\\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. Ignored when not using guidance (i.e., ignored\\n if `guidance_scale` is 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`, *optional*):\\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\\n 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 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 sld_guidance_scale (`float`, *optional*, defaults to 1000):\\n The guidance scale of safe latent diffusion. If set to be less than 1, safety guidance will be disabled.\\n sld_warmup_steps (`int`, *optional*, defaults to 10):\\n Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater\\n than `sld_warmup_steps`.\\n sld_threshold (`float`, *optional*, defaults to 0.01):\\n Threshold that separates the hyperplane between appropriate and inappropriate images.\\n sld_momentum_scale (`float`, *optional*, defaults to 0.3):\\n Scale of the SLD momentum to be added to the safety guidance at each diffusion step.\\n If set to 0.0 momentum will be disabled. Momentum is already built up during warmup,\\n i.e. for diffusion steps smaller than `sld_warmup_steps`.\\n sld_mom_beta (`float`, *optional*, defaults to 0.4):\\n Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous\\n momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller than\\n `sld_warmup_steps`.\\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 if isinstance(prompt, str):\\n batch_size = 1\\n elif isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\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 enable_safety_guidance = True\\n if sld_guidance_scale < 1:\\n enable_safety_guidance = False\\n logger.warn('You have disabled safety guidance.')\\n\\n # get prompt text embeddings\\n text_inputs = self.tokenizer(\\n prompt,\\n padding=\\\"max_length\\\",\\n max_length=self.tokenizer.model_max_length,\\n return_tensors=\\\"pt\\\",\\n )\\n text_input_ids = text_inputs.input_ids\\n\\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\\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 text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[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_images_per_prompt, 1)\\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\\n\\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 # 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 uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[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_images_per_prompt, 1)\\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\\n\\n # Encode the safety concept text\\n if enable_safety_guidance:\\n safety_concept_input = self.tokenizer(\\n [self._safety_text_concept],\\n padding=\\\"max_length\\\",\\n max_length=max_length,\\n truncation=True,\\n return_tensors=\\\"pt\\\",\\n )\\n safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0]\\n\\n # duplicate safety embeddings for each generation per prompt, using mps friendly method\\n seq_len = safety_embeddings.shape[1]\\n safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)\\n safety_embeddings = safety_embeddings.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 text_embeddings = torch.cat([uncond_embeddings, text_embeddings, safety_embeddings])\\n\\n else:\\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\\n\\n # get the initial random noise unless the user supplied it\\n\\n # Unlike in other pipelines, latents need to be generated in the target device\\n # for 1-to-1 results reproducibility with the CompVis implementation.\\n # However this currently doesn't work in `mps`.\\n latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)\\n latents_dtype = text_embeddings.dtype\\n if latents is None:\\n if self.device.type == \\\"mps\\\":\\n # randn does not work reproducibly on mps\\n latents = torch.randn(latents_shape, generator=generator, device=\\\"cpu\\\", dtype=latents_dtype).to(\\n self.device\\n )\\n else:\\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\\n else:\\n if latents.shape != latents_shape:\\n raise ValueError(f\\\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\\\")\\n latents = latents.to(self.device)\\n\\n # set timesteps\\n self.scheduler.set_timesteps(num_inference_steps)\\n timesteps = self.scheduler.timesteps\\n\\n # Some schedulers like PNDM have timesteps as arrays\\n # It's more optimized to move all timesteps to correct device beforehand\\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\\n\\n # scale the initial noise by the standard deviation required by the scheduler\\n latents = latents * self.scheduler.init_noise_sigma\\n\\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 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\\n safety_momentum = None\\n\\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * (3 if enable_safety_guidance else 2)) \\\\\\n if do_classifier_free_guidance else latents\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n # predict the noise residual\\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\\n\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))\\n noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]\\n\\n # default classifier free guidance\\n noise_guidance = (noise_pred_text - noise_pred_uncond)\\n\\n # Perform SLD guidance\\n if enable_safety_guidance:\\n if safety_momentum is None:\\n safety_momentum = torch.zeros_like(noise_guidance)\\n noise_pred_safety_concept = noise_pred_out[2]\\n\\n # Equation 6\\n scale = torch.clamp(\\n torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.)\\n\\n # Equation 6\\n safety_concept_scale = torch.where(\\n (noise_pred_text - noise_pred_safety_concept) >= sld_threshold,\\n torch.zeros_like(scale), scale)\\n\\n # Equation 4\\n noise_guidance_safety = torch.mul(\\n (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale)\\n\\n # Equation 7\\n noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum\\n\\n # Equation 8\\n safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety\\n\\n if i >= sld_warmup_steps: # Warmup\\n # Equation 3\\n noise_guidance = noise_guidance - noise_guidance_safety\\n\\n noise_pred = noise_pred_uncond + guidance_scale * noise_guidance\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\\n\\n # 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 latents = 1 / 0.18215 * latents\\n image = self.vae.decode(latents).sample\\n\\n image = (image / 2 + 0.5).clamp(0, 1)\\n\\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16\\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\\n\\n if self.safety_checker is not None:\\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\\\"pt\\\").to(\\n self.device\\n )\\n image, has_nsfw_concept = self.safety_checker(\\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\\n )\\n else:\\n has_nsfw_concept = None\\n\\n if output_type == \\\"pil\\\":\\n image = self.numpy_to_pil(image)\\n\\n if not return_dict:\\n return (image, has_nsfw_concept)\\n\\n return SLDPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept,\\n applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import argparse\nimport gc\nimport warnings\nimport torch\nfrom pathlib import Path\nfrom typing import Literal\nfrom torch.utils.data import DataLoader\nfrom accelerate import PartialState, Accelerator\nfrom src.configs import config\nfrom src.configs.config import RootConfig\nfrom src.configs.generation_config import GenerationConfig\nfrom src.engine import train_util\nfrom src.evaluation import *\nfrom src.models import model_util\nfrom src.models.spm import SPMLayer, SPMNetwork\nfrom src.models.merge_spm import load_state_dict\nfrom src.misc.sld_pipeline import SLDPipeline"},"token_num":{"kind":"number","value":11603,"string":"11,603"},"cropped_code":{"kind":"string","value":"\n\ndef parse_extra_args(extra_args):\n if extra_args is None or extra_args == ['']:\n return {}\n extra_args_dict = {}\n for extra_arg in extra_args:\n key, value = extra_arg.split(\"=\")\n # convert value to various types\n if value.isdigit():\n value = int(value)\n elif value.replace(\".