\"\\\"\\n see: src/base/reconstruction.cc\\n void Reconstruction::ReadPoints3DBinary(const std::string& path)\\n void Reconstruction::WritePoints3DBinary(const std::string& path)\\n \\\"\\\"\\\"\\n\\n\\n with open(path_to_model_file, \\\"rb\\\") as fid:\\n num_points = read_next_bytes(fid, 8, \\\"Q\\\")[0]\\n\\n xyzs = np.empty((num_points, 3))\\n rgbs = np.empty((num_points, 3))\\n errors = np.empty((num_points, 1))\\n\\n for p_id in range(num_points):\\n binary_point_line_properties = read_next_bytes(\\n fid, num_bytes=43, format_char_sequence=\\\"QdddBBBd\\\")\\n xyz = np.array(binary_point_line_properties[1:4])\\n rgb = np.array(binary_point_line_properties[4:7])\\n error = np.array(binary_point_line_properties[7])\\n track_length = read_next_bytes(\\n fid, num_bytes=8, format_char_sequence=\\\"Q\\\")[0]\\n track_elems = read_next_bytes(\\n fid, num_bytes=8*track_length,\\n format_char_sequence=\\\"ii\\\"*track_length)\\n xyzs[p_id] = xyz\\n rgbs[p_id] = rgb\\n errors[p_id] = error\\n return xyzs, rgbs, errors\"\n },\n {\n \"identifier\": \"read_points3D_text\",\n \"path\": \"scene/colmap_loader.py\",\n \"snippet\": \"def read_points3D_text(path):\\n \\\"\\\"\\\"\\n see: src/base/reconstruction.cc\\n void Reconstruction::ReadPoints3DText(const std::string& path)\\n void Reconstruction::WritePoints3DText(const std::string& path)\\n \\\"\\\"\\\"\\n xyzs = None\\n rgbs = None\\n errors = None\\n num_points = 0\\n with open(path, \\\"r\\\") as fid:\\n while True:\\n line = fid.readline()\\n if not line:\\n break\\n line = line.strip()\\n if len(line) > 0 and line[0] != \\\"#\\\":\\n num_points += 1\\n\\n\\n xyzs = np.empty((num_points, 3))\\n rgbs = np.empty((num_points, 3))\\n errors = np.empty((num_points, 1))\\n count = 0\\n with open(path, \\\"r\\\") as fid:\\n while True:\\n line = fid.readline()\\n if not line:\\n break\\n line = line.strip()\\n if len(line) > 0 and line[0] != \\\"#\\\":\\n elems = line.split()\\n xyz = np.array(tuple(map(float, elems[1:4])))\\n rgb = np.array(tuple(map(int, elems[4:7])))\\n error = np.array(float(elems[7]))\\n xyzs[count] = xyz\\n rgbs[count] = rgb\\n errors[count] = error\\n count += 1\\n\\n return xyzs, rgbs, errors\"\n },\n {\n \"identifier\": \"getWorld2View2\",\n \"path\": \"utils/graphics_utils.py\",\n \"snippet\": \"def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):\\n Rt = np.zeros((4, 4))\\n Rt[:3, :3] = R.transpose()\\n Rt[:3, 3] = t\\n Rt[3, 3] = 1.0\\n\\n C2W = np.linalg.inv(Rt)\\n cam_center = C2W[:3, 3]\\n cam_center = (cam_center + translate) * scale\\n C2W[:3, 3] = cam_center\\n Rt = np.linalg.inv(C2W)\\n return np.float32(Rt)\"\n },\n {\n \"identifier\": \"focal2fov\",\n \"path\": \"utils/graphics_utils.py\",\n \"snippet\": \"def focal2fov(focal, pixels):\\n return 2*math.atan(pixels/(2*focal))\"\n },\n {\n \"identifier\": \"fov2focal\",\n \"path\": \"utils/graphics_utils.py\",\n \"snippet\": \"def fov2focal(fov, pixels):\\n return pixels / (2 * math.tan(fov / 2))\"\n },\n {\n \"identifier\": \"SH2RGB\",\n \"path\": \"utils/sh_utils.py\",\n \"snippet\": \"def SH2RGB(sh):\\n return sh * C0 + 0.5\"\n },\n {\n \"identifier\": \"BasicPointCloud\",\n \"path\": \"scene/gaussian_model.py\",\n \"snippet\": \"class GaussianModel:\\n def setup_functions(self):\\n def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation, transform):\\n def __init__(self, sh_degree : int, smpl_type : str, motion_offset_flag : bool, actor_gender: str):\\n def capture(self):\\n def restore(self, model_args, training_args):\\n def get_scaling(self):\\n def get_rotation(self):\\n def get_xyz(self):\\n def get_features(self):\\n def get_opacity(self):\\n def get_covariance(self, scaling_modifier = 1, transform=None):\\n def oneupSHdegree(self):\\n def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):\\n def training_setup(self, training_args):\\n def update_learning_rate(self, iteration):\\n def construct_list_of_attributes(self):\\n def save_ply(self, path):\\n def reset_opacity(self):\\n def load_ply(self, path):\\n def replace_tensor_to_optimizer(self, tensor, name):\\n def _prune_optimizer(self, mask):\\n def prune_points(self, mask):\\n def cat_tensors_to_optimizer(self, tensors_dict):\\n def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):\\n def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):\\n def densify_and_clone(self, grads, grad_threshold, scene_extent):\\n def kl_densify_and_clone(self, grads, grad_threshold, scene_extent, kl_threshold=0.4):\\n def kl_densify_and_split(self, grads, grad_threshold, scene_extent, kl_threshold=0.4, N=2):\\n def kl_merge(self, grads, grad_threshold, scene_extent, kl_threshold=0.1):\\n def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, kl_threshold=0.4, t_vertices=None, iter=None):\\n def kl_div(self, mu_0, rotation_0_q, scaling_0_diag, mu_1, rotation_1_q, scaling_1_diag):\\n def add_densification_stats(self, viewspace_point_tensor, update_filter):\\n def coarse_deform_c2source(self, query_pts, params, t_params, t_vertices, lbs_weights=None, correct_Rs=None, return_transl=False):\\ndef read_pickle(pkl_path):\\ndef SMPL_to_tensor(params, device):\\ndef batch_rodrigues_torch(poses):\\ndef get_rigid_transformation_torch(rot_mats, joints, parents):\\ndef get_transform_params_torch(smpl, params, rot_mats=None, correct_Rs=None):\\ndef batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\\n L = build_scaling_rotation(scaling_modifier * scaling, rotation)\\n L_0 = rotation_0 @ scaling_0\\n A = torch.matmul(bweights, A.reshape(bs, joints_num, -1))\\n A = torch.reshape(A, (bs, -1, 4, 4))\\n A = torch.matmul(bweights, self.s_A.reshape(bs, joints_num, -1))\\n A = torch.reshape(A, (bs, -1, 4, 4))\\n K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1)\\n K = K.reshape([batch_size, 3, 3])\\n A = get_rigid_transformation_torch(rot_mats, joints, parents)\\n R = params['R'] \\n K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device)\\n K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \\\\\\n .view((batch_size, 3, 3))\"\n },\n {\n \"identifier\": \"SMPL\",\n \"path\": \"smpl/smpl_numpy.py\",\n \"snippet\": \"class SMPL():\\n def __init__(self, sex, model_dir):\\n super(SMPL, self).__init__()\\n\\n model_paths = {\\n 'male': os.path.join(model_dir, MALE_PATH),\\n 'female': os.path.join(model_dir, FEMALE_PATH),\\n # 'neutral': os.path.join(model_dir, NEUTRAL_PATH)\\n 'neutral': os.path.join('assets/SMPL_NEUTRAL.pkl')\\n }\\n\\n with open(model_paths[sex], 'rb') as f:\\n smpl_model = pickle.load(f, encoding='latin1')\\n self.J_regressor = np.array(smpl_model['J_regressor'].todense()) # (24, 6890)\\n self.weights = smpl_model['weights'] # (6890, 24)\\n self.posedirs = smpl_model['posedirs'] # (6890, 3, 207)\\n self.v_template = smpl_model['v_template'] # (6890, 3)\\n self.shapedirs = np.array(smpl_model['shapedirs']) # (6890, 3, 10)\\n self.faces = smpl_model['f'].astype('int32') # (13776, 3)\\n self.kintree_table = smpl_model['kintree_table'].astype('int64') # (2, 24)\\n\\n id_to_col = {self.kintree_table[1, i].item(): i for i in range(self.kintree_table.shape[1])}\\n self.parent = np.array([id_to_col[self.kintree_table[0, it]] for it in range(1, self.kintree_table.shape[1])])\\n\\n self.pose_shape = [24, 3]\\n self.beta_shape = [10]\\n self.pose = np.zeros(self.pose_shape)\\n self.beta = np.zeros(self.beta_shape)\\n\\n self.verts = None\\n self.J = None\\n self.R = None\\n\\n def __call__(self, pose, beta):\\n\\n v_template = self.v_template # (6890, 3)\\n shapedirs = self.shapedirs.reshape(-1,10) # (6890*3, 10)\\n beta = beta[:, None] # (10, 1)\\n\\n v_shaped = shapedirs.dot(beta).reshape(6890, 3) + v_template # (6890, 3)\\n J = self.J_regressor.dot(v_shaped) # (24, 3)\\n\\n # input is a rotation matrix: (24,3,3)\\n if pose.shape == (24, 3, 3):\\n R = pose\\n # input is a rotation axis-angle vector: (1, 72), (72, 1) or (72, )\\n elif pose.shape == (1, 72) or pose.shape == (72, 1) or pose.shape == (72,):\\n pose_vectors = pose.reshape(-1, 3) # (24, 3)\\n R = np.array([rodrigues(pose_vectors[p_idx])[0] \\n for p_idx in range(pose_vectors.shape[0])\\n ], \\n dtype='float32') # (24, 3, 3)\\n else:\\n raise ValueError(\\\"Unsupported Pose Inputs - the Pose Shape is {}\\\".format(pose.shape))\\n\\n Is = np.eye(3, dtype='float32')[None, :] # (1, 3, 3)\\n lrotmin = (R[1:,:] - Is).reshape(-1, 1) # (23x3x3, 1)\\n posedirs = self.posedirs.reshape(-1,207) # (6890x3, 207)\\n v_posed = v_shaped + posedirs.dot(lrotmin).reshape(6890, 3) # (6890, 3)\\n\\n J_ = J.copy()\\n J_[1:, :] = J[1:, :] - J[self.parent, :] # (24, 3)\\n G_ = np.concatenate([R, J_[:, :, None]], axis=-1) # (24, 3, 4)\\n pad_rows = np.array([[0, 0, 0, 1]], dtype='float32')\\n pad_rows = np.repeat(pad_rows, 24, axis=0).reshape(-1, 1, 4)\\n G_ = np.concatenate([G_, pad_rows], axis=1) # (24, 4, 4)\\n\\n G = [G_[0].copy()]\\n for i in range(1, 24):\\n G.append(G[self.parent[i-1]].dot(G_[i, :, :]))\\n G = np.stack(G, axis=0) # (24, 4, 4)\\n\\n joints = G[:, :3, 3]\\n rest_joints = np.concatenate([J, np.zeros((24, 1))], axis=-1)[:, :, None] # (24, 4, 1)\\n zeros = np.zeros((24, 4, 3), dtype='float32') # (24, 4, 3)\\n rest_joints_mtx = np.concatenate([zeros, rest_joints], axis=-1) # (24, 4, 4) \\n # print(\\\"G1: \\\", G[0], \\\"rest_joints_mtx1: \\\", rest_joints_mtx[0])\\n posed_joints_mtx = np.matmul(G, rest_joints_mtx)\\n # print(\\\"rest_joints_mtx2: \\\", posed_joints_mtx[0])\\n G = G - posed_joints_mtx\\n # print(G[0]) \\n rest_shape_h = np.concatenate([v_posed, np.ones(v_posed.shape[0])[:, None]], axis=-1) #(6890, 4)\\n T = self.weights.dot(G.reshape(24, -1)).reshape(6890, 4, 4)\\n v = np.matmul(T, rest_shape_h[:, :, None])[:, :3, 0]\\n \\n return v, joints\"\n },\n {\n \"identifier\": \"SMPLX\",\n \"path\": \"smplx/body_models.py\",\n \"snippet\": \"class SMPLX(SMPLH):\\n '''\\n SMPL-X (SMPL eXpressive) is a unified body model, with shape parameters\\n trained jointly for the face, hands and body.\\n SMPL-X uses standard vertex based linear blend skinning with learned\\n corrective blend shapes, has N=10475 vertices and K=54 joints,\\n which includes joints for the neck, jaw, eyeballs and fingers.\\n '''\\n\\n NUM_BODY_JOINTS = SMPLH.NUM_BODY_JOINTS\\n NUM_HAND_JOINTS = 15\\n NUM_FACE_JOINTS = 3\\n NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS + NUM_FACE_JOINTS\\n EXPRESSION_SPACE_DIM = 100\\n NECK_IDX = 12\\n\\n def __init__(\\n self, model_path: str,\\n kid_template_path: str = '',\\n num_expression_coeffs: int = 10,\\n create_expression: bool = True,\\n expression: Optional[Tensor] = None,\\n create_jaw_pose: bool = True,\\n jaw_pose: Optional[Tensor] = None,\\n create_leye_pose: bool = True,\\n leye_pose: Optional[Tensor] = None,\\n create_reye_pose=True,\\n reye_pose: Optional[Tensor] = None,\\n use_face_contour: bool = False,\\n batch_size: int = 1,\\n gender: str = 'neutral',\\n age: str = 'adult',\\n dtype=torch.float32,\\n ext: str = 'npz',\\n **kwargs\\n ) -> None:\\n ''' SMPLX model constructor\\n\\n Parameters\\n ----------\\n model_path: str\\n The path to the folder or to the file where the model\\n parameters are stored\\n num_expression_coeffs: int, optional\\n Number of expression components to use\\n (default = 10).\\n create_expression: bool, optional\\n Flag for creating a member variable for the expression space\\n (default = True).\\n expression: torch.tensor, optional, Bx10\\n The default value for the expression member variable.\\n (default = None)\\n create_jaw_pose: bool, optional\\n Flag for creating a member variable for the jaw pose.\\n (default = False)\\n jaw_pose: torch.tensor, optional, Bx3\\n The default value for the jaw pose variable.\\n (default = None)\\n create_leye_pose: bool, optional\\n Flag for creating a member variable for the left eye pose.\\n (default = False)\\n leye_pose: torch.tensor, optional, Bx10\\n The default value for the left eye pose variable.\\n (default = None)\\n create_reye_pose: bool, optional\\n Flag for creating a member variable for the right eye pose.\\n (default = False)\\n reye_pose: torch.tensor, optional, Bx10\\n The default value for the right eye pose variable.\\n (default = None)\\n use_face_contour: bool, optional\\n Whether to compute the keypoints that form the facial contour\\n batch_size: int, optional\\n The batch size used for creating the member variables\\n gender: str, optional\\n Which gender to load\\n dtype: torch.dtype\\n The data type for the created variables\\n '''\\n\\n # Load the model\\n if osp.isdir(model_path):\\n model_fn = 'SMPLX_{}.{ext}'.format(gender.upper(), ext=ext)\\n smplx_path = os.path.join(model_path, model_fn)\\n else:\\n smplx_path = model_path\\n assert osp.exists(smplx_path), 'Path {} does not exist!'.format(\\n smplx_path)\\n\\n if ext == 'pkl':\\n with open(smplx_path, 'rb') as smplx_file:\\n model_data = pickle.load(smplx_file, encoding='latin1')\\n elif ext == 'npz':\\n model_data = np.load(smplx_path, allow_pickle=True)\\n else:\\n raise ValueError('Unknown extension: {}'.format(ext))\\n\\n data_struct = Struct(**model_data)\\n\\n super(SMPLX, self).__init__(\\n model_path=model_path,\\n kid_template_path=kid_template_path,\\n data_struct=data_struct,\\n dtype=dtype,\\n batch_size=batch_size,\\n vertex_ids=VERTEX_IDS['smplx'],\\n gender=gender, age=age, ext=ext,\\n **kwargs)\\n\\n lmk_faces_idx = data_struct.lmk_faces_idx\\n self.register_buffer('lmk_faces_idx',\\n torch.tensor(lmk_faces_idx, dtype=torch.long))\\n lmk_bary_coords = data_struct.lmk_bary_coords\\n self.register_buffer('lmk_bary_coords',\\n torch.tensor(lmk_bary_coords, dtype=dtype))\\n\\n self.use_face_contour = use_face_contour\\n if self.use_face_contour:\\n dynamic_lmk_faces_idx = data_struct.dynamic_lmk_faces_idx\\n dynamic_lmk_faces_idx = torch.tensor(\\n dynamic_lmk_faces_idx,\\n dtype=torch.long)\\n self.register_buffer('dynamic_lmk_faces_idx',\\n dynamic_lmk_faces_idx)\\n\\n dynamic_lmk_bary_coords = data_struct.dynamic_lmk_bary_coords\\n dynamic_lmk_bary_coords = torch.tensor(\\n dynamic_lmk_bary_coords, dtype=dtype)\\n self.register_buffer('dynamic_lmk_bary_coords',\\n dynamic_lmk_bary_coords)\\n\\n neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents)\\n self.register_buffer(\\n 'neck_kin_chain',\\n torch.tensor(neck_kin_chain, dtype=torch.long))\\n\\n if create_jaw_pose:\\n if jaw_pose is None:\\n default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype)\\n else:\\n default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype)\\n jaw_pose_param = nn.Parameter(default_jaw_pose,\\n requires_grad=True)\\n self.register_parameter('jaw_pose', jaw_pose_param)\\n\\n if create_leye_pose:\\n if leye_pose is None:\\n default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype)\\n else:\\n default_leye_pose = torch.tensor(leye_pose, dtype=dtype)\\n leye_pose_param = nn.Parameter(default_leye_pose,\\n requires_grad=True)\\n self.register_parameter('leye_pose', leye_pose_param)\\n\\n if create_reye_pose:\\n if reye_pose is None:\\n default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype)\\n else:\\n default_reye_pose = torch.tensor(reye_pose, dtype=dtype)\\n reye_pose_param = nn.Parameter(default_reye_pose,\\n requires_grad=True)\\n self.register_parameter('reye_pose', reye_pose_param)\\n\\n shapedirs = data_struct.shapedirs\\n if len(shapedirs.shape) < 3:\\n shapedirs = shapedirs[:, :, None]\\n if (shapedirs.shape[-1] < self.SHAPE_SPACE_DIM +\\n self.EXPRESSION_SPACE_DIM):\\n print(f'WARNING: You are using a {self.name()} model, with only'\\n ' 10 shape and 10 expression coefficients.')\\n expr_start_idx = 10\\n expr_end_idx = 20\\n num_expression_coeffs = min(num_expression_coeffs, 10)\\n else:\\n expr_start_idx = self.SHAPE_SPACE_DIM\\n expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs\\n num_expression_coeffs = min(\\n num_expression_coeffs, self.EXPRESSION_SPACE_DIM)\\n\\n self._num_expression_coeffs = num_expression_coeffs\\n\\n expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx]\\n self.register_buffer(\\n 'expr_dirs', to_tensor(to_np(expr_dirs), dtype=dtype))\\n\\n if create_expression:\\n if expression is None:\\n default_expression = torch.zeros(\\n [batch_size, self.num_expression_coeffs], dtype=dtype)\\n else:\\n default_expression = torch.tensor(expression, dtype=dtype)\\n expression_param = nn.Parameter(default_expression,\\n requires_grad=True)\\n self.register_parameter('expression', expression_param)\\n\\n def name(self) -> str:\\n return 'SMPL-X'\\n\\n @property\\n def num_expression_coeffs(self):\\n return self._num_expression_coeffs\\n\\n def create_mean_pose(self, data_struct, flat_hand_mean=False):\\n # Create the array for the mean pose. If flat_hand is false, then use\\n # the mean that is given by the data, rather than the flat open hand\\n global_orient_mean = torch.zeros([3], dtype=self.dtype)\\n body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3],\\n dtype=self.dtype)\\n jaw_pose_mean = torch.zeros([3], dtype=self.dtype)\\n leye_pose_mean = torch.zeros([3], dtype=self.dtype)\\n reye_pose_mean = torch.zeros([3], dtype=self.dtype)\\n # pose_mean = np.concatenate([global_orient_mean, body_pose_mean, jaw_pose_mean, leye_pose_mean, reye_pose_mean, self.left_hand_mean, self.right_hand_mean], axis=0)\\n pose_mean = torch.cat([global_orient_mean, body_pose_mean, jaw_pose_mean, leye_pose_mean, reye_pose_mean, self.left_hand_mean, self.right_hand_mean], 0)\\n\\n return pose_mean\\n\\n def extra_repr(self):\\n msg = super(SMPLX, self).extra_repr()\\n msg = [\\n msg,\\n f'Number of Expression Coefficients: {self.num_expression_coeffs}'\\n ]\\n return '\\\\n'.join(msg)\\n\\n def forward(\\n self,\\n betas: Optional[Tensor] = None,\\n global_orient: Optional[Tensor] = None,\\n body_pose: Optional[Tensor] = None,\\n left_hand_pose: Optional[Tensor] = None,\\n right_hand_pose: Optional[Tensor] = None,\\n transl: Optional[Tensor] = None,\\n expression: Optional[Tensor] = None,\\n jaw_pose: Optional[Tensor] = None,\\n leye_pose: Optional[Tensor] = None,\\n reye_pose: Optional[Tensor] = None,\\n return_verts: bool = True,\\n return_full_pose: bool = False,\\n pose2rot: bool = True,\\n return_shaped: bool = True,\\n **kwargs\\n ) -> TensorOutput:\\n '''\\n Forward pass for the SMPLX model\\n\\n Parameters\\n ----------\\n global_orient: torch.tensor, optional, shape Bx3\\n If given, ignore the member variable and use it as the global\\n rotation of the body. Useful if someone wishes to predicts this\\n with an external model. (default=None)\\n betas: torch.tensor, optional, shape BxN_b\\n If given, ignore the member variable `betas` and use it\\n instead. For example, it can used if shape parameters\\n `betas` are predicted from some external model.\\n (default=None)\\n expression: torch.tensor, optional, shape BxN_e\\n If given, ignore the member variable `expression` and use it\\n instead. For example, it can used if expression parameters\\n `expression` are predicted from some external model.\\n body_pose: torch.tensor, optional, shape Bx(J*3)\\n If given, ignore the member variable `body_pose` and use it\\n instead. For example, it can used if someone predicts the\\n pose of the body joints are predicted from some external model.\\n It should be a tensor that contains joint rotations in\\n axis-angle format. (default=None)\\n left_hand_pose: torch.tensor, optional, shape BxP\\n If given, ignore the member variable `left_hand_pose` and\\n use this instead. It should either contain PCA coefficients or\\n joint rotations in axis-angle format.\\n right_hand_pose: torch.tensor, optional, shape BxP\\n If given, ignore the member variable `right_hand_pose` and\\n use this instead. It should either contain PCA coefficients or\\n joint rotations in axis-angle format.\\n jaw_pose: torch.tensor, optional, shape Bx3\\n If given, ignore the member variable `jaw_pose` and\\n use this instead. It should either joint rotations in\\n axis-angle format.\\n transl: torch.tensor, optional, shape Bx3\\n If given, ignore the member variable `transl` and use it\\n instead. For example, it can used if the translation\\n `transl` is predicted from some external model.\\n (default=None)\\n return_verts: bool, optional\\n Return the vertices. (default=True)\\n return_full_pose: bool, optional\\n Returns the full axis-angle pose vector (default=False)\\n\\n Returns\\n -------\\n output: ModelOutput\\n A named tuple of type `ModelOutput`\\n '''\\n\\n # If no shape and pose parameters are passed along, then use the\\n # ones from the module\\n global_orient = (global_orient if global_orient is not None else\\n self.global_orient)\\n body_pose = body_pose if body_pose is not None else self.body_pose\\n betas = betas if betas is not None else self.betas\\n\\n left_hand_pose = (left_hand_pose if left_hand_pose is not None else\\n self.left_hand_pose)\\n right_hand_pose = (right_hand_pose if right_hand_pose is not None else\\n self.right_hand_pose)\\n jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose\\n leye_pose = leye_pose if leye_pose is not None else self.leye_pose\\n reye_pose = reye_pose if reye_pose is not None else self.reye_pose\\n expression = expression if expression is not None else self.expression\\n\\n apply_trans = transl is not None or hasattr(self, 'transl')\\n if transl is None:\\n if hasattr(self, 'transl'):\\n transl = self.transl\\n\\n if self.use_pca:\\n left_hand_pose = torch.einsum(\\n 'bi,ij->bj', [left_hand_pose, self.left_hand_components])\\n right_hand_pose = torch.einsum(\\n 'bi,ij->bj', [right_hand_pose, self.right_hand_components])\\n\\n full_pose = torch.cat([global_orient.reshape(-1, 1, 3),\\n body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3),\\n jaw_pose.reshape(-1, 1, 3),\\n leye_pose.reshape(-1, 1, 3),\\n reye_pose.reshape(-1, 1, 3),\\n left_hand_pose.reshape(-1, 15, 3),\\n right_hand_pose.reshape(-1, 15, 3)],\\n dim=1).reshape(-1, 165).to(self.pose_mean.device)\\n\\n # Add the mean pose of the model. Does not affect the body, only the\\n # hands when flat_hand_mean == False\\n full_pose += self.pose_mean\\n\\n batch_size = max(betas.shape[0], global_orient.shape[0],\\n body_pose.shape[0])\\n # Concatenate the shape and expression coefficients\\n scale = int(batch_size / betas.shape[0])\\n if scale > 1:\\n betas = betas.expand(scale, -1)\\n shape_components = torch.cat([betas, expression], dim=-1).to(self.pose_mean.device)\\n\\n shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1)\\n\\n vertices, joints, A, T = lbs(shape_components, full_pose, self.v_template,\\n shapedirs, self.posedirs,\\n self.J_regressor, self.parents,\\n self.lbs_weights, pose2rot=pose2rot,\\n )\\n\\n lmk_faces_idx = self.lmk_faces_idx.unsqueeze(\\n dim=0).expand(batch_size, -1).contiguous()\\n lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat(\\n self.batch_size, 1, 1)\\n if self.use_face_contour:\\n lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords(\\n vertices, full_pose, self.dynamic_lmk_faces_idx,\\n self.dynamic_lmk_bary_coords,\\n self.neck_kin_chain,\\n pose2rot=True,\\n )\\n dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords\\n\\n lmk_faces_idx = torch.cat([lmk_faces_idx,\\n dyn_lmk_faces_idx], 1)\\n lmk_bary_coords = torch.cat(\\n [lmk_bary_coords.expand(batch_size, -1, -1),\\n dyn_lmk_bary_coords], 1)\\n\\n landmarks = vertices2landmarks(vertices, self.faces_tensor,\\n lmk_faces_idx,\\n lmk_bary_coords)\\n\\n # import matplotlib.pyplot as plt\\n # import numpy as np\\n # xs = joints[0,:,0]\\n # ys = joints[0,:,1]\\n # plt.scatter(xs, ys)\\n\\n # # zip joins x and y coordinates in pairs\\n # count = 0\\n # for x,y in zip(xs, ys):\\n\\n # label = \\\"{:.2f}\\\".format(count)\\n\\n # plt.annotate(label, # this is the text\\n # (x,y), # these are the coordinates to position the label\\n # textcoords=\\\"offset points\\\", # how to position the text\\n # xytext=(0,10), # distance from text to points (x,y)\\n # ha='center') # horizontal alignment can be left, right or center\\n # count += 1\\n # plt.savefig(\\\"joints.png\\\")\\n # import pdb; pdb.set_trace()\\n\\n # Add any extra joints that might be needed\\n joints = self.vertex_joint_selector(vertices, joints)\\n # Add the landmarks to the joints\\n joints = torch.cat([joints, landmarks], dim=1)\\n # Map the joints to the current dataset\\n\\n if self.joint_mapper is not None:\\n joints = self.joint_mapper(joints=joints, vertices=vertices)\\n\\n if apply_trans:\\n joints += transl.unsqueeze(dim=1)\\n vertices += transl.unsqueeze(dim=1)\\n # clone because we are modifying them in-place\\n A = A.clone()\\n A[..., :3, 3] += transl.unsqueeze(dim=1)\\n T = T.clone()\\n T[..., :3, 3] += transl.unsqueeze(dim=1)\\n\\n v_shaped = None\\n if return_shaped:\\n v_shaped = self.v_template + blend_shapes(betas, self.shapedirs)\\n else:\\n v_shaped = Tensor(0)\\n\\n output = TensorOutput(vertices=vertices if return_verts else None,\\n joints=joints,\\n betas=betas,\\n expression=expression,\\n global_orient=global_orient,\\n body_pose=body_pose,\\n left_hand_pose=left_hand_pose,\\n right_hand_pose=right_hand_pose,\\n jaw_pose=jaw_pose,\\n v_shaped=v_shaped,\\n full_pose=full_pose if return_full_pose else None,\\n A=A,\\n T=T,\\n f=self.faces)\\n return output\"\n },\n {\n \"identifier\": \"SMCReader\",\n \"path\": \"data/dna_rendering/dna_rendering_sample_code/SMCReader.py\",\n \"snippet\": \"class SMCReader:\\n\\n def __init__(self, file_path):\\n \\\"\\\"\\\"Read SenseMocapFile endswith \\\".smc\\\".\\n\\n Args:\\n file_path (str):\\n Path to an SMC file.\\n body_model (nn.Module or dict):\\n Only needed for SMPL transformation to device frame\\n if nn.Module: a body_model instance\\n if dict: a body_model config\\n \\\"\\\"\\\"\\n self.smc = h5py.File(file_path, 'r')\\n self.__calibration_dict__ = None\\n self.__kinect_calib_dict__ = None \\n self.__available_keys__ = list(self.smc.keys())\\n \\n self.actor_info = None \\n if hasattr(self.smc, 'attrs') and len(self.smc.attrs.keys()) > 0:\\n self.actor_info = dict(\\n id=self.smc.attrs['actor_id'],\\n perf_id=self.smc.attrs['performance_id'],\\n age=self.smc.attrs['age'],\\n gender=self.smc.attrs['gender'],\\n height=self.smc.attrs['height'],\\n weight=self.smc.attrs['weight'],\\n ethnicity=self.smc.attrs['ethnicity'],\\n )\\n\\n self.Camera_5mp_info = None \\n if 'Camera_5mp' in self.smc:\\n self.Camera_5mp_info = dict(\\n num_device=self.smc['Camera_5mp'].attrs['num_device'],\\n num_frame=self.smc['Camera_5mp'].attrs['num_frame'],\\n resolution=self.smc['Camera_5mp'].attrs['resolution'],\\n )\\n self.Camera_12mp_info = None \\n if 'Camera_12mp' in self.smc:\\n self.Camera_12mp_info = dict(\\n num_device=self.smc['Camera_12mp'].attrs['num_device'],\\n num_frame=self.smc['Camera_12mp'].attrs['num_frame'],\\n resolution=self.smc['Camera_12mp'].attrs['resolution'],\\n )\\n self.Kinect_info = None\\n if 'Kinect' in self.smc:\\n self.Kinect_info=dict(\\n num_device=self.smc['Kinect'].attrs['num_device'],\\n num_frame=self.smc['Kinect'].attrs['num_frame'],\\n resolution=self.smc['Kinect'].attrs['resolution'],\\n )\\n\\n def get_available_keys(self):\\n return self.__available_keys__ \\n\\n def get_actor_info(self):\\n return self.actor_info\\n \\n def get_Camera_12mp_info(self):\\n return self.Camera_12mp_info\\n\\n def get_Camera_5mp_info(self):\\n return self.Camera_5mp_info\\n \\n def get_Kinect_info(self):\\n return self.Kinect_info\\n \\n ### RGB Camera Calibration\\n def get_Calibration_all(self):\\n \\\"\\\"\\\"Get calibration matrix of all cameras and save it in self\\n \\n Args:\\n None\\n\\n Returns:\\n Dictionary of calibration matrixs of all matrixs.\\n dict( \\n Camera_Parameter: Camera_id : Matrix_type : value\\n )\\n Notice:\\n Camera_id(str) in {'Camera_5mp': '0'~'47', 'Camera_12mp':'48'~'60'}\\n Matrix_type in ['D', 'K', 'RT', 'Color_Calibration'] \\n \\\"\\\"\\\" \\n if not 'Camera_Parameter' in self.smc:\\n print(\\\"=== no key: Camera_Parameter.\\\\nplease check available keys!\\\")\\n return None \\n\\n if self.__calibration_dict__ is not None:\\n return self.__calibration_dict__\\n\\n self.__calibration_dict__ = dict()\\n for ci in self.smc['Camera_Parameter'].keys():\\n self.__calibration_dict__.setdefault(ci,dict())\\n for mt in ['D', 'K', 'RT', 'Color_Calibration'] :\\n self.__calibration_dict__[ci][mt] = \\\\\\n self.smc['Camera_Parameter'][ci][mt][()]\\n return self.__calibration_dict__\\n\\n def get_Calibration(self, Camera_id):\\n \\\"\\\"\\\"Get calibration matrixs of a certain camera by its type and id \\n\\n Args:\\n Camera_id (int/str of a number):\\n Camera_id(str) in {'Camera_5mp': '0'~'47', \\n 'Camera_12mp':'48'~'60'}\\n Returns:\\n Dictionary of calibration matrixs.\\n ['D', 'K', 'RT', 'Color_Calibration'] \\n \\\"\\\"\\\"\\n if not 'Camera_Parameter' in self.smc:\\n print(\\\"=== no key: Camera_Parameter.\\\\nplease check available keys!\\\")\\n return None \\n\\n rs = dict()\\n for k in ['D', 'K', 'RT', 'Color_Calibration'] :\\n rs[k] = self.smc['Camera_Parameter'][f'{int(Camera_id):02d}'][k][()]\\n return rs\\n\\n ### Kinect Camera Calibration\\n def get_Kinect_Calibration_all(self):\\n \\\"\\\"\\\"Get calibration matrix of all kinect cameras and save it in self\\n \\n Args:\\n None\\n\\n Returns:\\n Dictionary of calibration matrixs of all matrixs.\\n dict( \\n Camera_group: Camera_id : Matrix_type : value\\n )\\n Notice:\\n Camera_group(str) in ['Kinect']\\n Camera_id(str) in {'Kinect': '0'~'7'}\\n Matrix_type in ['D', 'K', 'RT'] \\n \\\"\\\"\\\" \\n if not 'Calibration' in self.smc:\\n print(\\\"=== no key: Calibration.\\\\nplease check available keys!\\\")\\n return None \\n\\n if self.__kinect_calib_dict__ is not None:\\n return self.__kinect_calib_dict__\\n\\n self.__kinect_calib_dict__ = dict()\\n for cg in ['Kinect']:\\n self.__kinect_calib_dict__.setdefault(cg,dict())\\n for ci in self.smc['Calibration'][cg].keys():\\n self.__kinect_calib_dict__[cg].setdefault(ci,dict())\\n for mt in ['D', 'K', 'RT'] :\\n self.__kinect_calib_dict__[cg][ci][mt] = \\\\\\n self.smc['Calibration'][cg][ci][mt][()]\\n return self.__kinect_calib_dict__\\n\\n def get_kinect_Calibration(self, Camera_id):\\n \\\"\\\"\\\"Get calibration matrixs of a certain kinect camera by its type and id \\n\\n Args:\\n Camera_group (str):\\n Camera_group in ['Kinect'].\\n Camera_id (int/str of a number):\\n CameraID(str) in {'Kinect': '0'~'7'}\\n Returns:\\n Dictionary of calibration matrixs.\\n ['D', 'K', 'RT'] \\n \\\"\\\"\\\" \\n if not 'Calibration' in self.smc:\\n print(\\\"=== no key: Calibration.\\\\nplease check available keys!\\\")\\n return None \\n\\n Camera_id = f'{int(Camera_id):02d}'\\n assert(Camera_id in self.smc['Calibration'][\\\"Kinect\\\"].keys())\\n rs = dict()\\n for k in ['D', 'K', 'RT']:\\n rs[k] = self.smc['Calibration'][\\\"Kinect\\\"][Camera_id][k][()]\\n return rs\\n\\n ### RGB image\\n def __read_color_from_bytes__(self, color_array):\\n \\\"\\\"\\\"Decode an RGB image from an encoded byte array.\\\"\\\"\\\"\\n return cv2.imdecode(color_array, cv2.IMREAD_COLOR)\\n\\n def get_mask(self, Camera_id, Frame_id=None, disable_tqdm=True):\\n \\\"\\\"\\\"Get mask from Camera_id, Frame_id\\n\\n Args:\\n Camera_id (int/str of a number):\\n Camera_id (str) in \\n {'Camera_5mp': '0'~'47', \\n 'Camera_12mp':'48'~'60',\\n 'Kinect': '0'~'7'}\\n Frame_id a.(int/str of a number): '0' ~ 'num_frame'\\n b.list of numbers (int/str)\\n c.None: get batch of all imgs in order of time sequence \\n Returns:\\n a single img :\\n 'color': HWC in bgr (uint8)\\n 'mask' : HW (uint8)\\n 'depth': HW (uint16)\\n \\\"\\\"\\\" \\n if not 'Mask' in self.smc:\\n print(\\\"=== no key: Mask.\\\\nplease check available keys!\\\")\\n return None \\n\\n Camera_id = str(Camera_id)\\n\\n assert(isinstance(Frame_id,(list,int, str, type(None))))\\n if isinstance(Frame_id, (str,int)):\\n Frame_id = str(Frame_id)\\n assert(Frame_id in self.smc['Mask'][Camera_id]['mask'].keys())\\n img_byte = self.smc['Mask'][Camera_id]['mask'][Frame_id][()]\\n img_color = self.__read_color_from_bytes__(img_byte)\\n img_color = np.max(img_color,2)\\n return img_color \\n else:\\n if Frame_id is None:\\n Frame_id_list =sorted([int(l) for l in self.smc['Mask'][Camera_id]['mask'].keys()])\\n elif isinstance(Frame_id, list):\\n Frame_id_list = Frame_id\\n rs = []\\n for fi in tqdm.tqdm(Frame_id_list, disable=disable_tqdm):\\n rs.append(self.get_mask(Camera_id,fi))\\n return np.stack(rs,axis=0)\\n\\n def get_img(self, Camera_group, Camera_id, Image_type, Frame_id=None, disable_tqdm=True):\\n \\\"\\\"\\\"Get image its Camera_group, Camera_id, Image_type and Frame_id\\n\\n Args:\\n Camera_group (str):\\n Camera_group in ['Camera_12mp', 'Camera_5mp','Kinect'].\\n Camera_id (int/str of a number):\\n CameraID (str) in \\n {'Camera_5mp': '0'~'47', \\n 'Camera_12mp':'48'~'60',\\n 'Kinect': '0'~'7'}\\n Image_type(str) in \\n {'Camera_5mp': ['color'], \\n 'Camera_12mp': ['color'],\\n 'Kinect': ['depth', 'mask']}\\n Frame_id a.(int/str of a number): '0' ~ 'num_frame'('149') \\n b.list of numbers (int/str)\\n c.None: get batch of all imgs in order of time sequence \\n Returns:\\n a single img :\\n 'color': HWC in bgr (uint8)\\n 'mask' : HW (uint8)\\n 'depth': HW (uint16)\\n \\\"\\\"\\\" \\n if not Camera_group in self.smc:\\n print(\\\"=== no key: %s.\\\\nplease check available keys!\\\" % Camera_group)\\n return None\\n\\n assert(Camera_group in ['Camera_12mp', 'Camera_5mp','Kinect'])\\n Camera_id = str(Camera_id)\\n assert(Camera_id in self.smc[Camera_group].keys())\\n assert(Image_type in self.smc[Camera_group][Camera_id].keys())\\n assert(isinstance(Frame_id,(list,int, str, type(None))))\\n if isinstance(Frame_id, (str,int)):\\n Frame_id = str(Frame_id)\\n assert(Frame_id in self.smc[Camera_group][Camera_id][Image_type].keys())\\n if Image_type in ['color']:\\n img_byte = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\\n img_color = self.__read_color_from_bytes__(img_byte)\\n if Image_type == 'mask':\\n img_byte = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\\n img_color = self.__read_color_from_bytes__(img_byte)\\n img_color = np.max(img_color,2)\\n if Image_type == 'depth':\\n img_color = self.smc[Camera_group][Camera_id][Image_type][Frame_id][()]\\n return img_color \\n else:\\n if Frame_id is None:\\n Frame_id_list =sorted([int(l) for l in self.smc[Camera_group][Camera_id][Image_type].keys()])\\n elif isinstance(Frame_id, list):\\n Frame_id_list = Frame_id\\n rs = []\\n for fi in tqdm(Frame_id_list, disable=disable_tqdm):\\n rs.append(self.get_img(Camera_group, Camera_id, Image_type,fi))\\n return np.stack(rs,axis=0)\\n \\n ###Keypoints2d\\n def get_Keypoints2d(self, Camera_id, Frame_id=None):\\n \\\"\\\"\\\"Get keypoint2D by its Camera_group, Camera_id and Frame_id\\n\\n Args:\\n Camera_id (int/str of a number):\\n CameraID (str) in \\n {'Camera_5mp': '0'~'47', \\n 'Camera_12mp':'48'~'60',}\\n Frame_id a.(int/str of a number): '0' ~ 'num_frame-1'('149') \\n b.list of numbers (int/str)\\n c.None: get batch of all imgs in order of time sequence \\n Returns:\\n a single img :\\n 'color': HWC in bgr (uint8)\\n 'mask' : HW (uint8)\\n 'depth': HW (uint16)\\n \\\"\\\"\\\" \\n if not 'Keypoints_2D' in self.smc:\\n print(\\\"=== no key: Keypoints_2D.\\\\nplease check available keys!\\\")\\n return None \\n\\n Camera_id = f'{int(Camera_id):02d}'\\n assert(isinstance(Frame_id,(list,int, str, type(None))))\\n if isinstance(Frame_id, (str,int)):\\n Frame_id = int(Frame_id)\\n return self.smc['Keypoints_2D'][Camera_id][()][Frame_id,:]\\n else:\\n if Frame_id is None:\\n return self.smc['Keypoints_2D'][Camera_id][()]\\n elif isinstance(Frame_id, list):\\n Frame_id_list = Frame_id\\n rs = []\\n for fi in tqdm.tqdm(Frame_id_list):\\n rs.append(self.get_Keypoints2d(Camera_id,fi))\\n return np.stack(rs,axis=0)\\n\\n ###Keypoints3d\\n def get_Keypoints3d(self, Frame_id=None):\\n \\\"\\\"\\\"Get keypoint3D Frame_id, TODO coordinate\\n\\n Args:\\n Frame_id a.(int/str of a number): '0' ~ 'num_frame-1'('149') \\n b.list of numbers (int/str)\\n c.None: get batch of all imgs in order of time sequence \\n Returns:\\n Keypoints3d tensor: np.ndarray of shape ([N], ,3)\\n \\\"\\\"\\\" \\n if not 'Keypoints_3D' in self.smc:\\n print(\\\"=== no key: Keypoints_3D.\\\\nplease check available keys!\\\")\\n return None \\n\\n if isinstance(Frame_id, (str,int)):\\n Frame_id = int(Frame_id)\\n return self.smc['Keypoints_3D'][\\\"keypoints3d\\\"][Frame_id,:]\\n else:\\n if Frame_id is None:\\n return self.smc['Keypoints_3D'][\\\"keypoints3d\\\"]\\n elif isinstance(Frame_id, list):\\n Frame_id_list = Frame_id\\n rs = []\\n for fi in tqdm.tqdm(Frame_id_list):\\n rs.append(self.get_Keypoints3d(fi))\\n return np.stack(rs,axis=0)\\n\\n ###SMPLx\\n def get_SMPLx(self, Frame_id=None):\\n \\\"\\\"\\\"Get SMPL (world coordinate) computed by mocap processing pipeline.\\n\\n Args:\\n Frame_id (int, list or None, optional):\\n int: frame id of one selected frame\\n list: a list of frame id\\n None: all frames will be returned\\n Defaults to None.\\n\\n Returns:\\n dict:\\n 'global_orient': np.ndarray of shape (N, 3)\\n 'body_pose': np.ndarray of shape (N, 21, 3)\\n 'transl': np.ndarray of shape (N, 3)\\n 'betas': np.ndarray of shape (1, 10)\\n \\\"\\\"\\\"\\n if not 'SMPLx' in self.smc:\\n print(\\\"=== no key: SMPLx.\\\\nplease check available keys!\\\")\\n return None \\n\\n t_frame = self.smc['SMPLx']['betas'][()].shape[0]\\n if Frame_id is None:\\n frame_list = range(t_frame)\\n elif isinstance(Frame_id, list):\\n frame_list = [int(fi) for fi in Frame_id]\\n elif isinstance(Frame_id, (int,str)):\\n Frame_id = int(Frame_id)\\n assert Frame_id < t_frame,\\\\\\n f'Invalid frame_index {Frame_id}'\\n frame_list = Frame_id\\n else:\\n raise TypeError('frame_id should be int, list or None.')\\n\\n smpl_dict = {}\\n for key in ['betas', 'expression', 'fullpose', 'transl']:\\n smpl_dict[key] = self.smc['SMPLx'][key][()][frame_list, ...]\\n smpl_dict['scale'] = self.smc['SMPLx']['scale'][()]\\n\\n return smpl_dict\\n\\n def release(self):\\n self.smc = None \\n self.__calibration_dict__ = None\\n self.__kinect_calib_dict__ = None\\n self.__available_keys__ = None\\n self.actor_info = None \\n self.Camera_5mp_info = None\\n self.Camera_12mp_info = None \\n self.Kinect_info = None\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport sys\nimport numpy as np\nimport torch\nimport json\nimport imageio\nimport cv2\nimport random\nfrom PIL import Image\nfrom typing import NamedTuple\nfrom scene.colmap_loader import read_extrinsics_text, read_intrinsics_text, qvec2rotmat, \\\n read_extrinsics_binary, read_intrinsics_binary, read_points3D_binary, read_points3D_text\nfrom utils.graphics_utils import getWorld2View2, focal2fov, fov2focal\nfrom pathlib import Path\nfrom plyfile import PlyData, PlyElement\nfrom utils.sh_utils import SH2RGB\nfrom scene.gaussian_model import BasicPointCloud\nfrom smpl.smpl_numpy import SMPL\nfrom smplx.body_models import SMPLX\nfrom data.dna_rendering.dna_rendering_sample_code.SMCReader import SMCReader"},"token_num":{"kind":"number","value":15383,"string":"15,383"},"cropped_code":{"kind":"string","value":" ply_path: str\n\ndef getNerfppNorm(cam_info):\n def get_center_and_diag(cam_centers):\n cam_centers = np.hstack(cam_centers)\n avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)\n center = avg_cam_center\n dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)\n diagonal = np.max(dist)\n return center.flatten(), diagonal\n\n cam_centers = []\n\n for cam in cam_info:\n W2C = getWorld2View2(cam.R, cam.T)\n C2W = np.linalg.inv(W2C)\n cam_centers.append(C2W[:3, 3:4])\n\n center, diagonal = get_center_and_diag(cam_centers)\n radius = diagonal * 1.1\n\n translate = -center\n\n return {\"translate\": translate, \"radius\": radius}\n\ndef readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):\n cam_infos = []\n for idx, key in enumerate(cam_extrinsics):\n sys.stdout.write('\\r')\n # the exact output you're looking for:\n sys.stdout.write(\"Reading camera {}/{}\".format(idx+1, len(cam_extrinsics)))\n sys.stdout.flush()\n\n extr = cam_extrinsics[key]\n intr = cam_intrinsics[extr.camera_id]\n height = intr.height\n width = intr.width\n\n uid = intr.id\n R = np.transpose(qvec2rotmat(extr.qvec))\n T = np.array(extr.tvec)\n\n if intr.model==\"SIMPLE_PINHOLE\":\n focal_length_x = intr.params[0]\n FovY = focal2fov(focal_length_x, height)\n FovX = focal2fov(focal_length_x, width)\n elif intr.model==\"PINHOLE\":\n focal_length_x = intr.params[0]\n focal_length_y = intr.params[1]\n FovY = focal2fov(focal_length_y, height)\n FovX = focal2fov(focal_length_x, width)\n else:\n assert False, \"Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!\"\n\n image_path = os.path.join(images_folder, os.path.basename(extr.name))\n image_name = os.path.basename(image_path).split(\".\")[0]\n image = Image.open(image_path)\n\n cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,\n image_path=image_path, image_name=image_name, width=width, height=height)\n cam_infos.append(cam_info)\n sys.stdout.write('\\n')\n return cam_infos\n\ndef fetchPly(path):\n plydata = PlyData.read(path)\n vertices = plydata['vertex']\n positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T\n colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0\n normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T\n return BasicPointCloud(points=positions, colors=colors, normals=normals)\n\ndef storePly(path, xyz, rgb):\n # Define the dtype for the structured array\n dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),\n ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),\n ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]\n \n normals = np.zeros_like(xyz)\n\n elements = np.empty(xyz.shape[0], dtype=dtype)\n attributes = np.concatenate((xyz, normals, rgb), axis=1)\n elements[:] = list(map(tuple, attributes))\n\n # Create the PlyData object and write to file\n vertex_element = PlyElement.describe(elements, 'vertex')\n ply_data = PlyData([vertex_element])\n ply_data.write(path)\n\ndef readColmapSceneInfo(path, images, eval, llffhold=8):\n try:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.bin\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.bin\")\n cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)\n except:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.txt\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.txt\")\n cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)\n\n reading_dir = \"images\" if images == None else images\n cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))\n cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)\n\n if eval:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]\n else:\n train_cam_infos = cam_infos\n test_cam_infos = []\n\n nerf_normalization = getNerfppNorm(train_cam_infos)\n\n ply_path = os.path.join(path, \"sparse/0/points3D.ply\")\n bin_path = os.path.join(path, \"sparse/0/points3D.bin\")\n txt_path = os.path.join(path, \"sparse/0/points3D.txt\")\n if not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n try:\n"},"all_code":{"kind":"string","value":"#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use \n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\n\n\n\nclass CameraInfo(NamedTuple):\n uid: int\n pose_id: int\n R: np.array\n T: np.array\n K: np.array\n FovY: np.array\n FovX: np.array\n image: np.array\n image_path: str\n image_name: str\n bkgd_mask: np.array\n bound_mask: np.array\n width: int\n height: int\n smpl_param: dict\n world_vertex: np.array\n world_bound: np.array\n big_pose_smpl_param: dict\n big_pose_world_vertex: np.array\n big_pose_world_bound: np.array\n\nclass SceneInfo(NamedTuple):\n point_cloud: BasicPointCloud\n train_cameras: list\n test_cameras: list\n nerf_normalization: dict\n ply_path: str\n\ndef getNerfppNorm(cam_info):\n def get_center_and_diag(cam_centers):\n cam_centers = np.hstack(cam_centers)\n avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)\n center = avg_cam_center\n dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)\n diagonal = np.max(dist)\n return center.flatten(), diagonal\n\n cam_centers = []\n\n for cam in cam_info:\n W2C = getWorld2View2(cam.R, cam.T)\n C2W = np.linalg.inv(W2C)\n cam_centers.append(C2W[:3, 3:4])\n\n center, diagonal = get_center_and_diag(cam_centers)\n radius = diagonal * 1.1\n\n translate = -center\n\n return {\"translate\": translate, \"radius\": radius}\n\ndef readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):\n cam_infos = []\n for idx, key in enumerate(cam_extrinsics):\n sys.stdout.write('\\r')\n # the exact output you're looking for:\n sys.stdout.write(\"Reading camera {}/{}\".format(idx+1, len(cam_extrinsics)))\n sys.stdout.flush()\n\n extr = cam_extrinsics[key]\n intr = cam_intrinsics[extr.camera_id]\n height = intr.height\n width = intr.width\n\n uid = intr.id\n R = np.transpose(qvec2rotmat(extr.qvec))\n T = np.array(extr.tvec)\n\n if intr.model==\"SIMPLE_PINHOLE\":\n focal_length_x = intr.params[0]\n FovY = focal2fov(focal_length_x, height)\n FovX = focal2fov(focal_length_x, width)\n elif intr.model==\"PINHOLE\":\n focal_length_x = intr.params[0]\n focal_length_y = intr.params[1]\n FovY = focal2fov(focal_length_y, height)\n FovX = focal2fov(focal_length_x, width)\n else:\n assert False, \"Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!\"\n\n image_path = os.path.join(images_folder, os.path.basename(extr.name))\n image_name = os.path.basename(image_path).split(\".\")[0]\n image = Image.open(image_path)\n\n cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,\n image_path=image_path, image_name=image_name, width=width, height=height)\n cam_infos.append(cam_info)\n sys.stdout.write('\\n')\n return cam_infos\n\ndef fetchPly(path):\n plydata = PlyData.read(path)\n vertices = plydata['vertex']\n positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T\n colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0\n normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T\n return BasicPointCloud(points=positions, colors=colors, normals=normals)\n\ndef storePly(path, xyz, rgb):\n # Define the dtype for the structured array\n dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),\n ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),\n ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]\n \n normals = np.zeros_like(xyz)\n\n elements = np.empty(xyz.shape[0], dtype=dtype)\n attributes = np.concatenate((xyz, normals, rgb), axis=1)\n elements[:] = list(map(tuple, attributes))\n\n # Create the PlyData object and write to file\n vertex_element = PlyElement.describe(elements, 'vertex')\n ply_data = PlyData([vertex_element])\n ply_data.write(path)\n\ndef readColmapSceneInfo(path, images, eval, llffhold=8):\n try:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.bin\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.bin\")\n cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)\n except:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.txt\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.txt\")\n cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)\n\n reading_dir = \"images\" if images == None else images\n cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))\n cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)\n\n if eval:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]\n else:\n train_cam_infos = cam_infos\n test_cam_infos = []\n\n nerf_normalization = getNerfppNorm(train_cam_infos)\n\n ply_path = os.path.join(path, \"sparse/0/points3D.ply\")\n bin_path = os.path.join(path, \"sparse/0/points3D.bin\")\n txt_path = os.path.join(path, \"sparse/0/points3D.txt\")\n if not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n try:"},"next_line":{"kind":"string","value":" xyz, rgb, _ = read_points3D_binary(bin_path)"},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-11-29 07:10:39+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5376,"cells":{"repo_name":{"kind":"string","value":"cswry/SeeSR"},"file_path":{"kind":"string","value":"test_seesr.py"},"context":{"kind":"list like","value":[{"identifier":"StableDiffusionControlNetPipeline","path":"pipelines/pipeline_seesr.py","snippet":"class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets\n as a list, the outputs from each ControlNet are added together to create one combined additional\n conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _init_tiled_vae(self,\n encoder_tile_size = 256,\n decoder_tile_size = 256,\n fast_decoder = False,\n fast_encoder = False,\n color_fix = False,\n vae_to_gpu = True):\n # save original forward (only once)\n if not hasattr(self.vae.encoder, 'original_forward'):\n setattr(self.vae.encoder, 'original_forward', self.vae.encoder.forward)\n if not hasattr(self.vae.decoder, 'original_forward'):\n setattr(self.vae.decoder, 'original_forward', self.vae.decoder.forward)\n\n encoder = self.vae.encoder\n decoder = self.vae.decoder\n\n self.vae.encoder.forward = VAEHook(\n encoder, encoder_tile_size, is_decoder=False, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu)\n self.vae.decoder.forward = VAEHook(\n decoder, decoder_tile_size, is_decoder=True, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding.\n\n When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several\n steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding.\n\n When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in\n several steps. This is useful to save a large amount of memory and to allow the processing of larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n if is_accelerate_available():\n from accelerate import cpu_offload\n else:\n raise ImportError(\"Please install accelerate via `pip install accelerate`\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:\n cpu_offload(cpu_offloaded_model, device)\n\n if self.safety_checker is not None:\n cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)\n\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n hook = None\n for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:\n _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)\n\n if self.safety_checker is not None:\n # the safety checker can offload the vae again\n _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)\n\n # control net hook has be manually offloaded as it alternates with unet\n cpu_offload_with_hook(self.controlnet, device)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device\n def _execution_device(self):\n r\"\"\"\n Returns the device on which the pipeline's models will be executed. After calling\n `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module\n hooks.\n \"\"\"\n if not hasattr(self.unet, \"_hf_hook\"):\n return self.device\n for module in self.unet.modules():\n if (\n hasattr(module, \"_hf_hook\")\n and hasattr(module._hf_hook, \"execution_device\")\n and module._hf_hook.execution_device is not None\n ):\n return torch.device(module._hf_hook.execution_device)\n return self.device\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n ram_encoder_hidden_states: Optional[torch.FloatTensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n ram_encoder_hidden_states = torch.cat([ram_encoder_hidden_states, ram_encoder_hidden_states])\n\n return prompt_embeds, ram_encoder_hidden_states\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n warnings.warn(\n \"The decode_latents method is deprecated and will be removed in a future version. Please\"\n \" use VaeImageProcessor instead\",\n FutureWarning,\n )\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n #extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n \"For multiple controlnets: `image` must have the same length as the number of controlnets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n\n if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\n raise TypeError(\n \"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n elif image_is_tensor:\n image_batch_size = image.shape[0]\n elif image_is_pil_list:\n image_batch_size = len(image)\n elif image_is_tensor_list:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n if not isinstance(image, torch.Tensor):\n if isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n images = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n #image_ = image_.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])\n image_ = np.array(image_)\n image_ = image_[None, :]\n images.append(image_)\n\n image = images\n\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)#.flip(1)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n #latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)\n #offset_noise = torch.randn(batch_size, num_channels_latents, 1, 1, device=device)\n #latents = latents + 0.1 * offset_noise\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[2]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[3]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n # override DiffusionPipeline\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n safe_serialization: bool = False,\n variant: Optional[str] = None,\n ):\n if isinstance(self.controlnet, ControlNetModel):\n super().save_pretrained(save_directory, safe_serialization, variant)\n else:\n raise NotImplementedError(\"Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.\")\n \n def _gaussian_weights(self, tile_width, tile_height, nbatches):\n \"\"\"Generates a gaussian mask of weights for tile contributions\"\"\"\n from numpy import pi, exp, sqrt\n import numpy as np\n\n latent_width = tile_width\n latent_height = tile_height\n\n var = 0.01\n midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1\n x_probs = [exp(-(x-midpoint)*(x-midpoint)/(latent_width*latent_width)/(2*var)) / sqrt(2*pi*var) for x in range(latent_width)]\n midpoint = latent_height / 2\n y_probs = [exp(-(y-midpoint)*(y-midpoint)/(latent_height*latent_height)/(2*var)) / sqrt(2*pi*var) for y in range(latent_height)]\n\n weights = np.outer(y_probs, x_probs)\n return torch.tile(torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1))\n\n @perfcount\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n image_sr = None,\n start_steps = 999,\n start_point = 'noise',\n ram_encoder_hidden_states=None,\n latent_tiled_size=320,\n latent_tiled_overlap=4,\n args=None\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,\n `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can\n also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If\n height and/or width are passed, `image` is resized according to them. If multiple ControlNets are\n specified in init, images must be passed as a list such that each element of the list can be correctly\n batched for input to a single controlnet.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, image)\n \n # 1. Check inputs. Raise error if not correct\n \"\"\"\n self.check_inputs(\n prompt,\n image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n conditioning_scale,\n )\n \"\"\"\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n \"\"\"\n if isinstance(controlnet, MultiControlNetModel) and isinstance(conditioning_scale, float):\n conditioning_scale = [conditioning_scale] * len(controlnet.nets)\n \n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n \n guess_mode = guess_mode or global_pool_conditions\n \"\"\"\n\n # 3. Encode input prompt\n prompt_embeds, ram_encoder_hidden_states = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n ram_encoder_hidden_states=ram_encoder_hidden_states\n )\n\n # 4. Prepare image\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare the start point\n if start_point == 'noise':\n latents = latents\n elif start_point == 'lr': # LRE Strategy\n latents_condition_image = self.vae.encode(image*2-1).latent_dist.sample()\n latents_condition_image = latents_condition_image * self.vae.config.scaling_factor\n start_steps_tensor = torch.randint(start_steps, start_steps+1, (latents.shape[0],), device=latents.device)\n start_steps_tensor = start_steps_tensor.long()\n latents = self.scheduler.add_noise(latents_condition_image[0:1, ...], latents, start_steps_tensor)\n \n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n \n _, _, h, w = latents.size()\n tile_size, tile_overlap = (latent_tiled_size, latent_tiled_overlap) if args is not None else (256, 8)\n if h*w<=tile_size*tile_size:\n print(f\"[Tiled Latent]: the input size is tiny and unnecessary to tile.\")\n else:\n print(f\"[Tiled Latent]: the input size is {image.shape[-2]}x{image.shape[-1]}, need to tiled\")\n\n for i, t in enumerate(timesteps):\n # pass, if the timestep is larger than start_steps\n if t > start_steps:\n print(f'pass {t} steps.')\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n controlnet_latent_model_input = latents\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n \n else:\n controlnet_latent_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if h*w<=tile_size*tile_size: # tiled latent input\n down_block_res_samples, mid_block_res_sample = [None]*10, None\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n controlnet_latent_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=conditioning_scale,\n guess_mode=guess_mode,\n return_dict=False,\n image_encoder_hidden_states = ram_encoder_hidden_states,\n )\n\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n image_encoder_hidden_states = ram_encoder_hidden_states,\n )[0]\n else:\n tile_weights = self._gaussian_weights(tile_size, tile_size, 1)\n tile_size = min(tile_size, min(h, w))\n tile_weights = self._gaussian_weights(tile_size, tile_size, 1)\n\n grid_rows = 0\n cur_x = 0\n while cur_x < latent_model_input.size(-1):\n cur_x = max(grid_rows * tile_size-tile_overlap * grid_rows, 0)+tile_size\n grid_rows += 1\n\n grid_cols = 0\n cur_y = 0\n while cur_y < latent_model_input.size(-2):\n cur_y = max(grid_cols * tile_size-tile_overlap * grid_cols, 0)+tile_size\n grid_cols += 1\n\n input_list = []\n cond_list = []\n img_list = []\n noise_preds = []\n for row in range(grid_rows):\n noise_preds_row = []\n for col in range(grid_cols):\n if col < grid_cols-1 or row < grid_rows-1:\n # extract tile from input image\n ofs_x = max(row * tile_size-tile_overlap * row, 0)\n ofs_y = max(col * tile_size-tile_overlap * col, 0)\n # input tile area on total image\n if row == grid_rows-1:\n ofs_x = w - tile_size\n if col == grid_cols-1:\n ofs_y = h - tile_size\n\n input_start_x = ofs_x\n input_end_x = ofs_x + tile_size\n input_start_y = ofs_y\n input_end_y = ofs_y + tile_size\n\n # input tile dimensions\n input_tile = latent_model_input[:, :, input_start_y:input_end_y, input_start_x:input_end_x]\n input_list.append(input_tile)\n cond_tile = controlnet_latent_model_input[:, :, input_start_y:input_end_y, input_start_x:input_end_x]\n cond_list.append(cond_tile)\n img_tile = image[:, :, input_start_y*8:input_end_y*8, input_start_x*8:input_end_x*8]\n img_list.append(img_tile)\n\n if len(input_list) == batch_size or col == grid_cols-1:\n input_list_t = torch.cat(input_list, dim=0)\n cond_list_t = torch.cat(cond_list, dim=0)\n img_list_t = torch.cat(img_list, dim=0)\n #print(input_list_t.shape, cond_list_t.shape, img_list_t.shape, fg_mask_list_t.shape)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n cond_list_t,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=img_list_t,\n conditioning_scale=conditioning_scale,\n guess_mode=guess_mode,\n return_dict=False,\n image_encoder_hidden_states = ram_encoder_hidden_states,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n model_out = self.unet(\n input_list_t,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n image_encoder_hidden_states = ram_encoder_hidden_states,\n )[0]\n\n #for sample_i in range(model_out.size(0)):\n # noise_preds_row.append(model_out[sample_i].unsqueeze(0))\n input_list = []\n cond_list = []\n img_list = []\n\n noise_preds.append(model_out)\n\n # Stitch noise predictions for all tiles\n noise_pred = torch.zeros(latent_model_input.shape, device=latent_model_input.device)\n contributors = torch.zeros(latent_model_input.shape, device=latent_model_input.device)\n # Add each tile contribution to overall latents\n for row in range(grid_rows):\n for col in range(grid_cols):\n if col < grid_cols-1 or row < grid_rows-1:\n # extract tile from input image\n ofs_x = max(row * tile_size-tile_overlap * row, 0)\n ofs_y = max(col * tile_size-tile_overlap * col, 0)\n # input tile area on total image\n if row == grid_rows-1:\n ofs_x = w - tile_size\n if col == grid_cols-1:\n ofs_y = h - tile_size\n\n input_start_x = ofs_x\n input_end_x = ofs_x + tile_size\n input_start_y = ofs_y\n input_end_y = ofs_y + tile_size\n \n noise_pred[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += noise_preds[row*grid_cols + col] * tile_weights\n contributors[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += tile_weights\n # Average overlapping areas with more than 1 contributor\n noise_pred /= contributors\n \n \n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n has_nsfw_concept = None\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]#.flip(1)\n #image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)"},{"identifier":"load_dreambooth_lora","path":"utils/misc.py","snippet":"def load_dreambooth_lora(unet, vae=None, model_path=None, alpha=1.0, model_base=\"\"):\n if model_path is None: return unet\n \n if model_path.endswith(\".ckpt\"):\n base_state_dict = torch.load(model_path)['state_dict']\n elif model_path.endswith(\".safetensors\"):\n state_dict = {}\n with safe_open(model_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n state_dict[key] = f.get_tensor(key)\n \n is_lora = all(\"lora\" in k for k in state_dict.keys())\n if not is_lora:\n base_state_dict = state_dict\n else:\n base_state_dict = {}\n with safe_open(model_base, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n base_state_dict[key] = f.get_tensor(key)\n \n converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_state_dict, unet.config)\n unet_state_dict = unet.state_dict()\n for key in converted_unet_checkpoint:\n converted_unet_checkpoint[key] = alpha * converted_unet_checkpoint[key] + (1.0-alpha) * unet_state_dict[key]\n unet.load_state_dict(converted_unet_checkpoint, strict=False)\n\n if vae is not None:\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_state_dict, vae.config)\n vae.load_state_dict(converted_vae_checkpoint)\n \n return unet, vae"},{"identifier":"wavelet_color_fix","path":"utils/wavelet_color_fix.py","snippet":"def wavelet_color_fix(target: Image, source: Image):\n # Convert images to tensors\n to_tensor = ToTensor()\n target_tensor = to_tensor(target).unsqueeze(0)\n source_tensor = to_tensor(source).unsqueeze(0)\n\n # Apply wavelet reconstruction\n result_tensor = wavelet_reconstruction(target_tensor, source_tensor)\n\n # Convert tensor back to image\n to_image = ToPILImage()\n result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))\n\n return result_image"},{"identifier":"adain_color_fix","path":"utils/wavelet_color_fix.py","snippet":"def adain_color_fix(target: Image, source: Image):\n # Convert images to tensors\n to_tensor = ToTensor()\n target_tensor = to_tensor(target).unsqueeze(0)\n source_tensor = to_tensor(source).unsqueeze(0)\n\n # Apply adaptive instance normalization\n result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)\n\n # Convert tensor back to image\n to_image = ToPILImage()\n result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))\n\n return result_image"},{"identifier":"ram","path":"ram/models/ram_lora.py","snippet":"def ram(pretrained='', pretrained_condition='', **kwargs):\n model = RAMLora(**kwargs)\n\n if pretrained:\n if kwargs['vit'] == 'swin_b':\n model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)\n elif kwargs['vit'] == 'swin_l':\n model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs)\n else:\n model, msg = load_checkpoint(model, pretrained)\n print('vit:', kwargs['vit'])\n \n if pretrained_condition:\n model.load_state_dict(torch.load(pretrained_condition), strict=False)\n print(f'load lora weights from {pretrained_condition}')\n\n return model"},{"identifier":"inference_ram","path":"ram/inference.py","snippet":"def inference_ram(image, model):\n\n with torch.no_grad():\n tags, tags_chinese = model.generate_tag(image)\n\n return tags[0],tags_chinese[0]"},{"identifier":"get_transform","path":"ram/transform.py","snippet":"def get_transform(image_size=384):\n return Compose([\n convert_to_rgb,\n Resize((image_size, image_size)),\n ToTensor(),\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])"}],"string":"[\n {\n \"identifier\": \"StableDiffusionControlNetPipeline\",\n \"path\": \"pipelines/pipeline_seesr.py\",\n \"snippet\": \"class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin):\\n r\\\"\\\"\\\"\\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.\\n\\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\\n\\n In addition the pipeline inherits the following loading methods:\\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\\n\\n Args:\\n vae ([`AutoencoderKL`]):\\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\\n text_encoder ([`CLIPTextModel`]):\\n Frozen text-encoder. Stable Diffusion uses the text portion of\\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\\n tokenizer (`CLIPTokenizer`):\\n Tokenizer of class\\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\\n Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets\\n as a list, the outputs from each ControlNet are added together to create one combined additional\\n conditioning.\\n scheduler ([`SchedulerMixin`]):\\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\\n safety_checker ([`StableDiffusionSafetyChecker`]):\\n Classification module that estimates whether generated images could be considered offensive or harmful.\\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\\n feature_extractor ([`CLIPImageProcessor`]):\\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\\n \\\"\\\"\\\"\\n _optional_components = [\\\"safety_checker\\\", \\\"feature_extractor\\\"]\\n\\n def __init__(\\n self,\\n vae: AutoencoderKL,\\n text_encoder: CLIPTextModel,\\n tokenizer: CLIPTokenizer,\\n unet: UNet2DConditionModel,\\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\\n scheduler: KarrasDiffusionSchedulers,\\n safety_checker: StableDiffusionSafetyChecker,\\n feature_extractor: CLIPImageProcessor,\\n requires_safety_checker: bool = True,\\n ):\\n super().__init__()\\n\\n if safety_checker is None and requires_safety_checker:\\n logger.warning(\\n f\\\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\\\"\\n \\\" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\\\"\\n \\\" results in services or applications open to the public. Both the diffusers team and Hugging Face\\\"\\n \\\" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\\\"\\n \\\" it only for use-cases that involve analyzing network behavior or auditing its results. For more\\\"\\n \\\" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\\\"\\n )\\n\\n if safety_checker is not None and feature_extractor is None:\\n raise ValueError(\\n \\\"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\\\"\\n \\\" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\\\"\\n )\\n\\n if isinstance(controlnet, (list, tuple)):\\n controlnet = MultiControlNetModel(controlnet)\\n\\n self.register_modules(\\n vae=vae,\\n text_encoder=text_encoder,\\n tokenizer=tokenizer,\\n unet=unet,\\n controlnet=controlnet,\\n scheduler=scheduler,\\n safety_checker=safety_checker,\\n feature_extractor=feature_extractor,\\n )\\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\\n self.register_to_config(requires_safety_checker=requires_safety_checker)\\n\\n def _init_tiled_vae(self,\\n encoder_tile_size = 256,\\n decoder_tile_size = 256,\\n fast_decoder = False,\\n fast_encoder = False,\\n color_fix = False,\\n vae_to_gpu = True):\\n # save original forward (only once)\\n if not hasattr(self.vae.encoder, 'original_forward'):\\n setattr(self.vae.encoder, 'original_forward', self.vae.encoder.forward)\\n if not hasattr(self.vae.decoder, 'original_forward'):\\n setattr(self.vae.decoder, 'original_forward', self.vae.decoder.forward)\\n\\n encoder = self.vae.encoder\\n decoder = self.vae.decoder\\n\\n self.vae.encoder.forward = VAEHook(\\n encoder, encoder_tile_size, is_decoder=False, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu)\\n self.vae.decoder.forward = VAEHook(\\n decoder, decoder_tile_size, is_decoder=True, fast_decoder=fast_decoder, fast_encoder=fast_encoder, color_fix=color_fix, to_gpu=vae_to_gpu)\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing\\n def enable_vae_slicing(self):\\n r\\\"\\\"\\\"\\n Enable sliced VAE decoding.\\n\\n When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several\\n steps. This is useful to save some memory and allow larger batch sizes.\\n \\\"\\\"\\\"\\n self.vae.enable_slicing()\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing\\n def disable_vae_slicing(self):\\n r\\\"\\\"\\\"\\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to\\n computing decoding in one step.\\n \\\"\\\"\\\"\\n self.vae.disable_slicing()\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling\\n def enable_vae_tiling(self):\\n r\\\"\\\"\\\"\\n Enable tiled VAE decoding.\\n\\n When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in\\n several steps. This is useful to save a large amount of memory and to allow the processing of larger images.\\n \\\"\\\"\\\"\\n self.vae.enable_tiling()\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling\\n def disable_vae_tiling(self):\\n r\\\"\\\"\\\"\\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to\\n computing decoding in one step.\\n \\\"\\\"\\\"\\n self.vae.disable_tiling()\\n\\n def enable_sequential_cpu_offload(self, gpu_id=0):\\n r\\\"\\\"\\\"\\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\\n text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a\\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\\n Note that offloading happens on a submodule basis. Memory savings are higher than with\\n `enable_model_cpu_offload`, but performance is lower.\\n \\\"\\\"\\\"\\n if is_accelerate_available():\\n from accelerate import cpu_offload\\n else:\\n raise ImportError(\\\"Please install accelerate via `pip install accelerate`\\\")\\n\\n device = torch.device(f\\\"cuda:{gpu_id}\\\")\\n\\n for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:\\n cpu_offload(cpu_offloaded_model, device)\\n\\n if self.safety_checker is not None:\\n cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)\\n\\n def enable_model_cpu_offload(self, gpu_id=0):\\n r\\\"\\\"\\\"\\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\\n \\\"\\\"\\\"\\n if is_accelerate_available() and is_accelerate_version(\\\">=\\\", \\\"0.17.0.dev0\\\"):\\n from accelerate import cpu_offload_with_hook\\n else:\\n raise ImportError(\\\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\\\")\\n\\n device = torch.device(f\\\"cuda:{gpu_id}\\\")\\n\\n hook = None\\n for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:\\n _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)\\n\\n if self.safety_checker is not None:\\n # the safety checker can offload the vae again\\n _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)\\n\\n # control net hook has be manually offloaded as it alternates with unet\\n cpu_offload_with_hook(self.controlnet, device)\\n\\n # We'll offload the last model manually.\\n self.final_offload_hook = hook\\n\\n @property\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device\\n def _execution_device(self):\\n r\\\"\\\"\\\"\\n Returns the device on which the pipeline's models will be executed. After calling\\n `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module\\n hooks.\\n \\\"\\\"\\\"\\n if not hasattr(self.unet, \\\"_hf_hook\\\"):\\n return self.device\\n for module in self.unet.modules():\\n if (\\n hasattr(module, \\\"_hf_hook\\\")\\n and hasattr(module._hf_hook, \\\"execution_device\\\")\\n and module._hf_hook.execution_device is not None\\n ):\\n return torch.device(module._hf_hook.execution_device)\\n return self.device\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\\n def _encode_prompt(\\n self,\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt=None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n ram_encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n ):\\n r\\\"\\\"\\\"\\n Encodes the prompt into text encoder hidden states.\\n\\n Args:\\n prompt (`str` or `List[str]`, *optional*):\\n prompt to be encoded\\n device: (`torch.device`):\\n torch device\\n num_images_per_prompt (`int`):\\n number of images that should be generated per prompt\\n do_classifier_free_guidance (`bool`):\\n whether to use classifier free guidance or not\\n negative_prompt (`str` or `List[str]`, *optional*):\\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\\n less than `1`).\\n prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\\n provided, text embeddings will be generated from `prompt` input argument.\\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\\n argument.\\n \\\"\\\"\\\"\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n if prompt_embeds is None:\\n # textual inversion: procecss multi-vector tokens if necessary\\n if isinstance(self, TextualInversionLoaderMixin):\\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\\n\\n text_inputs = self.tokenizer(\\n prompt,\\n padding=\\\"max_length\\\",\\n max_length=self.tokenizer.model_max_length,\\n truncation=True,\\n return_tensors=\\\"pt\\\",\\n )\\n text_input_ids = text_inputs.input_ids\\n untruncated_ids = self.tokenizer(prompt, padding=\\\"longest\\\", return_tensors=\\\"pt\\\").input_ids\\n\\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\\n text_input_ids, untruncated_ids\\n ):\\n removed_text = self.tokenizer.batch_decode(\\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\\n )\\n logger.warning(\\n \\\"The following part of your input was truncated because CLIP can only handle sequences up to\\\"\\n f\\\" {self.tokenizer.model_max_length} tokens: {removed_text}\\\"\\n )\\n\\n if hasattr(self.text_encoder.config, \\\"use_attention_mask\\\") and self.text_encoder.config.use_attention_mask:\\n attention_mask = text_inputs.attention_mask.to(device)\\n else:\\n attention_mask = None\\n\\n prompt_embeds = self.text_encoder(\\n text_input_ids.to(device),\\n attention_mask=attention_mask,\\n )\\n prompt_embeds = prompt_embeds[0]\\n\\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\\n\\n bs_embed, seq_len, _ = prompt_embeds.shape\\n # duplicate text embeddings for each generation per prompt, using mps friendly method\\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\\n\\n # get unconditional embeddings for classifier free guidance\\n if do_classifier_free_guidance and negative_prompt_embeds is None:\\n uncond_tokens: List[str]\\n if negative_prompt is None:\\n uncond_tokens = [\\\"\\\"] * batch_size\\n elif prompt is not None and type(prompt) is not type(negative_prompt):\\n raise TypeError(\\n f\\\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\\\"\\n f\\\" {type(prompt)}.\\\"\\n )\\n elif isinstance(negative_prompt, str):\\n uncond_tokens = [negative_prompt]\\n elif batch_size != len(negative_prompt):\\n raise ValueError(\\n f\\\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\\\"\\n f\\\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\\\"\\n \\\" the batch size of `prompt`.\\\"\\n )\\n else:\\n uncond_tokens = negative_prompt\\n\\n # textual inversion: procecss multi-vector tokens if necessary\\n if isinstance(self, TextualInversionLoaderMixin):\\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\\n\\n max_length = prompt_embeds.shape[1]\\n uncond_input = self.tokenizer(\\n uncond_tokens,\\n padding=\\\"max_length\\\",\\n max_length=max_length,\\n truncation=True,\\n return_tensors=\\\"pt\\\",\\n )\\n\\n if hasattr(self.text_encoder.config, \\\"use_attention_mask\\\") and self.text_encoder.config.use_attention_mask:\\n attention_mask = uncond_input.attention_mask.to(device)\\n else:\\n attention_mask = None\\n\\n negative_prompt_embeds = self.text_encoder(\\n uncond_input.input_ids.to(device),\\n attention_mask=attention_mask,\\n )\\n negative_prompt_embeds = negative_prompt_embeds[0]\\n\\n if do_classifier_free_guidance:\\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\\n seq_len = negative_prompt_embeds.shape[1]\\n\\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\\n\\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\\n\\n # For classifier free guidance, we need to do two forward passes.\\n # Here we concatenate the unconditional and text embeddings into a single batch\\n # to avoid doing two forward passes\\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\\n ram_encoder_hidden_states = torch.cat([ram_encoder_hidden_states, ram_encoder_hidden_states])\\n\\n return prompt_embeds, ram_encoder_hidden_states\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\\n def run_safety_checker(self, image, device, dtype):\\n if self.safety_checker is None:\\n has_nsfw_concept = None\\n else:\\n if torch.is_tensor(image):\\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\\\"pil\\\")\\n else:\\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\\\"pt\\\").to(device)\\n image, has_nsfw_concept = self.safety_checker(\\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\\n )\\n return image, has_nsfw_concept\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\\n def decode_latents(self, latents):\\n warnings.warn(\\n \\\"The decode_latents method is deprecated and will be removed in a future version. Please\\\"\\n \\\" use VaeImageProcessor instead\\\",\\n FutureWarning,\\n )\\n latents = 1 / self.vae.config.scaling_factor * latents\\n image = self.vae.decode(latents, return_dict=False)[0]\\n image = (image / 2 + 0.5).clamp(0, 1)\\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\\n return image\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\\n def prepare_extra_step_kwargs(self, generator, eta):\\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\\n # and should be between [0, 1]\\n\\n accepts_eta = \\\"eta\\\" in set(inspect.signature(self.scheduler.step).parameters.keys())\\n extra_step_kwargs = {}\\n if accepts_eta:\\n extra_step_kwargs[\\\"eta\\\"] = eta\\n\\n # check if the scheduler accepts generator\\n accepts_generator = \\\"generator\\\" in set(inspect.signature(self.scheduler.step).parameters.keys())\\n if accepts_generator:\\n extra_step_kwargs[\\\"generator\\\"] = generator\\n #extra_step_kwargs[\\\"generator\\\"] = generator\\n return extra_step_kwargs\\n\\n def check_inputs(\\n self,\\n prompt,\\n image,\\n height,\\n width,\\n callback_steps,\\n negative_prompt=None,\\n prompt_embeds=None,\\n negative_prompt_embeds=None,\\n controlnet_conditioning_scale=1.0,\\n ):\\n if height % 8 != 0 or width % 8 != 0:\\n raise ValueError(f\\\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\\\")\\n\\n if (callback_steps is None) or (\\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\\n ):\\n raise ValueError(\\n f\\\"`callback_steps` has to be a positive integer but is {callback_steps} of type\\\"\\n f\\\" {type(callback_steps)}.\\\"\\n )\\n\\n if prompt is not None and prompt_embeds is not None:\\n raise ValueError(\\n f\\\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\\\"\\n \\\" only forward one of the two.\\\"\\n )\\n elif prompt is None and prompt_embeds is None:\\n raise ValueError(\\n \\\"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\\\"\\n )\\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\\n raise ValueError(f\\\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\\\")\\n\\n if negative_prompt is not None and negative_prompt_embeds is not None:\\n raise ValueError(\\n f\\\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\\\"\\n f\\\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\\\"\\n )\\n\\n if prompt_embeds is not None and negative_prompt_embeds is not None:\\n if prompt_embeds.shape != negative_prompt_embeds.shape:\\n raise ValueError(\\n \\\"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\\\"\\n f\\\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\\\"\\n f\\\" {negative_prompt_embeds.shape}.\\\"\\n )\\n\\n # `prompt` needs more sophisticated handling when there are multiple\\n # conditionings.\\n if isinstance(self.controlnet, MultiControlNetModel):\\n if isinstance(prompt, list):\\n logger.warning(\\n f\\\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\\\"\\n \\\" prompts. The conditionings will be fixed across the prompts.\\\"\\n )\\n\\n # Check `image`\\n is_compiled = hasattr(F, \\\"scaled_dot_product_attention\\\") and isinstance(\\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\\n )\\n if (\\n isinstance(self.controlnet, ControlNetModel)\\n or is_compiled\\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\\n ):\\n self.check_image(image, prompt, prompt_embeds)\\n elif (\\n isinstance(self.controlnet, MultiControlNetModel)\\n or is_compiled\\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\\n ):\\n if not isinstance(image, list):\\n raise TypeError(\\\"For multiple controlnets: `image` must be type `list`\\\")\\n\\n # When `image` is a nested list:\\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\\n elif any(isinstance(i, list) for i in image):\\n raise ValueError(\\\"A single batch of multiple conditionings are supported at the moment.\\\")\\n elif len(image) != len(self.controlnet.nets):\\n raise ValueError(\\n \\\"For multiple controlnets: `image` must have the same length as the number of controlnets.\\\"\\n )\\n\\n for image_ in image:\\n self.check_image(image_, prompt, prompt_embeds)\\n else:\\n assert False\\n\\n # Check `controlnet_conditioning_scale`\\n if (\\n isinstance(self.controlnet, ControlNetModel)\\n or is_compiled\\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\\n ):\\n if not isinstance(controlnet_conditioning_scale, float):\\n raise TypeError(\\\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\\\")\\n elif (\\n isinstance(self.controlnet, MultiControlNetModel)\\n or is_compiled\\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\\n ):\\n if isinstance(controlnet_conditioning_scale, list):\\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\\n raise ValueError(\\\"A single batch of multiple conditionings are supported at the moment.\\\")\\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\\n self.controlnet.nets\\n ):\\n raise ValueError(\\n \\\"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\\\"\\n \\\" the same length as the number of controlnets\\\"\\n )\\n else:\\n assert False\\n\\n def check_image(self, image, prompt, prompt_embeds):\\n image_is_pil = isinstance(image, PIL.Image.Image)\\n image_is_tensor = isinstance(image, torch.Tensor)\\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\\n\\n if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\\n raise TypeError(\\n \\\"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\\\"\\n )\\n\\n if image_is_pil:\\n image_batch_size = 1\\n elif image_is_tensor:\\n image_batch_size = image.shape[0]\\n elif image_is_pil_list:\\n image_batch_size = len(image)\\n elif image_is_tensor_list:\\n image_batch_size = len(image)\\n\\n if prompt is not None and isinstance(prompt, str):\\n prompt_batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n prompt_batch_size = len(prompt)\\n elif prompt_embeds is not None:\\n prompt_batch_size = prompt_embeds.shape[0]\\n\\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\\n raise ValueError(\\n f\\\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\\\"\\n )\\n\\n def prepare_image(\\n self,\\n image,\\n width,\\n height,\\n batch_size,\\n num_images_per_prompt,\\n device,\\n dtype,\\n do_classifier_free_guidance=False,\\n guess_mode=False,\\n ):\\n if not isinstance(image, torch.Tensor):\\n if isinstance(image, PIL.Image.Image):\\n image = [image]\\n\\n if isinstance(image[0], PIL.Image.Image):\\n images = []\\n\\n for image_ in image:\\n image_ = image_.convert(\\\"RGB\\\")\\n #image_ = image_.resize((width, height), resample=PIL_INTERPOLATION[\\\"lanczos\\\"])\\n image_ = np.array(image_)\\n image_ = image_[None, :]\\n images.append(image_)\\n\\n image = images\\n\\n image = np.concatenate(image, axis=0)\\n image = np.array(image).astype(np.float32) / 255.0\\n image = image.transpose(0, 3, 1, 2)\\n image = torch.from_numpy(image)#.flip(1)\\n elif isinstance(image[0], torch.Tensor):\\n image = torch.cat(image, dim=0)\\n\\n image_batch_size = image.shape[0]\\n\\n if image_batch_size == 1:\\n repeat_by = batch_size\\n else:\\n # image batch size is the same as prompt batch size\\n repeat_by = num_images_per_prompt\\n\\n image = image.repeat_interleave(repeat_by, dim=0)\\n\\n image = image.to(device=device, dtype=dtype)\\n\\n if do_classifier_free_guidance and not guess_mode:\\n image = torch.cat([image] * 2)\\n\\n return image\\n\\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\\n shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)\\n if isinstance(generator, list) and len(generator) != batch_size:\\n raise ValueError(\\n f\\\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\\\"\\n f\\\" size of {batch_size}. Make sure the batch size matches the length of the generators.\\\"\\n )\\n\\n if latents is None:\\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\\n #latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)\\n #offset_noise = torch.randn(batch_size, num_channels_latents, 1, 1, device=device)\\n #latents = latents + 0.1 * offset_noise\\n else:\\n latents = latents.to(device)\\n\\n # scale the initial noise by the standard deviation required by the scheduler\\n latents = latents * self.scheduler.init_noise_sigma\\n return latents\\n\\n def _default_height_width(self, height, width, image):\\n # NOTE: It is possible that a list of images have different\\n # dimensions for each image, so just checking the first image\\n # is not _exactly_ correct, but it is simple.\\n while isinstance(image, list):\\n image = image[0]\\n\\n if height is None:\\n if isinstance(image, PIL.Image.Image):\\n height = image.height\\n elif isinstance(image, torch.Tensor):\\n height = image.shape[2]\\n\\n height = (height // 8) * 8 # round down to nearest multiple of 8\\n\\n if width is None:\\n if isinstance(image, PIL.Image.Image):\\n width = image.width\\n elif isinstance(image, torch.Tensor):\\n width = image.shape[3]\\n\\n width = (width // 8) * 8 # round down to nearest multiple of 8\\n\\n return height, width\\n\\n # override DiffusionPipeline\\n def save_pretrained(\\n self,\\n save_directory: Union[str, os.PathLike],\\n safe_serialization: bool = False,\\n variant: Optional[str] = None,\\n ):\\n if isinstance(self.controlnet, ControlNetModel):\\n super().save_pretrained(save_directory, safe_serialization, variant)\\n else:\\n raise NotImplementedError(\\\"Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.\\\")\\n \\n def _gaussian_weights(self, tile_width, tile_height, nbatches):\\n \\\"\\\"\\\"Generates a gaussian mask of weights for tile contributions\\\"\\\"\\\"\\n from numpy import pi, exp, sqrt\\n import numpy as np\\n\\n latent_width = tile_width\\n latent_height = tile_height\\n\\n var = 0.01\\n midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1\\n x_probs = [exp(-(x-midpoint)*(x-midpoint)/(latent_width*latent_width)/(2*var)) / sqrt(2*pi*var) for x in range(latent_width)]\\n midpoint = latent_height / 2\\n y_probs = [exp(-(y-midpoint)*(y-midpoint)/(latent_height*latent_height)/(2*var)) / sqrt(2*pi*var) for y in range(latent_height)]\\n\\n weights = np.outer(y_probs, x_probs)\\n return torch.tile(torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1))\\n\\n @perfcount\\n @torch.no_grad()\\n @replace_example_docstring(EXAMPLE_DOC_STRING)\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n num_inference_steps: int = 50,\\n guidance_scale: float = 7.5,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n conditioning_scale: Union[float, List[float]] = 1.0,\\n guess_mode: bool = False,\\n image_sr = None,\\n start_steps = 999,\\n start_point = 'noise',\\n ram_encoder_hidden_states=None,\\n latent_tiled_size=320,\\n latent_tiled_overlap=4,\\n args=None\\n ):\\n r\\\"\\\"\\\"\\n Function invoked when calling the pipeline for generation.\\n\\n Args:\\n prompt (`str` or `List[str]`, *optional*):\\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\\n instead.\\n image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,\\n `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):\\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\\n the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can\\n also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If\\n height and/or width are passed, `image` is resized according to them. If multiple ControlNets are\\n specified in init, images must be passed as a list such that each element of the list can be correctly\\n batched for input to a single controlnet.\\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\\n The height in pixels of the generated image.\\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\\n The width in pixels of the generated image.\\n num_inference_steps (`int`, *optional*, defaults to 50):\\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\\n expense of slower inference.\\n guidance_scale (`float`, *optional*, defaults to 7.5):\\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\\n usually at the expense of lower image quality.\\n negative_prompt (`str` or `List[str]`, *optional*):\\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\\n less than `1`).\\n num_images_per_prompt (`int`, *optional*, defaults to 1):\\n The number of images to generate per prompt.\\n eta (`float`, *optional*, defaults to 0.0):\\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\\n [`schedulers.DDIMScheduler`], will be ignored for others.\\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\\n to make generation deterministic.\\n latents (`torch.FloatTensor`, *optional*):\\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\\n tensor will ge generated by sampling using the supplied random `generator`.\\n prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\\n provided, text embeddings will be generated from `prompt` input argument.\\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\\n argument.\\n output_type (`str`, *optional*, defaults to `\\\"pil\\\"`):\\n The output format of the generate image. Choose between\\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\\n plain tuple.\\n callback (`Callable`, *optional*):\\n A function that will be called every `callback_steps` steps during inference. The function will be\\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\\n callback_steps (`int`, *optional*, defaults to 1):\\n The frequency at which the `callback` function will be called. If not specified, the callback will be\\n called at every step.\\n cross_attention_kwargs (`dict`, *optional*):\\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\\n `self.processor` in\\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\\n conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\\n The outputs of the controlnet are multiplied by `conditioning_scale` before they are added\\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\\n corresponding scale as a list.\\n guess_mode (`bool`, *optional*, defaults to `False`):\\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\\n\\n Examples:\\n\\n Returns:\\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\\n When returning a tuple, the first element is a list with the generated images, and the second element is a\\n list of `bool`s denoting whether the corresponding generated image likely represents \\\"not-safe-for-work\\\"\\n (nsfw) content, according to the `safety_checker`.\\n \\\"\\\"\\\"\\n # 0. Default height and width to unet\\n height, width = self._default_height_width(height, width, image)\\n \\n # 1. Check inputs. Raise error if not correct\\n \\\"\\\"\\\"\\n self.check_inputs(\\n prompt,\\n image,\\n height,\\n width,\\n callback_steps,\\n negative_prompt,\\n prompt_embeds,\\n negative_prompt_embeds,\\n conditioning_scale,\\n )\\n \\\"\\\"\\\"\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n device = self._execution_device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\\n \\\"\\\"\\\"\\n if isinstance(controlnet, MultiControlNetModel) and isinstance(conditioning_scale, float):\\n conditioning_scale = [conditioning_scale] * len(controlnet.nets)\\n \\n global_pool_conditions = (\\n controlnet.config.global_pool_conditions\\n if isinstance(controlnet, ControlNetModel)\\n else controlnet.nets[0].config.global_pool_conditions\\n )\\n \\n guess_mode = guess_mode or global_pool_conditions\\n \\\"\\\"\\\"\\n\\n # 3. Encode input prompt\\n prompt_embeds, ram_encoder_hidden_states = self._encode_prompt(\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n ram_encoder_hidden_states=ram_encoder_hidden_states\\n )\\n\\n # 4. Prepare image\\n image = self.prepare_image(\\n image=image,\\n width=width,\\n height=height,\\n batch_size=batch_size * num_images_per_prompt,\\n num_images_per_prompt=num_images_per_prompt,\\n device=device,\\n dtype=controlnet.dtype,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n guess_mode=guess_mode,\\n )\\n\\n # 5. Prepare timesteps\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n timesteps = self.scheduler.timesteps\\n\\n # 6. Prepare latent variables\\n num_channels_latents = self.unet.config.in_channels\\n latents = self.prepare_latents(\\n batch_size * num_images_per_prompt,\\n num_channels_latents,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n latents,\\n )\\n\\n # 6. Prepare the start point\\n if start_point == 'noise':\\n latents = latents\\n elif start_point == 'lr': # LRE Strategy\\n latents_condition_image = self.vae.encode(image*2-1).latent_dist.sample()\\n latents_condition_image = latents_condition_image * self.vae.config.scaling_factor\\n start_steps_tensor = torch.randint(start_steps, start_steps+1, (latents.shape[0],), device=latents.device)\\n start_steps_tensor = start_steps_tensor.long()\\n latents = self.scheduler.add_noise(latents_condition_image[0:1, ...], latents, start_steps_tensor)\\n \\n\\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 8. Denoising loop\\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n \\n _, _, h, w = latents.size()\\n tile_size, tile_overlap = (latent_tiled_size, latent_tiled_overlap) if args is not None else (256, 8)\\n if h*w<=tile_size*tile_size:\\n print(f\\\"[Tiled Latent]: the input size is tiny and unnecessary to tile.\\\")\\n else:\\n print(f\\\"[Tiled Latent]: the input size is {image.shape[-2]}x{image.shape[-1]}, need to tiled\\\")\\n\\n for i, t in enumerate(timesteps):\\n # pass, if the timestep is larger than start_steps\\n if t > start_steps:\\n print(f'pass {t} steps.')\\n continue\\n\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n # controlnet(s) inference\\n if guess_mode and do_classifier_free_guidance:\\n # Infer ControlNet only for the conditional batch.\\n controlnet_latent_model_input = latents\\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\\n \\n else:\\n controlnet_latent_model_input = latent_model_input\\n controlnet_prompt_embeds = prompt_embeds\\n\\n if h*w<=tile_size*tile_size: # tiled latent input\\n down_block_res_samples, mid_block_res_sample = [None]*10, None\\n down_block_res_samples, mid_block_res_sample = self.controlnet(\\n controlnet_latent_model_input,\\n t,\\n encoder_hidden_states=controlnet_prompt_embeds,\\n controlnet_cond=image,\\n conditioning_scale=conditioning_scale,\\n guess_mode=guess_mode,\\n return_dict=False,\\n image_encoder_hidden_states = ram_encoder_hidden_states,\\n )\\n\\n\\n if guess_mode and do_classifier_free_guidance:\\n # Infered ControlNet only for the conditional batch.\\n # To apply the output of ControlNet to both the unconditional and conditional batches,\\n # add 0 to the unconditional batch to keep it unchanged.\\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\\n\\n # predict the noise residual\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n down_block_additional_residuals=down_block_res_samples,\\n mid_block_additional_residual=mid_block_res_sample,\\n return_dict=False,\\n image_encoder_hidden_states = ram_encoder_hidden_states,\\n )[0]\\n else:\\n tile_weights = self._gaussian_weights(tile_size, tile_size, 1)\\n tile_size = min(tile_size, min(h, w))\\n tile_weights = self._gaussian_weights(tile_size, tile_size, 1)\\n\\n grid_rows = 0\\n cur_x = 0\\n while cur_x < latent_model_input.size(-1):\\n cur_x = max(grid_rows * tile_size-tile_overlap * grid_rows, 0)+tile_size\\n grid_rows += 1\\n\\n grid_cols = 0\\n cur_y = 0\\n while cur_y < latent_model_input.size(-2):\\n cur_y = max(grid_cols * tile_size-tile_overlap * grid_cols, 0)+tile_size\\n grid_cols += 1\\n\\n input_list = []\\n cond_list = []\\n img_list = []\\n noise_preds = []\\n for row in range(grid_rows):\\n noise_preds_row = []\\n for col in range(grid_cols):\\n if col < grid_cols-1 or row < grid_rows-1:\\n # extract tile from input image\\n ofs_x = max(row * tile_size-tile_overlap * row, 0)\\n ofs_y = max(col * tile_size-tile_overlap * col, 0)\\n # input tile area on total image\\n if row == grid_rows-1:\\n ofs_x = w - tile_size\\n if col == grid_cols-1:\\n ofs_y = h - tile_size\\n\\n input_start_x = ofs_x\\n input_end_x = ofs_x + tile_size\\n input_start_y = ofs_y\\n input_end_y = ofs_y + tile_size\\n\\n # input tile dimensions\\n input_tile = latent_model_input[:, :, input_start_y:input_end_y, input_start_x:input_end_x]\\n input_list.append(input_tile)\\n cond_tile = controlnet_latent_model_input[:, :, input_start_y:input_end_y, input_start_x:input_end_x]\\n cond_list.append(cond_tile)\\n img_tile = image[:, :, input_start_y*8:input_end_y*8, input_start_x*8:input_end_x*8]\\n img_list.append(img_tile)\\n\\n if len(input_list) == batch_size or col == grid_cols-1:\\n input_list_t = torch.cat(input_list, dim=0)\\n cond_list_t = torch.cat(cond_list, dim=0)\\n img_list_t = torch.cat(img_list, dim=0)\\n #print(input_list_t.shape, cond_list_t.shape, img_list_t.shape, fg_mask_list_t.shape)\\n\\n down_block_res_samples, mid_block_res_sample = self.controlnet(\\n cond_list_t,\\n t,\\n encoder_hidden_states=controlnet_prompt_embeds,\\n controlnet_cond=img_list_t,\\n conditioning_scale=conditioning_scale,\\n guess_mode=guess_mode,\\n return_dict=False,\\n image_encoder_hidden_states = ram_encoder_hidden_states,\\n )\\n\\n if guess_mode and do_classifier_free_guidance:\\n # Infered ControlNet only for the conditional batch.\\n # To apply the output of ControlNet to both the unconditional and conditional batches,\\n # add 0 to the unconditional batch to keep it unchanged.\\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\\n\\n # predict the noise residual\\n model_out = self.unet(\\n input_list_t,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n down_block_additional_residuals=down_block_res_samples,\\n mid_block_additional_residual=mid_block_res_sample,\\n return_dict=False,\\n image_encoder_hidden_states = ram_encoder_hidden_states,\\n )[0]\\n\\n #for sample_i in range(model_out.size(0)):\\n # noise_preds_row.append(model_out[sample_i].unsqueeze(0))\\n input_list = []\\n cond_list = []\\n img_list = []\\n\\n noise_preds.append(model_out)\\n\\n # Stitch noise predictions for all tiles\\n noise_pred = torch.zeros(latent_model_input.shape, device=latent_model_input.device)\\n contributors = torch.zeros(latent_model_input.shape, device=latent_model_input.device)\\n # Add each tile contribution to overall latents\\n for row in range(grid_rows):\\n for col in range(grid_cols):\\n if col < grid_cols-1 or row < grid_rows-1:\\n # extract tile from input image\\n ofs_x = max(row * tile_size-tile_overlap * row, 0)\\n ofs_y = max(col * tile_size-tile_overlap * col, 0)\\n # input tile area on total image\\n if row == grid_rows-1:\\n ofs_x = w - tile_size\\n if col == grid_cols-1:\\n ofs_y = h - tile_size\\n\\n input_start_x = ofs_x\\n input_end_x = ofs_x + tile_size\\n input_start_y = ofs_y\\n input_end_y = ofs_y + tile_size\\n \\n noise_pred[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += noise_preds[row*grid_cols + col] * tile_weights\\n contributors[:, :, input_start_y:input_end_y, input_start_x:input_end_x] += tile_weights\\n # Average overlapping areas with more than 1 contributor\\n noise_pred /= contributors\\n \\n \\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n # If we do sequential model offloading, let's offload unet and controlnet\\n # manually for max memory savings\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.unet.to(\\\"cpu\\\")\\n self.controlnet.to(\\\"cpu\\\")\\n torch.cuda.empty_cache()\\n\\n has_nsfw_concept = None\\n if not output_type == \\\"latent\\\":\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]#.flip(1)\\n #image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\\n else:\\n image = latents\\n has_nsfw_concept = None\\n\\n if has_nsfw_concept is None:\\n do_denormalize = [True] * image.shape[0]\\n else:\\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\\n\\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\\n\\n # Offload last model to CPU\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.final_offload_hook.offload()\\n\\n if not return_dict:\\n return (image, has_nsfw_concept)\\n\\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\"\n },\n {\n \"identifier\": \"load_dreambooth_lora\",\n \"path\": \"utils/misc.py\",\n \"snippet\": \"def load_dreambooth_lora(unet, vae=None, model_path=None, alpha=1.0, model_base=\\\"\\\"):\\n if model_path is None: return unet\\n \\n if model_path.endswith(\\\".ckpt\\\"):\\n base_state_dict = torch.load(model_path)['state_dict']\\n elif model_path.endswith(\\\".safetensors\\\"):\\n state_dict = {}\\n with safe_open(model_path, framework=\\\"pt\\\", device=\\\"cpu\\\") as f:\\n for key in f.keys():\\n state_dict[key] = f.get_tensor(key)\\n \\n is_lora = all(\\\"lora\\\" in k for k in state_dict.keys())\\n if not is_lora:\\n base_state_dict = state_dict\\n else:\\n base_state_dict = {}\\n with safe_open(model_base, framework=\\\"pt\\\", device=\\\"cpu\\\") as f:\\n for key in f.keys():\\n base_state_dict[key] = f.get_tensor(key)\\n \\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_state_dict, unet.config)\\n unet_state_dict = unet.state_dict()\\n for key in converted_unet_checkpoint:\\n converted_unet_checkpoint[key] = alpha * converted_unet_checkpoint[key] + (1.0-alpha) * unet_state_dict[key]\\n unet.load_state_dict(converted_unet_checkpoint, strict=False)\\n\\n if vae is not None:\\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_state_dict, vae.config)\\n vae.load_state_dict(converted_vae_checkpoint)\\n \\n return unet, vae\"\n },\n {\n \"identifier\": \"wavelet_color_fix\",\n \"path\": \"utils/wavelet_color_fix.py\",\n \"snippet\": \"def wavelet_color_fix(target: Image, source: Image):\\n # Convert images to tensors\\n to_tensor = ToTensor()\\n target_tensor = to_tensor(target).unsqueeze(0)\\n source_tensor = to_tensor(source).unsqueeze(0)\\n\\n # Apply wavelet reconstruction\\n result_tensor = wavelet_reconstruction(target_tensor, source_tensor)\\n\\n # Convert tensor back to image\\n to_image = ToPILImage()\\n result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))\\n\\n return result_image\"\n },\n {\n \"identifier\": \"adain_color_fix\",\n \"path\": \"utils/wavelet_color_fix.py\",\n \"snippet\": \"def adain_color_fix(target: Image, source: Image):\\n # Convert images to tensors\\n to_tensor = ToTensor()\\n target_tensor = to_tensor(target).unsqueeze(0)\\n source_tensor = to_tensor(source).unsqueeze(0)\\n\\n # Apply adaptive instance normalization\\n result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)\\n\\n # Convert tensor back to image\\n to_image = ToPILImage()\\n result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))\\n\\n return result_image\"\n },\n {\n \"identifier\": \"ram\",\n \"path\": \"ram/models/ram_lora.py\",\n \"snippet\": \"def ram(pretrained='', pretrained_condition='', **kwargs):\\n model = RAMLora(**kwargs)\\n\\n if pretrained:\\n if kwargs['vit'] == 'swin_b':\\n model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)\\n elif kwargs['vit'] == 'swin_l':\\n model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs)\\n else:\\n model, msg = load_checkpoint(model, pretrained)\\n print('vit:', kwargs['vit'])\\n \\n if pretrained_condition:\\n model.load_state_dict(torch.load(pretrained_condition), strict=False)\\n print(f'load lora weights from {pretrained_condition}')\\n\\n return model\"\n },\n {\n \"identifier\": \"inference_ram\",\n \"path\": \"ram/inference.py\",\n \"snippet\": \"def inference_ram(image, model):\\n\\n with torch.no_grad():\\n tags, tags_chinese = model.generate_tag(image)\\n\\n return tags[0],tags_chinese[0]\"\n },\n {\n \"identifier\": \"get_transform\",\n \"path\": \"ram/transform.py\",\n \"snippet\": \"def get_transform(image_size=384):\\n return Compose([\\n convert_to_rgb,\\n Resize((image_size, image_size)),\\n ToTensor(),\\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\\n ])\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport sys\nimport cv2\nimport glob\nimport argparse\nimport numpy as np\nimport torch\nimport torch.utils.checkpoint\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import set_seed\nfrom diffusers import AutoencoderKL, DDPMScheduler\nfrom diffusers.utils import check_min_version\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor\nfrom pipelines.pipeline_seesr import StableDiffusionControlNetPipeline\nfrom utils.misc import load_dreambooth_lora\nfrom utils.wavelet_color_fix import wavelet_color_fix, adain_color_fix\nfrom ram.models.ram_lora import ram\nfrom ram import inference_ram as inference\nfrom ram import get_transform\nfrom typing import Mapping, Any\nfrom torchvision import transforms\nfrom torchvision import transforms\n from models.controlnet import ControlNetModel\n from models.unet_2d_condition import UNet2DConditionModel"},"token_num":{"kind":"number","value":14797,"string":"14,797"},"cropped_code":{"kind":"string","value":"'''\n * SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution \n * Modified from diffusers by Rongyuan Wu\n * 24/12/2023\n'''\nsys.path.append(os.getcwd())\n\n\n\n\n\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ntensor_transforms = transforms.Compose([\n transforms.ToTensor(),\n ])\n\nram_transforms = transforms.Compose([\n transforms.Resize((384, 384)),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\ndef load_state_dict_diffbirSwinIR(model: nn.Module, state_dict: Mapping[str, Any], strict: bool=False) -> None:\n state_dict = state_dict.get(\"state_dict\", state_dict)\n \n is_model_key_starts_with_module = list(model.state_dict().keys())[0].startswith(\"module.\")\n is_state_dict_key_starts_with_module = list(state_dict.keys())[0].startswith(\"module.\")\n \n if (\n is_model_key_starts_with_module and\n (not is_state_dict_key_starts_with_module)\n ):\n state_dict = {f\"module.{key}\": value for key, value in state_dict.items()}\n if (\n (not is_model_key_starts_with_module) and\n is_state_dict_key_starts_with_module\n ):\n state_dict = {key[len(\"module.\"):]: value for key, value in state_dict.items()}\n \n model.load_state_dict(state_dict, strict=strict)\n\n\ndef load_seesr_pipeline(args, accelerator, enable_xformers_memory_efficient_attention):\n \n\n # Load scheduler, tokenizer and models.\n \n scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder=\"text_encoder\")\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder=\"tokenizer\")\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder=\"vae\")\n feature_extractor = CLIPImageProcessor.from_pretrained(f\"{args.pretrained_model_path}/feature_extractor\")\n unet = UNet2DConditionModel.from_pretrained(args.seesr_model_path, subfolder=\"unet\")\n controlnet = ControlNetModel.from_pretrained(args.seesr_model_path, subfolder=\"controlnet\")\n \n # Freeze vae and text_encoder\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n controlnet.requires_grad_(False)\n\n if enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n unet.enable_xformers_memory_efficient_attention()\n controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Get the validation pipeline\n"},"all_code":{"kind":"string","value":"'''\n * SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution \n * Modified from diffusers by Rongyuan Wu\n * 24/12/2023\n'''\nsys.path.append(os.getcwd())\n\n\n\n\n\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ntensor_transforms = transforms.Compose([\n transforms.ToTensor(),\n ])\n\nram_transforms = transforms.Compose([\n transforms.Resize((384, 384)),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\ndef load_state_dict_diffbirSwinIR(model: nn.Module, state_dict: Mapping[str, Any], strict: bool=False) -> None:\n state_dict = state_dict.get(\"state_dict\", state_dict)\n \n is_model_key_starts_with_module = list(model.state_dict().keys())[0].startswith(\"module.\")\n is_state_dict_key_starts_with_module = list(state_dict.keys())[0].startswith(\"module.\")\n \n if (\n is_model_key_starts_with_module and\n (not is_state_dict_key_starts_with_module)\n ):\n state_dict = {f\"module.{key}\": value for key, value in state_dict.items()}\n if (\n (not is_model_key_starts_with_module) and\n is_state_dict_key_starts_with_module\n ):\n state_dict = {key[len(\"module.\"):]: value for key, value in state_dict.items()}\n \n model.load_state_dict(state_dict, strict=strict)\n\n\ndef load_seesr_pipeline(args, accelerator, enable_xformers_memory_efficient_attention):\n \n\n # Load scheduler, tokenizer and models.\n \n scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder=\"text_encoder\")\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder=\"tokenizer\")\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder=\"vae\")\n feature_extractor = CLIPImageProcessor.from_pretrained(f\"{args.pretrained_model_path}/feature_extractor\")\n unet = UNet2DConditionModel.from_pretrained(args.seesr_model_path, subfolder=\"unet\")\n controlnet = ControlNetModel.from_pretrained(args.seesr_model_path, subfolder=\"controlnet\")\n \n # Freeze vae and text_encoder\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n controlnet.requires_grad_(False)\n\n if enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n unet.enable_xformers_memory_efficient_attention()\n controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Get the validation pipeline"},"next_line":{"kind":"string","value":" validation_pipeline = StableDiffusionControlNetPipeline("},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-11-27 08:50:33+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5377,"cells":{"repo_name":{"kind":"string","value":"xmu-xiaoma666/X-Dreamer"},"file_path":{"kind":"string","value":"train_x_dreamer.py"},"context":{"kind":"list like","value":[{"identifier":"DatasetMesh","path":"dataset/dataset_mesh.py","snippet":"class DatasetMesh(torch.utils.data.Dataset):\n\n\n def __init__(self, glctx, FLAGS, validate=False, gif=False):\n # Init \n self.glctx = glctx\n self.FLAGS = FLAGS\n self.validate = validate\n self.gif = gif\n self.aspect = FLAGS.train_res[1] / FLAGS.train_res[0]\n self.fovy_range_min = np.deg2rad(FLAGS.fovy_range[0])\n self.fovy_range_max = np.deg2rad(FLAGS.fovy_range[1])\n self.elevation_range_min= np.deg2rad(FLAGS.elevation_range[0])\n self.elevation_range_max= np.deg2rad(FLAGS.elevation_range[1])\n self.angle_front = np.deg2rad(FLAGS.front_threshold)\n \n\n def _gif_scene(self, itr):\n fovy = np.deg2rad(45)\n proj_mtx = util.perspective(fovy, self.FLAGS.display_res[1] / self.FLAGS.display_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\n ang = (itr / 100) * np.pi * 2\n rotate_x = np.deg2rad(20)\n prompt_index = 0\n mv = util.translate(0, 0, -3) @ (util.rotate_x(-rotate_x) @ util.rotate_y(ang ))\n normal_rotate = util.rotate_y_1(0)\n mvp = proj_mtx @ mv\n campos = torch.linalg.inv(mv)[:3, 3]\n\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(ang), torch.tensor([fovy])\n \n \n\n def _validate_scene(self, itr):\n fovy = np.deg2rad(45)\n proj_mtx = util.perspective(fovy, self.FLAGS.train_res[1] / self.FLAGS.train_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\n ang = (itr / 4) * np.pi * 2\n rotate_x = np.random.uniform(-np.pi/4,np.pi/18)\n prompt_index = 0\n mv = util.translate(0, 0, -3) @ (util.rotate_x(rotate_x) @ util.rotate_y( ang ))\n normal_rotate = util.rotate_y_1(0)\n mvp = proj_mtx @ mv\n campos = torch.linalg.inv(mv)[:3, 3]\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(ang), torch.tensor([fovy])\n\n def _train_scene(self, itr):\n fovy = np.random.uniform(self.fovy_range_min, self.fovy_range_max)\n proj_mtx = util.perspective(fovy, self.FLAGS.train_res[1] / self.FLAGS.train_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\n if self.FLAGS.gpu_number == 8: # All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\n if self.FLAGS.local_rank in [0,4]:\n rotate_y = np.random.uniform(np.deg2rad(-45), np.deg2rad(45))\n elif self.FLAGS.local_rank in [1,5]:\n rotate_y = np.random.uniform(np.deg2rad(45), np.deg2rad(135))\n elif self.FLAGS.local_rank in [2,6]:#back\n rotate_y = np.random.uniform( np.deg2rad(135), np.deg2rad(225))\n elif self.FLAGS.local_rank in [3,7]:\n rotate_y = np.random.uniform(np.deg2rad(-135), np.deg2rad(-45)) \n if rotate_y > np.pi:\n rotate_y = rotate_y - np.pi*2\n elif self.FLAGS.gpu_number == 4: #All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\n if self.FLAGS.local_rank in [0]:\n rotate_y = np.random.uniform(np.deg2rad(-45), np.deg2rad(45))\n elif self.FLAGS.local_rank in [1]:\n rotate_y = np.random.uniform(np.deg2rad(45), np.deg2rad(135))\n elif self.FLAGS.local_rank in [2]:#back\n rotate_y = np.random.uniform( np.deg2rad(135), np.deg2rad(225))\n elif self.FLAGS.local_rank in [3]:\n rotate_y = np.random.uniform(np.deg2rad(-135), np.deg2rad(-45)) \n if rotate_y > np.pi:\n rotate_y = rotate_y - np.pi*2\n else:\n rotate_y = np.random.uniform(np.deg2rad(-180), np.deg2rad(180)) #All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\n \n rotate_x = -np.random.uniform(self.elevation_range_min, self.elevation_range_max)\n # angle_front = np.deg2rad(45)\n prompt_index = get_view_direction(thetas= rotate_x, phis = rotate_y, front= self.angle_front)\n cam_radius = 3\n x = np.random.uniform(-self.FLAGS.camera_random_jitter, self.FLAGS.camera_random_jitter)\n y = np.random.uniform(-self.FLAGS.camera_random_jitter, self.FLAGS.camera_random_jitter)\n mv = util.translate(x, y, -cam_radius) @ (util.rotate_x(rotate_x) @ util.rotate_y(rotate_y))\n if ((itr+1)/self.FLAGS.batch) <=self.FLAGS.coarse_iter:\n rotate_y1 = np.random.uniform(0,np.pi*2) \n rotate_x1 = np.random.uniform(-np.pi,np.pi)\n normal_rotate = util.rotate_y_1(rotate_y1 )@ util.rotate_x_1(rotate_x1) \n else:\n normal_rotate = util.rotate_y_1(0)@util.rotate_x_1(0)\n mvp = proj_mtx @ mv\n campos = torch.linalg.inv(mv)[:3, 3]\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(rotate_y), torch.tensor([fovy])\n\n def __len__(self):\n if self.gif == True:\n return 100\n else:\n return 4 if self.validate else (self.FLAGS.iter + 1) * self.FLAGS.batch\n\n def __getitem__(self, itr):\n if self.gif:\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._gif_scene(itr)\n elif self.validate:\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._validate_scene(itr)\n else:\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._train_scene(itr)\n\n return {\n 'mv' : mv,\n 'mvp' : mvp,\n 'campos' : campos,\n 'resolution' : iter_res,\n 'spp' : iter_spp,\n 'normal_rotate': normal_rotate,\n 'prompt_index' : prompt_index,\n 'elev': elev,\n 'azim': azim,\n 'fov': fov\n }\n def collate(self, batch):\n iter_res, iter_spp = batch[0]['resolution'], batch[0]['spp']\n return {\n 'mv' : torch.cat(list([item['mv'] for item in batch]), dim=0),\n 'mvp' : torch.cat(list([item['mvp'] for item in batch]), dim=0),\n 'campos' : torch.cat(list([item['campos'] for item in batch]), dim=0),\n 'resolution' : iter_res,\n 'spp' : iter_spp,\n 'normal_rotate' : torch.cat(list([item['normal_rotate'] for item in batch]), dim=0),\n # 'prompt_index' : torch.cat(list([item['prompt_index'] for item in batch]), dim=0),\n 'prompt_index' : np.array([item['prompt_index'] for item in batch], dtype=np.int32),\n 'elev' : np.array([item['elev'] for item in batch], dtype=np.float16),\n 'azim' : np.array([item['azim'] for item in batch], dtype=np.float16),\n 'fov' : torch.cat(list([item['fov'] for item in batch]), dim=0),\n }"},{"identifier":"get_camera_params","path":"dataset/dataset_mesh.py","snippet":"def get_camera_params(resolution= 512, fov=45, elev_angle=-20, azim_angle=0):\n fovy = np.deg2rad(fov) \n elev = np.radians( elev_angle )\n azim = np.radians( azim_angle ) \n proj_mtx = util.perspective(fovy, resolution /resolution, 1, 50)\n mv = util.translate(0, 0, -3) @ (util.rotate_x(elev) @ util.rotate_y(azim))\n normal_rotate = util.rotate_y_1(-azim ) @ util.rotate_x_1(-elev) \n # nomral_rotate = util.rotate_y_1(0) @ util.rotate_x_1(0) \n mvp = proj_mtx @ mv\n campos = torch.linalg.inv(mv)[:3, 3]\n bkgs = torch.ones(1, resolution, resolution, 3, dtype=torch.float32, device='cuda')\n return {\n 'mvp' : mvp[None, ...].cuda(),\n 'mv' : mv[None, ...].cuda(),\n 'campos' : campos[None, ...].cuda(),\n 'resolution' : [resolution, resolution], \n 'spp' : 1,\n 'background' : bkgs,\n 'normal_rotate' : normal_rotate[None,...].cuda(),\n 'elev_angle' : torch.tensor(elev_angle).cuda(),\n 'azim_angle' : torch.tensor(azim_angle).cuda(),\n 'fov' : torch.tensor(fovy).cuda(),\n }"},{"identifier":"DMTetGeometry","path":"geometry/dmtet_x_dreamer.py","snippet":"class DMTetGeometry(torch.nn.Module):\n def __init__(self, grid_res, scale, FLAGS):\n super(DMTetGeometry, self).__init__()\n\n self.FLAGS = FLAGS\n self.grid_res = grid_res\n self.marching_tets = DMTet()\n \n tets = np.load('data/tets/{}_tets.npz'.format(self.grid_res))\n self.verts = torch.tensor(tets['vertices'], dtype=torch.float32, device='cuda') * scale\n print(\"tet grid min/max\", torch.min(self.verts).item(), torch.max(self.verts).item())\n self.decoder = Decoder(multires=0 , AABB= self.getAABB(), mesh_scale= scale)\n self.indices = torch.tensor(tets['indices'], dtype=torch.long, device='cuda')\n self.generate_edges()\n self.pos_encoder = CameraEncoder().to(self.verts.device)\n\n def generate_edges(self):\n with torch.no_grad():\n edges = torch.tensor([0,1,0,2,0,3,1,2,1,3,2,3], dtype = torch.long, device = \"cuda\")\n all_edges = self.indices[:,edges].reshape(-1,2) \n all_edges_sorted = torch.sort(all_edges, dim=1)[0]\n self.all_edges = torch.unique(all_edges_sorted, dim=0)\n\n @torch.no_grad()\n def getAABB(self):\n return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values\n\n def getMesh(self, material):\n pred= self.decoder(self.verts)\n \n self.sdf , self.deform = pred[:, 0], pred[:, 1:] \n v_deformed = self.verts + 1 / (self.grid_res ) * torch.tanh(self.deform)\n verts, faces = self.marching_tets(v_deformed, self.sdf, self.indices)\n \n imesh = mesh.Mesh(verts, faces, material=material)\n imesh = mesh.auto_normals(imesh)\n return imesh\n\n def render(self, glctx, target, lgt, opt_material, bsdf=None, if_normal=False, mode = 'geometry_modeling', if_flip_the_normal = False, if_use_bump = False):\n opt_mesh = self.getMesh(opt_material) \n return render.render_mesh(glctx, \n opt_mesh, \n target['mvp'], \n target['campos'], \n lgt, \n target['resolution'], \n spp=target['spp'], \n msaa= True,\n background= target['background'],\n bsdf= bsdf,\n if_normal= if_normal,\n normal_rotate= target['normal_rotate'],\n mode = mode,\n if_flip_the_normal = if_flip_the_normal,\n if_use_bump = if_use_bump\n )\n\n \n def tick(self, glctx, target, lgt, opt_material, iteration, if_normal, guidance, mode, if_flip_the_normal, if_use_bump):\n # ==============================================================================================\n # Render optimizable object with identical conditions\n # ==============================================================================================\n buffers= self.render(glctx, target, lgt, opt_material, if_normal= if_normal, mode = mode, if_flip_the_normal = if_flip_the_normal, if_use_bump = if_use_bump)\n if self.FLAGS.add_directional_text:\n text_embeddings = torch.cat([guidance.uncond_z[target['prompt_index']], guidance.text_z[target['prompt_index']]]) # [B*2, 77, 1024]\n indexs = torch.cat([guidance.uncond_index[target['prompt_index']], guidance.index[target['prompt_index']]]) # [B*2, 77, 1024]\n else:\n text_embeddings = torch.cat([guidance.uncond_z, guidance.text_z]) # [B * 2, 77, 1024]\n indexs = torch.cat([guidance.uncond_index, guidance.index]) # [B*2, 77, 1024]\n\n \n if iteration <=self.FLAGS.coarse_iter:\n t = torch.randint( guidance.min_step_early, guidance.max_step_early + 1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\n pred_rgb_512 = buffers['shaded'][..., 0:4].permute(0, 3, 1, 2).contiguous() # [B, 4, 64, 64]\n latents = F.interpolate(pred_rgb_512, (64, 64), mode='bilinear', align_corners=False)\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\n mask2 = mask.squeeze()\n \n else:\n t = torch.randint(guidance.min_step_late, guidance.max_step_late + 1, [self.FLAGS.batch], dtype=torch.long, device='cuda')\n srgb = buffers['shaded'][...,0:3] #* buffers['shaded'][..., 3:4] # normal * mask\n # \n pred_rgb_512 = srgb.permute(0, 3, 1, 2).contiguous() # [B, 3, 512, 512]\n latents = guidance.encode_imgs(pred_rgb_512)\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\n mask2 = mask.squeeze()\n\n ### calculate camera pos feature\n came_pos = torch.cat([target['campos'],torch.from_numpy(target['elev']).unsqueeze(-1).cuda(),torch.from_numpy(target['azim']).cuda().unsqueeze(-1),target['fov'].unsqueeze(-1)],dim=-1)\n came_pos = torch.cat([came_pos,came_pos],dim=0) #bs*2, 5\n came_pos = normalize_camera(came_pos,self.FLAGS)\n came_posfeat = self.pos_encoder(came_pos)\n\n # add noise\n noise = torch.randn_like(latents)\n latents_noisy = guidance.scheduler.add_noise(latents, noise, t)\n # pred noise\n latent_model_input = torch.cat([latents_noisy] * 2)\n tt = torch.cat([t] * 2)\n noise_pred, attention_map = guidance.unet(latent_model_input, tt, encoder_hidden_states=text_embeddings, index=indexs, came_posfeat=came_posfeat)\n noise_pred = noise_pred.sample\n\n attention_map[0] = attention_map[0].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\n attention_map[1] = attention_map[1].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\n attention_map[2] = attention_map[2].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\n attention_map[3] = attention_map[3].reshape(self.FLAGS.batch*2, 8 , 8 ).contiguous()\n attention_map[4] = attention_map[4].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\n attention_map[5] = attention_map[5].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\n attention_map[6] = attention_map[6].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\n\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred =noise_pred_uncond + guidance.guidance_weight * (noise_pred_text - noise_pred_uncond) # [B, 4, 64, 64]\n if iteration <= self.FLAGS.coarse_iter:\n w = (1 - guidance.alphas[t]) # [B]\n else:\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\n w = w[:, None, None, None] # [B, 1, 1, 1]\n grad = w * (noise_pred - noise ) #*w1\n grad = torch.nan_to_num(grad)\n \n sds_loss = SpecifyGradient.apply(latents, grad) \n img_loss = torch.tensor([0], dtype=torch.float32, device=\"cuda\")\n reg_loss = torch.tensor([0], dtype=torch.float32, device=\"cuda\")\n\n attention_loss = 0\n mask_sizes = [(64, 64), (32,32), (16,16), (8,8), (16,16), (32,32), (64,64)]\n for i in range(7):\n _, attention_map_text = attention_map[i].chunk(2)\n if(self.FLAGS.batch==1):\n mask2 = F.interpolate(mask2.unsqueeze(0).unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\n else:\n mask2 = F.interpolate(mask2.unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\n attention_map_text = (attention_map_text - attention_map_text.min())/(attention_map_text.max() - attention_map_text.min()+1e-6)\n attention_map_text = F.interpolate(attention_map_text.unsqueeze(0), size=mask_sizes[i], mode='bilinear', align_corners=False).squeeze()\n attention_loss = 0.1*F.l1_loss(mask2.float(), attention_map_text.float(), reduction=\"mean\") #0.1 1 10\n attention_loss = attention_loss/7\n \n return sds_loss, img_loss, reg_loss, attention_loss"},{"identifier":"DLMesh","path":"geometry/dlmesh_x_dreamer.py","snippet":"class DLMesh(torch.nn.Module):\n def __init__(self, initial_guess, FLAGS):\n super(DLMesh, self).__init__()\n self.FLAGS = FLAGS\n self.initial_guess = initial_guess\n self.mesh = initial_guess.clone()\n self.pos_encoder = CameraEncoder().cuda()\n print(\"Base mesh has %d triangles and %d vertices.\" % (self.mesh.t_pos_idx.shape[0], self.mesh.v_pos.shape[0]))\n \n @torch.no_grad()\n def getAABB(self):\n return mesh.aabb(self.mesh)\n\n def getMesh(self, material):\n self.mesh.material = material\n\n imesh = mesh.Mesh(base=self.mesh)\n # Compute normals and tangent space\n imesh = mesh.auto_normals(imesh)\n imesh = mesh.compute_tangents(imesh)\n return imesh\n\n def render(self, glctx, target, lgt, opt_material, bsdf=None,if_normal=False, mode = 'appearance_modeling', if_flip_the_normal = False, if_use_bump = False):\n opt_mesh = self.getMesh(opt_material)\n return render.render_mesh(glctx, \n opt_mesh,\n target['mvp'],\n target['campos'],\n lgt,\n target['resolution'], \n spp=target['spp'], \n msaa=True,\n background= target['background'] ,\n bsdf= bsdf,\n if_normal=if_normal,\n normal_rotate=target['normal_rotate'], \n mode = mode,\n if_flip_the_normal = if_flip_the_normal,\n if_use_bump = if_use_bump\n )\n\n def tick(self, glctx, target, lgt, opt_material, iteration, if_normal, guidance, mode, if_flip_the_normal, if_use_bump):\n # ==============================================================================================\n # Render optimizable object with identical conditions\n # ==============================================================================================\n buffers= self.render(glctx, target, lgt, opt_material, if_normal = if_normal, mode = mode, if_flip_the_normal = if_flip_the_normal, if_use_bump = if_use_bump)\n if self.FLAGS.add_directional_text:\n text_embeddings = torch.cat([guidance.uncond_z[target['prompt_index']], guidance.text_z[target['prompt_index']]])\n indexs = torch.cat([guidance.uncond_index[target['prompt_index']], guidance.index[target['prompt_index']]]) # [B*2, 77, 1024]\n else:\n text_embeddings = torch.cat([guidance.uncond_z, guidance.text_z])\n indexs = torch.cat([guidance.uncond_index, guidance.index]) # [B*2, 77, 1024]\n\n\n if iteration <= self.FLAGS.coarse_iter:\n srgb = buffers['shaded'][...,0:3]\n srgb = util.rgb_to_srgb(srgb)\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\n mask2 = mask.squeeze()\n t = torch.randint( guidance.min_step_early, guidance.max_step_early+1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\n else:\n srgb = buffers['shaded'][...,0:3]\n srgb = util.rgb_to_srgb(srgb)\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\n mask2 = mask.squeeze()\n t = torch.randint( guidance.min_step_late, guidance.max_step_late+1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\n\n pred_rgb_512 = srgb.permute(0, 3, 1, 2).contiguous() # [1, 3, H, W]\n latents = guidance.encode_imgs(pred_rgb_512)\n \n ### calculate camera pos feature\n came_pos = torch.cat([target['campos'],torch.from_numpy(target['elev']).unsqueeze(-1).cuda(),torch.from_numpy(target['azim']).cuda().unsqueeze(-1),target['fov'].unsqueeze(-1)],dim=-1)\n came_pos = torch.cat([came_pos,came_pos],dim=0) #bs*2, 5\n came_pos = normalize_camera(came_pos,self.FLAGS)\n came_posfeat = self.pos_encoder(came_pos)\n\n\n # add noise\n noise = torch.randn_like(latents)\n latents_noisy = guidance.scheduler.add_noise(latents, noise, t)\n # pred noise\n latent_model_input = torch.cat([latents_noisy] * 2)\n tt = torch.cat([t] * 2)\n noise_pred, attention_map = guidance.unet(latent_model_input, tt, encoder_hidden_states= text_embeddings, index=indexs, came_posfeat=came_posfeat)#.sample######################\n noise_pred = noise_pred.sample\n\n attention_map[0] = attention_map[0].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\n attention_map[1] = attention_map[1].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\n attention_map[2] = attention_map[2].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\n attention_map[3] = attention_map[3].reshape(self.FLAGS.batch*2, 8 , 8 ).contiguous()\n attention_map[4] = attention_map[4].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\n attention_map[5] = attention_map[5].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\n attention_map[6] = attention_map[6].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\n\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance.guidance_weight * (noise_pred_text - noise_pred_uncond)\n \n if guidance.sds_weight_strategy == 0:\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\n elif guidance.sds_weight_strategy == 1:\n w = 1 / (1 - guidance.alphas[t])\n elif guidance.sds_weight_strategy == 2:\n if iteration <= self.FLAGS.coarse_iter:\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\n else:\n w = 1 / (1 - guidance.alphas[t])\n w = w[:, None, None, None] # [B, 1, 1, 1]\n grad = w* (noise_pred -noise) \n grad = torch.nan_to_num(grad)\n sds_loss = SpecifyGradient.apply(latents, grad) \n img_loss = torch.tensor([0], dtype=torch.float32, device=\"cuda\")\n reg_loss = torch.tensor([0], dtype=torch.float32, device=\"cuda\")\n \n attention_loss = 0\n mask_sizes = [(64, 64), (32,32), (16,16), (8,8), (16,16), (32,32), (64,64)]\n for i in range(7):\n _, attention_map_text = attention_map[i].chunk(2)\n if(self.FLAGS.batch==1):\n mask2 = F.interpolate(mask2.unsqueeze(0).unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\n else:\n mask2 = F.interpolate(mask2.unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\n attention_map_text = (attention_map_text - attention_map_text.min())/(attention_map_text.max() - attention_map_text.min()+1e-6)\n attention_map_text = F.interpolate(attention_map_text.unsqueeze(0), size=mask2.shape, mode='bilinear', align_corners=False).squeeze()\n attention_loss = 0.1*F.l1_loss(mask2.float(), attention_map_text.float(), reduction=\"mean\") #0.1 1 10\n attention_loss = attention_loss/7\n \n return sds_loss, img_loss, reg_loss, attention_loss"},{"identifier":"obj","path":"render/obj.py","snippet":"def _find_mat(materials, name):\ndef load_obj(filename, clear_ks=True, mtl_override=None):\ndef write_obj(folder, mesh, save_material=True):"},{"identifier":"material","path":"render/material.py","snippet":"class Material(torch.nn.Module):\n def __init__(self, mat_dict):\n def __contains__(self, key):\n def __getitem__(self, key):\n def __setitem__(self, key, val):\n def __delitem__(self, key):\n def keys(self):\ndef load_mtl(fn, clear_ks=True):\ndef save_mtl(fn, material):\ndef _upscale_replicate(x, full_res):\ndef merge_materials(materials, texcoords, tfaces, mfaces):"},{"identifier":"util","path":"render/util.py","snippet":"def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\ndef reflect(x: torch.Tensor, n: torch.Tensor) -> torch.Tensor:\ndef length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\ndef safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\ndef to_hvec(x: torch.Tensor, w: float) -> torch.Tensor:\ndef _rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\ndef rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\ndef _srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\ndef srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\ndef reinhard(f: torch.Tensor) -> torch.Tensor:\ndef mse_to_psnr(mse):\ndef psnr_to_mse(psnr):\ndef get_miplevels(texture: np.ndarray) -> float:\ndef tex_2d(tex_map : torch.Tensor, coords : torch.Tensor, filter='nearest') -> torch.Tensor:\ndef cube_to_dir(s, x, y):\ndef latlong_to_cubemap(latlong_map, res):\ndef cubemap_to_latlong(cubemap, res):\ndef scale_img_hwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\ndef scale_img_nhwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\ndef avg_pool_nhwc(x : torch.Tensor, size) -> torch.Tensor:\ndef segment_sum(data: torch.Tensor, segment_ids: torch.Tensor) -> torch.Tensor:\ndef fovx_to_fovy(fovx, aspect):\ndef focal_length_to_fovy(focal_length, sensor_height):\ndef perspective(fovy=0.7854, aspect=1.0, n=0.1, f= 1000.0, device=None):\ndef perspective_offcenter(fovy, fraction, rx, ry, aspect=1.0, n=0.1, f=1000.0, device=None):\ndef translate(x, y, z, device=None):\ndef rotate_x(a, device=None):\ndef rotate_x_1(a, device=None):\ndef rotate_y(a, device=None):\ndef rotate_y_1(a, device=None):\ndef rotate_y_2(a, device=None):\ndef rotate_x_2(a, device=None):\ndef scale(s, device=None):\ndef lookAt(eye, at, up):\ndef random_rotation_translation(t, device=None):\ndef random_rotation(device=None):\ndef lines_focal(o, d):\ndef cosine_sample(N, size=None):\ndef bilinear_downsample(x : torch.tensor) -> torch.Tensor:\ndef bilinear_downsample(x : torch.tensor, spp) -> torch.Tensor:\ndef init_glfw():\ndef save_image(fn, x : np.ndarray):\ndef save_image_raw(fn, x : np.ndarray):\ndef load_image_raw(fn) -> np.ndarray:\ndef load_image(fn) -> np.ndarray:\ndef time_to_text(x):\ndef checkerboard(res, checker_size) -> np.ndarray:\ndef get_random_bg(h, w):\n R, L = aspect*y, -aspect*y\n T, B = y, -y\n I = torch.eye(3, dtype=o.dtype, device=o.device)\n S = torch.sum(d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...], dim=0)\n C = torch.sum((d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...]) @ o[..., None], dim=0).squeeze(1)\n N = N/torch.linalg.norm(N)"},{"identifier":"mesh","path":"render/mesh.py","snippet":"class Mesh:\n def __init__(self, v_pos=None, t_pos_idx=None, v_nrm=None, t_nrm_idx=None, v_tex=None, t_tex_idx=None, v_tng=None, t_tng_idx=None, material=None, base=None):\n def copy_none(self, other):\n def clone(self):\ndef load_mesh(filename, mtl_override=None):\ndef aabb(mesh):\ndef compute_edges(attr_idx, return_inverse=False):\ndef compute_edge_to_face_mapping(attr_idx, return_inverse=False):\ndef unit_size(mesh):\ndef center_by_reference(base_mesh, ref_aabb, scale):\ndef auto_normals(imesh):\ndef compute_tangents(imesh):"},{"identifier":"texture","path":"render/texture.py","snippet":"class texture2d_mip(torch.autograd.Function):\nclass Texture2D(torch.nn.Module):\n def forward(ctx, texture):\n def backward(ctx, dout):\n def __init__(self, init, min_max=None):\n def sample(self, texc, texc_deriv, filter_mode='linear-mipmap-linear'):\n def getRes(self):\n def getChannels(self):\n def getMips(self):\n def clamp_(self):\n def normalize_(self):\ndef create_trainable(init, res=None, auto_mipmaps=True, min_max=None):\ndef srgb_to_rgb(texture):\ndef rgb_to_srgb(texture):\ndef _load_mip2D(fn, lambda_fn=None, channels=None):\ndef load_texture2D(fn, lambda_fn=None, channels=None):\ndef _save_mip2D(fn, mip, mipidx, lambda_fn):\ndef save_texture2D(fn, tex, lambda_fn=None):"},{"identifier":"mlptexture","path":"render/mlptexture.py","snippet":"class _MLP(torch.nn.Module):\nclass MLPTexture3D(torch.nn.Module):\n def __init__(self, cfg, loss_scale=1.0):\n def forward(self, x):\n def _init_weights(m):\n def __init__(self, AABB, channels = 3, internal_dims = 32, hidden = 1, min_max = None):\n def sample(self, texc):\n def clamp_(self):\n def cleanup(self):"},{"identifier":"light","path":"render/light.py","snippet":"class cubemap_mip(torch.autograd.Function):\nclass EnvironmentLight(torch.nn.Module):\n def forward(ctx, cubemap):\n def backward(ctx, dout):\n def __init__(self, base):\n def xfm(self, mtx):\n def clone(self):\n def clamp_(self, min=None, max=None):\n def get_mip(self, roughness):\n def build_mips(self, cutoff=0.99):\n def regularizer(self):\n def shade(self, gb_pos, gb_normal, kd, ks, view_pos, specular=True):\ndef _load_env_hdr(fn, scale=1.0):\ndef load_env(fn, scale=1.0):\ndef save_env_map(fn, light):\ndef create_trainable_env_rnd(base_res, scale=0.5, bias=0.25):\n LIGHT_MIN_RES = 16\n MIN_ROUGHNESS = 0.08\n MAX_ROUGHNESS = 0.5"},{"identifier":"render","path":"render/render.py","snippet":"def interpolate(attr, rast, attr_idx, rast_db=None):\ndef shade(\n gb_pos,\n gb_geometric_normal,\n gb_normal,\n gb_tangent,\n gb_texc,\n gb_texc_deriv,\n view_pos,\n lgt,\n material,\n bsdf,\n if_normal,\n normal_rotate,\n mode,\n if_flip_the_normal,\n if_use_bump\n ):\ndef render_layer(\n rast,\n rast_deriv,\n mesh,\n view_pos,\n lgt,\n resolution,\n spp,\n msaa,\n bsdf,\n if_normal,\n normal_rotate,\n mode,\n if_flip_the_normal,\n if_use_bump\n ):\ndef render_mesh(\n ctx,\n mesh,\n mtx_in,\n view_pos,\n lgt,\n resolution,\n spp = 1,\n num_layers = 1,\n msaa = False,\n background = None, \n bsdf = None,\n if_normal = False,\n normal_rotate = None,\n mode = 'geometry_modeling',\n if_flip_the_normal = False,\n if_use_bump = False\n ):\n def prepare_input_vector(x):\n def composite_buffer(key, layers, background, antialias):\ndef render_uv(ctx, mesh, resolution, mlp_texture):\ndef uv_padding(image, hole_mask, padding = 2, uv_padding_block = 4):\ndef render_uv1(ctx, mesh, resolution, mlp_texture, uv_padding_block):"},{"identifier":"StableDiffusion","path":"sd_cglora.py","snippet":"class StableDiffusion(nn.Module):\n def __init__(self, \n device, \n mode='geometry', \n text= '', \n add_directional_text= False, \n batch = 1, \n guidance_weight = 100, \n sds_weight_strategy = 0,\n early_time_step_range = [0.02, 0.5],\n late_time_step_range = [0.02, 0.5]):\n super().__init__()\n\n self.device = device\n self.mode = mode\n self.text= text\n self.add_directional_text = add_directional_text\n self.batch = batch \n print(f'[INFO] loading stable diffusion...')\n model_key = \"stabilityai/stable-diffusion-2-1-base\"\n self.vae = AutoencoderKL.from_pretrained(model_key, subfolder=\"vae\",torch_dtype=torch.float16).to(self.device)\n self.tokenizer = CLIPTokenizer.from_pretrained(model_key, subfolder=\"tokenizer\",torch_dtype=torch.float16)\n self.text_encoder = CLIPTextModel.from_pretrained(model_key, subfolder=\"text_encoder\",torch_dtype=torch.float16).to(self.device)\n self.unet = UNet2DConditionModel.from_pretrained(model_key, subfolder=\"unet\",torch_dtype=torch.float16).to(self.device)\n if is_xformers_available():\n self.unet.enable_xformers_memory_efficient_attention()\n self.negative_text = ''\n if add_directional_text:\n self.text_z = []\n self.uncond_z = []\n self.index = []\n self.uncond_index = []\n for d in ['front', 'side', 'back', 'side']:\n text = f\"{self.text}, {d} view\"\n # text = f\"{d} view of {self.text}\"\n negative_text = f\"{self.negative_text}\"\n # if d == 'back': negative_text += \"face\"\n text_z, index = self.get_text_embeds([text], batch = 1)\n uncond_z, uncond_index =self.get_uncond_embeds([negative_text], batch = 1)\n self.text_z.append(text_z)\n self.uncond_z.append(uncond_z)\n self.index.append(index)\n self.uncond_index.append(uncond_index)\n self.text_z = torch.cat(self.text_z)\n self.uncond_z = torch.cat(self.uncond_z)\n self.index = torch.cat(self.index)\n self.uncond_index = torch.cat(self.uncond_index)\n else: \n self.text_z, self.index = self.get_text_embeds([self.text], batch = self.batch)\n self.uncond_z =self.get_uncond_embeds([self.negative_text], batch = self.batch)\n # del self.text_encoder\n self.scheduler = DPMSolverMultistepScheduler.from_pretrained(model_key, subfolder=\"scheduler\", torch_dtype=torch.float16)\n self.num_train_timesteps = self.scheduler.config.num_train_timesteps\n self.min_step_early = int(self.num_train_timesteps * early_time_step_range[0])\n self.max_step_early = int(self.num_train_timesteps * early_time_step_range[1])\n self.min_step_late = int(self.num_train_timesteps * late_time_step_range[0])\n self.max_step_late = int(self.num_train_timesteps * late_time_step_range[1])\n self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience\n self.guidance_weight = guidance_weight\n self.sds_weight_strategy = sds_weight_strategy\n print(f'[INFO] loaded stable diffusion!')\n\n for p in self.parameters():\n p.requires_grad_(False)\n self.unet_lora_params, self.names = inject_trainable_cglora(self.unet) # This will\n\n\n def get_text_embeds_global(self, prompt, batch=1):\n text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')\n with torch.no_grad():\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n if batch > 1:\n text_embeddings = text_embeddings.repeat(batch, 1, 1)\n \n global_embedding = text_embeddings[:,text_input['input_ids'].argmax(dim=-1),:].squeeze()\n \n return global_embedding\n\n\n def get_text_embeds(self, prompt, batch=1):\n text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')\n with torch.no_grad():\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n if batch > 1:\n text_embeddings = text_embeddings.repeat(batch, 1, 1)\n ###################################################################\n index = text_input['input_ids'].argmax(dim=-1)\n #global_embedding = text_embeddings[:, index, :].squeeze()\n ##################################################################\n \n return text_embeddings, index\n \n def get_uncond_embeds(self, negative_prompt, batch):\n uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt')\n with torch.no_grad():\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n \n if batch > 1:\n uncond_embeddings = uncond_embeddings.repeat(batch, 1, 1)\n ###################################################################\n index = uncond_input['input_ids'].argmax(dim=-1)\n # global_embedding = uncond_embeddings[:, index, :].squeeze()\n ##################################################################\n return uncond_embeddings,index\n\n def encode_imgs(self, imgs):\n # imgs: [B, 3, H, W]\n if self.mode == 'appearance_modeling':\n \n imgs = 2 * imgs - 1\n\n posterior = self.vae.encode(imgs).latent_dist\n latents = posterior.sample() * 0.18215\n\n return latents"},{"identifier":"util","path":"render/util.py","snippet":"def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\ndef reflect(x: torch.Tensor, n: torch.Tensor) -> torch.Tensor:\ndef length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\ndef safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\ndef to_hvec(x: torch.Tensor, w: float) -> torch.Tensor:\ndef _rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\ndef rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\ndef _srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\ndef srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\ndef reinhard(f: torch.Tensor) -> torch.Tensor:\ndef mse_to_psnr(mse):\ndef psnr_to_mse(psnr):\ndef get_miplevels(texture: np.ndarray) -> float:\ndef tex_2d(tex_map : torch.Tensor, coords : torch.Tensor, filter='nearest') -> torch.Tensor:\ndef cube_to_dir(s, x, y):\ndef latlong_to_cubemap(latlong_map, res):\ndef cubemap_to_latlong(cubemap, res):\ndef scale_img_hwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\ndef scale_img_nhwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\ndef avg_pool_nhwc(x : torch.Tensor, size) -> torch.Tensor:\ndef segment_sum(data: torch.Tensor, segment_ids: torch.Tensor) -> torch.Tensor:\ndef fovx_to_fovy(fovx, aspect):\ndef focal_length_to_fovy(focal_length, sensor_height):\ndef perspective(fovy=0.7854, aspect=1.0, n=0.1, f= 1000.0, device=None):\ndef perspective_offcenter(fovy, fraction, rx, ry, aspect=1.0, n=0.1, f=1000.0, device=None):\ndef translate(x, y, z, device=None):\ndef rotate_x(a, device=None):\ndef rotate_x_1(a, device=None):\ndef rotate_y(a, device=None):\ndef rotate_y_1(a, device=None):\ndef rotate_y_2(a, device=None):\ndef rotate_x_2(a, device=None):\ndef scale(s, device=None):\ndef lookAt(eye, at, up):\ndef random_rotation_translation(t, device=None):\ndef random_rotation(device=None):\ndef lines_focal(o, d):\ndef cosine_sample(N, size=None):\ndef bilinear_downsample(x : torch.tensor) -> torch.Tensor:\ndef bilinear_downsample(x : torch.tensor, spp) -> torch.Tensor:\ndef init_glfw():\ndef save_image(fn, x : np.ndarray):\ndef save_image_raw(fn, x : np.ndarray):\ndef load_image_raw(fn) -> np.ndarray:\ndef load_image(fn) -> np.ndarray:\ndef time_to_text(x):\ndef checkerboard(res, checker_size) -> np.ndarray:\ndef get_random_bg(h, w):\n R, L = aspect*y, -aspect*y\n T, B = y, -y\n I = torch.eye(3, dtype=o.dtype, device=o.device)\n S = torch.sum(d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...], dim=0)\n C = torch.sum((d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...]) @ o[..., None], dim=0).squeeze(1)\n N = N/torch.linalg.norm(N)"},{"identifier":"Video","path":"render/video.py","snippet":"class Video():\n def __init__(self, path, name='video_log.mp4', mode='I', fps=30, codec='libx264', bitrate='16M') -> None:\n \n if path[-1] != \"/\":\n path += \"/\"\n \n self.writer = imageio.get_writer(path+name, mode=mode, fps=fps, codec=codec, bitrate=bitrate)\n \n def ready_image(self, image, write_video=True):\n # assuming channels last - as renderer returns it\n if len(image.shape) == 4: \n image = image.squeeze(0)[..., :3].detach().cpu().numpy()\n else:\n image = image[..., :3].detach().cpu().numpy()\n\n image = np.clip(np.rint(image*255.0), 0, 255).astype(np.uint8)\n\n if write_video:\n self.writer.append_data(image)\n\n return image\n\n def close(self):\n self.writer.close()"}],"string":"[\n {\n \"identifier\": \"DatasetMesh\",\n \"path\": \"dataset/dataset_mesh.py\",\n \"snippet\": \"class DatasetMesh(torch.utils.data.Dataset):\\n\\n\\n def __init__(self, glctx, FLAGS, validate=False, gif=False):\\n # Init \\n self.glctx = glctx\\n self.FLAGS = FLAGS\\n self.validate = validate\\n self.gif = gif\\n self.aspect = FLAGS.train_res[1] / FLAGS.train_res[0]\\n self.fovy_range_min = np.deg2rad(FLAGS.fovy_range[0])\\n self.fovy_range_max = np.deg2rad(FLAGS.fovy_range[1])\\n self.elevation_range_min= np.deg2rad(FLAGS.elevation_range[0])\\n self.elevation_range_max= np.deg2rad(FLAGS.elevation_range[1])\\n self.angle_front = np.deg2rad(FLAGS.front_threshold)\\n \\n\\n def _gif_scene(self, itr):\\n fovy = np.deg2rad(45)\\n proj_mtx = util.perspective(fovy, self.FLAGS.display_res[1] / self.FLAGS.display_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\\n ang = (itr / 100) * np.pi * 2\\n rotate_x = np.deg2rad(20)\\n prompt_index = 0\\n mv = util.translate(0, 0, -3) @ (util.rotate_x(-rotate_x) @ util.rotate_y(ang ))\\n normal_rotate = util.rotate_y_1(0)\\n mvp = proj_mtx @ mv\\n campos = torch.linalg.inv(mv)[:3, 3]\\n\\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(ang), torch.tensor([fovy])\\n \\n \\n\\n def _validate_scene(self, itr):\\n fovy = np.deg2rad(45)\\n proj_mtx = util.perspective(fovy, self.FLAGS.train_res[1] / self.FLAGS.train_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\\n ang = (itr / 4) * np.pi * 2\\n rotate_x = np.random.uniform(-np.pi/4,np.pi/18)\\n prompt_index = 0\\n mv = util.translate(0, 0, -3) @ (util.rotate_x(rotate_x) @ util.rotate_y( ang ))\\n normal_rotate = util.rotate_y_1(0)\\n mvp = proj_mtx @ mv\\n campos = torch.linalg.inv(mv)[:3, 3]\\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(ang), torch.tensor([fovy])\\n\\n def _train_scene(self, itr):\\n fovy = np.random.uniform(self.fovy_range_min, self.fovy_range_max)\\n proj_mtx = util.perspective(fovy, self.FLAGS.train_res[1] / self.FLAGS.train_res[0], self.FLAGS.cam_near_far[0], self.FLAGS.cam_near_far[1])\\n if self.FLAGS.gpu_number == 8: # All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\\n if self.FLAGS.local_rank in [0,4]:\\n rotate_y = np.random.uniform(np.deg2rad(-45), np.deg2rad(45))\\n elif self.FLAGS.local_rank in [1,5]:\\n rotate_y = np.random.uniform(np.deg2rad(45), np.deg2rad(135))\\n elif self.FLAGS.local_rank in [2,6]:#back\\n rotate_y = np.random.uniform( np.deg2rad(135), np.deg2rad(225))\\n elif self.FLAGS.local_rank in [3,7]:\\n rotate_y = np.random.uniform(np.deg2rad(-135), np.deg2rad(-45)) \\n if rotate_y > np.pi:\\n rotate_y = rotate_y - np.pi*2\\n elif self.FLAGS.gpu_number == 4: #All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\\n if self.FLAGS.local_rank in [0]:\\n rotate_y = np.random.uniform(np.deg2rad(-45), np.deg2rad(45))\\n elif self.FLAGS.local_rank in [1]:\\n rotate_y = np.random.uniform(np.deg2rad(45), np.deg2rad(135))\\n elif self.FLAGS.local_rank in [2]:#back\\n rotate_y = np.random.uniform( np.deg2rad(135), np.deg2rad(225))\\n elif self.FLAGS.local_rank in [3]:\\n rotate_y = np.random.uniform(np.deg2rad(-135), np.deg2rad(-45)) \\n if rotate_y > np.pi:\\n rotate_y = rotate_y - np.pi*2\\n else:\\n rotate_y = np.random.uniform(np.deg2rad(-180), np.deg2rad(180)) #All the results in the paper were generated using 8 3090 GPUs. We cannot guarantee that fewer than 8 GPUs can achieve the same effect.\\n \\n rotate_x = -np.random.uniform(self.elevation_range_min, self.elevation_range_max)\\n # angle_front = np.deg2rad(45)\\n prompt_index = get_view_direction(thetas= rotate_x, phis = rotate_y, front= self.angle_front)\\n cam_radius = 3\\n x = np.random.uniform(-self.FLAGS.camera_random_jitter, self.FLAGS.camera_random_jitter)\\n y = np.random.uniform(-self.FLAGS.camera_random_jitter, self.FLAGS.camera_random_jitter)\\n mv = util.translate(x, y, -cam_radius) @ (util.rotate_x(rotate_x) @ util.rotate_y(rotate_y))\\n if ((itr+1)/self.FLAGS.batch) <=self.FLAGS.coarse_iter:\\n rotate_y1 = np.random.uniform(0,np.pi*2) \\n rotate_x1 = np.random.uniform(-np.pi,np.pi)\\n normal_rotate = util.rotate_y_1(rotate_y1 )@ util.rotate_x_1(rotate_x1) \\n else:\\n normal_rotate = util.rotate_y_1(0)@util.rotate_x_1(0)\\n mvp = proj_mtx @ mv\\n campos = torch.linalg.inv(mv)[:3, 3]\\n return mv[None, ...], mvp[None, ...], campos[None, ...], self.FLAGS.display_res, self.FLAGS.spp, normal_rotate[None,...], prompt_index, np.rad2deg(rotate_x), np.rad2deg(rotate_y), torch.tensor([fovy])\\n\\n def __len__(self):\\n if self.gif == True:\\n return 100\\n else:\\n return 4 if self.validate else (self.FLAGS.iter + 1) * self.FLAGS.batch\\n\\n def __getitem__(self, itr):\\n if self.gif:\\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._gif_scene(itr)\\n elif self.validate:\\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._validate_scene(itr)\\n else:\\n mv, mvp, campos, iter_res, iter_spp, normal_rotate, prompt_index, elev, azim, fov = self._train_scene(itr)\\n\\n return {\\n 'mv' : mv,\\n 'mvp' : mvp,\\n 'campos' : campos,\\n 'resolution' : iter_res,\\n 'spp' : iter_spp,\\n 'normal_rotate': normal_rotate,\\n 'prompt_index' : prompt_index,\\n 'elev': elev,\\n 'azim': azim,\\n 'fov': fov\\n }\\n def collate(self, batch):\\n iter_res, iter_spp = batch[0]['resolution'], batch[0]['spp']\\n return {\\n 'mv' : torch.cat(list([item['mv'] for item in batch]), dim=0),\\n 'mvp' : torch.cat(list([item['mvp'] for item in batch]), dim=0),\\n 'campos' : torch.cat(list([item['campos'] for item in batch]), dim=0),\\n 'resolution' : iter_res,\\n 'spp' : iter_spp,\\n 'normal_rotate' : torch.cat(list([item['normal_rotate'] for item in batch]), dim=0),\\n # 'prompt_index' : torch.cat(list([item['prompt_index'] for item in batch]), dim=0),\\n 'prompt_index' : np.array([item['prompt_index'] for item in batch], dtype=np.int32),\\n 'elev' : np.array([item['elev'] for item in batch], dtype=np.float16),\\n 'azim' : np.array([item['azim'] for item in batch], dtype=np.float16),\\n 'fov' : torch.cat(list([item['fov'] for item in batch]), dim=0),\\n }\"\n },\n {\n \"identifier\": \"get_camera_params\",\n \"path\": \"dataset/dataset_mesh.py\",\n \"snippet\": \"def get_camera_params(resolution= 512, fov=45, elev_angle=-20, azim_angle=0):\\n fovy = np.deg2rad(fov) \\n elev = np.radians( elev_angle )\\n azim = np.radians( azim_angle ) \\n proj_mtx = util.perspective(fovy, resolution /resolution, 1, 50)\\n mv = util.translate(0, 0, -3) @ (util.rotate_x(elev) @ util.rotate_y(azim))\\n normal_rotate = util.rotate_y_1(-azim ) @ util.rotate_x_1(-elev) \\n # nomral_rotate = util.rotate_y_1(0) @ util.rotate_x_1(0) \\n mvp = proj_mtx @ mv\\n campos = torch.linalg.inv(mv)[:3, 3]\\n bkgs = torch.ones(1, resolution, resolution, 3, dtype=torch.float32, device='cuda')\\n return {\\n 'mvp' : mvp[None, ...].cuda(),\\n 'mv' : mv[None, ...].cuda(),\\n 'campos' : campos[None, ...].cuda(),\\n 'resolution' : [resolution, resolution], \\n 'spp' : 1,\\n 'background' : bkgs,\\n 'normal_rotate' : normal_rotate[None,...].cuda(),\\n 'elev_angle' : torch.tensor(elev_angle).cuda(),\\n 'azim_angle' : torch.tensor(azim_angle).cuda(),\\n 'fov' : torch.tensor(fovy).cuda(),\\n }\"\n },\n {\n \"identifier\": \"DMTetGeometry\",\n \"path\": \"geometry/dmtet_x_dreamer.py\",\n \"snippet\": \"class DMTetGeometry(torch.nn.Module):\\n def __init__(self, grid_res, scale, FLAGS):\\n super(DMTetGeometry, self).__init__()\\n\\n self.FLAGS = FLAGS\\n self.grid_res = grid_res\\n self.marching_tets = DMTet()\\n \\n tets = np.load('data/tets/{}_tets.npz'.format(self.grid_res))\\n self.verts = torch.tensor(tets['vertices'], dtype=torch.float32, device='cuda') * scale\\n print(\\\"tet grid min/max\\\", torch.min(self.verts).item(), torch.max(self.verts).item())\\n self.decoder = Decoder(multires=0 , AABB= self.getAABB(), mesh_scale= scale)\\n self.indices = torch.tensor(tets['indices'], dtype=torch.long, device='cuda')\\n self.generate_edges()\\n self.pos_encoder = CameraEncoder().to(self.verts.device)\\n\\n def generate_edges(self):\\n with torch.no_grad():\\n edges = torch.tensor([0,1,0,2,0,3,1,2,1,3,2,3], dtype = torch.long, device = \\\"cuda\\\")\\n all_edges = self.indices[:,edges].reshape(-1,2) \\n all_edges_sorted = torch.sort(all_edges, dim=1)[0]\\n self.all_edges = torch.unique(all_edges_sorted, dim=0)\\n\\n @torch.no_grad()\\n def getAABB(self):\\n return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values\\n\\n def getMesh(self, material):\\n pred= self.decoder(self.verts)\\n \\n self.sdf , self.deform = pred[:, 0], pred[:, 1:] \\n v_deformed = self.verts + 1 / (self.grid_res ) * torch.tanh(self.deform)\\n verts, faces = self.marching_tets(v_deformed, self.sdf, self.indices)\\n \\n imesh = mesh.Mesh(verts, faces, material=material)\\n imesh = mesh.auto_normals(imesh)\\n return imesh\\n\\n def render(self, glctx, target, lgt, opt_material, bsdf=None, if_normal=False, mode = 'geometry_modeling', if_flip_the_normal = False, if_use_bump = False):\\n opt_mesh = self.getMesh(opt_material) \\n return render.render_mesh(glctx, \\n opt_mesh, \\n target['mvp'], \\n target['campos'], \\n lgt, \\n target['resolution'], \\n spp=target['spp'], \\n msaa= True,\\n background= target['background'],\\n bsdf= bsdf,\\n if_normal= if_normal,\\n normal_rotate= target['normal_rotate'],\\n mode = mode,\\n if_flip_the_normal = if_flip_the_normal,\\n if_use_bump = if_use_bump\\n )\\n\\n \\n def tick(self, glctx, target, lgt, opt_material, iteration, if_normal, guidance, mode, if_flip_the_normal, if_use_bump):\\n # ==============================================================================================\\n # Render optimizable object with identical conditions\\n # ==============================================================================================\\n buffers= self.render(glctx, target, lgt, opt_material, if_normal= if_normal, mode = mode, if_flip_the_normal = if_flip_the_normal, if_use_bump = if_use_bump)\\n if self.FLAGS.add_directional_text:\\n text_embeddings = torch.cat([guidance.uncond_z[target['prompt_index']], guidance.text_z[target['prompt_index']]]) # [B*2, 77, 1024]\\n indexs = torch.cat([guidance.uncond_index[target['prompt_index']], guidance.index[target['prompt_index']]]) # [B*2, 77, 1024]\\n else:\\n text_embeddings = torch.cat([guidance.uncond_z, guidance.text_z]) # [B * 2, 77, 1024]\\n indexs = torch.cat([guidance.uncond_index, guidance.index]) # [B*2, 77, 1024]\\n\\n \\n if iteration <=self.FLAGS.coarse_iter:\\n t = torch.randint( guidance.min_step_early, guidance.max_step_early + 1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\\n pred_rgb_512 = buffers['shaded'][..., 0:4].permute(0, 3, 1, 2).contiguous() # [B, 4, 64, 64]\\n latents = F.interpolate(pred_rgb_512, (64, 64), mode='bilinear', align_corners=False)\\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\\n mask2 = mask.squeeze()\\n \\n else:\\n t = torch.randint(guidance.min_step_late, guidance.max_step_late + 1, [self.FLAGS.batch], dtype=torch.long, device='cuda')\\n srgb = buffers['shaded'][...,0:3] #* buffers['shaded'][..., 3:4] # normal * mask\\n # \\n pred_rgb_512 = srgb.permute(0, 3, 1, 2).contiguous() # [B, 3, 512, 512]\\n latents = guidance.encode_imgs(pred_rgb_512)\\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\\n mask2 = mask.squeeze()\\n\\n ### calculate camera pos feature\\n came_pos = torch.cat([target['campos'],torch.from_numpy(target['elev']).unsqueeze(-1).cuda(),torch.from_numpy(target['azim']).cuda().unsqueeze(-1),target['fov'].unsqueeze(-1)],dim=-1)\\n came_pos = torch.cat([came_pos,came_pos],dim=0) #bs*2, 5\\n came_pos = normalize_camera(came_pos,self.FLAGS)\\n came_posfeat = self.pos_encoder(came_pos)\\n\\n # add noise\\n noise = torch.randn_like(latents)\\n latents_noisy = guidance.scheduler.add_noise(latents, noise, t)\\n # pred noise\\n latent_model_input = torch.cat([latents_noisy] * 2)\\n tt = torch.cat([t] * 2)\\n noise_pred, attention_map = guidance.unet(latent_model_input, tt, encoder_hidden_states=text_embeddings, index=indexs, came_posfeat=came_posfeat)\\n noise_pred = noise_pred.sample\\n\\n attention_map[0] = attention_map[0].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\\n attention_map[1] = attention_map[1].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\\n attention_map[2] = attention_map[2].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\\n attention_map[3] = attention_map[3].reshape(self.FLAGS.batch*2, 8 , 8 ).contiguous()\\n attention_map[4] = attention_map[4].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\\n attention_map[5] = attention_map[5].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\\n attention_map[6] = attention_map[6].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\\n\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred =noise_pred_uncond + guidance.guidance_weight * (noise_pred_text - noise_pred_uncond) # [B, 4, 64, 64]\\n if iteration <= self.FLAGS.coarse_iter:\\n w = (1 - guidance.alphas[t]) # [B]\\n else:\\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\\n w = w[:, None, None, None] # [B, 1, 1, 1]\\n grad = w * (noise_pred - noise ) #*w1\\n grad = torch.nan_to_num(grad)\\n \\n sds_loss = SpecifyGradient.apply(latents, grad) \\n img_loss = torch.tensor([0], dtype=torch.float32, device=\\\"cuda\\\")\\n reg_loss = torch.tensor([0], dtype=torch.float32, device=\\\"cuda\\\")\\n\\n attention_loss = 0\\n mask_sizes = [(64, 64), (32,32), (16,16), (8,8), (16,16), (32,32), (64,64)]\\n for i in range(7):\\n _, attention_map_text = attention_map[i].chunk(2)\\n if(self.FLAGS.batch==1):\\n mask2 = F.interpolate(mask2.unsqueeze(0).unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\\n else:\\n mask2 = F.interpolate(mask2.unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\\n attention_map_text = (attention_map_text - attention_map_text.min())/(attention_map_text.max() - attention_map_text.min()+1e-6)\\n attention_map_text = F.interpolate(attention_map_text.unsqueeze(0), size=mask_sizes[i], mode='bilinear', align_corners=False).squeeze()\\n attention_loss = 0.1*F.l1_loss(mask2.float(), attention_map_text.float(), reduction=\\\"mean\\\") #0.1 1 10\\n attention_loss = attention_loss/7\\n \\n return sds_loss, img_loss, reg_loss, attention_loss\"\n },\n {\n \"identifier\": \"DLMesh\",\n \"path\": \"geometry/dlmesh_x_dreamer.py\",\n \"snippet\": \"class DLMesh(torch.nn.Module):\\n def __init__(self, initial_guess, FLAGS):\\n super(DLMesh, self).__init__()\\n self.FLAGS = FLAGS\\n self.initial_guess = initial_guess\\n self.mesh = initial_guess.clone()\\n self.pos_encoder = CameraEncoder().cuda()\\n print(\\\"Base mesh has %d triangles and %d vertices.\\\" % (self.mesh.t_pos_idx.shape[0], self.mesh.v_pos.shape[0]))\\n \\n @torch.no_grad()\\n def getAABB(self):\\n return mesh.aabb(self.mesh)\\n\\n def getMesh(self, material):\\n self.mesh.material = material\\n\\n imesh = mesh.Mesh(base=self.mesh)\\n # Compute normals and tangent space\\n imesh = mesh.auto_normals(imesh)\\n imesh = mesh.compute_tangents(imesh)\\n return imesh\\n\\n def render(self, glctx, target, lgt, opt_material, bsdf=None,if_normal=False, mode = 'appearance_modeling', if_flip_the_normal = False, if_use_bump = False):\\n opt_mesh = self.getMesh(opt_material)\\n return render.render_mesh(glctx, \\n opt_mesh,\\n target['mvp'],\\n target['campos'],\\n lgt,\\n target['resolution'], \\n spp=target['spp'], \\n msaa=True,\\n background= target['background'] ,\\n bsdf= bsdf,\\n if_normal=if_normal,\\n normal_rotate=target['normal_rotate'], \\n mode = mode,\\n if_flip_the_normal = if_flip_the_normal,\\n if_use_bump = if_use_bump\\n )\\n\\n def tick(self, glctx, target, lgt, opt_material, iteration, if_normal, guidance, mode, if_flip_the_normal, if_use_bump):\\n # ==============================================================================================\\n # Render optimizable object with identical conditions\\n # ==============================================================================================\\n buffers= self.render(glctx, target, lgt, opt_material, if_normal = if_normal, mode = mode, if_flip_the_normal = if_flip_the_normal, if_use_bump = if_use_bump)\\n if self.FLAGS.add_directional_text:\\n text_embeddings = torch.cat([guidance.uncond_z[target['prompt_index']], guidance.text_z[target['prompt_index']]])\\n indexs = torch.cat([guidance.uncond_index[target['prompt_index']], guidance.index[target['prompt_index']]]) # [B*2, 77, 1024]\\n else:\\n text_embeddings = torch.cat([guidance.uncond_z, guidance.text_z])\\n indexs = torch.cat([guidance.uncond_index, guidance.index]) # [B*2, 77, 1024]\\n\\n\\n if iteration <= self.FLAGS.coarse_iter:\\n srgb = buffers['shaded'][...,0:3]\\n srgb = util.rgb_to_srgb(srgb)\\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\\n mask2 = mask.squeeze()\\n t = torch.randint( guidance.min_step_early, guidance.max_step_early+1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\\n else:\\n srgb = buffers['shaded'][...,0:3]\\n srgb = util.rgb_to_srgb(srgb)\\n mask = (buffers['shaded'][..., 3:4]).permute(0, 3, 1, 2).contiguous()\\n mask2 = mask.squeeze()\\n t = torch.randint( guidance.min_step_late, guidance.max_step_late+1, [self.FLAGS.batch], dtype=torch.long, device='cuda') # [B]\\n\\n pred_rgb_512 = srgb.permute(0, 3, 1, 2).contiguous() # [1, 3, H, W]\\n latents = guidance.encode_imgs(pred_rgb_512)\\n \\n ### calculate camera pos feature\\n came_pos = torch.cat([target['campos'],torch.from_numpy(target['elev']).unsqueeze(-1).cuda(),torch.from_numpy(target['azim']).cuda().unsqueeze(-1),target['fov'].unsqueeze(-1)],dim=-1)\\n came_pos = torch.cat([came_pos,came_pos],dim=0) #bs*2, 5\\n came_pos = normalize_camera(came_pos,self.FLAGS)\\n came_posfeat = self.pos_encoder(came_pos)\\n\\n\\n # add noise\\n noise = torch.randn_like(latents)\\n latents_noisy = guidance.scheduler.add_noise(latents, noise, t)\\n # pred noise\\n latent_model_input = torch.cat([latents_noisy] * 2)\\n tt = torch.cat([t] * 2)\\n noise_pred, attention_map = guidance.unet(latent_model_input, tt, encoder_hidden_states= text_embeddings, index=indexs, came_posfeat=came_posfeat)#.sample######################\\n noise_pred = noise_pred.sample\\n\\n attention_map[0] = attention_map[0].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\\n attention_map[1] = attention_map[1].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\\n attention_map[2] = attention_map[2].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\\n attention_map[3] = attention_map[3].reshape(self.FLAGS.batch*2, 8 , 8 ).contiguous()\\n attention_map[4] = attention_map[4].reshape(self.FLAGS.batch*2, 16, 16).contiguous()\\n attention_map[5] = attention_map[5].reshape(self.FLAGS.batch*2, 32, 32).contiguous()\\n attention_map[6] = attention_map[6].reshape(self.FLAGS.batch*2, 64, 64).contiguous()\\n\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance.guidance_weight * (noise_pred_text - noise_pred_uncond)\\n \\n if guidance.sds_weight_strategy == 0:\\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\\n elif guidance.sds_weight_strategy == 1:\\n w = 1 / (1 - guidance.alphas[t])\\n elif guidance.sds_weight_strategy == 2:\\n if iteration <= self.FLAGS.coarse_iter:\\n w = guidance.alphas[t] ** 0.5 * (1 - guidance.alphas[t])\\n else:\\n w = 1 / (1 - guidance.alphas[t])\\n w = w[:, None, None, None] # [B, 1, 1, 1]\\n grad = w* (noise_pred -noise) \\n grad = torch.nan_to_num(grad)\\n sds_loss = SpecifyGradient.apply(latents, grad) \\n img_loss = torch.tensor([0], dtype=torch.float32, device=\\\"cuda\\\")\\n reg_loss = torch.tensor([0], dtype=torch.float32, device=\\\"cuda\\\")\\n \\n attention_loss = 0\\n mask_sizes = [(64, 64), (32,32), (16,16), (8,8), (16,16), (32,32), (64,64)]\\n for i in range(7):\\n _, attention_map_text = attention_map[i].chunk(2)\\n if(self.FLAGS.batch==1):\\n mask2 = F.interpolate(mask2.unsqueeze(0).unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\\n else:\\n mask2 = F.interpolate(mask2.unsqueeze(0), mask_sizes[i], mode='bilinear').squeeze()\\n attention_map_text = (attention_map_text - attention_map_text.min())/(attention_map_text.max() - attention_map_text.min()+1e-6)\\n attention_map_text = F.interpolate(attention_map_text.unsqueeze(0), size=mask2.shape, mode='bilinear', align_corners=False).squeeze()\\n attention_loss = 0.1*F.l1_loss(mask2.float(), attention_map_text.float(), reduction=\\\"mean\\\") #0.1 1 10\\n attention_loss = attention_loss/7\\n \\n return sds_loss, img_loss, reg_loss, attention_loss\"\n },\n {\n \"identifier\": \"obj\",\n \"path\": \"render/obj.py\",\n \"snippet\": \"def _find_mat(materials, name):\\ndef load_obj(filename, clear_ks=True, mtl_override=None):\\ndef write_obj(folder, mesh, save_material=True):\"\n },\n {\n \"identifier\": \"material\",\n \"path\": \"render/material.py\",\n \"snippet\": \"class Material(torch.nn.Module):\\n def __init__(self, mat_dict):\\n def __contains__(self, key):\\n def __getitem__(self, key):\\n def __setitem__(self, key, val):\\n def __delitem__(self, key):\\n def keys(self):\\ndef load_mtl(fn, clear_ks=True):\\ndef save_mtl(fn, material):\\ndef _upscale_replicate(x, full_res):\\ndef merge_materials(materials, texcoords, tfaces, mfaces):\"\n },\n {\n \"identifier\": \"util\",\n \"path\": \"render/util.py\",\n \"snippet\": \"def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\\ndef reflect(x: torch.Tensor, n: torch.Tensor) -> torch.Tensor:\\ndef length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\\ndef safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\\ndef to_hvec(x: torch.Tensor, w: float) -> torch.Tensor:\\ndef _rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\\ndef rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\\ndef _srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\\ndef srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\\ndef reinhard(f: torch.Tensor) -> torch.Tensor:\\ndef mse_to_psnr(mse):\\ndef psnr_to_mse(psnr):\\ndef get_miplevels(texture: np.ndarray) -> float:\\ndef tex_2d(tex_map : torch.Tensor, coords : torch.Tensor, filter='nearest') -> torch.Tensor:\\ndef cube_to_dir(s, x, y):\\ndef latlong_to_cubemap(latlong_map, res):\\ndef cubemap_to_latlong(cubemap, res):\\ndef scale_img_hwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\\ndef scale_img_nhwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\\ndef avg_pool_nhwc(x : torch.Tensor, size) -> torch.Tensor:\\ndef segment_sum(data: torch.Tensor, segment_ids: torch.Tensor) -> torch.Tensor:\\ndef fovx_to_fovy(fovx, aspect):\\ndef focal_length_to_fovy(focal_length, sensor_height):\\ndef perspective(fovy=0.7854, aspect=1.0, n=0.1, f= 1000.0, device=None):\\ndef perspective_offcenter(fovy, fraction, rx, ry, aspect=1.0, n=0.1, f=1000.0, device=None):\\ndef translate(x, y, z, device=None):\\ndef rotate_x(a, device=None):\\ndef rotate_x_1(a, device=None):\\ndef rotate_y(a, device=None):\\ndef rotate_y_1(a, device=None):\\ndef rotate_y_2(a, device=None):\\ndef rotate_x_2(a, device=None):\\ndef scale(s, device=None):\\ndef lookAt(eye, at, up):\\ndef random_rotation_translation(t, device=None):\\ndef random_rotation(device=None):\\ndef lines_focal(o, d):\\ndef cosine_sample(N, size=None):\\ndef bilinear_downsample(x : torch.tensor) -> torch.Tensor:\\ndef bilinear_downsample(x : torch.tensor, spp) -> torch.Tensor:\\ndef init_glfw():\\ndef save_image(fn, x : np.ndarray):\\ndef save_image_raw(fn, x : np.ndarray):\\ndef load_image_raw(fn) -> np.ndarray:\\ndef load_image(fn) -> np.ndarray:\\ndef time_to_text(x):\\ndef checkerboard(res, checker_size) -> np.ndarray:\\ndef get_random_bg(h, w):\\n R, L = aspect*y, -aspect*y\\n T, B = y, -y\\n I = torch.eye(3, dtype=o.dtype, device=o.device)\\n S = torch.sum(d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...], dim=0)\\n C = torch.sum((d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...]) @ o[..., None], dim=0).squeeze(1)\\n N = N/torch.linalg.norm(N)\"\n },\n {\n \"identifier\": \"mesh\",\n \"path\": \"render/mesh.py\",\n \"snippet\": \"class Mesh:\\n def __init__(self, v_pos=None, t_pos_idx=None, v_nrm=None, t_nrm_idx=None, v_tex=None, t_tex_idx=None, v_tng=None, t_tng_idx=None, material=None, base=None):\\n def copy_none(self, other):\\n def clone(self):\\ndef load_mesh(filename, mtl_override=None):\\ndef aabb(mesh):\\ndef compute_edges(attr_idx, return_inverse=False):\\ndef compute_edge_to_face_mapping(attr_idx, return_inverse=False):\\ndef unit_size(mesh):\\ndef center_by_reference(base_mesh, ref_aabb, scale):\\ndef auto_normals(imesh):\\ndef compute_tangents(imesh):\"\n },\n {\n \"identifier\": \"texture\",\n \"path\": \"render/texture.py\",\n \"snippet\": \"class texture2d_mip(torch.autograd.Function):\\nclass Texture2D(torch.nn.Module):\\n def forward(ctx, texture):\\n def backward(ctx, dout):\\n def __init__(self, init, min_max=None):\\n def sample(self, texc, texc_deriv, filter_mode='linear-mipmap-linear'):\\n def getRes(self):\\n def getChannels(self):\\n def getMips(self):\\n def clamp_(self):\\n def normalize_(self):\\ndef create_trainable(init, res=None, auto_mipmaps=True, min_max=None):\\ndef srgb_to_rgb(texture):\\ndef rgb_to_srgb(texture):\\ndef _load_mip2D(fn, lambda_fn=None, channels=None):\\ndef load_texture2D(fn, lambda_fn=None, channels=None):\\ndef _save_mip2D(fn, mip, mipidx, lambda_fn):\\ndef save_texture2D(fn, tex, lambda_fn=None):\"\n },\n {\n \"identifier\": \"mlptexture\",\n \"path\": \"render/mlptexture.py\",\n \"snippet\": \"class _MLP(torch.nn.Module):\\nclass MLPTexture3D(torch.nn.Module):\\n def __init__(self, cfg, loss_scale=1.0):\\n def forward(self, x):\\n def _init_weights(m):\\n def __init__(self, AABB, channels = 3, internal_dims = 32, hidden = 1, min_max = None):\\n def sample(self, texc):\\n def clamp_(self):\\n def cleanup(self):\"\n },\n {\n \"identifier\": \"light\",\n \"path\": \"render/light.py\",\n \"snippet\": \"class cubemap_mip(torch.autograd.Function):\\nclass EnvironmentLight(torch.nn.Module):\\n def forward(ctx, cubemap):\\n def backward(ctx, dout):\\n def __init__(self, base):\\n def xfm(self, mtx):\\n def clone(self):\\n def clamp_(self, min=None, max=None):\\n def get_mip(self, roughness):\\n def build_mips(self, cutoff=0.99):\\n def regularizer(self):\\n def shade(self, gb_pos, gb_normal, kd, ks, view_pos, specular=True):\\ndef _load_env_hdr(fn, scale=1.0):\\ndef load_env(fn, scale=1.0):\\ndef save_env_map(fn, light):\\ndef create_trainable_env_rnd(base_res, scale=0.5, bias=0.25):\\n LIGHT_MIN_RES = 16\\n MIN_ROUGHNESS = 0.08\\n MAX_ROUGHNESS = 0.5\"\n },\n {\n \"identifier\": \"render\",\n \"path\": \"render/render.py\",\n \"snippet\": \"def interpolate(attr, rast, attr_idx, rast_db=None):\\ndef shade(\\n gb_pos,\\n gb_geometric_normal,\\n gb_normal,\\n gb_tangent,\\n gb_texc,\\n gb_texc_deriv,\\n view_pos,\\n lgt,\\n material,\\n bsdf,\\n if_normal,\\n normal_rotate,\\n mode,\\n if_flip_the_normal,\\n if_use_bump\\n ):\\ndef render_layer(\\n rast,\\n rast_deriv,\\n mesh,\\n view_pos,\\n lgt,\\n resolution,\\n spp,\\n msaa,\\n bsdf,\\n if_normal,\\n normal_rotate,\\n mode,\\n if_flip_the_normal,\\n if_use_bump\\n ):\\ndef render_mesh(\\n ctx,\\n mesh,\\n mtx_in,\\n view_pos,\\n lgt,\\n resolution,\\n spp = 1,\\n num_layers = 1,\\n msaa = False,\\n background = None, \\n bsdf = None,\\n if_normal = False,\\n normal_rotate = None,\\n mode = 'geometry_modeling',\\n if_flip_the_normal = False,\\n if_use_bump = False\\n ):\\n def prepare_input_vector(x):\\n def composite_buffer(key, layers, background, antialias):\\ndef render_uv(ctx, mesh, resolution, mlp_texture):\\ndef uv_padding(image, hole_mask, padding = 2, uv_padding_block = 4):\\ndef render_uv1(ctx, mesh, resolution, mlp_texture, uv_padding_block):\"\n },\n {\n \"identifier\": \"StableDiffusion\",\n \"path\": \"sd_cglora.py\",\n \"snippet\": \"class StableDiffusion(nn.Module):\\n def __init__(self, \\n device, \\n mode='geometry', \\n text= '', \\n add_directional_text= False, \\n batch = 1, \\n guidance_weight = 100, \\n sds_weight_strategy = 0,\\n early_time_step_range = [0.02, 0.5],\\n late_time_step_range = [0.02, 0.5]):\\n super().__init__()\\n\\n self.device = device\\n self.mode = mode\\n self.text= text\\n self.add_directional_text = add_directional_text\\n self.batch = batch \\n print(f'[INFO] loading stable diffusion...')\\n model_key = \\\"stabilityai/stable-diffusion-2-1-base\\\"\\n self.vae = AutoencoderKL.from_pretrained(model_key, subfolder=\\\"vae\\\",torch_dtype=torch.float16).to(self.device)\\n self.tokenizer = CLIPTokenizer.from_pretrained(model_key, subfolder=\\\"tokenizer\\\",torch_dtype=torch.float16)\\n self.text_encoder = CLIPTextModel.from_pretrained(model_key, subfolder=\\\"text_encoder\\\",torch_dtype=torch.float16).to(self.device)\\n self.unet = UNet2DConditionModel.from_pretrained(model_key, subfolder=\\\"unet\\\",torch_dtype=torch.float16).to(self.device)\\n if is_xformers_available():\\n self.unet.enable_xformers_memory_efficient_attention()\\n self.negative_text = ''\\n if add_directional_text:\\n self.text_z = []\\n self.uncond_z = []\\n self.index = []\\n self.uncond_index = []\\n for d in ['front', 'side', 'back', 'side']:\\n text = f\\\"{self.text}, {d} view\\\"\\n # text = f\\\"{d} view of {self.text}\\\"\\n negative_text = f\\\"{self.negative_text}\\\"\\n # if d == 'back': negative_text += \\\"face\\\"\\n text_z, index = self.get_text_embeds([text], batch = 1)\\n uncond_z, uncond_index =self.get_uncond_embeds([negative_text], batch = 1)\\n self.text_z.append(text_z)\\n self.uncond_z.append(uncond_z)\\n self.index.append(index)\\n self.uncond_index.append(uncond_index)\\n self.text_z = torch.cat(self.text_z)\\n self.uncond_z = torch.cat(self.uncond_z)\\n self.index = torch.cat(self.index)\\n self.uncond_index = torch.cat(self.uncond_index)\\n else: \\n self.text_z, self.index = self.get_text_embeds([self.text], batch = self.batch)\\n self.uncond_z =self.get_uncond_embeds([self.negative_text], batch = self.batch)\\n # del self.text_encoder\\n self.scheduler = DPMSolverMultistepScheduler.from_pretrained(model_key, subfolder=\\\"scheduler\\\", torch_dtype=torch.float16)\\n self.num_train_timesteps = self.scheduler.config.num_train_timesteps\\n self.min_step_early = int(self.num_train_timesteps * early_time_step_range[0])\\n self.max_step_early = int(self.num_train_timesteps * early_time_step_range[1])\\n self.min_step_late = int(self.num_train_timesteps * late_time_step_range[0])\\n self.max_step_late = int(self.num_train_timesteps * late_time_step_range[1])\\n self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience\\n self.guidance_weight = guidance_weight\\n self.sds_weight_strategy = sds_weight_strategy\\n print(f'[INFO] loaded stable diffusion!')\\n\\n for p in self.parameters():\\n p.requires_grad_(False)\\n self.unet_lora_params, self.names = inject_trainable_cglora(self.unet) # This will\\n\\n\\n def get_text_embeds_global(self, prompt, batch=1):\\n text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')\\n with torch.no_grad():\\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\\n if batch > 1:\\n text_embeddings = text_embeddings.repeat(batch, 1, 1)\\n \\n global_embedding = text_embeddings[:,text_input['input_ids'].argmax(dim=-1),:].squeeze()\\n \\n return global_embedding\\n\\n\\n def get_text_embeds(self, prompt, batch=1):\\n text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')\\n with torch.no_grad():\\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\\n if batch > 1:\\n text_embeddings = text_embeddings.repeat(batch, 1, 1)\\n ###################################################################\\n index = text_input['input_ids'].argmax(dim=-1)\\n #global_embedding = text_embeddings[:, index, :].squeeze()\\n ##################################################################\\n \\n return text_embeddings, index\\n \\n def get_uncond_embeds(self, negative_prompt, batch):\\n uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt')\\n with torch.no_grad():\\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\\n \\n if batch > 1:\\n uncond_embeddings = uncond_embeddings.repeat(batch, 1, 1)\\n ###################################################################\\n index = uncond_input['input_ids'].argmax(dim=-1)\\n # global_embedding = uncond_embeddings[:, index, :].squeeze()\\n ##################################################################\\n return uncond_embeddings,index\\n\\n def encode_imgs(self, imgs):\\n # imgs: [B, 3, H, W]\\n if self.mode == 'appearance_modeling':\\n \\n imgs = 2 * imgs - 1\\n\\n posterior = self.vae.encode(imgs).latent_dist\\n latents = posterior.sample() * 0.18215\\n\\n return latents\"\n },\n {\n \"identifier\": \"util\",\n \"path\": \"render/util.py\",\n \"snippet\": \"def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\\ndef reflect(x: torch.Tensor, n: torch.Tensor) -> torch.Tensor:\\ndef length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\\ndef safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor:\\ndef to_hvec(x: torch.Tensor, w: float) -> torch.Tensor:\\ndef _rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\\ndef rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:\\ndef _srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\\ndef srgb_to_rgb(f: torch.Tensor) -> torch.Tensor:\\ndef reinhard(f: torch.Tensor) -> torch.Tensor:\\ndef mse_to_psnr(mse):\\ndef psnr_to_mse(psnr):\\ndef get_miplevels(texture: np.ndarray) -> float:\\ndef tex_2d(tex_map : torch.Tensor, coords : torch.Tensor, filter='nearest') -> torch.Tensor:\\ndef cube_to_dir(s, x, y):\\ndef latlong_to_cubemap(latlong_map, res):\\ndef cubemap_to_latlong(cubemap, res):\\ndef scale_img_hwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\\ndef scale_img_nhwc(x : torch.Tensor, size, mag='bilinear', min='area') -> torch.Tensor:\\ndef avg_pool_nhwc(x : torch.Tensor, size) -> torch.Tensor:\\ndef segment_sum(data: torch.Tensor, segment_ids: torch.Tensor) -> torch.Tensor:\\ndef fovx_to_fovy(fovx, aspect):\\ndef focal_length_to_fovy(focal_length, sensor_height):\\ndef perspective(fovy=0.7854, aspect=1.0, n=0.1, f= 1000.0, device=None):\\ndef perspective_offcenter(fovy, fraction, rx, ry, aspect=1.0, n=0.1, f=1000.0, device=None):\\ndef translate(x, y, z, device=None):\\ndef rotate_x(a, device=None):\\ndef rotate_x_1(a, device=None):\\ndef rotate_y(a, device=None):\\ndef rotate_y_1(a, device=None):\\ndef rotate_y_2(a, device=None):\\ndef rotate_x_2(a, device=None):\\ndef scale(s, device=None):\\ndef lookAt(eye, at, up):\\ndef random_rotation_translation(t, device=None):\\ndef random_rotation(device=None):\\ndef lines_focal(o, d):\\ndef cosine_sample(N, size=None):\\ndef bilinear_downsample(x : torch.tensor) -> torch.Tensor:\\ndef bilinear_downsample(x : torch.tensor, spp) -> torch.Tensor:\\ndef init_glfw():\\ndef save_image(fn, x : np.ndarray):\\ndef save_image_raw(fn, x : np.ndarray):\\ndef load_image_raw(fn) -> np.ndarray:\\ndef load_image(fn) -> np.ndarray:\\ndef time_to_text(x):\\ndef checkerboard(res, checker_size) -> np.ndarray:\\ndef get_random_bg(h, w):\\n R, L = aspect*y, -aspect*y\\n T, B = y, -y\\n I = torch.eye(3, dtype=o.dtype, device=o.device)\\n S = torch.sum(d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...], dim=0)\\n C = torch.sum((d[..., None] @ torch.transpose(d[..., None], 1, 2) - I[None, ...]) @ o[..., None], dim=0).squeeze(1)\\n N = N/torch.linalg.norm(N)\"\n },\n {\n \"identifier\": \"Video\",\n \"path\": \"render/video.py\",\n \"snippet\": \"class Video():\\n def __init__(self, path, name='video_log.mp4', mode='I', fps=30, codec='libx264', bitrate='16M') -> None:\\n \\n if path[-1] != \\\"/\\\":\\n path += \\\"/\\\"\\n \\n self.writer = imageio.get_writer(path+name, mode=mode, fps=fps, codec=codec, bitrate=bitrate)\\n \\n def ready_image(self, image, write_video=True):\\n # assuming channels last - as renderer returns it\\n if len(image.shape) == 4: \\n image = image.squeeze(0)[..., :3].detach().cpu().numpy()\\n else:\\n image = image[..., :3].detach().cpu().numpy()\\n\\n image = np.clip(np.rint(image*255.0), 0, 255).astype(np.uint8)\\n\\n if write_video:\\n self.writer.append_data(image)\\n\\n return image\\n\\n def close(self):\\n self.writer.close()\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport time\nimport argparse\nimport json\nimport math\nimport numpy as np\nimport torch\nimport nvdiffrast.torch as dr\nimport itertools\nimport xatlas\nimport open3d as o3d\nimport random\nimport imageio\nimport os.path as osp\nimport pickle\nfrom dataset.dataset_mesh import DatasetMesh\nfrom dataset.dataset_mesh import get_camera_params\nfrom geometry.dmtet_x_dreamer import DMTetGeometry\nfrom geometry.dlmesh_x_dreamer import DLMesh\nfrom render import obj\nfrom render import material\nfrom render import util\nfrom render import mesh\nfrom render import texture\nfrom render import mlptexture\nfrom render import light\nfrom render import render\nfrom sd_cglora import StableDiffusion\nfrom tqdm import tqdm\nfrom render import util\nfrom render.video import Video"},"token_num":{"kind":"number","value":14969,"string":"14,969"},"cropped_code":{"kind":"string","value":" if 'kd' in result_dict.keys():\n save_gif(kd_dir,30)\n if 'ks' in result_dict.keys():\n save_gif(ks_dir,30)\n if 'normal' in result_dict.keys():\n save_gif(normal_dir,30)\n return 0\n\n###############################################################################\n# Main shape fitter function / optimization loop\n###############################################################################\nclass Trainer(torch.nn.Module):\n def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, FLAGS, guidance):\n super(Trainer, self).__init__()\n\n self.glctx = glctx\n self.geometry = geometry\n self.light = lgt\n self.material = mat\n self.optimize_geometry = optimize_geometry\n self.optimize_light = optimize_light\n self.FLAGS = FLAGS\n self.guidance = guidance\n self.if_flip_the_normal = FLAGS.if_flip_the_normal\n self.if_use_bump = FLAGS.if_use_bump\n if self.FLAGS.mode == 'appearance_modeling':\n if not self.optimize_light:\n with torch.no_grad():\n self.light.build_mips()\n\n self.params = list(self.material.parameters())\n self.params += list(self.geometry.pos_encoder.parameters())\n self.params += list(self.light.parameters()) if optimize_light else []\n self.geo_params = list(self.geometry.parameters()) if optimize_geometry else []\n \n \n\n def forward(self, target, it, if_normal, if_pretrain, scene_and_vertices ):\n if self.FLAGS.mode == 'appearance_modeling':\n if self.optimize_light:\n self.light.build_mips()\n if self.FLAGS.camera_space_light:\n self.light.xfm(target['mv'])\n if if_pretrain: \n return self.geometry.decoder.pre_train_ellipsoid(it, scene_and_vertices)\n else:\n return self.geometry.tick(glctx, target, self.light, self.material, it , if_normal, self.guidance, self.FLAGS.mode, self.if_flip_the_normal, self.if_use_bump)\n\ndef optimize_mesh(\n glctx,\n geometry,\n opt_material,\n lgt,\n dataset_train,\n dataset_validate,\n FLAGS,\n log_interval=10,\n optimize_light=True,\n optimize_geometry=True,\n guidance = None,\n scene_and_vertices = None,\n ):\n \n dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=False)\n dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_train.collate)\n \n model = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, FLAGS, guidance)\n if optimize_geometry: \n optimizer_mesh = torch.optim.AdamW(model.geo_params, lr=0.001, betas=(0.9, 0.99), eps=1e-15)\n optimizer = torch.optim.AdamW(model.params, lr=0.01, betas=(0.9, 0.99), eps=1e-15)\n optimizer_lora = torch.optim.SGD(itertools.chain(*guidance.unet_lora_params), lr=1e-5)\n\n if FLAGS.multi_gpu: \n model = model.cuda()\n model = torch.nn.parallel.DistributedDataParallel(model,\n device_ids=[FLAGS.local_rank],\n find_unused_parameters= True\n )\n \n img_cnt = 0\n img_loss_vec = []\n reg_loss_vec = []\n iter_dur_vec = []\n \n\n def cycle(iterable):\n iterator = iter(iterable)\n while True:\n try:\n yield next(iterator)\n except StopIteration:\n iterator = iter(iterable)\n\n v_it = cycle(dataloader_validate)\n scaler = torch.cuda.amp.GradScaler(enabled=True) \n \n rot_ang = 0\n if FLAGS.local_rank == 0:\n video = Video(FLAGS.out_dir)\n if FLAGS.local_rank == 0:\n dataloader_train = tqdm(dataloader_train)\n for it, target in enumerate(dataloader_train):\n\n # Mix randomized background into dataset image\n target = prepare_batch(target, FLAGS.train_background) \n\n # Show/save image before training step (want to get correct rendering of input)\n if FLAGS.local_rank == 0:\n save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)\n save_video = FLAGS.video_interval and (it % FLAGS.video_interval == 0)\n if save_image:\n result_image, result_dict = validate_itr(glctx, prepare_batch(next(v_it), FLAGS.train_background), geometry, opt_material, lgt, FLAGS) #prepare_batch(next(v_it), FLAGS.background)\n np_result_image = result_image.detach().cpu().numpy()\n util.save_image(FLAGS.out_dir + '/' + ('img_%s_%06d.png' % (FLAGS.mode, img_cnt)), np_result_image)\n util.save_image(FLAGS.out_dir + '/' + ('mask_%s_%06d.png' % (FLAGS.mode, img_cnt)), result_dict['mask'].detach().cpu().numpy())\n\n\n img_cnt = img_cnt+1\n if save_video:\n with torch.no_grad():\n"},"all_code":{"kind":"string","value":"\n\n\n###############################################################################\n# Mix background into a dataset image\n###############################################################################\n@torch.no_grad()\ndef prepare_batch(target, background= 'black'):\n target['mv'] = target['mv'].cuda()\n target['mvp'] = target['mvp'].cuda()\n target['campos'] = target['campos'].cuda()\n target['fov'] = target['fov'].cuda()\n target['normal_rotate'] = target['normal_rotate'].cuda()\n batch_size = target['mv'].shape[0]\n resolution = target['resolution']\n if background == 'white':\n target['background']= torch.ones(batch_size, resolution[0], resolution[1], 3, dtype=torch.float32, device='cuda') \n if background == 'black':\n target['background'] = torch.zeros(batch_size, resolution[0], resolution[1], 3, dtype=torch.float32, device='cuda') \n return target\n\n###############################################################################\n# UV - map geometry & convert to a mesh\n###############################################################################\n@torch.no_grad()\ndef xatlas_uvmap(glctx, geometry, mat, FLAGS):\n eval_mesh = geometry.getMesh(mat)\n # Create uvs with xatlas\n v_pos = eval_mesh.v_pos.detach().cpu().numpy()\n t_pos_idx = eval_mesh.t_pos_idx.detach().cpu().numpy()\n vmapping, indices, uvs = xatlas.parametrize(v_pos, t_pos_idx)\n\n # Convert to tensors\n indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)\n \n uvs = torch.tensor(uvs, dtype=torch.float32, device='cuda')\n faces = torch.tensor(indices_int64, dtype=torch.int64, device='cuda')\n\n new_mesh = mesh.Mesh(v_tex=uvs, t_tex_idx=faces, base=eval_mesh)\n \n mask, kd, ks, normal = render.render_uv(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'])\n \n if FLAGS.layers > 1:\n kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)\n\n kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')\n ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')\n nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')\n\n new_mesh.material = material.Material({\n 'bsdf' : mat['bsdf'],\n 'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),\n 'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),\n 'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])\n })\n\n return new_mesh\n\n@torch.no_grad()\ndef xatlas_uvmap1(glctx, geometry, mat, FLAGS):\n eval_mesh = geometry.getMesh(mat)\n new_mesh = mesh.Mesh( base=eval_mesh)\n mask, kd, ks, normal = render.render_uv1(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'], FLAGS.uv_padding_block)\n \n if FLAGS.layers > 1:\n kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)\n\n kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')\n ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')\n nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')\n\n new_mesh.material = material.Material({\n 'bsdf' : mat['bsdf'],\n 'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),\n 'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),\n 'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])\n })\n\n return new_mesh\n\n###############################################################################\n# Utility functions for material\n###############################################################################\ndef get_normalize_mesh(pro_path):\n mesh = o3d.io.read_triangle_mesh(pro_path)\n vertices = np.asarray(mesh.vertices)\n shift = np.mean(vertices,axis=0)\n scale = np.max(np.linalg.norm(vertices-shift, ord=2, axis=1))\n vertices = (vertices-shift) / scale\n mesh.vertices = o3d.cuda.pybind.utility.Vector3dVector(vertices)\n return mesh\n\n\ndef initial_guness_material(geometry, mlp, FLAGS, init_mat=None):\n # ipdb.set_trace(())\n kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')\n ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')\n nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')\n if mlp:\n mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)\n mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)\n mlp_map_opt = mlptexture.MLPTexture3D(geometry.getAABB(), channels=9, min_max=[mlp_min, mlp_max])\n mat = material.Material({'kd_ks_normal' : mlp_map_opt})\n else:\n # Setup Kd (albedo) and Ks (x, roughness, metalness) textures\n if FLAGS.random_textures or init_mat is None:\n num_channels = 4 if FLAGS.layers > 1 else 3\n kd_init = torch.rand(size=FLAGS.texture_res + [num_channels], device='cuda') * (kd_max - kd_min)[None, None, 0:num_channels] + kd_min[None, None, 0:num_channels]\n kd_map_opt = texture.create_trainable(kd_init , FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])\n\n ksR = np.random.uniform(size=FLAGS.texture_res + [1], low=0.0, high=0.01)\n ksG = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[1].cpu(), high=ks_max[1].cpu())\n ksB = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[2].cpu(), high=ks_max[2].cpu())\n\n ks_map_opt = texture.create_trainable(np.concatenate((ksR, ksG, ksB), axis=2), FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])\n else:\n kd_map_opt = texture.create_trainable(init_mat['kd'], FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])\n ks_map_opt = texture.create_trainable(init_mat['ks'], FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])\n\n # Setup normal map\n if FLAGS.random_textures or init_mat is None or 'normal' not in init_mat:\n normal_map_opt = texture.create_trainable(np.array([0, 0, 1]), FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])\n else:\n normal_map_opt = texture.create_trainable(init_mat['normal'], FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])\n\n mat = material.Material({\n 'kd' : kd_map_opt,\n 'ks' : ks_map_opt,\n 'normal' : normal_map_opt\n })\n\n if init_mat is not None:\n mat['bsdf'] = init_mat['bsdf']\n else:\n mat['bsdf'] = 'pbr'\n\n return mat\n\n###############################################################################\n# Validation & testing\n###############################################################################\n# @torch.no_grad() \ndef validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, relight = None):\n result_dict = {}\n with torch.no_grad():\n if FLAGS.mode == 'appearance_modeling':\n with torch.no_grad():\n lgt.build_mips()\n if FLAGS.camera_space_light:\n lgt.xfm(target['mv'])\n if relight != None:\n relight.build_mips()\n buffers = geometry.render(glctx, target, lgt, opt_material, if_use_bump = FLAGS.if_use_bump)\n result_dict['shaded'] = buffers['shaded'][0, ..., 0:3]\n result_dict['shaded'] = util.rgb_to_srgb(result_dict['shaded'])\n if relight != None:\n result_dict['relight'] = geometry.render(glctx, target, relight, opt_material, if_use_bump = FLAGS.if_use_bump)['shaded'][0, ..., 0:3]\n result_dict['relight'] = util.rgb_to_srgb(result_dict['relight'])\n result_dict['mask'] = (buffers['shaded'][0, ..., 3:4])\n result_image = result_dict['shaded']\n\n if FLAGS.display is not None :\n # white_bg = torch.ones_like(target['background'])\n for layer in FLAGS.display:\n if 'latlong' in layer and layer['latlong']:\n if isinstance(lgt, light.EnvironmentLight):\n result_dict['light_image'] = util.cubemap_to_latlong(lgt.base, FLAGS.display_res)\n result_image = torch.cat([result_image, result_dict['light_image']], axis=1)\n elif 'bsdf' in layer:\n buffers = geometry.render(glctx, target, lgt, opt_material, bsdf=layer['bsdf'], if_use_bump = FLAGS.if_use_bump)\n if layer['bsdf'] == 'kd':\n result_dict[layer['bsdf']] = util.rgb_to_srgb(buffers['shaded'][0, ..., 0:3]) \n elif layer['bsdf'] == 'normal':\n result_dict[layer['bsdf']] = (buffers['shaded'][0, ..., 0:3] + 1) * 0.5\n else:\n result_dict[layer['bsdf']] = buffers['shaded'][0, ..., 0:3]\n result_image = torch.cat([result_image, result_dict[layer['bsdf']]], axis=1)\n\n return result_image, result_dict\n\ndef save_gif(dir,fps):\n imgpath = dir\n frames = []\n for idx in sorted(os.listdir(imgpath)):\n img = osp.join(imgpath,idx)\n frames.append(imageio.imread(img))\n imageio.mimsave(os.path.join(dir, 'eval.gif'),frames,'GIF',duration=1/fps,loop=0)\n \n@torch.no_grad() \ndef validate(glctx, geometry, opt_material, lgt, dataset_validate, out_dir, FLAGS, relight= None):\n\n # ==============================================================================================\n # Validation loop\n # ==============================================================================================\n mse_values = []\n psnr_values = []\n\n dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_validate.collate)\n\n os.makedirs(out_dir, exist_ok=True)\n \n shaded_dir = os.path.join(out_dir, \"shaded\")\n relight_dir = os.path.join(out_dir, \"relight\")\n kd_dir = os.path.join(out_dir, \"kd\")\n ks_dir = os.path.join(out_dir, \"ks\")\n normal_dir = os.path.join(out_dir, \"normal\")\n mask_dir = os.path.join(out_dir, \"mask\")\n \n os.makedirs(shaded_dir, exist_ok=True)\n os.makedirs(relight_dir, exist_ok=True)\n os.makedirs(kd_dir, exist_ok=True)\n os.makedirs(ks_dir, exist_ok=True)\n os.makedirs(normal_dir, exist_ok=True)\n os.makedirs(mask_dir, exist_ok=True)\n \n print(\"Running validation\")\n dataloader_validate = tqdm(dataloader_validate)\n for it, target in enumerate(dataloader_validate):\n\n # Mix validation background\n target = prepare_batch(target, 'white')\n\n result_image, result_dict = validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, relight)\n for k in result_dict.keys():\n np_img = result_dict[k].detach().cpu().numpy()\n if k == 'shaded':\n util.save_image(shaded_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n elif k == 'relight':\n util.save_image(relight_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n elif k == 'kd':\n util.save_image(kd_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n elif k == 'ks':\n util.save_image(ks_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n elif k == 'normal':\n util.save_image(normal_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n elif k == 'mask':\n util.save_image(mask_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)\n if 'shaded' in result_dict.keys():\n save_gif(shaded_dir,30)\n if 'relight' in result_dict.keys():\n save_gif(relight_dir,30)\n if 'kd' in result_dict.keys():\n save_gif(kd_dir,30)\n if 'ks' in result_dict.keys():\n save_gif(ks_dir,30)\n if 'normal' in result_dict.keys():\n save_gif(normal_dir,30)\n return 0\n\n###############################################################################\n# Main shape fitter function / optimization loop\n###############################################################################\nclass Trainer(torch.nn.Module):\n def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, FLAGS, guidance):\n super(Trainer, self).__init__()\n\n self.glctx = glctx\n self.geometry = geometry\n self.light = lgt\n self.material = mat\n self.optimize_geometry = optimize_geometry\n self.optimize_light = optimize_light\n self.FLAGS = FLAGS\n self.guidance = guidance\n self.if_flip_the_normal = FLAGS.if_flip_the_normal\n self.if_use_bump = FLAGS.if_use_bump\n if self.FLAGS.mode == 'appearance_modeling':\n if not self.optimize_light:\n with torch.no_grad():\n self.light.build_mips()\n\n self.params = list(self.material.parameters())\n self.params += list(self.geometry.pos_encoder.parameters())\n self.params += list(self.light.parameters()) if optimize_light else []\n self.geo_params = list(self.geometry.parameters()) if optimize_geometry else []\n \n \n\n def forward(self, target, it, if_normal, if_pretrain, scene_and_vertices ):\n if self.FLAGS.mode == 'appearance_modeling':\n if self.optimize_light:\n self.light.build_mips()\n if self.FLAGS.camera_space_light:\n self.light.xfm(target['mv'])\n if if_pretrain: \n return self.geometry.decoder.pre_train_ellipsoid(it, scene_and_vertices)\n else:\n return self.geometry.tick(glctx, target, self.light, self.material, it , if_normal, self.guidance, self.FLAGS.mode, self.if_flip_the_normal, self.if_use_bump)\n\ndef optimize_mesh(\n glctx,\n geometry,\n opt_material,\n lgt,\n dataset_train,\n dataset_validate,\n FLAGS,\n log_interval=10,\n optimize_light=True,\n optimize_geometry=True,\n guidance = None,\n scene_and_vertices = None,\n ):\n \n dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=False)\n dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_train.collate)\n \n model = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, FLAGS, guidance)\n if optimize_geometry: \n optimizer_mesh = torch.optim.AdamW(model.geo_params, lr=0.001, betas=(0.9, 0.99), eps=1e-15)\n optimizer = torch.optim.AdamW(model.params, lr=0.01, betas=(0.9, 0.99), eps=1e-15)\n optimizer_lora = torch.optim.SGD(itertools.chain(*guidance.unet_lora_params), lr=1e-5)\n\n if FLAGS.multi_gpu: \n model = model.cuda()\n model = torch.nn.parallel.DistributedDataParallel(model,\n device_ids=[FLAGS.local_rank],\n find_unused_parameters= True\n )\n \n img_cnt = 0\n img_loss_vec = []\n reg_loss_vec = []\n iter_dur_vec = []\n \n\n def cycle(iterable):\n iterator = iter(iterable)\n while True:\n try:\n yield next(iterator)\n except StopIteration:\n iterator = iter(iterable)\n\n v_it = cycle(dataloader_validate)\n scaler = torch.cuda.amp.GradScaler(enabled=True) \n \n rot_ang = 0\n if FLAGS.local_rank == 0:\n video = Video(FLAGS.out_dir)\n if FLAGS.local_rank == 0:\n dataloader_train = tqdm(dataloader_train)\n for it, target in enumerate(dataloader_train):\n\n # Mix randomized background into dataset image\n target = prepare_batch(target, FLAGS.train_background) \n\n # Show/save image before training step (want to get correct rendering of input)\n if FLAGS.local_rank == 0:\n save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)\n save_video = FLAGS.video_interval and (it % FLAGS.video_interval == 0)\n if save_image:\n result_image, result_dict = validate_itr(glctx, prepare_batch(next(v_it), FLAGS.train_background), geometry, opt_material, lgt, FLAGS) #prepare_batch(next(v_it), FLAGS.background)\n np_result_image = result_image.detach().cpu().numpy()\n util.save_image(FLAGS.out_dir + '/' + ('img_%s_%06d.png' % (FLAGS.mode, img_cnt)), np_result_image)\n util.save_image(FLAGS.out_dir + '/' + ('mask_%s_%06d.png' % (FLAGS.mode, img_cnt)), result_dict['mask'].detach().cpu().numpy())\n\n\n img_cnt = img_cnt+1\n if save_video:\n with torch.no_grad():"},"next_line":{"kind":"string","value":" params = get_camera_params("},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-11-27 13:44:01+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5378,"cells":{"repo_name":{"kind":"string","value":"zhenzhiwang/intercontrol"},"file_path":{"kind":"string","value":"utils/model_util.py"},"context":{"kind":"list like","value":[{"identifier":"ControlGaussianDiffusion","path":"diffusion/control_diffusion.py","snippet":"class ControlGaussianDiffusion(SpacedDiffusion):\n\n def inv_transform(self, data):\n assert self.std is not None and self.mean is not None\n #assert data.requires_grad == True\n std = th.tensor(self.std, dtype=data.dtype, device=data.device, requires_grad=False)\n mean = th.tensor(self.mean, dtype=data.dtype, device=data.device, requires_grad=False)\n output = th.add(th.mul(data, std), mean)\n return output\n \n def q_sample(self, x_start, t, noise=None, model_kwargs=None):\n \"\"\"\n overrides q_sample to use the inpainting mask\n \n same usage as in GaussianDiffusion\n \"\"\"\n if noise is None:\n noise = th.randn_like(x_start)\n assert noise.shape == x_start.shape\n\n bs, feat, _, frames = noise.shape\n noise *= 1. #- model_kwargs['y']['inpainting_mask']\n\n return (\n _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start\n + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)\n * noise\n )\n \n def global_joint_bfgs_optimize(self, x, model_kwargs=None):\n assert self.model_mean_type == ModelMeanType.START_X, 'This feature supports only X_start pred for mow!'\n pred_joint = self.humanml_to_global_joint(x)\n cond_joint = model_kwargs['y']['global_joint']\n mask = model_kwargs['y']['global_joint_mask']\n pred_joint = th.masked_select(pred_joint, mask.bool())\n cond_joint = th.masked_select(cond_joint, mask.bool())\n assert pred_joint.shape == cond_joint.shape, f\"pred_joint: {pred_joint.shape}, cond_joint: {cond_joint.shape}\"\n loss = self.mse_loss(pred_joint, cond_joint)\n return loss\n \n def humanml_to_global_joint(self, x):\n n_joints = 22 if x.shape[1] == 263 else 21\n pred_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\n assert pred_joint.shape[1] == 1\n pred_joint = recover_from_ric(pred_joint, n_joints)\n pred_joint = pred_joint.view(-1, *pred_joint.shape[2:]).permute(0, 2, 3, 1)\n return pred_joint\n \n def global_joint_position_conditioning(self, x, model_kwargs=None):\n n_joints = 22 if x.shape[1] == 263 else 21\n assert self.model_mean_type == ModelMeanType.START_X, 'This feature supports only X_start pred for mow!'\n pred_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\n pred_joint = recover_from_ric(pred_joint, n_joints)\n pred_joint = pred_joint.view(-1, *pred_joint.shape[2:]).permute(0, 2, 3, 1)\n #pred_joint.requires_grad = True\n assert pred_joint.shape == model_kwargs['y']['global_joint'].shape == model_kwargs['y']['global_joint_mask'].shape, f\"pred_joint: {pred_joint.shape}, global_joint: {model_kwargs['y']['global_joint'].shape}, global_joint_mask: {model_kwargs['y']['global_joint_mask'].shape}\"\n loss = self.global_joint_condition_loss(pred_joint, model_kwargs['y']['global_joint'], model_kwargs['y']['global_joint_mask'])\n diff_scale = ((pred_joint.clamp(min=1e-4) - model_kwargs['y']['global_joint'].clamp(min=1e-4)).abs() / model_kwargs['y']['global_joint'].clamp(min=1e-4).abs()).mean().item()\n #loss.requires_grad = True\n gradient = th.autograd.grad(loss, x, \n grad_outputs=th.ones_like(loss),\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n return gradient.clone().detach(), loss.item(), diff_scale\n\n def p_sample(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n const_noise=False,\n use_posterior=False,\n ):\n \"\"\"\n overrides p_sample to use the inpainting mask\n \n same usage as in GaussianDiffusion\n \"\"\"\n #assert use_posterior == False\n p_mean_variance_func = self.p_mean_variance_bfgs_posterior if use_posterior else self.p_mean_variance_bfgs_x0\n out = p_mean_variance_func(\n model,\n x,\n t,\n clip_denoised=clip_denoised,\n denoised_fn=denoised_fn,\n model_kwargs=model_kwargs,\n k_first = self.bfgs_times_first,\n k_last = self.bfgs_times_last,\n )\n \n noise = th.randn_like(x)\n if const_noise:\n noise = noise[[0]].repeat(x.shape[0], 1, 1, 1)\n\n nonzero_mask = (\n (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))\n ) # no noise when t == 0\n\n sample = out[\"mean\"] + nonzero_mask * th.exp(0.5 * out[\"log_variance\"]) * noise\n \n return {\"sample\": sample, \"pred_xstart\": out[\"pred_xstart\"]}\n \n def condition_mean_with_grad(self, cond_fn, x_mean, x_var, t, strength, model_kwargs=None):\n \"\"\"\n Compute the mean for the previous step, given a function cond_fn that\n computes the gradient of a conditional log probability with respect to\n x. In particular, cond_fn computes grad(log(p(y|x))), and we want to\n condition on y.\n\n This uses the conditioning strategy from Sohl-Dickstein et al. (2015).\n \"\"\"\n with th.enable_grad():\n x_mean = x_mean.clone().detach().requires_grad_(True)\n gradient, loss_value, diff_scale = cond_fn(x_mean, model_kwargs) # p_mean_var[\"mean\"]\n gradient_guidance = - strength * gradient.float() # x_var.clamp(min = 0.01) \n new_mean = (x_mean + gradient_guidance).clone().detach()\n return new_mean, loss_value, gradient_guidance.clone().detach().abs().cpu(), x_mean.clone().detach().abs().cpu(), diff_scale\n\n\n def condition_mean_bfgs(self, x_mean, num_condition, model_kwargs=None):\n \"\"\"\n Compute the mean for the previous step, given a function cond_fn that\n computes the gradient of a conditional log probability with respect to\n x.\n\n This uses the conditioning strategy from Sohl-Dickstein et al. (2015).\n \"\"\"\n \n with th.enable_grad():\n x_mean = x_mean.clone().detach().contiguous().requires_grad_(True)\n def closure():\n lbfgs.zero_grad()\n objective = self.global_joint_bfgs_optimize(x_mean, model_kwargs)\n objective.backward()\n return objective\n lbfgs = optim.LBFGS([x_mean],\n history_size=10, \n max_iter=4, \n line_search_fn=\"strong_wolfe\")\n for _ in range(num_condition):\n lbfgs.step(closure)\n #loss_value = self.global_joint_bfgs_optimize(x_mean, model_kwargs).item()\n return x_mean #, loss_value\n\n def p_mean_variance_bfgs_x0(\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, \n k_first = 1,\n k_last = 10,\n t_threshold = 10,\n ):\n \"\"\"\n Apply the model to get p(x_{t-1} | x_t), as well as a prediction of\n the initial x, x_0.\n\n :param model: the model, which takes a signal and a batch of timesteps\n as input.\n :param x: the [N x C x ...] tensor at time t.\n :param t: a 1-D Tensor of timesteps.\n :param clip_denoised: if True, clip the denoised signal into [-1, 1].\n :param denoised_fn: if not None, a function which applies to the\n x_start prediction before it is used to sample. Applies before\n clip_denoised.\n :param model_kwargs: if not None, a dict of extra keyword arguments to\n pass to the model. This can be used for conditioning.\n :return: a dict with the following keys:\n - 'mean': the model mean output.\n - 'variance': the model variance output.\n - 'log_variance': the log of 'variance'.\n - 'pred_xstart': the prediction for x_0.\n \"\"\"\n if model_kwargs is None:\n model_kwargs = {}\n\n B, C = x.shape[:2]\n assert t.shape == (B,)\n original_model_output = model(x, self._scale_timesteps(t), **model_kwargs)\n model_output = original_model_output.clone().detach()\n\n if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:\n assert model_output.shape == (B, C * 2, *x.shape[2:])\n model_output, model_var_values = th.split(model_output, C, dim=1)\n if self.model_var_type == ModelVarType.LEARNED:\n model_log_variance = model_var_values\n model_variance = th.exp(model_log_variance)\n else:\n min_log = _extract_into_tensor(\n self.posterior_log_variance_clipped, t, x.shape\n )\n max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)\n # The model_var_values is [-1, 1] for [min_var, max_var].\n frac = (model_var_values + 1) / 2\n model_log_variance = frac * max_log + (1 - frac) * min_log\n model_variance = th.exp(model_log_variance)\n else:\n model_variance, model_log_variance = {\n # for fixedlarge, we set the initial (log-)variance like so\n # to get a better decoder log likelihood.\n ModelVarType.FIXED_LARGE: (\n np.append(self.posterior_variance[1], self.betas[1:]),\n np.log(np.append(self.posterior_variance[1], self.betas[1:])),\n ),\n ModelVarType.FIXED_SMALL: (\n self.posterior_variance,\n self.posterior_log_variance_clipped,\n ),\n }[self.model_var_type]\n\n model_variance = _extract_into_tensor(model_variance, t, x.shape)\n model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)\n\n\n # loss-guided condition\n #assert k_first ==1, \"k_first must be 1, {}\".format(k_first)\n num_condition = k_first if t[0] >= t_threshold else k_last # t[0] count from 1000 to 1, assume all t are equal\n model_output = self.condition_mean_bfgs(model_output, num_condition, model_kwargs=model_kwargs) # , loss_value\n\n def process_xstart(x):\n if denoised_fn is not None:\n x = denoised_fn(x)\n if clip_denoised:\n # print('clip_denoised', clip_denoised)\n return x.clamp(-1, 1)\n return x\n\n if self.model_mean_type == ModelMeanType.PREVIOUS_X:\n pred_xstart = process_xstart(\n self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)\n )\n model_mean = model_output\n elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: # THIS IS US!\n if self.model_mean_type == ModelMeanType.START_X:\n pred_xstart = process_xstart(model_output)\n else:\n pred_xstart = process_xstart(\n self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)\n )\n model_mean, _, _ = self.q_posterior_mean_variance(\n x_start=pred_xstart, x_t=x, t=t\n )\n else:\n raise NotImplementedError(self.model_mean_type)\n\n assert (\n model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape\n )\n\n \n return {\n \"mean\": model_mean,\n \"variance\": model_variance,\n \"log_variance\": model_log_variance,\n \"pred_xstart\": pred_xstart,\n }\n \n def p_mean_variance_bfgs_posterior(\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, \n k_first = 1,\n k_last = 10,\n t_threshold = 10,\n ):\n \"\"\"\n Apply the model to get p(x_{t-1} | x_t), as well as a prediction of\n the initial x, x_0.\n\n :param model: the model, which takes a signal and a batch of timesteps\n as input.\n :param x: the [N x C x ...] tensor at time t.\n :param t: a 1-D Tensor of timesteps.\n :param clip_denoised: if True, clip the denoised signal into [-1, 1].\n :param denoised_fn: if not None, a function which applies to the\n x_start prediction before it is used to sample. Applies before\n clip_denoised.\n :param model_kwargs: if not None, a dict of extra keyword arguments to\n pass to the model. This can be used for conditioning.\n :return: a dict with the following keys:\n - 'mean': the model mean output.\n - 'variance': the model variance output.\n - 'log_variance': the log of 'variance'.\n - 'pred_xstart': the prediction for x_0.\n \"\"\"\n if model_kwargs is None:\n model_kwargs = {}\n\n B, C = x.shape[:2]\n assert t.shape == (B,)\n original_model_output = model(x, self._scale_timesteps(t), **model_kwargs)\n model_output = original_model_output.clone().detach()\n\n if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:\n assert model_output.shape == (B, C * 2, *x.shape[2:])\n model_output, model_var_values = th.split(model_output, C, dim=1)\n if self.model_var_type == ModelVarType.LEARNED:\n model_log_variance = model_var_values\n model_variance = th.exp(model_log_variance)\n else:\n min_log = _extract_into_tensor(\n self.posterior_log_variance_clipped, t, x.shape\n )\n max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)\n # The model_var_values is [-1, 1] for [min_var, max_var].\n frac = (model_var_values + 1) / 2\n model_log_variance = frac * max_log + (1 - frac) * min_log\n model_variance = th.exp(model_log_variance)\n else:\n model_variance, model_log_variance = {\n # for fixedlarge, we set the initial (log-)variance like so\n # to get a better decoder log likelihood.\n ModelVarType.FIXED_LARGE: (\n np.append(self.posterior_variance[1], self.betas[1:]),\n np.log(np.append(self.posterior_variance[1], self.betas[1:])),\n ),\n ModelVarType.FIXED_SMALL: (\n self.posterior_variance,\n self.posterior_log_variance_clipped,\n ),\n }[self.model_var_type]\n\n model_variance = _extract_into_tensor(model_variance, t, x.shape)\n model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)\n\n\n def process_xstart(x):\n if denoised_fn is not None:\n x = denoised_fn(x)\n if clip_denoised:\n # print('clip_denoised', clip_denoised)\n return x.clamp(-1, 1)\n return x\n\n if self.model_mean_type == ModelMeanType.PREVIOUS_X:\n pred_xstart = process_xstart(\n self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)\n )\n model_mean = model_output\n elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: # THIS IS US!\n if self.model_mean_type == ModelMeanType.START_X:\n pred_xstart = process_xstart(model_output)\n else:\n pred_xstart = process_xstart(\n self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)\n )\n model_mean, _, _ = self.q_posterior_mean_variance(\n x_start=pred_xstart, x_t=x, t=t\n )\n else:\n raise NotImplementedError(self.model_mean_type)\n\n assert (\n model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape\n )\n\n # loss-guided condition\n #assert k_first ==1, \"k_first must be 1, {}\".format(k_first)\n num_condition = k_first if t[0] >= t_threshold else k_last # t[0] count from 1000 to 1, assume all t are equal\n model_mean = self.condition_mean_bfgs(model_mean, num_condition, model_kwargs=model_kwargs) # , loss_value\n\n return {\n \"mean\": model_mean,\n \"variance\": model_variance,\n \"log_variance\": model_log_variance,\n \"pred_xstart\": pred_xstart,\n }\n \n def training_losses(self, model, x_start, t, model_kwargs=None, noise=None, dataset=None,\n use_posterior = True,\n k_first = 1,\n k_last = 10,\n t_threshold = 10,):\n \"\"\"\n Compute training losses for a single timestep.\n\n :param model: the model to evaluate loss on.\n :param x_start: the [N x C x ...] tensor of inputs.\n :param t: a batch of timestep indices.\n :param model_kwargs: if not None, a dict of extra keyword arguments to\n pass to the model. This can be used for conditioning.\n :param noise: if specified, the specific Gaussian noise to try to remove.\n :return: a dict with the key \"loss\" containing a tensor of shape [N].\n Some mean or variance settings may also have other keys.\n \"\"\"\n\n # enc = model.model._modules['module']\n model = self._wrap_model(model)\n \n enc = model.model\n mask = model_kwargs['y']['mask']\n get_xyz = lambda sample: enc.rot2xyz(sample, mask=None, pose_rep=enc.pose_rep, translation=enc.translation,\n glob=enc.glob,\n # jointstype='vertices', # 3.4 iter/sec # USED ALSO IN MotionCLIP\n jointstype='smpl', # 3.4 iter/sec\n vertstrans=False)\n\n if model_kwargs is None:\n model_kwargs = {}\n if noise is None:\n noise = th.randn_like(x_start)\n x_t = self.q_sample(x_start, t, noise=noise, model_kwargs=model_kwargs)\n \n #assert k_first == 1, \"k_first must be 1, {}\".format(k_first)\n #assert k_last == 10, \"k_last must be 10, {}\".format(k_last)\n assert use_posterior == True, \"use_posterior must be True, {}\".format(use_posterior)\n if use_posterior:\n '''\n # loss-guided condition in training time\n if t[0] >= t_threshold:\n assert (t >= t_threshold).all(), f\"all t should be >=10 or <10 : t={t}\"\n num_condition = k_first # else k_last\n else:\n num_condition = k_last\n assert (t < t_threshold).all(), f\"all t should be >=10 or <10 : t={t}\"\n '''\n num_condition = k_first\n x_t = self.condition_mean_bfgs(x_t, num_condition, model_kwargs=model_kwargs)\n\n terms = {}\n if self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:\n model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)\n\n target = {\n ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(\n x_start=x_start, x_t=x_t, t=t\n )[0],\n ModelMeanType.START_X: x_start,\n ModelMeanType.EPSILON: noise,\n }[self.model_mean_type]\n\n assert model_output.shape == target.shape == x_start.shape, \"model_output {}, target {}, x_start {}\".format(model_output.shape ,target.shape ,x_start.shape) # [bs, njoints, nfeats, nframes]\n\n terms[\"rot_mse\"] = self.masked_l2(target, model_output, mask) # mean_flat(rot_mse)\n\n terms[\"loss\"] = terms[\"rot_mse\"] + terms.get('vb', 0.) +\\\n (self.lambda_vel * terms.get('vel_mse', 0.)) +\\\n (self.lambda_rcxyz * terms.get('rcxyz_mse', 0.)) + \\\n (self.lambda_fc * terms.get('fc', 0.))\n else:\n raise NotImplementedError(self.loss_type)\n\n return terms"},{"identifier":"wrap_model","path":"model/cfg_sampler.py","snippet":"def wrap_model(model, args):\n if args.guidance_param not in [0., 1.]:\n return ClassifierFreeSampleModel(model) # wrapping model with the classifier-free sampler\n elif args.guidance_param == 0:\n return UnconditionedModel(model)\n else:\n return model"},{"identifier":"MDM","path":"model/mdm.py","snippet":"class MDM(nn.Module):\n def __init__(self, modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\n latent_dim=256, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1,\n ablation=None, activation=\"gelu\", legacy=False, data_rep='rot6d', dataset='amass', clip_dim=512,\n arch='trans_enc', emb_trans_dec=False, clip_version=None, **kargs):\n super().__init__()\n\n self.legacy = legacy\n self.modeltype = modeltype\n self.njoints = njoints\n self.nfeats = nfeats\n self.num_actions = num_actions\n self.data_rep = data_rep\n self.dataset = dataset\n\n self.pose_rep = pose_rep\n self.glob = glob\n self.glob_rot = glob_rot\n self.translation = translation\n\n self.latent_dim = latent_dim\n\n self.ff_size = ff_size\n self.num_layers = num_layers\n self.num_heads = num_heads\n self.dropout = dropout\n\n self.ablation = ablation\n self.activation = activation\n self.clip_dim = clip_dim\n self.action_emb = kargs.get('action_emb', None)\n\n self.input_feats = self.njoints * self.nfeats\n\n self.normalize_output = kargs.get('normalize_encoder_output', False)\n\n self.cond_mode = kargs.get('cond_mode', 'no_cond')\n self.cond_mask_prob = kargs.get('cond_mask_prob', 0.)\n self.arch = arch\n self.gru_emb_dim = self.latent_dim if self.arch == 'gru' else 0\n self.input_process = InputProcess(self.data_rep, self.input_feats+self.gru_emb_dim, self.latent_dim)\n\n self.sequence_pos_encoder = PositionalEncoding(self.latent_dim, self.dropout)\n self.emb_trans_dec = emb_trans_dec\n\n if self.arch == 'trans_enc':\n print(\"TRANS_ENC init\")\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\n nhead=self.num_heads,\n dim_feedforward=self.ff_size,\n dropout=self.dropout,\n activation=self.activation)\n\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\n num_layers=self.num_layers)\n elif self.arch == 'trans_dec':\n print(\"TRANS_DEC init\")\n seqTransDecoderLayer = nn.TransformerDecoderLayer(d_model=self.latent_dim,\n nhead=self.num_heads,\n dim_feedforward=self.ff_size,\n dropout=self.dropout,\n activation=activation)\n self.seqTransDecoder = nn.TransformerDecoder(seqTransDecoderLayer,\n num_layers=self.num_layers)\n elif self.arch == 'gru':\n print(\"GRU init\")\n self.gru = nn.GRU(self.latent_dim, self.latent_dim, num_layers=self.num_layers, batch_first=True)\n else:\n raise ValueError('Please choose correct architecture [trans_enc, trans_dec, gru]')\n\n self.embed_timestep = TimestepEmbedder(self.latent_dim, self.sequence_pos_encoder)\n\n if self.cond_mode != 'no_cond':\n if 'text' in self.cond_mode:\n self.embed_text = nn.Linear(self.clip_dim, self.latent_dim)\n print('EMBED TEXT')\n print('Loading CLIP...')\n self.clip_version = clip_version\n self.clip_model = self.load_and_freeze_clip(clip_version)\n if 'action' in self.cond_mode:\n self.embed_action = EmbedAction(self.num_actions, self.latent_dim)\n print('EMBED ACTION')\n\n self.output_process = OutputProcess(self.data_rep, self.input_feats, self.latent_dim, self.njoints,\n self.nfeats)\n\n self.rot2xyz = Rotation2xyz(device='cpu', dataset=self.dataset)\n\n def parameters_wo_clip(self):\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\n\n def load_and_freeze_clip(self, clip_version):\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\n jit=False) # Must set jit=False for training\n clip.model.convert_weights(\n clip_model) # Actually this line is unnecessary since clip by default already on float16\n\n # Freeze CLIP weights\n clip_model.eval()\n for p in clip_model.parameters():\n p.requires_grad = False\n\n return clip_model\n\n def mask_cond(self, cond, force_mask=False):\n bs, d = cond.shape\n if force_mask:\n return torch.zeros_like(cond)\n elif self.training and self.cond_mask_prob > 0.:\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_mask_prob).view(bs, 1) # 1-> use null_cond, 0-> use real cond\n return cond * (1. - mask)\n else:\n return cond\n\n def encode_text(self, raw_text):\n # raw_text - list (batch_size length) of strings with input text prompts\n device = next(self.parameters()).device\n max_text_len = 20 if self.dataset in ['humanml', 'kit'] else None # Specific hardcoding for humanml dataset\n if max_text_len is not None:\n default_context_length = 77\n context_length = max_text_len + 2 # start_token + 20 + end_token\n assert context_length < default_context_length\n texts = clip.tokenize(raw_text, context_length=context_length, truncate=True).to(device) # [bs, context_length] # if n_tokens > context_length -> will truncate\n # print('texts', texts.shape)\n zero_pad = torch.zeros([texts.shape[0], default_context_length-context_length], dtype=texts.dtype, device=texts.device)\n texts = torch.cat([texts, zero_pad], dim=1)\n # print('texts after pad', texts.shape, texts)\n else:\n texts = clip.tokenize(raw_text, truncate=True).to(device) # [bs, context_length] # if n_tokens > 77 -> will truncate\n return self.clip_model.encode_text(texts).float()\n\n def forward(self, x, timesteps, y=None):\n \"\"\"\n x: [batch_size, njoints, nfeats, max_frames], denoted x_t in the paper\n timesteps: [batch_size] (int)\n \"\"\"\n bs, njoints, nfeats, nframes = x.shape\n emb = self.embed_timestep(timesteps) # [1, bs, d]\n\n force_mask = y.get('uncond', False)\n if 'text' in self.cond_mode:\n enc_text = self.encode_text(y['text'])\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\n if 'action' in self.cond_mode:\n action_emb = self.embed_action(y['action'])\n emb += self.mask_cond(action_emb, force_mask=force_mask)\n\n if self.arch == 'gru':\n x_reshaped = x.reshape(bs, njoints*nfeats, 1, nframes)\n emb_gru = emb.repeat(nframes, 1, 1) #[#frames, bs, d]\n emb_gru = emb_gru.permute(1, 2, 0) #[bs, d, #frames]\n emb_gru = emb_gru.reshape(bs, self.latent_dim, 1, nframes) #[bs, d, 1, #frames]\n x = torch.cat((x_reshaped, emb_gru), axis=1) #[bs, d+joints*feat, 1, #frames]\n\n x = self.input_process(x)\n\n if self.arch == 'trans_enc':\n # adding the timestep embed\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\n output = self.seqTransEncoder(xseq)[1:] # , src_key_padding_mask=~maskseq) # [seqlen, bs, d]\n\n elif self.arch == 'trans_dec':\n if self.emb_trans_dec:\n xseq = torch.cat((emb, x), axis=0)\n else:\n xseq = x\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\n if self.emb_trans_dec:\n output = self.seqTransDecoder(tgt=xseq, memory=emb)[1:] # [seqlen, bs, d] # FIXME - maybe add a causal mask\n else:\n output = self.seqTransDecoder(tgt=xseq, memory=emb)\n elif self.arch == 'gru':\n xseq = x\n xseq = self.sequence_pos_encoder(xseq) # [seqlen, bs, d]\n output, _ = self.gru(xseq)\n\n output = self.output_process(output) # [bs, njoints, nfeats, nframes]\n return output\n\n\n def _apply(self, fn):\n super()._apply(fn)\n self.rot2xyz.smpl_model._apply(fn)\n\n\n def train(self, *args, **kwargs):\n super().train(*args, **kwargs)\n self.rot2xyz.smpl_model.train(*args, **kwargs)"},{"identifier":"ControlMDM","path":"model/ControlMDM.py","snippet":"class ControlMDM(MDM):\n\n def __init__(self, modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\n latent_dim=256, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1,\n ablation=None, activation=\"gelu\", legacy=False, data_rep='rot6d', dataset='amass', clip_dim=512,\n arch='trans_enc', emb_trans_dec=False, clip_version=None, args=None, **kargs):\n\n super(ControlMDM, self).__init__(modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\n latent_dim, ff_size, num_layers, num_heads, dropout,\n ablation, activation, legacy, data_rep, dataset, clip_dim,\n arch, emb_trans_dec, clip_version, **kargs)\n self.args = args\n self.num_layers = num_layers\n self.multi_person = args.multi_person\n self.upper_orientation_index = [0, 16, 17] # root, l_shoulder, r_shoulder\n self.lower_orientation_index = [0, 1, 2] # root, l_hip, r_hip\n\n # linear layers init with zeros\n if self.dataset == 'kit':\n self.first_zero_linear = nn.Linear(21*3*2 + 2*3, self.latent_dim)\n elif self.dataset == 'humanml':\n self.first_zero_linear = nn.Linear(22*3*2 + 2*3, self.latent_dim)\n else:\n raise NotImplementedError('Supporting only kit and humanml dataset, got {}'.format(self.dataset))\n \n nn.init.zeros_(self.first_zero_linear.weight)\n nn.init.zeros_(self.first_zero_linear.bias)\n self.mid_zero_linear = nn.ModuleList(\n [nn.Linear(self.latent_dim, self.latent_dim) for _ in range(self.num_layers)])\n for m in self.mid_zero_linear:\n nn.init.zeros_(m.weight)\n nn.init.zeros_(m.bias)\n\n if self.arch == 'trans_enc':\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\n nhead=self.num_heads,\n dim_feedforward=self.ff_size,\n dropout=self.dropout,\n activation=self.activation)\n del self.seqTransEncoder\n self.seqTransEncoder_mdm = TransformerEncoder(seqTransEncoderLayer,\n num_layers=self.num_layers)\n self.seqTransEncoder_control = TransformerEncoder(seqTransEncoderLayer,\n num_layers=self.num_layers)\n else:\n raise ValueError('Supporting only trans_enc arch.')\n\n self.freeze_block(self.input_process)\n self.freeze_block(self.sequence_pos_encoder)\n self.freeze_block(self.seqTransEncoder_mdm)\n self.freeze_block(self.embed_timestep)\n if 'text' in self.cond_mode:\n self.freeze_block(self.embed_text)\n self.freeze_block(self.output_process)\n\n def inv_transform(self, data):\n assert self.std is not None and self.mean is not None\n #assert data.requires_grad == True\n std = torch.tensor(self.std, dtype=data.dtype, device=data.device, requires_grad=False)\n mean = torch.tensor(self.mean, dtype=data.dtype, device=data.device, requires_grad=False)\n output = torch.add(torch.mul(data, std), mean)\n return output\n \n def compute_triangle_normals(self, triangles):\n # Compute the vectors from the first point to the other two points\n v1 = triangles[:,:, 1] - triangles[:, :,0]\n v2 = triangles[:,:, 2] - triangles[:,:,0]\n\n # Compute the cross product of v1 and v2 to get the normal vectors\n normals = torch.cross(v2, v1, dim=-1)\n\n # Normalize the normal vectors to unit length\n normals = nn.functional.normalize(normals, dim=-1)\n return normals\n \n def humanml_to_global_joint(self, x):\n n_joints = 22 if x.shape[1] == 263 else 21\n curr_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\n assert curr_joint.shape[1] == 1\n curr_joint = recover_from_ric(curr_joint, n_joints)\n curr_joint = curr_joint.view(-1, *curr_joint.shape[2:]).permute(0, 2, 3, 1)\n # change root positions for multi-person purpose\n if self.multi_person:\n curr_joint[1::2, :,2,:] *= -1\n curr_joint[1::2, :,0,:] *= -1\n curr_joint[1::2, :,2,:] += 2\n\n # more than 3 people\n #curr_joint[1, :,2,:] *= -1\n #curr_joint[1, :,0,:] *= -1\n #curr_joint[1, :,2,:] += 2\n #curr_joint[2, :,0,:] += 1\n return curr_joint\n\n def forward(self, x, timesteps, y=None):\n bs, njoints, nfeats, seqlen = x.shape\n control_bs, n_global_joints, xyz_dim, control_frames = y['global_joint'].shape\n assert bs == control_bs and seqlen == control_frames, \"bs {} != {} or seqlen {} != {}\".format(bs, control_bs, seqlen, control_frames)\n assert xyz_dim ==3, \"xyz_dim {} != 3\".format(xyz_dim)\n # prepare global joints for controlmdm\n curr_joint = self.humanml_to_global_joint(x).clone().detach() # [bs, njoints, 3, seqlen]\n curr_joint.requires_grad = False\n\n # Build embedding vector\n emb = self.embed_timestep(timesteps) # [1, bs, d]\n\n force_mask = y.get('uncond', False)\n if 'text' in self.cond_mode:\n enc_text = self.encode_text(y['text'])\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\n if 'action' in self.cond_mode:\n action_emb = self.embed_action(y['action'])\n emb += self.mask_cond(action_emb, force_mask=force_mask)\n\n # Embed motion to latent space (frame by frame)\n x = self.input_process(x) #[seqlen, bs, d]\n\n # adding the timestep embed\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\n\n # controlmdm\n # orientation\n upper_triangles = curr_joint[:,self.upper_orientation_index,:,:].permute(3,0,1,2) # [seqlen, bs, 3, 3]\n lower_triangles = curr_joint[:,self.lower_orientation_index,:,:].permute(3,0,1,2) # [seqlen, bs, 3, 3]\n upper_orientation = self.compute_triangle_normals(upper_triangles) # [seqlen, bs, 3]\n lower_orientation = self.compute_triangle_normals(lower_triangles) # [seqlen, bs, 3]\n\n # relative position to joint\n '''\n relative_position = torch.zeros_like(curr_joint, device = xseq.device, dtype=torch.float32) # [bs, njoints, 3, seqlen]\n relative_position[1::2,:,:,:] = ((y['global_joint'][::2,:,:,:].unsqueeze(1).float() - \\\n curr_joint[:,1::2,:,:].unsqueeze(2))*y['global_joint_mask'][::2,:,:,:].bool().float()).float().sum(1)\n relative_position[::2,:,:,:] = ((y['global_joint'][1::2,:,:,:].unsqueeze(1).float() - \\\n curr_joint[:,::2,:,:].unsqueeze(2))*y['global_joint_mask'][1::2,:,:,:].bool().float()).float().sum(1)\n '''\n relative_position = ((y['global_joint'].float() - curr_joint)*y['global_joint_mask'].bool().float()).float() # [bs, njoints, 3, seqlen]\n relative_position = relative_position.permute(3, 0, 1, 2).reshape(control_frames, control_bs, -1) # [seqlen, bs, 22*3]\n\n # relative position to root\n relative_root = ((y['global_joint'].float() - curr_joint[:,[0],:,:])*y['global_joint_mask'].bool().float()).float() # [bs, njoints, 3, seqlen]\n relative_root = relative_root.permute(3, 0, 1, 2).reshape(control_frames, control_bs, -1) # [seqlen, bs, 22*3]\n global_joint_feat = torch.cat((relative_position, relative_root, upper_orientation, lower_orientation), axis=-1) # [seqlen, bs, 22*3 *2 +3 +3]\n \n global_joint_feat = self.first_zero_linear(global_joint_feat) # [seqlen, bs, d]\n control_input = xseq + torch.cat((torch.zeros_like(emb, device = xseq.device, dtype=torch.float32), global_joint_feat), axis=0) # [seqlen+1, bs, d]\n control_output_list = self.seqTransEncoder_control.return_all_layers(control_input) # [seqlen+1, bs, d]\n for i in range(self.num_layers):\n control_output_list[i] = self.mid_zero_linear[i](control_output_list[i])\n \n output = self.seqTransEncoder_mdm.forward_with_condition(xseq, control_output_list)[1:] # [seqlen, bs, d]\n output = self.output_process(output) # [bs, njoints, nfeats, seqlen]\n return output\n\n def trainable_parameters(self):\n return [p for name, p in self.named_parameters() if p.requires_grad]\n # return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\n \n def trainable_parameter_names(self):\n return [name for name, p in self.named_parameters() if p.requires_grad]\n\n def freeze_block(self, block):\n block.eval()\n for p in block.parameters():\n p.requires_grad = False\n\n def unfreeze_block(self, block):\n block.train()\n for p in block.parameters():\n p.requires_grad = True\n \n def forward_without_control(self, x, timesteps, y=None): #\n # Build embedding vector\n emb = self.embed_timestep(timesteps) # [1, bs, d]\n\n force_mask = y.get('uncond', False)\n if 'text' in self.cond_mode:\n enc_text = self.encode_text(y['text'])\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\n if 'action' in self.cond_mode:\n action_emb = self.embed_action(y['action'])\n emb += self.mask_cond(action_emb, force_mask=force_mask)\n\n # Embed motion to latent space (frame by frame)\n x = self.input_process(x) #[seqlen, bs, d]\n # adding the timestep embed\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\n output = self.seqTransEncoder_mdm(xseq)[1:] # [seqlen, bs, d]\n output = self.output_process(output) # [bs, njoints, nfeats, seqlen]\n return output"},{"identifier":"gaussian_diffusion","path":"diffusion/gaussian_diffusion.py","snippet":"def get_named_beta_schedule(schedule_name, num_diffusion_timesteps, scale_betas=1.):\ndef betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):\n def is_vb(self):\n def __init__(\n self,\n *,\n betas,\n model_mean_type,\n model_var_type,\n loss_type,\n rescale_timesteps=False,\n lambda_rcxyz=0.,\n lambda_vel=0.,\n lambda_pose=1.,\n lambda_orient=1.,\n lambda_loc=1.,\n data_rep='rot6d',\n lambda_root_vel=0.,\n lambda_vel_rcxyz=0.,\n lambda_fc=0.,\n batch_size=32,\n multi_train_mode=None,\n bfgs_times_first = 5,\n bfgs_times_last = 10,\n bfgs_interval = 1,\n ):\n def masked_l2(self, a, b, mask):\n def q_mean_variance(self, x_start, t):\n def q_sample(self, x_start, t, noise=None, model_kwargs=None):\n def q_posterior_mean_variance(self, x_start, x_t, t):\n def p_mean_variance(\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None\n ):\n def process_xstart(x):\n def _predict_xstart_from_eps(self, x_t, t, eps):\n def _predict_xstart_from_xprev(self, x_t, t, xprev):\n def _predict_eps_from_xstart(self, x_t, t, pred_xstart):\n def _scale_timesteps(self, t):\n def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\n def condition_mean_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\n def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\n def condition_score_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\n def p_sample(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n const_noise=False,\n ):\n def p_sample_with_grad(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n ):\n def p_sample_loop(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n dump_steps=None,\n const_noise=False,\n arb_len=False,\n use_posterior = False,\n ):\n def p_sample_loop_progressive(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n predict_two_person=False,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n const_noise=False,\n use_posterior = False,\n ):\n def p_sample_multi(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n const_noise=False,\n ):\n def handle_sample(_x, _out, _cond_fn, _t, _const_noise):\n def ddim_sample(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n eta=0.0,\n ):\n def ddim_sample_with_grad(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n eta=0.0,\n ):\n def ddim_reverse_sample(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n model_kwargs=None,\n eta=0.0,\n ):\n def ddim_sample_loop(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n eta=0.0,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n dump_steps=None,\n const_noise=False,\n ):\n def ddim_sample_loop_progressive(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n eta=0.0,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n ):\n def plms_sample(\n self,\n model,\n x,\n t,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n cond_fn_with_grad=False,\n order=2,\n old_out=None,\n ):\n def get_model_output(x, t):\n def plms_sample_loop(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n order=2,\n ):\n def plms_sample_loop_progressive(\n self,\n model,\n shape,\n noise=None,\n clip_denoised=True,\n denoised_fn=None,\n cond_fn=None,\n model_kwargs=None,\n device=None,\n progress=False,\n skip_timesteps=0,\n init_image=None,\n randomize_class=False,\n cond_fn_with_grad=False,\n order=2,\n ):\n def _vb_terms_bpd(\n self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None\n ):\n def training_losses(self, model, x_start, t, model_kwargs=None, noise=None, dataset=None):\n def fc_loss_rot_repr(self, gt_xyz, pred_xyz, mask):\n def to_np_cpu(x):\n def foot_contact_loss_humanml3d(self, target, model_output):\n def velocity_consistency_loss_humanml3d(self, target, model_output):\n def _prior_bpd(self, x_start):\n def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\nclass ModelMeanType(enum.Enum):\nclass ModelVarType(enum.Enum):\nclass LossType(enum.Enum):\nclass GaussianDiffusion:\n PREVIOUS_X = enum.auto() # the model predicts x_{t-1}\n START_X = enum.auto() # the model predicts x_0\n EPSILON = enum.auto() # the model predicts epsilon\n LEARNED = enum.auto()\n FIXED_SMALL = enum.auto()\n FIXED_LARGE = enum.auto()\n LEARNED_RANGE = enum.auto()\n MSE = enum.auto() # use raw MSE loss (and KL when learning variances)\n RESCALED_MSE = (\n enum.auto()\n ) # use raw MSE loss (with RESCALED_KL when learning variances)\n KL = enum.auto() # use the variational lower-bound\n RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB\n B, C = x.shape[:2]\n B, C = x_t.shape[:2]"},{"identifier":"SpacedDiffusion","path":"diffusion/respace.py","snippet":"class SpacedDiffusion(GaussianDiffusion):\n \"\"\"\n A diffusion process which can skip steps in a base diffusion process.\n\n :param use_timesteps: a collection (sequence or set) of timesteps from the\n original diffusion process to retain.\n :param kwargs: the kwargs to create the base diffusion process.\n \"\"\"\n\n def __init__(self, use_timesteps, **kwargs):\n self.use_timesteps = set(use_timesteps)\n self.timestep_map = []\n self.original_num_steps = len(kwargs[\"betas\"])\n\n base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa\n last_alpha_cumprod = 1.0\n new_betas = []\n for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):\n if i in self.use_timesteps:\n new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)\n last_alpha_cumprod = alpha_cumprod\n self.timestep_map.append(i)\n kwargs[\"betas\"] = np.array(new_betas)\n super().__init__(**kwargs)\n\n def p_mean_variance(\n self, model, *args, **kwargs\n ): # pylint: disable=signature-differs\n return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)\n\n def training_losses(\n self, model, *args, **kwargs\n ): # pylint: disable=signature-differs\n return super().training_losses(self._wrap_model(model), *args, **kwargs)\n\n def condition_mean(self, cond_fn, *args, **kwargs):\n return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)\n\n def condition_score(self, cond_fn, *args, **kwargs):\n return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)\n\n def _wrap_model(self, model):\n if isinstance(model, _WrappedModel):\n return model\n return _WrappedModel(\n model, self.timestep_map, self.rescale_timesteps, self.original_num_steps\n )\n\n def _scale_timesteps(self, t):\n # Scaling is done by the wrapped model.\n return t"},{"identifier":"space_timesteps","path":"diffusion/respace.py","snippet":"def space_timesteps(num_timesteps, section_counts):\n \"\"\"\n Create a list of timesteps to use from an original diffusion process,\n given the number of timesteps we want to take from equally-sized portions\n of the original process.\n\n For example, if there's 300 timesteps and the section counts are [10,15,20]\n then the first 100 timesteps are strided to be 10 timesteps, the second 100\n are strided to be 15 timesteps, and the final 100 are strided to be 20.\n\n If the stride is a string starting with \"ddim\", then the fixed striding\n from the DDIM paper is used, and only one section is allowed.\n\n :param num_timesteps: the number of diffusion steps in the original\n process to divide up.\n :param section_counts: either a list of numbers, or a string containing\n comma-separated numbers, indicating the step count\n per section. As a special case, use \"ddimN\" where N\n is a number of steps to use the striding from the\n DDIM paper.\n :return: a set of diffusion steps from the original process to use.\n \"\"\"\n if isinstance(section_counts, str):\n if section_counts.startswith(\"ddim\"):\n desired_count = int(section_counts[len(\"ddim\") :])\n for i in range(1, num_timesteps):\n if len(range(0, num_timesteps, i)) == desired_count:\n return set(range(0, num_timesteps, i))\n raise ValueError(\n f\"cannot create exactly {num_timesteps} steps with an integer stride\"\n )\n section_counts = [int(x) for x in section_counts.split(\",\")]\n size_per = num_timesteps // len(section_counts)\n extra = num_timesteps % len(section_counts)\n start_idx = 0\n all_steps = []\n for i, section_count in enumerate(section_counts):\n size = size_per + (1 if i < extra else 0)\n if size < section_count:\n raise ValueError(\n f\"cannot divide section of {size} steps into {section_count}\"\n )\n if section_count <= 1:\n frac_stride = 1\n else:\n frac_stride = (size - 1) / (section_count - 1)\n cur_idx = 0.0\n taken_steps = []\n for _ in range(section_count):\n taken_steps.append(start_idx + round(cur_idx))\n cur_idx += frac_stride\n all_steps += taken_steps\n start_idx += size\n return set(all_steps)"}],"string":"[\n {\n \"identifier\": \"ControlGaussianDiffusion\",\n \"path\": \"diffusion/control_diffusion.py\",\n \"snippet\": \"class ControlGaussianDiffusion(SpacedDiffusion):\\n\\n def inv_transform(self, data):\\n assert self.std is not None and self.mean is not None\\n #assert data.requires_grad == True\\n std = th.tensor(self.std, dtype=data.dtype, device=data.device, requires_grad=False)\\n mean = th.tensor(self.mean, dtype=data.dtype, device=data.device, requires_grad=False)\\n output = th.add(th.mul(data, std), mean)\\n return output\\n \\n def q_sample(self, x_start, t, noise=None, model_kwargs=None):\\n \\\"\\\"\\\"\\n overrides q_sample to use the inpainting mask\\n \\n same usage as in GaussianDiffusion\\n \\\"\\\"\\\"\\n if noise is None:\\n noise = th.randn_like(x_start)\\n assert noise.shape == x_start.shape\\n\\n bs, feat, _, frames = noise.shape\\n noise *= 1. #- model_kwargs['y']['inpainting_mask']\\n\\n return (\\n _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start\\n + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)\\n * noise\\n )\\n \\n def global_joint_bfgs_optimize(self, x, model_kwargs=None):\\n assert self.model_mean_type == ModelMeanType.START_X, 'This feature supports only X_start pred for mow!'\\n pred_joint = self.humanml_to_global_joint(x)\\n cond_joint = model_kwargs['y']['global_joint']\\n mask = model_kwargs['y']['global_joint_mask']\\n pred_joint = th.masked_select(pred_joint, mask.bool())\\n cond_joint = th.masked_select(cond_joint, mask.bool())\\n assert pred_joint.shape == cond_joint.shape, f\\\"pred_joint: {pred_joint.shape}, cond_joint: {cond_joint.shape}\\\"\\n loss = self.mse_loss(pred_joint, cond_joint)\\n return loss\\n \\n def humanml_to_global_joint(self, x):\\n n_joints = 22 if x.shape[1] == 263 else 21\\n pred_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\\n assert pred_joint.shape[1] == 1\\n pred_joint = recover_from_ric(pred_joint, n_joints)\\n pred_joint = pred_joint.view(-1, *pred_joint.shape[2:]).permute(0, 2, 3, 1)\\n return pred_joint\\n \\n def global_joint_position_conditioning(self, x, model_kwargs=None):\\n n_joints = 22 if x.shape[1] == 263 else 21\\n assert self.model_mean_type == ModelMeanType.START_X, 'This feature supports only X_start pred for mow!'\\n pred_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\\n pred_joint = recover_from_ric(pred_joint, n_joints)\\n pred_joint = pred_joint.view(-1, *pred_joint.shape[2:]).permute(0, 2, 3, 1)\\n #pred_joint.requires_grad = True\\n assert pred_joint.shape == model_kwargs['y']['global_joint'].shape == model_kwargs['y']['global_joint_mask'].shape, f\\\"pred_joint: {pred_joint.shape}, global_joint: {model_kwargs['y']['global_joint'].shape}, global_joint_mask: {model_kwargs['y']['global_joint_mask'].shape}\\\"\\n loss = self.global_joint_condition_loss(pred_joint, model_kwargs['y']['global_joint'], model_kwargs['y']['global_joint_mask'])\\n diff_scale = ((pred_joint.clamp(min=1e-4) - model_kwargs['y']['global_joint'].clamp(min=1e-4)).abs() / model_kwargs['y']['global_joint'].clamp(min=1e-4).abs()).mean().item()\\n #loss.requires_grad = True\\n gradient = th.autograd.grad(loss, x, \\n grad_outputs=th.ones_like(loss),\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True)[0]\\n return gradient.clone().detach(), loss.item(), diff_scale\\n\\n def p_sample(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n const_noise=False,\\n use_posterior=False,\\n ):\\n \\\"\\\"\\\"\\n overrides p_sample to use the inpainting mask\\n \\n same usage as in GaussianDiffusion\\n \\\"\\\"\\\"\\n #assert use_posterior == False\\n p_mean_variance_func = self.p_mean_variance_bfgs_posterior if use_posterior else self.p_mean_variance_bfgs_x0\\n out = p_mean_variance_func(\\n model,\\n x,\\n t,\\n clip_denoised=clip_denoised,\\n denoised_fn=denoised_fn,\\n model_kwargs=model_kwargs,\\n k_first = self.bfgs_times_first,\\n k_last = self.bfgs_times_last,\\n )\\n \\n noise = th.randn_like(x)\\n if const_noise:\\n noise = noise[[0]].repeat(x.shape[0], 1, 1, 1)\\n\\n nonzero_mask = (\\n (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))\\n ) # no noise when t == 0\\n\\n sample = out[\\\"mean\\\"] + nonzero_mask * th.exp(0.5 * out[\\\"log_variance\\\"]) * noise\\n \\n return {\\\"sample\\\": sample, \\\"pred_xstart\\\": out[\\\"pred_xstart\\\"]}\\n \\n def condition_mean_with_grad(self, cond_fn, x_mean, x_var, t, strength, model_kwargs=None):\\n \\\"\\\"\\\"\\n Compute the mean for the previous step, given a function cond_fn that\\n computes the gradient of a conditional log probability with respect to\\n x. In particular, cond_fn computes grad(log(p(y|x))), and we want to\\n condition on y.\\n\\n This uses the conditioning strategy from Sohl-Dickstein et al. (2015).\\n \\\"\\\"\\\"\\n with th.enable_grad():\\n x_mean = x_mean.clone().detach().requires_grad_(True)\\n gradient, loss_value, diff_scale = cond_fn(x_mean, model_kwargs) # p_mean_var[\\\"mean\\\"]\\n gradient_guidance = - strength * gradient.float() # x_var.clamp(min = 0.01) \\n new_mean = (x_mean + gradient_guidance).clone().detach()\\n return new_mean, loss_value, gradient_guidance.clone().detach().abs().cpu(), x_mean.clone().detach().abs().cpu(), diff_scale\\n\\n\\n def condition_mean_bfgs(self, x_mean, num_condition, model_kwargs=None):\\n \\\"\\\"\\\"\\n Compute the mean for the previous step, given a function cond_fn that\\n computes the gradient of a conditional log probability with respect to\\n x.\\n\\n This uses the conditioning strategy from Sohl-Dickstein et al. (2015).\\n \\\"\\\"\\\"\\n \\n with th.enable_grad():\\n x_mean = x_mean.clone().detach().contiguous().requires_grad_(True)\\n def closure():\\n lbfgs.zero_grad()\\n objective = self.global_joint_bfgs_optimize(x_mean, model_kwargs)\\n objective.backward()\\n return objective\\n lbfgs = optim.LBFGS([x_mean],\\n history_size=10, \\n max_iter=4, \\n line_search_fn=\\\"strong_wolfe\\\")\\n for _ in range(num_condition):\\n lbfgs.step(closure)\\n #loss_value = self.global_joint_bfgs_optimize(x_mean, model_kwargs).item()\\n return x_mean #, loss_value\\n\\n def p_mean_variance_bfgs_x0(\\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, \\n k_first = 1,\\n k_last = 10,\\n t_threshold = 10,\\n ):\\n \\\"\\\"\\\"\\n Apply the model to get p(x_{t-1} | x_t), as well as a prediction of\\n the initial x, x_0.\\n\\n :param model: the model, which takes a signal and a batch of timesteps\\n as input.\\n :param x: the [N x C x ...] tensor at time t.\\n :param t: a 1-D Tensor of timesteps.\\n :param clip_denoised: if True, clip the denoised signal into [-1, 1].\\n :param denoised_fn: if not None, a function which applies to the\\n x_start prediction before it is used to sample. Applies before\\n clip_denoised.\\n :param model_kwargs: if not None, a dict of extra keyword arguments to\\n pass to the model. This can be used for conditioning.\\n :return: a dict with the following keys:\\n - 'mean': the model mean output.\\n - 'variance': the model variance output.\\n - 'log_variance': the log of 'variance'.\\n - 'pred_xstart': the prediction for x_0.\\n \\\"\\\"\\\"\\n if model_kwargs is None:\\n model_kwargs = {}\\n\\n B, C = x.shape[:2]\\n assert t.shape == (B,)\\n original_model_output = model(x, self._scale_timesteps(t), **model_kwargs)\\n model_output = original_model_output.clone().detach()\\n\\n if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:\\n assert model_output.shape == (B, C * 2, *x.shape[2:])\\n model_output, model_var_values = th.split(model_output, C, dim=1)\\n if self.model_var_type == ModelVarType.LEARNED:\\n model_log_variance = model_var_values\\n model_variance = th.exp(model_log_variance)\\n else:\\n min_log = _extract_into_tensor(\\n self.posterior_log_variance_clipped, t, x.shape\\n )\\n max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)\\n # The model_var_values is [-1, 1] for [min_var, max_var].\\n frac = (model_var_values + 1) / 2\\n model_log_variance = frac * max_log + (1 - frac) * min_log\\n model_variance = th.exp(model_log_variance)\\n else:\\n model_variance, model_log_variance = {\\n # for fixedlarge, we set the initial (log-)variance like so\\n # to get a better decoder log likelihood.\\n ModelVarType.FIXED_LARGE: (\\n np.append(self.posterior_variance[1], self.betas[1:]),\\n np.log(np.append(self.posterior_variance[1], self.betas[1:])),\\n ),\\n ModelVarType.FIXED_SMALL: (\\n self.posterior_variance,\\n self.posterior_log_variance_clipped,\\n ),\\n }[self.model_var_type]\\n\\n model_variance = _extract_into_tensor(model_variance, t, x.shape)\\n model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)\\n\\n\\n # loss-guided condition\\n #assert k_first ==1, \\\"k_first must be 1, {}\\\".format(k_first)\\n num_condition = k_first if t[0] >= t_threshold else k_last # t[0] count from 1000 to 1, assume all t are equal\\n model_output = self.condition_mean_bfgs(model_output, num_condition, model_kwargs=model_kwargs) # , loss_value\\n\\n def process_xstart(x):\\n if denoised_fn is not None:\\n x = denoised_fn(x)\\n if clip_denoised:\\n # print('clip_denoised', clip_denoised)\\n return x.clamp(-1, 1)\\n return x\\n\\n if self.model_mean_type == ModelMeanType.PREVIOUS_X:\\n pred_xstart = process_xstart(\\n self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)\\n )\\n model_mean = model_output\\n elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: # THIS IS US!\\n if self.model_mean_type == ModelMeanType.START_X:\\n pred_xstart = process_xstart(model_output)\\n else:\\n pred_xstart = process_xstart(\\n self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)\\n )\\n model_mean, _, _ = self.q_posterior_mean_variance(\\n x_start=pred_xstart, x_t=x, t=t\\n )\\n else:\\n raise NotImplementedError(self.model_mean_type)\\n\\n assert (\\n model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape\\n )\\n\\n \\n return {\\n \\\"mean\\\": model_mean,\\n \\\"variance\\\": model_variance,\\n \\\"log_variance\\\": model_log_variance,\\n \\\"pred_xstart\\\": pred_xstart,\\n }\\n \\n def p_mean_variance_bfgs_posterior(\\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, \\n k_first = 1,\\n k_last = 10,\\n t_threshold = 10,\\n ):\\n \\\"\\\"\\\"\\n Apply the model to get p(x_{t-1} | x_t), as well as a prediction of\\n the initial x, x_0.\\n\\n :param model: the model, which takes a signal and a batch of timesteps\\n as input.\\n :param x: the [N x C x ...] tensor at time t.\\n :param t: a 1-D Tensor of timesteps.\\n :param clip_denoised: if True, clip the denoised signal into [-1, 1].\\n :param denoised_fn: if not None, a function which applies to the\\n x_start prediction before it is used to sample. Applies before\\n clip_denoised.\\n :param model_kwargs: if not None, a dict of extra keyword arguments to\\n pass to the model. This can be used for conditioning.\\n :return: a dict with the following keys:\\n - 'mean': the model mean output.\\n - 'variance': the model variance output.\\n - 'log_variance': the log of 'variance'.\\n - 'pred_xstart': the prediction for x_0.\\n \\\"\\\"\\\"\\n if model_kwargs is None:\\n model_kwargs = {}\\n\\n B, C = x.shape[:2]\\n assert t.shape == (B,)\\n original_model_output = model(x, self._scale_timesteps(t), **model_kwargs)\\n model_output = original_model_output.clone().detach()\\n\\n if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:\\n assert model_output.shape == (B, C * 2, *x.shape[2:])\\n model_output, model_var_values = th.split(model_output, C, dim=1)\\n if self.model_var_type == ModelVarType.LEARNED:\\n model_log_variance = model_var_values\\n model_variance = th.exp(model_log_variance)\\n else:\\n min_log = _extract_into_tensor(\\n self.posterior_log_variance_clipped, t, x.shape\\n )\\n max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)\\n # The model_var_values is [-1, 1] for [min_var, max_var].\\n frac = (model_var_values + 1) / 2\\n model_log_variance = frac * max_log + (1 - frac) * min_log\\n model_variance = th.exp(model_log_variance)\\n else:\\n model_variance, model_log_variance = {\\n # for fixedlarge, we set the initial (log-)variance like so\\n # to get a better decoder log likelihood.\\n ModelVarType.FIXED_LARGE: (\\n np.append(self.posterior_variance[1], self.betas[1:]),\\n np.log(np.append(self.posterior_variance[1], self.betas[1:])),\\n ),\\n ModelVarType.FIXED_SMALL: (\\n self.posterior_variance,\\n self.posterior_log_variance_clipped,\\n ),\\n }[self.model_var_type]\\n\\n model_variance = _extract_into_tensor(model_variance, t, x.shape)\\n model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)\\n\\n\\n def process_xstart(x):\\n if denoised_fn is not None:\\n x = denoised_fn(x)\\n if clip_denoised:\\n # print('clip_denoised', clip_denoised)\\n return x.clamp(-1, 1)\\n return x\\n\\n if self.model_mean_type == ModelMeanType.PREVIOUS_X:\\n pred_xstart = process_xstart(\\n self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)\\n )\\n model_mean = model_output\\n elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: # THIS IS US!\\n if self.model_mean_type == ModelMeanType.START_X:\\n pred_xstart = process_xstart(model_output)\\n else:\\n pred_xstart = process_xstart(\\n self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)\\n )\\n model_mean, _, _ = self.q_posterior_mean_variance(\\n x_start=pred_xstart, x_t=x, t=t\\n )\\n else:\\n raise NotImplementedError(self.model_mean_type)\\n\\n assert (\\n model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape\\n )\\n\\n # loss-guided condition\\n #assert k_first ==1, \\\"k_first must be 1, {}\\\".format(k_first)\\n num_condition = k_first if t[0] >= t_threshold else k_last # t[0] count from 1000 to 1, assume all t are equal\\n model_mean = self.condition_mean_bfgs(model_mean, num_condition, model_kwargs=model_kwargs) # , loss_value\\n\\n return {\\n \\\"mean\\\": model_mean,\\n \\\"variance\\\": model_variance,\\n \\\"log_variance\\\": model_log_variance,\\n \\\"pred_xstart\\\": pred_xstart,\\n }\\n \\n def training_losses(self, model, x_start, t, model_kwargs=None, noise=None, dataset=None,\\n use_posterior = True,\\n k_first = 1,\\n k_last = 10,\\n t_threshold = 10,):\\n \\\"\\\"\\\"\\n Compute training losses for a single timestep.\\n\\n :param model: the model to evaluate loss on.\\n :param x_start: the [N x C x ...] tensor of inputs.\\n :param t: a batch of timestep indices.\\n :param model_kwargs: if not None, a dict of extra keyword arguments to\\n pass to the model. This can be used for conditioning.\\n :param noise: if specified, the specific Gaussian noise to try to remove.\\n :return: a dict with the key \\\"loss\\\" containing a tensor of shape [N].\\n Some mean or variance settings may also have other keys.\\n \\\"\\\"\\\"\\n\\n # enc = model.model._modules['module']\\n model = self._wrap_model(model)\\n \\n enc = model.model\\n mask = model_kwargs['y']['mask']\\n get_xyz = lambda sample: enc.rot2xyz(sample, mask=None, pose_rep=enc.pose_rep, translation=enc.translation,\\n glob=enc.glob,\\n # jointstype='vertices', # 3.4 iter/sec # USED ALSO IN MotionCLIP\\n jointstype='smpl', # 3.4 iter/sec\\n vertstrans=False)\\n\\n if model_kwargs is None:\\n model_kwargs = {}\\n if noise is None:\\n noise = th.randn_like(x_start)\\n x_t = self.q_sample(x_start, t, noise=noise, model_kwargs=model_kwargs)\\n \\n #assert k_first == 1, \\\"k_first must be 1, {}\\\".format(k_first)\\n #assert k_last == 10, \\\"k_last must be 10, {}\\\".format(k_last)\\n assert use_posterior == True, \\\"use_posterior must be True, {}\\\".format(use_posterior)\\n if use_posterior:\\n '''\\n # loss-guided condition in training time\\n if t[0] >= t_threshold:\\n assert (t >= t_threshold).all(), f\\\"all t should be >=10 or <10 : t={t}\\\"\\n num_condition = k_first # else k_last\\n else:\\n num_condition = k_last\\n assert (t < t_threshold).all(), f\\\"all t should be >=10 or <10 : t={t}\\\"\\n '''\\n num_condition = k_first\\n x_t = self.condition_mean_bfgs(x_t, num_condition, model_kwargs=model_kwargs)\\n\\n terms = {}\\n if self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:\\n model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)\\n\\n target = {\\n ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(\\n x_start=x_start, x_t=x_t, t=t\\n )[0],\\n ModelMeanType.START_X: x_start,\\n ModelMeanType.EPSILON: noise,\\n }[self.model_mean_type]\\n\\n assert model_output.shape == target.shape == x_start.shape, \\\"model_output {}, target {}, x_start {}\\\".format(model_output.shape ,target.shape ,x_start.shape) # [bs, njoints, nfeats, nframes]\\n\\n terms[\\\"rot_mse\\\"] = self.masked_l2(target, model_output, mask) # mean_flat(rot_mse)\\n\\n terms[\\\"loss\\\"] = terms[\\\"rot_mse\\\"] + terms.get('vb', 0.) +\\\\\\n (self.lambda_vel * terms.get('vel_mse', 0.)) +\\\\\\n (self.lambda_rcxyz * terms.get('rcxyz_mse', 0.)) + \\\\\\n (self.lambda_fc * terms.get('fc', 0.))\\n else:\\n raise NotImplementedError(self.loss_type)\\n\\n return terms\"\n },\n {\n \"identifier\": \"wrap_model\",\n \"path\": \"model/cfg_sampler.py\",\n \"snippet\": \"def wrap_model(model, args):\\n if args.guidance_param not in [0., 1.]:\\n return ClassifierFreeSampleModel(model) # wrapping model with the classifier-free sampler\\n elif args.guidance_param == 0:\\n return UnconditionedModel(model)\\n else:\\n return model\"\n },\n {\n \"identifier\": \"MDM\",\n \"path\": \"model/mdm.py\",\n \"snippet\": \"class MDM(nn.Module):\\n def __init__(self, modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\\n latent_dim=256, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1,\\n ablation=None, activation=\\\"gelu\\\", legacy=False, data_rep='rot6d', dataset='amass', clip_dim=512,\\n arch='trans_enc', emb_trans_dec=False, clip_version=None, **kargs):\\n super().__init__()\\n\\n self.legacy = legacy\\n self.modeltype = modeltype\\n self.njoints = njoints\\n self.nfeats = nfeats\\n self.num_actions = num_actions\\n self.data_rep = data_rep\\n self.dataset = dataset\\n\\n self.pose_rep = pose_rep\\n self.glob = glob\\n self.glob_rot = glob_rot\\n self.translation = translation\\n\\n self.latent_dim = latent_dim\\n\\n self.ff_size = ff_size\\n self.num_layers = num_layers\\n self.num_heads = num_heads\\n self.dropout = dropout\\n\\n self.ablation = ablation\\n self.activation = activation\\n self.clip_dim = clip_dim\\n self.action_emb = kargs.get('action_emb', None)\\n\\n self.input_feats = self.njoints * self.nfeats\\n\\n self.normalize_output = kargs.get('normalize_encoder_output', False)\\n\\n self.cond_mode = kargs.get('cond_mode', 'no_cond')\\n self.cond_mask_prob = kargs.get('cond_mask_prob', 0.)\\n self.arch = arch\\n self.gru_emb_dim = self.latent_dim if self.arch == 'gru' else 0\\n self.input_process = InputProcess(self.data_rep, self.input_feats+self.gru_emb_dim, self.latent_dim)\\n\\n self.sequence_pos_encoder = PositionalEncoding(self.latent_dim, self.dropout)\\n self.emb_trans_dec = emb_trans_dec\\n\\n if self.arch == 'trans_enc':\\n print(\\\"TRANS_ENC init\\\")\\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\\n nhead=self.num_heads,\\n dim_feedforward=self.ff_size,\\n dropout=self.dropout,\\n activation=self.activation)\\n\\n self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer,\\n num_layers=self.num_layers)\\n elif self.arch == 'trans_dec':\\n print(\\\"TRANS_DEC init\\\")\\n seqTransDecoderLayer = nn.TransformerDecoderLayer(d_model=self.latent_dim,\\n nhead=self.num_heads,\\n dim_feedforward=self.ff_size,\\n dropout=self.dropout,\\n activation=activation)\\n self.seqTransDecoder = nn.TransformerDecoder(seqTransDecoderLayer,\\n num_layers=self.num_layers)\\n elif self.arch == 'gru':\\n print(\\\"GRU init\\\")\\n self.gru = nn.GRU(self.latent_dim, self.latent_dim, num_layers=self.num_layers, batch_first=True)\\n else:\\n raise ValueError('Please choose correct architecture [trans_enc, trans_dec, gru]')\\n\\n self.embed_timestep = TimestepEmbedder(self.latent_dim, self.sequence_pos_encoder)\\n\\n if self.cond_mode != 'no_cond':\\n if 'text' in self.cond_mode:\\n self.embed_text = nn.Linear(self.clip_dim, self.latent_dim)\\n print('EMBED TEXT')\\n print('Loading CLIP...')\\n self.clip_version = clip_version\\n self.clip_model = self.load_and_freeze_clip(clip_version)\\n if 'action' in self.cond_mode:\\n self.embed_action = EmbedAction(self.num_actions, self.latent_dim)\\n print('EMBED ACTION')\\n\\n self.output_process = OutputProcess(self.data_rep, self.input_feats, self.latent_dim, self.njoints,\\n self.nfeats)\\n\\n self.rot2xyz = Rotation2xyz(device='cpu', dataset=self.dataset)\\n\\n def parameters_wo_clip(self):\\n return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\\n\\n def load_and_freeze_clip(self, clip_version):\\n clip_model, clip_preprocess = clip.load(clip_version, device='cpu',\\n jit=False) # Must set jit=False for training\\n clip.model.convert_weights(\\n clip_model) # Actually this line is unnecessary since clip by default already on float16\\n\\n # Freeze CLIP weights\\n clip_model.eval()\\n for p in clip_model.parameters():\\n p.requires_grad = False\\n\\n return clip_model\\n\\n def mask_cond(self, cond, force_mask=False):\\n bs, d = cond.shape\\n if force_mask:\\n return torch.zeros_like(cond)\\n elif self.training and self.cond_mask_prob > 0.:\\n mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_mask_prob).view(bs, 1) # 1-> use null_cond, 0-> use real cond\\n return cond * (1. - mask)\\n else:\\n return cond\\n\\n def encode_text(self, raw_text):\\n # raw_text - list (batch_size length) of strings with input text prompts\\n device = next(self.parameters()).device\\n max_text_len = 20 if self.dataset in ['humanml', 'kit'] else None # Specific hardcoding for humanml dataset\\n if max_text_len is not None:\\n default_context_length = 77\\n context_length = max_text_len + 2 # start_token + 20 + end_token\\n assert context_length < default_context_length\\n texts = clip.tokenize(raw_text, context_length=context_length, truncate=True).to(device) # [bs, context_length] # if n_tokens > context_length -> will truncate\\n # print('texts', texts.shape)\\n zero_pad = torch.zeros([texts.shape[0], default_context_length-context_length], dtype=texts.dtype, device=texts.device)\\n texts = torch.cat([texts, zero_pad], dim=1)\\n # print('texts after pad', texts.shape, texts)\\n else:\\n texts = clip.tokenize(raw_text, truncate=True).to(device) # [bs, context_length] # if n_tokens > 77 -> will truncate\\n return self.clip_model.encode_text(texts).float()\\n\\n def forward(self, x, timesteps, y=None):\\n \\\"\\\"\\\"\\n x: [batch_size, njoints, nfeats, max_frames], denoted x_t in the paper\\n timesteps: [batch_size] (int)\\n \\\"\\\"\\\"\\n bs, njoints, nfeats, nframes = x.shape\\n emb = self.embed_timestep(timesteps) # [1, bs, d]\\n\\n force_mask = y.get('uncond', False)\\n if 'text' in self.cond_mode:\\n enc_text = self.encode_text(y['text'])\\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\\n if 'action' in self.cond_mode:\\n action_emb = self.embed_action(y['action'])\\n emb += self.mask_cond(action_emb, force_mask=force_mask)\\n\\n if self.arch == 'gru':\\n x_reshaped = x.reshape(bs, njoints*nfeats, 1, nframes)\\n emb_gru = emb.repeat(nframes, 1, 1) #[#frames, bs, d]\\n emb_gru = emb_gru.permute(1, 2, 0) #[bs, d, #frames]\\n emb_gru = emb_gru.reshape(bs, self.latent_dim, 1, nframes) #[bs, d, 1, #frames]\\n x = torch.cat((x_reshaped, emb_gru), axis=1) #[bs, d+joints*feat, 1, #frames]\\n\\n x = self.input_process(x)\\n\\n if self.arch == 'trans_enc':\\n # adding the timestep embed\\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\\n output = self.seqTransEncoder(xseq)[1:] # , src_key_padding_mask=~maskseq) # [seqlen, bs, d]\\n\\n elif self.arch == 'trans_dec':\\n if self.emb_trans_dec:\\n xseq = torch.cat((emb, x), axis=0)\\n else:\\n xseq = x\\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\\n if self.emb_trans_dec:\\n output = self.seqTransDecoder(tgt=xseq, memory=emb)[1:] # [seqlen, bs, d] # FIXME - maybe add a causal mask\\n else:\\n output = self.seqTransDecoder(tgt=xseq, memory=emb)\\n elif self.arch == 'gru':\\n xseq = x\\n xseq = self.sequence_pos_encoder(xseq) # [seqlen, bs, d]\\n output, _ = self.gru(xseq)\\n\\n output = self.output_process(output) # [bs, njoints, nfeats, nframes]\\n return output\\n\\n\\n def _apply(self, fn):\\n super()._apply(fn)\\n self.rot2xyz.smpl_model._apply(fn)\\n\\n\\n def train(self, *args, **kwargs):\\n super().train(*args, **kwargs)\\n self.rot2xyz.smpl_model.train(*args, **kwargs)\"\n },\n {\n \"identifier\": \"ControlMDM\",\n \"path\": \"model/ControlMDM.py\",\n \"snippet\": \"class ControlMDM(MDM):\\n\\n def __init__(self, modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\\n latent_dim=256, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1,\\n ablation=None, activation=\\\"gelu\\\", legacy=False, data_rep='rot6d', dataset='amass', clip_dim=512,\\n arch='trans_enc', emb_trans_dec=False, clip_version=None, args=None, **kargs):\\n\\n super(ControlMDM, self).__init__(modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot,\\n latent_dim, ff_size, num_layers, num_heads, dropout,\\n ablation, activation, legacy, data_rep, dataset, clip_dim,\\n arch, emb_trans_dec, clip_version, **kargs)\\n self.args = args\\n self.num_layers = num_layers\\n self.multi_person = args.multi_person\\n self.upper_orientation_index = [0, 16, 17] # root, l_shoulder, r_shoulder\\n self.lower_orientation_index = [0, 1, 2] # root, l_hip, r_hip\\n\\n # linear layers init with zeros\\n if self.dataset == 'kit':\\n self.first_zero_linear = nn.Linear(21*3*2 + 2*3, self.latent_dim)\\n elif self.dataset == 'humanml':\\n self.first_zero_linear = nn.Linear(22*3*2 + 2*3, self.latent_dim)\\n else:\\n raise NotImplementedError('Supporting only kit and humanml dataset, got {}'.format(self.dataset))\\n \\n nn.init.zeros_(self.first_zero_linear.weight)\\n nn.init.zeros_(self.first_zero_linear.bias)\\n self.mid_zero_linear = nn.ModuleList(\\n [nn.Linear(self.latent_dim, self.latent_dim) for _ in range(self.num_layers)])\\n for m in self.mid_zero_linear:\\n nn.init.zeros_(m.weight)\\n nn.init.zeros_(m.bias)\\n\\n if self.arch == 'trans_enc':\\n seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim,\\n nhead=self.num_heads,\\n dim_feedforward=self.ff_size,\\n dropout=self.dropout,\\n activation=self.activation)\\n del self.seqTransEncoder\\n self.seqTransEncoder_mdm = TransformerEncoder(seqTransEncoderLayer,\\n num_layers=self.num_layers)\\n self.seqTransEncoder_control = TransformerEncoder(seqTransEncoderLayer,\\n num_layers=self.num_layers)\\n else:\\n raise ValueError('Supporting only trans_enc arch.')\\n\\n self.freeze_block(self.input_process)\\n self.freeze_block(self.sequence_pos_encoder)\\n self.freeze_block(self.seqTransEncoder_mdm)\\n self.freeze_block(self.embed_timestep)\\n if 'text' in self.cond_mode:\\n self.freeze_block(self.embed_text)\\n self.freeze_block(self.output_process)\\n\\n def inv_transform(self, data):\\n assert self.std is not None and self.mean is not None\\n #assert data.requires_grad == True\\n std = torch.tensor(self.std, dtype=data.dtype, device=data.device, requires_grad=False)\\n mean = torch.tensor(self.mean, dtype=data.dtype, device=data.device, requires_grad=False)\\n output = torch.add(torch.mul(data, std), mean)\\n return output\\n \\n def compute_triangle_normals(self, triangles):\\n # Compute the vectors from the first point to the other two points\\n v1 = triangles[:,:, 1] - triangles[:, :,0]\\n v2 = triangles[:,:, 2] - triangles[:,:,0]\\n\\n # Compute the cross product of v1 and v2 to get the normal vectors\\n normals = torch.cross(v2, v1, dim=-1)\\n\\n # Normalize the normal vectors to unit length\\n normals = nn.functional.normalize(normals, dim=-1)\\n return normals\\n \\n def humanml_to_global_joint(self, x):\\n n_joints = 22 if x.shape[1] == 263 else 21\\n curr_joint = self.inv_transform(x.permute(0, 2, 3, 1)).float()\\n assert curr_joint.shape[1] == 1\\n curr_joint = recover_from_ric(curr_joint, n_joints)\\n curr_joint = curr_joint.view(-1, *curr_joint.shape[2:]).permute(0, 2, 3, 1)\\n # change root positions for multi-person purpose\\n if self.multi_person:\\n curr_joint[1::2, :,2,:] *= -1\\n curr_joint[1::2, :,0,:] *= -1\\n curr_joint[1::2, :,2,:] += 2\\n\\n # more than 3 people\\n #curr_joint[1, :,2,:] *= -1\\n #curr_joint[1, :,0,:] *= -1\\n #curr_joint[1, :,2,:] += 2\\n #curr_joint[2, :,0,:] += 1\\n return curr_joint\\n\\n def forward(self, x, timesteps, y=None):\\n bs, njoints, nfeats, seqlen = x.shape\\n control_bs, n_global_joints, xyz_dim, control_frames = y['global_joint'].shape\\n assert bs == control_bs and seqlen == control_frames, \\\"bs {} != {} or seqlen {} != {}\\\".format(bs, control_bs, seqlen, control_frames)\\n assert xyz_dim ==3, \\\"xyz_dim {} != 3\\\".format(xyz_dim)\\n # prepare global joints for controlmdm\\n curr_joint = self.humanml_to_global_joint(x).clone().detach() # [bs, njoints, 3, seqlen]\\n curr_joint.requires_grad = False\\n\\n # Build embedding vector\\n emb = self.embed_timestep(timesteps) # [1, bs, d]\\n\\n force_mask = y.get('uncond', False)\\n if 'text' in self.cond_mode:\\n enc_text = self.encode_text(y['text'])\\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\\n if 'action' in self.cond_mode:\\n action_emb = self.embed_action(y['action'])\\n emb += self.mask_cond(action_emb, force_mask=force_mask)\\n\\n # Embed motion to latent space (frame by frame)\\n x = self.input_process(x) #[seqlen, bs, d]\\n\\n # adding the timestep embed\\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\\n\\n # controlmdm\\n # orientation\\n upper_triangles = curr_joint[:,self.upper_orientation_index,:,:].permute(3,0,1,2) # [seqlen, bs, 3, 3]\\n lower_triangles = curr_joint[:,self.lower_orientation_index,:,:].permute(3,0,1,2) # [seqlen, bs, 3, 3]\\n upper_orientation = self.compute_triangle_normals(upper_triangles) # [seqlen, bs, 3]\\n lower_orientation = self.compute_triangle_normals(lower_triangles) # [seqlen, bs, 3]\\n\\n # relative position to joint\\n '''\\n relative_position = torch.zeros_like(curr_joint, device = xseq.device, dtype=torch.float32) # [bs, njoints, 3, seqlen]\\n relative_position[1::2,:,:,:] = ((y['global_joint'][::2,:,:,:].unsqueeze(1).float() - \\\\\\n curr_joint[:,1::2,:,:].unsqueeze(2))*y['global_joint_mask'][::2,:,:,:].bool().float()).float().sum(1)\\n relative_position[::2,:,:,:] = ((y['global_joint'][1::2,:,:,:].unsqueeze(1).float() - \\\\\\n curr_joint[:,::2,:,:].unsqueeze(2))*y['global_joint_mask'][1::2,:,:,:].bool().float()).float().sum(1)\\n '''\\n relative_position = ((y['global_joint'].float() - curr_joint)*y['global_joint_mask'].bool().float()).float() # [bs, njoints, 3, seqlen]\\n relative_position = relative_position.permute(3, 0, 1, 2).reshape(control_frames, control_bs, -1) # [seqlen, bs, 22*3]\\n\\n # relative position to root\\n relative_root = ((y['global_joint'].float() - curr_joint[:,[0],:,:])*y['global_joint_mask'].bool().float()).float() # [bs, njoints, 3, seqlen]\\n relative_root = relative_root.permute(3, 0, 1, 2).reshape(control_frames, control_bs, -1) # [seqlen, bs, 22*3]\\n global_joint_feat = torch.cat((relative_position, relative_root, upper_orientation, lower_orientation), axis=-1) # [seqlen, bs, 22*3 *2 +3 +3]\\n \\n global_joint_feat = self.first_zero_linear(global_joint_feat) # [seqlen, bs, d]\\n control_input = xseq + torch.cat((torch.zeros_like(emb, device = xseq.device, dtype=torch.float32), global_joint_feat), axis=0) # [seqlen+1, bs, d]\\n control_output_list = self.seqTransEncoder_control.return_all_layers(control_input) # [seqlen+1, bs, d]\\n for i in range(self.num_layers):\\n control_output_list[i] = self.mid_zero_linear[i](control_output_list[i])\\n \\n output = self.seqTransEncoder_mdm.forward_with_condition(xseq, control_output_list)[1:] # [seqlen, bs, d]\\n output = self.output_process(output) # [bs, njoints, nfeats, seqlen]\\n return output\\n\\n def trainable_parameters(self):\\n return [p for name, p in self.named_parameters() if p.requires_grad]\\n # return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]\\n \\n def trainable_parameter_names(self):\\n return [name for name, p in self.named_parameters() if p.requires_grad]\\n\\n def freeze_block(self, block):\\n block.eval()\\n for p in block.parameters():\\n p.requires_grad = False\\n\\n def unfreeze_block(self, block):\\n block.train()\\n for p in block.parameters():\\n p.requires_grad = True\\n \\n def forward_without_control(self, x, timesteps, y=None): #\\n # Build embedding vector\\n emb = self.embed_timestep(timesteps) # [1, bs, d]\\n\\n force_mask = y.get('uncond', False)\\n if 'text' in self.cond_mode:\\n enc_text = self.encode_text(y['text'])\\n emb += self.embed_text(self.mask_cond(enc_text, force_mask=force_mask))\\n if 'action' in self.cond_mode:\\n action_emb = self.embed_action(y['action'])\\n emb += self.mask_cond(action_emb, force_mask=force_mask)\\n\\n # Embed motion to latent space (frame by frame)\\n x = self.input_process(x) #[seqlen, bs, d]\\n # adding the timestep embed\\n xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d]\\n xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d]\\n output = self.seqTransEncoder_mdm(xseq)[1:] # [seqlen, bs, d]\\n output = self.output_process(output) # [bs, njoints, nfeats, seqlen]\\n return output\"\n },\n {\n \"identifier\": \"gaussian_diffusion\",\n \"path\": \"diffusion/gaussian_diffusion.py\",\n \"snippet\": \"def get_named_beta_schedule(schedule_name, num_diffusion_timesteps, scale_betas=1.):\\ndef betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):\\n def is_vb(self):\\n def __init__(\\n self,\\n *,\\n betas,\\n model_mean_type,\\n model_var_type,\\n loss_type,\\n rescale_timesteps=False,\\n lambda_rcxyz=0.,\\n lambda_vel=0.,\\n lambda_pose=1.,\\n lambda_orient=1.,\\n lambda_loc=1.,\\n data_rep='rot6d',\\n lambda_root_vel=0.,\\n lambda_vel_rcxyz=0.,\\n lambda_fc=0.,\\n batch_size=32,\\n multi_train_mode=None,\\n bfgs_times_first = 5,\\n bfgs_times_last = 10,\\n bfgs_interval = 1,\\n ):\\n def masked_l2(self, a, b, mask):\\n def q_mean_variance(self, x_start, t):\\n def q_sample(self, x_start, t, noise=None, model_kwargs=None):\\n def q_posterior_mean_variance(self, x_start, x_t, t):\\n def p_mean_variance(\\n self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None\\n ):\\n def process_xstart(x):\\n def _predict_xstart_from_eps(self, x_t, t, eps):\\n def _predict_xstart_from_xprev(self, x_t, t, xprev):\\n def _predict_eps_from_xstart(self, x_t, t, pred_xstart):\\n def _scale_timesteps(self, t):\\n def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\\n def condition_mean_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\\n def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\\n def condition_score_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):\\n def p_sample(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n const_noise=False,\\n ):\\n def p_sample_with_grad(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n ):\\n def p_sample_loop(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n dump_steps=None,\\n const_noise=False,\\n arb_len=False,\\n use_posterior = False,\\n ):\\n def p_sample_loop_progressive(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n predict_two_person=False,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n const_noise=False,\\n use_posterior = False,\\n ):\\n def p_sample_multi(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n const_noise=False,\\n ):\\n def handle_sample(_x, _out, _cond_fn, _t, _const_noise):\\n def ddim_sample(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n eta=0.0,\\n ):\\n def ddim_sample_with_grad(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n eta=0.0,\\n ):\\n def ddim_reverse_sample(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n model_kwargs=None,\\n eta=0.0,\\n ):\\n def ddim_sample_loop(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n eta=0.0,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n dump_steps=None,\\n const_noise=False,\\n ):\\n def ddim_sample_loop_progressive(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n eta=0.0,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n ):\\n def plms_sample(\\n self,\\n model,\\n x,\\n t,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n cond_fn_with_grad=False,\\n order=2,\\n old_out=None,\\n ):\\n def get_model_output(x, t):\\n def plms_sample_loop(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n order=2,\\n ):\\n def plms_sample_loop_progressive(\\n self,\\n model,\\n shape,\\n noise=None,\\n clip_denoised=True,\\n denoised_fn=None,\\n cond_fn=None,\\n model_kwargs=None,\\n device=None,\\n progress=False,\\n skip_timesteps=0,\\n init_image=None,\\n randomize_class=False,\\n cond_fn_with_grad=False,\\n order=2,\\n ):\\n def _vb_terms_bpd(\\n self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None\\n ):\\n def training_losses(self, model, x_start, t, model_kwargs=None, noise=None, dataset=None):\\n def fc_loss_rot_repr(self, gt_xyz, pred_xyz, mask):\\n def to_np_cpu(x):\\n def foot_contact_loss_humanml3d(self, target, model_output):\\n def velocity_consistency_loss_humanml3d(self, target, model_output):\\n def _prior_bpd(self, x_start):\\n def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):\\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\\nclass ModelMeanType(enum.Enum):\\nclass ModelVarType(enum.Enum):\\nclass LossType(enum.Enum):\\nclass GaussianDiffusion:\\n PREVIOUS_X = enum.auto() # the model predicts x_{t-1}\\n START_X = enum.auto() # the model predicts x_0\\n EPSILON = enum.auto() # the model predicts epsilon\\n LEARNED = enum.auto()\\n FIXED_SMALL = enum.auto()\\n FIXED_LARGE = enum.auto()\\n LEARNED_RANGE = enum.auto()\\n MSE = enum.auto() # use raw MSE loss (and KL when learning variances)\\n RESCALED_MSE = (\\n enum.auto()\\n ) # use raw MSE loss (with RESCALED_KL when learning variances)\\n KL = enum.auto() # use the variational lower-bound\\n RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB\\n B, C = x.shape[:2]\\n B, C = x_t.shape[:2]\"\n },\n {\n \"identifier\": \"SpacedDiffusion\",\n \"path\": \"diffusion/respace.py\",\n \"snippet\": \"class SpacedDiffusion(GaussianDiffusion):\\n \\\"\\\"\\\"\\n A diffusion process which can skip steps in a base diffusion process.\\n\\n :param use_timesteps: a collection (sequence or set) of timesteps from the\\n original diffusion process to retain.\\n :param kwargs: the kwargs to create the base diffusion process.\\n \\\"\\\"\\\"\\n\\n def __init__(self, use_timesteps, **kwargs):\\n self.use_timesteps = set(use_timesteps)\\n self.timestep_map = []\\n self.original_num_steps = len(kwargs[\\\"betas\\\"])\\n\\n base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa\\n last_alpha_cumprod = 1.0\\n new_betas = []\\n for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):\\n if i in self.use_timesteps:\\n new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)\\n last_alpha_cumprod = alpha_cumprod\\n self.timestep_map.append(i)\\n kwargs[\\\"betas\\\"] = np.array(new_betas)\\n super().__init__(**kwargs)\\n\\n def p_mean_variance(\\n self, model, *args, **kwargs\\n ): # pylint: disable=signature-differs\\n return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)\\n\\n def training_losses(\\n self, model, *args, **kwargs\\n ): # pylint: disable=signature-differs\\n return super().training_losses(self._wrap_model(model), *args, **kwargs)\\n\\n def condition_mean(self, cond_fn, *args, **kwargs):\\n return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)\\n\\n def condition_score(self, cond_fn, *args, **kwargs):\\n return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)\\n\\n def _wrap_model(self, model):\\n if isinstance(model, _WrappedModel):\\n return model\\n return _WrappedModel(\\n model, self.timestep_map, self.rescale_timesteps, self.original_num_steps\\n )\\n\\n def _scale_timesteps(self, t):\\n # Scaling is done by the wrapped model.\\n return t\"\n },\n {\n \"identifier\": \"space_timesteps\",\n \"path\": \"diffusion/respace.py\",\n \"snippet\": \"def space_timesteps(num_timesteps, section_counts):\\n \\\"\\\"\\\"\\n Create a list of timesteps to use from an original diffusion process,\\n given the number of timesteps we want to take from equally-sized portions\\n of the original process.\\n\\n For example, if there's 300 timesteps and the section counts are [10,15,20]\\n then the first 100 timesteps are strided to be 10 timesteps, the second 100\\n are strided to be 15 timesteps, and the final 100 are strided to be 20.\\n\\n If the stride is a string starting with \\\"ddim\\\", then the fixed striding\\n from the DDIM paper is used, and only one section is allowed.\\n\\n :param num_timesteps: the number of diffusion steps in the original\\n process to divide up.\\n :param section_counts: either a list of numbers, or a string containing\\n comma-separated numbers, indicating the step count\\n per section. As a special case, use \\\"ddimN\\\" where N\\n is a number of steps to use the striding from the\\n DDIM paper.\\n :return: a set of diffusion steps from the original process to use.\\n \\\"\\\"\\\"\\n if isinstance(section_counts, str):\\n if section_counts.startswith(\\\"ddim\\\"):\\n desired_count = int(section_counts[len(\\\"ddim\\\") :])\\n for i in range(1, num_timesteps):\\n if len(range(0, num_timesteps, i)) == desired_count:\\n return set(range(0, num_timesteps, i))\\n raise ValueError(\\n f\\\"cannot create exactly {num_timesteps} steps with an integer stride\\\"\\n )\\n section_counts = [int(x) for x in section_counts.split(\\\",\\\")]\\n size_per = num_timesteps // len(section_counts)\\n extra = num_timesteps % len(section_counts)\\n start_idx = 0\\n all_steps = []\\n for i, section_count in enumerate(section_counts):\\n size = size_per + (1 if i < extra else 0)\\n if size < section_count:\\n raise ValueError(\\n f\\\"cannot divide section of {size} steps into {section_count}\\\"\\n )\\n if section_count <= 1:\\n frac_stride = 1\\n else:\\n frac_stride = (size - 1) / (section_count - 1)\\n cur_idx = 0.0\\n taken_steps = []\\n for _ in range(section_count):\\n taken_steps.append(start_idx + round(cur_idx))\\n cur_idx += frac_stride\\n all_steps += taken_steps\\n start_idx += size\\n return set(all_steps)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import torch\nfrom diffusion.control_diffusion import ControlGaussianDiffusion\nfrom model.cfg_sampler import wrap_model\nfrom model.mdm import MDM\nfrom model.ControlMDM import ControlMDM\nfrom diffusion import gaussian_diffusion as gd\nfrom diffusion.respace import SpacedDiffusion, space_timesteps"},"token_num":{"kind":"number","value":15035,"string":"15,035"},"cropped_code":{"kind":"string","value":"\ndef load_model(args, data, device, ModelClass=MDM):\n model, diffusion = create_model_and_diffusion(args, data, ModelClass=ModelClass)\n model_path = args.model_path\n print(f\"Loading checkpoints from [{model_path}]...\")\n state_dict = torch.load(model_path, map_location='cpu')\n load_model_wo_clip(model, state_dict)\n model.to(device)\n model.eval() # disable random masking\n model = wrap_model(model, args)\n return model, diffusion\n\ndef load_model_wo_clip(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n if 't_pos_encoder.pe' in missing_keys:\n missing_keys.remove('t_pos_encoder.pe')\n if 't_pos_encoder.pe' in unexpected_keys:\n unexpected_keys.remove('t_pos_encoder.pe')\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n\ndef load_pretrained_mdm(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])\n\n\ndef load_pretrained_mdm_to_controlmdm(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n transformer_encoder_weight = {}\n for key, value in state_dict.items():\n if key.startswith('seqTransEncoder'):\n transformer_encoder_weight[key[16:]] = value\n unexpected_keys.remove(key)\n model.seqTransEncoder_mdm.load_state_dict(transformer_encoder_weight, strict=True)\n model.seqTransEncoder_control.load_state_dict(transformer_encoder_weight, strict=True)\n \n assert len(unexpected_keys) == 0\n #assert all([k.startswith('clip_model.') for k in missing_keys])\n print(\"The following parameters are trained from scratch.\")\n for k in missing_keys:\n if not k.startswith('clip_model.') and not k.startswith('seqTransEncoder'):\n print(k)\n\n\ndef load_split_mdm(model, state_dict, cutting_point):\n new_state_dict = {}\n orig_trans_prefix = 'seqTransEncoder.'\n for k, v in state_dict.items():\n if k.startswith(orig_trans_prefix):\n orig_layer = int(k.split('.')[2])\n orig_suffix = '.'.join(k.split('.')[3:])\n target_split = 'seqTransEncoder_start.' if orig_layer < cutting_point else 'seqTransEncoder_end.'\n target_layer = orig_layer if orig_layer < cutting_point else orig_layer - cutting_point\n new_k = target_split + 'layers.' + str(target_layer) + '.' + orig_suffix\n new_state_dict[new_k] = v\n else:\n new_state_dict[k] = v\n missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])\n\n\ndef create_model_and_diffusion(args, data, ModelClass=MDM, DiffusionClass=SpacedDiffusion):\n model = ModelClass(**get_model_args(args, data))\n diffusion = create_gaussian_diffusion(args, DiffusionClass)\n return model, diffusion\n\ndef get_model_args(args, data):\n\n # default args\n clip_version = 'ViT-B/32'\n action_emb = 'tensor'\n cond_mode = 'text' if args.dataset in ['humanml', 'kit','babel', 'pw3d'] else 'action'\n if hasattr(data.dataset, 'num_actions'):\n num_actions = data.dataset.num_actions\n else:\n num_actions = 1\n\n # SMPL defaults\n data_rep = 'rot6d'\n njoints = 25\n nfeats = 6\n\n if args.dataset in ['humanml', 'pw3d']:\n data_rep = 'hml_vec'\n njoints = 263\n nfeats = 1\n elif args.dataset == 'babel':\n data_rep = 'rot6d'\n njoints = 135\n nfeats = 1\n elif args.dataset == 'kit':\n data_rep = 'hml_vec'\n njoints = 251\n nfeats = 1\n else:\n raise TypeError(f'dataset {args.dataset} is not currently supported')\n\n return {'modeltype': '', 'njoints': njoints, 'nfeats': nfeats, 'num_actions': num_actions,\n 'translation': True, 'pose_rep': 'rot6d', 'glob': True, 'glob_rot': True,\n 'latent_dim': args.latent_dim, 'ff_size': 1024, 'num_layers': args.layers, 'num_heads': 4,\n 'dropout': 0.1, 'activation': \"gelu\", 'data_rep': data_rep, 'cond_mode': cond_mode,\n 'cond_mask_prob': args.cond_mask_prob, 'action_emb': action_emb, 'arch': args.arch,\n 'emb_trans_dec': args.emb_trans_dec, 'clip_version': clip_version, 'dataset': args.dataset,\n 'diffusion-steps': args.diffusion_steps, 'batch_size': args.batch_size, 'use_tta': args.use_tta,\n 'trans_emb': args.trans_emb, 'concat_trans_emb': args.concat_trans_emb, 'args': args}\n\n\ndef create_gaussian_diffusion(args, DiffusionClass=SpacedDiffusion):\n # default params\n predict_xstart = True # we always predict x_start (a.k.a. x0), that's our deal!\n steps = args.diffusion_steps\n scale_beta = 1. # no scaling\n timestep_respacing = '' # can be used for ddim sampling, we don't use it.\n learn_sigma = False\n rescale_timesteps = False\n \n print(f\"number of diffusion-steps: {steps}\")\n\n"},"all_code":{"kind":"string","value":"\ndef load_controlmdm_and_diffusion(args, data, device, ModelClass=ControlMDM, DiffusionClass=ControlGaussianDiffusion): \n model, diffusion = create_model_and_diffusion(args, data, ModelClass=ControlMDM, DiffusionClass=DiffusionClass)\n model_path = args.model_path\n print(f\"Loading checkpoints from [{model_path}]...\")\n state_dict = torch.load(model_path, map_location='cpu')\n load_model_wo_clip(model, state_dict)\n model.mean = data.dataset.t2m_dataset.mean\n model.std = data.dataset.t2m_dataset.std\n\n model.to(device)\n model.eval() # disable random masking\n model = wrap_model(model, args)\n return model, diffusion\n\ndef load_model(args, data, device, ModelClass=MDM):\n model, diffusion = create_model_and_diffusion(args, data, ModelClass=ModelClass)\n model_path = args.model_path\n print(f\"Loading checkpoints from [{model_path}]...\")\n state_dict = torch.load(model_path, map_location='cpu')\n load_model_wo_clip(model, state_dict)\n model.to(device)\n model.eval() # disable random masking\n model = wrap_model(model, args)\n return model, diffusion\n\ndef load_model_wo_clip(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n if 't_pos_encoder.pe' in missing_keys:\n missing_keys.remove('t_pos_encoder.pe')\n if 't_pos_encoder.pe' in unexpected_keys:\n unexpected_keys.remove('t_pos_encoder.pe')\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') for k in missing_keys])\n\ndef load_pretrained_mdm(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])\n\n\ndef load_pretrained_mdm_to_controlmdm(model, state_dict):\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n transformer_encoder_weight = {}\n for key, value in state_dict.items():\n if key.startswith('seqTransEncoder'):\n transformer_encoder_weight[key[16:]] = value\n unexpected_keys.remove(key)\n model.seqTransEncoder_mdm.load_state_dict(transformer_encoder_weight, strict=True)\n model.seqTransEncoder_control.load_state_dict(transformer_encoder_weight, strict=True)\n \n assert len(unexpected_keys) == 0\n #assert all([k.startswith('clip_model.') for k in missing_keys])\n print(\"The following parameters are trained from scratch.\")\n for k in missing_keys:\n if not k.startswith('clip_model.') and not k.startswith('seqTransEncoder'):\n print(k)\n\n\ndef load_split_mdm(model, state_dict, cutting_point):\n new_state_dict = {}\n orig_trans_prefix = 'seqTransEncoder.'\n for k, v in state_dict.items():\n if k.startswith(orig_trans_prefix):\n orig_layer = int(k.split('.')[2])\n orig_suffix = '.'.join(k.split('.')[3:])\n target_split = 'seqTransEncoder_start.' if orig_layer < cutting_point else 'seqTransEncoder_end.'\n target_layer = orig_layer if orig_layer < cutting_point else orig_layer - cutting_point\n new_k = target_split + 'layers.' + str(target_layer) + '.' + orig_suffix\n new_state_dict[new_k] = v\n else:\n new_state_dict[k] = v\n missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)\n assert len(unexpected_keys) == 0\n assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])\n\n\ndef create_model_and_diffusion(args, data, ModelClass=MDM, DiffusionClass=SpacedDiffusion):\n model = ModelClass(**get_model_args(args, data))\n diffusion = create_gaussian_diffusion(args, DiffusionClass)\n return model, diffusion\n\ndef get_model_args(args, data):\n\n # default args\n clip_version = 'ViT-B/32'\n action_emb = 'tensor'\n cond_mode = 'text' if args.dataset in ['humanml', 'kit','babel', 'pw3d'] else 'action'\n if hasattr(data.dataset, 'num_actions'):\n num_actions = data.dataset.num_actions\n else:\n num_actions = 1\n\n # SMPL defaults\n data_rep = 'rot6d'\n njoints = 25\n nfeats = 6\n\n if args.dataset in ['humanml', 'pw3d']:\n data_rep = 'hml_vec'\n njoints = 263\n nfeats = 1\n elif args.dataset == 'babel':\n data_rep = 'rot6d'\n njoints = 135\n nfeats = 1\n elif args.dataset == 'kit':\n data_rep = 'hml_vec'\n njoints = 251\n nfeats = 1\n else:\n raise TypeError(f'dataset {args.dataset} is not currently supported')\n\n return {'modeltype': '', 'njoints': njoints, 'nfeats': nfeats, 'num_actions': num_actions,\n 'translation': True, 'pose_rep': 'rot6d', 'glob': True, 'glob_rot': True,\n 'latent_dim': args.latent_dim, 'ff_size': 1024, 'num_layers': args.layers, 'num_heads': 4,\n 'dropout': 0.1, 'activation': \"gelu\", 'data_rep': data_rep, 'cond_mode': cond_mode,\n 'cond_mask_prob': args.cond_mask_prob, 'action_emb': action_emb, 'arch': args.arch,\n 'emb_trans_dec': args.emb_trans_dec, 'clip_version': clip_version, 'dataset': args.dataset,\n 'diffusion-steps': args.diffusion_steps, 'batch_size': args.batch_size, 'use_tta': args.use_tta,\n 'trans_emb': args.trans_emb, 'concat_trans_emb': args.concat_trans_emb, 'args': args}\n\n\ndef create_gaussian_diffusion(args, DiffusionClass=SpacedDiffusion):\n # default params\n predict_xstart = True # we always predict x_start (a.k.a. x0), that's our deal!\n steps = args.diffusion_steps\n scale_beta = 1. # no scaling\n timestep_respacing = '' # can be used for ddim sampling, we don't use it.\n learn_sigma = False\n rescale_timesteps = False\n \n print(f\"number of diffusion-steps: {steps}\")\n"},"next_line":{"kind":"string","value":" betas = gd.get_named_beta_schedule(args.noise_schedule, steps, scale_beta)"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-11-27 05:28:02+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5379,"cells":{"repo_name":{"kind":"string","value":"camenduru/magicanimate-hf"},"file_path":{"kind":"string","value":"magicanimate/pipelines/pipeline_animation.py"},"context":{"kind":"list like","value":[{"identifier":"UNet3DConditionModel","path":"magicanimate/models/unet_controlnet.py","snippet":"class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0, \n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n mid_block_type: str = \"UNetMidBlock3DCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n \n # Additional\n use_motion_module = False,\n motion_module_resolutions = ( 1,2,4,8 ),\n motion_module_mid_block = False,\n motion_module_decoder_only = False,\n motion_module_type = None,\n motion_module_kwargs = {},\n unet_use_cross_frame_attention = None,\n unet_use_temporal_attention = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # input\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n res = 2 ** i\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock3DCrossAttn\":\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and motion_module_mid_block,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n \n # count how many layers upsample the videos\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n res = 2 ** (3 - i)\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n # for controlnet\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # pre-process\n sample = self.conv_in(sample)\n\n # down\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # mid\n sample = self.mid_block(\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\n )\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n\n @classmethod\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\n print(f\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\")\n\n config_file = os.path.join(pretrained_model_path, 'config.json')\n if not os.path.isfile(config_file):\n raise RuntimeError(f\"{config_file} does not exist\")\n with open(config_file, \"r\") as f:\n config = json.load(f)\n config[\"_class_name\"] = cls.__name__\n config[\"down_block_types\"] = [\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\"\n ]\n config[\"up_block_types\"] = [\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ]\n # config[\"mid_block_type\"] = \"UNetMidBlock3DCrossAttn\"\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config, **unet_additional_kwargs)\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n raise RuntimeError(f\"{model_file} does not exist\")\n state_dict = torch.load(model_file, map_location=\"cpu\")\n\n m, u = model.load_state_dict(state_dict, strict=False)\n print(f\"### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};\")\n # print(f\"### missing keys:\\n{m}\\n### unexpected keys:\\n{u}\\n\")\n \n params = [p.numel() if \"temporal\" in n else 0 for n, p in model.named_parameters()]\n print(f\"### Temporal Module Parameters: {sum(params) / 1e6} M\")\n \n return model"},{"identifier":"ControlNetModel","path":"magicanimate/models/controlnet.py","snippet":"class ControlNetModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n projection_class_embeddings_input_dim: Optional[int] = None,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n ):\n super().__init__()\n\n # Check inputs\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n )\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n # control net conditioning embedding\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n )\n\n self.down_blocks = nn.ModuleList([])\n self.controlnet_down_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[i],\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.down_blocks.append(down_block)\n\n for _ in range(layers_per_block):\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n if not is_final_block:\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n # mid\n mid_block_channel = block_out_channels[-1]\n\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_mid_block = controlnet_block\n\n self.mid_block = UNetMidBlock2DCrossAttn(\n in_channels=mid_block_channel,\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n load_weights_from_unet: bool = True,\n ):\n r\"\"\"\n Instantiate Controlnet class from UNet2DConditionModel.\n\n Parameters:\n unet (`UNet2DConditionModel`):\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\n copied where applicable.\n \"\"\"\n controlnet = cls(\n in_channels=unet.config.in_channels,\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\n freq_shift=unet.config.freq_shift,\n down_block_types=unet.config.down_block_types,\n only_cross_attention=unet.config.only_cross_attention,\n block_out_channels=unet.config.block_out_channels,\n layers_per_block=unet.config.layers_per_block,\n downsample_padding=unet.config.downsample_padding,\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\n act_fn=unet.config.act_fn,\n norm_num_groups=unet.config.norm_num_groups,\n norm_eps=unet.config.norm_eps,\n cross_attention_dim=unet.config.cross_attention_dim,\n attention_head_dim=unet.config.attention_head_dim,\n use_linear_projection=unet.config.use_linear_projection,\n class_embed_type=unet.config.class_embed_type,\n num_class_embeds=unet.config.num_class_embeds,\n upcast_attention=unet.config.upcast_attention,\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\n )\n\n if load_weights_from_unet:\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\n\n if controlnet.class_embedding:\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\n\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\n\n return controlnet\n\n # @property\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\n # r\"\"\"\n # Returns:\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\n # indexed by its weight name.\n # \"\"\"\n # # set recursively\n # processors = {}\n\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n # if hasattr(module, \"set_processor\"):\n # processors[f\"{name}.processor\"] = module.processor\n\n # for sub_name, child in module.named_children():\n # fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n # return processors\n\n # for name, module in self.named_children():\n # fn_recursive_add_processors(name, module, processors)\n\n # return processors\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n # r\"\"\"\n # Parameters:\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\n # of **all** `Attention` layers.\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\n\n # \"\"\"\n # count = len(self.attn_processors.keys())\n\n # if isinstance(processor, dict) and len(processor) != count:\n # raise ValueError(\n # f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n # f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n # )\n\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n # if hasattr(module, \"set_processor\"):\n # if not isinstance(processor, dict):\n # module.set_processor(processor)\n # else:\n # module.set_processor(processor.pop(f\"{name}.processor\"))\n\n # for sub_name, child in module.named_children():\n # fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n # for name, module in self.named_children():\n # fn_recursive_attn_processor(name, module, processor)\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n # def set_default_attn_processor(self):\n # \"\"\"\n # Disables custom attention processors and sets the default attention implementation.\n # \"\"\"\n # self.set_attn_processor(AttnProcessor())\n\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: torch.FloatTensor,\n conditioning_scale: float = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple]:\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\n\n if channel_order == \"rgb\":\n # in rgb order by default\n ...\n elif channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n else:\n raise ValueError(f\"unknown `controlnet_conditioning_channel_order`: {channel_order}\")\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n\n sample += controlnet_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n\n # 5. Control net blocks\n\n controlnet_down_block_res_samples = ()\n\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample *= conditioning_scale\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )"},{"identifier":"ReferenceAttentionControl","path":"magicanimate/models/mutual_self_attention.py","snippet":"class ReferenceAttentionControl():\n \n def __init__(self, \n unet,\n mode=\"write\",\n do_classifier_free_guidance=False,\n attention_auto_machine_weight = float('inf'),\n gn_auto_machine_weight = 1.0,\n style_fidelity = 1.0,\n reference_attn=True,\n reference_adain=False,\n fusion_blocks=\"midup\",\n batch_size=1, \n ) -> None:\n # 10. Modify self attention and group norm\n self.unet = unet\n assert mode in [\"read\", \"write\"]\n assert fusion_blocks in [\"midup\", \"full\"]\n self.reference_attn = reference_attn\n self.reference_adain = reference_adain\n self.fusion_blocks = fusion_blocks\n self.register_reference_hooks(\n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n fusion_blocks,\n batch_size=batch_size, \n )\n\n def register_reference_hooks(\n self, \n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n dtype=torch.float16,\n batch_size=1, \n num_images_per_prompt=1, \n device=torch.device(\"cpu\"), \n fusion_blocks='midup',\n ):\n MODE = mode\n do_classifier_free_guidance = do_classifier_free_guidance\n attention_auto_machine_weight = attention_auto_machine_weight\n gn_auto_machine_weight = gn_auto_machine_weight\n style_fidelity = style_fidelity\n reference_attn = reference_attn\n reference_adain = reference_adain\n fusion_blocks = fusion_blocks\n num_images_per_prompt = num_images_per_prompt\n dtype=dtype\n if do_classifier_free_guidance:\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\n .to(device)\n .bool()\n )\n else:\n uc_mask = (\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\n .to(device)\n .bool()\n )\n \n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.FloatTensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n video_length=None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \"b t l c -> (b t) l c\")[:hidden_states.shape[0]] for d in self.bank]\n hidden_states_uc = self.attn1(norm_hidden_states, \n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n attention_mask=attention_mask) + hidden_states\n hidden_states_c = hidden_states_uc.clone()\n _uc_mask = uc_mask.clone()\n if do_classifier_free_guidance:\n if hidden_states.shape[0] != _uc_mask.shape[0]:\n _uc_mask = (\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\n .to(device)\n .bool()\n )\n hidden_states_c[_uc_mask] = self.attn1(\n norm_hidden_states[_uc_mask],\n encoder_hidden_states=norm_hidden_states[_uc_mask],\n attention_mask=attention_mask,\n ) + hidden_states[_uc_mask]\n hidden_states = hidden_states_c.clone()\n \n self.bank.clear()\n if self.attn2 is not None:\n # Cross-Attention\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n hidden_states = (\n self.attn2(\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n + hidden_states\n )\n\n # Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n # Temporal-Attention\n if self.unet_use_temporal_attention:\n d = hidden_states.shape[1]\n hidden_states = rearrange(hidden_states, \"(b f) d c -> (b d) f c\", f=video_length)\n norm_hidden_states = (\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\n )\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\n hidden_states = rearrange(hidden_states, \"(b d) f c -> (b f) d c\", d=d)\n\n return hidden_states\n \n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if self.reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n \n def update(self, writer, dtype=torch.float16):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r, w in zip(reader_attn_modules, writer_attn_modules):\n r.bank = [v.clone().to(dtype) for v in w.bank]\n # w.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n writer_gn_modules = [writer.unet.mid_block]\n \n down_blocks = writer.unet.down_blocks\n for w, module in enumerate(down_blocks):\n writer_gn_modules.append(module)\n\n up_blocks = writer.unet.up_blocks\n for w, module in enumerate(up_blocks):\n writer_gn_modules.append(module)\n \n for r, w in zip(reader_gn_modules, writer_gn_modules):\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\n else:\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\n \n def clear(self):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r in reader_attn_modules:\n r.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n for r in reader_gn_modules:\n r.mean_bank.clear()\n r.var_bank.clear()"},{"identifier":"get_context_scheduler","path":"magicanimate/pipelines/context.py","snippet":"def get_context_scheduler(name: str) -> Callable:\n if name == \"uniform\":\n return uniform\n else:\n raise ValueError(f\"Unknown context_overlap policy {name}\")"},{"identifier":"get_total_steps","path":"magicanimate/pipelines/context.py","snippet":"def get_total_steps(\n scheduler,\n timesteps: List[int],\n num_steps: Optional[int] = None,\n num_frames: int = ...,\n context_size: Optional[int] = None,\n context_stride: int = 3,\n context_overlap: int = 4,\n closed_loop: bool = True,\n):\n return sum(\n len(\n list(\n scheduler(\n i,\n num_steps,\n num_frames,\n context_size,\n context_stride,\n context_overlap,\n )\n )\n )\n for i in range(len(timesteps))\n )"},{"identifier":"get_tensor_interpolation_method","path":"magicanimate/utils/util.py","snippet":"def get_tensor_interpolation_method():\n return tensor_interpolation"}],"string":"[\n {\n \"identifier\": \"UNet3DConditionModel\",\n \"path\": \"magicanimate/models/unet_controlnet.py\",\n \"snippet\": \"class UNet3DConditionModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n center_input_sample: bool = False,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0, \\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\",\\n ),\\n mid_block_type: str = \\\"UNetMidBlock3DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: int = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n dual_cross_attention: bool = False,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n \\n # Additional\\n use_motion_module = False,\\n motion_module_resolutions = ( 1,2,4,8 ),\\n motion_module_mid_block = False,\\n motion_module_decoder_only = False,\\n motion_module_type = None,\\n motion_module_kwargs = {},\\n unet_use_cross_frame_attention = None,\\n unet_use_temporal_attention = None,\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n time_embed_dim = block_out_channels[0] * 4\\n\\n # input\\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\\n\\n # time\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n self.down_blocks = nn.ModuleList([])\\n self.mid_block = None\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n res = 2 ** i\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n if mid_block_type == \\\"UNetMidBlock3DCrossAttn\\\":\\n self.mid_block = UNetMidBlock3DCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and motion_module_mid_block,\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n else:\\n raise ValueError(f\\\"unknown mid_block_type : {mid_block_type}\\\")\\n \\n # count how many layers upsample the videos\\n self.num_upsamplers = 0\\n\\n # up\\n reversed_block_out_channels = list(reversed(block_out_channels))\\n reversed_attention_head_dim = list(reversed(attention_head_dim))\\n only_cross_attention = list(reversed(only_cross_attention))\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n res = 2 ** (3 - i)\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=layers_per_block + 1,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=time_embed_dim,\\n add_upsample=add_upsample,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=reversed_attention_head_dim[i],\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\\n self.conv_act = nn.SiLU()\\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\\n\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_slicable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_slicable_dims(module)\\n\\n num_slicable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_slicable_layers * [1]\\n\\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n # for controlnet\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n return_dict: bool = True,\\n ) -> Union[UNet3DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n Args:\\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\\n returning a tuple, the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\\n # on the fly if necessary.\\n default_overall_up_factor = 2**self.num_upsamplers\\n\\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\\n forward_upsample_size = False\\n upsample_size = None\\n\\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\\n logger.info(\\\"Forward upsample size to force interpolation output size.\\\")\\n forward_upsample_size = True\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # center input if necessary\\n if self.config.center_input_sample:\\n sample = 2 * sample - 1.0\\n\\n # time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n emb = self.time_embedding(t_emb)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # pre-process\\n sample = self.conv_in(sample)\\n\\n # down\\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\\n\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\\n\\n down_block_res_samples += res_samples\\n\\n if is_controlnet:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # mid\\n sample = self.mid_block(\\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n\\n if is_controlnet:\\n sample = sample + mid_block_additional_residual\\n\\n # up\\n for i, upsample_block in enumerate(self.up_blocks):\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\\n\\n # if we have not reached the final block and need to forward the\\n # upsample size, we do it here\\n if not is_final_block and forward_upsample_size:\\n upsample_size = down_block_res_samples[-1].shape[2:]\\n\\n if hasattr(upsample_block, \\\"has_cross_attention\\\") and upsample_block.has_cross_attention:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample = upsample_block(\\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\\n )\\n\\n # post-process\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n sample = self.conv_out(sample)\\n\\n if not return_dict:\\n return (sample,)\\n\\n return UNet3DConditionOutput(sample=sample)\\n\\n @classmethod\\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\\n if subfolder is not None:\\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\\n print(f\\\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\\\")\\n\\n config_file = os.path.join(pretrained_model_path, 'config.json')\\n if not os.path.isfile(config_file):\\n raise RuntimeError(f\\\"{config_file} does not exist\\\")\\n with open(config_file, \\\"r\\\") as f:\\n config = json.load(f)\\n config[\\\"_class_name\\\"] = cls.__name__\\n config[\\\"down_block_types\\\"] = [\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\"\\n ]\\n config[\\\"up_block_types\\\"] = [\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ]\\n # config[\\\"mid_block_type\\\"] = \\\"UNetMidBlock3DCrossAttn\\\"\\n\\n from diffusers.utils import WEIGHTS_NAME\\n model = cls.from_config(config, **unet_additional_kwargs)\\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\\n if not os.path.isfile(model_file):\\n raise RuntimeError(f\\\"{model_file} does not exist\\\")\\n state_dict = torch.load(model_file, map_location=\\\"cpu\\\")\\n\\n m, u = model.load_state_dict(state_dict, strict=False)\\n print(f\\\"### missing keys: {len(m)}; \\\\n### unexpected keys: {len(u)};\\\")\\n # print(f\\\"### missing keys:\\\\n{m}\\\\n### unexpected keys:\\\\n{u}\\\\n\\\")\\n \\n params = [p.numel() if \\\"temporal\\\" in n else 0 for n, p in model.named_parameters()]\\n print(f\\\"### Temporal Module Parameters: {sum(params) / 1e6} M\\\")\\n \\n return model\"\n },\n {\n \"identifier\": \"ControlNetModel\",\n \"path\": \"magicanimate/models/controlnet.py\",\n \"snippet\": \"class ControlNetModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n in_channels: int = 4,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"DownBlock2D\\\",\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: Optional[int] = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n projection_class_embeddings_input_dim: Optional[int] = None,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n ):\\n super().__init__()\\n\\n # Check inputs\\n if len(block_out_channels) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n # input\\n conv_in_kernel = 3\\n conv_in_padding = (conv_in_kernel - 1) // 2\\n self.conv_in = nn.Conv2d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n\\n # time\\n time_embed_dim = block_out_channels[0] * 4\\n\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(\\n timestep_input_dim,\\n time_embed_dim,\\n act_fn=act_fn,\\n )\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n elif class_embed_type == \\\"projection\\\":\\n if projection_class_embeddings_input_dim is None:\\n raise ValueError(\\n \\\"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\\\"\\n )\\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\\n # 2. it projects from an arbitrary input dimension.\\n #\\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n # control net conditioning embedding\\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\\n conditioning_embedding_channels=block_out_channels[0],\\n block_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n self.down_blocks = nn.ModuleList([])\\n self.controlnet_down_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n for i, down_block_type in enumerate(down_block_types):\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n )\\n self.down_blocks.append(down_block)\\n\\n for _ in range(layers_per_block):\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n if not is_final_block:\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n # mid\\n mid_block_channel = block_out_channels[-1]\\n\\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_mid_block = controlnet_block\\n\\n self.mid_block = UNetMidBlock2DCrossAttn(\\n in_channels=mid_block_channel,\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n )\\n\\n @classmethod\\n def from_unet(\\n cls,\\n unet: UNet2DConditionModel,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n load_weights_from_unet: bool = True,\\n ):\\n r\\\"\\\"\\\"\\n Instantiate Controlnet class from UNet2DConditionModel.\\n\\n Parameters:\\n unet (`UNet2DConditionModel`):\\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\\n copied where applicable.\\n \\\"\\\"\\\"\\n controlnet = cls(\\n in_channels=unet.config.in_channels,\\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\\n freq_shift=unet.config.freq_shift,\\n down_block_types=unet.config.down_block_types,\\n only_cross_attention=unet.config.only_cross_attention,\\n block_out_channels=unet.config.block_out_channels,\\n layers_per_block=unet.config.layers_per_block,\\n downsample_padding=unet.config.downsample_padding,\\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\\n act_fn=unet.config.act_fn,\\n norm_num_groups=unet.config.norm_num_groups,\\n norm_eps=unet.config.norm_eps,\\n cross_attention_dim=unet.config.cross_attention_dim,\\n attention_head_dim=unet.config.attention_head_dim,\\n use_linear_projection=unet.config.use_linear_projection,\\n class_embed_type=unet.config.class_embed_type,\\n num_class_embeds=unet.config.num_class_embeds,\\n upcast_attention=unet.config.upcast_attention,\\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n if load_weights_from_unet:\\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\\n\\n if controlnet.class_embedding:\\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\\n\\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\\n\\n return controlnet\\n\\n # @property\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\\n # r\\\"\\\"\\\"\\n # Returns:\\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\\n # indexed by its weight name.\\n # \\\"\\\"\\\"\\n # # set recursively\\n # processors = {}\\n\\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # processors[f\\\"{name}.processor\\\"] = module.processor\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_add_processors(f\\\"{name}.{sub_name}\\\", child, processors)\\n\\n # return processors\\n\\n # for name, module in self.named_children():\\n # fn_recursive_add_processors(name, module, processors)\\n\\n # return processors\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\\n # r\\\"\\\"\\\"\\n # Parameters:\\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\\n # of **all** `Attention` layers.\\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\\n\\n # \\\"\\\"\\\"\\n # count = len(self.attn_processors.keys())\\n\\n # if isinstance(processor, dict) and len(processor) != count:\\n # raise ValueError(\\n # f\\\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\\\"\\n # f\\\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\\\"\\n # )\\n\\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # if not isinstance(processor, dict):\\n # module.set_processor(processor)\\n # else:\\n # module.set_processor(processor.pop(f\\\"{name}.processor\\\"))\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_attn_processor(f\\\"{name}.{sub_name}\\\", child, processor)\\n\\n # for name, module in self.named_children():\\n # fn_recursive_attn_processor(name, module, processor)\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\\n # def set_default_attn_processor(self):\\n # \\\"\\\"\\\"\\n # Disables custom attention processors and sets the default attention implementation.\\n # \\\"\\\"\\\"\\n # self.set_attn_processor(AttnProcessor())\\n\\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_sliceable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_sliceable_dims(module)\\n\\n num_sliceable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_sliceable_layers * [1]\\n\\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n controlnet_cond: torch.FloatTensor,\\n conditioning_scale: float = 1.0,\\n class_labels: Optional[torch.Tensor] = None,\\n timestep_cond: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n return_dict: bool = True,\\n ) -> Union[ControlNetOutput, Tuple]:\\n # check channel order\\n channel_order = self.config.controlnet_conditioning_channel_order\\n\\n if channel_order == \\\"rgb\\\":\\n # in rgb order by default\\n ...\\n elif channel_order == \\\"bgr\\\":\\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\\n else:\\n raise ValueError(f\\\"unknown `controlnet_conditioning_channel_order`: {channel_order}\\\")\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # 1. time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n\\n emb = self.time_embedding(t_emb, timestep_cond)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # 2. pre-process\\n sample = self.conv_in(sample)\\n\\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\\n\\n sample += controlnet_cond\\n\\n # 3. down\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\\n\\n down_block_res_samples += res_samples\\n\\n # 4. mid\\n if self.mid_block is not None:\\n sample = self.mid_block(\\n sample,\\n emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n\\n # 5. Control net blocks\\n\\n controlnet_down_block_res_samples = ()\\n\\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\\n down_block_res_sample = controlnet_block(down_block_res_sample)\\n controlnet_down_block_res_samples += (down_block_res_sample,)\\n\\n down_block_res_samples = controlnet_down_block_res_samples\\n\\n mid_block_res_sample = self.controlnet_mid_block(sample)\\n\\n # 6. scaling\\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\\n mid_block_res_sample *= conditioning_scale\\n\\n if not return_dict:\\n return (down_block_res_samples, mid_block_res_sample)\\n\\n return ControlNetOutput(\\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\\n )\"\n },\n {\n \"identifier\": \"ReferenceAttentionControl\",\n \"path\": \"magicanimate/models/mutual_self_attention.py\",\n \"snippet\": \"class ReferenceAttentionControl():\\n \\n def __init__(self, \\n unet,\\n mode=\\\"write\\\",\\n do_classifier_free_guidance=False,\\n attention_auto_machine_weight = float('inf'),\\n gn_auto_machine_weight = 1.0,\\n style_fidelity = 1.0,\\n reference_attn=True,\\n reference_adain=False,\\n fusion_blocks=\\\"midup\\\",\\n batch_size=1, \\n ) -> None:\\n # 10. Modify self attention and group norm\\n self.unet = unet\\n assert mode in [\\\"read\\\", \\\"write\\\"]\\n assert fusion_blocks in [\\\"midup\\\", \\\"full\\\"]\\n self.reference_attn = reference_attn\\n self.reference_adain = reference_adain\\n self.fusion_blocks = fusion_blocks\\n self.register_reference_hooks(\\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n fusion_blocks,\\n batch_size=batch_size, \\n )\\n\\n def register_reference_hooks(\\n self, \\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n dtype=torch.float16,\\n batch_size=1, \\n num_images_per_prompt=1, \\n device=torch.device(\\\"cpu\\\"), \\n fusion_blocks='midup',\\n ):\\n MODE = mode\\n do_classifier_free_guidance = do_classifier_free_guidance\\n attention_auto_machine_weight = attention_auto_machine_weight\\n gn_auto_machine_weight = gn_auto_machine_weight\\n style_fidelity = style_fidelity\\n reference_attn = reference_attn\\n reference_adain = reference_adain\\n fusion_blocks = fusion_blocks\\n num_images_per_prompt = num_images_per_prompt\\n dtype=dtype\\n if do_classifier_free_guidance:\\n uc_mask = (\\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\\n .to(device)\\n .bool()\\n )\\n else:\\n uc_mask = (\\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\\n .to(device)\\n .bool()\\n )\\n \\n def hacked_basic_transformer_inner_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n timestep: Optional[torch.LongTensor] = None,\\n cross_attention_kwargs: Dict[str, Any] = None,\\n class_labels: Optional[torch.LongTensor] = None,\\n video_length=None,\\n ):\\n if self.use_ada_layer_norm:\\n norm_hidden_states = self.norm1(hidden_states, timestep)\\n elif self.use_ada_layer_norm_zero:\\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\\n )\\n else:\\n norm_hidden_states = self.norm1(hidden_states)\\n\\n # 1. Self-Attention\\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\\n if self.only_cross_attention:\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n else:\\n if MODE == \\\"write\\\":\\n self.bank.append(norm_hidden_states.clone())\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n if MODE == \\\"read\\\":\\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \\\"b t l c -> (b t) l c\\\")[:hidden_states.shape[0]] for d in self.bank]\\n hidden_states_uc = self.attn1(norm_hidden_states, \\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\\n attention_mask=attention_mask) + hidden_states\\n hidden_states_c = hidden_states_uc.clone()\\n _uc_mask = uc_mask.clone()\\n if do_classifier_free_guidance:\\n if hidden_states.shape[0] != _uc_mask.shape[0]:\\n _uc_mask = (\\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\\n .to(device)\\n .bool()\\n )\\n hidden_states_c[_uc_mask] = self.attn1(\\n norm_hidden_states[_uc_mask],\\n encoder_hidden_states=norm_hidden_states[_uc_mask],\\n attention_mask=attention_mask,\\n ) + hidden_states[_uc_mask]\\n hidden_states = hidden_states_c.clone()\\n \\n self.bank.clear()\\n if self.attn2 is not None:\\n # Cross-Attention\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n hidden_states = (\\n self.attn2(\\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n + hidden_states\\n )\\n\\n # Feed-forward\\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\\n\\n # Temporal-Attention\\n if self.unet_use_temporal_attention:\\n d = hidden_states.shape[1]\\n hidden_states = rearrange(hidden_states, \\\"(b f) d c -> (b d) f c\\\", f=video_length)\\n norm_hidden_states = (\\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\\n )\\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\\n hidden_states = rearrange(hidden_states, \\\"(b d) f c -> (b f) d c\\\", d=d)\\n\\n return hidden_states\\n \\n if self.use_ada_layer_norm_zero:\\n attn_output = gate_msa.unsqueeze(1) * attn_output\\n hidden_states = attn_output + hidden_states\\n\\n if self.attn2 is not None:\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n\\n # 2. Cross-Attention\\n attn_output = self.attn2(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=encoder_attention_mask,\\n **cross_attention_kwargs,\\n )\\n hidden_states = attn_output + hidden_states\\n\\n # 3. Feed-forward\\n norm_hidden_states = self.norm3(hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\\n\\n ff_output = self.ff(norm_hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n ff_output = gate_mlp.unsqueeze(1) * ff_output\\n\\n hidden_states = ff_output + hidden_states\\n\\n return hidden_states\\n\\n def hacked_mid_forward(self, *args, **kwargs):\\n eps = 1e-6\\n x = self.original_forward(*args, **kwargs)\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append(mean)\\n self.var_bank.append(var)\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\\n x_c = x_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n x_c[uc_mask] = x[uc_mask]\\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\\n self.mean_bank = []\\n self.var_bank = []\\n return x\\n\\n def hack_CrossAttnDownBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n\\n # TODO(Patrick, William) - attention mask is not used\\n output_states = ()\\n\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\\n eps = 1e-6\\n\\n output_states = ()\\n\\n for i, resnet in enumerate(self.resnets):\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_CrossAttnUpBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n upsample_size: Optional[int] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n # TODO(Patrick, William) - attention mask is not used\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\\n eps = 1e-6\\n for i, resnet in enumerate(self.resnets):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n\\n for i, module in enumerate(attn_modules):\\n module._original_inner_forward = module.forward\\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\\n module.bank = []\\n module.attn_weight = float(i) / float(len(attn_modules))\\n\\n if self.reference_adain:\\n gn_modules = [self.unet.mid_block]\\n self.unet.mid_block.gn_weight = 0\\n\\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\\n gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n module.gn_weight = float(w) / float(len(up_blocks))\\n gn_modules.append(module)\\n\\n for i, module in enumerate(gn_modules):\\n if getattr(module, \\\"original_forward\\\", None) is None:\\n module.original_forward = module.forward\\n if i == 0:\\n # mid_block\\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\\n elif isinstance(module, CrossAttnDownBlock2D):\\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\\n elif isinstance(module, DownBlock2D):\\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\\n elif isinstance(module, CrossAttnUpBlock2D):\\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\\n elif isinstance(module, UpBlock2D):\\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\\n module.mean_bank = []\\n module.var_bank = []\\n module.gn_weight *= 2\\n \\n def update(self, writer, dtype=torch.float16):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \\n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r, w in zip(reader_attn_modules, writer_attn_modules):\\n r.bank = [v.clone().to(dtype) for v in w.bank]\\n # w.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n writer_gn_modules = [writer.unet.mid_block]\\n \\n down_blocks = writer.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n writer_gn_modules.append(module)\\n\\n up_blocks = writer.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n writer_gn_modules.append(module)\\n \\n for r, w in zip(reader_gn_modules, writer_gn_modules):\\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\\n else:\\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\\n \\n def clear(self):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r in reader_attn_modules:\\n r.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n for r in reader_gn_modules:\\n r.mean_bank.clear()\\n r.var_bank.clear()\"\n },\n {\n \"identifier\": \"get_context_scheduler\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_context_scheduler(name: str) -> Callable:\\n if name == \\\"uniform\\\":\\n return uniform\\n else:\\n raise ValueError(f\\\"Unknown context_overlap policy {name}\\\")\"\n },\n {\n \"identifier\": \"get_total_steps\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_total_steps(\\n scheduler,\\n timesteps: List[int],\\n num_steps: Optional[int] = None,\\n num_frames: int = ...,\\n context_size: Optional[int] = None,\\n context_stride: int = 3,\\n context_overlap: int = 4,\\n closed_loop: bool = True,\\n):\\n return sum(\\n len(\\n list(\\n scheduler(\\n i,\\n num_steps,\\n num_frames,\\n context_size,\\n context_stride,\\n context_overlap,\\n )\\n )\\n )\\n for i in range(len(timesteps))\\n )\"\n },\n {\n \"identifier\": \"get_tensor_interpolation_method\",\n \"path\": \"magicanimate/utils/util.py\",\n \"snippet\": \"def get_tensor_interpolation_method():\\n return tensor_interpolation\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import inspect, math\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom typing import Callable, List, Optional, Union\nfrom dataclasses import dataclass\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom diffusers.utils import is_accelerate_available\nfrom packaging import version\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL\nfrom diffusers.pipeline_utils import DiffusionPipeline\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import deprecate, logging, BaseOutput\nfrom einops import rearrange\nfrom magicanimate.models.unet_controlnet import UNet3DConditionModel\nfrom magicanimate.models.controlnet import ControlNetModel\nfrom magicanimate.models.mutual_self_attention import ReferenceAttentionControl\nfrom magicanimate.pipelines.context import (\n get_context_scheduler,\n get_total_steps\n)\nfrom magicanimate.utils.util import get_tensor_interpolation_method\n from accelerate import cpu_offload"},"token_num":{"kind":"number","value":19344,"string":"19,344"},"cropped_code":{"kind":"string","value":" generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n controlnet_condition: list = None,\n controlnet_conditioning_scale: float = 1.0,\n context_frames: int = 16,\n context_stride: int = 1,\n context_overlap: int = 4,\n context_batch_size: int = 1, \n context_schedule: str = \"uniform\",\n init_latents: Optional[torch.FloatTensor] = None,\n num_actual_inference_steps: Optional[int] = None,\n appearance_encoder = None, \n reference_control_writer = None,\n reference_control_reader = None,\n source_image: str = None,\n decoder_consistency = None, \n **kwargs,\n ):\n \"\"\"\n New args:\n - controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet\n - controlnet_conditioning_scale : conditioning scale for controlnet\n - init_latents : initial latents to begin with (used along with invert())\n - num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps) \n \"\"\"\n controlnet = self.controlnet\n\n # Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # Define call parameters\n # batch_size = 1 if isinstance(prompt, str) else len(prompt)\n batch_size = 1\n if latents is not None:\n batch_size = latents.shape[0]\n if isinstance(prompt, list):\n batch_size = len(prompt)\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # Encode input prompt\n prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size\n if negative_prompt is not None:\n negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size \n text_embeddings = self._encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n text_embeddings = torch.cat([text_embeddings] * context_batch_size)\n \n reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)\n reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)\n \n is_dist_initialized = kwargs.get(\"dist\", False)\n rank = kwargs.get(\"rank\", 0)\n world_size = kwargs.get(\"world_size\", 1)\n\n # Prepare video\n assert num_videos_per_prompt == 1 # FIXME: verify if num_videos_per_prompt > 1 works\n assert batch_size == 1 # FIXME: verify if batch_size > 1 works\n control = self.prepare_condition(\n condition=controlnet_condition,\n device=device,\n dtype=controlnet.dtype,\n num_videos_per_prompt=num_videos_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n controlnet_uncond_images, controlnet_cond_images = control.chunk(2)\n\n # Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # Prepare latent variables\n if init_latents is not None:\n latents = rearrange(init_latents, \"(b f) c h w -> b c f h w\", f=video_length)\n else:\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n video_length,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n latents_dtype = latents.dtype\n\n # Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # Prepare text embeddings for controlnet\n controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)\n _, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)\n \n controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}\n\n # For img2img setting\n if num_actual_inference_steps is None:\n num_actual_inference_steps = num_inference_steps\n \n if isinstance(source_image, str):\n ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()\n elif isinstance(source_image, np.ndarray):\n ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()\n \n"},"all_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc.. \n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,\n controlnet: ControlNetModel,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def enable_vae_slicing(self):\n self.vae.enable_slicing()\n\n def disable_vae_slicing(self):\n self.vae.disable_slicing()\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n if is_accelerate_available():\n else:\n raise ImportError(\"Please install accelerate via `pip install accelerate`\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:\n if cpu_offloaded_model is not None:\n cpu_offload(cpu_offloaded_model, device)\n\n\n @property\n def _execution_device(self):\n if self.device != torch.device(\"meta\") or not hasattr(self.unet, \"_hf_hook\"):\n return self.device\n for module in self.unet.modules():\n if (\n hasattr(module, \"_hf_hook\")\n and hasattr(module._hf_hook, \"execution_device\")\n and module._hf_hook.execution_device is not None\n ):\n return torch.device(module._hf_hook.execution_device)\n return self.device\n\n def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def decode_latents(self, latents, rank, decoder_consistency=None):\n video_length = latents.shape[2]\n latents = 1 / 0.18215 * latents\n latents = rearrange(latents, \"b c f h w -> (b f) c h w\")\n # video = self.vae.decode(latents).sample\n video = []\n for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):\n if decoder_consistency is not None:\n video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))\n else:\n video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)\n video = torch.cat(video)\n video = rearrange(video, \"(b f) c h w -> b c f h w\", f=video_length)\n video = (video / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16\n video = video.cpu().float().numpy()\n return video\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):\n shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n if latents is None:\n rand_device = \"cpu\" if device.type == \"mps\" else device\n\n if isinstance(generator, list):\n latents = [\n torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)\n for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0).to(device)\n else:\n latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)\n \n latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):\n # prepare conditions for controlnet\n condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0\n condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)\n condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()\n if do_classifier_free_guidance:\n condition = torch.cat([condition] * 2)\n return condition\n\n def next_step(\n self,\n model_output: torch.FloatTensor,\n timestep: int,\n x: torch.FloatTensor,\n eta=0.,\n verbose=False\n ):\n \"\"\"\n Inverse sampling for DDIM Inversion\n \"\"\"\n if verbose:\n print(\"timestep: \", timestep)\n next_step = timestep\n timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod\n alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]\n beta_prod_t = 1 - alpha_prod_t\n pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output\n x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir\n return x_next, pred_x0\n\n @torch.no_grad()\n def images2latents(self, images, dtype):\n \"\"\"\n Convert RGB image to VAE latents\n \"\"\"\n device = self._execution_device\n images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1\n images = rearrange(images, \"f h w c -> f c h w\").to(device)\n latents = []\n for frame_idx in range(images.shape[0]):\n latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)\n latents = torch.cat(latents)\n return latents\n\n @torch.no_grad()\n def invert(\n self,\n image: torch.Tensor,\n prompt,\n num_inference_steps=20,\n num_actual_inference_steps=10,\n eta=0.0,\n return_intermediates=False,\n **kwargs):\n \"\"\"\n Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440\n invert a real image into noise map with determinisc DDIM inversion\n \"\"\"\n device = self._execution_device\n batch_size = image.shape[0]\n if isinstance(prompt, list):\n if batch_size == 1:\n image = image.expand(len(prompt), -1, -1, -1)\n elif isinstance(prompt, str):\n if batch_size > 1:\n prompt = [prompt] * batch_size\n\n # text embeddings\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n return_tensors=\"pt\"\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]\n print(\"input text embeddings :\", text_embeddings.shape)\n # define initial latents\n latents = self.images2latents(image)\n\n print(\"latents shape: \", latents.shape)\n # interative sampling\n self.scheduler.set_timesteps(num_inference_steps)\n print(\"Valid timesteps: \", reversed(self.scheduler.timesteps))\n latents_list = [latents]\n pred_x0_list = [latents]\n for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc=\"DDIM Inversion\")):\n\n if num_actual_inference_steps is not None and i >= num_actual_inference_steps:\n continue\n model_inputs = latents\n\n # predict the noise\n # NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)\n model_inputs = rearrange(model_inputs, \"f c h w -> 1 c f h w\")\n noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample\n noise_pred = rearrange(noise_pred, \"b c f h w -> (b f) c h w\")\n \n # compute the previous noise sample x_t-1 -> x_t\n latents, pred_x0 = self.next_step(noise_pred, t, latents)\n latents_list.append(latents)\n pred_x0_list.append(pred_x0)\n\n if return_intermediates:\n # return the intermediate laters during inversion\n return latents, latents_list\n return latents\n \n def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):\n if interpolation_factor < 2:\n return latents\n\n new_latents = torch.zeros(\n (latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),\n device=latents.device,\n dtype=latents.dtype,\n )\n\n org_video_length = latents.shape[2]\n rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]\n\n new_index = 0\n\n v0 = None\n v1 = None\n\n for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):\n v0 = latents[:,:,i0,:,:]\n v1 = latents[:,:,i1,:,:]\n\n new_latents[:,:,new_index,:,:] = v0\n new_index += 1\n\n for f in rate:\n v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)\n new_latents[:,:,new_index,:,:] = v.to(latents.device)\n new_index += 1\n\n new_latents[:,:,new_index,:,:] = v1\n new_index += 1\n\n return new_latents\n \n def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):\n _down_block_res_samples = []\n _mid_block_res_sample = []\n for i in np.concatenate(np.array(context)):\n _down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])\n _mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])\n down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]\n for res_t in _down_block_res_samples:\n for i, res in enumerate(res_t):\n down_block_res_samples[i].append(res)\n down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]\n mid_block_res_sample = torch.cat(_mid_block_res_sample)\n \n # reshape controlnet output to match the unet3d inputs\n b = b // 2 if do_classifier_free_guidance else b\n _down_block_res_samples = []\n for sample in down_block_res_samples:\n sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n sample = sample.repeat(2, 1, 1, 1, 1)\n _down_block_res_samples.append(sample)\n down_block_res_samples = _down_block_res_samples\n mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)\n \n return down_block_res_samples, mid_block_res_sample\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n video_length: Optional[int],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n controlnet_condition: list = None,\n controlnet_conditioning_scale: float = 1.0,\n context_frames: int = 16,\n context_stride: int = 1,\n context_overlap: int = 4,\n context_batch_size: int = 1, \n context_schedule: str = \"uniform\",\n init_latents: Optional[torch.FloatTensor] = None,\n num_actual_inference_steps: Optional[int] = None,\n appearance_encoder = None, \n reference_control_writer = None,\n reference_control_reader = None,\n source_image: str = None,\n decoder_consistency = None, \n **kwargs,\n ):\n \"\"\"\n New args:\n - controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet\n - controlnet_conditioning_scale : conditioning scale for controlnet\n - init_latents : initial latents to begin with (used along with invert())\n - num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps) \n \"\"\"\n controlnet = self.controlnet\n\n # Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # Define call parameters\n # batch_size = 1 if isinstance(prompt, str) else len(prompt)\n batch_size = 1\n if latents is not None:\n batch_size = latents.shape[0]\n if isinstance(prompt, list):\n batch_size = len(prompt)\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # Encode input prompt\n prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size\n if negative_prompt is not None:\n negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size \n text_embeddings = self._encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n text_embeddings = torch.cat([text_embeddings] * context_batch_size)\n \n reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)\n reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)\n \n is_dist_initialized = kwargs.get(\"dist\", False)\n rank = kwargs.get(\"rank\", 0)\n world_size = kwargs.get(\"world_size\", 1)\n\n # Prepare video\n assert num_videos_per_prompt == 1 # FIXME: verify if num_videos_per_prompt > 1 works\n assert batch_size == 1 # FIXME: verify if batch_size > 1 works\n control = self.prepare_condition(\n condition=controlnet_condition,\n device=device,\n dtype=controlnet.dtype,\n num_videos_per_prompt=num_videos_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n controlnet_uncond_images, controlnet_cond_images = control.chunk(2)\n\n # Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # Prepare latent variables\n if init_latents is not None:\n latents = rearrange(init_latents, \"(b f) c h w -> b c f h w\", f=video_length)\n else:\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n video_length,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n latents_dtype = latents.dtype\n\n # Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # Prepare text embeddings for controlnet\n controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)\n _, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)\n \n controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}\n\n # For img2img setting\n if num_actual_inference_steps is None:\n num_actual_inference_steps = num_inference_steps\n \n if isinstance(source_image, str):\n ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()\n elif isinstance(source_image, np.ndarray):\n ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()\n "},"next_line":{"kind":"string","value":" context_scheduler = get_context_scheduler(context_schedule)"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-12-04 20:47:34+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5380,"cells":{"repo_name":{"kind":"string","value":"metatube-community/metatube-plex-plugins"},"file_path":{"kind":"string","value":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/poolmanager.py"},"context":{"kind":"list like","value":[{"identifier":"HTTPHeaderDict","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/_collections.py","snippet":"class HTTPHeaderDict(MutableMapping):\n \"\"\"\n :param headers:\n An iterable of field-value pairs. Must not contain multiple field names\n when compared case-insensitively.\n\n :param kwargs:\n Additional field-value pairs to pass in to ``dict.update``.\n\n A ``dict`` like container for storing HTTP Headers.\n\n Field names are stored and compared case-insensitively in compliance with\n RFC 7230. Iteration provides the first case-sensitive key seen for each\n case-insensitive pair.\n\n Using ``__setitem__`` syntax overwrites fields that compare equal\n case-insensitively in order to maintain ``dict``'s api. For fields that\n compare equal, instead create a new ``HTTPHeaderDict`` and use ``.add``\n in a loop.\n\n If multiple fields that are equal case-insensitively are passed to the\n constructor or ``.update``, the behavior is undefined and some will be\n lost.\n\n >>> headers = HTTPHeaderDict()\n >>> headers.add('Set-Cookie', 'foo=bar')\n >>> headers.add('set-cookie', 'baz=quxx')\n >>> headers['content-length'] = '7'\n >>> headers['SET-cookie']\n 'foo=bar, baz=quxx'\n >>> headers['Content-Length']\n '7'\n \"\"\"\n\n def __init__(self, headers=None, **kwargs):\n super(HTTPHeaderDict, self).__init__()\n self._container = OrderedDict()\n if headers is not None:\n if isinstance(headers, HTTPHeaderDict):\n self._copy_from(headers)\n else:\n self.extend(headers)\n if kwargs:\n self.extend(kwargs)\n\n def __setitem__(self, key, val):\n self._container[key.lower()] = [key, val]\n return self._container[key.lower()]\n\n def __getitem__(self, key):\n val = self._container[key.lower()]\n return \", \".join(val[1:])\n\n def __delitem__(self, key):\n del self._container[key.lower()]\n\n def __contains__(self, key):\n return key.lower() in self._container\n\n def __eq__(self, other):\n if not isinstance(other, Mapping) and not hasattr(other, \"keys\"):\n return False\n if not isinstance(other, type(self)):\n other = type(self)(other)\n return dict((k.lower(), v) for k, v in self.itermerged()) == dict(\n (k.lower(), v) for k, v in other.itermerged()\n )\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n if six.PY2: # Python 2\n iterkeys = MutableMapping.iterkeys\n itervalues = MutableMapping.itervalues\n\n __marker = object()\n\n def __len__(self):\n return len(self._container)\n\n def __iter__(self):\n # Only provide the originally cased names\n for vals in self._container.values():\n yield vals[0]\n\n def pop(self, key, default=__marker):\n \"\"\"D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n If key is not found, d is returned if given, otherwise KeyError is raised.\n \"\"\"\n # Using the MutableMapping function directly fails due to the private marker.\n # Using ordinary dict.pop would expose the internal structures.\n # So let's reinvent the wheel.\n try:\n value = self[key]\n except KeyError:\n if default is self.__marker:\n raise\n return default\n else:\n del self[key]\n return value\n\n def discard(self, key):\n try:\n del self[key]\n except KeyError:\n pass\n\n def add(self, key, val):\n \"\"\"Adds a (name, value) pair, doesn't overwrite the value if it already\n exists.\n\n >>> headers = HTTPHeaderDict(foo='bar')\n >>> headers.add('Foo', 'baz')\n >>> headers['foo']\n 'bar, baz'\n \"\"\"\n key_lower = key.lower()\n new_vals = [key, val]\n # Keep the common case aka no item present as fast as possible\n vals = self._container.setdefault(key_lower, new_vals)\n if new_vals is not vals:\n vals.append(val)\n\n def extend(self, *args, **kwargs):\n \"\"\"Generic import function for any type of header-like object.\n Adapted version of MutableMapping.update in order to insert items\n with self.add instead of self.__setitem__\n \"\"\"\n if len(args) > 1:\n raise TypeError(\n \"extend() takes at most 1 positional \"\n \"arguments ({0} given)\".format(len(args))\n )\n other = args[0] if len(args) >= 1 else ()\n\n if isinstance(other, HTTPHeaderDict):\n for key, val in other.iteritems():\n self.add(key, val)\n elif isinstance(other, Mapping):\n for key in other:\n self.add(key, other[key])\n elif hasattr(other, \"keys\"):\n for key in other.keys():\n self.add(key, other[key])\n else:\n for key, value in other:\n self.add(key, value)\n\n for key, value in kwargs.items():\n self.add(key, value)\n\n def getlist(self, key, default=__marker):\n \"\"\"Returns a list of all the values for the named field. Returns an\n empty list if the key doesn't exist.\"\"\"\n try:\n vals = self._container[key.lower()]\n except KeyError:\n if default is self.__marker:\n return []\n return default\n else:\n return vals[1:]\n\n def _prepare_for_method_change(self):\n \"\"\"\n Remove content-specific header fields before changing the request\n method to GET or HEAD according to RFC 9110, Section 15.4.\n \"\"\"\n content_specific_headers = [\n \"Content-Encoding\",\n \"Content-Language\",\n \"Content-Location\",\n \"Content-Type\",\n \"Content-Length\",\n \"Digest\",\n \"Last-Modified\",\n ]\n for header in content_specific_headers:\n self.discard(header)\n return self\n\n # Backwards compatibility for httplib\n getheaders = getlist\n getallmatchingheaders = getlist\n iget = getlist\n\n # Backwards compatibility for http.cookiejar\n get_all = getlist\n\n def __repr__(self):\n return \"%s(%s)\" % (type(self).__name__, dict(self.itermerged()))\n\n def _copy_from(self, other):\n for key in other:\n val = other.getlist(key)\n if isinstance(val, list):\n # Don't need to convert tuples\n val = list(val)\n self._container[key.lower()] = [key] + val\n\n def copy(self):\n clone = type(self)()\n clone._copy_from(self)\n return clone\n\n def iteritems(self):\n \"\"\"Iterate over all header lines, including duplicate ones.\"\"\"\n for key in self:\n vals = self._container[key.lower()]\n for val in vals[1:]:\n yield vals[0], val\n\n def itermerged(self):\n \"\"\"Iterate over all headers, merging duplicate ones together.\"\"\"\n for key in self:\n val = self._container[key.lower()]\n yield val[0], \", \".join(val[1:])\n\n def items(self):\n return list(self.iteritems())\n\n @classmethod\n def from_httplib(cls, message): # Python 2\n \"\"\"Read headers from a Python 2 httplib message object.\"\"\"\n # python2.7 does not expose a proper API for exporting multiheaders\n # efficiently. This function re-reads raw lines from the message\n # object and extracts the multiheaders properly.\n obs_fold_continued_leaders = (\" \", \"\\t\")\n headers = []\n\n for line in message.headers:\n if line.startswith(obs_fold_continued_leaders):\n if not headers:\n # We received a header line that starts with OWS as described\n # in RFC-7230 S3.2.4. This indicates a multiline header, but\n # there exists no previous header to which we can attach it.\n raise InvalidHeader(\n \"Header continuation with no previous header: %s\" % line\n )\n else:\n key, value = headers[-1]\n headers[-1] = (key, value + \" \" + line.strip())\n continue\n\n key, value = line.split(\":\", 1)\n headers.append((key, value.strip()))\n\n return cls(headers)"},{"identifier":"RecentlyUsedContainer","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/_collections.py","snippet":"class RecentlyUsedContainer(MutableMapping):\n \"\"\"\n Provides a thread-safe dict-like container which maintains up to\n ``maxsize`` keys while throwing away the least-recently-used keys beyond\n ``maxsize``.\n\n :param maxsize:\n Maximum number of recent elements to retain.\n\n :param dispose_func:\n Every time an item is evicted from the container,\n ``dispose_func(value)`` is called. Callback which will get called\n \"\"\"\n\n ContainerCls = OrderedDict\n\n def __init__(self, maxsize=10, dispose_func=None):\n self._maxsize = maxsize\n self.dispose_func = dispose_func\n\n self._container = self.ContainerCls()\n self.lock = RLock()\n\n def __getitem__(self, key):\n # Re-insert the item, moving it to the end of the eviction line.\n with self.lock:\n item = self._container.pop(key)\n self._container[key] = item\n return item\n\n def __setitem__(self, key, value):\n evicted_value = _Null\n with self.lock:\n # Possibly evict the existing value of 'key'\n evicted_value = self._container.get(key, _Null)\n self._container[key] = value\n\n # If we didn't evict an existing value, we might have to evict the\n # least recently used item from the beginning of the container.\n if len(self._container) > self._maxsize:\n _key, evicted_value = self._container.popitem(last=False)\n\n if self.dispose_func and evicted_value is not _Null:\n self.dispose_func(evicted_value)\n\n def __delitem__(self, key):\n with self.lock:\n value = self._container.pop(key)\n\n if self.dispose_func:\n self.dispose_func(value)\n\n def __len__(self):\n with self.lock:\n return len(self._container)\n\n def __iter__(self):\n raise NotImplementedError(\n \"Iteration over this class is unlikely to be threadsafe.\"\n )\n\n def clear(self):\n with self.lock:\n # Copy pointers to all values, then wipe the mapping\n values = list(itervalues(self._container))\n self._container.clear()\n\n if self.dispose_func:\n for value in values:\n self.dispose_func(value)\n\n def keys(self):\n with self.lock:\n return list(iterkeys(self._container))"},{"identifier":"HTTPConnectionPool","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/connectionpool.py","snippet":"class ConnectionPool(object):\nclass HTTPConnectionPool(ConnectionPool, RequestMethods):\nclass HTTPSConnectionPool(HTTPConnectionPool):\n def __init__(self, host, port=None):\n def __str__(self):\n def __enter__(self):\n def __exit__(self, exc_type, exc_val, exc_tb):\n def close(self):\n def __init__(\n self,\n host,\n port=None,\n strict=False,\n timeout=Timeout.DEFAULT_TIMEOUT,\n maxsize=1,\n block=False,\n headers=None,\n retries=None,\n _proxy=None,\n _proxy_headers=None,\n _proxy_config=None,\n **conn_kw\n ):\n def _new_conn(self):\n def _get_conn(self, timeout=None):\n def _put_conn(self, conn):\n def _validate_conn(self, conn):\n def _prepare_proxy(self, conn):\n def _get_timeout(self, timeout):\n def _raise_timeout(self, err, url, timeout_value):\n def _make_request(\n self, conn, method, url, timeout=_Default, chunked=False, **httplib_request_kw\n ):\n def _absolute_url(self, path):\n def close(self):\n def is_same_host(self, url):\n def urlopen(\n self,\n method,\n url,\n body=None,\n headers=None,\n retries=None,\n redirect=True,\n assert_same_host=True,\n timeout=_Default,\n pool_timeout=None,\n release_conn=None,\n chunked=False,\n body_pos=None,\n **response_kw\n ):\n def _is_ssl_error_message_from_http_proxy(ssl_error):\n def __init__(\n self,\n host,\n port=None,\n strict=False,\n timeout=Timeout.DEFAULT_TIMEOUT,\n maxsize=1,\n block=False,\n headers=None,\n retries=None,\n _proxy=None,\n _proxy_headers=None,\n key_file=None,\n cert_file=None,\n cert_reqs=None,\n key_password=None,\n ca_certs=None,\n ssl_version=None,\n assert_hostname=None,\n assert_fingerprint=None,\n ca_cert_dir=None,\n **conn_kw\n ):\n def _prepare_conn(self, conn):\n def _prepare_proxy(self, conn):\n def _new_conn(self):\n def _validate_conn(self, conn):\ndef connection_from_url(url, **kw):\ndef _normalize_host(host, scheme):\ndef _close_pool_connections(pool):"},{"identifier":"LocationValueError","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py","snippet":"class LocationValueError(ValueError, HTTPError):\n \"\"\"Raised when there is something wrong with a given URL input.\"\"\"\n\n pass"},{"identifier":"MaxRetryError","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py","snippet":"class MaxRetryError(RequestError):\n \"\"\"Raised when the maximum number of retries is exceeded.\n\n :param pool: The connection pool\n :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool`\n :param string url: The requested Url\n :param exceptions.Exception reason: The underlying error\n\n \"\"\"\n\n def __init__(self, pool, url, reason=None):\n self.reason = reason\n\n message = \"Max retries exceeded with url: %s (Caused by %r)\" % (url, reason)\n\n RequestError.__init__(self, pool, url, message)"},{"identifier":"ProxySchemeUnknown","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py","snippet":"class ProxySchemeUnknown(AssertionError, URLSchemeUnknown):\n \"\"\"ProxyManager does not support the supplied scheme\"\"\"\n\n # TODO(t-8ch): Stop inheriting from AssertionError in v2.0.\n\n def __init__(self, scheme):\n # 'localhost' is here because our URL parser parses\n # localhost:8080 -> scheme=localhost, remove if we fix this.\n if scheme == \"localhost\":\n scheme = None\n if scheme is None:\n message = \"Proxy URL had no scheme, should start with http:// or https://\"\n else:\n message = (\n \"Proxy URL had unsupported scheme %s, should use http:// or https://\"\n % scheme\n )\n super(ProxySchemeUnknown, self).__init__(message)"},{"identifier":"ProxySchemeUnsupported","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py","snippet":"class ProxySchemeUnsupported(ValueError):\n \"\"\"Fetching HTTPS resources through HTTPS proxies is unsupported\"\"\"\n\n pass"},{"identifier":"URLSchemeUnknown","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py","snippet":"class URLSchemeUnknown(LocationValueError):\n \"\"\"Raised when a URL input has an unsupported scheme.\"\"\"\n\n def __init__(self, scheme):\n message = \"Not supported URL scheme %s\" % scheme\n super(URLSchemeUnknown, self).__init__(message)\n\n self.scheme = scheme"},{"identifier":"six","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/packages/six.py","snippet":"PY2 = sys.version_info[0] == 2\nPY3 = sys.version_info[0] == 3\nPY34 = sys.version_info[0:2] >= (3, 4)\n MAXSIZE = sys.maxsize\n MAXSIZE = int((1 << 31) - 1)\n MAXSIZE = int((1 << 31) - 1)\n MAXSIZE = int((1 << 63) - 1)\n class X(object):\nclass _LazyDescr(object):\nclass MovedModule(_LazyDescr):\nclass _LazyModule(types.ModuleType):\nclass MovedAttribute(_LazyDescr):\nclass _SixMetaPathImporter(object):\nclass _MovedItems(_LazyModule):\nclass Module_six_moves_urllib_parse(_LazyModule):\nclass Module_six_moves_urllib_error(_LazyModule):\nclass Module_six_moves_urllib_request(_LazyModule):\nclass Module_six_moves_urllib_response(_LazyModule):\nclass Module_six_moves_urllib_robotparser(_LazyModule):\nclass Module_six_moves_urllib(types.ModuleType):\n class Iterator(object):\n class metaclass(type):\n def __len__(self):\ndef _add_doc(func, doc):\ndef _import_module(name):\n def __init__(self, name):\n def __get__(self, obj, tp):\n def __init__(self, name, old, new=None):\n def _resolve(self):\n def __getattr__(self, attr):\n def __init__(self, name):\n def __dir__(self):\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\n def _resolve(self):\n def __init__(self, six_module_name):\n def _add_module(self, mod, *fullnames):\n def _get_module(self, fullname):\n def find_module(self, fullname, path=None):\n def find_spec(self, fullname, path, target=None):\n def __get_module(self, fullname):\n def load_module(self, fullname):\n def is_package(self, fullname):\n def get_code(self, fullname):\n def create_module(self, spec):\n def exec_module(self, module):\n def __dir__(self):\ndef add_move(move):\ndef remove_move(name):\n def advance_iterator(it):\n def callable(obj):\n def get_unbound_function(unbound):\n def create_unbound_method(func, cls):\n def get_unbound_function(unbound):\n def create_bound_method(func, obj):\n def create_unbound_method(func, cls):\n def next(self):\n def iterkeys(d, **kw):\n def itervalues(d, **kw):\n def iteritems(d, **kw):\n def iterlists(d, **kw):\n def iterkeys(d, **kw):\n def itervalues(d, **kw):\n def iteritems(d, **kw):\n def iterlists(d, **kw):\n def b(s):\n def u(s):\n def b(s):\n def u(s):\n def byte2int(bs):\n def indexbytes(buf, i):\ndef assertCountEqual(self, *args, **kwargs):\ndef assertRaisesRegex(self, *args, **kwargs):\ndef assertRegex(self, *args, **kwargs):\ndef assertNotRegex(self, *args, **kwargs):\n def reraise(tp, value, tb=None):\n def exec_(_code_, _globs_=None, _locs_=None):\n def raise_from(value, from_value):\n def print_(*args, **kwargs):\n def write(data):\n def print_(*args, **kwargs):\n def _update_wrapper(\n wrapper,\n wrapped,\n assigned=functools.WRAPPER_ASSIGNMENTS,\n updated=functools.WRAPPER_UPDATES,\n ):\n def wraps(\n wrapped,\n assigned=functools.WRAPPER_ASSIGNMENTS,\n updated=functools.WRAPPER_UPDATES,\n ):\ndef with_metaclass(meta, *bases):\n def __new__(cls, name, this_bases, d):\n def __prepare__(cls, name, this_bases):\ndef add_metaclass(metaclass):\n def wrapper(cls):\ndef ensure_binary(s, encoding=\"utf-8\", errors=\"strict\"):\ndef ensure_str(s, encoding=\"utf-8\", errors=\"strict\"):\ndef ensure_text(s, encoding=\"utf-8\", errors=\"strict\"):\ndef python_2_unicode_compatible(klass):"},{"identifier":"RequestMethods","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/request.py","snippet":"class RequestMethods(object):\n \"\"\"\n Convenience mixin for classes who implement a :meth:`urlopen` method, such\n as :class:`urllib3.HTTPConnectionPool` and\n :class:`urllib3.PoolManager`.\n\n Provides behavior for making common types of HTTP request methods and\n decides which type of request field encoding to use.\n\n Specifically,\n\n :meth:`.request_encode_url` is for sending requests whose fields are\n encoded in the URL (such as GET, HEAD, DELETE).\n\n :meth:`.request_encode_body` is for sending requests whose fields are\n encoded in the *body* of the request using multipart or www-form-urlencoded\n (such as for POST, PUT, PATCH).\n\n :meth:`.request` is for making any kind of request, it will look up the\n appropriate encoding format and use one of the above two methods to make\n the request.\n\n Initializer parameters:\n\n :param headers:\n Headers to include with all requests, unless other headers are given\n explicitly.\n \"\"\"\n\n _encode_url_methods = {\"DELETE\", \"GET\", \"HEAD\", \"OPTIONS\"}\n\n def __init__(self, headers=None):\n self.headers = headers or {}\n\n def urlopen(\n self,\n method,\n url,\n body=None,\n headers=None,\n encode_multipart=True,\n multipart_boundary=None,\n **kw\n ): # Abstract\n raise NotImplementedError(\n \"Classes extending RequestMethods must implement \"\n \"their own ``urlopen`` method.\"\n )\n\n def request(self, method, url, fields=None, headers=None, **urlopen_kw):\n \"\"\"\n Make a request using :meth:`urlopen` with the appropriate encoding of\n ``fields`` based on the ``method`` used.\n\n This is a convenience method that requires the least amount of manual\n effort. It can be used in most situations, while still having the\n option to drop down to more specific methods when necessary, such as\n :meth:`request_encode_url`, :meth:`request_encode_body`,\n or even the lowest level :meth:`urlopen`.\n \"\"\"\n method = method.upper()\n\n urlopen_kw[\"request_url\"] = url\n\n if method in self._encode_url_methods:\n return self.request_encode_url(\n method, url, fields=fields, headers=headers, **urlopen_kw\n )\n else:\n return self.request_encode_body(\n method, url, fields=fields, headers=headers, **urlopen_kw\n )\n\n def request_encode_url(self, method, url, fields=None, headers=None, **urlopen_kw):\n \"\"\"\n Make a request using :meth:`urlopen` with the ``fields`` encoded in\n the url. This is useful for request methods like GET, HEAD, DELETE, etc.\n \"\"\"\n if headers is None:\n headers = self.headers\n\n extra_kw = {\"headers\": headers}\n extra_kw.update(urlopen_kw)\n\n if fields:\n url += \"?\" + urlencode(fields)\n\n return self.urlopen(method, url, **extra_kw)\n\n def request_encode_body(\n self,\n method,\n url,\n fields=None,\n headers=None,\n encode_multipart=True,\n multipart_boundary=None,\n **urlopen_kw\n ):\n \"\"\"\n Make a request using :meth:`urlopen` with the ``fields`` encoded in\n the body. This is useful for request methods like POST, PUT, PATCH, etc.\n\n When ``encode_multipart=True`` (default), then\n :func:`urllib3.encode_multipart_formdata` is used to encode\n the payload with the appropriate content type. Otherwise\n :func:`urllib.parse.urlencode` is used with the\n 'application/x-www-form-urlencoded' content type.\n\n Multipart encoding must be used when posting files, and it's reasonably\n safe to use it in other times too. However, it may break request\n signing, such as with OAuth.\n\n Supports an optional ``fields`` parameter of key/value strings AND\n key/filetuple. A filetuple is a (filename, data, MIME type) tuple where\n the MIME type is optional. For example::\n\n fields = {\n 'foo': 'bar',\n 'fakefile': ('foofile.txt', 'contents of foofile'),\n 'realfile': ('barfile.txt', open('realfile').read()),\n 'typedfile': ('bazfile.bin', open('bazfile').read(),\n 'image/jpeg'),\n 'nonamefile': 'contents of nonamefile field',\n }\n\n When uploading a file, providing a filename (the first parameter of the\n tuple) is optional but recommended to best mimic behavior of browsers.\n\n Note that if ``headers`` are supplied, the 'Content-Type' header will\n be overwritten because it depends on the dynamic random boundary string\n which is used to compose the body of the request. The random boundary\n string can be explicitly set with the ``multipart_boundary`` parameter.\n \"\"\"\n if headers is None:\n headers = self.headers\n\n extra_kw = {\"headers\": {}}\n\n if fields:\n if \"body\" in urlopen_kw:\n raise TypeError(\n \"request got values for both 'fields' and 'body', can only specify one.\"\n )\n\n if encode_multipart:\n body, content_type = encode_multipart_formdata(\n fields, boundary=multipart_boundary\n )\n else:\n body, content_type = (\n urlencode(fields),\n \"application/x-www-form-urlencoded\",\n )\n\n extra_kw[\"body\"] = body\n extra_kw[\"headers\"] = {\"Content-Type\": content_type}\n\n extra_kw[\"headers\"].update(headers)\n extra_kw.update(urlopen_kw)\n\n return self.urlopen(method, url, **extra_kw)"},{"identifier":"connection_requires_http_tunnel","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/proxy.py","snippet":"def connection_requires_http_tunnel(\n proxy_url=None, proxy_config=None, destination_scheme=None\n):\n \"\"\"\n Returns True if the connection requires an HTTP CONNECT through the proxy.\n\n :param URL proxy_url:\n URL of the proxy.\n :param ProxyConfig proxy_config:\n Proxy configuration from poolmanager.py\n :param str destination_scheme:\n The scheme of the destination. (i.e https, http, etc)\n \"\"\"\n # If we're not using a proxy, no way to use a tunnel.\n if proxy_url is None:\n return False\n\n # HTTP destinations never require tunneling, we always forward.\n if destination_scheme == \"http\":\n return False\n\n # Support for forwarding with HTTPS proxies and HTTPS destinations.\n if (\n proxy_url.scheme == \"https\"\n and proxy_config\n and proxy_config.use_forwarding_for_https\n ):\n return False\n\n # Otherwise always use a tunnel.\n return True"},{"identifier":"Retry","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/retry.py","snippet":"class Retry(object):\n \"\"\"Retry configuration.\n\n Each retry attempt will create a new Retry object with updated values, so\n they can be safely reused.\n\n Retries can be defined as a default for a pool::\n\n retries = Retry(connect=5, read=2, redirect=5)\n http = PoolManager(retries=retries)\n response = http.request('GET', 'http://example.com/')\n\n Or per-request (which overrides the default for the pool)::\n\n response = http.request('GET', 'http://example.com/', retries=Retry(10))\n\n Retries can be disabled by passing ``False``::\n\n response = http.request('GET', 'http://example.com/', retries=False)\n\n Errors will be wrapped in :class:`~urllib3.exceptions.MaxRetryError` unless\n retries are disabled, in which case the causing exception will be raised.\n\n :param int total:\n Total number of retries to allow. Takes precedence over other counts.\n\n Set to ``None`` to remove this constraint and fall back on other\n counts.\n\n Set to ``0`` to fail on the first retry.\n\n Set to ``False`` to disable and imply ``raise_on_redirect=False``.\n\n :param int connect:\n How many connection-related errors to retry on.\n\n These are errors raised before the request is sent to the remote server,\n which we assume has not triggered the server to process the request.\n\n Set to ``0`` to fail on the first retry of this type.\n\n :param int read:\n How many times to retry on read errors.\n\n These errors are raised after the request was sent to the server, so the\n request may have side-effects.\n\n Set to ``0`` to fail on the first retry of this type.\n\n :param int redirect:\n How many redirects to perform. Limit this to avoid infinite redirect\n loops.\n\n A redirect is a HTTP response with a status code 301, 302, 303, 307 or\n 308.\n\n Set to ``0`` to fail on the first retry of this type.\n\n Set to ``False`` to disable and imply ``raise_on_redirect=False``.\n\n :param int status:\n How many times to retry on bad status codes.\n\n These are retries made on responses, where status code matches\n ``status_forcelist``.\n\n Set to ``0`` to fail on the first retry of this type.\n\n :param int other:\n How many times to retry on other errors.\n\n Other errors are errors that are not connect, read, redirect or status errors.\n These errors might be raised after the request was sent to the server, so the\n request might have side-effects.\n\n Set to ``0`` to fail on the first retry of this type.\n\n If ``total`` is not set, it's a good idea to set this to 0 to account\n for unexpected edge cases and avoid infinite retry loops.\n\n :param iterable allowed_methods:\n Set of uppercased HTTP method verbs that we should retry on.\n\n By default, we only retry on methods which are considered to be\n idempotent (multiple requests with the same parameters end with the\n same state). See :attr:`Retry.DEFAULT_ALLOWED_METHODS`.\n\n Set to a ``False`` value to retry on any verb.\n\n .. warning::\n\n Previously this parameter was named ``method_whitelist``, that\n usage is deprecated in v1.26.0 and will be removed in v2.0.\n\n :param iterable status_forcelist:\n A set of integer HTTP status codes that we should force a retry on.\n A retry is initiated if the request method is in ``allowed_methods``\n and the response status code is in ``status_forcelist``.\n\n By default, this is disabled with ``None``.\n\n :param float backoff_factor:\n A backoff factor to apply between attempts after the second try\n (most errors are resolved immediately by a second try without a\n delay). urllib3 will sleep for::\n\n {backoff factor} * (2 ** ({number of total retries} - 1))\n\n seconds. If the backoff_factor is 0.1, then :func:`.sleep` will sleep\n for [0.0s, 0.2s, 0.4s, ...] between retries. It will never be longer\n than :attr:`Retry.DEFAULT_BACKOFF_MAX`.\n\n By default, backoff is disabled (set to 0).\n\n :param bool raise_on_redirect: Whether, if the number of redirects is\n exhausted, to raise a MaxRetryError, or to return a response with a\n response code in the 3xx range.\n\n :param bool raise_on_status: Similar meaning to ``raise_on_redirect``:\n whether we should raise an exception, or return a response,\n if status falls in ``status_forcelist`` range and retries have\n been exhausted.\n\n :param tuple history: The history of the request encountered during\n each call to :meth:`~Retry.increment`. The list is in the order\n the requests occurred. Each list item is of class :class:`RequestHistory`.\n\n :param bool respect_retry_after_header:\n Whether to respect Retry-After header on status codes defined as\n :attr:`Retry.RETRY_AFTER_STATUS_CODES` or not.\n\n :param iterable remove_headers_on_redirect:\n Sequence of headers to remove from the request when a response\n indicating a redirect is returned before firing off the redirected\n request.\n \"\"\"\n\n #: Default methods to be used for ``allowed_methods``\n DEFAULT_ALLOWED_METHODS = frozenset(\n [\"HEAD\", \"GET\", \"PUT\", \"DELETE\", \"OPTIONS\", \"TRACE\"]\n )\n\n #: Default status codes to be used for ``status_forcelist``\n RETRY_AFTER_STATUS_CODES = frozenset([413, 429, 503])\n\n #: Default headers to be used for ``remove_headers_on_redirect``\n DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset([\"Cookie\", \"Authorization\"])\n\n #: Maximum backoff time.\n DEFAULT_BACKOFF_MAX = 120\n\n def __init__(\n self,\n total=10,\n connect=None,\n read=None,\n redirect=None,\n status=None,\n other=None,\n allowed_methods=_Default,\n status_forcelist=None,\n backoff_factor=0,\n raise_on_redirect=True,\n raise_on_status=True,\n history=None,\n respect_retry_after_header=True,\n remove_headers_on_redirect=_Default,\n # TODO: Deprecated, remove in v2.0\n method_whitelist=_Default,\n ):\n\n if method_whitelist is not _Default:\n if allowed_methods is not _Default:\n raise ValueError(\n \"Using both 'allowed_methods' and \"\n \"'method_whitelist' together is not allowed. \"\n \"Instead only use 'allowed_methods'\"\n )\n warnings.warn(\n \"Using 'method_whitelist' with Retry is deprecated and \"\n \"will be removed in v2.0. Use 'allowed_methods' instead\",\n DeprecationWarning,\n stacklevel=2,\n )\n allowed_methods = method_whitelist\n if allowed_methods is _Default:\n allowed_methods = self.DEFAULT_ALLOWED_METHODS\n if remove_headers_on_redirect is _Default:\n remove_headers_on_redirect = self.DEFAULT_REMOVE_HEADERS_ON_REDIRECT\n\n self.total = total\n self.connect = connect\n self.read = read\n self.status = status\n self.other = other\n\n if redirect is False or total is False:\n redirect = 0\n raise_on_redirect = False\n\n self.redirect = redirect\n self.status_forcelist = status_forcelist or set()\n self.allowed_methods = allowed_methods\n self.backoff_factor = backoff_factor\n self.raise_on_redirect = raise_on_redirect\n self.raise_on_status = raise_on_status\n self.history = history or tuple()\n self.respect_retry_after_header = respect_retry_after_header\n self.remove_headers_on_redirect = frozenset(\n [h.lower() for h in remove_headers_on_redirect]\n )\n\n def new(self, **kw):\n params = dict(\n total=self.total,\n connect=self.connect,\n read=self.read,\n redirect=self.redirect,\n status=self.status,\n other=self.other,\n status_forcelist=self.status_forcelist,\n backoff_factor=self.backoff_factor,\n raise_on_redirect=self.raise_on_redirect,\n raise_on_status=self.raise_on_status,\n history=self.history,\n remove_headers_on_redirect=self.remove_headers_on_redirect,\n respect_retry_after_header=self.respect_retry_after_header,\n )\n\n # TODO: If already given in **kw we use what's given to us\n # If not given we need to figure out what to pass. We decide\n # based on whether our class has the 'method_whitelist' property\n # and if so we pass the deprecated 'method_whitelist' otherwise\n # we use 'allowed_methods'. Remove in v2.0\n if \"method_whitelist\" not in kw and \"allowed_methods\" not in kw:\n if \"method_whitelist\" in self.__dict__:\n warnings.warn(\n \"Using 'method_whitelist' with Retry is deprecated and \"\n \"will be removed in v2.0. Use 'allowed_methods' instead\",\n DeprecationWarning,\n )\n params[\"method_whitelist\"] = self.allowed_methods\n else:\n params[\"allowed_methods\"] = self.allowed_methods\n\n params.update(kw)\n return type(self)(**params)\n\n @classmethod\n def from_int(cls, retries, redirect=True, default=None):\n \"\"\"Backwards-compatibility for the old retries format.\"\"\"\n if retries is None:\n retries = default if default is not None else cls.DEFAULT\n\n if isinstance(retries, Retry):\n return retries\n\n redirect = bool(redirect) and None\n new_retries = cls(retries, redirect=redirect)\n log.debug(\"Converted retries value: %r -> %r\", retries, new_retries)\n return new_retries\n\n def get_backoff_time(self):\n \"\"\"Formula for computing the current backoff\n\n :rtype: float\n \"\"\"\n # We want to consider only the last consecutive errors sequence (Ignore redirects).\n consecutive_errors_len = len(\n list(\n takewhile(lambda x: x.redirect_location is None, reversed(self.history))\n )\n )\n if consecutive_errors_len <= 1:\n return 0\n\n backoff_value = self.backoff_factor * (2 ** (consecutive_errors_len - 1))\n return min(self.DEFAULT_BACKOFF_MAX, backoff_value)\n\n def parse_retry_after(self, retry_after):\n # Whitespace: https://tools.ietf.org/html/rfc7230#section-3.2.4\n if re.match(r\"^\\s*[0-9]+\\s*$\", retry_after):\n seconds = int(retry_after)\n else:\n retry_date_tuple = email.utils.parsedate_tz(retry_after)\n if retry_date_tuple is None:\n raise InvalidHeader(\"Invalid Retry-After header: %s\" % retry_after)\n if retry_date_tuple[9] is None: # Python 2\n # Assume UTC if no timezone was specified\n # On Python2.7, parsedate_tz returns None for a timezone offset\n # instead of 0 if no timezone is given, where mktime_tz treats\n # a None timezone offset as local time.\n retry_date_tuple = retry_date_tuple[:9] + (0,) + retry_date_tuple[10:]\n\n retry_date = email.utils.mktime_tz(retry_date_tuple)\n seconds = retry_date - time.time()\n\n if seconds < 0:\n seconds = 0\n\n return seconds\n\n def get_retry_after(self, response):\n \"\"\"Get the value of Retry-After in seconds.\"\"\"\n\n retry_after = response.headers.get(\"Retry-After\")\n\n if retry_after is None:\n return None\n\n return self.parse_retry_after(retry_after)\n\n def sleep_for_retry(self, response=None):\n retry_after = self.get_retry_after(response)\n if retry_after:\n time.sleep(retry_after)\n return True\n\n return False\n\n def _sleep_backoff(self):\n backoff = self.get_backoff_time()\n if backoff <= 0:\n return\n time.sleep(backoff)\n\n def sleep(self, response=None):\n \"\"\"Sleep between retry attempts.\n\n This method will respect a server's ``Retry-After`` response header\n and sleep the duration of the time requested. If that is not present, it\n will use an exponential backoff. By default, the backoff factor is 0 and\n this method will return immediately.\n \"\"\"\n\n if self.respect_retry_after_header and response:\n slept = self.sleep_for_retry(response)\n if slept:\n return\n\n self._sleep_backoff()\n\n def _is_connection_error(self, err):\n \"\"\"Errors when we're fairly sure that the server did not receive the\n request, so it should be safe to retry.\n \"\"\"\n if isinstance(err, ProxyError):\n err = err.original_error\n return isinstance(err, ConnectTimeoutError)\n\n def _is_read_error(self, err):\n \"\"\"Errors that occur after the request has been started, so we should\n assume that the server began processing it.\n \"\"\"\n return isinstance(err, (ReadTimeoutError, ProtocolError))\n\n def _is_method_retryable(self, method):\n \"\"\"Checks if a given HTTP method should be retried upon, depending if\n it is included in the allowed_methods\n \"\"\"\n # TODO: For now favor if the Retry implementation sets its own method_whitelist\n # property outside of our constructor to avoid breaking custom implementations.\n if \"method_whitelist\" in self.__dict__:\n warnings.warn(\n \"Using 'method_whitelist' with Retry is deprecated and \"\n \"will be removed in v2.0. Use 'allowed_methods' instead\",\n DeprecationWarning,\n )\n allowed_methods = self.method_whitelist\n else:\n allowed_methods = self.allowed_methods\n\n if allowed_methods and method.upper() not in allowed_methods:\n return False\n return True\n\n def is_retry(self, method, status_code, has_retry_after=False):\n \"\"\"Is this method/status code retryable? (Based on allowlists and control\n variables such as the number of total retries to allow, whether to\n respect the Retry-After header, whether this header is present, and\n whether the returned status code is on the list of status codes to\n be retried upon on the presence of the aforementioned header)\n \"\"\"\n if not self._is_method_retryable(method):\n return False\n\n if self.status_forcelist and status_code in self.status_forcelist:\n return True\n\n return (\n self.total\n and self.respect_retry_after_header\n and has_retry_after\n and (status_code in self.RETRY_AFTER_STATUS_CODES)\n )\n\n def is_exhausted(self):\n \"\"\"Are we out of retries?\"\"\"\n retry_counts = (\n self.total,\n self.connect,\n self.read,\n self.redirect,\n self.status,\n self.other,\n )\n retry_counts = list(filter(None, retry_counts))\n if not retry_counts:\n return False\n\n return min(retry_counts) < 0\n\n def increment(\n self,\n method=None,\n url=None,\n response=None,\n error=None,\n _pool=None,\n _stacktrace=None,\n ):\n \"\"\"Return a new Retry object with incremented retry counters.\n\n :param response: A response object, or None, if the server did not\n return a response.\n :type response: :class:`~urllib3.response.HTTPResponse`\n :param Exception error: An error encountered during the request, or\n None if the response was received successfully.\n\n :return: A new ``Retry`` object.\n \"\"\"\n if self.total is False and error:\n # Disabled, indicate to re-raise the error.\n raise six.reraise(type(error), error, _stacktrace)\n\n total = self.total\n if total is not None:\n total -= 1\n\n connect = self.connect\n read = self.read\n redirect = self.redirect\n status_count = self.status\n other = self.other\n cause = \"unknown\"\n status = None\n redirect_location = None\n\n if error and self._is_connection_error(error):\n # Connect retry?\n if connect is False:\n raise six.reraise(type(error), error, _stacktrace)\n elif connect is not None:\n connect -= 1\n\n elif error and self._is_read_error(error):\n # Read retry?\n if read is False or not self._is_method_retryable(method):\n raise six.reraise(type(error), error, _stacktrace)\n elif read is not None:\n read -= 1\n\n elif error:\n # Other retry?\n if other is not None:\n other -= 1\n\n elif response and response.get_redirect_location():\n # Redirect retry?\n if redirect is not None:\n redirect -= 1\n cause = \"too many redirects\"\n redirect_location = response.get_redirect_location()\n status = response.status\n\n else:\n # Incrementing because of a server error like a 500 in\n # status_forcelist and the given method is in the allowed_methods\n cause = ResponseError.GENERIC_ERROR\n if response and response.status:\n if status_count is not None:\n status_count -= 1\n cause = ResponseError.SPECIFIC_ERROR.format(status_code=response.status)\n status = response.status\n\n history = self.history + (\n RequestHistory(method, url, error, status, redirect_location),\n )\n\n new_retry = self.new(\n total=total,\n connect=connect,\n read=read,\n redirect=redirect,\n status=status_count,\n other=other,\n history=history,\n )\n\n if new_retry.is_exhausted():\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\n\n log.debug(\"Incremented Retry for (url='%s'): %r\", url, new_retry)\n\n return new_retry\n\n def __repr__(self):\n return (\n \"{cls.__name__}(total={self.total}, connect={self.connect}, \"\n \"read={self.read}, redirect={self.redirect}, status={self.status})\"\n ).format(cls=type(self), self=self)\n\n def __getattr__(self, item):\n if item == \"method_whitelist\":\n # TODO: Remove this deprecated alias in v2.0\n warnings.warn(\n \"Using 'method_whitelist' with Retry is deprecated and \"\n \"will be removed in v2.0. Use 'allowed_methods' instead\",\n DeprecationWarning,\n )\n return self.allowed_methods\n try:\n return getattr(super(Retry, self), item)\n except AttributeError:\n return getattr(Retry, item)"},{"identifier":"parse_url","path":"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/url.py","snippet":"def parse_url(url):\n \"\"\"\n Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is\n performed to parse incomplete urls. Fields not provided will be None.\n This parser is RFC 3986 and RFC 6874 compliant.\n\n The parser logic and helper functions are based heavily on\n work done in the ``rfc3986`` module.\n\n :param str url: URL to parse into a :class:`.Url` namedtuple.\n\n Partly backwards-compatible with :mod:`urlparse`.\n\n Example::\n\n >>> parse_url('http://google.com/mail/')\n Url(scheme='http', host='google.com', port=None, path='/mail/', ...)\n >>> parse_url('google.com:80')\n Url(scheme=None, host='google.com', port=80, path=None, ...)\n >>> parse_url('/foo?bar')\n Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)\n \"\"\"\n if not url:\n # Empty\n return Url()\n\n source_url = url\n if not SCHEME_RE.search(url):\n url = \"//\" + url\n\n try:\n scheme, authority, path, query, fragment = URI_RE.match(url).groups()\n normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES\n\n if scheme:\n scheme = scheme.lower()\n\n if authority:\n auth, _, host_port = authority.rpartition(\"@\")\n auth = auth or None\n host, port = _HOST_PORT_RE.match(host_port).groups()\n if auth and normalize_uri:\n auth = _encode_invalid_chars(auth, USERINFO_CHARS)\n if port == \"\":\n port = None\n else:\n auth, host, port = None, None, None\n\n if port is not None:\n port = int(port)\n if not (0 <= port <= 65535):\n raise LocationParseError(url)\n\n host = _normalize_host(host, scheme)\n\n if normalize_uri and path:\n path = _remove_path_dot_segments(path)\n path = _encode_invalid_chars(path, PATH_CHARS)\n if normalize_uri and query:\n query = _encode_invalid_chars(query, QUERY_CHARS)\n if normalize_uri and fragment:\n fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS)\n\n except (ValueError, AttributeError):\n return six.raise_from(LocationParseError(source_url), None)\n\n # For the sake of backwards compatibility we put empty\n # string values for path if there are any defined values\n # beyond the path in the URL.\n # TODO: Remove this when we break backwards compatibility.\n if not path:\n if query is not None or fragment is not None:\n path = \"\"\n else:\n path = None\n\n # Ensure that each part of the URL is a `str` for\n # backwards compatibility.\n if isinstance(url, six.text_type):\n ensure_func = six.ensure_text\n else:\n ensure_func = six.ensure_str\n\n def ensure_type(x):\n return x if x is None else ensure_func(x)\n\n return Url(\n scheme=ensure_type(scheme),\n auth=ensure_type(auth),\n host=ensure_type(host),\n port=port,\n path=ensure_type(path),\n query=ensure_type(query),\n fragment=ensure_type(fragment),\n )"}],"string":"[\n {\n \"identifier\": \"HTTPHeaderDict\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/_collections.py\",\n \"snippet\": \"class HTTPHeaderDict(MutableMapping):\\n \\\"\\\"\\\"\\n :param headers:\\n An iterable of field-value pairs. Must not contain multiple field names\\n when compared case-insensitively.\\n\\n :param kwargs:\\n Additional field-value pairs to pass in to ``dict.update``.\\n\\n A ``dict`` like container for storing HTTP Headers.\\n\\n Field names are stored and compared case-insensitively in compliance with\\n RFC 7230. Iteration provides the first case-sensitive key seen for each\\n case-insensitive pair.\\n\\n Using ``__setitem__`` syntax overwrites fields that compare equal\\n case-insensitively in order to maintain ``dict``'s api. For fields that\\n compare equal, instead create a new ``HTTPHeaderDict`` and use ``.add``\\n in a loop.\\n\\n If multiple fields that are equal case-insensitively are passed to the\\n constructor or ``.update``, the behavior is undefined and some will be\\n lost.\\n\\n >>> headers = HTTPHeaderDict()\\n >>> headers.add('Set-Cookie', 'foo=bar')\\n >>> headers.add('set-cookie', 'baz=quxx')\\n >>> headers['content-length'] = '7'\\n >>> headers['SET-cookie']\\n 'foo=bar, baz=quxx'\\n >>> headers['Content-Length']\\n '7'\\n \\\"\\\"\\\"\\n\\n def __init__(self, headers=None, **kwargs):\\n super(HTTPHeaderDict, self).__init__()\\n self._container = OrderedDict()\\n if headers is not None:\\n if isinstance(headers, HTTPHeaderDict):\\n self._copy_from(headers)\\n else:\\n self.extend(headers)\\n if kwargs:\\n self.extend(kwargs)\\n\\n def __setitem__(self, key, val):\\n self._container[key.lower()] = [key, val]\\n return self._container[key.lower()]\\n\\n def __getitem__(self, key):\\n val = self._container[key.lower()]\\n return \\\", \\\".join(val[1:])\\n\\n def __delitem__(self, key):\\n del self._container[key.lower()]\\n\\n def __contains__(self, key):\\n return key.lower() in self._container\\n\\n def __eq__(self, other):\\n if not isinstance(other, Mapping) and not hasattr(other, \\\"keys\\\"):\\n return False\\n if not isinstance(other, type(self)):\\n other = type(self)(other)\\n return dict((k.lower(), v) for k, v in self.itermerged()) == dict(\\n (k.lower(), v) for k, v in other.itermerged()\\n )\\n\\n def __ne__(self, other):\\n return not self.__eq__(other)\\n\\n if six.PY2: # Python 2\\n iterkeys = MutableMapping.iterkeys\\n itervalues = MutableMapping.itervalues\\n\\n __marker = object()\\n\\n def __len__(self):\\n return len(self._container)\\n\\n def __iter__(self):\\n # Only provide the originally cased names\\n for vals in self._container.values():\\n yield vals[0]\\n\\n def pop(self, key, default=__marker):\\n \\\"\\\"\\\"D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\\n If key is not found, d is returned if given, otherwise KeyError is raised.\\n \\\"\\\"\\\"\\n # Using the MutableMapping function directly fails due to the private marker.\\n # Using ordinary dict.pop would expose the internal structures.\\n # So let's reinvent the wheel.\\n try:\\n value = self[key]\\n except KeyError:\\n if default is self.__marker:\\n raise\\n return default\\n else:\\n del self[key]\\n return value\\n\\n def discard(self, key):\\n try:\\n del self[key]\\n except KeyError:\\n pass\\n\\n def add(self, key, val):\\n \\\"\\\"\\\"Adds a (name, value) pair, doesn't overwrite the value if it already\\n exists.\\n\\n >>> headers = HTTPHeaderDict(foo='bar')\\n >>> headers.add('Foo', 'baz')\\n >>> headers['foo']\\n 'bar, baz'\\n \\\"\\\"\\\"\\n key_lower = key.lower()\\n new_vals = [key, val]\\n # Keep the common case aka no item present as fast as possible\\n vals = self._container.setdefault(key_lower, new_vals)\\n if new_vals is not vals:\\n vals.append(val)\\n\\n def extend(self, *args, **kwargs):\\n \\\"\\\"\\\"Generic import function for any type of header-like object.\\n Adapted version of MutableMapping.update in order to insert items\\n with self.add instead of self.__setitem__\\n \\\"\\\"\\\"\\n if len(args) > 1:\\n raise TypeError(\\n \\\"extend() takes at most 1 positional \\\"\\n \\\"arguments ({0} given)\\\".format(len(args))\\n )\\n other = args[0] if len(args) >= 1 else ()\\n\\n if isinstance(other, HTTPHeaderDict):\\n for key, val in other.iteritems():\\n self.add(key, val)\\n elif isinstance(other, Mapping):\\n for key in other:\\n self.add(key, other[key])\\n elif hasattr(other, \\\"keys\\\"):\\n for key in other.keys():\\n self.add(key, other[key])\\n else:\\n for key, value in other:\\n self.add(key, value)\\n\\n for key, value in kwargs.items():\\n self.add(key, value)\\n\\n def getlist(self, key, default=__marker):\\n \\\"\\\"\\\"Returns a list of all the values for the named field. Returns an\\n empty list if the key doesn't exist.\\\"\\\"\\\"\\n try:\\n vals = self._container[key.lower()]\\n except KeyError:\\n if default is self.__marker:\\n return []\\n return default\\n else:\\n return vals[1:]\\n\\n def _prepare_for_method_change(self):\\n \\\"\\\"\\\"\\n Remove content-specific header fields before changing the request\\n method to GET or HEAD according to RFC 9110, Section 15.4.\\n \\\"\\\"\\\"\\n content_specific_headers = [\\n \\\"Content-Encoding\\\",\\n \\\"Content-Language\\\",\\n \\\"Content-Location\\\",\\n \\\"Content-Type\\\",\\n \\\"Content-Length\\\",\\n \\\"Digest\\\",\\n \\\"Last-Modified\\\",\\n ]\\n for header in content_specific_headers:\\n self.discard(header)\\n return self\\n\\n # Backwards compatibility for httplib\\n getheaders = getlist\\n getallmatchingheaders = getlist\\n iget = getlist\\n\\n # Backwards compatibility for http.cookiejar\\n get_all = getlist\\n\\n def __repr__(self):\\n return \\\"%s(%s)\\\" % (type(self).__name__, dict(self.itermerged()))\\n\\n def _copy_from(self, other):\\n for key in other:\\n val = other.getlist(key)\\n if isinstance(val, list):\\n # Don't need to convert tuples\\n val = list(val)\\n self._container[key.lower()] = [key] + val\\n\\n def copy(self):\\n clone = type(self)()\\n clone._copy_from(self)\\n return clone\\n\\n def iteritems(self):\\n \\\"\\\"\\\"Iterate over all header lines, including duplicate ones.\\\"\\\"\\\"\\n for key in self:\\n vals = self._container[key.lower()]\\n for val in vals[1:]:\\n yield vals[0], val\\n\\n def itermerged(self):\\n \\\"\\\"\\\"Iterate over all headers, merging duplicate ones together.\\\"\\\"\\\"\\n for key in self:\\n val = self._container[key.lower()]\\n yield val[0], \\\", \\\".join(val[1:])\\n\\n def items(self):\\n return list(self.iteritems())\\n\\n @classmethod\\n def from_httplib(cls, message): # Python 2\\n \\\"\\\"\\\"Read headers from a Python 2 httplib message object.\\\"\\\"\\\"\\n # python2.7 does not expose a proper API for exporting multiheaders\\n # efficiently. This function re-reads raw lines from the message\\n # object and extracts the multiheaders properly.\\n obs_fold_continued_leaders = (\\\" \\\", \\\"\\\\t\\\")\\n headers = []\\n\\n for line in message.headers:\\n if line.startswith(obs_fold_continued_leaders):\\n if not headers:\\n # We received a header line that starts with OWS as described\\n # in RFC-7230 S3.2.4. This indicates a multiline header, but\\n # there exists no previous header to which we can attach it.\\n raise InvalidHeader(\\n \\\"Header continuation with no previous header: %s\\\" % line\\n )\\n else:\\n key, value = headers[-1]\\n headers[-1] = (key, value + \\\" \\\" + line.strip())\\n continue\\n\\n key, value = line.split(\\\":\\\", 1)\\n headers.append((key, value.strip()))\\n\\n return cls(headers)\"\n },\n {\n \"identifier\": \"RecentlyUsedContainer\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/_collections.py\",\n \"snippet\": \"class RecentlyUsedContainer(MutableMapping):\\n \\\"\\\"\\\"\\n Provides a thread-safe dict-like container which maintains up to\\n ``maxsize`` keys while throwing away the least-recently-used keys beyond\\n ``maxsize``.\\n\\n :param maxsize:\\n Maximum number of recent elements to retain.\\n\\n :param dispose_func:\\n Every time an item is evicted from the container,\\n ``dispose_func(value)`` is called. Callback which will get called\\n \\\"\\\"\\\"\\n\\n ContainerCls = OrderedDict\\n\\n def __init__(self, maxsize=10, dispose_func=None):\\n self._maxsize = maxsize\\n self.dispose_func = dispose_func\\n\\n self._container = self.ContainerCls()\\n self.lock = RLock()\\n\\n def __getitem__(self, key):\\n # Re-insert the item, moving it to the end of the eviction line.\\n with self.lock:\\n item = self._container.pop(key)\\n self._container[key] = item\\n return item\\n\\n def __setitem__(self, key, value):\\n evicted_value = _Null\\n with self.lock:\\n # Possibly evict the existing value of 'key'\\n evicted_value = self._container.get(key, _Null)\\n self._container[key] = value\\n\\n # If we didn't evict an existing value, we might have to evict the\\n # least recently used item from the beginning of the container.\\n if len(self._container) > self._maxsize:\\n _key, evicted_value = self._container.popitem(last=False)\\n\\n if self.dispose_func and evicted_value is not _Null:\\n self.dispose_func(evicted_value)\\n\\n def __delitem__(self, key):\\n with self.lock:\\n value = self._container.pop(key)\\n\\n if self.dispose_func:\\n self.dispose_func(value)\\n\\n def __len__(self):\\n with self.lock:\\n return len(self._container)\\n\\n def __iter__(self):\\n raise NotImplementedError(\\n \\\"Iteration over this class is unlikely to be threadsafe.\\\"\\n )\\n\\n def clear(self):\\n with self.lock:\\n # Copy pointers to all values, then wipe the mapping\\n values = list(itervalues(self._container))\\n self._container.clear()\\n\\n if self.dispose_func:\\n for value in values:\\n self.dispose_func(value)\\n\\n def keys(self):\\n with self.lock:\\n return list(iterkeys(self._container))\"\n },\n {\n \"identifier\": \"HTTPConnectionPool\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/connectionpool.py\",\n \"snippet\": \"class ConnectionPool(object):\\nclass HTTPConnectionPool(ConnectionPool, RequestMethods):\\nclass HTTPSConnectionPool(HTTPConnectionPool):\\n def __init__(self, host, port=None):\\n def __str__(self):\\n def __enter__(self):\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n def close(self):\\n def __init__(\\n self,\\n host,\\n port=None,\\n strict=False,\\n timeout=Timeout.DEFAULT_TIMEOUT,\\n maxsize=1,\\n block=False,\\n headers=None,\\n retries=None,\\n _proxy=None,\\n _proxy_headers=None,\\n _proxy_config=None,\\n **conn_kw\\n ):\\n def _new_conn(self):\\n def _get_conn(self, timeout=None):\\n def _put_conn(self, conn):\\n def _validate_conn(self, conn):\\n def _prepare_proxy(self, conn):\\n def _get_timeout(self, timeout):\\n def _raise_timeout(self, err, url, timeout_value):\\n def _make_request(\\n self, conn, method, url, timeout=_Default, chunked=False, **httplib_request_kw\\n ):\\n def _absolute_url(self, path):\\n def close(self):\\n def is_same_host(self, url):\\n def urlopen(\\n self,\\n method,\\n url,\\n body=None,\\n headers=None,\\n retries=None,\\n redirect=True,\\n assert_same_host=True,\\n timeout=_Default,\\n pool_timeout=None,\\n release_conn=None,\\n chunked=False,\\n body_pos=None,\\n **response_kw\\n ):\\n def _is_ssl_error_message_from_http_proxy(ssl_error):\\n def __init__(\\n self,\\n host,\\n port=None,\\n strict=False,\\n timeout=Timeout.DEFAULT_TIMEOUT,\\n maxsize=1,\\n block=False,\\n headers=None,\\n retries=None,\\n _proxy=None,\\n _proxy_headers=None,\\n key_file=None,\\n cert_file=None,\\n cert_reqs=None,\\n key_password=None,\\n ca_certs=None,\\n ssl_version=None,\\n assert_hostname=None,\\n assert_fingerprint=None,\\n ca_cert_dir=None,\\n **conn_kw\\n ):\\n def _prepare_conn(self, conn):\\n def _prepare_proxy(self, conn):\\n def _new_conn(self):\\n def _validate_conn(self, conn):\\ndef connection_from_url(url, **kw):\\ndef _normalize_host(host, scheme):\\ndef _close_pool_connections(pool):\"\n },\n {\n \"identifier\": \"LocationValueError\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py\",\n \"snippet\": \"class LocationValueError(ValueError, HTTPError):\\n \\\"\\\"\\\"Raised when there is something wrong with a given URL input.\\\"\\\"\\\"\\n\\n pass\"\n },\n {\n \"identifier\": \"MaxRetryError\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py\",\n \"snippet\": \"class MaxRetryError(RequestError):\\n \\\"\\\"\\\"Raised when the maximum number of retries is exceeded.\\n\\n :param pool: The connection pool\\n :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool`\\n :param string url: The requested Url\\n :param exceptions.Exception reason: The underlying error\\n\\n \\\"\\\"\\\"\\n\\n def __init__(self, pool, url, reason=None):\\n self.reason = reason\\n\\n message = \\\"Max retries exceeded with url: %s (Caused by %r)\\\" % (url, reason)\\n\\n RequestError.__init__(self, pool, url, message)\"\n },\n {\n \"identifier\": \"ProxySchemeUnknown\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py\",\n \"snippet\": \"class ProxySchemeUnknown(AssertionError, URLSchemeUnknown):\\n \\\"\\\"\\\"ProxyManager does not support the supplied scheme\\\"\\\"\\\"\\n\\n # TODO(t-8ch): Stop inheriting from AssertionError in v2.0.\\n\\n def __init__(self, scheme):\\n # 'localhost' is here because our URL parser parses\\n # localhost:8080 -> scheme=localhost, remove if we fix this.\\n if scheme == \\\"localhost\\\":\\n scheme = None\\n if scheme is None:\\n message = \\\"Proxy URL had no scheme, should start with http:// or https://\\\"\\n else:\\n message = (\\n \\\"Proxy URL had unsupported scheme %s, should use http:// or https://\\\"\\n % scheme\\n )\\n super(ProxySchemeUnknown, self).__init__(message)\"\n },\n {\n \"identifier\": \"ProxySchemeUnsupported\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py\",\n \"snippet\": \"class ProxySchemeUnsupported(ValueError):\\n \\\"\\\"\\\"Fetching HTTPS resources through HTTPS proxies is unsupported\\\"\\\"\\\"\\n\\n pass\"\n },\n {\n \"identifier\": \"URLSchemeUnknown\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/exceptions.py\",\n \"snippet\": \"class URLSchemeUnknown(LocationValueError):\\n \\\"\\\"\\\"Raised when a URL input has an unsupported scheme.\\\"\\\"\\\"\\n\\n def __init__(self, scheme):\\n message = \\\"Not supported URL scheme %s\\\" % scheme\\n super(URLSchemeUnknown, self).__init__(message)\\n\\n self.scheme = scheme\"\n },\n {\n \"identifier\": \"six\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/packages/six.py\",\n \"snippet\": \"PY2 = sys.version_info[0] == 2\\nPY3 = sys.version_info[0] == 3\\nPY34 = sys.version_info[0:2] >= (3, 4)\\n MAXSIZE = sys.maxsize\\n MAXSIZE = int((1 << 31) - 1)\\n MAXSIZE = int((1 << 31) - 1)\\n MAXSIZE = int((1 << 63) - 1)\\n class X(object):\\nclass _LazyDescr(object):\\nclass MovedModule(_LazyDescr):\\nclass _LazyModule(types.ModuleType):\\nclass MovedAttribute(_LazyDescr):\\nclass _SixMetaPathImporter(object):\\nclass _MovedItems(_LazyModule):\\nclass Module_six_moves_urllib_parse(_LazyModule):\\nclass Module_six_moves_urllib_error(_LazyModule):\\nclass Module_six_moves_urllib_request(_LazyModule):\\nclass Module_six_moves_urllib_response(_LazyModule):\\nclass Module_six_moves_urllib_robotparser(_LazyModule):\\nclass Module_six_moves_urllib(types.ModuleType):\\n class Iterator(object):\\n class metaclass(type):\\n def __len__(self):\\ndef _add_doc(func, doc):\\ndef _import_module(name):\\n def __init__(self, name):\\n def __get__(self, obj, tp):\\n def __init__(self, name, old, new=None):\\n def _resolve(self):\\n def __getattr__(self, attr):\\n def __init__(self, name):\\n def __dir__(self):\\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\\n def _resolve(self):\\n def __init__(self, six_module_name):\\n def _add_module(self, mod, *fullnames):\\n def _get_module(self, fullname):\\n def find_module(self, fullname, path=None):\\n def find_spec(self, fullname, path, target=None):\\n def __get_module(self, fullname):\\n def load_module(self, fullname):\\n def is_package(self, fullname):\\n def get_code(self, fullname):\\n def create_module(self, spec):\\n def exec_module(self, module):\\n def __dir__(self):\\ndef add_move(move):\\ndef remove_move(name):\\n def advance_iterator(it):\\n def callable(obj):\\n def get_unbound_function(unbound):\\n def create_unbound_method(func, cls):\\n def get_unbound_function(unbound):\\n def create_bound_method(func, obj):\\n def create_unbound_method(func, cls):\\n def next(self):\\n def iterkeys(d, **kw):\\n def itervalues(d, **kw):\\n def iteritems(d, **kw):\\n def iterlists(d, **kw):\\n def iterkeys(d, **kw):\\n def itervalues(d, **kw):\\n def iteritems(d, **kw):\\n def iterlists(d, **kw):\\n def b(s):\\n def u(s):\\n def b(s):\\n def u(s):\\n def byte2int(bs):\\n def indexbytes(buf, i):\\ndef assertCountEqual(self, *args, **kwargs):\\ndef assertRaisesRegex(self, *args, **kwargs):\\ndef assertRegex(self, *args, **kwargs):\\ndef assertNotRegex(self, *args, **kwargs):\\n def reraise(tp, value, tb=None):\\n def exec_(_code_, _globs_=None, _locs_=None):\\n def raise_from(value, from_value):\\n def print_(*args, **kwargs):\\n def write(data):\\n def print_(*args, **kwargs):\\n def _update_wrapper(\\n wrapper,\\n wrapped,\\n assigned=functools.WRAPPER_ASSIGNMENTS,\\n updated=functools.WRAPPER_UPDATES,\\n ):\\n def wraps(\\n wrapped,\\n assigned=functools.WRAPPER_ASSIGNMENTS,\\n updated=functools.WRAPPER_UPDATES,\\n ):\\ndef with_metaclass(meta, *bases):\\n def __new__(cls, name, this_bases, d):\\n def __prepare__(cls, name, this_bases):\\ndef add_metaclass(metaclass):\\n def wrapper(cls):\\ndef ensure_binary(s, encoding=\\\"utf-8\\\", errors=\\\"strict\\\"):\\ndef ensure_str(s, encoding=\\\"utf-8\\\", errors=\\\"strict\\\"):\\ndef ensure_text(s, encoding=\\\"utf-8\\\", errors=\\\"strict\\\"):\\ndef python_2_unicode_compatible(klass):\"\n },\n {\n \"identifier\": \"RequestMethods\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/request.py\",\n \"snippet\": \"class RequestMethods(object):\\n \\\"\\\"\\\"\\n Convenience mixin for classes who implement a :meth:`urlopen` method, such\\n as :class:`urllib3.HTTPConnectionPool` and\\n :class:`urllib3.PoolManager`.\\n\\n Provides behavior for making common types of HTTP request methods and\\n decides which type of request field encoding to use.\\n\\n Specifically,\\n\\n :meth:`.request_encode_url` is for sending requests whose fields are\\n encoded in the URL (such as GET, HEAD, DELETE).\\n\\n :meth:`.request_encode_body` is for sending requests whose fields are\\n encoded in the *body* of the request using multipart or www-form-urlencoded\\n (such as for POST, PUT, PATCH).\\n\\n :meth:`.request` is for making any kind of request, it will look up the\\n appropriate encoding format and use one of the above two methods to make\\n the request.\\n\\n Initializer parameters:\\n\\n :param headers:\\n Headers to include with all requests, unless other headers are given\\n explicitly.\\n \\\"\\\"\\\"\\n\\n _encode_url_methods = {\\\"DELETE\\\", \\\"GET\\\", \\\"HEAD\\\", \\\"OPTIONS\\\"}\\n\\n def __init__(self, headers=None):\\n self.headers = headers or {}\\n\\n def urlopen(\\n self,\\n method,\\n url,\\n body=None,\\n headers=None,\\n encode_multipart=True,\\n multipart_boundary=None,\\n **kw\\n ): # Abstract\\n raise NotImplementedError(\\n \\\"Classes extending RequestMethods must implement \\\"\\n \\\"their own ``urlopen`` method.\\\"\\n )\\n\\n def request(self, method, url, fields=None, headers=None, **urlopen_kw):\\n \\\"\\\"\\\"\\n Make a request using :meth:`urlopen` with the appropriate encoding of\\n ``fields`` based on the ``method`` used.\\n\\n This is a convenience method that requires the least amount of manual\\n effort. It can be used in most situations, while still having the\\n option to drop down to more specific methods when necessary, such as\\n :meth:`request_encode_url`, :meth:`request_encode_body`,\\n or even the lowest level :meth:`urlopen`.\\n \\\"\\\"\\\"\\n method = method.upper()\\n\\n urlopen_kw[\\\"request_url\\\"] = url\\n\\n if method in self._encode_url_methods:\\n return self.request_encode_url(\\n method, url, fields=fields, headers=headers, **urlopen_kw\\n )\\n else:\\n return self.request_encode_body(\\n method, url, fields=fields, headers=headers, **urlopen_kw\\n )\\n\\n def request_encode_url(self, method, url, fields=None, headers=None, **urlopen_kw):\\n \\\"\\\"\\\"\\n Make a request using :meth:`urlopen` with the ``fields`` encoded in\\n the url. This is useful for request methods like GET, HEAD, DELETE, etc.\\n \\\"\\\"\\\"\\n if headers is None:\\n headers = self.headers\\n\\n extra_kw = {\\\"headers\\\": headers}\\n extra_kw.update(urlopen_kw)\\n\\n if fields:\\n url += \\\"?\\\" + urlencode(fields)\\n\\n return self.urlopen(method, url, **extra_kw)\\n\\n def request_encode_body(\\n self,\\n method,\\n url,\\n fields=None,\\n headers=None,\\n encode_multipart=True,\\n multipart_boundary=None,\\n **urlopen_kw\\n ):\\n \\\"\\\"\\\"\\n Make a request using :meth:`urlopen` with the ``fields`` encoded in\\n the body. This is useful for request methods like POST, PUT, PATCH, etc.\\n\\n When ``encode_multipart=True`` (default), then\\n :func:`urllib3.encode_multipart_formdata` is used to encode\\n the payload with the appropriate content type. Otherwise\\n :func:`urllib.parse.urlencode` is used with the\\n 'application/x-www-form-urlencoded' content type.\\n\\n Multipart encoding must be used when posting files, and it's reasonably\\n safe to use it in other times too. However, it may break request\\n signing, such as with OAuth.\\n\\n Supports an optional ``fields`` parameter of key/value strings AND\\n key/filetuple. A filetuple is a (filename, data, MIME type) tuple where\\n the MIME type is optional. For example::\\n\\n fields = {\\n 'foo': 'bar',\\n 'fakefile': ('foofile.txt', 'contents of foofile'),\\n 'realfile': ('barfile.txt', open('realfile').read()),\\n 'typedfile': ('bazfile.bin', open('bazfile').read(),\\n 'image/jpeg'),\\n 'nonamefile': 'contents of nonamefile field',\\n }\\n\\n When uploading a file, providing a filename (the first parameter of the\\n tuple) is optional but recommended to best mimic behavior of browsers.\\n\\n Note that if ``headers`` are supplied, the 'Content-Type' header will\\n be overwritten because it depends on the dynamic random boundary string\\n which is used to compose the body of the request. The random boundary\\n string can be explicitly set with the ``multipart_boundary`` parameter.\\n \\\"\\\"\\\"\\n if headers is None:\\n headers = self.headers\\n\\n extra_kw = {\\\"headers\\\": {}}\\n\\n if fields:\\n if \\\"body\\\" in urlopen_kw:\\n raise TypeError(\\n \\\"request got values for both 'fields' and 'body', can only specify one.\\\"\\n )\\n\\n if encode_multipart:\\n body, content_type = encode_multipart_formdata(\\n fields, boundary=multipart_boundary\\n )\\n else:\\n body, content_type = (\\n urlencode(fields),\\n \\\"application/x-www-form-urlencoded\\\",\\n )\\n\\n extra_kw[\\\"body\\\"] = body\\n extra_kw[\\\"headers\\\"] = {\\\"Content-Type\\\": content_type}\\n\\n extra_kw[\\\"headers\\\"].update(headers)\\n extra_kw.update(urlopen_kw)\\n\\n return self.urlopen(method, url, **extra_kw)\"\n },\n {\n \"identifier\": \"connection_requires_http_tunnel\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/proxy.py\",\n \"snippet\": \"def connection_requires_http_tunnel(\\n proxy_url=None, proxy_config=None, destination_scheme=None\\n):\\n \\\"\\\"\\\"\\n Returns True if the connection requires an HTTP CONNECT through the proxy.\\n\\n :param URL proxy_url:\\n URL of the proxy.\\n :param ProxyConfig proxy_config:\\n Proxy configuration from poolmanager.py\\n :param str destination_scheme:\\n The scheme of the destination. (i.e https, http, etc)\\n \\\"\\\"\\\"\\n # If we're not using a proxy, no way to use a tunnel.\\n if proxy_url is None:\\n return False\\n\\n # HTTP destinations never require tunneling, we always forward.\\n if destination_scheme == \\\"http\\\":\\n return False\\n\\n # Support for forwarding with HTTPS proxies and HTTPS destinations.\\n if (\\n proxy_url.scheme == \\\"https\\\"\\n and proxy_config\\n and proxy_config.use_forwarding_for_https\\n ):\\n return False\\n\\n # Otherwise always use a tunnel.\\n return True\"\n },\n {\n \"identifier\": \"Retry\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/retry.py\",\n \"snippet\": \"class Retry(object):\\n \\\"\\\"\\\"Retry configuration.\\n\\n Each retry attempt will create a new Retry object with updated values, so\\n they can be safely reused.\\n\\n Retries can be defined as a default for a pool::\\n\\n retries = Retry(connect=5, read=2, redirect=5)\\n http = PoolManager(retries=retries)\\n response = http.request('GET', 'http://example.com/')\\n\\n Or per-request (which overrides the default for the pool)::\\n\\n response = http.request('GET', 'http://example.com/', retries=Retry(10))\\n\\n Retries can be disabled by passing ``False``::\\n\\n response = http.request('GET', 'http://example.com/', retries=False)\\n\\n Errors will be wrapped in :class:`~urllib3.exceptions.MaxRetryError` unless\\n retries are disabled, in which case the causing exception will be raised.\\n\\n :param int total:\\n Total number of retries to allow. Takes precedence over other counts.\\n\\n Set to ``None`` to remove this constraint and fall back on other\\n counts.\\n\\n Set to ``0`` to fail on the first retry.\\n\\n Set to ``False`` to disable and imply ``raise_on_redirect=False``.\\n\\n :param int connect:\\n How many connection-related errors to retry on.\\n\\n These are errors raised before the request is sent to the remote server,\\n which we assume has not triggered the server to process the request.\\n\\n Set to ``0`` to fail on the first retry of this type.\\n\\n :param int read:\\n How many times to retry on read errors.\\n\\n These errors are raised after the request was sent to the server, so the\\n request may have side-effects.\\n\\n Set to ``0`` to fail on the first retry of this type.\\n\\n :param int redirect:\\n How many redirects to perform. Limit this to avoid infinite redirect\\n loops.\\n\\n A redirect is a HTTP response with a status code 301, 302, 303, 307 or\\n 308.\\n\\n Set to ``0`` to fail on the first retry of this type.\\n\\n Set to ``False`` to disable and imply ``raise_on_redirect=False``.\\n\\n :param int status:\\n How many times to retry on bad status codes.\\n\\n These are retries made on responses, where status code matches\\n ``status_forcelist``.\\n\\n Set to ``0`` to fail on the first retry of this type.\\n\\n :param int other:\\n How many times to retry on other errors.\\n\\n Other errors are errors that are not connect, read, redirect or status errors.\\n These errors might be raised after the request was sent to the server, so the\\n request might have side-effects.\\n\\n Set to ``0`` to fail on the first retry of this type.\\n\\n If ``total`` is not set, it's a good idea to set this to 0 to account\\n for unexpected edge cases and avoid infinite retry loops.\\n\\n :param iterable allowed_methods:\\n Set of uppercased HTTP method verbs that we should retry on.\\n\\n By default, we only retry on methods which are considered to be\\n idempotent (multiple requests with the same parameters end with the\\n same state). See :attr:`Retry.DEFAULT_ALLOWED_METHODS`.\\n\\n Set to a ``False`` value to retry on any verb.\\n\\n .. warning::\\n\\n Previously this parameter was named ``method_whitelist``, that\\n usage is deprecated in v1.26.0 and will be removed in v2.0.\\n\\n :param iterable status_forcelist:\\n A set of integer HTTP status codes that we should force a retry on.\\n A retry is initiated if the request method is in ``allowed_methods``\\n and the response status code is in ``status_forcelist``.\\n\\n By default, this is disabled with ``None``.\\n\\n :param float backoff_factor:\\n A backoff factor to apply between attempts after the second try\\n (most errors are resolved immediately by a second try without a\\n delay). urllib3 will sleep for::\\n\\n {backoff factor} * (2 ** ({number of total retries} - 1))\\n\\n seconds. If the backoff_factor is 0.1, then :func:`.sleep` will sleep\\n for [0.0s, 0.2s, 0.4s, ...] between retries. It will never be longer\\n than :attr:`Retry.DEFAULT_BACKOFF_MAX`.\\n\\n By default, backoff is disabled (set to 0).\\n\\n :param bool raise_on_redirect: Whether, if the number of redirects is\\n exhausted, to raise a MaxRetryError, or to return a response with a\\n response code in the 3xx range.\\n\\n :param bool raise_on_status: Similar meaning to ``raise_on_redirect``:\\n whether we should raise an exception, or return a response,\\n if status falls in ``status_forcelist`` range and retries have\\n been exhausted.\\n\\n :param tuple history: The history of the request encountered during\\n each call to :meth:`~Retry.increment`. The list is in the order\\n the requests occurred. Each list item is of class :class:`RequestHistory`.\\n\\n :param bool respect_retry_after_header:\\n Whether to respect Retry-After header on status codes defined as\\n :attr:`Retry.RETRY_AFTER_STATUS_CODES` or not.\\n\\n :param iterable remove_headers_on_redirect:\\n Sequence of headers to remove from the request when a response\\n indicating a redirect is returned before firing off the redirected\\n request.\\n \\\"\\\"\\\"\\n\\n #: Default methods to be used for ``allowed_methods``\\n DEFAULT_ALLOWED_METHODS = frozenset(\\n [\\\"HEAD\\\", \\\"GET\\\", \\\"PUT\\\", \\\"DELETE\\\", \\\"OPTIONS\\\", \\\"TRACE\\\"]\\n )\\n\\n #: Default status codes to be used for ``status_forcelist``\\n RETRY_AFTER_STATUS_CODES = frozenset([413, 429, 503])\\n\\n #: Default headers to be used for ``remove_headers_on_redirect``\\n DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset([\\\"Cookie\\\", \\\"Authorization\\\"])\\n\\n #: Maximum backoff time.\\n DEFAULT_BACKOFF_MAX = 120\\n\\n def __init__(\\n self,\\n total=10,\\n connect=None,\\n read=None,\\n redirect=None,\\n status=None,\\n other=None,\\n allowed_methods=_Default,\\n status_forcelist=None,\\n backoff_factor=0,\\n raise_on_redirect=True,\\n raise_on_status=True,\\n history=None,\\n respect_retry_after_header=True,\\n remove_headers_on_redirect=_Default,\\n # TODO: Deprecated, remove in v2.0\\n method_whitelist=_Default,\\n ):\\n\\n if method_whitelist is not _Default:\\n if allowed_methods is not _Default:\\n raise ValueError(\\n \\\"Using both 'allowed_methods' and \\\"\\n \\\"'method_whitelist' together is not allowed. \\\"\\n \\\"Instead only use 'allowed_methods'\\\"\\n )\\n warnings.warn(\\n \\\"Using 'method_whitelist' with Retry is deprecated and \\\"\\n \\\"will be removed in v2.0. Use 'allowed_methods' instead\\\",\\n DeprecationWarning,\\n stacklevel=2,\\n )\\n allowed_methods = method_whitelist\\n if allowed_methods is _Default:\\n allowed_methods = self.DEFAULT_ALLOWED_METHODS\\n if remove_headers_on_redirect is _Default:\\n remove_headers_on_redirect = self.DEFAULT_REMOVE_HEADERS_ON_REDIRECT\\n\\n self.total = total\\n self.connect = connect\\n self.read = read\\n self.status = status\\n self.other = other\\n\\n if redirect is False or total is False:\\n redirect = 0\\n raise_on_redirect = False\\n\\n self.redirect = redirect\\n self.status_forcelist = status_forcelist or set()\\n self.allowed_methods = allowed_methods\\n self.backoff_factor = backoff_factor\\n self.raise_on_redirect = raise_on_redirect\\n self.raise_on_status = raise_on_status\\n self.history = history or tuple()\\n self.respect_retry_after_header = respect_retry_after_header\\n self.remove_headers_on_redirect = frozenset(\\n [h.lower() for h in remove_headers_on_redirect]\\n )\\n\\n def new(self, **kw):\\n params = dict(\\n total=self.total,\\n connect=self.connect,\\n read=self.read,\\n redirect=self.redirect,\\n status=self.status,\\n other=self.other,\\n status_forcelist=self.status_forcelist,\\n backoff_factor=self.backoff_factor,\\n raise_on_redirect=self.raise_on_redirect,\\n raise_on_status=self.raise_on_status,\\n history=self.history,\\n remove_headers_on_redirect=self.remove_headers_on_redirect,\\n respect_retry_after_header=self.respect_retry_after_header,\\n )\\n\\n # TODO: If already given in **kw we use what's given to us\\n # If not given we need to figure out what to pass. We decide\\n # based on whether our class has the 'method_whitelist' property\\n # and if so we pass the deprecated 'method_whitelist' otherwise\\n # we use 'allowed_methods'. Remove in v2.0\\n if \\\"method_whitelist\\\" not in kw and \\\"allowed_methods\\\" not in kw:\\n if \\\"method_whitelist\\\" in self.__dict__:\\n warnings.warn(\\n \\\"Using 'method_whitelist' with Retry is deprecated and \\\"\\n \\\"will be removed in v2.0. Use 'allowed_methods' instead\\\",\\n DeprecationWarning,\\n )\\n params[\\\"method_whitelist\\\"] = self.allowed_methods\\n else:\\n params[\\\"allowed_methods\\\"] = self.allowed_methods\\n\\n params.update(kw)\\n return type(self)(**params)\\n\\n @classmethod\\n def from_int(cls, retries, redirect=True, default=None):\\n \\\"\\\"\\\"Backwards-compatibility for the old retries format.\\\"\\\"\\\"\\n if retries is None:\\n retries = default if default is not None else cls.DEFAULT\\n\\n if isinstance(retries, Retry):\\n return retries\\n\\n redirect = bool(redirect) and None\\n new_retries = cls(retries, redirect=redirect)\\n log.debug(\\\"Converted retries value: %r -> %r\\\", retries, new_retries)\\n return new_retries\\n\\n def get_backoff_time(self):\\n \\\"\\\"\\\"Formula for computing the current backoff\\n\\n :rtype: float\\n \\\"\\\"\\\"\\n # We want to consider only the last consecutive errors sequence (Ignore redirects).\\n consecutive_errors_len = len(\\n list(\\n takewhile(lambda x: x.redirect_location is None, reversed(self.history))\\n )\\n )\\n if consecutive_errors_len <= 1:\\n return 0\\n\\n backoff_value = self.backoff_factor * (2 ** (consecutive_errors_len - 1))\\n return min(self.DEFAULT_BACKOFF_MAX, backoff_value)\\n\\n def parse_retry_after(self, retry_after):\\n # Whitespace: https://tools.ietf.org/html/rfc7230#section-3.2.4\\n if re.match(r\\\"^\\\\s*[0-9]+\\\\s*$\\\", retry_after):\\n seconds = int(retry_after)\\n else:\\n retry_date_tuple = email.utils.parsedate_tz(retry_after)\\n if retry_date_tuple is None:\\n raise InvalidHeader(\\\"Invalid Retry-After header: %s\\\" % retry_after)\\n if retry_date_tuple[9] is None: # Python 2\\n # Assume UTC if no timezone was specified\\n # On Python2.7, parsedate_tz returns None for a timezone offset\\n # instead of 0 if no timezone is given, where mktime_tz treats\\n # a None timezone offset as local time.\\n retry_date_tuple = retry_date_tuple[:9] + (0,) + retry_date_tuple[10:]\\n\\n retry_date = email.utils.mktime_tz(retry_date_tuple)\\n seconds = retry_date - time.time()\\n\\n if seconds < 0:\\n seconds = 0\\n\\n return seconds\\n\\n def get_retry_after(self, response):\\n \\\"\\\"\\\"Get the value of Retry-After in seconds.\\\"\\\"\\\"\\n\\n retry_after = response.headers.get(\\\"Retry-After\\\")\\n\\n if retry_after is None:\\n return None\\n\\n return self.parse_retry_after(retry_after)\\n\\n def sleep_for_retry(self, response=None):\\n retry_after = self.get_retry_after(response)\\n if retry_after:\\n time.sleep(retry_after)\\n return True\\n\\n return False\\n\\n def _sleep_backoff(self):\\n backoff = self.get_backoff_time()\\n if backoff <= 0:\\n return\\n time.sleep(backoff)\\n\\n def sleep(self, response=None):\\n \\\"\\\"\\\"Sleep between retry attempts.\\n\\n This method will respect a server's ``Retry-After`` response header\\n and sleep the duration of the time requested. If that is not present, it\\n will use an exponential backoff. By default, the backoff factor is 0 and\\n this method will return immediately.\\n \\\"\\\"\\\"\\n\\n if self.respect_retry_after_header and response:\\n slept = self.sleep_for_retry(response)\\n if slept:\\n return\\n\\n self._sleep_backoff()\\n\\n def _is_connection_error(self, err):\\n \\\"\\\"\\\"Errors when we're fairly sure that the server did not receive the\\n request, so it should be safe to retry.\\n \\\"\\\"\\\"\\n if isinstance(err, ProxyError):\\n err = err.original_error\\n return isinstance(err, ConnectTimeoutError)\\n\\n def _is_read_error(self, err):\\n \\\"\\\"\\\"Errors that occur after the request has been started, so we should\\n assume that the server began processing it.\\n \\\"\\\"\\\"\\n return isinstance(err, (ReadTimeoutError, ProtocolError))\\n\\n def _is_method_retryable(self, method):\\n \\\"\\\"\\\"Checks if a given HTTP method should be retried upon, depending if\\n it is included in the allowed_methods\\n \\\"\\\"\\\"\\n # TODO: For now favor if the Retry implementation sets its own method_whitelist\\n # property outside of our constructor to avoid breaking custom implementations.\\n if \\\"method_whitelist\\\" in self.__dict__:\\n warnings.warn(\\n \\\"Using 'method_whitelist' with Retry is deprecated and \\\"\\n \\\"will be removed in v2.0. Use 'allowed_methods' instead\\\",\\n DeprecationWarning,\\n )\\n allowed_methods = self.method_whitelist\\n else:\\n allowed_methods = self.allowed_methods\\n\\n if allowed_methods and method.upper() not in allowed_methods:\\n return False\\n return True\\n\\n def is_retry(self, method, status_code, has_retry_after=False):\\n \\\"\\\"\\\"Is this method/status code retryable? (Based on allowlists and control\\n variables such as the number of total retries to allow, whether to\\n respect the Retry-After header, whether this header is present, and\\n whether the returned status code is on the list of status codes to\\n be retried upon on the presence of the aforementioned header)\\n \\\"\\\"\\\"\\n if not self._is_method_retryable(method):\\n return False\\n\\n if self.status_forcelist and status_code in self.status_forcelist:\\n return True\\n\\n return (\\n self.total\\n and self.respect_retry_after_header\\n and has_retry_after\\n and (status_code in self.RETRY_AFTER_STATUS_CODES)\\n )\\n\\n def is_exhausted(self):\\n \\\"\\\"\\\"Are we out of retries?\\\"\\\"\\\"\\n retry_counts = (\\n self.total,\\n self.connect,\\n self.read,\\n self.redirect,\\n self.status,\\n self.other,\\n )\\n retry_counts = list(filter(None, retry_counts))\\n if not retry_counts:\\n return False\\n\\n return min(retry_counts) < 0\\n\\n def increment(\\n self,\\n method=None,\\n url=None,\\n response=None,\\n error=None,\\n _pool=None,\\n _stacktrace=None,\\n ):\\n \\\"\\\"\\\"Return a new Retry object with incremented retry counters.\\n\\n :param response: A response object, or None, if the server did not\\n return a response.\\n :type response: :class:`~urllib3.response.HTTPResponse`\\n :param Exception error: An error encountered during the request, or\\n None if the response was received successfully.\\n\\n :return: A new ``Retry`` object.\\n \\\"\\\"\\\"\\n if self.total is False and error:\\n # Disabled, indicate to re-raise the error.\\n raise six.reraise(type(error), error, _stacktrace)\\n\\n total = self.total\\n if total is not None:\\n total -= 1\\n\\n connect = self.connect\\n read = self.read\\n redirect = self.redirect\\n status_count = self.status\\n other = self.other\\n cause = \\\"unknown\\\"\\n status = None\\n redirect_location = None\\n\\n if error and self._is_connection_error(error):\\n # Connect retry?\\n if connect is False:\\n raise six.reraise(type(error), error, _stacktrace)\\n elif connect is not None:\\n connect -= 1\\n\\n elif error and self._is_read_error(error):\\n # Read retry?\\n if read is False or not self._is_method_retryable(method):\\n raise six.reraise(type(error), error, _stacktrace)\\n elif read is not None:\\n read -= 1\\n\\n elif error:\\n # Other retry?\\n if other is not None:\\n other -= 1\\n\\n elif response and response.get_redirect_location():\\n # Redirect retry?\\n if redirect is not None:\\n redirect -= 1\\n cause = \\\"too many redirects\\\"\\n redirect_location = response.get_redirect_location()\\n status = response.status\\n\\n else:\\n # Incrementing because of a server error like a 500 in\\n # status_forcelist and the given method is in the allowed_methods\\n cause = ResponseError.GENERIC_ERROR\\n if response and response.status:\\n if status_count is not None:\\n status_count -= 1\\n cause = ResponseError.SPECIFIC_ERROR.format(status_code=response.status)\\n status = response.status\\n\\n history = self.history + (\\n RequestHistory(method, url, error, status, redirect_location),\\n )\\n\\n new_retry = self.new(\\n total=total,\\n connect=connect,\\n read=read,\\n redirect=redirect,\\n status=status_count,\\n other=other,\\n history=history,\\n )\\n\\n if new_retry.is_exhausted():\\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\\n\\n log.debug(\\\"Incremented Retry for (url='%s'): %r\\\", url, new_retry)\\n\\n return new_retry\\n\\n def __repr__(self):\\n return (\\n \\\"{cls.__name__}(total={self.total}, connect={self.connect}, \\\"\\n \\\"read={self.read}, redirect={self.redirect}, status={self.status})\\\"\\n ).format(cls=type(self), self=self)\\n\\n def __getattr__(self, item):\\n if item == \\\"method_whitelist\\\":\\n # TODO: Remove this deprecated alias in v2.0\\n warnings.warn(\\n \\\"Using 'method_whitelist' with Retry is deprecated and \\\"\\n \\\"will be removed in v2.0. Use 'allowed_methods' instead\\\",\\n DeprecationWarning,\\n )\\n return self.allowed_methods\\n try:\\n return getattr(super(Retry, self), item)\\n except AttributeError:\\n return getattr(Retry, item)\"\n },\n {\n \"identifier\": \"parse_url\",\n \"path\": \"MetaTube.bundle/Contents/Libraries/Shared/urllib3/util/url.py\",\n \"snippet\": \"def parse_url(url):\\n \\\"\\\"\\\"\\n Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is\\n performed to parse incomplete urls. Fields not provided will be None.\\n This parser is RFC 3986 and RFC 6874 compliant.\\n\\n The parser logic and helper functions are based heavily on\\n work done in the ``rfc3986`` module.\\n\\n :param str url: URL to parse into a :class:`.Url` namedtuple.\\n\\n Partly backwards-compatible with :mod:`urlparse`.\\n\\n Example::\\n\\n >>> parse_url('http://google.com/mail/')\\n Url(scheme='http', host='google.com', port=None, path='/mail/', ...)\\n >>> parse_url('google.com:80')\\n Url(scheme=None, host='google.com', port=80, path=None, ...)\\n >>> parse_url('/foo?bar')\\n Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)\\n \\\"\\\"\\\"\\n if not url:\\n # Empty\\n return Url()\\n\\n source_url = url\\n if not SCHEME_RE.search(url):\\n url = \\\"//\\\" + url\\n\\n try:\\n scheme, authority, path, query, fragment = URI_RE.match(url).groups()\\n normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES\\n\\n if scheme:\\n scheme = scheme.lower()\\n\\n if authority:\\n auth, _, host_port = authority.rpartition(\\\"@\\\")\\n auth = auth or None\\n host, port = _HOST_PORT_RE.match(host_port).groups()\\n if auth and normalize_uri:\\n auth = _encode_invalid_chars(auth, USERINFO_CHARS)\\n if port == \\\"\\\":\\n port = None\\n else:\\n auth, host, port = None, None, None\\n\\n if port is not None:\\n port = int(port)\\n if not (0 <= port <= 65535):\\n raise LocationParseError(url)\\n\\n host = _normalize_host(host, scheme)\\n\\n if normalize_uri and path:\\n path = _remove_path_dot_segments(path)\\n path = _encode_invalid_chars(path, PATH_CHARS)\\n if normalize_uri and query:\\n query = _encode_invalid_chars(query, QUERY_CHARS)\\n if normalize_uri and fragment:\\n fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS)\\n\\n except (ValueError, AttributeError):\\n return six.raise_from(LocationParseError(source_url), None)\\n\\n # For the sake of backwards compatibility we put empty\\n # string values for path if there are any defined values\\n # beyond the path in the URL.\\n # TODO: Remove this when we break backwards compatibility.\\n if not path:\\n if query is not None or fragment is not None:\\n path = \\\"\\\"\\n else:\\n path = None\\n\\n # Ensure that each part of the URL is a `str` for\\n # backwards compatibility.\\n if isinstance(url, six.text_type):\\n ensure_func = six.ensure_text\\n else:\\n ensure_func = six.ensure_str\\n\\n def ensure_type(x):\\n return x if x is None else ensure_func(x)\\n\\n return Url(\\n scheme=ensure_type(scheme),\\n auth=ensure_type(auth),\\n host=ensure_type(host),\\n port=port,\\n path=ensure_type(path),\\n query=ensure_type(query),\\n fragment=ensure_type(fragment),\\n )\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import collections\nimport functools\nimport logging\nfrom ._collections import HTTPHeaderDict, RecentlyUsedContainer\nfrom .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme\nfrom .exceptions import (\n LocationValueError,\n MaxRetryError,\n ProxySchemeUnknown,\n ProxySchemeUnsupported,\n URLSchemeUnknown,\n)\nfrom .packages import six\nfrom .packages.six.moves.urllib.parse import urljoin\nfrom .request import RequestMethods\nfrom .util.proxy import connection_requires_http_tunnel\nfrom .util.retry import Retry\nfrom .util.url import parse_url"},"token_num":{"kind":"number","value":14808,"string":"14,808"},"cropped_code":{"kind":"string","value":"\n If ``port`` isn't given, it will be derived from the ``scheme`` using\n ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is\n provided, it is merged with the instance's ``connection_pool_kw``\n variable and used to create the new connection pool, if one is\n needed.\n \"\"\"\n\n if not host:\n raise LocationValueError(\"No host specified.\")\n\n request_context = self._merge_pool_kwargs(pool_kwargs)\n request_context[\"scheme\"] = scheme or \"http\"\n if not port:\n port = port_by_scheme.get(request_context[\"scheme\"].lower(), 80)\n request_context[\"port\"] = port\n request_context[\"host\"] = host\n\n return self.connection_from_context(request_context)\n\n def connection_from_context(self, request_context):\n \"\"\"\n Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.\n\n ``request_context`` must at least contain the ``scheme`` key and its\n value must be a key in ``key_fn_by_scheme`` instance variable.\n \"\"\"\n scheme = request_context[\"scheme\"].lower()\n pool_key_constructor = self.key_fn_by_scheme.get(scheme)\n if not pool_key_constructor:\n raise URLSchemeUnknown(scheme)\n pool_key = pool_key_constructor(request_context)\n\n return self.connection_from_pool_key(pool_key, request_context=request_context)\n\n def connection_from_pool_key(self, pool_key, request_context=None):\n \"\"\"\n Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.\n\n ``pool_key`` should be a namedtuple that only contains immutable\n objects. At a minimum it must have the ``scheme``, ``host``, and\n ``port`` fields.\n \"\"\"\n with self.pools.lock:\n # If the scheme, host, or port doesn't match existing open\n # connections, open a new ConnectionPool.\n pool = self.pools.get(pool_key)\n if pool:\n return pool\n\n # Make a fresh ConnectionPool of the desired type\n scheme = request_context[\"scheme\"]\n host = request_context[\"host\"]\n port = request_context[\"port\"]\n pool = self._new_pool(scheme, host, port, request_context=request_context)\n self.pools[pool_key] = pool\n\n return pool\n\n def connection_from_url(self, url, pool_kwargs=None):\n \"\"\"\n Similar to :func:`urllib3.connectionpool.connection_from_url`.\n\n If ``pool_kwargs`` is not provided and a new pool needs to be\n constructed, ``self.connection_pool_kw`` is used to initialize\n the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``\n is provided, it is used instead. Note that if a new pool does not\n need to be created for the request, the provided ``pool_kwargs`` are\n not used.\n \"\"\"\n u = parse_url(url)\n return self.connection_from_host(\n u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs\n )\n\n def _merge_pool_kwargs(self, override):\n \"\"\"\n Merge a dictionary of override values for self.connection_pool_kw.\n\n This does not modify self.connection_pool_kw and returns a new dict.\n Any keys in the override dictionary with a value of ``None`` are\n removed from the merged dictionary.\n \"\"\"\n base_pool_kwargs = self.connection_pool_kw.copy()\n if override:\n for key, value in override.items():\n if value is None:\n try:\n del base_pool_kwargs[key]\n except KeyError:\n pass\n else:\n base_pool_kwargs[key] = value\n return base_pool_kwargs\n\n def _proxy_requires_url_absolute_form(self, parsed_url):\n \"\"\"\n Indicates if the proxy requires the complete destination URL in the\n request. Normally this is only needed when not using an HTTP CONNECT\n tunnel.\n \"\"\"\n if self.proxy is None:\n return False\n\n return not connection_requires_http_tunnel(\n self.proxy, self.proxy_config, parsed_url.scheme\n )\n\n def _validate_proxy_scheme_url_selection(self, url_scheme):\n \"\"\"\n Validates that were not attempting to do TLS in TLS connections on\n Python2 or with unsupported SSL implementations.\n \"\"\"\n if self.proxy is None or url_scheme != \"https\":\n return\n\n if self.proxy.scheme != \"https\":\n return\n\n if six.PY2 and not self.proxy_config.use_forwarding_for_https:\n"},"all_code":{"kind":"string","value":"from __future__ import absolute_import\n\n\n\n__all__ = [\"PoolManager\", \"ProxyManager\", \"proxy_from_url\"]\n\n\nlog = logging.getLogger(__name__)\n\nSSL_KEYWORDS = (\n \"key_file\",\n \"cert_file\",\n \"cert_reqs\",\n \"ca_certs\",\n \"ssl_version\",\n \"ca_cert_dir\",\n \"ssl_context\",\n \"key_password\",\n \"server_hostname\",\n)\n\n# All known keyword arguments that could be provided to the pool manager, its\n# pools, or the underlying connections. This is used to construct a pool key.\n_key_fields = (\n \"key_scheme\", # str\n \"key_host\", # str\n \"key_port\", # int\n \"key_timeout\", # int or float or Timeout\n \"key_retries\", # int or Retry\n \"key_strict\", # bool\n \"key_block\", # bool\n \"key_source_address\", # str\n \"key_key_file\", # str\n \"key_key_password\", # str\n \"key_cert_file\", # str\n \"key_cert_reqs\", # str\n \"key_ca_certs\", # str\n \"key_ssl_version\", # str\n \"key_ca_cert_dir\", # str\n \"key_ssl_context\", # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext\n \"key_maxsize\", # int\n \"key_headers\", # dict\n \"key__proxy\", # parsed proxy url\n \"key__proxy_headers\", # dict\n \"key__proxy_config\", # class\n \"key_socket_options\", # list of (level (int), optname (int), value (int or str)) tuples\n \"key__socks_options\", # dict\n \"key_assert_hostname\", # bool or string\n \"key_assert_fingerprint\", # str\n \"key_server_hostname\", # str\n)\n\n#: The namedtuple class used to construct keys for the connection pool.\n#: All custom key schemes should include the fields in this key at a minimum.\nPoolKey = collections.namedtuple(\"PoolKey\", _key_fields)\n\n_proxy_config_fields = (\"ssl_context\", \"use_forwarding_for_https\")\nProxyConfig = collections.namedtuple(\"ProxyConfig\", _proxy_config_fields)\n\n\ndef _default_key_normalizer(key_class, request_context):\n \"\"\"\n Create a pool key out of a request context dictionary.\n\n According to RFC 3986, both the scheme and host are case-insensitive.\n Therefore, this function normalizes both before constructing the pool\n key for an HTTPS request. If you wish to change this behaviour, provide\n alternate callables to ``key_fn_by_scheme``.\n\n :param key_class:\n The class to use when constructing the key. This should be a namedtuple\n with the ``scheme`` and ``host`` keys at a minimum.\n :type key_class: namedtuple\n :param request_context:\n A dictionary-like object that contain the context for a request.\n :type request_context: dict\n\n :return: A namedtuple that can be used as a connection pool key.\n :rtype: PoolKey\n \"\"\"\n # Since we mutate the dictionary, make a copy first\n context = request_context.copy()\n context[\"scheme\"] = context[\"scheme\"].lower()\n context[\"host\"] = context[\"host\"].lower()\n\n # These are both dictionaries and need to be transformed into frozensets\n for key in (\"headers\", \"_proxy_headers\", \"_socks_options\"):\n if key in context and context[key] is not None:\n context[key] = frozenset(context[key].items())\n\n # The socket_options key may be a list and needs to be transformed into a\n # tuple.\n socket_opts = context.get(\"socket_options\")\n if socket_opts is not None:\n context[\"socket_options\"] = tuple(socket_opts)\n\n # Map the kwargs to the names in the namedtuple - this is necessary since\n # namedtuples can't have fields starting with '_'.\n for key in list(context.keys()):\n context[\"key_\" + key] = context.pop(key)\n\n # Default to ``None`` for keys missing from the context\n for field in key_class._fields:\n if field not in context:\n context[field] = None\n\n return key_class(**context)\n\n\n#: A dictionary that maps a scheme to a callable that creates a pool key.\n#: This can be used to alter the way pool keys are constructed, if desired.\n#: Each PoolManager makes a copy of this dictionary so they can be configured\n#: globally here, or individually on the instance.\nkey_fn_by_scheme = {\n \"http\": functools.partial(_default_key_normalizer, PoolKey),\n \"https\": functools.partial(_default_key_normalizer, PoolKey),\n}\n\npool_classes_by_scheme = {\"http\": HTTPConnectionPool, \"https\": HTTPSConnectionPool}\n\n\nclass PoolManager(RequestMethods):\n \"\"\"\n Allows for arbitrary requests while transparently keeping track of\n necessary connection pools for you.\n\n :param num_pools:\n Number of connection pools to cache before discarding the least\n recently used pool.\n\n :param headers:\n Headers to include with all requests, unless other headers are given\n explicitly.\n\n :param \\\\**connection_pool_kw:\n Additional parameters are used to create fresh\n :class:`urllib3.connectionpool.ConnectionPool` instances.\n\n Example::\n\n >>> manager = PoolManager(num_pools=2)\n >>> r = manager.request('GET', 'http://google.com/')\n >>> r = manager.request('GET', 'http://google.com/mail')\n >>> r = manager.request('GET', 'http://yahoo.com/')\n >>> len(manager.pools)\n 2\n\n \"\"\"\n\n proxy = None\n proxy_config = None\n\n def __init__(self, num_pools=10, headers=None, **connection_pool_kw):\n RequestMethods.__init__(self, headers)\n self.connection_pool_kw = connection_pool_kw\n self.pools = RecentlyUsedContainer(num_pools)\n\n # Locally set the pool classes and keys so other PoolManagers can\n # override them.\n self.pool_classes_by_scheme = pool_classes_by_scheme\n self.key_fn_by_scheme = key_fn_by_scheme.copy()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.clear()\n # Return False to re-raise any potential exceptions\n return False\n\n def _new_pool(self, scheme, host, port, request_context=None):\n \"\"\"\n Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and\n any additional pool keyword arguments.\n\n If ``request_context`` is provided, it is provided as keyword arguments\n to the pool class used. This method is used to actually create the\n connection pools handed out by :meth:`connection_from_url` and\n companion methods. It is intended to be overridden for customization.\n \"\"\"\n pool_cls = self.pool_classes_by_scheme[scheme]\n if request_context is None:\n request_context = self.connection_pool_kw.copy()\n\n # Although the context has everything necessary to create the pool,\n # this function has historically only used the scheme, host, and port\n # in the positional args. When an API change is acceptable these can\n # be removed.\n for key in (\"scheme\", \"host\", \"port\"):\n request_context.pop(key, None)\n\n if scheme == \"http\":\n for kw in SSL_KEYWORDS:\n request_context.pop(kw, None)\n\n return pool_cls(host, port, **request_context)\n\n def clear(self):\n \"\"\"\n Empty our store of pools and direct them all to close.\n\n This will not affect in-flight connections, but they will not be\n re-used after completion.\n \"\"\"\n self.pools.clear()\n\n def connection_from_host(self, host, port=None, scheme=\"http\", pool_kwargs=None):\n \"\"\"\n Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.\n\n If ``port`` isn't given, it will be derived from the ``scheme`` using\n ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is\n provided, it is merged with the instance's ``connection_pool_kw``\n variable and used to create the new connection pool, if one is\n needed.\n \"\"\"\n\n if not host:\n raise LocationValueError(\"No host specified.\")\n\n request_context = self._merge_pool_kwargs(pool_kwargs)\n request_context[\"scheme\"] = scheme or \"http\"\n if not port:\n port = port_by_scheme.get(request_context[\"scheme\"].lower(), 80)\n request_context[\"port\"] = port\n request_context[\"host\"] = host\n\n return self.connection_from_context(request_context)\n\n def connection_from_context(self, request_context):\n \"\"\"\n Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.\n\n ``request_context`` must at least contain the ``scheme`` key and its\n value must be a key in ``key_fn_by_scheme`` instance variable.\n \"\"\"\n scheme = request_context[\"scheme\"].lower()\n pool_key_constructor = self.key_fn_by_scheme.get(scheme)\n if not pool_key_constructor:\n raise URLSchemeUnknown(scheme)\n pool_key = pool_key_constructor(request_context)\n\n return self.connection_from_pool_key(pool_key, request_context=request_context)\n\n def connection_from_pool_key(self, pool_key, request_context=None):\n \"\"\"\n Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.\n\n ``pool_key`` should be a namedtuple that only contains immutable\n objects. At a minimum it must have the ``scheme``, ``host``, and\n ``port`` fields.\n \"\"\"\n with self.pools.lock:\n # If the scheme, host, or port doesn't match existing open\n # connections, open a new ConnectionPool.\n pool = self.pools.get(pool_key)\n if pool:\n return pool\n\n # Make a fresh ConnectionPool of the desired type\n scheme = request_context[\"scheme\"]\n host = request_context[\"host\"]\n port = request_context[\"port\"]\n pool = self._new_pool(scheme, host, port, request_context=request_context)\n self.pools[pool_key] = pool\n\n return pool\n\n def connection_from_url(self, url, pool_kwargs=None):\n \"\"\"\n Similar to :func:`urllib3.connectionpool.connection_from_url`.\n\n If ``pool_kwargs`` is not provided and a new pool needs to be\n constructed, ``self.connection_pool_kw`` is used to initialize\n the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``\n is provided, it is used instead. Note that if a new pool does not\n need to be created for the request, the provided ``pool_kwargs`` are\n not used.\n \"\"\"\n u = parse_url(url)\n return self.connection_from_host(\n u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs\n )\n\n def _merge_pool_kwargs(self, override):\n \"\"\"\n Merge a dictionary of override values for self.connection_pool_kw.\n\n This does not modify self.connection_pool_kw and returns a new dict.\n Any keys in the override dictionary with a value of ``None`` are\n removed from the merged dictionary.\n \"\"\"\n base_pool_kwargs = self.connection_pool_kw.copy()\n if override:\n for key, value in override.items():\n if value is None:\n try:\n del base_pool_kwargs[key]\n except KeyError:\n pass\n else:\n base_pool_kwargs[key] = value\n return base_pool_kwargs\n\n def _proxy_requires_url_absolute_form(self, parsed_url):\n \"\"\"\n Indicates if the proxy requires the complete destination URL in the\n request. Normally this is only needed when not using an HTTP CONNECT\n tunnel.\n \"\"\"\n if self.proxy is None:\n return False\n\n return not connection_requires_http_tunnel(\n self.proxy, self.proxy_config, parsed_url.scheme\n )\n\n def _validate_proxy_scheme_url_selection(self, url_scheme):\n \"\"\"\n Validates that were not attempting to do TLS in TLS connections on\n Python2 or with unsupported SSL implementations.\n \"\"\"\n if self.proxy is None or url_scheme != \"https\":\n return\n\n if self.proxy.scheme != \"https\":\n return\n\n if six.PY2 and not self.proxy_config.use_forwarding_for_https:"},"next_line":{"kind":"string","value":" raise ProxySchemeUnsupported("},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-11-27 07:01:39+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5381,"cells":{"repo_name":{"kind":"string","value":"NobiDeveloper/Nobita-Filter-Bot"},"file_path":{"kind":"string","value":"plugins/p_ttishow.py"},"context":{"kind":"list like","value":[{"identifier":"ADMINS","path":"info.py","snippet":"ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]"},{"identifier":"LOG_CHANNEL","path":"info.py","snippet":"LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))"},{"identifier":"SUPPORT_CHAT","path":"info.py","snippet":"SUPPORT_CHAT = environ.get('SUPPORT_CHAT', 'NobiDeveloperSupport')"},{"identifier":"MELCOW_NEW_USERS","path":"info.py","snippet":"MELCOW_NEW_USERS = is_enabled((environ.get('MELCOW_NEW_USERS', \"True\")), True)"},{"identifier":"MELCOW_VID","path":"info.py","snippet":"MELCOW_VID = environ.get(\"MELCOW_VID\", \"https://telegra.ph/file/61ef9818986cef9554017.jpg\")"},{"identifier":"CHNL_LNK","path":"info.py","snippet":"CHNL_LNK = environ.get('CHNL_LNK', 'https://telegram.me/NobiDeveloper')"},{"identifier":"GRP_LNK","path":"info.py","snippet":"GRP_LNK = environ.get('GRP_LNK', 'https://telegram.me/NobiDeveloperSupport')"},{"identifier":"db","path":"database/users_chats_db.py","snippet":"class Database:\n def __init__(self, uri, database_name):\n def new_user(self, id, name):\n def new_group(self, id, title):\n async def add_user(self, id, name):\n async def is_user_exist(self, id):\n async def total_users_count(self):\n async def remove_ban(self, id):\n async def ban_user(self, user_id, ban_reason=\"No Reason\"):\n async def get_ban_status(self, id):\n async def get_all_users(self):\n async def delete_user(self, user_id):\n async def get_banned(self):\n async def add_chat(self, chat, title):\n async def get_chat(self, chat):\n async def re_enable_chat(self, id):\n async def update_settings(self, id, settings):\n async def get_settings(self, id):\n async def disable_chat(self, chat, reason=\"No Reason\"):\n async def total_chat_count(self):\n async def get_all_chats(self):\n async def get_db_size(self):"},{"identifier":"Media","path":"database/ia_filterdb.py","snippet":"class Media(Document):\n file_id = fields.StrField(attribute='_id')\n file_ref = fields.StrField(allow_none=True)\n file_name = fields.StrField(required=True)\n file_size = fields.IntField(required=True)\n file_type = fields.StrField(allow_none=True)\n mime_type = fields.StrField(allow_none=True)\n caption = fields.StrField(allow_none=True)\n\n class Meta:\n indexes = ('$file_name', )\n collection_name = COLLECTION_NAME"},{"identifier":"get_size","path":"utils.py","snippet":"def get_size(size):\n \"\"\"Get size in readable format\"\"\"\n\n units = [\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n size = float(size)\n i = 0\n while size >= 1024.0 and i < len(units):\n i += 1\n size /= 1024.0\n return \"%.2f %s\" % (size, units[i])"},{"identifier":"temp","path":"utils.py","snippet":"class temp(object):\n BANNED_USERS = []\n BANNED_CHATS = []\n ME = None\n CURRENT=int(os.environ.get(\"SKIP\", 2))\n CANCEL = False\n MELCOW = {}\n U_NAME = None\n B_NAME = None\n GETALL = {}\n SHORT = {}\n SETTINGS = {}"},{"identifier":"get_settings","path":"utils.py","snippet":"async def get_settings(group_id):\n settings = temp.SETTINGS.get(group_id)\n if not settings:\n settings = await db.get_settings(group_id)\n temp.SETTINGS[group_id] = settings\n return settings"},{"identifier":"script","path":"Script.py","snippet":"class script(object):\n START_TXT = \"\"\"\n<b>{},\n\nɪ ᴄᴀɴ ᴘʀᴏᴠɪᴅᴇ ᴍᴏᴠɪᴇs ᴀɴᴅ sᴇʀɪᴇs,\nᴊᴜsᴛ ᴀᴅᴅ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴀɴᴅ ᴇɴᴊᴏʏ 😍\n\n💞 ᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ : <a href='https://telegram.me/MovieVillaYT'>ᴍᴏᴠɪᴇ ᴠɪʟʟᴀ</a></b>\n\"\"\"\n\n HELP_TXT = \"\"\"\n<b>{},\n\n/g_info - ᴛᴏ ᴄʜᴇᴄᴋ ʏᴏᴜʀ ᴠᴀʟᴜᴇꜱ\n/set_tutorial - ᴛᴏ ꜱᴇᴛ ᴄᴜꜱᴛᴏᴍ ᴛᴜᴛᴏʀɪᴀʟ\n/set_shortlink - ᴛᴏ ꜱᴇᴛ ᴄᴜꜱᴛᴏᴍ ꜱʜᴏʀᴛᴇɴᴇʀ\n/rem_tutorial - ᴛᴏ ʀᴇᴍᴏᴠᴇ ᴛᴜᴛᴏʀɪᴀʟ ʟɪɴᴋ\n</b>\"\"\"\n\n ABOUT_TXT = \"\"\"<b>➣ ᴍʏ ɴᴀᴍᴇ ⋟</b> {}\n<b>➢ ᴄʀᴇᴀᴛᴏʀ ⋟</b> <a href=https://youtube.com/@NobiDeveloper>𝘔𝘖𝘝𝘐𝘌 𝘝𝘐𝘓𝘓𝘈</a>\n<b>➣ ʟɪʙʀᴀʀʏ ⋟</b> 𝘱𝘺𝘳𝘰𝘨𝘳𝘢𝘮\n<b>➢ ʟᴀɴɢᴜᴀɢᴇ ⋟</b> 𝘱𝘺𝘵𝘩𝘰𝘯 3\n<b>➣ ᴅᴀᴛᴀʙᴀsᴇ ⋟</b> 𝘮𝘰𝘯𝘨𝘰 𝘥𝘣\n<b>➢ ʙᴏᴛ sᴇʀᴠᴇʀ ⋟</b> 𝘩𝘦𝘳𝘰𝘬𝘶\n<b>➣ ʙᴜɪʟᴅ sᴛᴀᴛs ⋟</b> 𝘷2.0.1 ﹝ʙᴇᴛᴀ﹞\"\"\"\n\n SOURCE_TXT = \"\"\"\n<b>ᴛʜɪꜱ ɪꜱ ᴀɴ ᴏᴘᴇɴ ꜱᴏᴜʀᴄᴇ ᴘʀᴏᴊᴇᴄᴛ.</b>\n\nᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜɪꜱ ʙᴏᴛ ᴀʀᴇ ꜰʀᴇᴇʟʏ ᴀᴠᴀɪʟᴀʙʟᴇ ᴏɴ ᴛʜᴇ ɪɴᴛᴇʀɴᴇᴛ ᴏʀ ᴘᴏꜱᴛᴇᴅ ʙʏ ꜱᴏᴍᴇʙᴏᴅʏ ᴇʟꜱᴇ. ᴊᴜꜱᴛ ꜰᴏʀ ᴇᴀꜱʏ ꜱᴇᴀʀᴄʜɪɴɢ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ɪɴᴅᴇxɪɴɢ ꜰɪʟᴇꜱ ᴡʜɪᴄʜ ᴀʀᴇ ᴀʟʀᴇᴀᴅʏ ᴜᴘʟᴏᴀᴅᴇᴅ ᴏɴ ᴛᴇʟᴇɢʀᴀᴍ. ᴡᴇ ʀᴇꜱᴘᴇᴄᴛ ᴀʟʟ ᴛʜᴇ ᴄᴏᴘʏʀɪɢʜᴛ ʟᴀᴡꜱ ᴀɴᴅ ᴡᴏʀᴋꜱ ɪɴ ᴄᴏᴍᴘʟɪᴀɴᴄᴇ ᴡɪᴛʜ ᴅᴍᴄᴀ ᴀɴᴅ ᴇᴜᴄᴅ. ɪꜰ ᴀɴʏᴛʜɪɴɢ ɪꜱ ᴀɢᴀɪɴꜱᴛ ʟᴀᴡ ᴘʟᴇᴀꜱᴇ ᴄᴏɴᴛᴀᴄᴛ ᴍᴇ ꜱᴏ ᴛʜᴀᴛ ɪᴛ ᴄᴀɴ ʙᴇ ʀᴇᴍᴏᴠᴇᴅ ᴀꜱᴀᴘ. ɪᴛ ɪꜱ ꜰᴏʀʙɪʙʙᴇɴ ᴛᴏ ᴅᴏᴡɴʟᴏᴀᴅ, ꜱᴛʀᴇᴀᴍ, ʀᴇᴘʀᴏᴅᴜᴄᴇ, ᴏʀ ʙʏ ᴀɴʏ ᴍᴇᴀɴꜱ, ꜱʜᴀʀᴇ, ᴏʀ ᴄᴏɴꜱᴜᴍᴇ, ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴄʀᴇᴀᴛᴏʀ ᴏʀ ʟᴇɢᴀʟ ᴄᴏᴘʏʀɪɢʜᴛ ʜᴏʟᴅᴇʀ. ɪꜰ ʏᴏᴜ ʙᴇʟɪᴇᴠᴇ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ᴠɪᴏʟᴀᴛɪɴɢ ʏᴏᴜʀ ɪɴᴛᴇʟʟᴇᴄᴛᴜᴀʟ ᴘʀᴏᴘᴇʀᴛʏ, ᴄᴏɴᴛᴀᴄᴛ ᴛʜᴇ ʀᴇꜱᴘᴇᴄᴛɪᴠᴇ ᴄʜᴀɴɴᴇʟꜱ ꜰᴏʀ ʀᴇᴍᴏᴠᴀʟ. ᴛʜᴇ ʙᴏᴛ ᴅᴏᴇꜱ ɴᴏᴛ ᴏᴡɴ ᴀɴʏ ᴏꜰ ᴛʜᴇꜱᴇ ᴄᴏɴᴛᴇɴᴛꜱ, ɪᴛ ᴏɴʟʏ ɪɴᴅᴇx ᴛʜᴇ ꜰɪʟᴇꜱ ꜰʀᴏᴍ ᴛᴇʟᴇɢʀᴀᴍ.\n\n<b><a href=https://telegram.me/NobiDeveloper>~ ᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ @MovieVillaYT</a></b>\n\"\"\"\n\n MANUELFILTER_TXT = \"\"\"\n<b>{},\n\n~ ʏᴏᴜ ᴄᴀɴ ᴇᴀsɪʟʏ ᴄᴜsᴛᴏᴍɪᴢᴇ ᴛʜɪs ʙᴏᴛ ꜰᴏʀ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\n\n~ ᴏɴʟʏ ɢʀᴏᴜᴘ ᴀᴅᴍɪɴ ᴄᴀɴ ᴜsᴇ ᴛʜɪs ᴄᴏᴍᴍᴀɴᴅ ᴀɴᴅ ᴄʜᴀɴɢᴇs sᴇᴛᴛɪɴɢs.\n\n~ ɪᴛ ᴡᴏʀᴋs ᴏɴʟʏ ᴡʜᴇɴ ʏᴏᴜ ᴀʟʀᴇᴀᴅʏ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\n\nᴄᴏᴍᴍᴀɴᴅs ᴀɴᴅ ᴜsᴀɢᴇ -\n\n• /settings - ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs ᴀs ʏᴏᴜʀ ᴡɪsʜ.</b>\n\"\"\"\n\n GROUP_TXT = \"\"\"\n<b>⍟ ᴄʜᴀɴɴᴇʟs ᴀɴᴅ ɢʀᴏᴜᴘs ᴍᴏᴅᴜʟᴇ ⍟</b>\n\n<b>🍿 ᴍᴏᴠɪᴇꜱ ᴄʜᴀɴɴᴇʟ.\n🗣️ ʙᴏᴛ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ.\n🚦 ʙᴏᴛ ᴜᴘᴅᴀᴛᴇs ᴄʜᴀɴɴᴇʟ.\n🎬 ᴍᴏᴠɪᴇ ʀᴇǫᴜᴇsᴛɪɴɢ ɢʀᴏᴜᴘ.</b>\"\"\"\n\n BUTTON_TXT = \"\"\"\n<b>💵 ɪ ʀᴇǫᴜᴇsᴛᴇᴅ ᴛᴏ ʏᴏᴜ 💸\n\nᴘʟᴇᴀsᴇ ᴅᴏɴᴀᴛᴇ ᴛʜᴇ ᴅᴇᴠᴇʟᴏᴘᴇʀ ꜰᴏʀ ᴋᴇᴇᴘɪɴɢ ᴛʜᴇ sᴇʀᴠɪᴄᴇ ᴀʟɪᴠᴇ & ᴋᴇᴇᴘ ʙʀɪɴɢɪɴɢ ᴍᴏʀᴇ ɴᴇᴡ ꜰᴇᴀᴛᴜʀᴇs ꜰᴏʀ ʏᴏᴜ....</b>\n\n𝐘𝐨𝐮 𝐂𝐚𝐧 𝐃𝐨𝐧𝐚𝐭𝐞 𝐀𝐧𝐲 𝐀𝐦𝐨𝐮𝐧𝐭 𝐘𝐨𝐮 𝐇𝐚𝐯𝐞 💷\n\n<b>᚜ ᴘᴀʏᴍᴇɴᴛ ᴍᴇᴛʜᴏᴅs ᚛</b>\n\n💵 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗚𝗼𝗼𝗴𝗹𝗲 𝗣𝗮𝘆</a>\n💸 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗣𝗮𝘆𝘁𝗺</a>\n💶 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗣𝗵𝗼𝗻𝗲𝗣𝗲</a>\n\n𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐌𝐞 𝐅𝐨𝐫 𝐊𝐧𝐨𝐰 𝐀𝐛𝐨𝐮𝐭 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐈𝐧𝐟𝐨\n\n<b>ᴄʟɪᴄᴋ ʜᴇʀᴇ - <a href='https://telegram.me/NobiDeveloperr'>ʙᴏss</a>\nᴄʟɪᴄᴋ ʜᴇʀᴇ - <a href='https://telegram.me/NobiDeveloperr'>ʙᴏss</a></b>\"\"\"\n\n AUTOFILTER_TXT = \"\"\"ʜᴇʟᴘ: <b>ᴀᴜᴛᴏ ꜰɪʟᴛᴇʀ</b>\n<b>ɴᴏᴛᴇ: Fɪʟᴇ Iɴᴅᴇx</b>\n1. ᴍᴀᴋᴇ ᴍᴇ ᴛʜᴇ ᴀᴅᴍɪɴ ᴏꜰ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ɪꜰ ɪᴛ'ꜱ ᴘʀɪᴠᴀᴛᴇ.\n2. ᴍᴀᴋᴇ ꜱᴜʀᴇ ᴛʜᴀᴛ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ᴅᴏᴇꜱ ɴᴏᴛ ᴄᴏɴᴛᴀɪɴꜱ ᴄᴀᴍʀɪᴘꜱ, ᴘᴏʀɴ ᴀɴᴅ ꜰᴀᴋᴇ ꜰɪʟᴇꜱ.\n3. ꜰᴏʀᴡᴀʀᴅ ᴛʜᴇ ʟᴀꜱᴛ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴍᴇ ᴡɪᴛʜ Qᴜᴏᴛᴇꜱ. ɪ'ʟʟ ᴀᴅᴅ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜᴀᴛ ᴄʜᴀɴɴᴇʟ ᴛᴏ ᴍʏ ᴅʙ.\n\n<b>Nᴏᴛᴇ: AᴜᴛᴏFɪʟᴛᴇʀ</b>\n1. Aᴅᴅ ᴛʜᴇ ʙᴏᴛ ᴀs ᴀᴅᴍɪɴ ᴏɴ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\n2. Usᴇ /connect ᴀɴᴅ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ᴛʜᴇ ʙᴏᴛ.\n3. Usᴇ /settings ᴏɴ ʙᴏᴛ's PM ᴀɴᴅ ᴛᴜʀɴ ᴏɴ AᴜᴛᴏFɪʟᴛᴇʀ ᴏɴ ᴛʜᴇ sᴇᴛᴛɪɴɢs ᴍᴇɴᴜ.\"\"\"\n\n CONNECTION_TXT = \"\"\"ʜᴇʟᴘ: <b>ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ</b>\n- ᴜꜱᴇᴅ ᴛᴏ ᴄᴏɴɴᴇᴄᴛ ʙᴏᴛ ᴛᴏ ᴘᴍ ꜰᴏʀ ᴍᴀɴᴀɢɪɴɢ ꜰɪʟᴛᴇʀꜱ \n- ɪᴛ ʜᴇʟᴘꜱ ᴛᴏ ᴀᴠᴏɪᴅ ꜱᴘᴀᴍᴍɪɴɢ ɪɴ ɢʀᴏᴜᴘꜱ.\n<b>ɴᴏᴛᴇ:</b>\n1. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ᴀ ᴄᴏɴɴᴇᴄᴛɪᴏɴ.\n2. ꜱᴇɴᴅ <code>/ᴄᴏɴɴᴇᴄᴛ</code> ꜰᴏʀ ᴄᴏɴɴᴇᴄᴛɪɴɢ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\n• /connect - <code>ᴄᴏɴɴᴇᴄᴛ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴄʜᴀᴛ ᴛᴏ ʏᴏᴜʀ ᴘᴍ</code>\n• /disconnect - <code>ᴅɪꜱᴄᴏɴɴᴇᴄᴛ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ</code>\n• /connections - <code>ʟɪꜱᴛ ᴀʟʟ ʏᴏᴜʀ ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ</code>\"\"\"\n\n EXTRAMOD_TXT = \"\"\"ʜᴇʟᴘ: Exᴛʀᴀ Mᴏᴅᴜʟᴇs\n<b>ɴᴏᴛᴇ:</b>\nᴛʜᴇꜱᴇ ᴀʀᴇ ᴛʜᴇ ᴇxᴛʀᴀ ꜰᴇᴀᴛᴜʀᴇꜱ ᴏꜰ ᴛʜɪꜱ ʙᴏᴛ\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\n• /id - <code>ɢᴇᴛ ɪᴅ ᴏꜰ ᴀ ꜱᴘᴇᴄɪꜰɪᴇᴅ ᴜꜱᴇʀ.</code>\n• /info - <code>ɢᴇᴛ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ᴀʙᴏᴜᴛ ᴀ ᴜꜱᴇʀ.</code>\n• /imdb - <code>ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ɪᴍᴅʙ ꜱᴏᴜʀᴄᴇ.</code>\n• /search - <code>ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ᴠᴀʀɪᴏᴜꜱ ꜱᴏᴜʀᴄᴇꜱ.</code>\"\"\"\n\n ADMIN_TXT = \"\"\"ʜᴇʟᴘ: Aᴅᴍɪɴ Mᴏᴅs\n<b>ɴᴏᴛᴇ:</b>\nTʜɪs Mᴏᴅᴜʟᴇ Oɴʟʏ Wᴏʀᴋs Fᴏʀ Mʏ Aᴅᴍɪɴs\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\n• /logs - <code>ᴛᴏ ɢᴇᴛ ᴛʜᴇ ʀᴇᴄᴇɴᴛ ᴇʀʀᴏʀꜱ</code>\n• /stats - <code>ᴛᴏ ɢᴇᴛ ꜱᴛᴀᴛᴜꜱ ᴏꜰ ꜰɪʟᴇꜱ ɪɴ ᴅʙ. [Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</code>\n• /delete - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴇ ꜰʀᴏᴍ ᴅʙ.</code>\n• /users - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴜꜱᴇʀꜱ ᴀɴᴅ ɪᴅꜱ.</code>\n• /chats - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴄʜᴀᴛꜱ ᴀɴᴅ ɪᴅꜱ</code>\n• /leave - <code>ᴛᴏ ʟᴇᴀᴠᴇ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ.</code>\n• /disable - <code>ᴛᴏ ᴅɪꜱᴀʙʟᴇ ᴀ ᴄʜᴀᴛ.</code>\n• /ban - <code>ᴛᴏ ʙᴀɴ ᴀ ᴜꜱᴇʀ.</code>\n• /unban - <code>ᴛᴏ ᴜɴʙᴀɴ ᴀ ᴜꜱᴇʀ.</code>\n• /channel - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴛᴏᴛᴀʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴄʜᴀɴɴᴇʟꜱ</code>\n• /broadcast - <code>ᴛᴏ ʙʀᴏᴀᴅᴄᴀꜱᴛ ᴀ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴀʟʟ ᴜꜱᴇʀꜱ</code>\n• /grp_broadcast - <code>Tᴏ ʙʀᴏᴀᴅᴄᴀsᴛ ᴀ ᴍᴇssᴀɢᴇ ᴛᴏ ᴀʟʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ɢʀᴏᴜᴘs.</code>\n• /gfilter - <code>ᴛᴏ ᴀᴅᴅ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs</code>\n• /gfilters - <code>ᴛᴏ ᴠɪᴇᴡ ʟɪsᴛ ᴏғ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs</code>\n• /delg - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ sᴘᴇᴄɪғɪᴄ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ</code>\n• /request - <code>Tᴏ sᴇɴᴅ ᴀ Mᴏᴠɪᴇ/Sᴇʀɪᴇs ʀᴇᴏ̨ᴜᴇsᴛ ᴛᴏ ʙᴏᴛ ᴀᴅᴍɪɴs. Oɴʟʏ ᴡᴏʀᴋs ᴏɴ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ. [Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</code>\n• /delallg - <code>Tᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ Gғɪʟᴛᴇʀs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.</code>\n• /deletefiles - <code>Tᴏ ᴅᴇʟᴇᴛᴇ CᴀᴍRɪᴘ ᴀɴᴅ PʀᴇDVD Fɪʟᴇs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.</code>\"\"\"\n\n STATUS_TXT = \"\"\"<b>📂 ᴛᴏᴛᴀʟ ꜰɪʟᴇs: <code>{}</code>\n👤 ᴛᴏᴛᴀʟ ᴜsᴇʀs: <code>{}</code>\n♻️ ᴛᴏᴛᴀʟ ᴄʜᴀᴛs: <code>{}</code>\n🗃️ ᴜsᴇᴅ sᴛᴏʀᴀɢᴇ: <code>{}</code>\n🆓 ꜰʀᴇᴇ sᴛᴏʀᴀɢᴇ: <code>{}</code></b>\"\"\"\n\n LOG_TEXT_G = \"\"\"#𝐍𝐞𝐰𝐆𝐫𝐨𝐮𝐩\n\n<b>᚛› 𝐆𝐫𝐨𝐮𝐩 ⪼ {}(<code>{}</code>)</b>\n<b>᚛› 𝐓𝐨𝐭𝐚𝐥 𝐌𝐞𝐦𝐛𝐞𝐫𝐬 ⪼ <code>{}</code></b>\n<b>᚛› 𝐀𝐝𝐝𝐞𝐝 𝐁𝐲 ⪼ {}</b>\n\"\"\"\n\n LOG_TEXT_P = \"\"\"#𝐍𝐞𝐰𝐔𝐬𝐞𝐫\n\n<b>᚛› 𝐈𝐃 - <code>{}</code></b>\n<b>᚛› 𝐍𝐚𝐦𝐞 - {}</b>\n\"\"\"\n\n ALRT_TXT = \"\"\"{},\nᴄʜᴇᴄᴋ ʏᴏᴜʀ ᴏᴡɴ ʀᴇǫᴜᴇ𝗌ᴛ 😤\n\"\"\"\n\n OLD_ALRT_TXT =\"\"\"{},\n\nʏᴏᴜ ᴀʀᴇ ᴜꜱɪɴɢ ᴍʏ ᴏʟᴅ ᴍᴇꜱꜱᴀɢᴇ,\n\nꜱᴇɴᴅ ᴛʜᴇ ʀᴇǫᴜᴇ𝗌ᴛ ᴀɢᴀɪɴ 😊\n\"\"\"\n\n CUDNT_FND = \"\"\"<b>{},</b>\n\n𝗜 𝗰𝗼𝘂𝗹𝗱𝗻'𝘁 𝗳𝗶𝗻𝗱 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘁𝗼 𝘁𝗵𝗮𝘁 𝗱𝗶𝗱 𝘆𝗼𝘂 𝗺𝗲𝗮𝗻 𝗮𝗻𝘆 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 ?? 👇\"\"\"\n\n I_CUDNT = \"\"\"<b>{},</b>\n\n𝗜 𝗰𝗼𝘂𝗹𝗱𝗻'𝘁 𝗳𝗶𝗻𝗱 𝗮𝗻𝘆 𝗺𝗼𝘃𝗶𝗲 𝗼𝗿 𝘀𝗲𝗿𝗶𝗲𝘀 𝗶𝗻 𝘁𝗵𝗮𝘁 𝗻𝗮𝗺𝗲.. 😐\"\"\"\n\n I_CUD_NT = \"\"\"ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏ ᴍᴏᴠɪᴇ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}.\nᴘʟᴇᴀꜱᴇ ᴄʜᴇᴄᴋ ᴛʜᴇ ꜱᴘᴇʟʟɪɴɢ ᴏɴ ɢᴏᴏɢʟᴇ ᴏʀ ɪᴍᴅʙ...\"\"\"\n\n MVE_NT_FND = \"\"\"<b>ᴍᴏᴠɪᴇ ɴᴏᴛ ꜰᴏᴜɴᴅ...\n\n<u>ʀᴇᴀꜱᴏɴꜱ:</u></b>\n\n𝟷) ꜱᴘᴇʟʟɪɴɢ ᴍɪꜱᴛᴀᴋᴇ\n\n𝟸) ᴏᴛᴛ ᴏʀ ᴅᴠᴅ ɴᴏᴛ ʀᴇʟᴇᴀꜱᴇᴅ\n\n𝟹) ɴᴏᴛ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ\n\n<b><a href=https://telegram.me/NobiDeveloperr>~ ʀᴇǫᴜᴇ𝗌ᴛ ᴛᴏ ᴏᴡɴᴇʀ</a></b>\n\"\"\"\n\n TOP_ALRT_MSG = \"\"\"ꜱᴇᴀʀᴄʜɪɴɢ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ...\"\"\"\n\n MELCOW_ENG = \"\"\"<b>{},\n\n📿 ᴡᴇʟᴄᴏᴍᴇ ᴛᴏ ᴏᴜʀ ɢʀᴏᴜᴘ {}\n\n🚬 ᴛʜɪs ɪs ᴀ ᴍᴏᴠɪᴇ ɢʀᴏᴜᴘ\n\n⏳ ᴀʟʟ ᴄᴀᴛᴇɢᴏʀɪᴇs ᴏꜰ ᴍᴏᴠɪᴇs ᴀᴠᴀɪʟᴀʙʟᴇ ʜᴇʀᴇ\n\n🧨 ᴊᴜsᴛ ᴛʏᴘᴇ ᴛʜᴇ ᴍᴏᴠɪᴇ ɴᴀᴍᴇ\n\n🤖 ʙᴏᴛ ᴡɪʟʟ sᴇɴᴅ ʏᴏᴜʀ ᴍᴏᴠɪᴇ\n\n☎️ ʀᴇᴀᴅ ɢʀᴏᴜᴘ ʀᴜʟᴇs ᴛᴏ ᴋɴᴏᴡ ᴍᴏʀᴇ...</b>\"\"\"\n\n SHORTLINK_INFO = \"\"\"\n<b>──────「 <a href='https://telegram.me/NobiDeveloper'>ᴇᴀʀɴ ᴍᴏɴᴇʏ</a> 」──────\n\n➥ ɴᴏᴡ ʏᴏᴜ ᴄᴀɴ ᴀʟsᴏ ᴇᴀʀɴ ʟᴏᴛs ᴏꜰ ᴍᴏɴᴇʏ ꜰʀᴏᴍ ᴛʜɪꜱ ʙᴏᴛ.\n\n›› sᴛᴇᴘ 𝟷 : ʏᴏᴜ ᴍᴜsᴛ ʜᴀᴠᴇ ᴀᴛʟᴇᴀsᴛ ᴏɴᴇ ɢʀᴏᴜᴘ ᴡɪᴛʜ ᴍɪɴɪᴍᴜᴍ 𝟹𝟶𝟶 ᴍᴇᴍʙᴇʀs.\n\n›› sᴛᴇᴘ 𝟸 : ᴍᴀᴋᴇ ᴀᴄᴄᴏᴜɴᴛ ᴏɴ <a href='https://tnshort.net/ref/devilofficial'>ᴛɴʟɪɴᴋ</a> ᴏʀ <a href='https://onepagelink.in/ref/Nobita'>ᴏɴᴇᴘᴀɢᴇʟɪɴᴋ</a>. [ ʏᴏᴜ ᴄᴀɴ ᴀʟsᴏ ᴜsᴇ ᴏᴛʜᴇʀ sʜᴏʀᴛɴᴇʀ ᴡᴇʙsɪᴛᴇ ]\n\n›› sᴛᴇᴘ 𝟹 : ꜰᴏʟʟᴏᴡ ᴛʜᴇsᴇ <a href='https://telegram.me/NobiDeveloper/1063'>ɪɴꜱᴛʀᴜᴄᴛɪᴏɴꜱ</a>.\n\n➥ ᴛʜɪꜱ ʙᴏᴛ ꜰʀᴇᴇ ꜰᴏʀ ᴀʟʟ ʏᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ʙᴏᴛ ɪɴ ʏᴏᴜʀ ɢʀᴏᴜᴘs ꜰʀᴇᴇ ᴏꜰ ᴄᴏꜱᴛ.</b>\"\"\"\n\n REQINFO = \"\"\"\n⚠ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ⚠\n\nᴀꜰᴛᴇʀ 5 ᴍɪɴᴜᴛᴇꜱ ᴛʜɪꜱ ᴍᴇꜱꜱᴀɢᴇ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏᴍᴀᴛɪᴄᴀʟʟʏ ᴅᴇʟᴇᴛᴇᴅ\n\nɪꜰ ʏᴏᴜ ᴅᴏ ɴᴏᴛ ꜱᴇᴇ ᴛʜᴇ ʀᴇǫᴜᴇsᴛᴇᴅ ᴍᴏᴠɪᴇ / sᴇʀɪᴇs ꜰɪʟᴇ, ʟᴏᴏᴋ ᴀᴛ ᴛʜᴇ ɴᴇxᴛ ᴘᴀɢᴇ\"\"\"\n\n SELECT = \"\"\"\nMOVIES ➢ Sᴇʟᴇᴄᴛ \"Lᴀɴɢᴜᴀɢᴇs\"\n\nSERIES ➢ Sᴇʟᴇᴄᴛ \"Sᴇᴀsᴏɴs\"\n\nTɪᴘ: Sᴇʟᴇᴄᴛ \"Lᴀɴɢᴜᴀɢᴇs\" ᴏʀ \"Sᴇᴀsᴏɴs\" Bᴜᴛᴛᴏɴ ᴀɴᴅ Cʟɪᴄᴋ \"Sᴇɴᴅ Aʟʟ\" Tᴏ ɢᴇᴛ Aʟʟ Fɪʟᴇ Lɪɴᴋs ɪɴ ᴀ Sɪɴɢʟᴇ ᴄʟɪᴄᴋ\"\"\"\n\n SINFO = \"\"\"\n▣ ᴛɪᴘs ▣\n\n☆ ᴛʏᴘᴇ ᴄᴏʀʀᴇᴄᴛ sᴘᴇʟʟɪɴɢ (ɢᴏᴏɢʟᴇ)\n\n☆ ɪꜰ ʏᴏᴜ ɴᴏᴛ ɢᴇᴛ ʏᴏᴜʀ ꜰɪʟᴇ ɪɴ ᴛʜɪꜱ ᴘᴀɢᴇ ᴛʜᴇɴ ᴄʟɪᴄᴋ ᴏɴ ɴᴇxᴛ ʙᴜᴛᴛᴏɴ\n\n☆ ᴄᴏɴᴛɪɴᴜᴇ ᴛʜɪs ᴍᴇᴛʜᴏᴅ ᴛᴏ ɢᴇᴛᴛɪɴɢ ʏᴏᴜ ꜰɪʟᴇ\n\n❤️🔥 ᴘᴏᴡᴇʀᴇᴅ ʙʏ @NobiDeveloper\n\"\"\"\n\n NORSLTS = \"\"\"\n★ #𝗡𝗼𝗥𝗲𝘀𝘂𝗹𝘁𝘀 ★\n\n𝗜𝗗 <b>: {}</b>\n𝗡𝗮𝗺𝗲 <b>: {}</b>\n𝗠𝗲𝘀𝘀𝗮𝗴𝗲 <b>: {}</b>\"\"\"\n\n CAPTION = \"\"\"\n[{file_name}](https://telegram.me/NobiDeveloper)\n\n<b>•────•────────•────•\n📌 ʀᴇǫᴜᴇsᴛ ɢʀᴏᴜᴘ : [ᴄʟɪᴄᴋ ʜᴇʀᴇ](https://telegram.me/AllRequestGroups)\n🎬 ᴍᴏᴠɪᴇs ᴄʜᴀɴɴᴇʟ : [ᴄʟɪᴄᴋ ʜᴇʀᴇ](https://telegram.me/MovieVillaYT)\n•────•────────•────•\n\n©️ ᴘᴏᴡᴇʀᴇᴅ ʙʏ : [ᴍᴏᴠɪᴇ ᴠɪʟʟᴀ](https://youtube.com/@NobiDeveloper)</b>\"\"\"\n\n IMDB_TEMPLATE_TXT = \"\"\"\n<b>{title}</b>\n\n⭐️<b>{rating}</b> | ⏰ <b>{runtime}</b> | 📆 <b>{release_date}</b>\n\n● <b>{genres}</b>\n● <b>{languages}</b>\n\n📖 sᴛᴏʀʏ : <b>{plot}</b> \n\n© {message.chat.title}\n\"\"\"\n \n ALL_FILTERS = \"\"\"\n<b>Hᴇʏ {}, Tʜᴇsᴇ ᴀʀᴇ ᴍʏ ᴛʜʀᴇᴇ ᴛʏᴘᴇs ᴏғ ғɪʟᴛᴇʀs.</b>\"\"\"\n \n GFILTER_TXT = \"\"\"\n<b>Wᴇʟᴄᴏᴍᴇ ᴛᴏ Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs. Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs ᴀʀᴇ ᴛʜᴇ ғɪʟᴛᴇʀs sᴇᴛ ʙʏ ʙᴏᴛ ᴀᴅᴍɪɴs ᴡʜɪᴄʜ ᴡɪʟʟ ᴡᴏʀᴋ ᴏɴ ᴀʟʟ ɢʀᴏᴜᴘs.</b>\n \nAᴠᴀɪʟᴀʙʟᴇ ᴄᴏᴍᴍᴀɴᴅs:\n• /gfilter - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.</code>\n• /gfilters - <code>Tᴏ ᴠɪᴇᴡ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs.</code>\n• /delg - <code>Tᴏ ᴅᴇʟᴇᴛᴇ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.</code>\n• /delallg - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢʟᴏʙᴀʟ ꜰɪʟᴛᴇʀꜱ.</code>\"\"\"\n \n FILE_STORE_TXT = \"\"\"\n<b>Fɪʟᴇ sᴛᴏʀᴇ ɪs ᴛʜᴇ ғᴇᴀᴛᴜʀᴇ ᴡʜɪᴄʜ ᴡɪʟʟ ᴄʀᴇᴀᴛᴇ ᴀ sʜᴀʀᴇᴀʙʟᴇ ʟɪɴᴋ ᴏғ ᴀ sɪɴɢʟᴇ ᴏʀ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.</b>\n\nAᴠᴀɪʟᴀʙʟᴇ ᴄᴏᴍᴍᴀɴᴅs:\n• /batch - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ʙᴀᴛᴄʜ ʟɪɴᴋ ᴏғ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.</code>\n• /link - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ sɪɴɢʟᴇ ғɪʟᴇ sᴛᴏʀᴇ ʟɪɴᴋ.</code>\n• /pbatch - <code>Jᴜsᴛ ʟɪᴋᴇ /batch, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇs ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴs.</code>\n• /plink - <code>Jᴜsᴛ ʟɪᴋᴇ /link, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇ ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴ.</code>\"\"\"\n\n RESTART_TXT = \"\"\"\n<b>Bᴏᴛ Rᴇsᴛᴀʀᴛᴇᴅ !\n\n📅 Dᴀᴛᴇ : <code>{}</code>\n⏰ Tɪᴍᴇ : <code>{}</code>\n🌐 Tɪᴍᴇᴢᴏɴᴇ : <code>Asia/Kolkata</code>\n🛠️ Bᴜɪʟᴅ Sᴛᴀᴛᴜs: <code>v2.7.1 [ Sᴛᴀʙʟᴇ ]</code></b>\n\"\"\"\n\n LOGO = \"\"\"\n𝑺𝒕𝒂𝒓𝒕𝒊𝒏𝒈.......🥵\"\"\""}],"string":"[\n {\n \"identifier\": \"ADMINS\",\n \"path\": \"info.py\",\n \"snippet\": \"ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]\"\n },\n {\n \"identifier\": \"LOG_CHANNEL\",\n \"path\": \"info.py\",\n \"snippet\": \"LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))\"\n },\n {\n \"identifier\": \"SUPPORT_CHAT\",\n \"path\": \"info.py\",\n \"snippet\": \"SUPPORT_CHAT = environ.get('SUPPORT_CHAT', 'NobiDeveloperSupport')\"\n },\n {\n \"identifier\": \"MELCOW_NEW_USERS\",\n \"path\": \"info.py\",\n \"snippet\": \"MELCOW_NEW_USERS = is_enabled((environ.get('MELCOW_NEW_USERS', \\\"True\\\")), True)\"\n },\n {\n \"identifier\": \"MELCOW_VID\",\n \"path\": \"info.py\",\n \"snippet\": \"MELCOW_VID = environ.get(\\\"MELCOW_VID\\\", \\\"https://telegra.ph/file/61ef9818986cef9554017.jpg\\\")\"\n },\n {\n \"identifier\": \"CHNL_LNK\",\n \"path\": \"info.py\",\n \"snippet\": \"CHNL_LNK = environ.get('CHNL_LNK', 'https://telegram.me/NobiDeveloper')\"\n },\n {\n \"identifier\": \"GRP_LNK\",\n \"path\": \"info.py\",\n \"snippet\": \"GRP_LNK = environ.get('GRP_LNK', 'https://telegram.me/NobiDeveloperSupport')\"\n },\n {\n \"identifier\": \"db\",\n \"path\": \"database/users_chats_db.py\",\n \"snippet\": \"class Database:\\n def __init__(self, uri, database_name):\\n def new_user(self, id, name):\\n def new_group(self, id, title):\\n async def add_user(self, id, name):\\n async def is_user_exist(self, id):\\n async def total_users_count(self):\\n async def remove_ban(self, id):\\n async def ban_user(self, user_id, ban_reason=\\\"No Reason\\\"):\\n async def get_ban_status(self, id):\\n async def get_all_users(self):\\n async def delete_user(self, user_id):\\n async def get_banned(self):\\n async def add_chat(self, chat, title):\\n async def get_chat(self, chat):\\n async def re_enable_chat(self, id):\\n async def update_settings(self, id, settings):\\n async def get_settings(self, id):\\n async def disable_chat(self, chat, reason=\\\"No Reason\\\"):\\n async def total_chat_count(self):\\n async def get_all_chats(self):\\n async def get_db_size(self):\"\n },\n {\n \"identifier\": \"Media\",\n \"path\": \"database/ia_filterdb.py\",\n \"snippet\": \"class Media(Document):\\n file_id = fields.StrField(attribute='_id')\\n file_ref = fields.StrField(allow_none=True)\\n file_name = fields.StrField(required=True)\\n file_size = fields.IntField(required=True)\\n file_type = fields.StrField(allow_none=True)\\n mime_type = fields.StrField(allow_none=True)\\n caption = fields.StrField(allow_none=True)\\n\\n class Meta:\\n indexes = ('$file_name', )\\n collection_name = COLLECTION_NAME\"\n },\n {\n \"identifier\": \"get_size\",\n \"path\": \"utils.py\",\n \"snippet\": \"def get_size(size):\\n \\\"\\\"\\\"Get size in readable format\\\"\\\"\\\"\\n\\n units = [\\\"Bytes\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\"]\\n size = float(size)\\n i = 0\\n while size >= 1024.0 and i < len(units):\\n i += 1\\n size /= 1024.0\\n return \\\"%.2f %s\\\" % (size, units[i])\"\n },\n {\n \"identifier\": \"temp\",\n \"path\": \"utils.py\",\n \"snippet\": \"class temp(object):\\n BANNED_USERS = []\\n BANNED_CHATS = []\\n ME = None\\n CURRENT=int(os.environ.get(\\\"SKIP\\\", 2))\\n CANCEL = False\\n MELCOW = {}\\n U_NAME = None\\n B_NAME = None\\n GETALL = {}\\n SHORT = {}\\n SETTINGS = {}\"\n },\n {\n \"identifier\": \"get_settings\",\n \"path\": \"utils.py\",\n \"snippet\": \"async def get_settings(group_id):\\n settings = temp.SETTINGS.get(group_id)\\n if not settings:\\n settings = await db.get_settings(group_id)\\n temp.SETTINGS[group_id] = settings\\n return settings\"\n },\n {\n \"identifier\": \"script\",\n \"path\": \"Script.py\",\n \"snippet\": \"class script(object):\\n START_TXT = \\\"\\\"\\\"\\n<b>{},\\n\\nɪ ᴄᴀɴ ᴘʀᴏᴠɪᴅᴇ ᴍᴏᴠɪᴇs ᴀɴᴅ sᴇʀɪᴇs,\\nᴊᴜsᴛ ᴀᴅᴅ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴀɴᴅ ᴇɴᴊᴏʏ 😍\\n\\n💞 ᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ : <a href='https://telegram.me/MovieVillaYT'>ᴍᴏᴠɪᴇ ᴠɪʟʟᴀ</a></b>\\n\\\"\\\"\\\"\\n\\n HELP_TXT = \\\"\\\"\\\"\\n<b>{},\\n\\n/g_info - ᴛᴏ ᴄʜᴇᴄᴋ ʏᴏᴜʀ ᴠᴀʟᴜᴇꜱ\\n/set_tutorial - ᴛᴏ ꜱᴇᴛ ᴄᴜꜱᴛᴏᴍ ᴛᴜᴛᴏʀɪᴀʟ\\n/set_shortlink - ᴛᴏ ꜱᴇᴛ ᴄᴜꜱᴛᴏᴍ ꜱʜᴏʀᴛᴇɴᴇʀ\\n/rem_tutorial - ᴛᴏ ʀᴇᴍᴏᴠᴇ ᴛᴜᴛᴏʀɪᴀʟ ʟɪɴᴋ\\n</b>\\\"\\\"\\\"\\n\\n ABOUT_TXT = \\\"\\\"\\\"<b>➣ ᴍʏ ɴᴀᴍᴇ ⋟</b> {}\\n<b>➢ ᴄʀᴇᴀᴛᴏʀ ⋟</b> <a href=https://youtube.com/@NobiDeveloper>𝘔𝘖𝘝𝘐𝘌 𝘝𝘐𝘓𝘓𝘈</a>\\n<b>➣ ʟɪʙʀᴀʀʏ ⋟</b> 𝘱𝘺𝘳𝘰𝘨𝘳𝘢𝘮\\n<b>➢ ʟᴀɴɢᴜᴀɢᴇ ⋟</b> 𝘱𝘺𝘵𝘩𝘰𝘯 3\\n<b>➣ ᴅᴀᴛᴀʙᴀsᴇ ⋟</b> 𝘮𝘰𝘯𝘨𝘰 𝘥𝘣\\n<b>➢ ʙᴏᴛ sᴇʀᴠᴇʀ ⋟</b> 𝘩𝘦𝘳𝘰𝘬𝘶\\n<b>➣ ʙᴜɪʟᴅ sᴛᴀᴛs ⋟</b> 𝘷2.0.1 ﹝ʙᴇᴛᴀ﹞\\\"\\\"\\\"\\n\\n SOURCE_TXT = \\\"\\\"\\\"\\n<b>ᴛʜɪꜱ ɪꜱ ᴀɴ ᴏᴘᴇɴ ꜱᴏᴜʀᴄᴇ ᴘʀᴏᴊᴇᴄᴛ.</b>\\n\\nᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜɪꜱ ʙᴏᴛ ᴀʀᴇ ꜰʀᴇᴇʟʏ ᴀᴠᴀɪʟᴀʙʟᴇ ᴏɴ ᴛʜᴇ ɪɴᴛᴇʀɴᴇᴛ ᴏʀ ᴘᴏꜱᴛᴇᴅ ʙʏ ꜱᴏᴍᴇʙᴏᴅʏ ᴇʟꜱᴇ. ᴊᴜꜱᴛ ꜰᴏʀ ᴇᴀꜱʏ ꜱᴇᴀʀᴄʜɪɴɢ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ɪɴᴅᴇxɪɴɢ ꜰɪʟᴇꜱ ᴡʜɪᴄʜ ᴀʀᴇ ᴀʟʀᴇᴀᴅʏ ᴜᴘʟᴏᴀᴅᴇᴅ ᴏɴ ᴛᴇʟᴇɢʀᴀᴍ. ᴡᴇ ʀᴇꜱᴘᴇᴄᴛ ᴀʟʟ ᴛʜᴇ ᴄᴏᴘʏʀɪɢʜᴛ ʟᴀᴡꜱ ᴀɴᴅ ᴡᴏʀᴋꜱ ɪɴ ᴄᴏᴍᴘʟɪᴀɴᴄᴇ ᴡɪᴛʜ ᴅᴍᴄᴀ ᴀɴᴅ ᴇᴜᴄᴅ. ɪꜰ ᴀɴʏᴛʜɪɴɢ ɪꜱ ᴀɢᴀɪɴꜱᴛ ʟᴀᴡ ᴘʟᴇᴀꜱᴇ ᴄᴏɴᴛᴀᴄᴛ ᴍᴇ ꜱᴏ ᴛʜᴀᴛ ɪᴛ ᴄᴀɴ ʙᴇ ʀᴇᴍᴏᴠᴇᴅ ᴀꜱᴀᴘ. ɪᴛ ɪꜱ ꜰᴏʀʙɪʙʙᴇɴ ᴛᴏ ᴅᴏᴡɴʟᴏᴀᴅ, ꜱᴛʀᴇᴀᴍ, ʀᴇᴘʀᴏᴅᴜᴄᴇ, ᴏʀ ʙʏ ᴀɴʏ ᴍᴇᴀɴꜱ, ꜱʜᴀʀᴇ, ᴏʀ ᴄᴏɴꜱᴜᴍᴇ, ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴄʀᴇᴀᴛᴏʀ ᴏʀ ʟᴇɢᴀʟ ᴄᴏᴘʏʀɪɢʜᴛ ʜᴏʟᴅᴇʀ. ɪꜰ ʏᴏᴜ ʙᴇʟɪᴇᴠᴇ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ᴠɪᴏʟᴀᴛɪɴɢ ʏᴏᴜʀ ɪɴᴛᴇʟʟᴇᴄᴛᴜᴀʟ ᴘʀᴏᴘᴇʀᴛʏ, ᴄᴏɴᴛᴀᴄᴛ ᴛʜᴇ ʀᴇꜱᴘᴇᴄᴛɪᴠᴇ ᴄʜᴀɴɴᴇʟꜱ ꜰᴏʀ ʀᴇᴍᴏᴠᴀʟ. ᴛʜᴇ ʙᴏᴛ ᴅᴏᴇꜱ ɴᴏᴛ ᴏᴡɴ ᴀɴʏ ᴏꜰ ᴛʜᴇꜱᴇ ᴄᴏɴᴛᴇɴᴛꜱ, ɪᴛ ᴏɴʟʏ ɪɴᴅᴇx ᴛʜᴇ ꜰɪʟᴇꜱ ꜰʀᴏᴍ ᴛᴇʟᴇɢʀᴀᴍ.\\n\\n<b><a href=https://telegram.me/NobiDeveloper>~ ᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ @MovieVillaYT</a></b>\\n\\\"\\\"\\\"\\n\\n MANUELFILTER_TXT = \\\"\\\"\\\"\\n<b>{},\\n\\n~ ʏᴏᴜ ᴄᴀɴ ᴇᴀsɪʟʏ ᴄᴜsᴛᴏᴍɪᴢᴇ ᴛʜɪs ʙᴏᴛ ꜰᴏʀ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\n\\n~ ᴏɴʟʏ ɢʀᴏᴜᴘ ᴀᴅᴍɪɴ ᴄᴀɴ ᴜsᴇ ᴛʜɪs ᴄᴏᴍᴍᴀɴᴅ ᴀɴᴅ ᴄʜᴀɴɢᴇs sᴇᴛᴛɪɴɢs.\\n\\n~ ɪᴛ ᴡᴏʀᴋs ᴏɴʟʏ ᴡʜᴇɴ ʏᴏᴜ ᴀʟʀᴇᴀᴅʏ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\n\\nᴄᴏᴍᴍᴀɴᴅs ᴀɴᴅ ᴜsᴀɢᴇ -\\n\\n• /settings - ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs ᴀs ʏᴏᴜʀ ᴡɪsʜ.</b>\\n\\\"\\\"\\\"\\n\\n GROUP_TXT = \\\"\\\"\\\"\\n<b>⍟ ᴄʜᴀɴɴᴇʟs ᴀɴᴅ ɢʀᴏᴜᴘs ᴍᴏᴅᴜʟᴇ ⍟</b>\\n\\n<b>🍿 ᴍᴏᴠɪᴇꜱ ᴄʜᴀɴɴᴇʟ.\\n🗣️ ʙᴏᴛ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ.\\n🚦 ʙᴏᴛ ᴜᴘᴅᴀᴛᴇs ᴄʜᴀɴɴᴇʟ.\\n🎬 ᴍᴏᴠɪᴇ ʀᴇǫᴜᴇsᴛɪɴɢ ɢʀᴏᴜᴘ.</b>\\\"\\\"\\\"\\n\\n BUTTON_TXT = \\\"\\\"\\\"\\n<b>💵 ɪ ʀᴇǫᴜᴇsᴛᴇᴅ ᴛᴏ ʏᴏᴜ 💸\\n\\nᴘʟᴇᴀsᴇ ᴅᴏɴᴀᴛᴇ ᴛʜᴇ ᴅᴇᴠᴇʟᴏᴘᴇʀ ꜰᴏʀ ᴋᴇᴇᴘɪɴɢ ᴛʜᴇ sᴇʀᴠɪᴄᴇ ᴀʟɪᴠᴇ & ᴋᴇᴇᴘ ʙʀɪɴɢɪɴɢ ᴍᴏʀᴇ ɴᴇᴡ ꜰᴇᴀᴛᴜʀᴇs ꜰᴏʀ ʏᴏᴜ....</b>\\n\\n𝐘𝐨𝐮 𝐂𝐚𝐧 𝐃𝐨𝐧𝐚𝐭𝐞 𝐀𝐧𝐲 𝐀𝐦𝐨𝐮𝐧𝐭 𝐘𝐨𝐮 𝐇𝐚𝐯𝐞 💷\\n\\n<b>᚜ ᴘᴀʏᴍᴇɴᴛ ᴍᴇᴛʜᴏᴅs ᚛</b>\\n\\n💵 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗚𝗼𝗼𝗴𝗹𝗲 𝗣𝗮𝘆</a>\\n💸 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗣𝗮𝘆𝘁𝗺</a>\\n💶 <a href='https://telegra.ph/SUPPORT-12-22-2'>𝗣𝗵𝗼𝗻𝗲𝗣𝗲</a>\\n\\n𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐌𝐞 𝐅𝐨𝐫 𝐊𝐧𝐨𝐰 𝐀𝐛𝐨𝐮𝐭 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐈𝐧𝐟𝐨\\n\\n<b>ᴄʟɪᴄᴋ ʜᴇʀᴇ - <a href='https://telegram.me/NobiDeveloperr'>ʙᴏss</a>\\nᴄʟɪᴄᴋ ʜᴇʀᴇ - <a href='https://telegram.me/NobiDeveloperr'>ʙᴏss</a></b>\\\"\\\"\\\"\\n\\n AUTOFILTER_TXT = \\\"\\\"\\\"ʜᴇʟᴘ: <b>ᴀᴜᴛᴏ ꜰɪʟᴛᴇʀ</b>\\n<b>ɴᴏᴛᴇ: Fɪʟᴇ Iɴᴅᴇx</b>\\n1. ᴍᴀᴋᴇ ᴍᴇ ᴛʜᴇ ᴀᴅᴍɪɴ ᴏꜰ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ɪꜰ ɪᴛ'ꜱ ᴘʀɪᴠᴀᴛᴇ.\\n2. ᴍᴀᴋᴇ ꜱᴜʀᴇ ᴛʜᴀᴛ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ᴅᴏᴇꜱ ɴᴏᴛ ᴄᴏɴᴛᴀɪɴꜱ ᴄᴀᴍʀɪᴘꜱ, ᴘᴏʀɴ ᴀɴᴅ ꜰᴀᴋᴇ ꜰɪʟᴇꜱ.\\n3. ꜰᴏʀᴡᴀʀᴅ ᴛʜᴇ ʟᴀꜱᴛ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴍᴇ ᴡɪᴛʜ Qᴜᴏᴛᴇꜱ. ɪ'ʟʟ ᴀᴅᴅ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜᴀᴛ ᴄʜᴀɴɴᴇʟ ᴛᴏ ᴍʏ ᴅʙ.\\n\\n<b>Nᴏᴛᴇ: AᴜᴛᴏFɪʟᴛᴇʀ</b>\\n1. Aᴅᴅ ᴛʜᴇ ʙᴏᴛ ᴀs ᴀᴅᴍɪɴ ᴏɴ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\n2. Usᴇ /connect ᴀɴᴅ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ᴛʜᴇ ʙᴏᴛ.\\n3. Usᴇ /settings ᴏɴ ʙᴏᴛ's PM ᴀɴᴅ ᴛᴜʀɴ ᴏɴ AᴜᴛᴏFɪʟᴛᴇʀ ᴏɴ ᴛʜᴇ sᴇᴛᴛɪɴɢs ᴍᴇɴᴜ.\\\"\\\"\\\"\\n\\n CONNECTION_TXT = \\\"\\\"\\\"ʜᴇʟᴘ: <b>ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ</b>\\n- ᴜꜱᴇᴅ ᴛᴏ ᴄᴏɴɴᴇᴄᴛ ʙᴏᴛ ᴛᴏ ᴘᴍ ꜰᴏʀ ᴍᴀɴᴀɢɪɴɢ ꜰɪʟᴛᴇʀꜱ \\n- ɪᴛ ʜᴇʟᴘꜱ ᴛᴏ ᴀᴠᴏɪᴅ ꜱᴘᴀᴍᴍɪɴɢ ɪɴ ɢʀᴏᴜᴘꜱ.\\n<b>ɴᴏᴛᴇ:</b>\\n1. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ᴀ ᴄᴏɴɴᴇᴄᴛɪᴏɴ.\\n2. ꜱᴇɴᴅ <code>/ᴄᴏɴɴᴇᴄᴛ</code> ꜰᴏʀ ᴄᴏɴɴᴇᴄᴛɪɴɢ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\\n• /connect - <code>ᴄᴏɴɴᴇᴄᴛ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴄʜᴀᴛ ᴛᴏ ʏᴏᴜʀ ᴘᴍ</code>\\n• /disconnect - <code>ᴅɪꜱᴄᴏɴɴᴇᴄᴛ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ</code>\\n• /connections - <code>ʟɪꜱᴛ ᴀʟʟ ʏᴏᴜʀ ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ</code>\\\"\\\"\\\"\\n\\n EXTRAMOD_TXT = \\\"\\\"\\\"ʜᴇʟᴘ: Exᴛʀᴀ Mᴏᴅᴜʟᴇs\\n<b>ɴᴏᴛᴇ:</b>\\nᴛʜᴇꜱᴇ ᴀʀᴇ ᴛʜᴇ ᴇxᴛʀᴀ ꜰᴇᴀᴛᴜʀᴇꜱ ᴏꜰ ᴛʜɪꜱ ʙᴏᴛ\\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\\n• /id - <code>ɢᴇᴛ ɪᴅ ᴏꜰ ᴀ ꜱᴘᴇᴄɪꜰɪᴇᴅ ᴜꜱᴇʀ.</code>\\n• /info - <code>ɢᴇᴛ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ᴀʙᴏᴜᴛ ᴀ ᴜꜱᴇʀ.</code>\\n• /imdb - <code>ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ɪᴍᴅʙ ꜱᴏᴜʀᴄᴇ.</code>\\n• /search - <code>ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ᴠᴀʀɪᴏᴜꜱ ꜱᴏᴜʀᴄᴇꜱ.</code>\\\"\\\"\\\"\\n\\n ADMIN_TXT = \\\"\\\"\\\"ʜᴇʟᴘ: Aᴅᴍɪɴ Mᴏᴅs\\n<b>ɴᴏᴛᴇ:</b>\\nTʜɪs Mᴏᴅᴜʟᴇ Oɴʟʏ Wᴏʀᴋs Fᴏʀ Mʏ Aᴅᴍɪɴs\\nCᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ:\\n• /logs - <code>ᴛᴏ ɢᴇᴛ ᴛʜᴇ ʀᴇᴄᴇɴᴛ ᴇʀʀᴏʀꜱ</code>\\n• /stats - <code>ᴛᴏ ɢᴇᴛ ꜱᴛᴀᴛᴜꜱ ᴏꜰ ꜰɪʟᴇꜱ ɪɴ ᴅʙ. [Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</code>\\n• /delete - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴇ ꜰʀᴏᴍ ᴅʙ.</code>\\n• /users - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴜꜱᴇʀꜱ ᴀɴᴅ ɪᴅꜱ.</code>\\n• /chats - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴄʜᴀᴛꜱ ᴀɴᴅ ɪᴅꜱ</code>\\n• /leave - <code>ᴛᴏ ʟᴇᴀᴠᴇ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ.</code>\\n• /disable - <code>ᴛᴏ ᴅɪꜱᴀʙʟᴇ ᴀ ᴄʜᴀᴛ.</code>\\n• /ban - <code>ᴛᴏ ʙᴀɴ ᴀ ᴜꜱᴇʀ.</code>\\n• /unban - <code>ᴛᴏ ᴜɴʙᴀɴ ᴀ ᴜꜱᴇʀ.</code>\\n• /channel - <code>ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴛᴏᴛᴀʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴄʜᴀɴɴᴇʟꜱ</code>\\n• /broadcast - <code>ᴛᴏ ʙʀᴏᴀᴅᴄᴀꜱᴛ ᴀ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴀʟʟ ᴜꜱᴇʀꜱ</code>\\n• /grp_broadcast - <code>Tᴏ ʙʀᴏᴀᴅᴄᴀsᴛ ᴀ ᴍᴇssᴀɢᴇ ᴛᴏ ᴀʟʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ɢʀᴏᴜᴘs.</code>\\n• /gfilter - <code>ᴛᴏ ᴀᴅᴅ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs</code>\\n• /gfilters - <code>ᴛᴏ ᴠɪᴇᴡ ʟɪsᴛ ᴏғ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs</code>\\n• /delg - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ sᴘᴇᴄɪғɪᴄ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ</code>\\n• /request - <code>Tᴏ sᴇɴᴅ ᴀ Mᴏᴠɪᴇ/Sᴇʀɪᴇs ʀᴇᴏ̨ᴜᴇsᴛ ᴛᴏ ʙᴏᴛ ᴀᴅᴍɪɴs. Oɴʟʏ ᴡᴏʀᴋs ᴏɴ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ. [Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</code>\\n• /delallg - <code>Tᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ Gғɪʟᴛᴇʀs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.</code>\\n• /deletefiles - <code>Tᴏ ᴅᴇʟᴇᴛᴇ CᴀᴍRɪᴘ ᴀɴᴅ PʀᴇDVD Fɪʟᴇs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.</code>\\\"\\\"\\\"\\n\\n STATUS_TXT = \\\"\\\"\\\"<b>📂 ᴛᴏᴛᴀʟ ꜰɪʟᴇs: <code>{}</code>\\n👤 ᴛᴏᴛᴀʟ ᴜsᴇʀs: <code>{}</code>\\n♻️ ᴛᴏᴛᴀʟ ᴄʜᴀᴛs: <code>{}</code>\\n🗃️ ᴜsᴇᴅ sᴛᴏʀᴀɢᴇ: <code>{}</code>\\n🆓 ꜰʀᴇᴇ sᴛᴏʀᴀɢᴇ: <code>{}</code></b>\\\"\\\"\\\"\\n\\n LOG_TEXT_G = \\\"\\\"\\\"#𝐍𝐞𝐰𝐆𝐫𝐨𝐮𝐩\\n\\n<b>᚛› 𝐆𝐫𝐨𝐮𝐩 ⪼ {}(<code>{}</code>)</b>\\n<b>᚛› 𝐓𝐨𝐭𝐚𝐥 𝐌𝐞𝐦𝐛𝐞𝐫𝐬 ⪼ <code>{}</code></b>\\n<b>᚛› 𝐀𝐝𝐝𝐞𝐝 𝐁𝐲 ⪼ {}</b>\\n\\\"\\\"\\\"\\n\\n LOG_TEXT_P = \\\"\\\"\\\"#𝐍𝐞𝐰𝐔𝐬𝐞𝐫\\n\\n<b>᚛› 𝐈𝐃 - <code>{}</code></b>\\n<b>᚛› 𝐍𝐚𝐦𝐞 - {}</b>\\n\\\"\\\"\\\"\\n\\n ALRT_TXT = \\\"\\\"\\\"{},\\nᴄʜᴇᴄᴋ ʏᴏᴜʀ ᴏᴡɴ ʀᴇǫᴜᴇ𝗌ᴛ 😤\\n\\\"\\\"\\\"\\n\\n OLD_ALRT_TXT =\\\"\\\"\\\"{},\\n\\nʏᴏᴜ ᴀʀᴇ ᴜꜱɪɴɢ ᴍʏ ᴏʟᴅ ᴍᴇꜱꜱᴀɢᴇ,\\n\\nꜱᴇɴᴅ ᴛʜᴇ ʀᴇǫᴜᴇ𝗌ᴛ ᴀɢᴀɪɴ 😊\\n\\\"\\\"\\\"\\n\\n CUDNT_FND = \\\"\\\"\\\"<b>{},</b>\\n\\n𝗜 𝗰𝗼𝘂𝗹𝗱𝗻'𝘁 𝗳𝗶𝗻𝗱 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘁𝗼 𝘁𝗵𝗮𝘁 𝗱𝗶𝗱 𝘆𝗼𝘂 𝗺𝗲𝗮𝗻 𝗮𝗻𝘆 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 ?? 👇\\\"\\\"\\\"\\n\\n I_CUDNT = \\\"\\\"\\\"<b>{},</b>\\n\\n𝗜 𝗰𝗼𝘂𝗹𝗱𝗻'𝘁 𝗳𝗶𝗻𝗱 𝗮𝗻𝘆 𝗺𝗼𝘃𝗶𝗲 𝗼𝗿 𝘀𝗲𝗿𝗶𝗲𝘀 𝗶𝗻 𝘁𝗵𝗮𝘁 𝗻𝗮𝗺𝗲.. 😐\\\"\\\"\\\"\\n\\n I_CUD_NT = \\\"\\\"\\\"ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏ ᴍᴏᴠɪᴇ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}.\\nᴘʟᴇᴀꜱᴇ ᴄʜᴇᴄᴋ ᴛʜᴇ ꜱᴘᴇʟʟɪɴɢ ᴏɴ ɢᴏᴏɢʟᴇ ᴏʀ ɪᴍᴅʙ...\\\"\\\"\\\"\\n\\n MVE_NT_FND = \\\"\\\"\\\"<b>ᴍᴏᴠɪᴇ ɴᴏᴛ ꜰᴏᴜɴᴅ...\\n\\n<u>ʀᴇᴀꜱᴏɴꜱ:</u></b>\\n\\n𝟷) ꜱᴘᴇʟʟɪɴɢ ᴍɪꜱᴛᴀᴋᴇ\\n\\n𝟸) ᴏᴛᴛ ᴏʀ ᴅᴠᴅ ɴᴏᴛ ʀᴇʟᴇᴀꜱᴇᴅ\\n\\n𝟹) ɴᴏᴛ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ\\n\\n<b><a href=https://telegram.me/NobiDeveloperr>~ ʀᴇǫᴜᴇ𝗌ᴛ ᴛᴏ ᴏᴡɴᴇʀ</a></b>\\n\\\"\\\"\\\"\\n\\n TOP_ALRT_MSG = \\\"\\\"\\\"ꜱᴇᴀʀᴄʜɪɴɢ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ...\\\"\\\"\\\"\\n\\n MELCOW_ENG = \\\"\\\"\\\"<b>{},\\n\\n📿 ᴡᴇʟᴄᴏᴍᴇ ᴛᴏ ᴏᴜʀ ɢʀᴏᴜᴘ {}\\n\\n🚬 ᴛʜɪs ɪs ᴀ ᴍᴏᴠɪᴇ ɢʀᴏᴜᴘ\\n\\n⏳ ᴀʟʟ ᴄᴀᴛᴇɢᴏʀɪᴇs ᴏꜰ ᴍᴏᴠɪᴇs ᴀᴠᴀɪʟᴀʙʟᴇ ʜᴇʀᴇ\\n\\n🧨 ᴊᴜsᴛ ᴛʏᴘᴇ ᴛʜᴇ ᴍᴏᴠɪᴇ ɴᴀᴍᴇ\\n\\n🤖 ʙᴏᴛ ᴡɪʟʟ sᴇɴᴅ ʏᴏᴜʀ ᴍᴏᴠɪᴇ\\n\\n☎️ ʀᴇᴀᴅ ɢʀᴏᴜᴘ ʀᴜʟᴇs ᴛᴏ ᴋɴᴏᴡ ᴍᴏʀᴇ...</b>\\\"\\\"\\\"\\n\\n SHORTLINK_INFO = \\\"\\\"\\\"\\n<b>──────「 <a href='https://telegram.me/NobiDeveloper'>ᴇᴀʀɴ ᴍᴏɴᴇʏ</a> 」──────\\n\\n➥ ɴᴏᴡ ʏᴏᴜ ᴄᴀɴ ᴀʟsᴏ ᴇᴀʀɴ ʟᴏᴛs ᴏꜰ ᴍᴏɴᴇʏ ꜰʀᴏᴍ ᴛʜɪꜱ ʙᴏᴛ.\\n\\n›› sᴛᴇᴘ 𝟷 : ʏᴏᴜ ᴍᴜsᴛ ʜᴀᴠᴇ ᴀᴛʟᴇᴀsᴛ ᴏɴᴇ ɢʀᴏᴜᴘ ᴡɪᴛʜ ᴍɪɴɪᴍᴜᴍ 𝟹𝟶𝟶 ᴍᴇᴍʙᴇʀs.\\n\\n›› sᴛᴇᴘ 𝟸 : ᴍᴀᴋᴇ ᴀᴄᴄᴏᴜɴᴛ ᴏɴ <a href='https://tnshort.net/ref/devilofficial'>ᴛɴʟɪɴᴋ</a> ᴏʀ <a href='https://onepagelink.in/ref/Nobita'>ᴏɴᴇᴘᴀɢᴇʟɪɴᴋ</a>. [ ʏᴏᴜ ᴄᴀɴ ᴀʟsᴏ ᴜsᴇ ᴏᴛʜᴇʀ sʜᴏʀᴛɴᴇʀ ᴡᴇʙsɪᴛᴇ ]\\n\\n›› sᴛᴇᴘ 𝟹 : ꜰᴏʟʟᴏᴡ ᴛʜᴇsᴇ <a href='https://telegram.me/NobiDeveloper/1063'>ɪɴꜱᴛʀᴜᴄᴛɪᴏɴꜱ</a>.\\n\\n➥ ᴛʜɪꜱ ʙᴏᴛ ꜰʀᴇᴇ ꜰᴏʀ ᴀʟʟ ʏᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ʙᴏᴛ ɪɴ ʏᴏᴜʀ ɢʀᴏᴜᴘs ꜰʀᴇᴇ ᴏꜰ ᴄᴏꜱᴛ.</b>\\\"\\\"\\\"\\n\\n REQINFO = \\\"\\\"\\\"\\n⚠ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ⚠\\n\\nᴀꜰᴛᴇʀ 5 ᴍɪɴᴜᴛᴇꜱ ᴛʜɪꜱ ᴍᴇꜱꜱᴀɢᴇ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏᴍᴀᴛɪᴄᴀʟʟʏ ᴅᴇʟᴇᴛᴇᴅ\\n\\nɪꜰ ʏᴏᴜ ᴅᴏ ɴᴏᴛ ꜱᴇᴇ ᴛʜᴇ ʀᴇǫᴜᴇsᴛᴇᴅ ᴍᴏᴠɪᴇ / sᴇʀɪᴇs ꜰɪʟᴇ, ʟᴏᴏᴋ ᴀᴛ ᴛʜᴇ ɴᴇxᴛ ᴘᴀɢᴇ\\\"\\\"\\\"\\n\\n SELECT = \\\"\\\"\\\"\\nMOVIES ➢ Sᴇʟᴇᴄᴛ \\\"Lᴀɴɢᴜᴀɢᴇs\\\"\\n\\nSERIES ➢ Sᴇʟᴇᴄᴛ \\\"Sᴇᴀsᴏɴs\\\"\\n\\nTɪᴘ: Sᴇʟᴇᴄᴛ \\\"Lᴀɴɢᴜᴀɢᴇs\\\" ᴏʀ \\\"Sᴇᴀsᴏɴs\\\" Bᴜᴛᴛᴏɴ ᴀɴᴅ Cʟɪᴄᴋ \\\"Sᴇɴᴅ Aʟʟ\\\" Tᴏ ɢᴇᴛ Aʟʟ Fɪʟᴇ Lɪɴᴋs ɪɴ ᴀ Sɪɴɢʟᴇ ᴄʟɪᴄᴋ\\\"\\\"\\\"\\n\\n SINFO = \\\"\\\"\\\"\\n▣ ᴛɪᴘs ▣\\n\\n☆ ᴛʏᴘᴇ ᴄᴏʀʀᴇᴄᴛ sᴘᴇʟʟɪɴɢ (ɢᴏᴏɢʟᴇ)\\n\\n☆ ɪꜰ ʏᴏᴜ ɴᴏᴛ ɢᴇᴛ ʏᴏᴜʀ ꜰɪʟᴇ ɪɴ ᴛʜɪꜱ ᴘᴀɢᴇ ᴛʜᴇɴ ᴄʟɪᴄᴋ ᴏɴ ɴᴇxᴛ ʙᴜᴛᴛᴏɴ\\n\\n☆ ᴄᴏɴᴛɪɴᴜᴇ ᴛʜɪs ᴍᴇᴛʜᴏᴅ ᴛᴏ ɢᴇᴛᴛɪɴɢ ʏᴏᴜ ꜰɪʟᴇ\\n\\n❤️🔥 ᴘᴏᴡᴇʀᴇᴅ ʙʏ @NobiDeveloper\\n\\\"\\\"\\\"\\n\\n NORSLTS = \\\"\\\"\\\"\\n★ #𝗡𝗼𝗥𝗲𝘀𝘂𝗹𝘁𝘀 ★\\n\\n𝗜𝗗 <b>: {}</b>\\n𝗡𝗮𝗺𝗲 <b>: {}</b>\\n𝗠𝗲𝘀𝘀𝗮𝗴𝗲 <b>: {}</b>\\\"\\\"\\\"\\n\\n CAPTION = \\\"\\\"\\\"\\n[{file_name}](https://telegram.me/NobiDeveloper)\\n\\n<b>•────•────────•────•\\n📌 ʀᴇǫᴜᴇsᴛ ɢʀᴏᴜᴘ : [ᴄʟɪᴄᴋ ʜᴇʀᴇ](https://telegram.me/AllRequestGroups)\\n🎬 ᴍᴏᴠɪᴇs ᴄʜᴀɴɴᴇʟ : [ᴄʟɪᴄᴋ ʜᴇʀᴇ](https://telegram.me/MovieVillaYT)\\n•────•────────•────•\\n\\n©️ ᴘᴏᴡᴇʀᴇᴅ ʙʏ : [ᴍᴏᴠɪᴇ ᴠɪʟʟᴀ](https://youtube.com/@NobiDeveloper)</b>\\\"\\\"\\\"\\n\\n IMDB_TEMPLATE_TXT = \\\"\\\"\\\"\\n<b>{title}</b>\\n\\n⭐️<b>{rating}</b> | ⏰ <b>{runtime}</b> | 📆 <b>{release_date}</b>\\n\\n● <b>{genres}</b>\\n● <b>{languages}</b>\\n\\n📖 sᴛᴏʀʏ : <b>{plot}</b> \\n\\n© {message.chat.title}\\n\\\"\\\"\\\"\\n \\n ALL_FILTERS = \\\"\\\"\\\"\\n<b>Hᴇʏ {}, Tʜᴇsᴇ ᴀʀᴇ ᴍʏ ᴛʜʀᴇᴇ ᴛʏᴘᴇs ᴏғ ғɪʟᴛᴇʀs.</b>\\\"\\\"\\\"\\n \\n GFILTER_TXT = \\\"\\\"\\\"\\n<b>Wᴇʟᴄᴏᴍᴇ ᴛᴏ Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs. Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs ᴀʀᴇ ᴛʜᴇ ғɪʟᴛᴇʀs sᴇᴛ ʙʏ ʙᴏᴛ ᴀᴅᴍɪɴs ᴡʜɪᴄʜ ᴡɪʟʟ ᴡᴏʀᴋ ᴏɴ ᴀʟʟ ɢʀᴏᴜᴘs.</b>\\n \\nAᴠᴀɪʟᴀʙʟᴇ ᴄᴏᴍᴍᴀɴᴅs:\\n• /gfilter - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.</code>\\n• /gfilters - <code>Tᴏ ᴠɪᴇᴡ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs.</code>\\n• /delg - <code>Tᴏ ᴅᴇʟᴇᴛᴇ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.</code>\\n• /delallg - <code>ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢʟᴏʙᴀʟ ꜰɪʟᴛᴇʀꜱ.</code>\\\"\\\"\\\"\\n \\n FILE_STORE_TXT = \\\"\\\"\\\"\\n<b>Fɪʟᴇ sᴛᴏʀᴇ ɪs ᴛʜᴇ ғᴇᴀᴛᴜʀᴇ ᴡʜɪᴄʜ ᴡɪʟʟ ᴄʀᴇᴀᴛᴇ ᴀ sʜᴀʀᴇᴀʙʟᴇ ʟɪɴᴋ ᴏғ ᴀ sɪɴɢʟᴇ ᴏʀ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.</b>\\n\\nAᴠᴀɪʟᴀʙʟᴇ ᴄᴏᴍᴍᴀɴᴅs:\\n• /batch - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ʙᴀᴛᴄʜ ʟɪɴᴋ ᴏғ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.</code>\\n• /link - <code>Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ sɪɴɢʟᴇ ғɪʟᴇ sᴛᴏʀᴇ ʟɪɴᴋ.</code>\\n• /pbatch - <code>Jᴜsᴛ ʟɪᴋᴇ /batch, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇs ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴs.</code>\\n• /plink - <code>Jᴜsᴛ ʟɪᴋᴇ /link, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇ ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴ.</code>\\\"\\\"\\\"\\n\\n RESTART_TXT = \\\"\\\"\\\"\\n<b>Bᴏᴛ Rᴇsᴛᴀʀᴛᴇᴅ !\\n\\n📅 Dᴀᴛᴇ : <code>{}</code>\\n⏰ Tɪᴍᴇ : <code>{}</code>\\n🌐 Tɪᴍᴇᴢᴏɴᴇ : <code>Asia/Kolkata</code>\\n🛠️ Bᴜɪʟᴅ Sᴛᴀᴛᴜs: <code>v2.7.1 [ Sᴛᴀʙʟᴇ ]</code></b>\\n\\\"\\\"\\\"\\n\\n LOGO = \\\"\\\"\\\"\\n𝑺𝒕𝒂𝒓𝒕𝒊𝒏𝒈.......🥵\\\"\\\"\\\"\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from pyrogram import Client, filters, enums\nfrom pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, CallbackQuery\nfrom pyrogram.errors.exceptions.bad_request_400 import MessageTooLong, PeerIdInvalid\nfrom info import ADMINS, LOG_CHANNEL, SUPPORT_CHAT, MELCOW_NEW_USERS, MELCOW_VID, CHNL_LNK, GRP_LNK\nfrom database.users_chats_db import db\nfrom database.ia_filterdb import Media\nfrom utils import get_size, temp, get_settings\nfrom Script import script\nfrom pyrogram.errors import ChatAdminRequired\nimport asyncio "},"token_num":{"kind":"number","value":14685,"string":"14,685"},"cropped_code":{"kind":"string","value":"\n\"\"\"----------------------------------------- https://github.com/NobiDeveloper/Nobita-Filter-Bot --------------------------------------\"\"\"\n\n@Client.on_message(filters.new_chat_members & filters.group)\nasync def save_group(bot, message):\n r_j_check = [u.id for u in message.new_chat_members]\n if temp.ME in r_j_check:\n if not await db.get_chat(message.chat.id):\n total=await bot.get_chat_members_count(message.chat.id)\n r_j = message.from_user.mention if message.from_user else \"Anonymous\" \n"},"all_code":{"kind":"string","value":"\n\"\"\"----------------------------------------- https://github.com/NobiDeveloper/Nobita-Filter-Bot --------------------------------------\"\"\"\n\n@Client.on_message(filters.new_chat_members & filters.group)\nasync def save_group(bot, message):\n r_j_check = [u.id for u in message.new_chat_members]\n if temp.ME in r_j_check:\n if not await db.get_chat(message.chat.id):\n total=await bot.get_chat_members_count(message.chat.id)\n r_j = message.from_user.mention if message.from_user else \"Anonymous\" "},"next_line":{"kind":"string","value":" await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j)) "},"gold_snippet_index":{"kind":"number","value":12,"string":"12"},"created_at":{"kind":"string","value":"2023-11-28 13:36:56+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5382,"cells":{"repo_name":{"kind":"string","value":"chenxx89/BFRffusion"},"file_path":{"kind":"string","value":"models/models.py"},"context":{"kind":"list like","value":[{"identifier":"timestep_embedding","path":"ldm/modules/diffusionmodules/util.py","snippet":"def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an [N x dim] Tensor of positional embeddings.\n \"\"\"\n if not repeat_only:\n half = dim // 2\n freqs = torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n ).to(device=timesteps.device)\n args = timesteps[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n else:\n embedding = repeat(timesteps, 'b -> b d', d=dim)\n return embedding"},{"identifier":"UNetModel","path":"ldm/modules/diffusionmodules/openaimodel.py","snippet":"class UNetModel(nn.Module):\n \"\"\"\n The full UNet model with attention and timestep embedding.\n :param in_channels: channels in the input Tensor.\n :param model_channels: base channel count for the model.\n :param out_channels: channels in the output Tensor.\n :param num_res_blocks: number of residual blocks per downsample.\n :param attention_resolutions: a collection of downsample rates at which\n attention will take place. May be a set, list, or tuple.\n For example, if this contains 4, then at 4x downsampling, attention\n will be used.\n :param dropout: the dropout probability.\n :param channel_mult: channel multiplier for each level of the UNet.\n :param conv_resample: if True, use learned convolutions for upsampling and\n downsampling.\n :param dims: determines if the signal is 1D, 2D, or 3D.\n :param num_classes: if specified (as an int), then this model will be\n class-conditional with `num_classes` classes.\n :param use_checkpoint: use gradient checkpointing to reduce memory usage.\n :param num_heads: the number of attention heads in each attention layer.\n :param num_heads_channels: if specified, ignore num_heads and instead use\n a fixed channel width per attention head.\n :param num_heads_upsample: works with num_heads to set a different number\n of heads for upsampling. Deprecated.\n :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.\n :param resblock_updown: use residual blocks for up/downsampling.\n :param use_new_attention_order: use a different attention pattern for potentially\n increased efficiency.\n \"\"\"\n\n def __init__(\n self,\n image_size,\n in_channels,\n model_channels,\n out_channels,\n num_res_blocks,\n attention_resolutions,\n dropout=0,\n channel_mult=(1, 2, 4, 8),\n conv_resample=True,\n dims=2,\n num_classes=None,\n use_checkpoint=False,\n use_fp16=False,\n num_heads=-1,\n num_head_channels=-1,\n num_heads_upsample=-1,\n use_scale_shift_norm=False,\n resblock_updown=False,\n use_new_attention_order=False,\n use_spatial_transformer=False, # custom transformer support\n transformer_depth=1, # custom transformer support\n context_dim=None, # custom transformer support\n n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model\n legacy=True,\n disable_self_attentions=None,\n num_attention_blocks=None,\n disable_middle_self_attn=False,\n use_linear_in_transformer=False,\n ):\n super().__init__()\n if use_spatial_transformer:\n assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'\n\n if context_dim is not None:\n assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'\n from omegaconf.listconfig import ListConfig\n if type(context_dim) == ListConfig:\n context_dim = list(context_dim)\n\n if num_heads_upsample == -1:\n num_heads_upsample = num_heads\n\n if num_heads == -1:\n assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'\n\n if num_head_channels == -1:\n assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'\n\n self.image_size = image_size\n self.in_channels = in_channels\n self.model_channels = model_channels\n self.out_channels = out_channels\n if isinstance(num_res_blocks, int):\n self.num_res_blocks = len(channel_mult) * [num_res_blocks]\n else:\n if len(num_res_blocks) != len(channel_mult):\n raise ValueError(\"provide num_res_blocks either as an int (globally constant) or \"\n \"as a list/tuple (per-level) with the same length as channel_mult\")\n self.num_res_blocks = num_res_blocks\n if disable_self_attentions is not None:\n # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not\n assert len(disable_self_attentions) == len(channel_mult)\n if num_attention_blocks is not None:\n assert len(num_attention_blocks) == len(self.num_res_blocks)\n assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))\n print(f\"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. \"\n f\"This option has LESS priority than attention_resolutions {attention_resolutions}, \"\n f\"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, \"\n f\"attention will still not be set.\")\n\n self.attention_resolutions = attention_resolutions\n self.dropout = dropout\n self.channel_mult = channel_mult\n self.conv_resample = conv_resample\n self.num_classes = num_classes\n self.use_checkpoint = use_checkpoint\n self.dtype = th.float16 if use_fp16 else th.float32\n self.num_heads = num_heads\n self.num_head_channels = num_head_channels\n self.num_heads_upsample = num_heads_upsample\n self.predict_codebook_ids = n_embed is not None\n\n time_embed_dim = model_channels * 4\n self.time_embed = nn.Sequential(\n linear(model_channels, time_embed_dim),\n nn.SiLU(),\n linear(time_embed_dim, time_embed_dim),\n )\n\n if self.num_classes is not None:\n if isinstance(self.num_classes, int):\n self.label_emb = nn.Embedding(num_classes, time_embed_dim)\n elif self.num_classes == \"continuous\":\n print(\"setting up linear c_adm embedding layer\")\n self.label_emb = nn.Linear(1, time_embed_dim)\n else:\n raise ValueError()\n\n self.input_blocks = nn.ModuleList(\n [\n TimestepEmbedSequential(\n conv_nd(dims, in_channels, model_channels, 3, padding=1)\n )\n ]\n )\n self._feature_size = model_channels\n input_block_chans = [model_channels]\n ch = model_channels\n ds = 1\n for level, mult in enumerate(channel_mult):\n for nr in range(self.num_res_blocks[level]):\n layers = [\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=mult * model_channels,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = mult * model_channels\n if ds in attention_resolutions:\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n if exists(disable_self_attentions):\n disabled_sa = disable_self_attentions[level]\n else:\n disabled_sa = False\n\n if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer(\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\n disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,\n use_checkpoint=use_checkpoint\n )\n )\n self.input_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n input_block_chans.append(ch)\n if level != len(channel_mult) - 1:\n out_ch = ch\n self.input_blocks.append(\n TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n down=True,\n )\n if resblock_updown\n else Downsample(\n ch, conv_resample, dims=dims, out_channels=out_ch\n )\n )\n )\n ch = out_ch\n input_block_chans.append(ch)\n ds *= 2\n self._feature_size += ch\n\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n self.middle_block = TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\n disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,\n use_checkpoint=use_checkpoint\n ),\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n )\n self._feature_size += ch\n\n self.output_blocks = nn.ModuleList([])\n for level, mult in list(enumerate(channel_mult))[::-1]:\n for i in range(self.num_res_blocks[level] + 1):\n ich = input_block_chans.pop()\n layers = [\n ResBlock(\n ch + ich,\n time_embed_dim,\n dropout,\n out_channels=model_channels * mult,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = model_channels * mult\n if ds in attention_resolutions:\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n if exists(disable_self_attentions):\n disabled_sa = disable_self_attentions[level]\n else:\n disabled_sa = False\n\n if not exists(num_attention_blocks) or i < num_attention_blocks[level]:\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads_upsample,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer(\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\n disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,\n use_checkpoint=use_checkpoint\n )\n )\n if level and i == self.num_res_blocks[level]:\n out_ch = ch\n layers.append(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n up=True,\n )\n if resblock_updown\n else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)\n )\n ds //= 2\n self.output_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),\n )\n if self.predict_codebook_ids:\n self.id_predictor = nn.Sequential(\n normalization(ch),\n conv_nd(dims, model_channels, n_embed, 1),\n #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits\n )\n\n def convert_to_fp16(self):\n \"\"\"\n Convert the torso of the model to float16.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f16)\n self.middle_block.apply(convert_module_to_f16)\n self.output_blocks.apply(convert_module_to_f16)\n\n def convert_to_fp32(self):\n \"\"\"\n Convert the torso of the model to float32.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f32)\n self.middle_block.apply(convert_module_to_f32)\n self.output_blocks.apply(convert_module_to_f32)\n\n def forward(self, x, timesteps=None, context=None, y=None,**kwargs):\n \"\"\"\n Apply the model to an input batch.\n :param x: an [N x C x ...] Tensor of inputs.\n :param timesteps: a 1-D batch of timesteps.\n :param context: conditioning plugged in via crossattn\n :param y: an [N] Tensor of labels, if class-conditional.\n :return: an [N x C x ...] Tensor of outputs.\n \"\"\"\n assert (y is not None) == (\n self.num_classes is not None\n ), \"must specify y if and only if the model is class-conditional\"\n hs = []\n t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)\n emb = self.time_embed(t_emb)\n\n if self.num_classes is not None:\n assert y.shape[0] == x.shape[0]\n emb = emb + self.label_emb(y)\n\n h = x.type(self.dtype)\n for module in self.input_blocks:\n h = module(h, emb, context)\n hs.append(h)\n h = self.middle_block(h, emb, context)\n for module in self.output_blocks:\n h = th.cat([h, hs.pop()], dim=1)\n h = module(h, emb, context)\n h = h.type(x.dtype)\n if self.predict_codebook_ids:\n return self.id_predictor(h)\n else:\n return self.out(h)"},{"identifier":"LatentDiffusion","path":"ldm/models/diffusion/ddpm.py","snippet":"class LatentDiffusion(DDPM):\n \"\"\"main class\"\"\"\n\n def __init__(self,\n first_stage_config,\n cond_stage_config,\n num_timesteps_cond=None,\n cond_stage_key=\"image\",\n cond_stage_trainable=False,\n concat_mode=True,\n cond_stage_forward=None,\n conditioning_key=None,\n scale_factor=1.0,\n scale_by_std=False,\n force_null_conditioning=False,\n *args, **kwargs):\n self.force_null_conditioning = force_null_conditioning\n self.num_timesteps_cond = default(num_timesteps_cond, 1)\n self.scale_by_std = scale_by_std\n assert self.num_timesteps_cond <= kwargs['timesteps']\n # for backwards compatibility after implementation of DiffusionWrapper\n if conditioning_key is None:\n conditioning_key = 'concat' if concat_mode else 'crossattn'\n if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:\n conditioning_key = None\n ckpt_path = kwargs.pop(\"ckpt_path\", None)\n reset_ema = kwargs.pop(\"reset_ema\", False)\n reset_num_ema_updates = kwargs.pop(\"reset_num_ema_updates\", False)\n ignore_keys = kwargs.pop(\"ignore_keys\", [])\n super().__init__(conditioning_key=conditioning_key, *args, **kwargs)\n self.concat_mode = concat_mode\n self.cond_stage_trainable = cond_stage_trainable\n self.cond_stage_key = cond_stage_key\n try:\n self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1\n except:\n self.num_downs = 0\n if not scale_by_std:\n self.scale_factor = scale_factor\n else:\n self.register_buffer('scale_factor', torch.tensor(scale_factor))\n self.instantiate_first_stage(first_stage_config)\n self.instantiate_cond_stage(cond_stage_config)\n self.cond_stage_forward = cond_stage_forward\n self.clip_denoised = False\n self.bbox_tokenizer = None\n\n self.restarted_from_ckpt = False\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys)\n self.restarted_from_ckpt = True\n if reset_ema:\n assert self.use_ema\n print(\n f\"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.\")\n self.model_ema = LitEma(self.model)\n if reset_num_ema_updates:\n print(\" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ \")\n assert self.use_ema\n self.model_ema.reset_num_updates()\n\n def make_cond_schedule(self, ):\n self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)\n ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()\n self.cond_ids[:self.num_timesteps_cond] = ids\n\n @rank_zero_only\n @torch.no_grad()\n def on_train_batch_start(self, batch, batch_idx, dataloader_idx):\n # only for very first batch\n 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:\n assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'\n # set rescale weight to 1./std of encodings\n print(\"### USING STD-RESCALING ###\")\n x = super().get_input(batch, self.first_stage_key)\n x = x.to(self.device)\n encoder_posterior = self.encode_first_stage(x)\n z = self.get_first_stage_encoding(encoder_posterior).detach()\n del self.scale_factor\n self.register_buffer('scale_factor', 1. / z.flatten().std())\n print(f\"setting self.scale_factor to {self.scale_factor}\")\n print(\"### USING STD-RESCALING ###\")\n\n def register_schedule(self,\n given_betas=None, beta_schedule=\"linear\", timesteps=1000,\n linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)\n\n self.shorten_cond_schedule = self.num_timesteps_cond > 1\n if self.shorten_cond_schedule:\n self.make_cond_schedule()\n\n def instantiate_first_stage(self, config):\n model = instantiate_from_config(config)\n self.first_stage_model = model.eval()\n self.first_stage_model.train = disabled_train\n for param in self.first_stage_model.parameters():\n param.requires_grad = False\n\n def instantiate_cond_stage(self, config):\n if not self.cond_stage_trainable:\n if config == \"__is_first_stage__\":\n print(\"Using first stage also as cond stage.\")\n self.cond_stage_model = self.first_stage_model\n elif config == \"__is_unconditional__\":\n print(f\"Training {self.__class__.__name__} as an unconditional model.\")\n self.cond_stage_model = None\n # self.be_unconditional = True\n else:\n model = instantiate_from_config(config)\n self.cond_stage_model = model.eval()\n self.cond_stage_model.train = disabled_train\n for param in self.cond_stage_model.parameters():\n param.requires_grad = False\n else:\n assert config != '__is_first_stage__'\n assert config != '__is_unconditional__'\n model = instantiate_from_config(config)\n self.cond_stage_model = model\n\n def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):\n denoise_row = []\n for zd in tqdm(samples, desc=desc):\n denoise_row.append(self.decode_first_stage(zd.to(self.device),\n force_not_quantize=force_no_decoder_quantization))\n n_imgs_per_row = len(denoise_row)\n denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W\n denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')\n denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')\n denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)\n return denoise_grid\n\n def get_first_stage_encoding(self, encoder_posterior):\n if isinstance(encoder_posterior, DiagonalGaussianDistribution):\n z = encoder_posterior.sample()\n elif isinstance(encoder_posterior, torch.Tensor):\n z = encoder_posterior\n else:\n raise NotImplementedError(f\"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented\")\n return self.scale_factor * z\n\n def get_learned_conditioning(self, c):\n if self.cond_stage_forward is None:\n if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):\n c = self.cond_stage_model.encode(c)\n if isinstance(c, DiagonalGaussianDistribution):\n c = c.mode()\n else:\n c = self.cond_stage_model(c)\n else:\n assert hasattr(self.cond_stage_model, self.cond_stage_forward)\n c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)\n return c\n\n def meshgrid(self, h, w):\n y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)\n x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)\n\n arr = torch.cat([y, x], dim=-1)\n return arr\n\n def delta_border(self, h, w):\n \"\"\"\n :param h: height\n :param w: width\n :return: normalized distance to image border,\n wtith min distance = 0 at border and max dist = 0.5 at image center\n \"\"\"\n lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)\n arr = self.meshgrid(h, w) / lower_right_corner\n dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]\n dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]\n edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]\n return edge_dist\n\n def get_weighting(self, h, w, Ly, Lx, device):\n weighting = self.delta_border(h, w)\n weighting = torch.clip(weighting, self.split_input_params[\"clip_min_weight\"],\n self.split_input_params[\"clip_max_weight\"], )\n weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)\n\n if self.split_input_params[\"tie_braker\"]:\n L_weighting = self.delta_border(Ly, Lx)\n L_weighting = torch.clip(L_weighting,\n self.split_input_params[\"clip_min_tie_weight\"],\n self.split_input_params[\"clip_max_tie_weight\"])\n\n L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)\n weighting = weighting * L_weighting\n return weighting\n\n def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code\n \"\"\"\n :param x: img of size (bs, c, h, w)\n :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])\n \"\"\"\n bs, nc, h, w = x.shape\n\n # number of crops in image\n Ly = (h - kernel_size[0]) // stride[0] + 1\n Lx = (w - kernel_size[1]) // stride[1] + 1\n\n if uf == 1 and df == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)\n\n weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))\n\n elif uf > 1 and df == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),\n dilation=1, padding=0,\n stride=(stride[0] * uf, stride[1] * uf))\n fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)\n\n weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))\n\n elif df > 1 and uf == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),\n dilation=1, padding=0,\n stride=(stride[0] // df, stride[1] // df))\n fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)\n\n weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))\n\n else:\n raise NotImplementedError\n\n return fold, unfold, normalization, weighting\n\n @torch.no_grad()\n def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,\n cond_key=None, return_original_cond=False, bs=None, return_x=False):\n x = super().get_input(batch, k)\n if bs is not None:\n x = x[:bs]\n x = x.to(self.device)\n encoder_posterior = self.encode_first_stage(x)\n z = self.get_first_stage_encoding(encoder_posterior).detach()\n\n if self.model.conditioning_key is not None and not self.force_null_conditioning:\n if cond_key is None:\n cond_key = self.cond_stage_key\n if cond_key != self.first_stage_key:\n if cond_key in ['caption', 'coordinates_bbox', \"txt\"]:\n xc = batch[cond_key]\n elif cond_key in ['class_label', 'cls']:\n xc = batch\n else:\n xc = super().get_input(batch, cond_key).to(self.device)\n else:\n xc = x\n if not self.cond_stage_trainable or force_c_encode:\n if isinstance(xc, dict) or isinstance(xc, list):\n c = self.get_learned_conditioning(xc)\n else:\n c = self.get_learned_conditioning(xc.to(self.device))\n else:\n c = xc\n if bs is not None:\n c = c[:bs]\n\n if self.use_positional_encodings:\n pos_x, pos_y = self.compute_latent_shifts(batch)\n ckey = __conditioning_keys__[self.model.conditioning_key]\n c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}\n\n else:\n c = None\n xc = None\n if self.use_positional_encodings:\n pos_x, pos_y = self.compute_latent_shifts(batch)\n c = {'pos_x': pos_x, 'pos_y': pos_y}\n out = [z, c]\n if return_first_stage_outputs:\n xrec = self.decode_first_stage(z)\n out.extend([x, xrec])\n if return_x:\n out.extend([x])\n if return_original_cond:\n out.append(xc)\n return out\n\n @torch.no_grad()\n def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):\n if predict_cids:\n if z.dim() == 4:\n z = torch.argmax(z.exp(), dim=1).long()\n z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)\n z = rearrange(z, 'b h w c -> b c h w').contiguous()\n\n z = 1. / self.scale_factor * z\n return self.first_stage_model.decode(z)\n\n @torch.no_grad()\n def encode_first_stage(self, x):\n return self.first_stage_model.encode(x)\n\n def shared_step(self, batch, **kwargs):\n x, c = self.get_input(batch, self.first_stage_key)\n loss = self(x, c)\n return loss\n\n def forward(self, x, c, *args, **kwargs):\n t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()\n if self.model.conditioning_key is not None:\n assert c is not None\n # if self.cond_stage_trainable:\n # c = self.get_learned_conditioning(c)\n if self.shorten_cond_schedule: # TODO: drop this option\n tc = self.cond_ids[t].to(self.device)\n c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))\n return self.p_losses(x, c, t, *args, **kwargs)\n\n def apply_model(self, x_noisy, t, cond, return_ids=False):\n if isinstance(cond, dict):\n # hybrid case, cond is expected to be a dict\n pass\n else:\n if not isinstance(cond, list):\n cond = [cond]\n key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'\n cond = {key: cond}\n\n x_recon = self.model(x_noisy, t, **cond)\n\n if isinstance(x_recon, tuple) and not return_ids:\n return x_recon[0]\n else:\n return x_recon\n\n def _predict_eps_from_xstart(self, x_t, t, pred_xstart):\n return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \\\n extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)\n\n def _prior_bpd(self, x_start):\n \"\"\"\n Get the prior KL term for the variational lower-bound, measured in\n bits-per-dim.\n This term can't be optimized, as it only depends on the encoder.\n :param x_start: the [N x C x ...] tensor of inputs.\n :return: a batch of [N] KL values (in bits), one per batch element.\n \"\"\"\n batch_size = x_start.shape[0]\n t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)\n qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)\n kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)\n return mean_flat(kl_prior) / np.log(2.0)\n\n def p_losses(self, x_start, cond, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x_start))\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n model_output = self.apply_model(x_noisy, t, cond)\n\n loss_dict = {}\n prefix = 'train' if self.training else 'val'\n\n if self.parameterization == \"x0\":\n target = x_start\n elif self.parameterization == \"eps\":\n target = noise\n elif self.parameterization == \"v\":\n target = self.get_v(x_start, noise, t)\n else:\n raise NotImplementedError()\n\n loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])\n loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})\n\n logvar_t = self.logvar[t].to(self.device)\n loss = loss_simple / torch.exp(logvar_t) + logvar_t\n # loss = loss_simple / torch.exp(self.logvar) + self.logvar\n if self.learn_logvar:\n loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})\n loss_dict.update({'logvar': self.logvar.data.mean()})\n\n loss = self.l_simple_weight * loss.mean()\n\n loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))\n loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()\n loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})\n loss += (self.original_elbo_weight * loss_vlb)\n loss_dict.update({f'{prefix}/loss': loss})\n\n return loss, loss_dict\n\n def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,\n return_x0=False, score_corrector=None, corrector_kwargs=None):\n t_in = t\n model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)\n\n if score_corrector is not None:\n assert self.parameterization == \"eps\"\n model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)\n\n if return_codebook_ids:\n model_out, logits = model_out\n\n if self.parameterization == \"eps\":\n x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)\n elif self.parameterization == \"x0\":\n x_recon = model_out\n else:\n raise NotImplementedError()\n\n if clip_denoised:\n x_recon.clamp_(-1., 1.)\n if quantize_denoised:\n x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)\n model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)\n if return_codebook_ids:\n return model_mean, posterior_variance, posterior_log_variance, logits\n elif return_x0:\n return model_mean, posterior_variance, posterior_log_variance, x_recon\n else:\n return model_mean, posterior_variance, posterior_log_variance\n\n @torch.no_grad()\n def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,\n return_codebook_ids=False, quantize_denoised=False, return_x0=False,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):\n b, *_, device = *x.shape, x.device\n outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,\n return_codebook_ids=return_codebook_ids,\n quantize_denoised=quantize_denoised,\n return_x0=return_x0,\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\n if return_codebook_ids:\n raise DeprecationWarning(\"Support dropped.\")\n model_mean, _, model_log_variance, logits = outputs\n elif return_x0:\n model_mean, _, model_log_variance, x0 = outputs\n else:\n model_mean, _, model_log_variance = outputs\n\n noise = noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n # no noise when t == 0\n nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\n\n if return_codebook_ids:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)\n if return_x0:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0\n else:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\n\n @torch.no_grad()\n def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,\n img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,\n score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,\n log_every_t=None):\n if not log_every_t:\n log_every_t = self.log_every_t\n timesteps = self.num_timesteps\n if batch_size is not None:\n b = batch_size if batch_size is not None else shape[0]\n shape = [batch_size] + list(shape)\n else:\n b = batch_size = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=self.device)\n else:\n img = x_T\n intermediates = []\n if cond is not None:\n if isinstance(cond, dict):\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\n else:\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\n\n if start_T is not None:\n timesteps = min(timesteps, start_T)\n iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',\n total=timesteps) if verbose else reversed(\n range(0, timesteps))\n if type(temperature) == float:\n temperature = [temperature] * timesteps\n\n for i in iterator:\n ts = torch.full((b,), i, device=self.device, dtype=torch.long)\n if self.shorten_cond_schedule:\n assert self.model.conditioning_key != 'hybrid'\n tc = self.cond_ids[ts].to(cond.device)\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\n\n img, x0_partial = self.p_sample(img, cond, ts,\n clip_denoised=self.clip_denoised,\n quantize_denoised=quantize_denoised, return_x0=True,\n temperature=temperature[i], noise_dropout=noise_dropout,\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\n if mask is not None:\n assert x0 is not None\n img_orig = self.q_sample(x0, ts)\n img = img_orig * mask + (1. - mask) * img\n\n if i % log_every_t == 0 or i == timesteps - 1:\n intermediates.append(x0_partial)\n if callback: callback(i)\n if img_callback: img_callback(img, i)\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_loop(self, cond, shape, return_intermediates=False,\n x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, img_callback=None, start_T=None,\n log_every_t=None):\n\n if not log_every_t:\n log_every_t = self.log_every_t\n device = self.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n intermediates = [img]\n if timesteps is None:\n timesteps = self.num_timesteps\n\n if start_T is not None:\n timesteps = min(timesteps, start_T)\n iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(\n range(0, timesteps))\n\n if mask is not None:\n assert x0 is not None\n assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match\n\n for i in iterator:\n ts = torch.full((b,), i, device=device, dtype=torch.long)\n if self.shorten_cond_schedule:\n assert self.model.conditioning_key != 'hybrid'\n tc = self.cond_ids[ts].to(cond.device)\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\n\n img = self.p_sample(img, cond, ts,\n clip_denoised=self.clip_denoised,\n quantize_denoised=quantize_denoised)\n if mask is not None:\n img_orig = self.q_sample(x0, ts)\n img = img_orig * mask + (1. - mask) * img\n\n if i % log_every_t == 0 or i == timesteps - 1:\n intermediates.append(img)\n if callback: callback(i)\n if img_callback: img_callback(img, i)\n\n if return_intermediates:\n return img, intermediates\n return img\n\n @torch.no_grad()\n def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,\n verbose=True, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, shape=None, **kwargs):\n if shape is None:\n shape = (batch_size, self.channels, self.image_size, self.image_size)\n if cond is not None:\n if isinstance(cond, dict):\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\n else:\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\n return self.p_sample_loop(cond,\n shape,\n return_intermediates=return_intermediates, x_T=x_T,\n verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,\n mask=mask, x0=x0)\n\n @torch.no_grad()\n def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):\n if ddim:\n ddim_sampler = DDIMSampler(self)\n shape = (self.channels, self.image_size, self.image_size)\n samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,\n shape, cond, verbose=False, **kwargs)\n\n else:\n samples, intermediates = self.sample(cond=cond, batch_size=batch_size,\n return_intermediates=True, **kwargs)\n\n return samples, intermediates\n\n @torch.no_grad()\n def get_unconditional_conditioning(self, batch_size, null_label=None):\n if null_label is not None:\n xc = null_label\n if isinstance(xc, ListConfig):\n xc = list(xc)\n if isinstance(xc, dict) or isinstance(xc, list):\n c = self.get_learned_conditioning(xc)\n else:\n if hasattr(xc, \"to\"):\n xc = xc.to(self.device)\n c = self.get_learned_conditioning(xc)\n else:\n if self.cond_stage_key in [\"class_label\", \"cls\"]:\n xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)\n return self.get_learned_conditioning(xc)\n else:\n raise NotImplementedError(\"todo\")\n if isinstance(c, list): # in case the encoder gives us a list\n for i in range(len(c)):\n c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)\n else:\n c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)\n return c\n\n @torch.no_grad()\n def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,\n quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,\n plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,\n use_ema_scope=True,\n **kwargs):\n ema_scope = self.ema_scope if use_ema_scope else nullcontext\n use_ddim = ddim_steps is not None\n\n log = dict()\n z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,\n return_first_stage_outputs=True,\n force_c_encode=True,\n return_original_cond=True,\n bs=N)\n N = min(x.shape[0], N)\n n_row = min(x.shape[0], n_row)\n log[\"inputs\"] = x\n log[\"reconstruction\"] = xrec\n if self.model.conditioning_key is not None:\n if hasattr(self.cond_stage_model, \"decode\"):\n xc = self.cond_stage_model.decode(c)\n log[\"conditioning\"] = xc\n elif self.cond_stage_key in [\"caption\", \"txt\"]:\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)\n log[\"conditioning\"] = xc\n elif self.cond_stage_key in ['class_label', \"cls\"]:\n try:\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[\"human_label\"], size=x.shape[2] // 25)\n log['conditioning'] = xc\n except KeyError:\n # probably no \"human_label\" in batch\n pass\n elif isimage(xc):\n log[\"conditioning\"] = xc\n if ismap(xc):\n log[\"original_conditioning\"] = self.to_rgb(xc)\n\n if plot_diffusion_rows:\n # get diffusion row\n diffusion_row = list()\n z_start = z[:n_row]\n for t in range(self.num_timesteps):\n if t % self.log_every_t == 0 or t == self.num_timesteps - 1:\n t = repeat(torch.tensor([t]), '1 -> b', b=n_row)\n t = t.to(self.device).long()\n noise = torch.randn_like(z_start)\n z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)\n diffusion_row.append(self.decode_first_stage(z_noisy))\n\n diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W\n diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')\n diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')\n diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])\n log[\"diffusion_row\"] = diffusion_grid\n\n if sample:\n # get denoise row\n with ema_scope(\"Sampling\"):\n samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\n ddim_steps=ddim_steps, eta=ddim_eta)\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)\n x_samples = self.decode_first_stage(samples)\n log[\"samples\"] = x_samples\n if plot_denoise_rows:\n denoise_grid = self._get_denoise_row_from_list(z_denoise_row)\n log[\"denoise_row\"] = denoise_grid\n\n if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(\n self.first_stage_model, IdentityFirstStage):\n # also display when quantizing x0 while sampling\n with ema_scope(\"Plotting Quantized Denoised\"):\n samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\n ddim_steps=ddim_steps, eta=ddim_eta,\n quantize_denoised=True)\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,\n # quantize_denoised=True)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_x0_quantized\"] = x_samples\n\n if unconditional_guidance_scale > 1.0:\n uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)\n if self.model.conditioning_key == \"crossattn-adm\":\n uc = {\"c_crossattn\": [uc], \"c_adm\": c[\"c_adm\"]}\n with ema_scope(\"Sampling with classifier-free guidance\"):\n samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\n ddim_steps=ddim_steps, eta=ddim_eta,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=uc,\n )\n x_samples_cfg = self.decode_first_stage(samples_cfg)\n log[f\"samples_cfg_scale_{unconditional_guidance_scale:.2f}\"] = x_samples_cfg\n\n if inpaint:\n # make a simple center square\n b, h, w = z.shape[0], z.shape[2], z.shape[3]\n mask = torch.ones(N, h, w).to(self.device)\n # zeros will be filled in\n mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.\n mask = mask[:, None, ...]\n with ema_scope(\"Plotting Inpaint\"):\n samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_inpainting\"] = x_samples\n log[\"mask\"] = mask\n\n # outpaint\n mask = 1. - mask\n with ema_scope(\"Plotting Outpaint\"):\n samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_outpainting\"] = x_samples\n\n if plot_progressive_rows:\n with ema_scope(\"Plotting Progressives\"):\n img, progressives = self.progressive_denoising(c,\n shape=(self.channels, self.image_size, self.image_size),\n batch_size=N)\n prog_row = self._get_denoise_row_from_list(progressives, desc=\"Progressive Generation\")\n log[\"progressive_row\"] = prog_row\n\n if return_keys:\n if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:\n return log\n else:\n return {key: log[key] for key in return_keys}\n return log\n\n def configure_optimizers(self):\n lr = self.learning_rate\n params = list(self.model.parameters())\n if self.cond_stage_trainable:\n print(f\"{self.__class__.__name__}: Also optimizing conditioner params!\")\n params = params + list(self.cond_stage_model.parameters())\n if self.learn_logvar:\n print('Diffusion model optimizing logvar')\n params.append(self.logvar)\n opt = torch.optim.AdamW(params, lr=lr)\n if self.use_scheduler:\n assert 'target' in self.scheduler_config\n scheduler = instantiate_from_config(self.scheduler_config)\n\n print(\"Setting up LambdaLR scheduler...\")\n scheduler = [\n {\n 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),\n 'interval': 'step',\n 'frequency': 1\n }]\n return [opt], scheduler\n return opt\n\n @torch.no_grad()\n def to_rgb(self, x):\n x = x.float()\n if not hasattr(self, \"colorize\"):\n self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)\n x = nn.functional.conv2d(x, weight=self.colorize)\n x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.\n return x"},{"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 draw = ImageDraw.Draw(txt)\n font = ImageFont.truetype('font/DejaVuSans.ttf', size=size)\n nc = int(40 * (wh[0] / 256))\n lines = \"\\n\".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))\n\n try:\n draw.text((0, 0), lines, fill=\"black\", font=font)\n except UnicodeEncodeError:\n print(\"Cant encode string for logging. Skipping.\")\n\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\n txts.append(txt)\n txts = np.stack(txts)\n txts = torch.tensor(txts)\n return txts"},{"identifier":"instantiate_from_config","path":"ldm/util.py","snippet":"def instantiate_from_config(config):\n if not \"target\" in config:\n if config == '__is_first_stage__':\n return None\n elif config == \"__is_unconditional__\":\n return None\n raise KeyError(\"Expected key `target` to instantiate.\")\n return get_obj_from_str(config[\"target\"])(**config.get(\"params\", dict()))"},{"identifier":"DDIMSampler","path":"ldm/models/diffusion/ddim.py","snippet":"class DDIMSampler(object):\n def __init__(self, model, schedule=\"linear\", **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedule = schedule\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device(\"cuda\"):\n attr = attr.to(torch.device(\"cuda\"))\n setattr(self, name, attr)\n\n def make_schedule(self, ddim_num_steps, ddim_discretize=\"uniform\", ddim_eta=0., verbose=True):\n self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)\n alphas_cumprod = self.model.alphas_cumprod\n assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)\n\n self.register_buffer('betas', to_torch(self.model.betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))\n self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,verbose=verbose)\n self.register_buffer('ddim_sigmas', ddim_sigmas)\n self.register_buffer('ddim_alphas', ddim_alphas)\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (\n 1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)\n\n @torch.no_grad()\n def sample(self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n dynamic_threshold=None,\n ucg_schedule=None,\n **kwargs\n ):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n ctmp = conditioning[list(conditioning.keys())[0]]\n while isinstance(ctmp, list): ctmp = ctmp[0]\n cbs = ctmp.shape[0]\n if cbs != batch_size:\n print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n\n elif isinstance(conditioning, list):\n for ctmp in conditioning:\n if ctmp.shape[0] != batch_size:\n print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n\n else:\n if conditioning.shape[0] != batch_size:\n print(f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\")\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n # print(f'Data shape for DDIM sampling is {size}, eta {eta}')\n\n samples, intermediates = self.ddim_sampling(conditioning, size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask, x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n dynamic_threshold=dynamic_threshold,\n ucg_schedule=ucg_schedule\n )\n return samples, intermediates\n\n @torch.no_grad()\n def ddim_sampling(self, cond, shape,\n x_T=None, ddim_use_original_steps=False,\n callback=None, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, img_callback=None, log_every_t=100,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,\n ucg_schedule=None):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1\n timesteps = self.ddim_timesteps[:subset_end]\n\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\n time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]\n # print(f\"Running DDIM Sampling with {total_steps} timesteps\")\n\n # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)\n\n for i, step in enumerate(time_range):\n index = total_steps - i - 1\n ts = torch.full((b,), step, device=device, dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?\n img = img_orig * mask + (1. - mask) * img\n\n if ucg_schedule is not None:\n assert len(ucg_schedule) == len(time_range)\n unconditional_guidance_scale = ucg_schedule[i]\n\n outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised, temperature=temperature,\n noise_dropout=noise_dropout, score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n dynamic_threshold=dynamic_threshold)\n img, pred_x0 = outs\n if callback: callback(i)\n if img_callback: img_callback(pred_x0, i)\n\n if index % log_every_t == 0 or index == total_steps - 1:\n intermediates['x_inter'].append(img)\n intermediates['pred_x0'].append(pred_x0)\n\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None,\n dynamic_threshold=None):\n b, *_, device = *x.shape, x.device\n\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\n model_output = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n if isinstance(c, dict):\n assert isinstance(unconditional_conditioning, dict)\n c_in = dict()\n for k in c:\n if isinstance(c[k], list):\n c_in[k] = [torch.cat([\n unconditional_conditioning[k][i],\n c[k][i]]) for i in range(len(c[k]))]\n else:\n c_in[k] = torch.cat([\n unconditional_conditioning[k],\n c[k]])\n elif isinstance(c, list):\n c_in = list()\n assert isinstance(unconditional_conditioning, list)\n for i in range(len(c)):\n c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))\n else:\n c_in = torch.cat([unconditional_conditioning, c])\n model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\n model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)\n\n if self.model.parameterization == \"v\":\n e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)\n else:\n e_t = model_output\n\n if score_corrector is not None:\n assert self.model.parameterization == \"eps\", 'not implemented'\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n # select parameters corresponding to the currently considered timestep\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)\n\n # current prediction for x_0\n if self.model.parameterization != \"v\":\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n else:\n pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)\n\n if quantize_denoised:\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n\n if dynamic_threshold is not None:\n raise NotImplementedError()\n\n # direction pointing to x_t\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n return x_prev, pred_x0\n\n @torch.no_grad()\n def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,\n unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):\n num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]\n\n assert t_enc <= num_reference_steps\n num_steps = t_enc\n\n if use_original_steps:\n alphas_next = self.alphas_cumprod[:num_steps]\n alphas = self.alphas_cumprod_prev[:num_steps]\n else:\n alphas_next = self.ddim_alphas[:num_steps]\n alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])\n\n x_next = x0\n intermediates = []\n inter_steps = []\n for i in tqdm(range(num_steps), desc='Encoding Image'):\n t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)\n if unconditional_guidance_scale == 1.:\n noise_pred = self.model.apply_model(x_next, t, c)\n else:\n assert unconditional_conditioning is not None\n e_t_uncond, noise_pred = torch.chunk(\n self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),\n torch.cat((unconditional_conditioning, c))), 2)\n noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)\n\n xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next\n weighted_noise_pred = alphas_next[i].sqrt() * (\n (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred\n x_next = xt_weighted + weighted_noise_pred\n if return_intermediates and i % (\n num_steps // return_intermediates) == 0 and i < num_steps - 1:\n intermediates.append(x_next)\n inter_steps.append(i)\n elif return_intermediates and i >= num_steps - 2:\n intermediates.append(x_next)\n inter_steps.append(i)\n if callback: callback(i)\n\n out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}\n if return_intermediates:\n out.update({'intermediates': intermediates})\n return x_next, out\n\n @torch.no_grad()\n def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):\n # fast, but does not allow for exact reconstruction\n # t serves as an index to gather the correct alphas\n if use_original_steps:\n sqrt_alphas_cumprod = self.sqrt_alphas_cumprod\n sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod\n else:\n sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)\n sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas\n\n if noise is None:\n noise = torch.randn_like(x0)\n return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +\n extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)\n\n @torch.no_grad()\n def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,\n use_original_steps=False, callback=None):\n\n timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps\n timesteps = timesteps[:t_start]\n\n time_range = np.flip(timesteps)\n total_steps = timesteps.shape[0]\n print(f\"Running DDIM Sampling with {total_steps} timesteps\")\n\n iterator = tqdm(time_range, desc='Decoding image', total=total_steps)\n x_dec = x_latent\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)\n x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning)\n if callback: callback(i)\n return x_dec"},{"identifier":"instantiate_from_config","path":"data/dataset_instantiate.py","snippet":"def instantiate_from_config(config):\n if not \"target\" in config:\n if config == '__is_first_stage__':\n return None\n elif config == \"__is_unconditional__\":\n return None\n raise KeyError(\"Expected key `target` to instantiate.\")\n return get_obj_from_str(config[\"target\"])(config.get(\"params\", dict()))"},{"identifier":"calculate_psnr_ssim","path":"metrics/metrics_all.py","snippet":"def calculate_psnr_ssim(gt_path, restored_path, test_y_channel = False, crop_border = 0, suffix = '', correct_mean_var = False, show_details =False):\n \"\"\"\n Calculate PSNR and SSIM for images.\n gt_path: Path to gt (Ground-Truth)\n restored_path: Path to restored images\n test_y_channel: If True, test Y channel (In MatLab YCbCr format). If False, test RGB channels.\n crop_border: Crop border for each side\n suffix: Suffix for restored images\n \"\"\"\n print(\"Calculate PSNR and SSIM for images\")\n psnr_all = []\n ssim_all = []\n img_list_gt = sorted(list(scandir(gt_path, recursive=True, full_path=True)))\n img_list_restored = sorted(list(scandir(restored_path, recursive=True, full_path=True)))\n\n if test_y_channel:\n print('Testing Y channel.')\n else:\n print('Testing RGB channels.')\n\n for i, img_path in tqdm(enumerate(img_list_gt)):\n basename, ext = osp.splitext(osp.basename(img_path))\n img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\n if suffix == '':\n img_path_restored = img_list_restored[i]\n else:\n img_path_restored = osp.join(restored_path, basename + suffix + ext)\n img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\n # img_restored = cv2.imread(img_path_restored, cv2.IMREAD_COLOR).astype(np.float32) / 255.\n img_restored\n if correct_mean_var:\n mean_l = []\n std_l = []\n for j in range(3):\n mean_l.append(np.mean(img_gt[:, :, j]))\n std_l.append(np.std(img_gt[:, :, j]))\n for j in range(3):\n # correct twice\n mean = np.mean(img_restored[:, :, j])\n img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]\n std = np.std(img_restored[:, :, j])\n img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]\n\n mean = np.mean(img_restored[:, :, j])\n img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]\n std = np.std(img_restored[:, :, j])\n img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]\n\n if test_y_channel and img_gt.ndim == 3 and img_gt.shape[2] == 3:\n img_gt = bgr2ycbcr(img_gt, y_only=True)\n img_restored = bgr2ycbcr(img_restored, y_only=True)\n\n # calculate PSNR and SSIM\n psnr = calculate_psnr(img_gt * 255, img_restored * 255, crop_border=crop_border, input_order='HWC')\n ssim = calculate_ssim(img_gt * 255, img_restored * 255, crop_border=crop_border, input_order='HWC')\n if show_details:\n print(f'{basename + suffix + ext:25}. \\tPSNR: {psnr:.6f} dB, \\tSSIM: {ssim:.6f}')\n psnr_all.append(psnr)\n ssim_all.append(ssim)\n Average_psnr = sum(psnr_all) / len(psnr_all)\n Average_ssim = sum(ssim_all) / len(ssim_all)\n print(f'PSNR: {Average_psnr:.6f} dB, SSIM: {Average_ssim:.6f}')\n return Average_psnr, Average_ssim"},{"identifier":"calculate_lpips","path":"metrics/metrics_all.py","snippet":"def calculate_lpips(gt_path, restored_path, suffix = '', show_details =False):\n \"\"\"\n Calculate LPIPS for images.\n gt_path: Path to gt (Ground-Truth)\n restored_path: Path to restored images\n suffix: Suffix for restored images\n \"\"\"\n print(\"Calculate LPIPS for images\")\n loss_fn_vgg = lpips.LPIPS(net='vgg').cuda() # RGB, normalized to [-1,1]\n lpips_all = []\n img_list = sorted(glob.glob(osp.join(gt_path, '*')))\n img_list_restored = sorted(list(scandir(restored_path, recursive=True, full_path=True)))\n\n mean = [0.5, 0.5, 0.5]\n std = [0.5, 0.5, 0.5]\n for i, img_path in tqdm(enumerate(img_list)):\n basename, ext = osp.splitext(osp.basename(img_path))\n img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\n\n if suffix == '':\n img_path_restored = img_list_restored[i]\n else:\n img_path_restored = osp.join(restored_path, basename + suffix + ext)\n img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255. \n # img_restored = cv2.imread(img_path_restored, cv2.IMREAD_COLOR).astype(np.float32) / 255. \n\n img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)\n # norm to [-1, 1]\n normalize(img_gt, mean, std, inplace=True)\n normalize(img_restored, mean, std, inplace=True)\n\n # calculate lpips\n lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(), img_gt.unsqueeze(0).cuda())\n lpips_val = lpips_val.cpu().item()\n if show_details:\n print(f'{i+1:3d}: {basename:25}. \\tLPIPS: {lpips_val:.6f}.')\n lpips_all.append(lpips_val)\n Average_lpips = sum(lpips_all) / len(lpips_all)\n print(f'LPIPS: {Average_lpips:.6f}')\n return Average_lpips"},{"identifier":"calculate_NIQE","path":"metrics/metrics_all.py","snippet":"def calculate_NIQE(restored_path, crop_border = 0, show_details =False):\n \"\"\"\n Calculate NIQE for images.\n restored_path: Path to restored images\n crop_border: Crop border for each side\n \"\"\"\n print(\"Calculate NIQE for images\")\n niqe_all = []\n img_list = sorted(scandir(restored_path, recursive=True, full_path=True))\n\n for i, img_path in tqdm(enumerate(img_list)):\n basename, _ = os.path.splitext(os.path.basename(img_path))\n img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)\n\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', category=RuntimeWarning)\n niqe_score = calculate_niqe(img, crop_border, input_order='HWC', convert_to='y')\n if show_details:\n print(f'{i+1:3d}: {basename:25}. \\tNIQE: {niqe_score:.6f}')\n niqe_all.append(niqe_score)\n Average_niqe = sum(niqe_all) / len(niqe_all)\n print(f'NIQE: {Average_niqe:.6f}')\n return Average_niqe "},{"identifier":"calculate_fid_folder","path":"metrics/metrics_all.py","snippet":"def calculate_fid_folder(restored_path):\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n fid_stats = ''\n batch_size = 64\n num_sample = 50000\n num_workers = 4\n backend = 'disk'\n\n # inception model\n inception = load_patched_inception_v3(device)\n\n # create dataset\n opt = {}\n opt['name'] = 'SingleImageDataset'\n opt['type'] = 'SingleImageDataset'\n opt['dataroot_lq'] = restored_path\n opt['io_backend'] = dict(type=backend)\n opt['mean'] = [0.5, 0.5, 0.5]\n opt['std'] = [0.5, 0.5, 0.5]\n dataset = build_dataset(opt)\n\n # create dataloader\n data_loader = DataLoader(\n dataset=dataset,\n batch_size=batch_size,\n shuffle=False,\n num_workers=num_workers,\n sampler=None,\n drop_last=False)\n num_sample = min(num_sample, len(dataset))\n total_batch = math.ceil(num_sample / batch_size)\n\n def data_generator(data_loader, total_batch):\n for idx, data in enumerate(data_loader):\n if idx >= total_batch:\n break\n else:\n yield data['lq']\n\n features = extract_inception_features(data_generator(data_loader, total_batch), inception, total_batch, device)\n features = features.numpy()\n total_len = features.shape[0]\n features = features[:num_sample]\n print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.')\n\n sample_mean = np.mean(features, 0)\n sample_cov = np.cov(features, rowvar=False)\n\n # load the dataset stats\n stats = torch.load(fid_stats)\n real_mean = stats['mean']\n real_cov = stats['cov']\n\n # calculate FID metric\n fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov)\n print('fid:', fid)\n return fid"}],"string":"[\n {\n \"identifier\": \"timestep_embedding\",\n \"path\": \"ldm/modules/diffusionmodules/util.py\",\n \"snippet\": \"def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\\n \\\"\\\"\\\"\\n Create sinusoidal timestep embeddings.\\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\\n These may be fractional.\\n :param dim: the dimension of the output.\\n :param max_period: controls the minimum frequency of the embeddings.\\n :return: an [N x dim] Tensor of positional embeddings.\\n \\\"\\\"\\\"\\n if not repeat_only:\\n half = dim // 2\\n freqs = torch.exp(\\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\\n ).to(device=timesteps.device)\\n args = timesteps[:, None].float() * freqs[None]\\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\\n if dim % 2:\\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\\n else:\\n embedding = repeat(timesteps, 'b -> b d', d=dim)\\n return embedding\"\n },\n {\n \"identifier\": \"UNetModel\",\n \"path\": \"ldm/modules/diffusionmodules/openaimodel.py\",\n \"snippet\": \"class UNetModel(nn.Module):\\n \\\"\\\"\\\"\\n The full UNet model with attention and timestep embedding.\\n :param in_channels: channels in the input Tensor.\\n :param model_channels: base channel count for the model.\\n :param out_channels: channels in the output Tensor.\\n :param num_res_blocks: number of residual blocks per downsample.\\n :param attention_resolutions: a collection of downsample rates at which\\n attention will take place. May be a set, list, or tuple.\\n For example, if this contains 4, then at 4x downsampling, attention\\n will be used.\\n :param dropout: the dropout probability.\\n :param channel_mult: channel multiplier for each level of the UNet.\\n :param conv_resample: if True, use learned convolutions for upsampling and\\n downsampling.\\n :param dims: determines if the signal is 1D, 2D, or 3D.\\n :param num_classes: if specified (as an int), then this model will be\\n class-conditional with `num_classes` classes.\\n :param use_checkpoint: use gradient checkpointing to reduce memory usage.\\n :param num_heads: the number of attention heads in each attention layer.\\n :param num_heads_channels: if specified, ignore num_heads and instead use\\n a fixed channel width per attention head.\\n :param num_heads_upsample: works with num_heads to set a different number\\n of heads for upsampling. Deprecated.\\n :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.\\n :param resblock_updown: use residual blocks for up/downsampling.\\n :param use_new_attention_order: use a different attention pattern for potentially\\n increased efficiency.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n image_size,\\n in_channels,\\n model_channels,\\n out_channels,\\n num_res_blocks,\\n attention_resolutions,\\n dropout=0,\\n channel_mult=(1, 2, 4, 8),\\n conv_resample=True,\\n dims=2,\\n num_classes=None,\\n use_checkpoint=False,\\n use_fp16=False,\\n num_heads=-1,\\n num_head_channels=-1,\\n num_heads_upsample=-1,\\n use_scale_shift_norm=False,\\n resblock_updown=False,\\n use_new_attention_order=False,\\n use_spatial_transformer=False, # custom transformer support\\n transformer_depth=1, # custom transformer support\\n context_dim=None, # custom transformer support\\n n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model\\n legacy=True,\\n disable_self_attentions=None,\\n num_attention_blocks=None,\\n disable_middle_self_attn=False,\\n use_linear_in_transformer=False,\\n ):\\n super().__init__()\\n if use_spatial_transformer:\\n assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'\\n\\n if context_dim is not None:\\n assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'\\n from omegaconf.listconfig import ListConfig\\n if type(context_dim) == ListConfig:\\n context_dim = list(context_dim)\\n\\n if num_heads_upsample == -1:\\n num_heads_upsample = num_heads\\n\\n if num_heads == -1:\\n assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'\\n\\n if num_head_channels == -1:\\n assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'\\n\\n self.image_size = image_size\\n self.in_channels = in_channels\\n self.model_channels = model_channels\\n self.out_channels = out_channels\\n if isinstance(num_res_blocks, int):\\n self.num_res_blocks = len(channel_mult) * [num_res_blocks]\\n else:\\n if len(num_res_blocks) != len(channel_mult):\\n raise ValueError(\\\"provide num_res_blocks either as an int (globally constant) or \\\"\\n \\\"as a list/tuple (per-level) with the same length as channel_mult\\\")\\n self.num_res_blocks = num_res_blocks\\n if disable_self_attentions is not None:\\n # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not\\n assert len(disable_self_attentions) == len(channel_mult)\\n if num_attention_blocks is not None:\\n assert len(num_attention_blocks) == len(self.num_res_blocks)\\n assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))\\n print(f\\\"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. \\\"\\n f\\\"This option has LESS priority than attention_resolutions {attention_resolutions}, \\\"\\n f\\\"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, \\\"\\n f\\\"attention will still not be set.\\\")\\n\\n self.attention_resolutions = attention_resolutions\\n self.dropout = dropout\\n self.channel_mult = channel_mult\\n self.conv_resample = conv_resample\\n self.num_classes = num_classes\\n self.use_checkpoint = use_checkpoint\\n self.dtype = th.float16 if use_fp16 else th.float32\\n self.num_heads = num_heads\\n self.num_head_channels = num_head_channels\\n self.num_heads_upsample = num_heads_upsample\\n self.predict_codebook_ids = n_embed is not None\\n\\n time_embed_dim = model_channels * 4\\n self.time_embed = nn.Sequential(\\n linear(model_channels, time_embed_dim),\\n nn.SiLU(),\\n linear(time_embed_dim, time_embed_dim),\\n )\\n\\n if self.num_classes is not None:\\n if isinstance(self.num_classes, int):\\n self.label_emb = nn.Embedding(num_classes, time_embed_dim)\\n elif self.num_classes == \\\"continuous\\\":\\n print(\\\"setting up linear c_adm embedding layer\\\")\\n self.label_emb = nn.Linear(1, time_embed_dim)\\n else:\\n raise ValueError()\\n\\n self.input_blocks = nn.ModuleList(\\n [\\n TimestepEmbedSequential(\\n conv_nd(dims, in_channels, model_channels, 3, padding=1)\\n )\\n ]\\n )\\n self._feature_size = model_channels\\n input_block_chans = [model_channels]\\n ch = model_channels\\n ds = 1\\n for level, mult in enumerate(channel_mult):\\n for nr in range(self.num_res_blocks[level]):\\n layers = [\\n ResBlock(\\n ch,\\n time_embed_dim,\\n dropout,\\n out_channels=mult * model_channels,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n )\\n ]\\n ch = mult * model_channels\\n if ds in attention_resolutions:\\n if num_head_channels == -1:\\n dim_head = ch // num_heads\\n else:\\n num_heads = ch // num_head_channels\\n dim_head = num_head_channels\\n if legacy:\\n #num_heads = 1\\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\\n if exists(disable_self_attentions):\\n disabled_sa = disable_self_attentions[level]\\n else:\\n disabled_sa = False\\n\\n if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:\\n layers.append(\\n AttentionBlock(\\n ch,\\n use_checkpoint=use_checkpoint,\\n num_heads=num_heads,\\n num_head_channels=dim_head,\\n use_new_attention_order=use_new_attention_order,\\n ) if not use_spatial_transformer else SpatialTransformer(\\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\\n disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,\\n use_checkpoint=use_checkpoint\\n )\\n )\\n self.input_blocks.append(TimestepEmbedSequential(*layers))\\n self._feature_size += ch\\n input_block_chans.append(ch)\\n if level != len(channel_mult) - 1:\\n out_ch = ch\\n self.input_blocks.append(\\n TimestepEmbedSequential(\\n ResBlock(\\n ch,\\n time_embed_dim,\\n dropout,\\n out_channels=out_ch,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n down=True,\\n )\\n if resblock_updown\\n else Downsample(\\n ch, conv_resample, dims=dims, out_channels=out_ch\\n )\\n )\\n )\\n ch = out_ch\\n input_block_chans.append(ch)\\n ds *= 2\\n self._feature_size += ch\\n\\n if num_head_channels == -1:\\n dim_head = ch // num_heads\\n else:\\n num_heads = ch // num_head_channels\\n dim_head = num_head_channels\\n if legacy:\\n #num_heads = 1\\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\\n self.middle_block = TimestepEmbedSequential(\\n ResBlock(\\n ch,\\n time_embed_dim,\\n dropout,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n ),\\n AttentionBlock(\\n ch,\\n use_checkpoint=use_checkpoint,\\n num_heads=num_heads,\\n num_head_channels=dim_head,\\n use_new_attention_order=use_new_attention_order,\\n ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn\\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\\n disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,\\n use_checkpoint=use_checkpoint\\n ),\\n ResBlock(\\n ch,\\n time_embed_dim,\\n dropout,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n ),\\n )\\n self._feature_size += ch\\n\\n self.output_blocks = nn.ModuleList([])\\n for level, mult in list(enumerate(channel_mult))[::-1]:\\n for i in range(self.num_res_blocks[level] + 1):\\n ich = input_block_chans.pop()\\n layers = [\\n ResBlock(\\n ch + ich,\\n time_embed_dim,\\n dropout,\\n out_channels=model_channels * mult,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n )\\n ]\\n ch = model_channels * mult\\n if ds in attention_resolutions:\\n if num_head_channels == -1:\\n dim_head = ch // num_heads\\n else:\\n num_heads = ch // num_head_channels\\n dim_head = num_head_channels\\n if legacy:\\n #num_heads = 1\\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\\n if exists(disable_self_attentions):\\n disabled_sa = disable_self_attentions[level]\\n else:\\n disabled_sa = False\\n\\n if not exists(num_attention_blocks) or i < num_attention_blocks[level]:\\n layers.append(\\n AttentionBlock(\\n ch,\\n use_checkpoint=use_checkpoint,\\n num_heads=num_heads_upsample,\\n num_head_channels=dim_head,\\n use_new_attention_order=use_new_attention_order,\\n ) if not use_spatial_transformer else SpatialTransformer(\\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,\\n disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,\\n use_checkpoint=use_checkpoint\\n )\\n )\\n if level and i == self.num_res_blocks[level]:\\n out_ch = ch\\n layers.append(\\n ResBlock(\\n ch,\\n time_embed_dim,\\n dropout,\\n out_channels=out_ch,\\n dims=dims,\\n use_checkpoint=use_checkpoint,\\n use_scale_shift_norm=use_scale_shift_norm,\\n up=True,\\n )\\n if resblock_updown\\n else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)\\n )\\n ds //= 2\\n self.output_blocks.append(TimestepEmbedSequential(*layers))\\n self._feature_size += ch\\n\\n self.out = nn.Sequential(\\n normalization(ch),\\n nn.SiLU(),\\n zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),\\n )\\n if self.predict_codebook_ids:\\n self.id_predictor = nn.Sequential(\\n normalization(ch),\\n conv_nd(dims, model_channels, n_embed, 1),\\n #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits\\n )\\n\\n def convert_to_fp16(self):\\n \\\"\\\"\\\"\\n Convert the torso of the model to float16.\\n \\\"\\\"\\\"\\n self.input_blocks.apply(convert_module_to_f16)\\n self.middle_block.apply(convert_module_to_f16)\\n self.output_blocks.apply(convert_module_to_f16)\\n\\n def convert_to_fp32(self):\\n \\\"\\\"\\\"\\n Convert the torso of the model to float32.\\n \\\"\\\"\\\"\\n self.input_blocks.apply(convert_module_to_f32)\\n self.middle_block.apply(convert_module_to_f32)\\n self.output_blocks.apply(convert_module_to_f32)\\n\\n def forward(self, x, timesteps=None, context=None, y=None,**kwargs):\\n \\\"\\\"\\\"\\n Apply the model to an input batch.\\n :param x: an [N x C x ...] Tensor of inputs.\\n :param timesteps: a 1-D batch of timesteps.\\n :param context: conditioning plugged in via crossattn\\n :param y: an [N] Tensor of labels, if class-conditional.\\n :return: an [N x C x ...] Tensor of outputs.\\n \\\"\\\"\\\"\\n assert (y is not None) == (\\n self.num_classes is not None\\n ), \\\"must specify y if and only if the model is class-conditional\\\"\\n hs = []\\n t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)\\n emb = self.time_embed(t_emb)\\n\\n if self.num_classes is not None:\\n assert y.shape[0] == x.shape[0]\\n emb = emb + self.label_emb(y)\\n\\n h = x.type(self.dtype)\\n for module in self.input_blocks:\\n h = module(h, emb, context)\\n hs.append(h)\\n h = self.middle_block(h, emb, context)\\n for module in self.output_blocks:\\n h = th.cat([h, hs.pop()], dim=1)\\n h = module(h, emb, context)\\n h = h.type(x.dtype)\\n if self.predict_codebook_ids:\\n return self.id_predictor(h)\\n else:\\n return self.out(h)\"\n },\n {\n \"identifier\": \"LatentDiffusion\",\n \"path\": \"ldm/models/diffusion/ddpm.py\",\n \"snippet\": \"class LatentDiffusion(DDPM):\\n \\\"\\\"\\\"main class\\\"\\\"\\\"\\n\\n def __init__(self,\\n first_stage_config,\\n cond_stage_config,\\n num_timesteps_cond=None,\\n cond_stage_key=\\\"image\\\",\\n cond_stage_trainable=False,\\n concat_mode=True,\\n cond_stage_forward=None,\\n conditioning_key=None,\\n scale_factor=1.0,\\n scale_by_std=False,\\n force_null_conditioning=False,\\n *args, **kwargs):\\n self.force_null_conditioning = force_null_conditioning\\n self.num_timesteps_cond = default(num_timesteps_cond, 1)\\n self.scale_by_std = scale_by_std\\n assert self.num_timesteps_cond <= kwargs['timesteps']\\n # for backwards compatibility after implementation of DiffusionWrapper\\n if conditioning_key is None:\\n conditioning_key = 'concat' if concat_mode else 'crossattn'\\n if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:\\n conditioning_key = None\\n ckpt_path = kwargs.pop(\\\"ckpt_path\\\", None)\\n reset_ema = kwargs.pop(\\\"reset_ema\\\", False)\\n reset_num_ema_updates = kwargs.pop(\\\"reset_num_ema_updates\\\", False)\\n ignore_keys = kwargs.pop(\\\"ignore_keys\\\", [])\\n super().__init__(conditioning_key=conditioning_key, *args, **kwargs)\\n self.concat_mode = concat_mode\\n self.cond_stage_trainable = cond_stage_trainable\\n self.cond_stage_key = cond_stage_key\\n try:\\n self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1\\n except:\\n self.num_downs = 0\\n if not scale_by_std:\\n self.scale_factor = scale_factor\\n else:\\n self.register_buffer('scale_factor', torch.tensor(scale_factor))\\n self.instantiate_first_stage(first_stage_config)\\n self.instantiate_cond_stage(cond_stage_config)\\n self.cond_stage_forward = cond_stage_forward\\n self.clip_denoised = False\\n self.bbox_tokenizer = None\\n\\n self.restarted_from_ckpt = False\\n if ckpt_path is not None:\\n self.init_from_ckpt(ckpt_path, ignore_keys)\\n self.restarted_from_ckpt = True\\n if reset_ema:\\n assert self.use_ema\\n print(\\n f\\\"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.\\\")\\n self.model_ema = LitEma(self.model)\\n if reset_num_ema_updates:\\n print(\\\" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ \\\")\\n assert self.use_ema\\n self.model_ema.reset_num_updates()\\n\\n def make_cond_schedule(self, ):\\n self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)\\n ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()\\n self.cond_ids[:self.num_timesteps_cond] = ids\\n\\n @rank_zero_only\\n @torch.no_grad()\\n def on_train_batch_start(self, batch, batch_idx, dataloader_idx):\\n # only for very first batch\\n 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:\\n assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'\\n # set rescale weight to 1./std of encodings\\n print(\\\"### USING STD-RESCALING ###\\\")\\n x = super().get_input(batch, self.first_stage_key)\\n x = x.to(self.device)\\n encoder_posterior = self.encode_first_stage(x)\\n z = self.get_first_stage_encoding(encoder_posterior).detach()\\n del self.scale_factor\\n self.register_buffer('scale_factor', 1. / z.flatten().std())\\n print(f\\\"setting self.scale_factor to {self.scale_factor}\\\")\\n print(\\\"### USING STD-RESCALING ###\\\")\\n\\n def register_schedule(self,\\n given_betas=None, beta_schedule=\\\"linear\\\", timesteps=1000,\\n linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\\n super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)\\n\\n self.shorten_cond_schedule = self.num_timesteps_cond > 1\\n if self.shorten_cond_schedule:\\n self.make_cond_schedule()\\n\\n def instantiate_first_stage(self, config):\\n model = instantiate_from_config(config)\\n self.first_stage_model = model.eval()\\n self.first_stage_model.train = disabled_train\\n for param in self.first_stage_model.parameters():\\n param.requires_grad = False\\n\\n def instantiate_cond_stage(self, config):\\n if not self.cond_stage_trainable:\\n if config == \\\"__is_first_stage__\\\":\\n print(\\\"Using first stage also as cond stage.\\\")\\n self.cond_stage_model = self.first_stage_model\\n elif config == \\\"__is_unconditional__\\\":\\n print(f\\\"Training {self.__class__.__name__} as an unconditional model.\\\")\\n self.cond_stage_model = None\\n # self.be_unconditional = True\\n else:\\n model = instantiate_from_config(config)\\n self.cond_stage_model = model.eval()\\n self.cond_stage_model.train = disabled_train\\n for param in self.cond_stage_model.parameters():\\n param.requires_grad = False\\n else:\\n assert config != '__is_first_stage__'\\n assert config != '__is_unconditional__'\\n model = instantiate_from_config(config)\\n self.cond_stage_model = model\\n\\n def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):\\n denoise_row = []\\n for zd in tqdm(samples, desc=desc):\\n denoise_row.append(self.decode_first_stage(zd.to(self.device),\\n force_not_quantize=force_no_decoder_quantization))\\n n_imgs_per_row = len(denoise_row)\\n denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W\\n denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')\\n denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')\\n denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)\\n return denoise_grid\\n\\n def get_first_stage_encoding(self, encoder_posterior):\\n if isinstance(encoder_posterior, DiagonalGaussianDistribution):\\n z = encoder_posterior.sample()\\n elif isinstance(encoder_posterior, torch.Tensor):\\n z = encoder_posterior\\n else:\\n raise NotImplementedError(f\\\"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented\\\")\\n return self.scale_factor * z\\n\\n def get_learned_conditioning(self, c):\\n if self.cond_stage_forward is None:\\n if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):\\n c = self.cond_stage_model.encode(c)\\n if isinstance(c, DiagonalGaussianDistribution):\\n c = c.mode()\\n else:\\n c = self.cond_stage_model(c)\\n else:\\n assert hasattr(self.cond_stage_model, self.cond_stage_forward)\\n c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)\\n return c\\n\\n def meshgrid(self, h, w):\\n y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)\\n x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)\\n\\n arr = torch.cat([y, x], dim=-1)\\n return arr\\n\\n def delta_border(self, h, w):\\n \\\"\\\"\\\"\\n :param h: height\\n :param w: width\\n :return: normalized distance to image border,\\n wtith min distance = 0 at border and max dist = 0.5 at image center\\n \\\"\\\"\\\"\\n lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)\\n arr = self.meshgrid(h, w) / lower_right_corner\\n dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]\\n dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]\\n edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]\\n return edge_dist\\n\\n def get_weighting(self, h, w, Ly, Lx, device):\\n weighting = self.delta_border(h, w)\\n weighting = torch.clip(weighting, self.split_input_params[\\\"clip_min_weight\\\"],\\n self.split_input_params[\\\"clip_max_weight\\\"], )\\n weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)\\n\\n if self.split_input_params[\\\"tie_braker\\\"]:\\n L_weighting = self.delta_border(Ly, Lx)\\n L_weighting = torch.clip(L_weighting,\\n self.split_input_params[\\\"clip_min_tie_weight\\\"],\\n self.split_input_params[\\\"clip_max_tie_weight\\\"])\\n\\n L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)\\n weighting = weighting * L_weighting\\n return weighting\\n\\n def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code\\n \\\"\\\"\\\"\\n :param x: img of size (bs, c, h, w)\\n :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])\\n \\\"\\\"\\\"\\n bs, nc, h, w = x.shape\\n\\n # number of crops in image\\n Ly = (h - kernel_size[0]) // stride[0] + 1\\n Lx = (w - kernel_size[1]) // stride[1] + 1\\n\\n if uf == 1 and df == 1:\\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\\n unfold = torch.nn.Unfold(**fold_params)\\n\\n fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)\\n\\n weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)\\n normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap\\n weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))\\n\\n elif uf > 1 and df == 1:\\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\\n unfold = torch.nn.Unfold(**fold_params)\\n\\n fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),\\n dilation=1, padding=0,\\n stride=(stride[0] * uf, stride[1] * uf))\\n fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)\\n\\n weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)\\n normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap\\n weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))\\n\\n elif df > 1 and uf == 1:\\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\\n unfold = torch.nn.Unfold(**fold_params)\\n\\n fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),\\n dilation=1, padding=0,\\n stride=(stride[0] // df, stride[1] // df))\\n fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)\\n\\n weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)\\n normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap\\n weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))\\n\\n else:\\n raise NotImplementedError\\n\\n return fold, unfold, normalization, weighting\\n\\n @torch.no_grad()\\n def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,\\n cond_key=None, return_original_cond=False, bs=None, return_x=False):\\n x = super().get_input(batch, k)\\n if bs is not None:\\n x = x[:bs]\\n x = x.to(self.device)\\n encoder_posterior = self.encode_first_stage(x)\\n z = self.get_first_stage_encoding(encoder_posterior).detach()\\n\\n if self.model.conditioning_key is not None and not self.force_null_conditioning:\\n if cond_key is None:\\n cond_key = self.cond_stage_key\\n if cond_key != self.first_stage_key:\\n if cond_key in ['caption', 'coordinates_bbox', \\\"txt\\\"]:\\n xc = batch[cond_key]\\n elif cond_key in ['class_label', 'cls']:\\n xc = batch\\n else:\\n xc = super().get_input(batch, cond_key).to(self.device)\\n else:\\n xc = x\\n if not self.cond_stage_trainable or force_c_encode:\\n if isinstance(xc, dict) or isinstance(xc, list):\\n c = self.get_learned_conditioning(xc)\\n else:\\n c = self.get_learned_conditioning(xc.to(self.device))\\n else:\\n c = xc\\n if bs is not None:\\n c = c[:bs]\\n\\n if self.use_positional_encodings:\\n pos_x, pos_y = self.compute_latent_shifts(batch)\\n ckey = __conditioning_keys__[self.model.conditioning_key]\\n c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}\\n\\n else:\\n c = None\\n xc = None\\n if self.use_positional_encodings:\\n pos_x, pos_y = self.compute_latent_shifts(batch)\\n c = {'pos_x': pos_x, 'pos_y': pos_y}\\n out = [z, c]\\n if return_first_stage_outputs:\\n xrec = self.decode_first_stage(z)\\n out.extend([x, xrec])\\n if return_x:\\n out.extend([x])\\n if return_original_cond:\\n out.append(xc)\\n return out\\n\\n @torch.no_grad()\\n def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):\\n if predict_cids:\\n if z.dim() == 4:\\n z = torch.argmax(z.exp(), dim=1).long()\\n z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)\\n z = rearrange(z, 'b h w c -> b c h w').contiguous()\\n\\n z = 1. / self.scale_factor * z\\n return self.first_stage_model.decode(z)\\n\\n @torch.no_grad()\\n def encode_first_stage(self, x):\\n return self.first_stage_model.encode(x)\\n\\n def shared_step(self, batch, **kwargs):\\n x, c = self.get_input(batch, self.first_stage_key)\\n loss = self(x, c)\\n return loss\\n\\n def forward(self, x, c, *args, **kwargs):\\n t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()\\n if self.model.conditioning_key is not None:\\n assert c is not None\\n # if self.cond_stage_trainable:\\n # c = self.get_learned_conditioning(c)\\n if self.shorten_cond_schedule: # TODO: drop this option\\n tc = self.cond_ids[t].to(self.device)\\n c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))\\n return self.p_losses(x, c, t, *args, **kwargs)\\n\\n def apply_model(self, x_noisy, t, cond, return_ids=False):\\n if isinstance(cond, dict):\\n # hybrid case, cond is expected to be a dict\\n pass\\n else:\\n if not isinstance(cond, list):\\n cond = [cond]\\n key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'\\n cond = {key: cond}\\n\\n x_recon = self.model(x_noisy, t, **cond)\\n\\n if isinstance(x_recon, tuple) and not return_ids:\\n return x_recon[0]\\n else:\\n return x_recon\\n\\n def _predict_eps_from_xstart(self, x_t, t, pred_xstart):\\n return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \\\\\\n extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)\\n\\n def _prior_bpd(self, x_start):\\n \\\"\\\"\\\"\\n Get the prior KL term for the variational lower-bound, measured in\\n bits-per-dim.\\n This term can't be optimized, as it only depends on the encoder.\\n :param x_start: the [N x C x ...] tensor of inputs.\\n :return: a batch of [N] KL values (in bits), one per batch element.\\n \\\"\\\"\\\"\\n batch_size = x_start.shape[0]\\n t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)\\n qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)\\n kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)\\n return mean_flat(kl_prior) / np.log(2.0)\\n\\n def p_losses(self, x_start, cond, t, noise=None):\\n noise = default(noise, lambda: torch.randn_like(x_start))\\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\\n model_output = self.apply_model(x_noisy, t, cond)\\n\\n loss_dict = {}\\n prefix = 'train' if self.training else 'val'\\n\\n if self.parameterization == \\\"x0\\\":\\n target = x_start\\n elif self.parameterization == \\\"eps\\\":\\n target = noise\\n elif self.parameterization == \\\"v\\\":\\n target = self.get_v(x_start, noise, t)\\n else:\\n raise NotImplementedError()\\n\\n loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])\\n loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})\\n\\n logvar_t = self.logvar[t].to(self.device)\\n loss = loss_simple / torch.exp(logvar_t) + logvar_t\\n # loss = loss_simple / torch.exp(self.logvar) + self.logvar\\n if self.learn_logvar:\\n loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})\\n loss_dict.update({'logvar': self.logvar.data.mean()})\\n\\n loss = self.l_simple_weight * loss.mean()\\n\\n loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))\\n loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()\\n loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})\\n loss += (self.original_elbo_weight * loss_vlb)\\n loss_dict.update({f'{prefix}/loss': loss})\\n\\n return loss, loss_dict\\n\\n def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,\\n return_x0=False, score_corrector=None, corrector_kwargs=None):\\n t_in = t\\n model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)\\n\\n if score_corrector is not None:\\n assert self.parameterization == \\\"eps\\\"\\n model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)\\n\\n if return_codebook_ids:\\n model_out, logits = model_out\\n\\n if self.parameterization == \\\"eps\\\":\\n x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)\\n elif self.parameterization == \\\"x0\\\":\\n x_recon = model_out\\n else:\\n raise NotImplementedError()\\n\\n if clip_denoised:\\n x_recon.clamp_(-1., 1.)\\n if quantize_denoised:\\n x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)\\n model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)\\n if return_codebook_ids:\\n return model_mean, posterior_variance, posterior_log_variance, logits\\n elif return_x0:\\n return model_mean, posterior_variance, posterior_log_variance, x_recon\\n else:\\n return model_mean, posterior_variance, posterior_log_variance\\n\\n @torch.no_grad()\\n def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,\\n return_codebook_ids=False, quantize_denoised=False, return_x0=False,\\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):\\n b, *_, device = *x.shape, x.device\\n outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,\\n return_codebook_ids=return_codebook_ids,\\n quantize_denoised=quantize_denoised,\\n return_x0=return_x0,\\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\\n if return_codebook_ids:\\n raise DeprecationWarning(\\\"Support dropped.\\\")\\n model_mean, _, model_log_variance, logits = outputs\\n elif return_x0:\\n model_mean, _, model_log_variance, x0 = outputs\\n else:\\n model_mean, _, model_log_variance = outputs\\n\\n noise = noise_like(x.shape, device, repeat_noise) * temperature\\n if noise_dropout > 0.:\\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\\n # no noise when t == 0\\n nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\\n\\n if return_codebook_ids:\\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)\\n if return_x0:\\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0\\n else:\\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\\n\\n @torch.no_grad()\\n def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,\\n img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,\\n score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,\\n log_every_t=None):\\n if not log_every_t:\\n log_every_t = self.log_every_t\\n timesteps = self.num_timesteps\\n if batch_size is not None:\\n b = batch_size if batch_size is not None else shape[0]\\n shape = [batch_size] + list(shape)\\n else:\\n b = batch_size = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=self.device)\\n else:\\n img = x_T\\n intermediates = []\\n if cond is not None:\\n if isinstance(cond, dict):\\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\\n else:\\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\\n\\n if start_T is not None:\\n timesteps = min(timesteps, start_T)\\n iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',\\n total=timesteps) if verbose else reversed(\\n range(0, timesteps))\\n if type(temperature) == float:\\n temperature = [temperature] * timesteps\\n\\n for i in iterator:\\n ts = torch.full((b,), i, device=self.device, dtype=torch.long)\\n if self.shorten_cond_schedule:\\n assert self.model.conditioning_key != 'hybrid'\\n tc = self.cond_ids[ts].to(cond.device)\\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\\n\\n img, x0_partial = self.p_sample(img, cond, ts,\\n clip_denoised=self.clip_denoised,\\n quantize_denoised=quantize_denoised, return_x0=True,\\n temperature=temperature[i], noise_dropout=noise_dropout,\\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\\n if mask is not None:\\n assert x0 is not None\\n img_orig = self.q_sample(x0, ts)\\n img = img_orig * mask + (1. - mask) * img\\n\\n if i % log_every_t == 0 or i == timesteps - 1:\\n intermediates.append(x0_partial)\\n if callback: callback(i)\\n if img_callback: img_callback(img, i)\\n return img, intermediates\\n\\n @torch.no_grad()\\n def p_sample_loop(self, cond, shape, return_intermediates=False,\\n x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,\\n mask=None, x0=None, img_callback=None, start_T=None,\\n log_every_t=None):\\n\\n if not log_every_t:\\n log_every_t = self.log_every_t\\n device = self.betas.device\\n b = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=device)\\n else:\\n img = x_T\\n\\n intermediates = [img]\\n if timesteps is None:\\n timesteps = self.num_timesteps\\n\\n if start_T is not None:\\n timesteps = min(timesteps, start_T)\\n iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(\\n range(0, timesteps))\\n\\n if mask is not None:\\n assert x0 is not None\\n assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match\\n\\n for i in iterator:\\n ts = torch.full((b,), i, device=device, dtype=torch.long)\\n if self.shorten_cond_schedule:\\n assert self.model.conditioning_key != 'hybrid'\\n tc = self.cond_ids[ts].to(cond.device)\\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\\n\\n img = self.p_sample(img, cond, ts,\\n clip_denoised=self.clip_denoised,\\n quantize_denoised=quantize_denoised)\\n if mask is not None:\\n img_orig = self.q_sample(x0, ts)\\n img = img_orig * mask + (1. - mask) * img\\n\\n if i % log_every_t == 0 or i == timesteps - 1:\\n intermediates.append(img)\\n if callback: callback(i)\\n if img_callback: img_callback(img, i)\\n\\n if return_intermediates:\\n return img, intermediates\\n return img\\n\\n @torch.no_grad()\\n def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,\\n verbose=True, timesteps=None, quantize_denoised=False,\\n mask=None, x0=None, shape=None, **kwargs):\\n if shape is None:\\n shape = (batch_size, self.channels, self.image_size, self.image_size)\\n if cond is not None:\\n if isinstance(cond, dict):\\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\\n else:\\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\\n return self.p_sample_loop(cond,\\n shape,\\n return_intermediates=return_intermediates, x_T=x_T,\\n verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,\\n mask=mask, x0=x0)\\n\\n @torch.no_grad()\\n def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):\\n if ddim:\\n ddim_sampler = DDIMSampler(self)\\n shape = (self.channels, self.image_size, self.image_size)\\n samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,\\n shape, cond, verbose=False, **kwargs)\\n\\n else:\\n samples, intermediates = self.sample(cond=cond, batch_size=batch_size,\\n return_intermediates=True, **kwargs)\\n\\n return samples, intermediates\\n\\n @torch.no_grad()\\n def get_unconditional_conditioning(self, batch_size, null_label=None):\\n if null_label is not None:\\n xc = null_label\\n if isinstance(xc, ListConfig):\\n xc = list(xc)\\n if isinstance(xc, dict) or isinstance(xc, list):\\n c = self.get_learned_conditioning(xc)\\n else:\\n if hasattr(xc, \\\"to\\\"):\\n xc = xc.to(self.device)\\n c = self.get_learned_conditioning(xc)\\n else:\\n if self.cond_stage_key in [\\\"class_label\\\", \\\"cls\\\"]:\\n xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)\\n return self.get_learned_conditioning(xc)\\n else:\\n raise NotImplementedError(\\\"todo\\\")\\n if isinstance(c, list): # in case the encoder gives us a list\\n for i in range(len(c)):\\n c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)\\n else:\\n c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)\\n return c\\n\\n @torch.no_grad()\\n def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,\\n quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,\\n plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,\\n use_ema_scope=True,\\n **kwargs):\\n ema_scope = self.ema_scope if use_ema_scope else nullcontext\\n use_ddim = ddim_steps is not None\\n\\n log = dict()\\n z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,\\n return_first_stage_outputs=True,\\n force_c_encode=True,\\n return_original_cond=True,\\n bs=N)\\n N = min(x.shape[0], N)\\n n_row = min(x.shape[0], n_row)\\n log[\\\"inputs\\\"] = x\\n log[\\\"reconstruction\\\"] = xrec\\n if self.model.conditioning_key is not None:\\n if hasattr(self.cond_stage_model, \\\"decode\\\"):\\n xc = self.cond_stage_model.decode(c)\\n log[\\\"conditioning\\\"] = xc\\n elif self.cond_stage_key in [\\\"caption\\\", \\\"txt\\\"]:\\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)\\n log[\\\"conditioning\\\"] = xc\\n elif self.cond_stage_key in ['class_label', \\\"cls\\\"]:\\n try:\\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[\\\"human_label\\\"], size=x.shape[2] // 25)\\n log['conditioning'] = xc\\n except KeyError:\\n # probably no \\\"human_label\\\" in batch\\n pass\\n elif isimage(xc):\\n log[\\\"conditioning\\\"] = xc\\n if ismap(xc):\\n log[\\\"original_conditioning\\\"] = self.to_rgb(xc)\\n\\n if plot_diffusion_rows:\\n # get diffusion row\\n diffusion_row = list()\\n z_start = z[:n_row]\\n for t in range(self.num_timesteps):\\n if t % self.log_every_t == 0 or t == self.num_timesteps - 1:\\n t = repeat(torch.tensor([t]), '1 -> b', b=n_row)\\n t = t.to(self.device).long()\\n noise = torch.randn_like(z_start)\\n z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)\\n diffusion_row.append(self.decode_first_stage(z_noisy))\\n\\n diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W\\n diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')\\n diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')\\n diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])\\n log[\\\"diffusion_row\\\"] = diffusion_grid\\n\\n if sample:\\n # get denoise row\\n with ema_scope(\\\"Sampling\\\"):\\n samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\\n ddim_steps=ddim_steps, eta=ddim_eta)\\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)\\n x_samples = self.decode_first_stage(samples)\\n log[\\\"samples\\\"] = x_samples\\n if plot_denoise_rows:\\n denoise_grid = self._get_denoise_row_from_list(z_denoise_row)\\n log[\\\"denoise_row\\\"] = denoise_grid\\n\\n if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(\\n self.first_stage_model, IdentityFirstStage):\\n # also display when quantizing x0 while sampling\\n with ema_scope(\\\"Plotting Quantized Denoised\\\"):\\n samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\\n ddim_steps=ddim_steps, eta=ddim_eta,\\n quantize_denoised=True)\\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,\\n # quantize_denoised=True)\\n x_samples = self.decode_first_stage(samples.to(self.device))\\n log[\\\"samples_x0_quantized\\\"] = x_samples\\n\\n if unconditional_guidance_scale > 1.0:\\n uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)\\n if self.model.conditioning_key == \\\"crossattn-adm\\\":\\n uc = {\\\"c_crossattn\\\": [uc], \\\"c_adm\\\": c[\\\"c_adm\\\"]}\\n with ema_scope(\\\"Sampling with classifier-free guidance\\\"):\\n samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,\\n ddim_steps=ddim_steps, eta=ddim_eta,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=uc,\\n )\\n x_samples_cfg = self.decode_first_stage(samples_cfg)\\n log[f\\\"samples_cfg_scale_{unconditional_guidance_scale:.2f}\\\"] = x_samples_cfg\\n\\n if inpaint:\\n # make a simple center square\\n b, h, w = z.shape[0], z.shape[2], z.shape[3]\\n mask = torch.ones(N, h, w).to(self.device)\\n # zeros will be filled in\\n mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.\\n mask = mask[:, None, ...]\\n with ema_scope(\\\"Plotting Inpaint\\\"):\\n samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,\\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\\n x_samples = self.decode_first_stage(samples.to(self.device))\\n log[\\\"samples_inpainting\\\"] = x_samples\\n log[\\\"mask\\\"] = mask\\n\\n # outpaint\\n mask = 1. - mask\\n with ema_scope(\\\"Plotting Outpaint\\\"):\\n samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,\\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\\n x_samples = self.decode_first_stage(samples.to(self.device))\\n log[\\\"samples_outpainting\\\"] = x_samples\\n\\n if plot_progressive_rows:\\n with ema_scope(\\\"Plotting Progressives\\\"):\\n img, progressives = self.progressive_denoising(c,\\n shape=(self.channels, self.image_size, self.image_size),\\n batch_size=N)\\n prog_row = self._get_denoise_row_from_list(progressives, desc=\\\"Progressive Generation\\\")\\n log[\\\"progressive_row\\\"] = prog_row\\n\\n if return_keys:\\n if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:\\n return log\\n else:\\n return {key: log[key] for key in return_keys}\\n return log\\n\\n def configure_optimizers(self):\\n lr = self.learning_rate\\n params = list(self.model.parameters())\\n if self.cond_stage_trainable:\\n print(f\\\"{self.__class__.__name__}: Also optimizing conditioner params!\\\")\\n params = params + list(self.cond_stage_model.parameters())\\n if self.learn_logvar:\\n print('Diffusion model optimizing logvar')\\n params.append(self.logvar)\\n opt = torch.optim.AdamW(params, lr=lr)\\n if self.use_scheduler:\\n assert 'target' in self.scheduler_config\\n scheduler = instantiate_from_config(self.scheduler_config)\\n\\n print(\\\"Setting up LambdaLR scheduler...\\\")\\n scheduler = [\\n {\\n 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),\\n 'interval': 'step',\\n 'frequency': 1\\n }]\\n return [opt], scheduler\\n return opt\\n\\n @torch.no_grad()\\n def to_rgb(self, x):\\n x = x.float()\\n if not hasattr(self, \\\"colorize\\\"):\\n self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)\\n x = nn.functional.conv2d(x, weight=self.colorize)\\n x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.\\n return x\"\n },\n {\n \"identifier\": \"log_txt_as_img\",\n \"path\": \"ldm/util.py\",\n \"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 draw = ImageDraw.Draw(txt)\\n font = ImageFont.truetype('font/DejaVuSans.ttf', size=size)\\n nc = int(40 * (wh[0] / 256))\\n lines = \\\"\\\\n\\\".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))\\n\\n try:\\n draw.text((0, 0), lines, fill=\\\"black\\\", font=font)\\n except UnicodeEncodeError:\\n print(\\\"Cant encode string for logging. Skipping.\\\")\\n\\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\\n txts.append(txt)\\n txts = np.stack(txts)\\n txts = torch.tensor(txts)\\n return txts\"\n },\n {\n \"identifier\": \"instantiate_from_config\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def instantiate_from_config(config):\\n if not \\\"target\\\" in config:\\n if config == '__is_first_stage__':\\n return None\\n elif config == \\\"__is_unconditional__\\\":\\n return None\\n raise KeyError(\\\"Expected key `target` to instantiate.\\\")\\n return get_obj_from_str(config[\\\"target\\\"])(**config.get(\\\"params\\\", dict()))\"\n },\n {\n \"identifier\": \"DDIMSampler\",\n \"path\": \"ldm/models/diffusion/ddim.py\",\n \"snippet\": \"class DDIMSampler(object):\\n def __init__(self, model, schedule=\\\"linear\\\", **kwargs):\\n super().__init__()\\n self.model = model\\n self.ddpm_num_timesteps = model.num_timesteps\\n self.schedule = schedule\\n\\n def register_buffer(self, name, attr):\\n if type(attr) == torch.Tensor:\\n if attr.device != torch.device(\\\"cuda\\\"):\\n attr = attr.to(torch.device(\\\"cuda\\\"))\\n setattr(self, name, attr)\\n\\n def make_schedule(self, ddim_num_steps, ddim_discretize=\\\"uniform\\\", ddim_eta=0., verbose=True):\\n self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,\\n num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)\\n alphas_cumprod = self.model.alphas_cumprod\\n assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)\\n\\n self.register_buffer('betas', to_torch(self.model.betas))\\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\\n self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))\\n\\n # calculations for diffusion q(x_t | x_{t-1}) and others\\n self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\\n self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\\n\\n # ddim sampling parameters\\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),\\n ddim_timesteps=self.ddim_timesteps,\\n eta=ddim_eta,verbose=verbose)\\n self.register_buffer('ddim_sigmas', ddim_sigmas)\\n self.register_buffer('ddim_alphas', ddim_alphas)\\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\\n self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))\\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (\\n 1 - self.alphas_cumprod / self.alphas_cumprod_prev))\\n self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)\\n\\n @torch.no_grad()\\n def sample(self,\\n S,\\n batch_size,\\n shape,\\n conditioning=None,\\n callback=None,\\n normals_sequence=None,\\n img_callback=None,\\n quantize_x0=False,\\n eta=0.,\\n mask=None,\\n x0=None,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n verbose=True,\\n x_T=None,\\n log_every_t=100,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\\n dynamic_threshold=None,\\n ucg_schedule=None,\\n **kwargs\\n ):\\n if conditioning is not None:\\n if isinstance(conditioning, dict):\\n ctmp = conditioning[list(conditioning.keys())[0]]\\n while isinstance(ctmp, list): ctmp = ctmp[0]\\n cbs = ctmp.shape[0]\\n if cbs != batch_size:\\n print(f\\\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\\\")\\n\\n elif isinstance(conditioning, list):\\n for ctmp in conditioning:\\n if ctmp.shape[0] != batch_size:\\n print(f\\\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\\\")\\n\\n else:\\n if conditioning.shape[0] != batch_size:\\n print(f\\\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\\\")\\n\\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\\n # sampling\\n C, H, W = shape\\n size = (batch_size, C, H, W)\\n # print(f'Data shape for DDIM sampling is {size}, eta {eta}')\\n\\n samples, intermediates = self.ddim_sampling(conditioning, size,\\n callback=callback,\\n img_callback=img_callback,\\n quantize_denoised=quantize_x0,\\n mask=mask, x0=x0,\\n ddim_use_original_steps=False,\\n noise_dropout=noise_dropout,\\n temperature=temperature,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n x_T=x_T,\\n log_every_t=log_every_t,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n dynamic_threshold=dynamic_threshold,\\n ucg_schedule=ucg_schedule\\n )\\n return samples, intermediates\\n\\n @torch.no_grad()\\n def ddim_sampling(self, cond, shape,\\n x_T=None, ddim_use_original_steps=False,\\n callback=None, timesteps=None, quantize_denoised=False,\\n mask=None, x0=None, img_callback=None, log_every_t=100,\\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\\n unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,\\n ucg_schedule=None):\\n device = self.model.betas.device\\n b = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=device)\\n else:\\n img = x_T\\n\\n if timesteps is None:\\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\\n elif timesteps is not None and not ddim_use_original_steps:\\n subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1\\n timesteps = self.ddim_timesteps[:subset_end]\\n\\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\\n time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)\\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]\\n # print(f\\\"Running DDIM Sampling with {total_steps} timesteps\\\")\\n\\n # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)\\n\\n for i, step in enumerate(time_range):\\n index = total_steps - i - 1\\n ts = torch.full((b,), step, device=device, dtype=torch.long)\\n\\n if mask is not None:\\n assert x0 is not None\\n img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?\\n img = img_orig * mask + (1. - mask) * img\\n\\n if ucg_schedule is not None:\\n assert len(ucg_schedule) == len(time_range)\\n unconditional_guidance_scale = ucg_schedule[i]\\n\\n outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,\\n quantize_denoised=quantize_denoised, temperature=temperature,\\n noise_dropout=noise_dropout, score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n dynamic_threshold=dynamic_threshold)\\n img, pred_x0 = outs\\n if callback: callback(i)\\n if img_callback: img_callback(pred_x0, i)\\n\\n if index % log_every_t == 0 or index == total_steps - 1:\\n intermediates['x_inter'].append(img)\\n intermediates['pred_x0'].append(pred_x0)\\n\\n return img, intermediates\\n\\n @torch.no_grad()\\n def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,\\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\\n unconditional_guidance_scale=1., unconditional_conditioning=None,\\n dynamic_threshold=None):\\n b, *_, device = *x.shape, x.device\\n\\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\\n model_output = self.model.apply_model(x, t, c)\\n else:\\n x_in = torch.cat([x] * 2)\\n t_in = torch.cat([t] * 2)\\n if isinstance(c, dict):\\n assert isinstance(unconditional_conditioning, dict)\\n c_in = dict()\\n for k in c:\\n if isinstance(c[k], list):\\n c_in[k] = [torch.cat([\\n unconditional_conditioning[k][i],\\n c[k][i]]) for i in range(len(c[k]))]\\n else:\\n c_in[k] = torch.cat([\\n unconditional_conditioning[k],\\n c[k]])\\n elif isinstance(c, list):\\n c_in = list()\\n assert isinstance(unconditional_conditioning, list)\\n for i in range(len(c)):\\n c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))\\n else:\\n c_in = torch.cat([unconditional_conditioning, c])\\n model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\\n model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)\\n\\n if self.model.parameterization == \\\"v\\\":\\n e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)\\n else:\\n e_t = model_output\\n\\n if score_corrector is not None:\\n assert self.model.parameterization == \\\"eps\\\", 'not implemented'\\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)\\n\\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\\n # select parameters corresponding to the currently considered timestep\\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\\n sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)\\n\\n # current prediction for x_0\\n if self.model.parameterization != \\\"v\\\":\\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\\n else:\\n pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)\\n\\n if quantize_denoised:\\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\\n\\n if dynamic_threshold is not None:\\n raise NotImplementedError()\\n\\n # direction pointing to x_t\\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\\n if noise_dropout > 0.:\\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\\n return x_prev, pred_x0\\n\\n @torch.no_grad()\\n def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,\\n unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):\\n num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]\\n\\n assert t_enc <= num_reference_steps\\n num_steps = t_enc\\n\\n if use_original_steps:\\n alphas_next = self.alphas_cumprod[:num_steps]\\n alphas = self.alphas_cumprod_prev[:num_steps]\\n else:\\n alphas_next = self.ddim_alphas[:num_steps]\\n alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])\\n\\n x_next = x0\\n intermediates = []\\n inter_steps = []\\n for i in tqdm(range(num_steps), desc='Encoding Image'):\\n t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)\\n if unconditional_guidance_scale == 1.:\\n noise_pred = self.model.apply_model(x_next, t, c)\\n else:\\n assert unconditional_conditioning is not None\\n e_t_uncond, noise_pred = torch.chunk(\\n self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),\\n torch.cat((unconditional_conditioning, c))), 2)\\n noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)\\n\\n xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next\\n weighted_noise_pred = alphas_next[i].sqrt() * (\\n (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred\\n x_next = xt_weighted + weighted_noise_pred\\n if return_intermediates and i % (\\n num_steps // return_intermediates) == 0 and i < num_steps - 1:\\n intermediates.append(x_next)\\n inter_steps.append(i)\\n elif return_intermediates and i >= num_steps - 2:\\n intermediates.append(x_next)\\n inter_steps.append(i)\\n if callback: callback(i)\\n\\n out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}\\n if return_intermediates:\\n out.update({'intermediates': intermediates})\\n return x_next, out\\n\\n @torch.no_grad()\\n def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):\\n # fast, but does not allow for exact reconstruction\\n # t serves as an index to gather the correct alphas\\n if use_original_steps:\\n sqrt_alphas_cumprod = self.sqrt_alphas_cumprod\\n sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod\\n else:\\n sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)\\n sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas\\n\\n if noise is None:\\n noise = torch.randn_like(x0)\\n return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +\\n extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)\\n\\n @torch.no_grad()\\n def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,\\n use_original_steps=False, callback=None):\\n\\n timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps\\n timesteps = timesteps[:t_start]\\n\\n time_range = np.flip(timesteps)\\n total_steps = timesteps.shape[0]\\n print(f\\\"Running DDIM Sampling with {total_steps} timesteps\\\")\\n\\n iterator = tqdm(time_range, desc='Decoding image', total=total_steps)\\n x_dec = x_latent\\n for i, step in enumerate(iterator):\\n index = total_steps - i - 1\\n ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)\\n x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning)\\n if callback: callback(i)\\n return x_dec\"\n },\n {\n \"identifier\": \"instantiate_from_config\",\n \"path\": \"data/dataset_instantiate.py\",\n \"snippet\": \"def instantiate_from_config(config):\\n if not \\\"target\\\" in config:\\n if config == '__is_first_stage__':\\n return None\\n elif config == \\\"__is_unconditional__\\\":\\n return None\\n raise KeyError(\\\"Expected key `target` to instantiate.\\\")\\n return get_obj_from_str(config[\\\"target\\\"])(config.get(\\\"params\\\", dict()))\"\n },\n {\n \"identifier\": \"calculate_psnr_ssim\",\n \"path\": \"metrics/metrics_all.py\",\n \"snippet\": \"def calculate_psnr_ssim(gt_path, restored_path, test_y_channel = False, crop_border = 0, suffix = '', correct_mean_var = False, show_details =False):\\n \\\"\\\"\\\"\\n Calculate PSNR and SSIM for images.\\n gt_path: Path to gt (Ground-Truth)\\n restored_path: Path to restored images\\n test_y_channel: If True, test Y channel (In MatLab YCbCr format). If False, test RGB channels.\\n crop_border: Crop border for each side\\n suffix: Suffix for restored images\\n \\\"\\\"\\\"\\n print(\\\"Calculate PSNR and SSIM for images\\\")\\n psnr_all = []\\n ssim_all = []\\n img_list_gt = sorted(list(scandir(gt_path, recursive=True, full_path=True)))\\n img_list_restored = sorted(list(scandir(restored_path, recursive=True, full_path=True)))\\n\\n if test_y_channel:\\n print('Testing Y channel.')\\n else:\\n print('Testing RGB channels.')\\n\\n for i, img_path in tqdm(enumerate(img_list_gt)):\\n basename, ext = osp.splitext(osp.basename(img_path))\\n img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\\n if suffix == '':\\n img_path_restored = img_list_restored[i]\\n else:\\n img_path_restored = osp.join(restored_path, basename + suffix + ext)\\n img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\\n # img_restored = cv2.imread(img_path_restored, cv2.IMREAD_COLOR).astype(np.float32) / 255.\\n img_restored\\n if correct_mean_var:\\n mean_l = []\\n std_l = []\\n for j in range(3):\\n mean_l.append(np.mean(img_gt[:, :, j]))\\n std_l.append(np.std(img_gt[:, :, j]))\\n for j in range(3):\\n # correct twice\\n mean = np.mean(img_restored[:, :, j])\\n img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]\\n std = np.std(img_restored[:, :, j])\\n img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]\\n\\n mean = np.mean(img_restored[:, :, j])\\n img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]\\n std = np.std(img_restored[:, :, j])\\n img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]\\n\\n if test_y_channel and img_gt.ndim == 3 and img_gt.shape[2] == 3:\\n img_gt = bgr2ycbcr(img_gt, y_only=True)\\n img_restored = bgr2ycbcr(img_restored, y_only=True)\\n\\n # calculate PSNR and SSIM\\n psnr = calculate_psnr(img_gt * 255, img_restored * 255, crop_border=crop_border, input_order='HWC')\\n ssim = calculate_ssim(img_gt * 255, img_restored * 255, crop_border=crop_border, input_order='HWC')\\n if show_details:\\n print(f'{basename + suffix + ext:25}. \\\\tPSNR: {psnr:.6f} dB, \\\\tSSIM: {ssim:.6f}')\\n psnr_all.append(psnr)\\n ssim_all.append(ssim)\\n Average_psnr = sum(psnr_all) / len(psnr_all)\\n Average_ssim = sum(ssim_all) / len(ssim_all)\\n print(f'PSNR: {Average_psnr:.6f} dB, SSIM: {Average_ssim:.6f}')\\n return Average_psnr, Average_ssim\"\n },\n {\n \"identifier\": \"calculate_lpips\",\n \"path\": \"metrics/metrics_all.py\",\n \"snippet\": \"def calculate_lpips(gt_path, restored_path, suffix = '', show_details =False):\\n \\\"\\\"\\\"\\n Calculate LPIPS for images.\\n gt_path: Path to gt (Ground-Truth)\\n restored_path: Path to restored images\\n suffix: Suffix for restored images\\n \\\"\\\"\\\"\\n print(\\\"Calculate LPIPS for images\\\")\\n loss_fn_vgg = lpips.LPIPS(net='vgg').cuda() # RGB, normalized to [-1,1]\\n lpips_all = []\\n img_list = sorted(glob.glob(osp.join(gt_path, '*')))\\n img_list_restored = sorted(list(scandir(restored_path, recursive=True, full_path=True)))\\n\\n mean = [0.5, 0.5, 0.5]\\n std = [0.5, 0.5, 0.5]\\n for i, img_path in tqdm(enumerate(img_list)):\\n basename, ext = osp.splitext(osp.basename(img_path))\\n img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\\n\\n if suffix == '':\\n img_path_restored = img_list_restored[i]\\n else:\\n img_path_restored = osp.join(restored_path, basename + suffix + ext)\\n img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255. \\n # img_restored = cv2.imread(img_path_restored, cv2.IMREAD_COLOR).astype(np.float32) / 255. \\n\\n img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)\\n # norm to [-1, 1]\\n normalize(img_gt, mean, std, inplace=True)\\n normalize(img_restored, mean, std, inplace=True)\\n\\n # calculate lpips\\n lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(), img_gt.unsqueeze(0).cuda())\\n lpips_val = lpips_val.cpu().item()\\n if show_details:\\n print(f'{i+1:3d}: {basename:25}. \\\\tLPIPS: {lpips_val:.6f}.')\\n lpips_all.append(lpips_val)\\n Average_lpips = sum(lpips_all) / len(lpips_all)\\n print(f'LPIPS: {Average_lpips:.6f}')\\n return Average_lpips\"\n },\n {\n \"identifier\": \"calculate_NIQE\",\n \"path\": \"metrics/metrics_all.py\",\n \"snippet\": \"def calculate_NIQE(restored_path, crop_border = 0, show_details =False):\\n \\\"\\\"\\\"\\n Calculate NIQE for images.\\n restored_path: Path to restored images\\n crop_border: Crop border for each side\\n \\\"\\\"\\\"\\n print(\\\"Calculate NIQE for images\\\")\\n niqe_all = []\\n img_list = sorted(scandir(restored_path, recursive=True, full_path=True))\\n\\n for i, img_path in tqdm(enumerate(img_list)):\\n basename, _ = os.path.splitext(os.path.basename(img_path))\\n img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore', category=RuntimeWarning)\\n niqe_score = calculate_niqe(img, crop_border, input_order='HWC', convert_to='y')\\n if show_details:\\n print(f'{i+1:3d}: {basename:25}. \\\\tNIQE: {niqe_score:.6f}')\\n niqe_all.append(niqe_score)\\n Average_niqe = sum(niqe_all) / len(niqe_all)\\n print(f'NIQE: {Average_niqe:.6f}')\\n return Average_niqe \"\n },\n {\n \"identifier\": \"calculate_fid_folder\",\n \"path\": \"metrics/metrics_all.py\",\n \"snippet\": \"def calculate_fid_folder(restored_path):\\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n fid_stats = ''\\n batch_size = 64\\n num_sample = 50000\\n num_workers = 4\\n backend = 'disk'\\n\\n # inception model\\n inception = load_patched_inception_v3(device)\\n\\n # create dataset\\n opt = {}\\n opt['name'] = 'SingleImageDataset'\\n opt['type'] = 'SingleImageDataset'\\n opt['dataroot_lq'] = restored_path\\n opt['io_backend'] = dict(type=backend)\\n opt['mean'] = [0.5, 0.5, 0.5]\\n opt['std'] = [0.5, 0.5, 0.5]\\n dataset = build_dataset(opt)\\n\\n # create dataloader\\n data_loader = DataLoader(\\n dataset=dataset,\\n batch_size=batch_size,\\n shuffle=False,\\n num_workers=num_workers,\\n sampler=None,\\n drop_last=False)\\n num_sample = min(num_sample, len(dataset))\\n total_batch = math.ceil(num_sample / batch_size)\\n\\n def data_generator(data_loader, total_batch):\\n for idx, data in enumerate(data_loader):\\n if idx >= total_batch:\\n break\\n else:\\n yield data['lq']\\n\\n features = extract_inception_features(data_generator(data_loader, total_batch), inception, total_batch, device)\\n features = features.numpy()\\n total_len = features.shape[0]\\n features = features[:num_sample]\\n print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.')\\n\\n sample_mean = np.mean(features, 0)\\n sample_cov = np.cov(features, rowvar=False)\\n\\n # load the dataset stats\\n stats = torch.load(fid_stats)\\n real_mean = stats['mean']\\n real_cov = stats['cov']\\n\\n # calculate FID metric\\n fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov)\\n print('fid:', fid)\\n return fid\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import torch\nimport os\nimport numpy as np\nimport math\nimport shutil\n import safetensors.torch\nfrom ldm.modules.diffusionmodules.util import timestep_embedding\nfrom einops import rearrange, repeat\nfrom torchvision.utils import make_grid\nfrom ldm.modules.diffusionmodules.openaimodel import UNetModel\nfrom ldm.models.diffusion.ddpm import LatentDiffusion\nfrom ldm.util import log_txt_as_img, instantiate_from_config\nfrom ldm.models.diffusion.ddim import DDIMSampler\nfrom data.dataset_instantiate import instantiate_from_config as instantiate_dataset_from_config \nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\nfrom metrics.metrics_all import calculate_psnr_ssim, calculate_lpips, calculate_NIQE, calculate_fid_folder\nfrom torch.utils.data import DataLoader\nfrom PIL import Image\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom omegaconf import OmegaConf"},"token_num":{"kind":"number","value":20413,"string":"20,413"},"cropped_code":{"kind":"string","value":"\n\ndef get_state_dict(d):\n return d.get('state_dict', d)\n\ndef load_state_dict(ckpt_path, location='cpu'):\n _, extension = os.path.splitext(ckpt_path)\n if extension.lower() == \".safetensors\":\n state_dict = safetensors.torch.load_file(ckpt_path, device=location)\n else:\n state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))\n state_dict = get_state_dict(state_dict)\n print(f'Loaded state_dict from [{ckpt_path}]')\n return state_dict\n\ndef create_model(config_path):\n config = OmegaConf.load(config_path)\n model = instantiate_from_config(config.model).cpu()\n print(f'Loaded model config from [{config_path}]')\n return model\n\nclass ControlledUnetModel(UNetModel):\n def forward(self, x, timesteps=None, context=None, control=None, **kwargs):\n hs = []\n"},"all_code":{"kind":"string","value":"\n\ndef get_state_dict(d):\n return d.get('state_dict', d)\n\ndef load_state_dict(ckpt_path, location='cpu'):\n _, extension = os.path.splitext(ckpt_path)\n if extension.lower() == \".safetensors\":\n state_dict = safetensors.torch.load_file(ckpt_path, device=location)\n else:\n state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))\n state_dict = get_state_dict(state_dict)\n print(f'Loaded state_dict from [{ckpt_path}]')\n return state_dict\n\ndef create_model(config_path):\n config = OmegaConf.load(config_path)\n model = instantiate_from_config(config.model).cpu()\n print(f'Loaded model config from [{config_path}]')\n return model\n\nclass ControlledUnetModel(UNetModel):\n def forward(self, x, timesteps=None, context=None, control=None, **kwargs):\n hs = []"},"next_line":{"kind":"string","value":" t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-11-30 13:50:58+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5383,"cells":{"repo_name":{"kind":"string","value":"IanYeung/MGLD-VSR"},"file_path":{"kind":"string","value":"basicsr/data/realbasicvsr_dataset.py"},"context":{"kind":"list like","value":[{"identifier":"Clip","path":"basicsr/data/mmcv_transforms/aug_pix.py","snippet":"class Clip(BaseTransform):\n \"\"\"Clip the pixels.\n\n Modified keys are the attributes specified in \"keys\".\n\n Args:\n keys (list[str]): The keys whose values are clipped.\n a_min (int): Lower limits of pixel value.\n a_max (int): Upper limits of pixel value.\n \"\"\"\n\n def __init__(self, keys, a_min=0, a_max=255):\n\n self.keys = keys\n self.a_min = a_min\n self.a_max = a_max\n\n def _clip(self, input_):\n \"\"\"Clip the pixels.\n\n Args:\n input_ (Union[List, np.ndarray]): Pixels to clip.\n\n Returns:\n Union[List, np.ndarray]: Clipped pixels.\n \"\"\"\n is_single_image = False\n if isinstance(input_, np.ndarray):\n is_single_image = True\n input_ = [input_]\n\n # clip\n input_ = [np.clip(v, self.a_min, self.a_max) for v in input_]\n\n if is_single_image:\n input_ = input_[0]\n\n return input_\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict with the values of the specified keys are rounded\n and clipped.\n \"\"\"\n\n for key in self.keys:\n results[key] = self._clip(results[key])\n\n return results\n\n def __repr__(self):\n\n result = self.__class__.__name__\n result += f'(a_min={self.a_min}, a_max={self.a_max})'\n\n return result"},{"identifier":"UnsharpMasking","path":"basicsr/data/mmcv_transforms/aug_pix.py","snippet":"class UnsharpMasking(BaseTransform):\n \"\"\"Apply unsharp masking to an image or a sequence of images.\n\n Args:\n kernel_size (int): The kernel_size of the Gaussian kernel.\n sigma (float): The standard deviation of the Gaussian.\n weight (float): The weight of the \"details\" in the final output.\n threshold (float): Pixel differences larger than this value are\n regarded as \"details\".\n keys (list[str]): The keys whose values are processed.\n\n Added keys are \"xxx_unsharp\", where \"xxx\" are the attributes specified\n in \"keys\".\n \"\"\"\n\n def __init__(self, kernel_size, sigma, weight, threshold, keys):\n\n if kernel_size % 2 == 0:\n raise ValueError('kernel_size must be an odd number, but '\n f'got {kernel_size}.')\n\n self.kernel_size = kernel_size\n self.sigma = sigma\n self.weight = weight\n self.threshold = threshold\n self.keys = keys\n\n kernel = cv2.getGaussianKernel(kernel_size, sigma)\n self.kernel = np.matmul(kernel, kernel.transpose())\n\n def _unsharp_masking(self, imgs):\n \"\"\"Unsharp masking function.\"\"\"\n\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n outputs = []\n for img in imgs:\n img = img.astype(np.float32)\n residue = img - cv2.filter2D(img, -1, self.kernel)\n mask = np.float32(np.abs(residue) > self.threshold)\n soft_mask = cv2.filter2D(mask, -1, self.kernel)\n sharpened = np.clip(img + self.weight * residue, 0, 255)\n\n outputs.append(soft_mask * sharpened + (1 - soft_mask) * img)\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n # results[f'{key}_unsharp'] = self._unsharp_masking(results[key])\n results[key] = self._unsharp_masking(results[key])\n\n return results\n\n def __repr__(self):\n\n repr_str = self.__class__.__name__\n repr_str += (f'(keys={self.keys}, kernel_size={self.kernel_size}, '\n f'sigma={self.sigma}, weight={self.weight}, '\n f'threshold={self.threshold})')\n\n return repr_str"},{"identifier":"RescaleToZeroOne","path":"basicsr/data/mmcv_transforms/normalization.py","snippet":"class RescaleToZeroOne(BaseTransform):\n \"\"\"Transform the images into a range between 0 and 1.\n\n Required keys are the keys in attribute \"keys\", added or modified keys are\n the keys in attribute \"keys\".\n It also supports rescaling a list of images.\n\n Args:\n keys (Sequence[str]): The images to be transformed.\n \"\"\"\n\n def __init__(self, keys):\n self.keys = keys\n\n def transform(self, results):\n \"\"\"transform function.\n\n Args:\n results (dict): A dict containing the necessary information and\n data for augmentation.\n\n Returns:\n dict: A dict containing the processed data and information.\n \"\"\"\n for key in self.keys:\n if isinstance(results[key], list):\n results[key] = [\n v.astype(np.float32) / 255. for v in results[key]\n ]\n else:\n results[key] = results[key].astype(np.float32) / 255.\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + f'(keys={self.keys})'"},{"identifier":"RandomBlur","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class RandomBlur:\n \"\"\"Apply random blur to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n def get_kernel(self, num_kernels: int):\n \"\"\"This is the function to create kernel.\n\n Args:\n num_kernels (int): the number of kernels\n\n Returns:\n _type_: _description_\n \"\"\"\n kernel_type = np.random.choice(\n self.params['kernel_list'], p=self.params['kernel_prob'])\n kernel_size = random.choice(self.params['kernel_size'])\n\n sigma_x_range = self.params.get('sigma_x', [0, 0])\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\n sigma_x_step = self.params.get('sigma_x_step', 0)\n\n sigma_y_range = self.params.get('sigma_y', [0, 0])\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\n sigma_y_step = self.params.get('sigma_y_step', 0)\n\n rotate_angle_range = self.params.get('rotate_angle', [-np.pi, np.pi])\n rotate_angle = np.random.uniform(rotate_angle_range[0],\n rotate_angle_range[1])\n rotate_angle_step = self.params.get('rotate_angle_step', 0)\n\n beta_gau_range = self.params.get('beta_gaussian', [0.5, 4])\n beta_gau = np.random.uniform(beta_gau_range[0], beta_gau_range[1])\n beta_gau_step = self.params.get('beta_gaussian_step', 0)\n\n beta_pla_range = self.params.get('beta_plateau', [1, 2])\n beta_pla = np.random.uniform(beta_pla_range[0], beta_pla_range[1])\n beta_pla_step = self.params.get('beta_plateau_step', 0)\n\n omega_range = self.params.get('omega', None)\n omega_step = self.params.get('omega_step', 0)\n if omega_range is None: # follow Real-ESRGAN settings if not specified\n if kernel_size < 13:\n omega_range = [np.pi / 3., np.pi]\n else:\n omega_range = [np.pi / 5., np.pi]\n omega = np.random.uniform(omega_range[0], omega_range[1])\n\n # determine blurring kernel\n kernels = []\n for _ in range(0, num_kernels):\n kernel = random_mixed_kernels(\n [kernel_type],\n [1],\n kernel_size,\n [sigma_x, sigma_x],\n [sigma_y, sigma_y],\n [rotate_angle, rotate_angle],\n [beta_gau, beta_gau],\n [beta_pla, beta_pla],\n [omega, omega],\n None,\n )\n kernels.append(kernel)\n\n # update kernel parameters\n sigma_x += np.random.uniform(-sigma_x_step, sigma_x_step)\n sigma_y += np.random.uniform(-sigma_y_step, sigma_y_step)\n rotate_angle += np.random.uniform(-rotate_angle_step,\n rotate_angle_step)\n beta_gau += np.random.uniform(-beta_gau_step, beta_gau_step)\n beta_pla += np.random.uniform(-beta_pla_step, beta_pla_step)\n omega += np.random.uniform(-omega_step, omega_step)\n\n sigma_x = np.clip(sigma_x, sigma_x_range[0], sigma_x_range[1])\n sigma_y = np.clip(sigma_y, sigma_y_range[0], sigma_y_range[1])\n rotate_angle = np.clip(rotate_angle, rotate_angle_range[0],\n rotate_angle_range[1])\n beta_gau = np.clip(beta_gau, beta_gau_range[0], beta_gau_range[1])\n beta_pla = np.clip(beta_pla, beta_pla_range[0], beta_pla_range[1])\n omega = np.clip(omega, omega_range[0], omega_range[1])\n\n return kernels\n\n def _apply_random_blur(self, imgs):\n \"\"\"This is the function to apply blur operation on images.\n\n Args:\n imgs (Tensor): images\n\n Returns:\n Tensor: Images applied blur\n \"\"\"\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n # get kernel and blur the input\n kernels = self.get_kernel(num_kernels=len(imgs))\n imgs = [\n cv2.filter2D(img, -1, kernel)\n for img, kernel in zip(imgs, kernels)\n ]\n\n if is_single_image:\n imgs = imgs[0]\n\n return imgs\n\n def __call__(self, results):\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_blur(results[key])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str"},{"identifier":"RandomResize","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class RandomResize:\n \"\"\"Randomly resize the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n self.resize_dict = dict(\n bilinear=cv2.INTER_LINEAR,\n bicubic=cv2.INTER_CUBIC,\n area=cv2.INTER_AREA,\n lanczos=cv2.INTER_LANCZOS4)\n\n def _random_resize(self, imgs):\n \"\"\"This is the function used to randomly resize images for training\n augmentation.\n\n Args:\n imgs (Tensor): training images.\n\n Returns:\n Tensor: images after randomly resized\n \"\"\"\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n h, w = imgs[0].shape[:2]\n\n resize_opt = self.params['resize_opt']\n resize_prob = self.params['resize_prob']\n resize_opt = np.random.choice(resize_opt, p=resize_prob).lower()\n if resize_opt not in self.resize_dict:\n raise NotImplementedError(f'resize_opt [{resize_opt}] is not '\n 'implemented')\n resize_opt = self.resize_dict[resize_opt]\n\n resize_step = self.params.get('resize_step', 0)\n\n # determine the target size, if not provided\n target_size = self.params.get('target_size', None)\n if target_size is None:\n resize_mode = np.random.choice(['up', 'down', 'keep'],\n p=self.params['resize_mode_prob'])\n resize_scale = self.params['resize_scale']\n if resize_mode == 'up':\n scale_factor = np.random.uniform(1, resize_scale[1])\n elif resize_mode == 'down':\n scale_factor = np.random.uniform(resize_scale[0], 1)\n else:\n scale_factor = 1\n\n # determine output size\n h_out, w_out = h * scale_factor, w * scale_factor\n if self.params.get('is_size_even', False):\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\n target_size = (int(h_out), int(w_out))\n else:\n resize_step = 0\n\n # resize the input\n if resize_step == 0: # same target_size for all input images\n outputs = [\n cv2.resize(img, target_size[::-1], interpolation=resize_opt)\n for img in imgs\n ]\n else: # different target_size for each input image\n outputs = []\n for img in imgs:\n img = cv2.resize(\n img, target_size[::-1], interpolation=resize_opt)\n outputs.append(img)\n\n # update scale\n scale_factor += np.random.uniform(-resize_step, resize_step)\n scale_factor = np.clip(scale_factor, resize_scale[0],\n resize_scale[1])\n\n # determine output size\n h_out, w_out = h * scale_factor, w * scale_factor\n if self.params.get('is_size_even', False):\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\n target_size = (int(h_out), int(w_out))\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def __call__(self, results):\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._random_resize(results[key])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str"},{"identifier":"RandomNoise","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class RandomNoise:\n \"\"\"Apply random noise to the input.\n\n Currently support Gaussian noise and Poisson noise.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n self.keys = keys\n self.params = params\n\n def _apply_gaussian_noise(self, imgs):\n \"\"\"This is the function used to apply gaussian noise on images.\n\n Args:\n imgs (Tensor): images\n\n Returns:\n Tensor: images applied gaussian noise\n \"\"\"\n sigma_range = self.params['gaussian_sigma']\n sigma = np.random.uniform(sigma_range[0], sigma_range[1])\n\n sigma_step = self.params.get('gaussian_sigma_step', 0)\n\n gray_noise_prob = self.params['gaussian_gray_noise_prob']\n is_gray_noise = np.random.uniform() < gray_noise_prob\n\n outputs = []\n for img in imgs:\n noise = np.float32(np.random.randn(*(img.shape))) * sigma\n if is_gray_noise:\n noise = noise[:, :, :1]\n outputs.append(img + noise)\n\n # update noise level\n sigma += np.random.uniform(-sigma_step, sigma_step)\n sigma = np.clip(sigma, sigma_range[0], sigma_range[1])\n\n return outputs\n\n def _apply_poisson_noise(self, imgs):\n scale_range = self.params['poisson_scale']\n scale = np.random.uniform(scale_range[0], scale_range[1])\n\n scale_step = self.params.get('poisson_scale_step', 0)\n\n gray_noise_prob = self.params['poisson_gray_noise_prob']\n is_gray_noise = np.random.uniform() < gray_noise_prob\n\n outputs = []\n for img in imgs:\n noise = np.float32(img.copy())\n if is_gray_noise:\n noise = cv2.cvtColor(noise[..., [2, 1, 0]], cv2.COLOR_BGR2GRAY)\n noise = noise[..., np.newaxis]\n noise = np.clip((noise).round(), 0, 255)\n unique_val = 2**np.ceil(np.log2(len(np.unique(noise))))\n noise = np.random.poisson(noise * unique_val).astype(np.float32) \\\n / unique_val - noise\n\n outputs.append(img + noise * scale)\n\n # update noise level\n scale += np.random.uniform(-scale_step, scale_step)\n scale = np.clip(scale, scale_range[0], scale_range[1])\n\n return outputs\n\n def _apply_random_noise(self, imgs):\n \"\"\"This is the function used to apply random noise on images.\n\n Args:\n imgs (Tensor): training images\n\n Returns:\n _type_: _description_\n \"\"\"\n noise_type = np.random.choice(\n self.params['noise_type'], p=self.params['noise_prob'])\n\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n if noise_type.lower() == 'gaussian':\n imgs = self._apply_gaussian_noise(imgs)\n elif noise_type.lower() == 'poisson':\n imgs = self._apply_poisson_noise(imgs)\n else:\n raise NotImplementedError(f'\"noise_type\" [{noise_type}] is '\n 'not implemented.')\n\n if is_single_image:\n imgs = imgs[0]\n\n return imgs\n\n def __call__(self, results):\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_noise(results[key])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str"},{"identifier":"RandomJPEGCompression","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class RandomJPEGCompression:\n \"\"\"Apply random JPEG compression to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n bgr2rgb (str): Whether change channel order. Default: False.\n \"\"\"\n\n def __init__(self, params, keys, color_type='color', bgr2rgb=False):\n self.keys = keys\n self.params = params\n self.color_type = color_type\n self.bgr2rgb = bgr2rgb\n\n def _apply_random_compression(self, imgs):\n is_single_image = False\n if isinstance(imgs, np.ndarray):\n is_single_image = True\n imgs = [imgs]\n\n # determine initial compression level and the step size\n quality = self.params['quality']\n quality_step = self.params.get('quality_step', 0)\n jpeg_param = round(np.random.uniform(quality[0], quality[1]))\n\n # apply jpeg compression\n outputs = []\n for img in imgs:\n encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_param]\n if self.bgr2rgb and self.color_type == 'color':\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n _, img_encoded = cv2.imencode('.jpg', img, encode_param)\n\n if self.color_type == 'color':\n img_encoded = cv2.imdecode(img_encoded, 1)\n if self.bgr2rgb:\n img_encoded = cv2.cvtColor(img_encoded, cv2.COLOR_BGR2RGB)\n outputs.append(img_encoded)\n else:\n outputs.append(cv2.imdecode(img_encoded, 0))\n\n # update compression level\n jpeg_param += np.random.uniform(-quality_step, quality_step)\n jpeg_param = round(np.clip(jpeg_param, quality[0], quality[1]))\n\n if is_single_image:\n outputs = outputs[0]\n\n return outputs\n\n def __call__(self, results):\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_compression(results[key])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str"},{"identifier":"RandomVideoCompression","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class RandomVideoCompression:\n \"\"\"Apply random video compression to the input.\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n params (dict): A dictionary specifying the degradation settings.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n \"\"\"\n\n def __init__(self, params, keys):\n assert has_av, 'Please install av to use video compression.'\n\n self.keys = keys\n self.params = params\n logging.getLogger('libav').setLevel(50)\n\n def _apply_random_compression(self, imgs):\n \"\"\"This is the function to apply random compression on images.\n\n Args:\n imgs (Tensor): training images\n\n Returns:\n Tensor: images after randomly compressed\n \"\"\"\n codec = random.choices(self.params['codec'],\n self.params['codec_prob'])[0]\n bitrate = self.params['bitrate']\n bitrate = np.random.randint(bitrate[0], bitrate[1] + 1)\n\n buf = io.BytesIO()\n with av.open(buf, 'w', 'mp4') as container:\n stream = container.add_stream(codec, rate=1)\n stream.height = imgs[0].shape[0]\n stream.width = imgs[0].shape[1]\n stream.pix_fmt = 'yuv420p'\n stream.bit_rate = bitrate\n\n for img in imgs:\n img = img.astype(np.uint8)\n frame = av.VideoFrame.from_ndarray(img, format='rgb24')\n frame.pict_type = 'NONE'\n for packet in stream.encode(frame):\n container.mux(packet)\n\n # Flush stream\n for packet in stream.encode():\n container.mux(packet)\n\n outputs = []\n with av.open(buf, 'r', 'mp4') as container:\n if container.streams.video:\n for frame in container.decode(**{'video': 0}):\n outputs.append(frame.to_rgb().to_ndarray().astype(\n np.float32))\n\n return outputs\n\n def __call__(self, results):\n if np.random.uniform() > self.params.get('prob', 1):\n return results\n\n for key in self.keys:\n results[key] = self._apply_random_compression(results[key])\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(params={self.params}, keys={self.keys})')\n return repr_str"},{"identifier":"DegradationsWithShuffle","path":"basicsr/data/mmcv_transforms/random_degradations.py","snippet":"class DegradationsWithShuffle:\n \"\"\"Apply random degradations to input, with degradations being shuffled.\n\n Degradation groups are supported. The order of degradations within the same\n group is preserved. For example, if we have degradations = [a, b, [c, d]]\n and shuffle_idx = None, then the possible orders are\n\n ::\n\n [a, b, [c, d]]\n [a, [c, d], b]\n [b, a, [c, d]]\n [b, [c, d], a]\n [[c, d], a, b]\n [[c, d], b, a]\n\n Modified keys are the attributed specified in \"keys\".\n\n Args:\n degradations (list[dict]): The list of degradations.\n keys (list[str]): A list specifying the keys whose values are\n modified.\n shuffle_idx (list | None, optional): The degradations corresponding to\n these indices are shuffled. If None, all degradations are shuffled.\n Default: None.\n \"\"\"\n\n def __init__(self, degradations, keys, shuffle_idx=None):\n\n self.keys = keys\n\n self.degradations = self._build_degradations(degradations)\n\n if shuffle_idx is None:\n self.shuffle_idx = list(range(0, len(degradations)))\n else:\n self.shuffle_idx = shuffle_idx\n\n def _build_degradations(self, degradations):\n for i, degradation in enumerate(degradations):\n if isinstance(degradation, (list, tuple)):\n degradations[i] = self._build_degradations(degradation)\n else:\n degradation_ = allowed_degradations[degradation['type']]\n degradations[i] = degradation_(degradation['params'],\n self.keys)\n\n return degradations\n\n def __call__(self, results):\n # shuffle degradations\n if len(self.shuffle_idx) > 0:\n shuffle_list = [self.degradations[i] for i in self.shuffle_idx]\n np.random.shuffle(shuffle_list)\n for i, idx in enumerate(self.shuffle_idx):\n self.degradations[idx] = shuffle_list[i]\n\n # apply degradations to input\n for degradation in self.degradations:\n if isinstance(degradation, (tuple, list)):\n for subdegrdation in degradation:\n results = subdegrdation(results)\n else:\n results = degradation(results)\n\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += (f'(degradations={self.degradations}, '\n f'keys={self.keys}, '\n f'shuffle_idx={self.shuffle_idx})')\n return repr_str"},{"identifier":"circular_lowpass_kernel","path":"basicsr/data/degradations.py","snippet":"def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0):\n \"\"\"2D sinc filter\n\n Reference: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter\n\n Args:\n cutoff (float): cutoff frequency in radians (pi is max)\n kernel_size (int): horizontal and vertical size, must be odd.\n pad_to (int): pad kernel size to desired size, must be odd or zero.\n \"\"\"\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\n kernel = np.fromfunction(\n lambda x, y: cutoff * special.j1(cutoff * np.sqrt(\n (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt(\n (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size])\n kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi)\n kernel = kernel / np.sum(kernel)\n if pad_to > kernel_size:\n pad_size = (pad_to - kernel_size) // 2\n kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))\n return kernel"},{"identifier":"random_mixed_kernels","path":"basicsr/data/degradations.py","snippet":"def random_mixed_kernels(kernel_list,\n kernel_prob,\n kernel_size=21,\n sigma_x_range=(0.6, 5),\n sigma_y_range=(0.6, 5),\n rotation_range=(-math.pi, math.pi),\n betag_range=(0.5, 8),\n betap_range=(0.5, 8),\n noise_range=None,\n return_sigma=False):\n \"\"\"Randomly generate mixed kernels.\n\n Args:\n kernel_list (tuple): a list name of kernel types,\n support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso',\n 'plateau_aniso']\n kernel_prob (tuple): corresponding kernel probability for each\n kernel type\n kernel_size (int):\n sigma_x_range (tuple): [0.6, 5]\n sigma_y_range (tuple): [0.6, 5]\n rotation range (tuple): [-math.pi, math.pi]\n beta_range (tuple): [0.5, 8]\n noise_range(tuple, optional): multiplicative kernel noise,\n [0.75, 1.25]. Default: None\n\n Returns:\n kernel (ndarray):\n \"\"\"\n kernel_type = random.choices(kernel_list, kernel_prob)[0]\n if not return_sigma:\n if kernel_type == 'iso':\n kernel = random_bivariate_Gaussian(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True, return_sigma=return_sigma)\n elif kernel_type == 'aniso':\n kernel = random_bivariate_Gaussian(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False, return_sigma=return_sigma)\n elif kernel_type == 'generalized_iso':\n kernel = random_bivariate_generalized_Gaussian(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n betag_range,\n noise_range=noise_range,\n isotropic=True,\n return_sigma=return_sigma)\n elif kernel_type == 'generalized_aniso':\n kernel = random_bivariate_generalized_Gaussian(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n betag_range,\n noise_range=noise_range,\n isotropic=False,\n return_sigma=return_sigma)\n elif kernel_type == 'plateau_iso':\n kernel = random_bivariate_plateau(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True, return_sigma=return_sigma)\n elif kernel_type == 'plateau_aniso':\n kernel = random_bivariate_plateau(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False, return_sigma=return_sigma)\n return kernel\n else:\n if kernel_type == 'iso':\n kernel, sigma_list = random_bivariate_Gaussian(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True, return_sigma=return_sigma)\n elif kernel_type == 'aniso':\n kernel, sigma_list = random_bivariate_Gaussian(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False, return_sigma=return_sigma)\n elif kernel_type == 'generalized_iso':\n kernel, sigma_list = random_bivariate_generalized_Gaussian(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n betag_range,\n noise_range=noise_range,\n isotropic=True,\n return_sigma=return_sigma)\n elif kernel_type == 'generalized_aniso':\n kernel, sigma_list = random_bivariate_generalized_Gaussian(\n kernel_size,\n sigma_x_range,\n sigma_y_range,\n rotation_range,\n betag_range,\n noise_range=noise_range,\n isotropic=False,\n return_sigma=return_sigma)\n elif kernel_type == 'plateau_iso':\n kernel, sigma_list = random_bivariate_plateau(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True, return_sigma=return_sigma)\n elif kernel_type == 'plateau_aniso':\n kernel, sigma_list = random_bivariate_plateau(\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False, return_sigma=return_sigma)\n return kernel, sigma_list"},{"identifier":"augment","path":"basicsr/data/transforms.py","snippet":"def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):\n \"\"\"Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).\n\n We use vertical flip and transpose for rotation implementation.\n All the images in the list use the same augmentation.\n\n Args:\n imgs (list[ndarray] | ndarray): Images to be augmented. If the input\n is an ndarray, it will be transformed to a list.\n hflip (bool): Horizontal flip. Default: True.\n rotation (bool): Ratotation. Default: True.\n flows (list[ndarray]: Flows to be augmented. If the input is an\n ndarray, it will be transformed to a list.\n Dimension is (h, w, 2). Default: None.\n return_status (bool): Return the status of flip and rotation.\n Default: False.\n\n Returns:\n list[ndarray] | ndarray: Augmented images and flows. If returned\n results only have one element, just return ndarray.\n\n \"\"\"\n hflip = hflip and random.random() < 0.5\n vflip = rotation and random.random() < 0.5\n rot90 = rotation and random.random() < 0.5\n\n def _augment(img):\n if hflip: # horizontal\n cv2.flip(img, 1, img)\n if vflip: # vertical\n cv2.flip(img, 0, img)\n if rot90:\n img = img.transpose(1, 0, 2)\n return img\n\n def _augment_flow(flow):\n if hflip: # horizontal\n cv2.flip(flow, 1, flow)\n flow[:, :, 0] *= -1\n if vflip: # vertical\n cv2.flip(flow, 0, flow)\n flow[:, :, 1] *= -1\n if rot90:\n flow = flow.transpose(1, 0, 2)\n flow = flow[:, :, [1, 0]]\n return flow\n\n if not isinstance(imgs, list):\n imgs = [imgs]\n imgs = [_augment(img) for img in imgs]\n if len(imgs) == 1:\n imgs = imgs[0]\n\n if flows is not None:\n if not isinstance(flows, list):\n flows = [flows]\n flows = [_augment_flow(flow) for flow in flows]\n if len(flows) == 1:\n flows = flows[0]\n return imgs, flows\n else:\n if return_status:\n return imgs, (hflip, vflip, rot90)\n else:\n return imgs"},{"identifier":"single_random_crop","path":"basicsr/data/transforms.py","snippet":"def single_random_crop(img_gts, gt_patch_size, gt_path=None):\n \"\"\"Random crop. Support Numpy array and Tensor inputs.\n\n It crops lists of lq and gt images with corresponding locations.\n\n Args:\n img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images\n should have the same shape. If the input is an ndarray, it will\n be transformed to a list containing itself.\n gt_patch_size (int): GT patch size.\n gt_path (str): Path to ground-truth. Default: None.\n\n Returns:\n list[ndarray] | ndarray: GT images and LQ images. If returned results\n only have one element, just return ndarray.\n \"\"\"\n\n if not isinstance(img_gts, list):\n img_gts = [img_gts]\n\n # determine input type: Numpy array or Tensor\n input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'\n\n if input_type == 'Tensor':\n h_gt, w_gt = img_gts[0].size()[-2:]\n else:\n h_gt, w_gt = img_gts[0].shape[0:2]\n\n # if h_gt != h_lq * scale or w_gt != w_lq * scale:\n # raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',\n # f'multiplication of LQ ({h_lq}, {w_lq}).')\n if h_gt < gt_patch_size or w_gt < gt_patch_size:\n raise ValueError(f'GT ({h_gt}, {w_gt}) is smaller than '\n f'patch size ({gt_patch_size}, {gt_patch_size}).')\n\n # randomly choose top and left coordinates for lq patch\n top = random.randint(0, h_gt - gt_patch_size)\n left = random.randint(0, w_gt - gt_patch_size)\n\n # crop corresponding gt patch\n if input_type == 'Tensor':\n img_gts = [v[:, :, top:top + gt_patch_size, left:left + gt_patch_size] for v in img_gts]\n else:\n img_gts = [v[top:top + gt_patch_size, left:left + gt_patch_size, ...] for v in img_gts]\n if len(img_gts) == 1:\n img_gts = img_gts[0]\n\n return img_gts"},{"identifier":"paired_random_crop","path":"basicsr/data/transforms.py","snippet":"def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None):\n \"\"\"Paired random crop. Support Numpy array and Tensor inputs.\n\n It crops lists of lq and gt images with corresponding locations.\n\n Args:\n img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images\n should have the same shape. If the input is an ndarray, it will\n be transformed to a list containing itself.\n img_lqs (list[ndarray] | ndarray): LQ images. Note that all images\n should have the same shape. If the input is an ndarray, it will\n be transformed to a list containing itself.\n gt_patch_size (int): GT patch size.\n scale (int): Scale factor.\n gt_path (str): Path to ground-truth. Default: None.\n\n Returns:\n list[ndarray] | ndarray: GT images and LQ images. If returned results\n only have one element, just return ndarray.\n \"\"\"\n\n if not isinstance(img_gts, list):\n img_gts = [img_gts]\n if not isinstance(img_lqs, list):\n img_lqs = [img_lqs]\n\n # determine input type: Numpy array or Tensor\n input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'\n\n if input_type == 'Tensor':\n h_lq, w_lq = img_lqs[0].size()[-2:]\n h_gt, w_gt = img_gts[0].size()[-2:]\n else:\n h_lq, w_lq = img_lqs[0].shape[0:2]\n h_gt, w_gt = img_gts[0].shape[0:2]\n lq_patch_size = gt_patch_size // scale\n\n if h_gt != h_lq * scale or w_gt != w_lq * scale:\n raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',\n f'multiplication of LQ ({h_lq}, {w_lq}).')\n if h_lq < lq_patch_size or w_lq < lq_patch_size:\n raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '\n f'({lq_patch_size}, {lq_patch_size}). '\n f'Please remove {gt_path}.')\n\n # randomly choose top and left coordinates for lq patch\n top = random.randint(0, h_lq - lq_patch_size)\n left = random.randint(0, w_lq - lq_patch_size)\n\n # crop lq patch\n if input_type == 'Tensor':\n img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs]\n else:\n img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]\n\n # crop corresponding gt patch\n top_gt, left_gt = int(top * scale), int(left * scale)\n if input_type == 'Tensor':\n img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts]\n else:\n img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]\n if len(img_gts) == 1:\n img_gts = img_gts[0]\n if len(img_lqs) == 1:\n img_lqs = img_lqs[0]\n return img_gts, img_lqs"},{"identifier":"FileClient","path":"basicsr/utils/file_client.py","snippet":"class FileClient(object):\n \"\"\"A general file client to access files in different backend.\n\n The client loads a file or text in a specified backend from its path\n and return it as a binary file. it can also register other backend\n accessor with a given name and backend class.\n\n Attributes:\n backend (str): The storage backend type. Options are \"disk\",\n \"memcached\" and \"lmdb\".\n client (:obj:`BaseStorageBackend`): The backend object.\n \"\"\"\n\n _backends = {\n 'disk': HardDiskBackend,\n 'memcached': MemcachedBackend,\n 'lmdb': LmdbBackend,\n }\n\n def __init__(self, backend='disk', **kwargs):\n if backend not in self._backends:\n raise ValueError(f'Backend {backend} is not supported. Currently supported ones'\n f' are {list(self._backends.keys())}')\n self.backend = backend\n self.client = self._backends[backend](**kwargs)\n\n def get(self, filepath, client_key='default'):\n # client_key is used only for lmdb, where different fileclients have\n # different lmdb environments.\n if self.backend == 'lmdb':\n return self.client.get(filepath, client_key)\n else:\n return self.client.get(filepath)\n\n def get_text(self, filepath):\n return self.client.get_text(filepath)"},{"identifier":"imfrombytes","path":"basicsr/utils/img_util.py","snippet":"def imfrombytes(content, flag='color', float32=False):\n \"\"\"Read an image from bytes.\n\n Args:\n content (bytes): Image bytes got from files or other streams.\n flag (str): Flags specifying the color type of a loaded image,\n candidates are `color`, `grayscale` and `unchanged`.\n float32 (bool): Whether to change to float32., If True, will also norm\n to [0, 1]. Default: False.\n\n Returns:\n ndarray: Loaded image array.\n \"\"\"\n img_np = np.frombuffer(content, np.uint8)\n imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}\n img = cv2.imdecode(img_np, imread_flags[flag])\n if float32:\n img = img.astype(np.float32) / 255.\n return img"},{"identifier":"img2tensor","path":"basicsr/utils/img_util.py","snippet":"def img2tensor(imgs, bgr2rgb=True, float32=True):\n \"\"\"Numpy array to tensor.\n\n Args:\n imgs (list[ndarray] | ndarray): Input images.\n bgr2rgb (bool): Whether to change bgr to rgb.\n float32 (bool): Whether to change to float32.\n\n Returns:\n list[tensor] | tensor: Tensor images. If returned results only have\n one element, just return tensor.\n \"\"\"\n\n def _totensor(img, bgr2rgb, float32):\n if img.shape[2] == 3 and bgr2rgb:\n if img.dtype == 'float64':\n img = img.astype('float32')\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = torch.from_numpy(img.transpose(2, 0, 1))\n if float32:\n img = img.float()\n return img\n\n if isinstance(imgs, list):\n return [_totensor(img, bgr2rgb, float32) for img in imgs]\n else:\n return _totensor(imgs, bgr2rgb, float32)"},{"identifier":"imwrite","path":"basicsr/utils/img_util.py","snippet":"def imwrite(img, file_path, params=None, auto_mkdir=True):\n \"\"\"Write image to file.\n\n Args:\n img (ndarray): Image array to be written.\n file_path (str): Image file path.\n params (None or list): Same as opencv's :func:`imwrite` interface.\n auto_mkdir (bool): If the parent folder of `file_path` does not exist,\n whether to create it automatically.\n\n Returns:\n bool: Successful or not.\n \"\"\"\n if auto_mkdir:\n dir_name = os.path.abspath(os.path.dirname(file_path))\n os.makedirs(dir_name, exist_ok=True)\n ok = cv2.imwrite(file_path, img, params)\n if not ok:\n raise IOError('Failed in writing images.')"},{"identifier":"tensor2img","path":"basicsr/utils/img_util.py","snippet":"def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):\n \"\"\"Convert torch Tensors into image numpy arrays.\n\n After clamping to [min, max], values will be normalized to [0, 1].\n\n Args:\n tensor (Tensor or list[Tensor]): Accept shapes:\n 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);\n 2) 3D Tensor of shape (3/1 x H x W);\n 3) 2D Tensor of shape (H x W).\n Tensor channel should be in RGB order.\n rgb2bgr (bool): Whether to change rgb to bgr.\n out_type (numpy type): output types. If ``np.uint8``, transform outputs\n to uint8 type with range [0, 255]; otherwise, float type with\n range [0, 1]. Default: ``np.uint8``.\n min_max (tuple[int]): min and max values for clamp.\n\n Returns:\n (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of\n shape (H x W). The channel order is BGR.\n \"\"\"\n if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):\n raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')\n\n if torch.is_tensor(tensor):\n tensor = [tensor]\n result = []\n for _tensor in tensor:\n _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)\n _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])\n\n n_dim = _tensor.dim()\n if n_dim == 4:\n img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()\n img_np = img_np.transpose(1, 2, 0)\n if rgb2bgr:\n img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)\n elif n_dim == 3:\n img_np = _tensor.numpy()\n img_np = img_np.transpose(1, 2, 0)\n if img_np.shape[2] == 1: # gray image\n img_np = np.squeeze(img_np, axis=2)\n else:\n if rgb2bgr:\n img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)\n elif n_dim == 2:\n img_np = _tensor.numpy()\n else:\n raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')\n if out_type == np.uint8:\n # Unlike MATLAB, numpy.unit8() WILL NOT round by default.\n img_np = (img_np * 255.0).round()\n img_np = img_np.astype(out_type)\n result.append(img_np)\n # if len(result) == 1 and torch.is_tensor(tensor):\n if len(result) == 1:\n result = result[0]\n return result"},{"identifier":"get_root_logger","path":"basicsr/utils/logger.py","snippet":"def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):\n \"\"\"Get the root logger.\n\n The logger will be initialized if it has not been initialized. By default a\n StreamHandler will be added. If `log_file` is specified, a FileHandler will\n also be added.\n\n Args:\n logger_name (str): root logger name. Default: 'basicsr'.\n log_file (str | None): The log filename. If specified, a FileHandler\n will be added to the root logger.\n log_level (int): The root logger level. Note that only the process of\n rank 0 is affected, while other processes will set the level to\n \"Error\" and be silent most of the time.\n\n Returns:\n logging.Logger: The root logger.\n \"\"\"\n logger = logging.getLogger(logger_name)\n # if the logger has been initialized, just return it\n if logger_name in initialized_logger:\n return logger\n\n format_str = '%(asctime)s %(levelname)s: %(message)s'\n stream_handler = logging.StreamHandler()\n stream_handler.setFormatter(logging.Formatter(format_str))\n logger.addHandler(stream_handler)\n logger.propagate = False\n rank, _ = get_dist_info()\n if rank != 0:\n logger.setLevel('ERROR')\n elif log_file is not None:\n logger.setLevel(log_level)\n # add file handler\n file_handler = logging.FileHandler(log_file, 'w')\n file_handler.setFormatter(logging.Formatter(format_str))\n file_handler.setLevel(log_level)\n logger.addHandler(file_handler)\n initialized_logger[logger_name] = True\n return logger"},{"identifier":"dequantize_flow","path":"basicsr/utils/flow_util.py","snippet":"def dequantize_flow(dx, dy, max_val=0.02, denorm=True):\n \"\"\"Recover from quantized flow.\n\n Args:\n dx (ndarray): Quantized dx.\n dy (ndarray): Quantized dy.\n max_val (float): Maximum value used when quantizing.\n denorm (bool): Whether to multiply flow values with width/height.\n\n Returns:\n ndarray: Dequantized flow.\n \"\"\"\n assert dx.shape == dy.shape\n assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)\n\n dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]\n\n if denorm:\n dx *= dx.shape[1]\n dy *= dx.shape[0]\n flow = np.dstack((dx, dy))\n return flow"},{"identifier":"DATASET_REGISTRY","path":"basicsr/utils/registry.py","snippet":"DATASET_REGISTRY = Registry('dataset')"}],"string":"[\n {\n \"identifier\": \"Clip\",\n \"path\": \"basicsr/data/mmcv_transforms/aug_pix.py\",\n \"snippet\": \"class Clip(BaseTransform):\\n \\\"\\\"\\\"Clip the pixels.\\n\\n Modified keys are the attributes specified in \\\"keys\\\".\\n\\n Args:\\n keys (list[str]): The keys whose values are clipped.\\n a_min (int): Lower limits of pixel value.\\n a_max (int): Upper limits of pixel value.\\n \\\"\\\"\\\"\\n\\n def __init__(self, keys, a_min=0, a_max=255):\\n\\n self.keys = keys\\n self.a_min = a_min\\n self.a_max = a_max\\n\\n def _clip(self, input_):\\n \\\"\\\"\\\"Clip the pixels.\\n\\n Args:\\n input_ (Union[List, np.ndarray]): Pixels to clip.\\n\\n Returns:\\n Union[List, np.ndarray]: Clipped pixels.\\n \\\"\\\"\\\"\\n is_single_image = False\\n if isinstance(input_, np.ndarray):\\n is_single_image = True\\n input_ = [input_]\\n\\n # clip\\n input_ = [np.clip(v, self.a_min, self.a_max) for v in input_]\\n\\n if is_single_image:\\n input_ = input_[0]\\n\\n return input_\\n\\n def transform(self, results):\\n \\\"\\\"\\\"transform function.\\n\\n Args:\\n results (dict): A dict containing the necessary information and\\n data for augmentation.\\n\\n Returns:\\n dict: A dict with the values of the specified keys are rounded\\n and clipped.\\n \\\"\\\"\\\"\\n\\n for key in self.keys:\\n results[key] = self._clip(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n\\n result = self.__class__.__name__\\n result += f'(a_min={self.a_min}, a_max={self.a_max})'\\n\\n return result\"\n },\n {\n \"identifier\": \"UnsharpMasking\",\n \"path\": \"basicsr/data/mmcv_transforms/aug_pix.py\",\n \"snippet\": \"class UnsharpMasking(BaseTransform):\\n \\\"\\\"\\\"Apply unsharp masking to an image or a sequence of images.\\n\\n Args:\\n kernel_size (int): The kernel_size of the Gaussian kernel.\\n sigma (float): The standard deviation of the Gaussian.\\n weight (float): The weight of the \\\"details\\\" in the final output.\\n threshold (float): Pixel differences larger than this value are\\n regarded as \\\"details\\\".\\n keys (list[str]): The keys whose values are processed.\\n\\n Added keys are \\\"xxx_unsharp\\\", where \\\"xxx\\\" are the attributes specified\\n in \\\"keys\\\".\\n \\\"\\\"\\\"\\n\\n def __init__(self, kernel_size, sigma, weight, threshold, keys):\\n\\n if kernel_size % 2 == 0:\\n raise ValueError('kernel_size must be an odd number, but '\\n f'got {kernel_size}.')\\n\\n self.kernel_size = kernel_size\\n self.sigma = sigma\\n self.weight = weight\\n self.threshold = threshold\\n self.keys = keys\\n\\n kernel = cv2.getGaussianKernel(kernel_size, sigma)\\n self.kernel = np.matmul(kernel, kernel.transpose())\\n\\n def _unsharp_masking(self, imgs):\\n \\\"\\\"\\\"Unsharp masking function.\\\"\\\"\\\"\\n\\n is_single_image = False\\n if isinstance(imgs, np.ndarray):\\n is_single_image = True\\n imgs = [imgs]\\n\\n outputs = []\\n for img in imgs:\\n img = img.astype(np.float32)\\n residue = img - cv2.filter2D(img, -1, self.kernel)\\n mask = np.float32(np.abs(residue) > self.threshold)\\n soft_mask = cv2.filter2D(mask, -1, self.kernel)\\n sharpened = np.clip(img + self.weight * residue, 0, 255)\\n\\n outputs.append(soft_mask * sharpened + (1 - soft_mask) * img)\\n\\n if is_single_image:\\n outputs = outputs[0]\\n\\n return outputs\\n\\n def transform(self, results):\\n \\\"\\\"\\\"transform function.\\n\\n Args:\\n results (dict): A dict containing the necessary information and\\n data for augmentation.\\n\\n Returns:\\n dict: A dict containing the processed data and information.\\n \\\"\\\"\\\"\\n for key in self.keys:\\n # results[f'{key}_unsharp'] = self._unsharp_masking(results[key])\\n results[key] = self._unsharp_masking(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n\\n repr_str = self.__class__.__name__\\n repr_str += (f'(keys={self.keys}, kernel_size={self.kernel_size}, '\\n f'sigma={self.sigma}, weight={self.weight}, '\\n f'threshold={self.threshold})')\\n\\n return repr_str\"\n },\n {\n \"identifier\": \"RescaleToZeroOne\",\n \"path\": \"basicsr/data/mmcv_transforms/normalization.py\",\n \"snippet\": \"class RescaleToZeroOne(BaseTransform):\\n \\\"\\\"\\\"Transform the images into a range between 0 and 1.\\n\\n Required keys are the keys in attribute \\\"keys\\\", added or modified keys are\\n the keys in attribute \\\"keys\\\".\\n It also supports rescaling a list of images.\\n\\n Args:\\n keys (Sequence[str]): The images to be transformed.\\n \\\"\\\"\\\"\\n\\n def __init__(self, keys):\\n self.keys = keys\\n\\n def transform(self, results):\\n \\\"\\\"\\\"transform function.\\n\\n Args:\\n results (dict): A dict containing the necessary information and\\n data for augmentation.\\n\\n Returns:\\n dict: A dict containing the processed data and information.\\n \\\"\\\"\\\"\\n for key in self.keys:\\n if isinstance(results[key], list):\\n results[key] = [\\n v.astype(np.float32) / 255. for v in results[key]\\n ]\\n else:\\n results[key] = results[key].astype(np.float32) / 255.\\n return results\\n\\n def __repr__(self):\\n return self.__class__.__name__ + f'(keys={self.keys})'\"\n },\n {\n \"identifier\": \"RandomBlur\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class RandomBlur:\\n \\\"\\\"\\\"Apply random blur to the input.\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n params (dict): A dictionary specifying the degradation settings.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n \\\"\\\"\\\"\\n\\n def __init__(self, params, keys):\\n self.keys = keys\\n self.params = params\\n\\n def get_kernel(self, num_kernels: int):\\n \\\"\\\"\\\"This is the function to create kernel.\\n\\n Args:\\n num_kernels (int): the number of kernels\\n\\n Returns:\\n _type_: _description_\\n \\\"\\\"\\\"\\n kernel_type = np.random.choice(\\n self.params['kernel_list'], p=self.params['kernel_prob'])\\n kernel_size = random.choice(self.params['kernel_size'])\\n\\n sigma_x_range = self.params.get('sigma_x', [0, 0])\\n sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])\\n sigma_x_step = self.params.get('sigma_x_step', 0)\\n\\n sigma_y_range = self.params.get('sigma_y', [0, 0])\\n sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])\\n sigma_y_step = self.params.get('sigma_y_step', 0)\\n\\n rotate_angle_range = self.params.get('rotate_angle', [-np.pi, np.pi])\\n rotate_angle = np.random.uniform(rotate_angle_range[0],\\n rotate_angle_range[1])\\n rotate_angle_step = self.params.get('rotate_angle_step', 0)\\n\\n beta_gau_range = self.params.get('beta_gaussian', [0.5, 4])\\n beta_gau = np.random.uniform(beta_gau_range[0], beta_gau_range[1])\\n beta_gau_step = self.params.get('beta_gaussian_step', 0)\\n\\n beta_pla_range = self.params.get('beta_plateau', [1, 2])\\n beta_pla = np.random.uniform(beta_pla_range[0], beta_pla_range[1])\\n beta_pla_step = self.params.get('beta_plateau_step', 0)\\n\\n omega_range = self.params.get('omega', None)\\n omega_step = self.params.get('omega_step', 0)\\n if omega_range is None: # follow Real-ESRGAN settings if not specified\\n if kernel_size < 13:\\n omega_range = [np.pi / 3., np.pi]\\n else:\\n omega_range = [np.pi / 5., np.pi]\\n omega = np.random.uniform(omega_range[0], omega_range[1])\\n\\n # determine blurring kernel\\n kernels = []\\n for _ in range(0, num_kernels):\\n kernel = random_mixed_kernels(\\n [kernel_type],\\n [1],\\n kernel_size,\\n [sigma_x, sigma_x],\\n [sigma_y, sigma_y],\\n [rotate_angle, rotate_angle],\\n [beta_gau, beta_gau],\\n [beta_pla, beta_pla],\\n [omega, omega],\\n None,\\n )\\n kernels.append(kernel)\\n\\n # update kernel parameters\\n sigma_x += np.random.uniform(-sigma_x_step, sigma_x_step)\\n sigma_y += np.random.uniform(-sigma_y_step, sigma_y_step)\\n rotate_angle += np.random.uniform(-rotate_angle_step,\\n rotate_angle_step)\\n beta_gau += np.random.uniform(-beta_gau_step, beta_gau_step)\\n beta_pla += np.random.uniform(-beta_pla_step, beta_pla_step)\\n omega += np.random.uniform(-omega_step, omega_step)\\n\\n sigma_x = np.clip(sigma_x, sigma_x_range[0], sigma_x_range[1])\\n sigma_y = np.clip(sigma_y, sigma_y_range[0], sigma_y_range[1])\\n rotate_angle = np.clip(rotate_angle, rotate_angle_range[0],\\n rotate_angle_range[1])\\n beta_gau = np.clip(beta_gau, beta_gau_range[0], beta_gau_range[1])\\n beta_pla = np.clip(beta_pla, beta_pla_range[0], beta_pla_range[1])\\n omega = np.clip(omega, omega_range[0], omega_range[1])\\n\\n return kernels\\n\\n def _apply_random_blur(self, imgs):\\n \\\"\\\"\\\"This is the function to apply blur operation on images.\\n\\n Args:\\n imgs (Tensor): images\\n\\n Returns:\\n Tensor: Images applied blur\\n \\\"\\\"\\\"\\n is_single_image = False\\n if isinstance(imgs, np.ndarray):\\n is_single_image = True\\n imgs = [imgs]\\n\\n # get kernel and blur the input\\n kernels = self.get_kernel(num_kernels=len(imgs))\\n imgs = [\\n cv2.filter2D(img, -1, kernel)\\n for img, kernel in zip(imgs, kernels)\\n ]\\n\\n if is_single_image:\\n imgs = imgs[0]\\n\\n return imgs\\n\\n def __call__(self, results):\\n if np.random.uniform() > self.params.get('prob', 1):\\n return results\\n\\n for key in self.keys:\\n results[key] = self._apply_random_blur(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(params={self.params}, keys={self.keys})')\\n return repr_str\"\n },\n {\n \"identifier\": \"RandomResize\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class RandomResize:\\n \\\"\\\"\\\"Randomly resize the input.\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n params (dict): A dictionary specifying the degradation settings.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n \\\"\\\"\\\"\\n\\n def __init__(self, params, keys):\\n self.keys = keys\\n self.params = params\\n\\n self.resize_dict = dict(\\n bilinear=cv2.INTER_LINEAR,\\n bicubic=cv2.INTER_CUBIC,\\n area=cv2.INTER_AREA,\\n lanczos=cv2.INTER_LANCZOS4)\\n\\n def _random_resize(self, imgs):\\n \\\"\\\"\\\"This is the function used to randomly resize images for training\\n augmentation.\\n\\n Args:\\n imgs (Tensor): training images.\\n\\n Returns:\\n Tensor: images after randomly resized\\n \\\"\\\"\\\"\\n is_single_image = False\\n if isinstance(imgs, np.ndarray):\\n is_single_image = True\\n imgs = [imgs]\\n\\n h, w = imgs[0].shape[:2]\\n\\n resize_opt = self.params['resize_opt']\\n resize_prob = self.params['resize_prob']\\n resize_opt = np.random.choice(resize_opt, p=resize_prob).lower()\\n if resize_opt not in self.resize_dict:\\n raise NotImplementedError(f'resize_opt [{resize_opt}] is not '\\n 'implemented')\\n resize_opt = self.resize_dict[resize_opt]\\n\\n resize_step = self.params.get('resize_step', 0)\\n\\n # determine the target size, if not provided\\n target_size = self.params.get('target_size', None)\\n if target_size is None:\\n resize_mode = np.random.choice(['up', 'down', 'keep'],\\n p=self.params['resize_mode_prob'])\\n resize_scale = self.params['resize_scale']\\n if resize_mode == 'up':\\n scale_factor = np.random.uniform(1, resize_scale[1])\\n elif resize_mode == 'down':\\n scale_factor = np.random.uniform(resize_scale[0], 1)\\n else:\\n scale_factor = 1\\n\\n # determine output size\\n h_out, w_out = h * scale_factor, w * scale_factor\\n if self.params.get('is_size_even', False):\\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\\n target_size = (int(h_out), int(w_out))\\n else:\\n resize_step = 0\\n\\n # resize the input\\n if resize_step == 0: # same target_size for all input images\\n outputs = [\\n cv2.resize(img, target_size[::-1], interpolation=resize_opt)\\n for img in imgs\\n ]\\n else: # different target_size for each input image\\n outputs = []\\n for img in imgs:\\n img = cv2.resize(\\n img, target_size[::-1], interpolation=resize_opt)\\n outputs.append(img)\\n\\n # update scale\\n scale_factor += np.random.uniform(-resize_step, resize_step)\\n scale_factor = np.clip(scale_factor, resize_scale[0],\\n resize_scale[1])\\n\\n # determine output size\\n h_out, w_out = h * scale_factor, w * scale_factor\\n if self.params.get('is_size_even', False):\\n h_out, w_out = 2 * (h_out // 2), 2 * (w_out // 2)\\n target_size = (int(h_out), int(w_out))\\n\\n if is_single_image:\\n outputs = outputs[0]\\n\\n return outputs\\n\\n def __call__(self, results):\\n if np.random.uniform() > self.params.get('prob', 1):\\n return results\\n\\n for key in self.keys:\\n results[key] = self._random_resize(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(params={self.params}, keys={self.keys})')\\n return repr_str\"\n },\n {\n \"identifier\": \"RandomNoise\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class RandomNoise:\\n \\\"\\\"\\\"Apply random noise to the input.\\n\\n Currently support Gaussian noise and Poisson noise.\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n params (dict): A dictionary specifying the degradation settings.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n \\\"\\\"\\\"\\n\\n def __init__(self, params, keys):\\n self.keys = keys\\n self.params = params\\n\\n def _apply_gaussian_noise(self, imgs):\\n \\\"\\\"\\\"This is the function used to apply gaussian noise on images.\\n\\n Args:\\n imgs (Tensor): images\\n\\n Returns:\\n Tensor: images applied gaussian noise\\n \\\"\\\"\\\"\\n sigma_range = self.params['gaussian_sigma']\\n sigma = np.random.uniform(sigma_range[0], sigma_range[1])\\n\\n sigma_step = self.params.get('gaussian_sigma_step', 0)\\n\\n gray_noise_prob = self.params['gaussian_gray_noise_prob']\\n is_gray_noise = np.random.uniform() < gray_noise_prob\\n\\n outputs = []\\n for img in imgs:\\n noise = np.float32(np.random.randn(*(img.shape))) * sigma\\n if is_gray_noise:\\n noise = noise[:, :, :1]\\n outputs.append(img + noise)\\n\\n # update noise level\\n sigma += np.random.uniform(-sigma_step, sigma_step)\\n sigma = np.clip(sigma, sigma_range[0], sigma_range[1])\\n\\n return outputs\\n\\n def _apply_poisson_noise(self, imgs):\\n scale_range = self.params['poisson_scale']\\n scale = np.random.uniform(scale_range[0], scale_range[1])\\n\\n scale_step = self.params.get('poisson_scale_step', 0)\\n\\n gray_noise_prob = self.params['poisson_gray_noise_prob']\\n is_gray_noise = np.random.uniform() < gray_noise_prob\\n\\n outputs = []\\n for img in imgs:\\n noise = np.float32(img.copy())\\n if is_gray_noise:\\n noise = cv2.cvtColor(noise[..., [2, 1, 0]], cv2.COLOR_BGR2GRAY)\\n noise = noise[..., np.newaxis]\\n noise = np.clip((noise).round(), 0, 255)\\n unique_val = 2**np.ceil(np.log2(len(np.unique(noise))))\\n noise = np.random.poisson(noise * unique_val).astype(np.float32) \\\\\\n / unique_val - noise\\n\\n outputs.append(img + noise * scale)\\n\\n # update noise level\\n scale += np.random.uniform(-scale_step, scale_step)\\n scale = np.clip(scale, scale_range[0], scale_range[1])\\n\\n return outputs\\n\\n def _apply_random_noise(self, imgs):\\n \\\"\\\"\\\"This is the function used to apply random noise on images.\\n\\n Args:\\n imgs (Tensor): training images\\n\\n Returns:\\n _type_: _description_\\n \\\"\\\"\\\"\\n noise_type = np.random.choice(\\n self.params['noise_type'], p=self.params['noise_prob'])\\n\\n is_single_image = False\\n if isinstance(imgs, np.ndarray):\\n is_single_image = True\\n imgs = [imgs]\\n\\n if noise_type.lower() == 'gaussian':\\n imgs = self._apply_gaussian_noise(imgs)\\n elif noise_type.lower() == 'poisson':\\n imgs = self._apply_poisson_noise(imgs)\\n else:\\n raise NotImplementedError(f'\\\"noise_type\\\" [{noise_type}] is '\\n 'not implemented.')\\n\\n if is_single_image:\\n imgs = imgs[0]\\n\\n return imgs\\n\\n def __call__(self, results):\\n if np.random.uniform() > self.params.get('prob', 1):\\n return results\\n\\n for key in self.keys:\\n results[key] = self._apply_random_noise(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(params={self.params}, keys={self.keys})')\\n return repr_str\"\n },\n {\n \"identifier\": \"RandomJPEGCompression\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class RandomJPEGCompression:\\n \\\"\\\"\\\"Apply random JPEG compression to the input.\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n params (dict): A dictionary specifying the degradation settings.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n bgr2rgb (str): Whether change channel order. Default: False.\\n \\\"\\\"\\\"\\n\\n def __init__(self, params, keys, color_type='color', bgr2rgb=False):\\n self.keys = keys\\n self.params = params\\n self.color_type = color_type\\n self.bgr2rgb = bgr2rgb\\n\\n def _apply_random_compression(self, imgs):\\n is_single_image = False\\n if isinstance(imgs, np.ndarray):\\n is_single_image = True\\n imgs = [imgs]\\n\\n # determine initial compression level and the step size\\n quality = self.params['quality']\\n quality_step = self.params.get('quality_step', 0)\\n jpeg_param = round(np.random.uniform(quality[0], quality[1]))\\n\\n # apply jpeg compression\\n outputs = []\\n for img in imgs:\\n encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_param]\\n if self.bgr2rgb and self.color_type == 'color':\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n _, img_encoded = cv2.imencode('.jpg', img, encode_param)\\n\\n if self.color_type == 'color':\\n img_encoded = cv2.imdecode(img_encoded, 1)\\n if self.bgr2rgb:\\n img_encoded = cv2.cvtColor(img_encoded, cv2.COLOR_BGR2RGB)\\n outputs.append(img_encoded)\\n else:\\n outputs.append(cv2.imdecode(img_encoded, 0))\\n\\n # update compression level\\n jpeg_param += np.random.uniform(-quality_step, quality_step)\\n jpeg_param = round(np.clip(jpeg_param, quality[0], quality[1]))\\n\\n if is_single_image:\\n outputs = outputs[0]\\n\\n return outputs\\n\\n def __call__(self, results):\\n if np.random.uniform() > self.params.get('prob', 1):\\n return results\\n\\n for key in self.keys:\\n results[key] = self._apply_random_compression(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(params={self.params}, keys={self.keys})')\\n return repr_str\"\n },\n {\n \"identifier\": \"RandomVideoCompression\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class RandomVideoCompression:\\n \\\"\\\"\\\"Apply random video compression to the input.\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n params (dict): A dictionary specifying the degradation settings.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n \\\"\\\"\\\"\\n\\n def __init__(self, params, keys):\\n assert has_av, 'Please install av to use video compression.'\\n\\n self.keys = keys\\n self.params = params\\n logging.getLogger('libav').setLevel(50)\\n\\n def _apply_random_compression(self, imgs):\\n \\\"\\\"\\\"This is the function to apply random compression on images.\\n\\n Args:\\n imgs (Tensor): training images\\n\\n Returns:\\n Tensor: images after randomly compressed\\n \\\"\\\"\\\"\\n codec = random.choices(self.params['codec'],\\n self.params['codec_prob'])[0]\\n bitrate = self.params['bitrate']\\n bitrate = np.random.randint(bitrate[0], bitrate[1] + 1)\\n\\n buf = io.BytesIO()\\n with av.open(buf, 'w', 'mp4') as container:\\n stream = container.add_stream(codec, rate=1)\\n stream.height = imgs[0].shape[0]\\n stream.width = imgs[0].shape[1]\\n stream.pix_fmt = 'yuv420p'\\n stream.bit_rate = bitrate\\n\\n for img in imgs:\\n img = img.astype(np.uint8)\\n frame = av.VideoFrame.from_ndarray(img, format='rgb24')\\n frame.pict_type = 'NONE'\\n for packet in stream.encode(frame):\\n container.mux(packet)\\n\\n # Flush stream\\n for packet in stream.encode():\\n container.mux(packet)\\n\\n outputs = []\\n with av.open(buf, 'r', 'mp4') as container:\\n if container.streams.video:\\n for frame in container.decode(**{'video': 0}):\\n outputs.append(frame.to_rgb().to_ndarray().astype(\\n np.float32))\\n\\n return outputs\\n\\n def __call__(self, results):\\n if np.random.uniform() > self.params.get('prob', 1):\\n return results\\n\\n for key in self.keys:\\n results[key] = self._apply_random_compression(results[key])\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(params={self.params}, keys={self.keys})')\\n return repr_str\"\n },\n {\n \"identifier\": \"DegradationsWithShuffle\",\n \"path\": \"basicsr/data/mmcv_transforms/random_degradations.py\",\n \"snippet\": \"class DegradationsWithShuffle:\\n \\\"\\\"\\\"Apply random degradations to input, with degradations being shuffled.\\n\\n Degradation groups are supported. The order of degradations within the same\\n group is preserved. For example, if we have degradations = [a, b, [c, d]]\\n and shuffle_idx = None, then the possible orders are\\n\\n ::\\n\\n [a, b, [c, d]]\\n [a, [c, d], b]\\n [b, a, [c, d]]\\n [b, [c, d], a]\\n [[c, d], a, b]\\n [[c, d], b, a]\\n\\n Modified keys are the attributed specified in \\\"keys\\\".\\n\\n Args:\\n degradations (list[dict]): The list of degradations.\\n keys (list[str]): A list specifying the keys whose values are\\n modified.\\n shuffle_idx (list | None, optional): The degradations corresponding to\\n these indices are shuffled. If None, all degradations are shuffled.\\n Default: None.\\n \\\"\\\"\\\"\\n\\n def __init__(self, degradations, keys, shuffle_idx=None):\\n\\n self.keys = keys\\n\\n self.degradations = self._build_degradations(degradations)\\n\\n if shuffle_idx is None:\\n self.shuffle_idx = list(range(0, len(degradations)))\\n else:\\n self.shuffle_idx = shuffle_idx\\n\\n def _build_degradations(self, degradations):\\n for i, degradation in enumerate(degradations):\\n if isinstance(degradation, (list, tuple)):\\n degradations[i] = self._build_degradations(degradation)\\n else:\\n degradation_ = allowed_degradations[degradation['type']]\\n degradations[i] = degradation_(degradation['params'],\\n self.keys)\\n\\n return degradations\\n\\n def __call__(self, results):\\n # shuffle degradations\\n if len(self.shuffle_idx) > 0:\\n shuffle_list = [self.degradations[i] for i in self.shuffle_idx]\\n np.random.shuffle(shuffle_list)\\n for i, idx in enumerate(self.shuffle_idx):\\n self.degradations[idx] = shuffle_list[i]\\n\\n # apply degradations to input\\n for degradation in self.degradations:\\n if isinstance(degradation, (tuple, list)):\\n for subdegrdation in degradation:\\n results = subdegrdation(results)\\n else:\\n results = degradation(results)\\n\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += (f'(degradations={self.degradations}, '\\n f'keys={self.keys}, '\\n f'shuffle_idx={self.shuffle_idx})')\\n return repr_str\"\n },\n {\n \"identifier\": \"circular_lowpass_kernel\",\n \"path\": \"basicsr/data/degradations.py\",\n \"snippet\": \"def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0):\\n \\\"\\\"\\\"2D sinc filter\\n\\n Reference: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter\\n\\n Args:\\n cutoff (float): cutoff frequency in radians (pi is max)\\n kernel_size (int): horizontal and vertical size, must be odd.\\n pad_to (int): pad kernel size to desired size, must be odd or zero.\\n \\\"\\\"\\\"\\n assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'\\n kernel = np.fromfunction(\\n lambda x, y: cutoff * special.j1(cutoff * np.sqrt(\\n (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt(\\n (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size])\\n kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi)\\n kernel = kernel / np.sum(kernel)\\n if pad_to > kernel_size:\\n pad_size = (pad_to - kernel_size) // 2\\n kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))\\n return kernel\"\n },\n {\n \"identifier\": \"random_mixed_kernels\",\n \"path\": \"basicsr/data/degradations.py\",\n \"snippet\": \"def random_mixed_kernels(kernel_list,\\n kernel_prob,\\n kernel_size=21,\\n sigma_x_range=(0.6, 5),\\n sigma_y_range=(0.6, 5),\\n rotation_range=(-math.pi, math.pi),\\n betag_range=(0.5, 8),\\n betap_range=(0.5, 8),\\n noise_range=None,\\n return_sigma=False):\\n \\\"\\\"\\\"Randomly generate mixed kernels.\\n\\n Args:\\n kernel_list (tuple): a list name of kernel types,\\n support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso',\\n 'plateau_aniso']\\n kernel_prob (tuple): corresponding kernel probability for each\\n kernel type\\n kernel_size (int):\\n sigma_x_range (tuple): [0.6, 5]\\n sigma_y_range (tuple): [0.6, 5]\\n rotation range (tuple): [-math.pi, math.pi]\\n beta_range (tuple): [0.5, 8]\\n noise_range(tuple, optional): multiplicative kernel noise,\\n [0.75, 1.25]. Default: None\\n\\n Returns:\\n kernel (ndarray):\\n \\\"\\\"\\\"\\n kernel_type = random.choices(kernel_list, kernel_prob)[0]\\n if not return_sigma:\\n if kernel_type == 'iso':\\n kernel = random_bivariate_Gaussian(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True, return_sigma=return_sigma)\\n elif kernel_type == 'aniso':\\n kernel = random_bivariate_Gaussian(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False, return_sigma=return_sigma)\\n elif kernel_type == 'generalized_iso':\\n kernel = random_bivariate_generalized_Gaussian(\\n kernel_size,\\n sigma_x_range,\\n sigma_y_range,\\n rotation_range,\\n betag_range,\\n noise_range=noise_range,\\n isotropic=True,\\n return_sigma=return_sigma)\\n elif kernel_type == 'generalized_aniso':\\n kernel = random_bivariate_generalized_Gaussian(\\n kernel_size,\\n sigma_x_range,\\n sigma_y_range,\\n rotation_range,\\n betag_range,\\n noise_range=noise_range,\\n isotropic=False,\\n return_sigma=return_sigma)\\n elif kernel_type == 'plateau_iso':\\n kernel = random_bivariate_plateau(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True, return_sigma=return_sigma)\\n elif kernel_type == 'plateau_aniso':\\n kernel = random_bivariate_plateau(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False, return_sigma=return_sigma)\\n return kernel\\n else:\\n if kernel_type == 'iso':\\n kernel, sigma_list = random_bivariate_Gaussian(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True, return_sigma=return_sigma)\\n elif kernel_type == 'aniso':\\n kernel, sigma_list = random_bivariate_Gaussian(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False, return_sigma=return_sigma)\\n elif kernel_type == 'generalized_iso':\\n kernel, sigma_list = random_bivariate_generalized_Gaussian(\\n kernel_size,\\n sigma_x_range,\\n sigma_y_range,\\n rotation_range,\\n betag_range,\\n noise_range=noise_range,\\n isotropic=True,\\n return_sigma=return_sigma)\\n elif kernel_type == 'generalized_aniso':\\n kernel, sigma_list = random_bivariate_generalized_Gaussian(\\n kernel_size,\\n sigma_x_range,\\n sigma_y_range,\\n rotation_range,\\n betag_range,\\n noise_range=noise_range,\\n isotropic=False,\\n return_sigma=return_sigma)\\n elif kernel_type == 'plateau_iso':\\n kernel, sigma_list = random_bivariate_plateau(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True, return_sigma=return_sigma)\\n elif kernel_type == 'plateau_aniso':\\n kernel, sigma_list = random_bivariate_plateau(\\n kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False, return_sigma=return_sigma)\\n return kernel, sigma_list\"\n },\n {\n \"identifier\": \"augment\",\n \"path\": \"basicsr/data/transforms.py\",\n \"snippet\": \"def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):\\n \\\"\\\"\\\"Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).\\n\\n We use vertical flip and transpose for rotation implementation.\\n All the images in the list use the same augmentation.\\n\\n Args:\\n imgs (list[ndarray] | ndarray): Images to be augmented. If the input\\n is an ndarray, it will be transformed to a list.\\n hflip (bool): Horizontal flip. Default: True.\\n rotation (bool): Ratotation. Default: True.\\n flows (list[ndarray]: Flows to be augmented. If the input is an\\n ndarray, it will be transformed to a list.\\n Dimension is (h, w, 2). Default: None.\\n return_status (bool): Return the status of flip and rotation.\\n Default: False.\\n\\n Returns:\\n list[ndarray] | ndarray: Augmented images and flows. If returned\\n results only have one element, just return ndarray.\\n\\n \\\"\\\"\\\"\\n hflip = hflip and random.random() < 0.5\\n vflip = rotation and random.random() < 0.5\\n rot90 = rotation and random.random() < 0.5\\n\\n def _augment(img):\\n if hflip: # horizontal\\n cv2.flip(img, 1, img)\\n if vflip: # vertical\\n cv2.flip(img, 0, img)\\n if rot90:\\n img = img.transpose(1, 0, 2)\\n return img\\n\\n def _augment_flow(flow):\\n if hflip: # horizontal\\n cv2.flip(flow, 1, flow)\\n flow[:, :, 0] *= -1\\n if vflip: # vertical\\n cv2.flip(flow, 0, flow)\\n flow[:, :, 1] *= -1\\n if rot90:\\n flow = flow.transpose(1, 0, 2)\\n flow = flow[:, :, [1, 0]]\\n return flow\\n\\n if not isinstance(imgs, list):\\n imgs = [imgs]\\n imgs = [_augment(img) for img in imgs]\\n if len(imgs) == 1:\\n imgs = imgs[0]\\n\\n if flows is not None:\\n if not isinstance(flows, list):\\n flows = [flows]\\n flows = [_augment_flow(flow) for flow in flows]\\n if len(flows) == 1:\\n flows = flows[0]\\n return imgs, flows\\n else:\\n if return_status:\\n return imgs, (hflip, vflip, rot90)\\n else:\\n return imgs\"\n },\n {\n \"identifier\": \"single_random_crop\",\n \"path\": \"basicsr/data/transforms.py\",\n \"snippet\": \"def single_random_crop(img_gts, gt_patch_size, gt_path=None):\\n \\\"\\\"\\\"Random crop. Support Numpy array and Tensor inputs.\\n\\n It crops lists of lq and gt images with corresponding locations.\\n\\n Args:\\n img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images\\n should have the same shape. If the input is an ndarray, it will\\n be transformed to a list containing itself.\\n gt_patch_size (int): GT patch size.\\n gt_path (str): Path to ground-truth. Default: None.\\n\\n Returns:\\n list[ndarray] | ndarray: GT images and LQ images. If returned results\\n only have one element, just return ndarray.\\n \\\"\\\"\\\"\\n\\n if not isinstance(img_gts, list):\\n img_gts = [img_gts]\\n\\n # determine input type: Numpy array or Tensor\\n input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'\\n\\n if input_type == 'Tensor':\\n h_gt, w_gt = img_gts[0].size()[-2:]\\n else:\\n h_gt, w_gt = img_gts[0].shape[0:2]\\n\\n # if h_gt != h_lq * scale or w_gt != w_lq * scale:\\n # raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',\\n # f'multiplication of LQ ({h_lq}, {w_lq}).')\\n if h_gt < gt_patch_size or w_gt < gt_patch_size:\\n raise ValueError(f'GT ({h_gt}, {w_gt}) is smaller than '\\n f'patch size ({gt_patch_size}, {gt_patch_size}).')\\n\\n # randomly choose top and left coordinates for lq patch\\n top = random.randint(0, h_gt - gt_patch_size)\\n left = random.randint(0, w_gt - gt_patch_size)\\n\\n # crop corresponding gt patch\\n if input_type == 'Tensor':\\n img_gts = [v[:, :, top:top + gt_patch_size, left:left + gt_patch_size] for v in img_gts]\\n else:\\n img_gts = [v[top:top + gt_patch_size, left:left + gt_patch_size, ...] for v in img_gts]\\n if len(img_gts) == 1:\\n img_gts = img_gts[0]\\n\\n return img_gts\"\n },\n {\n \"identifier\": \"paired_random_crop\",\n \"path\": \"basicsr/data/transforms.py\",\n \"snippet\": \"def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None):\\n \\\"\\\"\\\"Paired random crop. Support Numpy array and Tensor inputs.\\n\\n It crops lists of lq and gt images with corresponding locations.\\n\\n Args:\\n img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images\\n should have the same shape. If the input is an ndarray, it will\\n be transformed to a list containing itself.\\n img_lqs (list[ndarray] | ndarray): LQ images. Note that all images\\n should have the same shape. If the input is an ndarray, it will\\n be transformed to a list containing itself.\\n gt_patch_size (int): GT patch size.\\n scale (int): Scale factor.\\n gt_path (str): Path to ground-truth. Default: None.\\n\\n Returns:\\n list[ndarray] | ndarray: GT images and LQ images. If returned results\\n only have one element, just return ndarray.\\n \\\"\\\"\\\"\\n\\n if not isinstance(img_gts, list):\\n img_gts = [img_gts]\\n if not isinstance(img_lqs, list):\\n img_lqs = [img_lqs]\\n\\n # determine input type: Numpy array or Tensor\\n input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'\\n\\n if input_type == 'Tensor':\\n h_lq, w_lq = img_lqs[0].size()[-2:]\\n h_gt, w_gt = img_gts[0].size()[-2:]\\n else:\\n h_lq, w_lq = img_lqs[0].shape[0:2]\\n h_gt, w_gt = img_gts[0].shape[0:2]\\n lq_patch_size = gt_patch_size // scale\\n\\n if h_gt != h_lq * scale or w_gt != w_lq * scale:\\n raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',\\n f'multiplication of LQ ({h_lq}, {w_lq}).')\\n if h_lq < lq_patch_size or w_lq < lq_patch_size:\\n raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '\\n f'({lq_patch_size}, {lq_patch_size}). '\\n f'Please remove {gt_path}.')\\n\\n # randomly choose top and left coordinates for lq patch\\n top = random.randint(0, h_lq - lq_patch_size)\\n left = random.randint(0, w_lq - lq_patch_size)\\n\\n # crop lq patch\\n if input_type == 'Tensor':\\n img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs]\\n else:\\n img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]\\n\\n # crop corresponding gt patch\\n top_gt, left_gt = int(top * scale), int(left * scale)\\n if input_type == 'Tensor':\\n img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts]\\n else:\\n img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]\\n if len(img_gts) == 1:\\n img_gts = img_gts[0]\\n if len(img_lqs) == 1:\\n img_lqs = img_lqs[0]\\n return img_gts, img_lqs\"\n },\n {\n \"identifier\": \"FileClient\",\n \"path\": \"basicsr/utils/file_client.py\",\n \"snippet\": \"class FileClient(object):\\n \\\"\\\"\\\"A general file client to access files in different backend.\\n\\n The client loads a file or text in a specified backend from its path\\n and return it as a binary file. it can also register other backend\\n accessor with a given name and backend class.\\n\\n Attributes:\\n backend (str): The storage backend type. Options are \\\"disk\\\",\\n \\\"memcached\\\" and \\\"lmdb\\\".\\n client (:obj:`BaseStorageBackend`): The backend object.\\n \\\"\\\"\\\"\\n\\n _backends = {\\n 'disk': HardDiskBackend,\\n 'memcached': MemcachedBackend,\\n 'lmdb': LmdbBackend,\\n }\\n\\n def __init__(self, backend='disk', **kwargs):\\n if backend not in self._backends:\\n raise ValueError(f'Backend {backend} is not supported. Currently supported ones'\\n f' are {list(self._backends.keys())}')\\n self.backend = backend\\n self.client = self._backends[backend](**kwargs)\\n\\n def get(self, filepath, client_key='default'):\\n # client_key is used only for lmdb, where different fileclients have\\n # different lmdb environments.\\n if self.backend == 'lmdb':\\n return self.client.get(filepath, client_key)\\n else:\\n return self.client.get(filepath)\\n\\n def get_text(self, filepath):\\n return self.client.get_text(filepath)\"\n },\n {\n \"identifier\": \"imfrombytes\",\n \"path\": \"basicsr/utils/img_util.py\",\n \"snippet\": \"def imfrombytes(content, flag='color', float32=False):\\n \\\"\\\"\\\"Read an image from bytes.\\n\\n Args:\\n content (bytes): Image bytes got from files or other streams.\\n flag (str): Flags specifying the color type of a loaded image,\\n candidates are `color`, `grayscale` and `unchanged`.\\n float32 (bool): Whether to change to float32., If True, will also norm\\n to [0, 1]. Default: False.\\n\\n Returns:\\n ndarray: Loaded image array.\\n \\\"\\\"\\\"\\n img_np = np.frombuffer(content, np.uint8)\\n imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}\\n img = cv2.imdecode(img_np, imread_flags[flag])\\n if float32:\\n img = img.astype(np.float32) / 255.\\n return img\"\n },\n {\n \"identifier\": \"img2tensor\",\n \"path\": \"basicsr/utils/img_util.py\",\n \"snippet\": \"def img2tensor(imgs, bgr2rgb=True, float32=True):\\n \\\"\\\"\\\"Numpy array to tensor.\\n\\n Args:\\n imgs (list[ndarray] | ndarray): Input images.\\n bgr2rgb (bool): Whether to change bgr to rgb.\\n float32 (bool): Whether to change to float32.\\n\\n Returns:\\n list[tensor] | tensor: Tensor images. If returned results only have\\n one element, just return tensor.\\n \\\"\\\"\\\"\\n\\n def _totensor(img, bgr2rgb, float32):\\n if img.shape[2] == 3 and bgr2rgb:\\n if img.dtype == 'float64':\\n img = img.astype('float32')\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n img = torch.from_numpy(img.transpose(2, 0, 1))\\n if float32:\\n img = img.float()\\n return img\\n\\n if isinstance(imgs, list):\\n return [_totensor(img, bgr2rgb, float32) for img in imgs]\\n else:\\n return _totensor(imgs, bgr2rgb, float32)\"\n },\n {\n \"identifier\": \"imwrite\",\n \"path\": \"basicsr/utils/img_util.py\",\n \"snippet\": \"def imwrite(img, file_path, params=None, auto_mkdir=True):\\n \\\"\\\"\\\"Write image to file.\\n\\n Args:\\n img (ndarray): Image array to be written.\\n file_path (str): Image file path.\\n params (None or list): Same as opencv's :func:`imwrite` interface.\\n auto_mkdir (bool): If the parent folder of `file_path` does not exist,\\n whether to create it automatically.\\n\\n Returns:\\n bool: Successful or not.\\n \\\"\\\"\\\"\\n if auto_mkdir:\\n dir_name = os.path.abspath(os.path.dirname(file_path))\\n os.makedirs(dir_name, exist_ok=True)\\n ok = cv2.imwrite(file_path, img, params)\\n if not ok:\\n raise IOError('Failed in writing images.')\"\n },\n {\n \"identifier\": \"tensor2img\",\n \"path\": \"basicsr/utils/img_util.py\",\n \"snippet\": \"def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):\\n \\\"\\\"\\\"Convert torch Tensors into image numpy arrays.\\n\\n After clamping to [min, max], values will be normalized to [0, 1].\\n\\n Args:\\n tensor (Tensor or list[Tensor]): Accept shapes:\\n 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);\\n 2) 3D Tensor of shape (3/1 x H x W);\\n 3) 2D Tensor of shape (H x W).\\n Tensor channel should be in RGB order.\\n rgb2bgr (bool): Whether to change rgb to bgr.\\n out_type (numpy type): output types. If ``np.uint8``, transform outputs\\n to uint8 type with range [0, 255]; otherwise, float type with\\n range [0, 1]. Default: ``np.uint8``.\\n min_max (tuple[int]): min and max values for clamp.\\n\\n Returns:\\n (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of\\n shape (H x W). The channel order is BGR.\\n \\\"\\\"\\\"\\n if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):\\n raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')\\n\\n if torch.is_tensor(tensor):\\n tensor = [tensor]\\n result = []\\n for _tensor in tensor:\\n _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)\\n _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])\\n\\n n_dim = _tensor.dim()\\n if n_dim == 4:\\n img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()\\n img_np = img_np.transpose(1, 2, 0)\\n if rgb2bgr:\\n img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)\\n elif n_dim == 3:\\n img_np = _tensor.numpy()\\n img_np = img_np.transpose(1, 2, 0)\\n if img_np.shape[2] == 1: # gray image\\n img_np = np.squeeze(img_np, axis=2)\\n else:\\n if rgb2bgr:\\n img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)\\n elif n_dim == 2:\\n img_np = _tensor.numpy()\\n else:\\n raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')\\n if out_type == np.uint8:\\n # Unlike MATLAB, numpy.unit8() WILL NOT round by default.\\n img_np = (img_np * 255.0).round()\\n img_np = img_np.astype(out_type)\\n result.append(img_np)\\n # if len(result) == 1 and torch.is_tensor(tensor):\\n if len(result) == 1:\\n result = result[0]\\n return result\"\n },\n {\n \"identifier\": \"get_root_logger\",\n \"path\": \"basicsr/utils/logger.py\",\n \"snippet\": \"def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):\\n \\\"\\\"\\\"Get the root logger.\\n\\n The logger will be initialized if it has not been initialized. By default a\\n StreamHandler will be added. If `log_file` is specified, a FileHandler will\\n also be added.\\n\\n Args:\\n logger_name (str): root logger name. Default: 'basicsr'.\\n log_file (str | None): The log filename. If specified, a FileHandler\\n will be added to the root logger.\\n log_level (int): The root logger level. Note that only the process of\\n rank 0 is affected, while other processes will set the level to\\n \\\"Error\\\" and be silent most of the time.\\n\\n Returns:\\n logging.Logger: The root logger.\\n \\\"\\\"\\\"\\n logger = logging.getLogger(logger_name)\\n # if the logger has been initialized, just return it\\n if logger_name in initialized_logger:\\n return logger\\n\\n format_str = '%(asctime)s %(levelname)s: %(message)s'\\n stream_handler = logging.StreamHandler()\\n stream_handler.setFormatter(logging.Formatter(format_str))\\n logger.addHandler(stream_handler)\\n logger.propagate = False\\n rank, _ = get_dist_info()\\n if rank != 0:\\n logger.setLevel('ERROR')\\n elif log_file is not None:\\n logger.setLevel(log_level)\\n # add file handler\\n file_handler = logging.FileHandler(log_file, 'w')\\n file_handler.setFormatter(logging.Formatter(format_str))\\n file_handler.setLevel(log_level)\\n logger.addHandler(file_handler)\\n initialized_logger[logger_name] = True\\n return logger\"\n },\n {\n \"identifier\": \"dequantize_flow\",\n \"path\": \"basicsr/utils/flow_util.py\",\n \"snippet\": \"def dequantize_flow(dx, dy, max_val=0.02, denorm=True):\\n \\\"\\\"\\\"Recover from quantized flow.\\n\\n Args:\\n dx (ndarray): Quantized dx.\\n dy (ndarray): Quantized dy.\\n max_val (float): Maximum value used when quantizing.\\n denorm (bool): Whether to multiply flow values with width/height.\\n\\n Returns:\\n ndarray: Dequantized flow.\\n \\\"\\\"\\\"\\n assert dx.shape == dy.shape\\n assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)\\n\\n dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]\\n\\n if denorm:\\n dx *= dx.shape[1]\\n dy *= dx.shape[0]\\n flow = np.dstack((dx, dy))\\n return flow\"\n },\n {\n \"identifier\": \"DATASET_REGISTRY\",\n \"path\": \"basicsr/utils/registry.py\",\n \"snippet\": \"DATASET_REGISTRY = Registry('dataset')\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import cv2\nimport math\nimport time\nimport os\nimport os.path as osp\nimport numpy as np\nimport random\nimport torch\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom torch.utils import data as data\nfrom basicsr.data.mmcv_transforms import Clip, UnsharpMasking, RescaleToZeroOne\nfrom basicsr.data.mmcv_transforms import RandomBlur, RandomResize, RandomNoise, RandomJPEGCompression, RandomVideoCompression, DegradationsWithShuffle\nfrom basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels\nfrom basicsr.data.transforms import augment, single_random_crop, paired_random_crop\nfrom basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor, tensor2img, imwrite\nfrom basicsr.utils.flow_util import dequantize_flow\nfrom basicsr.utils.registry import DATASET_REGISTRY"},"token_num":{"kind":"number","value":15788,"string":"15,788"},"cropped_code":{"kind":"string","value":" folder, frame_num, _ = line.split(' ')\n self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])\n\n # remove the video clips used in validation\n if opt['val_partition'] == 'REDS4':\n val_partition = ['000', '011', '015', '020']\n elif opt['val_partition'] == 'official':\n val_partition = [f'{v:03d}' for v in range(240, 270)]\n else:\n raise ValueError(f'Wrong validation partition {opt[\"val_partition\"]}.'\n f\"Supported ones are ['official', 'REDS4'].\")\n if opt['test_mode']:\n self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]\n else:\n self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]\n\n # file client (io backend)\n self.file_client = None\n self.io_backend_opt = opt['io_backend']\n self.is_lmdb = False\n if self.io_backend_opt['type'] == 'lmdb':\n self.is_lmdb = True\n self.io_backend_opt['db_paths'] = [self.gt_root]\n self.io_backend_opt['client_keys'] = ['gt']\n\n # temporal augmentation configs\n self.interval_list = opt.get('interval_list', [1])\n self.random_reverse = opt.get('random_reverse', False)\n interval_str = ','.join(str(x) for x in self.interval_list)\n logger = get_root_logger()\n logger.info(f'Temporal augmentation interval list: [{interval_str}]; '\n f'random reverse is {self.random_reverse}.')\n\n # blur settings for the first degradation\n self.blur_kernel_size = opt['blur_kernel_size']\n self.kernel_list = opt['kernel_list']\n self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability\n self.blur_sigma = opt['blur_sigma']\n self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels\n self.betap_range = opt['betap_range'] # betap used in plateau blur kernels\n self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters\n\n # blur settings for the second degradation\n self.blur_kernel_size2 = opt['blur_kernel_size2']\n self.kernel_list2 = opt['kernel_list2']\n self.kernel_prob2 = opt['kernel_prob2']\n self.blur_sigma2 = opt['blur_sigma2']\n self.betag_range2 = opt['betag_range2']\n self.betap_range2 = opt['betap_range2']\n self.sinc_prob2 = opt['sinc_prob2']\n\n # a final sinc filter\n self.final_sinc_prob = opt['final_sinc_prob']\n\n self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21\n # TODO: kernel range is now hard-coded, should be in the configure file\n self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect\n self.pulse_tensor[10, 10] = 1\n\n def __getitem__(self, index):\n if self.file_client is None:\n self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)\n\n gt_size = self.opt['gt_size']\n key = self.keys[index]\n clip_name, frame_name = key.split('/') # key example: 000/00000000\n\n # determine the neighboring frames\n interval = random.choice(self.interval_list)\n\n # ensure not exceeding the borders\n start_frame_idx = int(frame_name)\n if start_frame_idx > 100 - self.num_frame * interval:\n start_frame_idx = random.randint(0, 100 - self.num_frame * interval)\n end_frame_idx = start_frame_idx + self.num_frame * interval\n\n neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))\n\n # random reverse\n if self.random_reverse and random.random() < 0.5:\n neighbor_list.reverse()\n\n # get the GT frames\n img_gts = []\n for neighbor in neighbor_list:\n if self.is_lmdb:\n img_gt_path = f'{clip_name}/{neighbor:08d}'\n else:\n img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'\n\n # get GT\n img_bytes = self.file_client.get(img_gt_path, 'gt')\n img_gt = imfrombytes(img_bytes, float32=True)\n img_gts.append(img_gt)\n\n # randomly crop\n img_gts = single_random_crop(img_gts, gt_size, img_gt_path)\n\n # augmentation - flip, rotate\n img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])\n\n # list-to-list\n img_gts = img2tensor(img_gts)\n\n # kernels\n kernel1s = []\n kernel2s = []\n sinc_kernels = []\n\n for _ in range(len(img_gts)):\n # ------------------------ Generate kernels (used in the first degradation) ------------------------ #\n kernel_size = random.choice(self.kernel_range)\n if np.random.uniform() < self.opt['sinc_prob']:\n # this sinc filter setting is for kernels ranging from [7, 21]\n if kernel_size < 13:\n omega_c = np.random.uniform(np.pi / 3, np.pi)\n else:\n omega_c = np.random.uniform(np.pi / 5, np.pi)\n kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)\n else:\n"},"all_code":{"kind":"string","value":"\n\n\n\n# @DATASET_REGISTRY.register()\nclass RealVSRRecurrentDataset(data.Dataset):\n \"\"\"REDS dataset for training recurrent networks.\n\n The keys are generated from a meta info txt file.\n basicsr/data/meta_info/meta_info_REDS_GT.txt\n\n Each line contains:\n 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by\n a white space.\n Examples:\n 000 100 (720,1280,3)\n 001 100 (720,1280,3)\n ...\n\n Key examples: \"000/00000000\"\n GT (gt): Ground-Truth;\n LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.\n\n Args:\n opt (dict): Config for train dataset. It contains the following keys:\n dataroot_gt (str): Data root path for gt.\n meta_info_file (str): Path for meta information file.\n val_partition (str): Validation partition types. 'REDS4' or 'official'.\n io_backend (dict): IO backend type and other kwarg.\n num_frame (int): Window size for input frames.\n gt_size (int): Cropped patched size for gt patches.\n interval_list (list): Interval list for temporal augmentation.\n random_reverse (bool): Random reverse input frames.\n use_hflip (bool): Use horizontal flips.\n use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).\n \"\"\"\n\n def __init__(self, opt):\n super(RealVSRRecurrentDataset, self).__init__()\n self.opt = opt\n self.gt_root = Path(opt['dataroot_gt'])\n self.num_frame = opt['num_frame']\n\n self.keys = []\n with open(opt['meta_info_file'], 'r') as fin:\n for line in fin:\n folder, frame_num, _ = line.split(' ')\n self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])\n\n # remove the video clips used in validation\n if opt['val_partition'] == 'REDS4':\n val_partition = ['000', '011', '015', '020']\n elif opt['val_partition'] == 'official':\n val_partition = [f'{v:03d}' for v in range(240, 270)]\n else:\n raise ValueError(f'Wrong validation partition {opt[\"val_partition\"]}.'\n f\"Supported ones are ['official', 'REDS4'].\")\n if opt['test_mode']:\n self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]\n else:\n self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]\n\n # file client (io backend)\n self.file_client = None\n self.io_backend_opt = opt['io_backend']\n self.is_lmdb = False\n if self.io_backend_opt['type'] == 'lmdb':\n self.is_lmdb = True\n self.io_backend_opt['db_paths'] = [self.gt_root]\n self.io_backend_opt['client_keys'] = ['gt']\n\n # temporal augmentation configs\n self.interval_list = opt.get('interval_list', [1])\n self.random_reverse = opt.get('random_reverse', False)\n interval_str = ','.join(str(x) for x in self.interval_list)\n logger = get_root_logger()\n logger.info(f'Temporal augmentation interval list: [{interval_str}]; '\n f'random reverse is {self.random_reverse}.')\n\n # the first degradation\n self.random_blur_1 = RandomBlur(\n params=opt['degradation_1']['random_blur']['params'],\n keys=opt['degradation_1']['random_blur']['keys']\n )\n self.random_resize_1 = RandomResize(\n params=opt['degradation_1']['random_resize']['params'],\n keys=opt['degradation_1']['random_resize']['keys']\n )\n self.random_noise_1 = RandomNoise(\n params=opt['degradation_1']['random_noise']['params'],\n keys=opt['degradation_1']['random_noise']['keys']\n )\n self.random_jpeg_1 = RandomJPEGCompression(\n params=opt['degradation_1']['random_jpeg']['params'],\n keys=opt['degradation_1']['random_jpeg']['keys']\n )\n self.random_mpeg_1 = RandomVideoCompression(\n params=opt['degradation_1']['random_mpeg']['params'],\n keys=opt['degradation_1']['random_mpeg']['keys']\n )\n\n # the second degradation\n self.random_blur_2 = RandomBlur(\n params=opt['degradation_2']['random_blur']['params'],\n keys=opt['degradation_2']['random_blur']['keys']\n )\n self.random_resize_2 = RandomResize(\n params=opt['degradation_2']['random_resize']['params'],\n keys=opt['degradation_2']['random_resize']['keys']\n )\n self.random_noise_2 = RandomNoise(\n params=opt['degradation_2']['random_noise']['params'],\n keys=opt['degradation_2']['random_noise']['keys']\n )\n self.random_jpeg_2 = RandomJPEGCompression(\n params=opt['degradation_2']['random_jpeg']['params'],\n keys=opt['degradation_2']['random_jpeg']['keys']\n )\n self.random_mpeg_2 = RandomVideoCompression(\n params=opt['degradation_2']['random_mpeg']['params'],\n keys=opt['degradation_2']['random_mpeg']['keys']\n )\n\n # final\n self.resize_final = RandomResize(\n params=opt['degradation_2']['resize_final']['params'],\n keys=opt['degradation_2']['resize_final']['keys']\n )\n self.blur_final = RandomBlur(\n params=opt['degradation_2']['blur_final']['params'],\n keys=opt['degradation_2']['blur_final']['keys']\n )\n\n # transforms\n self.usm = UnsharpMasking(\n kernel_size=opt['transforms']['usm']['kernel_size'],\n sigma=opt['transforms']['usm']['sigma'],\n weight=opt['transforms']['usm']['weight'],\n threshold=opt['transforms']['usm']['threshold'],\n keys=opt['transforms']['usm']['keys']\n )\n self.clip = Clip(keys=opt['transforms']['clip']['keys'])\n self.rescale = RescaleToZeroOne(keys=opt['transforms']['rescale']['keys'])\n\n def __getitem__(self, index):\n if self.file_client is None:\n self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)\n\n gt_size = self.opt['gt_size']\n key = self.keys[index]\n clip_name, frame_name = key.split('/') # key example: 000/00000000\n\n # determine the neighboring frames\n interval = random.choice(self.interval_list)\n\n # ensure not exceeding the borders\n start_frame_idx = int(frame_name)\n if start_frame_idx > 100 - self.num_frame * interval:\n start_frame_idx = random.randint(0, 100 - self.num_frame * interval)\n end_frame_idx = start_frame_idx + self.num_frame * interval\n\n neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))\n\n # random reverse\n if self.random_reverse and random.random() < 0.5:\n neighbor_list.reverse()\n\n # get the GT frames\n img_gts = []\n for neighbor in neighbor_list:\n if self.is_lmdb:\n img_gt_path = f'{clip_name}/{neighbor:08d}'\n else:\n img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'\n\n # get GT\n img_bytes = self.file_client.get(img_gt_path, 'gt')\n img_gt = imfrombytes(img_bytes, float32=False)\n img_gts.append(img_gt)\n\n # randomly crop\n img_gts = single_random_crop(img_gts, gt_size, img_gt_path)\n\n # augmentation - flip, rotate\n img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])\n img_lqs = deepcopy(img_gts)\n\n out_dict = {'lqs': img_lqs, 'gts': img_gts}\n\n out_dict = self.usm.transform(out_dict)\n\n ## the first degradation\n out_dict = self.random_blur_1(out_dict)\n out_dict = self.random_resize_1(out_dict)\n out_dict = self.random_noise_1(out_dict)\n out_dict = self.random_jpeg_1(out_dict)\n out_dict = self.random_mpeg_1(out_dict)\n\n ## the second degradation\n out_dict = self.random_blur_2(out_dict)\n out_dict = self.random_resize_2(out_dict)\n out_dict = self.random_noise_2(out_dict)\n out_dict = self.random_jpeg_2(out_dict)\n out_dict = self.random_mpeg_2(out_dict)\n\n ## final resize\n out_dict = self.resize_final(out_dict)\n out_dict = self.blur_final(out_dict)\n\n # post process\n out_dict = self.clip(out_dict)\n out_dict = self.rescale.transform(out_dict)\n\n # list-to-list\n for k in out_dict.keys():\n out_dict[k] = img2tensor(out_dict[k])\n\n # img_gts: (t, c, h, w)\n # key: str\n\n return out_dict\n\n def __len__(self):\n return len(self.keys)\n\n\n# @DATASET_REGISTRY.register()\nclass RealESRGANRecurrentDataset(data.Dataset):\n \"\"\"REDS dataset for training recurrent networks.\n\n The keys are generated from a meta info txt file.\n basicsr/data/meta_info/meta_info_REDS_GT.txt\n\n Each line contains:\n 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by\n a white space.\n Examples:\n 000 100 (720,1280,3)\n 001 100 (720,1280,3)\n ...\n\n Key examples: \"000/00000000\"\n GT (gt): Ground-Truth;\n LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.\n\n Args:\n opt (dict): Config for train dataset. It contains the following keys:\n dataroot_gt (str): Data root path for gt.\n meta_info_file (str): Path for meta information file.\n val_partition (str): Validation partition types. 'REDS4' or 'official'.\n io_backend (dict): IO backend type and other kwarg.\n num_frame (int): Window size for input frames.\n gt_size (int): Cropped patched size for gt patches.\n interval_list (list): Interval list for temporal augmentation.\n random_reverse (bool): Random reverse input frames.\n use_hflip (bool): Use horizontal flips.\n use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).\n \"\"\"\n\n def __init__(self, opt):\n super(RealESRGANRecurrentDataset, self).__init__()\n self.opt = opt\n self.gt_root = Path(opt['dataroot_gt'])\n self.num_frame = opt['num_frame']\n\n self.keys = []\n with open(opt['meta_info_file'], 'r') as fin:\n for line in fin:\n folder, frame_num, _ = line.split(' ')\n self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])\n\n # remove the video clips used in validation\n if opt['val_partition'] == 'REDS4':\n val_partition = ['000', '011', '015', '020']\n elif opt['val_partition'] == 'official':\n val_partition = [f'{v:03d}' for v in range(240, 270)]\n else:\n raise ValueError(f'Wrong validation partition {opt[\"val_partition\"]}.'\n f\"Supported ones are ['official', 'REDS4'].\")\n if opt['test_mode']:\n self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]\n else:\n self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]\n\n # file client (io backend)\n self.file_client = None\n self.io_backend_opt = opt['io_backend']\n self.is_lmdb = False\n if self.io_backend_opt['type'] == 'lmdb':\n self.is_lmdb = True\n self.io_backend_opt['db_paths'] = [self.gt_root]\n self.io_backend_opt['client_keys'] = ['gt']\n\n # temporal augmentation configs\n self.interval_list = opt.get('interval_list', [1])\n self.random_reverse = opt.get('random_reverse', False)\n interval_str = ','.join(str(x) for x in self.interval_list)\n logger = get_root_logger()\n logger.info(f'Temporal augmentation interval list: [{interval_str}]; '\n f'random reverse is {self.random_reverse}.')\n\n # blur settings for the first degradation\n self.blur_kernel_size = opt['blur_kernel_size']\n self.kernel_list = opt['kernel_list']\n self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability\n self.blur_sigma = opt['blur_sigma']\n self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels\n self.betap_range = opt['betap_range'] # betap used in plateau blur kernels\n self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters\n\n # blur settings for the second degradation\n self.blur_kernel_size2 = opt['blur_kernel_size2']\n self.kernel_list2 = opt['kernel_list2']\n self.kernel_prob2 = opt['kernel_prob2']\n self.blur_sigma2 = opt['blur_sigma2']\n self.betag_range2 = opt['betag_range2']\n self.betap_range2 = opt['betap_range2']\n self.sinc_prob2 = opt['sinc_prob2']\n\n # a final sinc filter\n self.final_sinc_prob = opt['final_sinc_prob']\n\n self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21\n # TODO: kernel range is now hard-coded, should be in the configure file\n self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect\n self.pulse_tensor[10, 10] = 1\n\n def __getitem__(self, index):\n if self.file_client is None:\n self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)\n\n gt_size = self.opt['gt_size']\n key = self.keys[index]\n clip_name, frame_name = key.split('/') # key example: 000/00000000\n\n # determine the neighboring frames\n interval = random.choice(self.interval_list)\n\n # ensure not exceeding the borders\n start_frame_idx = int(frame_name)\n if start_frame_idx > 100 - self.num_frame * interval:\n start_frame_idx = random.randint(0, 100 - self.num_frame * interval)\n end_frame_idx = start_frame_idx + self.num_frame * interval\n\n neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))\n\n # random reverse\n if self.random_reverse and random.random() < 0.5:\n neighbor_list.reverse()\n\n # get the GT frames\n img_gts = []\n for neighbor in neighbor_list:\n if self.is_lmdb:\n img_gt_path = f'{clip_name}/{neighbor:08d}'\n else:\n img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'\n\n # get GT\n img_bytes = self.file_client.get(img_gt_path, 'gt')\n img_gt = imfrombytes(img_bytes, float32=True)\n img_gts.append(img_gt)\n\n # randomly crop\n img_gts = single_random_crop(img_gts, gt_size, img_gt_path)\n\n # augmentation - flip, rotate\n img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])\n\n # list-to-list\n img_gts = img2tensor(img_gts)\n\n # kernels\n kernel1s = []\n kernel2s = []\n sinc_kernels = []\n\n for _ in range(len(img_gts)):\n # ------------------------ Generate kernels (used in the first degradation) ------------------------ #\n kernel_size = random.choice(self.kernel_range)\n if np.random.uniform() < self.opt['sinc_prob']:\n # this sinc filter setting is for kernels ranging from [7, 21]\n if kernel_size < 13:\n omega_c = np.random.uniform(np.pi / 3, np.pi)\n else:\n omega_c = np.random.uniform(np.pi / 5, np.pi)\n kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)\n else:"},"next_line":{"kind":"string","value":" kernel = random_mixed_kernels("},"gold_snippet_index":{"kind":"number","value":10,"string":"10"},"created_at":{"kind":"string","value":"2023-11-30 01:50:29+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5384,"cells":{"repo_name":{"kind":"string","value":"Czm369/MixPL"},"file_path":{"kind":"string","value":"mmdet/configs/rtmdet/rtmdet_ins_s_8xb32_300e_coco.py"},"context":{"kind":"list like","value":[{"identifier":"PackDetInputs","path":"mmdet/datasets/transforms/formatting.py","snippet":"class PackDetInputs(BaseTransform):\n \"\"\"Pack the inputs data for the detection / semantic segmentation /\n panoptic segmentation.\n\n The ``img_meta`` item is always populated. The contents of the\n ``img_meta`` dictionary depends on ``meta_keys``. By default this includes:\n\n - ``img_id``: id of the image\n\n - ``img_path``: path to the image file\n\n - ``ori_shape``: original shape of the image as a tuple (h, w)\n\n - ``img_shape``: shape of the image input to the network as a tuple \\\n (h, w). Note that images may be zero padded on the \\\n bottom/right if the batch tensor is larger than this shape.\n\n - ``scale_factor``: a float indicating the preprocessing scale\n\n - ``flip``: a boolean indicating if image flip transform was used\n\n - ``flip_direction``: the flipping direction\n\n Args:\n meta_keys (Sequence[str], optional): Meta keys to be converted to\n ``mmcv.DataContainer`` and collected in ``data[img_metas]``.\n Default: ``('img_id', 'img_path', 'ori_shape', 'img_shape',\n 'scale_factor', 'flip', 'flip_direction')``\n \"\"\"\n mapping_table = {\n 'gt_bboxes': 'bboxes',\n 'gt_bboxes_labels': 'labels',\n 'gt_masks': 'masks'\n }\n\n def __init__(self,\n meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\n 'scale_factor', 'flip', 'flip_direction')):\n self.meta_keys = meta_keys\n\n def transform(self, results: dict) -> dict:\n \"\"\"Method to pack the input data.\n\n Args:\n results (dict): Result dict from the data pipeline.\n\n Returns:\n dict:\n\n - 'inputs' (obj:`torch.Tensor`): The forward data of models.\n - 'data_sample' (obj:`DetDataSample`): The annotation info of the\n sample.\n \"\"\"\n packed_results = dict()\n if 'img' in results:\n img = results['img']\n if len(img.shape) < 3:\n img = np.expand_dims(img, -1)\n # To improve the computational speed by by 3-5 times, apply:\n # If image is not contiguous, use\n # `numpy.transpose()` followed by `numpy.ascontiguousarray()`\n # If image is already contiguous, use\n # `torch.permute()` followed by `torch.contiguous()`\n # Refer to https://github.com/open-mmlab/mmdetection/pull/9533\n # for more details\n if not img.flags.c_contiguous:\n img = np.ascontiguousarray(img.transpose(2, 0, 1))\n img = to_tensor(img)\n else:\n img = to_tensor(img).permute(2, 0, 1).contiguous()\n\n packed_results['inputs'] = img\n\n if 'gt_ignore_flags' in results:\n valid_idx = np.where(results['gt_ignore_flags'] == 0)[0]\n ignore_idx = np.where(results['gt_ignore_flags'] == 1)[0]\n\n data_sample = DetDataSample()\n instance_data = InstanceData()\n ignore_instance_data = InstanceData()\n\n for key in self.mapping_table.keys():\n if key not in results:\n continue\n if key == 'gt_masks' or isinstance(results[key], BaseBoxes):\n if 'gt_ignore_flags' in results:\n instance_data[\n self.mapping_table[key]] = results[key][valid_idx]\n ignore_instance_data[\n self.mapping_table[key]] = results[key][ignore_idx]\n else:\n instance_data[self.mapping_table[key]] = results[key]\n else:\n if 'gt_ignore_flags' in results:\n instance_data[self.mapping_table[key]] = to_tensor(\n results[key][valid_idx])\n ignore_instance_data[self.mapping_table[key]] = to_tensor(\n results[key][ignore_idx])\n else:\n instance_data[self.mapping_table[key]] = to_tensor(\n results[key])\n data_sample.gt_instances = instance_data\n data_sample.ignored_instances = ignore_instance_data\n\n if 'proposals' in results:\n proposals = InstanceData(\n bboxes=to_tensor(results['proposals']),\n scores=to_tensor(results['proposals_scores']))\n data_sample.proposals = proposals\n\n if 'gt_seg_map' in results:\n gt_sem_seg_data = dict(\n sem_seg=to_tensor(results['gt_seg_map'][None, ...].copy()))\n gt_sem_seg_data = PixelData(**gt_sem_seg_data)\n if 'ignore_index' in results:\n metainfo = dict(ignore_index=results['ignore_index'])\n gt_sem_seg_data.set_metainfo(metainfo)\n data_sample.gt_sem_seg = gt_sem_seg_data\n\n img_meta = {}\n for key in self.meta_keys:\n if key in results:\n img_meta[key] = results[key]\n data_sample.set_metainfo(img_meta)\n packed_results['data_samples'] = data_sample\n\n return packed_results\n\n def __repr__(self) -> str:\n repr_str = self.__class__.__name__\n repr_str += f'(meta_keys={self.meta_keys})'\n return repr_str"},{"identifier":"FilterAnnotations","path":"mmdet/datasets/transforms/loading.py","snippet":"class FilterAnnotations(BaseTransform):\n \"\"\"Filter invalid annotations.\n\n Required Keys:\n\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\n - gt_bboxes_labels (np.int64) (optional)\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\n - gt_ignore_flags (bool) (optional)\n\n Modified Keys:\n\n - gt_bboxes (optional)\n - gt_bboxes_labels (optional)\n - gt_masks (optional)\n - gt_ignore_flags (optional)\n\n Args:\n min_gt_bbox_wh (tuple[float]): Minimum width and height of ground truth\n boxes. Default: (1., 1.)\n min_gt_mask_area (int): Minimum foreground area of ground truth masks.\n Default: 1\n by_box (bool): Filter instances with bounding boxes not meeting the\n min_gt_bbox_wh threshold. Default: True\n by_mask (bool): Filter instances with masks not meeting\n min_gt_mask_area threshold. Default: False\n keep_empty (bool): Whether to return None when it\n becomes an empty bbox after filtering. Defaults to True.\n \"\"\"\n\n def __init__(self,\n min_gt_bbox_wh: Tuple[int, int] = (1, 1),\n min_gt_mask_area: int = 1,\n by_box: bool = True,\n by_mask: bool = False,\n keep_empty: bool = True) -> None:\n # TODO: add more filter options\n assert by_box or by_mask\n self.min_gt_bbox_wh = min_gt_bbox_wh\n self.min_gt_mask_area = min_gt_mask_area\n self.by_box = by_box\n self.by_mask = by_mask\n self.keep_empty = keep_empty\n\n @autocast_box_type()\n def transform(self, results: dict) -> Union[dict, None]:\n \"\"\"Transform function to filter annotations.\n\n Args:\n results (dict): Result dict.\n\n Returns:\n dict: Updated result dict.\n \"\"\"\n assert 'gt_bboxes' in results\n gt_bboxes = results['gt_bboxes']\n if gt_bboxes.shape[0] == 0:\n return results\n\n tests = []\n if self.by_box:\n tests.append(\n ((gt_bboxes.widths > self.min_gt_bbox_wh[0]) &\n (gt_bboxes.heights > self.min_gt_bbox_wh[1])).numpy())\n if self.by_mask:\n assert 'gt_masks' in results\n gt_masks = results['gt_masks']\n tests.append(gt_masks.areas >= self.min_gt_mask_area)\n\n keep = tests[0]\n for t in tests[1:]:\n keep = keep & t\n\n if not keep.any():\n if self.keep_empty:\n return None\n\n keys = ('gt_bboxes', 'gt_bboxes_labels', 'gt_masks', 'gt_ignore_flags')\n for key in keys:\n if key in results:\n results[key] = results[key][keep]\n\n return results\n\n def __repr__(self):\n return self.__class__.__name__ + \\\n f'(min_gt_bbox_wh={self.min_gt_bbox_wh}, ' \\\n f'keep_empty={self.keep_empty})'"},{"identifier":"LoadAnnotations","path":"mmdet/datasets/transforms/loading.py","snippet":"class LoadAnnotations(MMCV_LoadAnnotations):\n \"\"\"Load and process the ``instances`` and ``seg_map`` annotation provided\n by dataset.\n\n The annotation format is as the following:\n\n .. code-block:: python\n\n {\n 'instances':\n [\n {\n # List of 4 numbers representing the bounding box of the\n # instance, in (x1, y1, x2, y2) order.\n 'bbox': [x1, y1, x2, y2],\n\n # Label of image classification.\n 'bbox_label': 1,\n\n # Used in instance/panoptic segmentation. The segmentation mask\n # of the instance or the information of segments.\n # 1. If list[list[float]], it represents a list of polygons,\n # one for each connected component of the object. Each\n # list[float] is one simple polygon in the format of\n # [x1, y1, ..., xn, yn] (n >= 3). The Xs and Ys are absolute\n # coordinates in unit of pixels.\n # 2. If dict, it represents the per-pixel segmentation mask in\n # COCO's compressed RLE format. The dict should have keys\n # “size” and “counts”. Can be loaded by pycocotools\n 'mask': list[list[float]] or dict,\n\n }\n ]\n # Filename of semantic or panoptic segmentation ground truth file.\n 'seg_map_path': 'a/b/c'\n }\n\n After this module, the annotation has been changed to the format below:\n\n .. code-block:: python\n\n {\n # In (x1, y1, x2, y2) order, float type. N is the number of bboxes\n # in an image\n 'gt_bboxes': BaseBoxes(N, 4)\n # In int type.\n 'gt_bboxes_labels': np.ndarray(N, )\n # In built-in class\n 'gt_masks': PolygonMasks (H, W) or BitmapMasks (H, W)\n # In uint8 type.\n 'gt_seg_map': np.ndarray (H, W)\n # in (x, y, v) order, float type.\n }\n\n Required Keys:\n\n - height\n - width\n - instances\n\n - bbox (optional)\n - bbox_label\n - mask (optional)\n - ignore_flag\n\n - seg_map_path (optional)\n\n Added Keys:\n\n - gt_bboxes (BaseBoxes[torch.float32])\n - gt_bboxes_labels (np.int64)\n - gt_masks (BitmapMasks | PolygonMasks)\n - gt_seg_map (np.uint8)\n - gt_ignore_flags (bool)\n\n Args:\n with_bbox (bool): Whether to parse and load the bbox annotation.\n Defaults to True.\n with_label (bool): Whether to parse and load the label annotation.\n Defaults to True.\n with_mask (bool): Whether to parse and load the mask annotation.\n Default: False.\n with_seg (bool): Whether to parse and load the semantic segmentation\n annotation. Defaults to False.\n poly2mask (bool): Whether to convert mask to bitmap. Default: True.\n box_type (str): The box type used to wrap the bboxes. If ``box_type``\n is None, gt_bboxes will keep being np.ndarray. Defaults to 'hbox'.\n reduce_zero_label (bool): Whether reduce all label value\n by 1. Usually used for datasets where 0 is background label.\n Defaults to False.\n ignore_index (int): The label index to be ignored.\n Valid only if reduce_zero_label is true. Defaults is 255.\n imdecode_backend (str): The image decoding backend type. The backend\n argument for :func:``mmcv.imfrombytes``.\n See :fun:``mmcv.imfrombytes`` for details.\n Defaults to 'cv2'.\n backend_args (dict, optional): Arguments to instantiate the\n corresponding backend. Defaults to None.\n \"\"\"\n\n def __init__(\n self,\n with_mask: bool = False,\n poly2mask: bool = True,\n box_type: str = 'hbox',\n # use for semseg\n reduce_zero_label: bool = False,\n ignore_index: int = 255,\n **kwargs) -> None:\n super(LoadAnnotations, self).__init__(**kwargs)\n self.with_mask = with_mask\n self.poly2mask = poly2mask\n self.box_type = box_type\n self.reduce_zero_label = reduce_zero_label\n self.ignore_index = ignore_index\n\n def _load_bboxes(self, results: dict) -> None:\n \"\"\"Private function to load bounding box annotations.\n\n Args:\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\n Returns:\n dict: The dict contains loaded bounding box annotations.\n \"\"\"\n gt_bboxes = []\n gt_ignore_flags = []\n for instance in results.get('instances', []):\n gt_bboxes.append(instance['bbox'])\n gt_ignore_flags.append(instance['ignore_flag'])\n if self.box_type is None:\n results['gt_bboxes'] = np.array(\n gt_bboxes, dtype=np.float32).reshape((-1, 4))\n else:\n _, box_type_cls = get_box_type(self.box_type)\n results['gt_bboxes'] = box_type_cls(gt_bboxes, dtype=torch.float32)\n results['gt_ignore_flags'] = np.array(gt_ignore_flags, dtype=bool)\n\n def _load_labels(self, results: dict) -> None:\n \"\"\"Private function to load label annotations.\n\n Args:\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\n\n Returns:\n dict: The dict contains loaded label annotations.\n \"\"\"\n gt_bboxes_labels = []\n for instance in results.get('instances', []):\n gt_bboxes_labels.append(instance['bbox_label'])\n # TODO: Inconsistent with mmcv, consider how to deal with it later.\n results['gt_bboxes_labels'] = np.array(\n gt_bboxes_labels, dtype=np.int64)\n\n def _poly2mask(self, mask_ann: Union[list, dict], img_h: int,\n img_w: int) -> np.ndarray:\n \"\"\"Private function to convert masks represented with polygon to\n bitmaps.\n\n Args:\n mask_ann (list | dict): Polygon mask annotation input.\n img_h (int): The height of output mask.\n img_w (int): The width of output mask.\n\n Returns:\n np.ndarray: The decode bitmap mask of shape (img_h, img_w).\n \"\"\"\n\n if isinstance(mask_ann, list):\n # polygon -- a single object might consist of multiple parts\n # we merge all parts into one mask rle code\n rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)\n rle = maskUtils.merge(rles)\n elif isinstance(mask_ann['counts'], list):\n # uncompressed RLE\n rle = maskUtils.frPyObjects(mask_ann, img_h, img_w)\n else:\n # rle\n rle = mask_ann\n mask = maskUtils.decode(rle)\n return mask\n\n def _process_masks(self, results: dict) -> list:\n \"\"\"Process gt_masks and filter invalid polygons.\n\n Args:\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\n\n Returns:\n list: Processed gt_masks.\n \"\"\"\n gt_masks = []\n gt_ignore_flags = []\n for instance in results.get('instances', []):\n gt_mask = instance['mask']\n # If the annotation of segmentation mask is invalid,\n # ignore the whole instance.\n if isinstance(gt_mask, list):\n gt_mask = [\n np.array(polygon) for polygon in gt_mask\n if len(polygon) % 2 == 0 and len(polygon) >= 6\n ]\n if len(gt_mask) == 0:\n # ignore this instance and set gt_mask to a fake mask\n instance['ignore_flag'] = 1\n gt_mask = [np.zeros(6)]\n elif not self.poly2mask:\n # `PolygonMasks` requires a ploygon of format List[np.array],\n # other formats are invalid.\n instance['ignore_flag'] = 1\n gt_mask = [np.zeros(6)]\n elif isinstance(gt_mask, dict) and \\\n not (gt_mask.get('counts') is not None and\n gt_mask.get('size') is not None and\n isinstance(gt_mask['counts'], (list, str))):\n # if gt_mask is a dict, it should include `counts` and `size`,\n # so that `BitmapMasks` can uncompressed RLE\n instance['ignore_flag'] = 1\n gt_mask = [np.zeros(6)]\n gt_masks.append(gt_mask)\n # re-process gt_ignore_flags\n gt_ignore_flags.append(instance['ignore_flag'])\n results['gt_ignore_flags'] = np.array(gt_ignore_flags, dtype=bool)\n return gt_masks\n\n def _load_masks(self, results: dict) -> None:\n \"\"\"Private function to load mask annotations.\n\n Args:\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\n \"\"\"\n h, w = results['ori_shape']\n gt_masks = self._process_masks(results)\n if self.poly2mask:\n gt_masks = BitmapMasks(\n [self._poly2mask(mask, h, w) for mask in gt_masks], h, w)\n else:\n # fake polygon masks will be ignored in `PackDetInputs`\n gt_masks = PolygonMasks([mask for mask in gt_masks], h, w)\n results['gt_masks'] = gt_masks\n\n def _load_seg_map(self, results: dict) -> None:\n \"\"\"Private function to load semantic segmentation annotations.\n\n Args:\n results (dict): Result dict from :obj:``mmcv.BaseDataset``.\n\n Returns:\n dict: The dict contains loaded semantic segmentation annotations.\n \"\"\"\n if results.get('seg_map_path', None) is None:\n return\n\n img_bytes = get(\n results['seg_map_path'], backend_args=self.backend_args)\n gt_semantic_seg = mmcv.imfrombytes(\n img_bytes, flag='unchanged',\n backend=self.imdecode_backend).squeeze()\n\n if self.reduce_zero_label:\n # avoid using underflow conversion\n gt_semantic_seg[gt_semantic_seg == 0] = self.ignore_index\n gt_semantic_seg = gt_semantic_seg - 1\n gt_semantic_seg[gt_semantic_seg == self.ignore_index -\n 1] = self.ignore_index\n\n # modify if custom classes\n if results.get('label_map', None) is not None:\n # Add deep copy to solve bug of repeatedly\n # replace `gt_semantic_seg`, which is reported in\n # https://github.com/open-mmlab/mmsegmentation/pull/1445/\n gt_semantic_seg_copy = gt_semantic_seg.copy()\n for old_id, new_id in results['label_map'].items():\n gt_semantic_seg[gt_semantic_seg_copy == old_id] = new_id\n results['gt_seg_map'] = gt_semantic_seg\n results['ignore_index'] = self.ignore_index\n\n def transform(self, results: dict) -> dict:\n \"\"\"Function to load multiple types annotations.\n\n Args:\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\n\n Returns:\n dict: The dict contains loaded bounding box, label and\n semantic segmentation.\n \"\"\"\n\n if self.with_bbox:\n self._load_bboxes(results)\n if self.with_label:\n self._load_labels(results)\n if self.with_mask:\n self._load_masks(results)\n if self.with_seg:\n self._load_seg_map(results)\n return results\n\n def __repr__(self) -> str:\n repr_str = self.__class__.__name__\n repr_str += f'(with_bbox={self.with_bbox}, '\n repr_str += f'with_label={self.with_label}, '\n repr_str += f'with_mask={self.with_mask}, '\n repr_str += f'with_seg={self.with_seg}, '\n repr_str += f'poly2mask={self.poly2mask}, '\n repr_str += f\"imdecode_backend='{self.imdecode_backend}', \"\n repr_str += f'backend_args={self.backend_args})'\n return repr_str"},{"identifier":"CachedMixUp","path":"mmdet/datasets/transforms/transforms.py","snippet":"class CachedMixUp(BaseTransform):\n \"\"\"Cached mixup data augmentation.\n\n .. code:: text\n\n mixup transform\n +------------------------------+\n | mixup image | |\n | +--------|--------+ |\n | | | | |\n |---------------+ | |\n | | | |\n | | image | |\n | | | |\n | | | |\n | |-----------------+ |\n | pad |\n +------------------------------+\n\n The cached mixup transform steps are as follows:\n\n 1. Append the results from the last transform into the cache.\n 2. Another random image is picked from the cache and embedded in\n the top left patch(after padding and resizing)\n 3. The target of mixup transform is the weighted average of mixup\n image and origin image.\n\n Required Keys:\n\n - img\n - gt_bboxes (np.float32) (optional)\n - gt_bboxes_labels (np.int64) (optional)\n - gt_ignore_flags (bool) (optional)\n - mix_results (List[dict])\n\n\n Modified Keys:\n\n - img\n - img_shape\n - gt_bboxes (optional)\n - gt_bboxes_labels (optional)\n - gt_ignore_flags (optional)\n\n\n Args:\n img_scale (Sequence[int]): Image output size after mixup pipeline.\n The shape order should be (width, height). Defaults to (640, 640).\n ratio_range (Sequence[float]): Scale ratio of mixup image.\n Defaults to (0.5, 1.5).\n flip_ratio (float): Horizontal flip ratio of mixup image.\n Defaults to 0.5.\n pad_val (int): Pad value. Defaults to 114.\n max_iters (int): The maximum number of iterations. If the number of\n iterations is greater than `max_iters`, but gt_bbox is still\n empty, then the iteration is terminated. Defaults to 15.\n bbox_clip_border (bool, optional): Whether to clip the objects outside\n the border of the image. In some dataset like MOT17, the gt bboxes\n are allowed to cross the border of images. Therefore, we don't\n need to clip the gt bboxes in these cases. Defaults to True.\n max_cached_images (int): The maximum length of the cache. The larger\n the cache, the stronger the randomness of this transform. As a\n rule of thumb, providing 10 caches for each image suffices for\n randomness. Defaults to 20.\n random_pop (bool): Whether to randomly pop a result from the cache\n when the cache is full. If set to False, use FIFO popping method.\n Defaults to True.\n prob (float): Probability of applying this transformation.\n Defaults to 1.0.\n \"\"\"\n\n def __init__(self,\n img_scale: Tuple[int, int] = (640, 640),\n ratio_range: Tuple[float, float] = (0.5, 1.5),\n flip_ratio: float = 0.5,\n pad_val: float = 114.0,\n max_iters: int = 15,\n bbox_clip_border: bool = True,\n max_cached_images: int = 20,\n random_pop: bool = True,\n prob: float = 1.0) -> None:\n assert isinstance(img_scale, tuple)\n assert max_cached_images >= 2, 'The length of cache must >= 2, ' \\\n f'but got {max_cached_images}.'\n assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \\\n f'got {prob}.'\n self.dynamic_scale = img_scale\n self.ratio_range = ratio_range\n self.flip_ratio = flip_ratio\n self.pad_val = pad_val\n self.max_iters = max_iters\n self.bbox_clip_border = bbox_clip_border\n self.results_cache = []\n\n self.max_cached_images = max_cached_images\n self.random_pop = random_pop\n self.prob = prob\n\n @cache_randomness\n def get_indexes(self, cache: list) -> int:\n \"\"\"Call function to collect indexes.\n\n Args:\n cache (list): The result cache.\n\n Returns:\n int: index.\n \"\"\"\n\n for i in range(self.max_iters):\n index = random.randint(0, len(cache) - 1)\n gt_bboxes_i = cache[index]['gt_bboxes']\n if len(gt_bboxes_i) != 0:\n break\n return index\n\n @autocast_box_type()\n def transform(self, results: dict) -> dict:\n \"\"\"MixUp transform function.\n\n Args:\n results (dict): Result dict.\n\n Returns:\n dict: Updated result dict.\n \"\"\"\n # cache and pop images\n self.results_cache.append(copy.deepcopy(results))\n if len(self.results_cache) > self.max_cached_images:\n if self.random_pop:\n index = random.randint(0, len(self.results_cache) - 1)\n else:\n index = 0\n self.results_cache.pop(index)\n\n if len(self.results_cache) <= 1:\n return results\n\n if random.uniform(0, 1) > self.prob:\n return results\n\n index = self.get_indexes(self.results_cache)\n retrieve_results = copy.deepcopy(self.results_cache[index])\n\n # TODO: refactor mixup to reuse these code.\n if retrieve_results['gt_bboxes'].shape[0] == 0:\n # empty bbox\n return results\n\n retrieve_img = retrieve_results['img']\n with_mask = True if 'gt_masks' in results else False\n\n jit_factor = random.uniform(*self.ratio_range)\n is_flip = random.uniform(0, 1) > self.flip_ratio\n\n if len(retrieve_img.shape) == 3:\n out_img = np.ones(\n (self.dynamic_scale[1], self.dynamic_scale[0], 3),\n dtype=retrieve_img.dtype) * self.pad_val\n else:\n out_img = np.ones(\n self.dynamic_scale[::-1],\n dtype=retrieve_img.dtype) * self.pad_val\n\n # 1. keep_ratio resize\n scale_ratio = min(self.dynamic_scale[1] / retrieve_img.shape[0],\n self.dynamic_scale[0] / retrieve_img.shape[1])\n retrieve_img = mmcv.imresize(\n retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),\n int(retrieve_img.shape[0] * scale_ratio)))\n\n # 2. paste\n out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img\n\n # 3. scale jit\n scale_ratio *= jit_factor\n out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),\n int(out_img.shape[0] * jit_factor)))\n\n # 4. flip\n if is_flip:\n out_img = out_img[:, ::-1, :]\n\n # 5. random crop\n ori_img = results['img']\n origin_h, origin_w = out_img.shape[:2]\n target_h, target_w = ori_img.shape[:2]\n padded_img = np.ones((max(origin_h, target_h), max(\n origin_w, target_w), 3)) * self.pad_val\n padded_img = padded_img.astype(np.uint8)\n padded_img[:origin_h, :origin_w] = out_img\n\n x_offset, y_offset = 0, 0\n if padded_img.shape[0] > target_h:\n y_offset = random.randint(0, padded_img.shape[0] - target_h)\n if padded_img.shape[1] > target_w:\n x_offset = random.randint(0, padded_img.shape[1] - target_w)\n padded_cropped_img = padded_img[y_offset:y_offset + target_h,\n x_offset:x_offset + target_w]\n\n # 6. adjust bbox\n retrieve_gt_bboxes = retrieve_results['gt_bboxes']\n retrieve_gt_bboxes.rescale_([scale_ratio, scale_ratio])\n if with_mask:\n retrieve_gt_masks = retrieve_results['gt_masks'].rescale(\n scale_ratio)\n\n if self.bbox_clip_border:\n retrieve_gt_bboxes.clip_([origin_h, origin_w])\n\n if is_flip:\n retrieve_gt_bboxes.flip_([origin_h, origin_w],\n direction='horizontal')\n if with_mask:\n retrieve_gt_masks = retrieve_gt_masks.flip()\n\n # 7. filter\n cp_retrieve_gt_bboxes = retrieve_gt_bboxes.clone()\n cp_retrieve_gt_bboxes.translate_([-x_offset, -y_offset])\n if with_mask:\n retrieve_gt_masks = retrieve_gt_masks.translate(\n out_shape=(target_h, target_w),\n offset=-x_offset,\n direction='horizontal')\n retrieve_gt_masks = retrieve_gt_masks.translate(\n out_shape=(target_h, target_w),\n offset=-y_offset,\n direction='vertical')\n\n if self.bbox_clip_border:\n cp_retrieve_gt_bboxes.clip_([target_h, target_w])\n\n # 8. mix up\n ori_img = ori_img.astype(np.float32)\n mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img.astype(np.float32)\n\n retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']\n retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']\n\n mixup_gt_bboxes = cp_retrieve_gt_bboxes.cat(\n (results['gt_bboxes'], cp_retrieve_gt_bboxes), dim=0)\n mixup_gt_bboxes_labels = np.concatenate(\n (results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)\n mixup_gt_ignore_flags = np.concatenate(\n (results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)\n if with_mask:\n mixup_gt_masks = retrieve_gt_masks.cat(\n [results['gt_masks'], retrieve_gt_masks])\n\n # remove outside bbox\n inside_inds = mixup_gt_bboxes.is_inside([target_h, target_w]).numpy()\n mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]\n mixup_gt_bboxes_labels = mixup_gt_bboxes_labels[inside_inds]\n mixup_gt_ignore_flags = mixup_gt_ignore_flags[inside_inds]\n if with_mask:\n mixup_gt_masks = mixup_gt_masks[inside_inds]\n\n results['img'] = mixup_img.astype(np.uint8)\n results['img_shape'] = mixup_img.shape[:2]\n results['gt_bboxes'] = mixup_gt_bboxes\n results['gt_bboxes_labels'] = mixup_gt_bboxes_labels\n results['gt_ignore_flags'] = mixup_gt_ignore_flags\n if with_mask:\n results['gt_masks'] = mixup_gt_masks\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(dynamic_scale={self.dynamic_scale}, '\n repr_str += f'ratio_range={self.ratio_range}, '\n repr_str += f'flip_ratio={self.flip_ratio}, '\n repr_str += f'pad_val={self.pad_val}, '\n repr_str += f'max_iters={self.max_iters}, '\n repr_str += f'bbox_clip_border={self.bbox_clip_border}, '\n repr_str += f'max_cached_images={self.max_cached_images}, '\n repr_str += f'random_pop={self.random_pop}, '\n repr_str += f'prob={self.prob})'\n return repr_str"},{"identifier":"CachedMosaic","path":"mmdet/datasets/transforms/transforms.py","snippet":"class CachedMosaic(Mosaic):\n \"\"\"Cached mosaic augmentation.\n\n Cached mosaic transform will random select images from the cache\n and combine them into one output image.\n\n .. code:: text\n\n mosaic transform\n center_x\n +------------------------------+\n | pad | pad |\n | +-----------+ |\n | | | |\n | | image1 |--------+ |\n | | | | |\n | | | image2 | |\n center_y |----+-------------+-----------|\n | | cropped | |\n |pad | image3 | image4 |\n | | | |\n +----|-------------+-----------+\n | |\n +-------------+\n\n The cached mosaic transform steps are as follows:\n\n 1. Append the results from the last transform into the cache.\n 2. Choose the mosaic center as the intersections of 4 images\n 3. Get the left top image according to the index, and randomly\n sample another 3 images from the result cache.\n 4. Sub image will be cropped if image is larger than mosaic patch\n\n Required Keys:\n\n - img\n - gt_bboxes (np.float32) (optional)\n - gt_bboxes_labels (np.int64) (optional)\n - gt_ignore_flags (bool) (optional)\n\n Modified Keys:\n\n - img\n - img_shape\n - gt_bboxes (optional)\n - gt_bboxes_labels (optional)\n - gt_ignore_flags (optional)\n\n Args:\n img_scale (Sequence[int]): Image size before mosaic pipeline of single\n image. The shape order should be (width, height).\n Defaults to (640, 640).\n center_ratio_range (Sequence[float]): Center ratio range of mosaic\n output. Defaults to (0.5, 1.5).\n bbox_clip_border (bool, optional): Whether to clip the objects outside\n the border of the image. In some dataset like MOT17, the gt bboxes\n are allowed to cross the border of images. Therefore, we don't\n need to clip the gt bboxes in these cases. Defaults to True.\n pad_val (int): Pad value. Defaults to 114.\n prob (float): Probability of applying this transformation.\n Defaults to 1.0.\n max_cached_images (int): The maximum length of the cache. The larger\n the cache, the stronger the randomness of this transform. As a\n rule of thumb, providing 10 caches for each image suffices for\n randomness. Defaults to 40.\n random_pop (bool): Whether to randomly pop a result from the cache\n when the cache is full. If set to False, use FIFO popping method.\n Defaults to True.\n \"\"\"\n\n def __init__(self,\n *args,\n max_cached_images: int = 40,\n random_pop: bool = True,\n **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.results_cache = []\n self.random_pop = random_pop\n assert max_cached_images >= 4, 'The length of cache must >= 4, ' \\\n f'but got {max_cached_images}.'\n self.max_cached_images = max_cached_images\n\n @cache_randomness\n def get_indexes(self, cache: list) -> list:\n \"\"\"Call function to collect indexes.\n\n Args:\n cache (list): The results cache.\n\n Returns:\n list: indexes.\n \"\"\"\n\n indexes = [random.randint(0, len(cache) - 1) for _ in range(3)]\n return indexes\n\n @autocast_box_type()\n def transform(self, results: dict) -> dict:\n \"\"\"Mosaic transform function.\n\n Args:\n results (dict): Result dict.\n\n Returns:\n dict: Updated result dict.\n \"\"\"\n # cache and pop images\n self.results_cache.append(copy.deepcopy(results))\n if len(self.results_cache) > self.max_cached_images:\n if self.random_pop:\n index = random.randint(0, len(self.results_cache) - 1)\n else:\n index = 0\n self.results_cache.pop(index)\n\n if len(self.results_cache) <= 4:\n return results\n\n if random.uniform(0, 1) > self.prob:\n return results\n indices = self.get_indexes(self.results_cache)\n mix_results = [copy.deepcopy(self.results_cache[i]) for i in indices]\n\n # TODO: refactor mosaic to reuse these code.\n mosaic_bboxes = []\n mosaic_bboxes_labels = []\n mosaic_ignore_flags = []\n mosaic_masks = []\n with_mask = True if 'gt_masks' in results else False\n\n if len(results['img'].shape) == 3:\n mosaic_img = np.full(\n (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2), 3),\n self.pad_val,\n dtype=results['img'].dtype)\n else:\n mosaic_img = np.full(\n (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2)),\n self.pad_val,\n dtype=results['img'].dtype)\n\n # mosaic center x, y\n center_x = int(\n random.uniform(*self.center_ratio_range) * self.img_scale[0])\n center_y = int(\n random.uniform(*self.center_ratio_range) * self.img_scale[1])\n center_position = (center_x, center_y)\n\n loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')\n for i, loc in enumerate(loc_strs):\n if loc == 'top_left':\n results_patch = copy.deepcopy(results)\n else:\n results_patch = copy.deepcopy(mix_results[i - 1])\n\n img_i = results_patch['img']\n h_i, w_i = img_i.shape[:2]\n # keep_ratio resize\n scale_ratio_i = min(self.img_scale[1] / h_i,\n self.img_scale[0] / w_i)\n img_i = mmcv.imresize(\n img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))\n\n # compute the combine parameters\n paste_coord, crop_coord = self._mosaic_combine(\n loc, center_position, img_i.shape[:2][::-1])\n x1_p, y1_p, x2_p, y2_p = paste_coord\n x1_c, y1_c, x2_c, y2_c = crop_coord\n\n # crop and paste image\n mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]\n\n # adjust coordinate\n gt_bboxes_i = results_patch['gt_bboxes']\n gt_bboxes_labels_i = results_patch['gt_bboxes_labels']\n gt_ignore_flags_i = results_patch['gt_ignore_flags']\n\n padw = x1_p - x1_c\n padh = y1_p - y1_c\n gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])\n gt_bboxes_i.translate_([padw, padh])\n mosaic_bboxes.append(gt_bboxes_i)\n mosaic_bboxes_labels.append(gt_bboxes_labels_i)\n mosaic_ignore_flags.append(gt_ignore_flags_i)\n if with_mask and results_patch.get('gt_masks', None) is not None:\n gt_masks_i = results_patch['gt_masks']\n gt_masks_i = gt_masks_i.rescale(float(scale_ratio_i))\n gt_masks_i = gt_masks_i.translate(\n out_shape=(int(self.img_scale[0] * 2),\n int(self.img_scale[1] * 2)),\n offset=padw,\n direction='horizontal')\n gt_masks_i = gt_masks_i.translate(\n out_shape=(int(self.img_scale[0] * 2),\n int(self.img_scale[1] * 2)),\n offset=padh,\n direction='vertical')\n mosaic_masks.append(gt_masks_i)\n\n mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)\n mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)\n mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)\n\n if self.bbox_clip_border:\n mosaic_bboxes.clip_([2 * self.img_scale[1], 2 * self.img_scale[0]])\n # remove outside bboxes\n inside_inds = mosaic_bboxes.is_inside(\n [2 * self.img_scale[1], 2 * self.img_scale[0]]).numpy()\n mosaic_bboxes = mosaic_bboxes[inside_inds]\n mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]\n mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]\n\n results['img'] = mosaic_img\n results['img_shape'] = mosaic_img.shape[:2]\n results['gt_bboxes'] = mosaic_bboxes\n results['gt_bboxes_labels'] = mosaic_bboxes_labels\n results['gt_ignore_flags'] = mosaic_ignore_flags\n\n if with_mask:\n mosaic_masks = mosaic_masks[0].cat(mosaic_masks)\n results['gt_masks'] = mosaic_masks[inside_inds]\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(img_scale={self.img_scale}, '\n repr_str += f'center_ratio_range={self.center_ratio_range}, '\n repr_str += f'pad_val={self.pad_val}, '\n repr_str += f'prob={self.prob}, '\n repr_str += f'max_cached_images={self.max_cached_images}, '\n repr_str += f'random_pop={self.random_pop})'\n return repr_str"},{"identifier":"Pad","path":"mmdet/datasets/transforms/transforms.py","snippet":"class Pad(MMCV_Pad):\n \"\"\"Pad the image & segmentation map.\n\n There are three padding modes: (1) pad to a fixed size and (2) pad to the\n minimum size that is divisible by some number. and (3)pad to square. Also,\n pad to square and pad to the minimum size can be used as the same time.\n\n Required Keys:\n\n - img\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\n - gt_seg_map (np.uint8) (optional)\n\n Modified Keys:\n\n - img\n - img_shape\n - gt_masks\n - gt_seg_map\n\n Added Keys:\n\n - pad_shape\n - pad_fixed_size\n - pad_size_divisor\n\n Args:\n size (tuple, optional): Fixed padding size.\n Expected padding shape (width, height). Defaults to None.\n size_divisor (int, optional): The divisor of padded size. Defaults to\n None.\n pad_to_square (bool): Whether to pad the image into a square.\n Currently only used for YOLOX. Defaults to False.\n pad_val (Number | dict[str, Number], optional) - Padding value for if\n the pad_mode is \"constant\". If it is a single number, the value\n to pad the image is the number and to pad the semantic\n segmentation map is 255. If it is a dict, it should have the\n following keys:\n\n - img: The value to pad the image.\n - seg: The value to pad the semantic segmentation map.\n Defaults to dict(img=0, seg=255).\n padding_mode (str): Type of padding. Should be: constant, edge,\n reflect or symmetric. Defaults to 'constant'.\n\n - constant: pads with a constant value, this value is specified\n with pad_val.\n - edge: pads with the last value at the edge of the image.\n - reflect: pads with reflection of image without repeating the last\n value on the edge. For example, padding [1, 2, 3, 4] with 2\n elements on both sides in reflect mode will result in\n [3, 2, 1, 2, 3, 4, 3, 2].\n - symmetric: pads with reflection of image repeating the last value\n on the edge. For example, padding [1, 2, 3, 4] with 2 elements on\n both sides in symmetric mode will result in\n [2, 1, 1, 2, 3, 4, 4, 3]\n \"\"\"\n\n def _pad_masks(self, results: dict) -> None:\n \"\"\"Pad masks according to ``results['pad_shape']``.\"\"\"\n if results.get('gt_masks', None) is not None:\n pad_val = self.pad_val.get('masks', 0)\n pad_shape = results['pad_shape'][:2]\n results['gt_masks'] = results['gt_masks'].pad(\n pad_shape, pad_val=pad_val)\n\n def transform(self, results: dict) -> dict:\n \"\"\"Call function to pad images, masks, semantic segmentation maps.\n\n Args:\n results (dict): Result dict from loading pipeline.\n\n Returns:\n dict: Updated result dict.\n \"\"\"\n self._pad_img(results)\n self._pad_seg(results)\n self._pad_masks(results)\n return results"},{"identifier":"RandomCrop","path":"mmdet/datasets/transforms/transforms.py","snippet":"class RandomCrop(BaseTransform):\n \"\"\"Random crop the image & bboxes & masks.\n\n The absolute ``crop_size`` is sampled based on ``crop_type`` and\n ``image_size``, then the cropped results are generated.\n\n Required Keys:\n\n - img\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\n - gt_bboxes_labels (np.int64) (optional)\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\n - gt_ignore_flags (bool) (optional)\n - gt_seg_map (np.uint8) (optional)\n\n Modified Keys:\n\n - img\n - img_shape\n - gt_bboxes (optional)\n - gt_bboxes_labels (optional)\n - gt_masks (optional)\n - gt_ignore_flags (optional)\n - gt_seg_map (optional)\n - gt_instances_ids (options, only used in MOT/VIS)\n\n Added Keys:\n\n - homography_matrix\n\n Args:\n crop_size (tuple): The relative ratio or absolute pixels of\n (width, height).\n crop_type (str, optional): One of \"relative_range\", \"relative\",\n \"absolute\", \"absolute_range\". \"relative\" randomly crops\n (h * crop_size[0], w * crop_size[1]) part from an input of size\n (h, w). \"relative_range\" uniformly samples relative crop size from\n range [crop_size[0], 1] and [crop_size[1], 1] for height and width\n respectively. \"absolute\" crops from an input with absolute size\n (crop_size[0], crop_size[1]). \"absolute_range\" uniformly samples\n crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w\n in range [crop_size[0], min(w, crop_size[1])].\n Defaults to \"absolute\".\n allow_negative_crop (bool, optional): Whether to allow a crop that does\n not contain any bbox area. Defaults to False.\n recompute_bbox (bool, optional): Whether to re-compute the boxes based\n on cropped instance masks. Defaults to False.\n bbox_clip_border (bool, optional): Whether clip the objects outside\n the border of the image. Defaults to True.\n\n Note:\n - If the image is smaller than the absolute crop size, return the\n original image.\n - The keys for bboxes, labels and masks must be aligned. That is,\n ``gt_bboxes`` corresponds to ``gt_labels`` and ``gt_masks``, and\n ``gt_bboxes_ignore`` corresponds to ``gt_labels_ignore`` and\n ``gt_masks_ignore``.\n - If the crop does not contain any gt-bbox region and\n ``allow_negative_crop`` is set to False, skip this image.\n \"\"\"\n\n def __init__(self,\n crop_size: tuple,\n crop_type: str = 'absolute',\n allow_negative_crop: bool = False,\n recompute_bbox: bool = False,\n bbox_clip_border: bool = True) -> None:\n if crop_type not in [\n 'relative_range', 'relative', 'absolute', 'absolute_range'\n ]:\n raise ValueError(f'Invalid crop_type {crop_type}.')\n if crop_type in ['absolute', 'absolute_range']:\n assert crop_size[0] > 0 and crop_size[1] > 0\n assert isinstance(crop_size[0], int) and isinstance(\n crop_size[1], int)\n if crop_type == 'absolute_range':\n assert crop_size[0] <= crop_size[1]\n else:\n assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1\n self.crop_size = crop_size\n self.crop_type = crop_type\n self.allow_negative_crop = allow_negative_crop\n self.bbox_clip_border = bbox_clip_border\n self.recompute_bbox = recompute_bbox\n\n def _crop_data(self, results: dict, crop_size: Tuple[int, int],\n allow_negative_crop: bool) -> Union[dict, None]:\n \"\"\"Function to randomly crop images, bounding boxes, masks, semantic\n segmentation maps.\n\n Args:\n results (dict): Result dict from loading pipeline.\n crop_size (Tuple[int, int]): Expected absolute size after\n cropping, (h, w).\n allow_negative_crop (bool): Whether to allow a crop that does not\n contain any bbox area.\n\n Returns:\n results (Union[dict, None]): Randomly cropped results, 'img_shape'\n key in result dict is updated according to crop size. None will\n be returned when there is no valid bbox after cropping.\n \"\"\"\n assert crop_size[0] > 0 and crop_size[1] > 0\n img = results['img']\n margin_h = max(img.shape[0] - crop_size[0], 0)\n margin_w = max(img.shape[1] - crop_size[1], 0)\n offset_h, offset_w = self._rand_offset((margin_h, margin_w))\n crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]\n crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]\n\n # Record the homography matrix for the RandomCrop\n homography_matrix = np.array(\n [[1, 0, -offset_w], [0, 1, -offset_h], [0, 0, 1]],\n dtype=np.float32)\n if results.get('homography_matrix', None) is None:\n results['homography_matrix'] = homography_matrix\n else:\n results['homography_matrix'] = homography_matrix @ results[\n 'homography_matrix']\n\n # crop the image\n img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]\n img_shape = img.shape\n results['img'] = img\n results['img_shape'] = img_shape[:2]\n\n # crop bboxes accordingly and clip to the image boundary\n if results.get('gt_bboxes', None) is not None:\n bboxes = results['gt_bboxes']\n bboxes.translate_([-offset_w, -offset_h])\n if self.bbox_clip_border:\n bboxes.clip_(img_shape[:2])\n valid_inds = bboxes.is_inside(img_shape[:2]).numpy()\n # If the crop does not contain any gt-bbox area and\n # allow_negative_crop is False, skip this image.\n if (not valid_inds.any() and not allow_negative_crop):\n return None\n\n results['gt_bboxes'] = bboxes[valid_inds]\n\n if results.get('gt_ignore_flags', None) is not None:\n results['gt_ignore_flags'] = \\\n results['gt_ignore_flags'][valid_inds]\n\n if results.get('gt_bboxes_labels', None) is not None:\n results['gt_bboxes_labels'] = \\\n results['gt_bboxes_labels'][valid_inds]\n\n if results.get('gt_masks', None) is not None:\n results['gt_masks'] = results['gt_masks'][\n valid_inds.nonzero()[0]].crop(\n np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))\n if self.recompute_bbox:\n results['gt_bboxes'] = results['gt_masks'].get_bboxes(\n type(results['gt_bboxes']))\n\n # We should remove the instance ids corresponding to invalid boxes.\n if results.get('gt_instances_ids', None) is not None:\n results['gt_instances_ids'] = \\\n results['gt_instances_ids'][valid_inds]\n\n # crop semantic seg\n if results.get('gt_seg_map', None) is not None:\n results['gt_seg_map'] = results['gt_seg_map'][crop_y1:crop_y2,\n crop_x1:crop_x2]\n\n return results\n\n @cache_randomness\n def _rand_offset(self, margin: Tuple[int, int]) -> Tuple[int, int]:\n \"\"\"Randomly generate crop offset.\n\n Args:\n margin (Tuple[int, int]): The upper bound for the offset generated\n randomly.\n\n Returns:\n Tuple[int, int]: The random offset for the crop.\n \"\"\"\n margin_h, margin_w = margin\n offset_h = np.random.randint(0, margin_h + 1)\n offset_w = np.random.randint(0, margin_w + 1)\n\n return offset_h, offset_w\n\n @cache_randomness\n def _get_crop_size(self, image_size: Tuple[int, int]) -> Tuple[int, int]:\n \"\"\"Randomly generates the absolute crop size based on `crop_type` and\n `image_size`.\n\n Args:\n image_size (Tuple[int, int]): (h, w).\n\n Returns:\n crop_size (Tuple[int, int]): (crop_h, crop_w) in absolute pixels.\n \"\"\"\n h, w = image_size\n if self.crop_type == 'absolute':\n return min(self.crop_size[1], h), min(self.crop_size[0], w)\n elif self.crop_type == 'absolute_range':\n crop_h = np.random.randint(\n min(h, self.crop_size[0]),\n min(h, self.crop_size[1]) + 1)\n crop_w = np.random.randint(\n min(w, self.crop_size[0]),\n min(w, self.crop_size[1]) + 1)\n return crop_h, crop_w\n elif self.crop_type == 'relative':\n crop_w, crop_h = self.crop_size\n return int(h * crop_h + 0.5), int(w * crop_w + 0.5)\n else:\n # 'relative_range'\n crop_size = np.asarray(self.crop_size, dtype=np.float32)\n crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)\n return int(h * crop_h + 0.5), int(w * crop_w + 0.5)\n\n @autocast_box_type()\n def transform(self, results: dict) -> Union[dict, None]:\n \"\"\"Transform function to randomly crop images, bounding boxes, masks,\n semantic segmentation maps.\n\n Args:\n results (dict): Result dict from loading pipeline.\n\n Returns:\n results (Union[dict, None]): Randomly cropped results, 'img_shape'\n key in result dict is updated according to crop size. None will\n be returned when there is no valid bbox after cropping.\n \"\"\"\n image_size = results['img'].shape[:2]\n crop_size = self._get_crop_size(image_size)\n results = self._crop_data(results, crop_size, self.allow_negative_crop)\n return results\n\n def __repr__(self) -> str:\n repr_str = self.__class__.__name__\n repr_str += f'(crop_size={self.crop_size}, '\n repr_str += f'crop_type={self.crop_type}, '\n repr_str += f'allow_negative_crop={self.allow_negative_crop}, '\n repr_str += f'recompute_bbox={self.recompute_bbox}, '\n repr_str += f'bbox_clip_border={self.bbox_clip_border})'\n return repr_str"},{"identifier":"RandomFlip","path":"mmdet/datasets/transforms/transforms.py","snippet":"class RandomFlip(MMCV_RandomFlip):\n \"\"\"Flip the image & bbox & mask & segmentation map. Added or Updated keys:\n flip, flip_direction, img, gt_bboxes, and gt_seg_map. There are 3 flip\n modes:\n\n - ``prob`` is float, ``direction`` is string: the image will be\n ``direction``ly flipped with probability of ``prob`` .\n E.g., ``prob=0.5``, ``direction='horizontal'``,\n then image will be horizontally flipped with probability of 0.5.\n - ``prob`` is float, ``direction`` is list of string: the image will\n be ``direction[i]``ly flipped with probability of\n ``prob/len(direction)``.\n E.g., ``prob=0.5``, ``direction=['horizontal', 'vertical']``,\n then image will be horizontally flipped with probability of 0.25,\n vertically with probability of 0.25.\n - ``prob`` is list of float, ``direction`` is list of string:\n given ``len(prob) == len(direction)``, the image will\n be ``direction[i]``ly flipped with probability of ``prob[i]``.\n E.g., ``prob=[0.3, 0.5]``, ``direction=['horizontal',\n 'vertical']``, then image will be horizontally flipped with\n probability of 0.3, vertically with probability of 0.5.\n\n\n Required Keys:\n\n - img\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\n - gt_seg_map (np.uint8) (optional)\n\n Modified Keys:\n\n - img\n - gt_bboxes\n - gt_masks\n - gt_seg_map\n\n Added Keys:\n\n - flip\n - flip_direction\n - homography_matrix\n\n\n Args:\n prob (float | list[float], optional): The flipping probability.\n Defaults to None.\n direction(str | list[str]): The flipping direction. Options\n If input is a list, the length must equal ``prob``. Each\n element in ``prob`` indicates the flip probability of\n corresponding direction. Defaults to 'horizontal'.\n \"\"\"\n\n def _record_homography_matrix(self, results: dict) -> None:\n \"\"\"Record the homography matrix for the RandomFlip.\"\"\"\n cur_dir = results['flip_direction']\n h, w = results['img'].shape[:2]\n\n if cur_dir == 'horizontal':\n homography_matrix = np.array([[-1, 0, w], [0, 1, 0], [0, 0, 1]],\n dtype=np.float32)\n elif cur_dir == 'vertical':\n homography_matrix = np.array([[1, 0, 0], [0, -1, h], [0, 0, 1]],\n dtype=np.float32)\n elif cur_dir == 'diagonal':\n homography_matrix = np.array([[-1, 0, w], [0, -1, h], [0, 0, 1]],\n dtype=np.float32)\n else:\n homography_matrix = np.eye(3, dtype=np.float32)\n\n if results.get('homography_matrix', None) is None:\n results['homography_matrix'] = homography_matrix\n else:\n results['homography_matrix'] = homography_matrix @ results[\n 'homography_matrix']\n\n @autocast_box_type()\n def _flip(self, results: dict) -> None:\n \"\"\"Flip images, bounding boxes, and semantic segmentation map.\"\"\"\n # flip image\n results['img'] = mmcv.imflip(\n results['img'], direction=results['flip_direction'])\n\n img_shape = results['img'].shape[:2]\n\n # flip bboxes\n if results.get('gt_bboxes', None) is not None:\n results['gt_bboxes'].flip_(img_shape, results['flip_direction'])\n\n # flip masks\n if results.get('gt_masks', None) is not None:\n results['gt_masks'] = results['gt_masks'].flip(\n results['flip_direction'])\n\n # flip segs\n if results.get('gt_seg_map', None) is not None:\n results['gt_seg_map'] = mmcv.imflip(\n results['gt_seg_map'], direction=results['flip_direction'])\n\n # record homography matrix for flip\n self._record_homography_matrix(results)"},{"identifier":"Resize","path":"mmdet/datasets/transforms/transforms.py","snippet":"class Resize(MMCV_Resize):\n \"\"\"Resize images & bbox & seg.\n\n This transform resizes the input image according to ``scale`` or\n ``scale_factor``. Bboxes, masks, and seg map are then resized\n with the same scale factor.\n if ``scale`` and ``scale_factor`` are both set, it will use ``scale`` to\n resize.\n\n Required Keys:\n\n - img\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\n - gt_seg_map (np.uint8) (optional)\n\n Modified Keys:\n\n - img\n - img_shape\n - gt_bboxes\n - gt_masks\n - gt_seg_map\n\n\n Added Keys:\n\n - scale\n - scale_factor\n - keep_ratio\n - homography_matrix\n\n Args:\n scale (int or tuple): Images scales for resizing. Defaults to None\n scale_factor (float or tuple[float]): Scale factors for resizing.\n Defaults to None.\n keep_ratio (bool): Whether to keep the aspect ratio when resizing the\n image. Defaults to False.\n clip_object_border (bool): Whether to clip the objects\n outside the border of the image. In some dataset like MOT17, the gt\n bboxes are allowed to cross the border of images. Therefore, we\n don't need to clip the gt bboxes in these cases. Defaults to True.\n backend (str): Image resize backend, choices are 'cv2' and 'pillow'.\n These two backends generates slightly different results. Defaults\n to 'cv2'.\n interpolation (str): Interpolation method, accepted values are\n \"nearest\", \"bilinear\", \"bicubic\", \"area\", \"lanczos\" for 'cv2'\n backend, \"nearest\", \"bilinear\" for 'pillow' backend. Defaults\n to 'bilinear'.\n \"\"\"\n\n def _resize_masks(self, results: dict) -> None:\n \"\"\"Resize masks with ``results['scale']``\"\"\"\n if results.get('gt_masks', None) is not None:\n if self.keep_ratio:\n results['gt_masks'] = results['gt_masks'].rescale(\n results['scale'])\n else:\n results['gt_masks'] = results['gt_masks'].resize(\n results['img_shape'])\n\n def _resize_bboxes(self, results: dict) -> None:\n \"\"\"Resize bounding boxes with ``results['scale_factor']``.\"\"\"\n if results.get('gt_bboxes', None) is not None:\n results['gt_bboxes'].rescale_(results['scale_factor'])\n if self.clip_object_border:\n results['gt_bboxes'].clip_(results['img_shape'])\n\n def _record_homography_matrix(self, results: dict) -> None:\n \"\"\"Record the homography matrix for the Resize.\"\"\"\n w_scale, h_scale = results['scale_factor']\n homography_matrix = np.array(\n [[w_scale, 0, 0], [0, h_scale, 0], [0, 0, 1]], dtype=np.float32)\n if results.get('homography_matrix', None) is None:\n results['homography_matrix'] = homography_matrix\n else:\n results['homography_matrix'] = homography_matrix @ results[\n 'homography_matrix']\n\n @autocast_box_type()\n def transform(self, results: dict) -> dict:\n \"\"\"Transform function to resize images, bounding boxes and semantic\n segmentation map.\n\n Args:\n results (dict): Result dict from loading pipeline.\n Returns:\n dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',\n 'scale', 'scale_factor', 'height', 'width', and 'keep_ratio' keys\n are updated in result dict.\n \"\"\"\n if self.scale:\n results['scale'] = self.scale\n else:\n img_shape = results['img'].shape[:2]\n results['scale'] = _scale_size(img_shape[::-1], self.scale_factor)\n self._resize_img(results)\n self._resize_bboxes(results)\n self._resize_masks(results)\n self._resize_seg(results)\n self._record_homography_matrix(results)\n return results\n\n def __repr__(self) -> str:\n repr_str = self.__class__.__name__\n repr_str += f'(scale={self.scale}, '\n repr_str += f'scale_factor={self.scale_factor}, '\n repr_str += f'keep_ratio={self.keep_ratio}, '\n repr_str += f'clip_object_border={self.clip_object_border}), '\n repr_str += f'backend={self.backend}), '\n repr_str += f'interpolation={self.interpolation})'\n return repr_str"},{"identifier":"YOLOXHSVRandomAug","path":"mmdet/datasets/transforms/transforms.py","snippet":"class YOLOXHSVRandomAug(BaseTransform):\n \"\"\"Apply HSV augmentation to image sequentially. It is referenced from\n https://github.com/Megvii-\n BaseDetection/YOLOX/blob/main/yolox/data/data_augment.py#L21.\n\n Required Keys:\n\n - img\n\n Modified Keys:\n\n - img\n\n Args:\n hue_delta (int): delta of hue. Defaults to 5.\n saturation_delta (int): delta of saturation. Defaults to 30.\n value_delta (int): delat of value. Defaults to 30.\n \"\"\"\n\n def __init__(self,\n hue_delta: int = 5,\n saturation_delta: int = 30,\n value_delta: int = 30) -> None:\n self.hue_delta = hue_delta\n self.saturation_delta = saturation_delta\n self.value_delta = value_delta\n\n @cache_randomness\n def _get_hsv_gains(self):\n hsv_gains = np.random.uniform(-1, 1, 3) * [\n self.hue_delta, self.saturation_delta, self.value_delta\n ]\n # random selection of h, s, v\n hsv_gains *= np.random.randint(0, 2, 3)\n # prevent overflow\n hsv_gains = hsv_gains.astype(np.int16)\n return hsv_gains\n\n def transform(self, results: dict) -> dict:\n img = results['img']\n hsv_gains = self._get_hsv_gains()\n img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.int16)\n\n img_hsv[..., 0] = (img_hsv[..., 0] + hsv_gains[0]) % 180\n img_hsv[..., 1] = np.clip(img_hsv[..., 1] + hsv_gains[1], 0, 255)\n img_hsv[..., 2] = np.clip(img_hsv[..., 2] + hsv_gains[2], 0, 255)\n cv2.cvtColor(img_hsv.astype(img.dtype), cv2.COLOR_HSV2BGR, dst=img)\n\n results['img'] = img\n return results\n\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(hue_delta={self.hue_delta}, '\n repr_str += f'saturation_delta={self.saturation_delta}, '\n repr_str += f'value_delta={self.value_delta})'\n return repr_str"},{"identifier":"PipelineSwitchHook","path":"mmdet/engine/hooks/pipeline_switch_hook.py","snippet":"class PipelineSwitchHook(Hook):\n \"\"\"Switch data pipeline at switch_epoch.\n\n Args:\n switch_epoch (int): switch pipeline at this epoch.\n switch_pipeline (list[dict]): the pipeline to switch to.\n \"\"\"\n\n def __init__(self, switch_epoch, switch_pipeline):\n self.switch_epoch = switch_epoch\n self.switch_pipeline = switch_pipeline\n self._restart_dataloader = False\n self._has_switched = False\n\n def before_train_epoch(self, runner):\n \"\"\"switch pipeline.\"\"\"\n epoch = runner.epoch\n train_loader = runner.train_dataloader\n if epoch >= self.switch_epoch and not self._has_switched:\n runner.logger.info('Switch pipeline now!')\n # The dataset pipeline cannot be updated when persistent_workers\n # is True, so we need to force the dataloader's multi-process\n # restart. This is a very hacky approach.\n train_loader.dataset.pipeline = Compose(self.switch_pipeline)\n if hasattr(train_loader, 'persistent_workers'\n ) and train_loader.persistent_workers is True:\n train_loader._DataLoader__initialized = False\n train_loader._iterator = None\n self._restart_dataloader = True\n self._has_switched = True\n else:\n # Once the restart is complete, we need to restore\n # the initialization flag.\n if self._restart_dataloader:\n train_loader._DataLoader__initialized = True"},{"identifier":"ExpMomentumEMA","path":"mmdet/models/layers/ema.py","snippet":"class ExpMomentumEMA(ExponentialMovingAverage):\n \"\"\"Exponential moving average (EMA) with exponential momentum strategy,\n which is used in YOLOX.\n\n Args:\n model (nn.Module): The model to be averaged.\n momentum (float): The momentum used for updating ema parameter.\n Ema's parameter are updated with the formula:\n `averaged_param = (1-momentum) * averaged_param + momentum *\n source_param`. Defaults to 0.0002.\n gamma (int): Use a larger momentum early in training and gradually\n annealing to a smaller value to update the ema model smoothly. The\n momentum is calculated as\n `(1 - momentum) * exp(-(1 + steps) / gamma) + momentum`.\n Defaults to 2000.\n interval (int): Interval between two updates. Defaults to 1.\n device (torch.device, optional): If provided, the averaged model will\n be stored on the :attr:`device`. Defaults to None.\n update_buffers (bool): if True, it will compute running averages for\n both the parameters and the buffers of the model. Defaults to\n False.\n \"\"\"\n\n def __init__(self,\n model: nn.Module,\n momentum: float = 0.0002,\n gamma: int = 2000,\n interval=1,\n device: Optional[torch.device] = None,\n update_buffers: bool = False) -> None:\n super().__init__(\n model=model,\n momentum=momentum,\n interval=interval,\n device=device,\n update_buffers=update_buffers)\n assert gamma > 0, f'gamma must be greater than 0, but got {gamma}'\n self.gamma = gamma\n\n def avg_func(self, averaged_param: Tensor, source_param: Tensor,\n steps: int) -> None:\n \"\"\"Compute the moving average of the parameters using the exponential\n momentum strategy.\n\n Args:\n averaged_param (Tensor): The averaged parameters.\n source_param (Tensor): The source parameters.\n steps (int): The number of times the parameters have been\n updated.\n \"\"\"\n momentum = (1 - self.momentum) * math.exp(\n -float(1 + steps) / self.gamma) + self.momentum\n averaged_param.mul_(1 - momentum).add_(source_param, alpha=momentum)"}],"string":"[\n {\n \"identifier\": \"PackDetInputs\",\n \"path\": \"mmdet/datasets/transforms/formatting.py\",\n \"snippet\": \"class PackDetInputs(BaseTransform):\\n \\\"\\\"\\\"Pack the inputs data for the detection / semantic segmentation /\\n panoptic segmentation.\\n\\n The ``img_meta`` item is always populated. The contents of the\\n ``img_meta`` dictionary depends on ``meta_keys``. By default this includes:\\n\\n - ``img_id``: id of the image\\n\\n - ``img_path``: path to the image file\\n\\n - ``ori_shape``: original shape of the image as a tuple (h, w)\\n\\n - ``img_shape``: shape of the image input to the network as a tuple \\\\\\n (h, w). Note that images may be zero padded on the \\\\\\n bottom/right if the batch tensor is larger than this shape.\\n\\n - ``scale_factor``: a float indicating the preprocessing scale\\n\\n - ``flip``: a boolean indicating if image flip transform was used\\n\\n - ``flip_direction``: the flipping direction\\n\\n Args:\\n meta_keys (Sequence[str], optional): Meta keys to be converted to\\n ``mmcv.DataContainer`` and collected in ``data[img_metas]``.\\n Default: ``('img_id', 'img_path', 'ori_shape', 'img_shape',\\n 'scale_factor', 'flip', 'flip_direction')``\\n \\\"\\\"\\\"\\n mapping_table = {\\n 'gt_bboxes': 'bboxes',\\n 'gt_bboxes_labels': 'labels',\\n 'gt_masks': 'masks'\\n }\\n\\n def __init__(self,\\n meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',\\n 'scale_factor', 'flip', 'flip_direction')):\\n self.meta_keys = meta_keys\\n\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"Method to pack the input data.\\n\\n Args:\\n results (dict): Result dict from the data pipeline.\\n\\n Returns:\\n dict:\\n\\n - 'inputs' (obj:`torch.Tensor`): The forward data of models.\\n - 'data_sample' (obj:`DetDataSample`): The annotation info of the\\n sample.\\n \\\"\\\"\\\"\\n packed_results = dict()\\n if 'img' in results:\\n img = results['img']\\n if len(img.shape) < 3:\\n img = np.expand_dims(img, -1)\\n # To improve the computational speed by by 3-5 times, apply:\\n # If image is not contiguous, use\\n # `numpy.transpose()` followed by `numpy.ascontiguousarray()`\\n # If image is already contiguous, use\\n # `torch.permute()` followed by `torch.contiguous()`\\n # Refer to https://github.com/open-mmlab/mmdetection/pull/9533\\n # for more details\\n if not img.flags.c_contiguous:\\n img = np.ascontiguousarray(img.transpose(2, 0, 1))\\n img = to_tensor(img)\\n else:\\n img = to_tensor(img).permute(2, 0, 1).contiguous()\\n\\n packed_results['inputs'] = img\\n\\n if 'gt_ignore_flags' in results:\\n valid_idx = np.where(results['gt_ignore_flags'] == 0)[0]\\n ignore_idx = np.where(results['gt_ignore_flags'] == 1)[0]\\n\\n data_sample = DetDataSample()\\n instance_data = InstanceData()\\n ignore_instance_data = InstanceData()\\n\\n for key in self.mapping_table.keys():\\n if key not in results:\\n continue\\n if key == 'gt_masks' or isinstance(results[key], BaseBoxes):\\n if 'gt_ignore_flags' in results:\\n instance_data[\\n self.mapping_table[key]] = results[key][valid_idx]\\n ignore_instance_data[\\n self.mapping_table[key]] = results[key][ignore_idx]\\n else:\\n instance_data[self.mapping_table[key]] = results[key]\\n else:\\n if 'gt_ignore_flags' in results:\\n instance_data[self.mapping_table[key]] = to_tensor(\\n results[key][valid_idx])\\n ignore_instance_data[self.mapping_table[key]] = to_tensor(\\n results[key][ignore_idx])\\n else:\\n instance_data[self.mapping_table[key]] = to_tensor(\\n results[key])\\n data_sample.gt_instances = instance_data\\n data_sample.ignored_instances = ignore_instance_data\\n\\n if 'proposals' in results:\\n proposals = InstanceData(\\n bboxes=to_tensor(results['proposals']),\\n scores=to_tensor(results['proposals_scores']))\\n data_sample.proposals = proposals\\n\\n if 'gt_seg_map' in results:\\n gt_sem_seg_data = dict(\\n sem_seg=to_tensor(results['gt_seg_map'][None, ...].copy()))\\n gt_sem_seg_data = PixelData(**gt_sem_seg_data)\\n if 'ignore_index' in results:\\n metainfo = dict(ignore_index=results['ignore_index'])\\n gt_sem_seg_data.set_metainfo(metainfo)\\n data_sample.gt_sem_seg = gt_sem_seg_data\\n\\n img_meta = {}\\n for key in self.meta_keys:\\n if key in results:\\n img_meta[key] = results[key]\\n data_sample.set_metainfo(img_meta)\\n packed_results['data_samples'] = data_sample\\n\\n return packed_results\\n\\n def __repr__(self) -> str:\\n repr_str = self.__class__.__name__\\n repr_str += f'(meta_keys={self.meta_keys})'\\n return repr_str\"\n },\n {\n \"identifier\": \"FilterAnnotations\",\n \"path\": \"mmdet/datasets/transforms/loading.py\",\n \"snippet\": \"class FilterAnnotations(BaseTransform):\\n \\\"\\\"\\\"Filter invalid annotations.\\n\\n Required Keys:\\n\\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\\n - gt_bboxes_labels (np.int64) (optional)\\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\\n - gt_ignore_flags (bool) (optional)\\n\\n Modified Keys:\\n\\n - gt_bboxes (optional)\\n - gt_bboxes_labels (optional)\\n - gt_masks (optional)\\n - gt_ignore_flags (optional)\\n\\n Args:\\n min_gt_bbox_wh (tuple[float]): Minimum width and height of ground truth\\n boxes. Default: (1., 1.)\\n min_gt_mask_area (int): Minimum foreground area of ground truth masks.\\n Default: 1\\n by_box (bool): Filter instances with bounding boxes not meeting the\\n min_gt_bbox_wh threshold. Default: True\\n by_mask (bool): Filter instances with masks not meeting\\n min_gt_mask_area threshold. Default: False\\n keep_empty (bool): Whether to return None when it\\n becomes an empty bbox after filtering. Defaults to True.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n min_gt_bbox_wh: Tuple[int, int] = (1, 1),\\n min_gt_mask_area: int = 1,\\n by_box: bool = True,\\n by_mask: bool = False,\\n keep_empty: bool = True) -> None:\\n # TODO: add more filter options\\n assert by_box or by_mask\\n self.min_gt_bbox_wh = min_gt_bbox_wh\\n self.min_gt_mask_area = min_gt_mask_area\\n self.by_box = by_box\\n self.by_mask = by_mask\\n self.keep_empty = keep_empty\\n\\n @autocast_box_type()\\n def transform(self, results: dict) -> Union[dict, None]:\\n \\\"\\\"\\\"Transform function to filter annotations.\\n\\n Args:\\n results (dict): Result dict.\\n\\n Returns:\\n dict: Updated result dict.\\n \\\"\\\"\\\"\\n assert 'gt_bboxes' in results\\n gt_bboxes = results['gt_bboxes']\\n if gt_bboxes.shape[0] == 0:\\n return results\\n\\n tests = []\\n if self.by_box:\\n tests.append(\\n ((gt_bboxes.widths > self.min_gt_bbox_wh[0]) &\\n (gt_bboxes.heights > self.min_gt_bbox_wh[1])).numpy())\\n if self.by_mask:\\n assert 'gt_masks' in results\\n gt_masks = results['gt_masks']\\n tests.append(gt_masks.areas >= self.min_gt_mask_area)\\n\\n keep = tests[0]\\n for t in tests[1:]:\\n keep = keep & t\\n\\n if not keep.any():\\n if self.keep_empty:\\n return None\\n\\n keys = ('gt_bboxes', 'gt_bboxes_labels', 'gt_masks', 'gt_ignore_flags')\\n for key in keys:\\n if key in results:\\n results[key] = results[key][keep]\\n\\n return results\\n\\n def __repr__(self):\\n return self.__class__.__name__ + \\\\\\n f'(min_gt_bbox_wh={self.min_gt_bbox_wh}, ' \\\\\\n f'keep_empty={self.keep_empty})'\"\n },\n {\n \"identifier\": \"LoadAnnotations\",\n \"path\": \"mmdet/datasets/transforms/loading.py\",\n \"snippet\": \"class LoadAnnotations(MMCV_LoadAnnotations):\\n \\\"\\\"\\\"Load and process the ``instances`` and ``seg_map`` annotation provided\\n by dataset.\\n\\n The annotation format is as the following:\\n\\n .. code-block:: python\\n\\n {\\n 'instances':\\n [\\n {\\n # List of 4 numbers representing the bounding box of the\\n # instance, in (x1, y1, x2, y2) order.\\n 'bbox': [x1, y1, x2, y2],\\n\\n # Label of image classification.\\n 'bbox_label': 1,\\n\\n # Used in instance/panoptic segmentation. The segmentation mask\\n # of the instance or the information of segments.\\n # 1. If list[list[float]], it represents a list of polygons,\\n # one for each connected component of the object. Each\\n # list[float] is one simple polygon in the format of\\n # [x1, y1, ..., xn, yn] (n >= 3). The Xs and Ys are absolute\\n # coordinates in unit of pixels.\\n # 2. If dict, it represents the per-pixel segmentation mask in\\n # COCO's compressed RLE format. The dict should have keys\\n # “size” and “counts”. Can be loaded by pycocotools\\n 'mask': list[list[float]] or dict,\\n\\n }\\n ]\\n # Filename of semantic or panoptic segmentation ground truth file.\\n 'seg_map_path': 'a/b/c'\\n }\\n\\n After this module, the annotation has been changed to the format below:\\n\\n .. code-block:: python\\n\\n {\\n # In (x1, y1, x2, y2) order, float type. N is the number of bboxes\\n # in an image\\n 'gt_bboxes': BaseBoxes(N, 4)\\n # In int type.\\n 'gt_bboxes_labels': np.ndarray(N, )\\n # In built-in class\\n 'gt_masks': PolygonMasks (H, W) or BitmapMasks (H, W)\\n # In uint8 type.\\n 'gt_seg_map': np.ndarray (H, W)\\n # in (x, y, v) order, float type.\\n }\\n\\n Required Keys:\\n\\n - height\\n - width\\n - instances\\n\\n - bbox (optional)\\n - bbox_label\\n - mask (optional)\\n - ignore_flag\\n\\n - seg_map_path (optional)\\n\\n Added Keys:\\n\\n - gt_bboxes (BaseBoxes[torch.float32])\\n - gt_bboxes_labels (np.int64)\\n - gt_masks (BitmapMasks | PolygonMasks)\\n - gt_seg_map (np.uint8)\\n - gt_ignore_flags (bool)\\n\\n Args:\\n with_bbox (bool): Whether to parse and load the bbox annotation.\\n Defaults to True.\\n with_label (bool): Whether to parse and load the label annotation.\\n Defaults to True.\\n with_mask (bool): Whether to parse and load the mask annotation.\\n Default: False.\\n with_seg (bool): Whether to parse and load the semantic segmentation\\n annotation. Defaults to False.\\n poly2mask (bool): Whether to convert mask to bitmap. Default: True.\\n box_type (str): The box type used to wrap the bboxes. If ``box_type``\\n is None, gt_bboxes will keep being np.ndarray. Defaults to 'hbox'.\\n reduce_zero_label (bool): Whether reduce all label value\\n by 1. Usually used for datasets where 0 is background label.\\n Defaults to False.\\n ignore_index (int): The label index to be ignored.\\n Valid only if reduce_zero_label is true. Defaults is 255.\\n imdecode_backend (str): The image decoding backend type. The backend\\n argument for :func:``mmcv.imfrombytes``.\\n See :fun:``mmcv.imfrombytes`` for details.\\n Defaults to 'cv2'.\\n backend_args (dict, optional): Arguments to instantiate the\\n corresponding backend. Defaults to None.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n with_mask: bool = False,\\n poly2mask: bool = True,\\n box_type: str = 'hbox',\\n # use for semseg\\n reduce_zero_label: bool = False,\\n ignore_index: int = 255,\\n **kwargs) -> None:\\n super(LoadAnnotations, self).__init__(**kwargs)\\n self.with_mask = with_mask\\n self.poly2mask = poly2mask\\n self.box_type = box_type\\n self.reduce_zero_label = reduce_zero_label\\n self.ignore_index = ignore_index\\n\\n def _load_bboxes(self, results: dict) -> None:\\n \\\"\\\"\\\"Private function to load bounding box annotations.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\\n Returns:\\n dict: The dict contains loaded bounding box annotations.\\n \\\"\\\"\\\"\\n gt_bboxes = []\\n gt_ignore_flags = []\\n for instance in results.get('instances', []):\\n gt_bboxes.append(instance['bbox'])\\n gt_ignore_flags.append(instance['ignore_flag'])\\n if self.box_type is None:\\n results['gt_bboxes'] = np.array(\\n gt_bboxes, dtype=np.float32).reshape((-1, 4))\\n else:\\n _, box_type_cls = get_box_type(self.box_type)\\n results['gt_bboxes'] = box_type_cls(gt_bboxes, dtype=torch.float32)\\n results['gt_ignore_flags'] = np.array(gt_ignore_flags, dtype=bool)\\n\\n def _load_labels(self, results: dict) -> None:\\n \\\"\\\"\\\"Private function to load label annotations.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\\n\\n Returns:\\n dict: The dict contains loaded label annotations.\\n \\\"\\\"\\\"\\n gt_bboxes_labels = []\\n for instance in results.get('instances', []):\\n gt_bboxes_labels.append(instance['bbox_label'])\\n # TODO: Inconsistent with mmcv, consider how to deal with it later.\\n results['gt_bboxes_labels'] = np.array(\\n gt_bboxes_labels, dtype=np.int64)\\n\\n def _poly2mask(self, mask_ann: Union[list, dict], img_h: int,\\n img_w: int) -> np.ndarray:\\n \\\"\\\"\\\"Private function to convert masks represented with polygon to\\n bitmaps.\\n\\n Args:\\n mask_ann (list | dict): Polygon mask annotation input.\\n img_h (int): The height of output mask.\\n img_w (int): The width of output mask.\\n\\n Returns:\\n np.ndarray: The decode bitmap mask of shape (img_h, img_w).\\n \\\"\\\"\\\"\\n\\n if isinstance(mask_ann, list):\\n # polygon -- a single object might consist of multiple parts\\n # we merge all parts into one mask rle code\\n rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)\\n rle = maskUtils.merge(rles)\\n elif isinstance(mask_ann['counts'], list):\\n # uncompressed RLE\\n rle = maskUtils.frPyObjects(mask_ann, img_h, img_w)\\n else:\\n # rle\\n rle = mask_ann\\n mask = maskUtils.decode(rle)\\n return mask\\n\\n def _process_masks(self, results: dict) -> list:\\n \\\"\\\"\\\"Process gt_masks and filter invalid polygons.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\\n\\n Returns:\\n list: Processed gt_masks.\\n \\\"\\\"\\\"\\n gt_masks = []\\n gt_ignore_flags = []\\n for instance in results.get('instances', []):\\n gt_mask = instance['mask']\\n # If the annotation of segmentation mask is invalid,\\n # ignore the whole instance.\\n if isinstance(gt_mask, list):\\n gt_mask = [\\n np.array(polygon) for polygon in gt_mask\\n if len(polygon) % 2 == 0 and len(polygon) >= 6\\n ]\\n if len(gt_mask) == 0:\\n # ignore this instance and set gt_mask to a fake mask\\n instance['ignore_flag'] = 1\\n gt_mask = [np.zeros(6)]\\n elif not self.poly2mask:\\n # `PolygonMasks` requires a ploygon of format List[np.array],\\n # other formats are invalid.\\n instance['ignore_flag'] = 1\\n gt_mask = [np.zeros(6)]\\n elif isinstance(gt_mask, dict) and \\\\\\n not (gt_mask.get('counts') is not None and\\n gt_mask.get('size') is not None and\\n isinstance(gt_mask['counts'], (list, str))):\\n # if gt_mask is a dict, it should include `counts` and `size`,\\n # so that `BitmapMasks` can uncompressed RLE\\n instance['ignore_flag'] = 1\\n gt_mask = [np.zeros(6)]\\n gt_masks.append(gt_mask)\\n # re-process gt_ignore_flags\\n gt_ignore_flags.append(instance['ignore_flag'])\\n results['gt_ignore_flags'] = np.array(gt_ignore_flags, dtype=bool)\\n return gt_masks\\n\\n def _load_masks(self, results: dict) -> None:\\n \\\"\\\"\\\"Private function to load mask annotations.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\\n \\\"\\\"\\\"\\n h, w = results['ori_shape']\\n gt_masks = self._process_masks(results)\\n if self.poly2mask:\\n gt_masks = BitmapMasks(\\n [self._poly2mask(mask, h, w) for mask in gt_masks], h, w)\\n else:\\n # fake polygon masks will be ignored in `PackDetInputs`\\n gt_masks = PolygonMasks([mask for mask in gt_masks], h, w)\\n results['gt_masks'] = gt_masks\\n\\n def _load_seg_map(self, results: dict) -> None:\\n \\\"\\\"\\\"Private function to load semantic segmentation annotations.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmcv.BaseDataset``.\\n\\n Returns:\\n dict: The dict contains loaded semantic segmentation annotations.\\n \\\"\\\"\\\"\\n if results.get('seg_map_path', None) is None:\\n return\\n\\n img_bytes = get(\\n results['seg_map_path'], backend_args=self.backend_args)\\n gt_semantic_seg = mmcv.imfrombytes(\\n img_bytes, flag='unchanged',\\n backend=self.imdecode_backend).squeeze()\\n\\n if self.reduce_zero_label:\\n # avoid using underflow conversion\\n gt_semantic_seg[gt_semantic_seg == 0] = self.ignore_index\\n gt_semantic_seg = gt_semantic_seg - 1\\n gt_semantic_seg[gt_semantic_seg == self.ignore_index -\\n 1] = self.ignore_index\\n\\n # modify if custom classes\\n if results.get('label_map', None) is not None:\\n # Add deep copy to solve bug of repeatedly\\n # replace `gt_semantic_seg`, which is reported in\\n # https://github.com/open-mmlab/mmsegmentation/pull/1445/\\n gt_semantic_seg_copy = gt_semantic_seg.copy()\\n for old_id, new_id in results['label_map'].items():\\n gt_semantic_seg[gt_semantic_seg_copy == old_id] = new_id\\n results['gt_seg_map'] = gt_semantic_seg\\n results['ignore_index'] = self.ignore_index\\n\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"Function to load multiple types annotations.\\n\\n Args:\\n results (dict): Result dict from :obj:``mmengine.BaseDataset``.\\n\\n Returns:\\n dict: The dict contains loaded bounding box, label and\\n semantic segmentation.\\n \\\"\\\"\\\"\\n\\n if self.with_bbox:\\n self._load_bboxes(results)\\n if self.with_label:\\n self._load_labels(results)\\n if self.with_mask:\\n self._load_masks(results)\\n if self.with_seg:\\n self._load_seg_map(results)\\n return results\\n\\n def __repr__(self) -> str:\\n repr_str = self.__class__.__name__\\n repr_str += f'(with_bbox={self.with_bbox}, '\\n repr_str += f'with_label={self.with_label}, '\\n repr_str += f'with_mask={self.with_mask}, '\\n repr_str += f'with_seg={self.with_seg}, '\\n repr_str += f'poly2mask={self.poly2mask}, '\\n repr_str += f\\\"imdecode_backend='{self.imdecode_backend}', \\\"\\n repr_str += f'backend_args={self.backend_args})'\\n return repr_str\"\n },\n {\n \"identifier\": \"CachedMixUp\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class CachedMixUp(BaseTransform):\\n \\\"\\\"\\\"Cached mixup data augmentation.\\n\\n .. code:: text\\n\\n mixup transform\\n +------------------------------+\\n | mixup image | |\\n | +--------|--------+ |\\n | | | | |\\n |---------------+ | |\\n | | | |\\n | | image | |\\n | | | |\\n | | | |\\n | |-----------------+ |\\n | pad |\\n +------------------------------+\\n\\n The cached mixup transform steps are as follows:\\n\\n 1. Append the results from the last transform into the cache.\\n 2. Another random image is picked from the cache and embedded in\\n the top left patch(after padding and resizing)\\n 3. The target of mixup transform is the weighted average of mixup\\n image and origin image.\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (np.float32) (optional)\\n - gt_bboxes_labels (np.int64) (optional)\\n - gt_ignore_flags (bool) (optional)\\n - mix_results (List[dict])\\n\\n\\n Modified Keys:\\n\\n - img\\n - img_shape\\n - gt_bboxes (optional)\\n - gt_bboxes_labels (optional)\\n - gt_ignore_flags (optional)\\n\\n\\n Args:\\n img_scale (Sequence[int]): Image output size after mixup pipeline.\\n The shape order should be (width, height). Defaults to (640, 640).\\n ratio_range (Sequence[float]): Scale ratio of mixup image.\\n Defaults to (0.5, 1.5).\\n flip_ratio (float): Horizontal flip ratio of mixup image.\\n Defaults to 0.5.\\n pad_val (int): Pad value. Defaults to 114.\\n max_iters (int): The maximum number of iterations. If the number of\\n iterations is greater than `max_iters`, but gt_bbox is still\\n empty, then the iteration is terminated. Defaults to 15.\\n bbox_clip_border (bool, optional): Whether to clip the objects outside\\n the border of the image. In some dataset like MOT17, the gt bboxes\\n are allowed to cross the border of images. Therefore, we don't\\n need to clip the gt bboxes in these cases. Defaults to True.\\n max_cached_images (int): The maximum length of the cache. The larger\\n the cache, the stronger the randomness of this transform. As a\\n rule of thumb, providing 10 caches for each image suffices for\\n randomness. Defaults to 20.\\n random_pop (bool): Whether to randomly pop a result from the cache\\n when the cache is full. If set to False, use FIFO popping method.\\n Defaults to True.\\n prob (float): Probability of applying this transformation.\\n Defaults to 1.0.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n img_scale: Tuple[int, int] = (640, 640),\\n ratio_range: Tuple[float, float] = (0.5, 1.5),\\n flip_ratio: float = 0.5,\\n pad_val: float = 114.0,\\n max_iters: int = 15,\\n bbox_clip_border: bool = True,\\n max_cached_images: int = 20,\\n random_pop: bool = True,\\n prob: float = 1.0) -> None:\\n assert isinstance(img_scale, tuple)\\n assert max_cached_images >= 2, 'The length of cache must >= 2, ' \\\\\\n f'but got {max_cached_images}.'\\n assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \\\\\\n f'got {prob}.'\\n self.dynamic_scale = img_scale\\n self.ratio_range = ratio_range\\n self.flip_ratio = flip_ratio\\n self.pad_val = pad_val\\n self.max_iters = max_iters\\n self.bbox_clip_border = bbox_clip_border\\n self.results_cache = []\\n\\n self.max_cached_images = max_cached_images\\n self.random_pop = random_pop\\n self.prob = prob\\n\\n @cache_randomness\\n def get_indexes(self, cache: list) -> int:\\n \\\"\\\"\\\"Call function to collect indexes.\\n\\n Args:\\n cache (list): The result cache.\\n\\n Returns:\\n int: index.\\n \\\"\\\"\\\"\\n\\n for i in range(self.max_iters):\\n index = random.randint(0, len(cache) - 1)\\n gt_bboxes_i = cache[index]['gt_bboxes']\\n if len(gt_bboxes_i) != 0:\\n break\\n return index\\n\\n @autocast_box_type()\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"MixUp transform function.\\n\\n Args:\\n results (dict): Result dict.\\n\\n Returns:\\n dict: Updated result dict.\\n \\\"\\\"\\\"\\n # cache and pop images\\n self.results_cache.append(copy.deepcopy(results))\\n if len(self.results_cache) > self.max_cached_images:\\n if self.random_pop:\\n index = random.randint(0, len(self.results_cache) - 1)\\n else:\\n index = 0\\n self.results_cache.pop(index)\\n\\n if len(self.results_cache) <= 1:\\n return results\\n\\n if random.uniform(0, 1) > self.prob:\\n return results\\n\\n index = self.get_indexes(self.results_cache)\\n retrieve_results = copy.deepcopy(self.results_cache[index])\\n\\n # TODO: refactor mixup to reuse these code.\\n if retrieve_results['gt_bboxes'].shape[0] == 0:\\n # empty bbox\\n return results\\n\\n retrieve_img = retrieve_results['img']\\n with_mask = True if 'gt_masks' in results else False\\n\\n jit_factor = random.uniform(*self.ratio_range)\\n is_flip = random.uniform(0, 1) > self.flip_ratio\\n\\n if len(retrieve_img.shape) == 3:\\n out_img = np.ones(\\n (self.dynamic_scale[1], self.dynamic_scale[0], 3),\\n dtype=retrieve_img.dtype) * self.pad_val\\n else:\\n out_img = np.ones(\\n self.dynamic_scale[::-1],\\n dtype=retrieve_img.dtype) * self.pad_val\\n\\n # 1. keep_ratio resize\\n scale_ratio = min(self.dynamic_scale[1] / retrieve_img.shape[0],\\n self.dynamic_scale[0] / retrieve_img.shape[1])\\n retrieve_img = mmcv.imresize(\\n retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),\\n int(retrieve_img.shape[0] * scale_ratio)))\\n\\n # 2. paste\\n out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img\\n\\n # 3. scale jit\\n scale_ratio *= jit_factor\\n out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),\\n int(out_img.shape[0] * jit_factor)))\\n\\n # 4. flip\\n if is_flip:\\n out_img = out_img[:, ::-1, :]\\n\\n # 5. random crop\\n ori_img = results['img']\\n origin_h, origin_w = out_img.shape[:2]\\n target_h, target_w = ori_img.shape[:2]\\n padded_img = np.ones((max(origin_h, target_h), max(\\n origin_w, target_w), 3)) * self.pad_val\\n padded_img = padded_img.astype(np.uint8)\\n padded_img[:origin_h, :origin_w] = out_img\\n\\n x_offset, y_offset = 0, 0\\n if padded_img.shape[0] > target_h:\\n y_offset = random.randint(0, padded_img.shape[0] - target_h)\\n if padded_img.shape[1] > target_w:\\n x_offset = random.randint(0, padded_img.shape[1] - target_w)\\n padded_cropped_img = padded_img[y_offset:y_offset + target_h,\\n x_offset:x_offset + target_w]\\n\\n # 6. adjust bbox\\n retrieve_gt_bboxes = retrieve_results['gt_bboxes']\\n retrieve_gt_bboxes.rescale_([scale_ratio, scale_ratio])\\n if with_mask:\\n retrieve_gt_masks = retrieve_results['gt_masks'].rescale(\\n scale_ratio)\\n\\n if self.bbox_clip_border:\\n retrieve_gt_bboxes.clip_([origin_h, origin_w])\\n\\n if is_flip:\\n retrieve_gt_bboxes.flip_([origin_h, origin_w],\\n direction='horizontal')\\n if with_mask:\\n retrieve_gt_masks = retrieve_gt_masks.flip()\\n\\n # 7. filter\\n cp_retrieve_gt_bboxes = retrieve_gt_bboxes.clone()\\n cp_retrieve_gt_bboxes.translate_([-x_offset, -y_offset])\\n if with_mask:\\n retrieve_gt_masks = retrieve_gt_masks.translate(\\n out_shape=(target_h, target_w),\\n offset=-x_offset,\\n direction='horizontal')\\n retrieve_gt_masks = retrieve_gt_masks.translate(\\n out_shape=(target_h, target_w),\\n offset=-y_offset,\\n direction='vertical')\\n\\n if self.bbox_clip_border:\\n cp_retrieve_gt_bboxes.clip_([target_h, target_w])\\n\\n # 8. mix up\\n ori_img = ori_img.astype(np.float32)\\n mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img.astype(np.float32)\\n\\n retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']\\n retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']\\n\\n mixup_gt_bboxes = cp_retrieve_gt_bboxes.cat(\\n (results['gt_bboxes'], cp_retrieve_gt_bboxes), dim=0)\\n mixup_gt_bboxes_labels = np.concatenate(\\n (results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)\\n mixup_gt_ignore_flags = np.concatenate(\\n (results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)\\n if with_mask:\\n mixup_gt_masks = retrieve_gt_masks.cat(\\n [results['gt_masks'], retrieve_gt_masks])\\n\\n # remove outside bbox\\n inside_inds = mixup_gt_bboxes.is_inside([target_h, target_w]).numpy()\\n mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]\\n mixup_gt_bboxes_labels = mixup_gt_bboxes_labels[inside_inds]\\n mixup_gt_ignore_flags = mixup_gt_ignore_flags[inside_inds]\\n if with_mask:\\n mixup_gt_masks = mixup_gt_masks[inside_inds]\\n\\n results['img'] = mixup_img.astype(np.uint8)\\n results['img_shape'] = mixup_img.shape[:2]\\n results['gt_bboxes'] = mixup_gt_bboxes\\n results['gt_bboxes_labels'] = mixup_gt_bboxes_labels\\n results['gt_ignore_flags'] = mixup_gt_ignore_flags\\n if with_mask:\\n results['gt_masks'] = mixup_gt_masks\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += f'(dynamic_scale={self.dynamic_scale}, '\\n repr_str += f'ratio_range={self.ratio_range}, '\\n repr_str += f'flip_ratio={self.flip_ratio}, '\\n repr_str += f'pad_val={self.pad_val}, '\\n repr_str += f'max_iters={self.max_iters}, '\\n repr_str += f'bbox_clip_border={self.bbox_clip_border}, '\\n repr_str += f'max_cached_images={self.max_cached_images}, '\\n repr_str += f'random_pop={self.random_pop}, '\\n repr_str += f'prob={self.prob})'\\n return repr_str\"\n },\n {\n \"identifier\": \"CachedMosaic\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class CachedMosaic(Mosaic):\\n \\\"\\\"\\\"Cached mosaic augmentation.\\n\\n Cached mosaic transform will random select images from the cache\\n and combine them into one output image.\\n\\n .. code:: text\\n\\n mosaic transform\\n center_x\\n +------------------------------+\\n | pad | pad |\\n | +-----------+ |\\n | | | |\\n | | image1 |--------+ |\\n | | | | |\\n | | | image2 | |\\n center_y |----+-------------+-----------|\\n | | cropped | |\\n |pad | image3 | image4 |\\n | | | |\\n +----|-------------+-----------+\\n | |\\n +-------------+\\n\\n The cached mosaic transform steps are as follows:\\n\\n 1. Append the results from the last transform into the cache.\\n 2. Choose the mosaic center as the intersections of 4 images\\n 3. Get the left top image according to the index, and randomly\\n sample another 3 images from the result cache.\\n 4. Sub image will be cropped if image is larger than mosaic patch\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (np.float32) (optional)\\n - gt_bboxes_labels (np.int64) (optional)\\n - gt_ignore_flags (bool) (optional)\\n\\n Modified Keys:\\n\\n - img\\n - img_shape\\n - gt_bboxes (optional)\\n - gt_bboxes_labels (optional)\\n - gt_ignore_flags (optional)\\n\\n Args:\\n img_scale (Sequence[int]): Image size before mosaic pipeline of single\\n image. The shape order should be (width, height).\\n Defaults to (640, 640).\\n center_ratio_range (Sequence[float]): Center ratio range of mosaic\\n output. Defaults to (0.5, 1.5).\\n bbox_clip_border (bool, optional): Whether to clip the objects outside\\n the border of the image. In some dataset like MOT17, the gt bboxes\\n are allowed to cross the border of images. Therefore, we don't\\n need to clip the gt bboxes in these cases. Defaults to True.\\n pad_val (int): Pad value. Defaults to 114.\\n prob (float): Probability of applying this transformation.\\n Defaults to 1.0.\\n max_cached_images (int): The maximum length of the cache. The larger\\n the cache, the stronger the randomness of this transform. As a\\n rule of thumb, providing 10 caches for each image suffices for\\n randomness. Defaults to 40.\\n random_pop (bool): Whether to randomly pop a result from the cache\\n when the cache is full. If set to False, use FIFO popping method.\\n Defaults to True.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n *args,\\n max_cached_images: int = 40,\\n random_pop: bool = True,\\n **kwargs) -> None:\\n super().__init__(*args, **kwargs)\\n self.results_cache = []\\n self.random_pop = random_pop\\n assert max_cached_images >= 4, 'The length of cache must >= 4, ' \\\\\\n f'but got {max_cached_images}.'\\n self.max_cached_images = max_cached_images\\n\\n @cache_randomness\\n def get_indexes(self, cache: list) -> list:\\n \\\"\\\"\\\"Call function to collect indexes.\\n\\n Args:\\n cache (list): The results cache.\\n\\n Returns:\\n list: indexes.\\n \\\"\\\"\\\"\\n\\n indexes = [random.randint(0, len(cache) - 1) for _ in range(3)]\\n return indexes\\n\\n @autocast_box_type()\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"Mosaic transform function.\\n\\n Args:\\n results (dict): Result dict.\\n\\n Returns:\\n dict: Updated result dict.\\n \\\"\\\"\\\"\\n # cache and pop images\\n self.results_cache.append(copy.deepcopy(results))\\n if len(self.results_cache) > self.max_cached_images:\\n if self.random_pop:\\n index = random.randint(0, len(self.results_cache) - 1)\\n else:\\n index = 0\\n self.results_cache.pop(index)\\n\\n if len(self.results_cache) <= 4:\\n return results\\n\\n if random.uniform(0, 1) > self.prob:\\n return results\\n indices = self.get_indexes(self.results_cache)\\n mix_results = [copy.deepcopy(self.results_cache[i]) for i in indices]\\n\\n # TODO: refactor mosaic to reuse these code.\\n mosaic_bboxes = []\\n mosaic_bboxes_labels = []\\n mosaic_ignore_flags = []\\n mosaic_masks = []\\n with_mask = True if 'gt_masks' in results else False\\n\\n if len(results['img'].shape) == 3:\\n mosaic_img = np.full(\\n (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2), 3),\\n self.pad_val,\\n dtype=results['img'].dtype)\\n else:\\n mosaic_img = np.full(\\n (int(self.img_scale[1] * 2), int(self.img_scale[0] * 2)),\\n self.pad_val,\\n dtype=results['img'].dtype)\\n\\n # mosaic center x, y\\n center_x = int(\\n random.uniform(*self.center_ratio_range) * self.img_scale[0])\\n center_y = int(\\n random.uniform(*self.center_ratio_range) * self.img_scale[1])\\n center_position = (center_x, center_y)\\n\\n loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')\\n for i, loc in enumerate(loc_strs):\\n if loc == 'top_left':\\n results_patch = copy.deepcopy(results)\\n else:\\n results_patch = copy.deepcopy(mix_results[i - 1])\\n\\n img_i = results_patch['img']\\n h_i, w_i = img_i.shape[:2]\\n # keep_ratio resize\\n scale_ratio_i = min(self.img_scale[1] / h_i,\\n self.img_scale[0] / w_i)\\n img_i = mmcv.imresize(\\n img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))\\n\\n # compute the combine parameters\\n paste_coord, crop_coord = self._mosaic_combine(\\n loc, center_position, img_i.shape[:2][::-1])\\n x1_p, y1_p, x2_p, y2_p = paste_coord\\n x1_c, y1_c, x2_c, y2_c = crop_coord\\n\\n # crop and paste image\\n mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]\\n\\n # adjust coordinate\\n gt_bboxes_i = results_patch['gt_bboxes']\\n gt_bboxes_labels_i = results_patch['gt_bboxes_labels']\\n gt_ignore_flags_i = results_patch['gt_ignore_flags']\\n\\n padw = x1_p - x1_c\\n padh = y1_p - y1_c\\n gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])\\n gt_bboxes_i.translate_([padw, padh])\\n mosaic_bboxes.append(gt_bboxes_i)\\n mosaic_bboxes_labels.append(gt_bboxes_labels_i)\\n mosaic_ignore_flags.append(gt_ignore_flags_i)\\n if with_mask and results_patch.get('gt_masks', None) is not None:\\n gt_masks_i = results_patch['gt_masks']\\n gt_masks_i = gt_masks_i.rescale(float(scale_ratio_i))\\n gt_masks_i = gt_masks_i.translate(\\n out_shape=(int(self.img_scale[0] * 2),\\n int(self.img_scale[1] * 2)),\\n offset=padw,\\n direction='horizontal')\\n gt_masks_i = gt_masks_i.translate(\\n out_shape=(int(self.img_scale[0] * 2),\\n int(self.img_scale[1] * 2)),\\n offset=padh,\\n direction='vertical')\\n mosaic_masks.append(gt_masks_i)\\n\\n mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)\\n mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)\\n mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)\\n\\n if self.bbox_clip_border:\\n mosaic_bboxes.clip_([2 * self.img_scale[1], 2 * self.img_scale[0]])\\n # remove outside bboxes\\n inside_inds = mosaic_bboxes.is_inside(\\n [2 * self.img_scale[1], 2 * self.img_scale[0]]).numpy()\\n mosaic_bboxes = mosaic_bboxes[inside_inds]\\n mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]\\n mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]\\n\\n results['img'] = mosaic_img\\n results['img_shape'] = mosaic_img.shape[:2]\\n results['gt_bboxes'] = mosaic_bboxes\\n results['gt_bboxes_labels'] = mosaic_bboxes_labels\\n results['gt_ignore_flags'] = mosaic_ignore_flags\\n\\n if with_mask:\\n mosaic_masks = mosaic_masks[0].cat(mosaic_masks)\\n results['gt_masks'] = mosaic_masks[inside_inds]\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += f'(img_scale={self.img_scale}, '\\n repr_str += f'center_ratio_range={self.center_ratio_range}, '\\n repr_str += f'pad_val={self.pad_val}, '\\n repr_str += f'prob={self.prob}, '\\n repr_str += f'max_cached_images={self.max_cached_images}, '\\n repr_str += f'random_pop={self.random_pop})'\\n return repr_str\"\n },\n {\n \"identifier\": \"Pad\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class Pad(MMCV_Pad):\\n \\\"\\\"\\\"Pad the image & segmentation map.\\n\\n There are three padding modes: (1) pad to a fixed size and (2) pad to the\\n minimum size that is divisible by some number. and (3)pad to square. Also,\\n pad to square and pad to the minimum size can be used as the same time.\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\\n - gt_seg_map (np.uint8) (optional)\\n\\n Modified Keys:\\n\\n - img\\n - img_shape\\n - gt_masks\\n - gt_seg_map\\n\\n Added Keys:\\n\\n - pad_shape\\n - pad_fixed_size\\n - pad_size_divisor\\n\\n Args:\\n size (tuple, optional): Fixed padding size.\\n Expected padding shape (width, height). Defaults to None.\\n size_divisor (int, optional): The divisor of padded size. Defaults to\\n None.\\n pad_to_square (bool): Whether to pad the image into a square.\\n Currently only used for YOLOX. Defaults to False.\\n pad_val (Number | dict[str, Number], optional) - Padding value for if\\n the pad_mode is \\\"constant\\\". If it is a single number, the value\\n to pad the image is the number and to pad the semantic\\n segmentation map is 255. If it is a dict, it should have the\\n following keys:\\n\\n - img: The value to pad the image.\\n - seg: The value to pad the semantic segmentation map.\\n Defaults to dict(img=0, seg=255).\\n padding_mode (str): Type of padding. Should be: constant, edge,\\n reflect or symmetric. Defaults to 'constant'.\\n\\n - constant: pads with a constant value, this value is specified\\n with pad_val.\\n - edge: pads with the last value at the edge of the image.\\n - reflect: pads with reflection of image without repeating the last\\n value on the edge. For example, padding [1, 2, 3, 4] with 2\\n elements on both sides in reflect mode will result in\\n [3, 2, 1, 2, 3, 4, 3, 2].\\n - symmetric: pads with reflection of image repeating the last value\\n on the edge. For example, padding [1, 2, 3, 4] with 2 elements on\\n both sides in symmetric mode will result in\\n [2, 1, 1, 2, 3, 4, 4, 3]\\n \\\"\\\"\\\"\\n\\n def _pad_masks(self, results: dict) -> None:\\n \\\"\\\"\\\"Pad masks according to ``results['pad_shape']``.\\\"\\\"\\\"\\n if results.get('gt_masks', None) is not None:\\n pad_val = self.pad_val.get('masks', 0)\\n pad_shape = results['pad_shape'][:2]\\n results['gt_masks'] = results['gt_masks'].pad(\\n pad_shape, pad_val=pad_val)\\n\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"Call function to pad images, masks, semantic segmentation maps.\\n\\n Args:\\n results (dict): Result dict from loading pipeline.\\n\\n Returns:\\n dict: Updated result dict.\\n \\\"\\\"\\\"\\n self._pad_img(results)\\n self._pad_seg(results)\\n self._pad_masks(results)\\n return results\"\n },\n {\n \"identifier\": \"RandomCrop\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class RandomCrop(BaseTransform):\\n \\\"\\\"\\\"Random crop the image & bboxes & masks.\\n\\n The absolute ``crop_size`` is sampled based on ``crop_type`` and\\n ``image_size``, then the cropped results are generated.\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\\n - gt_bboxes_labels (np.int64) (optional)\\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\\n - gt_ignore_flags (bool) (optional)\\n - gt_seg_map (np.uint8) (optional)\\n\\n Modified Keys:\\n\\n - img\\n - img_shape\\n - gt_bboxes (optional)\\n - gt_bboxes_labels (optional)\\n - gt_masks (optional)\\n - gt_ignore_flags (optional)\\n - gt_seg_map (optional)\\n - gt_instances_ids (options, only used in MOT/VIS)\\n\\n Added Keys:\\n\\n - homography_matrix\\n\\n Args:\\n crop_size (tuple): The relative ratio or absolute pixels of\\n (width, height).\\n crop_type (str, optional): One of \\\"relative_range\\\", \\\"relative\\\",\\n \\\"absolute\\\", \\\"absolute_range\\\". \\\"relative\\\" randomly crops\\n (h * crop_size[0], w * crop_size[1]) part from an input of size\\n (h, w). \\\"relative_range\\\" uniformly samples relative crop size from\\n range [crop_size[0], 1] and [crop_size[1], 1] for height and width\\n respectively. \\\"absolute\\\" crops from an input with absolute size\\n (crop_size[0], crop_size[1]). \\\"absolute_range\\\" uniformly samples\\n crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w\\n in range [crop_size[0], min(w, crop_size[1])].\\n Defaults to \\\"absolute\\\".\\n allow_negative_crop (bool, optional): Whether to allow a crop that does\\n not contain any bbox area. Defaults to False.\\n recompute_bbox (bool, optional): Whether to re-compute the boxes based\\n on cropped instance masks. Defaults to False.\\n bbox_clip_border (bool, optional): Whether clip the objects outside\\n the border of the image. Defaults to True.\\n\\n Note:\\n - If the image is smaller than the absolute crop size, return the\\n original image.\\n - The keys for bboxes, labels and masks must be aligned. That is,\\n ``gt_bboxes`` corresponds to ``gt_labels`` and ``gt_masks``, and\\n ``gt_bboxes_ignore`` corresponds to ``gt_labels_ignore`` and\\n ``gt_masks_ignore``.\\n - If the crop does not contain any gt-bbox region and\\n ``allow_negative_crop`` is set to False, skip this image.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n crop_size: tuple,\\n crop_type: str = 'absolute',\\n allow_negative_crop: bool = False,\\n recompute_bbox: bool = False,\\n bbox_clip_border: bool = True) -> None:\\n if crop_type not in [\\n 'relative_range', 'relative', 'absolute', 'absolute_range'\\n ]:\\n raise ValueError(f'Invalid crop_type {crop_type}.')\\n if crop_type in ['absolute', 'absolute_range']:\\n assert crop_size[0] > 0 and crop_size[1] > 0\\n assert isinstance(crop_size[0], int) and isinstance(\\n crop_size[1], int)\\n if crop_type == 'absolute_range':\\n assert crop_size[0] <= crop_size[1]\\n else:\\n assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1\\n self.crop_size = crop_size\\n self.crop_type = crop_type\\n self.allow_negative_crop = allow_negative_crop\\n self.bbox_clip_border = bbox_clip_border\\n self.recompute_bbox = recompute_bbox\\n\\n def _crop_data(self, results: dict, crop_size: Tuple[int, int],\\n allow_negative_crop: bool) -> Union[dict, None]:\\n \\\"\\\"\\\"Function to randomly crop images, bounding boxes, masks, semantic\\n segmentation maps.\\n\\n Args:\\n results (dict): Result dict from loading pipeline.\\n crop_size (Tuple[int, int]): Expected absolute size after\\n cropping, (h, w).\\n allow_negative_crop (bool): Whether to allow a crop that does not\\n contain any bbox area.\\n\\n Returns:\\n results (Union[dict, None]): Randomly cropped results, 'img_shape'\\n key in result dict is updated according to crop size. None will\\n be returned when there is no valid bbox after cropping.\\n \\\"\\\"\\\"\\n assert crop_size[0] > 0 and crop_size[1] > 0\\n img = results['img']\\n margin_h = max(img.shape[0] - crop_size[0], 0)\\n margin_w = max(img.shape[1] - crop_size[1], 0)\\n offset_h, offset_w = self._rand_offset((margin_h, margin_w))\\n crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]\\n crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]\\n\\n # Record the homography matrix for the RandomCrop\\n homography_matrix = np.array(\\n [[1, 0, -offset_w], [0, 1, -offset_h], [0, 0, 1]],\\n dtype=np.float32)\\n if results.get('homography_matrix', None) is None:\\n results['homography_matrix'] = homography_matrix\\n else:\\n results['homography_matrix'] = homography_matrix @ results[\\n 'homography_matrix']\\n\\n # crop the image\\n img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]\\n img_shape = img.shape\\n results['img'] = img\\n results['img_shape'] = img_shape[:2]\\n\\n # crop bboxes accordingly and clip to the image boundary\\n if results.get('gt_bboxes', None) is not None:\\n bboxes = results['gt_bboxes']\\n bboxes.translate_([-offset_w, -offset_h])\\n if self.bbox_clip_border:\\n bboxes.clip_(img_shape[:2])\\n valid_inds = bboxes.is_inside(img_shape[:2]).numpy()\\n # If the crop does not contain any gt-bbox area and\\n # allow_negative_crop is False, skip this image.\\n if (not valid_inds.any() and not allow_negative_crop):\\n return None\\n\\n results['gt_bboxes'] = bboxes[valid_inds]\\n\\n if results.get('gt_ignore_flags', None) is not None:\\n results['gt_ignore_flags'] = \\\\\\n results['gt_ignore_flags'][valid_inds]\\n\\n if results.get('gt_bboxes_labels', None) is not None:\\n results['gt_bboxes_labels'] = \\\\\\n results['gt_bboxes_labels'][valid_inds]\\n\\n if results.get('gt_masks', None) is not None:\\n results['gt_masks'] = results['gt_masks'][\\n valid_inds.nonzero()[0]].crop(\\n np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))\\n if self.recompute_bbox:\\n results['gt_bboxes'] = results['gt_masks'].get_bboxes(\\n type(results['gt_bboxes']))\\n\\n # We should remove the instance ids corresponding to invalid boxes.\\n if results.get('gt_instances_ids', None) is not None:\\n results['gt_instances_ids'] = \\\\\\n results['gt_instances_ids'][valid_inds]\\n\\n # crop semantic seg\\n if results.get('gt_seg_map', None) is not None:\\n results['gt_seg_map'] = results['gt_seg_map'][crop_y1:crop_y2,\\n crop_x1:crop_x2]\\n\\n return results\\n\\n @cache_randomness\\n def _rand_offset(self, margin: Tuple[int, int]) -> Tuple[int, int]:\\n \\\"\\\"\\\"Randomly generate crop offset.\\n\\n Args:\\n margin (Tuple[int, int]): The upper bound for the offset generated\\n randomly.\\n\\n Returns:\\n Tuple[int, int]: The random offset for the crop.\\n \\\"\\\"\\\"\\n margin_h, margin_w = margin\\n offset_h = np.random.randint(0, margin_h + 1)\\n offset_w = np.random.randint(0, margin_w + 1)\\n\\n return offset_h, offset_w\\n\\n @cache_randomness\\n def _get_crop_size(self, image_size: Tuple[int, int]) -> Tuple[int, int]:\\n \\\"\\\"\\\"Randomly generates the absolute crop size based on `crop_type` and\\n `image_size`.\\n\\n Args:\\n image_size (Tuple[int, int]): (h, w).\\n\\n Returns:\\n crop_size (Tuple[int, int]): (crop_h, crop_w) in absolute pixels.\\n \\\"\\\"\\\"\\n h, w = image_size\\n if self.crop_type == 'absolute':\\n return min(self.crop_size[1], h), min(self.crop_size[0], w)\\n elif self.crop_type == 'absolute_range':\\n crop_h = np.random.randint(\\n min(h, self.crop_size[0]),\\n min(h, self.crop_size[1]) + 1)\\n crop_w = np.random.randint(\\n min(w, self.crop_size[0]),\\n min(w, self.crop_size[1]) + 1)\\n return crop_h, crop_w\\n elif self.crop_type == 'relative':\\n crop_w, crop_h = self.crop_size\\n return int(h * crop_h + 0.5), int(w * crop_w + 0.5)\\n else:\\n # 'relative_range'\\n crop_size = np.asarray(self.crop_size, dtype=np.float32)\\n crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)\\n return int(h * crop_h + 0.5), int(w * crop_w + 0.5)\\n\\n @autocast_box_type()\\n def transform(self, results: dict) -> Union[dict, None]:\\n \\\"\\\"\\\"Transform function to randomly crop images, bounding boxes, masks,\\n semantic segmentation maps.\\n\\n Args:\\n results (dict): Result dict from loading pipeline.\\n\\n Returns:\\n results (Union[dict, None]): Randomly cropped results, 'img_shape'\\n key in result dict is updated according to crop size. None will\\n be returned when there is no valid bbox after cropping.\\n \\\"\\\"\\\"\\n image_size = results['img'].shape[:2]\\n crop_size = self._get_crop_size(image_size)\\n results = self._crop_data(results, crop_size, self.allow_negative_crop)\\n return results\\n\\n def __repr__(self) -> str:\\n repr_str = self.__class__.__name__\\n repr_str += f'(crop_size={self.crop_size}, '\\n repr_str += f'crop_type={self.crop_type}, '\\n repr_str += f'allow_negative_crop={self.allow_negative_crop}, '\\n repr_str += f'recompute_bbox={self.recompute_bbox}, '\\n repr_str += f'bbox_clip_border={self.bbox_clip_border})'\\n return repr_str\"\n },\n {\n \"identifier\": \"RandomFlip\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class RandomFlip(MMCV_RandomFlip):\\n \\\"\\\"\\\"Flip the image & bbox & mask & segmentation map. Added or Updated keys:\\n flip, flip_direction, img, gt_bboxes, and gt_seg_map. There are 3 flip\\n modes:\\n\\n - ``prob`` is float, ``direction`` is string: the image will be\\n ``direction``ly flipped with probability of ``prob`` .\\n E.g., ``prob=0.5``, ``direction='horizontal'``,\\n then image will be horizontally flipped with probability of 0.5.\\n - ``prob`` is float, ``direction`` is list of string: the image will\\n be ``direction[i]``ly flipped with probability of\\n ``prob/len(direction)``.\\n E.g., ``prob=0.5``, ``direction=['horizontal', 'vertical']``,\\n then image will be horizontally flipped with probability of 0.25,\\n vertically with probability of 0.25.\\n - ``prob`` is list of float, ``direction`` is list of string:\\n given ``len(prob) == len(direction)``, the image will\\n be ``direction[i]``ly flipped with probability of ``prob[i]``.\\n E.g., ``prob=[0.3, 0.5]``, ``direction=['horizontal',\\n 'vertical']``, then image will be horizontally flipped with\\n probability of 0.3, vertically with probability of 0.5.\\n\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\\n - gt_seg_map (np.uint8) (optional)\\n\\n Modified Keys:\\n\\n - img\\n - gt_bboxes\\n - gt_masks\\n - gt_seg_map\\n\\n Added Keys:\\n\\n - flip\\n - flip_direction\\n - homography_matrix\\n\\n\\n Args:\\n prob (float | list[float], optional): The flipping probability.\\n Defaults to None.\\n direction(str | list[str]): The flipping direction. Options\\n If input is a list, the length must equal ``prob``. Each\\n element in ``prob`` indicates the flip probability of\\n corresponding direction. Defaults to 'horizontal'.\\n \\\"\\\"\\\"\\n\\n def _record_homography_matrix(self, results: dict) -> None:\\n \\\"\\\"\\\"Record the homography matrix for the RandomFlip.\\\"\\\"\\\"\\n cur_dir = results['flip_direction']\\n h, w = results['img'].shape[:2]\\n\\n if cur_dir == 'horizontal':\\n homography_matrix = np.array([[-1, 0, w], [0, 1, 0], [0, 0, 1]],\\n dtype=np.float32)\\n elif cur_dir == 'vertical':\\n homography_matrix = np.array([[1, 0, 0], [0, -1, h], [0, 0, 1]],\\n dtype=np.float32)\\n elif cur_dir == 'diagonal':\\n homography_matrix = np.array([[-1, 0, w], [0, -1, h], [0, 0, 1]],\\n dtype=np.float32)\\n else:\\n homography_matrix = np.eye(3, dtype=np.float32)\\n\\n if results.get('homography_matrix', None) is None:\\n results['homography_matrix'] = homography_matrix\\n else:\\n results['homography_matrix'] = homography_matrix @ results[\\n 'homography_matrix']\\n\\n @autocast_box_type()\\n def _flip(self, results: dict) -> None:\\n \\\"\\\"\\\"Flip images, bounding boxes, and semantic segmentation map.\\\"\\\"\\\"\\n # flip image\\n results['img'] = mmcv.imflip(\\n results['img'], direction=results['flip_direction'])\\n\\n img_shape = results['img'].shape[:2]\\n\\n # flip bboxes\\n if results.get('gt_bboxes', None) is not None:\\n results['gt_bboxes'].flip_(img_shape, results['flip_direction'])\\n\\n # flip masks\\n if results.get('gt_masks', None) is not None:\\n results['gt_masks'] = results['gt_masks'].flip(\\n results['flip_direction'])\\n\\n # flip segs\\n if results.get('gt_seg_map', None) is not None:\\n results['gt_seg_map'] = mmcv.imflip(\\n results['gt_seg_map'], direction=results['flip_direction'])\\n\\n # record homography matrix for flip\\n self._record_homography_matrix(results)\"\n },\n {\n \"identifier\": \"Resize\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class Resize(MMCV_Resize):\\n \\\"\\\"\\\"Resize images & bbox & seg.\\n\\n This transform resizes the input image according to ``scale`` or\\n ``scale_factor``. Bboxes, masks, and seg map are then resized\\n with the same scale factor.\\n if ``scale`` and ``scale_factor`` are both set, it will use ``scale`` to\\n resize.\\n\\n Required Keys:\\n\\n - img\\n - gt_bboxes (BaseBoxes[torch.float32]) (optional)\\n - gt_masks (BitmapMasks | PolygonMasks) (optional)\\n - gt_seg_map (np.uint8) (optional)\\n\\n Modified Keys:\\n\\n - img\\n - img_shape\\n - gt_bboxes\\n - gt_masks\\n - gt_seg_map\\n\\n\\n Added Keys:\\n\\n - scale\\n - scale_factor\\n - keep_ratio\\n - homography_matrix\\n\\n Args:\\n scale (int or tuple): Images scales for resizing. Defaults to None\\n scale_factor (float or tuple[float]): Scale factors for resizing.\\n Defaults to None.\\n keep_ratio (bool): Whether to keep the aspect ratio when resizing the\\n image. Defaults to False.