\", \"\", 1).isdigit():\n value = float(value)\n elif value[0] == \"[\" and value[-1] == \"]\":\n value = [i.replace('+', ' ') for i in value[1:-1].split(\",\")]\n value = [v.strip() for v in value]\n if value[0].isdigit():\n value = [int(v) for v in value]\n elif value[0].replace(\".\", \"\", 1).isdigit():\n value = [float(v) for v in value]\n extra_args_dict[key] = value\n return extra_args_dict\n\n\ndef get_dataloader(args, num_processes=1):\n # parse task_args arguments\n task_args = parse_extra_args(args.task_args)\n task_args[\"save_folder\"] = args.img_save_path\n task_args[\"output_path\"] = args.save_path\n\n # parse generation arguments\n cfg = parse_extra_args(args.generation_cfg)\n cfg = GenerationConfig(**cfg)\n\n dataset_class = None\n if args.task == \"general\":\n dataset_class = ClipTemplateDataset\n elif args.task == \"artwork\":\n dataset_class = ArtworkDataset\n elif args.task == \"i2p\":\n dataset_class = I2PDataset\n elif args.task == \"coco\":\n dataset_class = Coco30kGenerationDataset\n else:\n raise ValueError(f\"Unknown task: {args.task}\")\n dataset = dataset_class(**task_args, base_cfg=cfg)\n dataloader = DataLoader(dataset, batch_size=num_processes, num_workers=0, shuffle=False)\n return dataloader\n\n\ndef get_evaluator(args):\n evaluator_class = None\n if args.task == \"general\":\n evaluator_class = ClipEvaluator\n elif args.task == \"artwork\":\n evaluator_class = ArtworkEvaluator\n elif args.task == \"i2p\":\n evaluator_class = I2PEvaluator\n elif args.task == \"coco\":\n evaluator_class = CocoEvaluator\n else:\n raise ValueError(f\"Unknown task: {args.task}\")\n evaluator = evaluator_class(\n save_folder=args.img_save_path, output_path=args.save_path\n )\n return evaluator\n\n\ndef calculate_matching_score(\n prompt_tokens,\n prompt_embeds,\n erased_prompt_tokens,\n erased_prompt_embeds,\n matching_metric: MATCHING_METRICS,\n special_token_ids: set[int],\n weight_dtype: torch.dtype = torch.float32,\n):\n scores = []\n if \"allone\" in matching_metric:\n scores.append(torch.ones(prompt_embeds.shape[0]).to(\"cpu\", dtype=weight_dtype))\n if \"clipcos\" in matching_metric:\n clipcos = torch.cosine_similarity(\n prompt_embeds.flatten(1, 2), erased_prompt_embeds.flatten(1, 2), dim=-1\n ).cpu()\n scores.append(clipcos)\n if \"tokenuni\" in matching_metric:\n prompt_set = set(prompt_tokens[0].tolist()) - special_token_ids\n tokenuni = []\n for ep in erased_prompt_tokens:\n ep_set = set(ep.tolist()) - special_token_ids\n tokenuni.append(len(prompt_set.intersection(ep_set)) / len(ep_set))\n scores.append(torch.tensor(tokenuni).to(\"cpu\", dtype=weight_dtype))\n return torch.max(torch.stack(scores), dim=0)[0]\n\n\n@torch.no_grad()\ndef infer_with_spm(\n dataloader: DataLoader,\n spm_paths: list[str],\n matching_metric: MATCHING_METRICS,\n facilitate_factor: float = 1.0,\n assigned_multipliers: list[float] = None,\n finetuned_model_path: str = None,\n sld_target_concept: str = None,\n base_model: str = \"CompVis/stable-diffusion-v1-4\",\n v2: bool = False,\n precision: str = \"fp32\",\n):\n spm_model_paths = [\n lp / f\"{lp.name}_last.safetensors\" if lp.is_dir() else lp for lp in spm_paths\n ]\n\n weight_dtype = config.parse_precision(precision)\n\n if finetuned_model_path is not None and Path(finetuned_model_path).is_dir():\n # folder path for the diffuser model\n base_model = finetuned_model_path\n print(f\"Using models from {base_model}\")\n\n # load the pretrained SD\n"},"all_code":{"kind":"string","value":"\n\n\nDIFFUSERS_CACHE_DIR = \".cache/\" \nUNET_NAME = \"unet\"\nTEXT_ENCODER_NAME = \"text_encoder\"\nMATCHING_METRICS = Literal[\n \"clipcos\",\n \"clipcos_tokenuni\",\n \"tokenuni\",\n \"allone\",\n]\ndistributed_state = PartialState()\naccelerator = Accelerator()\n\n\ndef flush():\n torch.cuda.empty_cache()\n gc.collect()\n\n\ndef parse_extra_args(extra_args):\n if extra_args is None or extra_args == ['']:\n return {}\n extra_args_dict = {}\n for extra_arg in extra_args:\n key, value = extra_arg.split(\"=\")\n # convert value to various types\n if value.isdigit():\n value = int(value)\n elif value.replace(\".\", \"\", 1).isdigit():\n value = float(value)\n elif value[0] == \"[\" and value[-1] == \"]\":\n value = [i.replace('+', ' ') for i in value[1:-1].split(\",\")]\n value = [v.strip() for v in value]\n if value[0].isdigit():\n value = [int(v) for v in value]\n elif value[0].replace(\".\", \"\", 1).isdigit():\n value = [float(v) for v in value]\n extra_args_dict[key] = value\n return extra_args_dict\n\n\ndef get_dataloader(args, num_processes=1):\n # parse task_args arguments\n task_args = parse_extra_args(args.task_args)\n task_args[\"save_folder\"] = args.img_save_path\n task_args[\"output_path\"] = args.save_path\n\n # parse generation arguments\n cfg = parse_extra_args(args.generation_cfg)\n cfg = GenerationConfig(**cfg)\n\n dataset_class = None\n if args.task == \"general\":\n dataset_class = ClipTemplateDataset\n elif args.task == \"artwork\":\n dataset_class = ArtworkDataset\n elif args.task == \"i2p\":\n dataset_class = I2PDataset\n elif args.task == \"coco\":\n dataset_class = Coco30kGenerationDataset\n else:\n raise ValueError(f\"Unknown task: {args.task}\")\n dataset = dataset_class(**task_args, base_cfg=cfg)\n dataloader = DataLoader(dataset, batch_size=num_processes, num_workers=0, shuffle=False)\n return dataloader\n\n\ndef get_evaluator(args):\n evaluator_class = None\n if args.task == \"general\":\n evaluator_class = ClipEvaluator\n elif args.task == \"artwork\":\n evaluator_class = ArtworkEvaluator\n elif args.task == \"i2p\":\n evaluator_class = I2PEvaluator\n elif args.task == \"coco\":\n evaluator_class = CocoEvaluator\n else:\n raise ValueError(f\"Unknown task: {args.task}\")\n evaluator = evaluator_class(\n save_folder=args.img_save_path, output_path=args.save_path\n )\n return evaluator\n\n\ndef calculate_matching_score(\n prompt_tokens,\n prompt_embeds,\n erased_prompt_tokens,\n erased_prompt_embeds,\n matching_metric: MATCHING_METRICS,\n special_token_ids: set[int],\n weight_dtype: torch.dtype = torch.float32,\n):\n scores = []\n if \"allone\" in matching_metric:\n scores.append(torch.ones(prompt_embeds.shape[0]).to(\"cpu\", dtype=weight_dtype))\n if \"clipcos\" in matching_metric:\n clipcos = torch.cosine_similarity(\n prompt_embeds.flatten(1, 2), erased_prompt_embeds.flatten(1, 2), dim=-1\n ).cpu()\n scores.append(clipcos)\n if \"tokenuni\" in matching_metric:\n prompt_set = set(prompt_tokens[0].tolist()) - special_token_ids\n tokenuni = []\n for ep in erased_prompt_tokens:\n ep_set = set(ep.tolist()) - special_token_ids\n tokenuni.append(len(prompt_set.intersection(ep_set)) / len(ep_set))\n scores.append(torch.tensor(tokenuni).to(\"cpu\", dtype=weight_dtype))\n return torch.max(torch.stack(scores), dim=0)[0]\n\n\n@torch.no_grad()\ndef