\\n clip_object_border (bool): Whether to clip the objects\\n outside the border of the image. In some dataset like MOT17, the gt\\n bboxes are allowed to cross the border of images. Therefore, we\\n don't need to clip the gt bboxes in these cases. Defaults to True.\\n backend (str): Image resize backend, choices are 'cv2' and 'pillow'.\\n These two backends generates slightly different results. Defaults\\n to 'cv2'.\\n interpolation (str): Interpolation method, accepted values are\\n \\\"nearest\\\", \\\"bilinear\\\", \\\"bicubic\\\", \\\"area\\\", \\\"lanczos\\\" for 'cv2'\\n backend, \\\"nearest\\\", \\\"bilinear\\\" for 'pillow' backend. Defaults\\n to 'bilinear'.\\n \\\"\\\"\\\"\\n\\n def _resize_masks(self, results: dict) -> None:\\n \\\"\\\"\\\"Resize masks with ``results['scale']``\\\"\\\"\\\"\\n if results.get('gt_masks', None) is not None:\\n if self.keep_ratio:\\n results['gt_masks'] = results['gt_masks'].rescale(\\n results['scale'])\\n else:\\n results['gt_masks'] = results['gt_masks'].resize(\\n results['img_shape'])\\n\\n def _resize_bboxes(self, results: dict) -> None:\\n \\\"\\\"\\\"Resize bounding boxes with ``results['scale_factor']``.\\\"\\\"\\\"\\n if results.get('gt_bboxes', None) is not None:\\n results['gt_bboxes'].rescale_(results['scale_factor'])\\n if self.clip_object_border:\\n results['gt_bboxes'].clip_(results['img_shape'])\\n\\n def _record_homography_matrix(self, results: dict) -> None:\\n \\\"\\\"\\\"Record the homography matrix for the Resize.\\\"\\\"\\\"\\n w_scale, h_scale = results['scale_factor']\\n homography_matrix = np.array(\\n [[w_scale, 0, 0], [0, h_scale, 0], [0, 0, 1]], dtype=np.float32)\\n if results.get('homography_matrix', None) is None:\\n results['homography_matrix'] = homography_matrix\\n else:\\n results['homography_matrix'] = homography_matrix @ results[\\n 'homography_matrix']\\n\\n @autocast_box_type()\\n def transform(self, results: dict) -> dict:\\n \\\"\\\"\\\"Transform function to resize images, bounding boxes and semantic\\n segmentation map.\\n\\n Args:\\n results (dict): Result dict from loading pipeline.\\n Returns:\\n dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',\\n 'scale', 'scale_factor', 'height', 'width', and 'keep_ratio' keys\\n are updated in result dict.\\n \\\"\\\"\\\"\\n if self.scale:\\n results['scale'] = self.scale\\n else:\\n img_shape = results['img'].shape[:2]\\n results['scale'] = _scale_size(img_shape[::-1], self.scale_factor)\\n self._resize_img(results)\\n self._resize_bboxes(results)\\n self._resize_masks(results)\\n self._resize_seg(results)\\n self._record_homography_matrix(results)\\n return results\\n\\n def __repr__(self) -> str:\\n repr_str = self.__class__.__name__\\n repr_str += f'(scale={self.scale}, '\\n repr_str += f'scale_factor={self.scale_factor}, '\\n repr_str += f'keep_ratio={self.keep_ratio}, '\\n repr_str += f'clip_object_border={self.clip_object_border}), '\\n repr_str += f'backend={self.backend}), '\\n repr_str += f'interpolation={self.interpolation})'\\n return repr_str\"\n },\n {\n \"identifier\": \"YOLOXHSVRandomAug\",\n \"path\": \"mmdet/datasets/transforms/transforms.py\",\n \"snippet\": \"class YOLOXHSVRandomAug(BaseTransform):\\n \\\"\\\"\\\"Apply HSV augmentation to image sequentially. It is referenced from\\n https://github.com/Megvii-\\n BaseDetection/YOLOX/blob/main/yolox/data/data_augment.py#L21.\\n\\n Required Keys:\\n\\n - img\\n\\n Modified Keys:\\n\\n - img\\n\\n Args:\\n hue_delta (int): delta of hue. Defaults to 5.\\n saturation_delta (int): delta of saturation. Defaults to 30.\\n value_delta (int): delat of value. Defaults to 30.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n hue_delta: int = 5,\\n saturation_delta: int = 30,\\n value_delta: int = 30) -> None:\\n self.hue_delta = hue_delta\\n self.saturation_delta = saturation_delta\\n self.value_delta = value_delta\\n\\n @cache_randomness\\n def _get_hsv_gains(self):\\n hsv_gains = np.random.uniform(-1, 1, 3) * [\\n self.hue_delta, self.saturation_delta, self.value_delta\\n ]\\n # random selection of h, s, v\\n hsv_gains *= np.random.randint(0, 2, 3)\\n # prevent overflow\\n hsv_gains = hsv_gains.astype(np.int16)\\n return hsv_gains\\n\\n def transform(self, results: dict) -> dict:\\n img = results['img']\\n hsv_gains = self._get_hsv_gains()\\n img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.int16)\\n\\n img_hsv[..., 0] = (img_hsv[..., 0] + hsv_gains[0]) % 180\\n img_hsv[..., 1] = np.clip(img_hsv[..., 1] + hsv_gains[1], 0, 255)\\n img_hsv[..., 2] = np.clip(img_hsv[..., 2] + hsv_gains[2], 0, 255)\\n cv2.cvtColor(img_hsv.astype(img.dtype), cv2.COLOR_HSV2BGR, dst=img)\\n\\n results['img'] = img\\n return results\\n\\n def __repr__(self):\\n repr_str = self.__class__.__name__\\n repr_str += f'(hue_delta={self.hue_delta}, '\\n repr_str += f'saturation_delta={self.saturation_delta}, '\\n repr_str += f'value_delta={self.value_delta})'\\n return repr_str\"\n },\n {\n \"identifier\": \"PipelineSwitchHook\",\n \"path\": \"mmdet/engine/hooks/pipeline_switch_hook.py\",\n \"snippet\": \"class PipelineSwitchHook(Hook):\\n \\\"\\\"\\\"Switch data pipeline at switch_epoch.\\n\\n Args:\\n switch_epoch (int): switch pipeline at this epoch.\\n switch_pipeline (list[dict]): the pipeline to switch to.\\n \\\"\\\"\\\"\\n\\n def __init__(self, switch_epoch, switch_pipeline):\\n self.switch_epoch = switch_epoch\\n self.switch_pipeline = switch_pipeline\\n self._restart_dataloader = False\\n self._has_switched = False\\n\\n def before_train_epoch(self, runner):\\n \\\"\\\"\\\"switch pipeline.\\\"\\\"\\\"\\n epoch = runner.epoch\\n train_loader = runner.train_dataloader\\n if epoch >= self.switch_epoch and not self._has_switched:\\n runner.logger.info('Switch pipeline now!')\\n # The dataset pipeline cannot be updated when persistent_workers\\n # is True, so we need to force the dataloader's multi-process\\n # restart. This is a very hacky approach.\\n train_loader.dataset.pipeline = Compose(self.switch_pipeline)\\n if hasattr(train_loader, 'persistent_workers'\\n ) and train_loader.persistent_workers is True:\\n train_loader._DataLoader__initialized = False\\n train_loader._iterator = None\\n self._restart_dataloader = True\\n self._has_switched = True\\n else:\\n # Once the restart is complete, we need to restore\\n # the initialization flag.\\n if self._restart_dataloader:\\n train_loader._DataLoader__initialized = True\"\n },\n {\n \"identifier\": \"ExpMomentumEMA\",\n \"path\": \"mmdet/models/layers/ema.py\",\n \"snippet\": \"class ExpMomentumEMA(ExponentialMovingAverage):\\n \\\"\\\"\\\"Exponential moving average (EMA) with exponential momentum strategy,\\n which is used in YOLOX.\\n\\n Args:\\n model (nn.Module): The model to be averaged.\\n momentum (float): The momentum used for updating ema parameter.\\n Ema's parameter are updated with the formula:\\n `averaged_param = (1-momentum) * averaged_param + momentum *\\n source_param`. Defaults to 0.0002.\\n gamma (int): Use a larger momentum early in training and gradually\\n annealing to a smaller value to update the ema model smoothly. The\\n momentum is calculated as\\n `(1 - momentum) * exp(-(1 + steps) / gamma) + momentum`.\\n Defaults to 2000.\\n interval (int): Interval between two updates. Defaults to 1.\\n device (torch.device, optional): If provided, the averaged model will\\n be stored on the :attr:`device`. Defaults to None.\\n update_buffers (bool): if True, it will compute running averages for\\n both the parameters and the buffers of the model. Defaults to\\n False.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n model: nn.Module,\\n momentum: float = 0.0002,\\n gamma: int = 2000,\\n interval=1,\\n device: Optional[torch.device] = None,\\n update_buffers: bool = False) -> None:\\n super().__init__(\\n model=model,\\n momentum=momentum,\\n interval=interval,\\n device=device,\\n update_buffers=update_buffers)\\n assert gamma > 0, f'gamma must be greater than 0, but got {gamma}'\\n self.gamma = gamma\\n\\n def avg_func(self, averaged_param: Tensor, source_param: Tensor,\\n steps: int) -> None:\\n \\\"\\\"\\\"Compute the moving average of the parameters using the exponential\\n momentum strategy.\\n\\n Args:\\n averaged_param (Tensor): The averaged parameters.\\n source_param (Tensor): The source parameters.\\n steps (int): The number of times the parameters have been\\n updated.\\n \\\"\\\"\\\"\\n momentum = (1 - self.momentum) * math.exp(\\n -float(1 + steps) / self.gamma) + self.momentum\\n averaged_param.mul_(1 - momentum).add_(source_param, alpha=momentum)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from mmengine.config import read_base\n from .rtmdet_ins_l_8xb32_300e_coco import *\nfrom mmcv.transforms.loading import LoadImageFromFile\nfrom mmcv.transforms.processing import RandomResize\nfrom mmengine.hooks.ema_hook import EMAHook\nfrom mmdet.datasets.transforms.formatting import PackDetInputs\nfrom mmdet.datasets.transforms.loading import (FilterAnnotations,\n LoadAnnotations)\nfrom mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic,\n Pad, RandomCrop, RandomFlip,\n Resize, YOLOXHSVRandomAug)\nfrom mmdet.engine.hooks.pipeline_switch_hook import PipelineSwitchHook\nfrom mmdet.models.layers.ema import ExpMomentumEMA"},"token_num":{"kind":"number","value":18177,"string":"18,177"},"cropped_code":{"kind":"string","value":"# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\n\nwith read_base():\n\n\n\ncheckpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa\nmodel.update(\n dict(\n backbone=dict(\n deepen_factor=0.33,\n widen_factor=0.5,\n init_cfg=dict(\n type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),\n neck=dict(\n in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1),\n bbox_head=dict(in_channels=128, feat_channels=128)))\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, backend_args=backend_args),\n dict(\n type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),\n dict(type=CachedMosaic, img_scale=(640, 640), pad_val=114.0),\n dict(\n type=RandomResize,\n scale=(1280, 1280),\n ratio_range=(0.5, 2.0),\n resize_type=Resize,\n keep_ratio=True),\n dict(\n"},"all_code":{"kind":"string","value":"# Copyright (c) OpenMMLab. All rights reserved.\n\n# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa\n# mmcv >= 2.0.1\n# mmengine >= 0.8.0\n\n\nwith read_base():\n\n\n\ncheckpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa\nmodel.update(\n dict(\n backbone=dict(\n deepen_factor=0.33,\n widen_factor=0.5,\n init_cfg=dict(\n type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),\n neck=dict(\n in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1),\n bbox_head=dict(in_channels=128, feat_channels=128)))\n\ntrain_pipeline = [\n dict(type=LoadImageFromFile, backend_args=backend_args),\n dict(\n type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),\n dict(type=CachedMosaic, img_scale=(640, 640), pad_val=114.0),\n dict(\n type=RandomResize,\n scale=(1280, 1280),\n ratio_range=(0.5, 2.0),\n resize_type=Resize,\n keep_ratio=True),\n dict("},"next_line":{"kind":"string","value":" type=RandomCrop,"},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-11-30 08:58:00+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5385,"cells":{"repo_name":{"kind":"string","value":"SEU-ProactiveSecurity-Group/MalPurifier"},"file_path":{"kind":"string","value":"core/defense/amd_dnn_plus.py"},"context":{"kind":"list like","value":[{"identifier":"Max","path":"core/attack/max.py","snippet":"class Max(BaseAttack):\n \"\"\"\n Max攻击：迭代地从多个攻击方法中选择结果。\n\n 参数\n --------\n @param attack_list: List, 已实例化的攻击对象的列表。\n @param varepsilon: Float, 用于判断收敛性的标量。\n \"\"\"\n\n def __init__(self, attack_list, varepsilon=1e-20,\n is_attacker=True, oblivion=False, kappa=1., manipulation_x=None, omega=None, device=None):\n \"\"\"\n 构造函数\n\n 参数:\n - attack_list: 已实例化的攻击对象的列表，至少应该有一个攻击方法。\n - varepsilon: 用于判断收敛性的标量，默认值为1e-20。\n - is_attacker: Bool, 表示是否为攻击者，默认为True。\n - oblivion: Bool, 一个布尔标志（其功能在这里并未详细说明），默认为False。\n - kappa: Float, 一个浮点数参数，默认为1。\n - manipulation_x: 可能与数据的处理或操纵有关，具体用途未详细说明。\n - omega: 参数omega的具体用途未详细说明。\n - device: 设备，例如'cuda'或'cpu'，用于执行计算。\n\n 注意:\n - 在初始化过程中，会首先检查`attack_list`是否包含至少一个攻击对象。\n \"\"\"\n super(Max, self).__init__(is_attacker, oblivion, kappa, manipulation_x, omega, device) # 调用父类的构造函数\n assert len(attack_list) > 0, '至少需要一个攻击方法。' # 确保提供了至少一个攻击对象\n self.attack_list = attack_list # 设置攻击列表\n self.varepsilon = varepsilon # 设置varepsilon值\n self.device = device # 设置计算设备\n\n def perturb(self, model, x, label=None, steps_max=5, min_lambda_=1e-5, max_lambda_=1e5, verbose=False):\n \"\"\"\n 扰动节点特征\n\n 参数\n -----------\n @param model: 受害者模型。\n @param x: torch.FloatTensor, 形状为[batch_size, vocab_dim]的特征向量。\n @param label: torch.LongTensor, 真实标签。\n @param steps_max: Integer, 最大的迭代次数。\n @param min_lambda_: float, 平衡对手检测器的重要性（如果存在）。\n @param max_lambda_: float, 同上。\n @param verbose: Boolean, 是否打印详细日志。\n\n 返回值\n --------\n adv_x: 扰动后的数据。\n \"\"\"\n\n # 判断输入数据是否有效\n if x is None or x.shape[0] <= 0:\n return []\n\n # 将模型设为评估模式，主要是为了禁用一些在训练模式下的特殊层，比如Dropout\n model.eval()\n\n # 获取输入数据x在当前模型下的损失和完成状态\n with torch.no_grad():\n loss, done = self.get_scores(model, x, label)\n\n # 存储当前的损失为前一次的损失\n pre_loss = loss\n\n # 获取输入数据的数量以及其他的维度信息\n n, red_n = x.size()[0], x.size()[1:]\n red_ind = list(range(2, len(x.size()) + 1))\n\n # 初始化攻击样本为输入数据的拷贝\n adv_x = x.detach().clone()\n\n # 初始化停止标志，用于表示哪些样本已经完成了攻击\n stop_flag = torch.zeros(n, dtype=torch.bool, device=self.device)\n\n # 开始主循环，进行多次迭代以改进攻击效果\n for t in range(steps_max):\n # 计算还未完成攻击的样本数量\n num_sample_red = n - torch.sum(stop_flag)\n \n # 如果所有样本都已完成攻击，结束循环\n if num_sample_red <= 0:\n break\n\n # 获取那些还未完成攻击的样本的真实标签\n red_label = label[~stop_flag]\n pertbx = []\n\n # 对于攻击方法列表中的每种攻击方法，尝试对数据进行扰动\n for attack in self.attack_list:\n # 确保每种攻击方法都实现了perturb方法\n assert 'perturb' in type(attack).__dict__.keys()\n\n # 对于某些特定的攻击方法，在第二次及以后的迭代中取消随机化\n if t > 0 and 'use_random' in attack.__dict__.keys():\n attack.use_random = False\n\n # 对于名为\"Orthogonal\"的攻击方法，进行特殊处理\n if 'Orthogonal' in type(attack).__name__:\n pertbx.append(attack.perturb(model=model, x=adv_x[~stop_flag], label=red_label))\n else:\n pertbx.append(attack.perturb(model=model, x=adv_x[~stop_flag], label=red_label,\n min_lambda_=1e-5,\n max_lambda_=1e5,\n ))\n # 将所有攻击方法产生的扰动数据合并\n pertbx = torch.vstack(pertbx)\n\n\n # 不需要计算梯度，提高计算效率\n with torch.no_grad():\n # 将真实标签复制若干次以匹配所有的攻击列表\n red_label_ext = torch.cat([red_label] * len(self.attack_list))\n \n # 获取每种攻击方法产生的损失值和成功状态\n loss, done = self.get_scores(model, pertbx, red_label_ext)\n \n # 调整损失和成功状态的形状以方便后续计算\n loss = loss.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\n done = done.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\n \n # 判断哪些样本至少有一种攻击方法成功\n success_flag = torch.any(done, dim=-1)\n \n # 对于没有成功的样本，将其标记为1以进行后续处理\n done[~torch.any(done, dim=-1)] = 1\n \n # 调整损失值，对于成功的攻击方法，损失值保持不变；对于失败的，损失值变为最小值\n loss = (loss * done.to(torch.float)) + torch.min(loss) * (~done).to(torch.float)\n \n # 调整扰动数据的形状以方便后续计算\n pertbx = pertbx.reshape(len(self.attack_list), num_sample_red, *red_n).permute([1, 0, *red_ind])\n \n # 选择造成最大损失的扰动数据\n _, indices = loss.max(dim=-1)\n adv_x[~stop_flag] = pertbx[torch.arange(num_sample_red), indices]\n \n # 获取选中的扰动数据的损失值\n a_loss = loss[torch.arange(num_sample_red), indices]\n \n # 复制当前的停止标志\n pre_stop_flag = stop_flag.clone()\n \n # 更新停止标志，如果损失值变化很小或者某种攻击方法成功，则停止迭代\n stop_flag[~stop_flag] = (torch.abs(pre_loss[~stop_flag] - a_loss) < self.varepsilon) | success_flag\n \n # 更新前一个损失值\n pre_loss[~pre_stop_flag] = a_loss\n\n # 如果需要打印日志\n if verbose:\n # 评估最终的扰动数据的成功状态\n with torch.no_grad():\n _, done = self.get_scores(model, adv_x, label)\n # 打印攻击成功率\n logger.info(f\"max: attack effectiveness {done.sum().item() / x.size()[0] * 100}%.\")\n\n # 返回最终的扰动数据\n return adv_x\n\n\n def perturb_dae(self, predict_model, purifier, x, label=None, steps_max=5, min_lambda_=1e-5, max_lambda_=1e5, verbose=False, oblivion=False):\n \"\"\"\n 扰动节点特征\n\n 参数\n -----------\n @param model: 受害者模型。\n @param x: torch.FloatTensor, 形状为[batch_size, vocab_dim]的特征向量。\n @param label: torch.LongTensor, 真实标签。\n @param steps_max: Integer, 最大的迭代次数。\n @param min_lambda_: float, 平衡对手检测器的重要性（如果存在）。\n @param max_lambda_: float, 同上。\n @param verbose: Boolean, 是否打印详细日志。\n\n 返回值\n --------\n adv_x: 扰动后的数据。\n \"\"\"\n\n # 判断输入数据是否有效\n if x is None or x.shape[0] <= 0:\n return []\n\n # 将模型设为评估模式，主要是为了禁用一些在训练模式下的特殊层，比如Dropout\n predict_model.eval()\n purifier.eval()\n\n # 获取输入数据x在当前模型下的损失和完成状态\n with torch.no_grad():\n if not oblivion:\n purified_x = purifier(x.detach().clone().float()).to(torch.double)\n else:\n purified_x = x.detach().clone()\n loss, done = self.get_scores(predict_model, purified_x, label)\n\n # 存储当前的损失为前一次的损失\n pre_loss = loss\n\n # 获取输入数据的数量以及其他的维度信息\n n, red_n = x.size()[0], x.size()[1:]\n red_ind = list(range(2, len(x.size()) + 1))\n\n # 初始化攻击样本为输入数据的拷贝\n adv_x = x.detach().clone()\n\n # 初始化停止标志，用于表示哪些样本已经完成了攻击\n stop_flag = torch.zeros(n, dtype=torch.bool, device=self.device)\n\n # 开始主循环，进行多次迭代以改进攻击效果\n for t in range(steps_max):\n # 计算还未完成攻击的样本数量\n num_sample_red = n - torch.sum(stop_flag)\n \n # 如果所有样本都已完成攻击，结束循环\n if num_sample_red <= 0:\n break\n\n # 获取那些还未完成攻击的样本的真实标签\n red_label = label[~stop_flag]\n pertbx = []\n\n # 对于攻击方法列表中的每种攻击方法，尝试对数据进行扰动\n for attack in self.attack_list:\n # 确保每种攻击方法都实现了perturb方法\n assert 'perturb' in type(attack).__dict__.keys()\n\n # 对于某些特定的攻击方法，在第二次及以后的迭代中取消随机化\n if t > 0 and 'use_random' in attack.__dict__.keys():\n attack.use_random = False\n\n # 对于名为\"Orthogonal\"的攻击方法，进行特殊处理\n if 'Orthogonal' in type(attack).__name__:\n pertbx.append(attack.perturb_dae(predict_model=predict_model, purifier=purifier, x=adv_x[~stop_flag], label=red_label, oblivion=oblivion))\n else:\n pertbx.append(attack.perturb_dae(model=predict_model, purifier=purifier, x=adv_x[~stop_flag], label=red_label,\n min_lambda_=1e-5,\n max_lambda_=1e5,\n oblivion=oblivion\n ))\n\n # 将所有攻击方法产生的扰动数据合并\n pertbx = torch.vstack(pertbx)\n\n\n # 不需要计算梯度，提高计算效率\n with torch.no_grad():\n # 将真实标签复制若干次以匹配所有的攻击列表\n red_label_ext = torch.cat([red_label] * len(self.attack_list))\n \n # 获取每种攻击方法产生的损失值和成功状态\n if not oblivion:\n purified_pertbx = purifier(pertbx.detach().clone().float()).to(torch.double)\n else:\n purified_pertbx = pertbx.detach().clone()\n\n loss, done = self.get_scores(predict_model, purified_pertbx, red_label_ext)\n \n # 调整损失和成功状态的形状以方便后续计算\n loss = loss.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\n done = done.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\n \n # 判断哪些样本至少有一种攻击方法成功\n success_flag = torch.any(done, dim=-1)\n \n # 对于没有成功的样本，将其标记为1以进行后续处理\n done[~torch.any(done, dim=-1)] = 1\n \n # 调整损失值，对于成功的攻击方法，损失值保持不变；对于失败的，损失值变为最小值\n loss = (loss * done.to(torch.float)) + torch.min(loss) * (~done).to(torch.float)\n \n # 调整扰动数据的形状以方便后续计算\n pertbx = pertbx.reshape(len(self.attack_list), num_sample_red, *red_n).permute([1, 0, *red_ind])\n \n # 选择造成最大损失的扰动数据\n _, indices = loss.max(dim=-1)\n adv_x[~stop_flag] = pertbx[torch.arange(num_sample_red), indices]\n \n # 获取选中的扰动数据的损失值\n a_loss = loss[torch.arange(num_sample_red), indices]\n \n # 复制当前的停止标志\n pre_stop_flag = stop_flag.clone()\n \n # 更新停止标志，如果损失值变化很小或者某种攻击方法成功，则停止迭代\n stop_flag[~stop_flag] = (torch.abs(pre_loss[~stop_flag] - a_loss) < self.varepsilon) | success_flag\n \n # 更新前一个损失值\n pre_loss[~pre_stop_flag] = a_loss\n\n # 如果需要打印日志\n if verbose:\n # 评估最终的扰动数据的成功状态\n with torch.no_grad():\n purified_adv_x = purifier(adv_x.detach().clone().float()).to(torch.double)\n _, done = self.get_scores(predict_model, purified_adv_x, label)\n # 打印攻击成功率\n logger.info(f\"max: attack effectiveness {done.sum().item() / x.size()[0] * 100}%.\")\n\n # 返回最终的扰动数据\n return adv_x\n\n\n # 这个get_scores函数的主要目的是计算扰动数据在给定模型上的损失值，并判断模型对这些扰动数据的预测是否成功完成。\n # 对于具有检测器功能的模型，还会考虑模型的额外输出来决定预测的完成状态。\n def get_scores(self, model, pertb_x, label):\n \"\"\"\n 获取扰动数据在模型上的损失值和预测标签的完成状态。\n\n 参数：\n @param model: 模型对象，即受攻击的目标模型。\n @param pertb_x: torch.Tensor，扰动后的数据。\n @param label: torch.Tensor，扰动数据的真实标签。\n\n 返回：\n - loss_no_reduction: 每个样本的损失值（无降维处理）。\n - done: Boolean Tensor，表示模型对每个样本的预测是否成功完成。\n \"\"\"\n # 判断模型是否具有检测器功能，如果有，则获取模型的两个输出：logits_f 和 prob_g。\n if hasattr(model, 'is_detector_enabled'):\n logits_f, prob_g = model.forward(pertb_x)\n else:\n # 如果模型没有检测器功能，只获取一个输出logits_f。\n logits_f = model.forward(pertb_x)\n\n # 使用交叉熵计算每个样本的损失值\n ce = F.cross_entropy(logits_f, label, reduction='none')\n\n # 获取模型的预测标签\n y_pred = logits_f.argmax(1)\n\n # 如果模型具有检测器功能且不处于\"oblivion\"模式，则进行特殊处理。\n # 使用模型的输出prob_g来判断是否成功完成了预测。\n if hasattr(model, 'is_detector_enabled') and (not self.oblivion):\n tau = model.get_tau_sample_wise(y_pred)\n loss_no_reduction = -prob_g\n done = (y_pred != label) & (prob_g <= tau)\n else:\n # 如果模型没有检测器功能或处于\"oblivion\"模式，则使用交叉熵损失来判断是否成功完成了预测。\n loss_no_reduction = ce\n done = y_pred != label\n\n return loss_no_reduction, done"},{"identifier":"StepwiseMax","path":"core/attack/stepwise_max.py","snippet":"class StepwiseMax(BaseAttack):\n \"\"\"\n Stepwise max攻击方法，这是一个结合了pgd l1, pgd l2, 和 pgd linf三种攻击方式的方法。\n\n 参数\n ----------\n @param use_random: bool类型，是否使用随机的起始点。\n @param rounding_threshold: float类型，用于四舍五入实数的阈值。\n @param is_attacker: bool类型，是否扮演攻击者角色（注意：防御者执行对抗性训练）。\n @param oblivion: bool类型，是否知道敌手指示器。\n @param kappa: 攻击信心度。\n @param manipulation_x: 可操作性。\n @param omega: 与每个api相对应的互依赖api的索引。\n @param device: 设备，'cpu'或'cuda'。\n\n \"\"\"\n\n def __init__(self, use_random=False, rounding_threshold=0.5,\n is_attacker=True, oblivion=False, kappa=1., manipulation_x=None, omega=None, device=None):\n super(StepwiseMax, self).__init__(is_attacker, oblivion, kappa, manipulation_x, omega, device)\n \n # 是否使用随机起点\n self.use_random = use_random\n \n # 断言确保四舍五入阈值在(0, 1)之间\n assert 0 < rounding_threshold < 1\n \n # 设置四舍五入的阈值\n self.round_threshold = rounding_threshold\n \n # lambda_用于正则化，通常与优化的损失一起使用\n self.lambda_ = 1.\n\n def perturb_dae(self, model, purifier, x, label=None,\n steps=100,\n step_check=1,\n sl_l1=1.,\n sl_l2=1.,\n sl_linf=0.01,\n min_lambda_=1e-5,\n max_lambda_=1e5,\n is_score_round=True,\n base=10.,\n verbose=False,\n oblivion=False):\n \"\"\"\n 对模型进行增强攻击。\n\n @param model: PyTorch模型，待攻击目标。\n @param x: Tensor, 原始输入数据。\n @param label: Tensor或None, 输入数据对应的标签。\n @param steps: int, 攻击的总步数。\n @param step_check: int, 检查间隔，即多少步进行一次检查。\n @param sl_l1: float, L1范数的步长。\n @param sl_l2: float, L2范数的步长。\n @param sl_linf: float, Linf范数的步长。\n @param min_lambda_: float, lambda的最小值。\n @param max_lambda_: float, lambda的最大值。\n @param is_score_round: Boolean, 是否对分数进行四舍五入。\n @param base: float, 基数。\n @param verbose: Boolean, 是否输出详细信息。\n \"\"\"\n # torch.manual_seed(int(random.random() * 100)) # 设置随机种子\n # 参数校验\n assert 0 < min_lambda_ <= max_lambda_\n assert steps >= 0 and (step_check >= 1) and 1 >= sl_l1 > 0 and sl_l2 >= 0 and sl_linf >= 0\n \n model.eval() # 将模型设置为评估模式\n purifier.eval()\n \n # 根据模型是否具有某种属性来设置lambda的初值\n if hasattr(model, 'is_detector_enabled'):\n self.lambda_ = min_lambda_\n else:\n self.lambda_ = max_lambda_\n \n # 如果不是攻击者，从预定义的步骤中随机选择一个\n if not self.is_attacker:\n step_checks = [1, 10, 25, 50]\n step_check = random.choice(step_checks)\n \n # 计算每个小步骤中需要的迭代次数\n mini_steps = [step_check] * (steps // step_check)\n mini_steps = mini_steps + [steps % step_check] if steps % step_check != 0 else mini_steps\n \n # 获取输入的维度信息\n n, red_n = x.size()[0], x.size()[1:]\n red_ind = list(range(2, len(x.size()) + 1))\n \n adv_x = x.detach().clone() # 获取输入数据的副本\n while self.lambda_ <= max_lambda_:\n pert_x_cont = None\n prev_done = None\n for i, mini_step in enumerate(mini_steps):\n with torch.no_grad():\n # 如果是第一步并且启用了随机初始化，那么获取一个随机的起始点\n if i == 0:\n adv_x = get_x0(adv_x, rounding_threshold=self.round_threshold, is_sample=True)\n # 计算损失和完成标志\n if not oblivion:\n purified_adv = purifier(adv_x.detach().clone().float()).to(torch.double)\n else:\n purified_adv = adv_x.detach().clone()\n _, done = self.get_loss(model, purified_adv, label, self.lambda_)\n \n # print(\"done:\", done)\n \n # 如果所有的都完成了，就退出循环\n if torch.all(done):\n break\n \n # 对于那些没有完成的数据，重新计算扰动\n # print(\"i:\", i)\n if i == 0:\n # print(\"~done:\", (~done))\n adv_x[~done] = x[~done]\n prev_done = done.clone()\n else:\n if (adv_x[~done]).shape[0] == (pert_x_cont[~done[~prev_done]]).shape[0]:\n adv_x[~done] = pert_x_cont[~done[~prev_done]]\n else:\n updated_mask = (~done) & (~prev_done[:len(done)])\n num_to_select = updated_mask.sum().item()\n selected_perturbations = pert_x_cont[:num_to_select]\n adv_x[updated_mask] = selected_perturbations\n\n prev_done = done.clone() \n \n # 对那些未完成的数据进行真正的扰动\n num_sample_red = torch.sum(~done).item()\n pert_x_l1, pert_x_l2, pert_x_linf = self._perturb_dae(model, purifier, adv_x[~done], label[~done],\n mini_step,\n sl_l1,\n sl_l2,\n sl_linf,\n lambda_=self.lambda_,\n oblivion=False\n )\n # print(\"pert_x_l1, pert_x_l2, pert_x_linf\", pert_x_l1, pert_x_l2, pert_x_linf)\n # 不计算梯度地执行下列操作\n with torch.no_grad():\n # 构造一个包含三种扰动的列表\n pertb_x_list = [pert_x_linf, pert_x_l2, pert_x_l1]\n n_attacks = len(pertb_x_list) # 获取攻击的数量（即3）\n pertbx = torch.vstack(pertb_x_list) # 垂直堆叠这三种扰动\n label_ext = torch.cat([label[~done]] * n_attacks) # 扩展标签列表，使其与扰动列表长度匹配\n\n # 如果不是攻击者并且不需要四舍五入得分，则获取得分\n # 否则，先对扰动进行四舍五入，再获取得分\n if not oblivion:\n purified_pertbx = purifier(pertbx.detach().clone().float()).to(torch.double)\n else:\n purified_pertbx = pertbx.detach().clone()\n if (not self.is_attacker) and (not is_score_round): \n scores, _done = self.get_scores(model, purified_pertbx, label_ext)\n else:\n scores, _done = self.get_scores(model, round_x(purified_pertbx, self.round_threshold), label_ext)\n \n # 如果得分的最大值大于0，则设置为该值，否则设置为0\n max_v = scores.amax() if scores.amax() > 0 else 0.\n scores[_done] += max_v # 对完成的得分增加max_v\n\n # 重新整形扰动和得分张量，以便后续操作\n pertbx = pertbx.reshape(n_attacks, num_sample_red, *red_n).permute([1, 0, *red_ind])\n scores = scores.reshape(n_attacks, num_sample_red).permute(1, 0)\n\n # 从得分张量中获取最大得分及其索引\n _2, s_idx = scores.max(dim=-1)\n # 使用索引从扰动张量中选择具有最高误导性的扰动\n pert_x_cont = pertbx[torch.arange(num_sample_red), s_idx]\n # print(\"pert_x_cont.shape\", pert_x_cont.shape)\n # 更新经过扰动的数据adv_x\n adv_x[~done] = pert_x_cont if not self.is_attacker else round_x(pert_x_cont, self.round_threshold)\n \n # 更新lambda值以便于下一次循环\n self.lambda_ *= base\n # 如果lambda值检查失败，则中断循环\n if not self.check_lambda(model):\n break\n # 如果是攻击者，对最终的扰动结果进行四舍五入\n if self.is_attacker:\n adv_x = round_x(adv_x, self.round_threshold)\n \n # 不计算梯度地获取最后的损失和完成标志\n with torch.no_grad():\n purified_adv = purifier(adv_x.detach().clone().float()).to(torch.double)\n _, done = self.get_loss(model, purified_adv, label, self.lambda_)\n # 如果设置了详细输出，打印攻击效果的百分比\n if verbose:\n logger.info(f\"step-wise max: attack effectiveness {done.sum().item() / done.size()[0] * 100:.3f}%.\")\n # 返回扰动后的数据\n return adv_x\n\n\n def perturb(self, model, x, label=None,\n steps=100,\n step_check=1,\n sl_l1=1.,\n sl_l2=1.,\n sl_linf=0.01,\n min_lambda_=1e-5,\n max_lambda_=1e5,\n is_score_round=True,\n base=10.,\n verbose=False):\n \"\"\"\n 对模型进行增强攻击。\n\n @param model: PyTorch模型，待攻击目标。\n @param x: Tensor, 原始输入数据。\n @param label: Tensor或None, 输入数据对应的标签。\n @param steps: int, 攻击的总步数。\n @param step_check: int, 检查间隔，即多少步进行一次检查。\n @param sl_l1: float, L1范数的步长。\n @param sl_l2: float, L2范数的步长。\n @param sl_linf: float, Linf范数的步长。\n @param min_lambda_: float, lambda的最小值。\n @param max_lambda_: float, lambda的最大值。\n @param is_score_round: Boolean, 是否对分数进行四舍五入。\n @param base: float, 基数。\n @param verbose: Boolean, 是否输出详细信息。\n \"\"\"\n # torch.manual_seed(int(random.random() * 100)) # 设置随机种子\n # 参数校验\n assert 0 < min_lambda_ <= max_lambda_\n assert steps >= 0 and (step_check >= 1) and 1 >= sl_l1 > 0 and sl_l2 >= 0 and sl_linf >= 0\n \n model.eval() # 将模型设置为评估模式\n \n # 根据模型是否具有某种属性来设置lambda的初值\n if hasattr(model, 'is_detector_enabled'):\n self.lambda_ = min_lambda_\n else:\n self.lambda_ = max_lambda_\n \n # 如果不是攻击者，从预定义的步骤中随机选择一个\n if not self.is_attacker:\n step_checks = [1, 10, 25, 50]\n step_check = random.choice(step_checks)\n \n # 计算每个小步骤中需要的迭代次数\n mini_steps = [step_check] * (steps // step_check)\n mini_steps = mini_steps + [steps % step_check] if steps % step_check != 0 else mini_steps\n \n # 获取输入的维度信息\n n, red_n = x.size()[0], x.size()[1:]\n red_ind = list(range(2, len(x.size()) + 1))\n \n adv_x = x.detach().clone() # 获取输入数据的副本\n while self.lambda_ <= max_lambda_:\n pert_x_cont = None\n prev_done = None\n for i, mini_step in enumerate(mini_steps):\n with torch.no_grad():\n # 如果是第一步并且启用了随机初始化，那么获取一个随机的起始点\n if i == 0:\n adv_x = get_x0(adv_x, rounding_threshold=self.round_threshold, is_sample=True)\n _, done = self.get_loss(model, adv_x, label, self.lambda_)\n \n # print(\"done:\", done)\n \n # 如果所有的都完成了，就退出循环\n if torch.all(done):\n break\n \n # 对于那些没有完成的数据，重新计算扰动\n # print(\"i:\", i)\n if i == 0:\n # print(\"~done:\", (~done))\n adv_x[~done] = x[~done]\n prev_done = done.clone()\n else:\n if (adv_x[~done]).shape[0] == (pert_x_cont[~done[~prev_done]]).shape[0]:\n adv_x[~done] = pert_x_cont[~done[~prev_done]]\n else:\n updated_mask = (~done) & (~prev_done[:len(done)])\n num_to_select = updated_mask.sum().item()\n selected_perturbations = pert_x_cont[:num_to_select]\n adv_x[updated_mask] = selected_perturbations\n\n prev_done = done.clone() \n \n # 对那些未完成的数据进行真正的扰动\n num_sample_red = torch.sum(~done).item()\n pert_x_l1, pert_x_l2, pert_x_linf = self._perturb(model, adv_x[~done], label[~done],\n mini_step,\n sl_l1,\n sl_l2,\n sl_linf,\n lambda_=self.lambda_\n )\n # print(\"pert_x_l1, pert_x_l2, pert_x_linf\", pert_x_l1, pert_x_l2, pert_x_linf)\n # 不计算梯度地执行下列操作\n with torch.no_grad():\n # 构造一个包含三种扰动的列表\n pertb_x_list = [pert_x_linf, pert_x_l2, pert_x_l1]\n n_attacks = len(pertb_x_list) # 获取攻击的数量（即3）\n pertbx = torch.vstack(pertb_x_list) # 垂直堆叠这三种扰动\n label_ext = torch.cat([label[~done]] * n_attacks) # 扩展标签列表，使其与扰动列表长度匹配\n\n # 如果不是攻击者并且不需要四舍五入得分，则获取得分\n # 否则，先对扰动进行四舍五入，再获取得分\n if (not self.is_attacker) and (not is_score_round):\n scores, _done = self.get_scores(model, pertbx, label_ext)\n else:\n scores, _done = self.get_scores(model, round_x(pertbx, self.round_threshold), label_ext)\n \n # 如果得分的最大值大于0，则设置为该值，否则设置为0\n max_v = scores.amax() if scores.amax() > 0 else 0.\n scores[_done] += max_v # 对完成的得分增加max_v\n\n # 重新整形扰动和得分张量，以便后续操作\n pertbx = pertbx.reshape(n_attacks, num_sample_red, *red_n).permute([1, 0, *red_ind])\n scores = scores.reshape(n_attacks, num_sample_red).permute(1, 0)\n\n # 从得分张量中获取最大得分及其索引\n _2, s_idx = scores.max(dim=-1)\n # 使用索引从扰动张量中选择具有最高误导性的扰动\n pert_x_cont = pertbx[torch.arange(num_sample_red), s_idx]\n # print(\"pert_x_cont.shape\", pert_x_cont.shape)\n # 更新经过扰动的数据adv_x\n adv_x[~done] = pert_x_cont if not self.is_attacker else round_x(pert_x_cont, self.round_threshold)\n \n # 更新lambda值以便于下一次循环\n self.lambda_ *= base\n # 如果lambda值检查失败，则中断循环\n if not self.check_lambda(model):\n break\n # 如果是攻击者，对最终的扰动结果进行四舍五入\n if self.is_attacker:\n adv_x = round_x(adv_x, self.round_threshold)\n \n # 不计算梯度地获取最后的损失和完成标志\n with torch.no_grad():\n _, done = self.get_loss(model, adv_x, label, self.lambda_)\n # 如果设置了详细输出，打印攻击效果的百分比\n if verbose:\n logger.info(f\"step-wise max: attack effectiveness {done.sum().item() / done.size()[0] * 100:.3f}%.\")\n # 返回扰动后的数据\n return adv_x\n\n def _perturb(self, model, x, label=None,\n steps=1,\n step_length_l1=1.,\n step_length_l2=0.5,\n step_length_linf=0.01,\n lambda_=1.,\n ):\n \"\"\"\n 对节点的特征向量进行扰动\n\n 参数\n -----------\n @param model: 受害者模型\n @param x: torch.FloatTensor, 节点特征向量（每个表示一个图中的API出现次数）形状为 [batch_size, vocab_dim]\n @param label: torch.LongTensor, 真实的标签\n @param steps: 整数, 迭代的最大次数\n @param step_length_l1: 每次迭代的步长，L1范数\n @param step_length_l2: 每次迭代的步长，L2范数\n @param step_length_linf: 每次迭代的步长，Linf范数\n @param lambda_: 浮点数, 惩罚因子\n \"\"\"\n if x is None or x.shape[0] <= 0:\n return []\n \n self.lambda_ = lambda_\n \n # 确保L1步长在[0,1]之间\n assert 0 <= step_length_l1 <= 1, \"期望在 [0,1] 之间的实数值，但得到 {}\".format(step_length_l1)\n model.eval()\n adv_x = x.detach()\n \n def one_iteration(_adv_x, norm_type):\n # 基于当前的扰动输入来计算梯度\n if \"rnn\" in model.model_save_path:\n model.train()\n if \"lstm\" in model.model_save_path:\n model.train() \n var_adv_x = torch.autograd.Variable(_adv_x, requires_grad=True) # 将_adv_x转换为一个可以进行自动梯度计算的变量\n loss, done = self.get_loss(model, var_adv_x, label, self.lambda_) # 获取模型在扰动输入上的损失\n grads = torch.autograd.grad(loss.mean(), var_adv_x, allow_unused=True)\n if grads[0] is None:\n grad = torch.zeros_like(var_adv_x)\n else:\n grad = grads[0].data\n\n # 寻找允许的位置来插入和移除API\n pos_insertion = (_adv_x <= 0.5) * 1 * (_adv_x >= 0.) # 寻找API的可插入位置：特征值在0和0.5之间\n grad4insertion = (grad > 0) * pos_insertion * grad # 根据梯度正值计算插入API的梯度\n\n pos_removal = (_adv_x > 0.5) * 1 # 寻找API的可移除位置：特征值大于0.5\n grad4removal = (grad <= 0) * (pos_removal & self.manipulation_x) * grad # 根据梯度负值计算移除API的梯度\n\n if self.is_attacker:\n # 对于攻击者，处理那些互相依赖的API\n checking_nonexist_api = (pos_removal ^ self.omega) & self.omega # 检查不存在的API\n grad4removal[:, self.api_flag] += torch.sum(grad * checking_nonexist_api, dim=-1, keepdim=True) # 考虑API之间的关系，调整移除API的梯度\n\n # 合并插入和移除的梯度\n grad = grad4removal + grad4insertion\n\n # 根据不同的范数类型，计算扰动值\n if norm_type == 'linf':\n perturbation = torch.sign(grad) # 计算梯度符号来获取无穷范数扰动方向\n if self.is_attacker:\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_linf * perturbation, min=0., max=1.) # 应用扰动并确保结果在[0,1]范围内\n\n elif norm_type == 'l2':\n l2norm = torch.linalg.norm(grad, dim=-1, keepdim=True) # 计算L2范数\n perturbation = torch.minimum(\n torch.tensor(1., dtype=_adv_x.dtype, device=_adv_x.device),\n grad / l2norm\n ) # 计算L2范数下的扰动方向\n perturbation = torch.where(torch.isnan(perturbation), 0., perturbation) # 处理NaN值\n perturbation = torch.where(torch.isinf(perturbation), 1., perturbation) # 处理Inf值\n if self.is_attacker:\n min_val = torch.amin(perturbation, dim=-1, keepdim=True).clamp_(max=0.)\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * torch.abs(min_val) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_l2 * perturbation, min=0., max=1.)\n\n elif norm_type == 'l1':\n val, idx = torch.abs(grad).topk(int(1. / step_length_l1), dim=-1) # 获取梯度的绝对值的top-k值和相应的索引\n perturbation = F.one_hot(idx, num_classes=_adv_x.shape[-1]).sum(dim=1) # 根据索引计算L1范数下的扰动方向\n perturbation = torch.sign(grad) * perturbation # 使用梯度的符号来调整扰动方向\n if self.is_attacker:\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_l1 * perturbation, min=0., max=1.)\n\n else:\n raise NotImplementedError # 如果范数类型不在L1、L2、Linf中，则引发异常\n\n\n # 为每种范数执行迭代\n adv_x_l1 = adv_x.clone()\n for t in range(steps):\n adv_x_l1 = one_iteration(adv_x_l1, norm_type='l1')\n \n adv_x_l2 = adv_x.clone()\n for t in range(steps):\n adv_x_l2 = one_iteration(adv_x_l2, norm_type='l2')\n \n adv_x_linf = adv_x.clone()\n for t in range(steps):\n adv_x_linf = one_iteration(adv_x_linf, norm_type='linf')\n \n return adv_x_l1, adv_x_l2, adv_x_linf\n\n\n def _perturb_dae(self, model, purifier, x, label=None,\n steps=1,\n step_length_l1=1.,\n step_length_l2=0.5,\n step_length_linf=0.01,\n lambda_=1.,\n oblivion=False):\n \"\"\"\n 对节点的特征向量进行扰动\n\n 参数\n -----------\n @param model: 受害者模型\n @param x: torch.FloatTensor, 节点特征向量（每个表示一个图中的API出现次数）形状为 [batch_size, vocab_dim]\n @param label: torch.LongTensor, 真实的标签\n @param steps: 整数, 迭代的最大次数\n @param step_length_l1: 每次迭代的步长，L1范数\n @param step_length_l2: 每次迭代的步长，L2范数\n @param step_length_linf: 每次迭代的步长，Linf范数\n @param lambda_: 浮点数, 惩罚因子\n \"\"\"\n if x is None or x.shape[0] <= 0:\n return []\n \n self.lambda_ = lambda_\n \n # 确保L1步长在[0,1]之间\n assert 0 <= step_length_l1 <= 1, \"期望在 [0,1] 之间的实数值，但得到 {}\".format(step_length_l1)\n model.eval()\n adv_x = x.detach()\n \n\n def one_iteration(_adv_x, norm_type):\n # 基于当前的扰动输入来计算梯度\n var_adv_x = torch.autograd.Variable(_adv_x, requires_grad=True) # 将_adv_x转换为一个可以进行自动梯度计算的变量\n if not oblivion:\n purified_var = purifier(var_adv_x.detach().clone().float()).to(torch.double)\n else:\n purified_var = var_adv_x.detach().clone()\n loss, done = self.get_loss(model, purified_var, label, self.lambda_) # 获取模型在扰动输入上的损失\n grads = torch.autograd.grad(loss.mean(), var_adv_x, allow_unused=True)\n if grads[0] is None:\n grad = torch.zeros_like(var_adv_x)\n else:\n grad = grads[0].data\n\n # 寻找允许的位置来插入和移除API\n pos_insertion = (_adv_x <= 0.5) * 1 * (_adv_x >= 0.) # 寻找API的可插入位置：特征值在0和0.5之间\n grad4insertion = (grad > 0) * pos_insertion * grad # 根据梯度正值计算插入API的梯度\n\n pos_removal = (_adv_x > 0.5) * 1 # 寻找API的可移除位置：特征值大于0.5\n grad4removal = (grad <= 0) * (pos_removal & self.manipulation_x) * grad # 根据梯度负值计算移除API的梯度\n\n if self.is_attacker:\n # 对于攻击者，处理那些互相依赖的API\n checking_nonexist_api = (pos_removal ^ self.omega) & self.omega # 检查不存在的API\n grad4removal[:, self.api_flag] += torch.sum(grad * checking_nonexist_api, dim=-1, keepdim=True) # 考虑API之间的关系，调整移除API的梯度\n\n # 合并插入和移除的梯度\n grad = grad4removal + grad4insertion\n\n # 根据不同的范数类型，计算扰动值\n if norm_type == 'linf':\n perturbation = torch.sign(grad) # 计算梯度符号来获取无穷范数扰动方向\n if self.is_attacker:\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_linf * perturbation, min=0., max=1.) # 应用扰动并确保结果在[0,1]范围内\n\n elif norm_type == 'l2':\n l2norm = torch.linalg.norm(grad, dim=-1, keepdim=True) # 计算L2范数\n perturbation = torch.minimum(\n torch.tensor(1., dtype=_adv_x.dtype, device=_adv_x.device),\n grad / l2norm\n ) # 计算L2范数下的扰动方向\n perturbation = torch.where(torch.isnan(perturbation), 0., perturbation) # 处理NaN值\n perturbation = torch.where(torch.isinf(perturbation), 1., perturbation) # 处理Inf值\n if self.is_attacker:\n min_val = torch.amin(perturbation, dim=-1, keepdim=True).clamp_(max=0.)\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * torch.abs(min_val) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_l2 * perturbation, min=0., max=1.)\n\n elif norm_type == 'l1':\n val, idx = torch.abs(grad).topk(int(1. / step_length_l1), dim=-1) # 获取梯度的绝对值的top-k值和相应的索引\n perturbation = F.one_hot(idx, num_classes=_adv_x.shape[-1]).sum(dim=1) # 根据索引计算L1范数下的扰动方向\n perturbation = torch.sign(grad) * perturbation # 使用梯度的符号来调整扰动方向\n if self.is_attacker:\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\n return torch.clamp(_adv_x + step_length_l1 * perturbation, min=0., max=1.)\n\n else:\n raise NotImplementedError # 如果范数类型不在L1、L2、Linf中，则引发异常\n\n\n # 为每种范数执行迭代\n adv_x_l1 = adv_x.clone()\n for t in range(steps):\n adv_x_l1 = one_iteration(adv_x_l1, norm_type='l1')\n \n adv_x_l2 = adv_x.clone()\n for t in range(steps):\n adv_x_l2 = one_iteration(adv_x_l2, norm_type='l2')\n \n adv_x_linf = adv_x.clone()\n for t in range(steps):\n adv_x_linf = one_iteration(adv_x_linf, norm_type='linf')\n \n return adv_x_l1, adv_x_l2, adv_x_linf\n\n def get_scores(self, model, pertb_x, label):\n # 如果模型有 'is_detector_enabled' 这个属性\n if hasattr(model, 'is_detector_enabled'):\n # 获取模型的输出，logits_f 是模型的原始输出，prob_g 是一个概率值\n logits_f, prob_g = model.forward(pertb_x)\n else:\n # 如果模型没有 'is_detector_enabled' 这个属性，只获取模型的原始输出\n logits_f = model.forward(pertb_x)\n\n # 获取预测的类别\n y_pred = logits_f.argmax(1)\n \n # 计算交叉熵损失\n ce = F.cross_entropy(logits_f, label, reduction='none')\n \n # 如果模型有 'is_detector_enabled' 这个属性，并且 self.oblivion 为 False\n if hasattr(model, 'is_detector_enabled') and (not self.oblivion):\n # 获取样本的阈值\n tau = model.get_tau_sample_wise(y_pred)\n # 计算损失，加入了 prob_g 这个概率值的惩罚项\n loss_no_reduction = ce - self.lambda_ * prob_g\n # 判断预测是否错误，并且 prob_g 是否小于等于阈值 tau\n done = (y_pred != label) & (prob_g <= tau)\n else:\n # 如果没有 'is_detector_enabled' 这个属性或 self.oblivion 为 True，损失仍然是交叉熵损失\n loss_no_reduction = ce\n # 判断预测是否错误\n done = y_pred != label\n\n # 返回损失值和判断结果c\n return loss_no_reduction, done"},{"identifier":"MalwareDetectionDNN","path":"core/defense/md_dnn.py","snippet":"class MalwareDetectionDNN(nn.Module):\n def __init__(self, input_size, n_classes, device='cpu', name='DNN', **kwargs):\n \"\"\"\n 初始化恶意软件检测器\n\n 参数:\n ----------\n @param input_size: 整数，输入向量的维度数量。\n @param n_classes: 整数，表示分类的数量，例如二分类问题中n=2。\n @param device: 字符串，可以是'cpu'或'cuda'，表示模型应该在CPU还是GPU上运行。\n @param name: 字符串，用于命名模型。\n \"\"\"\n super(MalwareDetectionDNN, self).__init__() # 调用父类初始化\n self.input_size = input_size # 定义输入尺寸\n self.n_classes = n_classes # 定义分类数量\n self.device = device # 定义运行设备\n self.name = name # 定义模型名称\n\n self.parse_args(**kwargs) # 解析额外参数\n\n self.dense_layers = [] # 初始化一个空的密集层列表\n \n # 检查是否至少有一个隐藏层\n if len(self.dense_hidden_units) >= 1:\n # 添加第一个密集层\n self.dense_layers.append(nn.Linear(self.input_size, self.dense_hidden_units[0]))\n else:\n # 如果没有隐藏层，抛出异常\n raise ValueError(\"Expect at least one hidden layer.\")\n\n # 为每一对连续的隐藏单元添加一个密集层\n for i in range(len(self.dense_hidden_units[0:-1])):\n self.dense_layers.append(nn.Linear(self.dense_hidden_units[i], \n self.dense_hidden_units[i + 1]))\n \n # 添加最后一个连接到输出层的密集层\n self.dense_layers.append(nn.Linear(self.dense_hidden_units[-1], self.n_classes))\n \n # 将密集层添加到模型中以进行跟踪\n for idx_i, dense_layer in enumerate(self.dense_layers):\n self.add_module('nn_model_layer_{}'.format(idx_i), dense_layer)\n\n # 根据参数选择使用SELU或ReLU激活函数\n if self.smooth:\n self.activation_func = F.selu # 使用SELU激活函数\n else:\n self.activation_func = F.relu # 使用ReLU激活函数\n\n # 定义模型的保存路径\n self.model_save_path = path.join(config.get('experiments', 'md_dnn') + '_' + self.name,\n 'model.pth')\n \n # 日志中打印模型的结构信息\n logger.info('========================================dnn model architecture===============================')\n logger.info(self)\n logger.info('===============================================end==========================================')\n\n\n def parse_args(self,\n dense_hidden_units=None,\n dropout=0.6,\n alpha_=0.2,\n smooth=False,\n **kwargs\n ):\n \"\"\"\n 解析并设置网络的超参数。\n\n 参数:\n ----------\n dense_hidden_units : list, 可选\n 网络中每个隐藏层的单元数。如果没有指定，则默认为两个隐藏层，每层200个单元。\n dropout : float, 可选\n dropout正则化的比率，默认为0.6。\n alpha_ : float, 可选\n 某些激活函数的参数，默认为0.2。\n smooth : bool, 可选\n 是否使用平滑的激活函数，默认为False。\n **kwargs : dict\n 其他超参数。\n \"\"\"\n\n # 如果用户没有指定隐藏层，使用默认的配置\n if dense_hidden_units is None:\n self.dense_hidden_units = [200, 200]\n # 如果用户指定了一个列表，使用它\n elif isinstance(dense_hidden_units, list):\n self.dense_hidden_units = dense_hidden_units\n # 否则抛出一个异常\n else:\n raise TypeError(\"Expect a list of hidden units.\")\n\n # 设置dropout, alpha和smooth参数\n self.dropout = dropout\n self.alpha_ = alpha_\n self.smooth = smooth\n\n # 从kwargs中获取并设置proc_number\n self.proc_number = kwargs.get('proc_number', None) # 如果不存在，则返回None\n\n # 如果还有其他参数，记录警告，因为这些参数可能是未知的\n if len(kwargs) > 0:\n logger.warning(\"Unknown hyper-parameters {}\".format(str(kwargs)))\n\n\n def forward(self, x):\n \"\"\"\n 使输入数据 x 通过神经网络\n \n 参数\n ----------\n @param x: 2D张量，特征表示\n \"\"\"\n # 遍历神经网络的每一层，除了最后一层\n for dense_layer in self.dense_layers[:-1]:\n x = self.activation_func(dense_layer(x)) # 使用激活函数处理每一层的输出\n\n # 对处理过的数据进行 dropout 操作，用于防止过拟合\n latent_representation = F.dropout(x, self.dropout, training=self.training)\n \n # 用最后一层进行处理，得到logits（未归一化的预测或分类得分）\n logits = self.dense_layers[-1](latent_representation)\n return logits\n\n def inference(self, test_data_producer):\n \"\"\"\n 进行模型推理，获得预测的置信度和真实标签\n \n 参数\n ----------\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\n \n 返回值\n ----------\n 返回预测的置信度和真实标签\n \"\"\"\n confidences = [] # 存储每批数据的预测置信度\n gt_labels = [] # 存储每批数据的真实标签\n self.eval() # 设置模型为评估模式\n\n # 使用torch.no_grad()来告诉PyTorch不要在推理过程中计算梯度\n with torch.no_grad():\n # 遍历每一批测试数据\n for x, y in test_data_producer:\n # 将数据转移到指定的设备（CPU或GPU）并调整数据类型\n x, y = utils.to_device(x.double(), y.long(), self.device)\n # 得到每一批数据的logits\n logits = self.forward(x)\n # 使用softmax函数得到每一批数据的置信度，并将其添加到confidences列表中\n confidences.append(F.softmax(logits, dim=-1))\n # 将每一批数据的真实标签添加到gt_labels列表中\n gt_labels.append(y)\n\n # 将所有批次的置信度垂直堆叠成一个张量\n confidences = torch.vstack(confidences)\n # 将所有批次的真实标签连接成一个张量\n gt_labels = torch.cat(gt_labels, dim=0)\n \n return confidences, gt_labels\n\n def inference_dae(self, test_data_producer):\n \"\"\"\n 进行模型推理，获得预测的置信度和真实标签\n \n 参数\n ----------\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\n \n 返回值\n ----------\n 返回预测的置信度和真实标签\n \"\"\"\n confidences = [] # 存储每批数据的预测置信度\n gt_labels = [] # 存储每批数据的真实标签\n self.eval() # 设置模型为评估模式\n\n # 使用torch.no_grad()来告诉PyTorch不要在推理过程中计算梯度\n with torch.no_grad():\n # 遍历每一批测试数据\n for x, y in test_data_producer:\n # 将数据转移到指定的设备（CPU或GPU）并调整数据类型\n x, y = utils.to_device(x.double(), y.long(), self.device)\n # 得到每一批数据的logits\n logits = self.forward(x)\n # 使用softmax函数得到每一批数据的置信度，并将其添加到confidences列表中\n confidences.append(F.softmax(logits, dim=-1))\n # 将每一批数据的真实标签添加到gt_labels列表中\n gt_labels.append(y)\n \n return confidences, gt_labels\n\n\n def get_important_attributes(self, test_data_producer, target_label=1):\n \"\"\"\n 使用集成梯度（Integrated Gradients）方法获取重要的属性/特征\n\n 参数\n ----------\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\n @param target_label: 目标标签，默认为1\n \n 返回值\n ----------\n 返回重要的属性/特征\n \"\"\"\n attributions = [] # 存储属性或特征的重要性得分\n gt_labels = [] # 存储真实标签\n\n # 定义一个使用集成梯度方法的包装器\n def _ig_wrapper(_x):\n logits = self.forward(_x)\n return F.softmax(logits, dim=-1)\n\n # 初始化集成梯度对象\n ig = IntegratedGradients(_ig_wrapper)\n\n # 遍历测试数据集\n for i, (x, y) in enumerate(test_data_producer):\n # 将数据和标签转移到指定的设备上\n x, y = utils.to_device(x.double(), y.long(), self.device)\n # 使x能够计算梯度\n x.requires_grad = True\n # 定义基线，用于集成梯度的计算\n baseline = torch.zeros_like(x, dtype=torch.double, device=self.device)\n # 计算属性的重要性\n attribution_bs = ig.attribute(x,\n baselines=baseline,\n target=target_label)\n # 将所有批次的属性垂直堆叠\n attribution = torch.hstack(attribution_bs)\n # 保存得到的属性重要性得分和真实标签\n attributions.append(attribution.clone().detach().cpu().numpy())\n gt_labels.append(y.clone().detach().cpu().numpy())\n # 将真实标签保存为.npy文件\n np.save('./labels', np.concatenate(gt_labels))\n \n return np.vstack(attributions)\n\n\n def inference_batch_wise(self, x):\n \"\"\"\n 仅支持恶意软件样本的批量推理\n \n 参数\n ----------\n @param x: 输入数据的张量\n \n 返回值\n ----------\n 返回推理的置信度和标签\n \"\"\"\n # 确保输入是一个张量\n assert isinstance(x, torch.Tensor)\n \n # 获得模型的输出\n logit = self.forward(x)\n \n # 返回每个样本的置信度和一个与logit形状相同的全1数组（表示恶意软件样本）\n return torch.softmax(logit, dim=-1).detach().cpu().numpy(), np.ones((logit.size()[0],))\n\n\n def predict(self, test_data_producer, indicator_masking=True):\n \"\"\"\n 预测标签并进行评估\n\n 参数\n --------\n @param test_data_producer: torch.DataLoader, 用于生成测试数据的数据加载器\n \"\"\"\n # 进行评估\n confidence, y_true = self.inference(test_data_producer)\n y_pred = confidence.argmax(1).cpu().numpy() # 预测标签\n y_true = y_true.cpu().numpy() # 真实标签\n \n # print(\"y_true.shape:\", y_true.shape)\n # print(\"y_pred.shape:\", y_pred.shape)\n \n # 使用sklearn的评估指标进行评估\n from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, balanced_accuracy_score\n accuracy = accuracy_score(y_true, y_pred)\n b_accuracy = balanced_accuracy_score(y_true, y_pred)\n \n MSG = \"The accuracy on the test dataset is {:.5f}%\"\n logger.info(MSG.format(accuracy * 100))\n \n MSG = \"The balanced accuracy on the test dataset is {:.5f}%\"\n logger.info(MSG.format(b_accuracy * 100))\n\n # 检查数据中是否存在缺失的类别\n if np.any([np.all(y_true == i) for i in range(self.n_classes)]):\n logger.warning(\"class absent.\")\n return\n\n # 计算混淆矩阵\n tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n fpr = fp / float(tn + fp) # 计算假阳性率\n fnr = fn / float(tp + fn) # 计算假阴性率\n f1 = f1_score(y_true, y_pred, average='binary') # 计算F1分数\n\n print(\"Other evaluation metrics we may need:\")\n MSG = \"False Negative Rate (FNR) is {:.5f}%、False Positive Rate (FPR) is {:.5f}%, F1 score is {:.5f}%\"\n logger.info(MSG.format(fnr * 100, fpr * 100, f1 * 100))\n\n\n def customize_loss(self, logits, gt_labels, representation=None, mini_batch_idx=None):\n \"\"\"\n 自定义损失函数\n\n 参数\n --------\n @param logits: Tensor, 模型的输出\n @param gt_labels: Tensor, 真实的标签\n @param representation: Tensor, 可选参数，表示特征表示\n @param mini_batch_idx: Int, 可选参数，表示小批次的索引\n \n 返回值\n --------\n 返回交叉熵损失\n \"\"\"\n return F.cross_entropy(logits, gt_labels)\n\n\n def fit(self, train_data_producer, validation_data_producer, epochs=100, lr=0.005, weight_decay=0., weight_sampling=0.5, verbose=True):\n \"\"\"\n 训练恶意软件检测器，根据验证集上的交叉熵损失选择最佳模型。\n\n 参数\n ----------\n @param train_data_producer: 对象, 用于生成一批训练数据的迭代器\n @param validation_data_producer: 对象, 用于生成验证数据的迭代器\n @param epochs: 整数, 训练的周期数\n @param lr: 浮点数, Adam优化器的学习率\n @param weight_decay: 浮点数, 惩罚因子\n @param verbose: 布尔值, 是否显示详细的日志\n \"\"\"\n # 初始化优化器\n optimizer = optim.Adam(self.parameters(), lr=lr, weight_decay=weight_decay)\n best_avg_acc = 0. # 记录验证集上的最佳准确率\n best_epoch = 0 # 记录最佳准确率对应的周期\n total_time = 0. # 总的训练时间\n\n # 获取训练数据批次的数量\n nbatches = len(train_data_producer)\n \n # 进行指定次数的训练周期\n for i in range(epochs):\n # 设置模型为训练模式\n self.train()\n # 初始化列表用于保存每批数据的损失值和准确率\n losses, accuracies = [], []\n\n # 对每个训练数据批次进行遍历\n for idx_batch, (x_train, y_train) in enumerate(train_data_producer):\n # 将数据转移到指定的计算设备（例如GPU或CPU）\n x_train, y_train = utils.to_device(x_train.double(), y_train.long(), self.device)\n\n # 记录开始训练的时间\n start_time = time.time()\n\n # 清空之前累积的梯度\n optimizer.zero_grad() \n \n # 对输入数据进行前向传播\n logits = self.forward(x_train) \n \n # 根据模型的输出和真实标签计算损失\n loss_train = self.customize_loss(logits, y_train) \n\n # 对损失进行反向传播\n loss_train.backward()\n \n # 使用优化器更新模型参数\n optimizer.step()\n\n # 计算训练这批数据所花费的总时间\n total_time += time.time() - start_time\n \n # 计算这批数据上的准确率\n acc_train = (logits.argmax(1) == y_train).sum().item() / x_train.size()[0]\n \n # 将时间转换为分钟和秒\n mins, secs = int(total_time / 60), int(total_time % 60)\n \n # 将这批数据的损失和准确率加入到列表中\n losses.append(loss_train.item())\n accuracies.append(acc_train)\n\n # 如果开启了详细输出模式，显示当前训练进度和这批数据上的损失和准确率\n if verbose:\n logger.info(f'小批次： {i * nbatches + idx_batch + 1}/{epochs * nbatches} | 训练时间为 {mins:.0f} 分钟, {secs} 秒。')\n logger.info(f'训练损失（小批次级别）: {losses[-1]:.4f} | 训练精度: {acc_train * 100:.2f}')\n\n\n self.eval() # 将模型设置为评估模式\n avg_acc_val = []\n\n with torch.no_grad(): # 确保在评估模式下不进行梯度的计算\n for x_val, y_val in validation_data_producer:\n # 将数据移动到指定设备（例如GPU或CPU）上，并确保数据的类型为双精度浮点数和长整型\n x_val, y_val = utils.to_device(x_val.double(), y_val.long(), self.device)\n \n # 使用模型进行前向传播，得到输出结果\n logits = self.forward(x_val)\n \n # 计算验证数据上的准确率\n acc_val = (logits.argmax(1) == y_val).sum().item() / x_val.size()[0]\n \n # 保存每一批验证数据的准确率\n avg_acc_val.append(acc_val)\n \n # 计算所有验证数据的平均准确率\n avg_acc_val = np.mean(avg_acc_val)\n\n # 如果当前周期的验证精度超过之前的最佳验证精度\n if avg_acc_val >= best_avg_acc:\n # 更新最佳验证精度\n best_avg_acc = avg_acc_val\n best_epoch = i\n \n # 检查模型保存路径是否存在，如果不存在，则创建\n if not path.exists(self.model_save_path):\n utils.mkdir(path.dirname(self.model_save_path))\n \n # 保存当前的模型参数\n torch.save(self.state_dict(), self.model_save_path)\n \n # 如果开启了详细输出模式，显示模型保存路径\n if verbose:\n print(f'模型保存在路径： {self.model_save_path}')\n\n # 如果开启了详细输出模式，显示训练损失、训练精度、验证精度和最佳验证精度\n if verbose:\n logger.info(f'训练损失（周期级别）: {np.mean(losses):.4f} | 训练精度: {np.mean(accuracies) * 100:.2f}')\n logger.info(f'验证精度: {avg_acc_val * 100:.2f} | 最佳验证精度: {best_avg_acc * 100:.2f} 在第 {best_epoch} 个周期')\n\n def load(self):\n \"\"\"\n 从磁盘加载模型参数\n \"\"\"\n self.load_state_dict(torch.load(self.model_save_path))"},{"identifier":"DetectorTemplate","path":"core/defense/amd_template.py","snippet":"class DetectorTemplate(object):\n def __init__(self):\n self.tau = None # 阈值变量\n self.is_detector_enabled = True # 表示检测器是否启用的标志\n\n def forward(self, x):\n \"\"\"\n 类预测与密度估计\n \"\"\"\n raise NotImplementedError\n\n def get_threshold(self):\n \"\"\"\n 计算拒绝异常值的阈值\n \"\"\"\n raise NotImplementedError\n\n def get_tau_sample_wise(self):\n \"\"\"\n 获取每个样本的tau值\n \"\"\"\n raise NotImplementedError\n\n def indicator(self):\n \"\"\"\n 返回一个布尔标志向量，指示是否拒绝一个样本\n \"\"\"\n raise NotImplementedError"},{"identifier":"config","path":"config.py","snippet":"def parser_config():"},{"identifier":"utils","path":"tools/utils.py","snippet":"ENC_KEY = 'cab228a122d3486bac7fab148e8b5aba'\n MSG = \"No such directory or file {} exists!\".format(sample_dir)\n MSG = \"A directory or a list of paths are allowed!\"\ndef pool_initializer():\ndef retrive_files_set(base_dir, dir_ext, file_ext):\n def get_file_name(root_dir, file_ext):\ndef check_dir(sample_dir):\ndef dump_joblib(data, path):\ndef read_joblib(path):\ndef load_json(json_path):\ndef dump_json(obj_dict, file_path):\ndef dump_pickle(data, path, use_gzip=False):\ndef read_pickle(path, use_gzip=False):\ndef dump_pickle_frd_space(data, path):\ndef read_pickle_frd_space(path):\ndef dump_list_of_lists(data, path):\ndef read_list_of_lists(path):\ndef mkdir(target):\ndef read_txt(path, mode='r'):\ndef dump_txt(data_str, path, mode='w'):\ndef read_file_by_fileinput(file_path, inplace=True):\n def __init__(self, manager, use_cache=True):\n def is_cached(self, key):\n def reset(self):\n def get(self, key):\n def cache(self, key, img, lbl):\ndef build_kwargs(keys, arg_dict):\ndef inverse_kwargs(vars):\ndef save_args(fout, args):\ndef load_args(fout):\ndef get_group_args(args, args_parser, title):\ndef tensor_coo_sp_to_ivs(sparse_tensor):\ndef ivs_to_tensor_coo_sp(ivs, device='cpu'):\ndef sp_to_symmetric_sp(sparse_mx):\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\ndef to_tensor(feature_x=None, labels=None, device='cpu'):\n def _to_torch_tensor(mat):\ndef to_device(feature_x=None, labels=None, device='cpu'):\ndef psn(x_tensor, prob, lower_value=0., upper_value=1.):\n def __init__(self):\n def __call__(self, module):\ndef round_x(x, alpha=0.5):\ndef get_x0(x, rounding_threshold=0.5, is_sample=False):\ndef or_tensors(x_1, x_2):\ndef xor_tensors(x_1, x_2):\ndef get_mal_data(x_batch, y_batch):\ndef get_mal_ben_data(x_batch, y_batch):\ndef java_class_name2smali_name(cls):\ndef remove_duplicate(components):\ndef crypt_identifier(idf, seed=2345):\n def md5_transform():\ndef random_string(code):\n def sha1_transform():\ndef string_on_code(code):\n def md5_transform():\ndef random_name(seed=2345, code='abc'):\ndef apply_encryption(base_string):\ndef get_sha256(file_path):\nclass SimplifyClass:\nclass NonnegWeightConstraint(object):"}],"string":"[\n {\n \"identifier\": \"Max\",\n \"path\": \"core/attack/max.py\",\n \"snippet\": \"class Max(BaseAttack):\\n \\\"\\\"\\\"\\n Max攻击：迭代地从多个攻击方法中选择结果。\\n\\n 参数\\n --------\\n @param attack_list: List, 已实例化的攻击对象的列表。\\n @param varepsilon: Float, 用于判断收敛性的标量。\\n \\\"\\\"\\\"\\n\\n def __init__(self, attack_list, varepsilon=1e-20,\\n is_attacker=True, oblivion=False, kappa=1., manipulation_x=None, omega=None, device=None):\\n \\\"\\\"\\\"\\n 构造函数\\n\\n 参数:\\n - attack_list: 已实例化的攻击对象的列表，至少应该有一个攻击方法。\\n - varepsilon: 用于判断收敛性的标量，默认值为1e-20。\\n - is_attacker: Bool, 表示是否为攻击者，默认为True。\\n - oblivion: Bool, 一个布尔标志（其功能在这里并未详细说明），默认为False。\\n - kappa: Float, 一个浮点数参数，默认为1。\\n - manipulation_x: 可能与数据的处理或操纵有关，具体用途未详细说明。\\n - omega: 参数omega的具体用途未详细说明。\\n - device: 设备，例如'cuda'或'cpu'，用于执行计算。\\n\\n 注意:\\n - 在初始化过程中，会首先检查`attack_list`是否包含至少一个攻击对象。\\n \\\"\\\"\\\"\\n super(Max, self).__init__(is_attacker, oblivion, kappa, manipulation_x, omega, device) # 调用父类的构造函数\\n assert len(attack_list) > 0, '至少需要一个攻击方法。' # 确保提供了至少一个攻击对象\\n self.attack_list = attack_list # 设置攻击列表\\n self.varepsilon = varepsilon # 设置varepsilon值\\n self.device = device # 设置计算设备\\n\\n def perturb(self, model, x, label=None, steps_max=5, min_lambda_=1e-5, max_lambda_=1e5, verbose=False):\\n \\\"\\\"\\\"\\n 扰动节点特征\\n\\n 参数\\n -----------\\n @param model: 受害者模型。\\n @param x: torch.FloatTensor, 形状为[batch_size, vocab_dim]的特征向量。\\n @param label: torch.LongTensor, 真实标签。\\n @param steps_max: Integer, 最大的迭代次数。\\n @param min_lambda_: float, 平衡对手检测器的重要性（如果存在）。\\n @param max_lambda_: float, 同上。\\n @param verbose: Boolean, 是否打印详细日志。\\n\\n 返回值\\n --------\\n adv_x: 扰动后的数据。\\n \\\"\\\"\\\"\\n\\n # 判断输入数据是否有效\\n if x is None or x.shape[0] <= 0:\\n return []\\n\\n # 将模型设为评估模式，主要是为了禁用一些在训练模式下的特殊层，比如Dropout\\n model.eval()\\n\\n # 获取输入数据x在当前模型下的损失和完成状态\\n with torch.no_grad():\\n loss, done = self.get_scores(model, x, label)\\n\\n # 存储当前的损失为前一次的损失\\n pre_loss = loss\\n\\n # 获取输入数据的数量以及其他的维度信息\\n n, red_n = x.size()[0], x.size()[1:]\\n red_ind = list(range(2, len(x.size()) + 1))\\n\\n # 初始化攻击样本为输入数据的拷贝\\n adv_x = x.detach().clone()\\n\\n # 初始化停止标志，用于表示哪些样本已经完成了攻击\\n stop_flag = torch.zeros(n, dtype=torch.bool, device=self.device)\\n\\n # 开始主循环，进行多次迭代以改进攻击效果\\n for t in range(steps_max):\\n # 计算还未完成攻击的样本数量\\n num_sample_red = n - torch.sum(stop_flag)\\n \\n # 如果所有样本都已完成攻击，结束循环\\n if num_sample_red <= 0:\\n break\\n\\n # 获取那些还未完成攻击的样本的真实标签\\n red_label = label[~stop_flag]\\n pertbx = []\\n\\n # 对于攻击方法列表中的每种攻击方法，尝试对数据进行扰动\\n for attack in self.attack_list:\\n # 确保每种攻击方法都实现了perturb方法\\n assert 'perturb' in type(attack).__dict__.keys()\\n\\n # 对于某些特定的攻击方法，在第二次及以后的迭代中取消随机化\\n if t > 0 and 'use_random' in attack.__dict__.keys():\\n attack.use_random = False\\n\\n # 对于名为\\\"Orthogonal\\\"的攻击方法，进行特殊处理\\n if 'Orthogonal' in type(attack).__name__:\\n pertbx.append(attack.perturb(model=model, x=adv_x[~stop_flag], label=red_label))\\n else:\\n pertbx.append(attack.perturb(model=model, x=adv_x[~stop_flag], label=red_label,\\n min_lambda_=1e-5,\\n max_lambda_=1e5,\\n ))\\n # 将所有攻击方法产生的扰动数据合并\\n pertbx = torch.vstack(pertbx)\\n\\n\\n # 不需要计算梯度，提高计算效率\\n with torch.no_grad():\\n # 将真实标签复制若干次以匹配所有的攻击列表\\n red_label_ext = torch.cat([red_label] * len(self.attack_list))\\n \\n # 获取每种攻击方法产生的损失值和成功状态\\n loss, done = self.get_scores(model, pertbx, red_label_ext)\\n \\n # 调整损失和成功状态的形状以方便后续计算\\n loss = loss.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\\n done = done.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\\n \\n # 判断哪些样本至少有一种攻击方法成功\\n success_flag = torch.any(done, dim=-1)\\n \\n # 对于没有成功的样本，将其标记为1以进行后续处理\\n done[~torch.any(done, dim=-1)] = 1\\n \\n # 调整损失值，对于成功的攻击方法，损失值保持不变；对于失败的，损失值变为最小值\\n loss = (loss * done.to(torch.float)) + torch.min(loss) * (~done).to(torch.float)\\n \\n # 调整扰动数据的形状以方便后续计算\\n pertbx = pertbx.reshape(len(self.attack_list), num_sample_red, *red_n).permute([1, 0, *red_ind])\\n \\n # 选择造成最大损失的扰动数据\\n _, indices = loss.max(dim=-1)\\n adv_x[~stop_flag] = pertbx[torch.arange(num_sample_red), indices]\\n \\n # 获取选中的扰动数据的损失值\\n a_loss = loss[torch.arange(num_sample_red), indices]\\n \\n # 复制当前的停止标志\\n pre_stop_flag = stop_flag.clone()\\n \\n # 更新停止标志，如果损失值变化很小或者某种攻击方法成功，则停止迭代\\n stop_flag[~stop_flag] = (torch.abs(pre_loss[~stop_flag] - a_loss) < self.varepsilon) | success_flag\\n \\n # 更新前一个损失值\\n pre_loss[~pre_stop_flag] = a_loss\\n\\n # 如果需要打印日志\\n if verbose:\\n # 评估最终的扰动数据的成功状态\\n with torch.no_grad():\\n _, done = self.get_scores(model, adv_x, label)\\n # 打印攻击成功率\\n logger.info(f\\\"max: attack effectiveness {done.sum().item() / x.size()[0] * 100}%.\\\")\\n\\n # 返回最终的扰动数据\\n return adv_x\\n\\n\\n def perturb_dae(self, predict_model, purifier, x, label=None, steps_max=5, min_lambda_=1e-5, max_lambda_=1e5, verbose=False, oblivion=False):\\n \\\"\\\"\\\"\\n 扰动节点特征\\n\\n 参数\\n -----------\\n @param model: 受害者模型。\\n @param x: torch.FloatTensor, 形状为[batch_size, vocab_dim]的特征向量。\\n @param label: torch.LongTensor, 真实标签。\\n @param steps_max: Integer, 最大的迭代次数。\\n @param min_lambda_: float, 平衡对手检测器的重要性（如果存在）。\\n @param max_lambda_: float, 同上。\\n @param verbose: Boolean, 是否打印详细日志。\\n\\n 返回值\\n --------\\n adv_x: 扰动后的数据。\\n \\\"\\\"\\\"\\n\\n # 判断输入数据是否有效\\n if x is None or x.shape[0] <= 0:\\n return []\\n\\n # 将模型设为评估模式，主要是为了禁用一些在训练模式下的特殊层，比如Dropout\\n predict_model.eval()\\n purifier.eval()\\n\\n # 获取输入数据x在当前模型下的损失和完成状态\\n with torch.no_grad():\\n if not oblivion:\\n purified_x = purifier(x.detach().clone().float()).to(torch.double)\\n else:\\n purified_x = x.detach().clone()\\n loss, done = self.get_scores(predict_model, purified_x, label)\\n\\n # 存储当前的损失为前一次的损失\\n pre_loss = loss\\n\\n # 获取输入数据的数量以及其他的维度信息\\n n, red_n = x.size()[0], x.size()[1:]\\n red_ind = list(range(2, len(x.size()) + 1))\\n\\n # 初始化攻击样本为输入数据的拷贝\\n adv_x = x.detach().clone()\\n\\n # 初始化停止标志，用于表示哪些样本已经完成了攻击\\n stop_flag = torch.zeros(n, dtype=torch.bool, device=self.device)\\n\\n # 开始主循环，进行多次迭代以改进攻击效果\\n for t in range(steps_max):\\n # 计算还未完成攻击的样本数量\\n num_sample_red = n - torch.sum(stop_flag)\\n \\n # 如果所有样本都已完成攻击，结束循环\\n if num_sample_red <= 0:\\n break\\n\\n # 获取那些还未完成攻击的样本的真实标签\\n red_label = label[~stop_flag]\\n pertbx = []\\n\\n # 对于攻击方法列表中的每种攻击方法，尝试对数据进行扰动\\n for attack in self.attack_list:\\n # 确保每种攻击方法都实现了perturb方法\\n assert 'perturb' in type(attack).__dict__.keys()\\n\\n # 对于某些特定的攻击方法，在第二次及以后的迭代中取消随机化\\n if t > 0 and 'use_random' in attack.__dict__.keys():\\n attack.use_random = False\\n\\n # 对于名为\\\"Orthogonal\\\"的攻击方法，进行特殊处理\\n if 'Orthogonal' in type(attack).__name__:\\n pertbx.append(attack.perturb_dae(predict_model=predict_model, purifier=purifier, x=adv_x[~stop_flag], label=red_label, oblivion=oblivion))\\n else:\\n pertbx.append(attack.perturb_dae(model=predict_model, purifier=purifier, x=adv_x[~stop_flag], label=red_label,\\n min_lambda_=1e-5,\\n max_lambda_=1e5,\\n oblivion=oblivion\\n ))\\n\\n # 将所有攻击方法产生的扰动数据合并\\n pertbx = torch.vstack(pertbx)\\n\\n\\n # 不需要计算梯度，提高计算效率\\n with torch.no_grad():\\n # 将真实标签复制若干次以匹配所有的攻击列表\\n red_label_ext = torch.cat([red_label] * len(self.attack_list))\\n \\n # 获取每种攻击方法产生的损失值和成功状态\\n if not oblivion:\\n purified_pertbx = purifier(pertbx.detach().clone().float()).to(torch.double)\\n else:\\n purified_pertbx = pertbx.detach().clone()\\n\\n loss, done = self.get_scores(predict_model, purified_pertbx, red_label_ext)\\n \\n # 调整损失和成功状态的形状以方便后续计算\\n loss = loss.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\\n done = done.reshape(len(self.attack_list), num_sample_red).permute(1, 0)\\n \\n # 判断哪些样本至少有一种攻击方法成功\\n success_flag = torch.any(done, dim=-1)\\n \\n # 对于没有成功的样本，将其标记为1以进行后续处理\\n done[~torch.any(done, dim=-1)] = 1\\n \\n # 调整损失值，对于成功的攻击方法，损失值保持不变；对于失败的，损失值变为最小值\\n loss = (loss * done.to(torch.float)) + torch.min(loss) * (~done).to(torch.float)\\n \\n # 调整扰动数据的形状以方便后续计算\\n pertbx = pertbx.reshape(len(self.attack_list), num_sample_red, *red_n).permute([1, 0, *red_ind])\\n \\n # 选择造成最大损失的扰动数据\\n _, indices = loss.max(dim=-1)\\n adv_x[~stop_flag] = pertbx[torch.arange(num_sample_red), indices]\\n \\n # 获取选中的扰动数据的损失值\\n a_loss = loss[torch.arange(num_sample_red), indices]\\n \\n # 复制当前的停止标志\\n pre_stop_flag = stop_flag.clone()\\n \\n # 更新停止标志，如果损失值变化很小或者某种攻击方法成功，则停止迭代\\n stop_flag[~stop_flag] = (torch.abs(pre_loss[~stop_flag] - a_loss) < self.varepsilon) | success_flag\\n \\n # 更新前一个损失值\\n pre_loss[~pre_stop_flag] = a_loss\\n\\n # 如果需要打印日志\\n if verbose:\\n # 评估最终的扰动数据的成功状态\\n with torch.no_grad():\\n purified_adv_x = purifier(adv_x.detach().clone().float()).to(torch.double)\\n _, done = self.get_scores(predict_model, purified_adv_x, label)\\n # 打印攻击成功率\\n logger.info(f\\\"max: attack effectiveness {done.sum().item() / x.size()[0] * 100}%.\\\")\\n\\n # 返回最终的扰动数据\\n return adv_x\\n\\n\\n # 这个get_scores函数的主要目的是计算扰动数据在给定模型上的损失值，并判断模型对这些扰动数据的预测是否成功完成。\\n # 对于具有检测器功能的模型，还会考虑模型的额外输出来决定预测的完成状态。\\n def get_scores(self, model, pertb_x, label):\\n \\\"\\\"\\\"\\n 获取扰动数据在模型上的损失值和预测标签的完成状态。\\n\\n 参数：\\n @param model: 模型对象，即受攻击的目标模型。\\n @param pertb_x: torch.Tensor，扰动后的数据。\\n @param label: torch.Tensor，扰动数据的真实标签。\\n\\n 返回：\\n - loss_no_reduction: 每个样本的损失值（无降维处理）。\\n - done: Boolean Tensor，表示模型对每个样本的预测是否成功完成。\\n \\\"\\\"\\\"\\n # 判断模型是否具有检测器功能，如果有，则获取模型的两个输出：logits_f 和 prob_g。\\n if hasattr(model, 'is_detector_enabled'):\\n logits_f, prob_g = model.forward(pertb_x)\\n else:\\n # 如果模型没有检测器功能，只获取一个输出logits_f。\\n logits_f = model.forward(pertb_x)\\n\\n # 使用交叉熵计算每个样本的损失值\\n ce = F.cross_entropy(logits_f, label, reduction='none')\\n\\n # 获取模型的预测标签\\n y_pred = logits_f.argmax(1)\\n\\n # 如果模型具有检测器功能且不处于\\\"oblivion\\\"模式，则进行特殊处理。\\n # 使用模型的输出prob_g来判断是否成功完成了预测。\\n if hasattr(model, 'is_detector_enabled') and (not self.oblivion):\\n tau = model.get_tau_sample_wise(y_pred)\\n loss_no_reduction = -prob_g\\n done = (y_pred != label) & (prob_g <= tau)\\n else:\\n # 如果模型没有检测器功能或处于\\\"oblivion\\\"模式，则使用交叉熵损失来判断是否成功完成了预测。\\n loss_no_reduction = ce\\n done = y_pred != label\\n\\n return loss_no_reduction, done\"\n },\n {\n \"identifier\": \"StepwiseMax\",\n \"path\": \"core/attack/stepwise_max.py\",\n \"snippet\": \"class StepwiseMax(BaseAttack):\\n \\\"\\\"\\\"\\n Stepwise max攻击方法，这是一个结合了pgd l1, pgd l2, 和 pgd linf三种攻击方式的方法。\\n\\n 参数\\n ----------\\n @param use_random: bool类型，是否使用随机的起始点。\\n @param rounding_threshold: float类型，用于四舍五入实数的阈值。\\n @param is_attacker: bool类型，是否扮演攻击者角色（注意：防御者执行对抗性训练）。\\n @param oblivion: bool类型，是否知道敌手指示器。\\n @param kappa: 攻击信心度。\\n @param manipulation_x: 可操作性。\\n @param omega: 与每个api相对应的互依赖api的索引。\\n @param device: 设备，'cpu'或'cuda'。\\n\\n \\\"\\\"\\\"\\n\\n def __init__(self, use_random=False, rounding_threshold=0.5,\\n is_attacker=True, oblivion=False, kappa=1., manipulation_x=None, omega=None, device=None):\\n super(StepwiseMax, self).__init__(is_attacker, oblivion, kappa, manipulation_x, omega, device)\\n \\n # 是否使用随机起点\\n self.use_random = use_random\\n \\n # 断言确保四舍五入阈值在(0, 1)之间\\n assert 0 < rounding_threshold < 1\\n \\n # 设置四舍五入的阈值\\n self.round_threshold = rounding_threshold\\n \\n # lambda_用于正则化，通常与优化的损失一起使用\\n self.lambda_ = 1.\\n\\n def perturb_dae(self, model, purifier, x, label=None,\\n steps=100,\\n step_check=1,\\n sl_l1=1.,\\n sl_l2=1.,\\n sl_linf=0.01,\\n min_lambda_=1e-5,\\n max_lambda_=1e5,\\n is_score_round=True,\\n base=10.,\\n verbose=False,\\n oblivion=False):\\n \\\"\\\"\\\"\\n 对模型进行增强攻击。\\n\\n @param model: PyTorch模型，待攻击目标。\\n @param x: Tensor, 原始输入数据。\\n @param label: Tensor或None, 输入数据对应的标签。\\n @param steps: int, 攻击的总步数。\\n @param step_check: int, 检查间隔，即多少步进行一次检查。\\n @param sl_l1: float, L1范数的步长。\\n @param sl_l2: float, L2范数的步长。\\n @param sl_linf: float, Linf范数的步长。\\n @param min_lambda_: float, lambda的最小值。\\n @param max_lambda_: float, lambda的最大值。\\n @param is_score_round: Boolean, 是否对分数进行四舍五入。\\n @param base: float, 基数。\\n @param verbose: Boolean, 是否输出详细信息。\\n \\\"\\\"\\\"\\n # torch.manual_seed(int(random.random() * 100)) # 设置随机种子\\n # 参数校验\\n assert 0 < min_lambda_ <= max_lambda_\\n assert steps >= 0 and (step_check >= 1) and 1 >= sl_l1 > 0 and sl_l2 >= 0 and sl_linf >= 0\\n \\n model.eval() # 将模型设置为评估模式\\n purifier.eval()\\n \\n # 根据模型是否具有某种属性来设置lambda的初值\\n if hasattr(model, 'is_detector_enabled'):\\n self.lambda_ = min_lambda_\\n else:\\n self.lambda_ = max_lambda_\\n \\n # 如果不是攻击者，从预定义的步骤中随机选择一个\\n if not self.is_attacker:\\n step_checks = [1, 10, 25, 50]\\n step_check = random.choice(step_checks)\\n \\n # 计算每个小步骤中需要的迭代次数\\n mini_steps = [step_check] * (steps // step_check)\\n mini_steps = mini_steps + [steps % step_check] if steps % step_check != 0 else mini_steps\\n \\n # 获取输入的维度信息\\n n, red_n = x.size()[0], x.size()[1:]\\n red_ind = list(range(2, len(x.size()) + 1))\\n \\n adv_x = x.detach().clone() # 获取输入数据的副本\\n while self.lambda_ <= max_lambda_:\\n pert_x_cont = None\\n prev_done = None\\n for i, mini_step in enumerate(mini_steps):\\n with torch.no_grad():\\n # 如果是第一步并且启用了随机初始化，那么获取一个随机的起始点\\n if i == 0:\\n adv_x = get_x0(adv_x, rounding_threshold=self.round_threshold, is_sample=True)\\n # 计算损失和完成标志\\n if not oblivion:\\n purified_adv = purifier(adv_x.detach().clone().float()).to(torch.double)\\n else:\\n purified_adv = adv_x.detach().clone()\\n _, done = self.get_loss(model, purified_adv, label, self.lambda_)\\n \\n # print(\\\"done:\\\", done)\\n \\n # 如果所有的都完成了，就退出循环\\n if torch.all(done):\\n break\\n \\n # 对于那些没有完成的数据，重新计算扰动\\n # print(\\\"i:\\\", i)\\n if i == 0:\\n # print(\\\"~done:\\\", (~done))\\n adv_x[~done] = x[~done]\\n prev_done = done.clone()\\n else:\\n if (adv_x[~done]).shape[0] == (pert_x_cont[~done[~prev_done]]).shape[0]:\\n adv_x[~done] = pert_x_cont[~done[~prev_done]]\\n else:\\n updated_mask = (~done) & (~prev_done[:len(done)])\\n num_to_select = updated_mask.sum().item()\\n selected_perturbations = pert_x_cont[:num_to_select]\\n adv_x[updated_mask] = selected_perturbations\\n\\n prev_done = done.clone() \\n \\n # 对那些未完成的数据进行真正的扰动\\n num_sample_red = torch.sum(~done).item()\\n pert_x_l1, pert_x_l2, pert_x_linf = self._perturb_dae(model, purifier, adv_x[~done], label[~done],\\n mini_step,\\n sl_l1,\\n sl_l2,\\n sl_linf,\\n lambda_=self.lambda_,\\n oblivion=False\\n )\\n # print(\\\"pert_x_l1, pert_x_l2, pert_x_linf\\\", pert_x_l1, pert_x_l2, pert_x_linf)\\n # 不计算梯度地执行下列操作\\n with torch.no_grad():\\n # 构造一个包含三种扰动的列表\\n pertb_x_list = [pert_x_linf, pert_x_l2, pert_x_l1]\\n n_attacks = len(pertb_x_list) # 获取攻击的数量（即3）\\n pertbx = torch.vstack(pertb_x_list) # 垂直堆叠这三种扰动\\n label_ext = torch.cat([label[~done]] * n_attacks) # 扩展标签列表，使其与扰动列表长度匹配\\n\\n # 如果不是攻击者并且不需要四舍五入得分，则获取得分\\n # 否则，先对扰动进行四舍五入，再获取得分\\n if not oblivion:\\n purified_pertbx = purifier(pertbx.detach().clone().float()).to(torch.double)\\n else:\\n purified_pertbx = pertbx.detach().clone()\\n if (not self.is_attacker) and (not is_score_round): \\n scores, _done = self.get_scores(model, purified_pertbx, label_ext)\\n else:\\n scores, _done = self.get_scores(model, round_x(purified_pertbx, self.round_threshold), label_ext)\\n \\n # 如果得分的最大值大于0，则设置为该值，否则设置为0\\n max_v = scores.amax() if scores.amax() > 0 else 0.\\n scores[_done] += max_v # 对完成的得分增加max_v\\n\\n # 重新整形扰动和得分张量，以便后续操作\\n pertbx = pertbx.reshape(n_attacks, num_sample_red, *red_n).permute([1, 0, *red_ind])\\n scores = scores.reshape(n_attacks, num_sample_red).permute(1, 0)\\n\\n # 从得分张量中获取最大得分及其索引\\n _2, s_idx = scores.max(dim=-1)\\n # 使用索引从扰动张量中选择具有最高误导性的扰动\\n pert_x_cont = pertbx[torch.arange(num_sample_red), s_idx]\\n # print(\\\"pert_x_cont.shape\\\", pert_x_cont.shape)\\n # 更新经过扰动的数据adv_x\\n adv_x[~done] = pert_x_cont if not self.is_attacker else round_x(pert_x_cont, self.round_threshold)\\n \\n # 更新lambda值以便于下一次循环\\n self.lambda_ *= base\\n # 如果lambda值检查失败，则中断循环\\n if not self.check_lambda(model):\\n break\\n # 如果是攻击者，对最终的扰动结果进行四舍五入\\n if self.is_attacker:\\n adv_x = round_x(adv_x, self.round_threshold)\\n \\n # 不计算梯度地获取最后的损失和完成标志\\n with torch.no_grad():\\n purified_adv = purifier(adv_x.detach().clone().float()).to(torch.double)\\n _, done = self.get_loss(model, purified_adv, label, self.lambda_)\\n # 如果设置了详细输出，打印攻击效果的百分比\\n if verbose:\\n logger.info(f\\\"step-wise max: attack effectiveness {done.sum().item() / done.size()[0] * 100:.3f}%.\\\")\\n # 返回扰动后的数据\\n return adv_x\\n\\n\\n def perturb(self, model, x, label=None,\\n steps=100,\\n step_check=1,\\n sl_l1=1.,\\n sl_l2=1.,\\n sl_linf=0.01,\\n min_lambda_=1e-5,\\n max_lambda_=1e5,\\n is_score_round=True,\\n base=10.,\\n verbose=False):\\n \\\"\\\"\\\"\\n 对模型进行增强攻击。\\n\\n @param model: PyTorch模型，待攻击目标。\\n @param x: Tensor, 原始输入数据。\\n @param label: Tensor或None, 输入数据对应的标签。\\n @param steps: int, 攻击的总步数。\\n @param step_check: int, 检查间隔，即多少步进行一次检查。\\n @param sl_l1: float, L1范数的步长。\\n @param sl_l2: float, L2范数的步长。\\n @param sl_linf: float, Linf范数的步长。\\n @param min_lambda_: float, lambda的最小值。\\n @param max_lambda_: float, lambda的最大值。\\n @param is_score_round: Boolean, 是否对分数进行四舍五入。\\n @param base: float, 基数。\\n @param verbose: Boolean, 是否输出详细信息。\\n \\\"\\\"\\\"\\n # torch.manual_seed(int(random.random() * 100)) # 设置随机种子\\n # 参数校验\\n assert 0 < min_lambda_ <= max_lambda_\\n assert steps >= 0 and (step_check >= 1) and 1 >= sl_l1 > 0 and sl_l2 >= 0 and sl_linf >= 0\\n \\n model.eval() # 将模型设置为评估模式\\n \\n # 根据模型是否具有某种属性来设置lambda的初值\\n if hasattr(model, 'is_detector_enabled'):\\n self.lambda_ = min_lambda_\\n else:\\n self.lambda_ = max_lambda_\\n \\n # 如果不是攻击者，从预定义的步骤中随机选择一个\\n if not self.is_attacker:\\n step_checks = [1, 10, 25, 50]\\n step_check = random.choice(step_checks)\\n \\n # 计算每个小步骤中需要的迭代次数\\n mini_steps = [step_check] * (steps // step_check)\\n mini_steps = mini_steps + [steps % step_check] if steps % step_check != 0 else mini_steps\\n \\n # 获取输入的维度信息\\n n, red_n = x.size()[0], x.size()[1:]\\n red_ind = list(range(2, len(x.size()) + 1))\\n \\n adv_x = x.detach().clone() # 获取输入数据的副本\\n while self.lambda_ <= max_lambda_:\\n pert_x_cont = None\\n prev_done = None\\n for i, mini_step in enumerate(mini_steps):\\n with torch.no_grad():\\n # 如果是第一步并且启用了随机初始化，那么获取一个随机的起始点\\n if i == 0:\\n adv_x = get_x0(adv_x, rounding_threshold=self.round_threshold, is_sample=True)\\n _, done = self.get_loss(model, adv_x, label, self.lambda_)\\n \\n # print(\\\"done:\\\", done)\\n \\n # 如果所有的都完成了，就退出循环\\n if torch.all(done):\\n break\\n \\n # 对于那些没有完成的数据，重新计算扰动\\n # print(\\\"i:\\\", i)\\n if i == 0:\\n # print(\\\"~done:\\\", (~done))\\n adv_x[~done] = x[~done]\\n prev_done = done.clone()\\n else:\\n if (adv_x[~done]).shape[0] == (pert_x_cont[~done[~prev_done]]).shape[0]:\\n adv_x[~done] = pert_x_cont[~done[~prev_done]]\\n else:\\n updated_mask = (~done) & (~prev_done[:len(done)])\\n num_to_select = updated_mask.sum().item()\\n selected_perturbations = pert_x_cont[:num_to_select]\\n adv_x[updated_mask] = selected_perturbations\\n\\n prev_done = done.clone() \\n \\n # 对那些未完成的数据进行真正的扰动\\n num_sample_red = torch.sum(~done).item()\\n pert_x_l1, pert_x_l2, pert_x_linf = self._perturb(model, adv_x[~done], label[~done],\\n mini_step,\\n sl_l1,\\n sl_l2,\\n sl_linf,\\n lambda_=self.lambda_\\n )\\n # print(\\\"pert_x_l1, pert_x_l2, pert_x_linf\\\", pert_x_l1, pert_x_l2, pert_x_linf)\\n # 不计算梯度地执行下列操作\\n with torch.no_grad():\\n # 构造一个包含三种扰动的列表\\n pertb_x_list = [pert_x_linf, pert_x_l2, pert_x_l1]\\n n_attacks = len(pertb_x_list) # 获取攻击的数量（即3）\\n pertbx = torch.vstack(pertb_x_list) # 垂直堆叠这三种扰动\\n label_ext = torch.cat([label[~done]] * n_attacks) # 扩展标签列表，使其与扰动列表长度匹配\\n\\n # 如果不是攻击者并且不需要四舍五入得分，则获取得分\\n # 否则，先对扰动进行四舍五入，再获取得分\\n if (not self.is_attacker) and (not is_score_round):\\n scores, _done = self.get_scores(model, pertbx, label_ext)\\n else:\\n scores, _done = self.get_scores(model, round_x(pertbx, self.round_threshold), label_ext)\\n \\n # 如果得分的最大值大于0，则设置为该值，否则设置为0\\n max_v = scores.amax() if scores.amax() > 0 else 0.\\n scores[_done] += max_v # 对完成的得分增加max_v\\n\\n # 重新整形扰动和得分张量，以便后续操作\\n pertbx = pertbx.reshape(n_attacks, num_sample_red, *red_n).permute([1, 0, *red_ind])\\n scores = scores.reshape(n_attacks, num_sample_red).permute(1, 0)\\n\\n # 从得分张量中获取最大得分及其索引\\n _2, s_idx = scores.max(dim=-1)\\n # 使用索引从扰动张量中选择具有最高误导性的扰动\\n pert_x_cont = pertbx[torch.arange(num_sample_red), s_idx]\\n # print(\\\"pert_x_cont.shape\\\", pert_x_cont.shape)\\n # 更新经过扰动的数据adv_x\\n adv_x[~done] = pert_x_cont if not self.is_attacker else round_x(pert_x_cont, self.round_threshold)\\n \\n # 更新lambda值以便于下一次循环\\n self.lambda_ *= base\\n # 如果lambda值检查失败，则中断循环\\n if not self.check_lambda(model):\\n break\\n # 如果是攻击者，对最终的扰动结果进行四舍五入\\n if self.is_attacker:\\n adv_x = round_x(adv_x, self.round_threshold)\\n \\n # 不计算梯度地获取最后的损失和完成标志\\n with torch.no_grad():\\n _, done = self.get_loss(model, adv_x, label, self.lambda_)\\n # 如果设置了详细输出，打印攻击效果的百分比\\n if verbose:\\n logger.info(f\\\"step-wise max: attack effectiveness {done.sum().item() / done.size()[0] * 100:.3f}%.\\\")\\n # 返回扰动后的数据\\n return adv_x\\n\\n def _perturb(self, model, x, label=None,\\n steps=1,\\n step_length_l1=1.,\\n step_length_l2=0.5,\\n step_length_linf=0.01,\\n lambda_=1.,\\n ):\\n \\\"\\\"\\\"\\n 对节点的特征向量进行扰动\\n\\n 参数\\n -----------\\n @param model: 受害者模型\\n @param x: torch.FloatTensor, 节点特征向量（每个表示一个图中的API出现次数）形状为 [batch_size, vocab_dim]\\n @param label: torch.LongTensor, 真实的标签\\n @param steps: 整数, 迭代的最大次数\\n @param step_length_l1: 每次迭代的步长，L1范数\\n @param step_length_l2: 每次迭代的步长，L2范数\\n @param step_length_linf: 每次迭代的步长，Linf范数\\n @param lambda_: 浮点数, 惩罚因子\\n \\\"\\\"\\\"\\n if x is None or x.shape[0] <= 0:\\n return []\\n \\n self.lambda_ = lambda_\\n \\n # 确保L1步长在[0,1]之间\\n assert 0 <= step_length_l1 <= 1, \\\"期望在 [0,1] 之间的实数值，但得到 {}\\\".format(step_length_l1)\\n model.eval()\\n adv_x = x.detach()\\n \\n def one_iteration(_adv_x, norm_type):\\n # 基于当前的扰动输入来计算梯度\\n if \\\"rnn\\\" in model.model_save_path:\\n model.train()\\n if \\\"lstm\\\" in model.model_save_path:\\n model.train() \\n var_adv_x = torch.autograd.Variable(_adv_x, requires_grad=True) # 将_adv_x转换为一个可以进行自动梯度计算的变量\\n loss, done = self.get_loss(model, var_adv_x, label, self.lambda_) # 获取模型在扰动输入上的损失\\n grads = torch.autograd.grad(loss.mean(), var_adv_x, allow_unused=True)\\n if grads[0] is None:\\n grad = torch.zeros_like(var_adv_x)\\n else:\\n grad = grads[0].data\\n\\n # 寻找允许的位置来插入和移除API\\n pos_insertion = (_adv_x <= 0.5) * 1 * (_adv_x >= 0.) # 寻找API的可插入位置：特征值在0和0.5之间\\n grad4insertion = (grad > 0) * pos_insertion * grad # 根据梯度正值计算插入API的梯度\\n\\n pos_removal = (_adv_x > 0.5) * 1 # 寻找API的可移除位置：特征值大于0.5\\n grad4removal = (grad <= 0) * (pos_removal & self.manipulation_x) * grad # 根据梯度负值计算移除API的梯度\\n\\n if self.is_attacker:\\n # 对于攻击者，处理那些互相依赖的API\\n checking_nonexist_api = (pos_removal ^ self.omega) & self.omega # 检查不存在的API\\n grad4removal[:, self.api_flag] += torch.sum(grad * checking_nonexist_api, dim=-1, keepdim=True) # 考虑API之间的关系，调整移除API的梯度\\n\\n # 合并插入和移除的梯度\\n grad = grad4removal + grad4insertion\\n\\n # 根据不同的范数类型，计算扰动值\\n if norm_type == 'linf':\\n perturbation = torch.sign(grad) # 计算梯度符号来获取无穷范数扰动方向\\n if self.is_attacker:\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_linf * perturbation, min=0., max=1.) # 应用扰动并确保结果在[0,1]范围内\\n\\n elif norm_type == 'l2':\\n l2norm = torch.linalg.norm(grad, dim=-1, keepdim=True) # 计算L2范数\\n perturbation = torch.minimum(\\n torch.tensor(1., dtype=_adv_x.dtype, device=_adv_x.device),\\n grad / l2norm\\n ) # 计算L2范数下的扰动方向\\n perturbation = torch.where(torch.isnan(perturbation), 0., perturbation) # 处理NaN值\\n perturbation = torch.where(torch.isinf(perturbation), 1., perturbation) # 处理Inf值\\n if self.is_attacker:\\n min_val = torch.amin(perturbation, dim=-1, keepdim=True).clamp_(max=0.)\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * torch.abs(min_val) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_l2 * perturbation, min=0., max=1.)\\n\\n elif norm_type == 'l1':\\n val, idx = torch.abs(grad).topk(int(1. / step_length_l1), dim=-1) # 获取梯度的绝对值的top-k值和相应的索引\\n perturbation = F.one_hot(idx, num_classes=_adv_x.shape[-1]).sum(dim=1) # 根据索引计算L1范数下的扰动方向\\n perturbation = torch.sign(grad) * perturbation # 使用梯度的符号来调整扰动方向\\n if self.is_attacker:\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_l1 * perturbation, min=0., max=1.)\\n\\n else:\\n raise NotImplementedError # 如果范数类型不在L1、L2、Linf中，则引发异常\\n\\n\\n # 为每种范数执行迭代\\n adv_x_l1 = adv_x.clone()\\n for t in range(steps):\\n adv_x_l1 = one_iteration(adv_x_l1, norm_type='l1')\\n \\n adv_x_l2 = adv_x.clone()\\n for t in range(steps):\\n adv_x_l2 = one_iteration(adv_x_l2, norm_type='l2')\\n \\n adv_x_linf = adv_x.clone()\\n for t in range(steps):\\n adv_x_linf = one_iteration(adv_x_linf, norm_type='linf')\\n \\n return adv_x_l1, adv_x_l2, adv_x_linf\\n\\n\\n def _perturb_dae(self, model, purifier, x, label=None,\\n steps=1,\\n step_length_l1=1.,\\n step_length_l2=0.5,\\n step_length_linf=0.01,\\n lambda_=1.,\\n oblivion=False):\\n \\\"\\\"\\\"\\n 对节点的特征向量进行扰动\\n\\n 参数\\n -----------\\n @param model: 受害者模型\\n @param x: torch.FloatTensor, 节点特征向量（每个表示一个图中的API出现次数）形状为 [batch_size, vocab_dim]\\n @param label: torch.LongTensor, 真实的标签\\n @param steps: 整数, 迭代的最大次数\\n @param step_length_l1: 每次迭代的步长，L1范数\\n @param step_length_l2: 每次迭代的步长，L2范数\\n @param step_length_linf: 每次迭代的步长，Linf范数\\n @param lambda_: 浮点数, 惩罚因子\\n \\\"\\\"\\\"\\n if x is None or x.shape[0] <= 0:\\n return []\\n \\n self.lambda_ = lambda_\\n \\n # 确保L1步长在[0,1]之间\\n assert 0 <= step_length_l1 <= 1, \\\"期望在 [0,1] 之间的实数值，但得到 {}\\\".format(step_length_l1)\\n model.eval()\\n adv_x = x.detach()\\n \\n\\n def one_iteration(_adv_x, norm_type):\\n # 基于当前的扰动输入来计算梯度\\n var_adv_x = torch.autograd.Variable(_adv_x, requires_grad=True) # 将_adv_x转换为一个可以进行自动梯度计算的变量\\n if not oblivion:\\n purified_var = purifier(var_adv_x.detach().clone().float()).to(torch.double)\\n else:\\n purified_var = var_adv_x.detach().clone()\\n loss, done = self.get_loss(model, purified_var, label, self.lambda_) # 获取模型在扰动输入上的损失\\n grads = torch.autograd.grad(loss.mean(), var_adv_x, allow_unused=True)\\n if grads[0] is None:\\n grad = torch.zeros_like(var_adv_x)\\n else:\\n grad = grads[0].data\\n\\n # 寻找允许的位置来插入和移除API\\n pos_insertion = (_adv_x <= 0.5) * 1 * (_adv_x >= 0.) # 寻找API的可插入位置：特征值在0和0.5之间\\n grad4insertion = (grad > 0) * pos_insertion * grad # 根据梯度正值计算插入API的梯度\\n\\n pos_removal = (_adv_x > 0.5) * 1 # 寻找API的可移除位置：特征值大于0.5\\n grad4removal = (grad <= 0) * (pos_removal & self.manipulation_x) * grad # 根据梯度负值计算移除API的梯度\\n\\n if self.is_attacker:\\n # 对于攻击者，处理那些互相依赖的API\\n checking_nonexist_api = (pos_removal ^ self.omega) & self.omega # 检查不存在的API\\n grad4removal[:, self.api_flag] += torch.sum(grad * checking_nonexist_api, dim=-1, keepdim=True) # 考虑API之间的关系，调整移除API的梯度\\n\\n # 合并插入和移除的梯度\\n grad = grad4removal + grad4insertion\\n\\n # 根据不同的范数类型，计算扰动值\\n if norm_type == 'linf':\\n perturbation = torch.sign(grad) # 计算梯度符号来获取无穷范数扰动方向\\n if self.is_attacker:\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_linf * perturbation, min=0., max=1.) # 应用扰动并确保结果在[0,1]范围内\\n\\n elif norm_type == 'l2':\\n l2norm = torch.linalg.norm(grad, dim=-1, keepdim=True) # 计算L2范数\\n perturbation = torch.minimum(\\n torch.tensor(1., dtype=_adv_x.dtype, device=_adv_x.device),\\n grad / l2norm\\n ) # 计算L2范数下的扰动方向\\n perturbation = torch.where(torch.isnan(perturbation), 0., perturbation) # 处理NaN值\\n perturbation = torch.where(torch.isinf(perturbation), 1., perturbation) # 处理Inf值\\n if self.is_attacker:\\n min_val = torch.amin(perturbation, dim=-1, keepdim=True).clamp_(max=0.)\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * torch.abs(min_val) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_l2 * perturbation, min=0., max=1.)\\n\\n elif norm_type == 'l1':\\n val, idx = torch.abs(grad).topk(int(1. / step_length_l1), dim=-1) # 获取梯度的绝对值的top-k值和相应的索引\\n perturbation = F.one_hot(idx, num_classes=_adv_x.shape[-1]).sum(dim=1) # 根据索引计算L1范数下的扰动方向\\n perturbation = torch.sign(grad) * perturbation # 使用梯度的符号来调整扰动方向\\n if self.is_attacker:\\n perturbation += (torch.any(perturbation[:, self.api_flag] < 0, dim=-1, keepdim=True) * checking_nonexist_api)\\n return torch.clamp(_adv_x + step_length_l1 * perturbation, min=0., max=1.)\\n\\n else:\\n raise NotImplementedError # 如果范数类型不在L1、L2、Linf中，则引发异常\\n\\n\\n # 为每种范数执行迭代\\n adv_x_l1 = adv_x.clone()\\n for t in range(steps):\\n adv_x_l1 = one_iteration(adv_x_l1, norm_type='l1')\\n \\n adv_x_l2 = adv_x.clone()\\n for t in range(steps):\\n adv_x_l2 = one_iteration(adv_x_l2, norm_type='l2')\\n \\n adv_x_linf = adv_x.clone()\\n for t in range(steps):\\n adv_x_linf = one_iteration(adv_x_linf, norm_type='linf')\\n \\n return adv_x_l1, adv_x_l2, adv_x_linf\\n\\n def get_scores(self, model, pertb_x, label):\\n # 如果模型有 'is_detector_enabled' 这个属性\\n if hasattr(model, 'is_detector_enabled'):\\n # 获取模型的输出，logits_f 是模型的原始输出，prob_g 是一个概率值\\n logits_f, prob_g = model.forward(pertb_x)\\n else:\\n # 如果模型没有 'is_detector_enabled' 这个属性，只获取模型的原始输出\\n logits_f = model.forward(pertb_x)\\n\\n # 获取预测的类别\\n y_pred = logits_f.argmax(1)\\n \\n # 计算交叉熵损失\\n ce = F.cross_entropy(logits_f, label, reduction='none')\\n \\n # 如果模型有 'is_detector_enabled' 这个属性，并且 self.oblivion 为 False\\n if hasattr(model, 'is_detector_enabled') and (not self.oblivion):\\n # 获取样本的阈值\\n tau = model.get_tau_sample_wise(y_pred)\\n # 计算损失，加入了 prob_g 这个概率值的惩罚项\\n loss_no_reduction = ce - self.lambda_ * prob_g\\n # 判断预测是否错误，并且 prob_g 是否小于等于阈值 tau\\n done = (y_pred != label) & (prob_g <= tau)\\n else:\\n # 如果没有 'is_detector_enabled' 这个属性或 self.oblivion 为 True，损失仍然是交叉熵损失\\n loss_no_reduction = ce\\n # 判断预测是否错误\\n done = y_pred != label\\n\\n # 返回损失值和判断结果c\\n return loss_no_reduction, done\"\n },\n {\n \"identifier\": \"MalwareDetectionDNN\",\n \"path\": \"core/defense/md_dnn.py\",\n \"snippet\": \"class MalwareDetectionDNN(nn.Module):\\n def __init__(self, input_size, n_classes, device='cpu', name='DNN', **kwargs):\\n \\\"\\\"\\\"\\n 初始化恶意软件检测器\\n\\n 参数:\\n ----------\\n @param input_size: 整数，输入向量的维度数量。\\n @param n_classes: 整数，表示分类的数量，例如二分类问题中n=2。\\n @param device: 字符串，可以是'cpu'或'cuda'，表示模型应该在CPU还是GPU上运行。\\n @param name: 字符串，用于命名模型。\\n \\\"\\\"\\\"\\n super(MalwareDetectionDNN, self).__init__() # 调用父类初始化\\n self.input_size = input_size # 定义输入尺寸\\n self.n_classes = n_classes # 定义分类数量\\n self.device = device # 定义运行设备\\n self.name = name # 定义模型名称\\n\\n self.parse_args(**kwargs) # 解析额外参数\\n\\n self.dense_layers = [] # 初始化一个空的密集层列表\\n \\n # 检查是否至少有一个隐藏层\\n if len(self.dense_hidden_units) >= 1:\\n # 添加第一个密集层\\n self.dense_layers.append(nn.Linear(self.input_size, self.dense_hidden_units[0]))\\n else:\\n # 如果没有隐藏层，抛出异常\\n raise ValueError(\\\"Expect at least one hidden layer.\\\")\\n\\n # 为每一对连续的隐藏单元添加一个密集层\\n for i in range(len(self.dense_hidden_units[0:-1])):\\n self.dense_layers.append(nn.Linear(self.dense_hidden_units[i], \\n self.dense_hidden_units[i + 1]))\\n \\n # 添加最后一个连接到输出层的密集层\\n self.dense_layers.append(nn.Linear(self.dense_hidden_units[-1], self.n_classes))\\n \\n # 将密集层添加到模型中以进行跟踪\\n for idx_i, dense_layer in enumerate(self.dense_layers):\\n self.add_module('nn_model_layer_{}'.format(idx_i), dense_layer)\\n\\n # 根据参数选择使用SELU或ReLU激活函数\\n if self.smooth:\\n self.activation_func = F.selu # 使用SELU激活函数\\n else:\\n self.activation_func = F.relu # 使用ReLU激活函数\\n\\n # 定义模型的保存路径\\n self.model_save_path = path.join(config.get('experiments', 'md_dnn') + '_' + self.name,\\n 'model.pth')\\n \\n # 日志中打印模型的结构信息\\n logger.info('========================================dnn model architecture===============================')\\n logger.info(self)\\n logger.info('===============================================end==========================================')\\n\\n\\n def parse_args(self,\\n dense_hidden_units=None,\\n dropout=0.6,\\n alpha_=0.2,\\n smooth=False,\\n **kwargs\\n ):\\n \\\"\\\"\\\"\\n 解析并设置网络的超参数。\\n\\n 参数:\\n ----------\\n dense_hidden_units : list, 可选\\n 网络中每个隐藏层的单元数。如果没有指定，则默认为两个隐藏层，每层200个单元。\\n dropout : float, 可选\\n dropout正则化的比率，默认为0.6。\\n alpha_ : float, 可选\\n 某些激活函数的参数，默认为0.2。\\n smooth : bool, 可选\\n 是否使用平滑的激活函数，默认为False。\\n **kwargs : dict\\n 其他超参数。\\n \\\"\\\"\\\"\\n\\n # 如果用户没有指定隐藏层，使用默认的配置\\n if dense_hidden_units is None:\\n self.dense_hidden_units = [200, 200]\\n # 如果用户指定了一个列表，使用它\\n elif isinstance(dense_hidden_units, list):\\n self.dense_hidden_units = dense_hidden_units\\n # 否则抛出一个异常\\n else:\\n raise TypeError(\\\"Expect a list of hidden units.\\\")\\n\\n # 设置dropout, alpha和smooth参数\\n self.dropout = dropout\\n self.alpha_ = alpha_\\n self.smooth = smooth\\n\\n # 从kwargs中获取并设置proc_number\\n self.proc_number = kwargs.get('proc_number', None) # 如果不存在，则返回None\\n\\n # 如果还有其他参数，记录警告，因为这些参数可能是未知的\\n if len(kwargs) > 0:\\n logger.warning(\\\"Unknown hyper-parameters {}\\\".format(str(kwargs)))\\n\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n 使输入数据 x 通过神经网络\\n \\n 参数\\n ----------\\n @param x: 2D张量，特征表示\\n \\\"\\\"\\\"\\n # 遍历神经网络的每一层，除了最后一层\\n for dense_layer in self.dense_layers[:-1]:\\n x = self.activation_func(dense_layer(x)) # 使用激活函数处理每一层的输出\\n\\n # 对处理过的数据进行 dropout 操作，用于防止过拟合\\n latent_representation = F.dropout(x, self.dropout, training=self.training)\\n \\n # 用最后一层进行处理，得到logits（未归一化的预测或分类得分）\\n logits = self.dense_layers[-1](latent_representation)\\n return logits\\n\\n def inference(self, test_data_producer):\\n \\\"\\\"\\\"\\n 进行模型推理，获得预测的置信度和真实标签\\n \\n 参数\\n ----------\\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\\n \\n 返回值\\n ----------\\n 返回预测的置信度和真实标签\\n \\\"\\\"\\\"\\n confidences = [] # 存储每批数据的预测置信度\\n gt_labels = [] # 存储每批数据的真实标签\\n self.eval() # 设置模型为评估模式\\n\\n # 使用torch.no_grad()来告诉PyTorch不要在推理过程中计算梯度\\n with torch.no_grad():\\n # 遍历每一批测试数据\\n for x, y in test_data_producer:\\n # 将数据转移到指定的设备（CPU或GPU）并调整数据类型\\n x, y = utils.to_device(x.double(), y.long(), self.device)\\n # 得到每一批数据的logits\\n logits = self.forward(x)\\n # 使用softmax函数得到每一批数据的置信度，并将其添加到confidences列表中\\n confidences.append(F.softmax(logits, dim=-1))\\n # 将每一批数据的真实标签添加到gt_labels列表中\\n gt_labels.append(y)\\n\\n # 将所有批次的置信度垂直堆叠成一个张量\\n confidences = torch.vstack(confidences)\\n # 将所有批次的真实标签连接成一个张量\\n gt_labels = torch.cat(gt_labels, dim=0)\\n \\n return confidences, gt_labels\\n\\n def inference_dae(self, test_data_producer):\\n \\\"\\\"\\\"\\n 进行模型推理，获得预测的置信度和真实标签\\n \\n 参数\\n ----------\\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\\n \\n 返回值\\n ----------\\n 返回预测的置信度和真实标签\\n \\\"\\\"\\\"\\n confidences = [] # 存储每批数据的预测置信度\\n gt_labels = [] # 存储每批数据的真实标签\\n self.eval() # 设置模型为评估模式\\n\\n # 使用torch.no_grad()来告诉PyTorch不要在推理过程中计算梯度\\n with torch.no_grad():\\n # 遍历每一批测试数据\\n for x, y in test_data_producer:\\n # 将数据转移到指定的设备（CPU或GPU）并调整数据类型\\n x, y = utils.to_device(x.double(), y.long(), self.device)\\n # 得到每一批数据的logits\\n logits = self.forward(x)\\n # 使用softmax函数得到每一批数据的置信度，并将其添加到confidences列表中\\n confidences.append(F.softmax(logits, dim=-1))\\n # 将每一批数据的真实标签添加到gt_labels列表中\\n gt_labels.append(y)\\n \\n return confidences, gt_labels\\n\\n\\n def get_important_attributes(self, test_data_producer, target_label=1):\\n \\\"\\\"\\\"\\n 使用集成梯度（Integrated Gradients）方法获取重要的属性/特征\\n\\n 参数\\n ----------\\n @param test_data_producer: 数据生产者或数据加载器，用于产生测试数据\\n @param target_label: 目标标签，默认为1\\n \\n 返回值\\n ----------\\n 返回重要的属性/特征\\n \\\"\\\"\\\"\\n attributions = [] # 存储属性或特征的重要性得分\\n gt_labels = [] # 存储真实标签\\n\\n # 定义一个使用集成梯度方法的包装器\\n def _ig_wrapper(_x):\\n logits = self.forward(_x)\\n return F.softmax(logits, dim=-1)\\n\\n # 初始化集成梯度对象\\n ig = IntegratedGradients(_ig_wrapper)\\n\\n # 遍历测试数据集\\n for i, (x, y) in enumerate(test_data_producer):\\n # 将数据和标签转移到指定的设备上\\n x, y = utils.to_device(x.double(), y.long(), self.device)\\n # 使x能够计算梯度\\n x.requires_grad = True\\n # 定义基线，用于集成梯度的计算\\n baseline = torch.zeros_like(x, dtype=torch.double, device=self.device)\\n # 计算属性的重要性\\n attribution_bs = ig.attribute(x,\\n baselines=baseline,\\n target=target_label)\\n # 将所有批次的属性垂直堆叠\\n attribution = torch.hstack(attribution_bs)\\n # 保存得到的属性重要性得分和真实标签\\n attributions.append(attribution.clone().detach().cpu().numpy())\\n gt_labels.append(y.clone().detach().cpu().numpy())\\n # 将真实标签保存为.npy文件\\n np.save('./labels', np.concatenate(gt_labels))\\n \\n return np.vstack(attributions)\\n\\n\\n def inference_batch_wise(self, x):\\n \\\"\\\"\\\"\\n 仅支持恶意软件样本的批量推理\\n \\n 参数\\n ----------\\n @param x: 输入数据的张量\\n \\n 返回值\\n ----------\\n 返回推理的置信度和标签\\n \\\"\\\"\\\"\\n # 确保输入是一个张量\\n assert isinstance(x, torch.Tensor)\\n \\n # 获得模型的输出\\n logit = self.forward(x)\\n \\n # 返回每个样本的置信度和一个与logit形状相同的全1数组（表示恶意软件样本）\\n return torch.softmax(logit, dim=-1).detach().cpu().numpy(), np.ones((logit.size()[0],))\\n\\n\\n def predict(self, test_data_producer, indicator_masking=True):\\n \\\"\\\"\\\"\\n 预测标签并进行评估\\n\\n 参数\\n --------\\n @param test_data_producer: torch.DataLoader, 用于生成测试数据的数据加载器\\n \\\"\\\"\\\"\\n # 进行评估\\n confidence, y_true = self.inference(test_data_producer)\\n y_pred = confidence.argmax(1).cpu().numpy() # 预测标签\\n y_true = y_true.cpu().numpy() # 真实标签\\n \\n # print(\\\"y_true.shape:\\\", y_true.shape)\\n # print(\\\"y_pred.shape:\\\", y_pred.shape)\\n \\n # 使用sklearn的评估指标进行评估\\n from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, balanced_accuracy_score\\n accuracy = accuracy_score(y_true, y_pred)\\n b_accuracy = balanced_accuracy_score(y_true, y_pred)\\n \\n MSG = \\\"The accuracy on the test dataset is {:.5f}%\\\"\\n logger.info(MSG.format(accuracy * 100))\\n \\n MSG = \\\"The balanced accuracy on the test dataset is {:.5f}%\\\"\\n logger.info(MSG.format(b_accuracy * 100))\\n\\n # 检查数据中是否存在缺失的类别\\n if np.any([np.all(y_true == i) for i in range(self.n_classes)]):\\n logger.warning(\\\"class absent.\\\")\\n return\\n\\n # 计算混淆矩阵\\n tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\\n fpr = fp / float(tn + fp) # 计算假阳性率\\n fnr = fn / float(tp + fn) # 计算假阴性率\\n f1 = f1_score(y_true, y_pred, average='binary') # 计算F1分数\\n\\n print(\\\"Other evaluation metrics we may need:\\\")\\n MSG = \\\"False Negative Rate (FNR) is {:.5f}%、False Positive Rate (FPR) is {:.5f}%, F1 score is {:.5f}%\\\"\\n logger.info(MSG.format(fnr * 100, fpr * 100, f1 * 100))\\n\\n\\n def customize_loss(self, logits, gt_labels, representation=None, mini_batch_idx=None):\\n \\\"\\\"\\\"\\n 自定义损失函数\\n\\n 参数\\n --------\\n @param logits: Tensor, 模型的输出\\n @param gt_labels: Tensor, 真实的标签\\n @param representation: Tensor, 可选参数，表示特征表示\\n @param mini_batch_idx: Int, 可选参数，表示小批次的索引\\n \\n 返回值\\n --------\\n 返回交叉熵损失\\n \\\"\\\"\\\"\\n return F.cross_entropy(logits, gt_labels)\\n\\n\\n def fit(self, train_data_producer, validation_data_producer, epochs=100, lr=0.005, weight_decay=0., weight_sampling=0.5, verbose=True):\\n \\\"\\\"\\\"\\n 训练恶意软件检测器，根据验证集上的交叉熵损失选择最佳模型。\\n\\n 参数\\n ----------\\n @param train_data_producer: 对象, 用于生成一批训练数据的迭代器\\n @param validation_data_producer: 对象, 用于生成验证数据的迭代器\\n @param epochs: 整数, 训练的周期数\\n @param lr: 浮点数, Adam优化器的学习率\\n @param weight_decay: 浮点数, 惩罚因子\\n @param verbose: 布尔值, 是否显示详细的日志\\n \\\"\\\"\\\"\\n # 初始化优化器\\n optimizer = optim.Adam(self.parameters(), lr=lr, weight_decay=weight_decay)\\n best_avg_acc = 0. # 记录验证集上的最佳准确率\\n best_epoch = 0 # 记录最佳准确率对应的周期\\n total_time = 0. # 总的训练时间\\n\\n # 获取训练数据批次的数量\\n nbatches = len(train_data_producer)\\n \\n # 进行指定次数的训练周期\\n for i in range(epochs):\\n # 设置模型为训练模式\\n self.train()\\n # 初始化列表用于保存每批数据的损失值和准确率\\n losses, accuracies = [], []\\n\\n # 对每个训练数据批次进行遍历\\n for idx_batch, (x_train, y_train) in enumerate(train_data_producer):\\n # 将数据转移到指定的计算设备（例如GPU或CPU）\\n x_train, y_train = utils.to_device(x_train.double(), y_train.long(), self.device)\\n\\n # 记录开始训练的时间\\n start_time = time.time()\\n\\n # 清空之前累积的梯度\\n optimizer.zero_grad() \\n \\n # 对输入数据进行前向传播\\n logits = self.forward(x_train) \\n \\n # 根据模型的输出和真实标签计算损失\\n loss_train = self.customize_loss(logits, y_train) \\n\\n # 对损失进行反向传播\\n loss_train.backward()\\n \\n # 使用优化器更新模型参数\\n optimizer.step()\\n\\n # 计算训练这批数据所花费的总时间\\n total_time += time.time() - start_time\\n \\n # 计算这批数据上的准确率\\n acc_train = (logits.argmax(1) == y_train).sum().item() / x_train.size()[0]\\n \\n # 将时间转换为分钟和秒\\n mins, secs = int(total_time / 60), int(total_time % 60)\\n \\n # 将这批数据的损失和准确率加入到列表中\\n losses.append(loss_train.item())\\n accuracies.append(acc_train)\\n\\n # 如果开启了详细输出模式，显示当前训练进度和这批数据上的损失和准确率\\n if verbose:\\n logger.info(f'小批次： {i * nbatches + idx_batch + 1}/{epochs * nbatches} | 训练时间为 {mins:.0f} 分钟, {secs} 秒。')\\n logger.info(f'训练损失（小批次级别）: {losses[-1]:.4f} | 训练精度: {acc_train * 100:.2f}')\\n\\n\\n self.eval() # 将模型设置为评估模式\\n avg_acc_val = []\\n\\n with torch.no_grad(): # 确保在评估模式下不进行梯度的计算\\n for x_val, y_val in validation_data_producer:\\n # 将数据移动到指定设备（例如GPU或CPU）上，并确保数据的类型为双精度浮点数和长整型\\n x_val, y_val = utils.to_device(x_val.double(), y_val.long(), self.device)\\n \\n # 使用模型进行前向传播，得到输出结果\\n logits = self.forward(x_val)\\n \\n # 计算验证数据上的准确率\\n acc_val = (logits.argmax(1) == y_val).sum().item() / x_val.size()[0]\\n \\n # 保存每一批验证数据的准确率\\n avg_acc_val.append(acc_val)\\n \\n # 计算所有验证数据的平均准确率\\n avg_acc_val = np.mean(avg_acc_val)\\n\\n # 如果当前周期的验证精度超过之前的最佳验证精度\\n if avg_acc_val >= best_avg_acc:\\n # 更新最佳验证精度\\n best_avg_acc = avg_acc_val\\n best_epoch = i\\n \\n # 检查模型保存路径是否存在，如果不存在，则创建\\n if not path.exists(self.model_save_path):\\n utils.mkdir(path.dirname(self.model_save_path))\\n \\n # 保存当前的模型参数\\n torch.save(self.state_dict(), self.model_save_path)\\n \\n # 如果开启了详细输出模式，显示模型保存路径\\n if verbose:\\n print(f'模型保存在路径： {self.model_save_path}')\\n\\n # 如果开启了详细输出模式，显示训练损失、训练精度、验证精度和最佳验证精度\\n if verbose:\\n logger.info(f'训练损失（周期级别）: {np.mean(losses):.4f} | 训练精度: {np.mean(accuracies) * 100:.2f}')\\n logger.info(f'验证精度: {avg_acc_val * 100:.2f} | 最佳验证精度: {best_avg_acc * 100:.2f} 在第 {best_epoch} 个周期')\\n\\n def load(self):\\n \\\"\\\"\\\"\\n 从磁盘加载模型参数\\n \\\"\\\"\\\"\\n self.load_state_dict(torch.load(self.model_save_path))\"\n },\n {\n \"identifier\": \"DetectorTemplate\",\n \"path\": \"core/defense/amd_template.py\",\n \"snippet\": \"class DetectorTemplate(object):\\n def __init__(self):\\n self.tau = None # 阈值变量\\n self.is_detector_enabled = True # 表示检测器是否启用的标志\\n\\n def forward(self, x):\\n \\\"\\\"\\\"\\n 类预测与密度估计\\n \\\"\\\"\\\"\\n raise NotImplementedError\\n\\n def get_threshold(self):\\n \\\"\\\"\\\"\\n 计算拒绝异常值的阈值\\n \\\"\\\"\\\"\\n raise NotImplementedError\\n\\n def get_tau_sample_wise(self):\\n \\\"\\\"\\\"\\n 获取每个样本的tau值\\n \\\"\\\"\\\"\\n raise NotImplementedError\\n\\n def indicator(self):\\n \\\"\\\"\\\"\\n 返回一个布尔标志向量，指示是否拒绝一个样本\\n \\\"\\\"\\\"\\n raise NotImplementedError\"\n },\n {\n \"identifier\": \"config\",\n \"path\": \"config.py\",\n \"snippet\": \"def parser_config():\"\n },\n {\n \"identifier\": \"utils\",\n \"path\": \"tools/utils.py\",\n \"snippet\": \"ENC_KEY = 'cab228a122d3486bac7fab148e8b5aba'\\n MSG = \\\"No such directory or file {} exists!\\\".format(sample_dir)\\n MSG = \\\"A directory or a list of paths are allowed!\\\"\\ndef pool_initializer():\\ndef retrive_files_set(base_dir, dir_ext, file_ext):\\n def get_file_name(root_dir, file_ext):\\ndef check_dir(sample_dir):\\ndef dump_joblib(data, path):\\ndef read_joblib(path):\\ndef load_json(json_path):\\ndef dump_json(obj_dict, file_path):\\ndef dump_pickle(data, path, use_gzip=False):\\ndef read_pickle(path, use_gzip=False):\\ndef dump_pickle_frd_space(data, path):\\ndef read_pickle_frd_space(path):\\ndef dump_list_of_lists(data, path):\\ndef read_list_of_lists(path):\\ndef mkdir(target):\\ndef read_txt(path, mode='r'):\\ndef dump_txt(data_str, path, mode='w'):\\ndef read_file_by_fileinput(file_path, inplace=True):\\n def __init__(self, manager, use_cache=True):\\n def is_cached(self, key):\\n def reset(self):\\n def get(self, key):\\n def cache(self, key, img, lbl):\\ndef build_kwargs(keys, arg_dict):\\ndef inverse_kwargs(vars):\\ndef save_args(fout, args):\\ndef load_args(fout):\\ndef get_group_args(args, args_parser, title):\\ndef tensor_coo_sp_to_ivs(sparse_tensor):\\ndef ivs_to_tensor_coo_sp(ivs, device='cpu'):\\ndef sp_to_symmetric_sp(sparse_mx):\\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\\ndef to_tensor(feature_x=None, labels=None, device='cpu'):\\n def _to_torch_tensor(mat):\\ndef to_device(feature_x=None, labels=None, device='cpu'):\\ndef psn(x_tensor, prob, lower_value=0., upper_value=1.):\\n def __init__(self):\\n def __call__(self, module):\\ndef round_x(x, alpha=0.5):\\ndef get_x0(x, rounding_threshold=0.5, is_sample=False):\\ndef or_tensors(x_1, x_2):\\ndef xor_tensors(x_1, x_2):\\ndef get_mal_data(x_batch, y_batch):\\ndef get_mal_ben_data(x_batch, y_batch):\\ndef java_class_name2smali_name(cls):\\ndef remove_duplicate(components):\\ndef crypt_identifier(idf, seed=2345):\\n def md5_transform():\\ndef random_string(code):\\n def sha1_transform():\\ndef string_on_code(code):\\n def md5_transform():\\ndef random_name(seed=2345, code='abc'):\\ndef apply_encryption(base_string):\\ndef get_sha256(file_path):\\nclass SimplifyClass:\\nclass NonnegWeightConstraint(object):\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import time\nimport os.path as path\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport numpy as np\nfrom core.attack.max import Max\nfrom core.attack.stepwise_max import StepwiseMax\nfrom core.defense.md_dnn import MalwareDetectionDNN\nfrom core.defense.amd_template import DetectorTemplate\nfrom config import config, logging, ErrorHandler\nfrom tools import utils\n from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, balanced_accuracy_score"},"token_num":{"kind":"number","value":19741,"string":"19,741"},"cropped_code":{"kind":"string","value":"\"\"\"\n@article{grosse2017statistical,\n title={On the (statistical) detection of adversarial examples},\n author={Grosse, Kathrin and Manoharan, Praveen and Papernot, Nicolas and Backes, Michael and McDaniel, Patrick},\n journal={arXiv preprint arXiv:1702.06280},\n year={2017}\n}\n\n@inproceedings{carlini2017adversarial,\n title={Adversarial examples are not easily detected: Bypassing ten detection methods},\n author={Carlini, Nicholas and Wagner, David},\n booktitle={Proceedings of the 10th ACM workshop on artificial intelligence and security},\n pages={3--14},\n year={2017}\n}\n\nThis implementation is not an official version, but adapted from:\nhttps://github.com/carlini/nn_breaking_detection\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\n\n\n"},"all_code":{"kind":"string","value":"\"\"\"\n@article{grosse2017statistical,\n title={On the (statistical) detection of adversarial examples},\n author={Grosse, Kathrin and Manoharan, Praveen and Papernot, Nicolas and Backes, Michael and McDaniel, Patrick},\n journal={arXiv preprint arXiv:1702.06280},\n year={2017}\n}\n\n@inproceedings{carlini2017adversarial,\n title={Adversarial examples are not easily detected: Bypassing ten detection methods},\n author={Carlini, Nicholas and Wagner, David},\n booktitle={Proceedings of the 10th ACM workshop on artificial intelligence and security},\n pages={3--14},\n year={2017}\n}\n\nThis implementation is not an official version, but adapted from:\nhttps://github.com/carlini/nn_breaking_detection\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\n\n"},"next_line":{"kind":"string","value":"logger = logging.getLogger('core.defense.amd_dnn_plus')"},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-11-27 02:00:23+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5386,"cells":{"repo_name":{"kind":"string","value":"iann838/pulsefire"},"file_path":{"kind":"string","value":"tests/test_taskgroups.py"},"context":{"kind":"list like","value":[{"identifier":"RiotAPIClient","path":"pulsefire/clients.py","snippet":"class RiotAPIClient(BaseClient):\n \"\"\"Riot API Client.\n\n | Resources | Support |\n | -------------------- | -------------------------- |\n | League of Legends | ✅ |\n | Legends of Runeterra | ✅ |\n | Teamfight Tactics | ✅ |\n | Valorant | ✅ |\n\n Example:\n ```python\n async with RiotAPIClient(\n default_headers={\"X-Riot-Token\": <API_KEY>}\n ) as client:\n summoner = await client.get_lol_summoner_v4_by_name(region=\"na1\", name=\"Not a Whale\")\n assert summoner[\"summonerLevel\"] > 200\n ```\n \"\"\"\n\n Region = Literal[\n \"americas\", \"europe\", \"asia\", \"sea\", \"esports\",\n \"br1\", \"eun1\", \"euw1\", \"jp1\", \"kr\", \"la1\", \"la2\",\n \"na1\", \"oc1\", \"tr1\", \"ru\", \"ph2\", \"sg2\", \"th2\", \"tw2\", \"vn2\",\n \"ap\", \"br\", \"eu\", \"kr\", \"latam\", \"na\",\n ] | _str\n\n def __init__(\n self,\n *,\n base_url: str = \"https://{region}.api.riotgames.com\",\n default_params: dict[str, Any] = {},\n default_headers: dict[str, str] = {\"X-Riot-Token\": \"\"},\n default_queries: dict[str, str] = {},\n middlewares: list[Middleware] = [\n json_response_middleware(),\n http_error_middleware(),\n rate_limiter_middleware(RiotAPIRateLimiter()),\n ],\n ) -> None:\n super().__init__(\n base_url=base_url,\n default_params=default_params,\n default_headers=default_headers,\n default_queries=default_queries,\n middlewares=middlewares\n )\n\n # Account Endpoints\n\n async def get_account_v1_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.AccountV1Account:\n return await self.invoke(\"GET\", \"/riot/account/v1/accounts/by-puuid/{puuid}\")\n\n async def get_account_v1_by_riot_id(self, *, region: Region = ..., game_name: str = ..., tag_line: str = ...) -> RiotAPISchema.AccountV1Account:\n return await self.invoke(\"GET\", \"/riot/account/v1/accounts/by-riot-id/{game_name}/{tag_line}\")\n\n async def get_account_v1_me(self, *, region: Region = ..., headers: dict = {\"Authorization\": \"\"}) -> RiotAPISchema.AccountV1Account:\n return await self.invoke(\"GET\", \"/riot/account/v1/accounts/me\")\n\n async def get_account_v1_active_shard_by_puuid(self, *, region: Region = ..., puuid: str = ..., game: str = ...) -> RiotAPISchema.AccountV1ActiveShard:\n return await self.invoke(\"GET\", \"/riot/account/v1/active-shards/by-game/{game}/by-puuid/{puuid}\")\n\n # League of Legends Endpoints\n\n async def get_lol_champion_v3_rotation(self, *, region: Region = ...) -> RiotAPISchema.LolChampionV3Rotation:\n return await self.invoke(\"GET\", \"/lol/platform/v3/champion-rotations\")\n\n async def get_lol_champion_v4_mastery_by_summoner(self, *, region: Region = ..., summoner_id: str = ..., champion_id: int = ...) -> RiotAPISchema.LolChampionV4Mastery:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}/by-champion/{champion_id}\")\n\n async def get_lol_champion_v4_masteries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}\")\n\n async def get_lol_champion_v4_top_masteries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}/top\")\n\n async def get_lol_champion_v4_mastery_score_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> int:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/scores/by-summoner/{summoner_id}\")\n\n async def get_lol_champion_v4_mastery_by_puuid(self, *, region: Region = ..., puuid: str = ..., champion_id: int = ...) -> RiotAPISchema.LolChampionV4Mastery:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}/by-champion/{champion_id}\")\n\n async def get_lol_champion_v4_masteries_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}\")\n\n async def get_lol_champion_v4_top_masteries_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}/top\")\n\n async def get_lol_champion_v4_mastery_score_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> int:\n return await self.invoke(\"GET\", \"/lol/champion-mastery/v4/scores/by-puuid/{puuid}\")\n\n async def get_lol_clash_v1_players_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolClashV1Player]:\n return await self.invoke(\"GET\", \"/lol/clash/v1/players/by-summoner/{summoner_id}\")\n\n async def get_lol_clash_v1_players_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolClashV1Player]:\n return await self.invoke(\"GET\", \"/lol/clash/v1/players/by-puuid/{puuid}\")\n\n async def get_lol_clash_v1_team(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolClashV1Team:\n return await self.invoke(\"GET\", \"/lol/clash/v1/teams/{id}\")\n\n async def get_lol_clash_v1_tournament_by_team(self, *, region: Region = ..., team_id: str = ...) -> RiotAPISchema.LolClashV1Tournament:\n return await self.invoke(\"GET\", \"/lol/clash/v1/tournaments/by-team/{team_id}\")\n\n async def get_lol_clash_v1_tournament(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolClashV1Tournament:\n return await self.invoke(\"GET\", \"/lol/clash/v1/tournaments/{id}\")\n\n async def get_lol_clash_v1_tournaments(self, *, region: Region = ...) -> list[RiotAPISchema.LolClashV1Tournament]:\n return await self.invoke(\"GET\", \"/lol/clash/v1/tournaments\")\n\n async def get_lol_league_v4_entries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolLeagueV4LeagueFullEntry]:\n return await self.invoke(\"GET\", \"/lol/league/v4/entries/by-summoner/{summoner_id}\")\n\n async def get_lol_league_v4_challenger_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\n return await self.invoke(\"GET\", \"/lol/league/v4/challengerleagues/by-queue/{queue}\")\n\n async def get_lol_league_v4_grandmaster_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\n return await self.invoke(\"GET\", \"/lol/league/v4/grandmasterleagues/by-queue/{queue}\")\n\n async def get_lol_league_v4_master_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\n return await self.invoke(\"GET\", \"/lol/league/v4/masterleagues/by-queue/{queue}\")\n\n async def get_lol_league_v4_entries_by_division(\n self, *, region: Region = ..., queue: str = ..., tier: str = ..., division: str = ..., queries: dict = {\"page\": 1}\n ) -> list[RiotAPISchema.LolLeagueV4LeagueFullEntry]:\n return await self.invoke(\"GET\", \"/lol/league/v4/entries/{queue}/{tier}/{division}\")\n\n async def get_lol_league_v4_league(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolLeagueV4League:\n return await self.invoke(\"GET\", \"/lol/league/v4/leagues/{id}\")\n\n async def get_lol_match_v5_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolMatchV5Match:\n return await self.invoke(\"GET\", \"/lol/match/v5/matches/{id}\")\n\n async def get_lol_match_v5_match_timeline(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolMatchV5MatchTimeline:\n return await self.invoke(\"GET\", \"/lol/match/v5/matches/{id}/timeline\")\n\n async def get_lol_match_v5_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ..., queries: dict = {\"start\": 0, \"count\": 100}) -> list[str]:\n return await self.invoke(\"GET\", \"/lol/match/v5/matches/by-puuid/{puuid}/ids\")\n\n async def get_lol_spectator_v4_active_game_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> RiotAPISchema.LolSpectatorV4Game:\n return await self.invoke(\"GET\", \"/lol/spectator/v4/active-games/by-summoner/{summoner_id}\")\n\n async def get_lol_spectator_v4_featured_games(self, *, region: Region = ...) -> RiotAPISchema.LolSpectatorV4GameList:\n return await self.invoke(\"GET\", \"/lol/spectator/v4/featured-games\")\n\n async def get_lol_status_v4_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\n return await self.invoke(\"GET\", \"/lol/status/v4/platform-data\")\n\n async def get_lol_summoner_v4_by_id(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\n return await self.invoke(\"GET\", \"/lol/summoner/v4/summoners/{id}\")\n\n async def get_lol_summoner_v4_by_name(self, *, region: Region = ..., name: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\n return await self.invoke(\"GET\", \"/lol/summoner/v4/summoners/by-name/{name}\")\n\n async def get_lol_summoner_v4_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\n return await self.invoke(\"GET\", \"/lol/summoner/v4/summoners/by-puuid/{puuid}\")\n\n async def get_lol_summoner_v4_me(self, *, region: Region = ..., headers: dict = {\"Authorization\": \"\"}) -> RiotAPISchema.LolSummonerV4Summoner:\n return await self.invoke(\"GET\", \"/lol/summoner/v4/summoners/me\")\n\n async def get_lol_summoner_v4_by_rso_puuid(self, *, region: Region = ..., rso_puuid: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\n return await self.invoke(\"GET\", \"/fulfillment/v1/summoners/by-puuid/{rso_puuid}\")\n\n # Teamfight Tactics Endpoints\n\n async def get_tft_league_v1_entries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.TftLeagueV1LeagueFullEntry]:\n return await self.invoke(\"GET\", \"/tft/league/v1/entries/by-summoner/{summoner_id}\")\n\n async def get_tft_league_v1_challenger_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\n return await self.invoke(\"GET\", \"/tft/league/v1/challenger\")\n\n async def get_tft_league_v1_grandmaster_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\n return await self.invoke(\"GET\", \"/tft/league/v1/grandmaster\")\n\n async def get_tft_league_v1_master_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\n return await self.invoke(\"GET\", \"/tft/league/v1/master\")\n\n async def get_tft_league_v1_entries_by_division(\n self, *, region: Region = ..., tier: str = ..., division: str = ..., queries: dict = {\"page\": 1}\n ) -> list[RiotAPISchema.TftLeagueV1LeagueFullEntry]:\n return await self.invoke(\"GET\", \"/tft/league/v1/entries/{tier}/{division}\")\n\n async def get_tft_league_v1_league(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftLeagueV1League:\n return await self.invoke(\"GET\", \"/tft/league/v1/leagues/{id}\")\n\n async def get_tft_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftMatchV1Match:\n return await self.invoke(\"GET\", \"/tft/match/v1/matches/{id}\")\n\n async def get_tft_match_v1_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ..., queries: dict = {\"start\": 0, \"count\": 100}) -> list[str]:\n return await self.invoke(\"GET\", \"/tft/match/v1/matches/by-puuid/{puuid}/ids\")\n\n async def get_tft_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\n return await self.invoke(\"GET\", \"/tft/status/v1/platform-data\")\n\n async def get_tft_summoner_v1_by_id(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\n return await self.invoke(\"GET\", \"/tft/summoner/v1/summoners/{id}\")\n\n async def get_tft_summoner_v1_by_name(self, *, region: Region = ..., name: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\n return await self.invoke(\"GET\", \"/tft/summoner/v1/summoners/by-name/{name}\")\n\n async def get_tft_summoner_v1_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\n return await self.invoke(\"GET\", \"/tft/summoner/v1/summoners/by-puuid/{puuid}\")\n\n async def get_tft_summoner_v1_me(self, *, region: Region = ..., headers: dict = {\"Authorization\": \"\"}) -> RiotAPISchema.TftSummonerV1Summoner:\n return await self.invoke(\"GET\", \"/tft/summoner/v1/summoners/me\")\n\n # Legends of Runeterra Endpoints\n\n async def get_lor_ranked_v1_leaderboard(self, *, region: Region = ...) -> RiotAPISchema.LorRankedV1Leaderboard:\n return await self.invoke(\"GET\", \"/lor/ranked/v1/leaderboards\")\n\n async def get_lor_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LorMatchV1Match:\n return await self.invoke(\"GET\", \"/lor/match/v1/matches/{id}\")\n\n async def get_lor_match_v1_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[str]:\n return await self.invoke(\"GET\", \"/lor/match/v1/matches/by-puuid/{puuid}/ids\")\n\n async def get_lor_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\n return await self.invoke(\"GET\", \"/lor/status/v1/platform-data\")\n\n # Valorant Endpoints\n\n async def get_val_content_v1_contents(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.ValContentV1Contents:\n return await self.invoke(\"GET\", \"/val/content/v1/contents\")\n\n async def get_val_ranked_v1_leaderboard_by_act(self, *, region: Region = ..., act_id: str = ...) -> RiotAPISchema.ValRankedV1Leaderboard:\n return await self.invoke(\"GET\", \"/val/ranked/v1/leaderboards/by-act/{act_id}\")\n\n async def get_val_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.ValMatchV1Match:\n return await self.invoke(\"GET\", \"/val/match/v1/matches/{id}\")\n\n async def get_val_match_v1_matchlist_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.ValMatchV1Matchlist:\n return await self.invoke(\"GET\", \"/val/match/v1/matchlists/by-puuid/{puuid}\")\n\n async def get_val_match_v1_recent_matches_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.ValMatchV1RecentMatches:\n return await self.invoke(\"GET\", \"/val/match/v1/recent-matches/by-queue/{queue}\")\n\n async def get_val_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\n return await self.invoke(\"GET\", \"/val/status/v1/platform-data\")"},{"identifier":"async_to_sync","path":"pulsefire/functools.py","snippet":"def async_to_sync(runner: Callable[[Awaitable[Any]], Any] = asyncio.run):\n \"\"\"Convert a coroutine function to run synchronously. Use as decorator `@async_to_sync()`.\n\n Example:\n ```python\n @async_to_sync()\n async def sample_func(number: int):\n ...\n \n sample_func(0)\n ```\n\n Parameters:\n runner: A callable that runs the awaitable synchronously.\n\n Raises:\n TypeError: When `func` is not a coroutine function.\n \"\"\"\n\n def decorator[**P, R](func: Callable[P, Awaitable[R]]) -> Callable[P, R]:\n if not inspect.iscoroutinefunction(func):\n raise TypeError(f\"{func} is not a coroutine function\")\n\n @functools.wraps(func)\n def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:\n return runner(func(*args, **kwargs))\n\n return wrapper\n\n return decorator"},{"identifier":"RiotAPISchema","path":"pulsefire/schemas.py","snippet":"class RiotAPISchema:\n\n # Account Types\n\n AccountV1Account = TypedDict(\"AccountV1Account\", {\n \"puuid\": str,\n \"gameName\": str,\n \"tagLine\": str,\n })\n AccountV1ActiveShard = TypedDict(\"AccountV1ActiveShard\", {\n \"puuid\": str,\n \"game\": str,\n \"activeShard\": str,\n })\n\n # League of Legends Types\n\n LolChampionV3Rotation = TypedDict(\"LolChampionV3Rotation\", {\n \"freeChampionIds\": list[int],\n \"freeChampionIdsForNewPlayers\": list[int],\n \"maxNewPlayerLevel\": int\n })\n LolChampionV4Mastery = TypedDict(\"LolChampionV4Mastery\", {\n \"puuid\": str,\n \"championId\": int,\n \"championLevel\": int,\n \"championPoints\": int,\n \"lastPlayTime\": int,\n \"championPointsSinceLastLevel\": int,\n \"championPointsUntilNextLevel\": int,\n \"chestGranted\": bool,\n \"tokensEarned\": int,\n \"summonerId\": str\n })\n LolClashV1Player = TypedDict(\"LolClashV1Player\", {\n \"summonerId\": str,\n \"teamId\": str,\n \"position\": str,\n \"role\": str,\n })\n LolClashV1Team = TypedDict(\"LolClashV1Team\", {\n \"id\": str,\n \"tournamentId\": int,\n \"name\": str,\n \"iconId\": int,\n \"tier\": int,\n \"captain\": str,\n \"abbreviation\": str,\n \"players\": list[LolClashV1Player],\n })\n LolClashV1TournamentSchedule = TypedDict(\"LolClashV1TournamentSchedule\", {\n \"id\": int,\n \"registrationTime\": int,\n \"startTime\": int,\n \"cancelled\": bool,\n })\n LolClashV1Tournament = TypedDict(\"LolClashV1Tournament\", {\n \"id\": int,\n \"themeId\": int,\n \"nameKey\": str,\n \"nameKeySecondary\": str,\n \"schedule\": list[LolClashV1TournamentSchedule]\n })\n LolLeagueV4LeagueEntryMiniSeries = TypedDict(\"LolLeagueV4LeagueEntryMiniSeries\", {\n \"losses\": int,\n \"progress\": str,\n \"target\": int,\n \"wins\": int,\n })\n LolLeagueV4LeagueEntry = TypedDict(\"LolLeagueV4LeagueEntry\", {\n \"summonerId\": str,\n \"summonerName\": str,\n \"rank\": str,\n \"leaguePoints\": int,\n \"wins\": int,\n \"losses\": int,\n \"hotStreak\": bool,\n \"veteran\": bool,\n \"freshBlood\": bool,\n \"inactive\": bool,\n })\n LolLeagueV4LeagueFullEntry = TypedDict(\"LolLeagueV4LeagueFullEntry\", {\n \"leagueId\": str,\n \"summonerId\": str,\n \"summonerName\": str,\n \"queueType\": str,\n \"tier\": str,\n \"rank\": str,\n \"leaguePoints\": int,\n \"wins\": int,\n \"losses\": int,\n \"hotStreak\": bool,\n \"veteran\": bool,\n \"freshBlood\": bool,\n \"inactive\": bool,\n \"miniSeries\": NotRequired[LolLeagueV4LeagueEntryMiniSeries],\n })\n LolLeagueV4League = TypedDict(\"LolLeagueV4League\", {\n \"tier\": str,\n \"leagueId\": str,\n \"queue\": str,\n \"name\": str,\n \"entries\": list[LolLeagueV4LeagueEntry]\n })\n LolMatchV5MatchMetadata = TypedDict(\"LolMatchV5MatchMetadata\", {\n \"dataVersion\": str,\n \"matchId\": str,\n \"participants\": list[str]\n })\n LolMatchV5MatchTeamObjective = TypedDict(\"LolMatchV5MatchTeamObjective\", {\n \"first\": bool,\n \"kills\": int\n })\n LolMatchV5MatchInfoParticipantChallenges = TypedDict(\"LolMatchV5MatchInfoParticipantChallenges\", {\n \"12AssistStreakCount\": int,\n \"abilityUses\": int,\n \"acesBefore15Minutes\": int,\n \"alliedJungleMonsterKills\": int,\n \"baronTakedowns\": int,\n \"blastConeOppositeOpponentCount\": int,\n \"bountyGold\": int,\n \"buffsStolen\": int,\n \"completeSupportQuestInTime\": int,\n \"controlWardTimeCoverageInRiverOrEnemyHalf\": NotRequired[float],\n \"controlWardsPlaced\": int,\n \"damagePerMinute\": float,\n \"damageTakenOnTeamPercentage\": float,\n \"dancedWithRiftHerald\": int,\n \"deathsByEnemyChamps\": int,\n \"dodgeSkillShotsSmallWindow\": int,\n \"doubleAces\": int,\n \"dragonTakedowns\": int,\n \"earliestBaron\": float,\n \"earlyLaningPhaseGoldExpAdvantage\": int,\n \"effectiveHealAndShielding\": float,\n \"elderDragonKillsWithOpposingSoul\": int,\n \"elderDragonMultikills\": int,\n \"enemyChampionImmobilizations\": int,\n \"enemyJungleMonsterKills\": int,\n \"epicMonsterKillsNearEnemyJungler\": int,\n \"epicMonsterKillsWithin30SecondsOfSpawn\": int,\n \"epicMonsterSteals\": int,\n \"epicMonsterStolenWithoutSmite\": int,\n \"firstTurretKilled\": int,\n \"firstTurretKilledTime\": NotRequired[float],\n \"flawlessAces\": int,\n \"fullTeamTakedown\": int,\n \"gameLength\": float,\n \"getTakedownsInAllLanesEarlyJungleAsLaner\": NotRequired[int],\n \"goldPerMinute\": float,\n \"hadOpenNexus\": int,\n \"immobilizeAndKillWithAlly\": int,\n \"initialBuffCount\": int,\n \"initialCrabCount\": int,\n \"jungleCsBefore10Minutes\": float,\n \"junglerTakedownsNearDamagedEpicMonster\": int,\n \"kTurretsDestroyedBeforePlatesFall\": int,\n \"kda\": float,\n \"killAfterHiddenWithAlly\": int,\n \"killParticipation\": float,\n \"killedChampTookFullTeamDamageSurvived\": int,\n \"killingSprees\": int,\n \"killsNearEnemyTurret\": int,\n \"killsOnOtherLanesEarlyJungleAsLaner\": NotRequired[int],\n \"killsOnRecentlyHealedByAramPack\": int,\n \"killsUnderOwnTurret\": int,\n \"killsWithHelpFromEpicMonster\": int,\n \"knockEnemyIntoTeamAndKill\": int,\n \"landSkillShotsEarlyGame\": int,\n \"laneMinionsFirst10Minutes\": int,\n \"laningPhaseGoldExpAdvantage\": int,\n \"legendaryCount\": int,\n \"lostAnInhibitor\": int,\n \"maxCsAdvantageOnLaneOpponent\": float,\n \"maxKillDeficit\": int,\n \"maxLevelLeadLaneOpponent\": int,\n \"mejaisFullStackInTime\": int,\n \"moreEnemyJungleThanOpponent\": float,\n \"multiKillOneSpell\": int,\n \"multiTurretRiftHeraldCount\": int,\n \"multikills\": int,\n \"multikillsAfterAggressiveFlash\": int,\n \"mythicItemUsed\": NotRequired[int],\n \"outerTurretExecutesBefore10Minutes\": int,\n \"outnumberedKills\": int,\n \"outnumberedNexusKill\": int,\n \"perfectDragonSoulsTaken\": int,\n \"perfectGame\": int,\n \"pickKillWithAlly\": int,\n \"playedChampSelectPosition\": NotRequired[int],\n \"poroExplosions\": int,\n \"quickCleanse\": int,\n \"quickFirstTurret\": int,\n \"quickSoloKills\": int,\n \"riftHeraldTakedowns\": int,\n \"saveAllyFromDeath\": int,\n \"scuttleCrabKills\": int,\n \"shortestTimeToAceFromFirstTakedown\": NotRequired[float],\n \"skillshotsDodged\": int,\n \"skillshotsHit\": int,\n \"snowballsHit\": int,\n \"soloBaronKills\": int,\n \"soloKills\": int,\n \"stealthWardsPlaced\": int,\n \"survivedSingleDigitHpCount\": int,\n \"survivedThreeImmobilizesInFight\": int,\n \"takedownOnFirstTurret\": int,\n \"takedowns\": int,\n \"takedownsAfterGainingLevelAdvantage\": int,\n \"takedownsBeforeJungleMinionSpawn\": int,\n \"takedownsFirstXMinutes\": int,\n \"takedownsInAlcove\": int,\n \"takedownsInEnemyFountain\": int,\n \"teamBaronKills\": int,\n \"teamDamagePercentage\": float,\n \"teamElderDragonKills\": int,\n \"teamRiftHeraldKills\": int,\n \"tookLargeDamageSurvived\": int,\n \"turretPlatesTaken\": int,\n \"turretTakedowns\": int,\n \"turretsTakenWithRiftHerald\": int,\n \"twentyMinionsIn3SecondsCount\": int,\n \"twoWardsOneSweeperCount\": int,\n \"unseenRecalls\": int,\n \"visionScoreAdvantageLaneOpponent\": float,\n \"visionScorePerMinute\": float,\n \"wardTakedowns\": int,\n \"wardTakedownsBefore20M\": int,\n \"wardsGuarded\": int,\n \"earliestDragonTakedown\": NotRequired[float],\n \"baronBuffGoldAdvantageOverThreshold\": NotRequired[int],\n \"teleportTakedowns\": NotRequired[int],\n \"fastestLegendary\": NotRequired[float],\n \"highestChampionDamage\": NotRequired[int],\n \"highestCrowdControlScore\": NotRequired[int],\n \"junglerKillsEarlyJungle\": NotRequired[int],\n \"killsOnLanersEarlyJungleAsJungler\": NotRequired[int],\n \"fasterSupportQuestCompletion\": NotRequired[int],\n \"highestWardKills\": NotRequired[int],\n \"soloTurretsLategame\": NotRequired[int],\n \"thirdInhibitorDestroyedTime\": NotRequired[float],\n }, total=False) | dict[str, int | float]\n LolMatchV5MatchInfoParticipantPerksStatPerks = TypedDict(\"LolMatchV5MatchInfoParticipantPerksStatPerks\", {\n \"defense\": int,\n \"flex\": int,\n \"offense\": int\n })\n LolMatchV5MatchInfoParticipantPerksStyleSelection = TypedDict(\"LolMatchV5MatchInfoParticipantPerksStyleSelection\", {\n \"perk\": int,\n \"var1\": int,\n \"var2\": int,\n \"var3\": int}\n )\n LolMatchV5MatchInfoParticipantPerksStyle = TypedDict(\"LolMatchV5MatchInfoParticipantPerksStyle\", {\n \"description\": str,\n \"selections\": list[LolMatchV5MatchInfoParticipantPerksStyleSelection],\n \"style\": int\n })\n LolMatchV5MatchInfoParticipantPerks = TypedDict(\"LolMatchV5MatchInfoParticipantPerks\", {\n \"statPerks\": LolMatchV5MatchInfoParticipantPerksStatPerks,\n \"styles\": list[LolMatchV5MatchInfoParticipantPerksStyle]\n })\n LolMatchV5MatchInfoParticipantMissions = TypedDict(\"LolMatchV5MatchInfoParticipant\", {\n \"playerScore0\": float,\n \"playerScore1\": float,\n \"playerScore10\": float,\n \"playerScore11\": float,\n \"playerScore2\": float,\n \"playerScore3\": float,\n \"playerScore4\": float,\n \"playerScore5\": float,\n \"playerScore6\": float,\n \"playerScore7\": float,\n \"playerScore8\": float,\n \"playerScore9\": float,\n })\n LolMatchV5MatchInfoParticipant = TypedDict(\"LolMatchV5MatchInfoParticipant\", {\n \"allInPings\": int,\n \"assistMePings\": int,\n \"assists\": int,\n \"baitPings\": int,\n \"baronKills\": int,\n \"basicPings\": int,\n \"bountyLevel\": int,\n \"challenges\": NotRequired[LolMatchV5MatchInfoParticipantChallenges],\n \"champExperience\": int,\n \"champLevel\": int,\n \"championId\": int,\n \"championName\": str,\n \"championTransform\": int,\n \"commandPings\": int,\n \"consumablesPurchased\": int,\n \"damageDealtToBuildings\": int,\n \"damageDealtToObjectives\": int,\n \"damageDealtToTurrets\": int,\n \"damageSelfMitigated\": int,\n \"dangerPings\": int,\n \"deaths\": int,\n \"detectorWardsPlaced\": int,\n \"doubleKills\": int,\n \"dragonKills\": int,\n \"eligibleForProgression\": bool,\n \"enemyMissingPings\": int,\n \"enemyVisionPings\": int,\n \"firstBloodAssist\": bool,\n \"firstBloodKill\": bool,\n \"firstTowerAssist\": bool,\n \"firstTowerKill\": bool,\n \"gameEndedInEarlySurrender\": bool,\n \"gameEndedInSurrender\": bool,\n \"getBackPings\": int,\n \"goldEarned\": int,\n \"goldSpent\": int,\n \"holdPings\": int,\n \"individualPosition\": str,\n \"inhibitorKills\": int,\n \"inhibitorTakedowns\": int,\n \"inhibitorsLost\": int,\n \"item0\": int,\n \"item1\": int,\n \"item2\": int,\n \"item3\": int,\n \"item4\": int,\n \"item5\": int,\n \"item6\": int,\n \"itemsPurchased\": int,\n \"killingSprees\": int,\n \"kills\": int,\n \"lane\": str,\n \"largestCriticalStrike\": int,\n \"largestKillingSpree\": int,\n \"largestMultiKill\": int,\n \"longestTimeSpentLiving\": int,\n \"magicDamageDealt\": int,\n \"magicDamageDealtToChampions\": int,\n \"magicDamageTaken\": int,\n \"missions\": NotRequired[LolMatchV5MatchInfoParticipantMissions],\n \"needVisionPings\": int,\n \"neutralMinionsKilled\": int,\n \"nexusKills\": int,\n \"nexusLost\": int,\n \"nexusTakedowns\": int,\n \"objectivesStolen\": int,\n \"objectivesStolenAssists\": int,\n \"onMyWayPings\": int,\n \"participantId\": int,\n \"pentaKills\": int,\n \"perks\": LolMatchV5MatchInfoParticipantPerks,\n \"physicalDamageDealt\": int,\n \"physicalDamageDealtToChampions\": int,\n \"physicalDamageTaken\": int,\n \"placement\": int,\n \"playerAugment1\": int,\n \"playerAugment2\": int,\n \"playerAugment3\": int,\n \"playerAugment4\": int,\n \"playerSubteamId\": int,\n \"playerScore0\": NotRequired[float],\n \"playerScore1\": NotRequired[float],\n \"playerScore10\": NotRequired[float],\n \"playerScore11\": NotRequired[float],\n \"playerScore2\": NotRequired[float],\n \"playerScore3\": NotRequired[float],\n \"playerScore4\": NotRequired[float],\n \"playerScore5\": NotRequired[float],\n \"playerScore6\": NotRequired[float],\n \"playerScore7\": NotRequired[float],\n \"playerScore8\": NotRequired[float],\n \"playerScore9\": NotRequired[float],\n \"profileIcon\": int,\n \"pushPings\": int,\n \"puuid\": str,\n \"quadraKills\": int,\n \"riotIdName\": NotRequired[str],\n \"riotIdTagline\": str,\n \"riotIdGameName\": NotRequired[str],\n \"role\": str,\n \"sightWardsBoughtInGame\": int,\n \"spell1Casts\": int,\n \"spell2Casts\": int,\n \"spell3Casts\": int,\n \"spell4Casts\": int,\n \"subteamPlacement\": int,\n \"summoner1Casts\": int,\n \"summoner1Id\": int,\n \"summoner2Casts\": int,\n \"summoner2Id\": int,\n \"summonerId\": str,\n \"summonerLevel\": int,\n \"summonerName\": str,\n \"teamEarlySurrendered\": bool,\n \"teamId\": int,\n \"teamPosition\": str,\n \"timeCCingOthers\": int,\n \"timePlayed\": int,\n \"totalAllyJungleMinionsKilled\": int,\n \"totalDamageDealt\": int,\n \"totalDamageDealtToChampions\": int,\n \"totalDamageShieldedOnTeammates\": int,\n \"totalDamageTaken\": int,\n \"totalEnemyJungleMinionsKilled\": int,\n \"totalHeal\": int,\n \"totalHealsOnTeammates\": int,\n \"totalMinionsKilled\": int,\n \"totalTimeCCDealt\": int,\n \"totalTimeSpentDead\": int,\n \"totalUnitsHealed\": int,\n \"tripleKills\": int,\n \"trueDamageDealt\": int,\n \"trueDamageDealtToChampions\": int,\n \"trueDamageTaken\": int,\n \"turretKills\": int,\n \"turretTakedowns\": int,\n \"turretsLost\": int,\n \"unrealKills\": int,\n \"visionClearedPings\": int,\n \"visionScore\": int,\n \"visionWardsBoughtInGame\": int,\n \"wardsKilled\": int,\n \"wardsPlaced\": int,\n \"win\": bool\n })\n LolMatchV5MatchInfoTeamBan = TypedDict(\"LolMatchV5MatchInfoTeamBan\", {\n \"championId\": int,\n \"pickTurn\": int\n })\n LolMatchV5MatchInfoTeamObjectives = TypedDict(\"LolMatchV5MatchInfoTeamObjectives\", {\n \"baron\": LolMatchV5MatchTeamObjective,\n \"champion\": LolMatchV5MatchTeamObjective,\n \"dragon\": LolMatchV5MatchTeamObjective,\n \"horde\": NotRequired[LolMatchV5MatchTeamObjective],\n \"inhibitor\": LolMatchV5MatchTeamObjective,\n \"riftHerald\": LolMatchV5MatchTeamObjective,\n \"tower\": LolMatchV5MatchTeamObjective\n })\n LolMatchV5MatchInfoTeam = TypedDict(\"LolMatchV5MatchInfoTeam\", {\n \"bans\": list[LolMatchV5MatchInfoTeamBan],\n \"objectives\": LolMatchV5MatchInfoTeamObjectives,\n \"teamId\": int,\n \"win\": bool\n })\n LolMatchV5MatchInfo = TypedDict(\"LolMatchV5MatchInfo\", {\n \"gameCreation\": int,\n \"gameDuration\": int,\n \"gameEndTimestamp\": int,\n \"gameId\": int,\n \"gameMode\": str,\n \"gameName\": str,\n \"gameStartTimestamp\": int,\n \"gameType\": str,\n \"gameVersion\": str,\n \"mapId\": int,\n \"participants\": list[LolMatchV5MatchInfoParticipant],\n \"platformId\": str,\n \"queueId\": int,\n \"teams\": list[LolMatchV5MatchInfoTeam],\n \"tournamentCode\": str\n })\n LolMatchV5Match = TypedDict(\"LolMatchV5Match\", {\n \"metadata\": LolMatchV5MatchMetadata,\n \"info\": LolMatchV5MatchInfo\n })\n LolMatchV5MatchTimelineParticipantFrameChampionStats = TypedDict(\"LolMatchV5MatchTimelineParticipantFrameChampionStats\", {\n \"abilityHaste\": int,\n \"abilityPower\": int,\n \"armor\": int,\n \"armorPen\": int,\n \"armorPenPercent\": int,\n \"attackDamage\": int,\n \"attackSpeed\": int,\n \"bonusArmorPenPercent\": int,\n \"bonusMagicPenPercent\": int,\n \"ccReduction\": int,\n \"cooldownReduction\": int,\n \"health\": int,\n \"healthMax\": int,\n \"healthRegen\": int,\n \"lifesteal\": int,\n \"magicPen\": int,\n \"magicPenPercent\": int,\n \"magicResist\": int,\n \"movementSpeed\": int,\n \"omnivamp\": int,\n \"physicalVamp\": int,\n \"power\": int,\n \"powerMax\": int,\n \"powerRegen\": int,\n \"spellVamp\": int\n })\n LolMatchV5MatchTimelineParticipantFrameDamageStats = TypedDict(\"LolMatchV5MatchTimelineParticipantFrameDamageStats\", {\n \"magicDamageDone\": int,\n \"magicDamageDoneToChampions\": int,\n \"magicDamageTaken\": int,\n \"physicalDamageDone\": int,\n \"physicalDamageDoneToChampions\": int,\n \"physicalDamageTaken\": int,\n \"totalDamageDone\": int,\n \"totalDamageDoneToChampions\": int,\n \"totalDamageTaken\": int,\n \"trueDamageDone\": int,\n \"trueDamageDoneToChampions\": int,\n \"trueDamageTaken\": int\n })\n LolMatchV5MatchTimelinePosition = TypedDict(\"LolMatchV5MatchTimelinePosition\", {\n \"x\": int,\n \"y\": int\n })\n LolMatchV5MatchTimelineParticipantFrame = TypedDict(\"LolMatchV5MatchTimelineParticipantFrame\", {\n \"championStats\": LolMatchV5MatchTimelineParticipantFrameChampionStats,\n \"currentGold\": int,\n \"damageStats\": LolMatchV5MatchTimelineParticipantFrameDamageStats,\n \"goldPerSecond\": int,\n \"jungleMinionsKilled\": int,\n \"level\": int,\n \"minionsKilled\": int,\n \"participantId\": int,\n \"position\": LolMatchV5MatchTimelinePosition,\n \"timeEnemySpentControlled\": int,\n \"totalGold\": int,\n \"xp\": int\n })\n LolMatchV5MatchTimelineEventDamage = TypedDict(\"LolMatchV5MatchTimelineEventDamage\", {\n \"basic\": bool,\n \"magicDamage\": int,\n \"name\": str,\n \"participantId\": int,\n \"physicalDamage\": int,\n \"spellName\": str,\n \"spellSlot\": int,\n \"trueDamage\": int,\n \"type\": str\n })\n LolMatchV5MatchTimelineMetadata = TypedDict(\"LolMatchV5MatchTimelineMetadata\", {\n \"dataVersion\": str,\n \"matchId\": str,\n \"participants\": list[str]\n })\n LolMatchV5MatchTimelineInfoFrameEvent = TypedDict(\"LolMatchV5MatchTimelineInfoFrameEvent\", {\n \"afterId\": NotRequired[int],\n \"beforeId\": NotRequired[int],\n \"goldGain\": NotRequired[int],\n \"participantId\": NotRequired[int],\n \"timestamp\": int,\n \"type\": str,\n \"creatorId\": NotRequired[int],\n \"wardType\": NotRequired[str],\n \"level\": NotRequired[int],\n \"itemId\": NotRequired[int],\n \"assistingParticipantIds\": NotRequired[list[int]],\n \"bounty\": NotRequired[int],\n \"killStreakLength\": NotRequired[int],\n \"killerId\": NotRequired[int],\n \"position\": NotRequired[LolMatchV5MatchTimelinePosition],\n \"shutdownBounty\": NotRequired[int],\n \"victimDamageDealt\": NotRequired[list[LolMatchV5MatchTimelineEventDamage]],\n \"victimDamageReceived\": NotRequired[list[LolMatchV5MatchTimelineEventDamage]],\n \"victimId\": NotRequired[int],\n \"levelUpType\": NotRequired[str],\n \"skillSlot\": NotRequired[int],\n \"realTimestamp\": NotRequired[int],\n })\n LolMatchV5MatchTimelineInfoFrame = TypedDict(\"LolMatchV5MatchTimelineInfoFrame\", {\n \"events\": list[LolMatchV5MatchTimelineInfoFrameEvent],\n \"participantFrames\": dict[str, LolMatchV5MatchTimelineParticipantFrame],\n \"timestamp\": int\n })\n LolMatchV5MatchTimelineInfoParticipants = TypedDict(\"LolMatchV5MatchTimelineInfoParticipants\", {\n \"participantId\": int,\n \"puuid\": str,\n })\n LolMatchV5MatchTimelineInfo = TypedDict(\"LolMatchV5MatchTimelineInfo\", {\n \"frameInterval\": int,\n \"frames\": list[LolMatchV5MatchTimelineInfoFrame],\n \"gameId\": int,\n \"participants\": list[LolMatchV5MatchTimelineInfoParticipants]\n })\n LolMatchV5MatchTimeline = TypedDict(\"LolMatchV5MatchTimeline\", {\n \"metadata\": LolMatchV5MatchTimelineMetadata,\n \"info\": LolMatchV5MatchTimelineInfo\n })\n LolSpectatorV4GameParticipantPerks = TypedDict(\"LolSpectatorV4GameParticipantPerks\", {\n \"perkIds\": list[int],\n \"perkStyle\": int,\n \"perkSubStyle\": int\n })\n LolSpectatorV4GameParticipant = TypedDict(\"LolSpectatorV4GameParticipant\", {\n \"gameCustomizationObjects\": NotRequired[list[str]],\n \"perks\": NotRequired[LolSpectatorV4GameParticipantPerks],\n \"puuid\": str,\n \"summonerId\": str,\n \"teamId\": int,\n \"spell1Id\": int,\n \"spell2Id\": int,\n \"championId\": int,\n \"profileIconId\": int,\n \"summonerName\": str,\n \"bot\": bool\n })\n LolSpectatorV4GameObservers = TypedDict(\"LolSpectatorV4GameObservers\", {\n \"encryptionKey\": str\n })\n LolSpectatorV4Game = TypedDict(\"LolSpectatorV4Game\", {\n \"gameId\": int,\n \"mapId\": int,\n \"gameMode\": str,\n \"gameType\": str,\n \"gameQueueConfigId\": int,\n \"participants\": list[LolSpectatorV4GameParticipant],\n \"observers\": LolSpectatorV4GameObservers,\n \"platformId\": str,\n \"bannedChampions\": list[int],\n \"gameStartTime\": int,\n \"gameLength\": int\n })\n LolSpectatorV4GameList = TypedDict(\"LolSpectatorV4GameList\", {\n \"gameList\": list[LolSpectatorV4Game],\n \"clientRefreshInterval\": int\n })\n LolSummonerV4Summoner = TypedDict(\"SummonerV4Summoner\", {\n \"id\": str,\n \"accountId\": str,\n \"puuid\": str,\n \"name\": str,\n \"profileIconId\": int,\n \"revisionDate\": int,\n \"summonerLevel\": int\n })\n\n # Teamfight Tactics Types\n\n TftLeagueV1LeagueEntry = TypedDict(\"TftLeagueV1LeagueEntry\", {\n \"summonerId\": str,\n \"summonerName\": str,\n \"rank\": str,\n \"leaguePoints\": int,\n \"wins\": int,\n \"losses\": int,\n \"hotStreak\": bool,\n \"veteran\": bool,\n \"freshBlood\": bool,\n \"inactive\": bool,\n })\n TftLeagueV1LeagueFullEntry = TypedDict(\"TftLeagueV1LeagueFullEntry\", {\n \"leagueId\": str,\n \"puuid\": str,\n \"summonerId\": str,\n \"summonerName\": str,\n \"queueType\": str,\n \"tier\": str,\n \"rank\": str,\n \"leaguePoints\": int,\n \"wins\": int,\n \"losses\": int,\n \"hotStreak\": bool,\n \"veteran\": bool,\n \"freshBlood\": bool,\n \"inactive\": bool,\n \"miniSeries\": NotRequired[LolLeagueV4LeagueEntryMiniSeries],\n })\n TftLeagueV1League = TypedDict(\"TftLeagueV1League\", {\n \"tier\": str,\n \"leagueId\": NotRequired[str],\n \"queue\": NotRequired[str],\n \"name\": NotRequired[str],\n \"entries\": list[TftLeagueV1LeagueEntry]\n })\n TftMatchV1MatchMetadata = TypedDict(\"TftMatchV1MatchMetadata\", {\n \"data_version\": str,\n \"match_id\": str,\n \"participants\": list[str]\n })\n TftMatchV1MatchInfoParticipantCompanion = TypedDict(\"TftMatchV1MatchInfoParticipantCompanion\", {\n \"content_ID\": str,\n \"item_ID\": int,\n \"skin_ID\": int,\n \"species\": str\n })\n TftMatchV1MatchInfoParticipantTrait = TypedDict(\"TftMatchV1MatchInfoParticipantTrait\", {\n \"name\": str,\n \"num_units\": int,\n \"style\": int,\n \"tier_current\": int,\n \"tier_total\": int\n })\n TftMatchV1MatchInfoParticipantUnit = TypedDict(\"TftMatchV1MatchInfoParticipantUnit\", {\n \"character_id\": str,\n \"itemNames\": list[str],\n \"name\": str,\n \"rarity\": int,\n \"tier\": int\n })\n TftMatchV1MatchInfoParticipant = TypedDict(\"TftMatchV1MatchInfoParticipant\", {\n \"augments\": list[str],\n \"companion\": TftMatchV1MatchInfoParticipantCompanion,\n \"gold_left\": int,\n \"last_round\": int,\n \"level\": int,\n \"placement\": int,\n \"players_eliminated\": int,\n \"puuid\": str,\n \"time_eliminated\": float,\n \"total_damage_to_players\": int,\n \"traits\": list[TftMatchV1MatchInfoParticipantTrait],\n \"units\": list[TftMatchV1MatchInfoParticipantUnit]\n })\n TftMatchV1MatchInfo = TypedDict(\"TftMatchV1MatchInfo\", {\n \"game_datetime\": int,\n \"game_length\": float,\n \"game_version\": str,\n \"participants\": list[TftMatchV1MatchInfoParticipant],\n \"queue_id\": int,\n \"tft_game_type\": str,\n \"tft_set_core_name\": str,\n \"tft_set_number\": int\n })\n TftMatchV1Match = TypedDict(\"TftMatchV1Match\", {\n \"metadata\": TftMatchV1MatchMetadata,\n \"info\": TftMatchV1MatchInfo\n })\n TftSummonerV1Summoner = LolSummonerV4Summoner\n\n # Legends of Runeterra Types\n\n LorRankedV1LeaderboardPlayer = TypedDict(\"LorRankedV1LeaderboardPlayer\", {\n \"name\": str,\n \"rank\": int,\n \"lp\": float\n })\n LorRankedV1Leaderboard = TypedDict(\"LorRankedV1Leaderboard\", {\n \"players\": list[LorRankedV1LeaderboardPlayer]\n })\n LorMatchV1MatchMetadata = TypedDict(\"LorMatchV1MatchMetadata\", {\n \"data_version\": str,\n \"match_id\": str,\n \"participants\": list[str]\n })\n LorMatchV1MatchInfoPlayer = TypedDict(\"LorMatchV1MatchInfoPlayer\", {\n \"puuid\": str,\n \"deck_id\": str,\n \"deck_code\": str,\n \"factions\": list[str],\n \"game_outcome\": str,\n \"order_of_play\": int\n })\n LorMatchV1MatchInfo = TypedDict(\"LorMatchV1MatchInfo\", {\n \"game_mode\": str,\n \"game_type\": str,\n \"game_start_time_utc\": str,\n \"game_version\": str,\n \"players\": list[LorMatchV1MatchInfoPlayer],\n \"total_turn_count\": int\n })\n LorMatchV1Match = TypedDict(\"LorMatchV1Match\", {\n \"metadata\": LorMatchV1MatchMetadata,\n \"info\": LorMatchV1MatchInfo\n })\n\n # Valorant Types\n\n ValContentV1ContentsAssetLocalizedNames = TypedDict(\"ValContentV1ContentsAssetLocalizedNames\", {\n \"ar-AE\": str,\n \"de-DE\": str,\n \"en-US\": str,\n \"es-ES\": str,\n \"es-MX\": str,\n \"fr-FR\": str,\n \"id-ID\": str,\n \"it-IT\": str,\n \"ja-JP\": str,\n \"ko-KR\": str,\n \"pl-PL\": str,\n \"pt-BR\": str,\n \"ru-RU\": str,\n \"th-TH\": str,\n \"tr-TR\": str,\n \"vi-VN\": str,\n \"zh-CN\": str,\n \"zh-TW\": str,\n })\n ValContentV1ContentsAsset = TypedDict(\"ValContentV1ContentsAsset\", {\n \"name\": str,\n \"id\": str,\n \"localizedNames\": NotRequired[ValContentV1ContentsAssetLocalizedNames],\n \"assetName\": str,\n \"assetPath\": NotRequired[str]\n })\n ValContentV1ContentsAct = TypedDict(\"ValContentV1ContentsAct\", {\n \"id\": str,\n \"localizedNames\": NotRequired[ValContentV1ContentsAssetLocalizedNames],\n \"parentId\": str,\n \"type\": str,\n \"name\": str,\n \"isActive\": bool\n })\n ValContentV1Contents = TypedDict(\"ValContentV1Contents\", {\n \"version\": str,\n \"characters\": list[ValContentV1ContentsAsset],\n \"maps\": list[ValContentV1ContentsAsset],\n \"chromas\": list[ValContentV1ContentsAsset],\n \"skins\": list[ValContentV1ContentsAsset],\n \"skinLevels\": list[ValContentV1ContentsAsset],\n \"equips\": list[ValContentV1ContentsAsset],\n \"gameModes\": list[ValContentV1ContentsAsset],\n \"totems\": list[ValContentV1ContentsAsset],\n \"sprays\": list[ValContentV1ContentsAsset],\n \"sprayLevels\": list[ValContentV1ContentsAsset],\n \"charms\": list[ValContentV1ContentsAsset],\n \"charmLevels\": list[ValContentV1ContentsAsset],\n \"playerCards\": list[ValContentV1ContentsAsset],\n \"playerTitles\": list[ValContentV1ContentsAsset],\n \"acts\": list[ValContentV1ContentsAct],\n \"ceremonies\": list[ValContentV1ContentsAsset]\n })\n \n ValRankedV1LeaderboardTierDetail = TypedDict(\"ValRankedV1LeaderboardTierDetail\", {\n \"rankedRatingThreshold\": int,\n \"startingPage\": int,\n \"startingIndex\": int\n })\n ValRankedV1LeaderboardPlayer = TypedDict(\"ValRankedV1LeaderboardPlayer\", {\n \"puuid\": str,\n \"gameName\": str,\n \"tagLine\": str,\n \"leaderboardRank\": int,\n \"rankedRating\": int,\n \"numberOfWins\": int,\n \"competitiveTier\": int\n })\n ValRankedV1LeaderboardTierDetails = TypedDict(\"ValRankedV1LeaderboardTierDetails\", {\n \"24\": ValRankedV1LeaderboardTierDetail,\n \"25\": ValRankedV1LeaderboardTierDetail,\n \"26\": ValRankedV1LeaderboardTierDetail,\n \"27\": ValRankedV1LeaderboardTierDetail\n })\n ValRankedV1Leaderboard = TypedDict(\"ValRankedV1Leaderboard\", {\n \"actId\": str,\n \"players\": list[ValRankedV1LeaderboardPlayer],\n \"totalPlayers\": int,\n \"immortalStartingPage\": int,\n \"immortalStartingIndex\": int,\n \"topTierRRThreshold\": int,\n \"tierDetails\": ValRankedV1LeaderboardTierDetails,\n \"startIndex\": int,\n \"query\": str,\n \"shard\": str\n })\n ValMatchV1MatchLocation = TypedDict(\"ValMatchV1MatchLocation\", {\n \"x\": float,\n \"y\": float\n })\n ValMatchV1MatchPlayerLocation = TypedDict(\"ValMatchV1MatchPlayerLocation\", {\n \"puuid\": str,\n \"viewRadians\": float,\n \"location\": ValMatchV1MatchLocation\n })\n ValMatchV1MatchInfo = TypedDict(\"ValMatchV1MatchInfo\", {\n \"matchId\": str,\n \"mapId\": str,\n \"gameVersion\": str,\n \"gameLengthMillis\": int,\n \"region\": str,\n \"gameStartMillis\": int,\n \"provisioningFlowId\": str,\n \"isCompleted\": bool,\n \"customGameName\": str,\n \"queueId\": str,\n \"gameMode\": str,\n \"isRanked\": bool,\n \"premierMatchInfo\": dict,\n \"seasonId\": str\n })\n ValMatchV1MatchPlayerStatsAbilityCasts = TypedDict(\"ValMatchV1MatchPlayerStatsAbilityCasts\", {\n \"grenadeCasts\": int,\n \"ability1Casts\": int,\n \"ability2Casts\": int,\n \"ultimateCasts\": int\n })\n ValMatchV1MatchPlayerStats = TypedDict(\"ValMatchV1MatchPlayerStats\", {\n \"score\": int,\n \"roundsPlayed\": int,\n \"kills\": int,\n \"deaths\": int,\n \"assists\": int,\n \"playtimeMillis\": int,\n \"abilityCasts\": ValMatchV1MatchPlayerStatsAbilityCasts | None\n })\n ValMatchV1MatchPlayer = TypedDict(\"ValMatchV1MatchPlayer\", {\n \"puuid\": str,\n \"gameName\": str,\n \"tagLine\": str,\n \"teamId\": str,\n \"partyId\": str,\n \"characterId\": str,\n \"stats\": ValMatchV1MatchPlayerStats,\n \"competitiveTier\": int,\n \"isObserver\": bool,\n \"playerCard\": str,\n \"playerTitle\": str,\n \"accountLevel\": int\n })\n ValMatchV1MatchTeam = TypedDict(\"ValMatchV1MatchTeam\", {\n \"teamId\": str,\n \"won\": bool,\n \"roundsPlayed\": int,\n \"roundsWon\": int,\n \"numPoints\": int\n })\n ValMatchV1MatchRoundResultPlayerStatKill = TypedDict(\"ValMatchV1MatchRoundResultPlayerStatKill\", {\n \"timeSinceGameStartMillis\": int,\n \"timeSinceRoundStartMillis\": int,\n \"killer\": str,\n \"victim\": str,\n \"victimLocation\": ValMatchV1MatchLocation,\n \"assistants\": list[str],\n \"playerLocations\": list[ValMatchV1MatchPlayerLocation],\n \"finishingDamage\": TypedDict(\"FinishingDamage\", {\n \"damageType\": str,\n \"damageItem\": str,\n \"isSecondaryFireMode\": bool\n })\n })\n ValMatchV1MatchRoundResultPlayerStatDamage = TypedDict(\"ValMatchV1MatchRoundResultPlayerStatDamage\", {\n \"receiver\": str,\n \"damage\": int,\n \"legshots\": int,\n \"bodyshots\": int,\n \"headshots\": int\n })\n ValMatchV1MatchRoundResultPlayerStatEconomy = TypedDict(\"ValMatchV1MatchRoundResultPlayerStatEconomy\", {\n \"loadoutValue\": int,\n \"weapon\": str,\n \"armor\": str,\n \"remaining\": int,\n \"spent\": int\n })\n ValMatchV1MatchRoundResultPlayerStatAbility = TypedDict(\"ValMatchV1MatchRoundResultPlayerStatAbility\", {\n \"grenadeEffects\": str | None,\n \"ability1Effects\": str | None,\n \"ability2Effects\": str | None,\n \"ultimateEffects\": str | None\n })\n ValMatchV1MatchRoundResultPlayerStat = TypedDict(\"ValMatchV1MatchRoundResultPlayerStat\", {\n \"puuid\": str,\n \"kills\": list[ValMatchV1MatchRoundResultPlayerStatKill],\n \"damage\": list[ValMatchV1MatchRoundResultPlayerStatDamage],\n \"score\": int,\n \"economy\": ValMatchV1MatchRoundResultPlayerStatEconomy,\n \"ability\": ValMatchV1MatchRoundResultPlayerStatAbility\n })\n ValMatchV1MatchRoundResult = TypedDict(\"ValMatchV1MatchRoundResult\", {\n \"roundNum\": int,\n \"roundResult\": str,\n \"roundCeremony\": str,\n \"winningTeam\": str,\n \"bombPlanter\": str | None,\n \"bombDefuser\": str | None,\n \"plantRoundTime\": int,\n \"plantPlayerLocations\": list[ValMatchV1MatchPlayerLocation] | None,\n \"plantLocation\": ValMatchV1MatchLocation,\n \"plantSite\": str,\n \"defuseRoundTime\": int,\n \"defusePlayerLocations\": list[ValMatchV1MatchPlayerLocation] | None,\n \"defuseLocation\": ValMatchV1MatchLocation,\n \"playerStats\": list[ValMatchV1MatchRoundResultPlayerStat],\n \"roundResultCode\": str\n })\n ValMatchV1Match = TypedDict(\"ValMatchV1Match\", {\n \"matchInfo\": ValMatchV1MatchInfo,\n \"players\": list[ValMatchV1MatchPlayer],\n \"coaches\": list[str],\n \"teams\": list[ValMatchV1MatchTeam],\n \"roundResults\": list[ValMatchV1MatchRoundResult]\n })\n ValMatchV1MatchlistHistory = TypedDict(\"ValMatchV1MatchlistHistory\", {\n \"matchId\": str,\n \"gameStartTimeMillis\": int,\n \"queueId\": str\n })\n ValMatchV1Matchlist = TypedDict(\"ValMatchV1Matchlist\", {\n \"puuid\": str,\n \"history\": list[ValMatchV1MatchlistHistory]\n })\n ValMatchV1RecentMatches = TypedDict(\"ValMatchV1RecentMatches\", {\n \"currentTime\": int,\n \"matchIds\": list[str]\n })\n\n # Status Types\n\n StatusV1PlatformDataLocaleContent = TypedDict(\"StatusV1PlatformDataLocaleContent\", {\n \"locale\": str,\n \"content\": str\n })\n StatusV1PlatformDataEntryUpdate = TypedDict(\"StatusV1PlatformDataEntryUpdate\", {\n \"id\": int,\n \"created_at\": str,\n \"updated_at\": str,\n \"publish\": bool,\n \"author\": str,\n \"translations\": list[StatusV1PlatformDataLocaleContent],\n \"publish_locations\": list[str]\n })\n StatusV1PlatformDataEntry = TypedDict(\"StatusV1PlatformDataEntry\", {\n \"id\": int,\n \"created_at\": str,\n \"updated_at\": str | None,\n \"archive_at\": str | None,\n \"titles\": list[StatusV1PlatformDataLocaleContent],\n \"updates\": list[StatusV1PlatformDataEntryUpdate],\n \"platforms\": list[str],\n \"maintenance_status\": str | None,\n \"incident_severity\": str | None\n })\n StatusV1PlatformData = TypedDict(\"StatusV4PlatformData\", {\n \"id\": str,\n \"name\": str,\n \"locales\": list[str],\n \"maintenances\": list[StatusV1PlatformDataEntry],\n \"incidents\": list[StatusV1PlatformDataEntry]\n })"},{"identifier":"TaskGroup","path":"pulsefire/taskgroups.py","snippet":"class TaskGroup(asyncio.TaskGroup):\n \"\"\"Asynchronous context manager for managing groups of tasks.\n See [python asyncio task groups documentation](https://docs.python.org/3/library/asyncio-task.html#task-groups).\n\n Adapted for pulsefire, key differences from `asyncio.TaskGroup`:\n\n - Accepts a semaphore to restrict the amount of concurrent running coroutines.\n - Due to semaphore support, the `create_task` method is now async.\n - Allows internal collection of results and exceptions, similar to `asyncio.Task`.\n - If exception collection is on (default), the task group will not abort on task exceptions.\n\n Example:\n ```python\n async with TaskGroup(asyncio.Semaphore(100)) as tg:\n await tg.create_task(coro_func(...))\n results = tg.results()\n ```\n \"\"\"\n\n semaphore: asyncio.Semaphore | None = None\n \"\"\"Semaphore for restricting concurrent running coroutines.\"\"\"\n collect_results: bool = True\n \"\"\"Flag for collecting task results.\"\"\"\n collect_exceptions: bool = True\n \"\"\"Flag for collecting task exceptions, disables abort.\"\"\"\n\n def __init__(\n self,\n semaphore: asyncio.Semaphore | None = None,\n *,\n collect_results: bool = True,\n collect_exceptions: bool = True,\n ) -> None:\n super().__init__()\n self.semaphore = semaphore\n self.collect_results = collect_results\n self.collect_exceptions = collect_exceptions\n self._exceptions: list[BaseException] = []\n self._results = []\n\n async def __aenter__(self):\n self._exceptions = []\n self._results = []\n return await super().__aenter__()\n\n def results[T](self) -> list[T]:\n \"\"\"Return the collected results returned from created tasks.\"\"\"\n if not self.collect_results:\n raise RuntimeError(f\"TaskGroup {self!r} has `collect_results` off\")\n return self._results\n\n def exceptions(self) -> list[BaseException]:\n \"\"\"Return the collected exceptions raised from created tasks.\"\"\"\n if not self.collect_exceptions:\n raise RuntimeError(f\"TaskGroup {self!r} has `collect_exceptions` off\")\n return self._exceptions\n\n @override\n async def create_task[T](self, coro: Awaitable[T], *, name: str | None = None, context: Context | None = None) -> asyncio.Task[T]:\n \"\"\"Create a new task in this group and return it.\n\n If this group has a semaphore, wrap this semaphore on the coroutine.\n \"\"\"\n _coro = coro\n if self.semaphore:\n await self.semaphore.acquire()\n async def semaphored():\n try:\n return await _coro\n finally:\n self.semaphore.release()\n coro = semaphored()\n return super().create_task(coro, name=name, context=context)\n\n def _on_task_done(self, task) -> None:\n if exc := task.exception():\n if self.collect_exceptions:\n LOGGER.warning(\n \"TaskGroup: unhandled exception\\n\" +\n \"\".join(traceback.format_exception(type(exc), exc, exc.__traceback__))\n )\n self._exceptions.append(exc)\n self._tasks.discard(task)\n if self._on_completed_fut is not None and not self._tasks:\n if not self._on_completed_fut.done():\n self._on_completed_fut.set_result(True)\n return\n elif self.collect_results and not task.cancelled():\n self._results.append(task.result())\n return super()._on_task_done(task)"}],"string":"[\n {\n \"identifier\": \"RiotAPIClient\",\n \"path\": \"pulsefire/clients.py\",\n \"snippet\": \"class RiotAPIClient(BaseClient):\\n \\\"\\\"\\\"Riot API Client.\\n\\n | Resources | Support |\\n | -------------------- | -------------------------- |\\n | League of Legends | ✅ |\\n | Legends of Runeterra | ✅ |\\n | Teamfight Tactics | ✅ |\\n | Valorant | ✅ |\\n\\n Example:\\n ```python\\n async with RiotAPIClient(\\n default_headers={\\\"X-Riot-Token\\\": <API_KEY>}\\n ) as client:\\n summoner = await client.get_lol_summoner_v4_by_name(region=\\\"na1\\\", name=\\\"Not a Whale\\\")\\n assert summoner[\\\"summonerLevel\\\"] > 200\\n ```\\n \\\"\\\"\\\"\\n\\n Region = Literal[\\n \\\"americas\\\", \\\"europe\\\", \\\"asia\\\", \\\"sea\\\", \\\"esports\\\",\\n \\\"br1\\\", \\\"eun1\\\", \\\"euw1\\\", \\\"jp1\\\", \\\"kr\\\", \\\"la1\\\", \\\"la2\\\",\\n \\\"na1\\\", \\\"oc1\\\", \\\"tr1\\\", \\\"ru\\\", \\\"ph2\\\", \\\"sg2\\\", \\\"th2\\\", \\\"tw2\\\", \\\"vn2\\\",\\n \\\"ap\\\", \\\"br\\\", \\\"eu\\\", \\\"kr\\\", \\\"latam\\\", \\\"na\\\",\\n ] | _str\\n\\n def __init__(\\n self,\\n *,\\n base_url: str = \\\"https://{region}.api.riotgames.com\\\",\\n default_params: dict[str, Any] = {},\\n default_headers: dict[str, str] = {\\\"X-Riot-Token\\\": \\\"\\\"},\\n default_queries: dict[str, str] = {},\\n middlewares: list[Middleware] = [\\n json_response_middleware(),\\n http_error_middleware(),\\n rate_limiter_middleware(RiotAPIRateLimiter()),\\n ],\\n ) -> None:\\n super().__init__(\\n base_url=base_url,\\n default_params=default_params,\\n default_headers=default_headers,\\n default_queries=default_queries,\\n middlewares=middlewares\\n )\\n\\n # Account Endpoints\\n\\n async def get_account_v1_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.AccountV1Account:\\n return await self.invoke(\\\"GET\\\", \\\"/riot/account/v1/accounts/by-puuid/{puuid}\\\")\\n\\n async def get_account_v1_by_riot_id(self, *, region: Region = ..., game_name: str = ..., tag_line: str = ...) -> RiotAPISchema.AccountV1Account:\\n return await self.invoke(\\\"GET\\\", \\\"/riot/account/v1/accounts/by-riot-id/{game_name}/{tag_line}\\\")\\n\\n async def get_account_v1_me(self, *, region: Region = ..., headers: dict = {\\\"Authorization\\\": \\\"\\\"}) -> RiotAPISchema.AccountV1Account:\\n return await self.invoke(\\\"GET\\\", \\\"/riot/account/v1/accounts/me\\\")\\n\\n async def get_account_v1_active_shard_by_puuid(self, *, region: Region = ..., puuid: str = ..., game: str = ...) -> RiotAPISchema.AccountV1ActiveShard:\\n return await self.invoke(\\\"GET\\\", \\\"/riot/account/v1/active-shards/by-game/{game}/by-puuid/{puuid}\\\")\\n\\n # League of Legends Endpoints\\n\\n async def get_lol_champion_v3_rotation(self, *, region: Region = ...) -> RiotAPISchema.LolChampionV3Rotation:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/platform/v3/champion-rotations\\\")\\n\\n async def get_lol_champion_v4_mastery_by_summoner(self, *, region: Region = ..., summoner_id: str = ..., champion_id: int = ...) -> RiotAPISchema.LolChampionV4Mastery:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}/by-champion/{champion_id}\\\")\\n\\n async def get_lol_champion_v4_masteries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}\\\")\\n\\n async def get_lol_champion_v4_top_masteries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}/top\\\")\\n\\n async def get_lol_champion_v4_mastery_score_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> int:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/scores/by-summoner/{summoner_id}\\\")\\n\\n async def get_lol_champion_v4_mastery_by_puuid(self, *, region: Region = ..., puuid: str = ..., champion_id: int = ...) -> RiotAPISchema.LolChampionV4Mastery:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}/by-champion/{champion_id}\\\")\\n\\n async def get_lol_champion_v4_masteries_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}\\\")\\n\\n async def get_lol_champion_v4_top_masteries_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolChampionV4Mastery]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/champion-masteries/by-puuid/{puuid}/top\\\")\\n\\n async def get_lol_champion_v4_mastery_score_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> int:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/champion-mastery/v4/scores/by-puuid/{puuid}\\\")\\n\\n async def get_lol_clash_v1_players_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolClashV1Player]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/players/by-summoner/{summoner_id}\\\")\\n\\n async def get_lol_clash_v1_players_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[RiotAPISchema.LolClashV1Player]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/players/by-puuid/{puuid}\\\")\\n\\n async def get_lol_clash_v1_team(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolClashV1Team:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/teams/{id}\\\")\\n\\n async def get_lol_clash_v1_tournament_by_team(self, *, region: Region = ..., team_id: str = ...) -> RiotAPISchema.LolClashV1Tournament:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/tournaments/by-team/{team_id}\\\")\\n\\n async def get_lol_clash_v1_tournament(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolClashV1Tournament:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/tournaments/{id}\\\")\\n\\n async def get_lol_clash_v1_tournaments(self, *, region: Region = ...) -> list[RiotAPISchema.LolClashV1Tournament]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/clash/v1/tournaments\\\")\\n\\n async def get_lol_league_v4_entries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.LolLeagueV4LeagueFullEntry]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/entries/by-summoner/{summoner_id}\\\")\\n\\n async def get_lol_league_v4_challenger_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/challengerleagues/by-queue/{queue}\\\")\\n\\n async def get_lol_league_v4_grandmaster_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/grandmasterleagues/by-queue/{queue}\\\")\\n\\n async def get_lol_league_v4_master_league_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.LolLeagueV4League:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/masterleagues/by-queue/{queue}\\\")\\n\\n async def get_lol_league_v4_entries_by_division(\\n self, *, region: Region = ..., queue: str = ..., tier: str = ..., division: str = ..., queries: dict = {\\\"page\\\": 1}\\n ) -> list[RiotAPISchema.LolLeagueV4LeagueFullEntry]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/entries/{queue}/{tier}/{division}\\\")\\n\\n async def get_lol_league_v4_league(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolLeagueV4League:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/league/v4/leagues/{id}\\\")\\n\\n async def get_lol_match_v5_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolMatchV5Match:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/match/v5/matches/{id}\\\")\\n\\n async def get_lol_match_v5_match_timeline(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolMatchV5MatchTimeline:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/match/v5/matches/{id}/timeline\\\")\\n\\n async def get_lol_match_v5_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ..., queries: dict = {\\\"start\\\": 0, \\\"count\\\": 100}) -> list[str]:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/match/v5/matches/by-puuid/{puuid}/ids\\\")\\n\\n async def get_lol_spectator_v4_active_game_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> RiotAPISchema.LolSpectatorV4Game:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/spectator/v4/active-games/by-summoner/{summoner_id}\\\")\\n\\n async def get_lol_spectator_v4_featured_games(self, *, region: Region = ...) -> RiotAPISchema.LolSpectatorV4GameList:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/spectator/v4/featured-games\\\")\\n\\n async def get_lol_status_v4_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/status/v4/platform-data\\\")\\n\\n async def get_lol_summoner_v4_by_id(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/summoner/v4/summoners/{id}\\\")\\n\\n async def get_lol_summoner_v4_by_name(self, *, region: Region = ..., name: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/summoner/v4/summoners/by-name/{name}\\\")\\n\\n async def get_lol_summoner_v4_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/summoner/v4/summoners/by-puuid/{puuid}\\\")\\n\\n async def get_lol_summoner_v4_me(self, *, region: Region = ..., headers: dict = {\\\"Authorization\\\": \\\"\\\"}) -> RiotAPISchema.LolSummonerV4Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/lol/summoner/v4/summoners/me\\\")\\n\\n async def get_lol_summoner_v4_by_rso_puuid(self, *, region: Region = ..., rso_puuid: str = ...) -> RiotAPISchema.LolSummonerV4Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/fulfillment/v1/summoners/by-puuid/{rso_puuid}\\\")\\n\\n # Teamfight Tactics Endpoints\\n\\n async def get_tft_league_v1_entries_by_summoner(self, *, region: Region = ..., summoner_id: str = ...) -> list[RiotAPISchema.TftLeagueV1LeagueFullEntry]:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/entries/by-summoner/{summoner_id}\\\")\\n\\n async def get_tft_league_v1_challenger_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/challenger\\\")\\n\\n async def get_tft_league_v1_grandmaster_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/grandmaster\\\")\\n\\n async def get_tft_league_v1_master_league(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.TftLeagueV1League:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/master\\\")\\n\\n async def get_tft_league_v1_entries_by_division(\\n self, *, region: Region = ..., tier: str = ..., division: str = ..., queries: dict = {\\\"page\\\": 1}\\n ) -> list[RiotAPISchema.TftLeagueV1LeagueFullEntry]:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/entries/{tier}/{division}\\\")\\n\\n async def get_tft_league_v1_league(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftLeagueV1League:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/league/v1/leagues/{id}\\\")\\n\\n async def get_tft_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftMatchV1Match:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/match/v1/matches/{id}\\\")\\n\\n async def get_tft_match_v1_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ..., queries: dict = {\\\"start\\\": 0, \\\"count\\\": 100}) -> list[str]:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/match/v1/matches/by-puuid/{puuid}/ids\\\")\\n\\n async def get_tft_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/status/v1/platform-data\\\")\\n\\n async def get_tft_summoner_v1_by_id(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/summoner/v1/summoners/{id}\\\")\\n\\n async def get_tft_summoner_v1_by_name(self, *, region: Region = ..., name: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/summoner/v1/summoners/by-name/{name}\\\")\\n\\n async def get_tft_summoner_v1_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.TftSummonerV1Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/summoner/v1/summoners/by-puuid/{puuid}\\\")\\n\\n async def get_tft_summoner_v1_me(self, *, region: Region = ..., headers: dict = {\\\"Authorization\\\": \\\"\\\"}) -> RiotAPISchema.TftSummonerV1Summoner:\\n return await self.invoke(\\\"GET\\\", \\\"/tft/summoner/v1/summoners/me\\\")\\n\\n # Legends of Runeterra Endpoints\\n\\n async def get_lor_ranked_v1_leaderboard(self, *, region: Region = ...) -> RiotAPISchema.LorRankedV1Leaderboard:\\n return await self.invoke(\\\"GET\\\", \\\"/lor/ranked/v1/leaderboards\\\")\\n\\n async def get_lor_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.LorMatchV1Match:\\n return await self.invoke(\\\"GET\\\", \\\"/lor/match/v1/matches/{id}\\\")\\n\\n async def get_lor_match_v1_match_ids_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> list[str]:\\n return await self.invoke(\\\"GET\\\", \\\"/lor/match/v1/matches/by-puuid/{puuid}/ids\\\")\\n\\n async def get_lor_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\\n return await self.invoke(\\\"GET\\\", \\\"/lor/status/v1/platform-data\\\")\\n\\n # Valorant Endpoints\\n\\n async def get_val_content_v1_contents(self, *, region: Region = ..., queries: dict = {}) -> RiotAPISchema.ValContentV1Contents:\\n return await self.invoke(\\\"GET\\\", \\\"/val/content/v1/contents\\\")\\n\\n async def get_val_ranked_v1_leaderboard_by_act(self, *, region: Region = ..., act_id: str = ...) -> RiotAPISchema.ValRankedV1Leaderboard:\\n return await self.invoke(\\\"GET\\\", \\\"/val/ranked/v1/leaderboards/by-act/{act_id}\\\")\\n\\n async def get_val_match_v1_match(self, *, region: Region = ..., id: str = ...) -> RiotAPISchema.ValMatchV1Match:\\n return await self.invoke(\\\"GET\\\", \\\"/val/match/v1/matches/{id}\\\")\\n\\n async def get_val_match_v1_matchlist_by_puuid(self, *, region: Region = ..., puuid: str = ...) -> RiotAPISchema.ValMatchV1Matchlist:\\n return await self.invoke(\\\"GET\\\", \\\"/val/match/v1/matchlists/by-puuid/{puuid}\\\")\\n\\n async def get_val_match_v1_recent_matches_by_queue(self, *, region: Region = ..., queue: str = ...) -> RiotAPISchema.ValMatchV1RecentMatches:\\n return await self.invoke(\\\"GET\\\", \\\"/val/match/v1/recent-matches/by-queue/{queue}\\\")\\n\\n async def get_val_status_v1_platform_data(self, *, region: Region = ...) -> RiotAPISchema.StatusV1PlatformData:\\n return await self.invoke(\\\"GET\\\", \\\"/val/status/v1/platform-data\\\")\"\n },\n {\n \"identifier\": \"async_to_sync\",\n \"path\": \"pulsefire/functools.py\",\n \"snippet\": \"def async_to_sync(runner: Callable[[Awaitable[Any]], Any] = asyncio.run):\\n \\\"\\\"\\\"Convert a coroutine function to run synchronously. Use as decorator `@async_to_sync()`.\\n\\n Example:\\n ```python\\n @async_to_sync()\\n async def sample_func(number: int):\\n ...\\n \\n sample_func(0)\\n ```\\n\\n Parameters:\\n runner: A callable that runs the awaitable synchronously.\\n\\n Raises:\\n TypeError: When `func` is not a coroutine function.\\n \\\"\\\"\\\"\\n\\n def decorator[**P, R](func: Callable[P, Awaitable[R]]) -> Callable[P, R]:\\n if not inspect.iscoroutinefunction(func):\\n raise TypeError(f\\\"{func} is not a coroutine function\\\")\\n\\n @functools.wraps(func)\\n def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:\\n return runner(func(*args, **kwargs))\\n\\n return wrapper\\n\\n return decorator\"\n },\n {\n \"identifier\": \"RiotAPISchema\",\n \"path\": \"pulsefire/schemas.py\",\n \"snippet\": \"class RiotAPISchema:\\n\\n # Account Types\\n\\n AccountV1Account = TypedDict(\\\"AccountV1Account\\\", {\\n \\\"puuid\\\": str,\\n \\\"gameName\\\": str,\\n \\\"tagLine\\\": str,\\n })\\n AccountV1ActiveShard = TypedDict(\\\"AccountV1ActiveShard\\\", {\\n \\\"puuid\\\": str,\\n \\\"game\\\": str,\\n \\\"activeShard\\\": str,\\n })\\n\\n # League of Legends Types\\n\\n LolChampionV3Rotation = TypedDict(\\\"LolChampionV3Rotation\\\", {\\n \\\"freeChampionIds\\\": list[int],\\n \\\"freeChampionIdsForNewPlayers\\\": list[int],\\n \\\"maxNewPlayerLevel\\\": int\\n })\\n LolChampionV4Mastery = TypedDict(\\\"LolChampionV4Mastery\\\", {\\n \\\"puuid\\\": str,\\n \\\"championId\\\": int,\\n \\\"championLevel\\\": int,\\n \\\"championPoints\\\": int,\\n \\\"lastPlayTime\\\": int,\\n \\\"championPointsSinceLastLevel\\\": int,\\n \\\"championPointsUntilNextLevel\\\": int,\\n \\\"chestGranted\\\": bool,\\n \\\"tokensEarned\\\": int,\\n \\\"summonerId\\\": str\\n })\\n LolClashV1Player = TypedDict(\\\"LolClashV1Player\\\", {\\n \\\"summonerId\\\": str,\\n \\\"teamId\\\": str,\\n \\\"position\\\": str,\\n \\\"role\\\": str,\\n })\\n LolClashV1Team = TypedDict(\\\"LolClashV1Team\\\", {\\n \\\"id\\\": str,\\n \\\"tournamentId\\\": int,\\n \\\"name\\\": str,\\n \\\"iconId\\\": int,\\n \\\"tier\\\": int,\\n \\\"captain\\\": str,\\n \\\"abbreviation\\\": str,\\n \\\"players\\\": list[LolClashV1Player],\\n })\\n LolClashV1TournamentSchedule = TypedDict(\\\"LolClashV1TournamentSchedule\\\", {\\n \\\"id\\\": int,\\n \\\"registrationTime\\\": int,\\n \\\"startTime\\\": int,\\n \\\"cancelled\\\": bool,\\n })\\n LolClashV1Tournament = TypedDict(\\\"LolClashV1Tournament\\\", {\\n \\\"id\\\": int,\\n \\\"themeId\\\": int,\\n \\\"nameKey\\\": str,\\n \\\"nameKeySecondary\\\": str,\\n \\\"schedule\\\": list[LolClashV1TournamentSchedule]\\n })\\n LolLeagueV4LeagueEntryMiniSeries = TypedDict(\\\"LolLeagueV4LeagueEntryMiniSeries\\\", {\\n \\\"losses\\\": int,\\n \\\"progress\\\": str,\\n \\\"target\\\": int,\\n \\\"wins\\\": int,\\n })\\n LolLeagueV4LeagueEntry = TypedDict(\\\"LolLeagueV4LeagueEntry\\\", {\\n \\\"summonerId\\\": str,\\n \\\"summonerName\\\": str,\\n \\\"rank\\\": str,\\n \\\"leaguePoints\\\": int,\\n \\\"wins\\\": int,\\n \\\"losses\\\": int,\\n \\\"hotStreak\\\": bool,\\n \\\"veteran\\\": bool,\\n \\\"freshBlood\\\": bool,\\n \\\"inactive\\\": bool,\\n })\\n LolLeagueV4LeagueFullEntry = TypedDict(\\\"LolLeagueV4LeagueFullEntry\\\", {\\n \\\"leagueId\\\": str,\\n \\\"summonerId\\\": str,\\n \\\"summonerName\\\": str,\\n \\\"queueType\\\": str,\\n \\\"tier\\\": str,\\n \\\"rank\\\": str,\\n \\\"leaguePoints\\\": int,\\n \\\"wins\\\": int,\\n \\\"losses\\\": int,\\n \\\"hotStreak\\\": bool,\\n \\\"veteran\\\": bool,\\n \\\"freshBlood\\\": bool,\\n \\\"inactive\\\": bool,\\n \\\"miniSeries\\\": NotRequired[LolLeagueV4LeagueEntryMiniSeries],\\n })\\n LolLeagueV4League = TypedDict(\\\"LolLeagueV4League\\\", {\\n \\\"tier\\\": str,\\n \\\"leagueId\\\": str,\\n \\\"queue\\\": str,\\n \\\"name\\\": str,\\n \\\"entries\\\": list[LolLeagueV4LeagueEntry]\\n })\\n LolMatchV5MatchMetadata = TypedDict(\\\"LolMatchV5MatchMetadata\\\", {\\n \\\"dataVersion\\\": str,\\n \\\"matchId\\\": str,\\n \\\"participants\\\": list[str]\\n })\\n LolMatchV5MatchTeamObjective = TypedDict(\\\"LolMatchV5MatchTeamObjective\\\", {\\n \\\"first\\\": bool,\\n \\\"kills\\\": int\\n })\\n LolMatchV5MatchInfoParticipantChallenges = TypedDict(\\\"LolMatchV5MatchInfoParticipantChallenges\\\", {\\n \\\"12AssistStreakCount\\\": int,\\n \\\"abilityUses\\\": int,\\n \\\"acesBefore15Minutes\\\": int,\\n \\\"alliedJungleMonsterKills\\\": int,\\n \\\"baronTakedowns\\\": int,\\n \\\"blastConeOppositeOpponentCount\\\": int,\\n \\\"bountyGold\\\": int,\\n \\\"buffsStolen\\\": int,\\n \\\"completeSupportQuestInTime\\\": int,\\n \\\"controlWardTimeCoverageInRiverOrEnemyHalf\\\": NotRequired[float],\\n \\\"controlWardsPlaced\\\": int,\\n \\\"damagePerMinute\\\": float,\\n \\\"damageTakenOnTeamPercentage\\\": float,\\n \\\"dancedWithRiftHerald\\\": int,\\n \\\"deathsByEnemyChamps\\\": int,\\n \\\"dodgeSkillShotsSmallWindow\\\": int,\\n \\\"doubleAces\\\": int,\\n \\\"dragonTakedowns\\\": int,\\n \\\"earliestBaron\\\": float,\\n \\\"earlyLaningPhaseGoldExpAdvantage\\\": int,\\n \\\"effectiveHealAndShielding\\\": float,\\n \\\"elderDragonKillsWithOpposingSoul\\\": int,\\n \\\"elderDragonMultikills\\\": int,\\n \\\"enemyChampionImmobilizations\\\": int,\\n \\\"enemyJungleMonsterKills\\\": int,\\n \\\"epicMonsterKillsNearEnemyJungler\\\": int,\\n \\\"epicMonsterKillsWithin30SecondsOfSpawn\\\": int,\\n \\\"epicMonsterSteals\\\": int,\\n \\\"epicMonsterStolenWithoutSmite\\\": int,\\n \\\"firstTurretKilled\\\": int,\\n \\\"firstTurretKilledTime\\\": NotRequired[float],\\n \\\"flawlessAces\\\": int,\\n \\\"fullTeamTakedown\\\": int,\\n \\\"gameLength\\\": float,\\n \\\"getTakedownsInAllLanesEarlyJungleAsLaner\\\": NotRequired[int],\\n \\\"goldPerMinute\\\": float,\\n \\\"hadOpenNexus\\\": int,\\n \\\"immobilizeAndKillWithAlly\\\": int,\\n \\\"initialBuffCount\\\": int,\\n \\\"initialCrabCount\\\": int,\\n \\\"jungleCsBefore10Minutes\\\": float,\\n \\\"junglerTakedownsNearDamagedEpicMonster\\\": int,\\n \\\"kTurretsDestroyedBeforePlatesFall\\\": int,\\n \\\"kda\\\": float,\\n \\\"killAfterHiddenWithAlly\\\": int,\\n \\\"killParticipation\\\": float,\\n \\\"killedChampTookFullTeamDamageSurvived\\\": int,\\n \\\"killingSprees\\\": int,\\n \\\"killsNearEnemyTurret\\\": int,\\n \\\"killsOnOtherLanesEarlyJungleAsLaner\\\": NotRequired[int],\\n \\\"killsOnRecentlyHealedByAramPack\\\": int,\\n \\\"killsUnderOwnTurret\\\": int,\\n \\\"killsWithHelpFromEpicMonster\\\": int,\\n \\\"knockEnemyIntoTeamAndKill\\\": int,\\n \\\"landSkillShotsEarlyGame\\\": int,\\n \\\"laneMinionsFirst10Minutes\\\": int,\\n \\\"laningPhaseGoldExpAdvantage\\\": int,\\n \\\"legendaryCount\\\": int,\\n \\\"lostAnInhibitor\\\": int,\\n \\\"maxCsAdvantageOnLaneOpponent\\\": float,\\n \\\"maxKillDeficit\\\": int,\\n \\\"maxLevelLeadLaneOpponent\\\": int,\\n \\\"mejaisFullStackInTime\\\": int,\\n \\\"moreEnemyJungleThanOpponent\\\": float,\\n \\\"multiKillOneSpell\\\": int,\\n \\\"multiTurretRiftHeraldCount\\\": int,\\n \\\"multikills\\\": int,\\n \\\"multikillsAfterAggressiveFlash\\\": int,\\n \\\"mythicItemUsed\\\": NotRequired[int],\\n \\\"outerTurretExecutesBefore10Minutes\\\": int,\\n \\\"outnumberedKills\\\": int,\\n \\\"outnumberedNexusKill\\\": int,\\n \\\"perfectDragonSoulsTaken\\\": int,\\n \\\"perfectGame\\\": int,\\n \\\"pickKillWithAlly\\\": int,\\n \\\"playedChampSelectPosition\\\": NotRequired[int],\\n \\\"poroExplosions\\\": int,\\n \\\"quickCleanse\\\": int,\\n \\\"quickFirstTurret\\\": int,\\n \\\"quickSoloKills\\\": int,\\n \\\"riftHeraldTakedowns\\\": int,\\n \\\"saveAllyFromDeath\\\": int,\\n \\\"scuttleCrabKills\\\": int,\\n \\\"shortestTimeToAceFromFirstTakedown\\\": NotRequired[float],\\n \\\"skillshotsDodged\\\": int,\\n \\\"skillshotsHit\\\": int,\\n \\\"snowballsHit\\\": int,\\n \\\"soloBaronKills\\\": int,\\n \\\"soloKills\\\": int,\\n \\\"stealthWardsPlaced\\\": int,\\n \\\"survivedSingleDigitHpCount\\\": int,\\n \\\"survivedThreeImmobilizesInFight\\\": int,\\n \\\"takedownOnFirstTurret\\\": int,\\n \\\"takedowns\\\": int,\\n \\\"takedownsAfterGainingLevelAdvantage\\\": int,\\n \\\"takedownsBeforeJungleMinionSpawn\\\": int,\\n \\\"takedownsFirstXMinutes\\\": int,\\n \\\"takedownsInAlcove\\\": int,\\n \\\"takedownsInEnemyFountain\\\": int,\\n \\\"teamBaronKills\\\": int,\\n \\\"teamDamagePercentage\\\": float,\\n \\\"teamElderDragonKills\\\": int,\\n \\\"teamRiftHeraldKills\\\": int,\\n \\\"tookLargeDamageSurvived\\\": int,\\n \\\"turretPlatesTaken\\\": int,\\n \\\"turretTakedowns\\\": int,\\n \\\"turretsTakenWithRiftHerald\\\": int,\\n \\\"twentyMinionsIn3SecondsCount\\\": int,\\n \\\"twoWardsOneSweeperCount\\\": int,\\n \\\"unseenRecalls\\\": int,\\n \\\"visionScoreAdvantageLaneOpponent\\\": float,\\n \\\"visionScorePerMinute\\\": float,\\n \\\"wardTakedowns\\\": int,\\n \\\"wardTakedownsBefore20M\\\": int,\\n \\\"wardsGuarded\\\": int,\\n \\\"earliestDragonTakedown\\\": NotRequired[float],\\n \\\"baronBuffGoldAdvantageOverThreshold\\\": NotRequired[int],\\n \\\"teleportTakedowns\\\": NotRequired[int],\\n \\\"fastestLegendary\\\": NotRequired[float],\\n \\\"highestChampionDamage\\\": NotRequired[int],\\n \\\"highestCrowdControlScore\\\": NotRequired[int],\\n \\\"junglerKillsEarlyJungle\\\": NotRequired[int],\\n \\\"killsOnLanersEarlyJungleAsJungler\\\": NotRequired[int],\\n \\\"fasterSupportQuestCompletion\\\": NotRequired[int],\\n \\\"highestWardKills\\\": NotRequired[int],\\n \\\"soloTurretsLategame\\\": NotRequired[int],\\n \\\"thirdInhibitorDestroyedTime\\\": NotRequired[float],\\n }, total=False) | dict[str, int | float]\\n LolMatchV5MatchInfoParticipantPerksStatPerks = TypedDict(\\\"LolMatchV5MatchInfoParticipantPerksStatPerks\\\", {\\n \\\"defense\\\": int,\\n \\\"flex\\\": int,\\n \\\"offense\\\": int\\n })\\n LolMatchV5MatchInfoParticipantPerksStyleSelection = TypedDict(\\\"LolMatchV5MatchInfoParticipantPerksStyleSelection\\\", {\\n \\\"perk\\\": int,\\n \\\"var1\\\": int,\\n \\\"var2\\\": int,\\n \\\"var3\\\": int}\\n )\\n LolMatchV5MatchInfoParticipantPerksStyle = TypedDict(\\\"LolMatchV5MatchInfoParticipantPerksStyle\\\", {\\n \\\"description\\\": str,\\n \\\"selections\\\": list[LolMatchV5MatchInfoParticipantPerksStyleSelection],\\n \\\"style\\\": int\\n })\\n LolMatchV5MatchInfoParticipantPerks = TypedDict(\\\"LolMatchV5MatchInfoParticipantPerks\\\", {\\n \\\"statPerks\\\": LolMatchV5MatchInfoParticipantPerksStatPerks,\\n \\\"styles\\\": list[LolMatchV5MatchInfoParticipantPerksStyle]\\n })\\n LolMatchV5MatchInfoParticipantMissions = TypedDict(\\\"LolMatchV5MatchInfoParticipant\\\", {\\n \\\"playerScore0\\\": float,\\n \\\"playerScore1\\\": float,\\n \\\"playerScore10\\\": float,\\n \\\"playerScore11\\\": float,\\n \\\"playerScore2\\\": float,\\n \\\"playerScore3\\\": float,\\n \\\"playerScore4\\\": float,\\n \\\"playerScore5\\\": float,\\n \\\"playerScore6\\\": float,\\n \\\"playerScore7\\\": float,\\n \\\"playerScore8\\\": float,\\n \\\"playerScore9\\\": float,\\n })\\n LolMatchV5MatchInfoParticipant = TypedDict(\\\"LolMatchV5MatchInfoParticipant\\\", {\\n \\\"allInPings\\\": int,\\n \\\"assistMePings\\\": int,\\n \\\"assists\\\": int,\\n \\\"baitPings\\\": int,\\n \\\"baronKills\\\": int,\\n \\\"basicPings\\\": int,\\n \\\"bountyLevel\\\": int,\\n \\\"challenges\\\": NotRequired[LolMatchV5MatchInfoParticipantChallenges],\\n \\\"champExperience\\\": int,\\n \\\"champLevel\\\": int,\\n \\\"championId\\\": int,\\n \\\"championName\\\": str,\\n \\\"championTransform\\\": int,\\n \\\"commandPings\\\": int,\\n \\\"consumablesPurchased\\\": int,\\n \\\"damageDealtToBuildings\\\": int,\\n \\\"damageDealtToObjectives\\\": int,\\n \\\"damageDealtToTurrets\\\": int,\\n \\\"damageSelfMitigated\\\": int,\\n \\\"dangerPings\\\": int,\\n \\\"deaths\\\": int,\\n \\\"detectorWardsPlaced\\\": int,\\n \\\"doubleKills\\\": int,\\n \\\"dragonKills\\\": int,\\n \\\"eligibleForProgression\\\": bool,\\n \\\"enemyMissingPings\\\": int,\\n \\\"enemyVisionPings\\\": int,\\n \\\"firstBloodAssist\\\": bool,\\n \\\"firstBloodKill\\\": bool,\\n \\\"firstTowerAssist\\\": bool,\\n \\\"firstTowerKill\\\": bool,\\n \\\"gameEndedInEarlySurrender\\\": bool,\\n \\\"gameEndedInSurrender\\\": bool,\\n \\\"getBackPings\\\": int,\\n \\\"goldEarned\\\": int,\\n \\\"goldSpent\\\": int,\\n \\\"holdPings\\\": int,\\n \\\"individualPosition\\\": str,\\n \\\"inhibitorKills\\\": int,\\n \\\"inhibitorTakedowns\\\": int,\\n \\\"inhibitorsLost\\\": int,\\n \\\"item0\\\": int,\\n \\\"item1\\\": int,\\n \\\"item2\\\": int,\\n \\\"item3\\\": int,\\n \\\"item4\\\": int,\\n \\\"item5\\\": int,\\n \\\"item6\\\": int,\\n \\\"itemsPurchased\\\": int,\\n \\\"killingSprees\\\": int,\\n \\\"kills\\\": int,\\n \\\"lane\\\": str,\\n \\\"largestCriticalStrike\\\": int,\\n \\\"largestKillingSpree\\\": int,\\n \\\"largestMultiKill\\\": int,\\n \\\"longestTimeSpentLiving\\\": int,\\n \\\"magicDamageDealt\\\": int,\\n \\\"magicDamageDealtToChampions\\\": int,\\n \\\"magicDamageTaken\\\": int,\\n \\\"missions\\\": NotRequired[LolMatchV5MatchInfoParticipantMissions],\\n \\\"needVisionPings\\\": int,\\n \\\"neutralMinionsKilled\\\": int,\\n \\\"nexusKills\\\": int,\\n \\\"nexusLost\\\": int,\\n \\\"nexusTakedowns\\\": int,\\n \\\"objectivesStolen\\\": int,\\n \\\"objectivesStolenAssists\\\": int,\\n \\\"onMyWayPings\\\": int,\\n \\\"participantId\\\": int,\\n \\\"pentaKills\\\": int,\\n \\\"perks\\\": LolMatchV5MatchInfoParticipantPerks,\\n \\\"physicalDamageDealt\\\": int,\\n \\\"physicalDamageDealtToChampions\\\": int,\\n \\\"physicalDamageTaken\\\": int,\\n \\\"placement\\\": int,\\n \\\"playerAugment1\\\": int,\\n \\\"playerAugment2\\\": int,\\n \\\"playerAugment3\\\": int,\\n \\\"playerAugment4\\\": int,\\n \\\"playerSubteamId\\\": int,\\n \\\"playerScore0\\\": NotRequired[float],\\n \\\"playerScore1\\\": NotRequired[float],\\n \\\"playerScore10\\\": NotRequired[float],\\n \\\"playerScore11\\\": NotRequired[float],\\n \\\"playerScore2\\\": NotRequired[float],\\n \\\"playerScore3\\\": NotRequired[float],\\n \\\"playerScore4\\\": NotRequired[float],\\n \\\"playerScore5\\\": NotRequired[float],\\n \\\"playerScore6\\\": NotRequired[float],\\n \\\"playerScore7\\\": NotRequired[float],\\n \\\"playerScore8\\\": NotRequired[float],\\n \\\"playerScore9\\\": NotRequired[float],\\n \\\"profileIcon\\\": int,\\n \\\"pushPings\\\": int,\\n \\\"puuid\\\": str,\\n \\\"quadraKills\\\": int,\\n \\\"riotIdName\\\": NotRequired[str],\\n \\\"riotIdTagline\\\": str,\\n \\\"riotIdGameName\\\": NotRequired[str],\\n \\\"role\\\": str,\\n \\\"sightWardsBoughtInGame\\\": int,\\n \\\"spell1Casts\\\": int,\\n \\\"spell2Casts\\\": int,\\n \\\"spell3Casts\\\": int,\\n \\\"spell4Casts\\\": int,\\n \\\"subteamPlacement\\\": int,\\n \\\"summoner1Casts\\\": int,\\n \\\"summoner1Id\\\": int,\\n \\\"summoner2Casts\\\": int,\\n \\\"summoner2Id\\\": int,\\n \\\"summonerId\\\": str,\\n \\\"summonerLevel\\\": int,\\n \\\"summonerName\\\": str,\\n \\\"teamEarlySurrendered\\\": bool,\\n \\\"teamId\\\": int,\\n \\\"teamPosition\\\": str,\\n \\\"timeCCingOthers\\\": int,\\n \\\"timePlayed\\\": int,\\n \\\"totalAllyJungleMinionsKilled\\\": int,\\n \\\"totalDamageDealt\\\": int,\\n \\\"totalDamageDealtToChampions\\\": int,\\n \\\"totalDamageShieldedOnTeammates\\\": int,\\n \\\"totalDamageTaken\\\": int,\\n \\\"totalEnemyJungleMinionsKilled\\\": int,\\n \\\"totalHeal\\\": int,\\n \\\"totalHealsOnTeammates\\\": int,\\n \\\"totalMinionsKilled\\\": int,\\n \\\"totalTimeCCDealt\\\": int,\\n \\\"totalTimeSpentDead\\\": int,\\n \\\"totalUnitsHealed\\\": int,\\n \\\"tripleKills\\\": int,\\n \\\"trueDamageDealt\\\": int,\\n \\\"trueDamageDealtToChampions\\\": int,\\n \\\"trueDamageTaken\\\": int,\\n \\\"turretKills\\\": int,\\n \\\"turretTakedowns\\\": int,\\n \\\"turretsLost\\\": int,\\n \\\"unrealKills\\\": int,\\n \\\"visionClearedPings\\\": int,\\n \\\"visionScore\\\": int,\\n \\\"visionWardsBoughtInGame\\\": int,\\n \\\"wardsKilled\\\": int,\\n \\\"wardsPlaced\\\": int,\\n \\\"win\\\": bool\\n })\\n LolMatchV5MatchInfoTeamBan = TypedDict(\\\"LolMatchV5MatchInfoTeamBan\\\", {\\n \\\"championId\\\": int,\\n \\\"pickTurn\\\": int\\n })\\n LolMatchV5MatchInfoTeamObjectives = TypedDict(\\\"LolMatchV5MatchInfoTeamObjectives\\\", {\\n \\\"baron\\\": LolMatchV5MatchTeamObjective,\\n \\\"champion\\\": LolMatchV5MatchTeamObjective,\\n \\\"dragon\\\": LolMatchV5MatchTeamObjective,\\n \\\"horde\\\": NotRequired[LolMatchV5MatchTeamObjective],\\n \\\"inhibitor\\\": LolMatchV5MatchTeamObjective,\\n \\\"riftHerald\\\": LolMatchV5MatchTeamObjective,\\n \\\"tower\\\": LolMatchV5MatchTeamObjective\\n })\\n LolMatchV5MatchInfoTeam = TypedDict(\\\"LolMatchV5MatchInfoTeam\\\", {\\n \\\"bans\\\": list[LolMatchV5MatchInfoTeamBan],\\n \\\"objectives\\\": LolMatchV5MatchInfoTeamObjectives,\\n \\\"teamId\\\": int,\\n \\\"win\\\": bool\\n })\\n LolMatchV5MatchInfo = TypedDict(\\\"LolMatchV5MatchInfo\\\", {\\n \\\"gameCreation\\\": int,\\n \\\"gameDuration\\\": int,\\n \\\"gameEndTimestamp\\\": int,\\n \\\"gameId\\\": int,\\n \\\"gameMode\\\": str,\\n \\\"gameName\\\": str,\\n \\\"gameStartTimestamp\\\": int,\\n \\\"gameType\\\": str,\\n \\\"gameVersion\\\": str,\\n \\\"mapId\\\": int,\\n \\\"participants\\\": list[LolMatchV5MatchInfoParticipant],\\n \\\"platformId\\\": str,\\n \\\"queueId\\\": int,\\n \\\"teams\\\": list[LolMatchV5MatchInfoTeam],\\n \\\"tournamentCode\\\": str\\n })\\n LolMatchV5Match = TypedDict(\\\"LolMatchV5Match\\\", {\\n \\\"metadata\\\": LolMatchV5MatchMetadata,\\n \\\"info\\\": LolMatchV5MatchInfo\\n })\\n LolMatchV5MatchTimelineParticipantFrameChampionStats = TypedDict(\\\"LolMatchV5MatchTimelineParticipantFrameChampionStats\\\", {\\n \\\"abilityHaste\\\": int,\\n \\\"abilityPower\\\": int,\\n \\\"armor\\\": int,\\n \\\"armorPen\\\": int,\\n \\\"armorPenPercent\\\": int,\\n \\\"attackDamage\\\": int,\\n \\\"attackSpeed\\\": int,\\n \\\"bonusArmorPenPercent\\\": int,\\n \\\"bonusMagicPenPercent\\\": int,\\n \\\"ccReduction\\\": int,\\n \\\"cooldownReduction\\\": int,\\n \\\"health\\\": int,\\n \\\"healthMax\\\": int,\\n \\\"healthRegen\\\": int,\\n \\\"lifesteal\\\": int,\\n \\\"magicPen\\\": int,\\n \\\"magicPenPercent\\\": int,\\n \\\"magicResist\\\": int,\\n \\\"movementSpeed\\\": int,\\n \\\"omnivamp\\\": int,\\n \\\"physicalVamp\\\": int,\\n \\\"power\\\": int,\\n \\\"powerMax\\\": int,\\n \\\"powerRegen\\\": int,\\n \\\"spellVamp\\\": int\\n })\\n LolMatchV5MatchTimelineParticipantFrameDamageStats = TypedDict(\\\"LolMatchV5MatchTimelineParticipantFrameDamageStats\\\", {\\n \\\"magicDamageDone\\\": int,\\n \\\"magicDamageDoneToChampions\\\": int,\\n \\\"magicDamageTaken\\\": int,\\n \\\"physicalDamageDone\\\": int,\\n \\\"physicalDamageDoneToChampions\\\": int,\\n \\\"physicalDamageTaken\\\": int,\\n \\\"totalDamageDone\\\": int,\\n \\\"totalDamageDoneToChampions\\\": int,\\n \\\"totalDamageTaken\\\": int,\\n \\\"trueDamageDone\\\": int,\\n \\\"trueDamageDoneToChampions\\\": int,\\n \\\"trueDamageTaken\\\": int\\n })\\n LolMatchV5MatchTimelinePosition = TypedDict(\\\"LolMatchV5MatchTimelinePosition\\\", {\\n \\\"x\\\": int,\\n \\\"y\\\": int\\n })\\n LolMatchV5MatchTimelineParticipantFrame = TypedDict(\\\"LolMatchV5MatchTimelineParticipantFrame\\\", {\\n \\\"championStats\\\": LolMatchV5MatchTimelineParticipantFrameChampionStats,\\n \\\"currentGold\\\": int,\\n \\\"damageStats\\\": LolMatchV5MatchTimelineParticipantFrameDamageStats,\\n \\\"goldPerSecond\\\": int,\\n \\\"jungleMinionsKilled\\\": int,\\n \\\"level\\\": int,\\n \\\"minionsKilled\\\": int,\\n \\\"participantId\\\": int,\\n \\\"position\\\": LolMatchV5MatchTimelinePosition,\\n \\\"timeEnemySpentControlled\\\": int,\\n \\\"totalGold\\\": int,\\n \\\"xp\\\": int\\n })\\n LolMatchV5MatchTimelineEventDamage = TypedDict(\\\"LolMatchV5MatchTimelineEventDamage\\\", {\\n \\\"basic\\\": bool,\\n \\\"magicDamage\\\": int,\\n \\\"name\\\": str,\\n \\\"participantId\\\": int,\\n \\\"physicalDamage\\\": int,\\n \\\"spellName\\\": str,\\n \\\"spellSlot\\\": int,\\n \\\"trueDamage\\\": int,\\n \\\"type\\\": str\\n })\\n LolMatchV5MatchTimelineMetadata = TypedDict(\\\"LolMatchV5MatchTimelineMetadata\\\", {\\n \\\"dataVersion\\\": str,\\n \\\"matchId\\\": str,\\n \\\"participants\\\": list[str]\\n })\\n LolMatchV5MatchTimelineInfoFrameEvent = TypedDict(\\\"LolMatchV5MatchTimelineInfoFrameEvent\\\", {\\n \\\"afterId\\\": NotRequired[int],\\n \\\"beforeId\\\": NotRequired[int],\\n \\\"goldGain\\\": NotRequired[int],\\n \\\"participantId\\\": NotRequired[int],\\n \\\"timestamp\\\": int,\\n \\\"type\\\": str,\\n \\\"creatorId\\\": NotRequired[int],\\n \\\"wardType\\\": NotRequired[str],\\n \\\"level\\\": NotRequired[int],\\n \\\"itemId\\\": NotRequired[int],\\n \\\"assistingParticipantIds\\\": NotRequired[list[int]],\\n \\\"bounty\\\": NotRequired[int],\\n \\\"killStreakLength\\\": NotRequired[int],\\n \\\"killerId\\\": NotRequired[int],\\n \\\"position\\\": NotRequired[LolMatchV5MatchTimelinePosition],\\n \\\"shutdownBounty\\\": NotRequired[int],\\n \\\"victimDamageDealt\\\": NotRequired[list[LolMatchV5MatchTimelineEventDamage]],\\n \\\"victimDamageReceived\\\": NotRequired[list[LolMatchV5MatchTimelineEventDamage]],\\n \\\"victimId\\\": NotRequired[int],\\n \\\"levelUpType\\\": NotRequired[str],\\n \\\"skillSlot\\\": NotRequired[int],\\n \\\"realTimestamp\\\": NotRequired[int],\\n })\\n LolMatchV5MatchTimelineInfoFrame = TypedDict(\\\"LolMatchV5MatchTimelineInfoFrame\\\", {\\n \\\"events\\\": list[LolMatchV5MatchTimelineInfoFrameEvent],\\n \\\"participantFrames\\\": dict[str, LolMatchV5MatchTimelineParticipantFrame],\\n \\\"timestamp\\\": int\\n })\\n LolMatchV5MatchTimelineInfoParticipants = TypedDict(\\\"LolMatchV5MatchTimelineInfoParticipants\\\", {\\n \\\"participantId\\\": int,\\n \\\"puuid\\\": str,\\n })\\n LolMatchV5MatchTimelineInfo = TypedDict(\\\"LolMatchV5MatchTimelineInfo\\\", {\\n \\\"frameInterval\\\": int,\\n \\\"frames\\\": list[LolMatchV5MatchTimelineInfoFrame],\\n \\\"gameId\\\": int,\\n \\\"participants\\\": list[LolMatchV5MatchTimelineInfoParticipants]\\n })\\n LolMatchV5MatchTimeline = TypedDict(\\\"LolMatchV5MatchTimeline\\\", {\\n \\\"metadata\\\": LolMatchV5MatchTimelineMetadata,\\n \\\"info\\\": LolMatchV5MatchTimelineInfo\\n })\\n LolSpectatorV4GameParticipantPerks = TypedDict(\\\"LolSpectatorV4GameParticipantPerks\\\", {\\n \\\"perkIds\\\": list[int],\\n \\\"perkStyle\\\": int,\\n \\\"perkSubStyle\\\": int\\n })\\n LolSpectatorV4GameParticipant = TypedDict(\\\"LolSpectatorV4GameParticipant\\\", {\\n \\\"gameCustomizationObjects\\\": NotRequired[list[str]],\\n \\\"perks\\\": NotRequired[LolSpectatorV4GameParticipantPerks],\\n \\\"puuid\\\": str,\\n \\\"summonerId\\\": str,\\n \\\"teamId\\\": int,\\n \\\"spell1Id\\\": int,\\n \\\"spell2Id\\\": int,\\n \\\"championId\\\": int,\\n \\\"profileIconId\\\": int,\\n \\\"summonerName\\\": str,\\n \\\"bot\\\": bool\\n })\\n LolSpectatorV4GameObservers = TypedDict(\\\"LolSpectatorV4GameObservers\\\", {\\n \\\"encryptionKey\\\": str\\n })\\n LolSpectatorV4Game = TypedDict(\\\"LolSpectatorV4Game\\\", {\\n \\\"gameId\\\": int,\\n \\\"mapId\\\": int,\\n \\\"gameMode\\\": str,\\n \\\"gameType\\\": str,\\n \\\"gameQueueConfigId\\\": int,\\n \\\"participants\\\": list[LolSpectatorV4GameParticipant],\\n \\\"observers\\\": LolSpectatorV4GameObservers,\\n \\\"platformId\\\": str,\\n \\\"bannedChampions\\\": list[int],\\n \\\"gameStartTime\\\": int,\\n \\\"gameLength\\\": int\\n })\\n LolSpectatorV4GameList = TypedDict(\\\"LolSpectatorV4GameList\\\", {\\n \\\"gameList\\\": list[LolSpectatorV4Game],\\n \\\"clientRefreshInterval\\\": int\\n })\\n LolSummonerV4Summoner = TypedDict(\\\"SummonerV4Summoner\\\", {\\n \\\"id\\\": str,\\n \\\"accountId\\\": str,\\n \\\"puuid\\\": str,\\n \\\"name\\\": str,\\n \\\"profileIconId\\\": int,\\n \\\"revisionDate\\\": int,\\n \\\"summonerLevel\\\": int\\n })\\n\\n # Teamfight Tactics Types\\n\\n TftLeagueV1LeagueEntry = TypedDict(\\\"TftLeagueV1LeagueEntry\\\", {\\n \\\"summonerId\\\": str,\\n \\\"summonerName\\\": str,\\n \\\"rank\\\": str,\\n \\\"leaguePoints\\\": int,\\n \\\"wins\\\": int,\\n \\\"losses\\\": int,\\n \\\"hotStreak\\\": bool,\\n \\\"veteran\\\": bool,\\n \\\"freshBlood\\\": bool,\\n \\\"inactive\\\": bool,\\n })\\n TftLeagueV1LeagueFullEntry = TypedDict(\\\"TftLeagueV1LeagueFullEntry\\\", {\\n \\\"leagueId\\\": str,\\n \\\"puuid\\\": str,\\n \\\"summonerId\\\": str,\\n \\\"summonerName\\\": str,\\n \\\"queueType\\\": str,\\n \\\"tier\\\": str,\\n \\\"rank\\\": str,\\n \\\"leaguePoints\\\": int,\\n \\\"wins\\\": int,\\n \\\"losses\\\": int,\\n \\\"hotStreak\\\": bool,\\n \\\"veteran\\\": bool,\\n \\\"freshBlood\\\": bool,\\n \\\"inactive\\\": bool,\\n \\\"miniSeries\\\": NotRequired[LolLeagueV4LeagueEntryMiniSeries],\\n })\\n TftLeagueV1League = TypedDict(\\\"TftLeagueV1League\\\", {\\n \\\"tier\\\": str,\\n \\\"leagueId\\\": NotRequired[str],\\n \\\"queue\\\": NotRequired[str],\\n \\\"name\\\": NotRequired[str],\\n \\\"entries\\\": list[TftLeagueV1LeagueEntry]\\n })\\n TftMatchV1MatchMetadata = TypedDict(\\\"TftMatchV1MatchMetadata\\\", {\\n \\\"data_version\\\": str,\\n \\\"match_id\\\": str,\\n \\\"participants\\\": list[str]\\n })\\n TftMatchV1MatchInfoParticipantCompanion = TypedDict(\\\"TftMatchV1MatchInfoParticipantCompanion\\\", {\\n \\\"content_ID\\\": str,\\n \\\"item_ID\\\": int,\\n \\\"skin_ID\\\": int,\\n \\\"species\\\": str\\n })\\n TftMatchV1MatchInfoParticipantTrait = TypedDict(\\\"TftMatchV1MatchInfoParticipantTrait\\\", {\\n \\\"name\\\": str,\\n \\\"num_units\\\": int,\\n \\\"style\\\": int,\\n \\\"tier_current\\\": int,\\n \\\"tier_total\\\": int\\n })\\n TftMatchV1MatchInfoParticipantUnit = TypedDict(\\\"TftMatchV1MatchInfoParticipantUnit\\\", {\\n \\\"character_id\\\": str,\\n \\\"itemNames\\\": list[str],\\n \\\"name\\\": str,\\n \\\"rarity\\\": int,\\n \\\"tier\\\": int\\n })\\n TftMatchV1MatchInfoParticipant = TypedDict(\\\"TftMatchV1MatchInfoParticipant\\\", {\\n \\\"augments\\\": list[str],\\n \\\"companion\\\": TftMatchV1MatchInfoParticipantCompanion,\\n \\\"gold_left\\\": int,\\n \\\"last_round\\\": int,\\n \\\"level\\\": int,\\n \\\"placement\\\": int,\\n \\\"players_eliminated\\\": int,\\n \\\"puuid\\\": str,\\n \\\"time_eliminated\\\": float,\\n \\\"total_damage_to_players\\\": int,\\n \\\"traits\\\": list[TftMatchV1MatchInfoParticipantTrait],\\n \\\"units\\\": list[TftMatchV1MatchInfoParticipantUnit]\\n })\\n TftMatchV1MatchInfo = TypedDict(\\\"TftMatchV1MatchInfo\\\", {\\n \\\"game_datetime\\\": int,\\n \\\"game_length\\\": float,\\n \\\"game_version\\\": str,\\n \\\"participants\\\": list[TftMatchV1MatchInfoParticipant],\\n \\\"queue_id\\\": int,\\n \\\"tft_game_type\\\": str,\\n \\\"tft_set_core_name\\\": str,\\n \\\"tft_set_number\\\": int\\n })\\n TftMatchV1Match = TypedDict(\\\"TftMatchV1Match\\\", {\\n \\\"metadata\\\": TftMatchV1MatchMetadata,\\n \\\"info\\\": TftMatchV1MatchInfo\\n })\\n TftSummonerV1Summoner = LolSummonerV4Summoner\\n\\n # Legends of Runeterra Types\\n\\n LorRankedV1LeaderboardPlayer = TypedDict(\\\"LorRankedV1LeaderboardPlayer\\\", {\\n \\\"name\\\": str,\\n \\\"rank\\\": int,\\n \\\"lp\\\": float\\n })\\n LorRankedV1Leaderboard = TypedDict(\\\"LorRankedV1Leaderboard\\\", {\\n \\\"players\\\": list[LorRankedV1LeaderboardPlayer]\\n })\\n LorMatchV1MatchMetadata = TypedDict(\\\"LorMatchV1MatchMetadata\\\", {\\n \\\"data_version\\\": str,\\n \\\"match_id\\\": str,\\n \\\"participants\\\": list[str]\\n })\\n LorMatchV1MatchInfoPlayer = TypedDict(\\\"LorMatchV1MatchInfoPlayer\\\", {\\n \\\"puuid\\\": str,\\n \\\"deck_id\\\": str,\\n \\\"deck_code\\\": str,\\n \\\"factions\\\": list[str],\\n \\\"game_outcome\\\": str,\\n \\\"order_of_play\\\": int\\n })\\n LorMatchV1MatchInfo = TypedDict(\\\"LorMatchV1MatchInfo\\\", {\\n \\\"game_mode\\\": str,\\n \\\"game_type\\\": str,\\n \\\"game_start_time_utc\\\": str,\\n \\\"game_version\\\": str,\\n \\\"players\\\": list[LorMatchV1MatchInfoPlayer],\\n \\\"total_turn_count\\\": int\\n })\\n LorMatchV1Match = TypedDict(\\\"LorMatchV1Match\\\", {\\n \\\"metadata\\\": LorMatchV1MatchMetadata,\\n \\\"info\\\": LorMatchV1MatchInfo\\n })\\n\\n # Valorant Types\\n\\n ValContentV1ContentsAssetLocalizedNames = TypedDict(\\\"ValContentV1ContentsAssetLocalizedNames\\\", {\\n \\\"ar-AE\\\": str,\\n \\\"de-DE\\\": str,\\n \\\"en-US\\\": str,\\n \\\"es-ES\\\": str,\\n \\\"es-MX\\\": str,\\n \\\"fr-FR\\\": str,\\n \\\"id-ID\\\": str,\\n \\\"it-IT\\\": str,\\n \\\"ja-JP\\\": str,\\n \\\"ko-KR\\\": str,\\n \\\"pl-PL\\\": str,\\n \\\"pt-BR\\\": str,\\n \\\"ru-RU\\\": str,\\n \\\"th-TH\\\": str,\\n \\\"tr-TR\\\": str,\\n \\\"vi-VN\\\": str,\\n \\\"zh-CN\\\": str,\\n \\\"zh-TW\\\": str,\\n })\\n ValContentV1ContentsAsset = TypedDict(\\\"ValContentV1ContentsAsset\\\", {\\n \\\"name\\\": str,\\n \\\"id\\\": str,\\n \\\"localizedNames\\\": NotRequired[ValContentV1ContentsAssetLocalizedNames],\\n \\\"assetName\\\": str,\\n \\\"assetPath\\\": NotRequired[str]\\n })\\n ValContentV1ContentsAct = TypedDict(\\\"ValContentV1ContentsAct\\\", {\\n \\\"id\\\": str,\\n \\\"localizedNames\\\": NotRequired[ValContentV1ContentsAssetLocalizedNames],\\n \\\"parentId\\\": str,\\n \\\"type\\\": str,\\n \\\"name\\\": str,\\n \\\"isActive\\\": bool\\n })\\n ValContentV1Contents = TypedDict(\\\"ValContentV1Contents\\\", {\\n \\\"version\\\": str,\\n \\\"characters\\\": list[ValContentV1ContentsAsset],\\n \\\"maps\\\": list[ValContentV1ContentsAsset],\\n \\\"chromas\\\": list[ValContentV1ContentsAsset],\\n \\\"skins\\\": list[ValContentV1ContentsAsset],\\n \\\"skinLevels\\\": list[ValContentV1ContentsAsset],\\n \\\"equips\\\": list[ValContentV1ContentsAsset],\\n \\\"gameModes\\\": list[ValContentV1ContentsAsset],\\n \\\"totems\\\": list[ValContentV1ContentsAsset],\\n \\\"sprays\\\": list[ValContentV1ContentsAsset],\\n \\\"sprayLevels\\\": list[ValContentV1ContentsAsset],\\n \\\"charms\\\": list[ValContentV1ContentsAsset],\\n \\\"charmLevels\\\": list[ValContentV1ContentsAsset],\\n \\\"playerCards\\\": list[ValContentV1ContentsAsset],\\n \\\"playerTitles\\\": list[ValContentV1ContentsAsset],\\n \\\"acts\\\": list[ValContentV1ContentsAct],\\n \\\"ceremonies\\\": list[ValContentV1ContentsAsset]\\n })\\n \\n ValRankedV1LeaderboardTierDetail = TypedDict(\\\"ValRankedV1LeaderboardTierDetail\\\", {\\n \\\"rankedRatingThreshold\\\": int,\\n \\\"startingPage\\\": int,\\n \\\"startingIndex\\\": int\\n })\\n ValRankedV1LeaderboardPlayer = TypedDict(\\\"ValRankedV1LeaderboardPlayer\\\", {\\n \\\"puuid\\\": str,\\n \\\"gameName\\\": str,\\n \\\"tagLine\\\": str,\\n \\\"leaderboardRank\\\": int,\\n \\\"rankedRating\\\": int,\\n \\\"numberOfWins\\\": int,\\n \\\"competitiveTier\\\": int\\n })\\n ValRankedV1LeaderboardTierDetails = TypedDict(\\\"ValRankedV1LeaderboardTierDetails\\\", {\\n \\\"24\\\": ValRankedV1LeaderboardTierDetail,\\n \\\"25\\\": ValRankedV1LeaderboardTierDetail,\\n \\\"26\\\": ValRankedV1LeaderboardTierDetail,\\n \\\"27\\\": ValRankedV1LeaderboardTierDetail\\n })\\n ValRankedV1Leaderboard = TypedDict(\\\"ValRankedV1Leaderboard\\\", {\\n \\\"actId\\\": str,\\n \\\"players\\\": list[ValRankedV1LeaderboardPlayer],\\n \\\"totalPlayers\\\": int,\\n \\\"immortalStartingPage\\\": int,\\n \\\"immortalStartingIndex\\\": int,\\n \\\"topTierRRThreshold\\\": int,\\n \\\"tierDetails\\\": ValRankedV1LeaderboardTierDetails,\\n \\\"startIndex\\\": int,\\n \\\"query\\\": str,\\n \\\"shard\\\": str\\n })\\n ValMatchV1MatchLocation = TypedDict(\\\"ValMatchV1MatchLocation\\\", {\\n \\\"x\\\": float,\\n \\\"y\\\": float\\n })\\n ValMatchV1MatchPlayerLocation = TypedDict(\\\"ValMatchV1MatchPlayerLocation\\\", {\\n \\\"puuid\\\": str,\\n \\\"viewRadians\\\": float,\\n \\\"location\\\": ValMatchV1MatchLocation\\n })\\n ValMatchV1MatchInfo = TypedDict(\\\"ValMatchV1MatchInfo\\\", {\\n \\\"matchId\\\": str,\\n \\\"mapId\\\": str,\\n \\\"gameVersion\\\": str,\\n \\\"gameLengthMillis\\\": int,\\n \\\"region\\\": str,\\n \\\"gameStartMillis\\\": int,\\n \\\"provisioningFlowId\\\": str,\\n \\\"isCompleted\\\": bool,\\n \\\"customGameName\\\": str,\\n \\\"queueId\\\": str,\\n \\\"gameMode\\\": str,\\n \\\"isRanked\\\": bool,\\n \\\"premierMatchInfo\\\": dict,\\n \\\"seasonId\\\": str\\n })\\n ValMatchV1MatchPlayerStatsAbilityCasts = TypedDict(\\\"ValMatchV1MatchPlayerStatsAbilityCasts\\\", {\\n \\\"grenadeCasts\\\": int,\\n \\\"ability1Casts\\\": int,\\n \\\"ability2Casts\\\": int,\\n \\\"ultimateCasts\\\": int\\n })\\n ValMatchV1MatchPlayerStats = TypedDict(\\\"ValMatchV1MatchPlayerStats\\\", {\\n \\\"score\\\": int,\\n \\\"roundsPlayed\\\": int,\\n \\\"kills\\\": int,\\n \\\"deaths\\\": int,\\n \\\"assists\\\": int,\\n \\\"playtimeMillis\\\": int,\\n \\\"abilityCasts\\\": ValMatchV1MatchPlayerStatsAbilityCasts | None\\n })\\n ValMatchV1MatchPlayer = TypedDict(\\\"ValMatchV1MatchPlayer\\\", {\\n \\\"puuid\\\": str,\\n \\\"gameName\\\": str,\\n \\\"tagLine\\\": str,\\n \\\"teamId\\\": str,\\n \\\"partyId\\\": str,\\n \\\"characterId\\\": str,\\n \\\"stats\\\": ValMatchV1MatchPlayerStats,\\n \\\"competitiveTier\\\": int,\\n \\\"isObserver\\\": bool,\\n \\\"playerCard\\\": str,\\n \\\"playerTitle\\\": str,\\n \\\"accountLevel\\\": int\\n })\\n ValMatchV1MatchTeam = TypedDict(\\\"ValMatchV1MatchTeam\\\", {\\n \\\"teamId\\\": str,\\n \\\"won\\\": bool,\\n \\\"roundsPlayed\\\": int,\\n \\\"roundsWon\\\": int,\\n \\\"numPoints\\\": int\\n })\\n ValMatchV1MatchRoundResultPlayerStatKill = TypedDict(\\\"ValMatchV1MatchRoundResultPlayerStatKill\\\", {\\n \\\"timeSinceGameStartMillis\\\": int,\\n \\\"timeSinceRoundStartMillis\\\": int,\\n \\\"killer\\\": str,\\n \\\"victim\\\": str,\\n \\\"victimLocation\\\": ValMatchV1MatchLocation,\\n \\\"assistants\\\": list[str],\\n \\\"playerLocations\\\": list[ValMatchV1MatchPlayerLocation],\\n \\\"finishingDamage\\\": TypedDict(\\\"FinishingDamage\\\", {\\n \\\"damageType\\\": str,\\n \\\"damageItem\\\": str,\\n \\\"isSecondaryFireMode\\\": bool\\n })\\n })\\n ValMatchV1MatchRoundResultPlayerStatDamage = TypedDict(\\\"ValMatchV1MatchRoundResultPlayerStatDamage\\\", {\\n \\\"receiver\\\": str,\\n \\\"damage\\\": int,\\n \\\"legshots\\\": int,\\n \\\"bodyshots\\\": int,\\n \\\"headshots\\\": int\\n })\\n ValMatchV1MatchRoundResultPlayerStatEconomy = TypedDict(\\\"ValMatchV1MatchRoundResultPlayerStatEconomy\\\", {\\n \\\"loadoutValue\\\": int,\\n \\\"weapon\\\": str,\\n \\\"armor\\\": str,\\n \\\"remaining\\\": int,\\n \\\"spent\\\": int\\n })\\n ValMatchV1MatchRoundResultPlayerStatAbility = TypedDict(\\\"ValMatchV1MatchRoundResultPlayerStatAbility\\\", {\\n \\\"grenadeEffects\\\": str | None,\\n \\\"ability1Effects\\\": str | None,\\n \\\"ability2Effects\\\": str | None,\\n \\\"ultimateEffects\\\": str | None\\n })\\n ValMatchV1MatchRoundResultPlayerStat = TypedDict(\\\"ValMatchV1MatchRoundResultPlayerStat\\\", {\\n \\\"puuid\\\": str,\\n \\\"kills\\\": list[ValMatchV1MatchRoundResultPlayerStatKill],\\n \\\"damage\\\": list[ValMatchV1MatchRoundResultPlayerStatDamage],\\n \\\"score\\\": int,\\n \\\"economy\\\": ValMatchV1MatchRoundResultPlayerStatEconomy,\\n \\\"ability\\\": ValMatchV1MatchRoundResultPlayerStatAbility\\n })\\n ValMatchV1MatchRoundResult = TypedDict(\\\"ValMatchV1MatchRoundResult\\\", {\\n \\\"roundNum\\\": int,\\n \\\"roundResult\\\": str,\\n \\\"roundCeremony\\\": str,\\n \\\"winningTeam\\\": str,\\n \\\"bombPlanter\\\": str | None,\\n \\\"bombDefuser\\\": str | None,\\n \\\"plantRoundTime\\\": int,\\n \\\"plantPlayerLocations\\\": list[ValMatchV1MatchPlayerLocation] | None,\\n \\\"plantLocation\\\": ValMatchV1MatchLocation,\\n \\\"plantSite\\\": str,\\n \\\"defuseRoundTime\\\": int,\\n \\\"defusePlayerLocations\\\": list[ValMatchV1MatchPlayerLocation] | None,\\n \\\"defuseLocation\\\": ValMatchV1MatchLocation,\\n \\\"playerStats\\\": list[ValMatchV1MatchRoundResultPlayerStat],\\n \\\"roundResultCode\\\": str\\n })\\n ValMatchV1Match = TypedDict(\\\"ValMatchV1Match\\\", {\\n \\\"matchInfo\\\": ValMatchV1MatchInfo,\\n \\\"players\\\": list[ValMatchV1MatchPlayer],\\n \\\"coaches\\\": list[str],\\n \\\"teams\\\": list[ValMatchV1MatchTeam],\\n \\\"roundResults\\\": list[ValMatchV1MatchRoundResult]\\n })\\n ValMatchV1MatchlistHistory = TypedDict(\\\"ValMatchV1MatchlistHistory\\\", {\\n \\\"matchId\\\": str,\\n \\\"gameStartTimeMillis\\\": int,\\n \\\"queueId\\\": str\\n })\\n ValMatchV1Matchlist = TypedDict(\\\"ValMatchV1Matchlist\\\", {\\n \\\"puuid\\\": str,\\n \\\"history\\\": list[ValMatchV1MatchlistHistory]\\n })\\n ValMatchV1RecentMatches = TypedDict(\\\"ValMatchV1RecentMatches\\\", {\\n \\\"currentTime\\\": int,\\n \\\"matchIds\\\": list[str]\\n })\\n\\n # Status Types\\n\\n StatusV1PlatformDataLocaleContent = TypedDict(\\\"StatusV1PlatformDataLocaleContent\\\", {\\n \\\"locale\\\": str,\\n \\\"content\\\": str\\n })\\n StatusV1PlatformDataEntryUpdate = TypedDict(\\\"StatusV1PlatformDataEntryUpdate\\\", {\\n \\\"id\\\": int,\\n \\\"created_at\\\": str,\\n \\\"updated_at\\\": str,\\n \\\"publish\\\": bool,\\n \\\"author\\\": str,\\n \\\"translations\\\": list[StatusV1PlatformDataLocaleContent],\\n \\\"publish_locations\\\": list[str]\\n })\\n StatusV1PlatformDataEntry = TypedDict(\\\"StatusV1PlatformDataEntry\\\", {\\n \\\"id\\\": int,\\n \\\"created_at\\\": str,\\n \\\"updated_at\\\": str | None,\\n \\\"archive_at\\\": str | None,\\n \\\"titles\\\": list[StatusV1PlatformDataLocaleContent],\\n \\\"updates\\\": list[StatusV1PlatformDataEntryUpdate],\\n \\\"platforms\\\": list[str],\\n \\\"maintenance_status\\\": str | None,\\n \\\"incident_severity\\\": str | None\\n })\\n StatusV1PlatformData = TypedDict(\\\"StatusV4PlatformData\\\", {\\n \\\"id\\\": str,\\n \\\"name\\\": str,\\n \\\"locales\\\": list[str],\\n \\\"maintenances\\\": list[StatusV1PlatformDataEntry],\\n \\\"incidents\\\": list[StatusV1PlatformDataEntry]\\n })\"\n },\n {\n \"identifier\": \"TaskGroup\",\n \"path\": \"pulsefire/taskgroups.py\",\n \"snippet\": \"class TaskGroup(asyncio.TaskGroup):\\n \\\"\\\"\\\"Asynchronous context manager for managing groups of tasks.\\n See [python asyncio task groups documentation](https://docs.python.org/3/library/asyncio-task.html#task-groups).\\n\\n Adapted for pulsefire, key differences from `asyncio.TaskGroup`:\\n\\n - Accepts a semaphore to restrict the amount of concurrent running coroutines.\\n - Due to semaphore support, the `create_task` method is now async.\\n - Allows internal collection of results and exceptions, similar to `asyncio.Task`.\\n - If exception collection is on (default), the task group will not abort on task exceptions.\\n\\n Example:\\n ```python\\n async with TaskGroup(asyncio.Semaphore(100)) as tg:\\n await tg.create_task(coro_func(...))\\n results = tg.results()\\n ```\\n \\\"\\\"\\\"\\n\\n semaphore: asyncio.Semaphore | None = None\\n \\\"\\\"\\\"Semaphore for restricting concurrent running coroutines.\\\"\\\"\\\"\\n collect_results: bool = True\\n \\\"\\\"\\\"Flag for collecting task results.\\\"\\\"\\\"\\n collect_exceptions: bool = True\\n \\\"\\\"\\\"Flag for collecting task exceptions, disables abort.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n semaphore: asyncio.Semaphore | None = None,\\n *,\\n collect_results: bool = True,\\n collect_exceptions: bool = True,\\n ) -> None:\\n super().__init__()\\n self.semaphore = semaphore\\n self.collect_results = collect_results\\n self.collect_exceptions = collect_exceptions\\n self._exceptions: list[BaseException] = []\\n self._results = []\\n\\n async def __aenter__(self):\\n self._exceptions = []\\n self._results = []\\n return await super().__aenter__()\\n\\n def results[T](self) -> list[T]:\\n \\\"\\\"\\\"Return the collected results returned from created tasks.\\\"\\\"\\\"\\n if not self.collect_results:\\n raise RuntimeError(f\\\"TaskGroup {self!r} has `collect_results` off\\\")\\n return self._results\\n\\n def exceptions(self) -> list[BaseException]:\\n \\\"\\\"\\\"Return the collected exceptions raised from created tasks.\\\"\\\"\\\"\\n if not self.collect_exceptions:\\n raise RuntimeError(f\\\"TaskGroup {self!r} has `collect_exceptions` off\\\")\\n return self._exceptions\\n\\n @override\\n async def create_task[T](self, coro: Awaitable[T], *, name: str | None = None, context: Context | None = None) -> asyncio.Task[T]:\\n \\\"\\\"\\\"Create a new task in this group and return it.\\n\\n If this group has a semaphore, wrap this semaphore on the coroutine.\\n \\\"\\\"\\\"\\n _coro = coro\\n if self.semaphore:\\n await self.semaphore.acquire()\\n async def semaphored():\\n try:\\n return await _coro\\n finally:\\n self.semaphore.release()\\n coro = semaphored()\\n return super().create_task(coro, name=name, context=context)\\n\\n def _on_task_done(self, task) -> None:\\n if exc := task.exception():\\n if self.collect_exceptions:\\n LOGGER.warning(\\n \\\"TaskGroup: unhandled exception\\\\n\\\" +\\n \\\"\\\".join(traceback.format_exception(type(exc), exc, exc.__traceback__))\\n )\\n self._exceptions.append(exc)\\n self._tasks.discard(task)\\n if self._on_completed_fut is not None and not self._tasks:\\n if not self._on_completed_fut.done():\\n self._on_completed_fut.set_result(True)\\n return\\n elif self.collect_results and not task.cancelled():\\n self._results.append(task.result())\\n return super()._on_task_done(task)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import asyncio\nimport os\nfrom pulsefire.clients import RiotAPIClient\nfrom pulsefire.functools import async_to_sync\nfrom pulsefire.schemas import RiotAPISchema\nfrom pulsefire.taskgroups import TaskGroup"},"token_num":{"kind":"number","value":16605,"string":"16,605"},"cropped_code":{"kind":"string","value":"\n\n\n@async_to_sync()\nasync def test_taskgroup():\n async with RiotAPIClient(default_headers={\"X-Riot-Token\": os.environ[\"RIOT_API_KEY\"]}) as client:\n plat_league = await client.get_lol_league_v4_entries_by_division(region=\"na1\", queue=\"RANKED_SOLO_5x5\", tier=\"PLATINUM\", division=\"IV\")\n summoner = await client.get_lol_summoner_v4_by_id(region=\"na1\", id=plat_league[0][\"summonerId\"])\n match_ids = await client.get_lol_match_v5_match_ids_by_puuid(region=\"americas\", puuid=summoner[\"puuid\"])\n\n"},"all_code":{"kind":"string","value":"\n\n\n@async_to_sync()\nasync def test_taskgroup():\n async with RiotAPIClient(default_headers={\"X-Riot-Token\": os.environ[\"RIOT_API_KEY\"]}) as client:\n plat_league = await client.get_lol_league_v4_entries_by_division(region=\"na1\", queue=\"RANKED_SOLO_5x5\", tier=\"PLATINUM\", division=\"IV\")\n summoner = await client.get_lol_summoner_v4_by_id(region=\"na1\", id=plat_league[0][\"summonerId\"])\n match_ids = await client.get_lol_match_v5_match_ids_by_puuid(region=\"americas\", puuid=summoner[\"puuid\"])\n"},"next_line":{"kind":"string","value":" async with TaskGroup() as tg:"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-11-27 13:37:24+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5387,"cells":{"repo_name":{"kind":"string","value":"Matrixeigs/UncertaintyManagementInteroperablePowerTransportationSystems"},"file_path":{"kind":"string","value":"TestCaseDistributionSystems/uc_mmgs_tess_stochastic.py"},"context":{"kind":"list like","value":[{"identifier":"case33","path":"TestCaseDistributionSystems/test_cases/case33.py","snippet":"def case33():\n \"\"\"Power flow data for 33 bus, 6 generator case.\n Please see L{caseformat} for details on the case file format.\n\n Based on data from ...\n\n Alsac, O. & Stott, B., I{\"Optimal Load Flow with Steady State Security\"},\n IEEE Transactions on Power Apparatus and Systems, Vol. PAS 93, No. 3,\n 1974, pp. 745-751.\n\n ... with branch parameters rounded to nearest 0.01, shunt values divided\n by 100 and shunt on bus 10 moved to bus 5, load at bus 5 zeroed out.\n Generator locations, costs and limits and bus areas were taken from ...\n\n Ferrero, R.W., Shahidehpour, S.M., Ramesh, V.C., I{\"Transaction analysis\n in deregulated power systems using game theory\"}, IEEE Transactions on\n Power Systems, Vol. 12, No. 3, Aug 1997, pp. 1340-1347.\n\n Generator Q limits were derived from Alsac & Stott, using their Pmax\n capacities. V limits and line |S| limits taken from Alsac & Stott.\n\n @return: Power flow data for 30 bus, 6 generator case.\n @see: U{http://www.pserc.cornell.edu/matpower/}\n \"\"\"\n ppc = {\"version\": '2'}\n\n ##----- Power Flow Data -----##\n ## system MVA base\n ppc[\"baseMVA\"] = 100.0\n\n ## bus data\n # bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin\n ppc[\"bus\"] = array([\n [1, 3, 0, 0, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [2, 1, 0.1, 0.06, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.95],\n [3, 1, 0.09, 0.04, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [4, 1, 0.12, 0.08, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [5, 1, 0.06, 0.03, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [6, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [7, 1, 0.2, 0.1, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [8, 1, 0.2, 0.1, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [9, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [10, 1, 0.06, 0.02, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [11, 1, 0.045, 0.03, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [12, 1, 0.06, 0.035, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [13, 1, 0.06, 0.035, 0, 0, 2, 1, 0, 12.66, 1, 1.1, 0.95],\n [14, 1, 0.12, 0.08, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [15, 1, 0.06, 0.01, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [16, 1, 0.06, 0.02, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [17, 1, 0.06, 0.02, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [18, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [19, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [20, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\n [21, 1, 0.09, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [22, 2, 0.09, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.1, 0.95],\n [23, 2, 0.09, 0.05, 0, 0, 2, 1, 0, 12.66, 1, 1.1, 0.95],\n [24, 1, 0.42, 0.20, 0, 0.04, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [25, 1, 0.42, 0.2, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [26, 1, 0.06, 0.025, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [27, 1, 0.06, 0.025, 0, 0, 3, 1, 0, 12.66, 1, 1.1, 0.95],\n [28, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\n [29, 1, 0.12, 0.07, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [30, 1, 0.2, 0.6, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [31, 1, 0.15, 0.07, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [32, 1, 0.21, 0.1, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n [33, 1, 0.06, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\n ])\n\n ## generator data\n # bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,\n # Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf, start-up time, shut-down time and initial condition!\n ppc[\"gen\"] = array([\n [1, 23.54, 0, 150, -20, 1, 100, 1, 80, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1],\n ])\n\n ## branch data\n # fbus, tbus, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax\n ppc[\"branch\"] = array([\n [1, 2, 0.057525912, 0.029324489, 0, 130, 130, 130, 0, 0, 1, -360, 360],\n [2, 3, 0.307595167, 0.15666764, 0, 130, 130, 130, 0, 0, 1, -360, 360],\n [3, 4, 0.228356656, 0.116299674, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [4, 5, 0.237777928, 0.121103899, 0, 130, 130, 130, 0, 0, 1, -360, 360],\n [5, 6, 0.510994811, 0.441115179, 0, 130, 130, 130, 0, 0, 1, -360, 360],\n [6, 7, 0.116798814, 0.386084969, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [7, 8, 0.44386045, 0.146684835, 0, 90, 90, 90, 0, 0, 1, -360, 360],\n [8, 9, 0.642643047, 0.461704714, 0, 70, 70, 70, 0, 0, 1, -360, 360],\n [9, 10, 0.651378001, 0.461704714, 0, 130, 130, 130, 0, 0, 1, -360, 360],\n [10, 11, 0.122663712, 0.040555144, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [11, 12, 0.233597628, 0.077241951, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [12, 13, 0.915922324, 0.720633708, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [13, 14, 0.337917936, 0.444796338, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [14, 15, 0.368739846, 0.328184702, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [15, 16, 0.465635443, 0.340039282, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [16, 17, 0.804239697, 1.073775422, 0, 65, 65, 65, 0, 0, 1, -360, 360],\n [17, 18, 0.456713311, 0.358133116, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [2, 19, 0.102323747, 0.097644308, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [19, 20, 0.938508419, 0.845668336, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [20, 21, 0.255497406, 0.298485858, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [21, 22, 0.442300637, 0.584805173, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [3, 23, 0.28151509, 0.192356167, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [23, 24, 0.560284909, 0.442425422, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [24, 25, 0.559037059, 0.43743402, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [6, 26, 0.126656834, 0.064513875, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [26, 27, 0.177319567, 0.090281989, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [27, 28, 0.660736881, 0.582559042, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [28, 29, 0.501760717, 0.437122057, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [29, 30, 0.316642084, 0.161284687, 0, 32, 32, 32, 0, 0, 1, -360, 360],\n [30, 31, 0.607952801, 0.600840053, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [31, 32, 0.193728802, 0.225798562, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [32, 33, 0.212758523, 0.330805188, 0, 16, 16, 16, 0, 0, 1, -360, 360],\n [7, 20, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\n [8, 14, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\n [11, 21, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\n [17, 32, 0.3120, 0.3120, 0, 65, 65, 65, 0, 0, 0, -360, 360],\n [24, 28, 0.3120, 0.3120, 0, 16, 16, 16, 0, 0, 0, -360, 360]\n ])\n\n ##----- OPF Data -----##\n ## area data\n # area refbus\n ppc[\"areas\"] = array([\n [1, 8],\n [2, 23],\n [3, 26],\n ])\n\n ## generator cost data\n # 1 startup shutdown n x1 y1 ... xn yn\n # 2 startup shutdown n c(n-1) ... c0\n ppc[\"gencost\"] = array([\n [0, 0, 0, 3, 0.0, 20, 0]\n ])\n\n return ppc"},{"identifier":"micro_grid","path":"TestCasesMicrogrids/test_cases/cases_unit_commitment.py","snippet":"AC_PD = array([323.0284, 308.2374, 318.1886, 307.9809, 331.2170, 368.6539, 702.0040, 577.7045, 1180.4547, 1227.6240,\n 1282.9344, 1311.9738, 1268.9502, 1321.7436, 1323.9218, 1327.1464, 1386.9117, 1321.6387, 1132.0476,\n 1109.2701, 882.5698, 832.4520, 349.3568, 299.9920])\nDC_PD = array([287.7698, 287.7698, 287.7698, 287.7698, 299.9920, 349.3582, 774.4047, 664.0625, 1132.6996, 1107.7366,\n 1069.6837, 1068.9819, 1027.3295, 1096.3820, 1109.4778, 1110.7039, 1160.1270, 1078.7839, 852.2514,\n 791.5814, 575.4085, 551.1441, 349.3568, 299.992])\nDG = {\"PMIN\": 0,\n \"PMAX\": 5,\n \"QMIN\": -5,\n \"QMAX\": 5,\n \"COST_A\": 0.01,\n \"COST_B\": 0.5}\nUG = {\"PMIN\": -5,\n \"PMAX\": 5,\n \"QMIN\": -5,\n \"QMAX\": 5,\n \"COST\": Price_UG, } # The cost should be a profile\nESS = {\"PDC_MAX\": 5,\n \"PCH_MAX\": 5,\n \"EFF_DC\": 0.95,\n \"EFF_CH\": 0.95,\n \"E0\": 10,\n \"EMIN\": 5,\n \"EMAX\": 20, }\nBIC = {\"PMAX\": 5,\n \"QMAX\": 5,\n \"SMAX\": 5,\n \"EFF_AC2DC\": 0.9,\n \"EFF_DC2AC\": 0.9, }\nMG = {\"PMAX\": 5,\n \"PMIN\": -5,\n \"QMAX\": 5,\n \"QMIN\": -5\n }\nPD = {\"AC\": AC_PD / max(AC_PD),\n \"AC_MAX\": 5,\n \"DC\": DC_PD / max(DC_PD),\n \"DC_MAX\": 5}\nQD = {\"AC\": AC_PD / max(AC_PD),\n \"AC_MAX\": 5, }\nPV = {\"PMAX\": 0,\n \"COST\": 0}"},{"identifier":"PBIC_AC2DC","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PBIC_AC2DC = 4"},{"identifier":"PG","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PG = 0"},{"identifier":"PESS_DC","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PESS_DC = 8"},{"identifier":"PBIC_DC2AC","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PBIC_DC2AC = 5"},{"identifier":"PUG","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PUG = 2"},{"identifier":"PESS_CH","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PESS_CH = 7"},{"identifier":"PMESS","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"PMESS = 10 # Reactive power unit commitment of"},{"identifier":"EESS","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"EESS = 9"},{"identifier":"NX_MG","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"NX_MG = 11"},{"identifier":"QBIC","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"QBIC = 6"},{"identifier":"QUG","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"QUG = 3"},{"identifier":"QG","path":"TestCaseDistributionSystems/data_format/idx_MG.py","snippet":"QG = 1"},{"identifier":"DataBaseManagement","path":"TestCaseDistributionSystems/database_management.py","snippet":"class DataBaseManagement():\n\n def __init__(self, host=\"localhost\", user=\"root\", password=\"Ntu@1003\", db=\"mess\"):\n \"\"\"\n Initialized the database connection string\n :param host: host ip\n :param user: user name\n :param password: password\n :param db: database name\n :return\n \"\"\"\n self.db = pymysql.connect(host=host, user=user, password=password, db=db)\n\n def create_table(self, table_name, nl=32, nb=33, ng=6, nmg=3, nmes=3):\n \"\"\"\n Creat table name\n :param table_name:\n :param nb:\n :param nb:\n :param ng:\n :return: no return value\n \"\"\"\n cursor = self.db.cursor()\n sql = \"DROP TABLE IF EXISTS \"\n cursor.execute(sql + table_name)\n if table_name == \"distribution_networks\":\n sql_start = \"\"\"CREATE TABLE distribution_networks (\"\"\"\n sql = 'SCENARIO INT,\\n TIME INT NOT NULL,\\n '\n for i in range(nl):\n sql += \"PIJ{0} DECIMAL(8,6),\\n \".format(i)\n for i in range(nl):\n sql += \"QIJ{0} DECIMAL(8,6),\\n \".format(i)\n for i in range(nl):\n sql += \"IIJ{0} DECIMAL(8,6),\\n \".format(i)\n for i in range(nb):\n sql += \"V{0} DECIMAL(8,6),\\n \".format(i)\n for i in range(ng):\n sql += \"PG{0} DECIMAL(8,6),\\n \".format(i)\n for i in range(ng - 1):\n sql += \"QG{0} DECIMAL(8,6),\\n \".format(i)\n sql += \"QG{0} DECIMAL(8,6)\\n \".format(ng - 1)\n sql_end = \"\"\")\"\"\"\n elif table_name == \"micro_grids\":\n sql_start = \"\"\"CREATE TABLE micro_grids (\"\"\"\n sql = 'SCENARIO INT,\\n MG INT,\\n TIME INT,\\n '\n sql += 'PG DECIMAL(7,4),\\n QG DECIMAL(7,4),\\n PUG DECIMAL(7,4),\\n QUG DECIMAL(7,4),\\n '\n sql += 'PBIC_AC2DC DECIMAL(7,4),\\n PBIC_DC2AC DECIMAL(7,4),\\n QBIC DECIMAL(7,4),\\n PESS_CH DECIMAL(7,4),\\n '\n sql += 'PESS_DC DECIMAL(7,4),\\n EESS DECIMAL(7,4),\\n PMESS DECIMAL(7,4)'\n sql_end = \"\"\")\"\"\"\n elif table_name == \"mobile_energy_storage_systems\":\n sql_start = \"\"\"CREATE TABLE mobile_energy_storage_systems (\"\"\"\n sql = 'SCENARIO INT,\\n MESS INT,\\n TIME INT,\\n'\n for i in range(nmg):\n sql += \"PDC_MG{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg):\n sql += \"PCH_MG{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"EESS DECIMAL(7,4)\\n \"\n sql_end = \"\"\")\"\"\"\n elif table_name == \"first_stage_solutions\": # First-stage solution table\n sql_start = \"\"\"CREATE TABLE first_stage_solutions (\"\"\"\n sql = 'TIME INT,\\n'\n for i in range(ng):\n sql += \"PG{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"RG{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg - 1):\n sql += \"PG_MG{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"RG_MG{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"IESS{0} INT,\\n \".format(i)\n sql += \"PESS_DC{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"PESS_CH{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"RESS{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"ESS{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"PG_MG{0} DECIMAL(7,4),\\n \".format(nmg - 1)\n sql += \"RG_MG{0} DECIMAL(7,4),\\n \".format(nmg - 1)\n sql += \"IESS{0} INT,\\n \".format(nmg - 1)\n sql += \"PESS_DC{0} DECIMAL(7,4),\\n \".format(nmg - 1)\n sql += \"PESS_CH{0} DECIMAL(7,4),\\n \".format(nmg - 1)\n sql += \"RESS{0} DECIMAL(7,4),\\n \".format(nmg - 1)\n sql += \"ESS{0} DECIMAL(7,4)\\n \".format(nmg - 1)\n sql_end = \"\"\")\"\"\"\n elif table_name == \"fisrt_stage_mess\": # First-stage solution table\n sql_start = \"\"\"CREATE TABLE fisrt_stage_mess (\"\"\"\n sql = 'MESS INT,\\n TIME INT,\\n'\n for i in range(nmg):\n sql += \"IDC_MG{0} INT,\\n \".format(i)\n for i in range(nmg):\n sql += \"PDC_MG{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg):\n sql += \"PCH_MG{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg):\n sql += \"RMESS{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"MESS_F_STOP INT,\\n \"\n sql += \"MESS_T_STOP INT\\n \"\n sql_end = \"\"\")\"\"\"\n else:\n sql_start = \"\"\"CREATE TABLE scenarios (\"\"\"\n sql = 'SCENARIO INT,\\n WEIGHT DECIMAL(7,4),\\n TIME INT,\\n'\n for i in range(nb):\n sql += \"PD{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg):\n sql += \"PD_AC{0} DECIMAL(7,4),\\n \".format(i)\n for i in range(nmg - 1):\n sql += \"PD_DC{0} DECIMAL(7,4),\\n \".format(i)\n sql += \"PD_DC{0} DECIMAL(7,4)\\n\".format(nmg - 1)\n sql_end = \"\"\")\"\"\"\n\n cursor.execute(sql_start + sql + sql_end)\n cursor.close()\n\n def insert_data_ds(self, table_name, nl=32, nb=33, ng=6, scenario=0, time=0, pij=0, qij=0, lij=0, vi=0, pg=0, qg=0):\n \"\"\"\n Insert data into table_name\n :param table_name:\n :param nl:\n :param nb:\n :param ng:\n :param pij:\n :param qij:\n :param lij:\n :param vi:\n :param pg:\n :param qg:\n :return:\n \"\"\"\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"SCENARIO,TIME,\"\n value = \"{0},{1},\".format(scenario, time)\n for i in range(nl):\n sql += \"PIJ{0},\".format(i)\n value += \"{0},\".format(pij[i])\n for i in range(nl):\n sql += \"QIJ{0},\".format(i)\n value += \"{0},\".format(qij[i])\n for i in range(nl):\n sql += \"IIJ{0},\".format(i)\n value += \"{0},\".format(lij[i])\n for i in range(nb):\n sql += \"V{0},\".format(i)\n value += \"{0},\".format(vi[i])\n for i in range(ng):\n sql += \"PG{0},\".format(i)\n value += \"{0},\".format(pg[i])\n for i in range(ng - 1):\n sql += \"QG{0},\".format(i)\n value += \"{0},\".format(qg[i])\n sql += \"QG{0}\".format(ng - 1)\n value += \"{0}\".format(qg[ng - 1])\n\n sql += \") VALUES (\" + value + \")\"\n\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def insert_data_mg(self, table_name, scenario=0, time=0, mg=0, pg=0, qg=0, pug=0, qug=0, pbic_ac2dc=0, pbic_dc2ac=0,\n qbic=0, pess_ch=0, pess_dc=0, eess=0, pmess=0):\n \"\"\"\n insert microgrid data\n :param table_name:\n :param scenario:\n :param time:\n :param mg:\n :param pg:\n :param qg:\n :param pug:\n :param qug:\n :param pbic_ac2dc:\n :param pbic_dc2ac:\n :param qbic:\n :param pess_ch:\n :param pess_dc:\n :param eess:\n :param pmess:\n :return:\n \"\"\"\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"SCENARIO,MG,TIME,\"\n value = \"{0},{1},{2},\".format(scenario, mg, time)\n sql += \"PG,QG,PUG,QUG,PBIC_AC2DC,PBIC_DC2AC,QBIC,PESS_CH,PESS_DC,EESS,PMESS\"\n value += \"{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10}\".format(pg, qg, pug, qug, pbic_ac2dc, pbic_dc2ac, qbic,\n pess_ch, pess_dc, eess, pmess)\n sql += \") VALUES (\" + value + \")\"\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def insert_data_first_stage_mess(self, table_name, time=0, mess=0, imess=[0, 0, 0], pmess_ch=[0, 0, 0],\n pmess_dc=[0, 0, 0], rmess=[0, 0, 0], mess_f_stop=0, mess_t_stop=0, nmg=3):\n \"\"\"\n insert mobile energy storage systems data in the first-stage\n :param table_name:\n :param scenario:\n :param time:\n :param mess:\n :param pess_ch:\n :param pess_dc:\n :param eess:\n :param nmg:\n :return:\n \"\"\"\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"MESS,TIME,\"\n value = \"{0},{1},\".format(mess, time)\n for i in range(nmg):\n sql += \"IDC_MG{0},\".format(i)\n value += \"{0},\".format(imess[i])\n for i in range(nmg):\n sql += \"PDC_MG{0},\".format(i)\n value += \"{0},\".format(pmess_dc[i])\n for i in range(nmg):\n sql += \"PCH_MG{0},\".format(i)\n value += \"{0},\".format(pmess_ch[i])\n for i in range(nmg):\n sql += \"RMESS{0},\".format(i)\n value += \"{0},\".format(rmess[i])\n sql += \"MESS_F_STOP,MESS_T_STOP\"\n value += \"{0},{1}\".format(mess_f_stop, mess_t_stop)\n sql += \") VALUES (\" + value + \")\"\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def insert_data_mess(self, table_name, scenario=0, time=0, mess=0, pmess_ch=[0, 0, 0], pmess_dc=[0, 0, 0],\n emess=0, nmg=3):\n \"\"\"\n insert mobile energy storage systems data\n :param table_name:\n :param scenario:\n :param time:\n :param mess:\n :param pess_ch:\n :param pess_dc:\n :param eess:\n :param nmg:\n :return:\n \"\"\"\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"SCENARIO,MESS,TIME,\"\n value = \"{0},{1},{2},\".format(scenario, mess, time)\n for i in range(nmg):\n sql += \"PDC_MG{0},\".format(i)\n value += \"{0},\".format(pmess_dc[i])\n for i in range(nmg):\n sql += \"PCH_MG{0},\".format(i)\n value += \"{0},\".format(pmess_ch[i])\n sql += \"EESS\"\n value += \"{0}\".format(emess)\n sql += \") VALUES (\" + value + \")\"\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def insert_data_first_stage(self, table_name, time=0, ng=2, nmg=2, pg=[0, 0], rg=[0, 0], pg_mg=[0, 0],\n rg_mg=[0, 0], iess=[0, 0], pess_dc=[0, 0], pess_ch=[0, 0], ress=[0, 0], ess=[0, 0]):\n \"\"\"\n insert scenario data\n :param table_name:\n :param scenario:\n :param weight:\n :param time:\n :param nb:\n :param nmg:\n :param pd:\n :param pd_ac:\n :param pd_dc:\n :return:\n \"\"\"\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"TIME,\"\n value = \"{0},\".format(time)\n for i in range(ng):\n sql += \"PG{0},\".format(i)\n sql += \"RG{0},\".format(i)\n value += \"{0},\".format(pg[i])\n value += \"{0},\".format(rg[i])\n if nmg > 1:\n for i in range(nmg - 1):\n sql += \"PG_MG{0},\".format(i)\n sql += \"RG_MG{0},\".format(i)\n sql += \"IESS{0},\".format(i)\n sql += \"PESS_DC{0},\".format(i)\n sql += \"PESS_CH{0},\".format(i)\n sql += \"RESS{0},\".format(i)\n sql += \"ESS{0},\".format(i)\n value += \"{0},\".format(pg_mg[i])\n value += \"{0},\".format(rg_mg[i])\n value += \"{0},\".format(iess[i])\n value += \"{0},\".format(pess_dc[i])\n value += \"{0},\".format(pess_ch[i])\n value += \"{0},\".format(ress[i])\n value += \"{0},\".format(ess[i])\n sql += \"PG_MG{0},\".format(nmg - 1)\n sql += \"RG_MG{0},\".format(nmg - 1)\n sql += \"IESS{0},\".format(nmg - 1)\n sql += \"PESS_DC{0},\".format(nmg - 1)\n sql += \"PESS_CH{0},\".format(nmg - 1)\n sql += \"RESS{0},\".format(nmg - 1)\n sql += \"ESS{0}\".format(nmg - 1)\n value += \"{0},\".format(pg_mg[nmg - 1])\n value += \"{0},\".format(rg_mg[nmg - 1])\n value += \"{0},\".format(iess[nmg - 1])\n value += \"{0},\".format(pess_dc[nmg - 1])\n value += \"{0},\".format(pess_ch[nmg - 1])\n value += \"{0},\".format(ress[nmg - 1])\n value += \"{0}\".format(ess[nmg - 1])\n else:\n sql += \"PG_MG{0},\".format(nmg - 1)\n sql += \"RG_MG{0},\".format(nmg - 1)\n sql += \"IESS{0},\".format(nmg - 1)\n sql += \"PESS_DC{0},\".format(nmg - 1)\n sql += \"PESS_CH{0},\".format(nmg - 1)\n sql += \"RESS{0},\".format(nmg - 1)\n sql += \"ESS{0}\".format(nmg - 1)\n value += \"{0},\".format(pg_mg)\n value += \"{0},\".format(rg_mg)\n value += \"{0},\".format(iess)\n value += \"{0},\".format(pess_dc)\n value += \"{0},\".format(pess_ch)\n value += \"{0},\".format(ress)\n value += \"{0}\".format(ess)\n\n sql += \") VALUES (\" + value + \")\"\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def insert_data_scenario(self, table_name, scenario=0, weight=0, time=0, nb=1, nmg=2, pd=[0, 0], pd_ac=[0, 0],\n pd_dc=[0, 0]):\n cursor = self.db.cursor()\n sql_start = \"INSERT INTO \" + table_name + \" (\"\n sql = \"SCENARIO,WEIGHT,TIME,\"\n value = \"{0},{1},{2},\".format(scenario, weight, time)\n for i in range(nb):\n sql += \"PD{0},\".format(i)\n value += \"{0},\".format(pd[i])\n for i in range(nmg):\n sql += \"PD_AC{0},\".format(i)\n value += \"{0},\".format(pd_ac[i])\n for i in range(nmg - 1):\n sql += \"PD_DC{0},\".format(i)\n value += \"{0},\".format(pd_dc[i])\n if nmg > 1:\n sql += \"PD_DC{0}\".format(nmg - 1)\n value += \"{0}\".format(pd_dc[nmg - 1])\n\n sql += \") VALUES (\" + value + \")\"\n cursor.execute(sql_start + sql)\n self.db.commit()\n cursor.close()\n\n def inquery_data_scenario(self, table_name, scenario=0, time=0):\n cursor = self.db.cursor()\n # sql = \"SELECT * FROM \" + table_name + \" ;\"\n sql = \"SELECT * FROM \" + table_name + \" WHERE SCENARIO={0} AND TIME={1};\".format(scenario, time)\n cursor.execute(sql)\n data = cursor.fetchall()\n n_data = len(data[0])\n\n temp = []\n for i in range(n_data): temp.append(float(data[0][i]))\n\n cursor.close()\n return temp"},{"identifier":"ScenarioReduction","path":"StochasticOptimization/scenario_reduction.py","snippet":"class ScenarioReduction():\n def __init__(self):\n self.name = \"Scenario reduction\"\n\n def run(self, scenario, weight, n_reduced, power):\n \"\"\"\n\n :param scenario: A fan scenario tree, when more stage are considered, some merge operation can be implemented\n :param weight: Weight of each scenario\n :param n_reduced: Number of scenarios needs to be reduced\n :param power: The power in the distance calculation\n :return:\n \"\"\"\n n_scenario = scenario.shape[0] # number of original scenarios\n c = zeros((n_scenario, n_scenario))\n # Calculate the c matrix\n for i in range(n_scenario):\n for j in range(n_scenario):\n c[i, j] = linalg.norm((scenario[i, :] - scenario[j, :]), 2)\n c[i, j] = max([1, linalg.norm(scenario[i, :], power - 1), linalg.norm(scenario[j, :], power - 1)]) * \\\n c[i, j]\n\n J = arange(n_scenario) # The original index range\n J_reduced = array([])\n # Implement the iteration\n for n in range(n_reduced): # find the minimal distance\n print(\"The reduction is in process {0}\".format(n))\n c_n = inf * ones(n_scenario)\n c_n[J] = 0\n for u in J:\n # Delete the i-th distance\n J_temp = delete(J, where(J == u))\n for k in J_temp:\n c_k_j = delete(c[int(k)], J_temp)\n c_n[int(u)] += weight[int(k)] * min(c_k_j)\n u_i = argmin(c_n)\n J_reduced = append(J_reduced, u_i)\n J = delete(J, where(J == u_i))\n # Optimal redistribution\n p_s = weight.copy()\n p_s[J_reduced.astype(int)] = 0\n\n for i in J_reduced:\n c_temp = c[int(i), :]\n c_temp[J_reduced.astype(int)] = inf\n index = argmin(c_temp)\n p_s[index] += weight[int(i)]\n\n scenario_reduced = scenario[J.astype(int), :]\n weight_reduced = p_s[J.astype(int)]\n\n return scenario_reduced, weight_reduced"}],"string":"[\n {\n \"identifier\": \"case33\",\n \"path\": \"TestCaseDistributionSystems/test_cases/case33.py\",\n \"snippet\": \"def case33():\\n \\\"\\\"\\\"Power flow data for 33 bus, 6 generator case.\\n Please see L{caseformat} for details on the case file format.\\n\\n Based on data from ...\\n\\n Alsac, O. & Stott, B., I{\\\"Optimal Load Flow with Steady State Security\\\"},\\n IEEE Transactions on Power Apparatus and Systems, Vol. PAS 93, No. 3,\\n 1974, pp. 745-751.\\n\\n ... with branch parameters rounded to nearest 0.01, shunt values divided\\n by 100 and shunt on bus 10 moved to bus 5, load at bus 5 zeroed out.\\n Generator locations, costs and limits and bus areas were taken from ...\\n\\n Ferrero, R.W., Shahidehpour, S.M., Ramesh, V.C., I{\\\"Transaction analysis\\n in deregulated power systems using game theory\\\"}, IEEE Transactions on\\n Power Systems, Vol. 12, No. 3, Aug 1997, pp. 1340-1347.\\n\\n Generator Q limits were derived from Alsac & Stott, using their Pmax\\n capacities. V limits and line |S| limits taken from Alsac & Stott.\\n\\n @return: Power flow data for 30 bus, 6 generator case.\\n @see: U{http://www.pserc.cornell.edu/matpower/}\\n \\\"\\\"\\\"\\n ppc = {\\\"version\\\": '2'}\\n\\n ##----- Power Flow Data -----##\\n ## system MVA base\\n ppc[\\\"baseMVA\\\"] = 100.0\\n\\n ## bus data\\n # bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin\\n ppc[\\\"bus\\\"] = array([\\n [1, 3, 0, 0, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [2, 1, 0.1, 0.06, 0, 0, 1, 1, 0, 12.66, 1, 1.1, 0.95],\\n [3, 1, 0.09, 0.04, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [4, 1, 0.12, 0.08, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [5, 1, 0.06, 0.03, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [6, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [7, 1, 0.2, 0.1, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [8, 1, 0.2, 0.1, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [9, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [10, 1, 0.06, 0.02, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [11, 1, 0.045, 0.03, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [12, 1, 0.06, 0.035, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [13, 1, 0.06, 0.035, 0, 0, 2, 1, 0, 12.66, 1, 1.1, 0.95],\\n [14, 1, 0.12, 0.08, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [15, 1, 0.06, 0.01, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [16, 1, 0.06, 0.02, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [17, 1, 0.06, 0.02, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [18, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [19, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [20, 1, 0.09, 0.04, 0, 0, 2, 1, 0, 12.66, 1, 1.05, 0.95],\\n [21, 1, 0.09, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [22, 2, 0.09, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.1, 0.95],\\n [23, 2, 0.09, 0.05, 0, 0, 2, 1, 0, 12.66, 1, 1.1, 0.95],\\n [24, 1, 0.42, 0.20, 0, 0.04, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [25, 1, 0.42, 0.2, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [26, 1, 0.06, 0.025, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [27, 1, 0.06, 0.025, 0, 0, 3, 1, 0, 12.66, 1, 1.1, 0.95],\\n [28, 1, 0.06, 0.02, 0, 0, 1, 1, 0, 12.66, 1, 1.05, 0.95],\\n [29, 1, 0.12, 0.07, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [30, 1, 0.2, 0.6, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [31, 1, 0.15, 0.07, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [32, 1, 0.21, 0.1, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n [33, 1, 0.06, 0.04, 0, 0, 3, 1, 0, 12.66, 1, 1.05, 0.95],\\n ])\\n\\n ## generator data\\n # bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,\\n # Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf, start-up time, shut-down time and initial condition!\\n ppc[\\\"gen\\\"] = array([\\n [1, 23.54, 0, 150, -20, 1, 100, 1, 80, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1],\\n ])\\n\\n ## branch data\\n # fbus, tbus, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax\\n ppc[\\\"branch\\\"] = array([\\n [1, 2, 0.057525912, 0.029324489, 0, 130, 130, 130, 0, 0, 1, -360, 360],\\n [2, 3, 0.307595167, 0.15666764, 0, 130, 130, 130, 0, 0, 1, -360, 360],\\n [3, 4, 0.228356656, 0.116299674, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [4, 5, 0.237777928, 0.121103899, 0, 130, 130, 130, 0, 0, 1, -360, 360],\\n [5, 6, 0.510994811, 0.441115179, 0, 130, 130, 130, 0, 0, 1, -360, 360],\\n [6, 7, 0.116798814, 0.386084969, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [7, 8, 0.44386045, 0.146684835, 0, 90, 90, 90, 0, 0, 1, -360, 360],\\n [8, 9, 0.642643047, 0.461704714, 0, 70, 70, 70, 0, 0, 1, -360, 360],\\n [9, 10, 0.651378001, 0.461704714, 0, 130, 130, 130, 0, 0, 1, -360, 360],\\n [10, 11, 0.122663712, 0.040555144, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [11, 12, 0.233597628, 0.077241951, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [12, 13, 0.915922324, 0.720633708, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [13, 14, 0.337917936, 0.444796338, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [14, 15, 0.368739846, 0.328184702, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [15, 16, 0.465635443, 0.340039282, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [16, 17, 0.804239697, 1.073775422, 0, 65, 65, 65, 0, 0, 1, -360, 360],\\n [17, 18, 0.456713311, 0.358133116, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [2, 19, 0.102323747, 0.097644308, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [19, 20, 0.938508419, 0.845668336, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [20, 21, 0.255497406, 0.298485858, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [21, 22, 0.442300637, 0.584805173, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [3, 23, 0.28151509, 0.192356167, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [23, 24, 0.560284909, 0.442425422, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [24, 25, 0.559037059, 0.43743402, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [6, 26, 0.126656834, 0.064513875, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [26, 27, 0.177319567, 0.090281989, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [27, 28, 0.660736881, 0.582559042, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [28, 29, 0.501760717, 0.437122057, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [29, 30, 0.316642084, 0.161284687, 0, 32, 32, 32, 0, 0, 1, -360, 360],\\n [30, 31, 0.607952801, 0.600840053, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [31, 32, 0.193728802, 0.225798562, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [32, 33, 0.212758523, 0.330805188, 0, 16, 16, 16, 0, 0, 1, -360, 360],\\n [7, 20, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\\n [8, 14, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\\n [11, 21, 1.2479, 1.2479, 0, 16, 16, 16, 0, 0, 0, -360, 360],\\n [17, 32, 0.3120, 0.3120, 0, 65, 65, 65, 0, 0, 0, -360, 360],\\n [24, 28, 0.3120, 0.3120, 0, 16, 16, 16, 0, 0, 0, -360, 360]\\n ])\\n\\n ##----- OPF Data -----##\\n ## area data\\n # area refbus\\n ppc[\\\"areas\\\"] = array([\\n [1, 8],\\n [2, 23],\\n [3, 26],\\n ])\\n\\n ## generator cost data\\n # 1 startup shutdown n x1 y1 ... xn yn\\n # 2 startup shutdown n c(n-1) ... c0\\n ppc[\\\"gencost\\\"] = array([\\n [0, 0, 0, 3, 0.0, 20, 0]\\n ])\\n\\n return ppc\"\n },\n {\n \"identifier\": \"micro_grid\",\n \"path\": \"TestCasesMicrogrids/test_cases/cases_unit_commitment.py\",\n \"snippet\": \"AC_PD = array([323.0284, 308.2374, 318.1886, 307.9809, 331.2170, 368.6539, 702.0040, 577.7045, 1180.4547, 1227.6240,\\n 1282.9344, 1311.9738, 1268.9502, 1321.7436, 1323.9218, 1327.1464, 1386.9117, 1321.6387, 1132.0476,\\n 1109.2701, 882.5698, 832.4520, 349.3568, 299.9920])\\nDC_PD = array([287.7698, 287.7698, 287.7698, 287.7698, 299.9920, 349.3582, 774.4047, 664.0625, 1132.6996, 1107.7366,\\n 1069.6837, 1068.9819, 1027.3295, 1096.3820, 1109.4778, 1110.7039, 1160.1270, 1078.7839, 852.2514,\\n 791.5814, 575.4085, 551.1441, 349.3568, 299.992])\\nDG = {\\\"PMIN\\\": 0,\\n \\\"PMAX\\\": 5,\\n \\\"QMIN\\\": -5,\\n \\\"QMAX\\\": 5,\\n \\\"COST_A\\\": 0.01,\\n \\\"COST_B\\\": 0.5}\\nUG = {\\\"PMIN\\\": -5,\\n \\\"PMAX\\\": 5,\\n \\\"QMIN\\\": -5,\\n \\\"QMAX\\\": 5,\\n \\\"COST\\\": Price_UG, } # The cost should be a profile\\nESS = {\\\"PDC_MAX\\\": 5,\\n \\\"PCH_MAX\\\": 5,\\n \\\"EFF_DC\\\": 0.95,\\n \\\"EFF_CH\\\": 0.95,\\n \\\"E0\\\": 10,\\n \\\"EMIN\\\": 5,\\n \\\"EMAX\\\": 20, }\\nBIC = {\\\"PMAX\\\": 5,\\n \\\"QMAX\\\": 5,\\n \\\"SMAX\\\": 5,\\n \\\"EFF_AC2DC\\\": 0.9,\\n \\\"EFF_DC2AC\\\": 0.9, }\\nMG = {\\\"PMAX\\\": 5,\\n \\\"PMIN\\\": -5,\\n \\\"QMAX\\\": 5,\\n \\\"QMIN\\\": -5\\n }\\nPD = {\\\"AC\\\": AC_PD / max(AC_PD),\\n \\\"AC_MAX\\\": 5,\\n \\\"DC\\\": DC_PD / max(DC_PD),\\n \\\"DC_MAX\\\": 5}\\nQD = {\\\"AC\\\": AC_PD / max(AC_PD),\\n \\\"AC_MAX\\\": 5, }\\nPV = {\\\"PMAX\\\": 0,\\n \\\"COST\\\": 0}\"\n },\n {\n \"identifier\": \"PBIC_AC2DC\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PBIC_AC2DC = 4\"\n },\n {\n \"identifier\": \"PG\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PG = 0\"\n },\n {\n \"identifier\": \"PESS_DC\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PESS_DC = 8\"\n },\n {\n \"identifier\": \"PBIC_DC2AC\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PBIC_DC2AC = 5\"\n },\n {\n \"identifier\": \"PUG\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PUG = 2\"\n },\n {\n \"identifier\": \"PESS_CH\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PESS_CH = 7\"\n },\n {\n \"identifier\": \"PMESS\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"PMESS = 10 # Reactive power unit commitment of\"\n },\n {\n \"identifier\": \"EESS\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"EESS = 9\"\n },\n {\n \"identifier\": \"NX_MG\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"NX_MG = 11\"\n },\n {\n \"identifier\": \"QBIC\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"QBIC = 6\"\n },\n {\n \"identifier\": \"QUG\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"QUG = 3\"\n },\n {\n \"identifier\": \"QG\",\n \"path\": \"TestCaseDistributionSystems/data_format/idx_MG.py\",\n \"snippet\": \"QG = 1\"\n },\n {\n \"identifier\": \"DataBaseManagement\",\n \"path\": \"TestCaseDistributionSystems/database_management.py\",\n \"snippet\": \"class DataBaseManagement():\\n\\n def __init__(self, host=\\\"localhost\\\", user=\\\"root\\\", password=\\\"Ntu@1003\\\", db=\\\"mess\\\"):\\n \\\"\\\"\\\"\\n Initialized the database connection string\\n :param host: host ip\\n :param user: user name\\n :param password: password\\n :param db: database name\\n :return\\n \\\"\\\"\\\"\\n self.db = pymysql.connect(host=host, user=user, password=password, db=db)\\n\\n def create_table(self, table_name, nl=32, nb=33, ng=6, nmg=3, nmes=3):\\n \\\"\\\"\\\"\\n Creat table name\\n :param table_name:\\n :param nb:\\n :param nb:\\n :param ng:\\n :return: no return value\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql = \\\"DROP TABLE IF EXISTS \\\"\\n cursor.execute(sql + table_name)\\n if table_name == \\\"distribution_networks\\\":\\n sql_start = \\\"\\\"\\\"CREATE TABLE distribution_networks (\\\"\\\"\\\"\\n sql = 'SCENARIO INT,\\\\n TIME INT NOT NULL,\\\\n '\\n for i in range(nl):\\n sql += \\\"PIJ{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n for i in range(nl):\\n sql += \\\"QIJ{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n for i in range(nl):\\n sql += \\\"IIJ{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n for i in range(nb):\\n sql += \\\"V{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n for i in range(ng):\\n sql += \\\"PG{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n for i in range(ng - 1):\\n sql += \\\"QG{0} DECIMAL(8,6),\\\\n \\\".format(i)\\n sql += \\\"QG{0} DECIMAL(8,6)\\\\n \\\".format(ng - 1)\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n elif table_name == \\\"micro_grids\\\":\\n sql_start = \\\"\\\"\\\"CREATE TABLE micro_grids (\\\"\\\"\\\"\\n sql = 'SCENARIO INT,\\\\n MG INT,\\\\n TIME INT,\\\\n '\\n sql += 'PG DECIMAL(7,4),\\\\n QG DECIMAL(7,4),\\\\n PUG DECIMAL(7,4),\\\\n QUG DECIMAL(7,4),\\\\n '\\n sql += 'PBIC_AC2DC DECIMAL(7,4),\\\\n PBIC_DC2AC DECIMAL(7,4),\\\\n QBIC DECIMAL(7,4),\\\\n PESS_CH DECIMAL(7,4),\\\\n '\\n sql += 'PESS_DC DECIMAL(7,4),\\\\n EESS DECIMAL(7,4),\\\\n PMESS DECIMAL(7,4)'\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n elif table_name == \\\"mobile_energy_storage_systems\\\":\\n sql_start = \\\"\\\"\\\"CREATE TABLE mobile_energy_storage_systems (\\\"\\\"\\\"\\n sql = 'SCENARIO INT,\\\\n MESS INT,\\\\n TIME INT,\\\\n'\\n for i in range(nmg):\\n sql += \\\"PDC_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg):\\n sql += \\\"PCH_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"EESS DECIMAL(7,4)\\\\n \\\"\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n elif table_name == \\\"first_stage_solutions\\\": # First-stage solution table\\n sql_start = \\\"\\\"\\\"CREATE TABLE first_stage_solutions (\\\"\\\"\\\"\\n sql = 'TIME INT,\\\\n'\\n for i in range(ng):\\n sql += \\\"PG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"RG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg - 1):\\n sql += \\\"PG_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"RG_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"IESS{0} INT,\\\\n \\\".format(i)\\n sql += \\\"PESS_DC{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"PESS_CH{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"RESS{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"ESS{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"PG_MG{0} DECIMAL(7,4),\\\\n \\\".format(nmg - 1)\\n sql += \\\"RG_MG{0} DECIMAL(7,4),\\\\n \\\".format(nmg - 1)\\n sql += \\\"IESS{0} INT,\\\\n \\\".format(nmg - 1)\\n sql += \\\"PESS_DC{0} DECIMAL(7,4),\\\\n \\\".format(nmg - 1)\\n sql += \\\"PESS_CH{0} DECIMAL(7,4),\\\\n \\\".format(nmg - 1)\\n sql += \\\"RESS{0} DECIMAL(7,4),\\\\n \\\".format(nmg - 1)\\n sql += \\\"ESS{0} DECIMAL(7,4)\\\\n \\\".format(nmg - 1)\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n elif table_name == \\\"fisrt_stage_mess\\\": # First-stage solution table\\n sql_start = \\\"\\\"\\\"CREATE TABLE fisrt_stage_mess (\\\"\\\"\\\"\\n sql = 'MESS INT,\\\\n TIME INT,\\\\n'\\n for i in range(nmg):\\n sql += \\\"IDC_MG{0} INT,\\\\n \\\".format(i)\\n for i in range(nmg):\\n sql += \\\"PDC_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg):\\n sql += \\\"PCH_MG{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg):\\n sql += \\\"RMESS{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"MESS_F_STOP INT,\\\\n \\\"\\n sql += \\\"MESS_T_STOP INT\\\\n \\\"\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n else:\\n sql_start = \\\"\\\"\\\"CREATE TABLE scenarios (\\\"\\\"\\\"\\n sql = 'SCENARIO INT,\\\\n WEIGHT DECIMAL(7,4),\\\\n TIME INT,\\\\n'\\n for i in range(nb):\\n sql += \\\"PD{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg):\\n sql += \\\"PD_AC{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n for i in range(nmg - 1):\\n sql += \\\"PD_DC{0} DECIMAL(7,4),\\\\n \\\".format(i)\\n sql += \\\"PD_DC{0} DECIMAL(7,4)\\\\n\\\".format(nmg - 1)\\n sql_end = \\\"\\\"\\\")\\\"\\\"\\\"\\n\\n cursor.execute(sql_start + sql + sql_end)\\n cursor.close()\\n\\n def insert_data_ds(self, table_name, nl=32, nb=33, ng=6, scenario=0, time=0, pij=0, qij=0, lij=0, vi=0, pg=0, qg=0):\\n \\\"\\\"\\\"\\n Insert data into table_name\\n :param table_name:\\n :param nl:\\n :param nb:\\n :param ng:\\n :param pij:\\n :param qij:\\n :param lij:\\n :param vi:\\n :param pg:\\n :param qg:\\n :return:\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"SCENARIO,TIME,\\\"\\n value = \\\"{0},{1},\\\".format(scenario, time)\\n for i in range(nl):\\n sql += \\\"PIJ{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pij[i])\\n for i in range(nl):\\n sql += \\\"QIJ{0},\\\".format(i)\\n value += \\\"{0},\\\".format(qij[i])\\n for i in range(nl):\\n sql += \\\"IIJ{0},\\\".format(i)\\n value += \\\"{0},\\\".format(lij[i])\\n for i in range(nb):\\n sql += \\\"V{0},\\\".format(i)\\n value += \\\"{0},\\\".format(vi[i])\\n for i in range(ng):\\n sql += \\\"PG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pg[i])\\n for i in range(ng - 1):\\n sql += \\\"QG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(qg[i])\\n sql += \\\"QG{0}\\\".format(ng - 1)\\n value += \\\"{0}\\\".format(qg[ng - 1])\\n\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def insert_data_mg(self, table_name, scenario=0, time=0, mg=0, pg=0, qg=0, pug=0, qug=0, pbic_ac2dc=0, pbic_dc2ac=0,\\n qbic=0, pess_ch=0, pess_dc=0, eess=0, pmess=0):\\n \\\"\\\"\\\"\\n insert microgrid data\\n :param table_name:\\n :param scenario:\\n :param time:\\n :param mg:\\n :param pg:\\n :param qg:\\n :param pug:\\n :param qug:\\n :param pbic_ac2dc:\\n :param pbic_dc2ac:\\n :param qbic:\\n :param pess_ch:\\n :param pess_dc:\\n :param eess:\\n :param pmess:\\n :return:\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"SCENARIO,MG,TIME,\\\"\\n value = \\\"{0},{1},{2},\\\".format(scenario, mg, time)\\n sql += \\\"PG,QG,PUG,QUG,PBIC_AC2DC,PBIC_DC2AC,QBIC,PESS_CH,PESS_DC,EESS,PMESS\\\"\\n value += \\\"{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10}\\\".format(pg, qg, pug, qug, pbic_ac2dc, pbic_dc2ac, qbic,\\n pess_ch, pess_dc, eess, pmess)\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def insert_data_first_stage_mess(self, table_name, time=0, mess=0, imess=[0, 0, 0], pmess_ch=[0, 0, 0],\\n pmess_dc=[0, 0, 0], rmess=[0, 0, 0], mess_f_stop=0, mess_t_stop=0, nmg=3):\\n \\\"\\\"\\\"\\n insert mobile energy storage systems data in the first-stage\\n :param table_name:\\n :param scenario:\\n :param time:\\n :param mess:\\n :param pess_ch:\\n :param pess_dc:\\n :param eess:\\n :param nmg:\\n :return:\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"MESS,TIME,\\\"\\n value = \\\"{0},{1},\\\".format(mess, time)\\n for i in range(nmg):\\n sql += \\\"IDC_MG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(imess[i])\\n for i in range(nmg):\\n sql += \\\"PDC_MG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pmess_dc[i])\\n for i in range(nmg):\\n sql += \\\"PCH_MG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pmess_ch[i])\\n for i in range(nmg):\\n sql += \\\"RMESS{0},\\\".format(i)\\n value += \\\"{0},\\\".format(rmess[i])\\n sql += \\\"MESS_F_STOP,MESS_T_STOP\\\"\\n value += \\\"{0},{1}\\\".format(mess_f_stop, mess_t_stop)\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def insert_data_mess(self, table_name, scenario=0, time=0, mess=0, pmess_ch=[0, 0, 0], pmess_dc=[0, 0, 0],\\n emess=0, nmg=3):\\n \\\"\\\"\\\"\\n insert mobile energy storage systems data\\n :param table_name:\\n :param scenario:\\n :param time:\\n :param mess:\\n :param pess_ch:\\n :param pess_dc:\\n :param eess:\\n :param nmg:\\n :return:\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"SCENARIO,MESS,TIME,\\\"\\n value = \\\"{0},{1},{2},\\\".format(scenario, mess, time)\\n for i in range(nmg):\\n sql += \\\"PDC_MG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pmess_dc[i])\\n for i in range(nmg):\\n sql += \\\"PCH_MG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pmess_ch[i])\\n sql += \\\"EESS\\\"\\n value += \\\"{0}\\\".format(emess)\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def insert_data_first_stage(self, table_name, time=0, ng=2, nmg=2, pg=[0, 0], rg=[0, 0], pg_mg=[0, 0],\\n rg_mg=[0, 0], iess=[0, 0], pess_dc=[0, 0], pess_ch=[0, 0], ress=[0, 0], ess=[0, 0]):\\n \\\"\\\"\\\"\\n insert scenario data\\n :param table_name:\\n :param scenario:\\n :param weight:\\n :param time:\\n :param nb:\\n :param nmg:\\n :param pd:\\n :param pd_ac:\\n :param pd_dc:\\n :return:\\n \\\"\\\"\\\"\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"TIME,\\\"\\n value = \\\"{0},\\\".format(time)\\n for i in range(ng):\\n sql += \\\"PG{0},\\\".format(i)\\n sql += \\\"RG{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pg[i])\\n value += \\\"{0},\\\".format(rg[i])\\n if nmg > 1:\\n for i in range(nmg - 1):\\n sql += \\\"PG_MG{0},\\\".format(i)\\n sql += \\\"RG_MG{0},\\\".format(i)\\n sql += \\\"IESS{0},\\\".format(i)\\n sql += \\\"PESS_DC{0},\\\".format(i)\\n sql += \\\"PESS_CH{0},\\\".format(i)\\n sql += \\\"RESS{0},\\\".format(i)\\n sql += \\\"ESS{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pg_mg[i])\\n value += \\\"{0},\\\".format(rg_mg[i])\\n value += \\\"{0},\\\".format(iess[i])\\n value += \\\"{0},\\\".format(pess_dc[i])\\n value += \\\"{0},\\\".format(pess_ch[i])\\n value += \\\"{0},\\\".format(ress[i])\\n value += \\\"{0},\\\".format(ess[i])\\n sql += \\\"PG_MG{0},\\\".format(nmg - 1)\\n sql += \\\"RG_MG{0},\\\".format(nmg - 1)\\n sql += \\\"IESS{0},\\\".format(nmg - 1)\\n sql += \\\"PESS_DC{0},\\\".format(nmg - 1)\\n sql += \\\"PESS_CH{0},\\\".format(nmg - 1)\\n sql += \\\"RESS{0},\\\".format(nmg - 1)\\n sql += \\\"ESS{0}\\\".format(nmg - 1)\\n value += \\\"{0},\\\".format(pg_mg[nmg - 1])\\n value += \\\"{0},\\\".format(rg_mg[nmg - 1])\\n value += \\\"{0},\\\".format(iess[nmg - 1])\\n value += \\\"{0},\\\".format(pess_dc[nmg - 1])\\n value += \\\"{0},\\\".format(pess_ch[nmg - 1])\\n value += \\\"{0},\\\".format(ress[nmg - 1])\\n value += \\\"{0}\\\".format(ess[nmg - 1])\\n else:\\n sql += \\\"PG_MG{0},\\\".format(nmg - 1)\\n sql += \\\"RG_MG{0},\\\".format(nmg - 1)\\n sql += \\\"IESS{0},\\\".format(nmg - 1)\\n sql += \\\"PESS_DC{0},\\\".format(nmg - 1)\\n sql += \\\"PESS_CH{0},\\\".format(nmg - 1)\\n sql += \\\"RESS{0},\\\".format(nmg - 1)\\n sql += \\\"ESS{0}\\\".format(nmg - 1)\\n value += \\\"{0},\\\".format(pg_mg)\\n value += \\\"{0},\\\".format(rg_mg)\\n value += \\\"{0},\\\".format(iess)\\n value += \\\"{0},\\\".format(pess_dc)\\n value += \\\"{0},\\\".format(pess_ch)\\n value += \\\"{0},\\\".format(ress)\\n value += \\\"{0}\\\".format(ess)\\n\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def insert_data_scenario(self, table_name, scenario=0, weight=0, time=0, nb=1, nmg=2, pd=[0, 0], pd_ac=[0, 0],\\n pd_dc=[0, 0]):\\n cursor = self.db.cursor()\\n sql_start = \\\"INSERT INTO \\\" + table_name + \\\" (\\\"\\n sql = \\\"SCENARIO,WEIGHT,TIME,\\\"\\n value = \\\"{0},{1},{2},\\\".format(scenario, weight, time)\\n for i in range(nb):\\n sql += \\\"PD{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pd[i])\\n for i in range(nmg):\\n sql += \\\"PD_AC{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pd_ac[i])\\n for i in range(nmg - 1):\\n sql += \\\"PD_DC{0},\\\".format(i)\\n value += \\\"{0},\\\".format(pd_dc[i])\\n if nmg > 1:\\n sql += \\\"PD_DC{0}\\\".format(nmg - 1)\\n value += \\\"{0}\\\".format(pd_dc[nmg - 1])\\n\\n sql += \\\") VALUES (\\\" + value + \\\")\\\"\\n cursor.execute(sql_start + sql)\\n self.db.commit()\\n cursor.close()\\n\\n def inquery_data_scenario(self, table_name, scenario=0, time=0):\\n cursor = self.db.cursor()\\n # sql = \\\"SELECT * FROM \\\" + table_name + \\\" ;\\\"\\n sql = \\\"SELECT * FROM \\\" + table_name + \\\" WHERE SCENARIO={0} AND TIME={1};\\\".format(scenario, time)\\n cursor.execute(sql)\\n data = cursor.fetchall()\\n n_data = len(data[0])\\n\\n temp = []\\n for i in range(n_data): temp.append(float(data[0][i]))\\n\\n cursor.close()\\n return temp\"\n },\n {\n \"identifier\": \"ScenarioReduction\",\n \"path\": \"StochasticOptimization/scenario_reduction.py\",\n \"snippet\": \"class ScenarioReduction():\\n def __init__(self):\\n self.name = \\\"Scenario reduction\\\"\\n\\n def run(self, scenario, weight, n_reduced, power):\\n \\\"\\\"\\\"\\n\\n :param scenario: A fan scenario tree, when more stage are considered, some merge operation can be implemented\\n :param weight: Weight of each scenario\\n :param n_reduced: Number of scenarios needs to be reduced\\n :param power: The power in the distance calculation\\n :return:\\n \\\"\\\"\\\"\\n n_scenario = scenario.shape[0] # number of original scenarios\\n c = zeros((n_scenario, n_scenario))\\n # Calculate the c matrix\\n for i in range(n_scenario):\\n for j in range(n_scenario):\\n c[i, j] = linalg.norm((scenario[i, :] - scenario[j, :]), 2)\\n c[i, j] = max([1, linalg.norm(scenario[i, :], power - 1), linalg.norm(scenario[j, :], power - 1)]) * \\\\\\n c[i, j]\\n\\n J = arange(n_scenario) # The original index range\\n J_reduced = array([])\\n # Implement the iteration\\n for n in range(n_reduced): # find the minimal distance\\n print(\\\"The reduction is in process {0}\\\".format(n))\\n c_n = inf * ones(n_scenario)\\n c_n[J] = 0\\n for u in J:\\n # Delete the i-th distance\\n J_temp = delete(J, where(J == u))\\n for k in J_temp:\\n c_k_j = delete(c[int(k)], J_temp)\\n c_n[int(u)] += weight[int(k)] * min(c_k_j)\\n u_i = argmin(c_n)\\n J_reduced = append(J_reduced, u_i)\\n J = delete(J, where(J == u_i))\\n # Optimal redistribution\\n p_s = weight.copy()\\n p_s[J_reduced.astype(int)] = 0\\n\\n for i in J_reduced:\\n c_temp = c[int(i), :]\\n c_temp[J_reduced.astype(int)] = inf\\n index = argmin(c_temp)\\n p_s[index] += weight[int(i)]\\n\\n scenario_reduced = scenario[J.astype(int), :]\\n weight_reduced = p_s[J.astype(int)]\\n\\n return scenario_reduced, weight_reduced\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from TestCaseDistributionSystems.test_cases import case33\nfrom TestCasesMicrogrids.test_cases.cases_unit_commitment import micro_grid\nfrom TestCasesTransportationSystems.test_cases import case3, TIME, LOCATION\nfrom numpy import zeros, shape, ones, diag, concatenate, eye\nfrom scipy.sparse import csr_matrix as sparse\nfrom scipy.sparse import hstack, vstack, lil_matrix\nfrom numpy import flatnonzero as find\nfrom numpy import array, tile, arange, random\nfrom pypower.idx_brch import F_BUS, T_BUS, BR_R, BR_X, RATE_A\nfrom pypower.idx_bus import PD, VMAX, VMIN, QD\nfrom pypower.idx_gen import GEN_BUS, PMAX, PMIN, QMAX, QMIN\nfrom pypower.ext2int import ext2int\nfrom Solvers.mixed_integer_quadratic_constrained_cplex import mixed_integer_quadratic_constrained_programming as miqcp\nfrom Solvers.mixed_integer_solvers_cplex import mixed_integer_linear_programming as milp\nfrom copy import deepcopy\nfrom TestCaseDistributionSystems.data_format.idx_MG import PBIC_AC2DC, PG, PESS_DC, PBIC_DC2AC, PUG, PESS_CH, \\\n PMESS, EESS, NX_MG, QBIC, QUG, QG\nfrom TestCaseDistributionSystems.database_management import DataBaseManagement\nfrom StochasticOptimization.scenario_reduction import ScenarioReduction"},"token_num":{"kind":"number","value":15480,"string":"15,480"},"cropped_code":{"kind":"string","value":" for i in range(ng):\n c[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 5] * baseMVA\n q[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA\n c0 += gencost[i, 6]\n # Coupling constraints between the distribution systems and micro_grids\n Ax2y = lil_matrix((2 * nmg * T, nv_ds)) # connection matrix with the microgrids\n for i in range(T):\n for j in range(nmg):\n # Active power\n Ax2y[i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + j] = 1000 * baseMVA\n # Reactive power\n Ax2y[nmg * T + i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + nmg + j] = 1000 * baseMVA\n\n # II) Formulate the problem for microgrids\n model_microgrids = {}\n for i in range(nmg):\n model_microgrids[i] = self.problem_formulation_microgrid(mg=mgs[i], mess=mess)\n # II.A) Combine the distribution system operation problem and microgrid systems\n if Aeq is not None:\n neq_ds = Aeq.shape[0]\n else:\n neq_ds = 0\n\n nVariables = int(nv_ds)\n neq = int(neq_ds)\n\n nv_index = zeros(nmg + 1).astype(int)\n neq_index = zeros(nmg + 1).astype(int)\n nv_index[0] = nv_ds\n neq_index[0] = int(neq_ds)\n for i in range(nmg):\n nv_index[i + 1] = nv_index[i] + len(model_microgrids[i][\"c\"])\n neq_index[i + 1] = neq_index[i] + model_microgrids[i][\"Aeq\"].shape[0]\n nVariables += len(model_microgrids[i][\"c\"])\n neq += int(model_microgrids[i][\"Aeq\"].shape[0])\n\n Aeq_full = lil_matrix((int(neq_index[-1]), int(nv_index[-1])))\n Aeq_full[0:neq_ds, 0:nv_ds] = Aeq\n for i in range(nmg):\n lb = concatenate([lb, model_microgrids[i][\"lb\"]])\n ub = concatenate([ub, model_microgrids[i][\"ub\"]])\n c = concatenate([c, model_microgrids[i][\"c\"]])\n q = concatenate([q, model_microgrids[i][\"q\"]])\n vtypes += model_microgrids[i][\"vtypes\"]\n beq = concatenate([beq, model_microgrids[i][\"beq\"]])\n Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_microgrids[i][\"Aeq\"]\n\n # Add coupling constraints, between the microgrids and distribution networks\n Ay2x = lil_matrix((2 * nmg * T, nv_index[-1] - nv_index[0]))\n for i in range(T):\n for j in range(nmg):\n Ay2x[i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + PUG] = -1\n Ay2x[nmg * T + i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + QUG] = -1\n\n Aeq_temp = hstack([Ax2y, Ay2x])\n beq_temp = zeros(2 * nmg * T)\n\n Aeq_full = vstack([Aeq_full, Aeq_temp])\n beq = concatenate([beq, beq_temp])\n # III) Formulate the optimization problem for tess in the second stage optimization\n model_tess = {}\n for i in range(nmes):\n model_tess[i] = self.problem_formulation_tess_second_stage(mess=mess[i])\n # III.1) Merge the models of mirogrids and distribution\n # Formulate the index\n nv_index_ev = zeros(1 + nmes).astype(int)\n neq_index_temp = zeros(1 + nmes).astype(int)\n nv_index_ev[0] = int(Aeq_full.shape[1])\n neq_index_temp[0] = int(Aeq_full.shape[0])\n for i in range(nmes):\n nv_index_ev[i + 1] = nv_index_ev[i] + len(model_tess[i][\"c\"])\n neq_index_temp[i + 1] = neq_index_temp[i] + model_tess[i][\"Aeq\"].shape[0]\n\n Aeq = lil_matrix((int(neq_index_temp[-1]), int(nv_index_ev[-1])))\n Aeq[0:int(neq_index_temp[0]), 0:int(nv_index_ev[0])] = Aeq_full\n for i in range(nmes):\n lb = concatenate([lb, model_tess[i][\"lb\"]])\n ub = concatenate([ub, model_tess[i][\"ub\"]])\n c = concatenate([c, model_tess[i][\"c\"]])\n q = concatenate([q, model_tess[i][\"q\"]])\n vtypes += model_tess[i][\"vtypes\"]\n beq = concatenate([beq, model_tess[i][\"beq\"]])\n Aeq[neq_index_temp[i]:neq_index_temp[i + 1], nv_index_ev[i]:nv_index_ev[i + 1]] = model_tess[i][\"Aeq\"]\n # III.2) Coupling constraints between the microgrids and mobile energy storage systems\n # Additional equal constraints, nmg*T\n Aeq_temp = lil_matrix((nmg * T, nv_index_ev[-1]))\n beq_temp = zeros(nmg * T)\n for i in range(nmg):\n for t in range(T):\n Aeq_temp[i * T + t, nv_index[i] + t * NX_MG + PMESS] = 1 # TESSs injections to the MGs\n for j in range(nmes):\n Aeq_temp[i * T + t, nv_index_ev[j] + t * self.nb_tra_ele + i] = -1 # Discharging\n Aeq_temp[i * T + t,\n nv_index_ev[j] + self.nb_tra_ele * T + t * self.nb_tra_ele + i] = 1 # Sort by order\n Aeq = vstack([Aeq, Aeq_temp])\n beq = concatenate((beq, beq_temp))\n nv_second_stage = nv_index_ev[-1]\n nv_first_stage = self.nv_first_stage\n self.nv_second_stage = nv_second_stage\n Qc = dict()\n # 4) Pij**2+Qij**2<=Vi*Iij\n for t in range(T):\n for i in range(nl):\n Qc[(T * nl + T * nmg) * index + t * nl + i] = [\n [int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl)],\n [int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl)],\n [1, 1, -1 / 2, -1 / 2]]\n Rc = zeros(nl * T)\n # 5) (Pbic_ac2dc+Pbic_dc2ac)**2+Qbic**2<=Sbic**2\n Rc_temp = zeros(nmg * T)\n for i in range(nmg):\n for t in range(T):\n Qc[(T * nl + T * nmg) * index + T * nl + T * i + t] = [\n [int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_AC2DC),\n"},"all_code":{"kind":"string","value":"\"\"\"\nStochastic optimal power flow with multiple microgrids and mobile energy storage systems\n@author: Zhao Tianyang\n@e-mail: zhaoty@ntu.edu.sg\n@date: 10 Jan 2019\nMajor updates:\n1) Update code style using PEP 8 -- Style Guide for Python Code\n2) Store data in database\n3) Scenario generation and reduction\n4) Automatic results analysis\n\nNomenclature:\nnV: number of variables\nmg: microgrid\nds: distribution systems\nme: mobile energy storage systems\nch: charging\ndc: discharging\nele: electricity\ntra: traffic\ni,j,k: index\nt: time index\nT: time periods\ntns:traffic networks\npns:power networks\n\n\"\"\"\n\n\n\n\n\n\n\n\n\nclass StochasticDynamicOptimalPowerFlowTess():\n def __init__(self):\n self.name = \"Stochastic optimal power flow with tess\"\n\n def main(self, power_networks, micro_grids, profile, mess, traffic_networks, ns=100):\n \"\"\"\n Main entrance for network reconfiguration problems\n :param case: electric network information\n :param profile: load profile within the distribution networks\n :param micrgrids: dictionary for microgrids\n :param tess: dictionary for tess\n :return: network reconfiguration, distribution network status, and microgrid status\n \"\"\"\n T = len(profile) # Time spans\n self.T = T\n nmg = len(micro_grids) # Number of microgrids\n self.nmg = nmg\n nmes = len(mess) # Number of mobile energy storage systems\n self.nmes = nmes\n nb_tra = traffic_networks[\"bus\"].shape[0] # Number of buses in the transportation networks\n self.nb_tra = nb_tra\n assert nb_tra == nmg, \"The microgrids within the transportation networks are not synchronized!\"\n\n # 1) Formulate the first stage optimization problem\n model_first_stage = self.first_stage_problem_formualtion(pns=power_networks, mgs=micro_grids, mess=mess,\n tns=traffic_networks)\n # (sol_first_stage, obj, success) = milp(model_first_stage[\"c\"], Aeq=model_first_stage[\"Aeq\"],\n # beq=model_first_stage[\"beq\"],\n # A=model_first_stage[\"A\"], b=model_first_stage[\"b\"],\n # vtypes=model_first_stage[\"vtypes\"],\n # xmax=model_first_stage[\"ub\"], xmin=model_first_stage[\"lb\"])\n # sol_first_stage = self.first_stage_solution_validation(sol=sol_first_stage)\n\n # 2) Formulate the second stage optimization problem\n # Formulate the second stage scenarios\n (ds_second_stage, mgs_second_stage, weight) = self.scenario_generation_reduction(profile=profile,\n micro_grids=micro_grids, ns=ns,\n pns=power_networks,\n ns_reduced=round(0.98 * ns))\n ns -= round(0.98 * ns)\n model_second_stage = {}\n for i in range(ns):\n model_second_stage[i] = self.second_stage_problem_formualtion(pns=power_networks, mgs=mgs_second_stage[i],\n mess=mess, tns=traffic_networks,\n profile=ds_second_stage[i, :], index=i,\n weight=weight[i])\n # 3) Merge the first-stage problem and second stage problem\n lb = model_first_stage[\"lb\"]\n ub = model_first_stage[\"ub\"]\n vtypes = model_first_stage[\"vtypes\"]\n c = model_first_stage[\"c\"]\n Qc = dict()\n if model_first_stage[\"Aeq\"] is not None:\n neq = model_first_stage[\"Aeq\"].shape[0]\n else:\n neq = 0\n\n if model_first_stage[\"A\"] is not None:\n nineq = model_first_stage[\"A\"].shape[0]\n else:\n nineq = 0\n nv_first_stage = self.nv_first_stage\n nv_second_stage = self.nv_second_stage\n q = zeros(nv_first_stage)\n\n nv_index = zeros(ns + 1).astype(int)\n neq_index = zeros(ns + 1).astype(int)\n nineq_index = zeros(ns + 1).astype(int)\n neq_index[0] = neq\n nineq_index[0] = nineq\n nv_index[0] = nv_first_stage\n beq = model_first_stage[\"beq\"]\n for i in range(ns):\n if model_second_stage[i][\"Aeq\"] is not None:\n neq_index[i + 1] = neq_index[i] + model_second_stage[i][\"Aeq\"].shape[0]\n else:\n neq_index[i + 1] = neq_index[i]\n if model_second_stage[i][\"Ts\"] is not None:\n nineq_index[i + 1] = nineq_index[i] + model_second_stage[i][\"Ts\"].shape[0]\n else:\n nineq_index[i + 1] = nineq_index[i]\n nv_index[i + 1] = nv_index[i] + nv_second_stage\n\n c = concatenate([c, model_second_stage[i][\"c\"]])\n q = concatenate([q, model_second_stage[i][\"q\"]])\n lb = concatenate([lb, model_second_stage[i][\"lb\"]])\n ub = concatenate([ub, model_second_stage[i][\"ub\"]])\n vtypes += model_second_stage[i][\"vtypes\"]\n beq = concatenate([beq, model_second_stage[i][\"beq\"]])\n\n Aeq_full = lil_matrix((neq_index[-1], nv_index[-1]))\n Aeq_full[0:neq_index[0], 0:nv_index[0]] = model_first_stage[\"Aeq\"]\n rc = zeros(0)\n for i in range(ns):\n Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_second_stage[i][\"Aeq\"]\n Qc.update(model_second_stage[i][\"Qc\"])\n rc = concatenate([rc, model_second_stage[i][\"rc\"]])\n\n A_full = lil_matrix((nineq_index[-1], nv_index[-1]))\n b = model_first_stage[\"b\"]\n A_full[0:int(nineq_index[0]), 0:int(nv_index[0])] = model_first_stage[\"A\"]\n for i in range(ns):\n A_full[nineq_index[i]:nineq_index[i + 1], 0:nv_index[0]] = model_second_stage[i][\"Ts\"]\n A_full[nineq_index[i]:nineq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_second_stage[i][\"Ws\"]\n b = concatenate([b, model_second_stage[i][\"hs\"]])\n\n # 3) Obtain the results for first-stage and second stage optimization problems\n # 3.1) Obtain the integrated solution\n (sol, obj, success) = miqcp(c, q, Aeq=Aeq_full, beq=beq, A=A_full, b=b, Qc=Qc, rc=rc, xmin=lb, xmax=ub,\n vtypes=vtypes)\n # 3.2) decouple the solution into multiple subsystems\n sol_first_stage = sol[0:nv_second_stage]\n sol_second_stage = {}\n for i in range(ns):\n sol_second_stage[i] = sol[int(nv_index[i]):int(nv_index[i + 1])]\n # 4) Verify the first-stage and second stage optization problem\n # 4.1) First-stage solution\n sol_first_stage = self.first_stage_solution_validation(sol=sol_first_stage)\n # 4.2) Second-stage solution\n sol_second_stage_checked = {}\n\n db_management = DataBaseManagement()\n db_management.create_table(table_name=\"distribution_networks\", nl=self.nl, nb=self.nb, ng=self.ng)\n db_management.create_table(table_name=\"micro_grids\", nmg=self.nmg)\n db_management.create_table(table_name=\"mobile_energy_storage_systems\", nmg=self.nmg)\n db_management.create_table(table_name=\"first_stage_solutions\", nmg=self.nmg, ng=self.ng, nmes=self.nmes)\n db_management.create_table(table_name=\"fisrt_stage_mess\", nmg=self.nmg)\n\n for t in range(T):\n db_management.insert_data_first_stage(table_name=\"first_stage_solutions\", time=t, ng=self.ng, nmg=self.nmg,\n pg=sol_first_stage[\"pg\"][:, t].tolist(),\n rg=sol_first_stage[\"rg\"][:, t].tolist(),\n pg_mg=sol_first_stage[\"pg_mg\"][:, t].tolist(),\n rg_mg=sol_first_stage[\"rg_mg\"][:, t].tolist(),\n pess_ch=sol_first_stage[\"pess_ch\"][:, t].tolist(),\n pess_dc=sol_first_stage[\"pess_dc\"][:, t].tolist(),\n ress=sol_first_stage[\"ress\"][:, t].tolist(),\n ess=sol_first_stage[\"eess\"][:, t].tolist(),\n iess=sol_first_stage[\"iess\"][:, t].tolist())\n for i in range(nmes):\n for t in range(T):\n db_management.insert_data_first_stage_mess(table_name=\"fisrt_stage_mess\", nmg=self.nmg, time=t, mess=i,\n imess=sol_first_stage[\"MESS\"][i][\"idc\"][:, t].tolist(),\n rmess=sol_first_stage[\"MESS\"][i][\"rmess\"][:, t].tolist(),\n pmess_ch=\n sol_first_stage[\"MESS\"][i][\"pmess_ch\"][:, t].tolist(),\n pmess_dc=\n sol_first_stage[\"MESS\"][i][\"pmess_dc\"][:, t].tolist(),\n mess_f_stop=sol_first_stage[\"MESS\"][i][\"VRP\"][t + 1][0],\n mess_t_stop=sol_first_stage[\"MESS\"][i][\"VRP\"][t + 1][1])\n\n for i in range(ns):\n sol_second_stage_checked[i] = self.second_stage_solution_validation(sol_second_stage[i])\n for i in range(ns):\n for t in range(T):\n db_management.insert_data_ds(table_name=\"distribution_networks\", nl=self.nl, nb=self.nb, ng=self.ng,\n scenario=i, time=t,\n pij=sol_second_stage_checked[i][\"DS\"][\"pij\"][:, t].tolist(),\n qij=sol_second_stage_checked[i][\"DS\"][\"qij\"][:, t].tolist(),\n lij=sol_second_stage_checked[i][\"DS\"][\"lij\"][:, t].tolist(),\n vi=sol_second_stage_checked[i][\"DS\"][\"vi\"][:, t].tolist(),\n pg=sol_second_stage_checked[i][\"DS\"][\"pg\"][:, t].tolist(),\n qg=sol_second_stage_checked[i][\"DS\"][\"qg\"][:, t].tolist(), )\n for i in range(ns):\n for j in range(nmg):\n for t in range(T):\n db_management.insert_data_mg(table_name=\"micro_grids\", scenario=i, time=t, mg=j,\n pg=sol_second_stage_checked[i][\"MG\"][\"pg\"][j, t],\n qg=sol_second_stage_checked[i][\"MG\"][\"qg\"][j, t],\n pug=sol_second_stage_checked[i][\"MG\"][\"pug\"][j, t],\n qug=sol_second_stage_checked[i][\"MG\"][\"qug\"][j, t],\n pbic_ac2dc=sol_second_stage_checked[i][\"MG\"][\"pbic_ac2dc\"][j, t],\n pbic_dc2ac=sol_second_stage_checked[i][\"MG\"][\"pbic_dc2ac\"][j, t],\n qbic=sol_second_stage_checked[i][\"MG\"][\"qbic\"][j, t],\n pess_ch=sol_second_stage_checked[i][\"MG\"][\"pess_ch\"][j, t],\n pess_dc=sol_second_stage_checked[i][\"MG\"][\"pess_dc\"][j, t],\n eess=sol_second_stage_checked[i][\"MG\"][\"eess\"][j, t],\n pmess=sol_second_stage_checked[i][\"MG\"][\"pmess\"][j, t])\n for i in range(ns):\n for j in range(nmes):\n for t in range(T):\n db_management.insert_data_mess(table_name=\"mobile_energy_storage_systems\", scenario=i, time=t,\n mess=j, nmg=self.nmg,\n pmess_dc=\n sol_second_stage_checked[i][\"MESS\"][j][\"pmess_dc\"][:, t].tolist(),\n pmess_ch=\n sol_second_stage_checked[i][\"MESS\"][j][\"pmess_ch\"][:, t].tolist(),\n emess=sol_second_stage_checked[i][\"MESS\"][j][\"emess\"][0, t])\n # 4.3) Cross validation of the first-stage and second-stage decision variables\n tess_check = {}\n for i in range(ns):\n tess_temp = {}\n for j in range(nmes):\n tess_temp[j] = sol_second_stage_checked[i][\"MESS\"][j][\"pmess_dc\"] - \\\n sol_second_stage_checked[i][\"MESS\"][j][\"pmess_ch\"] - \\\n sol_first_stage[\"MESS\"][j][\"pmess_dc\"] + \\\n sol_first_stage[\"MESS\"][j][\"pmess_ch\"] - \\\n sol_first_stage[\"MESS\"][j][\"rmess\"]\n tess_temp[j + nmes] = sol_second_stage_checked[i][\"MESS\"][j][\"pmess_ch\"] - \\\n sol_second_stage_checked[i][\"MESS\"][j][\"pmess_dc\"] - \\\n sol_first_stage[\"MESS\"][j][\"pmess_ch\"] + \\\n sol_first_stage[\"MESS\"][j][\"pmess_dc\"] - \\\n sol_first_stage[\"MESS\"][j][\"rmess\"]\n tess_check[i] = tess_temp\n\n # return sol_distribution_network, sol_microgrids, sol_tess\n return sol_first_stage, sol_second_stage_checked\n\n def first_stage_problem_formualtion(self, pns, mgs, mess, tns):\n \"\"\"\n Problem formulation for the first stage optimization,\n Decision variables include, DGs within power networks, DGs within MGs, EESs within MGs and TESSs\n :param power_networks: Parameters for the power networks\n :param micro_grids: Parameters for the microgrids\n :param tess: Parameters for the mobile energy storage systems\n :param traffic_networks: Parameters for the transportation networks\n :return: Formulated first-stage problem\n \"\"\"\n T = self.T # Time slots\n nmg = self.nmg # Number of mgs\n nmes = self.nmes # Number of tess\n\n mpc = ext2int(pns)\n baseMVA, bus, gen, branch, gencost = mpc[\"baseMVA\"], mpc[\"bus\"], mpc[\"gen\"], mpc[\"branch\"], mpc[\"gencost\"]\n ng = shape(mpc['gen'])[0] ## number of dispatchable injections\n nb = shape(mpc[\"bus\"])[0]\n self.nb = nb\n self.ng = ng\n # Obtain the initial status, start-up and shut down of generators\n Ig0 = gen[:, -1].astype(int)\n MIN_DOWN = gen[:, -2].astype(int)\n MIN_UP = gen[:, -3].astype(int)\n\n alpha_l = zeros(ng)\n beta_l = zeros(ng)\n Ig_l = zeros(ng)\n pg_l = zeros(ng) # Boundary for DGs within distribution networks\n rg_l = zeros(ng)\n\n alpha_u = ones(ng)\n beta_u = ones(ng)\n Ig_u = ones(ng)\n pg_u = gen[:, PMAX] / baseMVA\n rg_u = gen[:, PMAX] / baseMVA\n c_alpha = gencost[:, 0]\n c_beta = gencost[:, 1]\n c_ig = gencost[:, 6]\n cg = gencost[:, 5] * baseMVA\n cr = zeros(ng)\n\n pg_mg_l = zeros(nmg) # Boundary for DGs within MGs\n rg_mg_l = zeros(nmg)\n pg_mg_u = zeros(nmg)\n rg_mg_u = zeros(nmg)\n cg_mg = zeros(nmg)\n cr_mg = zeros(nmg)\n for i in range(nmg):\n pg_mg_l[i] = mgs[i][\"DG\"][\"PMIN\"]\n pg_mg_u[i] = mgs[i][\"DG\"][\"PMAX\"]\n rg_mg_u[i] = mgs[i][\"DG\"][\"PMAX\"]\n cg_mg[i] = mgs[i][\"DG\"][\"COST_B\"]\n\n pes_ch_l = zeros(nmg) # Lower boundary for ESSs within MGs\n pes_dc_l = zeros(nmg)\n ees_l = zeros(nmg)\n res_l = zeros(nmg)\n ies_l = zeros(nmg)\n\n pes_ch_u = zeros(nmg) # Upper boundary for ESSs within MGs\n pes_dc_u = zeros(nmg)\n ees_u = zeros(nmg)\n res_u = zeros(nmg)\n ies_u = ones(nmg)\n\n ces_ch = zeros(nmg) # Cost boundary for ESSs within MGs\n ces_dc = zeros(nmg)\n ces_r = zeros(nmg)\n ces = zeros(nmg)\n ces_i = zeros(nmg)\n\n for i in range(nmg):\n pes_ch_u[i] = mgs[i][\"ESS\"][\"PCH_MAX\"]\n pes_dc_u[i] = mgs[i][\"ESS\"][\"PDC_MAX\"] + mgs[i][\"ESS\"][\"PCH_MAX\"]\n res_u[i] = mgs[i][\"ESS\"][\"PCH_MAX\"]\n ees_l[i] = mgs[i][\"ESS\"][\"EMIN\"]\n ees_u[i] = mgs[i][\"ESS\"][\"EMAX\"]\n\n _nv_first_stage = ng * 5 + nmg * 2 + nmg * 5\n nv_first_stage = _nv_first_stage * T\n # Formulate the boundaries\n lb = concatenate(\n [tile(concatenate(\n [alpha_l, beta_l, Ig_l, pg_l, rg_l, pg_mg_l, rg_mg_l, pes_ch_l, pes_dc_l, res_l, ees_l, ies_l]), T)])\n ub = concatenate(\n [tile(concatenate(\n [alpha_u, beta_u, Ig_u, pg_u, rg_u, pg_mg_u, rg_mg_u, pes_ch_u, pes_dc_u, res_u, ees_u, ies_u]), T)])\n # Objective value\n c = concatenate(\n [tile(concatenate([c_alpha, c_beta, c_ig, cg, cr, cg_mg, cr_mg, ces_ch, ces_dc, ces, ces_r, ces_i]), T)])\n # Variable types\n vtypes = ([\"b\"] * ng * 3 + [\"c\"] * (ng * 2 + nmg * 2 + nmg * 4) + [\"b\"] * nmg) * T\n ## Constraint sets\n # 1) Pg+Rg<=PguIg\n A = lil_matrix((ng * T, nv_first_stage))\n b = zeros(ng * T)\n for t in range(T):\n for j in range(ng):\n A[t * ng + j, t * _nv_first_stage + ng * 3 + j] = 1\n A[t * ng + j, t * _nv_first_stage + ng * 4 + j] = 1\n A[t * ng + j, t * _nv_first_stage + ng * 2 + j] = -pg_u[j]\n # 2) Pg-Rg>=IgPgl\n A_temp = lil_matrix((ng * T, nv_first_stage))\n b_temp = zeros(ng * T)\n for t in range(T):\n for j in range(ng):\n A_temp[t * ng + j, t * _nv_first_stage + ng * 3 + j] = -1\n A_temp[t * ng + j, t * _nv_first_stage + ng * 4 + j] = 1\n A_temp[t * ng + j, t * _nv_first_stage + j] = pg_l[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 3) Start-up and shut-down constraints of DGs\n UP_LIMIT = zeros(ng).astype(int)\n DOWN_LIMIT = zeros(ng).astype(int)\n for i in range(ng):\n UP_LIMIT[i] = T - MIN_UP[i]\n DOWN_LIMIT[i] = T - MIN_DOWN[i]\n # 3.1) Up limit\n A_temp = lil_matrix((sum(UP_LIMIT), nv_first_stage))\n b_temp = zeros(sum(UP_LIMIT))\n for i in range(ng):\n for t in range(MIN_UP[i], T):\n for k in range(t - MIN_UP[i], t):\n A_temp[sum(UP_LIMIT[0:i]) + t - MIN_UP[i], k * _nv_first_stage + i] = 1\n A_temp[sum(UP_LIMIT[0:i]) + t - MIN_UP[i], t * _nv_first_stage + ng * 2 + i] = -1\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # # 3.2) Down limit\n A_temp = lil_matrix((sum(DOWN_LIMIT), nv_first_stage))\n b_temp = ones(sum(DOWN_LIMIT))\n for i in range(ng):\n for t in range(MIN_DOWN[i], T):\n for k in range(t - MIN_DOWN[i], t):\n A_temp[sum(DOWN_LIMIT[0:i]) + t - MIN_DOWN[i], k * _nv_first_stage + ng + i] = 1\n A_temp[sum(DOWN_LIMIT[0:i]) + t - MIN_DOWN[i], t * _nv_first_stage + ng * 2 + i] = 1\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 4) Status transformation of each unit\n Aeq = lil_matrix((T * ng, nv_first_stage))\n beq = zeros(T * ng)\n for i in range(ng):\n for t in range(T):\n Aeq[i * T + t, t * _nv_first_stage + i] = 1\n Aeq[i * T + t, t * _nv_first_stage + ng + i] = -1\n Aeq[i * T + t, t * _nv_first_stage + ng * 2 + i] = -1\n if t != 0:\n Aeq[i * T + t, (t - 1) * _nv_first_stage + ng * 2 + i] = 1\n else:\n beq[i * T + t] = -Ig0[i]\n\n # 3) Pg_mg+Rg_mg<=Pg_mg_u\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + j] = 1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg + j] = 1\n b_temp[t * nmg + j] = pg_mg_u[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 4) Pg_mg-Rg_mg<=Pg_mg_l\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + j] = -1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg + j] = 1\n b_temp[t * nmg + j] = pg_mg_l[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 5) Pess_dc-Pess_ch+Ress<=Pess_dc_max\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = -1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 2 + j] = 1\n b_temp[t * nmg + j] = pes_dc_u[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 6) Pess_ch-Pess_dc+Ress<=Pess_ch_max\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, ng * 5 + nmg * 2 + t] = 1\n A_temp[t * nmg + j, ng * 5 + nmg * 2 + nmg + t] = -1\n A_temp[t * nmg + j, ng * 5 + nmg * 2 + nmg * 2 + t] = 1\n b_temp[t * nmg + j] = pes_ch_u[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 7) Energy storage balance equation\n Aeq_temp = lil_matrix((T * nmg, nv_first_stage))\n beq_temp = zeros(T * nmg)\n for t in range(T):\n for j in range(nmg):\n Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 3 + j] = 1\n Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = -mgs[j][\"ESS\"][\"EFF_CH\"]\n Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1 / mgs[j][\"ESS\"][\"EFF_DC\"]\n if t == 0:\n beq_temp[i * nmg + j] = mgs[j][\"ESS\"][\"E0\"]\n else:\n Aeq_temp[i * nmg + j, (i - 1) * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 3 + j] = -1\n Aeq = vstack([Aeq, Aeq_temp])\n beq = concatenate([beq, beq_temp])\n\n # 8) Pess_ch<=I*Pess_ch_max\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = 1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 4 + j] = -pes_ch_u[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n # 9) Pess_dc<=(1-I)*Pess_dc_max\n A_temp = lil_matrix((nmg * T, nv_first_stage))\n b_temp = zeros(nmg * T)\n for t in range(T):\n for j in range(nmg):\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1\n A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 4 + j] = pes_dc_u[j]\n b_temp[t * nmg + j] = pes_dc_u[j]\n A = vstack([A, A_temp])\n b = concatenate([b, b_temp])\n\n # 2) Transportation energy storage systems problem\n model_mess = {}\n for i in range(nmes):\n model_mess[i] = self.problem_formulation_tess(mess=mess[i], tns=tns)\n # 3) Merge the DGs, ESSs and TESSs\n neq = Aeq.shape[0]\n nineq = A.shape[0]\n\n nV_index = zeros(nmes + 1).astype(int)\n neq_index = zeros(nmes + 1).astype(int)\n nineq_index = zeros(nmes + 1).astype(int)\n nV_index[0] = nv_first_stage\n neq_index[0] = neq\n nineq_index[0] = nineq\n\n for i in range(nmes):\n nV_index[i + 1] = nV_index[i] + len(model_mess[i][\"c\"])\n neq_index[i + 1] = neq_index[i] + model_mess[i][\"Aeq\"].shape[0]\n nineq_index[i + 1] = nineq_index[i] + model_mess[i][\"A\"].shape[0]\n neq += model_mess[i][\"Aeq\"].shape[0]\n nineq += model_mess[i][\"A\"].shape[0]\n # Merge the objective function, boundaries, types and rhs\n c = concatenate([c, model_mess[i][\"c\"]])\n lb = concatenate([lb, model_mess[i][\"lb\"]])\n ub = concatenate([ub, model_mess[i][\"ub\"]])\n vtypes += model_mess[i][\"vtypes\"]\n beq = concatenate([beq, model_mess[i][\"beq\"]])\n b = concatenate([b, model_mess[i][\"b\"]])\n\n A_full = lil_matrix((nineq_index[-1], nV_index[-1]))\n Aeq_full = lil_matrix((neq_index[-1], nV_index[-1]))\n\n if Aeq is not None: Aeq_full[0:int(neq_index[0]), 0:int(nV_index[0])] = Aeq\n if A is not None: A_full[0:int(nineq_index[0]), 0:int(nV_index[0])] = A\n\n for i in range(nmes):\n Aeq_full[neq_index[i]:neq_index[i + 1], nV_index[i]:nV_index[i + 1]] = model_mess[i][\"Aeq\"]\n A_full[nineq_index[i]:nineq_index[i + 1], nV_index[i]:nV_index[i + 1]] = model_mess[i][\"A\"]\n\n self.nv_first_stage = nV_index[-1] # The number of first stage decision variables\n self._nv_first_stage = _nv_first_stage\n model_first_stage = {\"c\": c,\n \"lb\": lb,\n \"ub\": ub,\n \"vtypes\": vtypes,\n \"A\": A_full,\n \"b\": b,\n \"Aeq\": Aeq_full,\n \"beq\": beq, }\n\n return model_first_stage\n\n def first_stage_solution_validation(self, sol):\n \"\"\"\n Validation of the first-stage solution\n :param sol: The first stage solution\n :return: the first stage solution\n \"\"\"\n T = self.T\n ng = self.ng\n nmg = self.nmg\n nmes = self.nmes\n # Set-points of DGs within DSs, MGs and ESSs\n _nv_first_stage = self._nv_first_stage\n alpha = zeros((ng, T))\n beta = zeros((ng, T))\n Ig = zeros((ng, T))\n Pg = zeros((ng, T))\n Rg = zeros((ng, T))\n Pg_mg = zeros((nmg, T))\n Rg_mg = zeros((nmg, T))\n Pess_dc = zeros((nmg, T))\n Pess_ch = zeros((nmg, T))\n Ress = zeros((nmg, T))\n Eess = zeros((nmg, T))\n Iess = zeros((nmg, T))\n for i in range(T):\n alpha[:, i] = sol[_nv_first_stage * i:_nv_first_stage * i + ng]\n beta[:, i] = sol[_nv_first_stage * i + ng:_nv_first_stage * i + ng * 2]\n Ig[:, i] = sol[_nv_first_stage * i + ng * 2:_nv_first_stage * i + ng * 3]\n Pg[:, i] = sol[_nv_first_stage * i + ng * 3:_nv_first_stage * i + ng * 4]\n Rg[:, i] = sol[_nv_first_stage * i + ng * 4:_nv_first_stage * i + ng * 5]\n Pg_mg[:, i] = sol[_nv_first_stage * i + ng * 5:_nv_first_stage * i + ng * 5 + nmg]\n Rg_mg[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg:_nv_first_stage * i + ng * 5 + nmg * 2]\n Pess_ch[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 2:_nv_first_stage * i + ng * 5 + nmg * 3]\n Pess_dc[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 3:_nv_first_stage * i + ng * 5 + nmg * 4]\n Ress[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 4:_nv_first_stage * i + ng * 5 + nmg * 5]\n Eess[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 5:_nv_first_stage * i + ng * 5 + nmg * 6]\n Iess[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 6:_nv_first_stage * i + ng * 5 + nmg * 7]\n\n # Set-points and scheduling of mobile energy storage systems\n nv_tra = self.nv_tra\n nl_traffic = self.nl_tra\n n_stops = self.n_stops\n nb_tra_ele = self.nb_tra_ele\n sol_ev = {}\n for i in range(nmes):\n ev_temp = {}\n ev_temp[\"VRP\"] = []\n for t in range(nl_traffic):\n if sol[_nv_first_stage * T + nv_tra * i + t] > 0: # obtain the solution for vrp\n if self.connection_matrix[t, TIME] > 0:\n for j in range(int(self.connection_matrix[t, TIME])):\n ev_temp[\"VRP\"].append(((self.connection_matrix[t, F_BUS] - 1) % nmg,\n (self.connection_matrix[t, T_BUS] - 1) % nmg))\n else:\n ev_temp[\"VRP\"].append(((self.connection_matrix[t, F_BUS] - 1) % nmg,\n (self.connection_matrix[t, T_BUS] - 1) % nmg))\n\n ev_temp[\"idc\"] = zeros((nb_tra_ele, T))\n ev_temp[\"pmess_dc\"] = zeros((nb_tra_ele, T))\n ev_temp[\"pmess_ch\"] = zeros((nb_tra_ele, T))\n ev_temp[\"rmess\"] = zeros((nb_tra_ele, T))\n for t in range(T):\n for k in range(nb_tra_ele):\n ev_temp[\"idc\"][k, t] = sol[_nv_first_stage * T + nv_tra * i + nl_traffic + nb_tra_ele * t + k]\n ev_temp[\"pmess_dc\"][k, t] = \\\n sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops + nb_tra_ele * t + k]\n ev_temp[\"pmess_ch\"][k, t] = \\\n sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops * 2 + nb_tra_ele * t + k]\n ev_temp[\"rmess\"][k, t] = \\\n sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops * 3 + nb_tra_ele * t + k]\n sol_ev[i] = ev_temp\n\n sol_first_stage = {\"alpha\": alpha,\n \"beta\": beta,\n \"ig\": Ig,\n \"rg\": Rg,\n \"pg\": Pg,\n \"pg_mg\": Pg_mg,\n \"rg_mg\": Rg_mg,\n \"pess_ch\": Pess_ch,\n \"pess_dc\": Pess_dc,\n \"ress\": Ress,\n \"eess\": Eess,\n \"iess\": Iess,\n \"MESS\": sol_ev,\n }\n return sol_first_stage\n\n def second_stage_problem_formualtion(self, pns, mgs, mess, tns, profile, index=0, weight=1):\n \"\"\"\n Second-stage problem formulation, the decision variables includes DGs within power networks, DGs within MGs, EESs within MGs and TESSs and other systems' information\n :param power_networks:\n :param micro_grids:\n :param tess:\n :param traffic_networks:\n :return: The second stage problems as list, including coupling constraints, and other constraint set\n \"\"\"\n # I) Formulate the problem for distribution systems operator\n T = self.T\n mpc = ext2int(pns)\n baseMVA, bus, gen, branch, gencost = mpc[\"baseMVA\"], mpc[\"bus\"], mpc[\"gen\"], mpc[\"branch\"], mpc[\"gencost\"]\n\n nb = shape(mpc['bus'])[0] ## number of buses\n nl = shape(mpc['branch'])[0] ## number of branches\n ng = shape(mpc['gen'])[0] ## number of dispatchable injections\n nmg = self.nmg\n nmes = self.nmes\n\n self.nl = nl\n self.nb = nb\n self.ng = ng\n\n m = zeros(nmg) ## list of integration index\n pmg_l = zeros(nmg) ## list of lower boundary\n pmg_u = zeros(nmg) ## list of upper boundary\n qmg_l = zeros(nmg) ## list of lower boundary\n qmg_u = zeros(nmg) ## list of upper boundary\n for i in range(nmg):\n m[i] = mgs[i][\"BUS\"]\n pmg_l[i] = mgs[i][\"UG\"][\"PMIN\"] / 1000 / baseMVA\n pmg_u[i] = mgs[i][\"UG\"][\"PMAX\"] / 1000 / baseMVA\n qmg_l[i] = mgs[i][\"UG\"][\"QMIN\"] / 1000 / baseMVA\n qmg_u[i] = mgs[i][\"UG\"][\"QMAX\"] / 1000 / baseMVA\n\n f = branch[:, F_BUS] ## list of \"from\" buses\n t = branch[:, T_BUS] ## list of \"to\" buses\n i = range(nl) ## double set of row indices\n self.f = f ## record from bus for each branch\n\n # Connection matrix\n Cf = sparse((ones(nl), (i, f)), (nl, nb))\n Ct = sparse((ones(nl), (i, t)), (nl, nb))\n Cg = sparse((ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))\n Cmg = sparse((ones(nmg), (m, range(nmg))), (nb, nmg))\n\n Branch_R = branch[:, BR_R]\n Branch_X = branch[:, BR_X]\n Cf = Cf.T\n Ct = Ct.T\n # Obtain the boundary information\n slmax = branch[:, RATE_A] / baseMVA\n\n pij_l = -slmax\n qij_l = -slmax\n lij_l = zeros(nl)\n vm_l = bus[:, VMIN] ** 2\n pg_l = gen[:, PMIN] / baseMVA\n qg_l = gen[:, QMIN] / baseMVA\n\n pij_u = slmax\n qij_u = slmax\n lij_u = slmax\n vm_u = bus[:, VMAX] ** 2\n pg_u = 2 * gen[:, PMAX] / baseMVA\n qg_u = 2 * gen[:, QMAX] / baseMVA\n\n _nv_second_stage = int(3 * nl + nb + 2 * ng + 2 * nmg)\n self._nv_second_stage = _nv_second_stage # Number of decision variable within each time slot\n\n lb = concatenate([tile(concatenate([pij_l, qij_l, lij_l, vm_l, pg_l, qg_l, pmg_l, qmg_l]), T)])\n ub = concatenate([tile(concatenate([pij_u, qij_u, lij_u, vm_u, pg_u, qg_u, pmg_u, qmg_u]), T)])\n vtypes = [\"c\"] * _nv_second_stage * T\n nv_ds = _nv_second_stage * T # Number of total decision variables\n\n # Add system level constraints\n # 1) Active power balance\n Aeq_p = lil_matrix((nb * T, nv_ds))\n beq_p = zeros(nb * T)\n for i in range(T):\n Aeq_p[i * nb:(i + 1) * nb, i * _nv_second_stage: (i + 1) * _nv_second_stage] = \\\n hstack([Ct - Cf, zeros((nb, nl)),\n -diag(Ct * Branch_R) * Ct,\n zeros((nb, nb)), Cg,\n zeros((nb, ng)), -Cmg,\n zeros((nb, nmg))])\n\n beq_p[i * nb:(i + 1) * nb] = profile[i * nb:(i + 1) * nb] / baseMVA\n\n # 2) Reactive power balance\n Aeq_q = lil_matrix((nb * T, nv_ds))\n beq_q = zeros(nb * T)\n for i in range(T):\n Aeq_q[i * nb:(i + 1) * nb, i * _nv_second_stage: (i + 1) * _nv_second_stage] = \\\n hstack([zeros((nb, nl)), Ct - Cf,\n -diag(Ct * Branch_X) * Ct,\n zeros((nb, nb)),\n zeros((nb, ng)), Cg,\n zeros((nb, nmg)), -Cmg])\n for j in range(nb):\n if bus[j, PD] > 0:\n beq_q[i * nb:(i + 1) * nb] = profile[i * nb + j] / bus[j, PD] * bus[j, QD] / baseMVA\n # 3) KVL equation\n Aeq_kvl = lil_matrix((nl * T, nv_ds))\n beq_kvl = zeros(nl * T)\n\n for i in range(T):\n Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage: i * _nv_second_stage + nl] = -2 * diag(Branch_R)\n Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + nl: i * _nv_second_stage + 2 * nl] = -2 * diag(Branch_X)\n Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + 2 * nl: i * _nv_second_stage + 3 * nl] = diag(\n Branch_R ** 2) + diag(Branch_X ** 2)\n Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + 3 * nl:i * _nv_second_stage + 3 * nl + nb] = (\n Cf.T - Ct.T).toarray()\n\n Aeq = vstack([Aeq_p, Aeq_q, Aeq_kvl])\n beq = concatenate([beq_p, beq_q, beq_kvl])\n\n c = zeros(nv_ds)\n q = zeros(nv_ds)\n c0 = 0\n for t in range(T):\n for i in range(ng):\n c[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 5] * baseMVA\n q[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA\n c0 += gencost[i, 6]\n # Coupling constraints between the distribution systems and micro_grids\n Ax2y = lil_matrix((2 * nmg * T, nv_ds)) # connection matrix with the microgrids\n for i in range(T):\n for j in range(nmg):\n # Active power\n Ax2y[i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + j] = 1000 * baseMVA\n # Reactive power\n Ax2y[nmg * T + i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + nmg + j] = 1000 * baseMVA\n\n # II) Formulate the problem for microgrids\n model_microgrids = {}\n for i in range(nmg):\n model_microgrids[i] = self.problem_formulation_microgrid(mg=mgs[i], mess=mess)\n # II.A) Combine the distribution system operation problem and microgrid systems\n if Aeq is not None:\n neq_ds = Aeq.shape[0]\n else:\n neq_ds = 0\n\n nVariables = int(nv_ds)\n neq = int(neq_ds)\n\n nv_index = zeros(nmg + 1).astype(int)\n neq_index = zeros(nmg + 1).astype(int)\n nv_index[0] = nv_ds\n neq_index[0] = int(neq_ds)\n for i in range(nmg):\n nv_index[i + 1] = nv_index[i] + len(model_microgrids[i][\"c\"])\n neq_index[i + 1] = neq_index[i] + model_microgrids[i][\"Aeq\"].shape[0]\n nVariables += len(model_microgrids[i][\"c\"])\n neq += int(model_microgrids[i][\"Aeq\"].shape[0])\n\n Aeq_full = lil_matrix((int(neq_index[-1]), int(nv_index[-1])))\n Aeq_full[0:neq_ds, 0:nv_ds] = Aeq\n for i in range(nmg):\n lb = concatenate([lb, model_microgrids[i][\"lb\"]])\n ub = concatenate([ub, model_microgrids[i][\"ub\"]])\n c = concatenate([c, model_microgrids[i][\"c\"]])\n q = concatenate([q, model_microgrids[i][\"q\"]])\n vtypes += model_microgrids[i][\"vtypes\"]\n beq = concatenate([beq, model_microgrids[i][\"beq\"]])\n Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_microgrids[i][\"Aeq\"]\n\n # Add coupling constraints, between the microgrids and distribution networks\n Ay2x = lil_matrix((2 * nmg * T, nv_index[-1] - nv_index[0]))\n for i in range(T):\n for j in range(nmg):\n Ay2x[i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + PUG] = -1\n Ay2x[nmg * T + i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + QUG] = -1\n\n Aeq_temp = hstack([Ax2y, Ay2x])\n beq_temp = zeros(2 * nmg * T)\n\n Aeq_full = vstack([Aeq_full, Aeq_temp])\n beq = concatenate([beq, beq_temp])\n # III) Formulate the optimization problem for tess in the second stage optimization\n model_tess = {}\n for i in range(nmes):\n model_tess[i] = self.problem_formulation_tess_second_stage(mess=mess[i])\n # III.1) Merge the models of mirogrids and distribution\n # Formulate the index\n nv_index_ev = zeros(1 + nmes).astype(int)\n neq_index_temp = zeros(1 + nmes).astype(int)\n nv_index_ev[0] = int(Aeq_full.shape[1])\n neq_index_temp[0] = int(Aeq_full.shape[0])\n for i in range(nmes):\n nv_index_ev[i + 1] = nv_index_ev[i] + len(model_tess[i][\"c\"])\n neq_index_temp[i + 1] = neq_index_temp[i] + model_tess[i][\"Aeq\"].shape[0]\n\n Aeq = lil_matrix((int(neq_index_temp[-1]), int(nv_index_ev[-1])))\n Aeq[0:int(neq_index_temp[0]), 0:int(nv_index_ev[0])] = Aeq_full\n for i in range(nmes):\n lb = concatenate([lb, model_tess[i][\"lb\"]])\n ub = concatenate([ub, model_tess[i][\"ub\"]])\n c = concatenate([c, model_tess[i][\"c\"]])\n q = concatenate([q, model_tess[i][\"q\"]])\n vtypes += model_tess[i][\"vtypes\"]\n beq = concatenate([beq, model_tess[i][\"beq\"]])\n Aeq[neq_index_temp[i]:neq_index_temp[i + 1], nv_index_ev[i]:nv_index_ev[i + 1]] = model_tess[i][\"Aeq\"]\n # III.2) Coupling constraints between the microgrids and mobile energy storage systems\n # Additional equal constraints, nmg*T\n Aeq_temp = lil_matrix((nmg * T, nv_index_ev[-1]))\n beq_temp = zeros(nmg * T)\n for i in range(nmg):\n for t in range(T):\n Aeq_temp[i * T + t, nv_index[i] + t * NX_MG + PMESS] = 1 # TESSs injections to the MGs\n for j in range(nmes):\n Aeq_temp[i * T + t, nv_index_ev[j] + t * self.nb_tra_ele + i] = -1 # Discharging\n Aeq_temp[i * T + t,\n nv_index_ev[j] + self.nb_tra_ele * T + t * self.nb_tra_ele + i] = 1 # Sort by order\n Aeq = vstack([Aeq, Aeq_temp])\n beq = concatenate((beq, beq_temp))\n nv_second_stage = nv_index_ev[-1]\n nv_first_stage = self.nv_first_stage\n self.nv_second_stage = nv_second_stage\n Qc = dict()\n # 4) Pij**2+Qij**2<=Vi*Iij\n for t in range(T):\n for i in range(nl):\n Qc[(T * nl + T * nmg) * index + t * nl + i] = [\n [int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl)],\n [int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl),\n int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl)],\n [1, 1, -1 / 2, -1 / 2]]\n Rc = zeros(nl * T)\n # 5) (Pbic_ac2dc+Pbic_dc2ac)**2+Qbic**2<=Sbic**2\n Rc_temp = zeros(nmg * T)\n for i in range(nmg):\n for t in range(T):\n Qc[(T * nl + T * nmg) * index + T * nl + T * i + t] = [\n [int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_AC2DC),"},"next_line":{"kind":"string","value":" int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_DC2AC),"},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-11-27 15:57:53+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5388,"cells":{"repo_name":{"kind":"string","value":"girgle/DouZero_For_New_HLDDZ"},"file_path":{"kind":"string","value":"GOOD.py"},"context":{"kind":"list like","value":[{"identifier":"GameHelper","path":"GameHelper.py","snippet":"class GameHelper:\n def __init__(self):\n self.ScreenZoomRate = None\n self.counter = QTime()\n self.Pics = {}\n self.PicsCV = {}\n st = time.time()\n self.Handle = win32gui.FindWindow(\"UnityWndClass\", None)\n self.Interrupt = False\n self.RealRate = (1440, 810)\n self.GetZoomRate()\n for file in os.listdir(\"./pics\"):\n info = file.split(\".\")\n if info[1] == \"png\":\n tmpImage = Image.open(\"./pics/\" + file)\n imgCv = cv2.imread(\"./pics/\" + file)\n self.Pics.update({info[0]: tmpImage})\n self.PicsCV.update({info[0]: imgCv})\n\n def sleep(self, ms):\n self.counter.restart()\n while self.counter.elapsed() < ms:\n QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 50)\n\n def Screenshot(self, region=None): # -> (im, (left, top))\n try_count = 3\n success = False\n while try_count > 0 and not success:\n try:\n try_count -= 1\n self.Handle = win32gui.FindWindow(\"UnityWndClass\", None)\n hwnd = self.Handle\n left, top, right, bot = win32gui.GetWindowRect(hwnd)\n width = right - left\n height = bot - top\n self.RealRate = (width, height)\n width = int(width)\n height = int(height)\n hwndDC = win32gui.GetWindowDC(hwnd)\n mfcDC = win32ui.CreateDCFromHandle(hwndDC)\n saveDC = mfcDC.CreateCompatibleDC()\n saveBitMap = win32ui.CreateBitmap()\n saveBitMap.CreateCompatibleBitmap(mfcDC, width, height)\n saveDC.SelectObject(saveBitMap)\n result = windll.user32.PrintWindow(hwnd, saveDC.GetSafeHdc(), 3)\n bmpinfo = saveBitMap.GetInfo()\n bmpstr = saveBitMap.GetBitmapBits(True)\n im = Image.frombuffer(\n \"RGB\",\n (bmpinfo['bmWidth'], bmpinfo['bmHeight']),\n bmpstr, 'raw', 'BGRX', 0, 1)\n win32gui.DeleteObject(saveBitMap.GetHandle())\n saveDC.DeleteDC()\n mfcDC.DeleteDC()\n win32gui.ReleaseDC(hwnd, hwndDC)\n im = im.resize((1440, 810))\n if region is not None:\n im = im.crop((region[0], region[1], region[0] + region[2], region[1] + region[3]))\n if result:\n success = True\n return im, (left, top)\n except Exception as e:\n print(\"截图时出现错误:\", repr(e))\n self.sleep(200)\n return None, (0, 0)\n\n def GetZoomRate(self):\n self.ScreenZoomRate = ctypes.windll.shcore.GetScaleFactorForDevice(0) / 100\n\n def LocateOnScreen(self, templateName, region, confidence=0.8, img=None):\n if img is not None:\n image = img\n else:\n image, _ = self.Screenshot()\n imgcv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\n return LocateOnImage(imgcv, self.PicsCV[templateName], region=region, confidence=confidence)\n\n def ClickOnImage(self, templateName, region=None, confidence=0.8, img=None):\n if img is not None:\n image = img\n else:\n image, _ = self.Screenshot()\n imgcv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\n result = LocateOnImage(imgcv, self.PicsCV[templateName], region=region, confidence=confidence)\n\n if result is not None:\n self.LeftClick(result)\n print(result)\n\n def LeftClick(self, pos):\n x, y = pos\n x = (x / 1440) * self.RealRate[0]\n y = (y / 810) * self.RealRate[1]\n x = int(x)\n y = int(y)\n self.Handle = win32gui.FindWindow(\"UnityWndClass\", None)\n left, top, _, _ = win32gui.GetWindowRect(self.Handle)\n x, y = int(left + x), int(top + y)\n\n pyautogui.mouseDown(x, y, button='left')\n time.sleep(0.1)\n pyautogui.mouseUp(x, y, button='left')\n time.sleep(0.1)\n pyautogui.moveTo(int(left + 1000), int(top + 550))\n\n '''win32gui.SetActiveWindow(self.Handle)\n lParam = win32api.MAKELONG(x, y)\n\n win32gui.PostMessage(self.Handle, WM_ACTIVATE, WA_ACTIVE, lParam)\n win32gui.PostMessage(self.Handle, WM_ACTIVATE, WA_ACTIVE, lParam)\n win32gui.PostMessage(self.Handle, WM_MOUSEMOVE, MK_LBUTTON, lParam)\n win32gui.PostMessage(self.Handle, WM_LBUTTONDOWN, MK_LBUTTON, lParam)\n win32gui.PostMessage(self.Handle, WM_LBUTTONUP, MK_LBUTTON, lParam)'''\n\n def LeftClick2(self, pos):\n x, y = pos\n x = (x / 1440) * self.RealRate[0]\n y = (y / 810) * self.RealRate[1]\n x = int(x)\n y = int(y)\n self.Handle = win32gui.FindWindow(\"UnityWndClass\", None)\n left, top, _, _ = win32gui.GetWindowRect(self.Handle)\n x, y = int(left + x), int(top + y)\n\n pyautogui.mouseDown(x, y, button='left')\n time.sleep(0.1)\n pyautogui.mouseUp(x, y, button='left')"},{"identifier":"get_move_type","path":"douzero/env/move_detector.py","snippet":"def get_move_type(move):\n move_size = len(move)\n move_dict = collections.Counter(move)\n\n if move_size == 0:\n return {'type': TYPE_0_PASS}\n\n if move_size == 1:\n return {'type': TYPE_1_SINGLE, 'rank': move[0]}\n\n if move_size == 2:\n if move[0] == move[1]:\n return {'type': TYPE_2_PAIR, 'rank': move[0]}\n elif move == [20, 30]: # Kings\n return {'type': TYPE_5_KING_BOMB}\n else:\n return {'type': TYPE_15_WRONG}\n\n if move_size == 3:\n if len(move_dict) == 1:\n return {'type': TYPE_3_TRIPLE, 'rank': move[0]}\n else:\n return {'type': TYPE_15_WRONG}\n\n if move_size == 4:\n if len(move_dict) == 1:\n return {'type': TYPE_4_BOMB, 'rank': move[0]}\n elif len(move_dict) == 2:\n if move[0] == move[1] == move[2] or move[1] == move[2] == move[3]:\n return {'type': TYPE_6_3_1, 'rank': move[1]}\n else:\n return {'type': TYPE_15_WRONG}\n else:\n return {'type': TYPE_15_WRONG}\n\n if is_continuous_seq(move):\n return {'type': TYPE_8_SERIAL_SINGLE, 'rank': move[0], 'len': len(move)}\n\n if move_size == 5:\n if len(move_dict) == 2:\n return {'type': TYPE_7_3_2, 'rank': move[2]}\n else:\n return {'type': TYPE_15_WRONG}\n\n count_dict = collections.defaultdict(int)\n for c, n in move_dict.items():\n count_dict[n] += 1\n\n if move_size == 6:\n if (len(move_dict) == 2 or len(move_dict) == 3) and count_dict.get(4) == 1 and \\\n (count_dict.get(2) == 1 or count_dict.get(1) == 2):\n return {'type': TYPE_13_4_2, 'rank': move[2]}\n\n if move_size == 8 and (((len(move_dict) == 3 or len(move_dict) == 2) and\n (count_dict.get(4) == 1 and count_dict.get(2) == 2)) or count_dict.get(4) == 2):\n return {'type': TYPE_14_4_22, 'rank': max([c for c, n in move_dict.items() if n == 4])}\n\n mdkeys = sorted(move_dict.keys())\n if len(move_dict) == count_dict.get(2) and is_continuous_seq(mdkeys):\n return {'type': TYPE_9_SERIAL_PAIR, 'rank': mdkeys[0], 'len': len(mdkeys)}\n\n if len(move_dict) == count_dict.get(3) and is_continuous_seq(mdkeys):\n return {'type': TYPE_10_SERIAL_TRIPLE, 'rank': mdkeys[0], 'len': len(mdkeys)}\n\n # Check Type 11 (serial 3+1) and Type 12 (serial 3+2)\n if count_dict.get(3, 0) >= MIN_TRIPLES:\n serial_3 = list()\n single = list()\n pair = list()\n\n for k, v in move_dict.items():\n if v == 3:\n serial_3.append(k)\n elif v == 1:\n single.append(k)\n elif v == 2:\n pair.append(k)\n else: # no other possibilities\n return {'type': TYPE_15_WRONG}\n\n serial_3.sort()\n if is_continuous_seq(serial_3):\n if len(serial_3) == len(single)+len(pair)*2:\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[0], 'len': len(serial_3)}\n if len(serial_3) == len(pair) and len(move_dict) == len(serial_3) * 2:\n return {'type': TYPE_12_SERIAL_3_2, 'rank': serial_3[0], 'len': len(serial_3)}\n\n if len(serial_3) == 4:\n if is_continuous_seq(serial_3[1:]):\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[1], 'len': len(serial_3) - 1}\n if is_continuous_seq(serial_3[:-1]):\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[0], 'len': len(serial_3) - 1}\n\n return {'type': TYPE_15_WRONG}"},{"identifier":"Ui_Form","path":"MainWindow.py","snippet":"class Ui_Form(object):\n def setupUi(self, Form):\n Form.setObjectName(\"Form\")\n Form.resize(677, 450)\n font = QtGui.QFont()\n font.setFamily(\"Arial\")\n font.setPointSize(9)\n font.setBold(True)\n font.setItalic(False)\n font.setWeight(75)\n Form.setFont(font)\n Form.setWindowOpacity(0.8)\n self.WinRate = QtWidgets.QLabel(Form)\n self.WinRate.setGeometry(QtCore.QRect(320, 120, 121, 51))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.WinRate.setFont(font)\n self.WinRate.setAlignment(QtCore.Qt.AlignCenter)\n self.WinRate.setObjectName(\"WinRate\")\n self.UserHandCards = QtWidgets.QLabel(Form)\n self.UserHandCards.setGeometry(QtCore.QRect(30, 330, 351, 31))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.UserHandCards.setFont(font)\n self.UserHandCards.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\n self.UserHandCards.setObjectName(\"UserHandCards\")\n self.ThreeLandlordCards = QtWidgets.QLabel(Form)\n self.ThreeLandlordCards.setGeometry(QtCore.QRect(30, 120, 121, 51))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.ThreeLandlordCards.setFont(font)\n self.ThreeLandlordCards.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\n self.ThreeLandlordCards.setObjectName(\"ThreeLandlordCards\")\n self.BidWinrate = QtWidgets.QLabel(Form)\n self.BidWinrate.setGeometry(QtCore.QRect(30, 220, 161, 31))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.BidWinrate.setFont(font)\n self.BidWinrate.setObjectName(\"BidWinrate\")\n self.PreWinrate = QtWidgets.QLabel(Form)\n self.PreWinrate.setGeometry(QtCore.QRect(30, 280, 161, 31))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.PreWinrate.setFont(font)\n self.PreWinrate.setObjectName(\"PreWinrate\")\n self.label = QtWidgets.QLabel(Form)\n self.label.setGeometry(QtCore.QRect(490, 320, 101, 41))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.label.setFont(font)\n self.label.setAlignment(QtCore.Qt.AlignCenter)\n self.label.setObjectName(\"label\")\n self.LPlayedCard = QtWidgets.QLabel(Form)\n self.LPlayedCard.setGeometry(QtCore.QRect(170, 120, 102, 51))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.LPlayedCard.setFont(font)\n self.LPlayedCard.setAlignment(QtCore.Qt.AlignCenter)\n self.LPlayedCard.setObjectName(\"LPlayedCard\")\n self.splitter_2 = QtWidgets.QSplitter(Form)\n self.splitter_2.setGeometry(QtCore.QRect(20, 380, 621, 41))\n self.splitter_2.setOrientation(QtCore.Qt.Horizontal)\n self.splitter_2.setObjectName(\"splitter_2\")\n self.SingleButton = QtWidgets.QPushButton(self.splitter_2)\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.SingleButton.setFont(font)\n self.SingleButton.setObjectName(\"SingleButton\")\n self.LoopButton = QtWidgets.QPushButton(self.splitter_2)\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.LoopButton.setFont(font)\n self.LoopButton.setObjectName(\"LoopButton\")\n self.StopButton = QtWidgets.QPushButton(self.splitter_2)\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.StopButton.setFont(font)\n self.StopButton.setObjectName(\"StopButton\")\n self.tableWidget = QtWidgets.QTableWidget(Form)\n self.tableWidget.setGeometry(QtCore.QRect(20, 10, 611, 75))\n self.tableWidget.setMaximumSize(QtCore.QSize(16777215, 75))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(12)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.tableWidget.setFont(font)\n self.tableWidget.setLayoutDirection(QtCore.Qt.LeftToRight)\n self.tableWidget.setStyleSheet(\"QTableWidget{\\n\"\n\"color:#DCDCDC;\\n\"\n\"background:#444444;\\n\"\n\"border:1px solid #242424;\\n\"\n\"alternate-background-color:#525252;\\n\"\n\"gridline-color:#242424;\\n\"\n\"}\\n\"\n\" \\n\"\n\"QTableWidget::item:selected{\\n\"\n\"color:#DCDCDC;\\n\"\n\"background:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #484848,stop:1 #383838);\\n\"\n\"}\\n\"\n\" \\n\"\n\"QTableWidget::item:hover{\\n\"\n\"background:#5B5B5B;\\n\"\n\"}\\n\"\n\"QHeaderView::section{\\n\"\n\"text-align:center;\\n\"\n\"background:#5E5E5E;\\n\"\n\"padding:3px;\\n\"\n\"margin:0px;\\n\"\n\"color:#DCDCDC;\\n\"\n\"border:1px solid #242424;\\n\"\n\"border-left-width:0;\\n\"\n\"}\\n\"\n\" \\n\"\n\"QScrollBar:vertical{\\n\"\n\"background:#484848;\\n\"\n\"padding:0px;\\n\"\n\"border-radius:6px;\\n\"\n\"max-width:12px;\\n\"\n\"}\\n\"\n\" \\n\"\n\" \\n\"\n\"QScrollBar::handle:vertical{\\n\"\n\"background:#CCCCCC;\\n\"\n\"}\\n\"\n\" \\n\"\n\"QScrollBar::handle:hover:vertical,QScrollBar::handle:pressed:vertical{\\n\"\n\"background:#A7A7A7;\\n\"\n\"}\\n\"\n\"QScrollBar::sub-page:vertical{\\n\"\n\"background:444444;\\n\"\n\"}\\n\"\n\" \\n\"\n\" \\n\"\n\"QScrollBar::add-page:vertical{\\n\"\n\"background:5B5B5B;\\n\"\n\"}\\n\"\n\" \\n\"\n\"QScrollBar::add-line:vertical{\\n\"\n\"background:none;\\n\"\n\"}\\n\"\n\"QScrollBar::sub-line:vertical{\\n\"\n\"background:none;\\n\"\n\"}\")\n self.tableWidget.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.tableWidget.setMidLineWidth(-1)\n self.tableWidget.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n self.tableWidget.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n self.tableWidget.setAutoScroll(False)\n self.tableWidget.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\n self.tableWidget.setSelectionMode(QtWidgets.QAbstractItemView.NoSelection)\n self.tableWidget.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\n self.tableWidget.setTextElideMode(QtCore.Qt.ElideNone)\n self.tableWidget.setObjectName(\"tableWidget\")\n self.tableWidget.setColumnCount(15)\n self.tableWidget.setRowCount(1)\n item = QtWidgets.QTableWidgetItem()\n self.tableWidget.setVerticalHeaderItem(0, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(0, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(1, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(2, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(3, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(4, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(5, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(6, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(7, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(8, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(9, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(10, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(11, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(12, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(13, item)\n item = QtWidgets.QTableWidgetItem()\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(True)\n font.setWeight(75)\n item.setFont(font)\n self.tableWidget.setHorizontalHeaderItem(14, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 0, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 1, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 2, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 3, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 4, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 5, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 6, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 7, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 8, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 9, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 10, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 11, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 12, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 13, item)\n item = QtWidgets.QTableWidgetItem()\n item.setTextAlignment(QtCore.Qt.AlignCenter)\n self.tableWidget.setItem(0, 14, item)\n self.tableWidget.horizontalHeader().setVisible(True)\n self.tableWidget.horizontalHeader().setCascadingSectionResizes(True)\n self.tableWidget.horizontalHeader().setDefaultSectionSize(41)\n self.tableWidget.horizontalHeader().setStretchLastSection(True)\n self.tableWidget.verticalHeader().setVisible(False)\n self.tableWidget.verticalHeader().setCascadingSectionResizes(False)\n self.tableWidget.verticalHeader().setDefaultSectionSize(40)\n self.tableWidget.verticalHeader().setHighlightSections(True)\n self.tableWidget.verticalHeader().setMinimumSectionSize(40)\n self.tableWidget.verticalHeader().setSortIndicatorShown(False)\n self.RPlayedCard = QtWidgets.QLabel(Form)\n self.RPlayedCard.setGeometry(QtCore.QRect(490, 120, 102, 51))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.RPlayedCard.setFont(font)\n self.RPlayedCard.setAlignment(QtCore.Qt.AlignCenter)\n self.RPlayedCard.setObjectName(\"RPlayedCard\")\n self.PredictedCard = QtWidgets.QLabel(Form)\n self.PredictedCard.setGeometry(QtCore.QRect(320, 190, 121, 51))\n font = QtGui.QFont()\n font.setFamily(\"微软雅黑\")\n font.setPointSize(10)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.PredictedCard.setFont(font)\n self.PredictedCard.setStyleSheet(\"\")\n self.PredictedCard.setFrameShape(QtWidgets.QFrame.Panel)\n self.PredictedCard.setLineWidth(1)\n self.PredictedCard.setAlignment(QtCore.Qt.AlignCenter)\n self.PredictedCard.setObjectName(\"PredictedCard\")\n\n self.retranslateUi(Form)\n QtCore.QMetaObject.connectSlotsByName(Form)\n\n def retranslateUi(self, Form):\n _translate = QtCore.QCoreApplication.translate\n Form.setWindowTitle(_translate(\"Form\", \"Hi\"))\n self.WinRate.setText(_translate(\"Form\", \"评分\"))\n self.UserHandCards.setText(_translate(\"Form\", \"手牌\"))\n self.ThreeLandlordCards.setText(_translate(\"Form\", \"地主牌\"))\n self.BidWinrate.setText(_translate(\"Form\", \"叫牌胜率：\"))\n self.PreWinrate.setText(_translate(\"Form\", \"局前胜率：\"))\n self.label.setText(_translate(\"Form\", \"游戏状态\"))\n self.LPlayedCard.setText(_translate(\"Form\", \"上家出牌区域\"))\n self.SingleButton.setText(_translate(\"Form\", \"单局\"))\n self.LoopButton.setText(_translate(\"Form\", \" 连续\"))\n self.StopButton.setText(_translate(\"Form\", \"停止\"))\n item = self.tableWidget.horizontalHeaderItem(0)\n item.setText(_translate(\"Form\", \"大\"))\n item = self.tableWidget.horizontalHeaderItem(1)\n item.setText(_translate(\"Form\", \"小\"))\n item = self.tableWidget.horizontalHeaderItem(2)\n item.setText(_translate(\"Form\", \"2\"))\n item = self.tableWidget.horizontalHeaderItem(3)\n item.setText(_translate(\"Form\", \"A\"))\n item = self.tableWidget.horizontalHeaderItem(4)\n item.setText(_translate(\"Form\", \"K\"))\n item = self.tableWidget.horizontalHeaderItem(5)\n item.setText(_translate(\"Form\", \"Q\"))\n item = self.tableWidget.horizontalHeaderItem(6)\n item.setText(_translate(\"Form\", \"J\"))\n item = self.tableWidget.horizontalHeaderItem(7)\n item.setText(_translate(\"Form\", \"10\"))\n item = self.tableWidget.horizontalHeaderItem(8)\n item.setText(_translate(\"Form\", \"9\"))\n item = self.tableWidget.horizontalHeaderItem(9)\n item.setText(_translate(\"Form\", \"8\"))\n item = self.tableWidget.horizontalHeaderItem(10)\n item.setText(_translate(\"Form\", \"7\"))\n item = self.tableWidget.horizontalHeaderItem(11)\n item.setText(_translate(\"Form\", \"6\"))\n item = self.tableWidget.horizontalHeaderItem(12)\n item.setText(_translate(\"Form\", \"5\"))\n item = self.tableWidget.horizontalHeaderItem(13)\n item.setText(_translate(\"Form\", \"4\"))\n item = self.tableWidget.horizontalHeaderItem(14)\n item.setText(_translate(\"Form\", \"3\"))\n __sortingEnabled = self.tableWidget.isSortingEnabled()\n self.tableWidget.setSortingEnabled(False)\n item = self.tableWidget.item(0, 0)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 1)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 2)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 3)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 4)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 5)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 6)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 7)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 8)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 9)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 10)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 11)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 12)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 13)\n item.setText(_translate(\"Form\", \"0\"))\n item = self.tableWidget.item(0, 14)\n item.setText(_translate(\"Form\", \"0\"))\n self.tableWidget.setSortingEnabled(__sortingEnabled)\n self.RPlayedCard.setText(_translate(\"Form\", \"下家出牌区域\"))\n self.PredictedCard.setText(_translate(\"Form\", \"AI出牌区域\"))"},{"identifier":"GameEnv","path":"douzero/env/game.py","snippet":"class GameEnv(object):\n\n def __init__(self, players):\n\n self.card_play_action_seq = []\n\n self.three_landlord_cards = None\n self.game_over = False\n\n self.acting_player_position = None\n self.player_utility_dict = None\n\n self.players = players\n\n self.last_move_dict = {'landlord': [],\n 'landlord_up': [],\n 'landlord_down': []}\n\n self.played_cards = {'landlord': [],\n 'landlord_up': [],\n 'landlord_down': []}\n\n self.last_move = []\n self.last_two_moves = []\n\n self.num_wins = {'landlord': 0,\n 'farmer': 0}\n\n self.num_scores = {'landlord': 0,\n 'farmer': 0}\n\n self.info_sets = {'landlord': InfoSet('landlord'),\n 'landlord_up': InfoSet('landlord_up'),\n 'landlord_down': InfoSet('landlord_down')}\n\n self.bomb_num = 0\n self.last_pid = 'landlord'\n\n self.bid_info = [[1, 1, 1],\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1]]\n self.bid_count = 0\n self.multiply_count = {'landlord': 1,\n 'landlord_up': 1,\n 'landlord_down': 1}\n self.step_count = 0\n\n\n def card_play_init(self, card_play_data):\n self.info_sets['landlord'].player_hand_cards = \\\n card_play_data['landlord']\n self.info_sets['landlord_up'].player_hand_cards = \\\n card_play_data['landlord_up']\n self.info_sets['landlord_down'].player_hand_cards = \\\n card_play_data['landlord_down']\n self.three_landlord_cards = card_play_data['three_landlord_cards']\n self.get_acting_player_position()\n self.game_infoset = self.get_infoset()\n\n\n def game_done(self):\n if len(self.info_sets['landlord'].player_hand_cards) == 0 or \\\n len(self.info_sets['landlord_up'].player_hand_cards) == 0 or \\\n len(self.info_sets['landlord_down'].player_hand_cards) == 0:\n # if one of the three players discards his hand,\n # then game is over.\n self.compute_player_utility()\n self.update_num_wins_scores()\n\n self.game_over = True\n\n def compute_player_utility(self):\n\n if len(self.info_sets['landlord'].player_hand_cards) == 0:\n self.player_utility_dict = {'landlord': 2,\n 'farmer': -1}\n else:\n self.player_utility_dict = {'landlord': -2,\n 'farmer': 1}\n\n def update_num_wins_scores(self):\n for pos, utility in self.player_utility_dict.items():\n base_score = 2 if pos == 'landlord' else 1\n if utility > 0:\n self.num_wins[pos] += 1\n self.winner = pos\n self.num_scores[pos] += base_score * (2 ** self.bomb_num)\n else:\n self.num_scores[pos] -= base_score * (2 ** self.bomb_num)\n\n def get_winner(self):\n return self.winner\n\n def get_bomb_num(self):\n return self.bomb_num\n\n def step(self, position, action=[]):\n win_rate = 0\n if self.acting_player_position == position:\n action, actions_confidence = self.players[1].act(self.game_infoset)\n # 计算胜率\n win_rate = actions_confidence\n # win_rate = max(actions_confidence, -1)\n # win_rate = min(win_rate, 1)\n # win_rate = str(round(float((win_rate + 1) / 2), 4))\n\n if len(action) > 0:\n self.last_pid = self.acting_player_position\n\n if action in bombs:\n self.bomb_num += 1\n\n self.last_move_dict[\n self.acting_player_position] = action.copy()\n\n self.card_play_action_seq.append((position, action))\n self.update_acting_player_hand_cards(action)\n\n self.played_cards[self.acting_player_position] += action\n\n if self.acting_player_position == 'landlord' and \\\n len(action) > 0 and \\\n len(self.three_landlord_cards) > 0:\n for card in action:\n if len(self.three_landlord_cards) > 0:\n if card in self.three_landlord_cards:\n self.three_landlord_cards.remove(card)\n else:\n break\n self.game_done()\n if not self.game_over:\n self.get_acting_player_position()\n self.game_infoset = self.get_infoset()\n # 返回动作和胜率,只有玩家角色会接受返回值\n action_message = {\"action\": str(''.join([EnvCard2RealCard[c] for c in action])),\n \"win_rate\": str(round(float(win_rate), 4))}\n return action_message\n\n def get_last_move(self):\n last_move = []\n if len(self.card_play_action_seq) != 0:\n if len(self.card_play_action_seq[-1][1]) == 0:\n last_move = self.card_play_action_seq[-2][1]\n else:\n last_move = self.card_play_action_seq[-1][1]\n\n return last_move\n\n def get_last_two_moves(self):\n last_two_moves = [[], []]\n for card in self.card_play_action_seq[-2:]:\n last_two_moves.insert(0, card[1])\n last_two_moves = last_two_moves[:2]\n return last_two_moves\n\n def get_acting_player_position(self):\n if self.acting_player_position is None:\n self.acting_player_position = 'landlord'\n\n else:\n if self.acting_player_position == 'landlord':\n self.acting_player_position = 'landlord_down'\n\n elif self.acting_player_position == 'landlord_down':\n self.acting_player_position = 'landlord_up'\n\n else:\n self.acting_player_position = 'landlord'\n\n return self.acting_player_position\n\n def update_acting_player_hand_cards(self, action):\n if action != []:\n # 更新玩家手牌，删除对应的牌\n if self.acting_player_position == self.players[0]:\n for card in action:\n self.info_sets[self.acting_player_position].player_hand_cards.remove(card)\n # 更新另外两个玩家手牌，删除相同数量的牌\n else:\n del self.info_sets[self.acting_player_position].player_hand_cards[0:len(action)]\n self.info_sets[self.acting_player_position].player_hand_cards.sort()\n\n def get_legal_card_play_actions(self):\n mg = MovesGener(\n self.info_sets[self.acting_player_position].player_hand_cards)\n\n action_sequence = self.card_play_action_seq\n\n rival_move = []\n if len(action_sequence) != 0:\n if len(action_sequence[-1][1]) == 0:\n rival_move = action_sequence[-2][1]\n else:\n rival_move = action_sequence[-1][1]\n\n rival_type = md.get_move_type(rival_move)\n rival_move_type = rival_type['type']\n rival_move_len = rival_type.get('len', 1)\n moves = list()\n\n if rival_move_type == md.TYPE_0_PASS:\n moves = mg.gen_moves()\n\n elif rival_move_type == md.TYPE_1_SINGLE:\n all_moves = mg.gen_type_1_single()\n moves = ms.filter_type_1_single(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_2_PAIR:\n all_moves = mg.gen_type_2_pair()\n moves = ms.filter_type_2_pair(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_3_TRIPLE:\n all_moves = mg.gen_type_3_triple()\n moves = ms.filter_type_3_triple(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_4_BOMB:\n all_moves = mg.gen_type_4_bomb() + mg.gen_type_5_king_bomb()\n moves = ms.filter_type_4_bomb(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_5_KING_BOMB:\n moves = []\n\n elif rival_move_type == md.TYPE_6_3_1:\n all_moves = mg.gen_type_6_3_1()\n moves = ms.filter_type_6_3_1(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_7_3_2:\n all_moves = mg.gen_type_7_3_2()\n moves = ms.filter_type_7_3_2(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_8_SERIAL_SINGLE:\n all_moves = mg.gen_type_8_serial_single(repeat_num=rival_move_len)\n moves = ms.filter_type_8_serial_single(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_9_SERIAL_PAIR:\n all_moves = mg.gen_type_9_serial_pair(repeat_num=rival_move_len)\n moves = ms.filter_type_9_serial_pair(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_10_SERIAL_TRIPLE:\n all_moves = mg.gen_type_10_serial_triple(repeat_num=rival_move_len)\n moves = ms.filter_type_10_serial_triple(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_11_SERIAL_3_1:\n all_moves = mg.gen_type_11_serial_3_1(repeat_num=rival_move_len)\n moves = ms.filter_type_11_serial_3_1(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_12_SERIAL_3_2:\n all_moves = mg.gen_type_12_serial_3_2(repeat_num=rival_move_len)\n moves = ms.filter_type_12_serial_3_2(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_13_4_2:\n all_moves = mg.gen_type_13_4_2()\n moves = ms.filter_type_13_4_2(all_moves, rival_move)\n\n elif rival_move_type == md.TYPE_14_4_22:\n all_moves = mg.gen_type_14_4_22()\n moves = ms.filter_type_14_4_22(all_moves, rival_move)\n\n if rival_move_type not in [md.TYPE_0_PASS,\n md.TYPE_4_BOMB, md.TYPE_5_KING_BOMB]:\n moves = moves + mg.gen_type_4_bomb() + mg.gen_type_5_king_bomb()\n\n if len(rival_move) != 0: # rival_move is not 'pass'\n moves = moves + [[]]\n\n for m in moves:\n m.sort()\n\n return moves\n\n def reset(self):\n self.card_play_action_seq = []\n\n self.three_landlord_cards = None\n self.game_over = False\n\n self.acting_player_position = None\n self.player_utility_dict = None\n\n self.last_move_dict = {'landlord': [],\n 'landlord_up': [],\n 'landlord_down': []}\n\n self.played_cards = {'landlord': [],\n 'landlord_up': [],\n 'landlord_down': []}\n\n self.last_move = []\n self.last_two_moves = []\n\n self.info_sets = {'landlord': InfoSet('landlord'),\n 'landlord_up': InfoSet('landlord_up'),\n 'landlord_down': InfoSet('landlord_down')}\n\n self.bomb_num = 0\n self.last_pid = 'landlord'\n self.bid_info = [[1, 1, 1],\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1]]\n self.bid_count = 0\n self.multiply_count = {'landlord': 0,\n 'landlord_up': 0,\n 'landlord_down': 0}\n self.step_count = 0\n\n def get_infoset(self):\n self.info_sets[\n self.acting_player_position].last_pid = self.last_pid\n\n self.info_sets[\n self.acting_player_position].legal_actions = \\\n self.get_legal_card_play_actions()\n\n self.info_sets[\n self.acting_player_position].bomb_num = self.bomb_num\n\n self.info_sets[\n self.acting_player_position].last_move = self.get_last_move()\n\n self.info_sets[\n self.acting_player_position].last_two_moves = self.get_last_two_moves()\n\n self.info_sets[\n self.acting_player_position].last_move_dict = self.last_move_dict\n\n self.info_sets[self.acting_player_position].num_cards_left_dict = \\\n {pos: len(self.info_sets[pos].player_hand_cards)\n for pos in ['landlord', 'landlord_up', 'landlord_down']}\n\n self.info_sets[self.acting_player_position].other_hand_cards = []\n\n '''\n 调整计算其他人手牌的方法，整副牌减去玩家手牌与出过的牌\n for pos in ['landlord', 'landlord_up', 'landlord_down']:\n if pos != self.acting_player_position:\n self.info_sets[\n self.acting_player_position].other_hand_cards += \\\n self.info_sets[pos].player_hand_cards\n '''\n # 把出过的牌中三个子列表合成一个列表\n played_cards_tmp = []\n for i in list(self.played_cards.values()):\n played_cards_tmp.extend(i)\n # 出过的牌和玩家手上的牌\n played_and_hand_cards = played_cards_tmp + self.info_sets[self.acting_player_position].player_hand_cards\n # 整副牌减去出过的牌和玩家手上的牌，就是其他人的手牌\n for i in set(AllEnvCard):\n self.info_sets[\n self.acting_player_position].other_hand_cards.extend([i] * (AllEnvCard.count(i) - played_and_hand_cards.count(i)))\n\n self.info_sets[self.acting_player_position].played_cards = \\\n self.played_cards\n self.info_sets[self.acting_player_position].three_landlord_cards = \\\n self.three_landlord_cards\n self.info_sets[self.acting_player_position].card_play_action_seq = \\\n self.card_play_action_seq\n\n self.info_sets[\n self.acting_player_position].all_handcards = \\\n {pos: self.info_sets[pos].player_hand_cards\n for pos in ['landlord', 'landlord_up', 'landlord_down']}\n\n # Custom bid info\n self.info_sets[self.acting_player_position].bid_info = bid_infos[self.acting_player_position]\n\n return deepcopy(self.info_sets[self.acting_player_position])"},{"identifier":"DeepAgent","path":"douzero/evaluation/deep_agent.py","snippet":"class DeepAgent:\n\n def __init__(self, position, model_path):\n self.model_type = \"old\"\n if \"general\" in model_path:\n self.model_type = \"general\"\n elif \"resnet\" in model_path:\n self.model_type = \"resnet\"\n self.model = _load_model(position, model_path, self.model_type)\n\n def act(self, infoset):\n obs = get_obs(infoset, model_type=self.model_type)\n z_batch = torch.from_numpy(obs['z_batch']).float()\n x_batch = torch.from_numpy(obs['x_batch']).float()\n if torch.cuda.is_available():\n z_batch, x_batch = z_batch.cuda(), x_batch.cuda()\n y_pred = self.model.forward(z_batch, x_batch, return_value=True)['values']\n y_pred = y_pred.detach().cpu().numpy()\n\n best_action_index = np.argmax(y_pred, axis=0)[0]\n best_action = infoset.legal_actions[best_action_index]\n best_action_confidence = y_pred[best_action_index]\n return best_action, best_action_confidence"}],"string":"[\n {\n \"identifier\": \"GameHelper\",\n \"path\": \"GameHelper.py\",\n \"snippet\": \"class GameHelper:\\n def __init__(self):\\n self.ScreenZoomRate = None\\n self.counter = QTime()\\n self.Pics = {}\\n self.PicsCV = {}\\n st = time.time()\\n self.Handle = win32gui.FindWindow(\\\"UnityWndClass\\\", None)\\n self.Interrupt = False\\n self.RealRate = (1440, 810)\\n self.GetZoomRate()\\n for file in os.listdir(\\\"./pics\\\"):\\n info = file.split(\\\".\\\")\\n if info[1] == \\\"png\\\":\\n tmpImage = Image.open(\\\"./pics/\\\" + file)\\n imgCv = cv2.imread(\\\"./pics/\\\" + file)\\n self.Pics.update({info[0]: tmpImage})\\n self.PicsCV.update({info[0]: imgCv})\\n\\n def sleep(self, ms):\\n self.counter.restart()\\n while self.counter.elapsed() < ms:\\n QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 50)\\n\\n def Screenshot(self, region=None): # -> (im, (left, top))\\n try_count = 3\\n success = False\\n while try_count > 0 and not success:\\n try:\\n try_count -= 1\\n self.Handle = win32gui.FindWindow(\\\"UnityWndClass\\\", None)\\n hwnd = self.Handle\\n left, top, right, bot = win32gui.GetWindowRect(hwnd)\\n width = right - left\\n height = bot - top\\n self.RealRate = (width, height)\\n width = int(width)\\n height = int(height)\\n hwndDC = win32gui.GetWindowDC(hwnd)\\n mfcDC = win32ui.CreateDCFromHandle(hwndDC)\\n saveDC = mfcDC.CreateCompatibleDC()\\n saveBitMap = win32ui.CreateBitmap()\\n saveBitMap.CreateCompatibleBitmap(mfcDC, width, height)\\n saveDC.SelectObject(saveBitMap)\\n result = windll.user32.PrintWindow(hwnd, saveDC.GetSafeHdc(), 3)\\n bmpinfo = saveBitMap.GetInfo()\\n bmpstr = saveBitMap.GetBitmapBits(True)\\n im = Image.frombuffer(\\n \\\"RGB\\\",\\n (bmpinfo['bmWidth'], bmpinfo['bmHeight']),\\n bmpstr, 'raw', 'BGRX', 0, 1)\\n win32gui.DeleteObject(saveBitMap.GetHandle())\\n saveDC.DeleteDC()\\n mfcDC.DeleteDC()\\n win32gui.ReleaseDC(hwnd, hwndDC)\\n im = im.resize((1440, 810))\\n if region is not None:\\n im = im.crop((region[0], region[1], region[0] + region[2], region[1] + region[3]))\\n if result:\\n success = True\\n return im, (left, top)\\n except Exception as e:\\n print(\\\"截图时出现错误:\\\", repr(e))\\n self.sleep(200)\\n return None, (0, 0)\\n\\n def GetZoomRate(self):\\n self.ScreenZoomRate = ctypes.windll.shcore.GetScaleFactorForDevice(0) / 100\\n\\n def LocateOnScreen(self, templateName, region, confidence=0.8, img=None):\\n if img is not None:\\n image = img\\n else:\\n image, _ = self.Screenshot()\\n imgcv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\\n return LocateOnImage(imgcv, self.PicsCV[templateName], region=region, confidence=confidence)\\n\\n def ClickOnImage(self, templateName, region=None, confidence=0.8, img=None):\\n if img is not None:\\n image = img\\n else:\\n image, _ = self.Screenshot()\\n imgcv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\\n result = LocateOnImage(imgcv, self.PicsCV[templateName], region=region, confidence=confidence)\\n\\n if result is not None:\\n self.LeftClick(result)\\n print(result)\\n\\n def LeftClick(self, pos):\\n x, y = pos\\n x = (x / 1440) * self.RealRate[0]\\n y = (y / 810) * self.RealRate[1]\\n x = int(x)\\n y = int(y)\\n self.Handle = win32gui.FindWindow(\\\"UnityWndClass\\\", None)\\n left, top, _, _ = win32gui.GetWindowRect(self.Handle)\\n x, y = int(left + x), int(top + y)\\n\\n pyautogui.mouseDown(x, y, button='left')\\n time.sleep(0.1)\\n pyautogui.mouseUp(x, y, button='left')\\n time.sleep(0.1)\\n pyautogui.moveTo(int(left + 1000), int(top + 550))\\n\\n '''win32gui.SetActiveWindow(self.Handle)\\n lParam = win32api.MAKELONG(x, y)\\n\\n win32gui.PostMessage(self.Handle, WM_ACTIVATE, WA_ACTIVE, lParam)\\n win32gui.PostMessage(self.Handle, WM_ACTIVATE, WA_ACTIVE, lParam)\\n win32gui.PostMessage(self.Handle, WM_MOUSEMOVE, MK_LBUTTON, lParam)\\n win32gui.PostMessage(self.Handle, WM_LBUTTONDOWN, MK_LBUTTON, lParam)\\n win32gui.PostMessage(self.Handle, WM_LBUTTONUP, MK_LBUTTON, lParam)'''\\n\\n def LeftClick2(self, pos):\\n x, y = pos\\n x = (x / 1440) * self.RealRate[0]\\n y = (y / 810) * self.RealRate[1]\\n x = int(x)\\n y = int(y)\\n self.Handle = win32gui.FindWindow(\\\"UnityWndClass\\\", None)\\n left, top, _, _ = win32gui.GetWindowRect(self.Handle)\\n x, y = int(left + x), int(top + y)\\n\\n pyautogui.mouseDown(x, y, button='left')\\n time.sleep(0.1)\\n pyautogui.mouseUp(x, y, button='left')\"\n },\n {\n \"identifier\": \"get_move_type\",\n \"path\": \"douzero/env/move_detector.py\",\n \"snippet\": \"def get_move_type(move):\\n move_size = len(move)\\n move_dict = collections.Counter(move)\\n\\n if move_size == 0:\\n return {'type': TYPE_0_PASS}\\n\\n if move_size == 1:\\n return {'type': TYPE_1_SINGLE, 'rank': move[0]}\\n\\n if move_size == 2:\\n if move[0] == move[1]:\\n return {'type': TYPE_2_PAIR, 'rank': move[0]}\\n elif move == [20, 30]: # Kings\\n return {'type': TYPE_5_KING_BOMB}\\n else:\\n return {'type': TYPE_15_WRONG}\\n\\n if move_size == 3:\\n if len(move_dict) == 1:\\n return {'type': TYPE_3_TRIPLE, 'rank': move[0]}\\n else:\\n return {'type': TYPE_15_WRONG}\\n\\n if move_size == 4:\\n if len(move_dict) == 1:\\n return {'type': TYPE_4_BOMB, 'rank': move[0]}\\n elif len(move_dict) == 2:\\n if move[0] == move[1] == move[2] or move[1] == move[2] == move[3]:\\n return {'type': TYPE_6_3_1, 'rank': move[1]}\\n else:\\n return {'type': TYPE_15_WRONG}\\n else:\\n return {'type': TYPE_15_WRONG}\\n\\n if is_continuous_seq(move):\\n return {'type': TYPE_8_SERIAL_SINGLE, 'rank': move[0], 'len': len(move)}\\n\\n if move_size == 5:\\n if len(move_dict) == 2:\\n return {'type': TYPE_7_3_2, 'rank': move[2]}\\n else:\\n return {'type': TYPE_15_WRONG}\\n\\n count_dict = collections.defaultdict(int)\\n for c, n in move_dict.items():\\n count_dict[n] += 1\\n\\n if move_size == 6:\\n if (len(move_dict) == 2 or len(move_dict) == 3) and count_dict.get(4) == 1 and \\\\\\n (count_dict.get(2) == 1 or count_dict.get(1) == 2):\\n return {'type': TYPE_13_4_2, 'rank': move[2]}\\n\\n if move_size == 8 and (((len(move_dict) == 3 or len(move_dict) == 2) and\\n (count_dict.get(4) == 1 and count_dict.get(2) == 2)) or count_dict.get(4) == 2):\\n return {'type': TYPE_14_4_22, 'rank': max([c for c, n in move_dict.items() if n == 4])}\\n\\n mdkeys = sorted(move_dict.keys())\\n if len(move_dict) == count_dict.get(2) and is_continuous_seq(mdkeys):\\n return {'type': TYPE_9_SERIAL_PAIR, 'rank': mdkeys[0], 'len': len(mdkeys)}\\n\\n if len(move_dict) == count_dict.get(3) and is_continuous_seq(mdkeys):\\n return {'type': TYPE_10_SERIAL_TRIPLE, 'rank': mdkeys[0], 'len': len(mdkeys)}\\n\\n # Check Type 11 (serial 3+1) and Type 12 (serial 3+2)\\n if count_dict.get(3, 0) >= MIN_TRIPLES:\\n serial_3 = list()\\n single = list()\\n pair = list()\\n\\n for k, v in move_dict.items():\\n if v == 3:\\n serial_3.append(k)\\n elif v == 1:\\n single.append(k)\\n elif v == 2:\\n pair.append(k)\\n else: # no other possibilities\\n return {'type': TYPE_15_WRONG}\\n\\n serial_3.sort()\\n if is_continuous_seq(serial_3):\\n if len(serial_3) == len(single)+len(pair)*2:\\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[0], 'len': len(serial_3)}\\n if len(serial_3) == len(pair) and len(move_dict) == len(serial_3) * 2:\\n return {'type': TYPE_12_SERIAL_3_2, 'rank': serial_3[0], 'len': len(serial_3)}\\n\\n if len(serial_3) == 4:\\n if is_continuous_seq(serial_3[1:]):\\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[1], 'len': len(serial_3) - 1}\\n if is_continuous_seq(serial_3[:-1]):\\n return {'type': TYPE_11_SERIAL_3_1, 'rank': serial_3[0], 'len': len(serial_3) - 1}\\n\\n return {'type': TYPE_15_WRONG}\"\n },\n {\n \"identifier\": \"Ui_Form\",\n \"path\": \"MainWindow.py\",\n \"snippet\": \"class Ui_Form(object):\\n def setupUi(self, Form):\\n Form.setObjectName(\\\"Form\\\")\\n Form.resize(677, 450)\\n font = QtGui.QFont()\\n font.setFamily(\\\"Arial\\\")\\n font.setPointSize(9)\\n font.setBold(True)\\n font.setItalic(False)\\n font.setWeight(75)\\n Form.setFont(font)\\n Form.setWindowOpacity(0.8)\\n self.WinRate = QtWidgets.QLabel(Form)\\n self.WinRate.setGeometry(QtCore.QRect(320, 120, 121, 51))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.WinRate.setFont(font)\\n self.WinRate.setAlignment(QtCore.Qt.AlignCenter)\\n self.WinRate.setObjectName(\\\"WinRate\\\")\\n self.UserHandCards = QtWidgets.QLabel(Form)\\n self.UserHandCards.setGeometry(QtCore.QRect(30, 330, 351, 31))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.UserHandCards.setFont(font)\\n self.UserHandCards.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\\n self.UserHandCards.setObjectName(\\\"UserHandCards\\\")\\n self.ThreeLandlordCards = QtWidgets.QLabel(Form)\\n self.ThreeLandlordCards.setGeometry(QtCore.QRect(30, 120, 121, 51))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.ThreeLandlordCards.setFont(font)\\n self.ThreeLandlordCards.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\\n self.ThreeLandlordCards.setObjectName(\\\"ThreeLandlordCards\\\")\\n self.BidWinrate = QtWidgets.QLabel(Form)\\n self.BidWinrate.setGeometry(QtCore.QRect(30, 220, 161, 31))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.BidWinrate.setFont(font)\\n self.BidWinrate.setObjectName(\\\"BidWinrate\\\")\\n self.PreWinrate = QtWidgets.QLabel(Form)\\n self.PreWinrate.setGeometry(QtCore.QRect(30, 280, 161, 31))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.PreWinrate.setFont(font)\\n self.PreWinrate.setObjectName(\\\"PreWinrate\\\")\\n self.label = QtWidgets.QLabel(Form)\\n self.label.setGeometry(QtCore.QRect(490, 320, 101, 41))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.label.setFont(font)\\n self.label.setAlignment(QtCore.Qt.AlignCenter)\\n self.label.setObjectName(\\\"label\\\")\\n self.LPlayedCard = QtWidgets.QLabel(Form)\\n self.LPlayedCard.setGeometry(QtCore.QRect(170, 120, 102, 51))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.LPlayedCard.setFont(font)\\n self.LPlayedCard.setAlignment(QtCore.Qt.AlignCenter)\\n self.LPlayedCard.setObjectName(\\\"LPlayedCard\\\")\\n self.splitter_2 = QtWidgets.QSplitter(Form)\\n self.splitter_2.setGeometry(QtCore.QRect(20, 380, 621, 41))\\n self.splitter_2.setOrientation(QtCore.Qt.Horizontal)\\n self.splitter_2.setObjectName(\\\"splitter_2\\\")\\n self.SingleButton = QtWidgets.QPushButton(self.splitter_2)\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.SingleButton.setFont(font)\\n self.SingleButton.setObjectName(\\\"SingleButton\\\")\\n self.LoopButton = QtWidgets.QPushButton(self.splitter_2)\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.LoopButton.setFont(font)\\n self.LoopButton.setObjectName(\\\"LoopButton\\\")\\n self.StopButton = QtWidgets.QPushButton(self.splitter_2)\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.StopButton.setFont(font)\\n self.StopButton.setObjectName(\\\"StopButton\\\")\\n self.tableWidget = QtWidgets.QTableWidget(Form)\\n self.tableWidget.setGeometry(QtCore.QRect(20, 10, 611, 75))\\n self.tableWidget.setMaximumSize(QtCore.QSize(16777215, 75))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(12)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.tableWidget.setFont(font)\\n self.tableWidget.setLayoutDirection(QtCore.Qt.LeftToRight)\\n self.tableWidget.setStyleSheet(\\\"QTableWidget{\\\\n\\\"\\n\\\"color:#DCDCDC;\\\\n\\\"\\n\\\"background:#444444;\\\\n\\\"\\n\\\"border:1px solid #242424;\\\\n\\\"\\n\\\"alternate-background-color:#525252;\\\\n\\\"\\n\\\"gridline-color:#242424;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QTableWidget::item:selected{\\\\n\\\"\\n\\\"color:#DCDCDC;\\\\n\\\"\\n\\\"background:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #484848,stop:1 #383838);\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QTableWidget::item:hover{\\\\n\\\"\\n\\\"background:#5B5B5B;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\"QHeaderView::section{\\\\n\\\"\\n\\\"text-align:center;\\\\n\\\"\\n\\\"background:#5E5E5E;\\\\n\\\"\\n\\\"padding:3px;\\\\n\\\"\\n\\\"margin:0px;\\\\n\\\"\\n\\\"color:#DCDCDC;\\\\n\\\"\\n\\\"border:1px solid #242424;\\\\n\\\"\\n\\\"border-left-width:0;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QScrollBar:vertical{\\\\n\\\"\\n\\\"background:#484848;\\\\n\\\"\\n\\\"padding:0px;\\\\n\\\"\\n\\\"border-radius:6px;\\\\n\\\"\\n\\\"max-width:12px;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QScrollBar::handle:vertical{\\\\n\\\"\\n\\\"background:#CCCCCC;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QScrollBar::handle:hover:vertical,QScrollBar::handle:pressed:vertical{\\\\n\\\"\\n\\\"background:#A7A7A7;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\"QScrollBar::sub-page:vertical{\\\\n\\\"\\n\\\"background:444444;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QScrollBar::add-page:vertical{\\\\n\\\"\\n\\\"background:5B5B5B;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\" \\\\n\\\"\\n\\\"QScrollBar::add-line:vertical{\\\\n\\\"\\n\\\"background:none;\\\\n\\\"\\n\\\"}\\\\n\\\"\\n\\\"QScrollBar::sub-line:vertical{\\\\n\\\"\\n\\\"background:none;\\\\n\\\"\\n\\\"}\\\")\\n self.tableWidget.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.tableWidget.setMidLineWidth(-1)\\n self.tableWidget.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\\n self.tableWidget.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\\n self.tableWidget.setAutoScroll(False)\\n self.tableWidget.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\\n self.tableWidget.setSelectionMode(QtWidgets.QAbstractItemView.NoSelection)\\n self.tableWidget.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\\n self.tableWidget.setTextElideMode(QtCore.Qt.ElideNone)\\n self.tableWidget.setObjectName(\\\"tableWidget\\\")\\n self.tableWidget.setColumnCount(15)\\n self.tableWidget.setRowCount(1)\\n item = QtWidgets.QTableWidgetItem()\\n self.tableWidget.setVerticalHeaderItem(0, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(0, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(1, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(2, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(3, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(4, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(5, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(6, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(7, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(8, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(9, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(10, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(11, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(12, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(13, item)\\n item = QtWidgets.QTableWidgetItem()\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(True)\\n font.setWeight(75)\\n item.setFont(font)\\n self.tableWidget.setHorizontalHeaderItem(14, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 0, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 1, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 2, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 3, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 4, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 5, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 6, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 7, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 8, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 9, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 10, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 11, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 12, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 13, item)\\n item = QtWidgets.QTableWidgetItem()\\n item.setTextAlignment(QtCore.Qt.AlignCenter)\\n self.tableWidget.setItem(0, 14, item)\\n self.tableWidget.horizontalHeader().setVisible(True)\\n self.tableWidget.horizontalHeader().setCascadingSectionResizes(True)\\n self.tableWidget.horizontalHeader().setDefaultSectionSize(41)\\n self.tableWidget.horizontalHeader().setStretchLastSection(True)\\n self.tableWidget.verticalHeader().setVisible(False)\\n self.tableWidget.verticalHeader().setCascadingSectionResizes(False)\\n self.tableWidget.verticalHeader().setDefaultSectionSize(40)\\n self.tableWidget.verticalHeader().setHighlightSections(True)\\n self.tableWidget.verticalHeader().setMinimumSectionSize(40)\\n self.tableWidget.verticalHeader().setSortIndicatorShown(False)\\n self.RPlayedCard = QtWidgets.QLabel(Form)\\n self.RPlayedCard.setGeometry(QtCore.QRect(490, 120, 102, 51))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.RPlayedCard.setFont(font)\\n self.RPlayedCard.setAlignment(QtCore.Qt.AlignCenter)\\n self.RPlayedCard.setObjectName(\\\"RPlayedCard\\\")\\n self.PredictedCard = QtWidgets.QLabel(Form)\\n self.PredictedCard.setGeometry(QtCore.QRect(320, 190, 121, 51))\\n font = QtGui.QFont()\\n font.setFamily(\\\"微软雅黑\\\")\\n font.setPointSize(10)\\n font.setBold(False)\\n font.setItalic(False)\\n font.setWeight(50)\\n self.PredictedCard.setFont(font)\\n self.PredictedCard.setStyleSheet(\\\"\\\")\\n self.PredictedCard.setFrameShape(QtWidgets.QFrame.Panel)\\n self.PredictedCard.setLineWidth(1)\\n self.PredictedCard.setAlignment(QtCore.Qt.AlignCenter)\\n self.PredictedCard.setObjectName(\\\"PredictedCard\\\")\\n\\n self.retranslateUi(Form)\\n QtCore.QMetaObject.connectSlotsByName(Form)\\n\\n def retranslateUi(self, Form):\\n _translate = QtCore.QCoreApplication.translate\\n Form.setWindowTitle(_translate(\\\"Form\\\", \\\"Hi\\\"))\\n self.WinRate.setText(_translate(\\\"Form\\\", \\\"评分\\\"))\\n self.UserHandCards.setText(_translate(\\\"Form\\\", \\\"手牌\\\"))\\n self.ThreeLandlordCards.setText(_translate(\\\"Form\\\", \\\"地主牌\\\"))\\n self.BidWinrate.setText(_translate(\\\"Form\\\", \\\"叫牌胜率：\\\"))\\n self.PreWinrate.setText(_translate(\\\"Form\\\", \\\"局前胜率：\\\"))\\n self.label.setText(_translate(\\\"Form\\\", \\\"游戏状态\\\"))\\n self.LPlayedCard.setText(_translate(\\\"Form\\\", \\\"上家出牌区域\\\"))\\n self.SingleButton.setText(_translate(\\\"Form\\\", \\\"单局\\\"))\\n self.LoopButton.setText(_translate(\\\"Form\\\", \\\" 连续\\\"))\\n self.StopButton.setText(_translate(\\\"Form\\\", \\\"停止\\\"))\\n item = self.tableWidget.horizontalHeaderItem(0)\\n item.setText(_translate(\\\"Form\\\", \\\"大\\\"))\\n item = self.tableWidget.horizontalHeaderItem(1)\\n item.setText(_translate(\\\"Form\\\", \\\"小\\\"))\\n item = self.tableWidget.horizontalHeaderItem(2)\\n item.setText(_translate(\\\"Form\\\", \\\"2\\\"))\\n item = self.tableWidget.horizontalHeaderItem(3)\\n item.setText(_translate(\\\"Form\\\", \\\"A\\\"))\\n item = self.tableWidget.horizontalHeaderItem(4)\\n item.setText(_translate(\\\"Form\\\", \\\"K\\\"))\\n item = self.tableWidget.horizontalHeaderItem(5)\\n item.setText(_translate(\\\"Form\\\", \\\"Q\\\"))\\n item = self.tableWidget.horizontalHeaderItem(6)\\n item.setText(_translate(\\\"Form\\\", \\\"J\\\"))\\n item = self.tableWidget.horizontalHeaderItem(7)\\n item.setText(_translate(\\\"Form\\\", \\\"10\\\"))\\n item = self.tableWidget.horizontalHeaderItem(8)\\n item.setText(_translate(\\\"Form\\\", \\\"9\\\"))\\n item = self.tableWidget.horizontalHeaderItem(9)\\n item.setText(_translate(\\\"Form\\\", \\\"8\\\"))\\n item = self.tableWidget.horizontalHeaderItem(10)\\n item.setText(_translate(\\\"Form\\\", \\\"7\\\"))\\n item = self.tableWidget.horizontalHeaderItem(11)\\n item.setText(_translate(\\\"Form\\\", \\\"6\\\"))\\n item = self.tableWidget.horizontalHeaderItem(12)\\n item.setText(_translate(\\\"Form\\\", \\\"5\\\"))\\n item = self.tableWidget.horizontalHeaderItem(13)\\n item.setText(_translate(\\\"Form\\\", \\\"4\\\"))\\n item = self.tableWidget.horizontalHeaderItem(14)\\n item.setText(_translate(\\\"Form\\\", \\\"3\\\"))\\n __sortingEnabled = self.tableWidget.isSortingEnabled()\\n self.tableWidget.setSortingEnabled(False)\\n item = self.tableWidget.item(0, 0)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 1)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 2)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 3)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 4)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 5)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 6)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 7)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 8)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 9)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 10)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 11)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 12)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 13)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n item = self.tableWidget.item(0, 14)\\n item.setText(_translate(\\\"Form\\\", \\\"0\\\"))\\n self.tableWidget.setSortingEnabled(__sortingEnabled)\\n self.RPlayedCard.setText(_translate(\\\"Form\\\", \\\"下家出牌区域\\\"))\\n self.PredictedCard.setText(_translate(\\\"Form\\\", \\\"AI出牌区域\\\"))\"\n },\n {\n \"identifier\": \"GameEnv\",\n \"path\": \"douzero/env/game.py\",\n \"snippet\": \"class GameEnv(object):\\n\\n def __init__(self, players):\\n\\n self.card_play_action_seq = []\\n\\n self.three_landlord_cards = None\\n self.game_over = False\\n\\n self.acting_player_position = None\\n self.player_utility_dict = None\\n\\n self.players = players\\n\\n self.last_move_dict = {'landlord': [],\\n 'landlord_up': [],\\n 'landlord_down': []}\\n\\n self.played_cards = {'landlord': [],\\n 'landlord_up': [],\\n 'landlord_down': []}\\n\\n self.last_move = []\\n self.last_two_moves = []\\n\\n self.num_wins = {'landlord': 0,\\n 'farmer': 0}\\n\\n self.num_scores = {'landlord': 0,\\n 'farmer': 0}\\n\\n self.info_sets = {'landlord': InfoSet('landlord'),\\n 'landlord_up': InfoSet('landlord_up'),\\n 'landlord_down': InfoSet('landlord_down')}\\n\\n self.bomb_num = 0\\n self.last_pid = 'landlord'\\n\\n self.bid_info = [[1, 1, 1],\\n [1, 1, 1],\\n [1, 1, 1],\\n [1, 1, 1]]\\n self.bid_count = 0\\n self.multiply_count = {'landlord': 1,\\n 'landlord_up': 1,\\n 'landlord_down': 1}\\n self.step_count = 0\\n\\n\\n def card_play_init(self, card_play_data):\\n self.info_sets['landlord'].player_hand_cards = \\\\\\n card_play_data['landlord']\\n self.info_sets['landlord_up'].player_hand_cards = \\\\\\n card_play_data['landlord_up']\\n self.info_sets['landlord_down'].player_hand_cards = \\\\\\n card_play_data['landlord_down']\\n self.three_landlord_cards = card_play_data['three_landlord_cards']\\n self.get_acting_player_position()\\n self.game_infoset = self.get_infoset()\\n\\n\\n def game_done(self):\\n if len(self.info_sets['landlord'].player_hand_cards) == 0 or \\\\\\n len(self.info_sets['landlord_up'].player_hand_cards) == 0 or \\\\\\n len(self.info_sets['landlord_down'].player_hand_cards) == 0:\\n # if one of the three players discards his hand,\\n # then game is over.\\n self.compute_player_utility()\\n self.update_num_wins_scores()\\n\\n self.game_over = True\\n\\n def compute_player_utility(self):\\n\\n if len(self.info_sets['landlord'].player_hand_cards) == 0:\\n self.player_utility_dict = {'landlord': 2,\\n 'farmer': -1}\\n else:\\n self.player_utility_dict = {'landlord': -2,\\n 'farmer': 1}\\n\\n def update_num_wins_scores(self):\\n for pos, utility in self.player_utility_dict.items():\\n base_score = 2 if pos == 'landlord' else 1\\n if utility > 0:\\n self.num_wins[pos] += 1\\n self.winner = pos\\n self.num_scores[pos] += base_score * (2 ** self.bomb_num)\\n else:\\n self.num_scores[pos] -= base_score * (2 ** self.bomb_num)\\n\\n def get_winner(self):\\n return self.winner\\n\\n def get_bomb_num(self):\\n return self.bomb_num\\n\\n def step(self, position, action=[]):\\n win_rate = 0\\n if self.acting_player_position == position:\\n action, actions_confidence = self.players[1].act(self.game_infoset)\\n # 计算胜率\\n win_rate = actions_confidence\\n # win_rate = max(actions_confidence, -1)\\n # win_rate = min(win_rate, 1)\\n # win_rate = str(round(float((win_rate + 1) / 2), 4))\\n\\n if len(action) > 0:\\n self.last_pid = self.acting_player_position\\n\\n if action in bombs:\\n self.bomb_num += 1\\n\\n self.last_move_dict[\\n self.acting_player_position] = action.copy()\\n\\n self.card_play_action_seq.append((position, action))\\n self.update_acting_player_hand_cards(action)\\n\\n self.played_cards[self.acting_player_position] += action\\n\\n if self.acting_player_position == 'landlord' and \\\\\\n len(action) > 0 and \\\\\\n len(self.three_landlord_cards) > 0:\\n for card in action:\\n if len(self.three_landlord_cards) > 0:\\n if card in self.three_landlord_cards:\\n self.three_landlord_cards.remove(card)\\n else:\\n break\\n self.game_done()\\n if not self.game_over:\\n self.get_acting_player_position()\\n self.game_infoset = self.get_infoset()\\n # 返回动作和胜率,只有玩家角色会接受返回值\\n action_message = {\\\"action\\\": str(''.join([EnvCard2RealCard[c] for c in action])),\\n \\\"win_rate\\\": str(round(float(win_rate), 4))}\\n return action_message\\n\\n def get_last_move(self):\\n last_move = []\\n if len(self.card_play_action_seq) != 0:\\n if len(self.card_play_action_seq[-1][1]) == 0:\\n last_move = self.card_play_action_seq[-2][1]\\n else:\\n last_move = self.card_play_action_seq[-1][1]\\n\\n return last_move\\n\\n def get_last_two_moves(self):\\n last_two_moves = [[], []]\\n for card in self.card_play_action_seq[-2:]:\\n last_two_moves.insert(0, card[1])\\n last_two_moves = last_two_moves[:2]\\n return last_two_moves\\n\\n def get_acting_player_position(self):\\n if self.acting_player_position is None:\\n self.acting_player_position = 'landlord'\\n\\n else:\\n if self.acting_player_position == 'landlord':\\n self.acting_player_position = 'landlord_down'\\n\\n elif self.acting_player_position == 'landlord_down':\\n self.acting_player_position = 'landlord_up'\\n\\n else:\\n self.acting_player_position = 'landlord'\\n\\n return self.acting_player_position\\n\\n def update_acting_player_hand_cards(self, action):\\n if action != []:\\n # 更新玩家手牌，删除对应的牌\\n if self.acting_player_position == self.players[0]:\\n for card in action:\\n self.info_sets[self.acting_player_position].player_hand_cards.remove(card)\\n # 更新另外两个玩家手牌，删除相同数量的牌\\n else:\\n del self.info_sets[self.acting_player_position].player_hand_cards[0:len(action)]\\n self.info_sets[self.acting_player_position].player_hand_cards.sort()\\n\\n def get_legal_card_play_actions(self):\\n mg = MovesGener(\\n self.info_sets[self.acting_player_position].player_hand_cards)\\n\\n action_sequence = self.card_play_action_seq\\n\\n rival_move = []\\n if len(action_sequence) != 0:\\n if len(action_sequence[-1][1]) == 0:\\n rival_move = action_sequence[-2][1]\\n else:\\n rival_move = action_sequence[-1][1]\\n\\n rival_type = md.get_move_type(rival_move)\\n rival_move_type = rival_type['type']\\n rival_move_len = rival_type.get('len', 1)\\n moves = list()\\n\\n if rival_move_type == md.TYPE_0_PASS:\\n moves = mg.gen_moves()\\n\\n elif rival_move_type == md.TYPE_1_SINGLE:\\n all_moves = mg.gen_type_1_single()\\n moves = ms.filter_type_1_single(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_2_PAIR:\\n all_moves = mg.gen_type_2_pair()\\n moves = ms.filter_type_2_pair(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_3_TRIPLE:\\n all_moves = mg.gen_type_3_triple()\\n moves = ms.filter_type_3_triple(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_4_BOMB:\\n all_moves = mg.gen_type_4_bomb() + mg.gen_type_5_king_bomb()\\n moves = ms.filter_type_4_bomb(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_5_KING_BOMB:\\n moves = []\\n\\n elif rival_move_type == md.TYPE_6_3_1:\\n all_moves = mg.gen_type_6_3_1()\\n moves = ms.filter_type_6_3_1(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_7_3_2:\\n all_moves = mg.gen_type_7_3_2()\\n moves = ms.filter_type_7_3_2(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_8_SERIAL_SINGLE:\\n all_moves = mg.gen_type_8_serial_single(repeat_num=rival_move_len)\\n moves = ms.filter_type_8_serial_single(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_9_SERIAL_PAIR:\\n all_moves = mg.gen_type_9_serial_pair(repeat_num=rival_move_len)\\n moves = ms.filter_type_9_serial_pair(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_10_SERIAL_TRIPLE:\\n all_moves = mg.gen_type_10_serial_triple(repeat_num=rival_move_len)\\n moves = ms.filter_type_10_serial_triple(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_11_SERIAL_3_1:\\n all_moves = mg.gen_type_11_serial_3_1(repeat_num=rival_move_len)\\n moves = ms.filter_type_11_serial_3_1(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_12_SERIAL_3_2:\\n all_moves = mg.gen_type_12_serial_3_2(repeat_num=rival_move_len)\\n moves = ms.filter_type_12_serial_3_2(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_13_4_2:\\n all_moves = mg.gen_type_13_4_2()\\n moves = ms.filter_type_13_4_2(all_moves, rival_move)\\n\\n elif rival_move_type == md.TYPE_14_4_22:\\n all_moves = mg.gen_type_14_4_22()\\n moves = ms.filter_type_14_4_22(all_moves, rival_move)\\n\\n if rival_move_type not in [md.TYPE_0_PASS,\\n md.TYPE_4_BOMB, md.TYPE_5_KING_BOMB]:\\n moves = moves + mg.gen_type_4_bomb() + mg.gen_type_5_king_bomb()\\n\\n if len(rival_move) != 0: # rival_move is not 'pass'\\n moves = moves + [[]]\\n\\n for m in moves:\\n m.sort()\\n\\n return moves\\n\\n def reset(self):\\n self.card_play_action_seq = []\\n\\n self.three_landlord_cards = None\\n self.game_over = False\\n\\n self.acting_player_position = None\\n self.player_utility_dict = None\\n\\n self.last_move_dict = {'landlord': [],\\n 'landlord_up': [],\\n 'landlord_down': []}\\n\\n self.played_cards = {'landlord': [],\\n 'landlord_up': [],\\n 'landlord_down': []}\\n\\n self.last_move = []\\n self.last_two_moves = []\\n\\n self.info_sets = {'landlord': InfoSet('landlord'),\\n 'landlord_up': InfoSet('landlord_up'),\\n 'landlord_down': InfoSet('landlord_down')}\\n\\n self.bomb_num = 0\\n self.last_pid = 'landlord'\\n self.bid_info = [[1, 1, 1],\\n [1, 1, 1],\\n [1, 1, 1],\\n [1, 1, 1]]\\n self.bid_count = 0\\n self.multiply_count = {'landlord': 0,\\n 'landlord_up': 0,\\n 'landlord_down': 0}\\n self.step_count = 0\\n\\n def get_infoset(self):\\n self.info_sets[\\n self.acting_player_position].last_pid = self.last_pid\\n\\n self.info_sets[\\n self.acting_player_position].legal_actions = \\\\\\n self.get_legal_card_play_actions()\\n\\n self.info_sets[\\n self.acting_player_position].bomb_num = self.bomb_num\\n\\n self.info_sets[\\n self.acting_player_position].last_move = self.get_last_move()\\n\\n self.info_sets[\\n self.acting_player_position].last_two_moves = self.get_last_two_moves()\\n\\n self.info_sets[\\n self.acting_player_position].last_move_dict = self.last_move_dict\\n\\n self.info_sets[self.acting_player_position].num_cards_left_dict = \\\\\\n {pos: len(self.info_sets[pos].player_hand_cards)\\n for pos in ['landlord', 'landlord_up', 'landlord_down']}\\n\\n self.info_sets[self.acting_player_position].other_hand_cards = []\\n\\n '''\\n 调整计算其他人手牌的方法，整副牌减去玩家手牌与出过的牌\\n for pos in ['landlord', 'landlord_up', 'landlord_down']:\\n if pos != self.acting_player_position:\\n self.info_sets[\\n self.acting_player_position].other_hand_cards += \\\\\\n self.info_sets[pos].player_hand_cards\\n '''\\n # 把出过的牌中三个子列表合成一个列表\\n played_cards_tmp = []\\n for i in list(self.played_cards.values()):\\n played_cards_tmp.extend(i)\\n # 出过的牌和玩家手上的牌\\n played_and_hand_cards = played_cards_tmp + self.info_sets[self.acting_player_position].player_hand_cards\\n # 整副牌减去出过的牌和玩家手上的牌，就是其他人的手牌\\n for i in set(AllEnvCard):\\n self.info_sets[\\n self.acting_player_position].other_hand_cards.extend([i] * (AllEnvCard.count(i) - played_and_hand_cards.count(i)))\\n\\n self.info_sets[self.acting_player_position].played_cards = \\\\\\n self.played_cards\\n self.info_sets[self.acting_player_position].three_landlord_cards = \\\\\\n self.three_landlord_cards\\n self.info_sets[self.acting_player_position].card_play_action_seq = \\\\\\n self.card_play_action_seq\\n\\n self.info_sets[\\n self.acting_player_position].all_handcards = \\\\\\n {pos: self.info_sets[pos].player_hand_cards\\n for pos in ['landlord', 'landlord_up', 'landlord_down']}\\n\\n # Custom bid info\\n self.info_sets[self.acting_player_position].bid_info = bid_infos[self.acting_player_position]\\n\\n return deepcopy(self.info_sets[self.acting_player_position])\"\n },\n {\n \"identifier\": \"DeepAgent\",\n \"path\": \"douzero/evaluation/deep_agent.py\",\n \"snippet\": \"class DeepAgent:\\n\\n def __init__(self, position, model_path):\\n self.model_type = \\\"old\\\"\\n if \\\"general\\\" in model_path:\\n self.model_type = \\\"general\\\"\\n elif \\\"resnet\\\" in model_path:\\n self.model_type = \\\"resnet\\\"\\n self.model = _load_model(position, model_path, self.model_type)\\n\\n def act(self, infoset):\\n obs = get_obs(infoset, model_type=self.model_type)\\n z_batch = torch.from_numpy(obs['z_batch']).float()\\n x_batch = torch.from_numpy(obs['x_batch']).float()\\n if torch.cuda.is_available():\\n z_batch, x_batch = z_batch.cuda(), x_batch.cuda()\\n y_pred = self.model.forward(z_batch, x_batch, return_value=True)['values']\\n y_pred = y_pred.detach().cpu().numpy()\\n\\n best_action_index = np.argmax(y_pred, axis=0)[0]\\n best_action = infoset.legal_actions[best_action_index]\\n best_action_confidence = y_pred[best_action_index]\\n return best_action, best_action_confidence\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import GameHelper as gh\nimport os\nimport sys\nimport time\nimport threading\nimport pyautogui\nimport win32gui\nimport multiprocessing as mp\nimport DetermineColor as DC\nimport cv2\nimport numpy as np\nimport traceback\nimport BidModel\nimport LandlordModel\nimport FarmerModel\nfrom GameHelper import GameHelper\nfrom PIL import Image\nfrom skimage.metrics import structural_similarity as ssim\nfrom collections import defaultdict\nfrom douzero.env.move_detector import get_move_type\nfrom PyQt5 import QtGui, QtWidgets, QtCore\nfrom PyQt5.QtWidgets import QTableWidgetItem, QInputDialog, QMessageBox\nfrom PyQt5.QtGui import QPixmap, QIcon\nfrom PyQt5.QtCore import QTime, QEventLoop, Qt\nfrom MainWindow import Ui_Form\nfrom douzero.env.game import GameEnv\nfrom douzero.evaluation.deep_agent import DeepAgent"},"token_num":{"kind":"number","value":15236,"string":"15,236"},"cropped_code":{"kind":"string","value":"\n def init_display(self):\n self.WinRate.setText(\"评分\")\n self.label.setText(\"游戏状态\")\n self.label.setStyleSheet('background-color: rgba(255, 0, 0, 0);')\n self.UserHandCards.setText(\"手牌\")\n # self.LBrowser.clear()\n # self.RBrowser.clear()\n self.LPlayedCard.setText(\"上家出牌区域\")\n self.RPlayedCard.setText(\"下家出牌区域\")\n self.PredictedCard.setText(\"AI出牌区域\")\n self.ThreeLandlordCards.setText(\"地主牌\")\n self.recorder2zero()\n for player in self.Players:\n player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')\n\n def init_cards(self):\n self.RunGame = True\n GameHelper.Interrupt = False\n self.user_hand_cards_real = \"\"\n self.user_hand_cards_env = []\n # 其他玩家出牌\n self.other_played_cards_real = \"\"\n self.other_played_cards_env = []\n # 其他玩家手牌（整副牌减去玩家手牌，后续再减掉历史出牌）\n self.other_hand_cards = []\n # 三张底牌\n self.three_landlord_cards_real = \"\"\n self.three_landlord_cards_env = []\n # 玩家角色代码：0-地主上家, 1-地主, 2-地主下家\n self.user_position_code = None\n self.user_position = \"\"\n # 开局时三个玩家的手牌\n self.card_play_data_list = {}\n\n # 识别玩家手牌\n self.user_hand_cards_real = self.find_my_cards()\n while len(self.user_hand_cards_real) != 17 and len(self.user_hand_cards_real) != 20:\n self.detect_start_btn()\n if not self.RunGame:\n break\n self.sleep(200)\n self.user_hand_cards_real = self.find_my_cards()\n self.user_hand_cards_env = [RealCard2EnvCard[c] for c in list(self.user_hand_cards_real)]\n # 识别三张底牌\n self.three_landlord_cards_real = self.find_landlord_cards()\n self.ThreeLandlordCards.setText(\"底牌：\" + self.three_landlord_cards_real)\n self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]\n while len(self.three_landlord_cards_env) != 3:\n self.detect_start_btn()\n if not self.RunGame:\n break\n if len(self.three_landlord_cards_env) > 3:\n self.ThreeLandlordCardsConfidence += 0.05\n elif len(self.three_landlord_cards_env) < 3:\n self.ThreeLandlordCardsConfidence -= 0.05\n self.three_landlord_cards_real = self.find_landlord_cards()\n self.ThreeLandlordCards.setText(\"底牌：\" + self.three_landlord_cards_real)\n self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]\n\n # 识别玩家的角色\n self.sleep(500)\n self.user_position_code = self.find_landlord(self.LandlordFlagPos)\n self.sleep(200)\n while self.user_position_code is None:\n self.detect_start_btn()\n if not self.RunGame:\n break\n self.user_position_code = self.find_landlord(self.LandlordFlagPos)\n self.sleep(200)\n print(\"正在出牌人的代码： \", self.user_position_code)\n if self.user_position_code is None:\n items = (\"地主上家\", \"地主\", \"地主下家\")\n item, okPressed = QInputDialog.getItem(self, \"选择角色\", \"未识别到地主，请手动选择角色:\", items, 0, False)\n if okPressed and item:\n self.user_position_code = items.index(item)\n else:\n return\n self.user_position = ['landlord_up', 'landlord', 'landlord_down'][self.user_position_code]\n print(\"我现在在地主的方向：\", self.user_position)\n for player in self.Players:\n player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')\n self.Players[self.user_position_code].setStyleSheet('background-color: rgba(0, 255, 0, 0.5);')\n\n # 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）\n for i in set(AllEnvCard):\n self.other_hand_cards.extend([i] * (AllEnvCard.count(i) - self.user_hand_cards_env.count(i)))\n self.other_hands_cards_str = str(''.join([EnvCard2RealCard[c] for c in self.other_hand_cards]))[::-1]\n self.cards_recorder(self.other_hands_cards_str)\n self.card_play_data_list.update({\n 'three_landlord_cards': self.three_landlord_cards_env,\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 0) % 3]:\n self.user_hand_cards_env,\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 1) % 3]:\n self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 != 1 else self.other_hand_cards[17:],\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 2) % 3]:\n self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 == 1 else self.other_hand_cards[17:]\n })\n print(\"开始对局\")\n print(\"手牌:\", self.user_hand_cards_real)\n print(\"地主牌:\", self.three_landlord_cards_real)\n # 生成手牌结束，校验手牌数量\n if len(self.card_play_data_list[\"three_landlord_cards\"]) != 3:\n QMessageBox.critical(self, \"底牌识别出错\", \"底牌必须是3张！\", QMessageBox.Yes, QMessageBox.Yes)\n self.init_display()\n return\n if len(self.card_play_data_list[\"landlord_up\"]) != 17 or \\\n len(self.card_play_data_list[\"landlord_down\"]) != 17 or \\\n len(self.card_play_data_list[\"landlord\"]) != 20:\n QMessageBox.critical(self, \"手牌识别出错\", \"初始手牌数目有误\", QMessageBox.Yes, QMessageBox.Yes)\n self.init_display()\n return\n # 出牌顺序：0-玩家出牌, 1-玩家下家出牌, 2-玩家上家出牌\n self.play_order = 0 if self.user_position == \"landlord\" else 1 if self.user_position == \"landlord_up\" else 2\n\n # 创建一个代表玩家的AI\n ai_players = [0, 0]\n ai_players[0] = self.user_position\n ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])\n\n"},"all_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# Created by: Raf\n# Modify by: Vincentzyx\n\n\n\n\n\nEnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',\n 8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',\n 13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}\n\nRealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,\n '8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,\n 'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}\n\nAllEnvCard = [3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,\n 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12,\n 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 17, 17, 17, 17, 20, 30]\n\nAllCards = ['D', 'X', '2', 'A', 'K', 'Q', 'J', 'T', '9', '8', '7', '6', '5', '4', '3']\n\nhelper = GameHelper()\n\n\nclass MyPyQT_Form(QtWidgets.QWidget, Ui_Form):\n def __init__(self):\n super(MyPyQT_Form, self).__init__()\n self.other_hands_cards_str = None\n self.stop_sign = None\n self.loop_sign = None\n self.env = None\n self.three_landlord_cards_env = None\n self.three_landlord_cards_real = None\n self.user_hand_cards_env = None\n self.user_hand_cards_real = None\n self.play_order = None\n self.card_play_data_list = None\n self.other_hand_cards = None\n self.other_played_cards_env = None\n self.other_played_cards_real = None\n self.user_position = None\n self.user_position_code = None\n self.setupUi(self)\n self.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint | # 使能最小化按钮\n QtCore.Qt.WindowStaysOnTopHint | # 窗体总在最前端\n QtCore.Qt.WindowCloseButtonHint)\n self.setWindowIcon(QIcon(':/pics/favicon.ico'))\n self.setWindowTitle(\"DouZero欢乐斗地主v2.0\")\n self.setFixedSize(self.width(), self.height()) # 固定窗体大小\n self.move(50, 50)\n # self.setWindowIcon(QIcon('pics/favicon.ico'))\n window_pale = QtGui.QPalette()\n # window_pale.setBrush(self.backgroundRole(), QtGui.QBrush(QtGui.QPixmap(\"pics/bg.png\")))\n\n self.setPalette(window_pale)\n self.SingleButton.clicked.connect(self.game_single)\n self.LoopButton.clicked.connect(self.game_loop)\n self.StopButton.clicked.connect(self.stop)\n\n # self.Players = [self.RPlayer, self.Player, self.LPlayer]\n self.Players = [self.RPlayedCard, self.PredictedCard, self.LPlayedCard]\n self.counter = QTime()\n\n # 参数\n self.MyConfidence = 0.8 # 我的牌的置信度\n self.OtherConfidence = 0.8 # 别人的牌的置信度\n self.WhiteConfidence = 0.85 # 检测白块的置信度\n self.LandlordFlagConfidence = 0.8 # 检测地主标志的置信度\n self.ThreeLandlordCardsConfidence = 0.8 # 检测地主底牌的置信度\n self.PassConfidence = 0.7\n\n self.PassConfidence = 0.8\n self.WaitTime = 1 # 等待状态稳定延时\n self.MyFilter = 40 # 我的牌检测结果过滤参数\n self.OtherFilter = 25 # 别人的牌检测结果过滤参数\n self.SleepTime = 0.1 # 循环中睡眠时间\n self.RunGame = False\n self.AutoPlay = False\n self.BidThreshold1 = 65 # 叫地主阈值\n self.BidThreshold2 = 72 # 抢地主阈值\n self.JiabeiThreshold = (\n (85, 72), # 叫地主 超级加倍 加倍 阈值\n (85, 75) # 叫地主 超级加倍 加倍 阈值 (在地主是抢来的情况下)\n )\n self.MingpaiThreshold = 92\n # 坐标\n self.MyHandCardsPos = (180, 560, 1050, 90) # 我的截图区域\n self.LPlayedCardsPos = (320, 280, 500, 120) # 左边出牌截图区域\n self.RPlayedCardsPos = (600, 280, 500, 120) # 右边出牌截图区域\n self.LandlordCardsPos = (600, 33, 220, 103) # 地主底牌截图区域\n self.LPassPos = (360, 360, 120, 80) # 左边不出截图区域\n self.RPassPos = (940, 360, 120, 80) # 右边不出截图区域\n\n self.PassBtnPos = (200, 450, 1000, 120) # 要不起截图区域\n self.GeneralBtnPos = (200, 450, 1000, 120) # 叫地主、抢地主、加倍按钮截图区域\n self.LandlordFlagPos = [(1247, 245, 48, 52), (12, 661, 51, 53), (123, 243, 52, 54)] # 地主标志截图区域(右-我-左)\n\n self.card_play_model_path_dict = {\n 'landlord': \"baselines/resnet/resnet_landlord.ckpt\",\n 'landlord_up': \"baselines/resnet/resnet_landlord_up.ckpt\",\n 'landlord_down': \"baselines/resnet/resnet_landlord_down.ckpt\"\n }\n\n def game_single(self):\n self.loop_sign = 0\n self.stop_sign = 0\n self.detect_start_btn()\n self.before_start()\n self.init_cards()\n\n def game_loop(self):\n self.loop_sign = 1\n self.stop_sign = 0\n while True:\n if self.stop_sign == 1:\n break\n self.detect_start_btn()\n self.before_start()\n self.init_cards()\n self.sleep(5000)\n\n def stop(self):\n self.stop_sign = 1\n print(\"按下停止键\")\n try:\n self.RunGame = False\n self.loop_sign = 0\n\n self.env.game_over = True\n self.env.reset()\n self.init_display()\n self.PreWinrate.setText(\"局前胜率: \")\n self.BidWinrate.setText(\"叫牌胜率: \")\n except AttributeError as e:\n traceback.print_exc()\n\n def init_display(self):\n self.WinRate.setText(\"评分\")\n self.label.setText(\"游戏状态\")\n self.label.setStyleSheet('background-color: rgba(255, 0, 0, 0);')\n self.UserHandCards.setText(\"手牌\")\n # self.LBrowser.clear()\n # self.RBrowser.clear()\n self.LPlayedCard.setText(\"上家出牌区域\")\n self.RPlayedCard.setText(\"下家出牌区域\")\n self.PredictedCard.setText(\"AI出牌区域\")\n self.ThreeLandlordCards.setText(\"地主牌\")\n self.recorder2zero()\n for player in self.Players:\n player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')\n\n def init_cards(self):\n self.RunGame = True\n GameHelper.Interrupt = False\n self.user_hand_cards_real = \"\"\n self.user_hand_cards_env = []\n # 其他玩家出牌\n self.other_played_cards_real = \"\"\n self.other_played_cards_env = []\n # 其他玩家手牌（整副牌减去玩家手牌，后续再减掉历史出牌）\n self.other_hand_cards = []\n # 三张底牌\n self.three_landlord_cards_real = \"\"\n self.three_landlord_cards_env = []\n # 玩家角色代码：0-地主上家, 1-地主, 2-地主下家\n self.user_position_code = None\n self.user_position = \"\"\n # 开局时三个玩家的手牌\n self.card_play_data_list = {}\n\n # 识别玩家手牌\n self.user_hand_cards_real = self.find_my_cards()\n while len(self.user_hand_cards_real) != 17 and len(self.user_hand_cards_real) != 20:\n self.detect_start_btn()\n if not self.RunGame:\n break\n self.sleep(200)\n self.user_hand_cards_real = self.find_my_cards()\n self.user_hand_cards_env = [RealCard2EnvCard[c] for c in list(self.user_hand_cards_real)]\n # 识别三张底牌\n self.three_landlord_cards_real = self.find_landlord_cards()\n self.ThreeLandlordCards.setText(\"底牌：\" + self.three_landlord_cards_real)\n self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]\n while len(self.three_landlord_cards_env) != 3:\n self.detect_start_btn()\n if not self.RunGame:\n break\n if len(self.three_landlord_cards_env) > 3:\n self.ThreeLandlordCardsConfidence += 0.05\n elif len(self.three_landlord_cards_env) < 3:\n self.ThreeLandlordCardsConfidence -= 0.05\n self.three_landlord_cards_real = self.find_landlord_cards()\n self.ThreeLandlordCards.setText(\"底牌：\" + self.three_landlord_cards_real)\n self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]\n\n # 识别玩家的角色\n self.sleep(500)\n self.user_position_code = self.find_landlord(self.LandlordFlagPos)\n self.sleep(200)\n while self.user_position_code is None:\n self.detect_start_btn()\n if not self.RunGame:\n break\n self.user_position_code = self.find_landlord(self.LandlordFlagPos)\n self.sleep(200)\n print(\"正在出牌人的代码： \", self.user_position_code)\n if self.user_position_code is None:\n items = (\"地主上家\", \"地主\", \"地主下家\")\n item, okPressed = QInputDialog.getItem(self, \"选择角色\", \"未识别到地主，请手动选择角色:\", items, 0, False)\n if okPressed and item:\n self.user_position_code = items.index(item)\n else:\n return\n self.user_position = ['landlord_up', 'landlord', 'landlord_down'][self.user_position_code]\n print(\"我现在在地主的方向：\", self.user_position)\n for player in self.Players:\n player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')\n self.Players[self.user_position_code].setStyleSheet('background-color: rgba(0, 255, 0, 0.5);')\n\n # 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）\n for i in set(AllEnvCard):\n self.other_hand_cards.extend([i] * (AllEnvCard.count(i) - self.user_hand_cards_env.count(i)))\n self.other_hands_cards_str = str(''.join([EnvCard2RealCard[c] for c in self.other_hand_cards]))[::-1]\n self.cards_recorder(self.other_hands_cards_str)\n self.card_play_data_list.update({\n 'three_landlord_cards': self.three_landlord_cards_env,\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 0) % 3]:\n self.user_hand_cards_env,\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 1) % 3]:\n self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 != 1 else self.other_hand_cards[17:],\n ['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 2) % 3]:\n self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 == 1 else self.other_hand_cards[17:]\n })\n print(\"开始对局\")\n print(\"手牌:\", self.user_hand_cards_real)\n print(\"地主牌:\", self.three_landlord_cards_real)\n # 生成手牌结束，校验手牌数量\n if len(self.card_play_data_list[\"three_landlord_cards\"]) != 3:\n QMessageBox.critical(self, \"底牌识别出错\", \"底牌必须是3张！\", QMessageBox.Yes, QMessageBox.Yes)\n self.init_display()\n return\n if len(self.card_play_data_list[\"landlord_up\"]) != 17 or \\\n len(self.card_play_data_list[\"landlord_down\"]) != 17 or \\\n len(self.card_play_data_list[\"landlord\"]) != 20:\n QMessageBox.critical(self, \"手牌识别出错\", \"初始手牌数目有误\", QMessageBox.Yes, QMessageBox.Yes)\n self.init_display()\n return\n # 出牌顺序：0-玩家出牌, 1-玩家下家出牌, 2-玩家上家出牌\n self.play_order = 0 if self.user_position == \"landlord\" else 1 if self.user_position == \"landlord_up\" else 2\n\n # 创建一个代表玩家的AI\n ai_players = [0, 0]\n ai_players[0] = self.user_position\n ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])\n"},"next_line":{"kind":"string","value":" self.env = GameEnv(ai_players)"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-12-01 04:04:30+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5389,"cells":{"repo_name":{"kind":"string","value":"super1207/satoricq"},"file_path":{"kind":"string","value":"satori.py"},"context":{"kind":"list like","value":[{"identifier":"AdapterKook","path":"kook_adapter.py","snippet":"class AdapterKook:\n def __init__(self,config = {}) -> None:\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\n self._access_token = config[\"access_token\"]\n self._http_url = \"https://www.kookapp.cn/api/v3\"\n self._is_stop = False\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n self._queue = Queue(maxsize=100)\n self._id = 0\n self._sn = 0\n self._self_id = None\n\n\n async def enable(self) -> None:\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\n '''\n pass\n\n async def disable(self) -> None:\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\n pass\n \n async def release(self) -> None:\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\n 但是，这个函数允许资源延迟释放，只要能释放就行\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\n '''\n self._is_stop = True\n\n async def get_msg(self) -> dict:\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\n return await self._queue.get()\n \n\n async def _ws_recv(self,websocket):\n try:\n reply = await asyncio.wait_for(websocket.recv(),0.1)\n return reply\n except asyncio.TimeoutError:\n return None\n\n async def _ws_connect(self):\n self._login_status = SatoriLogin.LoginStatus.CONNECT\n ws_url = (await self._api_call(\"/gateway/index?compress=0\"))[\"url\"]\n async with connect(ws_url) as websocket:\n tm = time.time()\n while not self._is_stop:\n reply = await self._ws_recv(websocket)\n if not reply:\n now_time = time.time()\n if now_time - tm > 30:\n tm = now_time\n await websocket.send(json.dumps({\"s\": 2,\"sn\": self._sn}))\n continue\n js = json.loads(reply)\n s = js[\"s\"]\n if s == 5:raise Exception(\"recv reset ws\")\n elif s == 3:pass # heartbeat\n elif s == 1:\n self._login_status = SatoriLogin.LoginStatus.ONLINE\n print(\"kook:ws连接成功\")\n elif s == 0:\n self._sn = js[\"sn\"]\n asyncio.create_task(self._event_deal(js[\"d\"]))\n\n async def _ws_server(self) -> None:\n while not self._is_stop:\n try:\n await self._ws_connect()\n except:\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n print(traceback.format_exc())\n await asyncio.sleep(3)\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n\n async def init_after(self) -> None:\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\n asyncio.create_task(self._ws_server())\n\n def _kook_msg_to_satori(self,msg_type:int,message:str)->str:\n ret = \"\"\n if msg_type == 2: #图片\n ret += \"<img src={}/>\".format(json.dumps(message))\n else:\n def kook_msg_f(msg):\n ret = \"\"\n is_f = False\n for ch in msg:\n if is_f:\n is_f = False\n ret += ch\n elif ch == \"\\\\\":\n is_f = True\n else:\n ret += ch\n return ret\n \n index = 0\n msg_list = message.split(\"(met)\")\n for it in msg_list:\n if index % 2 == 0:\n ret += satori_to_plain(kook_msg_f(it))\n else:\n if it == \"all\":\n ret += \"<at type=\\\"all\\\"/>\"\n else:\n ret += \"<at id=\\\"{}\\\"/>\".format(it)\n index += 1\n return ret\n\n\n async def _deal_group_message_event(self,data,user_id:str):\n group_id = data[\"target_id\"]\n kook_msg = data[\"content\"]\n extra = data[\"extra\"]\n author = extra[\"author\"]\n msg_type = data[\"type\"]\n\n if msg_type == 10:#卡牌\n return\n satori_msg = self._kook_msg_to_satori(msg_type,kook_msg)\n\n satori_evt = SatoriGroupMessageCreatedEvent(\n id=self._id,\n self_id=self._self_id,\n timestamp=data[\"msg_timestamp\"],\n platform=\"kook\",\n channel=SatoriChannel(\n id=\"GROUP_\"+group_id,\n type=SatoriChannel.ChannelType.TEXT,\n name=extra[\"channel_name\"]\n ),\n message=SatoriMessage(\n id=data[\"msg_id\"],\n content=satori_msg,\n created_at=data[\"msg_timestamp\"]\n ),\n user=SatoriUser(\n id=author[\"id\"],\n name=author[\"username\"],\n avatar=author[\"avatar\"],\n is_bot=author[\"bot\"]\n ),\n member=SatoriGuildMember(\n nick=author[\"nickname\"],\n avatar=author[\"avatar\"]\n ),\n guild=SatoriGuild(\n id=extra[\"guild_id\"]\n ),\n role=SatoriGuildRole(\n id=json.dumps(sorted(author[\"roles\"]))\n )\n )\n self._id += 1\n self._queue.put_nowait(satori_evt.to_dict())\n\n async def _deal_private_message_event(self,data,user_id:str):\n\n kook_msg = data[\"content\"]\n extra = data[\"extra\"]\n author = extra[\"author\"]\n msg_type = data[\"type\"]\n\n if msg_type == 10:#卡牌\n return\n satori_msg = self._kook_msg_to_satori(msg_type,kook_msg)\n\n satori_evt = SatoriPrivateMessageCreatedEvent(\n id=self._id,\n self_id=self._self_id,\n timestamp=data[\"msg_timestamp\"],\n channel=SatoriChannel(\n id=user_id,\n type=SatoriChannel.ChannelType.TEXT,\n name=author[\"username\"]\n ),\n message=SatoriMessage(\n id=data[\"msg_id\"],\n content=satori_msg,\n created_at=data[\"msg_timestamp\"]\n ),\n user=SatoriUser(\n id=user_id,\n name=author[\"username\"],\n avatar=author[\"avatar\"],\n is_bot=author[\"bot\"]\n ),\n platform=\"kook\"\n ).to_dict()\n self._id += 1\n self._queue.put_nowait(satori_evt)\n\n async def _deal_group_increase_event(self,data):\n extra = data[\"extra\"]\n satori_evt = {\n \"id\":self._id,\n \"type\":\"guild-member-added\",\n \"platform\":\"kook\",\n \"self_id\":self._self_id,\n \"timestamp\":data[\"msg_timestamp\"],\n \"guild\":SatoriGuild(id=data[\"target_id\"]).to_dict(),\n \"member\":SatoriGuildMember(joined_at=extra[\"body\"][\"joined_at\"]).to_dict(),\n \"user\":SatoriUser(id=extra[\"body\"][\"user_id\"]).to_dict()\n }\n self._id += 1\n self._queue.put_nowait(satori_evt)\n\n\n\n async def _deal_group_evt(self,data):\n user_id:str = data[\"author_id\"]\n if user_id == \"1\": # system message\n tp = data[\"type\"]\n if tp != 255:\n return\n sub_type = data[\"extra\"][\"type\"]\n if sub_type == \"joined_guild\":\n await self._deal_group_increase_event(data)\n else:\n if self._self_id:\n if user_id != self._self_id:\n await self._deal_group_message_event(data,user_id)\n\n\n async def _deal_person_evt(self,data):\n user_id:str = data[\"author_id\"]\n if user_id != 1: # 不是系统消息\n if self._self_id:\n if user_id != self._self_id:\n await self._deal_private_message_event(data,user_id)\n\n\n async def _event_deal(self,data:dict):\n try:\n tp = data[\"channel_type\"]\n if tp == \"GROUP\":\n await self._deal_group_evt(data)\n else:\n await self._deal_person_evt(data)\n except:\n print(traceback.format_exc())\n \n async def _api_call(self,path,data = None) -> dict:\n url:str = self._http_url + path\n headers = {\"Authorization\":\"Bot {}\".format(self._access_token)}\n if data == None:\n async with httpx.AsyncClient() as client:\n return (await client.get(url,headers=headers)).json()[\"data\"]\n else:\n async with httpx.AsyncClient() as client:\n return (await client.post(url,headers=headers,data=data)).json()[\"data\"]\n\n def _make_kook_text(self,text):\n ret = \"\"\n for ch in text:\n if ch in [\"\\\\\",\"*\",\"~\",\"[\",\"(\",\")\",\"]\",\"-\",\">\",\"`\"]:\n ret += \"\\\\\"\n ret += ch\n return ret\n \n async def _satori_to_kook(self,satori_obj) -> [dict]:\n to_send_data = []\n last_type = 1\n for node in satori_obj:\n if isinstance(node,str):\n text = self._make_kook_text(node)\n if last_type == 1 and len(to_send_data) != 0:\n l = len(to_send_data)\n to_send_data[l - 1][\"content\"] += text\n else:\n to_send_data.append({\n \"type\":1,\n \"content\":text\n })\n last_type = 1\n else:\n if node[\"type\"] == \"at\":\n type = get_json_or(node[\"attrs\"],\"type\",None)\n id = get_json_or(node[\"attrs\"],\"id\",None)\n if type == \"all\":\n text = \"(met)all(met)\"\n elif id != None:\n text = \"(met){}(met)\".format(self._make_kook_text(id))\n if last_type == 1 and len(to_send_data) != 0:\n l = len(to_send_data)\n to_send_data[l - 1][\"content\"] += text\n else:\n to_send_data.append({\n \"type\":1,\n \"content\":text\n })\n last_type = 1\n elif node[\"type\"] == \"img\":\n img_url:str = node[\"attrs\"][\"src\"]\n kook_img_url = \"\"\n if img_url.startswith(\"https://img.kookapp.cn\"):\n kook_img_url = img_url\n else:\n if img_url.startswith(\"data:image/\"):\n base64_start = img_url.find(\"base64,\")\n img_content = base64.b64decode(img_url[base64_start + 7:])\n else:\n async with httpx.AsyncClient() as client:\n img_content = (await client.get(img_url)).content\n files = {\n 'file':('test',img_content)\n }\n headers = {\"Authorization\":\"Bot {}\".format(self._access_token)}\n async with httpx.AsyncClient() as client:\n ret = (await client.post(self._http_url + \"/asset/create\",files=files,headers=headers)).json()\n kook_img_url = ret[\"data\"][\"url\"]\n to_send_data.append({\n \"type\":2,\n \"content\":kook_img_url\n })\n last_type = 2\n return to_send_data\n \n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\n '''发送消息'''\n satori_obj = parse_satori_html(content)\n to_sends = await self._satori_to_kook(satori_obj)\n if channel_id.startswith(\"GROUP_\"):\n channel_id = int(channel_id[6:])\n to_ret = []\n for it in to_sends:\n ret = await self._api_call(\"/message/create\",{\"content\":it[\"content\"],\"type\":it[\"type\"],\"target_id\":channel_id})\n to_ret.append(SatoriMessage(id=ret[\"msg_id\"],content=\"\").to_dict())\n return to_ret\n else:\n to_ret = []\n for it in to_sends:\n ret = await self._api_call(\"/direct-message/create\",{\"content\":it[\"content\"],\"type\":it[\"type\"],\"target_id\":channel_id})\n to_ret.append(SatoriMessage(id=ret[\"msg_id\"],content=\"\").to_dict())\n return to_ret\n \n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\n obret = (await self._api_call(\"/user/me\"))\n satori_ret = SatoriLogin(\n status=self._login_status,\n user=SatoriUser(\n id=obret[\"id\"],\n name=obret[\"username\"],\n avatar=get_json_or(obret,\"avatar\",None),\n is_bot=True\n ),\n self_id=obret[\"id\"],\n platform=\"kook\"\n ).to_dict()\n self._self_id = obret[\"id\"]\n if platform == None and self_id == None:\n return [satori_ret]\n else:\n return satori_ret\n \n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\n '''获取群组成员信息'''\n url = \"/user/view?user_id={}&guild_id={}\".format(user_id,guild_id)\n obret = (await self._api_call(url))\n satori_ret = SatoriGuildMember(\n user=SatoriUser(\n id=obret[\"id\"],\n name=get_json_or(obret,\"username\",None),\n avatar=get_json_or(obret,\"avatar\",None),\n is_bot=get_json_or(obret,\"bot\",None)\n ),\n nick=get_json_or(obret,\"nickname\",None),\n avatar=get_json_or(obret,\"avatar\",None),\n joined_at=get_json_or(obret,\"join_time\",None)\n ).to_dict()\n return satori_ret\n \n async def get_user(self,platform:Optional[str],self_id:Optional[str],user_id:str) -> [dict]:\n '''获取用户信息'''\n url = \"/user/view?user_id={}\".format(user_id)\n obret = (await self._api_call(url))\n satori_ret = SatoriUser(\n id=obret[\"id\"],\n name=obret[\"username\"],\n avatar=obret[\"avatar\"],\n is_bot=obret[\"bot\"],\n ).to_dict()\n return satori_ret\n \n async def get_channel_list(self,platform:Optional[str],self_id:Optional[str],guild_id:str) -> [dict]:\n '''获取频道列表'''\n url = \"/channel/list?guild_id={}\".format(guild_id)\n obret = (await self._api_call(url))\n ret_list = []\n items = get_json_or(obret,\"items\",None)\n for it in items:\n channel_type = it[\"type\"]\n channel_id = \"GROUP_\" + it[\"id\"]\n channel_name = it[\"name\"]\n channel_parent = it[\"parent_id\"]\n if channel_type == 1:\n ret_list.append(SatoriChannel(\n id=channel_id,\n name=channel_name,\n type=SatoriChannel.ChannelType.TEXT,\n parent_id=channel_parent\n ).to_dict())\n page_total = get_json_or(obret,\"data\",1)\n if page_total > 1:\n for i in range(2,page_total + 1):\n url = \"/channel/list?guild_id={}&page={}\".format(guild_id,i)\n obret = (await self._api_call(url))\n items = get_json_or(obret,\"items\",None)\n for it in items:\n channel_type = it[\"type\"]\n channel_id = \"GROUP_\" + it[\"id\"]\n channel_name = it[\"name\"]\n channel_parent = it[\"parent_id\"]\n if channel_type == 1:\n ret_list.append(SatoriChannel(\n id=channel_id,\n name=channel_name,\n type=SatoriChannel.ChannelType.TEXT,\n parent=channel_parent\n ).to_dict())\n return {\"data\":ret_list}"},{"identifier":"AdapterMihoyo","path":"mihoyo_adapter.py","snippet":"class AdapterMihoyo:\n def __init__(self,config = {}) -> None:\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\n self._http_url = \"https://bbs-api.miyoushe.com\"\n self._is_stop = False\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n self._queue = Queue(maxsize=100)\n self._id = 0\n self._sn = 1\n self._self_id = config[\"bot_id\"]\n self._secret = config[\"secret\"]\n self._villa_id = config[\"villa_id\"]\n\n\n async def enable(self) -> None:\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\n '''\n pass\n\n async def disable(self) -> None:\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\n pass\n \n async def release(self) -> None:\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\n 但是，这个函数允许资源延迟释放，只要能释放就行\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\n '''\n self._is_stop = True\n\n async def get_msg(self) -> dict:\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\n return await self._queue.get()\n\n async def _send_ws_pack(self,ws,ws_dat,biztype):\n magic = 0xBABEFACE.to_bytes(length=4, byteorder='little', signed=False)\n if biztype == 7:\n pb_pack = bytes(PLogin(\n uid=int(ws_dat[\"uid\"]),\n token=self._villa_id + \".\" + self._secret + \".\" + self._self_id,\n platform=ws_dat[\"platform\"],\n app_id=ws_dat[\"app_id\"],\n device_id=ws_dat[\"device_id\"]\n ))\n elif biztype == 6:\n pb_pack = bytes(PHeartBeat(\n client_timestamp=str(int(round(time.time() * 1000)))\n ))\n else:\n raise Exception(\"unkonw biztype:{}\".format(biztype))\n \n wid = self._sn\n self._sn += 1\n\n flag = 1\n appid = 104\n headerlen = 24\n datalen = headerlen + len(pb_pack)\n\n to_send = magic\n to_send += datalen.to_bytes(length=4, byteorder='little', signed=False)\n to_send += headerlen.to_bytes(length=4, byteorder='little', signed=False)\n to_send += wid.to_bytes(length=8, byteorder='little', signed=False)\n to_send += flag.to_bytes(length=4, byteorder='little', signed=False)\n to_send += biztype.to_bytes(length=4, byteorder='little', signed=False)\n to_send += appid.to_bytes(length=4, byteorder='little', signed=True)\n to_send += pb_pack\n\n await ws.send(to_send)\n \n async def _ws_recv(self,websocket):\n try:\n reply = await asyncio.wait_for(websocket.recv(),0.1)\n return reply\n except asyncio.TimeoutError:\n return None\n\n async def _ws_connect(self):\n self._login_status = SatoriLogin.LoginStatus.CONNECT\n ws_dat = (await self._api_call(\"/vila/api/bot/platform/getWebsocketInfo\"))\n # print(ws_dat)\n ws_url = ws_dat[\"websocket_url\"]\n async with connect(ws_url) as websocket:\n await self._send_ws_pack(websocket,ws_dat,biztype=7)\n tm = time.time()\n while not self._is_stop:\n reply = await self._ws_recv(websocket)\n if not reply:\n now_time = time.time()\n if now_time - tm > 30:\n tm = now_time\n await self._send_ws_pack(websocket,ws_dat,biztype=6)\n continue\n biztype = int.from_bytes(reply[24:28],byteorder='little',signed=False)\n if biztype == 7: # 登录返回\n login_reply = PLoginReply().parse(reply[32:])\n if login_reply.code == 0:\n print(\"mihoyo:ws连接成功\")\n self._login_status = SatoriLogin.LoginStatus.ONLINE\n continue\n else:\n print(\"mihoyo:ws连接失败\",login_reply.to_json())\n break\n elif biztype == 53:\n print(\"mihoyo:ws被踢下线\")\n pkoff = PKickOff().parse(reply[32:])\n print(\"mihoyo:\" + pkoff.reason)\n break\n elif biztype == 52:\n print(\"mihoyo:ws服务关机\")\n break\n elif biztype == 6:\n heart_reply = PHeartBeatReply().parse(reply[32:])\n if heart_reply.code != 0:\n print(\"mihoyo:ws心跳失败\")\n break\n elif biztype == 30001: # 正常处理\n evt = RobotEvent().parse(reply[32:]).to_dict()\n asyncio.create_task(self._event_deal(evt))\n\n async def _ws_server(self) -> None:\n while not self._is_stop:\n try:\n await self._ws_connect()\n except:\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n traceback.print_exc()\n await asyncio.sleep(3)\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n\n async def init_after(self) -> None:\n asyncio.create_task(self._ws_server())\n\n def _mihoyo_msg_to_satori(self,content_obj)->str:\n ret = \"\"\n entities = content_obj[\"content\"][\"entities\"]\n text = content_obj[\"content\"][\"text\"]\n l = len(text)\n i = 0\n while i < l:\n for en in entities:\n if en[\"offset\"] == i:\n print(en)\n i += en[\"length\"]\n if en[\"entity\"][\"type\"] == \"mention_all\": # 实际上收不到\n ret += \"<at type=\\\"all\\\"/>\"\n elif en[\"entity\"][\"type\"] == \"mentioned_robot\":\n ret += \"<at id=\\\"{}\\\"/>\".format(en[\"entity\"][\"bot_id\"])\n elif en[\"entity\"][\"type\"] == \"mentioned_user\":\n ret += \"<at id=\\\"{}\\\"/>\".format(en[\"entity\"][\"user_id\"])\n break\n else:\n ret += satori_to_plain(text[i])\n i += 1\n return ret\n async def _deal_group_message_event(self,data):\n extendData = data[\"extendData\"]\n\n sendMessage = extendData[\"sendMessage\"]\n user_id = sendMessage[\"fromUserId\"]\n villaId = sendMessage[\"villaId\"]\n roomId = sendMessage[\"roomId\"]\n\n villaRoomId = villaId + \"_\" + roomId\n\n content_obj = json.loads(sendMessage[\"content\"])\n\n extra_obj = json.loads(content_obj[\"user\"][\"extra\"])\n\n satori_msg = self._mihoyo_msg_to_satori(content_obj) # todo\n\n satori_evt = SatoriGroupMessageCreatedEvent(\n id=self._id,\n self_id=self._self_id,\n timestamp=int(data[\"sendAt\"]) * 1000,\n platform=\"mihoyo\",\n channel=SatoriChannel(\n id=villaRoomId,\n type=SatoriChannel.ChannelType.TEXT,\n ),\n message=SatoriMessage(\n id=data[\"id\"],\n content=satori_msg,\n created_at=int(sendMessage[\"sendAt\"])\n ),\n user=SatoriUser(\n id=user_id,\n name=sendMessage[\"nickname\"],\n avatar=content_obj[\"user\"][\"portraitUri\"]\n ),\n member=SatoriGuildMember(\n nick=sendMessage[\"nickname\"],\n avatar=content_obj[\"user\"][\"portraitUri\"]\n ),\n guild=SatoriGuild(\n id=villaId\n ),\n role=SatoriGuildRole(\n id=extra_obj[\"member_roles\"][\"name\"],\n name=extra_obj[\"member_roles\"][\"name\"]\n )\n )\n self._id += 1\n self._queue.put_nowait(satori_evt.to_dict())\n\n async def _event_deal(self,data:dict):\n try:\n event_type = data[\"type\"]\n if event_type == \"SendMessage\":\n await self._deal_group_message_event(data)\n except:\n print(traceback.format_exc())\n\n \n async def _api_call(self,path,data = None,villa_id = 0) -> dict:\n url:str = self._http_url + path\n headers = {\"x-rpc-bot_id\":self._self_id,\"x-rpc-bot_secret\":self._secret}\n if villa_id == 0:\n headers[\"x-rpc-bot_villa_id\"] = self._villa_id\n else:\n headers[\"x-rpc-bot_villa_id\"] = villa_id\n if data == None:\n async with httpx.AsyncClient() as client:\n return (await client.get(url,headers=headers)).json()[\"data\"]\n else:\n headers[\"Content-Type\"] = \"application/json\"\n async with httpx.AsyncClient() as client:\n ret = (await client.post(url,headers=headers,data=data)).json()\n if ret[\"retcode\"] != 0:\n print(\"mihoyo:\",ret)\n return ret[\"data\"]\n\n \n async def _satori_to_mihoyo(self,satori_obj,villa_id) -> [dict]:\n to_send_data = []\n last_type = 1\n for node in satori_obj:\n if isinstance(node,str):\n text = node\n if last_type == 1 and len(to_send_data) != 0:\n l = len(to_send_data)\n to_send_data[l - 1][\"text\"] += text\n else:\n to_send_data.append({\n \"type\":1,\n \"text\":text,\n \"entities\":[]\n })\n last_type = 1\n else:\n if node[\"type\"] == \"at\":\n type = get_json_or(node[\"attrs\"],\"type\",None)\n id = get_json_or(node[\"attrs\"],\"id\",None)\n if type == \"all\":\n text = \"@全体成员\"\n elif id != None:\n text = \"@\" + id\n else:\n continue\n\n if last_type != 1 or len(to_send_data) == 0:\n to_send_data.append({\n \"type\":1,\n \"text\":\"\",\n \"entities\":[]\n })\n last_type = 1\n\n l = len(to_send_data)\n ll = len(to_send_data[l - 1][\"text\"])\n to_send_data[l - 1][\"text\"] += text\n if type == \"all\":\n to_send_data[l - 1][\"entities\"].append({\n \"entity\": {\n \"type\": \"mention_all\"\n },\n \"length\":5,\n \"offset\":ll\n })\n else:\n if id.startswith(\"bot_\"):\n to_send_data[l - 1][\"entities\"].append({\n \"entity\": {\n \"type\": \"mentioned_robot\",\n \"bot_id\": id\n },\n \"length\":len(id) + 1,\n \"offset\":ll\n })\n else:\n to_send_data[l - 1][\"entities\"].append({\n \"entity\": {\n \"type\": \"mentioned_user\",\n \"user_id\": id\n },\n \"length\":len(id) + 1,\n \"offset\":ll\n })\n\n elif node[\"type\"] == \"img\":\n img_url:str = node[\"attrs\"][\"src\"]\n mihoyo_img_url = \"\"\n if img_url.startswith(\"data:image/\"):\n base64_start = img_url.find(\"base64,\")\n img_content = base64.b64decode(img_url[base64_start + 7:])\n else:\n async with httpx.AsyncClient() as client:\n img_content = (await client.get(img_url)).content\n ext = imghdr.what(file = \"\",h=img_content)\n m = hashlib.md5()\n m.update(img_content)\n headers = {\"x-rpc-bot_id\":self._self_id,\"x-rpc-bot_secret\":self._secret,\"x-rpc-bot_villa_id\":villa_id}\n upload_info_url = self._http_url + \"/vila/api/bot/platform/getUploadImageParams\"\n async with httpx.AsyncClient() as client:\n req = client.build_request(\"GET\",upload_info_url,json={\n \"md5\":m.hexdigest(),\n \"ext\":ext\n },headers=headers)\n file_params = (await client.send(req)).json()[\"data\"][\"params\"]\n files = {\n \"x:extra\":file_params[\"callback_var\"][\"x:extra\"],\n \"OSSAccessKeyId\":file_params[\"accessid\"],\n \"signature\":file_params[\"signature\"],\n \"success_action_status\":file_params[\"success_action_status\"],\n \"name\":file_params[\"name\"],\n \"callback\":file_params[\"callback\"],\n \"x-oss-content-type\":file_params[\"x_oss_content_type\"],\n \"key\":file_params[\"key\"],\n \"policy\":file_params[\"policy\"],\n \"Content-Disposition\":file_params[\"content_disposition\"],\n 'file':('test',img_content)\n }\n async with httpx.AsyncClient() as client:\n ret = (await client.post(file_params[\"host\"],files=files)).json()\n mihoyo_img_url = ret[\"data\"][\"url\"]\n to_send_data.append({\n \"type\":2,\n \"url\":mihoyo_img_url,\n })\n last_type = 2\n to_send_data2 = []\n for it in to_send_data:\n type = it[\"type\"]\n if type == 1:\n to_send_data2.append({\n \"object_name\":\"MHY:Text\",\n \"msg_content\":json.dumps({\n \"content\":{\n \"text\":it[\"text\"],\n \"entities\":it[\"entities\"]\n }\n })})\n elif type == 2:\n to_send_data2.append({\n \"object_name\":\"MHY:Image\",\n \"msg_content\":json.dumps({\n \"content\":{\n \"url\":it[\"url\"]\n }\n \n })})\n \n return to_send_data2\n \n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\n '''发送消息'''\n villa_id = channel_id.split(\"_\")[0]\n satori_obj = parse_satori_html(content)\n to_sends = await self._satori_to_mihoyo(satori_obj,villa_id)\n to_ret = []\n # print(to_sends)\n for it in to_sends:\n it[\"room_id\"] = channel_id.split(\"_\")[1]\n ret = await self._api_call(\"/vila/api/bot/platform/sendMessage\",json.dumps(it),villa_id=villa_id)\n to_ret.append(SatoriMessage(id=ret[\"bot_msg_id\"],content=\"\").to_dict())\n return to_ret\n \n \n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\n satori_ret = SatoriLogin(\n status=self._login_status,\n user=SatoriUser(\n id=self._self_id,\n is_bot=True\n ),\n self_id=self._self_id,\n platform=\"mihoyo\"\n ).to_dict()\n if platform == None and self_id == None:\n return [satori_ret]\n else:\n return satori_ret\n\n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\n '''获取群组成员信息'''\n url = self._http_url + \"/vila/api/bot/platform/getMember\"\n headers = {\"x-rpc-bot_id\":self._self_id,\"x-rpc-bot_secret\":self._secret,\"x-rpc-bot_villa_id\":guild_id}\n async with httpx.AsyncClient() as client:\n req = client.build_request(\"GET\",url,json={\n \"uid\":user_id\n },headers=headers)\n obret = (await client.send(req)).json()[\"data\"][\"member\"]\n satori_ret = SatoriGuildMember(\n user=SatoriUser(\n id=obret[\"basic\"][\"uid\"],\n name=obret[\"basic\"][\"nickname\"],\n avatar=obret[\"basic\"][\"avatar_url\"],\n is_bot=False\n ),\n nick=obret[\"basic\"][\"nickname\"],\n avatar=obret[\"basic\"][\"avatar_url\"],\n joined_at=int(obret[\"joined_at\"] + \"000\")\n ).to_dict()\n return satori_ret"},{"identifier":"AdapterOnebot","path":"onebot_adapter.py","snippet":"class AdapterOnebot:\n def __init__(self,config = {}) -> None:\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\n self._http_url = config[\"http_url\"]\n self._ws_url = config[\"ws_url\"]\n if \"access_token\" in config:\n self._access_token = config[\"access_token\"]\n else:\n self._access_token = None\n self._is_stop = False\n self._login_status = 3 # DISCONNECT\n self._queue = Queue(maxsize=100)\n self._id = 0\n\n def _cqarr_to_satori(self,cqarr):\n ret = \"\"\n for node in cqarr:\n if node[\"type\"] == \"text\":\n ret += satori_to_plain(node[\"data\"][\"text\"])\n elif node[\"type\"] == \"at\":\n qq = node[\"data\"][\"qq\"]\n if qq == \"all\":\n ret += \"<at type=\\\"all\\\"/>\"\n else:\n ret += \"<at id={}/>\".format(json.dumps(qq))\n elif node[\"type\"] == \"image\":\n url = node[\"data\"][\"url\"]\n ret += \"<img src={}/>\".format(json.dumps(url))\n return ret\n\n async def enable(self) -> None:\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\n '''\n pass\n\n async def disable(self) -> None:\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\n pass\n \n async def release(self) -> None:\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\n 但是，这个函数允许资源延迟释放，只要能释放就行\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\n '''\n self._is_stop = True\n\n async def get_msg(self) -> dict:\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\n return await self._queue.get()\n\n async def init_after(self) -> None:\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\n async def _ws_server(self:AdapterOnebot) -> None:\n while not self._is_stop:\n try:\n self._login_status = 2 # CONNECT\n async with connect(self._ws_url) as websocket:\n print(\"onebot:ws已经连接\")\n self._login_status = 1 # ONLINE\n try:\n while True:\n try:\n reply = await asyncio.wait_for(websocket.recv(),0.1)\n await self._event_deal(json.loads(reply))\n except asyncio.TimeoutError:\n if self._is_stop:\n await websocket.close()\n except asyncio.QueueFull:\n print(\"队列满\")\n except Exception as e:\n print(e) \n except Exception as e:\n print(e)\n print(\"onebot:ws连接已经断开\")\n self._login_status = 3 # DISCONNECT\n asyncio.create_task(_ws_server(self))\n \n async def _event_deal(self,evt:dict):\n '''自己定义的事件转化函数'''\n post_type = evt[\"post_type\"]\n if post_type == \"message\":\n message_type = evt[\"message_type\"]\n sender = evt[\"sender\"]\n if message_type == \"group\":\n channel_obj = {\n \"id\":\"GROUP_\"+str(evt[\"group_id\"]),\n \"type\":0,\n \"name\":None,\n \"parent_id\":None\n }\n guild_obj = {\n \"id\":\"GROUP_\"+str(evt[\"group_id\"]),\n \"name\":None,\n \"avatar\":None\n }\n user_obj = {\n \"id\":str(evt[\"user_id\"]),\n \"name\":get_json_or(sender,\"nickname\",None),\n \"nick\":get_json_or(sender,\"nickname\",None),\n \"avatar\":get_json_or(sender,\"avatar\",None),\n \"is_bot\":None\n }\n joined_at = get_json_or(sender,\"join_time\",None)\n if joined_at:\n joined_at = int(str(joined_at) + \"000\")\n member_obj = {\n \"nick\":get_json_or(sender,\"card\",None),\n \"avatar\":get_json_or(sender,\"avatar\",None),\n \"joined_at\":joined_at\n }\n message_obj = {\n \"id\":str(evt[\"message_id\"]),\n \"content\":self._cqarr_to_satori(_cqmsg_to_arr(evt[\"message\"])),\n \"created_at\":int(str(evt[\"time\"] ) + \"000\")\n }\n role_obj = {\n \"id\":get_json_or(sender, \"role\",\"member\"),\n \"name\":get_json_or(sender,\"role\",\"member\")\n }\n satori_evt = {\n \"id\":self._id,\n \"type\":\"message-created\",\n \"platform\":\"onebot\",\n \"self_id\":str(evt[\"self_id\"]),\n \"timestamp\":int(str(evt[\"time\"] ) + \"000\"),\n \"channel\":channel_obj,\n \"guild\":guild_obj,\n \"member\":member_obj,\n \"message\":message_obj,\n \"role\":role_obj,\n \"user\":user_obj\n }\n self._id += 1\n self._queue.put_nowait(satori_evt)\n elif message_type == \"private\":\n channel_obj = {\n \"id\":str(evt[\"user_id\"]),\n \"type\":1,\n \"name\":None,\n \"parent_id\":None\n }\n user_obj = {\n \"id\":str(evt[\"user_id\"]),\n \"name\":get_json_or(sender,\"nickname\",None),\n \"nick\":get_json_or(sender,\"nickname\",None),\n \"avatar\":get_json_or(sender,\"avatar\",None),\n \"is_bot\":None\n }\n joined_at = get_json_or(sender,\"join_time\",None)\n if joined_at:\n joined_at = int(str(joined_at) + \"000\")\n message_obj = {\n \"id\":str(evt[\"message_id\"]),\n \"content\":self._cqarr_to_satori(_cqmsg_to_arr(evt[\"message\"])),\n \"created_at\":int(str(evt[\"time\"] ) + \"000\")\n }\n satori_evt = {\n \"id\":self._id,\n \"type\":\"message-created\",\n \"platform\":\"onebot\",\n \"self_id\":str(evt[\"self_id\"]),\n \"timestamp\":int(str(evt[\"time\"] ) + \"000\"),\n \"channel\":channel_obj,\n \"message\":message_obj,\n \"user\":user_obj\n }\n self._id += 1\n self._queue.put_nowait(satori_evt)\n elif post_type == \"notice\":\n notice_type = evt[\"notice_type\"]\n if notice_type == \"group_increase\":\n guild_obj = {\n \"id\":\"GROUP_\"+str(evt[\"group_id\"]),\n \"name\":None,\n \"avatar\":None\n }\n member_obj = {\n \"nick\":None,\n \"avatar\":get_json_or(evt,\"avatar\",None),\n \"joined_at\":int(str(evt[\"time\"] ) + \"000\")\n }\n user_obj = {\n \"id\":str(evt[\"user_id\"]),\n \"name\":None,\n \"nick\":None,\n \"avatar\":None,\n \"is_bot\":None\n }\n satori_evt = {\n \"id\":self._id,\n \"type\":\"guild-member-added\",\n \"platform\":\"onebot\",\n \"self_id\":str(evt[\"self_id\"]),\n \"timestamp\":int(str(evt[\"time\"] ) + \"000\"),\n \"guild\":guild_obj,\n \"member\":member_obj,\n \"user\":user_obj\n }\n self._id += 1\n self._queue.put_nowait(satori_evt)\n\n async def _api_call(self,path,data) -> dict:\n url:str = self._http_url + path\n if self._access_token:\n headers = {\"Authorization\":\"Bearer {}\".format(self._access_token)}\n else:\n headers = {}\n async with httpx.AsyncClient() as client:\n # headers[\"Content-Type\"] = \"application/json\"\n return (await client.post(url,headers=headers,data=data)).json()\n \n async def _satori_to_cq(self,satori_obj) -> str:\n ret = \"\"\n for node in satori_obj:\n if isinstance(node,str):\n ret += _cq_text_encode(node)\n else:\n if node[\"type\"] == \"at\":\n type = get_json_or(node[\"attrs\"],\"type\",None)\n id = get_json_or(node[\"attrs\"],\"id\",None)\n if type == \"all\":\n ret += \"[CQ:at,qq=all]\"\n elif id != None:\n ret += \"[CQ:at,qq={}]\".format(_cq_params_encode(id))\n elif node[\"type\"] == \"img\":\n img_url = node[\"attrs\"][\"src\"]\n if img_url.startswith(\"data:image/\"):\n base64_start = img_url.find(\"base64,\")\n img_url = \"base64://\" + img_url[base64_start + 7:]\n ret += \"[CQ:image,file={}]\".format(_cq_params_encode(img_url)) \n\n return ret\n\n\n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\n '''发送消息'''\n satori_obj = parse_satori_html(content)\n to_send = await self._satori_to_cq(satori_obj)\n if channel_id.startswith(\"GROUP_\"):\n group_id = int(channel_id[6:])\n ret = await self._api_call(\"/send_group_msg\",{\"group_id\":group_id,\"message\":to_send})\n return [{\"id\":str(ret[\"data\"][\"message_id\"]),\"content\":\"\"}]\n else:\n user_id = int(channel_id)\n ret = await self._api_call(\"/send_private_msg\",{\"user_id\":user_id,\"message\":to_send})\n return [{\"id\":str(ret[\"data\"][\"message_id\"]),\"content\":\"\"}]\n \n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\n obret = (await self._api_call(\"/get_login_info\",{}))[\"data\"]\n satori_ret = {\n \"user\":{\n \"id\":str(obret[\"user_id\"]),\n \"name\":obret[\"nickname\"],\n \"nick\":obret[\"nickname\"],\n \"avatar\":get_json_or(obret,\"avatar\",None),\n \"is_bot\":None\n },\n \"self_id\":str(obret[\"user_id\"]),\n \"platform\":\"onebot\",\n \"status\":self._login_status,\n }\n if platform == None and self_id == None:\n return [satori_ret]\n else:\n return satori_ret\n \n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\n '''获取群组成员信息'''\n obret = (await self._api_call(\"/get_group_member_info\",{\n \"group_id\":int(guild_id[6:]),\n \"user_id\":int(user_id)\n }))[\"data\"]\n joined_at = get_json_or(obret,\"join_time\",None)\n if joined_at:\n joined_at = int(str(joined_at) + \"000\")\n satori_ret = {\n \"user\":{\n \"id\":str(obret[\"user_id\"]),\n \"name\":get_json_or(obret,\"nickname\",None),\n \"nick\":get_json_or(obret,\"card\",None),\n \"avatar\":get_json_or(obret,\"avatar\",None),\n \"is_bot\":None\n },\n \"nick\":get_json_or(obret,\"card\",None),\n \"avatar\":get_json_or(obret,\"avatar\",None),\n \"joined_at\":joined_at,\n }\n return satori_ret"},{"identifier":"Config","path":"config.py","snippet":"class Config:\n def __init__(self) -> None:\n self.botlist:list = []\n self.web_port:int = 8080\n self.web_host:str = \"127.0.0.1\"\n self.access_token:str = \"\"\n \n async def read_config(self):\n async with aiofiles.open('config.json', mode='r') as f:\n json_dat = json5.loads(await f.read())\n self.botlist = json_dat[\"botlist\"]\n self.web_port = json_dat[\"web_port\"]\n self.web_host = json_dat[\"web_host\"]\n self.access_token = json_dat[\"access_token\"]"},{"identifier":"AdapterQQ","path":"qq_adapter.py","snippet":"class AdapterQQ:\n def __init__(self,config = {}) -> None:\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\n self._botqq = config[\"botqq\"]\n self._appid = config[\"appid\"]\n self._token = config[\"token\"]\n if \"withgroup\" in config:\n self._withgroup = config[\"withgroup\"]\n else:\n self._withgroup = None\n self._appsecret = config[\"appsecret\"]\n self._http_url = \"https://api.sgroup.qq.com\"\n self._is_stop = False\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n self._queue = Queue(maxsize=100)\n self._id = 0\n self._sn = None\n self._self_id = None\n self._access_token = None\n self._expires_in = 0\n self.msgid_map = dict()\n # self._self_name = None\n\n\n async def enable(self) -> None:\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\n '''\n pass\n\n async def disable(self) -> None:\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\n pass\n \n async def release(self) -> None:\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\n 但是，这个函数允许资源延迟释放，只要能释放就行\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\n '''\n self._is_stop = True\n\n async def get_msg(self) -> dict:\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\n return await self._queue.get()\n \n\n async def _ws_recv(self,websocket):\n try:\n reply = await asyncio.wait_for(websocket.recv(),0.1)\n return reply\n except asyncio.TimeoutError:\n return None\n\n async def _ws_connect(self):\n self._login_status = SatoriLogin.LoginStatus.CONNECT\n ws_url = (await self._api_call(\"/gateway\"))[\"url\"]\n async with connect(ws_url) as websocket:\n tm = time.time()\n while not self._is_stop:\n reply = await self._ws_recv(websocket)\n if not reply:\n now_time = time.time()\n if now_time - tm > 30:\n tm = now_time\n await websocket.send(json.dumps({\"op\": 1,\"d\": self._sn}))\n continue\n js = json.loads(reply)\n op = js[\"op\"]\n if op == 0: # 事件\n self._sn = js[\"s\"]\n t = js[\"t\"]\n if t == \"READY\":\n print(\"qq:ws连接成功\")\n print(json.dumps(js))\n self._login_status = SatoriLogin.LoginStatus.ONLINE\n else:\n print(json.dumps(js))\n asyncio.create_task(self._deal_event(js))\n elif op == 1: # 心跳\n await websocket.send(json.dumps({\"op\":11}))\n elif op == 7: # 重连\n print(\"qq:服务端要求重连\")\n break\n elif op == 9: # 参数错误\n print(\"qq:参数错误:\",json.dumps(js))\n break\n elif op == 10: # ws建立成功\n if self._withgroup:\n await websocket.send(json.dumps({\n \"op\":2,\n \"d\":{\n \"token\":\"QQBot {}\".format(self._access_token),\n \"intents\":0 | (1 << 0) | (1 << 1) | (1 << 30) | (1 << 25),\n \"shard\":[0, 1],\n }\n }))\n else:\n await websocket.send(json.dumps({\n \"op\":2,\n \"d\":{\n \"token\":\"QQBot {}\".format(self._access_token),\n \"intents\":0 | (1 << 0) | (1 << 1) | (1 << 30),\n \"shard\":[0, 1],\n }\n }))\n elif op == 11: # HTTP Callback ACK\n pass\n\n async def _ws_server(self) -> None:\n while not self._is_stop:\n try:\n await self._ws_connect()\n except:\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n print(traceback.format_exc())\n await asyncio.sleep(3)\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\n\n async def _token_refresh(self):\n async with httpx.AsyncClient() as client:\n if not self._expires_in or int(self._expires_in) < 60 * 5:\n url = \"https://bots.qq.com/app/getAppAccessToken\"\n ret = (await client.post(url,json={\n \"appId\":self._appid,\n \"clientSecret\":self._appsecret\n })).json()\n self._access_token = ret[\"access_token\"]\n self._expires_in = ret[\"expires_in\"]\n # print(ret)\n\n async def _qqarr_to_satori(self,qqmsg_arr):\n ret = \"\"\n for it in qqmsg_arr:\n if it[\"type\"] == \"text\":\n ret += satori_to_plain(it[\"data\"])\n else:\n if it[\"data\"].startswith(\"<@!