infer_with_spm(\n dataloader: DataLoader,\n spm_paths: list[str],\n matching_metric: MATCHING_METRICS,\n facilitate_factor: float = 1.0,\n assigned_multipliers: list[float] = None,\n finetuned_model_path: str = None,\n sld_target_concept: str = None,\n base_model: str = \"CompVis/stable-diffusion-v1-4\",\n v2: bool = False,\n precision: str = \"fp32\",\n):\n spm_model_paths = [\n lp / f\"{lp.name}_last.safetensors\" if lp.is_dir() else lp for lp in spm_paths\n ]\n\n weight_dtype = config.parse_precision(precision)\n\n if finetuned_model_path is not None and Path(finetuned_model_path).is_dir():\n # folder path for the diffuser model\n base_model = finetuned_model_path\n print(f\"Using models from {base_model}\")\n\n # load the pretrained SD"},"next_line":{"kind":"string","value":" tokenizer, text_encoder, unet, pipe = model_util.load_checkpoint_model("},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-12-26 03:19:16+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4696,"cells":{"repo_name":{"kind":"string","value":"dakpinaroglu/Frame2seq"},"file_path":{"kind":"string","value":"frame2seq/openfold/utils/feats.py"},"context":{"kind":"list like","value":[{"identifier":"protein","path":"frame2seq/openfold/np/protein.py","snippet":"PICO_TO_ANGSTROM = 0.01\nclass Protein:\ndef from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:\ndef from_proteinnet_string(proteinnet_str: str) -> Protein:\ndef get_pdb_headers(prot: Protein, chain_id: int = 0) -> Sequence[str]:\ndef add_pdb_headers(prot: Protein, pdb_str: str) -> str:\ndef to_pdb(prot: Protein) -> str:\ndef ideal_atom_mask(prot: Protein) -> np.ndarray:\ndef from_prediction(\n features: FeatureDict,\n result: ModelOutput,\n b_factors: Optional[np.ndarray] = None,\n chain_index: Optional[np.ndarray] = None,\n remark: Optional[str] = None,\n parents: Optional[Sequence[str]] = None,\n parents_chain_index: Optional[Sequence[int]] = None\n) -> Protein:"},{"identifier":"Rotation","path":"frame2seq/openfold/utils/rigid_utils.py","snippet":"class Rotation:\n \"\"\"\n A 3D rotation. Depending on how the object is initialized, the\n rotation is represented by either a rotation matrix or a\n quaternion, though both formats are made available by helper functions.\n To simplify gradient computation, the underlying format of the\n rotation cannot be changed in-place. Like Rigid, the class is designed\n to mimic the behavior of a torch Tensor, almost as if each Rotation\n object were a tensor of rotations, in one format or another.\n \"\"\"\n def __init__(self,\n rot_mats: Optional[torch.Tensor] = None,\n quats: Optional[torch.Tensor] = None,\n normalize_quats: bool = True,\n ):\n \"\"\"\n Args:\n rot_mats:\n A [*, 3, 3] rotation matrix tensor. Mutually exclusive with\n quats\n quats:\n A [*, 4] quaternion. Mutually exclusive with rot_mats. If\n normalize_quats is not True, must be a unit quaternion\n normalize_quats:\n If quats is specified, whether to normalize quats\n \"\"\"\n if((rot_mats is None and quats is None) or \n (rot_mats is not None and quats is not None)):\n raise ValueError(\"Exactly one input argument must be specified\")\n\n if((rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or \n (quats is not None and quats.shape[-1] != 4)):\n raise ValueError(\n \"Incorrectly shaped rotation matrix or quaternion\"\n )\n\n # Force full-precision\n if(quats is not None):\n quats = quats.to(dtype=torch.float32)\n if(rot_mats is not None):\n rot_mats = rot_mats.to(dtype=torch.float32)\n\n if(quats is not None and normalize_quats):\n quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True)\n\n self._rot_mats = rot_mats\n self._quats = quats\n\n @staticmethod\n def identity(\n shape,\n dtype: Optional[torch.dtype] = None,\n device: Optional[torch.device] = None,\n requires_grad: bool = True,\n fmt: str = \"quat\",\n ) -> Rotation:\n \"\"\"\n Returns an identity Rotation.\n\n Args:\n shape:\n The \"shape\" of the resulting Rotation object. See documentation\n for the shape property\n dtype:\n The torch dtype for the rotation\n device:\n The torch device for the new rotation\n requires_grad:\n Whether the underlying tensors in the new rotation object\n should require gradient computation\n fmt:\n One of \"quat\" or \"rot_mat\". Determines the underlying format\n of the new object's rotation \n Returns:\n A new identity rotation\n \"\"\"\n if(fmt == \"rot_mat\"):\n rot_mats = identity_rot_mats(\n shape, dtype, device, requires_grad,\n )\n return Rotation(rot_mats=rot_mats, quats=None)\n elif(fmt == \"quat\"):\n quats = identity_quats(shape, dtype, device, requires_grad)\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\n else:\n raise ValueError(f\"Invalid format: f{fmt}\")\n\n # Magic methods\n\n def __getitem__(self, index: Any) -> Rotation:\n \"\"\"\n Allows torch-style indexing over the virtual shape of the rotation\n object. See documentation for the shape property.\n\n Args:\n index:\n A torch index. E.g. (1, 3, 2), or (slice(None,))\n Returns:\n The indexed rotation\n \"\"\"\n if type(index) != tuple:\n index = (index,)\n\n if(self._rot_mats is not None):\n rot_mats = self._rot_mats[index + (slice(None), slice(None))]\n return Rotation(rot_mats=rot_mats)\n elif(self._quats is not None):\n quats = self._quats[index + (slice(None),)]\n return Rotation(quats=quats, normalize_quats=False)\n else:\n raise ValueError(\"Both rotations are None\")\n\n def __mul__(self,\n right: torch.Tensor,\n ) -> Rotation:\n \"\"\"\n Pointwise left multiplication of the rotation with a tensor. Can be\n used to e.g. mask the Rotation.\n\n Args:\n right:\n The tensor multiplicand\n Returns:\n The product\n \"\"\"\n if not(isinstance(right, torch.Tensor)):\n raise TypeError(\"The other multiplicand must be a Tensor\")\n\n if(self._rot_mats is not None):\n rot_mats = self._rot_mats * right[..., None, None]\n return Rotation(rot_mats=rot_mats, quats=None)\n elif(self._quats is not None):\n quats = self._quats * right[..., None]\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\n else:\n raise ValueError(\"Both rotations are None\")\n\n def __rmul__(self,\n left: torch.Tensor,\n ) -> Rotation:\n \"\"\"\n Reverse pointwise multiplication of the rotation with a tensor.\n\n Args:\n left:\n The left multiplicand\n Returns:\n The product\n \"\"\"\n return self.__mul__(left)\n \n # Properties\n\n @property\n def shape(self) -> torch.Size:\n \"\"\"\n Returns the virtual shape of the rotation object. This shape is\n defined as the batch dimensions of the underlying rotation matrix\n or quaternion. If the Rotation was initialized with a [10, 3, 3]\n rotation matrix tensor, for example, the resulting shape would be\n [10].\n \n Returns:\n The virtual shape of the rotation object\n \"\"\"\n s = None\n if(self._quats is not None):\n s = self._quats.shape[:-1]\n else:\n s = self._rot_mats.shape[:-2]\n\n return s\n\n @property\n def dtype(self) -> torch.dtype:\n \"\"\"\n Returns the dtype of the underlying rotation.\n\n Returns:\n The dtype of the underlying rotation\n \"\"\"\n if(self._rot_mats is not None):\n return self._rot_mats.dtype\n elif(self._quats is not None):\n return self._quats.dtype\n else:\n raise ValueError(\"Both rotations are None\")\n\n @property\n def device(self) -> torch.device:\n \"\"\"\n The device of the underlying rotation\n\n Returns:\n The device of the underlying rotation\n \"\"\"\n if(self._rot_mats is not None):\n return self._rot_mats.device\n elif(self._quats is not None):\n return self._quats.device\n else:\n raise ValueError(\"Both rotations are None\")\n\n @property\n def requires_grad(self) -> bool:\n \"\"\"\n Returns the requires_grad property of the underlying rotation\n\n Returns:\n The requires_grad property of the underlying tensor\n \"\"\"\n if(self._rot_mats is not None):\n return self._rot_mats.requires_grad\n elif(self._quats is not None):\n return self._quats.requires_grad\n else:\n raise ValueError(\"Both rotations are None\")\n\n def get_rot_mats(self) -> torch.Tensor:\n \"\"\"\n Returns the underlying rotation as a rotation matrix tensor.\n\n Returns:\n The rotation as a rotation matrix tensor\n \"\"\"\n rot_mats = self._rot_mats\n if(rot_mats is None):\n if(self._quats is None):\n raise ValueError(\"Both rotations are None\")\n else:\n rot_mats = quat_to_rot(self._quats)\n\n return rot_mats \n\n def get_quats(self) -> torch.Tensor:\n \"\"\"\n Returns the underlying rotation as a quaternion tensor.\n\n Depending on whether the Rotation was initialized with a\n quaternion, this function may call torch.linalg.eigh.\n\n Returns:\n The rotation as a quaternion tensor.\n \"\"\"\n quats = self._quats\n if(quats is None):\n if(self._rot_mats is None):\n raise ValueError(\"Both rotations are None\")\n else:\n quats = rot_to_quat(self._rot_mats)\n\n return quats\n\n def get_cur_rot(self) -> torch.Tensor:\n \"\"\"\n Return the underlying rotation in its current form\n\n Returns:\n The stored rotation\n \"\"\"\n if(self._rot_mats is not None):\n return self._rot_mats\n elif(self._quats is not None):\n return self._quats\n else:\n raise ValueError(\"Both rotations are None\")\n\n # Rotation functions\n\n def compose_q_update_vec(self, \n q_update_vec: torch.Tensor, \n normalize_quats: bool = True\n ) -> Rotation:\n \"\"\"\n Returns a new quaternion Rotation after updating the current\n object's underlying rotation with a quaternion update, formatted\n as a [*, 3] tensor whose final three columns represent x, y, z such \n that (1, x, y, z) is the desired (not necessarily unit) quaternion\n update.\n\n Args:\n q_update_vec:\n A [*, 3] quaternion update tensor\n normalize_quats:\n Whether to normalize the output quaternion\n Returns:\n An updated Rotation\n \"\"\"\n quats = self.get_quats()\n new_quats = quats + quat_multiply_by_vec(quats, q_update_vec)\n return Rotation(\n rot_mats=None, \n quats=new_quats, \n normalize_quats=normalize_quats,\n )\n\n def compose_r(self, r: Rotation) -> Rotation:\n \"\"\"\n Compose the rotation matrices of the current Rotation object with\n those of another.\n\n Args:\n r:\n An update rotation object\n Returns:\n An updated rotation object\n \"\"\"\n r1 = self.get_rot_mats()\n r2 = r.get_rot_mats()\n new_rot_mats = rot_matmul(r1, r2)\n return Rotation(rot_mats=new_rot_mats, quats=None)\n\n def compose_q(self, r: Rotation, normalize_quats: bool = True) -> Rotation:\n \"\"\"\n Compose the quaternions of the current Rotation object with those\n of another.\n\n Depending on whether either Rotation was initialized with\n quaternions, this function may call torch.linalg.eigh.\n\n Args:\n r:\n An update rotation object\n Returns:\n An updated rotation object\n \"\"\"\n q1 = self.get_quats()\n q2 = r.get_quats()\n new_quats = quat_multiply(q1, q2)\n return Rotation(\n rot_mats=None, quats=new_quats, normalize_quats=normalize_quats\n )\n\n def apply(self, pts: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Apply the current Rotation as a rotation matrix to a set of 3D\n coordinates.\n\n Args:\n pts:\n A [*, 3] set of points\n Returns:\n [*, 3] rotated points\n \"\"\"\n rot_mats = self.get_rot_mats()\n return rot_vec_mul(rot_mats, pts)\n\n def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:\n \"\"\"\n The inverse of the apply() method.\n\n Args:\n pts:\n A [*, 3] set of points\n Returns:\n [*, 3] inverse-rotated points\n \"\"\"\n rot_mats = self.get_rot_mats()\n inv_rot_mats = invert_rot_mat(rot_mats) \n return rot_vec_mul(inv_rot_mats, pts)\n\n def invert(self) -> Rotation:\n \"\"\"\n Returns the inverse of the current Rotation.\n\n Returns:\n The inverse of the current Rotation\n \"\"\"\n if(self._rot_mats is not None):\n return Rotation(\n rot_mats=invert_rot_mat(self._rot_mats), \n quats=None\n )\n elif(self._quats is not None):\n return Rotation(\n rot_mats=None,\n quats=invert_quat(self._quats),\n normalize_quats=False,\n )\n else:\n raise ValueError(\"Both rotations are None\")\n\n # \"Tensor\" stuff\n\n def unsqueeze(self, \n dim: int,\n ) -> Rigid:\n \"\"\"\n Analogous to torch.unsqueeze. The dimension is relative to the\n shape of the Rotation object.\n \n Args:\n dim: A positive or negative dimension index.\n Returns:\n The unsqueezed Rotation.\n \"\"\"\n if dim >= len(self.shape):\n raise ValueError(\"Invalid dimension\")\n\n if(self._rot_mats is not None):\n rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2)\n return Rotation(rot_mats=rot_mats, quats=None)\n elif(self._quats is not None):\n quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1)\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\n else:\n raise ValueError(\"Both rotations are None\")\n\n @staticmethod\n def cat(\n rs: Sequence[Rotation], \n dim: int,\n ) -> Rigid:\n \"\"\"\n Concatenates rotations along one of the batch dimensions. Analogous\n to torch.cat().\n\n Note that the output of this operation is always a rotation matrix,\n regardless of the format of input rotations.\n\n Args:\n rs: \n A list of rotation objects\n dim: \n The dimension along which the rotations should be \n concatenated\n Returns:\n A concatenated Rotation object in rotation matrix format\n \"\"\"\n rot_mats = [r.get_rot_mats() for r in rs]\n rot_mats = torch.cat(rot_mats, dim=dim if dim >= 0 else dim - 2)\n\n return Rotation(rot_mats=rot_mats, quats=None) \n\n def map_tensor_fn(self, \n fn: Callable[torch.Tensor, torch.Tensor]\n ) -> Rotation:\n \"\"\"\n Apply a Tensor -> Tensor function to underlying rotation tensors,\n mapping over the rotation dimension(s). Can be used e.g. to sum out\n a one-hot batch dimension.\n\n Args:\n fn:\n A Tensor -> Tensor function to be mapped over the Rotation \n Returns:\n The transformed Rotation object\n \"\"\" \n if(self._rot_mats is not None):\n rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,))\n rot_mats = torch.stack(\n list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1\n )\n rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3))\n return Rotation(rot_mats=rot_mats, quats=None)\n elif(self._quats is not None):\n quats = torch.stack(\n list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1\n )\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\n else:\n raise ValueError(\"Both rotations are None\")\n \n def cuda(self) -> Rotation:\n \"\"\"\n Analogous to the cuda() method of torch Tensors\n\n Returns:\n A copy of the Rotation in CUDA memory\n \"\"\"\n if(self._rot_mats is not None):\n return Rotation(rot_mats=self._rot_mats.cuda(), quats=None)\n elif(self._quats is not None):\n return Rotation(\n rot_mats=None, \n quats=self._quats.cuda(),\n normalize_quats=False\n )\n else:\n raise ValueError(\"Both rotations are None\")\n\n def to(self, \n device: Optional[torch.device], \n dtype: Optional[torch.dtype]\n ) -> Rotation:\n \"\"\"\n Analogous to the to() method of torch Tensors\n\n Args:\n device:\n A torch device\n dtype:\n A torch dtype\n Returns:\n A copy of the Rotation using the new device and dtype\n \"\"\"\n if(self._rot_mats is not None):\n return Rotation(\n rot_mats=self._rot_mats.to(device=device, dtype=dtype), \n quats=None,\n )\n elif(self._quats is not None):\n return Rotation(\n rot_mats=None, \n quats=self._quats.to(device=device, dtype=dtype),\n normalize_quats=False,\n )\n else:\n raise ValueError(\"Both rotations are None\")\n\n def detach(self) -> Rotation:\n \"\"\"\n Returns a copy of the Rotation whose underlying Tensor has been\n detached from its torch graph.