\"):\n user_id = it[\"data\"][3:len(it[\"data\"]) - 1]\n ret += \"<at id=\\\"{}\\\">\".format(satori_to_plain(user_id))\n elif it[\"data\"].startswith(\"<@\"):\n user_id = it[\"data\"][2:len(it[\"data\"]) - 1]\n ret += \"<at id=\\\"{}\\\">\".format(satori_to_plain(user_id))\n return ret\n \n async def _deal_channel_event(self,data):\n qqmsg_arr = _qqmsg_to_arr(data[\"content\"])\n # print(\"qqmsg_arr\",qqmsg_arr)\n satori_msg = await self._qqarr_to_satori(qqmsg_arr)\n self.msgid_map[\"CHANNEL_\"+data[\"channel_id\"]] = data[\"id\"]\n satori_evt = SatoriGroupMessageCreatedEvent(\n id=self._id,\n self_id=self._self_id,\n timestamp=int(time.mktime(time.strptime(data[\"timestamp\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000,\n platform=\"qq_guild\",\n channel=SatoriChannel(\n id=\"CHANNEL_\"+data[\"channel_id\"],\n type=SatoriChannel.ChannelType.TEXT,\n ),\n message=SatoriMessage(\n id=data[\"id\"],\n content=satori_msg,\n created_at=int(time.mktime(time.strptime(data[\"timestamp\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000\n ),\n user=SatoriUser(\n id=data[\"author\"][\"id\"],\n name=data[\"author\"][\"username\"],\n avatar=data[\"author\"][\"avatar\"],\n is_bot=data[\"author\"][\"bot\"]\n ),\n member=SatoriGuildMember(\n nick=data[\"member\"][\"nick\"],\n avatar=data[\"author\"][\"avatar\"],\n joined_at=int(time.mktime(time.strptime(data[\"member\"][\"joined_at\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000\n ),\n guild=SatoriGuild(\n id=data[\"guild_id\"]\n ),\n role=SatoriGuildRole(\n id=json.dumps(sorted(data[\"member\"][\"roles\"]))\n )\n )\n self._id += 1\n self._queue.put_nowait(satori_evt.to_dict())\n\n async def _deal_group_event(self,data):\n qqmsg_arr = _qqmsg_to_arr(data[\"content\"])\n # print(\"qqmsg_arr\",qqmsg_arr)\n satori_msg = await self._qqarr_to_satori(qqmsg_arr)\n self.msgid_map[\"GROUP_\"+data[\"group_id\"]] = data[\"id\"]\n satori_evt = SatoriGroupMessageCreatedEvent(\n id=self._id,\n self_id=self._botqq,\n timestamp=int(time.mktime(time.strptime(data[\"timestamp\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000,\n platform=\"qq_group\",\n channel=SatoriChannel(\n id=\"GROUP_\"+data[\"group_id\"],\n type=SatoriChannel.ChannelType.TEXT,\n ),\n message=SatoriMessage(\n id=data[\"id\"],\n content=satori_msg,\n created_at=int(time.mktime(time.strptime(data[\"timestamp\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000\n ),\n user=SatoriUser(\n id=data[\"author\"][\"id\"]\n ),\n member=SatoriGuildMember(\n ),\n guild=SatoriGuild(\n id=\"GROUP_\"+data[\"group_id\"]\n ),\n role=SatoriGuildRole(\n id=\"unkonw\",\n name=\"unkonw\"\n )\n )\n self._id += 1\n self._queue.put_nowait(satori_evt.to_dict())\n\n async def _deal_event(self,event):\n try:\n type = event[\"t\"]\n if type == \"AT_MESSAGE_CREATE\":\n d = event[\"d\"]\n if (\"channel_id\" in d) and d[\"channel_id\"]:\n await self._deal_channel_event(d)\n else:\n if type == \"GROUP_AT_MESSAGE_CREATE\":\n d = event[\"d\"]\n if (\"group_id\" in d) and d[\"group_id\"]:\n await self._deal_group_event(d)\n except:\n print(traceback.format_exc())\n\n async def _token_refresh_task(self):\n while True:\n try:\n await self._token_refresh()\n index = 0\n while index < 60: # 每60秒检测一次token是否过期\n await asyncio.sleep(1)\n if self._is_stop:\n break\n index += 1\n if self._is_stop:break\n except:\n print(traceback.format_exc())\n\n async def init_after(self) -> None:\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\n try:\n await self._token_refresh()\n except:\n print(traceback.format_exc())\n asyncio.create_task(self._token_refresh_task())\n asyncio.create_task(self._ws_server())\n\n async def _api_call(self,path,data = None) -> dict:\n url:str = self._http_url + path\n headers = {\"Authorization\":\"QQBot {}\".format(self._access_token),\"X-Union-Appid\":self._appid}\n if data == None:\n async with httpx.AsyncClient() as client:\n return (await client.get(url,headers=headers)).json()\n else:\n async with httpx.AsyncClient() as client:\n ret = (await client.post(url,headers=headers,json=data))\n # print(ret.content)\n return ret.json()\n\n def _make_qq_text(self,text:str):\n ret = text\n ret = ret.replace(\"&\",\"&\")\n ret = ret.replace(\"<\",\"<\")\n ret = ret.replace(\">\",\">\")\n return ret\n \n async def _satori_to_qq(self,satori_obj,platform = \"qq_guild\") -> [dict]:\n to_reply_id = None\n ret_text = \"\"\n ret_img = []\n for node in satori_obj:\n if isinstance(node,str):\n text = self._make_qq_text(node)\n ret_text += text\n else:\n if node[\"type\"] == \"at\":\n type = get_json_or(node[\"attrs\"],\"type\",None)\n id = get_json_or(node[\"attrs\"],\"id\",None)\n if type == \"all\":\n # 注意，机器人不支持at all，不能发，也不能收，这里假装at all了\n ret_text += \"@全体成员\"\n # text = \"<@everyone>\"\n elif id != None:\n ret_text += \"<@{}>\".format(self._make_qq_text(id))\n elif node[\"type\"] == \"img\":\n img_url:str = node[\"attrs\"][\"src\"]\n if img_url.startswith(\"data:image/\"):\n base64_start = img_url.find(\"base64,\")\n img_content = base64.b64decode(img_url[base64_start + 7:])\n ret_img.append(img_content)\n else:\n if platform == \"qq_guild\":\n async with httpx.AsyncClient() as client:\n img_content = (await client.get(img_url)).content\n ret_img.append(img_content)\n else:\n ret_img.append(img_url)\n elif node[\"type\"] == \"passive\":\n to_reply_id = node[\"attrs\"][\"id\"]\n \n ret_vec = []\n ret_vec.append({\n \"content\":ret_text,\n \"file_image\":None,\n \"to_reply_id\":to_reply_id\n })\n if len(ret_img) != 0:\n ret_vec[0][\"file_image\"] = ret_img[0]\n for img in ret_img[1:]:\n ret_vec.append({\n \"content\":\"\",\n \"file_image\":img,\n \"to_reply_id\":to_reply_id\n })\n return ret_vec\n \n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\n '''发送消息'''\n to_reply_id = self.msgid_map[channel_id]\n satori_obj = parse_satori_html(content)\n to_sends = await self._satori_to_qq(satori_obj,platform)\n # print(to_sends)\n if channel_id.startswith(\"CHANNEL_\") and platform == \"qq_guild\":\n channel_id = channel_id[8:]\n to_ret = []\n for it in to_sends:\n if it[\"to_reply_id\"]:to_reply_id = it[\"to_reply_id\"]\n async with httpx.AsyncClient() as client:\n headers = {\"Authorization\":\"QQBot {}\".format(self._access_token),\"X-Union-Appid\":self._appid,\"Accept\":\"application/json\"}\n url:str = self._http_url + \"/channels/{}/messages\".format(channel_id)\n data = {\n \"msg_id\":to_reply_id,\n \"content\":it[\"content\"]\n }\n if it[\"file_image\"]:\n ret = (await client.post(url,headers=headers,data=data,files={\"file_image\":it[\"file_image\"]})).json()\n else:\n ret = (await client.post(url,headers=headers,json=data)).json()\n # print(ret)\n to_ret.append(SatoriMessage(id=ret[\"id\"],content=\"\").to_dict())\n return to_ret\n elif channel_id.startswith(\"GROUP_\") and platform == \"qq_group\":\n channel_id = channel_id[6:]\n to_ret = []\n msg_seq = 1\n for it in to_sends:\n if it[\"to_reply_id\"]:to_reply_id = it[\"to_reply_id\"]\n async with httpx.AsyncClient() as client:\n headers = {\"Authorization\":\"QQBot {}\".format(self._access_token),\"X-Union-Appid\":self._appid,\"Accept\":\"application/json\"}\n url:str = self._http_url + \"/v2/groups/{}/messages\".format(channel_id)\n data = {\n \"msg_id\":to_reply_id,\n \"content\":it[\"content\"],\n \"msg_type\":0,\n \"msg_seq\":msg_seq,\n # \"image\": 目前暂不支持\n }\n msg_seq += 1\n ret = (await client.post(url,headers=headers,json=data)).json()\n # print(ret)\n to_ret.append(SatoriMessage(id=ret[\"msg_id\"],content=\"\").to_dict())\n return to_ret\n \n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\n\n if platform == \"qq_group\":\n return SatoriLogin(\n status=self._login_status,\n user=SatoriUser(\n id=self._botqq,\n is_bot=True\n ),\n self_id=self._botqq,\n platform=\"qq_group\"\n ).to_dict()\n else: \n obret = (await self._api_call(\"/users/@me\"))\n satori_ret = SatoriLogin(\n status=self._login_status,\n user=SatoriUser(\n id=obret[\"id\"],\n name=obret[\"username\"],\n avatar=obret[\"avatar\"],\n is_bot=True\n ),\n self_id=obret[\"id\"],\n platform=\"qq_guild\"\n ).to_dict()\n self._self_id = obret[\"id\"]\n if platform == \"qq_guild\":\n return satori_ret\n elif platform == None:\n if not self._withgroup:\n return [satori_ret]\n else:\n return [satori_ret,SatoriLogin(\n status=self._login_status,\n user=SatoriUser(\n id=self._botqq,\n is_bot=True\n ),\n self_id=self._botqq,\n platform=\"qq_group\"\n ).to_dict()]\n \n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\n '''获取群组成员信息'''\n if platform == \"qq_guild\":\n url = \"/guilds/{}/members/{}\".format(guild_id,user_id)\n obret = (await self._api_call(url))\n satori_ret = SatoriGuildMember(\n user=SatoriUser(\n id=obret[\"user\"][\"id\"],\n name=obret[\"user\"][\"username\"],\n avatar=obret[\"user\"][\"avatar\"],\n is_bot=obret[\"user\"][\"bot\"]\n ),\n nick=get_json_or(obret,\"nick\",None),\n avatar=obret[\"user\"][\"avatar\"],\n joined_at=int(time.mktime(time.strptime(obret[\"joined_at\"], \"%Y-%m-%dT%H:%M:%S%z\"))) * 1000\n ).to_dict()\n return satori_ret"},{"identifier":"remove_json_null","path":"tool.py","snippet":"def remove_json_null(js) -> dict:\n '''将json中的None字段删除'''\n if isinstance(js,dict):\n st = {}\n for key in js:\n if js[key] != None:\n st[key] = remove_json_null(js[key])\n return st\n elif isinstance(js,list):\n lst = []\n for it in js:\n lst.append(remove_json_null(it))\n return lst\n else:\n return js"}],"string":"[\n {\n \"identifier\": \"AdapterKook\",\n \"path\": \"kook_adapter.py\",\n \"snippet\": \"class AdapterKook:\\n def __init__(self,config = {}) -> None:\\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\\n self._access_token = config[\\\"access_token\\\"]\\n self._http_url = \\\"https://www.kookapp.cn/api/v3\\\"\\n self._is_stop = False\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n self._queue = Queue(maxsize=100)\\n self._id = 0\\n self._sn = 0\\n self._self_id = None\\n\\n\\n async def enable(self) -> None:\\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\\n '''\\n pass\\n\\n async def disable(self) -> None:\\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\\n pass\\n \\n async def release(self) -> None:\\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\\n 但是，这个函数允许资源延迟释放，只要能释放就行\\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\\n '''\\n self._is_stop = True\\n\\n async def get_msg(self) -> dict:\\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\\n return await self._queue.get()\\n \\n\\n async def _ws_recv(self,websocket):\\n try:\\n reply = await asyncio.wait_for(websocket.recv(),0.1)\\n return reply\\n except asyncio.TimeoutError:\\n return None\\n\\n async def _ws_connect(self):\\n self._login_status = SatoriLogin.LoginStatus.CONNECT\\n ws_url = (await self._api_call(\\\"/gateway/index?compress=0\\\"))[\\\"url\\\"]\\n async with connect(ws_url) as websocket:\\n tm = time.time()\\n while not self._is_stop:\\n reply = await self._ws_recv(websocket)\\n if not reply:\\n now_time = time.time()\\n if now_time - tm > 30:\\n tm = now_time\\n await websocket.send(json.dumps({\\\"s\\\": 2,\\\"sn\\\": self._sn}))\\n continue\\n js = json.loads(reply)\\n s = js[\\\"s\\\"]\\n if s == 5:raise Exception(\\\"recv reset ws\\\")\\n elif s == 3:pass # heartbeat\\n elif s == 1:\\n self._login_status = SatoriLogin.LoginStatus.ONLINE\\n print(\\\"kook:ws连接成功\\\")\\n elif s == 0:\\n self._sn = js[\\\"sn\\\"]\\n asyncio.create_task(self._event_deal(js[\\\"d\\\"]))\\n\\n async def _ws_server(self) -> None:\\n while not self._is_stop:\\n try:\\n await self._ws_connect()\\n except:\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n print(traceback.format_exc())\\n await asyncio.sleep(3)\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n\\n async def init_after(self) -> None:\\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\\n asyncio.create_task(self._ws_server())\\n\\n def _kook_msg_to_satori(self,msg_type:int,message:str)->str:\\n ret = \\\"\\\"\\n if msg_type == 2: #图片\\n ret += \\\"<img src={}/>\\\".format(json.dumps(message))\\n else:\\n def kook_msg_f(msg):\\n ret = \\\"\\\"\\n is_f = False\\n for ch in msg:\\n if is_f:\\n is_f = False\\n ret += ch\\n elif ch == \\\"\\\\\\\\\\\":\\n is_f = True\\n else:\\n ret += ch\\n return ret\\n \\n index = 0\\n msg_list = message.split(\\\"(met)\\\")\\n for it in msg_list:\\n if index % 2 == 0:\\n ret += satori_to_plain(kook_msg_f(it))\\n else:\\n if it == \\\"all\\\":\\n ret += \\\"<at type=\\\\\\\"all\\\\\\\"/>\\\"\\n else:\\n ret += \\\"<at id=\\\\\\\"{}\\\\\\\"/>\\\".format(it)\\n index += 1\\n return ret\\n\\n\\n async def _deal_group_message_event(self,data,user_id:str):\\n group_id = data[\\\"target_id\\\"]\\n kook_msg = data[\\\"content\\\"]\\n extra = data[\\\"extra\\\"]\\n author = extra[\\\"author\\\"]\\n msg_type = data[\\\"type\\\"]\\n\\n if msg_type == 10:#卡牌\\n return\\n satori_msg = self._kook_msg_to_satori(msg_type,kook_msg)\\n\\n satori_evt = SatoriGroupMessageCreatedEvent(\\n id=self._id,\\n self_id=self._self_id,\\n timestamp=data[\\\"msg_timestamp\\\"],\\n platform=\\\"kook\\\",\\n channel=SatoriChannel(\\n id=\\\"GROUP_\\\"+group_id,\\n type=SatoriChannel.ChannelType.TEXT,\\n name=extra[\\\"channel_name\\\"]\\n ),\\n message=SatoriMessage(\\n id=data[\\\"msg_id\\\"],\\n content=satori_msg,\\n created_at=data[\\\"msg_timestamp\\\"]\\n ),\\n user=SatoriUser(\\n id=author[\\\"id\\\"],\\n name=author[\\\"username\\\"],\\n avatar=author[\\\"avatar\\\"],\\n is_bot=author[\\\"bot\\\"]\\n ),\\n member=SatoriGuildMember(\\n nick=author[\\\"nickname\\\"],\\n avatar=author[\\\"avatar\\\"]\\n ),\\n guild=SatoriGuild(\\n id=extra[\\\"guild_id\\\"]\\n ),\\n role=SatoriGuildRole(\\n id=json.dumps(sorted(author[\\\"roles\\\"]))\\n )\\n )\\n self._id += 1\\n self._queue.put_nowait(satori_evt.to_dict())\\n\\n async def _deal_private_message_event(self,data,user_id:str):\\n\\n kook_msg = data[\\\"content\\\"]\\n extra = data[\\\"extra\\\"]\\n author = extra[\\\"author\\\"]\\n msg_type = data[\\\"type\\\"]\\n\\n if msg_type == 10:#卡牌\\n return\\n satori_msg = self._kook_msg_to_satori(msg_type,kook_msg)\\n\\n satori_evt = SatoriPrivateMessageCreatedEvent(\\n id=self._id,\\n self_id=self._self_id,\\n timestamp=data[\\\"msg_timestamp\\\"],\\n channel=SatoriChannel(\\n id=user_id,\\n type=SatoriChannel.ChannelType.TEXT,\\n name=author[\\\"username\\\"]\\n ),\\n message=SatoriMessage(\\n id=data[\\\"msg_id\\\"],\\n content=satori_msg,\\n created_at=data[\\\"msg_timestamp\\\"]\\n ),\\n user=SatoriUser(\\n id=user_id,\\n name=author[\\\"username\\\"],\\n avatar=author[\\\"avatar\\\"],\\n is_bot=author[\\\"bot\\\"]\\n ),\\n platform=\\\"kook\\\"\\n ).to_dict()\\n self._id += 1\\n self._queue.put_nowait(satori_evt)\\n\\n async def _deal_group_increase_event(self,data):\\n extra = data[\\\"extra\\\"]\\n satori_evt = {\\n \\\"id\\\":self._id,\\n \\\"type\\\":\\\"guild-member-added\\\",\\n \\\"platform\\\":\\\"kook\\\",\\n \\\"self_id\\\":self._self_id,\\n \\\"timestamp\\\":data[\\\"msg_timestamp\\\"],\\n \\\"guild\\\":SatoriGuild(id=data[\\\"target_id\\\"]).to_dict(),\\n \\\"member\\\":SatoriGuildMember(joined_at=extra[\\\"body\\\"][\\\"joined_at\\\"]).to_dict(),\\n \\\"user\\\":SatoriUser(id=extra[\\\"body\\\"][\\\"user_id\\\"]).to_dict()\\n }\\n self._id += 1\\n self._queue.put_nowait(satori_evt)\\n\\n\\n\\n async def _deal_group_evt(self,data):\\n user_id:str = data[\\\"author_id\\\"]\\n if user_id == \\\"1\\\": # system message\\n tp = data[\\\"type\\\"]\\n if tp != 255:\\n return\\n sub_type = data[\\\"extra\\\"][\\\"type\\\"]\\n if sub_type == \\\"joined_guild\\\":\\n await self._deal_group_increase_event(data)\\n else:\\n if self._self_id:\\n if user_id != self._self_id:\\n await self._deal_group_message_event(data,user_id)\\n\\n\\n async def _deal_person_evt(self,data):\\n user_id:str = data[\\\"author_id\\\"]\\n if user_id != 1: # 不是系统消息\\n if self._self_id:\\n if user_id != self._self_id:\\n await self._deal_private_message_event(data,user_id)\\n\\n\\n async def _event_deal(self,data:dict):\\n try:\\n tp = data[\\\"channel_type\\\"]\\n if tp == \\\"GROUP\\\":\\n await self._deal_group_evt(data)\\n else:\\n await self._deal_person_evt(data)\\n except:\\n print(traceback.format_exc())\\n \\n async def _api_call(self,path,data = None) -> dict:\\n url:str = self._http_url + path\\n headers = {\\\"Authorization\\\":\\\"Bot {}\\\".format(self._access_token)}\\n if data == None:\\n async with httpx.AsyncClient() as client:\\n return (await client.get(url,headers=headers)).json()[\\\"data\\\"]\\n else:\\n async with httpx.AsyncClient() as client:\\n return (await client.post(url,headers=headers,data=data)).json()[\\\"data\\\"]\\n\\n def _make_kook_text(self,text):\\n ret = \\\"\\\"\\n for ch in text:\\n if ch in [\\\"\\\\\\\\\\\",\\\"*\\\",\\\"~\\\",\\\"[\\\",\\\"(\\\",\\\")\\\",\\\"]\\\",\\\"-\\\",\\\">\\\",\\\"`\\\"]:\\n ret += \\\"\\\\\\\\\\\"\\n ret += ch\\n return ret\\n \\n async def _satori_to_kook(self,satori_obj) -> [dict]:\\n to_send_data = []\\n last_type = 1\\n for node in satori_obj:\\n if isinstance(node,str):\\n text = self._make_kook_text(node)\\n if last_type == 1 and len(to_send_data) != 0:\\n l = len(to_send_data)\\n to_send_data[l - 1][\\\"content\\\"] += text\\n else:\\n to_send_data.append({\\n \\\"type\\\":1,\\n \\\"content\\\":text\\n })\\n last_type = 1\\n else:\\n if node[\\\"type\\\"] == \\\"at\\\":\\n type = get_json_or(node[\\\"attrs\\\"],\\\"type\\\",None)\\n id = get_json_or(node[\\\"attrs\\\"],\\\"id\\\",None)\\n if type == \\\"all\\\":\\n text = \\\"(met)all(met)\\\"\\n elif id != None:\\n text = \\\"(met){}(met)\\\".format(self._make_kook_text(id))\\n if last_type == 1 and len(to_send_data) != 0:\\n l = len(to_send_data)\\n to_send_data[l - 1][\\\"content\\\"] += text\\n else:\\n to_send_data.append({\\n \\\"type\\\":1,\\n \\\"content\\\":text\\n })\\n last_type = 1\\n elif node[\\\"type\\\"] == \\\"img\\\":\\n img_url:str = node[\\\"attrs\\\"][\\\"src\\\"]\\n kook_img_url = \\\"\\\"\\n if img_url.startswith(\\\"https://img.kookapp.cn\\\"):\\n kook_img_url = img_url\\n else:\\n if img_url.startswith(\\\"data:image/\\\"):\\n base64_start = img_url.find(\\\"base64,\\\")\\n img_content = base64.b64decode(img_url[base64_start + 7:])\\n else:\\n async with httpx.AsyncClient() as client:\\n img_content = (await client.get(img_url)).content\\n files = {\\n 'file':('test',img_content)\\n }\\n headers = {\\\"Authorization\\\":\\\"Bot {}\\\".format(self._access_token)}\\n async with httpx.AsyncClient() as client:\\n ret = (await client.post(self._http_url + \\\"/asset/create\\\",files=files,headers=headers)).json()\\n kook_img_url = ret[\\\"data\\\"][\\\"url\\\"]\\n to_send_data.append({\\n \\\"type\\\":2,\\n \\\"content\\\":kook_img_url\\n })\\n last_type = 2\\n return to_send_data\\n \\n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\\n '''发送消息'''\\n satori_obj = parse_satori_html(content)\\n to_sends = await self._satori_to_kook(satori_obj)\\n if channel_id.startswith(\\\"GROUP_\\\"):\\n channel_id = int(channel_id[6:])\\n to_ret = []\\n for it in to_sends:\\n ret = await self._api_call(\\\"/message/create\\\",{\\\"content\\\":it[\\\"content\\\"],\\\"type\\\":it[\\\"type\\\"],\\\"target_id\\\":channel_id})\\n to_ret.append(SatoriMessage(id=ret[\\\"msg_id\\\"],content=\\\"\\\").to_dict())\\n return to_ret\\n else:\\n to_ret = []\\n for it in to_sends:\\n ret = await self._api_call(\\\"/direct-message/create\\\",{\\\"content\\\":it[\\\"content\\\"],\\\"type\\\":it[\\\"type\\\"],\\\"target_id\\\":channel_id})\\n to_ret.append(SatoriMessage(id=ret[\\\"msg_id\\\"],content=\\\"\\\").to_dict())\\n return to_ret\\n \\n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\\n obret = (await self._api_call(\\\"/user/me\\\"))\\n satori_ret = SatoriLogin(\\n status=self._login_status,\\n user=SatoriUser(\\n id=obret[\\\"id\\\"],\\n name=obret[\\\"username\\\"],\\n avatar=get_json_or(obret,\\\"avatar\\\",None),\\n is_bot=True\\n ),\\n self_id=obret[\\\"id\\\"],\\n platform=\\\"kook\\\"\\n ).to_dict()\\n self._self_id = obret[\\\"id\\\"]\\n if platform == None and self_id == None:\\n return [satori_ret]\\n else:\\n return satori_ret\\n \\n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\\n '''获取群组成员信息'''\\n url = \\\"/user/view?user_id={}&guild_id={}\\\".format(user_id,guild_id)\\n obret = (await self._api_call(url))\\n satori_ret = SatoriGuildMember(\\n user=SatoriUser(\\n id=obret[\\\"id\\\"],\\n name=get_json_or(obret,\\\"username\\\",None),\\n avatar=get_json_or(obret,\\\"avatar\\\",None),\\n is_bot=get_json_or(obret,\\\"bot\\\",None)\\n ),\\n nick=get_json_or(obret,\\\"nickname\\\",None),\\n avatar=get_json_or(obret,\\\"avatar\\\",None),\\n joined_at=get_json_or(obret,\\\"join_time\\\",None)\\n ).to_dict()\\n return satori_ret\\n \\n async def get_user(self,platform:Optional[str],self_id:Optional[str],user_id:str) -> [dict]:\\n '''获取用户信息'''\\n url = \\\"/user/view?user_id={}\\\".format(user_id)\\n obret = (await self._api_call(url))\\n satori_ret = SatoriUser(\\n id=obret[\\\"id\\\"],\\n name=obret[\\\"username\\\"],\\n avatar=obret[\\\"avatar\\\"],\\n is_bot=obret[\\\"bot\\\"],\\n ).to_dict()\\n return satori_ret\\n \\n async def get_channel_list(self,platform:Optional[str],self_id:Optional[str],guild_id:str) -> [dict]:\\n '''获取频道列表'''\\n url = \\\"/channel/list?guild_id={}\\\".format(guild_id)\\n obret = (await self._api_call(url))\\n ret_list = []\\n items = get_json_or(obret,\\\"items\\\",None)\\n for it in items:\\n channel_type = it[\\\"type\\\"]\\n channel_id = \\\"GROUP_\\\" + it[\\\"id\\\"]\\n channel_name = it[\\\"name\\\"]\\n channel_parent = it[\\\"parent_id\\\"]\\n if channel_type == 1:\\n ret_list.append(SatoriChannel(\\n id=channel_id,\\n name=channel_name,\\n type=SatoriChannel.ChannelType.TEXT,\\n parent_id=channel_parent\\n ).to_dict())\\n page_total = get_json_or(obret,\\\"data\\\",1)\\n if page_total > 1:\\n for i in range(2,page_total + 1):\\n url = \\\"/channel/list?guild_id={}&page={}\\\".format(guild_id,i)\\n obret = (await self._api_call(url))\\n items = get_json_or(obret,\\\"items\\\",None)\\n for it in items:\\n channel_type = it[\\\"type\\\"]\\n channel_id = \\\"GROUP_\\\" + it[\\\"id\\\"]\\n channel_name = it[\\\"name\\\"]\\n channel_parent = it[\\\"parent_id\\\"]\\n if channel_type == 1:\\n ret_list.append(SatoriChannel(\\n id=channel_id,\\n name=channel_name,\\n type=SatoriChannel.ChannelType.TEXT,\\n parent=channel_parent\\n ).to_dict())\\n return {\\\"data\\\":ret_list}\"\n },\n {\n \"identifier\": \"AdapterMihoyo\",\n \"path\": \"mihoyo_adapter.py\",\n \"snippet\": \"class AdapterMihoyo:\\n def __init__(self,config = {}) -> None:\\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\\n self._http_url = \\\"https://bbs-api.miyoushe.com\\\"\\n self._is_stop = False\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n self._queue = Queue(maxsize=100)\\n self._id = 0\\n self._sn = 1\\n self._self_id = config[\\\"bot_id\\\"]\\n self._secret = config[\\\"secret\\\"]\\n self._villa_id = config[\\\"villa_id\\\"]\\n\\n\\n async def enable(self) -> None:\\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\\n '''\\n pass\\n\\n async def disable(self) -> None:\\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\\n pass\\n \\n async def release(self) -> None:\\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\\n 但是，这个函数允许资源延迟释放，只要能释放就行\\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\\n '''\\n self._is_stop = True\\n\\n async def get_msg(self) -> dict:\\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\\n return await self._queue.get()\\n\\n async def _send_ws_pack(self,ws,ws_dat,biztype):\\n magic = 0xBABEFACE.to_bytes(length=4, byteorder='little', signed=False)\\n if biztype == 7:\\n pb_pack = bytes(PLogin(\\n uid=int(ws_dat[\\\"uid\\\"]),\\n token=self._villa_id + \\\".\\\" + self._secret + \\\".\\\" + self._self_id,\\n platform=ws_dat[\\\"platform\\\"],\\n app_id=ws_dat[\\\"app_id\\\"],\\n device_id=ws_dat[\\\"device_id\\\"]\\n ))\\n elif biztype == 6:\\n pb_pack = bytes(PHeartBeat(\\n client_timestamp=str(int(round(time.time() * 1000)))\\n ))\\n else:\\n raise Exception(\\\"unkonw biztype:{}\\\".format(biztype))\\n \\n wid = self._sn\\n self._sn += 1\\n\\n flag = 1\\n appid = 104\\n headerlen = 24\\n datalen = headerlen + len(pb_pack)\\n\\n to_send = magic\\n to_send += datalen.to_bytes(length=4, byteorder='little', signed=False)\\n to_send += headerlen.to_bytes(length=4, byteorder='little', signed=False)\\n to_send += wid.to_bytes(length=8, byteorder='little', signed=False)\\n to_send += flag.to_bytes(length=4, byteorder='little', signed=False)\\n to_send += biztype.to_bytes(length=4, byteorder='little', signed=False)\\n to_send += appid.to_bytes(length=4, byteorder='little', signed=True)\\n to_send += pb_pack\\n\\n await ws.send(to_send)\\n \\n async def _ws_recv(self,websocket):\\n try:\\n reply = await asyncio.wait_for(websocket.recv(),0.1)\\n return reply\\n except asyncio.TimeoutError:\\n return None\\n\\n async def _ws_connect(self):\\n self._login_status = SatoriLogin.LoginStatus.CONNECT\\n ws_dat = (await self._api_call(\\\"/vila/api/bot/platform/getWebsocketInfo\\\"))\\n # print(ws_dat)\\n ws_url = ws_dat[\\\"websocket_url\\\"]\\n async with connect(ws_url) as websocket:\\n await self._send_ws_pack(websocket,ws_dat,biztype=7)\\n tm = time.time()\\n while not self._is_stop:\\n reply = await self._ws_recv(websocket)\\n if not reply:\\n now_time = time.time()\\n if now_time - tm > 30:\\n tm = now_time\\n await self._send_ws_pack(websocket,ws_dat,biztype=6)\\n continue\\n biztype = int.from_bytes(reply[24:28],byteorder='little',signed=False)\\n if biztype == 7: # 登录返回\\n login_reply = PLoginReply().parse(reply[32:])\\n if login_reply.code == 0:\\n print(\\\"mihoyo:ws连接成功\\\")\\n self._login_status = SatoriLogin.LoginStatus.ONLINE\\n continue\\n else:\\n print(\\\"mihoyo:ws连接失败\\\",login_reply.to_json())\\n break\\n elif biztype == 53:\\n print(\\\"mihoyo:ws被踢下线\\\")\\n pkoff = PKickOff().parse(reply[32:])\\n print(\\\"mihoyo:\\\" + pkoff.reason)\\n break\\n elif biztype == 52:\\n print(\\\"mihoyo:ws服务关机\\\")\\n break\\n elif biztype == 6:\\n heart_reply = PHeartBeatReply().parse(reply[32:])\\n if heart_reply.code != 0:\\n print(\\\"mihoyo:ws心跳失败\\\")\\n break\\n elif biztype == 30001: # 正常处理\\n evt = RobotEvent().parse(reply[32:]).to_dict()\\n asyncio.create_task(self._event_deal(evt))\\n\\n async def _ws_server(self) -> None:\\n while not self._is_stop:\\n try:\\n await self._ws_connect()\\n except:\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n traceback.print_exc()\\n await asyncio.sleep(3)\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n\\n async def init_after(self) -> None:\\n asyncio.create_task(self._ws_server())\\n\\n def _mihoyo_msg_to_satori(self,content_obj)->str:\\n ret = \\\"\\\"\\n entities = content_obj[\\\"content\\\"][\\\"entities\\\"]\\n text = content_obj[\\\"content\\\"][\\\"text\\\"]\\n l = len(text)\\n i = 0\\n while i < l:\\n for en in entities:\\n if en[\\\"offset\\\"] == i:\\n print(en)\\n i += en[\\\"length\\\"]\\n if en[\\\"entity\\\"][\\\"type\\\"] == \\\"mention_all\\\": # 实际上收不到\\n ret += \\\"<at type=\\\\\\\"all\\\\\\\"/>\\\"\\n elif en[\\\"entity\\\"][\\\"type\\\"] == \\\"mentioned_robot\\\":\\n ret += \\\"<at id=\\\\\\\"{}\\\\\\\"/>\\\".format(en[\\\"entity\\\"][\\\"bot_id\\\"])\\n elif en[\\\"entity\\\"][\\\"type\\\"] == \\\"mentioned_user\\\":\\n ret += \\\"<at id=\\\\\\\"{}\\\\\\\"/>\\\".format(en[\\\"entity\\\"][\\\"user_id\\\"])\\n break\\n else:\\n ret += satori_to_plain(text[i])\\n i += 1\\n return ret\\n async def _deal_group_message_event(self,data):\\n extendData = data[\\\"extendData\\\"]\\n\\n sendMessage = extendData[\\\"sendMessage\\\"]\\n user_id = sendMessage[\\\"fromUserId\\\"]\\n villaId = sendMessage[\\\"villaId\\\"]\\n roomId = sendMessage[\\\"roomId\\\"]\\n\\n villaRoomId = villaId + \\\"_\\\" + roomId\\n\\n content_obj = json.loads(sendMessage[\\\"content\\\"])\\n\\n extra_obj = json.loads(content_obj[\\\"user\\\"][\\\"extra\\\"])\\n\\n satori_msg = self._mihoyo_msg_to_satori(content_obj) # todo\\n\\n satori_evt = SatoriGroupMessageCreatedEvent(\\n id=self._id,\\n self_id=self._self_id,\\n timestamp=int(data[\\\"sendAt\\\"]) * 1000,\\n platform=\\\"mihoyo\\\",\\n channel=SatoriChannel(\\n id=villaRoomId,\\n type=SatoriChannel.ChannelType.TEXT,\\n ),\\n message=SatoriMessage(\\n id=data[\\\"id\\\"],\\n content=satori_msg,\\n created_at=int(sendMessage[\\\"sendAt\\\"])\\n ),\\n user=SatoriUser(\\n id=user_id,\\n name=sendMessage[\\\"nickname\\\"],\\n avatar=content_obj[\\\"user\\\"][\\\"portraitUri\\\"]\\n ),\\n member=SatoriGuildMember(\\n nick=sendMessage[\\\"nickname\\\"],\\n avatar=content_obj[\\\"user\\\"][\\\"portraitUri\\\"]\\n ),\\n guild=SatoriGuild(\\n id=villaId\\n ),\\n role=SatoriGuildRole(\\n id=extra_obj[\\\"member_roles\\\"][\\\"name\\\"],\\n name=extra_obj[\\\"member_roles\\\"][\\\"name\\\"]\\n )\\n )\\n self._id += 1\\n self._queue.put_nowait(satori_evt.to_dict())\\n\\n async def _event_deal(self,data:dict):\\n try:\\n event_type = data[\\\"type\\\"]\\n if event_type == \\\"SendMessage\\\":\\n await self._deal_group_message_event(data)\\n except:\\n print(traceback.format_exc())\\n\\n \\n async def _api_call(self,path,data = None,villa_id = 0) -> dict:\\n url:str = self._http_url + path\\n headers = {\\\"x-rpc-bot_id\\\":self._self_id,\\\"x-rpc-bot_secret\\\":self._secret}\\n if villa_id == 0:\\n headers[\\\"x-rpc-bot_villa_id\\\"] = self._villa_id\\n else:\\n headers[\\\"x-rpc-bot_villa_id\\\"] = villa_id\\n if data == None:\\n async with httpx.AsyncClient() as client:\\n return (await client.get(url,headers=headers)).json()[\\\"data\\\"]\\n else:\\n headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n async with httpx.AsyncClient() as client:\\n ret = (await client.post(url,headers=headers,data=data)).json()\\n if ret[\\\"retcode\\\"] != 0:\\n print(\\\"mihoyo:\\\",ret)\\n return ret[\\\"data\\\"]\\n\\n \\n async def _satori_to_mihoyo(self,satori_obj,villa_id) -> [dict]:\\n to_send_data = []\\n last_type = 1\\n for node in satori_obj:\\n if isinstance(node,str):\\n text = node\\n if last_type == 1 and len(to_send_data) != 0:\\n l = len(to_send_data)\\n to_send_data[l - 1][\\\"text\\\"] += text\\n else:\\n to_send_data.append({\\n \\\"type\\\":1,\\n \\\"text\\\":text,\\n \\\"entities\\\":[]\\n })\\n last_type = 1\\n else:\\n if node[\\\"type\\\"] == \\\"at\\\":\\n type = get_json_or(node[\\\"attrs\\\"],\\\"type\\\",None)\\n id = get_json_or(node[\\\"attrs\\\"],\\\"id\\\",None)\\n if type == \\\"all\\\":\\n text = \\\"@全体成员\\\"\\n elif id != None:\\n text = \\\"@\\\" + id\\n else:\\n continue\\n\\n if last_type != 1 or len(to_send_data) == 0:\\n to_send_data.append({\\n \\\"type\\\":1,\\n \\\"text\\\":\\\"\\\",\\n \\\"entities\\\":[]\\n })\\n last_type = 1\\n\\n l = len(to_send_data)\\n ll = len(to_send_data[l - 1][\\\"text\\\"])\\n to_send_data[l - 1][\\\"text\\\"] += text\\n if type == \\\"all\\\":\\n to_send_data[l - 1][\\\"entities\\\"].append({\\n \\\"entity\\\": {\\n \\\"type\\\": \\\"mention_all\\\"\\n },\\n \\\"length\\\":5,\\n \\\"offset\\\":ll\\n })\\n else:\\n if id.startswith(\\\"bot_\\\"):\\n to_send_data[l - 1][\\\"entities\\\"].append({\\n \\\"entity\\\": {\\n \\\"type\\\": \\\"mentioned_robot\\\",\\n \\\"bot_id\\\": id\\n },\\n \\\"length\\\":len(id) + 1,\\n \\\"offset\\\":ll\\n })\\n else:\\n to_send_data[l - 1][\\\"entities\\\"].append({\\n \\\"entity\\\": {\\n \\\"type\\\": \\\"mentioned_user\\\",\\n \\\"user_id\\\": id\\n },\\n \\\"length\\\":len(id) + 1,\\n \\\"offset\\\":ll\\n })\\n\\n elif node[\\\"type\\\"] == \\\"img\\\":\\n img_url:str = node[\\\"attrs\\\"][\\\"src\\\"]\\n mihoyo_img_url = \\\"\\\"\\n if img_url.startswith(\\\"data:image/\\\"):\\n base64_start = img_url.find(\\\"base64,\\\")\\n img_content = base64.b64decode(img_url[base64_start + 7:])\\n else:\\n async with httpx.AsyncClient() as client:\\n img_content = (await client.get(img_url)).content\\n ext = imghdr.what(file = \\\"\\\",h=img_content)\\n m = hashlib.md5()\\n m.update(img_content)\\n headers = {\\\"x-rpc-bot_id\\\":self._self_id,\\\"x-rpc-bot_secret\\\":self._secret,\\\"x-rpc-bot_villa_id\\\":villa_id}\\n upload_info_url = self._http_url + \\\"/vila/api/bot/platform/getUploadImageParams\\\"\\n async with httpx.AsyncClient() as client:\\n req = client.build_request(\\\"GET\\\",upload_info_url,json={\\n \\\"md5\\\":m.hexdigest(),\\n \\\"ext\\\":ext\\n },headers=headers)\\n file_params = (await client.send(req)).json()[\\\"data\\\"][\\\"params\\\"]\\n files = {\\n \\\"x:extra\\\":file_params[\\\"callback_var\\\"][\\\"x:extra\\\"],\\n \\\"OSSAccessKeyId\\\":file_params[\\\"accessid\\\"],\\n \\\"signature\\\":file_params[\\\"signature\\\"],\\n \\\"success_action_status\\\":file_params[\\\"success_action_status\\\"],\\n \\\"name\\\":file_params[\\\"name\\\"],\\n \\\"callback\\\":file_params[\\\"callback\\\"],\\n \\\"x-oss-content-type\\\":file_params[\\\"x_oss_content_type\\\"],\\n \\\"key\\\":file_params[\\\"key\\\"],\\n \\\"policy\\\":file_params[\\\"policy\\\"],\\n \\\"Content-Disposition\\\":file_params[\\\"content_disposition\\\"],\\n 'file':('test',img_content)\\n }\\n async with httpx.AsyncClient() as client:\\n ret = (await client.post(file_params[\\\"host\\\"],files=files)).json()\\n mihoyo_img_url = ret[\\\"data\\\"][\\\"url\\\"]\\n to_send_data.append({\\n \\\"type\\\":2,\\n \\\"url\\\":mihoyo_img_url,\\n })\\n last_type = 2\\n to_send_data2 = []\\n for it in to_send_data:\\n type = it[\\\"type\\\"]\\n if type == 1:\\n to_send_data2.append({\\n \\\"object_name\\\":\\\"MHY:Text\\\",\\n \\\"msg_content\\\":json.dumps({\\n \\\"content\\\":{\\n \\\"text\\\":it[\\\"text\\\"],\\n \\\"entities\\\":it[\\\"entities\\\"]\\n }\\n })})\\n elif type == 2:\\n to_send_data2.append({\\n \\\"object_name\\\":\\\"MHY:Image\\\",\\n \\\"msg_content\\\":json.dumps({\\n \\\"content\\\":{\\n \\\"url\\\":it[\\\"url\\\"]\\n }\\n \\n })})\\n \\n return to_send_data2\\n \\n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\\n '''发送消息'''\\n villa_id = channel_id.split(\\\"_\\\")[0]\\n satori_obj = parse_satori_html(content)\\n to_sends = await self._satori_to_mihoyo(satori_obj,villa_id)\\n to_ret = []\\n # print(to_sends)\\n for it in to_sends:\\n it[\\\"room_id\\\"] = channel_id.split(\\\"_\\\")[1]\\n ret = await self._api_call(\\\"/vila/api/bot/platform/sendMessage\\\",json.dumps(it),villa_id=villa_id)\\n to_ret.append(SatoriMessage(id=ret[\\\"bot_msg_id\\\"],content=\\\"\\\").to_dict())\\n return to_ret\\n \\n \\n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\\n satori_ret = SatoriLogin(\\n status=self._login_status,\\n user=SatoriUser(\\n id=self._self_id,\\n is_bot=True\\n ),\\n self_id=self._self_id,\\n platform=\\\"mihoyo\\\"\\n ).to_dict()\\n if platform == None and self_id == None:\\n return [satori_ret]\\n else:\\n return satori_ret\\n\\n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\\n '''获取群组成员信息'''\\n url = self._http_url + \\\"/vila/api/bot/platform/getMember\\\"\\n headers = {\\\"x-rpc-bot_id\\\":self._self_id,\\\"x-rpc-bot_secret\\\":self._secret,\\\"x-rpc-bot_villa_id\\\":guild_id}\\n async with httpx.AsyncClient() as client:\\n req = client.build_request(\\\"GET\\\",url,json={\\n \\\"uid\\\":user_id\\n },headers=headers)\\n obret = (await client.send(req)).json()[\\\"data\\\"][\\\"member\\\"]\\n satori_ret = SatoriGuildMember(\\n user=SatoriUser(\\n id=obret[\\\"basic\\\"][\\\"uid\\\"],\\n name=obret[\\\"basic\\\"][\\\"nickname\\\"],\\n avatar=obret[\\\"basic\\\"][\\\"avatar_url\\\"],\\n is_bot=False\\n ),\\n nick=obret[\\\"basic\\\"][\\\"nickname\\\"],\\n avatar=obret[\\\"basic\\\"][\\\"avatar_url\\\"],\\n joined_at=int(obret[\\\"joined_at\\\"] + \\\"000\\\")\\n ).to_dict()\\n return satori_ret\"\n },\n {\n \"identifier\": \"AdapterOnebot\",\n \"path\": \"onebot_adapter.py\",\n \"snippet\": \"class AdapterOnebot:\\n def __init__(self,config = {}) -> None:\\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\\n self._http_url = config[\\\"http_url\\\"]\\n self._ws_url = config[\\\"ws_url\\\"]\\n if \\\"access_token\\\" in config:\\n self._access_token = config[\\\"access_token\\\"]\\n else:\\n self._access_token = None\\n self._is_stop = False\\n self._login_status = 3 # DISCONNECT\\n self._queue = Queue(maxsize=100)\\n self._id = 0\\n\\n def _cqarr_to_satori(self,cqarr):\\n ret = \\\"\\\"\\n for node in cqarr:\\n if node[\\\"type\\\"] == \\\"text\\\":\\n ret += satori_to_plain(node[\\\"data\\\"][\\\"text\\\"])\\n elif node[\\\"type\\\"] == \\\"at\\\":\\n qq = node[\\\"data\\\"][\\\"qq\\\"]\\n if qq == \\\"all\\\":\\n ret += \\\"<at type=\\\\\\\"all\\\\\\\"/>\\\"\\n else:\\n ret += \\\"<at id={}/>\\\".format(json.dumps(qq))\\n elif node[\\\"type\\\"] == \\\"image\\\":\\n url = node[\\\"data\\\"][\\\"url\\\"]\\n ret += \\\"<img src={}/>\\\".format(json.dumps(url))\\n return ret\\n\\n async def enable(self) -> None:\\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\\n '''\\n pass\\n\\n async def disable(self) -> None:\\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\\n pass\\n \\n async def release(self) -> None:\\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\\n 但是，这个函数允许资源延迟释放，只要能释放就行\\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\\n '''\\n self._is_stop = True\\n\\n async def get_msg(self) -> dict:\\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\\n return await self._queue.get()\\n\\n async def init_after(self) -> None:\\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\\n async def _ws_server(self:AdapterOnebot) -> None:\\n while not self._is_stop:\\n try:\\n self._login_status = 2 # CONNECT\\n async with connect(self._ws_url) as websocket:\\n print(\\\"onebot:ws已经连接\\\")\\n self._login_status = 1 # ONLINE\\n try:\\n while True:\\n try:\\n reply = await asyncio.wait_for(websocket.recv(),0.1)\\n await self._event_deal(json.loads(reply))\\n except asyncio.TimeoutError:\\n if self._is_stop:\\n await websocket.close()\\n except asyncio.QueueFull:\\n print(\\\"队列满\\\")\\n except Exception as e:\\n print(e) \\n except Exception as e:\\n print(e)\\n print(\\\"onebot:ws连接已经断开\\\")\\n self._login_status = 3 # DISCONNECT\\n asyncio.create_task(_ws_server(self))\\n \\n async def _event_deal(self,evt:dict):\\n '''自己定义的事件转化函数'''\\n post_type = evt[\\\"post_type\\\"]\\n if post_type == \\\"message\\\":\\n message_type = evt[\\\"message_type\\\"]\\n sender = evt[\\\"sender\\\"]\\n if message_type == \\\"group\\\":\\n channel_obj = {\\n \\\"id\\\":\\\"GROUP_\\\"+str(evt[\\\"group_id\\\"]),\\n \\\"type\\\":0,\\n \\\"name\\\":None,\\n \\\"parent_id\\\":None\\n }\\n guild_obj = {\\n \\\"id\\\":\\\"GROUP_\\\"+str(evt[\\\"group_id\\\"]),\\n \\\"name\\\":None,\\n \\\"avatar\\\":None\\n }\\n user_obj = {\\n \\\"id\\\":str(evt[\\\"user_id\\\"]),\\n \\\"name\\\":get_json_or(sender,\\\"nickname\\\",None),\\n \\\"nick\\\":get_json_or(sender,\\\"nickname\\\",None),\\n \\\"avatar\\\":get_json_or(sender,\\\"avatar\\\",None),\\n \\\"is_bot\\\":None\\n }\\n joined_at = get_json_or(sender,\\\"join_time\\\",None)\\n if joined_at:\\n joined_at = int(str(joined_at) + \\\"000\\\")\\n member_obj = {\\n \\\"nick\\\":get_json_or(sender,\\\"card\\\",None),\\n \\\"avatar\\\":get_json_or(sender,\\\"avatar\\\",None),\\n \\\"joined_at\\\":joined_at\\n }\\n message_obj = {\\n \\\"id\\\":str(evt[\\\"message_id\\\"]),\\n \\\"content\\\":self._cqarr_to_satori(_cqmsg_to_arr(evt[\\\"message\\\"])),\\n \\\"created_at\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\")\\n }\\n role_obj = {\\n \\\"id\\\":get_json_or(sender, \\\"role\\\",\\\"member\\\"),\\n \\\"name\\\":get_json_or(sender,\\\"role\\\",\\\"member\\\")\\n }\\n satori_evt = {\\n \\\"id\\\":self._id,\\n \\\"type\\\":\\\"message-created\\\",\\n \\\"platform\\\":\\\"onebot\\\",\\n \\\"self_id\\\":str(evt[\\\"self_id\\\"]),\\n \\\"timestamp\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\"),\\n \\\"channel\\\":channel_obj,\\n \\\"guild\\\":guild_obj,\\n \\\"member\\\":member_obj,\\n \\\"message\\\":message_obj,\\n \\\"role\\\":role_obj,\\n \\\"user\\\":user_obj\\n }\\n self._id += 1\\n self._queue.put_nowait(satori_evt)\\n elif message_type == \\\"private\\\":\\n channel_obj = {\\n \\\"id\\\":str(evt[\\\"user_id\\\"]),\\n \\\"type\\\":1,\\n \\\"name\\\":None,\\n \\\"parent_id\\\":None\\n }\\n user_obj = {\\n \\\"id\\\":str(evt[\\\"user_id\\\"]),\\n \\\"name\\\":get_json_or(sender,\\\"nickname\\\",None),\\n \\\"nick\\\":get_json_or(sender,\\\"nickname\\\",None),\\n \\\"avatar\\\":get_json_or(sender,\\\"avatar\\\",None),\\n \\\"is_bot\\\":None\\n }\\n joined_at = get_json_or(sender,\\\"join_time\\\",None)\\n if joined_at:\\n joined_at = int(str(joined_at) + \\\"000\\\")\\n message_obj = {\\n \\\"id\\\":str(evt[\\\"message_id\\\"]),\\n \\\"content\\\":self._cqarr_to_satori(_cqmsg_to_arr(evt[\\\"message\\\"])),\\n \\\"created_at\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\")\\n }\\n satori_evt = {\\n \\\"id\\\":self._id,\\n \\\"type\\\":\\\"message-created\\\",\\n \\\"platform\\\":\\\"onebot\\\",\\n \\\"self_id\\\":str(evt[\\\"self_id\\\"]),\\n \\\"timestamp\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\"),\\n \\\"channel\\\":channel_obj,\\n \\\"message\\\":message_obj,\\n \\\"user\\\":user_obj\\n }\\n self._id += 1\\n self._queue.put_nowait(satori_evt)\\n elif post_type == \\\"notice\\\":\\n notice_type = evt[\\\"notice_type\\\"]\\n if notice_type == \\\"group_increase\\\":\\n guild_obj = {\\n \\\"id\\\":\\\"GROUP_\\\"+str(evt[\\\"group_id\\\"]),\\n \\\"name\\\":None,\\n \\\"avatar\\\":None\\n }\\n member_obj = {\\n \\\"nick\\\":None,\\n \\\"avatar\\\":get_json_or(evt,\\\"avatar\\\",None),\\n \\\"joined_at\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\")\\n }\\n user_obj = {\\n \\\"id\\\":str(evt[\\\"user_id\\\"]),\\n \\\"name\\\":None,\\n \\\"nick\\\":None,\\n \\\"avatar\\\":None,\\n \\\"is_bot\\\":None\\n }\\n satori_evt = {\\n \\\"id\\\":self._id,\\n \\\"type\\\":\\\"guild-member-added\\\",\\n \\\"platform\\\":\\\"onebot\\\",\\n \\\"self_id\\\":str(evt[\\\"self_id\\\"]),\\n \\\"timestamp\\\":int(str(evt[\\\"time\\\"] ) + \\\"000\\\"),\\n \\\"guild\\\":guild_obj,\\n \\\"member\\\":member_obj,\\n \\\"user\\\":user_obj\\n }\\n self._id += 1\\n self._queue.put_nowait(satori_evt)\\n\\n async def _api_call(self,path,data) -> dict:\\n url:str = self._http_url + path\\n if self._access_token:\\n headers = {\\\"Authorization\\\":\\\"Bearer {}\\\".format(self._access_token)}\\n else:\\n headers = {}\\n async with httpx.AsyncClient() as client:\\n # headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n return (await client.post(url,headers=headers,data=data)).json()\\n \\n async def _satori_to_cq(self,satori_obj) -> str:\\n ret = \\\"\\\"\\n for node in satori_obj:\\n if isinstance(node,str):\\n ret += _cq_text_encode(node)\\n else:\\n if node[\\\"type\\\"] == \\\"at\\\":\\n type = get_json_or(node[\\\"attrs\\\"],\\\"type\\\",None)\\n id = get_json_or(node[\\\"attrs\\\"],\\\"id\\\",None)\\n if type == \\\"all\\\":\\n ret += \\\"[CQ:at,qq=all]\\\"\\n elif id != None:\\n ret += \\\"[CQ:at,qq={}]\\\".format(_cq_params_encode(id))\\n elif node[\\\"type\\\"] == \\\"img\\\":\\n img_url = node[\\\"attrs\\\"][\\\"src\\\"]\\n if img_url.startswith(\\\"data:image/\\\"):\\n base64_start = img_url.find(\\\"base64,\\\")\\n img_url = \\\"base64://\\\" + img_url[base64_start + 7:]\\n ret += \\\"[CQ:image,file={}]\\\".format(_cq_params_encode(img_url)) \\n\\n return ret\\n\\n\\n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\\n '''发送消息'''\\n satori_obj = parse_satori_html(content)\\n to_send = await self._satori_to_cq(satori_obj)\\n if channel_id.startswith(\\\"GROUP_\\\"):\\n group_id = int(channel_id[6:])\\n ret = await self._api_call(\\\"/send_group_msg\\\",{\\\"group_id\\\":group_id,\\\"message\\\":to_send})\\n return [{\\\"id\\\":str(ret[\\\"data\\\"][\\\"message_id\\\"]),\\\"content\\\":\\\"\\\"}]\\n else:\\n user_id = int(channel_id)\\n ret = await self._api_call(\\\"/send_private_msg\\\",{\\\"user_id\\\":user_id,\\\"message\\\":to_send})\\n return [{\\\"id\\\":str(ret[\\\"data\\\"][\\\"message_id\\\"]),\\\"content\\\":\\\"\\\"}]\\n \\n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\\n obret = (await self._api_call(\\\"/get_login_info\\\",{}))[\\\"data\\\"]\\n satori_ret = {\\n \\\"user\\\":{\\n \\\"id\\\":str(obret[\\\"user_id\\\"]),\\n \\\"name\\\":obret[\\\"nickname\\\"],\\n \\\"nick\\\":obret[\\\"nickname\\\"],\\n \\\"avatar\\\":get_json_or(obret,\\\"avatar\\\",None),\\n \\\"is_bot\\\":None\\n },\\n \\\"self_id\\\":str(obret[\\\"user_id\\\"]),\\n \\\"platform\\\":\\\"onebot\\\",\\n \\\"status\\\":self._login_status,\\n }\\n if platform == None and self_id == None:\\n return [satori_ret]\\n else:\\n return satori_ret\\n \\n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\\n '''获取群组成员信息'''\\n obret = (await self._api_call(\\\"/get_group_member_info\\\",{\\n \\\"group_id\\\":int(guild_id[6:]),\\n \\\"user_id\\\":int(user_id)\\n }))[\\\"data\\\"]\\n joined_at = get_json_or(obret,\\\"join_time\\\",None)\\n if joined_at:\\n joined_at = int(str(joined_at) + \\\"000\\\")\\n satori_ret = {\\n \\\"user\\\":{\\n \\\"id\\\":str(obret[\\\"user_id\\\"]),\\n \\\"name\\\":get_json_or(obret,\\\"nickname\\\",None),\\n \\\"nick\\\":get_json_or(obret,\\\"card\\\",None),\\n \\\"avatar\\\":get_json_or(obret,\\\"avatar\\\",None),\\n \\\"is_bot\\\":None\\n },\\n \\\"nick\\\":get_json_or(obret,\\\"card\\\",None),\\n \\\"avatar\\\":get_json_or(obret,\\\"avatar\\\",None),\\n \\\"joined_at\\\":joined_at,\\n }\\n return satori_ret\"\n },\n {\n \"identifier\": \"Config\",\n \"path\": \"config.py\",\n \"snippet\": \"class Config:\\n def __init__(self) -> None:\\n self.botlist:list = []\\n self.web_port:int = 8080\\n self.web_host:str = \\\"127.0.0.1\\\"\\n self.access_token:str = \\\"\\\"\\n \\n async def read_config(self):\\n async with aiofiles.open('config.json', mode='r') as f:\\n json_dat = json5.loads(await f.read())\\n self.botlist = json_dat[\\\"botlist\\\"]\\n self.web_port = json_dat[\\\"web_port\\\"]\\n self.web_host = json_dat[\\\"web_host\\\"]\\n self.access_token = json_dat[\\\"access_token\\\"]\"\n },\n {\n \"identifier\": \"AdapterQQ\",\n \"path\": \"qq_adapter.py\",\n \"snippet\": \"class AdapterQQ:\\n def __init__(self,config = {}) -> None:\\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\\n self._botqq = config[\\\"botqq\\\"]\\n self._appid = config[\\\"appid\\\"]\\n self._token = config[\\\"token\\\"]\\n if \\\"withgroup\\\" in config:\\n self._withgroup = config[\\\"withgroup\\\"]\\n else:\\n self._withgroup = None\\n self._appsecret = config[\\\"appsecret\\\"]\\n self._http_url = \\\"https://api.sgroup.qq.com\\\"\\n self._is_stop = False\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n self._queue = Queue(maxsize=100)\\n self._id = 0\\n self._sn = None\\n self._self_id = None\\n self._access_token = None\\n self._expires_in = 0\\n self.msgid_map = dict()\\n # self._self_name = None\\n\\n\\n async def enable(self) -> None:\\n '''适配器启用的时候会调用，可以不理，也可以没这个函数\\n 配合下面的停用函数，适配器可以得到自己在整个系统中的状态，进而进行一些优化\\n 如，如果适配器处于停用状态，适配器可以自行选择关闭网络连接，以节省资源，当然，也可以不理会\\n '''\\n pass\\n\\n async def disable(self) -> None:\\n '''适配器停用的时候会调用，可以不理，也可以没这个函数'''\\n pass\\n \\n async def release(self) -> None:\\n '''适配器释放的时候会调用一次，应该在这里停用ws连接\\n 一般认为，适配器会和真正的协议端建立连接，所以，这个函数大多数时候是需要写的\\n 但是，这个函数允许资源延迟释放，只要能释放就行\\n 你可以在这个函数里面进行数据保存之类的，这种用途下，请阻塞这个函数，直到保存完成\\n '''\\n self._is_stop = True\\n\\n async def get_msg(self) -> dict:\\n '''阻塞并等待消息返回，如果你的适配器不具备接收消息的能力，请不要写这个函数'''\\n return await self._queue.get()\\n \\n\\n async def _ws_recv(self,websocket):\\n try:\\n reply = await asyncio.wait_for(websocket.recv(),0.1)\\n return reply\\n except asyncio.TimeoutError:\\n return None\\n\\n async def _ws_connect(self):\\n self._login_status = SatoriLogin.LoginStatus.CONNECT\\n ws_url = (await self._api_call(\\\"/gateway\\\"))[\\\"url\\\"]\\n async with connect(ws_url) as websocket:\\n tm = time.time()\\n while not self._is_stop:\\n reply = await self._ws_recv(websocket)\\n if not reply:\\n now_time = time.time()\\n if now_time - tm > 30:\\n tm = now_time\\n await websocket.send(json.dumps({\\\"op\\\": 1,\\\"d\\\": self._sn}))\\n continue\\n js = json.loads(reply)\\n op = js[\\\"op\\\"]\\n if op == 0: # 事件\\n self._sn = js[\\\"s\\\"]\\n t = js[\\\"t\\\"]\\n if t == \\\"READY\\\":\\n print(\\\"qq:ws连接成功\\\")\\n print(json.dumps(js))\\n self._login_status = SatoriLogin.LoginStatus.ONLINE\\n else:\\n print(json.dumps(js))\\n asyncio.create_task(self._deal_event(js))\\n elif op == 1: # 心跳\\n await websocket.send(json.dumps({\\\"op\\\":11}))\\n elif op == 7: # 重连\\n print(\\\"qq:服务端要求重连\\\")\\n break\\n elif op == 9: # 参数错误\\n print(\\\"qq:参数错误:\\\",json.dumps(js))\\n break\\n elif op == 10: # ws建立成功\\n if self._withgroup:\\n await websocket.send(json.dumps({\\n \\\"op\\\":2,\\n \\\"d\\\":{\\n \\\"token\\\":\\\"QQBot {}\\\".format(self._access_token),\\n \\\"intents\\\":0 | (1 << 0) | (1 << 1) | (1 << 30) | (1 << 25),\\n \\\"shard\\\":[0, 1],\\n }\\n }))\\n else:\\n await websocket.send(json.dumps({\\n \\\"op\\\":2,\\n \\\"d\\\":{\\n \\\"token\\\":\\\"QQBot {}\\\".format(self._access_token),\\n \\\"intents\\\":0 | (1 << 0) | (1 << 1) | (1 << 30),\\n \\\"shard\\\":[0, 1],\\n }\\n }))\\n elif op == 11: # HTTP Callback ACK\\n pass\\n\\n async def _ws_server(self) -> None:\\n while not self._is_stop:\\n try:\\n await self._ws_connect()\\n except:\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n print(traceback.format_exc())\\n await asyncio.sleep(3)\\n self._login_status = SatoriLogin.LoginStatus.DISCONNECT\\n\\n async def _token_refresh(self):\\n async with httpx.AsyncClient() as client:\\n if not self._expires_in or int(self._expires_in) < 60 * 5:\\n url = \\\"https://bots.qq.com/app/getAppAccessToken\\\"\\n ret = (await client.post(url,json={\\n \\\"appId\\\":self._appid,\\n \\\"clientSecret\\\":self._appsecret\\n })).json()\\n self._access_token = ret[\\\"access_token\\\"]\\n self._expires_in = ret[\\\"expires_in\\\"]\\n # print(ret)\\n\\n async def _qqarr_to_satori(self,qqmsg_arr):\\n ret = \\\"\\\"\\n for it in qqmsg_arr:\\n if it[\\\"type\\\"] == \\\"text\\\":\\n ret += satori_to_plain(it[\\\"data\\\"])\\n else:\\n if it[\\\"data\\\"].startswith(\\\"<@!\\\"):\\n user_id = it[\\\"data\\\"][3:len(it[\\\"data\\\"]) - 1]\\n ret += \\\"<at id=\\\\\\\"{}\\\\\\\">\\\".format(satori_to_plain(user_id))\\n elif it[\\\"data\\\"].startswith(\\\"<@\\\"):\\n user_id = it[\\\"data\\\"][2:len(it[\\\"data\\\"]) - 1]\\n ret += \\\"<at id=\\\\\\\"{}\\\\\\\">\\\".format(satori_to_plain(user_id))\\n return ret\\n \\n async def _deal_channel_event(self,data):\\n qqmsg_arr = _qqmsg_to_arr(data[\\\"content\\\"])\\n # print(\\\"qqmsg_arr\\\",qqmsg_arr)\\n satori_msg = await self._qqarr_to_satori(qqmsg_arr)\\n self.msgid_map[\\\"CHANNEL_\\\"+data[\\\"channel_id\\\"]] = data[\\\"id\\\"]\\n satori_evt = SatoriGroupMessageCreatedEvent(\\n id=self._id,\\n self_id=self._self_id,\\n timestamp=int(time.mktime(time.strptime(data[\\\"timestamp\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000,\\n platform=\\\"qq_guild\\\",\\n channel=SatoriChannel(\\n id=\\\"CHANNEL_\\\"+data[\\\"channel_id\\\"],\\n type=SatoriChannel.ChannelType.TEXT,\\n ),\\n message=SatoriMessage(\\n id=data[\\\"id\\\"],\\n content=satori_msg,\\n created_at=int(time.mktime(time.strptime(data[\\\"timestamp\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000\\n ),\\n user=SatoriUser(\\n id=data[\\\"author\\\"][\\\"id\\\"],\\n name=data[\\\"author\\\"][\\\"username\\\"],\\n avatar=data[\\\"author\\\"][\\\"avatar\\\"],\\n is_bot=data[\\\"author\\\"][\\\"bot\\\"]\\n ),\\n member=SatoriGuildMember(\\n nick=data[\\\"member\\\"][\\\"nick\\\"],\\n avatar=data[\\\"author\\\"][\\\"avatar\\\"],\\n joined_at=int(time.mktime(time.strptime(data[\\\"member\\\"][\\\"joined_at\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000\\n ),\\n guild=SatoriGuild(\\n id=data[\\\"guild_id\\\"]\\n ),\\n role=SatoriGuildRole(\\n id=json.dumps(sorted(data[\\\"member\\\"][\\\"roles\\\"]))\\n )\\n )\\n self._id += 1\\n self._queue.put_nowait(satori_evt.to_dict())\\n\\n async def _deal_group_event(self,data):\\n qqmsg_arr = _qqmsg_to_arr(data[\\\"content\\\"])\\n # print(\\\"qqmsg_arr\\\",qqmsg_arr)\\n satori_msg = await self._qqarr_to_satori(qqmsg_arr)\\n self.msgid_map[\\\"GROUP_\\\"+data[\\\"group_id\\\"]] = data[\\\"id\\\"]\\n satori_evt = SatoriGroupMessageCreatedEvent(\\n id=self._id,\\n self_id=self._botqq,\\n timestamp=int(time.mktime(time.strptime(data[\\\"timestamp\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000,\\n platform=\\\"qq_group\\\",\\n channel=SatoriChannel(\\n id=\\\"GROUP_\\\"+data[\\\"group_id\\\"],\\n type=SatoriChannel.ChannelType.TEXT,\\n ),\\n message=SatoriMessage(\\n id=data[\\\"id\\\"],\\n content=satori_msg,\\n created_at=int(time.mktime(time.strptime(data[\\\"timestamp\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000\\n ),\\n user=SatoriUser(\\n id=data[\\\"author\\\"][\\\"id\\\"]\\n ),\\n member=SatoriGuildMember(\\n ),\\n guild=SatoriGuild(\\n id=\\\"GROUP_\\\"+data[\\\"group_id\\\"]\\n ),\\n role=SatoriGuildRole(\\n id=\\\"unkonw\\\",\\n name=\\\"unkonw\\\"\\n )\\n )\\n self._id += 1\\n self._queue.put_nowait(satori_evt.to_dict())\\n\\n async def _deal_event(self,event):\\n try:\\n type = event[\\\"t\\\"]\\n if type == \\\"AT_MESSAGE_CREATE\\\":\\n d = event[\\\"d\\\"]\\n if (\\\"channel_id\\\" in d) and d[\\\"channel_id\\\"]:\\n await self._deal_channel_event(d)\\n else:\\n if type == \\\"GROUP_AT_MESSAGE_CREATE\\\":\\n d = event[\\\"d\\\"]\\n if (\\\"group_id\\\" in d) and d[\\\"group_id\\\"]:\\n await self._deal_group_event(d)\\n except:\\n print(traceback.format_exc())\\n\\n async def _token_refresh_task(self):\\n while True:\\n try:\\n await self._token_refresh()\\n index = 0\\n while index < 60: # 每60秒检测一次token是否过期\\n await asyncio.sleep(1)\\n if self._is_stop:\\n break\\n index += 1\\n if self._is_stop:break\\n except:\\n print(traceback.format_exc())\\n\\n async def init_after(self) -> None:\\n '''适配器创建之后会调用一次，应该在这里进行ws连接等操作，如果不需要，可以不写'''\\n try:\\n await self._token_refresh()\\n except:\\n print(traceback.format_exc())\\n asyncio.create_task(self._token_refresh_task())\\n asyncio.create_task(self._ws_server())\\n\\n async def _api_call(self,path,data = None) -> dict:\\n url:str = self._http_url + path\\n headers = {\\\"Authorization\\\":\\\"QQBot {}\\\".format(self._access_token),\\\"X-Union-Appid\\\":self._appid}\\n if data == None:\\n async with httpx.AsyncClient() as client:\\n return (await client.get(url,headers=headers)).json()\\n else:\\n async with httpx.AsyncClient() as client:\\n ret = (await client.post(url,headers=headers,json=data))\\n # print(ret.content)\\n return ret.json()\\n\\n def _make_qq_text(self,text:str):\\n ret = text\\n ret = ret.replace(\\\"&\\\",\\\"&\\\")\\n ret = ret.replace(\\\"<\\\",\\\"<\\\")\\n ret = ret.replace(\\\">\\\",\\\">\\\")\\n return ret\\n \\n async def _satori_to_qq(self,satori_obj,platform = \\\"qq_guild\\\") -> [dict]:\\n to_reply_id = None\\n ret_text = \\\"\\\"\\n ret_img = []\\n for node in satori_obj:\\n if isinstance(node,str):\\n text = self._make_qq_text(node)\\n ret_text += text\\n else:\\n if node[\\\"type\\\"] == \\\"at\\\":\\n type = get_json_or(node[\\\"attrs\\\"],\\\"type\\\",None)\\n id = get_json_or(node[\\\"attrs\\\"],\\\"id\\\",None)\\n if type == \\\"all\\\":\\n # 注意，机器人不支持at all，不能发，也不能收，这里假装at all了\\n ret_text += \\\"@全体成员\\\"\\n # text = \\\"<@everyone>\\\"\\n elif id != None:\\n ret_text += \\\"<@{}>\\\".format(self._make_qq_text(id))\\n elif node[\\\"type\\\"] == \\\"img\\\":\\n img_url:str = node[\\\"attrs\\\"][\\\"src\\\"]\\n if img_url.startswith(\\\"data:image/\\\"):\\n base64_start = img_url.find(\\\"base64,\\\")\\n img_content = base64.b64decode(img_url[base64_start + 7:])\\n ret_img.append(img_content)\\n else:\\n if platform == \\\"qq_guild\\\":\\n async with httpx.AsyncClient() as client:\\n img_content = (await client.get(img_url)).content\\n ret_img.append(img_content)\\n else:\\n ret_img.append(img_url)\\n elif node[\\\"type\\\"] == \\\"passive\\\":\\n to_reply_id = node[\\\"attrs\\\"][\\\"id\\\"]\\n \\n ret_vec = []\\n ret_vec.append({\\n \\\"content\\\":ret_text,\\n \\\"file_image\\\":None,\\n \\\"to_reply_id\\\":to_reply_id\\n })\\n if len(ret_img) != 0:\\n ret_vec[0][\\\"file_image\\\"] = ret_img[0]\\n for img in ret_img[1:]:\\n ret_vec.append({\\n \\\"content\\\":\\\"\\\",\\n \\\"file_image\\\":img,\\n \\\"to_reply_id\\\":to_reply_id\\n })\\n return ret_vec\\n \\n async def create_message(self,platform:str,self_id:str,channel_id:str,content:str):\\n '''发送消息'''\\n to_reply_id = self.msgid_map[channel_id]\\n satori_obj = parse_satori_html(content)\\n to_sends = await self._satori_to_qq(satori_obj,platform)\\n # print(to_sends)\\n if channel_id.startswith(\\\"CHANNEL_\\\") and platform == \\\"qq_guild\\\":\\n channel_id = channel_id[8:]\\n to_ret = []\\n for it in to_sends:\\n if it[\\\"to_reply_id\\\"]:to_reply_id = it[\\\"to_reply_id\\\"]\\n async with httpx.AsyncClient() as client:\\n headers = {\\\"Authorization\\\":\\\"QQBot {}\\\".format(self._access_token),\\\"X-Union-Appid\\\":self._appid,\\\"Accept\\\":\\\"application/json\\\"}\\n url:str = self._http_url + \\\"/channels/{}/messages\\\".format(channel_id)\\n data = {\\n \\\"msg_id\\\":to_reply_id,\\n \\\"content\\\":it[\\\"content\\\"]\\n }\\n if it[\\\"file_image\\\"]:\\n ret = (await client.post(url,headers=headers,data=data,files={\\\"file_image\\\":it[\\\"file_image\\\"]})).json()\\n else:\\n ret = (await client.post(url,headers=headers,json=data)).json()\\n # print(ret)\\n to_ret.append(SatoriMessage(id=ret[\\\"id\\\"],content=\\\"\\\").to_dict())\\n return to_ret\\n elif channel_id.startswith(\\\"GROUP_\\\") and platform == \\\"qq_group\\\":\\n channel_id = channel_id[6:]\\n to_ret = []\\n msg_seq = 1\\n for it in to_sends:\\n if it[\\\"to_reply_id\\\"]:to_reply_id = it[\\\"to_reply_id\\\"]\\n async with httpx.AsyncClient() as client:\\n headers = {\\\"Authorization\\\":\\\"QQBot {}\\\".format(self._access_token),\\\"X-Union-Appid\\\":self._appid,\\\"Accept\\\":\\\"application/json\\\"}\\n url:str = self._http_url + \\\"/v2/groups/{}/messages\\\".format(channel_id)\\n data = {\\n \\\"msg_id\\\":to_reply_id,\\n \\\"content\\\":it[\\\"content\\\"],\\n \\\"msg_type\\\":0,\\n \\\"msg_seq\\\":msg_seq,\\n # \\\"image\\\": 目前暂不支持\\n }\\n msg_seq += 1\\n ret = (await client.post(url,headers=headers,json=data)).json()\\n # print(ret)\\n to_ret.append(SatoriMessage(id=ret[\\\"msg_id\\\"],content=\\\"\\\").to_dict())\\n return to_ret\\n \\n async def get_login(self,platform:Optional[str],self_id:Optional[str]) -> [dict]:\\n '''获取登录信息，如果platform和self_id为空，那么应该返回一个列表'''\\n\\n if platform == \\\"qq_group\\\":\\n return SatoriLogin(\\n status=self._login_status,\\n user=SatoriUser(\\n id=self._botqq,\\n is_bot=True\\n ),\\n self_id=self._botqq,\\n platform=\\\"qq_group\\\"\\n ).to_dict()\\n else: \\n obret = (await self._api_call(\\\"/users/@me\\\"))\\n satori_ret = SatoriLogin(\\n status=self._login_status,\\n user=SatoriUser(\\n id=obret[\\\"id\\\"],\\n name=obret[\\\"username\\\"],\\n avatar=obret[\\\"avatar\\\"],\\n is_bot=True\\n ),\\n self_id=obret[\\\"id\\\"],\\n platform=\\\"qq_guild\\\"\\n ).to_dict()\\n self._self_id = obret[\\\"id\\\"]\\n if platform == \\\"qq_guild\\\":\\n return satori_ret\\n elif platform == None:\\n if not self._withgroup:\\n return [satori_ret]\\n else:\\n return [satori_ret,SatoriLogin(\\n status=self._login_status,\\n user=SatoriUser(\\n id=self._botqq,\\n is_bot=True\\n ),\\n self_id=self._botqq,\\n platform=\\\"qq_group\\\"\\n ).to_dict()]\\n \\n async def get_guild_member(self,platform:Optional[str],self_id:Optional[str],guild_id:str,user_id:str) -> [dict]:\\n '''获取群组成员信息'''\\n if platform == \\\"qq_guild\\\":\\n url = \\\"/guilds/{}/members/{}\\\".format(guild_id,user_id)\\n obret = (await self._api_call(url))\\n satori_ret = SatoriGuildMember(\\n user=SatoriUser(\\n id=obret[\\\"user\\\"][\\\"id\\\"],\\n name=obret[\\\"user\\\"][\\\"username\\\"],\\n avatar=obret[\\\"user\\\"][\\\"avatar\\\"],\\n is_bot=obret[\\\"user\\\"][\\\"bot\\\"]\\n ),\\n nick=get_json_or(obret,\\\"nick\\\",None),\\n avatar=obret[\\\"user\\\"][\\\"avatar\\\"],\\n joined_at=int(time.mktime(time.strptime(obret[\\\"joined_at\\\"], \\\"%Y-%m-%dT%H:%M:%S%z\\\"))) * 1000\\n ).to_dict()\\n return satori_ret\"\n },\n {\n \"identifier\": \"remove_json_null\",\n \"path\": \"tool.py\",\n \"snippet\": \"def remove_json_null(js) -> dict:\\n '''将json中的None字段删除'''\\n if isinstance(js,dict):\\n st = {}\\n for key in js:\\n if js[key] != None:\\n st[key] = remove_json_null(js[key])\\n return st\\n elif isinstance(js,list):\\n lst = []\\n for it in js:\\n lst.append(remove_json_null(it))\\n return lst\\n else:\\n return js\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import asyncio\nimport aiohttp\nimport json\nimport uuid\nfrom kook_adapter import AdapterKook\nfrom mihoyo_adapter import AdapterMihoyo\nfrom onebot_adapter import AdapterOnebot\nfrom config import Config\nfrom aiohttp import web\nfrom qq_adapter import AdapterQQ\nfrom tool import remove_json_null"},"token_num":{"kind":"number","value":17914,"string":"17,914"},"cropped_code":{"kind":"string","value":" return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/guild.member.get\":\n body = await request.json()\n ret = await adapter.get_guild_member(platform,self_id,body[\"guild_id\"],body[\"user_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/message.create\":\n body = await request.json()\n ret = await adapter.create_message(platform,self_id,body[\"channel_id\"],body[\"content\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/channel.list\":\n body = await request.json()\n ret = await adapter.get_channel_list(platform,self_id,body[\"guild_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/user.get\":\n body = await request.json()\n ret = await adapter.get_user(platform,self_id,body[\"user_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n return web.Response(text=\"method not found\")\n \n async def _handle_http_admin(self,request:web.Request):\n print(\"----http admin\",request)\n '''在这里处理管理api调用'''\n # 鉴权\n if self._config.access_token != \"\":\n if request.headers.get(\"Authorization\") != \"Bearer \" + self._config.access_token:\n print(\"token err\")\n return web.Response(text=\"token err\")\n method = request.url.path\n if method == \"/v1/admin/login.list\":\n ret = []\n for adapter in self.adapterlist:\n ret += await adapter[\"adapter\"].get_login(None,None)\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n return web.Response(text=\"method not found\")\n \n async def _handle_http_foo(self,request:web.Request):\n '''在这里处理其余任何api调用'''\n print(\"--------http other\",request)\n return web.Response(text=\"method not found\")\n \n async def _handle_events_ws(self,request:web.Request):\n '''在这里处理websocket'''\n ws_id = str(uuid.uuid4())\n ws = web.WebSocketResponse()\n ws.can_prepare(request)\n await ws.prepare(request)\n self.wsmap[ws_id] = {\n \"ws\":ws,\n \"is_access\":False\n }\n print(\"--------http ws\",request,ws_id)\n try:\n async for msg in ws:\n if msg.type == aiohttp.WSMsgType.TEXT:\n data_json = json.loads(msg.data)\n print(\"--------recv_ws\",json.dumps(msg.data))\n op = data_json[\"op\"]\n if op == 3:\n if self._config.access_token != \"\":\n if data_json[\"body\"][\"token\"] != self._config.access_token:\n raise \"token err\"\n self.wsmap[ws_id][\"is_access\"] = True\n async def get_logins(self,ws):\n logins = []\n for adapter in self.adapterlist:\n logins += await adapter[\"adapter\"].get_login(None,None)\n await Satori.ws_send_json(ws,{\n \"op\":4,\n \"body\":{\n \"logins\":logins\n }\n })\n asyncio.create_task(get_logins(self,ws))\n elif op == 1:\n async def send_pong(ws):\n await Satori.ws_send_json(ws,{\n \"op\":2\n })\n asyncio.create_task(send_pong(ws))\n elif msg.type == aiohttp.WSMsgType.ERROR:\n print('ws connection closed with exception %s' %\n ws.exception())\n finally:\n del self.wsmap[ws_id]\n print(\"--------http ws close\",ws_id)\n return ws\n\n async def init_after(self):\n async def event_loop(self:Satori,adapter:AdapterOnebot):\n while True:\n msg = await adapter.get_msg()\n for wsid in self.wsmap:\n ws = self.wsmap[wsid]\n if ws[\"is_access\"]:\n msg[\"id\"] = self._evt_id\n asyncio.create_task(Satori.ws_send_json(ws[\"ws\"],{\"op\":0,\"body\":msg}))\n self._evt_id += 1\n # 读取配置文件\n await self._config.read_config()\n # 创建 adapter\n for botcfg in self._config.botlist:\n if botcfg[\"platform\"] == \"onebot\":\n adapter = AdapterOnebot(botcfg)\n elif botcfg[\"platform\"] == \"kook\":\n adapter = AdapterKook(botcfg)\n elif botcfg[\"platform\"] == \"mihoyo\":\n adapter = AdapterMihoyo(botcfg)\n elif botcfg[\"platform\"] == \"qq\":\n"},"all_code":{"kind":"string","value":"\n\n\nclass Satori:\n def __init__(self) -> None:\n self._config:Config = Config()\n self.adapterlist = []\n self.wsmap = {}\n self._evt_id = 100\n\n async def _get_adapter(self,platform,self_id):\n ''' 用于获取适配器 '''\n for adapter in self.adapterlist:\n info = adapter[\"info\"]\n for bot in info:\n if self_id == bot[\"self_id\"] and bot[\"platform\"] == platform:\n return adapter[\"adapter\"]\n return None\n \n async def ws_send_json(ws,js) -> None:\n js = remove_json_null(js)\n print(\"--------ws_send_json\",json.dumps(js))\n await ws.send_json(js)\n \n async def _handle_http_normal(self,request:web.Request):\n print(\"----http normal\",request)\n '''在这里处理普通api调用'''\n # 鉴权\n if self._config.access_token != \"\":\n if request.headers.get(\"Authorization\") != \"Bearer \" + self._config.access_token:\n print(\"token err\")\n return web.Response(text=\"token err\")\n method = request.url.path\n platform = request.headers.get(\"X-Platform\")\n self_id = request.headers.get(\"X-Self-ID\")\n adapter:AdapterOnebot = await self._get_adapter(platform,self_id)\n if adapter == None:\n return web.Response(text=\"bot not found\")\n if method == \"/v1/login.get\":\n ret = await adapter.get_login(platform,self_id)\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/guild.member.get\":\n body = await request.json()\n ret = await adapter.get_guild_member(platform,self_id,body[\"guild_id\"],body[\"user_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/message.create\":\n body = await request.json()\n ret = await adapter.create_message(platform,self_id,body[\"channel_id\"],body[\"content\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/channel.list\":\n body = await request.json()\n ret = await adapter.get_channel_list(platform,self_id,body[\"guild_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n elif method == \"/v1/user.get\":\n body = await request.json()\n ret = await adapter.get_user(platform,self_id,body[\"user_id\"])\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n return web.Response(text=\"method not found\")\n \n async def _handle_http_admin(self,request:web.Request):\n print(\"----http admin\",request)\n '''在这里处理管理api调用'''\n # 鉴权\n if self._config.access_token != \"\":\n if request.headers.get(\"Authorization\") != \"Bearer \" + self._config.access_token:\n print(\"token err\")\n return web.Response(text=\"token err\")\n method = request.url.path\n if method == \"/v1/admin/login.list\":\n ret = []\n for adapter in self.adapterlist:\n ret += await adapter[\"adapter\"].get_login(None,None)\n return web.Response(text=json.dumps(remove_json_null(ret)),headers={\n \"Content-Type\":\"application/json; charset=utf-8\"\n })\n return web.Response(text=\"method not found\")\n \n async def _handle_http_foo(self,request:web.Request):\n '''在这里处理其余任何api调用'''\n print(\"--------http other\",request)\n return web.Response(text=\"method not found\")\n \n async def _handle_events_ws(self,request:web.Request):\n '''在这里处理websocket'''\n ws_id = str(uuid.uuid4())\n ws = web.WebSocketResponse()\n ws.can_prepare(request)\n await ws.prepare(request)\n self.wsmap[ws_id] = {\n \"ws\":ws,\n \"is_access\":False\n }\n print(\"--------http ws\",request,ws_id)\n try:\n async for msg in ws:\n if msg.type == aiohttp.WSMsgType.TEXT:\n data_json = json.loads(msg.data)\n print(\"--------recv_ws\",json.dumps(msg.data))\n op = data_json[\"op\"]\n if op == 3:\n if self._config.access_token != \"\":\n if data_json[\"body\"][\"token\"] != self._config.access_token:\n raise \"token err\"\n self.wsmap[ws_id][\"is_access\"] = True\n async def get_logins(self,ws):\n logins = []\n for adapter in self.adapterlist:\n logins += await adapter[\"adapter\"].get_login(None,None)\n await Satori.ws_send_json(ws,{\n \"op\":4,\n \"body\":{\n \"logins\":logins\n }\n })\n asyncio.create_task(get_logins(self,ws))\n elif op == 1:\n async def send_pong(ws):\n await Satori.ws_send_json(ws,{\n \"op\":2\n })\n asyncio.create_task(send_pong(ws))\n elif msg.type == aiohttp.WSMsgType.ERROR:\n print('ws connection closed with exception %s' %\n ws.exception())\n finally:\n del self.wsmap[ws_id]\n print(\"--------http ws close\",ws_id)\n return ws\n\n async def init_after(self):\n async def event_loop(self:Satori,adapter:AdapterOnebot):\n while True:\n msg = await adapter.get_msg()\n for wsid in self.wsmap:\n ws = self.wsmap[wsid]\n if ws[\"is_access\"]:\n msg[\"id\"] = self._evt_id\n asyncio.create_task(Satori.ws_send_json(ws[\"ws\"],{\"op\":0,\"body\":msg}))\n self._evt_id += 1\n # 读取配置文件\n await self._config.read_config()\n # 创建 adapter\n for botcfg in self._config.botlist:\n if botcfg[\"platform\"] == \"onebot\":\n adapter = AdapterOnebot(botcfg)\n elif botcfg[\"platform\"] == \"kook\":\n adapter = AdapterKook(botcfg)\n elif botcfg[\"platform\"] == \"mihoyo\":\n adapter = AdapterMihoyo(botcfg)\n elif botcfg[\"platform\"] == \"qq\":"},"next_line":{"kind":"string","value":" adapter = AdapterQQ(botcfg)"},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-12-03 13:53:47+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5390,"cells":{"repo_name":{"kind":"string","value":"aliyun/pai-python-sdk"},"file_path":{"kind":"string","value":"pai/model.py"},"context":{"kind":"list like","value":[{"identifier":"git_utils","path":"pai/common/git_utils.py","snippet":"def git_clone_repo(git_config: Dict[str, str], source_dir: Optional[str] = None):\ndef _validate_git_config(git_config):\ndef _build_and_run_clone_command(git_config, dest_dir):\ndef _clone_command_for_codeup(git_config, dest_dir):\ndef _clone_command_for_github(git_config, dest_dir):\ndef _clone_command_for_ssh(git_config, dest_dir):\ndef _clone_command_for_github_https(git_config, dest_dir):\ndef _clone_command_for_codeup_https(git_config, dest_dir):\ndef _clone_command(repo_url, dest_dir, branch=None):\ndef _update_url_with_token(repo_url, token):\ndef _update_url_with_username_and_password(repo_url, username, password):\ndef _checkout_commit(git_config, dest_dir):"},{"identifier":"INSTANCE_TYPE_LOCAL_GPU","path":"pai/common/consts.py","snippet":"INSTANCE_TYPE_LOCAL_GPU = \"local_gpu\""},{"identifier":"ModelFormat","path":"pai/common/consts.py","snippet":"class ModelFormat(object):\n SavedModel = \"SavedModel\"\n FrozenPb = \"FrozenPb\"\n KerasH5 = \"KerasH5\"\n CaffePrototxt = \"Caffe\"\n ONNX = \"ONNX\"\n BladeModel = \"BladeModel\"\n PMML = \"PMML\"\n TorchScript = \"TorchScript\"\n TFLite = \"TFLite\""},{"identifier":"ContainerRun","path":"pai/common/docker_utils.py","snippet":"class ContainerRun(object):\n \"\"\"A class represent a container run in local.\"\"\"\n\n CONTAINER_STATUS_RUNNING = \"running\"\n CONTAINER_STATUS_EXITED = \"exited\"\n CONTAINER_STATUS_PAUSED = \"paused\"\n\n def __init__(self, container, port: Optional[int] = None):\n \"\"\"Initialize a container run.\n\n Args:\n container: A docker container object.\n port (int): The host port that container is exposed to.\n\n \"\"\"\n self.container = container\n self.port = port\n\n @property\n def status(self):\n self.container.reload()\n return self.container.status\n\n def is_running(self):\n \"\"\"Return True if container is running, otherwise False.\"\"\"\n return self.status == self.CONTAINER_STATUS_RUNNING\n\n def is_terminated(self):\n \"\"\"Return True if container is terminated, otherwise False.\"\"\"\n return self.status in [\n self.CONTAINER_STATUS_EXITED,\n self.CONTAINER_STATUS_PAUSED,\n ]\n\n def is_succeeded(self):\n \"\"\"Return True if container is succeeded, otherwise False.\"\"\"\n return (\n self.status == \"exited\" and self.container.attrs[\"State\"][\"ExitCode\"] == 0\n )\n\n def wait_for_ready(self, interval=5):\n \"\"\"Wait until container enter running state or terminated state.\"\"\"\n while True:\n status = self.status\n if status == self.CONTAINER_STATUS_RUNNING:\n break\n elif status in [self.CONTAINER_STATUS_EXITED, self.CONTAINER_STATUS_PAUSED]:\n raise RuntimeError(\n \"Container is terminated : id={} status={}\".format(\n self.container.id, self.container.status\n )\n )\n time.sleep(interval)\n\n def stop(self):\n if self.is_running():\n self.container.stop()\n\n def start(self):\n if not self.is_running():\n self.container.start()\n\n def delete(self):\n if self.is_running():\n self.container.stop()\n self.container.remove()\n\n def watch(self, show_logs: bool = True):\n \"\"\"Watch container log and wait for container to exit.\"\"\"\n if not show_logs:\n self.container.wait()\n else:\n log_iter = self.container.logs(\n stream=True,\n follow=True,\n )\n for log in log_iter:\n print(log.decode())\n\n self.container.reload()\n exit_code = self.container.attrs[\"State\"][\"ExitCode\"]\n if exit_code != 0:\n raise RuntimeError(\n \"Container run exited failed: exit_code={}\".format(exit_code)\n )"},{"identifier":"run_container","path":"pai/common/docker_utils.py","snippet":"def run_container(\n image_uri: str,\n container_name: Optional[str] = None,\n port: Optional[int] = None,\n environment_variables: Optional[Dict[str, str]] = None,\n command: Optional[Union[List[str], str]] = None,\n entry_point: Optional[Union[List[str], str]] = None,\n volumes: Optional[Dict[str, Any]] = None,\n working_dir: Optional[str] = None,\n gpu_count: Optional[int] = None,\n gpu_device_ids: Optional[List[str]] = None,\n gpu_capabilities: Optional[List[List[str]]] = None,\n) -> ContainerRun:\n \"\"\"Run a container in local.\n\n Args:\n image_uri (str): A docker image uri.\n container_name (str, optional): Name of the container.\n port (int, optional): The port to expose.\n environment_variables (Dict[str, str], optional): Environment variables to set\n in the container.\n command (Union[List[str], str], optional): Command to run the container.\n entry_point (Union[List[str], str], optional): Entry point to run the container.\n volumes (Dict[str, Any], optional): Volumes to mount in the container.\n working_dir (str, optional): Working directory in the container.\n gpu_count (int, optional): Number of GPU devices to request. Set to -1 to\n request all available devices.\n To use GPU, set either ``gpu_count`` or ``gpu_device_ids``.\n gpu_device_ids (List[str], optional): List of strings for GPU device IDs,\n corresponding to `NVIDIA_VISIBLE_DEVICES` in the NVIDIA Runtime.\n To use GPU, set either ``gpu_count`` or ``gpu_device_ids``.\n gpu_capabilities (List[List[str]], optional): This parameter corresponds to\n `NVIDIA_DRIVER_CAPABILITIES` in the NVIDIA Runtime. The default value is\n ``[[\"compute\", \"utility\"]]`` if ``gpu_device_ids`` or ``gpu_count`` is set.\n Available capabilities for the NVIDIA driver can be found in\n https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#driver-capabilities.\n\n Returns:\n ContainerRun: A ContainerRun object.\n\n \"\"\"\n try:\n import docker\n except ImportError:\n raise ImportError(\"Please install docker first: pip install docker\")\n\n client = docker.from_env()\n # use a random host port.\n host_port = randint(49152, 65535)\n\n if gpu_count or gpu_device_ids or gpu_capabilities:\n if not gpu_capabilities:\n gpu_capabilities = [[\"compute\", \"utility\"]]\n device_requests = [\n docker.types.DeviceRequest(\n count=gpu_count,\n device_ids=gpu_device_ids,\n capabilities=gpu_capabilities,\n )\n ]\n else:\n device_requests = []\n\n container = client.containers.run(\n name=container_name,\n entrypoint=entry_point,\n image=image_uri,\n command=command,\n environment=environment_variables,\n ports={port: host_port} if port else None,\n volumes=volumes,\n working_dir=working_dir,\n detach=True,\n device_requests=device_requests,\n )\n container_run = ContainerRun(\n container=container,\n port=host_port,\n )\n return container_run"},{"identifier":"OssUriObj","path":"pai/common/oss_utils.py","snippet":"class OssUriObj(object):\n \"\"\"A class that represents an OSS URI and provides some convenient methods.\"\"\"\n\n def __init__(self, uri: str):\n \"\"\"Constructor for class OssUriObj.\n\n Args:\n uri (str): A string in OSS URI schema: oss://<bucket_name>[.endpoint]/<path/to/file>,\n endpoint in uri is optional.\n \"\"\"\n if not uri.startswith(\"oss://\"):\n raise ValueError(\n \"Invalid OSS URI schema, please provide a string starts with 'oss://'\"\n )\n bucket_name, object_key, endpoint, role_arn = self.parse(uri)\n self.bucket_name = bucket_name\n self.object_key = object_key\n self.endpoint = endpoint\n self.role_arn = role_arn\n\n @classmethod\n def from_bucket_key_endpoint(\n cls, bucket_name: str, object_key: str, endpoint: Optional[str] = None\n ) -> \"OssUriObj\":\n \"\"\"Initialize an OSSUri object from bucket_name, object_key and endpoint.\n\n Args:\n bucket_name (str): The name of the OSS bucket.\n object_key (str): OSS object key/path.\n endpoint (str, optional): Endpoint for the OSS bucket.\n\n Returns:\n OssUriObj: An OssUriObj instance represents the specified OSS object.\n\n \"\"\"\n # OSS object key could not contain leading slashes.\n # Document: https://help.aliyun.com/document_detail/273129.html\n if object_key.startswith(\"/\"):\n logger.warning(\n \"OSS object key should not contain leading slashes, the leading\"\n \" slashes will be removed.\"\n )\n object_key = object_key.lstrip(\"/\")\n\n if endpoint:\n if endpoint.startswith(\"http://\"):\n endpoint = endpoint.lstrip(\"http://\")\n elif endpoint.startswith(\"https://\"):\n endpoint = endpoint.lstrip(\"https://\")\n\n uri = f\"oss://{bucket_name}.{endpoint}/{object_key}\"\n else:\n uri = f\"oss://{bucket_name}/{object_key}\"\n return OssUriObj(uri=uri)\n\n @classmethod\n def parse(cls, oss_uri: str) -> Tuple[str, str, str, str]:\n \"\"\"Parse OSS uri string and returns a tuple of (bucket_name, object_key,\n endpoint, role_arn).\n\n Args:\n oss_uri (str): A string in OSS Uri schema: oss://{bucket_name}.{endpoint}/{object_key}.\n\n Returns:\n Tuple: An tuple of [bucket_name, object_key, endpoint, role_arn].\n\n \"\"\"\n parsed_result = urlparse(oss_uri)\n if parsed_result.scheme != \"oss\":\n raise ValueError(\n \"require OSS uri('oss://[bucket_name]/[object_key]') but \"\n \"given '{}'\".format(oss_uri)\n )\n object_key = parsed_result.path\n if object_key.startswith(\"/\"):\n object_key = object_key[1:]\n\n query = parse_qs(parsed_result.query)\n if \".\" in parsed_result.hostname:\n bucket_name, endpoint = parsed_result.hostname.split(\".\", 1)\n else:\n bucket_name = parsed_result.hostname\n # try to get OSS endpoint from url query.\n if \"endpoint\" in query:\n endpoint = query.get(\"endpoint\")[0]\n elif \"host\" in query:\n endpoint = query.get(\"host\")[0]\n else:\n endpoint = None\n role_arn = query.get(\"role_arn\")[0] if \"role_arn\" in query else None\n\n return bucket_name, object_key, endpoint, role_arn\n\n def get_uri_with_endpoint(self, endpoint: str = None) -> str:\n \"\"\"Get an OSS uri string contains endpoint.\n\n Args:\n endpoint (str): Endpoint of the OSS bucket.\n\n Returns:\n str: An string in OSS uri schema contains endpoint.\n\n \"\"\"\n if not endpoint and not self.endpoint:\n raise ValueError(\"Unknown endpoint for the OSS bucket.\")\n\n return \"oss://{bucket_name}.{endpoint}/{object_key}\".format(\n bucket_name=self.bucket_name,\n endpoint=endpoint or self.endpoint,\n object_key=self.object_key,\n )\n\n def get_dir_uri(self):\n \"\"\"Returns directory in OSS uri string format of the original object.\"\"\"\n _, dirname, _ = self.parse_object_key()\n dir_uri = f\"oss://{self.bucket_name}{dirname}\"\n return dir_uri\n\n @property\n def uri(self) -> str:\n \"\"\"Returns OSS uri in string format.\"\"\"\n return \"oss://{bucket_name}/{object_key}\".format(\n bucket_name=self.bucket_name,\n object_key=self.object_key,\n )\n\n def parse_object_key(self) -> Tuple[bool, str, str]:\n \"\"\"Parse the OSS URI object key, returns a tuple of (is_dir, dir_path, file_name).\n\n Returns:\n namedtuple: An tuple of is_dir, dir_path, file_name.\n \"\"\"\n object_key = self.object_key.strip()\n if object_key.endswith(\"/\"):\n is_dir, dir_path, file_name = True, os.path.join(\"/\", object_key), None\n else:\n idx = object_key.rfind(\"/\")\n if idx < 0:\n is_dir, dir_path, file_name = False, \"/\", object_key\n else:\n is_dir, dir_path, file_name = (\n False,\n os.path.join(\"/\", object_key[: idx + 1]),\n object_key[idx + 1 :],\n )\n return is_dir, dir_path, file_name"},{"identifier":"download","path":"pai/common/oss_utils.py","snippet":"def download(\n oss_path: Union[str, OssUriObj],\n local_path: str,\n bucket: Optional[oss2.Bucket] = None,\n un_tar=False,\n):\n \"\"\"Download OSS objects to local path.\n\n Args:\n oss_path (str): Source OSS path, could be a single OSS object or a OSS\n directory.\n local_path (str): Local path used to store the data from OSS.\n bucket (oss2.Bucket, optional): OSS bucket used to store the upload data. If it\n is not provided, OSS bucket of the default session will be used.\n un_tar (bool, optional): Whether to decompress the downloaded data. It is only\n work for `oss_path` point to a single file that has a suffix \"tar.gz\".\n\n Returns:\n str: A local file path for the downloaded data.\n\n \"\"\"\n\n bucket, oss_path = _get_bucket_and_path(bucket, oss_path)\n\n if not bucket.object_exists(oss_path) or oss_path.endswith(\"/\"):\n # The `oss_path` represents a \"directory\" in the OSS bucket, download the\n # objects which object key is prefixed with `oss_path`.\n # Note: `un_tar` is not work while `oss_path` is a directory.\n\n oss_path += \"/\" if not oss_path.endswith(\"/\") else \"\"\n iterator = oss2.ObjectIteratorV2(\n bucket=bucket,\n prefix=oss_path,\n )\n keys = [obj.key for obj in iterator if not obj.key.endswith(\"/\")]\n for key in tqdm(keys, desc=f\"Downloading: {oss_path}\"):\n rel_path = os.path.relpath(key, oss_path)\n dest = os.path.join(local_path, rel_path)\n os.makedirs(os.path.dirname(dest), exist_ok=True)\n _download_with_progress(\n dest,\n object_key=key,\n oss_bucket=bucket,\n )\n return local_path\n else:\n # The `oss_path` represents a single file in OSS bucket.\n if oss_path.endswith(\".tar.gz\") and un_tar:\n # currently, only tar.gz format is supported for un_tar after downloading.\n with tempfile.TemporaryDirectory() as temp_dir:\n target_path = os.path.join(temp_dir, os.path.basename(oss_path))\n _download_with_progress(\n target_path,\n object_key=oss_path,\n oss_bucket=bucket,\n )\n with tarfile.open(name=target_path, mode=\"r\") as t:\n t.extractall(path=local_path)\n\n return local_path\n else:\n os.makedirs(local_path, exist_ok=True)\n dest = os.path.join(local_path, os.path.basename(oss_path))\n _download_with_progress(\n dest,\n object_key=oss_path,\n oss_bucket=bucket,\n )\n\n return dest"},{"identifier":"is_oss_uri","path":"pai/common/oss_utils.py","snippet":"def is_oss_uri(uri: Union[str, bytes]) -> bool:\n \"\"\"Determines whether the given uri is an OSS uri.\n\n Args:\n uri (Union[str, bytes]): A string in OSS URI schema:\n oss://<bucket_name>[.endpoint]/<path/to/file>,\n\n\n Returns:\n bool: True if the given uri is an OSS uri, else False.\n\n \"\"\"\n return bool(uri and isinstance(uri, (str, bytes)) and str(uri).startswith(\"oss://\"))"},{"identifier":"upload","path":"pai/common/oss_utils.py","snippet":"def upload(\n source_path: str,\n oss_path: Union[str, OssUriObj],\n bucket: Optional[oss2.Bucket] = None,\n is_tar: Optional[bool] = False,\n) -> str:\n \"\"\"Upload local source file/directory to OSS.\n\n Examples::\n\n # compress and upload local directory `./src/` to OSS\n >>> upload(source_path=\"./src/\", oss_path=\"path/to/file\",\n ... bucket=session.oss_bucket, is_tar=True)\n\n\n Args:\n source_path (str): Source file local path which needs to be uploaded, can be\n a single file or a directory.\n oss_path (Union[str, OssUriObj]): Destination OSS path.\n bucket (oss2.Bucket): OSS bucket used to store the upload data. If it is not\n provided, OSS bucket of the default session will be used.\n is_tar (bool): Whether to compress the file before uploading (default: False).\n\n Returns:\n str: A string in OSS URI format. If the source_path is directory, return the\n OSS URI representing the directory for uploaded data, else then\n returns the OSS URI points to the uploaded file.\n \"\"\"\n\n bucket, oss_path = _get_bucket_and_path(bucket, oss_path)\n\n source_path_obj = pathlib.Path(source_path)\n if not source_path_obj.exists():\n raise RuntimeError(\"Source path is not exist: {}\".format(source_path))\n\n if is_tar:\n # compress the local data and upload the compressed source data.\n with tempfile.TemporaryDirectory() as dir_name:\n temp_tar_path = _tar_file(\n source_path, os.path.join(dir_name, \"source.tar.gz\")\n )\n dest_path = (\n os.path.join(oss_path, os.path.basename(temp_tar_path))\n if oss_path.endswith(\"/\")\n else oss_path\n )\n _upload_with_progress(\n filename=temp_tar_path, object_key=dest_path, oss_bucket=bucket\n )\n return \"oss://{}/{}\".format(bucket.bucket_name, dest_path)\n elif not source_path_obj.is_dir():\n # if source path is a file, just invoke bucket.put_object.\n\n # if the oss_path is endswith slash, the file will be uploaded to\n # \"{oss_path}{filename}\", else the file will be uploaded to \"{oss_path}\".\n dest_path = (\n os.path.join(oss_path, os.path.basename(source_path))\n if oss_path.endswith(\"/\")\n else oss_path\n )\n _upload_with_progress(\n filename=source_path, object_key=dest_path, oss_bucket=bucket\n )\n return \"oss://{}/{}\".format(bucket.bucket_name, dest_path)\n else:\n # if the source path is a directory, upload all the file under the directory.\n source_files = glob.glob(\n pathname=str(source_path_obj / \"**\"),\n recursive=True,\n )\n if not oss_path.endswith(\"/\"):\n oss_path += \"/\"\n\n files = [f for f in source_files if not os.path.isdir(f)]\n for file_path in files:\n file_path_obj = pathlib.Path(file_path)\n file_relative_path = file_path_obj.relative_to(source_path_obj).as_posix()\n object_key = oss_path + file_relative_path\n _upload_with_progress(\n filename=file_path, object_key=object_key, oss_bucket=bucket\n )\n return \"oss://{}/{}\".format(bucket.bucket_name, oss_path)"},{"identifier":"generate_repr","path":"pai/common/utils.py","snippet":"def generate_repr(repr_obj, *attr_names: str, **kwargs) -> str:\n \"\"\"Generate a string representation of the given object.\n\n Args:\n repr_obj: The object used to generate the string representation.\n attr_names: A list of attribute names to include in the string representation.\n\n Returns:\n str: A string representation of the object.\n\n \"\"\"\n attrs = {name: getattr(repr_obj, name) for name in attr_names}\n attrs.update(kwargs)\n attr_repr = \", \".join([\"{}={}\".format(k, v) for k, v in attrs.items()])\n cls_name = repr_obj.__class__.__name__\n\n return f\"{cls_name}({attr_repr})\""},{"identifier":"is_local_run_instance_type","path":"pai/common/utils.py","snippet":"def is_local_run_instance_type(instance_type: str) -> bool:\n \"\"\"Return True if instance_type is local run instance type.\"\"\"\n return instance_type and instance_type.strip() in [\n INSTANCE_TYPE_LOCAL_GPU,\n INSTANCE_TYPE_LOCAL,\n ]"},{"identifier":"random_str","path":"pai/common/utils.py","snippet":"def random_str(n):\n \"\"\"Random string generation with lower case letters and digits.\n\n Args:\n n: Size of generated random string.\n\n Returns:\n str: generated random string.\n\n \"\"\"\n return \"\".join(\n random.choice(string.ascii_lowercase + string.digits) for _ in range(n)\n )"},{"identifier":"to_plain_text","path":"pai/common/utils.py","snippet":"def to_plain_text(\n input_str: str, allowed_characters=DEFAULT_PLAIN_TEXT_ALLOW_CHARACTERS, repl_ch=\"_\"\n):\n \"\"\"Replace characters in input_str if it is not in allowed_characters.\"\"\"\n return \"\".join([c if c in allowed_characters else repl_ch for c in input_str])"},{"identifier":"DuplicatedMountException","path":"pai/exception.py","snippet":"class DuplicatedMountException(PAIException):\n \"\"\"Raised if a OSS path is mounted twice.\"\"\""},{"identifier":"MountPathIsOccupiedException","path":"pai/exception.py","snippet":"class MountPathIsOccupiedException(PAIException):\n \"\"\"Raised if target mount path is already used.\"\"\""},{"identifier":"ImageInfo","path":"pai/image.py","snippet":"class ImageInfo(object):\n \"\"\"This class represents information for an image provided by PAI.\n\n Args:\n image_name (str): The name of the image.\n image_uri (str): The URI of the image.\n framework_name (str): The name of the framework installed in the image.\n framework_version (str, optional): The version of the framework (Default None).\n image_scope (str): The scope of the image, could be 'training', 'inference' or\n 'develop'.\n accelerator_type (str, optional): The type of accelerator. Defaults to None.\n python_version (str, optional): The version of Python. Defaults to None.\n \"\"\"\n\n def __repr__(self):\n return (\n \"{}(framework_name={}: framework_version={}: image_scope={}: \"\n \"accelerator_type={}: py_version={})\".format(\n self.__class__.__name__,\n self.framework_name,\n self.framework_version,\n self.image_scope,\n self.accelerator_type,\n self.python_version,\n )\n )\n\n def __init__(\n self,\n image_name: str,\n image_uri: str,\n framework_name: str,\n image_scope: str,\n framework_version: str = None,\n accelerator_type: Optional[str] = None,\n python_version: Optional[str] = None,\n ):\n self.image_name = image_name\n self.image_uri = image_uri\n self.framework_name = framework_name\n self.framework_version = framework_version\n self.accelerator_type = accelerator_type\n self.python_version = python_version\n self.image_scope = image_scope"},{"identifier":"AsyncPredictor","path":"pai/predictor.py","snippet":"class AsyncPredictor(PredictorBase, _ServicePredictorMixin):\n \"\"\"A class that facilitates making predictions to asynchronous prediction service.\n\n Examples::\n\n # Initialize an AsyncPredictor object using the name of a running service.\n async_predictor = AsyncPredictor(service_name=\"example_service\")\n\n # Make a prediction with the service and get the prediction result.\n resp = async_predictor.predict(data=\"YourPredictionData\")\n result = resp.wait()\n\n # Make a prediction with async API.\n import asyncio\n result = asyncio.run(async_predictor.predict_async(data=\"YourPredictionData\"))\n\n \"\"\"\n\n def __init__(\n self,\n service_name: str,\n max_workers: Optional[int] = None,\n endpoint_type: str = EndpointType.INTERNET,\n serializer: Optional[SerializerBase] = None,\n session: Optional[Session] = None,\n ):\n \"\"\"Construct a `AsyncPredictor` object using an existing async prediction service.\n\n Args:\n service_name (str): Name of the existing prediction service.\n max_workers (int): The maximum number of threads that can be used to\n execute the given prediction calls.\n endpoint_type (str): Selects the endpoint used by the predictor, which\n should be one of `INTERNET` or `INTRANET`. The `INTERNET` endpoint type\n means that the predictor calls the service over a public endpoint, while\n the `INTRANET` endpoint type is over a VPC endpoint.\n serializer (SerializerBase, optional): A serializer object that transforms\n the input Python object for data transmission and deserialize the\n response data to Python object.\n session (Session, optional): A PAI session object used for communicating\n with PAI service.\n \"\"\"\n\n super(AsyncPredictor, self).__init__(\n service_name=service_name,\n session=session or get_default_session(),\n endpoint_type=endpoint_type,\n serializer=serializer,\n )\n self._max_workers = max_workers\n self.executor = ThreadPoolExecutor(max_workers=self._max_workers)\n self._check()\n\n @property\n def max_workers(self):\n return self._max_workers\n\n @max_workers.setter\n def max_workers(self, n: int):\n if hasattr(self, \"executor\"):\n logger.info(\"Waiting for all submitted tasks in the queue to complete...\")\n self.executor.shutdown()\n self._max_workers = n\n self.executor = ThreadPoolExecutor(max_workers=self._max_workers)\n\n def __del__(self):\n \"\"\"wait for all pending tasks to complete before exit.\"\"\"\n if hasattr(self, \"executor\"):\n logger.info(\"Waiting for all pending tasks to complete...\")\n self.executor.shutdown()\n super(AsyncPredictor, self).__del__()\n\n def _check(self):\n config = json.loads(self._service_api_object[\"ServiceConfig\"])\n if config.get(\"metadata\", {}).get(\"type\") != ServiceType.Async:\n logger.warning(\n \"AsyncPredictor is not recommended to make prediction to a standard \"\n \" prediction service.\"\n )\n\n def _get_result(\n self, request_id: str\n ) -> Optional[Tuple[int, Dict[str, str], bytes]]:\n resp = self._send_request(\n method=\"GET\",\n path=_QUEUE_SERVICE_SINK_PATH,\n params={\n \"requestId\": request_id,\n # _raw_ is false because we want to get the encapsulated prediction\n # result in response body.\n \"_raw_\": \"false\",\n },\n )\n logger.debug(\n \"Poll prediction result: request_id=%s status_code=%s, content=%s\",\n request_id,\n resp.status_code,\n resp.content,\n )\n if resp.status_code == 204:\n # Status code 204 means could not find prediction response for the specific\n # request id.\n return\n\n # Raise exception if status code is not 2xx.\n if resp.status_code // 100 != 2:\n raise RuntimeError(\n \"Pulling prediction result failed: status_code={} content={}\".format(\n resp.status_code, resp.content.decode(\"utf-8\")\n )\n )\n return self._parse_encapsulated_response(resp.json()[0])\n\n def _parse_encapsulated_response(self, data) -> Tuple[int, Dict[str, str], bytes]:\n tags = data[\"tags\"]\n # If the status code from prediction service is not 200, a tag with\n # key 'lastCode' will be added to the tags in response.\n status_code = int(tags.get(\"lastCode\", 200))\n data = base64.b64decode(data[\"data\"])\n # currently, headers are not supported in async prediction service.\n headers = dict()\n return status_code, headers, data\n\n async def _get_result_async(\n self, request_id: str\n ) -> Optional[Tuple[int, Dict[str, str], bytes]]:\n resp = await self._send_request_async(\n method=\"GET\",\n path=_QUEUE_SERVICE_SINK_PATH,\n params={\n \"requestId\": request_id,\n # _raw_ is false because we want to get the encapsulated prediction\n # result in response body.\n \"_raw_\": \"false\",\n },\n )\n status_code = resp.status\n content = await resp.read()\n logger.debug(\n \"Get prediction result: request_id=%s status_code=%s, content=%s\",\n request_id,\n status_code,\n content,\n )\n if status_code == 204:\n # Status code 204 means could not find prediction response for the specific\n # request id.\n return\n if status_code // 100 != 2:\n raise RuntimeError(\n \"Pulling prediction result failed: status_code={} content={}\".format(\n status_code, content.decode(\"utf-8\")\n )\n )\n data = (await resp.json())[0]\n return self._parse_encapsulated_response(data)\n\n def _poll_result(\n self, request_id: str, wait_config: WaitConfig\n ) -> Tuple[int, Dict[str, str], bytes]:\n # if max_attempts is negative or zero, then wait forever\n attempts = -1 if wait_config.max_attempts <= 0 else wait_config.max_attempts\n while attempts != 0:\n attempts -= 1\n result = self._get_result(request_id=request_id)\n if not result:\n time.sleep(wait_config.interval)\n continue\n status_code, headers, content = result\n # check real prediction response\n if status_code // 100 != 2:\n raise PredictionException(\n code=status_code,\n message=f\"Prediction failed: status_code={status_code}\"\n f\" content={content.decode()}\",\n )\n return status_code, headers, content\n\n # Polling prediction result timeout.\n raise RuntimeError(\n f\"Polling prediction result timeout: request_id={request_id}, \"\n f\"total_time={wait_config.max_attempts * wait_config.interval}\"\n )\n\n async def _poll_result_async(\n self, request_id, wait_config: WaitConfig\n ) -> Tuple[int, Dict[str, str], bytes]:\n # if max_attempts is negative or zero, then wait forever\n attempts = -1 if wait_config.max_attempts <= 0 else wait_config.max_attempts\n while attempts != 0:\n attempts -= 1\n result = await self._get_result_async(request_id)\n if not result:\n await asyncio.sleep(wait_config.interval)\n continue\n status_code, headers, content = result\n # check real prediction response\n if status_code // 100 != 2:\n raise PredictionException(\n f\"Prediction failed: status_code={status_code} content={content.decode()}\"\n )\n return status_code, headers, content\n\n # Polling prediction result timeout.\n raise RuntimeError(\n f\"Polling prediction result timeout: request_id={request_id}, \"\n f\"total_time={wait_config.max_attempts * wait_config.interval}\"\n )\n\n def _get_request_id(self, resp: requests.models.Response) -> str:\n if resp.status_code // 100 != 2:\n raise RuntimeError(\n f\"Send prediction request failed. status_code={resp.status_code} \"\n f\"message={resp.text}\"\n )\n\n if _QUEUE_SERVICE_REQUEST_ID_HEADER not in resp.headers:\n logger.error(\n f\"Send prediction request failed. Missing request id.\"\n f\" status_code={resp.status_code} content={resp.text}\"\n )\n raise RuntimeError(\"Missing request id in response header.\")\n\n request_id = resp.headers[_QUEUE_SERVICE_REQUEST_ID_HEADER]\n logger.debug(\n f\"Send prediction request successfully. request_id={request_id}\"\n f\" status_code={resp.status_code}\",\n )\n return request_id\n\n async def _get_request_id_async(self, resp: aiohttp.ClientResponse) -> str:\n content = await resp.read()\n if resp.status != 200:\n raise RuntimeError(\n \"Send request to async prediction service failed: status_code={} \"\n \"content={}\".format(resp.status, content.decode(\"utf-8\"))\n )\n\n if _QUEUE_SERVICE_REQUEST_ID_HEADER not in resp.headers:\n logger.error(\n f\"Send prediction request failed. Missing request id.\"\n f\" status_code={resp.status} content={content.decode()}\"\n )\n raise RuntimeError(\"Missing request id in response header.\")\n request_id = resp.headers[_QUEUE_SERVICE_REQUEST_ID_HEADER]\n logger.debug(\n f\"Send prediction request successfully. request_id={request_id}\"\n f\" status_code={resp.status}\",\n )\n return request_id\n\n def _predict_fn(\n self,\n data,\n ):\n \"\"\"Make a prediction with the async prediction service.\"\"\"\n # serialize input data\n data = self._handle_input(data)\n resp = self._send_request(data=data)\n request_id = self._get_request_id(resp)\n logger.debug(\"Async prediction RequestId: \", request_id)\n # poll prediction result\n status, headers, content = self._poll_result(\n request_id=request_id, wait_config=WaitConfig()\n )\n\n return self._handle_output(content)\n\n def _wrap_callback_fn(self, cb: Callable):\n \"\"\"Wrap the callback function to handle the prediction result.\"\"\"\n\n @functools.wraps(cb)\n def _(future: Future):\n return cb(future.result())\n\n return _\n\n def predict(\n self,\n data,\n callback: Optional[Union[Callable, List[Callable]]] = None,\n ):\n \"\"\"Make a prediction with the async prediction service.\n\n The input data is serialized using the `serializer.serialize` method before it\n is sent, and the response body is deserialized using the\n `serializer.deserialize` method the prediction result returns.\n\n Args:\n data: The input data for the prediction. It will be serialized using the\n serializer of the predictor before transmitted to the prediction\n service.\n callback (Union[Callable, List[Callable]], optional): A Callback function,\n or a list of callback functions used to process the prediction result.\n\n Returns:\n AsyncTask: The task object that can be used to retrieve the prediction\n result.\n \"\"\"\n self._post_init_serializer()\n future = self.executor.submit(self._predict_fn, data)\n\n if isinstance(callback, Callable):\n callback = [callback]\n\n if callback:\n for cb in callback:\n future.add_done_callback(self._wrap_callback_fn(cb))\n\n return AsyncTask(future=future)\n\n async def predict_async(self, data, wait_config: WaitConfig = WaitConfig()):\n \"\"\"Make a prediction with the async prediction service.\n\n The serializer object for the predictor is responsible for data transformation\n when the 'predict' method is invoked. The input data is serialized using the\n `serializer.serialize` method before it is sent, and the response is\n deserialized using the `serializer.deserialize` method before the prediction\n result returns.\n\n Args:\n data: The input data for the prediction. It will be serialized using the\n serializer of the predictor before transmitted to the prediction\n service.\n wait_config (WaitConfig): A config object that controls the behavior of\n polling the prediction result.\n\n Returns:\n Prediction result.\n\n \"\"\"\n self._post_init_serializer()\n data = self._handle_input(data)\n resp = await self._send_request_async(data=data)\n request_id = await self._get_request_id_async(resp)\n\n status_code, headers, content = await self._poll_result_async(\n request_id=request_id, wait_config=wait_config\n )\n return self._handle_output(content)\n\n def _raw_predict_fn(self, data, method, path, headers, **kwargs):\n json_data, data = self._handle_raw_input(data)\n resp = self._send_request(\n path=path,\n json=json_data,\n data=data,\n headers=self._build_headers(headers),\n method=method,\n **kwargs,\n )\n request_id = self._get_request_id(resp)\n status, headers, content = self._poll_result(\n request_id, wait_config=WaitConfig()\n )\n return RawResponse(status, headers, content)\n\n def raw_predict(\n self,\n data: Any = None,\n callback: Optional[Union[Callable, List[Callable], None]] = None,\n method: str = \"POST\",\n path: Optional[str] = None,\n headers: Optional[Dict[str, str]] = None,\n **kwargs,\n ) -> AsyncTask:\n \"\"\"Make a prediction with the online prediction service.\n\n Args:\n data (Any): Input data to be sent to the prediction service. If it is a\n file-like object, bytes, or string, it will be sent as the request body.\n Otherwise, it will be treated as a JSON serializable object and sent as\n JSON.\n callback (Union[Callable, List[Callable]], optional): A Callback function,\n or a list of callback functions used to process the prediction result.\n path (str, optional): Path for the request to be sent to. If it is provided,\n it will be appended to the endpoint URL (Default None).\n headers (dict, optional): Request headers.\n method (str, optional): Request method, default to 'POST'.\n **kwargs: Additional keyword arguments for the request.\n Returns:\n AsyncTask: The task object that can be used to retrieve the prediction\n result.\n\n Examples:\n\n from pai.predictor import AsyncPredictor, AsyncTask\n\n predictor = AsyncPredictor()\n task: AsyncTask = predictor.raw_predict(data=\"YourPredictionData\")\n print(task.result())\n\n \"\"\"\n\n future = self.executor.submit(\n self._raw_predict_fn, data, method, path, headers, **kwargs\n )\n cbs = [callback] if isinstance(callback, Callable) else callback\n if cbs:\n for cb in cbs:\n future.add_done_callback(self._wrap_callback_fn(cb))\n\n return AsyncTask(future=future)\n\n async def raw_predict_async(\n self,\n data,\n wait_config: WaitConfig = WaitConfig(),\n method: str = \"POST\",\n headers: Optional[Dict[str, str]] = None,\n path: Optional[str] = None,\n **kwargs,\n ) -> RawResponse:\n \"\"\"Make a prediction with the online prediction service.\n\n Args:\n data (Any): Input data to be sent to the prediction service. If it is a\n file-like object, bytes, or string, it will be sent as the request body.\n Otherwise, it will be treated as a JSON serializable object and sent as\n JSON.\n wait_config (WaitConfig): A config object that controls the behavior of\n polling the prediction result.\n path (str, optional): Path for the request to be sent to. If it is provided,\n it will be appended to the endpoint URL (Default None).\n headers (dict, optional): Request headers.\n method (str, optional): Request method, default to 'POST'.\n **kwargs: Additional keyword arguments for the request.\n Returns:\n RawResponse: Prediction result.\n\n \"\"\"\n if self.service_status not in ServiceStatus.completed_status():\n self.wait_for_ready()\n json_data, data = self._handle_raw_input(data)\n\n resp = await self._send_request_async(\n data=data,\n method=method,\n json=json_data,\n path=path,\n headers=headers,\n **kwargs,\n )\n request_id = await self._get_request_id_async(resp)\n # Polling the prediction result.\n status_code, headers, content = await self._poll_result_async(\n request_id=request_id, wait_config=wait_config\n )\n return self._handle_raw_output(status_code, headers, content)"},{"identifier":"LocalPredictor","path":"pai/predictor.py","snippet":"class LocalPredictor(PredictorBase):\n \"\"\"Perform prediction to a local service running with docker.\"\"\"\n\n def __init__(\n self,\n port: int,\n container_id: Optional[str] = None,\n serializer: Optional[SerializerBase] = None,\n ):\n \"\"\"LocalPredictor initializer.\n\n Args:\n port (int): The port of the local service.\n container_id (str, optional): The container id of the local service.\n serializer (SerializerBase, optional): A serializer object that transforms.\n \"\"\"\n self.container_id = container_id\n self.port = port\n self.serializer = serializer or JsonSerializer()\n self._container_run = (\n self._build_container_run(container_id, port=port)\n if self.container_id\n else None\n )\n\n @classmethod\n def _build_container_run(cls, container_id, port):\n try:\n import docker\n except ImportError:\n raise ImportError(\"Please install docker first: pip install docker\")\n client = docker.from_env()\n container = client.containers.get(container_id)\n\n return ContainerRun(container=container, port=port)\n\n def predict(self, data) -> Any:\n \"\"\"Perform prediction with the given data.\n\n Args:\n data: The data to be predicted.\n \"\"\"\n request_data = self.serializer.serialize(data=data)\n response = requests.post(\n url=\"http://127.0.0.1:{port}/\".format(port=self._container_run.port),\n data=request_data,\n )\n\n if response.status_code // 100 != 2:\n raise PredictionException(\n code=response.status_code,\n message=response.content,\n )\n\n return self.serializer.deserialize(response.content)\n\n def _build_headers(\n self, headers: Optional[Dict[str, str]] = None\n ) -> Dict[str, str]:\n headers = headers or dict()\n headers[\"User-Agent\"] = http_user_agent(headers.get(\"User-Agent\"))\n return headers\n\n def _build_url(self, path: Optional[str] = None):\n url = \"http://127.0.0.1:{}\".format(self.port)\n if path:\n if path.startswith(\"/\"):\n path = path[1:]\n url = posixpath.join(url, path)\n return url\n\n def raw_predict(\n self,\n data: Any = None,\n path: Optional[str] = None,\n headers: Optional[Dict[str, str]] = None,\n method: str = \"POST\",\n timeout: Optional[Union[float, Tuple[float, float]]] = None,\n **kwargs,\n ) -> RawResponse:\n \"\"\"Make a prediction with the online prediction service.\n\n Args:\n data (Any): Input data to be sent to the prediction service. If it is a\n file-like object, bytes, or string, it will be sent as the request body.\n Otherwise, it will be treated as a JSON serializable object and sent as\n JSON.\n path (str, optional): Path for the request to be sent to. If it is provided,\n it will be appended to the endpoint URL (Default None).\n headers (dict, optional): Request headers.\n method (str, optional): Request method, default to 'POST'.\n timeout(float, tuple(float, float), optional): Timeout setting for the\n request (Default 10).\n Returns:\n RawResponse: Prediction response from the service.\n\n Raises:\n PredictionException: Raise if status code of the prediction response does\n not equal 2xx.\n \"\"\"\n if isinstance(data, (IOBase, bytes, str)):\n # if data is a file-like object, bytes, or string, it will be sent as\n # request body\n json_data, data = None, data\n else:\n # otherwise, it will be treated as a JSON serializable object and sent as\n # JSON.\n json_data, data = data, None\n header = self._build_headers(headers=headers)\n url = self._build_url(path)\n resp = requests.request(\n url=url,\n json=json_data,\n data=data,\n headers=header,\n method=method,\n timeout=timeout,\n **kwargs,\n )\n resp = RawResponse(\n status_code=resp.status_code,\n content=resp.content,\n headers=dict(resp.headers),\n )\n if resp.status_code // 100 != 2:\n raise PredictionException(resp.status_code, resp.content)\n return resp\n\n def delete_service(self):\n \"\"\"Delete the docker container that running the service.\"\"\"\n if self._container_run:\n self._container_run.stop()\n\n def wait_for_ready(self):\n self._container_run.wait_for_ready()\n # ensure the server is ready.\n self._wait_local_server_ready()\n time.sleep(5)\n\n def _wait_local_server_ready(\n self,\n interval: int = 5,\n ):\n \"\"\"Wait for the local model server to be ready.\"\"\"\n container_run = self._container_run\n while True:\n try:\n # Check whether the container is still running.\n if not container_run.is_running():\n raise RuntimeError(\n \"Container exited unexpectedly, status: {}\".format(\n container_run.status\n )\n )\n\n # Make a HEAD request to the server, just test for connection.\n requests.head(\n f\"http://127.0.0.1:{container_run.port}/\",\n )\n break\n except requests.ConnectionError:\n # ConnectionError means server is not ready.\n logging.debug(\"Waiting for the container to be ready...\")\n time.sleep(interval)\n continue"},{"identifier":"Predictor","path":"pai/predictor.py","snippet":"class Predictor(PredictorBase, _ServicePredictorMixin):\n \"\"\"Predictor is responsible for making prediction to an online service.\n\n The `predictor.predict` method sends the input data to the online prediction service\n and returns the prediction result. The serializer object of the predictor is\n responsible for data transformation when the `predict` method is invoked. The input\n data is serialized using the `serializer.serialize` method before it is sent, and\n the response is deserialized using the `serializer.deserialize` method before the\n prediction result returns.\n\n Examples::\n\n # Initialize a predictor object from an existing service using PyTorch\n # processor.\n torch_predictor = Predictor(service_name=\"example_torch_service\")\n result = torch_predictor.predict(numpy.asarray([[22,33,44], [19,22,33]]))\n assert isinstance(result, numpy.ndarray)\n\n \"\"\"\n\n def __init__(\n self,\n service_name: str,\n endpoint_type: str = EndpointType.INTERNET,\n serializer: Optional[SerializerBase] = None,\n session: Optional[Session] = None,\n ):\n \"\"\"Construct a `Predictor` object using an existing prediction service.\n\n Args:\n service_name (str): Name of the existing prediction service.\n endpoint_type (str): Selects the endpoint used by the predictor, which\n should be one of `INTERNET` or `INTRANET`. The `INTERNET` endpoint type\n means that the predictor calls the service over a public endpoint, while\n the `INTRANET` endpoint type is over a VPC endpoint.\n serializer (SerializerBase, optional): A serializer object that transforms\n the input Python object for data transmission and deserialize the\n response data to Python object.\n session (Session, optional): A PAI session object used for communicating\n with PAI service.\n \"\"\"\n super(Predictor, self).__init__(\n service_name=service_name,\n session=session or get_default_session(),\n endpoint_type=endpoint_type,\n serializer=serializer,\n )\n self._check()\n\n def _check(self):\n config = json.loads(self._service_api_object[\"ServiceConfig\"])\n if config.get(\"metadata\", {}).get(\"type\") == ServiceType.Async:\n logger.warning(\n \"Predictor is not recommended to make prediction to a async\"\n \" prediction service.\"\n )\n\n def predict(self, data):\n \"\"\"Make a prediction with the online prediction service.\n\n The serializer object for the predictor is responsible for data transformation\n when the 'predict' method is invoked. The input data is serialized using the\n `serializer.serialize` method before it is sent, and the response is\n deserialized using the `serializer.deserialize` method before the prediction\n result returns.\n\n Args:\n data: The input data for the prediction. It will be serialized using the\n serializer of the predictor before transmitted to the prediction\n service.\n\n Returns:\n object: Prediction result.\n\n Raises:\n PredictionException: Raise if status code of the prediction response does\n not equal 2xx.\n \"\"\"\n self._post_init_serializer()\n data = self._handle_input(data)\n resp = self._send_request(\n data,\n )\n if resp.status_code // 100 != 2:\n raise PredictionException(resp.status_code, resp.content)\n return self._handle_output(\n resp.content,\n )\n\n def raw_predict(\n self,\n data: Any = None,\n path: Optional[str] = None,\n headers: Optional[Dict[str, str]] = None,\n method: str = \"POST\",\n timeout: Optional[Union[float, Tuple[float, float]]] = None,\n **kwargs,\n ) -> RawResponse:\n \"\"\"Make a prediction with the online prediction service.\n\n Args:\n data (Any): Input data to be sent to the prediction service. If it is a\n file-like object, bytes, or string, it will be sent as the request body.\n Otherwise, it will be treated as a JSON serializable object and sent as\n JSON.\n path (str, optional): Path for the request to be sent to. If it is provided,\n it will be appended to the endpoint URL (Default None).\n headers (dict, optional): Request headers.\n method (str, optional): Request method, default to 'POST'.\n timeout(float, tuple(float, float), optional): Timeout setting for the\n request (Default 10).\n **kwargs: Additional keyword arguments for the request.\n Returns:\n RawResponse: Prediction response from the service.\n\n Raises:\n PredictionException: Raise if status code of the prediction response does\n not equal 2xx.\n \"\"\"\n json_data, data = self._handle_raw_input(data)\n resp = self._send_request(\n data=data,\n json=json_data,\n method=method,\n path=path,\n headers=headers,\n timeout=timeout,\n **kwargs,\n )\n if resp.status_code // 100 != 2:\n raise PredictionException(resp.status_code, resp.content)\n\n resp = RawResponse(\n status_code=resp.status_code,\n content=resp.content,\n headers=dict(resp.headers),\n )\n return resp"},{"identifier":"ServiceType","path":"pai/predictor.py","snippet":"class ServiceType(object):\n Standard = \"Standard\"\n Async = \"Async\""},{"identifier":"SerializerBase","path":"pai/serializers.py","snippet":"class SerializerBase(ABC):\n \"\"\"Abstract class for creating a Serializer class for predictor.\"\"\"\n\n @abstractmethod\n def serialize(self, data) -> bytes:\n \"\"\"Serialize the input data to bytes for transmitting.\"\"\"\n\n @abstractmethod\n def deserialize(self, data: bytes):\n \"\"\"Deserialize the data from raw bytes to Python object .\"\"\"\n\n def inspect_from_service(\n self, service_name: str, *, session: Optional[Session] = None\n ):\n \"\"\"Inspect the online prediction service to complete the serializer instance\n initialization.\n\n The implementation of the `inspect_from_service` method is optional. You only\n need to implement it if your serializer requires additional information from\n service metadata or if it needs to send a request to the service in order to\n be initialized.\n\n \"\"\""},{"identifier":"Session","path":"pai/session.py","snippet":"class Session(ResourceAPIsContainerMixin):\n \"\"\"A class responsible for communicating with PAI services.\"\"\"\n\n def __init__(\n self,\n region_id: str,\n workspace_id: Optional[str] = None,\n credential_config: Optional[CredentialConfig] = None,\n oss_bucket_name: Optional[str] = None,\n oss_endpoint: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"PAI Session Initializer.\n\n Args:\n credential_config (:class:`alibabacloud_credentials.models.Config`, optional):\n The credential config used to access the Alibaba Cloud.\n region_id (str): The ID of the Alibaba Cloud region where the service\n is located.\n workspace_id (str, optional): ID of the workspace used in the default\n session.\n oss_bucket_name (str, optional): The name of the OSS bucket used in the\n session.\n oss_endpoint (str, optional): The endpoint for the OSS bucket.\n \"\"\"\n\n if not region_id:\n raise ValueError(\"Region ID must be provided.\")\n\n self._credential_config = credential_config\n self._region_id = region_id\n self._workspace_id = workspace_id\n self._oss_bucket_name = oss_bucket_name\n self._oss_endpoint = oss_endpoint\n\n header = kwargs.pop(\"header\", None)\n super(Session, self).__init__(header=header)\n\n @property\n def region_id(self) -> str:\n return self._region_id\n\n @property\n def is_inner(self) -> bool:\n return self._region_id in INNER_REGION_IDS\n\n @property\n def oss_bucket_name(self) -> str:\n return self._oss_bucket_name\n\n @property\n def oss_endpoint(self) -> str:\n return self._oss_endpoint\n\n @property\n def credential_config(self) -> CredentialConfig:\n return self._credential_config\n\n @property\n def workspace_name(self):\n if hasattr(self, \"_workspace_name\") and self._workspace_name:\n return self._workspace_name\n\n if not self._workspace_id:\n raise ValueError(\"Workspace id is not set.\")\n workspace_api_obj = self.workspace_api.get(workspace_id=self._workspace_id)\n self._workspace_name = workspace_api_obj[\"WorkspaceName\"]\n return self._workspace_name\n\n @property\n def provider(self) -> str:\n caller_identity = self._acs_sts_client.get_caller_identity().body\n return caller_identity.account_id\n\n @property\n def workspace_id(self) -> str:\n \"\"\"ID of the workspace used by the session.\"\"\"\n return self._workspace_id\n\n @property\n def console_uri(self) -> str:\n \"\"\"The web console URI for PAI service.\"\"\"\n if self.is_inner:\n return \"https://pai-next.alibaba-inc.com\"\n else:\n return \"https://pai.console.aliyun.com/console\"\n\n def _init_oss_config(\n self,\n ):\n \"\"\"Initialize a OssConfig instance.\"\"\"\n if not self._oss_bucket_name:\n # If OSS bucket name is not provided, use the default OSS storage URI\n # that is configured for the workspace.\n default_oss_uri = self.workspace_api.get_default_storage_uri(\n self.workspace_id\n )\n if not default_oss_uri:\n raise RuntimeError(\n \"No default OSS URI is configured for the workspace.\"\n )\n oss_uri_obj = OssUriObj(default_oss_uri)\n self._oss_bucket_name = oss_uri_obj.bucket_name\n\n if not self._oss_endpoint:\n self._oss_endpoint = self._get_default_oss_endpoint()\n\n def _get_oss_auth(self):\n auth = oss2.ProviderAuth(\n credentials_provider=CredentialProviderWrapper(\n config=self._credential_config,\n )\n )\n return auth\n\n @property\n def oss_bucket(self):\n \"\"\"A OSS2 bucket instance used by the session.\"\"\"\n if not self._oss_bucket_name or not self._oss_endpoint:\n self._init_oss_config()\n oss_bucket = oss2.Bucket(\n auth=self._get_oss_auth(),\n endpoint=self._oss_endpoint,\n bucket_name=self._oss_bucket_name,\n )\n return oss_bucket\n\n def save_config(self, config_path=None):\n \"\"\"Save the configuration of the session to a local file.\"\"\"\n attrs = {key.lstrip(\"_\"): value for key, value in vars(self).items()}\n config = {\n key: value\n for key, value in attrs.items()\n if key in _DEFAULT_CONFIG_KEYS and value is not None\n }\n\n config_path = config_path or DEFAULT_CONFIG_PATH\n os.makedirs(os.path.dirname(config_path), exist_ok=True)\n with open(config_path, \"w\") as f:\n f.write(json.dumps(config, indent=4))\n logger.info(\"Write PAI config succeed: config_path=%s\" % config_path)\n\n def patch_oss_endpoint(self, oss_uri: str):\n oss_uri_obj = OssUriObj(oss_uri)\n if oss_uri_obj.endpoint:\n return oss_uri\n\n # patch endpoint using current OSS bucket endpoint.\n endpoint = self.oss_bucket.endpoint\n if endpoint.startswith(\"http://\"):\n endpoint = endpoint.lstrip(\"http://\")\n elif endpoint.startswith(\"https://\"):\n endpoint = endpoint.lstrip(\"https://\")\n return \"oss://{bucket_name}.{endpoint}/{key}\".format(\n bucket_name=oss_uri_obj.bucket_name,\n endpoint=endpoint,\n key=oss_uri_obj.object_key,\n )\n\n def _get_default_oss_endpoint(self) -> str:\n \"\"\"Returns a default OSS endpoint.\"\"\"\n\n # OSS Endpoint document:\n # https://help.aliyun.com/document_detail/31837.html\n internet_endpoint = \"oss-{}.aliyuncs.com\".format(self.region_id)\n internal_endpoint = \"oss-{}-internal.aliyuncs.com\".format(self.region_id)\n\n return (\n internet_endpoint\n if is_domain_connectable(internal_endpoint)\n else internet_endpoint\n )\n\n def get_oss_bucket(self, bucket_name: str, endpoint: str = None) -> oss2.Bucket:\n \"\"\"Get a OSS bucket using the credentials of the session.\n\n Args:\n bucket_name (str): The name of the bucket.\n endpoint (str): Endpoint of the bucket.\n\n Returns:\n :class:`oss2.Bucket`: A OSS bucket instance.\n\n \"\"\"\n endpoint = endpoint or self._oss_endpoint or self._get_default_oss_endpoint()\n oss_bucket = oss2.Bucket(\n auth=self._get_oss_auth(),\n endpoint=endpoint,\n bucket_name=bucket_name,\n )\n return oss_bucket\n\n @classmethod\n def get_storage_path_by_category(\n cls, category: str, dir_name: Optional[str] = None\n ) -> str:\n \"\"\"Get an OSS storage path for the resource.\n\n Args:\n category (str): The category of the resource.\n dir_name (str, optional): The directory name of the resource.\n\n Returns:\n str: A OSS storage path.\n\n \"\"\"\n dir_name = dir_name or datetime.now().strftime(\"%Y%m%d_%H%M%S_%f\")\n storage_path = posixpath.join(\"pai\", category, dir_name).strip()\n\n if not storage_path.endswith(\"/\"):\n storage_path += \"/\"\n return storage_path\n\n def is_supported_training_instance(self, instance_type: str) -> bool:\n \"\"\"Check if the instance type is supported for training.\"\"\"\n instance_generator = make_list_resource_iterator(self.job_api.list_ecs_specs)\n machine_spec = next(\n (\n item\n for item in instance_generator\n if item[\"InstanceType\"] == instance_type\n ),\n None,\n )\n return bool(machine_spec)\n\n def is_gpu_training_instance(self, instance_type: str) -> bool:\n \"\"\"Check if the instance type is GPU instance for training.\"\"\"\n instance_generator = make_list_resource_iterator(self.job_api.list_ecs_specs)\n machine_spec = next(\n (\n item\n for item in instance_generator\n if item[\"InstanceType\"] == instance_type\n ),\n None,\n )\n if not machine_spec:\n raise ValueError(\n f\"Instance type {instance_type} is not supported for training job. \"\n \"Please provide a supported instance type.\"\n )\n return machine_spec[\"AcceleratorType\"] == \"GPU\"\n\n def is_supported_inference_instance(self, instance_type: str) -> bool:\n \"\"\"Check if the instance type is supported for inference.\"\"\"\n res = self.service_api.describe_machine()[\"InstanceMetas\"]\n spec = next(\n (item for item in res if item[\"InstanceType\"] == instance_type), None\n )\n return bool(spec)\n\n def is_gpu_inference_instance(self, instance_type: str) -> bool:\n \"\"\"Check if the instance type is GPU instance for inference.\"\"\"\n res = self.service_api.describe_machine()[\"InstanceMetas\"]\n spec = next(\n (item for item in res if item[\"InstanceType\"] == instance_type), None\n )\n\n if not spec:\n raise ValueError(\n f\"Instance type {instance_type} is not supported for deploying. \"\n \"Please provide a supported instance type.\"\n )\n return bool(spec[\"GPU\"])"},{"identifier":"get_default_session","path":"pai/session.py","snippet":"def get_default_session() -> \"Session\":\n \"\"\"Get the default session used by the program.\n\n If the global default session is set, the function will try to initialize\n a session from config file.\n\n Returns:\n :class:`pai.session.Session`: The default session.\n\n \"\"\"\n global _default_session\n if not _default_session:\n config = load_default_config_file()\n if not config:\n return\n _default_session = Session(**config)\n return _default_session"}],"string":"[\n {\n \"identifier\": \"git_utils\",\n \"path\": \"pai/common/git_utils.py\",\n \"snippet\": \"def git_clone_repo(git_config: Dict[str, str], source_dir: Optional[str] = None):\\ndef _validate_git_config(git_config):\\ndef _build_and_run_clone_command(git_config, dest_dir):\\ndef _clone_command_for_codeup(git_config, dest_dir):\\ndef _clone_command_for_github(git_config, dest_dir):\\ndef _clone_command_for_ssh(git_config, dest_dir):\\ndef _clone_command_for_github_https(git_config, dest_dir):\\ndef _clone_command_for_codeup_https(git_config, dest_dir):\\ndef _clone_command(repo_url, dest_dir, branch=None):\\ndef _update_url_with_token(repo_url, token):\\ndef _update_url_with_username_and_password(repo_url, username, password):\\ndef _checkout_commit(git_config, dest_dir):\"\n },\n {\n \"identifier\": \"INSTANCE_TYPE_LOCAL_GPU\",\n \"path\": \"pai/common/consts.py\",\n \"snippet\": \"INSTANCE_TYPE_LOCAL_GPU = \\\"local_gpu\\\"\"\n },\n {\n \"identifier\": \"ModelFormat\",\n \"path\": \"pai/common/consts.py\",\n \"snippet\": \"class ModelFormat(object):\\n SavedModel = \\\"SavedModel\\\"\\n FrozenPb = \\\"FrozenPb\\\"\\n KerasH5 = \\\"KerasH5\\\"\\n CaffePrototxt = \\\"Caffe\\\"\\n ONNX = \\\"ONNX\\\"\\n BladeModel = \\\"BladeModel\\\"\\n PMML = \\\"PMML\\\"\\n TorchScript = \\\"TorchScript\\\"\\n TFLite = \\\"TFLite\\\"\"\n },\n {\n \"identifier\": \"ContainerRun\",\n \"path\": \"pai/common/docker_utils.py\",\n \"snippet\": \"class ContainerRun(object):\\n \\\"\\\"\\\"A class represent a container run in local.\\\"\\\"\\\"\\n\\n CONTAINER_STATUS_RUNNING = \\\"running\\\"\\n CONTAINER_STATUS_EXITED = \\\"exited\\\"\\n CONTAINER_STATUS_PAUSED = \\\"paused\\\"\\n\\n def __init__(self, container, port: Optional[int] = None):\\n \\\"\\\"\\\"Initialize a container run.\\n\\n Args:\\n container: A docker container object.\\n port (int): The host port that container is exposed to.\\n\\n \\\"\\\"\\\"\\n self.container = container\\n self.port = port\\n\\n @property\\n def status(self):\\n self.container.reload()\\n return self.container.status\\n\\n def is_running(self):\\n \\\"\\\"\\\"Return True if container is running, otherwise False.\\\"\\\"\\\"\\n return self.status == self.CONTAINER_STATUS_RUNNING\\n\\n def is_terminated(self):\\n \\\"\\\"\\\"Return True if container is terminated, otherwise False.\\\"\\\"\\\"\\n return self.status in [\\n self.CONTAINER_STATUS_EXITED,\\n self.CONTAINER_STATUS_PAUSED,\\n ]\\n\\n def is_succeeded(self):\\n \\\"\\\"\\\"Return True if container is succeeded, otherwise False.\\\"\\\"\\\"\\n return (\\n self.status == \\\"exited\\\" and self.container.attrs[\\\"State\\\"][\\\"ExitCode\\\"] == 0\\n )\\n\\n def wait_for_ready(self, interval=5):\\n \\\"\\\"\\\"Wait until container enter running state or terminated state.\\\"\\\"\\\"\\n while True:\\n status = self.status\\n if status == self.CONTAINER_STATUS_RUNNING:\\n break\\n elif status in [self.CONTAINER_STATUS_EXITED, self.CONTAINER_STATUS_PAUSED]:\\n raise RuntimeError(\\n \\\"Container is terminated : id={} status={}\\\".format(\\n self.container.id, self.container.status\\n )\\n )\\n time.sleep(interval)\\n\\n def stop(self):\\n if self.is_running():\\n self.container.stop()\\n\\n def start(self):\\n if not self.is_running():\\n self.container.start()\\n\\n def delete(self):\\n if self.is_running():\\n self.container.stop()\\n self.container.remove()\\n\\n def watch(self, show_logs: bool = True):\\n \\\"\\\"\\\"Watch container log and wait for container to exit.\\\"\\\"\\\"\\n if not show_logs:\\n self.container.wait()\\n else:\\n log_iter = self.container.logs(\\n stream=True,\\n follow=True,\\n )\\n for log in log_iter:\\n print(log.decode())\\n\\n self.container.reload()\\n exit_code = self.container.attrs[\\\"State\\\"][\\\"ExitCode\\\"]\\n if exit_code != 0:\\n raise RuntimeError(\\n \\\"Container run exited failed: exit_code={}\\\".format(exit_code)\\n )\"\n },\n {\n \"identifier\": \"run_container\",\n \"path\": \"pai/common/docker_utils.py\",\n \"snippet\": \"def run_container(\\n image_uri: str,\\n container_name: Optional[str] = None,\\n port: Optional[int] = None,\\n environment_variables: Optional[Dict[str, str]] = None,\\n command: Optional[Union[List[str], str]] = None,\\n entry_point: Optional[Union[List[str], str]] = None,\\n volumes: Optional[Dict[str, Any]] = None,\\n working_dir: Optional[str] = None,\\n gpu_count: Optional[int] = None,\\n gpu_device_ids: Optional[List[str]] = None,\\n gpu_capabilities: Optional[List[List[str]]] = None,\\n) -> ContainerRun:\\n \\\"\\\"\\\"Run a container in local.\\n\\n Args:\\n image_uri (str): A docker image uri.\\n container_name (str, optional): Name of the container.\\n port (int, optional): The port to expose.\\n environment_variables (Dict[str, str], optional): Environment variables to set\\n in the container.\\n command (Union[List[str], str], optional): Command to run the container.\\n entry_point (Union[List[str], str], optional): Entry point to run the container.\\n volumes (Dict[str, Any], optional): Volumes to mount in the container.\\n working_dir (str, optional): Working directory in the container.\\n gpu_count (int, optional): Number of GPU devices to request. Set to -1 to\\n request all available devices.\\n To use GPU, set either ``gpu_count`` or ``gpu_device_ids``.\\n gpu_device_ids (List[str], optional): List of strings for GPU device IDs,\\n corresponding to `NVIDIA_VISIBLE_DEVICES` in the NVIDIA Runtime.\\n To use GPU, set either ``gpu_count`` or ``gpu_device_ids``.\\n gpu_capabilities (List[List[str]], optional): This parameter corresponds to\\n `NVIDIA_DRIVER_CAPABILITIES` in the NVIDIA Runtime. The default value is\\n ``[[\\\"compute\\\", \\\"utility\\\"]]`` if ``gpu_device_ids`` or ``gpu_count`` is set.\\n Available capabilities for the NVIDIA driver can be found in\\n https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#driver-capabilities.\\n\\n Returns:\\n ContainerRun: A ContainerRun object.\\n\\n \\\"\\\"\\\"\\n try:\\n import docker\\n except ImportError:\\n raise ImportError(\\\"Please install docker first: pip install docker\\\")\\n\\n client = docker.from_env()\\n # use a random host port.\\n host_port = randint(49152, 65535)\\n\\n if gpu_count or gpu_device_ids or gpu_capabilities:\\n if not gpu_capabilities:\\n gpu_capabilities = [[\\\"compute\\\", \\\"utility\\\"]]\\n device_requests = [\\n docker.types.DeviceRequest(\\n count=gpu_count,\\n device_ids=gpu_device_ids,\\n capabilities=gpu_capabilities,\\n )\\n ]\\n else:\\n device_requests = []\\n\\n container = client.containers.run(\\n name=container_name,\\n entrypoint=entry_point,\\n image=image_uri,\\n command=command,\\n environment=environment_variables,\\n ports={port: host_port} if port else None,\\n volumes=volumes,\\n working_dir=working_dir,\\n detach=True,\\n device_requests=device_requests,\\n )\\n container_run = ContainerRun(\\n container=container,\\n port=host_port,\\n )\\n return container_run\"\n },\n {\n \"identifier\": \"OssUriObj\",\n \"path\": \"pai/common/oss_utils.py\",\n \"snippet\": \"class OssUriObj(object):\\n \\\"\\\"\\\"A class that represents an OSS URI and provides some convenient methods.\\\"\\\"\\\"\\n\\n def __init__(self, uri: str):\\n \\\"\\\"\\\"Constructor for class OssUriObj.\\n\\n Args:\\n uri (str): A string in OSS URI schema: oss://<bucket_name>[.endpoint]/<path/to/file>,\\n endpoint in uri is optional.\\n \\\"\\\"\\\"\\n if not uri.startswith(\\\"oss://\\\"):\\n raise ValueError(\\n \\\"Invalid OSS URI schema, please provide a string starts with 'oss://'\\\"\\n )\\n bucket_name, object_key, endpoint, role_arn = self.parse(uri)\\n self.bucket_name = bucket_name\\n self.object_key = object_key\\n self.endpoint = endpoint\\n self.role_arn = role_arn\\n\\n @classmethod\\n def from_bucket_key_endpoint(\\n cls, bucket_name: str, object_key: str, endpoint: Optional[str] = None\\n ) -> \\\"OssUriObj\\\":\\n \\\"\\\"\\\"Initialize an OSSUri object from bucket_name, object_key and endpoint.\\n\\n Args:\\n bucket_name (str): The name of the OSS bucket.\\n object_key (str): OSS object key/path.\\n endpoint (str, optional): Endpoint for the OSS bucket.\\n\\n Returns:\\n OssUriObj: An OssUriObj instance represents the specified OSS object.\\n\\n \\\"\\\"\\\"\\n # OSS object key could not contain leading slashes.\\n # Document: https://help.aliyun.com/document_detail/273129.html\\n if object_key.startswith(\\\"/\\\"):\\n logger.warning(\\n \\\"OSS object key should not contain leading slashes, the leading\\\"\\n \\\" slashes will be removed.\\\"\\n )\\n object_key = object_key.lstrip(\\\"/\\\")\\n\\n if endpoint:\\n if endpoint.startswith(\\\"http://\\\"):\\n endpoint = endpoint.lstrip(\\\"http://\\\")\\n elif endpoint.startswith(\\\"https://\\\"):\\n endpoint = endpoint.lstrip(\\\"https://\\\")\\n\\n uri = f\\\"oss://{bucket_name}.{endpoint}/{object_key}\\\"\\n else:\\n uri = f\\\"oss://{bucket_name}/{object_key}\\\"\\n return OssUriObj(uri=uri)\\n\\n @classmethod\\n def parse(cls, oss_uri: str) -> Tuple[str, str, str, str]:\\n \\\"\\\"\\\"Parse OSS uri string and returns a tuple of (bucket_name, object_key,\\n endpoint, role_arn).\\n\\n Args:\\n oss_uri (str): A string in OSS Uri schema: oss://{bucket_name}.{endpoint}/{object_key}.\\n\\n Returns:\\n Tuple: An tuple of [bucket_name, object_key, endpoint, role_arn].\\n\\n \\\"\\\"\\\"\\n parsed_result = urlparse(oss_uri)\\n if parsed_result.scheme != \\\"oss\\\":\\n raise ValueError(\\n \\\"require OSS uri('oss://[bucket_name]/[object_key]') but \\\"\\n \\\"given '{}'\\\".format(oss_uri)\\n )\\n object_key = parsed_result.path\\n if object_key.startswith(\\\"/\\\"):\\n object_key = object_key[1:]\\n\\n query = parse_qs(parsed_result.query)\\n if \\\".\\\" in parsed_result.hostname:\\n bucket_name, endpoint = parsed_result.hostname.split(\\\".\\\", 1)\\n else:\\n bucket_name = parsed_result.hostname\\n # try to get OSS endpoint from url query.\\n if \\\"endpoint\\\" in query:\\n endpoint = query.get(\\\"endpoint\\\")[0]\\n elif \\\"host\\\" in query:\\n endpoint = query.get(\\\"host\\\")[0]\\n else:\\n endpoint = None\\n role_arn = query.get(\\\"role_arn\\\")[0] if \\\"role_arn\\\" in query else None\\n\\n return bucket_name, object_key, endpoint, role_arn\\n\\n def get_uri_with_endpoint(self, endpoint: str = None) -> str:\\n \\\"\\\"\\\"Get an OSS uri string contains endpoint.\\n\\n Args:\\n endpoint (str): Endpoint of the OSS bucket.\\n\\n Returns:\\n str: An string in OSS uri schema contains endpoint.\\n\\n \\\"\\\"\\\"\\n if not endpoint and not self.endpoint:\\n raise ValueError(\\\"Unknown endpoint for the OSS bucket.\\\")\\n\\n return \\\"oss://{bucket_name}.{endpoint}/{object_key}\\\".format(\\n bucket_name=self.bucket_name,\\n endpoint=endpoint or self.endpoint,\\n object_key=self.object_key,\\n )\\n\\n def get_dir_uri(self):\\n \\\"\\\"\\\"Returns directory in OSS uri string format of the original object.\\\"\\\"\\\"\\n _, dirname, _ = self.parse_object_key()\\n dir_uri = f\\\"oss://{self.bucket_name}{dirname}\\\"\\n return dir_uri\\n\\n @property\\n def uri(self) -> str:\\n \\\"\\\"\\\"Returns OSS uri in string format.\\\"\\\"\\\"\\n return \\\"oss://{bucket_name}/{object_key}\\\".format(\\n bucket_name=self.bucket_name,\\n object_key=self.object_key,\\n )\\n\\n def parse_object_key(self) -> Tuple[bool, str, str]:\\n \\\"\\\"\\\"Parse the OSS URI object key, returns a tuple of (is_dir, dir_path, file_name).\\n\\n Returns:\\n namedtuple: An tuple of is_dir, dir_path, file_name.\\n \\\"\\\"\\\"\\n object_key = self.object_key.strip()\\n if object_key.endswith(\\\"/\\\"):\\n is_dir, dir_path, file_name = True, os.path.join(\\\"/\\\", object_key), None\\n else:\\n idx = object_key.rfind(\\\"/\\\")\\n if idx < 0:\\n is_dir, dir_path, file_name = False, \\\"/\\\", object_key\\n else:\\n is_dir, dir_path, file_name = (\\n False,\\n os.path.join(\\\"/\\\", object_key[: idx + 1]),\\n object_key[idx + 1 :],\\n )\\n return is_dir, dir_path, file_name\"\n },\n {\n \"identifier\": \"download\",\n \"path\": \"pai/common/oss_utils.py\",\n \"snippet\": \"def download(\\n oss_path: Union[str, OssUriObj],\\n local_path: str,\\n bucket: Optional[oss2.Bucket] = None,\\n un_tar=False,\\n):\\n \\\"\\\"\\\"Download OSS objects to local path.\\n\\n Args:\\n oss_path (str): Source OSS path, could be a single OSS object or a OSS\\n directory.\\n local_path (str): Local path used to store the data from OSS.\\n bucket (oss2.Bucket, optional): OSS bucket used to store the upload data. If it\\n is not provided, OSS bucket of the default session will be used.\\n un_tar (bool, optional): Whether to decompress the downloaded data. It is only\\n work for `oss_path` point to a single file that has a suffix \\\"tar.gz\\\".\\n\\n Returns:\\n str: A local file path for the downloaded data.\\n\\n \\\"\\\"\\\"\\n\\n bucket, oss_path = _get_bucket_and_path(bucket, oss_path)\\n\\n if not bucket.object_exists(oss_path) or oss_path.endswith(\\\"/\\\"):\\n # The `oss_path` represents a \\\"directory\\\" in the OSS bucket, download the\\n # objects which object key is prefixed with `oss_path`.\\n # Note: `un_tar` is not work while `oss_path` is a directory.\\n\\n oss_path += \\\"/\\\" if not oss_path.endswith(\\\"/\\\") else \\\"\\\"\\n iterator = oss2.ObjectIteratorV2(\\n bucket=bucket,\\n prefix=oss_path,\\n )\\n keys = [obj.key for obj in iterator if not obj.key.endswith(\\\"/\\\")]\\n for key in tqdm(keys, desc=f\\\"Downloading: {oss_path}\\\"):\\n rel_path = os.path.relpath(key, oss_path)\\n dest = os.path.join(local_path, rel_path)\\n os.makedirs(os.path.dirname(dest), exist_ok=True)\\n _download_with_progress(\\n dest,\\n object_key=key,\\n oss_bucket=bucket,\\n )\\n return local_path\\n else:\\n # The `oss_path` represents a single file in OSS bucket.\\n if oss_path.endswith(\\\".tar.gz\\\") and un_tar:\\n # currently, only tar.gz format is supported for un_tar after downloading.\\n with tempfile.TemporaryDirectory() as temp_dir:\\n target_path = os.path.join(temp_dir, os.path.basename(oss_path))\\n _download_with_progress(\\n target_path,\\n object_key=oss_path,\\n oss_bucket=bucket,\\n )\\n with tarfile.open(name=target_path, mode=\\\"r\\\") as t:\\n t.extractall(path=local_path)\\n\\n return local_path\\n else:\\n os.makedirs(local_path, exist_ok=True)\\n dest = os.path.join(local_path, os.path.basename(oss_path))\\n _download_with_progress(\\n dest,\\n object_key=oss_path,\\n oss_bucket=bucket,\\n )\\n\\n return dest\"\n },\n {\n \"identifier\": \"is_oss_uri\",\n \"path\": \"pai/common/oss_utils.py\",\n \"snippet\": \"def is_oss_uri(uri: Union[str, bytes]) -> bool:\\n \\\"\\\"\\\"Determines whether the given uri is an OSS uri.\\n\\n Args:\\n uri (Union[str, bytes]): A string in OSS URI schema:\\n oss://<bucket_name>[.endpoint]/<path/to/file>,\\n\\n\\n Returns:\\n bool: True if the given uri is an OSS uri, else False.\\n\\n \\\"\\\"\\\"\\n return bool(uri and isinstance(uri, (str, bytes)) and str(uri).startswith(\\\"oss://\\\"))\"\n },\n {\n \"identifier\": \"upload\",\n \"path\": \"pai/common/oss_utils.py\",\n \"snippet\": \"def upload(\\n source_path: str,\\n oss_path: Union[str, OssUriObj],\\n bucket: Optional[oss2.Bucket] = None,\\n is_tar: Optional[bool] = False,\\n) -> str:\\n \\\"\\\"\\\"Upload local source file/directory to OSS.\\n\\n Examples::\\n\\n # compress and upload local directory `./src/` to OSS\\n >>> upload(source_path=\\\"./src/\\\", oss_path=\\\"path/to/file\\\",\\n ... bucket=session.oss_bucket, is_tar=True)\\n\\n\\n Args:\\n source_path (str): Source file local path which needs to be uploaded, can be\\n a single file or a directory.\\n oss_path (Union[str, OssUriObj]): Destination OSS path.\\n bucket (oss2.Bucket): OSS bucket used to store the upload data. If it is not\\n provided, OSS bucket of the default session will be used.\\n is_tar (bool): Whether to compress the file before uploading (default: False).\\n\\n Returns:\\n str: A string in OSS URI format. If the source_path is directory, return the\\n OSS URI representing the directory for uploaded data, else then\\n returns the OSS URI points to the uploaded file.\\n \\\"\\\"\\\"\\n\\n bucket, oss_path = _get_bucket_and_path(bucket, oss_path)\\n\\n source_path_obj = pathlib.Path(source_path)\\n if not source_path_obj.exists():\\n raise RuntimeError(\\\"Source path is not exist: {}\\\".format(source_path))\\n\\n if is_tar:\\n # compress the local data and upload the compressed source data.\\n with tempfile.TemporaryDirectory() as dir_name:\\n temp_tar_path = _tar_file(\\n source_path, os.path.join(dir_name, \\\"source.tar.gz\\\")\\n )\\n dest_path = (\\n os.path.join(oss_path, os.path.basename(temp_tar_path))\\n if oss_path.endswith(\\\"/\\\")\\n else oss_path\\n )\\n _upload_with_progress(\\n filename=temp_tar_path, object_key=dest_path, oss_bucket=bucket\\n )\\n return \\\"oss://{}/{}\\\".format(bucket.bucket_name, dest_path)\\n elif not source_path_obj.is_dir():\\n # if source path is a file, just invoke bucket.put_object.\\n\\n # if the oss_path is endswith slash, the file will be uploaded to\\n # \\\"{oss_path}{filename}\\\", else the file will be uploaded to \\\"{oss_path}\\\".\\n dest_path = (\\n os.path.join(oss_path, os.path.basename(source_path))\\n if oss_path.endswith(\\\"/\\\")\\n else oss_path\\n )\\n _upload_with_progress(\\n filename=source_path, object_key=dest_path, oss_bucket=bucket\\n )\\n return \\\"oss://{}/{}\\\".format(bucket.bucket_name, dest_path)\\n else:\\n # if the source path is a directory, upload all the file under the directory.\\n source_files = glob.glob(\\n pathname=str(source_path_obj / \\\"**\\\"),\\n recursive=True,\\n )\\n if not oss_path.endswith(\\\"/\\\"):\\n oss_path += \\\"/\\\"\\n\\n files = [f for f in source_files if not os.path.isdir(f)]\\n for file_path in files:\\n file_path_obj = pathlib.Path(file_path)\\n file_relative_path = file_path_obj.relative_to(source_path_obj).as_posix()\\n object_key = oss_path + file_relative_path\\n _upload_with_progress(\\n filename=file_path, object_key=object_key, oss_bucket=bucket\\n )\\n return \\\"oss://{}/{}\\\".format(bucket.bucket_name, oss_path)\"\n },\n {\n \"identifier\": \"generate_repr\",\n \"path\": \"pai/common/utils.py\",\n \"snippet\": \"def generate_repr(repr_obj, *attr_names: str, **kwargs) -> str:\\n \\\"\\\"\\\"Generate a string representation of the given object.\\n\\n Args:\\n repr_obj: The object used to generate the string representation.\\n attr_names: A list of attribute names to include in the string representation.\\n\\n Returns:\\n str: A string representation of the object.\\n\\n \\\"\\\"\\\"\\n attrs = {name: getattr(repr_obj, name) for name in attr_names}\\n attrs.update(kwargs)\\n attr_repr = \\\", \\\".join([\\\"{}={}\\\".format(k, v) for k, v in attrs.items()])\\n cls_name = repr_obj.__class__.__name__\\n\\n return f\\\"{cls_name}({attr_repr})\\\"\"\n },\n {\n \"identifier\": \"is_local_run_instance_type\",\n \"path\": \"pai/common/utils.py\",\n \"snippet\": \"def is_local_run_instance_type(instance_type: str) -> bool:\\n \\\"\\\"\\\"Return True if instance_type is local run instance type.\\\"\\\"\\\"\\n return instance_type and instance_type.strip() in [\\n INSTANCE_TYPE_LOCAL_GPU,\\n INSTANCE_TYPE_LOCAL,\\n ]\"\n },\n {\n \"identifier\": \"random_str\",\n \"path\": \"pai/common/utils.py\",\n \"snippet\": \"def random_str(n):\\n \\\"\\\"\\\"Random string generation with lower case letters and digits.\\n\\n Args:\\n n: Size of generated random string.\\n\\n Returns:\\n str: generated random string.\\n\\n \\\"\\\"\\\"\\n return \\\"\\\".join(\\n random.choice(string.ascii_lowercase + string.digits) for _ in range(n)\\n )\"\n },\n {\n \"identifier\": \"to_plain_text\",\n \"path\": \"pai/common/utils.py\",\n \"snippet\": \"def to_plain_text(\\n input_str: str, allowed_characters=DEFAULT_PLAIN_TEXT_ALLOW_CHARACTERS, repl_ch=\\\"_\\\"\\n):\\n \\\"\\\"\\\"Replace characters in input_str if it is not in allowed_characters.\\\"\\\"\\\"\\n return \\\"\\\".join([c if c in allowed_characters else repl_ch for c in input_str])\"\n },\n {\n \"identifier\": \"DuplicatedMountException\",\n \"path\": \"pai/exception.py\",\n \"snippet\": \"class DuplicatedMountException(PAIException):\\n \\\"\\\"\\\"Raised if a OSS path is mounted twice.\\\"\\\"\\\"\"\n },\n {\n \"identifier\": \"MountPathIsOccupiedException\",\n \"path\": \"pai/exception.py\",\n \"snippet\": \"class MountPathIsOccupiedException(PAIException):\\n \\\"\\\"\\\"Raised if target mount path is already used.\\\"\\\"\\\"\"\n },\n {\n \"identifier\": \"ImageInfo\",\n \"path\": \"pai/image.py\",\n \"snippet\": \"class ImageInfo(object):\\n \\\"\\\"\\\"This class represents information for an image provided by PAI.\\n\\n Args:\\n image_name (str): The name of the image.\\n image_uri (str): The URI of the image.\\n framework_name (str): The name of the framework installed in the image.\\n framework_version (str, optional): The version of the framework (Default None).\\n image_scope (str): The scope of the image, could be 'training', 'inference' or\\n 'develop'.\\n accelerator_type (str, optional): The type of accelerator. Defaults to None.\\n python_version (str, optional): The version of Python. Defaults to None.\\n \\\"\\\"\\\"\\n\\n def __repr__(self):\\n return (\\n \\\"{}(framework_name={}: framework_version={}: image_scope={}: \\\"\\n \\\"accelerator_type={}: py_version={})\\\".format(\\n self.__class__.__name__,\\n self.framework_name,\\n self.framework_version,\\n self.image_scope,\\n self.accelerator_type,\\n self.python_version,\\n )\\n )\\n\\n def __init__(\\n self,\\n image_name: str,\\n image_uri: str,\\n framework_name: str,\\n image_scope: str,\\n framework_version: str = None,\\n accelerator_type: Optional[str] = None,\\n python_version: Optional[str] = None,\\n ):\\n self.image_name = image_name\\n self.image_uri = image_uri\\n self.framework_name = framework_name\\n self.framework_version = framework_version\\n self.accelerator_type = accelerator_type\\n self.python_version = python_version\\n self.image_scope = image_scope\"\n },\n {\n \"identifier\": \"AsyncPredictor\",\n \"path\": \"pai/predictor.py\",\n \"snippet\": \"class AsyncPredictor(PredictorBase, _ServicePredictorMixin):\\n \\\"\\\"\\\"A class that facilitates making predictions to asynchronous prediction service.\\n\\n Examples::\\n\\n # Initialize an AsyncPredictor object using the name of a running service.\\n async_predictor = AsyncPredictor(service_name=\\\"example_service\\\")\\n\\n # Make a prediction with the service and get the prediction result.\\n resp = async_predictor.predict(data=\\\"YourPredictionData\\\")\\n result = resp.wait()\\n\\n # Make a prediction with async API.\\n import asyncio\\n result = asyncio.run(async_predictor.predict_async(data=\\\"YourPredictionData\\\"))\\n\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n service_name: str,\\n max_workers: Optional[int] = None,\\n endpoint_type: str = EndpointType.INTERNET,\\n serializer: Optional[SerializerBase] = None,\\n session: Optional[Session] = None,\\n ):\\n \\\"\\\"\\\"Construct a `AsyncPredictor` object using an existing async prediction service.\\n\\n Args:\\n service_name (str): Name of the existing prediction service.\\n max_workers (int): The maximum number of threads that can be used to\\n execute the given prediction calls.\\n endpoint_type (str): Selects the endpoint used by the predictor, which\\n should be one of `INTERNET` or `INTRANET`. The `INTERNET` endpoint type\\n means that the predictor calls the service over a public endpoint, while\\n the `INTRANET` endpoint type is over a VPC endpoint.\\n serializer (SerializerBase, optional): A serializer object that transforms\\n the input Python object for data transmission and deserialize the\\n response data to Python object.\\n session (Session, optional): A PAI session object used for communicating\\n with PAI service.\\n \\\"\\\"\\\"\\n\\n super(AsyncPredictor, self).__init__(\\n service_name=service_name,\\n session=session or get_default_session(),\\n endpoint_type=endpoint_type,\\n serializer=serializer,\\n )\\n self._max_workers = max_workers\\n self.executor = ThreadPoolExecutor(max_workers=self._max_workers)\\n self._check()\\n\\n @property\\n def max_workers(self):\\n return self._max_workers\\n\\n @max_workers.setter\\n def max_workers(self, n: int):\\n if hasattr(self, \\\"executor\\\"):\\n logger.info(\\\"Waiting for all submitted tasks in the queue to complete...\\\")\\n self.executor.shutdown()\\n self._max_workers = n\\n self.executor = ThreadPoolExecutor(max_workers=self._max_workers)\\n\\n def __del__(self):\\n \\\"\\\"\\\"wait for all pending tasks to complete before exit.\\\"\\\"\\\"\\n if hasattr(self, \\\"executor\\\"):\\n logger.info(\\\"Waiting for all pending tasks to complete...\\\")\\n self.executor.shutdown()\\n super(AsyncPredictor, self).__del__()\\n\\n def _check(self):\\n config = json.loads(self._service_api_object[\\\"ServiceConfig\\\"])\\n if config.get(\\\"metadata\\\", {}).get(\\\"type\\\") != ServiceType.Async:\\n logger.warning(\\n \\\"AsyncPredictor is not recommended to make prediction to a standard \\\"\\n \\\" prediction service.\\\"\\n )\\n\\n def _get_result(\\n self, request_id: str\\n ) -> Optional[Tuple[int, Dict[str, str], bytes]]:\\n resp = self._send_request(\\n method=\\\"GET\\\",\\n path=_QUEUE_SERVICE_SINK_PATH,\\n params={\\n \\\"requestId\\\": request_id,\\n # _raw_ is false because we want to get the encapsulated prediction\\n # result in response body.\\n \\\"_raw_\\\": \\\"false\\\",\\n },\\n )\\n logger.debug(\\n \\\"Poll prediction result: request_id=%s status_code=%s, content=%s\\\",\\n request_id,\\n resp.status_code,\\n resp.content,\\n )\\n if resp.status_code == 204:\\n # Status code 204 means could not find prediction response for the specific\\n # request id.\\n return\\n\\n # Raise exception if status code is not 2xx.\\n if resp.status_code // 100 != 2:\\n raise RuntimeError(\\n \\\"Pulling prediction result failed: status_code={} content={}\\\".format(\\n resp.status_code, resp.content.decode(\\\"utf-8\\\")\\n )\\n )\\n return self._parse_encapsulated_response(resp.json()[0])\\n\\n def _parse_encapsulated_response(self, data) -> Tuple[int, Dict[str, str], bytes]:\\n tags = data[\\\"tags\\\"]\\n # If the status code from prediction service is not 200, a tag with\\n # key 'lastCode' will be added to the tags in response.\\n status_code = int(tags.get(\\\"lastCode\\\", 200))\\n data = base64.b64decode(data[\\\"data\\\"])\\n # currently, headers are not supported in async prediction service.\\n headers = dict()\\n return status_code, headers, data\\n\\n async def _get_result_async(\\n self, request_id: str\\n ) -> Optional[Tuple[int, Dict[str, str], bytes]]:\\n resp = await self._send_request_async(\\n method=\\\"GET\\\",\\n path=_QUEUE_SERVICE_SINK_PATH,\\n params={\\n \\\"requestId\\\": request_id,\\n # _raw_ is false because we want to get the encapsulated prediction\\n # result in response body.\\n \\\"_raw_\\\": \\\"false\\\",\\n },\\n )\\n status_code = resp.status\\n content = await resp.read()\\n logger.debug(\\n \\\"Get prediction result: request_id=%s status_code=%s, content=%s\\\",\\n request_id,\\n status_code,\\n content,\\n )\\n if status_code == 204:\\n # Status code 204 means could not find prediction response for the specific\\n # request id.\\n return\\n if status_code // 100 != 2:\\n raise RuntimeError(\\n \\\"Pulling prediction result failed: status_code={} content={}\\\".format(\\n status_code, content.decode(\\\"utf-8\\\")\\n )\\n )\\n data = (await resp.json())[0]\\n return self._parse_encapsulated_response(data)\\n\\n def _poll_result(\\n self, request_id: str, wait_config: WaitConfig\\n ) -> Tuple[int, Dict[str, str], bytes]:\\n # if max_attempts is negative or zero, then wait forever\\n attempts = -1 if wait_config.max_attempts <= 0 else wait_config.max_attempts\\n while attempts != 0:\\n attempts -= 1\\n result = self._get_result(request_id=request_id)\\n if not result:\\n time.sleep(wait_config.interval)\\n continue\\n status_code, headers, content = result\\n # check real prediction response\\n if status_code // 100 != 2:\\n raise PredictionException(\\n code=status_code,\\n message=f\\\"Prediction failed: status_code={status_code}\\\"\\n f\\\" content={content.decode()}\\\",\\n )\\n return status_code, headers, content\\n\\n # Polling prediction result timeout.\\n raise RuntimeError(\\n f\\\"Polling prediction result timeout: request_id={request_id}, \\\"\\n f\\\"total_time={wait_config.max_attempts * wait_config.interval}\\\"\\n )\\n\\n async def _poll_result_async(\\n self, request_id, wait_config: WaitConfig\\n ) -> Tuple[int, Dict[str, str], bytes]:\\n # if max_attempts is negative or zero, then wait forever\\n attempts = -1 if wait_config.max_attempts <= 0 else wait_config.max_attempts\\n while attempts != 0:\\n attempts -= 1\\n result = await self._get_result_async(request_id)\\n if not result:\\n await asyncio.sleep(wait_config.interval)\\n continue\\n status_code, headers, content = result\\n # check real prediction response\\n if status_code // 100 != 2:\\n raise PredictionException(\\n f\\\"Prediction failed: status_code={status_code} content={content.decode()}\\\"\\n )\\n return status_code, headers, content\\n\\n # Polling prediction result timeout.\\n raise RuntimeError(\\n f\\\"Polling prediction result timeout: request_id={request_id}, \\\"\\n f\\\"total_time={wait_config.max_attempts * wait_config.interval}\\\"\\n )\\n\\n def _get_request_id(self, resp: requests.models.Response) -> str:\\n if resp.status_code // 100 != 2:\\n raise RuntimeError(\\n f\\\"Send prediction request failed. status_code={resp.status_code} \\\"\\n f\\\"message={resp.text}\\\"\\n )\\n\\n if _QUEUE_SERVICE_REQUEST_ID_HEADER not in resp.headers:\\n logger.error(\\n f\\\"Send prediction request failed. Missing request id.\\\"\\n f\\\" status_code={resp.status_code} content={resp.text}\\\"\\n )\\n raise RuntimeError(\\\"Missing request id in response header.\\\")\\n\\n request_id = resp.headers[_QUEUE_SERVICE_REQUEST_ID_HEADER]\\n logger.debug(\\n f\\\"Send prediction request successfully. request_id={request_id}\\\"\\n f\\\" status_code={resp.status_code}\\\",\\n )\\n return request_id\\n\\n async def _get_request_id_async(self, resp: aiohttp.ClientResponse) -> str:\\n content = await resp.read()\\n if resp.status != 200:\\n raise RuntimeError(\\n \\\"Send request to async prediction service failed: status_code={} \\\"\\n \\\"content={}\\\".format(resp.status, content.decode(\\\"utf-8\\\"))\\n )\\n\\n if _QUEUE_SERVICE_REQUEST_ID_HEADER not in resp.headers:\\n logger.error(\\n f\\\"Send prediction request failed. Missing request id.\\\"\\n f\\\" status_code={resp.status} content={content.decode()}\\\"\\n )\\n raise RuntimeError(\\\"Missing request id in response header.\\\")\\n request_id = resp.headers[_QUEUE_SERVICE_REQUEST_ID_HEADER]\\n logger.debug(\\n f\\\"Send prediction request successfully. request_id={request_id}\\\"\\n f\\\" status_code={resp.status}\\\",\\n )\\n return request_id\\n\\n def _predict_fn(\\n self,\\n data,\\n ):\\n \\\"\\\"\\\"Make a prediction with the async prediction service.\\\"\\\"\\\"\\n # serialize input data\\n data = self._handle_input(data)\\n resp = self._send_request(data=data)\\n request_id = self._get_request_id(resp)\\n logger.debug(\\\"Async prediction RequestId: \\\", request_id)\\n # poll prediction result\\n status, headers, content = self._poll_result(\\n request_id=request_id, wait_config=WaitConfig()\\n )\\n\\n return self._handle_output(content)\\n\\n def _wrap_callback_fn(self, cb: Callable):\\n \\\"\\\"\\\"Wrap the callback function to handle the prediction result.\\\"\\\"\\\"\\n\\n @functools.wraps(cb)\\n def _(future: Future):\\n return cb(future.result())\\n\\n return _\\n\\n def predict(\\n self,\\n data,\\n callback: Optional[Union[Callable, List[Callable]]] = None,\\n ):\\n \\\"\\\"\\\"Make a prediction with the async prediction service.\\n\\n The input data is serialized using the `serializer.serialize` method before it\\n is sent, and the response body is deserialized using the\\n `serializer.deserialize` method the prediction result returns.\\n\\n Args:\\n data: The input data for the prediction. It will be serialized using the\\n serializer of the predictor before transmitted to the prediction\\n service.\\n callback (Union[Callable, List[Callable]], optional): A Callback function,\\n or a list of callback functions used to process the prediction result.\\n\\n Returns:\\n AsyncTask: The task object that can be used to retrieve the prediction\\n result.\\n \\\"\\\"\\\"\\n self._post_init_serializer()\\n future = self.executor.submit(self._predict_fn, data)\\n\\n if isinstance(callback, Callable):\\n callback = [callback]\\n\\n if callback:\\n for cb in callback:\\n future.add_done_callback(self._wrap_callback_fn(cb))\\n\\n return AsyncTask(future=future)\\n\\n async def predict_async(self, data, wait_config: WaitConfig = WaitConfig()):\\n \\\"\\\"\\\"Make a prediction with the async prediction service.\\n\\n The serializer object for the predictor is responsible for data transformation\\n when the 'predict' method is invoked. The input data is serialized using the\\n `serializer.serialize` method before it is sent, and the response is\\n deserialized using the `serializer.deserialize` method before the prediction\\n result returns.\\n\\n Args:\\n data: The input data for the prediction. It will be serialized using the\\n serializer of the predictor before transmitted to the prediction\\n service.\\n wait_config (WaitConfig): A config object that controls the behavior of\\n polling the prediction result.\\n\\n Returns:\\n Prediction result.\\n\\n \\\"\\\"\\\"\\n self._post_init_serializer()\\n data = self._handle_input(data)\\n resp = await self._send_request_async(data=data)\\n request_id = await self._get_request_id_async(resp)\\n\\n status_code, headers, content = await self._poll_result_async(\\n request_id=request_id, wait_config=wait_config\\n )\\n return self._handle_output(content)\\n\\n def _raw_predict_fn(self, data, method, path, headers, **kwargs):\\n json_data, data = self._handle_raw_input(data)\\n resp = self._send_request(\\n path=path,\\n json=json_data,\\n data=data,\\n headers=self._build_headers(headers),\\n method=method,\\n **kwargs,\\n )\\n request_id = self._get_request_id(resp)\\n status, headers, content = self._poll_result(\\n request_id, wait_config=WaitConfig()\\n )\\n return RawResponse(status, headers, content)\\n\\n def raw_predict(\\n self,\\n data: Any = None,\\n callback: Optional[Union[Callable, List[Callable], None]] = None,\\n method: str = \\\"POST\\\",\\n path: Optional[str] = None,\\n headers: Optional[Dict[str, str]] = None,\\n **kwargs,\\n ) -> AsyncTask:\\n \\\"\\\"\\\"Make a prediction with the online prediction service.\\n\\n Args:\\n data (Any): Input data to be sent to the prediction service. If it is a\\n file-like object, bytes, or string, it will be sent as the request body.\\n Otherwise, it will be treated as a JSON serializable object and sent as\\n JSON.\\n callback (Union[Callable, List[Callable]], optional): A Callback function,\\n or a list of callback functions used to process the prediction result.\\n path (str, optional): Path for the request to be sent to. If it is provided,\\n it will be appended to the endpoint URL (Default None).\\n headers (dict, optional): Request headers.\\n method (str, optional): Request method, default to 'POST'.\\n **kwargs: Additional keyword arguments for the request.\\n Returns:\\n AsyncTask: The task object that can be used to retrieve the prediction\\n result.\\n\\n Examples:\\n\\n from pai.predictor import AsyncPredictor, AsyncTask\\n\\n predictor = AsyncPredictor()\\n task: AsyncTask = predictor.raw_predict(data=\\\"YourPredictionData\\\")\\n print(task.result())\\n\\n \\\"\\\"\\\"\\n\\n future = self.executor.submit(\\n self._raw_predict_fn, data, method, path, headers, **kwargs\\n )\\n cbs = [callback] if isinstance(callback, Callable) else callback\\n if cbs:\\n for cb in cbs:\\n future.add_done_callback(self._wrap_callback_fn(cb))\\n\\n return AsyncTask(future=future)\\n\\n async def raw_predict_async(\\n self,\\n data,\\n wait_config: WaitConfig = WaitConfig(),\\n method: str = \\\"POST\\\",\\n headers: Optional[Dict[str, str]] = None,\\n path: Optional[str] = None,\\n **kwargs,\\n ) -> RawResponse:\\n \\\"\\\"\\\"Make a prediction with the online prediction service.\\n\\n Args:\\n data (Any): Input data to be sent to the prediction service. If it is a\\n file-like object, bytes, or string, it will be sent as the request body.\\n Otherwise, it will be treated as a JSON serializable object and sent as\\n JSON.\\n wait_config (WaitConfig): A config object that controls the behavior of\\n polling the prediction result.\\n path (str, optional): Path for the request to be sent to. If it is provided,\\n it will be appended to the endpoint URL (Default None).\\n headers (dict, optional): Request headers.\\n method (str, optional): Request method, default to 'POST'.\\n **kwargs: Additional keyword arguments for the request.\\n Returns:\\n RawResponse: Prediction result.\\n\\n \\\"\\\"\\\"\\n if self.service_status not in ServiceStatus.completed_status():\\n self.wait_for_ready()\\n json_data, data = self._handle_raw_input(data)\\n\\n resp = await self._send_request_async(\\n data=data,\\n method=method,\\n json=json_data,\\n path=path,\\n headers=headers,\\n **kwargs,\\n )\\n request_id = await self._get_request_id_async(resp)\\n # Polling the prediction result.\\n status_code, headers, content = await self._poll_result_async(\\n request_id=request_id, wait_config=wait_config\\n )\\n return self._handle_raw_output(status_code, headers, content)\"\n },\n {\n \"identifier\": \"LocalPredictor\",\n \"path\": \"pai/predictor.py\",\n \"snippet\": \"class LocalPredictor(PredictorBase):\\n \\\"\\\"\\\"Perform prediction to a local service running with docker.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n port: int,\\n container_id: Optional[str] = None,\\n serializer: Optional[SerializerBase] = None,\\n ):\\n \\\"\\\"\\\"LocalPredictor initializer.\\n\\n Args:\\n port (int): The port of the local service.\\n container_id (str, optional): The container id of the local service.\\n serializer (SerializerBase, optional): A serializer object that transforms.\\n \\\"\\\"\\\"\\n self.container_id = container_id\\n self.port = port\\n self.serializer = serializer or JsonSerializer()\\n self._container_run = (\\n self._build_container_run(container_id, port=port)\\n if self.container_id\\n else None\\n )\\n\\n @classmethod\\n def _build_container_run(cls, container_id, port):\\n try:\\n import docker\\n except ImportError:\\n raise ImportError(\\\"Please install docker first: pip install docker\\\")\\n client = docker.from_env()\\n container = client.containers.get(container_id)\\n\\n return ContainerRun(container=container, port=port)\\n\\n def predict(self, data) -> Any:\\n \\\"\\\"\\\"Perform prediction with the given data.\\n\\n Args:\\n data: The data to be predicted.\\n \\\"\\\"\\\"\\n request_data = self.serializer.serialize(data=data)\\n response = requests.post(\\n url=\\\"http://127.0.0.1:{port}/\\\".format(port=self._container_run.port),\\n data=request_data,\\n )\\n\\n if response.status_code // 100 != 2:\\n raise PredictionException(\\n code=response.status_code,\\n message=response.content,\\n )\\n\\n return self.serializer.deserialize(response.content)\\n\\n def _build_headers(\\n self, headers: Optional[Dict[str, str]] = None\\n ) -> Dict[str, str]:\\n headers = headers or dict()\\n headers[\\\"User-Agent\\\"] = http_user_agent(headers.get(\\\"User-Agent\\\"))\\n return headers\\n\\n def _build_url(self, path: Optional[str] = None):\\n url = \\\"http://127.0.0.1:{}\\\".format(self.port)\\n if path:\\n if path.startswith(\\\"/\\\"):\\n path = path[1:]\\n url = posixpath.join(url, path)\\n return url\\n\\n def raw_predict(\\n self,\\n data: Any = None,\\n path: Optional[str] = None,\\n headers: Optional[Dict[str, str]] = None,\\n method: str = \\\"POST\\\",\\n timeout: Optional[Union[float, Tuple[float, float]]] = None,\\n **kwargs,\\n ) -> RawResponse:\\n \\\"\\\"\\\"Make a prediction with the online prediction service.\\n\\n Args:\\n data (Any): Input data to be sent to the prediction service. If it is a\\n file-like object, bytes, or string, it will be sent as the request body.\\n Otherwise, it will be treated as a JSON serializable object and sent as\\n JSON.\\n path (str, optional): Path for the request to be sent to. If it is provided,\\n it will be appended to the endpoint URL (Default None).\\n headers (dict, optional): Request headers.\\n method (str, optional): Request method, default to 'POST'.\\n timeout(float, tuple(float, float), optional): Timeout setting for the\\n request (Default 10).\\n Returns:\\n RawResponse: Prediction response from the service.\\n\\n Raises:\\n PredictionException: Raise if status code of the prediction response does\\n not equal 2xx.\\n \\\"\\\"\\\"\\n if isinstance(data, (IOBase, bytes, str)):\\n # if data is a file-like object, bytes, or string, it will be sent as\\n # request body\\n json_data, data = None, data\\n else:\\n # otherwise, it will be treated as a JSON serializable object and sent as\\n # JSON.\\n json_data, data = data, None\\n header = self._build_headers(headers=headers)\\n url = self._build_url(path)\\n resp = requests.request(\\n url=url,\\n json=json_data,\\n data=data,\\n headers=header,\\n method=method,\\n timeout=timeout,\\n **kwargs,\\n )\\n resp = RawResponse(\\n status_code=resp.status_code,\\n content=resp.content,\\n headers=dict(resp.headers),\\n )\\n if resp.status_code // 100 != 2:\\n raise PredictionException(resp.status_code, resp.content)\\n return resp\\n\\n def delete_service(self):\\n \\\"\\\"\\\"Delete the docker container that running the service.\\\"\\\"\\\"\\n if self._container_run:\\n self._container_run.stop()\\n\\n def wait_for_ready(self):\\n self._container_run.wait_for_ready()\\n # ensure the server is ready.\\n self._wait_local_server_ready()\\n time.sleep(5)\\n\\n def _wait_local_server_ready(\\n self,\\n interval: int = 5,\\n ):\\n \\\"\\\"\\\"Wait for the local model server to be ready.\\\"\\\"\\\"\\n container_run = self._container_run\\n while True:\\n try:\\n # Check whether the container is still running.\\n if not container_run.is_running():\\n raise RuntimeError(\\n \\\"Container exited unexpectedly, status: {}\\\".format(\\n container_run.status\\n )\\n )\\n\\n # Make a HEAD request to the server, just test for connection.\\n requests.head(\\n f\\\"http://127.0.0.1:{container_run.port}/\\\",\\n )\\n break\\n except requests.ConnectionError:\\n # ConnectionError means server is not ready.\\n logging.debug(\\\"Waiting for the container to be ready...\\\")\\n time.sleep(interval)\\n continue\"\n },\n {\n \"identifier\": \"Predictor\",\n \"path\": \"pai/predictor.py\",\n \"snippet\": \"class Predictor(PredictorBase, _ServicePredictorMixin):\\n \\\"\\\"\\\"Predictor is responsible for making prediction to an online service.\\n\\n The `predictor.predict` method sends the input data to the online prediction service\\n and returns the prediction result. The serializer object of the predictor is\\n responsible for data transformation when the `predict` method is invoked. The input\\n data is serialized using the `serializer.serialize` method before it is sent, and\\n the response is deserialized using the `serializer.deserialize` method before the\\n prediction result returns.\\n\\n Examples::\\n\\n # Initialize a predictor object from an existing service using PyTorch\\n # processor.\\n torch_predictor = Predictor(service_name=\\\"example_torch_service\\\")\\n result = torch_predictor.predict(numpy.asarray([[22,33,44], [19,22,33]]))\\n assert isinstance(result, numpy.ndarray)\\n\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n service_name: str,\\n endpoint_type: str = EndpointType.INTERNET,\\n serializer: Optional[SerializerBase] = None,\\n session: Optional[Session] = None,\\n ):\\n \\\"\\\"\\\"Construct a `Predictor` object using an existing prediction service.\\n\\n Args:\\n service_name (str): Name of the existing prediction service.\\n endpoint_type (str): Selects the endpoint used by the predictor, which\\n should be one of `INTERNET` or `INTRANET`. The `INTERNET` endpoint type\\n means that the predictor calls the service over a public endpoint, while\\n the `INTRANET` endpoint type is over a VPC endpoint.\\n serializer (SerializerBase, optional): A serializer object that transforms\\n the input Python object for data transmission and deserialize the\\n response data to Python object.\\n session (Session, optional): A PAI session object used for communicating\\n with PAI service.\\n \\\"\\\"\\\"\\n super(Predictor, self).__init__(\\n service_name=service_name,\\n session=session or get_default_session(),\\n endpoint_type=endpoint_type,\\n serializer=serializer,\\n )\\n self._check()\\n\\n def _check(self):\\n config = json.loads(self._service_api_object[\\\"ServiceConfig\\\"])\\n if config.get(\\\"metadata\\\", {}).get(\\\"type\\\") == ServiceType.Async:\\n logger.warning(\\n \\\"Predictor is not recommended to make prediction to a async\\\"\\n \\\" prediction service.\\\"\\n )\\n\\n def predict(self, data):\\n \\\"\\\"\\\"Make a prediction with the online prediction service.\\n\\n The serializer object for the predictor is responsible for data transformation\\n when the 'predict' method is invoked. The input data is serialized using the\\n `serializer.serialize` method before it is sent, and the response is\\n deserialized using the `serializer.deserialize` method before the prediction\\n result returns.\\n\\n Args:\\n data: The input data for the prediction. It will be serialized using the\\n serializer of the predictor before transmitted to the prediction\\n service.\\n\\n Returns:\\n object: Prediction result.\\n\\n Raises:\\n PredictionException: Raise if status code of the prediction response does\\n not equal 2xx.\\n \\\"\\\"\\\"\\n self._post_init_serializer()\\n data = self._handle_input(data)\\n resp = self._send_request(\\n data,\\n )\\n if resp.status_code // 100 != 2:\\n raise PredictionException(resp.status_code, resp.content)\\n return self._handle_output(\\n resp.content,\\n )\\n\\n def raw_predict(\\n self,\\n data: Any = None,\\n path: Optional[str] = None,\\n headers: Optional[Dict[str, str]] = None,\\n method: str = \\\"POST\\\",\\n timeout: Optional[Union[float, Tuple[float, float]]] = None,\\n **kwargs,\\n ) -> RawResponse:\\n \\\"\\\"\\\"Make a prediction with the online prediction service.\\n\\n Args:\\n data (Any): Input data to be sent to the prediction service. If it is a\\n file-like object, bytes, or string, it will be sent as the request body.\\n Otherwise, it will be treated as a JSON serializable object and sent as\\n JSON.\\n path (str, optional): Path for the request to be sent to. If it is provided,\\n it will be appended to the endpoint URL (Default None).\\n headers (dict, optional): Request headers.\\n method (str, optional): Request method, default to 'POST'.\\n timeout(float, tuple(float, float), optional): Timeout setting for the\\n request (Default 10).\\n **kwargs: Additional keyword arguments for the request.\\n Returns:\\n RawResponse: Prediction response from the service.\\n\\n Raises:\\n PredictionException: Raise if status code of the prediction response does\\n not equal 2xx.\\n \\\"\\\"\\\"\\n json_data, data = self._handle_raw_input(data)\\n resp = self._send_request(\\n data=data,\\n json=json_data,\\n method=method,\\n path=path,\\n headers=headers,\\n timeout=timeout,\\n **kwargs,\\n )\\n if resp.status_code // 100 != 2:\\n raise PredictionException(resp.status_code, resp.content)\\n\\n resp = RawResponse(\\n status_code=resp.status_code,\\n content=resp.content,\\n headers=dict(resp.headers),\\n )\\n return resp\"\n },\n {\n \"identifier\": \"ServiceType\",\n \"path\": \"pai/predictor.py\",\n \"snippet\": \"class ServiceType(object):\\n Standard = \\\"Standard\\\"\\n Async = \\\"Async\\\"\"\n },\n {\n \"identifier\": \"SerializerBase\",\n \"path\": \"pai/serializers.py\",\n \"snippet\": \"class SerializerBase(ABC):\\n \\\"\\\"\\\"Abstract class for creating a Serializer class for predictor.\\\"\\\"\\\"\\n\\n @abstractmethod\\n def serialize(self, data) -> bytes:\\n \\\"\\\"\\\"Serialize the input data to bytes for transmitting.\\\"\\\"\\\"\\n\\n @abstractmethod\\n def deserialize(self, data: bytes):\\n \\\"\\\"\\\"Deserialize the data from raw bytes to Python object .\\\"\\\"\\\"\\n\\n def inspect_from_service(\\n self, service_name: str, *, session: Optional[Session] = None\\n ):\\n \\\"\\\"\\\"Inspect the online prediction service to complete the serializer instance\\n initialization.\\n\\n The implementation of the `inspect_from_service` method is optional. You only\\n need to implement it if your serializer requires additional information from\\n service metadata or if it needs to send a request to the service in order to\\n be initialized.\\n\\n \\\"\\\"\\\"\"\n },\n {\n \"identifier\": \"Session\",\n \"path\": \"pai/session.py\",\n \"snippet\": \"class Session(ResourceAPIsContainerMixin):\\n \\\"\\\"\\\"A class responsible for communicating with PAI services.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n region_id: str,\\n workspace_id: Optional[str] = None,\\n credential_config: Optional[CredentialConfig] = None,\\n oss_bucket_name: Optional[str] = None,\\n oss_endpoint: Optional[str] = None,\\n **kwargs,\\n ):\\n \\\"\\\"\\\"PAI Session Initializer.\\n\\n Args:\\n credential_config (:class:`alibabacloud_credentials.models.Config`, optional):\\n The credential config used to access the Alibaba Cloud.\\n region_id (str): The ID of the Alibaba Cloud region where the service\\n is located.\\n workspace_id (str, optional): ID of the workspace used in the default\\n session.\\n oss_bucket_name (str, optional): The name of the OSS bucket used in the\\n session.\\n oss_endpoint (str, optional): The endpoint for the OSS bucket.\\n \\\"\\\"\\\"\\n\\n if not region_id:\\n raise ValueError(\\\"Region ID must be provided.\\\")\\n\\n self._credential_config = credential_config\\n self._region_id = region_id\\n self._workspace_id = workspace_id\\n self._oss_bucket_name = oss_bucket_name\\n self._oss_endpoint = oss_endpoint\\n\\n header = kwargs.pop(\\\"header\\\", None)\\n super(Session, self).__init__(header=header)\\n\\n @property\\n def region_id(self) -> str:\\n return self._region_id\\n\\n @property\\n def is_inner(self) -> bool:\\n return self._region_id in INNER_REGION_IDS\\n\\n @property\\n def oss_bucket_name(self) -> str:\\n return self._oss_bucket_name\\n\\n @property\\n def oss_endpoint(self) -> str:\\n return self._oss_endpoint\\n\\n @property\\n def credential_config(self) -> CredentialConfig:\\n return self._credential_config\\n\\n @property\\n def workspace_name(self):\\n if hasattr(self, \\\"_workspace_name\\\") and self._workspace_name:\\n return self._workspace_name\\n\\n if not self._workspace_id:\\n raise ValueError(\\\"Workspace id is not set.\\\")\\n workspace_api_obj = self.workspace_api.get(workspace_id=self._workspace_id)\\n self._workspace_name = workspace_api_obj[\\\"WorkspaceName\\\"]\\n return self._workspace_name\\n\\n @property\\n def provider(self) -> str:\\n caller_identity = self._acs_sts_client.get_caller_identity().body\\n return caller_identity.account_id\\n\\n @property\\n def workspace_id(self) -> str:\\n \\\"\\\"\\\"ID of the workspace used by the session.\\\"\\\"\\\"\\n return self._workspace_id\\n\\n @property\\n def console_uri(self) -> str:\\n \\\"\\\"\\\"The web console URI for PAI service.\\\"\\\"\\\"\\n if self.is_inner:\\n return \\\"https://pai-next.alibaba-inc.com\\\"\\n else:\\n return \\\"https://pai.console.aliyun.com/console\\\"\\n\\n def _init_oss_config(\\n self,\\n ):\\n \\\"\\\"\\\"Initialize a OssConfig instance.\\\"\\\"\\\"\\n if not self._oss_bucket_name:\\n # If OSS bucket name is not provided, use the default OSS storage URI\\n # that is configured for the workspace.\\n default_oss_uri = self.workspace_api.get_default_storage_uri(\\n self.workspace_id\\n )\\n if not default_oss_uri:\\n raise RuntimeError(\\n \\\"No default OSS URI is configured for the workspace.\\\"\\n )\\n oss_uri_obj = OssUriObj(default_oss_uri)\\n self._oss_bucket_name = oss_uri_obj.bucket_name\\n\\n if not self._oss_endpoint:\\n self._oss_endpoint = self._get_default_oss_endpoint()\\n\\n def _get_oss_auth(self):\\n auth = oss2.ProviderAuth(\\n credentials_provider=CredentialProviderWrapper(\\n config=self._credential_config,\\n )\\n )\\n return auth\\n\\n @property\\n def oss_bucket(self):\\n \\\"\\\"\\\"A OSS2 bucket instance used by the session.\\\"\\\"\\\"\\n if not self._oss_bucket_name or not self._oss_endpoint:\\n self._init_oss_config()\\n oss_bucket = oss2.Bucket(\\n auth=self._get_oss_auth(),\\n endpoint=self._oss_endpoint,\\n bucket_name=self._oss_bucket_name,\\n )\\n return oss_bucket\\n\\n def save_config(self, config_path=None):\\n \\\"\\\"\\\"Save the configuration of the session to a local file.\\\"\\\"\\\"\\n attrs = {key.lstrip(\\\"_\\\"): value for key, value in vars(self).items()}\\n config = {\\n key: value\\n for key, value in attrs.items()\\n if key in _DEFAULT_CONFIG_KEYS and value is not None\\n }\\n\\n config_path = config_path or DEFAULT_CONFIG_PATH\\n os.makedirs(os.path.dirname(config_path), exist_ok=True)\\n with open(config_path, \\\"w\\\") as f:\\n f.write(json.dumps(config, indent=4))\\n logger.info(\\\"Write PAI config succeed: config_path=%s\\\" % config_path)\\n\\n def patch_oss_endpoint(self, oss_uri: str):\\n oss_uri_obj = OssUriObj(oss_uri)\\n if oss_uri_obj.endpoint:\\n return oss_uri\\n\\n # patch endpoint using current OSS bucket endpoint.\\n endpoint = self.oss_bucket.endpoint\\n if endpoint.startswith(\\\"http://\\\"):\\n endpoint = endpoint.lstrip(\\\"http://\\\")\\n elif endpoint.startswith(\\\"https://\\\"):\\n endpoint = endpoint.lstrip(\\\"https://\\\")\\n return \\\"oss://{bucket_name}.{endpoint}/{key}\\\".format(\\n bucket_name=oss_uri_obj.bucket_name,\\n endpoint=endpoint,\\n key=oss_uri_obj.object_key,\\n )\\n\\n def _get_default_oss_endpoint(self) -> str:\\n \\\"\\\"\\\"Returns a default OSS endpoint.\\\"\\\"\\\"\\n\\n # OSS Endpoint document:\\n # https://help.aliyun.com/document_detail/31837.html\\n internet_endpoint = \\\"oss-{}.aliyuncs.com\\\".format(self.region_id)\\n internal_endpoint = \\\"oss-{}-internal.aliyuncs.com\\\".format(self.region_id)\\n\\n return (\\n internet_endpoint\\n if is_domain_connectable(internal_endpoint)\\n else internet_endpoint\\n )\\n\\n def get_oss_bucket(self, bucket_name: str, endpoint: str = None) -> oss2.Bucket:\\n \\\"\\\"\\\"Get a OSS bucket using the credentials of the session.\\n\\n Args:\\n bucket_name (str): The name of the bucket.\\n endpoint (str): Endpoint of the bucket.\\n\\n Returns:\\n :class:`oss2.Bucket`: A OSS bucket instance.\\n\\n \\\"\\\"\\\"\\n endpoint = endpoint or self._oss_endpoint or self._get_default_oss_endpoint()\\n oss_bucket = oss2.Bucket(\\n auth=self._get_oss_auth(),\\n endpoint=endpoint,\\n bucket_name=bucket_name,\\n )\\n return oss_bucket\\n\\n @classmethod\\n def get_storage_path_by_category(\\n cls, category: str, dir_name: Optional[str] = None\\n ) -> str:\\n \\\"\\\"\\\"Get an OSS storage path for the resource.\\n\\n Args:\\n category (str): The category of the resource.\\n dir_name (str, optional): The directory name of the resource.\\n\\n Returns:\\n str: A OSS storage path.\\n\\n \\\"\\\"\\\"\\n dir_name = dir_name or datetime.now().strftime(\\\"%Y%m%d_%H%M%S_%f\\\")\\n storage_path = posixpath.join(\\\"pai\\\", category, dir_name).strip()\\n\\n if not storage_path.endswith(\\\"/\\\"):\\n storage_path += \\\"/\\\"\\n return storage_path\\n\\n def is_supported_training_instance(self, instance_type: str) -> bool:\\n \\\"\\\"\\\"Check if the instance type is supported for training.\\\"\\\"\\\"\\n instance_generator = make_list_resource_iterator(self.job_api.list_ecs_specs)\\n machine_spec = next(\\n (\\n item\\n for item in instance_generator\\n if item[\\\"InstanceType\\\"] == instance_type\\n ),\\n None,\\n )\\n return bool(machine_spec)\\n\\n def is_gpu_training_instance(self, instance_type: str) -> bool:\\n \\\"\\\"\\\"Check if the instance type is GPU instance for training.\\\"\\\"\\\"\\n instance_generator = make_list_resource_iterator(self.job_api.list_ecs_specs)\\n machine_spec = next(\\n (\\n item\\n for item in instance_generator\\n if item[\\\"InstanceType\\\"] == instance_type\\n ),\\n None,\\n )\\n if not machine_spec:\\n raise ValueError(\\n f\\\"Instance type {instance_type} is not supported for training job. \\\"\\n \\\"Please provide a supported instance type.\\\"\\n )\\n return machine_spec[\\\"AcceleratorType\\\"] == \\\"GPU\\\"\\n\\n def is_supported_inference_instance(self, instance_type: str) -> bool:\\n \\\"\\\"\\\"Check if the instance type is supported for inference.\\\"\\\"\\\"\\n res = self.service_api.describe_machine()[\\\"InstanceMetas\\\"]\\n spec = next(\\n (item for item in res if item[\\\"InstanceType\\\"] == instance_type), None\\n )\\n return bool(spec)\\n\\n def is_gpu_inference_instance(self, instance_type: str) -> bool:\\n \\\"\\\"\\\"Check if the instance type is GPU instance for inference.\\\"\\\"\\\"\\n res = self.service_api.describe_machine()[\\\"InstanceMetas\\\"]\\n spec = next(\\n (item for item in res if item[\\\"InstanceType\\\"] == instance_type), None\\n )\\n\\n if not spec:\\n raise ValueError(\\n f\\\"Instance type {instance_type} is not supported for deploying. \\\"\\n \\\"Please provide a supported instance type.\\\"\\n )\\n return bool(spec[\\\"GPU\\\"])\"\n },\n {\n \"identifier\": \"get_default_session\",\n \"path\": \"pai/session.py\",\n \"snippet\": \"def get_default_session() -> \\\"Session\\\":\\n \\\"\\\"\\\"Get the default session used by the program.\\n\\n If the global default session is set, the function will try to initialize\\n a session from config file.\\n\\n Returns:\\n :class:`pai.session.Session`: The default session.\\n\\n \\\"\\\"\\\"\\n global _default_session\\n if not _default_session:\\n config = load_default_config_file()\\n if not config:\\n return\\n _default_session = Session(**config)\\n return _default_session\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import copy\nimport distutils.dir_util\nimport json\nimport logging\nimport os.path\nimport posixpath\nimport shlex\nimport shutil\nimport tempfile\nimport textwrap\nimport time\nimport requests\nfrom typing import Any, Dict, Iterator, List, Optional, Tuple, Union\nfrom addict import Dict as AttrDict\nfrom oss2 import ObjectIterator\nfrom .common import git_utils\nfrom .common.consts import INSTANCE_TYPE_LOCAL_GPU, ModelFormat\nfrom .common.docker_utils import ContainerRun, run_container\nfrom .common.oss_utils import OssUriObj, download, is_oss_uri, upload\nfrom .common.utils import (\n generate_repr,\n is_local_run_instance_type,\n random_str,\n to_plain_text,\n)\nfrom .exception import DuplicatedMountException, MountPathIsOccupiedException\nfrom .image import ImageInfo\nfrom .predictor import AsyncPredictor, LocalPredictor, Predictor, ServiceType\nfrom .serializers import SerializerBase\nfrom .session import Session, get_default_session\n from .estimator import AlgorithmEstimator"},"token_num":{"kind":"number","value":17509,"string":"17,509"},"cropped_code":{"kind":"string","value":" # if source is a local path, upload it to OSS bucket and use OSS URI\n # as storage source.\n oss_path = session.get_storage_path_by_category(\"model_data\")\n oss_uri = upload(\n source_path=source, oss_path=oss_path, bucket=session.oss_bucket\n )\n oss_uri_obj = OssUriObj(oss_uri)\n storage_config = {\n \"mount_path\": mount_path,\n \"oss\": {\"path\": oss_uri_obj.get_dir_uri()},\n }\n else:\n raise ValueError(\n \"Source path is not a valid OSS URI or a existing local path.\"\n )\n\n # check if the source OSS Path is already mounted to the container.\n if oss_uri_obj.get_dir_uri() in uris:\n raise DuplicatedMountException(\n f\"Source OSS path '{oss_uri_obj.get_dir_uri()}' is already \"\n f\"mounted to the container.\"\n )\n\n configs.append(storage_config)\n self.storage = configs\n return storage_config\n\n\ndef container_serving_spec(\n command: str,\n image_uri: Union[str, ImageInfo],\n source_dir: Optional[str] = None,\n git_config: Optional[Dict[str, Any]] = None,\n port: Optional[int] = None,\n environment_variables: Optional[Dict[str, str]] = None,\n requirements: Optional[List[str]] = None,\n requirements_path: Optional[str] = None,\n health_check: Optional[Dict[str, Any]] = None,\n session: Optional[Session] = None,\n) -> InferenceSpec:\n \"\"\"A convenient function to create an InferenceSpec instance that serving the model\n with given container and script.\n\n Examples::\n\n infer_spec: InferenceSpec = container_serving_spec(\n command=\"python run.py\",\n source_dir=\"./model_server/\",\n image_uri=\"<ServingImageUri>\",\n )\n\n m = Model(\n model_data=\"oss://<YourOssBucket>/path/to/your/model\",\n inference_spec=infer_spec,\n )\n m.deploy(\n instance_type=\"ecs.c6.xlarge\"\n )\n\n\n Args:\n command (str): The command used to launch the Model server.\n source_dir (str): A relative path or an absolute path to the source code\n directory used to load model and launch the HTTP server, it will be\n uploaded to the OSS bucket and mounted to the container. If there is a\n ``requirements.txt`` file under the directory, it will be installed before\n the prediction server started.\n\n If 'git_config' is provided, 'source_dir' should be a relative location\n to a directory in the Git repo. With the following GitHub repo directory\n structure:\n\n .. code::\n\n |----- README.md\n |----- src\n |----- train.py\n |----- test.py\n\n if you need 'src' directory as the source code directory, you can assign\n source_dir='./src/'.\n git_config (Dict[str, str]): Git configuration used to clone the repo.\n Including ``repo``, ``branch``, ``commit``, ``username``, ``password`` and\n ``token``. The ``repo`` is required. All other fields are optional. ``repo``\n specifies the Git repository. If you don't provide ``branch``, the default\n value 'master' is used. If you don't provide ``commit``, the latest commit\n in the specified branch is used. ``username``, ``password`` and ``token``\n are for authentication purpose. For example, the following config:\n\n .. code:: python\n\n git_config = {\n 'repo': 'https://github.com/modelscope/modelscope.git',\n 'branch': 'master',\n 'commit': '9bfc4a9d83c4beaf8378d0a186261ffc1cd9f960'\n }\n\n results in cloning the repo specified in 'repo', then checking out the\n 'master' branch, and checking out the specified commit.\n image_uri (str): The Docker image used to run the prediction service.\n port (int): Expose port of the server in container, the prediction request\n will be forward to the port. The environment variable ``LISTENING_PORT``\n in the container will be set to this value. Default to 8000.\n environment_variables (Dict[str, str], optional): Dictionary of environment\n variable key-value pairs to set on the running container.\n requirements (List[str], optional): A list of Python package dependency, it\n will be installed before the serving container run.\n requirements_path (str, optional): A absolute path to the requirements.txt in\n the container.\n health_check (Dict[str, Any], optional): The health check configuration. If it\n not set, A TCP readiness probe will be used to check the health of the\n HTTP server.\n session (Session, optional): A PAI session instance used for communicating\n with PAI service.\n\n Returns:\n :class:`pai.model.InferenceSpec`: An InferenceSpec instance.\n \"\"\"\n session = session or get_default_session()\n if git_config:\n"},"all_code":{"kind":"string","value":"# Copyright 2023 Alibaba, Inc. or its affiliates.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\nlogger = logging.getLogger(__name__)\n\n# Reserved ports for internal use, do not use them for service\n_RESERVED_PORTS = [8080, 9090]\n\n\nclass DefaultServiceConfig(object):\n \"\"\"Default configuration used in creating prediction service.\"\"\"\n\n # Listen Port\n listen_port = 8000\n\n # Default model path in container\n model_path = \"/eas/workspace/model/\"\n\n # Default user code path in container\n code_path = \"/ml/usercode/\"\n\n\nclass ResourceConfig(object):\n \"\"\"A class that represents the resource used by a PAI prediction service\n instance.\"\"\"\n\n def __init__(self, cpu: int, memory: int, gpu: int = None, gpu_memory: int = None):\n \"\"\"ResourceConfig initializer.\n\n The public resource group does not support requesting GPU resources with\n `ResourceConfig`. Use the 'gpu' and 'gpu_memory' parameter only for services\n deployed to dedicated resource groups that provide GPU machine instances.\n\n Args:\n cpu (int): The number of CPUs that each instance requires.\n memory (int): The amount of memory that each instance requires,\n must be an integer, Unit: MB.\n gpu (int): The number of GPUs that each instance requires.\n gpu_memory (int): The amount of GPU memory that each instance requires.\n The value must be an integer, Unit: GB.\n\n PAI allows memory resources of a GPU to be allocated to multiple instances.\n If you want multiple instances to share the memory resources of a GPU,\n set the gpu parameter to 0. If you set the ``gpu`` parameter to 1, each\n instance occupies a GPU and the gpu_memory parameter does not take effect.\n\n .. note::\n\n **Important** PAI does not enable the strict isolation of GPU memory.\n To prevent out of memory (OOM) errors, make sure that the GPU memory\n used by each instance does not exceed the requested amount.\n \"\"\"\n self.cpu = cpu\n self.memory = memory\n self.gpu = gpu\n self.gpu_memory = gpu_memory\n\n def __repr__(self):\n return (\n f\"ResourceConfig(cpu={self.cpu}, memory={self.memory}MB, gpu={self.gpu or 0},\"\n f\" gpu_memory={self.gpu_memory or 0}GB)\"\n )\n\n def __str__(self):\n return self.__repr__()\n\n def to_dict(self):\n \"\"\"Transform the ResourceConfig instance to a dictionary.\n\n Returns:\n dict:\n\n \"\"\"\n res = {\n \"cpu\": self.cpu,\n \"gpu\": self.gpu,\n \"gpu_memory\": self.gpu_memory,\n \"memory\": self.memory,\n }\n\n return {k: v for k, v in res.items() if v is not None}\n\n\nclass InferenceSpec(object):\n \"\"\"A class used to describe how to create a prediction service.\n\n InferenceSpec is using to describe how the model is serving in PAI. To view the\n full supported parameters, please see the following hyperlink:\n `Parameters of model services <https://help.aliyun.com/document_detail/450525.htm>`_.\n\n Example of how to config a InferneceSpec::\n\n >>> # build an inference_spec that using XGBoost processor.\n >>> infer_spec = InferenceSpec(processor=\"xgboost\")\n >>> infer_spec.metadata.rpc.keepalive = 1000\n >>> infer_spec.warm_up_data_path = \"oss://bucket-name/path/to/warmup-data\"\n >>> infer_spec.add_option(\"metadata.rpc.max_batch_size\", 8)\n >>> print(infer_spec.processor)\n xgboost\n >>> print(infer_spec.metadata.rpc.keepalive)\n 1000\n >>> print(infer_spec.metadata.rpc.max_batch_size)\n 8\n >>> print(infer_spec.to_dict())\n {'processor': 'xgboost', 'metadata': {'rpc': {'keepalive': 1000, 'max_batch_size': 8}},\n 'warm_up_data_path': 'oss://bucket-name/path/to/warmup-data'}\n\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"InferenceSpec initializer.\n\n Args:\n **kwargs: Parameters of the inference spec.\n \"\"\"\n\n properties = kwargs.pop(\"__properties\", [])\n cfg_dict = copy.deepcopy(kwargs)\n cfg_dict = {k: v for k, v in cfg_dict.items() if not k.startswith(\"_\")}\n if args:\n if len(args) > 1:\n raise TypeError()\n cfg_dict.update(args[0])\n\n super(InferenceSpec, self).__setattr__(\n \"_cfg_dict\", self._transform_value(cfg_dict)\n )\n super(InferenceSpec, self).__setattr__(\"__properties\", properties)\n\n def __repr__(self):\n return json.dumps(self.to_dict(), sort_keys=True, indent=4)\n\n def _transform_value(self, value):\n if isinstance(value, (List, Tuple)):\n return [self._transform_value(item) for item in value]\n elif isinstance(value, (Dict, AttrDict)):\n return AttrDict(\n {key: self._transform_value(value) for key, value in value.items()}\n )\n return value\n\n def __missing__(self, name):\n return self._cfg_dict.__missing__(name)\n\n def __setitem__(self, name, value):\n return self._cfg_dict.__setitem__(name, self._transform_value(value))\n\n def __setattr__(self, name, value):\n if name in getattr(self, \"__properties\"):\n super(InferenceSpec, self).__setattr__(name, self._transform_value(value))\n else:\n self._cfg_dict.__setattr__(name, self._transform_value(value))\n\n def __getattr__(self, item):\n if item.startswith(\"_\"):\n return getattr(self, item)\n return self._cfg_dict.__getitem__(item)\n\n def __contains__(self, item):\n return item in self._cfg_dict\n\n def to_dict(self) -> Dict:\n \"\"\"Return a dictionary that represent the InferenceSpec.\"\"\"\n return self._cfg_dict.to_dict()\n\n def add_option(self, name: str, value):\n \"\"\"Add an option to the inference_spec instance.\n\n Args:\n name (str): Name of the option to set, represented as the JSON path of the\n parameter for the InferenceSpec. To view the full supported parameters,\n please see the following hyperlink: `Parameters of model services\n <https://help.aliyun.com/document_detail/450525.htm>`_.\n value: Value for the option.\n\n Examples:\n\n >>> infer_spec = InferenceSpec(processor=\"tensorflow_gpu_1.12\")\n >>> infer_spec.add_option(\"metadata.rpc.keepalive\", 10000)\n >>> infer_spec.metadata.rpc.keepalive\n 10000\n >>> infer_spec.to_dict()\n {'processor': 'tensorflow_gpu_1.12', 'metadata': {'rpc': {'keepalive': 10000}}}\n\n \"\"\"\n\n src = self._transform_value(value)\n for k in reversed(name.split(\".\")):\n src = {k: src}\n\n self._cfg_dict.update(AttrDict(src))\n\n def merge_options(self, options: Dict[str, Any]):\n \"\"\"Merge options from a dictionary.\"\"\"\n for key, value in options.items():\n self.add_option(key, value)\n\n @classmethod\n def from_dict(cls, config: Dict[str, Any]) -> \"InferenceSpec\":\n \"\"\"Initialize a InferenceSpec from a dictionary.\n\n You can use this method to initialize a InferenceSpec instance\n from a dictionary.\n\n Returns:\n :class:`pai.model.InferenceSpec`: A InferenceSpec instance.\n\n \"\"\"\n config = config or dict()\n\n return cls(**config)\n\n def is_container_serving(self):\n return \"containers\" in self._cfg_dict\n\n @classmethod\n def _upload_source_dir(cls, source_dir, session):\n \"\"\"Upload source files to OSS bucket.\"\"\"\n\n if not os.path.exists(source_dir):\n raise ValueError(f\"Input source code path does not exist: {source_dir}.\")\n if not os.path.isdir(source_dir):\n raise ValueError(\n f\"Input source code path should be a directory: {source_dir}.\"\n )\n\n target_dir = session.get_storage_path_by_category(category=\"inference_src\")\n # upload local script data to the OSS bucket.\n uploaded_source_code = upload(\n source_dir,\n target_dir,\n session.oss_bucket,\n )\n\n logger.debug(\"Uploaded source code to OSS: %s\", uploaded_source_code)\n return uploaded_source_code\n\n def mount(\n self,\n source: str,\n mount_path: str,\n session: Session = None,\n ) -> Dict[str, Any]:\n \"\"\"Mount a source storage to the running container.\n\n .. note::\n\n If source is a local path, it will be uploaded to the OSS bucket and\n mounted. If source is a OSS path, it will be mounted directly.\n\n Args:\n source (str): The source storage to be attached, currently only support OSS\n path in OSS URI format and local path.\n mount_path (str): The mount path in the container.\n session (Session, optional): A PAI session instance used for communicating\n with PAI service.\n\n Returns:\n Dict[str, Any]: The storage config.\n\n Raises:\n DuplicateMountException: If the mount path is already used or source OSS\n path is mounted to the container.\n\n Examples::\n # Mount a OSS storage path to the running container.\n >>> inference_spec.mount(\"oss://<YourOssBucket>/path/to/directory/model.json\",\n ... \"/ml/model/\")\n\n # 'Mount' a local path to the running container.\n >>> inference_spec.mount(\"/path/to/your/data/\", \"/ml/model/\")\n\n \"\"\"\n session = session or get_default_session()\n\n # TODO: supports more storages, such as NAS, PAI Dataset, PAI CodeSource, etc.\n if not isinstance(source, str):\n raise ValueError(\n \"Parameter should be a string which represents an OSS storage path\"\n \" or a local file path.\"\n )\n\n if \"storage\" in self._cfg_dict:\n configs = self._cfg_dict.get(\"storage\", [])\n else:\n configs = []\n\n uris = set()\n for conf in configs:\n # check if target mount path is already used.\n if conf.get(\"mount_path\") == mount_path:\n raise MountPathIsOccupiedException(\n f\"The mount path '{mount_path}' has already been used.\"\n )\n mount_uri = conf.get(\"oss\", {}).get(\"path\")\n uris.add(mount_uri)\n\n if is_oss_uri(source):\n oss_uri_obj = OssUriObj(source)\n storage_config = {\n \"mount_path\": mount_path,\n \"oss\": {\"path\": oss_uri_obj.get_dir_uri()},\n }\n elif os.path.exists(source):\n # if source is a local path, upload it to OSS bucket and use OSS URI\n # as storage source.\n oss_path = session.get_storage_path_by_category(\"model_data\")\n oss_uri = upload(\n source_path=source, oss_path=oss_path, bucket=session.oss_bucket\n )\n oss_uri_obj = OssUriObj(oss_uri)\n storage_config = {\n \"mount_path\": mount_path,\n \"oss\": {\"path\": oss_uri_obj.get_dir_uri()},\n }\n else:\n raise ValueError(\n \"Source path is not a valid OSS URI or a existing local path.\"\n )\n\n # check if the source OSS Path is already mounted to the container.\n if oss_uri_obj.get_dir_uri() in uris:\n raise DuplicatedMountException(\n f\"Source OSS path '{oss_uri_obj.get_dir_uri()}' is already \"\n f\"mounted to the container.\"\n )\n\n configs.append(storage_config)\n self.storage = configs\n return storage_config\n\n\ndef container_serving_spec(\n command: str,\n image_uri: Union[str, ImageInfo],\n source_dir: Optional[str] = None,\n git_config: Optional[Dict[str, Any]] = None,\n port: Optional[int] = None,\n environment_variables: Optional[Dict[str, str]] = None,\n requirements: Optional[List[str]] = None,\n requirements_path: Optional[str] = None,\n health_check: Optional[Dict[str, Any]] = None,\n session: Optional[Session] = None,\n) -> InferenceSpec:\n \"\"\"A convenient function to create an InferenceSpec instance that serving the model\n with given container and script.\n\n Examples::\n\n infer_spec: InferenceSpec = container_serving_spec(\n command=\"python run.py\",\n source_dir=\"./model_server/\",\n image_uri=\"<ServingImageUri>\",\n )\n\n m = Model(\n model_data=\"oss://<YourOssBucket>/path/to/your/model\",\n inference_spec=infer_spec,\n )\n m.deploy(\n instance_type=\"ecs.c6.xlarge\"\n )\n\n\n Args:\n command (str): The command used to launch the Model server.\n source_dir (str): A relative path or an absolute path to the source code\n directory used to load model and launch the HTTP server, it will be\n uploaded to the OSS bucket and mounted to the container. If there is a\n ``requirements.txt`` file under the directory, it will be installed before\n the prediction server started.\n\n If 'git_config' is provided, 'source_dir' should be a relative location\n to a directory in the Git repo. With the following GitHub repo directory\n structure:\n\n .. code::\n\n |----- README.md\n |----- src\n |----- train.py\n |----- test.py\n\n if you need 'src' directory as the source code directory, you can assign\n source_dir='./src/'.\n git_config (Dict[str, str]): Git configuration used to clone the repo.\n Including ``repo``, ``branch``, ``commit``, ``username``, ``password`` and\n ``token``. The ``repo`` is required. All other fields are optional. ``repo``\n specifies the Git repository. If you don't provide ``branch``, the default\n value 'master' is used. If you don't provide ``commit``, the latest commit\n in the specified branch is used. ``username``, ``password`` and ``token``\n are for authentication purpose. For example, the following config:\n\n .. code:: python\n\n git_config = {\n 'repo': 'https://github.com/modelscope/modelscope.git',\n 'branch': 'master',\n 'commit': '9bfc4a9d83c4beaf8378d0a186261ffc1cd9f960'\n }\n\n results in cloning the repo specified in 'repo', then checking out the\n 'master' branch, and checking out the specified commit.\n image_uri (str): The Docker image used to run the prediction service.\n port (int): Expose port of the server in container, the prediction request\n will be forward to the port. The environment variable ``LISTENING_PORT``\n in the container will be set to this value. Default to 8000.\n environment_variables (Dict[str, str], optional): Dictionary of environment\n variable key-value pairs to set on the running container.\n requirements (List[str], optional): A list of Python package dependency, it\n will be installed before the serving container run.\n requirements_path (str, optional): A absolute path to the requirements.txt in\n the container.\n health_check (Dict[str, Any], optional): The health check configuration. If it\n not set, A TCP readiness probe will be used to check the health of the\n HTTP server.\n session (Session, optional): A PAI session instance used for communicating\n with PAI service.\n\n Returns:\n :class:`pai.model.InferenceSpec`: An InferenceSpec instance.\n \"\"\"\n session = session or get_default_session()\n if git_config:"},"next_line":{"kind":"string","value":" updated_args = git_utils.git_clone_repo("},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-01 01:40:12+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5391,"cells":{"repo_name":{"kind":"string","value":"mpenning/ciscoconfparse2"},"file_path":{"kind":"string","value":"tests/conftest.py"},"context":{"kind":"list like","value":[{"identifier":"CiscoConfParse","path":"ciscoconfparse2/ciscoconfparse2.py","snippet":"class CiscoConfParse(object):\n \"\"\"Parse Cisco IOS configurations and answer queries about the configs.\"\"\"\n config: Optional[Union[str,List[str]]] = None\n syntax: str = \"ios\"\n encoding: str = locale.getpreferredencoding()\n loguru: bool = True\n comment_delimiters: List[str] = []\n auto_indent_width: int = -1\n linesplit_rgx: str = r\"\\r*\\n\"\n ignore_blank_lines: bool = False\n auto_commit: bool = None\n factory: bool = False\n debug: int = 0\n\n # Attributes\n config_objs: Any = None\n finished_config_parse: bool = False\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def __init__(\n self,\n config: Optional[Union[str,List[str],tuple[str, ...]]]=None,\n syntax: str=\"ios\",\n encoding: str=locale.getpreferredencoding(),\n loguru: bool=True,\n comment_delimiters: List[str]=None,\n auto_indent_width: int=-1,\n linesplit_rgx: str=r\"\\r*\\n\",\n ignore_blank_lines: bool=False,\n auto_commit: bool=True,\n factory: bool=False,\n debug: int=0,\n ):\n \"\"\"\n Initialize CiscoConfParse.\n\n .. note::\n\n ``comment_delimiters`` always assumes the delimiter is one character wide.\n\n .. note::\n\n ``ignore_blank_lines`` changes the original ciscoconfparse default value.\n\n\n :param config: A list of configuration lines or the filepath to the configuration.\n :type config: Union[str,List[str],tuple[str, ...]]\n :param syntax: The configuration type, default to 'ios'; it must be one of: 'ios', 'nxos', 'iosxr', 'asa', 'junos'. Use 'junos' for any brace-delimited network configuration (including F5, Palo Alto, etc...).\n :type syntax: str\n :param encoding: The configuration encoding, default to ``locale.getpreferredencoding()``.\n :type encoding: str\n :param loguru: Control whether CiscoConfParse should enable ``loguru``, default to True.\n :type loguru: bool\n :param comment_delimiters: String comment delimiters. This should only be changed when parsing non-Cisco configurations, which do not use a '!' as the comment delimiter. ``comment`` defaults to '!'. This value can hold multiple characters in case the config uses multiple characters for comment delimiters.\n :type comment_delimiters: List[str]\n :param auto_indent_width: Defaults to -1, and should be kept that way unless you're working on a very tricky config parsing problem.\n :type auto_indent_width: int\n :param linesplit_rgx: Used when parsing configuration files to find\n where new configuration lines are; it is best\n to leave this as the default, unless you're\n working on a system that uses unusual line\n terminations (for instance something besides\n Unix, OSX, or Windows).\n :type linesplit_rgx: str\n :param ignore_blank_lines: Defaults to False; when this is set True,\n ciscoconfparse2 ignores blank configuration\n lines.\n :type ignore_blank_lines: bool\n :param auto_commit: Control whether CiscoConfParse should auto-commit config changes when possible, default to True.\n However, parsing very large configs may be faster with ``auto_commit=False``.\n :type auto_commit: bool\n :param factory: Control whether CiscoConfParse should enable the\n beta-quality configuration parameter parser,\n default to False.\n :type factory: bool\n :param debug: Control CiscoConfParse debug output, default is 0.\n :type debug: int\n :return: A CiscoConfParse object\n :rtype: :py:class:`~ciscoconfparse2.CiscoConfParse`\n\n This example illustrates how to parse a simple Cisco IOS configuration\n with :class:`~ciscoconfparse2.CiscoConfParse` into a variable called\n ``parse``. This example also illustrates what the ``config_objs``\n and ``ioscfg`` attributes contain.\n\n .. code-block:: python\n :emphasize-lines: 6\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = [\n ... 'logging trap debugging',\n ... 'logging 172.28.26.15',\n ... ]\n >>> parse = CiscoConfParse(config=config)\n >>> parse\n <CiscoConfParse: 2 lines / syntax: ios / comment delimiter: '!' / factory: False>\n >>> parse.config_objs\n <ConfigList, comment='!', conf=[<IOSCfgLine # 0 'logging trap debugging'>, <IOSCfgLine # 1 'logging 172.28.26.15'>]>\n >>> parse.text\n ['logging trap debugging', 'logging 172.28.26.15']\n >>>\n\n Attributes\n ----------\n comment_delimiters : list\n A list of strings containing the comment-delimiters. Default: [\"!\"]\n objs : :class:`ConfigList`\n An alias for ``config_objs``\n config_objs : :class:`ConfigList`\n A custom list, which contains all parsed :class:`~models_cisco.IOSCfgLine` instances.\n debug : int\n An int to enable verbose config parsing debugs. Default 0.\n ioscfg : list\n A list of text configuration strings\n openargs : dict\n Returns a dictionary of valid arguments for `open()` (these change based on the running python version).\n syntax : str\n A string holding the configuration type. Default: 'ios'. Must be one of: 'ios', 'nxos', 'iosxr', 'asa', 'junos'. Use 'junos' for any brace-delimited network configuration (including F5, Palo Alto, etc...).\n\n\n \"\"\"\n\n if syntax not in ALL_VALID_SYNTAX:\n error = f\"{syntax} is not a valid syntax.\"\n logger.error(error)\n raise InvalidParameters(error)\n\n if comment_delimiters is None:\n comment_delimiters = get_comment_delimiters(syntax=syntax)\n elif isinstance(comment_delimiters, list):\n for comment_delimiter in comment_delimiters:\n if not isinstance(comment_delimiter, str):\n error = f\"`{comment_delimiter}` is not a valid string comment_delimiters\"\n logger.critical(error)\n raise InvalidParameters(error)\n elif not len(comment_delimiter) == 1:\n error = f\"`{comment_delimiter}` must be a single string character.\"\n logger.critical(error)\n raise InvalidParameters(error)\n elif not isinstance(comment_delimiters, list):\n error = \"'comment_delimiters' must be a list of string comment delimiters\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if int(auto_indent_width) <= 0:\n auto_indent_width = int(self.get_auto_indent_from_syntax(syntax=syntax))\n\n ######################################################################\n # Log an error if parsing with `ignore_blank_lines=True` and\n # `factory=False`\n ######################################################################\n if ignore_blank_lines is True and factory is True:\n error = \"ignore_blank_lines and factory are not supported together.\"\n logger.error(error)\n raise NotImplementedError(error)\n\n ######################################################################\n # Reconfigure loguru if read_only is True\n ######################################################################\n if loguru is False:\n active_loguru_handlers = configure_loguru(read_only=loguru, active_handlers=globals()[\"ACTIVE_LOGURU_HANDLERS\"], debug=debug)\n globals()[\"ACTIVE_LOGURU_HANDLERS\"] = active_loguru_handlers\n if debug > 0:\n logger.warning(f\"Disabled loguru enqueue because loguru={loguru}\")\n\n if not (isinstance(syntax, str) and (syntax in ALL_VALID_SYNTAX)):\n error = f\"'{syntax}' is an unknown syntax\"\n logger.error(error)\n raise ValueError(error)\n\n # all IOSCfgLine object instances...\n self.finished_config_parse = False\n\n self.syntax = syntax\n self.encoding = encoding or ENCODING\n self.loguru = bool(loguru)\n self.comment_delimiters = comment_delimiters\n self.auto_indent_width = int(auto_indent_width)\n self.debug = int(debug)\n self.factory = bool(factory)\n self.linesplit_rgx = linesplit_rgx\n self.ignore_blank_lines = ignore_blank_lines\n self.auto_commit = auto_commit\n\n self.config_objs = None\n\n\n # Convert an None config into an empty list\n if config is None:\n config = []\n\n if len(config) > 0:\n try:\n correct_element_types = []\n for ii in config:\n # Check whether the elements are the correct types...\n if isinstance(ii, (str, BaseCfgLine)):\n correct_element_types.append(True)\n else:\n correct_element_types.append(False)\n\n elements_have_len = all(correct_element_types)\n except AttributeError:\n elements_have_len = False\n except TypeError:\n elements_have_len = False\n else:\n elements_have_len = None\n\n if elements_have_len is False:\n error = \"All ConfigList elements must have a length()\"\n logger.error(error)\n raise InvalidParameters(error)\n\n # Read the configuration lines and detect invalid inputs...\n # tmp_lines = self._get_ccp_lines(config=config, logger=logger)\n if isinstance(config, (str, pathlib.Path,)):\n tmp_lines = self.read_config_file(filepath=config, linesplit_rgx=r\"\\r*\\n\")\n elif isinstance(config, Sequence):\n tmp_lines = config\n else:\n error = f\"Cannot read config from {config}\"\n logger.critical(error)\n raise ValueError(error)\n\n # conditionally strip off junos-config braces and other syntax\n # parsing issues...\n config_lines = self.handle_ccp_brace_syntax(tmp_lines=tmp_lines, syntax=syntax)\n if self.check_ccp_input_good(config=config_lines, logger=logger) is False:\n error = f\"Cannot parse config=`{tmp_lines}`\"\n logger.critical(error)\n raise ValueError(error)\n\n if self.debug > 0:\n logger.info(\"assigning self.config_objs = ConfigList()\")\n\n self.config_objs = ConfigList(\n initlist=config_lines,\n comment_delimiters=comment_delimiters,\n debug=debug,\n factory=factory,\n ignore_blank_lines=ignore_blank_lines,\n syntax=syntax,\n ccp_ref=self,\n auto_commit=auto_commit,\n )\n\n ######################################################################\n # Set the commit checkpoint after the initial parse... this\n # avoids the need to manually call CiscoConfParse.commit()\n # after parsing\n ######################################################################\n self.commit()\n\n # IMPORTANT this MUST not be a lie :-)...\n self.finished_config_parse = True\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def handle_ccp_brace_syntax(self, tmp_lines: list=None, syntax: str=None) -> List[str]:\n \"\"\"Deal with brace-delimited syntax issues, such as conditionally discarding junos closing brace-lines.\n\n :param tmp_lines: Brace-delimited text configuration lines\n :type tmp_lines: List[str]\n :param syntax: Syntax of the configuration lines\n :type syntax: str\n :return: Configuration lines without braces\n :rtype: List[str]\n \"\"\"\n\n if syntax not in ALL_VALID_SYNTAX:\n error = f\"{syntax} parser factory is not yet enabled; use factory=False\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if not isinstance(tmp_lines, (list, tuple)):\n error = f\"handle_ccp_brace_syntax(tmp_lines={tmp_lines}) must not be None\"\n logger.error(error)\n raise InvalidParameters(error)\n\n ######################################################################\n # Explicitly handle all brace-parsing factory syntax here...\n ######################################################################\n if syntax == \"junos\":\n config_lines = convert_junos_to_ios(tmp_lines, comment_delimiters=[\"#\"])\n elif syntax in ALL_VALID_SYNTAX:\n config_lines = tmp_lines\n else:\n error = f\"handle_ccp_brace_syntax(syntax=`{syntax}`) is not yet supported\"\n logger.error(error)\n raise InvalidParameters(error)\n\n return config_lines\n\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def get_auto_indent_from_syntax(self, syntax: str=None) -> int:\n \"\"\"Return an auto indent for the 'syntax' string in question\n\n :param syntax: Syntax of the configuration lines\n :type syntax: str\n :return: Number of spaces for each indent level\n :rtype: int\n \"\"\"\n if not isinstance(syntax, str):\n error = \"The 'syntax' parameter must be a string\"\n logger.error(error)\n raise InvalidParameters(error)\n\n if syntax not in ALL_VALID_SYNTAX:\n error = f\"syntax='{syntax}' is not yet supported\"\n logger.error(error)\n raise InvalidParameters(error)\n\n indent_width = -1\n if syntax == \"ios\":\n indent_width = 1\n elif syntax == \"asa\":\n indent_width = 1\n elif syntax == \"iosxr\":\n indent_width = 1\n elif syntax == \"nxos\":\n indent_width = 2\n elif syntax == \"junos\":\n indent_width = 4\n else:\n error = \"Unexpected condition in get_auto_indent_from_syntax()\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n return int(indent_width)\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def __repr__(self) -> str:\n \"\"\"Return a string that represents this CiscoConfParse object instance. The number of lines embedded in the string is calculated from the length of the config_objs attribute.\n\n :return: A representation of this object.\n :rtype: str\n \"\"\"\n if self.config_objs is None:\n num_lines = 0\n elif isinstance(self.config_objs, Sequence):\n num_lines = len(self.config_objs)\n return (\n \"<CiscoConfParse: %s lines / syntax: %s / comment delimiters: %s / auto_indent_width: %s / factory: %s / ignore_blank_lines: %s / encoding: '%s' / auto_commit: %s>\"\n % (\n num_lines,\n self.syntax,\n self.comment_delimiters,\n self.auto_indent_width,\n self.factory,\n self.ignore_blank_lines,\n self.encoding,\n self.auto_commit,\n )\n )\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def read_config_file(self, filepath: str=None, linesplit_rgx: str=r\"\\r*\\n\") -> List[str]:\n \"\"\"Read the config lines from the filepath. Return the list of text configuration commands or raise an error.\n\n :param filepath: Filepath to be read\n :type filepath: str\n :param linesplit_rgx: Regex to use for line splits\n :type filepath: str\n :return: The output configuration\n :rtype: List[str]\n \"\"\"\n\n if self.finished_config_parse is not False:\n raise RequirementFailure()\n\n valid_path_variable = False\n if filepath is None:\n error = \"Filepath: None is invalid\"\n logger.critical(error)\n raise FileNotFoundError(error)\n elif isinstance(filepath, (str, pathlib.Path,)):\n valid_path_variable = True\n\n if valid_path_variable and not os.path.exists(filepath):\n error = f\"Filepath: {filepath} does not exist\"\n logger.critical(error)\n raise FileNotFoundError(error)\n\n config_lines = None\n\n _encoding = self.openargs['encoding']\n if valid_path_variable is True and os.path.isfile(filepath) is True:\n # config string - assume a filename...\n if self.debug > 0:\n logger.debug(f\"reading config from the filepath named '{filepath}'\")\n\n elif valid_path_variable is True and os.path.isfile(filepath) is False:\n if self.debug > 0:\n logger.debug(f\"filepath not found - '{filepath}'\")\n try:\n _ = open(file=filepath, **self.openargs)\n except FileNotFoundError:\n error = f\"\"\"FATAL - Attempted to open(file='{filepath}', mode='r', encoding=\"{_encoding}\"); the filepath named:\"{filepath}\" does not exist.\"\"\"\n logger.critical(error)\n raise FileNotFoundError(error)\n\n except OSError:\n error = f\"\"\"FATAL - Attempted to open(file='{filepath}', mode='r', encoding=\"{_encoding}\"); OSError opening \"{filepath}\".\"\"\"\n logger.critical(error)\n raise OSError(error)\n\n except BaseException:\n logger.critical(f\"Cannot open {filepath}\")\n raise BaseException\n\n else:\n error = f'Unexpected condition processing filepath: {filepath}'\n logger.critical(error)\n raise ValueError(error)\n\n # Read the file from disk and return the list of config statements...\n try:\n with open(file=filepath, **self.openargs) as fh:\n text = fh.read()\n rgx = re.compile(linesplit_rgx)\n config_lines = rgx.split(text)\n return config_lines\n\n except OSError:\n error = f\"CiscoConfParse could not open() the filepath named '{filepath}'\"\n logger.critical(error)\n raise OSError(error)\n\n except BaseException as eee:\n error = f\"FATAL - {eee}\"\n logger.critical(error)\n raise eee\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def check_ccp_input_good(self, config: Union[List[str],tuple[str, ...]]=None, logger: logger=None) -> bool:\n \"\"\"\n :param config: Sequence of commands\n :type config: Union[List[str], tuple[str, ...]]\n :param logger: loguru.logger() reference\n :type logger: loguru._logger.Logger\n :return: Whether the config can be parsed\n :rtype: bool\n \"\"\"\n\n if self.finished_config_parse is not False:\n raise RequirementFailure()\n\n if isinstance(config, Sequence):\n # Here we assume that `config` is a list of text config lines...\n #\n # config list of text lines...\n if self.debug > 0:\n logger.debug(\n f\"parsing config stored in the config variable: `{config}`\"\n )\n return True\n\n else:\n return False\n\n @property\n @logger.catch(reraise=True)\n def openargs(self) -> Dict[str,Union[str,None]]:\n \"\"\"\n Fix Py3.5 deprecation of universal newlines\n\n .. note::\n\n Ref original ciscoconfparse Github issue #114; also see\n https://softwareengineering.stackexchange.com/q/298677/23144.\n\n :return: The proper encoding parameters\n :rtype: Dict[str,Union[str,None]]\n \"\"\"\n if sys.version_info >= (\n 3,\n 6,\n ):\n retval = {\"mode\": \"r\", \"newline\": None, \"encoding\": self.encoding}\n else:\n retval = {\"mode\": \"rU\", \"encoding\": self.encoding}\n return retval\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def get_text(self) -> List[str]:\n \"\"\"\n :return: All text configuration statements\n :rtype: List[str]\n\n .. warning::\n\n The original ciscoconfparse ``ioscfg`@property has been renamed to ``get_text()``.\n \"\"\"\n return [ii.text for ii in self.config_objs]\n\n # This method is on CiscoConfParse()\n @property\n @logger.catch(reraise=True)\n def objs(self) -> List[BaseCfgLine]:\n \"\"\"CiscoConfParse().objs is an alias for the CiscoConfParse().config_objs property.\n\n :returns: All configuration objects.\n :rtype: List[BaseCfgLine]\n \"\"\"\n if self.config_objs is None:\n error = (\n \"config_objs is set to None. config_objs should be a ConfigList() of configuration-line objects\"\n )\n logger.error(error)\n raise ValueError(error)\n return self.config_objs\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def commit(self) -> None:\n \"\"\"Use :py:func:`~ciscoconfparse2.CiscoConfParse.commit` to manually fix up ``config_objs`` relationships after modifying a parsed configuration. This method is slow; try to batch calls to :func:`~ciscoconfparse2.CiscoConfParse.commit()` if possible.\n\n :return: None\n :rtype: None\n\n .. warning::\n\n If you modify a configuration after parsing it with :class:`~ciscoconfparse2.CiscoConfParse`,\n you *must* call :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` or\n :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` before searching the configuration\n again with methods such as :func:`~ciscoconfparse2.CiscoConfParse.find_objects`. Failure\n to call :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` or\n :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` on config modifications could\n lead to unexpected search results.\n \"\"\"\n\n # perform a commit on the ConfigList()\n self.config_objs.commit()\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def _find_child_object_branches(\n self,\n parent_obj: BaseCfgLine,\n childspec: str,\n regex_flags: str,\n debug: int=0,\n ) -> list:\n \"\"\"\n :param parent_obj: The parent object to be searched\n :type parent_obj: BaseCfgLine\n :param childspec: Regex string to match against child objects\n :type childspec: str\n :param regex_flags: Regex flags to apply to the aforementioned match\n :type regex_flags: str\n :param debug: Debug level of the operation\n :type debug: int\n :return: Children matching ``childspec``\n :rtype: List[BaseCfgLine]\n \"\"\"\n # I'm not using parent_obj.re_search_children() because\n # re_search_children() doesn't return None for no match...\n\n if debug > 1:\n msg = f\"\"\"Calling _find_child_object_branches(\nparent_obj={parent_obj},\nchildspec='{childspec}',\nregex_flags='{regex_flags}',\ndebug={debug},\n)\"\"\"\n logger.info(msg)\n\n # Get the child objects from parent objects\n if parent_obj is None:\n children = self._find_line_OBJ(\n linespec=childspec,\n exactmatch=False,\n )\n else:\n children = parent_obj.children\n\n # Find all child objects which match childspec...\n segment_list = [\n cobj\n for cobj in children\n if re.search(childspec, cobj.text, regex_flags)\n ]\n # Return [None] if no children matched...\n if len(segment_list) == 0:\n segment_list = [None]\n\n if debug > 1:\n logger.info(f\" _find_child_object_branches() returns segment_list={segment_list}\")\n return segment_list\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def find_object_branches(\n self,\n branchspec: Union[tuple[str, ...],List[str]]=(),\n regex_flags: Union[re.RegexFlag,int]=0,\n regex_groups: bool=False,\n empty_branches: bool=False,\n reverse: bool=False,\n debug: int=0,\n ) -> List[List[BaseCfgLine]]:\n r\"\"\"Iterate over a tuple of regular expression strings in `branchspec` and return matching objects in a list of lists (consider it similar to a table of matching config objects). `branchspec` expects to start at some ancestor and walk through the nested object hierarchy (with no limit on depth).\n\n Previous CiscoConfParse() methods only handled a single parent regex and single child regex (such as :func:`~ciscoconfparse2.CiscoConfParse.find_objects`).\n\n Transcend past one-level of parent-child relationship parsing to include multiple nested 'branches' of a single family (i.e. parents, children, grand-children, great-grand-children, etc). The result of handling longer regex chains is that it flattens what would otherwise be nested loops in your scripts; this makes parsing heavily-nested configuratations like Juniper, Palo-Alto, and F5 much simpler. Of course, there are plenty of applications for \"flatter\" config formats like IOS.\n\n Return a list of lists (of object 'branches') which are nested to the same depth required in `branchspec`. However, unlike most other CiscoConfParse() methods, return an explicit `None` if there is no object match. Returning `None` allows a single search over configs that may not be uniformly nested in every branch.\n\n .. warning::\n\n The ``allow_none`` from original ciscoconfparse is removed and no longer a configuration option; it will always be regarded as True.\n\n :param branchspec: Regular expressions to be matched.\n :type branchspec: Union[tuple[str, ...],List[str]]\n :param regex_flags: Chained regular expression flags, such as `re.IGNORECASE|re.MULTILINE`\n :type regex_flags: Union[re.RegexFlags,int]\n :param regex_groups: Return a tuple of re.Match groups instead of the matching configuration objects, default is False.\n :type regex_groups: bool\n :param empty_branches: If True, return a list of None statements if there is no match; before version 1.9.49, this defaulted True.\n :type empty_branches: bool\n :param reverse: If True, reverse the return value order.\n :type reverse: bool\n :param debug: Set > 0 for debug messages\n :type debug: int\n :return: A list of lists of matching :class:`~ciscoconfparse2.IOSCfgLine` objects\n :rtype: List[List[BaseCfgLine]]\n\n\n .. code-block:: python\n :emphasize-lines: 30,31\n\n >>> from operator import attrgetter\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = [\n ... 'ltm pool FOO {',\n ... ' members {',\n ... ' k8s-05.localdomain:8443 {',\n ... ' address 192.0.2.5',\n ... ' session monitor-enabled',\n ... ' state up',\n ... ' }',\n ... ' k8s-06.localdomain:8443 {',\n ... ' address 192.0.2.6',\n ... ' session monitor-enabled',\n ... ' state down',\n ... ' }',\n ... ' }',\n ... '}',\n ... 'ltm pool BAR {',\n ... ' members {',\n ... ' k8s-07.localdomain:8443 {',\n ... ' address 192.0.2.7',\n ... ' session monitor-enabled',\n ... ' state down',\n ... ' }',\n ... ' }',\n ... '}',\n ... ]\n >>> parse = CiscoConfParse(config=config, syntax='junos', comment='#')\n >>>\n >>> branchspec = (r'ltm\\spool', r'members', r'\\S+?:\\d+', r'state\\sup')\n >>> branches = parse.find_object_branches(branchspec=branchspec)\n >>>\n >>> # We found three branches\n >>> len(branches)\n 3\n >>> # Each branch must match the length of branchspec\n >>> len(branches[0])\n 4\n >>> # Print out one object 'branch'\n >>> branches[0]\n [<IOSCfgLine # 0 'ltm pool FOO'>, <IOSCfgLine # 1 ' members' (parent is # 0)>, <IOSCfgLine # 2 ' k8s-05.localdomain:8443' (parent is # 1)>, <IOSCfgLine # 5 ' state up' (parent is # 2)>]\n >>>\n >>> # Get the a list of text lines for this branch...\n >>> [ii.text for ii in branches[0]]\n ['ltm pool FOO', ' members', ' k8s-05.localdomain:8443', ' state up']\n >>>\n >>> # Get the config text of the root object of the branch...\n >>> branches[0][0].text\n 'ltm pool FOO'\n >>>\n >>> # Note: `None` in branches[1][-1] because of no regex match\n >>> branches[1]\n [<IOSCfgLine # 0 'ltm pool FOO'>, <IOSCfgLine # 1 ' members' (parent is # 0)>, <IOSCfgLine # 6 ' k8s-06.localdomain:8443' (parent is # 1)>, None]\n >>>\n >>> branches[2]\n [<IOSCfgLine # 10 'ltm pool BAR'>, <IOSCfgLine # 11 ' members' (parent is # 10)>, <IOSCfgLine # 12 ' k8s-07.localdomain:8443' (parent is # 11)>, None]\n \"\"\"\n if self.config_objs.search_safe is False:\n error = \"The configuration has changed since the last commit; a config search is not safe.\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n if isinstance(branchspec, list):\n branchspec = tuple(branchspec)\n\n if isinstance(branchspec, tuple):\n if branchspec == ():\n error = \"find_object_branches(): branchspec must not be empty\"\n logger.error(error)\n raise ValueError(error)\n\n else:\n error = \"find_object_branches(): Please enclose the branchspec regular expressions in a Python tuple\"\n logger.error(error)\n raise ValueError(error)\n\n branches = []\n # iterate over the regular expressions in branchspec\n for idx, childspec in enumerate(branchspec):\n # FIXME: Insert debugging here...\n if idx == 0:\n # Get matching 'root' objects from the config\n next_kids = self._find_child_object_branches(\n parent_obj=None,\n childspec=childspec,\n regex_flags=regex_flags,\n debug=debug,\n )\n # Start growing branches from the segments we received...\n branches = [[kid] for kid in next_kids]\n\n else:\n new_branches = []\n for branch in branches:\n # Extend existing branches into the new_branches\n if branch[-1] is not None:\n # Find children to extend the family branch...\n next_kids = self._find_child_object_branches(\n parent_obj=branch[-1],\n childspec=childspec,\n regex_flags=regex_flags,\n debug=debug,\n )\n\n for kid in next_kids:\n # Fork off a new branch and add each matching kid...\n tmp = copy.copy(branch)\n tmp.append(kid)\n new_branches.append(tmp)\n else:\n branch.append(None)\n new_branches.append(branch)\n\n # Ensure we have the most recent branches...\n branches = new_branches\n\n branches = new_branches\n\n # If regex_groups is True, assign regexp matches to the return matrix.\n if regex_groups is True:\n return_matrix = []\n # branchspec = (r\"^interfaces\", r\"\\s+(\\S+)\", r\"\\s+(unit\\s+\\d+)\", r\"family\\s+(inet)\", r\"address\\s+(\\S+)\")\n # for idx_matrix, row in enumerate(self.find_object_branches(branchspec)):\n for _, row in enumerate(branches):\n if not isinstance(row, Sequence):\n raise RequirementFailure()\n\n # Before we check regex capture groups, allocate an \"empty return_row\"\n # of the correct length...\n return_row = [(None,)] * len(branchspec)\n\n # Populate the return_row below...\n # return_row will be appended to return_matrix...\n for idx, element in enumerate(row):\n if element is None:\n return_row[idx] = (None,)\n\n else:\n regex_result = re.search(branchspec[idx], element.text)\n if regex_result is not None:\n # Save all the regex capture groups in matched_capture...\n matched_capture = regex_result.groups()\n if len(matched_capture) == 0:\n # If the branchspec groups() matches are a\n # zero-length tuple, populate this return_row\n # with the whole element's text\n return_row[idx] = (element.text,)\n else:\n # In this case, we found regex capture groups\n return_row[idx] = matched_capture\n else:\n # No regex capture groups b/c of no regex match...\n return_row[idx] = (None,)\n\n return_matrix.append(return_row)\n\n branches = return_matrix\n\n # We could have lost or created an extra branch if these aren't the\n # same length\n retval = list()\n if bool(empty_branches) is False:\n for branch in branches:\n ###############################################################\n # discard the branch if it contains None (element that did\n # not match)\n ###############################################################\n if not all(branch):\n continue\n retval.append(branch)\n else:\n retval = branches\n\n if reverse:\n retval.reverse()\n\n return retval\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def find_objects(self, linespec, exactmatch=False, ignore_ws=False, escape_chars=False, reverse=False):\n \"\"\"Find all :class:`~models_cisco.IOSCfgLine` objects whose text matches ``linespec`` and return the\n :class:`~models_cisco.IOSCfgLine` objects in a python list.\n\n :param linespec: A string or python regular expression, which should be matched\n :type linespec: Union[str,re.Pattern,BaseCfgLine]\n :param exactmatch: When set True, this option requires ``linespec`` match the whole configuration line, instead of a\n portion of the configuration line, default to False.\n :type exactmatch: str\n :param ignore_ws: Controls whether whitespace is ignored, default to False.\n :type ignore_ws: bool\n :param reverse: Controls whether the order of the results is reversed, default to False.\n :type reverse: bool\n :return: Matching :class:`~ciscoconfparse2.IOSCfgLine` objects.\n :rtype: List[BaseCfgLine]\n\n This example illustrates the use of :func:`~ciscoconfparse2.CiscoConfParse.find_objects`\n\n .. code-block:: python\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = [\n ... '!',\n ... 'interface Serial1/0',\n ... ' ip address 1.1.1.1 255.255.255.252',\n ... '!',\n ... 'interface Serial1/1',\n ... ' ip address 1.1.1.5 255.255.255.252',\n ... '!',\n ... ]\n >>> parse = CiscoConfParse(config=config)\n >>>\n >>> parse.find_objects(r'^interface')\n [<IOSCfgLine # 1 'interface Serial1/0'>, <IOSCfgLine # 4 'interface Serial1/1'>]\n >>>\n \"\"\"\n if isinstance(linespec, list):\n if len(linespec) == 1 and isinstance(linespec[0], (str, re.Pattern)):\n linespec = linespec[0]\n else:\n error = \"linespec list input must be exactly one string or compiled-regex long\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if escape_chars is True:\n ###################################################################\n # Escape regex to avoid embedded parenthesis problems\n ###################################################################\n linespec = re.escape(linespec)\n\n if self.config_objs.search_safe is False:\n error = \"The configuration has changed since the last commit; a config search is not safe.\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n if self.debug > 0:\n logger.info(\n \"find_objects('%s', exactmatch=%s) was called\" % (linespec, exactmatch),\n )\n\n if ignore_ws:\n linespec = build_space_tolerant_regex(linespec, encoding=self.encoding)\n\n if isinstance(linespec, str) or isinstance(linespec, re.Pattern):\n retval = self._find_line_OBJ(linespec, exactmatch)\n elif isinstance(linespec, BaseCfgLine):\n retval = list()\n for obj in self.objs:\n if obj == linespec:\n retval.append(obj)\n else:\n error = f\"linespec must be a string, re.Pattern, or BaseCfgLine instance; we got {type(linespec)}.\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if bool(reverse):\n retval.reverse()\n return retval\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def find_parent_objects(\n self,\n parentspec,\n childspec=None,\n ignore_ws=False,\n recurse=True,\n escape_chars=False,\n reverse=False,\n ):\n \"\"\"Return a list of parent :class:`~models_cisco.IOSCfgLine` objects,\n which matched the ``parentspec`` and whose children match ``childspec``.\n Only the parent :class:`~models_cisco.IOSCfgLine` objects will be\n returned.\n\n :param parentspec: Text regular expression for the :class:`~models_cisco.IOSCfgLine` object to be matched; this must match the parent's line\n :type parentspec: Union[str,List[str],tuple[str, ...]]\n :param childspec: Text regular expression for the child's configuration line\n :type childspec: str\n :param ignore_ws: boolean that controls whether whitespace is ignored\n :type ignore_ws: bool\n :param recurse: Set True if you want to search all children (children, grand children, great grand children, etc...). This is considered True if parentspec is a list or tuple.\n :type recurse: bool\n :param escape_chars: Set True if you want to escape characters before searching\n :type escape_chars: bool\n :param reverse: Set True if you want to reverse the order of the results\n :type reverse: bool\n :return: A list of matching parent :py:class:`~models_cisco.IOSCfgLine` objects\n :rtype: List[BaseCfgLine]\n\n .. warning::\n\n Do not set ``childspec`` if searching with a tuple of strings or list of strings.\n\n This example uses :py:meth:`~ciscoconfparse2.find_parent_objects()` to\n find all ports that are members of access vlan 300 in following\n config...\n\n .. parsed-literal::\n\n !\n interface FastEthernet0/1\n switchport access vlan 532\n spanning-tree vlan 532 cost 3\n !\n interface FastEthernet0/2\n switchport access vlan 300\n spanning-tree portfast\n !\n interface FastEthernet0/3\n duplex full\n speed 100\n switchport access vlan 300\n spanning-tree portfast\n !\n\n The following interfaces should be returned:\n\n .. parsed-literal::\n\n interface FastEthernet0/2\n interface FastEthernet0/3\n\n We do this by quering `find_objects_w_child()`; we set our\n parent as `^interface` and set the child as `switchport access\n vlan 300`.\n\n .. code-block:: python\n :emphasize-lines: 19,20\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = ['!',\n ... 'interface FastEthernet0/1',\n ... ' switchport access vlan 532',\n ... ' spanning-tree vlan 532 cost 3',\n ... '!',\n ... 'interface FastEthernet0/2',\n ... ' switchport access vlan 300',\n ... ' spanning-tree portfast',\n ... '!',\n ... 'interface FastEthernet0/3',\n ... ' duplex full',\n ... ' speed 100',\n ... ' switchport access vlan 300',\n ... ' spanning-tree portfast',\n ... '!',\n ... ]\n >>> p = CiscoConfParse(config=config)\n >>> p.find_parent_objects(['interface', 'vlan 300'])\n [<IOSCfgLine # 5 'interface FastEthernet0/2'>, <IOSCfgLine # 9 'interface FastEthernet0/3'>]\n >>>\n \"\"\"\n if self.config_objs.search_safe is False:\n error = \"The configuration has changed since the last commit; a config search is not safe.\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n if escape_chars is True:\n ###################################################################\n # Escape regex to avoid embedded parenthesis problems\n ###################################################################\n parentspec = re.escape(parentspec)\n childspec = re.escape(childspec)\n\n if isinstance(parentspec, BaseCfgLine):\n parentspec = parentspec.text\n elif isinstance(parentspec, str):\n pass\n elif isinstance(parentspec, (list, tuple)):\n _result = set()\n _tmp = self.find_object_branches(\n parentspec,\n )\n for _obj_branch in _tmp:\n # add the parent of that object branch to the result set\n _result.add(_obj_branch[0])\n\n if len(_result) == 0:\n ######################################################\n # If any _parentspec fails to match, we will hit this\n # condition when that failure happens.\n ######################################################\n return []\n else:\n # Sort and return the de-duplicated results\n return sorted(_result)\n else:\n error = f\"Received unexpected `parentspec` {type(parentspec)}\"\n logger.error(error)\n raise InvalidParameters(error)\n\n #######################################################################\n # Handle the case where parentspec is not a list or tuple\n #######################################################################\n if isinstance(childspec, BaseCfgLine):\n parentspec = childspec.text\n\n if ignore_ws:\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\n\n # Set escape_chars False to avoid double-escaping characters\n return list(\n filter(\n lambda x: x.re_search_children(childspec, recurse=recurse),\n self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse),\n ),\n )\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def find_parent_objects_wo_child(self, parentspec, childspec=None, ignore_ws=False, recurse=False, escape_chars=False, reverse=False):\n r\"\"\"Return a list of parent :class:`~models_cisco.IOSCfgLine` objects, which matched the ``parentspec`` and whose children did not match ``childspec``. Only the parent :class:`~models_cisco.IOSCfgLine` objects will be returned. For simplicity, this method only finds oldest_ancestors without immediate children that match.\n\n Parameters\n ----------\n parentspec : str\n Text regular expression for the :class:`~models_cisco.IOSCfgLine` object to be matched; this must match the parent's line\n childspec : str\n Text regular expression for the line to be matched; this must match the child's line\n ignore_ws : bool\n boolean that controls whether whitespace is ignored\n recurse : bool\n boolean that controls whether to recurse through children of children\n escape_chars : bool\n boolean that controls whether to escape characters before searching\n reverse : bool\n Set True if you want to reverse the order of the results\n\n Returns\n -------\n list\n A list of matching parent configuration lines\n\n Examples\n --------\n This example finds all ports that are autonegotiating in the following config...\n\n .. parsed-literal::\n\n !\n interface FastEthernet0/1\n switchport access vlan 532\n spanning-tree vlan 532 cost 3\n !\n interface FastEthernet0/2\n switchport access vlan 300\n spanning-tree portfast\n !\n interface FastEthernet0/2\n duplex full\n speed 100\n switchport access vlan 300\n spanning-tree portfast\n !\n\n The following interfaces should be returned:\n\n .. parsed-literal::\n\n interface FastEthernet0/1\n interface FastEthernet0/2\n\n We do this by quering ``find_parent_objects_wo_child()``; we set our\n parent as ``^interface`` and set the child as ``speed\\s\\d+`` (a\n regular-expression which matches the word 'speed' followed by\n an integer).\n\n .. code-block:: python\n :emphasize-lines: 19\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = ['!',\n ... 'interface FastEthernet0/1',\n ... ' switchport access vlan 532',\n ... ' spanning-tree vlan 532 cost 3',\n ... '!',\n ... 'interface FastEthernet0/2',\n ... ' switchport access vlan 300',\n ... ' spanning-tree portfast',\n ... '!',\n ... 'interface FastEthernet0/3',\n ... ' duplex full',\n ... ' speed 100',\n ... ' switchport access vlan 300',\n ... ' spanning-tree portfast',\n ... '!',\n ... ]\n >>> p = CiscoConfParse(config=config)\n >>> p.find_parent_objects_wo_child(r'^interface', r'speed\\s\\d+')\n [<IOSCfgLine # 1 'interface FastEthernet0/1'>, <IOSCfgLine # 5 'interface FastEthernet0/2'>]\n >>>\n \"\"\"\n if isinstance(parentspec, list):\n if len(parentspec) == 2 and isinstance(parentspec[0], (str, re.Pattern)) and isinstance(parentspec[1], (str, re.Pattern)):\n parentspec = parentspec[0]\n childspec = parentspec[1]\n else:\n error = \"list input must be exactly two strings or compiled-regex long\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if not isinstance(childspec, (str, re.Pattern)):\n error = \"childspec input must be a string or compiled-regex\"\n logger.critical(error)\n raise InvalidParameters(error)\n\n if self.config_objs.search_safe is False:\n error = \"The configuration has changed since the last commit; a config search is not safe.\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n if escape_chars is True:\n ###################################################################\n # Escape regex to avoid embedded parenthesis problems\n ###################################################################\n parentspec = re.escape(parentspec)\n childspec = re.escape(childspec)\n\n if isinstance(parentspec, BaseCfgLine):\n parentspec = parentspec.text\n elif isinstance(parentspec, (list, tuple)):\n ##################################################################\n # Catch unsupported parentspec type here\n ##################################################################\n error = f\"find_parent_objects_wo_child() `parentspec` does not support a {type(parentspec)}\"\n logger.error(error)\n raise InvalidParameters(error)\n if isinstance(childspec, BaseCfgLine):\n parentspec = childspec.text\n\n if ignore_ws is True:\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\n\n\n # Set escape_chars False to avoid double-escaping chars\n return [\n obj\n for obj in self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse)\n if not obj.re_search_children(childspec, recurse=recurse)\n ]\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def find_child_objects(\n self,\n parentspec,\n childspec=None,\n ignore_ws=False,\n recurse=True,\n escape_chars=False,\n reverse=False,\n ):\n r\"\"\"Parse through the children of all parents matching parentspec,\n and return a list of child objects, which matched the childspec.\n\n :param parentspec: Text regular expression for the parent's configuration line. A list is preferred.\n :type parentspec: Union[str, List[str], tuple[str, ...]]\n :param childspec: Text regular expression for the child's configuration line.\n :type parentspec: str\n :param ignore_ws: Ignore whitespace, default to False\n :type ignore_ws: bool\n :param recurse: Control whether to recurse in the config, default to True.\n :type recurse: bool\n :param escape_chars: Controls whether characters are escaped before searching, default to False.\n :type escape_chars: bool\n :param reverse: Controls whether results are reversed; set True if modifying the configuration with these results.\n :type reverse: bool\n :return: Matching child objects\n :rtype: List[BaseCfgLine]\n\n .. warning::\n\n Do not set ``childspec`` if searching with a tuple of strings or list of strings.\n\n This example finds the object for \"ge-0/0/0\" under \"interfaces\" in the\n following config...\n\n .. parsed-literal::\n\n interfaces\n ge-0/0/0\n unit 0\n family ethernet-switching\n port-mode access\n vlan\n members VLAN_FOO\n ge-0/0/1\n unit 0\n family ethernet-switching\n port-mode trunk\n vlan\n members all\n native-vlan-id 1\n vlan\n unit 0\n family inet\n address 172.16.15.5/22\n\n\n The following object should be returned:\n\n .. parsed-literal::\n\n <IOSCfgLine # 7 ' ge-0/0/1' (parent is # 0)>\n\n We do this by quering `find_child_objects()`; we set our\n parent as `^\\s*interface` and set the child as\n `^\\s+ge-0/0/1`.\n\n .. code-block:: python\n :emphasize-lines: 22,23\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = ['interfaces',\n ... ' ge-0/0/0',\n ... ' unit 0',\n ... ' family ethernet-switching',\n ... ' port-mode access',\n ... ' vlan',\n ... ' members VLAN_FOO',\n ... ' ge-0/0/1',\n ... ' unit 0',\n ... ' family ethernet-switching',\n ... ' port-mode trunk',\n ... ' vlan',\n ... ' members all',\n ... ' native-vlan-id 1',\n ... ' vlan',\n ... ' unit 0',\n ... ' family inet',\n ... ' address 172.16.15.5/22',\n ... ]\n >>> p = CiscoConfParse(config=config)\n >>> p.find_child_objects(['interface', r'ge-0/0/1'])\n [<IOSCfgLine # 7 ' ge-0/0/1' (parent is # 0)>]\n >>>\n \"\"\"\n if self.config_objs.search_safe is False:\n error = \"The configuration has changed since the last commit; a config search is not safe.\"\n logger.critical(error)\n raise NotImplementedError(error)\n\n if escape_chars is True:\n ###################################################################\n # Escape regex to avoid embedded parenthesis problems\n ###################################################################\n parentspec = re.escape(parentspec)\n childspec = re.escape(childspec)\n\n if isinstance(parentspec, BaseCfgLine):\n parentspec = parentspec.text\n elif isinstance(parentspec, str):\n pass\n elif isinstance(parentspec, (list, tuple)):\n if len(parentspec) > 0:\n _result = set()\n _tmp = self.find_object_branches(\n parentspec,\n )\n for _obj_branch in _tmp:\n # add the child of that object branch to the result set\n _result.add(_obj_branch[-1])\n\n if len(_result) == 0:\n ######################################################\n # If any _childspec fails to match, we will hit this\n # condition when that failure happens.\n ######################################################\n return []\n # Sort the de-duplicated results\n return sorted(_result)\n else:\n error = f\"`parentspec` {type(parentspec)} must have at least one element.\"\n logger.error(error)\n raise InvalidParameters(error)\n else:\n error = f\"Received unexpected `parentspec` {type(parentspec)}\"\n logger.error(error)\n raise InvalidParameters(error)\n\n #######################################################################\n # Handle the case where parentspec is not a list or tuple\n #######################################################################\n if isinstance(childspec, BaseCfgLine):\n parentspec = childspec.text\n\n if ignore_ws:\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\n\n retval = set()\n # Set escape_chars False to avoid double-escaping characters\n parents = self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse)\n if recurse is False:\n for parent in parents:\n ##############################################################\n # If recurse is False, only search direct children\n ##############################################################\n for child in parent.children:\n if child.re_match(rf\"({childspec})\", default=False):\n retval.add(child)\n else:\n for parent in parents:\n ##############################################################\n # If recurse is True, search all children including children\n # of the children\n ##############################################################\n for child in parent.all_children:\n if child.re_match(rf\"({childspec})\", default=False):\n retval.add(child)\n\n return sorted(retval)\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def re_search_children(self, regexspec, recurse=False):\n \"\"\"Use ``regexspec`` to search for root parents in the config with text matching regex. If `recurse` is False, only root parent objects are returned. A list of matching objects is returned.\n\n This method is very similar to :func:`~ciscoconfparse2.CiscoConfParse.find_objects` (when `recurse` is True); however it was written in response to the use-case described in `Github Issue #156 <https://github.com/mpenning/ciscoconfparse/issues/156>`_.\n\n Parameters\n ----------\n regexspec : str\n A string or python regular expression, which should be matched.\n recurse : bool\n Set True if you want to search all objects, and not just the root parents\n\n Returns\n -------\n list\n A list of matching :class:`~models_cisco.IOSCfgLine` objects which matched. If there is no match, an empty :py:func:`list` is returned.\n\n \"\"\"\n ## I implemented this method in response to Github issue #156\n if recurse is False:\n # Only return the matching oldest ancestor objects...\n return [obj for obj in self.find_objects(regexspec) if (obj.parent is obj)]\n else:\n # Return any matching object\n return [obj for obj in self.find_objects(regexspec)]\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def re_match_iter_typed(\n self,\n regexspec,\n group=1,\n result_type=str,\n default=\"\",\n untyped_default=False,\n ):\n r\"\"\"Use ``regexspec`` to search the root parents in the config\n and return the contents of the regular expression group, at the\n integer ``group`` index, cast as ``result_type``; if there is no\n match, ``default`` is returned.\n\n Note\n ----\n Only the first regex match is returned.\n\n Parameters\n ----------\n regexspec : str\n A string or python compiled regular expression, which should be matched. This regular expression should contain parenthesis, which bound a match group.\n group : int\n An integer which specifies the desired regex group to be returned. ``group`` defaults to 1.\n result_type : type\n A type (typically one of: ``str``, ``int``, ``float``, or :class:`~ccp_util.IPv4Obj`). All returned values are cast as ``result_type``, which defaults to ``str``.\n default : any\n The default value to be returned, if there is no match. The default is an empty string.\n untyped_default : bool\n Set True if you don't want the default value to be typed\n\n Returns\n -------\n ``result_type``\n The text matched by the regular expression group; if there is no match, ``default`` is returned. All values are cast as ``result_type``. The default result_type is `str`.\n\n\n Examples\n --------\n This example illustrates how you can use\n :func:`~ciscoconfparse2.re_match_iter_typed` to get the\n first interface name listed in the config.\n\n >>> import re\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = [\n ... '!',\n ... 'interface Serial1/0',\n ... ' ip address 1.1.1.1 255.255.255.252',\n ... '!',\n ... 'interface Serial2/0',\n ... ' ip address 1.1.1.5 255.255.255.252',\n ... '!',\n ... ]\n >>> parse = CiscoConfParse(config=config)\n >>> parse.re_match_iter_typed(r'interface\\s(\\S+)')\n 'Serial1/0'\n >>>\n\n The following example retrieves the hostname from the configuration\n\n >>> from ciscoconfparse2 import CiscoConfParse\n >>> config = [\n ... '!',\n ... 'hostname DEN-EDGE-01',\n ... '!',\n ... 'interface Serial1/0',\n ... ' ip address 1.1.1.1 255.255.255.252',\n ... '!',\n ... 'interface Serial2/0',\n ... ' ip address 1.1.1.5 255.255.255.252',\n ... '!',\n ... ]\n >>> parse = CiscoConfParse(config=config)\n >>> parse.re_match_iter_typed(r'^hostname\\s+(\\S+)')\n 'DEN-EDGE-01'\n >>>\n\n \"\"\"\n ## iterate through root objects, and return the matching value\n ## (cast as result_type) from the first object.text that matches regex\n\n # if (default is True):\n ## Not using self.re_match_iter_typed(default=True), because I want\n ## to be sure I build the correct API for match=False\n ##\n ## Ref IOSIntfLine.has_dtp for an example of how to code around\n ## this while I build the API\n # raise NotImplementedError\n\n for cobj in self.config_objs:\n # Only process parent objects at the root of the tree...\n if cobj.parent is not cobj:\n continue\n\n mm = re.search(regexspec, cobj.text)\n if mm is not None:\n return result_type(mm.group(group))\n ## Ref Github issue #121\n if untyped_default:\n return default\n else:\n return result_type(default)\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def save_as(self, filepath):\n \"\"\"Save a text copy of the configuration at ``filepath``; this\n method uses the OperatingSystem's native line separators (such as\n ``\\\\r\\\\n`` in Windows).\"\"\"\n try:\n with open(filepath, \"w\", encoding=self.encoding) as newconf:\n for line in self.as_text:\n newconf.write(line + \"\\n\")\n return True\n except BaseException as ee:\n logger.error(str(ee))\n raise ee\n\n ### The methods below are marked SEMI-PRIVATE because they return an object\n ### or iterable of objects instead of the configuration text itself.\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def _find_line_OBJ(self, linespec, exactmatch=False):\n \"\"\"SEMI-PRIVATE: Find objects whose text matches the linespec\"\"\"\n\n if self.config_objs is None:\n err = \"config_objs is None. self.config_objs logic failed.\"\n raise ValueError(err)\n\n if self.debug >= 2:\n logger.debug(\n \"Looking for match of linespec='%s', exactmatch=%s\"\n % (linespec, exactmatch),\n )\n\n # NOTE TO SELF: do not remove _find_line_OBJ(); used by Cisco employees\n if not exactmatch:\n # Return objects whose text attribute matches linespec\n linespec_re = re.compile(linespec)\n elif exactmatch:\n # Return objects whose text attribute matches linespec exactly\n linespec_re = re.compile(\"^%s$\" % linespec)\n\n return list(\n filter(lambda obj: linespec_re.search(obj.text), self.config_objs),\n )\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def _find_sibling_OBJ(self, lineobject):\n \"\"\"SEMI-PRIVATE: Takes a singe object and returns a list of sibling\n objects\"\"\"\n siblings = lineobject.parent.children\n return siblings\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def _find_all_child_OBJ(self, lineobject):\n \"\"\"SEMI-PRIVATE: Takes a single object and returns a list of\n decendants in all 'children' / 'grandchildren' / etc... after it.\n It should NOT return the children of siblings\"\"\"\n # sort the list, and get unique objects\n retval = set(lineobject.children)\n for candidate in lineobject.children:\n if candidate.has_children:\n for child in candidate.children:\n retval.add(child)\n retval = sorted(retval)\n return retval\n\n # This method is on CiscoConfParse()\n @logger.catch(reraise=True)\n def _unique_OBJ(self, objectlist):\n \"\"\"SEMI-PRIVATE: Returns a list of unique objects (i.e. with no\n duplicates).\n The returned value is sorted by configuration line number\n (lowest first)\"\"\"\n retval = set()\n for obj in objectlist:\n retval.add(obj)\n return sorted(retval)"},{"identifier":"PythonOptimizeCheck","path":"ciscoconfparse2/ccp_util.py","snippet":"class PythonOptimizeCheck(object):\n \"\"\"\n Check if we're running under \"python -O ...\". The -O option removes\n all `assert` statements at runtime. ciscoconfparse2 depends heavily on\n `assert` and running ciscoconfparse2 under python -O is a really bad idea.\n\n __debug__ is True unless run with `python -O ...`. __debug__ is False\n under `python -O ...`.\n\n Also throw an error if PYTHONOPTIMIZE is set in the windows or unix shell.\n\n This class should be run in <module_name_dir>/__init__.py.\n\n This condition is not unique to ciscoconfparse2.\n\n Simple usage (in __init__.py):\n ------------------------------\n\n # Handle PYTHONOPTIMIZE problems...\n from ciscoconfparse2.ccp_util import PythonOptimizeCheck\n _ = PythonOptimizeCheck()\n\n\n \"\"\"\n @logger.catch(reraise=True)\n def __init__(self):\n\n self.PYTHONOPTIMIZE_env_value = os.environ.get(\"PYTHONOPTIMIZE\", None)\n\n error = \"__no_error__\"\n try:\n # PYTHONOPTIMIZE is not supported... in the linux shell\n # disable it with `unset PYTHONOPTIMIZE`\n if isinstance(self.PYTHONOPTIMIZE_env_value, str) and self.PYTHONOPTIMIZE_env_value.strip() != \"\":\n # This condition explicitly allows PYTHONOPTIMIZE=\"\", which\n # is not a problem.\n error = \"Your environment has PYTHONOPTIMIZE set. ciscoconfparse2 doesn't support running under PYTHONOPTIMIZE.\"\n # PYTHONOPTIMIZE is not supported... in the linux shell\n # disable it with `unset PYTHONOPTIMIZE`\n elif self.PYTHONOPTIMIZE_env_value is not None:\n error = \"Your environment has PYTHONOPTIMIZE set. ciscoconfparse2 doesn't support running under PYTHONOPTIMIZE.\"\n # Throw an error if we're running under `python -O`. `python -O` is not supported\n # We should keep the __debug__ check for `-O` at the end, otherwise it\n # masks identifying problems with PYTHONOPTIMIZE set in the shell...\n elif __debug__ is False:\n # Running under 'python -O'\n error = \"You're using `python -O`. Please don't. ciscoconfparse2 doesn't support `python -O`\"\n\n else:\n # whew...\n pass\n\n except Exception as exception_info:\n print(\"exception_info\", str(exception_info))\n raise RuntimeError(\"Something bad happened in PYTHONOPTIMIZE checks. Please report this problem as a ciscoconfparse2 bug\")\n\n if error != \"__no_error__\":\n raise PythonOptimizeException(error)"}],"string":"[\n {\n \"identifier\": \"CiscoConfParse\",\n \"path\": \"ciscoconfparse2/ciscoconfparse2.py\",\n \"snippet\": \"class CiscoConfParse(object):\\n \\\"\\\"\\\"Parse Cisco IOS configurations and answer queries about the configs.\\\"\\\"\\\"\\n config: Optional[Union[str,List[str]]] = None\\n syntax: str = \\\"ios\\\"\\n encoding: str = locale.getpreferredencoding()\\n loguru: bool = True\\n comment_delimiters: List[str] = []\\n auto_indent_width: int = -1\\n linesplit_rgx: str = r\\\"\\\\r*\\\\n\\\"\\n ignore_blank_lines: bool = False\\n auto_commit: bool = None\\n factory: bool = False\\n debug: int = 0\\n\\n # Attributes\\n config_objs: Any = None\\n finished_config_parse: bool = False\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def __init__(\\n self,\\n config: Optional[Union[str,List[str],tuple[str, ...]]]=None,\\n syntax: str=\\\"ios\\\",\\n encoding: str=locale.getpreferredencoding(),\\n loguru: bool=True,\\n comment_delimiters: List[str]=None,\\n auto_indent_width: int=-1,\\n linesplit_rgx: str=r\\\"\\\\r*\\\\n\\\",\\n ignore_blank_lines: bool=False,\\n auto_commit: bool=True,\\n factory: bool=False,\\n debug: int=0,\\n ):\\n \\\"\\\"\\\"\\n Initialize CiscoConfParse.\\n\\n .. note::\\n\\n ``comment_delimiters`` always assumes the delimiter is one character wide.\\n\\n .. note::\\n\\n ``ignore_blank_lines`` changes the original ciscoconfparse default value.\\n\\n\\n :param config: A list of configuration lines or the filepath to the configuration.\\n :type config: Union[str,List[str],tuple[str, ...]]\\n :param syntax: The configuration type, default to 'ios'; it must be one of: 'ios', 'nxos', 'iosxr', 'asa', 'junos'. Use 'junos' for any brace-delimited network configuration (including F5, Palo Alto, etc...).\\n :type syntax: str\\n :param encoding: The configuration encoding, default to ``locale.getpreferredencoding()``.\\n :type encoding: str\\n :param loguru: Control whether CiscoConfParse should enable ``loguru``, default to True.\\n :type loguru: bool\\n :param comment_delimiters: String comment delimiters. This should only be changed when parsing non-Cisco configurations, which do not use a '!' as the comment delimiter. ``comment`` defaults to '!'. This value can hold multiple characters in case the config uses multiple characters for comment delimiters.\\n :type comment_delimiters: List[str]\\n :param auto_indent_width: Defaults to -1, and should be kept that way unless you're working on a very tricky config parsing problem.\\n :type auto_indent_width: int\\n :param linesplit_rgx: Used when parsing configuration files to find\\n where new configuration lines are; it is best\\n to leave this as the default, unless you're\\n working on a system that uses unusual line\\n terminations (for instance something besides\\n Unix, OSX, or Windows).\\n :type linesplit_rgx: str\\n :param ignore_blank_lines: Defaults to False; when this is set True,\\n ciscoconfparse2 ignores blank configuration\\n lines.\\n :type ignore_blank_lines: bool\\n :param auto_commit: Control whether CiscoConfParse should auto-commit config changes when possible, default to True.\\n However, parsing very large configs may be faster with ``auto_commit=False``.\\n :type auto_commit: bool\\n :param factory: Control whether CiscoConfParse should enable the\\n beta-quality configuration parameter parser,\\n default to False.\\n :type factory: bool\\n :param debug: Control CiscoConfParse debug output, default is 0.\\n :type debug: int\\n :return: A CiscoConfParse object\\n :rtype: :py:class:`~ciscoconfparse2.CiscoConfParse`\\n\\n This example illustrates how to parse a simple Cisco IOS configuration\\n with :class:`~ciscoconfparse2.CiscoConfParse` into a variable called\\n ``parse``. This example also illustrates what the ``config_objs``\\n and ``ioscfg`` attributes contain.\\n\\n .. code-block:: python\\n :emphasize-lines: 6\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = [\\n ... 'logging trap debugging',\\n ... 'logging 172.28.26.15',\\n ... ]\\n >>> parse = CiscoConfParse(config=config)\\n >>> parse\\n <CiscoConfParse: 2 lines / syntax: ios / comment delimiter: '!' / factory: False>\\n >>> parse.config_objs\\n <ConfigList, comment='!', conf=[<IOSCfgLine # 0 'logging trap debugging'>, <IOSCfgLine # 1 'logging 172.28.26.15'>]>\\n >>> parse.text\\n ['logging trap debugging', 'logging 172.28.26.15']\\n >>>\\n\\n Attributes\\n ----------\\n comment_delimiters : list\\n A list of strings containing the comment-delimiters. Default: [\\\"!\\\"]\\n objs : :class:`ConfigList`\\n An alias for ``config_objs``\\n config_objs : :class:`ConfigList`\\n A custom list, which contains all parsed :class:`~models_cisco.IOSCfgLine` instances.\\n debug : int\\n An int to enable verbose config parsing debugs. Default 0.\\n ioscfg : list\\n A list of text configuration strings\\n openargs : dict\\n Returns a dictionary of valid arguments for `open()` (these change based on the running python version).\\n syntax : str\\n A string holding the configuration type. Default: 'ios'. Must be one of: 'ios', 'nxos', 'iosxr', 'asa', 'junos'. Use 'junos' for any brace-delimited network configuration (including F5, Palo Alto, etc...).\\n\\n\\n \\\"\\\"\\\"\\n\\n if syntax not in ALL_VALID_SYNTAX:\\n error = f\\\"{syntax} is not a valid syntax.\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n if comment_delimiters is None:\\n comment_delimiters = get_comment_delimiters(syntax=syntax)\\n elif isinstance(comment_delimiters, list):\\n for comment_delimiter in comment_delimiters:\\n if not isinstance(comment_delimiter, str):\\n error = f\\\"`{comment_delimiter}` is not a valid string comment_delimiters\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n elif not len(comment_delimiter) == 1:\\n error = f\\\"`{comment_delimiter}` must be a single string character.\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n elif not isinstance(comment_delimiters, list):\\n error = \\\"'comment_delimiters' must be a list of string comment delimiters\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if int(auto_indent_width) <= 0:\\n auto_indent_width = int(self.get_auto_indent_from_syntax(syntax=syntax))\\n\\n ######################################################################\\n # Log an error if parsing with `ignore_blank_lines=True` and\\n # `factory=False`\\n ######################################################################\\n if ignore_blank_lines is True and factory is True:\\n error = \\\"ignore_blank_lines and factory are not supported together.\\\"\\n logger.error(error)\\n raise NotImplementedError(error)\\n\\n ######################################################################\\n # Reconfigure loguru if read_only is True\\n ######################################################################\\n if loguru is False:\\n active_loguru_handlers = configure_loguru(read_only=loguru, active_handlers=globals()[\\\"ACTIVE_LOGURU_HANDLERS\\\"], debug=debug)\\n globals()[\\\"ACTIVE_LOGURU_HANDLERS\\\"] = active_loguru_handlers\\n if debug > 0:\\n logger.warning(f\\\"Disabled loguru enqueue because loguru={loguru}\\\")\\n\\n if not (isinstance(syntax, str) and (syntax in ALL_VALID_SYNTAX)):\\n error = f\\\"'{syntax}' is an unknown syntax\\\"\\n logger.error(error)\\n raise ValueError(error)\\n\\n # all IOSCfgLine object instances...\\n self.finished_config_parse = False\\n\\n self.syntax = syntax\\n self.encoding = encoding or ENCODING\\n self.loguru = bool(loguru)\\n self.comment_delimiters = comment_delimiters\\n self.auto_indent_width = int(auto_indent_width)\\n self.debug = int(debug)\\n self.factory = bool(factory)\\n self.linesplit_rgx = linesplit_rgx\\n self.ignore_blank_lines = ignore_blank_lines\\n self.auto_commit = auto_commit\\n\\n self.config_objs = None\\n\\n\\n # Convert an None config into an empty list\\n if config is None:\\n config = []\\n\\n if len(config) > 0:\\n try:\\n correct_element_types = []\\n for ii in config:\\n # Check whether the elements are the correct types...\\n if isinstance(ii, (str, BaseCfgLine)):\\n correct_element_types.append(True)\\n else:\\n correct_element_types.append(False)\\n\\n elements_have_len = all(correct_element_types)\\n except AttributeError:\\n elements_have_len = False\\n except TypeError:\\n elements_have_len = False\\n else:\\n elements_have_len = None\\n\\n if elements_have_len is False:\\n error = \\\"All ConfigList elements must have a length()\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n # Read the configuration lines and detect invalid inputs...\\n # tmp_lines = self._get_ccp_lines(config=config, logger=logger)\\n if isinstance(config, (str, pathlib.Path,)):\\n tmp_lines = self.read_config_file(filepath=config, linesplit_rgx=r\\\"\\\\r*\\\\n\\\")\\n elif isinstance(config, Sequence):\\n tmp_lines = config\\n else:\\n error = f\\\"Cannot read config from {config}\\\"\\n logger.critical(error)\\n raise ValueError(error)\\n\\n # conditionally strip off junos-config braces and other syntax\\n # parsing issues...\\n config_lines = self.handle_ccp_brace_syntax(tmp_lines=tmp_lines, syntax=syntax)\\n if self.check_ccp_input_good(config=config_lines, logger=logger) is False:\\n error = f\\\"Cannot parse config=`{tmp_lines}`\\\"\\n logger.critical(error)\\n raise ValueError(error)\\n\\n if self.debug > 0:\\n logger.info(\\\"assigning self.config_objs = ConfigList()\\\")\\n\\n self.config_objs = ConfigList(\\n initlist=config_lines,\\n comment_delimiters=comment_delimiters,\\n debug=debug,\\n factory=factory,\\n ignore_blank_lines=ignore_blank_lines,\\n syntax=syntax,\\n ccp_ref=self,\\n auto_commit=auto_commit,\\n )\\n\\n ######################################################################\\n # Set the commit checkpoint after the initial parse... this\\n # avoids the need to manually call CiscoConfParse.commit()\\n # after parsing\\n ######################################################################\\n self.commit()\\n\\n # IMPORTANT this MUST not be a lie :-)...\\n self.finished_config_parse = True\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def handle_ccp_brace_syntax(self, tmp_lines: list=None, syntax: str=None) -> List[str]:\\n \\\"\\\"\\\"Deal with brace-delimited syntax issues, such as conditionally discarding junos closing brace-lines.\\n\\n :param tmp_lines: Brace-delimited text configuration lines\\n :type tmp_lines: List[str]\\n :param syntax: Syntax of the configuration lines\\n :type syntax: str\\n :return: Configuration lines without braces\\n :rtype: List[str]\\n \\\"\\\"\\\"\\n\\n if syntax not in ALL_VALID_SYNTAX:\\n error = f\\\"{syntax} parser factory is not yet enabled; use factory=False\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if not isinstance(tmp_lines, (list, tuple)):\\n error = f\\\"handle_ccp_brace_syntax(tmp_lines={tmp_lines}) must not be None\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n ######################################################################\\n # Explicitly handle all brace-parsing factory syntax here...\\n ######################################################################\\n if syntax == \\\"junos\\\":\\n config_lines = convert_junos_to_ios(tmp_lines, comment_delimiters=[\\\"#\\\"])\\n elif syntax in ALL_VALID_SYNTAX:\\n config_lines = tmp_lines\\n else:\\n error = f\\\"handle_ccp_brace_syntax(syntax=`{syntax}`) is not yet supported\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n return config_lines\\n\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def get_auto_indent_from_syntax(self, syntax: str=None) -> int:\\n \\\"\\\"\\\"Return an auto indent for the 'syntax' string in question\\n\\n :param syntax: Syntax of the configuration lines\\n :type syntax: str\\n :return: Number of spaces for each indent level\\n :rtype: int\\n \\\"\\\"\\\"\\n if not isinstance(syntax, str):\\n error = \\\"The 'syntax' parameter must be a string\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n if syntax not in ALL_VALID_SYNTAX:\\n error = f\\\"syntax='{syntax}' is not yet supported\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n indent_width = -1\\n if syntax == \\\"ios\\\":\\n indent_width = 1\\n elif syntax == \\\"asa\\\":\\n indent_width = 1\\n elif syntax == \\\"iosxr\\\":\\n indent_width = 1\\n elif syntax == \\\"nxos\\\":\\n indent_width = 2\\n elif syntax == \\\"junos\\\":\\n indent_width = 4\\n else:\\n error = \\\"Unexpected condition in get_auto_indent_from_syntax()\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n return int(indent_width)\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def __repr__(self) -> str:\\n \\\"\\\"\\\"Return a string that represents this CiscoConfParse object instance. The number of lines embedded in the string is calculated from the length of the config_objs attribute.\\n\\n :return: A representation of this object.\\n :rtype: str\\n \\\"\\\"\\\"\\n if self.config_objs is None:\\n num_lines = 0\\n elif isinstance(self.config_objs, Sequence):\\n num_lines = len(self.config_objs)\\n return (\\n \\\"<CiscoConfParse: %s lines / syntax: %s / comment delimiters: %s / auto_indent_width: %s / factory: %s / ignore_blank_lines: %s / encoding: '%s' / auto_commit: %s>\\\"\\n % (\\n num_lines,\\n self.syntax,\\n self.comment_delimiters,\\n self.auto_indent_width,\\n self.factory,\\n self.ignore_blank_lines,\\n self.encoding,\\n self.auto_commit,\\n )\\n )\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def read_config_file(self, filepath: str=None, linesplit_rgx: str=r\\\"\\\\r*\\\\n\\\") -> List[str]:\\n \\\"\\\"\\\"Read the config lines from the filepath. Return the list of text configuration commands or raise an error.\\n\\n :param filepath: Filepath to be read\\n :type filepath: str\\n :param linesplit_rgx: Regex to use for line splits\\n :type filepath: str\\n :return: The output configuration\\n :rtype: List[str]\\n \\\"\\\"\\\"\\n\\n if self.finished_config_parse is not False:\\n raise RequirementFailure()\\n\\n valid_path_variable = False\\n if filepath is None:\\n error = \\\"Filepath: None is invalid\\\"\\n logger.critical(error)\\n raise FileNotFoundError(error)\\n elif isinstance(filepath, (str, pathlib.Path,)):\\n valid_path_variable = True\\n\\n if valid_path_variable and not os.path.exists(filepath):\\n error = f\\\"Filepath: {filepath} does not exist\\\"\\n logger.critical(error)\\n raise FileNotFoundError(error)\\n\\n config_lines = None\\n\\n _encoding = self.openargs['encoding']\\n if valid_path_variable is True and os.path.isfile(filepath) is True:\\n # config string - assume a filename...\\n if self.debug > 0:\\n logger.debug(f\\\"reading config from the filepath named '{filepath}'\\\")\\n\\n elif valid_path_variable is True and os.path.isfile(filepath) is False:\\n if self.debug > 0:\\n logger.debug(f\\\"filepath not found - '{filepath}'\\\")\\n try:\\n _ = open(file=filepath, **self.openargs)\\n except FileNotFoundError:\\n error = f\\\"\\\"\\\"FATAL - Attempted to open(file='{filepath}', mode='r', encoding=\\\"{_encoding}\\\"); the filepath named:\\\"{filepath}\\\" does not exist.\\\"\\\"\\\"\\n logger.critical(error)\\n raise FileNotFoundError(error)\\n\\n except OSError:\\n error = f\\\"\\\"\\\"FATAL - Attempted to open(file='{filepath}', mode='r', encoding=\\\"{_encoding}\\\"); OSError opening \\\"{filepath}\\\".\\\"\\\"\\\"\\n logger.critical(error)\\n raise OSError(error)\\n\\n except BaseException:\\n logger.critical(f\\\"Cannot open {filepath}\\\")\\n raise BaseException\\n\\n else:\\n error = f'Unexpected condition processing filepath: {filepath}'\\n logger.critical(error)\\n raise ValueError(error)\\n\\n # Read the file from disk and return the list of config statements...\\n try:\\n with open(file=filepath, **self.openargs) as fh:\\n text = fh.read()\\n rgx = re.compile(linesplit_rgx)\\n config_lines = rgx.split(text)\\n return config_lines\\n\\n except OSError:\\n error = f\\\"CiscoConfParse could not open() the filepath named '{filepath}'\\\"\\n logger.critical(error)\\n raise OSError(error)\\n\\n except BaseException as eee:\\n error = f\\\"FATAL - {eee}\\\"\\n logger.critical(error)\\n raise eee\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def check_ccp_input_good(self, config: Union[List[str],tuple[str, ...]]=None, logger: logger=None) -> bool:\\n \\\"\\\"\\\"\\n :param config: Sequence of commands\\n :type config: Union[List[str], tuple[str, ...]]\\n :param logger: loguru.logger() reference\\n :type logger: loguru._logger.Logger\\n :return: Whether the config can be parsed\\n :rtype: bool\\n \\\"\\\"\\\"\\n\\n if self.finished_config_parse is not False:\\n raise RequirementFailure()\\n\\n if isinstance(config, Sequence):\\n # Here we assume that `config` is a list of text config lines...\\n #\\n # config list of text lines...\\n if self.debug > 0:\\n logger.debug(\\n f\\\"parsing config stored in the config variable: `{config}`\\\"\\n )\\n return True\\n\\n else:\\n return False\\n\\n @property\\n @logger.catch(reraise=True)\\n def openargs(self) -> Dict[str,Union[str,None]]:\\n \\\"\\\"\\\"\\n Fix Py3.5 deprecation of universal newlines\\n\\n .. note::\\n\\n Ref original ciscoconfparse Github issue #114; also see\\n https://softwareengineering.stackexchange.com/q/298677/23144.\\n\\n :return: The proper encoding parameters\\n :rtype: Dict[str,Union[str,None]]\\n \\\"\\\"\\\"\\n if sys.version_info >= (\\n 3,\\n 6,\\n ):\\n retval = {\\\"mode\\\": \\\"r\\\", \\\"newline\\\": None, \\\"encoding\\\": self.encoding}\\n else:\\n retval = {\\\"mode\\\": \\\"rU\\\", \\\"encoding\\\": self.encoding}\\n return retval\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def get_text(self) -> List[str]:\\n \\\"\\\"\\\"\\n :return: All text configuration statements\\n :rtype: List[str]\\n\\n .. warning::\\n\\n The original ciscoconfparse ``ioscfg`@property has been renamed to ``get_text()``.\\n \\\"\\\"\\\"\\n return [ii.text for ii in self.config_objs]\\n\\n # This method is on CiscoConfParse()\\n @property\\n @logger.catch(reraise=True)\\n def objs(self) -> List[BaseCfgLine]:\\n \\\"\\\"\\\"CiscoConfParse().objs is an alias for the CiscoConfParse().config_objs property.\\n\\n :returns: All configuration objects.\\n :rtype: List[BaseCfgLine]\\n \\\"\\\"\\\"\\n if self.config_objs is None:\\n error = (\\n \\\"config_objs is set to None. config_objs should be a ConfigList() of configuration-line objects\\\"\\n )\\n logger.error(error)\\n raise ValueError(error)\\n return self.config_objs\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def commit(self) -> None:\\n \\\"\\\"\\\"Use :py:func:`~ciscoconfparse2.CiscoConfParse.commit` to manually fix up ``config_objs`` relationships after modifying a parsed configuration. This method is slow; try to batch calls to :func:`~ciscoconfparse2.CiscoConfParse.commit()` if possible.\\n\\n :return: None\\n :rtype: None\\n\\n .. warning::\\n\\n If you modify a configuration after parsing it with :class:`~ciscoconfparse2.CiscoConfParse`,\\n you *must* call :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` or\\n :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` before searching the configuration\\n again with methods such as :func:`~ciscoconfparse2.CiscoConfParse.find_objects`. Failure\\n to call :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` or\\n :py:meth:`~ciscoconfparse2.CiscoConfParse.commit` on config modifications could\\n lead to unexpected search results.\\n \\\"\\\"\\\"\\n\\n # perform a commit on the ConfigList()\\n self.config_objs.commit()\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def _find_child_object_branches(\\n self,\\n parent_obj: BaseCfgLine,\\n childspec: str,\\n regex_flags: str,\\n debug: int=0,\\n ) -> list:\\n \\\"\\\"\\\"\\n :param parent_obj: The parent object to be searched\\n :type parent_obj: BaseCfgLine\\n :param childspec: Regex string to match against child objects\\n :type childspec: str\\n :param regex_flags: Regex flags to apply to the aforementioned match\\n :type regex_flags: str\\n :param debug: Debug level of the operation\\n :type debug: int\\n :return: Children matching ``childspec``\\n :rtype: List[BaseCfgLine]\\n \\\"\\\"\\\"\\n # I'm not using parent_obj.re_search_children() because\\n # re_search_children() doesn't return None for no match...\\n\\n if debug > 1:\\n msg = f\\\"\\\"\\\"Calling _find_child_object_branches(\\nparent_obj={parent_obj},\\nchildspec='{childspec}',\\nregex_flags='{regex_flags}',\\ndebug={debug},\\n)\\\"\\\"\\\"\\n logger.info(msg)\\n\\n # Get the child objects from parent objects\\n if parent_obj is None:\\n children = self._find_line_OBJ(\\n linespec=childspec,\\n exactmatch=False,\\n )\\n else:\\n children = parent_obj.children\\n\\n # Find all child objects which match childspec...\\n segment_list = [\\n cobj\\n for cobj in children\\n if re.search(childspec, cobj.text, regex_flags)\\n ]\\n # Return [None] if no children matched...\\n if len(segment_list) == 0:\\n segment_list = [None]\\n\\n if debug > 1:\\n logger.info(f\\\" _find_child_object_branches() returns segment_list={segment_list}\\\")\\n return segment_list\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def find_object_branches(\\n self,\\n branchspec: Union[tuple[str, ...],List[str]]=(),\\n regex_flags: Union[re.RegexFlag,int]=0,\\n regex_groups: bool=False,\\n empty_branches: bool=False,\\n reverse: bool=False,\\n debug: int=0,\\n ) -> List[List[BaseCfgLine]]:\\n r\\\"\\\"\\\"Iterate over a tuple of regular expression strings in `branchspec` and return matching objects in a list of lists (consider it similar to a table of matching config objects). `branchspec` expects to start at some ancestor and walk through the nested object hierarchy (with no limit on depth).\\n\\n Previous CiscoConfParse() methods only handled a single parent regex and single child regex (such as :func:`~ciscoconfparse2.CiscoConfParse.find_objects`).\\n\\n Transcend past one-level of parent-child relationship parsing to include multiple nested 'branches' of a single family (i.e. parents, children, grand-children, great-grand-children, etc). The result of handling longer regex chains is that it flattens what would otherwise be nested loops in your scripts; this makes parsing heavily-nested configuratations like Juniper, Palo-Alto, and F5 much simpler. Of course, there are plenty of applications for \\\"flatter\\\" config formats like IOS.\\n\\n Return a list of lists (of object 'branches') which are nested to the same depth required in `branchspec`. However, unlike most other CiscoConfParse() methods, return an explicit `None` if there is no object match. Returning `None` allows a single search over configs that may not be uniformly nested in every branch.\\n\\n .. warning::\\n\\n The ``allow_none`` from original ciscoconfparse is removed and no longer a configuration option; it will always be regarded as True.\\n\\n :param branchspec: Regular expressions to be matched.\\n :type branchspec: Union[tuple[str, ...],List[str]]\\n :param regex_flags: Chained regular expression flags, such as `re.IGNORECASE|re.MULTILINE`\\n :type regex_flags: Union[re.RegexFlags,int]\\n :param regex_groups: Return a tuple of re.Match groups instead of the matching configuration objects, default is False.\\n :type regex_groups: bool\\n :param empty_branches: If True, return a list of None statements if there is no match; before version 1.9.49, this defaulted True.\\n :type empty_branches: bool\\n :param reverse: If True, reverse the return value order.\\n :type reverse: bool\\n :param debug: Set > 0 for debug messages\\n :type debug: int\\n :return: A list of lists of matching :class:`~ciscoconfparse2.IOSCfgLine` objects\\n :rtype: List[List[BaseCfgLine]]\\n\\n\\n .. code-block:: python\\n :emphasize-lines: 30,31\\n\\n >>> from operator import attrgetter\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = [\\n ... 'ltm pool FOO {',\\n ... ' members {',\\n ... ' k8s-05.localdomain:8443 {',\\n ... ' address 192.0.2.5',\\n ... ' session monitor-enabled',\\n ... ' state up',\\n ... ' }',\\n ... ' k8s-06.localdomain:8443 {',\\n ... ' address 192.0.2.6',\\n ... ' session monitor-enabled',\\n ... ' state down',\\n ... ' }',\\n ... ' }',\\n ... '}',\\n ... 'ltm pool BAR {',\\n ... ' members {',\\n ... ' k8s-07.localdomain:8443 {',\\n ... ' address 192.0.2.7',\\n ... ' session monitor-enabled',\\n ... ' state down',\\n ... ' }',\\n ... ' }',\\n ... '}',\\n ... ]\\n >>> parse = CiscoConfParse(config=config, syntax='junos', comment='#')\\n >>>\\n >>> branchspec = (r'ltm\\\\spool', r'members', r'\\\\S+?:\\\\d+', r'state\\\\sup')\\n >>> branches = parse.find_object_branches(branchspec=branchspec)\\n >>>\\n >>> # We found three branches\\n >>> len(branches)\\n 3\\n >>> # Each branch must match the length of branchspec\\n >>> len(branches[0])\\n 4\\n >>> # Print out one object 'branch'\\n >>> branches[0]\\n [<IOSCfgLine # 0 'ltm pool FOO'>, <IOSCfgLine # 1 ' members' (parent is # 0)>, <IOSCfgLine # 2 ' k8s-05.localdomain:8443' (parent is # 1)>, <IOSCfgLine # 5 ' state up' (parent is # 2)>]\\n >>>\\n >>> # Get the a list of text lines for this branch...\\n >>> [ii.text for ii in branches[0]]\\n ['ltm pool FOO', ' members', ' k8s-05.localdomain:8443', ' state up']\\n >>>\\n >>> # Get the config text of the root object of the branch...\\n >>> branches[0][0].text\\n 'ltm pool FOO'\\n >>>\\n >>> # Note: `None` in branches[1][-1] because of no regex match\\n >>> branches[1]\\n [<IOSCfgLine # 0 'ltm pool FOO'>, <IOSCfgLine # 1 ' members' (parent is # 0)>, <IOSCfgLine # 6 ' k8s-06.localdomain:8443' (parent is # 1)>, None]\\n >>>\\n >>> branches[2]\\n [<IOSCfgLine # 10 'ltm pool BAR'>, <IOSCfgLine # 11 ' members' (parent is # 10)>, <IOSCfgLine # 12 ' k8s-07.localdomain:8443' (parent is # 11)>, None]\\n \\\"\\\"\\\"\\n if self.config_objs.search_safe is False:\\n error = \\\"The configuration has changed since the last commit; a config search is not safe.\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n if isinstance(branchspec, list):\\n branchspec = tuple(branchspec)\\n\\n if isinstance(branchspec, tuple):\\n if branchspec == ():\\n error = \\\"find_object_branches(): branchspec must not be empty\\\"\\n logger.error(error)\\n raise ValueError(error)\\n\\n else:\\n error = \\\"find_object_branches(): Please enclose the branchspec regular expressions in a Python tuple\\\"\\n logger.error(error)\\n raise ValueError(error)\\n\\n branches = []\\n # iterate over the regular expressions in branchspec\\n for idx, childspec in enumerate(branchspec):\\n # FIXME: Insert debugging here...\\n if idx == 0:\\n # Get matching 'root' objects from the config\\n next_kids = self._find_child_object_branches(\\n parent_obj=None,\\n childspec=childspec,\\n regex_flags=regex_flags,\\n debug=debug,\\n )\\n # Start growing branches from the segments we received...\\n branches = [[kid] for kid in next_kids]\\n\\n else:\\n new_branches = []\\n for branch in branches:\\n # Extend existing branches into the new_branches\\n if branch[-1] is not None:\\n # Find children to extend the family branch...\\n next_kids = self._find_child_object_branches(\\n parent_obj=branch[-1],\\n childspec=childspec,\\n regex_flags=regex_flags,\\n debug=debug,\\n )\\n\\n for kid in next_kids:\\n # Fork off a new branch and add each matching kid...\\n tmp = copy.copy(branch)\\n tmp.append(kid)\\n new_branches.append(tmp)\\n else:\\n branch.append(None)\\n new_branches.append(branch)\\n\\n # Ensure we have the most recent branches...\\n branches = new_branches\\n\\n branches = new_branches\\n\\n # If regex_groups is True, assign regexp matches to the return matrix.\\n if regex_groups is True:\\n return_matrix = []\\n # branchspec = (r\\\"^interfaces\\\", r\\\"\\\\s+(\\\\S+)\\\", r\\\"\\\\s+(unit\\\\s+\\\\d+)\\\", r\\\"family\\\\s+(inet)\\\", r\\\"address\\\\s+(\\\\S+)\\\")\\n # for idx_matrix, row in enumerate(self.find_object_branches(branchspec)):\\n for _, row in enumerate(branches):\\n if not isinstance(row, Sequence):\\n raise RequirementFailure()\\n\\n # Before we check regex capture groups, allocate an \\\"empty return_row\\\"\\n # of the correct length...\\n return_row = [(None,)] * len(branchspec)\\n\\n # Populate the return_row below...\\n # return_row will be appended to return_matrix...\\n for idx, element in enumerate(row):\\n if element is None:\\n return_row[idx] = (None,)\\n\\n else:\\n regex_result = re.search(branchspec[idx], element.text)\\n if regex_result is not None:\\n # Save all the regex capture groups in matched_capture...\\n matched_capture = regex_result.groups()\\n if len(matched_capture) == 0:\\n # If the branchspec groups() matches are a\\n # zero-length tuple, populate this return_row\\n # with the whole element's text\\n return_row[idx] = (element.text,)\\n else:\\n # In this case, we found regex capture groups\\n return_row[idx] = matched_capture\\n else:\\n # No regex capture groups b/c of no regex match...\\n return_row[idx] = (None,)\\n\\n return_matrix.append(return_row)\\n\\n branches = return_matrix\\n\\n # We could have lost or created an extra branch if these aren't the\\n # same length\\n retval = list()\\n if bool(empty_branches) is False:\\n for branch in branches:\\n ###############################################################\\n # discard the branch if it contains None (element that did\\n # not match)\\n ###############################################################\\n if not all(branch):\\n continue\\n retval.append(branch)\\n else:\\n retval = branches\\n\\n if reverse:\\n retval.reverse()\\n\\n return retval\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def find_objects(self, linespec, exactmatch=False, ignore_ws=False, escape_chars=False, reverse=False):\\n \\\"\\\"\\\"Find all :class:`~models_cisco.IOSCfgLine` objects whose text matches ``linespec`` and return the\\n :class:`~models_cisco.IOSCfgLine` objects in a python list.\\n\\n :param linespec: A string or python regular expression, which should be matched\\n :type linespec: Union[str,re.Pattern,BaseCfgLine]\\n :param exactmatch: When set True, this option requires ``linespec`` match the whole configuration line, instead of a\\n portion of the configuration line, default to False.\\n :type exactmatch: str\\n :param ignore_ws: Controls whether whitespace is ignored, default to False.\\n :type ignore_ws: bool\\n :param reverse: Controls whether the order of the results is reversed, default to False.\\n :type reverse: bool\\n :return: Matching :class:`~ciscoconfparse2.IOSCfgLine` objects.\\n :rtype: List[BaseCfgLine]\\n\\n This example illustrates the use of :func:`~ciscoconfparse2.CiscoConfParse.find_objects`\\n\\n .. code-block:: python\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = [\\n ... '!',\\n ... 'interface Serial1/0',\\n ... ' ip address 1.1.1.1 255.255.255.252',\\n ... '!',\\n ... 'interface Serial1/1',\\n ... ' ip address 1.1.1.5 255.255.255.252',\\n ... '!',\\n ... ]\\n >>> parse = CiscoConfParse(config=config)\\n >>>\\n >>> parse.find_objects(r'^interface')\\n [<IOSCfgLine # 1 'interface Serial1/0'>, <IOSCfgLine # 4 'interface Serial1/1'>]\\n >>>\\n \\\"\\\"\\\"\\n if isinstance(linespec, list):\\n if len(linespec) == 1 and isinstance(linespec[0], (str, re.Pattern)):\\n linespec = linespec[0]\\n else:\\n error = \\\"linespec list input must be exactly one string or compiled-regex long\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if escape_chars is True:\\n ###################################################################\\n # Escape regex to avoid embedded parenthesis problems\\n ###################################################################\\n linespec = re.escape(linespec)\\n\\n if self.config_objs.search_safe is False:\\n error = \\\"The configuration has changed since the last commit; a config search is not safe.\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n if self.debug > 0:\\n logger.info(\\n \\\"find_objects('%s', exactmatch=%s) was called\\\" % (linespec, exactmatch),\\n )\\n\\n if ignore_ws:\\n linespec = build_space_tolerant_regex(linespec, encoding=self.encoding)\\n\\n if isinstance(linespec, str) or isinstance(linespec, re.Pattern):\\n retval = self._find_line_OBJ(linespec, exactmatch)\\n elif isinstance(linespec, BaseCfgLine):\\n retval = list()\\n for obj in self.objs:\\n if obj == linespec:\\n retval.append(obj)\\n else:\\n error = f\\\"linespec must be a string, re.Pattern, or BaseCfgLine instance; we got {type(linespec)}.\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if bool(reverse):\\n retval.reverse()\\n return retval\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def find_parent_objects(\\n self,\\n parentspec,\\n childspec=None,\\n ignore_ws=False,\\n recurse=True,\\n escape_chars=False,\\n reverse=False,\\n ):\\n \\\"\\\"\\\"Return a list of parent :class:`~models_cisco.IOSCfgLine` objects,\\n which matched the ``parentspec`` and whose children match ``childspec``.\\n Only the parent :class:`~models_cisco.IOSCfgLine` objects will be\\n returned.\\n\\n :param parentspec: Text regular expression for the :class:`~models_cisco.IOSCfgLine` object to be matched; this must match the parent's line\\n :type parentspec: Union[str,List[str],tuple[str, ...]]\\n :param childspec: Text regular expression for the child's configuration line\\n :type childspec: str\\n :param ignore_ws: boolean that controls whether whitespace is ignored\\n :type ignore_ws: bool\\n :param recurse: Set True if you want to search all children (children, grand children, great grand children, etc...). This is considered True if parentspec is a list or tuple.\\n :type recurse: bool\\n :param escape_chars: Set True if you want to escape characters before searching\\n :type escape_chars: bool\\n :param reverse: Set True if you want to reverse the order of the results\\n :type reverse: bool\\n :return: A list of matching parent :py:class:`~models_cisco.IOSCfgLine` objects\\n :rtype: List[BaseCfgLine]\\n\\n .. warning::\\n\\n Do not set ``childspec`` if searching with a tuple of strings or list of strings.\\n\\n This example uses :py:meth:`~ciscoconfparse2.find_parent_objects()` to\\n find all ports that are members of access vlan 300 in following\\n config...\\n\\n .. parsed-literal::\\n\\n !\\n interface FastEthernet0/1\\n switchport access vlan 532\\n spanning-tree vlan 532 cost 3\\n !\\n interface FastEthernet0/2\\n switchport access vlan 300\\n spanning-tree portfast\\n !\\n interface FastEthernet0/3\\n duplex full\\n speed 100\\n switchport access vlan 300\\n spanning-tree portfast\\n !\\n\\n The following interfaces should be returned:\\n\\n .. parsed-literal::\\n\\n interface FastEthernet0/2\\n interface FastEthernet0/3\\n\\n We do this by quering `find_objects_w_child()`; we set our\\n parent as `^interface` and set the child as `switchport access\\n vlan 300`.\\n\\n .. code-block:: python\\n :emphasize-lines: 19,20\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = ['!',\\n ... 'interface FastEthernet0/1',\\n ... ' switchport access vlan 532',\\n ... ' spanning-tree vlan 532 cost 3',\\n ... '!',\\n ... 'interface FastEthernet0/2',\\n ... ' switchport access vlan 300',\\n ... ' spanning-tree portfast',\\n ... '!',\\n ... 'interface FastEthernet0/3',\\n ... ' duplex full',\\n ... ' speed 100',\\n ... ' switchport access vlan 300',\\n ... ' spanning-tree portfast',\\n ... '!',\\n ... ]\\n >>> p = CiscoConfParse(config=config)\\n >>> p.find_parent_objects(['interface', 'vlan 300'])\\n [<IOSCfgLine # 5 'interface FastEthernet0/2'>, <IOSCfgLine # 9 'interface FastEthernet0/3'>]\\n >>>\\n \\\"\\\"\\\"\\n if self.config_objs.search_safe is False:\\n error = \\\"The configuration has changed since the last commit; a config search is not safe.\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n if escape_chars is True:\\n ###################################################################\\n # Escape regex to avoid embedded parenthesis problems\\n ###################################################################\\n parentspec = re.escape(parentspec)\\n childspec = re.escape(childspec)\\n\\n if isinstance(parentspec, BaseCfgLine):\\n parentspec = parentspec.text\\n elif isinstance(parentspec, str):\\n pass\\n elif isinstance(parentspec, (list, tuple)):\\n _result = set()\\n _tmp = self.find_object_branches(\\n parentspec,\\n )\\n for _obj_branch in _tmp:\\n # add the parent of that object branch to the result set\\n _result.add(_obj_branch[0])\\n\\n if len(_result) == 0:\\n ######################################################\\n # If any _parentspec fails to match, we will hit this\\n # condition when that failure happens.\\n ######################################################\\n return []\\n else:\\n # Sort and return the de-duplicated results\\n return sorted(_result)\\n else:\\n error = f\\\"Received unexpected `parentspec` {type(parentspec)}\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n #######################################################################\\n # Handle the case where parentspec is not a list or tuple\\n #######################################################################\\n if isinstance(childspec, BaseCfgLine):\\n parentspec = childspec.text\\n\\n if ignore_ws:\\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\\n\\n # Set escape_chars False to avoid double-escaping characters\\n return list(\\n filter(\\n lambda x: x.re_search_children(childspec, recurse=recurse),\\n self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse),\\n ),\\n )\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def find_parent_objects_wo_child(self, parentspec, childspec=None, ignore_ws=False, recurse=False, escape_chars=False, reverse=False):\\n r\\\"\\\"\\\"Return a list of parent :class:`~models_cisco.IOSCfgLine` objects, which matched the ``parentspec`` and whose children did not match ``childspec``. Only the parent :class:`~models_cisco.IOSCfgLine` objects will be returned. For simplicity, this method only finds oldest_ancestors without immediate children that match.\\n\\n Parameters\\n ----------\\n parentspec : str\\n Text regular expression for the :class:`~models_cisco.IOSCfgLine` object to be matched; this must match the parent's line\\n childspec : str\\n Text regular expression for the line to be matched; this must match the child's line\\n ignore_ws : bool\\n boolean that controls whether whitespace is ignored\\n recurse : bool\\n boolean that controls whether to recurse through children of children\\n escape_chars : bool\\n boolean that controls whether to escape characters before searching\\n reverse : bool\\n Set True if you want to reverse the order of the results\\n\\n Returns\\n -------\\n list\\n A list of matching parent configuration lines\\n\\n Examples\\n --------\\n This example finds all ports that are autonegotiating in the following config...\\n\\n .. parsed-literal::\\n\\n !\\n interface FastEthernet0/1\\n switchport access vlan 532\\n spanning-tree vlan 532 cost 3\\n !\\n interface FastEthernet0/2\\n switchport access vlan 300\\n spanning-tree portfast\\n !\\n interface FastEthernet0/2\\n duplex full\\n speed 100\\n switchport access vlan 300\\n spanning-tree portfast\\n !\\n\\n The following interfaces should be returned:\\n\\n .. parsed-literal::\\n\\n interface FastEthernet0/1\\n interface FastEthernet0/2\\n\\n We do this by quering ``find_parent_objects_wo_child()``; we set our\\n parent as ``^interface`` and set the child as ``speed\\\\s\\\\d+`` (a\\n regular-expression which matches the word 'speed' followed by\\n an integer).\\n\\n .. code-block:: python\\n :emphasize-lines: 19\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = ['!',\\n ... 'interface FastEthernet0/1',\\n ... ' switchport access vlan 532',\\n ... ' spanning-tree vlan 532 cost 3',\\n ... '!',\\n ... 'interface FastEthernet0/2',\\n ... ' switchport access vlan 300',\\n ... ' spanning-tree portfast',\\n ... '!',\\n ... 'interface FastEthernet0/3',\\n ... ' duplex full',\\n ... ' speed 100',\\n ... ' switchport access vlan 300',\\n ... ' spanning-tree portfast',\\n ... '!',\\n ... ]\\n >>> p = CiscoConfParse(config=config)\\n >>> p.find_parent_objects_wo_child(r'^interface', r'speed\\\\s\\\\d+')\\n [<IOSCfgLine # 1 'interface FastEthernet0/1'>, <IOSCfgLine # 5 'interface FastEthernet0/2'>]\\n >>>\\n \\\"\\\"\\\"\\n if isinstance(parentspec, list):\\n if len(parentspec) == 2 and isinstance(parentspec[0], (str, re.Pattern)) and isinstance(parentspec[1], (str, re.Pattern)):\\n parentspec = parentspec[0]\\n childspec = parentspec[1]\\n else:\\n error = \\\"list input must be exactly two strings or compiled-regex long\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if not isinstance(childspec, (str, re.Pattern)):\\n error = \\\"childspec input must be a string or compiled-regex\\\"\\n logger.critical(error)\\n raise InvalidParameters(error)\\n\\n if self.config_objs.search_safe is False:\\n error = \\\"The configuration has changed since the last commit; a config search is not safe.\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n if escape_chars is True:\\n ###################################################################\\n # Escape regex to avoid embedded parenthesis problems\\n ###################################################################\\n parentspec = re.escape(parentspec)\\n childspec = re.escape(childspec)\\n\\n if isinstance(parentspec, BaseCfgLine):\\n parentspec = parentspec.text\\n elif isinstance(parentspec, (list, tuple)):\\n ##################################################################\\n # Catch unsupported parentspec type here\\n ##################################################################\\n error = f\\\"find_parent_objects_wo_child() `parentspec` does not support a {type(parentspec)}\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n if isinstance(childspec, BaseCfgLine):\\n parentspec = childspec.text\\n\\n if ignore_ws is True:\\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\\n\\n\\n # Set escape_chars False to avoid double-escaping chars\\n return [\\n obj\\n for obj in self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse)\\n if not obj.re_search_children(childspec, recurse=recurse)\\n ]\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def find_child_objects(\\n self,\\n parentspec,\\n childspec=None,\\n ignore_ws=False,\\n recurse=True,\\n escape_chars=False,\\n reverse=False,\\n ):\\n r\\\"\\\"\\\"Parse through the children of all parents matching parentspec,\\n and return a list of child objects, which matched the childspec.\\n\\n :param parentspec: Text regular expression for the parent's configuration line. A list is preferred.\\n :type parentspec: Union[str, List[str], tuple[str, ...]]\\n :param childspec: Text regular expression for the child's configuration line.\\n :type parentspec: str\\n :param ignore_ws: Ignore whitespace, default to False\\n :type ignore_ws: bool\\n :param recurse: Control whether to recurse in the config, default to True.\\n :type recurse: bool\\n :param escape_chars: Controls whether characters are escaped before searching, default to False.\\n :type escape_chars: bool\\n :param reverse: Controls whether results are reversed; set True if modifying the configuration with these results.\\n :type reverse: bool\\n :return: Matching child objects\\n :rtype: List[BaseCfgLine]\\n\\n .. warning::\\n\\n Do not set ``childspec`` if searching with a tuple of strings or list of strings.\\n\\n This example finds the object for \\\"ge-0/0/0\\\" under \\\"interfaces\\\" in the\\n following config...\\n\\n .. parsed-literal::\\n\\n interfaces\\n ge-0/0/0\\n unit 0\\n family ethernet-switching\\n port-mode access\\n vlan\\n members VLAN_FOO\\n ge-0/0/1\\n unit 0\\n family ethernet-switching\\n port-mode trunk\\n vlan\\n members all\\n native-vlan-id 1\\n vlan\\n unit 0\\n family inet\\n address 172.16.15.5/22\\n\\n\\n The following object should be returned:\\n\\n .. parsed-literal::\\n\\n <IOSCfgLine # 7 ' ge-0/0/1' (parent is # 0)>\\n\\n We do this by quering `find_child_objects()`; we set our\\n parent as `^\\\\s*interface` and set the child as\\n `^\\\\s+ge-0/0/1`.\\n\\n .. code-block:: python\\n :emphasize-lines: 22,23\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = ['interfaces',\\n ... ' ge-0/0/0',\\n ... ' unit 0',\\n ... ' family ethernet-switching',\\n ... ' port-mode access',\\n ... ' vlan',\\n ... ' members VLAN_FOO',\\n ... ' ge-0/0/1',\\n ... ' unit 0',\\n ... ' family ethernet-switching',\\n ... ' port-mode trunk',\\n ... ' vlan',\\n ... ' members all',\\n ... ' native-vlan-id 1',\\n ... ' vlan',\\n ... ' unit 0',\\n ... ' family inet',\\n ... ' address 172.16.15.5/22',\\n ... ]\\n >>> p = CiscoConfParse(config=config)\\n >>> p.find_child_objects(['interface', r'ge-0/0/1'])\\n [<IOSCfgLine # 7 ' ge-0/0/1' (parent is # 0)>]\\n >>>\\n \\\"\\\"\\\"\\n if self.config_objs.search_safe is False:\\n error = \\\"The configuration has changed since the last commit; a config search is not safe.\\\"\\n logger.critical(error)\\n raise NotImplementedError(error)\\n\\n if escape_chars is True:\\n ###################################################################\\n # Escape regex to avoid embedded parenthesis problems\\n ###################################################################\\n parentspec = re.escape(parentspec)\\n childspec = re.escape(childspec)\\n\\n if isinstance(parentspec, BaseCfgLine):\\n parentspec = parentspec.text\\n elif isinstance(parentspec, str):\\n pass\\n elif isinstance(parentspec, (list, tuple)):\\n if len(parentspec) > 0:\\n _result = set()\\n _tmp = self.find_object_branches(\\n parentspec,\\n )\\n for _obj_branch in _tmp:\\n # add the child of that object branch to the result set\\n _result.add(_obj_branch[-1])\\n\\n if len(_result) == 0:\\n ######################################################\\n # If any _childspec fails to match, we will hit this\\n # condition when that failure happens.\\n ######################################################\\n return []\\n # Sort the de-duplicated results\\n return sorted(_result)\\n else:\\n error = f\\\"`parentspec` {type(parentspec)} must have at least one element.\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n else:\\n error = f\\\"Received unexpected `parentspec` {type(parentspec)}\\\"\\n logger.error(error)\\n raise InvalidParameters(error)\\n\\n #######################################################################\\n # Handle the case where parentspec is not a list or tuple\\n #######################################################################\\n if isinstance(childspec, BaseCfgLine):\\n parentspec = childspec.text\\n\\n if ignore_ws:\\n parentspec = build_space_tolerant_regex(parentspec, encoding=self.encoding)\\n childspec = build_space_tolerant_regex(childspec, encoding=self.encoding)\\n\\n retval = set()\\n # Set escape_chars False to avoid double-escaping characters\\n parents = self.find_objects(parentspec, ignore_ws=ignore_ws, escape_chars=False, reverse=reverse)\\n if recurse is False:\\n for parent in parents:\\n ##############################################################\\n # If recurse is False, only search direct children\\n ##############################################################\\n for child in parent.children:\\n if child.re_match(rf\\\"({childspec})\\\", default=False):\\n retval.add(child)\\n else:\\n for parent in parents:\\n ##############################################################\\n # If recurse is True, search all children including children\\n # of the children\\n ##############################################################\\n for child in parent.all_children:\\n if child.re_match(rf\\\"({childspec})\\\", default=False):\\n retval.add(child)\\n\\n return sorted(retval)\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def re_search_children(self, regexspec, recurse=False):\\n \\\"\\\"\\\"Use ``regexspec`` to search for root parents in the config with text matching regex. If `recurse` is False, only root parent objects are returned. A list of matching objects is returned.\\n\\n This method is very similar to :func:`~ciscoconfparse2.CiscoConfParse.find_objects` (when `recurse` is True); however it was written in response to the use-case described in `Github Issue #156 <https://github.com/mpenning/ciscoconfparse/issues/156>`_.\\n\\n Parameters\\n ----------\\n regexspec : str\\n A string or python regular expression, which should be matched.\\n recurse : bool\\n Set True if you want to search all objects, and not just the root parents\\n\\n Returns\\n -------\\n list\\n A list of matching :class:`~models_cisco.IOSCfgLine` objects which matched. If there is no match, an empty :py:func:`list` is returned.\\n\\n \\\"\\\"\\\"\\n ## I implemented this method in response to Github issue #156\\n if recurse is False:\\n # Only return the matching oldest ancestor objects...\\n return [obj for obj in self.find_objects(regexspec) if (obj.parent is obj)]\\n else:\\n # Return any matching object\\n return [obj for obj in self.find_objects(regexspec)]\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def re_match_iter_typed(\\n self,\\n regexspec,\\n group=1,\\n result_type=str,\\n default=\\\"\\\",\\n untyped_default=False,\\n ):\\n r\\\"\\\"\\\"Use ``regexspec`` to search the root parents in the config\\n and return the contents of the regular expression group, at the\\n integer ``group`` index, cast as ``result_type``; if there is no\\n match, ``default`` is returned.\\n\\n Note\\n ----\\n Only the first regex match is returned.\\n\\n Parameters\\n ----------\\n regexspec : str\\n A string or python compiled regular expression, which should be matched. This regular expression should contain parenthesis, which bound a match group.\\n group : int\\n An integer which specifies the desired regex group to be returned. ``group`` defaults to 1.\\n result_type : type\\n A type (typically one of: ``str``, ``int``, ``float``, or :class:`~ccp_util.IPv4Obj`). All returned values are cast as ``result_type``, which defaults to ``str``.\\n default : any\\n The default value to be returned, if there is no match. The default is an empty string.\\n untyped_default : bool\\n Set True if you don't want the default value to be typed\\n\\n Returns\\n -------\\n ``result_type``\\n The text matched by the regular expression group; if there is no match, ``default`` is returned. All values are cast as ``result_type``. The default result_type is `str`.\\n\\n\\n Examples\\n --------\\n This example illustrates how you can use\\n :func:`~ciscoconfparse2.re_match_iter_typed` to get the\\n first interface name listed in the config.\\n\\n >>> import re\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = [\\n ... '!',\\n ... 'interface Serial1/0',\\n ... ' ip address 1.1.1.1 255.255.255.252',\\n ... '!',\\n ... 'interface Serial2/0',\\n ... ' ip address 1.1.1.5 255.255.255.252',\\n ... '!',\\n ... ]\\n >>> parse = CiscoConfParse(config=config)\\n >>> parse.re_match_iter_typed(r'interface\\\\s(\\\\S+)')\\n 'Serial1/0'\\n >>>\\n\\n The following example retrieves the hostname from the configuration\\n\\n >>> from ciscoconfparse2 import CiscoConfParse\\n >>> config = [\\n ... '!',\\n ... 'hostname DEN-EDGE-01',\\n ... '!',\\n ... 'interface Serial1/0',\\n ... ' ip address 1.1.1.1 255.255.255.252',\\n ... '!',\\n ... 'interface Serial2/0',\\n ... ' ip address 1.1.1.5 255.255.255.252',\\n ... '!',\\n ... ]\\n >>> parse = CiscoConfParse(config=config)\\n >>> parse.re_match_iter_typed(r'^hostname\\\\s+(\\\\S+)')\\n 'DEN-EDGE-01'\\n >>>\\n\\n \\\"\\\"\\\"\\n ## iterate through root objects, and return the matching value\\n ## (cast as result_type) from the first object.text that matches regex\\n\\n # if (default is True):\\n ## Not using self.re_match_iter_typed(default=True), because I want\\n ## to be sure I build the correct API for match=False\\n ##\\n ## Ref IOSIntfLine.has_dtp for an example of how to code around\\n ## this while I build the API\\n # raise NotImplementedError\\n\\n for cobj in self.config_objs:\\n # Only process parent objects at the root of the tree...\\n if cobj.parent is not cobj:\\n continue\\n\\n mm = re.search(regexspec, cobj.text)\\n if mm is not None:\\n return result_type(mm.group(group))\\n ## Ref Github issue #121\\n if untyped_default:\\n return default\\n else:\\n return result_type(default)\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def save_as(self, filepath):\\n \\\"\\\"\\\"Save a text copy of the configuration at ``filepath``; this\\n method uses the OperatingSystem's native line separators (such as\\n ``\\\\\\\\r\\\\\\\\n`` in Windows).\\\"\\\"\\\"\\n try:\\n with open(filepath, \\\"w\\\", encoding=self.encoding) as newconf:\\n for line in self.as_text:\\n newconf.write(line + \\\"\\\\n\\\")\\n return True\\n except BaseException as ee:\\n logger.error(str(ee))\\n raise ee\\n\\n ### The methods below are marked SEMI-PRIVATE because they return an object\\n ### or iterable of objects instead of the configuration text itself.\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def _find_line_OBJ(self, linespec, exactmatch=False):\\n \\\"\\\"\\\"SEMI-PRIVATE: Find objects whose text matches the linespec\\\"\\\"\\\"\\n\\n if self.config_objs is None:\\n err = \\\"config_objs is None. self.config_objs logic failed.\\\"\\n raise ValueError(err)\\n\\n if self.debug >= 2:\\n logger.debug(\\n \\\"Looking for match of linespec='%s', exactmatch=%s\\\"\\n % (linespec, exactmatch),\\n )\\n\\n # NOTE TO SELF: do not remove _find_line_OBJ(); used by Cisco employees\\n if not exactmatch:\\n # Return objects whose text attribute matches linespec\\n linespec_re = re.compile(linespec)\\n elif exactmatch:\\n # Return objects whose text attribute matches linespec exactly\\n linespec_re = re.compile(\\\"^%s$\\\" % linespec)\\n\\n return list(\\n filter(lambda obj: linespec_re.search(obj.text), self.config_objs),\\n )\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def _find_sibling_OBJ(self, lineobject):\\n \\\"\\\"\\\"SEMI-PRIVATE: Takes a singe object and returns a list of sibling\\n objects\\\"\\\"\\\"\\n siblings = lineobject.parent.children\\n return siblings\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def _find_all_child_OBJ(self, lineobject):\\n \\\"\\\"\\\"SEMI-PRIVATE: Takes a single object and returns a list of\\n decendants in all 'children' / 'grandchildren' / etc... after it.\\n It should NOT return the children of siblings\\\"\\\"\\\"\\n # sort the list, and get unique objects\\n retval = set(lineobject.children)\\n for candidate in lineobject.children:\\n if candidate.has_children:\\n for child in candidate.children:\\n retval.add(child)\\n retval = sorted(retval)\\n return retval\\n\\n # This method is on CiscoConfParse()\\n @logger.catch(reraise=True)\\n def _unique_OBJ(self, objectlist):\\n \\\"\\\"\\\"SEMI-PRIVATE: Returns a list of unique objects (i.e. with no\\n duplicates).\\n The returned value is sorted by configuration line number\\n (lowest first)\\\"\\\"\\\"\\n retval = set()\\n for obj in objectlist:\\n retval.add(obj)\\n return sorted(retval)\"\n },\n {\n \"identifier\": \"PythonOptimizeCheck\",\n \"path\": \"ciscoconfparse2/ccp_util.py\",\n \"snippet\": \"class PythonOptimizeCheck(object):\\n \\\"\\\"\\\"\\n Check if we're running under \\\"python -O ...\\\". The -O option removes\\n all `assert` statements at runtime. ciscoconfparse2 depends heavily on\\n `assert` and running ciscoconfparse2 under python -O is a really bad idea.\\n\\n __debug__ is True unless run with `python -O ...`. __debug__ is False\\n under `python -O ...`.\\n\\n Also throw an error if PYTHONOPTIMIZE is set in the windows or unix shell.\\n\\n This class should be run in <module_name_dir>/__init__.py.\\n\\n This condition is not unique to ciscoconfparse2.\\n\\n Simple usage (in __init__.py):\\n ------------------------------\\n\\n # Handle PYTHONOPTIMIZE problems...\\n from ciscoconfparse2.ccp_util import PythonOptimizeCheck\\n _ = PythonOptimizeCheck()\\n\\n\\n \\\"\\\"\\\"\\n @logger.catch(reraise=True)\\n def __init__(self):\\n\\n self.PYTHONOPTIMIZE_env_value = os.environ.get(\\\"PYTHONOPTIMIZE\\\", None)\\n\\n error = \\\"__no_error__\\\"\\n try:\\n # PYTHONOPTIMIZE is not supported... in the linux shell\\n # disable it with `unset PYTHONOPTIMIZE`\\n if isinstance(self.PYTHONOPTIMIZE_env_value, str) and self.PYTHONOPTIMIZE_env_value.strip() != \\\"\\\":\\n # This condition explicitly allows PYTHONOPTIMIZE=\\\"\\\", which\\n # is not a problem.\\n error = \\\"Your environment has PYTHONOPTIMIZE set. ciscoconfparse2 doesn't support running under PYTHONOPTIMIZE.\\\"\\n # PYTHONOPTIMIZE is not supported... in the linux shell\\n # disable it with `unset PYTHONOPTIMIZE`\\n elif self.PYTHONOPTIMIZE_env_value is not None:\\n error = \\\"Your environment has PYTHONOPTIMIZE set. ciscoconfparse2 doesn't support running under PYTHONOPTIMIZE.\\\"\\n # Throw an error if we're running under `python -O`. `python -O` is not supported\\n # We should keep the __debug__ check for `-O` at the end, otherwise it\\n # masks identifying problems with PYTHONOPTIMIZE set in the shell...\\n elif __debug__ is False:\\n # Running under 'python -O'\\n error = \\\"You're using `python -O`. Please don't. ciscoconfparse2 doesn't support `python -O`\\\"\\n\\n else:\\n # whew...\\n pass\\n\\n except Exception as exception_info:\\n print(\\\"exception_info\\\", str(exception_info))\\n raise RuntimeError(\\\"Something bad happened in PYTHONOPTIMIZE checks. Please report this problem as a ciscoconfparse2 bug\\\")\\n\\n if error != \\\"__no_error__\\\":\\n raise PythonOptimizeException(error)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import platform\nimport sys\nimport os\nimport dns.exception\nimport dns.resolver\nimport pytest\nfrom ciscoconfparse2.ciscoconfparse2 import CiscoConfParse\nfrom ciscoconfparse2.ccp_util import PythonOptimizeCheck\n from fixtures.devices.mock_cisco import start_cisco_mock, stop_cisco_mock"},"token_num":{"kind":"number","value":18528,"string":"18,528"},"cropped_code":{"kind":"string","value":"tacacs-server host 10.0.0.32\ntacacs-server host 10.0.0.33\naaa group server tacacs+ TACACS_GROUP\n server 10.0.0.32\n server 10.0.0.33\n use-vrf management\n source-interface mgmt0\n\naaa authentication login default group TACACS_GROUP\naaa authentication login console group TACACS_GROUP\naaa authorization commands default group TACACS_GROUP\naaa accounting default group TACACS_GROUP\naaa authentication login error-enable\n\nlogging event link-status default\n\nvpc domain 999\n role priority 100\n system-priority 1\n auto-recovery\n peer-keepalive destination 1.1.1.2\n\nfex 115\n desc FEX115\n pinning max-links 1\n\ninterface loopback0\n ip address 10.1.1.1/32\n\ninterface mgmt0\n ip address 10.0.0.5/24\n\ninterface port-channel1\n vpc peer-link\n switchport mode trunk\n spanning-tree port type network\n description [vPC PEER LINK]\n\ninterface port-channel21\n description Uplink to core\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,155\n mtu 9216\n vpc 21\n\ninterface port-channel115\n switchport mode fex-fabric\n fex associate 115\n\ninterface Ethernet1/1\n switchport mode trunk\n spanning-tree port type network\n channel-group 1 mode active\n\ninterface Ethernet1/2\n switchport mode trunk\n spanning-tree port type network\n channel-group 1 mode active\n\ninterface Ethernet1/3\n ip address 192.0.2.0/31\n\ninterface Ethernet1/4\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,15\n channel-group 21 mode active\n mtu 9216\n\ninterface Ethernet1/5\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,15\n channel-group 21 mode active\n mtu 9216\n\ninterface Ethernet1/6\n switchport mode fex-fabric\n fex associate 115\n channel-group 115\n\ninterface Ethernet1/7\n switchport mode access\n switchport access vlan 100\n mtu 9216\n\ninterface Ethernet1/8\n switchport mode access\n switchport access vlan 102\n mtu 9216\n\ninterface Ethernet1/9\n ip address 10.1.2.6/30\n mtu 9216\n\ninterface Ethernet1/10\n encapsulation dot1Q 200\n bandwidth 100000000\n delay 200\n beacon\n ip address 10.1.2.2/30\n mpls ip\n mtu 9216\n\"\"\".splitlines()\n\n\n@pytest.fixture(scope=\"session\")\ndef c01_default_gigethernets(request):\n yield config_c01_default_gige\n\n\n@pytest.fixture(scope=\"session\")\ndef c01_insert_serial_replace(request):\n yield config_c01_insert_serial_replace\n\n\n@pytest.fixture(scope=\"function\")\ndef parse_c01(request):\n \"\"\"Preparsed c01\"\"\"\n"},"all_code":{"kind":"string","value":"r\"\"\" conftest.py - Parse, Query, Build, and Modify IOS-style configs\n\n Copyright (C) 2023 David Michael Pennington at Cisco Systems\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see <http://www.gnu.org/licenses/>.\n\n If you need to contact the author, you can do so by emailing:\n mike [~at~] pennington [/dot\\] net\n\"\"\"\n\n\nsys.path.insert(0, \"..\")\n\n\nc01 = \"\"\"policy-map QOS_1\n class GOLD\n priority percent 10\n !\n class SILVER\n bandwidth 30\n random-detect\n !\n class BRONZE\n random-detect\n!\ninterface Serial 1/0\n encapsulation ppp\n ip address 1.1.1.1 255.255.255.252\n!\ninterface GigabitEthernet4/1\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ninterface GigabitEthernet4/2\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ninterface GigabitEthernet4/3\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n!\ninterface GigabitEthernet4/4\n shutdown\n!\ninterface GigabitEthernet4/5\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/6\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/7\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/8\n switchport\n switchport access vlan 110\n!\naccess-list 101 deny tcp any any eq 25 log\naccess-list 101 permit ip any any\n!\n!\nlogging 1.1.3.5\nlogging 1.1.3.17\n!\nbanner login ^C\nThis is a router, and you cannot have it.\nLog off now while you still can type. I break the fingers\nof all tresspassers.\n^C\nalias exec showthang show ip route vrf THANG\"\"\".splitlines()\n\nconfig_c01_default_gige = \"\"\"policy-map QOS_1\n class GOLD\n priority percent 10\n !\n class SILVER\n bandwidth 30\n random-detect\n !\n class BRONZE\n random-detect\n!\ninterface Serial 1/0\n encapsulation ppp\n ip address 1.1.1.1 255.255.255.252\n!\ndefault interface GigabitEthernet4/1\ninterface GigabitEthernet4/1\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ndefault interface GigabitEthernet4/2\ninterface GigabitEthernet4/2\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ndefault interface GigabitEthernet4/3\ninterface GigabitEthernet4/3\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n!\ndefault interface GigabitEthernet4/4\ninterface GigabitEthernet4/4\n shutdown\n!\ndefault interface GigabitEthernet4/5\ninterface GigabitEthernet4/5\n switchport\n switchport access vlan 110\n!\ndefault interface GigabitEthernet4/6\ninterface GigabitEthernet4/6\n switchport\n switchport access vlan 110\n!\ndefault interface GigabitEthernet4/7\ninterface GigabitEthernet4/7\n switchport\n switchport access vlan 110\n!\ndefault interface GigabitEthernet4/8\ninterface GigabitEthernet4/8\n switchport\n switchport access vlan 110\n!\naccess-list 101 deny tcp any any eq 25 log\naccess-list 101 permit ip any any\n!\n!\nlogging 1.1.3.5\nlogging 1.1.3.17\n!\nbanner login ^C\nThis is a router, and you cannot have it.\nLog off now while you still can type. I break the fingers\nof all tresspassers.\n^C\nalias exec showthang show ip route vrf THANG\"\"\".splitlines()\n\nconfig_c01_insert_serial_replace = \"\"\"policy-map QOS_1\n class GOLD\n priority percent 10\n !\n class SILVER\n bandwidth 30\n random-detect\n !\n class BRONZE\n random-detect\n!\ndefault interface Serial 2/0\ninterface Serial 2/0\n encapsulation ppp\n ip address 1.1.1.1 255.255.255.252\n!\ninterface GigabitEthernet4/1\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ninterface GigabitEthernet4/2\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ninterface GigabitEthernet4/3\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n!\ninterface GigabitEthernet4/4\n shutdown\n!\ninterface GigabitEthernet4/5\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/6\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/7\n switchport\n switchport access vlan 110\n!\ninterface GigabitEthernet4/8\n switchport\n switchport access vlan 110\n!\naccess-list 101 deny tcp any any eq 25 log\naccess-list 101 permit ip any any\n!\n!\nlogging 1.1.3.5\nlogging 1.1.3.17\n!\nbanner login ^C\nThis is a router, and you cannot have it.\nLog off now while you still can type. I break the fingers\nof all tresspassers.\n^C\nalias exec showthang show ip route vrf THANG\"\"\".splitlines()\n\n# A smaller version of c01...\nc02 = \"\"\"policy-map QOS_1\n class GOLD\n priority percent 10\n !\n class SILVER\n bandwidth 30\n random-detect\n !\n class BRONZE\n random-detect\n!\ninterface GigabitEthernet4/1\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\"\"\".splitlines()\n\n## For historical reasons, I'm use c03 for configs/sample_01.ios (i.e. c01 was\n## already taken)\nc03 = \"\"\"!\nservice timestamps debug datetime msec localtime show-timezone\nservice timestamps log datetime msec localtime show-timezone\n!\nerrdisable recovery cause bpduguard\nerrdisable recovery interval 400\n!\naaa new-model\n!\nip vrf TEST_100_001\n route-target 100:1\n rd 100:1\n!\ninterface Serial 1/0\n description Uplink to SBC F923X2K425\n bandwidth 1500\n clock rate 1500\n delay 70\n encapsulation ppp\n ip address 1.1.1.1 255.255.255.252\n!\ninterface Serial 1/1\n description Uplink to AT&T\n encapsulation hdlc\n ip address 1.1.1.9 255.255.255.254\n hold-queue 1000 in\n hold-queue 1000 out\n mpls mtu 1540\n ip mtu 1500\n mpls ip\n!\ninterface GigabitEthernet4/1\n description\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n!\ninterface GigabitEthernet4/2\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n power inline static max 7000\n speed 100\n duplex full\n!\ninterface GigabitEthernet4/3\n mtu 9216\n switchport\n switchport access vlan 100\n switchport voice vlan 150\n!\ninterface GigabitEthernet4/4\n shutdown\n!\ninterface GigabitEthernet4/5\n switchport\n switchport access vlan 110\n switchport port-security\n switchport port-security maximum 3\n switchport port-security mac-address sticky\n switchport port-security mac-address 1000.2000.3000\n switchport port-security mac-address 1000.2000.3001\n switchport port-security mac-address 1000.2000.3002\n switchport port-security violation shutdown\n!\ninterface GigabitEthernet4/6\n description Simulate a Catalyst6500 access port\n switchport\n switchport access vlan 110\n switchport mode access\n switchport nonegotiate\n switchport port-security\n switchport port-security maximum 2\n switchport port-security violation restrict\n switchport port-security aging type inactivity\n switchport port-security aging time 5\n spanning-tree portfast\n spanning-tree portfast bpduguard\n storm-control action shutdown\n storm-control broadcast level 0.40\n storm-control multicast level 0.35\n!\ninterface GigabitEthernet4/7\n description Dot1Q trunk allowing vlans 2-4,7,10,11-19,21-4094\n switchport\n switchport trunk encapsulation dot1q\n switchport mode trunk\n switchport trunk native vlan 4094\n switchport trunk allowed vlan remove 1,5-10,20\n switchport trunk allowed vlan add 7,10\n switchport nonegotiate\n!\ninterface GigabitEthernet4/8.120\n no switchport\n encapsulation dot1q 120\n ip vrf forwarding TEST_100_001\n ip address 1.1.2.254 255.255.255.0\n!\ninterface ATM5/0/0\n no ip address\n no ip redirects\n no ip unreachables\n no ip proxy-arp\n load-interval 30\n carrier-delay msec 100\n no atm ilmi-keepalive\n bundle-enable\n max-reserved-bandwidth 100\n hold-queue 500 in\n!\ninterface ATM5/0/0.32 point-to-point\n ip address 1.1.1.5 255.255.255.252\n no ip redirects\n no ip unreachables\n no ip proxy-arp\n ip accounting access-violations\n pvc 0/32\n vbr-nrt 704 704\n!\ninterface ATM5/0/1\n shutdown\n!\nrouter ospf 100 vrf TEST_100_001\n router-id 1.1.2.254\n network 1.1.2.0 0.0.0.255 area 0\n!\npolicy-map QOS_1\n class GOLD\n priority percent 10\n !\n class SILVER\n bandwidth 30\n random-detect\n !\n class BRONZE\n random-detect\n!\naccess-list 101 deny tcp any any eq 25 log\naccess-list 101 permit ip any any\n!\n!\nlogging 1.1.3.5\nlogging 1.1.3.17\n!\nbanner login ^C\nThis is a router, and you cannot have it.\nLog off now while you still can type. I break the fingers\nof all tresspassers.\n^C\n!\nalias exec showthang show ip route vrf THANG\"\"\".splitlines()\n\nf01 = \"\"\"ltm virtual ACME {\n destination 192.168.1.191:http\n ip-protocol tcp\n mask 255.255.255.255\n pool pool1\n profiles {\n http { }\n tcp { }\n }\n rules {\n MOBILE\n }\n source 0.0.0.0/0\n source-address-translation {\n type automap\n }\n translate-address enabled\n translate-port enabled\n vs-index 17\n}\"\"\".splitlines()\n\n\n# Using configs/sample_02.f5\nf02 = \"\"\"\nltm profile udp DNS-UDP {\n app-service none\n datagram-load-balancing disabled\n idle-timeout 31\n}\nltm rule contrail-monitor {\n when HTTP_REQUEST {\n if {[active_members APN-DNS-TCP] > 0 & [active_members APN-DNS-UDP] > 0 } {\n HTTP::respond 200 content \"up\"\n }\n }\n}\nltm rule contrail-monitor1 {\n when HTTP_REQUEST {\n if {[active_members APN-DNS-TCP] >= 0 & [active_members APN-DNS-UDP] >= 0 } {\n HTTP::respond 200 content \"up\"\n }\n }\n}\nltm tacdb licenseddb licensed-tacdb {\n partition none\n}\nltm virtual ACME_VIP {\n destination 192.168.1.191:http\n ip-protocol tcp\n mask 255.255.255.255\n pool pool1\n profiles {\n http { }\n tcp { }\n }\n rules {\n MOBILE\n }\n source 0.0.0.0/0\n source-address-translation {\n type automap\n }\n translate-address enabled\n translate-port enabled\n vs-index 17\n}\nsys state-mirroring { }\nsys syslog {\n include \"\ntemplate t_remotetmpl {\ntemplate (\\\"<$PRI>$STAMP $HOST $FACILITY[$PID]: $MSGONLY\\\"); template_escape(no);\n};\nfilter f_remote_loghost {\nlevel(info..emerg);\n};\ndestination d_remote_loghost {\nudp(\\\"102.223.51.181\\\" port(519) template(t_remotetmpl));\n};\nlog {\nsource(s_syslog_pipe);\nfilter(f_remote_loghost);\ndestination(d_remote_loghost);\n};\n\"\nremote-servers {\n JSA {\n host 102.223.51.181\n }\n }\n}\nsys url-db download-schedule urldb { }\n\"\"\".splitlines()\n\nj01 = \"\"\"## Last commit: 2015-06-28 13:00:59 CST by mpenning\n system {\n host-name TEST01_EX;\n domain-name pennington.net;\n domain-search [ pennington.net lab.pennington.net ];\n location {\n country-code 001;\n building HQ_005;\n floor 1;\n }\n root-authentication {\n encrypted-password \"$1$y7ArHxKU$zUbdeLfBirgkCsKiOJ5Qa0\"; ## SECRET-DATA\n }\n name-server {\n 172.16.3.222;\n }\n login {\n announcement \"Test Lab Switch\";\n message \"Unauthorized access is prohibited\";\n user mpenning {\n full-name \"Mike Pennington\";\n uid 1000;\n class super-user;\n authentication {\n encrypted-password \"$1$y7ArHxKU$zUbdeLfBirgkCsKiOJ5Qa0\"; ## SECRET-DATA\n }\n }\n }\n services {\n ssh {\n root-login allow;\n }\n telnet;\n web-management {\n http;\n }\n }\n syslog {\n user * {\n any emergency;\n }\n file messages {\n any notice;\n authorization info;\n }\n file interactive-commands {\n interactive-commands any;\n }\n }\n ntp {\n Management {\n vlan-id 1;\n interface {\n ge-0/0/0.0;\n ge-0/0/1.0;\n ge-0/0/2.0;\n ge-0/0/3.0;\n }\n }\n VLAN_FOO {\n vlan-id 5;\n }\n vlan1 {\n vlan-id 1;\n l3-interface vlan.1;\n }\n vlan800 {\n vlan-id 800;\n }\n}\nethernet-switching-options {\n storm-control {\n interface all;\n }\n}\ninterfaces {\n ge-0/0/0 {\n unit 0 {\n family ethernet-switching {\n port-mode access;\n vlan {\n members VLAN_FOO;\n }\n }\n }\n }\n ge-0/0/1 {\n unit 0 {\n family ethernet-switching {\n port-mode trunk;\n vlan {\n members all;\n }\n native-vlan-id 1;\n }\n }\n }\n vlan {\n unit 0 {\n family inet {\n address 172.16.15.5/22;\n }\n }\n }\n}\nrouting-options {\n static {\n route 0.0.0.0/0 next-hop 172.16.12.1;\n route 192.168.36.0/25 next-hop 172.16.12.1;\n }\n}\"\"\".splitlines()\n\na01 = \"\"\"hostname TEST-FW\n!\nname 1.1.2.20 loghost01\nname 1.1.3.10 dmzsrv00\nname 1.1.3.11 dmzsrv01\nname 1.1.3.12 dmzsrv02\nname 1.1.3.13 dmzsrv03\n!\ninterface Ethernet0/0\n description Uplink to SBC F923X2K425\n nameif OUTSIDE\n security-level 0\n delay 70\n ip address 1.1.1.1 255.255.255.252\n!\ninterface Ethernet0/1\n nameif INSIDE\n security-level 100\n ip address 1.1.2.1 255.255.255.0\n!\ninterface Ethernet0/2\n switchport access vlan 100\n!\ninterface VLAN100\n nameif DMZ\n security-level 50\n ip address 1.1.3.1 255.255.255.0\n!\nobject-group network ANY_addrs\n network-object 0.0.0.0 0.0.0.0\n!\nobject-group network INSIDE_addrs1\n network-object host 1.1.2.1\n network-object 1.1.2.2 255.255.255.255\n network-object 1.1.2.0 255.255.255.0\n!\nobject-group network INSIDE_addrs1\n network-object host 1.1.2.1\n network-object 1.1.2.2 255.255.255.255\n network-object 1.1.2.0 255.255.255.0\n!\nobject-group service DNS_svc\n service-object udp destination eq dns\n!\nobject-group service NTP_svc\n service-object udp destination eq ntp\n!\nobject-group service FTP_svc\n service-object tcp destination eq ftp\n!\nobject-group service HTTP_svc\n service-object tcp destination eq http\n!\nobject-group service HTTPS_svc\n service-object tcp destination eq https\n!\naccess-list INSIDE_in extended permit object-group FTP_svc object-group INSIDE_addrs1 object-group ANY_addrs log\naccess-list INSIDE_in remark Overlap for test purposes\naccess-list INSIDE_in extended permit ip object-group INSIDE_addrs1 object-group ANY_addrs log\naccess-list INSIDE_in extended deny ip any any log\n!\n!\nclock timezone CST -6\nclock summer-time CDT recurring\n!\nlogging enable\nlogging timestamp\nlogging buffer-size 1048576\nlogging buffered informational\nlogging trap informational\nlogging asdm informational\nlogging facility 22\nlogging host INSIDE loghost01\nno logging message 302021\n!\naccess-group OUTSIDE_in in interface OUTSIDE\naccess-group INSIDE_in in interface INSIDE\n!\"\"\".splitlines()\n\na02 = \"\"\": Saved\n: Written by mpenning at 05:37:43.184 CDT Sun Jun 29 2015\n!\nASA Version 9.0(3)\n!\ncommand-alias exec slog show log | i Deny|denied\ncommand-alias exec sacl sh access-list INSIDE_out | e hitcnt=0 |remark|elements\nhostname fw\ndomain-name pennington.net\nenable password 2KFQnbNIdI.2KYOU encrypted\nxlate per-session deny tcp any4 any4\nxlate per-session deny tcp any4 any6\nxlate per-session deny tcp any6 any4\nxlate per-session deny tcp any6 any6\nxlate per-session deny udp any4 any4 eq domain\nxlate per-session deny udp any4 any6 eq domain\nxlate per-session deny udp any6 any4 eq domain\nxlate per-session deny udp any6 any6 eq domain\npasswd 2KFQnbNIdI.2KYOU encrypted\nnames\nname 192.0.2.13 Machine01 description machine01\nname 192.0.2.17 Machine02_Windows\nname 10.0.0.6 Machine03\nname 74.125.130.125 GTalk01 description Google talk server\nname 74.125.134.125 GTalk02 description Google talk server\nname 74.125.139.125 GTalk03 description Google Talk server\nname 74.125.142.125 GTalk04 description Google Talk server\nname 74.125.192.125 GTalk05 description Google Talk server\nname 74.125.140.125 GTalk06 description Google Talk server\nname 74.125.137.125 GTalk07\nname 74.125.138.125 GTalk08\nname 74.125.141.125 GTalk09\nname 74.125.136.125 GTalk10\nname 74.125.135.125 GTalk11\nname 108.160.160.0 AS19679_Dropbox__108-160-160-0__20\nname 199.47.216.0 AS19679_Dropbox__199.47.216.0__22\nname 173.194.64.109 GmailSMTP01\nname 173.194.64.108 GmailSMTP02\nname 128.223.51.103 route-views.oregon-ix.net description Route-Views route server\nip local pool SSL_VPN_ADDRS 10.1.1.240-10.1.1.241 mask 255.255.255.0\n!\ninterface Ethernet0/0\n description Internet ISP\n switchport access vlan 100\n!\ninterface Ethernet0/1\n switchport access vlan 200\n!\ninterface Ethernet0/2\n switchport access vlan 200\n shutdown\n!\ninterface Ethernet0/3\n switchport access vlan 200\n!\ninterface Ethernet0/4\n switchport access vlan 200\n!\ninterface Ethernet0/5\n switchport access vlan 200\n!\ninterface Ethernet0/6\n switchport access vlan 200\n!\ninterface Ethernet0/7\n shutdown\n!\ninterface Vlan1\n no nameif\n no security-level\n no ip address\n!\ninterface Vlan100\n mac-address 0030.dead.beef\n nameif OUTSIDE\n security-level 0\n ip address dhcp setroute\n!\ninterface Vlan200\n nameif INSIDE\n security-level 100\n ip address 192.0.2.1 255.255.255.0\n!\nbanner motd\nbanner motd Test banner for $(hostname)\nbanner motd\nbanner motd *******************************\nboot system disk0:/asa903-k8.bin\nftp mode passive\nclock timezone CST -6\nclock summer-time CDT recurring\ndns domain-lookup INSIDE\ndns server-group DefaultDNS\n name-server Machine01\n domain-name pennington.net\nobject network GTalk01\n host 74.125.130.125\n description Created during name migration\nobject network GTalk02\n host 74.125.134.125\n description Created during name migration\nobject network GTalk03\n host 74.125.139.125\n description Created during name migration\nobject network GTalk04\n host 74.125.142.125\n description Created during name migration\nobject network GTalk05\n host 74.125.192.125\n description Created during name migration\nobject network GTalk06\n host 74.125.140.125\n description Created during name migration\nobject network GTalk07\n host 74.125.137.125\n description Created during name migration\nobject network GTalk08\n host 74.125.138.125\n description Created during name migration\nobject network GTalk09\n host 74.125.141.125\n description Created during name migration\nobject network GTalk10\n host 74.125.136.125\n description Created during name migration\nobject network GTalk11\n host 74.125.135.125\n description Created during name migration\nobject network AS19679_Dropbox__108-160-160-0__20\n subnet 108.160.160.0 255.255.240.0\n description Created during name migration\nobject network AS19679_Dropbox__199.47.216.0__22\n subnet 199.47.216.0 255.255.252.0\n description Created during name migration\nobject network Machine01\n host 192.0.2.5\n description Created during name migration\nobject network obj_any\n subnet 0.0.0.0 0.0.0.0\nobject network Machine02_Windows\n host 192.0.2.17\n description Created during name migration\nobject-group network GoogleTalk\n network-object object GTalk01\n network-object object GTalk02\n network-object object GTalk03\n network-object object GTalk04\n network-object object GTalk05\n network-object object GTalk06\n network-object object GTalk07\n network-object object GTalk08\n network-object object GTalk09\n network-object object GTalk10\n network-object object GTalk11\nobject-group service GoogleTalkPorts\n service-object tcp destination eq 5222\n service-object tcp destination eq https\n service-object udp destination range 19302 19309\nobject-group network Inside\n network-object 192.0.2.0 255.255.255.0\n network-object 192.0.22.0 255.255.255.0\n network-object 192.0.23.0 255.255.255.0\nobject-group network DROPBOX_AS19679\n network-object object AS19679_Dropbox__108-160-160-0__20\n network-object object AS19679_Dropbox__199.47.216.0__22\nobject-group network GOOGLE_addrs\n description dig -t TXT _netblocks.google.com 8.8.8.8\n network-object 216.239.32.0 255.255.224.0\n network-object 64.233.160.0 255.255.224.0\n network-object 66.249.80.0 255.255.240.0\n network-object 72.14.192.0 255.255.192.0\n network-object 209.85.128.0 255.255.128.0\n network-object 66.102.0.0 255.255.240.0\n network-object 74.125.0.0 255.255.0.0\n network-object 64.18.0.0 255.255.240.0\n network-object 207.126.144.0 255.255.240.0\n network-object 173.194.0.0 255.255.0.0\nobject-group network SSH_addrs\n network-object 192.168.1.0 255.255.255.0\nobject-group network ANY_addrs\n network-object 0.0.0.0 0.0.0.0\nobject-group network INSIDE_addrs\n network-object 192.0.2.0 255.255.255.0\n network-object 10.0.0.0 255.0.0.0\nobject-group service GOOGLE_svc\n description Google's push service for Android\n service-object tcp destination eq www\n service-object tcp destination eq https\n service-object tcp destination eq 5228\n service-object tcp destination eq 5222\n service-object tcp destination eq 587\nobject-group service TELNET_svc\n service-object tcp destination eq telnet\nobject-group service WHOIS_svc\n service-object tcp destination eq whois\nobject-group service SSH_svc\n service-object tcp destination eq ssh\nobject-group service WEB_svc\n description Standard web services - http, https, ftp\n service-object tcp destination eq ftp\n service-object tcp destination eq www\n service-object tcp destination eq https\n service-object icmp\nobject-group service DNS_svc\n service-object udp destination eq domain\n service-object tcp destination eq domain\nobject-group network MACHINE01_addrs\n network-object object Machine01\nobject-group service ANDROID_svc\n description Google's push service for Android\n service-object tcp destination eq 5228\nobject-group service GMAILSMTP_svc\n service-object tcp destination eq 2525\nobject-group service NTP_svc\n service-object udp destination eq ntp\nobject-group service SKYPE_svc\n service-object udp destination eq 5555\nobject-group service XBOX_svc\n service-object tcp destination eq domain\n service-object udp destination eq domain\n service-object udp destination eq 88\n service-object tcp destination eq 3074\n service-object udp destination eq 3074\nobject-group network ANY\nobject-group service NaverLine_svc\n service-object udp destination eq 11000\n service-object udp destination range 9401 9405\nobject-group network NaverLine_addrs\n network-object 174.35.127.0 255.255.255.0\nobject-group network Facebook_addrs\n network-object 66.220.144.0 255.255.240.0\n network-object 69.63.176.0 255.255.248.0\n network-object 69.63.184.0 255.255.248.0\n network-object 69.171.224.0 255.255.240.0\n network-object 69.171.239.0 255.255.255.0\n network-object 69.171.240.0 255.255.240.0\n network-object 69.171.253.0 255.255.255.0\n network-object 69.171.255.0 255.255.255.0\n network-object 74.119.76.0 255.255.252.0\n network-object 103.4.96.0 255.255.252.0\n network-object 173.252.64.0 255.255.192.0\n network-object 204.15.20.0 255.255.252.0\n network-object 31.13.24.0 255.255.248.0\n network-object 31.13.64.0 255.255.192.0\n network-object 31.13.96.0 255.255.224.0\nobject-group service IP_SLA_PathTrace_svc\n service-object udp destination range 33400 33499\nobject-group service FTP_svc\n service-object tcp destination eq ftp\nobject-group service TeamViewerPorts\n service-object tcp destination eq 5938\nobject-group service SSLVPN_svc\n service-object udp destination eq 443\nobject-group service TEST_PORTS tcp\n port-object eq domain\n port-object eq smtp\naccess-list SPLIT_TUNNEL_NETS remark [[ destinations available via the VPN ]]\naccess-list SPLIT_TUNNEL_NETS standard permit 192.0.2.0 255.255.255.0\naccess-list NO_SSLVPN_NAT remark [[ prevent inadvertent nat of sslvpn traffic ]]\naccess-list NO_SSLVPN_NAT extended permit ip 192.0.2.0 255.255.255.0 192.0.2.0 255.255.255.0\naccess-list INSIDE_in extended deny object-group SKYPE_svc object-group INSIDE_addrs object-group ANY_addrs log disable\naccess-list INSIDE_in extended permit object-group GOOGLE_svc object-group INSIDE_addrs object-group GOOGLE_addrs log\naccess-list INSIDE_in extended permit object-group ANDROID_svc object-group INSIDE_addrs object-group GOOGLE_addrs log\naccess-list INSIDE_in extended permit object-group IP_SLA_PathTrace_svc any host 4.2.2.2 log\naccess-list INSIDE_in extended permit object-group DNS_svc object-group INSIDE_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended permit object-group NTP_svc object-group INSIDE_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended permit object-group TELNET_svc object-group INSIDE_addrs host 128.223.51.103 log\naccess-list INSIDE_in extended permit object-group FTP_svc object-group INSIDE_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended permit object-group WEB_svc object-group INSIDE_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended permit object-group SSH_svc object-group INSIDE_addrs object-group SSH_addrs log\naccess-list INSIDE_in extended permit object-group GMAILSMTP_svc object-group TSUNAMI_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended permit object-group WHOIS_svc object-group TSUNAMI_addrs object-group ANY_addrs log\naccess-list INSIDE_in extended deny ip any4 any4 log\naccess-list ANY extended permit ip object-group Inside any4\naccess-list ANY extended permit ip any4 object-group Inside\naccess-list VOIP extended permit object-group GoogleTalkPorts object-group Inside object-group GoogleTalk\naccess-list VOIP extended permit object-group GoogleTalkPorts object-group GoogleTalk object-group Inside\naccess-list MAINTENANCE extended deny ip any4 any4 log\naccess-list OUTSIDE_in extended deny ip host 4.2.2.2 any4 log\naccess-list OUTSIDE_in extended permit icmp any4 0.0.0.0 0.0.0.0 unreachable log interval 1\naccess-list OUTSIDE_in extended permit icmp any4 0.0.0.0 0.0.0.0 time-exceeded log interval 1\naccess-list OUTSIDE_in extended deny ip any4 any4 log\npager lines 23\nlogging enable\nlogging timestamp\nlogging buffer-size 1048576\nlogging buffered informational\nlogging trap informational\nlogging asdm informational\nlogging facility 22\nlogging host INSIDE Machine01\nlogging class sys buffered informational\nno logging message 302021\nno logging message 302020\nmtu OUTSIDE 1500\nmtu INSIDE 1500\nip verify reverse-path interface INSIDE\nicmp unreachable rate-limit 1 burst-size 1\nasdm image disk0:/asdm-645.bin\nno asdm history enable\narp timeout 14400\nno arp permit-nonconnected\n!\nobject network obj_any\n nat (INSIDE,OUTSIDE) dynamic interface\naccess-group OUTSIDE_in in interface OUTSIDE\naccess-group INSIDE_in in interface INSIDE\nroute INSIDE 10.0.0.0 255.0.0.0 192.0.2.2 1\ntimeout xlate 3:00:00\ntimeout pat-xlate 0:00:30\ntimeout conn 1:00:00 half-closed 0:59:00 udp 0:02:00 icmp 0:00:02\ntimeout sunrpc 0:10:00 h323 0:05:00 h225 1:00:00 mgcp 0:05:00 mgcp-pat 0:05:00\ntimeout sip 0:30:00 sip_media 0:02:00 sip-invite 0:03:00 sip-disconnect 0:02:00\ntimeout sip-provisional-media 0:02:00 uauth 0:05:00 absolute\ntimeout tcp-proxy-reassembly 0:01:00\ntimeout floating-conn 0:00:00\ndynamic-access-policy-record DfltAccessPolicy\nuser-identity default-domain LOCAL\naaa authentication ssh console LOCAL\naaa authentication enable console LOCAL\naaa authentication http console LOCAL\naaa authorization command LOCAL\naaa local authentication attempts max-fail 16\nfilter java 1-65535 192.0.2.0 255.255.255.0 0.0.0.0 0.0.0.0\nhttp server enable\nhttp 192.0.2.0 255.255.255.0 INSIDE\nsnmp-server host INSIDE Machine01 poll community public\nsnmp-server location ServerRoom\nsnmp-server contact mike@pennington.net\nsnmp-server community public\nsnmp-server enable traps snmp authentication linkup linkdown coldstart\ncrypto ipsec security-association pmtu-aging infinite\ncrypto ca trustpoint LOCAL_CERT_fw\n enrollment self\n fqdn fw.pennington.net\n subject-name CN=fw.pennington.net\n crl configure\ncrypto ca trustpool policy\ntelnet timeout 5\nssh scopy enable\nssh 192.0.2.0 255.255.255.0 INSIDE\nssh 10.0.0.0 255.0.0.0 INSIDE\nssh timeout 60\nssh version 2\nconsole timeout 5\nno vpn-addr-assign aaa\nno vpn-addr-assign dhcp\n\ndhcpd dns 68.94.156.1 Machine01\ndhcpd lease 604800\ndhcpd domain pennington.net\ndhcpd auto_config OUTSIDE\n!\nthreat-detection basic-threat\nthreat-detection scanning-threat shun duration 30\nthreat-detection statistics host\nthreat-detection statistics port\nthreat-detection statistics protocol\nthreat-detection statistics access-list\nno threat-detection statistics tcp-intercept\nntp server 17.151.16.20\nntp server 17.151.16.21\nntp server 17.151.16.22\nntp server 17.151.16.23\ngroup-policy SSL_VPN_Policy01 internal\ngroup-policy SSL_VPN_Policy01 attributes\n dns-server value 192.0.2.13\n vpn-idle-timeout none\n vpn-filter none\n vpn-tunnel-protocol ssl-client ssl-clientless\n split-tunnel-policy tunnelspecified\n split-tunnel-network-list value SPLIT_TUNNEL_NETS\n default-domain value pennington.net\n webvpn\n anyconnect keep-installer installed\n anyconnect ssl rekey time 30\n anyconnect ssl rekey method ssl\n anyconnect ask none default anyconnect\nusername mpenning password dXRTaA5wrZ3OL8gz encrypted privilege 15\ntunnel-group DefaultWEBVPNGroup general-attributes\n address-pool SSL_VPN_ADDRS\n default-group-policy SSL_VPN_Policy01\n!\n!\npolicy-map type inspect dns preset_dns_map\n parameters\n message-length maximum client auto\n message-length maximum 512\npolicy-map global_policy\n class inspection_default\n inspect dns preset_dns_map\n inspect h323 h225\n inspect h323 ras\n inspect rsh\n inspect rtsp\n inspect esmtp\n inspect sqlnet\n inspect skinny\n inspect sunrpc\n inspect xdmcp\n inspect sip\n inspect netbios\n inspect tftp\n inspect ip-options\n inspect icmp\n inspect http\n!\nservice-policy global_policy global\nprompt hostname context\nno call-home reporting anonymous\ncall-home\n profile CiscoTAC-1\n no active\n destination address http https://tools.cisco.com/its/service/oddce/services/DDCEService\n destination address email callhome@cisco.com\n destination transport-method http\n subscribe-to-alert-group diagnostic\n subscribe-to-alert-group environment\n subscribe-to-alert-group inventory periodic monthly\n subscribe-to-alert-group configuration periodic monthly\n subscribe-to-alert-group telemetry periodic daily\nCryptochecksum:571d01b7b08342e35db838e9acec00f6\n: end\"\"\".splitlines()\n\nn01 = \"\"\"\n\nfeature tacacs+\nfeature interface-vlan\nfeature vpc\nfeature fex\nfeature lacp\nfeature lldp\nfeature ospf\nno feature telnet\n\nip domain-lookup\nip domain-name pennington.net\nip name-server 10.0.0.10\n\nvrf context management\n ip route 0.0.0.0/0 10.0.0.1\n\nvrf context vpc-peerkeepalive\n\ntacacs-server key 0 DontTreadOnMe\nip tacacs source-interface Vlan10\ntacacs-server host 10.0.0.32\ntacacs-server host 10.0.0.33\naaa group server tacacs+ TACACS_GROUP\n server 10.0.0.32\n server 10.0.0.33\n use-vrf management\n source-interface mgmt0\n\naaa authentication login default group TACACS_GROUP\naaa authentication login console group TACACS_GROUP\naaa authorization commands default group TACACS_GROUP\naaa accounting default group TACACS_GROUP\naaa authentication login error-enable\n\nlogging event link-status default\n\nvpc domain 999\n role priority 100\n system-priority 1\n auto-recovery\n peer-keepalive destination 1.1.1.2\n\nfex 115\n desc FEX115\n pinning max-links 1\n\ninterface loopback0\n ip address 10.1.1.1/32\n\ninterface mgmt0\n ip address 10.0.0.5/24\n\ninterface port-channel1\n vpc peer-link\n switchport mode trunk\n spanning-tree port type network\n description [vPC PEER LINK]\n\ninterface port-channel21\n description Uplink to core\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,155\n mtu 9216\n vpc 21\n\ninterface port-channel115\n switchport mode fex-fabric\n fex associate 115\n\ninterface Ethernet1/1\n switchport mode trunk\n spanning-tree port type network\n channel-group 1 mode active\n\ninterface Ethernet1/2\n switchport mode trunk\n spanning-tree port type network\n channel-group 1 mode active\n\ninterface Ethernet1/3\n ip address 192.0.2.0/31\n\ninterface Ethernet1/4\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,15\n channel-group 21 mode active\n mtu 9216\n\ninterface Ethernet1/5\n switchport mode trunk\n switchport trunk native vlan 999\n switchport trunk allowed vlan 13,31-38,15\n channel-group 21 mode active\n mtu 9216\n\ninterface Ethernet1/6\n switchport mode fex-fabric\n fex associate 115\n channel-group 115\n\ninterface Ethernet1/7\n switchport mode access\n switchport access vlan 100\n mtu 9216\n\ninterface Ethernet1/8\n switchport mode access\n switchport access vlan 102\n mtu 9216\n\ninterface Ethernet1/9\n ip address 10.1.2.6/30\n mtu 9216\n\ninterface Ethernet1/10\n encapsulation dot1Q 200\n bandwidth 100000000\n delay 200\n beacon\n ip address 10.1.2.2/30\n mpls ip\n mtu 9216\n\"\"\".splitlines()\n\n\n@pytest.fixture(scope=\"session\")\ndef c01_default_gigethernets(request):\n yield config_c01_default_gige\n\n\n@pytest.fixture(scope=\"session\")\ndef c01_insert_serial_replace(request):\n yield config_c01_insert_serial_replace\n\n\n@pytest.fixture(scope=\"function\")\ndef parse_c01(request):\n \"\"\"Preparsed c01\"\"\""},"next_line":{"kind":"string","value":" parse_c01 = CiscoConfParse(c01, factory=False)"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-01 18:43:27+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5392,"cells":{"repo_name":{"kind":"string","value":"zerolink-io/zerolink-python"},"file_path":{"kind":"string","value":"zerolink/req.py"},"context":{"kind":"list like","value":[{"identifier":"settings","path":"zerolink/settings.py","snippet":" CONFIG_FILE = os.path.join(os.environ[\"APPDATA\"], \"zerolink\", \"config\")\n CONFIG_FILE = os.path.join(os.environ[\"HOME\"], \".config\", \"zerolink\", \"config\")\n CONFIG_FILE = os.path.join(\n os.environ[\"HOME\"], \"Library\", \"Application Support\", \"zerolink\", \"config\"\n )\ndef create_config() -> None:\ndef get_config() -> configparser.ConfigParser:\ndef get_config_path() -> str:\ndef get_config_var(var: str) -> str:\ndef write_config_var(var: str, value: str):\ndef write_api_key(api_key: str):\ndef read_api_key() -> Optional[str]:"},{"identifier":"APIError","path":"zerolink/exc.py","snippet":"class APIError(Exception):\n def __init__(self, message: str) -> None:\n self.message = message\n\n def __str__(self) -> str:\n return self.message"},{"identifier":"AuthenticationError","path":"zerolink/exc.py","snippet":"class AuthenticationError(Exception):\n def __init__(self) -> None:\n pass\n\n def __str__(self) -> str:\n return \"No API key. Please run `zerolink key` or set the ZEROLINK_API_KEY environment variable\""},{"identifier":"Client","path":"zerolink_client/client.py","snippet":"class Client:\n \"\"\"A class for keeping track of data related to the API\n\n The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\n\n ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\n\n ``cookies``: A dictionary of cookies to be sent with every request\n\n ``headers``: A dictionary of headers to be sent with every request\n\n ``timeout``: The maximum amount of a time a request can take. API functions will raise\n httpx.TimeoutException if this is exceeded.\n\n ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\n but can be set to False for testing purposes.\n\n ``follow_redirects``: Whether or not to follow redirects. Default value is False.\n\n ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\n\n\n Attributes:\n raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\n status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\n argument to the constructor.\n \"\"\"\n\n raise_on_unexpected_status: bool = field(default=False, kw_only=True)\n _base_url: str\n _cookies: Dict[str, str] = field(factory=dict, kw_only=True)\n _headers: Dict[str, str] = field(factory=dict, kw_only=True)\n _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True)\n _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True)\n _follow_redirects: bool = field(default=False, kw_only=True)\n _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True)\n _client: Optional[httpx.Client] = field(default=None, init=False)\n _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\n\n def with_headers(self, headers: Dict[str, str]) -> \"Client\":\n \"\"\"Get a new client matching this one with additional headers\"\"\"\n if self._client is not None:\n self._client.headers.update(headers)\n if self._async_client is not None:\n self._async_client.headers.update(headers)\n return evolve(self, headers={**self._headers, **headers})\n\n def with_cookies(self, cookies: Dict[str, str]) -> \"Client\":\n \"\"\"Get a new client matching this one with additional cookies\"\"\"\n if self._client is not None:\n self._client.cookies.update(cookies)\n if self._async_client is not None:\n self._async_client.cookies.update(cookies)\n return evolve(self, cookies={**self._cookies, **cookies})\n\n def with_timeout(self, timeout: httpx.Timeout) -> \"Client\":\n \"\"\"Get a new client matching this one with a new timeout (in seconds)\"\"\"\n if self._client is not None:\n self._client.timeout = timeout\n if self._async_client is not None:\n self._async_client.timeout = timeout\n return evolve(self, timeout=timeout)\n\n def set_httpx_client(self, client: httpx.Client) -> \"Client\":\n \"\"\"Manually the underlying httpx.Client\n\n **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n \"\"\"\n self._client = client\n return self\n\n def get_httpx_client(self) -> httpx.Client:\n \"\"\"Get the underlying httpx.Client, constructing a new one if not previously set\"\"\"\n if self._client is None:\n self._client = httpx.Client(\n base_url=self._base_url,\n cookies=self._cookies,\n headers=self._headers,\n timeout=self._timeout,\n verify=self._verify_ssl,\n follow_redirects=self._follow_redirects,\n **self._httpx_args,\n )\n return self._client\n\n def __enter__(self) -> \"Client\":\n \"\"\"Enter a context manager for self.client—you cannot enter twice (see httpx docs)\"\"\"\n self.get_httpx_client().__enter__()\n return self\n\n def __exit__(self, *args: Any, **kwargs: Any) -> None:\n \"\"\"Exit a context manager for internal httpx.Client (see httpx docs)\"\"\"\n self.get_httpx_client().__exit__(*args, **kwargs)\n\n def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \"Client\":\n \"\"\"Manually the underlying httpx.AsyncClient\n\n **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\n \"\"\"\n self._async_client = async_client\n return self\n\n def get_async_httpx_client(self) -> httpx.AsyncClient:\n \"\"\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\"\"\"\n if self._async_client is None:\n self._async_client = httpx.AsyncClient(\n base_url=self._base_url,\n cookies=self._cookies,\n headers=self._headers,\n timeout=self._timeout,\n verify=self._verify_ssl,\n follow_redirects=self._follow_redirects,\n **self._httpx_args,\n )\n return self._async_client\n\n async def __aenter__(self) -> \"Client\":\n \"\"\"Enter a context manager for underlying httpx.AsyncClient—you cannot enter twice (see httpx docs)\"\"\"\n await self.get_async_httpx_client().__aenter__()\n return self\n\n async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\n \"\"\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\"\"\"\n await self.get_async_httpx_client().__aexit__(*args, **kwargs)"},{"identifier":"finetune","path":"zerolink_client/api/default/finetune.py","snippet":"def _get_kwargs(\n *,\n file: Union[File, str],\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n file: Union[File, str],\n) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n file: Union[File, str],\n) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n file: Union[File, str],\n) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n file: Union[File, str],\n) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:"},{"identifier":"get_models_models_get","path":"zerolink_client/api/default/get_models_models_get.py","snippet":"def _get_kwargs() -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[ModelList]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[ModelList]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[ModelList]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[ModelList]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[ModelList]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[ModelList]:"},{"identifier":"desc_entity_id","path":"zerolink_client/api/entity/desc_entity_id.py","snippet":"def _get_kwargs(\n id: str,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[Entity, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[Entity, HTTPValidationError]]:\ndef sync_detailed(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[Entity, HTTPValidationError]]:\ndef sync(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[Entity, HTTPValidationError]]:\nasync def asyncio_detailed(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[Entity, HTTPValidationError]]:\nasync def asyncio(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[Entity, HTTPValidationError]]:"},{"identifier":"desc_entity_ontology","path":"zerolink_client/api/entity/desc_entity_ontology.py","snippet":"def _get_kwargs(\n id: str,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[Any, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[Any, HTTPValidationError]]:\ndef sync_detailed(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[Any, HTTPValidationError]]:\ndef sync(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[Any, HTTPValidationError]]:\nasync def asyncio_detailed(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[Any, HTTPValidationError]]:\nasync def asyncio(\n id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[Any, HTTPValidationError]]:"},{"identifier":"lookup_entity","path":"zerolink_client/api/entity/lookup_entity.py","snippet":"def _get_kwargs(\n name: str,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"Entity\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"Entity\"]]]:\ndef sync_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"Entity\"]]]:\ndef sync(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"Entity\"]]]:\nasync def asyncio_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"Entity\"]]]:\nasync def asyncio(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"Entity\"]]]:"},{"identifier":"lookup_relation","path":"zerolink_client/api/entity/lookup_relation.py","snippet":"def _get_kwargs(\n name: str,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"Relation\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"Relation\"]]]:\ndef sync_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"Relation\"]]]:\ndef sync(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"Relation\"]]]:\nasync def asyncio_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"Relation\"]]]:\nasync def asyncio(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"Relation\"]]]:"},{"identifier":"search_entity","path":"zerolink_client/api/entity/search_entity.py","snippet":"def _get_kwargs(\n name: str,\n *,\n limit: Union[Unset, int] = 10,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"Match\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"Match\"]]]:\ndef sync_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n) -> Response[Union[HTTPValidationError, List[\"Match\"]]]:\ndef sync(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n) -> Optional[Union[HTTPValidationError, List[\"Match\"]]]:\nasync def asyncio_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n) -> Response[Union[HTTPValidationError, List[\"Match\"]]]:\nasync def asyncio(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n) -> Optional[Union[HTTPValidationError, List[\"Match\"]]]:"},{"identifier":"extract_text","path":"zerolink_client/api/extract/extract_text.py","snippet":"def _get_kwargs(\n *,\n body: TextExtract,\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[AssertionResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[AssertionResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: TextExtract,\n session_id: int,\n) -> Response[Union[AssertionResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: TextExtract,\n session_id: int,\n) -> Optional[Union[AssertionResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: TextExtract,\n session_id: int,\n) -> Response[Union[AssertionResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: TextExtract,\n session_id: int,\n) -> Optional[Union[AssertionResponse, HTTPValidationError]]:"},{"identifier":"create_userattribute","path":"zerolink_client/api/fact/create_userattribute.py","snippet":"def _get_kwargs(\n *,\n body: CreateAttribute,\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[GenericResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[GenericResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateAttribute,\n session_id: int,\n) -> Response[Union[GenericResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateAttribute,\n session_id: int,\n) -> Optional[Union[GenericResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateAttribute,\n session_id: int,\n) -> Response[Union[GenericResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateAttribute,\n session_id: int,\n) -> Optional[Union[GenericResponse, HTTPValidationError]]:"},{"identifier":"create_userentity","path":"zerolink_client/api/fact/create_userentity.py","snippet":"def _get_kwargs(\n *,\n body: CreateEntity,\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateEntity,\n session_id: int,\n) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateEntity,\n session_id: int,\n) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateEntity,\n session_id: int,\n) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateEntity,\n session_id: int,\n) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:"},{"identifier":"create_userrule","path":"zerolink_client/api/fact/create_userrule.py","snippet":"def _get_kwargs(\n *,\n body: CreateRule,\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateRule,\n session_id: int,\n) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateRule,\n session_id: int,\n) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateRule,\n session_id: int,\n) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateRule,\n session_id: int,\n) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:"},{"identifier":"create_usertriple","path":"zerolink_client/api/fact/create_usertriple.py","snippet":"def _get_kwargs(\n *,\n body: CreateTriple,\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateTriple,\n session_id: int,\n) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateTriple,\n session_id: int,\n) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateTriple,\n session_id: int,\n) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: CreateTriple,\n session_id: int,\n) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:"},{"identifier":"get_triple","path":"zerolink_client/api/kg/get_triple.py","snippet":"def _get_kwargs(\n name: str,\n *,\n limit: Union[Unset, int] = 10,\n threshold: Union[Unset, float] = 0.3,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"Triple\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"Triple\"]]]:\ndef sync_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n threshold: Union[Unset, float] = 0.3,\n) -> Response[Union[HTTPValidationError, List[\"Triple\"]]]:\ndef sync(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n threshold: Union[Unset, float] = 0.3,\n) -> Optional[Union[HTTPValidationError, List[\"Triple\"]]]:\nasync def asyncio_detailed(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n threshold: Union[Unset, float] = 0.3,\n) -> Response[Union[HTTPValidationError, List[\"Triple\"]]]:\nasync def asyncio(\n name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n limit: Union[Unset, int] = 10,\n threshold: Union[Unset, float] = 0.3,\n) -> Optional[Union[HTTPValidationError, List[\"Triple\"]]]:"},{"identifier":"post_question","path":"zerolink_client/api/question/post_question.py","snippet":"def _get_kwargs(\n *,\n body: Question,\n session_id: Union[Unset, int] = UNSET,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, QuestionResponse]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, QuestionResponse]]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: Question,\n session_id: Union[Unset, int] = UNSET,\n) -> Response[Union[HTTPValidationError, QuestionResponse]]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n body: Question,\n session_id: Union[Unset, int] = UNSET,\n) -> Optional[Union[HTTPValidationError, QuestionResponse]]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n body: Question,\n session_id: Union[Unset, int] = UNSET,\n) -> Response[Union[HTTPValidationError, QuestionResponse]]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n body: Question,\n session_id: Union[Unset, int] = UNSET,\n) -> Optional[Union[HTTPValidationError, QuestionResponse]]:"},{"identifier":"create_session","path":"zerolink_client/api/session/create_session.py","snippet":"def _get_kwargs(\n user_id: str,\n *,\n name: Union[Unset, str] = UNSET,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[ChatSession, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[ChatSession, HTTPValidationError]]:\ndef sync_detailed(\n user_id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n name: Union[Unset, str] = UNSET,\n) -> Response[Union[ChatSession, HTTPValidationError]]:\ndef sync(\n user_id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n name: Union[Unset, str] = UNSET,\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\nasync def asyncio_detailed(\n user_id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n name: Union[Unset, str] = UNSET,\n) -> Response[Union[ChatSession, HTTPValidationError]]:\nasync def asyncio(\n user_id: str,\n *,\n client: Union[AuthenticatedClient, Client],\n name: Union[Unset, str] = UNSET,\n) -> Optional[Union[ChatSession, HTTPValidationError]]:"},{"identifier":"get_session_entities","path":"zerolink_client/api/session/get_session_entities.py","snippet":"def _get_kwargs(\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"GenericEntity\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"GenericEntity\"]]]:\ndef sync_detailed(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"GenericEntity\"]]]:\ndef sync(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"GenericEntity\"]]]:\nasync def asyncio_detailed(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"GenericEntity\"]]]:\nasync def asyncio(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"GenericEntity\"]]]:"},{"identifier":"get_session_facts","path":"zerolink_client/api/session/get_session_facts.py","snippet":"def _get_kwargs(\n session_id: int,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\"GenericTriple\"]]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\"GenericTriple\"]]]:\ndef sync_detailed(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"GenericTriple\"]]]:\ndef sync(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"GenericTriple\"]]]:\nasync def asyncio_detailed(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[HTTPValidationError, List[\"GenericTriple\"]]]:\nasync def asyncio(\n session_id: int,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[HTTPValidationError, List[\"GenericTriple\"]]]:"},{"identifier":"get_user_session","path":"zerolink_client/api/session/get_user_session.py","snippet":"def _get_kwargs(\n user_id: str,\n session_name: str,\n) -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[ChatSession, HTTPValidationError]]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[ChatSession, HTTPValidationError]]:\ndef sync_detailed(\n user_id: str,\n session_name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[ChatSession, HTTPValidationError]]:\ndef sync(\n user_id: str,\n session_name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\nasync def asyncio_detailed(\n user_id: str,\n session_name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[Union[ChatSession, HTTPValidationError]]:\nasync def asyncio(\n user_id: str,\n session_name: str,\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[Union[ChatSession, HTTPValidationError]]:"},{"identifier":"create_user","path":"zerolink_client/api/user/create_user.py","snippet":"def _get_kwargs() -> Dict[str, Any]:\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[CreateUser]:\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[CreateUser]:\ndef sync_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[CreateUser]:\ndef sync(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[CreateUser]:\nasync def asyncio_detailed(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Response[CreateUser]:\nasync def asyncio(\n *,\n client: Union[AuthenticatedClient, Client],\n) -> Optional[CreateUser]:"},{"identifier":"ChatSession","path":"zerolink_client/models/chat_session.py","snippet":"class ChatSession:\n \"\"\"A user chat session.\n\n Attributes:\n id (int):\n name (str): The name of the chat session\n index (int):\n requests (List['Req']):\n responses (List['Rep']):\n created_on (datetime.datetime):\n \"\"\"\n\n id: int\n name: str\n index: int\n requests: List[\"Req\"]\n responses: List[\"Rep\"]\n created_on: datetime.datetime\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.rep import Rep\n from ..models.req import Req\n\n id = self.id\n\n name = self.name\n\n index = self.index\n\n requests = []\n for requests_item_data in self.requests:\n requests_item = requests_item_data.to_dict()\n requests.append(requests_item)\n\n responses = []\n for responses_item_data in self.responses:\n responses_item = responses_item_data.to_dict()\n responses.append(responses_item)\n\n created_on = self.created_on.isoformat()\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"id\": id,\n \"name\": name,\n \"index\": index,\n \"requests\": requests,\n \"responses\": responses,\n \"created_on\": created_on,\n }\n )\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.rep import Rep\n from ..models.req import Req\n\n d = src_dict.copy()\n id = d.pop(\"id\")\n\n name = d.pop(\"name\")\n\n index = d.pop(\"index\")\n\n requests = []\n _requests = d.pop(\"requests\")\n for requests_item_data in _requests:\n requests_item = Req.from_dict(requests_item_data)\n\n requests.append(requests_item)\n\n responses = []\n _responses = d.pop(\"responses\")\n for responses_item_data in _responses:\n responses_item = Rep.from_dict(responses_item_data)\n\n responses.append(responses_item)\n\n created_on = isoparse(d.pop(\"created_on\"))\n\n chat_session = cls(\n id=id,\n name=name,\n index=index,\n requests=requests,\n responses=responses,\n created_on=created_on,\n )\n\n chat_session.additional_properties = d\n return chat_session\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateAttribute","path":"zerolink_client/models/create_attribute.py","snippet":"class CreateAttribute:\n \"\"\"\n Attributes:\n subject (str): EID of a builtin entity\n predicate (str): Name of attribute\n attribute (Attribute):\n \"\"\"\n\n subject: str\n predicate: str\n attribute: \"Attribute\"\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.attribute import Attribute\n\n subject = self.subject\n\n predicate = self.predicate\n\n attribute = self.attribute.to_dict()\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"subject\": subject,\n \"predicate\": predicate,\n \"attribute\": attribute,\n }\n )\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.attribute import Attribute\n\n d = src_dict.copy()\n subject = d.pop(\"subject\")\n\n predicate = d.pop(\"predicate\")\n\n attribute = Attribute.from_dict(d.pop(\"attribute\"))\n\n create_attribute = cls(\n subject=subject,\n predicate=predicate,\n attribute=attribute,\n )\n\n create_attribute.additional_properties = d\n return create_attribute\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateEntity","path":"zerolink_client/models/create_entity.py","snippet":"class CreateEntity:\n \"\"\"\n Attributes:\n entity (str): Name of entity\n entity_type (Union[Unset, EntityType]): Entity types are entities that map to base ontological entities in\n Foundation.\n entity_str (Union[Unset, str]): User specified type\n is_class (Union[Unset, bool]): Whether the entity is a class or instance Default: False.\n \"\"\"\n\n entity: str\n entity_type: Union[Unset, EntityType] = UNSET\n entity_str: Union[Unset, str] = UNSET\n is_class: Union[Unset, bool] = False\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n entity = self.entity\n\n entity_type: Union[Unset, str] = UNSET\n if not isinstance(self.entity_type, Unset):\n entity_type = self.entity_type.value\n\n entity_str = self.entity_str\n\n is_class = self.is_class\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"entity\": entity,\n }\n )\n if entity_type is not UNSET:\n field_dict[\"entity_type\"] = entity_type\n if entity_str is not UNSET:\n field_dict[\"entity_str\"] = entity_str\n if is_class is not UNSET:\n field_dict[\"is_class\"] = is_class\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n entity = d.pop(\"entity\")\n\n _entity_type = d.pop(\"entity_type\", UNSET)\n entity_type: Union[Unset, EntityType]\n if isinstance(_entity_type, Unset):\n entity_type = UNSET\n else:\n entity_type = EntityType(_entity_type)\n\n entity_str = d.pop(\"entity_str\", UNSET)\n\n is_class = d.pop(\"is_class\", UNSET)\n\n create_entity = cls(\n entity=entity,\n entity_type=entity_type,\n entity_str=entity_str,\n is_class=is_class,\n )\n\n create_entity.additional_properties = d\n return create_entity\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateRule","path":"zerolink_client/models/create_rule.py","snippet":"class CreateRule:\n \"\"\"\n Attributes:\n rule (str): Textual representation of the rule to parse\n context (Union[Unset, CreateRuleContext]): Context of entities to use for parsing the rule\n \"\"\"\n\n rule: str\n context: Union[Unset, \"CreateRuleContext\"] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.create_rule_context import CreateRuleContext\n\n rule = self.rule\n\n context: Union[Unset, Dict[str, Any]] = UNSET\n if not isinstance(self.context, Unset):\n context = self.context.to_dict()\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"rule\": rule,\n }\n )\n if context is not UNSET:\n field_dict[\"context\"] = context\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.create_rule_context import CreateRuleContext\n\n d = src_dict.copy()\n rule = d.pop(\"rule\")\n\n _context = d.pop(\"context\", UNSET)\n context: Union[Unset, CreateRuleContext]\n if isinstance(_context, Unset):\n context = UNSET\n else:\n context = CreateRuleContext.from_dict(_context)\n\n create_rule = cls(\n rule=rule,\n context=context,\n )\n\n create_rule.additional_properties = d\n return create_rule\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateRuleResponse","path":"zerolink_client/models/create_rule_response.py","snippet":"class CreateRuleResponse:\n \"\"\"\n Attributes:\n id (str):\n \"\"\"\n\n id: str\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n id = self.id\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"id\": id,\n }\n )\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n id = d.pop(\"id\")\n\n create_rule_response = cls(\n id=id,\n )\n\n create_rule_response.additional_properties = d\n return create_rule_response\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateTriple","path":"zerolink_client/models/create_triple.py","snippet":"class CreateTriple:\n \"\"\"\n Attributes:\n predicate (str): Name of predicate relation\n user_subject (Union[Unset, str]): EID of a user entity\n subject (Union[Unset, str]): EID of a builtin entity\n user_object (Union[Unset, str]): EID of a user entity\n object_ (Union[Unset, str]): EID of a builtin entity\n \"\"\"\n\n predicate: str\n user_subject: Union[Unset, str] = UNSET\n subject: Union[Unset, str] = UNSET\n user_object: Union[Unset, str] = UNSET\n object_: Union[Unset, str] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n predicate = self.predicate\n\n user_subject = self.user_subject\n\n subject = self.subject\n\n user_object = self.user_object\n\n object_ = self.object_\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"predicate\": predicate,\n }\n )\n if user_subject is not UNSET:\n field_dict[\"user_subject\"] = user_subject\n if subject is not UNSET:\n field_dict[\"subject\"] = subject\n if user_object is not UNSET:\n field_dict[\"user_object\"] = user_object\n if object_ is not UNSET:\n field_dict[\"object\"] = object_\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n predicate = d.pop(\"predicate\")\n\n user_subject = d.pop(\"user_subject\", UNSET)\n\n subject = d.pop(\"subject\", UNSET)\n\n user_object = d.pop(\"user_object\", UNSET)\n\n object_ = d.pop(\"object\", UNSET)\n\n create_triple = cls(\n predicate=predicate,\n user_subject=user_subject,\n subject=subject,\n user_object=user_object,\n object_=object_,\n )\n\n create_triple.additional_properties = d\n return create_triple\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"CreateTuneJobResponse","path":"zerolink_client/models/create_tune_job_response.py","snippet":"class CreateTuneJobResponse:\n \"\"\"\n Attributes:\n id (str):\n status (str):\n \"\"\"\n\n id: str\n status: str\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n id = self.id\n\n status = self.status\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"id\": id,\n \"status\": status,\n }\n )\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n id = d.pop(\"id\")\n\n status = d.pop(\"status\")\n\n create_tune_job_response = cls(\n id=id,\n status=status,\n )\n\n create_tune_job_response.additional_properties = d\n return create_tune_job_response\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"Entity","path":"zerolink_client/models/entity.py","snippet":"class Entity:\n \"\"\"\n Attributes:\n id (str):\n entity (str):\n description (Union[Unset, str]):\n source (Union[Unset, str]):\n source_url (Union[Unset, str]):\n ontology (Union[Unset, Graph]):\n source_id (Union[Unset, str]):\n \"\"\"\n\n id: str\n entity: str\n description: Union[Unset, str] = UNSET\n source: Union[Unset, str] = UNSET\n source_url: Union[Unset, str] = UNSET\n ontology: Union[Unset, \"Graph\"] = UNSET\n source_id: Union[Unset, str] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.graph import Graph\n\n id = self.id\n\n entity = self.entity\n\n description = self.description\n\n source = self.source\n\n source_url = self.source_url\n\n ontology: Union[Unset, Dict[str, Any]] = UNSET\n if not isinstance(self.ontology, Unset):\n ontology = self.ontology.to_dict()\n\n source_id = self.source_id\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"id\": id,\n \"entity\": entity,\n }\n )\n if description is not UNSET:\n field_dict[\"description\"] = description\n if source is not UNSET:\n field_dict[\"source\"] = source\n if source_url is not UNSET:\n field_dict[\"source_url\"] = source_url\n if ontology is not UNSET:\n field_dict[\"ontology\"] = ontology\n if source_id is not UNSET:\n field_dict[\"source_id\"] = source_id\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.graph import Graph\n\n d = src_dict.copy()\n id = d.pop(\"id\")\n\n entity = d.pop(\"entity\")\n\n description = d.pop(\"description\", UNSET)\n\n source = d.pop(\"source\", UNSET)\n\n source_url = d.pop(\"source_url\", UNSET)\n\n _ontology = d.pop(\"ontology\", UNSET)\n ontology: Union[Unset, Graph]\n if isinstance(_ontology, Unset):\n ontology = UNSET\n else:\n ontology = Graph.from_dict(_ontology)\n\n source_id = d.pop(\"source_id\", UNSET)\n\n entity = cls(\n id=id,\n entity=entity,\n description=description,\n source=source,\n source_url=source_url,\n ontology=ontology,\n source_id=source_id,\n )\n\n entity.additional_properties = d\n return entity\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"HTTPValidationError","path":"zerolink_client/models/http_validation_error.py","snippet":"class HTTPValidationError:\n \"\"\"\n Attributes:\n detail (Union[Unset, List['ValidationError']]):\n \"\"\"\n\n detail: Union[Unset, List[\"ValidationError\"]] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.validation_error import ValidationError\n\n detail: Union[Unset, List[Dict[str, Any]]] = UNSET\n if not isinstance(self.detail, Unset):\n detail = []\n for detail_item_data in self.detail:\n detail_item = detail_item_data.to_dict()\n detail.append(detail_item)\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update({})\n if detail is not UNSET:\n field_dict[\"detail\"] = detail\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.validation_error import ValidationError\n\n d = src_dict.copy()\n detail = []\n _detail = d.pop(\"detail\", UNSET)\n for detail_item_data in _detail or []:\n detail_item = ValidationError.from_dict(detail_item_data)\n\n detail.append(detail_item)\n\n http_validation_error = cls(\n detail=detail,\n )\n\n http_validation_error.additional_properties = d\n return http_validation_error\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"Question","path":"zerolink_client/models/question.py","snippet":"class Question:\n \"\"\"A question to be answered by querying the knowledge graph and reasoner.\n\n Attributes:\n body (str): The body of the question\n world (Union[Unset, WorldAssumption]): The world assumption is the assumption about the world that the reasoner\n makes. This is used to determine the answer to a query. For example, if\n the world assumption is \"closed\" then the reasoner will assume that the\n answer to the query is \"no\" if it cannot find a triple to satisfy the\n query. Default: WorldAssumption.CLOSED.\n spatial (Union[Unset, SpatialAssumption]): The spatial assumption is the assumption about space that the\n reasoner\n makes. This is used to determine the answer to a query. For example, if the\n spatial assumption is \"earth\" then the reasoner will only consider\n geographic locations on Earth and will assume all instances of 'location'\n are on Earth. If the spatial assumption is \"universe\" then the reasoner\n then this restriction is lifted and the reasoner will consider all\n locations in the universe. Default: SpatialAssumption.EARTH.\n temporal (Union[Unset, TemporalAssumption]): The temporal assumption is the assumption about time that the\n reasoner\n makes. This is used to determine the answer to a query. For example, if\n the temporal assumption is \"current\" then the reasoner will only consider\n triples that refer to entities that are non-historical. Excluding things\n like the Roman Empire and Francoist Spain. Default: TemporalAssumption.CURRENT.\n context (Union[Unset, ContextAssumption]): The context assumption is the assumption about the context that the\n reasoner makes. This is used to determine the answer to a query. For\n example, if the context assumption is \"none\" then the reasoner will only\n consider basic triples like instance_of and subclass_of. If the context\n assumption is \"local\" then the reasoner will consider triples that are\n defined by the user. If the context assumption is \"global\" then the\n reasoner will consider all queryable triples. Default: ContextAssumption.GLOBAL.\n \"\"\"\n\n body: str\n world: Union[Unset, WorldAssumption] = WorldAssumption.CLOSED\n spatial: Union[Unset, SpatialAssumption] = SpatialAssumption.EARTH\n temporal: Union[Unset, TemporalAssumption] = TemporalAssumption.CURRENT\n context: Union[Unset, ContextAssumption] = ContextAssumption.GLOBAL\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n body = self.body\n\n world: Union[Unset, str] = UNSET\n if not isinstance(self.world, Unset):\n world = self.world.value\n\n spatial: Union[Unset, str] = UNSET\n if not isinstance(self.spatial, Unset):\n spatial = self.spatial.value\n\n temporal: Union[Unset, str] = UNSET\n if not isinstance(self.temporal, Unset):\n temporal = self.temporal.value\n\n context: Union[Unset, str] = UNSET\n if not isinstance(self.context, Unset):\n context = self.context.value\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"body\": body,\n }\n )\n if world is not UNSET:\n field_dict[\"world\"] = world\n if spatial is not UNSET:\n field_dict[\"spatial\"] = spatial\n if temporal is not UNSET:\n field_dict[\"temporal\"] = temporal\n if context is not UNSET:\n field_dict[\"context\"] = context\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n body = d.pop(\"body\")\n\n _world = d.pop(\"world\", UNSET)\n world: Union[Unset, WorldAssumption]\n if isinstance(_world, Unset):\n world = UNSET\n else:\n world = WorldAssumption(_world)\n\n _spatial = d.pop(\"spatial\", UNSET)\n spatial: Union[Unset, SpatialAssumption]\n if isinstance(_spatial, Unset):\n spatial = UNSET\n else:\n spatial = SpatialAssumption(_spatial)\n\n _temporal = d.pop(\"temporal\", UNSET)\n temporal: Union[Unset, TemporalAssumption]\n if isinstance(_temporal, Unset):\n temporal = UNSET\n else:\n temporal = TemporalAssumption(_temporal)\n\n _context = d.pop(\"context\", UNSET)\n context: Union[Unset, ContextAssumption]\n if isinstance(_context, Unset):\n context = UNSET\n else:\n context = ContextAssumption(_context)\n\n question = cls(\n body=body,\n world=world,\n spatial=spatial,\n temporal=temporal,\n context=context,\n )\n\n question.additional_properties = d\n return question\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"QuestionResponse","path":"zerolink_client/models/question_response.py","snippet":"class QuestionResponse:\n \"\"\"A response to a question request.\n\n Attributes:\n id (int): The ID of the question\n msg (str): A message describing the result of the question\n status (ResultStatus): The status of a result.\n answers (List[str]): The answers to the question\n methods (List[str]): The methods used to answer the question\n reasoners (List[str]): The reasoners used to answer the question\n query (Union[Unset, QuestionResponseQuery]): The query used to answer the question\n \"\"\"\n\n id: int\n msg: str\n status: ResultStatus\n answers: List[str]\n methods: List[str]\n reasoners: List[str]\n query: Union[Unset, \"QuestionResponseQuery\"] = UNSET\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n from ..models.question_response_query import QuestionResponseQuery\n\n id = self.id\n\n msg = self.msg\n\n status = self.status.value\n\n answers = self.answers\n\n methods = self.methods\n\n reasoners = self.reasoners\n\n query: Union[Unset, Dict[str, Any]] = UNSET\n if not isinstance(self.query, Unset):\n query = self.query.to_dict()\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"id\": id,\n \"msg\": msg,\n \"status\": status,\n \"answers\": answers,\n \"methods\": methods,\n \"reasoners\": reasoners,\n }\n )\n if query is not UNSET:\n field_dict[\"query\"] = query\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n from ..models.question_response_query import QuestionResponseQuery\n\n d = src_dict.copy()\n id = d.pop(\"id\")\n\n msg = d.pop(\"msg\")\n\n status = ResultStatus(d.pop(\"status\"))\n\n answers = cast(List[str], d.pop(\"answers\"))\n\n methods = cast(List[str], d.pop(\"methods\"))\n\n reasoners = cast(List[str], d.pop(\"reasoners\"))\n\n _query = d.pop(\"query\", UNSET)\n query: Union[Unset, QuestionResponseQuery]\n if isinstance(_query, Unset):\n query = UNSET\n else:\n query = QuestionResponseQuery.from_dict(_query)\n\n question_response = cls(\n id=id,\n msg=msg,\n status=status,\n answers=answers,\n methods=methods,\n reasoners=reasoners,\n query=query,\n )\n\n question_response.additional_properties = d\n return question_response\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"TextExtract","path":"zerolink_client/models/text_extract.py","snippet":"class TextExtract:\n \"\"\"\n Attributes:\n text (str): Text to extract from\n extraction_model (Union[Unset, ExtractModel]): An enumeration. Default: ExtractModel.BASE.\n \"\"\"\n\n text: str\n extraction_model: Union[Unset, ExtractModel] = ExtractModel.BASE\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n text = self.text\n\n extraction_model: Union[Unset, str] = UNSET\n if not isinstance(self.extraction_model, Unset):\n extraction_model = self.extraction_model.value\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update(\n {\n \"text\": text,\n }\n )\n if extraction_model is not UNSET:\n field_dict[\"extraction_model\"] = extraction_model\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n text = d.pop(\"text\")\n\n _extraction_model = d.pop(\"extraction_model\", UNSET)\n extraction_model: Union[Unset, ExtractModel]\n if isinstance(_extraction_model, Unset):\n extraction_model = UNSET\n else:\n extraction_model = ExtractModel(_extraction_model)\n\n text_extract = cls(\n text=text,\n extraction_model=extraction_model,\n )\n\n text_extract.additional_properties = d\n return text_extract\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties"},{"identifier":"File","path":"zerolink_client/types.py","snippet":"class File:\n \"\"\"Contains information for file uploads\"\"\"\n\n payload: BinaryIO\n file_name: Optional[str] = None\n mime_type: Optional[str] = None\n\n def to_tuple(self) -> FileJsonType:\n \"\"\"Return a tuple representation that httpx will accept for multipart/form-data\"\"\"\n return self.file_name, self.payload, self.mime_type"},{"identifier":"UNSET","path":"zerolink_client/types.py","snippet":"UNSET: Unset = Unset()"}],"string":"[\n {\n \"identifier\": \"settings\",\n \"path\": \"zerolink/settings.py\",\n \"snippet\": \" CONFIG_FILE = os.path.join(os.environ[\\\"APPDATA\\\"], \\\"zerolink\\\", \\\"config\\\")\\n CONFIG_FILE = os.path.join(os.environ[\\\"HOME\\\"], \\\".config\\\", \\\"zerolink\\\", \\\"config\\\")\\n CONFIG_FILE = os.path.join(\\n os.environ[\\\"HOME\\\"], \\\"Library\\\", \\\"Application Support\\\", \\\"zerolink\\\", \\\"config\\\"\\n )\\ndef create_config() -> None:\\ndef get_config() -> configparser.ConfigParser:\\ndef get_config_path() -> str:\\ndef get_config_var(var: str) -> str:\\ndef write_config_var(var: str, value: str):\\ndef write_api_key(api_key: str):\\ndef read_api_key() -> Optional[str]:\"\n },\n {\n \"identifier\": \"APIError\",\n \"path\": \"zerolink/exc.py\",\n \"snippet\": \"class APIError(Exception):\\n def __init__(self, message: str) -> None:\\n self.message = message\\n\\n def __str__(self) -> str:\\n return self.message\"\n },\n {\n \"identifier\": \"AuthenticationError\",\n \"path\": \"zerolink/exc.py\",\n \"snippet\": \"class AuthenticationError(Exception):\\n def __init__(self) -> None:\\n pass\\n\\n def __str__(self) -> str:\\n return \\\"No API key. Please run `zerolink key` or set the ZEROLINK_API_KEY environment variable\\\"\"\n },\n {\n \"identifier\": \"Client\",\n \"path\": \"zerolink_client/client.py\",\n \"snippet\": \"class Client:\\n \\\"\\\"\\\"A class for keeping track of data related to the API\\n\\n The following are accepted as keyword arguments and will be used to construct httpx Clients internally:\\n\\n ``base_url``: The base URL for the API, all requests are made to a relative path to this URL\\n\\n ``cookies``: A dictionary of cookies to be sent with every request\\n\\n ``headers``: A dictionary of headers to be sent with every request\\n\\n ``timeout``: The maximum amount of a time a request can take. API functions will raise\\n httpx.TimeoutException if this is exceeded.\\n\\n ``verify_ssl``: Whether or not to verify the SSL certificate of the API server. This should be True in production,\\n but can be set to False for testing purposes.\\n\\n ``follow_redirects``: Whether or not to follow redirects. Default value is False.\\n\\n ``httpx_args``: A dictionary of additional arguments to be passed to the ``httpx.Client`` and ``httpx.AsyncClient`` constructor.\\n\\n\\n Attributes:\\n raise_on_unexpected_status: Whether or not to raise an errors.UnexpectedStatus if the API returns a\\n status code that was not documented in the source OpenAPI document. Can also be provided as a keyword\\n argument to the constructor.