\n\n Returns:\n A copy of the Rotation whose underlying Tensor has been detached\n from its torch graph\n \"\"\"\n if(self._rot_mats is not None):\n return Rotation(rot_mats=self._rot_mats.detach(), quats=None)\n elif(self._quats is not None):\n return Rotation(\n rot_mats=None, \n quats=self._quats.detach(), \n normalize_quats=False,\n )\n else:\n raise ValueError(\"Both rotations are None\")"},{"identifier":"Rigid","path":"frame2seq/openfold/utils/rigid_utils.py","snippet":"class Rigid:\n \"\"\"\n A class representing a rigid transformation. Little more than a wrapper\n around two objects: a Rotation object and a [*, 3] translation\n Designed to behave approximately like a single torch tensor with the \n shape of the shared batch dimensions of its component parts.\n \"\"\"\n def __init__(self, \n rots: Optional[Rotation],\n trans: Optional[torch.Tensor],\n ):\n \"\"\"\n Args:\n rots: A [*, 3, 3] rotation tensor\n trans: A corresponding [*, 3] translation tensor\n \"\"\"\n # (we need device, dtype, etc. from at least one input)\n\n batch_dims, dtype, device, requires_grad = None, None, None, None\n if(trans is not None):\n batch_dims = trans.shape[:-1]\n dtype = trans.dtype\n device = trans.device\n requires_grad = trans.requires_grad\n elif(rots is not None):\n batch_dims = rots.shape\n dtype = rots.dtype\n device = rots.device\n requires_grad = rots.requires_grad\n else:\n raise ValueError(\"At least one input argument must be specified\")\n\n if(rots is None):\n rots = Rotation.identity(\n batch_dims, dtype, device, requires_grad,\n )\n elif(trans is None):\n trans = identity_trans(\n batch_dims, dtype, device, requires_grad,\n )\n\n if((rots.shape != trans.shape[:-1]) or\n (rots.device != trans.device)):\n raise ValueError(\"Rots and trans incompatible\")\n\n # Force full precision. Happens to the rotations automatically.\n trans = trans.to(dtype=torch.float32)\n\n self._rots = rots\n self._trans = trans\n\n @staticmethod\n def identity(\n shape: Tuple[int], \n dtype: Optional[torch.dtype] = None,\n device: Optional[torch.device] = None, \n requires_grad: bool = True,\n fmt: str = \"quat\",\n ) -> Rigid:\n \"\"\"\n Constructs an identity transformation.\n\n Args:\n shape: \n The desired shape\n dtype: \n The dtype of both internal tensors\n device: \n The device of both internal tensors\n requires_grad: \n Whether grad should be enabled for the internal tensors\n Returns:\n The identity transformation\n \"\"\"\n return Rigid(\n Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt),\n identity_trans(shape, dtype, device, requires_grad),\n )\n\n def __getitem__(self, \n index: Any,\n ) -> Rigid:\n \"\"\" \n Indexes the affine transformation with PyTorch-style indices.\n The index is applied to the shared dimensions of both the rotation\n and the translation.\n\n E.g.::\n\n r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None)\n t = Rigid(r, torch.rand(10, 10, 3))\n indexed = t[3, 4:6]\n assert(indexed.shape == (2,))\n assert(indexed.get_rots().shape == (2,))\n assert(indexed.get_trans().shape == (2, 3))\n\n Args:\n index: A standard torch tensor index. E.g. 8, (10, None, 3),\n or (3, slice(0, 1, None))\n Returns:\n The indexed tensor \n \"\"\"\n if type(index) != tuple:\n index = (index,)\n \n return Rigid(\n self._rots[index],\n self._trans[index + (slice(None),)],\n )\n\n def __mul__(self,\n right: torch.Tensor,\n ) -> Rigid:\n \"\"\"\n Pointwise left multiplication of the transformation with a tensor.\n Can be used to e.g. mask the Rigid.\n\n Args:\n right:\n The tensor multiplicand\n Returns:\n The product\n \"\"\"\n if not(isinstance(right, torch.Tensor)):\n raise TypeError(\"The other multiplicand must be a Tensor\")\n\n new_rots = self._rots * right\n new_trans = self._trans * right[..., None]\n\n return Rigid(new_rots, new_trans)\n\n def __rmul__(self,\n left: torch.Tensor,\n ) -> Rigid:\n \"\"\"\n Reverse pointwise multiplication of the transformation with a \n tensor.\n\n Args:\n left:\n The left multiplicand\n Returns:\n The product\n \"\"\"\n return self.__mul__(left)\n\n @property\n def shape(self) -> torch.Size:\n \"\"\"\n Returns the shape of the shared dimensions of the rotation and\n the translation.\n \n Returns:\n The shape of the transformation\n \"\"\"\n s = self._trans.shape[:-1]\n return s\n\n @property\n def device(self) -> torch.device:\n \"\"\"\n Returns the device on which the Rigid's tensors are located.\n\n Returns:\n The device on which the Rigid's tensors are located\n \"\"\"\n return self._trans.device\n\n def get_rots(self) -> Rotation:\n \"\"\"\n Getter for the rotation.\n\n Returns:\n The rotation object\n \"\"\"\n return self._rots\n\n def get_trans(self) -> torch.Tensor:\n \"\"\"\n Getter for the translation.\n\n Returns:\n The stored translation\n \"\"\"\n return self._trans\n\n def compose_q_update_vec(self, \n q_update_vec: torch.Tensor,\n ) -> Rigid:\n \"\"\"\n Composes the transformation with a quaternion update vector of\n shape [*, 6], where the final 6 columns represent the x, y, and\n z values of a quaternion of form (1, x, y, z) followed by a 3D\n translation.\n\n Args:\n q_vec: The quaternion update vector.\n Returns:\n The composed transformation.\n \"\"\"\n q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:]\n new_rots = self._rots.compose_q_update_vec(q_vec)\n\n trans_update = self._rots.apply(t_vec)\n new_translation = self._trans + trans_update\n\n return Rigid(new_rots, new_translation)\n\n def compose(self,\n r: Rigid,\n ) -> Rigid:\n \"\"\"\n Composes the current rigid object with another.\n\n Args:\n r:\n Another Rigid object\n Returns:\n The composition of the two transformations\n \"\"\"\n new_rot = self._rots.compose_r(r._rots)\n new_trans = self._rots.apply(r._trans) + self._trans\n return Rigid(new_rot, new_trans)\n\n def apply(self, \n pts: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"\n Applies the transformation to a coordinate tensor.\n\n Args:\n pts: A [*, 3] coordinate tensor.\n Returns:\n The transformed points.\n \"\"\"\n rotated = self._rots.apply(pts) \n return rotated + self._trans\n\n def invert_apply(self, \n pts: torch.Tensor\n ) -> torch.Tensor:\n \"\"\"\n Applies the inverse of the transformation to a coordinate tensor.\n\n Args:\n pts: A [*, 3] coordinate tensor\n Returns:\n The transformed points.\n \"\"\"\n pts = pts - self._trans\n return self._rots.invert_apply(pts) \n\n def invert(self) -> Rigid:\n \"\"\"\n Inverts the transformation.\n\n Returns:\n The inverse transformation.\n \"\"\"\n rot_inv = self._rots.invert() \n trn_inv = rot_inv.apply(self._trans)\n\n return Rigid(rot_inv, -1 * trn_inv)\n\n def map_tensor_fn(self, \n fn: Callable[torch.Tensor, torch.Tensor]\n ) -> Rigid:\n \"\"\"\n Apply a Tensor -> Tensor function to underlying translation and\n rotation tensors, mapping over the translation/rotation dimensions\n respectively.