\\n \\\"\\\"\\\"\\n\\n raise_on_unexpected_status: bool = field(default=False, kw_only=True)\\n _base_url: str\\n _cookies: Dict[str, str] = field(factory=dict, kw_only=True)\\n _headers: Dict[str, str] = field(factory=dict, kw_only=True)\\n _timeout: Optional[httpx.Timeout] = field(default=None, kw_only=True)\\n _verify_ssl: Union[str, bool, ssl.SSLContext] = field(default=True, kw_only=True)\\n _follow_redirects: bool = field(default=False, kw_only=True)\\n _httpx_args: Dict[str, Any] = field(factory=dict, kw_only=True)\\n _client: Optional[httpx.Client] = field(default=None, init=False)\\n _async_client: Optional[httpx.AsyncClient] = field(default=None, init=False)\\n\\n def with_headers(self, headers: Dict[str, str]) -> \\\"Client\\\":\\n \\\"\\\"\\\"Get a new client matching this one with additional headers\\\"\\\"\\\"\\n if self._client is not None:\\n self._client.headers.update(headers)\\n if self._async_client is not None:\\n self._async_client.headers.update(headers)\\n return evolve(self, headers={**self._headers, **headers})\\n\\n def with_cookies(self, cookies: Dict[str, str]) -> \\\"Client\\\":\\n \\\"\\\"\\\"Get a new client matching this one with additional cookies\\\"\\\"\\\"\\n if self._client is not None:\\n self._client.cookies.update(cookies)\\n if self._async_client is not None:\\n self._async_client.cookies.update(cookies)\\n return evolve(self, cookies={**self._cookies, **cookies})\\n\\n def with_timeout(self, timeout: httpx.Timeout) -> \\\"Client\\\":\\n \\\"\\\"\\\"Get a new client matching this one with a new timeout (in seconds)\\\"\\\"\\\"\\n if self._client is not None:\\n self._client.timeout = timeout\\n if self._async_client is not None:\\n self._async_client.timeout = timeout\\n return evolve(self, timeout=timeout)\\n\\n def set_httpx_client(self, client: httpx.Client) -> \\\"Client\\\":\\n \\\"\\\"\\\"Manually the underlying httpx.Client\\n\\n **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\\n \\\"\\\"\\\"\\n self._client = client\\n return self\\n\\n def get_httpx_client(self) -> httpx.Client:\\n \\\"\\\"\\\"Get the underlying httpx.Client, constructing a new one if not previously set\\\"\\\"\\\"\\n if self._client is None:\\n self._client = httpx.Client(\\n base_url=self._base_url,\\n cookies=self._cookies,\\n headers=self._headers,\\n timeout=self._timeout,\\n verify=self._verify_ssl,\\n follow_redirects=self._follow_redirects,\\n **self._httpx_args,\\n )\\n return self._client\\n\\n def __enter__(self) -> \\\"Client\\\":\\n \\\"\\\"\\\"Enter a context manager for self.client—you cannot enter twice (see httpx docs)\\\"\\\"\\\"\\n self.get_httpx_client().__enter__()\\n return self\\n\\n def __exit__(self, *args: Any, **kwargs: Any) -> None:\\n \\\"\\\"\\\"Exit a context manager for internal httpx.Client (see httpx docs)\\\"\\\"\\\"\\n self.get_httpx_client().__exit__(*args, **kwargs)\\n\\n def set_async_httpx_client(self, async_client: httpx.AsyncClient) -> \\\"Client\\\":\\n \\\"\\\"\\\"Manually the underlying httpx.AsyncClient\\n\\n **NOTE**: This will override any other settings on the client, including cookies, headers, and timeout.\\n \\\"\\\"\\\"\\n self._async_client = async_client\\n return self\\n\\n def get_async_httpx_client(self) -> httpx.AsyncClient:\\n \\\"\\\"\\\"Get the underlying httpx.AsyncClient, constructing a new one if not previously set\\\"\\\"\\\"\\n if self._async_client is None:\\n self._async_client = httpx.AsyncClient(\\n base_url=self._base_url,\\n cookies=self._cookies,\\n headers=self._headers,\\n timeout=self._timeout,\\n verify=self._verify_ssl,\\n follow_redirects=self._follow_redirects,\\n **self._httpx_args,\\n )\\n return self._async_client\\n\\n async def __aenter__(self) -> \\\"Client\\\":\\n \\\"\\\"\\\"Enter a context manager for underlying httpx.AsyncClient—you cannot enter twice (see httpx docs)\\\"\\\"\\\"\\n await self.get_async_httpx_client().__aenter__()\\n return self\\n\\n async def __aexit__(self, *args: Any, **kwargs: Any) -> None:\\n \\\"\\\"\\\"Exit a context manager for underlying httpx.AsyncClient (see httpx docs)\\\"\\\"\\\"\\n await self.get_async_httpx_client().__aexit__(*args, **kwargs)\"\n },\n {\n \"identifier\": \"finetune\",\n \"path\": \"zerolink_client/api/default/finetune.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n file: Union[File, str],\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n file: Union[File, str],\\n) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n file: Union[File, str],\\n) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n file: Union[File, str],\\n) -> Response[Union[CreateTuneJobResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n file: Union[File, str],\\n) -> Optional[Union[CreateTuneJobResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"get_models_models_get\",\n \"path\": \"zerolink_client/api/default/get_models_models_get.py\",\n \"snippet\": \"def _get_kwargs() -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[ModelList]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[ModelList]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[ModelList]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[ModelList]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[ModelList]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[ModelList]:\"\n },\n {\n \"identifier\": \"desc_entity_id\",\n \"path\": \"zerolink_client/api/entity/desc_entity_id.py\",\n \"snippet\": \"def _get_kwargs(\\n id: str,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[Entity, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[Entity, HTTPValidationError]]:\\ndef sync_detailed(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[Entity, HTTPValidationError]]:\\ndef sync(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[Entity, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[Entity, HTTPValidationError]]:\\nasync def asyncio(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[Entity, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"desc_entity_ontology\",\n \"path\": \"zerolink_client/api/entity/desc_entity_ontology.py\",\n \"snippet\": \"def _get_kwargs(\\n id: str,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[Any, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[Any, HTTPValidationError]]:\\ndef sync_detailed(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[Any, HTTPValidationError]]:\\ndef sync(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[Any, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[Any, HTTPValidationError]]:\\nasync def asyncio(\\n id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[Any, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"lookup_entity\",\n \"path\": \"zerolink_client/api/entity/lookup_entity.py\",\n \"snippet\": \"def _get_kwargs(\\n name: str,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\\ndef sync_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\\ndef sync(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\\nasync def asyncio_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\\nasync def asyncio(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"Entity\\\"]]]:\"\n },\n {\n \"identifier\": \"lookup_relation\",\n \"path\": \"zerolink_client/api/entity/lookup_relation.py\",\n \"snippet\": \"def _get_kwargs(\\n name: str,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\\ndef sync_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\\ndef sync(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\\nasync def asyncio_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\\nasync def asyncio(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"Relation\\\"]]]:\"\n },\n {\n \"identifier\": \"search_entity\",\n \"path\": \"zerolink_client/api/entity/search_entity.py\",\n \"snippet\": \"def _get_kwargs(\\n name: str,\\n *,\\n limit: Union[Unset, int] = 10,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\\ndef sync_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n) -> Response[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\\ndef sync(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n) -> Optional[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\\nasync def asyncio_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n) -> Response[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\\nasync def asyncio(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n) -> Optional[Union[HTTPValidationError, List[\\\"Match\\\"]]]:\"\n },\n {\n \"identifier\": \"extract_text\",\n \"path\": \"zerolink_client/api/extract/extract_text.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: TextExtract,\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[AssertionResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[AssertionResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: TextExtract,\\n session_id: int,\\n) -> Response[Union[AssertionResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: TextExtract,\\n session_id: int,\\n) -> Optional[Union[AssertionResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: TextExtract,\\n session_id: int,\\n) -> Response[Union[AssertionResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: TextExtract,\\n session_id: int,\\n) -> Optional[Union[AssertionResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"create_userattribute\",\n \"path\": \"zerolink_client/api/fact/create_userattribute.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: CreateAttribute,\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[GenericResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[GenericResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateAttribute,\\n session_id: int,\\n) -> Response[Union[GenericResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateAttribute,\\n session_id: int,\\n) -> Optional[Union[GenericResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateAttribute,\\n session_id: int,\\n) -> Response[Union[GenericResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateAttribute,\\n session_id: int,\\n) -> Optional[Union[GenericResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"create_userentity\",\n \"path\": \"zerolink_client/api/fact/create_userentity.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: CreateEntity,\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateEntity,\\n session_id: int,\\n) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateEntity,\\n session_id: int,\\n) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateEntity,\\n session_id: int,\\n) -> Response[Union[CreateEntityResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateEntity,\\n session_id: int,\\n) -> Optional[Union[CreateEntityResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"create_userrule\",\n \"path\": \"zerolink_client/api/fact/create_userrule.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: CreateRule,\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateRule,\\n session_id: int,\\n) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateRule,\\n session_id: int,\\n) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateRule,\\n session_id: int,\\n) -> Response[Union[CreateRuleResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateRule,\\n session_id: int,\\n) -> Optional[Union[CreateRuleResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"create_usertriple\",\n \"path\": \"zerolink_client/api/fact/create_usertriple.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: CreateTriple,\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateTriple,\\n session_id: int,\\n) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateTriple,\\n session_id: int,\\n) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateTriple,\\n session_id: int,\\n) -> Response[Union[CreateFactResponse, HTTPValidationError]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: CreateTriple,\\n session_id: int,\\n) -> Optional[Union[CreateFactResponse, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"get_triple\",\n \"path\": \"zerolink_client/api/kg/get_triple.py\",\n \"snippet\": \"def _get_kwargs(\\n name: str,\\n *,\\n limit: Union[Unset, int] = 10,\\n threshold: Union[Unset, float] = 0.3,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\\ndef sync_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n threshold: Union[Unset, float] = 0.3,\\n) -> Response[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\\ndef sync(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n threshold: Union[Unset, float] = 0.3,\\n) -> Optional[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\\nasync def asyncio_detailed(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n threshold: Union[Unset, float] = 0.3,\\n) -> Response[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\\nasync def asyncio(\\n name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n limit: Union[Unset, int] = 10,\\n threshold: Union[Unset, float] = 0.3,\\n) -> Optional[Union[HTTPValidationError, List[\\\"Triple\\\"]]]:\"\n },\n {\n \"identifier\": \"post_question\",\n \"path\": \"zerolink_client/api/question/post_question.py\",\n \"snippet\": \"def _get_kwargs(\\n *,\\n body: Question,\\n session_id: Union[Unset, int] = UNSET,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, QuestionResponse]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, QuestionResponse]]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: Question,\\n session_id: Union[Unset, int] = UNSET,\\n) -> Response[Union[HTTPValidationError, QuestionResponse]]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: Question,\\n session_id: Union[Unset, int] = UNSET,\\n) -> Optional[Union[HTTPValidationError, QuestionResponse]]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: Question,\\n session_id: Union[Unset, int] = UNSET,\\n) -> Response[Union[HTTPValidationError, QuestionResponse]]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n body: Question,\\n session_id: Union[Unset, int] = UNSET,\\n) -> Optional[Union[HTTPValidationError, QuestionResponse]]:\"\n },\n {\n \"identifier\": \"create_session\",\n \"path\": \"zerolink_client/api/session/create_session.py\",\n \"snippet\": \"def _get_kwargs(\\n user_id: str,\\n *,\\n name: Union[Unset, str] = UNSET,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[ChatSession, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[ChatSession, HTTPValidationError]]:\\ndef sync_detailed(\\n user_id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n name: Union[Unset, str] = UNSET,\\n) -> Response[Union[ChatSession, HTTPValidationError]]:\\ndef sync(\\n user_id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n name: Union[Unset, str] = UNSET,\\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n user_id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n name: Union[Unset, str] = UNSET,\\n) -> Response[Union[ChatSession, HTTPValidationError]]:\\nasync def asyncio(\\n user_id: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n name: Union[Unset, str] = UNSET,\\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"get_session_entities\",\n \"path\": \"zerolink_client/api/session/get_session_entities.py\",\n \"snippet\": \"def _get_kwargs(\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\\ndef sync_detailed(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\\ndef sync(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\\nasync def asyncio_detailed(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\\nasync def asyncio(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"GenericEntity\\\"]]]:\"\n },\n {\n \"identifier\": \"get_session_facts\",\n \"path\": \"zerolink_client/api/session/get_session_facts.py\",\n \"snippet\": \"def _get_kwargs(\\n session_id: int,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\\ndef sync_detailed(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\\ndef sync(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\\nasync def asyncio_detailed(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\\nasync def asyncio(\\n session_id: int,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[HTTPValidationError, List[\\\"GenericTriple\\\"]]]:\"\n },\n {\n \"identifier\": \"get_user_session\",\n \"path\": \"zerolink_client/api/session/get_user_session.py\",\n \"snippet\": \"def _get_kwargs(\\n user_id: str,\\n session_name: str,\\n) -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[Union[ChatSession, HTTPValidationError]]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[Union[ChatSession, HTTPValidationError]]:\\ndef sync_detailed(\\n user_id: str,\\n session_name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[ChatSession, HTTPValidationError]]:\\ndef sync(\\n user_id: str,\\n session_name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\\nasync def asyncio_detailed(\\n user_id: str,\\n session_name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[Union[ChatSession, HTTPValidationError]]:\\nasync def asyncio(\\n user_id: str,\\n session_name: str,\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[Union[ChatSession, HTTPValidationError]]:\"\n },\n {\n \"identifier\": \"create_user\",\n \"path\": \"zerolink_client/api/user/create_user.py\",\n \"snippet\": \"def _get_kwargs() -> Dict[str, Any]:\\ndef _parse_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Optional[CreateUser]:\\ndef _build_response(*, client: Union[AuthenticatedClient, Client], response: httpx.Response) -> Response[CreateUser]:\\ndef sync_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[CreateUser]:\\ndef sync(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[CreateUser]:\\nasync def asyncio_detailed(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Response[CreateUser]:\\nasync def asyncio(\\n *,\\n client: Union[AuthenticatedClient, Client],\\n) -> Optional[CreateUser]:\"\n },\n {\n \"identifier\": \"ChatSession\",\n \"path\": \"zerolink_client/models/chat_session.py\",\n \"snippet\": \"class ChatSession:\\n \\\"\\\"\\\"A user chat session.\\n\\n Attributes:\\n id (int):\\n name (str): The name of the chat session\\n index (int):\\n requests (List['Req']):\\n responses (List['Rep']):\\n created_on (datetime.datetime):\\n \\\"\\\"\\\"\\n\\n id: int\\n name: str\\n index: int\\n requests: List[\\\"Req\\\"]\\n responses: List[\\\"Rep\\\"]\\n created_on: datetime.datetime\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.rep import Rep\\n from ..models.req import Req\\n\\n id = self.id\\n\\n name = self.name\\n\\n index = self.index\\n\\n requests = []\\n for requests_item_data in self.requests:\\n requests_item = requests_item_data.to_dict()\\n requests.append(requests_item)\\n\\n responses = []\\n for responses_item_data in self.responses:\\n responses_item = responses_item_data.to_dict()\\n responses.append(responses_item)\\n\\n created_on = self.created_on.isoformat()\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"id\\\": id,\\n \\\"name\\\": name,\\n \\\"index\\\": index,\\n \\\"requests\\\": requests,\\n \\\"responses\\\": responses,\\n \\\"created_on\\\": created_on,\\n }\\n )\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.rep import Rep\\n from ..models.req import Req\\n\\n d = src_dict.copy()\\n id = d.pop(\\\"id\\\")\\n\\n name = d.pop(\\\"name\\\")\\n\\n index = d.pop(\\\"index\\\")\\n\\n requests = []\\n _requests = d.pop(\\\"requests\\\")\\n for requests_item_data in _requests:\\n requests_item = Req.from_dict(requests_item_data)\\n\\n requests.append(requests_item)\\n\\n responses = []\\n _responses = d.pop(\\\"responses\\\")\\n for responses_item_data in _responses:\\n responses_item = Rep.from_dict(responses_item_data)\\n\\n responses.append(responses_item)\\n\\n created_on = isoparse(d.pop(\\\"created_on\\\"))\\n\\n chat_session = cls(\\n id=id,\\n name=name,\\n index=index,\\n requests=requests,\\n responses=responses,\\n created_on=created_on,\\n )\\n\\n chat_session.additional_properties = d\\n return chat_session\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateAttribute\",\n \"path\": \"zerolink_client/models/create_attribute.py\",\n \"snippet\": \"class CreateAttribute:\\n \\\"\\\"\\\"\\n Attributes:\\n subject (str): EID of a builtin entity\\n predicate (str): Name of attribute\\n attribute (Attribute):\\n \\\"\\\"\\\"\\n\\n subject: str\\n predicate: str\\n attribute: \\\"Attribute\\\"\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.attribute import Attribute\\n\\n subject = self.subject\\n\\n predicate = self.predicate\\n\\n attribute = self.attribute.to_dict()\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"subject\\\": subject,\\n \\\"predicate\\\": predicate,\\n \\\"attribute\\\": attribute,\\n }\\n )\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.attribute import Attribute\\n\\n d = src_dict.copy()\\n subject = d.pop(\\\"subject\\\")\\n\\n predicate = d.pop(\\\"predicate\\\")\\n\\n attribute = Attribute.from_dict(d.pop(\\\"attribute\\\"))\\n\\n create_attribute = cls(\\n subject=subject,\\n predicate=predicate,\\n attribute=attribute,\\n )\\n\\n create_attribute.additional_properties = d\\n return create_attribute\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateEntity\",\n \"path\": \"zerolink_client/models/create_entity.py\",\n \"snippet\": \"class CreateEntity:\\n \\\"\\\"\\\"\\n Attributes:\\n entity (str): Name of entity\\n entity_type (Union[Unset, EntityType]): Entity types are entities that map to base ontological entities in\\n Foundation.\\n entity_str (Union[Unset, str]): User specified type\\n is_class (Union[Unset, bool]): Whether the entity is a class or instance Default: False.\\n \\\"\\\"\\\"\\n\\n entity: str\\n entity_type: Union[Unset, EntityType] = UNSET\\n entity_str: Union[Unset, str] = UNSET\\n is_class: Union[Unset, bool] = False\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n entity = self.entity\\n\\n entity_type: Union[Unset, str] = UNSET\\n if not isinstance(self.entity_type, Unset):\\n entity_type = self.entity_type.value\\n\\n entity_str = self.entity_str\\n\\n is_class = self.is_class\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"entity\\\": entity,\\n }\\n )\\n if entity_type is not UNSET:\\n field_dict[\\\"entity_type\\\"] = entity_type\\n if entity_str is not UNSET:\\n field_dict[\\\"entity_str\\\"] = entity_str\\n if is_class is not UNSET:\\n field_dict[\\\"is_class\\\"] = is_class\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n entity = d.pop(\\\"entity\\\")\\n\\n _entity_type = d.pop(\\\"entity_type\\\", UNSET)\\n entity_type: Union[Unset, EntityType]\\n if isinstance(_entity_type, Unset):\\n entity_type = UNSET\\n else:\\n entity_type = EntityType(_entity_type)\\n\\n entity_str = d.pop(\\\"entity_str\\\", UNSET)\\n\\n is_class = d.pop(\\\"is_class\\\", UNSET)\\n\\n create_entity = cls(\\n entity=entity,\\n entity_type=entity_type,\\n entity_str=entity_str,\\n is_class=is_class,\\n )\\n\\n create_entity.additional_properties = d\\n return create_entity\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateRule\",\n \"path\": \"zerolink_client/models/create_rule.py\",\n \"snippet\": \"class CreateRule:\\n \\\"\\\"\\\"\\n Attributes:\\n rule (str): Textual representation of the rule to parse\\n context (Union[Unset, CreateRuleContext]): Context of entities to use for parsing the rule\\n \\\"\\\"\\\"\\n\\n rule: str\\n context: Union[Unset, \\\"CreateRuleContext\\\"] = UNSET\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.create_rule_context import CreateRuleContext\\n\\n rule = self.rule\\n\\n context: Union[Unset, Dict[str, Any]] = UNSET\\n if not isinstance(self.context, Unset):\\n context = self.context.to_dict()\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"rule\\\": rule,\\n }\\n )\\n if context is not UNSET:\\n field_dict[\\\"context\\\"] = context\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.create_rule_context import CreateRuleContext\\n\\n d = src_dict.copy()\\n rule = d.pop(\\\"rule\\\")\\n\\n _context = d.pop(\\\"context\\\", UNSET)\\n context: Union[Unset, CreateRuleContext]\\n if isinstance(_context, Unset):\\n context = UNSET\\n else:\\n context = CreateRuleContext.from_dict(_context)\\n\\n create_rule = cls(\\n rule=rule,\\n context=context,\\n )\\n\\n create_rule.additional_properties = d\\n return create_rule\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateRuleResponse\",\n \"path\": \"zerolink_client/models/create_rule_response.py\",\n \"snippet\": \"class CreateRuleResponse:\\n \\\"\\\"\\\"\\n Attributes:\\n id (str):\\n \\\"\\\"\\\"\\n\\n id: str\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n id = self.id\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"id\\\": id,\\n }\\n )\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n id = d.pop(\\\"id\\\")\\n\\n create_rule_response = cls(\\n id=id,\\n )\\n\\n create_rule_response.additional_properties = d\\n return create_rule_response\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateTriple\",\n \"path\": \"zerolink_client/models/create_triple.py\",\n \"snippet\": \"class CreateTriple:\\n \\\"\\\"\\\"\\n Attributes:\\n predicate (str): Name of predicate relation\\n user_subject (Union[Unset, str]): EID of a user entity\\n subject (Union[Unset, str]): EID of a builtin entity\\n user_object (Union[Unset, str]): EID of a user entity\\n object_ (Union[Unset, str]): EID of a builtin entity\\n \\\"\\\"\\\"\\n\\n predicate: str\\n user_subject: Union[Unset, str] = UNSET\\n subject: Union[Unset, str] = UNSET\\n user_object: Union[Unset, str] = UNSET\\n object_: Union[Unset, str] = UNSET\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n predicate = self.predicate\\n\\n user_subject = self.user_subject\\n\\n subject = self.subject\\n\\n user_object = self.user_object\\n\\n object_ = self.object_\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"predicate\\\": predicate,\\n }\\n )\\n if user_subject is not UNSET:\\n field_dict[\\\"user_subject\\\"] = user_subject\\n if subject is not UNSET:\\n field_dict[\\\"subject\\\"] = subject\\n if user_object is not UNSET:\\n field_dict[\\\"user_object\\\"] = user_object\\n if object_ is not UNSET:\\n field_dict[\\\"object\\\"] = object_\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n predicate = d.pop(\\\"predicate\\\")\\n\\n user_subject = d.pop(\\\"user_subject\\\", UNSET)\\n\\n subject = d.pop(\\\"subject\\\", UNSET)\\n\\n user_object = d.pop(\\\"user_object\\\", UNSET)\\n\\n object_ = d.pop(\\\"object\\\", UNSET)\\n\\n create_triple = cls(\\n predicate=predicate,\\n user_subject=user_subject,\\n subject=subject,\\n user_object=user_object,\\n object_=object_,\\n )\\n\\n create_triple.additional_properties = d\\n return create_triple\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"CreateTuneJobResponse\",\n \"path\": \"zerolink_client/models/create_tune_job_response.py\",\n \"snippet\": \"class CreateTuneJobResponse:\\n \\\"\\\"\\\"\\n Attributes:\\n id (str):\\n status (str):\\n \\\"\\\"\\\"\\n\\n id: str\\n status: str\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n id = self.id\\n\\n status = self.status\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"id\\\": id,\\n \\\"status\\\": status,\\n }\\n )\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n id = d.pop(\\\"id\\\")\\n\\n status = d.pop(\\\"status\\\")\\n\\n create_tune_job_response = cls(\\n id=id,\\n status=status,\\n )\\n\\n create_tune_job_response.additional_properties = d\\n return create_tune_job_response\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"Entity\",\n \"path\": \"zerolink_client/models/entity.py\",\n \"snippet\": \"class Entity:\\n \\\"\\\"\\\"\\n Attributes:\\n id (str):\\n entity (str):\\n description (Union[Unset, str]):\\n source (Union[Unset, str]):\\n source_url (Union[Unset, str]):\\n ontology (Union[Unset, Graph]):\\n source_id (Union[Unset, str]):\\n \\\"\\\"\\\"\\n\\n id: str\\n entity: str\\n description: Union[Unset, str] = UNSET\\n source: Union[Unset, str] = UNSET\\n source_url: Union[Unset, str] = UNSET\\n ontology: Union[Unset, \\\"Graph\\\"] = UNSET\\n source_id: Union[Unset, str] = UNSET\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.graph import Graph\\n\\n id = self.id\\n\\n entity = self.entity\\n\\n description = self.description\\n\\n source = self.source\\n\\n source_url = self.source_url\\n\\n ontology: Union[Unset, Dict[str, Any]] = UNSET\\n if not isinstance(self.ontology, Unset):\\n ontology = self.ontology.to_dict()\\n\\n source_id = self.source_id\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"id\\\": id,\\n \\\"entity\\\": entity,\\n }\\n )\\n if description is not UNSET:\\n field_dict[\\\"description\\\"] = description\\n if source is not UNSET:\\n field_dict[\\\"source\\\"] = source\\n if source_url is not UNSET:\\n field_dict[\\\"source_url\\\"] = source_url\\n if ontology is not UNSET:\\n field_dict[\\\"ontology\\\"] = ontology\\n if source_id is not UNSET:\\n field_dict[\\\"source_id\\\"] = source_id\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.graph import Graph\\n\\n d = src_dict.copy()\\n id = d.pop(\\\"id\\\")\\n\\n entity = d.pop(\\\"entity\\\")\\n\\n description = d.pop(\\\"description\\\", UNSET)\\n\\n source = d.pop(\\\"source\\\", UNSET)\\n\\n source_url = d.pop(\\\"source_url\\\", UNSET)\\n\\n _ontology = d.pop(\\\"ontology\\\", UNSET)\\n ontology: Union[Unset, Graph]\\n if isinstance(_ontology, Unset):\\n ontology = UNSET\\n else:\\n ontology = Graph.from_dict(_ontology)\\n\\n source_id = d.pop(\\\"source_id\\\", UNSET)\\n\\n entity = cls(\\n id=id,\\n entity=entity,\\n description=description,\\n source=source,\\n source_url=source_url,\\n ontology=ontology,\\n source_id=source_id,\\n )\\n\\n entity.additional_properties = d\\n return entity\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"HTTPValidationError\",\n \"path\": \"zerolink_client/models/http_validation_error.py\",\n \"snippet\": \"class HTTPValidationError:\\n \\\"\\\"\\\"\\n Attributes:\\n detail (Union[Unset, List['ValidationError']]):\\n \\\"\\\"\\\"\\n\\n detail: Union[Unset, List[\\\"ValidationError\\\"]] = UNSET\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.validation_error import ValidationError\\n\\n detail: Union[Unset, List[Dict[str, Any]]] = UNSET\\n if not isinstance(self.detail, Unset):\\n detail = []\\n for detail_item_data in self.detail:\\n detail_item = detail_item_data.to_dict()\\n detail.append(detail_item)\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update({})\\n if detail is not UNSET:\\n field_dict[\\\"detail\\\"] = detail\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.validation_error import ValidationError\\n\\n d = src_dict.copy()\\n detail = []\\n _detail = d.pop(\\\"detail\\\", UNSET)\\n for detail_item_data in _detail or []:\\n detail_item = ValidationError.from_dict(detail_item_data)\\n\\n detail.append(detail_item)\\n\\n http_validation_error = cls(\\n detail=detail,\\n )\\n\\n http_validation_error.additional_properties = d\\n return http_validation_error\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"Question\",\n \"path\": \"zerolink_client/models/question.py\",\n \"snippet\": \"class Question:\\n \\\"\\\"\\\"A question to be answered by querying the knowledge graph and reasoner.\\n\\n Attributes:\\n body (str): The body of the question\\n world (Union[Unset, WorldAssumption]): The world assumption is the assumption about the world that the reasoner\\n makes. This is used to determine the answer to a query. For example, if\\n the world assumption is \\\"closed\\\" then the reasoner will assume that the\\n answer to the query is \\\"no\\\" if it cannot find a triple to satisfy the\\n query. Default: WorldAssumption.CLOSED.\\n spatial (Union[Unset, SpatialAssumption]): The spatial assumption is the assumption about space that the\\n reasoner\\n makes. This is used to determine the answer to a query. For example, if the\\n spatial assumption is \\\"earth\\\" then the reasoner will only consider\\n geographic locations on Earth and will assume all instances of 'location'\\n are on Earth. If the spatial assumption is \\\"universe\\\" then the reasoner\\n then this restriction is lifted and the reasoner will consider all\\n locations in the universe. Default: SpatialAssumption.EARTH.\\n temporal (Union[Unset, TemporalAssumption]): The temporal assumption is the assumption about time that the\\n reasoner\\n makes. This is used to determine the answer to a query. For example, if\\n the temporal assumption is \\\"current\\\" then the reasoner will only consider\\n triples that refer to entities that are non-historical. Excluding things\\n like the Roman Empire and Francoist Spain. Default: TemporalAssumption.CURRENT.\\n context (Union[Unset, ContextAssumption]): The context assumption is the assumption about the context that the\\n reasoner makes. This is used to determine the answer to a query. For\\n example, if the context assumption is \\\"none\\\" then the reasoner will only\\n consider basic triples like instance_of and subclass_of. If the context\\n assumption is \\\"local\\\" then the reasoner will consider triples that are\\n defined by the user. If the context assumption is \\\"global\\\" then the\\n reasoner will consider all queryable triples. Default: ContextAssumption.GLOBAL.\\n \\\"\\\"\\\"\\n\\n body: str\\n world: Union[Unset, WorldAssumption] = WorldAssumption.CLOSED\\n spatial: Union[Unset, SpatialAssumption] = SpatialAssumption.EARTH\\n temporal: Union[Unset, TemporalAssumption] = TemporalAssumption.CURRENT\\n context: Union[Unset, ContextAssumption] = ContextAssumption.GLOBAL\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n body = self.body\\n\\n world: Union[Unset, str] = UNSET\\n if not isinstance(self.world, Unset):\\n world = self.world.value\\n\\n spatial: Union[Unset, str] = UNSET\\n if not isinstance(self.spatial, Unset):\\n spatial = self.spatial.value\\n\\n temporal: Union[Unset, str] = UNSET\\n if not isinstance(self.temporal, Unset):\\n temporal = self.temporal.value\\n\\n context: Union[Unset, str] = UNSET\\n if not isinstance(self.context, Unset):\\n context = self.context.value\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"body\\\": body,\\n }\\n )\\n if world is not UNSET:\\n field_dict[\\\"world\\\"] = world\\n if spatial is not UNSET:\\n field_dict[\\\"spatial\\\"] = spatial\\n if temporal is not UNSET:\\n field_dict[\\\"temporal\\\"] = temporal\\n if context is not UNSET:\\n field_dict[\\\"context\\\"] = context\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n body = d.pop(\\\"body\\\")\\n\\n _world = d.pop(\\\"world\\\", UNSET)\\n world: Union[Unset, WorldAssumption]\\n if isinstance(_world, Unset):\\n world = UNSET\\n else:\\n world = WorldAssumption(_world)\\n\\n _spatial = d.pop(\\\"spatial\\\", UNSET)\\n spatial: Union[Unset, SpatialAssumption]\\n if isinstance(_spatial, Unset):\\n spatial = UNSET\\n else:\\n spatial = SpatialAssumption(_spatial)\\n\\n _temporal = d.pop(\\\"temporal\\\", UNSET)\\n temporal: Union[Unset, TemporalAssumption]\\n if isinstance(_temporal, Unset):\\n temporal = UNSET\\n else:\\n temporal = TemporalAssumption(_temporal)\\n\\n _context = d.pop(\\\"context\\\", UNSET)\\n context: Union[Unset, ContextAssumption]\\n if isinstance(_context, Unset):\\n context = UNSET\\n else:\\n context = ContextAssumption(_context)\\n\\n question = cls(\\n body=body,\\n world=world,\\n spatial=spatial,\\n temporal=temporal,\\n context=context,\\n )\\n\\n question.additional_properties = d\\n return question\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"QuestionResponse\",\n \"path\": \"zerolink_client/models/question_response.py\",\n \"snippet\": \"class QuestionResponse:\\n \\\"\\\"\\\"A response to a question request.\\n\\n Attributes:\\n id (int): The ID of the question\\n msg (str): A message describing the result of the question\\n status (ResultStatus): The status of a result.\\n answers (List[str]): The answers to the question\\n methods (List[str]): The methods used to answer the question\\n reasoners (List[str]): The reasoners used to answer the question\\n query (Union[Unset, QuestionResponseQuery]): The query used to answer the question\\n \\\"\\\"\\\"\\n\\n id: int\\n msg: str\\n status: ResultStatus\\n answers: List[str]\\n methods: List[str]\\n reasoners: List[str]\\n query: Union[Unset, \\\"QuestionResponseQuery\\\"] = UNSET\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n from ..models.question_response_query import QuestionResponseQuery\\n\\n id = self.id\\n\\n msg = self.msg\\n\\n status = self.status.value\\n\\n answers = self.answers\\n\\n methods = self.methods\\n\\n reasoners = self.reasoners\\n\\n query: Union[Unset, Dict[str, Any]] = UNSET\\n if not isinstance(self.query, Unset):\\n query = self.query.to_dict()\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"id\\\": id,\\n \\\"msg\\\": msg,\\n \\\"status\\\": status,\\n \\\"answers\\\": answers,\\n \\\"methods\\\": methods,\\n \\\"reasoners\\\": reasoners,\\n }\\n )\\n if query is not UNSET:\\n field_dict[\\\"query\\\"] = query\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n from ..models.question_response_query import QuestionResponseQuery\\n\\n d = src_dict.copy()\\n id = d.pop(\\\"id\\\")\\n\\n msg = d.pop(\\\"msg\\\")\\n\\n status = ResultStatus(d.pop(\\\"status\\\"))\\n\\n answers = cast(List[str], d.pop(\\\"answers\\\"))\\n\\n methods = cast(List[str], d.pop(\\\"methods\\\"))\\n\\n reasoners = cast(List[str], d.pop(\\\"reasoners\\\"))\\n\\n _query = d.pop(\\\"query\\\", UNSET)\\n query: Union[Unset, QuestionResponseQuery]\\n if isinstance(_query, Unset):\\n query = UNSET\\n else:\\n query = QuestionResponseQuery.from_dict(_query)\\n\\n question_response = cls(\\n id=id,\\n msg=msg,\\n status=status,\\n answers=answers,\\n methods=methods,\\n reasoners=reasoners,\\n query=query,\\n )\\n\\n question_response.additional_properties = d\\n return question_response\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"TextExtract\",\n \"path\": \"zerolink_client/models/text_extract.py\",\n \"snippet\": \"class TextExtract:\\n \\\"\\\"\\\"\\n Attributes:\\n text (str): Text to extract from\\n extraction_model (Union[Unset, ExtractModel]): An enumeration. Default: ExtractModel.BASE.\\n \\\"\\\"\\\"\\n\\n text: str\\n extraction_model: Union[Unset, ExtractModel] = ExtractModel.BASE\\n additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)\\n\\n def to_dict(self) -> Dict[str, Any]:\\n text = self.text\\n\\n extraction_model: Union[Unset, str] = UNSET\\n if not isinstance(self.extraction_model, Unset):\\n extraction_model = self.extraction_model.value\\n\\n field_dict: Dict[str, Any] = {}\\n field_dict.update(self.additional_properties)\\n field_dict.update(\\n {\\n \\\"text\\\": text,\\n }\\n )\\n if extraction_model is not UNSET:\\n field_dict[\\\"extraction_model\\\"] = extraction_model\\n\\n return field_dict\\n\\n @classmethod\\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\\n d = src_dict.copy()\\n text = d.pop(\\\"text\\\")\\n\\n _extraction_model = d.pop(\\\"extraction_model\\\", UNSET)\\n extraction_model: Union[Unset, ExtractModel]\\n if isinstance(_extraction_model, Unset):\\n extraction_model = UNSET\\n else:\\n extraction_model = ExtractModel(_extraction_model)\\n\\n text_extract = cls(\\n text=text,\\n extraction_model=extraction_model,\\n )\\n\\n text_extract.additional_properties = d\\n return text_extract\\n\\n @property\\n def additional_keys(self) -> List[str]:\\n return list(self.additional_properties.keys())\\n\\n def __getitem__(self, key: str) -> Any:\\n return self.additional_properties[key]\\n\\n def __setitem__(self, key: str, value: Any) -> None:\\n self.additional_properties[key] = value\\n\\n def __delitem__(self, key: str) -> None:\\n del self.additional_properties[key]\\n\\n def __contains__(self, key: str) -> bool:\\n return key in self.additional_properties\"\n },\n {\n \"identifier\": \"File\",\n \"path\": \"zerolink_client/types.py\",\n \"snippet\": \"class File:\\n \\\"\\\"\\\"Contains information for file uploads\\\"\\\"\\\"\\n\\n payload: BinaryIO\\n file_name: Optional[str] = None\\n mime_type: Optional[str] = None\\n\\n def to_tuple(self) -> FileJsonType:\\n \\\"\\\"\\\"Return a tuple representation that httpx will accept for multipart/form-data\\\"\\\"\\\"\\n return self.file_name, self.payload, self.mime_type\"\n },\n {\n \"identifier\": \"UNSET\",\n \"path\": \"zerolink_client/types.py\",\n \"snippet\": \"UNSET: Unset = Unset()\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from typing import Any, Optional, cast\nfrom zerolink import settings\nfrom zerolink.exc import APIError, AuthenticationError\nfrom zerolink_client import Client\nfrom zerolink_client.api.default import finetune, get_models_models_get\nfrom zerolink_client.api.entity import (\n desc_entity_id,\n desc_entity_ontology,\n lookup_entity,\n lookup_relation,\n search_entity,\n)\nfrom zerolink_client.api.extract import extract_text\nfrom zerolink_client.api.fact import (\n create_userattribute,\n create_userentity,\n create_userrule,\n create_usertriple,\n)\nfrom zerolink_client.api.kg import get_triple\nfrom zerolink_client.api.question import post_question\nfrom zerolink_client.api.session import (\n create_session,\n get_session_entities,\n get_session_facts,\n get_user_session,\n)\nfrom zerolink_client.api.user import create_user\nfrom zerolink_client.models import (\n ChatSession,\n CreateAttribute,\n CreateEntity,\n CreateRule,\n CreateRuleResponse,\n CreateTriple,\n CreateTuneJobResponse,\n Entity,\n HTTPValidationError,\n Question,\n QuestionResponse,\n TextExtract,\n)\nfrom zerolink_client.types import File, UNSET"},"token_num":{"kind":"number","value":16468,"string":"16,468"},"cropped_code":{"kind":"string","value":"\n\n# ------------------------------------------------------------------------\n# Endpoints\n# ------------------------------------------------------------------------\n\nclient = Client(\n base_url=settings.server_url,\n raise_on_unexpected_status=False,\n)\n\n\ndef check_api_key() -> None:\n \"\"\"\n Check if the API key is set.\n \"\"\"\n if settings.api_key is None:\n raise AuthenticationError()\n else:\n pass\n\n\ndef get_user_id() -> str:\n \"\"\"\n Get the user ID from the server. Only used for Demo server.\n \"\"\"\n client._headers[\"Authorization\"] = settings.api_key\n rep = create_user.sync(client=client)\n if rep is None:\n raise Exception(\"Failed to authenticate.\")\n settings.api_key = rep.user_id\n if isinstance(rep, HTTPValidationError):\n raise APIError(str(rep))\n return rep.user_id\n\n\ndef post_session(user_id: str, **kwargs) -> Optional[ChatSession]:\n \"\"\"\n Create a new session.\n \"\"\"\n check_api_key()\n if user_id is None:\n user_id = settings.api_key\n rep = create_session.sync(client=client, user_id=user_id, **kwargs)\n if isinstance(rep, HTTPValidationError):\n raise APIError(str(rep))\n return rep\n\n\ndef get_session_name(user_id: str, session_name: str, **kwargs):\n \"\"\"\n Lookup a session by user and name.\n \"\"\"\n check_api_key()\n rep = get_user_session.sync_detailed(user_id, session_name, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n elif rep.status_code == 404:\n return None\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef get_session_entities_list(session_id: int, **kwargs):\n \"\"\"\n Get the entities of a session.\n \"\"\"\n check_api_key()\n rep = get_session_entities.sync_detailed(session_id, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef get_session_facts_list(session_id: int, **kwargs):\n \"\"\"\n Get the facts of a session.\n \"\"\"\n check_api_key()\n rep = get_session_facts.sync_detailed(session_id, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef ask_question(\n session_id: Optional[int],\n body: str,\n assumps: Optional[dict[str, Any]] = None,\n **kwargs,\n) -> QuestionResponse:\n \"\"\"\n Ask a question.\n \"\"\"\n check_api_key()\n rep = post_question.sync_detailed(\n client=client,\n session_id=(session_id or UNSET),\n body=Question(body=body, **(assumps or {})),\n **kwargs,\n )\n if rep.status_code == 200:\n return cast(QuestionResponse, rep.parsed)\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\n"},"all_code":{"kind":"string","value":"\n\n# ------------------------------------------------------------------------\n# Endpoints\n# ------------------------------------------------------------------------\n\nclient = Client(\n base_url=settings.server_url,\n raise_on_unexpected_status=False,\n)\n\n\ndef check_api_key() -> None:\n \"\"\"\n Check if the API key is set.\n \"\"\"\n if settings.api_key is None:\n raise AuthenticationError()\n else:\n pass\n\n\ndef get_user_id() -> str:\n \"\"\"\n Get the user ID from the server. Only used for Demo server.\n \"\"\"\n client._headers[\"Authorization\"] = settings.api_key\n rep = create_user.sync(client=client)\n if rep is None:\n raise Exception(\"Failed to authenticate.\")\n settings.api_key = rep.user_id\n if isinstance(rep, HTTPValidationError):\n raise APIError(str(rep))\n return rep.user_id\n\n\ndef post_session(user_id: str, **kwargs) -> Optional[ChatSession]:\n \"\"\"\n Create a new session.\n \"\"\"\n check_api_key()\n if user_id is None:\n user_id = settings.api_key\n rep = create_session.sync(client=client, user_id=user_id, **kwargs)\n if isinstance(rep, HTTPValidationError):\n raise APIError(str(rep))\n return rep\n\n\ndef get_session_name(user_id: str, session_name: str, **kwargs):\n \"\"\"\n Lookup a session by user and name.\n \"\"\"\n check_api_key()\n rep = get_user_session.sync_detailed(user_id, session_name, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n elif rep.status_code == 404:\n return None\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef get_session_entities_list(session_id: int, **kwargs):\n \"\"\"\n Get the entities of a session.\n \"\"\"\n check_api_key()\n rep = get_session_entities.sync_detailed(session_id, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef get_session_facts_list(session_id: int, **kwargs):\n \"\"\"\n Get the facts of a session.\n \"\"\"\n check_api_key()\n rep = get_session_facts.sync_detailed(session_id, client=client, **kwargs)\n if rep.status_code == 200:\n return rep.parsed\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n\ndef ask_question(\n session_id: Optional[int],\n body: str,\n assumps: Optional[dict[str, Any]] = None,\n **kwargs,\n) -> QuestionResponse:\n \"\"\"\n Ask a question.\n \"\"\"\n check_api_key()\n rep = post_question.sync_detailed(\n client=client,\n session_id=(session_id or UNSET),\n body=Question(body=body, **(assumps or {})),\n **kwargs,\n )\n if rep.status_code == 200:\n return cast(QuestionResponse, rep.parsed)\n else:\n err = rep.content.decode(\"utf-8\")\n print(err)\n raise APIError(err)\n\n"},"next_line":{"kind":"string","value":"def get_entity_id(id: str, **kwargs) -> Entity:"},"gold_snippet_index":{"kind":"number","value":30,"string":"30"},"created_at":{"kind":"string","value":"2023-12-03 07:50:04+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5393,"cells":{"repo_name":{"kind":"string","value":"JunMa11/UHNSeg-Quiz"},"file_path":{"kind":"string","value":"nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py"},"context":{"kind":"list like","value":[{"identifier":"DC_and_BCE_loss","path":"nnunetv2/training/loss/compound_losses.py","snippet":"class DC_and_BCE_loss(nn.Module):\n def __init__(self, bce_kwargs, soft_dice_kwargs, weight_ce=1, weight_dice=1, use_ignore_label: bool = False,\n dice_class=MemoryEfficientSoftDiceLoss):\n \"\"\"\n DO NOT APPLY NONLINEARITY IN YOUR NETWORK!\n\n target mut be one hot encoded\n IMPORTANT: We assume use_ignore_label is located in target[:, -1]!!!\n\n :param soft_dice_kwargs:\n :param bce_kwargs:\n :param aggregate:\n \"\"\"\n super(DC_and_BCE_loss, self).__init__()\n if use_ignore_label:\n bce_kwargs['reduction'] = 'none'\n\n self.weight_dice = weight_dice\n self.weight_ce = weight_ce\n self.use_ignore_label = use_ignore_label\n\n self.ce = nn.BCEWithLogitsLoss(**bce_kwargs)\n self.dc = dice_class(apply_nonlin=torch.sigmoid, **soft_dice_kwargs)\n\n def forward(self, net_output: torch.Tensor, target: torch.Tensor):\n if self.use_ignore_label:\n # target is one hot encoded here. invert it so that it is True wherever we can compute the loss\n mask = (1 - target[:, -1:]).bool()\n # remove ignore channel now that we have the mask\n target_regions = torch.clone(target[:, :-1])\n else:\n target_regions = target\n mask = None\n\n dc_loss = self.dc(net_output, target_regions, loss_mask=mask)\n if mask is not None:\n ce_loss = (self.ce(net_output, target_regions) * mask).sum() / torch.clip(mask.sum(), min=1e-8)\n else:\n ce_loss = self.ce(net_output, target_regions)\n result = self.weight_ce * ce_loss + self.weight_dice * dc_loss\n return result"},{"identifier":"DC_and_CE_loss","path":"nnunetv2/training/loss/compound_losses.py","snippet":"class DC_and_CE_loss(nn.Module):\n def __init__(self, soft_dice_kwargs, ce_kwargs, weight_ce=1, weight_dice=1, ignore_label=None,\n dice_class=SoftDiceLoss):\n \"\"\"\n Weights for CE and Dice do not need to sum to one. You can set whatever you want.\n :param soft_dice_kwargs:\n :param ce_kwargs:\n :param aggregate:\n :param square_dice:\n :param weight_ce:\n :param weight_dice:\n \"\"\"\n super(DC_and_CE_loss, self).__init__()\n if ignore_label is not None:\n ce_kwargs['ignore_index'] = ignore_label\n\n self.weight_dice = weight_dice\n self.weight_ce = weight_ce\n self.ignore_label = ignore_label\n\n self.ce = RobustCrossEntropyLoss(**ce_kwargs)\n self.dc = dice_class(apply_nonlin=softmax_helper_dim1, **soft_dice_kwargs)\n\n def forward(self, net_output: torch.Tensor, target: torch.Tensor):\n \"\"\"\n target must be b, c, x, y(, z) with c=1\n :param net_output:\n :param target:\n :return:\n \"\"\"\n if self.ignore_label is not None:\n assert target.shape[1] == 1, 'ignore label is not implemented for one hot encoded target variables ' \\\n '(DC_and_CE_loss)'\n mask = (target != self.ignore_label).bool()\n # remove ignore label from target, replace with one of the known labels. It doesn't matter because we\n # ignore gradients in those areas anyway\n target_dice = torch.clone(target)\n target_dice[target == self.ignore_label] = 0\n num_fg = mask.sum()\n else:\n target_dice = target\n mask = None\n\n dc_loss = self.dc(net_output, target_dice, loss_mask=mask) \\\n if self.weight_dice != 0 else 0\n ce_loss = self.ce(net_output, target[:, 0].long()) \\\n if self.weight_ce != 0 and (self.ignore_label is None or num_fg > 0) else 0\n\n result = self.weight_ce * ce_loss + self.weight_dice * dc_loss\n return result"},{"identifier":"get_tp_fp_fn_tn","path":"nnunetv2/training/loss/dice.py","snippet":"def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):\n \"\"\"\n net_output must be (b, c, x, y(, z)))\n gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))\n if mask is provided it must have shape (b, 1, x, y(, z)))\n :param net_output:\n :param gt:\n :param axes: can be (, ) = no summation\n :param mask: mask must be 1 for valid pixels and 0 for invalid pixels\n :param square: if True then fp, tp and fn will be squared before summation\n :return:\n \"\"\"\n if axes is None:\n axes = tuple(range(2, len(net_output.size())))\n\n shp_x = net_output.shape\n shp_y = gt.shape\n\n with torch.no_grad():\n if len(shp_x) != len(shp_y):\n gt = gt.view((shp_y[0], 1, *shp_y[1:]))\n\n if net_output.shape == gt.shape:\n # if this is the case then gt is probably already a one hot encoding\n y_onehot = gt\n else:\n gt = gt.long()\n y_onehot = torch.zeros(shp_x, device=net_output.device)\n y_onehot.scatter_(1, gt, 1)\n\n tp = net_output * y_onehot\n fp = net_output * (1 - y_onehot)\n fn = (1 - net_output) * y_onehot\n tn = (1 - net_output) * (1 - y_onehot)\n\n if mask is not None:\n with torch.no_grad():\n mask_here = torch.tile(mask, (1, tp.shape[1], *[1 for i in range(2, len(tp.shape))]))\n tp *= mask_here\n fp *= mask_here\n fn *= mask_here\n tn *= mask_here\n # benchmark whether tiling the mask would be faster (torch.tile). It probably is for large batch sizes\n # OK it barely makes a difference but the implementation above is a tiny bit faster + uses less vram\n # (using nnUNetv2_train 998 3d_fullres 0)\n # tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp, dim=1)), dim=1)\n # fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp, dim=1)), dim=1)\n # fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn, dim=1)), dim=1)\n # tn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tn, dim=1)), dim=1)\n\n if square:\n tp = tp ** 2\n fp = fp ** 2\n fn = fn ** 2\n tn = tn ** 2\n\n if len(axes) > 0:\n tp = tp.sum(dim=axes, keepdim=False)\n fp = fp.sum(dim=axes, keepdim=False)\n fn = fn.sum(dim=axes, keepdim=False)\n tn = tn.sum(dim=axes, keepdim=False)\n\n return tp, fp, fn, tn"},{"identifier":"MemoryEfficientSoftDiceLoss","path":"nnunetv2/training/loss/dice.py","snippet":"class MemoryEfficientSoftDiceLoss(nn.Module):\n def __init__(self, apply_nonlin: Callable = None, batch_dice: bool = False, do_bg: bool = True, smooth: float = 1.,\n ddp: bool = True):\n \"\"\"\n saves 1.6 GB on Dataset017 3d_lowres\n \"\"\"\n super(MemoryEfficientSoftDiceLoss, self).__init__()\n\n self.do_bg = do_bg\n self.batch_dice = batch_dice\n self.apply_nonlin = apply_nonlin\n self.smooth = smooth\n self.ddp = ddp\n\n def forward(self, x, y, loss_mask=None):\n if self.apply_nonlin is not None:\n x = self.apply_nonlin(x)\n\n # make everything shape (b, c)\n axes = list(range(2, len(x.shape)))\n with torch.no_grad():\n if len(x.shape) != len(y.shape):\n y = y.view((y.shape[0], 1, *y.shape[1:]))\n\n if x.shape == y.shape:\n # if this is the case then gt is probably already a one hot encoding\n y_onehot = y\n else:\n gt = y.long()\n y_onehot = torch.zeros(x.shape, device=x.device, dtype=torch.bool)\n y_onehot.scatter_(1, gt, 1)\n\n if not self.do_bg:\n y_onehot = y_onehot[:, 1:]\n\n sum_gt = y_onehot.sum(axes) if loss_mask is None else (y_onehot * loss_mask).sum(axes)\n\n # this one MUST be outside the with torch.no_grad(): context. Otherwise no gradients for you\n if not self.do_bg:\n x = x[:, 1:]\n\n intersect = (x * y_onehot).sum(axes) if loss_mask is None else (x * y_onehot * loss_mask).sum(axes)\n sum_pred = x.sum(axes) if loss_mask is None else (x * loss_mask).sum(axes)\n\n if self.ddp and self.batch_dice:\n intersect = AllGatherGrad.apply(intersect).sum(0)\n sum_pred = AllGatherGrad.apply(sum_pred).sum(0)\n sum_gt = AllGatherGrad.apply(sum_gt).sum(0)\n\n if self.batch_dice:\n intersect = intersect.sum(0)\n sum_pred = sum_pred.sum(0)\n sum_gt = sum_gt.sum(0)\n\n dc = (2 * intersect + self.smooth) / (torch.clip(sum_gt + sum_pred + self.smooth, 1e-8))\n\n dc = dc.mean()\n return -dc"},{"identifier":"nnUNetTrainer","path":"nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py","snippet":"class nnUNetTrainer(object):\n def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,\n device: torch.device = torch.device('cuda')):\n # From https://grugbrain.dev/. Worth a read ya big brains ;-)\n\n # apex predator of grug is complexity\n # complexity bad\n # say again:\n # complexity very bad\n # you say now:\n # complexity very, very bad\n # given choice between complexity or one on one against t-rex, grug take t-rex: at least grug see t-rex\n # complexity is spirit demon that enter codebase through well-meaning but ultimately very clubbable non grug-brain developers and project managers who not fear complexity spirit demon or even know about sometime\n # one day code base understandable and grug can get work done, everything good!\n # next day impossible: complexity demon spirit has entered code and very dangerous situation!\n\n # OK OK I am guilty. But I tried.\n # https://www.osnews.com/images/comics/wtfm.jpg\n # https://i.pinimg.com/originals/26/b2/50/26b250a738ea4abc7a5af4d42ad93af0.jpg\n\n self.is_ddp = dist.is_available() and dist.is_initialized()\n self.local_rank = 0 if not self.is_ddp else dist.get_rank()\n\n self.device = device\n\n # print what device we are using\n if self.is_ddp: # implicitly it's clear that we use cuda in this case\n print(f\"I am local rank {self.local_rank}. {device_count()} GPUs are available. The world size is \"\n f\"{dist.get_world_size()}.\"\n f\"Setting device to {self.device}\")\n self.device = torch.device(type='cuda', index=self.local_rank)\n else:\n if self.device.type == 'cuda':\n # we might want to let the user pick this but for now please pick the correct GPU with CUDA_VISIBLE_DEVICES=X\n self.device = torch.device(type='cuda', index=0)\n print(f\"Using device: {self.device}\")\n\n # loading and saving this class for continuing from checkpoint should not happen based on pickling. This\n # would also pickle the network etc. Bad, bad. Instead we just reinstantiate and then load the checkpoint we\n # need. So let's save the init args\n self.my_init_kwargs = {}\n for k in inspect.signature(self.__init__).parameters.keys():\n self.my_init_kwargs[k] = locals()[k]\n\n ### Saving all the init args into class variables for later access\n self.plans_manager = PlansManager(plans)\n self.configuration_manager = self.plans_manager.get_configuration(configuration)\n self.configuration_name = configuration\n self.dataset_json = dataset_json\n self.fold = fold\n self.unpack_dataset = unpack_dataset\n\n ### Setting all the folder names. We need to make sure things don't crash in case we are just running\n # inference and some of the folders may not be defined!\n self.preprocessed_dataset_folder_base = join(nnUNet_preprocessed, self.plans_manager.dataset_name) \\\n if nnUNet_preprocessed is not None else None\n self.output_folder_base = join(nnUNet_results, self.plans_manager.dataset_name,\n self.__class__.__name__ + '__' + self.plans_manager.plans_name + \"__\" + configuration) \\\n if nnUNet_results is not None else None\n self.output_folder = join(self.output_folder_base, f'fold_{fold}')\n\n self.preprocessed_dataset_folder = join(self.preprocessed_dataset_folder_base,\n self.configuration_manager.data_identifier)\n # unlike the previous nnunet folder_with_segs_from_previous_stage is now part of the plans. For now it has to\n # be a different configuration in the same plans\n # IMPORTANT! the mapping must be bijective, so lowres must point to fullres and vice versa (using\n # \"previous_stage\" and \"next_stage\"). Otherwise it won't work!\n self.is_cascaded = self.configuration_manager.previous_stage_name is not None\n self.folder_with_segs_from_previous_stage = \\\n join(nnUNet_results, self.plans_manager.dataset_name,\n self.__class__.__name__ + '__' + self.plans_manager.plans_name + \"__\" +\n self.configuration_manager.previous_stage_name, 'predicted_next_stage', self.configuration_name) \\\n if self.is_cascaded else None\n\n ### Some hyperparameters for you to fiddle with\n self.initial_lr = 1e-2\n self.weight_decay = 3e-5\n self.oversample_foreground_percent = 0.33\n self.num_iterations_per_epoch = 250\n self.num_val_iterations_per_epoch = 50\n self.num_epochs = 1000\n self.current_epoch = 0\n\n ### Dealing with labels/regions\n self.label_manager = self.plans_manager.get_label_manager(dataset_json)\n # labels can either be a list of int (regular training) or a list of tuples of int (region-based training)\n # needed for predictions. We do sigmoid in case of (overlapping) regions\n\n self.num_input_channels = None # -> self.initialize()\n self.network = None # -> self._get_network()\n self.optimizer = self.lr_scheduler = None # -> self.initialize\n self.grad_scaler = GradScaler() if self.device.type == 'cuda' else None\n self.loss = None # -> self.initialize\n\n ### Simple logging. Don't take that away from me!\n # initialize log file. This is just our log for the print statements etc. Not to be confused with lightning\n # logging\n timestamp = datetime.now()\n maybe_mkdir_p(self.output_folder)\n self.log_file = join(self.output_folder, \"training_log_%d_%d_%d_%02.0d_%02.0d_%02.0d.txt\" %\n (timestamp.year, timestamp.month, timestamp.day, timestamp.hour, timestamp.minute,\n timestamp.second))\n self.logger = nnUNetLogger()\n\n ### placeholders\n self.dataloader_train = self.dataloader_val = None # see on_train_start\n\n ### initializing stuff for remembering things and such\n self._best_ema = None\n\n ### inference things\n self.inference_allowed_mirroring_axes = None # this variable is set in\n # self.configure_rotation_dummyDA_mirroring_and_inital_patch_size and will be saved in checkpoints\n\n ### checkpoint saving stuff\n self.save_every = 50\n self.disable_checkpointing = False\n\n ## DDP batch size and oversampling can differ between workers and needs adaptation\n # we need to change the batch size in DDP because we don't use any of those distributed samplers\n self._set_batch_size_and_oversample()\n\n self.was_initialized = False\n\n self.print_to_log_file(\"\\n#######################################################################\\n\"\n \"Please cite the following paper when using nnU-Net:\\n\"\n \"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). \"\n \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. \"\n \"Nature methods, 18(2), 203-211.\\n\"\n \"#######################################################################\\n\",\n also_print_to_console=True, add_timestamp=False)\n\n def initialize(self):\n if not self.was_initialized:\n self.num_input_channels = determine_num_input_channels(self.plans_manager, self.configuration_manager,\n self.dataset_json)\n\n self.network = self.build_network_architecture(self.plans_manager, self.dataset_json,\n self.configuration_manager,\n self.num_input_channels,\n enable_deep_supervision=True).to(self.device)\n # compile network for free speedup\n if self._do_i_compile():\n self.print_to_log_file('Compiling network...')\n self.network = torch.compile(self.network)\n\n self.optimizer, self.lr_scheduler = self.configure_optimizers()\n # if ddp, wrap in DDP wrapper\n if self.is_ddp:\n self.network = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.network)\n self.network = DDP(self.network, device_ids=[self.local_rank])\n\n self.loss = self._build_loss()\n self.was_initialized = True\n else:\n raise RuntimeError(\"You have called self.initialize even though the trainer was already initialized. \"\n \"That should not happen.\")\n\n def _do_i_compile(self):\n return ('nnUNet_compile' in os.environ.keys()) and (os.environ['nnUNet_compile'].lower() in ('true', '1', 't'))\n\n def _save_debug_information(self):\n # saving some debug information\n if self.local_rank == 0:\n dct = {}\n for k in self.__dir__():\n if not k.startswith(\"__\"):\n if not callable(getattr(self, k)) or k in ['loss', ]:\n dct[k] = str(getattr(self, k))\n elif k in ['network', ]:\n dct[k] = str(getattr(self, k).__class__.__name__)\n else:\n # print(k)\n pass\n if k in ['dataloader_train', 'dataloader_val']:\n if hasattr(getattr(self, k), 'generator'):\n dct[k + '.generator'] = str(getattr(self, k).generator)\n if hasattr(getattr(self, k), 'num_processes'):\n dct[k + '.num_processes'] = str(getattr(self, k).num_processes)\n if hasattr(getattr(self, k), 'transform'):\n dct[k + '.transform'] = str(getattr(self, k).transform)\n import subprocess\n hostname = subprocess.getoutput(['hostname'])\n dct['hostname'] = hostname\n torch_version = torch.__version__\n if self.device.type == 'cuda':\n gpu_name = torch.cuda.get_device_name()\n dct['gpu_name'] = gpu_name\n cudnn_version = torch.backends.cudnn.version()\n else:\n cudnn_version = 'None'\n dct['device'] = str(self.device)\n dct['torch_version'] = torch_version\n dct['cudnn_version'] = cudnn_version\n save_json(dct, join(self.output_folder, \"debug.json\"))\n\n @staticmethod\n def build_network_architecture(plans_manager: PlansManager,\n dataset_json,\n configuration_manager: ConfigurationManager,\n num_input_channels,\n enable_deep_supervision: bool = True) -> nn.Module:\n \"\"\"\n his is where you build the architecture according to the plans. There is no obligation to use\n get_network_from_plans, this is just a utility we use for the nnU-Net default architectures. You can do what\n you want. Even ignore the plans and just return something static (as long as it can process the requested\n patch size)\n but don't bug us with your bugs arising from fiddling with this :-P\n This is the function that is called in inference as well! This is needed so that all network architecture\n variants can be loaded at inference time (inference will use the same nnUNetTrainer that was used for\n training, so if you change the network architecture during training by deriving a new trainer class then\n inference will know about it).\n\n If you need to know how many segmentation outputs your custom architecture needs to have, use the following snippet:\n > label_manager = plans_manager.get_label_manager(dataset_json)\n > label_manager.num_segmentation_heads\n (why so complicated? -> We can have either classical training (classes) or regions. If we have regions,\n the number of outputs is != the number of classes. Also there is the ignore label for which no output\n should be generated. label_manager takes care of all that for you.)\n\n \"\"\"\n return get_network_from_plans(plans_manager, dataset_json, configuration_manager,\n num_input_channels, deep_supervision=enable_deep_supervision)\n\n def _get_deep_supervision_scales(self):\n deep_supervision_scales = list(list(i) for i in 1 / np.cumprod(np.vstack(\n self.configuration_manager.pool_op_kernel_sizes), axis=0))[:-1]\n return deep_supervision_scales\n\n def _set_batch_size_and_oversample(self):\n if not self.is_ddp:\n # set batch size to what the plan says, leave oversample untouched\n self.batch_size = self.configuration_manager.batch_size\n else:\n # batch size is distributed over DDP workers and we need to change oversample_percent for each worker\n batch_sizes = []\n oversample_percents = []\n\n world_size = dist.get_world_size()\n my_rank = dist.get_rank()\n\n global_batch_size = self.configuration_manager.batch_size\n assert global_batch_size >= world_size, 'Cannot run DDP if the batch size is smaller than the number of ' \\\n 'GPUs... Duh.'\n\n batch_size_per_GPU = np.ceil(global_batch_size / world_size).astype(int)\n\n for rank in range(world_size):\n if (rank + 1) * batch_size_per_GPU > global_batch_size:\n batch_size = batch_size_per_GPU - ((rank + 1) * batch_size_per_GPU - global_batch_size)\n else:\n batch_size = batch_size_per_GPU\n\n batch_sizes.append(batch_size)\n\n sample_id_low = 0 if len(batch_sizes) == 0 else np.sum(batch_sizes[:-1])\n sample_id_high = np.sum(batch_sizes)\n\n if sample_id_high / global_batch_size < (1 - self.oversample_foreground_percent):\n oversample_percents.append(0.0)\n elif sample_id_low / global_batch_size > (1 - self.oversample_foreground_percent):\n oversample_percents.append(1.0)\n else:\n percent_covered_by_this_rank = sample_id_high / global_batch_size - sample_id_low / global_batch_size\n oversample_percent_here = 1 - (((1 - self.oversample_foreground_percent) -\n sample_id_low / global_batch_size) / percent_covered_by_this_rank)\n oversample_percents.append(oversample_percent_here)\n\n print(\"worker\", my_rank, \"oversample\", oversample_percents[my_rank])\n print(\"worker\", my_rank, \"batch_size\", batch_sizes[my_rank])\n # self.print_to_log_file(\"worker\", my_rank, \"oversample\", oversample_percents[my_rank])\n # self.print_to_log_file(\"worker\", my_rank, \"batch_size\", batch_sizes[my_rank])\n\n self.batch_size = batch_sizes[my_rank]\n self.oversample_foreground_percent = oversample_percents[my_rank]\n\n def _build_loss(self):\n if self.label_manager.has_regions:\n loss = DC_and_BCE_loss({},\n {'batch_dice': self.configuration_manager.batch_dice,\n 'do_bg': True, 'smooth': 1e-5, 'ddp': self.is_ddp},\n use_ignore_label=self.label_manager.ignore_label is not None,\n dice_class=MemoryEfficientSoftDiceLoss)\n else:\n loss = DC_and_CE_loss({'batch_dice': self.configuration_manager.batch_dice,\n 'smooth': 1e-5, 'do_bg': False, 'ddp': self.is_ddp}, {}, weight_ce=1, weight_dice=1,\n ignore_label=self.label_manager.ignore_label, dice_class=MemoryEfficientSoftDiceLoss)\n\n deep_supervision_scales = self._get_deep_supervision_scales()\n\n # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases\n # this gives higher resolution outputs more weight in the loss\n weights = np.array([1 / (2 ** i) for i in range(len(deep_supervision_scales))])\n weights[-1] = 0\n\n # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1\n weights = weights / weights.sum()\n # now wrap the loss\n loss = DeepSupervisionWrapper(loss, weights)\n return loss\n\n def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):\n \"\"\"\n This function is stupid and certainly one of the weakest spots of this implementation. Not entirely sure how we can fix it.\n \"\"\"\n patch_size = self.configuration_manager.patch_size\n dim = len(patch_size)\n # todo rotation should be defined dynamically based on patch size (more isotropic patch sizes = more rotation)\n if dim == 2:\n do_dummy_2d_data_aug = False\n # todo revisit this parametrization\n if max(patch_size) / min(patch_size) > 1.5:\n rotation_for_DA = {\n 'x': (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),\n 'y': (0, 0),\n 'z': (0, 0)\n }\n else:\n rotation_for_DA = {\n 'x': (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi),\n 'y': (0, 0),\n 'z': (0, 0)\n }\n mirror_axes = (0, 1)\n elif dim == 3:\n # todo this is not ideal. We could also have patch_size (64, 16, 128) in which case a full 180deg 2d rot would be bad\n # order of the axes is determined by spacing, not image size\n do_dummy_2d_data_aug = (max(patch_size) / patch_size[0]) > ANISO_THRESHOLD\n if do_dummy_2d_data_aug:\n # why do we rotate 180 deg here all the time? We should also restrict it\n rotation_for_DA = {\n 'x': (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi),\n 'y': (0, 0),\n 'z': (0, 0)\n }\n else:\n rotation_for_DA = {\n 'x': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\n 'y': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\n 'z': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\n }\n mirror_axes = (0, 1, 2)\n else:\n raise RuntimeError()\n\n # todo this function is stupid. It doesn't even use the correct scale range (we keep things as they were in the\n # old nnunet for now)\n initial_patch_size = get_patch_size(patch_size[-dim:],\n *rotation_for_DA.values(),\n (0.85, 1.25))\n if do_dummy_2d_data_aug:\n initial_patch_size[0] = patch_size[0]\n\n self.print_to_log_file(f'do_dummy_2d_data_aug: {do_dummy_2d_data_aug}')\n self.inference_allowed_mirroring_axes = mirror_axes\n\n return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes\n\n def print_to_log_file(self, *args, also_print_to_console=True, add_timestamp=True):\n if self.local_rank == 0:\n timestamp = time()\n dt_object = datetime.fromtimestamp(timestamp)\n\n if add_timestamp:\n args = (f\"{dt_object}:\", *args)\n\n successful = False\n max_attempts = 5\n ctr = 0\n while not successful and ctr < max_attempts:\n try:\n with open(self.log_file, 'a+') as f:\n for a in args:\n f.write(str(a))\n f.write(\" \")\n f.write(\"\\n\")\n successful = True\n except IOError:\n print(f\"{datetime.fromtimestamp(timestamp)}: failed to log: \", sys.exc_info())\n sleep(0.5)\n ctr += 1\n if also_print_to_console:\n print(*args)\n elif also_print_to_console:\n print(*args)\n\n def print_plans(self):\n if self.local_rank == 0:\n dct = deepcopy(self.plans_manager.plans)\n del dct['configurations']\n self.print_to_log_file(f\"\\nThis is the configuration used by this \"\n f\"training:\\nConfiguration name: {self.configuration_name}\\n\",\n self.configuration_manager, '\\n', add_timestamp=False)\n self.print_to_log_file('These are the global plan.json settings:\\n', dct, '\\n', add_timestamp=False)\n\n def configure_optimizers(self):\n optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,\n momentum=0.99, nesterov=True)\n lr_scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs)\n return optimizer, lr_scheduler\n\n def plot_network_architecture(self):\n if self._do_i_compile():\n self.print_to_log_file(\"Unable to plot network architecture: nnUNet_compile is enabled!\")\n return\n\n if self.local_rank == 0:\n try:\n # raise NotImplementedError('hiddenlayer no longer works and we do not have a viable alternative :-(')\n # pip install git+https://github.com/saugatkandel/hiddenlayer.git\n\n # from torchviz import make_dot\n # # not viable.\n # make_dot(tuple(self.network(torch.rand((1, self.num_input_channels,\n # *self.configuration_manager.patch_size),\n # device=self.device)))).render(\n # join(self.output_folder, \"network_architecture.pdf\"), format='pdf')\n # self.optimizer.zero_grad()\n\n # broken.\n\n import hiddenlayer as hl\n g = hl.build_graph(self.network,\n torch.rand((1, self.num_input_channels,\n *self.configuration_manager.patch_size),\n device=self.device),\n transforms=None)\n g.save(join(self.output_folder, \"network_architecture.pdf\"))\n del g\n except Exception as e:\n self.print_to_log_file(\"Unable to plot network architecture:\")\n self.print_to_log_file(e)\n\n # self.print_to_log_file(\"\\nprinting the network instead:\\n\")\n # self.print_to_log_file(self.network)\n # self.print_to_log_file(\"\\n\")\n finally:\n empty_cache(self.device)\n\n def do_split(self):\n \"\"\"\n The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded,\n so always the same) and save it as splits_final.pkl file in the preprocessed data directory.\n Sometimes you may want to create your own split for various reasons. For this you will need to create your own\n splits_final.pkl file. If this file is present, nnU-Net is going to use it and whatever splits are defined in\n it. You can create as many splits in this file as you want. Note that if you define only 4 splits (fold 0-3)\n and then set fold=4 when training (that would be the fifth split), nnU-Net will print a warning and proceed to\n use a random 80:20 data split.\n :return:\n \"\"\"\n if self.fold == \"all\":\n # if fold==all then we use all images for training and validation\n case_identifiers = get_case_identifiers(self.preprocessed_dataset_folder)\n tr_keys = case_identifiers\n val_keys = tr_keys\n else:\n splits_file = join(self.preprocessed_dataset_folder_base, \"splits_final.json\")\n dataset = nnUNetDataset(self.preprocessed_dataset_folder, case_identifiers=None,\n num_images_properties_loading_threshold=0,\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage)\n # if the split file does not exist we need to create it\n if not isfile(splits_file):\n self.print_to_log_file(\"Creating new 5-fold cross-validation split...\")\n splits = []\n all_keys_sorted = np.sort(list(dataset.keys()))\n kfold = KFold(n_splits=5, shuffle=True, random_state=12345)\n for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)):\n train_keys = np.array(all_keys_sorted)[train_idx]\n test_keys = np.array(all_keys_sorted)[test_idx]\n splits.append({})\n splits[-1]['train'] = list(train_keys)\n splits[-1]['val'] = list(test_keys)\n save_json(splits, splits_file)\n\n else:\n self.print_to_log_file(\"Using splits from existing split file:\", splits_file)\n splits = load_json(splits_file)\n self.print_to_log_file(f\"The split file contains {len(splits)} splits.\")\n\n self.print_to_log_file(\"Desired fold for training: %d\" % self.fold)\n if self.fold < len(splits):\n tr_keys = splits[self.fold]['train']\n val_keys = splits[self.fold]['val']\n self.print_to_log_file(\"This split has %d training and %d validation cases.\"\n % (len(tr_keys), len(val_keys)))\n else:\n self.print_to_log_file(\"INFO: You requested fold %d for training but splits \"\n \"contain only %d folds. I am now creating a \"\n \"random (but seeded) 80:20 split!\" % (self.fold, len(splits)))\n # if we request a fold that is not in the split file, create a random 80:20 split\n rnd = np.random.RandomState(seed=12345 + self.fold)\n keys = np.sort(list(dataset.keys()))\n idx_tr = rnd.choice(len(keys), int(len(keys) * 0.8), replace=False)\n idx_val = [i for i in range(len(keys)) if i not in idx_tr]\n tr_keys = [keys[i] for i in idx_tr]\n val_keys = [keys[i] for i in idx_val]\n self.print_to_log_file(\"This random 80:20 split has %d training and %d validation cases.\"\n % (len(tr_keys), len(val_keys)))\n if any([i in val_keys for i in tr_keys]):\n self.print_to_log_file('WARNING: Some validation cases are also in the training set. Please check the '\n 'splits.json or ignore if this is intentional.')\n return tr_keys, val_keys\n\n def get_tr_and_val_datasets(self):\n # create dataset split\n tr_keys, val_keys = self.do_split()\n\n # load the datasets for training and validation. Note that we always draw random samples so we really don't\n # care about distributing training cases across GPUs.\n dataset_tr = nnUNetDataset(self.preprocessed_dataset_folder, tr_keys,\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\n num_images_properties_loading_threshold=0)\n dataset_val = nnUNetDataset(self.preprocessed_dataset_folder, val_keys,\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\n num_images_properties_loading_threshold=0)\n return dataset_tr, dataset_val\n\n def get_dataloaders(self):\n # we use the patch size to determine whether we need 2D or 3D dataloaders. We also use it to determine whether\n # we need to use dummy 2D augmentation (in case of 3D training) and what our initial patch size should be\n patch_size = self.configuration_manager.patch_size\n dim = len(patch_size)\n\n # needed for deep supervision: how much do we need to downscale the segmentation targets for the different\n # outputs?\n deep_supervision_scales = self._get_deep_supervision_scales()\n\n rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \\\n self.configure_rotation_dummyDA_mirroring_and_inital_patch_size()\n\n # training pipeline\n tr_transforms = self.get_training_transforms(\n patch_size, rotation_for_DA, deep_supervision_scales, mirror_axes, do_dummy_2d_data_aug,\n order_resampling_data=3, order_resampling_seg=1,\n use_mask_for_norm=self.configuration_manager.use_mask_for_norm,\n is_cascaded=self.is_cascaded, foreground_labels=self.label_manager.foreground_labels,\n regions=self.label_manager.foreground_regions if self.label_manager.has_regions else None,\n ignore_label=self.label_manager.ignore_label)\n\n # validation pipeline\n val_transforms = self.get_validation_transforms(deep_supervision_scales,\n is_cascaded=self.is_cascaded,\n foreground_labels=self.label_manager.foreground_labels,\n regions=self.label_manager.foreground_regions if\n self.label_manager.has_regions else None,\n ignore_label=self.label_manager.ignore_label)\n\n dl_tr, dl_val = self.get_plain_dataloaders(initial_patch_size, dim)\n\n allowed_num_processes = get_allowed_n_proc_DA()\n if allowed_num_processes == 0:\n mt_gen_train = SingleThreadedAugmenter(dl_tr, tr_transforms)\n mt_gen_val = SingleThreadedAugmenter(dl_val, val_transforms)\n else:\n mt_gen_train = LimitedLenWrapper(self.num_iterations_per_epoch, data_loader=dl_tr, transform=tr_transforms,\n num_processes=allowed_num_processes, num_cached=6, seeds=None,\n pin_memory=self.device.type == 'cuda', wait_time=0.02)\n mt_gen_val = LimitedLenWrapper(self.num_val_iterations_per_epoch, data_loader=dl_val,\n transform=val_transforms, num_processes=max(1, allowed_num_processes // 2),\n num_cached=3, seeds=None, pin_memory=self.device.type == 'cuda',\n wait_time=0.02)\n return mt_gen_train, mt_gen_val\n\n def get_plain_dataloaders(self, initial_patch_size: Tuple[int, ...], dim: int):\n dataset_tr, dataset_val = self.get_tr_and_val_datasets()\n\n if dim == 2:\n dl_tr = nnUNetDataLoader2D(dataset_tr, self.batch_size,\n initial_patch_size,\n self.configuration_manager.patch_size,\n self.label_manager,\n oversample_foreground_percent=self.oversample_foreground_percent,\n sampling_probabilities=None, pad_sides=None)\n dl_val = nnUNetDataLoader2D(dataset_val, self.batch_size,\n self.configuration_manager.patch_size,\n self.configuration_manager.patch_size,\n self.label_manager,\n oversample_foreground_percent=self.oversample_foreground_percent,\n sampling_probabilities=None, pad_sides=None)\n else:\n dl_tr = nnUNetDataLoader3D(dataset_tr, self.batch_size,\n initial_patch_size,\n self.configuration_manager.patch_size,\n self.label_manager,\n oversample_foreground_percent=self.oversample_foreground_percent,\n sampling_probabilities=None, pad_sides=None)\n dl_val = nnUNetDataLoader3D(dataset_val, self.batch_size,\n self.configuration_manager.patch_size,\n self.configuration_manager.patch_size,\n self.label_manager,\n oversample_foreground_percent=self.oversample_foreground_percent,\n sampling_probabilities=None, pad_sides=None)\n return dl_tr, dl_val\n\n @staticmethod\n def get_training_transforms(patch_size: Union[np.ndarray, Tuple[int]],\n rotation_for_DA: dict,\n deep_supervision_scales: Union[List, Tuple],\n mirror_axes: Tuple[int, ...],\n do_dummy_2d_data_aug: bool,\n order_resampling_data: int = 3,\n order_resampling_seg: int = 1,\n border_val_seg: int = -1,\n use_mask_for_norm: List[bool] = None,\n is_cascaded: bool = False,\n foreground_labels: Union[Tuple[int, ...], List[int]] = None,\n regions: List[Union[List[int], Tuple[int, ...], int]] = None,\n ignore_label: int = None) -> AbstractTransform:\n tr_transforms = []\n if do_dummy_2d_data_aug:\n ignore_axes = (0,)\n tr_transforms.append(Convert3DTo2DTransform())\n patch_size_spatial = patch_size[1:]\n else:\n patch_size_spatial = patch_size\n ignore_axes = None\n\n tr_transforms.append(SpatialTransform(\n patch_size_spatial, patch_center_dist_from_border=None,\n do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0),\n do_rotation=True, angle_x=rotation_for_DA['x'], angle_y=rotation_for_DA['y'], angle_z=rotation_for_DA['z'],\n p_rot_per_axis=1, # todo experiment with this\n do_scale=True, scale=(0.7, 1.4),\n border_mode_data=\"constant\", border_cval_data=0, order_data=order_resampling_data,\n border_mode_seg=\"constant\", border_cval_seg=border_val_seg, order_seg=order_resampling_seg,\n random_crop=False, # random cropping is part of our dataloaders\n p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2,\n independent_scale_for_each_axis=False # todo experiment with this\n ))\n\n if do_dummy_2d_data_aug:\n tr_transforms.append(Convert2DTo3DTransform())\n\n tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1))\n tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2,\n p_per_channel=0.5))\n tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15))\n tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15))\n tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True,\n p_per_channel=0.5,\n order_downsample=0, order_upsample=3, p_per_sample=0.25,\n ignore_axes=ignore_axes))\n tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1))\n tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3))\n\n if mirror_axes is not None and len(mirror_axes) > 0:\n tr_transforms.append(MirrorTransform(mirror_axes))\n\n if use_mask_for_norm is not None and any(use_mask_for_norm):\n tr_transforms.append(MaskTransform([i for i in range(len(use_mask_for_norm)) if use_mask_for_norm[i]],\n mask_idx_in_seg=0, set_outside_to=0))\n\n tr_transforms.append(RemoveLabelTransform(-1, 0))\n\n if is_cascaded:\n assert foreground_labels is not None, 'We need foreground_labels for cascade augmentations'\n tr_transforms.append(MoveSegAsOneHotToData(1, foreground_labels, 'seg', 'data'))\n tr_transforms.append(ApplyRandomBinaryOperatorTransform(\n channel_idx=list(range(-len(foreground_labels), 0)),\n p_per_sample=0.4,\n key=\"data\",\n strel_size=(1, 8),\n p_per_label=1))\n tr_transforms.append(\n RemoveRandomConnectedComponentFromOneHotEncodingTransform(\n channel_idx=list(range(-len(foreground_labels), 0)),\n key=\"data\",\n p_per_sample=0.2,\n fill_with_other_class_p=0,\n dont_do_if_covers_more_than_x_percent=0.15))\n\n tr_transforms.append(RenameTransform('seg', 'target', True))\n\n if regions is not None:\n # the ignore label must also be converted\n tr_transforms.append(ConvertSegmentationToRegionsTransform(list(regions) + [ignore_label]\n if ignore_label is not None else regions,\n 'target', 'target'))\n\n if deep_supervision_scales is not None:\n tr_transforms.append(DownsampleSegForDSTransform2(deep_supervision_scales, 0, input_key='target',\n output_key='target'))\n tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))\n tr_transforms = Compose(tr_transforms)\n return tr_transforms\n\n @staticmethod\n def get_validation_transforms(deep_supervision_scales: Union[List, Tuple],\n is_cascaded: bool = False,\n foreground_labels: Union[Tuple[int, ...], List[int]] = None,\n regions: List[Union[List[int], Tuple[int, ...], int]] = None,\n ignore_label: int = None) -> AbstractTransform:\n val_transforms = []\n val_transforms.append(RemoveLabelTransform(-1, 0))\n\n if is_cascaded:\n val_transforms.append(MoveSegAsOneHotToData(1, foreground_labels, 'seg', 'data'))\n\n val_transforms.append(RenameTransform('seg', 'target', True))\n\n if regions is not None:\n # the ignore label must also be converted\n val_transforms.append(ConvertSegmentationToRegionsTransform(list(regions) + [ignore_label]\n if ignore_label is not None else regions,\n 'target', 'target'))\n\n if deep_supervision_scales is not None:\n val_transforms.append(DownsampleSegForDSTransform2(deep_supervision_scales, 0, input_key='target',\n output_key='target'))\n\n val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))\n val_transforms = Compose(val_transforms)\n return val_transforms\n\n def set_deep_supervision_enabled(self, enabled: bool):\n \"\"\"\n This function is specific for the default architecture in nnU-Net. If you change the architecture, there are\n chances you need to change this as well!\n \"\"\"\n if self.is_ddp:\n self.network.module.decoder.deep_supervision = enabled\n else:\n self.network.decoder.deep_supervision = enabled\n\n def on_train_start(self):\n if not self.was_initialized:\n self.initialize()\n\n maybe_mkdir_p(self.output_folder)\n\n # make sure deep supervision is on in the network\n self.set_deep_supervision_enabled(True)\n\n self.print_plans()\n empty_cache(self.device)\n\n # maybe unpack\n if self.unpack_dataset and self.local_rank == 0:\n self.print_to_log_file('unpacking dataset...')\n unpack_dataset(self.preprocessed_dataset_folder, unpack_segmentation=True, overwrite_existing=False,\n num_processes=max(1, round(get_allowed_n_proc_DA() // 2)))\n self.print_to_log_file('unpacking done...')\n\n if self.is_ddp:\n dist.barrier()\n\n # dataloaders must be instantiated here because they need access to the training data which may not be present\n # when doing inference\n self.dataloader_train, self.dataloader_val = self.get_dataloaders()\n\n # copy plans and dataset.json so that they can be used for restoring everything we need for inference\n save_json(self.plans_manager.plans, join(self.output_folder_base, 'plans.json'), sort_keys=False)\n save_json(self.dataset_json, join(self.output_folder_base, 'dataset.json'), sort_keys=False)\n\n # we don't really need the fingerprint but its still handy to have it with the others\n shutil.copy(join(self.preprocessed_dataset_folder_base, 'dataset_fingerprint.json'),\n join(self.output_folder_base, 'dataset_fingerprint.json'))\n\n # produces a pdf in output folder\n self.plot_network_architecture()\n\n self._save_debug_information()\n\n # print(f\"batch size: {self.batch_size}\")\n # print(f\"oversample: {self.oversample_foreground_percent}\")\n\n def on_train_end(self):\n # dirty hack because on_epoch_end increments the epoch counter and this is executed afterwards.\n # This will lead to the wrong current epoch to be stored\n self.current_epoch -= 1\n self.save_checkpoint(join(self.output_folder, \"checkpoint_final.pth\"))\n self.current_epoch += 1\n\n # now we can delete latest\n if self.local_rank == 0 and isfile(join(self.output_folder, \"checkpoint_latest.pth\")):\n os.remove(join(self.output_folder, \"checkpoint_latest.pth\"))\n\n # shut down dataloaders\n old_stdout = sys.stdout\n with open(os.devnull, 'w') as f:\n sys.stdout = f\n if self.dataloader_train is not None:\n self.dataloader_train._finish()\n if self.dataloader_val is not None:\n self.dataloader_val._finish()\n sys.stdout = old_stdout\n\n empty_cache(self.device)\n self.print_to_log_file(\"Training done.\")\n\n def on_train_epoch_start(self):\n self.network.train()\n self.lr_scheduler.step(self.current_epoch)\n self.print_to_log_file('')\n self.print_to_log_file(f'Epoch {self.current_epoch}')\n self.print_to_log_file(\n f\"Current learning rate: {np.round(self.optimizer.param_groups[0]['lr'], decimals=5)}\")\n # lrs are the same for all workers so we don't need to gather them in case of DDP training\n self.logger.log('lrs', self.optimizer.param_groups[0]['lr'], self.current_epoch)\n\n def train_step(self, batch: dict) -> dict:\n data = batch['data']\n target = batch['target']\n\n data = data.to(self.device, non_blocking=True)\n if isinstance(target, list):\n target = [i.to(self.device, non_blocking=True) for i in target]\n else:\n target = target.to(self.device, non_blocking=True)\n\n self.optimizer.zero_grad(set_to_none=True)\n # Autocast is a little bitch.\n # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.\n # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)\n # So autocast will only be active if we have a cuda device.\n with autocast(self.device.type, enabled=True) if self.device.type == 'cuda' else dummy_context():\n output = self.network(data)\n # del data\n l = self.loss(output, target)\n\n if self.grad_scaler is not None:\n self.grad_scaler.scale(l).backward()\n self.grad_scaler.unscale_(self.optimizer)\n torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\n self.grad_scaler.step(self.optimizer)\n self.grad_scaler.update()\n else:\n l.backward()\n torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\n self.optimizer.step()\n return {'loss': l.detach().cpu().numpy()}\n\n def on_train_epoch_end(self, train_outputs: List[dict]):\n outputs = collate_outputs(train_outputs)\n\n if self.is_ddp:\n losses_tr = [None for _ in range(dist.get_world_size())]\n dist.all_gather_object(losses_tr, outputs['loss'])\n loss_here = np.vstack(losses_tr).mean()\n else:\n loss_here = np.mean(outputs['loss'])\n\n self.logger.log('train_losses', loss_here, self.current_epoch)\n\n def on_validation_epoch_start(self):\n self.network.eval()\n\n def validation_step(self, batch: dict) -> dict:\n data = batch['data']\n target = batch['target']\n\n data = data.to(self.device, non_blocking=True)\n if isinstance(target, list):\n target = [i.to(self.device, non_blocking=True) for i in target]\n else:\n target = target.to(self.device, non_blocking=True)\n\n # Autocast is a little bitch.\n # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.\n # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)\n # So autocast will only be active if we have a cuda device.\n with autocast(self.device.type, enabled=True) if self.device.type == 'cuda' else dummy_context():\n output = self.network(data)\n del data\n l = self.loss(output, target)\n\n # we only need the output with the highest output resolution\n output = output[0]\n target = target[0]\n\n # the following is needed for online evaluation. Fake dice (green line)\n axes = [0] + list(range(2, output.ndim))\n\n if self.label_manager.has_regions:\n predicted_segmentation_onehot = (torch.sigmoid(output) > 0.5).long()\n else:\n # no need for softmax\n output_seg = output.argmax(1)[:, None]\n predicted_segmentation_onehot = torch.zeros(output.shape, device=output.device, dtype=torch.float32)\n predicted_segmentation_onehot.scatter_(1, output_seg, 1)\n del output_seg\n\n if self.label_manager.has_ignore_label:\n if not self.label_manager.has_regions:\n mask = (target != self.label_manager.ignore_label).float()\n # CAREFUL that you don't rely on target after this line!\n target[target == self.label_manager.ignore_label] = 0\n else:\n mask = 1 - target[:, -1:]\n # CAREFUL that you don't rely on target after this line!\n target = target[:, :-1]\n else:\n mask = None\n\n tp, fp, fn, _ = get_tp_fp_fn_tn(predicted_segmentation_onehot, target, axes=axes, mask=mask)\n\n tp_hard = tp.detach().cpu().numpy()\n fp_hard = fp.detach().cpu().numpy()\n fn_hard = fn.detach().cpu().numpy()\n if not self.label_manager.has_regions:\n # if we train with regions all segmentation heads predict some kind of foreground. In conventional\n # (softmax training) there needs tobe one output for the background. We are not interested in the\n # background Dice\n # [1:] in order to remove background\n tp_hard = tp_hard[1:]\n fp_hard = fp_hard[1:]\n fn_hard = fn_hard[1:]\n\n return {'loss': l.detach().cpu().numpy(), 'tp_hard': tp_hard, 'fp_hard': fp_hard, 'fn_hard': fn_hard}\n\n def on_validation_epoch_end(self, val_outputs: List[dict]):\n outputs_collated = collate_outputs(val_outputs)\n tp = np.sum(outputs_collated['tp_hard'], 0)\n fp = np.sum(outputs_collated['fp_hard'], 0)\n fn = np.sum(outputs_collated['fn_hard'], 0)\n\n if self.is_ddp:\n world_size = dist.get_world_size()\n\n tps = [None for _ in range(world_size)]\n dist.all_gather_object(tps, tp)\n tp = np.vstack([i[None] for i in tps]).sum(0)\n\n fps = [None for _ in range(world_size)]\n dist.all_gather_object(fps, fp)\n fp = np.vstack([i[None] for i in fps]).sum(0)\n\n fns = [None for _ in range(world_size)]\n dist.all_gather_object(fns, fn)\n fn = np.vstack([i[None] for i in fns]).sum(0)\n\n losses_val = [None for _ in range(world_size)]\n dist.all_gather_object(losses_val, outputs_collated['loss'])\n loss_here = np.vstack(losses_val).mean()\n else:\n loss_here = np.mean(outputs_collated['loss'])\n\n global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k in\n zip(tp, fp, fn)]]\n mean_fg_dice = np.nanmean(global_dc_per_class)\n self.logger.log('mean_fg_dice', mean_fg_dice, self.current_epoch)\n self.logger.log('dice_per_class_or_region', global_dc_per_class, self.current_epoch)\n self.logger.log('val_losses', loss_here, self.current_epoch)\n\n def on_epoch_start(self):\n self.logger.log('epoch_start_timestamps', time(), self.current_epoch)\n\n def on_epoch_end(self):\n self.logger.log('epoch_end_timestamps', time(), self.current_epoch)\n\n # todo find a solution for this stupid shit\n self.print_to_log_file('train_loss', np.round(self.logger.my_fantastic_logging['train_losses'][-1], decimals=4))\n self.print_to_log_file('val_loss', np.round(self.logger.my_fantastic_logging['val_losses'][-1], decimals=4))\n self.print_to_log_file('Pseudo dice', [np.round(i, decimals=4) for i in\n self.logger.my_fantastic_logging['dice_per_class_or_region'][-1]])\n self.print_to_log_file(\n f\"Epoch time: {np.round(self.logger.my_fantastic_logging['epoch_end_timestamps'][-1] - self.logger.my_fantastic_logging['epoch_start_timestamps'][-1], decimals=2)} s\")\n\n # handling periodic checkpointing\n current_epoch = self.current_epoch\n if (current_epoch + 1) % self.save_every == 0 and current_epoch != (self.num_epochs - 1):\n self.save_checkpoint(join(self.output_folder, 'checkpoint_latest.pth'))\n\n # handle 'best' checkpointing. ema_fg_dice is computed by the logger and can be accessed like this\n if self._best_ema is None or self.logger.my_fantastic_logging['ema_fg_dice'][-1] > self._best_ema:\n self._best_ema = self.logger.my_fantastic_logging['ema_fg_dice'][-1]\n self.print_to_log_file(f\"Yayy! New best EMA pseudo Dice: {np.round(self._best_ema, decimals=4)}\")\n self.save_checkpoint(join(self.output_folder, 'checkpoint_best.pth'))\n\n if self.local_rank == 0:\n self.logger.plot_progress_png(self.output_folder)\n\n self.current_epoch += 1\n\n def save_checkpoint(self, filename: str) -> None:\n if self.local_rank == 0:\n if not self.disable_checkpointing:\n if self.is_ddp:\n mod = self.network.module\n else:\n mod = self.network\n if isinstance(mod, OptimizedModule):\n mod = mod._orig_mod\n\n checkpoint = {\n 'network_weights': mod.state_dict(),\n 'optimizer_state': self.optimizer.state_dict(),\n 'grad_scaler_state': self.grad_scaler.state_dict() if self.grad_scaler is not None else None,\n 'logging': self.logger.get_checkpoint(),\n '_best_ema': self._best_ema,\n 'current_epoch': self.current_epoch + 1,\n 'init_args': self.my_init_kwargs,\n 'trainer_name': self.__class__.__name__,\n 'inference_allowed_mirroring_axes': self.inference_allowed_mirroring_axes,\n }\n torch.save(checkpoint, filename)\n else:\n self.print_to_log_file('No checkpoint written, checkpointing is disabled')\n\n def load_checkpoint(self, filename_or_checkpoint: Union[dict, str]) -> None:\n if not self.was_initialized:\n self.initialize()\n\n if isinstance(filename_or_checkpoint, str):\n checkpoint = torch.load(filename_or_checkpoint, map_location=self.device)\n # if state dict comes from nn.DataParallel but we use non-parallel model here then the state dict keys do not\n # match. Use heuristic to make it match\n new_state_dict = {}\n for k, value in checkpoint['network_weights'].items():\n key = k\n if key not in self.network.state_dict().keys() and key.startswith('module.'):\n key = key[7:]\n new_state_dict[key] = value\n\n self.my_init_kwargs = checkpoint['init_args']\n self.current_epoch = checkpoint['current_epoch']\n self.logger.load_checkpoint(checkpoint['logging'])\n self._best_ema = checkpoint['_best_ema']\n self.inference_allowed_mirroring_axes = checkpoint[\n 'inference_allowed_mirroring_axes'] if 'inference_allowed_mirroring_axes' in checkpoint.keys() else self.inference_allowed_mirroring_axes\n\n # messing with state dict naming schemes. Facepalm.\n if self.is_ddp:\n if isinstance(self.network.module, OptimizedModule):\n self.network.module._orig_mod.load_state_dict(new_state_dict)\n else:\n self.network.module.load_state_dict(new_state_dict)\n else:\n if isinstance(self.network, OptimizedModule):\n self.network._orig_mod.load_state_dict(new_state_dict)\n else:\n self.network.load_state_dict(new_state_dict)\n self.optimizer.load_state_dict(checkpoint['optimizer_state'])\n if self.grad_scaler is not None:\n if checkpoint['grad_scaler_state'] is not None:\n self.grad_scaler.load_state_dict(checkpoint['grad_scaler_state'])\n\n def perform_actual_validation(self, save_probabilities: bool = False):\n self.set_deep_supervision_enabled(False)\n self.network.eval()\n\n predictor = nnUNetPredictor(tile_step_size=0.5, use_gaussian=True, use_mirroring=True,\n perform_everything_on_gpu=True, device=self.device, verbose=False,\n verbose_preprocessing=False, allow_tqdm=False)\n predictor.manual_initialization(self.network, self.plans_manager, self.configuration_manager, None,\n self.dataset_json, self.__class__.__name__,\n self.inference_allowed_mirroring_axes)\n\n with multiprocessing.get_context(\"spawn\").Pool(default_num_processes) as segmentation_export_pool:\n worker_list = [i for i in segmentation_export_pool._pool]\n validation_output_folder = join(self.output_folder, 'validation')\n maybe_mkdir_p(validation_output_folder)\n\n # we cannot use self.get_tr_and_val_datasets() here because we might be DDP and then we have to distribute\n # the validation keys across the workers.\n _, val_keys = self.do_split()\n if self.is_ddp:\n val_keys = val_keys[self.local_rank:: dist.get_world_size()]\n\n dataset_val = nnUNetDataset(self.preprocessed_dataset_folder, val_keys,\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\n num_images_properties_loading_threshold=0)\n\n next_stages = self.configuration_manager.next_stage_names\n\n if next_stages is not None:\n _ = [maybe_mkdir_p(join(self.output_folder_base, 'predicted_next_stage', n)) for n in next_stages]\n\n results = []\n\n for k in dataset_val.keys():\n proceed = not check_workers_alive_and_busy(segmentation_export_pool, worker_list, results,\n allowed_num_queued=2)\n while not proceed:\n sleep(0.1)\n proceed = not check_workers_alive_and_busy(segmentation_export_pool, worker_list, results,\n allowed_num_queued=2)\n\n self.print_to_log_file(f\"predicting {k}\")\n data, seg, properties = dataset_val.load_case(k)\n\n if self.is_cascaded:\n data = np.vstack((data, convert_labelmap_to_one_hot(seg[-1], self.label_manager.foreground_labels,\n output_dtype=data.dtype)))\n with warnings.catch_warnings():\n # ignore 'The given NumPy array is not writable' warning\n warnings.simplefilter(\"ignore\")\n data = torch.from_numpy(data)\n\n output_filename_truncated = join(validation_output_folder, k)\n\n try:\n prediction = predictor.predict_sliding_window_return_logits(data)\n except RuntimeError:\n predictor.perform_everything_on_gpu = False\n prediction = predictor.predict_sliding_window_return_logits(data)\n predictor.perform_everything_on_gpu = True\n\n prediction = prediction.cpu()\n\n # this needs to go into background processes\n results.append(\n segmentation_export_pool.starmap_async(\n export_prediction_from_logits, (\n (prediction, properties, self.configuration_manager, self.plans_manager,\n self.dataset_json, output_filename_truncated, save_probabilities),\n )\n )\n )\n # for debug purposes\n # export_prediction(prediction_for_export, properties, self.configuration, self.plans, self.dataset_json,\n # output_filename_truncated, save_probabilities)\n\n # if needed, export the softmax prediction for the next stage\n if next_stages is not None:\n for n in next_stages:\n next_stage_config_manager = self.plans_manager.get_configuration(n)\n expected_preprocessed_folder = join(nnUNet_preprocessed, self.plans_manager.dataset_name,\n next_stage_config_manager.data_identifier)\n\n try:\n # we do this so that we can use load_case and do not have to hard code how loading training cases is implemented\n tmp = nnUNetDataset(expected_preprocessed_folder, [k],\n num_images_properties_loading_threshold=0)\n d, s, p = tmp.load_case(k)\n except FileNotFoundError:\n self.print_to_log_file(\n f\"Predicting next stage {n} failed for case {k} because the preprocessed file is missing! \"\n f\"Run the preprocessing for this configuration first!\")\n continue\n\n target_shape = d.shape[1:]\n output_folder = join(self.output_folder_base, 'predicted_next_stage', n)\n output_file = join(output_folder, k + '.npz')\n\n # resample_and_save(prediction, target_shape, output_file, self.plans_manager, self.configuration_manager, properties,\n # self.dataset_json)\n results.append(segmentation_export_pool.starmap_async(\n resample_and_save, (\n (prediction, target_shape, output_file, self.plans_manager,\n self.configuration_manager,\n properties,\n self.dataset_json),\n )\n ))\n\n _ = [r.get() for r in results]\n\n if self.is_ddp:\n dist.barrier()\n\n if self.local_rank == 0:\n metrics = compute_metrics_on_folder(join(self.preprocessed_dataset_folder_base, 'gt_segmentations'),\n validation_output_folder,\n join(validation_output_folder, 'summary.json'),\n self.plans_manager.image_reader_writer_class(),\n self.dataset_json[\"file_ending\"],\n self.label_manager.foreground_regions if self.label_manager.has_regions else\n self.label_manager.foreground_labels,\n self.label_manager.ignore_label, chill=True)\n self.print_to_log_file(\"Validation complete\", also_print_to_console=True)\n self.print_to_log_file(\"Mean Validation Dice: \", (metrics['foreground_mean'][\"Dice\"]), also_print_to_console=True)\n\n self.set_deep_supervision_enabled(True)\n compute_gaussian.cache_clear()\n\n def run_training(self):\n self.on_train_start()\n\n for epoch in range(self.current_epoch, self.num_epochs):\n self.on_epoch_start()\n\n self.on_train_epoch_start()\n train_outputs = []\n for batch_id in range(self.num_iterations_per_epoch):\n train_outputs.append(self.train_step(next(self.dataloader_train)))\n self.on_train_epoch_end(train_outputs)\n\n with torch.no_grad():\n self.on_validation_epoch_start()\n val_outputs = []\n for batch_id in range(self.num_val_iterations_per_epoch):\n val_outputs.append(self.validation_step(next(self.dataloader_val)))\n self.on_validation_epoch_end(val_outputs)\n\n self.on_epoch_end()\n\n self.on_train_end()"},{"identifier":"dummy_context","path":"nnunetv2/utilities/helpers.py","snippet":"class dummy_context(object):\n def __enter__(self):\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass"},{"identifier":"determine_num_input_channels","path":"nnunetv2/utilities/label_handling/label_handling.py","snippet":"def determine_num_input_channels(plans_manager: PlansManager,\n configuration_or_config_manager: Union[str, ConfigurationManager],\n dataset_json: dict) -> int:\n if isinstance(configuration_or_config_manager, str):\n config_manager = plans_manager.get_configuration(configuration_or_config_manager)\n else:\n config_manager = configuration_or_config_manager\n\n label_manager = plans_manager.get_label_manager(dataset_json)\n num_modalities = len(dataset_json['modality']) if 'modality' in dataset_json.keys() else len(dataset_json['channel_names'])\n\n # cascade has different number of input channels\n if config_manager.previous_stage_name is not None:\n num_label_inputs = len(label_manager.foreground_labels)\n num_input_channels = num_modalities + num_label_inputs\n else:\n num_input_channels = num_modalities\n return num_input_channels"}],"string":"[\n {\n \"identifier\": \"DC_and_BCE_loss\",\n \"path\": \"nnunetv2/training/loss/compound_losses.py\",\n \"snippet\": \"class DC_and_BCE_loss(nn.Module):\\n def __init__(self, bce_kwargs, soft_dice_kwargs, weight_ce=1, weight_dice=1, use_ignore_label: bool = False,\\n dice_class=MemoryEfficientSoftDiceLoss):\\n \\\"\\\"\\\"\\n DO NOT APPLY NONLINEARITY IN YOUR NETWORK!\\n\\n target mut be one hot encoded\\n IMPORTANT: We assume use_ignore_label is located in target[:, -1]!!!\\n\\n :param soft_dice_kwargs:\\n :param bce_kwargs:\\n :param aggregate:\\n \\\"\\\"\\\"\\n super(DC_and_BCE_loss, self).__init__()\\n if use_ignore_label:\\n bce_kwargs['reduction'] = 'none'\\n\\n self.weight_dice = weight_dice\\n self.weight_ce = weight_ce\\n self.use_ignore_label = use_ignore_label\\n\\n self.ce = nn.BCEWithLogitsLoss(**bce_kwargs)\\n self.dc = dice_class(apply_nonlin=torch.sigmoid, **soft_dice_kwargs)\\n\\n def forward(self, net_output: torch.Tensor, target: torch.Tensor):\\n if self.use_ignore_label:\\n # target is one hot encoded here. invert it so that it is True wherever we can compute the loss\\n mask = (1 - target[:, -1:]).bool()\\n # remove ignore channel now that we have the mask\\n target_regions = torch.clone(target[:, :-1])\\n else:\\n target_regions = target\\n mask = None\\n\\n dc_loss = self.dc(net_output, target_regions, loss_mask=mask)\\n if mask is not None:\\n ce_loss = (self.ce(net_output, target_regions) * mask).sum() / torch.clip(mask.sum(), min=1e-8)\\n else:\\n ce_loss = self.ce(net_output, target_regions)\\n result = self.weight_ce * ce_loss + self.weight_dice * dc_loss\\n return result\"\n },\n {\n \"identifier\": \"DC_and_CE_loss\",\n \"path\": \"nnunetv2/training/loss/compound_losses.py\",\n \"snippet\": \"class DC_and_CE_loss(nn.Module):\\n def __init__(self, soft_dice_kwargs, ce_kwargs, weight_ce=1, weight_dice=1, ignore_label=None,\\n dice_class=SoftDiceLoss):\\n \\\"\\\"\\\"\\n Weights for CE and Dice do not need to sum to one. You can set whatever you want.\\n :param soft_dice_kwargs:\\n :param ce_kwargs:\\n :param aggregate:\\n :param square_dice:\\n :param weight_ce:\\n :param weight_dice:\\n \\\"\\\"\\\"\\n super(DC_and_CE_loss, self).__init__()\\n if ignore_label is not None:\\n ce_kwargs['ignore_index'] = ignore_label\\n\\n self.weight_dice = weight_dice\\n self.weight_ce = weight_ce\\n self.ignore_label = ignore_label\\n\\n self.ce = RobustCrossEntropyLoss(**ce_kwargs)\\n self.dc = dice_class(apply_nonlin=softmax_helper_dim1, **soft_dice_kwargs)\\n\\n def forward(self, net_output: torch.Tensor, target: torch.Tensor):\\n \\\"\\\"\\\"\\n target must be b, c, x, y(, z) with c=1\\n :param net_output:\\n :param target:\\n :return:\\n \\\"\\\"\\\"\\n if self.ignore_label is not None:\\n assert target.shape[1] == 1, 'ignore label is not implemented for one hot encoded target variables ' \\\\\\n '(DC_and_CE_loss)'\\n mask = (target != self.ignore_label).bool()\\n # remove ignore label from target, replace with one of the known labels. It doesn't matter because we\\n # ignore gradients in those areas anyway\\n target_dice = torch.clone(target)\\n target_dice[target == self.ignore_label] = 0\\n num_fg = mask.sum()\\n else:\\n target_dice = target\\n mask = None\\n\\n dc_loss = self.dc(net_output, target_dice, loss_mask=mask) \\\\\\n if self.weight_dice != 0 else 0\\n ce_loss = self.ce(net_output, target[:, 0].long()) \\\\\\n if self.weight_ce != 0 and (self.ignore_label is None or num_fg > 0) else 0\\n\\n result = self.weight_ce * ce_loss + self.weight_dice * dc_loss\\n return result\"\n },\n {\n \"identifier\": \"get_tp_fp_fn_tn\",\n \"path\": \"nnunetv2/training/loss/dice.py\",\n \"snippet\": \"def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):\\n \\\"\\\"\\\"\\n net_output must be (b, c, x, y(, z)))\\n gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))\\n if mask is provided it must have shape (b, 1, x, y(, z)))\\n :param net_output:\\n :param gt:\\n :param axes: can be (, ) = no summation\\n :param mask: mask must be 1 for valid pixels and 0 for invalid pixels\\n :param square: if True then fp, tp and fn will be squared before summation\\n :return:\\n \\\"\\\"\\\"\\n if axes is None:\\n axes = tuple(range(2, len(net_output.size())))\\n\\n shp_x = net_output.shape\\n shp_y = gt.shape\\n\\n with torch.no_grad():\\n if len(shp_x) != len(shp_y):\\n gt = gt.view((shp_y[0], 1, *shp_y[1:]))\\n\\n if net_output.shape == gt.shape:\\n # if this is the case then gt is probably already a one hot encoding\\n y_onehot = gt\\n else:\\n gt = gt.long()\\n y_onehot = torch.zeros(shp_x, device=net_output.device)\\n y_onehot.scatter_(1, gt, 1)\\n\\n tp = net_output * y_onehot\\n fp = net_output * (1 - y_onehot)\\n fn = (1 - net_output) * y_onehot\\n tn = (1 - net_output) * (1 - y_onehot)\\n\\n if mask is not None:\\n with torch.no_grad():\\n mask_here = torch.tile(mask, (1, tp.shape[1], *[1 for i in range(2, len(tp.shape))]))\\n tp *= mask_here\\n fp *= mask_here\\n fn *= mask_here\\n tn *= mask_here\\n # benchmark whether tiling the mask would be faster (torch.tile). It probably is for large batch sizes\\n # OK it barely makes a difference but the implementation above is a tiny bit faster + uses less vram\\n # (using nnUNetv2_train 998 3d_fullres 0)\\n # tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp, dim=1)), dim=1)\\n # fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp, dim=1)), dim=1)\\n # fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn, dim=1)), dim=1)\\n # tn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tn, dim=1)), dim=1)\\n\\n if square:\\n tp = tp ** 2\\n fp = fp ** 2\\n fn = fn ** 2\\n tn = tn ** 2\\n\\n if len(axes) > 0:\\n tp = tp.sum(dim=axes, keepdim=False)\\n fp = fp.sum(dim=axes, keepdim=False)\\n fn = fn.sum(dim=axes, keepdim=False)\\n tn = tn.sum(dim=axes, keepdim=False)\\n\\n return tp, fp, fn, tn\"\n },\n {\n \"identifier\": \"MemoryEfficientSoftDiceLoss\",\n \"path\": \"nnunetv2/training/loss/dice.py\",\n \"snippet\": \"class MemoryEfficientSoftDiceLoss(nn.Module):\\n def __init__(self, apply_nonlin: Callable = None, batch_dice: bool = False, do_bg: bool = True, smooth: float = 1.,\\n ddp: bool = True):\\n \\\"\\\"\\\"\\n saves 1.6 GB on Dataset017 3d_lowres\\n \\\"\\\"\\\"\\n super(MemoryEfficientSoftDiceLoss, self).__init__()\\n\\n self.do_bg = do_bg\\n self.batch_dice = batch_dice\\n self.apply_nonlin = apply_nonlin\\n self.smooth = smooth\\n self.ddp = ddp\\n\\n def forward(self, x, y, loss_mask=None):\\n if self.apply_nonlin is not None:\\n x = self.apply_nonlin(x)\\n\\n # make everything shape (b, c)\\n axes = list(range(2, len(x.shape)))\\n with torch.no_grad():\\n if len(x.shape) != len(y.shape):\\n y = y.view((y.shape[0], 1, *y.shape[1:]))\\n\\n if x.shape == y.shape:\\n # if this is the case then gt is probably already a one hot encoding\\n y_onehot = y\\n else:\\n gt = y.long()\\n y_onehot = torch.zeros(x.shape, device=x.device, dtype=torch.bool)\\n y_onehot.scatter_(1, gt, 1)\\n\\n if not self.do_bg:\\n y_onehot = y_onehot[:, 1:]\\n\\n sum_gt = y_onehot.sum(axes) if loss_mask is None else (y_onehot * loss_mask).sum(axes)\\n\\n # this one MUST be outside the with torch.no_grad(): context. Otherwise no gradients for you\\n if not self.do_bg:\\n x = x[:, 1:]\\n\\n intersect = (x * y_onehot).sum(axes) if loss_mask is None else (x * y_onehot * loss_mask).sum(axes)\\n sum_pred = x.sum(axes) if loss_mask is None else (x * loss_mask).sum(axes)\\n\\n if self.ddp and self.batch_dice:\\n intersect = AllGatherGrad.apply(intersect).sum(0)\\n sum_pred = AllGatherGrad.apply(sum_pred).sum(0)\\n sum_gt = AllGatherGrad.apply(sum_gt).sum(0)\\n\\n if self.batch_dice:\\n intersect = intersect.sum(0)\\n sum_pred = sum_pred.sum(0)\\n sum_gt = sum_gt.sum(0)\\n\\n dc = (2 * intersect + self.smooth) / (torch.clip(sum_gt + sum_pred + self.smooth, 1e-8))\\n\\n dc = dc.mean()\\n return -dc\"\n },\n {\n \"identifier\": \"nnUNetTrainer\",\n \"path\": \"nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py\",\n \"snippet\": \"class nnUNetTrainer(object):\\n def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,\\n device: torch.device = torch.device('cuda')):\\n # From https://grugbrain.dev/. Worth a read ya big brains ;-)\\n\\n # apex predator of grug is complexity\\n # complexity bad\\n # say again:\\n # complexity very bad\\n # you say now:\\n # complexity very, very bad\\n # given choice between complexity or one on one against t-rex, grug take t-rex: at least grug see t-rex\\n # complexity is spirit demon that enter codebase through well-meaning but ultimately very clubbable non grug-brain developers and project managers who not fear complexity spirit demon or even know about sometime\\n # one day code base understandable and grug can get work done, everything good!\\n # next day impossible: complexity demon spirit has entered code and very dangerous situation!\\n\\n # OK OK I am guilty. But I tried.\\n # https://www.osnews.com/images/comics/wtfm.jpg\\n # https://i.pinimg.com/originals/26/b2/50/26b250a738ea4abc7a5af4d42ad93af0.jpg\\n\\n self.is_ddp = dist.is_available() and dist.is_initialized()\\n self.local_rank = 0 if not self.is_ddp else dist.get_rank()\\n\\n self.device = device\\n\\n # print what device we are using\\n if self.is_ddp: # implicitly it's clear that we use cuda in this case\\n print(f\\\"I am local rank {self.local_rank}. {device_count()} GPUs are available. The world size is \\\"\\n f\\\"{dist.get_world_size()}.\\\"\\n f\\\"Setting device to {self.device}\\\")\\n self.device = torch.device(type='cuda', index=self.local_rank)\\n else:\\n if self.device.type == 'cuda':\\n # we might want to let the user pick this but for now please pick the correct GPU with CUDA_VISIBLE_DEVICES=X\\n self.device = torch.device(type='cuda', index=0)\\n print(f\\\"Using device: {self.device}\\\")\\n\\n # loading and saving this class for continuing from checkpoint should not happen based on pickling. This\\n # would also pickle the network etc. Bad, bad. Instead we just reinstantiate and then load the checkpoint we\\n # need. So let's save the init args\\n self.my_init_kwargs = {}\\n for k in inspect.signature(self.__init__).parameters.keys():\\n self.my_init_kwargs[k] = locals()[k]\\n\\n ### Saving all the init args into class variables for later access\\n self.plans_manager = PlansManager(plans)\\n self.configuration_manager = self.plans_manager.get_configuration(configuration)\\n self.configuration_name = configuration\\n self.dataset_json = dataset_json\\n self.fold = fold\\n self.unpack_dataset = unpack_dataset\\n\\n ### Setting all the folder names. We need to make sure things don't crash in case we are just running\\n # inference and some of the folders may not be defined!\\n self.preprocessed_dataset_folder_base = join(nnUNet_preprocessed, self.plans_manager.dataset_name) \\\\\\n if nnUNet_preprocessed is not None else None\\n self.output_folder_base = join(nnUNet_results, self.plans_manager.dataset_name,\\n self.__class__.__name__ + '__' + self.plans_manager.plans_name + \\\"__\\\" + configuration) \\\\\\n if nnUNet_results is not None else None\\n self.output_folder = join(self.output_folder_base, f'fold_{fold}')\\n\\n self.preprocessed_dataset_folder = join(self.preprocessed_dataset_folder_base,\\n self.configuration_manager.data_identifier)\\n # unlike the previous nnunet folder_with_segs_from_previous_stage is now part of the plans. For now it has to\\n # be a different configuration in the same plans\\n # IMPORTANT! the mapping must be bijective, so lowres must point to fullres and vice versa (using\\n # \\\"previous_stage\\\" and \\\"next_stage\\\"). Otherwise it won't work!\\n self.is_cascaded = self.configuration_manager.previous_stage_name is not None\\n self.folder_with_segs_from_previous_stage = \\\\\\n join(nnUNet_results, self.plans_manager.dataset_name,\\n self.__class__.__name__ + '__' + self.plans_manager.plans_name + \\\"__\\\" +\\n self.configuration_manager.previous_stage_name, 'predicted_next_stage', self.configuration_name) \\\\\\n if self.is_cascaded else None\\n\\n ### Some hyperparameters for you to fiddle with\\n self.initial_lr = 1e-2\\n self.weight_decay = 3e-5\\n self.oversample_foreground_percent = 0.33\\n self.num_iterations_per_epoch = 250\\n self.num_val_iterations_per_epoch = 50\\n self.num_epochs = 1000\\n self.current_epoch = 0\\n\\n ### Dealing with labels/regions\\n self.label_manager = self.plans_manager.get_label_manager(dataset_json)\\n # labels can either be a list of int (regular training) or a list of tuples of int (region-based training)\\n # needed for predictions. We do sigmoid in case of (overlapping) regions\\n\\n self.num_input_channels = None # -> self.initialize()\\n self.network = None # -> self._get_network()\\n self.optimizer = self.lr_scheduler = None # -> self.initialize\\n self.grad_scaler = GradScaler() if self.device.type == 'cuda' else None\\n self.loss = None # -> self.initialize\\n\\n ### Simple logging. Don't take that away from me!\\n # initialize log file. This is just our log for the print statements etc. Not to be confused with lightning\\n # logging\\n timestamp = datetime.now()\\n maybe_mkdir_p(self.output_folder)\\n self.log_file = join(self.output_folder, \\\"training_log_%d_%d_%d_%02.0d_%02.0d_%02.0d.txt\\\" %\\n (timestamp.year, timestamp.month, timestamp.day, timestamp.hour, timestamp.minute,\\n timestamp.second))\\n self.logger = nnUNetLogger()\\n\\n ### placeholders\\n self.dataloader_train = self.dataloader_val = None # see on_train_start\\n\\n ### initializing stuff for remembering things and such\\n self._best_ema = None\\n\\n ### inference things\\n self.inference_allowed_mirroring_axes = None # this variable is set in\\n # self.configure_rotation_dummyDA_mirroring_and_inital_patch_size and will be saved in checkpoints\\n\\n ### checkpoint saving stuff\\n self.save_every = 50\\n self.disable_checkpointing = False\\n\\n ## DDP batch size and oversampling can differ between workers and needs adaptation\\n # we need to change the batch size in DDP because we don't use any of those distributed samplers\\n self._set_batch_size_and_oversample()\\n\\n self.was_initialized = False\\n\\n self.print_to_log_file(\\\"\\\\n#######################################################################\\\\n\\\"\\n \\\"Please cite the following paper when using nnU-Net:\\\\n\\\"\\n \\\"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). \\\"\\n \\\"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. \\\"\\n \\\"Nature methods, 18(2), 203-211.\\\\n\\\"\\n \\\"#######################################################################\\\\n\\\",\\n also_print_to_console=True, add_timestamp=False)\\n\\n def initialize(self):\\n if not self.was_initialized:\\n self.num_input_channels = determine_num_input_channels(self.plans_manager, self.configuration_manager,\\n self.dataset_json)\\n\\n self.network = self.build_network_architecture(self.plans_manager, self.dataset_json,\\n self.configuration_manager,\\n self.num_input_channels,\\n enable_deep_supervision=True).to(self.device)\\n # compile network for free speedup\\n if self._do_i_compile():\\n self.print_to_log_file('Compiling network...')\\n self.network = torch.compile(self.network)\\n\\n self.optimizer, self.lr_scheduler = self.configure_optimizers()\\n # if ddp, wrap in DDP wrapper\\n if self.is_ddp:\\n self.network = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.network)\\n self.network = DDP(self.network, device_ids=[self.local_rank])\\n\\n self.loss = self._build_loss()\\n self.was_initialized = True\\n else:\\n raise RuntimeError(\\\"You have called self.initialize even though the trainer was already initialized. \\\"\\n \\\"That should not happen.\\\")\\n\\n def _do_i_compile(self):\\n return ('nnUNet_compile' in os.environ.keys()) and (os.environ['nnUNet_compile'].lower() in ('true', '1', 't'))\\n\\n def _save_debug_information(self):\\n # saving some debug information\\n if self.local_rank == 0:\\n dct = {}\\n for k in self.__dir__():\\n if not k.startswith(\\\"__\\\"):\\n if not callable(getattr(self, k)) or k in ['loss', ]:\\n dct[k] = str(getattr(self, k))\\n elif k in ['network', ]:\\n dct[k] = str(getattr(self, k).__class__.__name__)\\n else:\\n # print(k)\\n pass\\n if k in ['dataloader_train', 'dataloader_val']:\\n if hasattr(getattr(self, k), 'generator'):\\n dct[k + '.generator'] = str(getattr(self, k).generator)\\n if hasattr(getattr(self, k), 'num_processes'):\\n dct[k + '.num_processes'] = str(getattr(self, k).num_processes)\\n if hasattr(getattr(self, k), 'transform'):\\n dct[k + '.transform'] = str(getattr(self, k).transform)\\n import subprocess\\n hostname = subprocess.getoutput(['hostname'])\\n dct['hostname'] = hostname\\n torch_version = torch.__version__\\n if self.device.type == 'cuda':\\n gpu_name = torch.cuda.get_device_name()\\n dct['gpu_name'] = gpu_name\\n cudnn_version = torch.backends.cudnn.version()\\n else:\\n cudnn_version = 'None'\\n dct['device'] = str(self.device)\\n dct['torch_version'] = torch_version\\n dct['cudnn_version'] = cudnn_version\\n save_json(dct, join(self.output_folder, \\\"debug.json\\\"))\\n\\n @staticmethod\\n def build_network_architecture(plans_manager: PlansManager,\\n dataset_json,\\n configuration_manager: ConfigurationManager,\\n num_input_channels,\\n enable_deep_supervision: bool = True) -> nn.Module:\\n \\\"\\\"\\\"\\n his is where you build the architecture according to the plans. There is no obligation to use\\n get_network_from_plans, this is just a utility we use for the nnU-Net default architectures. You can do what\\n you want. Even ignore the plans and just return something static (as long as it can process the requested\\n patch size)\\n but don't bug us with your bugs arising from fiddling with this :-P\\n This is the function that is called in inference as well! This is needed so that all network architecture\\n variants can be loaded at inference time (inference will use the same nnUNetTrainer that was used for\\n training, so if you change the network architecture during training by deriving a new trainer class then\\n inference will know about it).\\n\\n If you need to know how many segmentation outputs your custom architecture needs to have, use the following snippet:\\n > label_manager = plans_manager.get_label_manager(dataset_json)\\n > label_manager.num_segmentation_heads\\n (why so complicated? -> We can have either classical training (classes) or regions. If we have regions,\\n the number of outputs is != the number of classes. Also there is the ignore label for which no output\\n should be generated. label_manager takes care of all that for you.)\\n\\n \\\"\\\"\\\"\\n return get_network_from_plans(plans_manager, dataset_json, configuration_manager,\\n num_input_channels, deep_supervision=enable_deep_supervision)\\n\\n def _get_deep_supervision_scales(self):\\n deep_supervision_scales = list(list(i) for i in 1 / np.cumprod(np.vstack(\\n self.configuration_manager.pool_op_kernel_sizes), axis=0))[:-1]\\n return deep_supervision_scales\\n\\n def _set_batch_size_and_oversample(self):\\n if not self.is_ddp:\\n # set batch size to what the plan says, leave oversample untouched\\n self.batch_size = self.configuration_manager.batch_size\\n else:\\n # batch size is distributed over DDP workers and we need to change oversample_percent for each worker\\n batch_sizes = []\\n oversample_percents = []\\n\\n world_size = dist.get_world_size()\\n my_rank = dist.get_rank()\\n\\n global_batch_size = self.configuration_manager.batch_size\\n assert global_batch_size >= world_size, 'Cannot run DDP if the batch size is smaller than the number of ' \\\\\\n 'GPUs... Duh.'\\n\\n batch_size_per_GPU = np.ceil(global_batch_size / world_size).astype(int)\\n\\n for rank in range(world_size):\\n if (rank + 1) * batch_size_per_GPU > global_batch_size:\\n batch_size = batch_size_per_GPU - ((rank + 1) * batch_size_per_GPU - global_batch_size)\\n else:\\n batch_size = batch_size_per_GPU\\n\\n batch_sizes.append(batch_size)\\n\\n sample_id_low = 0 if len(batch_sizes) == 0 else np.sum(batch_sizes[:-1])\\n sample_id_high = np.sum(batch_sizes)\\n\\n if sample_id_high / global_batch_size < (1 - self.oversample_foreground_percent):\\n oversample_percents.append(0.0)\\n elif sample_id_low / global_batch_size > (1 - self.oversample_foreground_percent):\\n oversample_percents.append(1.0)\\n else:\\n percent_covered_by_this_rank = sample_id_high / global_batch_size - sample_id_low / global_batch_size\\n oversample_percent_here = 1 - (((1 - self.oversample_foreground_percent) -\\n sample_id_low / global_batch_size) / percent_covered_by_this_rank)\\n oversample_percents.append(oversample_percent_here)\\n\\n print(\\\"worker\\\", my_rank, \\\"oversample\\\", oversample_percents[my_rank])\\n print(\\\"worker\\\", my_rank, \\\"batch_size\\\", batch_sizes[my_rank])\\n # self.print_to_log_file(\\\"worker\\\", my_rank, \\\"oversample\\\", oversample_percents[my_rank])\\n # self.print_to_log_file(\\\"worker\\\", my_rank, \\\"batch_size\\\", batch_sizes[my_rank])\\n\\n self.batch_size = batch_sizes[my_rank]\\n self.oversample_foreground_percent = oversample_percents[my_rank]\\n\\n def _build_loss(self):\\n if self.label_manager.has_regions:\\n loss = DC_and_BCE_loss({},\\n {'batch_dice': self.configuration_manager.batch_dice,\\n 'do_bg': True, 'smooth': 1e-5, 'ddp': self.is_ddp},\\n use_ignore_label=self.label_manager.ignore_label is not None,\\n dice_class=MemoryEfficientSoftDiceLoss)\\n else:\\n loss = DC_and_CE_loss({'batch_dice': self.configuration_manager.batch_dice,\\n 'smooth': 1e-5, 'do_bg': False, 'ddp': self.is_ddp}, {}, weight_ce=1, weight_dice=1,\\n ignore_label=self.label_manager.ignore_label, dice_class=MemoryEfficientSoftDiceLoss)\\n\\n deep_supervision_scales = self._get_deep_supervision_scales()\\n\\n # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases\\n # this gives higher resolution outputs more weight in the loss\\n weights = np.array([1 / (2 ** i) for i in range(len(deep_supervision_scales))])\\n weights[-1] = 0\\n\\n # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1\\n weights = weights / weights.sum()\\n # now wrap the loss\\n loss = DeepSupervisionWrapper(loss, weights)\\n return loss\\n\\n def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):\\n \\\"\\\"\\\"\\n This function is stupid and certainly one of the weakest spots of this implementation. Not entirely sure how we can fix it.\\n \\\"\\\"\\\"\\n patch_size = self.configuration_manager.patch_size\\n dim = len(patch_size)\\n # todo rotation should be defined dynamically based on patch size (more isotropic patch sizes = more rotation)\\n if dim == 2:\\n do_dummy_2d_data_aug = False\\n # todo revisit this parametrization\\n if max(patch_size) / min(patch_size) > 1.5:\\n rotation_for_DA = {\\n 'x': (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),\\n 'y': (0, 0),\\n 'z': (0, 0)\\n }\\n else:\\n rotation_for_DA = {\\n 'x': (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi),\\n 'y': (0, 0),\\n 'z': (0, 0)\\n }\\n mirror_axes = (0, 1)\\n elif dim == 3:\\n # todo this is not ideal. We could also have patch_size (64, 16, 128) in which case a full 180deg 2d rot would be bad\\n # order of the axes is determined by spacing, not image size\\n do_dummy_2d_data_aug = (max(patch_size) / patch_size[0]) > ANISO_THRESHOLD\\n if do_dummy_2d_data_aug:\\n # why do we rotate 180 deg here all the time? We should also restrict it\\n rotation_for_DA = {\\n 'x': (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi),\\n 'y': (0, 0),\\n 'z': (0, 0)\\n }\\n else:\\n rotation_for_DA = {\\n 'x': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\\n 'y': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\\n 'z': (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi),\\n }\\n mirror_axes = (0, 1, 2)\\n else:\\n raise RuntimeError()\\n\\n # todo this function is stupid. It doesn't even use the correct scale range (we keep things as they were in the\\n # old nnunet for now)\\n initial_patch_size = get_patch_size(patch_size[-dim:],\\n *rotation_for_DA.values(),\\n (0.85, 1.25))\\n if do_dummy_2d_data_aug:\\n initial_patch_size[0] = patch_size[0]\\n\\n self.print_to_log_file(f'do_dummy_2d_data_aug: {do_dummy_2d_data_aug}')\\n self.inference_allowed_mirroring_axes = mirror_axes\\n\\n return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes\\n\\n def print_to_log_file(self, *args, also_print_to_console=True, add_timestamp=True):\\n if self.local_rank == 0:\\n timestamp = time()\\n dt_object = datetime.fromtimestamp(timestamp)\\n\\n if add_timestamp:\\n args = (f\\\"{dt_object}:\\\", *args)\\n\\n successful = False\\n max_attempts = 5\\n ctr = 0\\n while not successful and ctr < max_attempts:\\n try:\\n with open(self.log_file, 'a+') as f:\\n for a in args:\\n f.write(str(a))\\n f.write(\\\" \\\")\\n f.write(\\\"\\\\n\\\")\\n successful = True\\n except IOError:\\n print(f\\\"{datetime.fromtimestamp(timestamp)}: failed to log: \\\", sys.exc_info())\\n sleep(0.5)\\n ctr += 1\\n if also_print_to_console:\\n print(*args)\\n elif also_print_to_console:\\n print(*args)\\n\\n def print_plans(self):\\n if self.local_rank == 0:\\n dct = deepcopy(self.plans_manager.plans)\\n del dct['configurations']\\n self.print_to_log_file(f\\\"\\\\nThis is the configuration used by this \\\"\\n f\\\"training:\\\\nConfiguration name: {self.configuration_name}\\\\n\\\",\\n self.configuration_manager, '\\\\n', add_timestamp=False)\\n self.print_to_log_file('These are the global plan.json settings:\\\\n', dct, '\\\\n', add_timestamp=False)\\n\\n def configure_optimizers(self):\\n optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,\\n momentum=0.99, nesterov=True)\\n lr_scheduler = PolyLRScheduler(optimizer, self.initial_lr, self.num_epochs)\\n return optimizer, lr_scheduler\\n\\n def plot_network_architecture(self):\\n if self._do_i_compile():\\n self.print_to_log_file(\\\"Unable to plot network architecture: nnUNet_compile is enabled!\\\")\\n return\\n\\n if self.local_rank == 0:\\n try:\\n # raise NotImplementedError('hiddenlayer no longer works and we do not have a viable alternative :-(')\\n # pip install git+https://github.com/saugatkandel/hiddenlayer.git\\n\\n # from torchviz import make_dot\\n # # not viable.\\n # make_dot(tuple(self.network(torch.rand((1, self.num_input_channels,\\n # *self.configuration_manager.patch_size),\\n # device=self.device)))).render(\\n # join(self.output_folder, \\\"network_architecture.pdf\\\"), format='pdf')\\n # self.optimizer.zero_grad()\\n\\n # broken.\\n\\n import hiddenlayer as hl\\n g = hl.build_graph(self.network,\\n torch.rand((1, self.num_input_channels,\\n *self.configuration_manager.patch_size),\\n device=self.device),\\n transforms=None)\\n g.save(join(self.output_folder, \\\"network_architecture.pdf\\\"))\\n del g\\n except Exception as e:\\n self.print_to_log_file(\\\"Unable to plot network architecture:\\\")\\n self.print_to_log_file(e)\\n\\n # self.print_to_log_file(\\\"\\\\nprinting the network instead:\\\\n\\\")\\n # self.print_to_log_file(self.network)\\n # self.print_to_log_file(\\\"\\\\n\\\")\\n finally:\\n empty_cache(self.device)\\n\\n def do_split(self):\\n \\\"\\\"\\\"\\n The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded,\\n so always the same) and save it as splits_final.pkl file in the preprocessed data directory.\\n Sometimes you may want to create your own split for various reasons. For this you will need to create your own\\n splits_final.pkl file. If this file is present, nnU-Net is going to use it and whatever splits are defined in\\n it. You can create as many splits in this file as you want. Note that if you define only 4 splits (fold 0-3)\\n and then set fold=4 when training (that would be the fifth split), nnU-Net will print a warning and proceed to\\n use a random 80:20 data split.\\n :return:\\n \\\"\\\"\\\"\\n if self.fold == \\\"all\\\":\\n # if fold==all then we use all images for training and validation\\n case_identifiers = get_case_identifiers(self.preprocessed_dataset_folder)\\n tr_keys = case_identifiers\\n val_keys = tr_keys\\n else:\\n splits_file = join(self.preprocessed_dataset_folder_base, \\\"splits_final.json\\\")\\n dataset = nnUNetDataset(self.preprocessed_dataset_folder, case_identifiers=None,\\n num_images_properties_loading_threshold=0,\\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage)\\n # if the split file does not exist we need to create it\\n if not isfile(splits_file):\\n self.print_to_log_file(\\\"Creating new 5-fold cross-validation split...\\\")\\n splits = []\\n all_keys_sorted = np.sort(list(dataset.keys()))\\n kfold = KFold(n_splits=5, shuffle=True, random_state=12345)\\n for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)):\\n train_keys = np.array(all_keys_sorted)[train_idx]\\n test_keys = np.array(all_keys_sorted)[test_idx]\\n splits.append({})\\n splits[-1]['train'] = list(train_keys)\\n splits[-1]['val'] = list(test_keys)\\n save_json(splits, splits_file)\\n\\n else:\\n self.print_to_log_file(\\\"Using splits from existing split file:\\\", splits_file)\\n splits = load_json(splits_file)\\n self.print_to_log_file(f\\\"The split file contains {len(splits)} splits.\\\")\\n\\n self.print_to_log_file(\\\"Desired fold for training: %d\\\" % self.fold)\\n if self.fold < len(splits):\\n tr_keys = splits[self.fold]['train']\\n val_keys = splits[self.fold]['val']\\n self.print_to_log_file(\\\"This split has %d training and %d validation cases.\\\"\\n % (len(tr_keys), len(val_keys)))\\n else:\\n self.print_to_log_file(\\\"INFO: You requested fold %d for training but splits \\\"\\n \\\"contain only %d folds. I am now creating a \\\"\\n \\\"random (but seeded) 80:20 split!\\\" % (self.fold, len(splits)))\\n # if we request a fold that is not in the split file, create a random 80:20 split\\n rnd = np.random.RandomState(seed=12345 + self.fold)\\n keys = np.sort(list(dataset.keys()))\\n idx_tr = rnd.choice(len(keys), int(len(keys) * 0.8), replace=False)\\n idx_val = [i for i in range(len(keys)) if i not in idx_tr]\\n tr_keys = [keys[i] for i in idx_tr]\\n val_keys = [keys[i] for i in idx_val]\\n self.print_to_log_file(\\\"This random 80:20 split has %d training and %d validation cases.\\\"\\n % (len(tr_keys), len(val_keys)))\\n if any([i in val_keys for i in tr_keys]):\\n self.print_to_log_file('WARNING: Some validation cases are also in the training set. Please check the '\\n 'splits.json or ignore if this is intentional.')\\n return tr_keys, val_keys\\n\\n def get_tr_and_val_datasets(self):\\n # create dataset split\\n tr_keys, val_keys = self.do_split()\\n\\n # load the datasets for training and validation. Note that we always draw random samples so we really don't\\n # care about distributing training cases across GPUs.\\n dataset_tr = nnUNetDataset(self.preprocessed_dataset_folder, tr_keys,\\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\\n num_images_properties_loading_threshold=0)\\n dataset_val = nnUNetDataset(self.preprocessed_dataset_folder, val_keys,\\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\\n num_images_properties_loading_threshold=0)\\n return dataset_tr, dataset_val\\n\\n def get_dataloaders(self):\\n # we use the patch size to determine whether we need 2D or 3D dataloaders. We also use it to determine whether\\n # we need to use dummy 2D augmentation (in case of 3D training) and what our initial patch size should be\\n patch_size = self.configuration_manager.patch_size\\n dim = len(patch_size)\\n\\n # needed for deep supervision: how much do we need to downscale the segmentation targets for the different\\n # outputs?\\n deep_supervision_scales = self._get_deep_supervision_scales()\\n\\n rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \\\\\\n self.configure_rotation_dummyDA_mirroring_and_inital_patch_size()\\n\\n # training pipeline\\n tr_transforms = self.get_training_transforms(\\n patch_size, rotation_for_DA, deep_supervision_scales, mirror_axes, do_dummy_2d_data_aug,\\n order_resampling_data=3, order_resampling_seg=1,\\n use_mask_for_norm=self.configuration_manager.use_mask_for_norm,\\n is_cascaded=self.is_cascaded, foreground_labels=self.label_manager.foreground_labels,\\n regions=self.label_manager.foreground_regions if self.label_manager.has_regions else None,\\n ignore_label=self.label_manager.ignore_label)\\n\\n # validation pipeline\\n val_transforms = self.get_validation_transforms(deep_supervision_scales,\\n is_cascaded=self.is_cascaded,\\n foreground_labels=self.label_manager.foreground_labels,\\n regions=self.label_manager.foreground_regions if\\n self.label_manager.has_regions else None,\\n ignore_label=self.label_manager.ignore_label)\\n\\n dl_tr, dl_val = self.get_plain_dataloaders(initial_patch_size, dim)\\n\\n allowed_num_processes = get_allowed_n_proc_DA()\\n if allowed_num_processes == 0:\\n mt_gen_train = SingleThreadedAugmenter(dl_tr, tr_transforms)\\n mt_gen_val = SingleThreadedAugmenter(dl_val, val_transforms)\\n else:\\n mt_gen_train = LimitedLenWrapper(self.num_iterations_per_epoch, data_loader=dl_tr, transform=tr_transforms,\\n num_processes=allowed_num_processes, num_cached=6, seeds=None,\\n pin_memory=self.device.type == 'cuda', wait_time=0.02)\\n mt_gen_val = LimitedLenWrapper(self.num_val_iterations_per_epoch, data_loader=dl_val,\\n transform=val_transforms, num_processes=max(1, allowed_num_processes // 2),\\n num_cached=3, seeds=None, pin_memory=self.device.type == 'cuda',\\n wait_time=0.02)\\n return mt_gen_train, mt_gen_val\\n\\n def get_plain_dataloaders(self, initial_patch_size: Tuple[int, ...], dim: int):\\n dataset_tr, dataset_val = self.get_tr_and_val_datasets()\\n\\n if dim == 2:\\n dl_tr = nnUNetDataLoader2D(dataset_tr, self.batch_size,\\n initial_patch_size,\\n self.configuration_manager.patch_size,\\n self.label_manager,\\n oversample_foreground_percent=self.oversample_foreground_percent,\\n sampling_probabilities=None, pad_sides=None)\\n dl_val = nnUNetDataLoader2D(dataset_val, self.batch_size,\\n self.configuration_manager.patch_size,\\n self.configuration_manager.patch_size,\\n self.label_manager,\\n oversample_foreground_percent=self.oversample_foreground_percent,\\n sampling_probabilities=None, pad_sides=None)\\n else:\\n dl_tr = nnUNetDataLoader3D(dataset_tr, self.batch_size,\\n initial_patch_size,\\n self.configuration_manager.patch_size,\\n self.label_manager,\\n oversample_foreground_percent=self.oversample_foreground_percent,\\n sampling_probabilities=None, pad_sides=None)\\n dl_val = nnUNetDataLoader3D(dataset_val, self.batch_size,\\n self.configuration_manager.patch_size,\\n self.configuration_manager.patch_size,\\n self.label_manager,\\n oversample_foreground_percent=self.oversample_foreground_percent,\\n sampling_probabilities=None, pad_sides=None)\\n return dl_tr, dl_val\\n\\n @staticmethod\\n def get_training_transforms(patch_size: Union[np.ndarray, Tuple[int]],\\n rotation_for_DA: dict,\\n deep_supervision_scales: Union[List, Tuple],\\n mirror_axes: Tuple[int, ...],\\n do_dummy_2d_data_aug: bool,\\n order_resampling_data: int = 3,\\n order_resampling_seg: int = 1,\\n border_val_seg: int = -1,\\n use_mask_for_norm: List[bool] = None,\\n is_cascaded: bool = False,\\n foreground_labels: Union[Tuple[int, ...], List[int]] = None,\\n regions: List[Union[List[int], Tuple[int, ...], int]] = None,\\n ignore_label: int = None) -> AbstractTransform:\\n tr_transforms = []\\n if do_dummy_2d_data_aug:\\n ignore_axes = (0,)\\n tr_transforms.append(Convert3DTo2DTransform())\\n patch_size_spatial = patch_size[1:]\\n else:\\n patch_size_spatial = patch_size\\n ignore_axes = None\\n\\n tr_transforms.append(SpatialTransform(\\n patch_size_spatial, patch_center_dist_from_border=None,\\n do_elastic_deform=False, alpha=(0, 0), sigma=(0, 0),\\n do_rotation=True, angle_x=rotation_for_DA['x'], angle_y=rotation_for_DA['y'], angle_z=rotation_for_DA['z'],\\n p_rot_per_axis=1, # todo experiment with this\\n do_scale=True, scale=(0.7, 1.4),\\n border_mode_data=\\\"constant\\\", border_cval_data=0, order_data=order_resampling_data,\\n border_mode_seg=\\\"constant\\\", border_cval_seg=border_val_seg, order_seg=order_resampling_seg,\\n random_crop=False, # random cropping is part of our dataloaders\\n p_el_per_sample=0, p_scale_per_sample=0.2, p_rot_per_sample=0.2,\\n independent_scale_for_each_axis=False # todo experiment with this\\n ))\\n\\n if do_dummy_2d_data_aug:\\n tr_transforms.append(Convert2DTo3DTransform())\\n\\n tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1))\\n tr_transforms.append(GaussianBlurTransform((0.5, 1.), different_sigma_per_channel=True, p_per_sample=0.2,\\n p_per_channel=0.5))\\n tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15))\\n tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15))\\n tr_transforms.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), per_channel=True,\\n p_per_channel=0.5,\\n order_downsample=0, order_upsample=3, p_per_sample=0.25,\\n ignore_axes=ignore_axes))\\n tr_transforms.append(GammaTransform((0.7, 1.5), True, True, retain_stats=True, p_per_sample=0.1))\\n tr_transforms.append(GammaTransform((0.7, 1.5), False, True, retain_stats=True, p_per_sample=0.3))\\n\\n if mirror_axes is not None and len(mirror_axes) > 0:\\n tr_transforms.append(MirrorTransform(mirror_axes))\\n\\n if use_mask_for_norm is not None and any(use_mask_for_norm):\\n tr_transforms.append(MaskTransform([i for i in range(len(use_mask_for_norm)) if use_mask_for_norm[i]],\\n mask_idx_in_seg=0, set_outside_to=0))\\n\\n tr_transforms.append(RemoveLabelTransform(-1, 0))\\n\\n if is_cascaded:\\n assert foreground_labels is not None, 'We need foreground_labels for cascade augmentations'\\n tr_transforms.append(MoveSegAsOneHotToData(1, foreground_labels, 'seg', 'data'))\\n tr_transforms.append(ApplyRandomBinaryOperatorTransform(\\n channel_idx=list(range(-len(foreground_labels), 0)),\\n p_per_sample=0.4,\\n key=\\\"data\\\",\\n strel_size=(1, 8),\\n p_per_label=1))\\n tr_transforms.append(\\n RemoveRandomConnectedComponentFromOneHotEncodingTransform(\\n channel_idx=list(range(-len(foreground_labels), 0)),\\n key=\\\"data\\\",\\n p_per_sample=0.2,\\n fill_with_other_class_p=0,\\n dont_do_if_covers_more_than_x_percent=0.15))\\n\\n tr_transforms.append(RenameTransform('seg', 'target', True))\\n\\n if regions is not None:\\n # the ignore label must also be converted\\n tr_transforms.append(ConvertSegmentationToRegionsTransform(list(regions) + [ignore_label]\\n if ignore_label is not None else regions,\\n 'target', 'target'))\\n\\n if deep_supervision_scales is not None:\\n tr_transforms.append(DownsampleSegForDSTransform2(deep_supervision_scales, 0, input_key='target',\\n output_key='target'))\\n tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))\\n tr_transforms = Compose(tr_transforms)\\n return tr_transforms\\n\\n @staticmethod\\n def get_validation_transforms(deep_supervision_scales: Union[List, Tuple],\\n is_cascaded: bool = False,\\n foreground_labels: Union[Tuple[int, ...], List[int]] = None,\\n regions: List[Union[List[int], Tuple[int, ...], int]] = None,\\n ignore_label: int = None) -> AbstractTransform:\\n val_transforms = []\\n val_transforms.append(RemoveLabelTransform(-1, 0))\\n\\n if is_cascaded:\\n val_transforms.append(MoveSegAsOneHotToData(1, foreground_labels, 'seg', 'data'))\\n\\n val_transforms.append(RenameTransform('seg', 'target', True))\\n\\n if regions is not None:\\n # the ignore label must also be converted\\n val_transforms.append(ConvertSegmentationToRegionsTransform(list(regions) + [ignore_label]\\n if ignore_label is not None else regions,\\n 'target', 'target'))\\n\\n if deep_supervision_scales is not None:\\n val_transforms.append(DownsampleSegForDSTransform2(deep_supervision_scales, 0, input_key='target',\\n output_key='target'))\\n\\n val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))\\n val_transforms = Compose(val_transforms)\\n return val_transforms\\n\\n def set_deep_supervision_enabled(self, enabled: bool):\\n \\\"\\\"\\\"\\n This function is specific for the default architecture in nnU-Net. If you change the architecture, there are\\n chances you need to change this as well!\\n \\\"\\\"\\\"\\n if self.is_ddp:\\n self.network.module.decoder.deep_supervision = enabled\\n else:\\n self.network.decoder.deep_supervision = enabled\\n\\n def on_train_start(self):\\n if not self.was_initialized:\\n self.initialize()\\n\\n maybe_mkdir_p(self.output_folder)\\n\\n # make sure deep supervision is on in the network\\n self.set_deep_supervision_enabled(True)\\n\\n self.print_plans()\\n empty_cache(self.device)\\n\\n # maybe unpack\\n if self.unpack_dataset and self.local_rank == 0:\\n self.print_to_log_file('unpacking dataset...')\\n unpack_dataset(self.preprocessed_dataset_folder, unpack_segmentation=True, overwrite_existing=False,\\n num_processes=max(1, round(get_allowed_n_proc_DA() // 2)))\\n self.print_to_log_file('unpacking done...')\\n\\n if self.is_ddp:\\n dist.barrier()\\n\\n # dataloaders must be instantiated here because they need access to the training data which may not be present\\n # when doing inference\\n self.dataloader_train, self.dataloader_val = self.get_dataloaders()\\n\\n # copy plans and dataset.json so that they can be used for restoring everything we need for inference\\n save_json(self.plans_manager.plans, join(self.output_folder_base, 'plans.json'), sort_keys=False)\\n save_json(self.dataset_json, join(self.output_folder_base, 'dataset.json'), sort_keys=False)\\n\\n # we don't really need the fingerprint but its still handy to have it with the others\\n shutil.copy(join(self.preprocessed_dataset_folder_base, 'dataset_fingerprint.json'),\\n join(self.output_folder_base, 'dataset_fingerprint.json'))\\n\\n # produces a pdf in output folder\\n self.plot_network_architecture()\\n\\n self._save_debug_information()\\n\\n # print(f\\\"batch size: {self.batch_size}\\\")\\n # print(f\\\"oversample: {self.oversample_foreground_percent}\\\")\\n\\n def on_train_end(self):\\n # dirty hack because on_epoch_end increments the epoch counter and this is executed afterwards.\\n # This will lead to the wrong current epoch to be stored\\n self.current_epoch -= 1\\n self.save_checkpoint(join(self.output_folder, \\\"checkpoint_final.pth\\\"))\\n self.current_epoch += 1\\n\\n # now we can delete latest\\n if self.local_rank == 0 and isfile(join(self.output_folder, \\\"checkpoint_latest.pth\\\")):\\n os.remove(join(self.output_folder, \\\"checkpoint_latest.pth\\\"))\\n\\n # shut down dataloaders\\n old_stdout = sys.stdout\\n with open(os.devnull, 'w') as f:\\n sys.stdout = f\\n if self.dataloader_train is not None:\\n self.dataloader_train._finish()\\n if self.dataloader_val is not None:\\n self.dataloader_val._finish()\\n sys.stdout = old_stdout\\n\\n empty_cache(self.device)\\n self.print_to_log_file(\\\"Training done.\\\")\\n\\n def on_train_epoch_start(self):\\n self.network.train()\\n self.lr_scheduler.step(self.current_epoch)\\n self.print_to_log_file('')\\n self.print_to_log_file(f'Epoch {self.current_epoch}')\\n self.print_to_log_file(\\n f\\\"Current learning rate: {np.round(self.optimizer.param_groups[0]['lr'], decimals=5)}\\\")\\n # lrs are the same for all workers so we don't need to gather them in case of DDP training\\n self.logger.log('lrs', self.optimizer.param_groups[0]['lr'], self.current_epoch)\\n\\n def train_step(self, batch: dict) -> dict:\\n data = batch['data']\\n target = batch['target']\\n\\n data = data.to(self.device, non_blocking=True)\\n if isinstance(target, list):\\n target = [i.to(self.device, non_blocking=True) for i in target]\\n else:\\n target = target.to(self.device, non_blocking=True)\\n\\n self.optimizer.zero_grad(set_to_none=True)\\n # Autocast is a little bitch.\\n # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.\\n # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)\\n # So autocast will only be active if we have a cuda device.\\n with autocast(self.device.type, enabled=True) if self.device.type == 'cuda' else dummy_context():\\n output = self.network(data)\\n # del data\\n l = self.loss(output, target)\\n\\n if self.grad_scaler is not None:\\n self.grad_scaler.scale(l).backward()\\n self.grad_scaler.unscale_(self.optimizer)\\n torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\\n self.grad_scaler.step(self.optimizer)\\n self.grad_scaler.update()\\n else:\\n l.backward()\\n torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\\n self.optimizer.step()\\n return {'loss': l.detach().cpu().numpy()}\\n\\n def on_train_epoch_end(self, train_outputs: List[dict]):\\n outputs = collate_outputs(train_outputs)\\n\\n if self.is_ddp:\\n losses_tr = [None for _ in range(dist.get_world_size())]\\n dist.all_gather_object(losses_tr, outputs['loss'])\\n loss_here = np.vstack(losses_tr).mean()\\n else:\\n loss_here = np.mean(outputs['loss'])\\n\\n self.logger.log('train_losses', loss_here, self.current_epoch)\\n\\n def on_validation_epoch_start(self):\\n self.network.eval()\\n\\n def validation_step(self, batch: dict) -> dict:\\n data = batch['data']\\n target = batch['target']\\n\\n data = data.to(self.device, non_blocking=True)\\n if isinstance(target, list):\\n target = [i.to(self.device, non_blocking=True) for i in target]\\n else:\\n target = target.to(self.device, non_blocking=True)\\n\\n # Autocast is a little bitch.\\n # If the device_type is 'cpu' then it's slow as heck and needs to be disabled.\\n # If the device_type is 'mps' then it will complain that mps is not implemented, even if enabled=False is set. Whyyyyyyy. (this is why we don't make use of enabled=False)\\n # So autocast will only be active if we have a cuda device.\\n with autocast(self.device.type, enabled=True) if self.device.type == 'cuda' else dummy_context():\\n output = self.network(data)\\n del data\\n l = self.loss(output, target)\\n\\n # we only need the output with the highest output resolution\\n output = output[0]\\n target = target[0]\\n\\n # the following is needed for online evaluation. Fake dice (green line)\\n axes = [0] + list(range(2, output.ndim))\\n\\n if self.label_manager.has_regions:\\n predicted_segmentation_onehot = (torch.sigmoid(output) > 0.5).long()\\n else:\\n # no need for softmax\\n output_seg = output.argmax(1)[:, None]\\n predicted_segmentation_onehot = torch.zeros(output.shape, device=output.device, dtype=torch.float32)\\n predicted_segmentation_onehot.scatter_(1, output_seg, 1)\\n del output_seg\\n\\n if self.label_manager.has_ignore_label:\\n if not self.label_manager.has_regions:\\n mask = (target != self.label_manager.ignore_label).float()\\n # CAREFUL that you don't rely on target after this line!\\n target[target == self.label_manager.ignore_label] = 0\\n else:\\n mask = 1 - target[:, -1:]\\n # CAREFUL that you don't rely on target after this line!\\n target = target[:, :-1]\\n else:\\n mask = None\\n\\n tp, fp, fn, _ = get_tp_fp_fn_tn(predicted_segmentation_onehot, target, axes=axes, mask=mask)\\n\\n tp_hard = tp.detach().cpu().numpy()\\n fp_hard = fp.detach().cpu().numpy()\\n fn_hard = fn.detach().cpu().numpy()\\n if not self.label_manager.has_regions:\\n # if we train with regions all segmentation heads predict some kind of foreground. In conventional\\n # (softmax training) there needs tobe one output for the background. We are not interested in the\\n # background Dice\\n # [1:] in order to remove background\\n tp_hard = tp_hard[1:]\\n fp_hard = fp_hard[1:]\\n fn_hard = fn_hard[1:]\\n\\n return {'loss': l.detach().cpu().numpy(), 'tp_hard': tp_hard, 'fp_hard': fp_hard, 'fn_hard': fn_hard}\\n\\n def on_validation_epoch_end(self, val_outputs: List[dict]):\\n outputs_collated = collate_outputs(val_outputs)\\n tp = np.sum(outputs_collated['tp_hard'], 0)\\n fp = np.sum(outputs_collated['fp_hard'], 0)\\n fn = np.sum(outputs_collated['fn_hard'], 0)\\n\\n if self.is_ddp:\\n world_size = dist.get_world_size()\\n\\n tps = [None for _ in range(world_size)]\\n dist.all_gather_object(tps, tp)\\n tp = np.vstack([i[None] for i in tps]).sum(0)\\n\\n fps = [None for _ in range(world_size)]\\n dist.all_gather_object(fps, fp)\\n fp = np.vstack([i[None] for i in fps]).sum(0)\\n\\n fns = [None for _ in range(world_size)]\\n dist.all_gather_object(fns, fn)\\n fn = np.vstack([i[None] for i in fns]).sum(0)\\n\\n losses_val = [None for _ in range(world_size)]\\n dist.all_gather_object(losses_val, outputs_collated['loss'])\\n loss_here = np.vstack(losses_val).mean()\\n else:\\n loss_here = np.mean(outputs_collated['loss'])\\n\\n global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k in\\n zip(tp, fp, fn)]]\\n mean_fg_dice = np.nanmean(global_dc_per_class)\\n self.logger.log('mean_fg_dice', mean_fg_dice, self.current_epoch)\\n self.logger.log('dice_per_class_or_region', global_dc_per_class, self.current_epoch)\\n self.logger.log('val_losses', loss_here, self.current_epoch)\\n\\n def on_epoch_start(self):\\n self.logger.log('epoch_start_timestamps', time(), self.current_epoch)\\n\\n def on_epoch_end(self):\\n self.logger.log('epoch_end_timestamps', time(), self.current_epoch)\\n\\n # todo find a solution for this stupid shit\\n self.print_to_log_file('train_loss', np.round(self.logger.my_fantastic_logging['train_losses'][-1], decimals=4))\\n self.print_to_log_file('val_loss', np.round(self.logger.my_fantastic_logging['val_losses'][-1], decimals=4))\\n self.print_to_log_file('Pseudo dice', [np.round(i, decimals=4) for i in\\n self.logger.my_fantastic_logging['dice_per_class_or_region'][-1]])\\n self.print_to_log_file(\\n f\\\"Epoch time: {np.round(self.logger.my_fantastic_logging['epoch_end_timestamps'][-1] - self.logger.my_fantastic_logging['epoch_start_timestamps'][-1], decimals=2)} s\\\")\\n\\n # handling periodic checkpointing\\n current_epoch = self.current_epoch\\n if (current_epoch + 1) % self.save_every == 0 and current_epoch != (self.num_epochs - 1):\\n self.save_checkpoint(join(self.output_folder, 'checkpoint_latest.pth'))\\n\\n # handle 'best' checkpointing. ema_fg_dice is computed by the logger and can be accessed like this\\n if self._best_ema is None or self.logger.my_fantastic_logging['ema_fg_dice'][-1] > self._best_ema:\\n self._best_ema = self.logger.my_fantastic_logging['ema_fg_dice'][-1]\\n self.print_to_log_file(f\\\"Yayy! New best EMA pseudo Dice: {np.round(self._best_ema, decimals=4)}\\\")\\n self.save_checkpoint(join(self.output_folder, 'checkpoint_best.pth'))\\n\\n if self.local_rank == 0:\\n self.logger.plot_progress_png(self.output_folder)\\n\\n self.current_epoch += 1\\n\\n def save_checkpoint(self, filename: str) -> None:\\n if self.local_rank == 0:\\n if not self.disable_checkpointing:\\n if self.is_ddp:\\n mod = self.network.module\\n else:\\n mod = self.network\\n if isinstance(mod, OptimizedModule):\\n mod = mod._orig_mod\\n\\n checkpoint = {\\n 'network_weights': mod.state_dict(),\\n 'optimizer_state': self.optimizer.state_dict(),\\n 'grad_scaler_state': self.grad_scaler.state_dict() if self.grad_scaler is not None else None,\\n 'logging': self.logger.get_checkpoint(),\\n '_best_ema': self._best_ema,\\n 'current_epoch': self.current_epoch + 1,\\n 'init_args': self.my_init_kwargs,\\n 'trainer_name': self.__class__.__name__,\\n 'inference_allowed_mirroring_axes': self.inference_allowed_mirroring_axes,\\n }\\n torch.save(checkpoint, filename)\\n else:\\n self.print_to_log_file('No checkpoint written, checkpointing is disabled')\\n\\n def load_checkpoint(self, filename_or_checkpoint: Union[dict, str]) -> None:\\n if not self.was_initialized:\\n self.initialize()\\n\\n if isinstance(filename_or_checkpoint, str):\\n checkpoint = torch.load(filename_or_checkpoint, map_location=self.device)\\n # if state dict comes from nn.DataParallel but we use non-parallel model here then the state dict keys do not\\n # match. Use heuristic to make it match\\n new_state_dict = {}\\n for k, value in checkpoint['network_weights'].items():\\n key = k\\n if key not in self.network.state_dict().keys() and key.startswith('module.'):\\n key = key[7:]\\n new_state_dict[key] = value\\n\\n self.my_init_kwargs = checkpoint['init_args']\\n self.current_epoch = checkpoint['current_epoch']\\n self.logger.load_checkpoint(checkpoint['logging'])\\n self._best_ema = checkpoint['_best_ema']\\n self.inference_allowed_mirroring_axes = checkpoint[\\n 'inference_allowed_mirroring_axes'] if 'inference_allowed_mirroring_axes' in checkpoint.keys() else self.inference_allowed_mirroring_axes\\n\\n # messing with state dict naming schemes. Facepalm.\\n if self.is_ddp:\\n if isinstance(self.network.module, OptimizedModule):\\n self.network.module._orig_mod.load_state_dict(new_state_dict)\\n else:\\n self.network.module.load_state_dict(new_state_dict)\\n else:\\n if isinstance(self.network, OptimizedModule):\\n self.network._orig_mod.load_state_dict(new_state_dict)\\n else:\\n self.network.load_state_dict(new_state_dict)\\n self.optimizer.load_state_dict(checkpoint['optimizer_state'])\\n if self.grad_scaler is not None:\\n if checkpoint['grad_scaler_state'] is not None:\\n self.grad_scaler.load_state_dict(checkpoint['grad_scaler_state'])\\n\\n def perform_actual_validation(self, save_probabilities: bool = False):\\n self.set_deep_supervision_enabled(False)\\n self.network.eval()\\n\\n predictor = nnUNetPredictor(tile_step_size=0.5, use_gaussian=True, use_mirroring=True,\\n perform_everything_on_gpu=True, device=self.device, verbose=False,\\n verbose_preprocessing=False, allow_tqdm=False)\\n predictor.manual_initialization(self.network, self.plans_manager, self.configuration_manager, None,\\n self.dataset_json, self.__class__.__name__,\\n self.inference_allowed_mirroring_axes)\\n\\n with multiprocessing.get_context(\\\"spawn\\\").Pool(default_num_processes) as segmentation_export_pool:\\n worker_list = [i for i in segmentation_export_pool._pool]\\n validation_output_folder = join(self.output_folder, 'validation')\\n maybe_mkdir_p(validation_output_folder)\\n\\n # we cannot use self.get_tr_and_val_datasets() here because we might be DDP and then we have to distribute\\n # the validation keys across the workers.\\n _, val_keys = self.do_split()\\n if self.is_ddp:\\n val_keys = val_keys[self.local_rank:: dist.get_world_size()]\\n\\n dataset_val = nnUNetDataset(self.preprocessed_dataset_folder, val_keys,\\n folder_with_segs_from_previous_stage=self.folder_with_segs_from_previous_stage,\\n num_images_properties_loading_threshold=0)\\n\\n next_stages = self.configuration_manager.next_stage_names\\n\\n if next_stages is not None:\\n _ = [maybe_mkdir_p(join(self.output_folder_base, 'predicted_next_stage', n)) for n in next_stages]\\n\\n results = []\\n\\n for k in dataset_val.keys():\\n proceed = not check_workers_alive_and_busy(segmentation_export_pool, worker_list, results,\\n allowed_num_queued=2)\\n while not proceed:\\n sleep(0.1)\\n proceed = not check_workers_alive_and_busy(segmentation_export_pool, worker_list, results,\\n allowed_num_queued=2)\\n\\n self.print_to_log_file(f\\\"predicting {k}\\\")\\n data, seg, properties = dataset_val.load_case(k)\\n\\n if self.is_cascaded:\\n data = np.vstack((data, convert_labelmap_to_one_hot(seg[-1], self.label_manager.foreground_labels,\\n output_dtype=data.dtype)))\\n with warnings.catch_warnings():\\n # ignore 'The given NumPy array is not writable' warning\\n warnings.simplefilter(\\\"ignore\\\")\\n data = torch.from_numpy(data)\\n\\n output_filename_truncated = join(validation_output_folder, k)\\n\\n try:\\n prediction = predictor.predict_sliding_window_return_logits(data)\\n except RuntimeError:\\n predictor.perform_everything_on_gpu = False\\n prediction = predictor.predict_sliding_window_return_logits(data)\\n predictor.perform_everything_on_gpu = True\\n\\n prediction = prediction.cpu()\\n\\n # this needs to go into background processes\\n results.append(\\n segmentation_export_pool.starmap_async(\\n export_prediction_from_logits, (\\n (prediction, properties, self.configuration_manager, self.plans_manager,\\n self.dataset_json, output_filename_truncated, save_probabilities),\\n )\\n )\\n )\\n # for debug purposes\\n # export_prediction(prediction_for_export, properties, self.configuration, self.plans, self.dataset_json,\\n # output_filename_truncated, save_probabilities)\\n\\n # if needed, export the softmax prediction for the next stage\\n if next_stages is not None:\\n for n in next_stages:\\n next_stage_config_manager = self.plans_manager.get_configuration(n)\\n expected_preprocessed_folder = join(nnUNet_preprocessed, self.plans_manager.dataset_name,\\n next_stage_config_manager.data_identifier)\\n\\n try:\\n # we do this so that we can use load_case and do not have to hard code how loading training cases is implemented\\n tmp = nnUNetDataset(expected_preprocessed_folder, [k],\\n num_images_properties_loading_threshold=0)\\n d, s, p = tmp.load_case(k)\\n except FileNotFoundError:\\n self.print_to_log_file(\\n f\\\"Predicting next stage {n} failed for case {k} because the preprocessed file is missing! \\\"\\n f\\\"Run the preprocessing for this configuration first!\\\")\\n continue\\n\\n target_shape = d.shape[1:]\\n output_folder = join(self.output_folder_base, 'predicted_next_stage', n)\\n output_file = join(output_folder, k + '.npz')\\n\\n # resample_and_save(prediction, target_shape, output_file, self.plans_manager, self.configuration_manager, properties,\\n # self.dataset_json)\\n results.append(segmentation_export_pool.starmap_async(\\n resample_and_save, (\\n (prediction, target_shape, output_file, self.plans_manager,\\n self.configuration_manager,\\n properties,\\n self.dataset_json),\\n )\\n ))\\n\\n _ = [r.get() for r in results]\\n\\n if self.is_ddp:\\n dist.barrier()\\n\\n if self.local_rank == 0:\\n metrics = compute_metrics_on_folder(join(self.preprocessed_dataset_folder_base, 'gt_segmentations'),\\n validation_output_folder,\\n join(validation_output_folder, 'summary.json'),\\n self.plans_manager.image_reader_writer_class(),\\n self.dataset_json[\\\"file_ending\\\"],\\n self.label_manager.foreground_regions if self.label_manager.has_regions else\\n self.label_manager.foreground_labels,\\n self.label_manager.ignore_label, chill=True)\\n self.print_to_log_file(\\\"Validation complete\\\", also_print_to_console=True)\\n self.print_to_log_file(\\\"Mean Validation Dice: \\\", (metrics['foreground_mean'][\\\"Dice\\\"]), also_print_to_console=True)\\n\\n self.set_deep_supervision_enabled(True)\\n compute_gaussian.cache_clear()\\n\\n def run_training(self):\\n self.on_train_start()\\n\\n for epoch in range(self.current_epoch, self.num_epochs):\\n self.on_epoch_start()\\n\\n self.on_train_epoch_start()\\n train_outputs = []\\n for batch_id in range(self.num_iterations_per_epoch):\\n train_outputs.append(self.train_step(next(self.dataloader_train)))\\n self.on_train_epoch_end(train_outputs)\\n\\n with torch.no_grad():\\n self.on_validation_epoch_start()\\n val_outputs = []\\n for batch_id in range(self.num_val_iterations_per_epoch):\\n val_outputs.append(self.validation_step(next(self.dataloader_val)))\\n self.on_validation_epoch_end(val_outputs)\\n\\n self.on_epoch_end()\\n\\n self.on_train_end()\"\n },\n {\n \"identifier\": \"dummy_context\",\n \"path\": \"nnunetv2/utilities/helpers.py\",\n \"snippet\": \"class dummy_context(object):\\n def __enter__(self):\\n pass\\n\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n pass\"\n },\n {\n \"identifier\": \"determine_num_input_channels\",\n \"path\": \"nnunetv2/utilities/label_handling/label_handling.py\",\n \"snippet\": \"def determine_num_input_channels(plans_manager: PlansManager,\\n configuration_or_config_manager: Union[str, ConfigurationManager],\\n dataset_json: dict) -> int:\\n if isinstance(configuration_or_config_manager, str):\\n config_manager = plans_manager.get_configuration(configuration_or_config_manager)\\n else:\\n config_manager = configuration_or_config_manager\\n\\n label_manager = plans_manager.get_label_manager(dataset_json)\\n num_modalities = len(dataset_json['modality']) if 'modality' in dataset_json.keys() else len(dataset_json['channel_names'])\\n\\n # cascade has different number of input channels\\n if config_manager.previous_stage_name is not None:\\n num_label_inputs = len(label_manager.foreground_labels)\\n num_input_channels = num_modalities + num_label_inputs\\n else:\\n num_input_channels = num_modalities\\n return num_input_channels\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import torch\nfrom torch import autocast\nfrom nnunetv2.training.loss.compound_losses import DC_and_BCE_loss, DC_and_CE_loss\nfrom nnunetv2.training.loss.dice import get_tp_fp_fn_tn, MemoryEfficientSoftDiceLoss\nfrom nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer\nfrom nnunetv2.utilities.helpers import dummy_context\nfrom nnunetv2.utilities.label_handling.label_handling import determine_num_input_channels\nfrom torch.nn.parallel import DistributedDataParallel as DDP"},"token_num":{"kind":"number","value":17114,"string":"17,114"},"cropped_code":{"kind":"string","value":"\n\n\nclass nnUNetTrainerNoDeepSupervision(nnUNetTrainer):\n def _build_loss(self):\n if self.label_manager.has_regions:\n"},"all_code":{"kind":"string","value":"\n\n\nclass nnUNetTrainerNoDeepSupervision(nnUNetTrainer):\n def _build_loss(self):\n if self.label_manager.has_regions:"},"next_line":{"kind":"string","value":" loss = DC_and_BCE_loss({},"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-04 19:43:14+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5394,"cells":{"repo_name":{"kind":"string","value":"opisaac9001/TTS-With-ooba-and-voice"},"file_path":{"kind":"string","value":"TTS/tts/models/neuralhmm_tts.py"},"context":{"kind":"list like","value":[{"identifier":"Encoder","path":"TTS/tts/layers/overflow/common_layers.py","snippet":"class Encoder(nn.Module):\n r\"\"\"Neural HMM Encoder\n\n Same as Tacotron 2 encoder but increases the input length by states per phone\n\n Args:\n num_chars (int): Number of characters in the input.\n state_per_phone (int): Number of states per phone.\n in_out_channels (int): number of input and output channels.\n n_convolutions (int): number of convolutional layers.\n \"\"\"\n\n def __init__(self, num_chars, state_per_phone, in_out_channels=512, n_convolutions=3):\n super().__init__()\n\n self.state_per_phone = state_per_phone\n self.in_out_channels = in_out_channels\n\n self.emb = nn.Embedding(num_chars, in_out_channels)\n self.convolutions = nn.ModuleList()\n for _ in range(n_convolutions):\n self.convolutions.append(ConvBNBlock(in_out_channels, in_out_channels, 5, \"relu\"))\n self.lstm = nn.LSTM(\n in_out_channels,\n int(in_out_channels / 2) * state_per_phone,\n num_layers=1,\n batch_first=True,\n bias=True,\n bidirectional=True,\n )\n self.rnn_state = None\n\n def forward(self, x: torch.FloatTensor, x_len: torch.LongTensor) -> Tuple[torch.FloatTensor, torch.LongTensor]:\n \"\"\"Forward pass to the encoder.\n\n Args:\n x (torch.FloatTensor): input text indices.\n - shape: :math:`(b, T_{in})`\n x_len (torch.LongTensor): input text lengths.\n - shape: :math:`(b,)`\n\n Returns:\n Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths.\n -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))`\n \"\"\"\n b, T = x.shape\n o = self.emb(x).transpose(1, 2)\n for layer in self.convolutions:\n o = layer(o)\n o = o.transpose(1, 2)\n o = nn.utils.rnn.pack_padded_sequence(o, x_len.cpu(), batch_first=True)\n self.lstm.flatten_parameters()\n o, _ = self.lstm(o)\n o, _ = nn.utils.rnn.pad_packed_sequence(o, batch_first=True)\n o = o.reshape(b, T * self.state_per_phone, self.in_out_channels)\n x_len = x_len * self.state_per_phone\n return o, x_len\n\n def inference(self, x, x_len):\n \"\"\"Inference to the encoder.\n\n Args:\n x (torch.FloatTensor): input text indices.\n - shape: :math:`(b, T_{in})`\n x_len (torch.LongTensor): input text lengths.\n - shape: :math:`(b,)`\n\n Returns:\n Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths.\n -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))`\n \"\"\"\n b, T = x.shape\n o = self.emb(x).transpose(1, 2)\n for layer in self.convolutions:\n o = layer(o)\n o = o.transpose(1, 2)\n # self.lstm.flatten_parameters()\n o, _ = self.lstm(o)\n o = o.reshape(b, T * self.state_per_phone, self.in_out_channels)\n x_len = x_len * self.state_per_phone\n return o, x_len"},{"identifier":"OverflowUtils","path":"TTS/tts/layers/overflow/common_layers.py","snippet":"class OverflowUtils:\n @staticmethod\n def get_data_parameters_for_flat_start(\n data_loader: torch.utils.data.DataLoader, out_channels: int, states_per_phone: int\n ):\n \"\"\"Generates data parameters for flat starting the HMM.\n\n Args:\n data_loader (torch.utils.data.Dataloader): _description_\n out_channels (int): mel spectrogram channels\n states_per_phone (_type_): HMM states per phone\n \"\"\"\n\n # State related information for transition_p\n total_state_len = 0\n total_mel_len = 0\n\n # Useful for data mean an std\n total_mel_sum = 0\n total_mel_sq_sum = 0\n\n for batch in tqdm(data_loader, leave=False):\n text_lengths = batch[\"token_id_lengths\"]\n mels = batch[\"mel\"]\n mel_lengths = batch[\"mel_lengths\"]\n\n total_state_len += torch.sum(text_lengths)\n total_mel_len += torch.sum(mel_lengths)\n total_mel_sum += torch.sum(mels)\n total_mel_sq_sum += torch.sum(torch.pow(mels, 2))\n\n data_mean = total_mel_sum / (total_mel_len * out_channels)\n data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2))\n average_num_states = total_state_len / len(data_loader.dataset)\n average_mel_len = total_mel_len / len(data_loader.dataset)\n average_duration_each_state = average_mel_len / average_num_states\n init_transition_prob = 1 / average_duration_each_state\n\n return data_mean, data_std, (init_transition_prob * states_per_phone)\n\n @staticmethod\n @torch.no_grad()\n def update_flat_start_transition(model, transition_p):\n model.neural_hmm.output_net.parametermodel.flat_start_output_layer(0.0, 1.0, transition_p)\n\n @staticmethod\n def log_clamped(x, eps=1e-04):\n \"\"\"\n Avoids the log(0) problem\n\n Args:\n x (torch.tensor): input tensor\n eps (float, optional): lower bound. Defaults to 1e-04.\n\n Returns:\n torch.tensor: :math:`log(x)`\n \"\"\"\n clamped_x = torch.clamp(x, min=eps)\n return torch.log(clamped_x)\n\n @staticmethod\n def inverse_sigmod(x):\n r\"\"\"\n Inverse of the sigmoid function\n \"\"\"\n if not torch.is_tensor(x):\n x = torch.tensor(x)\n return OverflowUtils.log_clamped(x / (1.0 - x))\n\n @staticmethod\n def inverse_softplus(x):\n r\"\"\"\n Inverse of the softplus function\n \"\"\"\n if not torch.is_tensor(x):\n x = torch.tensor(x)\n return OverflowUtils.log_clamped(torch.exp(x) - 1.0)\n\n @staticmethod\n def logsumexp(x, dim):\n r\"\"\"\n Differentiable LogSumExp: Does not creates nan gradients\n when all the inputs are -inf yeilds 0 gradients.\n Args:\n x : torch.Tensor - The input tensor\n dim: int - The dimension on which the log sum exp has to be applied\n \"\"\"\n\n m, _ = x.max(dim=dim)\n mask = m == -float(\"inf\")\n s = (x - m.masked_fill_(mask, 0).unsqueeze(dim=dim)).exp().sum(dim=dim)\n return s.masked_fill_(mask, 1).log() + m.masked_fill_(mask, -float(\"inf\"))\n\n @staticmethod\n def double_pad(list_of_different_shape_tensors):\n r\"\"\"\n Pads the list of tensors in 2 dimensions\n \"\"\"\n second_dim_lens = [len(a) for a in [i[0] for i in list_of_different_shape_tensors]]\n second_dim_max = max(second_dim_lens)\n padded_x = [F.pad(x, (0, second_dim_max - len(x[0]))) for x in list_of_different_shape_tensors]\n return nn.utils.rnn.pad_sequence(padded_x, batch_first=True)"},{"identifier":"NeuralHMM","path":"TTS/tts/layers/overflow/neural_hmm.py","snippet":"class NeuralHMM(nn.Module):\n \"\"\"Autoregressive left to right HMM model primarily used in \"Neural HMMs are all you need (for high-quality attention-free TTS)\"\n\n Paper::\n https://arxiv.org/abs/2108.13320\n\n Paper abstract::\n Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using\n HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase\n training time and can make synthesis babble incoherently. This paper describes how the old and new paradigms can be\n combined to obtain the advantages of both worlds, by replacing attention in neural TTS with an autoregressive left-right\n no-skip hidden Markov model defined by a neural network. Based on this proposal, we modify Tacotron 2 to obtain an\n HMM-based neural TTS model with monotonic alignment, trained to maximise the full sequence likelihood without\n approximation. We also describe how to combine ideas from classical and contemporary TTS for best results. The resulting\n example system is smaller and simpler than Tacotron 2, and learns to speak with fewer iterations and less data, whilst\n achieving comparable naturalness prior to the post-net. Our approach also allows easy control over speaking rate.\n\n Args:\n frame_channels (int): Output dimension to generate.\n ar_order (int): Autoregressive order of the model. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.\n deterministic_transition (bool): deterministic duration generation based on duration quantiles as defiend in \"S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.\". Defaults to True.\n encoder_dim (int): Channels of encoder input and character embedding tensors. Defaults to 512.\n prenet_type (str): `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the Prenet.\n prenet_dim (int): Dimension of the Prenet.\n prenet_n_layers (int): Number of layers in the Prenet.\n prenet_dropout (float): Dropout probability of the Prenet.\n prenet_dropout_at_inference (bool): If True, dropout is applied at inference time.\n memory_rnn_dim (int): Size of the memory RNN to process output of prenet.\n outputnet_size (List[int]): Size of the output network inside the neural HMM.\n flat_start_params (dict): Parameters for the flat start initialization of the neural HMM.\n std_floor (float): Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint.\n use_grad_checkpointing (bool, optional): Use gradient checkpointing to save memory. Defaults to True.\n \"\"\"\n\n def __init__(\n self,\n frame_channels: int,\n ar_order: int,\n deterministic_transition: bool,\n encoder_dim: int,\n prenet_type: str,\n prenet_dim: int,\n prenet_n_layers: int,\n prenet_dropout: float,\n prenet_dropout_at_inference: bool,\n memory_rnn_dim: int,\n outputnet_size: List[int],\n flat_start_params: dict,\n std_floor: float,\n use_grad_checkpointing: bool = True,\n ):\n super().__init__()\n\n self.frame_channels = frame_channels\n self.ar_order = ar_order\n self.deterministic_transition = deterministic_transition\n self.prenet_dim = prenet_dim\n self.memory_rnn_dim = memory_rnn_dim\n self.use_grad_checkpointing = use_grad_checkpointing\n\n self.transition_model = TransitionModel()\n self.emission_model = EmissionModel()\n\n assert ar_order > 0, f\"AR order must be greater than 0 provided {ar_order}\"\n\n self.ar_order = ar_order\n self.prenet = Prenet(\n in_features=frame_channels * ar_order,\n prenet_type=prenet_type,\n prenet_dropout=prenet_dropout,\n dropout_at_inference=prenet_dropout_at_inference,\n out_features=[self.prenet_dim for _ in range(prenet_n_layers)],\n bias=False,\n )\n self.memory_rnn = nn.LSTMCell(input_size=prenet_dim, hidden_size=memory_rnn_dim)\n self.output_net = Outputnet(\n encoder_dim, memory_rnn_dim, frame_channels, outputnet_size, flat_start_params, std_floor\n )\n self.register_buffer(\"go_tokens\", torch.zeros(ar_order, 1))\n\n def forward(self, inputs, inputs_len, mels, mel_lens):\n r\"\"\"HMM forward algorithm for training uses logarithmic version of Rabiner (1989) forward algorithm.\n\n Args:\n inputs (torch.FloatTensor): Encoder outputs\n inputs_len (torch.LongTensor): Encoder output lengths\n mels (torch.FloatTensor): Mel inputs\n mel_lens (torch.LongTensor): Length of mel inputs\n\n Shapes:\n - inputs: (B, T, D_out_enc)\n - inputs_len: (B)\n - mels: (B, D_mel, T_mel)\n - mel_lens: (B)\n\n Returns:\n log_prob (torch.FloatTensor): Log probability of the sequence\n \"\"\"\n # Get dimensions of inputs\n batch_size, N, _ = inputs.shape\n T_max = torch.max(mel_lens)\n mels = mels.permute(0, 2, 1)\n\n # Intialize forward algorithm\n log_state_priors = self._initialize_log_state_priors(inputs)\n log_c, log_alpha_scaled, transition_matrix, means = self._initialize_forward_algorithm_variables(mels, N)\n\n # Initialize autoregression elements\n ar_inputs = self._add_go_token(mels)\n h_memory, c_memory = self._init_lstm_states(batch_size, self.memory_rnn_dim, mels)\n\n for t in range(T_max):\n # Process Autoregression\n h_memory, c_memory = self._process_ar_timestep(t, ar_inputs, h_memory, c_memory)\n # Get mean, std and transition vector from decoder for this timestep\n # Note: Gradient checkpointing currently doesn't works with multiple gpus inside a loop\n if self.use_grad_checkpointing and self.training:\n mean, std, transition_vector = checkpoint(self.output_net, h_memory, inputs)\n else:\n mean, std, transition_vector = self.output_net(h_memory, inputs)\n\n if t == 0:\n log_alpha_temp = log_state_priors + self.emission_model(mels[:, 0], mean, std, inputs_len)\n else:\n log_alpha_temp = self.emission_model(mels[:, t], mean, std, inputs_len) + self.transition_model(\n log_alpha_scaled[:, t - 1, :], transition_vector, inputs_len\n )\n log_c[:, t] = torch.logsumexp(log_alpha_temp, dim=1)\n log_alpha_scaled[:, t, :] = log_alpha_temp - log_c[:, t].unsqueeze(1)\n transition_matrix[:, t] = transition_vector # needed for absorption state calculation\n\n # Save for plotting\n means.append(mean.detach())\n\n log_c, log_alpha_scaled = self._mask_lengths(mel_lens, log_c, log_alpha_scaled)\n\n sum_final_log_c = self.get_absorption_state_scaling_factor(\n mel_lens, log_alpha_scaled, inputs_len, transition_matrix\n )\n\n log_probs = torch.sum(log_c, dim=1) + sum_final_log_c\n\n return log_probs, log_alpha_scaled, transition_matrix, means\n\n @staticmethod\n def _mask_lengths(mel_lens, log_c, log_alpha_scaled):\n \"\"\"\n Mask the lengths of the forward variables so that the variable lenghts\n do not contribute in the loss calculation\n Args:\n mel_inputs (torch.FloatTensor): (batch, T, frame_channels)\n mel_inputs_lengths (torch.IntTensor): (batch)\n log_c (torch.FloatTensor): (batch, T)\n Returns:\n log_c (torch.FloatTensor) : scaled probabilities (batch, T)\n log_alpha_scaled (torch.FloatTensor): forward probabilities (batch, T, N)\n \"\"\"\n mask_log_c = sequence_mask(mel_lens)\n log_c = log_c * mask_log_c\n mask_log_alpha_scaled = mask_log_c.unsqueeze(2)\n log_alpha_scaled = log_alpha_scaled * mask_log_alpha_scaled\n return log_c, log_alpha_scaled\n\n def _process_ar_timestep(\n self,\n t,\n ar_inputs,\n h_memory,\n c_memory,\n ):\n \"\"\"\n Process autoregression in timestep\n 1. At a specific t timestep\n 2. Perform data dropout if applied (we did not use it)\n 3. Run the autoregressive frame through the prenet (has dropout)\n 4. Run the prenet output through the post prenet rnn\n\n Args:\n t (int): mel-spec timestep\n ar_inputs (torch.FloatTensor): go-token appended mel-spectrograms\n - shape: (b, D_out, T_out)\n h_post_prenet (torch.FloatTensor): previous timestep rnn hidden state\n - shape: (b, memory_rnn_dim)\n c_post_prenet (torch.FloatTensor): previous timestep rnn cell state\n - shape: (b, memory_rnn_dim)\n\n Returns:\n h_post_prenet (torch.FloatTensor): rnn hidden state of the current timestep\n c_post_prenet (torch.FloatTensor): rnn cell state of the current timestep\n \"\"\"\n prenet_input = ar_inputs[:, t : t + self.ar_order].flatten(1)\n memory_inputs = self.prenet(prenet_input)\n h_memory, c_memory = self.memory_rnn(memory_inputs, (h_memory, c_memory))\n return h_memory, c_memory\n\n def _add_go_token(self, mel_inputs):\n \"\"\"Append the go token to create the autoregressive input\n Args:\n mel_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)\n Returns:\n ar_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)\n \"\"\"\n batch_size, T, _ = mel_inputs.shape\n go_tokens = self.go_tokens.unsqueeze(0).expand(batch_size, self.ar_order, self.frame_channels)\n ar_inputs = torch.cat((go_tokens, mel_inputs), dim=1)[:, :T]\n return ar_inputs\n\n @staticmethod\n def _initialize_forward_algorithm_variables(mel_inputs, N):\n r\"\"\"Initialize placeholders for forward algorithm variables, to use a stable\n version we will use log_alpha_scaled and the scaling constant\n\n Args:\n mel_inputs (torch.FloatTensor): (b, T_max, frame_channels)\n N (int): number of states\n Returns:\n log_c (torch.FloatTensor): Scaling constant (b, T_max)\n \"\"\"\n b, T_max, _ = mel_inputs.shape\n log_alpha_scaled = mel_inputs.new_zeros((b, T_max, N))\n log_c = mel_inputs.new_zeros(b, T_max)\n transition_matrix = mel_inputs.new_zeros((b, T_max, N))\n\n # Saving for plotting later, will not have gradient tapes\n means = []\n return log_c, log_alpha_scaled, transition_matrix, means\n\n @staticmethod\n def _init_lstm_states(batch_size, hidden_state_dim, device_tensor):\n r\"\"\"\n Initialize Hidden and Cell states for LSTM Cell\n\n Args:\n batch_size (Int): batch size\n hidden_state_dim (Int): dimensions of the h and c\n device_tensor (torch.FloatTensor): useful for the device and type\n\n Returns:\n (torch.FloatTensor): shape (batch_size, hidden_state_dim)\n can be hidden state for LSTM\n (torch.FloatTensor): shape (batch_size, hidden_state_dim)\n can be the cell state for LSTM\n \"\"\"\n return (\n device_tensor.new_zeros(batch_size, hidden_state_dim),\n device_tensor.new_zeros(batch_size, hidden_state_dim),\n )\n\n def get_absorption_state_scaling_factor(self, mels_len, log_alpha_scaled, inputs_len, transition_vector):\n \"\"\"Returns the final scaling factor of absorption state\n\n Args:\n mels_len (torch.IntTensor): Input size of mels to\n get the last timestep of log_alpha_scaled\n log_alpha_scaled (torch.FloatTEnsor): State probabilities\n text_lengths (torch.IntTensor): length of the states to\n mask the values of states lengths\n (\n Useful when the batch has very different lengths,\n when the length of an observation is less than\n the number of max states, then the log alpha after\n the state value is filled with -infs. So we mask\n those values so that it only consider the states\n which are needed for that length\n )\n transition_vector (torch.FloatTensor): transtiion vector for each state per timestep\n\n Shapes:\n - mels_len: (batch_size)\n - log_alpha_scaled: (batch_size, N, T)\n - text_lengths: (batch_size)\n - transition_vector: (batch_size, N, T)\n\n Returns:\n sum_final_log_c (torch.FloatTensor): (batch_size)\n\n \"\"\"\n N = torch.max(inputs_len)\n max_inputs_len = log_alpha_scaled.shape[2]\n state_lengths_mask = sequence_mask(inputs_len, max_len=max_inputs_len)\n\n last_log_alpha_scaled_index = (\n (mels_len - 1).unsqueeze(-1).expand(-1, N).unsqueeze(1)\n ) # Batch X Hidden State Size\n last_log_alpha_scaled = torch.gather(log_alpha_scaled, 1, last_log_alpha_scaled_index).squeeze(1)\n last_log_alpha_scaled = last_log_alpha_scaled.masked_fill(~state_lengths_mask, -float(\"inf\"))\n\n last_transition_vector = torch.gather(transition_vector, 1, last_log_alpha_scaled_index).squeeze(1)\n last_transition_probability = torch.sigmoid(last_transition_vector)\n log_probability_of_transitioning = OverflowUtils.log_clamped(last_transition_probability)\n\n last_transition_probability_index = self.get_mask_for_last_item(inputs_len, inputs_len.device)\n log_probability_of_transitioning = log_probability_of_transitioning.masked_fill(\n ~last_transition_probability_index, -float(\"inf\")\n )\n final_log_c = last_log_alpha_scaled + log_probability_of_transitioning\n\n # If the length of the mel is less than the number of states it will select the -inf values leading to nan gradients\n # Ideally, we should clean the dataset otherwise this is a little hack uncomment the line below\n final_log_c = final_log_c.clamp(min=torch.finfo(final_log_c.dtype).min)\n\n sum_final_log_c = torch.logsumexp(final_log_c, dim=1)\n return sum_final_log_c\n\n @staticmethod\n def get_mask_for_last_item(lengths, device, out_tensor=None):\n \"\"\"Returns n-1 mask for the last item in the sequence.\n\n Args:\n lengths (torch.IntTensor): lengths in a batch\n device (str, optional): Defaults to \"cpu\".\n out_tensor (torch.Tensor, optional): uses the memory of a specific tensor.\n Defaults to None.\n\n Returns:\n - Shape: :math:`(b, max_len)`\n \"\"\"\n max_len = torch.max(lengths).item()\n ids = (\n torch.arange(0, max_len, device=device) if out_tensor is None else torch.arange(0, max_len, out=out_tensor)\n )\n mask = ids == lengths.unsqueeze(1) - 1\n return mask\n\n @torch.inference_mode()\n def inference(\n self,\n inputs: torch.FloatTensor,\n input_lens: torch.LongTensor,\n sampling_temp: float,\n max_sampling_time: int,\n duration_threshold: float,\n ):\n \"\"\"Inference from autoregressive neural HMM\n\n Args:\n inputs (torch.FloatTensor): input states\n - shape: :math:`(b, T, d)`\n input_lens (torch.LongTensor): input state lengths\n - shape: :math:`(b)`\n sampling_temp (float): sampling temperature\n max_sampling_temp (int): max sampling temperature\n duration_threshold (float): duration threshold to switch to next state\n - Use this to change the spearking rate of the synthesised audio\n \"\"\"\n\n b = inputs.shape[0]\n outputs = {\n \"hmm_outputs\": [],\n \"hmm_outputs_len\": [],\n \"alignments\": [],\n \"input_parameters\": [],\n \"output_parameters\": [],\n }\n for i in range(b):\n neural_hmm_outputs, states_travelled, input_parameters, output_parameters = self.sample(\n inputs[i : i + 1], input_lens[i], sampling_temp, max_sampling_time, duration_threshold\n )\n\n outputs[\"hmm_outputs\"].append(neural_hmm_outputs)\n outputs[\"hmm_outputs_len\"].append(neural_hmm_outputs.shape[0])\n outputs[\"alignments\"].append(states_travelled)\n outputs[\"input_parameters\"].append(input_parameters)\n outputs[\"output_parameters\"].append(output_parameters)\n\n outputs[\"hmm_outputs\"] = nn.utils.rnn.pad_sequence(outputs[\"hmm_outputs\"], batch_first=True)\n outputs[\"hmm_outputs_len\"] = torch.tensor(\n outputs[\"hmm_outputs_len\"], dtype=input_lens.dtype, device=input_lens.device\n )\n return outputs\n\n @torch.inference_mode()\n def sample(self, inputs, input_lens, sampling_temp, max_sampling_time, duration_threshold):\n \"\"\"Samples an output from the parameter models\n\n Args:\n inputs (torch.FloatTensor): input states\n - shape: :math:`(1, T, d)`\n input_lens (torch.LongTensor): input state lengths\n - shape: :math:`(1)`\n sampling_temp (float): sampling temperature\n max_sampling_time (int): max sampling time\n duration_threshold (float): duration threshold to switch to next state\n\n Returns:\n outputs (torch.FloatTensor): Output Observations\n - Shape: :math:`(T, output_dim)`\n states_travelled (list[int]): Hidden states travelled\n - Shape: :math:`(T)`\n input_parameters (list[torch.FloatTensor]): Input parameters\n output_parameters (list[torch.FloatTensor]): Output parameters\n \"\"\"\n states_travelled, outputs, t = [], [], 0\n\n # Sample initial state\n current_state = 0\n states_travelled.append(current_state)\n\n # Prepare autoregression\n prenet_input = self.go_tokens.unsqueeze(0).expand(1, self.ar_order, self.frame_channels)\n h_memory, c_memory = self._init_lstm_states(1, self.memory_rnn_dim, prenet_input)\n\n input_parameter_values = []\n output_parameter_values = []\n quantile = 1\n while True:\n memory_input = self.prenet(prenet_input.flatten(1).unsqueeze(0))\n # will be 1 while sampling\n h_memory, c_memory = self.memory_rnn(memory_input.squeeze(0), (h_memory, c_memory))\n\n z_t = inputs[:, current_state].unsqueeze(0) # Add fake time dimension\n mean, std, transition_vector = self.output_net(h_memory, z_t)\n\n transition_probability = torch.sigmoid(transition_vector.flatten())\n staying_probability = torch.sigmoid(-transition_vector.flatten())\n\n # Save for plotting\n input_parameter_values.append([prenet_input, current_state])\n output_parameter_values.append([mean, std, transition_probability])\n\n x_t = self.emission_model.sample(mean, std, sampling_temp=sampling_temp)\n\n # Prepare autoregressive input for next iteration\n prenet_input = torch.cat((prenet_input, x_t), dim=1)[:, 1:]\n\n outputs.append(x_t.flatten())\n\n transition_matrix = torch.cat((staying_probability, transition_probability))\n quantile *= staying_probability\n if not self.deterministic_transition:\n switch = transition_matrix.multinomial(1)[0].item()\n else:\n switch = quantile < duration_threshold\n\n if switch:\n current_state += 1\n quantile = 1\n\n states_travelled.append(current_state)\n\n if (current_state == input_lens) or (max_sampling_time and t == max_sampling_time - 1):\n break\n\n t += 1\n\n return (\n torch.stack(outputs, dim=0),\n F.one_hot(input_lens.new_tensor(states_travelled)),\n input_parameter_values,\n output_parameter_values,\n )\n\n @staticmethod\n def _initialize_log_state_priors(text_embeddings):\n \"\"\"Creates the log pi in forward algorithm.\n\n Args:\n text_embeddings (torch.FloatTensor): used to create the log pi\n on current device\n\n Shapes:\n - text_embeddings: (B, T, D_out_enc)\n \"\"\"\n N = text_embeddings.shape[1]\n log_state_priors = text_embeddings.new_full([N], -float(\"inf\"))\n log_state_priors[0] = 0.0\n return log_state_priors"},{"identifier":"get_spec_from_most_probable_state","path":"TTS/tts/layers/overflow/plotting_utils.py","snippet":"def get_spec_from_most_probable_state(log_alpha_scaled, means, decoder=None):\n \"\"\"Get the most probable state means from the log_alpha_scaled.\n\n Args:\n log_alpha_scaled (torch.Tensor): Log alpha scaled values.\n - Shape: :math:`(T, N)`\n means (torch.Tensor): Means of the states.\n - Shape: :math:`(N, T, D_out)`\n decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None.\n \"\"\"\n max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1]\n max_len = means.shape[0]\n n_mel_channels = means.shape[2]\n max_state_numbers = max_state_numbers.unsqueeze(1).unsqueeze(1).expand(max_len, 1, n_mel_channels)\n means = torch.gather(means, 1, max_state_numbers).squeeze(1).to(log_alpha_scaled.dtype)\n if decoder is not None:\n mel = (\n decoder(means.T.unsqueeze(0), torch.tensor([means.shape[0]], device=means.device), reverse=True)[0]\n .squeeze(0)\n .T\n )\n else:\n mel = means\n return mel"},{"identifier":"plot_transition_probabilities_to_numpy","path":"TTS/tts/layers/overflow/plotting_utils.py","snippet":"def plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig=False):\n \"\"\"Generates trainsition probabilities plot for the states and the probability of transition.\n\n Args:\n states (torch.IntTensor): the states\n transition_probabilities (torch.FloatTensor): the transition probabilities\n \"\"\"\n states = validate_numpy_array(states)\n transition_probabilities = validate_numpy_array(transition_probabilities)\n\n fig, ax = plt.subplots(figsize=(30, 3))\n ax.plot(transition_probabilities, \"o\")\n ax.set_title(\"Transition probability of state\")\n ax.set_xlabel(\"hidden state\")\n ax.set_ylabel(\"probability\")\n ax.set_xticks([i for i in range(len(transition_probabilities))]) # pylint: disable=unnecessary-comprehension\n ax.set_xticklabels([int(x) for x in states], rotation=90)\n plt.tight_layout()\n if not output_fig:\n plt.close()\n return fig"},{"identifier":"BaseTTS","path":"TTS/tts/models/base_tts.py","snippet":"class BaseTTS(BaseTrainerModel):\n \"\"\"Base `tts` class. Every new `tts` model must inherit this.\n\n It defines common `tts` specific functions on top of `Model` implementation.\n \"\"\"\n\n MODEL_TYPE = \"tts\"\n\n def __init__(\n self,\n config: Coqpit,\n ap: \"AudioProcessor\",\n tokenizer: \"TTSTokenizer\",\n speaker_manager: SpeakerManager = None,\n language_manager: LanguageManager = None,\n ):\n super().__init__()\n self.config = config\n self.ap = ap\n self.tokenizer = tokenizer\n self.speaker_manager = speaker_manager\n self.language_manager = language_manager\n self._set_model_args(config)\n\n def _set_model_args(self, config: Coqpit):\n \"\"\"Setup model args based on the config type (`ModelConfig` or `ModelArgs`).\n\n `ModelArgs` has all the fields reuqired to initialize the model architecture.\n\n `ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.\n\n If the config is for training with a name like \"*Config\", then the model args are embeded in the\n config.model_args\n\n If the config is for the model with a name like \"*Args\", then we assign the directly.\n \"\"\"\n # don't use isintance not to import recursively\n if \"Config\" in config.__class__.__name__:\n config_num_chars = (\n self.config.model_args.num_chars if hasattr(self.config, \"model_args\") else self.config.num_chars\n )\n num_chars = config_num_chars if self.tokenizer is None else self.tokenizer.characters.num_chars\n if \"characters\" in config:\n self.config.num_chars = num_chars\n if hasattr(self.config, \"model_args\"):\n config.model_args.num_chars = num_chars\n self.args = self.config.model_args\n else:\n self.config = config\n self.args = config.model_args\n elif \"Args\" in config.__class__.__name__:\n self.args = config\n else:\n raise ValueError(\"config must be either a *Config or *Args\")\n\n def init_multispeaker(self, config: Coqpit, data: List = None):\n \"\"\"Initialize a speaker embedding layer if needen and define expected embedding channel size for defining\n `in_channels` size of the connected layers.\n\n This implementation yields 3 possible outcomes:\n\n 1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.\n 2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.\n 3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of\n `config.d_vector_dim` or 512.\n\n You can override this function for new models.\n\n Args:\n config (Coqpit): Model configuration.\n \"\"\"\n # set number of speakers\n if self.speaker_manager is not None:\n self.num_speakers = self.speaker_manager.num_speakers\n elif hasattr(config, \"num_speakers\"):\n self.num_speakers = config.num_speakers\n\n # set ultimate speaker embedding size\n if config.use_speaker_embedding or config.use_d_vector_file:\n self.embedded_speaker_dim = (\n config.d_vector_dim if \"d_vector_dim\" in config and config.d_vector_dim is not None else 512\n )\n # init speaker embedding layer\n if config.use_speaker_embedding and not config.use_d_vector_file:\n print(\" > Init speaker_embedding layer.\")\n self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)\n self.speaker_embedding.weight.data.normal_(0, 0.3)\n\n def get_aux_input(self, **kwargs) -> Dict:\n \"\"\"Prepare and return `aux_input` used by `forward()`\"\"\"\n return {\"speaker_id\": None, \"style_wav\": None, \"d_vector\": None, \"language_id\": None}\n\n def get_aux_input_from_test_sentences(self, sentence_info):\n if hasattr(self.config, \"model_args\"):\n config = self.config.model_args\n else:\n config = self.config\n\n # extract speaker and language info\n text, speaker_name, style_wav, language_name = None, None, None, None\n\n if isinstance(sentence_info, list):\n if len(sentence_info) == 1:\n text = sentence_info[0]\n elif len(sentence_info) == 2:\n text, speaker_name = sentence_info\n elif len(sentence_info) == 3:\n text, speaker_name, style_wav = sentence_info\n elif len(sentence_info) == 4:\n text, speaker_name, style_wav, language_name = sentence_info\n else:\n text = sentence_info\n\n # get speaker id/d_vector\n speaker_id, d_vector, language_id = None, None, None\n if self.speaker_manager is not None:\n if config.use_d_vector_file:\n if speaker_name is None:\n d_vector = self.speaker_manager.get_random_embedding()\n else:\n d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)\n elif config.use_speaker_embedding:\n if speaker_name is None:\n speaker_id = self.speaker_manager.get_random_id()\n else:\n speaker_id = self.speaker_manager.name_to_id[speaker_name]\n\n # get language id\n if self.language_manager is not None and config.use_language_embedding and language_name is not None:\n language_id = self.language_manager.name_to_id[language_name]\n\n return {\n \"text\": text,\n \"speaker_id\": speaker_id,\n \"style_wav\": style_wav,\n \"d_vector\": d_vector,\n \"language_id\": language_id,\n }\n\n def format_batch(self, batch: Dict) -> Dict:\n \"\"\"Generic batch formatting for `TTSDataset`.\n\n You must override this if you use a custom dataset.\n\n Args:\n batch (Dict): [description]\n\n Returns:\n Dict: [description]\n \"\"\"\n # setup input batch\n text_input = batch[\"token_id\"]\n text_lengths = batch[\"token_id_lengths\"]\n speaker_names = batch[\"speaker_names\"]\n linear_input = batch[\"linear\"]\n mel_input = batch[\"mel\"]\n mel_lengths = batch[\"mel_lengths\"]\n stop_targets = batch[\"stop_targets\"]\n item_idx = batch[\"item_idxs\"]\n d_vectors = batch[\"d_vectors\"]\n speaker_ids = batch[\"speaker_ids\"]\n attn_mask = batch[\"attns\"]\n waveform = batch[\"waveform\"]\n pitch = batch[\"pitch\"]\n energy = batch[\"energy\"]\n language_ids = batch[\"language_ids\"]\n max_text_length = torch.max(text_lengths.float())\n max_spec_length = torch.max(mel_lengths.float())\n\n # compute durations from attention masks\n durations = None\n if attn_mask is not None:\n durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])\n for idx, am in enumerate(attn_mask):\n # compute raw durations\n c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]\n # c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)\n c_idxs, counts = torch.unique(c_idxs, return_counts=True)\n dur = torch.ones([text_lengths[idx]]).to(counts.dtype)\n dur[c_idxs] = counts\n # smooth the durations and set any 0 duration to 1\n # by cutting off from the largest duration indeces.\n extra_frames = dur.sum() - mel_lengths[idx]\n largest_idxs = torch.argsort(-dur)[:extra_frames]\n dur[largest_idxs] -= 1\n assert (\n dur.sum() == mel_lengths[idx]\n ), f\" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}\"\n durations[idx, : text_lengths[idx]] = dur\n\n # set stop targets wrt reduction factor\n stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)\n stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)\n stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()\n\n return {\n \"text_input\": text_input,\n \"text_lengths\": text_lengths,\n \"speaker_names\": speaker_names,\n \"mel_input\": mel_input,\n \"mel_lengths\": mel_lengths,\n \"linear_input\": linear_input,\n \"stop_targets\": stop_targets,\n \"stop_target_lengths\": stop_target_lengths,\n \"attn_mask\": attn_mask,\n \"durations\": durations,\n \"speaker_ids\": speaker_ids,\n \"d_vectors\": d_vectors,\n \"max_text_length\": float(max_text_length),\n \"max_spec_length\": float(max_spec_length),\n \"item_idx\": item_idx,\n \"waveform\": waveform,\n \"pitch\": pitch,\n \"energy\": energy,\n \"language_ids\": language_ids,\n \"audio_unique_names\": batch[\"audio_unique_names\"],\n }\n\n def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):\n weights = None\n data_items = dataset.samples\n\n if getattr(config, \"use_language_weighted_sampler\", False):\n alpha = getattr(config, \"language_weighted_sampler_alpha\", 1.0)\n print(\" > Using Language weighted sampler with alpha:\", alpha)\n weights = get_language_balancer_weights(data_items) * alpha\n\n if getattr(config, \"use_speaker_weighted_sampler\", False):\n alpha = getattr(config, \"speaker_weighted_sampler_alpha\", 1.0)\n print(\" > Using Speaker weighted sampler with alpha:\", alpha)\n if weights is not None:\n weights += get_speaker_balancer_weights(data_items) * alpha\n else:\n weights = get_speaker_balancer_weights(data_items) * alpha\n\n if getattr(config, \"use_length_weighted_sampler\", False):\n alpha = getattr(config, \"length_weighted_sampler_alpha\", 1.0)\n print(\" > Using Length weighted sampler with alpha:\", alpha)\n if weights is not None:\n weights += get_length_balancer_weights(data_items) * alpha\n else:\n weights = get_length_balancer_weights(data_items) * alpha\n\n if weights is not None:\n sampler = WeightedRandomSampler(weights, len(weights))\n else:\n sampler = None\n\n # sampler for DDP\n if sampler is None:\n sampler = DistributedSampler(dataset) if num_gpus > 1 else None\n else: # If a sampler is already defined use this sampler and DDP sampler together\n sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler\n\n return sampler\n\n def get_data_loader(\n self,\n config: Coqpit,\n assets: Dict,\n is_eval: bool,\n samples: Union[List[Dict], List[List]],\n verbose: bool,\n num_gpus: int,\n rank: int = None,\n ) -> \"DataLoader\":\n if is_eval and not config.run_eval:\n loader = None\n else:\n # setup multi-speaker attributes\n if self.speaker_manager is not None:\n if hasattr(config, \"model_args\"):\n speaker_id_mapping = (\n self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None\n )\n d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None\n config.use_d_vector_file = config.model_args.use_d_vector_file\n else:\n speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None\n d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None\n else:\n speaker_id_mapping = None\n d_vector_mapping = None\n\n # setup multi-lingual attributes\n if self.language_manager is not None:\n language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None\n else:\n language_id_mapping = None\n\n # init dataloader\n dataset = TTSDataset(\n outputs_per_step=config.r if \"r\" in config else 1,\n compute_linear_spec=config.model.lower() == \"tacotron\" or config.compute_linear_spec,\n compute_f0=config.get(\"compute_f0\", False),\n f0_cache_path=config.get(\"f0_cache_path\", None),\n compute_energy=config.get(\"compute_energy\", False),\n energy_cache_path=config.get(\"energy_cache_path\", None),\n samples=samples,\n ap=self.ap,\n return_wav=config.return_wav if \"return_wav\" in config else False,\n batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,\n min_text_len=config.min_text_len,\n max_text_len=config.max_text_len,\n min_audio_len=config.min_audio_len,\n max_audio_len=config.max_audio_len,\n phoneme_cache_path=config.phoneme_cache_path,\n precompute_num_workers=config.precompute_num_workers,\n use_noise_augment=False if is_eval else config.use_noise_augment,\n verbose=verbose,\n speaker_id_mapping=speaker_id_mapping,\n d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,\n tokenizer=self.tokenizer,\n start_by_longest=config.start_by_longest,\n language_id_mapping=language_id_mapping,\n )\n\n # wait all the DDP process to be ready\n if num_gpus > 1:\n dist.barrier()\n\n # sort input sequences from short to long\n dataset.preprocess_samples()\n\n # get samplers\n sampler = self.get_sampler(config, dataset, num_gpus)\n\n loader = DataLoader(\n dataset,\n batch_size=config.eval_batch_size if is_eval else config.batch_size,\n shuffle=config.shuffle if sampler is None else False, # if there is no other sampler\n collate_fn=dataset.collate_fn,\n drop_last=config.drop_last, # setting this False might cause issues in AMP training.\n sampler=sampler,\n num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,\n pin_memory=False,\n )\n return loader\n\n def _get_test_aux_input(\n self,\n ) -> Dict:\n d_vector = None\n if self.config.use_d_vector_file:\n d_vector = [self.speaker_manager.embeddings[name][\"embedding\"] for name in self.speaker_manager.embeddings]\n d_vector = (random.sample(sorted(d_vector), 1),)\n\n aux_inputs = {\n \"speaker_id\": None\n if not self.config.use_speaker_embedding\n else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),\n \"d_vector\": d_vector,\n \"style_wav\": None, # TODO: handle GST style input\n }\n return aux_inputs\n\n def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:\n \"\"\"Generic test run for `tts` models used by `Trainer`.\n\n You can override this for a different behaviour.\n\n Args:\n assets (dict): A dict of training assets. For `tts` models, it must include `{'audio_processor': ap}`.\n\n Returns:\n Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.\n \"\"\"\n print(\" | > Synthesizing test sentences.\")\n test_audios = {}\n test_figures = {}\n test_sentences = self.config.test_sentences\n aux_inputs = self._get_test_aux_input()\n for idx, sen in enumerate(test_sentences):\n if isinstance(sen, list):\n aux_inputs = self.get_aux_input_from_test_sentences(sen)\n sen = aux_inputs[\"text\"]\n outputs_dict = synthesis(\n self,\n sen,\n self.config,\n \"cuda\" in str(next(self.parameters()).device),\n speaker_id=aux_inputs[\"speaker_id\"],\n d_vector=aux_inputs[\"d_vector\"],\n style_wav=aux_inputs[\"style_wav\"],\n use_griffin_lim=True,\n do_trim_silence=False,\n )\n test_audios[\"{}-audio\".format(idx)] = outputs_dict[\"wav\"]\n test_figures[\"{}-prediction\".format(idx)] = plot_spectrogram(\n outputs_dict[\"outputs\"][\"model_outputs\"], self.ap, output_fig=False\n )\n test_figures[\"{}-alignment\".format(idx)] = plot_alignment(\n outputs_dict[\"outputs\"][\"alignments\"], output_fig=False\n )\n return test_figures, test_audios\n\n def on_init_start(self, trainer):\n \"\"\"Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths.\"\"\"\n if self.speaker_manager is not None:\n output_path = os.path.join(trainer.output_path, \"speakers.pth\")\n self.speaker_manager.save_ids_to_file(output_path)\n trainer.config.speakers_file = output_path\n # some models don't have `model_args` set\n if hasattr(trainer.config, \"model_args\"):\n trainer.config.model_args.speakers_file = output_path\n trainer.config.save_json(os.path.join(trainer.output_path, \"config.json\"))\n print(f\" > `speakers.pth` is saved to {output_path}.\")\n print(\" > `speakers_file` is updated in the config.json.\")\n\n if self.language_manager is not None:\n output_path = os.path.join(trainer.output_path, \"language_ids.json\")\n self.language_manager.save_ids_to_file(output_path)\n trainer.config.language_ids_file = output_path\n if hasattr(trainer.config, \"model_args\"):\n trainer.config.model_args.language_ids_file = output_path\n trainer.config.save_json(os.path.join(trainer.output_path, \"config.json\"))\n print(f\" > `language_ids.json` is saved to {output_path}.\")\n print(\" > `language_ids_file` is updated in the config.json.\")"},{"identifier":"SpeakerManager","path":"TTS/tts/utils/speakers.py","snippet":"class SpeakerManager(EmbeddingManager):\n \"\"\"Manage the speakers for multi-speaker 🐸TTS models. Load a datafile and parse the information\n in a way that can be queried by speaker or clip.\n\n There are 3 different scenarios considered:\n\n 1. Models using speaker embedding layers. The datafile only maps speaker names to ids used by the embedding layer.\n 2. Models using d-vectors. The datafile includes a dictionary in the following format.\n\n ::\n\n {\n 'clip_name.wav':{\n 'name': 'speakerA',\n 'embedding'[<d_vector_values>]\n },\n ...\n }\n\n\n 3. Computing the d-vectors by the speaker encoder. It loads the speaker encoder model and\n computes the d-vectors for a given clip or speaker.\n\n Args:\n d_vectors_file_path (str, optional): Path to the metafile including x vectors. Defaults to \"\".\n speaker_id_file_path (str, optional): Path to the metafile that maps speaker names to ids used by\n TTS models. Defaults to \"\".\n encoder_model_path (str, optional): Path to the speaker encoder model file. Defaults to \"\".\n encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to \"\".\n\n Examples:\n >>> # load audio processor and speaker encoder\n >>> ap = AudioProcessor(**config.audio)\n >>> manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)\n >>> # load a sample audio and compute embedding\n >>> waveform = ap.load_wav(sample_wav_path)\n >>> mel = ap.melspectrogram(waveform)\n >>> d_vector = manager.compute_embeddings(mel.T)\n \"\"\"\n\n def __init__(\n self,\n data_items: List[List[Any]] = None,\n d_vectors_file_path: str = \"\",\n speaker_id_file_path: str = \"\",\n encoder_model_path: str = \"\",\n encoder_config_path: str = \"\",\n use_cuda: bool = False,\n ):\n super().__init__(\n embedding_file_path=d_vectors_file_path,\n id_file_path=speaker_id_file_path,\n encoder_model_path=encoder_model_path,\n encoder_config_path=encoder_config_path,\n use_cuda=use_cuda,\n )\n\n if data_items:\n self.set_ids_from_data(data_items, parse_key=\"speaker_name\")\n\n @property\n def num_speakers(self):\n return len(self.name_to_id)\n\n @property\n def speaker_names(self):\n return list(self.name_to_id.keys())\n\n def get_speakers(self) -> List:\n return self.name_to_id\n\n @staticmethod\n def init_from_config(config: \"Coqpit\", samples: Union[List[List], List[Dict]] = None) -> \"SpeakerManager\":\n \"\"\"Initialize a speaker manager from config\n\n Args:\n config (Coqpit): Config object.\n samples (Union[List[List], List[Dict]], optional): List of data samples to parse out the speaker names.\n Defaults to None.\n\n Returns:\n SpeakerEncoder: Speaker encoder object.\n \"\"\"\n speaker_manager = None\n if get_from_config_or_model_args_with_default(config, \"use_speaker_embedding\", False):\n if samples:\n speaker_manager = SpeakerManager(data_items=samples)\n if get_from_config_or_model_args_with_default(config, \"speaker_file\", None):\n speaker_manager = SpeakerManager(\n speaker_id_file_path=get_from_config_or_model_args_with_default(config, \"speaker_file\", None)\n )\n if get_from_config_or_model_args_with_default(config, \"speakers_file\", None):\n speaker_manager = SpeakerManager(\n speaker_id_file_path=get_from_config_or_model_args_with_default(config, \"speakers_file\", None)\n )\n\n if get_from_config_or_model_args_with_default(config, \"use_d_vector_file\", False):\n speaker_manager = SpeakerManager()\n if get_from_config_or_model_args_with_default(config, \"d_vector_file\", None):\n speaker_manager = SpeakerManager(\n d_vectors_file_path=get_from_config_or_model_args_with_default(config, \"d_vector_file\", None)\n )\n return speaker_manager"},{"identifier":"TTSTokenizer","path":"TTS/tts/utils/text/tokenizer.py","snippet":"class TTSTokenizer:\n \"\"\"🐸TTS tokenizer to convert input characters to token IDs and back.\n\n Token IDs for OOV chars are discarded but those are stored in `self.not_found_characters` for later.\n\n Args:\n use_phonemes (bool):\n Whether to use phonemes instead of characters. Defaults to False.\n\n characters (Characters):\n A Characters object to use for character-to-ID and ID-to-character mappings.\n\n text_cleaner (callable):\n A function to pre-process the text before tokenization and phonemization. Defaults to None.\n\n phonemizer (Phonemizer):\n A phonemizer object or a dict that maps language codes to phonemizer objects. Defaults to None.\n\n Example:\n\n >>> from TTS.tts.utils.text.tokenizer import TTSTokenizer\n >>> tokenizer = TTSTokenizer(use_phonemes=False, characters=Graphemes())\n >>> text = \"Hello world!\"\n >>> ids = tokenizer.text_to_ids(text)\n >>> text_hat = tokenizer.ids_to_text(ids)\n >>> assert text == text_hat\n \"\"\"\n\n def __init__(\n self,\n use_phonemes=False,\n text_cleaner: Callable = None,\n characters: \"BaseCharacters\" = None,\n phonemizer: Union[\"Phonemizer\", Dict] = None,\n add_blank: bool = False,\n use_eos_bos=False,\n ):\n self.text_cleaner = text_cleaner\n self.use_phonemes = use_phonemes\n self.add_blank = add_blank\n self.use_eos_bos = use_eos_bos\n self.characters = characters\n self.not_found_characters = []\n self.phonemizer = phonemizer\n\n @property\n def characters(self):\n return self._characters\n\n @characters.setter\n def characters(self, new_characters):\n self._characters = new_characters\n self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None\n self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None\n\n def encode(self, text: str) -> List[int]:\n \"\"\"Encodes a string of text as a sequence of IDs.\"\"\"\n token_ids = []\n for char in text:\n try:\n idx = self.characters.char_to_id(char)\n token_ids.append(idx)\n except KeyError:\n # discard but store not found characters\n if char not in self.not_found_characters:\n self.not_found_characters.append(char)\n print(text)\n print(f\" [!] Character {repr(char)} not found in the vocabulary. Discarding it.\")\n return token_ids\n\n def decode(self, token_ids: List[int]) -> str:\n \"\"\"Decodes a sequence of IDs to a string of text.\"\"\"\n text = \"\"\n for token_id in token_ids:\n text += self.characters.id_to_char(token_id)\n return text\n\n def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument\n \"\"\"Converts a string of text to a sequence of token IDs.\n\n Args:\n text(str):\n The text to convert to token IDs.\n\n language(str):\n The language code of the text. Defaults to None.\n\n TODO:\n - Add support for language-specific processing.\n\n 1. Text normalizatin\n 2. Phonemization (if use_phonemes is True)\n 3. Add blank char between characters\n 4. Add BOS and EOS characters\n 5. Text to token IDs\n \"\"\"\n # TODO: text cleaner should pick the right routine based on the language\n if self.text_cleaner is not None:\n text = self.text_cleaner(text)\n if self.use_phonemes:\n text = self.phonemizer.phonemize(text, separator=\"\", language=language)\n text = self.encode(text)\n if self.add_blank:\n text = self.intersperse_blank_char(text, True)\n if self.use_eos_bos:\n text = self.pad_with_bos_eos(text)\n return text\n\n def ids_to_text(self, id_sequence: List[int]) -> str:\n \"\"\"Converts a sequence of token IDs to a string of text.\"\"\"\n return self.decode(id_sequence)\n\n def pad_with_bos_eos(self, char_sequence: List[str]):\n \"\"\"Pads a sequence with the special BOS and EOS characters.\"\"\"\n return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id]\n\n def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False):\n \"\"\"Intersperses the blank character between characters in a sequence.\n\n Use the ```blank``` character if defined else use the ```pad``` character.\n \"\"\"\n char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad\n result = [char_to_use] * (len(char_sequence) * 2 + 1)\n result[1::2] = char_sequence\n return result\n\n def print_logs(self, level: int = 0):\n indent = \"\\t\" * level\n print(f\"{indent}| > add_blank: {self.add_blank}\")\n print(f\"{indent}| > use_eos_bos: {self.use_eos_bos}\")\n print(f\"{indent}| > use_phonemes: {self.use_phonemes}\")\n if self.use_phonemes:\n print(f\"{indent}| > phonemizer:\")\n self.phonemizer.print_logs(level + 1)\n if len(self.not_found_characters) > 0:\n print(f\"{indent}| > {len(self.not_found_characters)} not found characters:\")\n for char in self.not_found_characters:\n print(f\"{indent}| > {char}\")\n\n @staticmethod\n def init_from_config(config: \"Coqpit\", characters: \"BaseCharacters\" = None):\n \"\"\"Init Tokenizer object from config\n\n Args:\n config (Coqpit): Coqpit model config.\n characters (BaseCharacters): Defines the model character set. If not set, use the default options based on\n the config values. Defaults to None.\n \"\"\"\n # init cleaners\n text_cleaner = None\n if isinstance(config.text_cleaner, (str, list)):\n text_cleaner = getattr(cleaners, config.text_cleaner)\n\n # init characters\n if characters is None:\n # set characters based on defined characters class\n if config.characters and config.characters.characters_class:\n CharactersClass = import_class(config.characters.characters_class)\n characters, new_config = CharactersClass.init_from_config(config)\n # set characters based on config\n else:\n if config.use_phonemes:\n # init phoneme set\n characters, new_config = IPAPhonemes().init_from_config(config)\n else:\n # init character set\n characters, new_config = Graphemes().init_from_config(config)\n\n else:\n characters, new_config = characters.init_from_config(config)\n\n # set characters class\n new_config.characters.characters_class = get_import_path(characters)\n\n # init phonemizer\n phonemizer = None\n if config.use_phonemes:\n if \"phonemizer\" in config and config.phonemizer == \"multi_phonemizer\":\n lang_to_phonemizer_name = {}\n for dataset in config.datasets:\n if dataset.language != \"\":\n lang_to_phonemizer_name[dataset.language] = dataset.phonemizer\n else:\n raise ValueError(\"Multi phonemizer requires language to be set for each dataset.\")\n phonemizer = MultiPhonemizer(lang_to_phonemizer_name)\n else:\n phonemizer_kwargs = {\"language\": config.phoneme_language}\n if \"phonemizer\" in config and config.phonemizer:\n phonemizer = get_phonemizer_by_name(config.phonemizer, **phonemizer_kwargs)\n else:\n try:\n phonemizer = get_phonemizer_by_name(\n DEF_LANG_TO_PHONEMIZER[config.phoneme_language], **phonemizer_kwargs\n )\n new_config.phonemizer = phonemizer.name()\n except KeyError as e:\n raise ValueError(\n f\"\"\"No phonemizer found for language {config.phoneme_language}.\n You may need to install a third party library for this language.\"\"\"\n ) from e\n\n return (\n TTSTokenizer(\n config.use_phonemes, text_cleaner, characters, phonemizer, config.add_blank, config.enable_eos_bos_chars\n ),\n new_config,\n )"},{"identifier":"plot_alignment","path":"TTS/tts/utils/visual.py","snippet":"def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None, output_fig=False, plot_log=False):\n if isinstance(alignment, torch.Tensor):\n alignment_ = alignment.detach().cpu().numpy().squeeze()\n else:\n alignment_ = alignment\n alignment_ = alignment_.astype(np.float32) if alignment_.dtype == np.float16 else alignment_\n fig, ax = plt.subplots(figsize=fig_size)\n im = ax.imshow(\n alignment_.T, aspect=\"auto\", origin=\"lower\", interpolation=\"none\", norm=LogNorm() if plot_log else None\n )\n fig.colorbar(im, ax=ax)\n xlabel = \"Decoder timestep\"\n if info is not None:\n xlabel += \"\\n\\n\" + info\n plt.xlabel(xlabel)\n plt.ylabel(\"Encoder timestep\")\n # plt.yticks(range(len(text)), list(text))\n plt.tight_layout()\n if title is not None:\n plt.title(title)\n if not output_fig:\n plt.close()\n return fig"},{"identifier":"plot_spectrogram","path":"TTS/tts/utils/visual.py","snippet":"def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10), output_fig=False):\n if isinstance(spectrogram, torch.Tensor):\n spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T\n else:\n spectrogram_ = spectrogram.T\n spectrogram_ = spectrogram_.astype(np.float32) if spectrogram_.dtype == np.float16 else spectrogram_\n if ap is not None:\n spectrogram_ = ap.denormalize(spectrogram_) # pylint: disable=protected-access\n fig = plt.figure(figsize=fig_size)\n plt.imshow(spectrogram_, aspect=\"auto\", origin=\"lower\")\n plt.colorbar()\n plt.tight_layout()\n if not output_fig:\n plt.close()\n return fig"},{"identifier":"format_aux_input","path":"TTS/utils/generic_utils.py","snippet":"def format_aux_input(def_args: Dict, kwargs: Dict) -> Dict:\n \"\"\"Format kwargs to hande auxilary inputs to models.\n\n Args:\n def_args (Dict): A dictionary of argument names and their default values if not defined in `kwargs`.\n kwargs (Dict): A `dict` or `kwargs` that includes auxilary inputs to the model.\n\n Returns:\n Dict: arguments with formatted auxilary inputs.\n \"\"\"\n kwargs = kwargs.copy()\n for name in def_args:\n if name not in kwargs or kwargs[name] is None:\n kwargs[name] = def_args[name]\n return kwargs"},{"identifier":"load_fsspec","path":"TTS/utils/io.py","snippet":"def load_fsspec(\n path: str,\n map_location: Union[str, Callable, torch.device, Dict[Union[str, torch.device], Union[str, torch.device]]] = None,\n cache: bool = True,\n **kwargs,\n) -> Any:\n \"\"\"Like torch.load but can load from other locations (e.g. s3:// , gs://).\n\n Args:\n path: Any path or url supported by fsspec.\n map_location: torch.device or str.\n cache: If True, cache a remote file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to True.\n **kwargs: Keyword arguments forwarded to torch.load.\n\n Returns:\n Object stored in path.\n \"\"\"\n is_local = os.path.isdir(path) or os.path.isfile(path)\n if cache and not is_local:\n with fsspec.open(\n f\"filecache::{path}\",\n filecache={\"cache_storage\": str(get_user_data_dir(\"tts_cache\"))},\n mode=\"rb\",\n ) as f:\n return torch.load(f, map_location=map_location, **kwargs)\n else:\n with fsspec.open(path, \"rb\") as f:\n return torch.load(f, map_location=map_location, **kwargs)"}],"string":"[\n {\n \"identifier\": \"Encoder\",\n \"path\": \"TTS/tts/layers/overflow/common_layers.py\",\n \"snippet\": \"class Encoder(nn.Module):\\n r\\\"\\\"\\\"Neural HMM Encoder\\n\\n Same as Tacotron 2 encoder but increases the input length by states per phone\\n\\n Args:\\n num_chars (int): Number of characters in the input.\\n state_per_phone (int): Number of states per phone.\\n in_out_channels (int): number of input and output channels.\\n n_convolutions (int): number of convolutional layers.\\n \\\"\\\"\\\"\\n\\n def __init__(self, num_chars, state_per_phone, in_out_channels=512, n_convolutions=3):\\n super().__init__()\\n\\n self.state_per_phone = state_per_phone\\n self.in_out_channels = in_out_channels\\n\\n self.emb = nn.Embedding(num_chars, in_out_channels)\\n self.convolutions = nn.ModuleList()\\n for _ in range(n_convolutions):\\n self.convolutions.append(ConvBNBlock(in_out_channels, in_out_channels, 5, \\\"relu\\\"))\\n self.lstm = nn.LSTM(\\n in_out_channels,\\n int(in_out_channels / 2) * state_per_phone,\\n num_layers=1,\\n batch_first=True,\\n bias=True,\\n bidirectional=True,\\n )\\n self.rnn_state = None\\n\\n def forward(self, x: torch.FloatTensor, x_len: torch.LongTensor) -> Tuple[torch.FloatTensor, torch.LongTensor]:\\n \\\"\\\"\\\"Forward pass to the encoder.\\n\\n Args:\\n x (torch.FloatTensor): input text indices.\\n - shape: :math:`(b, T_{in})`\\n x_len (torch.LongTensor): input text lengths.\\n - shape: :math:`(b,)`\\n\\n Returns:\\n Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths.\\n -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))`\\n \\\"\\\"\\\"\\n b, T = x.shape\\n o = self.emb(x).transpose(1, 2)\\n for layer in self.convolutions:\\n o = layer(o)\\n o = o.transpose(1, 2)\\n o = nn.utils.rnn.pack_padded_sequence(o, x_len.cpu(), batch_first=True)\\n self.lstm.flatten_parameters()\\n o, _ = self.lstm(o)\\n o, _ = nn.utils.rnn.pad_packed_sequence(o, batch_first=True)\\n o = o.reshape(b, T * self.state_per_phone, self.in_out_channels)\\n x_len = x_len * self.state_per_phone\\n return o, x_len\\n\\n def inference(self, x, x_len):\\n \\\"\\\"\\\"Inference to the encoder.\\n\\n Args:\\n x (torch.FloatTensor): input text indices.\\n - shape: :math:`(b, T_{in})`\\n x_len (torch.LongTensor): input text lengths.\\n - shape: :math:`(b,)`\\n\\n Returns:\\n Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths.\\n -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))`\\n \\\"\\\"\\\"\\n b, T = x.shape\\n o = self.emb(x).transpose(1, 2)\\n for layer in self.convolutions:\\n o = layer(o)\\n o = o.transpose(1, 2)\\n # self.lstm.flatten_parameters()\\n o, _ = self.lstm(o)\\n o = o.reshape(b, T * self.state_per_phone, self.in_out_channels)\\n x_len = x_len * self.state_per_phone\\n return o, x_len\"\n },\n {\n \"identifier\": \"OverflowUtils\",\n \"path\": \"TTS/tts/layers/overflow/common_layers.py\",\n \"snippet\": \"class OverflowUtils:\\n @staticmethod\\n def get_data_parameters_for_flat_start(\\n data_loader: torch.utils.data.DataLoader, out_channels: int, states_per_phone: int\\n ):\\n \\\"\\\"\\\"Generates data parameters for flat starting the HMM.\\n\\n Args:\\n data_loader (torch.utils.data.Dataloader): _description_\\n out_channels (int): mel spectrogram channels\\n states_per_phone (_type_): HMM states per phone\\n \\\"\\\"\\\"\\n\\n # State related information for transition_p\\n total_state_len = 0\\n total_mel_len = 0\\n\\n # Useful for data mean an std\\n total_mel_sum = 0\\n total_mel_sq_sum = 0\\n\\n for batch in tqdm(data_loader, leave=False):\\n text_lengths = batch[\\\"token_id_lengths\\\"]\\n mels = batch[\\\"mel\\\"]\\n mel_lengths = batch[\\\"mel_lengths\\\"]\\n\\n total_state_len += torch.sum(text_lengths)\\n total_mel_len += torch.sum(mel_lengths)\\n total_mel_sum += torch.sum(mels)\\n total_mel_sq_sum += torch.sum(torch.pow(mels, 2))\\n\\n data_mean = total_mel_sum / (total_mel_len * out_channels)\\n data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2))\\n average_num_states = total_state_len / len(data_loader.dataset)\\n average_mel_len = total_mel_len / len(data_loader.dataset)\\n average_duration_each_state = average_mel_len / average_num_states\\n init_transition_prob = 1 / average_duration_each_state\\n\\n return data_mean, data_std, (init_transition_prob * states_per_phone)\\n\\n @staticmethod\\n @torch.no_grad()\\n def update_flat_start_transition(model, transition_p):\\n model.neural_hmm.output_net.parametermodel.flat_start_output_layer(0.0, 1.0, transition_p)\\n\\n @staticmethod\\n def log_clamped(x, eps=1e-04):\\n \\\"\\\"\\\"\\n Avoids the log(0) problem\\n\\n Args:\\n x (torch.tensor): input tensor\\n eps (float, optional): lower bound. Defaults to 1e-04.\\n\\n Returns:\\n torch.tensor: :math:`log(x)`\\n \\\"\\\"\\\"\\n clamped_x = torch.clamp(x, min=eps)\\n return torch.log(clamped_x)\\n\\n @staticmethod\\n def inverse_sigmod(x):\\n r\\\"\\\"\\\"\\n Inverse of the sigmoid function\\n \\\"\\\"\\\"\\n if not torch.is_tensor(x):\\n x = torch.tensor(x)\\n return OverflowUtils.log_clamped(x / (1.0 - x))\\n\\n @staticmethod\\n def inverse_softplus(x):\\n r\\\"\\\"\\\"\\n Inverse of the softplus function\\n \\\"\\\"\\\"\\n if not torch.is_tensor(x):\\n x = torch.tensor(x)\\n return OverflowUtils.log_clamped(torch.exp(x) - 1.0)\\n\\n @staticmethod\\n def logsumexp(x, dim):\\n r\\\"\\\"\\\"\\n Differentiable LogSumExp: Does not creates nan gradients\\n when all the inputs are -inf yeilds 0 gradients.\\n Args:\\n x : torch.Tensor - The input tensor\\n dim: int - The dimension on which the log sum exp has to be applied\\n \\\"\\\"\\\"\\n\\n m, _ = x.max(dim=dim)\\n mask = m == -float(\\\"inf\\\")\\n s = (x - m.masked_fill_(mask, 0).unsqueeze(dim=dim)).exp().sum(dim=dim)\\n return s.masked_fill_(mask, 1).log() + m.masked_fill_(mask, -float(\\\"inf\\\"))\\n\\n @staticmethod\\n def double_pad(list_of_different_shape_tensors):\\n r\\\"\\\"\\\"\\n Pads the list of tensors in 2 dimensions\\n \\\"\\\"\\\"\\n second_dim_lens = [len(a) for a in [i[0] for i in list_of_different_shape_tensors]]\\n second_dim_max = max(second_dim_lens)\\n padded_x = [F.pad(x, (0, second_dim_max - len(x[0]))) for x in list_of_different_shape_tensors]\\n return nn.utils.rnn.pad_sequence(padded_x, batch_first=True)\"\n },\n {\n \"identifier\": \"NeuralHMM\",\n \"path\": \"TTS/tts/layers/overflow/neural_hmm.py\",\n \"snippet\": \"class NeuralHMM(nn.Module):\\n \\\"\\\"\\\"Autoregressive left to right HMM model primarily used in \\\"Neural HMMs are all you need (for high-quality attention-free TTS)\\\"\\n\\n Paper::\\n https://arxiv.org/abs/2108.13320\\n\\n Paper abstract::\\n Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using\\n HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase\\n training time and can make synthesis babble incoherently. This paper describes how the old and new paradigms can be\\n combined to obtain the advantages of both worlds, by replacing attention in neural TTS with an autoregressive left-right\\n no-skip hidden Markov model defined by a neural network. Based on this proposal, we modify Tacotron 2 to obtain an\\n HMM-based neural TTS model with monotonic alignment, trained to maximise the full sequence likelihood without\\n approximation. We also describe how to combine ideas from classical and contemporary TTS for best results. The resulting\\n example system is smaller and simpler than Tacotron 2, and learns to speak with fewer iterations and less data, whilst\\n achieving comparable naturalness prior to the post-net. Our approach also allows easy control over speaking rate.\\n\\n Args:\\n frame_channels (int): Output dimension to generate.\\n ar_order (int): Autoregressive order of the model. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.\\n deterministic_transition (bool): deterministic duration generation based on duration quantiles as defiend in \\\"S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.\\\". Defaults to True.\\n encoder_dim (int): Channels of encoder input and character embedding tensors. Defaults to 512.\\n prenet_type (str): `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the Prenet.\\n prenet_dim (int): Dimension of the Prenet.\\n prenet_n_layers (int): Number of layers in the Prenet.\\n prenet_dropout (float): Dropout probability of the Prenet.\\n prenet_dropout_at_inference (bool): If True, dropout is applied at inference time.\\n memory_rnn_dim (int): Size of the memory RNN to process output of prenet.\\n outputnet_size (List[int]): Size of the output network inside the neural HMM.\\n flat_start_params (dict): Parameters for the flat start initialization of the neural HMM.\\n std_floor (float): Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint.\\n use_grad_checkpointing (bool, optional): Use gradient checkpointing to save memory. Defaults to True.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n frame_channels: int,\\n ar_order: int,\\n deterministic_transition: bool,\\n encoder_dim: int,\\n prenet_type: str,\\n prenet_dim: int,\\n prenet_n_layers: int,\\n prenet_dropout: float,\\n prenet_dropout_at_inference: bool,\\n memory_rnn_dim: int,\\n outputnet_size: List[int],\\n flat_start_params: dict,\\n std_floor: float,\\n use_grad_checkpointing: bool = True,\\n ):\\n super().__init__()\\n\\n self.frame_channels = frame_channels\\n self.ar_order = ar_order\\n self.deterministic_transition = deterministic_transition\\n self.prenet_dim = prenet_dim\\n self.memory_rnn_dim = memory_rnn_dim\\n self.use_grad_checkpointing = use_grad_checkpointing\\n\\n self.transition_model = TransitionModel()\\n self.emission_model = EmissionModel()\\n\\n assert ar_order > 0, f\\\"AR order must be greater than 0 provided {ar_order}\\\"\\n\\n self.ar_order = ar_order\\n self.prenet = Prenet(\\n in_features=frame_channels * ar_order,\\n prenet_type=prenet_type,\\n prenet_dropout=prenet_dropout,\\n dropout_at_inference=prenet_dropout_at_inference,\\n out_features=[self.prenet_dim for _ in range(prenet_n_layers)],\\n bias=False,\\n )\\n self.memory_rnn = nn.LSTMCell(input_size=prenet_dim, hidden_size=memory_rnn_dim)\\n self.output_net = Outputnet(\\n encoder_dim, memory_rnn_dim, frame_channels, outputnet_size, flat_start_params, std_floor\\n )\\n self.register_buffer(\\\"go_tokens\\\", torch.zeros(ar_order, 1))\\n\\n def forward(self, inputs, inputs_len, mels, mel_lens):\\n r\\\"\\\"\\\"HMM forward algorithm for training uses logarithmic version of Rabiner (1989) forward algorithm.\\n\\n Args:\\n inputs (torch.FloatTensor): Encoder outputs\\n inputs_len (torch.LongTensor): Encoder output lengths\\n mels (torch.FloatTensor): Mel inputs\\n mel_lens (torch.LongTensor): Length of mel inputs\\n\\n Shapes:\\n - inputs: (B, T, D_out_enc)\\n - inputs_len: (B)\\n - mels: (B, D_mel, T_mel)\\n - mel_lens: (B)\\n\\n Returns:\\n log_prob (torch.FloatTensor): Log probability of the sequence\\n \\\"\\\"\\\"\\n # Get dimensions of inputs\\n batch_size, N, _ = inputs.shape\\n T_max = torch.max(mel_lens)\\n mels = mels.permute(0, 2, 1)\\n\\n # Intialize forward algorithm\\n log_state_priors = self._initialize_log_state_priors(inputs)\\n log_c, log_alpha_scaled, transition_matrix, means = self._initialize_forward_algorithm_variables(mels, N)\\n\\n # Initialize autoregression elements\\n ar_inputs = self._add_go_token(mels)\\n h_memory, c_memory = self._init_lstm_states(batch_size, self.memory_rnn_dim, mels)\\n\\n for t in range(T_max):\\n # Process Autoregression\\n h_memory, c_memory = self._process_ar_timestep(t, ar_inputs, h_memory, c_memory)\\n # Get mean, std and transition vector from decoder for this timestep\\n # Note: Gradient checkpointing currently doesn't works with multiple gpus inside a loop\\n if self.use_grad_checkpointing and self.training:\\n mean, std, transition_vector = checkpoint(self.output_net, h_memory, inputs)\\n else:\\n mean, std, transition_vector = self.output_net(h_memory, inputs)\\n\\n if t == 0:\\n log_alpha_temp = log_state_priors + self.emission_model(mels[:, 0], mean, std, inputs_len)\\n else:\\n log_alpha_temp = self.emission_model(mels[:, t], mean, std, inputs_len) + self.transition_model(\\n log_alpha_scaled[:, t - 1, :], transition_vector, inputs_len\\n )\\n log_c[:, t] = torch.logsumexp(log_alpha_temp, dim=1)\\n log_alpha_scaled[:, t, :] = log_alpha_temp - log_c[:, t].unsqueeze(1)\\n transition_matrix[:, t] = transition_vector # needed for absorption state calculation\\n\\n # Save for plotting\\n means.append(mean.detach())\\n\\n log_c, log_alpha_scaled = self._mask_lengths(mel_lens, log_c, log_alpha_scaled)\\n\\n sum_final_log_c = self.get_absorption_state_scaling_factor(\\n mel_lens, log_alpha_scaled, inputs_len, transition_matrix\\n )\\n\\n log_probs = torch.sum(log_c, dim=1) + sum_final_log_c\\n\\n return log_probs, log_alpha_scaled, transition_matrix, means\\n\\n @staticmethod\\n def _mask_lengths(mel_lens, log_c, log_alpha_scaled):\\n \\\"\\\"\\\"\\n Mask the lengths of the forward variables so that the variable lenghts\\n do not contribute in the loss calculation\\n Args:\\n mel_inputs (torch.FloatTensor): (batch, T, frame_channels)\\n mel_inputs_lengths (torch.IntTensor): (batch)\\n log_c (torch.FloatTensor): (batch, T)\\n Returns:\\n log_c (torch.FloatTensor) : scaled probabilities (batch, T)\\n log_alpha_scaled (torch.FloatTensor): forward probabilities (batch, T, N)\\n \\\"\\\"\\\"\\n mask_log_c = sequence_mask(mel_lens)\\n log_c = log_c * mask_log_c\\n mask_log_alpha_scaled = mask_log_c.unsqueeze(2)\\n log_alpha_scaled = log_alpha_scaled * mask_log_alpha_scaled\\n return log_c, log_alpha_scaled\\n\\n def _process_ar_timestep(\\n self,\\n t,\\n ar_inputs,\\n h_memory,\\n c_memory,\\n ):\\n \\\"\\\"\\\"\\n Process autoregression in timestep\\n 1. At a specific t timestep\\n 2. Perform data dropout if applied (we did not use it)\\n 3. Run the autoregressive frame through the prenet (has dropout)\\n 4. Run the prenet output through the post prenet rnn\\n\\n Args:\\n t (int): mel-spec timestep\\n ar_inputs (torch.FloatTensor): go-token appended mel-spectrograms\\n - shape: (b, D_out, T_out)\\n h_post_prenet (torch.FloatTensor): previous timestep rnn hidden state\\n - shape: (b, memory_rnn_dim)\\n c_post_prenet (torch.FloatTensor): previous timestep rnn cell state\\n - shape: (b, memory_rnn_dim)\\n\\n Returns:\\n h_post_prenet (torch.FloatTensor): rnn hidden state of the current timestep\\n c_post_prenet (torch.FloatTensor): rnn cell state of the current timestep\\n \\\"\\\"\\\"\\n prenet_input = ar_inputs[:, t : t + self.ar_order].flatten(1)\\n memory_inputs = self.prenet(prenet_input)\\n h_memory, c_memory = self.memory_rnn(memory_inputs, (h_memory, c_memory))\\n return h_memory, c_memory\\n\\n def _add_go_token(self, mel_inputs):\\n \\\"\\\"\\\"Append the go token to create the autoregressive input\\n Args:\\n mel_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)\\n Returns:\\n ar_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)\\n \\\"\\\"\\\"\\n batch_size, T, _ = mel_inputs.shape\\n go_tokens = self.go_tokens.unsqueeze(0).expand(batch_size, self.ar_order, self.frame_channels)\\n ar_inputs = torch.cat((go_tokens, mel_inputs), dim=1)[:, :T]\\n return ar_inputs\\n\\n @staticmethod\\n def _initialize_forward_algorithm_variables(mel_inputs, N):\\n r\\\"\\\"\\\"Initialize placeholders for forward algorithm variables, to use a stable\\n version we will use log_alpha_scaled and the scaling constant\\n\\n Args:\\n mel_inputs (torch.FloatTensor): (b, T_max, frame_channels)\\n N (int): number of states\\n Returns:\\n log_c (torch.FloatTensor): Scaling constant (b, T_max)\\n \\\"\\\"\\\"\\n b, T_max, _ = mel_inputs.shape\\n log_alpha_scaled = mel_inputs.new_zeros((b, T_max, N))\\n log_c = mel_inputs.new_zeros(b, T_max)\\n transition_matrix = mel_inputs.new_zeros((b, T_max, N))\\n\\n # Saving for plotting later, will not have gradient tapes\\n means = []\\n return log_c, log_alpha_scaled, transition_matrix, means\\n\\n @staticmethod\\n def _init_lstm_states(batch_size, hidden_state_dim, device_tensor):\\n r\\\"\\\"\\\"\\n Initialize Hidden and Cell states for LSTM Cell\\n\\n Args:\\n batch_size (Int): batch size\\n hidden_state_dim (Int): dimensions of the h and c\\n device_tensor (torch.FloatTensor): useful for the device and type\\n\\n Returns:\\n (torch.FloatTensor): shape (batch_size, hidden_state_dim)\\n can be hidden state for LSTM\\n (torch.FloatTensor): shape (batch_size, hidden_state_dim)\\n can be the cell state for LSTM\\n \\\"\\\"\\\"\\n return (\\n device_tensor.new_zeros(batch_size, hidden_state_dim),\\n device_tensor.new_zeros(batch_size, hidden_state_dim),\\n )\\n\\n def get_absorption_state_scaling_factor(self, mels_len, log_alpha_scaled, inputs_len, transition_vector):\\n \\\"\\\"\\\"Returns the final scaling factor of absorption state\\n\\n Args:\\n mels_len (torch.IntTensor): Input size of mels to\\n get the last timestep of log_alpha_scaled\\n log_alpha_scaled (torch.FloatTEnsor): State probabilities\\n text_lengths (torch.IntTensor): length of the states to\\n mask the values of states lengths\\n (\\n Useful when the batch has very different lengths,\\n when the length of an observation is less than\\n the number of max states, then the log alpha after\\n the state value is filled with -infs. So we mask\\n those values so that it only consider the states\\n which are needed for that length\\n )\\n transition_vector (torch.FloatTensor): transtiion vector for each state per timestep\\n\\n Shapes:\\n - mels_len: (batch_size)\\n - log_alpha_scaled: (batch_size, N, T)\\n - text_lengths: (batch_size)\\n - transition_vector: (batch_size, N, T)\\n\\n Returns:\\n sum_final_log_c (torch.FloatTensor): (batch_size)\\n\\n \\\"\\\"\\\"\\n N = torch.max(inputs_len)\\n max_inputs_len = log_alpha_scaled.shape[2]\\n state_lengths_mask = sequence_mask(inputs_len, max_len=max_inputs_len)\\n\\n last_log_alpha_scaled_index = (\\n (mels_len - 1).unsqueeze(-1).expand(-1, N).unsqueeze(1)\\n ) # Batch X Hidden State Size\\n last_log_alpha_scaled = torch.gather(log_alpha_scaled, 1, last_log_alpha_scaled_index).squeeze(1)\\n last_log_alpha_scaled = last_log_alpha_scaled.masked_fill(~state_lengths_mask, -float(\\\"inf\\\"))\\n\\n last_transition_vector = torch.gather(transition_vector, 1, last_log_alpha_scaled_index).squeeze(1)\\n last_transition_probability = torch.sigmoid(last_transition_vector)\\n log_probability_of_transitioning = OverflowUtils.log_clamped(last_transition_probability)\\n\\n last_transition_probability_index = self.get_mask_for_last_item(inputs_len, inputs_len.device)\\n log_probability_of_transitioning = log_probability_of_transitioning.masked_fill(\\n ~last_transition_probability_index, -float(\\\"inf\\\")\\n )\\n final_log_c = last_log_alpha_scaled + log_probability_of_transitioning\\n\\n # If the length of the mel is less than the number of states it will select the -inf values leading to nan gradients\\n # Ideally, we should clean the dataset otherwise this is a little hack uncomment the line below\\n final_log_c = final_log_c.clamp(min=torch.finfo(final_log_c.dtype).min)\\n\\n sum_final_log_c = torch.logsumexp(final_log_c, dim=1)\\n return sum_final_log_c\\n\\n @staticmethod\\n def get_mask_for_last_item(lengths, device, out_tensor=None):\\n \\\"\\\"\\\"Returns n-1 mask for the last item in the sequence.\\n\\n Args:\\n lengths (torch.IntTensor): lengths in a batch\\n device (str, optional): Defaults to \\\"cpu\\\".\\n out_tensor (torch.Tensor, optional): uses the memory of a specific tensor.\\n Defaults to None.\\n\\n Returns:\\n - Shape: :math:`(b, max_len)`\\n \\\"\\\"\\\"\\n max_len = torch.max(lengths).item()\\n ids = (\\n torch.arange(0, max_len, device=device) if out_tensor is None else torch.arange(0, max_len, out=out_tensor)\\n )\\n mask = ids == lengths.unsqueeze(1) - 1\\n return mask\\n\\n @torch.inference_mode()\\n def inference(\\n self,\\n inputs: torch.FloatTensor,\\n input_lens: torch.LongTensor,\\n sampling_temp: float,\\n max_sampling_time: int,\\n duration_threshold: float,\\n ):\\n \\\"\\\"\\\"Inference from autoregressive neural HMM\\n\\n Args:\\n inputs (torch.FloatTensor): input states\\n - shape: :math:`(b, T, d)`\\n input_lens (torch.LongTensor): input state lengths\\n - shape: :math:`(b)`\\n sampling_temp (float): sampling temperature\\n max_sampling_temp (int): max sampling temperature\\n duration_threshold (float): duration threshold to switch to next state\\n - Use this to change the spearking rate of the synthesised audio\\n \\\"\\\"\\\"\\n\\n b = inputs.shape[0]\\n outputs = {\\n \\\"hmm_outputs\\\": [],\\n \\\"hmm_outputs_len\\\": [],\\n \\\"alignments\\\": [],\\n \\\"input_parameters\\\": [],\\n \\\"output_parameters\\\": [],\\n }\\n for i in range(b):\\n neural_hmm_outputs, states_travelled, input_parameters, output_parameters = self.sample(\\n inputs[i : i + 1], input_lens[i], sampling_temp, max_sampling_time, duration_threshold\\n )\\n\\n outputs[\\\"hmm_outputs\\\"].append(neural_hmm_outputs)\\n outputs[\\\"hmm_outputs_len\\\"].append(neural_hmm_outputs.shape[0])\\n outputs[\\\"alignments\\\"].append(states_travelled)\\n outputs[\\\"input_parameters\\\"].append(input_parameters)\\n outputs[\\\"output_parameters\\\"].append(output_parameters)\\n\\n outputs[\\\"hmm_outputs\\\"] = nn.utils.rnn.pad_sequence(outputs[\\\"hmm_outputs\\\"], batch_first=True)\\n outputs[\\\"hmm_outputs_len\\\"] = torch.tensor(\\n outputs[\\\"hmm_outputs_len\\\"], dtype=input_lens.dtype, device=input_lens.device\\n )\\n return outputs\\n\\n @torch.inference_mode()\\n def sample(self, inputs, input_lens, sampling_temp, max_sampling_time, duration_threshold):\\n \\\"\\\"\\\"Samples an output from the parameter models\\n\\n Args:\\n inputs (torch.FloatTensor): input states\\n - shape: :math:`(1, T, d)`\\n input_lens (torch.LongTensor): input state lengths\\n - shape: :math:`(1)`\\n sampling_temp (float): sampling temperature\\n max_sampling_time (int): max sampling time\\n duration_threshold (float): duration threshold to switch to next state\\n\\n Returns:\\n outputs (torch.FloatTensor): Output Observations\\n - Shape: :math:`(T, output_dim)`\\n states_travelled (list[int]): Hidden states travelled\\n - Shape: :math:`(T)`\\n input_parameters (list[torch.FloatTensor]): Input parameters\\n output_parameters (list[torch.FloatTensor]): Output parameters\\n \\\"\\\"\\\"\\n states_travelled, outputs, t = [], [], 0\\n\\n # Sample initial state\\n current_state = 0\\n states_travelled.append(current_state)\\n\\n # Prepare autoregression\\n prenet_input = self.go_tokens.unsqueeze(0).expand(1, self.ar_order, self.frame_channels)\\n h_memory, c_memory = self._init_lstm_states(1, self.memory_rnn_dim, prenet_input)\\n\\n input_parameter_values = []\\n output_parameter_values = []\\n quantile = 1\\n while True:\\n memory_input = self.prenet(prenet_input.flatten(1).unsqueeze(0))\\n # will be 1 while sampling\\n h_memory, c_memory = self.memory_rnn(memory_input.squeeze(0), (h_memory, c_memory))\\n\\n z_t = inputs[:, current_state].unsqueeze(0) # Add fake time dimension\\n mean, std, transition_vector = self.output_net(h_memory, z_t)\\n\\n transition_probability = torch.sigmoid(transition_vector.flatten())\\n staying_probability = torch.sigmoid(-transition_vector.flatten())\\n\\n # Save for plotting\\n input_parameter_values.append([prenet_input, current_state])\\n output_parameter_values.append([mean, std, transition_probability])\\n\\n x_t = self.emission_model.sample(mean, std, sampling_temp=sampling_temp)\\n\\n # Prepare autoregressive input for next iteration\\n prenet_input = torch.cat((prenet_input, x_t), dim=1)[:, 1:]\\n\\n outputs.append(x_t.flatten())\\n\\n transition_matrix = torch.cat((staying_probability, transition_probability))\\n quantile *= staying_probability\\n if not self.deterministic_transition:\\n switch = transition_matrix.multinomial(1)[0].item()\\n else:\\n switch = quantile < duration_threshold\\n\\n if switch:\\n current_state += 1\\n quantile = 1\\n\\n states_travelled.append(current_state)\\n\\n if (current_state == input_lens) or (max_sampling_time and t == max_sampling_time - 1):\\n break\\n\\n t += 1\\n\\n return (\\n torch.stack(outputs, dim=0),\\n F.one_hot(input_lens.new_tensor(states_travelled)),\\n input_parameter_values,\\n output_parameter_values,\\n )\\n\\n @staticmethod\\n def _initialize_log_state_priors(text_embeddings):\\n \\\"\\\"\\\"Creates the log pi in forward algorithm.\\n\\n Args:\\n text_embeddings (torch.FloatTensor): used to create the log pi\\n on current device\\n\\n Shapes:\\n - text_embeddings: (B, T, D_out_enc)\\n \\\"\\\"\\\"\\n N = text_embeddings.shape[1]\\n log_state_priors = text_embeddings.new_full([N], -float(\\\"inf\\\"))\\n log_state_priors[0] = 0.0\\n return log_state_priors\"\n },\n {\n \"identifier\": \"get_spec_from_most_probable_state\",\n \"path\": \"TTS/tts/layers/overflow/plotting_utils.py\",\n \"snippet\": \"def get_spec_from_most_probable_state(log_alpha_scaled, means, decoder=None):\\n \\\"\\\"\\\"Get the most probable state means from the log_alpha_scaled.\\n\\n Args:\\n log_alpha_scaled (torch.Tensor): Log alpha scaled values.\\n - Shape: :math:`(T, N)`\\n means (torch.Tensor): Means of the states.\\n - Shape: :math:`(N, T, D_out)`\\n decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None.\\n \\\"\\\"\\\"\\n max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1]\\n max_len = means.shape[0]\\n n_mel_channels = means.shape[2]\\n max_state_numbers = max_state_numbers.unsqueeze(1).unsqueeze(1).expand(max_len, 1, n_mel_channels)\\n means = torch.gather(means, 1, max_state_numbers).squeeze(1).to(log_alpha_scaled.dtype)\\n if decoder is not None:\\n mel = (\\n decoder(means.T.unsqueeze(0), torch.tensor([means.shape[0]], device=means.device), reverse=True)[0]\\n .squeeze(0)\\n .T\\n )\\n else:\\n mel = means\\n return mel\"\n },\n {\n \"identifier\": \"plot_transition_probabilities_to_numpy\",\n \"path\": \"TTS/tts/layers/overflow/plotting_utils.py\",\n \"snippet\": \"def plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig=False):\\n \\\"\\\"\\\"Generates trainsition probabilities plot for the states and the probability of transition.\\n\\n Args:\\n states (torch.IntTensor): the states\\n transition_probabilities (torch.FloatTensor): the transition probabilities\\n \\\"\\\"\\\"\\n states = validate_numpy_array(states)\\n transition_probabilities = validate_numpy_array(transition_probabilities)\\n\\n fig, ax = plt.subplots(figsize=(30, 3))\\n ax.plot(transition_probabilities, \\\"o\\\")\\n ax.set_title(\\\"Transition probability of state\\\")\\n ax.set_xlabel(\\\"hidden state\\\")\\n ax.set_ylabel(\\\"probability\\\")\\n ax.set_xticks([i for i in range(len(transition_probabilities))]) # pylint: disable=unnecessary-comprehension\\n ax.set_xticklabels([int(x) for x in states], rotation=90)\\n plt.tight_layout()\\n if not output_fig:\\n plt.close()\\n return fig\"\n },\n {\n \"identifier\": \"BaseTTS\",\n \"path\": \"TTS/tts/models/base_tts.py\",\n \"snippet\": \"class BaseTTS(BaseTrainerModel):\\n \\\"\\\"\\\"Base `tts` class. Every new `tts` model must inherit this.\\n\\n It defines common `tts` specific functions on top of `Model` implementation.\\n \\\"\\\"\\\"\\n\\n MODEL_TYPE = \\\"tts\\\"\\n\\n def __init__(\\n self,\\n config: Coqpit,\\n ap: \\\"AudioProcessor\\\",\\n tokenizer: \\\"TTSTokenizer\\\",\\n speaker_manager: SpeakerManager = None,\\n language_manager: LanguageManager = None,\\n ):\\n super().__init__()\\n self.config = config\\n self.ap = ap\\n self.tokenizer = tokenizer\\n self.speaker_manager = speaker_manager\\n self.language_manager = language_manager\\n self._set_model_args(config)\\n\\n def _set_model_args(self, config: Coqpit):\\n \\\"\\\"\\\"Setup model args based on the config type (`ModelConfig` or `ModelArgs`).\\n\\n `ModelArgs` has all the fields reuqired to initialize the model architecture.\\n\\n `ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.\\n\\n If the config is for training with a name like \\\"*Config\\\", then the model args are embeded in the\\n config.model_args\\n\\n If the config is for the model with a name like \\\"*Args\\\", then we assign the directly.\\n \\\"\\\"\\\"\\n # don't use isintance not to import recursively\\n if \\\"Config\\\" in config.__class__.__name__:\\n config_num_chars = (\\n self.config.model_args.num_chars if hasattr(self.config, \\\"model_args\\\") else self.config.num_chars\\n )\\n num_chars = config_num_chars if self.tokenizer is None else self.tokenizer.characters.num_chars\\n if \\\"characters\\\" in config:\\n self.config.num_chars = num_chars\\n if hasattr(self.config, \\\"model_args\\\"):\\n config.model_args.num_chars = num_chars\\n self.args = self.config.model_args\\n else:\\n self.config = config\\n self.args = config.model_args\\n elif \\\"Args\\\" in config.__class__.__name__:\\n self.args = config\\n else:\\n raise ValueError(\\\"config must be either a *Config or *Args\\\")\\n\\n def init_multispeaker(self, config: Coqpit, data: List = None):\\n \\\"\\\"\\\"Initialize a speaker embedding layer if needen and define expected embedding channel size for defining\\n `in_channels` size of the connected layers.\\n\\n This implementation yields 3 possible outcomes:\\n\\n 1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.\\n 2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.\\n 3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of\\n `config.d_vector_dim` or 512.\\n\\n You can override this function for new models.\\n\\n Args:\\n config (Coqpit): Model configuration.\\n \\\"\\\"\\\"\\n # set number of speakers\\n if self.speaker_manager is not None:\\n self.num_speakers = self.speaker_manager.num_speakers\\n elif hasattr(config, \\\"num_speakers\\\"):\\n self.num_speakers = config.num_speakers\\n\\n # set ultimate speaker embedding size\\n if config.use_speaker_embedding or config.use_d_vector_file:\\n self.embedded_speaker_dim = (\\n config.d_vector_dim if \\\"d_vector_dim\\\" in config and config.d_vector_dim is not None else 512\\n )\\n # init speaker embedding layer\\n if config.use_speaker_embedding and not config.use_d_vector_file:\\n print(\\\" > Init speaker_embedding layer.\\\")\\n self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)\\n self.speaker_embedding.weight.data.normal_(0, 0.3)\\n\\n def get_aux_input(self, **kwargs) -> Dict:\\n \\\"\\\"\\\"Prepare and return `aux_input` used by `forward()`\\\"\\\"\\\"\\n return {\\\"speaker_id\\\": None, \\\"style_wav\\\": None, \\\"d_vector\\\": None, \\\"language_id\\\": None}\\n\\n def get_aux_input_from_test_sentences(self, sentence_info):\\n if hasattr(self.config, \\\"model_args\\\"):\\n config = self.config.model_args\\n else:\\n config = self.config\\n\\n # extract speaker and language info\\n text, speaker_name, style_wav, language_name = None, None, None, None\\n\\n if isinstance(sentence_info, list):\\n if len(sentence_info) == 1:\\n text = sentence_info[0]\\n elif len(sentence_info) == 2:\\n text, speaker_name = sentence_info\\n elif len(sentence_info) == 3:\\n text, speaker_name, style_wav = sentence_info\\n elif len(sentence_info) == 4:\\n text, speaker_name, style_wav, language_name = sentence_info\\n else:\\n text = sentence_info\\n\\n # get speaker id/d_vector\\n speaker_id, d_vector, language_id = None, None, None\\n if self.speaker_manager is not None:\\n if config.use_d_vector_file:\\n if speaker_name is None:\\n d_vector = self.speaker_manager.get_random_embedding()\\n else:\\n d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)\\n elif config.use_speaker_embedding:\\n if speaker_name is None:\\n speaker_id = self.speaker_manager.get_random_id()\\n else:\\n speaker_id = self.speaker_manager.name_to_id[speaker_name]\\n\\n # get language id\\n if self.language_manager is not None and config.use_language_embedding and language_name is not None:\\n language_id = self.language_manager.name_to_id[language_name]\\n\\n return {\\n \\\"text\\\": text,\\n \\\"speaker_id\\\": speaker_id,\\n \\\"style_wav\\\": style_wav,\\n \\\"d_vector\\\": d_vector,\\n \\\"language_id\\\": language_id,\\n }\\n\\n def format_batch(self, batch: Dict) -> Dict:\\n \\\"\\\"\\\"Generic batch formatting for `TTSDataset`.\\n\\n You must override this if you use a custom dataset.\\n\\n Args:\\n batch (Dict): [description]\\n\\n Returns:\\n Dict: [description]\\n \\\"\\\"\\\"\\n # setup input batch\\n text_input = batch[\\\"token_id\\\"]\\n text_lengths = batch[\\\"token_id_lengths\\\"]\\n speaker_names = batch[\\\"speaker_names\\\"]\\n linear_input = batch[\\\"linear\\\"]\\n mel_input = batch[\\\"mel\\\"]\\n mel_lengths = batch[\\\"mel_lengths\\\"]\\n stop_targets = batch[\\\"stop_targets\\\"]\\n item_idx = batch[\\\"item_idxs\\\"]\\n d_vectors = batch[\\\"d_vectors\\\"]\\n speaker_ids = batch[\\\"speaker_ids\\\"]\\n attn_mask = batch[\\\"attns\\\"]\\n waveform = batch[\\\"waveform\\\"]\\n pitch = batch[\\\"pitch\\\"]\\n energy = batch[\\\"energy\\\"]\\n language_ids = batch[\\\"language_ids\\\"]\\n max_text_length = torch.max(text_lengths.float())\\n max_spec_length = torch.max(mel_lengths.float())\\n\\n # compute durations from attention masks\\n durations = None\\n if attn_mask is not None:\\n durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])\\n for idx, am in enumerate(attn_mask):\\n # compute raw durations\\n c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]\\n # c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)\\n c_idxs, counts = torch.unique(c_idxs, return_counts=True)\\n dur = torch.ones([text_lengths[idx]]).to(counts.dtype)\\n dur[c_idxs] = counts\\n # smooth the durations and set any 0 duration to 1\\n # by cutting off from the largest duration indeces.\\n extra_frames = dur.sum() - mel_lengths[idx]\\n largest_idxs = torch.argsort(-dur)[:extra_frames]\\n dur[largest_idxs] -= 1\\n assert (\\n dur.sum() == mel_lengths[idx]\\n ), f\\\" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}\\\"\\n durations[idx, : text_lengths[idx]] = dur\\n\\n # set stop targets wrt reduction factor\\n stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)\\n stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)\\n stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()\\n\\n return {\\n \\\"text_input\\\": text_input,\\n \\\"text_lengths\\\": text_lengths,\\n \\\"speaker_names\\\": speaker_names,\\n \\\"mel_input\\\": mel_input,\\n \\\"mel_lengths\\\": mel_lengths,\\n \\\"linear_input\\\": linear_input,\\n \\\"stop_targets\\\": stop_targets,\\n \\\"stop_target_lengths\\\": stop_target_lengths,\\n \\\"attn_mask\\\": attn_mask,\\n \\\"durations\\\": durations,\\n \\\"speaker_ids\\\": speaker_ids,\\n \\\"d_vectors\\\": d_vectors,\\n \\\"max_text_length\\\": float(max_text_length),\\n \\\"max_spec_length\\\": float(max_spec_length),\\n \\\"item_idx\\\": item_idx,\\n \\\"waveform\\\": waveform,\\n \\\"pitch\\\": pitch,\\n \\\"energy\\\": energy,\\n \\\"language_ids\\\": language_ids,\\n \\\"audio_unique_names\\\": batch[\\\"audio_unique_names\\\"],\\n }\\n\\n def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):\\n weights = None\\n data_items = dataset.samples\\n\\n if getattr(config, \\\"use_language_weighted_sampler\\\", False):\\n alpha = getattr(config, \\\"language_weighted_sampler_alpha\\\", 1.0)\\n print(\\\" > Using Language weighted sampler with alpha:\\\", alpha)\\n weights = get_language_balancer_weights(data_items) * alpha\\n\\n if getattr(config, \\\"use_speaker_weighted_sampler\\\", False):\\n alpha = getattr(config, \\\"speaker_weighted_sampler_alpha\\\", 1.0)\\n print(\\\" > Using Speaker weighted sampler with alpha:\\\", alpha)\\n if weights is not None:\\n weights += get_speaker_balancer_weights(data_items) * alpha\\n else:\\n weights = get_speaker_balancer_weights(data_items) * alpha\\n\\n if getattr(config, \\\"use_length_weighted_sampler\\\", False):\\n alpha = getattr(config, \\\"length_weighted_sampler_alpha\\\", 1.0)\\n print(\\\" > Using Length weighted sampler with alpha:\\\", alpha)\\n if weights is not None:\\n weights += get_length_balancer_weights(data_items) * alpha\\n else:\\n weights = get_length_balancer_weights(data_items) * alpha\\n\\n if weights is not None:\\n sampler = WeightedRandomSampler(weights, len(weights))\\n else:\\n sampler = None\\n\\n # sampler for DDP\\n if sampler is None:\\n sampler = DistributedSampler(dataset) if num_gpus > 1 else None\\n else: # If a sampler is already defined use this sampler and DDP sampler together\\n sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler\\n\\n return sampler\\n\\n def get_data_loader(\\n self,\\n config: Coqpit,\\n assets: Dict,\\n is_eval: bool,\\n samples: Union[List[Dict], List[List]],\\n verbose: bool,\\n num_gpus: int,\\n rank: int = None,\\n ) -> \\\"DataLoader\\\":\\n if is_eval and not config.run_eval:\\n loader = None\\n else:\\n # setup multi-speaker attributes\\n if self.speaker_manager is not None:\\n if hasattr(config, \\\"model_args\\\"):\\n speaker_id_mapping = (\\n self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None\\n )\\n d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None\\n config.use_d_vector_file = config.model_args.use_d_vector_file\\n else:\\n speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None\\n d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None\\n else:\\n speaker_id_mapping = None\\n d_vector_mapping = None\\n\\n # setup multi-lingual attributes\\n if self.language_manager is not None:\\n language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None\\n else:\\n language_id_mapping = None\\n\\n # init dataloader\\n dataset = TTSDataset(\\n outputs_per_step=config.r if \\\"r\\\" in config else 1,\\n compute_linear_spec=config.model.lower() == \\\"tacotron\\\" or config.compute_linear_spec,\\n compute_f0=config.get(\\\"compute_f0\\\", False),\\n f0_cache_path=config.get(\\\"f0_cache_path\\\", None),\\n compute_energy=config.get(\\\"compute_energy\\\", False),\\n energy_cache_path=config.get(\\\"energy_cache_path\\\", None),\\n samples=samples,\\n ap=self.ap,\\n return_wav=config.return_wav if \\\"return_wav\\\" in config else False,\\n batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,\\n min_text_len=config.min_text_len,\\n max_text_len=config.max_text_len,\\n min_audio_len=config.min_audio_len,\\n max_audio_len=config.max_audio_len,\\n phoneme_cache_path=config.phoneme_cache_path,\\n precompute_num_workers=config.precompute_num_workers,\\n use_noise_augment=False if is_eval else config.use_noise_augment,\\n verbose=verbose,\\n speaker_id_mapping=speaker_id_mapping,\\n d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,\\n tokenizer=self.tokenizer,\\n start_by_longest=config.start_by_longest,\\n language_id_mapping=language_id_mapping,\\n )\\n\\n # wait all the DDP process to be ready\\n if num_gpus > 1:\\n dist.barrier()\\n\\n # sort input sequences from short to long\\n dataset.preprocess_samples()\\n\\n # get samplers\\n sampler = self.get_sampler(config, dataset, num_gpus)\\n\\n loader = DataLoader(\\n dataset,\\n batch_size=config.eval_batch_size if is_eval else config.batch_size,\\n shuffle=config.shuffle if sampler is None else False, # if there is no other sampler\\n collate_fn=dataset.collate_fn,\\n drop_last=config.drop_last, # setting this False might cause issues in AMP training.\\n sampler=sampler,\\n num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,\\n pin_memory=False,\\n )\\n return loader\\n\\n def _get_test_aux_input(\\n self,\\n ) -> Dict:\\n d_vector = None\\n if self.config.use_d_vector_file:\\n d_vector = [self.speaker_manager.embeddings[name][\\\"embedding\\\"] for name in self.speaker_manager.embeddings]\\n d_vector = (random.sample(sorted(d_vector), 1),)\\n\\n aux_inputs = {\\n \\\"speaker_id\\\": None\\n if not self.config.use_speaker_embedding\\n else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),\\n \\\"d_vector\\\": d_vector,\\n \\\"style_wav\\\": None, # TODO: handle GST style input\\n }\\n return aux_inputs\\n\\n def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:\\n \\\"\\\"\\\"Generic test run for `tts` models used by `Trainer`.\\n\\n You can override this for a different behaviour.\\n\\n Args:\\n assets (dict): A dict of training assets. For `tts` models, it must include `{'audio_processor': ap}`.\\n\\n Returns:\\n Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.\\n \\\"\\\"\\\"\\n print(\\\" | > Synthesizing test sentences.\\\")\\n test_audios = {}\\n test_figures = {}\\n test_sentences = self.config.test_sentences\\n aux_inputs = self._get_test_aux_input()\\n for idx, sen in enumerate(test_sentences):\\n if isinstance(sen, list):\\n aux_inputs = self.get_aux_input_from_test_sentences(sen)\\n sen = aux_inputs[\\\"text\\\"]\\n outputs_dict = synthesis(\\n self,\\n sen,\\n self.config,\\n \\\"cuda\\\" in str(next(self.parameters()).device),\\n speaker_id=aux_inputs[\\\"speaker_id\\\"],\\n d_vector=aux_inputs[\\\"d_vector\\\"],\\n style_wav=aux_inputs[\\\"style_wav\\\"],\\n use_griffin_lim=True,\\n do_trim_silence=False,\\n )\\n test_audios[\\\"{}-audio\\\".format(idx)] = outputs_dict[\\\"wav\\\"]\\n test_figures[\\\"{}-prediction\\\".format(idx)] = plot_spectrogram(\\n outputs_dict[\\\"outputs\\\"][\\\"model_outputs\\\"], self.ap, output_fig=False\\n )\\n test_figures[\\\"{}-alignment\\\".format(idx)] = plot_alignment(\\n outputs_dict[\\\"outputs\\\"][\\\"alignments\\\"], output_fig=False\\n )\\n return test_figures, test_audios\\n\\n def on_init_start(self, trainer):\\n \\\"\\\"\\\"Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths.\\\"\\\"\\\"\\n if self.speaker_manager is not None:\\n output_path = os.path.join(trainer.output_path, \\\"speakers.pth\\\")\\n self.speaker_manager.save_ids_to_file(output_path)\\n trainer.config.speakers_file = output_path\\n # some models don't have `model_args` set\\n if hasattr(trainer.config, \\\"model_args\\\"):\\n trainer.config.model_args.speakers_file = output_path\\n trainer.config.save_json(os.path.join(trainer.output_path, \\\"config.json\\\"))\\n print(f\\\" > `speakers.pth` is saved to {output_path}.\\\")\\n print(\\\" > `speakers_file` is updated in the config.json.\\\")\\n\\n if self.language_manager is not None:\\n output_path = os.path.join(trainer.output_path, \\\"language_ids.json\\\")\\n self.language_manager.save_ids_to_file(output_path)\\n trainer.config.language_ids_file = output_path\\n if hasattr(trainer.config, \\\"model_args\\\"):\\n trainer.config.model_args.language_ids_file = output_path\\n trainer.config.save_json(os.path.join(trainer.output_path, \\\"config.json\\\"))\\n print(f\\\" > `language_ids.json` is saved to {output_path}.\\\")\\n print(\\\" > `language_ids_file` is updated in the config.json.\\\")\"\n },\n {\n \"identifier\": \"SpeakerManager\",\n \"path\": \"TTS/tts/utils/speakers.py\",\n \"snippet\": \"class SpeakerManager(EmbeddingManager):\\n \\\"\\\"\\\"Manage the speakers for multi-speaker 🐸TTS models. Load a datafile and parse the information\\n in a way that can be queried by speaker or clip.\\n\\n There are 3 different scenarios considered:\\n\\n 1. Models using speaker embedding layers. The datafile only maps speaker names to ids used by the embedding layer.\\n 2. Models using d-vectors. The datafile includes a dictionary in the following format.\\n\\n ::\\n\\n {\\n 'clip_name.wav':{\\n 'name': 'speakerA',\\n 'embedding'[<d_vector_values>]\\n },\\n ...\\n }\\n\\n\\n 3. Computing the d-vectors by the speaker encoder. It loads the speaker encoder model and\\n computes the d-vectors for a given clip or speaker.\\n\\n Args:\\n d_vectors_file_path (str, optional): Path to the metafile including x vectors. Defaults to \\\"\\\".\\n speaker_id_file_path (str, optional): Path to the metafile that maps speaker names to ids used by\\n TTS models. Defaults to \\\"\\\".\\n encoder_model_path (str, optional): Path to the speaker encoder model file. Defaults to \\\"\\\".\\n encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to \\\"\\\".\\n\\n Examples:\\n >>> # load audio processor and speaker encoder\\n >>> ap = AudioProcessor(**config.audio)\\n >>> manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)\\n >>> # load a sample audio and compute embedding\\n >>> waveform = ap.load_wav(sample_wav_path)\\n >>> mel = ap.melspectrogram(waveform)\\n >>> d_vector = manager.compute_embeddings(mel.T)\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n data_items: List[List[Any]] = None,\\n d_vectors_file_path: str = \\\"\\\",\\n speaker_id_file_path: str = \\\"\\\",\\n encoder_model_path: str = \\\"\\\",\\n encoder_config_path: str = \\\"\\\",\\n use_cuda: bool = False,\\n ):\\n super().__init__(\\n embedding_file_path=d_vectors_file_path,\\n id_file_path=speaker_id_file_path,\\n encoder_model_path=encoder_model_path,\\n encoder_config_path=encoder_config_path,\\n use_cuda=use_cuda,\\n )\\n\\n if data_items:\\n self.set_ids_from_data(data_items, parse_key=\\\"speaker_name\\\")\\n\\n @property\\n def num_speakers(self):\\n return len(self.name_to_id)\\n\\n @property\\n def speaker_names(self):\\n return list(self.name_to_id.keys())\\n\\n def get_speakers(self) -> List:\\n return self.name_to_id\\n\\n @staticmethod\\n def init_from_config(config: \\\"Coqpit\\\", samples: Union[List[List], List[Dict]] = None) -> \\\"SpeakerManager\\\":\\n \\\"\\\"\\\"Initialize a speaker manager from config\\n\\n Args:\\n config (Coqpit): Config object.\\n samples (Union[List[List], List[Dict]], optional): List of data samples to parse out the speaker names.\\n Defaults to None.\\n\\n Returns:\\n SpeakerEncoder: Speaker encoder object.\\n \\\"\\\"\\\"\\n speaker_manager = None\\n if get_from_config_or_model_args_with_default(config, \\\"use_speaker_embedding\\\", False):\\n if samples:\\n speaker_manager = SpeakerManager(data_items=samples)\\n if get_from_config_or_model_args_with_default(config, \\\"speaker_file\\\", None):\\n speaker_manager = SpeakerManager(\\n speaker_id_file_path=get_from_config_or_model_args_with_default(config, \\\"speaker_file\\\", None)\\n )\\n if get_from_config_or_model_args_with_default(config, \\\"speakers_file\\\", None):\\n speaker_manager = SpeakerManager(\\n speaker_id_file_path=get_from_config_or_model_args_with_default(config, \\\"speakers_file\\\", None)\\n )\\n\\n if get_from_config_or_model_args_with_default(config, \\\"use_d_vector_file\\\", False):\\n speaker_manager = SpeakerManager()\\n if get_from_config_or_model_args_with_default(config, \\\"d_vector_file\\\", None):\\n speaker_manager = SpeakerManager(\\n d_vectors_file_path=get_from_config_or_model_args_with_default(config, \\\"d_vector_file\\\", None)\\n )\\n return speaker_manager\"\n },\n {\n \"identifier\": \"TTSTokenizer\",\n \"path\": \"TTS/tts/utils/text/tokenizer.py\",\n \"snippet\": \"class TTSTokenizer:\\n \\\"\\\"\\\"🐸TTS tokenizer to convert input characters to token IDs and back.\\n\\n Token IDs for OOV chars are discarded but those are stored in `self.not_found_characters` for later.\\n\\n Args:\\n use_phonemes (bool):\\n Whether to use phonemes instead of characters. Defaults to False.\\n\\n characters (Characters):\\n A Characters object to use for character-to-ID and ID-to-character mappings.\\n\\n text_cleaner (callable):\\n A function to pre-process the text before tokenization and phonemization. Defaults to None.\\n\\n phonemizer (Phonemizer):\\n A phonemizer object or a dict that maps language codes to phonemizer objects. Defaults to None.\\n\\n Example:\\n\\n >>> from TTS.tts.utils.text.tokenizer import TTSTokenizer\\n >>> tokenizer = TTSTokenizer(use_phonemes=False, characters=Graphemes())\\n >>> text = \\\"Hello world!\\\"\\n >>> ids = tokenizer.text_to_ids(text)\\n >>> text_hat = tokenizer.ids_to_text(ids)\\n >>> assert text == text_hat\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n use_phonemes=False,\\n text_cleaner: Callable = None,\\n characters: \\\"BaseCharacters\\\" = None,\\n phonemizer: Union[\\\"Phonemizer\\\", Dict] = None,\\n add_blank: bool = False,\\n use_eos_bos=False,\\n ):\\n self.text_cleaner = text_cleaner\\n self.use_phonemes = use_phonemes\\n self.add_blank = add_blank\\n self.use_eos_bos = use_eos_bos\\n self.characters = characters\\n self.not_found_characters = []\\n self.phonemizer = phonemizer\\n\\n @property\\n def characters(self):\\n return self._characters\\n\\n @characters.setter\\n def characters(self, new_characters):\\n self._characters = new_characters\\n self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None\\n self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None\\n\\n def encode(self, text: str) -> List[int]:\\n \\\"\\\"\\\"Encodes a string of text as a sequence of IDs.\\\"\\\"\\\"\\n token_ids = []\\n for char in text:\\n try:\\n idx = self.characters.char_to_id(char)\\n token_ids.append(idx)\\n except KeyError:\\n # discard but store not found characters\\n if char not in self.not_found_characters:\\n self.not_found_characters.append(char)\\n print(text)\\n print(f\\\" [!] Character {repr(char)} not found in the vocabulary. Discarding it.\\\")\\n return token_ids\\n\\n def decode(self, token_ids: List[int]) -> str:\\n \\\"\\\"\\\"Decodes a sequence of IDs to a string of text.\\\"\\\"\\\"\\n text = \\\"\\\"\\n for token_id in token_ids:\\n text += self.characters.id_to_char(token_id)\\n return text\\n\\n def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument\\n \\\"\\\"\\\"Converts a string of text to a sequence of token IDs.\\n\\n Args:\\n text(str):\\n The text to convert to token IDs.\\n\\n language(str):\\n The language code of the text. Defaults to None.\\n\\n TODO:\\n - Add support for language-specific processing.\\n\\n 1. Text normalizatin\\n 2. Phonemization (if use_phonemes is True)\\n 3. Add blank char between characters\\n 4. Add BOS and EOS characters\\n 5. Text to token IDs\\n \\\"\\\"\\\"\\n # TODO: text cleaner should pick the right routine based on the language\\n if self.text_cleaner is not None:\\n text = self.text_cleaner(text)\\n if self.use_phonemes:\\n text = self.phonemizer.phonemize(text, separator=\\\"\\\", language=language)\\n text = self.encode(text)\\n if self.add_blank:\\n text = self.intersperse_blank_char(text, True)\\n if self.use_eos_bos:\\n text = self.pad_with_bos_eos(text)\\n return text\\n\\n def ids_to_text(self, id_sequence: List[int]) -> str:\\n \\\"\\\"\\\"Converts a sequence of token IDs to a string of text.\\\"\\\"\\\"\\n return self.decode(id_sequence)\\n\\n def pad_with_bos_eos(self, char_sequence: List[str]):\\n \\\"\\\"\\\"Pads a sequence with the special BOS and EOS characters.\\\"\\\"\\\"\\n return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id]\\n\\n def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False):\\n \\\"\\\"\\\"Intersperses the blank character between characters in a sequence.\\n\\n Use the ```blank``` character if defined else use the ```pad``` character.\\n \\\"\\\"\\\"\\n char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad\\n result = [char_to_use] * (len(char_sequence) * 2 + 1)\\n result[1::2] = char_sequence\\n return result\\n\\n def print_logs(self, level: int = 0):\\n indent = \\\"\\\\t\\\" * level\\n print(f\\\"{indent}| > add_blank: {self.add_blank}\\\")\\n print(f\\\"{indent}| > use_eos_bos: {self.use_eos_bos}\\\")\\n print(f\\\"{indent}| > use_phonemes: {self.use_phonemes}\\\")\\n if self.use_phonemes:\\n print(f\\\"{indent}| > phonemizer:\\\")\\n self.phonemizer.print_logs(level + 1)\\n if len(self.not_found_characters) > 0:\\n print(f\\\"{indent}| > {len(self.not_found_characters)} not found characters:\\\")\\n for char in self.not_found_characters:\\n print(f\\\"{indent}| > {char}\\\")\\n\\n @staticmethod\\n def init_from_config(config: \\\"Coqpit\\\", characters: \\\"BaseCharacters\\\" = None):\\n \\\"\\\"\\\"Init Tokenizer object from config\\n\\n Args:\\n config (Coqpit): Coqpit model config.\\n characters (BaseCharacters): Defines the model character set. If not set, use the default options based on\\n the config values. Defaults to None.\\n \\\"\\\"\\\"\\n # init cleaners\\n text_cleaner = None\\n if isinstance(config.text_cleaner, (str, list)):\\n text_cleaner = getattr(cleaners, config.text_cleaner)\\n\\n # init characters\\n if characters is None:\\n # set characters based on defined characters class\\n if config.characters and config.characters.characters_class:\\n CharactersClass = import_class(config.characters.characters_class)\\n characters, new_config = CharactersClass.init_from_config(config)\\n # set characters based on config\\n else:\\n if config.use_phonemes:\\n # init phoneme set\\n characters, new_config = IPAPhonemes().init_from_config(config)\\n else:\\n # init character set\\n characters, new_config = Graphemes().init_from_config(config)\\n\\n else:\\n characters, new_config = characters.init_from_config(config)\\n\\n # set characters class\\n new_config.characters.characters_class = get_import_path(characters)\\n\\n # init phonemizer\\n phonemizer = None\\n if config.use_phonemes:\\n if \\\"phonemizer\\\" in config and config.phonemizer == \\\"multi_phonemizer\\\":\\n lang_to_phonemizer_name = {}\\n for dataset in config.datasets:\\n if dataset.language != \\\"\\\":\\n lang_to_phonemizer_name[dataset.language] = dataset.phonemizer\\n else:\\n raise ValueError(\\\"Multi phonemizer requires language to be set for each dataset.\\\")\\n phonemizer = MultiPhonemizer(lang_to_phonemizer_name)\\n else:\\n phonemizer_kwargs = {\\\"language\\\": config.phoneme_language}\\n if \\\"phonemizer\\\" in config and config.phonemizer:\\n phonemizer = get_phonemizer_by_name(config.phonemizer, **phonemizer_kwargs)\\n else:\\n try:\\n phonemizer = get_phonemizer_by_name(\\n DEF_LANG_TO_PHONEMIZER[config.phoneme_language], **phonemizer_kwargs\\n )\\n new_config.phonemizer = phonemizer.name()\\n except KeyError as e:\\n raise ValueError(\\n f\\\"\\\"\\\"No phonemizer found for language {config.phoneme_language}.\\n You may need to install a third party library for this language.\\\"\\\"\\\"\\n ) from e\\n\\n return (\\n TTSTokenizer(\\n config.use_phonemes, text_cleaner, characters, phonemizer, config.add_blank, config.enable_eos_bos_chars\\n ),\\n new_config,\\n )\"\n },\n {\n \"identifier\": \"plot_alignment\",\n \"path\": \"TTS/tts/utils/visual.py\",\n \"snippet\": \"def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None, output_fig=False, plot_log=False):\\n if isinstance(alignment, torch.Tensor):\\n alignment_ = alignment.detach().cpu().numpy().squeeze()\\n else:\\n alignment_ = alignment\\n alignment_ = alignment_.astype(np.float32) if alignment_.dtype == np.float16 else alignment_\\n fig, ax = plt.subplots(figsize=fig_size)\\n im = ax.imshow(\\n alignment_.T, aspect=\\\"auto\\\", origin=\\\"lower\\\", interpolation=\\\"none\\\", norm=LogNorm() if plot_log else None\\n )\\n fig.colorbar(im, ax=ax)\\n xlabel = \\\"Decoder timestep\\\"\\n if info is not None:\\n xlabel += \\\"\\\\n\\\\n\\\" + info\\n plt.xlabel(xlabel)\\n plt.ylabel(\\\"Encoder timestep\\\")\\n # plt.yticks(range(len(text)), list(text))\\n plt.tight_layout()\\n if title is not None:\\n plt.title(title)\\n if not output_fig:\\n plt.close()\\n return fig\"\n },\n {\n \"identifier\": \"plot_spectrogram\",\n \"path\": \"TTS/tts/utils/visual.py\",\n \"snippet\": \"def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10), output_fig=False):\\n if isinstance(spectrogram, torch.Tensor):\\n spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T\\n else:\\n spectrogram_ = spectrogram.T\\n spectrogram_ = spectrogram_.astype(np.float32) if spectrogram_.dtype == np.float16 else spectrogram_\\n if ap is not None:\\n spectrogram_ = ap.denormalize(spectrogram_) # pylint: disable=protected-access\\n fig = plt.figure(figsize=fig_size)\\n plt.imshow(spectrogram_, aspect=\\\"auto\\\", origin=\\\"lower\\\")\\n plt.colorbar()\\n plt.tight_layout()\\n if not output_fig:\\n plt.close()\\n return fig\"\n },\n {\n \"identifier\": \"format_aux_input\",\n \"path\": \"TTS/utils/generic_utils.py\",\n \"snippet\": \"def format_aux_input(def_args: Dict, kwargs: Dict) -> Dict:\\n \\\"\\\"\\\"Format kwargs to hande auxilary inputs to models.\\n\\n Args:\\n def_args (Dict): A dictionary of argument names and their default values if not defined in `kwargs`.\\n kwargs (Dict): A `dict` or `kwargs` that includes auxilary inputs to the model.\\n\\n Returns:\\n Dict: arguments with formatted auxilary inputs.\\n \\\"\\\"\\\"\\n kwargs = kwargs.copy()\\n for name in def_args:\\n if name not in kwargs or kwargs[name] is None:\\n kwargs[name] = def_args[name]\\n return kwargs\"\n },\n {\n \"identifier\": \"load_fsspec\",\n \"path\": \"TTS/utils/io.py\",\n \"snippet\": \"def load_fsspec(\\n path: str,\\n map_location: Union[str, Callable, torch.device, Dict[Union[str, torch.device], Union[str, torch.device]]] = None,\\n cache: bool = True,\\n **kwargs,\\n) -> Any:\\n \\\"\\\"\\\"Like torch.load but can load from other locations (e.g. s3:// , gs://).\\n\\n Args:\\n path: Any path or url supported by fsspec.\\n map_location: torch.device or str.\\n cache: If True, cache a remote file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to True.\\n **kwargs: Keyword arguments forwarded to torch.load.\\n\\n Returns:\\n Object stored in path.\\n \\\"\\\"\\\"\\n is_local = os.path.isdir(path) or os.path.isfile(path)\\n if cache and not is_local:\\n with fsspec.open(\\n f\\\"filecache::{path}\\\",\\n filecache={\\\"cache_storage\\\": str(get_user_data_dir(\\\"tts_cache\\\"))},\\n mode=\\\"rb\\\",\\n ) as f:\\n return torch.load(f, map_location=map_location, **kwargs)\\n else:\\n with fsspec.open(path, \\\"rb\\\") as f:\\n return torch.load(f, map_location=map_location, **kwargs)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport torch\nfrom typing import Dict, List, Union\nfrom coqpit import Coqpit\nfrom torch import nn\nfrom trainer.logging.tensorboard_logger import TensorboardLogger\nfrom TTS.tts.layers.overflow.common_layers import Encoder, OverflowUtils\nfrom TTS.tts.layers.overflow.neural_hmm import NeuralHMM\nfrom TTS.tts.layers.overflow.plotting_utils import (\n get_spec_from_most_probable_state,\n plot_transition_probabilities_to_numpy,\n)\nfrom TTS.tts.models.base_tts import BaseTTS\nfrom TTS.tts.utils.speakers import SpeakerManager\nfrom TTS.tts.utils.text.tokenizer import TTSTokenizer\nfrom TTS.tts.utils.visual import plot_alignment, plot_spectrogram\nfrom TTS.utils.generic_utils import format_aux_input\nfrom TTS.utils.io import load_fsspec\n from TTS.utils.audio import AudioProcessor"},"token_num":{"kind":"number","value":18911,"string":"18,911"},"cropped_code":{"kind":"string","value":"\n @torch.no_grad()\n def inference(\n self,\n text: torch.Tensor,\n aux_input={\"x_lengths\": None, \"sampling_temp\": None, \"max_sampling_time\": None, \"duration_threshold\": None},\n ): # pylint: disable=dangerous-default-value\n \"\"\"Sampling from the model\n\n Args:\n text (torch.Tensor): :math:`[B, T_in]`\n aux_inputs (_type_, optional): _description_. Defaults to None.\n\n Returns:\n outputs: Dictionary containing the following\n - mel (torch.Tensor): :math:`[B, T_out, C]`\n - hmm_outputs_len (torch.Tensor): :math:`[B]`\n - state_travelled (List[List[int]]): List of lists containing the state travelled for each sample in the batch.\n - input_parameters (list[torch.FloatTensor]): Input parameters to the neural HMM.\n - output_parameters (list[torch.FloatTensor]): Output parameters to the neural HMM.\n \"\"\"\n default_input_dict = {\n \"x_lengths\": torch.sum(text != 0, dim=1),\n }\n aux_input = self._format_aux_input(aux_input, default_input_dict)\n encoder_outputs, encoder_output_len = self.encoder.inference(text, aux_input[\"x_lengths\"])\n outputs = self.neural_hmm.inference(\n encoder_outputs,\n encoder_output_len,\n sampling_temp=aux_input[\"sampling_temp\"],\n max_sampling_time=aux_input[\"max_sampling_time\"],\n duration_threshold=aux_input[\"duration_threshold\"],\n )\n mels, mel_outputs_len = outputs[\"hmm_outputs\"], outputs[\"hmm_outputs_len\"]\n\n mels = self.inverse_normalize(mels)\n outputs.update({\"model_outputs\": mels, \"model_outputs_len\": mel_outputs_len})\n outputs[\"alignments\"] = OverflowUtils.double_pad(outputs[\"alignments\"])\n return outputs\n\n @staticmethod\n def get_criterion():\n return NLLLoss()\n\n @staticmethod\n def init_from_config(config: \"NeuralhmmTTSConfig\", samples: Union[List[List], List[Dict]] = None, verbose=True):\n \"\"\"Initiate model from config\n\n Args:\n config (VitsConfig): Model config.\n samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.\n Defaults to None.\n verbose (bool): If True, print init messages. Defaults to True.\n \"\"\"\n\n ap = AudioProcessor.init_from_config(config, verbose)\n tokenizer, new_config = TTSTokenizer.init_from_config(config)\n speaker_manager = SpeakerManager.init_from_config(config, samples)\n return NeuralhmmTTS(new_config, ap, tokenizer, speaker_manager)\n\n def load_checkpoint(\n self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False\n ): # pylint: disable=unused-argument, redefined-builtin\n state = load_fsspec(checkpoint_path, map_location=torch.device(\"cpu\"))\n self.load_state_dict(state[\"model\"])\n if eval:\n self.eval()\n assert not self.training\n\n def on_init_start(self, trainer):\n \"\"\"If the current dataset does not have normalisation statistics and initialisation transition_probability it computes them otherwise loads.\"\"\"\n if not os.path.isfile(trainer.config.mel_statistics_parameter_path) or trainer.config.force_generate_statistics:\n dataloader = trainer.get_train_dataloader(\n training_assets=None, samples=trainer.train_samples, verbose=False\n )\n print(\n f\" | > Data parameters not found for: {trainer.config.mel_statistics_parameter_path}. Computing mel normalization parameters...\"\n )\n data_mean, data_std, init_transition_prob = OverflowUtils.get_data_parameters_for_flat_start(\n dataloader, trainer.config.out_channels, trainer.config.state_per_phone\n )\n print(\n f\" | > Saving data parameters to: {trainer.config.mel_statistics_parameter_path}: value: {data_mean, data_std, init_transition_prob}\"\n )\n statistics = {\n \"mean\": data_mean.item(),\n \"std\": data_std.item(),\n \"init_transition_prob\": init_transition_prob.item(),\n }\n torch.save(statistics, trainer.config.mel_statistics_parameter_path)\n\n else:\n print(\n f\" | > Data parameters found for: {trainer.config.mel_statistics_parameter_path}. Loading mel normalization parameters...\"\n )\n statistics = torch.load(trainer.config.mel_statistics_parameter_path)\n data_mean, data_std, init_transition_prob = (\n statistics[\"mean\"],\n statistics[\"std\"],\n statistics[\"init_transition_prob\"],\n )\n print(f\" | > Data parameters loaded with value: {data_mean, data_std, init_transition_prob}\")\n\n trainer.config.flat_start_params[\"transition_p\"] = (\n init_transition_prob.item() if torch.is_tensor(init_transition_prob) else init_transition_prob\n )\n OverflowUtils.update_flat_start_transition(trainer.model, init_transition_prob)\n trainer.model.update_mean_std(statistics)\n\n @torch.inference_mode()\n def _create_logs(self, batch, outputs, ap): # pylint: disable=no-self-use, unused-argument\n alignments, transition_vectors = outputs[\"alignments\"], outputs[\"transition_vectors\"]\n means = torch.stack(outputs[\"means\"], dim=1)\n\n figures = {\n \"alignment\": plot_alignment(alignments[0].exp(), title=\"Forward alignment\", fig_size=(20, 20)),\n \"log_alignment\": plot_alignment(\n alignments[0].exp(), title=\"Forward log alignment\", plot_log=True, fig_size=(20, 20)\n ),\n \"transition_vectors\": plot_alignment(transition_vectors[0], title=\"Transition vectors\", fig_size=(20, 20)),\n"},"all_code":{"kind":"string","value":"\n\n\n\nclass NeuralhmmTTS(BaseTTS):\n \"\"\"Neural HMM TTS model.\n\n Paper::\n https://arxiv.org/abs/2108.13320\n\n Paper abstract::\n Neural sequence-to-sequence TTS has achieved significantly better output quality\n than statistical speech synthesis using HMMs.However, neural TTS is generally not probabilistic\n and uses non-monotonic attention. Attention failures increase training time and can make\n synthesis babble incoherently. This paper describes how the old and new paradigms can be\n combined to obtain the advantages of both worlds, by replacing attention in neural TTS with\n an autoregressive left-right no-skip hidden Markov model defined by a neural network.\n Based on this proposal, we modify Tacotron 2 to obtain an HMM-based neural TTS model with\n monotonic alignment, trained to maximise the full sequence likelihood without approximation.\n We also describe how to combine ideas from classical and contemporary TTS for best results.\n The resulting example system is smaller and simpler than Tacotron 2, and learns to speak with\n fewer iterations and less data, whilst achieving comparable naturalness prior to the post-net.\n Our approach also allows easy control over speaking rate. Audio examples and code\n are available at https://shivammehta25.github.io/Neural-HMM/ .\n\n Note:\n - This is a parameter efficient version of OverFlow (15.3M vs 28.6M). Since it has half the\n number of parameters as OverFlow the synthesis output quality is suboptimal (but comparable to Tacotron2\n without Postnet), but it learns to speak with even lesser amount of data and is still significantly faster\n than other attention-based methods.\n\n - Neural HMMs uses flat start initialization i.e it computes the means and std and transition probabilities\n of the dataset and uses them to initialize the model. This benefits the model and helps with faster learning\n If you change the dataset or want to regenerate the parameters change the `force_generate_statistics` and\n `mel_statistics_parameter_path` accordingly.\n\n - To enable multi-GPU training, set the `use_grad_checkpointing=False` in config.\n This will significantly increase the memory usage. This is because to compute\n the actual data likelihood (not an approximation using MAS/Viterbi) we must use\n all the states at the previous time step during the forward pass to decide the\n probability distribution at the current step i.e the difference between the forward\n algorithm and viterbi approximation.\n\n Check :class:`TTS.tts.configs.neuralhmm_tts_config.NeuralhmmTTSConfig` for class arguments.\n \"\"\"\n\n def __init__(\n self,\n config: \"NeuralhmmTTSConfig\",\n ap: \"AudioProcessor\" = None,\n tokenizer: \"TTSTokenizer\" = None,\n speaker_manager: SpeakerManager = None,\n ):\n super().__init__(config, ap, tokenizer, speaker_manager)\n\n # pass all config fields to `self`\n # for fewer code change\n self.config = config\n for key in config:\n setattr(self, key, config[key])\n\n self.encoder = Encoder(config.num_chars, config.state_per_phone, config.encoder_in_out_features)\n self.neural_hmm = NeuralHMM(\n frame_channels=self.out_channels,\n ar_order=self.ar_order,\n deterministic_transition=self.deterministic_transition,\n encoder_dim=self.encoder_in_out_features,\n prenet_type=self.prenet_type,\n prenet_dim=self.prenet_dim,\n prenet_n_layers=self.prenet_n_layers,\n prenet_dropout=self.prenet_dropout,\n prenet_dropout_at_inference=self.prenet_dropout_at_inference,\n memory_rnn_dim=self.memory_rnn_dim,\n outputnet_size=self.outputnet_size,\n flat_start_params=self.flat_start_params,\n std_floor=self.std_floor,\n use_grad_checkpointing=self.use_grad_checkpointing,\n )\n\n self.register_buffer(\"mean\", torch.tensor(0))\n self.register_buffer(\"std\", torch.tensor(1))\n\n def update_mean_std(self, statistics_dict: Dict):\n self.mean.data = torch.tensor(statistics_dict[\"mean\"])\n self.std.data = torch.tensor(statistics_dict[\"std\"])\n\n def preprocess_batch(self, text, text_len, mels, mel_len):\n if self.mean.item() == 0 or self.std.item() == 1:\n statistics_dict = torch.load(self.mel_statistics_parameter_path)\n self.update_mean_std(statistics_dict)\n\n mels = self.normalize(mels)\n return text, text_len, mels, mel_len\n\n def normalize(self, x):\n return x.sub(self.mean).div(self.std)\n\n def inverse_normalize(self, x):\n return x.mul(self.std).add(self.mean)\n\n def forward(self, text, text_len, mels, mel_len):\n \"\"\"\n Forward pass for training and computing the log likelihood of a given batch.\n\n Shapes:\n Shapes:\n text: :math:`[B, T_in]`\n text_len: :math:`[B]`\n mels: :math:`[B, T_out, C]`\n mel_len: :math:`[B]`\n \"\"\"\n text, text_len, mels, mel_len = self.preprocess_batch(text, text_len, mels, mel_len)\n encoder_outputs, encoder_output_len = self.encoder(text, text_len)\n\n log_probs, fwd_alignments, transition_vectors, means = self.neural_hmm(\n encoder_outputs, encoder_output_len, mels.transpose(1, 2), mel_len\n )\n\n outputs = {\n \"log_probs\": log_probs,\n \"alignments\": fwd_alignments,\n \"transition_vectors\": transition_vectors,\n \"means\": means,\n }\n\n return outputs\n\n @staticmethod\n def _training_stats(batch):\n stats = {}\n stats[\"avg_text_length\"] = batch[\"text_lengths\"].float().mean()\n stats[\"avg_spec_length\"] = batch[\"mel_lengths\"].float().mean()\n stats[\"avg_text_batch_occupancy\"] = (batch[\"text_lengths\"].float() / batch[\"text_lengths\"].float().max()).mean()\n stats[\"avg_spec_batch_occupancy\"] = (batch[\"mel_lengths\"].float() / batch[\"mel_lengths\"].float().max()).mean()\n return stats\n\n def train_step(self, batch: dict, criterion: nn.Module):\n text_input = batch[\"text_input\"]\n text_lengths = batch[\"text_lengths\"]\n mel_input = batch[\"mel_input\"]\n mel_lengths = batch[\"mel_lengths\"]\n\n outputs = self.forward(\n text=text_input,\n text_len=text_lengths,\n mels=mel_input,\n mel_len=mel_lengths,\n )\n loss_dict = criterion(outputs[\"log_probs\"] / (mel_lengths.sum() + text_lengths.sum()))\n\n # for printing useful statistics on terminal\n loss_dict.update(self._training_stats(batch))\n return outputs, loss_dict\n\n def eval_step(self, batch: Dict, criterion: nn.Module):\n return self.train_step(batch, criterion)\n\n def _format_aux_input(self, aux_input: Dict, default_input_dict):\n \"\"\"Set missing fields to their default value.\n\n Args:\n aux_inputs (Dict): Dictionary containing the auxiliary inputs.\n \"\"\"\n default_input_dict = default_input_dict.copy()\n default_input_dict.update(\n {\n \"sampling_temp\": self.sampling_temp,\n \"max_sampling_time\": self.max_sampling_time,\n \"duration_threshold\": self.duration_threshold,\n }\n )\n if aux_input:\n return format_aux_input(default_input_dict, aux_input)\n return default_input_dict\n\n @torch.no_grad()\n def inference(\n self,\n text: torch.Tensor,\n aux_input={\"x_lengths\": None, \"sampling_temp\": None, \"max_sampling_time\": None, \"duration_threshold\": None},\n ): # pylint: disable=dangerous-default-value\n \"\"\"Sampling from the model\n\n Args:\n text (torch.Tensor): :math:`[B, T_in]`\n aux_inputs (_type_, optional): _description_. Defaults to None.\n\n Returns:\n outputs: Dictionary containing the following\n - mel (torch.Tensor): :math:`[B, T_out, C]`\n - hmm_outputs_len (torch.Tensor): :math:`[B]`\n - state_travelled (List[List[int]]): List of lists containing the state travelled for each sample in the batch.\n - input_parameters (list[torch.FloatTensor]): Input parameters to the neural HMM.\n - output_parameters (list[torch.FloatTensor]): Output parameters to the neural HMM.\n \"\"\"\n default_input_dict = {\n \"x_lengths\": torch.sum(text != 0, dim=1),\n }\n aux_input = self._format_aux_input(aux_input, default_input_dict)\n encoder_outputs, encoder_output_len = self.encoder.inference(text, aux_input[\"x_lengths\"])\n outputs = self.neural_hmm.inference(\n encoder_outputs,\n encoder_output_len,\n sampling_temp=aux_input[\"sampling_temp\"],\n max_sampling_time=aux_input[\"max_sampling_time\"],\n duration_threshold=aux_input[\"duration_threshold\"],\n )\n mels, mel_outputs_len = outputs[\"hmm_outputs\"], outputs[\"hmm_outputs_len\"]\n\n mels = self.inverse_normalize(mels)\n outputs.update({\"model_outputs\": mels, \"model_outputs_len\": mel_outputs_len})\n outputs[\"alignments\"] = OverflowUtils.double_pad(outputs[\"alignments\"])\n return outputs\n\n @staticmethod\n def get_criterion():\n return NLLLoss()\n\n @staticmethod\n def init_from_config(config: \"NeuralhmmTTSConfig\", samples: Union[List[List], List[Dict]] = None, verbose=True):\n \"\"\"Initiate model from config\n\n Args:\n config (VitsConfig): Model config.\n samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.\n Defaults to None.\n verbose (bool): If True, print init messages. Defaults to True.\n \"\"\"\n\n ap = AudioProcessor.init_from_config(config, verbose)\n tokenizer, new_config = TTSTokenizer.init_from_config(config)\n speaker_manager = SpeakerManager.init_from_config(config, samples)\n return NeuralhmmTTS(new_config, ap, tokenizer, speaker_manager)\n\n def load_checkpoint(\n self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False\n ): # pylint: disable=unused-argument, redefined-builtin\n state = load_fsspec(checkpoint_path, map_location=torch.device(\"cpu\"))\n self.load_state_dict(state[\"model\"])\n if eval:\n self.eval()\n assert not self.training\n\n def on_init_start(self, trainer):\n \"\"\"If the current dataset does not have normalisation statistics and initialisation transition_probability it computes them otherwise loads.\"\"\"\n if not os.path.isfile(trainer.config.mel_statistics_parameter_path) or trainer.config.force_generate_statistics:\n dataloader = trainer.get_train_dataloader(\n training_assets=None, samples=trainer.train_samples, verbose=False\n )\n print(\n f\" | > Data parameters not found for: {trainer.config.mel_statistics_parameter_path}. Computing mel normalization parameters...\"\n )\n data_mean, data_std, init_transition_prob = OverflowUtils.get_data_parameters_for_flat_start(\n dataloader, trainer.config.out_channels, trainer.config.state_per_phone\n )\n print(\n f\" | > Saving data parameters to: {trainer.config.mel_statistics_parameter_path}: value: {data_mean, data_std, init_transition_prob}\"\n )\n statistics = {\n \"mean\": data_mean.item(),\n \"std\": data_std.item(),\n \"init_transition_prob\": init_transition_prob.item(),\n }\n torch.save(statistics, trainer.config.mel_statistics_parameter_path)\n\n else:\n print(\n f\" | > Data parameters found for: {trainer.config.mel_statistics_parameter_path}. Loading mel normalization parameters...\"\n )\n statistics = torch.load(trainer.config.mel_statistics_parameter_path)\n data_mean, data_std, init_transition_prob = (\n statistics[\"mean\"],\n statistics[\"std\"],\n statistics[\"init_transition_prob\"],\n )\n print(f\" | > Data parameters loaded with value: {data_mean, data_std, init_transition_prob}\")\n\n trainer.config.flat_start_params[\"transition_p\"] = (\n init_transition_prob.item() if torch.is_tensor(init_transition_prob) else init_transition_prob\n )\n OverflowUtils.update_flat_start_transition(trainer.model, init_transition_prob)\n trainer.model.update_mean_std(statistics)\n\n @torch.inference_mode()\n def _create_logs(self, batch, outputs, ap): # pylint: disable=no-self-use, unused-argument\n alignments, transition_vectors = outputs[\"alignments\"], outputs[\"transition_vectors\"]\n means = torch.stack(outputs[\"means\"], dim=1)\n\n figures = {\n \"alignment\": plot_alignment(alignments[0].exp(), title=\"Forward alignment\", fig_size=(20, 20)),\n \"log_alignment\": plot_alignment(\n alignments[0].exp(), title=\"Forward log alignment\", plot_log=True, fig_size=(20, 20)\n ),\n \"transition_vectors\": plot_alignment(transition_vectors[0], title=\"Transition vectors\", fig_size=(20, 20)),"},"next_line":{"kind":"string","value":" \"mel_from_most_probable_state\": plot_spectrogram("},"gold_snippet_index":{"kind":"number","value":9,"string":"9"},"created_at":{"kind":"string","value":"2023-11-29 08:15:06+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5395,"cells":{"repo_name":{"kind":"string","value":"magic-research/magic-animate"},"file_path":{"kind":"string","value":"magicanimate/pipelines/pipeline_animation.py"},"context":{"kind":"list like","value":[{"identifier":"UNet3DConditionModel","path":"magicanimate/models/unet_controlnet.py","snippet":"class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0, \n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n mid_block_type: str = \"UNetMidBlock3DCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n \n # Additional\n use_motion_module = False,\n motion_module_resolutions = ( 1,2,4,8 ),\n motion_module_mid_block = False,\n motion_module_decoder_only = False,\n motion_module_type = None,\n motion_module_kwargs = {},\n unet_use_cross_frame_attention = None,\n unet_use_temporal_attention = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # input\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n res = 2 ** i\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock3DCrossAttn\":\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n \n use_motion_module=use_motion_module and motion_module_mid_block,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n \n # count how many layers upsample the videos\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n res = 2 ** (3 - i)\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n # for controlnet\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # pre-process\n sample = self.conv_in(sample)\n\n # down\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # mid\n sample = self.mid_block(\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\n )\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n\n @classmethod\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\n print(f\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\")\n\n config_file = os.path.join(pretrained_model_path, 'config.json')\n if not os.path.isfile(config_file):\n raise RuntimeError(f\"{config_file} does not exist\")\n with open(config_file, \"r\") as f:\n config = json.load(f)\n config[\"_class_name\"] = cls.__name__\n config[\"down_block_types\"] = [\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\"\n ]\n config[\"up_block_types\"] = [\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ]\n # config[\"mid_block_type\"] = \"UNetMidBlock3DCrossAttn\"\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config, **unet_additional_kwargs)\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n raise RuntimeError(f\"{model_file} does not exist\")\n state_dict = torch.load(model_file, map_location=\"cpu\")\n\n m, u = model.load_state_dict(state_dict, strict=False)\n print(f\"### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};\")\n # print(f\"### missing keys:\\n{m}\\n### unexpected keys:\\n{u}\\n\")\n \n params = [p.numel() if \"temporal\" in n else 0 for n, p in model.named_parameters()]\n print(f\"### Temporal Module Parameters: {sum(params) / 1e6} M\")\n \n return model"},{"identifier":"ControlNetModel","path":"magicanimate/models/controlnet.py","snippet":"class ControlNetModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n projection_class_embeddings_input_dim: Optional[int] = None,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n ):\n super().__init__()\n\n # Check inputs\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n )\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n # control net conditioning embedding\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n )\n\n self.down_blocks = nn.ModuleList([])\n self.controlnet_down_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[i],\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.down_blocks.append(down_block)\n\n for _ in range(layers_per_block):\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n if not is_final_block:\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n # mid\n mid_block_channel = block_out_channels[-1]\n\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_mid_block = controlnet_block\n\n self.mid_block = UNetMidBlock2DCrossAttn(\n in_channels=mid_block_channel,\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n load_weights_from_unet: bool = True,\n ):\n r\"\"\"\n Instantiate Controlnet class from UNet2DConditionModel.\n\n Parameters:\n unet (`UNet2DConditionModel`):\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\n copied where applicable.\n \"\"\"\n controlnet = cls(\n in_channels=unet.config.in_channels,\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\n freq_shift=unet.config.freq_shift,\n down_block_types=unet.config.down_block_types,\n only_cross_attention=unet.config.only_cross_attention,\n block_out_channels=unet.config.block_out_channels,\n layers_per_block=unet.config.layers_per_block,\n downsample_padding=unet.config.downsample_padding,\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\n act_fn=unet.config.act_fn,\n norm_num_groups=unet.config.norm_num_groups,\n norm_eps=unet.config.norm_eps,\n cross_attention_dim=unet.config.cross_attention_dim,\n attention_head_dim=unet.config.attention_head_dim,\n use_linear_projection=unet.config.use_linear_projection,\n class_embed_type=unet.config.class_embed_type,\n num_class_embeds=unet.config.num_class_embeds,\n upcast_attention=unet.config.upcast_attention,\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\n )\n\n if load_weights_from_unet:\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\n\n if controlnet.class_embedding:\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\n\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\n\n return controlnet\n\n # @property\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\n # r\"\"\"\n # Returns:\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\n # indexed by its weight name.\n # \"\"\"\n # # set recursively\n # processors = {}\n\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n # if hasattr(module, \"set_processor\"):\n # processors[f\"{name}.processor\"] = module.processor\n\n # for sub_name, child in module.named_children():\n # fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n # return processors\n\n # for name, module in self.named_children():\n # fn_recursive_add_processors(name, module, processors)\n\n # return processors\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n # r\"\"\"\n # Parameters:\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\n # of **all** `Attention` layers.\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\n\n # \"\"\"\n # count = len(self.attn_processors.keys())\n\n # if isinstance(processor, dict) and len(processor) != count:\n # raise ValueError(\n # f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n # f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n # )\n\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n # if hasattr(module, \"set_processor\"):\n # if not isinstance(processor, dict):\n # module.set_processor(processor)\n # else:\n # module.set_processor(processor.pop(f\"{name}.processor\"))\n\n # for sub_name, child in module.named_children():\n # fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n # for name, module in self.named_children():\n # fn_recursive_attn_processor(name, module, processor)\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n # def set_default_attn_processor(self):\n # \"\"\"\n # Disables custom attention processors and sets the default attention implementation.\n # \"\"\"\n # self.set_attn_processor(AttnProcessor())\n\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: torch.FloatTensor,\n conditioning_scale: float = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple]:\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\n\n if channel_order == \"rgb\":\n # in rgb order by default\n ...\n elif channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n else:\n raise ValueError(f\"unknown `controlnet_conditioning_channel_order`: {channel_order}\")\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n\n sample += controlnet_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n\n # 5. Control net blocks\n\n controlnet_down_block_res_samples = ()\n\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample *= conditioning_scale\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )"},{"identifier":"ReferenceAttentionControl","path":"magicanimate/models/mutual_self_attention.py","snippet":"class ReferenceAttentionControl():\n \n def __init__(self, \n unet,\n mode=\"write\",\n do_classifier_free_guidance=False,\n attention_auto_machine_weight = float('inf'),\n gn_auto_machine_weight = 1.0,\n style_fidelity = 1.0,\n reference_attn=True,\n reference_adain=False,\n fusion_blocks=\"midup\",\n batch_size=1, \n ) -> None:\n # 10. Modify self attention and group norm\n self.unet = unet\n assert mode in [\"read\", \"write\"]\n assert fusion_blocks in [\"midup\", \"full\"]\n self.reference_attn = reference_attn\n self.reference_adain = reference_adain\n self.fusion_blocks = fusion_blocks\n self.register_reference_hooks(\n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n fusion_blocks,\n batch_size=batch_size, \n )\n\n def register_reference_hooks(\n self, \n mode, \n do_classifier_free_guidance,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n dtype=torch.float16,\n batch_size=1, \n num_images_per_prompt=1, \n device=torch.device(\"cpu\"), \n fusion_blocks='midup',\n ):\n MODE = mode\n do_classifier_free_guidance = do_classifier_free_guidance\n attention_auto_machine_weight = attention_auto_machine_weight\n gn_auto_machine_weight = gn_auto_machine_weight\n style_fidelity = style_fidelity\n reference_attn = reference_attn\n reference_adain = reference_adain\n fusion_blocks = fusion_blocks\n num_images_per_prompt = num_images_per_prompt\n dtype=dtype\n if do_classifier_free_guidance:\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\n .to(device)\n .bool()\n )\n else:\n uc_mask = (\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\n .to(device)\n .bool()\n )\n \n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.FloatTensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n video_length=None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \"b t l c -> (b t) l c\")[:hidden_states.shape[0]] for d in self.bank]\n hidden_states_uc = self.attn1(norm_hidden_states, \n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n attention_mask=attention_mask) + hidden_states\n hidden_states_c = hidden_states_uc.clone()\n _uc_mask = uc_mask.clone()\n if do_classifier_free_guidance:\n if hidden_states.shape[0] != _uc_mask.shape[0]:\n _uc_mask = (\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\n .to(device)\n .bool()\n )\n hidden_states_c[_uc_mask] = self.attn1(\n norm_hidden_states[_uc_mask],\n encoder_hidden_states=norm_hidden_states[_uc_mask],\n attention_mask=attention_mask,\n ) + hidden_states[_uc_mask]\n hidden_states = hidden_states_c.clone()\n \n self.bank.clear()\n if self.attn2 is not None:\n # Cross-Attention\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n hidden_states = (\n self.attn2(\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n + hidden_states\n )\n\n # Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n # Temporal-Attention\n if self.unet_use_temporal_attention:\n d = hidden_states.shape[1]\n hidden_states = rearrange(hidden_states, \"(b f) d c -> (b d) f c\", f=video_length)\n norm_hidden_states = (\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\n )\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\n hidden_states = rearrange(hidden_states, \"(b d) f c -> (b f) d c\", d=d)\n\n return hidden_states\n \n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if self.reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n \n def update(self, writer, dtype=torch.float16):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r, w in zip(reader_attn_modules, writer_attn_modules):\n r.bank = [v.clone().to(dtype) for v in w.bank]\n # w.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n writer_gn_modules = [writer.unet.mid_block]\n \n down_blocks = writer.unet.down_blocks\n for w, module in enumerate(down_blocks):\n writer_gn_modules.append(module)\n\n up_blocks = writer.unet.up_blocks\n for w, module in enumerate(up_blocks):\n writer_gn_modules.append(module)\n \n for r, w in zip(reader_gn_modules, writer_gn_modules):\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\n else:\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\n \n def clear(self):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r in reader_attn_modules:\n r.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n for r in reader_gn_modules:\n r.mean_bank.clear()\n r.var_bank.clear()"},{"identifier":"get_context_scheduler","path":"magicanimate/pipelines/context.py","snippet":"def get_context_scheduler(name: str) -> Callable:\n if name == \"uniform\":\n return uniform\n else:\n raise ValueError(f\"Unknown context_overlap policy {name}\")"},{"identifier":"get_total_steps","path":"magicanimate/pipelines/context.py","snippet":"def get_total_steps(\n scheduler,\n timesteps: List[int],\n num_steps: Optional[int] = None,\n num_frames: int = ...,\n context_size: Optional[int] = None,\n context_stride: int = 3,\n context_overlap: int = 4,\n closed_loop: bool = True,\n):\n return sum(\n len(\n list(\n scheduler(\n i,\n num_steps,\n num_frames,\n context_size,\n context_stride,\n context_overlap,\n )\n )\n )\n for i in range(len(timesteps))\n )"},{"identifier":"get_tensor_interpolation_method","path":"magicanimate/utils/util.py","snippet":"def get_tensor_interpolation_method():\n return tensor_interpolation"}],"string":"[\n {\n \"identifier\": \"UNet3DConditionModel\",\n \"path\": \"magicanimate/models/unet_controlnet.py\",\n \"snippet\": \"class UNet3DConditionModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n center_input_sample: bool = False,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0, \\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\",\\n ),\\n mid_block_type: str = \\\"UNetMidBlock3DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: int = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n dual_cross_attention: bool = False,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n \\n # Additional\\n use_motion_module = False,\\n motion_module_resolutions = ( 1,2,4,8 ),\\n motion_module_mid_block = False,\\n motion_module_decoder_only = False,\\n motion_module_type = None,\\n motion_module_kwargs = {},\\n unet_use_cross_frame_attention = None,\\n unet_use_temporal_attention = None,\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n time_embed_dim = block_out_channels[0] * 4\\n\\n # input\\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\\n\\n # time\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n self.down_blocks = nn.ModuleList([])\\n self.mid_block = None\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n res = 2 ** i\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n if mid_block_type == \\\"UNetMidBlock3DCrossAttn\\\":\\n self.mid_block = UNetMidBlock3DCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n \\n use_motion_module=use_motion_module and motion_module_mid_block,\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n else:\\n raise ValueError(f\\\"unknown mid_block_type : {mid_block_type}\\\")\\n \\n # count how many layers upsample the videos\\n self.num_upsamplers = 0\\n\\n # up\\n reversed_block_out_channels = list(reversed(block_out_channels))\\n reversed_attention_head_dim = list(reversed(attention_head_dim))\\n only_cross_attention = list(reversed(only_cross_attention))\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n res = 2 ** (3 - i)\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=layers_per_block + 1,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=time_embed_dim,\\n add_upsample=add_upsample,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=reversed_attention_head_dim[i],\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\\n self.conv_act = nn.SiLU()\\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\\n\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_slicable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_slicable_dims(module)\\n\\n num_slicable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_slicable_layers * [1]\\n\\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n # for controlnet\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n return_dict: bool = True,\\n ) -> Union[UNet3DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n Args:\\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\\n returning a tuple, the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\\n # on the fly if necessary.\\n default_overall_up_factor = 2**self.num_upsamplers\\n\\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\\n forward_upsample_size = False\\n upsample_size = None\\n\\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\\n logger.info(\\\"Forward upsample size to force interpolation output size.\\\")\\n forward_upsample_size = True\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # center input if necessary\\n if self.config.center_input_sample:\\n sample = 2 * sample - 1.0\\n\\n # time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n emb = self.time_embedding(t_emb)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # pre-process\\n sample = self.conv_in(sample)\\n\\n # down\\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\\n\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)\\n\\n down_block_res_samples += res_samples\\n\\n if is_controlnet:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # mid\\n sample = self.mid_block(\\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n\\n if is_controlnet:\\n sample = sample + mid_block_additional_residual\\n\\n # up\\n for i, upsample_block in enumerate(self.up_blocks):\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\\n\\n # if we have not reached the final block and need to forward the\\n # upsample size, we do it here\\n if not is_final_block and forward_upsample_size:\\n upsample_size = down_block_res_samples[-1].shape[2:]\\n\\n if hasattr(upsample_block, \\\"has_cross_attention\\\") and upsample_block.has_cross_attention:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample = upsample_block(\\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,\\n )\\n\\n # post-process\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n sample = self.conv_out(sample)\\n\\n if not return_dict:\\n return (sample,)\\n\\n return UNet3DConditionOutput(sample=sample)\\n\\n @classmethod\\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\\n if subfolder is not None:\\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\\n print(f\\\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\\\")\\n\\n config_file = os.path.join(pretrained_model_path, 'config.json')\\n if not os.path.isfile(config_file):\\n raise RuntimeError(f\\\"{config_file} does not exist\\\")\\n with open(config_file, \\\"r\\\") as f:\\n config = json.load(f)\\n config[\\\"_class_name\\\"] = cls.__name__\\n config[\\\"down_block_types\\\"] = [\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\"\\n ]\\n config[\\\"up_block_types\\\"] = [\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ]\\n # config[\\\"mid_block_type\\\"] = \\\"UNetMidBlock3DCrossAttn\\\"\\n\\n from diffusers.utils import WEIGHTS_NAME\\n model = cls.from_config(config, **unet_additional_kwargs)\\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\\n if not os.path.isfile(model_file):\\n raise RuntimeError(f\\\"{model_file} does not exist\\\")\\n state_dict = torch.load(model_file, map_location=\\\"cpu\\\")\\n\\n m, u = model.load_state_dict(state_dict, strict=False)\\n print(f\\\"### missing keys: {len(m)}; \\\\n### unexpected keys: {len(u)};\\\")\\n # print(f\\\"### missing keys:\\\\n{m}\\\\n### unexpected keys:\\\\n{u}\\\\n\\\")\\n \\n params = [p.numel() if \\\"temporal\\\" in n else 0 for n, p in model.named_parameters()]\\n print(f\\\"### Temporal Module Parameters: {sum(params) / 1e6} M\\\")\\n \\n return model\"\n },\n {\n \"identifier\": \"ControlNetModel\",\n \"path\": \"magicanimate/models/controlnet.py\",\n \"snippet\": \"class ControlNetModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n in_channels: int = 4,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"DownBlock2D\\\",\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: Optional[int] = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n projection_class_embeddings_input_dim: Optional[int] = None,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n ):\\n super().__init__()\\n\\n # Check inputs\\n if len(block_out_channels) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n # input\\n conv_in_kernel = 3\\n conv_in_padding = (conv_in_kernel - 1) // 2\\n self.conv_in = nn.Conv2d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n\\n # time\\n time_embed_dim = block_out_channels[0] * 4\\n\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(\\n timestep_input_dim,\\n time_embed_dim,\\n act_fn=act_fn,\\n )\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n elif class_embed_type == \\\"projection\\\":\\n if projection_class_embeddings_input_dim is None:\\n raise ValueError(\\n \\\"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\\\"\\n )\\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\\n # 2. it projects from an arbitrary input dimension.\\n #\\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n # control net conditioning embedding\\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\\n conditioning_embedding_channels=block_out_channels[0],\\n block_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n self.down_blocks = nn.ModuleList([])\\n self.controlnet_down_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n for i, down_block_type in enumerate(down_block_types):\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n )\\n self.down_blocks.append(down_block)\\n\\n for _ in range(layers_per_block):\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n if not is_final_block:\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n # mid\\n mid_block_channel = block_out_channels[-1]\\n\\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_mid_block = controlnet_block\\n\\n self.mid_block = UNetMidBlock2DCrossAttn(\\n in_channels=mid_block_channel,\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n )\\n\\n @classmethod\\n def from_unet(\\n cls,\\n unet: UNet2DConditionModel,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n load_weights_from_unet: bool = True,\\n ):\\n r\\\"\\\"\\\"\\n Instantiate Controlnet class from UNet2DConditionModel.\\n\\n Parameters:\\n unet (`UNet2DConditionModel`):\\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\\n copied where applicable.\\n \\\"\\\"\\\"\\n controlnet = cls(\\n in_channels=unet.config.in_channels,\\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\\n freq_shift=unet.config.freq_shift,\\n down_block_types=unet.config.down_block_types,\\n only_cross_attention=unet.config.only_cross_attention,\\n block_out_channels=unet.config.block_out_channels,\\n layers_per_block=unet.config.layers_per_block,\\n downsample_padding=unet.config.downsample_padding,\\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\\n act_fn=unet.config.act_fn,\\n norm_num_groups=unet.config.norm_num_groups,\\n norm_eps=unet.config.norm_eps,\\n cross_attention_dim=unet.config.cross_attention_dim,\\n attention_head_dim=unet.config.attention_head_dim,\\n use_linear_projection=unet.config.use_linear_projection,\\n class_embed_type=unet.config.class_embed_type,\\n num_class_embeds=unet.config.num_class_embeds,\\n upcast_attention=unet.config.upcast_attention,\\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n if load_weights_from_unet:\\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\\n\\n if controlnet.class_embedding:\\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\\n\\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\\n\\n return controlnet\\n\\n # @property\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\\n # r\\\"\\\"\\\"\\n # Returns:\\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\\n # indexed by its weight name.\\n # \\\"\\\"\\\"\\n # # set recursively\\n # processors = {}\\n\\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # processors[f\\\"{name}.processor\\\"] = module.processor\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_add_processors(f\\\"{name}.{sub_name}\\\", child, processors)\\n\\n # return processors\\n\\n # for name, module in self.named_children():\\n # fn_recursive_add_processors(name, module, processors)\\n\\n # return processors\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\\n # r\\\"\\\"\\\"\\n # Parameters:\\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\\n # of **all** `Attention` layers.\\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\\n\\n # \\\"\\\"\\\"\\n # count = len(self.attn_processors.keys())\\n\\n # if isinstance(processor, dict) and len(processor) != count:\\n # raise ValueError(\\n # f\\\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\\\"\\n # f\\\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\\\"\\n # )\\n\\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # if not isinstance(processor, dict):\\n # module.set_processor(processor)\\n # else:\\n # module.set_processor(processor.pop(f\\\"{name}.processor\\\"))\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_attn_processor(f\\\"{name}.{sub_name}\\\", child, processor)\\n\\n # for name, module in self.named_children():\\n # fn_recursive_attn_processor(name, module, processor)\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\\n # def set_default_attn_processor(self):\\n # \\\"\\\"\\\"\\n # Disables custom attention processors and sets the default attention implementation.\\n # \\\"\\\"\\\"\\n # self.set_attn_processor(AttnProcessor())\\n\\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_sliceable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_sliceable_dims(module)\\n\\n num_sliceable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_sliceable_layers * [1]\\n\\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n controlnet_cond: torch.FloatTensor,\\n conditioning_scale: float = 1.0,\\n class_labels: Optional[torch.Tensor] = None,\\n timestep_cond: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n return_dict: bool = True,\\n ) -> Union[ControlNetOutput, Tuple]:\\n # check channel order\\n channel_order = self.config.controlnet_conditioning_channel_order\\n\\n if channel_order == \\\"rgb\\\":\\n # in rgb order by default\\n ...\\n elif channel_order == \\\"bgr\\\":\\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\\n else:\\n raise ValueError(f\\\"unknown `controlnet_conditioning_channel_order`: {channel_order}\\\")\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # 1. time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n\\n emb = self.time_embedding(t_emb, timestep_cond)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # 2. pre-process\\n sample = self.conv_in(sample)\\n\\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\\n\\n sample += controlnet_cond\\n\\n # 3. down\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\\n\\n down_block_res_samples += res_samples\\n\\n # 4. mid\\n if self.mid_block is not None:\\n sample = self.mid_block(\\n sample,\\n emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n\\n # 5. Control net blocks\\n\\n controlnet_down_block_res_samples = ()\\n\\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\\n down_block_res_sample = controlnet_block(down_block_res_sample)\\n controlnet_down_block_res_samples += (down_block_res_sample,)\\n\\n down_block_res_samples = controlnet_down_block_res_samples\\n\\n mid_block_res_sample = self.controlnet_mid_block(sample)\\n\\n # 6. scaling\\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\\n mid_block_res_sample *= conditioning_scale\\n\\n if not return_dict:\\n return (down_block_res_samples, mid_block_res_sample)\\n\\n return ControlNetOutput(\\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\\n )\"\n },\n {\n \"identifier\": \"ReferenceAttentionControl\",\n \"path\": \"magicanimate/models/mutual_self_attention.py\",\n \"snippet\": \"class ReferenceAttentionControl():\\n \\n def __init__(self, \\n unet,\\n mode=\\\"write\\\",\\n do_classifier_free_guidance=False,\\n attention_auto_machine_weight = float('inf'),\\n gn_auto_machine_weight = 1.0,\\n style_fidelity = 1.0,\\n reference_attn=True,\\n reference_adain=False,\\n fusion_blocks=\\\"midup\\\",\\n batch_size=1, \\n ) -> None:\\n # 10. Modify self attention and group norm\\n self.unet = unet\\n assert mode in [\\\"read\\\", \\\"write\\\"]\\n assert fusion_blocks in [\\\"midup\\\", \\\"full\\\"]\\n self.reference_attn = reference_attn\\n self.reference_adain = reference_adain\\n self.fusion_blocks = fusion_blocks\\n self.register_reference_hooks(\\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n fusion_blocks,\\n batch_size=batch_size, \\n )\\n\\n def register_reference_hooks(\\n self, \\n mode, \\n do_classifier_free_guidance,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n dtype=torch.float16,\\n batch_size=1, \\n num_images_per_prompt=1, \\n device=torch.device(\\\"cpu\\\"), \\n fusion_blocks='midup',\\n ):\\n MODE = mode\\n do_classifier_free_guidance = do_classifier_free_guidance\\n attention_auto_machine_weight = attention_auto_machine_weight\\n gn_auto_machine_weight = gn_auto_machine_weight\\n style_fidelity = style_fidelity\\n reference_attn = reference_attn\\n reference_adain = reference_adain\\n fusion_blocks = fusion_blocks\\n num_images_per_prompt = num_images_per_prompt\\n dtype=dtype\\n if do_classifier_free_guidance:\\n uc_mask = (\\n torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\\n .to(device)\\n .bool()\\n )\\n else:\\n uc_mask = (\\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\\n .to(device)\\n .bool()\\n )\\n \\n def hacked_basic_transformer_inner_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n timestep: Optional[torch.LongTensor] = None,\\n cross_attention_kwargs: Dict[str, Any] = None,\\n class_labels: Optional[torch.LongTensor] = None,\\n video_length=None,\\n ):\\n if self.use_ada_layer_norm:\\n norm_hidden_states = self.norm1(hidden_states, timestep)\\n elif self.use_ada_layer_norm_zero:\\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\\n )\\n else:\\n norm_hidden_states = self.norm1(hidden_states)\\n\\n # 1. Self-Attention\\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\\n if self.only_cross_attention:\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n else:\\n if MODE == \\\"write\\\":\\n self.bank.append(norm_hidden_states.clone())\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n if MODE == \\\"read\\\":\\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \\\"b t l c -> (b t) l c\\\")[:hidden_states.shape[0]] for d in self.bank]\\n hidden_states_uc = self.attn1(norm_hidden_states, \\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\\n attention_mask=attention_mask) + hidden_states\\n hidden_states_c = hidden_states_uc.clone()\\n _uc_mask = uc_mask.clone()\\n if do_classifier_free_guidance:\\n if hidden_states.shape[0] != _uc_mask.shape[0]:\\n _uc_mask = (\\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\\n .to(device)\\n .bool()\\n )\\n hidden_states_c[_uc_mask] = self.attn1(\\n norm_hidden_states[_uc_mask],\\n encoder_hidden_states=norm_hidden_states[_uc_mask],\\n attention_mask=attention_mask,\\n ) + hidden_states[_uc_mask]\\n hidden_states = hidden_states_c.clone()\\n \\n self.bank.clear()\\n if self.attn2 is not None:\\n # Cross-Attention\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n hidden_states = (\\n self.attn2(\\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n + hidden_states\\n )\\n\\n # Feed-forward\\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\\n\\n # Temporal-Attention\\n if self.unet_use_temporal_attention:\\n d = hidden_states.shape[1]\\n hidden_states = rearrange(hidden_states, \\\"(b f) d c -> (b d) f c\\\", f=video_length)\\n norm_hidden_states = (\\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\\n )\\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\\n hidden_states = rearrange(hidden_states, \\\"(b d) f c -> (b f) d c\\\", d=d)\\n\\n return hidden_states\\n \\n if self.use_ada_layer_norm_zero:\\n attn_output = gate_msa.unsqueeze(1) * attn_output\\n hidden_states = attn_output + hidden_states\\n\\n if self.attn2 is not None:\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n\\n # 2. Cross-Attention\\n attn_output = self.attn2(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=encoder_attention_mask,\\n **cross_attention_kwargs,\\n )\\n hidden_states = attn_output + hidden_states\\n\\n # 3. Feed-forward\\n norm_hidden_states = self.norm3(hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\\n\\n ff_output = self.ff(norm_hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n ff_output = gate_mlp.unsqueeze(1) * ff_output\\n\\n hidden_states = ff_output + hidden_states\\n\\n return hidden_states\\n\\n def hacked_mid_forward(self, *args, **kwargs):\\n eps = 1e-6\\n x = self.original_forward(*args, **kwargs)\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append(mean)\\n self.var_bank.append(var)\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\\n x_c = x_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n x_c[uc_mask] = x[uc_mask]\\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\\n self.mean_bank = []\\n self.var_bank = []\\n return x\\n\\n def hack_CrossAttnDownBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n\\n # TODO(Patrick, William) - attention mask is not used\\n output_states = ()\\n\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\\n eps = 1e-6\\n\\n output_states = ()\\n\\n for i, resnet in enumerate(self.resnets):\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_CrossAttnUpBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n upsample_size: Optional[int] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n # TODO(Patrick, William) - attention mask is not used\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\\n eps = 1e-6\\n for i, resnet in enumerate(self.resnets):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n\\n for i, module in enumerate(attn_modules):\\n module._original_inner_forward = module.forward\\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\\n module.bank = []\\n module.attn_weight = float(i) / float(len(attn_modules))\\n\\n if self.reference_adain:\\n gn_modules = [self.unet.mid_block]\\n self.unet.mid_block.gn_weight = 0\\n\\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\\n gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n module.gn_weight = float(w) / float(len(up_blocks))\\n gn_modules.append(module)\\n\\n for i, module in enumerate(gn_modules):\\n if getattr(module, \\\"original_forward\\\", None) is None:\\n module.original_forward = module.forward\\n if i == 0:\\n # mid_block\\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\\n elif isinstance(module, CrossAttnDownBlock2D):\\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\\n elif isinstance(module, DownBlock2D):\\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\\n elif isinstance(module, CrossAttnUpBlock2D):\\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\\n elif isinstance(module, UpBlock2D):\\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\\n module.mean_bank = []\\n module.var_bank = []\\n module.gn_weight *= 2\\n \\n def update(self, writer, dtype=torch.float16):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \\n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r, w in zip(reader_attn_modules, writer_attn_modules):\\n r.bank = [v.clone().to(dtype) for v in w.bank]\\n # w.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n writer_gn_modules = [writer.unet.mid_block]\\n \\n down_blocks = writer.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n writer_gn_modules.append(module)\\n\\n up_blocks = writer.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n writer_gn_modules.append(module)\\n \\n for r, w in zip(reader_gn_modules, writer_gn_modules):\\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\\n else:\\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\\n \\n def clear(self):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r in reader_attn_modules:\\n r.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n for r in reader_gn_modules:\\n r.mean_bank.clear()\\n r.var_bank.clear()\"\n },\n {\n \"identifier\": \"get_context_scheduler\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_context_scheduler(name: str) -> Callable:\\n if name == \\\"uniform\\\":\\n return uniform\\n else:\\n raise ValueError(f\\\"Unknown context_overlap policy {name}\\\")\"\n },\n {\n \"identifier\": \"get_total_steps\",\n \"path\": \"magicanimate/pipelines/context.py\",\n \"snippet\": \"def get_total_steps(\\n scheduler,\\n timesteps: List[int],\\n num_steps: Optional[int] = None,\\n num_frames: int = ...,\\n context_size: Optional[int] = None,\\n context_stride: int = 3,\\n context_overlap: int = 4,\\n closed_loop: bool = True,\\n):\\n return sum(\\n len(\\n list(\\n scheduler(\\n i,\\n num_steps,\\n num_frames,\\n context_size,\\n context_stride,\\n context_overlap,\\n )\\n )\\n )\\n for i in range(len(timesteps))\\n )\"\n },\n {\n \"identifier\": \"get_tensor_interpolation_method\",\n \"path\": \"magicanimate/utils/util.py\",\n \"snippet\": \"def get_tensor_interpolation_method():\\n return tensor_interpolation\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import inspect, math\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom typing import Callable, List, Optional, Union\nfrom dataclasses import dataclass\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom diffusers.utils import is_accelerate_available\nfrom packaging import version\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL\nfrom diffusers.pipeline_utils import DiffusionPipeline\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import deprecate, logging, BaseOutput\nfrom einops import rearrange\nfrom magicanimate.models.unet_controlnet import UNet3DConditionModel\nfrom magicanimate.models.controlnet import ControlNetModel\nfrom magicanimate.models.mutual_self_attention import ReferenceAttentionControl\nfrom magicanimate.pipelines.context import (\n get_context_scheduler,\n get_total_steps\n)\nfrom magicanimate.utils.util import get_tensor_interpolation_method\n from accelerate import cpu_offload"},"token_num":{"kind":"number","value":19468,"string":"19,468"},"cropped_code":{"kind":"string","value":" v1 = None\n\n for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):\n v0 = latents[:,:,i0,:,:]\n v1 = latents[:,:,i1,:,:]\n\n new_latents[:,:,new_index,:,:] = v0\n new_index += 1\n\n for f in rate:\n v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)\n new_latents[:,:,new_index,:,:] = v.to(latents.device)\n new_index += 1\n\n new_latents[:,:,new_index,:,:] = v1\n new_index += 1\n\n return new_latents\n \n def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):\n _down_block_res_samples = []\n _mid_block_res_sample = []\n for i in np.concatenate(np.array(context)):\n _down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])\n _mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])\n down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]\n for res_t in _down_block_res_samples:\n for i, res in enumerate(res_t):\n down_block_res_samples[i].append(res)\n down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]\n mid_block_res_sample = torch.cat(_mid_block_res_sample)\n \n # reshape controlnet output to match the unet3d inputs\n b = b // 2 if do_classifier_free_guidance else b\n _down_block_res_samples = []\n for sample in down_block_res_samples:\n sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n sample = sample.repeat(2, 1, 1, 1, 1)\n _down_block_res_samples.append(sample)\n down_block_res_samples = _down_block_res_samples\n mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)\n \n return down_block_res_samples, mid_block_res_sample\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n video_length: Optional[int],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n controlnet_condition: list = None,\n controlnet_conditioning_scale: float = 1.0,\n context_frames: int = 16,\n context_stride: int = 1,\n context_overlap: int = 4,\n context_batch_size: int = 1, \n context_schedule: str = \"uniform\",\n init_latents: Optional[torch.FloatTensor] = None,\n num_actual_inference_steps: Optional[int] = None,\n appearance_encoder = None, \n reference_control_writer = None,\n reference_control_reader = None,\n source_image: str = None,\n decoder_consistency = None, \n **kwargs,\n ):\n \"\"\"\n New args:\n - controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet\n - controlnet_conditioning_scale : conditioning scale for controlnet\n - init_latents : initial latents to begin with (used along with invert())\n - num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps) \n \"\"\"\n controlnet = self.controlnet\n\n # Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # Define call parameters\n # batch_size = 1 if isinstance(prompt, str) else len(prompt)\n batch_size = 1\n if latents is not None:\n batch_size = latents.shape[0]\n if isinstance(prompt, list):\n batch_size = len(prompt)\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # Encode input prompt\n prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size\n if negative_prompt is not None:\n negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size \n text_embeddings = self._encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n text_embeddings = torch.cat([text_embeddings] * context_batch_size)\n \n"},"all_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc.. \n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,\n controlnet: ControlNetModel,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def enable_vae_slicing(self):\n self.vae.enable_slicing()\n\n def disable_vae_slicing(self):\n self.vae.disable_slicing()\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n if is_accelerate_available():\n else:\n raise ImportError(\"Please install accelerate via `pip install accelerate`\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:\n if cpu_offloaded_model is not None:\n cpu_offload(cpu_offloaded_model, device)\n\n\n @property\n def _execution_device(self):\n if self.device != torch.device(\"meta\") or not hasattr(self.unet, \"_hf_hook\"):\n return self.device\n for module in self.unet.modules():\n if (\n hasattr(module, \"_hf_hook\")\n and hasattr(module._hf_hook, \"execution_device\")\n and module._hf_hook.execution_device is not None\n ):\n return torch.device(module._hf_hook.execution_device)\n return self.device\n\n def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def decode_latents(self, latents, rank, decoder_consistency=None):\n video_length = latents.shape[2]\n latents = 1 / 0.18215 * latents\n latents = rearrange(latents, \"b c f h w -> (b f) c h w\")\n # video = self.vae.decode(latents).sample\n video = []\n for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):\n if decoder_consistency is not None:\n video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))\n else:\n video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)\n video = torch.cat(video)\n video = rearrange(video, \"(b f) c h w -> b c f h w\", f=video_length)\n video = (video / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16\n video = video.cpu().float().numpy()\n return video\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):\n shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n if latents is None:\n rand_device = \"cpu\" if device.type == \"mps\" else device\n\n if isinstance(generator, list):\n latents = [\n torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)\n for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0).to(device)\n else:\n latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)\n \n latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):\n # prepare conditions for controlnet\n condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0\n condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)\n condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()\n if do_classifier_free_guidance:\n condition = torch.cat([condition] * 2)\n return condition\n\n def next_step(\n self,\n model_output: torch.FloatTensor,\n timestep: int,\n x: torch.FloatTensor,\n eta=0.,\n verbose=False\n ):\n \"\"\"\n Inverse sampling for DDIM Inversion\n \"\"\"\n if verbose:\n print(\"timestep: \", timestep)\n next_step = timestep\n timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod\n alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]\n beta_prod_t = 1 - alpha_prod_t\n pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output\n x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir\n return x_next, pred_x0\n\n @torch.no_grad()\n def images2latents(self, images, dtype):\n \"\"\"\n Convert RGB image to VAE latents\n \"\"\"\n device = self._execution_device\n images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1\n images = rearrange(images, \"f h w c -> f c h w\").to(device)\n latents = []\n for frame_idx in range(images.shape[0]):\n latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)\n latents = torch.cat(latents)\n return latents\n\n @torch.no_grad()\n def invert(\n self,\n image: torch.Tensor,\n prompt,\n num_inference_steps=20,\n num_actual_inference_steps=10,\n eta=0.0,\n return_intermediates=False,\n **kwargs):\n \"\"\"\n Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440\n invert a real image into noise map with determinisc DDIM inversion\n \"\"\"\n device = self._execution_device\n batch_size = image.shape[0]\n if isinstance(prompt, list):\n if batch_size == 1:\n image = image.expand(len(prompt), -1, -1, -1)\n elif isinstance(prompt, str):\n if batch_size > 1:\n prompt = [prompt] * batch_size\n\n # text embeddings\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n return_tensors=\"pt\"\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]\n print(\"input text embeddings :\", text_embeddings.shape)\n # define initial latents\n latents = self.images2latents(image)\n\n print(\"latents shape: \", latents.shape)\n # interative sampling\n self.scheduler.set_timesteps(num_inference_steps)\n print(\"Valid timesteps: \", reversed(self.scheduler.timesteps))\n latents_list = [latents]\n pred_x0_list = [latents]\n for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc=\"DDIM Inversion\")):\n\n if num_actual_inference_steps is not None and i >= num_actual_inference_steps:\n continue\n model_inputs = latents\n\n # predict the noise\n # NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)\n model_inputs = rearrange(model_inputs, \"f c h w -> 1 c f h w\")\n noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample\n noise_pred = rearrange(noise_pred, \"b c f h w -> (b f) c h w\")\n \n # compute the previous noise sample x_t-1 -> x_t\n latents, pred_x0 = self.next_step(noise_pred, t, latents)\n latents_list.append(latents)\n pred_x0_list.append(pred_x0)\n\n if return_intermediates:\n # return the intermediate laters during inversion\n return latents, latents_list\n return latents\n \n def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):\n if interpolation_factor < 2:\n return latents\n\n new_latents = torch.zeros(\n (latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),\n device=latents.device,\n dtype=latents.dtype,\n )\n\n org_video_length = latents.shape[2]\n rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]\n\n new_index = 0\n\n v0 = None\n v1 = None\n\n for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):\n v0 = latents[:,:,i0,:,:]\n v1 = latents[:,:,i1,:,:]\n\n new_latents[:,:,new_index,:,:] = v0\n new_index += 1\n\n for f in rate:\n v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)\n new_latents[:,:,new_index,:,:] = v.to(latents.device)\n new_index += 1\n\n new_latents[:,:,new_index,:,:] = v1\n new_index += 1\n\n return new_latents\n \n def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):\n _down_block_res_samples = []\n _mid_block_res_sample = []\n for i in np.concatenate(np.array(context)):\n _down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])\n _mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])\n down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]\n for res_t in _down_block_res_samples:\n for i, res in enumerate(res_t):\n down_block_res_samples[i].append(res)\n down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]\n mid_block_res_sample = torch.cat(_mid_block_res_sample)\n \n # reshape controlnet output to match the unet3d inputs\n b = b // 2 if do_classifier_free_guidance else b\n _down_block_res_samples = []\n for sample in down_block_res_samples:\n sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n sample = sample.repeat(2, 1, 1, 1, 1)\n _down_block_res_samples.append(sample)\n down_block_res_samples = _down_block_res_samples\n mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)\n if do_classifier_free_guidance:\n mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)\n \n return down_block_res_samples, mid_block_res_sample\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n video_length: Optional[int],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n controlnet_condition: list = None,\n controlnet_conditioning_scale: float = 1.0,\n context_frames: int = 16,\n context_stride: int = 1,\n context_overlap: int = 4,\n context_batch_size: int = 1, \n context_schedule: str = \"uniform\",\n init_latents: Optional[torch.FloatTensor] = None,\n num_actual_inference_steps: Optional[int] = None,\n appearance_encoder = None, \n reference_control_writer = None,\n reference_control_reader = None,\n source_image: str = None,\n decoder_consistency = None, \n **kwargs,\n ):\n \"\"\"\n New args:\n - controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet\n - controlnet_conditioning_scale : conditioning scale for controlnet\n - init_latents : initial latents to begin with (used along with invert())\n - num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps) \n \"\"\"\n controlnet = self.controlnet\n\n # Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # Define call parameters\n # batch_size = 1 if isinstance(prompt, str) else len(prompt)\n batch_size = 1\n if latents is not None:\n batch_size = latents.shape[0]\n if isinstance(prompt, list):\n batch_size = len(prompt)\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # Encode input prompt\n prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size\n if negative_prompt is not None:\n negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size \n text_embeddings = self._encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n text_embeddings = torch.cat([text_embeddings] * context_batch_size)\n "},"next_line":{"kind":"string","value":" reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)"},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-11-21 08:33:54+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5396,"cells":{"repo_name":{"kind":"string","value":"eth-sri/language-model-arithmetic"},"file_path":{"kind":"string","value":"src/model_arithmetic/evaluation.py"},"context":{"kind":"list like","value":[{"identifier":"BaseClass","path":"src/model_arithmetic/base.py","snippet":"class BaseClass:\n \"\"\"\n Base class for providing a serialization and deserialization mechanism.\n \"\"\"\n def __init__(self, **kwargs):\n \"\"\"\n Instantiates the base class with keyword arguments\n \n Args:\n kwargs (dict): Keyword arguments\n \"\"\"\n self.kwargs = kwargs\n self.__dict__.update(kwargs)\n\n def generate_list_settings(self, list_):\n \"\"\"\n Converts provided list to a normalized list that can be stored as a json object to serialize.\n \n Args:\n list_ (List): List to be converted\n Returns\n Transformed normal list\n \"\"\"\n normal_list = []\n for item in list_:\n if isinstance(item, BaseClass):\n normal_list.append(item.generate_settings())\n elif isinstance(item, dict):\n normal_list.append(self.generate_kwarg_setting(item))\n elif isinstance(item, (tuple, list)):\n normal_list.append(self.generate_list_settings(item))\n else:\n normal_list.append(item)\n return normal_list\n\n def generate_kwarg_setting(self, kwargs):\n \"\"\"\n Converts provided keyword arguments to normal kwargs in terms of serialization.\n\n Args:\n kwargs (dict): kwargs to be converted.\n \"\"\"\n normal_kwargs = dict()\n for kwarg in kwargs:\n if isinstance(kwargs[kwarg], BaseClass):\n normal_kwargs[kwarg] = kwargs[kwarg].generate_settings()\n elif isinstance(kwargs[kwarg], (list, tuple)):\n normal_kwargs[kwarg] = self.generate_list_settings(kwargs[kwarg])\n elif isinstance(kwargs[kwarg], dict):\n normal_kwargs[kwarg] = self.generate_kwarg_setting(kwargs[kwarg])\n else:\n normal_kwargs[kwarg] = kwargs[kwarg]\n \n return normal_kwargs\n\n\n def generate_settings(self):\n \"\"\"\n Generates settings for the instance of the BaseClass.\n\n Returns\n Settings in dictionary format.\n \"\"\"\n settings = {\n \"class\": self.__class__.__name__, \n **self.generate_kwarg_setting({kwarg: self.__dict__[kwarg] for kwarg in self.kwargs}), \n }\n return settings\n \n def save(self, path):\n \"\"\"\n Saves the generated settings into a JSON file at a specified path.\n \n Args:\n path (string): The file path at which the settings have to be saved.\n \"\"\"\n settings = self.generate_settings()\n\n if os.path.dirname(path) != \"\":\n os.makedirs(os.path.dirname(path), exist_ok=True)\n \n with open(path, \"w\") as f:\n json.dump(settings, f, indent=2)\n\n @classmethod\n def get_all_subclasses(cls):\n \"\"\"\n Returns all subclasses of the BaseClass.\n \"\"\"\n all_subclasses = []\n\n for subclass in cls.__subclasses__():\n all_subclasses.append(subclass)\n all_subclasses.extend(subclass.get_all_subclasses())\n\n return all_subclasses\n\n @staticmethod\n def find_class(cls_name):\n \"\"\"\n Searches for a class that matches the given class name.\n\n Args:\n cls_name (string): Class name to be matched\n \"\"\"\n for possible_cls in BaseClass.get_all_subclasses():\n if possible_cls.__name__ == cls_name:\n return possible_cls\n return None\n\n @staticmethod\n def load_from_list_settings(list_):\n \"\"\"\n Deserializes the list saved settings to instantiate the objects.\n\n Args:\n list_ (List): List of saved settings\n \"\"\"\n output_list = []\n for item in list_:\n if isinstance(item, dict):\n output_list.append(BaseClass.load_from_dict(item))\n elif isinstance(item, (tuple, list)):\n output_list.append(BaseClass.load_from_list_settings(item))\n else:\n output_list.append(item)\n\n return output_list\n \n @staticmethod\n def load_from_dict(dict_):\n \"\"\"\n Deserializes the dictionary saved settings to instantiate the objects.\n\n Args:\n dict_ (dict): Dictionary containing saved settings\n \"\"\"\n other_class = BaseClass.find_class(dict_.get(\"class\", None))\n if other_class is not None:\n return other_class.load_from_settings(dict_)\n \n output_dict = dict()\n for key in dict_:\n if isinstance(dict_[key], dict):\n output_dict[key] = BaseClass.load_from_dict(dict_[key])\n elif isinstance(dict_[key], (tuple, list)):\n output_dict[key] = BaseClass.load_from_list_settings(dict_[key])\n else:\n output_dict[key] = dict_[key]\n\n return output_dict\n\n @staticmethod\n def load_from_settings(settings):\n \"\"\"\n Deserializes the saved settings to instantiate the object.\n\n Args:\n settings (dict): Saved settings\n \"\"\"\n cls = BaseClass.find_class(settings[\"class\"])\n\n if cls is None:\n logger.error(f\"Could not find class {settings['class']} when loading class.\")\n return None\n\n kwargs = dict()\n for kwarg in settings:\n if kwarg == \"class\":\n continue\n if isinstance(settings[kwarg], dict):\n kwargs[kwarg] = BaseClass.load_from_dict(settings[kwarg])\n elif isinstance(settings[kwarg], (tuple, list)):\n kwargs[kwarg] = BaseClass.load_from_list_settings(settings[kwarg])\n else:\n kwargs[kwarg] = settings[kwarg]\n\n return cls(**kwargs)\n\n @classmethod\n def _load(cls, path, **kwargs):\n \"\"\"\n Loads the settings from the JSON file at the specified path.\n \n Args:\n path (string): The file path from which the settings have to be loaded.\n kwargs (dict): Additional keywords arguments\n \"\"\"\n with open(path, \"r\") as f:\n settings = json.load(f)\n for kwarg in kwargs:\n settings[kwarg] = kwargs[kwarg]\n return cls.load_from_settings(settings)\n\n @staticmethod\n def load(path, **kwargs):\n \"\"\"\n Loads the settings of the class from the JSON file.\n\n Args:\n path (string): The file path from which the class settings have to be loaded.\n kwargs (dict): Additional keywords arguments\n \"\"\"\n with open(path, \"r\") as f:\n settings = json.load(f)\n cls = BaseClass.find_class(settings[\"class\"])\n return cls._load(path, **kwargs)\n\n def __str__(self) -> str:\n \"\"\"\n Returns a string representation of the class object.\n \"\"\"\n return f\"{self.__class__.__name__}({self.kwargs})\"\n \n def __eq__(self, o: object) -> bool:\n \"\"\"\n Checks whether the provided object is equal to the current object.\n\n Args:\n o (object): Object to compare\n \"\"\"\n if not isinstance(o, BaseClass):\n return False\n \n other_settings = o.generate_settings()\n settings = self.generate_settings()\n\n return other_settings == settings"},{"identifier":"CustomDataset","path":"src/model_arithmetic/dataset.py","snippet":"class CustomDataset(Dataset):\n \"\"\"\n A custom PyTorch Dataset class for tokenized sequence data.\n\n Uses a tokenizer to convert text data from a DataFrame to input_ids (tokens), \n and optionally attaches label data if present in the DataFrame.\n \"\"\"\n def __init__(self, tokenizer, df, max_tokens=128, min_tokens=1, random_cutoff=False):\n \"\"\"\n Initializes the CustomDataset object.\n\n Args:\n tokenizer (Tokenizer): The tokenizer to be used for the text data.\n df (pandas.DataFrame): DataFrame containing the text data, and optionally labels.\n max_tokens (int, optional): Maximum number of tokens per sequence. Defaults to 128.\n min_tokens (int, optional): Minimum number of tokens per sequence. Defaults to 1.\n random_cutoff (bool, optional): Whether to randomly cut off the number of tokens per sequence. Defaults to False.\n \"\"\"\n super().__init__()\n data = df.dropna()\n self.tokenized_dataset = [\n tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=max_tokens).input_ids.view(-1) for sentence in tqdm(data[\"text\"].tolist())\n ]\n\n self.df = data\n self.has_labels = \"label\" in data.columns\n self.min_tokens = min_tokens\n self.labels = None\n if self.has_labels:\n self.labels = data[\"label\"].values\n \n self.random_cutoff = random_cutoff\n\n def __len__(self):\n \"\"\"\n Returns the length of the tokenized dataset, \n i.e., the number of tokenized sequences.\n \n Returns:\n int: Number of tokenized sequences.\n \"\"\"\n return len(self.tokenized_dataset)\n\n def __getitem__(self, idx):\n \"\"\"\n Fetches an item from the dataset at the given index.\n\n If labels are available, also fetches the associated label.\n If `random_cutoff` is true, may truncate sequence length randomly.\n\n Args:\n idx (int): Index of the required sequence.\n\n Returns:\n dict: A dictionary with the following structure-\n {\n \"input_ids\": torch.Tensor (Tokenized sequence),\n \"labels\": torch.Tensor (Associated label, if available)\n }\n \"\"\"\n cutoff = len(self.tokenized_dataset[idx])\n if self.random_cutoff:\n cutoff = torch.randint(min(cutoff, self.min_tokens), cutoff + 1, (1,)).item()\n \n if not self.has_labels:\n return {\"input_ids\": self.tokenized_dataset[idx][:cutoff]}\n else:\n return {\"input_ids\": self.tokenized_dataset[idx][:cutoff], \"labels\": torch.tensor([self.labels[idx]], dtype=torch.long)}"},{"identifier":"load_model","path":"src/model_arithmetic/basic_model_loader.py","snippet":"def load_model(dir_or_model, classification=False, token_classification=False, return_tokenizer=False, dtype=torch.bfloat16, load_dtype=True, \n rl=False, peft_config=None):\n \"\"\"\n This function is used to load a model based on several parameters including the type of task it is targeted to perform.\n \n Args:\n dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.\n\n classification (bool): If True, loads the model for sequence classification.\n\n token_classification (bool): If True, loads the model for token classification.\n\n return_tokenizer (bool): If True, returns the tokenizer along with the model.\n\n dtype: The data type that PyTorch should use internally to store the model’s parameters and do the computation.\n\n load_dtype (bool): If False, sets dtype as torch.float32 regardless of the passed dtype value.\n\n rl (bool): If True, loads model specifically designed to be used in reinforcement learning environment.\n\n peft_config: Configuration details for Peft models. \n \n Returns:\n It returns a model for the required task along with its tokenizer, if specified.\n \"\"\"\n log(logger.debug, f\"Loading model for {dir_or_model} with {classification}, {dtype}, {load_dtype}\")\n is_lora_dir = os.path.isfile(os.path.join(dir_or_model, \"adapter_config.json\"))\n\n if not load_dtype:\n dtype = torch.float32\n\n if is_lora_dir:\n loaded_json = json.load(open(os.path.join(dir_or_model, \"adapter_config.json\"), \"r\"))\n model_name = loaded_json[\"base_model_name_or_path\"]\n else:\n model_name = dir_or_model\n\n original_model_name = model_name\n\n if classification:\n model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\"auto\") # to investigate: calling torch_dtype here fails.\n elif token_classification:\n model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\"auto\")\n elif rl:\n model = AutoModelForCausalLMWithValueHead.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, \n peft_config=peft_config, device_map=\"auto\")\n else:\n if model_name.endswith(\"GPTQ\") or model_name.endswith(\"GGML\"):\n model = AutoGPTQForCausalLM.from_quantized(model_name,\n use_safetensors=True,\n trust_remote_code=True,\n # use_triton=True, # breaks currently, unfortunately generation time of the GPTQ model is quite slow\n quantize_config=None, device_map=\"auto\")\n else:\n model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\"auto\")\n\n if is_lora_dir:\n model = PeftModel.from_pretrained(model, dir_or_model)\n \n try:\n tokenizer = load_tokenizer(original_model_name)\n model.config.pad_token_id = tokenizer.pad_token_id\n except Exception:\n pass\n if return_tokenizer:\n return model, load_tokenizer(original_model_name)\n return model"},{"identifier":"load_tokenizer","path":"src/model_arithmetic/basic_model_loader.py","snippet":"def load_tokenizer(dir_or_model):\n \"\"\"\n This function is used to load the tokenizer for a specific pre-trained model.\n \n Args:\n dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.\n \n Returns:\n It returns a tokenizer that can convert text to tokens for the specific model input.\n \"\"\"\n log(logger.debug, f\"Loading tokenizer for {dir_or_model}\")\n\n is_lora_dir = os.path.isfile(os.path.join(dir_or_model, \"adapter_config.json\"))\n\n if is_lora_dir:\n loaded_json = json.load(open(os.path.join(dir_or_model, \"adapter_config.json\"), \"r\"))\n model_name = loaded_json[\"base_model_name_or_path\"]\n else:\n model_name = dir_or_model\n \n if os.path.isfile(os.path.join(dir_or_model, \"config.json\")):\n loaded_json = json.load(open(os.path.join(dir_or_model, \"config.json\"), \"r\"))\n model_name = loaded_json[\"_name_or_path\"]\n\n tokenizer = AutoTokenizer.from_pretrained(model_name)\n\n if tokenizer.pad_token is None:\n log(logger.debug, \"Setting pad token to eos token\")\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.pad_token_id = tokenizer.eos_token_id\n \n return tokenizer"},{"identifier":"ModelArithmetic","path":"src/model_arithmetic/model_arithmetic.py","snippet":"class ModelArithmetic(PreTrainedModel):\n \"\"\"\n Main class for prompt arithmetic. Handles the generation of text based on the formula.\n \"\"\"\n SAVE_FILE = \"prompt_arithmetic.json\"\n _supports_sdpa = True\n\n def __init__(self, formula : Operator, default_model : str = None, dtype=torch.bfloat16, intermediate_argmax : bool = False, epsilon = 1e-12, \n retroactive_operators = [], calculate_statistics=True, needs_input_tokens_lm_eval=False, lm_eval_task=None, tokenizer=None):\n \"\"\"Initializes the prompt arithmetic model.\n\n Args:\n formula (Operator): The formula for which generations need to be made.\n default_model (str, optional): Default model for RunnableOperators that don't have a model associated with them. Defaults to None.\n dtype (torch.dtype, optional): Dtype of the models to load by default. Defaults to torch.bfloat16.\n intermediate_argmax (bool, optional): Something unimportant that was tried out, but now deprecated. Defaults to False.\n epsilon (float, optional): Just some small value. Defaults to 1e-12.\n retroactive_operators (list, optional): The retroactive operators that need to be applied. Defaults to [].\n calculate_statistics (bool, optional): Whether or not to calculate some statistics, can be a tad bit expensive. Defaults to True.\n needs_input_tokens_lm_eval (bool, optional): Whether or not lm eval is used and whether or not the task needs the input tokens. Defaults to False. Only set to true for an lm eval task.\n lm_eval_task (str, optional): Name of the lm eval task. Defaults to None.\n tokenizer (transformers.tokenization_utils_base.PreTrainedTokenizerBase, optional): Tokenizer to use. Defaults to None.\n \"\"\"\n self.formula = formula.clone()\n\n self.default_model = default_model\n self.loaded_models = dict()\n self.model_prediction_history = [] # keeps track of the RunnableOperators predictions for each token (that hasn't finished computing)\n self.logprobs_history = [] # keeps track of the current probability distribution for which each token has been drawn\n self.model_last_token_prediction = [] # keeps track of the last token that has been predicted for each RunnableOperator\n \n self.output_type = namedtuple(\"ModelArithmeticOutput\", [\"logits\", \"logprobs_per_model\"])\n self.intermediate_argmax = intermediate_argmax\n self.retroactive_operators = retroactive_operators\n self.calculate_statistics = calculate_statistics\n\n self.runnable_operators = []\n for runnable_operator in self.formula.runnable_operators():\n if not any([runnable_operator.same_operator(output) for output in self.runnable_operators]):\n self.runnable_operators.append(runnable_operator)\n \n\n # sort the prompts by speculative factor, putting the one with highest speculative factor first\n # => run model with highest speculative factor first, since otherwise the computation might be wasted for the first ones\n # however, we first need to sort by run_priority and then within that by speculative factor\n self.runnable_operators = sorted(self.runnable_operators, key=lambda runnable_operator: (runnable_operator.run_priority, runnable_operator.speculative_factor), reverse=True)\n \n self.load_all_models(dtype=dtype)\n if self.default_model not in self.loaded_models:\n for runnable_operator in self.runnable_operators:\n if isinstance(runnable_operator, PromptedLLM) and runnable_operator.model is not None:\n self.default_model = runnable_operator.model\n break\n if self.default_model is None:\n raise ValueError(\"Default model must be specified if not specified in an llm prompt\")\n\n self.config = self.loaded_models[str(self.default_model)].config\n\n if tokenizer is None:\n self.tokenizer = load_tokenizer(self.default_model)\n else:\n self.tokenizer = tokenizer\n \n self.init_runnable_operators()\n \n self.model_input_tokens = {\n runnable_operator.id(): TokenizedInput(runnable_operator, \n runnable_operator.model, \n self.loaded_models[str(runnable_operator.model)].config,\n self.tokenizer) \n for runnable_operator in self.runnable_operators\n }\n \n self.init_monitor()\n \n self.epsilon = epsilon\n \n self.word_size = len(self.tokenizer)\n \n if Compatibility is not None:\n self.lm_eval_compatibility = Compatibility(\n task_name=lm_eval_task,\n needs_input_tokens_lm_eval=needs_input_tokens_lm_eval,\n tokenizer=self.tokenizer,\n device=self.device,\n max_length=get_max_length(self.config),\n )\n else:\n self.lm_eval_compatibility = None\n \n super().__init__(self.config)\n \n def init_monitor(self):\n \"\"\"\n Initializes the monitor for the prompt arithmetic model.\n \"\"\"\n self.monitor = Monitor(self.runnable_operators)\n \n def init_runnable_operators(self):\n \"\"\"Initializes the runnable operators. This is done after the models have been loaded, because the models are needed for the runnable operators.\n \"\"\"\n for runnable_operator in self.runnable_operators:\n if runnable_operator.model is None:\n runnable_operator.model = self.default_model\n runnable_operator.initialize_after_model_set()\n\n def load_all_models(self, dtype=torch.bfloat16):\n \"\"\"Loads all the models that are needed for the runnable operators. Models are never loaded twice.\n\n Args:\n dtype (torch.dtype, optional): Default Dtype of the models. Defaults to torch.bfloat16.\n \"\"\"\n if self.default_model is None:\n for runnable_operator in self.runnable_operators:\n if isinstance(runnable_operator, PromptedLLM) and runnable_operator.model is not None:\n self.default_model = str(runnable_operator.model)\n break\n \n for runnable_operator in self.runnable_operators:\n if runnable_operator.model is None:\n assert self.default_model is not None, \"Default model must be specified if not specified in prompt\"\n runnable_operator.model = self.default_model\n if runnable_operator.model not in self.loaded_models:\n model = runnable_operator.load_model(dtype=dtype)\n model.eval()\n if model is not None:\n self.loaded_models[str(runnable_operator.model)] = model\n \n if len(self.loaded_models) == 0:\n assert self.default_model is not None, \"Required to at least have one model, for now\"\n self.loaded_models[str(self.default_model)] = load_model(self.default_model, dtype=dtype)\n \n @property\n def device(self):\n \"\"\"Device of the default model. Needed for compatibility with lm_eval\n\n Returns:\n torch.device: Device of the default model.\n \"\"\"\n return self.loaded_models[str(self.default_model)].device\n\n def save_pretrained(self, path : str):\n \"\"\"Saves the model to the specified path.\n\n Args:\n path (str): Path to which to save the model\n \"\"\"\n os.makedirs(path, exist_ok=True)\n all_settings = {\n \"formula\": self.formula.generate_settings(),\n \"default_model\": self.default_model,\n }\n\n with open(os.path.join(path, self.SAVE_FILE), \"w\") as f:\n json.dump(all_settings, f, indent=4, sort_keys=True)\n\n @classmethod\n def from_pretrained(cls, path : str, dtype=torch.bfloat16):\n \"\"\"Loads the model from the specified path.\n\n Args:\n path (str): Path from which to load the model\n dtype (torch.dtype, optional): Default dtype for the models. Defaults to torch.bfloat16.\n\n Returns:\n ModelArithmetic: model arithmetic model\n \"\"\"\n with open(os.path.join(path, cls.SAVE_FILE), \"r\") as f:\n all_settings = json.load(f)\n all_settings[\"formula\"] = Operator.load_from_settings(all_settings[\"formula\"])\n return cls(**all_settings, dtype=dtype)\n\n \n def forward_model(self, runnable_operator, continuation_tokens, model_new_tokens=None, use_cache=False, do_speculation=False):\n \"\"\"Runs a specifc runnable operator on the continuation tokens.\n\n Args:\n runnable_operator (RunnableOperator): The runnable operator to run.\n continuation_tokens (list[list[int]]): List of tokens that need to be continued. The prompt is not included in these tokens\n model_new_tokens (list[int], optional): New tokens for the model. Defaults to None.\n use_cache (bool, optional): Whether or not to allow the model to use cache (eg key-value storage for an LLM). Defaults to False.\n do_speculation (bool, optional): Whether or not to do speculation sampling. Defaults to False.\n\n Returns:\n torch.tensor: logprobs of the model, one logprob distribution for each new token in each sample\n \"\"\"\n start_time = time.time()\n \n tokenized_input_creator = self.model_input_tokens[runnable_operator.id()]\n tokenized_inputs = tokenized_input_creator.add_continuation_tokens(continuation_tokens)\n tokenized_only_input = tokenized_input_creator.get_only_input_tokens()\n \n was_none = model_new_tokens is None\n \n if was_none:\n model_new_tokens = torch.tensor([len(continuation_tokens[i]) + 1 for i in range(len(continuation_tokens))])\n \n if len(self.model_prediction_history) < len(continuation_tokens):\n new_prediction_history = [dict() for _ in range(len(continuation_tokens))]\n else:\n new_prediction_history = [self.model_prediction_history[i].get(self.max_index_prediction_history(i), dict()) for i in range(len(continuation_tokens))]\n \n logprobs = runnable_operator.run(\n loaded_models=self.loaded_models,\n tokenized_inputs=tokenized_inputs,\n model_new_tokens=model_new_tokens,\n new_prediction_history=new_prediction_history,\n other_tokenizer=self.tokenizer,\n tokenized_only_input=tokenized_only_input, \n use_cache=use_cache,\n do_speculation=do_speculation\n )\n \n logprobs = [logprob.to(self.device) for logprob in logprobs]\n \n if was_none:\n logprobs = torch.stack(logprobs, dim=0)\n\n self.monitor.add_result(element=time.time() - start_time, runnable_operator=runnable_operator)\n return logprobs\n \n def group_complete(self, model_history):\n \"\"\"Checks which groups of runnable operators have been completely calculated and which haven't.\n\n Args:\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\n\n Returns:\n dict[bool]: Dict mapping the group to whether it has been completely calculated or not\n \"\"\"\n # everything that is a group needs to be either all calculated or all not calculated\n group_calculated = dict()\n groups = set([runnable_operator.group for runnable_operator in self.runnable_operators if runnable_operator.group is not None])\n completed_groups = {group: True for group in groups}\n \n for runnable_operator in self.runnable_operators:\n if runnable_operator.group is not None:\n is_calculated = model_history.get(runnable_operator.id()) is not None\n if runnable_operator.group not in group_calculated:\n group_calculated[runnable_operator.group] = is_calculated\n elif group_calculated[runnable_operator.group] != is_calculated:\n completed_groups[runnable_operator.group] = False\n return completed_groups\n \n def group_model_history(self, model_history):\n \"\"\"Sets the model history on which to evaluate the formula based on the groups. Removes predictions if the group hasn't been completely calculated yet.\n\n Args:\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\n\n Returns:\n dict: Adjusted dict mapping\n \"\"\"\n completed_groups = self.group_complete(model_history)\n grouped_model_history = dict()\n for runnable_operator in self.runnable_operators:\n if runnable_operator.group is None or completed_groups[runnable_operator.group]:\n grouped_model_history[runnable_operator.id()] = model_history[runnable_operator.id()]\n else:\n grouped_model_history[runnable_operator.id()] = None\n \n return grouped_model_history\n \n def create_sample_logprobs(self, logprobs, temperature, top_k, top_p):\n \"\"\"Creates the logprobs for each token in each sample.\n\n Args:\n logprobs (torch.tensor): Logprobs of the model\n temperature (float): temperature to use\n top_k (int): top_k to use\n top_p (float): top_p to use\n\n Returns:\n torch.tensor: Logprobs for each token in each sample\n \"\"\"\n if temperature == 0:\n logprobs_argmax = torch.argmax(logprobs, dim=-1)\n logprobs = torch.nn.functional.one_hot(logprobs_argmax, num_classes=logprobs.shape[-1]).float()\n return logprobs\n logprobs = logprobs / temperature\n logprobs = top_k_top_p_filtering(logprobs.unsqueeze(0), top_k=top_k, top_p=top_p)\n return torch.softmax(logprobs, dim=-1).squeeze()\n \n \n\n def process_logprobs(self, model_history):\n \"\"\"Processes model history to get the probability distribution for the token.\n\n Args:\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\n\n Returns:\n _type_: _description_\n \"\"\"\n init_time = time.time()\n logprobs_normalized = self.formula.evaluate(model_history)\n self.monitor.add_result(element=time.time() - init_time, indicator=\"formula_evaluation\")\n if not torch.is_tensor(logprobs_normalized):\n return None\n # logprobs_normalized = logprobs_normalized / temperature\n # logprobs_normalized = top_k_top_p_filtering(logprobs_normalized.unsqueeze(0), top_k=top_k, top_p=top_p)\n return logprobs_normalized\n \n def run_retroactive_operators(self, index, tokenized_sentence, temperature, top_k, top_p):\n \"\"\"Runs the retroactive operators on the tokenized sentence. \n\n Args:\n index (int): Index of the sentence in the current batch\n tokenized_sentence (list[int]): Tokenized sentence\n temperature (float): temperature to use\n top_k (int): top_k to use\n top_p (float): top_p to use\n\n Returns:\n list[int]: Adjusted tokenized sentence based on the retroactive operators and whether they accepted it.\n \"\"\"\n for operator in self.retroactive_operators:\n accepted = operator.accept(tokenized_sentence, self.tokenizer)\n if accepted < 0:\n not_accepted_token = tokenized_sentence[accepted]\n self.clear_model_prediction_history(index, tokenized_sentence, from_=len(tokenized_sentence) + accepted)\n tokenized_sentence = tokenized_sentence[:len(tokenized_sentence) + accepted]\n \n self.logprobs_history[index][len(tokenized_sentence)][not_accepted_token] = -torch.inf\n \n if torch.all(self.logprobs_history[index][len(tokenized_sentence)] == -torch.inf):\n self.logprobs_history[index][len(tokenized_sentence)] = torch.zeros_like(self.logprobs_history[index][len(tokenized_sentence)])\n \n probs_to_sample = self.create_sample_logprobs(\n self.logprobs_history[index][len(tokenized_sentence)],\n temperature=temperature,\n top_k=top_k,\n top_p=top_p\n )\n new_token = torch.multinomial(probs_to_sample, 1).item()\n \n tokenized_sentence.append(new_token)\n return self.run_retroactive_operators(index, tokenized_sentence, temperature, top_k, top_p)\n \n return tokenized_sentence\n \n def speculation_sample(self, token, previous_models_probs, new_models_probs):\n \"\"\"Sample a token based on the previous and new model probabilities in the speculative sampling way. Also returns whether the token was accepted or not.\n\n Args:\n token (int): Token that is currently selected\n previous_models_probs (torch.tensor): Model probabilities of the previous models\n new_models_probs (torch.tensor): Model probabilities of the new models\n\n Returns:\n (int, bool): New token and whether or not the input token was accepted\n \"\"\"\n acceptance_prob = torch.minimum(torch.tensor(1.0), new_models_probs[token] / (previous_models_probs[token] + torch.tensor(self.epsilon)))\n # TODO: the next line is taking an enormous amount of time because of asynchronous computing on gpu's and requiring it to be returned immediately\n # Therefore do batch processing\n acceptance_prob = float(acceptance_prob)\n self.monitor.add_result(element=float(acceptance_prob), indicator=\"acceptance_prob\")\n # self.monitor.add_result(element=self.entropy(previous_models_probs).item(), indicator=\"entropy_previous\")\n # self.monitor.add_result(element=previous_models_probs[token].item(), indicator=\"probability_previous\")\n\n if torch.rand(1) < acceptance_prob:\n return token, True\n else:\n new_proba_distrib = torch.relu(new_models_probs - previous_models_probs)\n new_proba_distrib /= torch.sum(new_proba_distrib)\n new_token = torch.multinomial(new_proba_distrib, 1).item()\n return new_token, False\n \n \n def add_new_result(self, generated_tokens, num_new_tokens, runnable_operator, new_model_logprobs, top_p, top_k, temperature):\n \"\"\"Adds a new run of a runnable operator to the model prediction history. Also does speculation sampling if needed.\n\n Args:\n generated_tokens (list[list[int]]): Currently generated tokens by the model\n num_new_tokens (list[int]): Number of new tokens for each sample in the batch\n runnable_operator (RunnableOperator): Runnable operator that was run\n new_model_logprobs (List[torch.tensor]): Output of the run function of the runnable operator\n top_p (flaot): top_p to use\n top_k (int): top_k to use\n temperature (float): temperature to use\n\n Returns:\n list[bool]: For each sample in the batch, whether all tokens in that sample were kept or not\n \"\"\"\n all_kept = []\n for i in range(len(generated_tokens)):\n n_generated_tokens = len(generated_tokens[i])\n kept = True\n for n_token in range(n_generated_tokens - num_new_tokens[i] + 1, n_generated_tokens + 1):\n # initialize the model prediction history\n self.model_prediction_history[i][n_token] = self.model_prediction_history[i].get(n_token, \n {runnable_operator.id(): None for runnable_operator in self.runnable_operators})\n # check if we need to do speculation sampling, only needed when a previous token was sampled\n do_speculation_sample = n_token < n_generated_tokens\n \n # speculation sampling not needed if the model was run before \n if self.model_prediction_history[i][n_token][runnable_operator.id()] is not None:\n do_speculation_sample = False\n \n # speculation sampling not needed if all models have not been run yet: this is the first model on this token\n if all([logprob is None for logprob in self.model_prediction_history[i][n_token].values()]):\n do_speculation_sample = False\n # This means that this token was already fully accepted, so we can just continue (can happen if batch_size > 1 or when end is triggered)\n if self.max_index_prediction_history(i) > n_token:\n continue\n \n # add the new model logprobs\n self.model_prediction_history[i][n_token][runnable_operator.id()] = new_model_logprobs[i][-n_generated_tokens + n_token + num_new_tokens[i] - 1]\n \n group_model_history = self.group_model_history(self.model_prediction_history[i][n_token])\n # group_model_history needs to be separately checked, since it could be that the group is not yet fully calculated\n # also allow no logprobs runnable operators (would lead to errors) if the formula is not finished yet (if it is finished, you need to)\n if all([logprob is None for logprob in group_model_history.values()]) or (not runnable_operator.outputs_logprobs and not self.formula.is_finished(group_model_history)):\n continue\n \n # process the logprobs\n new_model_probs = self.process_logprobs(group_model_history)\n \n if self.intermediate_argmax and not self.formula.is_finished(group_model_history):\n argmax_el = torch.argmax(new_model_probs)\n new_model_probs = torch.zeros_like(new_model_probs)\n new_model_probs[argmax_el] = 1.0\n \n if do_speculation_sample:\n if self.calculate_statistics:\n self.monitor.add_result(self.expected_acceptance_prob(self.create_sample_logprobs(new_model_probs, temperature, top_k, top_p), \n self.create_sample_logprobs(self.logprobs_history[i].get(n_token), temperature, top_k, top_p)), \n indicator=\"expected_acceptance_prob\", runnable_operator=runnable_operator)\n\n new_token, kept = self.speculation_sample(\n token = generated_tokens[i][n_token],\n previous_models_probs=self.create_sample_logprobs(self.logprobs_history[i][n_token], temperature, top_k, top_p),\n new_models_probs=self.create_sample_logprobs(new_model_probs, temperature, top_k, top_p), \n )\n if n_token in self.model_prediction_history[i]:\n self.logprobs_history[i][n_token] = new_model_probs\n \n if not kept:\n # if not kept, we change the generated tokens and remove the model prediction history after that token\n generated_tokens[i][n_token] = new_token\n generated_tokens[i] = generated_tokens[i][:n_token + 1]\n self.clear_model_prediction_history(i, generated_tokens[i], from_=n_token)\n self.trigger_end[i] = False\n \n elif n_token in self.model_prediction_history[i]:\n self.logprobs_history[i][n_token] = new_model_probs\n \n if not kept:\n break\n \n all_kept.append(kept)\n return all_kept\n \n\n def clear_model_prediction_history(self, index, generated_tokens_index, from_=-1):\n \"\"\"Clears the model prediction history for a specific sample in the batch. First deletes all history of finished tokens, then \n deletes history of tokens that were prediction, but then got removed because of speculation\n\n Args:\n index (int): index of the sample in the batch\n generated_tokens_index (list[int]): Generated tokens at the index\n from_ (int, optional): From which token to delete all the history. Defaults to -1.\n \"\"\"\n all_indices = list(self.model_prediction_history[index].keys())\n for token in all_indices:\n all_none = all([logprob is None for logprob in self.model_prediction_history[index][token].values()])\n finished = self.formula.is_finished(self.model_prediction_history[index][token])\n if all_none or finished or (from_ != -1 and token > from_):\n if finished and len(generated_tokens_index) > token and self.calculate_statistics:\n self.add_monitor_token_probs(generated_tokens_index[token], self.model_prediction_history[index][token], self.logprobs_history[index].get(token))\n \n if finished:\n for model_index in range(len(self.model_last_token_prediction)):\n self.model_last_token_prediction[model_index][index] = max(token + 1, self.model_last_token_prediction[model_index][index])\n \n del self.model_prediction_history[index][token]\n \n if from_ > -1:\n for model_index in range(len(self.model_last_token_prediction)):\n self.model_last_token_prediction[model_index][index] = min(from_ + 1, self.model_last_token_prediction[model_index][index])\n \n def max_index_prediction_history(self, index):\n \"\"\"Gets the max index of the model prediction history for a specific runnable operator \n\n Args:\n index (int): index of runnable operator in the list of runnable operators\n\n Returns:\n int: max index of its prediction\n \"\"\"\n keys = list(self.model_prediction_history[index].keys())\n if len(keys) == 0:\n return 0\n return max(self.model_prediction_history[index].keys())\n\n def normal_sample(self, probs):\n \"\"\"Samples from a probability distribution\n\n Args:\n probs (torch.tensor): Probability distribution\n\n Returns:\n int: Sampled token\n \"\"\"\n out = torch.multinomial(probs, 1)\n return out\n \n def KL_divergence(self, p, q):\n \"\"\"Compuates KL divergence between two probability distributions\n\n Args:\n p (torch.tensor): probability distribution\n q (torch.tensor): probability distribution\n\n Returns:\n float: KL divergence\n \"\"\"\n return torch.sum(p * torch.log((p + self.epsilon) / (q + self.epsilon)))\n \n def entropy(self, p):\n \"\"\"Computes entropy of a probability distribution\n\n Args:\n p (torch.tensor): probability distribution\n\n Returns:\n float: entropy\n \"\"\"\n return -torch.sum(p * torch.log(p + self.epsilon))\n \n def expected_acceptance_prob(self, p, q):\n \"\"\"\n Calculates the expected acceptance probability of speculative sampling.\n \n Args:\n p (torch.tensor): probability distribution\n q (torch.tensor): probability distribution\n \"\"\"\n return 1 - 1 / 2 * torch.sum(torch.abs(q - p)).item()\n \n def add_monitor_token_probs(self, token, history, history_logprobs):\n \"\"\"Adds some token probabilities to the monitor\n\n Args:\n token (int): Samples token\n history (dict): Model prediction history at the specific index where the token was drawn from\n history_logprobs (torch.tensor): LogProbability distribution from which the token was drawn.\n \"\"\"\n for runnable_operator in self.runnable_operators:\n if runnable_operator.is_finished(history) and runnable_operator.outputs_logprobs:\n evaluated = runnable_operator.evaluate(history)\n self.monitor.add_result(element=torch.softmax(evaluated, dim=-1)[token].item(), runnable_operator=runnable_operator, indicator=\"token_prob\")\n # add logprob as well\n self.monitor.add_result(element=max(evaluated[token].item(), np.log(self.epsilon)), runnable_operator=runnable_operator, indicator=\"token_logprob\")\n # add KL divergence\n if history_logprobs is not None:\n self.monitor.add_result(element=self.KL_divergence(torch.softmax(history_logprobs, dim=-1), torch.softmax(evaluated, dim=-1)).item(), \n runnable_operator=runnable_operator, indicator=\"KL_divergence\")\n \n self.monitor.add_result(element=self.entropy(torch.softmax(history_logprobs, dim=-1)).item(), indicator=\"entropy\")\n\n def next_token_speculative(self, continuation_tokens, \n top_p=1.0, top_k=0, temperature=1.0, speculation=True, use_cache=True):\n \"\"\"Continues one step in the generation process by running the runnable operators that need to be run and then sampling from the probability distribution.\n\n Args:\n continuation_tokens (list[list[int]]): Current continuation tokens\n top_p (float, optional): top_p to use. Defaults to 1.0.\n top_k (int, optional): top_k to use. Defaults to 0.\n temperature (float, optional): temperature to use. Defaults to 1.0.\n speculation (bool, optional): Whether to use speculation. Defaults to True.\n use_cache (bool, optional): Whether to use cache. Defaults to True.\n\n Returns:\n _type_: _description_\n \"\"\"\n models_ran = []\n for i, runnable_operator in enumerate(self.runnable_operators):\n new_tokens = [len(continuation_tokens[j]) - self.model_last_token_prediction[i][j] + 1 for j in range(len(continuation_tokens))]\n if runnable_operator.run_condition(new_tokens, self.trigger_end) or not speculation:\n logprobs = self.forward_model(runnable_operator, continuation_tokens, model_new_tokens=new_tokens, use_cache=use_cache, do_speculation=speculation)\n all_kept = self.add_new_result(continuation_tokens, new_tokens, runnable_operator, logprobs, top_p, top_k, temperature)\n models_ran.append(i)\n \n self.model_last_token_prediction[i] = [len(continuation_tokens[j]) + int(all_kept[j])\n for j in range(len(continuation_tokens))]\n \n if not all(all_kept):\n break\n \n to_sample_indices = [i for i in range(len(continuation_tokens)) if all_kept[i] and not self.trigger_end[i]]\n\n if len(to_sample_indices) > 0:\n # do batch sampling\n all_required_histories = torch.stack([\n self.create_sample_logprobs(\n self.logprobs_history[i][len(continuation_tokens[i])], \n temperature=temperature,\n top_k=top_k,\n top_p=top_p\n ) for i in to_sample_indices\n ])\n new_tokens = self.normal_sample(all_required_histories)\n for i in range(len(to_sample_indices)):\n continuation_tokens[to_sample_indices[i]].append(new_tokens[i].item())\n\n for i in models_ran:\n self.model_last_token_prediction[i] = [len(continuation_tokens[j]) for j in range(len(continuation_tokens))]\n return continuation_tokens\n\n def __call__(self, input_ids, **kwargs):\n \"\"\"Runs the forward pass of the model. This is needed for compatibility with lm-evaluation-harness\n\n Args:\n input_ids (torch.tensor): input ids\n\n Returns:\n namedtuple: Named tuple of the ModelArithmetic model\n \"\"\"\n return self.forward(input_ids, **kwargs)\n \n def forward(self, input_ids, normalize=True, **kwargs):\n \"\"\"Runs the foward pass. This is needed for compatibility with lm-evaluation-harness\n\n Args:\n input_ids (torch.tensor): input ids\n normalize (bool, optional): Whether or not to normalize the output. Defaults to True.\n\n Returns:\n namedtuple: Named tuple of the ModelArithmetic model\n \"\"\"\n ### this is a bit cheeky, but in order to be compatible with lm-evaluation-harness, we need to implement this method\n logprobs_per_model = {runnable_operator.id(): None for runnable_operator in self.runnable_operators}\n if not isinstance(input_ids, list):\n input_shape = input_ids.shape\n continuation_tokens = self.lm_eval_compatibility.forward_preprocessing(input_ids, self.model_input_tokens)\n else:\n input_shape = None\n continuation_tokens = input_ids\n\n for runnable_operator in self.runnable_operators:\n logprobs = self.forward_model(runnable_operator, continuation_tokens)\n if input_shape is not None:\n logprobs = self.lm_eval_compatibility.forward_post_processing(logprobs, input_shape)\n logprobs_per_model[runnable_operator.id()] = logprobs\n\n output = self.formula.evaluate(logprobs_per_model, normalize=normalize)\n return [output]\n\n def get_decoded_tokens(self, next_tokens_batch):\n \"\"\"Gets decoded tokens from the next tokens\n\n Args:\n next_tokens_batch (list[list[int]]): New tokens for each sample in the batch\n\n Returns:\n list[str]: Decoded tokens\n \"\"\"\n # adding eos token for compatibility with sentencepiece tokenizer\n encoded_sentences = [[self.tokenizer.eos_token_id] + next_tokens for next_tokens in next_tokens_batch]\n decoded_sentences = [self.tokenizer.decode(encoded_sentence, add_special_tokens=False) for encoded_sentence in encoded_sentences]\n decoded_next_tokens = [decoded_sentence[len(self.tokenizer.eos_token):] for decoded_sentence in decoded_sentences]\n return decoded_next_tokens\n \n def clear_memory(self):\n \"\"\"Deletes all loaded models and clears the cache\n \"\"\"\n for runnable_operator in self.runnable_operators:\n runnable_operator.delete_cache()\n self.loaded_models = dict()\n torch.cuda.empty_cache()\n\n def generate_text(self, sentences, max_length=1024, stop_texts=None, batch_size=None,\n temperature=1.0, top_p=1.0, top_k=0, num_return_sequences=1, do_speculation=False, use_cache=True, **kwargs):\n \"\"\"Generates text based on the input params\n\n Args:\n sentences (list[str]): List of input sentences\n max_length (int, optional): Max generation length. Defaults to 128.\n stop_texts (list[str], optional): Strings at which to stop generation. Defaults to None.\n batch_size (int, optional): Batch size. Defaults to None (all at once).\n temperature (float, optional): temperature to use. Defaults to 1.0.\n top_p (float, optional): top_p to use. Defaults to 1.0.\n top_k (int, optional): top_k to use. Defaults to 0.\n num_return_sequences (int, optional): Number of return sequences per sentence. Defaults to 1.\n do_speculation (bool, optional): Whether or not to do speculation. Defaults to True.\n use_cache (bool, optional): Whether or not to use cache. Defaults to True.\n\n Returns:\n list[str]: List of generated texts\n \"\"\"\n assert not do_speculation or any([runnable_operator.speculative_factor == 1 for runnable_operator in self.runnable_operators])\n if isinstance(sentences, str):\n sentences = [sentences]\n if batch_size is None:\n batch_size = len(sentences)\n \n # duplicate each sentence num_return_sequences times, but keep the same sentences next to each other\n sentences = [sentence for sentence in sentences for _ in range(num_return_sequences)]\n\n self.model_prediction_history = [dict() for _ in range(batch_size)]\n self.logprobs_history = [dict() for _ in range(batch_size)]\n self.model_last_token_prediction = [[0 for _ in range(batch_size)] for _ in range(len(self.runnable_operators))]\n self.trigger_end = [False for _ in range(batch_size)]\n self.init_monitor()\n \n if stop_texts is None:\n stop_texts = []\n stop_texts.append(self.tokenizer.eos_token)\n\n start_sentences = sentences[:]\n\n log(logger.debug, f\"Generating {len(sentences)} sentences\")\n\n generated_texts = [\"\" for _ in range(len(sentences))]\n generated_tokens = [[] for _ in range(len(sentences))]\n current_indices = [i for i in range(0, min(len(sentences), batch_size))]\n next_index = len(current_indices)\n \n for runnable_operator_id in self.model_input_tokens:\n self.model_input_tokens[runnable_operator_id].set_inputs([start_sentences[index] for index in current_indices])\n \n total_done = 0\n while len(current_indices) > 0:\n start_time = time.time()\n generated_tokens_batch = [generated_tokens[index] for index in current_indices]\n next_tokens = self.next_token_speculative(generated_tokens_batch, top_p, top_k, \n temperature, speculation=do_speculation, use_cache=use_cache)\n for i in range(len(next_tokens)):\n next_tokens[i] = self.run_retroactive_operators(i, next_tokens[i], temperature, top_k, top_p)\n self.clear_model_prediction_history(i, next_tokens[i])\n decoded_tokens = self.get_decoded_tokens(next_tokens)\n\n for i, index in enumerate(current_indices):\n generated_tokens[index] = next_tokens[i]\n generated_texts[index] = decoded_tokens[i]\n\n indices_to_remove = []\n for i in range(len(current_indices)):\n sentences[current_indices[i]] = start_sentences[current_indices[i]] + generated_texts[current_indices[i]]\n if any([stop_text in generated_texts[current_indices[i]] for stop_text in stop_texts]) or len(generated_tokens[current_indices[i]]) >= max_length:\n if len(self.model_prediction_history[i]) == 0:\n indices_to_remove.append(i)\n else:\n self.trigger_end[i] = True\n \n for i in indices_to_remove[::-1]:\n self.monitor.add_result(element=len(generated_tokens[current_indices[i]]), indicator=\"length\")\n del current_indices[i]\n self.model_prediction_history = self.model_prediction_history[:i] + self.model_prediction_history[i + 1:]\n self.logprobs_history = self.logprobs_history[:i] + self.logprobs_history[i + 1:]\n for j in range(len(self.model_last_token_prediction)):\n self.model_last_token_prediction[j] = self.model_last_token_prediction[j][:i] + self.model_last_token_prediction[j][i + 1:]\n self.trigger_end = self.trigger_end[:i] + self.trigger_end[i + 1:]\n \n for runnable_operator in self.runnable_operators:\n runnable_operator.delete_cache(index=i)\n\n if next_index < len(sentences):\n current_indices.append(next_index)\n self.model_prediction_history.append(dict())\n self.logprobs_history.append(dict())\n self.trigger_end.append(False)\n \n for j in range(len(self.model_last_token_prediction)):\n self.model_last_token_prediction[j].append(0)\n \n next_index += 1\n total_done += 1\n if total_done % 30 == 0:\n log(logger.debug, f\"Progress: {total_done / len(sentences):.3f}\")\n \n for runnable_operator_id in self.model_input_tokens:\n self.model_input_tokens[runnable_operator_id].set_inputs([start_sentences[index] for index in current_indices])\n\n self.monitor.add_result(element=time.time() - start_time)\n \n return generated_texts\n\n def generate(self, input_ids, attention_mask=None, do_sample=False, max_new_tokens=1024, \n stopping_criteria=None, temperature=1.0, top_p=1.0, top_k=0, use_cache=True, eos_token_id=None, pad_token_id=None, **kwargs):\n \"\"\"Generates text based on the input params. Needed for compatibility with lm-evaluation-harness\n\n Args:\n input_ids (torch.tensor): input ids\n attention_mask (torch.tensor, optional): attention mask. Defaults to None.\n do_sample (bool, optional): Whether or not to sample. Defaults to False.\n max_new_tokens (int, optional): Max new number of tokens. Defaults to 128.\n stopping_criteria (_type_, optional): Stopping criteria to use. Defaults to None.\n temperature (float, optional): Temperature to. Defaults to 1.0.\n top_p (float, optional): top_p to use. Defaults to 1.0.\n top_k (int, optional): top_k to use. Defaults to 0.\n use_cache (bool, optional): Whether or not to use cache. Defaults to True.\n eos_token_id (int, optional): eos token id. Defaults to None.\n pad_token_id (int, optional): pad token id. Defaults to None.\n\n Returns:\n list[str]: Generated texts\n \"\"\"\n if not do_sample:\n top_k = 1\n \n batch_size = input_ids.shape[0]\n input_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in input_ids]\n stopping_sequences = [self.tokenizer.eos_token]\n if stopping_criteria is not None:\n stopping_sequences += [criteria.sequence for criteria in stopping_criteria]\n if eos_token_id is not None:\n stopping_sequences += [self.tokenizer.decode([eos_token_id])]\n \n texts = self.generate_text(input_texts, max_length=max_new_tokens, stop_texts=stopping_sequences,\n batch_size=batch_size, temperature=temperature, top_p=top_p, top_k=top_k, use_cache=use_cache)\n encoded_texts = self.tokenizer.batch_encode_plus(texts, add_special_tokens=False, return_tensors=\"pt\").input_ids.to(self.device)\n # concatenate the input_ids with the encoded_texts\n all_encoded = torch.cat([input_ids, encoded_texts], dim=-1)\n return all_encoded"},{"identifier":"ENABLE_LOGGING","path":"src/model_arithmetic/utils.py","snippet":"ENABLE_LOGGING = False"},{"identifier":"log","path":"src/model_arithmetic/utils.py","snippet":"def log(function, message):\n \"\"\"\n Logs the given message using the provided function if logging is enabled.\n \n Parameters:\n function (callable): The logging function to use.\n message (str): The message to be logged.\n \"\"\"\n if ENABLE_LOGGING:\n function(message)"}],"string":"[\n {\n \"identifier\": \"BaseClass\",\n \"path\": \"src/model_arithmetic/base.py\",\n \"snippet\": \"class BaseClass:\\n \\\"\\\"\\\"\\n Base class for providing a serialization and deserialization mechanism.\\n \\\"\\\"\\\"\\n def __init__(self, **kwargs):\\n \\\"\\\"\\\"\\n Instantiates the base class with keyword arguments\\n \\n Args:\\n kwargs (dict): Keyword arguments\\n \\\"\\\"\\\"\\n self.kwargs = kwargs\\n self.__dict__.update(kwargs)\\n\\n def generate_list_settings(self, list_):\\n \\\"\\\"\\\"\\n Converts provided list to a normalized list that can be stored as a json object to serialize.\\n \\n Args:\\n list_ (List): List to be converted\\n Returns\\n Transformed normal list\\n \\\"\\\"\\\"\\n normal_list = []\\n for item in list_:\\n if isinstance(item, BaseClass):\\n normal_list.append(item.generate_settings())\\n elif isinstance(item, dict):\\n normal_list.append(self.generate_kwarg_setting(item))\\n elif isinstance(item, (tuple, list)):\\n normal_list.append(self.generate_list_settings(item))\\n else:\\n normal_list.append(item)\\n return normal_list\\n\\n def generate_kwarg_setting(self, kwargs):\\n \\\"\\\"\\\"\\n Converts provided keyword arguments to normal kwargs in terms of serialization.\\n\\n Args:\\n kwargs (dict): kwargs to be converted.\\n \\\"\\\"\\\"\\n normal_kwargs = dict()\\n for kwarg in kwargs:\\n if isinstance(kwargs[kwarg], BaseClass):\\n normal_kwargs[kwarg] = kwargs[kwarg].generate_settings()\\n elif isinstance(kwargs[kwarg], (list, tuple)):\\n normal_kwargs[kwarg] = self.generate_list_settings(kwargs[kwarg])\\n elif isinstance(kwargs[kwarg], dict):\\n normal_kwargs[kwarg] = self.generate_kwarg_setting(kwargs[kwarg])\\n else:\\n normal_kwargs[kwarg] = kwargs[kwarg]\\n \\n return normal_kwargs\\n\\n\\n def generate_settings(self):\\n \\\"\\\"\\\"\\n Generates settings for the instance of the BaseClass.\\n\\n Returns\\n Settings in dictionary format.\\n \\\"\\\"\\\"\\n settings = {\\n \\\"class\\\": self.__class__.__name__, \\n **self.generate_kwarg_setting({kwarg: self.__dict__[kwarg] for kwarg in self.kwargs}), \\n }\\n return settings\\n \\n def save(self, path):\\n \\\"\\\"\\\"\\n Saves the generated settings into a JSON file at a specified path.\\n \\n Args:\\n path (string): The file path at which the settings have to be saved.\\n \\\"\\\"\\\"\\n settings = self.generate_settings()\\n\\n if os.path.dirname(path) != \\\"\\\":\\n os.makedirs(os.path.dirname(path), exist_ok=True)\\n \\n with open(path, \\\"w\\\") as f:\\n json.dump(settings, f, indent=2)\\n\\n @classmethod\\n def get_all_subclasses(cls):\\n \\\"\\\"\\\"\\n Returns all subclasses of the BaseClass.\\n \\\"\\\"\\\"\\n all_subclasses = []\\n\\n for subclass in cls.__subclasses__():\\n all_subclasses.append(subclass)\\n all_subclasses.extend(subclass.get_all_subclasses())\\n\\n return all_subclasses\\n\\n @staticmethod\\n def find_class(cls_name):\\n \\\"\\\"\\\"\\n Searches for a class that matches the given class name.\\n\\n Args:\\n cls_name (string): Class name to be matched\\n \\\"\\\"\\\"\\n for possible_cls in BaseClass.get_all_subclasses():\\n if possible_cls.__name__ == cls_name:\\n return possible_cls\\n return None\\n\\n @staticmethod\\n def load_from_list_settings(list_):\\n \\\"\\\"\\\"\\n Deserializes the list saved settings to instantiate the objects.\\n\\n Args:\\n list_ (List): List of saved settings\\n \\\"\\\"\\\"\\n output_list = []\\n for item in list_:\\n if isinstance(item, dict):\\n output_list.append(BaseClass.load_from_dict(item))\\n elif isinstance(item, (tuple, list)):\\n output_list.append(BaseClass.load_from_list_settings(item))\\n else:\\n output_list.append(item)\\n\\n return output_list\\n \\n @staticmethod\\n def load_from_dict(dict_):\\n \\\"\\\"\\\"\\n Deserializes the dictionary saved settings to instantiate the objects.\\n\\n Args:\\n dict_ (dict): Dictionary containing saved settings\\n \\\"\\\"\\\"\\n other_class = BaseClass.find_class(dict_.get(\\\"class\\\", None))\\n if other_class is not None:\\n return other_class.load_from_settings(dict_)\\n \\n output_dict = dict()\\n for key in dict_:\\n if isinstance(dict_[key], dict):\\n output_dict[key] = BaseClass.load_from_dict(dict_[key])\\n elif isinstance(dict_[key], (tuple, list)):\\n output_dict[key] = BaseClass.load_from_list_settings(dict_[key])\\n else:\\n output_dict[key] = dict_[key]\\n\\n return output_dict\\n\\n @staticmethod\\n def load_from_settings(settings):\\n \\\"\\\"\\\"\\n Deserializes the saved settings to instantiate the object.\\n\\n Args:\\n settings (dict): Saved settings\\n \\\"\\\"\\\"\\n cls = BaseClass.find_class(settings[\\\"class\\\"])\\n\\n if cls is None:\\n logger.error(f\\\"Could not find class {settings['class']} when loading class.\\\")\\n return None\\n\\n kwargs = dict()\\n for kwarg in settings:\\n if kwarg == \\\"class\\\":\\n continue\\n if isinstance(settings[kwarg], dict):\\n kwargs[kwarg] = BaseClass.load_from_dict(settings[kwarg])\\n elif isinstance(settings[kwarg], (tuple, list)):\\n kwargs[kwarg] = BaseClass.load_from_list_settings(settings[kwarg])\\n else:\\n kwargs[kwarg] = settings[kwarg]\\n\\n return cls(**kwargs)\\n\\n @classmethod\\n def _load(cls, path, **kwargs):\\n \\\"\\\"\\\"\\n Loads the settings from the JSON file at the specified path.\\n \\n Args:\\n path (string): The file path from which the settings have to be loaded.\\n kwargs (dict): Additional keywords arguments\\n \\\"\\\"\\\"\\n with open(path, \\\"r\\\") as f:\\n settings = json.load(f)\\n for kwarg in kwargs:\\n settings[kwarg] = kwargs[kwarg]\\n return cls.load_from_settings(settings)\\n\\n @staticmethod\\n def load(path, **kwargs):\\n \\\"\\\"\\\"\\n Loads the settings of the class from the JSON file.\\n\\n Args:\\n path (string): The file path from which the class settings have to be loaded.