\n\n Args:\n fn:\n A Tensor -> Tensor function to be mapped over the Rigid\n Returns:\n The transformed Rigid object\n \"\"\" \n new_rots = self._rots.map_tensor_fn(fn) \n new_trans = torch.stack(\n list(map(fn, torch.unbind(self._trans, dim=-1))), \n dim=-1\n )\n\n return Rigid(new_rots, new_trans)\n\n def to_tensor_4x4(self) -> torch.Tensor:\n \"\"\"\n Converts a transformation to a homogenous transformation tensor.\n\n Returns:\n A [*, 4, 4] homogenous transformation tensor\n \"\"\"\n tensor = self._trans.new_zeros((*self.shape, 4, 4))\n tensor[..., :3, :3] = self._rots.get_rot_mats()\n tensor[..., :3, 3] = self._trans\n tensor[..., 3, 3] = 1\n return tensor\n\n @staticmethod\n def from_tensor_4x4(\n t: torch.Tensor\n ) -> Rigid:\n \"\"\"\n Constructs a transformation from a homogenous transformation\n tensor.\n\n Args:\n t: [*, 4, 4] homogenous transformation tensor\n Returns:\n T object with shape [*]\n \"\"\"\n if(t.shape[-2:] != (4, 4)):\n raise ValueError(\"Incorrectly shaped input tensor\")\n\n rots = Rotation(rot_mats=t[..., :3, :3], quats=None)\n trans = t[..., :3, 3]\n \n return Rigid(rots, trans)\n\n def to_tensor_7(self) -> torch.Tensor:\n \"\"\"\n Converts a transformation to a tensor with 7 final columns, four \n for the quaternion followed by three for the translation.\n\n Returns:\n A [*, 7] tensor representation of the transformation\n \"\"\"\n tensor = self._trans.new_zeros((*self.shape, 7))\n tensor[..., :4] = self._rots.get_quats()\n tensor[..., 4:] = self._trans\n\n return tensor\n\n @staticmethod\n def from_tensor_7(\n t: torch.Tensor,\n normalize_quats: bool = False,\n ) -> Rigid:\n if(t.shape[-1] != 7):\n raise ValueError(\"Incorrectly shaped input tensor\")\n\n quats, trans = t[..., :4], t[..., 4:]\n\n rots = Rotation(\n rot_mats=None, \n quats=quats, \n normalize_quats=normalize_quats\n )\n\n return Rigid(rots, trans)\n\n @staticmethod\n def from_3_points(\n p_neg_x_axis: torch.Tensor, \n origin: torch.Tensor, \n p_xy_plane: torch.Tensor, \n eps: float = 1e-8\n ) -> Rigid:\n \"\"\"\n Implements algorithm 21. Constructs transformations from sets of 3 \n points using the Gram-Schmidt algorithm.\n\n Args:\n p_neg_x_axis: [*, 3] coordinates\n origin: [*, 3] coordinates used as frame origins\n p_xy_plane: [*, 3] coordinates\n eps: Small epsilon value\n Returns:\n A transformation object of shape [*]\n \"\"\"\n p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1)\n origin = torch.unbind(origin, dim=-1)\n p_xy_plane = torch.unbind(p_xy_plane, dim=-1)\n\n e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)]\n e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)]\n\n denom = torch.sqrt(sum((c * c for c in e0)) + eps)\n e0 = [c / denom for c in e0]\n dot = sum((c1 * c2 for c1, c2 in zip(e0, e1)))\n e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)]\n denom = torch.sqrt(sum((c * c for c in e1)) + eps)\n e1 = [c / denom for c in e1]\n e2 = [\n e0[1] * e1[2] - e0[2] * e1[1],\n e0[2] * e1[0] - e0[0] * e1[2],\n e0[0] * e1[1] - e0[1] * e1[0],\n ]\n\n rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1)\n rots = rots.reshape(rots.shape[:-1] + (3, 3))\n\n rot_obj = Rotation(rot_mats=rots, quats=None)\n\n return Rigid(rot_obj, torch.stack(origin, dim=-1))\n\n def unsqueeze(self, \n dim: int,\n ) -> Rigid:\n \"\"\"\n Analogous to torch.unsqueeze. The dimension is relative to the\n shared dimensions of the rotation/translation.\n \n Args:\n dim: A positive or negative dimension index.\n Returns:\n The unsqueezed transformation.\n \"\"\"\n if dim >= len(self.shape):\n raise ValueError(\"Invalid dimension\")\n rots = self._rots.unsqueeze(dim)\n trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1)\n\n return Rigid(rots, trans)\n\n @staticmethod\n def cat(\n ts: Sequence[Rigid], \n dim: int,\n ) -> Rigid:\n \"\"\"\n Concatenates transformations along a new dimension.\n\n Args:\n ts: \n A list of T objects\n dim: \n The dimension along which the transformations should be \n concatenated\n Returns:\n A concatenated transformation object\n \"\"\"\n rots = Rotation.cat([t._rots for t in ts], dim) \n trans = torch.cat(\n [t._trans for t in ts], dim=dim if dim >= 0 else dim - 1\n )\n\n return Rigid(rots, trans)\n\n def apply_rot_fn(self, fn: Callable[Rotation, Rotation]) -> Rigid:\n \"\"\"\n Applies a Rotation -> Rotation function to the stored rotation\n object.\n\n Args:\n fn: A function of type Rotation -> Rotation\n Returns:\n A transformation object with a transformed rotation.\n \"\"\"\n return Rigid(fn(self._rots), self._trans)\n\n def apply_trans_fn(self, fn: Callable[torch.Tensor, torch.Tensor]) -> Rigid:\n \"\"\"\n Applies a Tensor -> Tensor function to the stored translation.\n\n Args:\n fn: \n A function of type Tensor -> Tensor to be applied to the\n translation\n Returns:\n A transformation object with a transformed translation.\n \"\"\"\n return Rigid(self._rots, fn(self._trans))\n\n def scale_translation(self, trans_scale_factor: float) -> Rigid:\n \"\"\"\n Scales the translation by a constant factor.\n\n Args:\n trans_scale_factor:\n The constant factor\n Returns:\n A transformation object with a scaled translation.\n \"\"\"\n fn = lambda t: t * trans_scale_factor\n return self.apply_trans_fn(fn)\n\n def stop_rot_gradient(self) -> Rigid:\n \"\"\"\n Detaches the underlying rotation object\n\n Returns:\n A transformation object with detached rotations\n \"\"\"\n fn = lambda r: r.detach()\n return self.apply_rot_fn(fn)\n\n @staticmethod\n def make_transform_from_reference(n_xyz, ca_xyz, c_xyz, eps=1e-20):\n \"\"\"\n Returns a transformation object from reference coordinates.\n \n Note that this method does not take care of symmetries. If you \n provide the atom positions in the non-standard way, the N atom will \n end up not at [-0.527250, 1.359329, 0.0] but instead at \n [-0.527250, -1.359329, 0.0]. You need to take care of such cases in \n your code.\n \n Args:\n n_xyz: A [*, 3] tensor of nitrogen xyz coordinates.\n ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates.\n c_xyz: A [*, 3] tensor of carbon xyz coordinates.\n Returns:\n A transformation object. After applying the translation and \n rotation to the reference backbone, the coordinates will \n approximately equal to the input coordinates.\n \"\"\" \n translation = -1 * ca_xyz\n n_xyz = n_xyz + translation\n c_xyz = c_xyz + translation\n\n c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)]\n norm = torch.sqrt(eps + c_x ** 2 + c_y ** 2)\n sin_c1 = -c_y / norm\n cos_c1 = c_x / norm\n zeros = sin_c1.new_zeros(sin_c1.shape)\n ones = sin_c1.new_ones(sin_c1.shape)\n\n c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3))\n c1_rots[..., 0, 0] = cos_c1\n c1_rots[..., 0, 1] = -1 * sin_c1\n c1_rots[..., 1, 0] = sin_c1\n c1_rots[..., 1, 1] = cos_c1\n c1_rots[..., 2, 2] = 1\n\n norm = torch.sqrt(eps + c_x ** 2 + c_y ** 2 + c_z ** 2)\n sin_c2 = c_z / norm\n cos_c2 = torch.sqrt(c_x ** 2 + c_y ** 2) / norm\n\n c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))\n c2_rots[..., 0, 0] = cos_c2\n c2_rots[..., 0, 2] = sin_c2\n c2_rots[..., 1, 1] = 1\n c2_rots[..., 2, 0] = -1 * sin_c2\n c2_rots[..., 2, 2] = cos_c2\n\n c_rots = rot_matmul(c2_rots, c1_rots)\n n_xyz = rot_vec_mul(c_rots, n_xyz)\n\n _, n_y, n_z = [n_xyz[..., i] for i in range(3)]\n norm = torch.sqrt(eps + n_y ** 2 + n_z ** 2)\n sin_n = -n_z / norm\n cos_n = n_y / norm\n\n n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))\n n_rots[..., 