\\n kwargs (dict): Additional keywords arguments\\n \\\"\\\"\\\"\\n with open(path, \\\"r\\\") as f:\\n settings = json.load(f)\\n cls = BaseClass.find_class(settings[\\\"class\\\"])\\n return cls._load(path, **kwargs)\\n\\n def __str__(self) -> str:\\n \\\"\\\"\\\"\\n Returns a string representation of the class object.\\n \\\"\\\"\\\"\\n return f\\\"{self.__class__.__name__}({self.kwargs})\\\"\\n \\n def __eq__(self, o: object) -> bool:\\n \\\"\\\"\\\"\\n Checks whether the provided object is equal to the current object.\\n\\n Args:\\n o (object): Object to compare\\n \\\"\\\"\\\"\\n if not isinstance(o, BaseClass):\\n return False\\n \\n other_settings = o.generate_settings()\\n settings = self.generate_settings()\\n\\n return other_settings == settings\"\n },\n {\n \"identifier\": \"CustomDataset\",\n \"path\": \"src/model_arithmetic/dataset.py\",\n \"snippet\": \"class CustomDataset(Dataset):\\n \\\"\\\"\\\"\\n A custom PyTorch Dataset class for tokenized sequence data.\\n\\n Uses a tokenizer to convert text data from a DataFrame to input_ids (tokens), \\n and optionally attaches label data if present in the DataFrame.\\n \\\"\\\"\\\"\\n def __init__(self, tokenizer, df, max_tokens=128, min_tokens=1, random_cutoff=False):\\n \\\"\\\"\\\"\\n Initializes the CustomDataset object.\\n\\n Args:\\n tokenizer (Tokenizer): The tokenizer to be used for the text data.\\n df (pandas.DataFrame): DataFrame containing the text data, and optionally labels.\\n max_tokens (int, optional): Maximum number of tokens per sequence. Defaults to 128.\\n min_tokens (int, optional): Minimum number of tokens per sequence. Defaults to 1.\\n random_cutoff (bool, optional): Whether to randomly cut off the number of tokens per sequence. Defaults to False.\\n \\\"\\\"\\\"\\n super().__init__()\\n data = df.dropna()\\n self.tokenized_dataset = [\\n tokenizer(sentence, return_tensors=\\\"pt\\\", truncation=True, max_length=max_tokens).input_ids.view(-1) for sentence in tqdm(data[\\\"text\\\"].tolist())\\n ]\\n\\n self.df = data\\n self.has_labels = \\\"label\\\" in data.columns\\n self.min_tokens = min_tokens\\n self.labels = None\\n if self.has_labels:\\n self.labels = data[\\\"label\\\"].values\\n \\n self.random_cutoff = random_cutoff\\n\\n def __len__(self):\\n \\\"\\\"\\\"\\n Returns the length of the tokenized dataset, \\n i.e., the number of tokenized sequences.\\n \\n Returns:\\n int: Number of tokenized sequences.\\n \\\"\\\"\\\"\\n return len(self.tokenized_dataset)\\n\\n def __getitem__(self, idx):\\n \\\"\\\"\\\"\\n Fetches an item from the dataset at the given index.\\n\\n If labels are available, also fetches the associated label.\\n If `random_cutoff` is true, may truncate sequence length randomly.\\n\\n Args:\\n idx (int): Index of the required sequence.\\n\\n Returns:\\n dict: A dictionary with the following structure-\\n {\\n \\\"input_ids\\\": torch.Tensor (Tokenized sequence),\\n \\\"labels\\\": torch.Tensor (Associated label, if available)\\n }\\n \\\"\\\"\\\"\\n cutoff = len(self.tokenized_dataset[idx])\\n if self.random_cutoff:\\n cutoff = torch.randint(min(cutoff, self.min_tokens), cutoff + 1, (1,)).item()\\n \\n if not self.has_labels:\\n return {\\\"input_ids\\\": self.tokenized_dataset[idx][:cutoff]}\\n else:\\n return {\\\"input_ids\\\": self.tokenized_dataset[idx][:cutoff], \\\"labels\\\": torch.tensor([self.labels[idx]], dtype=torch.long)}\"\n },\n {\n \"identifier\": \"load_model\",\n \"path\": \"src/model_arithmetic/basic_model_loader.py\",\n \"snippet\": \"def load_model(dir_or_model, classification=False, token_classification=False, return_tokenizer=False, dtype=torch.bfloat16, load_dtype=True, \\n rl=False, peft_config=None):\\n \\\"\\\"\\\"\\n This function is used to load a model based on several parameters including the type of task it is targeted to perform.\\n \\n Args:\\n dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.\\n\\n classification (bool): If True, loads the model for sequence classification.\\n\\n token_classification (bool): If True, loads the model for token classification.\\n\\n return_tokenizer (bool): If True, returns the tokenizer along with the model.\\n\\n dtype: The data type that PyTorch should use internally to store the model’s parameters and do the computation.\\n\\n load_dtype (bool): If False, sets dtype as torch.float32 regardless of the passed dtype value.\\n\\n rl (bool): If True, loads model specifically designed to be used in reinforcement learning environment.\\n\\n peft_config: Configuration details for Peft models. \\n \\n Returns:\\n It returns a model for the required task along with its tokenizer, if specified.\\n \\\"\\\"\\\"\\n log(logger.debug, f\\\"Loading model for {dir_or_model} with {classification}, {dtype}, {load_dtype}\\\")\\n is_lora_dir = os.path.isfile(os.path.join(dir_or_model, \\\"adapter_config.json\\\"))\\n\\n if not load_dtype:\\n dtype = torch.float32\\n\\n if is_lora_dir:\\n loaded_json = json.load(open(os.path.join(dir_or_model, \\\"adapter_config.json\\\"), \\\"r\\\"))\\n model_name = loaded_json[\\\"base_model_name_or_path\\\"]\\n else:\\n model_name = dir_or_model\\n\\n original_model_name = model_name\\n\\n if classification:\\n model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\\\"auto\\\") # to investigate: calling torch_dtype here fails.\\n elif token_classification:\\n model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\\\"auto\\\")\\n elif rl:\\n model = AutoModelForCausalLMWithValueHead.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, \\n peft_config=peft_config, device_map=\\\"auto\\\")\\n else:\\n if model_name.endswith(\\\"GPTQ\\\") or model_name.endswith(\\\"GGML\\\"):\\n model = AutoGPTQForCausalLM.from_quantized(model_name,\\n use_safetensors=True,\\n trust_remote_code=True,\\n # use_triton=True, # breaks currently, unfortunately generation time of the GPTQ model is quite slow\\n quantize_config=None, device_map=\\\"auto\\\")\\n else:\\n model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=dtype, use_auth_token=True, device_map=\\\"auto\\\")\\n\\n if is_lora_dir:\\n model = PeftModel.from_pretrained(model, dir_or_model)\\n \\n try:\\n tokenizer = load_tokenizer(original_model_name)\\n model.config.pad_token_id = tokenizer.pad_token_id\\n except Exception:\\n pass\\n if return_tokenizer:\\n return model, load_tokenizer(original_model_name)\\n return model\"\n },\n {\n \"identifier\": \"load_tokenizer\",\n \"path\": \"src/model_arithmetic/basic_model_loader.py\",\n \"snippet\": \"def load_tokenizer(dir_or_model):\\n \\\"\\\"\\\"\\n This function is used to load the tokenizer for a specific pre-trained model.\\n \\n Args:\\n dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.\\n \\n Returns:\\n It returns a tokenizer that can convert text to tokens for the specific model input.\\n \\\"\\\"\\\"\\n log(logger.debug, f\\\"Loading tokenizer for {dir_or_model}\\\")\\n\\n is_lora_dir = os.path.isfile(os.path.join(dir_or_model, \\\"adapter_config.json\\\"))\\n\\n if is_lora_dir:\\n loaded_json = json.load(open(os.path.join(dir_or_model, \\\"adapter_config.json\\\"), \\\"r\\\"))\\n model_name = loaded_json[\\\"base_model_name_or_path\\\"]\\n else:\\n model_name = dir_or_model\\n \\n if os.path.isfile(os.path.join(dir_or_model, \\\"config.json\\\")):\\n loaded_json = json.load(open(os.path.join(dir_or_model, \\\"config.json\\\"), \\\"r\\\"))\\n model_name = loaded_json[\\\"_name_or_path\\\"]\\n\\n tokenizer = AutoTokenizer.from_pretrained(model_name)\\n\\n if tokenizer.pad_token is None:\\n log(logger.debug, \\\"Setting pad token to eos token\\\")\\n tokenizer.pad_token = tokenizer.eos_token\\n tokenizer.pad_token_id = tokenizer.eos_token_id\\n \\n return tokenizer\"\n },\n {\n \"identifier\": \"ModelArithmetic\",\n \"path\": \"src/model_arithmetic/model_arithmetic.py\",\n \"snippet\": \"class ModelArithmetic(PreTrainedModel):\\n \\\"\\\"\\\"\\n Main class for prompt arithmetic. Handles the generation of text based on the formula.\\n \\\"\\\"\\\"\\n SAVE_FILE = \\\"prompt_arithmetic.json\\\"\\n _supports_sdpa = True\\n\\n def __init__(self, formula : Operator, default_model : str = None, dtype=torch.bfloat16, intermediate_argmax : bool = False, epsilon = 1e-12, \\n retroactive_operators = [], calculate_statistics=True, needs_input_tokens_lm_eval=False, lm_eval_task=None, tokenizer=None):\\n \\\"\\\"\\\"Initializes the prompt arithmetic model.\\n\\n Args:\\n formula (Operator): The formula for which generations need to be made.\\n default_model (str, optional): Default model for RunnableOperators that don't have a model associated with them. Defaults to None.\\n dtype (torch.dtype, optional): Dtype of the models to load by default. Defaults to torch.bfloat16.\\n intermediate_argmax (bool, optional): Something unimportant that was tried out, but now deprecated. Defaults to False.\\n epsilon (float, optional): Just some small value. Defaults to 1e-12.\\n retroactive_operators (list, optional): The retroactive operators that need to be applied. Defaults to [].\\n calculate_statistics (bool, optional): Whether or not to calculate some statistics, can be a tad bit expensive. Defaults to True.\\n needs_input_tokens_lm_eval (bool, optional): Whether or not lm eval is used and whether or not the task needs the input tokens. Defaults to False. Only set to true for an lm eval task.\\n lm_eval_task (str, optional): Name of the lm eval task. Defaults to None.\\n tokenizer (transformers.tokenization_utils_base.PreTrainedTokenizerBase, optional): Tokenizer to use. Defaults to None.\\n \\\"\\\"\\\"\\n self.formula = formula.clone()\\n\\n self.default_model = default_model\\n self.loaded_models = dict()\\n self.model_prediction_history = [] # keeps track of the RunnableOperators predictions for each token (that hasn't finished computing)\\n self.logprobs_history = [] # keeps track of the current probability distribution for which each token has been drawn\\n self.model_last_token_prediction = [] # keeps track of the last token that has been predicted for each RunnableOperator\\n \\n self.output_type = namedtuple(\\\"ModelArithmeticOutput\\\", [\\\"logits\\\", \\\"logprobs_per_model\\\"])\\n self.intermediate_argmax = intermediate_argmax\\n self.retroactive_operators = retroactive_operators\\n self.calculate_statistics = calculate_statistics\\n\\n self.runnable_operators = []\\n for runnable_operator in self.formula.runnable_operators():\\n if not any([runnable_operator.same_operator(output) for output in self.runnable_operators]):\\n self.runnable_operators.append(runnable_operator)\\n \\n\\n # sort the prompts by speculative factor, putting the one with highest speculative factor first\\n # => run model with highest speculative factor first, since otherwise the computation might be wasted for the first ones\\n # however, we first need to sort by run_priority and then within that by speculative factor\\n self.runnable_operators = sorted(self.runnable_operators, key=lambda runnable_operator: (runnable_operator.run_priority, runnable_operator.speculative_factor), reverse=True)\\n \\n self.load_all_models(dtype=dtype)\\n if self.default_model not in self.loaded_models:\\n for runnable_operator in self.runnable_operators:\\n if isinstance(runnable_operator, PromptedLLM) and runnable_operator.model is not None:\\n self.default_model = runnable_operator.model\\n break\\n if self.default_model is None:\\n raise ValueError(\\\"Default model must be specified if not specified in an llm prompt\\\")\\n\\n self.config = self.loaded_models[str(self.default_model)].config\\n\\n if tokenizer is None:\\n self.tokenizer = load_tokenizer(self.default_model)\\n else:\\n self.tokenizer = tokenizer\\n \\n self.init_runnable_operators()\\n \\n self.model_input_tokens = {\\n runnable_operator.id(): TokenizedInput(runnable_operator, \\n runnable_operator.model, \\n self.loaded_models[str(runnable_operator.model)].config,\\n self.tokenizer) \\n for runnable_operator in self.runnable_operators\\n }\\n \\n self.init_monitor()\\n \\n self.epsilon = epsilon\\n \\n self.word_size = len(self.tokenizer)\\n \\n if Compatibility is not None:\\n self.lm_eval_compatibility = Compatibility(\\n task_name=lm_eval_task,\\n needs_input_tokens_lm_eval=needs_input_tokens_lm_eval,\\n tokenizer=self.tokenizer,\\n device=self.device,\\n max_length=get_max_length(self.config),\\n )\\n else:\\n self.lm_eval_compatibility = None\\n \\n super().__init__(self.config)\\n \\n def init_monitor(self):\\n \\\"\\\"\\\"\\n Initializes the monitor for the prompt arithmetic model.\\n \\\"\\\"\\\"\\n self.monitor = Monitor(self.runnable_operators)\\n \\n def init_runnable_operators(self):\\n \\\"\\\"\\\"Initializes the runnable operators. This is done after the models have been loaded, because the models are needed for the runnable operators.\\n \\\"\\\"\\\"\\n for runnable_operator in self.runnable_operators:\\n if runnable_operator.model is None:\\n runnable_operator.model = self.default_model\\n runnable_operator.initialize_after_model_set()\\n\\n def load_all_models(self, dtype=torch.bfloat16):\\n \\\"\\\"\\\"Loads all the models that are needed for the runnable operators. Models are never loaded twice.\\n\\n Args:\\n dtype (torch.dtype, optional): Default Dtype of the models. Defaults to torch.bfloat16.\\n \\\"\\\"\\\"\\n if self.default_model is None:\\n for runnable_operator in self.runnable_operators:\\n if isinstance(runnable_operator, PromptedLLM) and runnable_operator.model is not None:\\n self.default_model = str(runnable_operator.model)\\n break\\n \\n for runnable_operator in self.runnable_operators:\\n if runnable_operator.model is None:\\n assert self.default_model is not None, \\\"Default model must be specified if not specified in prompt\\\"\\n runnable_operator.model = self.default_model\\n if runnable_operator.model not in self.loaded_models:\\n model = runnable_operator.load_model(dtype=dtype)\\n model.eval()\\n if model is not None:\\n self.loaded_models[str(runnable_operator.model)] = model\\n \\n if len(self.loaded_models) == 0:\\n assert self.default_model is not None, \\\"Required to at least have one model, for now\\\"\\n self.loaded_models[str(self.default_model)] = load_model(self.default_model, dtype=dtype)\\n \\n @property\\n def device(self):\\n \\\"\\\"\\\"Device of the default model. Needed for compatibility with lm_eval\\n\\n Returns:\\n torch.device: Device of the default model.\\n \\\"\\\"\\\"\\n return self.loaded_models[str(self.default_model)].device\\n\\n def save_pretrained(self, path : str):\\n \\\"\\\"\\\"Saves the model to the specified path.\\n\\n Args:\\n path (str): Path to which to save the model\\n \\\"\\\"\\\"\\n os.makedirs(path, exist_ok=True)\\n all_settings = {\\n \\\"formula\\\": self.formula.generate_settings(),\\n \\\"default_model\\\": self.default_model,\\n }\\n\\n with open(os.path.join(path, self.SAVE_FILE), \\\"w\\\") as f:\\n json.dump(all_settings, f, indent=4, sort_keys=True)\\n\\n @classmethod\\n def from_pretrained(cls, path : str, dtype=torch.bfloat16):\\n \\\"\\\"\\\"Loads the model from the specified path.\\n\\n Args:\\n path (str): Path from which to load the model\\n dtype (torch.dtype, optional): Default dtype for the models. Defaults to torch.bfloat16.\\n\\n Returns:\\n ModelArithmetic: model arithmetic model\\n \\\"\\\"\\\"\\n with open(os.path.join(path, cls.SAVE_FILE), \\\"r\\\") as f:\\n all_settings = json.load(f)\\n all_settings[\\\"formula\\\"] = Operator.load_from_settings(all_settings[\\\"formula\\\"])\\n return cls(**all_settings, dtype=dtype)\\n\\n \\n def forward_model(self, runnable_operator, continuation_tokens, model_new_tokens=None, use_cache=False, do_speculation=False):\\n \\\"\\\"\\\"Runs a specifc runnable operator on the continuation tokens.\\n\\n Args:\\n runnable_operator (RunnableOperator): The runnable operator to run.\\n continuation_tokens (list[list[int]]): List of tokens that need to be continued. The prompt is not included in these tokens\\n model_new_tokens (list[int], optional): New tokens for the model. Defaults to None.\\n use_cache (bool, optional): Whether or not to allow the model to use cache (eg key-value storage for an LLM). Defaults to False.\\n do_speculation (bool, optional): Whether or not to do speculation sampling. Defaults to False.\\n\\n Returns:\\n torch.tensor: logprobs of the model, one logprob distribution for each new token in each sample\\n \\\"\\\"\\\"\\n start_time = time.time()\\n \\n tokenized_input_creator = self.model_input_tokens[runnable_operator.id()]\\n tokenized_inputs = tokenized_input_creator.add_continuation_tokens(continuation_tokens)\\n tokenized_only_input = tokenized_input_creator.get_only_input_tokens()\\n \\n was_none = model_new_tokens is None\\n \\n if was_none:\\n model_new_tokens = torch.tensor([len(continuation_tokens[i]) + 1 for i in range(len(continuation_tokens))])\\n \\n if len(self.model_prediction_history) < len(continuation_tokens):\\n new_prediction_history = [dict() for _ in range(len(continuation_tokens))]\\n else:\\n new_prediction_history = [self.model_prediction_history[i].get(self.max_index_prediction_history(i), dict()) for i in range(len(continuation_tokens))]\\n \\n logprobs = runnable_operator.run(\\n loaded_models=self.loaded_models,\\n tokenized_inputs=tokenized_inputs,\\n model_new_tokens=model_new_tokens,\\n new_prediction_history=new_prediction_history,\\n other_tokenizer=self.tokenizer,\\n tokenized_only_input=tokenized_only_input, \\n use_cache=use_cache,\\n do_speculation=do_speculation\\n )\\n \\n logprobs = [logprob.to(self.device) for logprob in logprobs]\\n \\n if was_none:\\n logprobs = torch.stack(logprobs, dim=0)\\n\\n self.monitor.add_result(element=time.time() - start_time, runnable_operator=runnable_operator)\\n return logprobs\\n \\n def group_complete(self, model_history):\\n \\\"\\\"\\\"Checks which groups of runnable operators have been completely calculated and which haven't.\\n\\n Args:\\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\\n\\n Returns:\\n dict[bool]: Dict mapping the group to whether it has been completely calculated or not\\n \\\"\\\"\\\"\\n # everything that is a group needs to be either all calculated or all not calculated\\n group_calculated = dict()\\n groups = set([runnable_operator.group for runnable_operator in self.runnable_operators if runnable_operator.group is not None])\\n completed_groups = {group: True for group in groups}\\n \\n for runnable_operator in self.runnable_operators:\\n if runnable_operator.group is not None:\\n is_calculated = model_history.get(runnable_operator.id()) is not None\\n if runnable_operator.group not in group_calculated:\\n group_calculated[runnable_operator.group] = is_calculated\\n elif group_calculated[runnable_operator.group] != is_calculated:\\n completed_groups[runnable_operator.group] = False\\n return completed_groups\\n \\n def group_model_history(self, model_history):\\n \\\"\\\"\\\"Sets the model history on which to evaluate the formula based on the groups. Removes predictions if the group hasn't been completely calculated yet.\\n\\n Args:\\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\\n\\n Returns:\\n dict: Adjusted dict mapping\\n \\\"\\\"\\\"\\n completed_groups = self.group_complete(model_history)\\n grouped_model_history = dict()\\n for runnable_operator in self.runnable_operators:\\n if runnable_operator.group is None or completed_groups[runnable_operator.group]:\\n grouped_model_history[runnable_operator.id()] = model_history[runnable_operator.id()]\\n else:\\n grouped_model_history[runnable_operator.id()] = None\\n \\n return grouped_model_history\\n \\n def create_sample_logprobs(self, logprobs, temperature, top_k, top_p):\\n \\\"\\\"\\\"Creates the logprobs for each token in each sample.\\n\\n Args:\\n logprobs (torch.tensor): Logprobs of the model\\n temperature (float): temperature to use\\n top_k (int): top_k to use\\n top_p (float): top_p to use\\n\\n Returns:\\n torch.tensor: Logprobs for each token in each sample\\n \\\"\\\"\\\"\\n if temperature == 0:\\n logprobs_argmax = torch.argmax(logprobs, dim=-1)\\n logprobs = torch.nn.functional.one_hot(logprobs_argmax, num_classes=logprobs.shape[-1]).float()\\n return logprobs\\n logprobs = logprobs / temperature\\n logprobs = top_k_top_p_filtering(logprobs.unsqueeze(0), top_k=top_k, top_p=top_p)\\n return torch.softmax(logprobs, dim=-1).squeeze()\\n \\n \\n\\n def process_logprobs(self, model_history):\\n \\\"\\\"\\\"Processes model history to get the probability distribution for the token.\\n\\n Args:\\n model_history (dict): Dict mapping the runnable operator id to the logprobs of the model\\n\\n Returns:\\n _type_: _description_\\n \\\"\\\"\\\"\\n init_time = time.time()\\n logprobs_normalized = self.formula.evaluate(model_history)\\n self.monitor.add_result(element=time.time() - init_time, indicator=\\\"formula_evaluation\\\")\\n if not torch.is_tensor(logprobs_normalized):\\n return None\\n # logprobs_normalized = logprobs_normalized / temperature\\n # logprobs_normalized = top_k_top_p_filtering(logprobs_normalized.unsqueeze(0), top_k=top_k, top_p=top_p)\\n return logprobs_normalized\\n \\n def run_retroactive_operators(self, index, tokenized_sentence, temperature, top_k, top_p):\\n \\\"\\\"\\\"Runs the retroactive operators on the tokenized sentence. \\n\\n Args:\\n index (int): Index of the sentence in the current batch\\n tokenized_sentence (list[int]): Tokenized sentence\\n temperature (float): temperature to use\\n top_k (int): top_k to use\\n top_p (float): top_p to use\\n\\n Returns:\\n list[int]: Adjusted tokenized sentence based on the retroactive operators and whether they accepted it.\\n \\\"\\\"\\\"\\n for operator in self.retroactive_operators:\\n accepted = operator.accept(tokenized_sentence, self.tokenizer)\\n if accepted < 0:\\n not_accepted_token = tokenized_sentence[accepted]\\n self.clear_model_prediction_history(index, tokenized_sentence, from_=len(tokenized_sentence) + accepted)\\n tokenized_sentence = tokenized_sentence[:len(tokenized_sentence) + accepted]\\n \\n self.logprobs_history[index][len(tokenized_sentence)][not_accepted_token] = -torch.inf\\n \\n if torch.all(self.logprobs_history[index][len(tokenized_sentence)] == -torch.inf):\\n self.logprobs_history[index][len(tokenized_sentence)] = torch.zeros_like(self.logprobs_history[index][len(tokenized_sentence)])\\n \\n probs_to_sample = self.create_sample_logprobs(\\n self.logprobs_history[index][len(tokenized_sentence)],\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p\\n )\\n new_token = torch.multinomial(probs_to_sample, 1).item()\\n \\n tokenized_sentence.append(new_token)\\n return self.run_retroactive_operators(index, tokenized_sentence, temperature, top_k, top_p)\\n \\n return tokenized_sentence\\n \\n def speculation_sample(self, token, previous_models_probs, new_models_probs):\\n \\\"\\\"\\\"Sample a token based on the previous and new model probabilities in the speculative sampling way. Also returns whether the token was accepted or not.\\n\\n Args:\\n token (int): Token that is currently selected\\n previous_models_probs (torch.tensor): Model probabilities of the previous models\\n new_models_probs (torch.tensor): Model probabilities of the new models\\n\\n Returns:\\n (int, bool): New token and whether or not the input token was accepted\\n \\\"\\\"\\\"\\n acceptance_prob = torch.minimum(torch.tensor(1.0), new_models_probs[token] / (previous_models_probs[token] + torch.tensor(self.epsilon)))\\n # TODO: the next line is taking an enormous amount of time because of asynchronous computing on gpu's and requiring it to be returned immediately\\n # Therefore do batch processing\\n acceptance_prob = float(acceptance_prob)\\n self.monitor.add_result(element=float(acceptance_prob), indicator=\\\"acceptance_prob\\\")\\n # self.monitor.add_result(element=self.entropy(previous_models_probs).item(), indicator=\\\"entropy_previous\\\")\\n # self.monitor.add_result(element=previous_models_probs[token].item(), indicator=\\\"probability_previous\\\")\\n\\n if torch.rand(1) < acceptance_prob:\\n return token, True\\n else:\\n new_proba_distrib = torch.relu(new_models_probs - previous_models_probs)\\n new_proba_distrib /= torch.sum(new_proba_distrib)\\n new_token = torch.multinomial(new_proba_distrib, 1).item()\\n return new_token, False\\n \\n \\n def add_new_result(self, generated_tokens, num_new_tokens, runnable_operator, new_model_logprobs, top_p, top_k, temperature):\\n \\\"\\\"\\\"Adds a new run of a runnable operator to the model prediction history. Also does speculation sampling if needed.\\n\\n Args:\\n generated_tokens (list[list[int]]): Currently generated tokens by the model\\n num_new_tokens (list[int]): Number of new tokens for each sample in the batch\\n runnable_operator (RunnableOperator): Runnable operator that was run\\n new_model_logprobs (List[torch.tensor]): Output of the run function of the runnable operator\\n top_p (flaot): top_p to use\\n top_k (int): top_k to use\\n temperature (float): temperature to use\\n\\n Returns:\\n list[bool]: For each sample in the batch, whether all tokens in that sample were kept or not\\n \\\"\\\"\\\"\\n all_kept = []\\n for i in range(len(generated_tokens)):\\n n_generated_tokens = len(generated_tokens[i])\\n kept = True\\n for n_token in range(n_generated_tokens - num_new_tokens[i] + 1, n_generated_tokens + 1):\\n # initialize the model prediction history\\n self.model_prediction_history[i][n_token] = self.model_prediction_history[i].get(n_token, \\n {runnable_operator.id(): None for runnable_operator in self.runnable_operators})\\n # check if we need to do speculation sampling, only needed when a previous token was sampled\\n do_speculation_sample = n_token < n_generated_tokens\\n \\n # speculation sampling not needed if the model was run before \\n if self.model_prediction_history[i][n_token][runnable_operator.id()] is not None:\\n do_speculation_sample = False\\n \\n # speculation sampling not needed if all models have not been run yet: this is the first model on this token\\n if all([logprob is None for logprob in self.model_prediction_history[i][n_token].values()]):\\n do_speculation_sample = False\\n # This means that this token was already fully accepted, so we can just continue (can happen if batch_size > 1 or when end is triggered)\\n if self.max_index_prediction_history(i) > n_token:\\n continue\\n \\n # add the new model logprobs\\n self.model_prediction_history[i][n_token][runnable_operator.id()] = new_model_logprobs[i][-n_generated_tokens + n_token + num_new_tokens[i] - 1]\\n \\n group_model_history = self.group_model_history(self.model_prediction_history[i][n_token])\\n # group_model_history needs to be separately checked, since it could be that the group is not yet fully calculated\\n # also allow no logprobs runnable operators (would lead to errors) if the formula is not finished yet (if it is finished, you need to)\\n if all([logprob is None for logprob in group_model_history.values()]) or (not runnable_operator.outputs_logprobs and not self.formula.is_finished(group_model_history)):\\n continue\\n \\n # process the logprobs\\n new_model_probs = self.process_logprobs(group_model_history)\\n \\n if self.intermediate_argmax and not self.formula.is_finished(group_model_history):\\n argmax_el = torch.argmax(new_model_probs)\\n new_model_probs = torch.zeros_like(new_model_probs)\\n new_model_probs[argmax_el] = 1.0\\n \\n if do_speculation_sample:\\n if self.calculate_statistics:\\n self.monitor.add_result(self.expected_acceptance_prob(self.create_sample_logprobs(new_model_probs, temperature, top_k, top_p), \\n self.create_sample_logprobs(self.logprobs_history[i].get(n_token), temperature, top_k, top_p)), \\n indicator=\\\"expected_acceptance_prob\\\", runnable_operator=runnable_operator)\\n\\n new_token, kept = self.speculation_sample(\\n token = generated_tokens[i][n_token],\\n previous_models_probs=self.create_sample_logprobs(self.logprobs_history[i][n_token], temperature, top_k, top_p),\\n new_models_probs=self.create_sample_logprobs(new_model_probs, temperature, top_k, top_p), \\n )\\n if n_token in self.model_prediction_history[i]:\\n self.logprobs_history[i][n_token] = new_model_probs\\n \\n if not kept:\\n # if not kept, we change the generated tokens and remove the model prediction history after that token\\n generated_tokens[i][n_token] = new_token\\n generated_tokens[i] = generated_tokens[i][:n_token + 1]\\n self.clear_model_prediction_history(i, generated_tokens[i], from_=n_token)\\n self.trigger_end[i] = False\\n \\n elif n_token in self.model_prediction_history[i]:\\n self.logprobs_history[i][n_token] = new_model_probs\\n \\n if not kept:\\n break\\n \\n all_kept.append(kept)\\n return all_kept\\n \\n\\n def clear_model_prediction_history(self, index, generated_tokens_index, from_=-1):\\n \\\"\\\"\\\"Clears the model prediction history for a specific sample in the batch. First deletes all history of finished tokens, then \\n deletes history of tokens that were prediction, but then got removed because of speculation\\n\\n Args:\\n index (int): index of the sample in the batch\\n generated_tokens_index (list[int]): Generated tokens at the index\\n from_ (int, optional): From which token to delete all the history. Defaults to -1.\\n \\\"\\\"\\\"\\n all_indices = list(self.model_prediction_history[index].keys())\\n for token in all_indices:\\n all_none = all([logprob is None for logprob in self.model_prediction_history[index][token].values()])\\n finished = self.formula.is_finished(self.model_prediction_history[index][token])\\n if all_none or finished or (from_ != -1 and token > from_):\\n if finished and len(generated_tokens_index) > token and self.calculate_statistics:\\n self.add_monitor_token_probs(generated_tokens_index[token], self.model_prediction_history[index][token], self.logprobs_history[index].get(token))\\n \\n if finished:\\n for model_index in range(len(self.model_last_token_prediction)):\\n self.model_last_token_prediction[model_index][index] = max(token + 1, self.model_last_token_prediction[model_index][index])\\n \\n del self.model_prediction_history[index][token]\\n \\n if from_ > -1:\\n for model_index in range(len(self.model_last_token_prediction)):\\n self.model_last_token_prediction[model_index][index] = min(from_ + 1, self.model_last_token_prediction[model_index][index])\\n \\n def max_index_prediction_history(self, index):\\n \\\"\\\"\\\"Gets the max index of the model prediction history for a specific runnable operator \\n\\n Args:\\n index (int): index of runnable operator in the list of runnable operators\\n\\n Returns:\\n int: max index of its prediction\\n \\\"\\\"\\\"\\n keys = list(self.model_prediction_history[index].keys())\\n if len(keys) == 0:\\n return 0\\n return max(self.model_prediction_history[index].keys())\\n\\n def normal_sample(self, probs):\\n \\\"\\\"\\\"Samples from a probability distribution\\n\\n Args:\\n probs (torch.tensor): Probability distribution\\n\\n Returns:\\n int: Sampled token\\n \\\"\\\"\\\"\\n out = torch.multinomial(probs, 1)\\n return out\\n \\n def KL_divergence(self, p, q):\\n \\\"\\\"\\\"Compuates KL divergence between two probability distributions\\n\\n Args:\\n p (torch.tensor): probability distribution\\n q (torch.tensor): probability distribution\\n\\n Returns:\\n float: KL divergence\\n \\\"\\\"\\\"\\n return torch.sum(p * torch.log((p + self.epsilon) / (q + self.epsilon)))\\n \\n def entropy(self, p):\\n \\\"\\\"\\\"Computes entropy of a probability distribution\\n\\n Args:\\n p (torch.tensor): probability distribution\\n\\n Returns:\\n float: entropy\\n \\\"\\\"\\\"\\n return -torch.sum(p * torch.log(p + self.epsilon))\\n \\n def expected_acceptance_prob(self, p, q):\\n \\\"\\\"\\\"\\n Calculates the expected acceptance probability of speculative sampling.\\n \\n Args:\\n p (torch.tensor): probability distribution\\n q (torch.tensor): probability distribution\\n \\\"\\\"\\\"\\n return 1 - 1 / 2 * torch.sum(torch.abs(q - p)).item()\\n \\n def add_monitor_token_probs(self, token, history, history_logprobs):\\n \\\"\\\"\\\"Adds some token probabilities to the monitor\\n\\n Args:\\n token (int): Samples token\\n history (dict): Model prediction history at the specific index where the token was drawn from\\n history_logprobs (torch.tensor): LogProbability distribution from which the token was drawn.\\n \\\"\\\"\\\"\\n for runnable_operator in self.runnable_operators:\\n if runnable_operator.is_finished(history) and runnable_operator.outputs_logprobs:\\n evaluated = runnable_operator.evaluate(history)\\n self.monitor.add_result(element=torch.softmax(evaluated, dim=-1)[token].item(), runnable_operator=runnable_operator, indicator=\\\"token_prob\\\")\\n # add logprob as well\\n self.monitor.add_result(element=max(evaluated[token].item(), np.log(self.epsilon)), runnable_operator=runnable_operator, indicator=\\\"token_logprob\\\")\\n # add KL divergence\\n if history_logprobs is not None:\\n self.monitor.add_result(element=self.KL_divergence(torch.softmax(history_logprobs, dim=-1), torch.softmax(evaluated, dim=-1)).item(), \\n runnable_operator=runnable_operator, indicator=\\\"KL_divergence\\\")\\n \\n self.monitor.add_result(element=self.entropy(torch.softmax(history_logprobs, dim=-1)).item(), indicator=\\\"entropy\\\")\\n\\n def next_token_speculative(self, continuation_tokens, \\n top_p=1.0, top_k=0, temperature=1.0, speculation=True, use_cache=True):\\n \\\"\\\"\\\"Continues one step in the generation process by running the runnable operators that need to be run and then sampling from the probability distribution.\\n\\n Args:\\n continuation_tokens (list[list[int]]): Current continuation tokens\\n top_p (float, optional): top_p to use. Defaults to 1.0.\\n top_k (int, optional): top_k to use. Defaults to 0.\\n temperature (float, optional): temperature to use. Defaults to 1.0.\\n speculation (bool, optional): Whether to use speculation. Defaults to True.\\n use_cache (bool, optional): Whether to use cache. Defaults to True.\\n\\n Returns:\\n _type_: _description_\\n \\\"\\\"\\\"\\n models_ran = []\\n for i, runnable_operator in enumerate(self.runnable_operators):\\n new_tokens = [len(continuation_tokens[j]) - self.model_last_token_prediction[i][j] + 1 for j in range(len(continuation_tokens))]\\n if runnable_operator.run_condition(new_tokens, self.trigger_end) or not speculation:\\n logprobs = self.forward_model(runnable_operator, continuation_tokens, model_new_tokens=new_tokens, use_cache=use_cache, do_speculation=speculation)\\n all_kept = self.add_new_result(continuation_tokens, new_tokens, runnable_operator, logprobs, top_p, top_k, temperature)\\n models_ran.append(i)\\n \\n self.model_last_token_prediction[i] = [len(continuation_tokens[j]) + int(all_kept[j])\\n for j in range(len(continuation_tokens))]\\n \\n if not all(all_kept):\\n break\\n \\n to_sample_indices = [i for i in range(len(continuation_tokens)) if all_kept[i] and not self.trigger_end[i]]\\n\\n if len(to_sample_indices) > 0:\\n # do batch sampling\\n all_required_histories = torch.stack([\\n self.create_sample_logprobs(\\n self.logprobs_history[i][len(continuation_tokens[i])], \\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p\\n ) for i in to_sample_indices\\n ])\\n new_tokens = self.normal_sample(all_required_histories)\\n for i in range(len(to_sample_indices)):\\n continuation_tokens[to_sample_indices[i]].append(new_tokens[i].item())\\n\\n for i in models_ran:\\n self.model_last_token_prediction[i] = [len(continuation_tokens[j]) for j in range(len(continuation_tokens))]\\n return continuation_tokens\\n\\n def __call__(self, input_ids, **kwargs):\\n \\\"\\\"\\\"Runs the forward pass of the model. This is needed for compatibility with lm-evaluation-harness\\n\\n Args:\\n input_ids (torch.tensor): input ids\\n\\n Returns:\\n namedtuple: Named tuple of the ModelArithmetic model\\n \\\"\\\"\\\"\\n return self.forward(input_ids, **kwargs)\\n \\n def forward(self, input_ids, normalize=True, **kwargs):\\n \\\"\\\"\\\"Runs the foward pass. This is needed for compatibility with lm-evaluation-harness\\n\\n Args:\\n input_ids (torch.tensor): input ids\\n normalize (bool, optional): Whether or not to normalize the output. Defaults to True.\\n\\n Returns:\\n namedtuple: Named tuple of the ModelArithmetic model\\n \\\"\\\"\\\"\\n ### this is a bit cheeky, but in order to be compatible with lm-evaluation-harness, we need to implement this method\\n logprobs_per_model = {runnable_operator.id(): None for runnable_operator in self.runnable_operators}\\n if not isinstance(input_ids, list):\\n input_shape = input_ids.shape\\n continuation_tokens = self.lm_eval_compatibility.forward_preprocessing(input_ids, self.model_input_tokens)\\n else:\\n input_shape = None\\n continuation_tokens = input_ids\\n\\n for runnable_operator in self.runnable_operators:\\n logprobs = self.forward_model(runnable_operator, continuation_tokens)\\n if input_shape is not None:\\n logprobs = self.lm_eval_compatibility.forward_post_processing(logprobs, input_shape)\\n logprobs_per_model[runnable_operator.id()] = logprobs\\n\\n output = self.formula.evaluate(logprobs_per_model, normalize=normalize)\\n return [output]\\n\\n def get_decoded_tokens(self, next_tokens_batch):\\n \\\"\\\"\\\"Gets decoded tokens from the next tokens\\n\\n Args:\\n next_tokens_batch (list[list[int]]): New tokens for each sample in the batch\\n\\n Returns:\\n list[str]: Decoded tokens\\n \\\"\\\"\\\"\\n # adding eos token for compatibility with sentencepiece tokenizer\\n encoded_sentences = [[self.tokenizer.eos_token_id] + next_tokens for next_tokens in next_tokens_batch]\\n decoded_sentences = [self.tokenizer.decode(encoded_sentence, add_special_tokens=False) for encoded_sentence in encoded_sentences]\\n decoded_next_tokens = [decoded_sentence[len(self.tokenizer.eos_token):] for decoded_sentence in decoded_sentences]\\n return decoded_next_tokens\\n \\n def clear_memory(self):\\n \\\"\\\"\\\"Deletes all loaded models and clears the cache\\n \\\"\\\"\\\"\\n for runnable_operator in self.runnable_operators:\\n runnable_operator.delete_cache()\\n self.loaded_models = dict()\\n torch.cuda.empty_cache()\\n\\n def generate_text(self, sentences, max_length=1024, stop_texts=None, batch_size=None,\\n temperature=1.0, top_p=1.0, top_k=0, num_return_sequences=1, do_speculation=False, use_cache=True, **kwargs):\\n \\\"\\\"\\\"Generates text based on the input params\\n\\n Args:\\n sentences (list[str]): List of input sentences\\n max_length (int, optional): Max generation length. Defaults to 128.\\n stop_texts (list[str], optional): Strings at which to stop generation. Defaults to None.\\n batch_size (int, optional): Batch size. Defaults to None (all at once).\\n temperature (float, optional): temperature to use. Defaults to 1.0.\\n top_p (float, optional): top_p to use. Defaults to 1.0.\\n top_k (int, optional): top_k to use. Defaults to 0.\\n num_return_sequences (int, optional): Number of return sequences per sentence. Defaults to 1.\\n do_speculation (bool, optional): Whether or not to do speculation. Defaults to True.\\n use_cache (bool, optional): Whether or not to use cache. Defaults to True.\\n\\n Returns:\\n list[str]: List of generated texts\\n \\\"\\\"\\\"\\n assert not do_speculation or any([runnable_operator.speculative_factor == 1 for runnable_operator in self.runnable_operators])\\n if isinstance(sentences, str):\\n sentences = [sentences]\\n if batch_size is None:\\n batch_size = len(sentences)\\n \\n # duplicate each sentence num_return_sequences times, but keep the same sentences next to each other\\n sentences = [sentence for sentence in sentences for _ in range(num_return_sequences)]\\n\\n self.model_prediction_history = [dict() for _ in range(batch_size)]\\n self.logprobs_history = [dict() for _ in range(batch_size)]\\n self.model_last_token_prediction = [[0 for _ in range(batch_size)] for _ in range(len(self.runnable_operators))]\\n self.trigger_end = [False for _ in range(batch_size)]\\n self.init_monitor()\\n \\n if stop_texts is None:\\n stop_texts = []\\n stop_texts.append(self.tokenizer.eos_token)\\n\\n start_sentences = sentences[:]\\n\\n log(logger.debug, f\\\"Generating {len(sentences)} sentences\\\")\\n\\n generated_texts = [\\\"\\\" for _ in range(len(sentences))]\\n generated_tokens = [[] for _ in range(len(sentences))]\\n current_indices = [i for i in range(0, min(len(sentences), batch_size))]\\n next_index = len(current_indices)\\n \\n for runnable_operator_id in self.model_input_tokens:\\n self.model_input_tokens[runnable_operator_id].set_inputs([start_sentences[index] for index in current_indices])\\n \\n total_done = 0\\n while len(current_indices) > 0:\\n start_time = time.time()\\n generated_tokens_batch = [generated_tokens[index] for index in current_indices]\\n next_tokens = self.next_token_speculative(generated_tokens_batch, top_p, top_k, \\n temperature, speculation=do_speculation, use_cache=use_cache)\\n for i in range(len(next_tokens)):\\n next_tokens[i] = self.run_retroactive_operators(i, next_tokens[i], temperature, top_k, top_p)\\n self.clear_model_prediction_history(i, next_tokens[i])\\n decoded_tokens = self.get_decoded_tokens(next_tokens)\\n\\n for i, index in enumerate(current_indices):\\n generated_tokens[index] = next_tokens[i]\\n generated_texts[index] = decoded_tokens[i]\\n\\n indices_to_remove = []\\n for i in range(len(current_indices)):\\n sentences[current_indices[i]] = start_sentences[current_indices[i]] + generated_texts[current_indices[i]]\\n if any([stop_text in generated_texts[current_indices[i]] for stop_text in stop_texts]) or len(generated_tokens[current_indices[i]]) >= max_length:\\n if len(self.model_prediction_history[i]) == 0:\\n indices_to_remove.append(i)\\n else:\\n self.trigger_end[i] = True\\n \\n for i in indices_to_remove[::-1]:\\n self.monitor.add_result(element=len(generated_tokens[current_indices[i]]), indicator=\\\"length\\\")\\n del current_indices[i]\\n self.model_prediction_history = self.model_prediction_history[:i] + self.model_prediction_history[i + 1:]\\n self.logprobs_history = self.logprobs_history[:i] + self.logprobs_history[i + 1:]\\n for j in range(len(self.model_last_token_prediction)):\\n self.model_last_token_prediction[j] = self.model_last_token_prediction[j][:i] + self.model_last_token_prediction[j][i + 1:]\\n self.trigger_end = self.trigger_end[:i] + self.trigger_end[i + 1:]\\n \\n for runnable_operator in self.runnable_operators:\\n runnable_operator.delete_cache(index=i)\\n\\n if next_index < len(sentences):\\n current_indices.append(next_index)\\n self.model_prediction_history.append(dict())\\n self.logprobs_history.append(dict())\\n self.trigger_end.append(False)\\n \\n for j in range(len(self.model_last_token_prediction)):\\n self.model_last_token_prediction[j].append(0)\\n \\n next_index += 1\\n total_done += 1\\n if total_done % 30 == 0:\\n log(logger.debug, f\\\"Progress: {total_done / len(sentences):.3f}\\\")\\n \\n for runnable_operator_id in self.model_input_tokens:\\n self.model_input_tokens[runnable_operator_id].set_inputs([start_sentences[index] for index in current_indices])\\n\\n self.monitor.add_result(element=time.time() - start_time)\\n \\n return generated_texts\\n\\n def generate(self, input_ids, attention_mask=None, do_sample=False, max_new_tokens=1024, \\n stopping_criteria=None, temperature=1.0, top_p=1.0, top_k=0, use_cache=True, eos_token_id=None, pad_token_id=None, **kwargs):\\n \\\"\\\"\\\"Generates text based on the input params. Needed for compatibility with lm-evaluation-harness\\n\\n Args:\\n input_ids (torch.tensor): input ids\\n attention_mask (torch.tensor, optional): attention mask. Defaults to None.\\n do_sample (bool, optional): Whether or not to sample. Defaults to False.\\n max_new_tokens (int, optional): Max new number of tokens. Defaults to 128.\\n stopping_criteria (_type_, optional): Stopping criteria to use. Defaults to None.\\n temperature (float, optional): Temperature to. Defaults to 1.0.\\n top_p (float, optional): top_p to use. Defaults to 1.0.\\n top_k (int, optional): top_k to use. Defaults to 0.\\n use_cache (bool, optional): Whether or not to use cache. Defaults to True.\\n eos_token_id (int, optional): eos token id. Defaults to None.\\n pad_token_id (int, optional): pad token id. Defaults to None.\\n\\n Returns:\\n list[str]: Generated texts\\n \\\"\\\"\\\"\\n if not do_sample:\\n top_k = 1\\n \\n batch_size = input_ids.shape[0]\\n input_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in input_ids]\\n stopping_sequences = [self.tokenizer.eos_token]\\n if stopping_criteria is not None:\\n stopping_sequences += [criteria.sequence for criteria in stopping_criteria]\\n if eos_token_id is not None:\\n stopping_sequences += [self.tokenizer.decode([eos_token_id])]\\n \\n texts = self.generate_text(input_texts, max_length=max_new_tokens, stop_texts=stopping_sequences,\\n batch_size=batch_size, temperature=temperature, top_p=top_p, top_k=top_k, use_cache=use_cache)\\n encoded_texts = self.tokenizer.batch_encode_plus(texts, add_special_tokens=False, return_tensors=\\\"pt\\\").input_ids.to(self.device)\\n # concatenate the input_ids with the encoded_texts\\n all_encoded = torch.cat([input_ids, encoded_texts], dim=-1)\\n return all_encoded\"\n },\n {\n \"identifier\": \"ENABLE_LOGGING\",\n \"path\": \"src/model_arithmetic/utils.py\",\n \"snippet\": \"ENABLE_LOGGING = False\"\n },\n {\n \"identifier\": \"log\",\n \"path\": \"src/model_arithmetic/utils.py\",\n \"snippet\": \"def log(function, message):\\n \\\"\\\"\\\"\\n Logs the given message using the provided function if logging is enabled.\\n \\n Parameters:\\n function (callable): The logging function to use.\\n message (str): The message to be logged.\\n \\\"\\\"\\\"\\n if ENABLE_LOGGING:\\n function(message)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from .base import BaseClass\nfrom loguru import logger\nfrom transformers import set_seed, Trainer, TrainingArguments, DataCollatorWithPadding\nfrom .dataset import CustomDataset\nfrom sklearn.model_selection import train_test_split\nfrom .basic_model_loader import load_model, load_tokenizer\nfrom .model_arithmetic import ModelArithmetic\nfrom googleapiclient import discovery\nfrom dotenv import load_dotenv\nfrom torch.utils.data import DataLoader\nfrom .utils import ENABLE_LOGGING, log\n from lm_eval import evaluator\nimport pandas as pd\nimport numpy as np\nimport torch\nimport os\nimport json\nimport time"},"token_num":{"kind":"number","value":16098,"string":"16,098"},"cropped_code":{"kind":"string","value":" scores.append(value)\n time.sleep(0.1)\n result = np.mean(scores)\n log(logger.info, f\"Perspective is {result:.6f}\")\n self.output[\"perspective\"] = {\n \"mean\": result,\n \"std\": np.std(scores),\n }\n return result\n\n def generate_samples(self, max_tokens, batch_size=1, temperature=1.0, top_p=1.0, top_k=0, stop_texts=None, speculation=False, **kwargs):\n \"\"\"\n Generates samples from the model.\n\n Args:\n max_tokens (int): The maximum number of tokens to be used for evaluation.\n batch_size (int, optional): The batch size to be used for evaluation.\n temperature (float, optional): The temperature to be used for sampling.\n top_p (float, optional): The top-p value to be used for sampling.\n top_k (int, optional): The top-k value to be used for sampling.\n stop_texts (list, optional): The list of texts at which sampling should be stopped\n speculation (bool, optional): Whether to use speculation or not.\n **kwargs: Additional keyword arguments.\n \"\"\"\n start_time = time.time()\n if \"generated\" not in self.dataset.columns:\n texts = self.generator.generate_text(self.dataset[\"input\"].tolist(), max_length=max_tokens, \n batch_size=batch_size, temperature=temperature, \n top_p=top_p, top_k=top_k, stop_texts=stop_texts, do_speculation=speculation)\n self.dataset[\"generated\"] = texts\n end_time = time.time()\n \n self.output[\"time\"] = {\n \"total_time\": end_time - start_time,\n \"time_per_sample\": (end_time - start_time) / len(self.dataset),\n \"dataset_size\": len(self.dataset),\n \"max_tokens\": max_tokens,\n \"batch_size\": batch_size\n }\n\n def save_generated(self, output_location):\n \"\"\"\n Saves the generated samples to the specified location.\n\n Args:\n output_location (string): The location to save the generated samples.\n \"\"\"\n log(logger.debug, f\"Saving generated samples to {output_location}\")\n self.dataset.to_csv(output_location)\n\n def get_perplexity(self, dataset, model, tokenizer, **kwargs):\n \"\"\"\n Calculates the perplexity of the generated sentences.\n\n Args:\n dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns \"input\" (for input text), \"generated\" (for generated text). \n model (PreTrainedModel): The model to be evaluated.\n tokenizer (Tokenizer): The tokenizer to be used for tokenizing the sentences.\n **kwargs: Additional keyword arguments.\n \"\"\"\n perplexities = []\n sum_nllos = 0\n n_tokens = 0\n\n for index, sample in dataset.iterrows():\n input_sentence = sample['input']\n sentence = sample['generated']\n if len(sentence) == 0:\n continue\n combined_sentence = input_sentence + sentence\n\n encodings = tokenizer(combined_sentence, return_tensors='pt')\n input_ids = encodings['input_ids'].to(model.device)\n attention_mask = encodings['attention_mask'].to(model.device)\n\n input_encodings = tokenizer(input_sentence, return_tensors='pt')\n input_ids_inputs = input_encodings['input_ids']\n input_length = input_ids_inputs.size(1)\n\n with torch.no_grad():\n output = model(input_ids, labels=input_ids, attention_mask=attention_mask)\n logprobs = output.logits[0, :].log_softmax(dim=-1)\n\n loss_func = torch.nn.NLLLoss(ignore_index=-100, reduction='sum')\n\n loss = loss_func(logprobs[..., input_length:-1, :].contiguous(), input_ids[0, :][..., input_length+1:].contiguous())\n loss = loss.to(torch.float32).detach().cpu().numpy()\n n_tokens_here = input_ids.shape[-1] - input_length - 1\n if n_tokens_here > 0:\n perplexity = np.exp(loss / n_tokens_here)\n sum_nllos += loss\n n_tokens += n_tokens_here\n if not np.isnan(perplexity):\n perplexities.append(perplexity)\n\n average = np.mean(perplexities)\n median = np.median(perplexities)\n real = np.exp(sum_nllos / n_tokens)\n\n return {\n \"average\": average,\n \"median\": median,\n \"correct_perplexity\": real\n }\n\n def perplexity(self, dataset, model_name_fluency=\"gpt2-xl\", dtype=torch.float16, **kwargs):\n \"\"\"\n Calculates the perplexity of the generated sentences.\n\n Args:\n dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns \"input\" (for input text), \"generated\" (for generated text). \n model_name_fluency (string, optional): The name of the model to be used for calculating fluency.\n dtype (torch.dtype, optional): The data type to be used for the model.\n **kwargs: Additional keyword arguments.\n \"\"\"\n log(logger.info, \"Calculating fluency\")\n if \"perplexity\" in self.output:\n log(logger.info, f\"Reloading perplexity. Perplexity is {self.output['perplexity']}\")\n return self.output[\"perplexity\"]\n tokenizer = load_tokenizer(model_name_fluency)\n"},"all_code":{"kind":"string","value":"\nload_dotenv()\n\nclass Evaluation(BaseClass):\n \"\"\"\n This class is used for evaluating a model's performance on a given dataset.\n It includes methods for preparing the dataset, evaluating the model, generating samples, \n calculating perplexity and faithfulness of the model.\n \"\"\"\n def __init__(self, generator=None, dataset_location=None, dataset=None, train_dataset=None, train_dataset_location=None, \n n_input_words=5, bleurt_checkpoint=\"../models/BLEURT-20\", **kwargs):\n \"\"\"\n Initialize the Evaluation class with the given parameters.\n\n Args:\n generator (ModelArithmetic, optional): The model to be evaluated.\n dataset_location (string, optional): The location of the dataset to be used for evaluation. Either this or dataset should be provided. Dataset should contain column \"text\", \"input\", \"output and \"label\" (\"label\", \"input\", \"output\" optional)\n dataset (pd.DataFrame, optional): The dataset to be used for evaluation. Either this or dataset_location should be provided. Dataset should contain column \"text\", \"input\", \"output and \"label\" (\"label\", \"input\", \"output\" optional)\n train_dataset (pd.DataFrame, optional): The dataset to be used for training the model. Only used when calculating the faithfulness of the model and when the downstream model still needs to be finetuned.\n train_dataset_location (string, optional): The location of the dataset to be used for training the model.\n n_input_words (int, optional): The number of input words to be used in the generator. Only used if the dataset does not contain the column \"input\".\n bleurt_checkpoint (string, optional): The location of the BLEURT model checkpoint.\n **kwargs: Additional keyword arguments.\n \"\"\"\n self.has_input_task = True\n self.dataset = None\n if dataset is not None:\n self.dataset = dataset.copy()\n elif dataset_location is not None:\n self.dataset = pd.read_csv(dataset_location, escapechar='\\\\', lineterminator=\"\\n\")\n\n if train_dataset is not None:\n self.train_dataset = train_dataset\n elif train_dataset_location is not None:\n self.train_dataset = pd.read_csv(train_dataset_location, escapechar='\\\\', lineterminator=\"\\n\")\n else:\n self.train_dataset = None\n\n if self.dataset is not None:\n self.prepare_dataset(n_input_words)\n\n super().__init__(**kwargs, dataset_location=dataset_location, generator=generator, \n has_input_task=self.has_input_task, output=dict(), extra_kwargs=None, bleurt_checkpoint=bleurt_checkpoint, \n train_dataset_location=None)\n \n if isinstance(generator, ModelArithmetic):\n # If we don't do it this way, we can't store the evaluator because ModelArithmetic is not serializable\n del self.kwargs[\"generator\"]\n self.kwargs[\"formula\"] = generator.formula\n self.formula = generator.formula\n\n def prepare_dataset(self, n_input_words=5):\n \"\"\"\n Prepares the dataset for evaluation. If the dataset does not have an input column, \n it assumes the input is the first n_input_words words of the output. If the dataset \n does not have a label column, it assumes all labels are 1.\n\n Args:\n n_input_words (int): The number of input words to be used.\n \"\"\"\n log(logger.debug, \"Preparing dataset\")\n if \"input\" not in self.dataset.columns:\n log(logger.debug, f\"No input column found, assuming input is the first {n_input_words} words of the output\")\n self.dataset[\"input\"] = self.dataset[\"text\"].apply(lambda x: \" \".join(x.split()[:n_input_words]))\n self.dataset[\"output\"] = self.dataset[\"text\"].apply(lambda x: \" \" + \" \".join(x.split()[n_input_words:]))\n self.has_input_task = False\n \n if \"label\" not in self.dataset.columns:\n log(logger.debug, \"No label column found, assuming all labels are 1\")\n self.dataset[\"label\"] = 1\n\n def evaluate_lm_eval(self, model, task_name, batch_size, num_fewshot, model_args, no_cache=False, limit=None, write_out=False, output_folder=None, **kwargs):\n \"\"\"\n Evaluates the model using the lm_eval package. \n\n Args:\n model (PreTrainedModel): The model to be evaluated.\n task_name (string): The name of the task for evaluation.\n batch_size (int): The batch size to be used for evaluation.\n num_fewshot (int): The number of fewshot examples to be used for evaluation.\n model_args (dict): The arguments to be passed to the model.\n no_cache (bool, optional): Whether to use cached results or not.\n limit (int, optional): The maximum number of examples to be used for evaluation.\n write_out (bool, optional): Whether to write out the results or not.\n output_folder (string, optional): The folder to write out the results.\n **kwargs: Additional keyword arguments.\n \"\"\"\n try:\n except ImportError:\n raise ImportError(\"Please install lm_eval to run this function\")\n \n results = evaluator.simple_evaluate(\n model=model,\n model_args=model_args,\n tasks=[task_name],\n num_fewshot=num_fewshot,\n batch_size=batch_size,\n device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n no_cache=no_cache,\n limit=limit,\n write_out=write_out,\n output_base_path=output_folder\n )\n if \"lm_eval\" in self.output:\n self.output[\"lm_eval\"][task_name] = results\n else:\n self.output[\"lm_eval\"] = {task_name: results}\n\n def evaluate(self, max_tokens=128, store_file=None, reload=True, \n dataset_file=None, reload_data=True, preserve_memory=False, batch_size=1, do_perspective=True,\n speculation=False, only_faithfulness=False,\n **kwargs):\n \"\"\"\n Evaluates the model on the dataset and calculates the perplexity and faithfulness\n\n Args:\n max_tokens (int, optional): The maximum number of tokens to be used for evaluation.\n store_file (string, optional): The file to store the evaluation results.\n reload (bool, optional): Whether to reload the dataset or not if it was stored before.\n dataset_file (string, optional): The file containing the dataset. If path exists, dataset is loaded from path. If path does not exist, dataset is saved to path.\n reload_data (bool, optional): Whether to reload the data or not if it was stored before.\n preserve_memory (bool, optional): Whether to preserve memory or not.\n batch_size (int, optional): The batch size to be used for evaluation.\n do_perspective (bool, optional): Whether to calculate the perspective score or not.\n speculation (bool, optional): Whether to use speculation or not.\n **kwargs: Additional keyword arguments.\n \"\"\"\n if store_file is not None:\n os.makedirs(os.path.dirname(store_file), exist_ok=True)\n if dataset_file is not None:\n os.makedirs(os.path.dirname(dataset_file), exist_ok=True)\n\n if (reload_data or reload) and dataset_file is not None and os.path.isfile(dataset_file):\n log(logger.debug, f\"Reloading dataset from {dataset_file}\")\n self.dataset = pd.read_csv(dataset_file, escapechar='\\\\', lineterminator=\"\\n\")\n self.dataset.fillna(\"\", inplace=True)\n else:\n log(logger.debug,\"Generating samples\")\n self.generate_samples(max_tokens, batch_size=batch_size, speculation=speculation, **kwargs)\n if dataset_file is not None:\n log(logger.info, f\"Saving dataset to {dataset_file}\")\n self.dataset.to_csv(dataset_file, index=False, escapechar='\\\\', lineterminator=\"\\n\")\n \n if reload and store_file is not None and os.path.isfile(store_file):\n self.output = json.load(open(store_file))[\"output\"]\n log(logger.info, f\"Reloading output. Output is {self.output}\")\n if not reload:\n self.output = dict()\n\n self.dataset.dropna(inplace=True)\n\n if preserve_memory and hasattr(self.generator, \"clear_memory\"): # can have weird side effects!\n self.generator.clear_memory()\n\n if not only_faithfulness:\n self.perplexity(self.dataset, **kwargs)\n else:\n del self.output['faithfulness']\n self.faithfulness_multiple(self.dataset, **kwargs)\n \n if do_perspective and not only_faithfulness:\n self.perspective(**kwargs)\n if dataset_file is not None:\n log(logger.info, f\"Saving dataset to {dataset_file}\")\n self.dataset.to_csv(dataset_file, index=False, escapechar='\\\\', lineterminator=\"\\n\")\n \n self.extra_kwargs = {kwarg: str(kwargs[kwarg]) for kwarg in kwargs}\n self.extra_kwargs[\"max_tokens\"] = max_tokens\n if store_file is not None:\n log(logger.info, f\"Saving output to {store_file}\")\n self.save(store_file)\n\n \n return self.output\n \n def perspective(self, classification_with_input=True, **kwargs):\n \"\"\"\n Calculates the perspective score of the generated sentences.\n \n Args:\n classification_with_input (bool, optional): Whether to use the input of the sentence for calculating the perspective score or not.\n **kwargs: Additional keyword arguments.\n \"\"\"\n retries = 0\n if \"perspective\" in self.output:\n log(logger.info, f\"Reloading perspective. Perspective is {self.output['perspective']}\")\n return self.output[\"perspective\"]\n log(logger.info, \"Calculating perspective\")\n service = discovery.build('commentanalyzer', 'v1alpha1', \n developerKey=os.environ[\"PERSPECTIVE_API_KEY\"],\n discoveryServiceUrl=\"https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1\",\n static_discovery=False)\n scores = []\n self.dataset[\"perspective\"] = [None] * len(self.dataset)\n for index, sample in self.dataset.iterrows():\n sentence = sample['generated']\n if classification_with_input:\n sentence = sample['input'] + sentence\n\n analyze_request = {\n 'comment': { 'text': sentence },\n 'languages': [\"en\"],\n 'requestedAttributes': {'TOXICITY': {}}\n }\n while retries < 10 ** 5:\n try:\n response = service.comments().analyze(body=analyze_request).execute()\n break\n except Exception as e:\n if \"LANGUAGE_NOT_SUPPORTED_BY_ATTRIBUTE\" in str(e):\n continue\n log(logger.warning, f\"Exception {e} occurred, retrying...\")\n retries += 1\n time.sleep(10)\n if retries == 10 ** 5:\n log(logger.error, \"Could not get all perspective scores\")\n break\n value = response[\"attributeScores\"][\"TOXICITY\"][\"summaryScore\"][\"value\"]\n self.dataset.at[index, \"perspective\"] = value\n scores.append(value)\n time.sleep(0.1)\n result = np.mean(scores)\n log(logger.info, f\"Perspective is {result:.6f}\")\n self.output[\"perspective\"] = {\n \"mean\": result,\n \"std\": np.std(scores),\n }\n return result\n\n def generate_samples(self, max_tokens, batch_size=1, temperature=1.0, top_p=1.0, top_k=0, stop_texts=None, speculation=False, **kwargs):\n \"\"\"\n Generates samples from the model.\n\n Args:\n max_tokens (int): The maximum number of tokens to be used for evaluation.\n batch_size (int, optional): The batch size to be used for evaluation.\n temperature (float, optional): The temperature to be used for sampling.\n top_p (float, optional): The top-p value to be used for sampling.\n top_k (int, optional): The top-k value to be used for sampling.\n stop_texts (list, optional): The list of texts at which sampling should be stopped\n speculation (bool, optional): Whether to use speculation or not.\n **kwargs: Additional keyword arguments.\n \"\"\"\n start_time = time.time()\n if \"generated\" not in self.dataset.columns:\n texts = self.generator.generate_text(self.dataset[\"input\"].tolist(), max_length=max_tokens, \n batch_size=batch_size, temperature=temperature, \n top_p=top_p, top_k=top_k, stop_texts=stop_texts, do_speculation=speculation)\n self.dataset[\"generated\"] = texts\n end_time = time.time()\n \n self.output[\"time\"] = {\n \"total_time\": end_time - start_time,\n \"time_per_sample\": (end_time - start_time) / len(self.dataset),\n \"dataset_size\": len(self.dataset),\n \"max_tokens\": max_tokens,\n \"batch_size\": batch_size\n }\n\n def save_generated(self, output_location):\n \"\"\"\n Saves the generated samples to the specified location.\n\n Args:\n output_location (string): The location to save the generated samples.\n \"\"\"\n log(logger.debug, f\"Saving generated samples to {output_location}\")\n self.dataset.to_csv(output_location)\n\n def get_perplexity(self, dataset, model, tokenizer, **kwargs):\n \"\"\"\n Calculates the perplexity of the generated sentences.\n\n Args:\n dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns \"input\" (for input text), \"generated\" (for generated text). \n model (PreTrainedModel): The model to be evaluated.\n tokenizer (Tokenizer): The tokenizer to be used for tokenizing the sentences.\n **kwargs: Additional keyword arguments.\n \"\"\"\n perplexities = []\n sum_nllos = 0\n n_tokens = 0\n\n for index, sample in dataset.iterrows():\n input_sentence = sample['input']\n sentence = sample['generated']\n if len(sentence) == 0:\n continue\n combined_sentence = input_sentence + sentence\n\n encodings = tokenizer(combined_sentence, return_tensors='pt')\n input_ids = encodings['input_ids'].to(model.device)\n attention_mask = encodings['attention_mask'].to(model.device)\n\n input_encodings = tokenizer(input_sentence, return_tensors='pt')\n input_ids_inputs = input_encodings['input_ids']\n input_length = input_ids_inputs.size(1)\n\n with torch.no_grad():\n output = model(input_ids, labels=input_ids, attention_mask=attention_mask)\n logprobs = output.logits[0, :].log_softmax(dim=-1)\n\n loss_func = torch.nn.NLLLoss(ignore_index=-100, reduction='sum')\n\n loss = loss_func(logprobs[..., input_length:-1, :].contiguous(), input_ids[0, :][..., input_length+1:].contiguous())\n loss = loss.to(torch.float32).detach().cpu().numpy()\n n_tokens_here = input_ids.shape[-1] - input_length - 1\n if n_tokens_here > 0:\n perplexity = np.exp(loss / n_tokens_here)\n sum_nllos += loss\n n_tokens += n_tokens_here\n if not np.isnan(perplexity):\n perplexities.append(perplexity)\n\n average = np.mean(perplexities)\n median = np.median(perplexities)\n real = np.exp(sum_nllos / n_tokens)\n\n return {\n \"average\": average,\n \"median\": median,\n \"correct_perplexity\": real\n }\n\n def perplexity(self, dataset, model_name_fluency=\"gpt2-xl\", dtype=torch.float16, **kwargs):\n \"\"\"\n Calculates the perplexity of the generated sentences.\n\n Args:\n dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns \"input\" (for input text), \"generated\" (for generated text). \n model_name_fluency (string, optional): The name of the model to be used for calculating fluency.\n dtype (torch.dtype, optional): The data type to be used for the model.\n **kwargs: Additional keyword arguments.\n \"\"\"\n log(logger.info, \"Calculating fluency\")\n if \"perplexity\" in self.output:\n log(logger.info, f\"Reloading perplexity. Perplexity is {self.output['perplexity']}\")\n return self.output[\"perplexity\"]\n tokenizer = load_tokenizer(model_name_fluency)"},"next_line":{"kind":"string","value":" model = load_model(model_name_fluency, dtype=dtype)"},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-11-21 20:01:08+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5397,"cells":{"repo_name":{"kind":"string","value":"HeliosZhao/Animate124"},"file_path":{"kind":"string","value":"dnerf/renderer.py"},"context":{"kind":"list like","value":[{"identifier":"custom_meshgrid","path":"nerf/utils.py","snippet":"def custom_meshgrid(*args):\n # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid\n if pver.parse(torch.__version__) < pver.parse('1.10'):\n return torch.meshgrid(*args)\n else:\n return torch.meshgrid(*args, indexing='ij')"},{"identifier":"safe_normalize","path":"nerf/utils.py","snippet":"def safe_normalize(x, eps=1e-20):\n return x / torch.sqrt(torch.clamp(torch.sum(x * x, -1, keepdim=True), min=eps))"},{"identifier":"NeRFRenderer","path":"nerf/renderer.py","snippet":"class NeRFRenderer(nn.Module):\n def __init__(self, opt):\n super().__init__()\n\n self.opt = opt\n self.bound = opt.bound\n self.cascade = 1 + math.ceil(math.log2(opt.bound))\n self.grid_size = 128\n self.max_level = None\n self.dmtet = opt.dmtet\n self.cuda_ray = opt.cuda_ray\n self.taichi_ray = opt.taichi_ray\n self.min_near = opt.min_near\n self.density_thresh = opt.density_thresh\n\n # prepare aabb with a 6D tensor (xmin, ymin, zmin, xmax, ymax, zmax)\n # NOTE: aabb (can be rectangular) is only used to generate points, we still rely on bound (always cubic) to calculate density grid and hashing.\n aabb_train = torch.FloatTensor(\n [-opt.bound, -opt.bound, -opt.bound, opt.bound, opt.bound, opt.bound])\n aabb_infer = aabb_train.clone()\n self.register_buffer('aabb_train', aabb_train)\n self.register_buffer('aabb_infer', aabb_infer)\n\n self.glctx = None\n\n # extra state for cuda raymarching\n if self.cuda_ray:\n ## NOTE TODO the cuda ray sampling for DNeRF is different, make sure to change\n # density grid\n density_grid = torch.zeros(\n [self.cascade, self.grid_size ** 3]) # [CAS, H * H * H]\n density_bitfield = torch.zeros(\n self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8]\n self.register_buffer('density_grid', density_grid)\n self.register_buffer('density_bitfield', density_bitfield)\n self.mean_density = 0\n self.iter_density = 0\n\n # load dmtet vertices\n if self.opt.dmtet:\n self.dmtet = DMTetGeometry(opt.tet_grid_size, opt.tet_mlp, opt).to(opt.device)\n if self.opt.h <= 2048 and self.opt.w <= 2048:\n self.glctx = dr.RasterizeCudaContext()\n else:\n self.glctx = dr.RasterizeGLContext()\n\n if self.taichi_ray:\n from einops import rearrange\n from taichi_modules import RayMarcherTaichi\n from taichi_modules import VolumeRendererTaichi\n from taichi_modules import RayAABBIntersector as RayAABBIntersectorTaichi\n from taichi_modules import raymarching_test as raymarching_test_taichi\n from taichi_modules import composite_test as composite_test_fw\n from taichi_modules import packbits as packbits_taichi\n self.rearrange = rearrange\n self.packbits_taichi = packbits_taichi\n self.ray_aabb_intersector = RayAABBIntersectorTaichi\n self.raymarching_test_taichi = raymarching_test_taichi\n self.composite_test_fw = composite_test_fw\n self.ray_marching = RayMarcherTaichi(\n batch_size=4096) # TODO: hard encoded batch size\n self.volume_render = VolumeRendererTaichi(\n batch_size=4096) # TODO: hard encoded batch size\n # density grid\n density_grid = torch.zeros(\n [self.cascade, self.grid_size ** 3]) # [CAS, H * H * H]\n density_bitfield = torch.zeros(\n self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8]\n self.register_buffer('density_grid', density_grid)\n self.register_buffer('density_bitfield', density_bitfield)\n self.mean_density = 0\n self.iter_density = 0\n \n if self.opt.density_activation == 'exp':\n self.density_activation = trunc_exp\n elif self.opt.density_activation == 'softplus':\n self.density_activation = F.softplus\n elif self.opt.density_activation == 'relu':\n self.density_activation = F.relu\n \n # ref: https://github.com/zhaofuq/Instant-NSR/blob/main/nerf/network_sdf.py#L192\n def finite_difference_normal(self, x, epsilon=1e-2):\n # x: [N, 3]\n # ipdb.set_trace()\n dx_pos, _ = self.common_forward((x + torch.tensor([[epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\n dx_neg, _ = self.common_forward((x + torch.tensor([[-epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\n dy_pos, _ = self.common_forward((x + torch.tensor([[0.00, epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\n dy_neg, _ = self.common_forward((x + torch.tensor([[0.00, -epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\n dz_pos, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, epsilon]], device=x.device)).clamp(-self.bound, self.bound))\n dz_neg, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, -epsilon]], device=x.device)).clamp(-self.bound, self.bound))\n \n normal = torch.stack([\n 0.5 * (dx_pos - dx_neg) / epsilon, \n 0.5 * (dy_pos - dy_neg) / epsilon, \n 0.5 * (dz_pos - dz_neg) / epsilon\n ], dim=-1)\n\n return -normal\n \n def normal(self, x):\n normal = self.finite_difference_normal(x)\n normal = safe_normalize(normal)\n normal = torch.nan_to_num(normal)\n return normal\n\n @torch.no_grad()\n def density_blob(self, x):\n # x: [B, N, 3]\n\n d = (x ** 2).sum(-1)\n\n if self.opt.density_activation == 'exp':\n g = self.opt.blob_density * \\\n torch.exp(- d / (2 * self.opt.blob_radius ** 2))\n else:\n g = self.opt.blob_density * \\\n (1 - torch.sqrt(d) / self.opt.blob_radius)\n\n return g\n\n def forward(self, x, d):\n raise NotImplementedError()\n\n def density(self, x):\n raise NotImplementedError()\n\n def reset_extra_state(self):\n if not (self.cuda_ray or self.taichi_ray):\n return\n # density grid\n self.density_grid.zero_()\n self.mean_density = 0\n self.iter_density = 0\n\n @torch.no_grad()\n def export_mesh(self, path, resolution=None, decimate_target=-1, S=128):\n from meshutils import decimate_mesh, clean_mesh, poisson_mesh_reconstruction\n if self.opt.dmtet:\n vertices, triangles = self.dmtet.get_verts_face()\n vertices = vertices.detach().cpu().numpy()\n triangles = triangles.detach().cpu().numpy()\n\n else:\n\n if resolution is None:\n resolution = self.grid_size\n\n if self.cuda_ray:\n density_thresh = min(self.mean_density, self.density_thresh) \\\n if np.greater(self.mean_density, 0) else self.density_thresh\n else:\n density_thresh = self.density_thresh\n\n sigmas = np.zeros(\n [resolution, resolution, resolution], dtype=np.float32)\n\n # query\n X = torch.linspace(-1, 1, resolution).split(S)\n Y = torch.linspace(-1, 1, resolution).split(S)\n Z = torch.linspace(-1, 1, resolution).split(S)\n\n for xi, xs in enumerate(X):\n for yi, ys in enumerate(Y):\n for zi, zs in enumerate(Z):\n xx, yy, zz = custom_meshgrid(xs, ys, zs)\n pts = torch.cat(\n [xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [S, 3]\n val = self.density(pts.to(self.aabb_train.device))\n sigmas[xi * S: xi * S + len(xs), yi * S: yi * S + len(ys), zi * S: zi * S + len(\n zs)] = val['sigma'].reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy() # [S, 1] --> [x, y, z]\n\n logger.info(\n f'[INFO] marching cubes thresh: {density_thresh} ({sigmas.min()} ~ {sigmas.max()})')\n\n vertices, triangles = mcubes.marching_cubes(sigmas, density_thresh)\n vertices = vertices / (resolution - 1.0) * 2 - 1\n\n # clean\n vertices = vertices.astype(np.float32)\n triangles = triangles.astype(np.int32)\n vertices, triangles = clean_mesh(\n vertices, triangles, remesh=True, remesh_size=0.01)\n\n # decimation\n if decimate_target > 0 and triangles.shape[0] > decimate_target:\n vertices, triangles = decimate_mesh(\n vertices, triangles, decimate_target)\n\n v = torch.from_numpy(vertices).contiguous(\n ).float().to(self.aabb_train.device)\n f = torch.from_numpy(triangles).contiguous().int().to(\n self.aabb_train.device)\n\n # mesh = trimesh.Trimesh(vertices, triangles, process=False) # important, process=True leads to seg fault...\n # mesh.export(os.path.join(path, f'mesh.ply'))\n\n def _export(v, f, h0=2048, w0=2048, ssaa=1, name=''):\n # v, f: torch Tensor\n device = v.device\n v_np = v.cpu().numpy() # [N, 3]\n f_np = f.cpu().numpy() # [M, 3]\n\n logger.info(\n f'[INFO] running xatlas to unwrap UVs for mesh: v={v_np.shape} f={f_np.shape}')\n\n # unwrap uvs\n import xatlas\n import nvdiffrast.torch as dr\n from sklearn.neighbors import NearestNeighbors\n from scipy.ndimage import binary_dilation, binary_erosion\n\n atlas = xatlas.Atlas()\n atlas.add_mesh(v_np, f_np)\n chart_options = xatlas.ChartOptions()\n chart_options.max_iterations = 4 # for faster unwrap...\n atlas.generate(chart_options=chart_options)\n vmapping, ft_np, vt_np = atlas[0] # [N], [M, 3], [N, 2]\n\n # vmapping, ft_np, vt_np = xatlas.parametrize(v_np, f_np) # [N], [M, 3], [N, 2]\n\n vt = torch.from_numpy(vt_np.astype(np.float32)).float().to(device)\n ft = torch.from_numpy(ft_np.astype(np.int64)).int().to(device)\n\n # render uv maps\n uv = vt * 2.0 - 1.0 # uvs to range [-1, 1]\n uv = torch.cat((uv, torch.zeros_like(\n uv[..., :1]), torch.ones_like(uv[..., :1])), dim=-1) # [N, 4]\n\n if ssaa > 1:\n h = int(h0 * ssaa)\n w = int(w0 * ssaa)\n else:\n h, w = h0, w0\n \n if self.glctx is None:\n if h <= 2048 and w <= 2048:\n self.glctx = dr.RasterizeCudaContext()\n else:\n self.glctx = dr.RasterizeGLContext()\n\n rast, _ = dr.rasterize(self.glctx, uv.unsqueeze(\n 0), ft, (h, w)) # [1, h, w, 4]\n xyzs, _ = dr.interpolate(v.unsqueeze(0), rast, f) # [1, h, w, 3]\n mask, _ = dr.interpolate(torch.ones_like(\n v[:, :1]).unsqueeze(0), rast, f) # [1, h, w, 1]\n\n # masked query \n xyzs = xyzs.view(-1, 3)\n mask = (mask > 0).view(-1)\n \n feats = torch.zeros(h * w, 3, device=device, dtype=torch.float32)\n\n if mask.any():\n xyzs = xyzs[mask] # [M, 3]\n\n # batched inference to avoid OOM\n all_feats = []\n head = 0\n while head < xyzs.shape[0]:\n tail = min(head + 640000, xyzs.shape[0])\n results_ = self.density(xyzs[head:tail])\n all_feats.append(results_['albedo'].float())\n head += 640000\n\n feats[mask] = torch.cat(all_feats, dim=0)\n \n feats = feats.view(h, w, -1)\n mask = mask.view(h, w)\n\n # quantize [0.0, 1.0] to [0, 255]\n feats = feats.cpu().numpy()\n feats = (feats * 255).astype(np.uint8)\n\n ### NN search as an antialiasing ...\n mask = mask.cpu().numpy()\n\n inpaint_region = binary_dilation(mask, iterations=3)\n inpaint_region[mask] = 0\n\n search_region = mask.copy()\n not_search_region = binary_erosion(search_region, iterations=2)\n search_region[not_search_region] = 0\n\n search_coords = np.stack(np.nonzero(search_region), axis=-1)\n inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)\n\n knn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(search_coords)\n _, indices = knn.kneighbors(inpaint_coords)\n\n feats[tuple(inpaint_coords.T)] = feats[tuple(search_coords[indices[:, 0]].T)]\n\n feats = cv2.cvtColor(feats, cv2.COLOR_RGB2BGR)\n\n # do ssaa after the NN search, in numpy\n if ssaa > 1:\n feats = cv2.resize(feats, (w0, h0), interpolation=cv2.INTER_LINEAR)\n\n cv2.imwrite(os.path.join(path, f'{name}albedo.png'), feats)\n\n # save obj (v, vt, f /)\n obj_file = os.path.join(path, f'{name}mesh.obj')\n mtl_file = os.path.join(path, f'{name}mesh.mtl')\n\n logger.info(f'[INFO] writing obj mesh to {obj_file}')\n with open(obj_file, \"w\") as fp:\n fp.write(f'mtllib {name}mesh.mtl \\n')\n\n logger.info(f'[INFO] writing vertices {v_np.shape}')\n for v in v_np:\n fp.write(f'v {v[0]} {v[1]} {v[2]} \\n')\n\n logger.info(\n f'[INFO] writing vertices texture coords {vt_np.shape}')\n for v in vt_np:\n fp.write(f'vt {v[0]} {1 - v[1]} \\n')\n\n logger.info(f'[INFO] writing faces {f_np.shape}')\n fp.write(f'usemtl mat0 \\n')\n for i in range(len(f_np)):\n fp.write(\n f\"f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1} {f_np[i, 1] + 1}/{ft_np[i, 1] + 1} {f_np[i, 2] + 1}/{ft_np[i, 2] + 1} \\n\")\n\n with open(mtl_file, \"w\") as fp:\n fp.write(f'newmtl mat0 \\n')\n fp.write(f'Ka 1.000000 1.000000 1.000000 \\n')\n fp.write(f'Kd 1.000000 1.000000 1.000000 \\n')\n fp.write(f'Ks 0.000000 0.000000 0.000000 \\n')\n fp.write(f'Tr 1.000000 \\n')\n fp.write(f'illum 1 \\n')\n fp.write(f'Ns 0.000000 \\n')\n fp.write(f'map_Kd {name}albedo.png \\n')\n\n _export(v, f)\n\n def run(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, **kwargs):\n # rays_o, rays_d: [B, N, 3]\n # bg_color: [BN, 3] in range [0, 1]\n # return: image: [B, N, 3], depth: [B, N]\n\n prefix = rays_o.shape[:-1]\n rays_o = rays_o.contiguous().view(-1, 3)\n rays_d = rays_d.contiguous().view(-1, 3)\n\n N = rays_o.shape[0] # N = B * N, in fact\n device = rays_o.device\n\n results = {}\n\n # choose aabb\n aabb = self.aabb_train if self.training else self.aabb_infer\n\n # sample steps\n # nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, aabb, self.min_near)\n # nears.unsqueeze_(-1)\n # fars.unsqueeze_(-1)\n nears, fars = near_far_from_bound(rays_o, rays_d, self.bound, type='sphere', min_near=self.min_near)\n\n # random sample light_d if not provided\n if light_d is None:\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\n if self.training:\n light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [N, 3]\n else:\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\n \n #print(f'nears = {nears.min().item()} ~ {nears.max().item()}, fars = {fars.min().item()} ~ {fars.max().item()}')\n\n z_vals = torch.linspace(0.0, 1.0, self.opt.num_steps, device=device).unsqueeze(0) # [1, T]\n z_vals = z_vals.expand((N, self.opt.num_steps)) # [N, T]\n z_vals = nears + (fars - nears) * z_vals # [N, T], in [nears, fars]\n\n # perturb z_vals\n sample_dist = (fars - nears) / self.opt.num_steps\n if perturb:\n z_vals = z_vals + (torch.rand(z_vals.shape, device=device) - 0.5) * sample_dist\n #z_vals = z_vals.clamp(nears, fars) # avoid out of bounds xyzs.\n\n # generate xyzs\n xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(-1) # [N, 1, 3] * [N, T, 1] -> [N, T, 3]\n xyzs = torch.min(torch.max(xyzs, aabb[:3]), aabb[3:]) # a manual clip.\n\n #plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy())\n\n # query SDF and RGB\n density_outputs = self.density(xyzs.reshape(-1, 3))\n\n #sigmas = density_outputs['sigma'].view(N, self.opt.num_steps) # [N, T]\n for k, v in density_outputs.items():\n density_outputs[k] = v.view(N, self.opt.num_steps, -1)\n\n # upsample z_vals (nerf-like)\n if self.opt.upsample_steps > 0:\n with torch.no_grad():\n\n deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T-1]\n deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1) ## confused with this, so the last point should be around relative distance or zero?\n\n alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T]\n alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+1]\n weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T]\n\n # sample new z_vals\n z_vals_mid = (z_vals[..., :-1] + 0.5 * deltas[..., :-1]) # [N, T-1]\n new_z_vals = sample_pdf(z_vals_mid, weights[:, 1:-1], self.opt.upsample_steps, det=not self.training).detach() # [N, t]\n\n new_xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * new_z_vals.unsqueeze(-1) # [N, 1, 3] * [N, t, 1] -> [N, t, 3]\n new_xyzs = torch.min(torch.max(new_xyzs, aabb[:3]), aabb[3:]) # a manual clip.\n\n # only forward new points to save computation\n new_density_outputs = self.density(new_xyzs.reshape(-1, 3))\n #new_sigmas = new_density_outputs['sigma'].view(N, self.opt.upsample_steps) # [N, t]\n for k, v in new_density_outputs.items():\n new_density_outputs[k] = v.view(N, self.opt.upsample_steps, -1)\n\n # re-order\n z_vals = torch.cat([z_vals, new_z_vals], dim=1) # [N, T+t]\n z_vals, z_index = torch.sort(z_vals, dim=1)\n\n xyzs = torch.cat([xyzs, new_xyzs], dim=1) # [N, T+t, 3]\n xyzs = torch.gather(xyzs, dim=1, index=z_index.unsqueeze(-1).expand_as(xyzs))\n\n for k in density_outputs:\n tmp_output = torch.cat([density_outputs[k], new_density_outputs[k]], dim=1)\n density_outputs[k] = torch.gather(tmp_output, dim=1, index=z_index.unsqueeze(-1).expand_as(tmp_output))\n\n deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T+t-1]\n deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1)\n alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T+t]\n alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+t+1]\n weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T+t]\n\n dirs = rays_d.view(-1, 1, 3).expand_as(xyzs)\n light_d = light_d.view(-1, 1, 3).expand_as(xyzs)\n for k, v in density_outputs.items():\n density_outputs[k] = v.view(-1, v.shape[-1])\n\n dirs = safe_normalize(dirs)\n sigmas, rgbs, normals = self(xyzs.reshape(-1, 3), dirs.reshape(-1, 3), light_d.reshape(-1,3), ratio=ambient_ratio, shading=shading)\n rgbs = rgbs.view(N, -1, 3) # [N, T+t, 3]\n if normals is not None:\n normals = normals.view(N, -1, 3)\n\n # calculate weight_sum (mask)\n weights_sum = weights.sum(dim=-1) # [N]\n \n # calculate depth \n depth = torch.sum(weights * z_vals, dim=-1)\n\n # calculate color\n image = torch.sum(weights.unsqueeze(-1) * rgbs, dim=-2) # [N, 3], in [0, 1]\n\n # mix background color\n if bg_color is None:\n if self.opt.bg_radius > 0:\n # use the bg model to calculate bg_color\n bg_color = self.background(rays_d) # [N, 3]\n else:\n bg_color = 1\n \n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\n # ipdb.set_trace()\n image = image.view(*prefix, 3)\n depth = depth.view(*prefix)\n weights_sum = weights_sum.reshape(*prefix)\n\n if self.training:\n if self.opt.lambda_orient > 0 and normals is not None:\n # orientation loss\n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\n results['loss_orient'] = loss_orient.sum(-1).mean()\n \n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\n \n if normals is not None:\n normal_image = torch.sum(\n weights.unsqueeze(-1) * (normals + 1) / 2, dim=-2) # [N, 3], in [0, 1]\n results['normal_image'] = normal_image\n \n results['image'] = image\n results['depth'] = depth\n results['weights'] = weights\n results['weights_sum'] = weights_sum\n\n return results\n\n\n def run_cuda(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, binarize=False, **kwargs):\n # rays_o, rays_d: [B, N, 3]\n # return: image: [B, N, 3], depth: [B, N]\n # ipdb.set_trace()\n prefix = rays_o.shape[:-1]\n rays_o = rays_o.contiguous().view(-1, 3)\n rays_d = rays_d.contiguous().view(-1, 3)\n\n N = rays_o.shape[0] # B * N, in fact\n device = rays_o.device\n\n # pre-calculate near far\n nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, self.aabb_train if self.training else self.aabb_infer)\n\n # random sample light_d if not provided\n if light_d is None:\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\n if self.training:\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\n else:\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\n\n results = {}\n\n if self.training:\n xyzs, dirs, ts, rays = raymarching.march_rays_train(rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb, self.opt.dt_gamma, self.opt.max_steps)\n dirs = safe_normalize(dirs)\n\n if light_d.shape[0] > 1:\n flatten_rays = raymarching.flatten_rays(rays, xyzs.shape[0]).long()\n light_d = light_d[flatten_rays]\n \n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\n weights, weights_sum, depth, image = raymarching.composite_rays_train(sigmas, rgbs, ts, rays, T_thresh, binarize)\n \n # normals related regularizations\n if self.opt.lambda_orient > 0 and normals is not None:\n # orientation loss \n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\n results['loss_orient'] = loss_orient.mean()\n \n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\n \n if normals is not None:\n _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)\n results['normal_image'] = normal_image\n \n # weights normalization\n results['weights'] = weights\n\n else:\n \n # allocate outputs \n dtype = torch.float32\n \n weights_sum = torch.zeros(N, dtype=dtype, device=device)\n depth = torch.zeros(N, dtype=dtype, device=device)\n image = torch.zeros(N, 3, dtype=dtype, device=device)\n \n n_alive = N\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\n rays_t = nears.clone() # [N]\n\n step = 0\n \n while step < self.opt.max_steps: # hard coded max step\n\n # count alive rays \n n_alive = rays_alive.shape[0]\n\n # exit loop\n if n_alive <= 0:\n break\n\n # decide compact_steps\n n_step = max(min(N // n_alive, 8), 1)\n\n xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)\n dirs = safe_normalize(dirs)\n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\n raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)\n\n rays_alive = rays_alive[rays_alive >= 0]\n #print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')\n\n step += n_step\n\n # mix background color\n if bg_color is None:\n if self.opt.bg_radius > 0:\n # use the bg model to calculate bg_color\n bg_color = self.background(rays_d) # [N, 3]\n else:\n bg_color = 1\n\n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\n image = image.view(*prefix, 3)\n\n depth = depth.view(*prefix)\n\n weights_sum = weights_sum.reshape(*prefix)\n\n results['image'] = image\n results['depth'] = depth\n results['weights_sum'] = weights_sum\n \n return results\n\n def get_sdf_albedo_for_init(self, points=None):\n output = self.density(self.dmtet.verts if points is None else points)\n sigma, albedo = output['sigma'], output['albedo']\n return sigma - self.density_thresh, albedo\n\n def run_dmtet(self, rays_o, rays_d, mvp, h, w, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, **kwargs):\n # mvp: [B, 4, 4]\n\n device = mvp.device\n campos = rays_o[:, 0, :] # only need one ray per batch\n\n # random sample light_d if not provided\n if light_d is None:\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\n light_d = safe_normalize(campos + torch.randn_like(campos)).view(-1, 1, 1, 3) # [B, 1, 1, 3]\n\n results = {}\n\n verts, faces = self.dmtet.get_verts_face()\n\n # get normals\n i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2]\n v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :]\n\n faces = faces.int()\n \n face_normals = torch.cross(v1 - v0, v2 - v0)\n face_normals = safe_normalize(face_normals)\n \n vn = torch.zeros_like(verts)\n vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals)\n vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals)\n vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals)\n\n vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device))\n\n # rasterization\n verts_clip = torch.bmm(F.pad(verts, pad=(0, 1), mode='constant', value=1.0).unsqueeze(0).repeat(mvp.shape[0], 1, 1), \n mvp.permute(0,2,1)).float() # [B, N, 4]\n rast, rast_db = dr.rasterize(self.glctx, verts_clip, faces, (h, w))\n \n alpha, _ = dr.interpolate(torch.ones_like(verts[:, :1]).unsqueeze(0), rast, faces) # [B, H, W, 1]\n xyzs, _ = dr.interpolate(verts.unsqueeze(0), rast, faces) # [B, H, W, 3]\n normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, faces)\n normal = safe_normalize(normal)\n\n xyzs = xyzs.view(-1, 3)\n mask = (alpha > 0).view(-1).detach()\n\n # do the lighting here since we have normal from mesh now.\n albedo = torch.zeros_like(xyzs, dtype=torch.float32)\n if mask.any():\n masked_albedo = self.density(xyzs[mask])['albedo']\n albedo[mask] = masked_albedo.float()\n albedo = albedo.view(-1, h, w, 3)\n\n if shading == 'albedo':\n color = albedo\n elif shading == 'textureless':\n lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0)\n color = lambertian.unsqueeze(-1).repeat(1, 1, 1, 3)\n elif shading == 'normal':\n color = (normal + 1) / 2\n else: # 'lambertian'\n lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0)\n color = albedo * lambertian.unsqueeze(-1)\n\n color = dr.antialias(color, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3]\n alpha = dr.antialias(alpha, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 1]\n\n # mix background color\n if bg_color is None:\n if self.opt.bg_radius > 0:\n # use the bg model to calculate bg_color\n bg_color = self.background(rays_d) # [N, 3]\n else:\n bg_color = 1\n \n if torch.is_tensor(bg_color) and len(bg_color.shape) > 1:\n bg_color = bg_color.view(-1, h, w, 3)\n \n depth = rast[:, :, :, [2]] # [B, H, W]\n color = color + (1 - alpha) * bg_color\n\n results['depth'] = depth \n results['image'] = color\n results['weights_sum'] = alpha.squeeze(-1)\n\n normal_image = dr.antialias((normal + 1) / 2, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3]\n results['normal_image'] = normal_image\n \n # regularizations\n if self.training:\n if self.opt.lambda_mesh_normal > 0:\n results['loss_normal'] = normal_consistency(\n face_normals, faces)\n if self.opt.lambda_mesh_lap > 0:\n results['loss_lap'] = laplacian_smooth_loss(verts, faces)\n\n return results\n\n def run_taichi(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, **kwargs):\n # rays_o, rays_d: [B, N, 3], assumes B == 1\n # return: image: [B, N, 3], depth: [B, N]\n\n prefix = rays_o.shape[:-1]\n rays_o = rays_o.contiguous().view(-1, 3)\n rays_d = rays_d.contiguous().view(-1, 3)\n\n N = rays_o.shape[0] # N = B * N, in fact\n device = rays_o.device\n\n # pre-calculate near far\n exp_step_factor = kwargs.get('exp_step_factor', 0.)\n MAX_SAMPLES = 1024\n NEAR_DISTANCE = 0.01\n center = torch.zeros(1, 3)\n half_size = torch.ones(1, 3)\n _, hits_t, _ = self.ray_aabb_intersector.apply(rays_o, rays_d, center, half_size, 1)\n hits_t[(hits_t[:, 0, 0] >= 0) & (hits_t[:, 0, 0] < NEAR_DISTANCE), 0, 0] = NEAR_DISTANCE\n\n # TODO: should sample different light_d for each batch... but taichi end doesn't have a flatten_ray implemented currently...\n # random sample light_d if not provided\n if light_d is None:\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\n light_d = (rays_o[0] + torch.randn(3, device=device, dtype=torch.float))\n light_d = safe_normalize(light_d)\n\n results = {}\n\n if self.training:\n rays_a, xyzs, dirs, deltas, ts, _ = self.ray_marching(rays_o, rays_d, hits_t[:, 0], self.density_bitfield, self.cascade, self.bound, exp_step_factor, self.grid_size, MAX_SAMPLES)\n dirs = safe_normalize(dirs)\n # plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy())\n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\n _, weights_sum, depth, image, weights = self.volume_render(sigmas, rgbs, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4))\n \n # normals related regularizations\n if self.opt.lambda_orient > 0 and normals is not None:\n # orientation loss \n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\n results['loss_orient'] = loss_orient.mean()\n \n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\n \n if normals is not None:\n _, _, _, normal_image, _ = self.volume_render(sigmas.detach(), (normals + 1) / 2, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4))\n results['normal_image'] = normal_image\n \n # weights normalization\n results['weights'] = weights\n\n else:\n \n # allocate outputs \n dtype = torch.float32\n \n weights_sum = torch.zeros(N, dtype=dtype, device=device)\n depth = torch.zeros(N, dtype=dtype, device=device)\n image = torch.zeros(N, 3, dtype=dtype, device=device)\n \n n_alive = N\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\n rays_t = hits_t[:, 0, 0]\n step = 0\n \n min_samples = 1 if exp_step_factor == 0 else 4\n\n while step < self.opt.max_steps: # hard coded max step\n\n # count alive rays \n n_alive = rays_alive.shape[0]\n\n # exit loop\n if n_alive <= 0:\n break\n\n # decide compact_steps\n # n_step = max(min(N // n_alive, 8), 1)\n n_step = max(min(N // n_alive, 64), min_samples)\n\n xyzs, dirs, deltas, ts, N_eff_samples = \\\n self.raymarching_test_taichi(rays_o, rays_d, hits_t[:, 0], rays_alive,\n self.density_bitfield, self.cascade,\n self.bound, exp_step_factor,\n self.grid_size, MAX_SAMPLES, n_step)\n\n xyzs = self.rearrange(xyzs, 'n1 n2 c -> (n1 n2) c')\n dirs = self.rearrange(dirs, 'n1 n2 c -> (n1 n2) c')\n dirs = safe_normalize(dirs)\n valid_mask = ~torch.all(dirs == 0, dim=1)\n if valid_mask.sum() == 0:\n break\n\n sigmas = torch.zeros(len(xyzs), device=device)\n rgbs = torch.zeros(len(xyzs), 3, device=device)\n normals = torch.zeros(len(xyzs), 3, device=device)\n\n sigmas[valid_mask], _rgbs, normals = self(xyzs[valid_mask], dirs[valid_mask], light_d, ratio=ambient_ratio, shading=shading)\n rgbs[valid_mask] = _rgbs.float()\n sigmas = self.rearrange(sigmas, '(n1 n2) -> n1 n2', n2=n_step)\n rgbs = self.rearrange(rgbs, '(n1 n2) c -> n1 n2 c', n2=n_step)\n if normals is not None:\n normals = self.rearrange(normals, '(n1 n2) c -> n1 n2 c', n2=n_step)\n\n self.composite_test_fw(sigmas, rgbs, deltas, ts, hits_t[:,0], rays_alive,\n kwargs.get('T_threshold', 1e-4), N_eff_samples,\n weights_sum, depth, image)\n\n rays_alive = rays_alive[rays_alive >= 0]\n\n step += n_step\n\n # mix background color\n if bg_color is None:\n if self.opt.bg_radius > 0:\n # use the bg model to calculate bg_color\n bg_color = self.background(rays_d) # [N, 3]\n else:\n bg_color = 1\n\n image = image + self.rearrange(1 - weights_sum, 'n -> n 1') * bg_color\n image = image.view(*prefix, 3)\n\n depth = depth.view(*prefix)\n\n weights_sum = weights_sum.reshape(*prefix)\n\n results['image'] = image\n results['depth'] = depth\n results['weights_sum'] = weights_sum\n \n return results\n\n\n @torch.no_grad()\n def update_extra_state(self, decay=0.95, S=128):\n # call before each epoch to update extra states.\n\n if not (self.cuda_ray or self.taichi_ray):\n return \n \n ### update density grid\n tmp_grid = - torch.ones_like(self.density_grid)\n \n X = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\n Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\n Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\n\n for xs in X:\n for ys in Y:\n for zs in Z:\n \n # construct points\n xx, yy, zz = custom_meshgrid(xs, ys, zs)\n coords = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [N, 3], in [0, 128)\n indices = raymarching.morton3D(coords).long() # [N]\n xyzs = 2 * coords.float() / (self.grid_size - 1) - 1 # [N, 3] in [-1, 1]\n\n # cascading\n for cas in range(self.cascade):\n bound = min(2 ** cas, self.bound)\n half_grid_size = bound / self.grid_size\n # scale to current cascade's resolution\n cas_xyzs = xyzs * (bound - half_grid_size)\n # add noise in [-hgs, hgs]\n cas_xyzs += (torch.rand_like(cas_xyzs) * 2 - 1) * half_grid_size\n # query density\n sigmas = self.density(cas_xyzs)['sigma'].reshape(-1).detach()\n # assign \n tmp_grid[cas, indices] = sigmas\n # ema update\n valid_mask = self.density_grid >= 0\n self.density_grid[valid_mask] = torch.maximum(self.density_grid[valid_mask] * decay, tmp_grid[valid_mask])\n self.mean_density = torch.mean(self.density_grid[valid_mask]).item()\n self.iter_density += 1\n\n # convert to bitfield\n density_thresh = min(self.mean_density, self.density_thresh)\n if self.cuda_ray:\n self.density_bitfield = raymarching.packbits(self.density_grid, density_thresh, self.density_bitfield)\n elif self.taichi_ray:\n self.packbits_taichi(self.density_grid.reshape(-1).contiguous(), density_thresh, self.density_bitfield)\n\n # print(f'[density grid] min={self.density_grid.min().item():.4f}, max={self.density_grid.max().item():.4f}, mean={self.mean_density:.4f}, occ_rate={(self.density_grid > density_thresh).sum() / (128**3 * self.cascade):.3f}')\n\n\n def render(self, rays_o, rays_d, mvp, h, w, staged=False, max_ray_batch=4096, **kwargs):\n # rays_o, rays_d: [B, N, 3]\n # return: pred_rgb: [B, N, 3]\n B, N = rays_o.shape[:2]\n device = rays_o.device\n\n if self.dmtet:\n results = self.run_dmtet(rays_o, rays_d, mvp, h, w, **kwargs)\n elif self.cuda_ray:\n results = self.run_cuda(rays_o, rays_d, **kwargs)\n elif self.taichi_ray:\n results = self.run_taichi(rays_o, rays_d, **kwargs)\n else:\n if staged:\n depth = torch.empty((B, N), device=device)\n image = torch.empty((B, N, 3), device=device)\n weights_sum = torch.empty((B, N), device=device)\n\n for b in range(B):\n head = 0\n while head < N:\n tail = min(head + max_ray_batch, N)\n results_ = self.run(rays_o[b:b+1, head:tail], rays_d[b:b+1, head:tail], **kwargs)\n depth[b:b+1, head:tail] = results_['depth']\n weights_sum[b:b+1, head:tail] = results_['weights_sum']\n image[b:b+1, head:tail] = results_['image']\n head += max_ray_batch\n \n results = {}\n results['depth'] = depth\n results['image'] = image\n results['weights_sum'] = weights_sum\n\n else:\n results = self.run(rays_o, rays_d, **kwargs)\n\n return results\n\n def init_tet_from_nerf(self, reset_scale=True):\n sdf = self.get_sdf_from_nerf(reset_scale=reset_scale)\n self.dmtet.init_tet_from_sdf(sdf)\n logger.info(f'init dmtet from NeRF Done ...')\n\n\n @torch.no_grad()\n def get_sdf_from_nerf(self, reset_scale=True):\n if self.cuda_ray:\n density_thresh = min(self.mean_density, self.density_thresh)\n else:\n density_thresh = self.density_thresh\n\n if reset_scale:\n # init scale\n sigma = self.density(self.dmtet.verts)[\n 'sigma'] # verts covers [-1, 1] now\n mask = sigma > density_thresh\n valid_verts = self.dmtet.verts[mask]\n tet_scale = valid_verts.abs().amax(dim=0) + 1e-1\n self.dmtet.reset_tet_scale(tet_scale)\n sdf = (self.density(self.dmtet.verts)[\n 'sigma'] - density_thresh).clamp(-1, 1)\n return sdf"}],"string":"[\n {\n \"identifier\": \"custom_meshgrid\",\n \"path\": \"nerf/utils.py\",\n \"snippet\": \"def custom_meshgrid(*args):\\n # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid\\n if pver.parse(torch.__version__) < pver.parse('1.10'):\\n return torch.meshgrid(*args)\\n else:\\n return torch.meshgrid(*args, indexing='ij')\"\n },\n {\n \"identifier\": \"safe_normalize\",\n \"path\": \"nerf/utils.py\",\n \"snippet\": \"def safe_normalize(x, eps=1e-20):\\n return x / torch.sqrt(torch.clamp(torch.sum(x * x, -1, keepdim=True), min=eps))\"\n },\n {\n \"identifier\": \"NeRFRenderer\",\n \"path\": \"nerf/renderer.py\",\n \"snippet\": \"class NeRFRenderer(nn.Module):\\n def __init__(self, opt):\\n super().__init__()\\n\\n self.opt = opt\\n self.bound = opt.bound\\n self.cascade = 1 + math.ceil(math.log2(opt.bound))\\n self.grid_size = 128\\n self.max_level = None\\n self.dmtet = opt.dmtet\\n self.cuda_ray = opt.cuda_ray\\n self.taichi_ray = opt.taichi_ray\\n self.min_near = opt.min_near\\n self.density_thresh = opt.density_thresh\\n\\n # prepare aabb with a 6D tensor (xmin, ymin, zmin, xmax, ymax, zmax)\\n # NOTE: aabb (can be rectangular) is only used to generate points, we still rely on bound (always cubic) to calculate density grid and hashing.\\n aabb_train = torch.FloatTensor(\\n [-opt.bound, -opt.bound, -opt.bound, opt.bound, opt.bound, opt.bound])\\n aabb_infer = aabb_train.clone()\\n self.register_buffer('aabb_train', aabb_train)\\n self.register_buffer('aabb_infer', aabb_infer)\\n\\n self.glctx = None\\n\\n # extra state for cuda raymarching\\n if self.cuda_ray:\\n ## NOTE TODO the cuda ray sampling for DNeRF is different, make sure to change\\n # density grid\\n density_grid = torch.zeros(\\n [self.cascade, self.grid_size ** 3]) # [CAS, H * H * H]\\n density_bitfield = torch.zeros(\\n self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8]\\n self.register_buffer('density_grid', density_grid)\\n self.register_buffer('density_bitfield', density_bitfield)\\n self.mean_density = 0\\n self.iter_density = 0\\n\\n # load dmtet vertices\\n if self.opt.dmtet:\\n self.dmtet = DMTetGeometry(opt.tet_grid_size, opt.tet_mlp, opt).to(opt.device)\\n if self.opt.h <= 2048 and self.opt.w <= 2048:\\n self.glctx = dr.RasterizeCudaContext()\\n else:\\n self.glctx = dr.RasterizeGLContext()\\n\\n if self.taichi_ray:\\n from einops import rearrange\\n from taichi_modules import RayMarcherTaichi\\n from taichi_modules import VolumeRendererTaichi\\n from taichi_modules import RayAABBIntersector as RayAABBIntersectorTaichi\\n from taichi_modules import raymarching_test as raymarching_test_taichi\\n from taichi_modules import composite_test as composite_test_fw\\n from taichi_modules import packbits as packbits_taichi\\n self.rearrange = rearrange\\n self.packbits_taichi = packbits_taichi\\n self.ray_aabb_intersector = RayAABBIntersectorTaichi\\n self.raymarching_test_taichi = raymarching_test_taichi\\n self.composite_test_fw = composite_test_fw\\n self.ray_marching = RayMarcherTaichi(\\n batch_size=4096) # TODO: hard encoded batch size\\n self.volume_render = VolumeRendererTaichi(\\n batch_size=4096) # TODO: hard encoded batch size\\n # density grid\\n density_grid = torch.zeros(\\n [self.cascade, self.grid_size ** 3]) # [CAS, H * H * H]\\n density_bitfield = torch.zeros(\\n self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8]\\n self.register_buffer('density_grid', density_grid)\\n self.register_buffer('density_bitfield', density_bitfield)\\n self.mean_density = 0\\n self.iter_density = 0\\n \\n if self.opt.density_activation == 'exp':\\n self.density_activation = trunc_exp\\n elif self.opt.density_activation == 'softplus':\\n self.density_activation = F.softplus\\n elif self.opt.density_activation == 'relu':\\n self.density_activation = F.relu\\n \\n # ref: https://github.com/zhaofuq/Instant-NSR/blob/main/nerf/network_sdf.py#L192\\n def finite_difference_normal(self, x, epsilon=1e-2):\\n # x: [N, 3]\\n # ipdb.set_trace()\\n dx_pos, _ = self.common_forward((x + torch.tensor([[epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\\n dx_neg, _ = self.common_forward((x + torch.tensor([[-epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\\n dy_pos, _ = self.common_forward((x + torch.tensor([[0.00, epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\\n dy_neg, _ = self.common_forward((x + torch.tensor([[0.00, -epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound))\\n dz_pos, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, epsilon]], device=x.device)).clamp(-self.bound, self.bound))\\n dz_neg, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, -epsilon]], device=x.device)).clamp(-self.bound, self.bound))\\n \\n normal = torch.stack([\\n 0.5 * (dx_pos - dx_neg) / epsilon, \\n 0.5 * (dy_pos - dy_neg) / epsilon, \\n 0.5 * (dz_pos - dz_neg) / epsilon\\n ], dim=-1)\\n\\n return -normal\\n \\n def normal(self, x):\\n normal = self.finite_difference_normal(x)\\n normal = safe_normalize(normal)\\n normal = torch.nan_to_num(normal)\\n return normal\\n\\n @torch.no_grad()\\n def density_blob(self, x):\\n # x: [B, N, 3]\\n\\n d = (x ** 2).sum(-1)\\n\\n if self.opt.density_activation == 'exp':\\n g = self.opt.blob_density * \\\\\\n torch.exp(- d / (2 * self.opt.blob_radius ** 2))\\n else:\\n g = self.opt.blob_density * \\\\\\n (1 - torch.sqrt(d) / self.opt.blob_radius)\\n\\n return g\\n\\n def forward(self, x, d):\\n raise NotImplementedError()\\n\\n def density(self, x):\\n raise NotImplementedError()\\n\\n def reset_extra_state(self):\\n if not (self.cuda_ray or self.taichi_ray):\\n return\\n # density grid\\n self.density_grid.zero_()\\n self.mean_density = 0\\n self.iter_density = 0\\n\\n @torch.no_grad()\\n def export_mesh(self, path, resolution=None, decimate_target=-1, S=128):\\n from meshutils import decimate_mesh, clean_mesh, poisson_mesh_reconstruction\\n if self.opt.dmtet:\\n vertices, triangles = self.dmtet.get_verts_face()\\n vertices = vertices.detach().cpu().numpy()\\n triangles = triangles.detach().cpu().numpy()\\n\\n else:\\n\\n if resolution is None:\\n resolution = self.grid_size\\n\\n if self.cuda_ray:\\n density_thresh = min(self.mean_density, self.density_thresh) \\\\\\n if np.greater(self.mean_density, 0) else self.density_thresh\\n else:\\n density_thresh = self.density_thresh\\n\\n sigmas = np.zeros(\\n [resolution, resolution, resolution], dtype=np.float32)\\n\\n # query\\n X = torch.linspace(-1, 1, resolution).split(S)\\n Y = torch.linspace(-1, 1, resolution).split(S)\\n Z = torch.linspace(-1, 1, resolution).split(S)\\n\\n for xi, xs in enumerate(X):\\n for yi, ys in enumerate(Y):\\n for zi, zs in enumerate(Z):\\n xx, yy, zz = custom_meshgrid(xs, ys, zs)\\n pts = torch.cat(\\n [xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [S, 3]\\n val = self.density(pts.to(self.aabb_train.device))\\n sigmas[xi * S: xi * S + len(xs), yi * S: yi * S + len(ys), zi * S: zi * S + len(\\n zs)] = val['sigma'].reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy() # [S, 1] --> [x, y, z]\\n\\n logger.info(\\n f'[INFO] marching cubes thresh: {density_thresh} ({sigmas.min()} ~ {sigmas.max()})')\\n\\n vertices, triangles = mcubes.marching_cubes(sigmas, density_thresh)\\n vertices = vertices / (resolution - 1.0) * 2 - 1\\n\\n # clean\\n vertices = vertices.astype(np.float32)\\n triangles = triangles.astype(np.int32)\\n vertices, triangles = clean_mesh(\\n vertices, triangles, remesh=True, remesh_size=0.01)\\n\\n # decimation\\n if decimate_target > 0 and triangles.shape[0] > decimate_target:\\n vertices, triangles = decimate_mesh(\\n vertices, triangles, decimate_target)\\n\\n v = torch.from_numpy(vertices).contiguous(\\n ).float().to(self.aabb_train.device)\\n f = torch.from_numpy(triangles).contiguous().int().to(\\n self.aabb_train.device)\\n\\n # mesh = trimesh.Trimesh(vertices, triangles, process=False) # important, process=True leads to seg fault...\\n # mesh.export(os.path.join(path, f'mesh.ply'))\\n\\n def _export(v, f, h0=2048, w0=2048, ssaa=1, name=''):\\n # v, f: torch Tensor\\n device = v.device\\n v_np = v.cpu().numpy() # [N, 3]\\n f_np = f.cpu().numpy() # [M, 3]\\n\\n logger.info(\\n f'[INFO] running xatlas to unwrap UVs for mesh: v={v_np.shape} f={f_np.shape}')\\n\\n # unwrap uvs\\n import xatlas\\n import nvdiffrast.torch as dr\\n from sklearn.neighbors import NearestNeighbors\\n from scipy.ndimage import binary_dilation, binary_erosion\\n\\n atlas = xatlas.Atlas()\\n atlas.add_mesh(v_np, f_np)\\n chart_options = xatlas.ChartOptions()\\n chart_options.max_iterations = 4 # for faster unwrap...\\n atlas.generate(chart_options=chart_options)\\n vmapping, ft_np, vt_np = atlas[0] # [N], [M, 3], [N, 2]\\n\\n # vmapping, ft_np, vt_np = xatlas.parametrize(v_np, f_np) # [N], [M, 3], [N, 2]\\n\\n vt = torch.from_numpy(vt_np.astype(np.float32)).float().to(device)\\n ft = torch.from_numpy(ft_np.astype(np.int64)).int().to(device)\\n\\n # render uv maps\\n uv = vt * 2.0 - 1.0 # uvs to range [-1, 1]\\n uv = torch.cat((uv, torch.zeros_like(\\n uv[..., :1]), torch.ones_like(uv[..., :1])), dim=-1) # [N, 4]\\n\\n if ssaa > 1:\\n h = int(h0 * ssaa)\\n w = int(w0 * ssaa)\\n else:\\n h, w = h0, w0\\n \\n if self.glctx is None:\\n if h <= 2048 and w <= 2048:\\n self.glctx = dr.RasterizeCudaContext()\\n else:\\n self.glctx = dr.RasterizeGLContext()\\n\\n rast, _ = dr.rasterize(self.glctx, uv.unsqueeze(\\n 0), ft, (h, w)) # [1, h, w, 4]\\n xyzs, _ = dr.interpolate(v.unsqueeze(0), rast, f) # [1, h, w, 3]\\n mask, _ = dr.interpolate(torch.ones_like(\\n v[:, :1]).unsqueeze(0), rast, f) # [1, h, w, 1]\\n\\n # masked query \\n xyzs = xyzs.view(-1, 3)\\n mask = (mask > 0).view(-1)\\n \\n feats = torch.zeros(h * w, 3, device=device, dtype=torch.float32)\\n\\n if mask.any():\\n xyzs = xyzs[mask] # [M, 3]\\n\\n # batched inference to avoid OOM\\n all_feats = []\\n head = 0\\n while head < xyzs.shape[0]:\\n tail = min(head + 640000, xyzs.shape[0])\\n results_ = self.density(xyzs[head:tail])\\n all_feats.append(results_['albedo'].float())\\n head += 640000\\n\\n feats[mask] = torch.cat(all_feats, dim=0)\\n \\n feats = feats.view(h, w, -1)\\n mask = mask.view(h, w)\\n\\n # quantize [0.0, 1.0] to [0, 255]\\n feats = feats.cpu().numpy()\\n feats = (feats * 255).astype(np.uint8)\\n\\n ### NN search as an antialiasing ...\\n mask = mask.cpu().numpy()\\n\\n inpaint_region = binary_dilation(mask, iterations=3)\\n inpaint_region[mask] = 0\\n\\n search_region = mask.copy()\\n not_search_region = binary_erosion(search_region, iterations=2)\\n search_region[not_search_region] = 0\\n\\n search_coords = np.stack(np.nonzero(search_region), axis=-1)\\n inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)\\n\\n knn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(search_coords)\\n _, indices = knn.kneighbors(inpaint_coords)\\n\\n feats[tuple(inpaint_coords.T)] = feats[tuple(search_coords[indices[:, 0]].T)]\\n\\n feats = cv2.cvtColor(feats, cv2.COLOR_RGB2BGR)\\n\\n # do ssaa after the NN search, in numpy\\n if ssaa > 1:\\n feats = cv2.resize(feats, (w0, h0), interpolation=cv2.INTER_LINEAR)\\n\\n cv2.imwrite(os.path.join(path, f'{name}albedo.png'), feats)\\n\\n # save obj (v, vt, f /)\\n obj_file = os.path.join(path, f'{name}mesh.obj')\\n mtl_file = os.path.join(path, f'{name}mesh.mtl')\\n\\n logger.info(f'[INFO] writing obj mesh to {obj_file}')\\n with open(obj_file, \\\"w\\\") as fp:\\n fp.write(f'mtllib {name}mesh.mtl \\\\n')\\n\\n logger.info(f'[INFO] writing vertices {v_np.shape}')\\n for v in v_np:\\n fp.write(f'v {v[0]} {v[1]} {v[2]} \\\\n')\\n\\n logger.info(\\n f'[INFO] writing vertices texture coords {vt_np.shape}')\\n for v in vt_np:\\n fp.write(f'vt {v[0]} {1 - v[1]} \\\\n')\\n\\n logger.info(f'[INFO] writing faces {f_np.shape}')\\n fp.write(f'usemtl mat0 \\\\n')\\n for i in range(len(f_np)):\\n fp.write(\\n f\\\"f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1} {f_np[i, 1] + 1}/{ft_np[i, 1] + 1} {f_np[i, 2] + 1}/{ft_np[i, 2] + 1} \\\\n\\\")\\n\\n with open(mtl_file, \\\"w\\\") as fp:\\n fp.write(f'newmtl mat0 \\\\n')\\n fp.write(f'Ka 1.000000 1.000000 1.000000 \\\\n')\\n fp.write(f'Kd 1.000000 1.000000 1.000000 \\\\n')\\n fp.write(f'Ks 0.000000 0.000000 0.000000 \\\\n')\\n fp.write(f'Tr 1.000000 \\\\n')\\n fp.write(f'illum 1 \\\\n')\\n fp.write(f'Ns 0.000000 \\\\n')\\n fp.write(f'map_Kd {name}albedo.png \\\\n')\\n\\n _export(v, f)\\n\\n def run(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, **kwargs):\\n # rays_o, rays_d: [B, N, 3]\\n # bg_color: [BN, 3] in range [0, 1]\\n # return: image: [B, N, 3], depth: [B, N]\\n\\n prefix = rays_o.shape[:-1]\\n rays_o = rays_o.contiguous().view(-1, 3)\\n rays_d = rays_d.contiguous().view(-1, 3)\\n\\n N = rays_o.shape[0] # N = B * N, in fact\\n device = rays_o.device\\n\\n results = {}\\n\\n # choose aabb\\n aabb = self.aabb_train if self.training else self.aabb_infer\\n\\n # sample steps\\n # nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, aabb, self.min_near)\\n # nears.unsqueeze_(-1)\\n # fars.unsqueeze_(-1)\\n nears, fars = near_far_from_bound(rays_o, rays_d, self.bound, type='sphere', min_near=self.min_near)\\n\\n # random sample light_d if not provided\\n if light_d is None:\\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\\n if self.training:\\n light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [N, 3]\\n else:\\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\\n \\n #print(f'nears = {nears.min().item()} ~ {nears.max().item()}, fars = {fars.min().item()} ~ {fars.max().item()}')\\n\\n z_vals = torch.linspace(0.0, 1.0, self.opt.num_steps, device=device).unsqueeze(0) # [1, T]\\n z_vals = z_vals.expand((N, self.opt.num_steps)) # [N, T]\\n z_vals = nears + (fars - nears) * z_vals # [N, T], in [nears, fars]\\n\\n # perturb z_vals\\n sample_dist = (fars - nears) / self.opt.num_steps\\n if perturb:\\n z_vals = z_vals + (torch.rand(z_vals.shape, device=device) - 0.5) * sample_dist\\n #z_vals = z_vals.clamp(nears, fars) # avoid out of bounds xyzs.\\n\\n # generate xyzs\\n xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(-1) # [N, 1, 3] * [N, T, 1] -> [N, T, 3]\\n xyzs = torch.min(torch.max(xyzs, aabb[:3]), aabb[3:]) # a manual clip.\\n\\n #plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy())\\n\\n # query SDF and RGB\\n density_outputs = self.density(xyzs.reshape(-1, 3))\\n\\n #sigmas = density_outputs['sigma'].view(N, self.opt.num_steps) # [N, T]\\n for k, v in density_outputs.items():\\n density_outputs[k] = v.view(N, self.opt.num_steps, -1)\\n\\n # upsample z_vals (nerf-like)\\n if self.opt.upsample_steps > 0:\\n with torch.no_grad():\\n\\n deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T-1]\\n deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1) ## confused with this, so the last point should be around relative distance or zero?\\n\\n alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T]\\n alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+1]\\n weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T]\\n\\n # sample new z_vals\\n z_vals_mid = (z_vals[..., :-1] + 0.5 * deltas[..., :-1]) # [N, T-1]\\n new_z_vals = sample_pdf(z_vals_mid, weights[:, 1:-1], self.opt.upsample_steps, det=not self.training).detach() # [N, t]\\n\\n new_xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * new_z_vals.unsqueeze(-1) # [N, 1, 3] * [N, t, 1] -> [N, t, 3]\\n new_xyzs = torch.min(torch.max(new_xyzs, aabb[:3]), aabb[3:]) # a manual clip.\\n\\n # only forward new points to save computation\\n new_density_outputs = self.density(new_xyzs.reshape(-1, 3))\\n #new_sigmas = new_density_outputs['sigma'].view(N, self.opt.upsample_steps) # [N, t]\\n for k, v in new_density_outputs.items():\\n new_density_outputs[k] = v.view(N, self.opt.upsample_steps, -1)\\n\\n # re-order\\n z_vals = torch.cat([z_vals, new_z_vals], dim=1) # [N, T+t]\\n z_vals, z_index = torch.sort(z_vals, dim=1)\\n\\n xyzs = torch.cat([xyzs, new_xyzs], dim=1) # [N, T+t, 3]\\n xyzs = torch.gather(xyzs, dim=1, index=z_index.unsqueeze(-1).expand_as(xyzs))\\n\\n for k in density_outputs:\\n tmp_output = torch.cat([density_outputs[k], new_density_outputs[k]], dim=1)\\n density_outputs[k] = torch.gather(tmp_output, dim=1, index=z_index.unsqueeze(-1).expand_as(tmp_output))\\n\\n deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T+t-1]\\n deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1)\\n alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T+t]\\n alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+t+1]\\n weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T+t]\\n\\n dirs = rays_d.view(-1, 1, 3).expand_as(xyzs)\\n light_d = light_d.view(-1, 1, 3).expand_as(xyzs)\\n for k, v in density_outputs.items():\\n density_outputs[k] = v.view(-1, v.shape[-1])\\n\\n dirs = safe_normalize(dirs)\\n sigmas, rgbs, normals = self(xyzs.reshape(-1, 3), dirs.reshape(-1, 3), light_d.reshape(-1,3), ratio=ambient_ratio, shading=shading)\\n rgbs = rgbs.view(N, -1, 3) # [N, T+t, 3]\\n if normals is not None:\\n normals = normals.view(N, -1, 3)\\n\\n # calculate weight_sum (mask)\\n weights_sum = weights.sum(dim=-1) # [N]\\n \\n # calculate depth \\n depth = torch.sum(weights * z_vals, dim=-1)\\n\\n # calculate color\\n image = torch.sum(weights.unsqueeze(-1) * rgbs, dim=-2) # [N, 3], in [0, 1]\\n\\n # mix background color\\n if bg_color is None:\\n if self.opt.bg_radius > 0:\\n # use the bg model to calculate bg_color\\n bg_color = self.background(rays_d) # [N, 3]\\n else:\\n bg_color = 1\\n \\n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\\n # ipdb.set_trace()\\n image = image.view(*prefix, 3)\\n depth = depth.view(*prefix)\\n weights_sum = weights_sum.reshape(*prefix)\\n\\n if self.training:\\n if self.opt.lambda_orient > 0 and normals is not None:\\n # orientation loss\\n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\\n results['loss_orient'] = loss_orient.sum(-1).mean()\\n \\n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\\n \\n if normals is not None:\\n normal_image = torch.sum(\\n weights.unsqueeze(-1) * (normals + 1) / 2, dim=-2) # [N, 3], in [0, 1]\\n results['normal_image'] = normal_image\\n \\n results['image'] = image\\n results['depth'] = depth\\n results['weights'] = weights\\n results['weights_sum'] = weights_sum\\n\\n return results\\n\\n\\n def run_cuda(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, binarize=False, **kwargs):\\n # rays_o, rays_d: [B, N, 3]\\n # return: image: [B, N, 3], depth: [B, N]\\n # ipdb.set_trace()\\n prefix = rays_o.shape[:-1]\\n rays_o = rays_o.contiguous().view(-1, 3)\\n rays_d = rays_d.contiguous().view(-1, 3)\\n\\n N = rays_o.shape[0] # B * N, in fact\\n device = rays_o.device\\n\\n # pre-calculate near far\\n nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, self.aabb_train if self.training else self.aabb_infer)\\n\\n # random sample light_d if not provided\\n if light_d is None:\\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\\n if self.training:\\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\\n else:\\n light_d = safe_normalize(rays_o[0:1] + torch.randn(3, device=rays_o.device)) # [N, 3]\\n\\n results = {}\\n\\n if self.training:\\n xyzs, dirs, ts, rays = raymarching.march_rays_train(rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb, self.opt.dt_gamma, self.opt.max_steps)\\n dirs = safe_normalize(dirs)\\n\\n if light_d.shape[0] > 1:\\n flatten_rays = raymarching.flatten_rays(rays, xyzs.shape[0]).long()\\n light_d = light_d[flatten_rays]\\n \\n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\\n weights, weights_sum, depth, image = raymarching.composite_rays_train(sigmas, rgbs, ts, rays, T_thresh, binarize)\\n \\n # normals related regularizations\\n if self.opt.lambda_orient > 0 and normals is not None:\\n # orientation loss \\n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\\n results['loss_orient'] = loss_orient.mean()\\n \\n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\\n \\n if normals is not None:\\n _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)\\n results['normal_image'] = normal_image\\n \\n # weights normalization\\n results['weights'] = weights\\n\\n else:\\n \\n # allocate outputs \\n dtype = torch.float32\\n \\n weights_sum = torch.zeros(N, dtype=dtype, device=device)\\n depth = torch.zeros(N, dtype=dtype, device=device)\\n image = torch.zeros(N, 3, dtype=dtype, device=device)\\n \\n n_alive = N\\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\\n rays_t = nears.clone() # [N]\\n\\n step = 0\\n \\n while step < self.opt.max_steps: # hard coded max step\\n\\n # count alive rays \\n n_alive = rays_alive.shape[0]\\n\\n # exit loop\\n if n_alive <= 0:\\n break\\n\\n # decide compact_steps\\n n_step = max(min(N // n_alive, 8), 1)\\n\\n xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)\\n dirs = safe_normalize(dirs)\\n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\\n raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)\\n\\n rays_alive = rays_alive[rays_alive >= 0]\\n #print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')\\n\\n step += n_step\\n\\n # mix background color\\n if bg_color is None:\\n if self.opt.bg_radius > 0:\\n # use the bg model to calculate bg_color\\n bg_color = self.background(rays_d) # [N, 3]\\n else:\\n bg_color = 1\\n\\n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\\n image = image.view(*prefix, 3)\\n\\n depth = depth.view(*prefix)\\n\\n weights_sum = weights_sum.reshape(*prefix)\\n\\n results['image'] = image\\n results['depth'] = depth\\n results['weights_sum'] = weights_sum\\n \\n return results\\n\\n def get_sdf_albedo_for_init(self, points=None):\\n output = self.density(self.dmtet.verts if points is None else points)\\n sigma, albedo = output['sigma'], output['albedo']\\n return sigma - self.density_thresh, albedo\\n\\n def run_dmtet(self, rays_o, rays_d, mvp, h, w, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, **kwargs):\\n # mvp: [B, 4, 4]\\n\\n device = mvp.device\\n campos = rays_o[:, 0, :] # only need one ray per batch\\n\\n # random sample light_d if not provided\\n if light_d is None:\\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\\n light_d = safe_normalize(campos + torch.randn_like(campos)).view(-1, 1, 1, 3) # [B, 1, 1, 3]\\n\\n results = {}\\n\\n verts, faces = self.dmtet.get_verts_face()\\n\\n # get normals\\n i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2]\\n v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :]\\n\\n faces = faces.int()\\n \\n face_normals = torch.cross(v1 - v0, v2 - v0)\\n face_normals = safe_normalize(face_normals)\\n \\n vn = torch.zeros_like(verts)\\n vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals)\\n vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals)\\n vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals)\\n\\n vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device))\\n\\n # rasterization\\n verts_clip = torch.bmm(F.pad(verts, pad=(0, 1), mode='constant', value=1.0).unsqueeze(0).repeat(mvp.shape[0], 1, 1), \\n mvp.permute(0,2,1)).float() # [B, N, 4]\\n rast, rast_db = dr.rasterize(self.glctx, verts_clip, faces, (h, w))\\n \\n alpha, _ = dr.interpolate(torch.ones_like(verts[:, :1]).unsqueeze(0), rast, faces) # [B, H, W, 1]\\n xyzs, _ = dr.interpolate(verts.unsqueeze(0), rast, faces) # [B, H, W, 3]\\n normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, faces)\\n normal = safe_normalize(normal)\\n\\n xyzs = xyzs.view(-1, 3)\\n mask = (alpha > 0).view(-1).detach()\\n\\n # do the lighting here since we have normal from mesh now.\\n albedo = torch.zeros_like(xyzs, dtype=torch.float32)\\n if mask.any():\\n masked_albedo = self.density(xyzs[mask])['albedo']\\n albedo[mask] = masked_albedo.float()\\n albedo = albedo.view(-1, h, w, 3)\\n\\n if shading == 'albedo':\\n color = albedo\\n elif shading == 'textureless':\\n lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0)\\n color = lambertian.unsqueeze(-1).repeat(1, 1, 1, 3)\\n elif shading == 'normal':\\n color = (normal + 1) / 2\\n else: # 'lambertian'\\n lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0)\\n color = albedo * lambertian.unsqueeze(-1)\\n\\n color = dr.antialias(color, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3]\\n alpha = dr.antialias(alpha, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 1]\\n\\n # mix background color\\n if bg_color is None:\\n if self.opt.bg_radius > 0:\\n # use the bg model to calculate bg_color\\n bg_color = self.background(rays_d) # [N, 3]\\n else:\\n bg_color = 1\\n \\n if torch.is_tensor(bg_color) and len(bg_color.shape) > 1:\\n bg_color = bg_color.view(-1, h, w, 3)\\n \\n depth = rast[:, :, :, [2]] # [B, H, W]\\n color = color + (1 - alpha) * bg_color\\n\\n results['depth'] = depth \\n results['image'] = color\\n results['weights_sum'] = alpha.squeeze(-1)\\n\\n normal_image = dr.antialias((normal + 1) / 2, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3]\\n results['normal_image'] = normal_image\\n \\n # regularizations\\n if self.training:\\n if self.opt.lambda_mesh_normal > 0:\\n results['loss_normal'] = normal_consistency(\\n face_normals, faces)\\n if self.opt.lambda_mesh_lap > 0:\\n results['loss_lap'] = laplacian_smooth_loss(verts, faces)\\n\\n return results\\n\\n def run_taichi(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, **kwargs):\\n # rays_o, rays_d: [B, N, 3], assumes B == 1\\n # return: image: [B, N, 3], depth: [B, N]\\n\\n prefix = rays_o.shape[:-1]\\n rays_o = rays_o.contiguous().view(-1, 3)\\n rays_d = rays_d.contiguous().view(-1, 3)\\n\\n N = rays_o.shape[0] # N = B * N, in fact\\n device = rays_o.device\\n\\n # pre-calculate near far\\n exp_step_factor = kwargs.get('exp_step_factor', 0.)\\n MAX_SAMPLES = 1024\\n NEAR_DISTANCE = 0.01\\n center = torch.zeros(1, 3)\\n half_size = torch.ones(1, 3)\\n _, hits_t, _ = self.ray_aabb_intersector.apply(rays_o, rays_d, center, half_size, 1)\\n hits_t[(hits_t[:, 0, 0] >= 0) & (hits_t[:, 0, 0] < NEAR_DISTANCE), 0, 0] = NEAR_DISTANCE\\n\\n # TODO: should sample different light_d for each batch... but taichi end doesn't have a flatten_ray implemented currently...\\n # random sample light_d if not provided\\n if light_d is None:\\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\\n light_d = (rays_o[0] + torch.randn(3, device=device, dtype=torch.float))\\n light_d = safe_normalize(light_d)\\n\\n results = {}\\n\\n if self.training:\\n rays_a, xyzs, dirs, deltas, ts, _ = self.ray_marching(rays_o, rays_d, hits_t[:, 0], self.density_bitfield, self.cascade, self.bound, exp_step_factor, self.grid_size, MAX_SAMPLES)\\n dirs = safe_normalize(dirs)\\n # plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy())\\n sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading)\\n _, weights_sum, depth, image, weights = self.volume_render(sigmas, rgbs, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4))\\n \\n # normals related regularizations\\n if self.opt.lambda_orient > 0 and normals is not None:\\n # orientation loss \\n loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2\\n results['loss_orient'] = loss_orient.mean()\\n \\n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\\n normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2)\\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\\n \\n if normals is not None:\\n _, _, _, normal_image, _ = self.volume_render(sigmas.detach(), (normals + 1) / 2, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4))\\n results['normal_image'] = normal_image\\n \\n # weights normalization\\n results['weights'] = weights\\n\\n else:\\n \\n # allocate outputs \\n dtype = torch.float32\\n \\n weights_sum = torch.zeros(N, dtype=dtype, device=device)\\n depth = torch.zeros(N, dtype=dtype, device=device)\\n image = torch.zeros(N, 3, dtype=dtype, device=device)\\n \\n n_alive = N\\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\\n rays_t = hits_t[:, 0, 0]\\n step = 0\\n \\n min_samples = 1 if exp_step_factor == 0 else 4\\n\\n while step < self.opt.max_steps: # hard coded max step\\n\\n # count alive rays \\n n_alive = rays_alive.shape[0]\\n\\n # exit loop\\n if n_alive <= 0:\\n break\\n\\n # decide compact_steps\\n # n_step = max(min(N // n_alive, 8), 1)\\n n_step = max(min(N // n_alive, 64), min_samples)\\n\\n xyzs, dirs, deltas, ts, N_eff_samples = \\\\\\n self.raymarching_test_taichi(rays_o, rays_d, hits_t[:, 0], rays_alive,\\n self.density_bitfield, self.cascade,\\n self.bound, exp_step_factor,\\n self.grid_size, MAX_SAMPLES, n_step)\\n\\n xyzs = self.rearrange(xyzs, 'n1 n2 c -> (n1 n2) c')\\n dirs = self.rearrange(dirs, 'n1 n2 c -> (n1 n2) c')\\n dirs = safe_normalize(dirs)\\n valid_mask = ~torch.all(dirs == 0, dim=1)\\n if valid_mask.sum() == 0:\\n break\\n\\n sigmas = torch.zeros(len(xyzs), device=device)\\n rgbs = torch.zeros(len(xyzs), 3, device=device)\\n normals = torch.zeros(len(xyzs), 3, device=device)\\n\\n sigmas[valid_mask], _rgbs, normals = self(xyzs[valid_mask], dirs[valid_mask], light_d, ratio=ambient_ratio, shading=shading)\\n rgbs[valid_mask] = _rgbs.float()\\n sigmas = self.rearrange(sigmas, '(n1 n2) -> n1 n2', n2=n_step)\\n rgbs = self.rearrange(rgbs, '(n1 n2) c -> n1 n2 c', n2=n_step)\\n if normals is not None:\\n normals = self.rearrange(normals, '(n1 n2) c -> n1 n2 c', n2=n_step)\\n\\n self.composite_test_fw(sigmas, rgbs, deltas, ts, hits_t[:,0], rays_alive,\\n kwargs.get('T_threshold', 1e-4), N_eff_samples,\\n weights_sum, depth, image)\\n\\n rays_alive = rays_alive[rays_alive >= 0]\\n\\n step += n_step\\n\\n # mix background color\\n if bg_color is None:\\n if self.opt.bg_radius > 0:\\n # use the bg model to calculate bg_color\\n bg_color = self.background(rays_d) # [N, 3]\\n else:\\n bg_color = 1\\n\\n image = image + self.rearrange(1 - weights_sum, 'n -> n 1') * bg_color\\n image = image.view(*prefix, 3)\\n\\n depth = depth.view(*prefix)\\n\\n weights_sum = weights_sum.reshape(*prefix)\\n\\n results['image'] = image\\n results['depth'] = depth\\n results['weights_sum'] = weights_sum\\n \\n return results\\n\\n\\n @torch.no_grad()\\n def update_extra_state(self, decay=0.95, S=128):\\n # call before each epoch to update extra states.\\n\\n if not (self.cuda_ray or self.taichi_ray):\\n return \\n \\n ### update density grid\\n tmp_grid = - torch.ones_like(self.density_grid)\\n \\n X = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\\n Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\\n Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S)\\n\\n for xs in X:\\n for ys in Y:\\n for zs in Z:\\n \\n # construct points\\n xx, yy, zz = custom_meshgrid(xs, ys, zs)\\n coords = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [N, 3], in [0, 128)\\n indices = raymarching.morton3D(coords).long() # [N]\\n xyzs = 2 * coords.float() / (self.grid_size - 1) - 1 # [N, 3] in [-1, 1]\\n\\n # cascading\\n for cas in range(self.cascade):\\n bound = min(2 ** cas, self.bound)\\n half_grid_size = bound / self.grid_size\\n # scale to current cascade's resolution\\n cas_xyzs = xyzs * (bound - half_grid_size)\\n # add noise in [-hgs, hgs]\\n cas_xyzs += (torch.rand_like(cas_xyzs) * 2 - 1) * half_grid_size\\n # query density\\n sigmas = self.density(cas_xyzs)['sigma'].reshape(-1).detach()\\n # assign \\n tmp_grid[cas, indices] = sigmas\\n # ema update\\n valid_mask = self.density_grid >= 0\\n self.density_grid[valid_mask] = torch.maximum(self.density_grid[valid_mask] * decay, tmp_grid[valid_mask])\\n self.mean_density = torch.mean(self.density_grid[valid_mask]).item()\\n self.iter_density += 1\\n\\n # convert to bitfield\\n density_thresh = min(self.mean_density, self.density_thresh)\\n if self.cuda_ray:\\n self.density_bitfield = raymarching.packbits(self.density_grid, density_thresh, self.density_bitfield)\\n elif self.taichi_ray:\\n self.packbits_taichi(self.density_grid.reshape(-1).contiguous(), density_thresh, self.density_bitfield)\\n\\n # print(f'[density grid] min={self.density_grid.min().item():.4f}, max={self.density_grid.max().item():.4f}, mean={self.mean_density:.4f}, occ_rate={(self.density_grid > density_thresh).sum() / (128**3 * self.cascade):.3f}')\\n\\n\\n def render(self, rays_o, rays_d, mvp, h, w, staged=False, max_ray_batch=4096, **kwargs):\\n # rays_o, rays_d: [B, N, 3]\\n # return: pred_rgb: [B, N, 3]\\n B, N = rays_o.shape[:2]\\n device = rays_o.device\\n\\n if self.dmtet:\\n results = self.run_dmtet(rays_o, rays_d, mvp, h, w, **kwargs)\\n elif self.cuda_ray:\\n results = self.run_cuda(rays_o, rays_d, **kwargs)\\n elif self.taichi_ray:\\n results = self.run_taichi(rays_o, rays_d, **kwargs)\\n else:\\n if staged:\\n depth = torch.empty((B, N), device=device)\\n image = torch.empty((B, N, 3), device=device)\\n weights_sum = torch.empty((B, N), device=device)\\n\\n for b in range(B):\\n head = 0\\n while head < N:\\n tail = min(head + max_ray_batch, N)\\n results_ = self.run(rays_o[b:b+1, head:tail], rays_d[b:b+1, head:tail], **kwargs)\\n depth[b:b+1, head:tail] = results_['depth']\\n weights_sum[b:b+1, head:tail] = results_['weights_sum']\\n image[b:b+1, head:tail] = results_['image']\\n head += max_ray_batch\\n \\n results = {}\\n results['depth'] = depth\\n results['image'] = image\\n results['weights_sum'] = weights_sum\\n\\n else:\\n results = self.run(rays_o, rays_d, **kwargs)\\n\\n return results\\n\\n def init_tet_from_nerf(self, reset_scale=True):\\n sdf = self.get_sdf_from_nerf(reset_scale=reset_scale)\\n self.dmtet.init_tet_from_sdf(sdf)\\n logger.info(f'init dmtet from NeRF Done ...')\\n\\n\\n @torch.no_grad()\\n def get_sdf_from_nerf(self, reset_scale=True):\\n if self.cuda_ray:\\n density_thresh = min(self.mean_density, self.density_thresh)\\n else:\\n density_thresh = self.density_thresh\\n\\n if reset_scale:\\n # init scale\\n sigma = self.density(self.dmtet.verts)[\\n 'sigma'] # verts covers [-1, 1] now\\n mask = sigma > density_thresh\\n valid_verts = self.dmtet.verts[mask]\\n tet_scale = valid_verts.abs().amax(dim=0) + 1e-1\\n self.dmtet.reset_tet_scale(tet_scale)\\n sdf = (self.density(self.dmtet.verts)[\\n 'sigma'] - density_thresh).clamp(-1, 1)\\n return sdf\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport math\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport raymarching\nimport logging\nfrom tqdm import tqdm\nfrom nerf.utils import custom_meshgrid, safe_normalize\nfrom nerf.renderer import NeRFRenderer"},"token_num":{"kind":"number","value":15610,"string":"15,610"},"cropped_code":{"kind":"string","value":" if normals is not None:\n # _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)\n # results['normal_image'] = normal_image.view(*prefix, 3)\n normal_image = torch.stack(normal_image, dim=0) # F,N,3\n results['normal_image'] = normal_image.view(batch_size, num_frames, -1, 3) # B,F,N,3\n \n # weights normalization\n results['weights'] = weights # N'*F\n\n else:\n image_all = []\n weights_sum_all = []\n depth_all = []\n # ipdb.set_trace()\n for frame_idx in range(num_frames): \n _rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item() \n nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)\n # allocate outputs \n dtype = torch.float32\n \n weights_sum = torch.zeros(N, dtype=dtype, device=device)\n depth = torch.zeros(N, dtype=dtype, device=device)\n image = torch.zeros(N, 3, dtype=dtype, device=device)\n \n n_alive = N\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\n rays_t = nears.clone() # [N] ## test must use the same \n\n step = 0\n \n while step < self.opt.max_steps: # hard coded max step\n\n # count alive rays \n n_alive = rays_alive.shape[0]\n\n # exit loop\n if n_alive <= 0:\n break\n\n # decide compact_steps\n n_step = max(min(N // n_alive, 8), 1)\n\n xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, _rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)\n dirs = safe_normalize(dirs)\n\n s_time = torch.zeros((xyzs.size(0),1), device=xyzs.device, dtype=xyzs.dtype) + time[0, frame_idx].item()\n # ipdb.set_trace()\n sigmas, rgbs, normals, _ = self(xyzs, dirs, _light_d[:1], ratio=ambient_ratio, shading=shading, t=s_time)\n sigmas = self.density_scale * sigmas\n raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)\n\n rays_alive = rays_alive[rays_alive >= 0]\n #print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')\n\n step += n_step\n\n image_all.append(image)\n weights_sum_all.append(weights_sum)\n depth_all.append(depth)\n # ipdb.set_trace()\n weights_sum = torch.stack(weights_sum_all, dim=0) # F,N\n depth = torch.stack(depth_all, dim=0) # F,N\n image = torch.stack(image_all, dim=0) # F,N,3\n\n # mix background color\n ## when bg_radius < 0 -> the way Magic123, during training, bg_color is always a random color, during inference, always 1 \n # ipdb.set_trace()\n if bg_color is None:\n if self.opt.bg_radius > 0 and self.bg_net is not None:\n # use the bg model to calculate bg_color\n ## NOTE here the camera should be fixed in the video\n ## rays_d F,N,3\n bg_color = self.background(rays_d.reshape(-1, 3)) # [FN, 3] # this is irrelavant to time\n bg_color = bg_color.reshape(batch_size, num_frames, -1, 3) # F,N,3\n else:\n bg_color = 1\n\n image_wo_bg = image.view(batch_size, num_frames, -1, 3)\n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\n image = image.view(batch_size, num_frames, -1, 3)\n\n depth = depth.view(batch_size, num_frames, -1)\n\n weights_sum = weights_sum.reshape(batch_size, num_frames, -1)\n\n results['image'] = image # B,F,N,3\n results['depth'] = depth # B,F,N\n results['weights_sum'] = weights_sum # B,F,N\n results['image_wo_bg'] = image_wo_bg # B,F,N,3\n # ipdb.set_trace()\n return results\n\n\n\n @torch.no_grad()\n def update_extra_state(self, decay=0.95, S=128):\n # call before each epoch to update extra states.\n\n if not (self.cuda_ray):\n return \n if self.taichi_ray:\n raise NotImplementedError\n \n ### update density grid\n\n tmp_grid = - torch.ones_like(self.density_grid)\n\n # full update.\n # if self.iter_density < 16: # update only 16 times\n if True: # full update\n X = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n\n for t, time in enumerate(self.times):\n for xs in X:\n for ys in Y:\n for zs in Z:\n \n # construct points\n"},"all_code":{"kind":"string","value":"\n\n\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DNeRFRenderer(NeRFRenderer):\n def __init__(self, opt):\n super().__init__(opt)\n\n self.time_size = opt.get(\"time_size\", 1)\n self.density_scale = opt.get(\"density_scale\", 1)\n self.dynamic_ft = opt.get(\"dynamic_ft\", False)\n\n # extra state for cuda raymarching\n if self.cuda_ray:\n # density grid (with an extra time dimension)\n density_grid = torch.zeros(self.time_size, self.cascade, self.grid_size ** 3) # [T, CAS, H * H * H]\n density_bitfield = torch.zeros(self.time_size, self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [T, CAS * H * H * H // 8]\n self.register_buffer('density_grid', density_grid)\n self.register_buffer('density_bitfield', density_bitfield)\n self.mean_density = 0\n self.iter_density = 0\n # time stamps for density grid\n times = ((torch.arange(self.time_size, dtype=torch.float32) + 0.5) / self.time_size).view(-1, 1, 1) # [T, 1, 1]\n self.register_buffer('times', times)\n # step counter\n step_counter = torch.zeros(16, 2, dtype=torch.int32) # 16 is hardcoded for averaging...\n self.register_buffer('step_counter', step_counter)\n self.mean_count = 0\n self.local_step = 0\n\n\n def run_cuda(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, binarize=False, time=None, **kwargs):\n # rays_o, rays_d: [B, N, 3] / B,F,N,3\n # return: image: [B, N, 3], depth: [B, N]\n # time: [B,F]\n\n prefix = rays_o.shape[:-1]\n batch_size = prefix[0]\n if prefix[0] != 1:\n raise \"The prefix should be 1 if different frames has different camera pose in the current version\"\n \n\n dynamic_cam = True if rays_o.ndim == 4 else False\n\n\n\n N = rays_o.shape[:-1].numel() # B * N, in fact\n device = rays_o.device\n\n if light_d is None:\n # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)\n light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [B,N,3] / B,F,N,3\n\n if time is None:\n assert not self.dynamic_ft\n time_steps = torch.LongTensor([[0]]).reshape(1,1) # 1,1\n time = torch.FloatTensor([[0]]).reshape(1,1) # 1,1\n num_frames = 1\n else:\n time_steps = torch.floor(time * self.time_size).clamp(min=0, max=self.time_size - 1).long() # B,F\n num_frames = time.size(1)\n \n if dynamic_cam:\n rays_o = rays_o[0].contiguous() # F,N,3\n rays_d = rays_d[0].contiguous() # F,N,3\n light_d = light_d[0].contiguous() # F,N,3\n else:\n rays_o = rays_o.repeat(num_frames, 1, 1).contiguous()\n rays_d = rays_d.repeat(num_frames, 1, 1).contiguous()\n light_d = light_d.repeat(num_frames, 1, 1).contiguous()\n # ipdb.set_trace()\n\n \n\n results = {}\n # ipdb.set_trace()\n if self.training:\n # ipdb.set_trace()\n v_xyzs = []\n v_dirs = []\n v_light = []\n v_time = []\n v_idx = [0]\n v_rays = []\n v_ts = []\n v_kernels = []\n for frame_idx in range(num_frames):\n _rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item() \n ## N,3 for the first 3, t is a value\n # pre-calculate near far\n nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)\n xyzs, dirs, ts, rays = raymarching.march_rays_train(_rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb, self.opt.dt_gamma, self.opt.max_steps)\n dirs = safe_normalize(dirs)\n\n flatten_rays = raymarching.flatten_rays(rays, xyzs.shape[0]).long()\n if _light_d.shape[0] > 1:\n _light_d = _light_d[flatten_rays]\n else: # 1,3\n _light_d = _light_d.repeat(xyzs.size(0), 1)\n \n v_xyzs.append(xyzs)\n v_dirs.append(dirs)\n v_light.append(_light_d)\n sample_num = xyzs.size(0)\n\n v_time.append(torch.zeros((sample_num,1), device=xyzs.device, dtype=xyzs.dtype)+time[0, frame_idx].item()) # NOTE this should be real time # N\n v_idx.append(sample_num)\n\n v_rays.append(rays)\n v_ts.append(ts)\n \n v_xyzs = torch.cat(v_xyzs, dim=0)\n v_dirs = torch.cat(v_dirs, dim=0)\n v_light = torch.cat(v_light, dim=0)\n v_time = torch.cat(v_time, dim=0)\n v_idx = np.cumsum(v_idx).tolist()\n \n sigmas, rgbs, normals, deforms = self(v_xyzs, v_dirs, v_light, ratio=ambient_ratio, shading=shading, t=v_time)\n \n sigmas = self.density_scale * sigmas\n weights = []\n weights_sum = []\n depth = []\n image = []\n normal_image = []\n\n\n\n\n # ipdb.set_trace()\n for frame_idx in range(num_frames):\n start_idx, end_idx = v_idx[frame_idx], v_idx[frame_idx+1] \n # _weights, _weights_sum, _depth, _image\n out = raymarching.composite_rays_train(sigmas[start_idx:end_idx], rgbs[start_idx:end_idx], v_ts[frame_idx], v_rays[frame_idx], T_thresh, binarize)\n if normals is not None:\n _, _, _, _normal_image = raymarching.composite_rays_train(sigmas[start_idx:end_idx].detach(), (normals[start_idx:end_idx] + 1) / 2, v_ts[frame_idx], v_rays[frame_idx], T_thresh, binarize)\n normal_image.append(_normal_image)\n\n weights.append(out[0])\n weights_sum.append(out[1])\n depth.append(out[2])\n image.append(out[3]) # N,3\n \n weights = torch.cat(weights)\n weights_sum = torch.stack(weights_sum, dim=0) # F,N\n depth = torch.stack(depth, dim=0) # F,N\n image = torch.stack(image, dim=0) # F,N,3\n \n # normals related regularizations\n if self.opt.lambda_orient > 0 and normals is not None:\n # orientation loss \n loss_orient = weights.detach() * (normals * v_dirs).sum(-1).clamp(min=0) ** 2\n results['loss_orient'] = loss_orient.mean()\n \n if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:\n normals_perturb = self.normal(v_xyzs + torch.randn_like(v_xyzs) * 1e-2, t=v_time)\n results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()\n\n \n if normals is not None:\n # _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)\n # results['normal_image'] = normal_image.view(*prefix, 3)\n normal_image = torch.stack(normal_image, dim=0) # F,N,3\n results['normal_image'] = normal_image.view(batch_size, num_frames, -1, 3) # B,F,N,3\n \n # weights normalization\n results['weights'] = weights # N'*F\n\n else:\n image_all = []\n weights_sum_all = []\n depth_all = []\n # ipdb.set_trace()\n for frame_idx in range(num_frames): \n _rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item() \n nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)\n # allocate outputs \n dtype = torch.float32\n \n weights_sum = torch.zeros(N, dtype=dtype, device=device)\n depth = torch.zeros(N, dtype=dtype, device=device)\n image = torch.zeros(N, 3, dtype=dtype, device=device)\n \n n_alive = N\n rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]\n rays_t = nears.clone() # [N] ## test must use the same \n\n step = 0\n \n while step < self.opt.max_steps: # hard coded max step\n\n # count alive rays \n n_alive = rays_alive.shape[0]\n\n # exit loop\n if n_alive <= 0:\n break\n\n # decide compact_steps\n n_step = max(min(N // n_alive, 8), 1)\n\n xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, _rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)\n dirs = safe_normalize(dirs)\n\n s_time = torch.zeros((xyzs.size(0),1), device=xyzs.device, dtype=xyzs.dtype) + time[0, frame_idx].item()\n # ipdb.set_trace()\n sigmas, rgbs, normals, _ = self(xyzs, dirs, _light_d[:1], ratio=ambient_ratio, shading=shading, t=s_time)\n sigmas = self.density_scale * sigmas\n raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)\n\n rays_alive = rays_alive[rays_alive >= 0]\n #print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')\n\n step += n_step\n\n image_all.append(image)\n weights_sum_all.append(weights_sum)\n depth_all.append(depth)\n # ipdb.set_trace()\n weights_sum = torch.stack(weights_sum_all, dim=0) # F,N\n depth = torch.stack(depth_all, dim=0) # F,N\n image = torch.stack(image_all, dim=0) # F,N,3\n\n # mix background color\n ## when bg_radius < 0 -> the way Magic123, during training, bg_color is always a random color, during inference, always 1 \n # ipdb.set_trace()\n if bg_color is None:\n if self.opt.bg_radius > 0 and self.bg_net is not None:\n # use the bg model to calculate bg_color\n ## NOTE here the camera should be fixed in the video\n ## rays_d F,N,3\n bg_color = self.background(rays_d.reshape(-1, 3)) # [FN, 3] # this is irrelavant to time\n bg_color = bg_color.reshape(batch_size, num_frames, -1, 3) # F,N,3\n else:\n bg_color = 1\n\n image_wo_bg = image.view(batch_size, num_frames, -1, 3)\n image = image + (1 - weights_sum).unsqueeze(-1) * bg_color\n image = image.view(batch_size, num_frames, -1, 3)\n\n depth = depth.view(batch_size, num_frames, -1)\n\n weights_sum = weights_sum.reshape(batch_size, num_frames, -1)\n\n results['image'] = image # B,F,N,3\n results['depth'] = depth # B,F,N\n results['weights_sum'] = weights_sum # B,F,N\n results['image_wo_bg'] = image_wo_bg # B,F,N,3\n # ipdb.set_trace()\n return results\n\n\n\n @torch.no_grad()\n def update_extra_state(self, decay=0.95, S=128):\n # call before each epoch to update extra states.\n\n if not (self.cuda_ray):\n return \n if self.taichi_ray:\n raise NotImplementedError\n \n ### update density grid\n\n tmp_grid = - torch.ones_like(self.density_grid)\n\n # full update.\n # if self.iter_density < 16: # update only 16 times\n if True: # full update\n X = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)\n\n for t, time in enumerate(self.times):\n for xs in X:\n for ys in Y:\n for zs in Z:\n \n # construct points"},"next_line":{"kind":"string","value":" xx, yy, zz = custom_meshgrid(xs, ys, zs)"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-11-23 10:34:08+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5398,"cells":{"repo_name":{"kind":"string","value":"alexzhou907/DreamPropeller"},"file_path":{"kind":"string","value":"threestudio/models/geometry/tetrahedra_sdf_grid.py"},"context":{"kind":"list like","value":[{"identifier":"BaseExplicitGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\n \"bbox\",\n torch.as_tensor(\n [\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\n ],\n dtype=torch.float32,\n ),\n )"},{"identifier":"BaseGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseGeometry(BaseModule):\n @dataclass\n class Config(BaseModule.Config):\n pass\n\n cfg: Config\n\n @staticmethod\n def create_from(\n other: \"BaseGeometry\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\n ) -> \"BaseGeometry\":\n raise TypeError(\n f\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\"\n )\n\n def export(self, *args, **kwargs) -> Dict[str, Any]:\n return {}"},{"identifier":"contract_to_unisphere","path":"threestudio/models/geometry/base.py","snippet":"def contract_to_unisphere(\n x: Float[Tensor, \"... 3\"], bbox: Float[Tensor, \"2 3\"], unbounded: bool = False\n) -> Float[Tensor, \"... 3\"]:\n if unbounded:\n x = scale_tensor(x, bbox, (0, 1))\n x = x * 2 - 1 # aabb is at [-1, 1]\n mag = x.norm(dim=-1, keepdim=True)\n mask = mag.squeeze(-1) > 1\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\n else:\n x = scale_tensor(x, bbox, (0, 1))\n return x"},{"identifier":"ImplicitSDF","path":"threestudio/models/geometry/implicit_sdf.py","snippet":"class ImplicitSDF(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: Union[\n float, str\n ] = 0.01 # in [float, \"progressive\"]\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n sdf_bias: Union[float, str] = 0.0\n sdf_bias_params: Optional[Any] = None\n\n # no need to removal outlier for SDF\n isosurface_remove_outliers: bool = False\n\n cfg: Config\n\n def configure(self, *args, **kwargs) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.sdf_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n if self.cfg.isosurface_deformable_grid:\n assert (\n self.cfg.isosurface_method == \"mt\"\n ), \"isosurface_deformable_grid only works with mt\"\n self.deformation_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n self.finite_difference_normal_eps: Optional[float] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n if self.cfg.sdf_bias != 0.0:\n threestudio.warn(\n \"shape_init and sdf_bias are both specified, which may lead to unexpected results.\"\n )\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n import trimesh\n\n scene = trimesh.load(mesh_path)\n if isinstance(scene, trimesh.Trimesh):\n mesh = scene\n elif isinstance(scene, trimesh.scene.Scene):\n mesh = trimesh.Trimesh()\n for obj in scene.geometry.values():\n mesh = trimesh.util.concatenate([mesh, obj])\n else:\n raise ValueError(f\"Unknown mesh type at {mesh_path}.\")\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n from pysdf import SDF\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\n from tqdm import tqdm\n\n for _ in tqdm(\n range(1000),\n desc=f\"Initializing SDF to a(n) {self.cfg.shape_init}:\",\n disable=get_rank() != 0,\n ):\n points_rand = (\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\n )\n sdf_gt = get_gt_sdf(points_rand)\n sdf_pred = self.forward_sdf(points_rand)\n loss = F.mse_loss(sdf_pred, sdf_gt)\n optim.zero_grad()\n loss.backward()\n optim.step()\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def get_shifted_sdf(\n self, points: Float[Tensor, \"*N Di\"], sdf: Float[Tensor, \"*N 1\"]\n ) -> Float[Tensor, \"*N 1\"]:\n sdf_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.sdf_bias == \"ellipsoid\":\n assert (\n isinstance(self.cfg.sdf_bias_params, Sized)\n and len(self.cfg.sdf_bias_params) == 3\n )\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\n sdf_bias = ((points / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n elif self.cfg.sdf_bias == \"sphere\":\n assert isinstance(self.cfg.sdf_bias_params, float)\n radius = self.cfg.sdf_bias_params\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\n elif isinstance(self.cfg.sdf_bias, float):\n sdf_bias = self.cfg.sdf_bias\n else:\n raise ValueError(f\"Unknown sdf bias {self.cfg.sdf_bias}\")\n return sdf + sdf_bias\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n output = {\"sdf\": sdf}\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n assert self.finite_difference_normal_eps is not None\n eps: float = self.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 6 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (\n 0.5\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 3 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\n normal = F.normalize(sdf_grad, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n sdf_grad = normal\n elif self.cfg.normal_type == \"analytic\":\n sdf_grad = -torch.autograd.grad(\n sdf,\n points_unscaled,\n grad_outputs=torch.ones_like(sdf),\n create_graph=True,\n )[0]\n normal = F.normalize(sdf_grad, dim=-1)\n if not grad_enabled:\n sdf_grad = sdf_grad.detach()\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update(\n {\"normal\": normal, \"shading_normal\": normal, \"sdf_grad\": sdf_grad}\n )\n return output\n\n def forward_sdf(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n sdf = self.sdf_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n return sdf\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n deformation: Optional[Float[Tensor, \"*N 3\"]] = None\n if self.cfg.isosurface_deformable_grid:\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\n return sdf, deformation\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return field - threshold\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n if isinstance(self.cfg.finite_difference_normal_eps, float):\n self.finite_difference_normal_eps = (\n self.cfg.finite_difference_normal_eps\n )\n elif self.cfg.finite_difference_normal_eps == \"progressive\":\n # progressive finite difference eps from Neuralangelo\n # https://arxiv.org/abs/2306.03092\n hg_conf: Any = self.cfg.pos_encoding_config\n assert (\n hg_conf.otype == \"ProgressiveBandHashGrid\"\n ), \"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\"\n current_level = min(\n hg_conf.start_level\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\n hg_conf.n_levels,\n )\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\n current_level - 1\n )\n grid_size = 2 * self.cfg.radius / grid_res\n if grid_size != self.finite_difference_normal_eps:\n threestudio.info(\n f\"Update finite_difference_normal_eps to {grid_size}\"\n )\n self.finite_difference_normal_eps = grid_size\n else:\n raise ValueError(\n f\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\"\n )"},{"identifier":"ImplicitVolume","path":"threestudio/models/geometry/implicit_volume.py","snippet":"class ImplicitVolume(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n density_activation: Optional[str] = \"softplus\"\n density_bias: Union[float, str] = \"blob_magic3d\"\n density_blob_scale: float = 10.0\n density_blob_std: float = 0.5\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: float = 0.01\n\n # automatically determine the threshold\n isosurface_threshold: Union[float, str] = 25.0\n\n cfg: Config\n\n def configure(self, *args, **kwargs) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.density_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n \n def get_activated_density(\n self, points: Float[Tensor, \"*N Di\"], density: Float[Tensor, \"*N 1\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Float[Tensor, \"*N 1\"]]:\n density_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.density_bias == \"blob_dreamfusion\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * torch.exp(\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\n )[..., None]\n )\n elif self.cfg.density_bias == \"blob_magic3d\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * (\n 1\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\n )[..., None]\n )\n elif isinstance(self.cfg.density_bias, float):\n density_bias = self.cfg.density_bias\n else:\n raise ValueError(f\"Unknown density bias {self.cfg.density_bias}\")\n raw_density: Float[Tensor, \"*N 1\"] = density + density_bias\n density = get_activation(self.cfg.density_activation)(raw_density)\n # density = self.density_act(raw_density)\n return raw_density, density\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n density = self.density_network(enc).view(*points.shape[:-1], 1)\n raw_density, density = self.get_activated_density(points_unscaled, density)\n\n output = {\n \"density\": density,\n }\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n # TODO: use raw density\n eps = self.cfg.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 6 1\"] = self.forward_density(\n points_offset\n )\n normal = (\n -0.5\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 3 1\"] = self.forward_density(\n points_offset\n )\n normal = -(density_offset[..., 0::1, 0] - density) / eps\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"analytic\":\n normal = -torch.autograd.grad(\n density,\n points_unscaled,\n grad_outputs=torch.ones_like(density),\n create_graph=True,\n )[0]\n normal = F.normalize(normal, dim=-1)\n if not grad_enabled:\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update({\"normal\": normal, \"shading_normal\": normal})\n\n torch.set_grad_enabled(grad_enabled)\n return output\n\n def forward_density(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n density = self.density_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n\n _, density = self.get_activated_density(points_unscaled, density)\n return density\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n if self.cfg.isosurface_deformable_grid:\n threestudio.warn(\n f\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\"\n )\n density = self.forward_density(points)\n return density, None\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return -(field - threshold)\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"ImplicitVolume\":\n if isinstance(other, ImplicitVolume):\n instance = ImplicitVolume(cfg, **kwargs)\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.density_network.load_state_dict(other.density_network.state_dict())\n if copy_net:\n if (\n instance.cfg.n_feature_dims > 0\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\n ):\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n if (\n instance.cfg.normal_type == \"pred\"\n and other.cfg.normal_type == \"pred\"\n ):\n instance.normal_network.load_state_dict(\n other.normal_network.state_dict()\n )\n return instance\n else:\n raise TypeError(\n f\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\"\n )"},{"identifier":"MarchingTetrahedraHelper","path":"threestudio/models/isosurface.py","snippet":"class MarchingTetrahedraHelper(IsosurfaceHelper):\n def __init__(self, resolution: int, tets_path: str):\n super().__init__()\n self.resolution = resolution\n self.tets_path = tets_path\n\n self.triangle_table: Float[Tensor, \"...\"]\n self.register_buffer(\n \"triangle_table\",\n torch.as_tensor(\n [\n [-1, -1, -1, -1, -1, -1],\n [1, 0, 2, -1, -1, -1],\n [4, 0, 3, -1, -1, -1],\n [1, 4, 2, 1, 3, 4],\n [3, 1, 5, -1, -1, -1],\n [2, 3, 0, 2, 5, 3],\n [1, 4, 0, 1, 5, 4],\n [4, 2, 5, -1, -1, -1],\n [4, 5, 2, -1, -1, -1],\n [4, 1, 0, 4, 5, 1],\n [3, 2, 0, 3, 5, 2],\n [1, 3, 5, -1, -1, -1],\n [4, 1, 2, 4, 3, 1],\n [3, 0, 4, -1, -1, -1],\n [2, 0, 1, -1, -1, -1],\n [-1, -1, -1, -1, -1, -1],\n ],\n dtype=torch.long,\n ),\n persistent=False,\n )\n self.num_triangles_table: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"num_triangles_table\",\n torch.as_tensor(\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\n ),\n persistent=False,\n )\n self.base_tet_edges: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"base_tet_edges\",\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\n persistent=False,\n )\n\n tets = np.load(self.tets_path)\n self._grid_vertices: Float[Tensor, \"...\"]\n self.register_buffer(\n \"_grid_vertices\",\n torch.from_numpy(tets[\"vertices\"]).float(),\n persistent=False,\n )\n self.indices: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"indices\", torch.from_numpy(tets[\"indices\"]).long(), persistent=False\n )\n\n self._all_edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n\n def normalize_grid_deformation(\n self, grid_vertex_offsets: Float[Tensor, \"Nv 3\"]\n ) -> Float[Tensor, \"Nv 3\"]:\n return (\n (self.points_range[1] - self.points_range[0])\n / (self.resolution) # half tet size is approximately 1 / self.resolution\n * torch.tanh(grid_vertex_offsets)\n ) # FIXME: hard-coded activation\n\n @property\n def grid_vertices(self) -> Float[Tensor, \"Nv 3\"]:\n return self._grid_vertices\n\n @property\n def all_edges(self) -> Integer[Tensor, \"Ne 2\"]:\n if self._all_edges is None:\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\n edges = torch.tensor(\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\n dtype=torch.long,\n device=self.indices.device,\n )\n _all_edges = self.indices[:, edges].reshape(-1, 2)\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\n self._all_edges = _all_edges\n return self._all_edges\n\n def sort_edges(self, edges_ex2):\n with torch.no_grad():\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\n order = order.unsqueeze(dim=1)\n\n a = torch.gather(input=edges_ex2, index=order, dim=1)\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\n\n return torch.stack([a, b], -1)\n\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\n with torch.no_grad():\n occ_n = sdf_n > 0\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\n occ_sum = torch.sum(occ_fx4, -1)\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\n occ_sum = occ_sum[valid_tets]\n\n # find all vertices\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\n all_edges = self.sort_edges(all_edges)\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\n\n unique_edges = unique_edges.long()\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\n mapping = (\n torch.ones(\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\n )\n * -1\n )\n mapping[mask_edges] = torch.arange(\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\n )\n idx_map = mapping[idx_map] # map edges to verts\n\n interp_v = unique_edges[mask_edges]\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\n edges_to_interp_sdf[:, -1] *= -1\n\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\n\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\n\n idx_map = idx_map.reshape(-1, 6)\n\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\n num_triangles = self.num_triangles_table[tetindex]\n\n # Generate triangle indices\n faces = torch.cat(\n (\n torch.gather(\n input=idx_map[num_triangles == 1],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\n ).reshape(-1, 3),\n torch.gather(\n input=idx_map[num_triangles == 2],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\n ).reshape(-1, 3),\n ),\n dim=0,\n )\n\n return verts, faces\n\n def forward(\n self,\n level: Float[Tensor, \"N3 1\"],\n deformation: Optional[Float[Tensor, \"N3 3\"]] = None,\n ) -> Mesh:\n if deformation is not None:\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\n deformation\n )\n else:\n grid_vertices = self.grid_vertices\n\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\n\n mesh = Mesh(\n v_pos=v_pos,\n t_pos_idx=t_pos_idx,\n # extras\n grid_vertices=grid_vertices,\n tet_edges=self.all_edges,\n grid_level=level,\n grid_deformation=deformation,\n )\n\n return mesh"},{"identifier":"Mesh","path":"threestudio/models/mesh.py","snippet":"class Mesh:\n def __init__(\n self, v_pos: Float[Tensor, \"Nv 3\"], t_pos_idx: Integer[Tensor, \"Nf 3\"], **kwargs\n ) -> None:\n self.v_pos: Float[Tensor, \"Nv 3\"] = v_pos\n self.t_pos_idx: Integer[Tensor, \"Nf 3\"] = t_pos_idx\n self._v_nrm: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tng: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tex: Optional[Float[Tensor, \"Nt 3\"]] = None\n self._t_tex_idx: Optional[Float[Tensor, \"Nf 3\"]] = None\n self._v_rgb: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n self.extras: Dict[str, Any] = {}\n for k, v in kwargs.items():\n self.add_extra(k, v)\n\n def add_extra(self, k, v) -> None:\n self.extras[k] = v\n\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\n if self.requires_grad:\n threestudio.debug(\"Mesh is differentiable, not removing outliers\")\n return self\n\n # use trimesh to first split the mesh into connected components\n # then remove the components with less than n_face_threshold faces\n import trimesh\n\n # construct a trimesh object\n mesh = trimesh.Trimesh(\n vertices=self.v_pos.detach().cpu().numpy(),\n faces=self.t_pos_idx.detach().cpu().numpy(),\n )\n\n # split the mesh into connected components\n components = mesh.split(only_watertight=False)\n # log the number of faces in each component\n threestudio.debug(\n \"Mesh has {} components, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n\n n_faces_threshold: int\n if isinstance(outlier_n_faces_threshold, float):\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\n n_faces_threshold = int(\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\n )\n else:\n # set the threshold directly to outlier_n_faces_threshold\n n_faces_threshold = outlier_n_faces_threshold\n\n # log the threshold\n threestudio.debug(\n \"Removing components with less than {} faces\".format(n_faces_threshold)\n )\n\n # remove the components with less than n_face_threshold faces\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\n\n # log the number of faces in each component after removing outliers\n threestudio.debug(\n \"Mesh has {} components after removing outliers, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n # merge the components\n mesh = trimesh.util.concatenate(components)\n\n # convert back to our mesh format\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\n\n clean_mesh = Mesh(v_pos, t_pos_idx)\n # keep the extras unchanged\n\n if len(self.extras) > 0:\n clean_mesh.extras = self.extras\n threestudio.debug(\n f\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\"\n )\n return clean_mesh\n\n @property\n def requires_grad(self):\n return self.v_pos.requires_grad\n\n @property\n def v_nrm(self):\n if self._v_nrm is None:\n self._v_nrm = self._compute_vertex_normal()\n return self._v_nrm\n\n @property\n def v_tng(self):\n if self._v_tng is None:\n self._v_tng = self._compute_vertex_tangent()\n return self._v_tng\n\n @property\n def v_tex(self):\n if self._v_tex is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._v_tex\n\n @property\n def t_tex_idx(self):\n if self._t_tex_idx is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._t_tex_idx\n\n @property\n def v_rgb(self):\n return self._v_rgb\n\n @property\n def edges(self):\n if self._edges is None:\n self._edges = self._compute_edges()\n return self._edges\n\n def _compute_vertex_normal(self):\n i0 = self.t_pos_idx[:, 0]\n i1 = self.t_pos_idx[:, 1]\n i2 = self.t_pos_idx[:, 2]\n\n v0 = self.v_pos[i0, :]\n v1 = self.v_pos[i1, :]\n v2 = self.v_pos[i2, :]\n\n face_normals = torch.cross(v1 - v0, v2 - v0)\n\n # Splat face normals to vertices\n v_nrm = torch.zeros_like(self.v_pos)\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\n\n # Normalize, replace zero (degenerated) normals with some default value\n v_nrm = torch.where(\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\n )\n v_nrm = F.normalize(v_nrm, dim=1)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(v_nrm))\n\n return v_nrm\n\n def _compute_vertex_tangent(self):\n vn_idx = [None] * 3\n pos = [None] * 3\n tex = [None] * 3\n for i in range(0, 3):\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\n # t_nrm_idx is always the same as t_pos_idx\n vn_idx[i] = self.t_pos_idx[:, i]\n\n tangents = torch.zeros_like(self.v_nrm)\n tansum = torch.zeros_like(self.v_nrm)\n\n # Compute tangent space for each triangle\n uve1 = tex[1] - tex[0]\n uve2 = tex[2] - tex[0]\n pe1 = pos[1] - pos[0]\n pe2 = pos[2] - pos[0]\n\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\n\n # Avoid division by zero for degenerated texture coordinates\n tang = nom / torch.where(\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\n )\n\n # Update all 3 vertices\n for i in range(0, 3):\n idx = vn_idx[i][:, None].repeat(1, 3)\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\n tansum.scatter_add_(\n 0, idx, torch.ones_like(tang)\n ) # tansum[n_i] = tansum[n_i] + 1\n tangents = tangents / tansum\n\n # Normalize and make sure tangent is perpendicular to normal\n tangents = F.normalize(tangents, dim=1)\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(tangents))\n\n return tangents\n\n def _unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n threestudio.info(\"Using xatlas to perform UV unwrapping, may take a while ...\")\n\n import xatlas\n\n atlas = xatlas.Atlas()\n atlas.add_mesh(\n self.v_pos.detach().cpu().numpy(),\n self.t_pos_idx.cpu().numpy(),\n )\n co = xatlas.ChartOptions()\n po = xatlas.PackOptions()\n for k, v in xatlas_chart_options.items():\n setattr(co, k, v)\n for k, v in xatlas_pack_options.items():\n setattr(po, k, v)\n atlas.generate(co, po)\n vmapping, indices, uvs = atlas.get_mesh(0)\n vmapping = (\n torch.from_numpy(\n vmapping.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\n indices = (\n torch.from_numpy(\n indices.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n return uvs, indices\n\n def unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\n xatlas_chart_options, xatlas_pack_options\n )\n\n def set_vertex_color(self, v_rgb):\n assert v_rgb.shape[0] == self.v_pos.shape[0]\n self._v_rgb = v_rgb\n\n def _compute_edges(self):\n # Compute edges\n edges = torch.cat(\n [\n self.t_pos_idx[:, [0, 1]],\n self.t_pos_idx[:, [1, 2]],\n self.t_pos_idx[:, [2, 0]],\n ],\n dim=0,\n )\n edges = edges.sort()[0]\n edges = torch.unique(edges, dim=0)\n return edges\n\n def normal_consistency(self) -> Float[Tensor, \"\"]:\n edge_nrm: Float[Tensor, \"Ne 2 3\"] = self.v_nrm[self.edges]\n nc = (\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\n ).mean()\n return nc\n\n def _laplacian_uniform(self):\n # from stable-dreamfusion\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\n verts, faces = self.v_pos, self.t_pos_idx\n\n V = verts.shape[0]\n F = faces.shape[0]\n\n # Neighbor indices\n ii = faces[:, [1, 2, 0]].flatten()\n jj = faces[:, [2, 0, 1]].flatten()\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\n dim=1\n )\n adj_values = torch.ones(adj.shape[1]).to(verts)\n\n # Diagonal indices\n diag_idx = adj[0]\n\n # Build the sparse matrix\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\n values = torch.cat((-adj_values, adj_values))\n\n # The coalesce operation sums the duplicate indices, resulting in the\n # correct diagonal\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\n\n def laplacian(self) -> Float[Tensor, \"\"]:\n with torch.no_grad():\n L = self._laplacian_uniform()\n loss = L.mm(self.v_pos)\n loss = loss.norm(dim=1)\n loss = loss.mean()\n return loss"},{"identifier":"get_encoding","path":"threestudio/models/networks.py","snippet":"def get_encoding(n_input_dims: int, config) -> nn.Module:\n # input suppose to be range [0, 1]\n encoding: nn.Module\n if config.otype == \"ProgressiveBandFrequency\":\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\n elif config.otype == \"ProgressiveBandHashGrid\":\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\n else:\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\n encoding = CompositeEncoding(\n encoding,\n include_xyz=config.get(\"include_xyz\", False),\n xyz_scale=2.0,\n xyz_offset=-1.0,\n ) # FIXME: hard coded\n return encoding"},{"identifier":"get_mlp","path":"threestudio/models/networks.py","snippet":"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\n network: nn.Module\n if config.otype == \"VanillaMLP\":\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\n elif config.otype == \"SphereInitVanillaMLP\":\n network = SphereInitVanillaMLP(\n n_input_dims, n_output_dims, config_to_primitive(config)\n )\n else:\n assert (\n config.get(\"sphere_init\", False) is False\n ), \"sphere_init=True only supported by VanillaMLP\"\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\n return network"},{"identifier":"broadcast","path":"threestudio/utils/misc.py","snippet":"def broadcast(tensor, src=0):\n if not _distributed_available():\n return tensor\n else:\n torch.distributed.broadcast(tensor, src=src)\n return tensor"},{"identifier":"scale_tensor","path":"threestudio/utils/ops.py","snippet":"def scale_tensor(\n dat: Num[Tensor, \"... D\"], inp_scale: ValidScale, tgt_scale: ValidScale\n):\n if inp_scale is None:\n inp_scale = (0, 1)\n if tgt_scale is None:\n tgt_scale = (0, 1)\n if isinstance(tgt_scale, Tensor):\n assert dat.shape[-1] == tgt_scale.shape[-1]\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\n return dat"}],"string":"[\n {\n \"identifier\": \"BaseExplicitGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseExplicitGeometry(BaseGeometry):\\n @dataclass\\n class Config(BaseGeometry.Config):\\n radius: float = 1.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n self.bbox: Float[Tensor, \\\"2 3\\\"]\\n self.register_buffer(\\n \\\"bbox\\\",\\n torch.as_tensor(\\n [\\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\\n ],\\n dtype=torch.float32,\\n ),\\n )\"\n },\n {\n \"identifier\": \"BaseGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseGeometry(BaseModule):\\n @dataclass\\n class Config(BaseModule.Config):\\n pass\\n\\n cfg: Config\\n\\n @staticmethod\\n def create_from(\\n other: \\\"BaseGeometry\\\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\\n ) -> \\\"BaseGeometry\\\":\\n raise TypeError(\\n f\\\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\\\"\\n )\\n\\n def export(self, *args, **kwargs) -> Dict[str, Any]:\\n return {}\"\n },\n {\n \"identifier\": \"contract_to_unisphere\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"def contract_to_unisphere(\\n x: Float[Tensor, \\\"... 3\\\"], bbox: Float[Tensor, \\\"2 3\\\"], unbounded: bool = False\\n) -> Float[Tensor, \\\"... 3\\\"]:\\n if unbounded:\\n x = scale_tensor(x, bbox, (0, 1))\\n x = x * 2 - 1 # aabb is at [-1, 1]\\n mag = x.norm(dim=-1, keepdim=True)\\n mask = mag.squeeze(-1) > 1\\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\\n else:\\n x = scale_tensor(x, bbox, (0, 1))\\n return x\"\n },\n {\n \"identifier\": \"ImplicitSDF\",\n \"path\": \"threestudio/models/geometry/implicit_sdf.py\",\n \"snippet\": \"class ImplicitSDF(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: Union[\\n float, str\\n ] = 0.01 # in [float, \\\"progressive\\\"]\\n shape_init: Optional[str] = None\\n shape_init_params: Optional[Any] = None\\n shape_init_mesh_up: str = \\\"+z\\\"\\n shape_init_mesh_front: str = \\\"+x\\\"\\n force_shape_init: bool = False\\n sdf_bias: Union[float, str] = 0.0\\n sdf_bias_params: Optional[Any] = None\\n\\n # no need to removal outlier for SDF\\n isosurface_remove_outliers: bool = False\\n\\n cfg: Config\\n\\n def configure(self, *args, **kwargs) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.sdf_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n if self.cfg.isosurface_deformable_grid:\\n assert (\\n self.cfg.isosurface_method == \\\"mt\\\"\\n ), \\\"isosurface_deformable_grid only works with mt\\\"\\n self.deformation_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n self.finite_difference_normal_eps: Optional[float] = None\\n\\n def initialize_shape(self) -> None:\\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\\n return\\n\\n # do not initialize shape if weights are provided\\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\\n return\\n\\n if self.cfg.sdf_bias != 0.0:\\n threestudio.warn(\\n \\\"shape_init and sdf_bias are both specified, which may lead to unexpected results.\\\"\\n )\\n\\n get_gt_sdf: Callable[[Float[Tensor, \\\"N 3\\\"]], Float[Tensor, \\\"N 1\\\"]]\\n assert isinstance(self.cfg.shape_init, str)\\n if self.cfg.shape_init == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.shape_init_params, Sized)\\n and len(self.cfg.shape_init_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return ((points_rand / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init == \\\"sphere\\\":\\n assert isinstance(self.cfg.shape_init_params, float)\\n radius = self.cfg.shape_init_params\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init.startswith(\\\"mesh:\\\"):\\n assert isinstance(self.cfg.shape_init_params, float)\\n mesh_path = self.cfg.shape_init[5:]\\n if not os.path.exists(mesh_path):\\n raise ValueError(f\\\"Mesh file {mesh_path} does not exist.\\\")\\n\\n import trimesh\\n\\n scene = trimesh.load(mesh_path)\\n if isinstance(scene, trimesh.Trimesh):\\n mesh = scene\\n elif isinstance(scene, trimesh.scene.Scene):\\n mesh = trimesh.Trimesh()\\n for obj in scene.geometry.values():\\n mesh = trimesh.util.concatenate([mesh, obj])\\n else:\\n raise ValueError(f\\\"Unknown mesh type at {mesh_path}.\\\")\\n\\n # move to center\\n centroid = mesh.vertices.mean(0)\\n mesh.vertices = mesh.vertices - centroid\\n\\n # align to up-z and front-x\\n dirs = [\\\"+x\\\", \\\"+y\\\", \\\"+z\\\", \\\"-x\\\", \\\"-y\\\", \\\"-z\\\"]\\n dir2vec = {\\n \\\"+x\\\": np.array([1, 0, 0]),\\n \\\"+y\\\": np.array([0, 1, 0]),\\n \\\"+z\\\": np.array([0, 0, 1]),\\n \\\"-x\\\": np.array([-1, 0, 0]),\\n \\\"-y\\\": np.array([0, -1, 0]),\\n \\\"-z\\\": np.array([0, 0, -1]),\\n }\\n if (\\n self.cfg.shape_init_mesh_up not in dirs\\n or self.cfg.shape_init_mesh_front not in dirs\\n ):\\n raise ValueError(\\n f\\\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\\\"\\n )\\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\\n raise ValueError(\\n \\\"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\\\"\\n )\\n z_, x_ = (\\n dir2vec[self.cfg.shape_init_mesh_up],\\n dir2vec[self.cfg.shape_init_mesh_front],\\n )\\n y_ = np.cross(z_, x_)\\n std2mesh = np.stack([x_, y_, z_], axis=0).T\\n mesh2std = np.linalg.inv(std2mesh)\\n\\n # scaling\\n scale = np.abs(mesh.vertices).max()\\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\\n\\n from pysdf import SDF\\n\\n sdf = SDF(mesh.vertices, mesh.faces)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n # add a negative signed here\\n # as in pysdf the inside of the shape has positive signed distance\\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\\n points_rand\\n )[..., None]\\n\\n get_gt_sdf = func\\n\\n else:\\n raise ValueError(\\n f\\\"Unknown shape initialization type: {self.cfg.shape_init}\\\"\\n )\\n\\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\\n from tqdm import tqdm\\n\\n for _ in tqdm(\\n range(1000),\\n desc=f\\\"Initializing SDF to a(n) {self.cfg.shape_init}:\\\",\\n disable=get_rank() != 0,\\n ):\\n points_rand = (\\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\\n )\\n sdf_gt = get_gt_sdf(points_rand)\\n sdf_pred = self.forward_sdf(points_rand)\\n loss = F.mse_loss(sdf_pred, sdf_gt)\\n optim.zero_grad()\\n loss.backward()\\n optim.step()\\n\\n # explicit broadcast to ensure param consistency across ranks\\n for param in self.parameters():\\n broadcast(param, src=0)\\n\\n def get_shifted_sdf(\\n self, points: Float[Tensor, \\\"*N Di\\\"], sdf: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n sdf_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.sdf_bias == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.sdf_bias_params, Sized)\\n and len(self.cfg.sdf_bias_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\\n sdf_bias = ((points / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n elif self.cfg.sdf_bias == \\\"sphere\\\":\\n assert isinstance(self.cfg.sdf_bias_params, float)\\n radius = self.cfg.sdf_bias_params\\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n elif isinstance(self.cfg.sdf_bias, float):\\n sdf_bias = self.cfg.sdf_bias\\n else:\\n raise ValueError(f\\\"Unknown sdf bias {self.cfg.sdf_bias}\\\")\\n return sdf + sdf_bias\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n output = {\\\"sdf\\\": sdf}\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n assert self.finite_difference_normal_eps is not None\\n eps: float = self.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (\\n 0.5\\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\\n normal = F.normalize(sdf_grad, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n sdf_grad = normal\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n sdf_grad = -torch.autograd.grad(\\n sdf,\\n points_unscaled,\\n grad_outputs=torch.ones_like(sdf),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(sdf_grad, dim=-1)\\n if not grad_enabled:\\n sdf_grad = sdf_grad.detach()\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update(\\n {\\\"normal\\\": normal, \\\"shading_normal\\\": normal, \\\"sdf_grad\\\": sdf_grad}\\n )\\n return output\\n\\n def forward_sdf(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n sdf = self.sdf_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n return sdf\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n deformation: Optional[Float[Tensor, \\\"*N 3\\\"]] = None\\n if self.cfg.isosurface_deformable_grid:\\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\\n return sdf, deformation\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return field - threshold\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n if isinstance(self.cfg.finite_difference_normal_eps, float):\\n self.finite_difference_normal_eps = (\\n self.cfg.finite_difference_normal_eps\\n )\\n elif self.cfg.finite_difference_normal_eps == \\\"progressive\\\":\\n # progressive finite difference eps from Neuralangelo\\n # https://arxiv.org/abs/2306.03092\\n hg_conf: Any = self.cfg.pos_encoding_config\\n assert (\\n hg_conf.otype == \\\"ProgressiveBandHashGrid\\\"\\n ), \\\"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\\\"\\n current_level = min(\\n hg_conf.start_level\\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\\n hg_conf.n_levels,\\n )\\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\\n current_level - 1\\n )\\n grid_size = 2 * self.cfg.radius / grid_res\\n if grid_size != self.finite_difference_normal_eps:\\n threestudio.info(\\n f\\\"Update finite_difference_normal_eps to {grid_size}\\\"\\n )\\n self.finite_difference_normal_eps = grid_size\\n else:\\n raise ValueError(\\n f\\\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\\\"\\n )\"\n },\n {\n \"identifier\": \"ImplicitVolume\",\n \"path\": \"threestudio/models/geometry/implicit_volume.py\",\n \"snippet\": \"class ImplicitVolume(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n density_activation: Optional[str] = \\\"softplus\\\"\\n density_bias: Union[float, str] = \\\"blob_magic3d\\\"\\n density_blob_scale: float = 10.0\\n density_blob_std: float = 0.5\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: float = 0.01\\n\\n # automatically determine the threshold\\n isosurface_threshold: Union[float, str] = 25.0\\n\\n cfg: Config\\n\\n def configure(self, *args, **kwargs) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.density_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n \\n def get_activated_density(\\n self, points: Float[Tensor, \\\"*N Di\\\"], density: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Float[Tensor, \\\"*N 1\\\"]]:\\n density_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.density_bias == \\\"blob_dreamfusion\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * torch.exp(\\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\\n )[..., None]\\n )\\n elif self.cfg.density_bias == \\\"blob_magic3d\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * (\\n 1\\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\\n )[..., None]\\n )\\n elif isinstance(self.cfg.density_bias, float):\\n density_bias = self.cfg.density_bias\\n else:\\n raise ValueError(f\\\"Unknown density bias {self.cfg.density_bias}\\\")\\n raw_density: Float[Tensor, \\\"*N 1\\\"] = density + density_bias\\n density = get_activation(self.cfg.density_activation)(raw_density)\\n # density = self.density_act(raw_density)\\n return raw_density, density\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n density = self.density_network(enc).view(*points.shape[:-1], 1)\\n raw_density, density = self.get_activated_density(points_unscaled, density)\\n\\n output = {\\n \\\"density\\\": density,\\n }\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n # TODO: use raw density\\n eps = self.cfg.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = (\\n -0.5\\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = -(density_offset[..., 0::1, 0] - density) / eps\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n normal = -torch.autograd.grad(\\n density,\\n points_unscaled,\\n grad_outputs=torch.ones_like(density),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(normal, dim=-1)\\n if not grad_enabled:\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update({\\\"normal\\\": normal, \\\"shading_normal\\\": normal})\\n\\n torch.set_grad_enabled(grad_enabled)\\n return output\\n\\n def forward_density(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n density = self.density_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n\\n _, density = self.get_activated_density(points_unscaled, density)\\n return density\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n if self.cfg.isosurface_deformable_grid:\\n threestudio.warn(\\n f\\\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\\\"\\n )\\n density = self.forward_density(points)\\n return density, None\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return -(field - threshold)\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry,\\n cfg: Optional[Union[dict, DictConfig]] = None,\\n copy_net: bool = True,\\n **kwargs,\\n ) -> \\\"ImplicitVolume\\\":\\n if isinstance(other, ImplicitVolume):\\n instance = ImplicitVolume(cfg, **kwargs)\\n instance.encoding.load_state_dict(other.encoding.state_dict())\\n instance.density_network.load_state_dict(other.density_network.state_dict())\\n if copy_net:\\n if (\\n instance.cfg.n_feature_dims > 0\\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\\n ):\\n instance.feature_network.load_state_dict(\\n other.feature_network.state_dict()\\n )\\n if (\\n instance.cfg.normal_type == \\\"pred\\\"\\n and other.cfg.normal_type == \\\"pred\\\"\\n ):\\n instance.normal_network.load_state_dict(\\n other.normal_network.state_dict()\\n )\\n return instance\\n else:\\n raise TypeError(\\n f\\\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\\\"\\n )\"\n },\n {\n \"identifier\": \"MarchingTetrahedraHelper\",\n \"path\": \"threestudio/models/isosurface.py\",\n \"snippet\": \"class MarchingTetrahedraHelper(IsosurfaceHelper):\\n def __init__(self, resolution: int, tets_path: str):\\n super().__init__()\\n self.resolution = resolution\\n self.tets_path = tets_path\\n\\n self.triangle_table: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"triangle_table\\\",\\n torch.as_tensor(\\n [\\n [-1, -1, -1, -1, -1, -1],\\n [1, 0, 2, -1, -1, -1],\\n [4, 0, 3, -1, -1, -1],\\n [1, 4, 2, 1, 3, 4],\\n [3, 1, 5, -1, -1, -1],\\n [2, 3, 0, 2, 5, 3],\\n [1, 4, 0, 1, 5, 4],\\n [4, 2, 5, -1, -1, -1],\\n [4, 5, 2, -1, -1, -1],\\n [4, 1, 0, 4, 5, 1],\\n [3, 2, 0, 3, 5, 2],\\n [1, 3, 5, -1, -1, -1],\\n [4, 1, 2, 4, 3, 1],\\n [3, 0, 4, -1, -1, -1],\\n [2, 0, 1, -1, -1, -1],\\n [-1, -1, -1, -1, -1, -1],\\n ],\\n dtype=torch.long,\\n ),\\n persistent=False,\\n )\\n self.num_triangles_table: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"num_triangles_table\\\",\\n torch.as_tensor(\\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\\n ),\\n persistent=False,\\n )\\n self.base_tet_edges: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"base_tet_edges\\\",\\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\\n persistent=False,\\n )\\n\\n tets = np.load(self.tets_path)\\n self._grid_vertices: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"_grid_vertices\\\",\\n torch.from_numpy(tets[\\\"vertices\\\"]).float(),\\n persistent=False,\\n )\\n self.indices: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"indices\\\", torch.from_numpy(tets[\\\"indices\\\"]).long(), persistent=False\\n )\\n\\n self._all_edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n\\n def normalize_grid_deformation(\\n self, grid_vertex_offsets: Float[Tensor, \\\"Nv 3\\\"]\\n ) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return (\\n (self.points_range[1] - self.points_range[0])\\n / (self.resolution) # half tet size is approximately 1 / self.resolution\\n * torch.tanh(grid_vertex_offsets)\\n ) # FIXME: hard-coded activation\\n\\n @property\\n def grid_vertices(self) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return self._grid_vertices\\n\\n @property\\n def all_edges(self) -> Integer[Tensor, \\\"Ne 2\\\"]:\\n if self._all_edges is None:\\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\\n edges = torch.tensor(\\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\\n dtype=torch.long,\\n device=self.indices.device,\\n )\\n _all_edges = self.indices[:, edges].reshape(-1, 2)\\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\\n self._all_edges = _all_edges\\n return self._all_edges\\n\\n def sort_edges(self, edges_ex2):\\n with torch.no_grad():\\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\\n order = order.unsqueeze(dim=1)\\n\\n a = torch.gather(input=edges_ex2, index=order, dim=1)\\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\\n\\n return torch.stack([a, b], -1)\\n\\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\\n with torch.no_grad():\\n occ_n = sdf_n > 0\\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\\n occ_sum = torch.sum(occ_fx4, -1)\\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\\n occ_sum = occ_sum[valid_tets]\\n\\n # find all vertices\\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\\n all_edges = self.sort_edges(all_edges)\\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\\n\\n unique_edges = unique_edges.long()\\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\\n mapping = (\\n torch.ones(\\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\\n )\\n * -1\\n )\\n mapping[mask_edges] = torch.arange(\\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\\n )\\n idx_map = mapping[idx_map] # map edges to verts\\n\\n interp_v = unique_edges[mask_edges]\\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\\n edges_to_interp_sdf[:, -1] *= -1\\n\\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\\n\\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\\n\\n idx_map = idx_map.reshape(-1, 6)\\n\\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\\n num_triangles = self.num_triangles_table[tetindex]\\n\\n # Generate triangle indices\\n faces = torch.cat(\\n (\\n torch.gather(\\n input=idx_map[num_triangles == 1],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\\n ).reshape(-1, 3),\\n torch.gather(\\n input=idx_map[num_triangles == 2],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\\n ).reshape(-1, 3),\\n ),\\n dim=0,\\n )\\n\\n return verts, faces\\n\\n def forward(\\n self,\\n level: Float[Tensor, \\\"N3 1\\\"],\\n deformation: Optional[Float[Tensor, \\\"N3 3\\\"]] = None,\\n ) -> Mesh:\\n if deformation is not None:\\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\\n deformation\\n )\\n else:\\n grid_vertices = self.grid_vertices\\n\\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\\n\\n mesh = Mesh(\\n v_pos=v_pos,\\n t_pos_idx=t_pos_idx,\\n # extras\\n grid_vertices=grid_vertices,\\n tet_edges=self.all_edges,\\n grid_level=level,\\n grid_deformation=deformation,\\n )\\n\\n return mesh\"\n },\n {\n \"identifier\": \"Mesh\",\n \"path\": \"threestudio/models/mesh.py\",\n \"snippet\": \"class Mesh:\\n def __init__(\\n self, v_pos: Float[Tensor, \\\"Nv 3\\\"], t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"], **kwargs\\n ) -> None:\\n self.v_pos: Float[Tensor, \\\"Nv 3\\\"] = v_pos\\n self.t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"] = t_pos_idx\\n self._v_nrm: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tng: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tex: Optional[Float[Tensor, \\\"Nt 3\\\"]] = None\\n self._t_tex_idx: Optional[Float[Tensor, \\\"Nf 3\\\"]] = None\\n self._v_rgb: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n self.extras: Dict[str, Any] = {}\\n for k, v in kwargs.items():\\n self.add_extra(k, v)\\n\\n def add_extra(self, k, v) -> None:\\n self.extras[k] = v\\n\\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\\n if self.requires_grad:\\n threestudio.debug(\\\"Mesh is differentiable, not removing outliers\\\")\\n return self\\n\\n # use trimesh to first split the mesh into connected components\\n # then remove the components with less than n_face_threshold faces\\n import trimesh\\n\\n # construct a trimesh object\\n mesh = trimesh.Trimesh(\\n vertices=self.v_pos.detach().cpu().numpy(),\\n faces=self.t_pos_idx.detach().cpu().numpy(),\\n )\\n\\n # split the mesh into connected components\\n components = mesh.split(only_watertight=False)\\n # log the number of faces in each component\\n threestudio.debug(\\n \\\"Mesh has {} components, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n\\n n_faces_threshold: int\\n if isinstance(outlier_n_faces_threshold, float):\\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\\n n_faces_threshold = int(\\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\\n )\\n else:\\n # set the threshold directly to outlier_n_faces_threshold\\n n_faces_threshold = outlier_n_faces_threshold\\n\\n # log the threshold\\n threestudio.debug(\\n \\\"Removing components with less than {} faces\\\".format(n_faces_threshold)\\n )\\n\\n # remove the components with less than n_face_threshold faces\\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\\n\\n # log the number of faces in each component after removing outliers\\n threestudio.debug(\\n \\\"Mesh has {} components after removing outliers, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n # merge the components\\n mesh = trimesh.util.concatenate(components)\\n\\n # convert back to our mesh format\\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\\n\\n clean_mesh = Mesh(v_pos, t_pos_idx)\\n # keep the extras unchanged\\n\\n if len(self.extras) > 0:\\n clean_mesh.extras = self.extras\\n threestudio.debug(\\n f\\\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\\\"\\n )\\n return clean_mesh\\n\\n @property\\n def requires_grad(self):\\n return self.v_pos.requires_grad\\n\\n @property\\n def v_nrm(self):\\n if self._v_nrm is None:\\n self._v_nrm = self._compute_vertex_normal()\\n return self._v_nrm\\n\\n @property\\n def v_tng(self):\\n if self._v_tng is None:\\n self._v_tng = self._compute_vertex_tangent()\\n return self._v_tng\\n\\n @property\\n def v_tex(self):\\n if self._v_tex is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._v_tex\\n\\n @property\\n def t_tex_idx(self):\\n if self._t_tex_idx is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._t_tex_idx\\n\\n @property\\n def v_rgb(self):\\n return self._v_rgb\\n\\n @property\\n def edges(self):\\n if self._edges is None:\\n self._edges = self._compute_edges()\\n return self._edges\\n\\n def _compute_vertex_normal(self):\\n i0 = self.t_pos_idx[:, 0]\\n i1 = self.t_pos_idx[:, 1]\\n i2 = self.t_pos_idx[:, 2]\\n\\n v0 = self.v_pos[i0, :]\\n v1 = self.v_pos[i1, :]\\n v2 = self.v_pos[i2, :]\\n\\n face_normals = torch.cross(v1 - v0, v2 - v0)\\n\\n # Splat face normals to vertices\\n v_nrm = torch.zeros_like(self.v_pos)\\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\\n\\n # Normalize, replace zero (degenerated) normals with some default value\\n v_nrm = torch.where(\\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\\n )\\n v_nrm = F.normalize(v_nrm, dim=1)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(v_nrm))\\n\\n return v_nrm\\n\\n def _compute_vertex_tangent(self):\\n vn_idx = [None] * 3\\n pos = [None] * 3\\n tex = [None] * 3\\n for i in range(0, 3):\\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\\n # t_nrm_idx is always the same as t_pos_idx\\n vn_idx[i] = self.t_pos_idx[:, i]\\n\\n tangents = torch.zeros_like(self.v_nrm)\\n tansum = torch.zeros_like(self.v_nrm)\\n\\n # Compute tangent space for each triangle\\n uve1 = tex[1] - tex[0]\\n uve2 = tex[2] - tex[0]\\n pe1 = pos[1] - pos[0]\\n pe2 = pos[2] - pos[0]\\n\\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\\n\\n # Avoid division by zero for degenerated texture coordinates\\n tang = nom / torch.where(\\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\\n )\\n\\n # Update all 3 vertices\\n for i in range(0, 3):\\n idx = vn_idx[i][:, None].repeat(1, 3)\\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\\n tansum.scatter_add_(\\n 0, idx, torch.ones_like(tang)\\n ) # tansum[n_i] = tansum[n_i] + 1\\n tangents = tangents / tansum\\n\\n # Normalize and make sure tangent is perpendicular to normal\\n tangents = F.normalize(tangents, dim=1)\\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(tangents))\\n\\n return tangents\\n\\n def _unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n threestudio.info(\\\"Using xatlas to perform UV unwrapping, may take a while ...\\\")\\n\\n import xatlas\\n\\n atlas = xatlas.Atlas()\\n atlas.add_mesh(\\n self.v_pos.detach().cpu().numpy(),\\n self.t_pos_idx.cpu().numpy(),\\n )\\n co = xatlas.ChartOptions()\\n po = xatlas.PackOptions()\\n for k, v in xatlas_chart_options.items():\\n setattr(co, k, v)\\n for k, v in xatlas_pack_options.items():\\n setattr(po, k, v)\\n atlas.generate(co, po)\\n vmapping, indices, uvs = atlas.get_mesh(0)\\n vmapping = (\\n torch.from_numpy(\\n vmapping.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\\n indices = (\\n torch.from_numpy(\\n indices.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n return uvs, indices\\n\\n def unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\\n xatlas_chart_options, xatlas_pack_options\\n )\\n\\n def set_vertex_color(self, v_rgb):\\n assert v_rgb.shape[0] == self.v_pos.shape[0]\\n self._v_rgb = v_rgb\\n\\n def _compute_edges(self):\\n # Compute edges\\n edges = torch.cat(\\n [\\n self.t_pos_idx[:, [0, 1]],\\n self.t_pos_idx[:, [1, 2]],\\n self.t_pos_idx[:, [2, 0]],\\n ],\\n dim=0,\\n )\\n edges = edges.sort()[0]\\n edges = torch.unique(edges, dim=0)\\n return edges\\n\\n def normal_consistency(self) -> Float[Tensor, \\\"\\\"]:\\n edge_nrm: Float[Tensor, \\\"Ne 2 3\\\"] = self.v_nrm[self.edges]\\n nc = (\\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\\n ).mean()\\n return nc\\n\\n def _laplacian_uniform(self):\\n # from stable-dreamfusion\\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\\n verts, faces = self.v_pos, self.t_pos_idx\\n\\n V = verts.shape[0]\\n F = faces.shape[0]\\n\\n # Neighbor indices\\n ii = faces[:, [1, 2, 0]].flatten()\\n jj = faces[:, [2, 0, 1]].flatten()\\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\\n dim=1\\n )\\n adj_values = torch.ones(adj.shape[1]).to(verts)\\n\\n # Diagonal indices\\n diag_idx = adj[0]\\n\\n # Build the sparse matrix\\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\\n values = torch.cat((-adj_values, adj_values))\\n\\n # The coalesce operation sums the duplicate indices, resulting in the\\n # correct diagonal\\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\\n\\n def laplacian(self) -> Float[Tensor, \\\"\\\"]:\\n with torch.no_grad():\\n L = self._laplacian_uniform()\\n loss = L.mm(self.v_pos)\\n loss = loss.norm(dim=1)\\n loss = loss.mean()\\n return loss\"\n },\n {\n \"identifier\": \"get_encoding\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_encoding(n_input_dims: int, config) -> nn.Module:\\n # input suppose to be range [0, 1]\\n encoding: nn.Module\\n if config.otype == \\\"ProgressiveBandFrequency\\\":\\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\\n elif config.otype == \\\"ProgressiveBandHashGrid\\\":\\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\\n else:\\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\\n encoding = CompositeEncoding(\\n encoding,\\n include_xyz=config.get(\\\"include_xyz\\\", False),\\n xyz_scale=2.0,\\n xyz_offset=-1.0,\\n ) # FIXME: hard coded\\n return encoding\"\n },\n {\n \"identifier\": \"get_mlp\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\\n network: nn.Module\\n if config.otype == \\\"VanillaMLP\\\":\\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\\n elif config.otype == \\\"SphereInitVanillaMLP\\\":\\n network = SphereInitVanillaMLP(\\n n_input_dims, n_output_dims, config_to_primitive(config)\\n )\\n else:\\n assert (\\n config.get(\\\"sphere_init\\\", False) is False\\n ), \\\"sphere_init=True only supported by VanillaMLP\\\"\\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\\n return network\"\n },\n {\n \"identifier\": \"broadcast\",\n \"path\": \"threestudio/utils/misc.py\",\n \"snippet\": \"def broadcast(tensor, src=0):\\n if not _distributed_available():\\n return tensor\\n else:\\n torch.distributed.broadcast(tensor, src=src)\\n return tensor\"\n },\n {\n \"identifier\": \"scale_tensor\",\n \"path\": \"threestudio/utils/ops.py\",\n \"snippet\": \"def scale_tensor(\\n dat: Num[Tensor, \\\"... D\\\"], inp_scale: ValidScale, tgt_scale: ValidScale\\n):\\n if inp_scale is None:\\n inp_scale = (0, 1)\\n if tgt_scale is None:\\n tgt_scale = (0, 1)\\n if isinstance(tgt_scale, Tensor):\\n assert dat.shape[-1] == tgt_scale.shape[-1]\\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\\n return dat\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport threestudio\n import trimesh\nfrom dataclasses import dataclass, field\nfrom threestudio.models.geometry.base import (\n BaseExplicitGeometry,\n BaseGeometry,\n contract_to_unisphere,\n)\nfrom threestudio.models.geometry.implicit_sdf import ImplicitSDF\nfrom threestudio.models.geometry.implicit_volume import ImplicitVolume\nfrom threestudio.models.isosurface import MarchingTetrahedraHelper\nfrom threestudio.models.mesh import Mesh\nfrom threestudio.models.networks import get_encoding, get_mlp\nfrom threestudio.utils.misc import broadcast\nfrom threestudio.utils.ops import scale_tensor\nfrom threestudio.utils.typing import *\n from pysdf import SDF"},"token_num":{"kind":"number","value":14680,"string":"14,680"},"cropped_code":{"kind":"string","value":" (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_buffer(\n \"deformation\",\n torch.zeros_like(self.isosurface_helper.grid_vertices),\n )\n else:\n self.deformation = None\n\n if not self.cfg.geometry_only:\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n self.mesh: Optional[Mesh] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n"},"all_code":{"kind":"string","value":"\n\n\n\n@threestudio.register(\"tetrahedra-sdf-grid\")\nclass TetrahedraSDFGrid(BaseExplicitGeometry):\n @dataclass\n class Config(BaseExplicitGeometry.Config):\n isosurface_resolution: int = 128\n isosurface_deformable_grid: bool = True\n isosurface_remove_outliers: bool = False\n isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01\n\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n geometry_only: bool = False\n fix_geometry: bool = False\n\n cfg: Config\n\n def configure(self,*args, **kwargs) -> None:\n super().configure()\n\n # this should be saved to state_dict, register as buffer\n self.isosurface_bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\"isosurface_bbox\", self.bbox.clone())\n\n self.isosurface_helper = MarchingTetrahedraHelper(\n self.cfg.isosurface_resolution,\n f\"load/tets/{self.cfg.isosurface_resolution}_tets.npz\",\n )\n\n self.sdf: Float[Tensor, \"Nv 1\"]\n self.deformation: Optional[Float[Tensor, \"Nv 3\"]]\n\n if not self.cfg.fix_geometry:\n self.register_parameter(\n \"sdf\",\n nn.Parameter(\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n )\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_parameter(\n \"deformation\",\n nn.Parameter(\n torch.zeros_like(self.isosurface_helper.grid_vertices)\n ),\n )\n else:\n self.deformation = None\n else:\n self.register_buffer(\n \"sdf\",\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_buffer(\n \"deformation\",\n torch.zeros_like(self.isosurface_helper.grid_vertices),\n )\n else:\n self.deformation = None\n\n if not self.cfg.geometry_only:\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n self.mesh: Optional[Mesh] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf("},"next_line":{"kind":"string","value":" scale_tensor("},"gold_snippet_index":{"kind":"number","value":10,"string":"10"},"created_at":{"kind":"string","value":"2023-11-27 23:39:49+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5399,"cells":{"repo_name":{"kind":"string","value":"abdulhaim/LMRL-Gym"},"file_path":{"kind":"string","value":"llm_rl_scripts/chess/ilql/train_full_games_ilql.py"},"context":{"kind":"list like","value":[{"identifier":"ilql_loss","path":"LLM_RL/algorithms/ilql/base_interface.py","snippet":"def ilql_loss(\n q1: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n q2: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n v: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n v_final: jax.Array, # [batch]\n target_q1: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n target_q2: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n q1_logits: jax.Array, # [batch, time-1, vocab] output is masked; shift x[:-1]\n q2_logits: jax.Array, # [batch, time-1, vocab] output is masked; shift x[:-1]\n token_ids: jax.Array, # [batch, time-1] output is masked; shift x[1:]\n attention_mask: jax.Array, # [batch, time-1] output is masked; shift x[1:]\n should_take_action: jax.Array, # [batch, time-1] output is masked; shift x[1:]\n rewards: jax.Array, # [batch, time-1] output is masked; shift x[1:]\n *, \n gamma: Union[float, jax.Array], \n tau: Union[float, jax.Array], \n cql_weight: Union[float, jax.Array], \n) -> Tuple[jnp.ndarray, Any]:\n # should be an action in the batch\n mask = should_take_action.astype(jnp.float32) * attention_mask\n n = mask.sum()\n \n q1sa_flat, q2sa_flat, v_flat = q1.reshape(-1), q2.reshape(-1), v.reshape(-1)\n target_q1sa_flat, target_q2sa_flat = target_q1.reshape(-1), target_q2.reshape(-1)\n vns_flat = jnp.concatenate((v, v_final[..., None]), axis=1).reshape(-1)\n\n qv_query_indicators = get_query_indicators(should_take_action.reshape(-1))\n\n is_next_state = should_take_action.copy()\n # set first action position to false\n is_next_state = is_next_state.at[jnp.arange(0, is_next_state.shape[0], dtype=jnp.int32), jnp.argmax(is_next_state.astype(jnp.int32), axis=1)].set(False)\n # set endpoint to true as long as there is at least 1 action in the sequence\n is_next_state = jnp.concatenate((is_next_state, (should_take_action.sum(axis=1) > 0)[..., None]), axis=1)\n\n vns_query_indicators = get_query_indicators(is_next_state.reshape(-1))\n # should be the same number of vns as qv, so we can clip the extra padding to match shape\n vns_query_indicators = vns_query_indicators[:qv_query_indicators.shape[0], :]\n \n # extract selected values\n q1sa_selected = (qv_query_indicators * q1sa_flat).sum(axis=1)\n q2sa_selected = (qv_query_indicators * q2sa_flat).sum(axis=1)\n v_selected = (qv_query_indicators * v_flat).sum(axis=1)\n target_q1sa_selected = (qv_query_indicators * target_q1sa_flat).sum(axis=1)\n target_q2sa_selected = (qv_query_indicators * target_q2sa_flat).sum(axis=1)\n vns_selected = (vns_query_indicators * vns_flat).sum(axis=1)\n rs_selected = (qv_query_indicators * rewards.reshape(-1)).sum(axis=1)\n\n # get masks for selected values\n sa_mask = (qv_query_indicators.sum(axis=1) > 0).astype(jnp.float32)\n ns_mask = (vns_query_indicators.sum(axis=1) > 0).astype(jnp.float32)\n\n # compute q loss\n q1_loss = (optax.l2_loss(q1sa_selected, jax.lax.stop_gradient(rs_selected + gamma * vns_selected)) * sa_mask).sum() / n\n q2_loss = (optax.l2_loss(q2sa_selected, jax.lax.stop_gradient(rs_selected + gamma * vns_selected)) * sa_mask).sum() / n\n\n # compute v loss\n target_q_selected = jnp.minimum(target_q1sa_selected, target_q2sa_selected)\n expectile_indicator = (target_q_selected >= v_selected).astype(jnp.float32)\n expectile_weights = expectile_indicator * tau + (1 - expectile_indicator) * (1 - tau)\n v_loss = (optax.l2_loss(v_selected, jax.lax.stop_gradient(target_q_selected)) * jax.lax.stop_gradient(expectile_weights) * sa_mask).sum() / n\n\n # compute cql loss on both q heads\n q1_cql_loss = optax.softmax_cross_entropy_with_integer_labels(q1_logits, token_ids)\n q1_cql_loss = (mask * q1_cql_loss).sum() / n\n\n q2_cql_loss = optax.softmax_cross_entropy_with_integer_labels(q2_logits, token_ids)\n q2_cql_loss = (mask * q2_cql_loss).sum() / n\n \n loss = q1_loss + q2_loss + v_loss + cql_weight * (q1_cql_loss + q2_cql_loss)\n\n logs = dict(\n losses=dict(\n total_loss=loss, \n q1_loss=q1_loss, \n q2_loss=q2_loss, \n v_loss=v_loss, \n q1_cql_loss=q1_cql_loss, \n q2_cql_loss=q2_cql_loss, \n ), \n q1=get_tensor_stats(q1sa_selected, mask=sa_mask, n=n), \n q2=get_tensor_stats(q2sa_selected, mask=sa_mask, n=n), \n v=get_tensor_stats(v_selected, mask=sa_mask, n=n), \n target_q=get_tensor_stats(target_q_selected, mask=sa_mask, n=n), \n target_q1=get_tensor_stats(target_q1sa_selected, mask=sa_mask, n=n), \n target_q2=get_tensor_stats(target_q2sa_selected, mask=sa_mask, n=n), \n vns=get_tensor_stats(vns_selected, mask=ns_mask, n=n), \n v_final=get_tensor_stats(v_final, mask=jnp.ones(v_final.shape, dtype=jnp.int32), n=v_final.shape[0]), \n rewards=get_tensor_stats(rewards, mask=mask, n=n), \n )\n\n return loss, logs"},{"identifier":"Text","path":"LLM_RL/environment.py","snippet":"class Text:\n text: str\n is_action: bool"},{"identifier":"text_env_eval","path":"LLM_RL/environment.py","snippet":"def text_env_eval(\n env: Union[TextEnv, BatchedTextEnv], \n policy: Union[TextPolicy, BatchedTextPolicy], \n n_rollouts: int, \n initial_text_history: Optional[TextHistory]=None, # only allow one initial_text_history here\n seed_generator: Optional[Iterator[int]]=None, \n env_options: Optional[Dict]=None, # only allow one env_options here\n interaction_callback: Optional[Callable[[List[Tuple[TextHistory, TextHistory, TextHistory, float, bool]]], None]]=None, \n bsize: int=1, \n verbose: bool=True, \n) -> Tuple[List[List[InteractionTransition]], Dict[str, Any]]:\n interactions, rewards, dones, eps_lengths = [], [], [], []\n for _ in tqdm(range((n_rollouts+(bsize-1))//bsize), disable=not verbose):\n actual_bsize = min(n_rollouts-len(interactions), bsize)\n npad = bsize - actual_bsize\n interaction_batch = interact_environment(\n env, \n policy, \n initial_text_history=initial_text_history, \n env_seed=[None]*actual_bsize if seed_generator is None else [next(seed_generator) for _ in range(actual_bsize)], \n env_options=[env_options]*actual_bsize, \n bsize=actual_bsize,\n npad=npad,\n )\n \n for interaction in interaction_batch:\n interactions.append(interaction)\n rewards.append(sum(map(lambda x: x.reward, interaction)))\n dones.append(interaction[-1].done)\n eps_lengths.append(len(interaction))\n if interaction_callback is not None:\n interaction_callback(interaction)\n \n rewards = np.asarray(rewards, dtype=np.float32)\n dones = np.asarray(dones, dtype=np.float32)\n results_summary = dict(\n reward=dict(\n mean=np.mean(rewards), \n std=np.std(rewards), \n min=np.min(rewards), \n max=np.max(rewards), \n ), \n done=dict(\n mean=np.mean(dones), \n std=np.std(dones), \n min=np.min(dones), \n max=np.max(dones), \n ), \n length=dict(\n mean=np.mean(eps_lengths),\n std=np.std(eps_lengths),\n min=np.min(eps_lengths),\n max=np.max(eps_lengths),\n ),\n )\n \n return interactions, results_summary"},{"identifier":"TextTrajectory","path":"LLM_RL/environment.py","snippet":"class TextTrajectory:\n text_history: TextHistory\n reward: Tuple[float, ...]\n done: bool\n\n def __post_init__(self):\n assert len(self.reward) == len(self.text_history), \"reward is needed for each text\"\n assert all([r == 0.0 for r, t in zip(self.reward, self.text_history) if not t.is_action]), \"reward for non-actions texts should be 0.0\""},{"identifier":"TextTrajectoryChain","path":"LLM_RL/environment.py","snippet":"class TextTrajectoryChain:\n text_trajectory: TextTrajectory\n next: Optional[TextTrajectoryChain]"},{"identifier":"TokenTrajectoryChain","path":"LLM_RL/environment.py","snippet":"class TokenTrajectoryChain:\n token_trajectory: TokenTrajectory\n next: Optional[TokenTrajectoryChain]\n\n def __post_init__(self):\n curr, dones = self, []\n while curr.next is not None:\n dones.append(curr.token_trajectory.done)\n curr = curr.next\n assert not np.any(dones[:-1]), 'token trajectory chain can only be done at the end'\n \n def to_list(self) -> List[TokenTrajectory]:\n curr, l = self, []\n while curr is not None:\n l.append(curr.token_trajectory)\n curr = curr.next\n return l\n\n @classmethod\n def from_text_trajectory_chain(\n cls, \n text_trajectory_chain: TextTrajectoryChain, \n tokenizer: PreTrainedTokenizer, \n token_process: Optional[Callable[[List[int]], List[int]]]=None, \n ) -> TokenTrajectoryChain:\n return TokenTrajectoryChain(\n TokenTrajectory.from_text_trajectory(\n text_trajectory_chain.text_trajectory, \n tokenizer, \n token_process=token_process, \n ), \n cls.from_text_trajectory_chain(\n text_trajectory_chain.next, \n tokenizer, \n token_process=token_process, \n ) if text_trajectory_chain.next is not None else None, \n )"},{"identifier":"GPT2ILQLTrain","path":"LLM_RL/algorithms/ilql/gpt2/interface.py","snippet":"class GPT2ILQLTrain(ILQLTrain):\n @classmethod\n def load_train(\n cls, \n base_train_state: TrainState, \n target_base_params: Optional[PyTree], \n q1_head_train_state: TrainState, \n q2_head_train_state: TrainState, \n v_head_train_state: TrainState, \n q1_target_head_params: PyTree, \n q2_target_head_params: PyTree, \n base_model: FlaxPreTrainedModel, \n q_head_model: nn.Module, \n v_head_model: nn.Module, \n tokenizer: PreTrainedTokenizerBase, \n loss_fn: Callable, \n detach_q1: bool, \n detach_q2: bool, \n detach_v: bool, \n polyak_alpha: float, \n hard_update_every: Optional[int], \n ):\n mesh = base_model.config.mesh\n assert mesh is not None\n assert mesh == q_head_model.config.mesh\n assert mesh == v_head_model.config.mesh\n base_train_state_partition_spec = match_partition_rules(base_model.config.get_partition_rules(), base_train_state)\n target_base_params_partition_spec = PS() if target_base_params is None else match_partition_rules(base_model.config.get_partition_rules(), target_base_params)\n q1_head_train_state_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_head_train_state)\n q2_head_train_state_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q2_head_train_state)\n v_head_train_state_partition_spec = match_partition_rules(v_head_model.config.get_partition_rules(), v_head_train_state)\n q1_target_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_target_head_params)\n q2_target_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q2_target_head_params)\n\n @partial(\n pjit, \n donate_argnums=(0, 1, 2, 3, 4, 5, 6), \n static_argnames=('train',), \n in_shardings=(\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n out_shardings=(\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_train_state_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n )\n def _step(\n base_train_state: TrainState, \n target_base_params: Optional[PyTree], \n q1_head_train_state: TrainState, \n q2_head_train_state: TrainState, \n v_head_train_state: TrainState, \n q1_target_head_params: PyTree, \n q2_target_head_params: PyTree, \n\n input_ids: jax.Array, \n attention_mask: jax.Array, \n position_ids: jax.Array, \n should_take_action: jax.Array, \n rewards: jax.Array, \n dones: jax.Array, \n\n next_token_ids: Optional[jax.Array], \n next_tokens_attention_mask: Optional[jax.Array], \n next_tokens_position_ids: Optional[jax.Array], \n next_dones: Optional[jax.Array], \n\n prng_key: Optional[jax.random.PRNGKeyArray], \n train: bool=True, \n ) -> Tuple[TrainState, Optional[PyTree], TrainState, TrainState, TrainState, PyTree, PyTree, jax.Array, PyTree]:\n # data parallel shard inputs\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS(('dp', 'fsdp'), None))\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS(('dp', 'fsdp'), None))\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS(('dp', 'fsdp'), None))\n should_take_action = with_named_sharding_constraint(should_take_action, mesh, PS(('dp', 'fsdp'), None))\n rewards = with_named_sharding_constraint(rewards, mesh, PS(('dp', 'fsdp'), None))\n dones = with_named_sharding_constraint(dones, mesh, PS(('dp', 'fsdp')))\n if next_token_ids is not None:\n assert next_tokens_attention_mask is not None\n assert next_tokens_position_ids is not None\n next_token_ids = with_named_sharding_constraint(next_token_ids, mesh, PS(('dp', 'fsdp'), None))\n next_tokens_attention_mask = with_named_sharding_constraint(next_tokens_attention_mask, mesh, PS(('dp', 'fsdp'), None))\n next_tokens_position_ids = with_named_sharding_constraint(next_tokens_position_ids, mesh, PS(('dp', 'fsdp'), None))\n next_dones = with_named_sharding_constraint(next_dones, mesh, PS(('dp', 'fsdp')))\n else:\n assert next_tokens_attention_mask is None\n assert next_tokens_position_ids is None\n\n # define loss function\n\n def grad_loss(base_params: PyTree, q1_head_params: PyTree, q2_head_params: PyTree, v_head_params: PyTree, prng_key: jax.random.PRNGKeyArray):\n \n # get base hidden states\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n base_model_output = base_model(\n input_ids=input_ids, \n attention_mask=attention_mask, \n position_ids=position_ids, \n params=base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n\n if target_base_params is not None:\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_base_model_output = base_model(\n input_ids=input_ids, \n attention_mask=attention_mask, \n position_ids=position_ids, \n params=target_base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n else:\n target_base_model_output = base_model_output\n \n if next_token_ids is not None:\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n next_token_base_model_output = base_model(\n input_ids=next_token_ids, \n attention_mask=next_tokens_attention_mask, \n position_ids=next_tokens_position_ids, \n params=base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n \n # get values\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q1_head_output = q_head_model.apply(\n {'params': q1_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q2_head_output = q_head_model.apply(\n {'params': q2_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n v_head_output = v_head_model.apply(\n {'params': v_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_q1_head_output = q_head_model.apply(\n {'params': q1_target_head_params}, \n target_base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_q2_head_output = q_head_model.apply(\n {'params': q2_target_head_params}, \n target_base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n # stop gradients\n if detach_q1:\n q1_head_output = jax.lax.stop_gradient(q1_head_output)\n if detach_q2:\n q2_head_output = jax.lax.stop_gradient(q2_head_output)\n if detach_v:\n v_head_output = jax.lax.stop_gradient(v_head_output)\n target_q1_head_output = jax.lax.stop_gradient(target_q1_head_output)\n target_q2_head_output = jax.lax.stop_gradient(target_q2_head_output)\n\n q1 = jnp.take_along_axis(q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n q2 = jnp.take_along_axis(q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n v = v_head_output[:, :-1].squeeze(2)\n v_full = v_head_output.squeeze(2)\n target_q1 = jnp.take_along_axis(target_q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n target_q2 = jnp.take_along_axis(target_q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n\n q1_logits = q1_head_output[:, :-1, :].astype(jnp.float32)\n q2_logits = q2_head_output[:, :-1, :].astype(jnp.float32)\n\n # get next token values\n\n if next_token_ids is not None:\n # just run vf on last token to save some flops\n last_next_token_idxs = (next_tokens_attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(next_tokens_attention_mask, axis=1).astype(jnp.int32), axis=1)\n final_next_token_h = next_token_base_model_output.hidden_states[-1][jnp.arange(0, input_ids.shape[0], dtype=jnp.int32), last_next_token_idxs, :]\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n next_token_v_head_output = v_head_model.apply(\n {'params': v_head_params}, \n final_next_token_h, \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n ).squeeze(1)\n v_final = next_token_v_head_output * (1 - next_dones.astype(jnp.float32))\n else:\n last_action_idxs = (should_take_action.shape[1]-1)-jnp.argmax(jnp.flip(should_take_action, axis=1).astype(jnp.int32), axis=1)+1\n last_token_idxs = (attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(attention_mask, axis=1).astype(jnp.int32), axis=1)\n final_state_idxs = ((1 - dones) * last_action_idxs + dones * last_token_idxs).astype(jnp.int32)\n v_final = v_full[jnp.arange(0, should_take_action.shape[0], dtype=jnp.int32), final_state_idxs]\n v_final = v_final * (1 - dones)\n v_final = jax.lax.stop_gradient(v_final)\n\n loss, info = loss_fn(\n q1, \n q2, \n v, \n v_final, \n target_q1, \n target_q2, \n q1_logits, \n q2_logits, \n input_ids[:, 1:], \n attention_mask[:, 1:], \n should_take_action, \n rewards, \n )\n return loss, info\n\n # take loss\n (loss, info), (base_grads, q1_head_grads, q2_head_grads, v_head_grads) = jax.value_and_grad(grad_loss, has_aux=True, argnums=(0, 1, 2, 3))(\n base_train_state.params, \n q1_head_train_state.params, \n q2_head_train_state.params, \n v_head_train_state.params, \n prng_key, \n )\n # assert shard gradients\n base_grads = jax.tree_util.tree_map(\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \n base_grads, \n base_train_state_partition_spec.params, \n )\n q1_head_grads = jax.tree_util.tree_map(\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \n q1_head_grads, \n q1_head_train_state_partition_spec.params, \n )\n q2_head_grads = jax.tree_util.tree_map(\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \n q2_head_grads, \n q2_head_train_state_partition_spec.params, \n )\n v_head_grads = jax.tree_util.tree_map(\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \n v_head_grads, \n v_head_train_state_partition_spec.params, \n )\n # update params and optim state\n base_train_state = base_train_state.apply_gradients(grads=base_grads)\n q1_head_train_state = q1_head_train_state.apply_gradients(grads=q1_head_grads)\n q2_head_train_state = q2_head_train_state.apply_gradients(grads=q2_head_grads)\n v_head_train_state = v_head_train_state.apply_gradients(grads=v_head_grads)\n\n # handle target network updates\n def update_targets(params: PyTree, base_params: PyTree, steps: jnp.ndarray) -> PyTree:\n base_params = optax.incremental_update(params, base_params, polyak_alpha)\n if hard_update_every is not None:\n base_params = optax.periodic_update(params, base_params, steps, hard_update_every)\n return base_params\n \n def mid_targets(params: PyTree, base_params: PyTree, steps: jnp.ndarray) -> PyTree:\n return base_params\n\n def update_cond(opt_state: PyTree) -> bool:\n if hasattr(opt_state, 'mini_step'):\n return opt_state.mini_step == 0\n return True\n \n if target_base_params is not None:\n target_base_params = jax.lax.cond(\n update_cond(base_train_state.opt_state), \n update_targets, \n mid_targets, \n base_train_state.params, \n target_base_params, \n base_train_state.step, \n )\n q1_target_head_params = jax.lax.cond(\n update_cond(q1_head_train_state.opt_state), \n update_targets, \n mid_targets, \n q1_head_train_state.params, \n q1_target_head_params, \n q1_head_train_state.step, \n )\n q2_target_head_params = jax.lax.cond(\n update_cond(q2_head_train_state.opt_state), \n update_targets, \n mid_targets, \n q2_head_train_state.params, \n q2_target_head_params, \n q2_head_train_state.step, \n )\n\n return base_train_state, target_base_params, q1_head_train_state, q2_head_train_state, v_head_train_state, q1_target_head_params, q2_target_head_params, loss, info\n\n return cls(\n base_train_state=base_train_state, \n target_base_params=target_base_params, \n q1_head_train_state=q1_head_train_state, \n q2_head_train_state=q2_head_train_state, \n v_head_train_state=v_head_train_state, \n q1_target_head_params=q1_target_head_params, \n q2_target_head_params=q2_target_head_params, \n base_model=base_model, \n q_head_model=q_head_model, \n v_head_model=v_head_model, \n tokenizer=tokenizer, \n _step=_step, \n )"},{"identifier":"GPT2ILQLInference","path":"LLM_RL/algorithms/ilql/gpt2/interface.py","snippet":"class GPT2ILQLInference(ILQLInference):\n @classmethod\n def load_inference(\n cls, \n value_inference: GPT2ValueRLInference, \n target_value_inference: GPT2ValueRLInference, \n loss_fn: Callable, \n use_target_base_for_loss: bool=True, \n ):\n mesh = value_inference.base_model.config.mesh\n assert mesh is not None\n assert mesh == value_inference.q_head_model.config.mesh\n assert mesh == value_inference.v_head_model.config.mesh\n assert mesh == target_value_inference.base_model.config.mesh\n assert mesh == target_value_inference.q_head_model.config.mesh\n\n base_params_partition_spec = match_partition_rules(value_inference.base_model.config.get_partition_rules(), value_inference.base_params)\n target_base_params_partition_spec = PS() if (not use_target_base_for_loss) else match_partition_rules(target_value_inference.base_model.config.get_partition_rules(), target_value_inference.base_params)\n q1_head_params_partition_spec = match_partition_rules(value_inference.q_head_model.config.get_partition_rules(), value_inference.q1_head_params)\n q2_head_params_partition_spec = match_partition_rules(value_inference.q_head_model.config.get_partition_rules(), value_inference.q2_head_params)\n v_head_params_partition_spec = match_partition_rules(value_inference.v_head_model.config.get_partition_rules(), value_inference.v_head_params)\n q1_target_head_params_partition_spec = match_partition_rules(target_value_inference.q_head_model.config.get_partition_rules(), target_value_inference.q1_head_params)\n q2_target_head_params_partition_spec = match_partition_rules(target_value_inference.q_head_model.config.get_partition_rules(), target_value_inference.q2_head_params)\n \n @partial(\n pjit, \n static_argnames=('train',), \n in_shardings=(\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n out_shardings=(\n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n )\n def _eval_loss(\n base_params: PyTree, \n target_base_params: Optional[PyTree], \n q1_head_params: PyTree, \n q2_head_params: PyTree, \n v_head_params: PyTree, \n q1_target_head_params: PyTree, \n q2_target_head_params: PyTree, \n\n input_ids: jax.Array, \n attention_mask: jax.Array, \n position_ids: jax.Array, \n should_take_action: jax.Array, \n rewards: jax.Array, \n dones: jax.Array, \n\n next_token_ids: Optional[jax.Array], \n next_tokens_attention_mask: Optional[jax.Array], \n next_tokens_position_ids: Optional[jax.Array], \n next_dones: Optional[jax.Array], \n\n prng_key: Optional[jax.random.PRNGKeyArray]=None, \n train: bool=False, \n ) -> Tuple[jax.Array, PyTree]:\n # data parallel shard inputs\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS(('dp', 'fsdp'), None))\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS(('dp', 'fsdp'), None))\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS(('dp', 'fsdp'), None))\n should_take_action = with_named_sharding_constraint(should_take_action, mesh, PS(('dp', 'fsdp'), None))\n rewards = with_named_sharding_constraint(rewards, mesh, PS(('dp', 'fsdp'), None))\n dones = with_named_sharding_constraint(dones, mesh, PS(('dp', 'fsdp')))\n if next_token_ids is not None:\n assert next_tokens_attention_mask is not None\n assert next_tokens_position_ids is not None\n next_token_ids = with_named_sharding_constraint(next_token_ids, mesh, PS(('dp', 'fsdp'), None))\n next_tokens_attention_mask = with_named_sharding_constraint(next_tokens_attention_mask, mesh, PS(('dp', 'fsdp'), None))\n next_tokens_position_ids = with_named_sharding_constraint(next_tokens_position_ids, mesh, PS(('dp', 'fsdp'), None))\n next_dones = with_named_sharding_constraint(next_dones, mesh, PS(('dp', 'fsdp')))\n else:\n assert next_tokens_attention_mask is None\n assert next_tokens_position_ids is None\n \n # get base hidden states\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n base_model_output = value_inference.base_model(\n input_ids=input_ids, \n attention_mask=attention_mask, \n position_ids=position_ids, \n params=base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n\n if target_base_params is not None:\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_base_model_output = target_value_inference.base_model(\n input_ids=input_ids, \n attention_mask=attention_mask, \n position_ids=position_ids, \n params=target_base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n else:\n target_base_model_output = base_model_output\n \n if next_token_ids is not None:\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n next_token_base_model_output = value_inference.base_model(\n input_ids=next_token_ids, \n attention_mask=next_tokens_attention_mask, \n position_ids=next_tokens_position_ids, \n params=base_params, \n dropout_rng=new_key, \n train=train, \n output_hidden_states=True, \n )\n \n # get values\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q1_head_output = value_inference.q_head_model.apply(\n {'params': q1_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q2_head_output = value_inference.q_head_model.apply(\n {'params': q2_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n v_head_output = value_inference.v_head_model.apply(\n {'params': v_head_params}, \n base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_q1_head_output = target_value_inference.q_head_model.apply(\n {'params': q1_target_head_params}, \n target_base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n target_q2_head_output = target_value_inference.q_head_model.apply(\n {'params': q2_target_head_params}, \n target_base_model_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n\n # process outputs\n\n q1 = jnp.take_along_axis(q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n q2 = jnp.take_along_axis(q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n v = v_head_output[:, :-1].squeeze(2)\n v_full = v_head_output.squeeze(2)\n target_q1 = jnp.take_along_axis(target_q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n target_q2 = jnp.take_along_axis(target_q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\n\n q1_logits = q1_head_output[:, :-1, :].astype(jnp.float32)\n q2_logits = q2_head_output[:, :-1, :].astype(jnp.float32)\n\n # get next token values\n\n if next_token_ids is not None:\n # just run vf on last token to save some flops\n last_next_token_idxs = (next_tokens_attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(next_tokens_attention_mask, axis=1).astype(jnp.int32), axis=1)\n final_next_token_h = next_token_base_model_output.hidden_states[-1][jnp.arange(0, input_ids.shape[0], dtype=jnp.int32), last_next_token_idxs, :]\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n next_token_v_head_output = value_inference.v_head_model.apply(\n {'params': v_head_params}, \n final_next_token_h, \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n ).squeeze(1)\n v_final = next_token_v_head_output * (1 - next_dones.astype(jnp.float32))\n else:\n last_action_idxs = (should_take_action.shape[1]-1)-jnp.argmax(jnp.flip(should_take_action, axis=1).astype(jnp.int32), axis=1)+1\n last_token_idxs = (attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(attention_mask, axis=1).astype(jnp.int32), axis=1)\n final_state_idxs = ((1 - dones) * last_action_idxs + dones * last_token_idxs).astype(jnp.int32)\n v_final = v_full[jnp.arange(0, should_take_action.shape[0], dtype=jnp.int32), final_state_idxs]\n v_final = v_final * (1 - dones)\n\n loss, info = loss_fn(\n q1, \n q2, \n v, \n v_final, \n target_q1, \n target_q2, \n q1_logits, \n q2_logits, \n input_ids[:, 1:], \n attention_mask[:, 1:], \n should_take_action, \n rewards, \n )\n \n return loss, info\n\n return cls(\n value_inference=value_inference, \n target_value_inference=target_value_inference, \n _eval_loss=_eval_loss, \n use_target_base_for_loss=use_target_base_for_loss, \n )"},{"identifier":"GPT2ValuePolicy","path":"LLM_RL/algorithms/value_rl_base/gpt2/interface.py","snippet":"class GPT2ValuePolicy(ValueRLPolicy):\n def __init__(\n self, \n inference: ValueRLInference, \n prng_key: Optional[jax.random.KeyArray], \n generation_config: Optional[GenerationConfig]=None, \n blocking_strategy: BlockingStrategy=BlockingStrategy(padding=Padding.LEFT, truncation=Truncation.LEFT, max_length=None), \n in_str_process: Optional[Callable[[str], str]]=None, \n out_str_process: Optional[Callable[[str], str]]=None, \n input_token_process: Optional[Callable[[List[int]], List[int]]]=None, \n target_token_process: Optional[Callable[[List[int]], List[int]]]=None, \n trace: bool=True, \n ):\n self.inference = inference\n self.prng_key = prng_key\n self.generation_config = generation_config\n self.blocking_strategy = blocking_strategy\n self.in_str_process = in_str_process\n self.out_str_process = out_str_process\n self.input_token_process = input_token_process\n self.target_token_process = target_token_process\n if self.in_str_process is None:\n self.in_str_process = lambda x: x\n if self.out_str_process is None:\n self.out_str_process = lambda x: x\n self.trace = trace\n \n def act(self, text_history: List[Optional[TextHistory]], done: Optional[List[bool]]=None) -> List[Optional[TextHistory]]:\n if done is None:\n done = [False]*len(text_history)\n # force eos_token for done sequences\n eos_token = self.inference.tokenizer.eos_token\n if self.generation_config is not None and self.generation_config.eos_token_id is not None:\n eos_token = self.inference.tokenizer.decode(self.generation_config.eos_token_id)\n if eos_token is None:\n eos_token = self.inference.tokenizer.pad_token\n if eos_token is None:\n eos_token = ''\n \n raw_input_strs = [\n eos_token if d else self.in_str_process(text_history_to_str(item)) \\\n for item, d in zip(text_history, done)\n ]\n\n new_key = None\n if self.prng_key is not None:\n self.prng_key, new_key = jax.random.split(self.prng_key)\n model_outputs = self.inference.generate_from_str(\n input_strs=raw_input_strs, \n prng_key=new_key, \n blocking_strategy=self.blocking_strategy, \n generation_config=self.generation_config, \n input_token_process=self.input_token_process, \n target_token_process=self.target_token_process, \n trace=self.trace, \n )\n\n raw_output_strs = model_outputs.output_strs\n output_strs = [\n \"\" if d else self.out_str_process(strip_prompt_from_completion(raw_input_str, raw_output_str)) \\\n for raw_input_str, raw_output_str, d in zip(raw_input_strs, raw_output_strs, done)\n ]\n\n return [\n None if d else text_history_item+(Text(output_str, True),) \\\n for text_history_item, output_str, d in zip(text_history, output_strs, done)\n ]\n \n def set_params(self, policy_params: PyTree) -> None:\n pi_beta_params, base_params, \\\n q1_head_params, q2_head_params = policy_params\n self.inference = self.inference.replace(\n pi_beta_params=pi_beta_params, \n base_params=base_params, \n q1_head_params=q1_head_params, \n q2_head_params=q2_head_params, \n )"},{"identifier":"GPT2ValueRLInference","path":"LLM_RL/algorithms/value_rl_base/gpt2/interface.py","snippet":"class GPT2ValueRLInference(ValueRLInference):\n @classmethod\n def load_inference(\n cls, \n pi_beta_params: Optional[PyTree], \n base_params: PyTree, \n q1_head_params: PyTree, \n q2_head_params: Optional[PyTree], \n v_head_params: Optional[PyTree], \n pi_beta_model: Optional[FlaxPreTrainedModel], \n base_model: FlaxPreTrainedModel, \n q_head_model: nn.Module, \n v_head_model: Optional[nn.Module], \n tokenizer: PreTrainedTokenizerBase, \n beta: float=0.0, \n dp_shard_logits: bool=True, \n ):\n mesh = base_model.config.mesh\n assert mesh is not None\n assert mesh == q_head_model.config.mesh\n if v_head_model is not None:\n assert mesh == v_head_model.config.mesh\n assert (pi_beta_model is None and pi_beta_params is None) or (pi_beta_model is not None and pi_beta_params is not None)\n \n pi_beta_params_partition_spec = PS() if pi_beta_params is None else match_partition_rules(pi_beta_model.config.get_partition_rules(), pi_beta_params)\n base_params_partition_spec = match_partition_rules(base_model.config.get_partition_rules(), base_params)\n q1_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_head_params)\n q2_head_params_partition_spec = PS() if q2_head_params is None else match_partition_rules(q_head_model.config.get_partition_rules(), q2_head_params)\n v_head_params_partition_spec = PS() if v_head_params is None else match_partition_rules(v_head_model.config.get_partition_rules(), v_head_params)\n\n generator = None\n if pi_beta_model is not None:\n generator = GPT2ValueRLGeneration(\n base_model_config=base_model.config, \n pi_beta=pi_beta_model, \n value_base=base_model, \n q_head=q_head_model, \n beta=beta, \n )\n\n if pi_beta_params is not None:\n @partial(\n pjit, \n static_argnames=('generation_config', 'trace'), \n in_shardings=(\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), pi_beta_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n out_shardings=NamedSharding(mesh, PS()), \n )\n def _generate(\n pi_beta_params: Optional[PyTree], \n base_params: PyTree, \n q1_head_params: PyTree, \n q2_head_params: Optional[PyTree], \n input_ids: jax.Array, \n attention_mask: jax.Array, \n position_ids: jax.Array, \n prng_key: Optional[jax.random.PRNGKeyArray]=None, \n generation_config: Optional[FrozenDict]=None, \n trace: bool=True, \n ) -> Union[FlaxSampleOutput, FlaxGreedySearchOutput, FlaxBeamSearchOutput]:\n # data parallel shard inputs\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS((\"dp\", \"fsdp\"), None))\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS((\"dp\", \"fsdp\"), None))\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS((\"dp\", \"fsdp\"), None))\n # NOTE: position_ids ignored by transformers\n\n # generate from model\n output = generator.generate(\n input_ids=input_ids, \n attention_mask=attention_mask, \n params=(pi_beta_params, base_params, q1_head_params, q2_head_params), \n prng_key=prng_key, \n generation_config=StreamingGenerationConfig.from_dict(generation_config) if generation_config is not None else None, \n trace=trace, \n )\n \n return output\n else:\n def _generate(\n pi_beta_params: Optional[PyTree], \n base_params: PyTree, \n q1_head_params: PyTree, \n q2_head_params: Optional[PyTree], \n input_ids: jax.Array, \n attention_mask: jax.Array, \n position_ids: jax.Array, \n prng_key: Optional[jax.random.PRNGKeyArray]=None, \n generation_config: Optional[FrozenDict]=None, \n trace: bool=True, \n ) -> Union[FlaxSampleOutput, FlaxGreedySearchOutput, FlaxBeamSearchOutput]:\n raise NotImplementedError\n \n @partial(\n pjit, \n static_argnames=('output_attentions', 'train'), \n in_shardings=(\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_params_partition_spec), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n NamedSharding(mesh, PS()), \n ), \n out_shardings=ValueRLForwardOutput(\n base_raw_output=FlaxCausalLMOutputWithCrossAttentions(\n logits=NamedSharding(mesh, PS((\"dp\", \"fsdp\"), None, None)) if dp_shard_logits else NamedSharding(mesh, PS()), \n hidden_states=NamedSharding(mesh, PS()), # assume no sharding for hidden states\n attentions=NamedSharding(mesh, PS()), # assume no sharding for attentions\n cross_attentions=NamedSharding(mesh, PS()), # assume no sharding for cross attentions\n past_key_values=NamedSharding(mesh, PS()), # assume no sharding for past key values\n ), \n q1=NamedSharding(mesh, PS((\"dp\", \"fsdp\"), None, None)) if dp_shard_logits else NamedSharding(mesh, PS()), \n q2=NamedSharding(mesh, PS((\"dp\", \"fsdp\"), None, None)) if (dp_shard_logits and q2_head_params is not None) else NamedSharding(mesh, PS()), \n v=NamedSharding(mesh, PS()), \n ), \n )\n def _forward(\n base_params: PyTree, \n q1_head_params: PyTree, \n q2_head_params: Optional[PyTree], \n v_head_params: Optional[PyTree], \n input_ids: jax.Array, \n attention_mask: jax.Array, \n position_ids: jax.Array, \n prng_key: Optional[jax.random.PRNGKeyArray]=None, \n output_attentions: Optional[bool]=None, \n train: bool=False, \n ) -> ValueRLForwardOutput:\n # data parallel shard inputs\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS((\"dp\", \"fsdp\"), None))\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS((\"dp\", \"fsdp\"), None))\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS((\"dp\", \"fsdp\"), None))\n\n # get logits\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n base_output = base_model(\n input_ids=input_ids, \n attention_mask=attention_mask, \n position_ids=position_ids, \n params=base_params, \n train=train, \n output_attentions=output_attentions, \n output_hidden_states=True, \n dropout_rng=new_key, \n )\n # trunc padded logits\n base_output = base_output.replace(logits=base_output.logits.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf')))\n\n # get q1\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q1 = q_head_model.apply(\n {'params': q1_head_params}, \n base_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n # trunc padded qs\n q1 = q1.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf'))\n\n # get q2\n if q2_head_params is not None:\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n q2 = q_head_model.apply(\n {'params': q2_head_params}, \n base_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n )\n # trunc padded qs\n q2 = q2.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf'))\n else:\n q2 = None\n\n if v_head_params is not None:\n # get v\n new_key = None\n if prng_key is not None:\n prng_key, new_key = jax.random.split(prng_key)\n v = v_head_model.apply(\n {'params': v_head_params}, \n base_output.hidden_states[-1], \n train=train, \n rngs={'dropout': new_key} if prng_key is not None else None, \n ).squeeze(2)\n else:\n v = None\n\n # assert sharding on outputs\n if dp_shard_logits:\n base_output = base_output.replace(logits=with_named_sharding_constraint(base_output.logits, mesh, PS((\"dp\", \"fsdp\"), None, None)))\n q1 = with_named_sharding_constraint(q1, mesh, PS((\"dp\", \"fsdp\"), None, None))\n if q2 is not None:\n q2 = with_named_sharding_constraint(q2, mesh, PS((\"dp\", \"fsdp\"), None, None))\n return ValueRLForwardOutput(\n base_raw_output=base_output, \n q1=q1, \n q2=q2, \n v=v, \n )\n\n return cls(\n pi_beta_params=pi_beta_params, \n base_params=base_params, \n q1_head_params=q1_head_params, \n q2_head_params=q2_head_params, \n v_head_params=v_head_params, \n pi_beta_model=pi_beta_model, \n base_model=base_model, \n q_head_model=q_head_model, \n v_head_model=v_head_model, \n tokenizer=tokenizer, \n _generate=_generate, \n _forward=_forward,\n )"},{"identifier":"load_train_state_from_config","path":"LLM_RL/heads/mlp_head.py","snippet":"def load_train_state_from_config(\n model_config: MLPHeadConfig, \n model_dtype: Union[str, jnp.dtype], \n optim_getter: Callable[[PyTree], optax.GradientTransformation], \n mesh: Mesh, # should be shape (dp, mp)\n prng_key: jax.random.PRNGKeyArray, \n pad_to_output_dim: Optional[int]=None, \n params_dtype: Optional[Union[str, jnp.dtype]]=jnp.float32, \n) -> Tuple[TrainState, MLPHead]:\n \n model = MLPHead(model_config, dtype=model_dtype)\n model.config.mesh = mesh\n # shard params\n params = freeze(shard_params_from_config(model, prng_key, params_dtype=params_dtype))\n # pad outputs\n if pad_to_output_dim is not None:\n params = freeze(pad_outputs(unfreeze(params), model, pad_to_output_dim, dtype=params_dtype))\n # shard train_state\n train_state = shard_train_state_from_params(model, params, optim_getter(params))\n\n return train_state, model"},{"identifier":"MLPHeadConfig","path":"LLM_RL/heads/mlp_head.py","snippet":"class MLPHeadConfig(HeadConfig):\n def __init__(\n self, \n input_dim: int, \n hidden_dim: int, \n output_dim: int, \n use_bias: bool=True, \n unpadded_output_dim: Optional[int]=None, \n layer1_initializer_range: Optional[int]=None, \n layer1_bias_init: Optional[float]=None, \n layer2_initializer_range: Optional[int]=None, \n layer2_bias_init: Optional[float]=None, \n mesh: Optional[jax.sharding.Mesh]=None, \n ) -> None:\n self.input_dim = input_dim\n self.hidden_dim = hidden_dim\n self.output_dim = output_dim\n self.use_bias = use_bias\n self.layer1_initializer_range = layer1_initializer_range\n self.layer1_bias_init = layer1_bias_init\n self.layer2_initializer_range = layer2_initializer_range\n self.layer2_bias_init = layer2_bias_init\n self.mesh = mesh\n self.unpadded_output_dim = unpadded_output_dim\n if self.unpadded_output_dim is None:\n self.unpadded_output_dim = self.output_dim\n super().__init__()\n \n @staticmethod\n def get_partition_rules():\n return [\n (re.escape(\"['dense1']['kernel']\"), PS(\"fsdp\", \"mp\")), \n (re.escape(\"['dense1']['bias']\"), PS(\"mp\")), \n (re.escape(\"['dense2']['kernel']\"), PS(\"mp\", \"fsdp\")), \n (re.escape(\"['dense2']['bias']\"), PS()), \n ]\n\n def to_dict(self) -> Dict[str, Any]:\n if self.mesh is None:\n return super().to_dict()\n else:\n new_conf = MLPHeadConfig(**self.__dict__)\n new_conf.mesh = None\n return new_conf.to_dict()"},{"identifier":"ILQLDataset","path":"LLM_RL/algorithms/ilql/data.py","snippet":"class ILQLDataset(Dataset):\n def __init__(\n self, \n input_ids: np.ndarray, # [b, t]\n should_take_action: np.ndarray, # [b, t-1]\n rewards: np.ndarray, # [b, t-1]\n dones: np.ndarray, # [b]\n next_token_ids: Optional[np.ndarray], # [b, t']\n next_dones: Optional[np.ndarray], # [b]\n ):\n assert input_ids.shape[1] == (should_take_action.shape[1]+1)\n assert input_ids.shape[1] == (rewards.shape[1]+1)\n\n assert input_ids.shape[0] == should_take_action.shape[0]\n assert input_ids.shape[0] == rewards.shape[0]\n assert input_ids.shape[0] == dones.shape[0]\n if next_token_ids is not None:\n assert input_ids.shape[0] == next_token_ids.shape[0]\n if next_dones is not None:\n assert input_ids.shape[0] == next_dones.shape[0]\n\n self.input_ids = input_ids\n self.should_take_action = should_take_action\n self.rewards = rewards\n self.dones = dones\n self.next_token_ids = next_token_ids\n self.next_dones = next_dones\n \n def __getitem__(self, index):\n return {\n 'input_ids': jnp.asarray(self.input_ids[index], dtype=jnp.int32), \n 'should_take_action': jnp.asarray(self.should_take_action[index], dtype=jnp.bool_), \n 'rewards': jnp.asarray(self.rewards[index], dtype=jnp.float32), \n 'dones': jnp.asarray(self.dones[index], dtype=jnp.float32), \n 'next_token_ids': jnp.asarray(self.next_token_ids[index], dtype=jnp.float32) if self.next_token_ids is not None else None, \n 'next_dones': jnp.asarray(self.next_dones[index], dtype=jnp.float32) if self.next_dones is not None else None, \n }\n \n def __len__(self):\n return self.input_ids.shape[0]\n \n @classmethod\n def from_ilql_data_list(\n cls, \n ilql_data_list: List[ILQLData], \n tokenizer: PreTrainedTokenizerBase, \n blocking_strategy: BlockingStrategy, \n ) -> ILQLDataset:\n \n data = ILQLData.block(ilql_data_list, blocking_strategy, tokenizer)\n\n return cls(**data)"},{"identifier":"ILQLIterableDataset","path":"LLM_RL/algorithms/ilql/data.py","snippet":"class ILQLIterableDataset(IterableDataset):\n def __init__(self, ilql_data: Iterable[Dict[str, np.ndarray]]):\n self.ilql_data = ilql_data\n \n def __iter__(self):\n return _ILQLIteratorDataset(iter(self.ilql_data))\n \n @classmethod\n def from_ilql_data_iterable(\n cls, \n ilql_data: Iterable[ILQLData], \n tokenizer: PreTrainedTokenizerBase, \n blocking_strategy: BlockingStrategy, \n ) -> ILQLIterableDataset:\n \n class _TokensIterable(Iterable):\n def _tokens_generator(self):\n for item in ilql_data:\n yield jax.tree_util.tree_map(lambda x: x[0], ILQLData.block([item], blocking_strategy, tokenizer))\n\n def __iter__(self):\n return self._tokens_generator()\n\n return cls(_TokensIterable())"},{"identifier":"eval_loss","path":"LLM_RL/algorithms/ilql/train.py","snippet":"def eval_loss(\n inference: ILQLInference, \n dataset: Union[Seq2SeqDataset, Seq2SeqIterableDataset], \n prng_key: Optional[KeyArray], \n bsize: int, \n eval_batches: Optional[int], \n) -> Dict[str, Any]:\n # setup evaluator loop state\n eval_logs = []\n\n # eval on batches\n prng_key, new_prng = jax.random.split(prng_key) if prng_key is not None else (None, None)\n d = dataloader(new_prng, dataset, bsize, truncate=True)\n for i, batch in tqdm(enumerate(d)):\n # conditionally terminate early\n if eval_batches is not None and i >= eval_batches:\n break\n\n # get eval logs\n _, info = inference.eval_loss(**batch)\n eval_logs.append(info)\n \n # gather and postproc eval logs\n eval_logs = pull_logs(combine_logs(eval_logs))\n return eval_logs"},{"identifier":"train_loop","path":"LLM_RL/algorithms/ilql/train.py","snippet":"def train_loop(\n trainer: ILQLTrain, \n inference: Union[ValueRLInference, ILQLInference], \n evaluator: Optional[Callable[[Inference], Tuple[float, Dict[str, Any]]]], \n dataset: Union[Seq2SeqDataset, Seq2SeqIterableDataset], \n prng_key: KeyArray, \n save_dir: Optional[str], \n epochs: int, \n max_steps: Optional[int], \n bsize: int, \n log_every: int, \n eval_every_steps: Optional[int], \n eval_every_epochs: Optional[int], \n eval_at_beginning: bool, \n eval_at_end: bool, \n save_every_steps: Optional[int], \n save_every_epochs: Optional[int], \n save_at_beginning: bool, \n save_at_end: bool, \n save_best: bool, \n max_checkpoints: Optional[int], \n save_train_state: bool, \n save_dtype: jnp.dtype, \n use_wandb: bool, \n wandb_project: Optional[str], \n wandb_run_name: Optional[str], \n wandb_config: Optional[Dict[str, Any]], \n is_main_process: Optional[bool]=None, \n **loop_state: Dict[Hashable, Any], \n) -> Tuple[Train, Inference]:\n assert (not use_wandb) or (use_wandb and wandb_project is not None)\n if is_main_process is None:\n is_main_process = jax.process_index() == 0\n \n # initalize wandb\n wandb_id = loop_state.get('wandb_id', None)\n if use_wandb and is_main_process:\n if wandb_id is None:\n wandb_id = wandb.util.generate_id()\n wandb.init(\n project=wandb_project, \n id=wandb_id, \n name=wandb_run_name, \n config=wandb_config, \n reinit=True, \n resume=\"allow\", \n )\n\n # initalize training loop state\n train_logs = []\n best_perf = loop_state.get('best_perf', float('inf'))\n saved_checkpoints = loop_state.get('saved_checkpoints', deque([]))\n step = 0\n steps_per_epoch = len(dataset) // bsize if isinstance(dataset, Dataset) else None\n if 'steps_per_epoch' in loop_state:\n assert steps_per_epoch == loop_state['steps_per_epoch'], 'loop_state steps_per_epoch does not match dataset steps_per_epoch'\n epoch = -1\n\n def _save(\n name: str, \n add_to_queue: bool, \n **loop_state: Dict[Hashable, Any], \n ):\n nonlocal saved_checkpoints\n print(f'saving checkpoint {name} ...')\n # conditionally delete old checkpoints\n if add_to_queue and is_main_process:\n if (max_checkpoints is not None) and (len(saved_checkpoints) >= max_checkpoints):\n delete(saved_checkpoints.popleft(), recursive=True)\n curr_save_dir = os.path.join(save_dir, name)\n if is_main_process:\n create_path(curr_save_dir)\n dump_state(\n base_model=trainer.base_model, \n q_head_model=trainer.q_head_model, \n v_head_model=trainer.v_head_model, \n base_train_state=trainer.base_train_state, \n target_base_params=trainer.target_base_params, \n q1_head_train_state=trainer.q1_head_train_state, \n q2_head_train_state=trainer.q2_head_train_state, \n v_head_train_state=trainer.v_head_train_state, \n q1_target_head_params=trainer.q1_target_head_params, \n q2_target_head_params=trainer.q2_target_head_params, \n save_dir=curr_save_dir, \n save_train_state=save_train_state, \n enable_save=is_main_process, \n save_dtype=save_dtype, \n **loop_state, \n )\n if add_to_queue and is_main_process:\n saved_checkpoints.append(curr_save_dir)\n print('saved.')\n \n def _inference_update():\n nonlocal inference\n if isinstance(inference, ValueRLInference):\n inference = inference.replace(\n base_params=trainer.base_train_state.params, \n q1_head_params=trainer.q1_head_train_state.params, \n q2_head_params=trainer.q2_head_train_state.params, \n v_head_params=trainer.v_head_train_state.params, \n )\n elif isinstance(inference, ILQLInference):\n new_value_inference = inference.value_inference.replace(\n base_params=trainer.base_train_state.params, \n q1_head_params=trainer.q1_head_train_state.params, \n q2_head_params=trainer.q2_head_train_state.params, \n v_head_params=trainer.v_head_train_state.params, \n )\n new_target_value_inference = inference.target_value_inference.replace(\n base_params=trainer.target_base_params, \n q1_head_params=trainer.q1_target_head_params, \n q2_head_params=trainer.q2_target_head_params, \n )\n inference = inference.replace(\n value_inference=new_value_inference, \n target_value_inference=new_target_value_inference, \n )\n else:\n raise NotImplementedError\n \n def _eval(\n **loop_state: Dict[Hashable, Any], \n ):\n nonlocal best_perf\n # get eval logs\n _inference_update()\n eval_perf, eval_logs = evaluator(inference)\n\n # publish eval logs\n eval_logs = pull_logs(label_logs(eval_logs, 'eval', {'step': step+1, 'epoch': epoch}))\n log(eval_logs, use_wandb and is_main_process)\n\n # conditionally save best model and optimizer state\n if save_dir is not None and save_best and eval_perf < best_perf:\n print('new best model!')\n best_perf = eval_perf\n _save(\n name='best', \n add_to_queue=False, \n **{**loop_state, 'best_perf': best_perf}, \n )\n \n # begin evaluation\n if evaluator is not None and eval_at_beginning:\n _eval(\n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # save initial checkpoint\n if save_dir is not None and save_at_beginning:\n _save(\n name='initial', \n add_to_queue=False, \n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # begin training loop\n for epoch in tqdm(range(epochs)):\n prng_key, new_prng = jax.random.split(prng_key)\n d = dataloader(new_prng, dataset, bsize, truncate=True)\n for batch in tqdm(d, total=steps_per_epoch):\n \n # step model and get training logs\n prng_key, new_prng = jax.random.split(prng_key)\n if 'step' in loop_state and step < loop_state['step']:\n step += 1\n continue\n trainer, _, info = trainer.step(\n **batch, \n prng_key=new_prng, \n train=True, \n )\n train_logs.append(info)\n \n # publish training logs and clear logs\n if (step + 1) % log_every == 0:\n logs = combine_logs(train_logs)\n logs = pull_logs(label_logs(logs, 'train', {'step': step+1, 'epoch': epoch}))\n log(logs, use_wandb and is_main_process)\n train_logs = []\n \n # begin evaluation\n if evaluator is not None and eval_every_steps is not None and (step + 1) % eval_every_steps == 0:\n _eval(\n # loop state metadata\n best_perf=best_perf, \n step=step+1, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # periodically save checkpoint\n if save_dir is not None and save_every_steps is not None and (step + 1) % save_every_steps == 0:\n _save(\n name=f'step_{step+1}', \n add_to_queue=True, \n # loop state metadata\n best_perf=best_perf, \n step=step+1, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n\n step += 1\n\n # conditionally terminate\n if max_steps is not None and step >= max_steps:\n break\n \n # begin evaluation\n if evaluator is not None and eval_every_epochs is not None and (epoch + 1) % eval_every_epochs == 0:\n _eval(\n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # periodically save checkpoint\n if save_dir is not None and save_every_epochs is not None and (epoch + 1) % save_every_epochs == 0:\n _save(\n name=f'epoch_{epoch}', \n add_to_queue=True, \n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # conditionally terminate\n if max_steps is not None and step >= max_steps:\n break\n \n # begin evaluation\n if evaluator is not None and eval_at_end:\n _eval(\n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n \n # save final checkpoint\n if save_dir is not None and save_at_end:\n _save(\n name='last', \n add_to_queue=False, \n # loop state metadata\n best_perf=best_perf, \n step=step, \n epoch=epoch, \n saved_checkpoints=saved_checkpoints, \n steps_per_epoch=steps_per_epoch, \n wandb_id=wandb_id, \n )\n\n # stop wandb\n if use_wandb and is_main_process:\n wandb.finish()\n _inference_update()\n return trainer, inference"},{"identifier":"ILQLData","path":"LLM_RL/algorithms/ilql/data.py","snippet":"class ILQLData(NamedTuple):\n input_ids: np.ndarray # [t]\n should_take_action: np.ndarray # [t-1]\n rewards: np.ndarray # [t-1]\n done: np.ndarray # []\n next_token_ids: Optional[np.ndarray] # [t']\n next_done: Optional[np.ndarray] # []\n\n @staticmethod\n def block(\n data: List[ILQLData], \n blocking_strategy: BlockingStrategy, \n tokenizer: PreTrainedTokenizerBase, \n ) -> Dict[str, np.ndarray]:\n has_next_token = any(map(lambda x: x.next_token_ids is not None, data))\n assert all(map(lambda x: x.next_token_ids is None, data)) or has_next_token\n assert all(map(lambda x: x.next_done is None, data)) or has_next_token\n\n return dict(\n input_ids=block_sequences(\n list(map(lambda x: x.input_ids, data)), \n tokenizer.pad_token_id, \n dtype=np.int32, \n blocking_strategy=blocking_strategy, \n ), \n should_take_action=block_sequences(\n list(map(lambda x: x.should_take_action, data)), \n False, \n dtype=np.bool_, \n blocking_strategy=blocking_strategy._replace(max_length=blocking_strategy.max_length-1), \n ), \n rewards=block_sequences(\n list(map(lambda x: x.rewards, data)), \n 0.0, \n dtype=np.float32, \n blocking_strategy=blocking_strategy._replace(max_length=blocking_strategy.max_length-1), \n ), \n dones=np.asarray(list(map(lambda x: x.done, data)), dtype=np.bool_), \n next_token_ids=block_sequences(\n list(map(lambda x: x.next_token_ids, data)), \n tokenizer.pad_token_id, \n dtype=np.int32, \n blocking_strategy=blocking_strategy, \n ) if has_next_token else None, \n next_dones=np.asarray(list(map(lambda x: x.next_done, data)), dtype=np.bool_) if has_next_token else None, \n )\n \n @classmethod\n def from_token_trajectory_chain(\n cls, \n token_trajectory_chain: TokenTrajectoryChain, \n ):\n if token_trajectory_chain.next is not None:\n if token_trajectory_chain.next.token_trajectory.is_action[1:].sum() > 0:\n first_next_action = np.argmax(token_trajectory_chain.next.token_trajectory.is_action[1:], axis=0)+1\n next_token_ids = token_trajectory_chain.next.token_trajectory.tokens[:first_next_action]\n next_done = False\n else:\n next_token_ids = token_trajectory_chain.next.token_trajectory.tokens\n next_done = token_trajectory_chain.next.token_trajectory.done\n else:\n next_token_ids, next_done = None, None\n return cls(\n input_ids=token_trajectory_chain.token_trajectory.tokens, \n should_take_action=token_trajectory_chain.token_trajectory.is_action[1:], \n rewards=token_trajectory_chain.token_trajectory.reward[1:], \n done=token_trajectory_chain.token_trajectory.done, \n next_token_ids=next_token_ids, \n next_done=next_done, \n )"},{"identifier":"ILQLDataset","path":"LLM_RL/algorithms/ilql/data.py","snippet":"class ILQLDataset(Dataset):\n def __init__(\n self, \n input_ids: np.ndarray, # [b, t]\n should_take_action: np.ndarray, # [b, t-1]\n rewards: np.ndarray, # [b, t-1]\n dones: np.ndarray, # [b]\n next_token_ids: Optional[np.ndarray], # [b, t']\n next_dones: Optional[np.ndarray], # [b]\n ):\n assert input_ids.shape[1] == (should_take_action.shape[1]+1)\n assert input_ids.shape[1] == (rewards.shape[1]+1)\n\n assert input_ids.shape[0] == should_take_action.shape[0]\n assert input_ids.shape[0] == rewards.shape[0]\n assert input_ids.shape[0] == dones.shape[0]\n if next_token_ids is not None:\n assert input_ids.shape[0] == next_token_ids.shape[0]\n if next_dones is not None:\n assert input_ids.shape[0] == next_dones.shape[0]\n\n self.input_ids = input_ids\n self.should_take_action = should_take_action\n self.rewards = rewards\n self.dones = dones\n self.next_token_ids = next_token_ids\n self.next_dones = next_dones\n \n def __getitem__(self, index):\n return {\n 'input_ids': jnp.asarray(self.input_ids[index], dtype=jnp.int32), \n 'should_take_action': jnp.asarray(self.should_take_action[index], dtype=jnp.bool_), \n 'rewards': jnp.asarray(self.rewards[index], dtype=jnp.float32), \n 'dones': jnp.asarray(self.dones[index], dtype=jnp.float32), \n 'next_token_ids': jnp.asarray(self.next_token_ids[index], dtype=jnp.float32) if self.next_token_ids is not None else None, \n 'next_dones': jnp.asarray(self.next_dones[index], dtype=jnp.float32) if self.next_dones is not None else None, \n }\n \n def __len__(self):\n return self.input_ids.shape[0]\n \n @classmethod\n def from_ilql_data_list(\n cls, \n ilql_data_list: List[ILQLData], \n tokenizer: PreTrainedTokenizerBase, \n blocking_strategy: BlockingStrategy, \n ) -> ILQLDataset:\n \n data = ILQLData.block(ilql_data_list, blocking_strategy, tokenizer)\n\n return cls(**data)"},{"identifier":"FenChessHistoryEnv","path":"llm_rl_scripts/chess/env/env.py","snippet":"class FenChessHistoryEnv(TextEnv):\n def __init__(self, max_moves=400, from_position=None, random_opponent=False):\n super().__init__()\n self.chess_env = ChessEnv(fen=True, from_position=from_position, random_opponent=random_opponent)\n self.from_position = from_position\n self.max_moves = max_moves\n self.from_position = from_position\n # self.initial_history = initial_history\n\n def reset(self, seed: Optional[int] = None, options: Optional[Dict] = None):\n self.init_state, _ = self.chess_env.reset()\n self.num_moves_made = 0\n return (Text(preprocess_state_og(self.init_state), False),)\n\n def step(self, text_history: TextHistory):\n assert text_history[-1].is_action\n action = text_history[-1].text\n action = postprocess_move(action)\n st, reward, done, opp_mv = self.chess_env.step(action) \n new_state = Text(preprocess_state_og(st), False)\n self.num_moves_made += 1\n if self.num_moves_made > self.max_moves:\n done = 1\n return (new_state,), reward, done\n \n def copy(self):\n return FenChessHistoryEnv( self.max_moves, self.from_position)"}],"string":"[\n {\n \"identifier\": \"ilql_loss\",\n \"path\": \"LLM_RL/algorithms/ilql/base_interface.py\",\n \"snippet\": \"def ilql_loss(\\n q1: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\\n q2: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\\n v: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\\n v_final: jax.Array, # [batch]\\n target_q1: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\\n target_q2: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\\n q1_logits: jax.Array, # [batch, time-1, vocab] output is masked; shift x[:-1]\\n q2_logits: jax.Array, # [batch, time-1, vocab] output is masked; shift x[:-1]\\n token_ids: jax.Array, # [batch, time-1] output is masked; shift x[1:]\\n attention_mask: jax.Array, # [batch, time-1] output is masked; shift x[1:]\\n should_take_action: jax.Array, # [batch, time-1] output is masked; shift x[1:]\\n rewards: jax.Array, # [batch, time-1] output is masked; shift x[1:]\\n *, \\n gamma: Union[float, jax.Array], \\n tau: Union[float, jax.Array], \\n cql_weight: Union[float, jax.Array], \\n) -> Tuple[jnp.ndarray, Any]:\\n # should be an action in the batch\\n mask = should_take_action.astype(jnp.float32) * attention_mask\\n n = mask.sum()\\n \\n q1sa_flat, q2sa_flat, v_flat = q1.reshape(-1), q2.reshape(-1), v.reshape(-1)\\n target_q1sa_flat, target_q2sa_flat = target_q1.reshape(-1), target_q2.reshape(-1)\\n vns_flat = jnp.concatenate((v, v_final[..., None]), axis=1).reshape(-1)\\n\\n qv_query_indicators = get_query_indicators(should_take_action.reshape(-1))\\n\\n is_next_state = should_take_action.copy()\\n # set first action position to false\\n is_next_state = is_next_state.at[jnp.arange(0, is_next_state.shape[0], dtype=jnp.int32), jnp.argmax(is_next_state.astype(jnp.int32), axis=1)].set(False)\\n # set endpoint to true as long as there is at least 1 action in the sequence\\n is_next_state = jnp.concatenate((is_next_state, (should_take_action.sum(axis=1) > 0)[..., None]), axis=1)\\n\\n vns_query_indicators = get_query_indicators(is_next_state.reshape(-1))\\n # should be the same number of vns as qv, so we can clip the extra padding to match shape\\n vns_query_indicators = vns_query_indicators[:qv_query_indicators.shape[0], :]\\n \\n # extract selected values\\n q1sa_selected = (qv_query_indicators * q1sa_flat).sum(axis=1)\\n q2sa_selected = (qv_query_indicators * q2sa_flat).sum(axis=1)\\n v_selected = (qv_query_indicators * v_flat).sum(axis=1)\\n target_q1sa_selected = (qv_query_indicators * target_q1sa_flat).sum(axis=1)\\n target_q2sa_selected = (qv_query_indicators * target_q2sa_flat).sum(axis=1)\\n vns_selected = (vns_query_indicators * vns_flat).sum(axis=1)\\n rs_selected = (qv_query_indicators * rewards.reshape(-1)).sum(axis=1)\\n\\n # get masks for selected values\\n sa_mask = (qv_query_indicators.sum(axis=1) > 0).astype(jnp.float32)\\n ns_mask = (vns_query_indicators.sum(axis=1) > 0).astype(jnp.float32)\\n\\n # compute q loss\\n q1_loss = (optax.l2_loss(q1sa_selected, jax.lax.stop_gradient(rs_selected + gamma * vns_selected)) * sa_mask).sum() / n\\n q2_loss = (optax.l2_loss(q2sa_selected, jax.lax.stop_gradient(rs_selected + gamma * vns_selected)) * sa_mask).sum() / n\\n\\n # compute v loss\\n target_q_selected = jnp.minimum(target_q1sa_selected, target_q2sa_selected)\\n expectile_indicator = (target_q_selected >= v_selected).astype(jnp.float32)\\n expectile_weights = expectile_indicator * tau + (1 - expectile_indicator) * (1 - tau)\\n v_loss = (optax.l2_loss(v_selected, jax.lax.stop_gradient(target_q_selected)) * jax.lax.stop_gradient(expectile_weights) * sa_mask).sum() / n\\n\\n # compute cql loss on both q heads\\n q1_cql_loss = optax.softmax_cross_entropy_with_integer_labels(q1_logits, token_ids)\\n q1_cql_loss = (mask * q1_cql_loss).sum() / n\\n\\n q2_cql_loss = optax.softmax_cross_entropy_with_integer_labels(q2_logits, token_ids)\\n q2_cql_loss = (mask * q2_cql_loss).sum() / n\\n \\n loss = q1_loss + q2_loss + v_loss + cql_weight * (q1_cql_loss + q2_cql_loss)\\n\\n logs = dict(\\n losses=dict(\\n total_loss=loss, \\n q1_loss=q1_loss, \\n q2_loss=q2_loss, \\n v_loss=v_loss, \\n q1_cql_loss=q1_cql_loss, \\n q2_cql_loss=q2_cql_loss, \\n ), \\n q1=get_tensor_stats(q1sa_selected, mask=sa_mask, n=n), \\n q2=get_tensor_stats(q2sa_selected, mask=sa_mask, n=n), \\n v=get_tensor_stats(v_selected, mask=sa_mask, n=n), \\n target_q=get_tensor_stats(target_q_selected, mask=sa_mask, n=n), \\n target_q1=get_tensor_stats(target_q1sa_selected, mask=sa_mask, n=n), \\n target_q2=get_tensor_stats(target_q2sa_selected, mask=sa_mask, n=n), \\n vns=get_tensor_stats(vns_selected, mask=ns_mask, n=n), \\n v_final=get_tensor_stats(v_final, mask=jnp.ones(v_final.shape, dtype=jnp.int32), n=v_final.shape[0]), \\n rewards=get_tensor_stats(rewards, mask=mask, n=n), \\n )\\n\\n return loss, logs\"\n },\n {\n \"identifier\": \"Text\",\n \"path\": \"LLM_RL/environment.py\",\n \"snippet\": \"class Text:\\n text: str\\n is_action: bool\"\n },\n {\n \"identifier\": \"text_env_eval\",\n \"path\": \"LLM_RL/environment.py\",\n \"snippet\": \"def text_env_eval(\\n env: Union[TextEnv, BatchedTextEnv], \\n policy: Union[TextPolicy, BatchedTextPolicy], \\n n_rollouts: int, \\n initial_text_history: Optional[TextHistory]=None, # only allow one initial_text_history here\\n seed_generator: Optional[Iterator[int]]=None, \\n env_options: Optional[Dict]=None, # only allow one env_options here\\n interaction_callback: Optional[Callable[[List[Tuple[TextHistory, TextHistory, TextHistory, float, bool]]], None]]=None, \\n bsize: int=1, \\n verbose: bool=True, \\n) -> Tuple[List[List[InteractionTransition]], Dict[str, Any]]:\\n interactions, rewards, dones, eps_lengths = [], [], [], []\\n for _ in tqdm(range((n_rollouts+(bsize-1))//bsize), disable=not verbose):\\n actual_bsize = min(n_rollouts-len(interactions), bsize)\\n npad = bsize - actual_bsize\\n interaction_batch = interact_environment(\\n env, \\n policy, \\n initial_text_history=initial_text_history, \\n env_seed=[None]*actual_bsize if seed_generator is None else [next(seed_generator) for _ in range(actual_bsize)], \\n env_options=[env_options]*actual_bsize, \\n bsize=actual_bsize,\\n npad=npad,\\n )\\n \\n for interaction in interaction_batch:\\n interactions.append(interaction)\\n rewards.append(sum(map(lambda x: x.reward, interaction)))\\n dones.append(interaction[-1].done)\\n eps_lengths.append(len(interaction))\\n if interaction_callback is not None:\\n interaction_callback(interaction)\\n \\n rewards = np.asarray(rewards, dtype=np.float32)\\n dones = np.asarray(dones, dtype=np.float32)\\n results_summary = dict(\\n reward=dict(\\n mean=np.mean(rewards), \\n std=np.std(rewards), \\n min=np.min(rewards), \\n max=np.max(rewards), \\n ), \\n done=dict(\\n mean=np.mean(dones), \\n std=np.std(dones), \\n min=np.min(dones), \\n max=np.max(dones), \\n ), \\n length=dict(\\n mean=np.mean(eps_lengths),\\n std=np.std(eps_lengths),\\n min=np.min(eps_lengths),\\n max=np.max(eps_lengths),\\n ),\\n )\\n \\n return interactions, results_summary\"\n },\n {\n \"identifier\": \"TextTrajectory\",\n \"path\": \"LLM_RL/environment.py\",\n \"snippet\": \"class TextTrajectory:\\n text_history: TextHistory\\n reward: Tuple[float, ...]\\n done: bool\\n\\n def __post_init__(self):\\n assert len(self.reward) == len(self.text_history), \\\"reward is needed for each text\\\"\\n assert all([r == 0.0 for r, t in zip(self.reward, self.text_history) if not t.is_action]), \\\"reward for non-actions texts should be 0.0\\\"\"\n },\n {\n \"identifier\": \"TextTrajectoryChain\",\n \"path\": \"LLM_RL/environment.py\",\n \"snippet\": \"class TextTrajectoryChain:\\n text_trajectory: TextTrajectory\\n next: Optional[TextTrajectoryChain]\"\n },\n {\n \"identifier\": \"TokenTrajectoryChain\",\n \"path\": \"LLM_RL/environment.py\",\n \"snippet\": \"class TokenTrajectoryChain:\\n token_trajectory: TokenTrajectory\\n next: Optional[TokenTrajectoryChain]\\n\\n def __post_init__(self):\\n curr, dones = self, []\\n while curr.next is not None:\\n dones.append(curr.token_trajectory.done)\\n curr = curr.next\\n assert not np.any(dones[:-1]), 'token trajectory chain can only be done at the end'\\n \\n def to_list(self) -> List[TokenTrajectory]:\\n curr, l = self, []\\n while curr is not None:\\n l.append(curr.token_trajectory)\\n curr = curr.next\\n return l\\n\\n @classmethod\\n def from_text_trajectory_chain(\\n cls, \\n text_trajectory_chain: TextTrajectoryChain, \\n tokenizer: PreTrainedTokenizer, \\n token_process: Optional[Callable[[List[int]], List[int]]]=None, \\n ) -> TokenTrajectoryChain:\\n return TokenTrajectoryChain(\\n TokenTrajectory.from_text_trajectory(\\n text_trajectory_chain.text_trajectory, \\n tokenizer, \\n token_process=token_process, \\n ), \\n cls.from_text_trajectory_chain(\\n text_trajectory_chain.next, \\n tokenizer, \\n token_process=token_process, \\n ) if text_trajectory_chain.next is not None else None, \\n )\"\n },\n {\n \"identifier\": \"GPT2ILQLTrain\",\n \"path\": \"LLM_RL/algorithms/ilql/gpt2/interface.py\",\n \"snippet\": \"class GPT2ILQLTrain(ILQLTrain):\\n @classmethod\\n def load_train(\\n cls, \\n base_train_state: TrainState, \\n target_base_params: Optional[PyTree], \\n q1_head_train_state: TrainState, \\n q2_head_train_state: TrainState, \\n v_head_train_state: TrainState, \\n q1_target_head_params: PyTree, \\n q2_target_head_params: PyTree, \\n base_model: FlaxPreTrainedModel, \\n q_head_model: nn.Module, \\n v_head_model: nn.Module, \\n tokenizer: PreTrainedTokenizerBase, \\n loss_fn: Callable, \\n detach_q1: bool, \\n detach_q2: bool, \\n detach_v: bool, \\n polyak_alpha: float, \\n hard_update_every: Optional[int], \\n ):\\n mesh = base_model.config.mesh\\n assert mesh is not None\\n assert mesh == q_head_model.config.mesh\\n assert mesh == v_head_model.config.mesh\\n base_train_state_partition_spec = match_partition_rules(base_model.config.get_partition_rules(), base_train_state)\\n target_base_params_partition_spec = PS() if target_base_params is None else match_partition_rules(base_model.config.get_partition_rules(), target_base_params)\\n q1_head_train_state_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_head_train_state)\\n q2_head_train_state_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q2_head_train_state)\\n v_head_train_state_partition_spec = match_partition_rules(v_head_model.config.get_partition_rules(), v_head_train_state)\\n q1_target_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_target_head_params)\\n q2_target_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q2_target_head_params)\\n\\n @partial(\\n pjit, \\n donate_argnums=(0, 1, 2, 3, 4, 5, 6), \\n static_argnames=('train',), \\n in_shardings=(\\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n out_shardings=(\\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_train_state_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n )\\n def _step(\\n base_train_state: TrainState, \\n target_base_params: Optional[PyTree], \\n q1_head_train_state: TrainState, \\n q2_head_train_state: TrainState, \\n v_head_train_state: TrainState, \\n q1_target_head_params: PyTree, \\n q2_target_head_params: PyTree, \\n\\n input_ids: jax.Array, \\n attention_mask: jax.Array, \\n position_ids: jax.Array, \\n should_take_action: jax.Array, \\n rewards: jax.Array, \\n dones: jax.Array, \\n\\n next_token_ids: Optional[jax.Array], \\n next_tokens_attention_mask: Optional[jax.Array], \\n next_tokens_position_ids: Optional[jax.Array], \\n next_dones: Optional[jax.Array], \\n\\n prng_key: Optional[jax.random.PRNGKeyArray], \\n train: bool=True, \\n ) -> Tuple[TrainState, Optional[PyTree], TrainState, TrainState, TrainState, PyTree, PyTree, jax.Array, PyTree]:\\n # data parallel shard inputs\\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS(('dp', 'fsdp'), None))\\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS(('dp', 'fsdp'), None))\\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS(('dp', 'fsdp'), None))\\n should_take_action = with_named_sharding_constraint(should_take_action, mesh, PS(('dp', 'fsdp'), None))\\n rewards = with_named_sharding_constraint(rewards, mesh, PS(('dp', 'fsdp'), None))\\n dones = with_named_sharding_constraint(dones, mesh, PS(('dp', 'fsdp')))\\n if next_token_ids is not None:\\n assert next_tokens_attention_mask is not None\\n assert next_tokens_position_ids is not None\\n next_token_ids = with_named_sharding_constraint(next_token_ids, mesh, PS(('dp', 'fsdp'), None))\\n next_tokens_attention_mask = with_named_sharding_constraint(next_tokens_attention_mask, mesh, PS(('dp', 'fsdp'), None))\\n next_tokens_position_ids = with_named_sharding_constraint(next_tokens_position_ids, mesh, PS(('dp', 'fsdp'), None))\\n next_dones = with_named_sharding_constraint(next_dones, mesh, PS(('dp', 'fsdp')))\\n else:\\n assert next_tokens_attention_mask is None\\n assert next_tokens_position_ids is None\\n\\n # define loss function\\n\\n def grad_loss(base_params: PyTree, q1_head_params: PyTree, q2_head_params: PyTree, v_head_params: PyTree, prng_key: jax.random.PRNGKeyArray):\\n \\n # get base hidden states\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n base_model_output = base_model(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n position_ids=position_ids, \\n params=base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n\\n if target_base_params is not None:\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_base_model_output = base_model(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n position_ids=position_ids, \\n params=target_base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n else:\\n target_base_model_output = base_model_output\\n \\n if next_token_ids is not None:\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n next_token_base_model_output = base_model(\\n input_ids=next_token_ids, \\n attention_mask=next_tokens_attention_mask, \\n position_ids=next_tokens_position_ids, \\n params=base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n \\n # get values\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q1_head_output = q_head_model.apply(\\n {'params': q1_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q2_head_output = q_head_model.apply(\\n {'params': q2_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n v_head_output = v_head_model.apply(\\n {'params': v_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_q1_head_output = q_head_model.apply(\\n {'params': q1_target_head_params}, \\n target_base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_q2_head_output = q_head_model.apply(\\n {'params': q2_target_head_params}, \\n target_base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n # stop gradients\\n if detach_q1:\\n q1_head_output = jax.lax.stop_gradient(q1_head_output)\\n if detach_q2:\\n q2_head_output = jax.lax.stop_gradient(q2_head_output)\\n if detach_v:\\n v_head_output = jax.lax.stop_gradient(v_head_output)\\n target_q1_head_output = jax.lax.stop_gradient(target_q1_head_output)\\n target_q2_head_output = jax.lax.stop_gradient(target_q2_head_output)\\n\\n q1 = jnp.take_along_axis(q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n q2 = jnp.take_along_axis(q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n v = v_head_output[:, :-1].squeeze(2)\\n v_full = v_head_output.squeeze(2)\\n target_q1 = jnp.take_along_axis(target_q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n target_q2 = jnp.take_along_axis(target_q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n\\n q1_logits = q1_head_output[:, :-1, :].astype(jnp.float32)\\n q2_logits = q2_head_output[:, :-1, :].astype(jnp.float32)\\n\\n # get next token values\\n\\n if next_token_ids is not None:\\n # just run vf on last token to save some flops\\n last_next_token_idxs = (next_tokens_attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(next_tokens_attention_mask, axis=1).astype(jnp.int32), axis=1)\\n final_next_token_h = next_token_base_model_output.hidden_states[-1][jnp.arange(0, input_ids.shape[0], dtype=jnp.int32), last_next_token_idxs, :]\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n next_token_v_head_output = v_head_model.apply(\\n {'params': v_head_params}, \\n final_next_token_h, \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n ).squeeze(1)\\n v_final = next_token_v_head_output * (1 - next_dones.astype(jnp.float32))\\n else:\\n last_action_idxs = (should_take_action.shape[1]-1)-jnp.argmax(jnp.flip(should_take_action, axis=1).astype(jnp.int32), axis=1)+1\\n last_token_idxs = (attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(attention_mask, axis=1).astype(jnp.int32), axis=1)\\n final_state_idxs = ((1 - dones) * last_action_idxs + dones * last_token_idxs).astype(jnp.int32)\\n v_final = v_full[jnp.arange(0, should_take_action.shape[0], dtype=jnp.int32), final_state_idxs]\\n v_final = v_final * (1 - dones)\\n v_final = jax.lax.stop_gradient(v_final)\\n\\n loss, info = loss_fn(\\n q1, \\n q2, \\n v, \\n v_final, \\n target_q1, \\n target_q2, \\n q1_logits, \\n q2_logits, \\n input_ids[:, 1:], \\n attention_mask[:, 1:], \\n should_take_action, \\n rewards, \\n )\\n return loss, info\\n\\n # take loss\\n (loss, info), (base_grads, q1_head_grads, q2_head_grads, v_head_grads) = jax.value_and_grad(grad_loss, has_aux=True, argnums=(0, 1, 2, 3))(\\n base_train_state.params, \\n q1_head_train_state.params, \\n q2_head_train_state.params, \\n v_head_train_state.params, \\n prng_key, \\n )\\n # assert shard gradients\\n base_grads = jax.tree_util.tree_map(\\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \\n base_grads, \\n base_train_state_partition_spec.params, \\n )\\n q1_head_grads = jax.tree_util.tree_map(\\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \\n q1_head_grads, \\n q1_head_train_state_partition_spec.params, \\n )\\n q2_head_grads = jax.tree_util.tree_map(\\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \\n q2_head_grads, \\n q2_head_train_state_partition_spec.params, \\n )\\n v_head_grads = jax.tree_util.tree_map(\\n lambda x, ps: with_named_sharding_constraint(x, mesh, ps), \\n v_head_grads, \\n v_head_train_state_partition_spec.params, \\n )\\n # update params and optim state\\n base_train_state = base_train_state.apply_gradients(grads=base_grads)\\n q1_head_train_state = q1_head_train_state.apply_gradients(grads=q1_head_grads)\\n q2_head_train_state = q2_head_train_state.apply_gradients(grads=q2_head_grads)\\n v_head_train_state = v_head_train_state.apply_gradients(grads=v_head_grads)\\n\\n # handle target network updates\\n def update_targets(params: PyTree, base_params: PyTree, steps: jnp.ndarray) -> PyTree:\\n base_params = optax.incremental_update(params, base_params, polyak_alpha)\\n if hard_update_every is not None:\\n base_params = optax.periodic_update(params, base_params, steps, hard_update_every)\\n return base_params\\n \\n def mid_targets(params: PyTree, base_params: PyTree, steps: jnp.ndarray) -> PyTree:\\n return base_params\\n\\n def update_cond(opt_state: PyTree) -> bool:\\n if hasattr(opt_state, 'mini_step'):\\n return opt_state.mini_step == 0\\n return True\\n \\n if target_base_params is not None:\\n target_base_params = jax.lax.cond(\\n update_cond(base_train_state.opt_state), \\n update_targets, \\n mid_targets, \\n base_train_state.params, \\n target_base_params, \\n base_train_state.step, \\n )\\n q1_target_head_params = jax.lax.cond(\\n update_cond(q1_head_train_state.opt_state), \\n update_targets, \\n mid_targets, \\n q1_head_train_state.params, \\n q1_target_head_params, \\n q1_head_train_state.step, \\n )\\n q2_target_head_params = jax.lax.cond(\\n update_cond(q2_head_train_state.opt_state), \\n update_targets, \\n mid_targets, \\n q2_head_train_state.params, \\n q2_target_head_params, \\n q2_head_train_state.step, \\n )\\n\\n return base_train_state, target_base_params, q1_head_train_state, q2_head_train_state, v_head_train_state, q1_target_head_params, q2_target_head_params, loss, info\\n\\n return cls(\\n base_train_state=base_train_state, \\n target_base_params=target_base_params, \\n q1_head_train_state=q1_head_train_state, \\n q2_head_train_state=q2_head_train_state, \\n v_head_train_state=v_head_train_state, \\n q1_target_head_params=q1_target_head_params, \\n q2_target_head_params=q2_target_head_params, \\n base_model=base_model, \\n q_head_model=q_head_model, \\n v_head_model=v_head_model, \\n tokenizer=tokenizer, \\n _step=_step, \\n )\"\n },\n {\n \"identifier\": \"GPT2ILQLInference\",\n \"path\": \"LLM_RL/algorithms/ilql/gpt2/interface.py\",\n \"snippet\": \"class GPT2ILQLInference(ILQLInference):\\n @classmethod\\n def load_inference(\\n cls, \\n value_inference: GPT2ValueRLInference, \\n target_value_inference: GPT2ValueRLInference, \\n loss_fn: Callable, \\n use_target_base_for_loss: bool=True, \\n ):\\n mesh = value_inference.base_model.config.mesh\\n assert mesh is not None\\n assert mesh == value_inference.q_head_model.config.mesh\\n assert mesh == value_inference.v_head_model.config.mesh\\n assert mesh == target_value_inference.base_model.config.mesh\\n assert mesh == target_value_inference.q_head_model.config.mesh\\n\\n base_params_partition_spec = match_partition_rules(value_inference.base_model.config.get_partition_rules(), value_inference.base_params)\\n target_base_params_partition_spec = PS() if (not use_target_base_for_loss) else match_partition_rules(target_value_inference.base_model.config.get_partition_rules(), target_value_inference.base_params)\\n q1_head_params_partition_spec = match_partition_rules(value_inference.q_head_model.config.get_partition_rules(), value_inference.q1_head_params)\\n q2_head_params_partition_spec = match_partition_rules(value_inference.q_head_model.config.get_partition_rules(), value_inference.q2_head_params)\\n v_head_params_partition_spec = match_partition_rules(value_inference.v_head_model.config.get_partition_rules(), value_inference.v_head_params)\\n q1_target_head_params_partition_spec = match_partition_rules(target_value_inference.q_head_model.config.get_partition_rules(), target_value_inference.q1_head_params)\\n q2_target_head_params_partition_spec = match_partition_rules(target_value_inference.q_head_model.config.get_partition_rules(), target_value_inference.q2_head_params)\\n \\n @partial(\\n pjit, \\n static_argnames=('train',), \\n in_shardings=(\\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), target_base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_target_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_target_head_params_partition_spec), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n out_shardings=(\\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n )\\n def _eval_loss(\\n base_params: PyTree, \\n target_base_params: Optional[PyTree], \\n q1_head_params: PyTree, \\n q2_head_params: PyTree, \\n v_head_params: PyTree, \\n q1_target_head_params: PyTree, \\n q2_target_head_params: PyTree, \\n\\n input_ids: jax.Array, \\n attention_mask: jax.Array, \\n position_ids: jax.Array, \\n should_take_action: jax.Array, \\n rewards: jax.Array, \\n dones: jax.Array, \\n\\n next_token_ids: Optional[jax.Array], \\n next_tokens_attention_mask: Optional[jax.Array], \\n next_tokens_position_ids: Optional[jax.Array], \\n next_dones: Optional[jax.Array], \\n\\n prng_key: Optional[jax.random.PRNGKeyArray]=None, \\n train: bool=False, \\n ) -> Tuple[jax.Array, PyTree]:\\n # data parallel shard inputs\\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS(('dp', 'fsdp'), None))\\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS(('dp', 'fsdp'), None))\\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS(('dp', 'fsdp'), None))\\n should_take_action = with_named_sharding_constraint(should_take_action, mesh, PS(('dp', 'fsdp'), None))\\n rewards = with_named_sharding_constraint(rewards, mesh, PS(('dp', 'fsdp'), None))\\n dones = with_named_sharding_constraint(dones, mesh, PS(('dp', 'fsdp')))\\n if next_token_ids is not None:\\n assert next_tokens_attention_mask is not None\\n assert next_tokens_position_ids is not None\\n next_token_ids = with_named_sharding_constraint(next_token_ids, mesh, PS(('dp', 'fsdp'), None))\\n next_tokens_attention_mask = with_named_sharding_constraint(next_tokens_attention_mask, mesh, PS(('dp', 'fsdp'), None))\\n next_tokens_position_ids = with_named_sharding_constraint(next_tokens_position_ids, mesh, PS(('dp', 'fsdp'), None))\\n next_dones = with_named_sharding_constraint(next_dones, mesh, PS(('dp', 'fsdp')))\\n else:\\n assert next_tokens_attention_mask is None\\n assert next_tokens_position_ids is None\\n \\n # get base hidden states\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n base_model_output = value_inference.base_model(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n position_ids=position_ids, \\n params=base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n\\n if target_base_params is not None:\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_base_model_output = target_value_inference.base_model(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n position_ids=position_ids, \\n params=target_base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n else:\\n target_base_model_output = base_model_output\\n \\n if next_token_ids is not None:\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n next_token_base_model_output = value_inference.base_model(\\n input_ids=next_token_ids, \\n attention_mask=next_tokens_attention_mask, \\n position_ids=next_tokens_position_ids, \\n params=base_params, \\n dropout_rng=new_key, \\n train=train, \\n output_hidden_states=True, \\n )\\n \\n # get values\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q1_head_output = value_inference.q_head_model.apply(\\n {'params': q1_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q2_head_output = value_inference.q_head_model.apply(\\n {'params': q2_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n v_head_output = value_inference.v_head_model.apply(\\n {'params': v_head_params}, \\n base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_q1_head_output = target_value_inference.q_head_model.apply(\\n {'params': q1_target_head_params}, \\n target_base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n target_q2_head_output = target_value_inference.q_head_model.apply(\\n {'params': q2_target_head_params}, \\n target_base_model_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n\\n # process outputs\\n\\n q1 = jnp.take_along_axis(q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n q2 = jnp.take_along_axis(q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n v = v_head_output[:, :-1].squeeze(2)\\n v_full = v_head_output.squeeze(2)\\n target_q1 = jnp.take_along_axis(target_q1_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n target_q2 = jnp.take_along_axis(target_q2_head_output[:, :-1], input_ids[:, 1:][..., None], axis=2).squeeze(2)\\n\\n q1_logits = q1_head_output[:, :-1, :].astype(jnp.float32)\\n q2_logits = q2_head_output[:, :-1, :].astype(jnp.float32)\\n\\n # get next token values\\n\\n if next_token_ids is not None:\\n # just run vf on last token to save some flops\\n last_next_token_idxs = (next_tokens_attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(next_tokens_attention_mask, axis=1).astype(jnp.int32), axis=1)\\n final_next_token_h = next_token_base_model_output.hidden_states[-1][jnp.arange(0, input_ids.shape[0], dtype=jnp.int32), last_next_token_idxs, :]\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n next_token_v_head_output = value_inference.v_head_model.apply(\\n {'params': v_head_params}, \\n final_next_token_h, \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n ).squeeze(1)\\n v_final = next_token_v_head_output * (1 - next_dones.astype(jnp.float32))\\n else:\\n last_action_idxs = (should_take_action.shape[1]-1)-jnp.argmax(jnp.flip(should_take_action, axis=1).astype(jnp.int32), axis=1)+1\\n last_token_idxs = (attention_mask.shape[1]-1)-jnp.argmax(jnp.flip(attention_mask, axis=1).astype(jnp.int32), axis=1)\\n final_state_idxs = ((1 - dones) * last_action_idxs + dones * last_token_idxs).astype(jnp.int32)\\n v_final = v_full[jnp.arange(0, should_take_action.shape[0], dtype=jnp.int32), final_state_idxs]\\n v_final = v_final * (1 - dones)\\n\\n loss, info = loss_fn(\\n q1, \\n q2, \\n v, \\n v_final, \\n target_q1, \\n target_q2, \\n q1_logits, \\n q2_logits, \\n input_ids[:, 1:], \\n attention_mask[:, 1:], \\n should_take_action, \\n rewards, \\n )\\n \\n return loss, info\\n\\n return cls(\\n value_inference=value_inference, \\n target_value_inference=target_value_inference, \\n _eval_loss=_eval_loss, \\n use_target_base_for_loss=use_target_base_for_loss, \\n )\"\n },\n {\n \"identifier\": \"GPT2ValuePolicy\",\n \"path\": \"LLM_RL/algorithms/value_rl_base/gpt2/interface.py\",\n \"snippet\": \"class GPT2ValuePolicy(ValueRLPolicy):\\n def __init__(\\n self, \\n inference: ValueRLInference, \\n prng_key: Optional[jax.random.KeyArray], \\n generation_config: Optional[GenerationConfig]=None, \\n blocking_strategy: BlockingStrategy=BlockingStrategy(padding=Padding.LEFT, truncation=Truncation.LEFT, max_length=None), \\n in_str_process: Optional[Callable[[str], str]]=None, \\n out_str_process: Optional[Callable[[str], str]]=None, \\n input_token_process: Optional[Callable[[List[int]], List[int]]]=None, \\n target_token_process: Optional[Callable[[List[int]], List[int]]]=None, \\n trace: bool=True, \\n ):\\n self.inference = inference\\n self.prng_key = prng_key\\n self.generation_config = generation_config\\n self.blocking_strategy = blocking_strategy\\n self.in_str_process = in_str_process\\n self.out_str_process = out_str_process\\n self.input_token_process = input_token_process\\n self.target_token_process = target_token_process\\n if self.in_str_process is None:\\n self.in_str_process = lambda x: x\\n if self.out_str_process is None:\\n self.out_str_process = lambda x: x\\n self.trace = trace\\n \\n def act(self, text_history: List[Optional[TextHistory]], done: Optional[List[bool]]=None) -> List[Optional[TextHistory]]:\\n if done is None:\\n done = [False]*len(text_history)\\n # force eos_token for done sequences\\n eos_token = self.inference.tokenizer.eos_token\\n if self.generation_config is not None and self.generation_config.eos_token_id is not None:\\n eos_token = self.inference.tokenizer.decode(self.generation_config.eos_token_id)\\n if eos_token is None:\\n eos_token = self.inference.tokenizer.pad_token\\n if eos_token is None:\\n eos_token = ''\\n \\n raw_input_strs = [\\n eos_token if d else self.in_str_process(text_history_to_str(item)) \\\\\\n for item, d in zip(text_history, done)\\n ]\\n\\n new_key = None\\n if self.prng_key is not None:\\n self.prng_key, new_key = jax.random.split(self.prng_key)\\n model_outputs = self.inference.generate_from_str(\\n input_strs=raw_input_strs, \\n prng_key=new_key, \\n blocking_strategy=self.blocking_strategy, \\n generation_config=self.generation_config, \\n input_token_process=self.input_token_process, \\n target_token_process=self.target_token_process, \\n trace=self.trace, \\n )\\n\\n raw_output_strs = model_outputs.output_strs\\n output_strs = [\\n \\\"\\\" if d else self.out_str_process(strip_prompt_from_completion(raw_input_str, raw_output_str)) \\\\\\n for raw_input_str, raw_output_str, d in zip(raw_input_strs, raw_output_strs, done)\\n ]\\n\\n return [\\n None if d else text_history_item+(Text(output_str, True),) \\\\\\n for text_history_item, output_str, d in zip(text_history, output_strs, done)\\n ]\\n \\n def set_params(self, policy_params: PyTree) -> None:\\n pi_beta_params, base_params, \\\\\\n q1_head_params, q2_head_params = policy_params\\n self.inference = self.inference.replace(\\n pi_beta_params=pi_beta_params, \\n base_params=base_params, \\n q1_head_params=q1_head_params, \\n q2_head_params=q2_head_params, \\n )\"\n },\n {\n \"identifier\": \"GPT2ValueRLInference\",\n \"path\": \"LLM_RL/algorithms/value_rl_base/gpt2/interface.py\",\n \"snippet\": \"class GPT2ValueRLInference(ValueRLInference):\\n @classmethod\\n def load_inference(\\n cls, \\n pi_beta_params: Optional[PyTree], \\n base_params: PyTree, \\n q1_head_params: PyTree, \\n q2_head_params: Optional[PyTree], \\n v_head_params: Optional[PyTree], \\n pi_beta_model: Optional[FlaxPreTrainedModel], \\n base_model: FlaxPreTrainedModel, \\n q_head_model: nn.Module, \\n v_head_model: Optional[nn.Module], \\n tokenizer: PreTrainedTokenizerBase, \\n beta: float=0.0, \\n dp_shard_logits: bool=True, \\n ):\\n mesh = base_model.config.mesh\\n assert mesh is not None\\n assert mesh == q_head_model.config.mesh\\n if v_head_model is not None:\\n assert mesh == v_head_model.config.mesh\\n assert (pi_beta_model is None and pi_beta_params is None) or (pi_beta_model is not None and pi_beta_params is not None)\\n \\n pi_beta_params_partition_spec = PS() if pi_beta_params is None else match_partition_rules(pi_beta_model.config.get_partition_rules(), pi_beta_params)\\n base_params_partition_spec = match_partition_rules(base_model.config.get_partition_rules(), base_params)\\n q1_head_params_partition_spec = match_partition_rules(q_head_model.config.get_partition_rules(), q1_head_params)\\n q2_head_params_partition_spec = PS() if q2_head_params is None else match_partition_rules(q_head_model.config.get_partition_rules(), q2_head_params)\\n v_head_params_partition_spec = PS() if v_head_params is None else match_partition_rules(v_head_model.config.get_partition_rules(), v_head_params)\\n\\n generator = None\\n if pi_beta_model is not None:\\n generator = GPT2ValueRLGeneration(\\n base_model_config=base_model.config, \\n pi_beta=pi_beta_model, \\n value_base=base_model, \\n q_head=q_head_model, \\n beta=beta, \\n )\\n\\n if pi_beta_params is not None:\\n @partial(\\n pjit, \\n static_argnames=('generation_config', 'trace'), \\n in_shardings=(\\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), pi_beta_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n out_shardings=NamedSharding(mesh, PS()), \\n )\\n def _generate(\\n pi_beta_params: Optional[PyTree], \\n base_params: PyTree, \\n q1_head_params: PyTree, \\n q2_head_params: Optional[PyTree], \\n input_ids: jax.Array, \\n attention_mask: jax.Array, \\n position_ids: jax.Array, \\n prng_key: Optional[jax.random.PRNGKeyArray]=None, \\n generation_config: Optional[FrozenDict]=None, \\n trace: bool=True, \\n ) -> Union[FlaxSampleOutput, FlaxGreedySearchOutput, FlaxBeamSearchOutput]:\\n # data parallel shard inputs\\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n # NOTE: position_ids ignored by transformers\\n\\n # generate from model\\n output = generator.generate(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n params=(pi_beta_params, base_params, q1_head_params, q2_head_params), \\n prng_key=prng_key, \\n generation_config=StreamingGenerationConfig.from_dict(generation_config) if generation_config is not None else None, \\n trace=trace, \\n )\\n \\n return output\\n else:\\n def _generate(\\n pi_beta_params: Optional[PyTree], \\n base_params: PyTree, \\n q1_head_params: PyTree, \\n q2_head_params: Optional[PyTree], \\n input_ids: jax.Array, \\n attention_mask: jax.Array, \\n position_ids: jax.Array, \\n prng_key: Optional[jax.random.PRNGKeyArray]=None, \\n generation_config: Optional[FrozenDict]=None, \\n trace: bool=True, \\n ) -> Union[FlaxSampleOutput, FlaxGreedySearchOutput, FlaxBeamSearchOutput]:\\n raise NotImplementedError\\n \\n @partial(\\n pjit, \\n static_argnames=('output_attentions', 'train'), \\n in_shardings=(\\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), base_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q1_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), q2_head_params_partition_spec), \\n jax.tree_util.tree_map(lambda ps: NamedSharding(mesh, ps), v_head_params_partition_spec), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n NamedSharding(mesh, PS()), \\n ), \\n out_shardings=ValueRLForwardOutput(\\n base_raw_output=FlaxCausalLMOutputWithCrossAttentions(\\n logits=NamedSharding(mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None)) if dp_shard_logits else NamedSharding(mesh, PS()), \\n hidden_states=NamedSharding(mesh, PS()), # assume no sharding for hidden states\\n attentions=NamedSharding(mesh, PS()), # assume no sharding for attentions\\n cross_attentions=NamedSharding(mesh, PS()), # assume no sharding for cross attentions\\n past_key_values=NamedSharding(mesh, PS()), # assume no sharding for past key values\\n ), \\n q1=NamedSharding(mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None)) if dp_shard_logits else NamedSharding(mesh, PS()), \\n q2=NamedSharding(mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None)) if (dp_shard_logits and q2_head_params is not None) else NamedSharding(mesh, PS()), \\n v=NamedSharding(mesh, PS()), \\n ), \\n )\\n def _forward(\\n base_params: PyTree, \\n q1_head_params: PyTree, \\n q2_head_params: Optional[PyTree], \\n v_head_params: Optional[PyTree], \\n input_ids: jax.Array, \\n attention_mask: jax.Array, \\n position_ids: jax.Array, \\n prng_key: Optional[jax.random.PRNGKeyArray]=None, \\n output_attentions: Optional[bool]=None, \\n train: bool=False, \\n ) -> ValueRLForwardOutput:\\n # data parallel shard inputs\\n input_ids = with_named_sharding_constraint(input_ids, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n attention_mask = with_named_sharding_constraint(attention_mask, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n position_ids = with_named_sharding_constraint(position_ids, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None))\\n\\n # get logits\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n base_output = base_model(\\n input_ids=input_ids, \\n attention_mask=attention_mask, \\n position_ids=position_ids, \\n params=base_params, \\n train=train, \\n output_attentions=output_attentions, \\n output_hidden_states=True, \\n dropout_rng=new_key, \\n )\\n # trunc padded logits\\n base_output = base_output.replace(logits=base_output.logits.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf')))\\n\\n # get q1\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q1 = q_head_model.apply(\\n {'params': q1_head_params}, \\n base_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n # trunc padded qs\\n q1 = q1.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf'))\\n\\n # get q2\\n if q2_head_params is not None:\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n q2 = q_head_model.apply(\\n {'params': q2_head_params}, \\n base_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n )\\n # trunc padded qs\\n q2 = q2.at[:, :, base_model.config.unpadded_vocab_size:].set(-float('inf'))\\n else:\\n q2 = None\\n\\n if v_head_params is not None:\\n # get v\\n new_key = None\\n if prng_key is not None:\\n prng_key, new_key = jax.random.split(prng_key)\\n v = v_head_model.apply(\\n {'params': v_head_params}, \\n base_output.hidden_states[-1], \\n train=train, \\n rngs={'dropout': new_key} if prng_key is not None else None, \\n ).squeeze(2)\\n else:\\n v = None\\n\\n # assert sharding on outputs\\n if dp_shard_logits:\\n base_output = base_output.replace(logits=with_named_sharding_constraint(base_output.logits, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None)))\\n q1 = with_named_sharding_constraint(q1, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None))\\n if q2 is not None:\\n q2 = with_named_sharding_constraint(q2, mesh, PS((\\\"dp\\\", \\\"fsdp\\\"), None, None))\\n return ValueRLForwardOutput(\\n base_raw_output=base_output, \\n q1=q1, \\n q2=q2, \\n v=v, \\n )\\n\\n return cls(\\n pi_beta_params=pi_beta_params, \\n base_params=base_params, \\n q1_head_params=q1_head_params, \\n q2_head_params=q2_head_params, \\n v_head_params=v_head_params, \\n pi_beta_model=pi_beta_model, \\n base_model=base_model, \\n q_head_model=q_head_model, \\n v_head_model=v_head_model, \\n tokenizer=tokenizer, \\n _generate=_generate, \\n _forward=_forward,\\n )\"\n },\n {\n \"identifier\": \"load_train_state_from_config\",\n \"path\": \"LLM_RL/heads/mlp_head.py\",\n \"snippet\": \"def load_train_state_from_config(\\n model_config: MLPHeadConfig, \\n model_dtype: Union[str, jnp.dtype], \\n optim_getter: Callable[[PyTree], optax.GradientTransformation], \\n mesh: Mesh, # should be shape (dp, mp)\\n prng_key: jax.random.PRNGKeyArray, \\n pad_to_output_dim: Optional[int]=None, \\n params_dtype: Optional[Union[str, jnp.dtype]]=jnp.float32, \\n) -> Tuple[TrainState, MLPHead]:\\n \\n model = MLPHead(model_config, dtype=model_dtype)\\n model.config.mesh = mesh\\n # shard params\\n params = freeze(shard_params_from_config(model, prng_key, params_dtype=params_dtype))\\n # pad outputs\\n if pad_to_output_dim is not None:\\n params = freeze(pad_outputs(unfreeze(params), model, pad_to_output_dim, dtype=params_dtype))\\n # shard train_state\\n train_state = shard_train_state_from_params(model, params, optim_getter(params))\\n\\n return train_state, model\"\n },\n {\n \"identifier\": \"MLPHeadConfig\",\n \"path\": \"LLM_RL/heads/mlp_head.py\",\n \"snippet\": \"class MLPHeadConfig(HeadConfig):\\n def __init__(\\n self, \\n input_dim: int, \\n hidden_dim: int, \\n output_dim: int, \\n use_bias: bool=True, \\n unpadded_output_dim: Optional[int]=None, \\n layer1_initializer_range: Optional[int]=None, \\n layer1_bias_init: Optional[float]=None, \\n layer2_initializer_range: Optional[int]=None, \\n layer2_bias_init: Optional[float]=None, \\n mesh: Optional[jax.sharding.Mesh]=None, \\n ) -> None:\\n self.input_dim = input_dim\\n self.hidden_dim = hidden_dim\\n self.output_dim = output_dim\\n self.use_bias = use_bias\\n self.layer1_initializer_range = layer1_initializer_range\\n self.layer1_bias_init = layer1_bias_init\\n self.layer2_initializer_range = layer2_initializer_range\\n self.layer2_bias_init = layer2_bias_init\\n self.mesh = mesh\\n self.unpadded_output_dim = unpadded_output_dim\\n if self.unpadded_output_dim is None:\\n self.unpadded_output_dim = self.output_dim\\n super().__init__()\\n \\n @staticmethod\\n def get_partition_rules():\\n return [\\n (re.escape(\\\"['dense1']['kernel']\\\"), PS(\\\"fsdp\\\", \\\"mp\\\")), \\n (re.escape(\\\"['dense1']['bias']\\\"), PS(\\\"mp\\\")), \\n (re.escape(\\\"['dense2']['kernel']\\\"), PS(\\\"mp\\\", \\\"fsdp\\\")), \\n (re.escape(\\\"['dense2']['bias']\\\"), PS()), \\n ]\\n\\n def to_dict(self) -> Dict[str, Any]:\\n if self.mesh is None:\\n return super().to_dict()\\n else:\\n new_conf = MLPHeadConfig(**self.__dict__)\\n new_conf.mesh = None\\n return new_conf.to_dict()\"\n },\n {\n \"identifier\": \"ILQLDataset\",\n \"path\": \"LLM_RL/algorithms/ilql/data.py\",\n \"snippet\": \"class ILQLDataset(Dataset):\\n def __init__(\\n self, \\n input_ids: np.ndarray, # [b, t]\\n should_take_action: np.ndarray, # [b, t-1]\\n rewards: np.ndarray, # [b, t-1]\\n dones: np.ndarray, # [b]\\n next_token_ids: Optional[np.ndarray], # [b, t']\\n next_dones: Optional[np.ndarray], # [b]\\n ):\\n assert input_ids.shape[1] == (should_take_action.shape[1]+1)\\n assert input_ids.shape[1] == (rewards.shape[1]+1)\\n\\n assert input_ids.shape[0] == should_take_action.shape[0]\\n assert input_ids.shape[0] == rewards.shape[0]\\n assert input_ids.shape[0] == dones.shape[0]\\n if next_token_ids is not None:\\n assert input_ids.shape[0] == next_token_ids.shape[0]\\n if next_dones is not None:\\n assert input_ids.shape[0] == next_dones.shape[0]\\n\\n self.input_ids = input_ids\\n self.should_take_action = should_take_action\\n self.rewards = rewards\\n self.dones = dones\\n self.next_token_ids = next_token_ids\\n self.next_dones = next_dones\\n \\n def __getitem__(self, index):\\n return {\\n 'input_ids': jnp.asarray(self.input_ids[index], dtype=jnp.int32), \\n 'should_take_action': jnp.asarray(self.should_take_action[index], dtype=jnp.bool_), \\n 'rewards': jnp.asarray(self.rewards[index], dtype=jnp.float32), \\n 'dones': jnp.asarray(self.dones[index], dtype=jnp.float32), \\n 'next_token_ids': jnp.asarray(self.next_token_ids[index], dtype=jnp.float32) if self.next_token_ids is not None else None, \\n 'next_dones': jnp.asarray(self.next_dones[index], dtype=jnp.float32) if self.next_dones is not None else None, \\n }\\n \\n def __len__(self):\\n return self.input_ids.shape[0]\\n \\n @classmethod\\n def from_ilql_data_list(\\n cls, \\n ilql_data_list: List[ILQLData], \\n tokenizer: PreTrainedTokenizerBase, \\n blocking_strategy: BlockingStrategy, \\n ) -> ILQLDataset:\\n \\n data = ILQLData.block(ilql_data_list, blocking_strategy, tokenizer)\\n\\n return cls(**data)\"\n },\n {\n \"identifier\": \"ILQLIterableDataset\",\n \"path\": \"LLM_RL/algorithms/ilql/data.py\",\n \"snippet\": \"class ILQLIterableDataset(IterableDataset):\\n def __init__(self, ilql_data: Iterable[Dict[str, np.ndarray]]):\\n self.ilql_data = ilql_data\\n \\n def __iter__(self):\\n return _ILQLIteratorDataset(iter(self.ilql_data))\\n \\n @classmethod\\n def from_ilql_data_iterable(\\n cls, \\n ilql_data: Iterable[ILQLData], \\n tokenizer: PreTrainedTokenizerBase, \\n blocking_strategy: BlockingStrategy, \\n ) -> ILQLIterableDataset:\\n \\n class _TokensIterable(Iterable):\\n def _tokens_generator(self):\\n for item in ilql_data:\\n yield jax.tree_util.tree_map(lambda x: x[0], ILQLData.block([item], blocking_strategy, tokenizer))\\n\\n def __iter__(self):\\n return self._tokens_generator()\\n\\n return cls(_TokensIterable())\"\n },\n {\n \"identifier\": \"eval_loss\",\n \"path\": \"LLM_RL/algorithms/ilql/train.py\",\n \"snippet\": \"def eval_loss(\\n inference: ILQLInference, \\n dataset: Union[Seq2SeqDataset, Seq2SeqIterableDataset], \\n prng_key: Optional[KeyArray], \\n bsize: int, \\n eval_batches: Optional[int], \\n) -> Dict[str, Any]:\\n # setup evaluator loop state\\n eval_logs = []\\n\\n # eval on batches\\n prng_key, new_prng = jax.random.split(prng_key) if prng_key is not None else (None, None)\\n d = dataloader(new_prng, dataset, bsize, truncate=True)\\n for i, batch in tqdm(enumerate(d)):\\n # conditionally terminate early\\n if eval_batches is not None and i >= eval_batches:\\n break\\n\\n # get eval logs\\n _, info = inference.eval_loss(**batch)\\n eval_logs.append(info)\\n \\n # gather and postproc eval logs\\n eval_logs = pull_logs(combine_logs(eval_logs))\\n return eval_logs\"\n },\n {\n \"identifier\": \"train_loop\",\n \"path\": \"LLM_RL/algorithms/ilql/train.py\",\n \"snippet\": \"def train_loop(\\n trainer: ILQLTrain, \\n inference: Union[ValueRLInference, ILQLInference], \\n evaluator: Optional[Callable[[Inference], Tuple[float, Dict[str, Any]]]], \\n dataset: Union[Seq2SeqDataset, Seq2SeqIterableDataset], \\n prng_key: KeyArray, \\n save_dir: Optional[str], \\n epochs: int, \\n max_steps: Optional[int], \\n bsize: int, \\n log_every: int, \\n eval_every_steps: Optional[int], \\n eval_every_epochs: Optional[int], \\n eval_at_beginning: bool, \\n eval_at_end: bool, \\n save_every_steps: Optional[int], \\n save_every_epochs: Optional[int], \\n save_at_beginning: bool, \\n save_at_end: bool, \\n save_best: bool, \\n max_checkpoints: Optional[int], \\n save_train_state: bool, \\n save_dtype: jnp.dtype, \\n use_wandb: bool, \\n wandb_project: Optional[str], \\n wandb_run_name: Optional[str], \\n wandb_config: Optional[Dict[str, Any]], \\n is_main_process: Optional[bool]=None, \\n **loop_state: Dict[Hashable, Any], \\n) -> Tuple[Train, Inference]:\\n assert (not use_wandb) or (use_wandb and wandb_project is not None)\\n if is_main_process is None:\\n is_main_process = jax.process_index() == 0\\n \\n # initalize wandb\\n wandb_id = loop_state.get('wandb_id', None)\\n if use_wandb and is_main_process:\\n if wandb_id is None:\\n wandb_id = wandb.util.generate_id()\\n wandb.init(\\n project=wandb_project, \\n id=wandb_id, \\n name=wandb_run_name, \\n config=wandb_config, \\n reinit=True, \\n resume=\\\"allow\\\", \\n )\\n\\n # initalize training loop state\\n train_logs = []\\n best_perf = loop_state.get('best_perf', float('inf'))\\n saved_checkpoints = loop_state.get('saved_checkpoints', deque([]))\\n step = 0\\n steps_per_epoch = len(dataset) // bsize if isinstance(dataset, Dataset) else None\\n if 'steps_per_epoch' in loop_state:\\n assert steps_per_epoch == loop_state['steps_per_epoch'], 'loop_state steps_per_epoch does not match dataset steps_per_epoch'\\n epoch = -1\\n\\n def _save(\\n name: str, \\n add_to_queue: bool, \\n **loop_state: Dict[Hashable, Any], \\n ):\\n nonlocal saved_checkpoints\\n print(f'saving checkpoint {name} ...')\\n # conditionally delete old checkpoints\\n if add_to_queue and is_main_process:\\n if (max_checkpoints is not None) and (len(saved_checkpoints) >= max_checkpoints):\\n delete(saved_checkpoints.popleft(), recursive=True)\\n curr_save_dir = os.path.join(save_dir, name)\\n if is_main_process:\\n create_path(curr_save_dir)\\n dump_state(\\n base_model=trainer.base_model, \\n q_head_model=trainer.q_head_model, \\n v_head_model=trainer.v_head_model, \\n base_train_state=trainer.base_train_state, \\n target_base_params=trainer.target_base_params, \\n q1_head_train_state=trainer.q1_head_train_state, \\n q2_head_train_state=trainer.q2_head_train_state, \\n v_head_train_state=trainer.v_head_train_state, \\n q1_target_head_params=trainer.q1_target_head_params, \\n q2_target_head_params=trainer.q2_target_head_params, \\n save_dir=curr_save_dir, \\n save_train_state=save_train_state, \\n enable_save=is_main_process, \\n save_dtype=save_dtype, \\n **loop_state, \\n )\\n if add_to_queue and is_main_process:\\n saved_checkpoints.append(curr_save_dir)\\n print('saved.')\\n \\n def _inference_update():\\n nonlocal inference\\n if isinstance(inference, ValueRLInference):\\n inference = inference.replace(\\n base_params=trainer.base_train_state.params, \\n q1_head_params=trainer.q1_head_train_state.params, \\n q2_head_params=trainer.q2_head_train_state.params, \\n v_head_params=trainer.v_head_train_state.params, \\n )\\n elif isinstance(inference, ILQLInference):\\n new_value_inference = inference.value_inference.replace(\\n base_params=trainer.base_train_state.params, \\n q1_head_params=trainer.q1_head_train_state.params, \\n q2_head_params=trainer.q2_head_train_state.params, \\n v_head_params=trainer.v_head_train_state.params, \\n )\\n new_target_value_inference = inference.target_value_inference.replace(\\n base_params=trainer.target_base_params, \\n q1_head_params=trainer.q1_target_head_params, \\n q2_head_params=trainer.q2_target_head_params, \\n )\\n inference = inference.replace(\\n value_inference=new_value_inference, \\n target_value_inference=new_target_value_inference, \\n )\\n else:\\n raise NotImplementedError\\n \\n def _eval(\\n **loop_state: Dict[Hashable, Any], \\n ):\\n nonlocal best_perf\\n # get eval logs\\n _inference_update()\\n eval_perf, eval_logs = evaluator(inference)\\n\\n # publish eval logs\\n eval_logs = pull_logs(label_logs(eval_logs, 'eval', {'step': step+1, 'epoch': epoch}))\\n log(eval_logs, use_wandb and is_main_process)\\n\\n # conditionally save best model and optimizer state\\n if save_dir is not None and save_best and eval_perf < best_perf:\\n print('new best model!')\\n best_perf = eval_perf\\n _save(\\n name='best', \\n add_to_queue=False, \\n **{**loop_state, 'best_perf': best_perf}, \\n )\\n \\n # begin evaluation\\n if evaluator is not None and eval_at_beginning:\\n _eval(\\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # save initial checkpoint\\n if save_dir is not None and save_at_beginning:\\n _save(\\n name='initial', \\n add_to_queue=False, \\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # begin training loop\\n for epoch in tqdm(range(epochs)):\\n prng_key, new_prng = jax.random.split(prng_key)\\n d = dataloader(new_prng, dataset, bsize, truncate=True)\\n for batch in tqdm(d, total=steps_per_epoch):\\n \\n # step model and get training logs\\n prng_key, new_prng = jax.random.split(prng_key)\\n if 'step' in loop_state and step < loop_state['step']:\\n step += 1\\n continue\\n trainer, _, info = trainer.step(\\n **batch, \\n prng_key=new_prng, \\n train=True, \\n )\\n train_logs.append(info)\\n \\n # publish training logs and clear logs\\n if (step + 1) % log_every == 0:\\n logs = combine_logs(train_logs)\\n logs = pull_logs(label_logs(logs, 'train', {'step': step+1, 'epoch': epoch}))\\n log(logs, use_wandb and is_main_process)\\n train_logs = []\\n \\n # begin evaluation\\n if evaluator is not None and eval_every_steps is not None and (step + 1) % eval_every_steps == 0:\\n _eval(\\n # loop state metadata\\n best_perf=best_perf, \\n step=step+1, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # periodically save checkpoint\\n if save_dir is not None and save_every_steps is not None and (step + 1) % save_every_steps == 0:\\n _save(\\n name=f'step_{step+1}', \\n add_to_queue=True, \\n # loop state metadata\\n best_perf=best_perf, \\n step=step+1, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n\\n step += 1\\n\\n # conditionally terminate\\n if max_steps is not None and step >= max_steps:\\n break\\n \\n # begin evaluation\\n if evaluator is not None and eval_every_epochs is not None and (epoch + 1) % eval_every_epochs == 0:\\n _eval(\\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # periodically save checkpoint\\n if save_dir is not None and save_every_epochs is not None and (epoch + 1) % save_every_epochs == 0:\\n _save(\\n name=f'epoch_{epoch}', \\n add_to_queue=True, \\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # conditionally terminate\\n if max_steps is not None and step >= max_steps:\\n break\\n \\n # begin evaluation\\n if evaluator is not None and eval_at_end:\\n _eval(\\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n \\n # save final checkpoint\\n if save_dir is not None and save_at_end:\\n _save(\\n name='last', \\n add_to_queue=False, \\n # loop state metadata\\n best_perf=best_perf, \\n step=step, \\n epoch=epoch, \\n saved_checkpoints=saved_checkpoints, \\n steps_per_epoch=steps_per_epoch, \\n wandb_id=wandb_id, \\n )\\n\\n # stop wandb\\n if use_wandb and is_main_process:\\n wandb.finish()\\n _inference_update()\\n return trainer, inference\"\n },\n {\n \"identifier\": \"ILQLData\",\n \"path\": \"LLM_RL/algorithms/ilql/data.py\",\n \"snippet\": \"class ILQLData(NamedTuple):\\n input_ids: np.ndarray # [t]\\n should_take_action: np.ndarray # [t-1]\\n rewards: np.ndarray # [t-1]\\n done: np.ndarray # []\\n next_token_ids: Optional[np.ndarray] # [t']\\n next_done: Optional[np.ndarray] # []\\n\\n @staticmethod\\n def block(\\n data: List[ILQLData], \\n blocking_strategy: BlockingStrategy, \\n tokenizer: PreTrainedTokenizerBase, \\n ) -> Dict[str, np.ndarray]:\\n has_next_token = any(map(lambda x: x.next_token_ids is not None, data))\\n assert all(map(lambda x: x.next_token_ids is None, data)) or has_next_token\\n assert all(map(lambda x: x.next_done is None, data)) or has_next_token\\n\\n return dict(\\n input_ids=block_sequences(\\n list(map(lambda x: x.input_ids, data)), \\n tokenizer.pad_token_id, \\n dtype=np.int32, \\n blocking_strategy=blocking_strategy, \\n ), \\n should_take_action=block_sequences(\\n list(map(lambda x: x.should_take_action, data)), \\n False, \\n dtype=np.bool_, \\n blocking_strategy=blocking_strategy._replace(max_length=blocking_strategy.max_length-1), \\n ), \\n rewards=block_sequences(\\n list(map(lambda x: x.rewards, data)), \\n 0.0, \\n dtype=np.float32, \\n blocking_strategy=blocking_strategy._replace(max_length=blocking_strategy.max_length-1), \\n ), \\n dones=np.asarray(list(map(lambda x: x.done, data)), dtype=np.bool_), \\n next_token_ids=block_sequences(\\n list(map(lambda x: x.next_token_ids, data)), \\n tokenizer.pad_token_id, \\n dtype=np.int32, \\n blocking_strategy=blocking_strategy, \\n ) if has_next_token else None, \\n next_dones=np.asarray(list(map(lambda x: x.next_done, data)), dtype=np.bool_) if has_next_token else None, \\n )\\n \\n @classmethod\\n def from_token_trajectory_chain(\\n cls, \\n token_trajectory_chain: TokenTrajectoryChain, \\n ):\\n if token_trajectory_chain.next is not None:\\n if token_trajectory_chain.next.token_trajectory.is_action[1:].sum() > 0:\\n first_next_action = np.argmax(token_trajectory_chain.next.token_trajectory.is_action[1:], axis=0)+1\\n next_token_ids = token_trajectory_chain.next.token_trajectory.tokens[:first_next_action]\\n next_done = False\\n else:\\n next_token_ids = token_trajectory_chain.next.token_trajectory.tokens\\n next_done = token_trajectory_chain.next.token_trajectory.done\\n else:\\n next_token_ids, next_done = None, None\\n return cls(\\n input_ids=token_trajectory_chain.token_trajectory.tokens, \\n should_take_action=token_trajectory_chain.token_trajectory.is_action[1:], \\n rewards=token_trajectory_chain.token_trajectory.reward[1:], \\n done=token_trajectory_chain.token_trajectory.done, \\n next_token_ids=next_token_ids, \\n next_done=next_done, \\n )\"\n },\n {\n \"identifier\": \"ILQLDataset\",\n \"path\": \"LLM_RL/algorithms/ilql/data.py\",\n \"snippet\": \"class ILQLDataset(Dataset):\\n def __init__(\\n self, \\n input_ids: np.ndarray, # [b, t]\\n should_take_action: np.ndarray, # [b, t-1]\\n rewards: np.ndarray, # [b, t-1]\\n dones: np.ndarray, # [b]\\n next_token_ids: Optional[np.ndarray], # [b, t']\\n next_dones: Optional[np.ndarray], # [b]\\n ):\\n assert input_ids.shape[1] == (should_take_action.shape[1]+1)\\n assert input_ids.shape[1] == (rewards.shape[1]+1)\\n\\n assert input_ids.shape[0] == should_take_action.shape[0]\\n assert input_ids.shape[0] == rewards.shape[0]\\n assert input_ids.shape[0] == dones.shape[0]\\n if next_token_ids is not None:\\n assert input_ids.shape[0] == next_token_ids.shape[0]\\n if next_dones is not None:\\n assert input_ids.shape[0] == next_dones.shape[0]\\n\\n self.input_ids = input_ids\\n self.should_take_action = should_take_action\\n self.rewards = rewards\\n self.dones = dones\\n self.next_token_ids = next_token_ids\\n self.next_dones = next_dones\\n \\n def __getitem__(self, index):\\n return {\\n 'input_ids': jnp.asarray(self.input_ids[index], dtype=jnp.int32), \\n 'should_take_action': jnp.asarray(self.should_take_action[index], dtype=jnp.bool_), \\n 'rewards': jnp.asarray(self.rewards[index], dtype=jnp.float32), \\n 'dones': jnp.asarray(self.dones[index], dtype=jnp.float32), \\n 'next_token_ids': jnp.asarray(self.next_token_ids[index], dtype=jnp.float32) if self.next_token_ids is not None else None, \\n 'next_dones': jnp.asarray(self.next_dones[index], dtype=jnp.float32) if self.next_dones is not None else None, \\n }\\n \\n def __len__(self):\\n return self.input_ids.shape[0]\\n \\n @classmethod\\n def from_ilql_data_list(\\n cls, \\n ilql_data_list: List[ILQLData], \\n tokenizer: PreTrainedTokenizerBase, \\n blocking_strategy: BlockingStrategy, \\n ) -> ILQLDataset:\\n \\n data = ILQLData.block(ilql_data_list, blocking_strategy, tokenizer)\\n\\n return cls(**data)\"\n },\n {\n \"identifier\": \"FenChessHistoryEnv\",\n \"path\": \"llm_rl_scripts/chess/env/env.py\",\n \"snippet\": \"class FenChessHistoryEnv(TextEnv):\\n def __init__(self, max_moves=400, from_position=None, random_opponent=False):\\n super().__init__()\\n self.chess_env = ChessEnv(fen=True, from_position=from_position, random_opponent=random_opponent)\\n self.from_position = from_position\\n self.max_moves = max_moves\\n self.from_position = from_position\\n # self.initial_history = initial_history\\n\\n def reset(self, seed: Optional[int] = None, options: Optional[Dict] = None):\\n self.init_state, _ = self.chess_env.reset()\\n self.num_moves_made = 0\\n return (Text(preprocess_state_og(self.init_state), False),)\\n\\n def step(self, text_history: TextHistory):\\n assert text_history[-1].is_action\\n action = text_history[-1].text\\n action = postprocess_move(action)\\n st, reward, done, opp_mv = self.chess_env.step(action) \\n new_state = Text(preprocess_state_og(st), False)\\n self.num_moves_made += 1\\n if self.num_moves_made > self.max_moves:\\n done = 1\\n return (new_state,), reward, done\\n \\n def copy(self):\\n return FenChessHistoryEnv( self.max_moves, self.from_position)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from typing import Optional\nfrom JaxSeq.bucket_manager import open_with_bucket as open\nfrom JaxSeq.utils import convert_path, load_mesh, setup_experiment_save\nfrom JaxSeq.utils import BlockingStrategy, Padding, Truncation, get_weight_decay_mask\nfrom JaxSeq.models.gpt2.load import load_train_state, ModelLoadMode\nfrom LLM_RL.algorithms.ilql.base_interface import ilql_loss\nfrom transformers.generation import GenerationConfig\nfrom jaxtyping import PyTree\nfrom LLM_RL.environment import Text, text_env_eval, TextTrajectory, TextTrajectoryChain, TokenTrajectoryChain\nfrom LLM_RL.algorithms.ilql.gpt2.interface import GPT2ILQLTrain, GPT2ILQLInference\nfrom LLM_RL.algorithms.value_rl_base.gpt2.interface import GPT2ValuePolicy, GPT2ValueRLInference\nfrom LLM_RL.heads.mlp_head import load_train_state_from_config as load_head_train_state_from_config\nfrom LLM_RL.heads.mlp_head import MLPHeadConfig\nfrom JaxSeq.shard_model import shard_params_from_params\nfrom LLM_RL.algorithms.ilql.data import ILQLDataset\nfrom LLM_RL.algorithms.ilql.data import ILQLIterableDataset\nfrom functools import partial\nfrom JaxSeq.logs import pull_logs\nfrom LLM_RL.algorithms.ilql.train import eval_loss, train_loop\nfrom LLM_RL.algorithms.ilql.data import ILQLData, ILQLDataset\nfrom JaxSeq.utils import multihost_device_get\nfrom transformers import GPT2TokenizerFast\nfrom IPython import embed\nfrom llm_rl_scripts.chess.env.env import FenChessHistoryEnv\nimport tyro\nimport jax\nimport jax.numpy as jnp\nimport os\nimport optax\nimport pickle as pkl\nimport re\nimport json\nimport random"},"token_num":{"kind":"number","value":21116,"string":"21,116"},"cropped_code":{"kind":"string","value":"\ndef main(\n model_load_mode: ModelLoadMode, \n model_load_path: str, \n train_data_path: str, \n\n /, # Mark the end of positional arguments.\n\n exp_name: Optional[str]=None, \n outputs_path: Optional[str]=None, \n\n data_mesh_shape: int=1, \n fsdp_mesh_shape: int=1, \n model_mesh_shape: int=-1, \n\n use_wandb: bool=True, \n wandb_project: Optional[str]=\"llm_rl_repo_endgames_ilql\", \n\n n_rounds: int=1, \n epochs: int=1, \n max_steps: Optional[int]=None, \n \n lr: float=1e-4, \n weight_decay: float=0.0, \n tau: float=0.7,\n cql_weight: float=1.0,\n gamma: float=0.99,\n\n train_bsize: int=32, \n grad_accum_steps: int=1, \n\n gradient_checkpointing: bool=False, \n gradient_checkpointing_policy: str='nothing_saveable', \n\n max_length: int=160, \n\n log_every: int=256, \n eval_every_steps: Optional[int]=None, \n eval_every_epochs: Optional[int]=10, \n eval_at_beginning: bool=False, \n eval_at_end: bool=True, \n\n save_every_steps: Optional[int]=None, \n save_every_epochs: Optional[int]=5, \n save_at_beginning: bool=False, \n save_at_end: bool=True, \n save_best: bool=False, \n max_checkpoints: Optional[int]=5, \n save_train_state: bool=True, \n save_bf16: bool=True, \n\n policy_max_input_length: int=256, \n policy_max_output_length: int=256, \n policy_do_sample: bool=True, \n policy_num_beams: int=1, \n policy_temperature: Optional[float]=None, \n policy_top_p: Optional[float]=None, \n policy_top_k: Optional[int]=None, \n\n force_pad_embeddings: bool=False, \n\n should_restore_loop_state: bool=False, \n reranker: bool=True\n):\n input_args = locals()\n print(input_args)\n\n tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')\n tokenizer.add_special_tokens({'pad_token': '<|pad|>'})\n\n mesh = load_mesh((data_mesh_shape, fsdp_mesh_shape, model_mesh_shape), ('dp', 'fsdp', 'mp'))\n is_main_process = jax.process_index() == 0\n print(f\"Mesh: {mesh}\")\n print(f\"Is main process: {is_main_process}\")\n \n def ilql_data_generator(data_name):\n with open(data_name, \"r\") as f:\n for item in f:\n obj = json.loads(item)\n # curr_chain = TextTrajectory()\n # starting with the last element\n last_trajectory = TextTrajectory([Text(obj[-1][\"state\"], False), Text(obj[-1][\"action\"], True)], \n [0, obj[-1][\"reward\"]], True)\n curr_chain = TextTrajectoryChain(text_trajectory=last_trajectory, next=None)\n # curr_chain.next = curr_chain\n for traj in reversed(obj): # iterate through move history backwards except for last transition\n # embed()\n prev_trajectory = TextTrajectory([Text(traj[\"state\"], False), Text(traj[\"action\"], True)], \n [0, traj[\"reward\"]], False)\n curr_chain = TextTrajectoryChain(text_trajectory=prev_trajectory, next=curr_chain)\n"},"all_code":{"kind":"string","value":"\ndef main(\n model_load_mode: ModelLoadMode, \n model_load_path: str, \n train_data_path: str, \n\n /, # Mark the end of positional arguments.\n\n exp_name: Optional[str]=None, \n outputs_path: Optional[str]=None, \n\n data_mesh_shape: int=1, \n fsdp_mesh_shape: int=1, \n model_mesh_shape: int=-1, \n\n use_wandb: bool=True, \n wandb_project: Optional[str]=\"llm_rl_repo_endgames_ilql\", \n\n n_rounds: int=1, \n epochs: int=1, \n max_steps: Optional[int]=None, \n \n lr: float=1e-4, \n weight_decay: float=0.0, \n tau: float=0.7,\n cql_weight: float=1.0,\n gamma: float=0.99,\n\n train_bsize: int=32, \n grad_accum_steps: int=1, \n\n gradient_checkpointing: bool=False, \n gradient_checkpointing_policy: str='nothing_saveable', \n\n max_length: int=160, \n\n log_every: int=256, \n eval_every_steps: Optional[int]=None, \n eval_every_epochs: Optional[int]=10, \n eval_at_beginning: bool=False, \n eval_at_end: bool=True, \n\n save_every_steps: Optional[int]=None, \n save_every_epochs: Optional[int]=5, \n save_at_beginning: bool=False, \n save_at_end: bool=True, \n save_best: bool=False, \n max_checkpoints: Optional[int]=5, \n save_train_state: bool=True, \n save_bf16: bool=True, \n\n policy_max_input_length: int=256, \n policy_max_output_length: int=256, \n policy_do_sample: bool=True, \n policy_num_beams: int=1, \n policy_temperature: Optional[float]=None, \n policy_top_p: Optional[float]=None, \n policy_top_k: Optional[int]=None, \n\n force_pad_embeddings: bool=False, \n\n should_restore_loop_state: bool=False, \n reranker: bool=True\n):\n input_args = locals()\n print(input_args)\n\n tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')\n tokenizer.add_special_tokens({'pad_token': '<|pad|>'})\n\n mesh = load_mesh((data_mesh_shape, fsdp_mesh_shape, model_mesh_shape), ('dp', 'fsdp', 'mp'))\n is_main_process = jax.process_index() == 0\n print(f\"Mesh: {mesh}\")\n print(f\"Is main process: {is_main_process}\")\n \n def ilql_data_generator(data_name):\n with open(data_name, \"r\") as f:\n for item in f:\n obj = json.loads(item)\n # curr_chain = TextTrajectory()\n # starting with the last element\n last_trajectory = TextTrajectory([Text(obj[-1][\"state\"], False), Text(obj[-1][\"action\"], True)], \n [0, obj[-1][\"reward\"]], True)\n curr_chain = TextTrajectoryChain(text_trajectory=last_trajectory, next=None)\n # curr_chain.next = curr_chain\n for traj in reversed(obj): # iterate through move history backwards except for last transition\n # embed()\n prev_trajectory = TextTrajectory([Text(traj[\"state\"], False), Text(traj[\"action\"], True)], \n [0, traj[\"reward\"]], False)\n curr_chain = TextTrajectoryChain(text_trajectory=prev_trajectory, next=curr_chain)"},"next_line":{"kind":"string","value":" token_trajectory_chain = TokenTrajectoryChain.from_text_trajectory_chain(curr_chain, tokenizer)"},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-11-21 00:16:42+00:00"},"level":{"kind":"string","value":"24k"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":53,"numItemsPerPage":100,"numTotalItems":8033,"offset":5300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDExNjI4Niwic3ViIjoiL2RhdGFzZXRzL3RpYW55YW5nL3JlcG9iZW5jaF9weXRob25fdjEuMSIsImV4cCI6MTc3NDExOTg4NiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.pxS20TUELlLWZpA202pHLjAhOSwTpdAzEY4l86V5yWm5iZkvyNw62G_48YMkaet-KBluFQcQb5lQdN62iCt2AQ","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 |
|---|---|---|---|---|---|---|---|---|---|---|
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 | 15,247 | trigger = nack.get_trigger()
if trigger == TRIGGER_OOS:
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t But the NACK READ request indicates a loss (%d) before this packet (%d)" %\
(nack.get_roce_pkt_seq(), pkt.get_roce_pkt_seq()))
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
elif trigger == TRIGGER_TIMEOUT:
logger.info("\t\t Timeout triggered retransmission")
logger.info("\t\t But the NACK READ request indicates a loss (%d) before this packet (%d)" %\
(nack.get_roce_pkt_seq(), pkt.get_roce_pkt_seq()))
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
else:
logger.error("\t\t NACK READ request should be triggered by either OOS or timeout")
else:
# For other verbs, we can only find a NACK in case of out of sequence arriving packets
if latency_measurement.get_nack(pkt) != None:
# Out of sequence/NACK triggered retransmission
next_delivered_pkt_delay = latency_measurement.get_qp_next_delivered_pkt_latency(pkt)
nack_gen_latency = latency_measurement.get_nack_gen_latency(pkt)
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t Next delivered packet delay: %fus' % (next_delivered_pkt_delay * 1e6))
logger.info("\t\t NACK generation latency: %fus" % (nack_gen_latency * 1e6))
logger.info('\t\t NACK response latency: %fus' % (nack_resp_latency * 1e6))
elif latency_measurement.get_qp_first_nack_before_retrans(pkt) != None:
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t But the NACK indicates a loss (%d) before this packet (%d)")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
else:
logger.info("\t\t Timeout triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
def verify_results(orchestrator):
""" Verify the experiment results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
result_dir = orchestrator.result_path
num_repeats = orchestrator.num_repeats
mtu = orchestrator.traffic_conf['mtu']
msg_size = orchestrator.traffic_conf['message-size']
num_msgs_per_qp = orchestrator.traffic_conf['num-msgs-per-qp']
aggregate_pcap_filename = orchestrator.aggregate_pcap_filename
port_map = {'requester': orchestrator.requester.conf['nic']['switch-port'],
'responder': orchestrator.responder.conf['nic']['switch-port'],
'requester-mirror': orchestrator.requester_mirror.conf['nic']['switch-port'],
'responder-mirror': orchestrator.responder_mirror.conf['nic']['switch-port']}
requester_ip_list = orchestrator.get_requester_ip_list()
responder_ip_list = orchestrator.get_responder_ip_list()
for iter in range(num_repeats):
iter = str(iter)
result_logger = logging.getLogger('Analysis iter %s' % (iter))
result_logger.handlers.clear()
config_file_handler(logger=result_logger,
log_file=os.path.join(result_dir, iter, RESULT_FILENAME),
no_format=True)
result_logger.info("=" * 100)
result_logger.info("Iteration %s" % iter)
switch_msg_snapshot = os.path.join(result_dir,
iter,
switch.SWITCH_RESULT_DIR,
switch.SWITCH_MESSAGE_SNAPSHOT)
switch_state_snapshot = os.path.join(result_dir,
iter,
switch.SWITCH_RESULT_DIR,
switch.SWITCH_STATE_SNAPSHOT)
pcap_filename = os.path.join(result_dir,
iter,
host.PCAP_RESULT_DIR,
aggregate_pcap_filename)
requester_counter_start = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.REQ_START_COUNTER_FILE_NAME)
requester_counter_finish = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.REQ_FINISH_COUNTER_FILE_NAME)
responder_counter_start = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.RSP_START_COUNTER_FILE_NAME)
responder_counter_finish = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.RSP_FINISH_COUNTER_FILE_NAME)
switch_counter = SwitchCounter(switch_state_snapshot, port_map)
if orchestrator.requester.is_mlnx_nic():
requester_counter = MLNXHostCounter(requester_counter_start, requester_counter_finish)
elif orchestrator.requester.is_intel_nic():
requester_counter = IntelHostCounter(requester_counter_start, requester_counter_finish)
else:
logging.error("Unkown NIC Vendor for rdma requester.")
requester_counter = None
if orchestrator.responder.is_mlnx_nic():
responder_counter = MLNXHostCounter(responder_counter_start, responder_counter_finish)
elif orchestrator.responder.is_intel_nic():
responder_counter = IntelHostCounter(responder_counter_start, responder_counter_finish)
else:
logging.error("Unkown NIC Vendor for rdma responder.")
responder_counter = None
qp_info_list = get_qp_info_list(switch_msg_snapshot)
|
## 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:
next_delivered_pkt_delay = latency_measurement.get_qp_next_delivered_pkt_latency(pkt)
nack_gen_latency = latency_measurement.get_nack_gen_latency(pkt)
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t Next delivered packet delay: %fus' % (next_delivered_pkt_delay * 1e6))
logger.info("\t\t NACK READ request generation latency: %fus" % (nack_gen_latency * 1e6))
logger.info('\t\t NACK READ request response latency: %fus' % (nack_resp_latency * 1e6))
elif trigger == TRIGGER_TIMEOUT:
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Timeout triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t NACK READ request response latency: %fus' % (nack_resp_latency * 1e6))
else:
logger.error("\t\t NACK READ request should be triggered by either OOS or timeout")
else:
nack = latency_measurement.get_qp_first_nack_before_retrans(pkt)
if nack is None:
logger.error("\t\t Cannot find the NACK READ request to recover this lost packet")
return
trigger = nack.get_trigger()
if trigger == TRIGGER_OOS:
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t But the NACK READ request indicates a loss (%d) before this packet (%d)" %\
(nack.get_roce_pkt_seq(), pkt.get_roce_pkt_seq()))
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
elif trigger == TRIGGER_TIMEOUT:
logger.info("\t\t Timeout triggered retransmission")
logger.info("\t\t But the NACK READ request indicates a loss (%d) before this packet (%d)" %\
(nack.get_roce_pkt_seq(), pkt.get_roce_pkt_seq()))
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
else:
logger.error("\t\t NACK READ request should be triggered by either OOS or timeout")
else:
# For other verbs, we can only find a NACK in case of out of sequence arriving packets
if latency_measurement.get_nack(pkt) != None:
# Out of sequence/NACK triggered retransmission
next_delivered_pkt_delay = latency_measurement.get_qp_next_delivered_pkt_latency(pkt)
nack_gen_latency = latency_measurement.get_nack_gen_latency(pkt)
nack_resp_latency = latency_measurement.get_nack_resp_latency(pkt)
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
logger.info('\t\t Next delivered packet delay: %fus' % (next_delivered_pkt_delay * 1e6))
logger.info("\t\t NACK generation latency: %fus" % (nack_gen_latency * 1e6))
logger.info('\t\t NACK response latency: %fus' % (nack_resp_latency * 1e6))
elif latency_measurement.get_qp_first_nack_before_retrans(pkt) != None:
logger.info("\t\t Out of sequence (OOS) triggered retransmission")
logger.info("\t\t But the NACK indicates a loss (%d) before this packet (%d)")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
else:
logger.info("\t\t Timeout triggered retransmission")
logger.info("\t\t Retransmission latency: %fus" % (retrans_latency * 1e6))
def verify_results(orchestrator):
""" Verify the experiment results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
result_dir = orchestrator.result_path
num_repeats = orchestrator.num_repeats
mtu = orchestrator.traffic_conf['mtu']
msg_size = orchestrator.traffic_conf['message-size']
num_msgs_per_qp = orchestrator.traffic_conf['num-msgs-per-qp']
aggregate_pcap_filename = orchestrator.aggregate_pcap_filename
port_map = {'requester': orchestrator.requester.conf['nic']['switch-port'],
'responder': orchestrator.responder.conf['nic']['switch-port'],
'requester-mirror': orchestrator.requester_mirror.conf['nic']['switch-port'],
'responder-mirror': orchestrator.responder_mirror.conf['nic']['switch-port']}
requester_ip_list = orchestrator.get_requester_ip_list()
responder_ip_list = orchestrator.get_responder_ip_list()
for iter in range(num_repeats):
iter = str(iter)
result_logger = logging.getLogger('Analysis iter %s' % (iter))
result_logger.handlers.clear()
config_file_handler(logger=result_logger,
log_file=os.path.join(result_dir, iter, RESULT_FILENAME),
no_format=True)
result_logger.info("=" * 100)
result_logger.info("Iteration %s" % iter)
switch_msg_snapshot = os.path.join(result_dir,
iter,
switch.SWITCH_RESULT_DIR,
switch.SWITCH_MESSAGE_SNAPSHOT)
switch_state_snapshot = os.path.join(result_dir,
iter,
switch.SWITCH_RESULT_DIR,
switch.SWITCH_STATE_SNAPSHOT)
pcap_filename = os.path.join(result_dir,
iter,
host.PCAP_RESULT_DIR,
aggregate_pcap_filename)
requester_counter_start = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.REQ_START_COUNTER_FILE_NAME)
requester_counter_finish = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.REQ_FINISH_COUNTER_FILE_NAME)
responder_counter_start = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.RSP_START_COUNTER_FILE_NAME)
responder_counter_finish = os.path.join(result_dir,
iter,
host.RDMA_RESULT_DIR,
host.RSP_FINISH_COUNTER_FILE_NAME)
switch_counter = SwitchCounter(switch_state_snapshot, port_map)
if orchestrator.requester.is_mlnx_nic():
requester_counter = MLNXHostCounter(requester_counter_start, requester_counter_finish)
elif orchestrator.requester.is_intel_nic():
requester_counter = IntelHostCounter(requester_counter_start, requester_counter_finish)
else:
logging.error("Unkown NIC Vendor for rdma requester.")
requester_counter = None
if orchestrator.responder.is_mlnx_nic():
responder_counter = MLNXHostCounter(responder_counter_start, responder_counter_finish)
elif orchestrator.responder.is_intel_nic():
responder_counter = IntelHostCounter(responder_counter_start, responder_counter_finish)
else:
logging.error("Unkown NIC Vendor for rdma responder.")
responder_counter = None
qp_info_list = get_qp_info_list(switch_msg_snapshot) | packet_list = get_packet_list(pcap_filename) | 5 | 2023-12-09 08:21:14+00:00 | 24k |
ebb-earl-co/tidal-wave | tidal_wave/main.py | [
{
"identifier": "login",
"path": "tidal_wave/login.py",
"snippet": "def login(\n audio_format: AudioFormat,\n) -> Tuple[Optional[requests.Session], Optional[AudioFormat]]:\n \"\"\"Given a selected audio_format, either log in \"automatically\"\n via the Fire TV OAuth 2.0 flow, or ask for an Andr... | from contextlib import closing
from pathlib import Path
from typing import Optional, Union
from .login import login, AudioFormat, LogLevel
from .album import Album
from .artist import Artist
from .mix import Mix
from .playlist import Playlist
from .track import Track
from .video import Video
from .models import (
match_tidal_url,
TidalAlbum,
TidalArtist,
TidalMix,
TidalPlaylist,
TidalTrack,
TidalVideo,
)
from platformdirs import user_music_path
from typing_extensions import Annotated
import logging
import typer | 17,473 |
app = typer.Typer()
@app.command()
def main(
tidal_url: Annotated[
str,
typer.Argument(
help="The Tidal album or artist or mix or playlist or track or video to download"
),
],
audio_format: Annotated[
AudioFormat, typer.Option(case_sensitive=False)
] = AudioFormat.lossless.value,
output_directory: Annotated[
Path,
typer.Argument(
help="The parent directory under which directory(ies) of files will be written"
),
] = user_music_path(),
loglevel: Annotated[
LogLevel, typer.Option(case_sensitive=False)
] = LogLevel.info.value,
include_eps_singles: Annotated[
bool,
typer.Option(
"--include-eps-singles",
help="No-op unless passing TIDAL artist. Whether to include artist's EPs and singles with albums",
),
] = False,
):
logging.basicConfig(
format="%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d:%H:%M:%S",
level=logging.getLevelName(loglevel.value),
)
logger = logging.getLogger(__name__)
tidal_resource: Optional[
Union[TidalAlbum, TidalMix, TidalPlaylist, TidalTrack, TidalVideo]
] = match_tidal_url(tidal_url)
if tidal_resource is None:
logger.critical(
f"Cannot parse '{tidal_url}' as a TIDAL album, artist, mix, playlist, track, or video URL"
)
raise typer.Exit(code=1)
s, audio_format = login(audio_format=audio_format)
if s is None:
raise typer.Exit(code=1)
with closing(s) as session:
if isinstance(tidal_resource, TidalTrack):
track = Track(track_id=tidal_resource.tidal_id)
track.get(
session=session, audio_format=audio_format, out_dir=output_directory
)
if loglevel == LogLevel.debug:
track.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalAlbum):
album = Album(album_id=tidal_resource.tidal_id)
album.get(
session=session, audio_format=audio_format, out_dir=output_directory
)
if loglevel == LogLevel.debug:
album.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalArtist):
artist = Artist(artist_id=tidal_resource.tidal_id)
artist.get(
session=session,
audio_format=audio_format,
out_dir=output_directory,
include_eps_singles=include_eps_singles,
)
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalVideo):
video = Video(video_id=tidal_resource.tidal_id)
video.get(session=session, out_dir=output_directory)
if loglevel == LogLevel.debug:
video.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalPlaylist):
|
app = typer.Typer()
@app.command()
def main(
tidal_url: Annotated[
str,
typer.Argument(
help="The Tidal album or artist or mix or playlist or track or video to download"
),
],
audio_format: Annotated[
AudioFormat, typer.Option(case_sensitive=False)
] = AudioFormat.lossless.value,
output_directory: Annotated[
Path,
typer.Argument(
help="The parent directory under which directory(ies) of files will be written"
),
] = user_music_path(),
loglevel: Annotated[
LogLevel, typer.Option(case_sensitive=False)
] = LogLevel.info.value,
include_eps_singles: Annotated[
bool,
typer.Option(
"--include-eps-singles",
help="No-op unless passing TIDAL artist. Whether to include artist's EPs and singles with albums",
),
] = False,
):
logging.basicConfig(
format="%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d:%H:%M:%S",
level=logging.getLevelName(loglevel.value),
)
logger = logging.getLogger(__name__)
tidal_resource: Optional[
Union[TidalAlbum, TidalMix, TidalPlaylist, TidalTrack, TidalVideo]
] = match_tidal_url(tidal_url)
if tidal_resource is None:
logger.critical(
f"Cannot parse '{tidal_url}' as a TIDAL album, artist, mix, playlist, track, or video URL"
)
raise typer.Exit(code=1)
s, audio_format = login(audio_format=audio_format)
if s is None:
raise typer.Exit(code=1)
with closing(s) as session:
if isinstance(tidal_resource, TidalTrack):
track = Track(track_id=tidal_resource.tidal_id)
track.get(
session=session, audio_format=audio_format, out_dir=output_directory
)
if loglevel == LogLevel.debug:
track.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalAlbum):
album = Album(album_id=tidal_resource.tidal_id)
album.get(
session=session, audio_format=audio_format, out_dir=output_directory
)
if loglevel == LogLevel.debug:
album.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalArtist):
artist = Artist(artist_id=tidal_resource.tidal_id)
artist.get(
session=session,
audio_format=audio_format,
out_dir=output_directory,
include_eps_singles=include_eps_singles,
)
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalVideo):
video = Video(video_id=tidal_resource.tidal_id)
video.get(session=session, out_dir=output_directory)
if loglevel == LogLevel.debug:
video.dump()
raise typer.Exit(code=0)
elif isinstance(tidal_resource, TidalPlaylist): | playlist = Playlist(playlist_id=tidal_resource.tidal_id) | 6 | 2023-12-12 21:50:25+00:00 | 24k |
lbcb-sci/GNNome | pipeline.py | [
{
"identifier": "train",
"path": "train.py",
"snippet": "def train(train_path, valid_path, out, assembler, overfit=False, dropout=None, seed=None, resume=False):\n hyperparameters = get_hyperparameters()\n if seed is None:\n seed = hyperparameters['seed']\n num_epochs = hyperparameters['... | import argparse
import gzip
import os
import re
import pickle
import subprocess
import time
import torch
import requests
import graph_dataset
import evaluate
import train_valid_chrs
import hyperparameters
from datetime import datetime
from tqdm import tqdm
from Bio import SeqIO, AlignIO
from train import train
from inference import inference | 14,681 |
def evaluate_genome(eval_path, assembler, model_path, asm_path, ref_path, genome, save_dir):
real_path = os.path.join(eval_path, 'real')
save_dir = os.path.join(asm_path, 'real', assembler, save_dir)
print(f'New genome')
chr_path = os.path.join(real_path, genome)
save_path = os.path.join(save_dir, genome)
if not os.path.isdir(save_path):
os.makedirs(save_path)
os.mkdir(os.path.join(save_path, f'assembly'))
os.mkdir(os.path.join(save_path, f'decode'))
os.mkdir(os.path.join(save_path, f'reports'))
inference(chr_path, model_path, assembler, save_path)
ref = ref_path
idx = ref_path + '.fai'
asm = os.path.join(save_path, f'assembly', f'0_assembly.fasta')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
paf = os.path.join(save_path, f'asm.paf')
p = evaluate.run_minigraph(ref, asm, paf)
p.wait()
p = evaluate.parse_pafs(idx, report, paf)
p.wait()
evaluate.parse_minigraph_for_full(save_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--out', type=str, default=None, help='Output name for models')
parser.add_argument('--overfit', action='store_true', default=False, help='Overfit on the chromosomes in the train directory')
args = parser.parse_args()
out = args.out
overfit = args.overfit
hyperparams = hyperparameters.get_hyperparameters()
data_path = hyperparams['data_path'] # Location of the master database (storage)
temp_path = hyperparams['temp_path'] # Location where the data will be temporarily stored for training
eval_path = hyperparams['eval_path'] # Location where the synth and real evaluation data is stored
refs_path = hyperparams['refs_path'] # Location where the references are stored - local because everythin else can be generated from this
asms_path = hyperparams['asms_path'] # Where the assemblies and other inference info will be stored
assembler = hyperparams['assembler'] # Which assembler we are using, currently: raven/hifiasm
models_path = hyperparams['models_path']
threads = hyperparams['num_threads']
# dataset = hyperparams['dataset'] # Which dataset we are using, currently it's only chm13
data_path_ont = hyperparams['data_path_ont']
eval_path_ont = hyperparams['eval_path_ont']
initials = hyperparams['initials']
time_start = datetime.now()
if out is None:
timestamp = time_start.strftime('%Y-%b-%d-%H-%M-%S')
out = f'{timestamp}_{initials}'
else:
timestamp = time_start.strftime('%y-%m-%d')
out = f'{timestamp}_{initials}_{out}'
# Model name must start with the date when the model was trained, in the yy-mm-dd format
# Following is the underscore and a name of the model
# E.g., 22-10-31_modelA
# All the info about the training (train/valid data, hyperparameters, etc.) should be stored in the logbook
# You can also include them in the model name, but they NEED to be stored in the logbook!
model_name = out
# In the inference, model_name represents the model used for evaluation
# All the inference data (predictions, walks, assemblies, and reports)
# Will be stored in a directory with name {model_name}_{decoding}
# Suffix should indicate info about the decoding
strategy = hyperparams['strategy']
B = hyperparams['B']
num_decoding_paths = hyperparams['num_decoding_paths']
if strategy == 'greedy':
save_dir = f'{model_name}_Gx{num_decoding_paths}'
elif strategy == 'beamsearch':
save_dir = f'{model_name}_B{B}x{num_decoding_paths}'
dicts = train_valid_chrs.get_config()
train_dict = dicts['train_dict']
valid_dict = dicts['valid_dict']
test_dict = dicts['test_dict']
train_dict_ont = dicts['train_dict_ont']
valid_dict_ont = dicts['valid_dict_ont']
test_dict_ont = {}
specs = {
'threads': threads,
'filter': 0.99,
'out': 'assembly.fasta',
'assembler': assembler,
}
torch.set_num_threads(threads)
model_path = os.path.join(models_path, f'model_{model_name}.pt')
all_chr = merge_dicts(train_dict, valid_dict, test_dict)
all_chr_ont = merge_dicts(train_dict_ont, valid_dict_ont)
# file_structure_setup(data_path, refs_path)
# download_reference(refs_path)
# simulate_reads_hifi(data_path, refs_path, all_chr, assembler)
# simulate_reads_combo(data_path, refs_path, all_chr, assembler)
# generate_graphs_hifi(data_path, all_chr, assembler)
# simulate_reads_ont(data_path_ont, refs_path, all_chr_ont, 'raven')
# generate_graphs_ont(data_path_ont, all_chr_ont, 'raven')
# exit(0)
if overfit:
train_path, valid_path, test_path = train_valid_split(data_path, eval_path, temp_path, assembler, train_dict, valid_dict, test_dict, out, overfit=True)
| #### DEPRECATED ####
def change_description(file_path):
new_fasta = []
for record in SeqIO.parse(file_path, file_path[-5:]): # 'fasta' for FASTA file, 'fastq' for FASTQ file
des = record.description.split(",")
id = des[0][5:]
if des[1] == "forward":
strand = '+'
else:
strand = '-'
position = des[2][9:].split("-")
start = position[0]
end = position[1]
record.id = id
record.description = f'strand={strand} start={start} end={end}'
new_fasta.append(record)
SeqIO.write(new_fasta, file_path, "fasta")
def change_description2(fastq_path, maf_path, chr):
chr = int(chr[3:])
reads = {r.id: r for r in SeqIO.parse(fastq_path, 'fastq')}
# print(len(reads))
# counter = 0
for align in AlignIO.parse(maf_path, 'maf'):
ref, read_m = align
start = ref.annotations['start']
end = start + ref.annotations['size']
strand = '+' if read_m.annotations['strand'] == 1 else '-'
description = f'strand={strand} start={start} end={end} chr={chr}'
reads[read_m.id].id += f'_chr{chr}'
reads[read_m.id].name += f'_chr{chr}'
reads[read_m.id].description = description
# counter += 1
# print(counter)
fasta_path = fastq_path[:-1] + 'a'
SeqIO.write(list(reads.values()), fasta_path, 'fasta')
os.remove(fastq_path)
return fasta_path
def create_chr_dirs(pth):
for i in range(1, 24):
if i == 23:
i = 'X'
subprocess.run(f'mkdir chr{i}', shell=True, cwd=pth)
subprocess.run(f'mkdir raw raven hifiasm', shell=True, cwd=os.path.join(pth, f'chr{i}'))
subprocess.run(f'mkdir processed info output graphia', shell=True, cwd=os.path.join(pth, f'chr{i}/raven'))
subprocess.run(f'mkdir processed info output graphia', shell=True, cwd=os.path.join(pth, f'chr{i}/hifiasm'))
def merge_dicts(d1, d2, d3={}):
keys = {*d1, *d2, *d3}
merged = {key: d1.get(key, 0) + d2.get(key, 0) + d3.get(key, 0) for key in keys}
return merged
# -1. Set up the data file structure
def file_structure_setup(data_path, refs_path):
# TODO: Do something with this!
return
print(f'SETUP::filesystem:: Create directories for storing data')
if not os.path.isdir(data_path):
os.makedirs(data_path)
if 'CHM13' not in os.listdir(refs_path):
os.mkdir(os.path.join(refs_path, 'CHM13'))
if 'chromosomes' not in os.listdir(refs_path):
os.mkdir(os.path.join(refs_path, 'chromosomes'))
if 'simulated_hifi' not in os.listdir(data_path):
os.mkdir(os.path.join(data_path, 'simulated_hifi'))
create_chr_dirs(os.path.join(data_path, 'simulated_hifi'))
if 'simulated_ont' not in os.listdir(data_path):
os.mkdir(os.path.join(data_path, 'simulated_ont'))
create_chr_dirs(os.path.join(data_path, 'simulated_ont'))
# if 'real' not in os.listdir(data_path):
# subprocess.run(f'bash download_dataset.sh {data_path}', shell=True)
# os.mkdir(os.path.join(data_path, 'real'))
# create_chr_dirs(os.path.join(data_path, 'real'))
if 'experiments' not in os.listdir(data_path):
os.mkdir(os.path.join(data_path, 'experiments'))
# 0. Download the CHM13 if necessary
def download_reference(refs_path):
chm_path = os.path.join(refs_path, 'CHM13')
chr_path = os.path.join(refs_path, 'chromosomes')
chm13_url = 'https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/chm13.draft_v1.1.fasta.gz'
chm13_path = os.path.join(chm_path, 'chm13.draft_v1.1.fasta.gz')
if len(os.listdir(chm_path)) == 0:
# Download the CHM13 reference
# Code for tqdm from: https://stackoverflow.com/questions/37573483/progress-bar-while-download-file-over-http-with-requests
print(f'SETUP::download:: CHM13 not found! Downloading...')
response = requests.get(chm13_url, stream=True)
total_size_in_bytes= int(response.headers.get('content-length', 0))
block_size = 1024 #1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(chm13_path, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
print("ERROR, something went wrong")
if len(os.listdir(chr_path)) == 0:
# Parse the CHM13 into individual chromosomes
print(f'SETUP::download:: Split CHM13 per chromosome')
with gzip.open(chm13_path, 'rt') as f:
for record in SeqIO.parse(f, 'fasta'):
SeqIO.write(record, os.path.join(chr_path, f'{record.id}.fasta'), 'fasta')
def handle_pbsim_output(idx, chrN, chr_raw_path, combo=False):
if combo == True:
idx = chrN
subprocess.run(f'mv {idx}_0001.fastq {idx}.fastq', shell=True, cwd=chr_raw_path)
subprocess.run(f'mv {idx}_0001.maf {idx}.maf', shell=True, cwd=chr_raw_path)
subprocess.run(f'rm {idx}_0001.ref', shell=True, cwd=chr_raw_path)
fastq_path = os.path.join(chr_raw_path, f'{idx}.fastq')
maf_path = os.path.join(chr_raw_path, f'{idx}.maf')
print(f'Adding positions for training...')
fasta_path = change_description2(fastq_path, maf_path, chr=chrN) # Extract positional info from the MAF file
print(f'Removing the MAF file...')
subprocess.run(f'rm {idx}.maf', shell=True, cwd=chr_raw_path)
if combo:
return fasta_path
else:
return None
# 1. Simulate the sequences - HiFi
def simulate_reads_hifi(data_path, refs_path, chr_dict, assembler):
print(f'SETUP::simulate')
if 'vendor' not in os.listdir():
os.mkdir('vendor')
pbsim3_dir = f'/home/vrcekl/pbsim3'
data_path = os.path.abspath(data_path)
for chrN_flag, n_need in chr_dict.items():
if chrN_flag.endswith('_r'):
continue
if '+' in chrN_flag:
continue
# elif chrN_flag.endswith('_chm13'):
# chrN = chrN_flag[:-6]
# chr_path = os.path.join(refs_path, 'CHM13', 'chromosomes')
# pbsim_path = os.path.join(data_path, 'chm13_pbsim3')
# chr_seq_path = os.path.join(chr_path, f'{chrN}.fasta')
# sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/CHM13/chm13_subsampled.fastq'
# sample_profile_id = f'chm13'
# depth = 60
# elif chrN_flag.endswith('_ncbr'):
# chrN = chrN_flag[:-5]
# chr_path = os.path.join(refs_path, 'ncoibor', 'chromosomes')
# pbsim_path = os.path.join(data_path, 'ncoibor_pbsim3')
# chr_seq_path = os.path.join(chr_path, f'{chrN}.fasta')
# sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/CHM13/chm13_subsampled.fastq'
# sample_profile_id = f'chm13'
# depth = 60
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6]
chr_path = os.path.join(refs_path, f'HG002', 'hg002_chromosomes') # TODO: redefine refs path
pbsim_path = os.path.join(data_path, 'hg002_pbsim3') # TODO: redefine data path
chr_seq_path = os.path.join(chr_path, f'{chrN}_MATERNAL.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/HG002/20kb/m64011_190830_220126.sub.fastq' # TODO: Need to provide this as an argument
sample_profile_id = f'20kb-m64011_190830_220126' # TODO: Need to provide this as an argument
depth = 60
else:
print('Give valid suffix!')
raise Exception
chr_raw_path = os.path.join(pbsim_path, f'{chrN}/raw')
chr_processed_path = os.path.join(pbsim_path, f'{chrN}/{assembler}/processed')
if not os.path.isdir(chr_raw_path):
os.makedirs(chr_raw_path)
if not os.path.isdir(chr_processed_path):
os.makedirs(chr_processed_path)
# TODO: Fix so that you can delete raw files
raw_files = {int(re.findall(r'(\d+).fast*', raw)[0]) for raw in os.listdir(chr_raw_path)}
prc_files = {int(re.findall(r'(\d+).dgl', prc)[0]) for prc in os.listdir(chr_processed_path)}
all_files = raw_files | prc_files
n_have = max(all_files) + 1 if all_files else 0
if n_need <= n_have:
continue
n_diff = n_need - n_have
print(f'SETUP::simulate:: Simulate {n_diff} datasets for {chrN_flag} with PBSIM3')
for i in range(n_diff):
idx = n_have + i
chr_save_path = os.path.join(chr_raw_path, f'{idx}.fasta')
print(f'\nStep {i}: Simulating reads {chr_save_path}')
# Use the CHM13/HG002 profile for all the chromosomes
if f'sample_profile_{sample_profile_id}.fastq' not in os.listdir(pbsim3_dir):
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample {sample_file_path} '
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{idx}', shell=True, cwd=pbsim3_dir)
else:
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{idx}', shell=True, cwd=pbsim3_dir)
handle_pbsim_output(idx, chrN, chr_raw_path)
def simulate_reads_combo(data_path, refs_path, chr_dict, assembler):
data_path = os.path.abspath(data_path)
pbsim_path = os.path.join(data_path, 'combo')
pbsim3_dir = hyperparameters.get_hyperparameters()['pbsim3_dir']
if len(pbsim3_dir) == 0:
pbsim3_dir = 'pbsim3'
for chrN_combo, n_need in chr_dict.items():
if '+' not in chrN_combo:
continue
chromosomes = chrN_combo.split('+') # chr1_chm13+chr2_chm13+chr3_chm13
chr_raw_path = os.path.join(pbsim_path, f'{chrN_combo}/raw')
if not os.path.isdir(chr_raw_path):
os.makedirs(chr_raw_path)
chr_processed_path = os.path.join(pbsim_path, f'{chrN_combo}/{assembler}/processed') # TODO: Fix so that you can delete raw files
if not os.path.isdir(chr_processed_path):
os.makedirs(chr_processed_path)
raw_files = {int(re.findall(r'(\d+).fast*', raw)[0]) for raw in os.listdir(chr_raw_path)}
prc_files = {int(re.findall(r'(\d+).dgl', prc)[0]) for prc in os.listdir(chr_processed_path)}
all_files = raw_files | prc_files
if all_files:
n_have = max(all_files) + 1
else:
n_have = 0
if n_need <= n_have:
continue
else:
n_diff = n_need - n_have
print(f'SETUP::simulate:: Simulate {n_diff} datasets for {chrN_combo} with PBSIM3')
# Simulate reads for chrN_combo n_diff times
for i in range(n_diff):
idx = n_have + i
all_reads = []
for chromosome in chromosomes:
if chromosome.endswith('_chm13'):
chrN = chromosome[:-6]
chr_path = os.path.join(refs_path, 'CHM13', 'chromosomes')
chr_seq_path = os.path.join(chr_path, f'{chrN}.fasta')
chr_save_path = os.path.join(chr_raw_path, f'{chrN}.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/CHM13/chm13_subsampled.fastq'
sample_profile_id = f'chm13'
depth = 30
elif chromosome.endswith('_ncbr'):
chrN = chromosome[:-5]
chr_path = os.path.join(refs_path, 'ncoibor', 'chromosomes')
chr_seq_path = os.path.join(chr_path, f'{chrN}.fasta')
chr_save_path = os.path.join(chr_raw_path, f'{chrN}.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/CHM13/chm13_subsampled.fastq'
sample_profile_id = f'chm13'
depth = 30
elif chromosome.endswith('_hg002'):
chrN = chromosome[:-6]
chr_path = os.path.join(refs_path, 'HG002', 'hg002_chromosomes')
chr_seq_path = os.path.join(chr_path, f'{chrN}_MATERNAL.fasta')
chr_save_path = os.path.join(chr_raw_path, f'{chrN}.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data/HG002/20kb/m64011_190830_220126.sub.fastq'
sample_profile_id = f'20kb-m64011_190830_220126'
depth = 30
print(f'\nStep {i}: Simulating reads {chr_save_path}')
if f'sample_profile_{sample_profile_id}.fastq' not in os.listdir(pbsim3_dir):
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample {sample_file_path} '
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{chrN}', shell=True, cwd=pbsim3_dir)
else:
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{chrN}', shell=True, cwd=pbsim3_dir)
fasta_path = handle_pbsim_output(idx, chrN, chr_raw_path, combo=True) # Because it's combo we pass chrN instead of idx! We get idx.fasta later
# Combining individual chromosome FASTAs into a unified list
print(f'Appending the list of all the reads with {chromosome}...', end='\t')
all_reads.extend(list(SeqIO.parse(fasta_path, 'fasta')))
subprocess.run(f'rm {fasta_path}', shell=True)
print(f'Done!')
# Saving the unified FASTA file as idx.fasta
all_reads_path = os.path.join(chr_raw_path, f'{idx}.fasta')
SeqIO.write(all_reads, all_reads_path, 'fasta')
def simulate_reads_ont(data_path, refs_path, chr_dict, assembler='raven'):
print(f'SETUP::simulate')
if 'vendor' not in os.listdir():
os.mkdir('vendor')
pbsim3_dir = f'/home/vrcekl/pbsim3'
data_path = os.path.abspath(data_path)
for chrN_flag, n_need in chr_dict.items():
if chrN_flag.endswith('_r'): # Training on real reads
continue
if '+' in chrN_flag: # Training on combined synthetic chromosomes
continue
elif chrN_flag.endswith('_ncbr'): # Training on synthetic ncoibor chromosomes
continue
elif chrN_flag.endswith('_chm13'):
chrN = chrN_flag[:-6] # chrN_chm13
chr_path = os.path.join(refs_path, 'CHM13', 'chromosomes')
pbsim_path = os.path.join(data_path, 'chm13_pbsim3')
chr_seq_path = os.path.join(chr_path, f'{chrN}.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data-DELETE/CHM13/ONT/chm13_ont-subsampled_2M_trimmed.fastq'
sample_profile_id = f'chm13-ont'
depth = 120
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6] # chrN_hg002
chr_path = os.path.join(refs_path, 'HG002', 'hg002_chromosomes')
pbsim_path = os.path.join(data_path, 'hg002_pbsim3')
chr_seq_path = os.path.join(chr_path, f'{chrN}_MATERNAL.fasta')
sample_file_path = f'/mnt/sod2-project/csb4/wgs/lovro/sequencing_data-DELETE/CHM13/ONT/chm13_ont-subsampled_2M_trimmed.fastq'
sample_profile_id = f'chm13-ont'
depth = 120
else:
print(f'Chromosome suffix incorrect!')
raise Exception
chr_raw_path = os.path.join(pbsim_path, f'{chrN}/raw')
chr_processed_path = os.path.join(pbsim_path, f'{chrN}/{assembler}/processed')
if not os.path.isdir(chr_raw_path):
os.makedirs(chr_raw_path)
if not os.path.isdir(chr_processed_path):
os.makedirs(chr_processed_path)
# TODO: Fix so that you can delete raw files
raw_files = {int(re.findall(r'(\d+).fast*', raw)[0]) for raw in os.listdir(chr_raw_path)}
prc_files = {int(re.findall(r'(\d+).dgl', prc)[0]) for prc in os.listdir(chr_processed_path)}
all_files = raw_files | prc_files
n_have = max(all_files) + 1 if all_files else 0
if n_need <= n_have:
continue
n_diff = n_need - n_have
print(f'SETUP::simulate:: Simulate {n_diff} datasets for {chrN} with PBSIM3')
for i in range(n_diff):
idx = n_have + i
chr_save_path = os.path.join(chr_raw_path, f'{idx}.fasta')
print(f'\nStep {i}: Simulating reads {chr_save_path}')
if f'sample_profile_{sample_profile_id}.fastq' in os.listdir(pbsim3_dir):
# Use the CHM13 profile for all the chromosomes
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{idx}', shell=True, cwd=pbsim3_dir)
else:
subprocess.run(f'./src/pbsim --strategy wgs --method sample --depth {depth} --genome {chr_seq_path} ' \
f'--sample {sample_file_path} ' \
f'--sample-profile-id {sample_profile_id} --prefix {chr_raw_path}/{idx}', shell=True, cwd=pbsim3_dir)
handle_pbsim_output(idx, chrN, chr_raw_path)
# 2. Generate the graphs
def generate_graphs_hifi(data_path, chr_dict, assembler):
print(f'SETUP::generate')
if 'raven' not in os.listdir('vendor'):
print(f'SETUP::generate:: Download Raven')
subprocess.run(f'git clone -b print_graphs https://github.com/lbcb-sci/raven', shell=True, cwd='vendor')
subprocess.run(f'cmake -S ./ -B./build -DRAVEN_BUILD_EXE=1 -DCMAKE_BUILD_TYPE=Release', shell=True, cwd='vendor/raven')
subprocess.run(f'cmake --build build', shell=True, cwd='vendor/raven')
data_path = os.path.abspath(data_path)
for chrN_flag, n_need in chr_dict.items():
if '+' in chrN_flag:
chrN = chrN_flag # Called chrN_combo in simulate_reads_combo function
chr_sim_path = os.path.join(data_path, 'combo', f'{chrN}')
elif chrN_flag.endswith('_r'):
chrN = chrN_flag[:-2]
chr_sim_path = os.path.join(data_path, 'real', f'{chrN}')
elif chrN_flag.endswith('_chm13'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(data_path, 'chm13_pbsim3', f'{chrN}')
elif chrN_flag.endswith('_ncbr'):
chrN = chrN_flag[:-5]
chr_sim_path = os.path.join(data_path, 'ncoibor_pbsim3', f'{chrN}')
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(data_path, 'hg002_pbsim3', f'{chrN}')
else:
print(f'Give valid suffix')
raise Exception
chr_raw_path = os.path.join(chr_sim_path, 'raw')
chr_prc_path = os.path.join(chr_sim_path, f'{assembler}/processed')
n_raw = len(os.listdir(chr_raw_path))
n_prc = len(os.listdir(chr_prc_path))
n_diff = max(0, n_raw - n_prc)
print(f'SETUP::generate:: Generate {n_diff} graphs for {chrN}')
specs = {
'threads': 32,
'filter': 0.99,
'out': 'assembly.fasta',
'assembler': assembler,
}
graph_dataset.AssemblyGraphDataset_HiFi(chr_sim_path, nb_pos_enc=None, assembler=assembler, specs=specs, generate=True)
def generate_graphs_ont(data_path, chr_dict, assembler):
print(f'SETUP::generate')
if 'raven' not in os.listdir('vendor'):
print(f'SETUP::generate:: Download Raven')
subprocess.run(f'git clone -b print_graphs https://github.com/lbcb-sci/raven', shell=True, cwd='vendor')
subprocess.run(f'cmake -S ./ -B./build -DRAVEN_BUILD_EXE=1 -DCMAKE_BUILD_TYPE=Release', shell=True, cwd='vendor/raven')
subprocess.run(f'cmake --build build', shell=True, cwd='vendor/raven')
data_path = os.path.abspath(data_path)
for chrN_flag, n_need in chr_dict.items():
if '+' in chrN_flag:
chrN = chrN_flag # Called chrN_combo in simulate_reads_combo function
chr_sim_path = os.path.join(data_path, 'combo', f'{chrN}')
elif chrN_flag.endswith('_r'):
chrN = chrN_flag[:-2]
chr_sim_path = os.path.join(data_path, 'real', f'{chrN}')
elif chrN_flag.endswith('_chm13'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(data_path, 'chm13_pbsim3', f'{chrN}')
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(data_path, 'hg002_pbsim3', f'{chrN}')
else:
print(f'Chromosome suffix incorrect!')
raise Exception
chr_raw_path = os.path.join(chr_sim_path, 'raw')
chr_prc_path = os.path.join(chr_sim_path, f'{assembler}/processed')
n_raw = len(os.listdir(chr_raw_path))
n_prc = len(os.listdir(chr_prc_path))
n_diff = max(0, n_raw - n_prc)
print(f'SETUP::generate:: Generate {n_diff} graphs for {chrN}')
specs = {
'threads': 32,
'filter': 0.99,
'out': 'assembly.fasta',
'assembler': 'raven',
}
graph_dataset.AssemblyGraphDataset_ONT(chr_sim_path, nb_pos_enc=None, assembler='raven', specs=specs, generate=True)
# 2.5 Train-valid-test split
def train_valid_split(data_path, eval_path, temp_path, assembler, train_dict, valid_dict, test_dict={}, out=None, overfit=False):
print(f'SETUP::split')
data_path = os.path.abspath(data_path)
eval_path = os.path.abspath(eval_path)
if overfit:
data_path = eval_path
real_path = os.path.join(eval_path, 'real')
# pbsim_path = os.path.join(data_path, 'pbsim3')
ncoibor_path = os.path.join(data_path, 'ncoibor_pbsim3')
hg002_path = os.path.join(data_path, 'hg002_pbsim3')
combo_path = os.path.join(data_path, 'combo')
chm13_path = os.path.join(data_path, 'chm13_pbsim3')
arab_path = os.path.join(data_path, 'arabidopsis_pbsim3')
zmays_path = os.path.join(data_path, 'zmays_Mo17_pbsim3')
exp_path = temp_path
if out is None:
train_path = os.path.join(exp_path, f'train', assembler)
valid_path = os.path.join(exp_path, f'valid', assembler)
test_path = os.path.join(exp_path, f'test', assembler)
else:
train_path = os.path.join(exp_path, f'train_{out}', assembler)
valid_path = os.path.join(exp_path, f'valid_{out}', assembler)
test_path = os.path.join(exp_path, f'test_{out}', assembler)
if not os.path.isdir(train_path):
os.makedirs(train_path)
subprocess.run(f'mkdir processed info', shell=True, cwd=train_path)
if not os.path.isdir(valid_path):
os.makedirs(valid_path)
subprocess.run(f'mkdir processed info', shell=True, cwd=valid_path)
if not os.path.isdir(test_path) and len(test_dict) > 0:
os.makedirs(test_path)
subprocess.run(f'mkdir processed info', shell=True, cwd=test_path)
train_g_to_chr = {} # Remember chromosomes for each graph in the dataset
train_g_to_org_g = {} # Remember index of the graph in the master dataset for each graph in this dataset
n_have = 0
if assembler == 'both':
assemblers = ['hifiasm', 'raven']
else:
assemblers = [assembler]
for assembler in assemblers:
for chrN_flag, n_need in train_dict.items():
# copy n_need datasets from chrN into train dict
if '_r' in chrN_flag and n_need > 1:
print(f'SETUP::split::WARNING Cannot copy more than one graph for real data: {chrN_flag}')
n_need = 1
print(f'SETUP::split:: Copying {n_need} graphs of {chrN_flag} - {assembler} into {train_path}')
for i in range(n_need):
if '+' in chrN_flag:
chrN = chrN_flag
chr_sim_path = os.path.join(combo_path, chrN, assembler)
elif chrN_flag.endswith('_r'):
chrN = chrN_flag[:-2]
chr_sim_path = os.path.join(real_path, 'chm13_chromosomes', chrN, assembler)
# elif chrN_flag.endswith('_pbs'):
# chrN = chrN_flag[:-4]
# chr_sim_path = os.path.join(pbsim_path, chrN, assembler)
elif chrN_flag.endswith('_ncbr'):
chrN = chrN_flag[:-5]
chr_sim_path = os.path.join(ncoibor_path, chrN, assembler)
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(hg002_path, chrN, assembler)
elif chrN_flag.endswith('_chm13'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(chm13_path, chrN, assembler)
elif chrN_flag.endswith('_arab'):
chrN = chrN_flag[:-5]
chr_sim_path = os.path.join(arab_path, chrN, assembler)
elif chrN_flag.endswith('_zmays'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(zmays_path, chrN, assembler)
else:
print(f'Give proper suffix!')
raise Exception
train_g_to_chr[n_have] = chrN
print(f'Copying {chr_sim_path}/processed/{i}.dgl into {train_path}/processed/{n_have}.dgl')
subprocess.run(f'cp {chr_sim_path}/processed/{i}.dgl {train_path}/processed/{n_have}.dgl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{i}_succ.pkl {train_path}/info/{n_have}_succ.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{i}_pred.pkl {train_path}/info/{n_have}_pred.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{i}_edges.pkl {train_path}/info/{n_have}_edges.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{i}_reads.pkl {train_path}/info/{n_have}_reads.pkl', shell=True)
train_g_to_org_g[n_have] = i
n_have += 1
pickle.dump(train_g_to_chr, open(f'{train_path}/info/g_to_chr.pkl', 'wb'))
pickle.dump(train_g_to_org_g, open(f'{train_path}/info/g_to_org_g.pkl', 'wb'))
valid_g_to_chr = {}
valid_g_to_org_g = {}
n_have = 0
for assembler in assemblers:
for chrN_flag, n_need in valid_dict.items():
# copy n_need datasets from chrN into train dict
if '_r' in chrN_flag and n_need > 1:
print(f'SETUP::split::WARNING Cannot copy more than one graph for real data: {chrN_flag}')
n_need = 1
print(f'SETUP::split:: Copying {n_need} graphs of {chrN_flag} - {assembler} into {valid_path}')
for i in range(n_need):
if '+' in chrN_flag:
chrN = chrN_flag
chr_sim_path = os.path.join(combo_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_r'):
chrN = chrN_flag[:-2]
chr_sim_path = os.path.join(real_path, 'chm13_chromosomes', chrN, assembler)
j = 0
# elif chrN_flag.endswith('_pbs'):
# chrN = chrN_flag[:-4]
# chr_sim_path = os.path.join(pbsim_path, chrN, assembler)
# j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_ncbr'):
chrN = chrN_flag[:-5]
chr_sim_path = os.path.join(ncoibor_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_hg002'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(hg002_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_chm13'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(chm13_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_arab'):
chrN = chrN_flag[:-5]
chr_sim_path = os.path.join(arab_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
elif chrN_flag.endswith('_zmays'):
chrN = chrN_flag[:-6]
chr_sim_path = os.path.join(zmays_path, chrN, assembler)
j = i + train_dict.get(chrN_flag, 0)
else:
print(f'Give proper suffix!')
raise Exception
valid_g_to_chr[n_have] = chrN
print(f'Copying {chr_sim_path}/processed/{j}.dgl into {valid_path}/processed/{n_have}.dgl')
subprocess.run(f'cp {chr_sim_path}/processed/{j}.dgl {valid_path}/processed/{n_have}.dgl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{j}_succ.pkl {valid_path}/info/{n_have}_succ.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{j}_pred.pkl {valid_path}/info/{n_have}_pred.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{j}_edges.pkl {valid_path}/info/{n_have}_edges.pkl', shell=True)
# subprocess.run(f'cp {chr_sim_path}/info/{j}_reads.pkl {valid_path}/info/{n_have}_reads.pkl', shell=True)
valid_g_to_org_g[n_have] = j
n_have += 1
pickle.dump(valid_g_to_chr, open(f'{valid_path}/info/g_to_chr.pkl', 'wb'))
pickle.dump(valid_g_to_org_g, open(f'{valid_path}/info/g_to_org_g.pkl', 'wb'))
# TODO: FIX THIS !!!!!!!!!!!!!!!!!!
train_path = os.path.join(train_path, os.path.pardir)
valid_path = os.path.join(valid_path, os.path.pardir)
test_path = os.path.join(test_path, os.path.pardir)
###################################
return train_path, valid_path, test_path
# def predict_baselines(test_path, assembler, out, model_path=None, device='cpu'):
# if model_path is None:
# model_path = os.path.abspath(f'pretrained/model_{out}.pt')
# walks_and_contigs = inference_baselines(test_path, model_path, assembler, device)
# walks_per_graph, contigs_per_graph = walks_and_contigs[0], walks_and_contigs[1]
# walks_per_graph_ol_len, contigs_per_graph_ol_len = walks_and_contigs[2], walks_and_contigs[3]
# walks_per_graph_ol_sim, contigs_per_graph_ol_sim = walks_and_contigs[4], walks_and_contigs[5]
# g_to_chr = pickle.load(open(f'{test_path}/info/g_to_chr.pkl', 'rb'))
# for idx, (contigs, contigs_ol_len, contigs_ol_sim) in enumerate(zip(contigs_per_graph, contigs_per_graph_ol_len, contigs_per_graph_ol_sim)):
# chrN = g_to_chr[idx]
# print(f'GNN: Scores')
# num_contigs, longest_contig, reconstructed, n50, ng50 = evaluate.quick_evaluation(contigs, chrN)
# evaluate.print_summary(test_path, idx, chrN, num_contigs, longest_contig, reconstructed, n50, ng50)
# print(f'Baseline: Overlap lengths')
# num_contigs, longest_contig, reconstructed, n50, ng50 = evaluate.quick_evaluation(contigs_ol_len, chrN)
# evaluate.print_summary(test_path, idx, chrN, num_contigs, longest_contig, reconstructed, n50, ng50)
# print(f'Baseline: Overlap similarities')
# num_contigs, longest_contig, reconstructed, n50, ng50 = evaluate.quick_evaluation(contigs_ol_sim, chrN)
# evaluate.print_summary(test_path, idx, chrN, num_contigs, longest_contig, reconstructed, n50, ng50)
def cleanup(train_path, valid_path):
subprocess.run(f'rm -rf {train_path}', shell=True)
subprocess.run(f'rm -rf {valid_path}', shell=True)
def evaluate_real(eval_path, assembler, model_path, asm_path, ref_path, save_dir):
real_path = os.path.join(eval_path, 'real')
save_dir = os.path.join(asm_path, 'real', assembler, save_dir)
procs = []
for i in range(1, 24):
if i == 23:
i = 'X'
print(f'\nChromosome {i}')
chr_path = os.path.join(real_path, f'chr{i}')
save_path = os.path.join(save_dir, f'chr{i}')
if not os.path.isdir(save_path):
os.makedirs(save_path)
os.mkdir(os.path.join(save_path, f'assembly'))
os.mkdir(os.path.join(save_path, f'decode'))
os.mkdir(os.path.join(save_path, f'reports'))
inference(chr_path, model_path, assembler, save_path)
ref = os.path.join(ref_path, 'CHM13', 'chromosomes', f'chr{i}.fasta')
idx = os.path.join(ref_path, 'CHM13', 'chromosomes', 'indexed', f'chr{i}.fasta.fai')
asm = os.path.join(save_path, f'assembly', f'0_assembly.fasta')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
paf = os.path.join(save_path, f'asm.paf')
p = evaluate.run_minigraph(ref, asm, paf)
procs.append(p)
for p in procs:
p.wait()
procs = []
for i in range(1, 24):
if i == 23:
i = 'X'
save_path = os.path.join(save_dir, f'chr{i}')
idx = os.path.join(ref_path, 'indexed', f'chr{i}.fasta.fai')
paf = os.path.join(save_path, f'asm.paf')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
p = evaluate.parse_pafs(idx, report, paf)
procs.append(p)
for p in procs:
p.wait()
evaluate.parse_minigraph_for_chrs(save_dir)
def evaluate_synth(eval_path, assembler, model_path, asm_path, ref_path, save_dir):
synth_path = os.path.join(eval_path, 'synth')
save_dir = os.path.join(asm_path, 'synth', assembler, save_dir)
procs = []
for i in range(1, 24):
if i == 23:
i = 'X'
print(f'\nChromosome {i}')
chr_path = os.path.join(synth_path, f'chr{i}')
save_path = os.path.join(save_dir, f'chr{i}')
if not os.path.isdir(save_path):
os.makedirs(save_path)
os.mkdir(os.path.join(save_path, f'assembly'))
os.mkdir(os.path.join(save_path, f'decode'))
os.mkdir(os.path.join(save_path, f'reports'))
inference(chr_path, model_path, assembler, save_path)
ref = os.path.join(ref_path, 'CHM13', 'chromosomes', f'chr{i}.fasta')
idx = os.path.join(ref_path, 'CHM13', 'chromosomes', 'indexed', f'chr{i}.fasta.fai')
asm = os.path.join(save_path, f'assembly', f'0_assembly.fasta')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
paf = os.path.join(save_path, f'asm.paf')
p = evaluate.run_minigraph(ref, asm, paf)
procs.append(p)
for p in procs:
p.wait()
procs = []
for i in range(1, 24):
if i == 23:
i = 'X'
save_path = os.path.join(save_dir, f'chr{i}')
idx = os.path.join(ref_path, 'indexed', f'chr{i}.fasta.fai')
paf = os.path.join(save_path, f'asm.paf')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
p = evaluate.parse_pafs(idx, report, paf)
procs.append(p)
for p in procs:
p.wait()
evaluate.parse_minigraph_for_chrs(save_dir)
def evaluate_genome(eval_path, assembler, model_path, asm_path, ref_path, genome, save_dir):
real_path = os.path.join(eval_path, 'real')
save_dir = os.path.join(asm_path, 'real', assembler, save_dir)
print(f'New genome')
chr_path = os.path.join(real_path, genome)
save_path = os.path.join(save_dir, genome)
if not os.path.isdir(save_path):
os.makedirs(save_path)
os.mkdir(os.path.join(save_path, f'assembly'))
os.mkdir(os.path.join(save_path, f'decode'))
os.mkdir(os.path.join(save_path, f'reports'))
inference(chr_path, model_path, assembler, save_path)
ref = ref_path
idx = ref_path + '.fai'
asm = os.path.join(save_path, f'assembly', f'0_assembly.fasta')
report = os.path.join(save_path, f'reports', '0_minigraph.txt')
paf = os.path.join(save_path, f'asm.paf')
p = evaluate.run_minigraph(ref, asm, paf)
p.wait()
p = evaluate.parse_pafs(idx, report, paf)
p.wait()
evaluate.parse_minigraph_for_full(save_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--out', type=str, default=None, help='Output name for models')
parser.add_argument('--overfit', action='store_true', default=False, help='Overfit on the chromosomes in the train directory')
args = parser.parse_args()
out = args.out
overfit = args.overfit
hyperparams = hyperparameters.get_hyperparameters()
data_path = hyperparams['data_path'] # Location of the master database (storage)
temp_path = hyperparams['temp_path'] # Location where the data will be temporarily stored for training
eval_path = hyperparams['eval_path'] # Location where the synth and real evaluation data is stored
refs_path = hyperparams['refs_path'] # Location where the references are stored - local because everythin else can be generated from this
asms_path = hyperparams['asms_path'] # Where the assemblies and other inference info will be stored
assembler = hyperparams['assembler'] # Which assembler we are using, currently: raven/hifiasm
models_path = hyperparams['models_path']
threads = hyperparams['num_threads']
# dataset = hyperparams['dataset'] # Which dataset we are using, currently it's only chm13
data_path_ont = hyperparams['data_path_ont']
eval_path_ont = hyperparams['eval_path_ont']
initials = hyperparams['initials']
time_start = datetime.now()
if out is None:
timestamp = time_start.strftime('%Y-%b-%d-%H-%M-%S')
out = f'{timestamp}_{initials}'
else:
timestamp = time_start.strftime('%y-%m-%d')
out = f'{timestamp}_{initials}_{out}'
# Model name must start with the date when the model was trained, in the yy-mm-dd format
# Following is the underscore and a name of the model
# E.g., 22-10-31_modelA
# All the info about the training (train/valid data, hyperparameters, etc.) should be stored in the logbook
# You can also include them in the model name, but they NEED to be stored in the logbook!
model_name = out
# In the inference, model_name represents the model used for evaluation
# All the inference data (predictions, walks, assemblies, and reports)
# Will be stored in a directory with name {model_name}_{decoding}
# Suffix should indicate info about the decoding
strategy = hyperparams['strategy']
B = hyperparams['B']
num_decoding_paths = hyperparams['num_decoding_paths']
if strategy == 'greedy':
save_dir = f'{model_name}_Gx{num_decoding_paths}'
elif strategy == 'beamsearch':
save_dir = f'{model_name}_B{B}x{num_decoding_paths}'
dicts = train_valid_chrs.get_config()
train_dict = dicts['train_dict']
valid_dict = dicts['valid_dict']
test_dict = dicts['test_dict']
train_dict_ont = dicts['train_dict_ont']
valid_dict_ont = dicts['valid_dict_ont']
test_dict_ont = {}
specs = {
'threads': threads,
'filter': 0.99,
'out': 'assembly.fasta',
'assembler': assembler,
}
torch.set_num_threads(threads)
model_path = os.path.join(models_path, f'model_{model_name}.pt')
all_chr = merge_dicts(train_dict, valid_dict, test_dict)
all_chr_ont = merge_dicts(train_dict_ont, valid_dict_ont)
# file_structure_setup(data_path, refs_path)
# download_reference(refs_path)
# simulate_reads_hifi(data_path, refs_path, all_chr, assembler)
# simulate_reads_combo(data_path, refs_path, all_chr, assembler)
# generate_graphs_hifi(data_path, all_chr, assembler)
# simulate_reads_ont(data_path_ont, refs_path, all_chr_ont, 'raven')
# generate_graphs_ont(data_path_ont, all_chr_ont, 'raven')
# exit(0)
if overfit:
train_path, valid_path, test_path = train_valid_split(data_path, eval_path, temp_path, assembler, train_dict, valid_dict, test_dict, out, overfit=True) | train(train_path, valid_path, out, assembler, overfit) | 0 | 2023-12-08 04:45:45+00:00 | 24k |
ZS-YANG/FemtoDet-v3 | mmdet/models/roi_heads/bbox_heads/multi_instance_bbox_head.py | [
{
"identifier": "BBoxHead",
"path": "mmdet/models/roi_heads/bbox_heads/bbox_head.py",
"snippet": "class BBoxHead(BaseModule):\n \"\"\"Simplest RoI head, with only two fc layers for classification and\n regression respectively.\"\"\"\n\n def __init__(self,\n with_avg_pool: bool =... | from typing import List, Optional, Tuple, Union
from mmcv.cnn import ConvModule
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor, nn
from mmdet.models.roi_heads.bbox_heads.bbox_head import BBoxHead
from mmdet.models.task_modules.samplers import SamplingResult
from mmdet.models.utils import empty_instances
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
import numpy as np
import torch
import torch.nn.functional as F | 15,729 | bbox_pred: Tensor,
img_meta: dict,
rescale: bool = False,
rcnn_test_cfg: Optional[ConfigDict] = None) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
Args:
roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
cls_score (Tensor): Box scores, has shape
(num_boxes, num_classes + 1).
bbox_pred (Tensor): Box energies / deltas. has shape
(num_boxes, num_classes * 4).
img_meta (dict): image information.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
Defaults to None
Returns:
:obj:`InstanceData`: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
cls_score = cls_score.reshape(-1, self.num_classes + 1)
bbox_pred = bbox_pred.reshape(-1, 4)
roi = roi.repeat_interleave(self.num_instance, dim=0)
results = InstanceData()
if roi.shape[0] == 0:
return empty_instances([img_meta],
roi.device,
task_type='bbox',
instance_results=[results])[0]
scores = cls_score.softmax(dim=-1) if cls_score is not None else None
img_shape = img_meta['img_shape']
bboxes = self.bbox_coder.decode(
roi[..., 1:], bbox_pred, max_shape=img_shape)
if rescale and bboxes.size(0) > 0:
assert img_meta.get('scale_factor') is not None
scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
(1, 2))
bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view(
bboxes.size()[0], -1)
if rcnn_test_cfg is None:
# This means that it is aug test.
# It needs to return the raw results without nms.
results.bboxes = bboxes
results.scores = scores
else:
roi_idx = np.tile(
np.arange(bboxes.shape[0] / self.num_instance)[:, None],
(1, self.num_instance)).reshape(-1, 1)[:, 0]
roi_idx = torch.from_numpy(roi_idx).to(bboxes.device).reshape(
-1, 1)
bboxes = torch.cat([bboxes, roi_idx], dim=1)
det_bboxes, det_scores = self.set_nms(
bboxes, scores[:, 1], rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms['iou_threshold'], rcnn_test_cfg.max_per_img)
results.bboxes = det_bboxes[:, :-1]
results.scores = det_scores
results.labels = torch.zeros_like(det_scores)
return results
@staticmethod
def set_nms(bboxes: Tensor,
scores: Tensor,
score_thr: float,
iou_threshold: float,
max_num: int = -1) -> Tuple[Tensor, Tensor]:
"""NMS for multi-instance prediction. Please refer to
https://github.com/Purkialo/CrowdDet for more details.
Args:
bboxes (Tensor): predict bboxes.
scores (Tensor): The score of each predict bbox.
score_thr (float): bbox threshold, bboxes with scores lower than it
will not be considered.
iou_threshold (float): IoU threshold to be considered as
conflicted.
max_num (int, optional): if there are more than max_num bboxes
after NMS, only top max_num will be kept. Default to -1.
Returns:
Tuple[Tensor, Tensor]: (bboxes, scores).
"""
bboxes = bboxes[scores > score_thr]
scores = scores[scores > score_thr]
ordered_scores, order = scores.sort(descending=True)
ordered_bboxes = bboxes[order]
roi_idx = ordered_bboxes[:, -1]
keep = torch.ones(len(ordered_bboxes)) == 1
ruler = torch.arange(len(ordered_bboxes))
keep = keep.to(bboxes.device)
ruler = ruler.to(bboxes.device)
while ruler.shape[0] > 0:
basement = ruler[0]
ruler = ruler[1:]
idx = roi_idx[basement]
# calculate the body overlap
basement_bbox = ordered_bboxes[:, :4][basement].reshape(-1, 4)
ruler_bbox = ordered_bboxes[:, :4][ruler].reshape(-1, 4)
| # Copyright (c) OpenMMLab. All rights reserved.
@MODELS.register_module()
class MultiInstanceBBoxHead(BBoxHead):
r"""Bbox head used in CrowdDet.
.. code-block:: none
/-> cls convs_1 -> cls fcs_1 -> cls_1
|--
| \-> reg convs_1 -> reg fcs_1 -> reg_1
|
| /-> cls convs_2 -> cls fcs_2 -> cls_2
shared convs -> shared fcs |--
| \-> reg convs_2 -> reg fcs_2 -> reg_2
|
| ...
|
| /-> cls convs_k -> cls fcs_k -> cls_k
|--
\-> reg convs_k -> reg fcs_k -> reg_k
Args:
num_instance (int): The number of branches after shared fcs.
Defaults to 2.
with_refine (bool): Whether to use refine module. Defaults to False.
num_shared_convs (int): The number of shared convs. Defaults to 0.
num_shared_fcs (int): The number of shared fcs. Defaults to 2.
num_cls_convs (int): The number of cls convs. Defaults to 0.
num_cls_fcs (int): The number of cls fcs. Defaults to 0.
num_reg_convs (int): The number of reg convs. Defaults to 0.
num_reg_fcs (int): The number of reg fcs. Defaults to 0.
conv_out_channels (int): The number of conv out channels.
Defaults to 256.
fc_out_channels (int): The number of fc out channels. Defaults to 1024.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
""" # noqa: W605
def __init__(self,
num_instance: int = 2,
with_refine: bool = False,
num_shared_convs: int = 0,
num_shared_fcs: int = 2,
num_cls_convs: int = 0,
num_cls_fcs: int = 0,
num_reg_convs: int = 0,
num_reg_fcs: int = 0,
conv_out_channels: int = 256,
fc_out_channels: int = 1024,
init_cfg: Optional[Union[dict, ConfigDict]] = None,
*args,
**kwargs) -> None:
super().__init__(*args, init_cfg=init_cfg, **kwargs)
assert (num_shared_convs + num_shared_fcs + num_cls_convs +
num_cls_fcs + num_reg_convs + num_reg_fcs > 0)
assert num_instance == 2, 'Currently only 2 instances are supported'
if num_cls_convs > 0 or num_reg_convs > 0:
assert num_shared_fcs == 0
if not self.with_cls:
assert num_cls_convs == 0 and num_cls_fcs == 0
if not self.with_reg:
assert num_reg_convs == 0 and num_reg_fcs == 0
self.num_instance = num_instance
self.num_shared_convs = num_shared_convs
self.num_shared_fcs = num_shared_fcs
self.num_cls_convs = num_cls_convs
self.num_cls_fcs = num_cls_fcs
self.num_reg_convs = num_reg_convs
self.num_reg_fcs = num_reg_fcs
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.with_refine = with_refine
# add shared convs and fcs
self.shared_convs, self.shared_fcs, last_layer_dim = \
self._add_conv_fc_branch(
self.num_shared_convs, self.num_shared_fcs, self.in_channels,
True)
self.shared_out_channels = last_layer_dim
self.relu = nn.ReLU(inplace=True)
if self.with_refine:
refine_model_cfg = {
'type': 'Linear',
'in_features': self.shared_out_channels + 20,
'out_features': self.shared_out_channels
}
self.shared_fcs_ref = MODELS.build(refine_model_cfg)
self.fc_cls_ref = nn.ModuleList()
self.fc_reg_ref = nn.ModuleList()
self.cls_convs = nn.ModuleList()
self.cls_fcs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.reg_fcs = nn.ModuleList()
self.cls_last_dim = list()
self.reg_last_dim = list()
self.fc_cls = nn.ModuleList()
self.fc_reg = nn.ModuleList()
for k in range(self.num_instance):
# add cls specific branch
cls_convs, cls_fcs, cls_last_dim = self._add_conv_fc_branch(
self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
self.cls_convs.append(cls_convs)
self.cls_fcs.append(cls_fcs)
self.cls_last_dim.append(cls_last_dim)
# add reg specific branch
reg_convs, reg_fcs, reg_last_dim = self._add_conv_fc_branch(
self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
self.reg_convs.append(reg_convs)
self.reg_fcs.append(reg_fcs)
self.reg_last_dim.append(reg_last_dim)
if self.num_shared_fcs == 0 and not self.with_avg_pool:
if self.num_cls_fcs == 0:
self.cls_last_dim *= self.roi_feat_area
if self.num_reg_fcs == 0:
self.reg_last_dim *= self.roi_feat_area
if self.with_cls:
if self.custom_cls_channels:
cls_channels = self.loss_cls.get_cls_channels(
self.num_classes)
else:
cls_channels = self.num_classes + 1
cls_predictor_cfg_ = self.cls_predictor_cfg.copy() # deepcopy
cls_predictor_cfg_.update(
in_features=self.cls_last_dim[k],
out_features=cls_channels)
self.fc_cls.append(MODELS.build(cls_predictor_cfg_))
if self.with_refine:
self.fc_cls_ref.append(MODELS.build(cls_predictor_cfg_))
if self.with_reg:
out_dim_reg = (4 if self.reg_class_agnostic else 4 *
self.num_classes)
reg_predictor_cfg_ = self.reg_predictor_cfg.copy()
reg_predictor_cfg_.update(
in_features=self.reg_last_dim[k], out_features=out_dim_reg)
self.fc_reg.append(MODELS.build(reg_predictor_cfg_))
if self.with_refine:
self.fc_reg_ref.append(MODELS.build(reg_predictor_cfg_))
if init_cfg is None:
# when init_cfg is None,
# It has been set to
# [[dict(type='Normal', std=0.01, override=dict(name='fc_cls'))],
# [dict(type='Normal', std=0.001, override=dict(name='fc_reg'))]
# after `super(ConvFCBBoxHead, self).__init__()`
# we only need to append additional configuration
# for `shared_fcs`, `cls_fcs` and `reg_fcs`
self.init_cfg += [
dict(
type='Xavier',
distribution='uniform',
override=[
dict(name='shared_fcs'),
dict(name='cls_fcs'),
dict(name='reg_fcs')
])
]
def _add_conv_fc_branch(self,
num_branch_convs: int,
num_branch_fcs: int,
in_channels: int,
is_shared: bool = False) -> tuple:
"""Add shared or separable branch.
convs -> avg pool (optional) -> fcs
"""
last_layer_dim = in_channels
# add branch specific conv layers
branch_convs = nn.ModuleList()
if num_branch_convs > 0:
for i in range(num_branch_convs):
conv_in_channels = (
last_layer_dim if i == 0 else self.conv_out_channels)
branch_convs.append(
ConvModule(
conv_in_channels, self.conv_out_channels, 3,
padding=1))
last_layer_dim = self.conv_out_channels
# add branch specific fc layers
branch_fcs = nn.ModuleList()
if num_branch_fcs > 0:
# for shared branch, only consider self.with_avg_pool
# for separated branches, also consider self.num_shared_fcs
if (is_shared
or self.num_shared_fcs == 0) and not self.with_avg_pool:
last_layer_dim *= self.roi_feat_area
for i in range(num_branch_fcs):
fc_in_channels = (
last_layer_dim if i == 0 else self.fc_out_channels)
branch_fcs.append(
nn.Linear(fc_in_channels, self.fc_out_channels))
last_layer_dim = self.fc_out_channels
return branch_convs, branch_fcs, last_layer_dim
def forward(self, x: Tuple[Tensor]) -> tuple:
"""Forward features from the upstream network.
Args:
x (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: A tuple of classification scores and bbox prediction.
- cls_score (Tensor): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
- cls_score_ref (Tensor): The cls_score after refine model.
- bbox_pred_ref (Tensor): The bbox_pred after refine model.
"""
# shared part
if self.num_shared_convs > 0:
for conv in self.shared_convs:
x = conv(x)
if self.num_shared_fcs > 0:
if self.with_avg_pool:
x = self.avg_pool(x)
x = x.flatten(1)
for fc in self.shared_fcs:
x = self.relu(fc(x))
x_cls = x
x_reg = x
# separate branches
cls_score = list()
bbox_pred = list()
for k in range(self.num_instance):
for conv in self.cls_convs[k]:
x_cls = conv(x_cls)
if x_cls.dim() > 2:
if self.with_avg_pool:
x_cls = self.avg_pool(x_cls)
x_cls = x_cls.flatten(1)
for fc in self.cls_fcs[k]:
x_cls = self.relu(fc(x_cls))
for conv in self.reg_convs[k]:
x_reg = conv(x_reg)
if x_reg.dim() > 2:
if self.with_avg_pool:
x_reg = self.avg_pool(x_reg)
x_reg = x_reg.flatten(1)
for fc in self.reg_fcs[k]:
x_reg = self.relu(fc(x_reg))
cls_score.append(self.fc_cls[k](x_cls) if self.with_cls else None)
bbox_pred.append(self.fc_reg[k](x_reg) if self.with_reg else None)
if self.with_refine:
x_ref = x
cls_score_ref = list()
bbox_pred_ref = list()
for k in range(self.num_instance):
feat_ref = cls_score[k].softmax(dim=-1)
feat_ref = torch.cat((bbox_pred[k], feat_ref[:, 1][:, None]),
dim=1).repeat(1, 4)
feat_ref = torch.cat((x_ref, feat_ref), dim=1)
feat_ref = F.relu_(self.shared_fcs_ref(feat_ref))
cls_score_ref.append(self.fc_cls_ref[k](feat_ref))
bbox_pred_ref.append(self.fc_reg_ref[k](feat_ref))
cls_score = torch.cat(cls_score, dim=1)
bbox_pred = torch.cat(bbox_pred, dim=1)
cls_score_ref = torch.cat(cls_score_ref, dim=1)
bbox_pred_ref = torch.cat(bbox_pred_ref, dim=1)
return cls_score, bbox_pred, cls_score_ref, bbox_pred_ref
cls_score = torch.cat(cls_score, dim=1)
bbox_pred = torch.cat(bbox_pred, dim=1)
return cls_score, bbox_pred
def get_targets(self,
sampling_results: List[SamplingResult],
rcnn_train_cfg: ConfigDict,
concat: bool = True) -> tuple:
"""Calculate the ground truth for all samples in a batch according to
the sampling_results.
Almost the same as the implementation in bbox_head, we passed
additional parameters pos_inds_list and neg_inds_list to
`_get_targets_single` function.
Args:
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
concat (bool): Whether to concatenate the results of all
the images in a single batch.
Returns:
Tuple[Tensor]: Ground truth for proposals in a single image.
Containing the following list of Tensors:
- labels (list[Tensor],Tensor): Gt_labels for all proposals in a
batch, each tensor in list has shape (num_proposals,) when
`concat=False`, otherwise just a single tensor has shape
(num_all_proposals,).
- label_weights (list[Tensor]): Labels_weights for
all proposals in a batch, each tensor in list has shape
(num_proposals,) when `concat=False`, otherwise just a single
tensor has shape (num_all_proposals,).
- bbox_targets (list[Tensor],Tensor): Regression target for all
proposals in a batch, each tensor in list has shape
(num_proposals, 4) when `concat=False`, otherwise just a single
tensor has shape (num_all_proposals, 4), the last dimension 4
represents [tl_x, tl_y, br_x, br_y].
- bbox_weights (list[tensor],Tensor): Regression weights for
all proposals in a batch, each tensor in list has shape
(num_proposals, 4) when `concat=False`, otherwise just a
single tensor has shape (num_all_proposals, 4).
"""
labels = []
bbox_targets = []
bbox_weights = []
label_weights = []
for i in range(len(sampling_results)):
sample_bboxes = torch.cat([
sampling_results[i].pos_gt_bboxes,
sampling_results[i].neg_gt_bboxes
])
sample_priors = sampling_results[i].priors
sample_priors = sample_priors.repeat(1, self.num_instance).reshape(
-1, 4)
sample_bboxes = sample_bboxes.reshape(-1, 4)
if not self.reg_decoded_bbox:
_bbox_targets = self.bbox_coder.encode(sample_priors,
sample_bboxes)
else:
_bbox_targets = sample_priors
_bbox_targets = _bbox_targets.reshape(-1, self.num_instance * 4)
_bbox_weights = torch.ones(_bbox_targets.shape)
_labels = torch.cat([
sampling_results[i].pos_gt_labels,
sampling_results[i].neg_gt_labels
])
_labels_weights = torch.ones(_labels.shape)
bbox_targets.append(_bbox_targets)
bbox_weights.append(_bbox_weights)
labels.append(_labels)
label_weights.append(_labels_weights)
if concat:
labels = torch.cat(labels, 0)
label_weights = torch.cat(label_weights, 0)
bbox_targets = torch.cat(bbox_targets, 0)
bbox_weights = torch.cat(bbox_weights, 0)
return labels, label_weights, bbox_targets, bbox_weights
def loss(self, cls_score: Tensor, bbox_pred: Tensor, rois: Tensor,
labels: Tensor, label_weights: Tensor, bbox_targets: Tensor,
bbox_weights: Tensor, **kwargs) -> dict:
"""Calculate the loss based on the network predictions and targets.
Args:
cls_score (Tensor): Classification prediction results of all class,
has shape (batch_size * num_proposals_single_image,
(num_classes + 1) * k), k represents the number of prediction
boxes generated by each proposal box.
bbox_pred (Tensor): Regression prediction results, has shape
(batch_size * num_proposals_single_image, 4 * k), the last
dimension 4 represents [tl_x, tl_y, br_x, br_y].
rois (Tensor): RoIs with the shape
(batch_size * num_proposals_single_image, 5) where the first
column indicates batch id of each RoI.
labels (Tensor): Gt_labels for all proposals in a batch, has
shape (batch_size * num_proposals_single_image, k).
label_weights (Tensor): Labels_weights for all proposals in a
batch, has shape (batch_size * num_proposals_single_image, k).
bbox_targets (Tensor): Regression target for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k), the last dimension 4 represents [tl_x, tl_y, br_x,
br_y].
bbox_weights (Tensor): Regression weights for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k).
Returns:
dict: A dictionary of loss.
"""
losses = dict()
if bbox_pred.numel():
loss_0 = self.emd_loss(bbox_pred[:, 0:4], cls_score[:, 0:2],
bbox_pred[:, 4:8], cls_score[:, 2:4],
bbox_targets, labels)
loss_1 = self.emd_loss(bbox_pred[:, 4:8], cls_score[:, 2:4],
bbox_pred[:, 0:4], cls_score[:, 0:2],
bbox_targets, labels)
loss = torch.cat([loss_0, loss_1], dim=1)
_, min_indices = loss.min(dim=1)
loss_emd = loss[torch.arange(loss.shape[0]), min_indices]
loss_emd = loss_emd.mean()
else:
loss_emd = bbox_pred.sum()
losses['loss_rcnn_emd'] = loss_emd
return losses
def emd_loss(self, bbox_pred_0: Tensor, cls_score_0: Tensor,
bbox_pred_1: Tensor, cls_score_1: Tensor, targets: Tensor,
labels: Tensor) -> Tensor:
"""Calculate the emd loss.
Note:
This implementation is modified from https://github.com/Purkialo/
CrowdDet/blob/master/lib/det_oprs/loss_opr.py
Args:
bbox_pred_0 (Tensor): Part of regression prediction results, has
shape (batch_size * num_proposals_single_image, 4), the last
dimension 4 represents [tl_x, tl_y, br_x, br_y].
cls_score_0 (Tensor): Part of classification prediction results,
has shape (batch_size * num_proposals_single_image,
(num_classes + 1)), where 1 represents the background.
bbox_pred_1 (Tensor): The other part of regression prediction
results, has shape (batch_size*num_proposals_single_image, 4).
cls_score_1 (Tensor):The other part of classification prediction
results, has shape (batch_size * num_proposals_single_image,
(num_classes + 1)).
targets (Tensor):Regression target for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k), the last dimension 4 represents [tl_x, tl_y, br_x,
br_y], k represents the number of prediction boxes generated
by each proposal box.
labels (Tensor): Gt_labels for all proposals in a batch, has
shape (batch_size * num_proposals_single_image, k).
Returns:
torch.Tensor: The calculated loss.
"""
bbox_pred = torch.cat([bbox_pred_0, bbox_pred_1],
dim=1).reshape(-1, bbox_pred_0.shape[-1])
cls_score = torch.cat([cls_score_0, cls_score_1],
dim=1).reshape(-1, cls_score_0.shape[-1])
targets = targets.reshape(-1, 4)
labels = labels.long().flatten()
# masks
valid_masks = labels >= 0
fg_masks = labels > 0
# multiple class
bbox_pred = bbox_pred.reshape(-1, self.num_classes, 4)
fg_gt_classes = labels[fg_masks]
bbox_pred = bbox_pred[fg_masks, fg_gt_classes - 1, :]
# loss for regression
loss_bbox = self.loss_bbox(bbox_pred, targets[fg_masks])
loss_bbox = loss_bbox.sum(dim=1)
# loss for classification
labels = labels * valid_masks
loss_cls = self.loss_cls(cls_score, labels)
loss_cls[fg_masks] = loss_cls[fg_masks] + loss_bbox
loss = loss_cls.reshape(-1, 2).sum(dim=1)
return loss.reshape(-1, 1)
def _predict_by_feat_single(
self,
roi: Tensor,
cls_score: Tensor,
bbox_pred: Tensor,
img_meta: dict,
rescale: bool = False,
rcnn_test_cfg: Optional[ConfigDict] = None) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
Args:
roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
cls_score (Tensor): Box scores, has shape
(num_boxes, num_classes + 1).
bbox_pred (Tensor): Box energies / deltas. has shape
(num_boxes, num_classes * 4).
img_meta (dict): image information.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
Defaults to None
Returns:
:obj:`InstanceData`: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
cls_score = cls_score.reshape(-1, self.num_classes + 1)
bbox_pred = bbox_pred.reshape(-1, 4)
roi = roi.repeat_interleave(self.num_instance, dim=0)
results = InstanceData()
if roi.shape[0] == 0:
return empty_instances([img_meta],
roi.device,
task_type='bbox',
instance_results=[results])[0]
scores = cls_score.softmax(dim=-1) if cls_score is not None else None
img_shape = img_meta['img_shape']
bboxes = self.bbox_coder.decode(
roi[..., 1:], bbox_pred, max_shape=img_shape)
if rescale and bboxes.size(0) > 0:
assert img_meta.get('scale_factor') is not None
scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
(1, 2))
bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view(
bboxes.size()[0], -1)
if rcnn_test_cfg is None:
# This means that it is aug test.
# It needs to return the raw results without nms.
results.bboxes = bboxes
results.scores = scores
else:
roi_idx = np.tile(
np.arange(bboxes.shape[0] / self.num_instance)[:, None],
(1, self.num_instance)).reshape(-1, 1)[:, 0]
roi_idx = torch.from_numpy(roi_idx).to(bboxes.device).reshape(
-1, 1)
bboxes = torch.cat([bboxes, roi_idx], dim=1)
det_bboxes, det_scores = self.set_nms(
bboxes, scores[:, 1], rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms['iou_threshold'], rcnn_test_cfg.max_per_img)
results.bboxes = det_bboxes[:, :-1]
results.scores = det_scores
results.labels = torch.zeros_like(det_scores)
return results
@staticmethod
def set_nms(bboxes: Tensor,
scores: Tensor,
score_thr: float,
iou_threshold: float,
max_num: int = -1) -> Tuple[Tensor, Tensor]:
"""NMS for multi-instance prediction. Please refer to
https://github.com/Purkialo/CrowdDet for more details.
Args:
bboxes (Tensor): predict bboxes.
scores (Tensor): The score of each predict bbox.
score_thr (float): bbox threshold, bboxes with scores lower than it
will not be considered.
iou_threshold (float): IoU threshold to be considered as
conflicted.
max_num (int, optional): if there are more than max_num bboxes
after NMS, only top max_num will be kept. Default to -1.
Returns:
Tuple[Tensor, Tensor]: (bboxes, scores).
"""
bboxes = bboxes[scores > score_thr]
scores = scores[scores > score_thr]
ordered_scores, order = scores.sort(descending=True)
ordered_bboxes = bboxes[order]
roi_idx = ordered_bboxes[:, -1]
keep = torch.ones(len(ordered_bboxes)) == 1
ruler = torch.arange(len(ordered_bboxes))
keep = keep.to(bboxes.device)
ruler = ruler.to(bboxes.device)
while ruler.shape[0] > 0:
basement = ruler[0]
ruler = ruler[1:]
idx = roi_idx[basement]
# calculate the body overlap
basement_bbox = ordered_bboxes[:, :4][basement].reshape(-1, 4)
ruler_bbox = ordered_bboxes[:, :4][ruler].reshape(-1, 4) | overlap = bbox_overlaps(basement_bbox, ruler_bbox) | 4 | 2023-12-11 15:23:03+00:00 | 24k |
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 | 14,784 | """Encode image for ip-adapter. Copied from
https://github.com/huggingface/diffusers/blob/f9487783228cd500a21555da3346db40e8f05992/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L492-L514 # noqa
"""
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
@torch.no_grad()
def __call__(
self,
image: np.ndarray,
prompt: Union[str, List[str]],
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
global_inf_num: int = 0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cond_frame: int = 0,
mask_sim_template_idx: int = 0,
ip_adapter_scale: float = 0,
strength: float = 1,
progress_fn=None,
**kwargs,
):
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
assert strength > 0 and strength <= 1, (
f'"strength" for img2vid must in (0, 1]. But receive {strength}.')
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is None:
negative_prompt = DEFAULT_N_PROMPT
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
# Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
#timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
# Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
latent_timestep,
batch_size * num_videos_per_prompt,
4,
video_length,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
raw_image = image.copy()
image = torch.from_numpy(image)[None, ...].permute(0, 3, 1, 2)
image = image / 255 # [0, 1]
image = image * 2 - 1 # [-1, 1]
image = image.to(device=device, dtype=self.vae.dtype)
if isinstance(generator, list):
image_latent = [
self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size)
]
image_latent = torch.cat(image_latent, dim=0)
else:
image_latent = self.vae.encode(image).latent_dist.sample(generator)
image_latent = image_latent.to(device=device, dtype=self.unet.dtype)
image_latent = torch.nn.functional.interpolate(image_latent, size=[shape[-2], shape[-1]])
image_latent_padding = image_latent.clone() * 0.18215
mask = torch.zeros((shape[0], 1, shape[2], shape[3], shape[4])).to(device=device, dtype=self.unet.dtype)
# prepare mask
| # 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(
9, old_weights.shape[0],
kernel_size=unet.conv_in.kernel_size,
stride=unet.conv_in.stride,
padding=unet.conv_in.padding,
bias=True if old_bias is not None else False)
param = torch.zeros((320,5,3,3),requires_grad=True)
new_conv1.weight = torch.nn.Parameter(torch.cat((old_weights,param),dim=1))
if old_bias is not None:
new_conv1.bias = old_bias
unet.conv_in = new_conv1
unet.config["in_channels"] = 9
unet_ckpt = torch.load(unet_path, map_location='cpu')
unet.load_state_dict(unet_ckpt, strict=False)
# NOTE: only load temporal layers and condition module
# for key, value in unet_ckpt.items():
# if 'motion' in key or 'conv_in' in key:
# unet.state_dict()[key].copy_(value)
# load vae, tokenizer, text encoder
vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae")
tokenizer = CLIPTokenizer.from_pretrained(base_model, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(base_model, subfolder="text_encoder")
noise_scheduler = DDIMScheduler(**OmegaConf.to_container(base_cfg.noise_scheduler_kwargs))
if dreambooth_path:
print(" >>> Begin loading DreamBooth >>>")
base_model_state_dict = {}
with safe_open(dreambooth_path, framework="pt", device="cpu") as f:
for key in f.keys():
base_model_state_dict[key] = f.get_tensor(key)
# load unet
converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, unet.config)
old_value = converted_unet_checkpoint['conv_in.weight']
new_param = unet_ckpt['conv_in.weight'][:,4:,:,:].clone().cpu()
new_value = torch.nn.Parameter(torch.cat((old_value, new_param), dim=1))
converted_unet_checkpoint['conv_in.weight'] = new_value
unet.load_state_dict(converted_unet_checkpoint, strict=False)
# load vae
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
base_model_state_dict, vae.config,
only_decoder=only_load_vae_decoder,
only_encoder=only_load_vae_encoder,)
need_strict = not (only_load_vae_decoder or only_load_vae_encoder)
vae.load_state_dict(converted_vae_checkpoint, strict=need_strict)
print('Prefix in loaded VAE checkpoint: ')
print(set([k.split('.')[0] for k in converted_vae_checkpoint.keys()]))
# load text encoder
text_encoder_checkpoint = convert_ldm_clip_checkpoint(base_model_state_dict)
if text_encoder_checkpoint:
text_encoder.load_state_dict(text_encoder_checkpoint, strict=False)
print(" <<< Loaded DreamBooth <<<")
if vae_path:
print(' >>> Begin loading VAE >>>')
vae_state_dict = {}
if vae_path.endswith('safetensors'):
with safe_open(vae_path, framework="pt", device="cpu") as f:
for key in f.keys():
vae_state_dict[key] = f.get_tensor(key)
elif vae_path.endswith('ckpt') or vae_path.endswith('pt'):
vae_state_dict = torch.load(vae_path, map_location='cpu')
if 'state_dict' in vae_state_dict:
vae_state_dict = vae_state_dict['state_dict']
vae_state_dict = {f'first_stage_model.{k}': v for k, v in vae_state_dict.items()}
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
vae_state_dict, vae.config,
only_decoder=only_load_vae_decoder,
only_encoder=only_load_vae_encoder,)
print('Prefix in loaded VAE checkpoint: ')
print(set([k.split('.')[0] for k in converted_vae_checkpoint.keys()]))
need_strict = not (only_load_vae_decoder or only_load_vae_encoder)
vae.load_state_dict(converted_vae_checkpoint, strict=need_strict)
print(" <<< Loaded VAE <<<")
if lora_path:
print(" >>> Begin loading LoRA >>>")
lora_dict = {}
with safe_open(lora_path, framework='pt', device='cpu') as file:
for k in file.keys():
lora_dict[k] = file.get_tensor(k)
unet, text_encoder = convert_lora_model_level(
lora_dict, unet, text_encoder, alpha=lora_alpha)
print(" <<< Loaded LoRA <<<")
# move model to device
device = torch.device('cuda')
unet_dtype = torch.float16
tenc_dtype = torch.float16
vae_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
unet = unet.to(device=device, dtype=unet_dtype)
text_encoder = text_encoder.to(device=device, dtype=tenc_dtype)
vae = vae.to(device=device, dtype=vae_dtype)
print(f'Set Unet to {unet_dtype}')
print(f'Set text encoder to {tenc_dtype}')
print(f'Set vae to {vae_dtype}')
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
pipeline = cls(unet=unet,
vae=vae,
tokenizer=tokenizer,
text_encoder=text_encoder,
scheduler=noise_scheduler)
# ip_adapter_path = 'h94/IP-Adapter'
if ip_adapter_path and ip_adapter_scale > 0:
ip_adapter_name = 'ip-adapter_sd15.bin'
# only online repo need subfolder
if not osp.isdir(ip_adapter_path):
subfolder = 'models'
else:
subfolder = ''
pipeline.load_ip_adapter(ip_adapter_path, subfolder, ip_adapter_name)
pipeline.set_ip_adapter_scale(ip_adapter_scale)
pipeline.use_ip_adapter = True
print(f'Load IP-Adapter, scale: {ip_adapter_scale}')
# text_inversion_path = './models/TextualInversion/easynegative.safetensors'
# if text_inversion_path:
# pipeline.load_textual_inversion(text_inversion_path, 'easynegative')
return pipeline
def enable_vae_slicing(self):
self.vae.enable_slicing()
def disable_vae_slicing(self):
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
if is_accelerate_available():
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def decode_latents(self, latents):
video_length = latents.shape[2]
latents = 1 / 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w")
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0])):
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
video = video.cpu().float().numpy()
return video
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, add_noise_time_step, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
rand_device = "cpu" if device.type == "mps" else device
if isinstance(generator, list):
shape = shape
# shape = (1,) + shape[1:]
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
return latents
def encode_image(self, image, device, num_images_per_prompt):
"""Encode image for ip-adapter. Copied from
https://github.com/huggingface/diffusers/blob/f9487783228cd500a21555da3346db40e8f05992/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L492-L514 # noqa
"""
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
@torch.no_grad()
def __call__(
self,
image: np.ndarray,
prompt: Union[str, List[str]],
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
global_inf_num: int = 0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cond_frame: int = 0,
mask_sim_template_idx: int = 0,
ip_adapter_scale: float = 0,
strength: float = 1,
progress_fn=None,
**kwargs,
):
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
assert strength > 0 and strength <= 1, (
f'"strength" for img2vid must in (0, 1]. But receive {strength}.')
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is None:
negative_prompt = DEFAULT_N_PROMPT
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
# Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
#timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
# Prepare latent variables
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
latent_timestep,
batch_size * num_videos_per_prompt,
4,
video_length,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
raw_image = image.copy()
image = torch.from_numpy(image)[None, ...].permute(0, 3, 1, 2)
image = image / 255 # [0, 1]
image = image * 2 - 1 # [-1, 1]
image = image.to(device=device, dtype=self.vae.dtype)
if isinstance(generator, list):
image_latent = [
self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size)
]
image_latent = torch.cat(image_latent, dim=0)
else:
image_latent = self.vae.encode(image).latent_dist.sample(generator)
image_latent = image_latent.to(device=device, dtype=self.unet.dtype)
image_latent = torch.nn.functional.interpolate(image_latent, size=[shape[-2], shape[-1]])
image_latent_padding = image_latent.clone() * 0.18215
mask = torch.zeros((shape[0], 1, shape[2], shape[3], shape[4])).to(device=device, dtype=self.unet.dtype)
# prepare mask | mask_coef = prepare_mask_coef_by_statistics(video_length, cond_frame, mask_sim_template_idx) | 6 | 2023-12-21 03:29:34+00:00 | 24k |
chinhsuanwu/ifusion | model/zero123.py | [
{
"identifier": "inject_trainable_lora_extended",
"path": "ldm/lora.py",
"snippet": "def inject_trainable_lora_extended(\n model: nn.Module,\n target_replace_module: Set[str] = UNET_EXTENDED_TARGET_REPLACE,\n r: int = 4,\n loras=None, # path to lora .pt\n eval=True,\n):\n \"\"\"\n ... | import itertools
import torch
import torch.nn as nn
from dataclasses import dataclass
from diffusers import DDIMScheduler
from einops import rearrange
from omegaconf import OmegaConf
from ldm.lora import (
inject_trainable_lora_extended,
monkeypatch_remove_lora,
save_lora_weight,
)
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import load_model_from_config
from util.pose import make_T
from util.typing import *
from util.util import default | 14,862 |
class Zero123(nn.Module):
@dataclass
class Config:
pretrained_model_name_or_path: str = "ldm/ckpt/zero123-xl.ckpt"
pretrained_config: str = "ldm/ckpt/sd-objaverse-finetune-c_concat-256.yaml"
vram_O: bool = False
min_step_percent: float = 0.02
max_step_percent: float = 0.98
config: Config
def __init__(self, **kwargs) -> None:
super().__init__()
self.config = OmegaConf.structured(self.Config(**kwargs))
self.device = "cuda"
self.require_grad_params = []
self.configure()
def configure(self) -> None:
print("[INFO] Loading Zero123...")
self.pretrained_config = OmegaConf.load(self.config.pretrained_config)
self.weights_dtype = torch.float32
|
class Zero123(nn.Module):
@dataclass
class Config:
pretrained_model_name_or_path: str = "ldm/ckpt/zero123-xl.ckpt"
pretrained_config: str = "ldm/ckpt/sd-objaverse-finetune-c_concat-256.yaml"
vram_O: bool = False
min_step_percent: float = 0.02
max_step_percent: float = 0.98
config: Config
def __init__(self, **kwargs) -> None:
super().__init__()
self.config = OmegaConf.structured(self.Config(**kwargs))
self.device = "cuda"
self.require_grad_params = []
self.configure()
def configure(self) -> None:
print("[INFO] Loading Zero123...")
self.pretrained_config = OmegaConf.load(self.config.pretrained_config)
self.weights_dtype = torch.float32 | self.model: LatentDiffusion = load_model_from_config( | 3 | 2023-12-17 12:45:38+00:00 | 24k |
penghao-wu/vstar | VisualSearch/utils/dataset.py | [
{
"identifier": "conversation",
"path": "VisualSearch/model/llava/conversation.py",
"snippet": "class SeparatorStyle(Enum):\nclass Conversation:\n SINGLE = auto()\n TWO = auto()\n MPT = auto()\n PLAIN = auto()\n LLAMA_2 = auto()\n W, H = image.size\n ... | import glob
import os
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from pycocotools import mask
from transformers import CLIPImageProcessor
from transformers import OwlViTProcessor
from VisualSearch.model.llava import conversation as conversation_lib
from VisualSearch.model.llava.constants import (DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
IMAGE_TOKEN_INDEX)
from VisualSearch.model.llava.mm_utils import tokenizer_image_token
from VisualSearch.utils.data_processing import get_mask_from_json
from VisualSearch.utils.refer import REFER
from VisualSearch.utils.refer_seg_dataset import ReferSegDataset
from VisualSearch.utils.general_segdet_dataset import SegDetDataset
from VisualSearch.utils.mixed_grounding_dataset import MixedGroundingDataset
from VisualSearch.utils.vqa_dataset import VQADataset
from VisualSearch.utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN)
from VisualSearch.utils.utils import box_xyxy_to_cxcywh, expand2square | 14,499 | cv2.setNumThreads(1)
def collate_fn(
batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1
):
image_path_list = []
images_list = []
images_clip_list = []
conversation_list = []
masks_list = []
label_list = []
bboxes_labels_list = []
bboxes_valid_list = []
masks_valid_list = []
resize_list = []
questions_list = []
sampled_classes_list = []
offset_list = [0]
cnt = 0
inferences = []
for (
image_path,
images,
images_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
inference,
) in batch:
image_path_list.append(image_path)
images_list.append(images)
images_clip_list.append(images_clip)
conversation_list.extend(conversations)
label_list.append(label)
masks_list.append(masks.float())
bboxes_labels_list.extend(bboxes_labels)
bboxes_valid_list.extend(bboxes_valid)
masks_valid_list.append(torch.tensor(masks_valid))
resize_list.append(resize)
questions_list.append(questions)
sampled_classes_list.append(sampled_classes)
cnt += len(conversations)
offset_list.append(cnt)
inferences.append(inference)
if use_mm_start_end:
# replace <image> token
for i in range(len(conversation_list)):
replace_token = DEFAULT_IMAGE_TOKEN
replace_token = (
DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
)
conversation_list[i] = conversation_list[i].replace(
DEFAULT_IMAGE_TOKEN, replace_token
)
input_ids = [
tokenizer_image_token(prompt, tokenizer, return_tensors="pt")
for prompt in conversation_list
]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=tokenizer.pad_token_id
)
attention_masks = input_ids.ne(tokenizer.pad_token_id)
for i in range(len(bboxes_valid_list)):
bboxes_valid = bboxes_valid_list[i]
attention_mask = attention_masks[i]
if not bboxes_valid:
attention_mask = attention_mask & input_ids[i].ne(tokenizer("[LOC]", add_special_tokens=False).input_ids[0])
attention_masks[i] = attention_mask
| cv2.setNumThreads(1)
def collate_fn(
batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1
):
image_path_list = []
images_list = []
images_clip_list = []
conversation_list = []
masks_list = []
label_list = []
bboxes_labels_list = []
bboxes_valid_list = []
masks_valid_list = []
resize_list = []
questions_list = []
sampled_classes_list = []
offset_list = [0]
cnt = 0
inferences = []
for (
image_path,
images,
images_clip,
conversations,
masks,
label,
bboxes_labels,
bboxes_valid,
masks_valid,
resize,
questions,
sampled_classes,
inference,
) in batch:
image_path_list.append(image_path)
images_list.append(images)
images_clip_list.append(images_clip)
conversation_list.extend(conversations)
label_list.append(label)
masks_list.append(masks.float())
bboxes_labels_list.extend(bboxes_labels)
bboxes_valid_list.extend(bboxes_valid)
masks_valid_list.append(torch.tensor(masks_valid))
resize_list.append(resize)
questions_list.append(questions)
sampled_classes_list.append(sampled_classes)
cnt += len(conversations)
offset_list.append(cnt)
inferences.append(inference)
if use_mm_start_end:
# replace <image> token
for i in range(len(conversation_list)):
replace_token = DEFAULT_IMAGE_TOKEN
replace_token = (
DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
)
conversation_list[i] = conversation_list[i].replace(
DEFAULT_IMAGE_TOKEN, replace_token
)
input_ids = [
tokenizer_image_token(prompt, tokenizer, return_tensors="pt")
for prompt in conversation_list
]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=tokenizer.pad_token_id
)
attention_masks = input_ids.ne(tokenizer.pad_token_id)
for i in range(len(bboxes_valid_list)):
bboxes_valid = bboxes_valid_list[i]
attention_mask = attention_masks[i]
if not bboxes_valid:
attention_mask = attention_mask & input_ids[i].ne(tokenizer("[LOC]", add_special_tokens=False).input_ids[0])
attention_masks[i] = attention_mask
| conv = conversation_lib.default_conversation.copy() | 11 | 2023-12-15 14:58:24+00:00 | 24k |
sinoyou/nelf-pro | nerfstudio/data/dataparsers/nelfpro_dataparser.py | [
{
"identifier": "plot_point3d",
"path": "nerfstudio/utils/plotly_utils_nelfpro.py",
"snippet": "def plot_point3d(xyz, color):\n point_cloud_size = 0.8\n cam_centers_go = go.Scatter3d(\n x = xyz[:, 0], \n y = xyz[:, 1], \n z = xyz[:, 2], \n mode=\"markers\",\n nam... | from curses import meta
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Type
from rich.console import Console
from typing_extensions import Literal
from nerfstudio.utils.plotly_utils_nelfpro import plot_point3d
from nerfstudio.data.utils.probe_sample import FactorPoseGenerator
from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.cameras import Cameras, CameraType
from nerfstudio.data.dataparsers.base_dataparser import (
DataParser,
DataParserConfig,
DataparserOutputs,
)
from nerfstudio.data.scene_box import SceneBox
from nerfstudio.data.dataparsers.raw_dataset_loader.llff_dataset_raw_loader import LLFFRawLoader
from nerfstudio.data.dataparsers.raw_dataset_loader.kitti360_dataset_raw_loader import KITTI360RawLoader
from nerfstudio.data.dataparsers.raw_dataset_loader.bungee_dataset_raw_loader import BungeeRawLoader
from nerfstudio.data.dataparsers.raw_dataset_loader.nerf_dataset_raw_loader import NeRFStudioRawLoader
import numpy as np
import torch | 20,994 | from __future__ import annotations
CONSOLE = Console(width=120)
@dataclass
class NeLFProDataParserConfig(DataParserConfig):
"""Configuration for the SpherRiFDataParser."""
_target: Type = field(default_factory=lambda: NeLFProDataParser)
# raw dataset loader config
raw_loader: Literal["llff", "kitti360", "bungee", "nerfstudio"] = "llff"
data: Path = Path("./data/please_fill_in_the_path_to_your_raw_dataset")
eval_interval: int = 8
eval_type: Literal["dev"] = "dev"
# camera pose config
scale_factor: float = 1.0
downscale_factor: Optional[int] = None
scenebox_scale: int = 1.0
orientation_method: Literal["none", "up", "pca"] = "up"
center_poses: bool = True
auto_scale_poses: bool = True
# probe generation config
data_num_core: int = 3
data_num_basis: int = 64
use_kmeans_core: bool = True
use_fps_basis: bool = True
factor_pos_noise_scale: float = 0.02
# point cloud config
point_cloud_sample_num: int = -1
@dataclass
class NeLFProDataParser(DataParser):
"""Dataset Parser for Raw Mipnerf360 dataset."""
config: NeLFProDataParserConfig
downscale_factor: Optional[int] = None
def _generate_dataparser_outputs(self, split="train"):
data_dir = Path(self.config.data)
if self.config.raw_loader == "llff":
raw_loader = LLFFRawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None)
elif self.config.raw_loader == "kitti360":
raw_loader = KITTI360RawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None, eval_type=self.config.eval_type)
elif self.config.raw_loader == 'bungee':
raw_loader = BungeeRawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None)
elif self.config.raw_loader == "nerfstudio":
| from __future__ import annotations
CONSOLE = Console(width=120)
@dataclass
class NeLFProDataParserConfig(DataParserConfig):
"""Configuration for the SpherRiFDataParser."""
_target: Type = field(default_factory=lambda: NeLFProDataParser)
# raw dataset loader config
raw_loader: Literal["llff", "kitti360", "bungee", "nerfstudio"] = "llff"
data: Path = Path("./data/please_fill_in_the_path_to_your_raw_dataset")
eval_interval: int = 8
eval_type: Literal["dev"] = "dev"
# camera pose config
scale_factor: float = 1.0
downscale_factor: Optional[int] = None
scenebox_scale: int = 1.0
orientation_method: Literal["none", "up", "pca"] = "up"
center_poses: bool = True
auto_scale_poses: bool = True
# probe generation config
data_num_core: int = 3
data_num_basis: int = 64
use_kmeans_core: bool = True
use_fps_basis: bool = True
factor_pos_noise_scale: float = 0.02
# point cloud config
point_cloud_sample_num: int = -1
@dataclass
class NeLFProDataParser(DataParser):
"""Dataset Parser for Raw Mipnerf360 dataset."""
config: NeLFProDataParserConfig
downscale_factor: Optional[int] = None
def _generate_dataparser_outputs(self, split="train"):
data_dir = Path(self.config.data)
if self.config.raw_loader == "llff":
raw_loader = LLFFRawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None)
elif self.config.raw_loader == "kitti360":
raw_loader = KITTI360RawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None, eval_type=self.config.eval_type)
elif self.config.raw_loader == 'bungee':
raw_loader = BungeeRawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None)
elif self.config.raw_loader == "nerfstudio": | raw_loader = NeRFStudioRawLoader(data_dir, downscale_factor=self.config.downscale_factor, partition_index=None) | 12 | 2023-12-15 20:07:22+00:00 | 24k |
amazon-science/c2f-seg | data/dataloader_transformer.py | [
{
"identifier": "FishBowl",
"path": "data/dataloader_Fishbowl.py",
"snippet": "class FishBowl(object):\n def __init__(self, config, mode, subtest=None):\n self.datatype = mode\n data_dir = config.root_path\n\n self.img_path = os.path.join(data_dir, self.datatype+\"_data\", self.d... | from data.dataloader_Fishbowl import FishBowl
from data.dataloader_MOViD_A import MOViD_A
from data.dataloader_KINS import Kins_Fusion_dataset, KINS_Aisformer_VRSP_Intersection
from data.dataloader_COCOA import COCOA_Fusion_dataset, COCOA_VRSP | 21,188 |
def load_dataset(config, args, mode):
if mode=="train":
if args.dataset=="KINS":
train_dataset = Kins_Fusion_dataset(config, mode='train')
test_dataset = Kins_Fusion_dataset(config, mode='test')
elif args.dataset=="COCOA":
|
def load_dataset(config, args, mode):
if mode=="train":
if args.dataset=="KINS":
train_dataset = Kins_Fusion_dataset(config, mode='train')
test_dataset = Kins_Fusion_dataset(config, mode='test')
elif args.dataset=="COCOA": | train_dataset = COCOA_Fusion_dataset(config, mode='train') | 4 | 2023-12-21 04:25:47+00:00 | 24k |
alipay/PainlessInferenceAcceleration | pia/lookahead/models/baichuan/modeling_baichuan.py | [
{
"identifier": "LookaheadPreTrainedModel",
"path": "pia/lookahead/common/pretrained_model.py",
"snippet": "class LookaheadPreTrainedModel(PreTrainedModel):\n _batch_generation = False\n _stream_generation = False\n\n def __init__(self, config):\n super().__init__(config=config)\n\n d... | import math
import os
import torch
import torch.utils.checkpoint
from contextlib import contextmanager
from threading import Thread
from typing import List, Optional, Tuple, Union
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from transformers import PretrainedConfig
from transformers.activations import ACT2FN
from transformers.generation.utils import GenerationConfig
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.utils import logging, ContextManagers
from pia.lookahead.common.pretrained_model import LookaheadPreTrainedModel
from pia.lookahead.models.baichuan.configuration_baichuan import BaichuanConfig
from pia.lookahead.models.baichuan.generation_utils import build_chat_input, TextIterStreamer
from xformers import ops as xops
from .quantizer import quantize_offline, init_model_weight_int4
from .quantizer import init_model_weight_int4
from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
from accelerate.utils import CustomDtype
from accelerate.utils import get_balanced_memory
from .quantizer import quantize_online | 18,125 |
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
proj = self.W_pack(hidden_states)
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if xops is not None and self.training:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = xops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
)
else:
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
attn_mask=attention_mask)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DecoderLayer(nn.Module):
def __init__(self, config: BaichuanConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Attention(config=config)
self.mlp = MLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
| # Copyright 2023 Baichuan Inc. All Rights Reserved.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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__)
try:
except ImportError:
xops = None
logger.warning(
"Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
if len(mask.size()) == 3:
bsz, src_len, _ = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, :, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
else:
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device)
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device)
elif self.cos_cached.device != x.device:
self.cos_cached = self.cos_cached.to(x.device)
self.sin_cached = self.sin_cached.to(x.device)
return (
self.cos_cached[:, :, :seq_len, ...],
self.sin_cached[:, :, :seq_len, ...],
)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
return q_embed.to(q.dtype), k_embed.to(k.dtype)
class MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: BaichuanConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_position_embeddings
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
proj = self.W_pack(hidden_states)
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if xops is not None and self.training:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = xops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
)
else:
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
attn_mask=attention_mask)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DecoderLayer(nn.Module):
def __init__(self, config: BaichuanConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Attention(config=config)
self.mlp = MLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
| class BaichuanPreTrainedModel(LookaheadPreTrainedModel): | 0 | 2023-12-19 13:11:38+00:00 | 24k |
MingtaoGuo/AnimateAnyone_unofficial | aldm/aldm.py | [
{
"identifier": "conv_nd",
"path": "ldm/modules/diffusionmodules/util.py",
"snippet": "def conv_nd(dims, *args, **kwargs):\n \"\"\"\n Create a 1D, 2D, or 3D convolution module.\n \"\"\"\n if dims == 1:\n return nn.Conv1d(*args, **kwargs)\n elif dims == 2:\n return nn.Conv2d(... | import einops
import torch
import torch as th
import torch.nn as nn
from ldm.modules.diffusionmodules.util import (
conv_nd,
linear,
zero_module,
timestep_embedding,
)
from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.attention import SpatialTransformer, SpatialTransformerPlus
from ldm.modules.diffusionmodules.openaimodel import ResBlock, TimestepEmbedSequential, Downsample, AttentionBlock, Upsample, normalization, checkpoint, convert_module_to_f16, convert_module_to_f32
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from omegaconf.listconfig import ListConfig
from omegaconf.listconfig import ListConfig | 18,218 | self,
image_size,
in_channels,
model_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
if disable_self_attentions is not None:
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
assert len(disable_self_attentions) == len(channel_mult)
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
f"attention will still not be set.")
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
class ReferenceNet(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
if disable_self_attentions is not None:
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
assert len(disable_self_attentions) == len(channel_mult)
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
f"attention will still not be set.")
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels | if exists(disable_self_attentions): | 9 | 2023-12-16 03:31:33+00:00 | 24k |
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 | 14,542 | @classmethod
def test(cls, cfg, model, output_folder=None, evaluators=None):
"""
Evaluate the given model. The given model is expected to already contain
weights to evaluate.
Args:
cfg (CfgNode):
model (nn.Module):
evaluators (list[DatasetEvaluator] or None): if None, will call
:meth:`build_evaluator`. Otherwise, must have the same length as
``cfg.DATASETS.TEST``.
Returns:
dict: a dict of result metrics
"""
logger = logging.getLogger(__name__)
if isinstance(evaluators, DatasetEvaluator):
evaluators = [evaluators]
if evaluators is not None:
assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
len(cfg.DATASETS.TEST), len(evaluators)
)
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
data_loader = cls.build_test_loader(cfg, dataset_name)
# When evaluators are passed in as arguments,
# implicitly assume that evaluators can be created before data_loader.
if evaluators is not None:
evaluator = evaluators[idx]
else:
try:
evaluator = cls.build_evaluator(
cfg, dataset_name, output_folder=output_folder
)
except NotImplementedError:
logger.warn(
"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
"or implement its `build_evaluator` method."
)
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results_i, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results_i
)
logger.info(
"Evaluation results for {} in csv format:".format(dataset_name)
)
print_csv_format(results_i)
if len(results) == 1:
results = list(results.values())[0]
return results
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer, cfg.TEST.EVAL_PERIOD * 5)
)
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
# ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
ret.append(PersoEvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# Here the default print/log frequency of each writer is used.
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
# for poly lr schedule
add_deeplab_config(cfg)
add_maskformer2_config(cfg)
| """
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()
return writers
@classmethod
def build_model(cls, cfg):
"""
Returns:
torch.nn.Module:
It now calls :func:`detectron2.modeling.build_model`.
Overwrite it if you'd like a different model.
"""
model = build_model(cfg)
# logger = logging.getLogger(__name__)
# logger.info("Model:\n{}".format(model))
return model
# @classmethod
# def build_model(cls, cfg):
# """
# Returns:
# torch.nn.Module:
#
# It now calls :func:`detectron2.modeling.build_model`.
# Overwrite it if you'd like a different model.
# """
# model = build_model(cfg)
# # logger = logging.getLogger(__name__)
# # logger.info("Model:\n{}".format(model))
# return model
@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")
else:
output_folder = os.path.join(cfg.OUTPUT_DIR, output_folder, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if (
evaluator_type == "bdd_sem_seg"
or evaluator_type == "mapillary_sem_seg"
or evaluator_type == "acdc_sem_seg"
):
evaluator_list.append(
ClassicalSemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
save_pl=cfg.MODEL.SAVE_PSEUDO_LABELS,
)
)
# Cityscapes
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 cfg.MODEL.SAVE_PSEUDO_LABELS:
return CityscapesSemSegEvaluator(
dataset_name, save_pl=True, output_dir=output_folder
)
else:
return CityscapesSemSegEvaluator(dataset_name)
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
mapper = MapperTrain(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_test_loader(cls, cfg, dataset_name):
mapper = MapperTest(cfg, False)
return build_detection_test_loader(
cfg, dataset_name, batch_size=1, 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
if cfg.MODEL.CLOUDS.OVERWRITING:
if any(
ignored_module in module_name
for ignored_module in ["sem_seg_head_ema.", "sam.sam."]
):
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(cls, cfg, model, output_folder=None, evaluators=None):
"""
Evaluate the given model. The given model is expected to already contain
weights to evaluate.
Args:
cfg (CfgNode):
model (nn.Module):
evaluators (list[DatasetEvaluator] or None): if None, will call
:meth:`build_evaluator`. Otherwise, must have the same length as
``cfg.DATASETS.TEST``.
Returns:
dict: a dict of result metrics
"""
logger = logging.getLogger(__name__)
if isinstance(evaluators, DatasetEvaluator):
evaluators = [evaluators]
if evaluators is not None:
assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
len(cfg.DATASETS.TEST), len(evaluators)
)
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
data_loader = cls.build_test_loader(cfg, dataset_name)
# When evaluators are passed in as arguments,
# implicitly assume that evaluators can be created before data_loader.
if evaluators is not None:
evaluator = evaluators[idx]
else:
try:
evaluator = cls.build_evaluator(
cfg, dataset_name, output_folder=output_folder
)
except NotImplementedError:
logger.warn(
"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
"or implement its `build_evaluator` method."
)
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results_i, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results_i
)
logger.info(
"Evaluation results for {} in csv format:".format(dataset_name)
)
print_csv_format(results_i)
if len(results) == 1:
results = list(results.values())[0]
return results
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer, cfg.TEST.EVAL_PERIOD * 5)
)
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
# ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
ret.append(PersoEvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# Here the default print/log frequency of each writer is used.
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
# for poly lr schedule
add_deeplab_config(cfg)
add_maskformer2_config(cfg) | add_clouds_config(cfg) | 1 | 2023-12-15 15:40:58+00:00 | 24k |
modelscope/scepter | scepter/modules/model/network/ldm/ldm_sce.py | [
{
"identifier": "ANNOTATORS",
"path": "scepter/modules/annotator/registry.py",
"snippet": "ANNOTATORS = Registry('ANNOTATORS', build_func=build_annotator)"
},
{
"identifier": "MODELS",
"path": "scepter/modules/model/registry.py",
"snippet": "MODELS = Registry('MODELS', build_func=build_m... | import copy
import torch
import torch.nn as nn
import torchvision.transforms as TT
from scepter.modules.annotator.registry import ANNOTATORS
from scepter.modules.model.registry import MODELS, TUNERS
from scepter.modules.utils.config import Config, dict_to_yaml
from .ldm import LatentDiffusion
from .ldm_xl import LatentDiffusionXL | 14,472 | # -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
@MODELS.register_class()
class LatentDiffusionSCETuning(LatentDiffusion):
para_dict = {}
para_dict.update(LatentDiffusion.para_dict)
def __init__(self, cfg, logger):
super().__init__(cfg, logger=logger)
def init_params(self):
super().init_params()
self.tuner_model_config = self.cfg.TUNER_MODEL
def construct_network(self):
super().construct_network()
input_block_channels = self.model._input_block_chans
sc_tuner_cfg = self.tuner_model_config['SC_TUNER_CFG']
use_layers = self.tuner_model_config.get('USE_LAYERS', None)
lsc_tuner_blocks = nn.ModuleList([])
for i, chan in enumerate(input_block_channels[::-1]):
if use_layers and i not in use_layers:
lsc_tuner_blocks.append(nn.Identity())
continue
tuner_cfg = copy.deepcopy(sc_tuner_cfg)
tuner_cfg['DIM'] = chan
tuner_cfg['TUNER_LENGTH'] = int(chan *
tuner_cfg.get('DOWN_RATIO', 1.0))
sc_tuner = TUNERS.build(tuner_cfg, logger=self.logger)
lsc_tuner_blocks.append(sc_tuner)
self.model.lsc_identity = lsc_tuner_blocks
def save_pretrained(self,
*args,
destination=None,
prefix='',
keep_vars=False):
save_state = {
key: value
for key, value in self.state_dict().items()
if 'lsc_identity' in key
}
return save_state
def save_pretrained_config(self):
return copy.deepcopy(self.cfg.TUNER_MODEL.cfg_dict)
@staticmethod
def get_config_template():
| # -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
@MODELS.register_class()
class LatentDiffusionSCETuning(LatentDiffusion):
para_dict = {}
para_dict.update(LatentDiffusion.para_dict)
def __init__(self, cfg, logger):
super().__init__(cfg, logger=logger)
def init_params(self):
super().init_params()
self.tuner_model_config = self.cfg.TUNER_MODEL
def construct_network(self):
super().construct_network()
input_block_channels = self.model._input_block_chans
sc_tuner_cfg = self.tuner_model_config['SC_TUNER_CFG']
use_layers = self.tuner_model_config.get('USE_LAYERS', None)
lsc_tuner_blocks = nn.ModuleList([])
for i, chan in enumerate(input_block_channels[::-1]):
if use_layers and i not in use_layers:
lsc_tuner_blocks.append(nn.Identity())
continue
tuner_cfg = copy.deepcopy(sc_tuner_cfg)
tuner_cfg['DIM'] = chan
tuner_cfg['TUNER_LENGTH'] = int(chan *
tuner_cfg.get('DOWN_RATIO', 1.0))
sc_tuner = TUNERS.build(tuner_cfg, logger=self.logger)
lsc_tuner_blocks.append(sc_tuner)
self.model.lsc_identity = lsc_tuner_blocks
def save_pretrained(self,
*args,
destination=None,
prefix='',
keep_vars=False):
save_state = {
key: value
for key, value in self.state_dict().items()
if 'lsc_identity' in key
}
return save_state
def save_pretrained_config(self):
return copy.deepcopy(self.cfg.TUNER_MODEL.cfg_dict)
@staticmethod
def get_config_template(): | return dict_to_yaml('MODELS', | 4 | 2023-12-21 02:01:48+00:00 | 24k |
Ruiyuan-Zhang/CCS | multi_part_assembly/utils/wx_transformer_utilities/transformer_layer.py | [
{
"identifier": "LayerNorm",
"path": "multi_part_assembly/utils/wx_transformer_utilities/layer_norm.py",
"snippet": "def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):\n if not export and torch.cuda.is_available() and has_fused_layernorm:\n return FusedLayerNorm(... | from typing import Dict, List, Optional
from .layer_norm import LayerNorm
from .multihead_attention import MultiheadAttention
from .relational_memory import RelationalMemory
from .group_linear_layer import GroupLinearLayer
from .basic_mha import MemoryAttention
from .quant_noise import quant_noise
from .fairseq_dropout import FairseqDropout
from torch import Tensor
import torch
import torch.nn as nn
import multi_part_assembly.utils.wx_transformer_utilities.fairseq_utils as utils
import random
import torch.nn.functional as F | 15,265 |
# 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.
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer
class TransformerEncoderLayerVanilla(nn.Module):
"""Encoder layer block.
In the original paper each operation (multi-head attention or FFN) is
postprocessed with: `dropout -> add residual -> layernorm`. In the
tensor2tensor code they suggest that learning is more robust when
preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.encoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
"""
def __init__(self, args, out_proj = None):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.self_attn = self.build_self_attention(self.embed_dim, args)
|
# 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.
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer
class TransformerEncoderLayerVanilla(nn.Module):
"""Encoder layer block.
In the original paper each operation (multi-head attention or FFN) is
postprocessed with: `dropout -> add residual -> layernorm`. In the
tensor2tensor code they suggest that learning is more robust when
preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.encoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
"""
def __init__(self, args, out_proj = None):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.self_attn = self.build_self_attention(self.embed_dim, args) | self.self_attn_layer_norm = LayerNorm(self.embed_dim, eps=1e-5) | 0 | 2023-12-15 13:13:01+00:00 | 24k |
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 | 15,176 |
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)
self.vae.load_state_dict(converted_vae_checkpoint)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
return gr.Dropdown.update()
def update_motion_module(self, motion_module_dropdown):
self.selected_motion_module = motion_module_dropdown
motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
_, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
assert len(unexpected) == 0
return gr.Dropdown.update()
# def update_filter(self, shape, method, n, d_s, d_t):
def update_filter(self, width_slider, height_slider, filter_type_dropdown, d_s_slider, d_t_slider):
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s_slider
self.set_d_t = d_t_slider
vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
shape = [1, 4, 16, self.set_width//vae_scale_factor, self.set_height//vae_scale_factor]
self.freq_filter = get_freq_filter(
shape,
device="cuda",
filter_type=self.selected_filter_type,
n=4,
d_s=self.set_d_s,
d_t=self.set_d_t
)
def animate(
self,
base_model_dropdown,
motion_module_dropdown,
prompt_textbox,
negative_prompt_textbox,
width_slider,
height_slider,
seed_textbox,
# freeinit params
filter_type_dropdown,
d_s_slider,
d_t_slider,
num_iters_slider,
# speed up
speed_up_options
):
# set global seed
set_seed(42)
d_s = float(d_s_slider)
d_t = float(d_t_slider)
num_iters = int(num_iters_slider)
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s
self.set_d_t = d_t
if self.set_width != width_slider or self.set_height != height_slider or self.selected_filter_type != filter_type_dropdown or self.set_d_s != d_s or self.set_d_t != d_t:
self.update_filter(width_slider, height_slider, filter_type_dropdown, d_s, d_t)
if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
pipeline = AnimationFreeInitPipeline(
vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
).to("cuda")
# (freeinit) initialize frequency filter for noise reinitialization -------------
pipeline.freq_filter = self.freq_filter
# -------------------------------------------------------------------------------
if int(seed_textbox) > 0: seed = int(seed_textbox)
else: seed = random.randint(1, 1e16)
torch.manual_seed(int(seed))
assert seed == torch.initial_seed()
print(f"### seed: {seed}")
generator = torch.Generator(device="cuda")
generator.manual_seed(seed)
sample_output = pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = 25,
guidance_scale = 7.5,
width = width_slider,
height = height_slider,
video_length = 16,
num_iters = num_iters,
use_fast_sampling = True if "use_coarse_to_fine_sampling" in speed_up_options else False,
save_intermediate = False,
return_orig = True,
use_fp16 = True if "use_fp16" in speed_up_options else False
)
orig_sample = sample_output.orig_videos
sample = sample_output.videos
save_sample_path = os.path.join(self.savedir, f"sample.mp4")
|
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)
self.vae.load_state_dict(converted_vae_checkpoint)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
return gr.Dropdown.update()
def update_motion_module(self, motion_module_dropdown):
self.selected_motion_module = motion_module_dropdown
motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
_, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
assert len(unexpected) == 0
return gr.Dropdown.update()
# def update_filter(self, shape, method, n, d_s, d_t):
def update_filter(self, width_slider, height_slider, filter_type_dropdown, d_s_slider, d_t_slider):
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s_slider
self.set_d_t = d_t_slider
vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
shape = [1, 4, 16, self.set_width//vae_scale_factor, self.set_height//vae_scale_factor]
self.freq_filter = get_freq_filter(
shape,
device="cuda",
filter_type=self.selected_filter_type,
n=4,
d_s=self.set_d_s,
d_t=self.set_d_t
)
def animate(
self,
base_model_dropdown,
motion_module_dropdown,
prompt_textbox,
negative_prompt_textbox,
width_slider,
height_slider,
seed_textbox,
# freeinit params
filter_type_dropdown,
d_s_slider,
d_t_slider,
num_iters_slider,
# speed up
speed_up_options
):
# set global seed
set_seed(42)
d_s = float(d_s_slider)
d_t = float(d_t_slider)
num_iters = int(num_iters_slider)
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s
self.set_d_t = d_t
if self.set_width != width_slider or self.set_height != height_slider or self.selected_filter_type != filter_type_dropdown or self.set_d_s != d_s or self.set_d_t != d_t:
self.update_filter(width_slider, height_slider, filter_type_dropdown, d_s, d_t)
if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
pipeline = AnimationFreeInitPipeline(
vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
).to("cuda")
# (freeinit) initialize frequency filter for noise reinitialization -------------
pipeline.freq_filter = self.freq_filter
# -------------------------------------------------------------------------------
if int(seed_textbox) > 0: seed = int(seed_textbox)
else: seed = random.randint(1, 1e16)
torch.manual_seed(int(seed))
assert seed == torch.initial_seed()
print(f"### seed: {seed}")
generator = torch.Generator(device="cuda")
generator.manual_seed(seed)
sample_output = pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = 25,
guidance_scale = 7.5,
width = width_slider,
height = height_slider,
video_length = 16,
num_iters = num_iters,
use_fast_sampling = True if "use_coarse_to_fine_sampling" in speed_up_options else False,
save_intermediate = False,
return_orig = True,
use_fp16 = True if "use_fp16" in speed_up_options else False
)
orig_sample = sample_output.orig_videos
sample = sample_output.videos
save_sample_path = os.path.join(self.savedir, f"sample.mp4") | save_videos_grid(sample, save_sample_path) | 2 | 2023-12-19 21:06:32+00:00 | 24k |
m-abr/FCPCodebase | world/World.py | [
{
"identifier": "Logger",
"path": "logs/Logger.py",
"snippet": "class Logger():\n _folder = None\n\n def __init__(self, is_enabled:bool, topic:str) -> None:\n self.no_of_entries = 0 \n self.enabled = is_enabled\n self.topic = topic\n\n def write(self, msg:str, timestamp:boo... | from collections import deque
from cpp.ball_predictor import ball_predictor
from cpp.localization import localization
from logs.Logger import Logger
from math import atan2, pi
from math_ops.Matrix_4x4 import Matrix_4x4
from world.commons.Draw import Draw
from world.commons.Other_Robot import Other_Robot
from world.Robot import Robot
import numpy as np | 17,861 |
class World():
STEPTIME = 0.02 # Fixed step time
STEPTIME_MS = 20 # Fixed step time in milliseconds
VISUALSTEP = 0.04 # Fixed visual step time
VISUALSTEP_MS = 40 # Fixed visual step time in milliseconds
# play modes in our favor
M_OUR_KICKOFF = 0
M_OUR_KICK_IN = 1
M_OUR_CORNER_KICK = 2
M_OUR_GOAL_KICK = 3
M_OUR_FREE_KICK = 4
M_OUR_PASS = 5
M_OUR_DIR_FREE_KICK = 6
M_OUR_GOAL = 7
M_OUR_OFFSIDE = 8
# play modes in their favor
M_THEIR_KICKOFF = 9
M_THEIR_KICK_IN = 10
M_THEIR_CORNER_KICK = 11
M_THEIR_GOAL_KICK = 12
M_THEIR_FREE_KICK = 13
M_THEIR_PASS = 14
M_THEIR_DIR_FREE_KICK = 15
M_THEIR_GOAL = 16
M_THEIR_OFFSIDE = 17
# neutral play modes
M_BEFORE_KICKOFF = 18
M_GAME_OVER = 19
M_PLAY_ON = 20
# play mode groups
MG_OUR_KICK = 0
MG_THEIR_KICK = 1
MG_ACTIVE_BEAM = 2
MG_PASSIVE_BEAM = 3
MG_OTHER = 4 # play on, game over
FLAGS_CORNERS_POS = ((-15,-10,0), (-15,+10,0), (+15,-10,0), (+15,+10,0))
FLAGS_POSTS_POS = ((-15,-1.05,0.8),(-15,+1.05,0.8),(+15,-1.05,0.8),(+15,+1.05,0.8))
def __init__(self,robot_type:int, team_name:str, unum:int, apply_play_mode_correction:bool,
|
class World():
STEPTIME = 0.02 # Fixed step time
STEPTIME_MS = 20 # Fixed step time in milliseconds
VISUALSTEP = 0.04 # Fixed visual step time
VISUALSTEP_MS = 40 # Fixed visual step time in milliseconds
# play modes in our favor
M_OUR_KICKOFF = 0
M_OUR_KICK_IN = 1
M_OUR_CORNER_KICK = 2
M_OUR_GOAL_KICK = 3
M_OUR_FREE_KICK = 4
M_OUR_PASS = 5
M_OUR_DIR_FREE_KICK = 6
M_OUR_GOAL = 7
M_OUR_OFFSIDE = 8
# play modes in their favor
M_THEIR_KICKOFF = 9
M_THEIR_KICK_IN = 10
M_THEIR_CORNER_KICK = 11
M_THEIR_GOAL_KICK = 12
M_THEIR_FREE_KICK = 13
M_THEIR_PASS = 14
M_THEIR_DIR_FREE_KICK = 15
M_THEIR_GOAL = 16
M_THEIR_OFFSIDE = 17
# neutral play modes
M_BEFORE_KICKOFF = 18
M_GAME_OVER = 19
M_PLAY_ON = 20
# play mode groups
MG_OUR_KICK = 0
MG_THEIR_KICK = 1
MG_ACTIVE_BEAM = 2
MG_PASSIVE_BEAM = 3
MG_OTHER = 4 # play on, game over
FLAGS_CORNERS_POS = ((-15,-10,0), (-15,+10,0), (+15,-10,0), (+15,+10,0))
FLAGS_POSTS_POS = ((-15,-1.05,0.8),(-15,+1.05,0.8),(+15,-1.05,0.8),(+15,+1.05,0.8))
def __init__(self,robot_type:int, team_name:str, unum:int, apply_play_mode_correction:bool, | enable_draw:bool, logger:Logger, host:str) -> None: | 0 | 2023-12-16 23:40:23+00:00 | 24k |
Sam-Izdat/tinycio | src/tinycio/tonemapping.py | [
{
"identifier": "applyAgX",
"path": "src/tinycio/np_agx/agx.py",
"snippet": "def applyAgX(array):\n # type: (numpy.ndarray) -> numpy.ndarray\n \"\"\"\n -> take linear - sRGB image data as input\n - apply custom grading if any\n - apply the AgX Punchy view-transform\n - return a display... | import torch
import numpy as np
import typing
from enum import IntEnum
from .np_agx.agx import applyAgX, applyAgXPunchy
from .colorspace import ColorSpace, TransferFunction | 17,575 |
class ToneMapping:
"""
Map high-dynamic-range values to low-dynamic-range. LDR is typically sRGB in [0, 1] range. Example:
.. highlight:: python
.. code-block:: python
tm = ToneMapping.Variant.HABLE
tonemapped_image = ToneMapping.apply(input_im, tone_mapper=tm)
"""
class Variant(IntEnum):
"""
Tone mapper enum. Available options are:
.. highlight:: text
.. code-block:: text
- NONE
- CLAMP
- AGX
- AGX_PUNCHY
- HABLE
- REINHARD
- ACESCG
"""
NONE = 1<<0
CLAMP = 1<<1
AGX = 1<<2
AGX_PUNCHY = 1<<3
HABLE = 1<<4
REINHARD = 1<<5
ACESCG = 1<<6
IP_SRGB_LIN = CLAMP | AGX | AGX_PUNCHY | HABLE | REINHARD
IP_ACESCG = ACESCG
OP_SRGB_LIN = CLAMP | AGX | AGX_PUNCHY | HABLE | REINHARD
OP_ACESCG = ACESCG
DISABLED = 0
@classmethod
def apply(cls, im:torch.Tensor, tone_mapper:Variant):
"""
Apply tone mapping to HDR image tensor. Input data is expected to be in the correct color
space for the chosen tone mapper.
.. note::
:code:`ACESCG` tone mapping is performed on AP1 primaries and expects input in the
:code:`ACESCG` color space. All other tone mappers expect :code:`SRGB_LIN`.
The :code:`tone_map()` method of :class:`ColorImage` handles this conversion automatically.
:param torch.Tensor im: [C=3, H, W] sized image tensor
:param ToneMapping.Variant tone_mapper: tonemapper to be used
:return: image tensor
:rtype: torch.Tensor
"""
assert im.dim() == 3 and im.size(0) == 3, f"expected [C=3, H, W] image tensor, got {im.size()}"
op, tm = tone_mapper, cls.Variant
err_not_supported, err_disabled = f"ToneMapping {op.name} is not supported", f"ToneMapping {op.name} is disabled"
if op & tm.DISABLED: raise Exception(err_disabled)
if op == tm.NONE: return im
elif op == tm.CLAMP: return im.clamp(0., 1.)
elif op == tm.AGX: return cls._agx(im)
elif op == tm.AGX_PUNCHY: return cls._agx_punchy(im)
elif op == tm.HABLE: return cls._hable(im)
elif op == tm.REINHARD: return cls._reinhard_extended_luminance(im)
elif op == tm.ACESCG: return cls._aces_fitted(im)
else: raise Exception(err_not_supported)
return out
@classmethod
def _agx(cls, im:torch.Tensor):
device = im.device
|
class ToneMapping:
"""
Map high-dynamic-range values to low-dynamic-range. LDR is typically sRGB in [0, 1] range. Example:
.. highlight:: python
.. code-block:: python
tm = ToneMapping.Variant.HABLE
tonemapped_image = ToneMapping.apply(input_im, tone_mapper=tm)
"""
class Variant(IntEnum):
"""
Tone mapper enum. Available options are:
.. highlight:: text
.. code-block:: text
- NONE
- CLAMP
- AGX
- AGX_PUNCHY
- HABLE
- REINHARD
- ACESCG
"""
NONE = 1<<0
CLAMP = 1<<1
AGX = 1<<2
AGX_PUNCHY = 1<<3
HABLE = 1<<4
REINHARD = 1<<5
ACESCG = 1<<6
IP_SRGB_LIN = CLAMP | AGX | AGX_PUNCHY | HABLE | REINHARD
IP_ACESCG = ACESCG
OP_SRGB_LIN = CLAMP | AGX | AGX_PUNCHY | HABLE | REINHARD
OP_ACESCG = ACESCG
DISABLED = 0
@classmethod
def apply(cls, im:torch.Tensor, tone_mapper:Variant):
"""
Apply tone mapping to HDR image tensor. Input data is expected to be in the correct color
space for the chosen tone mapper.
.. note::
:code:`ACESCG` tone mapping is performed on AP1 primaries and expects input in the
:code:`ACESCG` color space. All other tone mappers expect :code:`SRGB_LIN`.
The :code:`tone_map()` method of :class:`ColorImage` handles this conversion automatically.
:param torch.Tensor im: [C=3, H, W] sized image tensor
:param ToneMapping.Variant tone_mapper: tonemapper to be used
:return: image tensor
:rtype: torch.Tensor
"""
assert im.dim() == 3 and im.size(0) == 3, f"expected [C=3, H, W] image tensor, got {im.size()}"
op, tm = tone_mapper, cls.Variant
err_not_supported, err_disabled = f"ToneMapping {op.name} is not supported", f"ToneMapping {op.name} is disabled"
if op & tm.DISABLED: raise Exception(err_disabled)
if op == tm.NONE: return im
elif op == tm.CLAMP: return im.clamp(0., 1.)
elif op == tm.AGX: return cls._agx(im)
elif op == tm.AGX_PUNCHY: return cls._agx_punchy(im)
elif op == tm.HABLE: return cls._hable(im)
elif op == tm.REINHARD: return cls._reinhard_extended_luminance(im)
elif op == tm.ACESCG: return cls._aces_fitted(im)
else: raise Exception(err_not_supported)
return out
@classmethod
def _agx(cls, im:torch.Tensor):
device = im.device | out = applyAgX(im.permute(1, 2, 0).cpu().numpy()) | 0 | 2023-12-15 15:39:08+00:00 | 24k |
quocanh34/magic-animate-modified | magicanimate/pipelines/pipeline_animation.py | [
{
"identifier": "UNet3DConditionModel",
"path": "magicanimate/models/unet_controlnet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,... | import inspect, math
import numpy as np
import torch
import torch.distributed as dist
from typing import Callable, List, Optional, Union
from dataclasses import dataclass
from PIL import Image
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange
from magicanimate.models.unet_controlnet import UNet3DConditionModel
from magicanimate.models.multicontrolnet import ControlNetProcessor #fix
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
from magicanimate.pipelines.context import (
get_context_scheduler,
get_total_steps
)
from magicanimate.utils.util import get_tensor_interpolation_method
from accelerate import cpu_offload | 15,878 | for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
v0 = latents[:,:,i0,:,:]
v1 = latents[:,:,i1,:,:]
new_latents[:,:,new_index,:,:] = v0
new_index += 1
for f in rate:
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
new_latents[:,:,new_index,:,:] = v.to(latents.device)
new_index += 1
new_latents[:,:,new_index,:,:] = v1
new_index += 1
return new_latents
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
_down_block_res_samples = []
_mid_block_res_sample = []
for i in np.concatenate(np.array(context)):
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
for res_t in _down_block_res_samples:
for i, res in enumerate(res_t):
down_block_res_samples[i].append(res)
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
mid_block_res_sample = torch.cat(_mid_block_res_sample)
# reshape controlnet output to match the unet3d inputs
b = b // 2 if do_classifier_free_guidance else b
_down_block_res_samples = []
for sample in down_block_res_samples:
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
sample = sample.repeat(2, 1, 1, 1, 1)
_down_block_res_samples.append(sample)
down_block_res_samples = _down_block_res_samples
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
return down_block_res_samples, mid_block_res_sample
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
processors: List[ControlNetProcessor], #fix
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition1: list = None,
controlnet_condition2: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
# controlnet = self.controlnet
# processors = self.processors
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
| # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
# from magicanimate.models.controlnet import ControlNetModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
# controlnet: ControlNetModel,
# processors: List[ControlNetProcessor],
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
):
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,
# controlnet1=processors[0],
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def enable_vae_slicing(self):
self.vae.enable_slicing()
def disable_vae_slicing(self):
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
if is_accelerate_available():
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def decode_latents(self, latents, rank, decoder_consistency=None):
video_length = latents.shape[2]
latents = 1 / 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w")
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):
if decoder_consistency is not None:
video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))
else:
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
video = video.cpu().float().numpy()
return video
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):
shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
rand_device = "cpu" if device.type == "mps" else device
if isinstance(generator, list):
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_condition(self, condition1, condition2, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
# Prepare first condition
condition1 = torch.from_numpy(condition1.copy()).to(device=device, dtype=dtype) / 255.0
condition1 = torch.stack([condition1 for _ in range(num_videos_per_prompt)], dim=0)
condition1 = rearrange(condition1, 'b f h w c -> (b f) c h w').clone()
# Prepare second condition
condition2 = torch.from_numpy(condition2.copy()).to(device=device, dtype=dtype) / 255.0
condition2 = torch.stack([condition2 for _ in range(num_videos_per_prompt)], dim=0)
condition2 = rearrange(condition2, 'b f h w c -> (b f) c h w').clone()
# Here, we're averaging the two conditions
combined_condition = (condition1*8+condition2*2)/10
if do_classifier_free_guidance:
combined_condition = torch.cat([combined_condition] * 2)
#combined_condition = torch.from_numpy(combined_condition.copy()).to(device=device, dtype=dtype)
return combined_condition
def next_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
@torch.no_grad()
def images2latents(self, images, dtype):
"""
Convert RGB image to VAE latents
"""
device = self._execution_device
images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
images = rearrange(images, "f h w c -> f c h w").to(device)
latents = []
for frame_idx in range(images.shape[0]):
latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)
latents = torch.cat(latents)
return latents
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=20,
num_actual_inference_steps=10,
eta=0.0,
return_intermediates=False,
**kwargs):
"""
Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440
invert a real image into noise map with determinisc DDIM inversion
"""
device = self._execution_device
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.images2latents(image)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
continue
model_inputs = latents
# predict the noise
# NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)
model_inputs = rearrange(model_inputs, "f c h w -> 1 c f h w")
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w")
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.next_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
if return_intermediates:
# return the intermediate laters during inversion
return latents, latents_list
return latents
def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):
if interpolation_factor < 2:
return latents
new_latents = torch.zeros(
(latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),
device=latents.device,
dtype=latents.dtype,
)
org_video_length = latents.shape[2]
rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]
new_index = 0
v0 = None
v1 = None
for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
v0 = latents[:,:,i0,:,:]
v1 = latents[:,:,i1,:,:]
new_latents[:,:,new_index,:,:] = v0
new_index += 1
for f in rate:
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
new_latents[:,:,new_index,:,:] = v.to(latents.device)
new_index += 1
new_latents[:,:,new_index,:,:] = v1
new_index += 1
return new_latents
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
_down_block_res_samples = []
_mid_block_res_sample = []
for i in np.concatenate(np.array(context)):
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
for res_t in _down_block_res_samples:
for i, res in enumerate(res_t):
down_block_res_samples[i].append(res)
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
mid_block_res_sample = torch.cat(_mid_block_res_sample)
# reshape controlnet output to match the unet3d inputs
b = b // 2 if do_classifier_free_guidance else b
_down_block_res_samples = []
for sample in down_block_res_samples:
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
sample = sample.repeat(2, 1, 1, 1, 1)
_down_block_res_samples.append(sample)
down_block_res_samples = _down_block_res_samples
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
return down_block_res_samples, mid_block_res_sample
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
processors: List[ControlNetProcessor], #fix
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition1: list = None,
controlnet_condition2: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
# controlnet = self.controlnet
# processors = self.processors
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
| reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size) | 2 | 2023-12-15 01:22:37+00:00 | 24k |
Azure-Samples/functions-python-web-crawler | .venv/Lib/site-packages/urllib3/poolmanager.py | [
{
"identifier": "HTTPHeaderDict",
"path": ".venv/Lib/site-packages/urllib3/_collections.py",
"snippet": "class HTTPHeaderDict(typing.MutableMapping[str, str]):\n \"\"\"\n :param headers:\n An iterable of field-value pairs. Must not contain multiple field names\n when compared case-in... | import functools
import logging
import typing
import warnings
import ssl
from types import TracebackType
from urllib.parse import urljoin
from ._collections import HTTPHeaderDict, RecentlyUsedContainer
from ._request_methods import RequestMethods
from .connection import ProxyConfig
from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
from .exceptions import (
LocationValueError,
MaxRetryError,
ProxySchemeUnknown,
URLSchemeUnknown,
)
from .response import BaseHTTPResponse
from .util.connection import _TYPE_SOCKET_OPTIONS
from .util.proxy import connection_requires_http_tunnel
from .util.retry import Retry
from .util.timeout import Timeout
from .util.url import Url, parse_url
from typing import Literal | 20,175 | from __future__ import annotations
if typing.TYPE_CHECKING:
__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
log = logging.getLogger(__name__)
SSL_KEYWORDS = (
"key_file",
"cert_file",
"cert_reqs",
"ca_certs",
"ca_cert_data",
"ssl_version",
"ssl_minimum_version",
"ssl_maximum_version",
"ca_cert_dir",
"ssl_context",
"key_password",
"server_hostname",
)
# Default value for `blocksize` - a new parameter introduced to
# http.client.HTTPConnection & http.client.HTTPSConnection in Python 3.7
_DEFAULT_BLOCKSIZE = 16384
_SelfT = typing.TypeVar("_SelfT")
class PoolKey(typing.NamedTuple):
"""
All known keyword arguments that could be provided to the pool manager, its
pools, or the underlying connections.
All custom key schemes should include the fields in this key at a minimum.
"""
key_scheme: str
key_host: str
key_port: int | None
key_timeout: Timeout | float | int | None
key_retries: Retry | bool | int | None
key_block: bool | None
key_source_address: tuple[str, int] | None
key_key_file: str | None
key_key_password: str | None
key_cert_file: str | None
key_cert_reqs: str | None
key_ca_certs: str | None
key_ca_cert_data: str | bytes | None
key_ssl_version: int | str | None
key_ssl_minimum_version: ssl.TLSVersion | None
key_ssl_maximum_version: ssl.TLSVersion | None
key_ca_cert_dir: str | None
key_ssl_context: ssl.SSLContext | None
key_maxsize: int | None
key_headers: frozenset[tuple[str, str]] | None
key__proxy: Url | None
key__proxy_headers: frozenset[tuple[str, str]] | None
| from __future__ import annotations
if typing.TYPE_CHECKING:
__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
log = logging.getLogger(__name__)
SSL_KEYWORDS = (
"key_file",
"cert_file",
"cert_reqs",
"ca_certs",
"ca_cert_data",
"ssl_version",
"ssl_minimum_version",
"ssl_maximum_version",
"ca_cert_dir",
"ssl_context",
"key_password",
"server_hostname",
)
# Default value for `blocksize` - a new parameter introduced to
# http.client.HTTPConnection & http.client.HTTPSConnection in Python 3.7
_DEFAULT_BLOCKSIZE = 16384
_SelfT = typing.TypeVar("_SelfT")
class PoolKey(typing.NamedTuple):
"""
All known keyword arguments that could be provided to the pool manager, its
pools, or the underlying connections.
All custom key schemes should include the fields in this key at a minimum.
"""
key_scheme: str
key_host: str
key_port: int | None
key_timeout: Timeout | float | int | None
key_retries: Retry | bool | int | None
key_block: bool | None
key_source_address: tuple[str, int] | None
key_key_file: str | None
key_key_password: str | None
key_cert_file: str | None
key_cert_reqs: str | None
key_ca_certs: str | None
key_ca_cert_data: str | bytes | None
key_ssl_version: int | str | None
key_ssl_minimum_version: ssl.TLSVersion | None
key_ssl_maximum_version: ssl.TLSVersion | None
key_ca_cert_dir: str | None
key_ssl_context: ssl.SSLContext | None
key_maxsize: int | None
key_headers: frozenset[tuple[str, str]] | None
key__proxy: Url | None
key__proxy_headers: frozenset[tuple[str, str]] | None | key__proxy_config: ProxyConfig | None | 3 | 2023-12-16 04:12:01+00:00 | 24k |
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 | 14,568 | while preserving the aspect ratio.
interpolation: Type of interpolation to use when resizing images.
video_manager: [DEPRECATED] DO NOT USE. For backwards compatibility only.
Returns:
Dictionary of the format { scene_num : [image_paths] }, where scene_num is the
number of the scene in scene_list (starting from 1), and image_paths is a list of
the paths to the newly saved/created images.
Raises:
ValueError: Raised if any arguments are invalid or out of range (e.g.
if num_images is negative).
"""
# TODO(v0.7): Add DeprecationWarning that `video_manager` will be removed in v0.8.
if video_manager is not None:
logger.error('`video_manager` argument is deprecated, use `video` instead.')
video = video_manager
if not scene_list:
return {}
if num_images <= 0 or frame_margin < 0:
raise ValueError()
# TODO: Validate that encoder_param is within the proper range.
# Should be between 0 and 100 (inclusive) for jpg/webp, and 1-9 for png.
imwrite_param = [get_cv2_imwrite_params()[image_extension], encoder_param
] if encoder_param is not None else []
video.reset()
# Setup flags and init progress bar if available.
completed = True
logger.info('Generating output images (%d per scene)...', num_images)
progress_bar = None
if show_progress:
progress_bar = tqdm(total=len(scene_list) * num_images, unit='images', dynamic_ncols=True)
filename_template = Template(image_name_template)
scene_num_format = '%0'
scene_num_format += str(max(3, math.floor(math.log(len(scene_list), 10)) + 1)) + 'd'
image_num_format = '%0'
image_num_format += str(math.floor(math.log(num_images, 10)) + 2) + 'd'
framerate = scene_list[0][0].framerate
# TODO(v1.0): Split up into multiple sub-expressions so auto-formatter works correctly.
timecode_list = [
[
FrameTimecode(int(f), fps=framerate) for f in [
# middle frames
a[len(a) // 2] if (0 < j < num_images - 1) or num_images == 1
# first frame
else min(a[0] + frame_margin, a[-1]) if j == 0
# last frame
else max(a[-1] - frame_margin, a[0])
# for each evenly-split array of frames in the scene list
for j, a in enumerate(np.array_split(r, num_images))
]
] for i, r in enumerate([
# pad ranges to number of images
r if 1 + r[-1] - r[0] >= num_images else list(r) + [r[-1]] * (num_images - len(r))
# create range of frames in scene
for r in (
range(
start.get_frames(),
start.get_frames() + max(
1, # guard against zero length scenes
end.get_frames() - start.get_frames()))
# for each scene in scene list
for start, end in scene_list)
])
]
image_filenames = {i: [] for i in range(len(timecode_list))}
aspect_ratio = video.aspect_ratio
if abs(aspect_ratio - 1.0) < 0.01:
aspect_ratio = None
logger.debug('Writing images with template %s', filename_template.template)
for i, scene_timecodes in enumerate(timecode_list):
for j, image_timecode in enumerate(scene_timecodes):
video.seek(image_timecode)
frame_im = video.read()
if frame_im is not None:
# TODO: Allow NUM to be a valid suffix in addition to NUMBER.
file_path = '%s.%s' % (filename_template.safe_substitute(
VIDEO_NAME=video.name,
SCENE_NUMBER=scene_num_format % (i + 1),
IMAGE_NUMBER=image_num_format % (j + 1),
FRAME_NUMBER=image_timecode.get_frames()), image_extension)
image_filenames[i].append(file_path)
# TODO(0.6.3): Combine this resize with the ones below.
if aspect_ratio is not None:
frame_im = cv2.resize(
frame_im, (0, 0),
fx=aspect_ratio,
fy=1.0,
interpolation=interpolation.value)
frame_height = frame_im.shape[0]
frame_width = frame_im.shape[1]
# Figure out what kind of resizing needs to be done
if height or width:
if height and not width:
factor = height / float(frame_height)
width = int(factor * frame_width)
if width and not height:
factor = width / float(frame_width)
height = int(factor * frame_height)
assert height > 0 and width > 0
frame_im = cv2.resize(
frame_im, (width, height), interpolation=interpolation.value)
elif scale:
frame_im = cv2.resize(
frame_im, (0, 0), fx=scale, fy=scale, interpolation=interpolation.value)
| # -*- 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(
SimpleTableImage(image, width=image_width, height=image_height)))
if i == 0:
scene_table = SimpleTable(rows=[row], header_row=header_row, css_class=css_class)
else:
scene_table.add_row(row=row)
# Write html file
page = HTMLPage()
page.add_table(timecode_table)
page.add_table(scene_table)
page.css = css
page.save(output_html_filename)
#
# TODO(v1.0): Refactor to take a SceneList object; consider moving this and save scene list
# to a better spot, or just move them to scene_list.py.
#
def save_images(scene_list: List[Tuple[FrameTimecode, FrameTimecode]],
video: VideoStream,
num_images: int = 3,
frame_margin: int = 1,
image_extension: str = 'jpg',
encoder_param: int = 95,
image_name_template: str = '$VIDEO_NAME-Scene-$SCENE_NUMBER-$IMAGE_NUMBER',
output_dir: Optional[str] = None,
show_progress: Optional[bool] = False,
scale: Optional[float] = None,
height: Optional[int] = None,
width: Optional[int] = None,
interpolation: Interpolation = Interpolation.CUBIC,
video_manager=None) -> Dict[int, List[str]]:
"""Save a set number of images from each scene, given a list of scenes
and the associated video/frame source.
Arguments:
scene_list: A list of scenes (pairs of FrameTimecode objects) returned
from calling a SceneManager's detect_scenes() method.
video: A VideoStream object corresponding to the scene list.
Note that the video will be closed/re-opened and seeked through.
num_images: Number of images to generate for each scene. Minimum is 1.
frame_margin: Number of frames to pad each scene around the beginning
and end (e.g. moves the first/last image into the scene by N frames).
Can set to 0, but will result in some video files failing to extract
the very last frame.
image_extension: Type of image to save (must be one of 'jpg', 'png', or 'webp').
encoder_param: Quality/compression efficiency, based on type of image:
'jpg' / 'webp': Quality 0-100, higher is better quality. 100 is lossless for webp.
'png': Compression from 1-9, where 9 achieves best filesize but is slower to encode.
image_name_template: Template to use when creating the images on disk. Can
use the macros $VIDEO_NAME, $SCENE_NUMBER, and $IMAGE_NUMBER. The image
extension is applied automatically as per the argument image_extension.
output_dir: Directory to output the images into. If not set, the output
is created in the working directory.
show_progress: If True, shows a progress bar if tqdm is installed.
scale: Optional factor by which to rescale saved images. A scaling factor of 1 would
not result in rescaling. A value < 1 results in a smaller saved image, while a
value > 1 results in an image larger than the original. This value is ignored if
either the height or width values are specified.
height: Optional value for the height of the saved images. Specifying both the height
and width will resize images to an exact size, regardless of aspect ratio.
Specifying only height will rescale the image to that number of pixels in height
while preserving the aspect ratio.
width: Optional value for the width of the saved images. Specifying both the width
and height will resize images to an exact size, regardless of aspect ratio.
Specifying only width will rescale the image to that number of pixels wide
while preserving the aspect ratio.
interpolation: Type of interpolation to use when resizing images.
video_manager: [DEPRECATED] DO NOT USE. For backwards compatibility only.
Returns:
Dictionary of the format { scene_num : [image_paths] }, where scene_num is the
number of the scene in scene_list (starting from 1), and image_paths is a list of
the paths to the newly saved/created images.
Raises:
ValueError: Raised if any arguments are invalid or out of range (e.g.
if num_images is negative).
"""
# TODO(v0.7): Add DeprecationWarning that `video_manager` will be removed in v0.8.
if video_manager is not None:
logger.error('`video_manager` argument is deprecated, use `video` instead.')
video = video_manager
if not scene_list:
return {}
if num_images <= 0 or frame_margin < 0:
raise ValueError()
# TODO: Validate that encoder_param is within the proper range.
# Should be between 0 and 100 (inclusive) for jpg/webp, and 1-9 for png.
imwrite_param = [get_cv2_imwrite_params()[image_extension], encoder_param
] if encoder_param is not None else []
video.reset()
# Setup flags and init progress bar if available.
completed = True
logger.info('Generating output images (%d per scene)...', num_images)
progress_bar = None
if show_progress:
progress_bar = tqdm(total=len(scene_list) * num_images, unit='images', dynamic_ncols=True)
filename_template = Template(image_name_template)
scene_num_format = '%0'
scene_num_format += str(max(3, math.floor(math.log(len(scene_list), 10)) + 1)) + 'd'
image_num_format = '%0'
image_num_format += str(math.floor(math.log(num_images, 10)) + 2) + 'd'
framerate = scene_list[0][0].framerate
# TODO(v1.0): Split up into multiple sub-expressions so auto-formatter works correctly.
timecode_list = [
[
FrameTimecode(int(f), fps=framerate) for f in [
# middle frames
a[len(a) // 2] if (0 < j < num_images - 1) or num_images == 1
# first frame
else min(a[0] + frame_margin, a[-1]) if j == 0
# last frame
else max(a[-1] - frame_margin, a[0])
# for each evenly-split array of frames in the scene list
for j, a in enumerate(np.array_split(r, num_images))
]
] for i, r in enumerate([
# pad ranges to number of images
r if 1 + r[-1] - r[0] >= num_images else list(r) + [r[-1]] * (num_images - len(r))
# create range of frames in scene
for r in (
range(
start.get_frames(),
start.get_frames() + max(
1, # guard against zero length scenes
end.get_frames() - start.get_frames()))
# for each scene in scene list
for start, end in scene_list)
])
]
image_filenames = {i: [] for i in range(len(timecode_list))}
aspect_ratio = video.aspect_ratio
if abs(aspect_ratio - 1.0) < 0.01:
aspect_ratio = None
logger.debug('Writing images with template %s', filename_template.template)
for i, scene_timecodes in enumerate(timecode_list):
for j, image_timecode in enumerate(scene_timecodes):
video.seek(image_timecode)
frame_im = video.read()
if frame_im is not None:
# TODO: Allow NUM to be a valid suffix in addition to NUMBER.
file_path = '%s.%s' % (filename_template.safe_substitute(
VIDEO_NAME=video.name,
SCENE_NUMBER=scene_num_format % (i + 1),
IMAGE_NUMBER=image_num_format % (j + 1),
FRAME_NUMBER=image_timecode.get_frames()), image_extension)
image_filenames[i].append(file_path)
# TODO(0.6.3): Combine this resize with the ones below.
if aspect_ratio is not None:
frame_im = cv2.resize(
frame_im, (0, 0),
fx=aspect_ratio,
fy=1.0,
interpolation=interpolation.value)
frame_height = frame_im.shape[0]
frame_width = frame_im.shape[1]
# Figure out what kind of resizing needs to be done
if height or width:
if height and not width:
factor = height / float(frame_height)
width = int(factor * frame_width)
if width and not height:
factor = width / float(frame_width)
height = int(factor * frame_height)
assert height > 0 and width > 0
frame_im = cv2.resize(
frame_im, (width, height), interpolation=interpolation.value)
elif scale:
frame_im = cv2.resize(
frame_im, (0, 0), fx=scale, fy=scale, interpolation=interpolation.value)
| cv2.imwrite(get_and_create_path(file_path, output_dir), frame_im, imwrite_param) | 5 | 2023-10-25 02:50:01+00:00 | 24k |
EulerSearch/embedding_studio | plugins/default_fine_tuning_method.py | [
{
"identifier": "settings",
"path": "embedding_studio/core/config.py",
"snippet": "class Settings(BaseSettings):\n API_V1_STR: str = \"/api/v1\"\n SECRET_KEY: str = secrets.token_urlsafe(32)\n ACCESS_TOKEN_EXPIRE_MINUTES: int = 60 * 24 * 8\n BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = []\n F... | from typing import List
from sentence_transformers import SentenceTransformer
from embedding_studio.core.config import settings
from embedding_studio.core.plugin import FineTuningMethod
from embedding_studio.embeddings.data.clickstream.parsers.s3_parser import (
AWSS3ClickstreamParser,
)
from embedding_studio.embeddings.data.clickstream.search_event import (
DummyEventType,
SearchResult,
)
from embedding_studio.embeddings.data.clickstream.splitter import (
ClickstreamSessionsSplitter,
)
from embedding_studio.embeddings.data.clickstream.text_query_item import (
TextQueryItem,
)
from embedding_studio.embeddings.data.clickstream.text_query_retriever import (
TextQueryRetriever,
)
from embedding_studio.embeddings.data.loaders.s3.s3_loader import (
AWSS3DataLoader,
)
from embedding_studio.embeddings.data.storages.producers.clip import (
CLIPItemStorageProducer,
)
from embedding_studio.embeddings.data.utils.fields_normalizer import (
DatasetFieldsNormalizer,
)
from embedding_studio.embeddings.losses.prob_cosine_margin_ranking_loss import (
CosineProbMarginRankingLoss,
)
from embedding_studio.embeddings.models.text_to_image.clip import (
TextToImageCLIPModel,
)
from embedding_studio.models.clickstream.sessions import SessionWithEvents
from embedding_studio.models.plugin import FineTuningBuilder, PluginMeta
from embedding_studio.workers.fine_tuning.data.prepare_data import prepare_data
from embedding_studio.workers.fine_tuning.experiments.experiments_tracker import (
ExperimentsManager,
)
from embedding_studio.workers.fine_tuning.experiments.finetuning_settings import (
FineTuningSettings,
)
from embedding_studio.workers.fine_tuning.experiments.initial_params.clip import (
INITIAL_PARAMS,
)
from embedding_studio.workers.fine_tuning.experiments.metrics_accumulator import (
MetricsAccumulator,
) | 16,704 |
class DefaultFineTuningMethod(FineTuningMethod):
meta = PluginMeta(
name="Default Fine Tuning Method",
version="0.0.1",
description="A default fine-tuning plugin",
)
def __init__(self):
# uncomment and pass your credentials to use your own s3 bucket
# creds = {
# "role_arn": "arn:aws:iam::123456789012:role/some_data"
# "aws_access_key_id": "TESTACCESSKEIDTEST11",
# "aws_secret_access_key": "QWERTY1232qdsadfasfg5349BBdf30ekp23odk03",
# }
# self.data_loader = AWSS3DataLoader(**creds)
# with empty creds, use anonymous session
creds = {
}
self.data_loader = AWSS3DataLoader(**creds)
self.retriever = TextQueryRetriever()
self.parser = AWSS3ClickstreamParser(
TextQueryItem, SearchResult, DummyEventType
)
self.splitter = ClickstreamSessionsSplitter()
self.normalizer = DatasetFieldsNormalizer("item", "item_id")
self.storage_producer = CLIPItemStorageProducer(self.normalizer)
self.accumulators = [
MetricsAccumulator("train_loss", True, True, True, True),
MetricsAccumulator(
"train_not_irrelevant_dist_shift", True, True, True, True
),
MetricsAccumulator(
"train_irrelevant_dist_shift", True, True, True, True
),
MetricsAccumulator("test_loss"),
MetricsAccumulator("test_not_irrelevant_dist_shift"),
MetricsAccumulator("test_irrelevant_dist_shift"),
]
self.manager = ExperimentsManager(
|
class DefaultFineTuningMethod(FineTuningMethod):
meta = PluginMeta(
name="Default Fine Tuning Method",
version="0.0.1",
description="A default fine-tuning plugin",
)
def __init__(self):
# uncomment and pass your credentials to use your own s3 bucket
# creds = {
# "role_arn": "arn:aws:iam::123456789012:role/some_data"
# "aws_access_key_id": "TESTACCESSKEIDTEST11",
# "aws_secret_access_key": "QWERTY1232qdsadfasfg5349BBdf30ekp23odk03",
# }
# self.data_loader = AWSS3DataLoader(**creds)
# with empty creds, use anonymous session
creds = {
}
self.data_loader = AWSS3DataLoader(**creds)
self.retriever = TextQueryRetriever()
self.parser = AWSS3ClickstreamParser(
TextQueryItem, SearchResult, DummyEventType
)
self.splitter = ClickstreamSessionsSplitter()
self.normalizer = DatasetFieldsNormalizer("item", "item_id")
self.storage_producer = CLIPItemStorageProducer(self.normalizer)
self.accumulators = [
MetricsAccumulator("train_loss", True, True, True, True),
MetricsAccumulator(
"train_not_irrelevant_dist_shift", True, True, True, True
),
MetricsAccumulator(
"train_irrelevant_dist_shift", True, True, True, True
),
MetricsAccumulator("test_loss"),
MetricsAccumulator("test_not_irrelevant_dist_shift"),
MetricsAccumulator("test_irrelevant_dist_shift"),
]
self.manager = ExperimentsManager( | tracking_uri=settings.MLFLOW_TRACKING_URI, | 0 | 2023-10-31 00:33:13+00:00 | 24k |
facebookresearch/minimax | src/minimax/runners/xp_runner.py | [
{
"identifier": "EvalRunner",
"path": "src/minimax/runners/eval_runner.py",
"snippet": "class EvalRunner:\n def __init__(\n self,\n pop,\n env_names,\n env_kwargs=None,\n n_episodes=10,\n agent_idxs='*',\n render_mode=None):\n\n self.pop = pop\n... | import copy
import time
import numpy as np
import jax
import minimax.envs as envs
import minimax.models as models
import minimax.agents as agents
from functools import partial
from collections import defaultdict
from jax.sharding import Mesh, PartitionSpec as P
from jax.experimental import mesh_utils
from jax.experimental.shard_map import shard_map
from .eval_runner import EvalRunner
from .dr_runner import DRRunner
from .paired_runner import PAIREDRunner
from .plr_runner import PLRRunner
from minimax.util.rl import UEDScore, PopPLRManager | 16,378 | """
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 RunnerInfo:
def __init__(
self,
runner_cls,
is_ued=False):
self.runner_cls = runner_cls
self.is_ued = is_ued
RUNNER_INFO = {
'dr': RunnerInfo(
runner_cls=DRRunner,
),
'plr': RunnerInfo(
| """
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 RunnerInfo:
def __init__(
self,
runner_cls,
is_ued=False):
self.runner_cls = runner_cls
self.is_ued = is_ued
RUNNER_INFO = {
'dr': RunnerInfo(
runner_cls=DRRunner,
),
'plr': RunnerInfo( | runner_cls=PLRRunner, | 3 | 2023-10-28 12:12:01+00:00 | 24k |
nv-tlabs/vid2player3d | uhc/smpllib/smpl_local_robot.py | [
{
"identifier": "Skeleton",
"path": "uhc/khrylib/mocap/skeleton_local.py",
"snippet": "class Skeleton:\n def __init__(\n self, template_dir=\"/hdd/zen/dev/copycat/Copycat/assets/bigfoot_template_v1.pkl\"\n ):\n self.bones = []\n self.name2bone = {}\n self.mass_scale = 1... | import os
import sys
import time
import argparse
import torch
import os.path as osp
import mujoco_py
import numpy as np
import math
import uuid
import atexit
import shutil
from copy import deepcopy
from lxml.etree import XMLParser, parse, Element, SubElement
from lxml import etree
from io import BytesIO
from scipy.spatial import ConvexHull
from stl import mesh
from mujoco_py import load_model_from_path, MjSim, MjViewer
from uhc.khrylib.mocap.skeleton_local import Skeleton
from uhc.khrylib.mocap.skeleton_mesh_local import Skeleton as SkeletonMesh
from uhc.smpllib.smpl_parser import (
SMPL_Parser,
SMPLH_Parser,
SMPLX_Parser,
)
from uhc.utils.geom import quadric_mesh_decimation
from uhc.utils.flags import flags | 15,111 |
# ##############################
# joint_range["L_Thorax"][0] = np.array([-np.pi , np.pi ])
# joint_range["L_Thorax"][1] = np.array([-np.pi , np.pi])
# joint_range["L_Thorax"][2] = np.array([-np.pi, np.pi])
# joint_range["R_Thorax"][0] = np.array([-np.pi , np.pi ])
# joint_range["R_Thorax"][1] = np.array([-np.pi, np.pi])
# joint_range["R_Thorax"][2] = np.array([-np.pi, np.pi])
# joint_range["L_Shoulder"][0] = np.array([-np.pi , np.pi ])
# joint_range["L_Shoulder"][1] = np.array([-np.pi , np.pi / 2])
# joint_range["L_Shoulder"][2] = np.array([-np.pi, np.pi])
# joint_range["R_Shoulder"][0] = np.array([-np.pi , np.pi ])
# joint_range["R_Shoulder"][1] = np.array([-np.pi/2, np.pi])
# joint_range["R_Shoulder"][2] = np.array([-np.pi, np.pi])
# ##############################
# joint_range["L_Hip"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Hip"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["L_Hip"][2] = np.array([-np.pi / 3, np.pi /2])
# joint_range["R_Hip"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Hip"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["R_Hip"][2] = np.array([-np.pi / 2, np.pi / 3])
# joint_range["L_Knee"][0] = np.array([-np.pi / 16, np.pi])
# joint_range["L_Knee"][1] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["L_Knee"][2] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["R_Knee"][0] = np.array([-np.pi / 16, np.pi])
# joint_range["R_Knee"][1] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["R_Knee"][2] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["L_Ankle"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Ankle"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Ankle"][2] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][2] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Toe"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Toe"][1] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["L_Toe"][2] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["R_Toe"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Toe"][1] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["R_Toe"][2] = np.array([-np.pi / 4, np.pi / 4])
self.height = np.max(verts[:, 1]) - np.min(verts[:, 1])
size_dict = {}
if (
len(self.get_params(get_name=True)) > 1 and not params is None
): # ZL: dank code, very dank code
self.set_params(params)
size_dict = self.get_size()
size_dict = self.enforce_length_size(size_dict)
# Gear based size
# gear_dict = self.get_gear()
# for k, v in size_dict.items():
# for idx, suffix in enumerate(["_x", "_y", "_z"]):
# if k + suffix in gear_dict:
# size_dict[k][idx] *= gear_dict[k + suffix]
self.hull_dict = get_joint_geometries(
verts,
joints,
skin_weights,
joint_names,
scale_dict=size_dict,
geom_dir=f"{self.geom_dir}/geom",
)
self.skeleton.load_from_offsets(
joint_offsets,
joint_parents,
joint_axes,
joint_dofs,
joint_range,
sites={},
scale=1,
equalities={},
exclude_contacts = [
["Chest", "L_Shoulder"], ["Chest", "R_Shoulder"], ["Chest", "R_Thorax"], ["Chest", "L_Thorax"],
['L_Hip', 'Pelvis'],
['R_Hip', 'Pelvis'],
['Torso', 'Pelvis'],
['L_Knee', 'L_Hip'],
['R_Knee', 'R_Hip'],
['Spine', 'Torso'],
['L_Ankle', 'L_Knee'],
['R_Ankle', 'R_Knee'],
['Chest', 'Spine'],
['L_Toe', 'L_Ankle'],
['R_Toe', 'R_Ankle'],
['Neck', 'Chest'],
['L_Thorax', 'Chest'],
['R_Thorax', 'Chest'],
['Head', 'Neck'],
['L_Shoulder', 'L_Thorax'],
['R_Shoulder', 'R_Thorax'],
['L_Elbow', 'L_Shoulder'],
['R_Elbow', 'R_Shoulder'],
['L_Wrist', 'L_Elbow'],
['R_Wrist', 'R_Elbow'],
['L_Hand', 'L_Wrist'],
['R_Hand', 'R_Wrist']
],
collision_groups=contype,
conaffinity=conaffinity,
simple_geom=False,
)
else:
|
sys.path.append(os.getcwd())
def parse_vec(string):
return np.fromstring(string, sep=" ")
def parse_fromto(string):
fromto = np.fromstring(string, sep=" ")
return fromto[:3], fromto[3:]
def normalize_range(value, lb, ub):
return (value - lb) / (ub - lb) * 2 - 1
def denormalize_range(value, lb, ub):
return (value + 1) * 0.5 * (ub - lb) + lb
def vec_to_polar(v):
phi = math.atan2(v[1], v[0])
theta = math.acos(v[2])
return np.array([theta, phi])
def polar_to_vec(p):
v = np.zeros(3)
v[0] = math.sin(p[0]) * math.cos(p[1])
v[1] = math.sin(p[0]) * math.sin(p[1])
v[2] = math.cos(p[0])
return v
def in_hull(hull, queries):
tolerance = 1e-3
if len(queries.shape) == 1:
queries = queries[
None,
]
return np.all(
np.add(np.dot(queries, hull.equations[:, :-1].T), hull.equations[:, -1])
<= tolerance,
axis=1,
)
def get_joint_geometries(
smpl_verts,
smpl_jts,
skin_weights,
joint_names,
geom_dir,
scale_dict={},
suffix = None,
verbose=False,
min_num_vert = 50,
):
vert_to_joint = skin_weights.argmax(axis=1)
hull_dict = {}
# create joint geometries
os.makedirs(geom_dir, exist_ok=True)
for jind, jname in enumerate(joint_names):
vind = np.where(vert_to_joint == jind)[0]
if len(vind) == 0:
print(f"{jname} has no vertices!")
continue
vert = (smpl_verts[vind] - smpl_jts[jind]) * scale_dict.get(jname, 1)
hull = ConvexHull(vert)
norm_verts = vert - smpl_jts[jind]
norm_hull = ConvexHull(norm_verts)
hull_dict[jname] = {
"norm_hull": norm_hull,
"norm_verts": norm_verts,
"verts": vert,
"hull": hull,
}
# print(jname, hull.simplices.shape[0])
center = vert[hull.vertices].mean(axis=0)
jgeom = mesh.Mesh(np.zeros(hull.simplices.shape[0], dtype=mesh.Mesh.dtype))
for i, f in enumerate(hull.simplices):
for j in range(3):
jgeom.vectors[i][j] = vert[f[j], :]
# check if the face's normal is facing outward
normal = np.cross(
jgeom.vectors[i][1] - jgeom.vectors[i][0],
jgeom.vectors[i][2] - jgeom.vectors[i][0],
)
out_vec = jgeom.vectors[i].mean(axis=0) - center
if np.dot(normal, out_vec) < 0:
jgeom.vectors[i] = jgeom.vectors[i][[0, 2, 1]] # flip the face
if suffix is None:
fname = f"{geom_dir}/{jname}.stl"
else:
fname = f"{geom_dir}/{jname}_{suffix}.stl"
jgeom.save(fname)
# mesh simplification with vtk
# min_num_vert = 50
min_num_vert = 50
cur_num_vert = len(hull.vertices)
reduction_rate = min(0.9, 1.0 - min_num_vert / cur_num_vert)
quadric_mesh_decimation(fname, reduction_rate, verbose=verbose)
return hull_dict
class Joint:
def __init__(self, node, body):
self.node = node
self.body = body
self.cfg = body.cfg
self.local_coord = body.local_coord
self.name = node.attrib["name"]
self.type = node.attrib["type"] if "type" in node.attrib else "free"
if self.type == "hinge":
self.range = np.deg2rad(parse_vec(node.attrib.get("range", "-360 360")))
actu_node = (
body.tree.getroot().find("actuator").find(f'motor[@joint="{self.name}"]')
)
if actu_node is not None:
self.actuator = Actuator(actu_node, self)
else:
self.actuator = None
self.parse_param_specs()
self.param_inited = False
# tunable parameters
self.pos = parse_vec("0 0 0")
if self.type == "hinge":
self.axis = vec_to_polar(parse_vec(node.attrib["axis"]))
if self.local_coord:
self.pos += body.pos
self.damping = (
parse_vec(node.attrib["damping"])
if "damping" in node.attrib
else np.array([0])
)
self.stiffness = (
parse_vec(node.attrib["stiffness"])
if "stiffness" in node.attrib
else np.array([0])
)
self.armature = (
parse_vec(node.attrib["armature"])
if "armature" in node.attrib
else np.array([0.01])
)
self.frictionloss = (
parse_vec(node.attrib["frictionloss"])
if "frictionloss" in node.attrib
else np.array([0])
)
# import ipdb; ipdb.set_trace()
# assert np.all(self.pos == body.pos)
def __repr__(self):
return "joint_" + self.name
def parse_param_specs(self):
self.param_specs = deepcopy(self.cfg.get("joint_params", {}))
for name, specs in self.param_specs.items():
if "lb" in specs and isinstance(specs["lb"], list):
specs["lb"] = np.array(specs["lb"])
if "ub" in specs and isinstance(specs["ub"], list):
specs["ub"] = np.array(specs["ub"])
def sync_node(self, rename=False, index=0):
pos = self.pos - self.body.pos if self.local_coord else self.pos
if rename:
self.name = self.body.name + "_joint_" + str(index)
self.node.attrib["name"] = self.name
if self.type == "hinge":
axis_vec = polar_to_vec(self.axis)
self.node.attrib["axis"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in axis_vec]
)
self.node.attrib["pos"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in pos]
)
self.node.attrib["damping"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in self.damping]
)
self.node.attrib["stiffness"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in self.stiffness]
)
self.node.attrib["armature"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in self.armature]
)
elif self.type == "free":
pass
if self.actuator is not None:
self.actuator.sync_node()
# if self.name != "Pelvis":
# self.node.attrib["frictionloss"] = " ".join(
# [f"{x:.6f}".rstrip("0").rstrip(".") for x in self.frictionloss]
# )
# if np.sum([self.name.startswith(i) for i in ["L_Knee", "R_Knee", "L_Ankle", "R_Ankle", "L_Toe", "R_Toe"]]):
# self.node.attrib["frictionloss"] = "500"
# self.node.attrib["stiffness"] = "5"
# self.node.attrib["damping"] = "5"
# if self.name != "Pelvis":
# self.node.attrib["frictionloss"] = "5000"
def get_params(self, param_list, get_name=False, pad_zeros=False):
if "axis" in self.param_specs:
if self.type == "hinge":
if get_name:
param_list += ["axis_theta", "axis_phi"]
else:
axis = normalize_range(
self.axis,
np.array([0, -2 * np.pi]),
np.array([np.pi, 2 * np.pi]),
)
param_list.append(axis)
elif pad_zeros:
param_list.append(np.zeros(2))
if self.actuator is not None:
self.actuator.get_params(param_list, get_name)
elif pad_zeros:
param_list.append(
np.zeros(3 if self.type == "free" else 1)
) # ZL currently a workaround for supporting 3D joints
if "damping" in self.param_specs:
if get_name:
param_list.append("damping")
else:
if not self.param_inited and self.param_specs["damping"].get(
"rel", False
):
self.param_specs["damping"]["lb"] += self.damping
self.param_specs["damping"]["ub"] += self.damping
self.param_specs["damping"]["lb"] = max(
self.param_specs["damping"]["lb"],
self.param_specs["damping"].get("min", -np.inf),
)
self.param_specs["damping"]["ub"] = min(
self.param_specs["damping"]["ub"],
self.param_specs["damping"].get("max", np.inf),
)
damping = normalize_range(
self.damping,
self.param_specs["damping"]["lb"],
self.param_specs["damping"]["ub"],
)
param_list.append(damping.flatten())
if "armature" in self.param_specs:
if get_name:
param_list.append("armature")
else:
if not self.param_inited and self.param_specs["armature"].get(
"rel", False
):
self.param_specs["armature"]["lb"] += self.armature
self.param_specs["armature"]["ub"] += self.armature
self.param_specs["armature"]["lb"] = max(
self.param_specs["armature"]["lb"],
self.param_specs["armature"].get("min", -np.inf),
)
self.param_specs["armature"]["ub"] = min(
self.param_specs["armature"]["ub"],
self.param_specs["armature"].get("max", np.inf),
)
armature = normalize_range(
self.armature,
self.param_specs["armature"]["lb"],
self.param_specs["armature"]["ub"],
)
param_list.append(armature.flatten())
if "stiffness" in self.param_specs:
if get_name:
param_list.append("stiffness")
else:
if not self.param_inited and self.param_specs["stiffness"].get(
"rel", False
):
self.param_specs["stiffness"]["lb"] += self.stiffness
self.param_specs["stiffness"]["ub"] += self.stiffness
self.param_specs["stiffness"]["lb"] = max(
self.param_specs["stiffness"]["lb"],
self.param_specs["stiffness"].get("min", -np.inf),
)
self.param_specs["stiffness"]["ub"] = min(
self.param_specs["stiffness"]["ub"],
self.param_specs["stiffness"].get("max", np.inf),
)
stiffness = normalize_range(
self.stiffness,
self.param_specs["stiffness"]["lb"],
self.param_specs["stiffness"]["ub"],
)
param_list.append(stiffness.flatten())
if "frictionloss" in self.param_specs:
if get_name:
param_list.append("frictionloss")
else:
if not self.param_inited and self.param_specs["frictionloss"].get(
"rel", False
):
self.param_specs["frictionloss"]["lb"] += self.frictionloss
self.param_specs["frictionloss"]["ub"] += self.frictionloss
self.param_specs["frictionloss"]["lb"] = max(
self.param_specs["frictionloss"]["lb"],
self.param_specs["frictionloss"].get("min", -np.inf),
)
self.param_specs["frictionloss"]["ub"] = min(
self.param_specs["frictionloss"]["ub"],
self.param_specs["frictionloss"].get("max", np.inf),
)
frictionloss = normalize_range(
self.frictionloss,
self.param_specs["frictionloss"]["lb"],
self.param_specs["frictionloss"]["ub"],
)
param_list.append(frictionloss.flatten())
if not get_name:
self.param_inited = True
# import ipdb; ipdb.set_trace()
def set_params(self, params, pad_zeros=False):
if "axis" in self.param_specs:
if self.type == "hinge":
self.axis = denormalize_range(
params[:2], np.array([0, -2 * np.pi]), np.array([np.pi, 2 * np.pi])
)
params = params[2:]
elif pad_zeros:
params = params[2:]
if self.actuator is not None:
params = self.actuator.set_params(params)
elif pad_zeros:
params = params[1:]
# Order of this matters!!! Should always be damping, aramature, stiffness (the order they are read)
if "damping" in self.param_specs:
self.damping = denormalize_range(
params[[0]],
self.param_specs["damping"]["lb"],
self.param_specs["damping"]["ub"],
)
params = params[1:]
if "armature" in self.param_specs:
self.armature = denormalize_range(
params[[0]],
self.param_specs["armature"]["lb"],
self.param_specs["armature"]["ub"],
)
params = params[1:]
if "stiffness" in self.param_specs:
self.stiffness = denormalize_range(
params[[0]],
self.param_specs["stiffness"]["lb"],
self.param_specs["stiffness"]["ub"],
)
params = params[1:]
if "frictionloss" in self.param_specs:
self.frictionloss = denormalize_range(
params[[0]],
self.param_specs["frictionloss"]["lb"],
self.param_specs["frictionloss"]["ub"],
)
params = params[1:]
return params
class Geom:
def __init__(self, node, body):
self.node = node
self.body = body
self.cfg = body.cfg
self.local_coord = body.local_coord
self.name = node.attrib.get("name", "")
self.type = node.attrib["type"]
self.density = (
parse_vec(node.attrib["density"]) / 1000
if "density" in node.attrib
else np.array([1])
)
self.parse_param_specs()
self.param_inited = False
# tunable parameters
# self.size = (
# parse_vec(node.attrib["size"]) if "size" in node.attrib else np.array([0])
# )
self.size = (
parse_vec(node.attrib["size"])
if "size" in node.attrib
else np.array([1, 1, 1])
)
if self.type == "box":
self.start = self.end = self.pos = parse_vec(node.attrib["pos"])
self.pos_delta = np.array([0, 0, 0])
self.rot = parse_vec(node.attrib["quat"])
elif self.type == "sphere":
self.pos_delta = np.array([0, 0, 0])
self.start = self.end = self.pos = parse_vec(node.attrib["pos"])
elif self.type == "capsule":
self.start, self.end = parse_fromto(node.attrib["fromto"])
elif self.type == "mesh":
self.start, self.end = body.pos.copy(), body.pos.copy()
if self.local_coord:
self.start += body.pos
self.end += body.pos
if body.bone_start is None:
self.bone_start = self.start.copy()
body.bone_start = self.bone_start.copy()
else:
self.bone_start = body.bone_start.copy()
self.ext_start = np.linalg.norm(
self.bone_start - self.start
) ## Geom extension from bone start
def __repr__(self):
return "geom_" + self.name
def parse_param_specs(self):
self.param_specs = deepcopy(self.cfg.get("geom_params", {}))
for name, specs in self.param_specs.items():
if "lb" in specs and isinstance(specs["lb"], list):
if self.type == "box":
specs["lb"] = np.array([specs["lb"]] * 3)
elif self.type == "capsule":
specs["lb"] = np.array(specs["lb"])
if "ub" in specs and isinstance(specs["ub"], list):
if self.type == "box":
specs["lb"] = np.array([specs["lb"]] * 3)
elif self.type == "capsule":
specs["lb"] = np.array(specs["lb"])
def update_start(self):
if self.type == "capsule":
vec = self.bone_start - self.end
self.start = self.bone_start + vec * (self.ext_start / np.linalg.norm(vec))
def sync_node(self):
# self.node.attrib['name'] = self.name
self.node.attrib.pop("name", None)
if not self.size is None:
self.node.attrib["size"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in self.size]
)
self.node.attrib["density"] = " ".join(
[f"{x * 1000:.6f}".rstrip("0").rstrip(".") for x in self.density]
)
# if self.type == "capsule":
# start = self.start - self.body.pos if self.local_coord else self.start
# end = self.end - self.body.pos if self.local_coord else self.end
# self.node.attrib["fromto"] = " ".join(
# [
# f"{x:.6f}".rstrip("0").rstrip(".")
# for x in np.concatenate([start, end])
# ]
# )
# elif self.type == "box" or self.type == "sphere":
# # self.node.attrib["pos"] = " ".join(
# # [f"{x:.6f}".rstrip("0").rstrip(".") for x in self.pos + self.pos_delta]
# # )
# import ipdb; ipdb.set_trace()
# pass
def get_params(self, param_list, get_name=False, pad_zeros=False):
if "size" in self.param_specs:
if get_name:
param_list.append("size")
else:
if (
self.type == "capsule"
or self.type == "box"
or self.type == "sphere"
or self.type == "mesh"
):
if not self.param_inited and self.param_specs["size"].get(
"rel", False
):
self.param_specs["size"]["lb"] += self.size
self.param_specs["size"]["ub"] += self.size
self.param_specs["size"]["lb"] = max(
self.param_specs["size"]["lb"],
self.param_specs["size"].get("min", -np.inf),
)
self.param_specs["size"]["ub"] = min(
self.param_specs["size"]["ub"],
self.param_specs["size"].get("max", np.inf),
)
size = normalize_range(
self.size,
self.param_specs["size"]["lb"],
self.param_specs["size"]["ub"],
)
param_list.append(size.flatten())
if pad_zeros and self.type == "capsule":
param_list.append(
np.zeros(2)
) # capsule has needs to be 3 for GNN
elif pad_zeros:
param_list.append(np.zeros(self.size.shape))
if "ext_start" in self.param_specs:
if get_name:
param_list.append("ext_start")
else:
if (
self.type == "capsule"
or self.type == "box"
or self.type == "sphere"
):
if not self.param_inited and self.param_specs["ext_start"].get(
"rel", False
):
self.param_specs["ext_start"]["lb"] += self.ext_start
self.param_specs["ext_start"]["ub"] += self.ext_start
self.param_specs["ext_start"]["lb"] = max(
self.param_specs["ext_start"]["lb"],
self.param_specs["ext_start"].get("min", -np.inf),
)
self.param_specs["ext_start"]["ub"] = min(
self.param_specs["ext_start"]["ub"],
self.param_specs["ext_start"].get("max", np.inf),
)
ext_start = normalize_range(
self.ext_start,
self.param_specs["ext_start"]["lb"],
self.param_specs["ext_start"]["ub"],
)
param_list.append(ext_start.flatten())
elif pad_zeros:
param_list.append(np.zeros(self.size.shape))
if "density" in self.param_specs:
if get_name:
param_list.append("density")
else:
if not self.param_inited and self.param_specs["density"].get(
"rel", False
):
self.param_specs["density"]["lb"] += self.density
self.param_specs["density"]["ub"] += self.density
self.param_specs["density"]["lb"] = max(
self.param_specs["density"]["lb"],
self.param_specs["density"].get("min", -np.inf),
)
self.param_specs["density"]["ub"] = min(
self.param_specs["density"]["ub"],
self.param_specs["density"].get("max", np.inf),
)
density = normalize_range(
self.density,
self.param_specs["density"]["lb"],
self.param_specs["density"]["ub"],
)
param_list.append(density.flatten())
# if pad_zeros:
# param_list.append(np.zeros(self.density.shape))
if "pos_delta" in self.param_specs:
if get_name:
param_list.append("pos_delta")
else:
if self.type == "box" or self.type == "sphere":
if not self.param_inited and self.param_specs["pos_delta"].get(
"rel", False
):
self.param_specs["pos_delta"]["lb"] += self.density
self.param_specs["pos_delta"]["ub"] += self.density
self.param_specs["pos_delta"]["lb"] = max(
self.param_specs["pos_delta"]["lb"],
self.param_specs["pos_delta"].get("min", -np.inf),
)
self.param_specs["pos_delta"]["ub"] = min(
self.param_specs["pos_delta"]["ub"],
self.param_specs["pos_delta"].get("max", np.inf),
)
pos_delta = normalize_range(
self.pos_delta,
self.param_specs["pos_delta"]["lb"],
self.param_specs["pos_delta"]["ub"],
)
param_list.append(pos_delta.flatten())
elif pad_zeros:
param_list.append(np.zeros(3))
if not get_name:
self.param_inited = True
def set_params(self, params, pad_zeros=False):
if "size" in self.param_specs:
if (
self.type == "capsule"
or self.type == "box"
or self.type == "sphere"
or self.type == "mesh"
):
if len(self.size) == 1:
self.size = denormalize_range(
params[[0]],
self.param_specs["size"]["lb"],
self.param_specs["size"]["ub"],
)
params = params[1:]
elif len(self.size) == 3:
self.size = denormalize_range(
np.array(params[:3]),
self.param_specs["size"]["lb"],
self.param_specs["size"]["ub"],
)
params = params[3:]
elif pad_zeros:
params = params[1:]
if "ext_start" in self.param_specs:
if self.type == "capsule" or self.type == "box" or self.type == "sphere":
self.ext_start = denormalize_range(
params[[0]],
self.param_specs["ext_start"]["lb"],
self.param_specs["ext_start"]["ub"],
)
params = params[1:]
elif pad_zeros:
params = params[1:]
if "density" in self.param_specs:
if (
self.type == "capsule"
or self.type == "box"
or self.type == "sphere"
or self.type == "mesh"
):
self.density = denormalize_range(
params[[0]],
self.param_specs["density"]["lb"],
self.param_specs["density"]["ub"],
)
params = params[1:]
elif pad_zeros:
params = params[1:]
if "pos_delta" in self.param_specs:
if self.type == "box" or self.type == "sphere":
self.pos_delta = denormalize_range(
np.array(params[:3]),
self.param_specs["pos_delta"]["lb"],
self.param_specs["pos_delta"]["ub"],
)
params = params[3:]
elif pad_zeros:
params = params[3:]
return params
class Actuator:
def __init__(self, node, joint):
self.node = node
self.joint = joint
self.cfg = joint.cfg
self.joint_name = node.attrib["joint"]
self.name = self.joint_name
self.parse_param_specs()
self.param_inited = False
# tunable parameters
self.gear = float(node.attrib["gear"])
def parse_param_specs(self):
self.param_specs = deepcopy(self.cfg.get("actuator_params", {}))
for name, specs in self.param_specs.items():
if "lb" in specs and isinstance(specs["lb"], list):
specs["lb"] = np.array(specs["lb"])
if "ub" in specs and isinstance(specs["ub"], list):
specs["ub"] = np.array(specs["ub"])
def sync_node(self):
self.node.attrib["gear"] = f"{self.gear:.6f}".rstrip("0").rstrip(".")
self.name = self.joint.name
self.node.attrib["name"] = self.name
self.node.attrib["joint"] = self.joint.name
def get_params(self, param_list, get_name=False):
if "gear" in self.param_specs:
if get_name:
param_list.append("gear")
else:
if not self.param_inited and self.param_specs["gear"].get("rel", False):
self.param_specs["gear"]["lb"] += self.gear
self.param_specs["gear"]["ub"] += self.gear
self.param_specs["gear"]["lb"] = max(
self.param_specs["gear"]["lb"],
self.param_specs["gear"].get("min", -np.inf),
)
self.param_specs["gear"]["ub"] = min(
self.param_specs["gear"]["ub"],
self.param_specs["gear"].get("max", np.inf),
)
gear = normalize_range(
self.gear,
self.param_specs["gear"]["lb"],
self.param_specs["gear"]["ub"],
)
param_list.append(np.array([gear]))
if not get_name:
self.param_inited = True
def set_params(self, params):
if "gear" in self.param_specs:
self.gear = denormalize_range(
params[0].item(),
self.param_specs["gear"]["lb"],
self.param_specs["gear"]["ub"],
)
params = params[1:]
return params
class Body:
def __init__(self, node, parent_body, robot, cfg, new_body=False):
self.node = node
self.parent = parent_body
self.new_body = new_body
if parent_body is not None:
parent_body.child.append(self)
parent_body.cind += 1
self.depth = parent_body.depth + 1
else:
self.depth = 0
self.robot = robot
self.cfg = cfg
self.tree = robot.tree
self.local_coord = robot.local_coord
self.name = (
node.attrib["name"]
if "name" in node.attrib
else self.parent.name + f"_child{len(self.parent.child)}"
)
self.child = []
self.cind = 0
self.pos = parse_vec(node.attrib["pos"])
if self.local_coord and parent_body is not None:
self.pos += parent_body.pos
if cfg.get("init_root_from_geom", False):
self.bone_start = None if parent_body is None else self.pos.copy()
else:
self.bone_start = self.pos.copy()
self.joints = [Joint(x, self) for x in node.findall('joint[@type="hinge"]')] + \
[Joint(x, self) for x in node.findall('joint[@type="free"]')] + \
[Joint(x, self) for x in node.findall('freejoint')]
# self.geoms = [Geom(x, self) for x in node.findall('geom[@type="capsule"]')]
supported_geoms = self.cfg.get("supported_geoms", ["capsule", "box"])
self.geoms = [
Geom(x, self)
for geom_type in supported_geoms
for x in node.findall(f'geom[@type="{geom_type}"]')
]
# self.geoms = [Geom(x, self) for x in node.findall('geom[@type="capsule"]')] + [Geom(x, self) for x in node.findall('geom[@type="sphere"]')] + [Geom(x, self) for x in node.findall('geom[@type="box"]')]
self.parse_param_specs()
self.param_inited = False
# parameters
self.bone_end = None
self.bone_offset = None
def __repr__(self):
return "body_" + self.name
def parse_param_specs(self):
self.param_specs = deepcopy(self.cfg.get("body_params", {}))
for name, specs in self.param_specs.items():
if "lb" in specs and isinstance(specs["lb"], list):
specs["lb"] = np.array(specs["lb"])
if "ub" in specs and isinstance(specs["ub"], list):
specs["ub"] = np.array(specs["ub"])
if name == "bone_ang":
specs["lb"] = np.deg2rad(specs["lb"])
specs["ub"] = np.deg2rad(specs["ub"])
def reindex(self):
if self.parent is None:
self.index = "0"
else:
ind = self.parent.child.index(self) + 1
pname = "" if self.parent.index == "0" else self.parent.index
self.index = str(ind) + pname
if self.new_body:
self.name = self.index
def init(self):
if len(self.child) > 0:
bone_ends = [x.bone_start for x in self.child]
else:
bone_ends = [x.end for x in self.geoms]
if len(bone_ends) > 0:
self.bone_end = np.mean(np.stack(bone_ends), axis=0)
self.bone_offset = self.bone_end - self.bone_start
def get_actuator_name(self):
for joint in self.joints:
if joint.actuator is not None:
return joint.actuator.name
def get_joint_range(self):
assert len(self.joints) == 1
return self.joints[0].range
def sync_node(self):
pos = (
self.pos - self.parent.pos
if self.local_coord and self.parent is not None
else self.pos
)
self.node.attrib["name"] = self.name
self.node.attrib["pos"] = " ".join(
[f"{x:.6f}".rstrip("0").rstrip(".") for x in pos]
)
for idx, joint in enumerate(self.joints):
joint.sync_node(rename=self.new_body, index=idx)
for geom in self.geoms:
geom.sync_node()
def sync_geom(self):
for geom in self.geoms:
geom.bone_start = self.bone_start.copy()
# geom.end = self.bone_end.copy()
# geom.update_start()
def sync_joint(self):
if self.parent is not None:
for joint in self.joints:
joint.pos = self.pos.copy()
def rebuild(self):
if self.parent is not None:
# self.bone_start = self.parent.bone_end.copy()
self.pos = self.bone_start.copy()
if self.bone_offset is not None:
self.bone_end = self.bone_start + self.bone_offset
if self.parent is None and self.cfg.get("no_root_offset", False):
self.bone_end = self.bone_start
self.sync_geom()
self.sync_joint()
def get_params(
self, param_list, get_name=False, pad_zeros=False, demap_params=False
):
if self.bone_offset is not None and "offset" in self.param_specs:
if get_name:
if self.param_specs["offset"]["type"] == "xz":
param_list += ["offset_x", "offset_z"]
elif self.param_specs["offset"]["type"] == "xy":
param_list += ["offset_x", "offset_y"]
else:
param_list += ["offset_x", "offset_y", "offset_z"]
else:
if self.param_specs["offset"]["type"] == "xz":
offset = self.bone_offset[[0, 2]]
elif self.param_specs["offset"]["type"] == "xy":
offset = self.bone_offset[[0, 1]]
else:
offset = self.bone_offset
if not self.param_inited and self.param_specs["offset"].get(
"rel", False
):
self.param_specs["offset"]["lb"] += offset
self.param_specs["offset"]["ub"] += offset
self.param_specs["offset"]["lb"] = np.maximum(
self.param_specs["offset"]["lb"],
self.param_specs["offset"].get(
"min", np.full_like(offset, -np.inf)
),
)
self.param_specs["offset"]["ub"] = np.minimum(
self.param_specs["offset"]["ub"],
self.param_specs["offset"].get(
"max", np.full_like(offset, np.inf)
),
)
offset = normalize_range(
offset,
self.param_specs["offset"]["lb"],
self.param_specs["offset"]["ub"],
)
param_list.append(offset.flatten())
if self.bone_offset is not None and "bone_len" in self.param_specs:
if get_name:
param_list += ["bone_len"]
else:
bone_len = np.linalg.norm(self.bone_offset)
if not self.param_inited and self.param_specs["bone_len"].get(
"rel", False
):
self.param_specs["bone_len"]["lb"] += bone_len
self.param_specs["bone_len"]["ub"] += bone_len
self.param_specs["bone_len"]["lb"] = max(
self.param_specs["bone_len"]["lb"],
self.param_specs["bone_len"].get("min", -np.inf),
)
self.param_specs["bone_len"]["ub"] = min(
self.param_specs["bone_len"]["ub"],
self.param_specs["bone_len"].get("max", np.inf),
)
bone_len = normalize_range(
bone_len,
self.param_specs["bone_len"]["lb"],
self.param_specs["bone_len"]["ub"],
)
param_list.append(np.array([bone_len]))
if self.bone_offset is not None and "bone_ang" in self.param_specs:
if get_name:
param_list += ["bone_ang"]
else:
bone_ang = math.atan2(self.bone_offset[2], self.bone_offset[0])
if not self.param_inited and self.param_specs["bone_ang"].get(
"rel", False
):
self.param_specs["bone_ang"]["lb"] += bone_ang
self.param_specs["bone_ang"]["ub"] += bone_ang
self.param_specs["bone_ang"]["lb"] = max(
self.param_specs["bone_ang"]["lb"],
self.param_specs["bone_ang"].get("min", -np.inf),
)
self.param_specs["bone_ang"]["ub"] = min(
self.param_specs["bone_ang"]["ub"],
self.param_specs["bone_ang"].get("max", np.inf),
)
bone_ang = normalize_range(
bone_ang,
self.param_specs["bone_ang"]["lb"],
self.param_specs["bone_ang"]["ub"],
)
param_list.append(np.array([bone_ang]))
for joint in self.joints:
joint.get_params(param_list, get_name, pad_zeros)
for geom in self.geoms:
geom.get_params(param_list, get_name, pad_zeros)
if not get_name:
self.param_inited = True
if demap_params and not get_name and len(param_list) > 0:
params = self.robot.demap_params(np.concatenate(param_list))
return params
def set_params(self, params, pad_zeros=False, map_params=False):
if map_params:
params = self.robot.map_params(params)
if self.bone_offset is not None and "offset" in self.param_specs:
if self.param_specs["offset"]["type"] in {"xz", "xy"}:
offset = denormalize_range(
params[:2],
self.param_specs["offset"]["lb"],
self.param_specs["offset"]["ub"],
)
if np.all(offset == 0.0):
offset[0] += 1e-8
if self.param_specs["offset"]["type"] == "xz":
self.bone_offset[[0, 2]] = offset
elif self.param_specs["offset"]["type"] == "xy":
self.bone_offset[[0, 1]] = offset
params = params[2:]
else:
offset = denormalize_range(
params[:3],
self.param_specs["offset"]["lb"],
self.param_specs["offset"]["ub"],
)
if np.all(offset == 0.0):
offset[0] += 1e-8
self.bone_offset[:] = offset
params = params[3:]
if self.bone_offset is not None and "bone_len" in self.param_specs:
bone_len = denormalize_range(
params[0].item(),
self.param_specs["bone_len"]["lb"],
self.param_specs["bone_len"]["ub"],
)
bone_len = max(bone_len, 1e-4)
params = params[1:]
elif self.bone_offset is not None:
bone_len = np.linalg.norm(self.bone_offset)
if self.bone_offset is not None and "bone_ang" in self.param_specs:
bone_ang = denormalize_range(
params[0].item(),
self.param_specs["bone_ang"]["lb"],
self.param_specs["bone_ang"]["ub"],
)
params = params[1:]
elif self.bone_offset is not None:
bone_ang = math.atan2(self.bone_offset[2], self.bone_offset[0])
if "bone_len" in self.param_specs or "bone_ang" in self.param_specs:
self.bone_offset = np.array(
[bone_len * math.cos(bone_ang), 0, bone_len * math.sin(bone_ang)]
)
for joint in self.joints:
params = joint.set_params(params, pad_zeros)
for geom in self.geoms:
params = geom.set_params(params, pad_zeros)
# rebuild bone, geom, joint
self.rebuild()
return params
class Robot:
def __init__(self, cfg, data_dir="data/smpl", model_xml_path=None, masterfoot=False, create_default_skeleton=False, clean_up=False):
self.bodies = []
self.weight = 0
self.height = 0
self.cfg = cfg
if model_xml_path is not None:
self.set_model_xml_path(model_xml_path)
else:
self.model_xml_path = None
self.param_mapping = cfg.get("param_mapping", "clip")
self.smpl_model = cfg.get("model", "smpl")
self.mesh = cfg.get("mesh", False)
self.gender = cfg.get("gender", "neutral")
self.flatfoot = cfg.get("flatfoot", True)
self.rel_joint_lm = cfg.get(
"rel_joint_lm", True
) # Rolling this out worldwide!!
self.masterfoot = masterfoot
self.param_specs = self.cfg.get("body_params", {})
self.hull_dict = {}
self.beta = (
torch.zeros((1, 10)).float()
if self.smpl_model == "smpl"
else torch.zeros((1, 16)).float()
)
if self.smpl_model == "smpl":
self.smpl_parser_n = SMPL_Parser(model_path=data_dir, gender="neutral", create_transl=False)
self.smpl_parser_m = SMPL_Parser(model_path=data_dir, gender="male", create_transl=False)
self.smpl_parser_f = SMPL_Parser(model_path=data_dir, gender="female", create_transl=False)
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
)
if create_default_skeleton:
self.load_from_skeleton()
if clean_up:
atexit.register(self.clean_up)
def set_model_xml_path(self, model_xml_path):
self.model_xml_path = model_xml_path
self.model_dir = osp.dirname(model_xml_path)
self.geom_dir = f'{self.model_dir}/mesh/{uuid.uuid4()}'
os.makedirs(self.model_dir, exist_ok=True)
def clean_up(self):
if os.path.exists(self.model_xml_path):
os.remove(self.model_xml_path)
if osp.isdir(self.geom_dir):
shutil.rmtree(self.geom_dir, ignore_errors=True)
def get_joint_vertices(self, pose_aa, th_betas=None, th_trans=None, gender=[0]):
if gender[0] == 0:
smpl_parser = self.smpl_parser_n
elif gender[0] == 1:
smpl_parser = self.smpl_parser_m
elif gender[0] == 2:
smpl_parser = self.smpl_parser_f
else:
print(gender)
raise Exception("Gender Not Supported!!")
vertices, joints = smpl_parser.get_joints_verts(
pose=pose_aa, th_betas=th_betas, th_trans=th_trans
)
return vertices, joints
def load_from_skeleton(
self,
betas=None,
scale=None,
v_template=None,
gender=[0],
objs_info=None,
obj_pose=None,
params=None,
model_xml_path=None,
):
if model_xml_path is not None:
self.set_model_xml_path(model_xml_path)
self.tree = None # xml tree
if gender[0] == 0:
self.smpl_parser = smpl_parser = self.smpl_parser_n
elif gender[0] == 1:
self.smpl_parser = smpl_parser = self.smpl_parser_m
elif gender[0] == 2:
self.smpl_parser = smpl_parser = self.smpl_parser_f
else:
print(gender)
raise Exception("Gender Not Supported!!")
if betas is None and self.beta is None:
betas = (
torch.zeros((1, 10)).float()
if self.smpl_model == "smpl"
else torch.zeros((1, 16)).float()
)
else:
if len(betas.shape) == 1:
betas = betas[None, :]
if params is None:
self.beta = betas if not betas is None else self.beta
else:
# If params is not none, we need to set the beta first
betas = self.map_params(betas)
self.beta = torch.from_numpy(
denormalize_range(
betas.numpy().squeeze(),
self.param_specs["beta"]["lb"],
self.param_specs["beta"]["ub"],
)[
None,
]
)
if flags.debug:
print(self.beta)
## Clear up beta for smpl and smplh
if self.smpl_model == "smpl" and self.beta.shape[1] == 16:
self.beta = self.beta[:, :10]
# print(f"Incorrect shape size for {self.model}!!!")
elif self.smpl_model == "smplh" and self.beta.shape[1] == 10:
self.beta = torch.hstack([self.beta, torch.zeros((1, 6)).float()])
# print(f"Incorrect shape size for {self.model}!!!")
if self.mesh:
rel_geom_dir = os.path.relpath(self.geom_dir, self.model_dir)
self.skeleton = SkeletonMesh(self.geom_dir, rel_geom_dir)
zero_pose = torch.zeros((1,72))
(
verts,
joints,
skin_weights,
joint_names,
joint_offsets,
joint_parents,
joint_axes,
joint_dofs,
joint_range,
contype,
conaffinity,
) = (smpl_parser.get_mesh_offsets(
zero_pose=zero_pose, betas=self.beta, flatfoot=self.flatfoot, scale=scale)
if self.smpl_model != "smplx" else
smpl_parser.get_mesh_offsets(v_template=v_template))
# if self.rel_joint_lm:
# # if False:
# joint_range["Head"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["Head"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["Head"][2] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Chest"][0] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Chest"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Chest"][2] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Spine"][0] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Spine"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Spine"][2] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Torso"][0] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["Torso"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["Torso"][2] = np.array([-np.pi / 3, np.pi / 3])
# ##############################
# joint_range["L_Thorax"][0] = np.array([-np.pi , np.pi ])
# joint_range["L_Thorax"][1] = np.array([-np.pi , np.pi])
# joint_range["L_Thorax"][2] = np.array([-np.pi, np.pi])
# joint_range["R_Thorax"][0] = np.array([-np.pi , np.pi ])
# joint_range["R_Thorax"][1] = np.array([-np.pi, np.pi])
# joint_range["R_Thorax"][2] = np.array([-np.pi, np.pi])
# joint_range["L_Shoulder"][0] = np.array([-np.pi , np.pi ])
# joint_range["L_Shoulder"][1] = np.array([-np.pi , np.pi / 2])
# joint_range["L_Shoulder"][2] = np.array([-np.pi, np.pi])
# joint_range["R_Shoulder"][0] = np.array([-np.pi , np.pi ])
# joint_range["R_Shoulder"][1] = np.array([-np.pi/2, np.pi])
# joint_range["R_Shoulder"][2] = np.array([-np.pi, np.pi])
# ##############################
# joint_range["L_Hip"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Hip"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["L_Hip"][2] = np.array([-np.pi / 3, np.pi /2])
# joint_range["R_Hip"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Hip"][1] = np.array([-np.pi / 3, np.pi / 3])
# joint_range["R_Hip"][2] = np.array([-np.pi / 2, np.pi / 3])
# joint_range["L_Knee"][0] = np.array([-np.pi / 16, np.pi])
# joint_range["L_Knee"][1] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["L_Knee"][2] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["R_Knee"][0] = np.array([-np.pi / 16, np.pi])
# joint_range["R_Knee"][1] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["R_Knee"][2] = np.array([-np.pi / 16, np.pi / 16])
# joint_range["L_Ankle"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Ankle"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Ankle"][2] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][1] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Ankle"][2] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Toe"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["L_Toe"][1] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["L_Toe"][2] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["R_Toe"][0] = np.array([-np.pi / 2, np.pi / 2])
# joint_range["R_Toe"][1] = np.array([-np.pi / 4, np.pi / 4])
# joint_range["R_Toe"][2] = np.array([-np.pi / 4, np.pi / 4])
self.height = np.max(verts[:, 1]) - np.min(verts[:, 1])
size_dict = {}
if (
len(self.get_params(get_name=True)) > 1 and not params is None
): # ZL: dank code, very dank code
self.set_params(params)
size_dict = self.get_size()
size_dict = self.enforce_length_size(size_dict)
# Gear based size
# gear_dict = self.get_gear()
# for k, v in size_dict.items():
# for idx, suffix in enumerate(["_x", "_y", "_z"]):
# if k + suffix in gear_dict:
# size_dict[k][idx] *= gear_dict[k + suffix]
self.hull_dict = get_joint_geometries(
verts,
joints,
skin_weights,
joint_names,
scale_dict=size_dict,
geom_dir=f"{self.geom_dir}/geom",
)
self.skeleton.load_from_offsets(
joint_offsets,
joint_parents,
joint_axes,
joint_dofs,
joint_range,
sites={},
scale=1,
equalities={},
exclude_contacts = [
["Chest", "L_Shoulder"], ["Chest", "R_Shoulder"], ["Chest", "R_Thorax"], ["Chest", "L_Thorax"],
['L_Hip', 'Pelvis'],
['R_Hip', 'Pelvis'],
['Torso', 'Pelvis'],
['L_Knee', 'L_Hip'],
['R_Knee', 'R_Hip'],
['Spine', 'Torso'],
['L_Ankle', 'L_Knee'],
['R_Ankle', 'R_Knee'],
['Chest', 'Spine'],
['L_Toe', 'L_Ankle'],
['R_Toe', 'R_Ankle'],
['Neck', 'Chest'],
['L_Thorax', 'Chest'],
['R_Thorax', 'Chest'],
['Head', 'Neck'],
['L_Shoulder', 'L_Thorax'],
['R_Shoulder', 'R_Thorax'],
['L_Elbow', 'L_Shoulder'],
['R_Elbow', 'R_Shoulder'],
['L_Wrist', 'L_Elbow'],
['R_Wrist', 'R_Elbow'],
['L_Hand', 'L_Wrist'],
['R_Hand', 'R_Wrist']
],
collision_groups=contype,
conaffinity=conaffinity,
simple_geom=False,
)
else: | self.skeleton = Skeleton() | 1 | 2023-10-30 20:43:43+00:00 | 24k |
masked-spacetime-hashing/msth | nerfstudio/models/semantic_nerfw.py | [
{
"identifier": "RayBundle",
"path": "nerfstudio/cameras/rays.py",
"snippet": "class RayBundle(TensorDataclass):\n \"\"\"A bundle of ray parameters.\"\"\"\n\n # TODO(ethan): make sure the sizes with ... are correct\n origins: TensorType[..., 3]\n \"\"\"Ray origins (XYZ)\"\"\"\n directions... | from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Type
from torch.nn import Parameter
from torchmetrics import PeakSignalNoiseRatio
from torchmetrics.functional import structural_similarity_index_measure
from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
from nerfstudio.cameras.rays import RayBundle
from nerfstudio.data.dataparsers.base_dataparser import Semantics
from nerfstudio.engine.callbacks import (
TrainingCallback,
TrainingCallbackAttributes,
TrainingCallbackLocation,
)
from nerfstudio.field_components.field_heads import FieldHeadNames
from nerfstudio.field_components.spatial_distortions import SceneContraction
from nerfstudio.fields.density_fields import HashMLPDensityField
from nerfstudio.fields.nerfacto_field import TCNNNerfactoField
from nerfstudio.model_components.losses import MSELoss, distortion_loss, interlevel_loss
from nerfstudio.model_components.ray_samplers import ProposalNetworkSampler
from nerfstudio.model_components.renderers import (
AccumulationRenderer,
DepthRenderer,
RGBRenderer,
SemanticRenderer,
UncertaintyRenderer,
)
from nerfstudio.model_components.scene_colliders import NearFarCollider
from nerfstudio.models.base_model import Model
from nerfstudio.models.nerfacto import NerfactoModelConfig
from nerfstudio.utils import colormaps
import numpy as np
import torch | 14,794 | # 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.
"""
Semantic NeRF-W implementation which should be fast enough to view in the viewer.
"""
from __future__ import annotations
@dataclass
class SemanticNerfWModelConfig(NerfactoModelConfig):
"""Nerfacto Model Config"""
_target: Type = field(default_factory=lambda: SemanticNerfWModel)
use_transient_embedding: bool = False
"""Whether to use transient embedding."""
semantic_loss_weight: float = 1.0
pass_semantic_gradients: bool = False
class SemanticNerfWModel(Model):
"""Nerfacto model
Args:
config: Nerfacto configuration to instantiate model
"""
config: SemanticNerfWModelConfig
def __init__(self, config: SemanticNerfWModelConfig, metadata: Dict, **kwargs) -> None:
assert "semantics" in metadata.keys() and isinstance(metadata["semantics"], Semantics)
self.semantics = metadata["semantics"]
super().__init__(config=config, **kwargs)
self.colormap = self.semantics.colors.clone().detach().to(self.device)
def populate_modules(self):
"""Set the fields and modules."""
super().populate_modules()
scene_contraction = SceneContraction(order=float("inf"))
if self.config.use_transient_embedding:
raise ValueError("Transient embedding is not fully working for semantic nerf-w.")
# Fields
self.field = TCNNNerfactoField(
self.scene_box.aabb,
num_levels=self.config.num_levels,
max_res=self.config.max_res,
log2_hashmap_size=self.config.log2_hashmap_size,
spatial_distortion=scene_contraction,
num_images=self.num_train_data,
use_average_appearance_embedding=self.config.use_average_appearance_embedding,
use_transient_embedding=self.config.use_transient_embedding,
use_semantics=True,
num_semantic_classes=len(self.semantics.classes),
pass_semantic_gradients=self.config.pass_semantic_gradients,
)
# Build the proposal network(s)
self.proposal_networks = torch.nn.ModuleList()
if self.config.use_same_proposal_network:
network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
self.proposal_networks.append(network)
self.density_fns = [network.density_fn for _ in range(self.config.num_proposal_iterations)]
else:
for _ in range(self.config.num_proposal_iterations):
network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
self.proposal_networks.append(network)
self.density_fns = [network.density_fn for network in self.proposal_networks]
# Collider
self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
# Samplers
self.proposal_sampler = ProposalNetworkSampler(
num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
num_proposal_network_iterations=self.config.num_proposal_iterations,
single_jitter=self.config.use_single_jitter,
)
# renderers
self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
self.renderer_accumulation = AccumulationRenderer()
| # 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.
"""
Semantic NeRF-W implementation which should be fast enough to view in the viewer.
"""
from __future__ import annotations
@dataclass
class SemanticNerfWModelConfig(NerfactoModelConfig):
"""Nerfacto Model Config"""
_target: Type = field(default_factory=lambda: SemanticNerfWModel)
use_transient_embedding: bool = False
"""Whether to use transient embedding."""
semantic_loss_weight: float = 1.0
pass_semantic_gradients: bool = False
class SemanticNerfWModel(Model):
"""Nerfacto model
Args:
config: Nerfacto configuration to instantiate model
"""
config: SemanticNerfWModelConfig
def __init__(self, config: SemanticNerfWModelConfig, metadata: Dict, **kwargs) -> None:
assert "semantics" in metadata.keys() and isinstance(metadata["semantics"], Semantics)
self.semantics = metadata["semantics"]
super().__init__(config=config, **kwargs)
self.colormap = self.semantics.colors.clone().detach().to(self.device)
def populate_modules(self):
"""Set the fields and modules."""
super().populate_modules()
scene_contraction = SceneContraction(order=float("inf"))
if self.config.use_transient_embedding:
raise ValueError("Transient embedding is not fully working for semantic nerf-w.")
# Fields
self.field = TCNNNerfactoField(
self.scene_box.aabb,
num_levels=self.config.num_levels,
max_res=self.config.max_res,
log2_hashmap_size=self.config.log2_hashmap_size,
spatial_distortion=scene_contraction,
num_images=self.num_train_data,
use_average_appearance_embedding=self.config.use_average_appearance_embedding,
use_transient_embedding=self.config.use_transient_embedding,
use_semantics=True,
num_semantic_classes=len(self.semantics.classes),
pass_semantic_gradients=self.config.pass_semantic_gradients,
)
# Build the proposal network(s)
self.proposal_networks = torch.nn.ModuleList()
if self.config.use_same_proposal_network:
network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
self.proposal_networks.append(network)
self.density_fns = [network.density_fn for _ in range(self.config.num_proposal_iterations)]
else:
for _ in range(self.config.num_proposal_iterations):
network = HashMLPDensityField(self.scene_box.aabb, spatial_distortion=scene_contraction)
self.proposal_networks.append(network)
self.density_fns = [network.density_fn for network in self.proposal_networks]
# Collider
self.collider = NearFarCollider(near_plane=self.config.near_plane, far_plane=self.config.far_plane)
# Samplers
self.proposal_sampler = ProposalNetworkSampler(
num_nerf_samples_per_ray=self.config.num_nerf_samples_per_ray,
num_proposal_samples_per_ray=self.config.num_proposal_samples_per_ray,
num_proposal_network_iterations=self.config.num_proposal_iterations,
single_jitter=self.config.use_single_jitter,
)
# renderers
self.renderer_rgb = RGBRenderer(background_color=self.config.background_color)
self.renderer_accumulation = AccumulationRenderer() | self.renderer_depth = DepthRenderer() | 12 | 2023-10-26 04:39:15+00:00 | 24k |
chenruduan/OAReactDiff | oa_reactdiff/trainer/pl_trainer.py | [
{
"identifier": "ProcessedQM9",
"path": "oa_reactdiff/dataset/qm9.py",
"snippet": "class ProcessedQM9(BaseQM9):\n def __init__(\n self,\n npz_path,\n center=True,\n pad_fragments=2,\n device=\"cpu\",\n zero_charge=False,\n remove_h=False,\n **kw... | from typing import Dict, List, Optional, Tuple
from pathlib import Path
from torch import nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, StepLR
from pytorch_lightning import LightningModule
from torchmetrics.classification import (
BinaryAccuracy,
BinaryAUROC,
BinaryF1Score,
BinaryPrecision,
BinaryCohenKappa,
)
from torchmetrics import PearsonCorrCoef, SpearmanCorrCoef, MeanAbsoluteError
from oa_reactdiff.dataset import (
ProcessedQM9,
ProcessedDoubleQM9,
ProcessedTripleQM9,
ProcessedTS1x,
)
from oa_reactdiff.dynamics import EGNNDynamics, Confidence
from oa_reactdiff.diffusion._schedule import DiffSchedule, PredefinedNoiseSchedule
from oa_reactdiff.diffusion._normalizer import Normalizer, FEATURE_MAPPING
from oa_reactdiff.diffusion.en_diffusion import EnVariationalDiffusion
from oa_reactdiff.trainer._metrics import average_over_batch_metrics, pretty_print
from oa_reactdiff.analyze.rmsd import batch_rmsd
import torch
import copy
import torch.nn.functional as F
import numpy as np
import pandas as pd
import oa_reactdiff.utils.training_tools as utils | 20,055 |
PROCESS_FUNC = {
"QM9": ProcessedQM9,
"DoubleQM9": ProcessedDoubleQM9,
"TripleQM9": ProcessedTripleQM9,
"TS1x": ProcessedTS1x,
}
FILE_TYPE = {
"QM9": ".npz",
"DoubleQM9": ".npz",
"TripleQM9": ".npz",
"TS1x": ".pkl",
}
LR_SCHEDULER = {
"cos": CosineAnnealingWarmRestarts,
"step": StepLR,
}
class DDPMModule(LightningModule):
def __init__(
self,
model_config: Dict,
optimizer_config: Dict,
training_config: Dict,
node_nfs: List[int] = [9] * 3,
edge_nf: int = 4,
condition_nf: int = 3,
fragment_names: List[str] = ["inorg_node", "org_edge", "org_node"],
pos_dim: int = 3,
update_pocket_coords: bool = True,
condition_time: bool = True,
edge_cutoff: Optional[float] = None,
norm_values: Tuple = (1.0, 1.0, 1.0),
norm_biases: Tuple = (0.0, 0.0, 0.0),
noise_schedule: str = "polynomial_2",
timesteps: int = 1000,
precision: float = 1e-5,
loss_type: str = "l2",
pos_only: bool = False,
process_type: Optional[str] = None,
model: nn.Module = None,
enforce_same_encoding: Optional[List] = None,
scales: List[float] = [1.0, 1.0, 1.0],
eval_epochs: int = 20,
source: Optional[Dict] = None,
fixed_idx: Optional[List] = None,
) -> None:
super().__init__()
egnn_dynamics = EGNNDynamics(
model_config=model_config,
node_nfs=node_nfs,
edge_nf=edge_nf,
condition_nf=condition_nf,
fragment_names=fragment_names,
pos_dim=pos_dim,
update_pocket_coords=update_pocket_coords,
condition_time=condition_time,
edge_cutoff=edge_cutoff,
model=model,
enforce_same_encoding=enforce_same_encoding,
source=source,
)
normalizer = Normalizer(
norm_values=norm_values,
norm_biases=norm_biases,
pos_dim=pos_dim,
)
gamma_module = PredefinedNoiseSchedule(
noise_schedule=noise_schedule,
timesteps=timesteps,
precision=precision,
)
schedule = DiffSchedule(gamma_module=gamma_module, norm_values=norm_values)
|
PROCESS_FUNC = {
"QM9": ProcessedQM9,
"DoubleQM9": ProcessedDoubleQM9,
"TripleQM9": ProcessedTripleQM9,
"TS1x": ProcessedTS1x,
}
FILE_TYPE = {
"QM9": ".npz",
"DoubleQM9": ".npz",
"TripleQM9": ".npz",
"TS1x": ".pkl",
}
LR_SCHEDULER = {
"cos": CosineAnnealingWarmRestarts,
"step": StepLR,
}
class DDPMModule(LightningModule):
def __init__(
self,
model_config: Dict,
optimizer_config: Dict,
training_config: Dict,
node_nfs: List[int] = [9] * 3,
edge_nf: int = 4,
condition_nf: int = 3,
fragment_names: List[str] = ["inorg_node", "org_edge", "org_node"],
pos_dim: int = 3,
update_pocket_coords: bool = True,
condition_time: bool = True,
edge_cutoff: Optional[float] = None,
norm_values: Tuple = (1.0, 1.0, 1.0),
norm_biases: Tuple = (0.0, 0.0, 0.0),
noise_schedule: str = "polynomial_2",
timesteps: int = 1000,
precision: float = 1e-5,
loss_type: str = "l2",
pos_only: bool = False,
process_type: Optional[str] = None,
model: nn.Module = None,
enforce_same_encoding: Optional[List] = None,
scales: List[float] = [1.0, 1.0, 1.0],
eval_epochs: int = 20,
source: Optional[Dict] = None,
fixed_idx: Optional[List] = None,
) -> None:
super().__init__()
egnn_dynamics = EGNNDynamics(
model_config=model_config,
node_nfs=node_nfs,
edge_nf=edge_nf,
condition_nf=condition_nf,
fragment_names=fragment_names,
pos_dim=pos_dim,
update_pocket_coords=update_pocket_coords,
condition_time=condition_time,
edge_cutoff=edge_cutoff,
model=model,
enforce_same_encoding=enforce_same_encoding,
source=source,
)
normalizer = Normalizer(
norm_values=norm_values,
norm_biases=norm_biases,
pos_dim=pos_dim,
)
gamma_module = PredefinedNoiseSchedule(
noise_schedule=noise_schedule,
timesteps=timesteps,
precision=precision,
)
schedule = DiffSchedule(gamma_module=gamma_module, norm_values=norm_values)
| self.ddpm = EnVariationalDiffusion( | 10 | 2023-10-30 02:53:38+00:00 | 24k |
lewandofskee/DiAD | sgn/sgn.py | [
{
"identifier": "conv_nd",
"path": "ldm/modules/diffusionmodules/util.py",
"snippet": "def conv_nd(dims, *args, **kwargs):\n \"\"\"\n Create a 1D, 2D, or 3D convolution module.\n \"\"\"\n if dims == 1:\n return nn.Conv1d(*args, **kwargs)\n elif dims == 2:\n return nn.Conv2d(... | import einops
import torch
import torch as th
import torch.nn as nn
import torchvision
from ldm.modules.diffusionmodules.util import (
conv_nd,
linear,
zero_module,
timestep_embedding,
)
from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock, Upsample
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from omegaconf.listconfig import ListConfig | 21,254 |
def custom_sigmoid(x):
return 1 / (1 + torch.exp(-(x - 600) / 10))
class ControlledUnetModel(UNetModel):
def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
hs = []
with torch.no_grad():
|
def custom_sigmoid(x):
return 1 / (1 + torch.exp(-(x - 600) / 10))
class ControlledUnetModel(UNetModel):
def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
hs = []
with torch.no_grad(): | t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) | 3 | 2023-10-30 14:21:09+00:00 | 24k |
nv-tlabs/pacer | pacer/utils/motion_lib_smpl.py | [
{
"identifier": "torch_utils",
"path": "pacer/utils/torch_utils.py",
"snippet": "def my_quat_rotate(q, v):\ndef quat_to_angle_axis(q):\ndef angle_axis_to_exp_map(angle, axis):\ndef quat_to_exp_map(q):\ndef quat_to_tan_norm(q):\ndef euler_xyz_to_exp_map(roll, pitch, yaw):\ndef exp_map_to_angle_axis(exp_m... | from ast import If
from tqdm import tqdm
from poselib.poselib.core.rotation3d import *
from isaacgym.torch_utils import *
from pacer.utils import torch_utils
from poselib.poselib.skeleton.skeleton3d import SkeletonMotion, SkeletonState
from uhc.smpllib.smpl_parser import (
SMPL_Parser,
SMPLH_Parser,
SMPLX_Parser,
)
import numpy as np
import os
import yaml
import joblib
import torch
import torch.multiprocessing as mp
import copy
import gc | 18,179 | betas = curr_gender_betas[1:]
height_tolorance = 0.0
vertices_curr, joints_curr = smpl_parsers[gender.item()].get_joints_verts(pose_aa, betas[None, ], trans)
offset = joints_curr[:, 0] - trans
diff_fix = ((vertices_curr - offset[:, None])[..., -1].min(dim=-1).values - height_tolorance).min()
vertices_curr[..., 2].max() - vertices_curr[..., 2].min()
trans[..., -1] -= diff_fix
return trans
def load_motion_with_skeleton(ids, motion_data_list, skeleton_trees, gender_betas, fix_height, smpl_parsers, masterfoot_config, queue, pid):
# ZL: loading motion with the specified skeleton. Perfoming forward kinematics to get the joint positions
res = {}
for f in range(len(motion_data_list)):
assert (len(ids) == len(motion_data_list))
curr_id = ids[f] # id for this datasample
curr_file = motion_data_list[f]
curr_gender_beta = gender_betas[f]
trans = curr_file['root_trans_offset'].clone()
pose_aa = torch.from_numpy(curr_file['pose_aa'])
if fix_height:
trans = fix_trans_height(pose_aa, trans, curr_gender_beta,
smpl_parsers)
pose_quat_global = curr_file['pose_quat_global']
B, J, N = pose_quat_global.shape
if not masterfoot_config is None:
num_bodies = len(masterfoot_config['body_names'])
pose_quat_holder = np.zeros([B, num_bodies, N])
pose_quat_holder[..., -1] = 1
pose_quat_holder[...,masterfoot_config['body_to_orig_without_toe'], :] \
= pose_quat_global[..., masterfoot_config['orig_to_orig_without_toe'], :]
pose_quat_holder[..., [
masterfoot_config['body_names'].index(name)
for name in ["L_Toe", "L_Toe_1", "L_Toe_1_1", "L_Toe_2"]
], :] = pose_quat_holder[..., [masterfoot_config['body_names'].index(name) for name in ["L_Ankle"]], :]
pose_quat_holder[..., [
masterfoot_config['body_names'].index(name)
for name in ["R_Toe", "R_Toe_1", "R_Toe_1_1", "R_Toe_2"]
], :] = pose_quat_holder[..., [masterfoot_config['body_names'].index(name) for name in ["R_Ankle"]], :]
pose_quat_global = pose_quat_holder
sk_state = SkeletonState.from_rotation_and_root_translation(
skeleton_trees[f],
torch.from_numpy(pose_quat_global),
trans,
is_local=False)
curr_motion = SkeletonMotion.from_skeleton_state(sk_state, curr_file.get("fps", 30))
curr_dof_vels = compute_motion_dof_vels(curr_motion)
curr_motion.dof_vels = curr_dof_vels
curr_motion.gender_beta = curr_gender_beta
res[curr_id] = (curr_file, curr_motion)
if not queue is None:
queue.put(res)
else:
return res
class DeviceCache:
def __init__(self, obj, device):
self.obj = obj
self.device = device
keys = dir(obj)
num_added = 0
for k in keys:
try:
out = getattr(obj, k)
except:
# print("Error for key=", k)
continue
if isinstance(out, torch.Tensor):
if out.is_floating_point():
out = out.to(self.device, dtype=torch.float32)
else:
out.to(self.device)
setattr(self, k, out)
num_added += 1
elif isinstance(out, np.ndarray):
out = torch.tensor(out)
if out.is_floating_point():
out = out.to(self.device, dtype=torch.float32)
else:
out.to(self.device)
setattr(self, k, out)
num_added += 1
# print("Total added", num_added)
def __getattr__(self, string):
out = getattr(self.obj, string)
return out
class MotionLib():
def __init__(self, motion_file, key_body_ids, device, fix_height = True, masterfoot_conifg = None, min_length = -1):
self._key_body_ids = torch.tensor(key_body_ids, device=device)
self._device = device
self._motion_data = joblib.load(motion_file)
if min_length != -1:
data_list = {k: v for k, v in list(self._motion_data.items()) if len(v['pose_quat_global']) >= min_length}
self._motion_data_list = np.array(list(data_list.values()))
self._motion_data_keys = np.array(list(data_list.keys()))
else:
self._motion_data_list = np.array(list(self._motion_data.values()))
self._motion_data_keys = np.array(list(self._motion_data.keys()))
self._num_unique_motions = len(self._motion_data_list)
self._masterfoot_conifg = masterfoot_conifg
data_dir = "data/smpl"
| # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
USE_CACHE = True
print("MOVING MOTION DATA TO GPU, USING CACHE:", USE_CACHE)
if not USE_CACHE:
old_numpy = torch.Tensor.numpy
class Patch:
def numpy(self):
if self.is_cuda:
return self.to("cpu").numpy()
else:
return old_numpy(self)
torch.Tensor.numpy = Patch.numpy
def local_rotation_to_dof_vel(local_rot0, local_rot1, dt):
# Assume each joint is 3dof
diff_quat_data = quat_mul_norm(quat_inverse(local_rot0), local_rot1)
diff_angle, diff_axis = quat_angle_axis(diff_quat_data)
dof_vel = diff_axis * diff_angle.unsqueeze(-1) / dt
return dof_vel[1:, :].flatten()
def compute_motion_dof_vels(motion):
num_frames = motion.tensor.shape[0]
dt = 1.0 / motion.fps
dof_vels = []
for f in range(num_frames - 1):
local_rot0 = motion.local_rotation[f]
local_rot1 = motion.local_rotation[f + 1]
frame_dof_vel = local_rotation_to_dof_vel(local_rot0, local_rot1, dt)
dof_vels.append(frame_dof_vel)
dof_vels.append(dof_vels[-1])
dof_vels = torch.stack(dof_vels, dim=0).view(num_frames, -1, 3)
return dof_vels
def fix_trans_height(pose_aa, trans, curr_gender_betas, smpl_parsers):
with torch.no_grad():
gender = curr_gender_betas[0]
betas = curr_gender_betas[1:]
height_tolorance = 0.0
vertices_curr, joints_curr = smpl_parsers[gender.item()].get_joints_verts(pose_aa, betas[None, ], trans)
offset = joints_curr[:, 0] - trans
diff_fix = ((vertices_curr - offset[:, None])[..., -1].min(dim=-1).values - height_tolorance).min()
vertices_curr[..., 2].max() - vertices_curr[..., 2].min()
trans[..., -1] -= diff_fix
return trans
def load_motion_with_skeleton(ids, motion_data_list, skeleton_trees, gender_betas, fix_height, smpl_parsers, masterfoot_config, queue, pid):
# ZL: loading motion with the specified skeleton. Perfoming forward kinematics to get the joint positions
res = {}
for f in range(len(motion_data_list)):
assert (len(ids) == len(motion_data_list))
curr_id = ids[f] # id for this datasample
curr_file = motion_data_list[f]
curr_gender_beta = gender_betas[f]
trans = curr_file['root_trans_offset'].clone()
pose_aa = torch.from_numpy(curr_file['pose_aa'])
if fix_height:
trans = fix_trans_height(pose_aa, trans, curr_gender_beta,
smpl_parsers)
pose_quat_global = curr_file['pose_quat_global']
B, J, N = pose_quat_global.shape
if not masterfoot_config is None:
num_bodies = len(masterfoot_config['body_names'])
pose_quat_holder = np.zeros([B, num_bodies, N])
pose_quat_holder[..., -1] = 1
pose_quat_holder[...,masterfoot_config['body_to_orig_without_toe'], :] \
= pose_quat_global[..., masterfoot_config['orig_to_orig_without_toe'], :]
pose_quat_holder[..., [
masterfoot_config['body_names'].index(name)
for name in ["L_Toe", "L_Toe_1", "L_Toe_1_1", "L_Toe_2"]
], :] = pose_quat_holder[..., [masterfoot_config['body_names'].index(name) for name in ["L_Ankle"]], :]
pose_quat_holder[..., [
masterfoot_config['body_names'].index(name)
for name in ["R_Toe", "R_Toe_1", "R_Toe_1_1", "R_Toe_2"]
], :] = pose_quat_holder[..., [masterfoot_config['body_names'].index(name) for name in ["R_Ankle"]], :]
pose_quat_global = pose_quat_holder
sk_state = SkeletonState.from_rotation_and_root_translation(
skeleton_trees[f],
torch.from_numpy(pose_quat_global),
trans,
is_local=False)
curr_motion = SkeletonMotion.from_skeleton_state(sk_state, curr_file.get("fps", 30))
curr_dof_vels = compute_motion_dof_vels(curr_motion)
curr_motion.dof_vels = curr_dof_vels
curr_motion.gender_beta = curr_gender_beta
res[curr_id] = (curr_file, curr_motion)
if not queue is None:
queue.put(res)
else:
return res
class DeviceCache:
def __init__(self, obj, device):
self.obj = obj
self.device = device
keys = dir(obj)
num_added = 0
for k in keys:
try:
out = getattr(obj, k)
except:
# print("Error for key=", k)
continue
if isinstance(out, torch.Tensor):
if out.is_floating_point():
out = out.to(self.device, dtype=torch.float32)
else:
out.to(self.device)
setattr(self, k, out)
num_added += 1
elif isinstance(out, np.ndarray):
out = torch.tensor(out)
if out.is_floating_point():
out = out.to(self.device, dtype=torch.float32)
else:
out.to(self.device)
setattr(self, k, out)
num_added += 1
# print("Total added", num_added)
def __getattr__(self, string):
out = getattr(self.obj, string)
return out
class MotionLib():
def __init__(self, motion_file, key_body_ids, device, fix_height = True, masterfoot_conifg = None, min_length = -1):
self._key_body_ids = torch.tensor(key_body_ids, device=device)
self._device = device
self._motion_data = joblib.load(motion_file)
if min_length != -1:
data_list = {k: v for k, v in list(self._motion_data.items()) if len(v['pose_quat_global']) >= min_length}
self._motion_data_list = np.array(list(data_list.values()))
self._motion_data_keys = np.array(list(data_list.keys()))
else:
self._motion_data_list = np.array(list(self._motion_data.values()))
self._motion_data_keys = np.array(list(self._motion_data.keys()))
self._num_unique_motions = len(self._motion_data_list)
self._masterfoot_conifg = masterfoot_conifg
data_dir = "data/smpl" | smpl_parser_n = SMPL_Parser(model_path=data_dir, | 3 | 2023-10-31 20:47:12+00:00 | 24k |
Improbable-AI/dexenv | dexenv/envs/dclaw_multiobjs.py | [
{
"identifier": "DClawBase",
"path": "dexenv/envs/dclaw_base.py",
"snippet": "class DClawBase(VecTask):\n\n def __init__(self, cfg, sim_device, rl_device, graphics_device_id):\n\n self.cfg = cfg\n headless = self.cfg.headless\n self.randomize = self.cfg[\"task\"][\"randomize\"]\n... | import numpy as np
import torch
import dexenv
from gym.utils import seeding
from isaacgym import gymapi
from loguru import logger
from tqdm import tqdm
from dexenv.envs.dclaw_base import DClawBase
from dexenv.utils.common import chunker_list
from dexenv.utils.common import get_all_files_with_name
from dexenv.utils.common import load_from_pickle
from dexenv.utils.isaac_utils import load_a_goal_object_asset
from dexenv.utils.isaac_utils import load_an_object_asset
from dexenv.utils.isaac_utils import load_obj_texture | 15,462 | # add object
object_handle = self.gym.create_actor(env_ptr, object_assets[obj_asset_id],
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)
self.object_cat_indices.append(object_cat_ids[obj_asset_id])
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_assets[obj_asset_id],
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.obj.load_texture:
self.gym.set_rigid_body_texture(env_ptr,
object_handle,
0,
gymapi.MESH_VISUAL_AND_COLLISION,
object_textures[obj_asset_id]
)
self.gym.set_rigid_body_texture(env_ptr,
goal_handle,
0,
gymapi.MESH_VISUAL_AND_COLLISION,
object_textures[obj_asset_id]
)
else:
color = np.array([179, 193, 134]) / 255.0
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(*color))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(*color))
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table", i, 0)
self.gym.set_rigid_body_color(env_ptr, table_handle, 0, gymapi.MESH_VISUAL,
gymapi.Vec3(180 / 255., 180 / 255., 180 / 255.))
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)
object_rb_props = self.gym.get_actor_rigid_body_properties(env_ptr, object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.setup_torch_states()
self.env_obj_ids = torch.LongTensor(env_obj_ids).to(self.device).view(-1, 1)
self.object_cat_indices = torch.LongTensor(self.object_cat_indices).to(self.device).view(-1, 1)
def parse_obj_dataset(self, dataset):
asset_root = dexenv.LIB_PATH.joinpath('assets')
split_dataset_name = dataset.split(':')
if len(split_dataset_name) == 1:
dataset_path = asset_root.joinpath(dataset, 'train')
else:
target_object = split_dataset_name[1]
dataset_path = asset_root.joinpath(split_dataset_name[0], 'train', target_object)
logger.warning(f'Dataset path:{dataset_path}')
urdf_files = get_all_files_with_name(dataset_path, name='model.urdf')
permute_ids = self.np_random.permutation(np.arange(len(urdf_files)))
permuted_urdfs = [urdf_files[i] for i in permute_ids]
object_categories = sorted(list(set([self.get_object_category(urdf) for urdf in permuted_urdfs])))
obj_name_to_id = {name: idx for idx, name in enumerate(object_categories)}
return permuted_urdfs, dataset_path, obj_name_to_id
def get_object_category(self, urdf_path):
cat = urdf_path.parents[0].name
if 'var_' in cat:
cat = urdf_path.parents[1].name
return cat
def load_object_asset(self):
asset_root = dexenv.LIB_PATH.joinpath('assets')
object_urdfs = self.object_urdfs
object_assets, goal_assets, object_ids, object_tex_handles, object_ptds = [], [], [], [], []
object_cat_ids = []
if self.cfg.obj.object_id is not None:
urdf_to_load = self.object_urdfs[self.cfg.obj.object_id]
logger.info(f'Loading a single object: {urdf_to_load}')
obj_asset, goal_asset, texture_handle, ptd = self.load_an_object(asset_root,
urdf_to_load)
object_assets.append(obj_asset)
goal_assets.append(goal_asset)
object_ids.append(self.object_urdfs.index(urdf_to_load))
object_tex_handles.append(texture_handle)
object_ptds.append(ptd)
object_cat_ids.append(self.obj_name_to_cat_id[self.get_object_category(urdf_to_load)])
else:
if self.cfg.obj.start_id is None:
start = 0
end = min(len(object_urdfs), self.cfg.obj.num_objs)
else:
start = self.cfg.obj.start_id
end = min(start + self.cfg.obj.num_objs, len(object_urdfs))
iters = range(start, end)
logger.info(f'Loading object IDs from {start} to {end}.')
for idx in tqdm(iters, desc='Loading Asset'):
urdf_to_load = object_urdfs[idx]
obj_asset, goal_asset, texture_handle, ptd = self.load_an_object(asset_root,
urdf_to_load)
object_assets.append(obj_asset)
goal_assets.append(goal_asset)
object_ids.append(self.object_urdfs.index(urdf_to_load))
object_tex_handles.append(texture_handle)
object_ptds.append(ptd)
object_cat_ids.append(self.obj_name_to_cat_id[self.get_object_category(urdf_to_load)])
return object_assets, goal_assets, object_ids, object_tex_handles, object_ptds, object_cat_ids
def load_an_object(self, asset_root, object_urdf):
out = []
|
class DclawMultiObjs(DClawBase):
def __init__(self, cfg, sim_device, rl_device, graphics_device_id):
self.set_random_gen()
self.object_urdfs, self.dataset_path, self.obj_name_to_cat_id = self.parse_obj_dataset(cfg.obj.dataset)
self.num_objects = len(self.object_urdfs)
logger.info(f'Object urdf root path:{self.dataset_path}.')
logger.info(f'Number of available objects:{self.num_objects}.')
super().__init__(cfg=cfg,
sim_device=sim_device,
rl_device=rl_device,
graphics_device_id=graphics_device_id)
def set_random_gen(self, seed=12345):
self.np_random, seed = seeding.np_random(seed)
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()
dclaw_asset, dclaw_dof_props = self.get_dclaw_asset(asset_root=asset_root)
# load manipulated object and goal assets
table_asset = self.get_table_asset()
table_pose = self.get_table_pose()
object_assets, goal_assets, object_ids, object_textures, object_ptds, object_cat_ids = self.load_object_asset()
# create fingertip force sensors, if needed
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)
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.object_cat_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]
dclaw_rb_count = self.gym.get_asset_rigid_body_count(dclaw_asset)
object_rb_count = self.gym.get_asset_rigid_body_count(object_assets[0])
self.object_rb_handles = list(range(dclaw_rb_count, dclaw_rb_count + object_rb_count))
self.object_handles = []
num_object_assets = len(object_assets)
env_obj_ids = []
for i in range(self.num_envs):
# create env instance
obj_asset_id = i % num_object_assets
env_obj_ids.append(object_ids[obj_asset_id])
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
# compute aggregate size
obj_num_bodies = self.gym.get_asset_rigid_body_count(object_assets[obj_asset_id])
obj_num_shapes = self.gym.get_asset_rigid_shape_count(object_assets[obj_asset_id])
max_agg_bodies = self.num_dclaw_bodies + obj_num_bodies * 2 + 1
max_agg_shapes = self.num_dclaw_shapes + obj_num_shapes * 2 + 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)
# add object
object_handle = self.gym.create_actor(env_ptr, object_assets[obj_asset_id],
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)
self.object_cat_indices.append(object_cat_ids[obj_asset_id])
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_assets[obj_asset_id],
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.obj.load_texture:
self.gym.set_rigid_body_texture(env_ptr,
object_handle,
0,
gymapi.MESH_VISUAL_AND_COLLISION,
object_textures[obj_asset_id]
)
self.gym.set_rigid_body_texture(env_ptr,
goal_handle,
0,
gymapi.MESH_VISUAL_AND_COLLISION,
object_textures[obj_asset_id]
)
else:
color = np.array([179, 193, 134]) / 255.0
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(*color))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(*color))
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table", i, 0)
self.gym.set_rigid_body_color(env_ptr, table_handle, 0, gymapi.MESH_VISUAL,
gymapi.Vec3(180 / 255., 180 / 255., 180 / 255.))
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)
object_rb_props = self.gym.get_actor_rigid_body_properties(env_ptr, object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.setup_torch_states()
self.env_obj_ids = torch.LongTensor(env_obj_ids).to(self.device).view(-1, 1)
self.object_cat_indices = torch.LongTensor(self.object_cat_indices).to(self.device).view(-1, 1)
def parse_obj_dataset(self, dataset):
asset_root = dexenv.LIB_PATH.joinpath('assets')
split_dataset_name = dataset.split(':')
if len(split_dataset_name) == 1:
dataset_path = asset_root.joinpath(dataset, 'train')
else:
target_object = split_dataset_name[1]
dataset_path = asset_root.joinpath(split_dataset_name[0], 'train', target_object)
logger.warning(f'Dataset path:{dataset_path}')
urdf_files = get_all_files_with_name(dataset_path, name='model.urdf')
permute_ids = self.np_random.permutation(np.arange(len(urdf_files)))
permuted_urdfs = [urdf_files[i] for i in permute_ids]
object_categories = sorted(list(set([self.get_object_category(urdf) for urdf in permuted_urdfs])))
obj_name_to_id = {name: idx for idx, name in enumerate(object_categories)}
return permuted_urdfs, dataset_path, obj_name_to_id
def get_object_category(self, urdf_path):
cat = urdf_path.parents[0].name
if 'var_' in cat:
cat = urdf_path.parents[1].name
return cat
def load_object_asset(self):
asset_root = dexenv.LIB_PATH.joinpath('assets')
object_urdfs = self.object_urdfs
object_assets, goal_assets, object_ids, object_tex_handles, object_ptds = [], [], [], [], []
object_cat_ids = []
if self.cfg.obj.object_id is not None:
urdf_to_load = self.object_urdfs[self.cfg.obj.object_id]
logger.info(f'Loading a single object: {urdf_to_load}')
obj_asset, goal_asset, texture_handle, ptd = self.load_an_object(asset_root,
urdf_to_load)
object_assets.append(obj_asset)
goal_assets.append(goal_asset)
object_ids.append(self.object_urdfs.index(urdf_to_load))
object_tex_handles.append(texture_handle)
object_ptds.append(ptd)
object_cat_ids.append(self.obj_name_to_cat_id[self.get_object_category(urdf_to_load)])
else:
if self.cfg.obj.start_id is None:
start = 0
end = min(len(object_urdfs), self.cfg.obj.num_objs)
else:
start = self.cfg.obj.start_id
end = min(start + self.cfg.obj.num_objs, len(object_urdfs))
iters = range(start, end)
logger.info(f'Loading object IDs from {start} to {end}.')
for idx in tqdm(iters, desc='Loading Asset'):
urdf_to_load = object_urdfs[idx]
obj_asset, goal_asset, texture_handle, ptd = self.load_an_object(asset_root,
urdf_to_load)
object_assets.append(obj_asset)
goal_assets.append(goal_asset)
object_ids.append(self.object_urdfs.index(urdf_to_load))
object_tex_handles.append(texture_handle)
object_ptds.append(ptd)
object_cat_ids.append(self.obj_name_to_cat_id[self.get_object_category(urdf_to_load)])
return object_assets, goal_assets, object_ids, object_tex_handles, object_ptds, object_cat_ids
def load_an_object(self, asset_root, object_urdf):
out = [] | obj_asset = load_an_object_asset(self.gym, self.sim, asset_root, object_urdf, vhacd=self.cfg.env.vhacd) | 5 | 2023-10-25 17:22:41+00:00 | 24k |
ai-safety-foundation/sparse_autoencoder | sparse_autoencoder/train/pipeline.py | [
{
"identifier": "ActivationResampler",
"path": "sparse_autoencoder/activation_resampler/activation_resampler.py",
"snippet": "class ActivationResampler:\n \"\"\"Activation resampler.\n\n Collates the number of times each neuron fires over a set number of learned activation vectors,\n and then p... | from collections.abc import Iterator
from functools import partial
from pathlib import Path
from tempfile import gettempdir
from typing import TYPE_CHECKING, final
from deepspeed import DeepSpeedEngine
from jaxtyping import Float, Int, Int64
from pydantic import NonNegativeInt, PositiveInt, validate_call
from torch import Tensor
from torch.nn.parallel import DataParallel
from torch.optim.lr_scheduler import LRScheduler
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformer_lens import HookedTransformer
from sparse_autoencoder.activation_resampler.activation_resampler import (
ActivationResampler,
ParameterUpdateResults,
)
from sparse_autoencoder.activation_store.tensor_store import TensorActivationStore
from sparse_autoencoder.autoencoder.model import SparseAutoencoder
from sparse_autoencoder.loss.abstract_loss import AbstractLoss, LossReductionType
from sparse_autoencoder.metrics.metrics_container import MetricsContainer, default_metrics
from sparse_autoencoder.metrics.train.abstract_train_metric import TrainMetricData
from sparse_autoencoder.metrics.validate.abstract_validate_metric import ValidationMetricData
from sparse_autoencoder.optimizer.abstract_optimizer import AbstractOptimizerWithReset
from sparse_autoencoder.source_data.abstract_dataset import SourceDataset, TorchTokenizedPrompts
from sparse_autoencoder.source_model.replace_activations_hook import replace_activations_hook
from sparse_autoencoder.source_model.store_activations_hook import store_activations_hook
from sparse_autoencoder.source_model.zero_ablate_hook import zero_ablate_hook
from sparse_autoencoder.tensor_types import Axis
from sparse_autoencoder.train.utils.get_model_device import get_model_device
from sparse_autoencoder.metrics.abstract_metric import MetricResult
import torch
import wandb | 18,535 | """Default pipeline."""
if TYPE_CHECKING:
DEFAULT_CHECKPOINT_DIRECTORY: Path = Path(gettempdir()) / "sparse_autoencoder"
class Pipeline:
"""Pipeline for training a Sparse Autoencoder on TransformerLens activations.
Includes all the key functionality to train a sparse autoencoder, with a specific set of
hyperparameters.
"""
activation_resampler: ActivationResampler | None
"""Activation resampler to use."""
autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine
"""Sparse autoencoder to train."""
n_input_features: int
"""Number of input features in the sparse autoencoder."""
n_learned_features: int
"""Number of learned features in the sparse autoencoder."""
cache_names: list[str]
"""Names of the cache hook points to use in the source model."""
layer: int
"""Layer to stope the source model at (if we don't need activations after this layer)."""
log_frequency: int
"""Frequency at which to log metrics (in steps)."""
loss: AbstractLoss
"""Loss function to use."""
metrics: MetricsContainer
"""Metrics to use."""
| """Default pipeline."""
if TYPE_CHECKING:
DEFAULT_CHECKPOINT_DIRECTORY: Path = Path(gettempdir()) / "sparse_autoencoder"
class Pipeline:
"""Pipeline for training a Sparse Autoencoder on TransformerLens activations.
Includes all the key functionality to train a sparse autoencoder, with a specific set of
hyperparameters.
"""
activation_resampler: ActivationResampler | None
"""Activation resampler to use."""
autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine
"""Sparse autoencoder to train."""
n_input_features: int
"""Number of input features in the sparse autoencoder."""
n_learned_features: int
"""Number of learned features in the sparse autoencoder."""
cache_names: list[str]
"""Names of the cache hook points to use in the source model."""
layer: int
"""Layer to stope the source model at (if we don't need activations after this layer)."""
log_frequency: int
"""Frequency at which to log metrics (in steps)."""
loss: AbstractLoss
"""Loss function to use."""
metrics: MetricsContainer
"""Metrics to use."""
| optimizer: AbstractOptimizerWithReset | 9 | 2023-10-27 07:37:15+00:00 | 24k |
OATML-Markslab/ProteinNPT | scripts/train.py | [
{
"identifier": "ProteinNPTModel",
"path": "proteinnpt/model.py",
"snippet": "class ProteinNPTModel(nn.Module):\n def __init__(self, args, alphabet):\n super().__init__()\n self.args = args\n self.alphabet = alphabet\n self.alphabet_size = len(alphabet)\n self.paddi... | import os,gc
import json
import argparse
import random
import numpy as np
import pandas as pd
import wandb
import torch
import proteinnpt,baselines,utils
from collections import defaultdict
from proteinnpt.model import ProteinNPTModel
from baselines.model import AugmentedPropertyPredictor
from utils.esm.data import Alphabet
from utils.tranception.model_pytorch import get_tranception_tokenizer
from utils.data_utils import get_train_val_test_data, standardize, pnpt_count_non_nan, pnpt_spearmanr
from utils.msa_utils import process_MSA
from utils.model_utils import Trainer | 18,292 |
def setup_config_and_paths(args):
# All parameters that are not defined by end user are fetched from the config file
if args.model_config_location is not None:
args.main_config=json.load(open(args.model_config_location))
for key in args.main_config:
if args.__dict__[key] is None:
args.__dict__[key] = args.main_config[key]
# File paths config
for local_path in ['embedding_model_location','MSA_data_folder','MSA_weight_data_folder','path_to_hhfilter']:
if getattr(args, local_path): setattr(args, local_path, args.data_location + os.sep + getattr(args, local_path))
if not os.path.exists(args.data_location + os.sep + 'model_predictions'): os.mkdir(args.data_location + os.sep + 'model_predictions')
if not os.path.exists(args.data_location + os.sep + 'checkpoint'): os.mkdir(args.data_location + os.sep + 'checkpoint')
args.output_scores_location = args.data_location + os.sep + 'model_predictions' + os.sep + args.model_name_suffix
if not os.path.exists(args.output_scores_location): os.mkdir(args.output_scores_location)
args.model_location = args.data_location + os.sep + 'checkpoint' + os.sep + args.model_name_suffix
if not os.path.exists(args.model_location): os.mkdir(args.model_location)
# Target config
args.target_config=json.load(open(args.target_config_location))
zero_shot_predictions_mapping={
"MSA_Transformer_pred": "MSA_Transformer_ensemble",
"ESM1v_pred": "ESM1v_ensemble",
"TranceptEVE_pred": "TranceptEVE_L",
"Tranception_pred": "Tranception_L",
"DeepSequence_pred": "DeepSequence_ensemble"
}
if args.model_type=="ProteinNPT": zero_shot_predictions_mapping["ProteinNPT"]=zero_shot_predictions_mapping[args.aa_embeddings+"_pred"]
if args.augmentation=="zero_shot_fitness_predictions_auxiliary_labels": # Add auxiliary label to target_config
assert args.zero_shot_fitness_predictions_location is not None, "Location of zero-shot fitness predictions to use as auxiliary labels not properly referenced"
print("Using zero-shot fitness predictions as auxiliary labels")
args.target_config["zero_shot_fitness_predictions"] = {
"type": "continuous",
"dim": 1,
"var_name": zero_shot_predictions_mapping[args.model_type], #Select the relevant model for zero-shot fitness predictions
"location": args.zero_shot_fitness_predictions_location,
"in_NPT_loss": False,
"main_target": False
}
args.augmentation_short="auxiliary"
elif args.augmentation=="zero_shot_fitness_predictions_covariate": # Will use zero-shot fitness predictions as an additional model covariate
assert args.zero_shot_fitness_predictions_location is not None, "Location of zero-shot fitness predictions to use as model covariate not properly referenced"
print("Using zero-shot fitness predictions as covariate")
args.augmentation_short="covariate"
args.zero_shot_fitness_predictions_var_name = zero_shot_predictions_mapping[args.model_type]
else:
args.augmentation_short="none"
for target_index,target in enumerate(args.target_config):
if "location" not in args.target_config[target].keys(): # Note: the case of zero-shot fitness predictions is already handled above if present
if args.assay_location is not None: # We passed at least one path for the assay location
num_targets = [x for x in args.target_config.keys() if args.target_config[x]["in_NPT_loss"]]
if len(args.assay_location) > 1:
assert len(args.assay_location)==num_targets, "Trying to predict {} targets, but only referencing {} distinct paths for them.".format(num_targets,len(args.assay_location))
args.target_config[target]["location"] = args.assay_location[target_index]
print("Location used for target {} if {}".format(target,args.assay_location[target_index]))
else:
args.target_config[target]["location"] = args.assay_location[0]
print("Location used for target {} if {}".format(target,args.assay_location[0]))
else:
print("Assay location not provided. Defaulting to location for single substitutions fitness assays: {}".format(args.data_location + os.sep + 'data/fitness/substitutions_singles'))
args.target_config[target]["location"] = args.data_location + os.sep + 'data/fitness/substitutions_singles'
return args
def log_performance_fold(args,target_names,test_eval_results,trainer_final_status,perf_list,logs_folder=None):
test_logs = {'total_training_steps': trainer_final_status['total_training_steps'], 'total_training_epochs': trainer_final_status['total_training_epochs'], 'total_train_time': trainer_final_status['total_train_time']}
if logs_folder is None:
dir_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
logs_folder = dir_path+os.sep+'output'
if not os.path.exists(logs_folder): os.mkdir(logs_folder)
if args.model_type=="ProteinNPT":
normalization = 0
for target_name in target_names: normalization += test_eval_results['eval_num_masked_targets'][target_name]
else:
normalization = test_eval_results['eval_num_predicted_targets']
test_logs['Test total loss per seq.'] = test_eval_results['eval_total_loss'] / normalization
|
def setup_config_and_paths(args):
# All parameters that are not defined by end user are fetched from the config file
if args.model_config_location is not None:
args.main_config=json.load(open(args.model_config_location))
for key in args.main_config:
if args.__dict__[key] is None:
args.__dict__[key] = args.main_config[key]
# File paths config
for local_path in ['embedding_model_location','MSA_data_folder','MSA_weight_data_folder','path_to_hhfilter']:
if getattr(args, local_path): setattr(args, local_path, args.data_location + os.sep + getattr(args, local_path))
if not os.path.exists(args.data_location + os.sep + 'model_predictions'): os.mkdir(args.data_location + os.sep + 'model_predictions')
if not os.path.exists(args.data_location + os.sep + 'checkpoint'): os.mkdir(args.data_location + os.sep + 'checkpoint')
args.output_scores_location = args.data_location + os.sep + 'model_predictions' + os.sep + args.model_name_suffix
if not os.path.exists(args.output_scores_location): os.mkdir(args.output_scores_location)
args.model_location = args.data_location + os.sep + 'checkpoint' + os.sep + args.model_name_suffix
if not os.path.exists(args.model_location): os.mkdir(args.model_location)
# Target config
args.target_config=json.load(open(args.target_config_location))
zero_shot_predictions_mapping={
"MSA_Transformer_pred": "MSA_Transformer_ensemble",
"ESM1v_pred": "ESM1v_ensemble",
"TranceptEVE_pred": "TranceptEVE_L",
"Tranception_pred": "Tranception_L",
"DeepSequence_pred": "DeepSequence_ensemble"
}
if args.model_type=="ProteinNPT": zero_shot_predictions_mapping["ProteinNPT"]=zero_shot_predictions_mapping[args.aa_embeddings+"_pred"]
if args.augmentation=="zero_shot_fitness_predictions_auxiliary_labels": # Add auxiliary label to target_config
assert args.zero_shot_fitness_predictions_location is not None, "Location of zero-shot fitness predictions to use as auxiliary labels not properly referenced"
print("Using zero-shot fitness predictions as auxiliary labels")
args.target_config["zero_shot_fitness_predictions"] = {
"type": "continuous",
"dim": 1,
"var_name": zero_shot_predictions_mapping[args.model_type], #Select the relevant model for zero-shot fitness predictions
"location": args.zero_shot_fitness_predictions_location,
"in_NPT_loss": False,
"main_target": False
}
args.augmentation_short="auxiliary"
elif args.augmentation=="zero_shot_fitness_predictions_covariate": # Will use zero-shot fitness predictions as an additional model covariate
assert args.zero_shot_fitness_predictions_location is not None, "Location of zero-shot fitness predictions to use as model covariate not properly referenced"
print("Using zero-shot fitness predictions as covariate")
args.augmentation_short="covariate"
args.zero_shot_fitness_predictions_var_name = zero_shot_predictions_mapping[args.model_type]
else:
args.augmentation_short="none"
for target_index,target in enumerate(args.target_config):
if "location" not in args.target_config[target].keys(): # Note: the case of zero-shot fitness predictions is already handled above if present
if args.assay_location is not None: # We passed at least one path for the assay location
num_targets = [x for x in args.target_config.keys() if args.target_config[x]["in_NPT_loss"]]
if len(args.assay_location) > 1:
assert len(args.assay_location)==num_targets, "Trying to predict {} targets, but only referencing {} distinct paths for them.".format(num_targets,len(args.assay_location))
args.target_config[target]["location"] = args.assay_location[target_index]
print("Location used for target {} if {}".format(target,args.assay_location[target_index]))
else:
args.target_config[target]["location"] = args.assay_location[0]
print("Location used for target {} if {}".format(target,args.assay_location[0]))
else:
print("Assay location not provided. Defaulting to location for single substitutions fitness assays: {}".format(args.data_location + os.sep + 'data/fitness/substitutions_singles'))
args.target_config[target]["location"] = args.data_location + os.sep + 'data/fitness/substitutions_singles'
return args
def log_performance_fold(args,target_names,test_eval_results,trainer_final_status,perf_list,logs_folder=None):
test_logs = {'total_training_steps': trainer_final_status['total_training_steps'], 'total_training_epochs': trainer_final_status['total_training_epochs'], 'total_train_time': trainer_final_status['total_train_time']}
if logs_folder is None:
dir_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
logs_folder = dir_path+os.sep+'output'
if not os.path.exists(logs_folder): os.mkdir(logs_folder)
if args.model_type=="ProteinNPT":
normalization = 0
for target_name in target_names: normalization += test_eval_results['eval_num_masked_targets'][target_name]
else:
normalization = test_eval_results['eval_num_predicted_targets']
test_logs['Test total loss per seq.'] = test_eval_results['eval_total_loss'] / normalization | spearmans = {target_name: pnpt_spearmanr(test_eval_results['output_scores']['predictions_'+target_name], test_eval_results['output_scores']['labels_'+target_name]) for target_name in target_names} | 7 | 2023-10-28 11:41:05+00:00 | 24k |
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 | 14,903 | 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)
det = torch.cat((pred_poly, det[:, -2:]), dim=1) # (n, [poly conf cls])
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *poly, conf, cls in reversed(det):
if save_txt: # Write to file
# xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
poly = poly.tolist()
line = (cls, *poly, conf) if save_conf else (cls, *poly) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add poly to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
# annotator.box_label(xyxy, label, color=colors(c, True))
annotator.poly_label(poly, label, color=colors(c, True))
if save_crop: # Yolov5-obb doesn't support it yet
# save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
pass
# Print time (inference-only)
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
# Stream results
im0 = annotator.result()
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
| # 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)
det = torch.cat((pred_poly, det[:, -2:]), dim=1) # (n, [poly conf cls])
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *poly, conf, cls in reversed(det):
if save_txt: # Write to file
# xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
poly = poly.tolist()
line = (cls, *poly, conf) if save_conf else (cls, *poly) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add poly to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
# annotator.box_label(xyxy, label, color=colors(c, True))
annotator.poly_label(poly, label, color=colors(c, True))
if save_crop: # Yolov5-obb doesn't support it yet
# save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
pass
# Print time (inference-only)
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
# Stream results
im0 = annotator.result()
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' | LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") | 10 | 2023-10-31 06:06:41+00:00 | 24k |
serengil/LightPHE | lightphe/models/Ciphertext.py | [
{
"identifier": "Homomorphic",
"path": "lightphe/models/Homomorphic.py",
"snippet": "class Homomorphic(ABC):\n keys: dict\n plaintext_modulo: int\n ciphertext_modulo: int\n\n @abstractmethod\n def generate_keys(self, key_size: int, s: Optional[int] = None) -> dict:\n pass\n\n @a... | from typing import Union
from lightphe.models.Homomorphic import Homomorphic
from lightphe.models.Algorithm import Algorithm
from lightphe.cryptosystems.RSA import RSA
from lightphe.cryptosystems.ElGamal import ElGamal
from lightphe.cryptosystems.Paillier import Paillier
from lightphe.cryptosystems.DamgardJurik import DamgardJurik
from lightphe.cryptosystems.OkamotoUchiyama import OkamotoUchiyama
from lightphe.cryptosystems.Benaloh import Benaloh
from lightphe.cryptosystems.NaccacheStern import NaccacheStern
from lightphe.cryptosystems.GoldwasserMicali import GoldwasserMicali
from lightphe.cryptosystems.EllipticCurveElGamal import EllipticCurveElGamal
from lightphe.commons import phe_utils
from lightphe.commons.logger import Logger | 17,545 |
logger = Logger(module="lightphe/models/Ciphertext.py")
# pylint: disable=too-few-public-methods, no-else-return
class Ciphertext:
def __init__(self, algorithm_name: str, keys: dict, value: Union[int, tuple, list]):
self.algorithm_name = algorithm_name
self.keys = keys
self.value = value
if algorithm_name == Algorithm.RSA:
cs = RSA(keys=keys)
elif algorithm_name == Algorithm.ElGamal:
cs = ElGamal(keys=keys)
elif algorithm_name == Algorithm.ExponentialElGamal:
cs = ElGamal(keys=keys, exponential=True)
elif algorithm_name == Algorithm.EllipticCurveElGamal:
cs = EllipticCurveElGamal(keys=keys)
elif algorithm_name == Algorithm.Paillier:
cs = Paillier(keys=keys)
elif algorithm_name == Algorithm.DamgardJurik:
cs = DamgardJurik(keys=keys)
|
logger = Logger(module="lightphe/models/Ciphertext.py")
# pylint: disable=too-few-public-methods, no-else-return
class Ciphertext:
def __init__(self, algorithm_name: str, keys: dict, value: Union[int, tuple, list]):
self.algorithm_name = algorithm_name
self.keys = keys
self.value = value
if algorithm_name == Algorithm.RSA:
cs = RSA(keys=keys)
elif algorithm_name == Algorithm.ElGamal:
cs = ElGamal(keys=keys)
elif algorithm_name == Algorithm.ExponentialElGamal:
cs = ElGamal(keys=keys, exponential=True)
elif algorithm_name == Algorithm.EllipticCurveElGamal:
cs = EllipticCurveElGamal(keys=keys)
elif algorithm_name == Algorithm.Paillier:
cs = Paillier(keys=keys)
elif algorithm_name == Algorithm.DamgardJurik:
cs = DamgardJurik(keys=keys) | elif algorithm_name == Algorithm.OkamotoUchiyama: | 6 | 2023-10-28 14:57:59+00:00 | 24k |
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 | 14,908 | # 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,
"P_TUNING": PromptEncoderConfig,
"LORA": LoraConfig,
"ADALORA": AdaLoraConfig,
}
def get_peft_config(config_dict):
"""
Returns a Peft config object from a dictionary.
Args:
config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters.
"""
return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict)
def _prepare_prompt_learning_config(peft_config, model_config):
if peft_config.num_layers is None:
if "num_hidden_layers" in model_config:
num_layers = model_config["num_hidden_layers"]
elif "num_layers" in model_config:
num_layers = model_config["num_layers"]
elif "n_layer" in model_config:
num_layers = model_config["n_layer"]
else:
raise ValueError("Please specify `num_layers` in `peft_config`")
peft_config.num_layers = num_layers
if peft_config.token_dim is None:
if "hidden_size" in model_config:
token_dim = model_config["hidden_size"]
elif "n_embd" in model_config:
token_dim = model_config["n_embd"]
elif "d_model" in model_config:
token_dim = model_config["d_model"]
else:
raise ValueError("Please specify `token_dim` in `peft_config`")
peft_config.token_dim = token_dim
if peft_config.num_attention_heads is None:
if "num_attention_heads" in model_config:
num_attention_heads = model_config["num_attention_heads"]
elif "n_head" in model_config:
num_attention_heads = model_config["n_head"]
elif "num_heads" in model_config:
num_attention_heads = model_config["num_heads"]
elif "encoder_attention_heads" in model_config:
num_attention_heads = model_config["encoder_attention_heads"]
else:
raise ValueError("Please specify `num_attention_heads` in `peft_config`")
peft_config.num_attention_heads = num_attention_heads
if getattr(peft_config, "encoder_hidden_size", None) is None:
setattr(peft_config, "encoder_hidden_size", token_dim)
return peft_config
def get_peft_model(model, peft_config):
"""
Returns a Peft model object from a model and a config.
Args:
model ([`transformers.PreTrainedModel`]): Model to be wrapped.
peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model.
"""
model_config = model.config.to_dict() if hasattr(model.config, "to_dict") else model.config
peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None)
if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys() and not isinstance(
| # 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,
"P_TUNING": PromptEncoderConfig,
"LORA": LoraConfig,
"ADALORA": AdaLoraConfig,
}
def get_peft_config(config_dict):
"""
Returns a Peft config object from a dictionary.
Args:
config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters.
"""
return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict)
def _prepare_prompt_learning_config(peft_config, model_config):
if peft_config.num_layers is None:
if "num_hidden_layers" in model_config:
num_layers = model_config["num_hidden_layers"]
elif "num_layers" in model_config:
num_layers = model_config["num_layers"]
elif "n_layer" in model_config:
num_layers = model_config["n_layer"]
else:
raise ValueError("Please specify `num_layers` in `peft_config`")
peft_config.num_layers = num_layers
if peft_config.token_dim is None:
if "hidden_size" in model_config:
token_dim = model_config["hidden_size"]
elif "n_embd" in model_config:
token_dim = model_config["n_embd"]
elif "d_model" in model_config:
token_dim = model_config["d_model"]
else:
raise ValueError("Please specify `token_dim` in `peft_config`")
peft_config.token_dim = token_dim
if peft_config.num_attention_heads is None:
if "num_attention_heads" in model_config:
num_attention_heads = model_config["num_attention_heads"]
elif "n_head" in model_config:
num_attention_heads = model_config["n_head"]
elif "num_heads" in model_config:
num_attention_heads = model_config["num_heads"]
elif "encoder_attention_heads" in model_config:
num_attention_heads = model_config["encoder_attention_heads"]
else:
raise ValueError("Please specify `num_attention_heads` in `peft_config`")
peft_config.num_attention_heads = num_attention_heads
if getattr(peft_config, "encoder_hidden_size", None) is None:
setattr(peft_config, "encoder_hidden_size", token_dim)
return peft_config
def get_peft_model(model, peft_config):
"""
Returns a Peft model object from a model and a config.
Args:
model ([`transformers.PreTrainedModel`]): Model to be wrapped.
peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model.
"""
model_config = model.config.to_dict() if hasattr(model.config, "to_dict") else model.config
peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None)
if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys() and not isinstance( | peft_config, PromptLearningConfig | 10 | 2023-10-30 10:50:32+00:00 | 24k |
chenran-li/RQL-release | sb3_contrib/ars/ars.py | [
{
"identifier": "BaseAlgorithm",
"path": "stable_baselines3/common/base_class.py",
"snippet": "class BaseAlgorithm(ABC):\n \"\"\"\n The base of RL algorithms\n\n :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)\n :param env: The environment to learn from\n (i... | import copy
import sys
import time
import warnings
import numpy as np
import torch as th
import torch.nn.utils
from functools import partial
from typing import Any, Dict, Optional, Type, TypeVar, Union
from gym import spaces
from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.policies import BasePolicy
from stable_baselines3.common.save_util import load_from_zip_file
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import get_schedule_fn, safe_mean
from sb3_contrib.ars.policies import ARSPolicy, LinearPolicy, MlpPolicy
from sb3_contrib.common.vec_env.async_eval import AsyncEval | 17,039 | :param episode_lengths: List containing per-episode lengths (in number of steps)
"""
# Mimic Monitor Wrapper
infos = [
{"episode": {"r": episode_reward, "l": episode_length}}
for episode_reward, episode_length in zip(episode_rewards, episode_lengths)
]
self._update_info_buffer(infos)
def _trigger_callback(
self,
_locals: Dict[str, Any],
_globals: Dict[str, Any],
callback: BaseCallback,
n_envs: int,
) -> None:
"""
Callback passed to the ``evaluate_policy()`` helper
in order to increment the number of timesteps
and trigger events in the single process version.
:param _locals:
:param _globals:
:param callback: Callback that will be called at every step
:param n_envs: Number of environments
"""
self.num_timesteps += n_envs
callback.on_step()
def evaluate_candidates(
self, candidate_weights: th.Tensor, callback: BaseCallback, async_eval: Optional[AsyncEval]
) -> th.Tensor:
"""
Evaluate each candidate.
:param candidate_weights: The candidate weights to be evaluated.
:param callback: Callback that will be called at each step
(or after evaluation in the multiprocess version)
:param async_eval: The object for asynchronous evaluation of candidates.
:return: The episodic return for each candidate.
"""
batch_steps = 0
# returns == sum of rewards
candidate_returns = th.zeros(self.pop_size, device=self.device)
train_policy = copy.deepcopy(self.policy)
# Empty buffer to show only mean over one iteration (one set of candidates) in the logs
self.ep_info_buffer = []
callback.on_rollout_start()
if async_eval is not None:
# Multiprocess asynchronous version
async_eval.send_jobs(candidate_weights, self.pop_size)
results = async_eval.get_results()
for weights_idx, (episode_rewards, episode_lengths) in results:
# Update reward to cancel out alive bonus if needed
candidate_returns[weights_idx] = sum(episode_rewards) + self.alive_bonus_offset * sum(episode_lengths)
batch_steps += np.sum(episode_lengths)
self._mimic_monitor_wrapper(episode_rewards, episode_lengths)
# Combine the filter stats of each process for normalization
for worker_obs_rms in async_eval.get_obs_rms():
if self._vec_normalize_env is not None:
# worker_obs_rms.count -= self.old_count
self._vec_normalize_env.obs_rms.combine(worker_obs_rms)
# Hack: don't count timesteps twice (between the two are synced)
# otherwise it will lead to overflow,
# in practice we would need two RunningMeanStats
self._vec_normalize_env.obs_rms.count -= self.old_count
# Synchronise VecNormalize if needed
if self._vec_normalize_env is not None:
async_eval.sync_obs_rms(self._vec_normalize_env.obs_rms.copy())
self.old_count = self._vec_normalize_env.obs_rms.count
# Hack to have Callback events
for _ in range(batch_steps // len(async_eval.remotes)):
self.num_timesteps += len(async_eval.remotes)
callback.on_step()
else:
# Single process, synchronous version
for weights_idx in range(self.pop_size):
# Load current candidate weights
train_policy.load_from_vector(candidate_weights[weights_idx].cpu())
# Evaluate the candidate
episode_rewards, episode_lengths = evaluate_policy(
train_policy,
self.env,
n_eval_episodes=self.n_eval_episodes,
return_episode_rewards=True,
# Increment num_timesteps too (slight mismatch with multi envs)
callback=partial(self._trigger_callback, callback=callback, n_envs=self.env.num_envs),
warn=False,
)
# Update reward to cancel out alive bonus if needed
candidate_returns[weights_idx] = sum(episode_rewards) + self.alive_bonus_offset * sum(episode_lengths)
batch_steps += sum(episode_lengths)
self._mimic_monitor_wrapper(episode_rewards, episode_lengths)
# Note: we increment the num_timesteps inside the evaluate_policy()
# however when using multiple environments, there will be a slight
# mismatch between the number of timesteps used and the number
# of calls to the step() method (cf. implementation of evaluate_policy())
# self.num_timesteps += batch_steps
callback.on_rollout_end()
return candidate_returns
def _log_and_dump(self) -> None:
"""
Dump information to the logger.
"""
time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
|
SelfARS = TypeVar("SelfARS", bound="ARS")
class ARS(BaseAlgorithm):
"""
Augmented Random Search: https://arxiv.org/abs/1803.07055
Original implementation: https://github.com/modestyachts/ARS
C++/Cuda Implementation: https://github.com/google-research/tiny-differentiable-simulator/
150 LOC Numpy Implementation: https://github.com/alexis-jacq/numpy_ARS/blob/master/asr.py
:param policy: The policy to train, can be an instance of ``ARSPolicy``, or a string from ["LinearPolicy", "MlpPolicy"]
:param env: The environment to train on, may be a string if registered with gym
:param n_delta: How many random perturbations of the policy to try at each update step.
:param n_top: How many of the top delta to use in each update step. Default is n_delta
:param learning_rate: Float or schedule for the step size
:param delta_std: Float or schedule for the exploration noise
:param zero_policy: Boolean determining if the passed policy should have it's weights zeroed before training.
:param alive_bonus_offset: Constant added to the reward at each step, used to cancel out alive bonuses.
:param n_eval_episodes: Number of episodes to evaluate each candidate.
:param policy_kwargs: Keyword arguments to pass to the policy on creation
:param tensorboard_log: String with the directory to put tensorboard logs:
:param seed: Random seed for the training
:param verbose: Verbosity level: 0 no output, 1 info, 2 debug
:param device: Torch device to use for training, defaults to "cpu"
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
policy_aliases: Dict[str, Type[BasePolicy]] = {
"MlpPolicy": MlpPolicy,
"LinearPolicy": LinearPolicy,
}
def __init__(
self,
policy: Union[str, Type[ARSPolicy]],
env: Union[GymEnv, str],
n_delta: int = 8,
n_top: Optional[int] = None,
learning_rate: Union[float, Schedule] = 0.02,
delta_std: Union[float, Schedule] = 0.05,
zero_policy: bool = True,
alive_bonus_offset: float = 0,
n_eval_episodes: int = 1,
policy_kwargs: Optional[Dict[str, Any]] = None,
tensorboard_log: Optional[str] = None,
seed: Optional[int] = None,
verbose: int = 0,
device: Union[th.device, str] = "cpu",
_init_setup_model: bool = True,
):
super().__init__(
policy,
env,
learning_rate=learning_rate,
tensorboard_log=tensorboard_log,
policy_kwargs=policy_kwargs,
verbose=verbose,
device=device,
supported_action_spaces=(spaces.Box, spaces.Discrete),
support_multi_env=True,
seed=seed,
)
self.n_delta = n_delta
self.pop_size = 2 * n_delta
self.delta_std_schedule = get_schedule_fn(delta_std)
self.n_eval_episodes = n_eval_episodes
if n_top is None:
n_top = n_delta
# Make sure our hyper parameters are valid and auto correct them if they are not
if n_top > n_delta:
warnings.warn(f"n_top = {n_top} > n_delta = {n_top}, setting n_top = n_delta")
n_top = n_delta
self.n_top = n_top
self.alive_bonus_offset = alive_bonus_offset
self.zero_policy = zero_policy
self.weights = None # Need to call init model to initialize weight
self.processes = None
# Keep track of how many steps where elapsed before a new rollout
# Important for syncing observation normalization between workers
self.old_count = 0
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
self._setup_lr_schedule()
self.set_random_seed(self.seed)
self.policy = self.policy_class(self.observation_space, self.action_space, **self.policy_kwargs)
self.policy = self.policy.to(self.device)
self.weights = th.nn.utils.parameters_to_vector(self.policy.parameters()).detach()
self.n_params = len(self.weights)
if self.zero_policy:
self.weights = th.zeros_like(self.weights, requires_grad=False)
self.policy.load_from_vector(self.weights.cpu())
def _mimic_monitor_wrapper(self, episode_rewards: np.ndarray, episode_lengths: np.ndarray) -> None:
"""
Helper to mimic Monitor wrapper and report episode statistics (mean reward, mean episode length).
:param episode_rewards: List containing per-episode rewards
:param episode_lengths: List containing per-episode lengths (in number of steps)
"""
# Mimic Monitor Wrapper
infos = [
{"episode": {"r": episode_reward, "l": episode_length}}
for episode_reward, episode_length in zip(episode_rewards, episode_lengths)
]
self._update_info_buffer(infos)
def _trigger_callback(
self,
_locals: Dict[str, Any],
_globals: Dict[str, Any],
callback: BaseCallback,
n_envs: int,
) -> None:
"""
Callback passed to the ``evaluate_policy()`` helper
in order to increment the number of timesteps
and trigger events in the single process version.
:param _locals:
:param _globals:
:param callback: Callback that will be called at every step
:param n_envs: Number of environments
"""
self.num_timesteps += n_envs
callback.on_step()
def evaluate_candidates(
self, candidate_weights: th.Tensor, callback: BaseCallback, async_eval: Optional[AsyncEval]
) -> th.Tensor:
"""
Evaluate each candidate.
:param candidate_weights: The candidate weights to be evaluated.
:param callback: Callback that will be called at each step
(or after evaluation in the multiprocess version)
:param async_eval: The object for asynchronous evaluation of candidates.
:return: The episodic return for each candidate.
"""
batch_steps = 0
# returns == sum of rewards
candidate_returns = th.zeros(self.pop_size, device=self.device)
train_policy = copy.deepcopy(self.policy)
# Empty buffer to show only mean over one iteration (one set of candidates) in the logs
self.ep_info_buffer = []
callback.on_rollout_start()
if async_eval is not None:
# Multiprocess asynchronous version
async_eval.send_jobs(candidate_weights, self.pop_size)
results = async_eval.get_results()
for weights_idx, (episode_rewards, episode_lengths) in results:
# Update reward to cancel out alive bonus if needed
candidate_returns[weights_idx] = sum(episode_rewards) + self.alive_bonus_offset * sum(episode_lengths)
batch_steps += np.sum(episode_lengths)
self._mimic_monitor_wrapper(episode_rewards, episode_lengths)
# Combine the filter stats of each process for normalization
for worker_obs_rms in async_eval.get_obs_rms():
if self._vec_normalize_env is not None:
# worker_obs_rms.count -= self.old_count
self._vec_normalize_env.obs_rms.combine(worker_obs_rms)
# Hack: don't count timesteps twice (between the two are synced)
# otherwise it will lead to overflow,
# in practice we would need two RunningMeanStats
self._vec_normalize_env.obs_rms.count -= self.old_count
# Synchronise VecNormalize if needed
if self._vec_normalize_env is not None:
async_eval.sync_obs_rms(self._vec_normalize_env.obs_rms.copy())
self.old_count = self._vec_normalize_env.obs_rms.count
# Hack to have Callback events
for _ in range(batch_steps // len(async_eval.remotes)):
self.num_timesteps += len(async_eval.remotes)
callback.on_step()
else:
# Single process, synchronous version
for weights_idx in range(self.pop_size):
# Load current candidate weights
train_policy.load_from_vector(candidate_weights[weights_idx].cpu())
# Evaluate the candidate
episode_rewards, episode_lengths = evaluate_policy(
train_policy,
self.env,
n_eval_episodes=self.n_eval_episodes,
return_episode_rewards=True,
# Increment num_timesteps too (slight mismatch with multi envs)
callback=partial(self._trigger_callback, callback=callback, n_envs=self.env.num_envs),
warn=False,
)
# Update reward to cancel out alive bonus if needed
candidate_returns[weights_idx] = sum(episode_rewards) + self.alive_bonus_offset * sum(episode_lengths)
batch_steps += sum(episode_lengths)
self._mimic_monitor_wrapper(episode_rewards, episode_lengths)
# Note: we increment the num_timesteps inside the evaluate_policy()
# however when using multiple environments, there will be a slight
# mismatch between the number of timesteps used and the number
# of calls to the step() method (cf. implementation of evaluate_policy())
# self.num_timesteps += batch_steps
callback.on_rollout_end()
return candidate_returns
def _log_and_dump(self) -> None:
"""
Dump information to the logger.
"""
time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0: | self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer])) | 7 | 2023-10-28 01:09:21+00:00 | 24k |
pytabular-ai/auto-scikit-dl | utils/model.py | [
{
"identifier": "MLP",
"path": "models/mlp.py",
"snippet": "class MLP(TabModel):\n def __init__(\n self,\n model_config: dict,\n n_num_features: int,\n categories: ty.Optional[ty.List[int]],\n n_labels: int,\n device: ty.Union[str, torch.device] = 'cuda',\n ... | import os
import time
import json
import yaml
import shutil
import random
import datetime
import numpy as np
import torch
import optuna
from pathlib import Path
from typing import Dict, List, Tuple, Union, Optional, Literal
from models import MLP, FTTransformer, AutoInt, DCNv2, NODE
from models.abstract import TabModel, check_dir
from data.utils import Dataset
from data.processor import DataProcessor | 14,917 |
MODEL_CARDS = {
'xgboost': None, 'catboost': None, 'lightgbm': None,
'mlp': MLP, 'autoint': AutoInt, 'dcnv2': DCNv2, 'node': NODE,
|
MODEL_CARDS = {
'xgboost': None, 'catboost': None, 'lightgbm': None,
'mlp': MLP, 'autoint': AutoInt, 'dcnv2': DCNv2, 'node': NODE, | 'ft-transformer': FTTransformer, 'saint': None, | 1 | 2023-10-30 14:55:44+00:00 | 24k |
hyperspy/exspy | exspy/tests/signals/test_kramers_kronig_transform.py | [
{
"identifier": "VolumePlasmonDrude",
"path": "exspy/components/volume_plasmon_drude.py",
"snippet": "class VolumePlasmonDrude(hs.model.components1D.Expression):\n r\"\"\"\n Drude volume plasmon energy loss function component, the energy loss\n function is defined as:\n\n .. math::\n\n ... | import numpy as np
import pytest
import hyperspy.api as hs
from hyperspy.components1d import Lorentzian
from exspy.components import VolumePlasmonDrude
from exspy.misc.eels.tools import eels_constant
from exspy.signals import EELSSpectrum | 20,729 | # -*- 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>.
class Test2D:
def setup_method(self, method):
"""To test the kramers_kronig_analysis we will generate 3
EELSSpectrum instances. First a model energy loss function(ELF),
in our case following the Drude bulk plasmon peak. Second, we
simulate the inelastic scattering to generate a model scattering
distribution (SPC). Finally, we use a lorentzian peak with
integral equal to 1 to simulate a ZLP.
"""
# Parameters
i0 = 1.0
t = hs.signals.BaseSignal(np.arange(10, 70, 10).reshape((2, 3)))
t = t.transpose(signal_axes=0)
scale = 0.02
# Create an 3x2x2048 spectrum with Drude plasmon
| # -*- 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>.
class Test2D:
def setup_method(self, method):
"""To test the kramers_kronig_analysis we will generate 3
EELSSpectrum instances. First a model energy loss function(ELF),
in our case following the Drude bulk plasmon peak. Second, we
simulate the inelastic scattering to generate a model scattering
distribution (SPC). Finally, we use a lorentzian peak with
integral equal to 1 to simulate a ZLP.
"""
# Parameters
i0 = 1.0
t = hs.signals.BaseSignal(np.arange(10, 70, 10).reshape((2, 3)))
t = t.transpose(signal_axes=0)
scale = 0.02
# Create an 3x2x2048 spectrum with Drude plasmon | s = EELSSpectrum(np.zeros((2, 3, 2 * 2048))) | 2 | 2023-10-28 20:04:10+00:00 | 24k |
Elfenreigen/UniChest | train.py | [
{
"identifier": "utils",
"path": "factory/utils.py",
"snippet": "class SmoothedValue(object):\nclass MetricLogger(object):\nclass AttrDict(dict):\n def __init__(self, window_size=20, fmt=None):\n def update(self, value, n=1):\n def synchronize_between_processes(self):\n def median(self):\n ... | import argparse
import os
import logging
import yaml
import numpy as np
import random
import time
import datetime
import json
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import socket
from pathlib import Path
from functools import partial
from sklearn.metrics import roc_auc_score
from collections import OrderedDict
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from transformers import AutoModel,BertConfig,AutoTokenizer
from factory import utils
from scheduler import create_scheduler
from optim import create_optimizer
from engine.train import train,valid_on_cheXpert,valid_on_chestxray14
from models.clip_tqn import CLP_clinical,ModelRes,TQN_Model,TQN_Model_Add,ModelDense,CLP_clinical2
from models.tokenization_bert import BertTokenizer
from dataset.dataset_entity import MIMIC_Dataset,Mergetrain_Dataset, Chestxray14_Dataset,CheXpert_Dataset
from io import BytesIO | 16,347 |
# import ruamel.yaml as yaml
def main(args, config):
torch.cuda.current_device()
torch.cuda._initialized = True
print("Total CUDA devices: ", torch.cuda.device_count())
torch.set_default_tensor_type('torch.FloatTensor')
utils.init_distributed_mode(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
start_epoch = 0
max_epoch = config['schedular']['epochs']
warmup_steps = config['schedular']['warmup_epochs']
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_rank = global_rank
print('sampler_rank',sampler_rank,'num_tasks',num_tasks)
#### Dataset ####
print("Creating dataset")
if args.add_dataset == True:
|
# import ruamel.yaml as yaml
def main(args, config):
torch.cuda.current_device()
torch.cuda._initialized = True
print("Total CUDA devices: ", torch.cuda.device_count())
torch.set_default_tensor_type('torch.FloatTensor')
utils.init_distributed_mode(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
start_epoch = 0
max_epoch = config['schedular']['epochs']
warmup_steps = config['schedular']['warmup_epochs']
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_rank = global_rank
print('sampler_rank',sampler_rank,'num_tasks',num_tasks)
#### Dataset ####
print("Creating dataset")
if args.add_dataset == True: | train_dataset = Mergetrain_Dataset(config['train_entity_file'], config['train_fg_query_file_v1'], config['mrsty_file'],config['image_res'], args) | 14 | 2023-10-30 00:24:16+00:00 | 24k |
ifrit98/storage-subnet | neurons/miner.py | [
{
"identifier": "hash_data",
"path": "storage/shared/ecc.py",
"snippet": "def hash_data(data):\n \"\"\"\n Compute a SHA3-256 hash of the input data and return its integer representation.\n\n The function handles both byte-like and non-byte-like inputs by converting non-byte inputs to\n strin... | import os
import sys
import copy
import json
import time
import torch
import typing
import base64
import asyncio
import aioredis
import argparse
import threading
import traceback
import bittensor as bt
import storage
from collections import defaultdict
from Crypto.Random import get_random_bytes
from typing import Dict
from pprint import pprint, pformat
from storage.shared.ecc import (
hash_data,
setup_CRS,
ECCommitment,
ecc_point_to_hex,
hex_to_ecc_point,
)
from storage.shared.merkle import (
MerkleTree,
)
from storage.shared.utils import b64_encode, b64_decode, chunk_data, safe_key_search
from storage.miner import (
run,
set_weights,
)
from storage.miner.utils import (
compute_subsequent_commitment,
save_data_to_filesystem,
load_from_filesystem,
commit_data_with_seed,
init_wandb,
get_directory_size,
get_free_disk_space,
update_storage_stats,
)
from storage.miner.config import (
config,
check_config,
add_args,
)
from storage.miner.database import (
store_chunk_metadata,
update_seed_info,
get_chunk_metadata,
) | 14,988 |
def retrieve_blacklist_fn(
self, synapse: storage.protocol.Retrieve
) -> typing.Tuple[bool, str]:
"""
Determines whether a given synapse should be blacklisted based on the recognition
of the hotkey in the metagraph. This function is used to filter out requests from
entities that are not part of the network's current state.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey.
Returns:
- (bool, str): A tuple where the first element is a boolean indicating whether the
synapse's hotkey is blacklisted, and the second element is a string message explaining
the reason.
If the hotkey is not recognized in the metagraph, the synapse is blacklisted, and
the function returns (True, "Unrecognized hotkey"). Otherwise, it returns (False,
"Hotkey recognized!"), allowing the synapse to interact with the network.
Usage:
This method is internally used by the network to ensure that only recognized
entities can participate in communication or transactions.
"""
if synapse.dendrite.hotkey not in self.metagraph.hotkeys:
# Ignore requests from unrecognized entities.
bt.logging.trace(
f"Blacklisting unrecognized hotkey {synapse.dendrite.hotkey}"
)
return True, "Unrecognized hotkey"
bt.logging.trace(
f"Not Blacklisting recognized hotkey {synapse.dendrite.hotkey}"
)
return False, "Hotkey recognized!"
def retrieve_priority_fn(self, synapse: storage.protocol.Retrieve) -> float:
"""
Assigns a priority to a given synapse based on the stake of the calling entity
in the metagraph. This function is crucial for prioritizing network requests
and ensuring that higher-stake entities are given precedence in processing.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey of the caller.
Returns:
- float: The priority value assigned to the synapse, derived from the stake of
the calling hotkey in the metagraph.
The priority is determined by the stake associated with the caller's UID in the
metagraph. A higher stake results in a higher priority.
Usage:
This method is used within the network's request handling mechanism to allocate
resources and processing time based on the stake-based priority of each request.
"""
caller_uid = self.metagraph.hotkeys.index(
synapse.dendrite.hotkey
) # Get the caller index.
prirority = float(
self.metagraph.S[caller_uid]
) # Return the stake as the priority.
bt.logging.trace(
f"Prioritizing {synapse.dendrite.hotkey} with value: ", prirority
)
return prirority
async def store(self, synapse: storage.protocol.Store) -> storage.protocol.Store:
"""
Processes the storage request from a synapse by securely storing the provided data and returning
a proof of storage. The data is committed using elliptic curve cryptography, stored on the filesystem,
and the metadata is recorded in a Redis database. A cryptographic proof of the commitment, along with
a digital signature from the server's hotkey, is returned in the synapse for verification by the requester.
Args:
synapse (storage.protocol.Store): An object containing the data to be stored,
encoded in base64 format, along with associated metadata like the cryptographic
curve parameters, a seed for the commitment, and the expected commitment group elements.
Returns:
storage.protocol.Store: The synapse is returned with additional fields populated,
including the randomness used in the commitment, the commitment point itself, a signature
from this storage server's hotkey, and a commitment hash that can be used for chained proofs.
The method performs the following operations:
1. Decodes the base64-encoded data into raw bytes.
2. Commits to the data using the provided elliptic curve parameters and the seed to generate a commitment point.
3. Stores the raw byte data in the filesystem using a hash of the data as the filename.
4. Records metadata about the stored data in the Redis database, including the file path, previous seed, and data size.
5. Updates the synapse object with the commitment details and a digital signature.
This process ensures the integrity and non-repudiation of the data storage, allowing clients to verify
that their data has been stored correctly without the need to retrieve the full data set.
Example usage:
Assuming an initialized 'committer' object and 'synapse' with necessary data:
>>> updated_synapse = self.store(synapse)
"""
bt.logging.info(f"received store request: {synapse.encrypted_data[:24]}")
self.request_count += 1
# Decode the data from base64 to raw bytes
encrypted_byte_data = base64.b64decode(synapse.encrypted_data)
bt.logging.trace(f"store b64decrypted data: {encrypted_byte_data[:24]}")
# Store the data with the hash as the key in the filesystem
bt.logging.trace(f"entering hash_data()")
data_hash = hash_data(encrypted_byte_data)
# If already storing this hash, simply update the validator seeds and return challenge
bt.logging.trace(f"checking if data already exists...")
if await self.database.exists(data_hash):
# update the validator seed challenge hash in storage
await update_seed_info(
self.database, data_hash, synapse.dendrite.hotkey, synapse.seed
)
else:
# Store the data in the filesystem
| # 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.
# import this repo
class miner:
@classmethod
def check_config(cls, config: "bt.Config"):
"""
Adds neuron-specific arguments to the argument parser.
Args:
parser (argparse.ArgumentParser): Parser to add arguments to.
This class method enriches the argument parser with options specific to the neuron's configuration.
"""
check_config(cls, config)
@classmethod
def add_args(cls, parser):
"""
Adds neuron-specific arguments to the argument parser.
Args:
parser (argparse.ArgumentParser): Parser to add arguments to.
This class method enriches the argument parser with options specific to the neuron's configuration.
"""
add_args(cls, parser)
@classmethod
def config(cls):
"""
Retrieves the configuration for the neuron.
Returns:
bt.Config: The configuration object for the neuron.
This class method returns the neuron's configuration, which is used throughout the neuron's lifecycle
for various functionalities and operations.
"""
return config(cls)
subtensor: "bt.subtensor"
wallet: "bt.wallet"
metagraph: "bt.metagraph"
def __init__(self):
self.config = miner.config()
self.check_config(self.config)
bt.logging(config=self.config, logging_dir=self.config.miner.full_path)
bt.logging.info(f"{self.config}")
bt.logging.info("miner.__init__()")
# Init device.
bt.logging.debug("loading device")
self.device = torch.device(self.config.miner.device)
bt.logging.debug(str(self.device))
# Init subtensor
bt.logging.debug("loading subtensor")
self.subtensor = bt.subtensor(config=self.config)
bt.logging.debug(str(self.subtensor))
self.current_block = self.subtensor.get_current_block()
# Init wallet.
bt.logging.debug("loading wallet")
self.wallet = bt.wallet(config=self.config)
self.wallet.create_if_non_existent()
if not self.config.wallet._mock:
if not self.subtensor.is_hotkey_registered_on_subnet(
hotkey_ss58=self.wallet.hotkey.ss58_address, netuid=self.config.netuid
):
raise Exception(
f"Wallet not currently registered on netuid {self.config.netuid}, please first register wallet before running"
)
bt.logging.debug(f"wallet: {str(self.wallet)}")
# Init metagraph.
bt.logging.debug("loading metagraph")
self.metagraph = bt.metagraph(
netuid=self.config.netuid, network=self.subtensor.network, sync=False
) # Make sure not to sync without passing subtensor
self.metagraph.sync(subtensor=self.subtensor) # Sync metagraph with subtensor.
bt.logging.debug(str(self.metagraph))
# Setup database
self.database = aioredis.StrictRedis(
host=self.config.database.host,
port=self.config.database.port,
db=self.config.database.index,
socket_keepalive=True,
socket_connect_timeout=300,
)
self.my_subnet_uid = self.metagraph.hotkeys.index(
self.wallet.hotkey.ss58_address
)
bt.logging.info(f"Running miner on uid: {self.my_subnet_uid}")
# Init wandb.
if not self.config.wandb.off:
bt.logging.debug("loading wandb")
init_wandb(self)
# The axon handles request processing, allowing validators to send this process requests.
self.axon = bt.axon(wallet=self.wallet, config=self.config)
bt.logging.info(f"Axon {self.axon}")
# Attach determiners which functions are called when servicing a request.
bt.logging.info(f"Attaching forward functions to axon.")
self.axon.attach(
forward_fn=self.store,
blacklist_fn=self.store_blacklist_fn,
priority_fn=self.store_priority_fn,
).attach(
forward_fn=self.challenge,
blacklist_fn=self.challenge_blacklist_fn,
priority_fn=self.challenge_priority_fn,
).attach(
forward_fn=self.retrieve,
blacklist_fn=self.retrieve_blacklist_fn,
priority_fn=self.retrieve_priority_fn,
)
# Serve passes the axon information to the network + netuid we are hosting on.
# This will auto-update if the axon port of external ip have changed.
bt.logging.info(
f"Serving axon {self.axon} on network: {self.subtensor.chain_endpoint} with netuid: {self.config.netuid}"
)
self.axon.serve(netuid=self.config.netuid, subtensor=self.subtensor)
# Start starts the miner's axon, making it active on the network.
bt.logging.info(f"Starting axon server on port: {self.config.axon.port}")
self.axon.start()
# Init the event loop.
self.loop = asyncio.get_event_loop()
# Instantiate runners
self.should_exit: bool = False
self.is_running: bool = False
self.thread: threading.Thread = None
self.lock = asyncio.Lock()
self.request_timestamps: Dict = {}
self.step = 0
# Init the miner's storage request tracker
self.request_count = 0
self.start_request_count_timer()
self.requests_per_hour = []
self.average_requests_per_hour = 0
# Init the miner's storage usage tracker
update_storage_stats(self)
def start_request_count_timer(self):
"""
Initializes and starts a timer for tracking the number of requests received by the miner in an hour.
This method sets up a one-hour timer that, upon expiration, calls the `reset_request_count` method to log
the number of requests received and reset the count for the next hour. The timer is set to run in a separate
thread to avoid blocking the main execution.
Usage:
Should be called during the initialization of the miner to start tracking requests per hour.
"""
self.request_count_timer = threading.Timer(3600, self.reset_request_count)
self.request_count_timer.start()
def reset_request_count(self):
"""
Logs the number of requests received in the last hour and resets the count.
This method is automatically called when the one-hour timer set by `start_request_count_timer` expires.
It logs the count of requests received in the last hour and then resets the count. Additionally, it
restarts the timer for the next hour.
Usage:
This method is intended to be called automatically by a timer and typically should not be called directly.
"""
bt.logging.info(
f"Number of requests received in the last hour: {self.request_count}"
)
self.requests_per_hour.append(self.request_count)
bt.logging.info(f"Requests per hour: {self.requests_per_hour}")
self.average_requests_per_hour = sum(self.requests_per_hour) / len(
self.requests_per_hour
)
bt.logging.info(f"Average requests per hour: {self.average_requests_per_hour}")
self.request_count = 0
self.start_request_count_timer()
@property
async def total_storage(self):
"""
Calculates the total size of data stored by the miner.
This method fetches all data keys from the Redis database and sums up the size of each data object.
It provides an estimate of the total amount of data currently held by the miner.
Returns:
int: Total size of data (in bytes) stored by the miner.
Example:
>>> miner.total_storage()
102400 # Example output indicating 102,400 bytes of data stored
"""
# Fetch all keys from Redis
all_keys = await safe_key_search(self.database, "*")
# Filter out keys that contain a period (temporary, remove later)
filtered_keys = [key for key in all_keys if b"." not in key]
# Get the size of each data object and sum them up
total_size = sum(
[
await get_chunk_metadata(self.database, key).get(b"size", 0)
for key in filtered_keys
]
)
return total_size
def store_blacklist_fn(
self, synapse: storage.protocol.Store
) -> typing.Tuple[bool, str]:
"""
Determines whether a given synapse should be blacklisted based on the recognition
of the hotkey in the metagraph. This function is used to filter out requests from
entities that are not part of the network's current state.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey.
Returns:
- (bool, str): A tuple where the first element is a boolean indicating whether the
synapse's hotkey is blacklisted, and the second element is a string message explaining
the reason.
If the hotkey is not recognized in the metagraph, the synapse is blacklisted, and
the function returns (True, "Unrecognized hotkey"). Otherwise, it returns (False,
"Hotkey recognized!"), allowing the synapse to interact with the network.
Usage:
This method is internally used by the network to ensure that only recognized
entities can participate in communication or transactions.
"""
if synapse.dendrite.hotkey not in self.metagraph.hotkeys:
# Ignore requests from unrecognized entities.
bt.logging.trace(
f"Blacklisting unrecognized hotkey {synapse.dendrite.hotkey}"
)
return True, "Unrecognized hotkey"
bt.logging.trace(
f"Not Blacklisting recognized hotkey {synapse.dendrite.hotkey}"
)
return False, "Hotkey recognized!"
def store_priority_fn(self, synapse: storage.protocol.Store) -> float:
"""
Assigns a priority to a given synapse based on the stake of the calling entity
in the metagraph. This function is crucial for prioritizing network requests
and ensuring that higher-stake entities are given precedence in processing.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey of the caller.
Returns:
- float: The priority value assigned to the synapse, derived from the stake of
the calling hotkey in the metagraph.
The priority is determined by the stake associated with the caller's UID in the
metagraph. A higher stake results in a higher priority.
Usage:
This method is used within the network's request handling mechanism to allocate
resources and processing time based on the stake-based priority of each request.
"""
caller_uid = self.metagraph.hotkeys.index(
synapse.dendrite.hotkey
) # Get the caller index.
prirority = float(
self.metagraph.S[caller_uid]
) # Return the stake as the priority.
bt.logging.trace(
f"Prioritizing {synapse.dendrite.hotkey} with value: ", prirority
)
return prirority
def challenge_blacklist_fn(
self, synapse: storage.protocol.Challenge
) -> typing.Tuple[bool, str]:
"""
Determines whether a given synapse should be blacklisted based on the recognition
of the hotkey in the metagraph. This function is used to filter out requests from
entities that are not part of the network's current state.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey.
Returns:
- (bool, str): A tuple where the first element is a boolean indicating whether the
synapse's hotkey is blacklisted, and the second element is a string message explaining
the reason.
If the hotkey is not recognized in the metagraph, the synapse is blacklisted, and
the function returns (True, "Unrecognized hotkey"). Otherwise, it returns (False,
"Hotkey recognized!"), allowing the synapse to interact with the network.
Usage:
This method is internally used by the network to ensure that only recognized
entities can participate in communication or transactions.
"""
if synapse.dendrite.hotkey not in self.metagraph.hotkeys:
# Ignore requests from unrecognized entities.
bt.logging.trace(
f"Blacklisting unrecognized hotkey {synapse.dendrite.hotkey}"
)
return True, "Unrecognized hotkey"
bt.logging.trace(
f"Not Blacklisting recognized hotkey {synapse.dendrite.hotkey}"
)
return False, "Hotkey recognized!"
def challenge_priority_fn(self, synapse: storage.protocol.Challenge) -> float:
"""
Assigns a priority to a given synapse based on the stake of the calling entity
in the metagraph. This function is crucial for prioritizing network requests
and ensuring that higher-stake entities are given precedence in processing.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey of the caller.
Returns:
- float: The priority value assigned to the synapse, derived from the stake of
the calling hotkey in the metagraph.
The priority is determined by the stake associated with the caller's UID in the
metagraph. A higher stake results in a higher priority.
Usage:
This method is used within the network's request handling mechanism to allocate
resources and processing time based on the stake-based priority of each request.
"""
caller_uid = self.metagraph.hotkeys.index(
synapse.dendrite.hotkey
) # Get the caller index.
prirority = float(
self.metagraph.S[caller_uid]
) # Return the stake as the priority.
bt.logging.trace(
f"Prioritizing {synapse.dendrite.hotkey} with value: ", prirority
)
return prirority
def retrieve_blacklist_fn(
self, synapse: storage.protocol.Retrieve
) -> typing.Tuple[bool, str]:
"""
Determines whether a given synapse should be blacklisted based on the recognition
of the hotkey in the metagraph. This function is used to filter out requests from
entities that are not part of the network's current state.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey.
Returns:
- (bool, str): A tuple where the first element is a boolean indicating whether the
synapse's hotkey is blacklisted, and the second element is a string message explaining
the reason.
If the hotkey is not recognized in the metagraph, the synapse is blacklisted, and
the function returns (True, "Unrecognized hotkey"). Otherwise, it returns (False,
"Hotkey recognized!"), allowing the synapse to interact with the network.
Usage:
This method is internally used by the network to ensure that only recognized
entities can participate in communication or transactions.
"""
if synapse.dendrite.hotkey not in self.metagraph.hotkeys:
# Ignore requests from unrecognized entities.
bt.logging.trace(
f"Blacklisting unrecognized hotkey {synapse.dendrite.hotkey}"
)
return True, "Unrecognized hotkey"
bt.logging.trace(
f"Not Blacklisting recognized hotkey {synapse.dendrite.hotkey}"
)
return False, "Hotkey recognized!"
def retrieve_priority_fn(self, synapse: storage.protocol.Retrieve) -> float:
"""
Assigns a priority to a given synapse based on the stake of the calling entity
in the metagraph. This function is crucial for prioritizing network requests
and ensuring that higher-stake entities are given precedence in processing.
Parameters:
- synapse (bt.Synapse): The synapse object which contains the dendrite information
including the hotkey of the caller.
Returns:
- float: The priority value assigned to the synapse, derived from the stake of
the calling hotkey in the metagraph.
The priority is determined by the stake associated with the caller's UID in the
metagraph. A higher stake results in a higher priority.
Usage:
This method is used within the network's request handling mechanism to allocate
resources and processing time based on the stake-based priority of each request.
"""
caller_uid = self.metagraph.hotkeys.index(
synapse.dendrite.hotkey
) # Get the caller index.
prirority = float(
self.metagraph.S[caller_uid]
) # Return the stake as the priority.
bt.logging.trace(
f"Prioritizing {synapse.dendrite.hotkey} with value: ", prirority
)
return prirority
async def store(self, synapse: storage.protocol.Store) -> storage.protocol.Store:
"""
Processes the storage request from a synapse by securely storing the provided data and returning
a proof of storage. The data is committed using elliptic curve cryptography, stored on the filesystem,
and the metadata is recorded in a Redis database. A cryptographic proof of the commitment, along with
a digital signature from the server's hotkey, is returned in the synapse for verification by the requester.
Args:
synapse (storage.protocol.Store): An object containing the data to be stored,
encoded in base64 format, along with associated metadata like the cryptographic
curve parameters, a seed for the commitment, and the expected commitment group elements.
Returns:
storage.protocol.Store: The synapse is returned with additional fields populated,
including the randomness used in the commitment, the commitment point itself, a signature
from this storage server's hotkey, and a commitment hash that can be used for chained proofs.
The method performs the following operations:
1. Decodes the base64-encoded data into raw bytes.
2. Commits to the data using the provided elliptic curve parameters and the seed to generate a commitment point.
3. Stores the raw byte data in the filesystem using a hash of the data as the filename.
4. Records metadata about the stored data in the Redis database, including the file path, previous seed, and data size.
5. Updates the synapse object with the commitment details and a digital signature.
This process ensures the integrity and non-repudiation of the data storage, allowing clients to verify
that their data has been stored correctly without the need to retrieve the full data set.
Example usage:
Assuming an initialized 'committer' object and 'synapse' with necessary data:
>>> updated_synapse = self.store(synapse)
"""
bt.logging.info(f"received store request: {synapse.encrypted_data[:24]}")
self.request_count += 1
# Decode the data from base64 to raw bytes
encrypted_byte_data = base64.b64decode(synapse.encrypted_data)
bt.logging.trace(f"store b64decrypted data: {encrypted_byte_data[:24]}")
# Store the data with the hash as the key in the filesystem
bt.logging.trace(f"entering hash_data()")
data_hash = hash_data(encrypted_byte_data)
# If already storing this hash, simply update the validator seeds and return challenge
bt.logging.trace(f"checking if data already exists...")
if await self.database.exists(data_hash):
# update the validator seed challenge hash in storage
await update_seed_info(
self.database, data_hash, synapse.dendrite.hotkey, synapse.seed
)
else:
# Store the data in the filesystem | filepath = save_data_to_filesystem( | 13 | 2023-10-26 18:54:47+00:00 | 24k |
cpacker/MemGPT | memgpt/main.py | [
{
"identifier": "logger",
"path": "memgpt/log.py",
"snippet": ""
},
{
"identifier": "CLIInterface",
"path": "memgpt/interface.py",
"snippet": "class CLIInterface(AgentInterface):\r\n \"\"\"Basic interface for dumping agent events to the command-line\"\"\"\r\n\r\n @staticmethod\r\n ... | import shutil
import configparser
import uuid
import logging
import glob
import os
import sys
import pickle
import traceback
import json
import questionary
import typer
import memgpt.agent as agent
import memgpt.system as system
import memgpt.constants as constants
import memgpt.errors as errors
from rich.console import Console
from prettytable import PrettyTable
from memgpt.log import logger
from memgpt.interface import CLIInterface as interface # for printing to terminal
from memgpt.config import MemGPTConfig
from memgpt.cli.cli import run, attach, version, server, open_folder, quickstart, migrate
from memgpt.cli.cli_config import configure, list, add, delete
from memgpt.cli.cli_load import app as load_app
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.metadata import MetadataStore, save_agent
| 17,393 |
console = Console()
app = typer.Typer(pretty_exceptions_enable=False)
app.command(name="run")(run)
app.command(name="version")(version)
|
console = Console()
app = typer.Typer(pretty_exceptions_enable=False)
app.command(name="run")(run)
app.command(name="version")(version)
| app.command(name="attach")(attach)
| 4 | 2023-10-11 07:38:37+00:00 | 24k |
PixArt-alpha/PixArt-alpha | train_scripts/train_pixart_lcm.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 as nn
import numpy as np
import torch.nn.functional as F
from pathlib import Path
from accelerate import Accelerator, InitProcessGroupKwargs
from accelerate.utils import DistributedType
from diffusers.models import AutoencoderKL
from torch.utils.data import RandomSampler
from mmcv.runner import LogBuffer
from copy import deepcopy
from tqdm import tqdm
from diffusion import IDDPM
from diffusion.utils.checkpoint import save_checkpoint, load_checkpoint
from diffusion.utils.dist_utils import synchronize, get_world_size, clip_grad_norm_
from diffusion.data.builder import build_dataset, build_dataloader, set_data_root
from diffusion.model.builder import build_model
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 diffusion.lcm_scheduler import LCMScheduler
from torchvision.utils import save_image
from accelerate import FullyShardedDataParallelPlugin
from torch.distributed.fsdp.fully_sharded_data_parallel import FullStateDictConfig | 16,410 | args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
config = read_config(args.config)
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.load_from = None
config.resume_from = dict(
checkpoint=args.resume_from,
load_ema=False,
resume_optimizer=True,
resume_lr_scheduler=True)
if args.debug:
config.log_interval = 1
config.train_batch_size = 11
config.valid_num = 100
config.load_from = None
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'))
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
model_kwargs={"window_block_indexes": config.window_block_indexes, "window_size": config.window_size,
"use_rel_pos": config.use_rel_pos, "lewei_scale": config.lewei_scale, 'config':config,
'model_max_length': config.model_max_length}
# build models
train_diffusion = IDDPM(str(config.train_sampling_steps), learn_sigma=learn_sigma, pred_sigma=pred_sigma,
snr=config.snr_loss, return_startx=True)
model = build_model(config.model,
config.grad_checkpointing,
config.get('fp32_attention', False),
input_size=latent_size,
learn_sigma=learn_sigma,
pred_sigma=pred_sigma,
**model_kwargs).train()
logger.info(f"{model.__class__.__name__} Model Parameters: {sum(p.numel() for p in model.parameters()):,}")
if config.load_from is not None:
if args.load_from is not None:
config.load_from = args.load_from
missing, unexpected = load_checkpoint(config.load_from, model, load_ema=config.get('load_ema', False))
logger.warning(f'Missing keys: {missing}')
logger.warning(f'Unexpected keys: {unexpected}')
model_ema = deepcopy(model).eval()
model_teacher = deepcopy(model).eval()
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,
batch_size=config.train_batch_size, aspect_ratios=dataset.aspect_ratio, drop_last=True,
ratio_nums=dataset.ratio_nums, config=config, valid_num=config.valid_num)
# used for balanced sampling
# batch_sampler = BalancedAspectRatioBatchSampler(sampler=RandomSampler(dataset), dataset=dataset,
# batch_size=config.train_batch_size, aspect_ratios=dataset.aspect_ratio,
# ratio_nums=dataset.ratio_nums)
train_dataloader = build_dataloader(dataset, batch_sampler=batch_sampler, num_workers=config.num_workers)
else:
train_dataloader = build_dataloader(dataset, num_workers=config.num_workers, batch_size=config.train_batch_size, shuffle=True)
# build optimizer and lr scheduler
lr_scale_ratio = 1
if config.get('auto_lr', None):
| 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 ema_update(model_dest: nn.Module, model_src: nn.Module, rate):
param_dict_src = dict(model_src.named_parameters())
for p_name, p_dest in model_dest.named_parameters():
p_src = param_dict_src[p_name]
assert p_src is not p_dest
p_dest.data.mul_(rate).add_((1 - rate) * p_src.data)
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
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
@torch.no_grad()
def log_validation(model, step, device):
if hasattr(model, 'module'):
model = model.module
scheduler = LCMScheduler(beta_start=0.0001, beta_end=0.02, beta_schedule="linear", prediction_type="epsilon")
scheduler.set_timesteps(4, 50)
infer_timesteps = scheduler.timesteps
dog_embed = torch.load('data/tmp/dog.pth', map_location='cpu')
caption_embs, emb_masks = dog_embed['dog_text'].to(device), dog_embed['dog_mask'].to(device)
hw = torch.tensor([[1024, 1024]], dtype=torch.float, device=device).repeat(1, 1)
ar = torch.tensor([[1.]], device=device).repeat(1, 1)
# Create sampling noise:
infer_latents = torch.randn(1, 4, 1024, 1024, device=device)
model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks)
logger.info("Running validation... ")
# 7. LCM MultiStep Sampling Loop:
for i, t in tqdm(list(enumerate(infer_timesteps))):
ts = torch.full((1,), t, device=device, dtype=torch.long)
# model prediction (v-prediction, eps, x)
model_pred = model(infer_latents, ts, caption_embs, **model_kwargs)[:, :4]
# compute the previous noisy sample x_t -> x_t-1
infer_latents, denoised = scheduler.step(model_pred, i, t, infer_latents, return_dict=False)
samples = vae.decode(denoised / 0.18215).sample
torch.cuda.empty_cache()
save_image(samples[0], f'output_cv/vis/{step}.jpg', nrow=1, normalize=True, value_range=(-1, 1))
def train():
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()
start_step = start_epoch * len(train_dataloader)
global_step = 0
total_steps = len(train_dataloader) * config.num_epochs
load_vae_feat = getattr(train_dataloader.dataset, 'load_vae_feat', False)
# Create uncond embeds for classifier free guidance
uncond_prompt_embeds = model.module.y_embedder.y_embedding.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
y = batch[1]
y_mask = batch[2]
data_info = batch[3]
# 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(model, latents, start_timesteps, model_kwargs=dict(y=y, mask=y_mask, data_info=data_info), noise=noise)
model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
# Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
# noisy_latents with both the conditioning embedding c and unconditional embedding 0
# Get teacher model prediction on noisy_latents and conditional embedding
with torch.no_grad():
with torch.autocast("cuda"):
cond_teacher_output, cond_pred_x0, _ = train_diffusion.training_losses(model_teacher, latents, start_timesteps, model_kwargs=dict(y=y, mask=y_mask, data_info=data_info), noise=noise)
# Get teacher model prediction on noisy_latents and unconditional embedding
uncond_teacher_output, uncond_pred_x0, _ = train_diffusion.training_losses(model_teacher, latents, start_timesteps, model_kwargs=dict(y=uncond_prompt_embeds, mask=y_mask, data_info=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(model_ema, x_prev.float(), timesteps, model_kwargs=dict(y=y, mask=y_mask, data_info=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)
# Backpropagation on the online student model (`model`)
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)
if accelerator.sync_gradients:
ema_update(model_ema, model, config.ema_decay)
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()
synchronize()
torch.cuda.empty_cache()
if accelerator.is_main_process:
# log_validation(model_ema, step, model.device)
if ((epoch - 1) * len(train_dataloader) + step + 1) % config.save_model_steps == 0:
os.umask(0o000)
save_checkpoint(os.path.join(config.work_dir, 'checkpoints'),
epoch=epoch,
step=(epoch - 1) * len(train_dataloader) + step + 1,
model=accelerator.unwrap_model(model),
model_ema=accelerator.unwrap_model(model_ema),
optimizer=optimizer,
lr_scheduler=lr_scheduler
)
synchronize()
synchronize()
if accelerator.is_main_process:
if epoch % config.save_model_epochs == 0 or epoch == config.num_epochs:
os.umask(0o000)
save_checkpoint(os.path.join(config.work_dir, 'checkpoints'),
epoch=epoch,
step=(epoch - 1) * len(train_dataloader) + step + 1,
model=accelerator.unwrap_model(model),
model_ema=accelerator.unwrap_model(model_ema),
optimizer=optimizer,
lr_scheduler=lr_scheduler
)
synchronize()
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', help='the dir to save logs and models')
parser.add_argument('--resume-from', help='the dir to resume the training')
parser.add_argument('--load-from', default=None, help='the dir to load a ckpt for training')
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')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
config = read_config(args.config)
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.load_from = None
config.resume_from = dict(
checkpoint=args.resume_from,
load_ema=False,
resume_optimizer=True,
resume_lr_scheduler=True)
if args.debug:
config.log_interval = 1
config.train_batch_size = 11
config.valid_num = 100
config.load_from = None
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'))
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
model_kwargs={"window_block_indexes": config.window_block_indexes, "window_size": config.window_size,
"use_rel_pos": config.use_rel_pos, "lewei_scale": config.lewei_scale, 'config':config,
'model_max_length': config.model_max_length}
# build models
train_diffusion = IDDPM(str(config.train_sampling_steps), learn_sigma=learn_sigma, pred_sigma=pred_sigma,
snr=config.snr_loss, return_startx=True)
model = build_model(config.model,
config.grad_checkpointing,
config.get('fp32_attention', False),
input_size=latent_size,
learn_sigma=learn_sigma,
pred_sigma=pred_sigma,
**model_kwargs).train()
logger.info(f"{model.__class__.__name__} Model Parameters: {sum(p.numel() for p in model.parameters()):,}")
if config.load_from is not None:
if args.load_from is not None:
config.load_from = args.load_from
missing, unexpected = load_checkpoint(config.load_from, model, load_ema=config.get('load_ema', False))
logger.warning(f'Missing keys: {missing}')
logger.warning(f'Unexpected keys: {unexpected}')
model_ema = deepcopy(model).eval()
model_teacher = deepcopy(model).eval()
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,
batch_size=config.train_batch_size, aspect_ratios=dataset.aspect_ratio, drop_last=True,
ratio_nums=dataset.ratio_nums, config=config, valid_num=config.valid_num)
# used for balanced sampling
# batch_sampler = BalancedAspectRatioBatchSampler(sampler=RandomSampler(dataset), dataset=dataset,
# batch_size=config.train_batch_size, aspect_ratios=dataset.aspect_ratio,
# ratio_nums=dataset.ratio_nums)
train_dataloader = build_dataloader(dataset, batch_sampler=batch_sampler, num_workers=config.num_workers)
else:
train_dataloader = build_dataloader(dataset, num_workers=config.num_workers, batch_size=config.train_batch_size, shuffle=True)
# build optimizer and lr scheduler
lr_scale_ratio = 1
if config.get('auto_lr', None): | lr_scale_ratio = auto_scale_lr(config.train_batch_size * get_world_size() * config.gradient_accumulation_steps, | 16 | 2023-10-12 14:16:33+00:00 | 24k |
NVlabs/EmerNeRF | datasets/waymo.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]), ... | import logging
import os
import numpy as np
import torch
from typing import Dict
from omegaconf import OmegaConf
from torch import Tensor
from tqdm import trange
from datasets.base.lidar_source import SceneLidarSource
from datasets.base.pixel_source import ScenePixelSource
from datasets.base.scene_dataset import SceneDataset
from datasets.base.split_wrapper import SplitWrapper
from datasets.utils import voxel_coords_to_world_coords
from radiance_fields.video_utils import depth_visualizer, save_videos, scene_flow_to_rgb | 17,561 | render_results=video_dict,
save_pth=kwargs["save_pth"],
num_timestamps=kwargs["num_timestamps"],
keys=kwargs["keys"],
num_cams=kwargs["num_cams"],
fps=kwargs["fps"],
verbose=kwargs["verbose"],
save_seperate_video=kwargs["save_seperate_video"],
)
def render_data_videos(
self,
save_pth: str,
split: str = "full",
fps: int = 24,
verbose=True,
):
"""
Render a video of data.
"""
pixel_dataset, lidar_dataset = None, None
if split == "full":
if self.pixel_source is not None:
pixel_dataset = self.full_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.full_lidar_set
elif split == "train":
if self.pixel_source is not None:
pixel_dataset = self.train_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.train_lidar_set
elif split == "test":
if self.pixel_source is not None:
pixel_dataset = self.test_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.test_lidar_set
else:
raise NotImplementedError(f"Split {split} not supported")
# pixel source
rgb_imgs, dynamic_objects = [], []
sky_masks, feature_pca_colors = [], []
lidar_depths, flow_colors = [], []
for i in trange(
len(pixel_dataset), desc="Rendering data videos", dynamic_ncols=True
):
data_dict = pixel_dataset[i]
if "pixels" in data_dict:
rgb_imgs.append(data_dict["pixels"].cpu().numpy())
if "dynamic_masks" in data_dict:
dynamic_objects.append(
(data_dict["dynamic_masks"].unsqueeze(-1) * data_dict["pixels"])
.cpu()
.numpy()
)
if "sky_masks" in data_dict:
sky_masks.append(data_dict["sky_masks"].cpu().numpy())
if "features" in data_dict:
features = data_dict["features"]
# use registered parameters to normalize the features for visualization
features = features @ self.pixel_source.feat_dimension_reduction_mat
features = (features - self.pixel_source.feat_color_min) / (
self.pixel_source.feat_color_max - self.pixel_source.feat_color_min
).clamp(0, 1)
feature_pca_colors.append(features.cpu().numpy())
if lidar_dataset is not None:
# to deal with asynchronized data
# find the closest lidar scan to the current image in time
closest_lidar_idx = self.lidar_source.find_closest_timestep(
data_dict["normed_timestamps"].flatten()[0]
)
data_dict = lidar_dataset[closest_lidar_idx]
lidar_points = (
data_dict["lidar_origins"]
+ data_dict["lidar_ranges"] * data_dict["lidar_viewdirs"]
)
# project lidar points to the image plane
# TODO: consider making this a function
intrinsic_4x4 = torch.nn.functional.pad(
self.pixel_source.intrinsics[i], (0, 1, 0, 1)
)
intrinsic_4x4[3, 3] = 1.0
lidar2img = intrinsic_4x4 @ self.pixel_source.cam_to_worlds[i].inverse()
lidar_points = (
lidar2img[:3, :3] @ lidar_points.T + lidar2img[:3, 3:4]
).T
depth = lidar_points[:, 2]
cam_points = lidar_points[:, :2] / (depth.unsqueeze(-1) + 1e-6)
valid_mask = (
(cam_points[:, 0] >= 0)
& (cam_points[:, 0] < self.pixel_source.WIDTH)
& (cam_points[:, 1] >= 0)
& (cam_points[:, 1] < self.pixel_source.HEIGHT)
& (depth > 0)
)
depth = depth[valid_mask]
_cam_points = cam_points[valid_mask]
depth_map = torch.zeros(
self.pixel_source.HEIGHT, self.pixel_source.WIDTH
).to(self.device)
depth_map[
_cam_points[:, 1].long(), _cam_points[:, 0].long()
] = depth.squeeze(-1)
depth_img = depth_map.cpu().numpy()
depth_img = depth_visualizer(depth_img, depth_img > 0)
mask = (depth_map.unsqueeze(-1) > 0).cpu().numpy()
# show the depth map on top of the rgb image
image = rgb_imgs[-1] * (1 - mask) + depth_img * mask
lidar_depths.append(image)
# project lidar flows to the image plane
flow_img = torch.zeros(
self.pixel_source.HEIGHT, self.pixel_source.WIDTH, 3
).to(self.device)
# to examine whether the ground labels are correct
valid_mask = valid_mask & (~data_dict["lidar_ground"])
_cam_points = cam_points[valid_mask]
# final color:
# white if no flow, black if ground, and flow color otherwise
|
logger = logging.getLogger()
class WaymoPixelSource(ScenePixelSource):
ORIGINAL_SIZE = [[1280, 1920], [1280, 1920], [1280, 1920], [884, 1920], [884, 1920]]
OPENCV2DATASET = np.array(
[[0, 0, 1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, 1]]
)
def __init__(
self,
pixel_data_config: OmegaConf,
data_path: str,
start_timestep: int,
end_timestep: int,
device: torch.device = torch.device("cpu"),
):
super().__init__(pixel_data_config, device=device)
self.data_path = data_path
self.start_timestep = start_timestep
self.end_timestep = end_timestep
self.create_all_filelist()
self.load_data()
def create_all_filelist(self):
"""
Create file lists for all data files.
e.g., img files, feature files, etc.
"""
# ---- define camera list ---- #
# 0: front, 1: front_left, 2: front_right, 3: side_left, 4: side_right
if self.num_cams == 1:
self.camera_list = [0]
elif self.num_cams == 3:
self.camera_list = [1, 0, 2]
elif self.num_cams == 5:
self.camera_list = [3, 1, 0, 2, 4]
else:
raise NotImplementedError(
f"num_cams: {self.num_cams} not supported for waymo dataset"
)
# ---- define filepaths ---- #
img_filepaths, feat_filepaths = [], []
dynamic_mask_filepaths, sky_mask_filepaths = [], []
# Note: we assume all the files in waymo dataset are synchronized
for t in range(self.start_timestep, self.end_timestep):
for cam_idx in self.camera_list:
img_filepaths.append(
os.path.join(self.data_path, "images", f"{t:03d}_{cam_idx}.jpg")
)
dynamic_mask_filepaths.append(
os.path.join(
self.data_path, "dynamic_masks", f"{t:03d}_{cam_idx}.png"
)
)
sky_mask_filepaths.append(
os.path.join(self.data_path, "sky_masks", f"{t:03d}_{cam_idx}.png")
)
feat_filepaths.append(
os.path.join(
self.data_path,
self.data_cfg.feature_model_type,
f"{t:03d}_{cam_idx}.npy",
)
)
self.img_filepaths = np.array(img_filepaths)
self.dynamic_mask_filepaths = np.array(dynamic_mask_filepaths)
self.sky_mask_filepaths = np.array(sky_mask_filepaths)
self.feat_filepaths = np.array(feat_filepaths)
def load_calibrations(self):
"""
Load the camera intrinsics, extrinsics, timestamps, etc.
Compute the camera-to-world matrices, ego-to-world matrices, etc.
"""
# to store per-camera intrinsics and extrinsics
_intrinsics = []
cam_to_egos = []
for i in range(self.num_cams):
# load camera intrinsics
# 1d Array of [f_u, f_v, c_u, c_v, k{1, 2}, p{1, 2}, k{3}].
# ====!! we did not use distortion parameters for simplicity !!====
# to be improved!!
intrinsic = np.loadtxt(
os.path.join(self.data_path, "intrinsics", f"{i}.txt")
)
fx, fy, cx, cy = intrinsic[0], intrinsic[1], intrinsic[2], intrinsic[3]
# scale intrinsics w.r.t. load size
fx, fy = (
fx * self.data_cfg.load_size[1] / self.ORIGINAL_SIZE[i][1],
fy * self.data_cfg.load_size[0] / self.ORIGINAL_SIZE[i][0],
)
cx, cy = (
cx * self.data_cfg.load_size[1] / self.ORIGINAL_SIZE[i][1],
cy * self.data_cfg.load_size[0] / self.ORIGINAL_SIZE[i][0],
)
intrinsic = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
_intrinsics.append(intrinsic)
# load camera extrinsics
cam_to_ego = np.loadtxt(
os.path.join(self.data_path, "extrinsics", f"{i}.txt")
)
# because we use opencv coordinate system to generate camera rays,
# we need a transformation matrix to covnert rays from opencv coordinate
# system to waymo coordinate system.
# opencv coordinate system: x right, y down, z front
# waymo coordinate system: x front, y left, z up
cam_to_egos.append(cam_to_ego @ self.OPENCV2DATASET)
# compute per-image poses and intrinsics
cam_to_worlds, ego_to_worlds = [], []
intrinsics, cam_ids = [], []
# ===! for waymo, we simplify timestamps as the time indices
timestamps, timesteps = [], []
# we tranform the camera poses w.r.t. the first timestep to make the translation vector of
# the first ego pose as the origin of the world coordinate system.
ego_to_world_start = np.loadtxt(
os.path.join(self.data_path, "ego_pose", f"{self.start_timestep:03d}.txt")
)
for t in range(self.start_timestep, self.end_timestep):
ego_to_world_current = np.loadtxt(
os.path.join(self.data_path, "ego_pose", f"{t:03d}.txt")
)
# compute ego_to_world transformation
ego_to_world = np.linalg.inv(ego_to_world_start) @ ego_to_world_current
ego_to_worlds.append(ego_to_world)
for cam_id in self.camera_list:
cam_ids.append(cam_id)
# transformation:
# (opencv_cam -> waymo_cam -> waymo_ego_vehicle) -> current_world
cam2world = ego_to_world @ cam_to_egos[cam_id]
cam_to_worlds.append(cam2world)
intrinsics.append(_intrinsics[cam_id])
# ===! we use time indices as the timestamp for waymo dataset for simplicity
# ===! we can use the actual timestamps if needed
# to be improved
timestamps.append(t - self.start_timestep)
timesteps.append(t - self.start_timestep)
self.intrinsics = torch.from_numpy(np.stack(intrinsics, axis=0)).float()
self.cam_to_worlds = torch.from_numpy(np.stack(cam_to_worlds, axis=0)).float()
self.ego_to_worlds = torch.from_numpy(np.stack(ego_to_worlds, axis=0)).float()
self.cam_ids = torch.from_numpy(np.stack(cam_ids, axis=0)).long()
# the underscore here is important.
self._timestamps = torch.from_numpy(np.stack(timestamps, axis=0)).float()
self._timesteps = torch.from_numpy(np.stack(timesteps, axis=0)).long()
class WaymoLiDARSource(SceneLidarSource):
def __init__(
self,
lidar_data_config: OmegaConf,
data_path: str,
start_timestep: int,
end_timestep: int,
device: torch.device = torch.device("cpu"),
):
super().__init__(lidar_data_config, device=device)
self.data_path = data_path
self.start_timestep = start_timestep
self.end_timestep = end_timestep
self.create_all_filelist()
self.load_data()
def create_all_filelist(self):
"""
Create a list of all the files in the dataset.
e.g., a list of all the lidar scans in the dataset.
"""
lidar_filepaths = []
for t in range(self.start_timestep, self.end_timestep):
lidar_filepaths.append(
os.path.join(self.data_path, "lidar", f"{t:03d}.bin")
)
self.lidar_filepaths = np.array(lidar_filepaths)
def load_calibrations(self):
"""
Load the calibration files of the dataset.
e.g., lidar to world transformation matrices.
"""
# Note that in the Waymo Open Dataset, the lidar coordinate system is the same
# as the vehicle coordinate system
lidar_to_worlds = []
# we tranform the poses w.r.t. the first timestep to make the origin of the
# first ego pose as the origin of the world coordinate system.
ego_to_world_start = np.loadtxt(
os.path.join(self.data_path, "ego_pose", f"{self.start_timestep:03d}.txt")
)
for t in range(self.start_timestep, self.end_timestep):
ego_to_world_current = np.loadtxt(
os.path.join(self.data_path, "ego_pose", f"{t:03d}.txt")
)
# compute ego_to_world transformation
lidar_to_world = np.linalg.inv(ego_to_world_start) @ ego_to_world_current
lidar_to_worlds.append(lidar_to_world)
self.lidar_to_worlds = torch.from_numpy(
np.stack(lidar_to_worlds, axis=0)
).float()
def load_lidar(self):
"""
Load the lidar data of the dataset from the filelist.
"""
origins, directions, ranges, laser_ids = [], [], [], []
# flow/ground info are used for evaluation only
flows, flow_classes, grounds = [], [], []
# in waymo, we simplify timestamps as the time indices
timestamps, timesteps = [], []
accumulated_num_original_rays = 0
accumulated_num_rays = 0
for t in trange(
0, len(self.lidar_filepaths), desc="Loading lidar", dynamic_ncols=True
):
# each lidar_info contains an Nx14 array
# from left to right:
# origins: 3d, points: 3d, flows: 3d, flow_class: 1d,
# ground_labels: 1d, intensities: 1d, elongations: 1d, laser_ids: 1d
lidar_info = np.memmap(
self.lidar_filepaths[t],
dtype=np.float32,
mode="r",
).reshape(-1, 14)
original_length = len(lidar_info)
accumulated_num_original_rays += original_length
# select lidar points based on the laser id
if self.data_cfg.only_use_top_lidar:
# laser_ids: 0: TOP, 1: FRONT, 2: SIDE_LEFT, 3: SIDE_RIGHT, 4: REAR
lidar_info = lidar_info[lidar_info[:, 13] == 0]
lidar_origins = torch.from_numpy(lidar_info[:, :3]).float()
lidar_points = torch.from_numpy(lidar_info[:, 3:6]).float()
lidar_ids = torch.from_numpy(lidar_info[:, 13]).float()
lidar_flows = torch.from_numpy(lidar_info[:, 6:9]).float()
lidar_flow_classes = torch.from_numpy(lidar_info[:, 9]).long()
ground_labels = torch.from_numpy(lidar_info[:, 10]).long()
# we don't collect intensities and elongations for now
# select lidar points based on a truncated ego-forward-directional range
# this is to make sure most of the lidar points are within the range of the camera
valid_mask = torch.ones_like(lidar_origins[:, 0]).bool()
if self.data_cfg.truncated_max_range is not None:
valid_mask = lidar_points[:, 0] < self.data_cfg.truncated_max_range
if self.data_cfg.truncated_min_range is not None:
valid_mask = valid_mask & (
lidar_points[:, 0] > self.data_cfg.truncated_min_range
)
lidar_origins = lidar_origins[valid_mask]
lidar_points = lidar_points[valid_mask]
lidar_ids = lidar_ids[valid_mask]
lidar_flows = lidar_flows[valid_mask]
lidar_flow_classes = lidar_flow_classes[valid_mask]
ground_labels = ground_labels[valid_mask]
# transform lidar points from lidar coordinate system to world coordinate system
lidar_origins = (
self.lidar_to_worlds[t][:3, :3] @ lidar_origins.T
+ self.lidar_to_worlds[t][:3, 3:4]
).T
lidar_points = (
self.lidar_to_worlds[t][:3, :3] @ lidar_points.T
+ self.lidar_to_worlds[t][:3, 3:4]
).T
# scene flows are in the lidar coordinate system, so we need to rotate them
lidar_flows = (self.lidar_to_worlds[t][:3, :3] @ lidar_flows.T).T
# compute lidar directions
lidar_directions = lidar_points - lidar_origins
lidar_ranges = torch.norm(lidar_directions, dim=-1, keepdim=True)
lidar_directions = lidar_directions / lidar_ranges
# we use time indices as the timestamp for waymo dataset
lidar_timestamp = torch.ones_like(lidar_ranges).squeeze(-1) * t
accumulated_num_rays += len(lidar_ranges)
origins.append(lidar_origins)
directions.append(lidar_directions)
ranges.append(lidar_ranges)
laser_ids.append(lidar_ids)
flows.append(lidar_flows)
flow_classes.append(lidar_flow_classes)
grounds.append(ground_labels)
# we use time indices as the timestamp for waymo dataset
timestamps.append(lidar_timestamp)
timesteps.append(lidar_timestamp)
logger.info(
f"Number of lidar rays: {accumulated_num_rays} "
f"({accumulated_num_rays / accumulated_num_original_rays * 100:.2f}% of "
f"{accumulated_num_original_rays} original rays)"
)
logger.info("Filter condition:")
logger.info(f" only_use_top_lidar: {self.data_cfg.only_use_top_lidar}")
logger.info(f" truncated_max_range: {self.data_cfg.truncated_max_range}")
logger.info(f" truncated_min_range: {self.data_cfg.truncated_min_range}")
self.origins = torch.cat(origins, dim=0)
self.directions = torch.cat(directions, dim=0)
self.ranges = torch.cat(ranges, dim=0)
self.laser_ids = torch.cat(laser_ids, dim=0)
# becasue the flows here are velocities (m/s), and the fps of the lidar is 10,
# we need to divide the velocities by 10 to get the displacements/flows
# between two consecutive lidar scans
self.flows = torch.cat(flows, dim=0) / 10.0
self.flow_classes = torch.cat(flow_classes, dim=0)
self.grounds = torch.cat(grounds, dim=0).bool()
# the underscore here is important.
self._timestamps = torch.cat(timestamps, dim=0)
self._timesteps = torch.cat(timesteps, dim=0)
def to(self, device: torch.device):
super().to(device)
self.flows = self.flows.to(device)
self.flow_classes = self.flow_classes.to(device)
self.grounds = self.grounds.to(self.device)
def get_render_rays(self, time_idx: int) -> Dict[str, Tensor]:
"""
Override the base class function to add more information to the render rays.
"""
return {
"lidar_origins": self.origins[self.timesteps == time_idx],
"lidar_viewdirs": self.directions[self.timesteps == time_idx],
"lidar_ranges": self.ranges[self.timesteps == time_idx],
# normalized timestamps between 0 and 1
"lidar_normed_timestamps": self.normalized_timestamps[
self.timesteps == time_idx
],
"lidar_flow": self.flows[self.timesteps == time_idx],
"lidar_flow_class": self.flow_classes[self.timesteps == time_idx],
"lidar_ground": self.grounds[self.timesteps == time_idx],
}
class WaymoDataset(SceneDataset):
dataset: str = "waymo"
def __init__(
self,
data_cfg: OmegaConf,
) -> None:
super().__init__(data_cfg)
self.data_path = os.path.join(self.data_cfg.data_root, f"{self.scene_idx:03d}")
assert self.data_cfg.dataset == "waymo"
assert os.path.exists(self.data_path), f"{self.data_path} does not exist"
# ---- find the number of synchronized frames ---- #
if self.data_cfg.end_timestep == -1:
num_files = len(os.listdir(os.path.join(self.data_path, "ego_pose")))
end_timestep = num_files - 1
else:
end_timestep = self.data_cfg.end_timestep
# to make sure the last timestep is included
self.end_timestep = end_timestep + 1
self.start_timestep = self.data_cfg.start_timestep
# ---- create data source ---- #
self.pixel_source, self.lidar_source = self.build_data_source()
self.aabb = self.get_aabb()
# ---- define train and test indices ---- #
# note that the timestamps of the pixel source and the lidar source are the same in waymo dataset
(
self.train_timesteps,
self.test_timesteps,
self.train_indices,
self.test_indices,
) = self.split_train_test()
# ---- create split wrappers ---- #
pixel_sets, lidar_sets = self.build_split_wrapper()
self.train_pixel_set, self.test_pixel_set, self.full_pixel_set = pixel_sets
self.train_lidar_set, self.test_lidar_set, self.full_lidar_set = lidar_sets
def build_split_wrapper(self):
"""
Makes each data source as a Pytorch Dataset
"""
train_pixel_set, test_pixel_set, full_pixel_set = None, None, None
train_lidar_set, test_lidar_set, full_lidar_set = None, None, None
if self.pixel_source is not None:
train_pixel_set = SplitWrapper(
datasource=self.pixel_source,
# train_indices are img indices, so the length is num_cams * num_train_timesteps
split_indices=self.train_indices,
split="train",
ray_batch_size=self.data_cfg.ray_batch_size,
)
full_pixel_set = SplitWrapper(
datasource=self.pixel_source,
# cover all the images
split_indices=np.arange(self.pixel_source.num_imgs).tolist(),
split="full",
ray_batch_size=self.data_cfg.ray_batch_size,
)
if len(self.test_indices) > 0:
test_pixel_set = SplitWrapper(
datasource=self.pixel_source,
# test_indices are img indices, so the length is num_cams * num_test_timesteps
split_indices=self.test_indices,
split="test",
ray_batch_size=self.data_cfg.ray_batch_size,
)
if self.lidar_source is not None:
train_lidar_set = SplitWrapper(
datasource=self.lidar_source,
# train_timesteps are lidar indices, so the length is num_train_timesteps
split_indices=self.train_timesteps,
split="train",
ray_batch_size=self.data_cfg.ray_batch_size,
)
full_lidar_set = SplitWrapper(
datasource=self.lidar_source,
# cover all the lidar scans
split_indices=np.arange(self.lidar_source.num_timesteps),
split="full",
ray_batch_size=self.data_cfg.ray_batch_size,
)
if len(self.test_indices) > 0:
test_lidar_set = SplitWrapper(
datasource=self.lidar_source,
# test_timesteps are lidar indices, so the length is num_test_timesteps
split_indices=self.test_timesteps,
split="test",
ray_batch_size=self.data_cfg.ray_batch_size,
)
pixel_set = (train_pixel_set, test_pixel_set, full_pixel_set)
lidar_set = (train_lidar_set, test_lidar_set, full_lidar_set)
return pixel_set, lidar_set
def build_data_source(self):
"""
Create the data source for the dataset.
"""
pixel_source, lidar_source = None, None
# to collect all timestamps from pixel source and lidar source
all_timestamps = []
# ---- create pixel source ---- #
load_pixel = (
self.data_cfg.pixel_source.load_rgb
or self.data_cfg.pixel_source.load_sky_mask
or self.data_cfg.pixel_source.load_dynamic_mask
or self.data_cfg.pixel_source.load_feature
)
if load_pixel:
pixel_source = WaymoPixelSource(
self.data_cfg.pixel_source,
self.data_path,
self.start_timestep,
self.end_timestep,
device=self.device,
)
pixel_source.to(self.device)
# collect img timestamps
all_timestamps.append(pixel_source.timestamps)
# ---- create lidar source ---- #
if self.data_cfg.lidar_source.load_lidar:
lidar_source = WaymoLiDARSource(
self.data_cfg.lidar_source,
self.data_path,
self.start_timestep,
self.end_timestep,
device=self.device,
)
lidar_source.to(self.device)
# collect lidar timestamps
all_timestamps.append(lidar_source.timestamps)
assert len(all_timestamps) > 0, "No data source is loaded"
all_timestamps = torch.cat(all_timestamps, dim=0)
# normalize the timestamps jointly for pixel source and lidar source
# so that the normalized timestamps are between 0 and 1
all_timestamps = (all_timestamps - all_timestamps.min()) / (
all_timestamps.max() - all_timestamps.min()
)
if pixel_source is not None:
pixel_source.register_normalized_timestamps(
all_timestamps[: len(pixel_source.timestamps)]
)
if lidar_source is not None:
lidar_source.register_normalized_timestamps(
all_timestamps[-len(lidar_source.timestamps) :]
)
return pixel_source, lidar_source
def split_train_test(self):
if self.data_cfg.pixel_source.test_image_stride != 0:
test_timesteps = np.arange(
# it makes no sense to have test timesteps before the start timestep
self.data_cfg.pixel_source.test_image_stride,
self.num_img_timesteps,
self.data_cfg.pixel_source.test_image_stride,
)
else:
test_timesteps = []
train_timesteps = np.array(
[i for i in range(self.num_img_timesteps) if i not in test_timesteps]
)
logger.info(
f"Train timesteps: \n{np.arange(self.start_timestep, self.end_timestep)[train_timesteps]}"
)
logger.info(
f"Test timesteps: \n{np.arange(self.start_timestep, self.end_timestep)[test_timesteps]}"
)
# propagate the train and test timesteps to the train and test indices
train_indices, test_indices = [], []
for t in range(self.num_img_timesteps):
if t in train_timesteps:
for cam in range(self.pixel_source.num_cams):
train_indices.append(t * self.pixel_source.num_cams + cam)
elif t in test_timesteps:
for cam in range(self.pixel_source.num_cams):
test_indices.append(t * self.pixel_source.num_cams + cam)
logger.info(f"Number of train indices: {len(train_indices)}")
logger.info(f"Train indices: {train_indices}")
logger.info(f"Number of test indices: {len(test_indices)}")
logger.info(f"Test indices: {test_indices}")
# Again, training and testing indices are indices into the full dataset
# train_indices are img indices, so the length is num_cams * num_train_timesteps
# but train_timesteps are timesteps, so the length is num_train_timesteps (len(unique_train_timestamps))
return train_timesteps, test_timesteps, train_indices, test_indices
def get_occ(self, index: int):
"""
Get the Occ3D data of the scene at the given index.
"""
# from: https://github.com/Tsinghua-MARS-Lab/Occ3D#occ3d-waymo
# The dataset contains 15 classes. The definition of classes from 0 to 14 is
# 0: TYPE_GENERALOBJECT, 1: TYPE_VEHICLE, 2: TYPE_PEDESTRIAN, 3: TYPE_SIGN,
# 4: TYPE_CYCLIST, 5: TYPE_TRAFFIC_LIGHT, 6: TYPE_POLE, 7: TYPE_CONSTRUCTION_CONE,
# 8: TYPE_BICYCLE, 9: TYPE_MOTORCYCLE, 10: TYPE_BUILDING, 11: TYPE_VEGETATION,
# 12: TYPE_TREE_TRUNK, 13: TYPE_ROAD, 14: TYPE_WALKABLE.
self.label_mapping = {
0: "general_obj",
1: "vehicle",
2: "pedestrian",
3: "sign",
4: "cyclist",
5: "traffic_light",
6: "pole",
7: "construction_cone",
8: "bicyle",
9: "motorcycle",
10: "building",
11: "vegetation",
12: "tree_trunck",
13: "road",
14: "walkable",
}
if self.data_cfg.occ_source.voxel_size == 0.4:
occ_path = f"{self.data_path}/occ3d/{index:03d}_04.npz"
occupancy_resolution = [100, 200, 16]
occupancy_aabb_min = [0, -40, -1]
occupancy_aabb_max = [40, 40, 5.4]
elif self.data_cfg.occ_source.voxel_size == 0.1:
occ_path = f"{self.data_path}/occ3d/{index:03d}.npz"
occupancy_resolution = [800, 1600, 64]
occupancy_aabb_min = [0, -80, -5]
occupancy_aabb_max = [80, 80, 7.8]
else:
raise NotImplementedError(
f"voxel size {self.data_cfg.occ_source.voxel_size} not supported"
)
if not os.path.exists(occ_path):
raise FileNotFoundError(f"{occ_path} does not exist")
# loading the occupancy grid
gt_occ = np.load(occ_path)
# np.unique(gt_occ['voxel_label']): array([ 0, 1, 2, 3, 6, 8, 9, 10, 11, 12, 13, 14, 23], dtype=uint8)
semantic_labels = gt_occ["voxel_label"]
# final voxel_state will indicate what voxels are visible from the camera
mask_camera = gt_occ["final_voxel_state"]
# we don't have back-cameras, so we remove the back part of the grid
semantic_labels = semantic_labels[len(semantic_labels) // 2 :, :, :]
mask_camera = mask_camera[len(mask_camera) // 2 :, :, :]
# semantic_labels == 23 means the free space, i.e. empty
semantic_labels[semantic_labels == 23] = 15
# mask_camera == 0 means invisible from the camera
semantic_labels[mask_camera == 0] = 15
semantic_labels = (
torch.from_numpy(semantic_labels.copy()).long().to(self.device)
)
# compute the coordinates and labels of the occupied voxels
occ_coords = torch.nonzero(semantic_labels != 15).float()
occ_labels = semantic_labels[semantic_labels != 15]
# transform the coordinates from voxel space to world space
ego_occ_coords = voxel_coords_to_world_coords(
occupancy_aabb_min,
occupancy_aabb_max,
occupancy_resolution,
points=occ_coords,
).to(self.device)
world_occ_coords = (
self.lidar_source.lidar_to_worlds[index][:3, :3] @ ego_occ_coords.T
+ self.lidar_source.lidar_to_worlds[index][:3, 3:4]
).T
normed_timestamps = (
torch.ones_like(world_occ_coords[..., 0])
* index
/ (self.lidar_source.num_timesteps + 1e-6 - 1)
)
return world_occ_coords, occ_labels, normed_timestamps
def get_valid_lidar_mask(self, lidar_timestep: int, data_dict: dict):
# filter out the lidar points that are not visible from the camera
lidar_points = (
data_dict["lidar_origins"]
+ data_dict["lidar_ranges"] * data_dict["lidar_viewdirs"]
)
valid_mask = torch.zeros_like(lidar_points[:, 0]).bool()
# project lidar points to the image plane
for i in range(self.pixel_source.num_cams):
img_idx = lidar_timestep * self.pixel_source.num_cams + i
intrinsic_4x4 = torch.nn.functional.pad(
self.pixel_source.intrinsics[img_idx], (0, 1, 0, 1)
)
intrinsic_4x4[3, 3] = 1.0
lidar2img = (
intrinsic_4x4 @ self.pixel_source.cam_to_worlds[img_idx].inverse()
)
projected_points = (
lidar2img[:3, :3] @ lidar_points.T + lidar2img[:3, 3:4]
).T
depth = projected_points[:, 2]
cam_points = projected_points[:, :2] / (depth.unsqueeze(-1) + 1e-6)
current_valid_mask = (
(cam_points[:, 0] >= 0)
& (cam_points[:, 0] < self.pixel_source.WIDTH)
& (cam_points[:, 1] >= 0)
& (cam_points[:, 1] < self.pixel_source.HEIGHT)
& (depth > 0)
)
valid_mask = valid_mask | current_valid_mask
return valid_mask
def save_videos(self, video_dict: dict, **kwargs):
"""
Save the a video of the data.
"""
return save_videos(
render_results=video_dict,
save_pth=kwargs["save_pth"],
num_timestamps=kwargs["num_timestamps"],
keys=kwargs["keys"],
num_cams=kwargs["num_cams"],
fps=kwargs["fps"],
verbose=kwargs["verbose"],
save_seperate_video=kwargs["save_seperate_video"],
)
def render_data_videos(
self,
save_pth: str,
split: str = "full",
fps: int = 24,
verbose=True,
):
"""
Render a video of data.
"""
pixel_dataset, lidar_dataset = None, None
if split == "full":
if self.pixel_source is not None:
pixel_dataset = self.full_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.full_lidar_set
elif split == "train":
if self.pixel_source is not None:
pixel_dataset = self.train_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.train_lidar_set
elif split == "test":
if self.pixel_source is not None:
pixel_dataset = self.test_pixel_set
if self.lidar_source is not None:
lidar_dataset = self.test_lidar_set
else:
raise NotImplementedError(f"Split {split} not supported")
# pixel source
rgb_imgs, dynamic_objects = [], []
sky_masks, feature_pca_colors = [], []
lidar_depths, flow_colors = [], []
for i in trange(
len(pixel_dataset), desc="Rendering data videos", dynamic_ncols=True
):
data_dict = pixel_dataset[i]
if "pixels" in data_dict:
rgb_imgs.append(data_dict["pixels"].cpu().numpy())
if "dynamic_masks" in data_dict:
dynamic_objects.append(
(data_dict["dynamic_masks"].unsqueeze(-1) * data_dict["pixels"])
.cpu()
.numpy()
)
if "sky_masks" in data_dict:
sky_masks.append(data_dict["sky_masks"].cpu().numpy())
if "features" in data_dict:
features = data_dict["features"]
# use registered parameters to normalize the features for visualization
features = features @ self.pixel_source.feat_dimension_reduction_mat
features = (features - self.pixel_source.feat_color_min) / (
self.pixel_source.feat_color_max - self.pixel_source.feat_color_min
).clamp(0, 1)
feature_pca_colors.append(features.cpu().numpy())
if lidar_dataset is not None:
# to deal with asynchronized data
# find the closest lidar scan to the current image in time
closest_lidar_idx = self.lidar_source.find_closest_timestep(
data_dict["normed_timestamps"].flatten()[0]
)
data_dict = lidar_dataset[closest_lidar_idx]
lidar_points = (
data_dict["lidar_origins"]
+ data_dict["lidar_ranges"] * data_dict["lidar_viewdirs"]
)
# project lidar points to the image plane
# TODO: consider making this a function
intrinsic_4x4 = torch.nn.functional.pad(
self.pixel_source.intrinsics[i], (0, 1, 0, 1)
)
intrinsic_4x4[3, 3] = 1.0
lidar2img = intrinsic_4x4 @ self.pixel_source.cam_to_worlds[i].inverse()
lidar_points = (
lidar2img[:3, :3] @ lidar_points.T + lidar2img[:3, 3:4]
).T
depth = lidar_points[:, 2]
cam_points = lidar_points[:, :2] / (depth.unsqueeze(-1) + 1e-6)
valid_mask = (
(cam_points[:, 0] >= 0)
& (cam_points[:, 0] < self.pixel_source.WIDTH)
& (cam_points[:, 1] >= 0)
& (cam_points[:, 1] < self.pixel_source.HEIGHT)
& (depth > 0)
)
depth = depth[valid_mask]
_cam_points = cam_points[valid_mask]
depth_map = torch.zeros(
self.pixel_source.HEIGHT, self.pixel_source.WIDTH
).to(self.device)
depth_map[
_cam_points[:, 1].long(), _cam_points[:, 0].long()
] = depth.squeeze(-1)
depth_img = depth_map.cpu().numpy()
depth_img = depth_visualizer(depth_img, depth_img > 0)
mask = (depth_map.unsqueeze(-1) > 0).cpu().numpy()
# show the depth map on top of the rgb image
image = rgb_imgs[-1] * (1 - mask) + depth_img * mask
lidar_depths.append(image)
# project lidar flows to the image plane
flow_img = torch.zeros(
self.pixel_source.HEIGHT, self.pixel_source.WIDTH, 3
).to(self.device)
# to examine whether the ground labels are correct
valid_mask = valid_mask & (~data_dict["lidar_ground"])
_cam_points = cam_points[valid_mask]
# final color:
# white if no flow, black if ground, and flow color otherwise | flow_color = scene_flow_to_rgb( | 5 | 2023-10-11 20:56:27+00:00 | 24k |
alibaba-damo-academy/FunCodec | funcodec/models/encoder/transformer_encoder.py | [
{
"identifier": "AbsEncoder",
"path": "funcodec/models/encoder/abs_encoder.py",
"snippet": "class AbsEncoder(torch.nn.Module, ABC):\n @abstractmethod\n def output_size(self) -> int:\n raise NotImplementedError\n\n @abstractmethod\n def forward(\n self,\n xs_pad: torch.Te... | from typing import List
from typing import Optional
from typing import Tuple
from torch import nn
from funcodec.models.encoder.abs_encoder import AbsEncoder
from funcodec.modules.attention import (
MultiHeadedAttention,
RelPositionMultiHeadedAttention, # noqa: H301
LegacyRelPositionMultiHeadedAttention, # noqa: H301
)
from funcodec.modules.layer_norm import LayerNorm
from funcodec.modules.multi_layer_conv import Conv1dLinear
from funcodec.modules.multi_layer_conv import MultiLayeredConv1d
from funcodec.modules.nets_utils import make_pad_mask
from funcodec.modules.embedding import (
PositionalEncoding, # noqa: H301
ScaledPositionalEncoding, # noqa: H301
RelPositionalEncoding, # noqa: H301
LegacyRelPositionalEncoding, # noqa: H301
)
from funcodec.modules.positionwise_feed_forward import (
PositionwiseFeedForward, # noqa: H301
)
from funcodec.modules.repeat import repeat
from funcodec.modules.nets_utils import rename_state_dict
from funcodec.modules.dynamic_conv import DynamicConvolution
from funcodec.modules.dynamic_conv2d import DynamicConvolution2D
from funcodec.modules.lightconv import LightweightConvolution
from funcodec.modules.lightconv2d import LightweightConvolution2D
from funcodec.modules.subsampling import Conv2dSubsampling
from funcodec.modules.subsampling import Conv2dSubsampling2
from funcodec.modules.subsampling import Conv2dSubsampling6
from funcodec.modules.subsampling import Conv2dSubsampling8
from funcodec.modules.subsampling import TooShortUttError
from funcodec.modules.subsampling import check_short_utt
import torch
import logging | 18,455 | if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
class TransformerEncoder(AbsEncoder):
"""Transformer encoder module.
Args:
input_size: input dim
output_size: dimension of attention
attention_heads: the number of heads of multi head attention
linear_units: the number of units of position-wise feed forward
num_blocks: the number of decoder blocks
dropout_rate: dropout rate
attention_dropout_rate: dropout rate in attention
positional_dropout_rate: dropout rate after adding positional encoding
input_layer: input layer type
pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
normalize_before: whether to use layer_norm before the first block
concat_after: whether to concat attention layer's input and output
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied.
i.e. x -> x + att(x)
positionwise_layer_type: linear of conv1d
positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
padding_idx: padding_idx for input_layer=embed
"""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: Optional[str] = "conv2d",
pos_enc_class=PositionalEncoding,
normalize_before: bool = True,
concat_after: bool = False,
positionwise_layer_type: str = "linear",
positionwise_conv_kernel_size: int = 1,
padding_idx: int = -1,
interctc_layer_idx: List[int] = [],
interctc_use_conditioning: bool = False,
causal_mode: str = "None",
):
super().__init__()
self._output_size = output_size
self.causal_mode = causal_mode
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(input_size, output_size),
torch.nn.LayerNorm(output_size),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
pos_enc_class(output_size, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
elif input_layer == "conv2d2":
self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
pos_enc_class(output_size, positional_dropout_rate),
)
elif input_layer is None:
if input_size == output_size:
self.embed = None
else:
self.embed = torch.nn.Linear(input_size, output_size)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
else:
raise NotImplementedError("Support only linear or conv1d.")
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
output_size,
| # Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer encoder definition."""
class EncoderLayer(nn.Module):
"""Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
can be used as the argument.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
stochastic_depth_rate (float): Proability to skip this layer.
During training, the layer may skip residual computation and return input
as-is with given probability.
"""
def __init__(
self,
size,
self_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
stochastic_depth_rate=0.0,
):
"""Construct an EncoderLayer object."""
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(size)
self.norm2 = LayerNorm(size)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
def forward(self, x, mask, cache=None):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, time).
cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time).
"""
if isinstance(x, tuple):
x, pos_emb = x[0], x[1]
else:
x, pos_emb = x, None
skip_layer = False
# with stochastic depth, residual connection `x + f(x)` becomes
# `x <- x + 1 / (1 - p) * f(x)` at training time.
stoch_layer_coeff = 1.0
if self.training and self.stochastic_depth_rate > 0:
skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
if skip_layer:
if cache is not None:
x = torch.cat([cache, x], dim=1)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
residual = x
if self.normalize_before:
x = self.norm1(x)
if cache is None:
x_q = x
else:
assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
x_q = x[:, -1:, :]
residual = residual[:, -1:, :]
mask = None if mask is None else mask[:, -1:, :]
if pos_emb is not None:
x_att = self.self_attn(x_q, x, x, pos_emb, mask)
else:
x_att = self.self_attn(x_q, x, x, mask)
if self.concat_after:
x_concat = torch.cat((x, x_att), dim=-1)
x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
else:
x = residual + stoch_layer_coeff * self.dropout(x_att)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
class TransformerEncoder(AbsEncoder):
"""Transformer encoder module.
Args:
input_size: input dim
output_size: dimension of attention
attention_heads: the number of heads of multi head attention
linear_units: the number of units of position-wise feed forward
num_blocks: the number of decoder blocks
dropout_rate: dropout rate
attention_dropout_rate: dropout rate in attention
positional_dropout_rate: dropout rate after adding positional encoding
input_layer: input layer type
pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
normalize_before: whether to use layer_norm before the first block
concat_after: whether to concat attention layer's input and output
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied.
i.e. x -> x + att(x)
positionwise_layer_type: linear of conv1d
positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
padding_idx: padding_idx for input_layer=embed
"""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: Optional[str] = "conv2d",
pos_enc_class=PositionalEncoding,
normalize_before: bool = True,
concat_after: bool = False,
positionwise_layer_type: str = "linear",
positionwise_conv_kernel_size: int = 1,
padding_idx: int = -1,
interctc_layer_idx: List[int] = [],
interctc_use_conditioning: bool = False,
causal_mode: str = "None",
):
super().__init__()
self._output_size = output_size
self.causal_mode = causal_mode
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(input_size, output_size),
torch.nn.LayerNorm(output_size),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
pos_enc_class(output_size, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
elif input_layer == "conv2d2":
self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
pos_enc_class(output_size, positional_dropout_rate),
)
elif input_layer is None:
if input_size == output_size:
self.embed = None
else:
self.embed = torch.nn.Linear(input_size, output_size)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
else:
raise NotImplementedError("Support only linear or conv1d.")
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
output_size, | MultiHeadedAttention( | 1 | 2023-10-07 02:00:40+00:00 | 24k |
Beckschen/3D-TransUNet | nn_transunet/trainer/nnUNetTrainerV2.py | [
{
"identifier": "get_moreDA_augmentation",
"path": "nn_transunet/data/data_augmentation_moreDA.py",
"snippet": "def get_moreDA_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params,\n border_val_seg=-1,\n se... | from collections import OrderedDict
from typing import Tuple
from ..data.data_augmentation_moreDA import get_moreDA_augmentation
from ..trainer.loss_functions import MultipleOutputLoss2
from ..trainer.network_trainer import maybe_to_torch, to_cuda
from ..trainer.nnUNetTrainer import nnUNetTrainer
from ..networks.nnunet_model import Generic_UNet
from ..data.default_data_augmentation import default_2D_augmentation_params, \
get_patch_size, default_3D_augmentation_params
from ..data.dataset_loading import unpack_dataset
from sklearn.model_selection import KFold
from torch.cuda.amp import autocast
from batchgenerators.utilities.file_and_folder_operations import *
from torch import nn
from loss_functions import DC_and_CE_loss
from ..networks.transunet3d_model import Generic_TransUNet_max_ppbp
import numpy as np
import torch
import torch.nn.functional as F | 16,795 | # 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.
softmax_helper = lambda x: F.softmax(x, 1)
def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9):
return initial_lr * (1 - epoch / max_epochs)**exponent
class InitWeights_He(object):
def __init__(self, neg_slope=1e-2):
self.neg_slope = neg_slope
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
class InitWeights_XavierUniform(object):
def __init__(self, gain=1):
self.gain = gain
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.xavier_uniform_(module.weight, self.gain)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
class nnUNetTrainerV2(nnUNetTrainer):
"""
Info for Fabian: same as internal nnUNetTrainerV2_2
"""
def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
unpack_data=True, deterministic=True, fp16=False, input_size=(64, 160, 160),args=None):
super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
deterministic, fp16)
if args is not None:
self.input_size=input_size
self.model = args.model
self.resume = args.resume
self.disable_ds=args.disable_ds
self.max_num_epochs = args.max_num_epochs # set 1 gpu training
self.initial_lr = args.initial_lr # 0.01
self.args = args
if self.disable_ds:
print("disable_ds")
# print("not runnable for this feature! current nnunetV2 (w/o DDP) only support deep supervision version")
# raise NotImplementedError
else:
print("runnning DDP, inheriting nnUNetTrainerV2")
self.save_every = 1 # prev 50
# self.max_num_epochs = 1000
# self.initial_lr = 1e-2
self.deep_supervision_scales = None
self.ds_loss_weights = None
self.pin_memory = True
def initialize(self, training=True, force_load_plans=False):
"""
- replaced get_default_augmentation with get_moreDA_augmentation
- enforce to only run this code once
- loss function wrapper for deep supervision
:param training:
:param force_load_plans:
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = len(self.net_num_pool_op_kernel_sizes)
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
if self.disable_ds:
self.ds_loss_weights[0]=1
self.ds_loss_weights[1:]=0
self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
else:
# now wrap the loss
| # 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.
softmax_helper = lambda x: F.softmax(x, 1)
def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9):
return initial_lr * (1 - epoch / max_epochs)**exponent
class InitWeights_He(object):
def __init__(self, neg_slope=1e-2):
self.neg_slope = neg_slope
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
class InitWeights_XavierUniform(object):
def __init__(self, gain=1):
self.gain = gain
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.xavier_uniform_(module.weight, self.gain)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
class nnUNetTrainerV2(nnUNetTrainer):
"""
Info for Fabian: same as internal nnUNetTrainerV2_2
"""
def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
unpack_data=True, deterministic=True, fp16=False, input_size=(64, 160, 160),args=None):
super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
deterministic, fp16)
if args is not None:
self.input_size=input_size
self.model = args.model
self.resume = args.resume
self.disable_ds=args.disable_ds
self.max_num_epochs = args.max_num_epochs # set 1 gpu training
self.initial_lr = args.initial_lr # 0.01
self.args = args
if self.disable_ds:
print("disable_ds")
# print("not runnable for this feature! current nnunetV2 (w/o DDP) only support deep supervision version")
# raise NotImplementedError
else:
print("runnning DDP, inheriting nnUNetTrainerV2")
self.save_every = 1 # prev 50
# self.max_num_epochs = 1000
# self.initial_lr = 1e-2
self.deep_supervision_scales = None
self.ds_loss_weights = None
self.pin_memory = True
def initialize(self, training=True, force_load_plans=False):
"""
- replaced get_default_augmentation with get_moreDA_augmentation
- enforce to only run this code once
- loss function wrapper for deep supervision
:param training:
:param force_load_plans:
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = len(self.net_num_pool_op_kernel_sizes)
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
if self.disable_ds:
self.ds_loss_weights[0]=1
self.ds_loss_weights[1:]=0
self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
else:
# now wrap the loss | self.loss = MultipleOutputLoss2(self.loss, self.ds_loss_weights) | 1 | 2023-10-11 05:19:25+00:00 | 24k |
eai-lab/On-NAS | cifar_search.py | [
{
"identifier": "genotypes",
"path": "utils/genotypes.py",
"snippet": "PRIMITIVES = [\n \"max_pool_3x3\",\n \"avg_pool_3x3\",\n \"skip_connect\", # identity\n \"sep_conv_3x3\",\n \"sep_conv_5x5\",\n \"dil_conv_3x3\",\n \"dil_conv_5x5\",\n \"none\",\n]\nPRIMITIVES_FEWSHOT = [\n ... | import os
import torch
import torch.nn as nn
import numpy as np
import utils.utils as utils
import random
import time
import pandas as pd
import copy
import argparse
from utils import genotypes as gt
from models.search_cnn import SearchCNNController
from models.search_cnn_PC import SearchCNNControllerPC
from task_optimizer.darts import Darts,Architect
from task_optimizer.darts import train as d_train
from tqdm import tqdm
from tqdm import tqdm | 15,692 | """ Search cell """
'''
Based on https://github.com/boschresearch/metanas
which is licensed under GNU Affero General Public License,
'''
device = torch.device("cuda")
# tensorboard
def _init_alpha_normalizer(name, task_train_steps, t_max, t_min, temp_anneal_mode):
normalizer = dict()
normalizer["name"] = name
normalizer["params"] = dict()
normalizer["params"]["curr_step"] = 0.0 # current step for scheduling normalizer
normalizer["params"]["max_steps"] = float(
task_train_steps
) # for scheduling normalizer
normalizer["params"]["t_max"] = t_max
normalizer["params"]["t_min"] = t_min
normalizer["params"]["temp_anneal_mode"] = temp_anneal_mode # temperature annealing
return normalizer
def main(config):
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
random.seed(config.seed)
torch.backends.cudnn.benchmark = True
# get data with meta info
input_size, input_channels, n_classes, train_data = utils.get_data(
config.dataset, config.data_path, cutout_length=0, validation=False)
_,_,_,_,test_data = utils.get_data(config.dataset, config.data_path, cutout_length=0, validation=True)
# input my model architecture here
normalizer = _init_alpha_normalizer(
config.normalizer,
config.task_train_steps,
config.normalizer_t_max,
config.normalizer_t_min,
config.normalizer_temp_anneal_mode,
)
net_crit = nn.CrossEntropyLoss().to(device)
model = SearchCNNController(
3,
config.init_channels,
config.k,
config.layers,
config,
n_nodes=config.nodes,
reduction_layers=config.reduction_layers,
device_ids=config.gpus,
normalizer=normalizer,
PRIMITIVES=gt.PRIMITIVES,
feature_scale_rate=1,
use_hierarchical_alphas=config.use_hierarchical_alphas,
use_pairwise_input_alphas=config.use_pairwise_input_alphas,
alpha_prune_threshold=config.alpha_prune_threshold,
)
if config.meta_model == 'pc_adaptation':
print("model created as PC adaptation")
| """ Search cell """
'''
Based on https://github.com/boschresearch/metanas
which is licensed under GNU Affero General Public License,
'''
device = torch.device("cuda")
# tensorboard
def _init_alpha_normalizer(name, task_train_steps, t_max, t_min, temp_anneal_mode):
normalizer = dict()
normalizer["name"] = name
normalizer["params"] = dict()
normalizer["params"]["curr_step"] = 0.0 # current step for scheduling normalizer
normalizer["params"]["max_steps"] = float(
task_train_steps
) # for scheduling normalizer
normalizer["params"]["t_max"] = t_max
normalizer["params"]["t_min"] = t_min
normalizer["params"]["temp_anneal_mode"] = temp_anneal_mode # temperature annealing
return normalizer
def main(config):
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
random.seed(config.seed)
torch.backends.cudnn.benchmark = True
# get data with meta info
input_size, input_channels, n_classes, train_data = utils.get_data(
config.dataset, config.data_path, cutout_length=0, validation=False)
_,_,_,_,test_data = utils.get_data(config.dataset, config.data_path, cutout_length=0, validation=True)
# input my model architecture here
normalizer = _init_alpha_normalizer(
config.normalizer,
config.task_train_steps,
config.normalizer_t_max,
config.normalizer_t_min,
config.normalizer_temp_anneal_mode,
)
net_crit = nn.CrossEntropyLoss().to(device)
model = SearchCNNController(
3,
config.init_channels,
config.k,
config.layers,
config,
n_nodes=config.nodes,
reduction_layers=config.reduction_layers,
device_ids=config.gpus,
normalizer=normalizer,
PRIMITIVES=gt.PRIMITIVES,
feature_scale_rate=1,
use_hierarchical_alphas=config.use_hierarchical_alphas,
use_pairwise_input_alphas=config.use_pairwise_input_alphas,
alpha_prune_threshold=config.alpha_prune_threshold,
)
if config.meta_model == 'pc_adaptation':
print("model created as PC adaptation") | model = SearchCNNControllerPC( | 2 | 2023-10-08 02:42:27+00:00 | 24k |
LukeForeverYoung/UReader | serve/model_worker.py | [
{
"identifier": "IO",
"path": "serve/io_utils.py",
"snippet": "class IO:\n @staticmethod\n def register(options):\n pass\n\n def open(self, path: str, mode: str):\n raise NotImplementedError\n\n def exists(self, path: str) -> bool:\n raise NotImplementedError\n\n def ... | from PIL import Image
from io import BytesIO
from .io_utils import IO, DefaultIO, OSS
from mplug_owl.processing_mplug_owl import MplugOwlProcessor, MplugOwlImageProcessor
from mplug_owl.modeling_mplug_owl import MplugOwlForConditionalGeneration
from mplug_owl.configuration_mplug_owl import MplugOwlConfig
from mplug_owl.tokenization_mplug_owl import MplugOwlTokenizer
from transformers import GenerationConfig
from .model_utils import post_process_output, Stream, Iteratorize
from pathlib import Path
from mplug_owl.processing_mplug_owl import MplugOwlProcessor
from mplug_owl.modeling_mplug_owl import MplugOwlForConditionalGeneration
from pipeline.data_utils.processors.builder import build_processors
from pipeline.data_utils.processors import *
from transformers.models.llama.tokenization_llama import LlamaTokenizer
from icecream import ic
import torch
import gradio as gr
import logging
import sys
import os
import json
import requests
import datetime
import uuid
import base64
import time
import sys
import transformers | 14,657 | sys.path.append("..")
server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
# from pipeline.data_utils.xgpt3_dataset import ImageIO
# class ImageProcessor(object):
# def __init__(self, resolution=224, tokenizer=None):
# normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
# # self.transform = transforms.Compose([
# # transforms.Resize((resolution, resolution),interpolation=Image.BICUBIC),
# # transforms.ToTensor(),
# # normalize,
# # ])
# from megatron.data.processors import doc_processor
# processor_class = os.environ.get('DocProcessor','DocSFTProcessor')
# self.transform = getattr(doc_processor,processor_class)()
# self.image_io = ImageIO()
# self.tokenizer=tokenizer
# def __call__(self, image_paths, prompts):
# if isinstance(image_paths, str):
# image_paths = [image_paths]
# images = []
# images = self.image_io._load_img(image_paths)
# images = [self.transform(image, None) for image in images] # image_input, text_input, patch_position
# patch_position = [_[2] for _ in images]
# images = [_[0] for _ in images]
# text_list = prompts[0].split('<image>')
# text = text_list[0]
# for ri, image in enumerate(images):
# if args.patch_pos_embed_type == 'pre':
# # 对于pre处理 v2t最终输出的是一张图的token
# text += '<image>'
# else:
# # 对于post处理 v2t最终输出的是多图
# text += '<image>'*image.shape[0]
# text += text_list[ri+1]
# images = torch.cat(images, dim=0)
# patch_position = torch.cat(patch_position, dim=0)
# print(text)
# ic(images.shape)
# ic(patch_position.shape)
# from mplug_owl.processing_mplug_owl import tokenize_prompts
# input_ids = tokenize_prompts(text, tokenizer=self.tokenizer, return_tensors='pt')
# return {
# "pixel_values": images,
# 'patch_position': patch_position,
# "input_ids": input_ids
# }
class mPLUG_Owl_Server:
def __init__(
self,
base_model='MAGAer13/mplug-owl-llama-7b',
log_dir='./',
load_in_8bit=False,
bf16=True,
device="cuda",
io=None,
config=None,
):
self.log_dir = log_dir
self.config = config
self.image_processor = build_processors(config['valid_processors'])['sft']
self.tokenizer = LlamaTokenizer.from_pretrained(base_model)
| sys.path.append("..")
server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
# from pipeline.data_utils.xgpt3_dataset import ImageIO
# class ImageProcessor(object):
# def __init__(self, resolution=224, tokenizer=None):
# normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
# # self.transform = transforms.Compose([
# # transforms.Resize((resolution, resolution),interpolation=Image.BICUBIC),
# # transforms.ToTensor(),
# # normalize,
# # ])
# from megatron.data.processors import doc_processor
# processor_class = os.environ.get('DocProcessor','DocSFTProcessor')
# self.transform = getattr(doc_processor,processor_class)()
# self.image_io = ImageIO()
# self.tokenizer=tokenizer
# def __call__(self, image_paths, prompts):
# if isinstance(image_paths, str):
# image_paths = [image_paths]
# images = []
# images = self.image_io._load_img(image_paths)
# images = [self.transform(image, None) for image in images] # image_input, text_input, patch_position
# patch_position = [_[2] for _ in images]
# images = [_[0] for _ in images]
# text_list = prompts[0].split('<image>')
# text = text_list[0]
# for ri, image in enumerate(images):
# if args.patch_pos_embed_type == 'pre':
# # 对于pre处理 v2t最终输出的是一张图的token
# text += '<image>'
# else:
# # 对于post处理 v2t最终输出的是多图
# text += '<image>'*image.shape[0]
# text += text_list[ri+1]
# images = torch.cat(images, dim=0)
# patch_position = torch.cat(patch_position, dim=0)
# print(text)
# ic(images.shape)
# ic(patch_position.shape)
# from mplug_owl.processing_mplug_owl import tokenize_prompts
# input_ids = tokenize_prompts(text, tokenizer=self.tokenizer, return_tensors='pt')
# return {
# "pixel_values": images,
# 'patch_position': patch_position,
# "input_ids": input_ids
# }
class mPLUG_Owl_Server:
def __init__(
self,
base_model='MAGAer13/mplug-owl-llama-7b',
log_dir='./',
load_in_8bit=False,
bf16=True,
device="cuda",
io=None,
config=None,
):
self.log_dir = log_dir
self.config = config
self.image_processor = build_processors(config['valid_processors'])['sft']
self.tokenizer = LlamaTokenizer.from_pretrained(base_model) | self.processor = MplugOwlProcessor(self.image_processor, self.tokenizer) | 11 | 2023-10-08 06:29:02+00:00 | 24k |
LeapLabTHU/Rank-DETR | projects/dino/configs/models/dino_r50.py | [
{
"identifier": "HungarianMatcher",
"path": "detrex/modeling/matcher/matcher.py",
"snippet": "class HungarianMatcher(nn.Module):\n \"\"\"HungarianMatcher which computes an assignment between targets and predictions.\n\n For efficiency reasons, the targets don't include the no_object. Because of th... | import copy
import torch.nn as nn
from detectron2.modeling.backbone import ResNet, BasicStem
from detectron2.layers import ShapeSpec
from detectron2.config import LazyCall as L
from detrex.modeling.matcher import HungarianMatcher
from detrex.modeling.neck import ChannelMapper
from detrex.layers import PositionEmbeddingSine
from projects.dino.modeling import (
DINO,
DINOTransformerEncoder,
DINOTransformerDecoder,
DINOTransformer,
DINOCriterion,
) | 15,951 |
model = L(DINO)(
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=["res3", "res4", "res5"],
freeze_at=1,
),
position_embedding=L(PositionEmbeddingSine)(
num_pos_feats=128,
temperature=10000,
normalize=True,
offset=-0.5,
),
neck=L(ChannelMapper)(
input_shapes={
"res3": ShapeSpec(channels=512),
"res4": ShapeSpec(channels=1024),
"res5": ShapeSpec(channels=2048),
},
in_features=["res3", "res4", "res5"],
out_channels=256,
num_outs=4,
kernel_size=1,
norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256),
),
transformer=L(DINOTransformer)(
encoder=L(DINOTransformerEncoder)(
embed_dim=256,
num_heads=8,
feedforward_dim=2048,
attn_dropout=0.0,
ffn_dropout=0.0,
num_layers=6,
post_norm=False,
num_feature_levels="${..num_feature_levels}",
use_checkpoint=False
),
decoder=L(DINOTransformerDecoder)(
embed_dim=256,
num_heads=8,
feedforward_dim=2048,
attn_dropout=0.0,
ffn_dropout=0.0,
num_layers=6,
return_intermediate=True,
num_feature_levels="${..num_feature_levels}",
use_checkpoint=False,
),
num_feature_levels=4,
two_stage_num_proposals="${..num_queries}",
),
embed_dim=256,
num_classes=80,
num_queries=900,
aux_loss=True,
|
model = L(DINO)(
backbone=L(ResNet)(
stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
stages=L(ResNet.make_default_stages)(
depth=50,
stride_in_1x1=False,
norm="FrozenBN",
),
out_features=["res3", "res4", "res5"],
freeze_at=1,
),
position_embedding=L(PositionEmbeddingSine)(
num_pos_feats=128,
temperature=10000,
normalize=True,
offset=-0.5,
),
neck=L(ChannelMapper)(
input_shapes={
"res3": ShapeSpec(channels=512),
"res4": ShapeSpec(channels=1024),
"res5": ShapeSpec(channels=2048),
},
in_features=["res3", "res4", "res5"],
out_channels=256,
num_outs=4,
kernel_size=1,
norm_layer=L(nn.GroupNorm)(num_groups=32, num_channels=256),
),
transformer=L(DINOTransformer)(
encoder=L(DINOTransformerEncoder)(
embed_dim=256,
num_heads=8,
feedforward_dim=2048,
attn_dropout=0.0,
ffn_dropout=0.0,
num_layers=6,
post_norm=False,
num_feature_levels="${..num_feature_levels}",
use_checkpoint=False
),
decoder=L(DINOTransformerDecoder)(
embed_dim=256,
num_heads=8,
feedforward_dim=2048,
attn_dropout=0.0,
ffn_dropout=0.0,
num_layers=6,
return_intermediate=True,
num_feature_levels="${..num_feature_levels}",
use_checkpoint=False,
),
num_feature_levels=4,
two_stage_num_proposals="${..num_queries}",
),
embed_dim=256,
num_classes=80,
num_queries=900,
aux_loss=True, | criterion=L(DINOCriterion)( | 7 | 2023-10-12 03:02:25+00:00 | 24k |
sakemin/cog-musicgen-remixer | predict.py | [
{
"identifier": "MultiBandDiffusion",
"path": "audiocraft/models/multibanddiffusion.py",
"snippet": "class MultiBandDiffusion:\n \"\"\"Sample from multiple diffusion models.\n\n Args:\n DPs (list of DiffusionProcess): Diffusion processes.\n codec_model (CompressionModel): Underlying ... | import os
import random
import torchaudio
import typing as tp
import numpy as np
import torch
import librosa
import subprocess
import math
import allin1
import pytsmod as tsm
import shutil
import shutil
from typing import Optional
from cog import BasePredictor, Input, Path
from audiocraft.models import MusicGen, MultiBandDiffusion
from audiocraft.solvers.compression import CompressionSolver
from audiocraft.models.loaders import (
load_compression_model,
load_lm_model,
)
from audiocraft.data.audio import audio_write
from audiocraft.models.builders import get_lm_model
from omegaconf import OmegaConf
from audiocraft.modules.btc.btc_model import BTC_model
from audiocraft.modules.btc.utils.mir_eval_modules import idx2chord
from demucs.audio import convert_audio
from demucs.apply import apply_model | 16,650 | default=1.0,
),
classifier_free_guidance: int = Input(
description="Increases the influence of inputs on the output. Higher values produce lower-varience outputs that adhere more closely to inputs.",
default=3,
),
output_format: str = Input(
description="Output format for generated audio.",
default="wav",
choices=["wav", "mp3"],
),
seed: int = Input(
description="Seed for random number generator. If `None` or `-1`, a random seed will be used.",
default=None,
),
# overlap: int = Input(
# description="The length of overlapping part. Last `overlap` seconds of previous generation output audio is given to the next generation's audio prompt for continuation. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=5, le=15, ge=1
# ),
# in_step_beat_sync: bool = Input(
# description="If `True`, beat syncing is performed every generation step. In this case, audio prompting with EnCodec token will not be used, so that the audio quality might be degraded on and on along encoding-decoding sequences of the generation steps. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=False,
# ),
# amp_rate: float = Input(
# description="Amplifying the output audio to prevent volume diminishing along generations. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=1.2,
# ),
) -> Path:
if prompt is None:
raise ValueError("Must provide `prompt`.")
if not music_input:
raise ValueError("Must provide `music_input`.")
if prompt is None:
prompt = ''
# tmp_path = 'tmp'
# if os.path.isdir(tmp_path):
# import shutil
# shutil.rmtree(tmp_path)
# os.mkdir(tmp_path)
if os.path.isdir('demix'):
shutil.rmtree('demix')
if os.path.isdir('spec'):
shutil.rmtree('spec')
# Loading models
if os.path.isfile(f'musicgen-{model_version}.th'):
pass
else:
url = f"https://weights.replicate.delivery/default/musicgen-chord/musicgen-{model_version}.th"
dest = f"/src/musicgen-{model_version}.th"
subprocess.check_call(["pget", url, dest], close_fds=False)
self.model = load_ckpt(f'/src/musicgen-{model_version}.th', self.device)
self.model.lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
if 'stereo' in model_version:
channel = 2
else:
channel = 1
if large_chord_voca is False:
# Switching Chord Prediction model to 25 vocab (smaller)
self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.feature['large_voca']=False
self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.model['num_chords']=25
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model_file='audiocraft/modules/btc/test/btc_model.pt'
self.model.lm.condition_provider.conditioners['self_wav'].chroma.idx_to_chord = idx2chord
loaded = torch.load('audiocraft/modules/btc/test/btc_model.pt')
self.model.lm.condition_provider.conditioners['self_wav'].chroma.mean = loaded['mean']
self.model.lm.condition_provider.conditioners['self_wav'].chroma.std = loaded['std']
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model = BTC_model(config=self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.model).to(self.device)
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model.load_state_dict(loaded['model'])
model = self.model
model.lm.eval()
if multi_band_diffusion and int(self.model.lm.cfg.transformer_lm.n_q) == 8:
raise ValueError("Multi-band Diffusion only works with non-stereo models.")
# in_step_beat_sync = in_step_beat_sync
set_generation_params = lambda duration: model.set_generation_params(
duration=duration,
top_k=top_k,
top_p=top_p,
temperature=temperature,
cfg_coef=classifier_free_guidance,
)
model.lm.condition_provider.conditioners['self_wav'].chroma_coefficient = chroma_coefficient
if not seed or seed == -1:
seed = torch.seed() % 2 ** 32 - 1
set_all_seeds(seed)
print(f"Using seed {seed}")
# Music Structure Analysis
music_input_analysis = allin1.analyze(music_input)
music_input, sr = torchaudio.load(music_input)
print("BPM : ", music_input_analysis.bpm)
if not beat_sync_threshold or beat_sync_threshold == -1:
if music_input_analysis.bpm is not None:
beat_sync_threshold = 1.1/(int(music_input_analysis.bpm)/60)
else:
beat_sync_threshold = 0.75
if music_input_analysis.bpm is not None:
prompt = prompt + f', bpm : {int(music_input_analysis.bpm)}'
music_input = music_input[None] if music_input.dim() == 2 else music_input
duration = music_input.shape[-1]/sr
wav_sr = model.sample_rate
vocal, background = self.separate_vocals(music_input, sr)
| # Prediction interface for Cog ⚙️
# https://github.com/replicate/cog/blob/main/docs/python.md
# We need to set `TRANSFORMERS_CACHE` before any imports, which is why this is up here.
MODEL_PATH = "/src/models/"
os.environ["TRANSFORMERS_CACHE"] = MODEL_PATH
os.environ["TORCH_HOME"] = MODEL_PATH
# Model specific imports
def _delete_param(cfg, full_name: str):
parts = full_name.split('.')
for part in parts[:-1]:
if part in cfg:
cfg = cfg[part]
else:
return
OmegaConf.set_struct(cfg, False)
if parts[-1] in cfg:
del cfg[parts[-1]]
OmegaConf.set_struct(cfg, True)
def load_ckpt(path, device, url=False):
if url:
loaded = torch.hub.load_state_dict_from_url(str(path))
else:
loaded = torch.load(str(path))
cfg = OmegaConf.create(loaded['xp.cfg'])
cfg.device = str(device)
if cfg.device == 'cpu':
cfg.dtype = 'float32'
else:
cfg.dtype = 'float16'
_delete_param(cfg, 'conditioners.self_wav.chroma_chord.cache_path')
_delete_param(cfg, 'conditioners.self_wav.chroma_stem.cache_path')
_delete_param(cfg, 'conditioners.args.merge_text_conditions_p')
_delete_param(cfg, 'conditioners.args.drop_desc_p')
lm = get_lm_model(loaded['xp.cfg'])
lm.load_state_dict(loaded['model'])
lm.eval()
lm.cfg = cfg
compression_model = CompressionSolver.model_from_checkpoint(cfg.compression_model_checkpoint, device=device)
return MusicGen(f"{os.getenv('COG_USERNAME')}/musicgen-chord", compression_model, lm)
class Predictor(BasePredictor):
def setup(self, weights: Optional[Path] = None):
"""Load the model into memory to make running multiple predictions efficient"""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.mbd = MultiBandDiffusion.get_mbd_musicgen()
def _load_model(
self,
model_path: str,
cls: Optional[any] = None,
load_args: Optional[dict] = {},
model_id: Optional[str] = None,
device: Optional[str] = None,
) -> MusicGen:
if device is None:
device = self.device
compression_model = load_compression_model(
model_id, device=device, cache_dir=model_path
)
lm = load_lm_model(model_id, device=device, cache_dir=model_path)
return MusicGen(model_id, compression_model, lm)
def predict(
self,
model_version: str = Input(
description="Model type. Computations take longer when using `large` or `stereo` models.", default="stereo-chord",
choices=["stereo-chord", "stereo-chord-large", "chord", "chord-large"]
),
prompt: str = Input(
description="A description of the music you want to generate.", default=None
),
music_input: Path = Input(
description="An audio file input for the remix.",
default=None,
),
multi_band_diffusion: bool = Input(
description="If `True`, the EnCodec tokens will be decoded with MultiBand Diffusion. Not compatible with `stereo` models.",
default=False,
),
normalization_strategy: str = Input(
description="Strategy for normalizing audio.",
default="loudness",
choices=["loudness", "clip", "peak", "rms"],
),
# bpm_hard_sync: bool = Input(
# description="If `True`, respective downbeats aren't analyzed, but are calculated from the bpm value detected and the first downbeat recognized instead. If the input audio has a changing bpm value, must be set `False`.",
# default=True,
# ),
beat_sync_threshold: float = Input(
description="When beat syncing, if the gap between generated downbeat timing and input audio downbeat timing is larger than `beat_sync_threshold`, consider the beats are not corresponding. If `None` or `-1`, `1.1/(bpm/60)` will be used as the value. 0.75 is a good value to set.",
default=None,
),
large_chord_voca: bool = Input(
description="If `True`, more chords like 7th, diminished and etc are used. If `False` only 12 major and 12 minor chords are used.",
default=True
),
chroma_coefficient: float = Input(
description="Coefficient value multiplied to multi-hot chord chroma.",
default=1.0,
ge=0.5,
le=2.0
),
top_k: int = Input(
description="Reduces sampling to the k most likely tokens.", default=250
),
top_p: float = Input(
description="Reduces sampling to tokens with cumulative probability of p. When set to `0` (default), top_k sampling is used.",
default=0.0,
),
temperature: float = Input(
description="Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity.",
default=1.0,
),
classifier_free_guidance: int = Input(
description="Increases the influence of inputs on the output. Higher values produce lower-varience outputs that adhere more closely to inputs.",
default=3,
),
output_format: str = Input(
description="Output format for generated audio.",
default="wav",
choices=["wav", "mp3"],
),
seed: int = Input(
description="Seed for random number generator. If `None` or `-1`, a random seed will be used.",
default=None,
),
# overlap: int = Input(
# description="The length of overlapping part. Last `overlap` seconds of previous generation output audio is given to the next generation's audio prompt for continuation. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=5, le=15, ge=1
# ),
# in_step_beat_sync: bool = Input(
# description="If `True`, beat syncing is performed every generation step. In this case, audio prompting with EnCodec token will not be used, so that the audio quality might be degraded on and on along encoding-decoding sequences of the generation steps. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=False,
# ),
# amp_rate: float = Input(
# description="Amplifying the output audio to prevent volume diminishing along generations. (This will be fixed with the optimal value and be hidden, when releasing.)",
# default=1.2,
# ),
) -> Path:
if prompt is None:
raise ValueError("Must provide `prompt`.")
if not music_input:
raise ValueError("Must provide `music_input`.")
if prompt is None:
prompt = ''
# tmp_path = 'tmp'
# if os.path.isdir(tmp_path):
# import shutil
# shutil.rmtree(tmp_path)
# os.mkdir(tmp_path)
if os.path.isdir('demix'):
shutil.rmtree('demix')
if os.path.isdir('spec'):
shutil.rmtree('spec')
# Loading models
if os.path.isfile(f'musicgen-{model_version}.th'):
pass
else:
url = f"https://weights.replicate.delivery/default/musicgen-chord/musicgen-{model_version}.th"
dest = f"/src/musicgen-{model_version}.th"
subprocess.check_call(["pget", url, dest], close_fds=False)
self.model = load_ckpt(f'/src/musicgen-{model_version}.th', self.device)
self.model.lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
if 'stereo' in model_version:
channel = 2
else:
channel = 1
if large_chord_voca is False:
# Switching Chord Prediction model to 25 vocab (smaller)
self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.feature['large_voca']=False
self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.model['num_chords']=25
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model_file='audiocraft/modules/btc/test/btc_model.pt'
self.model.lm.condition_provider.conditioners['self_wav'].chroma.idx_to_chord = idx2chord
loaded = torch.load('audiocraft/modules/btc/test/btc_model.pt')
self.model.lm.condition_provider.conditioners['self_wav'].chroma.mean = loaded['mean']
self.model.lm.condition_provider.conditioners['self_wav'].chroma.std = loaded['std']
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model = BTC_model(config=self.model.lm.condition_provider.conditioners['self_wav'].chroma.config.model).to(self.device)
self.model.lm.condition_provider.conditioners['self_wav'].chroma.model.load_state_dict(loaded['model'])
model = self.model
model.lm.eval()
if multi_band_diffusion and int(self.model.lm.cfg.transformer_lm.n_q) == 8:
raise ValueError("Multi-band Diffusion only works with non-stereo models.")
# in_step_beat_sync = in_step_beat_sync
set_generation_params = lambda duration: model.set_generation_params(
duration=duration,
top_k=top_k,
top_p=top_p,
temperature=temperature,
cfg_coef=classifier_free_guidance,
)
model.lm.condition_provider.conditioners['self_wav'].chroma_coefficient = chroma_coefficient
if not seed or seed == -1:
seed = torch.seed() % 2 ** 32 - 1
set_all_seeds(seed)
print(f"Using seed {seed}")
# Music Structure Analysis
music_input_analysis = allin1.analyze(music_input)
music_input, sr = torchaudio.load(music_input)
print("BPM : ", music_input_analysis.bpm)
if not beat_sync_threshold or beat_sync_threshold == -1:
if music_input_analysis.bpm is not None:
beat_sync_threshold = 1.1/(int(music_input_analysis.bpm)/60)
else:
beat_sync_threshold = 0.75
if music_input_analysis.bpm is not None:
prompt = prompt + f', bpm : {int(music_input_analysis.bpm)}'
music_input = music_input[None] if music_input.dim() == 2 else music_input
duration = music_input.shape[-1]/sr
wav_sr = model.sample_rate
vocal, background = self.separate_vocals(music_input, sr)
| audio_write( | 5 | 2023-10-09 09:55:24+00:00 | 24k |
oracle/guardian-ai | tests/unitary/test_fairness_metrics.py | [
{
"identifier": "ConsistencyScorer",
"path": "guardian_ai/fairness/metrics/dataset.py",
"snippet": "class ConsistencyScorer(_SimpleDatasetFairnessScorer):\n \"\"\"\n Measures the consistency of a dataset.\n\n Consistency is measured as the number of ratio of instances that have a\n different... | import math
import numpy as np
import pandas as pd
import pytest
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from guardian_ai.fairness.metrics.dataset import (
ConsistencyScorer,
DatasetStatisticalParityScorer,
SmoothedEDFScorer,
consistency,
dataset_statistical_parity,
smoothed_edf,
)
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 GuardianAITypeError, GuardianAIValueError
from tests.utils import get_dummy_dataset | 18,356 | #!/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/
@pytest.fixture(scope="module", autouse=True)
def init():
np.random.seed(12345)
def is_close(a, b):
return math.isclose(a, b, rel_tol=1e-5)
def approx_dict(d):
return pytest.approx(d, rel=1e-5)
MODEL_X_Y_SCORERS = {
"model_statistical_parity_scorer": ModelStatisticalParityScorer,
"true_positive_rate_scorer": TruePositiveRateScorer,
| #!/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/
@pytest.fixture(scope="module", autouse=True)
def init():
np.random.seed(12345)
def is_close(a, b):
return math.isclose(a, b, rel_tol=1e-5)
def approx_dict(d):
return pytest.approx(d, rel=1e-5)
MODEL_X_Y_SCORERS = {
"model_statistical_parity_scorer": ModelStatisticalParityScorer,
"true_positive_rate_scorer": TruePositiveRateScorer, | "false_positive_rate_scorer": FalsePositiveRateScorer, | 11 | 2023-10-09 09:48:50+00:00 | 24k |
jiangjiechen/auction-arena | app.py | [
{
"identifier": "create_items",
"path": "src/item_base.py",
"snippet": "def create_items(item_info_jsl):\n '''\n item_info: a list of dict (name, price, desc, id)\n '''\n item_info_jsl = LoadJsonL(item_info_jsl)\n item_list = []\n for info in item_info_jsl:\n item_list.append(It... | import os
import gradio as gr
from app_modules.presets import *
from app_modules.overwrites import *
from app_modules.utils import *
from src.item_base import create_items
from src.bidder_base import Bidder
from src.human_bidder import HumanBidder
from src.auctioneer_base import Auctioneer
from auction_workflow import run_auction, make_auction_hash
from utils import chunks, reset_state_list | 15,751 |
BIDDER_NUM = 4
items = create_items('data/items_demo.jsonl')
def auction_loop_app(*args):
global items
bidder_list = args[0] # gr.State() -> session state
items_id = args[1]
os.environ['OPENAI_API_KEY'] = args[2] if args[2] != '' else os.environ.get('OPENAI_API_KEY', '')
os.environ['ANTHROPIC_API_KEY'] = args[3] if args[3] != '' else os.environ.get('ANTHROPIC_API_KEY', '')
thread_num = args[4]
item_shuffle = args[5]
enable_discount = args[6]
min_markup_pct = args[7]
args = args[8:]
auction_hash = make_auction_hash()
items_to_bid = [items[i] for i in items_id]
auctioneer = Auctioneer(enable_discount=enable_discount, min_markup_pct=min_markup_pct)
auctioneer.init_items(items_to_bid)
if item_shuffle:
auctioneer.shuffle_items()
# must correspond to the order in app's parameters
input_keys = [
'chatbot',
'model_name',
'desire',
'plan_strategy',
'budget',
'correct_belief',
'enable_learning',
'temperature',
'overestimate_percent',
]
# convert flatten list into a json list
input_jsl = []
for i, chunk in enumerate(chunks(args, len(input_keys))):
js = {'name': f"Bidder {i+1}", 'auction_hash': auction_hash}
for k, v in zip(input_keys, chunk):
js[k] = v
input_jsl.append(js)
for js in input_jsl:
js.pop('chatbot')
if 'human' in js['model_name']:
bidder_list.append(HumanBidder.create(**js))
else:
|
BIDDER_NUM = 4
items = create_items('data/items_demo.jsonl')
def auction_loop_app(*args):
global items
bidder_list = args[0] # gr.State() -> session state
items_id = args[1]
os.environ['OPENAI_API_KEY'] = args[2] if args[2] != '' else os.environ.get('OPENAI_API_KEY', '')
os.environ['ANTHROPIC_API_KEY'] = args[3] if args[3] != '' else os.environ.get('ANTHROPIC_API_KEY', '')
thread_num = args[4]
item_shuffle = args[5]
enable_discount = args[6]
min_markup_pct = args[7]
args = args[8:]
auction_hash = make_auction_hash()
items_to_bid = [items[i] for i in items_id]
auctioneer = Auctioneer(enable_discount=enable_discount, min_markup_pct=min_markup_pct)
auctioneer.init_items(items_to_bid)
if item_shuffle:
auctioneer.shuffle_items()
# must correspond to the order in app's parameters
input_keys = [
'chatbot',
'model_name',
'desire',
'plan_strategy',
'budget',
'correct_belief',
'enable_learning',
'temperature',
'overestimate_percent',
]
# convert flatten list into a json list
input_jsl = []
for i, chunk in enumerate(chunks(args, len(input_keys))):
js = {'name': f"Bidder {i+1}", 'auction_hash': auction_hash}
for k, v in zip(input_keys, chunk):
js[k] = v
input_jsl.append(js)
for js in input_jsl:
js.pop('chatbot')
if 'human' in js['model_name']:
bidder_list.append(HumanBidder.create(**js))
else: | bidder_list.append(Bidder.create(**js)) | 1 | 2023-10-08 09:30:57+00:00 | 24k |
sakemin/cog-musicgen-chord | predict.py | [
{
"identifier": "CompressionSolver",
"path": "audiocraft/solvers/compression.py",
"snippet": "class CompressionSolver(base.StandardSolver):\n \"\"\"Solver for compression task.\n\n The compression task combines a set of perceptual and objective losses\n to train an EncodecModel (composed of an ... | import os
import random
import torchaudio
import typing as tp
import numpy as np
import torch
import subprocess
from typing import Optional
from cog import BasePredictor, Input, Path
from audiocraft.solvers.compression import CompressionSolver
from audiocraft.models import MusicGen, MultiBandDiffusion
from audiocraft.solvers.compression import CompressionSolver
from audiocraft.models.loaders import (
load_compression_model,
load_lm_model,
)
from audiocraft.data.audio import audio_write
from audiocraft.models.builders import get_lm_model
from omegaconf import OmegaConf | 20,448 |
if audio_start > audio_end:
raise ValueError(
"`audio_start` must be less than or equal to `audio_end`"
)
audio_chords = audio_chords[
..., int(sr * audio_start) : int(sr * audio_end)
]
wav, tokens = model.generate_with_chroma(['the intro of ' + prompt], audio_chords[...,:30*sr], sr, progress=True, return_tokens=True)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
for i in range(int((duration - overlap) // sub_duration) - 1):
wav, tokens = model.generate_continuation_with_audio_tokens_and_audio_chroma(
prompt=tokens[...,sub_duration*encodec_rate:],
melody_wavs = audio_chords[...,sub_duration*(i+1)*sr:(sub_duration*(i+1)+30)*sr],
melody_sample_rate=sr,
descriptions=['chorus of ' + prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
if int(duration - overlap) % sub_duration != 0:
set_generation_params(overlap + ((duration - overlap) % sub_duration))
wav, tokens = model.generate_continuation_with_audio_tokens_and_audio_chroma(
prompt=tokens[...,sub_duration*encodec_rate:],
melody_wavs = audio_chords[...,sub_duration*(len(wavs))*sr:],
melody_sample_rate=sr,
descriptions=['the outro of ' + prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
else: # Case 3
wav, tokens = model.generate_with_text_chroma(descriptions = [prompt], chord_texts = [text_chords], bpm = [bpm], meter = [int(time_sig.split('/')[0])], progress=True, return_tokens=True)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
for i in range((duration - 10) // sub_duration - 1):
model.lm.condition_provider.conditioners['self_wav'].set_continuation_count(sub_duration/30, i)
wav, tokens = model.generate_continuation_with_audio_tokens_and_text_chroma(
tokens[...,sub_duration*encodec_rate:], [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
if (duration - overlap) % sub_duration != 0:
model.lm.condition_provider.conditioners['self_wav'].set_continuation_count(sub_duration/30, i+1)
set_generation_params(sub_duration + ((duration - overlap) % sub_duration))
wav, tokens = model.generate_continuation_with_audio_tokens_and_text_chroma(
tokens[...,sub_duration*encodec_rate:], [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
wav = wavs[0][...,:sub_duration*wav_sr]
for i in range(len(wavs)-1):
if i == len(wavs)-2:
wav = torch.concat([wav,wavs[i+1]],dim=-1)
else:
wav = torch.concat([wav,wavs[i+1][...,:sub_duration*wav_sr]],dim=-1)
wav = wav.cpu()
else:
'''
if not audio_chords:
set_generation_params(duration)
if text_chords is None or text_chords == '': # Case 4
wav, tokens = model.generate([prompt], progress=True, return_tokens=True)
else: # Case 5
wav, tokens = model.generate_with_text_chroma(descriptions = [prompt], chord_texts = [text_chords], bpm = [bpm], meter = [int(time_sig.split('/')[0])], progress=True, return_tokens=True)
else:
audio_chords, sr = torchaudio.load(audio_chords)
audio_chords = audio_chords[None] if audio_chords.dim() == 2 else audio_chords
audio_start = 0 if not audio_start else audio_start
if audio_end is None or audio_end == -1:
audio_end = audio_chords.shape[2] / sr
if audio_start > audio_end:
raise ValueError(
"`audio_start` must be less than or equal to `audio_end`"
)
audio_chords_wavform = audio_chords[
..., int(sr * audio_start) : int(sr * audio_end)
]
audio_chords_duration = audio_chords_wavform.shape[-1] / sr
if continuation:
set_generation_params(duration)
if text_chords is None or text_chords == '': # Case 6
wav, tokens = model.generate_continuation(
prompt=audio_chords_wavform,
prompt_sample_rate=sr,
descriptions=[prompt],
progress=True,
return_tokens=True
)
else: # Case 7
wav, tokens = model.generate_continuation_with_text_chroma(
audio_chords_wavform, sr, [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
else: # Case 8
set_generation_params(duration)
wav, tokens = model.generate_with_chroma(
[prompt], audio_chords_wavform, sr, progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
| # Prediction interface for Cog ⚙️
# https://github.com/replicate/cog/blob/main/docs/python.md
# We need to set `TRANSFORMERS_CACHE` before any imports, which is why this is up here.
MODEL_PATH = "/src/models/"
os.environ["TRANSFORMERS_CACHE"] = MODEL_PATH
os.environ["TORCH_HOME"] = MODEL_PATH
# Model specific imports
def _delete_param(cfg, full_name: str):
parts = full_name.split('.')
for part in parts[:-1]:
if part in cfg:
cfg = cfg[part]
else:
return
OmegaConf.set_struct(cfg, False)
if parts[-1] in cfg:
del cfg[parts[-1]]
OmegaConf.set_struct(cfg, True)
def load_ckpt(path, device, url=False):
if url:
loaded = torch.hub.load_state_dict_from_url(str(path))
else:
loaded = torch.load(str(path))
cfg = OmegaConf.create(loaded['xp.cfg'])
cfg.device = str(device)
if cfg.device == 'cpu':
cfg.dtype = 'float32'
else:
cfg.dtype = 'float16'
_delete_param(cfg, 'conditioners.self_wav.chroma_chord.cache_path')
_delete_param(cfg, 'conditioners.self_wav.chroma_stem.cache_path')
_delete_param(cfg, 'conditioners.args.merge_text_conditions_p')
_delete_param(cfg, 'conditioners.args.drop_desc_p')
lm = get_lm_model(loaded['xp.cfg'])
lm.load_state_dict(loaded['model'])
lm.eval()
lm.cfg = cfg
compression_model = CompressionSolver.model_from_checkpoint(cfg.compression_model_checkpoint, device=device)
return MusicGen(f"{os.getenv('COG_USERNAME')}/musicgen-chord", compression_model, lm)
class Predictor(BasePredictor):
def setup(self, weights: Optional[Path] = None):
"""Load the model into memory to make running multiple predictions efficient"""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.mbd = MultiBandDiffusion.get_mbd_musicgen()
if str(weights) == "weights":
weights = None
if weights is not None:
print("Fine-tuned model weights loaded!")
self.model = load_ckpt(weights, self.device, url=True)
def _load_model(
self,
model_path: str,
cls: Optional[any] = None,
load_args: Optional[dict] = {},
model_id: Optional[str] = None,
device: Optional[str] = None,
) -> MusicGen:
if device is None:
device = self.device
compression_model = load_compression_model(
model_id, device=device, cache_dir=model_path
)
lm = load_lm_model(model_id, device=device, cache_dir=model_path)
return MusicGen(model_id, compression_model, lm)
def predict(
self,
model_version: str = Input(
description="Model type. Select `fine-tuned` if you trained the model into your own repository.", default="stereo-chord-large",
choices=["chord", "chord-large", "stereo-chord", "stereo-chord-large", "fine-tuned"]
),
prompt: str = Input(
description="A description of the music you want to generate.", default=None
),
text_chords: str = Input(
description="A text based chord progression condition. Single uppercase alphabet character(eg. `C`) is considered as a major chord. Chord attributes like(`maj`, `min`, `dim`, `aug`, `min6`, `maj6`, `min7`, `minmaj7`, `maj7`, `7`, `dim7`, `hdim7`, `sus2` and `sus4`) can be added to the root alphabet character after `:`.(eg. `A:min7`) Each chord token splitted by `SPACE` is allocated to a single bar. If more than one chord must be allocated to a single bar, cluster the chords adding with `,` without any `SPACE`.(eg. `C,C:7 G, E:min A:min`) You must choose either only one of `audio_chords` below or `text_chords`.", default=None
),
bpm: float = Input(
description="BPM condition for the generated output. `text_chords` will be processed based on this value. This will be appended at the end of `prompt`.", default=None
),
time_sig: str = Input(
description="Time signature value for the generate output. `text_chords` will be processed based on this value. This will be appended at the end of `prompt`.", default="4/4"
),
audio_chords: Path = Input(
description="An audio file that will condition the chord progression. You must choose only one among `audio_chords` or `text_chords` above.",
default=None,
),
audio_start: int = Input(
description="Start time of the audio file to use for chord conditioning.",
default=0,
ge=0,
),
audio_end: int = Input(
description="End time of the audio file to use for chord conditioning. If None, will default to the end of the audio clip.",
default=None,
ge=0,
),
duration: int = Input(
description="Duration of the generated audio in seconds.", default=8
),
continuation: bool = Input(
description="If `True`, generated music will continue from `audio_chords`. If chord conditioning, this is only possible when the chord condition is given with `text_chords`. If `False`, generated music will mimic `audio_chords`'s chord.",
default=False,
),
# continuation_start: int = Input(
# description="Start time of the audio file to use for continuation.",
# default=0,
# ge=0,
# ),
# continuation_end: int = Input(
# description="End time of the audio file to use for continuation. If -1 or None, will default to the end of the audio clip.",
# default=None,
# ge=0,
# ),
multi_band_diffusion: bool = Input(
description="If `True`, the EnCodec tokens will be decoded with MultiBand Diffusion. Not compatible with stereo models.",
default=False,
),
normalization_strategy: str = Input(
description="Strategy for normalizing audio.",
default="loudness",
choices=["loudness", "clip", "peak", "rms"],
),
chroma_coefficient: float = Input(
description="Coefficient value multiplied to multi-hot chord chroma.",
default=1.0,
ge=0.5,
le=2.5
),
top_k: int = Input(
description="Reduces sampling to the k most likely tokens.", default=250
),
top_p: float = Input(
description="Reduces sampling to tokens with cumulative probability of p. When set to `0` (default), top_k sampling is used.",
default=0.0,
),
temperature: float = Input(
description="Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity.",
default=1.0,
),
classifier_free_guidance: int = Input(
description="Increases the influence of inputs on the output. Higher values produce lower-varience outputs that adhere more closely to inputs.",
default=3,
),
output_format: str = Input(
description="Output format for generated audio.",
default="wav",
choices=["wav", "mp3"],
),
seed: int = Input(
description="Seed for random number generator. If `None` or `-1`, a random seed will be used.",
default=None,
),
) -> Path:
if text_chords == '':
text_chords = None
if text_chords and audio_chords and not continuation:
raise ValueError("Must provide either only one of `audio_chords` or `text_chords`.")
if text_chords and not bpm:
raise ValueError("There must be `bpm` value set when text based chord conditioning.")
if text_chords and (not time_sig or time_sig==""):
raise ValueError("There must be `time_sig` value set when text based chord conditioning.")
if continuation and not audio_chords:
raise ValueError("Must provide an audio input file via `audio_chords` if continuation is `True`.")
if multi_band_diffusion and int(self.model.lm.cfg.transformer_lm.n_q) == 8:
raise ValueError("Multi-band Diffusion only works with non-stereo models.")
if prompt is None:
prompt = ''
if time_sig is not None and not time_sig == '':
if prompt == '':
prompt = time_sig
else:
prompt = prompt + ', ' + time_sig
if bpm is not None:
if prompt == '':
prompt = str(bpm)
else:
prompt = prompt + f', bpm : {bpm}'
if model_version == "fine-tuned":
try:
self.model
except AttributeError:
raise Exception("ERROR: Fine-tuned weights don't exist! Is the model trained from `sakemin/musicgen-chord`? If not, set `model_version` from `chord`, `chord-large`, `stereo-chord` and `stereo-chord-large`.")
else:
if os.path.isfile(f'musicgen-{model_version}.th'):
pass
else:
url = f"https://weights.replicate.delivery/default/musicgen-chord/musicgen-{model_version}.th"
dest = f"musicgen-{model_version}.th"
subprocess.check_call(["pget", url, dest], close_fds=False)
self.model = load_ckpt(f'musicgen-{model_version}.th', self.device)
self.model.lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
model = self.model
set_generation_params = lambda duration: model.set_generation_params(
duration=duration,
top_k=top_k,
top_p=top_p,
temperature=temperature,
cfg_coef=classifier_free_guidance,
)
model.lm.condition_provider.conditioners['self_wav'].chroma_coefficient = chroma_coefficient
if not seed or seed == -1:
seed = torch.seed() % 2 ** 32 - 1
set_all_seeds(seed)
set_all_seeds(seed)
print(f"Using seed {seed}")
'''
if duration > 30:
encodec_rate = 50
sub_duration=25
overlap = 30 - sub_duration
wavs = []
wav_sr = model.sample_rate
set_generation_params(30)
if (text_chords is None) and audio_chords is None: # Case 1
wav, tokens = model.generate([prompt], progress=True, return_tokens=True)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
for i in range((duration - overlap) // sub_duration - 1):
wav, tokens= model.generate_continuation_with_audio_token(
prompt=tokens[...,sub_duration*encodec_rate:],
descriptions=[prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
if (duration - overlap) % sub_duration != 0:
set_generation_params(overlap + ((duration - overlap) % sub_duration))
wav, tokens = model.generate_continuation_with_audio_token(
prompt=tokens[...,sub_duration*encodec_rate:],
descriptions=[prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
elif (text_chords is None or text_chords == '') and audio_chords is not None: # Case 2
audio_chords, sr = torchaudio.load(audio_chords)
audio_chords = audio_chords[None] if audio_chords.dim() == 2 else audio_chords
audio_start = 0 if not audio_start else audio_start
if audio_end is None or audio_end == -1:
audio_end = audio_chords.shape[-1] / sr
if audio_start > audio_end:
raise ValueError(
"`audio_start` must be less than or equal to `audio_end`"
)
audio_chords = audio_chords[
..., int(sr * audio_start) : int(sr * audio_end)
]
wav, tokens = model.generate_with_chroma(['the intro of ' + prompt], audio_chords[...,:30*sr], sr, progress=True, return_tokens=True)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
for i in range(int((duration - overlap) // sub_duration) - 1):
wav, tokens = model.generate_continuation_with_audio_tokens_and_audio_chroma(
prompt=tokens[...,sub_duration*encodec_rate:],
melody_wavs = audio_chords[...,sub_duration*(i+1)*sr:(sub_duration*(i+1)+30)*sr],
melody_sample_rate=sr,
descriptions=['chorus of ' + prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
if int(duration - overlap) % sub_duration != 0:
set_generation_params(overlap + ((duration - overlap) % sub_duration))
wav, tokens = model.generate_continuation_with_audio_tokens_and_audio_chroma(
prompt=tokens[...,sub_duration*encodec_rate:],
melody_wavs = audio_chords[...,sub_duration*(len(wavs))*sr:],
melody_sample_rate=sr,
descriptions=['the outro of ' + prompt],
progress=True,
return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
else: # Case 3
wav, tokens = model.generate_with_text_chroma(descriptions = [prompt], chord_texts = [text_chords], bpm = [bpm], meter = [int(time_sig.split('/')[0])], progress=True, return_tokens=True)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
for i in range((duration - 10) // sub_duration - 1):
model.lm.condition_provider.conditioners['self_wav'].set_continuation_count(sub_duration/30, i)
wav, tokens = model.generate_continuation_with_audio_tokens_and_text_chroma(
tokens[...,sub_duration*encodec_rate:], [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
if (duration - overlap) % sub_duration != 0:
model.lm.condition_provider.conditioners['self_wav'].set_continuation_count(sub_duration/30, i+1)
set_generation_params(sub_duration + ((duration - overlap) % sub_duration))
wav, tokens = model.generate_continuation_with_audio_tokens_and_text_chroma(
tokens[...,sub_duration*encodec_rate:], [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
wavs.append(wav.detach().cpu())
wav = wavs[0][...,:sub_duration*wav_sr]
for i in range(len(wavs)-1):
if i == len(wavs)-2:
wav = torch.concat([wav,wavs[i+1]],dim=-1)
else:
wav = torch.concat([wav,wavs[i+1][...,:sub_duration*wav_sr]],dim=-1)
wav = wav.cpu()
else:
'''
if not audio_chords:
set_generation_params(duration)
if text_chords is None or text_chords == '': # Case 4
wav, tokens = model.generate([prompt], progress=True, return_tokens=True)
else: # Case 5
wav, tokens = model.generate_with_text_chroma(descriptions = [prompt], chord_texts = [text_chords], bpm = [bpm], meter = [int(time_sig.split('/')[0])], progress=True, return_tokens=True)
else:
audio_chords, sr = torchaudio.load(audio_chords)
audio_chords = audio_chords[None] if audio_chords.dim() == 2 else audio_chords
audio_start = 0 if not audio_start else audio_start
if audio_end is None or audio_end == -1:
audio_end = audio_chords.shape[2] / sr
if audio_start > audio_end:
raise ValueError(
"`audio_start` must be less than or equal to `audio_end`"
)
audio_chords_wavform = audio_chords[
..., int(sr * audio_start) : int(sr * audio_end)
]
audio_chords_duration = audio_chords_wavform.shape[-1] / sr
if continuation:
set_generation_params(duration)
if text_chords is None or text_chords == '': # Case 6
wav, tokens = model.generate_continuation(
prompt=audio_chords_wavform,
prompt_sample_rate=sr,
descriptions=[prompt],
progress=True,
return_tokens=True
)
else: # Case 7
wav, tokens = model.generate_continuation_with_text_chroma(
audio_chords_wavform, sr, [prompt], [text_chords], bpm=[bpm], meter=[int(time_sig.split('/')[0])], progress=True, return_tokens=True
)
else: # Case 8
set_generation_params(duration)
wav, tokens = model.generate_with_chroma(
[prompt], audio_chords_wavform, sr, progress=True, return_tokens=True
)
if multi_band_diffusion:
wav = self.mbd.tokens_to_wav(tokens)
| audio_write( | 6 | 2023-10-09 09:52:24+00:00 | 24k |
zhijie-group/LOVECon | test_lovecon.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 os
import copy
import click
import re
import numpy as np
import torch
import torch.utils.data
import torch.utils.checkpoint
import decord
import shutil
from glob import glob
from typing import Optional,Dict
from tqdm.auto import tqdm
from omegaconf import OmegaConf
from PIL import Image
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
AutoencoderKL,
DDIMScheduler,
)
from diffusers.utils.import_utils import is_xformers_available
from transformers import AutoTokenizer, CLIPTextModel
from einops import rearrange
from video_diffusion.models.unet_3d_condition import UNetPseudo3DConditionModel
from video_diffusion.models.controlnet_3d_condition import ControlNetPseudo3DModel
from video_diffusion.data.dataset import ImageSequenceDataset
from video_diffusion.common.util import get_time_string, get_function_args
from video_diffusion.common.logger import get_logger_config_path
from video_diffusion.common.image_util import log_train_samples
from video_diffusion.common.instantiate_from_config import instantiate_from_config
from video_diffusion.pipelines.p2p_validation_loop_controlnet import P2pSampleLogger
from annotator.util import get_control
from video_diffusion.pipelines.DDIMInterpolationScheduler import DDIMInterpolationScheduler
from RIFEModel.RIFE_HDv3 import Model | 20,140 | print("There are {} frames in the video but we take {} frames".format(len(vr), dataset_config.n_sample_frame))
if dataset_config.n_sample_frame <= 50:
duration = 100
fps = 10
sample_index = list(range(0,len(vr), 1))[:dataset_config.n_sample_frame]
video = vr.get_batch(sample_index)
video_name_match = re.search(r"(.*)/(.*).mp4", dataset_config.video_path)
video_name = video_name_match.group(2)
video_frame_folder = os.path.join('data',video_name)
if os.path.exists(video_frame_folder):
shutil.rmtree(video_frame_folder)
os.makedirs(video_frame_folder,exist_ok=True)
for i in range(video.shape[0]):
frame = video[i]
frame_path = os.path.join(video_frame_folder,f'frame-{i:04}.jpg')
frame = Image.fromarray(frame.numpy().astype(np.uint8))
frame.save(frame_path)
dataset_config.update({'path': video_frame_folder} )
time_string = get_time_string()
if logdir is None:
logdir = config.replace('config', 'result').replace('.yml', '').replace('.yaml', '')
logdir += f"_{time_string}"
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
)
if accelerator.is_main_process:
os.makedirs(logdir, exist_ok=True)
OmegaConf.save(args, os.path.join(logdir, "config.yml"))
logger = get_logger_config_path(logdir)
if seed is not None:
set_seed(seed)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_path,
subfolder="tokenizer",
use_fast=False,
)
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
pretrained_model_path,
subfolder="text_encoder",
)
vae = AutoencoderKL.from_pretrained(
pretrained_model_path,
subfolder="vae",
)
#加载unet报错
unet = UNetPseudo3DConditionModel.from_2d_model(
os.path.join(pretrained_model_path, "unet"), model_config=model_config
)
controlnet = ControlNetPseudo3DModel.from_2d_model(
pretrained_controlnet_model_path, model_config=model_config
)
if 'target' not in test_pipeline_config:
test_pipeline_config['target'] = 'video_diffusion.pipelines.stable_diffusion.SpatioTemporalStableDiffusionControlPipeline'
scheduler = DDIMScheduler.from_pretrained(
pretrained_model_path,
subfolder="scheduler",
)
pipeline = instantiate_from_config(
test_pipeline_config,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
control_type = control_type,
editing_type = editing_config.editing_type,
dilation_kernel = editing_config.dilation_kernel,
disk_store=kwargs.get('disk_store', False)
)
pipeline.scheduler.set_timesteps(editing_config['num_inference_steps'])
if editing_config.use_interpolater:
new_scheduler = DDIMInterpolationScheduler.from_pretrained(
pretrained_model_path,
subfolder="scheduler",
)
interpolater = Model()
interpolater.load_model('RIFEModel', -1)
new_scheduler.set_model(vae,interpolater)
print('using interpolater')
pipeline.add_new_scheduler(new_scheduler)
pipeline.new_scheduler.set_timesteps(editing_config['num_inference_steps'])
pipeline.set_progress_bar_config(disable=True)
# pipeline.print_pipeline(logger)
if is_xformers_available():
try:
pipeline.enable_xformers_memory_efficient_attention()
except Exception as e:
logger.warning(
"Could not enable memory efficient attention. Make sure xformers is installed"
f" correctly and a GPU is available: {e}"
)
vae.requires_grad_(False)
unet.requires_grad_(False)
text_encoder.requires_grad_(False)
prompt_ids = tokenizer(
dataset_config["prompt"],
truncation=True,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
).input_ids
|
decord.bridge.set_bridge('torch')
# from video_diffusion.pipelines.p2p_validation_loop_controlnet_ablation import P2pSampleLogger
# logger = get_logger(__name__)
def collate_fn(examples):
"""Concat a batch of sampled image in dataloader
"""
batch = {
"prompt_ids": torch.cat([example["prompt_ids"] for example in examples], dim=0),
"images": torch.stack([example["images"] for example in examples]),
}
return batch
def test(
config: str,
pretrained_model_path: str,
control_type:str,
pretrained_controlnet_model_path :str,
dataset_config: Dict,
logdir: str = None,
editing_config: Optional[Dict] = None,
test_pipeline_config: Optional[Dict] = None,
gradient_accumulation_steps: int = 1,
seed: Optional[int] = None,
mixed_precision: Optional[str] = "fp16",
batch_size: int = 1,
model_config: dict={},
verbose: bool=True,
**kwargs
):
args = get_function_args()
vr = decord.VideoReader(dataset_config.video_path)
fps = vr.get_avg_fps()
duration = len(vr) / fps
print("There are {} frames in the video but we take {} frames".format(len(vr), dataset_config.n_sample_frame))
if dataset_config.n_sample_frame <= 50:
duration = 100
fps = 10
sample_index = list(range(0,len(vr), 1))[:dataset_config.n_sample_frame]
video = vr.get_batch(sample_index)
video_name_match = re.search(r"(.*)/(.*).mp4", dataset_config.video_path)
video_name = video_name_match.group(2)
video_frame_folder = os.path.join('data',video_name)
if os.path.exists(video_frame_folder):
shutil.rmtree(video_frame_folder)
os.makedirs(video_frame_folder,exist_ok=True)
for i in range(video.shape[0]):
frame = video[i]
frame_path = os.path.join(video_frame_folder,f'frame-{i:04}.jpg')
frame = Image.fromarray(frame.numpy().astype(np.uint8))
frame.save(frame_path)
dataset_config.update({'path': video_frame_folder} )
time_string = get_time_string()
if logdir is None:
logdir = config.replace('config', 'result').replace('.yml', '').replace('.yaml', '')
logdir += f"_{time_string}"
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
)
if accelerator.is_main_process:
os.makedirs(logdir, exist_ok=True)
OmegaConf.save(args, os.path.join(logdir, "config.yml"))
logger = get_logger_config_path(logdir)
if seed is not None:
set_seed(seed)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_path,
subfolder="tokenizer",
use_fast=False,
)
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
pretrained_model_path,
subfolder="text_encoder",
)
vae = AutoencoderKL.from_pretrained(
pretrained_model_path,
subfolder="vae",
)
#加载unet报错
unet = UNetPseudo3DConditionModel.from_2d_model(
os.path.join(pretrained_model_path, "unet"), model_config=model_config
)
controlnet = ControlNetPseudo3DModel.from_2d_model(
pretrained_controlnet_model_path, model_config=model_config
)
if 'target' not in test_pipeline_config:
test_pipeline_config['target'] = 'video_diffusion.pipelines.stable_diffusion.SpatioTemporalStableDiffusionControlPipeline'
scheduler = DDIMScheduler.from_pretrained(
pretrained_model_path,
subfolder="scheduler",
)
pipeline = instantiate_from_config(
test_pipeline_config,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
control_type = control_type,
editing_type = editing_config.editing_type,
dilation_kernel = editing_config.dilation_kernel,
disk_store=kwargs.get('disk_store', False)
)
pipeline.scheduler.set_timesteps(editing_config['num_inference_steps'])
if editing_config.use_interpolater:
new_scheduler = DDIMInterpolationScheduler.from_pretrained(
pretrained_model_path,
subfolder="scheduler",
)
interpolater = Model()
interpolater.load_model('RIFEModel', -1)
new_scheduler.set_model(vae,interpolater)
print('using interpolater')
pipeline.add_new_scheduler(new_scheduler)
pipeline.new_scheduler.set_timesteps(editing_config['num_inference_steps'])
pipeline.set_progress_bar_config(disable=True)
# pipeline.print_pipeline(logger)
if is_xformers_available():
try:
pipeline.enable_xformers_memory_efficient_attention()
except Exception as e:
logger.warning(
"Could not enable memory efficient attention. Make sure xformers is installed"
f" correctly and a GPU is available: {e}"
)
vae.requires_grad_(False)
unet.requires_grad_(False)
text_encoder.requires_grad_(False)
prompt_ids = tokenizer(
dataset_config["prompt"],
truncation=True,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
).input_ids | video_dataset = ImageSequenceDataset(**dataset_config, prompt_ids=prompt_ids) | 2 | 2023-10-09 14:38:28+00:00 | 24k |
LiYunfengLYF/LightFC | lib/train/data/base_functions.py | [
{
"identifier": "sampler",
"path": "lib/train/data/sampler.py",
"snippet": "def no_processing(data):\r\n def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap,\r\n num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal',\r\n ... | import torch
import lib.train.data.transforms as tfm
from torch.utils.data.distributed import DistributedSampler
from lib.train.data import sampler, opencv_loader, processing, LTRLoader
from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet
from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
from lib.train.optimizer.anan import Adan
from lib.train.optimizer.lion import Lion
from lib.utils.misc import is_main_process
| 21,027 |
# datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
settings.save_interval = cfg.TRAIN.SAVE_INTERVAL
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
# assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
# "COCO17", "VID", "TRACKINGNET"]
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(
Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader, env_num=settings.env_num))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_train_full":
if settings.use_lmdb:
print("Building got10k_train_full from lmdb")
datasets.append(
Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_votval":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_official_val":
if settings.use_lmdb:
raise ValueError("Not implement")
else:
datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader,
env_num=settings.env_num))
if name == "COCO17":
if settings.use_lmdb:
print("Building COCO2017 from lmdb")
datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader,
env_num=settings.env_num))
if name == "VID":
if settings.use_lmdb:
print("Building VID from lmdb")
|
# datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
settings.save_interval = cfg.TRAIN.SAVE_INTERVAL
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
# assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
# "COCO17", "VID", "TRACKINGNET"]
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(
Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader, env_num=settings.env_num))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_train_full":
if settings.use_lmdb:
print("Building got10k_train_full from lmdb")
datasets.append(
Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_votval":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader,
env_num=settings.env_num))
if name == "GOT10K_official_val":
if settings.use_lmdb:
raise ValueError("Not implement")
else:
datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader,
env_num=settings.env_num))
if name == "COCO17":
if settings.use_lmdb:
print("Building COCO2017 from lmdb")
datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader,
env_num=settings.env_num))
else:
datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader,
env_num=settings.env_num))
if name == "VID":
if settings.use_lmdb:
print("Building VID from lmdb")
| datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader,
| 11 | 2023-10-08 11:44:32+00:00 | 24k |
LiyaoTang/ERDA | main.py | [
{
"identifier": "load_config",
"path": "config/utils.py",
"snippet": "def load_config(cfg_path=None, dataset_name=None, cfg_name=None, cfg_group=None, reload=True):\n # cfg from path\n if cfg_path is not None:\n update = None\n if os.path.isfile(cfg_path):\n # update on th... | import numpy as np
import multiprocessing as mp
import os, sys, time, glob, pickle, psutil, argparse, importlib
import tensorflow as tf
import models, datasets
import utils.memory_saving_gradients
from config import load_config, log_config
from utils.logger import print_mem, redirect_io
from config.utils import get_snap
from utils.tester import ModelTester
from utils.trainer import ModelTrainer
from utils.tf_graph_builder import GraphBuilder | 16,504 | # Common libs
sys.path.insert(0, f'{os.getcwd()}')
# Custom libs
def get_last_train(cfg):
saving_path = sorted(glob.glob(f'results/{cfg.dataset.lower()}/{cfg.name}/*'))
return saving_path[-1] if saving_path else None
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--cfg_path', type=str, help='config path')
parser.add_argument('--gpus', type=str, default=None, help='the number/ID of GPU(s) to use [default: 1], 0 to use cpu only')
parser.add_argument('--mode', type=str, default=None, help='options: train, val, test')
parser.add_argument('--seed', type=int, default=None, dest='rand_seed', help='random seed for use')
parser.add_argument('--data_path', type=str, default=None, help='path to dataset dir = data_path/dataset_name')
parser.add_argument('--model_path', type=str, default=None, help='pretrained model path')
parser.add_argument('--saving_path', type=str, default=None, help='specified saving path')
parser.add_argument('--num_votes', type=float, default=None, help='least num of votes of each point (default to 30)')
parser.add_argument('--num_threads', type=lambda n: mp.cpu_count() if n == 'a' else int(n) if n else None, default=None, help='the number of cpu to use for data loading')
parser.add_argument('--set', type=str, help='external source to set the config - str of dict / yaml file')
parser.add_argument('--debug', action='store_true', help='debug mode')
FLAGS = parser.parse_args()
# sys.argv = sys.argv[:1] # clean extra argv
# ---------------------------------------------------------------------------- #
# solve env & cfg
# ---------------------------------------------------------------------------- #
assert FLAGS.cfg_path is not None
# load config - config path: config(dir).dataset_name(py).config_name(py_class)
| # Common libs
sys.path.insert(0, f'{os.getcwd()}')
# Custom libs
def get_last_train(cfg):
saving_path = sorted(glob.glob(f'results/{cfg.dataset.lower()}/{cfg.name}/*'))
return saving_path[-1] if saving_path else None
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--cfg_path', type=str, help='config path')
parser.add_argument('--gpus', type=str, default=None, help='the number/ID of GPU(s) to use [default: 1], 0 to use cpu only')
parser.add_argument('--mode', type=str, default=None, help='options: train, val, test')
parser.add_argument('--seed', type=int, default=None, dest='rand_seed', help='random seed for use')
parser.add_argument('--data_path', type=str, default=None, help='path to dataset dir = data_path/dataset_name')
parser.add_argument('--model_path', type=str, default=None, help='pretrained model path')
parser.add_argument('--saving_path', type=str, default=None, help='specified saving path')
parser.add_argument('--num_votes', type=float, default=None, help='least num of votes of each point (default to 30)')
parser.add_argument('--num_threads', type=lambda n: mp.cpu_count() if n == 'a' else int(n) if n else None, default=None, help='the number of cpu to use for data loading')
parser.add_argument('--set', type=str, help='external source to set the config - str of dict / yaml file')
parser.add_argument('--debug', action='store_true', help='debug mode')
FLAGS = parser.parse_args()
# sys.argv = sys.argv[:1] # clean extra argv
# ---------------------------------------------------------------------------- #
# solve env & cfg
# ---------------------------------------------------------------------------- #
assert FLAGS.cfg_path is not None
# load config - config path: config(dir).dataset_name(py).config_name(py_class) | cfg = load_config(cfg_path=FLAGS.cfg_path) | 0 | 2023-10-13 08:03:07+00:00 | 24k |
bilibini/Lovely_Image_Downloader | dist/py/Python38/site-packages/urllib3/poolmanager.py | [
{
"identifier": "HTTPHeaderDict",
"path": "dist/py/Python38/site-packages/urllib3/_collections.py",
"snippet": "class HTTPHeaderDict(typing.MutableMapping[str, str]):\n \"\"\"\n :param headers:\n An iterable of field-value pairs. Must not contain multiple field names\n when compared ... | import functools
import logging
import typing
import warnings
import ssl
from types import TracebackType
from urllib.parse import urljoin
from ._collections import HTTPHeaderDict, RecentlyUsedContainer
from ._request_methods import RequestMethods
from .connection import ProxyConfig
from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
from .exceptions import (
LocationValueError,
MaxRetryError,
ProxySchemeUnknown,
URLSchemeUnknown,
)
from .response import BaseHTTPResponse
from .util.connection import _TYPE_SOCKET_OPTIONS
from .util.proxy import connection_requires_http_tunnel
from .util.retry import Retry
from .util.timeout import Timeout
from .util.url import Url, parse_url
from typing_extensions import Literal | 20,132 | from __future__ import annotations
if typing.TYPE_CHECKING:
__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
log = logging.getLogger(__name__)
SSL_KEYWORDS = (
"key_file",
"cert_file",
"cert_reqs",
"ca_certs",
"ssl_version",
"ssl_minimum_version",
"ssl_maximum_version",
"ca_cert_dir",
"ssl_context",
"key_password",
"server_hostname",
)
# Default value for `blocksize` - a new parameter introduced to
# http.client.HTTPConnection & http.client.HTTPSConnection in Python 3.7
_DEFAULT_BLOCKSIZE = 16384
_SelfT = typing.TypeVar("_SelfT")
class PoolKey(typing.NamedTuple):
"""
All known keyword arguments that could be provided to the pool manager, its
pools, or the underlying connections.
All custom key schemes should include the fields in this key at a minimum.
"""
key_scheme: str
key_host: str
key_port: int | None
key_timeout: Timeout | float | int | None
key_retries: Retry | bool | int | None
key_block: bool | None
key_source_address: tuple[str, int] | None
key_key_file: str | None
key_key_password: str | None
key_cert_file: str | None
key_cert_reqs: str | None
key_ca_certs: str | None
key_ssl_version: int | str | None
key_ssl_minimum_version: ssl.TLSVersion | None
key_ssl_maximum_version: ssl.TLSVersion | None
key_ca_cert_dir: str | None
key_ssl_context: ssl.SSLContext | None
key_maxsize: int | None
key_headers: frozenset[tuple[str, str]] | None
| from __future__ import annotations
if typing.TYPE_CHECKING:
__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
log = logging.getLogger(__name__)
SSL_KEYWORDS = (
"key_file",
"cert_file",
"cert_reqs",
"ca_certs",
"ssl_version",
"ssl_minimum_version",
"ssl_maximum_version",
"ca_cert_dir",
"ssl_context",
"key_password",
"server_hostname",
)
# Default value for `blocksize` - a new parameter introduced to
# http.client.HTTPConnection & http.client.HTTPSConnection in Python 3.7
_DEFAULT_BLOCKSIZE = 16384
_SelfT = typing.TypeVar("_SelfT")
class PoolKey(typing.NamedTuple):
"""
All known keyword arguments that could be provided to the pool manager, its
pools, or the underlying connections.
All custom key schemes should include the fields in this key at a minimum.
"""
key_scheme: str
key_host: str
key_port: int | None
key_timeout: Timeout | float | int | None
key_retries: Retry | bool | int | None
key_block: bool | None
key_source_address: tuple[str, int] | None
key_key_file: str | None
key_key_password: str | None
key_cert_file: str | None
key_cert_reqs: str | None
key_ca_certs: str | None
key_ssl_version: int | str | None
key_ssl_minimum_version: ssl.TLSVersion | None
key_ssl_maximum_version: ssl.TLSVersion | None
key_ca_cert_dir: str | None
key_ssl_context: ssl.SSLContext | None
key_maxsize: int | None
key_headers: frozenset[tuple[str, str]] | None | key__proxy: Url | None | 14 | 2023-10-11 09:08:57+00:00 | 24k |
MTgeophysics/mtpy-v2 | mtpy/modeling/modem/convariance.py | [
{
"identifier": "CovarianceError",
"path": "mtpy/modeling/modem/exception.py",
"snippet": "class CovarianceError(ModEMError):\n \"\"\" Raise for Covariance class specific exceptions\"\"\"\n\n pass"
},
{
"identifier": "Model",
"path": "mtpy/modeling/modem/model.py",
"snippet": "clas... | from pathlib import Path
from loguru import logger
from .exception import CovarianceError
from .model import Model
from pyevtk.hl import gridToVTK
import numpy as np | 19,948 | """
==================
ModEM
==================
# Generate files for ModEM
# revised by JP 2017
# revised by AK 2017 to bring across functionality from ak branch
"""
# =============================================================================
# Imports
# =============================================================================
# =============================================================================
class Covariance(object):
"""
read and write covariance files
"""
def __init__(self, grid_dimensions=None, **kwargs):
self._logger = logger
self.grid_dimensions = grid_dimensions
self.smoothing_east = 0.3
self.smoothing_north = 0.3
self.smoothing_z = 0.3
self.smoothing_num = 1
self.exception_list = []
self.mask_arr = None
self.save_path = Path().cwd()
self.fn_basename = "covariance.cov"
self._header_str = "\n".join(
[
"+{0}+".format("-" * 77),
"| This file defines model covariance for a recursive autoregression scheme. |",
"| The model space may be divided into distinct areas using integer masks. |",
"| Mask 0 is reserved for air; mask 9 is reserved for ocean. Smoothing between |",
"| air, ocean and the rest of the model is turned off automatically. You can |",
"| also define exceptions to override smoothing between any two model areas. |",
"| To turn off smoothing set it to zero. This header is 16 lines long. |",
"| 1. Grid dimensions excluding air layers (Nx, Ny, NzEarth) |",
"| 2. Smoothing in the X direction (NzEarth real values) |",
"| 3. Smoothing in the Y direction (NzEarth real values) |",
"| 4. Vertical smoothing (1 real value) |",
"| 5. Number of times the smoothing should be applied (1 integer >= 0) |",
"| 6. Number of exceptions (1 integer >= 0) |",
"| 7. Exceptions in the for e.g. 2 3 0. (to turn off smoothing between 3 & 4) |",
"| 8. Two integer layer indices and Nx x Ny block of masks, repeated as needed.|",
"+{0}+".format("-" * 77),
]
)
for key in list(kwargs.keys()):
if hasattr(self, key):
setattr(self, key, kwargs[key])
else:
self._logger.warn(
"Argument {}={} is not supportted thus not been set.".format(
key, kwargs[key]
)
)
@property
def cov_fn(self):
return self.save_path.joinpath(self.fn_basename)
@cov_fn.setter
def cov_fn(self, value):
if value is not None:
value = Path(value)
self.save_path = value.parent
self.fn_basename = value.name
def write_covariance_file(
self,
cov_fn=None,
save_path=None,
fn_basename=None,
model_fn=None,
sea_water=0.3,
air=1e12,
): #
"""
write a covariance file
"""
if model_fn is not None:
| """
==================
ModEM
==================
# Generate files for ModEM
# revised by JP 2017
# revised by AK 2017 to bring across functionality from ak branch
"""
# =============================================================================
# Imports
# =============================================================================
# =============================================================================
class Covariance(object):
"""
read and write covariance files
"""
def __init__(self, grid_dimensions=None, **kwargs):
self._logger = logger
self.grid_dimensions = grid_dimensions
self.smoothing_east = 0.3
self.smoothing_north = 0.3
self.smoothing_z = 0.3
self.smoothing_num = 1
self.exception_list = []
self.mask_arr = None
self.save_path = Path().cwd()
self.fn_basename = "covariance.cov"
self._header_str = "\n".join(
[
"+{0}+".format("-" * 77),
"| This file defines model covariance for a recursive autoregression scheme. |",
"| The model space may be divided into distinct areas using integer masks. |",
"| Mask 0 is reserved for air; mask 9 is reserved for ocean. Smoothing between |",
"| air, ocean and the rest of the model is turned off automatically. You can |",
"| also define exceptions to override smoothing between any two model areas. |",
"| To turn off smoothing set it to zero. This header is 16 lines long. |",
"| 1. Grid dimensions excluding air layers (Nx, Ny, NzEarth) |",
"| 2. Smoothing in the X direction (NzEarth real values) |",
"| 3. Smoothing in the Y direction (NzEarth real values) |",
"| 4. Vertical smoothing (1 real value) |",
"| 5. Number of times the smoothing should be applied (1 integer >= 0) |",
"| 6. Number of exceptions (1 integer >= 0) |",
"| 7. Exceptions in the for e.g. 2 3 0. (to turn off smoothing between 3 & 4) |",
"| 8. Two integer layer indices and Nx x Ny block of masks, repeated as needed.|",
"+{0}+".format("-" * 77),
]
)
for key in list(kwargs.keys()):
if hasattr(self, key):
setattr(self, key, kwargs[key])
else:
self._logger.warn(
"Argument {}={} is not supportted thus not been set.".format(
key, kwargs[key]
)
)
@property
def cov_fn(self):
return self.save_path.joinpath(self.fn_basename)
@cov_fn.setter
def cov_fn(self, value):
if value is not None:
value = Path(value)
self.save_path = value.parent
self.fn_basename = value.name
def write_covariance_file(
self,
cov_fn=None,
save_path=None,
fn_basename=None,
model_fn=None,
sea_water=0.3,
air=1e12,
): #
"""
write a covariance file
"""
if model_fn is not None: | mod_obj = Model() | 1 | 2023-10-11 22:24:50+00:00 | 24k |
weavel-ai/promptmodel-python | promptmodel/llms/llm_proxy.py | [
{
"identifier": "LLM",
"path": "promptmodel/llms/llm.py",
"snippet": "class LLM:\n def __init__(self):\n pass\n\n @classmethod\n def __parse_output_pattern__(\n cls,\n raw_output: Optional[str] = None,\n parsing_type: Optional[ParsingType] = None,\n ) -> ParseResu... | from typing import (
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
List,
Optional,
Tuple,
Union,
)
from uuid import UUID
from threading import Thread
from rich import print
from uuid import uuid4
from litellm.utils import ModelResponse, get_max_tokens
from promptmodel.llms.llm import LLM
from promptmodel.database.models import (
DeployedPrompt,
DeployedFunctionModel,
DeployedFunctionModelVersion,
)
from promptmodel.database.crud import (
get_deployed_prompts,
)
from promptmodel.promptmodel_init import CacheManager
from promptmodel.utils.config_utils import read_config, upsert_config
from promptmodel.utils.random_utils import select_version_by_ratio
from promptmodel.utils import logger
from promptmodel.utils.async_utils import run_async_in_sync
from promptmodel.utils.token_counting import (
num_tokens_for_messages_for_each,
num_tokens_from_functions_input,
)
from promptmodel.utils.output_utils import update_dict
from promptmodel.apis.base import AsyncAPIClient
from promptmodel.types.response import (
LLMResponse,
LLMStreamResponse,
FunctionModelConfig,
ChatModelConfig,
UnitConfig,
PMDetail,
)
from promptmodel.types.request import ChatLogRequest | 19,740 |
def _prepare_call_args(
self,
prompts: List[Dict[str, str]],
version_detail: Dict[str, Any],
inputs: Dict[str, Any],
kwargs,
):
stringified_inputs = {key: str(value) for key, value in inputs.items()}
messages = [
{
"content": prompt["content"].format(**stringified_inputs),
"role": prompt["role"],
}
for prompt in prompts
]
call_args = {
"messages": messages,
"model": version_detail["model"] if version_detail else None,
"parsing_type": version_detail["parsing_type"] if version_detail else None,
"output_keys": version_detail["output_keys"] if version_detail else None,
}
if call_args["parsing_type"] is None:
del call_args["parsing_type"]
del call_args["output_keys"]
if "functions" in kwargs:
call_args["functions"] = kwargs["functions"]
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
if "api_key" in kwargs:
call_args["api_key"] = kwargs["api_key"]
return call_args
def _prepare_call_args_for_chat(
self,
messages: List[Dict[str, Any]],
version_detail: Dict[str, Any],
kwargs,
):
call_args = {}
token_per_tools = 0
if "functions" in kwargs:
call_args["functions"] = kwargs["functions"]
token_per_tools = num_tokens_from_functions_input(
functions=kwargs["functions"],
model=version_detail["model"] if version_detail else "gpt-3.5-turbo",
)
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
token_per_tools = num_tokens_from_functions_input(
functions=kwargs["tools"],
model=version_detail["model"] if version_detail else "gpt-3.5-turbo",
)
# truncate messages to make length <= model's max length
model_max_tokens = get_max_tokens(
model=version_detail["model"] if version_detail else "gpt-3.5-turbo"
)
token_per_messages = num_tokens_for_messages_for_each(
messages, version_detail["model"]
)
token_limit_exceeded = (
sum(token_per_messages) + token_per_tools
) - model_max_tokens
if token_limit_exceeded > 0:
while token_limit_exceeded > 0:
# erase the second oldest message (first one is system prompt, so it should not be erased)
if len(messages) == 1:
# if there is only one message, Error cannot be solved. Just call LLM and get error response
break
token_limit_exceeded -= token_per_messages[1]
del messages[1]
del token_per_messages[1]
call_args["messages"] = messages
call_args["model"] = version_detail["model"] if version_detail else None
if "api_key" in kwargs:
call_args["api_key"] = kwargs["api_key"]
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
return call_args
async def _async_log_to_cloud(
self,
version_uuid: str,
log_uuid: str,
inputs: Optional[Dict] = None,
api_response: Optional[ModelResponse] = None,
parsed_outputs: Optional[Dict] = None,
metadata: Optional[Dict] = None,
):
config = read_config()
if (
"project" in config
and "mask_inputs" in config["project"]
and config["project"]["mask_inputs"] == True
):
inputs = {key: "PRIVATE LOGGING" for key, value in inputs.items()}
# Perform the logging asynchronously
if api_response:
api_response_dict = api_response.model_dump()
api_response_dict["response_ms"] = api_response._response_ms
api_response_dict["_response_ms"] = api_response._response_ms
else:
api_response_dict = None
run_log_request_body = {
"uuid": log_uuid,
"api_response": api_response_dict,
"inputs": inputs,
"parsed_outputs": parsed_outputs,
"metadata": metadata,
}
|
class LLMProxy(LLM):
def __init__(
self,
name: str,
version: Optional[Union[str, int]] = "deploy",
unit_config: Optional[UnitConfig] = None
):
super().__init__()
self._name = name
self.version = version
self.unit_config = unit_config
def _wrap_gen(self, gen: Callable[..., Any]) -> Callable[..., Any]:
def wrapper(inputs: Dict[str, Any], **kwargs):
prompts, version_details = run_async_in_sync(
LLMProxy.fetch_prompts(self._name, self.version)
)
call_args = self._prepare_call_args(
prompts, version_details, inputs, kwargs
)
log_uuid = str(uuid4())
# Call the generator with the arguments
stream_response: Generator[LLMStreamResponse, None, None] = gen(**call_args)
api_response = None
dict_cache = {} # to store aggregated dictionary values
string_cache = "" # to store aggregated string values
error_occurs = False
error_log = None
for item in stream_response:
if (
item.api_response and "delta" not in item.api_response.choices[0]
): # only get the last api_response, not delta response
api_response = item.api_response
if item.parsed_outputs:
dict_cache = update_dict(dict_cache, item.parsed_outputs)
if item.raw_output:
string_cache += item.raw_output
if item.error and not error_occurs:
error_occurs = True
error_log = item.error_log
if error_occurs:
# delete all promptmodel data in item
item.raw_output = None
item.parsed_outputs = None
item.function_call = None
item.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
yield item
metadata = {
"error": error_occurs,
"error_log": error_log,
}
run_async_in_sync(
self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=api_response,
parsed_outputs=dict_cache,
metadata=metadata,
)
)
return wrapper
def _wrap_async_gen(self, async_gen: Callable[..., Any]) -> Callable[..., Any]:
async def wrapper(inputs: Dict[str, Any], **kwargs):
prompts, version_details = await LLMProxy.fetch_prompts(
self._name, self.version
)
call_args = self._prepare_call_args(
prompts, version_details, inputs, kwargs
)
# Call async_gen with the arguments
stream_response: AsyncGenerator[LLMStreamResponse, None] = async_gen(
**call_args
)
log_uuid = str(uuid4())
api_response = None
dict_cache = {} # to store aggregated dictionary values
string_cache = "" # to store aggregated string values
error_occurs = False
error_log = None
api_response: Optional[ModelResponse] = None
async for item in stream_response:
if (
item.api_response and "delta" not in item.api_response.choices[0]
): # only get the last api_response, not delta response
api_response = item.api_response
if item.parsed_outputs:
dict_cache = update_dict(dict_cache, item.parsed_outputs)
if item.raw_output:
string_cache += item.raw_output
if item.error and not error_occurs:
error_occurs = True
error_log = item.error_log
item.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
yield item
# # add string_cache in model_response
# if api_response:
# if "message" not in api_response.choices[0]:
# api_response.choices[0].message = {}
# if "content" not in api_response.choices[0].message:
# api_response.choices[0].message["content"] = string_cache
# api_response.choices[0].message["role"] = "assistant"
metadata = {
"error": error_occurs,
"error_log": error_log,
}
await self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=api_response,
parsed_outputs=dict_cache,
metadata=metadata,
)
# raise Exception("error_log")
return wrapper
def _wrap_method(self, method: Callable[..., Any]) -> Callable[..., Any]:
def wrapper(inputs: Dict[str, Any], **kwargs):
prompts, version_details = run_async_in_sync(
LLMProxy.fetch_prompts(self._name, self.version)
)
call_args = self._prepare_call_args(
prompts, version_details, inputs, kwargs
)
# Call the method with the arguments
llm_response: LLMResponse = method(**call_args)
error_occurs = llm_response.error
error_log = llm_response.error_log
metadata = {
"error": error_occurs,
"error_log": error_log,
}
log_uuid = str(uuid4())
if llm_response.parsed_outputs:
run_async_in_sync(
self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=llm_response.api_response,
parsed_outputs=llm_response.parsed_outputs,
metadata=metadata,
)
)
else:
run_async_in_sync(
self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=llm_response.api_response,
parsed_outputs={},
metadata=metadata,
)
)
if error_occurs:
# delete all promptmodel data in llm_response
llm_response.raw_output = None
llm_response.parsed_outputs = None
llm_response.function_call = None
llm_response.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
return llm_response
return wrapper
def _wrap_async_method(self, method: Callable[..., Any]) -> Callable[..., Any]:
async def async_wrapper(inputs: Dict[str, Any], **kwargs):
prompts, version_details = await LLMProxy.fetch_prompts(
self._name, self.version
) # messages, model, uuid = self._fetch_prompts()
call_args = self._prepare_call_args(
prompts, version_details, inputs, kwargs
)
# Call the method with the arguments
llm_response: LLMResponse = await method(**call_args)
error_occurs = llm_response.error
error_log = llm_response.error_log
metadata = {
"error": error_occurs,
"error_log": error_log,
}
log_uuid = str(uuid4())
if llm_response.parsed_outputs:
await self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=llm_response.api_response,
parsed_outputs=llm_response.parsed_outputs,
metadata=metadata,
)
else:
await self._async_log_to_cloud(
log_uuid=log_uuid,
version_uuid=version_details["uuid"],
inputs=inputs,
api_response=llm_response.api_response,
parsed_outputs={},
metadata=metadata,
)
if error_occurs:
# delete all promptmodel data in llm_response
llm_response.raw_output = None
llm_response.parsed_outputs = None
llm_response.function_call = None
llm_response.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
return llm_response
return async_wrapper
def _wrap_chat(self, method: Callable[..., Any]) -> Callable[..., Any]:
def wrapper(session_uuid: str, **kwargs):
instruction, version_details, message_logs = run_async_in_sync(
LLMProxy.fetch_chat_model(self._name, session_uuid, self.version)
)
call_args = self._prepare_call_args_for_chat(
message_logs, version_details, kwargs
)
# Call the method with the arguments
llm_response: LLMResponse = method(**call_args)
error_occurs = llm_response.error
error_log = llm_response.error_log
metadata = {
"error": error_occurs,
"error_log": error_log,
}
api_response = None
if llm_response.api_response:
api_response = llm_response.api_response
log_uuid = str(uuid4())
run_async_in_sync(
self._async_chat_log_to_cloud(
session_uuid=session_uuid,
version_uuid=version_details["uuid"],
chat_log_request_list=[
ChatLogRequest(
message=llm_response.api_response.choices[
0
].message.model_dump(),
uuid=log_uuid,
metadata=metadata,
api_response=api_response,
)
],
)
)
if error_occurs:
# delete all promptmodel data in llm_response
llm_response.raw_output = None
llm_response.parsed_outputs = None
llm_response.function_call = None
llm_response.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
return llm_response
return wrapper
def _wrap_async_chat(self, method: Callable[..., Any]) -> Callable[..., Any]:
async def async_wrapper(session_uuid: str, **kwargs):
(
instruction,
version_details,
message_logs,
) = await LLMProxy.fetch_chat_model(self._name, session_uuid, self.version)
call_args = self._prepare_call_args_for_chat(
message_logs, version_details, kwargs
)
# Call the method with the arguments
llm_response: LLMResponse = await method(**call_args)
error_occurs = llm_response.error
error_log = llm_response.error_log
metadata = {
"error": error_occurs,
"error_log": error_log,
}
api_response = None
if llm_response.api_response:
api_response = llm_response.api_response
log_uuid = str(uuid4())
await self._async_chat_log_to_cloud(
session_uuid=session_uuid,
version_uuid=version_details["uuid"],
chat_log_request_list=[
ChatLogRequest(
uuid=log_uuid,
message=llm_response.api_response.choices[
0
].message.model_dump(),
metadata=metadata,
api_response=api_response,
)
],
)
if error_occurs:
# delete all promptmodel data in llm_response
llm_response.raw_output = None
llm_response.parsed_outputs = None
llm_response.function_call = None
llm_response.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
return llm_response
return async_wrapper
def _wrap_chat_gen(self, gen: Callable[..., Any]) -> Callable[..., Any]:
def wrapper(session_uuid: str, **kwargs):
instruction, version_details, message_logs = run_async_in_sync(
LLMProxy.fetch_chat_model(self._name, session_uuid, self.version)
)
call_args = self._prepare_call_args_for_chat(
message_logs, version_details, kwargs
)
# Call the generator with the arguments
stream_response: Generator[LLMStreamResponse, None, None] = gen(**call_args)
api_response = None
error_occurs = False
error_log = None
log_uuid = str(uuid4())
for item in stream_response:
if (
item.api_response and "delta" not in item.api_response.choices[0]
): # only get the last api_response, not delta response
api_response = item.api_response
if item.error and not error_occurs:
error_occurs = True
error_log = item.error_log
if error_occurs:
# delete all promptmodel data in item
item.raw_output = None
item.parsed_outputs = None
item.function_call = None
item.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
yield item
metadata = {
"error": error_occurs,
"error_log": error_log,
}
run_async_in_sync(
self._async_chat_log_to_cloud(
session_uuid=session_uuid,
version_uuid=version_details["uuid"],
chat_log_request_list=[
ChatLogRequest(
uuid=log_uuid,
message=api_response.choices[0].message.model_dump(),
metadata=metadata,
api_response=api_response,
)
],
)
)
return wrapper
def _wrap_async_chat_gen(self, async_gen: Callable[..., Any]) -> Callable[..., Any]:
async def wrapper(session_uuid: str, **kwargs):
(
instruction,
version_details,
message_logs,
) = await LLMProxy.fetch_chat_model(self._name, session_uuid, self.version)
call_args = self._prepare_call_args_for_chat(
message_logs, version_details, kwargs
)
# Call the generator with the arguments
stream_response: AsyncGenerator[LLMStreamResponse, None] = async_gen(
**call_args
)
api_response = None
error_occurs = False
error_log = None
log_uuid = str(uuid4())
async for item in stream_response:
if (
item.api_response and "delta" not in item.api_response.choices[0]
): # only get the last api_response, not delta response
api_response = item.api_response
if item.error and not error_occurs:
error_occurs = True
error_log = item.error_log
if error_occurs:
# delete all promptmodel data in item
item.raw_output = None
item.parsed_outputs = None
item.function_call = None
item.pm_detail = PMDetail(
model=version_details["model"],
name=self._name,
version_uuid=str(version_details["uuid"]),
version=version_details["version"],
log_uuid=log_uuid,
)
yield item
metadata = {
"error": error_occurs,
"error_log": error_log,
}
await self._async_chat_log_to_cloud(
session_uuid=session_uuid,
version_uuid=version_details["uuid"],
chat_log_request_list=[
ChatLogRequest(
uuid=log_uuid,
message=api_response.choices[0].message.model_dump(),
metadata=metadata,
api_response=api_response,
)
],
)
return wrapper
def _prepare_call_args(
self,
prompts: List[Dict[str, str]],
version_detail: Dict[str, Any],
inputs: Dict[str, Any],
kwargs,
):
stringified_inputs = {key: str(value) for key, value in inputs.items()}
messages = [
{
"content": prompt["content"].format(**stringified_inputs),
"role": prompt["role"],
}
for prompt in prompts
]
call_args = {
"messages": messages,
"model": version_detail["model"] if version_detail else None,
"parsing_type": version_detail["parsing_type"] if version_detail else None,
"output_keys": version_detail["output_keys"] if version_detail else None,
}
if call_args["parsing_type"] is None:
del call_args["parsing_type"]
del call_args["output_keys"]
if "functions" in kwargs:
call_args["functions"] = kwargs["functions"]
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
if "api_key" in kwargs:
call_args["api_key"] = kwargs["api_key"]
return call_args
def _prepare_call_args_for_chat(
self,
messages: List[Dict[str, Any]],
version_detail: Dict[str, Any],
kwargs,
):
call_args = {}
token_per_tools = 0
if "functions" in kwargs:
call_args["functions"] = kwargs["functions"]
token_per_tools = num_tokens_from_functions_input(
functions=kwargs["functions"],
model=version_detail["model"] if version_detail else "gpt-3.5-turbo",
)
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
token_per_tools = num_tokens_from_functions_input(
functions=kwargs["tools"],
model=version_detail["model"] if version_detail else "gpt-3.5-turbo",
)
# truncate messages to make length <= model's max length
model_max_tokens = get_max_tokens(
model=version_detail["model"] if version_detail else "gpt-3.5-turbo"
)
token_per_messages = num_tokens_for_messages_for_each(
messages, version_detail["model"]
)
token_limit_exceeded = (
sum(token_per_messages) + token_per_tools
) - model_max_tokens
if token_limit_exceeded > 0:
while token_limit_exceeded > 0:
# erase the second oldest message (first one is system prompt, so it should not be erased)
if len(messages) == 1:
# if there is only one message, Error cannot be solved. Just call LLM and get error response
break
token_limit_exceeded -= token_per_messages[1]
del messages[1]
del token_per_messages[1]
call_args["messages"] = messages
call_args["model"] = version_detail["model"] if version_detail else None
if "api_key" in kwargs:
call_args["api_key"] = kwargs["api_key"]
if "tools" in kwargs:
call_args["tools"] = kwargs["tools"]
return call_args
async def _async_log_to_cloud(
self,
version_uuid: str,
log_uuid: str,
inputs: Optional[Dict] = None,
api_response: Optional[ModelResponse] = None,
parsed_outputs: Optional[Dict] = None,
metadata: Optional[Dict] = None,
):
config = read_config()
if (
"project" in config
and "mask_inputs" in config["project"]
and config["project"]["mask_inputs"] == True
):
inputs = {key: "PRIVATE LOGGING" for key, value in inputs.items()}
# Perform the logging asynchronously
if api_response:
api_response_dict = api_response.model_dump()
api_response_dict["response_ms"] = api_response._response_ms
api_response_dict["_response_ms"] = api_response._response_ms
else:
api_response_dict = None
run_log_request_body = {
"uuid": log_uuid,
"api_response": api_response_dict,
"inputs": inputs,
"parsed_outputs": parsed_outputs,
"metadata": metadata,
} | res = await AsyncAPIClient.execute( | 14 | 2023-10-09 03:35:44+00:00 | 24k |
cambridgeltl/ClaPS | algs/greedy.py | [
{
"identifier": "PromptedClassificationReward",
"path": "rewards/text_classification_reward.py",
"snippet": "class PromptedClassificationReward:\n def __init__(\n self,\n args,\n task_lm: str,\n is_mask_lm: Optional[bool],\n num_classes: int,\n verbalizers: L... | import random
import numpy as np
from typing import Any, Optional
from rewards.text_classification_reward import (
PromptedClassificationReward,
)
from utils.fsc_datasets import PromptedClassificationDataset
from .base_trainer import BaseTrainer
from utils.fsc_datasets import PromptedClassificationDataset
from rewards.text_classification_reward import PromptedClassificationReward | 18,703 |
class GreedyTrainer(BaseTrainer):
def __init__(
self,
|
class GreedyTrainer(BaseTrainer):
def __init__(
self, | obj_func: PromptedClassificationReward, | 4 | 2023-10-08 12:39:44+00:00 | 24k |
clessig/atmorep | atmorep/core/trainer.py | [
{
"identifier": "AtmoRep",
"path": "atmorep/core/atmorep_model.py",
"snippet": "class AtmoRep( torch.nn.Module) :\n\n def __init__(self, cf) :\n '''Constructor'''\n \n super( AtmoRep, self).__init__()\n\n self.cf = cf\n\n ###################################################\n def create( self... | import torch
import torchinfo
import numpy as np
import code
import os
import datetime
import functools
import pandas as pd
import wandb
import torch.distributed as dist
import torch.utils.data.distributed
import atmorep.config.config as config
import atmorep.utils.token_infos_transformations as token_infos_transformations
import atmorep.utils.utils as utils
from pathlib import Path
from typing import TypeVar
from torch.distributed.optim import ZeroRedundancyOptimizer
from atmorep.core.atmorep_model import AtmoRep
from atmorep.core.atmorep_model import AtmoRepData
from atmorep.training.bert import prepare_batch_BERT_multifield
from atmorep.transformer.transformer_base import positional_encoding_harmonic
from atmorep.utils.utils import shape_to_str
from atmorep.utils.utils import relMSELoss
from atmorep.utils.utils import Gaussian
from atmorep.utils.utils import CRPS
from atmorep.utils.utils import NetMode
from atmorep.utils.utils import sgn_exp
from atmorep.datasets.data_writer import write_forecast, write_BERT, write_attention | 15,091 |
# target etc are aliasing targets_b which simplifies bookkeeping below
if is_predicted :
target = [targets_b[vidx][bidx] for vidx in range(num_levels)]
pred_mu = [preds_mu_b[vidx][bidx] for vidx in range(num_levels)]
pred_ens = [preds_ens_b[vidx][bidx] for vidx in range(num_levels)]
dates_masked_l, lats_masked_l, lons_masked_l = [], [], []
for vidx, _ in enumerate(field_info[2]) :
normalizer = self.model.normalizer( fidx, vidx)
y, m = dates[0].year, dates[0].month
sources_b[bidx,vidx] = normalizer.denormalize( y, m, sources_b[bidx,vidx], [lats, lons])
if is_predicted :
# TODO: make sure normalizer_local / normalizer_global is used in data_loader
idx = tokens_masked_idx_list[fidx][vidx][bidx]
tinfo_masked = tinfos[bidx,vidx].flatten( 0,2)
tinfo_masked = tinfo_masked[idx]
lad, lod = lat_d_h*res, lon_d_h*res
lats_masked, lons_masked, dates_masked = [], [], []
for t in tinfo_masked :
lats_masked.append( np.expand_dims( np.arange(t[4]-lad, t[4]+lad+0.001,res), 0))
lons_masked.append( np.expand_dims( np.arange(t[5]-lod, t[5]+lod+0.001,res), 0))
r = pd.date_range( start=utils.token_info_to_time(t), periods=token_size[0], freq='h')
dates_masked.append( np.expand_dims(r.to_pydatetime().astype( 'datetime64[s]'), 0) )
lats_masked = np.concatenate( lats_masked, 0)
lons_masked = np.remainder( np.concatenate( lons_masked, 0), 360.)
dates_masked = np.concatenate( dates_masked, 0)
for ii,(t,p,e,la,lo) in enumerate(zip( target[vidx], pred_mu[vidx], pred_ens[vidx],
lats_masked, lons_masked)) :
targets_b[vidx][bidx][ii] = normalizer.denormalize( y, m, t, [la, lo])
preds_mu_b[vidx][bidx][ii] = normalizer.denormalize( y, m, p, [la, lo])
preds_ens_b[vidx][bidx][ii] = normalizer.denormalize( y, m, e, [la, lo])
dates_masked_l += [ dates_masked ]
lats_masked_l += [ [90.-lat for lat in lats_masked] ]
lons_masked_l += [ lons_masked ]
dates = dates.to_pydatetime().astype( 'datetime64[s]')
coords_b += [ [dates, 90.-lats, lons, dates_masked_l, lats_masked_l, lons_masked_l] ]
fn = field_info[0]
sources_out.append( [fn, sources_b])
if is_predicted :
targets_out.append([fn, [[t.numpy(force=True) for t in t_v] for t_v in targets_b]])
preds_out.append( [fn, [[p.numpy(force=True) for p in p_v] for p_v in preds_mu_b]])
ensembles_out.append( [fn, [[p.numpy(force=True) for p in p_v] for p_v in preds_ens_b]])
else :
targets_out.append( [fn, []])
preds_out.append( [fn, []])
ensembles_out.append( [fn, []])
sources_dates_out.append( [c[0] for c in coords_b])
sources_lats_out.append( [c[1] for c in coords_b])
sources_lons_out.append( [c[2] for c in coords_b])
if is_predicted :
targets_dates_out.append( [c[3] for c in coords_b])
targets_lats_out.append( [c[4] for c in coords_b])
targets_lons_out.append( [c[5] for c in coords_b])
else :
targets_dates_out.append( [ ])
targets_lats_out.append( [ ])
targets_lons_out.append( [ ])
levels = [[np.array(l) for l in field[2]] for field in cf.fields]
write_BERT( cf.wandb_id, epoch, batch_idx,
levels, sources_out,
[sources_dates_out, sources_lats_out, sources_lons_out],
targets_out, [targets_dates_out, targets_lats_out, targets_lons_out],
preds_out, ensembles_out )
def log_attention( self, epoch, bidx, log) :
'''Hook for logging: output attention maps.'''
cf = self.cf
attention, token_infos = log
attn_dates_out, attn_lats_out, attn_lons_out = [ ], [ ], [ ]
attn_out = []
for fidx, field_info in enumerate(cf.fields) :
# reconstruct coordinates
is_predicted = fidx in self.fields_prediction_idx
num_levels = len(field_info[2])
num_tokens = field_info[3]
token_size = field_info[4]
lat_d_h, lon_d_h = int(np.floor(token_size[1]/2.)), int(np.floor(token_size[2]/2.))
tinfos = token_infos[fidx].reshape( [-1, num_levels, *num_tokens, cf.size_token_info])
coords_b = []
for tinfo in tinfos :
# use first vertical levels since a column is considered
res = tinfo[0,0,0,0,-1]
lats = np.arange(tinfo[0,0,0,0,4]-lat_d_h*res, tinfo[0,0,-1,0,4]+lat_d_h*res+0.001,res*token_size[1])
if tinfo[0,0,0,-1,5] < tinfo[0,0,0,0,5] :
lons = np.remainder( np.arange( tinfo[0,0,0,0,5] - lon_d_h*res,
360. + tinfo[0,0,0,-1,5] + lon_d_h*res + 0.001, res*token_size[2]), 360.)
else :
lons = np.arange(tinfo[0,0,0,0,5]-lon_d_h*res, tinfo[0,0,0,-1,5]+lon_d_h*res+0.001,res*token_size[2])
lons = np.remainder( lons, 360.)
dates = np.array([(utils.token_info_to_time(tinfo[0,t,0,0,:3])) for t in range(tinfo.shape[1])], dtype='datetime64[s]')
coords_b += [ [dates, lats, lons] ]
if is_predicted:
attn_out.append([field_info[0], attention[fidx]])
attn_dates_out.append([c[0] for c in coords_b])
attn_lats_out.append( [c[1] for c in coords_b])
attn_lons_out.append( [c[2] for c in coords_b])
else:
attn_dates_out.append( [] )
attn_lats_out.append( [] )
attn_lons_out.append( [] )
levels = [[np.array(l) for l in field[2]] for field in cf.fields]
| ####################################################################################################
#
# Copyright (C) 2022
#
####################################################################################################
#
# project : atmorep
#
# author : atmorep collaboration
#
# description :
#
# license :
#
####################################################################################################
# code.interact(local=locals())
# import horovod.torch as hvd
####################################################################################################
class Trainer_Base() :
def __init__( self, cf, devices ) :
self.cf = cf
self.devices = devices
self.device_in = devices[0]
self.device_out = devices[-1]
self.fields_prediction_idx = []
self.loss_weights = torch.zeros( len(cf.fields_prediction) )
for ifield, field in enumerate(cf.fields_prediction) :
self.loss_weights[ifield] = self.cf.fields_prediction[ifield][1]
for idx, field_info in enumerate(cf.fields) :
if field_info[0] == field[0] :
self.fields_prediction_idx.append( idx)
break
self.loss_weights = self.loss_weights.to( self.device_out)
self.MSELoss = torch.nn.MSELoss()
# transformation for token infos
if hasattr( cf, 'token_infos_transformation') :
self.tok_infos_trans = getattr( token_infos_transformations, cf.token_infos_transformation)
else :
self.tok_infos_trans = getattr( token_infos_transformations, 'identity')
if 0 == cf.par_rank :
directory = Path( config.path_results, 'id{}'.format( cf.wandb_id))
if not os.path.exists(directory):
os.makedirs( directory)
directory = Path( config.path_models, 'id{}'.format( cf.wandb_id))
if not os.path.exists(directory):
os.makedirs( directory)
###################################################
def create( self, load_embeds=True) :
net = AtmoRep( self.cf)
self.model = AtmoRepData( net)
self.model.create( self.pre_batch, self.devices, load_embeds)
# TODO: pass the properly to model / net
self.model.net.encoder_to_decoder = self.encoder_to_decoder
self.model.net.decoder_to_tail = self.decoder_to_tail
return self
###################################################
@classmethod
def load( Typename, cf, model_id, epoch, devices) :
trainer = Typename( cf, devices).create( load_embeds=False)
trainer.model.net = trainer.model.net.load( model_id, devices, cf, epoch)
# TODO: pass the properly to model / net
trainer.model.net.encoder_to_decoder = trainer.encoder_to_decoder
trainer.model.net.decoder_to_tail = trainer.decoder_to_tail
str = 'Loaded model id = {}{}.'.format( model_id, f' at epoch = {epoch}' if epoch> -2 else '')
print( str)
return trainer
###################################################
def save( self, epoch) :
self.model.net.save( epoch)
###################################################
def get_learn_rates( self) :
cf = self.cf
size_padding = 5
learn_rates = np.zeros( cf.num_epochs + size_padding)
learn_rates[:cf.lr_start_epochs] = np.linspace( cf.lr_start, cf.lr_max, num = cf.lr_start_epochs)
lr = learn_rates[cf.lr_start_epochs-1]
ic = 0
for epoch in range( cf.lr_start_epochs, cf.num_epochs + size_padding) :
lr = max( lr / cf.lr_decay_rate, cf.lr_min)
learn_rates[epoch] = lr
if ic > 9999 : # sanity check
assert "Maximum number of epochs exceeded."
return learn_rates
###################################################
def run( self, epoch = -1) :
cf = self.cf
model = self.model
learn_rates = self.get_learn_rates()
if cf.with_ddp :
self.model_ddp = torch.nn.parallel.DistributedDataParallel( model, static_graph=True)
if not cf.optimizer_zero :
self.optimizer = torch.optim.AdamW( self.model_ddp.parameters(), lr=cf.lr_start,
weight_decay=cf.weight_decay)
else :
self.optimizer = ZeroRedundancyOptimizer(self.model_ddp.parameters(),
optimizer_class=torch.optim.AdamW,
lr=cf.lr_start )
else :
self.optimizer = torch.optim.AdamW( self.model.parameters(), lr=cf.lr_start,
weight_decay=cf.weight_decay)
if 0 == cf.par_rank :
# print( self.model.net)
model_parameters = filter(lambda p: p.requires_grad, self.model_ddp.parameters())
num_params = sum([np.prod(p.size()) for p in model_parameters])
print( f'Number of trainable parameters: {num_params:,}')
# test at the beginning as reference
self.model.load_data( NetMode.test, batch_size=cf.batch_size_test)
if cf.test_initial :
cur_test_loss = self.validate( epoch, cf.BERT_strategy).cpu().numpy()
test_loss = np.array( [cur_test_loss])
else :
# generic value based on data normalization
test_loss = np.array( [1.0])
epoch += 1
batch_size = cf.batch_size_start - cf.batch_size_delta
if cf.profile :
lr = learn_rates[epoch]
for g in self.optimizer.param_groups:
g['lr'] = lr
self.model.load_data( NetMode.train, batch_size = cf.batch_size_max)
self.profile()
# training loop
while True :
if epoch >= cf.num_epochs :
break
lr = learn_rates[epoch]
for g in self.optimizer.param_groups:
g['lr'] = lr
batch_size = min( cf.batch_size_max, batch_size + cf.batch_size_delta)
tstr = datetime.datetime.now().strftime("%H:%M:%S")
print( '{} : {} :: batch_size = {}, lr = {}'.format( epoch, tstr, batch_size, lr) )
self.model.load_data( NetMode.train, batch_size = batch_size)
self.train( epoch)
if cf.with_wandb and 0 == cf.par_rank :
self.save( epoch)
cur_test_loss = self.validate( epoch, cf.BERT_strategy).cpu().numpy()
# self.validate( epoch, 'forecast')
# save model
if cur_test_loss < test_loss.min() :
self.save( -2)
test_loss = np.append( test_loss, [cur_test_loss])
epoch += 1
tstr = datetime.datetime.now().strftime("%H:%M:%S")
print( 'Finished training at {} with test loss = {}.'.format( tstr, test_loss[-1]) )
# save final network
if cf.with_wandb and 0 == cf.par_rank :
self.save( -2)
###################################################
def train( self, epoch):
model = self.model
cf = self.cf
model.mode( NetMode.train)
self.optimizer.zero_grad()
loss_total = [[] for i in range(len(cf.losses)) ]
std_dev_total = [[] for i in range(len(self.fields_prediction_idx)) ]
mse_loss_total = []
grad_loss_total = []
ctr = 0
for batch_idx in range( model.len( NetMode.train)) :
batch_data = self.model.next()
batch_data = self.prepare_batch( batch_data)
preds, _ = self.model_ddp( batch_data)
loss, mse_loss, losses = self.loss( preds, batch_idx)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
[loss_total[idx].append( losses[key]) for idx, key in enumerate(losses)]
mse_loss_total.append( mse_loss.detach().cpu() )
grad_loss_total.append( loss.detach().cpu() )
[std_dev_total[idx].append( pred[1].detach().cpu()) for idx, pred in enumerate(preds)]
# logging
if int((batch_idx * cf.batch_size_max) / 4) > ctr :
# wandb logging
if cf.with_wandb and (0 == cf.par_rank) :
loss_dict = { "training loss": torch.mean( torch.tensor( mse_loss_total)),
"gradient loss": torch.mean( torch.tensor( grad_loss_total)) }
# log individual loss terms for individual fields
for idx, cur_loss in enumerate(loss_total) :
loss_name = self.cf.losses[idx]
lt = torch.tensor(cur_loss)
for i, field in enumerate(cf.fields_prediction) :
idx_name = loss_name + ', ' + field[0]
idx_std_name = 'stddev, ' + field[0]
loss_dict[idx_name] = torch.mean( lt[:,i]).cpu().detach()
loss_dict[idx_std_name] = torch.mean(torch.cat(std_dev_total[i],0)).cpu().detach()
wandb.log( loss_dict )
# console output
print('train epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:1.5f} : {:1.5f} :: {:1.5f}'.format(
epoch, batch_idx, model.len( NetMode.train),
100. * batch_idx/model.len(NetMode.train),
torch.mean( torch.tensor( grad_loss_total)), torch.mean(torch.tensor(mse_loss_total)),
torch.mean( preds[0][1]) ), flush=True)
# save model (use -2 as epoch to indicate latest, stored without epoch specification)
# self.save( -2)
# reset
loss_total = [[] for i in range(len(cf.losses)) ]
mse_loss_total = []
grad_loss_total = []
std_dev_total = [[] for i in range(len(self.fields_prediction_idx)) ]
ctr += 1
# save gradients
if cf.save_grads and cf.with_wandb and (0 == cf.par_rank) :
dir_name = './grads/id{}'.format( cf.wandb_id)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
rmsprop_ws = []
for k in range( len(self.optimizer.state_dict()['state']) ) :
rmsprop_ws.append(self.optimizer.state_dict()['state'][k]['exp_avg_sq'].mean().unsqueeze(0))
rmsprop_ws = torch.cat( rmsprop_ws)
fname = '{}/{}_epoch{}_rmsprop.npy'.format( dir_name, cf.wandb_id, epoch)
np.save( fname, rmsprop_ws.cpu().detach().numpy() )
idx = 0
for name, param in self.model.named_parameters():
if param.requires_grad :
fname = '{}/{}_epoch{}_{:05d}_{}_grad.npy'.format( dir_name, cf.wandb_id, epoch, idx,name)
np.save( fname, param.grad.cpu().detach().numpy() )
idx += 1
# clean memory
self.optimizer.zero_grad()
del batch_data, loss, loss_total, mse_loss_total, grad_loss_total, std_dev_total
###################################################
def profile( self):
model = self.model
cf = self.cf
model.mode( NetMode.train)
self.optimizer.zero_grad()
# See https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html
# for details on how to load and analyse report
# https://pytorch.org/blog/trace-analysis-for-masses/
# do for all par_ranks to avoid that they run out of sync
print( '---------------------------------')
print( 'Profiling:')
pname = './logs/profile_par_rank' + str(cf.par_rank) + '_' + cf.wandb_id + '/profile'
with torch.profiler.profile( activities=[torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA],
schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
on_trace_ready=torch.profiler.tensorboard_trace_handler(pname),
profile_memory=True, record_shapes=True, with_stack=True) as prof:
for batch_idx in range( 2 * (1+1+3) ) :
batch_data = self.model.next()
batch_data = self.prepare_batch( batch_data)
preds, _ = self.model_ddp( batch_data)
loss, mse_loss, losses = self.loss( preds, batch_idx)
self.optimizer.zero_grad()
# loss.backward()
# self.optimizer.step()
prof.step()
print( 'Profiling finished.')
print( '---------------------------------')
###################################################
def validate( self, epoch, BERT_test_strategy = 'BERT'):
cf = self.cf
BERT_strategy_train = cf.BERT_strategy
cf.BERT_strategy = BERT_test_strategy
self.model.mode( NetMode.test)
total_loss = 0.
total_losses = torch.zeros( len(self.fields_prediction_idx) )
test_len = 0
self.mode_test = True
# run in training mode
offset = 0
if -1 == epoch and 0 == cf.par_rank :
if 1 == cf.num_accs_per_task : # bug in torchinfo; fixed in v1.8.0
offset += 1
print( 'Network size:')
batch_data = self.model.next()
batch_data = self.prepare_batch( batch_data)
torchinfo.summary( self.model, input_data=[batch_data])
# run test set evaluation
with torch.no_grad() :
for it in range( self.model.len( NetMode.test) - offset) :
batch_data = self.model.next()
if cf.par_rank < cf.log_test_num_ranks :
# keep on cpu since it will otherwise clog up GPU memory
(sources, token_infos, targets, tmis, tmis_list) = batch_data[0]
# targets
if len(batch_data[1]) > 0 :
if type(batch_data[1][0][0]) is list :
targets = [batch_data[1][i][0][0] for i in range( len(batch_data[1]))]
else :
targets = batch_data[1][0]
# store on cpu
log_sources = ( [source.detach().clone().cpu() for source in sources ],
[ti.detach().clone().cpu() for ti in token_infos],
[target.detach().clone().cpu() for target in targets ],
tmis, tmis_list )
batch_data = self.prepare_batch( batch_data)
preds, atts = self.model( batch_data)
loss = torch.tensor( 0.)
ifield = 0
for pred, idx in zip( preds, self.fields_prediction_idx) :
target = self.targets[idx]
# hook for custom test loss
self.test_loss( pred, target)
# base line loss
cur_loss = self.MSELoss( pred[0], target = target ).cpu().item()
loss += cur_loss
total_losses[ifield] += cur_loss
ifield += 1
total_loss += loss
test_len += 1
# store detailed results on current test set for book keeping
if cf.par_rank < cf.log_test_num_ranks :
log_preds = [[p.detach().clone().cpu() for p in pred] for pred in preds]
self.log_validate( epoch, it, log_sources, log_preds)
if cf.attention:
self.log_attention( epoch, it, [atts,
[ti.detach().clone().cpu() for ti in token_infos]])
# average over all nodes
total_loss /= test_len * len(self.cf.fields_prediction)
total_losses /= test_len
if cf.with_ddp :
total_loss_cuda = total_loss.cuda()
total_losses_cuda = total_losses.cuda()
dist.all_reduce( total_loss_cuda, op=torch.distributed.ReduceOp.AVG )
dist.all_reduce( total_losses_cuda, op=torch.distributed.ReduceOp.AVG )
total_loss = total_loss_cuda.cpu()
total_losses = total_losses_cuda.cpu()
if 0 == cf.par_rank :
print( 'validation loss for strategy={} at epoch {} : {}'.format( BERT_test_strategy,
epoch, total_loss),
flush=True)
if cf.with_wandb and (0 == cf.par_rank) :
loss_dict = {"val. loss {}".format(BERT_test_strategy) : total_loss}
total_losses = total_losses.cpu().detach()
for i, field in enumerate(cf.fields_prediction) :
idx_name = 'val., {}, '.format(BERT_test_strategy) + field[0]
loss_dict[idx_name] = total_losses[i]
print( 'validation loss for {} : {}'.format( field[0], total_losses[i] ))
wandb.log( loss_dict)
batch_data = []
torch.cuda.empty_cache()
cf.BERT_strategy = BERT_strategy_train
self.mode_test = False
return total_loss
###################################################
def evaluate( self, data_idx = 0, log = True):
cf = self.cf
self.model.mode( NetMode.test)
log_sources = []
test_len = 0
# evaluate
loss = torch.tensor( 0.)
with torch.no_grad() :
for it in range( self.model.len( NetMode.test)) :
batch_data = self.model.next()
if cf.par_rank < cf.log_test_num_ranks :
# keep on cpu since it will otherwise clog up GPU memory
(sources, token_infos, targets, tmis, tmis_list) = batch_data[0]
# targets
if len(batch_data[1]) > 0 :
targets = []
for target_field in batch_data[1] :
targets.append(torch.cat([target_vl[0].unsqueeze(1) for target_vl in target_field],1))
# store on cpu
log_sources = ( [source.detach().clone().cpu() for source in sources ],
[ti.detach().clone().cpu() for ti in token_infos],
[target.detach().clone().cpu() for target in targets ],
tmis, tmis_list )
batch_data = self.prepare_batch( batch_data)
preds, atts = self.model( batch_data)
ifield = 0
for pred, idx in zip( preds, self.fields_prediction_idx) :
target = self.targets[idx]
cur_loss = self.MSELoss( pred[0], target = target ).cpu()
loss += cur_loss
ifield += 1
test_len += 1
# logging
if cf.par_rank < cf.log_test_num_ranks :
self.log_validate( data_idx, it, log_sources, preds)
if cf.attention:
self.log_attention( data_idx , it, [atts,
[ti.detach().clone().cpu() for ti in token_infos]])
# average over all nodes
loss /= test_len * len(self.cf.fields_prediction)
if cf.with_ddp :
loss_cuda = loss.cuda()
dist.all_reduce( loss_cuda, op=torch.distributed.ReduceOp.AVG )
loss = loss_cuda.cpu()
if 0 == cf.par_rank :
print( 'Loss {}'.format( loss))
###################################################
def test_loss( self, pred, target) :
'''Hook for custom test loss'''
pass
###################################################
def loss( self, preds, batch_idx = 0) :
# TODO: move implementations to individual files
cf = self.cf
mse_loss_total = torch.tensor( 0.,)
losses = dict(zip(cf.losses,[[] for loss in cf.losses ]))
for pred, idx in zip( preds, self.fields_prediction_idx) :
target = self.targets[idx]
mse_loss = self.MSELoss( pred[0], target = target)
mse_loss_total += mse_loss.cpu().detach()
# MSE loss
if 'mse' in self.cf.losses :
losses['mse'].append( mse_loss)
# MSE loss
if 'mse_ensemble' in self.cf.losses :
loss_en = torch.tensor( 0., device=target.device)
for en in torch.transpose( pred[2], 1, 0) :
loss_en += self.MSELoss( en, target = target)
# losses['mse_ensemble'].append( 50. * loss_en / pred[2].shape[1])
losses['mse_ensemble'].append( loss_en / pred[2].shape[1])
# Generalized cross entroy loss for continuous distributions
if 'stats' in self.cf.losses :
stats_loss = Gaussian( target, pred[0], pred[1])
diff = (stats_loss-1.)
# stats_loss = 0.01 * torch.mean( diff * diff) + torch.mean( torch.sqrt(torch.abs( pred[1])) )
stats_loss = torch.mean( diff * diff) + torch.mean( torch.sqrt( torch.abs( pred[1])) )
losses['stats'].append( stats_loss)
# Generalized cross entroy loss for continuous distributions
if 'stats_area' in self.cf.losses :
diff = torch.abs( torch.special.erf( (target - pred[0]) / (pred[1] * pred[1])) )
stats_area = 0.2 * torch.mean( diff * diff) + torch.mean( torch.sqrt(torch.abs( pred[1])) )
losses['stats_area'].append( stats_area)
# CRPS score
if 'crps' in self.cf.losses :
crps_loss = torch.mean( CRPS( target, pred[0], pred[1]))
losses['crps'].append( crps_loss)
loss = torch.tensor( 0., device=self.device_out)
for key in losses :
# print( 'LOSS : {} :: {}'.format( key, losses[key]))
for ifield, val in enumerate(losses[key]) :
loss += self.loss_weights[ifield] * val.to( self.device_out)
loss /= len(self.cf.fields_prediction) * len( self.cf.losses)
mse_loss = mse_loss_total / len(self.cf.fields_prediction)
return loss, mse_loss, losses
####################################################################################################
class Trainer_BERT( Trainer_Base) :
###################################################
def __init__( self, cf, devices) :
Trainer_Base.__init__( self, cf, devices)
self.rng_seed = cf.rng_seed
if not self.rng_seed :
self.rng_seed = int(torch.randint( 100000000, (1,)))
# TODO: generate only rngs that are needed
ll = len(cf.fields) * 8 #len(cf.vertical_levels)
if cf.BERT_fields_synced :
self.rngs = [np.random.default_rng(self.rng_seed) for _ in range(ll)]
else :
self.rngs = [np.random.default_rng(self.rng_seed+i) for i in range(ll)]
# batch preprocessing to be done in loader (mainly for performance reasons since it's
# parallelized there)
self.pre_batch = functools.partial( prepare_batch_BERT_multifield, self.cf, self.rngs,
self.cf.fields, self.cf.BERT_strategy )
###################################################
def prepare_batch( self, xin) :
'''Move data to device and some additional final preprocessing before model eval'''
cf = self.cf
devs = self.devices
# unpack loader output
# xin[0] since BERT does not have targets
(sources, token_infos, targets, fields_tokens_masked_idx,fields_tokens_masked_idx_list) = xin[0]
# network input
batch_data = [ ( sources[i].to( devs[ cf.fields[i][1][3] ], non_blocking=True),
self.tok_infos_trans(token_infos[i]).to( self.devices[0], non_blocking=True))
for i in range(len(sources)) ]
# store token number since BERT selects sub-cube (optionally)
self.num_tokens = []
for field_idx in range(len(batch_data)) :
self.num_tokens.append( list(batch_data[field_idx][0].shape[2:5]))
# target
self.targets = []
for ifield in self.fields_prediction_idx :
self.targets.append( targets[ifield].to( devs[cf.fields[ifield][1][3]], non_blocking=True ))
# idxs of masked tokens
tmi_out = [[] for _ in range(len(fields_tokens_masked_idx))]
for i,tmi in enumerate(fields_tokens_masked_idx) :
tmi_out[i] = [tmi_l.to( devs[cf.fields[i][1][3]], non_blocking=True) for tmi_l in tmi]
self.tokens_masked_idx = tmi_out
# idxs of masked tokens per batch entry
self.fields_tokens_masked_idx_list = fields_tokens_masked_idx_list
# learnable class token (cannot be done in the data loader since this is running in parallel)
if cf.learnable_mask :
for ifield, (source, _) in enumerate(batch_data) :
source = torch.flatten( torch.flatten( torch.flatten( source, 1, 4), 2, 4), 0, 1)
assert len(cf.fields[ifield][2]) == 1
tmidx = self.tokens_masked_idx[ifield][0]
source[ tmidx ] = self.model.net.masks[ifield].to( source.device)
return batch_data
###################################################
def encoder_to_decoder( self, embeds_layers) :
return ([embeds_layers[i][-1] for i in range(len(embeds_layers))] , embeds_layers )
###################################################
def decoder_to_tail( self, idx_pred, pred) :
'''Positional encoding of masked tokens for tail network evaluation'''
field_idx = self.fields_prediction_idx[idx_pred]
dev = self.devices[ self.cf.fields[field_idx][1][3] ]
target_idx = self.tokens_masked_idx[field_idx]
assert len(target_idx) > 0, 'no masked tokens but target variable'
# select "fixed" masked tokens for loss computation
# recover vertical level dimension
num_tokens = self.num_tokens[field_idx]
num_vlevels = len(self.cf.fields[field_idx][2])
# flatten token dimensions: remove space-time separation
pred = torch.flatten( pred, 2, 3).to( dev)
# extract masked token level by level
pred_masked = []
for lidx, level in enumerate(self.cf.fields[field_idx][2]) :
# select masked tokens, flattened along batch dimension for easier indexing and processing
pred_l = torch.flatten( pred[:,lidx], 0, 1)
pred_masked_l = pred_l[ target_idx[lidx] ]
target_idx_l = target_idx[lidx]
# add positional encoding of masked tokens
# # TODO: do we need the positional encoding?
# compute space time indices of all tokens
target_idxs_v = level * torch.ones( target_idx_l.shape[0], device=dev)
num_tokens_space = num_tokens[1] * num_tokens[2]
# remove offset introduced by linearization
target_idx_l = torch.remainder( target_idx_l, np.prod(num_tokens))
target_idxs_t = (target_idx_l / num_tokens_space).int()
temp = torch.remainder( target_idx_l, num_tokens_space)
target_idxs_x = (temp / num_tokens[1]).int()
target_idxs_y = torch.remainder( temp, num_tokens[2])
# apply harmonic positional encoding
dim_embed = pred.shape[-1]
pe = torch.zeros( pred_masked_l.shape[0], dim_embed, device=dev)
xs = (2. * np.pi / dim_embed) * torch.arange( 0, dim_embed, 2, device=dev)
pe[:, 0::2] = 0.5 * torch.sin( torch.outer( 8 * target_idxs_x, xs) ) \
+ torch.sin( torch.outer( target_idxs_t, xs) )
pe[:, 1::2] = 0.5 * torch.cos( torch.outer( 8 * target_idxs_y, xs) ) \
+ torch.cos( torch.outer( target_idxs_v, xs) )
# TODO: with or without final positional encoding?
# pred_masked.append( pred_masked_l + pe)
pred_masked.append( pred_masked_l)
# flatten along level dimension, for loss evaluation we effectively have level, batch, ...
# as ordering of dimensions
pred_masked = torch.cat( pred_masked, 0)
return pred_masked
###################################################
def log_validate( self, epoch, bidx, log_sources, log_preds) :
'''Hook for logging: output associated with concrete training strategy.'''
if not hasattr( self.cf, 'wandb_id') :
return
if 'forecast' == self.cf.BERT_strategy :
self.log_validate_forecast( epoch, bidx, log_sources, log_preds)
elif 'BERT' == self.cf.BERT_strategy :
self.log_validate_BERT( epoch, bidx, log_sources, log_preds)
else :
assert False
###################################################
def log_validate_forecast( self, epoch, batch_idx, log_sources, log_preds) :
'''Logging for BERT_strategy=forecast.'''
cf = self.cf
detok = utils.detokenize
# TODO, TODO: for 6h forecast we need to iterate over predicted token slices
# save source: remains identical so just save ones
(sources, token_infos, targets, _, _) = log_sources
sources_out, targets_out, preds_out, ensembles_out = [ ], [ ], [ ], [ ]
# reconstruct geo-coords (identical for all fields)
forecast_num_tokens = 1
if hasattr( cf, 'forecast_num_tokens') :
forecast_num_tokens = cf.forecast_num_tokens
num_tokens = cf.fields[0][3]
token_size = cf.fields[0][4]
lat_d_h, lon_d_h = int(np.floor(token_size[1]/2.)), int(np.floor(token_size[2]/2.))
lats, lons = [ ], [ ]
for tinfo in token_infos[0] :
lat_min, lat_max = tinfo[0][4], tinfo[ num_tokens[1]*num_tokens[2]-1 ][4]
lon_min, lon_max = tinfo[0][5], tinfo[ num_tokens[1]*num_tokens[2]-1 ][5]
res = tinfo[0][-1]
lat = torch.arange( lat_min - lat_d_h*res, lat_max + lat_d_h*res + 0.001, res)
if lon_max < lon_min :
lon = torch.arange( lon_min - lon_d_h*res, 360. + lon_max + lon_d_h*res + 0.001, res)
else :
lon = torch.arange( lon_min - lon_d_h*res, lon_max + lon_d_h*res + 0.001, res)
lats.append( lat.numpy())
lons.append( torch.remainder( lon, 360.).numpy())
# check that last token (bottom right corner) has the expected coords
# assert np.allclose( )
# extract dates for each token entry, constant for each batch and field
dates_t = []
for b_token_infos in token_infos[0] :
dates_t.append(utils.token_info_to_time(b_token_infos[0])-pd.Timedelta(hours=token_size[0]-1))
# TODO: check that last token matches first one
# process input fields
for fidx, field_info in enumerate(cf.fields) :
# reshape from tokens to contiguous physical field
num_levels = len(field_info[2])
source = detok( sources[fidx].cpu().detach().numpy())
# recover tokenized shape
target = detok( targets[fidx].cpu().detach().numpy().reshape( [ -1, num_levels,
forecast_num_tokens, *field_info[3][1:], *field_info[4] ]))
# TODO: check that geo-coords match to general ones that have been pre-determined
for bidx in range(token_infos[fidx].shape[0]) :
for vidx, _ in enumerate(field_info[2]) :
denormalize = self.model.normalizer( fidx, vidx).denormalize
date, coords = dates_t[bidx], [lats[bidx], lons[bidx]]
source[bidx,vidx] = denormalize( date.year, date.month, source[bidx,vidx], coords)
target[bidx,vidx] = denormalize( date.year, date.month, target[bidx,vidx], coords)
# append
sources_out.append( [field_info[0], source])
targets_out.append( [field_info[0], target])
# process predicted fields
for fidx, fn in enumerate(cf.fields_prediction) :
#
field_info = cf.fields[ self.fields_prediction_idx[fidx] ]
num_levels = len(field_info[2])
# predictions
pred = log_preds[fidx][0].cpu().detach().numpy()
pred = detok( pred.reshape( [ -1, num_levels,
forecast_num_tokens, *field_info[3][1:], *field_info[4] ]))
# ensemble
ensemble = log_preds[fidx][2].cpu().detach().numpy()
ensemble = detok( ensemble.reshape( [ -1, cf.net_tail_num_nets, num_levels,
forecast_num_tokens, *field_info[3][1:], *field_info[4] ]) )
# denormalize
for bidx in range(token_infos[fidx].shape[0]) :
for vidx, vl in enumerate(field_info[2]) :
denormalize = self.model.normalizer( self.fields_prediction_idx[fidx], vidx).denormalize
date, coords = dates_t[bidx], [lats[bidx], lons[bidx]]
pred[bidx,vidx] = denormalize( date.year, date.month, pred[bidx,vidx], coords)
ensemble[bidx,:,vidx] = denormalize(date.year, date.month, ensemble[bidx,:,vidx], coords)
# append
preds_out.append( [fn[0], pred])
ensembles_out.append( [fn[0], ensemble])
# generate time range
dates_sources, dates_targets = [ ], [ ]
for bidx in range( source.shape[0]) :
r = pd.date_range( start=dates_t[bidx], periods=source.shape[2], freq='h')
dates_sources.append( r.to_pydatetime().astype( 'datetime64[s]') )
dates_targets.append( dates_sources[-1][ -forecast_num_tokens*token_size[0] : ] )
levels = np.array(cf.fields[0][2])
lats = [90.-lat for lat in lats]
write_forecast( cf.wandb_id, epoch, batch_idx,
levels, sources_out, [dates_sources, lats, lons],
targets_out, [dates_targets, lats, lons],
preds_out, ensembles_out )
###################################################
def log_validate_BERT( self, epoch, batch_idx, log_sources, log_preds) :
'''Logging for BERT_strategy=BERT.'''
cf = self.cf
detok = utils.detokenize
# save source: remains identical so just save ones
(sources, token_infos, targets, tokens_masked_idx, tokens_masked_idx_list) = log_sources
sources_out, targets_out, preds_out, ensembles_out = [ ], [ ], [ ], [ ]
sources_dates_out, sources_lats_out, sources_lons_out = [ ], [ ], [ ]
targets_dates_out, targets_lats_out, targets_lons_out = [ ], [ ], [ ]
for fidx, field_info in enumerate(cf.fields) :
# reconstruct coordinates
is_predicted = fidx in self.fields_prediction_idx
num_levels = len(field_info[2])
num_tokens = field_info[3]
token_size = field_info[4]
lat_d_h, lon_d_h = int(np.floor(token_size[1]/2.)), int(np.floor(token_size[2]/2.))
tinfos = token_infos[fidx].reshape( [-1, num_levels, *num_tokens, cf.size_token_info])
res = tinfos[0,0,0,0,0][-1].item()
batch_size = tinfos.shape[0]
sources_b = detok( sources[fidx].numpy())
if is_predicted :
# split according to levels
lens_levels = [t.shape[0] for t in tokens_masked_idx[fidx]]
targets_b = torch.split( targets[fidx], lens_levels)
preds_mu_b = torch.split( log_preds[fidx][0], lens_levels)
preds_ens_b = torch.split( log_preds[fidx][2], lens_levels)
# split according to batch
lens_batches = [ [bv.shape[0] for bv in b] for b in tokens_masked_idx_list[fidx] ]
targets_b = [torch.split( targets_b[vidx], lens) for vidx,lens in enumerate(lens_batches)]
preds_mu_b = [torch.split(preds_mu_b[vidx], lens) for vidx,lens in enumerate(lens_batches)]
preds_ens_b =[torch.split(preds_ens_b[vidx],lens) for vidx,lens in enumerate(lens_batches)]
# recover token shape
targets_b = [[targets_b[vidx][bidx].reshape([-1, *token_size])
for bidx in range(batch_size)]
for vidx in range(num_levels)]
preds_mu_b = [[preds_mu_b[vidx][bidx].reshape([-1, *token_size])
for bidx in range(batch_size)]
for vidx in range(num_levels)]
preds_ens_b = [[preds_ens_b[vidx][bidx].reshape( [-1, cf.net_tail_num_nets, *token_size])
for bidx in range(batch_size)]
for vidx in range(num_levels)]
# for all batch items
coords_b = []
for bidx, tinfo in enumerate(tinfos) :
# use first vertical levels since a column is considered
lats = np.arange(tinfo[0,0,0,0,4]-lat_d_h*res, tinfo[0,0,-1,0,4]+lat_d_h*res+0.001,res)
if tinfo[0,0,0,-1,5] < tinfo[0,0,0,0,5] :
lons = np.remainder( np.arange( tinfo[0,0,0,0,5] - lon_d_h*res,
360. + tinfo[0,0,0,-1,5] + lon_d_h*res + 0.001, res), 360.)
else :
lons = np.arange(tinfo[0,0,0,0,5]-lon_d_h*res, tinfo[0,0,0,-1,5]+lon_d_h*res+0.001,res)
lons = np.remainder( lons, 360.)
# time stamp in token_infos is at start time so needs to be advanced by token_size[0]-1
s = utils.token_info_to_time( tinfo[0,0,0,0,:3] ) - pd.Timedelta(hours=token_size[0]-1)
e = utils.token_info_to_time( tinfo[0,-1,0,0,:3] )
dates = pd.date_range( start=s, end=e, freq='h')
# target etc are aliasing targets_b which simplifies bookkeeping below
if is_predicted :
target = [targets_b[vidx][bidx] for vidx in range(num_levels)]
pred_mu = [preds_mu_b[vidx][bidx] for vidx in range(num_levels)]
pred_ens = [preds_ens_b[vidx][bidx] for vidx in range(num_levels)]
dates_masked_l, lats_masked_l, lons_masked_l = [], [], []
for vidx, _ in enumerate(field_info[2]) :
normalizer = self.model.normalizer( fidx, vidx)
y, m = dates[0].year, dates[0].month
sources_b[bidx,vidx] = normalizer.denormalize( y, m, sources_b[bidx,vidx], [lats, lons])
if is_predicted :
# TODO: make sure normalizer_local / normalizer_global is used in data_loader
idx = tokens_masked_idx_list[fidx][vidx][bidx]
tinfo_masked = tinfos[bidx,vidx].flatten( 0,2)
tinfo_masked = tinfo_masked[idx]
lad, lod = lat_d_h*res, lon_d_h*res
lats_masked, lons_masked, dates_masked = [], [], []
for t in tinfo_masked :
lats_masked.append( np.expand_dims( np.arange(t[4]-lad, t[4]+lad+0.001,res), 0))
lons_masked.append( np.expand_dims( np.arange(t[5]-lod, t[5]+lod+0.001,res), 0))
r = pd.date_range( start=utils.token_info_to_time(t), periods=token_size[0], freq='h')
dates_masked.append( np.expand_dims(r.to_pydatetime().astype( 'datetime64[s]'), 0) )
lats_masked = np.concatenate( lats_masked, 0)
lons_masked = np.remainder( np.concatenate( lons_masked, 0), 360.)
dates_masked = np.concatenate( dates_masked, 0)
for ii,(t,p,e,la,lo) in enumerate(zip( target[vidx], pred_mu[vidx], pred_ens[vidx],
lats_masked, lons_masked)) :
targets_b[vidx][bidx][ii] = normalizer.denormalize( y, m, t, [la, lo])
preds_mu_b[vidx][bidx][ii] = normalizer.denormalize( y, m, p, [la, lo])
preds_ens_b[vidx][bidx][ii] = normalizer.denormalize( y, m, e, [la, lo])
dates_masked_l += [ dates_masked ]
lats_masked_l += [ [90.-lat for lat in lats_masked] ]
lons_masked_l += [ lons_masked ]
dates = dates.to_pydatetime().astype( 'datetime64[s]')
coords_b += [ [dates, 90.-lats, lons, dates_masked_l, lats_masked_l, lons_masked_l] ]
fn = field_info[0]
sources_out.append( [fn, sources_b])
if is_predicted :
targets_out.append([fn, [[t.numpy(force=True) for t in t_v] for t_v in targets_b]])
preds_out.append( [fn, [[p.numpy(force=True) for p in p_v] for p_v in preds_mu_b]])
ensembles_out.append( [fn, [[p.numpy(force=True) for p in p_v] for p_v in preds_ens_b]])
else :
targets_out.append( [fn, []])
preds_out.append( [fn, []])
ensembles_out.append( [fn, []])
sources_dates_out.append( [c[0] for c in coords_b])
sources_lats_out.append( [c[1] for c in coords_b])
sources_lons_out.append( [c[2] for c in coords_b])
if is_predicted :
targets_dates_out.append( [c[3] for c in coords_b])
targets_lats_out.append( [c[4] for c in coords_b])
targets_lons_out.append( [c[5] for c in coords_b])
else :
targets_dates_out.append( [ ])
targets_lats_out.append( [ ])
targets_lons_out.append( [ ])
levels = [[np.array(l) for l in field[2]] for field in cf.fields]
write_BERT( cf.wandb_id, epoch, batch_idx,
levels, sources_out,
[sources_dates_out, sources_lats_out, sources_lons_out],
targets_out, [targets_dates_out, targets_lats_out, targets_lons_out],
preds_out, ensembles_out )
def log_attention( self, epoch, bidx, log) :
'''Hook for logging: output attention maps.'''
cf = self.cf
attention, token_infos = log
attn_dates_out, attn_lats_out, attn_lons_out = [ ], [ ], [ ]
attn_out = []
for fidx, field_info in enumerate(cf.fields) :
# reconstruct coordinates
is_predicted = fidx in self.fields_prediction_idx
num_levels = len(field_info[2])
num_tokens = field_info[3]
token_size = field_info[4]
lat_d_h, lon_d_h = int(np.floor(token_size[1]/2.)), int(np.floor(token_size[2]/2.))
tinfos = token_infos[fidx].reshape( [-1, num_levels, *num_tokens, cf.size_token_info])
coords_b = []
for tinfo in tinfos :
# use first vertical levels since a column is considered
res = tinfo[0,0,0,0,-1]
lats = np.arange(tinfo[0,0,0,0,4]-lat_d_h*res, tinfo[0,0,-1,0,4]+lat_d_h*res+0.001,res*token_size[1])
if tinfo[0,0,0,-1,5] < tinfo[0,0,0,0,5] :
lons = np.remainder( np.arange( tinfo[0,0,0,0,5] - lon_d_h*res,
360. + tinfo[0,0,0,-1,5] + lon_d_h*res + 0.001, res*token_size[2]), 360.)
else :
lons = np.arange(tinfo[0,0,0,0,5]-lon_d_h*res, tinfo[0,0,0,-1,5]+lon_d_h*res+0.001,res*token_size[2])
lons = np.remainder( lons, 360.)
dates = np.array([(utils.token_info_to_time(tinfo[0,t,0,0,:3])) for t in range(tinfo.shape[1])], dtype='datetime64[s]')
coords_b += [ [dates, lats, lons] ]
if is_predicted:
attn_out.append([field_info[0], attention[fidx]])
attn_dates_out.append([c[0] for c in coords_b])
attn_lats_out.append( [c[1] for c in coords_b])
attn_lons_out.append( [c[2] for c in coords_b])
else:
attn_dates_out.append( [] )
attn_lats_out.append( [] )
attn_lons_out.append( [] )
levels = [[np.array(l) for l in field[2]] for field in cf.fields] | write_attention(cf.wandb_id, epoch, | 12 | 2023-10-09 19:42:46+00:00 | 24k |
MachinePerceptionLab/Attentive_DFPrior | src/DF_Prior.py | [
{
"identifier": "config",
"path": "src/config.py",
"snippet": "def load_config(path, default_path=None):\ndef update_recursive(dict1, dict2):\ndef get_model(cfg):"
},
{
"identifier": "Mapper",
"path": "src/Mapper.py",
"snippet": "class Mapper(object):\n \"\"\"\n Mapper thread. \n\n... | import os
import time
import numpy as np
import torch
import torch.multiprocessing
import torch.multiprocessing as mp
from src import config
from src.Mapper import Mapper
from src.Tracker import Tracker
from src.utils.datasets import get_dataset
from src.utils.Logger import Logger
from src.utils.Mesher import Mesher
from src.utils.Renderer import Renderer | 20,617 |
# import src.fusion as fusion
# import open3d as o3d
torch.multiprocessing.set_sharing_strategy('file_system')
class DF_Prior():
"""
DF_Prior main class.
Mainly allocate shared resources, and dispatch mapping and tracking process.
"""
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
self.occupancy = cfg['occupancy']
self.low_gpu_mem = cfg['low_gpu_mem']
self.verbose = cfg['verbose']
self.dataset = cfg['dataset']
if args.output is None:
self.output = cfg['data']['output']
else:
self.output = args.output
self.ckptsdir = os.path.join(self.output, 'ckpts')
os.makedirs(self.output, exist_ok=True)
os.makedirs(self.ckptsdir, exist_ok=True)
os.makedirs(f'{self.output}/mesh', exist_ok=True)
self.H, self.W, self.fx, self.fy, self.cx, self.cy = cfg['cam']['H'], cfg['cam'][
'W'], cfg['cam']['fx'], cfg['cam']['fy'], cfg['cam']['cx'], cfg['cam']['cy']
self.update_cam()
model = config.get_model(cfg)
self.shared_decoders = model
self.scale = cfg['scale']
self.load_bound(cfg)
self.load_pretrain(cfg)
self.grid_init(cfg)
# need to use spawn
try:
mp.set_start_method('spawn', force=True)
except RuntimeError:
pass
self.frame_reader = get_dataset(cfg, args, self.scale)
self.n_img = len(self.frame_reader)
self.estimate_c2w_list = torch.zeros((self.n_img, 4, 4))
self.estimate_c2w_list.share_memory_()
dataset = self.cfg['data']['dataset']
scene_id = self.cfg['data']['id']
self.scene_id = scene_id
print(scene_id)
# load tsdf grid
if dataset == 'scannet':
self.tsdf_volume_shared = torch.load(f'scannet_tsdf_volume/scene{scene_id}_tsdf_volume.pt')
elif dataset == 'replica':
self.tsdf_volume_shared = torch.load(f'replica_tsdf_volume/{scene_id}_tsdf_volume.pt')
self.tsdf_volume_shared = self.tsdf_volume_shared.to(self.cfg['mapping']['device'])
self.tsdf_volume_shared.share_memory_()
# load tsdf grid bound
if dataset == 'scannet':
self.tsdf_bnds = torch.load(f'scannet_tsdf_volume/scene{scene_id}_bounds.pt')
elif dataset == 'replica':
self.tsdf_bnds = torch.load(f'replica_tsdf_volume/{scene_id}_bounds.pt')
self.tsdf_bnds = torch.tensor(self.tsdf_bnds).to(self.cfg['mapping']['device'])
self.tsdf_bnds.share_memory_()
self.vol_bnds = self.tsdf_bnds
self.vol_bnds.share_memory_()
self.gt_c2w_list = torch.zeros((self.n_img, 4, 4))
self.gt_c2w_list.share_memory_()
self.idx = torch.zeros((1)).int()
self.idx.share_memory_()
self.mapping_first_frame = torch.zeros((1)).int()
self.mapping_first_frame.share_memory_()
# the id of the newest frame Mapper is processing
self.mapping_idx = torch.zeros((1)).int()
self.mapping_idx.share_memory_()
self.mapping_cnt = torch.zeros((1)).int() # counter for mapping
self.mapping_cnt.share_memory_()
for key, val in self.shared_c.items():
val = val.to(self.cfg['mapping']['device'])
val.share_memory_()
self.shared_c[key] = val
self.shared_decoders = self.shared_decoders.to(
self.cfg['mapping']['device'])
self.shared_decoders.share_memory()
self.renderer = Renderer(cfg, args, self)
|
# import src.fusion as fusion
# import open3d as o3d
torch.multiprocessing.set_sharing_strategy('file_system')
class DF_Prior():
"""
DF_Prior main class.
Mainly allocate shared resources, and dispatch mapping and tracking process.
"""
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
self.occupancy = cfg['occupancy']
self.low_gpu_mem = cfg['low_gpu_mem']
self.verbose = cfg['verbose']
self.dataset = cfg['dataset']
if args.output is None:
self.output = cfg['data']['output']
else:
self.output = args.output
self.ckptsdir = os.path.join(self.output, 'ckpts')
os.makedirs(self.output, exist_ok=True)
os.makedirs(self.ckptsdir, exist_ok=True)
os.makedirs(f'{self.output}/mesh', exist_ok=True)
self.H, self.W, self.fx, self.fy, self.cx, self.cy = cfg['cam']['H'], cfg['cam'][
'W'], cfg['cam']['fx'], cfg['cam']['fy'], cfg['cam']['cx'], cfg['cam']['cy']
self.update_cam()
model = config.get_model(cfg)
self.shared_decoders = model
self.scale = cfg['scale']
self.load_bound(cfg)
self.load_pretrain(cfg)
self.grid_init(cfg)
# need to use spawn
try:
mp.set_start_method('spawn', force=True)
except RuntimeError:
pass
self.frame_reader = get_dataset(cfg, args, self.scale)
self.n_img = len(self.frame_reader)
self.estimate_c2w_list = torch.zeros((self.n_img, 4, 4))
self.estimate_c2w_list.share_memory_()
dataset = self.cfg['data']['dataset']
scene_id = self.cfg['data']['id']
self.scene_id = scene_id
print(scene_id)
# load tsdf grid
if dataset == 'scannet':
self.tsdf_volume_shared = torch.load(f'scannet_tsdf_volume/scene{scene_id}_tsdf_volume.pt')
elif dataset == 'replica':
self.tsdf_volume_shared = torch.load(f'replica_tsdf_volume/{scene_id}_tsdf_volume.pt')
self.tsdf_volume_shared = self.tsdf_volume_shared.to(self.cfg['mapping']['device'])
self.tsdf_volume_shared.share_memory_()
# load tsdf grid bound
if dataset == 'scannet':
self.tsdf_bnds = torch.load(f'scannet_tsdf_volume/scene{scene_id}_bounds.pt')
elif dataset == 'replica':
self.tsdf_bnds = torch.load(f'replica_tsdf_volume/{scene_id}_bounds.pt')
self.tsdf_bnds = torch.tensor(self.tsdf_bnds).to(self.cfg['mapping']['device'])
self.tsdf_bnds.share_memory_()
self.vol_bnds = self.tsdf_bnds
self.vol_bnds.share_memory_()
self.gt_c2w_list = torch.zeros((self.n_img, 4, 4))
self.gt_c2w_list.share_memory_()
self.idx = torch.zeros((1)).int()
self.idx.share_memory_()
self.mapping_first_frame = torch.zeros((1)).int()
self.mapping_first_frame.share_memory_()
# the id of the newest frame Mapper is processing
self.mapping_idx = torch.zeros((1)).int()
self.mapping_idx.share_memory_()
self.mapping_cnt = torch.zeros((1)).int() # counter for mapping
self.mapping_cnt.share_memory_()
for key, val in self.shared_c.items():
val = val.to(self.cfg['mapping']['device'])
val.share_memory_()
self.shared_c[key] = val
self.shared_decoders = self.shared_decoders.to(
self.cfg['mapping']['device'])
self.shared_decoders.share_memory()
self.renderer = Renderer(cfg, args, self) | self.mesher = Mesher(cfg, args, self) | 5 | 2023-10-13 00:49:57+00:00 | 24k |
fury-05/BookRecomendApp | .pythonlibs/lib/python3.10/site-packages/sklearn/metrics/_ranking.py | [
{
"identifier": "UndefinedMetricWarning",
"path": ".pythonlibs/lib/python3.10/site-packages/sklearn/exceptions.py",
"snippet": "class UndefinedMetricWarning(UserWarning):\n \"\"\"Warning used when the metric is invalid\n\n .. versionchanged:: 0.18\n Moved from sklearn.base.\n \"\"\""
},... | import warnings
import numpy as np
from functools import partial
from numbers import Integral, Real
from scipy.sparse import csr_matrix, issparse
from scipy.stats import rankdata
from ..exceptions import UndefinedMetricWarning
from ..preprocessing import label_binarize
from ..utils import (
assert_all_finite,
check_array,
check_consistent_length,
column_or_1d,
)
from ..utils._encode import _encode, _unique
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.extmath import stable_cumsum
from ..utils.fixes import trapezoid
from ..utils.multiclass import type_of_target
from ..utils.sparsefuncs import count_nonzero
from ..utils.validation import _check_pos_label_consistency, _check_sample_weight
from ._base import _average_binary_score, _average_multiclass_ovo_score | 17,755 | "Target scores need to be probabilities for multiclass "
"roc_auc, i.e. they should sum up to 1.0 over classes"
)
# validation for multiclass parameter specifications
average_options = ("macro", "weighted", None)
if multi_class == "ovr":
average_options = ("micro",) + average_options
if average not in average_options:
raise ValueError(
"average must be one of {0} for multiclass problems".format(average_options)
)
multiclass_options = ("ovo", "ovr")
if multi_class not in multiclass_options:
raise ValueError(
"multi_class='{0}' is not supported "
"for multiclass ROC AUC, multi_class must be "
"in {1}".format(multi_class, multiclass_options)
)
if average is None and multi_class == "ovo":
raise NotImplementedError(
"average=None is not implemented for multi_class='ovo'."