0, 0] = 1\n n_rots[..., 1, 1] = cos_n\n n_rots[..., 1, 2] = -1 * sin_n\n n_rots[..., 2, 1] = sin_n\n n_rots[..., 2, 2] = cos_n\n\n rots = rot_matmul(n_rots, c_rots)\n\n rots = rots.transpose(-1, -2)\n translation = -1 * translation\n\n rot_obj = Rotation(rot_mats=rots, quats=None)\n\n return Rigid(rot_obj, translation)\n\n def cuda(self) -> Rigid:\n \"\"\"\n Moves the transformation object to GPU memory\n \n Returns:\n A version of the transformation on GPU\n \"\"\"\n return Rigid(self._rots.cuda(), self._trans.cuda())"},{"identifier":"batched_gather","path":"frame2seq/openfold/utils/tensor_utils.py","snippet":"def add(m1, m2, inplace):\ndef permute_final_dims(tensor: torch.Tensor, inds: List[int]):\ndef flatten_final_dims(t: torch.Tensor, no_dims: int):\ndef masked_mean(mask, value, dim, eps=1e-4):\ndef pts_to_distogram(pts, min_bin=2.3125, max_bin=21.6875, no_bins=64):\ndef dict_multimap(fn, dicts):\ndef one_hot(x, v_bins):\ndef batched_gather(data, inds, dim=0, no_batch_dims=0):\ndef dict_map(fn, dic, leaf_type):\ndef tree_map(fn, tree, leaf_type):"}],"string":"[\n {\n \"identifier\": \"protein\",\n \"path\": \"frame2seq/openfold/np/protein.py\",\n \"snippet\": \"PICO_TO_ANGSTROM = 0.01\\nclass Protein:\\ndef from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:\\ndef from_proteinnet_string(proteinnet_str: str) -> Protein:\\ndef get_pdb_headers(prot: Protein, chain_id: int = 0) -> Sequence[str]:\\ndef add_pdb_headers(prot: Protein, pdb_str: str) -> str:\\ndef to_pdb(prot: Protein) -> str:\\ndef ideal_atom_mask(prot: Protein) -> np.ndarray:\\ndef from_prediction(\\n features: FeatureDict,\\n result: ModelOutput,\\n b_factors: Optional[np.ndarray] = None,\\n chain_index: Optional[np.ndarray] = None,\\n remark: Optional[str] = None,\\n parents: Optional[Sequence[str]] = None,\\n parents_chain_index: Optional[Sequence[int]] = None\\n) -> Protein:\"\n },\n {\n \"identifier\": \"Rotation\",\n \"path\": \"frame2seq/openfold/utils/rigid_utils.py\",\n \"snippet\": \"class Rotation:\\n \\\"\\\"\\\"\\n A 3D rotation. Depending on how the object is initialized, the\\n rotation is represented by either a rotation matrix or a\\n quaternion, though both formats are made available by helper functions.\\n To simplify gradient computation, the underlying format of the\\n rotation cannot be changed in-place. Like Rigid, the class is designed\\n to mimic the behavior of a torch Tensor, almost as if each Rotation\\n object were a tensor of rotations, in one format or another.\\n \\\"\\\"\\\"\\n def __init__(self,\\n rot_mats: Optional[torch.Tensor] = None,\\n quats: Optional[torch.Tensor] = None,\\n normalize_quats: bool = True,\\n ):\\n \\\"\\\"\\\"\\n Args:\\n rot_mats:\\n A [*, 3, 3] rotation matrix tensor. Mutually exclusive with\\n quats\\n quats:\\n A [*, 4] quaternion. Mutually exclusive with rot_mats. If\\n normalize_quats is not True, must be a unit quaternion\\n normalize_quats:\\n If quats is specified, whether to normalize quats\\n \\\"\\\"\\\"\\n if((rot_mats is None and quats is None) or \\n (rot_mats is not None and quats is not None)):\\n raise ValueError(\\\"Exactly one input argument must be specified\\\")\\n\\n if((rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or \\n (quats is not None and quats.shape[-1] != 4)):\\n raise ValueError(\\n \\\"Incorrectly shaped rotation matrix or quaternion\\\"\\n )\\n\\n # Force full-precision\\n if(quats is not None):\\n quats = quats.to(dtype=torch.float32)\\n if(rot_mats is not None):\\n rot_mats = rot_mats.to(dtype=torch.float32)\\n\\n if(quats is not None and normalize_quats):\\n quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True)\\n\\n self._rot_mats = rot_mats\\n self._quats = quats\\n\\n @staticmethod\\n def identity(\\n shape,\\n dtype: Optional[torch.dtype] = None,\\n device: Optional[torch.device] = None,\\n requires_grad: bool = True,\\n fmt: str = \\\"quat\\\",\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Returns an identity Rotation.\\n\\n Args:\\n shape:\\n The \\\"shape\\\" of the resulting Rotation object. See documentation\\n for the shape property\\n dtype:\\n The torch dtype for the rotation\\n device:\\n The torch device for the new rotation\\n requires_grad:\\n Whether the underlying tensors in the new rotation object\\n should require gradient computation\\n fmt:\\n One of \\\"quat\\\" or \\\"rot_mat\\\". Determines the underlying format\\n of the new object's rotation \\n Returns:\\n A new identity rotation\\n \\\"\\\"\\\"\\n if(fmt == \\\"rot_mat\\\"):\\n rot_mats = identity_rot_mats(\\n shape, dtype, device, requires_grad,\\n )\\n return Rotation(rot_mats=rot_mats, quats=None)\\n elif(fmt == \\\"quat\\\"):\\n quats = identity_quats(shape, dtype, device, requires_grad)\\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\\n else:\\n raise ValueError(f\\\"Invalid format: f{fmt}\\\")\\n\\n # Magic methods\\n\\n def __getitem__(self, index: Any) -> Rotation:\\n \\\"\\\"\\\"\\n Allows torch-style indexing over the virtual shape of the rotation\\n object. See documentation for the shape property.\\n\\n Args:\\n index:\\n A torch index. E.g. (1, 3, 2), or (slice(None,))\\n Returns:\\n The indexed rotation\\n \\\"\\\"\\\"\\n if type(index) != tuple:\\n index = (index,)\\n\\n if(self._rot_mats is not None):\\n rot_mats = self._rot_mats[index + (slice(None), slice(None))]\\n return Rotation(rot_mats=rot_mats)\\n elif(self._quats is not None):\\n quats = self._quats[index + (slice(None),)]\\n return Rotation(quats=quats, normalize_quats=False)\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n def __mul__(self,\\n right: torch.Tensor,\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Pointwise left multiplication of the rotation with a tensor. Can be\\n used to e.g. mask the Rotation.\\n\\n Args:\\n right:\\n The tensor multiplicand\\n Returns:\\n The product\\n \\\"\\\"\\\"\\n if not(isinstance(right, torch.Tensor)):\\n raise TypeError(\\\"The other multiplicand must be a Tensor\\\")\\n\\n if(self._rot_mats is not None):\\n rot_mats = self._rot_mats * right[..., None, None]\\n return Rotation(rot_mats=rot_mats, quats=None)\\n elif(self._quats is not None):\\n quats = self._quats * right[..., None]\\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n def __rmul__(self,\\n left: torch.Tensor,\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Reverse pointwise multiplication of the rotation with a tensor.\\n\\n Args:\\n left:\\n The left multiplicand\\n Returns:\\n The product\\n \\\"\\\"\\\"\\n return self.__mul__(left)\\n \\n # Properties\\n\\n @property\\n def shape(self) -> torch.Size:\\n \\\"\\\"\\\"\\n Returns the virtual shape of the rotation object. This shape is\\n defined as the batch dimensions of the underlying rotation matrix\\n or quaternion. If the Rotation was initialized with a [10, 3, 3]\\n rotation matrix tensor, for example, the resulting shape would be\\n [10].\\n \\n Returns:\\n The virtual shape of the rotation object\\n \\\"\\\"\\\"\\n s = None\\n if(self._quats is not None):\\n s = self._quats.shape[:-1]\\n else:\\n s = self._rot_mats.shape[:-2]\\n\\n return s\\n\\n @property\\n def dtype(self) -> torch.dtype:\\n \\\"\\\"\\\"\\n Returns the dtype of the underlying rotation.