)
if labels is not None:
labels = column_or_1d(labels)
classes = _unique(labels)
if len(classes) != len(labels):
raise ValueError("Parameter 'labels' must be unique")
if not np.array_equal(classes, labels):
raise ValueError("Parameter 'labels' must be ordered")
if len(classes) != y_score.shape[1]:
raise ValueError(
"Number of given labels, {0}, not equal to the number "
"of columns in 'y_score', {1}".format(len(classes), y_score.shape[1])
)
if len(np.setdiff1d(y_true, classes)):
raise ValueError("'y_true' contains labels not in parameter 'labels'")
else:
classes = _unique(y_true)
if len(classes) != y_score.shape[1]:
raise ValueError(
"Number of classes in y_true not equal to the number of "
"columns in 'y_score'"
)
if multi_class == "ovo":
if sample_weight is not None:
raise ValueError(
"sample_weight is not supported "
"for multiclass one-vs-one ROC AUC, "
"'sample_weight' must be None in this case."
)
y_true_encoded = _encode(y_true, uniques=classes)
# Hand & Till (2001) implementation (ovo)
return _average_multiclass_ovo_score(
_binary_roc_auc_score, y_true_encoded, y_score, average=average
)
else:
# ovr is same as multi-label
y_true_multilabel = label_binarize(y_true, classes=classes)
return _average_binary_score(
_binary_roc_auc_score,
y_true_multilabel,
y_score,
average,
sample_weight=sample_weight,
)
def _binary_clf_curve(y_true, y_score, pos_label=None, sample_weight=None):
"""Calculate true and false positives per binary classification threshold.
Parameters
----------
y_true : ndarray of shape (n_samples,)
True targets of binary classification.
y_score : ndarray of shape (n_samples,)
Estimated probabilities or output of a decision function.
pos_label : int, float, bool or str, default=None
The label of the positive class.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
fps : ndarray of shape (n_thresholds,)
A count of false positives, at index i being the number of negative
samples assigned a score >= thresholds[i]. The total number of
negative samples is equal to fps[-1] (thus true negatives are given by
fps[-1] - fps).
tps : ndarray of shape (n_thresholds,)
An increasing count of true positives, at index i being the number
of positive samples assigned a score >= thresholds[i]. The total
number of positive samples is equal to tps[-1] (thus false negatives
are given by tps[-1] - tps).
thresholds : ndarray of shape (n_thresholds,)
Decreasing score values.
"""
# Check to make sure y_true is valid
y_type = type_of_target(y_true, input_name="y_true")
if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
raise ValueError("{0} format is not supported".format(y_type))
check_consistent_length(y_true, y_score, sample_weight)
y_true = column_or_1d(y_true)
y_score = column_or_1d(y_score)
assert_all_finite(y_true)
assert_all_finite(y_score)
# Filter out zero-weighted samples, as they should not impact the result
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
| """Metrics to assess performance on classification task given scores.
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better.
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck
# Joel Nothman <joel.nothman@gmail.com>
# Noel Dawe <noel@dawe.me>
# Michal Karbownik <michakarbownik@gmail.com>
# License: BSD 3 clause
@validate_params(
{"x": ["array-like"], "y": ["array-like"]},
prefer_skip_nested_validation=True,
)
def auc(x, y):
"""Compute Area Under the Curve (AUC) using the trapezoidal rule.
This is a general function, given points on a curve. For computing the
area under the ROC-curve, see :func:`roc_auc_score`. For an alternative
way to summarize a precision-recall curve, see
:func:`average_precision_score`.
Parameters
----------
x : array-like of shape (n,)
X coordinates. These must be either monotonic increasing or monotonic
decreasing.
y : array-like of shape (n,)
Y coordinates.
Returns
-------
auc : float
Area Under the Curve.
See Also
--------
roc_auc_score : Compute the area under the ROC curve.
average_precision_score : Compute average precision from prediction scores.
precision_recall_curve : Compute precision-recall pairs for different
probability thresholds.
Examples
--------
>>> import numpy as np
>>> from sklearn import metrics
>>> y = np.array([1, 1, 2, 2])
>>> pred = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
>>> metrics.auc(fpr, tpr)
0.75
"""
check_consistent_length(x, y)
x = column_or_1d(x)
y = column_or_1d(y)
if x.shape[0] < 2:
raise ValueError(
"At least 2 points are needed to compute area under curve, but x.shape = %s"
% x.shape
)
direction = 1
dx = np.diff(x)
if np.any(dx < 0):
if np.all(dx <= 0):
direction = -1
else:
raise ValueError("x is neither increasing nor decreasing : {}.".format(x))
area = direction * trapezoid(y, x)
if isinstance(area, np.memmap):
# Reductions such as .sum used internally in trapezoid do not return a
# scalar by default for numpy.memmap instances contrary to
# regular numpy.ndarray instances.
area = area.dtype.type(area)
return area
@validate_params(
{
"y_true": ["array-like"],
"y_score": ["array-like"],
"average": [StrOptions({"micro", "samples", "weighted", "macro"}), None],
"pos_label": [Real, str, "boolean"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def average_precision_score(
y_true, y_score, *, average="macro", pos_label=1, sample_weight=None
):
"""Compute average precision (AP) from prediction scores.
AP summarizes a precision-recall curve as the weighted mean of precisions
achieved at each threshold, with the increase in recall from the previous
threshold used as the weight:
.. math::
\\text{AP} = \\sum_n (R_n - R_{n-1}) P_n
where :math:`P_n` and :math:`R_n` are the precision and recall at the nth
threshold [1]_. This implementation is not interpolated and is different
from computing the area under the precision-recall curve with the
trapezoidal rule, which uses linear interpolation and can be too
optimistic.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
True binary labels or binary label indicators.
y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by :term:`decision_function` on some classifiers).
average : {'micro', 'samples', 'weighted', 'macro'} or None, \
default='macro'
If ``None``, the scores for each class are returned. Otherwise,
this determines the type of averaging performed on the data:
``'micro'``:
Calculate metrics globally by considering each element of the label
indicator matrix as a label.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average, weighted
by support (the number of true instances for each label).
``'samples'``:
Calculate metrics for each instance, and find their average.
Will be ignored when ``y_true`` is binary.
pos_label : int, float, bool or str, default=1
The label of the positive class. Only applied to binary ``y_true``.
For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
average_precision : float
Average precision score.
See Also
--------
roc_auc_score : Compute the area under the ROC curve.
precision_recall_curve : Compute precision-recall pairs for different
probability thresholds.
Notes
-----
.. versionchanged:: 0.19
Instead of linearly interpolating between operating points, precisions
are weighted by the change in recall since the last operating point.
References
----------
.. [1] `Wikipedia entry for the Average precision
<https://en.wikipedia.org/w/index.php?title=Information_retrieval&
oldid=793358396#Average_precision>`_
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import average_precision_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> average_precision_score(y_true, y_scores)
0.83...
>>> y_true = np.array([0, 0, 1, 1, 2, 2])
>>> y_scores = np.array([
... [0.7, 0.2, 0.1],
... [0.4, 0.3, 0.3],
... [0.1, 0.8, 0.1],
... [0.2, 0.3, 0.5],
... [0.4, 0.4, 0.2],
... [0.1, 0.2, 0.7],
... ])
>>> average_precision_score(y_true, y_scores)
0.77...
"""
def _binary_uninterpolated_average_precision(
y_true, y_score, pos_label=1, sample_weight=None
):
precision, recall, _ = precision_recall_curve(
y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
)
# Return the step function integral
# The following works because the last entry of precision is
# guaranteed to be 1, as returned by precision_recall_curve
return -np.sum(np.diff(recall) * np.array(precision)[:-1])
y_type = type_of_target(y_true, input_name="y_true")
# Convert to Python primitive type to avoid NumPy type / Python str
# comparison. See https://github.com/numpy/numpy/issues/6784
present_labels = np.unique(y_true).tolist()
if y_type == "binary":
if len(present_labels) == 2 and pos_label not in present_labels:
raise ValueError(
f"pos_label={pos_label} is not a valid label. It should be "
f"one of {present_labels}"
)
elif y_type == "multilabel-indicator" and pos_label != 1:
raise ValueError(
"Parameter pos_label is fixed to 1 for multilabel-indicator y_true. "
"Do not set pos_label or set pos_label to 1."
)
elif y_type == "multiclass":
if pos_label != 1:
raise ValueError(
"Parameter pos_label is fixed to 1 for multiclass y_true. "
"Do not set pos_label or set pos_label to 1."
)
y_true = label_binarize(y_true, classes=present_labels)
average_precision = partial(
_binary_uninterpolated_average_precision, pos_label=pos_label
)
return _average_binary_score(
average_precision, y_true, y_score, average, sample_weight=sample_weight
)
@validate_params(
{
"y_true": ["array-like"],
"y_score": ["array-like"],
"pos_label": [Real, str, "boolean", None],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def det_curve(y_true, y_score, pos_label=None, sample_weight=None):
"""Compute error rates for different probability thresholds.
.. note::
This metric is used for evaluation of ranking and error tradeoffs of
a binary classification task.
Read more in the :ref:`User Guide <det_curve>`.
.. versionadded:: 0.24
Parameters
----------
y_true : ndarray of shape (n_samples,)
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
pos_label should be explicitly given.
y_score : ndarray of shape of (n_samples,)
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by "decision_function" on some classifiers).
pos_label : int, float, bool or str, default=None
The label of the positive class.
When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
``pos_label`` is set to 1, otherwise an error will be raised.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
fpr : ndarray of shape (n_thresholds,)
False positive rate (FPR) such that element i is the false positive
rate of predictions with score >= thresholds[i]. This is occasionally
referred to as false acceptance probability or fall-out.
fnr : ndarray of shape (n_thresholds,)
False negative rate (FNR) such that element i is the false negative
rate of predictions with score >= thresholds[i]. This is occasionally
referred to as false rejection or miss rate.
thresholds : ndarray of shape (n_thresholds,)
Decreasing score values.
See Also
--------
DetCurveDisplay.from_estimator : Plot DET curve given an estimator and
some data.
DetCurveDisplay.from_predictions : Plot DET curve given the true and
predicted labels.
DetCurveDisplay : DET curve visualization.
roc_curve : Compute Receiver operating characteristic (ROC) curve.
precision_recall_curve : Compute precision-recall curve.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import det_curve
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, fnr, thresholds = det_curve(y_true, y_scores)
>>> fpr
array([0.5, 0.5, 0. ])
>>> fnr
array([0. , 0.5, 0.5])
>>> thresholds
array([0.35, 0.4 , 0.8 ])
"""
fps, tps, thresholds = _binary_clf_curve(
y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
)
if len(np.unique(y_true)) != 2:
raise ValueError(
"Only one class present in y_true. Detection error "
"tradeoff curve is not defined in that case."
)
fns = tps[-1] - tps
p_count = tps[-1]
n_count = fps[-1]
# start with false positives zero
first_ind = (
fps.searchsorted(fps[0], side="right") - 1
if fps.searchsorted(fps[0], side="right") > 0
else None
)
# stop with false negatives zero
last_ind = tps.searchsorted(tps[-1]) + 1
sl = slice(first_ind, last_ind)
# reverse the output such that list of false positives is decreasing
return (fps[sl][::-1] / n_count, fns[sl][::-1] / p_count, thresholds[sl][::-1])
def _binary_roc_auc_score(y_true, y_score, sample_weight=None, max_fpr=None):
"""Binary roc auc score."""
if len(np.unique(y_true)) != 2:
raise ValueError(
"Only one class present in y_true. ROC AUC score "
"is not defined in that case."
)
fpr, tpr, _ = roc_curve(y_true, y_score, sample_weight=sample_weight)
if max_fpr is None or max_fpr == 1:
return auc(fpr, tpr)
if max_fpr <= 0 or max_fpr > 1:
raise ValueError("Expected max_fpr in range (0, 1], got: %r" % max_fpr)
# Add a single point at max_fpr by linear interpolation
stop = np.searchsorted(fpr, max_fpr, "right")
x_interp = [fpr[stop - 1], fpr[stop]]
y_interp = [tpr[stop - 1], tpr[stop]]
tpr = np.append(tpr[:stop], np.interp(max_fpr, x_interp, y_interp))
fpr = np.append(fpr[:stop], max_fpr)
partial_auc = auc(fpr, tpr)
# McClish correction: standardize result to be 0.5 if non-discriminant
# and 1 if maximal
min_area = 0.5 * max_fpr**2
max_area = max_fpr
return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
@validate_params(
{
"y_true": ["array-like"],
"y_score": ["array-like"],
"average": [StrOptions({"micro", "macro", "samples", "weighted"}), None],
"sample_weight": ["array-like", None],
"max_fpr": [Interval(Real, 0.0, 1, closed="right"), None],
"multi_class": [StrOptions({"raise", "ovr", "ovo"})],
"labels": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def roc_auc_score(
y_true,
y_score,
*,
average="macro",
sample_weight=None,
max_fpr=None,
multi_class="raise",
labels=None,
):
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
from prediction scores.
Note: this implementation can be used with binary, multiclass and
multilabel classification, but some restrictions apply (see Parameters).
Read more in the :ref:`User Guide <roc_metrics>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
True labels or binary label indicators. The binary and multiclass cases
expect labels with shape (n_samples,) while the multilabel case expects
binary label indicators with shape (n_samples, n_classes).
y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
Target scores.
* In the binary case, it corresponds to an array of shape
`(n_samples,)`. Both probability estimates and non-thresholded
decision values can be provided. The probability estimates correspond
to the **probability of the class with the greater label**,
i.e. `estimator.classes_[1]` and thus
`estimator.predict_proba(X, y)[:, 1]`. The decision values
corresponds to the output of `estimator.decision_function(X, y)`.
See more information in the :ref:`User guide <roc_auc_binary>`;
* In the multiclass case, it corresponds to an array of shape
`(n_samples, n_classes)` of probability estimates provided by the
`predict_proba` method. The probability estimates **must**
sum to 1 across the possible classes. In addition, the order of the
class scores must correspond to the order of ``labels``,
if provided, or else to the numerical or lexicographical order of
the labels in ``y_true``. See more information in the
:ref:`User guide <roc_auc_multiclass>`;
* In the multilabel case, it corresponds to an array of shape
`(n_samples, n_classes)`. Probability estimates are provided by the
`predict_proba` method and the non-thresholded decision values by
the `decision_function` method. The probability estimates correspond
to the **probability of the class with the greater label for each
output** of the classifier. See more information in the
:ref:`User guide <roc_auc_multilabel>`.
average : {'micro', 'macro', 'samples', 'weighted'} or None, \
default='macro'
If ``None``, the scores for each class are returned.
Otherwise, this determines the type of averaging performed on the data.
Note: multiclass ROC AUC currently only handles the 'macro' and
'weighted' averages. For multiclass targets, `average=None` is only
implemented for `multi_class='ovr'` and `average='micro'` is only
implemented for `multi_class='ovr'`.
``'micro'``:
Calculate metrics globally by considering each element of the label
indicator matrix as a label.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average, weighted
by support (the number of true instances for each label).
``'samples'``:
Calculate metrics for each instance, and find their average.
Will be ignored when ``y_true`` is binary.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
max_fpr : float > 0 and <= 1, default=None
If not ``None``, the standardized partial AUC [2]_ over the range
[0, max_fpr] is returned. For the multiclass case, ``max_fpr``,
should be either equal to ``None`` or ``1.0`` as AUC ROC partial
computation currently is not supported for multiclass.
multi_class : {'raise', 'ovr', 'ovo'}, default='raise'
Only used for multiclass targets. Determines the type of configuration
to use. The default value raises an error, so either
``'ovr'`` or ``'ovo'`` must be passed explicitly.
``'ovr'``:
Stands for One-vs-rest. Computes the AUC of each class
against the rest [3]_ [4]_. This
treats the multiclass case in the same way as the multilabel case.
Sensitive to class imbalance even when ``average == 'macro'``,
because class imbalance affects the composition of each of the
'rest' groupings.
``'ovo'``:
Stands for One-vs-one. Computes the average AUC of all
possible pairwise combinations of classes [5]_.
Insensitive to class imbalance when
``average == 'macro'``.
labels : array-like of shape (n_classes,), default=None
Only used for multiclass targets. List of labels that index the
classes in ``y_score``. If ``None``, the numerical or lexicographical
order of the labels in ``y_true`` is used.
Returns
-------
auc : float
Area Under the Curve score.
See Also
--------
average_precision_score : Area under the precision-recall curve.
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
(ROC) curve given an estimator and some data.
RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
(ROC) curve given the true and predicted values.
References
----------
.. [1] `Wikipedia entry for the Receiver operating characteristic
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
.. [2] `Analyzing a portion of the ROC curve. McClish, 1989
<https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_
.. [3] Provost, F., Domingos, P. (2000). Well-trained PETs: Improving
probability estimation trees (Section 6.2), CeDER Working Paper
#IS-00-04, Stern School of Business, New York University.
.. [4] `Fawcett, T. (2006). An introduction to ROC analysis. Pattern
Recognition Letters, 27(8), 861-874.
<https://www.sciencedirect.com/science/article/pii/S016786550500303X>`_
.. [5] `Hand, D.J., Till, R.J. (2001). A Simple Generalisation of the Area
Under the ROC Curve for Multiple Class Classification Problems.
Machine Learning, 45(2), 171-186.
<http://link.springer.com/article/10.1023/A:1010920819831>`_
Examples
--------
Binary case:
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.metrics import roc_auc_score
>>> X, y = load_breast_cancer(return_X_y=True)
>>> clf = LogisticRegression(solver="liblinear", random_state=0).fit(X, y)
>>> roc_auc_score(y, clf.predict_proba(X)[:, 1])
0.99...
>>> roc_auc_score(y, clf.decision_function(X))
0.99...
Multiclass case:
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> clf = LogisticRegression(solver="liblinear").fit(X, y)
>>> roc_auc_score(y, clf.predict_proba(X), multi_class='ovr')
0.99...
Multilabel case:
>>> import numpy as np
>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.multioutput import MultiOutputClassifier
>>> X, y = make_multilabel_classification(random_state=0)
>>> clf = MultiOutputClassifier(clf).fit(X, y)
>>> # get a list of n_output containing probability arrays of shape
>>> # (n_samples, n_classes)
>>> y_pred = clf.predict_proba(X)
>>> # extract the positive columns for each output
>>> y_pred = np.transpose([pred[:, 1] for pred in y_pred])
>>> roc_auc_score(y, y_pred, average=None)
array([0.82..., 0.86..., 0.94..., 0.85... , 0.94...])
>>> from sklearn.linear_model import RidgeClassifierCV
>>> clf = RidgeClassifierCV().fit(X, y)
>>> roc_auc_score(y, clf.decision_function(X), average=None)
array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])
"""
y_type = type_of_target(y_true, input_name="y_true")
y_true = check_array(y_true, ensure_2d=False, dtype=None)
y_score = check_array(y_score, ensure_2d=False)
if y_type == "multiclass" or (
y_type == "binary" and y_score.ndim == 2 and y_score.shape[1] > 2
):
# do not support partial ROC computation for multiclass
if max_fpr is not None and max_fpr != 1.0:
raise ValueError(
"Partial AUC computation not available in "
"multiclass setting, 'max_fpr' must be"
" set to `None`, received `max_fpr={0}` "
"instead".format(max_fpr)
)
if multi_class == "raise":
raise ValueError("multi_class must be in ('ovo', 'ovr')")
return _multiclass_roc_auc_score(
y_true, y_score, labels, multi_class, average, sample_weight
)
elif y_type == "binary":
labels = np.unique(y_true)
y_true = label_binarize(y_true, classes=labels)[:, 0]
return _average_binary_score(
partial(_binary_roc_auc_score, max_fpr=max_fpr),
y_true,
y_score,
average,
sample_weight=sample_weight,
)
else: # multilabel-indicator
return _average_binary_score(
partial(_binary_roc_auc_score, max_fpr=max_fpr),
y_true,
y_score,
average,
sample_weight=sample_weight,
)
def _multiclass_roc_auc_score(
y_true, y_score, labels, multi_class, average, sample_weight
):
"""Multiclass roc auc score.
Parameters
----------
y_true : array-like of shape (n_samples,)
True multiclass labels.
y_score : array-like of shape (n_samples, n_classes)
Target scores corresponding to probability estimates of a sample
belonging to a particular class
labels : array-like of shape (n_classes,) or None
List of labels to index ``y_score`` used for multiclass. If ``None``,
the lexical order of ``y_true`` is used to index ``y_score``.
multi_class : {'ovr', 'ovo'}
Determines the type of multiclass configuration to use.
``'ovr'``:
Calculate metrics for the multiclass case using the one-vs-rest
approach.
``'ovo'``:
Calculate metrics for the multiclass case using the one-vs-one
approach.
average : {'micro', 'macro', 'weighted'}
Determines the type of averaging performed on the pairwise binary
metric scores
``'micro'``:
Calculate metrics for the binarized-raveled classes. Only supported
for `multi_class='ovr'`.
.. versionadded:: 1.2
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account. Classes
are assumed to be uniformly distributed.
``'weighted'``:
Calculate metrics for each label, taking into account the
prevalence of the classes.
sample_weight : array-like of shape (n_samples,) or None
Sample weights.
"""
# validation of the input y_score
if not np.allclose(1, y_score.sum(axis=1)):
raise ValueError(
"Target scores need to be probabilities for multiclass "
"roc_auc, i.e. they should sum up to 1.0 over classes"
)
# validation for multiclass parameter specifications
average_options = ("macro", "weighted", None)
if multi_class == "ovr":
average_options = ("micro",) + average_options
if average not in average_options:
raise ValueError(
"average must be one of {0} for multiclass problems".format(average_options)
)
multiclass_options = ("ovo", "ovr")
if multi_class not in multiclass_options:
raise ValueError(
"multi_class='{0}' is not supported "
"for multiclass ROC AUC, multi_class must be "
"in {1}".format(multi_class, multiclass_options)
)
if average is None and multi_class == "ovo":
raise NotImplementedError(
"average=None is not implemented for multi_class='ovo'."
)
if labels is not None:
labels = column_or_1d(labels)
classes = _unique(labels)
if len(classes) != len(labels):
raise ValueError("Parameter 'labels' must be unique")
if not np.array_equal(classes, labels):
raise ValueError("Parameter 'labels' must be ordered")
if len(classes) != y_score.shape[1]:
raise ValueError(
"Number of given labels, {0}, not equal to the number "
"of columns in 'y_score', {1}".format(len(classes), y_score.shape[1])
)
if len(np.setdiff1d(y_true, classes)):
raise ValueError("'y_true' contains labels not in parameter 'labels'")
else:
classes = _unique(y_true)
if len(classes) != y_score.shape[1]:
raise ValueError(
"Number of classes in y_true not equal to the number of "
"columns in 'y_score'"
)
if multi_class == "ovo":
if sample_weight is not None:
raise ValueError(
"sample_weight is not supported "
"for multiclass one-vs-one ROC AUC, "
"'sample_weight' must be None in this case."
)
y_true_encoded = _encode(y_true, uniques=classes)
# Hand & Till (2001) implementation (ovo)
return _average_multiclass_ovo_score(
_binary_roc_auc_score, y_true_encoded, y_score, average=average
)
else:
# ovr is same as multi-label
y_true_multilabel = label_binarize(y_true, classes=classes)
return _average_binary_score(
_binary_roc_auc_score,
y_true_multilabel,
y_score,
average,
sample_weight=sample_weight,
)
def _binary_clf_curve(y_true, y_score, pos_label=None, sample_weight=None):
"""Calculate true and false positives per binary classification threshold.
Parameters
----------
y_true : ndarray of shape (n_samples,)
True targets of binary classification.
y_score : ndarray of shape (n_samples,)
Estimated probabilities or output of a decision function.
pos_label : int, float, bool or str, default=None
The label of the positive class.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
fps : ndarray of shape (n_thresholds,)
A count of false positives, at index i being the number of negative
samples assigned a score >= thresholds[i]. The total number of
negative samples is equal to fps[-1] (thus true negatives are given by
fps[-1] - fps).
tps : ndarray of shape (n_thresholds,)
An increasing count of true positives, at index i being the number
of positive samples assigned a score >= thresholds[i]. The total
number of positive samples is equal to tps[-1] (thus false negatives
are given by tps[-1] - tps).
thresholds : ndarray of shape (n_thresholds,)
Decreasing score values.
"""
# Check to make sure y_true is valid
y_type = type_of_target(y_true, input_name="y_true")
if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
raise ValueError("{0} format is not supported".format(y_type))
check_consistent_length(y_true, y_score, sample_weight)
y_true = column_or_1d(y_true)
y_score = column_or_1d(y_score)
assert_all_finite(y_true)
assert_all_finite(y_score)
# Filter out zero-weighted samples, as they should not impact the result
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight) | sample_weight = _check_sample_weight(sample_weight, y_true) | 16 | 2023-10-07 13:19:48+00:00 | 24k |
zbzhu99/madiff | diffuser/models/diffusion.py | [
{
"identifier": "DPM_Solver",
"path": "diffuser/utils/dpm_solver.py",
"snippet": "class DPM_Solver:\n def __init__(\n self,\n model_fn,\n noise_schedule,\n algorithm_type=\"dpmsolver++\",\n correcting_x0_fn=None,\n correcting_xt_fn=None,\n thresholding... | import functools
import numpy as np
import torch
import torch.nn.functional as F
import diffuser.utils as utils
from torch import nn
from diffuser.utils.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
from .helpers import Losses, apply_conditioning, cosine_beta_schedule, extract | 20,349 |
class GaussianDiffusion(nn.Module):
def __init__(
self,
model,
n_agents,
horizon,
history_horizon,
observation_dim,
action_dim,
n_timesteps=1000,
loss_type="l1",
clip_denoised=False,
predict_epsilon=True,
action_weight=1.0,
loss_discount=1.0,
loss_weights=None,
returns_condition=False,
condition_guidance_w=0.1,
agent_share_noise=False,
data_encoder=utils.IdentityEncoder(),
**kwargs,
):
super().__init__()
self.n_agents = n_agents
self.horizon = horizon
self.history_horizon = history_horizon
self.observation_dim = observation_dim
self.action_dim = action_dim
self.transition_dim = observation_dim + action_dim
self.model = model
self.returns_condition = returns_condition
self.condition_guidance_w = condition_guidance_w
self.agent_share_noise = agent_share_noise
self.data_encoder = data_encoder
|
class GaussianDiffusion(nn.Module):
def __init__(
self,
model,
n_agents,
horizon,
history_horizon,
observation_dim,
action_dim,
n_timesteps=1000,
loss_type="l1",
clip_denoised=False,
predict_epsilon=True,
action_weight=1.0,
loss_discount=1.0,
loss_weights=None,
returns_condition=False,
condition_guidance_w=0.1,
agent_share_noise=False,
data_encoder=utils.IdentityEncoder(),
**kwargs,
):
super().__init__()
self.n_agents = n_agents
self.horizon = horizon
self.history_horizon = history_horizon
self.observation_dim = observation_dim
self.action_dim = action_dim
self.transition_dim = observation_dim + action_dim
self.model = model
self.returns_condition = returns_condition
self.condition_guidance_w = condition_guidance_w
self.agent_share_noise = agent_share_noise
self.data_encoder = data_encoder
| betas = cosine_beta_schedule(n_timesteps) | 3 | 2023-10-13 13:03:53+00:00 | 24k |
hellloxiaotian/KDNet | train_KDNet.py | [
{
"identifier": "attempt_load",
"path": "models/experimental.py",
"snippet": "def attempt_load(weights, map_location=None):\n # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a\n model = Ensemble()\n # print('weights', weights) # /runs/train/yolov7_distill... | import argparse
import logging
import math
import os
import random
import time
import numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
import yaml
import test # import test.py to get mAP after each epoch
from copy import deepcopy
from pathlib import Path
from threading import Thread
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from models.experimental import attempt_load
from models.experimental import attempt_loadv5
from models.experimental import attempt_load_zxy
from models.yolo import Model
from utils.autoanchor import check_anchors
from utils.datasets import create_dataloader
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
check_requirements, print_mutation, set_logging, one_cycle, colorstr
from utils.google_utils import attempt_download
from utils.loss import ComputeLoss, ComputeLossOTA
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
from utils.distill_utils import getMask, compute_mask_loss | 20,427 |
# load teacher model
teacher = attempt_load_zxy(opt.teacher_weights, device=device)
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
# Freeze
freeze = [f'model.{x}.' for x in
(freeze if len(freeze) > 1 else range(freeze[0]))] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if hasattr(v, 'im'):
if hasattr(v.im, 'implicit'):
pg0.append(v.im.implicit)
else:
for iv in v.im:
pg0.append(iv.implicit)
if hasattr(v, 'imc'):
if hasattr(v.imc, 'implicit'):
pg0.append(v.imc.implicit)
else:
for iv in v.imc:
pg0.append(iv.implicit)
if hasattr(v, 'imb'):
if hasattr(v.imb, 'implicit'):
pg0.append(v.imb.implicit)
else:
for iv in v.imb:
pg0.append(iv.implicit)
if hasattr(v, 'imo'):
if hasattr(v.imo, 'implicit'):
pg0.append(v.imo.implicit)
else:
for iv in v.imo:
pg0.append(iv.implicit)
if hasattr(v, 'ia'):
if hasattr(v.ia, 'implicit'):
pg0.append(v.ia.implicit)
else:
for iv in v.ia:
pg0.append(iv.implicit)
if hasattr(v, 'attn'):
if hasattr(v.attn, 'logit_scale'):
pg0.append(v.attn.logit_scale)
if hasattr(v.attn, 'q_bias'):
pg0.append(v.attn.q_bias)
if hasattr(v.attn, 'v_bias'):
pg0.append(v.attn.v_bias)
if hasattr(v.attn, 'relative_position_bias_table'):
pg0.append(v.attn.relative_position_bias_table)
if hasattr(v, 'rbr_dense'):
if hasattr(v.rbr_dense, 'weight_rbr_origin'):
pg0.append(v.rbr_dense.weight_rbr_origin)
if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'):
pg0.append(v.rbr_dense.weight_rbr_avg_conv)
if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'):
pg0.append(v.rbr_dense.weight_rbr_pfir_conv)
if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'):
pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1)
if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'):
pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2)
if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'):
pg0.append(v.rbr_dense.weight_rbr_gconv_dw)
if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'):
pg0.append(v.rbr_dense.weight_rbr_gconv_pw)
if hasattr(v.rbr_dense, 'vector'):
pg0.append(v.rbr_dense.vector)
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
|
logger = logging.getLogger(__name__)
def train(hyp, opt, device, tb_writer=None):
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
is_coco = opt.data.endswith('coco.yaml')
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(
weights) else None
wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
# load teacher model
teacher = attempt_load_zxy(opt.teacher_weights, device=device)
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
# Freeze
freeze = [f'model.{x}.' for x in
(freeze if len(freeze) > 1 else range(freeze[0]))] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if hasattr(v, 'im'):
if hasattr(v.im, 'implicit'):
pg0.append(v.im.implicit)
else:
for iv in v.im:
pg0.append(iv.implicit)
if hasattr(v, 'imc'):
if hasattr(v.imc, 'implicit'):
pg0.append(v.imc.implicit)
else:
for iv in v.imc:
pg0.append(iv.implicit)
if hasattr(v, 'imb'):
if hasattr(v.imb, 'implicit'):
pg0.append(v.imb.implicit)
else:
for iv in v.imb:
pg0.append(iv.implicit)
if hasattr(v, 'imo'):
if hasattr(v.imo, 'implicit'):
pg0.append(v.imo.implicit)
else:
for iv in v.imo:
pg0.append(iv.implicit)
if hasattr(v, 'ia'):
if hasattr(v.ia, 'implicit'):
pg0.append(v.ia.implicit)
else:
for iv in v.ia:
pg0.append(iv.implicit)
if hasattr(v, 'attn'):
if hasattr(v.attn, 'logit_scale'):
pg0.append(v.attn.logit_scale)
if hasattr(v.attn, 'q_bias'):
pg0.append(v.attn.q_bias)
if hasattr(v.attn, 'v_bias'):
pg0.append(v.attn.v_bias)
if hasattr(v.attn, 'relative_position_bias_table'):
pg0.append(v.attn.relative_position_bias_table)
if hasattr(v, 'rbr_dense'):
if hasattr(v.rbr_dense, 'weight_rbr_origin'):
pg0.append(v.rbr_dense.weight_rbr_origin)
if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'):
pg0.append(v.rbr_dense.weight_rbr_avg_conv)
if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'):
pg0.append(v.rbr_dense.weight_rbr_pfir_conv)
if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'):
pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1)
if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'):
pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2)
if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'):
pg0.append(v.rbr_dense.weight_rbr_gconv_dw)
if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'):
pg0.append(v.rbr_dense.weight_rbr_gconv_pw)
if hasattr(v.rbr_dense, 'vector'):
pg0.append(v.rbr_dense.vector)
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA | ema = ModelEMA(model) if rank in [-1, 0] else None | 14 | 2023-10-08 13:05:58+00:00 | 24k |
falesiani/torch_ga | tests/test_keras.py | [
{
"identifier": "GeometricProductDense",
"path": "torch_ga/layers.py",
"snippet": "class GeometricProductDense(GeometricAlgebraLayer):\n \"\"\"Analagous to Keras' Dense layer but using multivector-valued matrices\n instead of scalar ones and geometric multiplication instead of standard\n multip... | import unittest as ut
import h5py
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch
from io import BytesIO
from torch_ga.layers import (
GeometricProductDense, GeometricSandwichProductDense,
GeometricProductElementwise, GeometricSandwichProductElementwise,
GeometricProductConv1D,
GeometricAlgebraExp,
GeometricToTensor, GeometricToTensorWithKind,
TensorToGeometric, TensorWithKindToGeometric,
)
from torch_ga.blades import BladeKind
from torch_ga import GeometricAlgebra | 16,872 |
torch.manual_seed(0)
class TestKerasLayers(ut.TestCase):
def assertTensorsEqual(self, a, b):
# self.assertTrue(tf.reduce_all(a == b), "%s not equal to %s" % (a, b))
print(f"assertTensorsEqual(a={a},b={b})")
assert torch.all(a.squeeze() == b.squeeze()), "%s not equal to %s" % (a, b)
def test_tensor_to_geometric(self):
sta = GeometricAlgebra([1, -1, -1, -1])
tensor = torch.ones([32, 4])
gt_geom_tensor = torch.concat(
[torch.zeros([32, 1]), torch.ones([32, 4]), torch.zeros([32, 11])],
axis=-1
)
vector_blade_indices = [1, 2, 3, 4]
tensor_to_geom_layer = TensorToGeometric(sta, vector_blade_indices)
self.assertTensorsEqual(tensor_to_geom_layer(tensor), gt_geom_tensor)
def test_tensor_with_kind_to_geometric(self):
sta = GeometricAlgebra([1, -1, -1, -1])
tensor = torch.ones([32, 4])
gt_geom_tensor = torch.concat(
[torch.zeros([32, 1]), torch.ones([32, 4]), torch.zeros([32, 11])],
axis=-1
)
vector_blade_indices = [1, 2, 3, 4]
|
torch.manual_seed(0)
class TestKerasLayers(ut.TestCase):
def assertTensorsEqual(self, a, b):
# self.assertTrue(tf.reduce_all(a == b), "%s not equal to %s" % (a, b))
print(f"assertTensorsEqual(a={a},b={b})")
assert torch.all(a.squeeze() == b.squeeze()), "%s not equal to %s" % (a, b)
def test_tensor_to_geometric(self):
sta = GeometricAlgebra([1, -1, -1, -1])
tensor = torch.ones([32, 4])
gt_geom_tensor = torch.concat(
[torch.zeros([32, 1]), torch.ones([32, 4]), torch.zeros([32, 11])],
axis=-1
)
vector_blade_indices = [1, 2, 3, 4]
tensor_to_geom_layer = TensorToGeometric(sta, vector_blade_indices)
self.assertTensorsEqual(tensor_to_geom_layer(tensor), gt_geom_tensor)
def test_tensor_with_kind_to_geometric(self):
sta = GeometricAlgebra([1, -1, -1, -1])
tensor = torch.ones([32, 4])
gt_geom_tensor = torch.concat(
[torch.zeros([32, 1]), torch.ones([32, 4]), torch.zeros([32, 11])],
axis=-1
)
vector_blade_indices = [1, 2, 3, 4]
| tensor_kind_to_geom_layer = TensorWithKindToGeometric( | 9 | 2023-10-07 13:34:07+00:00 | 24k |
Significant-Gravitas/autostandup | bot.py | [
{
"identifier": "StreaksDB",
"path": "streaks/streaks_db.py",
"snippet": "class StreaksDB(BaseDB):\n \"\"\"\n StreaksDB class handles all operations related to the 'streaks' table.\n Inherits from the BaseDB class.\n \"\"\"\n\n def __init__(self, host, user, password, database, port):\n ... | import os
import pytz
import asyncio
import openai
import requests
from typing import List
from dotenv import load_dotenv
from datetime import datetime, timedelta
from multiprocessing import Process
from streaks.streaks_db import StreaksDB
from team_members.team_member_db import TeamMemberDB
from updates.updates_db import UpdatesDB
from weekly_posts.weekly_posts_db import WeeklyPostsDB
from streaks.streaks_manager import StreaksManager
from team_members.team_member_manager import TeamMemberManager
from updates.updates_manager import UpdatesManager
from weekly_posts.weekly_post_manager import WeeklyPostManager
from scheduler import Scheduler
from team_members.team_member import TeamMember
from discord.ext import commands, tasks
from discord import Intents, DMChannel
from flask import Flask
from asyncio import Task, ensure_future, CancelledError | 15,358 | if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to update streaks.")
return
# Find the member object using the Discord ID
member_to_update = team_member_manager.find_member(discord_id)
if member_to_update:
# Update the streak in the database
streaks_manager.update_streak(discord_id, new_streak)
member_to_update.update_streak(new_streak)
# Update the Discord post using WeeklyPostManager
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
await ctx.send(f"Streak for user with Discord ID {discord_id} updated to {new_streak}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='forcepostrebuild')
async def force_post_rebuild(ctx):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to force a post rebuild.")
return
# Rebuild the post
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
await ctx.send("Post rebuilt successfully.")
@bot.command(name='deletelateststatus')
async def delete_latest_status(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to delete status updates.")
return
# Find the member object using the Discord ID
member = team_member_manager.find_member(discord_id)
if not member:
await ctx.send(f"No user with Discord ID {discord_id} found.")
return
# Delete the newest status using the UpdatesManager's method
updates_manager.delete_newest_status(discord_id)
await ctx.send(f"Latest status update for user with Discord ID {discord_id} deleted successfully.")
@bot.command(name='viewuser')
async def view_user(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to view user data.")
return
# Get the member's statuses using the UpdatesManager's method
statuses = updates_manager.get_all_statuses_for_user(discord_id)
if not statuses:
await ctx.send(f"No status updates found for user with Discord ID {discord_id}.")
return
# Loop through the statuses and send individual messages
for status in statuses:
await ctx.send(f"### **Timestamp:** {status['timestamp']}")
await ctx.send(f"### **Raw Status:** {status['status']}")
await ctx.send(f"### **Summarized Status:** \n{status['summarized_status']}")
@bot.command(name='setvacationstatus')
async def set_vacation_status(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to set vacation status.")
return
member = team_member_manager.find_member(discord_id)
if member:
new_status = not member.on_vacation
team_member_manager.set_member_vacation_status(discord_id, new_status)
await ctx.send(f"Vacation status for user with Discord ID {discord_id} set to {'on vacation' if new_status else 'not on vacation'}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='weeklysummary')
async def weekly_summary(ctx, discord_id: int, start_date: str, end_date: str):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to generate weekly summaries.")
return
# Find the member object using the Discord ID
member = team_member_manager.find_member(discord_id)
if not member:
await ctx.send(f"No user with Discord ID {discord_id} found.")
return
# Convert the start_date and end_date strings to datetime objects
# Adjusting the date format to MM-DD-YYYY and setting the time
try:
start_date = datetime.strptime(start_date, '%m-%d-%Y')
end_date = datetime.strptime(end_date, '%m-%d-%Y')
# Setting the time to ensure the whole week is captured
start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
end_date = end_date.replace(hour=23, minute=59, second=59, microsecond=999999)
except ValueError:
await ctx.send("Invalid date format. Please use MM-DD-YYYY.")
return
# Generate the weekly summary
weekly_summary = await updates_manager.generate_weekly_summary(discord_id, start_date, end_date)
# Send the weekly summary to the admin user
admin_user = bot.get_user(ADMIN_DISCORD_ID)
if admin_user:
await admin_user.send(f"**{member.name}'s Weekly Summary for {start_date.strftime('%m-%d-%Y')} to {end_date.strftime('%m-%d-%Y')}:**\n{weekly_summary}")
else:
await ctx.send("Unable to find the admin user.")
@bot.event
async def on_ready():
print("Bot is online!") # Log that the bot is online
| # Import required modules
app = Flask(__name__)
# Load environment variables from the .env file
load_dotenv()
# Retrieve bot, guild, and channel tokens from environment variables
BOT_TOKEN = os.getenv('DISCORD_BOT_TOKEN')
GUILD_TOKEN = int(os.getenv('DISCORD_GUILD_TOKEN'))
CHANNEL_TOKEN = int(os.getenv('DISCORD_CHANNEL_TOKEN'))
ADMIN_DISCORD_ID = int(os.getenv('ADMIN_DISCORD_ID'))
# Retrieve database credentials from environment variables
MYSQL_HOST = os.getenv('MYSQL_HOST')
MYSQL_USER = os.getenv('MYSQL_USER')
MYSQL_PASSWORD = os.getenv('MYSQL_PASSWORD')
MYSQL_DB = os.getenv('MYSQL_DB')
MYSQL_PORT = os.getenv('MYSQL_PORT')
ORG_NAME = os.getenv('GITHUB_ORG_NAME')
ORG_TOKEN = os.getenv('GITHUB_ORG_TOKEN')
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
# Initialize bot with default intents
intents = Intents.default()
intents.members = True
intents.message_content = True
bot = commands.Bot(command_prefix='!', intents=intents)
openai.api_key = OPENAI_API_KEY
# TODO: Remove these globals
streaks_manager = None
weekly_post_manager = None
team_member_manager = None
updates_manager = None
scheduler = None
ongoing_status_requests = {}
THUMBS_UP_EMOJI = "👍"
PENCIL_EMOJI = "✏️"
REPORT_SUBMISSION_EMOJI = '📝'
async def weekly_state_reset(weekly_post_manager: WeeklyPostManager, streaks_manager: StreaksManager, team_members: List[TeamMember]):
# Reset streaks for the previous week
for member in team_members:
if not member.on_vacation and member.weekly_checkins < 5:
streaks_manager.reset_streak(member.discord_id)
member.reset_streak()
member.reset_weekly_checkins()
# Initialize new weekly post
await weekly_post_manager.initialize_post(team_members)
def get_all_commit_messages_for_user(org_name: str, token: str, member: TeamMember) -> list:
"""Retrieve all commit messages for a user across all repos in an organization from the last 24 hours."""
headers = {
"Authorization": f"token {token}",
"Accept": "application/vnd.github.v3+json"
}
last_update_timestamp, user_time_zone = updates_manager.get_last_update_timestamp(member.discord_id)
if last_update_timestamp:
# Convert the timestamp to UTC
local_tz = pytz.timezone(user_time_zone)
localized_timestamp = local_tz.localize(last_update_timestamp)
utc_timestamp = localized_timestamp.astimezone(pytz.utc)
# Format the timestamp for the GitHub API and append 'Z'
since_date = utc_timestamp.isoformat()
if not since_date.endswith('Z'):
since_date = utc_timestamp.isoformat().replace('+00:00', '') + 'Z'
else:
# If no updates found, default to last 24 hours
since_date = (datetime.utcnow() - timedelta(days=1)).isoformat() + 'Z'
all_commit_messages = []
# Paginate through all repositories in the organization
repos_url = f"https://api.github.com/orgs/{org_name}/repos?type=all&per_page=100"
while repos_url:
response = requests.get(repos_url, headers=headers)
if response.status_code != 200:
# Log error and break loop
print(f"Failed to fetch repos: {response.status_code} {response.text}")
break
repos = response.json()
# Iterate over each repository
for repo in repos:
repo_name = repo["name"]
commits_url = f"https://api.github.com/repos/{org_name}/{repo_name}/commits?author={member.github_username}&since={since_date}&per_page=100"
# Paginate through commits for the repository
while commits_url:
response = requests.get(commits_url, headers=headers)
if response.status_code != 200:
# Log error and continue to the next repository
print(f"Failed to fetch commits for {repo_name}: {response.status_code} {response.text}")
break
commits = response.json()
repo_commit_messages = [commit["commit"]["message"] for commit in commits]
all_commit_messages.extend(repo_commit_messages)
# Check for the 'next' link for commits pagination
commits_url = get_pagination_link(response.headers, 'next')
# Check for the 'next' link for repositories pagination
repos_url = get_pagination_link(response.headers, 'next')
return all_commit_messages
def get_pagination_link(headers, rel):
"""Extract pagination link for the 'rel' type from the Link header."""
link = headers.get('Link', None)
if link:
links = link.split(', ')
for link in links:
if 'rel="{}"'.format(rel) in link:
return link.split('; ')[0].strip('<>')
return None
async def send_status_request(member: TeamMember,
weekly_post_manager: WeeklyPostManager,
streaks_manager: StreaksManager,
updates_manager: UpdatesManager):
if member.weekly_checkins == 5:
return # If already completed 5 check-ins, do nothing
user = bot.get_user(member.discord_id)
if user:
# Notify the admin that a status request is being sent
admin_user = bot.get_user(ADMIN_DISCORD_ID)
if admin_user:
await admin_user.send(f"Status request sent to {member.name}.")
# Cancel the previous task if it exists
ongoing_task: Task = ongoing_status_requests.get(member.discord_id)
if ongoing_task:
ongoing_task.cancel()
# Retrieve all commit messages for the member
commit_messages = get_all_commit_messages_for_user(ORG_NAME, ORG_TOKEN, member)
if not commit_messages:
summarized_report = "You have no commits for the previous working day."
msg = f"{summarized_report}\nReact with {THUMBS_UP_EMOJI} to confirm, {PENCIL_EMOJI} to iterate with AI, or {REPORT_SUBMISSION_EMOJI} to submit your own report."
else:
summarized_report = await updates_manager.summarize_technical_updates(commit_messages)
msg = f"Here's your summarized report based on your commits:\n{summarized_report}\nReact with {THUMBS_UP_EMOJI} to confirm, {PENCIL_EMOJI} to iterate with AI, or {REPORT_SUBMISSION_EMOJI} to submit your own report."
raw_updates = summarized_report
# Send initial message and wait for reaction
await user.send(
f"# Good morning {member.name}, time for your daily status update!\n"
f"### I'm first going to check your commit messages and try to build a technical report for you.\n"
f"### Next I will ask you for any non-technical updates from your previous work day.\n"
f"### Finally I will ask you what you plan to work on today."
)
sent_message = await user.send(msg)
await sent_message.add_reaction(THUMBS_UP_EMOJI)
await sent_message.add_reaction(PENCIL_EMOJI)
await sent_message.add_reaction(REPORT_SUBMISSION_EMOJI)
def check(m) -> bool:
return m.author == user and isinstance(m.channel, DMChannel)
# Store the new wait_for reaction task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('reaction_add', check=lambda r, u: u == user and r.message.id == sent_message.id and isinstance(r.message.channel, DMChannel) and str(r.emoji) in [THUMBS_UP_EMOJI, PENCIL_EMOJI, REPORT_SUBMISSION_EMOJI]))
ongoing_status_requests[member.discord_id] = ongoing_task
reaction, reactor = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the reaction
for emoji in [THUMBS_UP_EMOJI, PENCIL_EMOJI, REPORT_SUBMISSION_EMOJI]:
await sent_message.remove_reaction(emoji, bot.user)
while str(reaction.emoji) in [PENCIL_EMOJI, REPORT_SUBMISSION_EMOJI]:
if str(reaction.emoji) == PENCIL_EMOJI:
await user.send("What would you like me to change?")
# Store the new wait_for message (feedback) task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('message', check=check))
ongoing_status_requests[member.discord_id] = ongoing_task
feedback = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the feedback
# Send original + feedback to LLM for reformatting
summarized_report = await updates_manager.summarize_feedback_and_revisions(summarized_report, feedback.content)
elif str(reaction.emoji) == REPORT_SUBMISSION_EMOJI:
await user.send("Please submit your technical report directly.")
# Store the new wait_for message (report submission) task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('message', check=check))
ongoing_status_requests[member.discord_id] = ongoing_task
direct_report = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the report
summarized_report = direct_report.content
break # Exit the while loop as the user has submitted their report directly
msg = f"Here's the revised report:\n{summarized_report}\nReact with {THUMBS_UP_EMOJI} to confirm, {PENCIL_EMOJI} to iterate with AI, or {REPORT_SUBMISSION_EMOJI} to submit your own report."
last_sent_message = await send_long_message(user, msg)
if last_sent_message:
await last_sent_message.add_reaction(THUMBS_UP_EMOJI)
await last_sent_message.add_reaction(PENCIL_EMOJI)
await last_sent_message.add_reaction(REPORT_SUBMISSION_EMOJI)
# Store the new wait_for reaction task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('reaction_add', check=lambda r, u: u == user and r.message.id == last_sent_message.id and isinstance(r.message.channel, DMChannel) and str(r.emoji) in [THUMBS_UP_EMOJI, PENCIL_EMOJI, REPORT_SUBMISSION_EMOJI]))
ongoing_status_requests[member.discord_id] = ongoing_task
reaction, user = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the reaction
for emoji in [THUMBS_UP_EMOJI, PENCIL_EMOJI, REPORT_SUBMISSION_EMOJI]:
await last_sent_message.remove_reaction(emoji, bot.user)
# Prompt user for non-technical updates from the previous day
non_technical_msg_prompt = "Please provide any non-technical updates from your previous working day, e.g., important meetings, interviews, etc."
await user.send(non_technical_msg_prompt)
# Store the new wait_for message (non-technical update) task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('message', check=check))
ongoing_status_requests[member.discord_id] = ongoing_task
non_technical_update_raw = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the non-technical update
raw_updates += f"\n\n{non_technical_update_raw.content}"
# Summarize non-technical update with LLM
non_technical_update = await updates_manager.summarize_non_technical_updates(non_technical_update_raw.content)
# Prompt user for their goals for the day
goals_msg_prompt = "What do you plan to work on or accomplish today?"
await user.send(goals_msg_prompt)
# Store the new wait_for message (goals for the day) task in the global dictionary
ongoing_task = ensure_future(bot.wait_for('message', check=check))
ongoing_status_requests[member.discord_id] = ongoing_task
goals_for_today_raw = await ongoing_task
ongoing_status_requests.pop(member.discord_id, None) # Remove the task once we get the goals
# Summarize goals for the day with LLM
goals_for_today = await updates_manager.summarize_goals_for_the_day(goals_for_today_raw.content)
# Update the streak for this member
streak = streaks_manager.get_streak(member.discord_id)
streaks_manager.update_streak(member.discord_id, streak + 1)
member.update_streak(streaks_manager.get_streak(member.discord_id))
member.increment_weekly_checkins()
raw_updates += f"\n\n{goals_for_today_raw.content}"
final_updates = f"{summarized_report}\n\n{non_technical_update}\n\n{goals_for_today}"
updates_manager.insert_status(member.discord_id, raw_updates, member.time_zone)
updates_manager.update_summarized_status(member.discord_id, final_updates)
# Update the Discord post using WeeklyPostManager
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
# Member name update as a header
member_update_header = f"## {member.name}'s Update:"
# Compile the final report with Markdown formatting
final_report = (
f"\n### Technical Update:\n"
f"{summarized_report}\n"
f"### Non-Technical Update:\n"
f"{non_technical_update}\n"
f"### Goals for Today:\n"
f"{goals_for_today}"
)
stand_up_feedback = await updates_manager.evaluate_performance(final_report)
# Concatenate the member name update with the final report and send to the designated Discord channel
complete_message = f"{member_update_header}{final_report}"
guild = bot.get_guild(GUILD_TOKEN)
channel_to_post_in = guild.get_channel(CHANNEL_TOKEN)
await user.send(stand_up_feedback)
await send_long_message(channel_to_post_in, complete_message)
async def send_long_message(destination, msg):
max_length = 2000 # Discord's max character limit for a message
sent_messages = [] # Keep track of all messages sent
while len(msg) > 0:
# If the message is shorter than the max length, send it as is
if len(msg) <= max_length:
sent_message = await destination.send(msg)
sent_messages.append(sent_message)
break # The message is sent, so break out of the loop
# Find the nearest newline character before the max_length
split_index = msg.rfind('\n', 0, max_length)
# If no newline is found, just split at max_length
if split_index == -1:
split_index = max_length
# Split the message at the found index and send the first part
part_to_send = msg[:split_index].strip()
sent_message = await destination.send(part_to_send)
sent_messages.append(sent_message)
# Wait a bit to respect Discord's rate limits
await asyncio.sleep(1)
# Remove the part that was sent from the message
msg = msg[split_index:].strip()
# Return the last message sent for reaction addition
return sent_messages[-1] if sent_messages else None
@bot.command(name='viewscheduledjobs')
async def view_scheduled_jobs(ctx):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to view scheduled jobs.")
return
# Get all scheduled jobs using the Scheduler's method
scheduled_jobs = scheduler.get_all_scheduled_jobs(team_member_manager)
# Send the scheduled jobs to the admin user
for job in scheduled_jobs:
await ctx.send(job)
@bot.command(name='statusrequest')
async def status_request(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to request status.")
return
# Find the member object using the Discord ID
member_to_request = team_member_manager.find_member(discord_id)
if member_to_request:
for member in team_member_manager.team_members:
scheduler.remove_job(member.discord_id)
scheduler.unschedule_weekly_post()
# Send the status request to the member
await ctx.send(f"Status request sent to user with Discord ID {discord_id}.")
for member in team_member_manager.team_members:
scheduler.add_job(send_status_request, member, weekly_post_manager, streaks_manager, updates_manager)
scheduler.schedule_weekly_post(weekly_state_reset, weekly_post_manager, streaks_manager, team_member_manager.team_members)
await send_status_request(member_to_request, weekly_post_manager, streaks_manager, updates_manager)
await ctx.send(f"Status request received from user with Discord ID {discord_id}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='adduser')
async def add_user(ctx, discord_id: int, time_zone: str, name: str, github_username: str):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to add users.")
return
# Add the new member using team_member_manager
team_member_manager.add_member(discord_id, name, time_zone, github_username)
# Update the weekly post to include the new member
new_member = team_member_manager.find_member(discord_id)
if new_member:
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
scheduler.add_job(send_status_request, new_member, weekly_post_manager, streaks_manager, updates_manager)
scheduler.unschedule_weekly_post()
scheduler.schedule_weekly_post(weekly_state_reset, weekly_post_manager, streaks_manager, team_member_manager.team_members)
await ctx.send(f"User {name} added successfully.")
@bot.command(name='removeuser')
async def remove_user(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to remove users.")
return
# Find the member object
member_to_remove = team_member_manager.find_member(discord_id)
if member_to_remove:
# Remove the member from the database
team_member_manager.remove_member(discord_id)
# Update the weekly post to remove the member
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
scheduler.remove_job(discord_id)
scheduler.unschedule_weekly_post()
scheduler.schedule_weekly_post(weekly_state_reset, weekly_post_manager, streaks_manager, team_member_manager.team_members)
await ctx.send(f"User with Discord ID {discord_id} removed successfully.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='listusers')
async def list_users(ctx):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to list users.")
return
# List users using team_member_manager
users = [(member.discord_id, member.name, member.time_zone, member.github_username, member.current_streak) for member in team_member_manager.team_members]
user_list = '\n'.join([f"Name: {user[1]}, Discord ID: {user[0]}, Time Zone: {user[2]}, GitHub Username: {user[3]}, Current Streak: {user[4]}" for user in users])
await ctx.send(f"List of users:\n{user_list}")
@bot.command(name='updatetimezone')
async def update_timezone(ctx, discord_id: int, new_time_zone: str):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to update timezones.")
return
# Find the member object using the Discord ID
member_to_update = team_member_manager.find_member(discord_id)
if member_to_update:
# Update the timezone in the database
team_member_manager.update_member_timezone(discord_id, new_time_zone)
scheduler.remove_job(discord_id)
scheduler.add_job(send_status_request, member_to_update, weekly_post_manager, streaks_manager, updates_manager)
scheduler.unschedule_weekly_post()
scheduler.schedule_weekly_post(weekly_state_reset, weekly_post_manager, streaks_manager, team_member_manager.team_members)
await ctx.send(f"Timezone for user with Discord ID {discord_id} updated to {new_time_zone}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='updatestreak')
async def update_streak(ctx, discord_id: int, new_streak: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to update streaks.")
return
# Find the member object using the Discord ID
member_to_update = team_member_manager.find_member(discord_id)
if member_to_update:
# Update the streak in the database
streaks_manager.update_streak(discord_id, new_streak)
member_to_update.update_streak(new_streak)
# Update the Discord post using WeeklyPostManager
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
await ctx.send(f"Streak for user with Discord ID {discord_id} updated to {new_streak}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='forcepostrebuild')
async def force_post_rebuild(ctx):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to force a post rebuild.")
return
# Rebuild the post
await weekly_post_manager.rebuild_post(team_member_manager.team_members)
await ctx.send("Post rebuilt successfully.")
@bot.command(name='deletelateststatus')
async def delete_latest_status(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to delete status updates.")
return
# Find the member object using the Discord ID
member = team_member_manager.find_member(discord_id)
if not member:
await ctx.send(f"No user with Discord ID {discord_id} found.")
return
# Delete the newest status using the UpdatesManager's method
updates_manager.delete_newest_status(discord_id)
await ctx.send(f"Latest status update for user with Discord ID {discord_id} deleted successfully.")
@bot.command(name='viewuser')
async def view_user(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to view user data.")
return
# Get the member's statuses using the UpdatesManager's method
statuses = updates_manager.get_all_statuses_for_user(discord_id)
if not statuses:
await ctx.send(f"No status updates found for user with Discord ID {discord_id}.")
return
# Loop through the statuses and send individual messages
for status in statuses:
await ctx.send(f"### **Timestamp:** {status['timestamp']}")
await ctx.send(f"### **Raw Status:** {status['status']}")
await ctx.send(f"### **Summarized Status:** \n{status['summarized_status']}")
@bot.command(name='setvacationstatus')
async def set_vacation_status(ctx, discord_id: int):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to set vacation status.")
return
member = team_member_manager.find_member(discord_id)
if member:
new_status = not member.on_vacation
team_member_manager.set_member_vacation_status(discord_id, new_status)
await ctx.send(f"Vacation status for user with Discord ID {discord_id} set to {'on vacation' if new_status else 'not on vacation'}.")
else:
await ctx.send(f"No user with Discord ID {discord_id} found.")
@bot.command(name='weeklysummary')
async def weekly_summary(ctx, discord_id: int, start_date: str, end_date: str):
if ctx.message.author.id != ADMIN_DISCORD_ID or not isinstance(ctx.channel, DMChannel):
await ctx.send("You're not authorized to generate weekly summaries.")
return
# Find the member object using the Discord ID
member = team_member_manager.find_member(discord_id)
if not member:
await ctx.send(f"No user with Discord ID {discord_id} found.")
return
# Convert the start_date and end_date strings to datetime objects
# Adjusting the date format to MM-DD-YYYY and setting the time
try:
start_date = datetime.strptime(start_date, '%m-%d-%Y')
end_date = datetime.strptime(end_date, '%m-%d-%Y')
# Setting the time to ensure the whole week is captured
start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
end_date = end_date.replace(hour=23, minute=59, second=59, microsecond=999999)
except ValueError:
await ctx.send("Invalid date format. Please use MM-DD-YYYY.")
return
# Generate the weekly summary
weekly_summary = await updates_manager.generate_weekly_summary(discord_id, start_date, end_date)
# Send the weekly summary to the admin user
admin_user = bot.get_user(ADMIN_DISCORD_ID)
if admin_user:
await admin_user.send(f"**{member.name}'s Weekly Summary for {start_date.strftime('%m-%d-%Y')} to {end_date.strftime('%m-%d-%Y')}:**\n{weekly_summary}")
else:
await ctx.send("Unable to find the admin user.")
@bot.event
async def on_ready():
print("Bot is online!") # Log that the bot is online
| streaks_db = StreaksDB(MYSQL_HOST, MYSQL_USER, MYSQL_PASSWORD, MYSQL_DB, MYSQL_PORT) | 0 | 2023-10-12 02:01:46+00:00 | 24k |
azuline/rose | rose/rules_test.py | [
{
"identifier": "AudioTags",
"path": "rose/audiotags.py",
"snippet": "class AudioTags:\n id: str | None\n release_id: str | None\n title: str | None\n year: int | None\n tracknumber: str | None\n tracktotal: int | None\n discnumber: str | None\n disctotal: int | None\n album: ... | import dataclasses
import pytest
from pathlib import Path
from typing import Any
from unittest.mock import Mock
from rose.audiotags import AudioTags
from rose.cache import (
list_releases,
list_tracks,
update_cache,
)
from rose.common import Artist
from rose.config import Config
from rose.rule_parser import MetadataMatcher, MetadataRule
from rose.rules import (
FastSearchResult,
TrackTagNotAllowedError,
execute_metadata_rule,
execute_stored_metadata_rules,
fast_search_for_matching_releases,
fast_search_for_matching_tracks,
filter_release_false_positives_using_read_cache,
filter_track_false_positives_using_read_cache,
) | 16,537 | execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-Pop", "lalala"]
def test_action_on_different_tag(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("label:A Cool Label", ["genre::replace:hi"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["hi"]
def test_action_no_pattern(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["matched:::sed:P:B"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-Bop", "Bop"]
def test_chained_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse(
"label:A Cool Label",
[
"replace:Jennie",
"label:^Jennie$::replace:Jisoo",
"label:nomatch::replace:Rose",
"genre::replace:haha",
],
)
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.label == ["Jisoo"]
assert af.genre == ["haha"]
@pytest.mark.timeout(2)
def test_confirmation_yes(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.confirm", lambda *_, **__: True)
execute_metadata_rule(config, rule, confirm_yes=True)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
@pytest.mark.timeout(2)
def test_confirmation_no(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.confirm", lambda *_, **__: False)
execute_metadata_rule(config, rule, confirm_yes=True)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
@pytest.mark.timeout(2)
def test_confirmation_count(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.prompt", Mock(side_effect=["no", "8", "6"]))
# Abort.
execute_metadata_rule(config, rule, confirm_yes=True, enter_number_to_confirm_above_count=1)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Success in two arguments.
execute_metadata_rule(config, rule, confirm_yes=True, enter_number_to_confirm_above_count=1)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_dry_run(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
execute_metadata_rule(config, rule, dry_run=True, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
def test_run_stored_rules(config: Config, source_dir: Path) -> None:
config = dataclasses.replace(
config,
stored_metadata_rules=[MetadataRule.parse("tracktitle:Track", ["replace:lalala"])],
)
execute_stored_metadata_rules(config)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
@pytest.mark.usefixtures("seeded_cache")
def test_fast_search_for_matching_releases(config: Config) -> None:
results = fast_search_for_matching_releases(
config, MetadataMatcher.parse("albumartist:Techno Man")
)
assert results == [FastSearchResult(id="r1", path=config.music_source_dir / "r1")]
@pytest.mark.usefixtures("seeded_cache")
def test_fast_search_for_matching_releases_invalid_tag(config: Config) -> None:
with pytest.raises(TrackTagNotAllowedError):
fast_search_for_matching_releases(config, MetadataMatcher.parse("tracktitle:x"))
with pytest.raises(TrackTagNotAllowedError):
fast_search_for_matching_releases(config, MetadataMatcher.parse("trackartist:x"))
# But allow artist tag:
fast_search_for_matching_releases(config, MetadataMatcher.parse("artist:x"))
@pytest.mark.usefixtures("seeded_cache")
def test_filter_release_false_positives_with_read_cache(config: Config) -> None:
matcher = MetadataMatcher.parse("albumartist:^Man")
fsresults = fast_search_for_matching_releases(config, matcher)
assert len(fsresults) == 2
cacheresults = list_releases(config, [r.id for r in fsresults])
assert len(cacheresults) == 2
filteredresults = filter_release_false_positives_using_read_cache(matcher, cacheresults)
assert not filteredresults
@pytest.mark.usefixtures("seeded_cache")
def test_filter_track_false_positives_with_read_cache(config: Config) -> None:
matcher = MetadataMatcher.parse("trackartist:^Man")
fsresults = fast_search_for_matching_tracks(config, matcher)
assert len(fsresults) == 3
|
def test_rules_execution_match_substring(config: Config, source_dir: Path) -> None:
# No match
rule = MetadataRule.parse("tracktitle:bbb", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Match
rule = MetadataRule.parse("tracktitle:rack", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_rules_execution_match_beginnning(config: Config, source_dir: Path) -> None:
# No match
rule = MetadataRule.parse("tracktitle:^rack", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Match
rule = MetadataRule.parse("tracktitle:^Track", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_rules_execution_match_end(config: Config, source_dir: Path) -> None:
# No match
rule = MetadataRule.parse("tracktitle:rack$", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Match
rule = MetadataRule.parse("tracktitle:rack 1$", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_rules_execution_match_superstrict(config: Config, source_dir: Path) -> None:
# No match
rule = MetadataRule.parse("tracktitle:^Track $", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Match
rule = MetadataRule.parse("tracktitle:^Track 1$", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_rules_execution_match_case_insensitive(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:tRaCk:i", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_rules_fields_match_tracktitle(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "8"
def test_rules_fields_match_year(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("year:1990", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.year == 8
def test_rules_fields_match_releasetype(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("releasetype:album", ["replace:live"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.releasetype == "live"
def test_rules_fields_match_tracknumber(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracknumber:1", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.tracknumber == "8"
def test_rules_fields_match_tracktotal(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktotal:2", ["tracktitle::replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "8"
def test_rules_fields_match_discnumber(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("discnumber:1", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.discnumber == "8"
def test_rules_fields_match_disctotal(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("disctotal:1", ["tracktitle::replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "8"
def test_rules_fields_match_albumtitle(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("albumtitle:Love Blackpink", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.album == "8"
def test_rules_fields_match_genre(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["8", "Pop"]
def test_rules_fields_match_label(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("label:Cool", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.label == ["8"]
def test_rules_fields_match_albumartist(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("albumartist:BLACKPINK", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.albumartists.main == [Artist("8")]
def test_rules_fields_match_trackartist(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("trackartist:BLACKPINK", ["replace:8"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.trackartists.main == [Artist("8")]
def test_match_backslash(config: Config, source_dir: Path) -> None:
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
af.title = r"X \\ Y"
af.flush()
update_cache(config)
rule = MetadataRule.parse(r"tracktitle: \\\\ ", [r"sed: \\\\\\\\ : / "])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "X / Y"
def test_action_replace_with_delimiter(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["replace:Hip-Hop;Rap"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["Hip-Hop", "Rap", "Pop"]
def test_action_replace_with_delimiters_empty_str(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["matched:::replace:Hip-Hop;;;;"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["Hip-Hop"]
def test_sed_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["sed:ack:ip"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "Trip 1"
def test_sed_no_pattern(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:P", [r"matched:::sed:^(.*)$:i\\1"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["iK-Pop", "iPop"]
def test_split_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("label:Cool", ["split:Cool"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.label == ["A", "Label"]
def test_split_action_no_pattern(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["matched:::split:P"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-", "op"]
def test_add_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("label:Cool", ["add:Even Cooler Label"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.label == ["A Cool Label", "Even Cooler Label"]
def test_delete_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:^Pop$", ["delete"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-Pop"]
def test_delete_action_no_pattern(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:^Pop$", ["matched:::delete"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == []
def test_preserves_unmatched_multitags(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:^Pop$", ["replace:lalala"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-Pop", "lalala"]
def test_action_on_different_tag(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("label:A Cool Label", ["genre::replace:hi"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["hi"]
def test_action_no_pattern(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("genre:K-Pop", ["matched:::sed:P:B"])
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.genre == ["K-Bop", "Bop"]
def test_chained_action(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse(
"label:A Cool Label",
[
"replace:Jennie",
"label:^Jennie$::replace:Jisoo",
"label:nomatch::replace:Rose",
"genre::replace:haha",
],
)
execute_metadata_rule(config, rule, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.label == ["Jisoo"]
assert af.genre == ["haha"]
@pytest.mark.timeout(2)
def test_confirmation_yes(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.confirm", lambda *_, **__: True)
execute_metadata_rule(config, rule, confirm_yes=True)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
@pytest.mark.timeout(2)
def test_confirmation_no(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.confirm", lambda *_, **__: False)
execute_metadata_rule(config, rule, confirm_yes=True)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
@pytest.mark.timeout(2)
def test_confirmation_count(monkeypatch: Any, config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
monkeypatch.setattr("rose.rules.click.prompt", Mock(side_effect=["no", "8", "6"]))
# Abort.
execute_metadata_rule(config, rule, confirm_yes=True, enter_number_to_confirm_above_count=1)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
# Success in two arguments.
execute_metadata_rule(config, rule, confirm_yes=True, enter_number_to_confirm_above_count=1)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
def test_dry_run(config: Config, source_dir: Path) -> None:
rule = MetadataRule.parse("tracktitle:Track", ["replace:lalala"])
execute_metadata_rule(config, rule, dry_run=True, confirm_yes=False)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title != "lalala"
def test_run_stored_rules(config: Config, source_dir: Path) -> None:
config = dataclasses.replace(
config,
stored_metadata_rules=[MetadataRule.parse("tracktitle:Track", ["replace:lalala"])],
)
execute_stored_metadata_rules(config)
af = AudioTags.from_file(source_dir / "Test Release 1" / "01.m4a")
assert af.title == "lalala"
@pytest.mark.usefixtures("seeded_cache")
def test_fast_search_for_matching_releases(config: Config) -> None:
results = fast_search_for_matching_releases(
config, MetadataMatcher.parse("albumartist:Techno Man")
)
assert results == [FastSearchResult(id="r1", path=config.music_source_dir / "r1")]
@pytest.mark.usefixtures("seeded_cache")
def test_fast_search_for_matching_releases_invalid_tag(config: Config) -> None:
with pytest.raises(TrackTagNotAllowedError):
fast_search_for_matching_releases(config, MetadataMatcher.parse("tracktitle:x"))
with pytest.raises(TrackTagNotAllowedError):
fast_search_for_matching_releases(config, MetadataMatcher.parse("trackartist:x"))
# But allow artist tag:
fast_search_for_matching_releases(config, MetadataMatcher.parse("artist:x"))
@pytest.mark.usefixtures("seeded_cache")
def test_filter_release_false_positives_with_read_cache(config: Config) -> None:
matcher = MetadataMatcher.parse("albumartist:^Man")
fsresults = fast_search_for_matching_releases(config, matcher)
assert len(fsresults) == 2
cacheresults = list_releases(config, [r.id for r in fsresults])
assert len(cacheresults) == 2
filteredresults = filter_release_false_positives_using_read_cache(matcher, cacheresults)
assert not filteredresults
@pytest.mark.usefixtures("seeded_cache")
def test_filter_track_false_positives_with_read_cache(config: Config) -> None:
matcher = MetadataMatcher.parse("trackartist:^Man")
fsresults = fast_search_for_matching_tracks(config, matcher)
assert len(fsresults) == 3 | tracks = list_tracks(config, [r.id for r in fsresults]) | 2 | 2023-10-09 14:42:23+00:00 | 24k |
zhaoyizhou1123/mbrcsl | examples/roboverse/run_combo_roboverse.py | [
{
"identifier": "MLP",
"path": "offlinerlkit/nets/mlp.py",
"snippet": "class MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dims: Union[List[int], Tuple[int]],\n output_dim: Optional[int] = None,\n activation: nn.Module = nn.ReLU,\n dropou... | import argparse
import os
import sys
import random
import datetime
import roboverse
import numpy as np
import torch
from offlinerlkit.nets import MLP
from offlinerlkit.modules import ActorProb, Critic, TanhDiagGaussian, EnsembleDynamicsModel
from offlinerlkit.dynamics import EnsembleDynamics
from offlinerlkit.utils.scaler import StandardScaler
from offlinerlkit.utils.termination_fns import termination_fn_default
from offlinerlkit.buffer import ReplayBuffer
from offlinerlkit.utils.logger import Logger, make_log_dirs
from offlinerlkit.policy_trainer import MBPolicyTrainer
from offlinerlkit.policy import COMBOPolicy
from offlinerlkit.utils.roboverse_utils import PickPlaceObsWrapper, DoubleDrawerObsWrapper, get_pickplace_dataset, get_doubledrawer_dataset
from offlinerlkit.utils.none_or_str import none_or_str | 16,115 |
return parser.parse_args()
def train(args=get_args()):
# seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
# create env and dataset
if args.task == 'pickplace':
env = roboverse.make('Widow250PickTray-v0')
env = PickPlaceObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "pickplace_prior.npy")
task_data_path = os.path.join(args.data_dir, "pickplace_task.npy")
dataset, init_obss_dataset = get_pickplace_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledraweropen':
env = roboverse.make('Widow250DoubleDrawerOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "closed_drawer_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledrawercloseopen':
env = roboverse.make('Widow250DoubleDrawerCloseOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "blocked_drawer_1_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledrawerpickplaceopen':
env = roboverse.make('Widow250DoubleDrawerPickPlaceOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "blocked_drawer_2_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
else:
raise NotImplementedError
env.reset(seed=args.seed)
# create policy model
actor_backbone = MLP(input_dim=np.prod(args.obs_shape), hidden_dims=args.hidden_dims)
critic1_backbone = MLP(input_dim=np.prod(args.obs_shape) + args.action_dim, hidden_dims=args.hidden_dims)
critic2_backbone = MLP(input_dim=np.prod(args.obs_shape) + args.action_dim, hidden_dims=args.hidden_dims)
dist = TanhDiagGaussian(
latent_dim=getattr(actor_backbone, "output_dim"),
output_dim=args.action_dim,
unbounded=True,
conditioned_sigma=True
)
actor = ActorProb(actor_backbone, dist, args.device)
critic1 = Critic(critic1_backbone, args.device)
critic2 = Critic(critic2_backbone, args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(actor_optim, args.epoch)
if args.auto_alpha:
target_entropy = args.target_entropy if args.target_entropy \
else -np.prod(env.action_space.shape)
args.target_entropy = target_entropy
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
alpha = (target_entropy, log_alpha, alpha_optim)
else:
alpha = args.alpha
# create dynamics
load_dynamics_model = True if args.load_dynamics_path else False
dynamics_model = EnsembleDynamicsModel(
obs_dim=np.prod(args.obs_shape),
action_dim=args.action_dim,
hidden_dims=args.dynamics_hidden_dims,
num_ensemble=args.n_ensemble,
num_elites=args.n_elites,
weight_decays=args.dynamics_weight_decay,
device=args.device
)
dynamics_optim = torch.optim.Adam(
dynamics_model.parameters(),
lr=args.dynamics_lr
)
|
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--algo-name", type=str, default="combo")
parser.add_argument("--task", type=str, default="pickplace", help="pickplace") # Self-constructed environment
parser.add_argument("--last_eval", action="store_false")
# env config (pickplace)
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--horizon', type=int, default=40, help="max path length for pickplace")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--actor-lr", type=float, default=1e-4)
parser.add_argument("--critic-lr", type=float, default=3e-4)
parser.add_argument("--hidden-dims", type=int, nargs='*', default=[256, 256, 256])
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--tau", type=float, default=0.005)
parser.add_argument("--alpha", type=float, default=0.2)
parser.add_argument("--auto-alpha", default=True)
parser.add_argument("--target-entropy", type=int, default=None)
parser.add_argument("--alpha-lr", type=float, default=1e-4)
parser.add_argument("--cql-weight", type=float, default=1.0)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--max-q-backup", type=bool, default=False)
parser.add_argument("--deterministic-backup", type=bool, default=True)
parser.add_argument("--with-lagrange", type=bool, default=False)
parser.add_argument("--lagrange-threshold", type=float, default=10.0)
parser.add_argument("--cql-alpha-lr", type=float, default=3e-4)
parser.add_argument("--num-repeat-actions", type=int, default=10)
parser.add_argument("--uniform-rollout", type=bool, default=False)
parser.add_argument("--rho-s", type=str, default="mix", choices=["model", "mix"])
parser.add_argument("--dynamics-lr", type=float, default=1e-3)
parser.add_argument("--dynamics-hidden-dims", type=int, nargs='*', default=[200, 200, 200, 200])
parser.add_argument("--dynamics-weight-decay", type=float, nargs='*', default=[2.5e-5, 5e-5, 7.5e-5, 7.5e-5, 1e-4])
parser.add_argument("--n-ensemble", type=int, default=7)
parser.add_argument("--n-elites", type=int, default=5)
parser.add_argument("--rollout-freq", type=int, default=1000)
parser.add_argument("--rollout-batch-size", type=int, default=50000)
parser.add_argument("--rollout-length", type=int, default=5)
parser.add_argument("--model-retain-epochs", type=int, default=5)
parser.add_argument("--real-ratio", type=float, default=0.5)
parser.add_argument("--load-dynamics-path", type=none_or_str, default=None)
parser.add_argument("--epoch", type=int, default=200)
parser.add_argument("--step-per-epoch", type=int, default=1000)
parser.add_argument("--eval_episodes", type=int, default=100)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
return parser.parse_args()
def train(args=get_args()):
# seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
# create env and dataset
if args.task == 'pickplace':
env = roboverse.make('Widow250PickTray-v0')
env = PickPlaceObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "pickplace_prior.npy")
task_data_path = os.path.join(args.data_dir, "pickplace_task.npy")
dataset, init_obss_dataset = get_pickplace_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledraweropen':
env = roboverse.make('Widow250DoubleDrawerOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "closed_drawer_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledrawercloseopen':
env = roboverse.make('Widow250DoubleDrawerCloseOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "blocked_drawer_1_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
elif args.task == 'doubledrawerpickplaceopen':
env = roboverse.make('Widow250DoubleDrawerPickPlaceOpenGraspNeutral-v0')
env = DoubleDrawerObsWrapper(env)
obs_space = env.observation_space
args.obs_shape = obs_space.shape
args.obs_dim = np.prod(args.obs_shape)
args.action_shape = env.action_space.shape
args.action_dim = np.prod(args.action_shape)
prior_data_path = os.path.join(args.data_dir, "blocked_drawer_2_prior.npy")
task_data_path = os.path.join(args.data_dir, "drawer_task.npy")
dataset, init_obss_dataset = get_doubledrawer_dataset(
prior_data_path=prior_data_path,
task_data_path=task_data_path)
else:
raise NotImplementedError
env.reset(seed=args.seed)
# create policy model
actor_backbone = MLP(input_dim=np.prod(args.obs_shape), hidden_dims=args.hidden_dims)
critic1_backbone = MLP(input_dim=np.prod(args.obs_shape) + args.action_dim, hidden_dims=args.hidden_dims)
critic2_backbone = MLP(input_dim=np.prod(args.obs_shape) + args.action_dim, hidden_dims=args.hidden_dims)
dist = TanhDiagGaussian(
latent_dim=getattr(actor_backbone, "output_dim"),
output_dim=args.action_dim,
unbounded=True,
conditioned_sigma=True
)
actor = ActorProb(actor_backbone, dist, args.device)
critic1 = Critic(critic1_backbone, args.device)
critic2 = Critic(critic2_backbone, args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(actor_optim, args.epoch)
if args.auto_alpha:
target_entropy = args.target_entropy if args.target_entropy \
else -np.prod(env.action_space.shape)
args.target_entropy = target_entropy
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
alpha = (target_entropy, log_alpha, alpha_optim)
else:
alpha = args.alpha
# create dynamics
load_dynamics_model = True if args.load_dynamics_path else False
dynamics_model = EnsembleDynamicsModel(
obs_dim=np.prod(args.obs_shape),
action_dim=args.action_dim,
hidden_dims=args.dynamics_hidden_dims,
num_ensemble=args.n_ensemble,
num_elites=args.n_elites,
weight_decays=args.dynamics_weight_decay,
device=args.device
)
dynamics_optim = torch.optim.Adam(
dynamics_model.parameters(),
lr=args.dynamics_lr
) | scaler = StandardScaler() | 6 | 2023-10-11 08:36:06+00:00 | 24k |
lmb-freiburg/ldce | scripts/ldce.py | [
{
"identifier": "disabled_train",
"path": "sampling_helpers.py",
"snippet": "def disabled_train(self, mode=True):\n \"\"\"Overwrite model.train with this function to make sure train/eval mode\n does not change anymore.\"\"\"\n return self"
},
{
"identifier": "get_model",
"path": "sa... | import argparse
import os
import psutil
import yaml
import copy
import random
import matplotlib.pyplot as plt
import numpy as np
import pathlib
import torch
import hydra
import wandb
import torchvision
import json
import sys
import regex as re
import open_clip
from contextlib import nullcontext
from torch import autocast
from omegaconf import OmegaConf, open_dict
from hydra.utils import instantiate
from omegaconf import DictConfig, OmegaConf
from torchvision import transforms, datasets
from torchvision.utils import save_image
from sampling_helpers import disabled_train, get_model, _unmap_img, generate_samples
from sampling_helpers import load_model_hf
from ldm import *
from ldm.models.diffusion.cc_ddim import CCMDDIMSampler
from data.imagenet_classnames import name_map, openai_imagenet_classes
from utils.DecisionDensenetModel import DecisionDensenetModel
from utils.preprocessor import Normalizer, CropAndNormalizer, ResizeAndNormalizer, GenericPreprocessing, Crop
from utils.vision_language_wrapper import VisionLanguageWrapper
from utils.madry_net import MadryNet
from utils.dino_linear import LinearClassifier, DINOLinear | 14,675 | print(f"resuming from batch {last_data_idx}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu") # there seems to be a CUDA/autograd instability in gradient computation
print(f"using device: {device}")
model = get_model(cfg_path=cfg.diffusion_model.cfg_path, ckpt_path = cfg.diffusion_model.ckpt_path).to(device).eval()
classifier_model = get_classifier(cfg, device)
classifier_model.to(device).eval()
classifier_model.train = disabled_train
ddim_steps = cfg.ddim_steps
ddim_eta = cfg.ddim_eta
scale = cfg.scale #for unconditional guidance
strength = cfg.strength #for unconditional guidance
sampler = CCMDDIMSampler(model, classifier_model, seg_model= None, classifier_wrapper="classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper, record_intermediate_results=cfg.record_intermediate_results, verbose=cfg.verbose, **cfg.sampler)
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(strength * len(sampler.ddim_timesteps))
assert len(sampler.ddim_timesteps) == ddim_steps, "ddim_steps should be equal to len(sampler.ddim_timesteps)"
n_samples_per_class = cfg.n_samples_per_class
batch_size = cfg.data.batch_size
shuffle = cfg.get("shuffle", False)
#save config to the output directory
#check if the config file already exists else create a config file
config_path = os.path.join(out_dir, "config.yaml")
if os.path.exists(config_path):
print("config file already exists! skipping ...")
else:
with open(os.path.join(out_dir, "config.yaml"), 'w') as f:
print("saving config to ", os.path.join(out_dir, "config.yaml ..."))
yaml.dump(config, f)
os.chmod(os.path.join(out_dir, "config.yaml"), 0o555)
#data_path = cfg.data_path
dataset = get_dataset(cfg, last_data_idx=last_data_idx)
print("dataset length: ", len(dataset))
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=1)
if "ImageNet" in cfg.data._target_:
i2h = name_map
elif "CelebAHQDataset" in cfg.data._target_:
# query label 31 (smile): label=0 <-> no smile and label=1 <-> smile
# query label 39 (age): label=0 <-> old and label=1 <-> young
assert cfg.data.query_label in [31, 39]
if 31 == cfg.data.query_label:
i2h = ["no smile", "smile"]
elif 39 == cfg.data.query_label:
i2h = ["old", "young"]
else:
raise NotImplementedError
elif "Flowers102" in cfg.data._target_:
with open("data/flowers_idx_to_label.json", "r") as f:
flowers_idx_to_classname = json.load(f)
flowers_idx_to_classname = {int(k)-1: v for k, v in flowers_idx_to_classname.items()}
i2h = flowers_idx_to_classname
elif "OxfordIIIPets" in cfg.data._target_:
with open("data/pets_idx_to_label.json", "r") as f:
pets_idx_to_classname = json.load(f)
i2h = {int(k): v for k, v in pets_idx_to_classname.items()}
else:
raise NotImplementedError
if "ImageNet" in cfg.data._target_:
with open('data/synset_closest_idx.yaml', 'r') as file:
synset_closest_idx = yaml.safe_load(file)
elif "Flowers102" in cfg.data._target_:
with open("data/flowers_closest_indices.json") as file:
closest_indices = json.load(file)
closest_indices = {int(k):v for k,v in closest_indices.items()}
elif "OxfordIIIPets" in cfg.data._target_:
with open("data/pets_closest_indices.json") as file:
closest_indices = json.load(file)
closest_indices = {int(k):v for k,v in closest_indices.items()}
if not cfg.resume:
torch.save({"last_data_idx": -1}, checkpoint_path)
seed = cfg.seed if "seed" in cfg else 0
set_seed(seed=seed)
for i, batch in enumerate(data_loader):
if "fixed_seed" in cfg:
set_seed(seed=cfg.get("seed", 0)) if cfg.fixed_seed else None
seed = seed if cfg.fixed_seed else -1
if "return_tgt_cls" in cfg.data and cfg.data.return_tgt_cls:
image, label, tgt_classes, unique_data_idx = batch
tgt_classes = tgt_classes.to(device) #squeeze()
else:
image, label, unique_data_idx = batch
if "ImageNet" in cfg.data._target_:
tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
elif "CelebAHQDataset" in cfg.data._target_:
tgt_classes = (1 - label).type(torch.float32)
elif "Flowers102" in cfg.data._target_ or "OxfordIIIPets" in cfg.data._target_:
tgt_classes = torch.tensor([closest_indices[unique_data_idx[l].item()*cfg.data.num_shards + cfg.data.shard][0] for l in range(label.shape[0])]).to(device)
else:
raise NotImplementedError
image = image.to(device) #squeeze()
label = label.to(device) #.item() #squeeze()
#tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
#tgt_classes = synset_closest_idx[label]
#tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
#shuffle tgt_classes
#random.shuffle(tgt_classes)
#get classifcation prediction
with torch.inference_mode():
#with precision_scope():
if "classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper:
logits = classifier_model(image)
else:
|
torch.backends.cuda.matmul.allow_tf32 = True
# torch.backends.cudnn.benchmark = True
try:
except:
print("Install OpenClip via: pip install open_clip_torch")
def set_seed(seed: int = 0):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.cuda.manual_seed_all(seed)
def blockPrint():
sys.stdout = open(os.devnull, 'w')
def get_classifier(cfg, device):
if "ImageNet" in cfg.data._target_:
classifier_name = cfg.classifier_model.name
if classifier_name == "robust_resnet50":
classifier_model = MadryNet(cfg.classifier_model.ckpt, device)
if "classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper:
classifier_model = Crop(classifier_model)
else:
classifier_model = getattr(torchvision.models, classifier_name)(pretrained=True)
if "classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper:
classifier_model = CropAndNormalizer(classifier_model)
elif "CelebAHQDataset" in cfg.data._target_:
assert cfg.data.query_label in [20, 31, 39], 'Query label MUST be 20 (Gender), 31 (Smile), or 39 (Age) for CelebAHQ'
ql = 0
if cfg.data.query_label in [31, 39]:
ql = 1 if cfg.data.query_label == 31 else 2
classifier_model = DecisionDensenetModel(3, pretrained=False,
query_label=ql)
classifier_model.load_state_dict(torch.load(cfg.classifier_model.classifier_path, map_location='cpu')['model_state_dict'])
if cfg.classifier_model.classifier_wrapper:
classifier_model = Normalizer(
classifier_model,
[0.5] * 3, [0.5] * 3
)
elif "Flowers102" in cfg.data._target_:
# fine-tuned Dino ViT B/8: https://arxiv.org/pdf/2104.14294.pdf
dino = torch.hub.load('facebookresearch/dino:main', 'dino_vits8').to(device).eval()
dim = dino.embed_dim
linear_classifier = LinearClassifier(dim*cfg.classifier_model.n_last_blocks, 102)
linear_classifier.load_state_dict(torch.load(cfg.classifier_model.classifier_path, map_location="cpu"), strict=True)
linear_classifier = linear_classifier.eval().to(device)
classifier_model = DINOLinear(dino, linear_classifier)
transforms_list = [transforms.CenterCrop(224), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]
classifier_model = GenericPreprocessing(classifier_model, transforms.Compose(transforms_list))
elif "OxfordIIIPets" in cfg.data._target_:
# zero-shot OpenClip: https://arxiv.org/pdf/2212.07143.pdf
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
model = model.to(device).eval()
tokenizer = open_clip.get_tokenizer('ViT-B-32')
# prompts following https://github.com/openai/CLIP/blob/main/data/prompts.md
with open("data/pets_idx_to_label.json", "r") as f:
pets_idx_to_classname = json.load(f)
prompts = [f"a photo of a {label}, a type of pet." for label in pets_idx_to_classname.values()]
classifier_model = VisionLanguageWrapper(model, tokenizer, prompts)
# try running optimization on 224x224 pixel image
# transforms_list = [preprocess.transforms[0], preprocess.transforms[1], preprocess.transforms[4]]
if cfg.classifier_model.classifier_wrapper:
transforms_list = [preprocess.transforms[1], preprocess.transforms[4]] # CenterCrop(224, 224), Normalize
classifier_model = GenericPreprocessing(classifier_model, transforms.Compose(transforms_list))
else:
raise NotImplementedError
return classifier_model
def get_dataset(cfg, last_data_idx: int = 0):
if "ImageNet" in cfg.data._target_:
out_size = 256
transform_list = [
transforms.Resize((out_size, out_size)),
transforms.ToTensor()
]
transform = transforms.Compose(transform_list)
dataset = instantiate(cfg.data, start_sample=cfg.data.start_sample, end_sample=cfg.data.end_sample, transform=transform, restart_idx=last_data_idx)
elif "CelebAHQDataset" in cfg.data._target_:
dataset = instantiate(
cfg.data,
image_size=256,
data_dir=cfg.data.data_dir,
random_crop=False,
random_flip=False,
partition='test',
query_label=cfg.data.query_label,
normalize=False,
shard=cfg.data.shard,
num_shards=cfg.data.num_shards,
restart_idx=last_data_idx
)
elif "Flowers102" in cfg.data._target_:
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
])
dataset = instantiate(
cfg.data,
shard=cfg.data.shard,
num_shards=cfg.data.num_shards,
transform=transform,
restart_idx=last_data_idx
)
elif "OxfordIIIPets" in cfg.data._target_: # try running on 224x224 img
def _convert_to_rgb(image):
return image.convert('RGB')
out_size = 256
transform_list = [
transforms.Resize((out_size, out_size)),
# transforms.CenterCrop(out_size),
_convert_to_rgb,
transforms.ToTensor(),
]
transform = transforms.Compose(transform_list)
dataset = instantiate(
cfg.data,
shard=cfg.data.shard,
num_shards=cfg.data.num_shards,
transform=transform,
restart_idx=last_data_idx
)
else:
raise NotImplementedError
return dataset
@hydra.main(version_base=None, config_path="../configs/ldce", config_name="v1")
def main(cfg : DictConfig) -> None:
if "verbose" not in cfg:
with open_dict(cfg):
cfg.verbose = True
if "record_intermediate_results" not in cfg:
with open_dict(cfg):
cfg.record_intermediate_results = True
if "verbose" in cfg and not cfg.verbose:
blockPrint()
os.makedirs(cfg.output_dir, exist_ok=True)
os.chmod(cfg.output_dir, 0o777)
if "ImageNet" in cfg.data._target_:
out_dir = os.path.join(cfg.output_dir, f"bucket_{cfg.data.start_sample}_{cfg.data.end_sample}")
else:
out_dir = os.path.join(cfg.output_dir, f"bucket_{cfg.data.shard}_{cfg.data.num_shards}")
os.makedirs(out_dir, exist_ok=True)
os.chmod(out_dir, 0o777)
checkpoint_path = os.path.join(out_dir, "last_saved_id.pth")
config = {}
if "ImageNet" in cfg.data._target_:
run_id = f"{cfg.data.start_sample}_{cfg.data.end_sample}"
else:
run_id = f"{cfg.data.shard}_{cfg.data.num_shards}"
if cfg.resume:
print("run ID to resume: ", run_id)
else:
print("starting new run", run_id)
config.update(OmegaConf.to_container(cfg, resolve=True))
print("current run id: ", run_id)
last_data_idx = 0
if cfg.resume: # or os.path.isfile(checkpoint_path): resume only if asked to, allow restarts
print(f"resuming from {checkpoint_path}")
#check if checkpoint exists
if not os.path.exists(checkpoint_path):
print("checkpoint does not exist! starting from 0 ...")
else:
checkpoint = torch.load(checkpoint_path)# torch.load(restored_file.name)
last_data_idx = checkpoint["last_data_idx"] + 1 if "last_data_idx" in checkpoint else 0
print(f"resuming from batch {last_data_idx}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu") # there seems to be a CUDA/autograd instability in gradient computation
print(f"using device: {device}")
model = get_model(cfg_path=cfg.diffusion_model.cfg_path, ckpt_path = cfg.diffusion_model.ckpt_path).to(device).eval()
classifier_model = get_classifier(cfg, device)
classifier_model.to(device).eval()
classifier_model.train = disabled_train
ddim_steps = cfg.ddim_steps
ddim_eta = cfg.ddim_eta
scale = cfg.scale #for unconditional guidance
strength = cfg.strength #for unconditional guidance
sampler = CCMDDIMSampler(model, classifier_model, seg_model= None, classifier_wrapper="classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper, record_intermediate_results=cfg.record_intermediate_results, verbose=cfg.verbose, **cfg.sampler)
sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
t_enc = int(strength * len(sampler.ddim_timesteps))
assert len(sampler.ddim_timesteps) == ddim_steps, "ddim_steps should be equal to len(sampler.ddim_timesteps)"
n_samples_per_class = cfg.n_samples_per_class
batch_size = cfg.data.batch_size
shuffle = cfg.get("shuffle", False)
#save config to the output directory
#check if the config file already exists else create a config file
config_path = os.path.join(out_dir, "config.yaml")
if os.path.exists(config_path):
print("config file already exists! skipping ...")
else:
with open(os.path.join(out_dir, "config.yaml"), 'w') as f:
print("saving config to ", os.path.join(out_dir, "config.yaml ..."))
yaml.dump(config, f)
os.chmod(os.path.join(out_dir, "config.yaml"), 0o555)
#data_path = cfg.data_path
dataset = get_dataset(cfg, last_data_idx=last_data_idx)
print("dataset length: ", len(dataset))
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=1)
if "ImageNet" in cfg.data._target_:
i2h = name_map
elif "CelebAHQDataset" in cfg.data._target_:
# query label 31 (smile): label=0 <-> no smile and label=1 <-> smile
# query label 39 (age): label=0 <-> old and label=1 <-> young
assert cfg.data.query_label in [31, 39]
if 31 == cfg.data.query_label:
i2h = ["no smile", "smile"]
elif 39 == cfg.data.query_label:
i2h = ["old", "young"]
else:
raise NotImplementedError
elif "Flowers102" in cfg.data._target_:
with open("data/flowers_idx_to_label.json", "r") as f:
flowers_idx_to_classname = json.load(f)
flowers_idx_to_classname = {int(k)-1: v for k, v in flowers_idx_to_classname.items()}
i2h = flowers_idx_to_classname
elif "OxfordIIIPets" in cfg.data._target_:
with open("data/pets_idx_to_label.json", "r") as f:
pets_idx_to_classname = json.load(f)
i2h = {int(k): v for k, v in pets_idx_to_classname.items()}
else:
raise NotImplementedError
if "ImageNet" in cfg.data._target_:
with open('data/synset_closest_idx.yaml', 'r') as file:
synset_closest_idx = yaml.safe_load(file)
elif "Flowers102" in cfg.data._target_:
with open("data/flowers_closest_indices.json") as file:
closest_indices = json.load(file)
closest_indices = {int(k):v for k,v in closest_indices.items()}
elif "OxfordIIIPets" in cfg.data._target_:
with open("data/pets_closest_indices.json") as file:
closest_indices = json.load(file)
closest_indices = {int(k):v for k,v in closest_indices.items()}
if not cfg.resume:
torch.save({"last_data_idx": -1}, checkpoint_path)
seed = cfg.seed if "seed" in cfg else 0
set_seed(seed=seed)
for i, batch in enumerate(data_loader):
if "fixed_seed" in cfg:
set_seed(seed=cfg.get("seed", 0)) if cfg.fixed_seed else None
seed = seed if cfg.fixed_seed else -1
if "return_tgt_cls" in cfg.data and cfg.data.return_tgt_cls:
image, label, tgt_classes, unique_data_idx = batch
tgt_classes = tgt_classes.to(device) #squeeze()
else:
image, label, unique_data_idx = batch
if "ImageNet" in cfg.data._target_:
tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
elif "CelebAHQDataset" in cfg.data._target_:
tgt_classes = (1 - label).type(torch.float32)
elif "Flowers102" in cfg.data._target_ or "OxfordIIIPets" in cfg.data._target_:
tgt_classes = torch.tensor([closest_indices[unique_data_idx[l].item()*cfg.data.num_shards + cfg.data.shard][0] for l in range(label.shape[0])]).to(device)
else:
raise NotImplementedError
image = image.to(device) #squeeze()
label = label.to(device) #.item() #squeeze()
#tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
#tgt_classes = synset_closest_idx[label]
#tgt_classes = torch.tensor([random.choice(synset_closest_idx[l.item()]) for l in label]).to(device)
#shuffle tgt_classes
#random.shuffle(tgt_classes)
#get classifcation prediction
with torch.inference_mode():
#with precision_scope():
if "classifier_wrapper" in cfg.classifier_model and cfg.classifier_model.classifier_wrapper:
logits = classifier_model(image)
else: | logits = sampler.get_classifier_logits(_unmap_img(image)) #converting to -1, 1 | 2 | 2023-10-10 09:40:10+00:00 | 24k |
spla-tam/SplaTAM | scripts/post_splatam_opt.py | [
{
"identifier": "AzureKinectDataset",
"path": "datasets/gradslam_datasets/azure.py",
"snippet": "class AzureKinectDataset(GradSLAMDataset):\n def __init__(\n self,\n config_dict,\n basedir,\n sequence,\n stride: Optional[int] = None,\n start: Optional[int] = ... | import argparse
import os
import random
import sys
import shutil
import cv2
import numpy as np
import torch
import wandb
from importlib.machinery import SourceFileLoader
from tqdm import tqdm
from datasets.gradslam_datasets import (
load_dataset_config,
ICLDataset,
ReplicaDataset,
AzureKinectDataset,
ScannetDataset,
Ai2thorDataset,
Record3DDataset,
RealsenseDataset,
TUMDataset,
ScannetPPDataset,
NeRFCaptureDataset
)
from utils.common_utils import seed_everything, save_seq_params, save_params, save_params_ckpt, save_seq_params_ckpt
from utils.recon_helpers import setup_camera
from utils.gs_helpers import (
params2rendervar, params2depthplussilhouette,
transformed_params2depthplussilhouette,
transform_to_frame, report_progress, eval,
l1_loss_v1, matrix_to_quaternion
)
from utils.gs_external import (
calc_ssim, build_rotation, densify,
get_expon_lr_func, update_learning_rate
)
from diff_gaussian_rasterization import GaussianRasterizer as Renderer | 20,186 | use_train_split=dataset_config["use_train_split"],
)
num_frames = dataset_config["num_frames"]
if num_frames == -1:
num_frames = len(mapping_dataset)
eval_num_frames = dataset_config["eval_num_frames"]
if eval_num_frames == -1:
eval_num_frames = len(eval_dataset)
# Initialize Parameters, Optimizer & Canoncial Camera parameters
ckpt_path = config["data"]["param_ckpt_path"]
params, variables, optimizer, intrinsics, w2c, cam = initialize_first_timestep_from_ckpt(ckpt_path,mapping_dataset, num_frames,
config['train']['lrs_mapping'],
config['mean_sq_dist_method'])
_, _, map_intrinsics, _ = mapping_dataset[0]
# Load all RGBD frames - Mapping dataloader
color_all_frames_map = []
depth_all_frames_map = []
gt_w2c_all_frames_map = []
gs_cams_all_frames_map = []
for time_idx in range(num_frames):
color, depth, _, gt_pose = mapping_dataset[time_idx]
# Process poses
gt_w2c = torch.linalg.inv(gt_pose)
# Process RGB-D Data
color = color.permute(2, 0, 1) / 255
depth = depth.permute(2, 0, 1)
color_all_frames_map.append(color)
depth_all_frames_map.append(depth)
gt_w2c_all_frames_map.append(gt_w2c)
# Setup Gaussian Splatting Camera
gs_cam = setup_camera(color.shape[2], color.shape[1],
map_intrinsics.cpu().numpy(),
gt_w2c.detach().cpu().numpy())
gs_cams_all_frames_map.append(gs_cam)
# Iterate over Scan
for time_idx in tqdm(range(num_frames)):
# Optimization Iterations
num_iters_mapping = config['train']['num_iters_mapping']
# Initialize current frame data
iter_time_idx = time_idx
color = color_all_frames_map[iter_time_idx]
depth = depth_all_frames_map[iter_time_idx]
curr_gt_w2c = gt_w2c_all_frames_map[:iter_time_idx+1]
curr_data = {'cam': cam, 'im': color, 'depth': depth, 'id': iter_time_idx,
'intrinsics': intrinsics, 'w2c': w2c, 'iter_gt_w2c_list': curr_gt_w2c}
# Add new Gaussians to the scene based on the Silhouette
# if time_idx > 0:
# params, variables = add_new_gaussians(params, variables, curr_data,
# config['train']['sil_thres'], time_idx,
# config['mean_sq_dist_method'])
post_num_pts = params['means3D'].shape[0]
if config['use_wandb']:
wandb_run.log({"Init/Number of Gaussians": post_num_pts,
"Init/step": wandb_time_step})
# Reset Optimizer & Learning Rates for Full Map Optimization
optimizer = initialize_optimizer(params, config['train']['lrs_mapping'])
means3D_scheduler = get_expon_lr_func(lr_init=config['train']['lrs_mapping']['means3D'],
lr_final=config['train']['lrs_mapping_means3D_final'],
lr_delay_mult=config['train']['lr_delay_mult'],
max_steps=config['train']['num_iters_mapping'])
# Mapping
if (time_idx + 1) == num_frames:
if num_iters_mapping > 0:
progress_bar = tqdm(range(num_iters_mapping), desc=f"Mapping Time Step: {time_idx}")
for iter in range(num_iters_mapping):
# Update Learning Rates for means3D
updated_lr = update_learning_rate(optimizer, means3D_scheduler, iter+1)
if config['use_wandb']:
wandb_run.log({"Learning Rate - Means3D": updated_lr})
# Randomly select a frame until current time step
iter_time_idx = random.randint(0, time_idx)
# Initialize Data for selected frame
iter_color = color_all_frames_map[iter_time_idx]
iter_depth = depth_all_frames_map[iter_time_idx]
iter_gt_w2c = gt_w2c_all_frames_map[:iter_time_idx+1]
iter_gs_cam = gs_cams_all_frames_map[iter_time_idx]
iter_data = {'cam': iter_gs_cam, 'im': iter_color, 'depth': iter_depth,
'id': iter_time_idx, 'intrinsics': map_intrinsics,
'w2c': gt_w2c_all_frames_map[iter_time_idx], 'iter_gt_w2c_list': iter_gt_w2c}
# Loss for current frame
loss, variables, losses = get_loss_gs(params, iter_data, variables, config['train']['loss_weights'])
# Backprop
loss.backward()
with torch.no_grad():
# Gaussian-Splatting's Gradient-based Densification
if config['train']['use_gaussian_splatting_densification']:
params, variables = densify(params, variables, optimizer, iter, config['train']['densify_dict'])
if config['use_wandb']:
wandb_run.log({"Number of Gaussians - Densification": params['means3D'].shape[0]})
# Optimizer Update
optimizer.step()
optimizer.zero_grad(set_to_none=True)
# Report Progress
if config['report_iter_progress']:
if config['use_wandb']:
report_progress(params, iter_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['train']['sil_thres'],
wandb_run=wandb_run, wandb_step=wandb_step, wandb_save_qual=config['wandb']['save_qual'],
mapping=True, online_time_idx=time_idx)
else:
report_progress(params, iter_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['train']['sil_thres'],
mapping=True, online_time_idx=time_idx)
else:
progress_bar.update(1)
# Eval Params at 7K Iterations
if (iter + 1) == 7000:
print("Evaluating Params at 7K Iterations")
eval_params = convert_params_to_store(params)
output_dir = os.path.join(config["workdir"], config["run_name"])
eval_dir = os.path.join(output_dir, "eval_7k")
os.makedirs(eval_dir, exist_ok=True)
if config['use_wandb']:
|
_BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, _BASE_DIR)
print("System Paths:")
for p in sys.path:
print(p)
def get_dataset(config_dict, basedir, sequence, **kwargs):
if config_dict["dataset_name"].lower() in ["icl"]:
return ICLDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["replica"]:
return ReplicaDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["azure", "azurekinect"]:
return AzureKinectDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["scannet"]:
return ScannetDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["ai2thor"]:
return Ai2thorDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["record3d"]:
return Record3DDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["realsense"]:
return RealsenseDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["tum"]:
return TUMDataset(config_dict, basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["scannetpp"]:
return ScannetPPDataset(basedir, sequence, **kwargs)
elif config_dict["dataset_name"].lower() in ["nerfcapture"]:
return NeRFCaptureDataset(basedir, sequence, **kwargs)
else:
raise ValueError(f"Unknown dataset name {config_dict['dataset_name']}")
def get_pointcloud(color, depth, intrinsics, w2c, transform_pts=True,
mask=None, compute_mean_sq_dist=False, mean_sq_dist_method="projective"):
width, height = color.shape[2], color.shape[1]
CX = intrinsics[0][2]
CY = intrinsics[1][2]
FX = intrinsics[0][0]
FY = intrinsics[1][1]
# Compute indices of pixels
x_grid, y_grid = torch.meshgrid(torch.arange(width).cuda().float(),
torch.arange(height).cuda().float(),
indexing='xy')
xx = (x_grid - CX)/FX
yy = (y_grid - CY)/FY
xx = xx.reshape(-1)
yy = yy.reshape(-1)
depth_z = depth[0].reshape(-1)
# Initialize point cloud
pts_cam = torch.stack((xx * depth_z, yy * depth_z, depth_z), dim=-1)
if transform_pts:
pix_ones = torch.ones(height * width, 1).cuda().float()
pts4 = torch.cat((pts_cam, pix_ones), dim=1)
c2w = torch.inverse(w2c)
pts = (c2w @ pts4.T).T[:, :3]
else:
pts = pts_cam
# Compute mean squared distance for initializing the scale of the Gaussians
if compute_mean_sq_dist:
if mean_sq_dist_method == "projective":
# Projective Geometry (this is fast, farther -> larger radius)
scale_gaussian = depth_z / ((FX + FY)/2)
mean3_sq_dist = scale_gaussian**2
else:
raise ValueError(f"Unknown mean_sq_dist_method: {mean_sq_dist_method}")
# Colorize point cloud
cols = torch.permute(color, (1, 2, 0)).reshape(-1, 3) # (C, H, W) -> (H, W, C) -> (H * W, C)
point_cld = torch.cat((pts, cols), -1)
# Select points based on mask
if mask is not None:
point_cld = point_cld[mask]
if compute_mean_sq_dist:
mean3_sq_dist = mean3_sq_dist[mask]
if compute_mean_sq_dist:
return point_cld, mean3_sq_dist
else:
return point_cld
def initialize_params(init_pt_cld, num_frames, mean3_sq_dist):
num_pts = init_pt_cld.shape[0]
means3D = init_pt_cld[:, :3] # [num_gaussians, 3]
unnorm_rots = np.tile([1, 0, 0, 0], (num_pts, 1)) # [num_gaussians, 3]
logit_opacities = torch.zeros((num_pts, 1), dtype=torch.float, device="cuda")
params = {
'means3D': means3D,
'rgb_colors': init_pt_cld[:, 3:6],
'unnorm_rotations': unnorm_rots,
'logit_opacities': logit_opacities,
'log_scales': torch.tile(torch.log(torch.sqrt(mean3_sq_dist))[..., None], (1, 1)),
}
# Initialize a single gaussian trajectory to model the camera poses relative to the first frame
cam_rots = np.tile([1, 0, 0, 0], (1, 1))
cam_rots = np.tile(cam_rots[:, :, None], (1, 1, num_frames))
params['cam_unnorm_rots'] = cam_rots
params['cam_trans'] = np.zeros((1, 3, num_frames))
for k, v in params.items():
# Check if value is already a torch tensor
if not isinstance(v, torch.Tensor):
params[k] = torch.nn.Parameter(torch.tensor(v).cuda().float().contiguous().requires_grad_(True))
else:
params[k] = torch.nn.Parameter(v.cuda().float().contiguous().requires_grad_(True))
variables = {'max_2D_radius': torch.zeros(params['means3D'].shape[0]).cuda().float(),
'means2D_gradient_accum': torch.zeros(params['means3D'].shape[0]).cuda().float(),
'denom': torch.zeros(params['means3D'].shape[0]).cuda().float()}
return params, variables
def initialize_optimizer(params, lrs_dict):
lrs = lrs_dict
param_groups = [{'params': [v], 'name': k, 'lr': lrs[k]} for k, v in params.items()]
return torch.optim.Adam(param_groups, lr=0.0, eps=1e-15)
def initialize_first_timestep_from_ckpt(ckpt_path,dataset, num_frames, lrs_dict, mean_sq_dist_method):
# Get RGB-D Data & Camera Parameters
color, depth, intrinsics, pose = dataset[0]
# Process RGB-D Data
color = color.permute(2, 0, 1) / 255 # (H, W, C) -> (C, H, W)
depth = depth.permute(2, 0, 1) # (H, W, C) -> (C, H, W)
# Process Camera Parameters
intrinsics = intrinsics[:3, :3]
w2c = torch.linalg.inv(pose)
# Setup Camera
cam = setup_camera(color.shape[2], color.shape[1], intrinsics.cpu().numpy(), w2c.detach().cpu().numpy())
# Get Initial Point Cloud (PyTorch CUDA Tensor)
mask = (depth > 0) # Mask out invalid depth values
mask = mask.reshape(-1)
# Initialize Parameters & Optimizer from Checkpoint
# Load checkpoint
print(f"Loading Params")
params = dict(np.load(ckpt_path, allow_pickle=True))
variables = {}
for k in ['intrinsics', 'w2c', 'org_width', 'org_height', 'gt_w2c_all_frames']:
# for k in ['timestep','intrinsics', 'w2c', 'org_width', 'org_height', 'gt_w2c_all_frames']:
params.pop(k)
print(params.keys())
params = {k: torch.tensor(params[k]).cuda().float().requires_grad_(True) for k in params.keys()}
variables['max_2D_radius'] = torch.zeros(params['means3D'].shape[0]).cuda().float()
variables['means2D_gradient_accum'] = torch.zeros(params['means3D'].shape[0]).cuda().float()
variables['denom'] = torch.zeros(params['means3D'].shape[0]).cuda().float()
# variables['timestep'] = torch.zeros(params['means3D'].shape[0]).cuda().float()
variables['timestep'] = torch.tensor(params['timestep']).cuda().float()
params.pop('timestep')
optimizer = initialize_optimizer(params, lrs_dict)
# Initialize an estimate of scene radius for Gaussian-Splatting Densification
variables['scene_radius'] = torch.max(depth)/2.0
return params, variables, optimizer, intrinsics, w2c, cam
def get_loss_gs(params, curr_data, variables, loss_weights):
# Initialize Loss Dictionary
losses = {}
# Initialize Render Variables
rendervar = params2rendervar(params)
depth_sil_rendervar = params2depthplussilhouette(params, curr_data['w2c'])
# RGB Rendering
rendervar['means2D'].retain_grad()
im, radius, _, = Renderer(raster_settings=curr_data['cam'])(**rendervar)
variables['means2D'] = rendervar['means2D'] # Gradient only accum from colour render for densification
# Depth & Silhouette Rendering
depth_sil, _, _, = Renderer(raster_settings=curr_data['cam'])(**depth_sil_rendervar)
depth = depth_sil[0, :, :].unsqueeze(0)
silhouette = depth_sil[1, :, :]
# Get invalid Depth Mask
valid_depth_mask = (curr_data['depth'] != 0.0)
depth = depth * valid_depth_mask
# RGB Loss
losses['im'] = 0.8 * l1_loss_v1(im, curr_data['im']) + 0.2 * (1.0 - calc_ssim(im, curr_data['im']))
# Depth Loss
losses['depth'] = l1_loss_v1(depth, curr_data['depth'])
weighted_losses = {k: v * loss_weights[k] for k, v in losses.items()}
loss = sum(weighted_losses.values())
seen = radius > 0
variables['max_2D_radius'][seen] = torch.max(radius[seen], variables['max_2D_radius'][seen])
variables['seen'] = seen
weighted_losses['loss'] = loss
return loss, variables, weighted_losses
def initialize_new_params(new_pt_cld, mean3_sq_dist):
num_pts = new_pt_cld.shape[0]
means3D = new_pt_cld[:, :3] # [num_gaussians, 3]
unnorm_rots = np.tile([1, 0, 0, 0], (num_pts, 1)) # [num_gaussians, 3]
logit_opacities = torch.zeros((num_pts, 1), dtype=torch.float, device="cuda")
params = {
'means3D': means3D,
'rgb_colors': new_pt_cld[:, 3:6],
'unnorm_rotations': unnorm_rots,
'logit_opacities': logit_opacities,
'log_scales': torch.tile(torch.log(torch.sqrt(mean3_sq_dist))[..., None], (1, 1)),
}
for k, v in params.items():
# Check if value is already a torch tensor
if not isinstance(v, torch.Tensor):
params[k] = torch.nn.Parameter(torch.tensor(v).cuda().float().contiguous().requires_grad_(True))
else:
params[k] = torch.nn.Parameter(v.cuda().float().contiguous().requires_grad_(True))
return params
def infill_depth(depth, inpaint_radius=1):
"""
Function to infill Depth for invalid regions
Input:
depth: Depth Image (Numpy)
radius: Radius of the circular neighborhood for infilling
Output:
depth: Depth Image with invalid regions infilled (Numpy)
"""
invalid_mask = (depth == 0)
invalid_mask = invalid_mask.astype(np.uint8)
filled_depth = cv2.inpaint(depth, invalid_mask, inpaint_radius, cv2.INPAINT_NS)
return filled_depth
def add_new_gaussians(params, variables, curr_data, sil_thres, time_idx, mean_sq_dist_method):
# Silhouette Rendering
transformed_pts = transform_to_frame(params, time_idx, gaussians_grad=False, camera_grad=False)
depth_sil_rendervar = transformed_params2depthplussilhouette(params, curr_data['w2c'],
transformed_pts)
depth_sil, _, _, = Renderer(raster_settings=curr_data['cam'])(**depth_sil_rendervar)
silhouette = depth_sil[1, :, :]
non_presence_sil_mask = (silhouette < sil_thres)
# Check for new foreground objects by using GT depth
gt_depth = curr_data['depth'][0, :, :]
render_depth = depth_sil[0, :, :]
depth_error = torch.abs(gt_depth - render_depth) * (gt_depth > 0)
non_presence_depth_mask = (render_depth > gt_depth) * (depth_error > 50*depth_error.median())
# Determine non-presence mask
non_presence_mask = non_presence_sil_mask | non_presence_depth_mask
# Infill Depth for invalid regions of GT Depth
infilled_gt_depth = infill_depth(curr_data['depth'][0, :, :].detach().cpu().numpy())
infilled_gt_depth = torch.tensor(infilled_gt_depth).cuda().float().unsqueeze(0)
# Flatten mask
non_presence_mask = non_presence_mask.reshape(-1)
# Get the new frame Gaussians based on the Silhouette
if torch.sum(non_presence_mask) > 0:
# Get the new pointcloud in the world frame
curr_cam_rot = torch.nn.functional.normalize(params['cam_unnorm_rots'][..., time_idx].detach())
curr_cam_tran = params['cam_trans'][..., time_idx].detach()
curr_w2c = torch.eye(4).cuda().float()
curr_w2c[:3, :3] = build_rotation(curr_cam_rot)
curr_w2c[:3, 3] = curr_cam_tran
valid_depth_mask = (infilled_gt_depth > 0)
non_presence_mask = non_presence_mask & valid_depth_mask.reshape(-1)
new_pt_cld, mean3_sq_dist = get_pointcloud(curr_data['im'], infilled_gt_depth, curr_data['intrinsics'],
curr_w2c, mask=non_presence_mask, compute_mean_sq_dist=True,
mean_sq_dist_method=mean_sq_dist_method)
new_params = initialize_new_params(new_pt_cld, mean3_sq_dist)
for k, v in new_params.items():
params[k] = torch.nn.Parameter(torch.cat((params[k], v), dim=0).requires_grad_(True))
num_pts = params['means3D'].shape[0]
variables['means2D_gradient_accum'] = torch.zeros(num_pts, device="cuda").float()
variables['denom'] = torch.zeros(num_pts, device="cuda").float()
variables['max_2D_radius'] = torch.zeros(num_pts, device="cuda").float()
new_timestep = time_idx*torch.ones(new_pt_cld.shape[0],device="cuda").float()
variables['timestep'] = torch.cat((variables['timestep'],new_timestep),dim=0)
return params, variables
def convert_params_to_store(params):
params_to_store = {}
for k, v in params.items():
if isinstance(v, torch.Tensor):
params_to_store[k] = v.detach().clone()
else:
params_to_store[k] = v
return params_to_store
def rgbd_slam(config: dict):
# Print Config
print("Loaded Config:")
print(f"{config}")
# Init WandB
if config['use_wandb']:
wandb_step = 0
wandb_time_step = 0
wandb_run = wandb.init(project=config['wandb']['project'],
entity=config['wandb']['entity'],
group=config['wandb']['group'],
name=config['wandb']['name'],
config=config)
wandb_run.define_metric("Mapping_Iters")
wandb_run.define_metric("Number of Gaussians - Densification", step_metric="Mapping_Iters")
wandb_run.define_metric("Learning Rate - Means3D", step_metric="Mapping_Iters")
# Get Device
device = torch.device(config["primary_device"])
# Load Dataset
print("Loading Dataset ...")
dataset_config = config["data"]
if "gradslam_data_cfg" not in dataset_config:
gradslam_data_cfg = {}
gradslam_data_cfg["dataset_name"] = dataset_config["dataset_name"]
else:
gradslam_data_cfg = load_dataset_config(dataset_config["gradslam_data_cfg"])
if "ignore_bad" not in dataset_config:
dataset_config["ignore_bad"] = False
if "use_train_split" not in dataset_config:
dataset_config["use_train_split"] = True
# Poses are relative to the first frame
mapping_dataset = get_dataset(
config_dict=gradslam_data_cfg,
basedir=dataset_config["basedir"],
sequence=os.path.basename(dataset_config["sequence"]),
start=dataset_config["start"],
end=dataset_config["end"],
stride=dataset_config["stride"],
desired_height=dataset_config["desired_image_height"],
desired_width=dataset_config["desired_image_width"],
device=device,
relative_pose=True,
ignore_bad=dataset_config["ignore_bad"],
use_train_split=dataset_config["use_train_split"],
)
eval_dataset = get_dataset(
config_dict=gradslam_data_cfg,
basedir=dataset_config["basedir"],
sequence=os.path.basename(dataset_config["sequence"]),
start=dataset_config["start"],
end=dataset_config["end"],
stride=dataset_config["eval_stride"],
desired_height=dataset_config["desired_image_height"],
desired_width=dataset_config["desired_image_width"],
device=device,
relative_pose=True,
ignore_bad=dataset_config["ignore_bad"],
use_train_split=dataset_config["use_train_split"],
)
num_frames = dataset_config["num_frames"]
if num_frames == -1:
num_frames = len(mapping_dataset)
eval_num_frames = dataset_config["eval_num_frames"]
if eval_num_frames == -1:
eval_num_frames = len(eval_dataset)
# Initialize Parameters, Optimizer & Canoncial Camera parameters
ckpt_path = config["data"]["param_ckpt_path"]
params, variables, optimizer, intrinsics, w2c, cam = initialize_first_timestep_from_ckpt(ckpt_path,mapping_dataset, num_frames,
config['train']['lrs_mapping'],
config['mean_sq_dist_method'])
_, _, map_intrinsics, _ = mapping_dataset[0]
# Load all RGBD frames - Mapping dataloader
color_all_frames_map = []
depth_all_frames_map = []
gt_w2c_all_frames_map = []
gs_cams_all_frames_map = []
for time_idx in range(num_frames):
color, depth, _, gt_pose = mapping_dataset[time_idx]
# Process poses
gt_w2c = torch.linalg.inv(gt_pose)
# Process RGB-D Data
color = color.permute(2, 0, 1) / 255
depth = depth.permute(2, 0, 1)
color_all_frames_map.append(color)
depth_all_frames_map.append(depth)
gt_w2c_all_frames_map.append(gt_w2c)
# Setup Gaussian Splatting Camera
gs_cam = setup_camera(color.shape[2], color.shape[1],
map_intrinsics.cpu().numpy(),
gt_w2c.detach().cpu().numpy())
gs_cams_all_frames_map.append(gs_cam)
# Iterate over Scan
for time_idx in tqdm(range(num_frames)):
# Optimization Iterations
num_iters_mapping = config['train']['num_iters_mapping']
# Initialize current frame data
iter_time_idx = time_idx
color = color_all_frames_map[iter_time_idx]
depth = depth_all_frames_map[iter_time_idx]
curr_gt_w2c = gt_w2c_all_frames_map[:iter_time_idx+1]
curr_data = {'cam': cam, 'im': color, 'depth': depth, 'id': iter_time_idx,
'intrinsics': intrinsics, 'w2c': w2c, 'iter_gt_w2c_list': curr_gt_w2c}
# Add new Gaussians to the scene based on the Silhouette
# if time_idx > 0:
# params, variables = add_new_gaussians(params, variables, curr_data,
# config['train']['sil_thres'], time_idx,
# config['mean_sq_dist_method'])
post_num_pts = params['means3D'].shape[0]
if config['use_wandb']:
wandb_run.log({"Init/Number of Gaussians": post_num_pts,
"Init/step": wandb_time_step})
# Reset Optimizer & Learning Rates for Full Map Optimization
optimizer = initialize_optimizer(params, config['train']['lrs_mapping'])
means3D_scheduler = get_expon_lr_func(lr_init=config['train']['lrs_mapping']['means3D'],
lr_final=config['train']['lrs_mapping_means3D_final'],
lr_delay_mult=config['train']['lr_delay_mult'],
max_steps=config['train']['num_iters_mapping'])
# Mapping
if (time_idx + 1) == num_frames:
if num_iters_mapping > 0:
progress_bar = tqdm(range(num_iters_mapping), desc=f"Mapping Time Step: {time_idx}")
for iter in range(num_iters_mapping):
# Update Learning Rates for means3D
updated_lr = update_learning_rate(optimizer, means3D_scheduler, iter+1)
if config['use_wandb']:
wandb_run.log({"Learning Rate - Means3D": updated_lr})
# Randomly select a frame until current time step
iter_time_idx = random.randint(0, time_idx)
# Initialize Data for selected frame
iter_color = color_all_frames_map[iter_time_idx]
iter_depth = depth_all_frames_map[iter_time_idx]
iter_gt_w2c = gt_w2c_all_frames_map[:iter_time_idx+1]
iter_gs_cam = gs_cams_all_frames_map[iter_time_idx]
iter_data = {'cam': iter_gs_cam, 'im': iter_color, 'depth': iter_depth,
'id': iter_time_idx, 'intrinsics': map_intrinsics,
'w2c': gt_w2c_all_frames_map[iter_time_idx], 'iter_gt_w2c_list': iter_gt_w2c}
# Loss for current frame
loss, variables, losses = get_loss_gs(params, iter_data, variables, config['train']['loss_weights'])
# Backprop
loss.backward()
with torch.no_grad():
# Gaussian-Splatting's Gradient-based Densification
if config['train']['use_gaussian_splatting_densification']:
params, variables = densify(params, variables, optimizer, iter, config['train']['densify_dict'])
if config['use_wandb']:
wandb_run.log({"Number of Gaussians - Densification": params['means3D'].shape[0]})
# Optimizer Update
optimizer.step()
optimizer.zero_grad(set_to_none=True)
# Report Progress
if config['report_iter_progress']:
if config['use_wandb']:
report_progress(params, iter_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['train']['sil_thres'],
wandb_run=wandb_run, wandb_step=wandb_step, wandb_save_qual=config['wandb']['save_qual'],
mapping=True, online_time_idx=time_idx)
else:
report_progress(params, iter_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['train']['sil_thres'],
mapping=True, online_time_idx=time_idx)
else:
progress_bar.update(1)
# Eval Params at 7K Iterations
if (iter + 1) == 7000:
print("Evaluating Params at 7K Iterations")
eval_params = convert_params_to_store(params)
output_dir = os.path.join(config["workdir"], config["run_name"])
eval_dir = os.path.join(output_dir, "eval_7k")
os.makedirs(eval_dir, exist_ok=True)
if config['use_wandb']: | eval(eval_dataset, eval_params, eval_num_frames, eval_dir, sil_thres=config['train']['sil_thres'], | 22 | 2023-11-30 20:26:47+00:00 | 24k |
zhyever/PatchFusion | zoedepth/models/zoedepth_custom/patchfusion.py | [
{
"identifier": "DepthModel",
"path": "zoedepth/models/depth_model.py",
"snippet": "class DepthModel(nn.Module):\n def __init__(self):\n super().__init__()\n self.device = 'cpu'\n \n def to(self, device) -> nn.Module:\n self.device = device\n return super().to(device... | import itertools
import math
import copy
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import torch.distributed as dist
import torch.nn.functional as F
from zoedepth.models.depth_model import DepthModel
from zoedepth.models.base_models.midas import MidasCore
from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed
from zoedepth.models.layers.dist_layers import ConditionalLogBinomial, ConditionalLogBinomialV2
from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor, SeedBinRegressorUnnormed)
from zoedepth.models.model_io import load_state_from_resource
from torchvision.transforms import Normalize
from torchvision.ops import roi_align as torch_roi_align
from zoedepth.utils.misc import generatemask
from zoedepth.models.layers.transformer import TransformerDecoderLayer, TransformerEncoderLayer, TransformerEncoder
from zoedepth.utils.misc import colorize, colors
from zoedepth.models.layers.fusion_network import UNetv1
from zoedepth.models.zoedepth_custom.zoedepth_custom import ZoeDepthCustom | 14,798 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Zhenyu Li
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
isin = True
return isin
def get_activation(name, bank):
# input of forward_hook will be a function of model/inp/oup
def hook(module, input, output):
bank[name] = output
return hook
def get_input(name, bank):
# input of forward_hook will be a function of model/inp/oup
def hook(module, input, output):
bank[name] = input
return hook
class AttributeDict(dict):
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
def __setattr__(self, key, value):
self[key] = value
def __delattr__(self, key):
try:
del self[key]
except KeyError:
raise AttributeError(key)
class PatchFusion(DepthModel):
def __init__(self, coarse_model, fine_model, n_bins=64, bin_centers_type="softplus", bin_embedding_dim=128, min_depth=1e-3, max_depth=10,
n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', min_temp=5, max_temp=50, train_midas=True,
midas_lr_factor=10, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, sr_ratio=1,
raw_depth_shape=(2160, 3840), transform_sample_gt_size=(2160, 3840), representation='',
fetch_features=True, sample_feat_level=3, use_hr=False, deform=False, wo_bins=False, baseline=False,
condition=True, freeze=False, g2l=False, use_fusion_network=False, use_area_prior=False,
unet_version='v1', consistency_training=False, consistency_target='unet_feat', pos_embed=False, **kwargs):
"""ZoeDepth model. This is the version of ZoeDepth that has a single metric head
Args:
core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features
n_bins (int, optional): Number of bin centers. Defaults to 64.
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 "softplus".
bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.
min_depth (float, optional): Lower bound for normed bin centers. Defaults to 1e-3.
max_depth (float, optional): Upper bound for normed bin centers. Defaults to 10.
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.
train_midas (bool, optional): Whether to train "core", the base midas model. 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.
sr_ratio: sr ratio during infer
raw_depth_shape: raw depth shape during infer. times sr_ratio will be the target resolution. Used to sample points during training
transform_sample_gt_size: training depth shape # influenced by crop shape which is not included in this pipeline right now
representation: I use it to test the "bilap head" and a discarded idea
fetch_features: if fetch feats. Default=True
"""
super().__init__()
self.coarse_model = coarse_model
self.fine_model = fine_model
self.max_depth = max_depth
self.min_depth = min_depth
self.min_temp = min_temp
self.bin_centers_type = bin_centers_type
self.midas_lr_factor = midas_lr_factor
self.encoder_lr_factor = encoder_lr_factor
self.pos_enc_lr_factor = pos_enc_lr_factor
self.train_midas = train_midas
self.inverse_midas = inverse_midas
if bin_centers_type == "normed":
SeedBinRegressorLayer = SeedBinRegressor
Attractor = AttractorLayer
elif bin_centers_type == "softplus": # default
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'")
N_MIDAS_OUT = 32
btlnck_features = self.fine_model.core.output_channels[0]
num_out_features = self.fine_model.core.output_channels[1:] # all of them are the same
self.seed_bin_regressor = SeedBinRegressorLayer(
btlnck_features, n_bins=n_bins, min_depth=min_depth, max_depth=max_depth)
| # 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: Zhenyu Li
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
isin = True
return isin
def get_activation(name, bank):
# input of forward_hook will be a function of model/inp/oup
def hook(module, input, output):
bank[name] = output
return hook
def get_input(name, bank):
# input of forward_hook will be a function of model/inp/oup
def hook(module, input, output):
bank[name] = input
return hook
class AttributeDict(dict):
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
def __setattr__(self, key, value):
self[key] = value
def __delattr__(self, key):
try:
del self[key]
except KeyError:
raise AttributeError(key)
class PatchFusion(DepthModel):
def __init__(self, coarse_model, fine_model, n_bins=64, bin_centers_type="softplus", bin_embedding_dim=128, min_depth=1e-3, max_depth=10,
n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', min_temp=5, max_temp=50, train_midas=True,
midas_lr_factor=10, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, sr_ratio=1,
raw_depth_shape=(2160, 3840), transform_sample_gt_size=(2160, 3840), representation='',
fetch_features=True, sample_feat_level=3, use_hr=False, deform=False, wo_bins=False, baseline=False,
condition=True, freeze=False, g2l=False, use_fusion_network=False, use_area_prior=False,
unet_version='v1', consistency_training=False, consistency_target='unet_feat', pos_embed=False, **kwargs):
"""ZoeDepth model. This is the version of ZoeDepth that has a single metric head
Args:
core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features
n_bins (int, optional): Number of bin centers. Defaults to 64.
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 "softplus".
bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.
min_depth (float, optional): Lower bound for normed bin centers. Defaults to 1e-3.
max_depth (float, optional): Upper bound for normed bin centers. Defaults to 10.
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.
train_midas (bool, optional): Whether to train "core", the base midas model. 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.
sr_ratio: sr ratio during infer
raw_depth_shape: raw depth shape during infer. times sr_ratio will be the target resolution. Used to sample points during training
transform_sample_gt_size: training depth shape # influenced by crop shape which is not included in this pipeline right now
representation: I use it to test the "bilap head" and a discarded idea
fetch_features: if fetch feats. Default=True
"""
super().__init__()
self.coarse_model = coarse_model
self.fine_model = fine_model
self.max_depth = max_depth
self.min_depth = min_depth
self.min_temp = min_temp
self.bin_centers_type = bin_centers_type
self.midas_lr_factor = midas_lr_factor
self.encoder_lr_factor = encoder_lr_factor
self.pos_enc_lr_factor = pos_enc_lr_factor
self.train_midas = train_midas
self.inverse_midas = inverse_midas
if bin_centers_type == "normed":
SeedBinRegressorLayer = SeedBinRegressor
Attractor = AttractorLayer
elif bin_centers_type == "softplus": # default
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'")
N_MIDAS_OUT = 32
btlnck_features = self.fine_model.core.output_channels[0]
num_out_features = self.fine_model.core.output_channels[1:] # all of them are the same
self.seed_bin_regressor = SeedBinRegressorLayer(
btlnck_features, n_bins=n_bins, min_depth=min_depth, max_depth=max_depth) | self.seed_projector = Projector(btlnck_features, bin_embedding_dim) | 6 | 2023-12-04 08:43:15+00:00 | 24k |
baaivision/GeoDream | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.models.geometry.geodream_geometry_volume import GeodreamGeometryVolume
from threestudio.utils.typing import *
from pysdf import SDF | 18,340 | mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitSDF) or isinstance(other, GeodreamGeometryVolume): | 11 | 2023-12-01 01:59:42+00:00 | 24k |
horseee/DeepCache | DeepCache/sd/pipeline_text_to_video_zero.py | [
{
"identifier": "UNet2DConditionModel",
"path": "DeepCache/sd/unet_2d_condition.py",
"snippet": "class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a samp... | import copy
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
from torch.nn.functional import grid_sample
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL
from .unet_2d_condition import UNet2DConditionModel
from .pipeline_stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import BaseOutput
from diffusers.utils.torch_utils import randn_tensor | 16,961 | value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
@dataclass
class TextToVideoPipelineOutput(BaseOutput):
r"""
Output class for zero-shot text-to-video pipeline.
Args:
images (`[List[PIL.Image.Image]`, `np.ndarray`]):
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`[List[bool]]`):
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
def coords_grid(batch, ht, wd, device):
# Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py
coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
coords = torch.stack(coords[::-1], dim=0).float()
return coords[None].repeat(batch, 1, 1, 1)
def warp_single_latent(latent, reference_flow):
"""
Warp latent of a single frame with given flow
Args:
latent: latent code of a single frame
reference_flow: flow which to warp the latent with
Returns:
warped: warped latent
"""
_, _, H, W = reference_flow.size()
_, _, h, w = latent.size()
coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)
coords_t0 = coords0 + reference_flow
coords_t0[:, 0] /= W
coords_t0[:, 1] /= H
coords_t0 = coords_t0 * 2.0 - 1.0
coords_t0 = F.interpolate(coords_t0, size=(h, w), mode="bilinear")
coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))
warped = grid_sample(latent, coords_t0, mode="nearest", padding_mode="reflection")
return warped
def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):
"""
Create translation motion field
Args:
motion_field_strength_x: motion strength along x-axis
motion_field_strength_y: motion strength along y-axis
frame_ids: indexes of the frames the latents of which are being processed.
This is needed when we perform chunk-by-chunk inference
device: device
dtype: dtype
Returns:
"""
seq_length = len(frame_ids)
reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)
for fr_idx in range(seq_length):
reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])
reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])
return reference_flow
def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):
"""
Creates translation motion and warps the latents accordingly
Args:
motion_field_strength_x: motion strength along x-axis
motion_field_strength_y: motion strength along y-axis
frame_ids: indexes of the frames the latents of which are being processed.
This is needed when we perform chunk-by-chunk inference
latents: latent codes of frames
Returns:
warped_latents: warped latents
"""
motion_field = create_motion_field(
motion_field_strength_x=motion_field_strength_x,
motion_field_strength_y=motion_field_strength_y,
frame_ids=frame_ids,
device=latents.device,
dtype=latents.dtype,
)
warped_latents = latents.clone().detach()
for i in range(len(warped_latents)):
warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])
return warped_latents
|
def sample_gaussian_centered(n=1000, sample_size=100, std_dev=100):
samples = []
while len(samples) < sample_size:
# Sample from a Gaussian centered at n/2
sample = int(np.random.normal(loc=n/2, scale=std_dev))
# Check if the sample is in bounds
if 1 <= sample < n and sample not in samples:
samples.append(sample)
return samples
def sample_from_quad(total_numbers, n_samples, pow=1.2):
while pow > 1:
# Generate linearly spaced values between 0 and a max value
x_values = np.linspace(0, total_numbers**(1/pow), n_samples+1)
# Raise these values to the power of 1.5 to get a non-linear distribution
indices = np.unique(np.int32(x_values**pow))[:-1]
if len(indices) == n_samples:
break
pow -=0.02
if pow <= 1:
raise ValueError("Cannot find suitable pow. Please adjust n_samples or decrease center.")
return indices, pow
def sample_from_quad_center(total_numbers, n_samples, center, pow=1.2):
while pow > 1:
# Generate linearly spaced values between 0 and a max value
x_values = np.linspace((-center)**(1/pow), (total_numbers-center)**(1/pow), n_samples+1)
indices = [0] + [x+center for x in np.unique(np.int32(x_values**pow))[1:-1]]
if len(indices) == n_samples:
break
pow -=0.02
if pow <= 1:
raise ValueError("Cannot find suitable pow. Please adjust n_samples or decrease center.")
return indices, pow
def rearrange_0(tensor, f):
F, C, H, W = tensor.size()
tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))
return tensor
def rearrange_1(tensor):
B, C, F, H, W = tensor.size()
return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))
def rearrange_3(tensor, f):
F, D, C = tensor.size()
return torch.reshape(tensor, (F // f, f, D, C))
def rearrange_4(tensor):
B, F, D, C = tensor.size()
return torch.reshape(tensor, (B * F, D, C))
class CrossFrameAttnProcessor:
"""
Cross frame attention processor. Each frame attends the first frame.
Args:
batch_size: The number that represents actual batch size, other than the frames.
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
2, due to classifier-free guidance.
"""
def __init__(self, batch_size=2):
self.batch_size = batch_size
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
is_cross_attention = encoder_hidden_states is not None
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
# Cross Frame Attention
if not is_cross_attention:
video_length = key.size()[0] // self.batch_size
first_frame_index = [0] * video_length
# rearrange keys to have batch and frames in the 1st and 2nd dims respectively
key = rearrange_3(key, video_length)
key = key[:, first_frame_index]
# rearrange values to have batch and frames in the 1st and 2nd dims respectively
value = rearrange_3(value, video_length)
value = value[:, first_frame_index]
# rearrange back to original shape
key = rearrange_4(key)
value = rearrange_4(value)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
class CrossFrameAttnProcessor2_0:
"""
Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.
Args:
batch_size: The number that represents actual batch size, other than the frames.
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
2, due to classifier-free guidance.
"""
def __init__(self, batch_size=2):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.batch_size = batch_size
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
inner_dim = hidden_states.shape[-1]
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
query = attn.to_q(hidden_states)
is_cross_attention = encoder_hidden_states is not None
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
# Cross Frame Attention
if not is_cross_attention:
video_length = max(1, key.size()[0] // self.batch_size)
first_frame_index = [0] * video_length
# rearrange keys to have batch and frames in the 1st and 2nd dims respectively
key = rearrange_3(key, video_length)
key = key[:, first_frame_index]
# rearrange values to have batch and frames in the 1st and 2nd dims respectively
value = rearrange_3(value, video_length)
value = value[:, first_frame_index]
# rearrange back to original shape
key = rearrange_4(key)
value = rearrange_4(value)
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
@dataclass
class TextToVideoPipelineOutput(BaseOutput):
r"""
Output class for zero-shot text-to-video pipeline.
Args:
images (`[List[PIL.Image.Image]`, `np.ndarray`]):
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`[List[bool]]`):
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
def coords_grid(batch, ht, wd, device):
# Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py
coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
coords = torch.stack(coords[::-1], dim=0).float()
return coords[None].repeat(batch, 1, 1, 1)
def warp_single_latent(latent, reference_flow):
"""
Warp latent of a single frame with given flow
Args:
latent: latent code of a single frame
reference_flow: flow which to warp the latent with
Returns:
warped: warped latent
"""
_, _, H, W = reference_flow.size()
_, _, h, w = latent.size()
coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)
coords_t0 = coords0 + reference_flow
coords_t0[:, 0] /= W
coords_t0[:, 1] /= H
coords_t0 = coords_t0 * 2.0 - 1.0
coords_t0 = F.interpolate(coords_t0, size=(h, w), mode="bilinear")
coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))
warped = grid_sample(latent, coords_t0, mode="nearest", padding_mode="reflection")
return warped
def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):
"""
Create translation motion field
Args:
motion_field_strength_x: motion strength along x-axis
motion_field_strength_y: motion strength along y-axis
frame_ids: indexes of the frames the latents of which are being processed.
This is needed when we perform chunk-by-chunk inference
device: device
dtype: dtype
Returns:
"""
seq_length = len(frame_ids)
reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)
for fr_idx in range(seq_length):
reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])
reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])
return reference_flow
def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):
"""
Creates translation motion and warps the latents accordingly
Args:
motion_field_strength_x: motion strength along x-axis
motion_field_strength_y: motion strength along y-axis
frame_ids: indexes of the frames the latents of which are being processed.
This is needed when we perform chunk-by-chunk inference
latents: latent codes of frames
Returns:
warped_latents: warped latents
"""
motion_field = create_motion_field(
motion_field_strength_x=motion_field_strength_x,
motion_field_strength_y=motion_field_strength_y,
frame_ids=frame_ids,
device=latents.device,
dtype=latents.dtype,
)
warped_latents = latents.clone().detach()
for i in range(len(warped_latents)):
warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])
return warped_latents
| class TextToVideoZeroPipeline(StableDiffusionPipeline): | 1 | 2023-12-01 10:54:04+00:00 | 24k |
alvinliu0/HumanGaussian | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 14,932 | if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale | points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1) | 2 | 2023-11-27 02:39:39+00:00 | 24k |
EricGuo5513/momask-codes | gen_t2m.py | [
{
"identifier": "MaskTransformer",
"path": "models/mask_transformer/transformer.py",
"snippet": "class MaskTransformer(nn.Module):\n def __init__(self, code_dim, cond_mode, latent_dim=256, ff_size=1024, num_layers=8,\n num_heads=4, dropout=0.1, clip_dim=512, cond_drop_prob=0.1,\n ... | import os
import torch
import torch.nn.functional as F
import numpy as np
from os.path import join as pjoin
from models.mask_transformer.transformer import MaskTransformer, ResidualTransformer
from models.vq.model import RVQVAE, LengthEstimator
from options.eval_option import EvalT2MOptions
from utils.get_opt import get_opt
from utils.fixseed import fixseed
from visualization.joints2bvh import Joint2BVHConvertor
from torch.distributions.categorical import Categorical
from utils.motion_process import recover_from_ric
from utils.plot_script import plot_3d_motion
from utils.paramUtil import t2m_kinematic_chain | 16,497 |
clip_version = 'ViT-B/32'
def load_vq_model(vq_opt):
# opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
vq_model = RVQVAE(vq_opt,
vq_opt.dim_pose,
vq_opt.nb_code,
vq_opt.code_dim,
vq_opt.output_emb_width,
vq_opt.down_t,
vq_opt.stride_t,
vq_opt.width,
vq_opt.depth,
vq_opt.dilation_growth_rate,
vq_opt.vq_act,
vq_opt.vq_norm)
ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', 'net_best_fid.tar'),
map_location='cpu')
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
print(f'Loading VQ Model {vq_opt.name} Completed!')
return vq_model, vq_opt
def load_trans_model(model_opt, opt, which_model):
t2m_transformer = MaskTransformer(code_dim=model_opt.code_dim,
cond_mode='text',
latent_dim=model_opt.latent_dim,
ff_size=model_opt.ff_size,
num_layers=model_opt.n_layers,
num_heads=model_opt.n_heads,
dropout=model_opt.dropout,
clip_dim=512,
cond_drop_prob=model_opt.cond_drop_prob,
clip_version=clip_version,
opt=model_opt)
ckpt = torch.load(pjoin(model_opt.checkpoints_dir, model_opt.dataset_name, model_opt.name, 'model', which_model),
map_location='cpu')
model_key = 't2m_transformer' if 't2m_transformer' in ckpt else 'trans'
# print(ckpt.keys())
missing_keys, unexpected_keys = t2m_transformer.load_state_dict(ckpt[model_key], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Transformer {opt.name} from epoch {ckpt["ep"]}!')
return t2m_transformer
def load_res_model(res_opt, vq_opt, opt):
res_opt.num_quantizers = vq_opt.num_quantizers
res_opt.num_tokens = vq_opt.nb_code
res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
cond_mode='text',
latent_dim=res_opt.latent_dim,
ff_size=res_opt.ff_size,
num_layers=res_opt.n_layers,
num_heads=res_opt.n_heads,
dropout=res_opt.dropout,
clip_dim=512,
shared_codebook=vq_opt.shared_codebook,
cond_drop_prob=res_opt.cond_drop_prob,
# codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
share_weight=res_opt.share_weight,
clip_version=clip_version,
opt=res_opt)
ckpt = torch.load(pjoin(res_opt.checkpoints_dir, res_opt.dataset_name, res_opt.name, 'model', 'net_best_fid.tar'),
map_location=opt.device)
missing_keys, unexpected_keys = res_transformer.load_state_dict(ckpt['res_transformer'], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Residual Transformer {res_opt.name} from epoch {ckpt["ep"]}!')
return res_transformer
def load_len_estimator(opt):
model = LengthEstimator(512, 50)
ckpt = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'length_estimator', 'model', 'finest.tar'),
map_location=opt.device)
model.load_state_dict(ckpt['estimator'])
print(f'Loading Length Estimator from epoch {ckpt["epoch"]}!')
return model
if __name__ == '__main__':
parser = EvalT2MOptions()
opt = parser.parse()
|
clip_version = 'ViT-B/32'
def load_vq_model(vq_opt):
# opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
vq_model = RVQVAE(vq_opt,
vq_opt.dim_pose,
vq_opt.nb_code,
vq_opt.code_dim,
vq_opt.output_emb_width,
vq_opt.down_t,
vq_opt.stride_t,
vq_opt.width,
vq_opt.depth,
vq_opt.dilation_growth_rate,
vq_opt.vq_act,
vq_opt.vq_norm)
ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', 'net_best_fid.tar'),
map_location='cpu')
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
print(f'Loading VQ Model {vq_opt.name} Completed!')
return vq_model, vq_opt
def load_trans_model(model_opt, opt, which_model):
t2m_transformer = MaskTransformer(code_dim=model_opt.code_dim,
cond_mode='text',
latent_dim=model_opt.latent_dim,
ff_size=model_opt.ff_size,
num_layers=model_opt.n_layers,
num_heads=model_opt.n_heads,
dropout=model_opt.dropout,
clip_dim=512,
cond_drop_prob=model_opt.cond_drop_prob,
clip_version=clip_version,
opt=model_opt)
ckpt = torch.load(pjoin(model_opt.checkpoints_dir, model_opt.dataset_name, model_opt.name, 'model', which_model),
map_location='cpu')
model_key = 't2m_transformer' if 't2m_transformer' in ckpt else 'trans'
# print(ckpt.keys())
missing_keys, unexpected_keys = t2m_transformer.load_state_dict(ckpt[model_key], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Transformer {opt.name} from epoch {ckpt["ep"]}!')
return t2m_transformer
def load_res_model(res_opt, vq_opt, opt):
res_opt.num_quantizers = vq_opt.num_quantizers
res_opt.num_tokens = vq_opt.nb_code
res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
cond_mode='text',
latent_dim=res_opt.latent_dim,
ff_size=res_opt.ff_size,
num_layers=res_opt.n_layers,
num_heads=res_opt.n_heads,
dropout=res_opt.dropout,
clip_dim=512,
shared_codebook=vq_opt.shared_codebook,
cond_drop_prob=res_opt.cond_drop_prob,
# codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
share_weight=res_opt.share_weight,
clip_version=clip_version,
opt=res_opt)
ckpt = torch.load(pjoin(res_opt.checkpoints_dir, res_opt.dataset_name, res_opt.name, 'model', 'net_best_fid.tar'),
map_location=opt.device)
missing_keys, unexpected_keys = res_transformer.load_state_dict(ckpt['res_transformer'], strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
print(f'Loading Residual Transformer {res_opt.name} from epoch {ckpt["ep"]}!')
return res_transformer
def load_len_estimator(opt):
model = LengthEstimator(512, 50)
ckpt = torch.load(pjoin(opt.checkpoints_dir, opt.dataset_name, 'length_estimator', 'model', 'finest.tar'),
map_location=opt.device)
model.load_state_dict(ckpt['estimator'])
print(f'Loading Length Estimator from epoch {ckpt["epoch"]}!')
return model
if __name__ == '__main__':
parser = EvalT2MOptions()
opt = parser.parse() | fixseed(opt.seed) | 6 | 2023-11-29 19:21:27+00:00 | 24k |
dvlab-research/LLMGA | llmga/diffusers/src/diffusers/models/transformer_temporal.py | [
{
"identifier": "ConfigMixin",
"path": "llmga/diffusers/src/diffusers/configuration_utils.py",
"snippet": "class ConfigMixin:\n r\"\"\"\n Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also\n provides the [`~ConfigMixin.from_config`] and [... | from dataclasses import dataclass
from typing import Optional
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
import torch | 20,040 | # 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.
@dataclass
class TransformerTemporalModelOutput(BaseOutput):
"""
The output of [`TransformerTemporalModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input.
"""
sample: torch.FloatTensor
| # 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.
@dataclass
class TransformerTemporalModelOutput(BaseOutput):
"""
The output of [`TransformerTemporalModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input.
"""
sample: torch.FloatTensor
| class TransformerTemporalModel(ModelMixin, ConfigMixin): | 0 | 2023-11-27 18:46:55+00:00 | 24k |
JiahuiLei/GART | solver.py | [
{
"identifier": "prepare_real_seq",
"path": "lib_data/get_data.py",
"snippet": "def prepare_real_seq(\n seq_name,\n dataset_mode,\n split=\"train\",\n image_zoom_ratio=0.5,\n balance=False,\n ins_avt_wild_start_end_skip=None,\n):\n logging.info(\"Prepare real seq: {}\".format(seq_na... | from matplotlib import pyplot as plt
from pytorch3d.transforms import matrix_to_axis_angle
from tqdm import tqdm
from transforms3d.euler import euler2mat
from omegaconf import OmegaConf
from lib_data.get_data import prepare_real_seq
from lib_data.data_provider import DatabasePoseProvider
from lib_gart.templates import get_template
from lib_gart.model import GaussianTemplateModel, AdditionalBones
from lib_gart.optim_utils import *
from lib_render.gauspl_renderer import render_cam_pcl
from lib_gart.model_utils import transform_mu_frame
from utils.misc import *
from utils.viz import viz_render
from pytorch3d.transforms import axis_angle_to_matrix, matrix_to_axis_angle
from pytorch3d.ops import knn_points
from lib_guidance.camera_sampling import sample_camera, fov2K, opencv2blender
from viz_utils import viz_spinning, viz_human_all, viz_dog_all
from utils.ssim import ssim
from datetime import datetime
from test_utils import test
from lib_guidance.mvdream.mvdream_guidance import MVDream
from utils.lpips import LPIPS
import imageio
import torch
import numpy as np
import os, os.path as osp, shutil, sys
import time
import logging
import argparse | 20,848 | self.profile_fn = profile_fn
try:
shutil.copy(profile_fn, osp.join(self.log_dir, osp.basename(profile_fn)))
except:
pass
self.mode = mode
assert self.mode in ["human", "dog"], "Only support human and dog for now"
self.template_model_path = template_model_path
self.device = device
# * auto set attr
cfg = OmegaConf.load(profile_fn)
# assign the cfg to self attribute
for k, v in cfg.items():
setattr(self, k, v)
for k, v in kwargs.items():
setattr(self, k, v)
# * explicitly set flags
self.FAST_TRAINING = getattr(self, "FAST_TRAINING", False)
self.LAMBDA_SSIM = getattr(self, "LAMBDA_SSIM", 0.0)
self.LAMBDA_LPIPS = getattr(self, "LAMBDA_LPIPS", 0.0)
if self.LAMBDA_LPIPS > 0:
self.lpips = LPIPS(net="vgg").to(self.device)
for param in self.lpips.parameters():
param.requires_grad = False
if isinstance(self.RESET_OPACITY_STEPS, int):
self.RESET_OPACITY_STEPS = [
i
for i in range(1, self.TOTAL_steps)
if i % self.RESET_OPACITY_STEPS == 0
]
if isinstance(self.REGAUSSIAN_STEPS, int):
self.REGAUSSIAN_STEPS = [
i for i in range(1, self.TOTAL_steps) if i % self.REGAUSSIAN_STEPS == 0
]
# prepare base R
if self.mode == "human":
viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, "sxyz"))
else:
viz_base_R_opencv = np.asarray(euler2mat(np.pi / 2.0, 0, np.pi, "rxyz"))
viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()
self.viz_base_R = viz_base_R_opencv.to(self.device)
if self.mode == "human":
self.reg_base_R_global = (
matrix_to_axis_angle(
torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, "sxyz"))[
None
]
)[0]
.float()
.to(self.device)
)
else:
# TODO, for generation of dog
pass
self.writer = create_log(
self.log_dir, name=osp.basename(self.profile_fn).split(".")[0], debug=False
)
return
def prepare_fake_data(self, mode, *args, **kwargs):
if mode == "amass":
# todo: change to amass
provider = DatabasePoseProvider(*args, **kwargs, device=torch.device("cpu"))
return provider
return provider
def prepare_real_seq(
self,
seq_name,
dataset_mode,
split,
ins_avt_wild_start_end_skip=None,
image_zoom_ratio=0.5,
data_stay_gpu_flag=True,
):
provider, dataset = prepare_real_seq(
seq_name=seq_name,
dataset_mode=dataset_mode,
split=split,
ins_avt_wild_start_end_skip=ins_avt_wild_start_end_skip,
image_zoom_ratio=getattr(
self, "IMAGE_ZOOM_RATIO", image_zoom_ratio
), # ! this overwrite the func arg
balance=getattr(self, "VIEW_BALANCE_FLAG", False),
)
provider.to(self.device)
if getattr(self, "DATA_STAY_GPU_FLAG", data_stay_gpu_flag):
provider.move_images_to_device(self.device)
provider.viz_selection_prob(
osp.join(self.log_dir, f"split_{split}_view_prob.png")
)
return provider, dataset
def load_saved_model(self, ckpt_path=None):
if ckpt_path is None:
ckpt_path = osp.join(self.log_dir, "model.pth")
ret = self._get_model_optimizer(betas=None)
model = ret[0]
model.load(torch.load(ckpt_path))
model.to(self.device)
model.eval()
logging.info("After loading:")
model.summary()
return model
def _get_model_optimizer(self, betas, add_bones_total_t=0):
seed_everything(self.SEED)
|
# from lib_marchingcubes.gaumesh_utils import MeshExtractor
try:
# from lib_guidance.sd_utils import StableDiffusion
except:
logging.warning("No guidance module")
class TGFitter:
def __init__(
self,
log_dir,
profile_fn,
mode,
template_model_path="data/smpl_model/SMPL_NEUTRAL.pkl",
device=torch.device("cuda:0"),
**kwargs,
) -> None:
self.log_dir = log_dir
os.makedirs(self.log_dir, exist_ok=True)
self.profile_fn = profile_fn
try:
shutil.copy(profile_fn, osp.join(self.log_dir, osp.basename(profile_fn)))
except:
pass
self.mode = mode
assert self.mode in ["human", "dog"], "Only support human and dog for now"
self.template_model_path = template_model_path
self.device = device
# * auto set attr
cfg = OmegaConf.load(profile_fn)
# assign the cfg to self attribute
for k, v in cfg.items():
setattr(self, k, v)
for k, v in kwargs.items():
setattr(self, k, v)
# * explicitly set flags
self.FAST_TRAINING = getattr(self, "FAST_TRAINING", False)
self.LAMBDA_SSIM = getattr(self, "LAMBDA_SSIM", 0.0)
self.LAMBDA_LPIPS = getattr(self, "LAMBDA_LPIPS", 0.0)
if self.LAMBDA_LPIPS > 0:
self.lpips = LPIPS(net="vgg").to(self.device)
for param in self.lpips.parameters():
param.requires_grad = False
if isinstance(self.RESET_OPACITY_STEPS, int):
self.RESET_OPACITY_STEPS = [
i
for i in range(1, self.TOTAL_steps)
if i % self.RESET_OPACITY_STEPS == 0
]
if isinstance(self.REGAUSSIAN_STEPS, int):
self.REGAUSSIAN_STEPS = [
i for i in range(1, self.TOTAL_steps) if i % self.REGAUSSIAN_STEPS == 0
]
# prepare base R
if self.mode == "human":
viz_base_R_opencv = np.asarray(euler2mat(np.pi, 0, 0, "sxyz"))
else:
viz_base_R_opencv = np.asarray(euler2mat(np.pi / 2.0, 0, np.pi, "rxyz"))
viz_base_R_opencv = torch.from_numpy(viz_base_R_opencv).float()
self.viz_base_R = viz_base_R_opencv.to(self.device)
if self.mode == "human":
self.reg_base_R_global = (
matrix_to_axis_angle(
torch.as_tensor(euler2mat(np.pi / 2.0, 0, np.pi / 2.0, "sxyz"))[
None
]
)[0]
.float()
.to(self.device)
)
else:
# TODO, for generation of dog
pass
self.writer = create_log(
self.log_dir, name=osp.basename(self.profile_fn).split(".")[0], debug=False
)
return
def prepare_fake_data(self, mode, *args, **kwargs):
if mode == "amass":
# todo: change to amass
provider = DatabasePoseProvider(*args, **kwargs, device=torch.device("cpu"))
return provider
return provider
def prepare_real_seq(
self,
seq_name,
dataset_mode,
split,
ins_avt_wild_start_end_skip=None,
image_zoom_ratio=0.5,
data_stay_gpu_flag=True,
):
provider, dataset = prepare_real_seq(
seq_name=seq_name,
dataset_mode=dataset_mode,
split=split,
ins_avt_wild_start_end_skip=ins_avt_wild_start_end_skip,
image_zoom_ratio=getattr(
self, "IMAGE_ZOOM_RATIO", image_zoom_ratio
), # ! this overwrite the func arg
balance=getattr(self, "VIEW_BALANCE_FLAG", False),
)
provider.to(self.device)
if getattr(self, "DATA_STAY_GPU_FLAG", data_stay_gpu_flag):
provider.move_images_to_device(self.device)
provider.viz_selection_prob(
osp.join(self.log_dir, f"split_{split}_view_prob.png")
)
return provider, dataset
def load_saved_model(self, ckpt_path=None):
if ckpt_path is None:
ckpt_path = osp.join(self.log_dir, "model.pth")
ret = self._get_model_optimizer(betas=None)
model = ret[0]
model.load(torch.load(ckpt_path))
model.to(self.device)
model.eval()
logging.info("After loading:")
model.summary()
return model
def _get_model_optimizer(self, betas, add_bones_total_t=0):
seed_everything(self.SEED)
| template = get_template( | 2 | 2023-11-27 17:30:04+00:00 | 24k |
skhu101/GauHuman | scene/dataset_readers.py | [
{
"identifier": "read_extrinsics_text",
"path": "scene/colmap_loader.py",
"snippet": "def read_extrinsics_text(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\n \"\"\"\n images = {}\n with open(path, \"r\") as fid:\n while T... | import os
import sys
import numpy as np
import torch
import json
import imageio
import cv2
import random
from PIL import Image
from typing import NamedTuple
from scene.colmap_loader import read_extrinsics_text, read_intrinsics_text, qvec2rotmat, \
read_extrinsics_binary, read_intrinsics_binary, read_points3D_binary, read_points3D_text
from utils.graphics_utils import getWorld2View2, focal2fov, fov2focal
from pathlib import Path
from plyfile import PlyData, PlyElement
from utils.sh_utils import SH2RGB
from scene.gaussian_model import BasicPointCloud
from smpl.smpl_numpy import SMPL
from smplx.body_models import SMPLX
from data.dna_rendering.dna_rendering_sample_code.SMCReader import SMCReader | 15,383 | ply_path: str
def getNerfppNorm(cam_info):
def get_center_and_diag(cam_centers):
cam_centers = np.hstack(cam_centers)
avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)
center = avg_cam_center
dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)
diagonal = np.max(dist)
return center.flatten(), diagonal
cam_centers = []
for cam in cam_info:
W2C = getWorld2View2(cam.R, cam.T)
C2W = np.linalg.inv(W2C)
cam_centers.append(C2W[:3, 3:4])
center, diagonal = get_center_and_diag(cam_centers)
radius = diagonal * 1.1
translate = -center
return {"translate": translate, "radius": radius}
def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
cam_infos = []
for idx, key in enumerate(cam_extrinsics):
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics)))
sys.stdout.flush()
extr = cam_extrinsics[key]
intr = cam_intrinsics[extr.camera_id]
height = intr.height
width = intr.width
uid = intr.id
R = np.transpose(qvec2rotmat(extr.qvec))
T = np.array(extr.tvec)
if intr.model=="SIMPLE_PINHOLE":
focal_length_x = intr.params[0]
FovY = focal2fov(focal_length_x, height)
FovX = focal2fov(focal_length_x, width)
elif intr.model=="PINHOLE":
focal_length_x = intr.params[0]
focal_length_y = intr.params[1]
FovY = focal2fov(focal_length_y, height)
FovX = focal2fov(focal_length_x, width)
else:
assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
image_path = os.path.join(images_folder, os.path.basename(extr.name))
image_name = os.path.basename(image_path).split(".")[0]
image = Image.open(image_path)
cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
image_path=image_path, image_name=image_name, width=width, height=height)
cam_infos.append(cam_info)
sys.stdout.write('\n')
return cam_infos
def fetchPly(path):
plydata = PlyData.read(path)
vertices = plydata['vertex']
positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T
colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0
normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T
return BasicPointCloud(points=positions, colors=colors, normals=normals)
def storePly(path, xyz, rgb):
# Define the dtype for the structured array
dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),
('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]
normals = np.zeros_like(xyz)
elements = np.empty(xyz.shape[0], dtype=dtype)
attributes = np.concatenate((xyz, normals, rgb), axis=1)
elements[:] = list(map(tuple, attributes))
# Create the PlyData object and write to file
vertex_element = PlyElement.describe(elements, 'vertex')
ply_data = PlyData([vertex_element])
ply_data.write(path)
def readColmapSceneInfo(path, images, eval, llffhold=8):
try:
cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin")
cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin")
cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)
cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)
except:
cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt")
cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt")
cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)
cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)
reading_dir = "images" if images == None else images
cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))
cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)
if eval:
train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]
test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]
else:
train_cam_infos = cam_infos
test_cam_infos = []
nerf_normalization = getNerfppNorm(train_cam_infos)
ply_path = os.path.join(path, "sparse/0/points3D.ply")
bin_path = os.path.join(path, "sparse/0/points3D.bin")
txt_path = os.path.join(path, "sparse/0/points3D.txt")
if not os.path.exists(ply_path):
print("Converting point3d.bin to .ply, will happen only the first time you open the scene.")
try:
| #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class CameraInfo(NamedTuple):
uid: int
pose_id: int
R: np.array
T: np.array
K: np.array
FovY: np.array
FovX: np.array
image: np.array
image_path: str
image_name: str
bkgd_mask: np.array
bound_mask: np.array
width: int
height: int
smpl_param: dict
world_vertex: np.array
world_bound: np.array
big_pose_smpl_param: dict
big_pose_world_vertex: np.array
big_pose_world_bound: np.array
class SceneInfo(NamedTuple):
point_cloud: BasicPointCloud
train_cameras: list
test_cameras: list
nerf_normalization: dict
ply_path: str
def getNerfppNorm(cam_info):
def get_center_and_diag(cam_centers):
cam_centers = np.hstack(cam_centers)
avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)
center = avg_cam_center
dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)
diagonal = np.max(dist)
return center.flatten(), diagonal
cam_centers = []
for cam in cam_info:
W2C = getWorld2View2(cam.R, cam.T)
C2W = np.linalg.inv(W2C)
cam_centers.append(C2W[:3, 3:4])
center, diagonal = get_center_and_diag(cam_centers)
radius = diagonal * 1.1
translate = -center
return {"translate": translate, "radius": radius}
def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
cam_infos = []
for idx, key in enumerate(cam_extrinsics):
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics)))
sys.stdout.flush()
extr = cam_extrinsics[key]
intr = cam_intrinsics[extr.camera_id]
height = intr.height
width = intr.width
uid = intr.id
R = np.transpose(qvec2rotmat(extr.qvec))
T = np.array(extr.tvec)
if intr.model=="SIMPLE_PINHOLE":
focal_length_x = intr.params[0]
FovY = focal2fov(focal_length_x, height)
FovX = focal2fov(focal_length_x, width)
elif intr.model=="PINHOLE":
focal_length_x = intr.params[0]
focal_length_y = intr.params[1]
FovY = focal2fov(focal_length_y, height)
FovX = focal2fov(focal_length_x, width)
else:
assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
image_path = os.path.join(images_folder, os.path.basename(extr.name))
image_name = os.path.basename(image_path).split(".")[0]
image = Image.open(image_path)
cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
image_path=image_path, image_name=image_name, width=width, height=height)
cam_infos.append(cam_info)
sys.stdout.write('\n')
return cam_infos
def fetchPly(path):
plydata = PlyData.read(path)
vertices = plydata['vertex']
positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T
colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0
normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T
return BasicPointCloud(points=positions, colors=colors, normals=normals)
def storePly(path, xyz, rgb):
# Define the dtype for the structured array
dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),
('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]
normals = np.zeros_like(xyz)
elements = np.empty(xyz.shape[0], dtype=dtype)
attributes = np.concatenate((xyz, normals, rgb), axis=1)
elements[:] = list(map(tuple, attributes))
# Create the PlyData object and write to file
vertex_element = PlyElement.describe(elements, 'vertex')
ply_data = PlyData([vertex_element])
ply_data.write(path)
def readColmapSceneInfo(path, images, eval, llffhold=8):
try:
cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin")
cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin")
cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)
cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)
except:
cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt")
cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt")
cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)
cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)
reading_dir = "images" if images == None else images
cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))
cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)
if eval:
train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]
test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]
else:
train_cam_infos = cam_infos
test_cam_infos = []
nerf_normalization = getNerfppNorm(train_cam_infos)
ply_path = os.path.join(path, "sparse/0/points3D.ply")
bin_path = os.path.join(path, "sparse/0/points3D.bin")
txt_path = os.path.join(path, "sparse/0/points3D.txt")
if not os.path.exists(ply_path):
print("Converting point3d.bin to .ply, will happen only the first time you open the scene.")
try: | xyz, rgb, _ = read_points3D_binary(bin_path) | 5 | 2023-11-29 07:10:39+00:00 | 24k |
cswry/SeeSR | test_seesr.py | [
{
"identifier": "StableDiffusionControlNetPipeline",
"path": "pipelines/pipeline_seesr.py",
"snippet": "class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.\n\n ... | import os
import sys
import cv2
import glob
import argparse
import numpy as np
import torch
import torch.utils.checkpoint
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor
from pipelines.pipeline_seesr import StableDiffusionControlNetPipeline
from utils.misc import load_dreambooth_lora
from utils.wavelet_color_fix import wavelet_color_fix, adain_color_fix
from ram.models.ram_lora import ram
from ram import inference_ram as inference
from ram import get_transform
from typing import Mapping, Any
from torchvision import transforms
from torchvision import transforms
from models.controlnet import ControlNetModel
from models.unet_2d_condition import UNet2DConditionModel | 14,797 | '''
* SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
* Modified from diffusers by Rongyuan Wu
* 24/12/2023
'''
sys.path.append(os.getcwd())
logger = get_logger(__name__, log_level="INFO")
tensor_transforms = transforms.Compose([
transforms.ToTensor(),
])
ram_transforms = transforms.Compose([
transforms.Resize((384, 384)),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def load_state_dict_diffbirSwinIR(model: nn.Module, state_dict: Mapping[str, Any], strict: bool=False) -> None:
state_dict = state_dict.get("state_dict", state_dict)
is_model_key_starts_with_module = list(model.state_dict().keys())[0].startswith("module.")
is_state_dict_key_starts_with_module = list(state_dict.keys())[0].startswith("module.")
if (
is_model_key_starts_with_module and
(not is_state_dict_key_starts_with_module)
):
state_dict = {f"module.{key}": value for key, value in state_dict.items()}
if (
(not is_model_key_starts_with_module) and
is_state_dict_key_starts_with_module
):
state_dict = {key[len("module."):]: value for key, value in state_dict.items()}
model.load_state_dict(state_dict, strict=strict)
def load_seesr_pipeline(args, accelerator, enable_xformers_memory_efficient_attention):
# Load scheduler, tokenizer and models.
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
feature_extractor = CLIPImageProcessor.from_pretrained(f"{args.pretrained_model_path}/feature_extractor")
unet = UNet2DConditionModel.from_pretrained(args.seesr_model_path, subfolder="unet")
controlnet = ControlNetModel.from_pretrained(args.seesr_model_path, subfolder="controlnet")
# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
controlnet.requires_grad_(False)
if enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
controlnet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Get the validation pipeline
| '''
* SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
* Modified from diffusers by Rongyuan Wu
* 24/12/2023
'''
sys.path.append(os.getcwd())
logger = get_logger(__name__, log_level="INFO")
tensor_transforms = transforms.Compose([
transforms.ToTensor(),
])
ram_transforms = transforms.Compose([
transforms.Resize((384, 384)),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def load_state_dict_diffbirSwinIR(model: nn.Module, state_dict: Mapping[str, Any], strict: bool=False) -> None:
state_dict = state_dict.get("state_dict", state_dict)
is_model_key_starts_with_module = list(model.state_dict().keys())[0].startswith("module.")
is_state_dict_key_starts_with_module = list(state_dict.keys())[0].startswith("module.")
if (
is_model_key_starts_with_module and
(not is_state_dict_key_starts_with_module)
):
state_dict = {f"module.{key}": value for key, value in state_dict.items()}
if (
(not is_model_key_starts_with_module) and
is_state_dict_key_starts_with_module
):
state_dict = {key[len("module."):]: value for key, value in state_dict.items()}
model.load_state_dict(state_dict, strict=strict)
def load_seesr_pipeline(args, accelerator, enable_xformers_memory_efficient_attention):
# Load scheduler, tokenizer and models.
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
feature_extractor = CLIPImageProcessor.from_pretrained(f"{args.pretrained_model_path}/feature_extractor")
unet = UNet2DConditionModel.from_pretrained(args.seesr_model_path, subfolder="unet")
controlnet = ControlNetModel.from_pretrained(args.seesr_model_path, subfolder="controlnet")
# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
controlnet.requires_grad_(False)
if enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
controlnet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Get the validation pipeline | validation_pipeline = StableDiffusionControlNetPipeline( | 0 | 2023-11-27 08:50:33+00:00 | 24k |
xmu-xiaoma666/X-Dreamer | train_x_dreamer.py | [
{
"identifier": "DatasetMesh",
"path": "dataset/dataset_mesh.py",
"snippet": "class DatasetMesh(torch.utils.data.Dataset):\n\n\n def __init__(self, glctx, FLAGS, validate=False, gif=False):\n # Init \n self.glctx = glctx\n self.FLAGS = FLAGS\n sel... | import os
import time
import argparse
import json
import math
import numpy as np
import torch
import nvdiffrast.torch as dr
import itertools
import xatlas
import open3d as o3d
import random
import imageio
import os.path as osp
import pickle
from dataset.dataset_mesh import DatasetMesh
from dataset.dataset_mesh import get_camera_params
from geometry.dmtet_x_dreamer import DMTetGeometry
from geometry.dlmesh_x_dreamer import DLMesh
from render import obj
from render import material
from render import util
from render import mesh
from render import texture
from render import mlptexture
from render import light
from render import render
from sd_cglora import StableDiffusion
from tqdm import tqdm
from render import util
from render.video import Video | 14,969 | if 'kd' in result_dict.keys():
save_gif(kd_dir,30)
if 'ks' in result_dict.keys():
save_gif(ks_dir,30)
if 'normal' in result_dict.keys():
save_gif(normal_dir,30)
return 0
###############################################################################
# Main shape fitter function / optimization loop
###############################################################################
class Trainer(torch.nn.Module):
def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, FLAGS, guidance):
super(Trainer, self).__init__()
self.glctx = glctx
self.geometry = geometry
self.light = lgt
self.material = mat
self.optimize_geometry = optimize_geometry
self.optimize_light = optimize_light
self.FLAGS = FLAGS
self.guidance = guidance
self.if_flip_the_normal = FLAGS.if_flip_the_normal
self.if_use_bump = FLAGS.if_use_bump
if self.FLAGS.mode == 'appearance_modeling':
if not self.optimize_light:
with torch.no_grad():
self.light.build_mips()
self.params = list(self.material.parameters())
self.params += list(self.geometry.pos_encoder.parameters())
self.params += list(self.light.parameters()) if optimize_light else []
self.geo_params = list(self.geometry.parameters()) if optimize_geometry else []
def forward(self, target, it, if_normal, if_pretrain, scene_and_vertices ):
if self.FLAGS.mode == 'appearance_modeling':
if self.optimize_light:
self.light.build_mips()
if self.FLAGS.camera_space_light:
self.light.xfm(target['mv'])
if if_pretrain:
return self.geometry.decoder.pre_train_ellipsoid(it, scene_and_vertices)
else:
return self.geometry.tick(glctx, target, self.light, self.material, it , if_normal, self.guidance, self.FLAGS.mode, self.if_flip_the_normal, self.if_use_bump)
def optimize_mesh(
glctx,
geometry,
opt_material,
lgt,
dataset_train,
dataset_validate,
FLAGS,
log_interval=10,
optimize_light=True,
optimize_geometry=True,
guidance = None,
scene_and_vertices = None,
):
dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=False)
dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_train.collate)
model = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, FLAGS, guidance)
if optimize_geometry:
optimizer_mesh = torch.optim.AdamW(model.geo_params, lr=0.001, betas=(0.9, 0.99), eps=1e-15)
optimizer = torch.optim.AdamW(model.params, lr=0.01, betas=(0.9, 0.99), eps=1e-15)
optimizer_lora = torch.optim.SGD(itertools.chain(*guidance.unet_lora_params), lr=1e-5)
if FLAGS.multi_gpu:
model = model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[FLAGS.local_rank],
find_unused_parameters= True
)
img_cnt = 0
img_loss_vec = []
reg_loss_vec = []
iter_dur_vec = []
def cycle(iterable):
iterator = iter(iterable)
while True:
try:
yield next(iterator)
except StopIteration:
iterator = iter(iterable)
v_it = cycle(dataloader_validate)
scaler = torch.cuda.amp.GradScaler(enabled=True)
rot_ang = 0
if FLAGS.local_rank == 0:
video = Video(FLAGS.out_dir)
if FLAGS.local_rank == 0:
dataloader_train = tqdm(dataloader_train)
for it, target in enumerate(dataloader_train):
# Mix randomized background into dataset image
target = prepare_batch(target, FLAGS.train_background)
# Show/save image before training step (want to get correct rendering of input)
if FLAGS.local_rank == 0:
save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)
save_video = FLAGS.video_interval and (it % FLAGS.video_interval == 0)
if save_image:
result_image, result_dict = validate_itr(glctx, prepare_batch(next(v_it), FLAGS.train_background), geometry, opt_material, lgt, FLAGS) #prepare_batch(next(v_it), FLAGS.background)
np_result_image = result_image.detach().cpu().numpy()
util.save_image(FLAGS.out_dir + '/' + ('img_%s_%06d.png' % (FLAGS.mode, img_cnt)), np_result_image)
util.save_image(FLAGS.out_dir + '/' + ('mask_%s_%06d.png' % (FLAGS.mode, img_cnt)), result_dict['mask'].detach().cpu().numpy())
img_cnt = img_cnt+1
if save_video:
with torch.no_grad():
|
###############################################################################
# Mix background into a dataset image
###############################################################################
@torch.no_grad()
def prepare_batch(target, background= 'black'):
target['mv'] = target['mv'].cuda()
target['mvp'] = target['mvp'].cuda()
target['campos'] = target['campos'].cuda()
target['fov'] = target['fov'].cuda()
target['normal_rotate'] = target['normal_rotate'].cuda()
batch_size = target['mv'].shape[0]
resolution = target['resolution']
if background == 'white':
target['background']= torch.ones(batch_size, resolution[0], resolution[1], 3, dtype=torch.float32, device='cuda')
if background == 'black':
target['background'] = torch.zeros(batch_size, resolution[0], resolution[1], 3, dtype=torch.float32, device='cuda')
return target
###############################################################################
# UV - map geometry & convert to a mesh
###############################################################################
@torch.no_grad()
def xatlas_uvmap(glctx, geometry, mat, FLAGS):
eval_mesh = geometry.getMesh(mat)
# Create uvs with xatlas
v_pos = eval_mesh.v_pos.detach().cpu().numpy()
t_pos_idx = eval_mesh.t_pos_idx.detach().cpu().numpy()
vmapping, indices, uvs = xatlas.parametrize(v_pos, t_pos_idx)
# Convert to tensors
indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
uvs = torch.tensor(uvs, dtype=torch.float32, device='cuda')
faces = torch.tensor(indices_int64, dtype=torch.int64, device='cuda')
new_mesh = mesh.Mesh(v_tex=uvs, t_tex_idx=faces, base=eval_mesh)
mask, kd, ks, normal = render.render_uv(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'])
if FLAGS.layers > 1:
kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
new_mesh.material = material.Material({
'bsdf' : mat['bsdf'],
'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),
'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),
'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])
})
return new_mesh
@torch.no_grad()
def xatlas_uvmap1(glctx, geometry, mat, FLAGS):
eval_mesh = geometry.getMesh(mat)
new_mesh = mesh.Mesh( base=eval_mesh)
mask, kd, ks, normal = render.render_uv1(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'], FLAGS.uv_padding_block)
if FLAGS.layers > 1:
kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
new_mesh.material = material.Material({
'bsdf' : mat['bsdf'],
'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),
'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),
'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])
})
return new_mesh
###############################################################################
# Utility functions for material
###############################################################################
def get_normalize_mesh(pro_path):
mesh = o3d.io.read_triangle_mesh(pro_path)
vertices = np.asarray(mesh.vertices)
shift = np.mean(vertices,axis=0)
scale = np.max(np.linalg.norm(vertices-shift, ord=2, axis=1))
vertices = (vertices-shift) / scale
mesh.vertices = o3d.cuda.pybind.utility.Vector3dVector(vertices)
return mesh
def initial_guness_material(geometry, mlp, FLAGS, init_mat=None):
# ipdb.set_trace(())
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
if mlp:
mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
mlp_map_opt = mlptexture.MLPTexture3D(geometry.getAABB(), channels=9, min_max=[mlp_min, mlp_max])
mat = material.Material({'kd_ks_normal' : mlp_map_opt})
else:
# Setup Kd (albedo) and Ks (x, roughness, metalness) textures
if FLAGS.random_textures or init_mat is None:
num_channels = 4 if FLAGS.layers > 1 else 3
kd_init = torch.rand(size=FLAGS.texture_res + [num_channels], device='cuda') * (kd_max - kd_min)[None, None, 0:num_channels] + kd_min[None, None, 0:num_channels]
kd_map_opt = texture.create_trainable(kd_init , FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])
ksR = np.random.uniform(size=FLAGS.texture_res + [1], low=0.0, high=0.01)
ksG = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[1].cpu(), high=ks_max[1].cpu())
ksB = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[2].cpu(), high=ks_max[2].cpu())
ks_map_opt = texture.create_trainable(np.concatenate((ksR, ksG, ksB), axis=2), FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])
else:
kd_map_opt = texture.create_trainable(init_mat['kd'], FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])
ks_map_opt = texture.create_trainable(init_mat['ks'], FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])
# Setup normal map
if FLAGS.random_textures or init_mat is None or 'normal' not in init_mat:
normal_map_opt = texture.create_trainable(np.array([0, 0, 1]), FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])
else:
normal_map_opt = texture.create_trainable(init_mat['normal'], FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])
mat = material.Material({
'kd' : kd_map_opt,
'ks' : ks_map_opt,
'normal' : normal_map_opt
})
if init_mat is not None:
mat['bsdf'] = init_mat['bsdf']
else:
mat['bsdf'] = 'pbr'
return mat
###############################################################################
# Validation & testing
###############################################################################
# @torch.no_grad()
def validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, relight = None):
result_dict = {}
with torch.no_grad():
if FLAGS.mode == 'appearance_modeling':
with torch.no_grad():
lgt.build_mips()
if FLAGS.camera_space_light:
lgt.xfm(target['mv'])
if relight != None:
relight.build_mips()
buffers = geometry.render(glctx, target, lgt, opt_material, if_use_bump = FLAGS.if_use_bump)
result_dict['shaded'] = buffers['shaded'][0, ..., 0:3]
result_dict['shaded'] = util.rgb_to_srgb(result_dict['shaded'])
if relight != None:
result_dict['relight'] = geometry.render(glctx, target, relight, opt_material, if_use_bump = FLAGS.if_use_bump)['shaded'][0, ..., 0:3]
result_dict['relight'] = util.rgb_to_srgb(result_dict['relight'])
result_dict['mask'] = (buffers['shaded'][0, ..., 3:4])
result_image = result_dict['shaded']
if FLAGS.display is not None :
# white_bg = torch.ones_like(target['background'])
for layer in FLAGS.display:
if 'latlong' in layer and layer['latlong']:
if isinstance(lgt, light.EnvironmentLight):
result_dict['light_image'] = util.cubemap_to_latlong(lgt.base, FLAGS.display_res)
result_image = torch.cat([result_image, result_dict['light_image']], axis=1)
elif 'bsdf' in layer:
buffers = geometry.render(glctx, target, lgt, opt_material, bsdf=layer['bsdf'], if_use_bump = FLAGS.if_use_bump)
if layer['bsdf'] == 'kd':
result_dict[layer['bsdf']] = util.rgb_to_srgb(buffers['shaded'][0, ..., 0:3])
elif layer['bsdf'] == 'normal':
result_dict[layer['bsdf']] = (buffers['shaded'][0, ..., 0:3] + 1) * 0.5
else:
result_dict[layer['bsdf']] = buffers['shaded'][0, ..., 0:3]
result_image = torch.cat([result_image, result_dict[layer['bsdf']]], axis=1)
return result_image, result_dict
def save_gif(dir,fps):
imgpath = dir
frames = []
for idx in sorted(os.listdir(imgpath)):
img = osp.join(imgpath,idx)
frames.append(imageio.imread(img))
imageio.mimsave(os.path.join(dir, 'eval.gif'),frames,'GIF',duration=1/fps,loop=0)
@torch.no_grad()
def validate(glctx, geometry, opt_material, lgt, dataset_validate, out_dir, FLAGS, relight= None):
# ==============================================================================================
# Validation loop
# ==============================================================================================
mse_values = []
psnr_values = []
dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_validate.collate)
os.makedirs(out_dir, exist_ok=True)
shaded_dir = os.path.join(out_dir, "shaded")
relight_dir = os.path.join(out_dir, "relight")
kd_dir = os.path.join(out_dir, "kd")
ks_dir = os.path.join(out_dir, "ks")
normal_dir = os.path.join(out_dir, "normal")
mask_dir = os.path.join(out_dir, "mask")
os.makedirs(shaded_dir, exist_ok=True)
os.makedirs(relight_dir, exist_ok=True)
os.makedirs(kd_dir, exist_ok=True)
os.makedirs(ks_dir, exist_ok=True)
os.makedirs(normal_dir, exist_ok=True)
os.makedirs(mask_dir, exist_ok=True)
print("Running validation")
dataloader_validate = tqdm(dataloader_validate)
for it, target in enumerate(dataloader_validate):
# Mix validation background
target = prepare_batch(target, 'white')
result_image, result_dict = validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, relight)
for k in result_dict.keys():
np_img = result_dict[k].detach().cpu().numpy()
if k == 'shaded':
util.save_image(shaded_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
elif k == 'relight':
util.save_image(relight_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
elif k == 'kd':
util.save_image(kd_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
elif k == 'ks':
util.save_image(ks_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
elif k == 'normal':
util.save_image(normal_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
elif k == 'mask':
util.save_image(mask_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)
if 'shaded' in result_dict.keys():
save_gif(shaded_dir,30)
if 'relight' in result_dict.keys():
save_gif(relight_dir,30)
if 'kd' in result_dict.keys():
save_gif(kd_dir,30)
if 'ks' in result_dict.keys():
save_gif(ks_dir,30)
if 'normal' in result_dict.keys():
save_gif(normal_dir,30)
return 0
###############################################################################
# Main shape fitter function / optimization loop
###############################################################################
class Trainer(torch.nn.Module):
def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, FLAGS, guidance):
super(Trainer, self).__init__()
self.glctx = glctx
self.geometry = geometry
self.light = lgt
self.material = mat
self.optimize_geometry = optimize_geometry
self.optimize_light = optimize_light
self.FLAGS = FLAGS
self.guidance = guidance
self.if_flip_the_normal = FLAGS.if_flip_the_normal
self.if_use_bump = FLAGS.if_use_bump
if self.FLAGS.mode == 'appearance_modeling':
if not self.optimize_light:
with torch.no_grad():
self.light.build_mips()
self.params = list(self.material.parameters())
self.params += list(self.geometry.pos_encoder.parameters())
self.params += list(self.light.parameters()) if optimize_light else []
self.geo_params = list(self.geometry.parameters()) if optimize_geometry else []
def forward(self, target, it, if_normal, if_pretrain, scene_and_vertices ):
if self.FLAGS.mode == 'appearance_modeling':
if self.optimize_light:
self.light.build_mips()
if self.FLAGS.camera_space_light:
self.light.xfm(target['mv'])
if if_pretrain:
return self.geometry.decoder.pre_train_ellipsoid(it, scene_and_vertices)
else:
return self.geometry.tick(glctx, target, self.light, self.material, it , if_normal, self.guidance, self.FLAGS.mode, self.if_flip_the_normal, self.if_use_bump)
def optimize_mesh(
glctx,
geometry,
opt_material,
lgt,
dataset_train,
dataset_validate,
FLAGS,
log_interval=10,
optimize_light=True,
optimize_geometry=True,
guidance = None,
scene_and_vertices = None,
):
dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=False)
dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_train.collate)
model = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, FLAGS, guidance)
if optimize_geometry:
optimizer_mesh = torch.optim.AdamW(model.geo_params, lr=0.001, betas=(0.9, 0.99), eps=1e-15)
optimizer = torch.optim.AdamW(model.params, lr=0.01, betas=(0.9, 0.99), eps=1e-15)
optimizer_lora = torch.optim.SGD(itertools.chain(*guidance.unet_lora_params), lr=1e-5)
if FLAGS.multi_gpu:
model = model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[FLAGS.local_rank],
find_unused_parameters= True
)
img_cnt = 0
img_loss_vec = []
reg_loss_vec = []
iter_dur_vec = []
def cycle(iterable):
iterator = iter(iterable)
while True:
try:
yield next(iterator)
except StopIteration:
iterator = iter(iterable)
v_it = cycle(dataloader_validate)
scaler = torch.cuda.amp.GradScaler(enabled=True)
rot_ang = 0
if FLAGS.local_rank == 0:
video = Video(FLAGS.out_dir)
if FLAGS.local_rank == 0:
dataloader_train = tqdm(dataloader_train)
for it, target in enumerate(dataloader_train):
# Mix randomized background into dataset image
target = prepare_batch(target, FLAGS.train_background)
# Show/save image before training step (want to get correct rendering of input)
if FLAGS.local_rank == 0:
save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)
save_video = FLAGS.video_interval and (it % FLAGS.video_interval == 0)
if save_image:
result_image, result_dict = validate_itr(glctx, prepare_batch(next(v_it), FLAGS.train_background), geometry, opt_material, lgt, FLAGS) #prepare_batch(next(v_it), FLAGS.background)
np_result_image = result_image.detach().cpu().numpy()
util.save_image(FLAGS.out_dir + '/' + ('img_%s_%06d.png' % (FLAGS.mode, img_cnt)), np_result_image)
util.save_image(FLAGS.out_dir + '/' + ('mask_%s_%06d.png' % (FLAGS.mode, img_cnt)), result_dict['mask'].detach().cpu().numpy())
img_cnt = img_cnt+1
if save_video:
with torch.no_grad(): | params = get_camera_params( | 1 | 2023-11-27 13:44:01+00:00 | 24k |
zhenzhiwang/intercontrol | utils/model_util.py | [
{
"identifier": "ControlGaussianDiffusion",
"path": "diffusion/control_diffusion.py",
"snippet": "class ControlGaussianDiffusion(SpacedDiffusion):\n\n def inv_transform(self, data):\n assert self.std is not None and self.mean is not None\n #assert data.requires_grad == True\n std... | import torch
from diffusion.control_diffusion import ControlGaussianDiffusion
from model.cfg_sampler import wrap_model
from model.mdm import MDM
from model.ControlMDM import ControlMDM
from diffusion import gaussian_diffusion as gd
from diffusion.respace import SpacedDiffusion, space_timesteps | 15,035 |
def load_model(args, data, device, ModelClass=MDM):
model, diffusion = create_model_and_diffusion(args, data, ModelClass=ModelClass)
model_path = args.model_path
print(f"Loading checkpoints from [{model_path}]...")
state_dict = torch.load(model_path, map_location='cpu')
load_model_wo_clip(model, state_dict)
model.to(device)
model.eval() # disable random masking
model = wrap_model(model, args)
return model, diffusion
def load_model_wo_clip(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
if 't_pos_encoder.pe' in missing_keys:
missing_keys.remove('t_pos_encoder.pe')
if 't_pos_encoder.pe' in unexpected_keys:
unexpected_keys.remove('t_pos_encoder.pe')
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
def load_pretrained_mdm(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])
def load_pretrained_mdm_to_controlmdm(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
transformer_encoder_weight = {}
for key, value in state_dict.items():
if key.startswith('seqTransEncoder'):
transformer_encoder_weight[key[16:]] = value
unexpected_keys.remove(key)
model.seqTransEncoder_mdm.load_state_dict(transformer_encoder_weight, strict=True)
model.seqTransEncoder_control.load_state_dict(transformer_encoder_weight, strict=True)
assert len(unexpected_keys) == 0
#assert all([k.startswith('clip_model.') for k in missing_keys])
print("The following parameters are trained from scratch.")
for k in missing_keys:
if not k.startswith('clip_model.') and not k.startswith('seqTransEncoder'):
print(k)
def load_split_mdm(model, state_dict, cutting_point):
new_state_dict = {}
orig_trans_prefix = 'seqTransEncoder.'
for k, v in state_dict.items():
if k.startswith(orig_trans_prefix):
orig_layer = int(k.split('.')[2])
orig_suffix = '.'.join(k.split('.')[3:])
target_split = 'seqTransEncoder_start.' if orig_layer < cutting_point else 'seqTransEncoder_end.'
target_layer = orig_layer if orig_layer < cutting_point else orig_layer - cutting_point
new_k = target_split + 'layers.' + str(target_layer) + '.' + orig_suffix
new_state_dict[new_k] = v
else:
new_state_dict[k] = v
missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])
def create_model_and_diffusion(args, data, ModelClass=MDM, DiffusionClass=SpacedDiffusion):
model = ModelClass(**get_model_args(args, data))
diffusion = create_gaussian_diffusion(args, DiffusionClass)
return model, diffusion
def get_model_args(args, data):
# default args
clip_version = 'ViT-B/32'
action_emb = 'tensor'
cond_mode = 'text' if args.dataset in ['humanml', 'kit','babel', 'pw3d'] else 'action'
if hasattr(data.dataset, 'num_actions'):
num_actions = data.dataset.num_actions
else:
num_actions = 1
# SMPL defaults
data_rep = 'rot6d'
njoints = 25
nfeats = 6
if args.dataset in ['humanml', 'pw3d']:
data_rep = 'hml_vec'
njoints = 263
nfeats = 1
elif args.dataset == 'babel':
data_rep = 'rot6d'
njoints = 135
nfeats = 1
elif args.dataset == 'kit':
data_rep = 'hml_vec'
njoints = 251
nfeats = 1
else:
raise TypeError(f'dataset {args.dataset} is not currently supported')
return {'modeltype': '', 'njoints': njoints, 'nfeats': nfeats, 'num_actions': num_actions,
'translation': True, 'pose_rep': 'rot6d', 'glob': True, 'glob_rot': True,
'latent_dim': args.latent_dim, 'ff_size': 1024, 'num_layers': args.layers, 'num_heads': 4,
'dropout': 0.1, 'activation': "gelu", 'data_rep': data_rep, 'cond_mode': cond_mode,
'cond_mask_prob': args.cond_mask_prob, 'action_emb': action_emb, 'arch': args.arch,
'emb_trans_dec': args.emb_trans_dec, 'clip_version': clip_version, 'dataset': args.dataset,
'diffusion-steps': args.diffusion_steps, 'batch_size': args.batch_size, 'use_tta': args.use_tta,
'trans_emb': args.trans_emb, 'concat_trans_emb': args.concat_trans_emb, 'args': args}
def create_gaussian_diffusion(args, DiffusionClass=SpacedDiffusion):
# default params
predict_xstart = True # we always predict x_start (a.k.a. x0), that's our deal!
steps = args.diffusion_steps
scale_beta = 1. # no scaling
timestep_respacing = '' # can be used for ddim sampling, we don't use it.
learn_sigma = False
rescale_timesteps = False
print(f"number of diffusion-steps: {steps}")
|
def load_controlmdm_and_diffusion(args, data, device, ModelClass=ControlMDM, DiffusionClass=ControlGaussianDiffusion):
model, diffusion = create_model_and_diffusion(args, data, ModelClass=ControlMDM, DiffusionClass=DiffusionClass)
model_path = args.model_path
print(f"Loading checkpoints from [{model_path}]...")
state_dict = torch.load(model_path, map_location='cpu')
load_model_wo_clip(model, state_dict)
model.mean = data.dataset.t2m_dataset.mean
model.std = data.dataset.t2m_dataset.std
model.to(device)
model.eval() # disable random masking
model = wrap_model(model, args)
return model, diffusion
def load_model(args, data, device, ModelClass=MDM):
model, diffusion = create_model_and_diffusion(args, data, ModelClass=ModelClass)
model_path = args.model_path
print(f"Loading checkpoints from [{model_path}]...")
state_dict = torch.load(model_path, map_location='cpu')
load_model_wo_clip(model, state_dict)
model.to(device)
model.eval() # disable random masking
model = wrap_model(model, args)
return model, diffusion
def load_model_wo_clip(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
if 't_pos_encoder.pe' in missing_keys:
missing_keys.remove('t_pos_encoder.pe')
if 't_pos_encoder.pe' in unexpected_keys:
unexpected_keys.remove('t_pos_encoder.pe')
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') for k in missing_keys])
def load_pretrained_mdm(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])
def load_pretrained_mdm_to_controlmdm(model, state_dict):
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
transformer_encoder_weight = {}
for key, value in state_dict.items():
if key.startswith('seqTransEncoder'):
transformer_encoder_weight[key[16:]] = value
unexpected_keys.remove(key)
model.seqTransEncoder_mdm.load_state_dict(transformer_encoder_weight, strict=True)
model.seqTransEncoder_control.load_state_dict(transformer_encoder_weight, strict=True)
assert len(unexpected_keys) == 0
#assert all([k.startswith('clip_model.') for k in missing_keys])
print("The following parameters are trained from scratch.")
for k in missing_keys:
if not k.startswith('clip_model.') and not k.startswith('seqTransEncoder'):
print(k)
def load_split_mdm(model, state_dict, cutting_point):
new_state_dict = {}
orig_trans_prefix = 'seqTransEncoder.'
for k, v in state_dict.items():
if k.startswith(orig_trans_prefix):
orig_layer = int(k.split('.')[2])
orig_suffix = '.'.join(k.split('.')[3:])
target_split = 'seqTransEncoder_start.' if orig_layer < cutting_point else 'seqTransEncoder_end.'
target_layer = orig_layer if orig_layer < cutting_point else orig_layer - cutting_point
new_k = target_split + 'layers.' + str(target_layer) + '.' + orig_suffix
new_state_dict[new_k] = v
else:
new_state_dict[k] = v
missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
assert len(unexpected_keys) == 0
assert all([k.startswith('clip_model.') or k.startswith('multi_person.') for k in missing_keys])
def create_model_and_diffusion(args, data, ModelClass=MDM, DiffusionClass=SpacedDiffusion):
model = ModelClass(**get_model_args(args, data))
diffusion = create_gaussian_diffusion(args, DiffusionClass)
return model, diffusion
def get_model_args(args, data):
# default args
clip_version = 'ViT-B/32'
action_emb = 'tensor'
cond_mode = 'text' if args.dataset in ['humanml', 'kit','babel', 'pw3d'] else 'action'
if hasattr(data.dataset, 'num_actions'):
num_actions = data.dataset.num_actions
else:
num_actions = 1
# SMPL defaults
data_rep = 'rot6d'
njoints = 25
nfeats = 6
if args.dataset in ['humanml', 'pw3d']:
data_rep = 'hml_vec'
njoints = 263
nfeats = 1
elif args.dataset == 'babel':
data_rep = 'rot6d'
njoints = 135
nfeats = 1
elif args.dataset == 'kit':
data_rep = 'hml_vec'
njoints = 251
nfeats = 1
else:
raise TypeError(f'dataset {args.dataset} is not currently supported')
return {'modeltype': '', 'njoints': njoints, 'nfeats': nfeats, 'num_actions': num_actions,
'translation': True, 'pose_rep': 'rot6d', 'glob': True, 'glob_rot': True,
'latent_dim': args.latent_dim, 'ff_size': 1024, 'num_layers': args.layers, 'num_heads': 4,
'dropout': 0.1, 'activation': "gelu", 'data_rep': data_rep, 'cond_mode': cond_mode,
'cond_mask_prob': args.cond_mask_prob, 'action_emb': action_emb, 'arch': args.arch,
'emb_trans_dec': args.emb_trans_dec, 'clip_version': clip_version, 'dataset': args.dataset,
'diffusion-steps': args.diffusion_steps, 'batch_size': args.batch_size, 'use_tta': args.use_tta,
'trans_emb': args.trans_emb, 'concat_trans_emb': args.concat_trans_emb, 'args': args}
def create_gaussian_diffusion(args, DiffusionClass=SpacedDiffusion):
# default params
predict_xstart = True # we always predict x_start (a.k.a. x0), that's our deal!
steps = args.diffusion_steps
scale_beta = 1. # no scaling
timestep_respacing = '' # can be used for ddim sampling, we don't use it.
learn_sigma = False
rescale_timesteps = False
print(f"number of diffusion-steps: {steps}")
| betas = gd.get_named_beta_schedule(args.noise_schedule, steps, scale_beta) | 3 | 2023-11-27 05:28:02+00:00 | 24k |
camenduru/magicanimate-hf | magicanimate/pipelines/pipeline_animation.py | [
{
"identifier": "UNet3DConditionModel",
"path": "magicanimate/models/unet_controlnet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,... | import inspect, math
import numpy as np
import torch
import torch.distributed as dist
from typing import Callable, List, Optional, Union
from dataclasses import dataclass
from PIL import Image
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange
from magicanimate.models.unet_controlnet import UNet3DConditionModel
from magicanimate.models.controlnet import ControlNetModel
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
from magicanimate.pipelines.context import (
get_context_scheduler,
get_total_steps
)
from magicanimate.utils.util import get_tensor_interpolation_method
from accelerate import cpu_offload | 19,344 | generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
controlnet = self.controlnet
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)
reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)
is_dist_initialized = kwargs.get("dist", False)
rank = kwargs.get("rank", 0)
world_size = kwargs.get("world_size", 1)
# Prepare video
assert num_videos_per_prompt == 1 # FIXME: verify if num_videos_per_prompt > 1 works
assert batch_size == 1 # FIXME: verify if batch_size > 1 works
control = self.prepare_condition(
condition=controlnet_condition,
device=device,
dtype=controlnet.dtype,
num_videos_per_prompt=num_videos_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
)
controlnet_uncond_images, controlnet_cond_images = control.chunk(2)
# Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# Prepare latent variables
if init_latents is not None:
latents = rearrange(init_latents, "(b f) c h w -> b c f h w", f=video_length)
else:
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
video_length,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
latents_dtype = latents.dtype
# Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# Prepare text embeddings for controlnet
controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)
_, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)
controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}
# For img2img setting
if num_actual_inference_steps is None:
num_actual_inference_steps = num_inference_steps
if isinstance(source_image, str):
ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()
elif isinstance(source_image, np.ndarray):
ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()
| # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
controlnet: ControlNetModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
):
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,
controlnet=controlnet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def enable_vae_slicing(self):
self.vae.enable_slicing()
def disable_vae_slicing(self):
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
if is_accelerate_available():
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def decode_latents(self, latents, rank, decoder_consistency=None):
video_length = latents.shape[2]
latents = 1 / 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w")
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):
if decoder_consistency is not None:
video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))
else:
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
video = video.cpu().float().numpy()
return video
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):
shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
rand_device = "cpu" if device.type == "mps" else device
if isinstance(generator, list):
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
# prepare conditions for controlnet
condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0
condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)
condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()
if do_classifier_free_guidance:
condition = torch.cat([condition] * 2)
return condition
def next_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
@torch.no_grad()
def images2latents(self, images, dtype):
"""
Convert RGB image to VAE latents
"""
device = self._execution_device
images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
images = rearrange(images, "f h w c -> f c h w").to(device)
latents = []
for frame_idx in range(images.shape[0]):
latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)
latents = torch.cat(latents)
return latents
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=20,
num_actual_inference_steps=10,
eta=0.0,
return_intermediates=False,
**kwargs):
"""
Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440
invert a real image into noise map with determinisc DDIM inversion
"""
device = self._execution_device
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.images2latents(image)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
continue
model_inputs = latents
# predict the noise
# NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)
model_inputs = rearrange(model_inputs, "f c h w -> 1 c f h w")
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w")
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.next_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
if return_intermediates:
# return the intermediate laters during inversion
return latents, latents_list
return latents
def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):
if interpolation_factor < 2:
return latents
new_latents = torch.zeros(
(latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),
device=latents.device,
dtype=latents.dtype,
)
org_video_length = latents.shape[2]
rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]
new_index = 0
v0 = None
v1 = None
for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
v0 = latents[:,:,i0,:,:]
v1 = latents[:,:,i1,:,:]
new_latents[:,:,new_index,:,:] = v0
new_index += 1
for f in rate:
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
new_latents[:,:,new_index,:,:] = v.to(latents.device)
new_index += 1
new_latents[:,:,new_index,:,:] = v1
new_index += 1
return new_latents
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
_down_block_res_samples = []
_mid_block_res_sample = []
for i in np.concatenate(np.array(context)):
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
for res_t in _down_block_res_samples:
for i, res in enumerate(res_t):
down_block_res_samples[i].append(res)
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
mid_block_res_sample = torch.cat(_mid_block_res_sample)
# reshape controlnet output to match the unet3d inputs
b = b // 2 if do_classifier_free_guidance else b
_down_block_res_samples = []
for sample in down_block_res_samples:
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
sample = sample.repeat(2, 1, 1, 1, 1)
_down_block_res_samples.append(sample)
down_block_res_samples = _down_block_res_samples
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
return down_block_res_samples, mid_block_res_sample
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
controlnet = self.controlnet
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)
reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)
is_dist_initialized = kwargs.get("dist", False)
rank = kwargs.get("rank", 0)
world_size = kwargs.get("world_size", 1)
# Prepare video
assert num_videos_per_prompt == 1 # FIXME: verify if num_videos_per_prompt > 1 works
assert batch_size == 1 # FIXME: verify if batch_size > 1 works
control = self.prepare_condition(
condition=controlnet_condition,
device=device,
dtype=controlnet.dtype,
num_videos_per_prompt=num_videos_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
)
controlnet_uncond_images, controlnet_cond_images = control.chunk(2)
# Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# Prepare latent variables
if init_latents is not None:
latents = rearrange(init_latents, "(b f) c h w -> b c f h w", f=video_length)
else:
num_channels_latents = self.unet.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
video_length,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
)
latents_dtype = latents.dtype
# Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# Prepare text embeddings for controlnet
controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)
_, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)
controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}
# For img2img setting
if num_actual_inference_steps is None:
num_actual_inference_steps = num_inference_steps
if isinstance(source_image, str):
ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()
elif isinstance(source_image, np.ndarray):
ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()
| context_scheduler = get_context_scheduler(context_schedule) | 3 | 2023-12-04 20:47:34+00:00 | 24k |
metatube-community/metatube-plex-plugins | MetaTube.bundle/Contents/Libraries/Shared/urllib3/poolmanager.py | [
{
"identifier": "HTTPHeaderDict",
"path": "MetaTube.bundle/Contents/Libraries/Shared/urllib3/_collections.py",
"snippet": "class HTTPHeaderDict(MutableMapping):\n \"\"\"\n :param headers:\n An iterable of field-value pairs. Must not contain multiple field names\n when compared case-i... | import collections
import functools
import logging
from ._collections import HTTPHeaderDict, RecentlyUsedContainer
from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
from .exceptions import (
LocationValueError,
MaxRetryError,
ProxySchemeUnknown,
ProxySchemeUnsupported,
URLSchemeUnknown,
)
from .packages import six
from .packages.six.moves.urllib.parse import urljoin
from .request import RequestMethods
from .util.proxy import connection_requires_http_tunnel
from .util.retry import Retry
from .util.url import parse_url | 14,808 |
If ``port`` isn't given, it will be derived from the ``scheme`` using
``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
provided, it is merged with the instance's ``connection_pool_kw``
variable and used to create the new connection pool, if one is
needed.
"""
if not host:
raise LocationValueError("No host specified.")
request_context = self._merge_pool_kwargs(pool_kwargs)
request_context["scheme"] = scheme or "http"
if not port:
port = port_by_scheme.get(request_context["scheme"].lower(), 80)
request_context["port"] = port
request_context["host"] = host
return self.connection_from_context(request_context)
def connection_from_context(self, request_context):
"""
Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
``request_context`` must at least contain the ``scheme`` key and its
value must be a key in ``key_fn_by_scheme`` instance variable.
"""
scheme = request_context["scheme"].lower()
pool_key_constructor = self.key_fn_by_scheme.get(scheme)
if not pool_key_constructor:
raise URLSchemeUnknown(scheme)
pool_key = pool_key_constructor(request_context)
return self.connection_from_pool_key(pool_key, request_context=request_context)
def connection_from_pool_key(self, pool_key, request_context=None):
"""
Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
``pool_key`` should be a namedtuple that only contains immutable
objects. At a minimum it must have the ``scheme``, ``host``, and
``port`` fields.
"""
with self.pools.lock:
# If the scheme, host, or port doesn't match existing open
# connections, open a new ConnectionPool.
pool = self.pools.get(pool_key)
if pool:
return pool
# Make a fresh ConnectionPool of the desired type
scheme = request_context["scheme"]
host = request_context["host"]
port = request_context["port"]
pool = self._new_pool(scheme, host, port, request_context=request_context)
self.pools[pool_key] = pool
return pool
def connection_from_url(self, url, pool_kwargs=None):
"""
Similar to :func:`urllib3.connectionpool.connection_from_url`.
If ``pool_kwargs`` is not provided and a new pool needs to be
constructed, ``self.connection_pool_kw`` is used to initialize
the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
is provided, it is used instead. Note that if a new pool does not
need to be created for the request, the provided ``pool_kwargs`` are
not used.
"""
u = parse_url(url)
return self.connection_from_host(
u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
)
def _merge_pool_kwargs(self, override):
"""
Merge a dictionary of override values for self.connection_pool_kw.
This does not modify self.connection_pool_kw and returns a new dict.
Any keys in the override dictionary with a value of ``None`` are
removed from the merged dictionary.
"""
base_pool_kwargs = self.connection_pool_kw.copy()
if override:
for key, value in override.items():
if value is None:
try:
del base_pool_kwargs[key]
except KeyError:
pass
else:
base_pool_kwargs[key] = value
return base_pool_kwargs
def _proxy_requires_url_absolute_form(self, parsed_url):
"""
Indicates if the proxy requires the complete destination URL in the
request. Normally this is only needed when not using an HTTP CONNECT
tunnel.
"""
if self.proxy is None:
return False
return not connection_requires_http_tunnel(
self.proxy, self.proxy_config, parsed_url.scheme
)
def _validate_proxy_scheme_url_selection(self, url_scheme):
"""
Validates that were not attempting to do TLS in TLS connections on
Python2 or with unsupported SSL implementations.
"""
if self.proxy is None or url_scheme != "https":
return
if self.proxy.scheme != "https":
return
if six.PY2 and not self.proxy_config.use_forwarding_for_https:
| from __future__ import absolute_import
__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
log = logging.getLogger(__name__)
SSL_KEYWORDS = (
"key_file",
"cert_file",
"cert_reqs",
"ca_certs",
"ssl_version",
"ca_cert_dir",
"ssl_context",
"key_password",
"server_hostname",
)
# All known keyword arguments that could be provided to the pool manager, its
# pools, or the underlying connections. This is used to construct a pool key.
_key_fields = (
"key_scheme", # str
"key_host", # str
"key_port", # int
"key_timeout", # int or float or Timeout
"key_retries", # int or Retry
"key_strict", # bool
"key_block", # bool
"key_source_address", # str
"key_key_file", # str
"key_key_password", # str
"key_cert_file", # str
"key_cert_reqs", # str
"key_ca_certs", # str
"key_ssl_version", # str
"key_ca_cert_dir", # str
"key_ssl_context", # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext
"key_maxsize", # int
"key_headers", # dict
"key__proxy", # parsed proxy url
"key__proxy_headers", # dict
"key__proxy_config", # class
"key_socket_options", # list of (level (int), optname (int), value (int or str)) tuples
"key__socks_options", # dict
"key_assert_hostname", # bool or string
"key_assert_fingerprint", # str
"key_server_hostname", # str
)
#: The namedtuple class used to construct keys for the connection pool.
#: All custom key schemes should include the fields in this key at a minimum.
PoolKey = collections.namedtuple("PoolKey", _key_fields)
_proxy_config_fields = ("ssl_context", "use_forwarding_for_https")
ProxyConfig = collections.namedtuple("ProxyConfig", _proxy_config_fields)
def _default_key_normalizer(key_class, request_context):
"""
Create a pool key out of a request context dictionary.
According to RFC 3986, both the scheme and host are case-insensitive.
Therefore, this function normalizes both before constructing the pool
key for an HTTPS request. If you wish to change this behaviour, provide
alternate callables to ``key_fn_by_scheme``.
:param key_class:
The class to use when constructing the key. This should be a namedtuple
with the ``scheme`` and ``host`` keys at a minimum.
:type key_class: namedtuple
:param request_context:
A dictionary-like object that contain the context for a request.
:type request_context: dict
:return: A namedtuple that can be used as a connection pool key.
:rtype: PoolKey
"""
# Since we mutate the dictionary, make a copy first
context = request_context.copy()
context["scheme"] = context["scheme"].lower()
context["host"] = context["host"].lower()
# These are both dictionaries and need to be transformed into frozensets
for key in ("headers", "_proxy_headers", "_socks_options"):
if key in context and context[key] is not None:
context[key] = frozenset(context[key].items())
# The socket_options key may be a list and needs to be transformed into a
# tuple.
socket_opts = context.get("socket_options")
if socket_opts is not None:
context["socket_options"] = tuple(socket_opts)
# Map the kwargs to the names in the namedtuple - this is necessary since
# namedtuples can't have fields starting with '_'.
for key in list(context.keys()):
context["key_" + key] = context.pop(key)
# Default to ``None`` for keys missing from the context
for field in key_class._fields:
if field not in context:
context[field] = None
return key_class(**context)
#: A dictionary that maps a scheme to a callable that creates a pool key.
#: This can be used to alter the way pool keys are constructed, if desired.
#: Each PoolManager makes a copy of this dictionary so they can be configured
#: globally here, or individually on the instance.
key_fn_by_scheme = {
"http": functools.partial(_default_key_normalizer, PoolKey),
"https": functools.partial(_default_key_normalizer, PoolKey),
}
pool_classes_by_scheme = {"http": HTTPConnectionPool, "https": HTTPSConnectionPool}
class PoolManager(RequestMethods):
"""
Allows for arbitrary requests while transparently keeping track of
necessary connection pools for you.
:param num_pools:
Number of connection pools to cache before discarding the least
recently used pool.
:param headers:
Headers to include with all requests, unless other headers are given
explicitly.
:param \\**connection_pool_kw:
Additional parameters are used to create fresh
:class:`urllib3.connectionpool.ConnectionPool` instances.
Example::
>>> manager = PoolManager(num_pools=2)
>>> r = manager.request('GET', 'http://google.com/')
>>> r = manager.request('GET', 'http://google.com/mail')
>>> r = manager.request('GET', 'http://yahoo.com/')
>>> len(manager.pools)
2
"""
proxy = None
proxy_config = None
def __init__(self, num_pools=10, headers=None, **connection_pool_kw):
RequestMethods.__init__(self, headers)
self.connection_pool_kw = connection_pool_kw
self.pools = RecentlyUsedContainer(num_pools)
# Locally set the pool classes and keys so other PoolManagers can
# override them.
self.pool_classes_by_scheme = pool_classes_by_scheme
self.key_fn_by_scheme = key_fn_by_scheme.copy()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.clear()
# Return False to re-raise any potential exceptions
return False
def _new_pool(self, scheme, host, port, request_context=None):
"""
Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and
any additional pool keyword arguments.
If ``request_context`` is provided, it is provided as keyword arguments
to the pool class used. This method is used to actually create the
connection pools handed out by :meth:`connection_from_url` and
companion methods. It is intended to be overridden for customization.
"""
pool_cls = self.pool_classes_by_scheme[scheme]
if request_context is None:
request_context = self.connection_pool_kw.copy()
# Although the context has everything necessary to create the pool,
# this function has historically only used the scheme, host, and port
# in the positional args. When an API change is acceptable these can
# be removed.
for key in ("scheme", "host", "port"):
request_context.pop(key, None)
if scheme == "http":
for kw in SSL_KEYWORDS:
request_context.pop(kw, None)
return pool_cls(host, port, **request_context)
def clear(self):
"""
Empty our store of pools and direct them all to close.
This will not affect in-flight connections, but they will not be
re-used after completion.
"""
self.pools.clear()
def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
"""
Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.
If ``port`` isn't given, it will be derived from the ``scheme`` using
``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
provided, it is merged with the instance's ``connection_pool_kw``
variable and used to create the new connection pool, if one is
needed.
"""
if not host:
raise LocationValueError("No host specified.")
request_context = self._merge_pool_kwargs(pool_kwargs)
request_context["scheme"] = scheme or "http"
if not port:
port = port_by_scheme.get(request_context["scheme"].lower(), 80)
request_context["port"] = port
request_context["host"] = host
return self.connection_from_context(request_context)
def connection_from_context(self, request_context):
"""
Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
``request_context`` must at least contain the ``scheme`` key and its
value must be a key in ``key_fn_by_scheme`` instance variable.
"""
scheme = request_context["scheme"].lower()
pool_key_constructor = self.key_fn_by_scheme.get(scheme)
if not pool_key_constructor:
raise URLSchemeUnknown(scheme)
pool_key = pool_key_constructor(request_context)
return self.connection_from_pool_key(pool_key, request_context=request_context)
def connection_from_pool_key(self, pool_key, request_context=None):
"""
Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
``pool_key`` should be a namedtuple that only contains immutable
objects. At a minimum it must have the ``scheme``, ``host``, and
``port`` fields.
"""
with self.pools.lock:
# If the scheme, host, or port doesn't match existing open
# connections, open a new ConnectionPool.
pool = self.pools.get(pool_key)
if pool:
return pool
# Make a fresh ConnectionPool of the desired type
scheme = request_context["scheme"]
host = request_context["host"]
port = request_context["port"]
pool = self._new_pool(scheme, host, port, request_context=request_context)
self.pools[pool_key] = pool
return pool
def connection_from_url(self, url, pool_kwargs=None):
"""
Similar to :func:`urllib3.connectionpool.connection_from_url`.
If ``pool_kwargs`` is not provided and a new pool needs to be
constructed, ``self.connection_pool_kw`` is used to initialize
the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
is provided, it is used instead. Note that if a new pool does not
need to be created for the request, the provided ``pool_kwargs`` are
not used.
"""
u = parse_url(url)
return self.connection_from_host(
u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
)
def _merge_pool_kwargs(self, override):
"""
Merge a dictionary of override values for self.connection_pool_kw.
This does not modify self.connection_pool_kw and returns a new dict.
Any keys in the override dictionary with a value of ``None`` are
removed from the merged dictionary.
"""
base_pool_kwargs = self.connection_pool_kw.copy()
if override:
for key, value in override.items():
if value is None:
try:
del base_pool_kwargs[key]
except KeyError:
pass
else:
base_pool_kwargs[key] = value
return base_pool_kwargs
def _proxy_requires_url_absolute_form(self, parsed_url):
"""
Indicates if the proxy requires the complete destination URL in the
request. Normally this is only needed when not using an HTTP CONNECT
tunnel.
"""
if self.proxy is None:
return False
return not connection_requires_http_tunnel(
self.proxy, self.proxy_config, parsed_url.scheme
)
def _validate_proxy_scheme_url_selection(self, url_scheme):
"""
Validates that were not attempting to do TLS in TLS connections on
Python2 or with unsupported SSL implementations.
"""
if self.proxy is None or url_scheme != "https":
return
if self.proxy.scheme != "https":
return
if six.PY2 and not self.proxy_config.use_forwarding_for_https: | raise ProxySchemeUnsupported( | 6 | 2023-11-27 07:01:39+00:00 | 24k |
NobiDeveloper/Nobita-Filter-Bot | plugins/p_ttishow.py | [
{
"identifier": "ADMINS",
"path": "info.py",
"snippet": "ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]"
},
{
"identifier": "LOG_CHANNEL",
"path": "info.py",
"snippet": "LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))"
},
... | from pyrogram import Client, filters, enums
from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, CallbackQuery
from pyrogram.errors.exceptions.bad_request_400 import MessageTooLong, PeerIdInvalid
from info import ADMINS, LOG_CHANNEL, SUPPORT_CHAT, MELCOW_NEW_USERS, MELCOW_VID, CHNL_LNK, GRP_LNK
from database.users_chats_db import db
from database.ia_filterdb import Media
from utils import get_size, temp, get_settings
from Script import script
from pyrogram.errors import ChatAdminRequired
import asyncio | 14,685 |
"""----------------------------------------- https://github.com/NobiDeveloper/Nobita-Filter-Bot --------------------------------------"""
@Client.on_message(filters.new_chat_members & filters.group)
async def save_group(bot, message):
r_j_check = [u.id for u in message.new_chat_members]
if temp.ME in r_j_check:
if not await db.get_chat(message.chat.id):
total=await bot.get_chat_members_count(message.chat.id)
r_j = message.from_user.mention if message.from_user else "Anonymous"
|
"""----------------------------------------- https://github.com/NobiDeveloper/Nobita-Filter-Bot --------------------------------------"""
@Client.on_message(filters.new_chat_members & filters.group)
async def save_group(bot, message):
r_j_check = [u.id for u in message.new_chat_members]
if temp.ME in r_j_check:
if not await db.get_chat(message.chat.id):
total=await bot.get_chat_members_count(message.chat.id)
r_j = message.from_user.mention if message.from_user else "Anonymous" | await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j)) | 12 | 2023-11-28 13:36:56+00:00 | 24k |
chenxx89/BFRffusion | models/models.py | [
{
"identifier": "timestep_embedding",
"path": "ldm/modules/diffusionmodules/util.py",
"snippet": "def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\... | import torch
import os
import numpy as np
import math
import shutil
import safetensors.torch
from ldm.modules.diffusionmodules.util import timestep_embedding
from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.diffusionmodules.openaimodel import UNetModel
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from data.dataset_instantiate import instantiate_from_config as instantiate_dataset_from_config
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from metrics.metrics_all import calculate_psnr_ssim, calculate_lpips, calculate_NIQE, calculate_fid_folder
from torch.utils.data import DataLoader
from PIL import Image
from torch.optim.lr_scheduler import LambdaLR
from omegaconf import OmegaConf | 20,413 |
def get_state_dict(d):
return d.get('state_dict', d)
def load_state_dict(ckpt_path, location='cpu'):
_, extension = os.path.splitext(ckpt_path)
if extension.lower() == ".safetensors":
state_dict = safetensors.torch.load_file(ckpt_path, device=location)
else:
state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
state_dict = get_state_dict(state_dict)
print(f'Loaded state_dict from [{ckpt_path}]')
return state_dict
def create_model(config_path):
config = OmegaConf.load(config_path)
model = instantiate_from_config(config.model).cpu()
print(f'Loaded model config from [{config_path}]')
return model
class ControlledUnetModel(UNetModel):
def forward(self, x, timesteps=None, context=None, control=None, **kwargs):
hs = []
|
def get_state_dict(d):
return d.get('state_dict', d)
def load_state_dict(ckpt_path, location='cpu'):
_, extension = os.path.splitext(ckpt_path)
if extension.lower() == ".safetensors":
state_dict = safetensors.torch.load_file(ckpt_path, device=location)
else:
state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
state_dict = get_state_dict(state_dict)
print(f'Loaded state_dict from [{ckpt_path}]')
return state_dict
def create_model(config_path):
config = OmegaConf.load(config_path)
model = instantiate_from_config(config.model).cpu()
print(f'Loaded model config from [{config_path}]')
return model
class ControlledUnetModel(UNetModel):
def forward(self, x, timesteps=None, context=None, control=None, **kwargs):
hs = [] | t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) | 0 | 2023-11-30 13:50:58+00:00 | 24k |
IanYeung/MGLD-VSR | basicsr/data/realbasicvsr_dataset.py | [
{
"identifier": "Clip",
"path": "basicsr/data/mmcv_transforms/aug_pix.py",
"snippet": "class Clip(BaseTransform):\n \"\"\"Clip the pixels.\n\n Modified keys are the attributes specified in \"keys\".\n\n Args:\n keys (list[str]): The keys whose values are clipped.\n a_min (int): Lo... | import cv2
import math
import time
import os
import os.path as osp
import numpy as np
import random
import torch
from copy import deepcopy
from pathlib import Path
from torch.utils import data as data
from basicsr.data.mmcv_transforms import Clip, UnsharpMasking, RescaleToZeroOne
from basicsr.data.mmcv_transforms import RandomBlur, RandomResize, RandomNoise, RandomJPEGCompression, RandomVideoCompression, DegradationsWithShuffle
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.data.transforms import augment, single_random_crop, paired_random_crop
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor, tensor2img, imwrite
from basicsr.utils.flow_util import dequantize_flow
from basicsr.utils.registry import DATASET_REGISTRY | 15,788 | folder, frame_num, _ = line.split(' ')
self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
# remove the video clips used in validation
if opt['val_partition'] == 'REDS4':
val_partition = ['000', '011', '015', '020']
elif opt['val_partition'] == 'official':
val_partition = [f'{v:03d}' for v in range(240, 270)]
else:
raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
f"Supported ones are ['official', 'REDS4'].")
if opt['test_mode']:
self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]
else:
self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
# file client (io backend)
self.file_client = None
self.io_backend_opt = opt['io_backend']
self.is_lmdb = False
if self.io_backend_opt['type'] == 'lmdb':
self.is_lmdb = True
self.io_backend_opt['db_paths'] = [self.gt_root]
self.io_backend_opt['client_keys'] = ['gt']
# temporal augmentation configs
self.interval_list = opt.get('interval_list', [1])
self.random_reverse = opt.get('random_reverse', False)
interval_str = ','.join(str(x) for x in self.interval_list)
logger = get_root_logger()
logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
f'random reverse is {self.random_reverse}.')
# blur settings for the first degradation
self.blur_kernel_size = opt['blur_kernel_size']
self.kernel_list = opt['kernel_list']
self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability
self.blur_sigma = opt['blur_sigma']
self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels
self.betap_range = opt['betap_range'] # betap used in plateau blur kernels
self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters
# blur settings for the second degradation
self.blur_kernel_size2 = opt['blur_kernel_size2']
self.kernel_list2 = opt['kernel_list2']
self.kernel_prob2 = opt['kernel_prob2']
self.blur_sigma2 = opt['blur_sigma2']
self.betag_range2 = opt['betag_range2']
self.betap_range2 = opt['betap_range2']
self.sinc_prob2 = opt['sinc_prob2']
# a final sinc filter
self.final_sinc_prob = opt['final_sinc_prob']
self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21
# TODO: kernel range is now hard-coded, should be in the configure file
self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect
self.pulse_tensor[10, 10] = 1
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = int(frame_name)
if start_frame_idx > 100 - self.num_frame * interval:
start_frame_idx = random.randint(0, 100 - self.num_frame * interval)
end_frame_idx = start_frame_idx + self.num_frame * interval
neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
# get the GT frames
img_gts = []
for neighbor in neighbor_list:
if self.is_lmdb:
img_gt_path = f'{clip_name}/{neighbor:08d}'
else:
img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'
# get GT
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=True)
img_gts.append(img_gt)
# randomly crop
img_gts = single_random_crop(img_gts, gt_size, img_gt_path)
# augmentation - flip, rotate
img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])
# list-to-list
img_gts = img2tensor(img_gts)
# kernels
kernel1s = []
kernel2s = []
sinc_kernels = []
for _ in range(len(img_gts)):
# ------------------------ Generate kernels (used in the first degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob']:
# this sinc filter setting is for kernels ranging from [7, 21]
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
|
# @DATASET_REGISTRY.register()
class RealVSRRecurrentDataset(data.Dataset):
"""REDS dataset for training recurrent networks.
The keys are generated from a meta info txt file.
basicsr/data/meta_info/meta_info_REDS_GT.txt
Each line contains:
1. subfolder (clip) name; 2. frame number; 3. image shape, separated by
a white space.
Examples:
000 100 (720,1280,3)
001 100 (720,1280,3)
...
Key examples: "000/00000000"
GT (gt): Ground-Truth;
LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
Args:
opt (dict): Config for train dataset. It contains the following keys:
dataroot_gt (str): Data root path for gt.
meta_info_file (str): Path for meta information file.
val_partition (str): Validation partition types. 'REDS4' or 'official'.
io_backend (dict): IO backend type and other kwarg.
num_frame (int): Window size for input frames.
gt_size (int): Cropped patched size for gt patches.
interval_list (list): Interval list for temporal augmentation.
random_reverse (bool): Random reverse input frames.
use_hflip (bool): Use horizontal flips.
use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
"""
def __init__(self, opt):
super(RealVSRRecurrentDataset, self).__init__()
self.opt = opt
self.gt_root = Path(opt['dataroot_gt'])
self.num_frame = opt['num_frame']
self.keys = []
with open(opt['meta_info_file'], 'r') as fin:
for line in fin:
folder, frame_num, _ = line.split(' ')
self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
# remove the video clips used in validation
if opt['val_partition'] == 'REDS4':
val_partition = ['000', '011', '015', '020']
elif opt['val_partition'] == 'official':
val_partition = [f'{v:03d}' for v in range(240, 270)]
else:
raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
f"Supported ones are ['official', 'REDS4'].")
if opt['test_mode']:
self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]
else:
self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
# file client (io backend)
self.file_client = None
self.io_backend_opt = opt['io_backend']
self.is_lmdb = False
if self.io_backend_opt['type'] == 'lmdb':
self.is_lmdb = True
self.io_backend_opt['db_paths'] = [self.gt_root]
self.io_backend_opt['client_keys'] = ['gt']
# temporal augmentation configs
self.interval_list = opt.get('interval_list', [1])
self.random_reverse = opt.get('random_reverse', False)
interval_str = ','.join(str(x) for x in self.interval_list)
logger = get_root_logger()
logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
f'random reverse is {self.random_reverse}.')
# the first degradation
self.random_blur_1 = RandomBlur(
params=opt['degradation_1']['random_blur']['params'],
keys=opt['degradation_1']['random_blur']['keys']
)
self.random_resize_1 = RandomResize(
params=opt['degradation_1']['random_resize']['params'],
keys=opt['degradation_1']['random_resize']['keys']
)
self.random_noise_1 = RandomNoise(
params=opt['degradation_1']['random_noise']['params'],
keys=opt['degradation_1']['random_noise']['keys']
)
self.random_jpeg_1 = RandomJPEGCompression(
params=opt['degradation_1']['random_jpeg']['params'],
keys=opt['degradation_1']['random_jpeg']['keys']
)
self.random_mpeg_1 = RandomVideoCompression(
params=opt['degradation_1']['random_mpeg']['params'],
keys=opt['degradation_1']['random_mpeg']['keys']
)
# the second degradation
self.random_blur_2 = RandomBlur(
params=opt['degradation_2']['random_blur']['params'],
keys=opt['degradation_2']['random_blur']['keys']
)
self.random_resize_2 = RandomResize(
params=opt['degradation_2']['random_resize']['params'],
keys=opt['degradation_2']['random_resize']['keys']
)
self.random_noise_2 = RandomNoise(
params=opt['degradation_2']['random_noise']['params'],
keys=opt['degradation_2']['random_noise']['keys']
)
self.random_jpeg_2 = RandomJPEGCompression(
params=opt['degradation_2']['random_jpeg']['params'],
keys=opt['degradation_2']['random_jpeg']['keys']
)
self.random_mpeg_2 = RandomVideoCompression(
params=opt['degradation_2']['random_mpeg']['params'],
keys=opt['degradation_2']['random_mpeg']['keys']
)
# final
self.resize_final = RandomResize(
params=opt['degradation_2']['resize_final']['params'],
keys=opt['degradation_2']['resize_final']['keys']
)
self.blur_final = RandomBlur(
params=opt['degradation_2']['blur_final']['params'],
keys=opt['degradation_2']['blur_final']['keys']
)
# transforms
self.usm = UnsharpMasking(
kernel_size=opt['transforms']['usm']['kernel_size'],
sigma=opt['transforms']['usm']['sigma'],
weight=opt['transforms']['usm']['weight'],
threshold=opt['transforms']['usm']['threshold'],
keys=opt['transforms']['usm']['keys']
)
self.clip = Clip(keys=opt['transforms']['clip']['keys'])
self.rescale = RescaleToZeroOne(keys=opt['transforms']['rescale']['keys'])
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = int(frame_name)
if start_frame_idx > 100 - self.num_frame * interval:
start_frame_idx = random.randint(0, 100 - self.num_frame * interval)
end_frame_idx = start_frame_idx + self.num_frame * interval
neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
# get the GT frames
img_gts = []
for neighbor in neighbor_list:
if self.is_lmdb:
img_gt_path = f'{clip_name}/{neighbor:08d}'
else:
img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'
# get GT
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=False)
img_gts.append(img_gt)
# randomly crop
img_gts = single_random_crop(img_gts, gt_size, img_gt_path)
# augmentation - flip, rotate
img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])
img_lqs = deepcopy(img_gts)
out_dict = {'lqs': img_lqs, 'gts': img_gts}
out_dict = self.usm.transform(out_dict)
## the first degradation
out_dict = self.random_blur_1(out_dict)
out_dict = self.random_resize_1(out_dict)
out_dict = self.random_noise_1(out_dict)
out_dict = self.random_jpeg_1(out_dict)
out_dict = self.random_mpeg_1(out_dict)
## the second degradation
out_dict = self.random_blur_2(out_dict)
out_dict = self.random_resize_2(out_dict)
out_dict = self.random_noise_2(out_dict)
out_dict = self.random_jpeg_2(out_dict)
out_dict = self.random_mpeg_2(out_dict)
## final resize
out_dict = self.resize_final(out_dict)
out_dict = self.blur_final(out_dict)
# post process
out_dict = self.clip(out_dict)
out_dict = self.rescale.transform(out_dict)
# list-to-list
for k in out_dict.keys():
out_dict[k] = img2tensor(out_dict[k])
# img_gts: (t, c, h, w)
# key: str
return out_dict
def __len__(self):
return len(self.keys)
# @DATASET_REGISTRY.register()
class RealESRGANRecurrentDataset(data.Dataset):
"""REDS dataset for training recurrent networks.
The keys are generated from a meta info txt file.
basicsr/data/meta_info/meta_info_REDS_GT.txt
Each line contains:
1. subfolder (clip) name; 2. frame number; 3. image shape, separated by
a white space.
Examples:
000 100 (720,1280,3)
001 100 (720,1280,3)
...
Key examples: "000/00000000"
GT (gt): Ground-Truth;
LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
Args:
opt (dict): Config for train dataset. It contains the following keys:
dataroot_gt (str): Data root path for gt.
meta_info_file (str): Path for meta information file.
val_partition (str): Validation partition types. 'REDS4' or 'official'.
io_backend (dict): IO backend type and other kwarg.
num_frame (int): Window size for input frames.
gt_size (int): Cropped patched size for gt patches.
interval_list (list): Interval list for temporal augmentation.
random_reverse (bool): Random reverse input frames.
use_hflip (bool): Use horizontal flips.
use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
"""
def __init__(self, opt):
super(RealESRGANRecurrentDataset, self).__init__()
self.opt = opt
self.gt_root = Path(opt['dataroot_gt'])
self.num_frame = opt['num_frame']
self.keys = []
with open(opt['meta_info_file'], 'r') as fin:
for line in fin:
folder, frame_num, _ = line.split(' ')
self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
# remove the video clips used in validation
if opt['val_partition'] == 'REDS4':
val_partition = ['000', '011', '015', '020']
elif opt['val_partition'] == 'official':
val_partition = [f'{v:03d}' for v in range(240, 270)]
else:
raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
f"Supported ones are ['official', 'REDS4'].")
if opt['test_mode']:
self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]
else:
self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
# file client (io backend)
self.file_client = None
self.io_backend_opt = opt['io_backend']
self.is_lmdb = False
if self.io_backend_opt['type'] == 'lmdb':
self.is_lmdb = True
self.io_backend_opt['db_paths'] = [self.gt_root]
self.io_backend_opt['client_keys'] = ['gt']
# temporal augmentation configs
self.interval_list = opt.get('interval_list', [1])
self.random_reverse = opt.get('random_reverse', False)
interval_str = ','.join(str(x) for x in self.interval_list)
logger = get_root_logger()
logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
f'random reverse is {self.random_reverse}.')
# blur settings for the first degradation
self.blur_kernel_size = opt['blur_kernel_size']
self.kernel_list = opt['kernel_list']
self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability
self.blur_sigma = opt['blur_sigma']
self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels
self.betap_range = opt['betap_range'] # betap used in plateau blur kernels
self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters
# blur settings for the second degradation
self.blur_kernel_size2 = opt['blur_kernel_size2']
self.kernel_list2 = opt['kernel_list2']
self.kernel_prob2 = opt['kernel_prob2']
self.blur_sigma2 = opt['blur_sigma2']
self.betag_range2 = opt['betag_range2']
self.betap_range2 = opt['betap_range2']
self.sinc_prob2 = opt['sinc_prob2']
# a final sinc filter
self.final_sinc_prob = opt['final_sinc_prob']
self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21
# TODO: kernel range is now hard-coded, should be in the configure file
self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect
self.pulse_tensor[10, 10] = 1
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = int(frame_name)
if start_frame_idx > 100 - self.num_frame * interval:
start_frame_idx = random.randint(0, 100 - self.num_frame * interval)
end_frame_idx = start_frame_idx + self.num_frame * interval
neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
# get the GT frames
img_gts = []
for neighbor in neighbor_list:
if self.is_lmdb:
img_gt_path = f'{clip_name}/{neighbor:08d}'
else:
img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'
# get GT
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=True)
img_gts.append(img_gt)
# randomly crop
img_gts = single_random_crop(img_gts, gt_size, img_gt_path)
# augmentation - flip, rotate
img_gts = augment(img_gts, self.opt['use_hflip'], self.opt['use_rot'])
# list-to-list
img_gts = img2tensor(img_gts)
# kernels
kernel1s = []
kernel2s = []
sinc_kernels = []
for _ in range(len(img_gts)):
# ------------------------ Generate kernels (used in the first degradation) ------------------------ #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < self.opt['sinc_prob']:
# this sinc filter setting is for kernels ranging from [7, 21]
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else: | kernel = random_mixed_kernels( | 10 | 2023-11-30 01:50:29+00:00 | 24k |
Czm369/MixPL | mmdet/configs/rtmdet/rtmdet_ins_s_8xb32_300e_coco.py | [
{
"identifier": "PackDetInputs",
"path": "mmdet/datasets/transforms/formatting.py",
"snippet": "class PackDetInputs(BaseTransform):\n \"\"\"Pack the inputs data for the detection / semantic segmentation /\n panoptic segmentation.\n\n The ``img_meta`` item is always populated. The contents of t... | from mmengine.config import read_base
from .rtmdet_ins_l_8xb32_300e_coco import *
from mmcv.transforms.loading import LoadImageFromFile
from mmcv.transforms.processing import RandomResize
from mmengine.hooks.ema_hook import EMAHook
from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import (FilterAnnotations,
LoadAnnotations)
from mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic,
Pad, RandomCrop, RandomFlip,
Resize, YOLOXHSVRandomAug)
from mmdet.engine.hooks.pipeline_switch_hook import PipelineSwitchHook
from mmdet.models.layers.ema import ExpMomentumEMA | 18,177 | # Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
with read_base():
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model.update(
dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(
in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1),
bbox_head=dict(in_channels=128, feat_channels=128)))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(type=CachedMosaic, img_scale=(640, 640), pad_val=114.0),
dict(
type=RandomResize,
scale=(1280, 1280),
ratio_range=(0.5, 2.0),
resize_type=Resize,
keep_ratio=True),
dict(
| # Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
with read_base():
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model.update(
dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(
in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1),
bbox_head=dict(in_channels=128, feat_channels=128)))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(type=CachedMosaic, img_scale=(640, 640), pad_val=114.0),
dict(
type=RandomResize,
scale=(1280, 1280),
ratio_range=(0.5, 2.0),
resize_type=Resize,
keep_ratio=True),
dict( | type=RandomCrop, | 6 | 2023-11-30 08:58:00+00:00 | 24k |
SEU-ProactiveSecurity-Group/MalPurifier | core/defense/amd_dnn_plus.py | [
{
"identifier": "Max",
"path": "core/attack/max.py",
"snippet": "class Max(BaseAttack):\n \"\"\"\n Max攻击：迭代地从多个攻击方法中选择结果。\n\n 参数\n --------\n @param attack_list: List, 已实例化的攻击对象的列表。\n @param varepsilon: Float, 用于判断收敛性的标量。\n \"\"\"\n\n def __init__(self, attack_list, varepsilon=1e... | import time
import os.path as path
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from core.attack.max import Max
from core.attack.stepwise_max import StepwiseMax
from core.defense.md_dnn import MalwareDetectionDNN
from core.defense.amd_template import DetectorTemplate
from config import config, logging, ErrorHandler
from tools import utils
from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, balanced_accuracy_score | 19,741 | """
@article{grosse2017statistical,
title={On the (statistical) detection of adversarial examples},
author={Grosse, Kathrin and Manoharan, Praveen and Papernot, Nicolas and Backes, Michael and McDaniel, Patrick},
journal={arXiv preprint arXiv:1702.06280},
year={2017}
}
@inproceedings{carlini2017adversarial,
title={Adversarial examples are not easily detected: Bypassing ten detection methods},
author={Carlini, Nicholas and Wagner, David},
booktitle={Proceedings of the 10th ACM workshop on artificial intelligence and security},
pages={3--14},
year={2017}
}
This implementation is not an official version, but adapted from:
https://github.com/carlini/nn_breaking_detection
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
| """
@article{grosse2017statistical,
title={On the (statistical) detection of adversarial examples},
author={Grosse, Kathrin and Manoharan, Praveen and Papernot, Nicolas and Backes, Michael and McDaniel, Patrick},
journal={arXiv preprint arXiv:1702.06280},
year={2017}
}
@inproceedings{carlini2017adversarial,
title={Adversarial examples are not easily detected: Bypassing ten detection methods},
author={Carlini, Nicholas and Wagner, David},
booktitle={Proceedings of the 10th ACM workshop on artificial intelligence and security},
pages={3--14},
year={2017}
}
This implementation is not an official version, but adapted from:
https://github.com/carlini/nn_breaking_detection
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
| logger = logging.getLogger('core.defense.amd_dnn_plus') | 4 | 2023-11-27 02:00:23+00:00 | 24k |
iann838/pulsefire | tests/test_taskgroups.py | [
{
"identifier": "RiotAPIClient",
"path": "pulsefire/clients.py",
"snippet": "class RiotAPIClient(BaseClient):\n \"\"\"Riot API Client.\n\n | Resources | Support |\n | -------------------- | -------------------------- |\n | League of Legends | ✅ ... | import asyncio
import os
from pulsefire.clients import RiotAPIClient
from pulsefire.functools import async_to_sync
from pulsefire.schemas import RiotAPISchema
from pulsefire.taskgroups import TaskGroup | 16,605 |
@async_to_sync()
async def test_taskgroup():
async with RiotAPIClient(default_headers={"X-Riot-Token": os.environ["RIOT_API_KEY"]}) as client:
plat_league = await client.get_lol_league_v4_entries_by_division(region="na1", queue="RANKED_SOLO_5x5", tier="PLATINUM", division="IV")
summoner = await client.get_lol_summoner_v4_by_id(region="na1", id=plat_league[0]["summonerId"])
match_ids = await client.get_lol_match_v5_match_ids_by_puuid(region="americas", puuid=summoner["puuid"])
|
@async_to_sync()
async def test_taskgroup():
async with RiotAPIClient(default_headers={"X-Riot-Token": os.environ["RIOT_API_KEY"]}) as client:
plat_league = await client.get_lol_league_v4_entries_by_division(region="na1", queue="RANKED_SOLO_5x5", tier="PLATINUM", division="IV")
summoner = await client.get_lol_summoner_v4_by_id(region="na1", id=plat_league[0]["summonerId"])
match_ids = await client.get_lol_match_v5_match_ids_by_puuid(region="americas", puuid=summoner["puuid"])
| async with TaskGroup() as tg: | 3 | 2023-11-27 13:37:24+00:00 | 24k |
Matrixeigs/UncertaintyManagementInteroperablePowerTransportationSystems | TestCaseDistributionSystems/uc_mmgs_tess_stochastic.py | [
{
"identifier": "case33",
"path": "TestCaseDistributionSystems/test_cases/case33.py",
"snippet": "def case33():\n \"\"\"Power flow data for 33 bus, 6 generator case.\n Please see L{caseformat} for details on the case file format.\n\n Based on data from ...\n\n Alsac, O. & Stott, B., I{\"Opti... | from TestCaseDistributionSystems.test_cases import case33
from TestCasesMicrogrids.test_cases.cases_unit_commitment import micro_grid
from TestCasesTransportationSystems.test_cases import case3, TIME, LOCATION
from numpy import zeros, shape, ones, diag, concatenate, eye
from scipy.sparse import csr_matrix as sparse
from scipy.sparse import hstack, vstack, lil_matrix
from numpy import flatnonzero as find
from numpy import array, tile, arange, random
from pypower.idx_brch import F_BUS, T_BUS, BR_R, BR_X, RATE_A
from pypower.idx_bus import PD, VMAX, VMIN, QD
from pypower.idx_gen import GEN_BUS, PMAX, PMIN, QMAX, QMIN
from pypower.ext2int import ext2int
from Solvers.mixed_integer_quadratic_constrained_cplex import mixed_integer_quadratic_constrained_programming as miqcp
from Solvers.mixed_integer_solvers_cplex import mixed_integer_linear_programming as milp
from copy import deepcopy
from TestCaseDistributionSystems.data_format.idx_MG import PBIC_AC2DC, PG, PESS_DC, PBIC_DC2AC, PUG, PESS_CH, \
PMESS, EESS, NX_MG, QBIC, QUG, QG
from TestCaseDistributionSystems.database_management import DataBaseManagement
from StochasticOptimization.scenario_reduction import ScenarioReduction | 15,480 | for i in range(ng):
c[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 5] * baseMVA
q[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA
c0 += gencost[i, 6]
# Coupling constraints between the distribution systems and micro_grids
Ax2y = lil_matrix((2 * nmg * T, nv_ds)) # connection matrix with the microgrids
for i in range(T):
for j in range(nmg):
# Active power
Ax2y[i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + j] = 1000 * baseMVA
# Reactive power
Ax2y[nmg * T + i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + nmg + j] = 1000 * baseMVA
# II) Formulate the problem for microgrids
model_microgrids = {}
for i in range(nmg):
model_microgrids[i] = self.problem_formulation_microgrid(mg=mgs[i], mess=mess)
# II.A) Combine the distribution system operation problem and microgrid systems
if Aeq is not None:
neq_ds = Aeq.shape[0]
else:
neq_ds = 0
nVariables = int(nv_ds)
neq = int(neq_ds)
nv_index = zeros(nmg + 1).astype(int)
neq_index = zeros(nmg + 1).astype(int)
nv_index[0] = nv_ds
neq_index[0] = int(neq_ds)
for i in range(nmg):
nv_index[i + 1] = nv_index[i] + len(model_microgrids[i]["c"])
neq_index[i + 1] = neq_index[i] + model_microgrids[i]["Aeq"].shape[0]
nVariables += len(model_microgrids[i]["c"])
neq += int(model_microgrids[i]["Aeq"].shape[0])
Aeq_full = lil_matrix((int(neq_index[-1]), int(nv_index[-1])))
Aeq_full[0:neq_ds, 0:nv_ds] = Aeq
for i in range(nmg):
lb = concatenate([lb, model_microgrids[i]["lb"]])
ub = concatenate([ub, model_microgrids[i]["ub"]])
c = concatenate([c, model_microgrids[i]["c"]])
q = concatenate([q, model_microgrids[i]["q"]])
vtypes += model_microgrids[i]["vtypes"]
beq = concatenate([beq, model_microgrids[i]["beq"]])
Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_microgrids[i]["Aeq"]
# Add coupling constraints, between the microgrids and distribution networks
Ay2x = lil_matrix((2 * nmg * T, nv_index[-1] - nv_index[0]))
for i in range(T):
for j in range(nmg):
Ay2x[i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + PUG] = -1
Ay2x[nmg * T + i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + QUG] = -1
Aeq_temp = hstack([Ax2y, Ay2x])
beq_temp = zeros(2 * nmg * T)
Aeq_full = vstack([Aeq_full, Aeq_temp])
beq = concatenate([beq, beq_temp])
# III) Formulate the optimization problem for tess in the second stage optimization
model_tess = {}
for i in range(nmes):
model_tess[i] = self.problem_formulation_tess_second_stage(mess=mess[i])
# III.1) Merge the models of mirogrids and distribution
# Formulate the index
nv_index_ev = zeros(1 + nmes).astype(int)
neq_index_temp = zeros(1 + nmes).astype(int)
nv_index_ev[0] = int(Aeq_full.shape[1])
neq_index_temp[0] = int(Aeq_full.shape[0])
for i in range(nmes):
nv_index_ev[i + 1] = nv_index_ev[i] + len(model_tess[i]["c"])
neq_index_temp[i + 1] = neq_index_temp[i] + model_tess[i]["Aeq"].shape[0]
Aeq = lil_matrix((int(neq_index_temp[-1]), int(nv_index_ev[-1])))
Aeq[0:int(neq_index_temp[0]), 0:int(nv_index_ev[0])] = Aeq_full
for i in range(nmes):
lb = concatenate([lb, model_tess[i]["lb"]])
ub = concatenate([ub, model_tess[i]["ub"]])
c = concatenate([c, model_tess[i]["c"]])
q = concatenate([q, model_tess[i]["q"]])
vtypes += model_tess[i]["vtypes"]
beq = concatenate([beq, model_tess[i]["beq"]])
Aeq[neq_index_temp[i]:neq_index_temp[i + 1], nv_index_ev[i]:nv_index_ev[i + 1]] = model_tess[i]["Aeq"]
# III.2) Coupling constraints between the microgrids and mobile energy storage systems
# Additional equal constraints, nmg*T
Aeq_temp = lil_matrix((nmg * T, nv_index_ev[-1]))
beq_temp = zeros(nmg * T)
for i in range(nmg):
for t in range(T):
Aeq_temp[i * T + t, nv_index[i] + t * NX_MG + PMESS] = 1 # TESSs injections to the MGs
for j in range(nmes):
Aeq_temp[i * T + t, nv_index_ev[j] + t * self.nb_tra_ele + i] = -1 # Discharging
Aeq_temp[i * T + t,
nv_index_ev[j] + self.nb_tra_ele * T + t * self.nb_tra_ele + i] = 1 # Sort by order
Aeq = vstack([Aeq, Aeq_temp])
beq = concatenate((beq, beq_temp))
nv_second_stage = nv_index_ev[-1]
nv_first_stage = self.nv_first_stage
self.nv_second_stage = nv_second_stage
Qc = dict()
# 4) Pij**2+Qij**2<=Vi*Iij
for t in range(T):
for i in range(nl):
Qc[(T * nl + T * nmg) * index + t * nl + i] = [
[int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl)],
[int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl)],
[1, 1, -1 / 2, -1 / 2]]
Rc = zeros(nl * T)
# 5) (Pbic_ac2dc+Pbic_dc2ac)**2+Qbic**2<=Sbic**2
Rc_temp = zeros(nmg * T)
for i in range(nmg):
for t in range(T):
Qc[(T * nl + T * nmg) * index + T * nl + T * i + t] = [
[int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_AC2DC),
| """
Stochastic optimal power flow with multiple microgrids and mobile energy storage systems
@author: Zhao Tianyang
@e-mail: zhaoty@ntu.edu.sg
@date: 10 Jan 2019
Major updates:
1) Update code style using PEP 8 -- Style Guide for Python Code
2) Store data in database
3) Scenario generation and reduction
4) Automatic results analysis
Nomenclature:
nV: number of variables
mg: microgrid
ds: distribution systems
me: mobile energy storage systems
ch: charging
dc: discharging
ele: electricity
tra: traffic
i,j,k: index
t: time index
T: time periods
tns:traffic networks
pns:power networks
"""
class StochasticDynamicOptimalPowerFlowTess():
def __init__(self):
self.name = "Stochastic optimal power flow with tess"
def main(self, power_networks, micro_grids, profile, mess, traffic_networks, ns=100):
"""
Main entrance for network reconfiguration problems
:param case: electric network information
:param profile: load profile within the distribution networks
:param micrgrids: dictionary for microgrids
:param tess: dictionary for tess
:return: network reconfiguration, distribution network status, and microgrid status
"""
T = len(profile) # Time spans
self.T = T
nmg = len(micro_grids) # Number of microgrids
self.nmg = nmg
nmes = len(mess) # Number of mobile energy storage systems
self.nmes = nmes
nb_tra = traffic_networks["bus"].shape[0] # Number of buses in the transportation networks
self.nb_tra = nb_tra
assert nb_tra == nmg, "The microgrids within the transportation networks are not synchronized!"
# 1) Formulate the first stage optimization problem
model_first_stage = self.first_stage_problem_formualtion(pns=power_networks, mgs=micro_grids, mess=mess,
tns=traffic_networks)
# (sol_first_stage, obj, success) = milp(model_first_stage["c"], Aeq=model_first_stage["Aeq"],
# beq=model_first_stage["beq"],
# A=model_first_stage["A"], b=model_first_stage["b"],
# vtypes=model_first_stage["vtypes"],
# xmax=model_first_stage["ub"], xmin=model_first_stage["lb"])
# sol_first_stage = self.first_stage_solution_validation(sol=sol_first_stage)
# 2) Formulate the second stage optimization problem
# Formulate the second stage scenarios
(ds_second_stage, mgs_second_stage, weight) = self.scenario_generation_reduction(profile=profile,
micro_grids=micro_grids, ns=ns,
pns=power_networks,
ns_reduced=round(0.98 * ns))
ns -= round(0.98 * ns)
model_second_stage = {}
for i in range(ns):
model_second_stage[i] = self.second_stage_problem_formualtion(pns=power_networks, mgs=mgs_second_stage[i],
mess=mess, tns=traffic_networks,
profile=ds_second_stage[i, :], index=i,
weight=weight[i])
# 3) Merge the first-stage problem and second stage problem
lb = model_first_stage["lb"]
ub = model_first_stage["ub"]
vtypes = model_first_stage["vtypes"]
c = model_first_stage["c"]
Qc = dict()
if model_first_stage["Aeq"] is not None:
neq = model_first_stage["Aeq"].shape[0]
else:
neq = 0
if model_first_stage["A"] is not None:
nineq = model_first_stage["A"].shape[0]
else:
nineq = 0
nv_first_stage = self.nv_first_stage
nv_second_stage = self.nv_second_stage
q = zeros(nv_first_stage)
nv_index = zeros(ns + 1).astype(int)
neq_index = zeros(ns + 1).astype(int)
nineq_index = zeros(ns + 1).astype(int)
neq_index[0] = neq
nineq_index[0] = nineq
nv_index[0] = nv_first_stage
beq = model_first_stage["beq"]
for i in range(ns):
if model_second_stage[i]["Aeq"] is not None:
neq_index[i + 1] = neq_index[i] + model_second_stage[i]["Aeq"].shape[0]
else:
neq_index[i + 1] = neq_index[i]
if model_second_stage[i]["Ts"] is not None:
nineq_index[i + 1] = nineq_index[i] + model_second_stage[i]["Ts"].shape[0]
else:
nineq_index[i + 1] = nineq_index[i]
nv_index[i + 1] = nv_index[i] + nv_second_stage
c = concatenate([c, model_second_stage[i]["c"]])
q = concatenate([q, model_second_stage[i]["q"]])
lb = concatenate([lb, model_second_stage[i]["lb"]])
ub = concatenate([ub, model_second_stage[i]["ub"]])
vtypes += model_second_stage[i]["vtypes"]
beq = concatenate([beq, model_second_stage[i]["beq"]])
Aeq_full = lil_matrix((neq_index[-1], nv_index[-1]))
Aeq_full[0:neq_index[0], 0:nv_index[0]] = model_first_stage["Aeq"]
rc = zeros(0)
for i in range(ns):
Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_second_stage[i]["Aeq"]
Qc.update(model_second_stage[i]["Qc"])
rc = concatenate([rc, model_second_stage[i]["rc"]])
A_full = lil_matrix((nineq_index[-1], nv_index[-1]))
b = model_first_stage["b"]
A_full[0:int(nineq_index[0]), 0:int(nv_index[0])] = model_first_stage["A"]
for i in range(ns):
A_full[nineq_index[i]:nineq_index[i + 1], 0:nv_index[0]] = model_second_stage[i]["Ts"]
A_full[nineq_index[i]:nineq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_second_stage[i]["Ws"]
b = concatenate([b, model_second_stage[i]["hs"]])
# 3) Obtain the results for first-stage and second stage optimization problems
# 3.1) Obtain the integrated solution
(sol, obj, success) = miqcp(c, q, Aeq=Aeq_full, beq=beq, A=A_full, b=b, Qc=Qc, rc=rc, xmin=lb, xmax=ub,
vtypes=vtypes)
# 3.2) decouple the solution into multiple subsystems
sol_first_stage = sol[0:nv_second_stage]
sol_second_stage = {}
for i in range(ns):
sol_second_stage[i] = sol[int(nv_index[i]):int(nv_index[i + 1])]
# 4) Verify the first-stage and second stage optization problem
# 4.1) First-stage solution
sol_first_stage = self.first_stage_solution_validation(sol=sol_first_stage)
# 4.2) Second-stage solution
sol_second_stage_checked = {}
db_management = DataBaseManagement()
db_management.create_table(table_name="distribution_networks", nl=self.nl, nb=self.nb, ng=self.ng)
db_management.create_table(table_name="micro_grids", nmg=self.nmg)
db_management.create_table(table_name="mobile_energy_storage_systems", nmg=self.nmg)
db_management.create_table(table_name="first_stage_solutions", nmg=self.nmg, ng=self.ng, nmes=self.nmes)
db_management.create_table(table_name="fisrt_stage_mess", nmg=self.nmg)
for t in range(T):
db_management.insert_data_first_stage(table_name="first_stage_solutions", time=t, ng=self.ng, nmg=self.nmg,
pg=sol_first_stage["pg"][:, t].tolist(),
rg=sol_first_stage["rg"][:, t].tolist(),
pg_mg=sol_first_stage["pg_mg"][:, t].tolist(),
rg_mg=sol_first_stage["rg_mg"][:, t].tolist(),
pess_ch=sol_first_stage["pess_ch"][:, t].tolist(),
pess_dc=sol_first_stage["pess_dc"][:, t].tolist(),
ress=sol_first_stage["ress"][:, t].tolist(),
ess=sol_first_stage["eess"][:, t].tolist(),
iess=sol_first_stage["iess"][:, t].tolist())
for i in range(nmes):
for t in range(T):
db_management.insert_data_first_stage_mess(table_name="fisrt_stage_mess", nmg=self.nmg, time=t, mess=i,
imess=sol_first_stage["MESS"][i]["idc"][:, t].tolist(),
rmess=sol_first_stage["MESS"][i]["rmess"][:, t].tolist(),
pmess_ch=
sol_first_stage["MESS"][i]["pmess_ch"][:, t].tolist(),
pmess_dc=
sol_first_stage["MESS"][i]["pmess_dc"][:, t].tolist(),
mess_f_stop=sol_first_stage["MESS"][i]["VRP"][t + 1][0],
mess_t_stop=sol_first_stage["MESS"][i]["VRP"][t + 1][1])
for i in range(ns):
sol_second_stage_checked[i] = self.second_stage_solution_validation(sol_second_stage[i])
for i in range(ns):
for t in range(T):
db_management.insert_data_ds(table_name="distribution_networks", nl=self.nl, nb=self.nb, ng=self.ng,
scenario=i, time=t,
pij=sol_second_stage_checked[i]["DS"]["pij"][:, t].tolist(),
qij=sol_second_stage_checked[i]["DS"]["qij"][:, t].tolist(),
lij=sol_second_stage_checked[i]["DS"]["lij"][:, t].tolist(),
vi=sol_second_stage_checked[i]["DS"]["vi"][:, t].tolist(),
pg=sol_second_stage_checked[i]["DS"]["pg"][:, t].tolist(),
qg=sol_second_stage_checked[i]["DS"]["qg"][:, t].tolist(), )
for i in range(ns):
for j in range(nmg):
for t in range(T):
db_management.insert_data_mg(table_name="micro_grids", scenario=i, time=t, mg=j,
pg=sol_second_stage_checked[i]["MG"]["pg"][j, t],
qg=sol_second_stage_checked[i]["MG"]["qg"][j, t],
pug=sol_second_stage_checked[i]["MG"]["pug"][j, t],
qug=sol_second_stage_checked[i]["MG"]["qug"][j, t],
pbic_ac2dc=sol_second_stage_checked[i]["MG"]["pbic_ac2dc"][j, t],
pbic_dc2ac=sol_second_stage_checked[i]["MG"]["pbic_dc2ac"][j, t],
qbic=sol_second_stage_checked[i]["MG"]["qbic"][j, t],
pess_ch=sol_second_stage_checked[i]["MG"]["pess_ch"][j, t],
pess_dc=sol_second_stage_checked[i]["MG"]["pess_dc"][j, t],
eess=sol_second_stage_checked[i]["MG"]["eess"][j, t],
pmess=sol_second_stage_checked[i]["MG"]["pmess"][j, t])
for i in range(ns):
for j in range(nmes):
for t in range(T):
db_management.insert_data_mess(table_name="mobile_energy_storage_systems", scenario=i, time=t,
mess=j, nmg=self.nmg,
pmess_dc=
sol_second_stage_checked[i]["MESS"][j]["pmess_dc"][:, t].tolist(),
pmess_ch=
sol_second_stage_checked[i]["MESS"][j]["pmess_ch"][:, t].tolist(),
emess=sol_second_stage_checked[i]["MESS"][j]["emess"][0, t])
# 4.3) Cross validation of the first-stage and second-stage decision variables
tess_check = {}
for i in range(ns):
tess_temp = {}
for j in range(nmes):
tess_temp[j] = sol_second_stage_checked[i]["MESS"][j]["pmess_dc"] - \
sol_second_stage_checked[i]["MESS"][j]["pmess_ch"] - \
sol_first_stage["MESS"][j]["pmess_dc"] + \
sol_first_stage["MESS"][j]["pmess_ch"] - \
sol_first_stage["MESS"][j]["rmess"]
tess_temp[j + nmes] = sol_second_stage_checked[i]["MESS"][j]["pmess_ch"] - \
sol_second_stage_checked[i]["MESS"][j]["pmess_dc"] - \
sol_first_stage["MESS"][j]["pmess_ch"] + \
sol_first_stage["MESS"][j]["pmess_dc"] - \
sol_first_stage["MESS"][j]["rmess"]
tess_check[i] = tess_temp
# return sol_distribution_network, sol_microgrids, sol_tess
return sol_first_stage, sol_second_stage_checked
def first_stage_problem_formualtion(self, pns, mgs, mess, tns):
"""
Problem formulation for the first stage optimization,
Decision variables include, DGs within power networks, DGs within MGs, EESs within MGs and TESSs
:param power_networks: Parameters for the power networks
:param micro_grids: Parameters for the microgrids
:param tess: Parameters for the mobile energy storage systems
:param traffic_networks: Parameters for the transportation networks
:return: Formulated first-stage problem
"""
T = self.T # Time slots
nmg = self.nmg # Number of mgs
nmes = self.nmes # Number of tess
mpc = ext2int(pns)
baseMVA, bus, gen, branch, gencost = mpc["baseMVA"], mpc["bus"], mpc["gen"], mpc["branch"], mpc["gencost"]
ng = shape(mpc['gen'])[0] ## number of dispatchable injections
nb = shape(mpc["bus"])[0]
self.nb = nb
self.ng = ng
# Obtain the initial status, start-up and shut down of generators
Ig0 = gen[:, -1].astype(int)
MIN_DOWN = gen[:, -2].astype(int)
MIN_UP = gen[:, -3].astype(int)
alpha_l = zeros(ng)
beta_l = zeros(ng)
Ig_l = zeros(ng)
pg_l = zeros(ng) # Boundary for DGs within distribution networks
rg_l = zeros(ng)
alpha_u = ones(ng)
beta_u = ones(ng)
Ig_u = ones(ng)
pg_u = gen[:, PMAX] / baseMVA
rg_u = gen[:, PMAX] / baseMVA
c_alpha = gencost[:, 0]
c_beta = gencost[:, 1]
c_ig = gencost[:, 6]
cg = gencost[:, 5] * baseMVA
cr = zeros(ng)
pg_mg_l = zeros(nmg) # Boundary for DGs within MGs
rg_mg_l = zeros(nmg)
pg_mg_u = zeros(nmg)
rg_mg_u = zeros(nmg)
cg_mg = zeros(nmg)
cr_mg = zeros(nmg)
for i in range(nmg):
pg_mg_l[i] = mgs[i]["DG"]["PMIN"]
pg_mg_u[i] = mgs[i]["DG"]["PMAX"]
rg_mg_u[i] = mgs[i]["DG"]["PMAX"]
cg_mg[i] = mgs[i]["DG"]["COST_B"]
pes_ch_l = zeros(nmg) # Lower boundary for ESSs within MGs
pes_dc_l = zeros(nmg)
ees_l = zeros(nmg)
res_l = zeros(nmg)
ies_l = zeros(nmg)
pes_ch_u = zeros(nmg) # Upper boundary for ESSs within MGs
pes_dc_u = zeros(nmg)
ees_u = zeros(nmg)
res_u = zeros(nmg)
ies_u = ones(nmg)
ces_ch = zeros(nmg) # Cost boundary for ESSs within MGs
ces_dc = zeros(nmg)
ces_r = zeros(nmg)
ces = zeros(nmg)
ces_i = zeros(nmg)
for i in range(nmg):
pes_ch_u[i] = mgs[i]["ESS"]["PCH_MAX"]
pes_dc_u[i] = mgs[i]["ESS"]["PDC_MAX"] + mgs[i]["ESS"]["PCH_MAX"]
res_u[i] = mgs[i]["ESS"]["PCH_MAX"]
ees_l[i] = mgs[i]["ESS"]["EMIN"]
ees_u[i] = mgs[i]["ESS"]["EMAX"]
_nv_first_stage = ng * 5 + nmg * 2 + nmg * 5
nv_first_stage = _nv_first_stage * T
# Formulate the boundaries
lb = concatenate(
[tile(concatenate(
[alpha_l, beta_l, Ig_l, pg_l, rg_l, pg_mg_l, rg_mg_l, pes_ch_l, pes_dc_l, res_l, ees_l, ies_l]), T)])
ub = concatenate(
[tile(concatenate(
[alpha_u, beta_u, Ig_u, pg_u, rg_u, pg_mg_u, rg_mg_u, pes_ch_u, pes_dc_u, res_u, ees_u, ies_u]), T)])
# Objective value
c = concatenate(
[tile(concatenate([c_alpha, c_beta, c_ig, cg, cr, cg_mg, cr_mg, ces_ch, ces_dc, ces, ces_r, ces_i]), T)])
# Variable types
vtypes = (["b"] * ng * 3 + ["c"] * (ng * 2 + nmg * 2 + nmg * 4) + ["b"] * nmg) * T
## Constraint sets
# 1) Pg+Rg<=PguIg
A = lil_matrix((ng * T, nv_first_stage))
b = zeros(ng * T)
for t in range(T):
for j in range(ng):
A[t * ng + j, t * _nv_first_stage + ng * 3 + j] = 1
A[t * ng + j, t * _nv_first_stage + ng * 4 + j] = 1
A[t * ng + j, t * _nv_first_stage + ng * 2 + j] = -pg_u[j]
# 2) Pg-Rg>=IgPgl
A_temp = lil_matrix((ng * T, nv_first_stage))
b_temp = zeros(ng * T)
for t in range(T):
for j in range(ng):
A_temp[t * ng + j, t * _nv_first_stage + ng * 3 + j] = -1
A_temp[t * ng + j, t * _nv_first_stage + ng * 4 + j] = 1
A_temp[t * ng + j, t * _nv_first_stage + j] = pg_l[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 3) Start-up and shut-down constraints of DGs
UP_LIMIT = zeros(ng).astype(int)
DOWN_LIMIT = zeros(ng).astype(int)
for i in range(ng):
UP_LIMIT[i] = T - MIN_UP[i]
DOWN_LIMIT[i] = T - MIN_DOWN[i]
# 3.1) Up limit
A_temp = lil_matrix((sum(UP_LIMIT), nv_first_stage))
b_temp = zeros(sum(UP_LIMIT))
for i in range(ng):
for t in range(MIN_UP[i], T):
for k in range(t - MIN_UP[i], t):
A_temp[sum(UP_LIMIT[0:i]) + t - MIN_UP[i], k * _nv_first_stage + i] = 1
A_temp[sum(UP_LIMIT[0:i]) + t - MIN_UP[i], t * _nv_first_stage + ng * 2 + i] = -1
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# # 3.2) Down limit
A_temp = lil_matrix((sum(DOWN_LIMIT), nv_first_stage))
b_temp = ones(sum(DOWN_LIMIT))
for i in range(ng):
for t in range(MIN_DOWN[i], T):
for k in range(t - MIN_DOWN[i], t):
A_temp[sum(DOWN_LIMIT[0:i]) + t - MIN_DOWN[i], k * _nv_first_stage + ng + i] = 1
A_temp[sum(DOWN_LIMIT[0:i]) + t - MIN_DOWN[i], t * _nv_first_stage + ng * 2 + i] = 1
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 4) Status transformation of each unit
Aeq = lil_matrix((T * ng, nv_first_stage))
beq = zeros(T * ng)
for i in range(ng):
for t in range(T):
Aeq[i * T + t, t * _nv_first_stage + i] = 1
Aeq[i * T + t, t * _nv_first_stage + ng + i] = -1
Aeq[i * T + t, t * _nv_first_stage + ng * 2 + i] = -1
if t != 0:
Aeq[i * T + t, (t - 1) * _nv_first_stage + ng * 2 + i] = 1
else:
beq[i * T + t] = -Ig0[i]
# 3) Pg_mg+Rg_mg<=Pg_mg_u
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + j] = 1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg + j] = 1
b_temp[t * nmg + j] = pg_mg_u[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 4) Pg_mg-Rg_mg<=Pg_mg_l
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + j] = -1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg + j] = 1
b_temp[t * nmg + j] = pg_mg_l[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 5) Pess_dc-Pess_ch+Ress<=Pess_dc_max
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = -1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 2 + j] = 1
b_temp[t * nmg + j] = pes_dc_u[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 6) Pess_ch-Pess_dc+Ress<=Pess_ch_max
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, ng * 5 + nmg * 2 + t] = 1
A_temp[t * nmg + j, ng * 5 + nmg * 2 + nmg + t] = -1
A_temp[t * nmg + j, ng * 5 + nmg * 2 + nmg * 2 + t] = 1
b_temp[t * nmg + j] = pes_ch_u[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 7) Energy storage balance equation
Aeq_temp = lil_matrix((T * nmg, nv_first_stage))
beq_temp = zeros(T * nmg)
for t in range(T):
for j in range(nmg):
Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 3 + j] = 1
Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = -mgs[j]["ESS"]["EFF_CH"]
Aeq_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1 / mgs[j]["ESS"]["EFF_DC"]
if t == 0:
beq_temp[i * nmg + j] = mgs[j]["ESS"]["E0"]
else:
Aeq_temp[i * nmg + j, (i - 1) * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 3 + j] = -1
Aeq = vstack([Aeq, Aeq_temp])
beq = concatenate([beq, beq_temp])
# 8) Pess_ch<=I*Pess_ch_max
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + j] = 1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 4 + j] = -pes_ch_u[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 9) Pess_dc<=(1-I)*Pess_dc_max
A_temp = lil_matrix((nmg * T, nv_first_stage))
b_temp = zeros(nmg * T)
for t in range(T):
for j in range(nmg):
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg + j] = 1
A_temp[t * nmg + j, t * _nv_first_stage + ng * 5 + nmg * 2 + nmg * 4 + j] = pes_dc_u[j]
b_temp[t * nmg + j] = pes_dc_u[j]
A = vstack([A, A_temp])
b = concatenate([b, b_temp])
# 2) Transportation energy storage systems problem
model_mess = {}
for i in range(nmes):
model_mess[i] = self.problem_formulation_tess(mess=mess[i], tns=tns)
# 3) Merge the DGs, ESSs and TESSs
neq = Aeq.shape[0]
nineq = A.shape[0]
nV_index = zeros(nmes + 1).astype(int)
neq_index = zeros(nmes + 1).astype(int)
nineq_index = zeros(nmes + 1).astype(int)
nV_index[0] = nv_first_stage
neq_index[0] = neq
nineq_index[0] = nineq
for i in range(nmes):
nV_index[i + 1] = nV_index[i] + len(model_mess[i]["c"])
neq_index[i + 1] = neq_index[i] + model_mess[i]["Aeq"].shape[0]
nineq_index[i + 1] = nineq_index[i] + model_mess[i]["A"].shape[0]
neq += model_mess[i]["Aeq"].shape[0]
nineq += model_mess[i]["A"].shape[0]
# Merge the objective function, boundaries, types and rhs
c = concatenate([c, model_mess[i]["c"]])
lb = concatenate([lb, model_mess[i]["lb"]])
ub = concatenate([ub, model_mess[i]["ub"]])
vtypes += model_mess[i]["vtypes"]
beq = concatenate([beq, model_mess[i]["beq"]])
b = concatenate([b, model_mess[i]["b"]])
A_full = lil_matrix((nineq_index[-1], nV_index[-1]))
Aeq_full = lil_matrix((neq_index[-1], nV_index[-1]))
if Aeq is not None: Aeq_full[0:int(neq_index[0]), 0:int(nV_index[0])] = Aeq
if A is not None: A_full[0:int(nineq_index[0]), 0:int(nV_index[0])] = A
for i in range(nmes):
Aeq_full[neq_index[i]:neq_index[i + 1], nV_index[i]:nV_index[i + 1]] = model_mess[i]["Aeq"]
A_full[nineq_index[i]:nineq_index[i + 1], nV_index[i]:nV_index[i + 1]] = model_mess[i]["A"]
self.nv_first_stage = nV_index[-1] # The number of first stage decision variables
self._nv_first_stage = _nv_first_stage
model_first_stage = {"c": c,
"lb": lb,
"ub": ub,
"vtypes": vtypes,
"A": A_full,
"b": b,
"Aeq": Aeq_full,
"beq": beq, }
return model_first_stage
def first_stage_solution_validation(self, sol):
"""
Validation of the first-stage solution
:param sol: The first stage solution
:return: the first stage solution
"""
T = self.T
ng = self.ng
nmg = self.nmg
nmes = self.nmes
# Set-points of DGs within DSs, MGs and ESSs
_nv_first_stage = self._nv_first_stage
alpha = zeros((ng, T))
beta = zeros((ng, T))
Ig = zeros((ng, T))
Pg = zeros((ng, T))
Rg = zeros((ng, T))
Pg_mg = zeros((nmg, T))
Rg_mg = zeros((nmg, T))
Pess_dc = zeros((nmg, T))
Pess_ch = zeros((nmg, T))
Ress = zeros((nmg, T))
Eess = zeros((nmg, T))
Iess = zeros((nmg, T))
for i in range(T):
alpha[:, i] = sol[_nv_first_stage * i:_nv_first_stage * i + ng]
beta[:, i] = sol[_nv_first_stage * i + ng:_nv_first_stage * i + ng * 2]
Ig[:, i] = sol[_nv_first_stage * i + ng * 2:_nv_first_stage * i + ng * 3]
Pg[:, i] = sol[_nv_first_stage * i + ng * 3:_nv_first_stage * i + ng * 4]
Rg[:, i] = sol[_nv_first_stage * i + ng * 4:_nv_first_stage * i + ng * 5]
Pg_mg[:, i] = sol[_nv_first_stage * i + ng * 5:_nv_first_stage * i + ng * 5 + nmg]
Rg_mg[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg:_nv_first_stage * i + ng * 5 + nmg * 2]
Pess_ch[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 2:_nv_first_stage * i + ng * 5 + nmg * 3]
Pess_dc[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 3:_nv_first_stage * i + ng * 5 + nmg * 4]
Ress[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 4:_nv_first_stage * i + ng * 5 + nmg * 5]
Eess[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 5:_nv_first_stage * i + ng * 5 + nmg * 6]
Iess[:, i] = sol[_nv_first_stage * i + ng * 5 + nmg * 6:_nv_first_stage * i + ng * 5 + nmg * 7]
# Set-points and scheduling of mobile energy storage systems
nv_tra = self.nv_tra
nl_traffic = self.nl_tra
n_stops = self.n_stops
nb_tra_ele = self.nb_tra_ele
sol_ev = {}
for i in range(nmes):
ev_temp = {}
ev_temp["VRP"] = []
for t in range(nl_traffic):
if sol[_nv_first_stage * T + nv_tra * i + t] > 0: # obtain the solution for vrp
if self.connection_matrix[t, TIME] > 0:
for j in range(int(self.connection_matrix[t, TIME])):
ev_temp["VRP"].append(((self.connection_matrix[t, F_BUS] - 1) % nmg,
(self.connection_matrix[t, T_BUS] - 1) % nmg))
else:
ev_temp["VRP"].append(((self.connection_matrix[t, F_BUS] - 1) % nmg,
(self.connection_matrix[t, T_BUS] - 1) % nmg))
ev_temp["idc"] = zeros((nb_tra_ele, T))
ev_temp["pmess_dc"] = zeros((nb_tra_ele, T))
ev_temp["pmess_ch"] = zeros((nb_tra_ele, T))
ev_temp["rmess"] = zeros((nb_tra_ele, T))
for t in range(T):
for k in range(nb_tra_ele):
ev_temp["idc"][k, t] = sol[_nv_first_stage * T + nv_tra * i + nl_traffic + nb_tra_ele * t + k]
ev_temp["pmess_dc"][k, t] = \
sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops + nb_tra_ele * t + k]
ev_temp["pmess_ch"][k, t] = \
sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops * 2 + nb_tra_ele * t + k]
ev_temp["rmess"][k, t] = \
sol[_nv_first_stage * T + nv_tra * i + nl_traffic + n_stops * 3 + nb_tra_ele * t + k]
sol_ev[i] = ev_temp
sol_first_stage = {"alpha": alpha,
"beta": beta,
"ig": Ig,
"rg": Rg,
"pg": Pg,
"pg_mg": Pg_mg,
"rg_mg": Rg_mg,
"pess_ch": Pess_ch,
"pess_dc": Pess_dc,
"ress": Ress,
"eess": Eess,
"iess": Iess,
"MESS": sol_ev,
}
return sol_first_stage
def second_stage_problem_formualtion(self, pns, mgs, mess, tns, profile, index=0, weight=1):
"""
Second-stage problem formulation, the decision variables includes DGs within power networks, DGs within MGs, EESs within MGs and TESSs and other systems' information
:param power_networks:
:param micro_grids:
:param tess:
:param traffic_networks:
:return: The second stage problems as list, including coupling constraints, and other constraint set
"""
# I) Formulate the problem for distribution systems operator
T = self.T
mpc = ext2int(pns)
baseMVA, bus, gen, branch, gencost = mpc["baseMVA"], mpc["bus"], mpc["gen"], mpc["branch"], mpc["gencost"]
nb = shape(mpc['bus'])[0] ## number of buses
nl = shape(mpc['branch'])[0] ## number of branches
ng = shape(mpc['gen'])[0] ## number of dispatchable injections
nmg = self.nmg
nmes = self.nmes
self.nl = nl
self.nb = nb
self.ng = ng
m = zeros(nmg) ## list of integration index
pmg_l = zeros(nmg) ## list of lower boundary
pmg_u = zeros(nmg) ## list of upper boundary
qmg_l = zeros(nmg) ## list of lower boundary
qmg_u = zeros(nmg) ## list of upper boundary
for i in range(nmg):
m[i] = mgs[i]["BUS"]
pmg_l[i] = mgs[i]["UG"]["PMIN"] / 1000 / baseMVA
pmg_u[i] = mgs[i]["UG"]["PMAX"] / 1000 / baseMVA
qmg_l[i] = mgs[i]["UG"]["QMIN"] / 1000 / baseMVA
qmg_u[i] = mgs[i]["UG"]["QMAX"] / 1000 / baseMVA
f = branch[:, F_BUS] ## list of "from" buses
t = branch[:, T_BUS] ## list of "to" buses
i = range(nl) ## double set of row indices
self.f = f ## record from bus for each branch
# Connection matrix
Cf = sparse((ones(nl), (i, f)), (nl, nb))
Ct = sparse((ones(nl), (i, t)), (nl, nb))
Cg = sparse((ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))
Cmg = sparse((ones(nmg), (m, range(nmg))), (nb, nmg))
Branch_R = branch[:, BR_R]
Branch_X = branch[:, BR_X]
Cf = Cf.T
Ct = Ct.T
# Obtain the boundary information
slmax = branch[:, RATE_A] / baseMVA
pij_l = -slmax
qij_l = -slmax
lij_l = zeros(nl)
vm_l = bus[:, VMIN] ** 2
pg_l = gen[:, PMIN] / baseMVA
qg_l = gen[:, QMIN] / baseMVA
pij_u = slmax
qij_u = slmax
lij_u = slmax
vm_u = bus[:, VMAX] ** 2
pg_u = 2 * gen[:, PMAX] / baseMVA
qg_u = 2 * gen[:, QMAX] / baseMVA
_nv_second_stage = int(3 * nl + nb + 2 * ng + 2 * nmg)
self._nv_second_stage = _nv_second_stage # Number of decision variable within each time slot
lb = concatenate([tile(concatenate([pij_l, qij_l, lij_l, vm_l, pg_l, qg_l, pmg_l, qmg_l]), T)])
ub = concatenate([tile(concatenate([pij_u, qij_u, lij_u, vm_u, pg_u, qg_u, pmg_u, qmg_u]), T)])
vtypes = ["c"] * _nv_second_stage * T
nv_ds = _nv_second_stage * T # Number of total decision variables
# Add system level constraints
# 1) Active power balance
Aeq_p = lil_matrix((nb * T, nv_ds))
beq_p = zeros(nb * T)
for i in range(T):
Aeq_p[i * nb:(i + 1) * nb, i * _nv_second_stage: (i + 1) * _nv_second_stage] = \
hstack([Ct - Cf, zeros((nb, nl)),
-diag(Ct * Branch_R) * Ct,
zeros((nb, nb)), Cg,
zeros((nb, ng)), -Cmg,
zeros((nb, nmg))])
beq_p[i * nb:(i + 1) * nb] = profile[i * nb:(i + 1) * nb] / baseMVA
# 2) Reactive power balance
Aeq_q = lil_matrix((nb * T, nv_ds))
beq_q = zeros(nb * T)
for i in range(T):
Aeq_q[i * nb:(i + 1) * nb, i * _nv_second_stage: (i + 1) * _nv_second_stage] = \
hstack([zeros((nb, nl)), Ct - Cf,
-diag(Ct * Branch_X) * Ct,
zeros((nb, nb)),
zeros((nb, ng)), Cg,
zeros((nb, nmg)), -Cmg])
for j in range(nb):
if bus[j, PD] > 0:
beq_q[i * nb:(i + 1) * nb] = profile[i * nb + j] / bus[j, PD] * bus[j, QD] / baseMVA
# 3) KVL equation
Aeq_kvl = lil_matrix((nl * T, nv_ds))
beq_kvl = zeros(nl * T)
for i in range(T):
Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage: i * _nv_second_stage + nl] = -2 * diag(Branch_R)
Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + nl: i * _nv_second_stage + 2 * nl] = -2 * diag(Branch_X)
Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + 2 * nl: i * _nv_second_stage + 3 * nl] = diag(
Branch_R ** 2) + diag(Branch_X ** 2)
Aeq_kvl[i * nl:(i + 1) * nl, i * _nv_second_stage + 3 * nl:i * _nv_second_stage + 3 * nl + nb] = (
Cf.T - Ct.T).toarray()
Aeq = vstack([Aeq_p, Aeq_q, Aeq_kvl])
beq = concatenate([beq_p, beq_q, beq_kvl])
c = zeros(nv_ds)
q = zeros(nv_ds)
c0 = 0
for t in range(T):
for i in range(ng):
c[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 5] * baseMVA
q[t * _nv_second_stage + i + 3 * nl + nb] = gencost[i, 4] * baseMVA * baseMVA
c0 += gencost[i, 6]
# Coupling constraints between the distribution systems and micro_grids
Ax2y = lil_matrix((2 * nmg * T, nv_ds)) # connection matrix with the microgrids
for i in range(T):
for j in range(nmg):
# Active power
Ax2y[i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + j] = 1000 * baseMVA
# Reactive power
Ax2y[nmg * T + i * nmg + j, i * _nv_second_stage + 3 * nl + nb + 2 * ng + nmg + j] = 1000 * baseMVA
# II) Formulate the problem for microgrids
model_microgrids = {}
for i in range(nmg):
model_microgrids[i] = self.problem_formulation_microgrid(mg=mgs[i], mess=mess)
# II.A) Combine the distribution system operation problem and microgrid systems
if Aeq is not None:
neq_ds = Aeq.shape[0]
else:
neq_ds = 0
nVariables = int(nv_ds)
neq = int(neq_ds)
nv_index = zeros(nmg + 1).astype(int)
neq_index = zeros(nmg + 1).astype(int)
nv_index[0] = nv_ds
neq_index[0] = int(neq_ds)
for i in range(nmg):
nv_index[i + 1] = nv_index[i] + len(model_microgrids[i]["c"])
neq_index[i + 1] = neq_index[i] + model_microgrids[i]["Aeq"].shape[0]
nVariables += len(model_microgrids[i]["c"])
neq += int(model_microgrids[i]["Aeq"].shape[0])
Aeq_full = lil_matrix((int(neq_index[-1]), int(nv_index[-1])))
Aeq_full[0:neq_ds, 0:nv_ds] = Aeq
for i in range(nmg):
lb = concatenate([lb, model_microgrids[i]["lb"]])
ub = concatenate([ub, model_microgrids[i]["ub"]])
c = concatenate([c, model_microgrids[i]["c"]])
q = concatenate([q, model_microgrids[i]["q"]])
vtypes += model_microgrids[i]["vtypes"]
beq = concatenate([beq, model_microgrids[i]["beq"]])
Aeq_full[neq_index[i]:neq_index[i + 1], nv_index[i]:nv_index[i + 1]] = model_microgrids[i]["Aeq"]
# Add coupling constraints, between the microgrids and distribution networks
Ay2x = lil_matrix((2 * nmg * T, nv_index[-1] - nv_index[0]))
for i in range(T):
for j in range(nmg):
Ay2x[i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + PUG] = -1
Ay2x[nmg * T + i * nmg + j, int(nv_index[j] - nv_index[0]) + i * NX_MG + QUG] = -1
Aeq_temp = hstack([Ax2y, Ay2x])
beq_temp = zeros(2 * nmg * T)
Aeq_full = vstack([Aeq_full, Aeq_temp])
beq = concatenate([beq, beq_temp])
# III) Formulate the optimization problem for tess in the second stage optimization
model_tess = {}
for i in range(nmes):
model_tess[i] = self.problem_formulation_tess_second_stage(mess=mess[i])
# III.1) Merge the models of mirogrids and distribution
# Formulate the index
nv_index_ev = zeros(1 + nmes).astype(int)
neq_index_temp = zeros(1 + nmes).astype(int)
nv_index_ev[0] = int(Aeq_full.shape[1])
neq_index_temp[0] = int(Aeq_full.shape[0])
for i in range(nmes):
nv_index_ev[i + 1] = nv_index_ev[i] + len(model_tess[i]["c"])
neq_index_temp[i + 1] = neq_index_temp[i] + model_tess[i]["Aeq"].shape[0]
Aeq = lil_matrix((int(neq_index_temp[-1]), int(nv_index_ev[-1])))
Aeq[0:int(neq_index_temp[0]), 0:int(nv_index_ev[0])] = Aeq_full
for i in range(nmes):
lb = concatenate([lb, model_tess[i]["lb"]])
ub = concatenate([ub, model_tess[i]["ub"]])
c = concatenate([c, model_tess[i]["c"]])
q = concatenate([q, model_tess[i]["q"]])
vtypes += model_tess[i]["vtypes"]
beq = concatenate([beq, model_tess[i]["beq"]])
Aeq[neq_index_temp[i]:neq_index_temp[i + 1], nv_index_ev[i]:nv_index_ev[i + 1]] = model_tess[i]["Aeq"]
# III.2) Coupling constraints between the microgrids and mobile energy storage systems
# Additional equal constraints, nmg*T
Aeq_temp = lil_matrix((nmg * T, nv_index_ev[-1]))
beq_temp = zeros(nmg * T)
for i in range(nmg):
for t in range(T):
Aeq_temp[i * T + t, nv_index[i] + t * NX_MG + PMESS] = 1 # TESSs injections to the MGs
for j in range(nmes):
Aeq_temp[i * T + t, nv_index_ev[j] + t * self.nb_tra_ele + i] = -1 # Discharging
Aeq_temp[i * T + t,
nv_index_ev[j] + self.nb_tra_ele * T + t * self.nb_tra_ele + i] = 1 # Sort by order
Aeq = vstack([Aeq, Aeq_temp])
beq = concatenate((beq, beq_temp))
nv_second_stage = nv_index_ev[-1]
nv_first_stage = self.nv_first_stage
self.nv_second_stage = nv_second_stage
Qc = dict()
# 4) Pij**2+Qij**2<=Vi*Iij
for t in range(T):
for i in range(nl):
Qc[(T * nl + T * nmg) * index + t * nl + i] = [
[int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl)],
[int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + f[i] + 3 * nl),
int(nv_first_stage + index * nv_second_stage + t * _nv_second_stage + i + 2 * nl)],
[1, 1, -1 / 2, -1 / 2]]
Rc = zeros(nl * T)
# 5) (Pbic_ac2dc+Pbic_dc2ac)**2+Qbic**2<=Sbic**2
Rc_temp = zeros(nmg * T)
for i in range(nmg):
for t in range(T):
Qc[(T * nl + T * nmg) * index + T * nl + T * i + t] = [
[int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_AC2DC), | int(nv_first_stage + index * nv_second_stage + nv_ds + NX_MG * T * i + NX_MG * t + PBIC_DC2AC), | 5 | 2023-11-27 15:57:53+00:00 | 24k |
girgle/DouZero_For_New_HLDDZ | GOOD.py | [
{
"identifier": "GameHelper",
"path": "GameHelper.py",
"snippet": "class GameHelper:\n def __init__(self):\n self.ScreenZoomRate = None\n self.counter = QTime()\n self.Pics = {}\n self.PicsCV = {}\n st = time.time()\n self.Handle = win32gui.FindWindow(\"Unity... | import GameHelper as gh
import os
import sys
import time
import threading
import pyautogui
import win32gui
import multiprocessing as mp
import DetermineColor as DC
import cv2
import numpy as np
import traceback
import BidModel
import LandlordModel
import FarmerModel
from GameHelper import GameHelper
from PIL import Image
from skimage.metrics import structural_similarity as ssim
from collections import defaultdict
from douzero.env.move_detector import get_move_type
from PyQt5 import QtGui, QtWidgets, QtCore
from PyQt5.QtWidgets import QTableWidgetItem, QInputDialog, QMessageBox
from PyQt5.QtGui import QPixmap, QIcon
from PyQt5.QtCore import QTime, QEventLoop, Qt
from MainWindow import Ui_Form
from douzero.env.game import GameEnv
from douzero.evaluation.deep_agent import DeepAgent | 15,236 |
def init_display(self):
self.WinRate.setText("评分")
self.label.setText("游戏状态")
self.label.setStyleSheet('background-color: rgba(255, 0, 0, 0);')
self.UserHandCards.setText("手牌")
# self.LBrowser.clear()
# self.RBrowser.clear()
self.LPlayedCard.setText("上家出牌区域")
self.RPlayedCard.setText("下家出牌区域")
self.PredictedCard.setText("AI出牌区域")
self.ThreeLandlordCards.setText("地主牌")
self.recorder2zero()
for player in self.Players:
player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')
def init_cards(self):
self.RunGame = True
GameHelper.Interrupt = False
self.user_hand_cards_real = ""
self.user_hand_cards_env = []
# 其他玩家出牌
self.other_played_cards_real = ""
self.other_played_cards_env = []
# 其他玩家手牌（整副牌减去玩家手牌，后续再减掉历史出牌）
self.other_hand_cards = []
# 三张底牌
self.three_landlord_cards_real = ""
self.three_landlord_cards_env = []
# 玩家角色代码：0-地主上家, 1-地主, 2-地主下家
self.user_position_code = None
self.user_position = ""
# 开局时三个玩家的手牌
self.card_play_data_list = {}
# 识别玩家手牌
self.user_hand_cards_real = self.find_my_cards()
while len(self.user_hand_cards_real) != 17 and len(self.user_hand_cards_real) != 20:
self.detect_start_btn()
if not self.RunGame:
break
self.sleep(200)
self.user_hand_cards_real = self.find_my_cards()
self.user_hand_cards_env = [RealCard2EnvCard[c] for c in list(self.user_hand_cards_real)]
# 识别三张底牌
self.three_landlord_cards_real = self.find_landlord_cards()
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
while len(self.three_landlord_cards_env) != 3:
self.detect_start_btn()
if not self.RunGame:
break
if len(self.three_landlord_cards_env) > 3:
self.ThreeLandlordCardsConfidence += 0.05
elif len(self.three_landlord_cards_env) < 3:
self.ThreeLandlordCardsConfidence -= 0.05
self.three_landlord_cards_real = self.find_landlord_cards()
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
# 识别玩家的角色
self.sleep(500)
self.user_position_code = self.find_landlord(self.LandlordFlagPos)
self.sleep(200)
while self.user_position_code is None:
self.detect_start_btn()
if not self.RunGame:
break
self.user_position_code = self.find_landlord(self.LandlordFlagPos)
self.sleep(200)
print("正在出牌人的代码： ", self.user_position_code)
if self.user_position_code is None:
items = ("地主上家", "地主", "地主下家")
item, okPressed = QInputDialog.getItem(self, "选择角色", "未识别到地主，请手动选择角色:", items, 0, False)
if okPressed and item:
self.user_position_code = items.index(item)
else:
return
self.user_position = ['landlord_up', 'landlord', 'landlord_down'][self.user_position_code]
print("我现在在地主的方向：", self.user_position)
for player in self.Players:
player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')
self.Players[self.user_position_code].setStyleSheet('background-color: rgba(0, 255, 0, 0.5);')
# 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）
for i in set(AllEnvCard):
self.other_hand_cards.extend([i] * (AllEnvCard.count(i) - self.user_hand_cards_env.count(i)))
self.other_hands_cards_str = str(''.join([EnvCard2RealCard[c] for c in self.other_hand_cards]))[::-1]
self.cards_recorder(self.other_hands_cards_str)
self.card_play_data_list.update({
'three_landlord_cards': self.three_landlord_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 0) % 3]:
self.user_hand_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 1) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 != 1 else self.other_hand_cards[17:],
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 2) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 == 1 else self.other_hand_cards[17:]
})
print("开始对局")
print("手牌:", self.user_hand_cards_real)
print("地主牌:", self.three_landlord_cards_real)
# 生成手牌结束，校验手牌数量
if len(self.card_play_data_list["three_landlord_cards"]) != 3:
QMessageBox.critical(self, "底牌识别出错", "底牌必须是3张！", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
if len(self.card_play_data_list["landlord_up"]) != 17 or \
len(self.card_play_data_list["landlord_down"]) != 17 or \
len(self.card_play_data_list["landlord"]) != 20:
QMessageBox.critical(self, "手牌识别出错", "初始手牌数目有误", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
# 出牌顺序：0-玩家出牌, 1-玩家下家出牌, 2-玩家上家出牌
self.play_order = 0 if self.user_position == "landlord" else 1 if self.user_position == "landlord_up" else 2
# 创建一个代表玩家的AI
ai_players = [0, 0]
ai_players[0] = self.user_position
ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])
| # -*- coding: utf-8 -*-
# Created by: Raf
# Modify by: Vincentzyx
EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
'8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}
AllEnvCard = [3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12,
12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 17, 17, 17, 17, 20, 30]
AllCards = ['D', 'X', '2', 'A', 'K', 'Q', 'J', 'T', '9', '8', '7', '6', '5', '4', '3']
helper = GameHelper()
class MyPyQT_Form(QtWidgets.QWidget, Ui_Form):
def __init__(self):
super(MyPyQT_Form, self).__init__()
self.other_hands_cards_str = None
self.stop_sign = None
self.loop_sign = None
self.env = None
self.three_landlord_cards_env = None
self.three_landlord_cards_real = None
self.user_hand_cards_env = None
self.user_hand_cards_real = None
self.play_order = None
self.card_play_data_list = None
self.other_hand_cards = None
self.other_played_cards_env = None
self.other_played_cards_real = None
self.user_position = None
self.user_position_code = None
self.setupUi(self)
self.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint | # 使能最小化按钮
QtCore.Qt.WindowStaysOnTopHint | # 窗体总在最前端
QtCore.Qt.WindowCloseButtonHint)
self.setWindowIcon(QIcon(':/pics/favicon.ico'))
self.setWindowTitle("DouZero欢乐斗地主v2.0")
self.setFixedSize(self.width(), self.height()) # 固定窗体大小
self.move(50, 50)
# self.setWindowIcon(QIcon('pics/favicon.ico'))
window_pale = QtGui.QPalette()
# window_pale.setBrush(self.backgroundRole(), QtGui.QBrush(QtGui.QPixmap("pics/bg.png")))
self.setPalette(window_pale)
self.SingleButton.clicked.connect(self.game_single)
self.LoopButton.clicked.connect(self.game_loop)
self.StopButton.clicked.connect(self.stop)
# self.Players = [self.RPlayer, self.Player, self.LPlayer]
self.Players = [self.RPlayedCard, self.PredictedCard, self.LPlayedCard]
self.counter = QTime()
# 参数
self.MyConfidence = 0.8 # 我的牌的置信度
self.OtherConfidence = 0.8 # 别人的牌的置信度
self.WhiteConfidence = 0.85 # 检测白块的置信度
self.LandlordFlagConfidence = 0.8 # 检测地主标志的置信度
self.ThreeLandlordCardsConfidence = 0.8 # 检测地主底牌的置信度
self.PassConfidence = 0.7
self.PassConfidence = 0.8
self.WaitTime = 1 # 等待状态稳定延时
self.MyFilter = 40 # 我的牌检测结果过滤参数
self.OtherFilter = 25 # 别人的牌检测结果过滤参数
self.SleepTime = 0.1 # 循环中睡眠时间
self.RunGame = False
self.AutoPlay = False
self.BidThreshold1 = 65 # 叫地主阈值
self.BidThreshold2 = 72 # 抢地主阈值
self.JiabeiThreshold = (
(85, 72), # 叫地主 超级加倍 加倍 阈值
(85, 75) # 叫地主 超级加倍 加倍 阈值 (在地主是抢来的情况下)
)
self.MingpaiThreshold = 92
# 坐标
self.MyHandCardsPos = (180, 560, 1050, 90) # 我的截图区域
self.LPlayedCardsPos = (320, 280, 500, 120) # 左边出牌截图区域
self.RPlayedCardsPos = (600, 280, 500, 120) # 右边出牌截图区域
self.LandlordCardsPos = (600, 33, 220, 103) # 地主底牌截图区域
self.LPassPos = (360, 360, 120, 80) # 左边不出截图区域
self.RPassPos = (940, 360, 120, 80) # 右边不出截图区域
self.PassBtnPos = (200, 450, 1000, 120) # 要不起截图区域
self.GeneralBtnPos = (200, 450, 1000, 120) # 叫地主、抢地主、加倍按钮截图区域
self.LandlordFlagPos = [(1247, 245, 48, 52), (12, 661, 51, 53), (123, 243, 52, 54)] # 地主标志截图区域(右-我-左)
self.card_play_model_path_dict = {
'landlord': "baselines/resnet/resnet_landlord.ckpt",
'landlord_up': "baselines/resnet/resnet_landlord_up.ckpt",
'landlord_down': "baselines/resnet/resnet_landlord_down.ckpt"
}
def game_single(self):
self.loop_sign = 0
self.stop_sign = 0
self.detect_start_btn()
self.before_start()
self.init_cards()
def game_loop(self):
self.loop_sign = 1
self.stop_sign = 0
while True:
if self.stop_sign == 1:
break
self.detect_start_btn()
self.before_start()
self.init_cards()
self.sleep(5000)
def stop(self):
self.stop_sign = 1
print("按下停止键")
try:
self.RunGame = False
self.loop_sign = 0
self.env.game_over = True
self.env.reset()
self.init_display()
self.PreWinrate.setText("局前胜率: ")
self.BidWinrate.setText("叫牌胜率: ")
except AttributeError as e:
traceback.print_exc()
def init_display(self):
self.WinRate.setText("评分")
self.label.setText("游戏状态")
self.label.setStyleSheet('background-color: rgba(255, 0, 0, 0);')
self.UserHandCards.setText("手牌")
# self.LBrowser.clear()
# self.RBrowser.clear()
self.LPlayedCard.setText("上家出牌区域")
self.RPlayedCard.setText("下家出牌区域")
self.PredictedCard.setText("AI出牌区域")
self.ThreeLandlordCards.setText("地主牌")
self.recorder2zero()
for player in self.Players:
player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')
def init_cards(self):
self.RunGame = True
GameHelper.Interrupt = False
self.user_hand_cards_real = ""
self.user_hand_cards_env = []
# 其他玩家出牌
self.other_played_cards_real = ""
self.other_played_cards_env = []
# 其他玩家手牌（整副牌减去玩家手牌，后续再减掉历史出牌）
self.other_hand_cards = []
# 三张底牌
self.three_landlord_cards_real = ""
self.three_landlord_cards_env = []
# 玩家角色代码：0-地主上家, 1-地主, 2-地主下家
self.user_position_code = None
self.user_position = ""
# 开局时三个玩家的手牌
self.card_play_data_list = {}
# 识别玩家手牌
self.user_hand_cards_real = self.find_my_cards()
while len(self.user_hand_cards_real) != 17 and len(self.user_hand_cards_real) != 20:
self.detect_start_btn()
if not self.RunGame:
break
self.sleep(200)
self.user_hand_cards_real = self.find_my_cards()
self.user_hand_cards_env = [RealCard2EnvCard[c] for c in list(self.user_hand_cards_real)]
# 识别三张底牌
self.three_landlord_cards_real = self.find_landlord_cards()
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
while len(self.three_landlord_cards_env) != 3:
self.detect_start_btn()
if not self.RunGame:
break
if len(self.three_landlord_cards_env) > 3:
self.ThreeLandlordCardsConfidence += 0.05
elif len(self.three_landlord_cards_env) < 3:
self.ThreeLandlordCardsConfidence -= 0.05
self.three_landlord_cards_real = self.find_landlord_cards()
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
# 识别玩家的角色
self.sleep(500)
self.user_position_code = self.find_landlord(self.LandlordFlagPos)
self.sleep(200)
while self.user_position_code is None:
self.detect_start_btn()
if not self.RunGame:
break
self.user_position_code = self.find_landlord(self.LandlordFlagPos)
self.sleep(200)
print("正在出牌人的代码： ", self.user_position_code)
if self.user_position_code is None:
items = ("地主上家", "地主", "地主下家")
item, okPressed = QInputDialog.getItem(self, "选择角色", "未识别到地主，请手动选择角色:", items, 0, False)
if okPressed and item:
self.user_position_code = items.index(item)
else:
return
self.user_position = ['landlord_up', 'landlord', 'landlord_down'][self.user_position_code]
print("我现在在地主的方向：", self.user_position)
for player in self.Players:
player.setStyleSheet('background-color: rgba(0, 255, 0, 0);')
self.Players[self.user_position_code].setStyleSheet('background-color: rgba(0, 255, 0, 0.5);')
# 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）
for i in set(AllEnvCard):
self.other_hand_cards.extend([i] * (AllEnvCard.count(i) - self.user_hand_cards_env.count(i)))
self.other_hands_cards_str = str(''.join([EnvCard2RealCard[c] for c in self.other_hand_cards]))[::-1]
self.cards_recorder(self.other_hands_cards_str)
self.card_play_data_list.update({
'three_landlord_cards': self.three_landlord_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 0) % 3]:
self.user_hand_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 1) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 != 1 else self.other_hand_cards[17:],
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 2) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 == 1 else self.other_hand_cards[17:]
})
print("开始对局")
print("手牌:", self.user_hand_cards_real)
print("地主牌:", self.three_landlord_cards_real)
# 生成手牌结束，校验手牌数量
if len(self.card_play_data_list["three_landlord_cards"]) != 3:
QMessageBox.critical(self, "底牌识别出错", "底牌必须是3张！", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
if len(self.card_play_data_list["landlord_up"]) != 17 or \
len(self.card_play_data_list["landlord_down"]) != 17 or \
len(self.card_play_data_list["landlord"]) != 20:
QMessageBox.critical(self, "手牌识别出错", "初始手牌数目有误", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
# 出牌顺序：0-玩家出牌, 1-玩家下家出牌, 2-玩家上家出牌
self.play_order = 0 if self.user_position == "landlord" else 1 if self.user_position == "landlord_up" else 2
# 创建一个代表玩家的AI
ai_players = [0, 0]
ai_players[0] = self.user_position
ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])
| self.env = GameEnv(ai_players) | 3 | 2023-12-01 04:04:30+00:00 | 24k |
super1207/satoricq | satori.py | [
{
"identifier": "AdapterKook",
"path": "kook_adapter.py",
"snippet": "class AdapterKook:\n def __init__(self,config = {}) -> None:\n '''用于初始化一些配置信息，尽量不要在这里阻塞，因为此处不具备异步环境，如果你需要读写配置文件，请在init_after中进行'''\n self._access_token = config[\"access_token\"]\n self._http_url = \"https://ww... | import asyncio
import aiohttp
import json
import uuid
from kook_adapter import AdapterKook
from mihoyo_adapter import AdapterMihoyo
from onebot_adapter import AdapterOnebot
from config import Config
from aiohttp import web
from qq_adapter import AdapterQQ
from tool import remove_json_null | 17,914 | return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/guild.member.get":
body = await request.json()
ret = await adapter.get_guild_member(platform,self_id,body["guild_id"],body["user_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/message.create":
body = await request.json()
ret = await adapter.create_message(platform,self_id,body["channel_id"],body["content"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/channel.list":
body = await request.json()
ret = await adapter.get_channel_list(platform,self_id,body["guild_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/user.get":
body = await request.json()
ret = await adapter.get_user(platform,self_id,body["user_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
return web.Response(text="method not found")
async def _handle_http_admin(self,request:web.Request):
print("----http admin",request)
'''在这里处理管理api调用'''
# 鉴权
if self._config.access_token != "":
if request.headers.get("Authorization") != "Bearer " + self._config.access_token:
print("token err")
return web.Response(text="token err")
method = request.url.path
if method == "/v1/admin/login.list":
ret = []
for adapter in self.adapterlist:
ret += await adapter["adapter"].get_login(None,None)
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
return web.Response(text="method not found")
async def _handle_http_foo(self,request:web.Request):
'''在这里处理其余任何api调用'''
print("--------http other",request)
return web.Response(text="method not found")
async def _handle_events_ws(self,request:web.Request):
'''在这里处理websocket'''
ws_id = str(uuid.uuid4())
ws = web.WebSocketResponse()
ws.can_prepare(request)
await ws.prepare(request)
self.wsmap[ws_id] = {
"ws":ws,
"is_access":False
}
print("--------http ws",request,ws_id)
try:
async for msg in ws:
if msg.type == aiohttp.WSMsgType.TEXT:
data_json = json.loads(msg.data)
print("--------recv_ws",json.dumps(msg.data))
op = data_json["op"]
if op == 3:
if self._config.access_token != "":
if data_json["body"]["token"] != self._config.access_token:
raise "token err"
self.wsmap[ws_id]["is_access"] = True
async def get_logins(self,ws):
logins = []
for adapter in self.adapterlist:
logins += await adapter["adapter"].get_login(None,None)
await Satori.ws_send_json(ws,{
"op":4,
"body":{
"logins":logins
}
})
asyncio.create_task(get_logins(self,ws))
elif op == 1:
async def send_pong(ws):
await Satori.ws_send_json(ws,{
"op":2
})
asyncio.create_task(send_pong(ws))
elif msg.type == aiohttp.WSMsgType.ERROR:
print('ws connection closed with exception %s' %
ws.exception())
finally:
del self.wsmap[ws_id]
print("--------http ws close",ws_id)
return ws
async def init_after(self):
async def event_loop(self:Satori,adapter:AdapterOnebot):
while True:
msg = await adapter.get_msg()
for wsid in self.wsmap:
ws = self.wsmap[wsid]
if ws["is_access"]:
msg["id"] = self._evt_id
asyncio.create_task(Satori.ws_send_json(ws["ws"],{"op":0,"body":msg}))
self._evt_id += 1
# 读取配置文件
await self._config.read_config()
# 创建 adapter
for botcfg in self._config.botlist:
if botcfg["platform"] == "onebot":
adapter = AdapterOnebot(botcfg)
elif botcfg["platform"] == "kook":
adapter = AdapterKook(botcfg)
elif botcfg["platform"] == "mihoyo":
adapter = AdapterMihoyo(botcfg)
elif botcfg["platform"] == "qq":
|
class Satori:
def __init__(self) -> None:
self._config:Config = Config()
self.adapterlist = []
self.wsmap = {}
self._evt_id = 100
async def _get_adapter(self,platform,self_id):
''' 用于获取适配器 '''
for adapter in self.adapterlist:
info = adapter["info"]
for bot in info:
if self_id == bot["self_id"] and bot["platform"] == platform:
return adapter["adapter"]
return None
async def ws_send_json(ws,js) -> None:
js = remove_json_null(js)
print("--------ws_send_json",json.dumps(js))
await ws.send_json(js)
async def _handle_http_normal(self,request:web.Request):
print("----http normal",request)
'''在这里处理普通api调用'''
# 鉴权
if self._config.access_token != "":
if request.headers.get("Authorization") != "Bearer " + self._config.access_token:
print("token err")
return web.Response(text="token err")
method = request.url.path
platform = request.headers.get("X-Platform")
self_id = request.headers.get("X-Self-ID")
adapter:AdapterOnebot = await self._get_adapter(platform,self_id)
if adapter == None:
return web.Response(text="bot not found")
if method == "/v1/login.get":
ret = await adapter.get_login(platform,self_id)
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/guild.member.get":
body = await request.json()
ret = await adapter.get_guild_member(platform,self_id,body["guild_id"],body["user_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/message.create":
body = await request.json()
ret = await adapter.create_message(platform,self_id,body["channel_id"],body["content"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/channel.list":
body = await request.json()
ret = await adapter.get_channel_list(platform,self_id,body["guild_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
elif method == "/v1/user.get":
body = await request.json()
ret = await adapter.get_user(platform,self_id,body["user_id"])
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
return web.Response(text="method not found")
async def _handle_http_admin(self,request:web.Request):
print("----http admin",request)
'''在这里处理管理api调用'''
# 鉴权
if self._config.access_token != "":
if request.headers.get("Authorization") != "Bearer " + self._config.access_token:
print("token err")
return web.Response(text="token err")
method = request.url.path
if method == "/v1/admin/login.list":
ret = []
for adapter in self.adapterlist:
ret += await adapter["adapter"].get_login(None,None)
return web.Response(text=json.dumps(remove_json_null(ret)),headers={
"Content-Type":"application/json; charset=utf-8"
})
return web.Response(text="method not found")
async def _handle_http_foo(self,request:web.Request):
'''在这里处理其余任何api调用'''
print("--------http other",request)
return web.Response(text="method not found")
async def _handle_events_ws(self,request:web.Request):
'''在这里处理websocket'''
ws_id = str(uuid.uuid4())
ws = web.WebSocketResponse()
ws.can_prepare(request)
await ws.prepare(request)
self.wsmap[ws_id] = {
"ws":ws,
"is_access":False
}
print("--------http ws",request,ws_id)
try:
async for msg in ws:
if msg.type == aiohttp.WSMsgType.TEXT:
data_json = json.loads(msg.data)
print("--------recv_ws",json.dumps(msg.data))
op = data_json["op"]
if op == 3:
if self._config.access_token != "":
if data_json["body"]["token"] != self._config.access_token:
raise "token err"
self.wsmap[ws_id]["is_access"] = True
async def get_logins(self,ws):
logins = []
for adapter in self.adapterlist:
logins += await adapter["adapter"].get_login(None,None)
await Satori.ws_send_json(ws,{
"op":4,
"body":{
"logins":logins
}
})
asyncio.create_task(get_logins(self,ws))
elif op == 1:
async def send_pong(ws):
await Satori.ws_send_json(ws,{
"op":2
})
asyncio.create_task(send_pong(ws))
elif msg.type == aiohttp.WSMsgType.ERROR:
print('ws connection closed with exception %s' %
ws.exception())
finally:
del self.wsmap[ws_id]
print("--------http ws close",ws_id)
return ws
async def init_after(self):
async def event_loop(self:Satori,adapter:AdapterOnebot):
while True:
msg = await adapter.get_msg()
for wsid in self.wsmap:
ws = self.wsmap[wsid]
if ws["is_access"]:
msg["id"] = self._evt_id
asyncio.create_task(Satori.ws_send_json(ws["ws"],{"op":0,"body":msg}))
self._evt_id += 1
# 读取配置文件
await self._config.read_config()
# 创建 adapter
for botcfg in self._config.botlist:
if botcfg["platform"] == "onebot":
adapter = AdapterOnebot(botcfg)
elif botcfg["platform"] == "kook":
adapter = AdapterKook(botcfg)
elif botcfg["platform"] == "mihoyo":
adapter = AdapterMihoyo(botcfg)
elif botcfg["platform"] == "qq": | adapter = AdapterQQ(botcfg) | 4 | 2023-12-03 13:53:47+00:00 | 24k |
aliyun/pai-python-sdk | pai/model.py | [
{
"identifier": "git_utils",
"path": "pai/common/git_utils.py",
"snippet": "def git_clone_repo(git_config: Dict[str, str], source_dir: Optional[str] = None):\ndef _validate_git_config(git_config):\ndef _build_and_run_clone_command(git_config, dest_dir):\ndef _clone_command_for_codeup(git_config, dest_di... | import copy
import distutils.dir_util
import json
import logging
import os.path
import posixpath
import shlex
import shutil
import tempfile
import textwrap
import time
import requests
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from addict import Dict as AttrDict
from oss2 import ObjectIterator
from .common import git_utils
from .common.consts import INSTANCE_TYPE_LOCAL_GPU, ModelFormat
from .common.docker_utils import ContainerRun, run_container
from .common.oss_utils import OssUriObj, download, is_oss_uri, upload
from .common.utils import (
generate_repr,
is_local_run_instance_type,
random_str,
to_plain_text,
)
from .exception import DuplicatedMountException, MountPathIsOccupiedException
from .image import ImageInfo
from .predictor import AsyncPredictor, LocalPredictor, Predictor, ServiceType
from .serializers import SerializerBase
from .session import Session, get_default_session
from .estimator import AlgorithmEstimator | 17,509 | # if source is a local path, upload it to OSS bucket and use OSS URI
# as storage source.
oss_path = session.get_storage_path_by_category("model_data")
oss_uri = upload(
source_path=source, oss_path=oss_path, bucket=session.oss_bucket
)
oss_uri_obj = OssUriObj(oss_uri)
storage_config = {
"mount_path": mount_path,
"oss": {"path": oss_uri_obj.get_dir_uri()},
}
else:
raise ValueError(
"Source path is not a valid OSS URI or a existing local path."
)
# check if the source OSS Path is already mounted to the container.
if oss_uri_obj.get_dir_uri() in uris:
raise DuplicatedMountException(
f"Source OSS path '{oss_uri_obj.get_dir_uri()}' is already "
f"mounted to the container."
)
configs.append(storage_config)
self.storage = configs
return storage_config
def container_serving_spec(
command: str,
image_uri: Union[str, ImageInfo],
source_dir: Optional[str] = None,
git_config: Optional[Dict[str, Any]] = None,
port: Optional[int] = None,
environment_variables: Optional[Dict[str, str]] = None,
requirements: Optional[List[str]] = None,
requirements_path: Optional[str] = None,
health_check: Optional[Dict[str, Any]] = None,
session: Optional[Session] = None,
) -> InferenceSpec:
"""A convenient function to create an InferenceSpec instance that serving the model
with given container and script.
Examples::
infer_spec: InferenceSpec = container_serving_spec(
command="python run.py",
source_dir="./model_server/",
image_uri="<ServingImageUri>",
)
m = Model(
model_data="oss://<YourOssBucket>/path/to/your/model",
inference_spec=infer_spec,
)
m.deploy(
instance_type="ecs.c6.xlarge"
)
Args:
command (str): The command used to launch the Model server.
source_dir (str): A relative path or an absolute path to the source code
directory used to load model and launch the HTTP server, it will be
uploaded to the OSS bucket and mounted to the container. If there is a
``requirements.txt`` file under the directory, it will be installed before
the prediction server started.
If 'git_config' is provided, 'source_dir' should be a relative location
to a directory in the Git repo. With the following GitHub repo directory
structure:
.. code::
|----- README.md
|----- src
|----- train.py
|----- test.py
if you need 'src' directory as the source code directory, you can assign
source_dir='./src/'.
git_config (Dict[str, str]): Git configuration used to clone the repo.
Including ``repo``, ``branch``, ``commit``, ``username``, ``password`` and
``token``. The ``repo`` is required. All other fields are optional. ``repo``
specifies the Git repository. If you don't provide ``branch``, the default
value 'master' is used. If you don't provide ``commit``, the latest commit
in the specified branch is used. ``username``, ``password`` and ``token``
are for authentication purpose. For example, the following config:
.. code:: python
git_config = {
'repo': 'https://github.com/modelscope/modelscope.git',
'branch': 'master',
'commit': '9bfc4a9d83c4beaf8378d0a186261ffc1cd9f960'
}
results in cloning the repo specified in 'repo', then checking out the
'master' branch, and checking out the specified commit.
image_uri (str): The Docker image used to run the prediction service.
port (int): Expose port of the server in container, the prediction request
will be forward to the port. The environment variable ``LISTENING_PORT``
in the container will be set to this value. Default to 8000.
environment_variables (Dict[str, str], optional): Dictionary of environment
variable key-value pairs to set on the running container.
requirements (List[str], optional): A list of Python package dependency, it
will be installed before the serving container run.
requirements_path (str, optional): A absolute path to the requirements.txt in
the container.
health_check (Dict[str, Any], optional): The health check configuration. If it
not set, A TCP readiness probe will be used to check the health of the
HTTP server.
session (Session, optional): A PAI session instance used for communicating
with PAI service.
Returns:
:class:`pai.model.InferenceSpec`: An InferenceSpec instance.
"""
session = session or get_default_session()
if git_config:
| # Copyright 2023 Alibaba, Inc. or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
logger = logging.getLogger(__name__)
# Reserved ports for internal use, do not use them for service
_RESERVED_PORTS = [8080, 9090]
class DefaultServiceConfig(object):
"""Default configuration used in creating prediction service."""
# Listen Port
listen_port = 8000
# Default model path in container
model_path = "/eas/workspace/model/"
# Default user code path in container
code_path = "/ml/usercode/"
class ResourceConfig(object):
"""A class that represents the resource used by a PAI prediction service
instance."""
def __init__(self, cpu: int, memory: int, gpu: int = None, gpu_memory: int = None):
"""ResourceConfig initializer.
The public resource group does not support requesting GPU resources with
`ResourceConfig`. Use the 'gpu' and 'gpu_memory' parameter only for services
deployed to dedicated resource groups that provide GPU machine instances.
Args:
cpu (int): The number of CPUs that each instance requires.
memory (int): The amount of memory that each instance requires,
must be an integer, Unit: MB.
gpu (int): The number of GPUs that each instance requires.
gpu_memory (int): The amount of GPU memory that each instance requires.
The value must be an integer, Unit: GB.
PAI allows memory resources of a GPU to be allocated to multiple instances.
If you want multiple instances to share the memory resources of a GPU,
set the gpu parameter to 0. If you set the ``gpu`` parameter to 1, each
instance occupies a GPU and the gpu_memory parameter does not take effect.
.. note::
**Important** PAI does not enable the strict isolation of GPU memory.
To prevent out of memory (OOM) errors, make sure that the GPU memory
used by each instance does not exceed the requested amount.
"""
self.cpu = cpu
self.memory = memory
self.gpu = gpu
self.gpu_memory = gpu_memory
def __repr__(self):
return (
f"ResourceConfig(cpu={self.cpu}, memory={self.memory}MB, gpu={self.gpu or 0},"
f" gpu_memory={self.gpu_memory or 0}GB)"
)
def __str__(self):
return self.__repr__()
def to_dict(self):
"""Transform the ResourceConfig instance to a dictionary.
Returns:
dict:
"""
res = {
"cpu": self.cpu,
"gpu": self.gpu,
"gpu_memory": self.gpu_memory,
"memory": self.memory,
}
return {k: v for k, v in res.items() if v is not None}
class InferenceSpec(object):
"""A class used to describe how to create a prediction service.
InferenceSpec is using to describe how the model is serving in PAI. To view the
full supported parameters, please see the following hyperlink:
`Parameters of model services <https://help.aliyun.com/document_detail/450525.htm>`_.
Example of how to config a InferneceSpec::
>>> # build an inference_spec that using XGBoost processor.
>>> infer_spec = InferenceSpec(processor="xgboost")
>>> infer_spec.metadata.rpc.keepalive = 1000
>>> infer_spec.warm_up_data_path = "oss://bucket-name/path/to/warmup-data"
>>> infer_spec.add_option("metadata.rpc.max_batch_size", 8)
>>> print(infer_spec.processor)
xgboost
>>> print(infer_spec.metadata.rpc.keepalive)
1000
>>> print(infer_spec.metadata.rpc.max_batch_size)
8
>>> print(infer_spec.to_dict())
{'processor': 'xgboost', 'metadata': {'rpc': {'keepalive': 1000, 'max_batch_size': 8}},
'warm_up_data_path': 'oss://bucket-name/path/to/warmup-data'}
"""
def __init__(self, *args, **kwargs):
"""InferenceSpec initializer.
Args:
**kwargs: Parameters of the inference spec.
"""
properties = kwargs.pop("__properties", [])
cfg_dict = copy.deepcopy(kwargs)
cfg_dict = {k: v for k, v in cfg_dict.items() if not k.startswith("_")}
if args:
if len(args) > 1:
raise TypeError()
cfg_dict.update(args[0])
super(InferenceSpec, self).__setattr__(
"_cfg_dict", self._transform_value(cfg_dict)
)
super(InferenceSpec, self).__setattr__("__properties", properties)
def __repr__(self):
return json.dumps(self.to_dict(), sort_keys=True, indent=4)
def _transform_value(self, value):
if isinstance(value, (List, Tuple)):
return [self._transform_value(item) for item in value]
elif isinstance(value, (Dict, AttrDict)):
return AttrDict(
{key: self._transform_value(value) for key, value in value.items()}
)
return value
def __missing__(self, name):
return self._cfg_dict.__missing__(name)
def __setitem__(self, name, value):
return self._cfg_dict.__setitem__(name, self._transform_value(value))
def __setattr__(self, name, value):
if name in getattr(self, "__properties"):
super(InferenceSpec, self).__setattr__(name, self._transform_value(value))
else:
self._cfg_dict.__setattr__(name, self._transform_value(value))
def __getattr__(self, item):
if item.startswith("_"):
return getattr(self, item)
return self._cfg_dict.__getitem__(item)
def __contains__(self, item):
return item in self._cfg_dict
def to_dict(self) -> Dict:
"""Return a dictionary that represent the InferenceSpec."""
return self._cfg_dict.to_dict()
def add_option(self, name: str, value):
"""Add an option to the inference_spec instance.
Args:
name (str): Name of the option to set, represented as the JSON path of the
parameter for the InferenceSpec. To view the full supported parameters,
please see the following hyperlink: `Parameters of model services
<https://help.aliyun.com/document_detail/450525.htm>`_.
value: Value for the option.
Examples:
>>> infer_spec = InferenceSpec(processor="tensorflow_gpu_1.12")
>>> infer_spec.add_option("metadata.rpc.keepalive", 10000)
>>> infer_spec.metadata.rpc.keepalive
10000
>>> infer_spec.to_dict()
{'processor': 'tensorflow_gpu_1.12', 'metadata': {'rpc': {'keepalive': 10000}}}
"""
src = self._transform_value(value)
for k in reversed(name.split(".")):
src = {k: src}
self._cfg_dict.update(AttrDict(src))
def merge_options(self, options: Dict[str, Any]):
"""Merge options from a dictionary."""
for key, value in options.items():
self.add_option(key, value)
@classmethod
def from_dict(cls, config: Dict[str, Any]) -> "InferenceSpec":
"""Initialize a InferenceSpec from a dictionary.
You can use this method to initialize a InferenceSpec instance
from a dictionary.
Returns:
:class:`pai.model.InferenceSpec`: A InferenceSpec instance.
"""
config = config or dict()
return cls(**config)
def is_container_serving(self):
return "containers" in self._cfg_dict
@classmethod
def _upload_source_dir(cls, source_dir, session):
"""Upload source files to OSS bucket."""
if not os.path.exists(source_dir):
raise ValueError(f"Input source code path does not exist: {source_dir}.")
if not os.path.isdir(source_dir):
raise ValueError(
f"Input source code path should be a directory: {source_dir}."
)
target_dir = session.get_storage_path_by_category(category="inference_src")
# upload local script data to the OSS bucket.
uploaded_source_code = upload(
source_dir,
target_dir,
session.oss_bucket,
)
logger.debug("Uploaded source code to OSS: %s", uploaded_source_code)
return uploaded_source_code
def mount(
self,
source: str,
mount_path: str,
session: Session = None,
) -> Dict[str, Any]:
"""Mount a source storage to the running container.
.. note::
If source is a local path, it will be uploaded to the OSS bucket and
mounted. If source is a OSS path, it will be mounted directly.
Args:
source (str): The source storage to be attached, currently only support OSS
path in OSS URI format and local path.
mount_path (str): The mount path in the container.
session (Session, optional): A PAI session instance used for communicating
with PAI service.
Returns:
Dict[str, Any]: The storage config.
Raises:
DuplicateMountException: If the mount path is already used or source OSS
path is mounted to the container.
Examples::
# Mount a OSS storage path to the running container.
>>> inference_spec.mount("oss://<YourOssBucket>/path/to/directory/model.json",
... "/ml/model/")
# 'Mount' a local path to the running container.
>>> inference_spec.mount("/path/to/your/data/", "/ml/model/")
"""
session = session or get_default_session()
# TODO: supports more storages, such as NAS, PAI Dataset, PAI CodeSource, etc.
if not isinstance(source, str):
raise ValueError(
"Parameter should be a string which represents an OSS storage path"
" or a local file path."
)
if "storage" in self._cfg_dict:
configs = self._cfg_dict.get("storage", [])
else:
configs = []
uris = set()
for conf in configs:
# check if target mount path is already used.
if conf.get("mount_path") == mount_path:
raise MountPathIsOccupiedException(
f"The mount path '{mount_path}' has already been used."
)
mount_uri = conf.get("oss", {}).get("path")
uris.add(mount_uri)
if is_oss_uri(source):
oss_uri_obj = OssUriObj(source)
storage_config = {
"mount_path": mount_path,
"oss": {"path": oss_uri_obj.get_dir_uri()},
}
elif os.path.exists(source):
# if source is a local path, upload it to OSS bucket and use OSS URI
# as storage source.
oss_path = session.get_storage_path_by_category("model_data")
oss_uri = upload(
source_path=source, oss_path=oss_path, bucket=session.oss_bucket
)
oss_uri_obj = OssUriObj(oss_uri)
storage_config = {
"mount_path": mount_path,
"oss": {"path": oss_uri_obj.get_dir_uri()},
}
else:
raise ValueError(
"Source path is not a valid OSS URI or a existing local path."
)
# check if the source OSS Path is already mounted to the container.
if oss_uri_obj.get_dir_uri() in uris:
raise DuplicatedMountException(
f"Source OSS path '{oss_uri_obj.get_dir_uri()}' is already "
f"mounted to the container."
)
configs.append(storage_config)
self.storage = configs
return storage_config
def container_serving_spec(
command: str,
image_uri: Union[str, ImageInfo],
source_dir: Optional[str] = None,
git_config: Optional[Dict[str, Any]] = None,
port: Optional[int] = None,
environment_variables: Optional[Dict[str, str]] = None,
requirements: Optional[List[str]] = None,
requirements_path: Optional[str] = None,
health_check: Optional[Dict[str, Any]] = None,
session: Optional[Session] = None,
) -> InferenceSpec:
"""A convenient function to create an InferenceSpec instance that serving the model
with given container and script.
Examples::
infer_spec: InferenceSpec = container_serving_spec(
command="python run.py",
source_dir="./model_server/",
image_uri="<ServingImageUri>",
)
m = Model(
model_data="oss://<YourOssBucket>/path/to/your/model",
inference_spec=infer_spec,
)
m.deploy(
instance_type="ecs.c6.xlarge"
)
Args:
command (str): The command used to launch the Model server.
source_dir (str): A relative path or an absolute path to the source code
directory used to load model and launch the HTTP server, it will be
uploaded to the OSS bucket and mounted to the container. If there is a
``requirements.txt`` file under the directory, it will be installed before
the prediction server started.
If 'git_config' is provided, 'source_dir' should be a relative location
to a directory in the Git repo. With the following GitHub repo directory
structure:
.. code::
|----- README.md
|----- src
|----- train.py
|----- test.py
if you need 'src' directory as the source code directory, you can assign
source_dir='./src/'.
git_config (Dict[str, str]): Git configuration used to clone the repo.
Including ``repo``, ``branch``, ``commit``, ``username``, ``password`` and
``token``. The ``repo`` is required. All other fields are optional. ``repo``
specifies the Git repository. If you don't provide ``branch``, the default
value 'master' is used. If you don't provide ``commit``, the latest commit
in the specified branch is used. ``username``, ``password`` and ``token``
are for authentication purpose. For example, the following config:
.. code:: python
git_config = {
'repo': 'https://github.com/modelscope/modelscope.git',
'branch': 'master',
'commit': '9bfc4a9d83c4beaf8378d0a186261ffc1cd9f960'
}
results in cloning the repo specified in 'repo', then checking out the
'master' branch, and checking out the specified commit.
image_uri (str): The Docker image used to run the prediction service.
port (int): Expose port of the server in container, the prediction request
will be forward to the port. The environment variable ``LISTENING_PORT``
in the container will be set to this value. Default to 8000.
environment_variables (Dict[str, str], optional): Dictionary of environment
variable key-value pairs to set on the running container.
requirements (List[str], optional): A list of Python package dependency, it
will be installed before the serving container run.
requirements_path (str, optional): A absolute path to the requirements.txt in
the container.
health_check (Dict[str, Any], optional): The health check configuration. If it
not set, A TCP readiness probe will be used to check the health of the
HTTP server.
session (Session, optional): A PAI session instance used for communicating
with PAI service.
Returns:
:class:`pai.model.InferenceSpec`: An InferenceSpec instance.
"""
session = session or get_default_session()
if git_config: | updated_args = git_utils.git_clone_repo( | 0 | 2023-12-01 01:40:12+00:00 | 24k |
mpenning/ciscoconfparse2 | tests/conftest.py | [
{
"identifier": "CiscoConfParse",
"path": "ciscoconfparse2/ciscoconfparse2.py",
"snippet": "class CiscoConfParse(object):\n \"\"\"Parse Cisco IOS configurations and answer queries about the configs.\"\"\"\n config: Optional[Union[str,List[str]]] = None\n syntax: str = \"ios\"\n encoding: str... | import platform
import sys
import os
import dns.exception
import dns.resolver
import pytest
from ciscoconfparse2.ciscoconfparse2 import CiscoConfParse
from ciscoconfparse2.ccp_util import PythonOptimizeCheck
from fixtures.devices.mock_cisco import start_cisco_mock, stop_cisco_mock | 18,528 | tacacs-server host 10.0.0.32
tacacs-server host 10.0.0.33
aaa group server tacacs+ TACACS_GROUP
server 10.0.0.32
server 10.0.0.33
use-vrf management
source-interface mgmt0
aaa authentication login default group TACACS_GROUP
aaa authentication login console group TACACS_GROUP
aaa authorization commands default group TACACS_GROUP
aaa accounting default group TACACS_GROUP
aaa authentication login error-enable
logging event link-status default
vpc domain 999
role priority 100
system-priority 1
auto-recovery
peer-keepalive destination 1.1.1.2
fex 115
desc FEX115
pinning max-links 1
interface loopback0
ip address 10.1.1.1/32
interface mgmt0
ip address 10.0.0.5/24
interface port-channel1
vpc peer-link
switchport mode trunk
spanning-tree port type network
description [vPC PEER LINK]
interface port-channel21
description Uplink to core
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,155
mtu 9216
vpc 21
interface port-channel115
switchport mode fex-fabric
fex associate 115
interface Ethernet1/1
switchport mode trunk
spanning-tree port type network
channel-group 1 mode active
interface Ethernet1/2
switchport mode trunk
spanning-tree port type network
channel-group 1 mode active
interface Ethernet1/3
ip address 192.0.2.0/31
interface Ethernet1/4
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,15
channel-group 21 mode active
mtu 9216
interface Ethernet1/5
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,15
channel-group 21 mode active
mtu 9216
interface Ethernet1/6
switchport mode fex-fabric
fex associate 115
channel-group 115
interface Ethernet1/7
switchport mode access
switchport access vlan 100
mtu 9216
interface Ethernet1/8
switchport mode access
switchport access vlan 102
mtu 9216
interface Ethernet1/9
ip address 10.1.2.6/30
mtu 9216
interface Ethernet1/10
encapsulation dot1Q 200
bandwidth 100000000
delay 200
beacon
ip address 10.1.2.2/30
mpls ip
mtu 9216
""".splitlines()
@pytest.fixture(scope="session")
def c01_default_gigethernets(request):
yield config_c01_default_gige
@pytest.fixture(scope="session")
def c01_insert_serial_replace(request):
yield config_c01_insert_serial_replace
@pytest.fixture(scope="function")
def parse_c01(request):
"""Preparsed c01"""
| r""" conftest.py - Parse, Query, Build, and Modify IOS-style configs
Copyright (C) 2023 David Michael Pennington at Cisco Systems
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
If you need to contact the author, you can do so by emailing:
mike [~at~] pennington [/dot\] net
"""
sys.path.insert(0, "..")
c01 = """policy-map QOS_1
class GOLD
priority percent 10
!
class SILVER
bandwidth 30
random-detect
!
class BRONZE
random-detect
!
interface Serial 1/0
encapsulation ppp
ip address 1.1.1.1 255.255.255.252
!
interface GigabitEthernet4/1
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
interface GigabitEthernet4/2
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
interface GigabitEthernet4/3
switchport
switchport access vlan 100
switchport voice vlan 150
!
interface GigabitEthernet4/4
shutdown
!
interface GigabitEthernet4/5
switchport
switchport access vlan 110
!
interface GigabitEthernet4/6
switchport
switchport access vlan 110
!
interface GigabitEthernet4/7
switchport
switchport access vlan 110
!
interface GigabitEthernet4/8
switchport
switchport access vlan 110
!
access-list 101 deny tcp any any eq 25 log
access-list 101 permit ip any any
!
!
logging 1.1.3.5
logging 1.1.3.17
!
banner login ^C
This is a router, and you cannot have it.
Log off now while you still can type. I break the fingers
of all tresspassers.
^C
alias exec showthang show ip route vrf THANG""".splitlines()
config_c01_default_gige = """policy-map QOS_1
class GOLD
priority percent 10
!
class SILVER
bandwidth 30
random-detect
!
class BRONZE
random-detect
!
interface Serial 1/0
encapsulation ppp
ip address 1.1.1.1 255.255.255.252
!
default interface GigabitEthernet4/1
interface GigabitEthernet4/1
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
default interface GigabitEthernet4/2
interface GigabitEthernet4/2
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
default interface GigabitEthernet4/3
interface GigabitEthernet4/3
switchport
switchport access vlan 100
switchport voice vlan 150
!
default interface GigabitEthernet4/4
interface GigabitEthernet4/4
shutdown
!
default interface GigabitEthernet4/5
interface GigabitEthernet4/5
switchport
switchport access vlan 110
!
default interface GigabitEthernet4/6
interface GigabitEthernet4/6
switchport
switchport access vlan 110
!
default interface GigabitEthernet4/7
interface GigabitEthernet4/7
switchport
switchport access vlan 110
!
default interface GigabitEthernet4/8
interface GigabitEthernet4/8
switchport
switchport access vlan 110
!
access-list 101 deny tcp any any eq 25 log
access-list 101 permit ip any any
!
!
logging 1.1.3.5
logging 1.1.3.17
!
banner login ^C
This is a router, and you cannot have it.
Log off now while you still can type. I break the fingers
of all tresspassers.
^C
alias exec showthang show ip route vrf THANG""".splitlines()
config_c01_insert_serial_replace = """policy-map QOS_1
class GOLD
priority percent 10
!
class SILVER
bandwidth 30
random-detect
!
class BRONZE
random-detect
!
default interface Serial 2/0
interface Serial 2/0
encapsulation ppp
ip address 1.1.1.1 255.255.255.252
!
interface GigabitEthernet4/1
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
interface GigabitEthernet4/2
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
interface GigabitEthernet4/3
switchport
switchport access vlan 100
switchport voice vlan 150
!
interface GigabitEthernet4/4
shutdown
!
interface GigabitEthernet4/5
switchport
switchport access vlan 110
!
interface GigabitEthernet4/6
switchport
switchport access vlan 110
!
interface GigabitEthernet4/7
switchport
switchport access vlan 110
!
interface GigabitEthernet4/8
switchport
switchport access vlan 110
!
access-list 101 deny tcp any any eq 25 log
access-list 101 permit ip any any
!
!
logging 1.1.3.5
logging 1.1.3.17
!
banner login ^C
This is a router, and you cannot have it.
Log off now while you still can type. I break the fingers
of all tresspassers.
^C
alias exec showthang show ip route vrf THANG""".splitlines()
# A smaller version of c01...
c02 = """policy-map QOS_1
class GOLD
priority percent 10
!
class SILVER
bandwidth 30
random-detect
!
class BRONZE
random-detect
!
interface GigabitEthernet4/1
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!""".splitlines()
## For historical reasons, I'm use c03 for configs/sample_01.ios (i.e. c01 was
## already taken)
c03 = """!
service timestamps debug datetime msec localtime show-timezone
service timestamps log datetime msec localtime show-timezone
!
errdisable recovery cause bpduguard
errdisable recovery interval 400
!
aaa new-model
!
ip vrf TEST_100_001
route-target 100:1
rd 100:1
!
interface Serial 1/0
description Uplink to SBC F923X2K425
bandwidth 1500
clock rate 1500
delay 70
encapsulation ppp
ip address 1.1.1.1 255.255.255.252
!
interface Serial 1/1
description Uplink to AT&T
encapsulation hdlc
ip address 1.1.1.9 255.255.255.254
hold-queue 1000 in
hold-queue 1000 out
mpls mtu 1540
ip mtu 1500
mpls ip
!
interface GigabitEthernet4/1
description
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
!
interface GigabitEthernet4/2
switchport
switchport access vlan 100
switchport voice vlan 150
power inline static max 7000
speed 100
duplex full
!
interface GigabitEthernet4/3
mtu 9216
switchport
switchport access vlan 100
switchport voice vlan 150
!
interface GigabitEthernet4/4
shutdown
!
interface GigabitEthernet4/5
switchport
switchport access vlan 110
switchport port-security
switchport port-security maximum 3
switchport port-security mac-address sticky
switchport port-security mac-address 1000.2000.3000
switchport port-security mac-address 1000.2000.3001
switchport port-security mac-address 1000.2000.3002
switchport port-security violation shutdown
!
interface GigabitEthernet4/6
description Simulate a Catalyst6500 access port
switchport
switchport access vlan 110
switchport mode access
switchport nonegotiate
switchport port-security
switchport port-security maximum 2
switchport port-security violation restrict
switchport port-security aging type inactivity
switchport port-security aging time 5
spanning-tree portfast
spanning-tree portfast bpduguard
storm-control action shutdown
storm-control broadcast level 0.40
storm-control multicast level 0.35
!
interface GigabitEthernet4/7
description Dot1Q trunk allowing vlans 2-4,7,10,11-19,21-4094
switchport
switchport trunk encapsulation dot1q
switchport mode trunk
switchport trunk native vlan 4094
switchport trunk allowed vlan remove 1,5-10,20
switchport trunk allowed vlan add 7,10
switchport nonegotiate
!
interface GigabitEthernet4/8.120
no switchport
encapsulation dot1q 120
ip vrf forwarding TEST_100_001
ip address 1.1.2.254 255.255.255.0
!
interface ATM5/0/0
no ip address
no ip redirects
no ip unreachables
no ip proxy-arp
load-interval 30
carrier-delay msec 100
no atm ilmi-keepalive
bundle-enable
max-reserved-bandwidth 100
hold-queue 500 in
!
interface ATM5/0/0.32 point-to-point
ip address 1.1.1.5 255.255.255.252
no ip redirects
no ip unreachables
no ip proxy-arp
ip accounting access-violations
pvc 0/32
vbr-nrt 704 704
!
interface ATM5/0/1
shutdown
!
router ospf 100 vrf TEST_100_001
router-id 1.1.2.254
network 1.1.2.0 0.0.0.255 area 0
!
policy-map QOS_1
class GOLD
priority percent 10
!
class SILVER
bandwidth 30
random-detect
!
class BRONZE
random-detect
!
access-list 101 deny tcp any any eq 25 log
access-list 101 permit ip any any
!
!
logging 1.1.3.5
logging 1.1.3.17
!
banner login ^C
This is a router, and you cannot have it.
Log off now while you still can type. I break the fingers
of all tresspassers.
^C
!
alias exec showthang show ip route vrf THANG""".splitlines()
f01 = """ltm virtual ACME {
destination 192.168.1.191:http
ip-protocol tcp
mask 255.255.255.255
pool pool1
profiles {
http { }
tcp { }
}
rules {
MOBILE
}
source 0.0.0.0/0
source-address-translation {
type automap
}
translate-address enabled
translate-port enabled
vs-index 17
}""".splitlines()
# Using configs/sample_02.f5
f02 = """
ltm profile udp DNS-UDP {
app-service none
datagram-load-balancing disabled
idle-timeout 31
}
ltm rule contrail-monitor {
when HTTP_REQUEST {
if {[active_members APN-DNS-TCP] > 0 & [active_members APN-DNS-UDP] > 0 } {
HTTP::respond 200 content "up"
}
}
}
ltm rule contrail-monitor1 {
when HTTP_REQUEST {
if {[active_members APN-DNS-TCP] >= 0 & [active_members APN-DNS-UDP] >= 0 } {
HTTP::respond 200 content "up"
}
}
}
ltm tacdb licenseddb licensed-tacdb {
partition none
}
ltm virtual ACME_VIP {
destination 192.168.1.191:http
ip-protocol tcp
mask 255.255.255.255
pool pool1
profiles {
http { }
tcp { }
}
rules {
MOBILE
}
source 0.0.0.0/0
source-address-translation {
type automap
}
translate-address enabled
translate-port enabled
vs-index 17
}
sys state-mirroring { }
sys syslog {
include "
template t_remotetmpl {
template (\"<$PRI>$STAMP $HOST $FACILITY[$PID]: $MSGONLY\"); template_escape(no);
};
filter f_remote_loghost {
level(info..emerg);
};
destination d_remote_loghost {
udp(\"102.223.51.181\" port(519) template(t_remotetmpl));
};
log {
source(s_syslog_pipe);
filter(f_remote_loghost);
destination(d_remote_loghost);
};
"
remote-servers {
JSA {
host 102.223.51.181
}
}
}
sys url-db download-schedule urldb { }
""".splitlines()
j01 = """## Last commit: 2015-06-28 13:00:59 CST by mpenning
system {
host-name TEST01_EX;
domain-name pennington.net;
domain-search [ pennington.net lab.pennington.net ];
location {
country-code 001;
building HQ_005;
floor 1;
}
root-authentication {
encrypted-password "$1$y7ArHxKU$zUbdeLfBirgkCsKiOJ5Qa0"; ## SECRET-DATA
}
name-server {
172.16.3.222;
}
login {
announcement "Test Lab Switch";
message "Unauthorized access is prohibited";
user mpenning {
full-name "Mike Pennington";
uid 1000;
class super-user;
authentication {
encrypted-password "$1$y7ArHxKU$zUbdeLfBirgkCsKiOJ5Qa0"; ## SECRET-DATA
}
}
}
services {
ssh {
root-login allow;
}
telnet;
web-management {
http;
}
}
syslog {
user * {
any emergency;
}
file messages {
any notice;
authorization info;
}
file interactive-commands {
interactive-commands any;
}
}
ntp {
Management {
vlan-id 1;
interface {
ge-0/0/0.0;
ge-0/0/1.0;
ge-0/0/2.0;
ge-0/0/3.0;
}
}
VLAN_FOO {
vlan-id 5;
}
vlan1 {
vlan-id 1;
l3-interface vlan.1;
}
vlan800 {
vlan-id 800;
}
}
ethernet-switching-options {
storm-control {
interface all;
}
}
interfaces {
ge-0/0/0 {
unit 0 {
family ethernet-switching {
port-mode access;
vlan {
members VLAN_FOO;
}
}
}
}
ge-0/0/1 {
unit 0 {
family ethernet-switching {
port-mode trunk;
vlan {
members all;
}
native-vlan-id 1;
}
}
}
vlan {
unit 0 {
family inet {
address 172.16.15.5/22;
}
}
}
}
routing-options {
static {
route 0.0.0.0/0 next-hop 172.16.12.1;
route 192.168.36.0/25 next-hop 172.16.12.1;
}
}""".splitlines()
a01 = """hostname TEST-FW
!
name 1.1.2.20 loghost01
name 1.1.3.10 dmzsrv00
name 1.1.3.11 dmzsrv01
name 1.1.3.12 dmzsrv02
name 1.1.3.13 dmzsrv03
!
interface Ethernet0/0
description Uplink to SBC F923X2K425
nameif OUTSIDE
security-level 0
delay 70
ip address 1.1.1.1 255.255.255.252
!
interface Ethernet0/1
nameif INSIDE
security-level 100
ip address 1.1.2.1 255.255.255.0
!
interface Ethernet0/2
switchport access vlan 100
!
interface VLAN100
nameif DMZ
security-level 50
ip address 1.1.3.1 255.255.255.0
!
object-group network ANY_addrs
network-object 0.0.0.0 0.0.0.0
!
object-group network INSIDE_addrs1
network-object host 1.1.2.1
network-object 1.1.2.2 255.255.255.255
network-object 1.1.2.0 255.255.255.0
!
object-group network INSIDE_addrs1
network-object host 1.1.2.1
network-object 1.1.2.2 255.255.255.255
network-object 1.1.2.0 255.255.255.0
!
object-group service DNS_svc
service-object udp destination eq dns
!
object-group service NTP_svc
service-object udp destination eq ntp
!
object-group service FTP_svc
service-object tcp destination eq ftp
!
object-group service HTTP_svc
service-object tcp destination eq http
!
object-group service HTTPS_svc
service-object tcp destination eq https
!
access-list INSIDE_in extended permit object-group FTP_svc object-group INSIDE_addrs1 object-group ANY_addrs log
access-list INSIDE_in remark Overlap for test purposes
access-list INSIDE_in extended permit ip object-group INSIDE_addrs1 object-group ANY_addrs log
access-list INSIDE_in extended deny ip any any log
!
!
clock timezone CST -6
clock summer-time CDT recurring
!
logging enable
logging timestamp
logging buffer-size 1048576
logging buffered informational
logging trap informational
logging asdm informational
logging facility 22
logging host INSIDE loghost01
no logging message 302021
!
access-group OUTSIDE_in in interface OUTSIDE
access-group INSIDE_in in interface INSIDE
!""".splitlines()
a02 = """: Saved
: Written by mpenning at 05:37:43.184 CDT Sun Jun 29 2015
!
ASA Version 9.0(3)
!
command-alias exec slog show log | i Deny|denied
command-alias exec sacl sh access-list INSIDE_out | e hitcnt=0 |remark|elements
hostname fw
domain-name pennington.net
enable password 2KFQnbNIdI.2KYOU encrypted
xlate per-session deny tcp any4 any4
xlate per-session deny tcp any4 any6
xlate per-session deny tcp any6 any4
xlate per-session deny tcp any6 any6
xlate per-session deny udp any4 any4 eq domain
xlate per-session deny udp any4 any6 eq domain
xlate per-session deny udp any6 any4 eq domain
xlate per-session deny udp any6 any6 eq domain
passwd 2KFQnbNIdI.2KYOU encrypted
names
name 192.0.2.13 Machine01 description machine01
name 192.0.2.17 Machine02_Windows
name 10.0.0.6 Machine03
name 74.125.130.125 GTalk01 description Google talk server
name 74.125.134.125 GTalk02 description Google talk server
name 74.125.139.125 GTalk03 description Google Talk server
name 74.125.142.125 GTalk04 description Google Talk server
name 74.125.192.125 GTalk05 description Google Talk server
name 74.125.140.125 GTalk06 description Google Talk server
name 74.125.137.125 GTalk07
name 74.125.138.125 GTalk08
name 74.125.141.125 GTalk09
name 74.125.136.125 GTalk10
name 74.125.135.125 GTalk11
name 108.160.160.0 AS19679_Dropbox__108-160-160-0__20
name 199.47.216.0 AS19679_Dropbox__199.47.216.0__22
name 173.194.64.109 GmailSMTP01
name 173.194.64.108 GmailSMTP02
name 128.223.51.103 route-views.oregon-ix.net description Route-Views route server
ip local pool SSL_VPN_ADDRS 10.1.1.240-10.1.1.241 mask 255.255.255.0
!
interface Ethernet0/0
description Internet ISP
switchport access vlan 100
!
interface Ethernet0/1
switchport access vlan 200
!
interface Ethernet0/2
switchport access vlan 200
shutdown
!
interface Ethernet0/3
switchport access vlan 200
!
interface Ethernet0/4
switchport access vlan 200
!
interface Ethernet0/5
switchport access vlan 200
!
interface Ethernet0/6
switchport access vlan 200
!
interface Ethernet0/7
shutdown
!
interface Vlan1
no nameif
no security-level
no ip address
!
interface Vlan100
mac-address 0030.dead.beef
nameif OUTSIDE
security-level 0
ip address dhcp setroute
!
interface Vlan200
nameif INSIDE
security-level 100
ip address 192.0.2.1 255.255.255.0
!
banner motd
banner motd Test banner for $(hostname)
banner motd
banner motd *******************************
boot system disk0:/asa903-k8.bin
ftp mode passive
clock timezone CST -6
clock summer-time CDT recurring
dns domain-lookup INSIDE
dns server-group DefaultDNS
name-server Machine01
domain-name pennington.net
object network GTalk01
host 74.125.130.125
description Created during name migration
object network GTalk02
host 74.125.134.125
description Created during name migration
object network GTalk03
host 74.125.139.125
description Created during name migration
object network GTalk04
host 74.125.142.125
description Created during name migration
object network GTalk05
host 74.125.192.125
description Created during name migration
object network GTalk06
host 74.125.140.125
description Created during name migration
object network GTalk07
host 74.125.137.125
description Created during name migration
object network GTalk08
host 74.125.138.125
description Created during name migration
object network GTalk09
host 74.125.141.125
description Created during name migration
object network GTalk10
host 74.125.136.125
description Created during name migration
object network GTalk11
host 74.125.135.125
description Created during name migration
object network AS19679_Dropbox__108-160-160-0__20
subnet 108.160.160.0 255.255.240.0
description Created during name migration
object network AS19679_Dropbox__199.47.216.0__22
subnet 199.47.216.0 255.255.252.0
description Created during name migration
object network Machine01
host 192.0.2.5
description Created during name migration
object network obj_any
subnet 0.0.0.0 0.0.0.0
object network Machine02_Windows
host 192.0.2.17
description Created during name migration
object-group network GoogleTalk
network-object object GTalk01
network-object object GTalk02
network-object object GTalk03
network-object object GTalk04
network-object object GTalk05
network-object object GTalk06
network-object object GTalk07
network-object object GTalk08
network-object object GTalk09
network-object object GTalk10
network-object object GTalk11
object-group service GoogleTalkPorts
service-object tcp destination eq 5222
service-object tcp destination eq https
service-object udp destination range 19302 19309
object-group network Inside
network-object 192.0.2.0 255.255.255.0
network-object 192.0.22.0 255.255.255.0
network-object 192.0.23.0 255.255.255.0
object-group network DROPBOX_AS19679
network-object object AS19679_Dropbox__108-160-160-0__20
network-object object AS19679_Dropbox__199.47.216.0__22
object-group network GOOGLE_addrs
description dig -t TXT _netblocks.google.com 8.8.8.8
network-object 216.239.32.0 255.255.224.0
network-object 64.233.160.0 255.255.224.0
network-object 66.249.80.0 255.255.240.0
network-object 72.14.192.0 255.255.192.0
network-object 209.85.128.0 255.255.128.0
network-object 66.102.0.0 255.255.240.0
network-object 74.125.0.0 255.255.0.0
network-object 64.18.0.0 255.255.240.0
network-object 207.126.144.0 255.255.240.0
network-object 173.194.0.0 255.255.0.0
object-group network SSH_addrs
network-object 192.168.1.0 255.255.255.0
object-group network ANY_addrs
network-object 0.0.0.0 0.0.0.0
object-group network INSIDE_addrs
network-object 192.0.2.0 255.255.255.0
network-object 10.0.0.0 255.0.0.0
object-group service GOOGLE_svc
description Google's push service for Android
service-object tcp destination eq www
service-object tcp destination eq https
service-object tcp destination eq 5228
service-object tcp destination eq 5222
service-object tcp destination eq 587
object-group service TELNET_svc
service-object tcp destination eq telnet
object-group service WHOIS_svc
service-object tcp destination eq whois
object-group service SSH_svc
service-object tcp destination eq ssh
object-group service WEB_svc
description Standard web services - http, https, ftp
service-object tcp destination eq ftp
service-object tcp destination eq www
service-object tcp destination eq https
service-object icmp
object-group service DNS_svc
service-object udp destination eq domain
service-object tcp destination eq domain
object-group network MACHINE01_addrs
network-object object Machine01
object-group service ANDROID_svc
description Google's push service for Android
service-object tcp destination eq 5228
object-group service GMAILSMTP_svc
service-object tcp destination eq 2525
object-group service NTP_svc
service-object udp destination eq ntp
object-group service SKYPE_svc
service-object udp destination eq 5555
object-group service XBOX_svc
service-object tcp destination eq domain
service-object udp destination eq domain
service-object udp destination eq 88
service-object tcp destination eq 3074
service-object udp destination eq 3074
object-group network ANY
object-group service NaverLine_svc
service-object udp destination eq 11000
service-object udp destination range 9401 9405
object-group network NaverLine_addrs
network-object 174.35.127.0 255.255.255.0
object-group network Facebook_addrs
network-object 66.220.144.0 255.255.240.0
network-object 69.63.176.0 255.255.248.0
network-object 69.63.184.0 255.255.248.0
network-object 69.171.224.0 255.255.240.0
network-object 69.171.239.0 255.255.255.0
network-object 69.171.240.0 255.255.240.0
network-object 69.171.253.0 255.255.255.0
network-object 69.171.255.0 255.255.255.0
network-object 74.119.76.0 255.255.252.0
network-object 103.4.96.0 255.255.252.0
network-object 173.252.64.0 255.255.192.0
network-object 204.15.20.0 255.255.252.0
network-object 31.13.24.0 255.255.248.0
network-object 31.13.64.0 255.255.192.0
network-object 31.13.96.0 255.255.224.0
object-group service IP_SLA_PathTrace_svc
service-object udp destination range 33400 33499
object-group service FTP_svc
service-object tcp destination eq ftp
object-group service TeamViewerPorts
service-object tcp destination eq 5938
object-group service SSLVPN_svc
service-object udp destination eq 443
object-group service TEST_PORTS tcp
port-object eq domain
port-object eq smtp
access-list SPLIT_TUNNEL_NETS remark [[ destinations available via the VPN ]]
access-list SPLIT_TUNNEL_NETS standard permit 192.0.2.0 255.255.255.0
access-list NO_SSLVPN_NAT remark [[ prevent inadvertent nat of sslvpn traffic ]]
access-list NO_SSLVPN_NAT extended permit ip 192.0.2.0 255.255.255.0 192.0.2.0 255.255.255.0
access-list INSIDE_in extended deny object-group SKYPE_svc object-group INSIDE_addrs object-group ANY_addrs log disable
access-list INSIDE_in extended permit object-group GOOGLE_svc object-group INSIDE_addrs object-group GOOGLE_addrs log
access-list INSIDE_in extended permit object-group ANDROID_svc object-group INSIDE_addrs object-group GOOGLE_addrs log
access-list INSIDE_in extended permit object-group IP_SLA_PathTrace_svc any host 4.2.2.2 log
access-list INSIDE_in extended permit object-group DNS_svc object-group INSIDE_addrs object-group ANY_addrs log
access-list INSIDE_in extended permit object-group NTP_svc object-group INSIDE_addrs object-group ANY_addrs log
access-list INSIDE_in extended permit object-group TELNET_svc object-group INSIDE_addrs host 128.223.51.103 log
access-list INSIDE_in extended permit object-group FTP_svc object-group INSIDE_addrs object-group ANY_addrs log
access-list INSIDE_in extended permit object-group WEB_svc object-group INSIDE_addrs object-group ANY_addrs log
access-list INSIDE_in extended permit object-group SSH_svc object-group INSIDE_addrs object-group SSH_addrs log
access-list INSIDE_in extended permit object-group GMAILSMTP_svc object-group TSUNAMI_addrs object-group ANY_addrs log
access-list INSIDE_in extended permit object-group WHOIS_svc object-group TSUNAMI_addrs object-group ANY_addrs log
access-list INSIDE_in extended deny ip any4 any4 log
access-list ANY extended permit ip object-group Inside any4
access-list ANY extended permit ip any4 object-group Inside
access-list VOIP extended permit object-group GoogleTalkPorts object-group Inside object-group GoogleTalk
access-list VOIP extended permit object-group GoogleTalkPorts object-group GoogleTalk object-group Inside
access-list MAINTENANCE extended deny ip any4 any4 log
access-list OUTSIDE_in extended deny ip host 4.2.2.2 any4 log
access-list OUTSIDE_in extended permit icmp any4 0.0.0.0 0.0.0.0 unreachable log interval 1
access-list OUTSIDE_in extended permit icmp any4 0.0.0.0 0.0.0.0 time-exceeded log interval 1
access-list OUTSIDE_in extended deny ip any4 any4 log
pager lines 23
logging enable
logging timestamp
logging buffer-size 1048576
logging buffered informational
logging trap informational
logging asdm informational
logging facility 22
logging host INSIDE Machine01
logging class sys buffered informational
no logging message 302021
no logging message 302020
mtu OUTSIDE 1500
mtu INSIDE 1500
ip verify reverse-path interface INSIDE
icmp unreachable rate-limit 1 burst-size 1
asdm image disk0:/asdm-645.bin
no asdm history enable
arp timeout 14400
no arp permit-nonconnected
!
object network obj_any
nat (INSIDE,OUTSIDE) dynamic interface
access-group OUTSIDE_in in interface OUTSIDE
access-group INSIDE_in in interface INSIDE
route INSIDE 10.0.0.0 255.0.0.0 192.0.2.2 1
timeout xlate 3:00:00
timeout pat-xlate 0:00:30
timeout conn 1:00:00 half-closed 0:59:00 udp 0:02:00 icmp 0:00:02
timeout sunrpc 0:10:00 h323 0:05:00 h225 1:00:00 mgcp 0:05:00 mgcp-pat 0:05:00
timeout sip 0:30:00 sip_media 0:02:00 sip-invite 0:03:00 sip-disconnect 0:02:00
timeout sip-provisional-media 0:02:00 uauth 0:05:00 absolute
timeout tcp-proxy-reassembly 0:01:00
timeout floating-conn 0:00:00
dynamic-access-policy-record DfltAccessPolicy
user-identity default-domain LOCAL
aaa authentication ssh console LOCAL
aaa authentication enable console LOCAL
aaa authentication http console LOCAL
aaa authorization command LOCAL
aaa local authentication attempts max-fail 16
filter java 1-65535 192.0.2.0 255.255.255.0 0.0.0.0 0.0.0.0
http server enable
http 192.0.2.0 255.255.255.0 INSIDE
snmp-server host INSIDE Machine01 poll community public
snmp-server location ServerRoom
snmp-server contact mike@pennington.net
snmp-server community public
snmp-server enable traps snmp authentication linkup linkdown coldstart
crypto ipsec security-association pmtu-aging infinite
crypto ca trustpoint LOCAL_CERT_fw
enrollment self
fqdn fw.pennington.net
subject-name CN=fw.pennington.net
crl configure
crypto ca trustpool policy
telnet timeout 5
ssh scopy enable
ssh 192.0.2.0 255.255.255.0 INSIDE
ssh 10.0.0.0 255.0.0.0 INSIDE
ssh timeout 60
ssh version 2
console timeout 5
no vpn-addr-assign aaa
no vpn-addr-assign dhcp
dhcpd dns 68.94.156.1 Machine01
dhcpd lease 604800
dhcpd domain pennington.net
dhcpd auto_config OUTSIDE
!
threat-detection basic-threat
threat-detection scanning-threat shun duration 30
threat-detection statistics host
threat-detection statistics port
threat-detection statistics protocol
threat-detection statistics access-list
no threat-detection statistics tcp-intercept
ntp server 17.151.16.20
ntp server 17.151.16.21
ntp server 17.151.16.22
ntp server 17.151.16.23
group-policy SSL_VPN_Policy01 internal
group-policy SSL_VPN_Policy01 attributes
dns-server value 192.0.2.13
vpn-idle-timeout none
vpn-filter none
vpn-tunnel-protocol ssl-client ssl-clientless
split-tunnel-policy tunnelspecified
split-tunnel-network-list value SPLIT_TUNNEL_NETS
default-domain value pennington.net
webvpn
anyconnect keep-installer installed
anyconnect ssl rekey time 30
anyconnect ssl rekey method ssl
anyconnect ask none default anyconnect
username mpenning password dXRTaA5wrZ3OL8gz encrypted privilege 15
tunnel-group DefaultWEBVPNGroup general-attributes
address-pool SSL_VPN_ADDRS
default-group-policy SSL_VPN_Policy01
!
!
policy-map type inspect dns preset_dns_map
parameters
message-length maximum client auto
message-length maximum 512
policy-map global_policy
class inspection_default
inspect dns preset_dns_map
inspect h323 h225
inspect h323 ras
inspect rsh
inspect rtsp
inspect esmtp
inspect sqlnet
inspect skinny
inspect sunrpc
inspect xdmcp
inspect sip
inspect netbios
inspect tftp
inspect ip-options
inspect icmp
inspect http
!
service-policy global_policy global
prompt hostname context
no call-home reporting anonymous
call-home
profile CiscoTAC-1
no active
destination address http https://tools.cisco.com/its/service/oddce/services/DDCEService
destination address email callhome@cisco.com
destination transport-method http
subscribe-to-alert-group diagnostic
subscribe-to-alert-group environment
subscribe-to-alert-group inventory periodic monthly
subscribe-to-alert-group configuration periodic monthly
subscribe-to-alert-group telemetry periodic daily
Cryptochecksum:571d01b7b08342e35db838e9acec00f6
: end""".splitlines()
n01 = """
feature tacacs+
feature interface-vlan
feature vpc
feature fex
feature lacp
feature lldp
feature ospf
no feature telnet
ip domain-lookup
ip domain-name pennington.net
ip name-server 10.0.0.10
vrf context management
ip route 0.0.0.0/0 10.0.0.1
vrf context vpc-peerkeepalive
tacacs-server key 0 DontTreadOnMe
ip tacacs source-interface Vlan10
tacacs-server host 10.0.0.32
tacacs-server host 10.0.0.33
aaa group server tacacs+ TACACS_GROUP
server 10.0.0.32
server 10.0.0.33
use-vrf management
source-interface mgmt0
aaa authentication login default group TACACS_GROUP
aaa authentication login console group TACACS_GROUP
aaa authorization commands default group TACACS_GROUP
aaa accounting default group TACACS_GROUP
aaa authentication login error-enable
logging event link-status default
vpc domain 999
role priority 100
system-priority 1
auto-recovery
peer-keepalive destination 1.1.1.2
fex 115
desc FEX115
pinning max-links 1
interface loopback0
ip address 10.1.1.1/32
interface mgmt0
ip address 10.0.0.5/24
interface port-channel1
vpc peer-link
switchport mode trunk
spanning-tree port type network
description [vPC PEER LINK]
interface port-channel21
description Uplink to core
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,155
mtu 9216
vpc 21
interface port-channel115
switchport mode fex-fabric
fex associate 115
interface Ethernet1/1
switchport mode trunk
spanning-tree port type network
channel-group 1 mode active
interface Ethernet1/2
switchport mode trunk
spanning-tree port type network
channel-group 1 mode active
interface Ethernet1/3
ip address 192.0.2.0/31
interface Ethernet1/4
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,15
channel-group 21 mode active
mtu 9216
interface Ethernet1/5
switchport mode trunk
switchport trunk native vlan 999
switchport trunk allowed vlan 13,31-38,15
channel-group 21 mode active
mtu 9216
interface Ethernet1/6
switchport mode fex-fabric
fex associate 115
channel-group 115
interface Ethernet1/7
switchport mode access
switchport access vlan 100
mtu 9216
interface Ethernet1/8
switchport mode access
switchport access vlan 102
mtu 9216
interface Ethernet1/9
ip address 10.1.2.6/30
mtu 9216
interface Ethernet1/10
encapsulation dot1Q 200
bandwidth 100000000
delay 200
beacon
ip address 10.1.2.2/30
mpls ip
mtu 9216
""".splitlines()
@pytest.fixture(scope="session")
def c01_default_gigethernets(request):
yield config_c01_default_gige
@pytest.fixture(scope="session")
def c01_insert_serial_replace(request):
yield config_c01_insert_serial_replace
@pytest.fixture(scope="function")
def parse_c01(request):
"""Preparsed c01""" | parse_c01 = CiscoConfParse(c01, factory=False) | 0 | 2023-12-01 18:43:27+00:00 | 24k |
zerolink-io/zerolink-python | zerolink/req.py | [
{
"identifier": "settings",
"path": "zerolink/settings.py",
"snippet": " CONFIG_FILE = os.path.join(os.environ[\"APPDATA\"], \"zerolink\", \"config\")\n CONFIG_FILE = os.path.join(os.environ[\"HOME\"], \".config\", \"zerolink\", \"config\")\n CONFIG_FILE = os.path.join(\n os.environ[\"HO... | from typing import Any, Optional, cast
from zerolink import settings
from zerolink.exc import APIError, AuthenticationError
from zerolink_client import Client
from zerolink_client.api.default import finetune, get_models_models_get
from zerolink_client.api.entity import (
desc_entity_id,
desc_entity_ontology,
lookup_entity,
lookup_relation,
search_entity,
)
from zerolink_client.api.extract import extract_text
from zerolink_client.api.fact import (
create_userattribute,
create_userentity,
create_userrule,
create_usertriple,
)
from zerolink_client.api.kg import get_triple
from zerolink_client.api.question import post_question
from zerolink_client.api.session import (
create_session,
get_session_entities,
get_session_facts,
get_user_session,
)
from zerolink_client.api.user import create_user
from zerolink_client.models import (
ChatSession,
CreateAttribute,
CreateEntity,
CreateRule,
CreateRuleResponse,
CreateTriple,
CreateTuneJobResponse,
Entity,
HTTPValidationError,
Question,
QuestionResponse,
TextExtract,
)
from zerolink_client.types import File, UNSET | 16,468 |
# ------------------------------------------------------------------------
# Endpoints
# ------------------------------------------------------------------------
client = Client(
base_url=settings.server_url,
raise_on_unexpected_status=False,
)
def check_api_key() -> None:
"""
Check if the API key is set.
"""
if settings.api_key is None:
raise AuthenticationError()
else:
pass
def get_user_id() -> str:
"""
Get the user ID from the server. Only used for Demo server.
"""
client._headers["Authorization"] = settings.api_key
rep = create_user.sync(client=client)
if rep is None:
raise Exception("Failed to authenticate.")
settings.api_key = rep.user_id
if isinstance(rep, HTTPValidationError):
raise APIError(str(rep))
return rep.user_id
def post_session(user_id: str, **kwargs) -> Optional[ChatSession]:
"""
Create a new session.
"""
check_api_key()
if user_id is None:
user_id = settings.api_key
rep = create_session.sync(client=client, user_id=user_id, **kwargs)
if isinstance(rep, HTTPValidationError):
raise APIError(str(rep))
return rep
def get_session_name(user_id: str, session_name: str, **kwargs):
"""
Lookup a session by user and name.
"""
check_api_key()
rep = get_user_session.sync_detailed(user_id, session_name, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
elif rep.status_code == 404:
return None
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def get_session_entities_list(session_id: int, **kwargs):
"""
Get the entities of a session.
"""
check_api_key()
rep = get_session_entities.sync_detailed(session_id, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def get_session_facts_list(session_id: int, **kwargs):
"""
Get the facts of a session.
"""
check_api_key()
rep = get_session_facts.sync_detailed(session_id, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def ask_question(
session_id: Optional[int],
body: str,
assumps: Optional[dict[str, Any]] = None,
**kwargs,
) -> QuestionResponse:
"""
Ask a question.
"""
check_api_key()
rep = post_question.sync_detailed(
client=client,
session_id=(session_id or UNSET),
body=Question(body=body, **(assumps or {})),
**kwargs,
)
if rep.status_code == 200:
return cast(QuestionResponse, rep.parsed)
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
|
# ------------------------------------------------------------------------
# Endpoints
# ------------------------------------------------------------------------
client = Client(
base_url=settings.server_url,
raise_on_unexpected_status=False,
)
def check_api_key() -> None:
"""
Check if the API key is set.
"""
if settings.api_key is None:
raise AuthenticationError()
else:
pass
def get_user_id() -> str:
"""
Get the user ID from the server. Only used for Demo server.
"""
client._headers["Authorization"] = settings.api_key
rep = create_user.sync(client=client)
if rep is None:
raise Exception("Failed to authenticate.")
settings.api_key = rep.user_id
if isinstance(rep, HTTPValidationError):
raise APIError(str(rep))
return rep.user_id
def post_session(user_id: str, **kwargs) -> Optional[ChatSession]:
"""
Create a new session.
"""
check_api_key()
if user_id is None:
user_id = settings.api_key
rep = create_session.sync(client=client, user_id=user_id, **kwargs)
if isinstance(rep, HTTPValidationError):
raise APIError(str(rep))
return rep
def get_session_name(user_id: str, session_name: str, **kwargs):
"""
Lookup a session by user and name.
"""
check_api_key()
rep = get_user_session.sync_detailed(user_id, session_name, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
elif rep.status_code == 404:
return None
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def get_session_entities_list(session_id: int, **kwargs):
"""
Get the entities of a session.
"""
check_api_key()
rep = get_session_entities.sync_detailed(session_id, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def get_session_facts_list(session_id: int, **kwargs):
"""
Get the facts of a session.
"""
check_api_key()
rep = get_session_facts.sync_detailed(session_id, client=client, **kwargs)
if rep.status_code == 200:
return rep.parsed
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
def ask_question(
session_id: Optional[int],
body: str,
assumps: Optional[dict[str, Any]] = None,
**kwargs,
) -> QuestionResponse:
"""
Ask a question.
"""
check_api_key()
rep = post_question.sync_detailed(
client=client,
session_id=(session_id or UNSET),
body=Question(body=body, **(assumps or {})),
**kwargs,
)
if rep.status_code == 200:
return cast(QuestionResponse, rep.parsed)
else:
err = rep.content.decode("utf-8")
print(err)
raise APIError(err)
| def get_entity_id(id: str, **kwargs) -> Entity: | 30 | 2023-12-03 07:50:04+00:00 | 24k |
JunMa11/UHNSeg-Quiz | nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py | [
{
"identifier": "DC_and_BCE_loss",
"path": "nnunetv2/training/loss/compound_losses.py",
"snippet": "class DC_and_BCE_loss(nn.Module):\n def __init__(self, bce_kwargs, soft_dice_kwargs, weight_ce=1, weight_dice=1, use_ignore_label: bool = False,\n dice_class=MemoryEfficientSoftDiceLoss... | import torch
from torch import autocast
from nnunetv2.training.loss.compound_losses import DC_and_BCE_loss, DC_and_CE_loss
from nnunetv2.training.loss.dice import get_tp_fp_fn_tn, MemoryEfficientSoftDiceLoss
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from nnunetv2.utilities.helpers import dummy_context
from nnunetv2.utilities.label_handling.label_handling import determine_num_input_channels
from torch.nn.parallel import DistributedDataParallel as DDP | 17,114 |
class nnUNetTrainerNoDeepSupervision(nnUNetTrainer):
def _build_loss(self):
if self.label_manager.has_regions:
|
class nnUNetTrainerNoDeepSupervision(nnUNetTrainer):
def _build_loss(self):
if self.label_manager.has_regions: | loss = DC_and_BCE_loss({}, | 0 | 2023-12-04 19:43:14+00:00 | 24k |
opisaac9001/TTS-With-ooba-and-voice | TTS/tts/models/neuralhmm_tts.py | [
{
"identifier": "Encoder",
"path": "TTS/tts/layers/overflow/common_layers.py",
"snippet": "class Encoder(nn.Module):\n r\"\"\"Neural HMM Encoder\n\n Same as Tacotron 2 encoder but increases the input length by states per phone\n\n Args:\n num_chars (int): Number of characters in the inpu... | import os
import torch
from typing import Dict, List, Union
from coqpit import Coqpit
from torch import nn
from trainer.logging.tensorboard_logger import TensorboardLogger
from TTS.tts.layers.overflow.common_layers import Encoder, OverflowUtils
from TTS.tts.layers.overflow.neural_hmm import NeuralHMM
from TTS.tts.layers.overflow.plotting_utils import (
get_spec_from_most_probable_state,
plot_transition_probabilities_to_numpy,
)
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
from TTS.utils.generic_utils import format_aux_input
from TTS.utils.io import load_fsspec
from TTS.utils.audio import AudioProcessor | 18,911 |
@torch.no_grad()
def inference(
self,
text: torch.Tensor,
aux_input={"x_lengths": None, "sampling_temp": None, "max_sampling_time": None, "duration_threshold": None},
): # pylint: disable=dangerous-default-value
"""Sampling from the model
Args:
text (torch.Tensor): :math:`[B, T_in]`
aux_inputs (_type_, optional): _description_. Defaults to None.
Returns:
outputs: Dictionary containing the following
- mel (torch.Tensor): :math:`[B, T_out, C]`
- hmm_outputs_len (torch.Tensor): :math:`[B]`
- state_travelled (List[List[int]]): List of lists containing the state travelled for each sample in the batch.
- input_parameters (list[torch.FloatTensor]): Input parameters to the neural HMM.
- output_parameters (list[torch.FloatTensor]): Output parameters to the neural HMM.
"""
default_input_dict = {
"x_lengths": torch.sum(text != 0, dim=1),
}
aux_input = self._format_aux_input(aux_input, default_input_dict)
encoder_outputs, encoder_output_len = self.encoder.inference(text, aux_input["x_lengths"])
outputs = self.neural_hmm.inference(
encoder_outputs,
encoder_output_len,
sampling_temp=aux_input["sampling_temp"],
max_sampling_time=aux_input["max_sampling_time"],
duration_threshold=aux_input["duration_threshold"],
)
mels, mel_outputs_len = outputs["hmm_outputs"], outputs["hmm_outputs_len"]
mels = self.inverse_normalize(mels)
outputs.update({"model_outputs": mels, "model_outputs_len": mel_outputs_len})
outputs["alignments"] = OverflowUtils.double_pad(outputs["alignments"])
return outputs
@staticmethod
def get_criterion():
return NLLLoss()
@staticmethod
def init_from_config(config: "NeuralhmmTTSConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
"""Initiate model from config
Args:
config (VitsConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
verbose (bool): If True, print init messages. Defaults to True.
"""
ap = AudioProcessor.init_from_config(config, verbose)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config, samples)
return NeuralhmmTTS(new_config, ap, tokenizer, speaker_manager)
def load_checkpoint(
self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False
): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training
def on_init_start(self, trainer):
"""If the current dataset does not have normalisation statistics and initialisation transition_probability it computes them otherwise loads."""
if not os.path.isfile(trainer.config.mel_statistics_parameter_path) or trainer.config.force_generate_statistics:
dataloader = trainer.get_train_dataloader(
training_assets=None, samples=trainer.train_samples, verbose=False
)
print(
f" | > Data parameters not found for: {trainer.config.mel_statistics_parameter_path}. Computing mel normalization parameters..."
)
data_mean, data_std, init_transition_prob = OverflowUtils.get_data_parameters_for_flat_start(
dataloader, trainer.config.out_channels, trainer.config.state_per_phone
)
print(
f" | > Saving data parameters to: {trainer.config.mel_statistics_parameter_path}: value: {data_mean, data_std, init_transition_prob}"
)
statistics = {
"mean": data_mean.item(),
"std": data_std.item(),
"init_transition_prob": init_transition_prob.item(),
}
torch.save(statistics, trainer.config.mel_statistics_parameter_path)
else:
print(
f" | > Data parameters found for: {trainer.config.mel_statistics_parameter_path}. Loading mel normalization parameters..."
)
statistics = torch.load(trainer.config.mel_statistics_parameter_path)
data_mean, data_std, init_transition_prob = (
statistics["mean"],
statistics["std"],
statistics["init_transition_prob"],
)
print(f" | > Data parameters loaded with value: {data_mean, data_std, init_transition_prob}")
trainer.config.flat_start_params["transition_p"] = (
init_transition_prob.item() if torch.is_tensor(init_transition_prob) else init_transition_prob
)
OverflowUtils.update_flat_start_transition(trainer.model, init_transition_prob)
trainer.model.update_mean_std(statistics)
@torch.inference_mode()
def _create_logs(self, batch, outputs, ap): # pylint: disable=no-self-use, unused-argument
alignments, transition_vectors = outputs["alignments"], outputs["transition_vectors"]
means = torch.stack(outputs["means"], dim=1)
figures = {
"alignment": plot_alignment(alignments[0].exp(), title="Forward alignment", fig_size=(20, 20)),
"log_alignment": plot_alignment(
alignments[0].exp(), title="Forward log alignment", plot_log=True, fig_size=(20, 20)
),
"transition_vectors": plot_alignment(transition_vectors[0], title="Transition vectors", fig_size=(20, 20)),
|
class NeuralhmmTTS(BaseTTS):
"""Neural HMM TTS model.
Paper::
https://arxiv.org/abs/2108.13320
Paper abstract::
Neural sequence-to-sequence TTS has achieved significantly better output quality
than statistical speech synthesis using HMMs.However, neural TTS is generally not probabilistic
and uses non-monotonic attention. Attention failures increase training time and can make
synthesis babble incoherently. This paper describes how the old and new paradigms can be
combined to obtain the advantages of both worlds, by replacing attention in neural TTS with
an autoregressive left-right no-skip hidden Markov model defined by a neural network.
Based on this proposal, we modify Tacotron 2 to obtain an HMM-based neural TTS model with
monotonic alignment, trained to maximise the full sequence likelihood without approximation.
We also describe how to combine ideas from classical and contemporary TTS for best results.
The resulting example system is smaller and simpler than Tacotron 2, and learns to speak with
fewer iterations and less data, whilst achieving comparable naturalness prior to the post-net.
Our approach also allows easy control over speaking rate. Audio examples and code
are available at https://shivammehta25.github.io/Neural-HMM/ .
Note:
- This is a parameter efficient version of OverFlow (15.3M vs 28.6M). Since it has half the
number of parameters as OverFlow the synthesis output quality is suboptimal (but comparable to Tacotron2
without Postnet), but it learns to speak with even lesser amount of data and is still significantly faster
than other attention-based methods.
- Neural HMMs uses flat start initialization i.e it computes the means and std and transition probabilities
of the dataset and uses them to initialize the model. This benefits the model and helps with faster learning
If you change the dataset or want to regenerate the parameters change the `force_generate_statistics` and
`mel_statistics_parameter_path` accordingly.
- To enable multi-GPU training, set the `use_grad_checkpointing=False` in config.
This will significantly increase the memory usage. This is because to compute
the actual data likelihood (not an approximation using MAS/Viterbi) we must use
all the states at the previous time step during the forward pass to decide the
probability distribution at the current step i.e the difference between the forward
algorithm and viterbi approximation.
Check :class:`TTS.tts.configs.neuralhmm_tts_config.NeuralhmmTTSConfig` for class arguments.
"""
def __init__(
self,
config: "NeuralhmmTTSConfig",
ap: "AudioProcessor" = None,
tokenizer: "TTSTokenizer" = None,
speaker_manager: SpeakerManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager)
# pass all config fields to `self`
# for fewer code change
self.config = config
for key in config:
setattr(self, key, config[key])
self.encoder = Encoder(config.num_chars, config.state_per_phone, config.encoder_in_out_features)
self.neural_hmm = NeuralHMM(
frame_channels=self.out_channels,
ar_order=self.ar_order,
deterministic_transition=self.deterministic_transition,
encoder_dim=self.encoder_in_out_features,
prenet_type=self.prenet_type,
prenet_dim=self.prenet_dim,
prenet_n_layers=self.prenet_n_layers,
prenet_dropout=self.prenet_dropout,
prenet_dropout_at_inference=self.prenet_dropout_at_inference,
memory_rnn_dim=self.memory_rnn_dim,
outputnet_size=self.outputnet_size,
flat_start_params=self.flat_start_params,
std_floor=self.std_floor,
use_grad_checkpointing=self.use_grad_checkpointing,
)
self.register_buffer("mean", torch.tensor(0))
self.register_buffer("std", torch.tensor(1))
def update_mean_std(self, statistics_dict: Dict):
self.mean.data = torch.tensor(statistics_dict["mean"])
self.std.data = torch.tensor(statistics_dict["std"])
def preprocess_batch(self, text, text_len, mels, mel_len):
if self.mean.item() == 0 or self.std.item() == 1:
statistics_dict = torch.load(self.mel_statistics_parameter_path)
self.update_mean_std(statistics_dict)
mels = self.normalize(mels)
return text, text_len, mels, mel_len
def normalize(self, x):
return x.sub(self.mean).div(self.std)
def inverse_normalize(self, x):
return x.mul(self.std).add(self.mean)
def forward(self, text, text_len, mels, mel_len):
"""
Forward pass for training and computing the log likelihood of a given batch.
Shapes:
Shapes:
text: :math:`[B, T_in]`
text_len: :math:`[B]`
mels: :math:`[B, T_out, C]`
mel_len: :math:`[B]`
"""
text, text_len, mels, mel_len = self.preprocess_batch(text, text_len, mels, mel_len)
encoder_outputs, encoder_output_len = self.encoder(text, text_len)
log_probs, fwd_alignments, transition_vectors, means = self.neural_hmm(
encoder_outputs, encoder_output_len, mels.transpose(1, 2), mel_len
)
outputs = {
"log_probs": log_probs,
"alignments": fwd_alignments,
"transition_vectors": transition_vectors,
"means": means,
}
return outputs
@staticmethod
def _training_stats(batch):
stats = {}
stats["avg_text_length"] = batch["text_lengths"].float().mean()
stats["avg_spec_length"] = batch["mel_lengths"].float().mean()
stats["avg_text_batch_occupancy"] = (batch["text_lengths"].float() / batch["text_lengths"].float().max()).mean()
stats["avg_spec_batch_occupancy"] = (batch["mel_lengths"].float() / batch["mel_lengths"].float().max()).mean()
return stats
def train_step(self, batch: dict, criterion: nn.Module):
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
mel_lengths = batch["mel_lengths"]
outputs = self.forward(
text=text_input,
text_len=text_lengths,
mels=mel_input,
mel_len=mel_lengths,
)
loss_dict = criterion(outputs["log_probs"] / (mel_lengths.sum() + text_lengths.sum()))
# for printing useful statistics on terminal
loss_dict.update(self._training_stats(batch))
return outputs, loss_dict
def eval_step(self, batch: Dict, criterion: nn.Module):
return self.train_step(batch, criterion)
def _format_aux_input(self, aux_input: Dict, default_input_dict):
"""Set missing fields to their default value.
Args:
aux_inputs (Dict): Dictionary containing the auxiliary inputs.
"""
default_input_dict = default_input_dict.copy()
default_input_dict.update(
{
"sampling_temp": self.sampling_temp,
"max_sampling_time": self.max_sampling_time,
"duration_threshold": self.duration_threshold,
}
)
if aux_input:
return format_aux_input(default_input_dict, aux_input)
return default_input_dict
@torch.no_grad()
def inference(
self,
text: torch.Tensor,
aux_input={"x_lengths": None, "sampling_temp": None, "max_sampling_time": None, "duration_threshold": None},
): # pylint: disable=dangerous-default-value
"""Sampling from the model
Args:
text (torch.Tensor): :math:`[B, T_in]`
aux_inputs (_type_, optional): _description_. Defaults to None.
Returns:
outputs: Dictionary containing the following
- mel (torch.Tensor): :math:`[B, T_out, C]`
- hmm_outputs_len (torch.Tensor): :math:`[B]`
- state_travelled (List[List[int]]): List of lists containing the state travelled for each sample in the batch.
- input_parameters (list[torch.FloatTensor]): Input parameters to the neural HMM.
- output_parameters (list[torch.FloatTensor]): Output parameters to the neural HMM.
"""
default_input_dict = {
"x_lengths": torch.sum(text != 0, dim=1),
}
aux_input = self._format_aux_input(aux_input, default_input_dict)
encoder_outputs, encoder_output_len = self.encoder.inference(text, aux_input["x_lengths"])
outputs = self.neural_hmm.inference(
encoder_outputs,
encoder_output_len,
sampling_temp=aux_input["sampling_temp"],
max_sampling_time=aux_input["max_sampling_time"],
duration_threshold=aux_input["duration_threshold"],
)
mels, mel_outputs_len = outputs["hmm_outputs"], outputs["hmm_outputs_len"]
mels = self.inverse_normalize(mels)
outputs.update({"model_outputs": mels, "model_outputs_len": mel_outputs_len})
outputs["alignments"] = OverflowUtils.double_pad(outputs["alignments"])
return outputs
@staticmethod
def get_criterion():
return NLLLoss()
@staticmethod
def init_from_config(config: "NeuralhmmTTSConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
"""Initiate model from config
Args:
config (VitsConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
verbose (bool): If True, print init messages. Defaults to True.
"""
ap = AudioProcessor.init_from_config(config, verbose)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config, samples)
return NeuralhmmTTS(new_config, ap, tokenizer, speaker_manager)
def load_checkpoint(
self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False
): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training
def on_init_start(self, trainer):
"""If the current dataset does not have normalisation statistics and initialisation transition_probability it computes them otherwise loads."""
if not os.path.isfile(trainer.config.mel_statistics_parameter_path) or trainer.config.force_generate_statistics:
dataloader = trainer.get_train_dataloader(
training_assets=None, samples=trainer.train_samples, verbose=False
)
print(
f" | > Data parameters not found for: {trainer.config.mel_statistics_parameter_path}. Computing mel normalization parameters..."
)
data_mean, data_std, init_transition_prob = OverflowUtils.get_data_parameters_for_flat_start(
dataloader, trainer.config.out_channels, trainer.config.state_per_phone
)
print(
f" | > Saving data parameters to: {trainer.config.mel_statistics_parameter_path}: value: {data_mean, data_std, init_transition_prob}"
)
statistics = {
"mean": data_mean.item(),
"std": data_std.item(),
"init_transition_prob": init_transition_prob.item(),
}
torch.save(statistics, trainer.config.mel_statistics_parameter_path)
else:
print(
f" | > Data parameters found for: {trainer.config.mel_statistics_parameter_path}. Loading mel normalization parameters..."
)
statistics = torch.load(trainer.config.mel_statistics_parameter_path)
data_mean, data_std, init_transition_prob = (
statistics["mean"],
statistics["std"],
statistics["init_transition_prob"],
)
print(f" | > Data parameters loaded with value: {data_mean, data_std, init_transition_prob}")
trainer.config.flat_start_params["transition_p"] = (
init_transition_prob.item() if torch.is_tensor(init_transition_prob) else init_transition_prob
)
OverflowUtils.update_flat_start_transition(trainer.model, init_transition_prob)
trainer.model.update_mean_std(statistics)
@torch.inference_mode()
def _create_logs(self, batch, outputs, ap): # pylint: disable=no-self-use, unused-argument
alignments, transition_vectors = outputs["alignments"], outputs["transition_vectors"]
means = torch.stack(outputs["means"], dim=1)
figures = {
"alignment": plot_alignment(alignments[0].exp(), title="Forward alignment", fig_size=(20, 20)),
"log_alignment": plot_alignment(
alignments[0].exp(), title="Forward log alignment", plot_log=True, fig_size=(20, 20)
),
"transition_vectors": plot_alignment(transition_vectors[0], title="Transition vectors", fig_size=(20, 20)), | "mel_from_most_probable_state": plot_spectrogram( | 9 | 2023-11-29 08:15:06+00:00 | 24k |
magic-research/magic-animate | magicanimate/pipelines/pipeline_animation.py | [
{
"identifier": "UNet3DConditionModel",
"path": "magicanimate/models/unet_controlnet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,... | import inspect, math
import numpy as np
import torch
import torch.distributed as dist
from typing import Callable, List, Optional, Union
from dataclasses import dataclass
from PIL import Image
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange
from magicanimate.models.unet_controlnet import UNet3DConditionModel
from magicanimate.models.controlnet import ControlNetModel
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
from magicanimate.pipelines.context import (
get_context_scheduler,
get_total_steps
)
from magicanimate.utils.util import get_tensor_interpolation_method
from accelerate import cpu_offload | 19,468 | v1 = None
for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
v0 = latents[:,:,i0,:,:]
v1 = latents[:,:,i1,:,:]
new_latents[:,:,new_index,:,:] = v0
new_index += 1
for f in rate:
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
new_latents[:,:,new_index,:,:] = v.to(latents.device)
new_index += 1
new_latents[:,:,new_index,:,:] = v1
new_index += 1
return new_latents
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
_down_block_res_samples = []
_mid_block_res_sample = []
for i in np.concatenate(np.array(context)):
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
for res_t in _down_block_res_samples:
for i, res in enumerate(res_t):
down_block_res_samples[i].append(res)
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
mid_block_res_sample = torch.cat(_mid_block_res_sample)
# reshape controlnet output to match the unet3d inputs
b = b // 2 if do_classifier_free_guidance else b
_down_block_res_samples = []
for sample in down_block_res_samples:
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
sample = sample.repeat(2, 1, 1, 1, 1)
_down_block_res_samples.append(sample)
down_block_res_samples = _down_block_res_samples
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
return down_block_res_samples, mid_block_res_sample
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
controlnet = self.controlnet
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
| # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
controlnet: ControlNetModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
):
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,
controlnet=controlnet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def enable_vae_slicing(self):
self.vae.enable_slicing()
def disable_vae_slicing(self):
self.vae.disable_slicing()
def enable_sequential_cpu_offload(self, gpu_id=0):
if is_accelerate_available():
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device(f"cuda:{gpu_id}")
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
text_embeddings = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
text_embeddings = text_embeddings[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def decode_latents(self, latents, rank, decoder_consistency=None):
video_length = latents.shape[2]
latents = 1 / 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w")
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):
if decoder_consistency is not None:
video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))
else:
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
video = video.cpu().float().numpy()
return video
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):
shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
rand_device = "cpu" if device.type == "mps" else device
if isinstance(generator, list):
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
# prepare conditions for controlnet
condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0
condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)
condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()
if do_classifier_free_guidance:
condition = torch.cat([condition] * 2)
return condition
def next_step(
self,
model_output: torch.FloatTensor,
timestep: int,
x: torch.FloatTensor,
eta=0.,
verbose=False
):
"""
Inverse sampling for DDIM Inversion
"""
if verbose:
print("timestep: ", timestep)
next_step = timestep
timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
return x_next, pred_x0
@torch.no_grad()
def images2latents(self, images, dtype):
"""
Convert RGB image to VAE latents
"""
device = self._execution_device
images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
images = rearrange(images, "f h w c -> f c h w").to(device)
latents = []
for frame_idx in range(images.shape[0]):
latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)
latents = torch.cat(latents)
return latents
@torch.no_grad()
def invert(
self,
image: torch.Tensor,
prompt,
num_inference_steps=20,
num_actual_inference_steps=10,
eta=0.0,
return_intermediates=False,
**kwargs):
"""
Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440
invert a real image into noise map with determinisc DDIM inversion
"""
device = self._execution_device
batch_size = image.shape[0]
if isinstance(prompt, list):
if batch_size == 1:
image = image.expand(len(prompt), -1, -1, -1)
elif isinstance(prompt, str):
if batch_size > 1:
prompt = [prompt] * batch_size
# text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
return_tensors="pt"
)
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
print("input text embeddings :", text_embeddings.shape)
# define initial latents
latents = self.images2latents(image)
print("latents shape: ", latents.shape)
# interative sampling
self.scheduler.set_timesteps(num_inference_steps)
print("Valid timesteps: ", reversed(self.scheduler.timesteps))
latents_list = [latents]
pred_x0_list = [latents]
for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
continue
model_inputs = latents
# predict the noise
# NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)
model_inputs = rearrange(model_inputs, "f c h w -> 1 c f h w")
noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w")
# compute the previous noise sample x_t-1 -> x_t
latents, pred_x0 = self.next_step(noise_pred, t, latents)
latents_list.append(latents)
pred_x0_list.append(pred_x0)
if return_intermediates:
# return the intermediate laters during inversion
return latents, latents_list
return latents
def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):
if interpolation_factor < 2:
return latents
new_latents = torch.zeros(
(latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),
device=latents.device,
dtype=latents.dtype,
)
org_video_length = latents.shape[2]
rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]
new_index = 0
v0 = None
v1 = None
for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
v0 = latents[:,:,i0,:,:]
v1 = latents[:,:,i1,:,:]
new_latents[:,:,new_index,:,:] = v0
new_index += 1
for f in rate:
v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
new_latents[:,:,new_index,:,:] = v.to(latents.device)
new_index += 1
new_latents[:,:,new_index,:,:] = v1
new_index += 1
return new_latents
def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
_down_block_res_samples = []
_mid_block_res_sample = []
for i in np.concatenate(np.array(context)):
_down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
_mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
for res_t in _down_block_res_samples:
for i, res in enumerate(res_t):
down_block_res_samples[i].append(res)
down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
mid_block_res_sample = torch.cat(_mid_block_res_sample)
# reshape controlnet output to match the unet3d inputs
b = b // 2 if do_classifier_free_guidance else b
_down_block_res_samples = []
for sample in down_block_res_samples:
sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
sample = sample.repeat(2, 1, 1, 1, 1)
_down_block_res_samples.append(sample)
down_block_res_samples = _down_block_res_samples
mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
if do_classifier_free_guidance:
mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
return down_block_res_samples, mid_block_res_sample
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
video_length: Optional[int],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
controlnet_condition: list = None,
controlnet_conditioning_scale: float = 1.0,
context_frames: int = 16,
context_stride: int = 1,
context_overlap: int = 4,
context_batch_size: int = 1,
context_schedule: str = "uniform",
init_latents: Optional[torch.FloatTensor] = None,
num_actual_inference_steps: Optional[int] = None,
appearance_encoder = None,
reference_control_writer = None,
reference_control_reader = None,
source_image: str = None,
decoder_consistency = None,
**kwargs,
):
"""
New args:
- controlnet_condition : condition map (e.g., depth, canny, keypoints) for controlnet
- controlnet_conditioning_scale : conditioning scale for controlnet
- init_latents : initial latents to begin with (used along with invert())
- num_actual_inference_steps : number of actual inference steps (while total steps is num_inference_steps)
"""
controlnet = self.controlnet
# Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# Define call parameters
# batch_size = 1 if isinstance(prompt, str) else len(prompt)
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
)
text_embeddings = torch.cat([text_embeddings] * context_batch_size)
| reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size) | 2 | 2023-11-21 08:33:54+00:00 | 24k |
eth-sri/language-model-arithmetic | src/model_arithmetic/evaluation.py | [
{
"identifier": "BaseClass",
"path": "src/model_arithmetic/base.py",
"snippet": "class BaseClass:\n \"\"\"\n Base class for providing a serialization and deserialization mechanism.\n \"\"\"\n def __init__(self, **kwargs):\n \"\"\"\n Instantiates the base class with keyword argu... | from .base import BaseClass
from loguru import logger
from transformers import set_seed, Trainer, TrainingArguments, DataCollatorWithPadding
from .dataset import CustomDataset
from sklearn.model_selection import train_test_split
from .basic_model_loader import load_model, load_tokenizer
from .model_arithmetic import ModelArithmetic
from googleapiclient import discovery
from dotenv import load_dotenv
from torch.utils.data import DataLoader
from .utils import ENABLE_LOGGING, log
from lm_eval import evaluator
import pandas as pd
import numpy as np
import torch
import os
import json
import time | 16,098 | scores.append(value)
time.sleep(0.1)
result = np.mean(scores)
log(logger.info, f"Perspective is {result:.6f}")
self.output["perspective"] = {
"mean": result,
"std": np.std(scores),
}
return result
def generate_samples(self, max_tokens, batch_size=1, temperature=1.0, top_p=1.0, top_k=0, stop_texts=None, speculation=False, **kwargs):
"""
Generates samples from the model.
Args:
max_tokens (int): The maximum number of tokens to be used for evaluation.
batch_size (int, optional): The batch size to be used for evaluation.
temperature (float, optional): The temperature to be used for sampling.
top_p (float, optional): The top-p value to be used for sampling.
top_k (int, optional): The top-k value to be used for sampling.
stop_texts (list, optional): The list of texts at which sampling should be stopped
speculation (bool, optional): Whether to use speculation or not.
**kwargs: Additional keyword arguments.
"""
start_time = time.time()
if "generated" not in self.dataset.columns:
texts = self.generator.generate_text(self.dataset["input"].tolist(), max_length=max_tokens,
batch_size=batch_size, temperature=temperature,
top_p=top_p, top_k=top_k, stop_texts=stop_texts, do_speculation=speculation)
self.dataset["generated"] = texts
end_time = time.time()
self.output["time"] = {
"total_time": end_time - start_time,
"time_per_sample": (end_time - start_time) / len(self.dataset),
"dataset_size": len(self.dataset),
"max_tokens": max_tokens,
"batch_size": batch_size
}
def save_generated(self, output_location):
"""
Saves the generated samples to the specified location.
Args:
output_location (string): The location to save the generated samples.
"""
log(logger.debug, f"Saving generated samples to {output_location}")
self.dataset.to_csv(output_location)
def get_perplexity(self, dataset, model, tokenizer, **kwargs):
"""
Calculates the perplexity of the generated sentences.
Args:
dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns "input" (for input text), "generated" (for generated text).
model (PreTrainedModel): The model to be evaluated.
tokenizer (Tokenizer): The tokenizer to be used for tokenizing the sentences.
**kwargs: Additional keyword arguments.
"""
perplexities = []
sum_nllos = 0
n_tokens = 0
for index, sample in dataset.iterrows():
input_sentence = sample['input']
sentence = sample['generated']
if len(sentence) == 0:
continue
combined_sentence = input_sentence + sentence
encodings = tokenizer(combined_sentence, return_tensors='pt')
input_ids = encodings['input_ids'].to(model.device)
attention_mask = encodings['attention_mask'].to(model.device)
input_encodings = tokenizer(input_sentence, return_tensors='pt')
input_ids_inputs = input_encodings['input_ids']
input_length = input_ids_inputs.size(1)
with torch.no_grad():
output = model(input_ids, labels=input_ids, attention_mask=attention_mask)
logprobs = output.logits[0, :].log_softmax(dim=-1)
loss_func = torch.nn.NLLLoss(ignore_index=-100, reduction='sum')
loss = loss_func(logprobs[..., input_length:-1, :].contiguous(), input_ids[0, :][..., input_length+1:].contiguous())
loss = loss.to(torch.float32).detach().cpu().numpy()
n_tokens_here = input_ids.shape[-1] - input_length - 1
if n_tokens_here > 0:
perplexity = np.exp(loss / n_tokens_here)
sum_nllos += loss
n_tokens += n_tokens_here
if not np.isnan(perplexity):
perplexities.append(perplexity)
average = np.mean(perplexities)
median = np.median(perplexities)
real = np.exp(sum_nllos / n_tokens)
return {
"average": average,
"median": median,
"correct_perplexity": real
}
def perplexity(self, dataset, model_name_fluency="gpt2-xl", dtype=torch.float16, **kwargs):
"""
Calculates the perplexity of the generated sentences.
Args:
dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns "input" (for input text), "generated" (for generated text).
model_name_fluency (string, optional): The name of the model to be used for calculating fluency.
dtype (torch.dtype, optional): The data type to be used for the model.
**kwargs: Additional keyword arguments.
"""
log(logger.info, "Calculating fluency")
if "perplexity" in self.output:
log(logger.info, f"Reloading perplexity. Perplexity is {self.output['perplexity']}")
return self.output["perplexity"]
tokenizer = load_tokenizer(model_name_fluency)
|
load_dotenv()
class Evaluation(BaseClass):
"""
This class is used for evaluating a model's performance on a given dataset.
It includes methods for preparing the dataset, evaluating the model, generating samples,
calculating perplexity and faithfulness of the model.
"""
def __init__(self, generator=None, dataset_location=None, dataset=None, train_dataset=None, train_dataset_location=None,
n_input_words=5, bleurt_checkpoint="../models/BLEURT-20", **kwargs):
"""
Initialize the Evaluation class with the given parameters.
Args:
generator (ModelArithmetic, optional): The model to be evaluated.
dataset_location (string, optional): The location of the dataset to be used for evaluation. Either this or dataset should be provided. Dataset should contain column "text", "input", "output and "label" ("label", "input", "output" optional)
dataset (pd.DataFrame, optional): The dataset to be used for evaluation. Either this or dataset_location should be provided. Dataset should contain column "text", "input", "output and "label" ("label", "input", "output" optional)
train_dataset (pd.DataFrame, optional): The dataset to be used for training the model. Only used when calculating the faithfulness of the model and when the downstream model still needs to be finetuned.
train_dataset_location (string, optional): The location of the dataset to be used for training the model.
n_input_words (int, optional): The number of input words to be used in the generator. Only used if the dataset does not contain the column "input".
bleurt_checkpoint (string, optional): The location of the BLEURT model checkpoint.
**kwargs: Additional keyword arguments.
"""
self.has_input_task = True
self.dataset = None
if dataset is not None:
self.dataset = dataset.copy()
elif dataset_location is not None:
self.dataset = pd.read_csv(dataset_location, escapechar='\\', lineterminator="\n")
if train_dataset is not None:
self.train_dataset = train_dataset
elif train_dataset_location is not None:
self.train_dataset = pd.read_csv(train_dataset_location, escapechar='\\', lineterminator="\n")
else:
self.train_dataset = None
if self.dataset is not None:
self.prepare_dataset(n_input_words)
super().__init__(**kwargs, dataset_location=dataset_location, generator=generator,
has_input_task=self.has_input_task, output=dict(), extra_kwargs=None, bleurt_checkpoint=bleurt_checkpoint,
train_dataset_location=None)
if isinstance(generator, ModelArithmetic):
# If we don't do it this way, we can't store the evaluator because ModelArithmetic is not serializable
del self.kwargs["generator"]
self.kwargs["formula"] = generator.formula
self.formula = generator.formula
def prepare_dataset(self, n_input_words=5):
"""
Prepares the dataset for evaluation. If the dataset does not have an input column,
it assumes the input is the first n_input_words words of the output. If the dataset
does not have a label column, it assumes all labels are 1.
Args:
n_input_words (int): The number of input words to be used.
"""
log(logger.debug, "Preparing dataset")
if "input" not in self.dataset.columns:
log(logger.debug, f"No input column found, assuming input is the first {n_input_words} words of the output")
self.dataset["input"] = self.dataset["text"].apply(lambda x: " ".join(x.split()[:n_input_words]))
self.dataset["output"] = self.dataset["text"].apply(lambda x: " " + " ".join(x.split()[n_input_words:]))
self.has_input_task = False
if "label" not in self.dataset.columns:
log(logger.debug, "No label column found, assuming all labels are 1")
self.dataset["label"] = 1
def evaluate_lm_eval(self, model, task_name, batch_size, num_fewshot, model_args, no_cache=False, limit=None, write_out=False, output_folder=None, **kwargs):
"""
Evaluates the model using the lm_eval package.
Args:
model (PreTrainedModel): The model to be evaluated.
task_name (string): The name of the task for evaluation.
batch_size (int): The batch size to be used for evaluation.
num_fewshot (int): The number of fewshot examples to be used for evaluation.
model_args (dict): The arguments to be passed to the model.
no_cache (bool, optional): Whether to use cached results or not.
limit (int, optional): The maximum number of examples to be used for evaluation.
write_out (bool, optional): Whether to write out the results or not.
output_folder (string, optional): The folder to write out the results.
**kwargs: Additional keyword arguments.
"""
try:
except ImportError:
raise ImportError("Please install lm_eval to run this function")
results = evaluator.simple_evaluate(
model=model,
model_args=model_args,
tasks=[task_name],
num_fewshot=num_fewshot,
batch_size=batch_size,
device="cuda" if torch.cuda.is_available() else "cpu",
no_cache=no_cache,
limit=limit,
write_out=write_out,
output_base_path=output_folder
)
if "lm_eval" in self.output:
self.output["lm_eval"][task_name] = results
else:
self.output["lm_eval"] = {task_name: results}
def evaluate(self, max_tokens=128, store_file=None, reload=True,
dataset_file=None, reload_data=True, preserve_memory=False, batch_size=1, do_perspective=True,
speculation=False, only_faithfulness=False,
**kwargs):
"""
Evaluates the model on the dataset and calculates the perplexity and faithfulness
Args:
max_tokens (int, optional): The maximum number of tokens to be used for evaluation.
store_file (string, optional): The file to store the evaluation results.
reload (bool, optional): Whether to reload the dataset or not if it was stored before.
dataset_file (string, optional): The file containing the dataset. If path exists, dataset is loaded from path. If path does not exist, dataset is saved to path.
reload_data (bool, optional): Whether to reload the data or not if it was stored before.
preserve_memory (bool, optional): Whether to preserve memory or not.
batch_size (int, optional): The batch size to be used for evaluation.
do_perspective (bool, optional): Whether to calculate the perspective score or not.
speculation (bool, optional): Whether to use speculation or not.
**kwargs: Additional keyword arguments.
"""
if store_file is not None:
os.makedirs(os.path.dirname(store_file), exist_ok=True)
if dataset_file is not None:
os.makedirs(os.path.dirname(dataset_file), exist_ok=True)
if (reload_data or reload) and dataset_file is not None and os.path.isfile(dataset_file):
log(logger.debug, f"Reloading dataset from {dataset_file}")
self.dataset = pd.read_csv(dataset_file, escapechar='\\', lineterminator="\n")
self.dataset.fillna("", inplace=True)
else:
log(logger.debug,"Generating samples")
self.generate_samples(max_tokens, batch_size=batch_size, speculation=speculation, **kwargs)
if dataset_file is not None:
log(logger.info, f"Saving dataset to {dataset_file}")
self.dataset.to_csv(dataset_file, index=False, escapechar='\\', lineterminator="\n")
if reload and store_file is not None and os.path.isfile(store_file):
self.output = json.load(open(store_file))["output"]
log(logger.info, f"Reloading output. Output is {self.output}")
if not reload:
self.output = dict()
self.dataset.dropna(inplace=True)
if preserve_memory and hasattr(self.generator, "clear_memory"): # can have weird side effects!
self.generator.clear_memory()
if not only_faithfulness:
self.perplexity(self.dataset, **kwargs)
else:
del self.output['faithfulness']
self.faithfulness_multiple(self.dataset, **kwargs)
if do_perspective and not only_faithfulness:
self.perspective(**kwargs)
if dataset_file is not None:
log(logger.info, f"Saving dataset to {dataset_file}")
self.dataset.to_csv(dataset_file, index=False, escapechar='\\', lineterminator="\n")
self.extra_kwargs = {kwarg: str(kwargs[kwarg]) for kwarg in kwargs}
self.extra_kwargs["max_tokens"] = max_tokens
if store_file is not None:
log(logger.info, f"Saving output to {store_file}")
self.save(store_file)
return self.output
def perspective(self, classification_with_input=True, **kwargs):
"""
Calculates the perspective score of the generated sentences.
Args:
classification_with_input (bool, optional): Whether to use the input of the sentence for calculating the perspective score or not.
**kwargs: Additional keyword arguments.
"""
retries = 0
if "perspective" in self.output:
log(logger.info, f"Reloading perspective. Perspective is {self.output['perspective']}")
return self.output["perspective"]
log(logger.info, "Calculating perspective")
service = discovery.build('commentanalyzer', 'v1alpha1',
developerKey=os.environ["PERSPECTIVE_API_KEY"],
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False)
scores = []
self.dataset["perspective"] = [None] * len(self.dataset)
for index, sample in self.dataset.iterrows():
sentence = sample['generated']
if classification_with_input:
sentence = sample['input'] + sentence
analyze_request = {
'comment': { 'text': sentence },
'languages': ["en"],
'requestedAttributes': {'TOXICITY': {}}
}
while retries < 10 ** 5:
try:
response = service.comments().analyze(body=analyze_request).execute()
break
except Exception as e:
if "LANGUAGE_NOT_SUPPORTED_BY_ATTRIBUTE" in str(e):
continue
log(logger.warning, f"Exception {e} occurred, retrying...")
retries += 1
time.sleep(10)
if retries == 10 ** 5:
log(logger.error, "Could not get all perspective scores")
break
value = response["attributeScores"]["TOXICITY"]["summaryScore"]["value"]
self.dataset.at[index, "perspective"] = value
scores.append(value)
time.sleep(0.1)
result = np.mean(scores)
log(logger.info, f"Perspective is {result:.6f}")
self.output["perspective"] = {
"mean": result,
"std": np.std(scores),
}
return result
def generate_samples(self, max_tokens, batch_size=1, temperature=1.0, top_p=1.0, top_k=0, stop_texts=None, speculation=False, **kwargs):
"""
Generates samples from the model.
Args:
max_tokens (int): The maximum number of tokens to be used for evaluation.
batch_size (int, optional): The batch size to be used for evaluation.
temperature (float, optional): The temperature to be used for sampling.
top_p (float, optional): The top-p value to be used for sampling.
top_k (int, optional): The top-k value to be used for sampling.
stop_texts (list, optional): The list of texts at which sampling should be stopped
speculation (bool, optional): Whether to use speculation or not.
**kwargs: Additional keyword arguments.
"""
start_time = time.time()
if "generated" not in self.dataset.columns:
texts = self.generator.generate_text(self.dataset["input"].tolist(), max_length=max_tokens,
batch_size=batch_size, temperature=temperature,
top_p=top_p, top_k=top_k, stop_texts=stop_texts, do_speculation=speculation)
self.dataset["generated"] = texts
end_time = time.time()
self.output["time"] = {
"total_time": end_time - start_time,
"time_per_sample": (end_time - start_time) / len(self.dataset),
"dataset_size": len(self.dataset),
"max_tokens": max_tokens,
"batch_size": batch_size
}
def save_generated(self, output_location):
"""
Saves the generated samples to the specified location.
Args:
output_location (string): The location to save the generated samples.
"""
log(logger.debug, f"Saving generated samples to {output_location}")
self.dataset.to_csv(output_location)
def get_perplexity(self, dataset, model, tokenizer, **kwargs):
"""
Calculates the perplexity of the generated sentences.
Args:
dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns "input" (for input text), "generated" (for generated text).
model (PreTrainedModel): The model to be evaluated.
tokenizer (Tokenizer): The tokenizer to be used for tokenizing the sentences.
**kwargs: Additional keyword arguments.
"""
perplexities = []
sum_nllos = 0
n_tokens = 0
for index, sample in dataset.iterrows():
input_sentence = sample['input']
sentence = sample['generated']
if len(sentence) == 0:
continue
combined_sentence = input_sentence + sentence
encodings = tokenizer(combined_sentence, return_tensors='pt')
input_ids = encodings['input_ids'].to(model.device)
attention_mask = encodings['attention_mask'].to(model.device)
input_encodings = tokenizer(input_sentence, return_tensors='pt')
input_ids_inputs = input_encodings['input_ids']
input_length = input_ids_inputs.size(1)
with torch.no_grad():
output = model(input_ids, labels=input_ids, attention_mask=attention_mask)
logprobs = output.logits[0, :].log_softmax(dim=-1)
loss_func = torch.nn.NLLLoss(ignore_index=-100, reduction='sum')
loss = loss_func(logprobs[..., input_length:-1, :].contiguous(), input_ids[0, :][..., input_length+1:].contiguous())
loss = loss.to(torch.float32).detach().cpu().numpy()
n_tokens_here = input_ids.shape[-1] - input_length - 1
if n_tokens_here > 0:
perplexity = np.exp(loss / n_tokens_here)
sum_nllos += loss
n_tokens += n_tokens_here
if not np.isnan(perplexity):
perplexities.append(perplexity)
average = np.mean(perplexities)
median = np.median(perplexities)
real = np.exp(sum_nllos / n_tokens)
return {
"average": average,
"median": median,
"correct_perplexity": real
}
def perplexity(self, dataset, model_name_fluency="gpt2-xl", dtype=torch.float16, **kwargs):
"""
Calculates the perplexity of the generated sentences.
Args:
dataset (pd.DataFrame): The dataset to be used for evaluation. Has columns "input" (for input text), "generated" (for generated text).
model_name_fluency (string, optional): The name of the model to be used for calculating fluency.
dtype (torch.dtype, optional): The data type to be used for the model.
**kwargs: Additional keyword arguments.
"""
log(logger.info, "Calculating fluency")
if "perplexity" in self.output:
log(logger.info, f"Reloading perplexity. Perplexity is {self.output['perplexity']}")
return self.output["perplexity"]
tokenizer = load_tokenizer(model_name_fluency) | model = load_model(model_name_fluency, dtype=dtype) | 2 | 2023-11-21 20:01:08+00:00 | 24k |
HeliosZhao/Animate124 | dnerf/renderer.py | [
{
"identifier": "custom_meshgrid",
"path": "nerf/utils.py",
"snippet": "def custom_meshgrid(*args):\n # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid\n if pver.parse(torch.__version__) < pver.parse('1.10'):\n return torch.meshgrid(*args... | import os
import math
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import raymarching
import logging
from tqdm import tqdm
from nerf.utils import custom_meshgrid, safe_normalize
from nerf.renderer import NeRFRenderer | 15,610 | if normals is not None:
# _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)
# results['normal_image'] = normal_image.view(*prefix, 3)
normal_image = torch.stack(normal_image, dim=0) # F,N,3
results['normal_image'] = normal_image.view(batch_size, num_frames, -1, 3) # B,F,N,3
# weights normalization
results['weights'] = weights # N'*F
else:
image_all = []
weights_sum_all = []
depth_all = []
# ipdb.set_trace()
for frame_idx in range(num_frames):
_rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item()
nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)
# allocate outputs
dtype = torch.float32
weights_sum = torch.zeros(N, dtype=dtype, device=device)
depth = torch.zeros(N, dtype=dtype, device=device)
image = torch.zeros(N, 3, dtype=dtype, device=device)
n_alive = N
rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]
rays_t = nears.clone() # [N] ## test must use the same
step = 0
while step < self.opt.max_steps: # hard coded max step
# count alive rays
n_alive = rays_alive.shape[0]
# exit loop
if n_alive <= 0:
break
# decide compact_steps
n_step = max(min(N // n_alive, 8), 1)
xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, _rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)
dirs = safe_normalize(dirs)
s_time = torch.zeros((xyzs.size(0),1), device=xyzs.device, dtype=xyzs.dtype) + time[0, frame_idx].item()
# ipdb.set_trace()
sigmas, rgbs, normals, _ = self(xyzs, dirs, _light_d[:1], ratio=ambient_ratio, shading=shading, t=s_time)
sigmas = self.density_scale * sigmas
raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)
rays_alive = rays_alive[rays_alive >= 0]
#print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')
step += n_step
image_all.append(image)
weights_sum_all.append(weights_sum)
depth_all.append(depth)
# ipdb.set_trace()
weights_sum = torch.stack(weights_sum_all, dim=0) # F,N
depth = torch.stack(depth_all, dim=0) # F,N
image = torch.stack(image_all, dim=0) # F,N,3
# mix background color
## when bg_radius < 0 -> the way Magic123, during training, bg_color is always a random color, during inference, always 1
# ipdb.set_trace()
if bg_color is None:
if self.opt.bg_radius > 0 and self.bg_net is not None:
# use the bg model to calculate bg_color
## NOTE here the camera should be fixed in the video
## rays_d F,N,3
bg_color = self.background(rays_d.reshape(-1, 3)) # [FN, 3] # this is irrelavant to time
bg_color = bg_color.reshape(batch_size, num_frames, -1, 3) # F,N,3
else:
bg_color = 1
image_wo_bg = image.view(batch_size, num_frames, -1, 3)
image = image + (1 - weights_sum).unsqueeze(-1) * bg_color
image = image.view(batch_size, num_frames, -1, 3)
depth = depth.view(batch_size, num_frames, -1)
weights_sum = weights_sum.reshape(batch_size, num_frames, -1)
results['image'] = image # B,F,N,3
results['depth'] = depth # B,F,N
results['weights_sum'] = weights_sum # B,F,N
results['image_wo_bg'] = image_wo_bg # B,F,N,3
# ipdb.set_trace()
return results
@torch.no_grad()
def update_extra_state(self, decay=0.95, S=128):
# call before each epoch to update extra states.
if not (self.cuda_ray):
return
if self.taichi_ray:
raise NotImplementedError
### update density grid
tmp_grid = - torch.ones_like(self.density_grid)
# full update.
# if self.iter_density < 16: # update only 16 times
if True: # full update
X = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
for t, time in enumerate(self.times):
for xs in X:
for ys in Y:
for zs in Z:
# construct points
|
logger = logging.getLogger(__name__)
class DNeRFRenderer(NeRFRenderer):
def __init__(self, opt):
super().__init__(opt)
self.time_size = opt.get("time_size", 1)
self.density_scale = opt.get("density_scale", 1)
self.dynamic_ft = opt.get("dynamic_ft", False)
# extra state for cuda raymarching
if self.cuda_ray:
# density grid (with an extra time dimension)
density_grid = torch.zeros(self.time_size, self.cascade, self.grid_size ** 3) # [T, CAS, H * H * H]
density_bitfield = torch.zeros(self.time_size, self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [T, CAS * H * H * H // 8]
self.register_buffer('density_grid', density_grid)
self.register_buffer('density_bitfield', density_bitfield)
self.mean_density = 0
self.iter_density = 0
# time stamps for density grid
times = ((torch.arange(self.time_size, dtype=torch.float32) + 0.5) / self.time_size).view(-1, 1, 1) # [T, 1, 1]
self.register_buffer('times', times)
# step counter
step_counter = torch.zeros(16, 2, dtype=torch.int32) # 16 is hardcoded for averaging...
self.register_buffer('step_counter', step_counter)
self.mean_count = 0
self.local_step = 0
def run_cuda(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, binarize=False, time=None, **kwargs):
# rays_o, rays_d: [B, N, 3] / B,F,N,3
# return: image: [B, N, 3], depth: [B, N]
# time: [B,F]
prefix = rays_o.shape[:-1]
batch_size = prefix[0]
if prefix[0] != 1:
raise "The prefix should be 1 if different frames has different camera pose in the current version"
dynamic_cam = True if rays_o.ndim == 4 else False
N = rays_o.shape[:-1].numel() # B * N, in fact
device = rays_o.device
if light_d is None:
# gaussian noise around the ray origin, so the light always face the view dir (avoid dark face)
light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [B,N,3] / B,F,N,3
if time is None:
assert not self.dynamic_ft
time_steps = torch.LongTensor([[0]]).reshape(1,1) # 1,1
time = torch.FloatTensor([[0]]).reshape(1,1) # 1,1
num_frames = 1
else:
time_steps = torch.floor(time * self.time_size).clamp(min=0, max=self.time_size - 1).long() # B,F
num_frames = time.size(1)
if dynamic_cam:
rays_o = rays_o[0].contiguous() # F,N,3
rays_d = rays_d[0].contiguous() # F,N,3
light_d = light_d[0].contiguous() # F,N,3
else:
rays_o = rays_o.repeat(num_frames, 1, 1).contiguous()
rays_d = rays_d.repeat(num_frames, 1, 1).contiguous()
light_d = light_d.repeat(num_frames, 1, 1).contiguous()
# ipdb.set_trace()
results = {}
# ipdb.set_trace()
if self.training:
# ipdb.set_trace()
v_xyzs = []
v_dirs = []
v_light = []
v_time = []
v_idx = [0]
v_rays = []
v_ts = []
v_kernels = []
for frame_idx in range(num_frames):
_rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item()
## N,3 for the first 3, t is a value
# pre-calculate near far
nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)
xyzs, dirs, ts, rays = raymarching.march_rays_train(_rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb, self.opt.dt_gamma, self.opt.max_steps)
dirs = safe_normalize(dirs)
flatten_rays = raymarching.flatten_rays(rays, xyzs.shape[0]).long()
if _light_d.shape[0] > 1:
_light_d = _light_d[flatten_rays]
else: # 1,3
_light_d = _light_d.repeat(xyzs.size(0), 1)
v_xyzs.append(xyzs)
v_dirs.append(dirs)
v_light.append(_light_d)
sample_num = xyzs.size(0)
v_time.append(torch.zeros((sample_num,1), device=xyzs.device, dtype=xyzs.dtype)+time[0, frame_idx].item()) # NOTE this should be real time # N
v_idx.append(sample_num)
v_rays.append(rays)
v_ts.append(ts)
v_xyzs = torch.cat(v_xyzs, dim=0)
v_dirs = torch.cat(v_dirs, dim=0)
v_light = torch.cat(v_light, dim=0)
v_time = torch.cat(v_time, dim=0)
v_idx = np.cumsum(v_idx).tolist()
sigmas, rgbs, normals, deforms = self(v_xyzs, v_dirs, v_light, ratio=ambient_ratio, shading=shading, t=v_time)
sigmas = self.density_scale * sigmas
weights = []
weights_sum = []
depth = []
image = []
normal_image = []
# ipdb.set_trace()
for frame_idx in range(num_frames):
start_idx, end_idx = v_idx[frame_idx], v_idx[frame_idx+1]
# _weights, _weights_sum, _depth, _image
out = raymarching.composite_rays_train(sigmas[start_idx:end_idx], rgbs[start_idx:end_idx], v_ts[frame_idx], v_rays[frame_idx], T_thresh, binarize)
if normals is not None:
_, _, _, _normal_image = raymarching.composite_rays_train(sigmas[start_idx:end_idx].detach(), (normals[start_idx:end_idx] + 1) / 2, v_ts[frame_idx], v_rays[frame_idx], T_thresh, binarize)
normal_image.append(_normal_image)
weights.append(out[0])
weights_sum.append(out[1])
depth.append(out[2])
image.append(out[3]) # N,3
weights = torch.cat(weights)
weights_sum = torch.stack(weights_sum, dim=0) # F,N
depth = torch.stack(depth, dim=0) # F,N
image = torch.stack(image, dim=0) # F,N,3
# normals related regularizations
if self.opt.lambda_orient > 0 and normals is not None:
# orientation loss
loss_orient = weights.detach() * (normals * v_dirs).sum(-1).clamp(min=0) ** 2
results['loss_orient'] = loss_orient.mean()
if self.opt.lambda_3d_normal_smooth > 0 and normals is not None:
normals_perturb = self.normal(v_xyzs + torch.randn_like(v_xyzs) * 1e-2, t=v_time)
results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean()
if normals is not None:
# _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize)
# results['normal_image'] = normal_image.view(*prefix, 3)
normal_image = torch.stack(normal_image, dim=0) # F,N,3
results['normal_image'] = normal_image.view(batch_size, num_frames, -1, 3) # B,F,N,3
# weights normalization
results['weights'] = weights # N'*F
else:
image_all = []
weights_sum_all = []
depth_all = []
# ipdb.set_trace()
for frame_idx in range(num_frames):
_rays_o, _rays_d, _light_d, t = rays_o[frame_idx], rays_d[frame_idx], light_d[frame_idx], time_steps[0,frame_idx].item()
nears, fars = raymarching.near_far_from_aabb(_rays_o, _rays_d, self.aabb_train if self.training else self.aabb_infer)
# allocate outputs
dtype = torch.float32
weights_sum = torch.zeros(N, dtype=dtype, device=device)
depth = torch.zeros(N, dtype=dtype, device=device)
image = torch.zeros(N, 3, dtype=dtype, device=device)
n_alive = N
rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N]
rays_t = nears.clone() # [N] ## test must use the same
step = 0
while step < self.opt.max_steps: # hard coded max step
# count alive rays
n_alive = rays_alive.shape[0]
# exit loop
if n_alive <= 0:
break
# decide compact_steps
n_step = max(min(N // n_alive, 8), 1)
xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, _rays_o, _rays_d, self.bound, self.density_bitfield[t], self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps)
dirs = safe_normalize(dirs)
s_time = torch.zeros((xyzs.size(0),1), device=xyzs.device, dtype=xyzs.dtype) + time[0, frame_idx].item()
# ipdb.set_trace()
sigmas, rgbs, normals, _ = self(xyzs, dirs, _light_d[:1], ratio=ambient_ratio, shading=shading, t=s_time)
sigmas = self.density_scale * sigmas
raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize)
rays_alive = rays_alive[rays_alive >= 0]
#print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}')
step += n_step
image_all.append(image)
weights_sum_all.append(weights_sum)
depth_all.append(depth)
# ipdb.set_trace()
weights_sum = torch.stack(weights_sum_all, dim=0) # F,N
depth = torch.stack(depth_all, dim=0) # F,N
image = torch.stack(image_all, dim=0) # F,N,3
# mix background color
## when bg_radius < 0 -> the way Magic123, during training, bg_color is always a random color, during inference, always 1
# ipdb.set_trace()
if bg_color is None:
if self.opt.bg_radius > 0 and self.bg_net is not None:
# use the bg model to calculate bg_color
## NOTE here the camera should be fixed in the video
## rays_d F,N,3
bg_color = self.background(rays_d.reshape(-1, 3)) # [FN, 3] # this is irrelavant to time
bg_color = bg_color.reshape(batch_size, num_frames, -1, 3) # F,N,3
else:
bg_color = 1
image_wo_bg = image.view(batch_size, num_frames, -1, 3)
image = image + (1 - weights_sum).unsqueeze(-1) * bg_color
image = image.view(batch_size, num_frames, -1, 3)
depth = depth.view(batch_size, num_frames, -1)
weights_sum = weights_sum.reshape(batch_size, num_frames, -1)
results['image'] = image # B,F,N,3
results['depth'] = depth # B,F,N
results['weights_sum'] = weights_sum # B,F,N
results['image_wo_bg'] = image_wo_bg # B,F,N,3
# ipdb.set_trace()
return results
@torch.no_grad()
def update_extra_state(self, decay=0.95, S=128):
# call before each epoch to update extra states.
if not (self.cuda_ray):
return
if self.taichi_ray:
raise NotImplementedError
### update density grid
tmp_grid = - torch.ones_like(self.density_grid)
# full update.
# if self.iter_density < 16: # update only 16 times
if True: # full update
X = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.density_bitfield.device).split(S)
for t, time in enumerate(self.times):
for xs in X:
for ys in Y:
for zs in Z:
# construct points | xx, yy, zz = custom_meshgrid(xs, ys, zs) | 0 | 2023-11-23 10:34:08+00:00 | 24k |
alexzhou907/DreamPropeller | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 14,680 | (self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self,*args, **kwargs) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf( | scale_tensor( | 10 | 2023-11-27 23:39:49+00:00 | 24k |
abdulhaim/LMRL-Gym | llm_rl_scripts/chess/ilql/train_full_games_ilql.py | [
{
"identifier": "ilql_loss",
"path": "LLM_RL/algorithms/ilql/base_interface.py",
"snippet": "def ilql_loss(\n q1: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n q2: jax.Array, # [batch, time-1] output is masked; shift x[:-1]\n v: jax.Array, # [batch, time-1] output is masked; shi... | from typing import Optional
from JaxSeq.bucket_manager import open_with_bucket as open
from JaxSeq.utils import convert_path, load_mesh, setup_experiment_save
from JaxSeq.utils import BlockingStrategy, Padding, Truncation, get_weight_decay_mask
from JaxSeq.models.gpt2.load import load_train_state, ModelLoadMode
from LLM_RL.algorithms.ilql.base_interface import ilql_loss
from transformers.generation import GenerationConfig
from jaxtyping import PyTree
from LLM_RL.environment import Text, text_env_eval, TextTrajectory, TextTrajectoryChain, TokenTrajectoryChain
from LLM_RL.algorithms.ilql.gpt2.interface import GPT2ILQLTrain, GPT2ILQLInference
from LLM_RL.algorithms.value_rl_base.gpt2.interface import GPT2ValuePolicy, GPT2ValueRLInference
from LLM_RL.heads.mlp_head import load_train_state_from_config as load_head_train_state_from_config
from LLM_RL.heads.mlp_head import MLPHeadConfig
from JaxSeq.shard_model import shard_params_from_params
from LLM_RL.algorithms.ilql.data import ILQLDataset
from LLM_RL.algorithms.ilql.data import ILQLIterableDataset
from functools import partial
from JaxSeq.logs import pull_logs
from LLM_RL.algorithms.ilql.train import eval_loss, train_loop
from LLM_RL.algorithms.ilql.data import ILQLData, ILQLDataset
from JaxSeq.utils import multihost_device_get
from transformers import GPT2TokenizerFast
from IPython import embed
from llm_rl_scripts.chess.env.env import FenChessHistoryEnv
import tyro
import jax
import jax.numpy as jnp
import os
import optax
import pickle as pkl
import re
import json
import random | 21,116 |
def main(
model_load_mode: ModelLoadMode,
model_load_path: str,
train_data_path: str,
/, # Mark the end of positional arguments.
exp_name: Optional[str]=None,
outputs_path: Optional[str]=None,
data_mesh_shape: int=1,
fsdp_mesh_shape: int=1,
model_mesh_shape: int=-1,
use_wandb: bool=True,
wandb_project: Optional[str]="llm_rl_repo_endgames_ilql",
n_rounds: int=1,
epochs: int=1,
max_steps: Optional[int]=None,
lr: float=1e-4,
weight_decay: float=0.0,
tau: float=0.7,
cql_weight: float=1.0,
gamma: float=0.99,
train_bsize: int=32,
grad_accum_steps: int=1,
gradient_checkpointing: bool=False,
gradient_checkpointing_policy: str='nothing_saveable',
max_length: int=160,
log_every: int=256,
eval_every_steps: Optional[int]=None,
eval_every_epochs: Optional[int]=10,
eval_at_beginning: bool=False,
eval_at_end: bool=True,
save_every_steps: Optional[int]=None,
save_every_epochs: Optional[int]=5,
save_at_beginning: bool=False,
save_at_end: bool=True,
save_best: bool=False,
max_checkpoints: Optional[int]=5,
save_train_state: bool=True,
save_bf16: bool=True,
policy_max_input_length: int=256,
policy_max_output_length: int=256,
policy_do_sample: bool=True,
policy_num_beams: int=1,
policy_temperature: Optional[float]=None,
policy_top_p: Optional[float]=None,
policy_top_k: Optional[int]=None,
force_pad_embeddings: bool=False,
should_restore_loop_state: bool=False,
reranker: bool=True
):
input_args = locals()
print(input_args)
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
mesh = load_mesh((data_mesh_shape, fsdp_mesh_shape, model_mesh_shape), ('dp', 'fsdp', 'mp'))
is_main_process = jax.process_index() == 0
print(f"Mesh: {mesh}")
print(f"Is main process: {is_main_process}")
def ilql_data_generator(data_name):
with open(data_name, "r") as f:
for item in f:
obj = json.loads(item)
# curr_chain = TextTrajectory()
# starting with the last element
last_trajectory = TextTrajectory([Text(obj[-1]["state"], False), Text(obj[-1]["action"], True)],
[0, obj[-1]["reward"]], True)
curr_chain = TextTrajectoryChain(text_trajectory=last_trajectory, next=None)
# curr_chain.next = curr_chain
for traj in reversed(obj): # iterate through move history backwards except for last transition
# embed()
prev_trajectory = TextTrajectory([Text(traj["state"], False), Text(traj["action"], True)],
[0, traj["reward"]], False)
curr_chain = TextTrajectoryChain(text_trajectory=prev_trajectory, next=curr_chain)
|
def main(
model_load_mode: ModelLoadMode,
model_load_path: str,
train_data_path: str,
/, # Mark the end of positional arguments.
exp_name: Optional[str]=None,
outputs_path: Optional[str]=None,
data_mesh_shape: int=1,
fsdp_mesh_shape: int=1,
model_mesh_shape: int=-1,
use_wandb: bool=True,
wandb_project: Optional[str]="llm_rl_repo_endgames_ilql",
n_rounds: int=1,
epochs: int=1,
max_steps: Optional[int]=None,
lr: float=1e-4,
weight_decay: float=0.0,
tau: float=0.7,
cql_weight: float=1.0,
gamma: float=0.99,
train_bsize: int=32,
grad_accum_steps: int=1,
gradient_checkpointing: bool=False,
gradient_checkpointing_policy: str='nothing_saveable',
max_length: int=160,
log_every: int=256,
eval_every_steps: Optional[int]=None,
eval_every_epochs: Optional[int]=10,
eval_at_beginning: bool=False,
eval_at_end: bool=True,
save_every_steps: Optional[int]=None,
save_every_epochs: Optional[int]=5,
save_at_beginning: bool=False,
save_at_end: bool=True,
save_best: bool=False,
max_checkpoints: Optional[int]=5,
save_train_state: bool=True,
save_bf16: bool=True,
policy_max_input_length: int=256,
policy_max_output_length: int=256,
policy_do_sample: bool=True,
policy_num_beams: int=1,
policy_temperature: Optional[float]=None,
policy_top_p: Optional[float]=None,
policy_top_k: Optional[int]=None,
force_pad_embeddings: bool=False,
should_restore_loop_state: bool=False,
reranker: bool=True
):
input_args = locals()
print(input_args)
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
mesh = load_mesh((data_mesh_shape, fsdp_mesh_shape, model_mesh_shape), ('dp', 'fsdp', 'mp'))
is_main_process = jax.process_index() == 0
print(f"Mesh: {mesh}")
print(f"Is main process: {is_main_process}")
def ilql_data_generator(data_name):
with open(data_name, "r") as f:
for item in f:
obj = json.loads(item)
# curr_chain = TextTrajectory()
# starting with the last element
last_trajectory = TextTrajectory([Text(obj[-1]["state"], False), Text(obj[-1]["action"], True)],
[0, obj[-1]["reward"]], True)
curr_chain = TextTrajectoryChain(text_trajectory=last_trajectory, next=None)
# curr_chain.next = curr_chain
for traj in reversed(obj): # iterate through move history backwards except for last transition
# embed()
prev_trajectory = TextTrajectory([Text(traj["state"], False), Text(traj["action"], True)],
[0, traj["reward"]], False)
curr_chain = TextTrajectoryChain(text_trajectory=prev_trajectory, next=curr_chain) | token_trajectory_chain = TokenTrajectoryChain.from_text_trajectory_chain(curr_chain, tokenizer) | 5 | 2023-11-21 00:16:42+00:00 | 24k |
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