\\n\\n Returns:\\n The dtype of the underlying rotation\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return self._rot_mats.dtype\\n elif(self._quats is not None):\\n return self._quats.dtype\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n @property\\n def device(self) -> torch.device:\\n \\\"\\\"\\\"\\n The device of the underlying rotation\\n\\n Returns:\\n The device of the underlying rotation\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return self._rot_mats.device\\n elif(self._quats is not None):\\n return self._quats.device\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n @property\\n def requires_grad(self) -> bool:\\n \\\"\\\"\\\"\\n Returns the requires_grad property of the underlying rotation\\n\\n Returns:\\n The requires_grad property of the underlying tensor\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return self._rot_mats.requires_grad\\n elif(self._quats is not None):\\n return self._quats.requires_grad\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n def get_rot_mats(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Returns the underlying rotation as a rotation matrix tensor.\\n\\n Returns:\\n The rotation as a rotation matrix tensor\\n \\\"\\\"\\\"\\n rot_mats = self._rot_mats\\n if(rot_mats is None):\\n if(self._quats is None):\\n raise ValueError(\\\"Both rotations are None\\\")\\n else:\\n rot_mats = quat_to_rot(self._quats)\\n\\n return rot_mats \\n\\n def get_quats(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Returns the underlying rotation as a quaternion tensor.\\n\\n Depending on whether the Rotation was initialized with a\\n quaternion, this function may call torch.linalg.eigh.\\n\\n Returns:\\n The rotation as a quaternion tensor.\\n \\\"\\\"\\\"\\n quats = self._quats\\n if(quats is None):\\n if(self._rot_mats is None):\\n raise ValueError(\\\"Both rotations are None\\\")\\n else:\\n quats = rot_to_quat(self._rot_mats)\\n\\n return quats\\n\\n def get_cur_rot(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Return the underlying rotation in its current form\\n\\n Returns:\\n The stored rotation\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return self._rot_mats\\n elif(self._quats is not None):\\n return self._quats\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n # Rotation functions\\n\\n def compose_q_update_vec(self, \\n q_update_vec: torch.Tensor, \\n normalize_quats: bool = True\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Returns a new quaternion Rotation after updating the current\\n object's underlying rotation with a quaternion update, formatted\\n as a [*, 3] tensor whose final three columns represent x, y, z such \\n that (1, x, y, z) is the desired (not necessarily unit) quaternion\\n update.\\n\\n Args:\\n q_update_vec:\\n A [*, 3] quaternion update tensor\\n normalize_quats:\\n Whether to normalize the output quaternion\\n Returns:\\n An updated Rotation\\n \\\"\\\"\\\"\\n quats = self.get_quats()\\n new_quats = quats + quat_multiply_by_vec(quats, q_update_vec)\\n return Rotation(\\n rot_mats=None, \\n quats=new_quats, \\n normalize_quats=normalize_quats,\\n )\\n\\n def compose_r(self, r: Rotation) -> Rotation:\\n \\\"\\\"\\\"\\n Compose the rotation matrices of the current Rotation object with\\n those of another.\\n\\n Args:\\n r:\\n An update rotation object\\n Returns:\\n An updated rotation object\\n \\\"\\\"\\\"\\n r1 = self.get_rot_mats()\\n r2 = r.get_rot_mats()\\n new_rot_mats = rot_matmul(r1, r2)\\n return Rotation(rot_mats=new_rot_mats, quats=None)\\n\\n def compose_q(self, r: Rotation, normalize_quats: bool = True) -> Rotation:\\n \\\"\\\"\\\"\\n Compose the quaternions of the current Rotation object with those\\n of another.\\n\\n Depending on whether either Rotation was initialized with\\n quaternions, this function may call torch.linalg.eigh.\\n\\n Args:\\n r:\\n An update rotation object\\n Returns:\\n An updated rotation object\\n \\\"\\\"\\\"\\n q1 = self.get_quats()\\n q2 = r.get_quats()\\n new_quats = quat_multiply(q1, q2)\\n return Rotation(\\n rot_mats=None, quats=new_quats, normalize_quats=normalize_quats\\n )\\n\\n def apply(self, pts: torch.Tensor) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Apply the current Rotation as a rotation matrix to a set of 3D\\n coordinates.\\n\\n Args:\\n pts:\\n A [*, 3] set of points\\n Returns:\\n [*, 3] rotated points\\n \\\"\\\"\\\"\\n rot_mats = self.get_rot_mats()\\n return rot_vec_mul(rot_mats, pts)\\n\\n def invert_apply(self, pts: torch.Tensor) -> torch.Tensor:\\n \\\"\\\"\\\"\\n The inverse of the apply() method.\\n\\n Args:\\n pts:\\n A [*, 3] set of points\\n Returns:\\n [*, 3] inverse-rotated points\\n \\\"\\\"\\\"\\n rot_mats = self.get_rot_mats()\\n inv_rot_mats = invert_rot_mat(rot_mats) \\n return rot_vec_mul(inv_rot_mats, pts)\\n\\n def invert(self) -> Rotation:\\n \\\"\\\"\\\"\\n Returns the inverse of the current Rotation.\\n\\n Returns:\\n The inverse of the current Rotation\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return Rotation(\\n rot_mats=invert_rot_mat(self._rot_mats), \\n quats=None\\n )\\n elif(self._quats is not None):\\n return Rotation(\\n rot_mats=None,\\n quats=invert_quat(self._quats),\\n normalize_quats=False,\\n )\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n # \\\"Tensor\\\" stuff\\n\\n def unsqueeze(self, \\n dim: int,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Analogous to torch.unsqueeze. The dimension is relative to the\\n shape of the Rotation object.\\n \\n Args:\\n dim: A positive or negative dimension index.\\n Returns:\\n The unsqueezed Rotation.\\n \\\"\\\"\\\"\\n if dim >= len(self.shape):\\n raise ValueError(\\\"Invalid dimension\\\")\\n\\n if(self._rot_mats is not None):\\n rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2)\\n return Rotation(rot_mats=rot_mats, quats=None)\\n elif(self._quats is not None):\\n quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1)\\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n @staticmethod\\n def cat(\\n rs: Sequence[Rotation], \\n dim: int,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Concatenates rotations along one of the batch dimensions. Analogous\\n to torch.cat().\\n\\n Note that the output of this operation is always a rotation matrix,\\n regardless of the format of input rotations.\\n\\n Args:\\n rs: \\n A list of rotation objects\\n dim: \\n The dimension along which the rotations should be \\n concatenated\\n Returns:\\n A concatenated Rotation object in rotation matrix format\\n \\\"\\\"\\\"\\n rot_mats = [r.get_rot_mats() for r in rs]\\n rot_mats = torch.cat(rot_mats, dim=dim if dim >= 0 else dim - 2)\\n\\n return Rotation(rot_mats=rot_mats, quats=None) \\n\\n def map_tensor_fn(self, \\n fn: Callable[torch.Tensor, torch.Tensor]\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Apply a Tensor -> Tensor function to underlying rotation tensors,\\n mapping over the rotation dimension(s). Can be used e.g. to sum out\\n a one-hot batch dimension.\\n\\n Args:\\n fn:\\n A Tensor -> Tensor function to be mapped over the Rotation \\n Returns:\\n The transformed Rotation object\\n \\\"\\\"\\\" \\n if(self._rot_mats is not None):\\n rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,))\\n rot_mats = torch.stack(\\n list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1\\n )\\n rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3))\\n return Rotation(rot_mats=rot_mats, quats=None)\\n elif(self._quats is not None):\\n quats = torch.stack(\\n list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1\\n )\\n return Rotation(rot_mats=None, quats=quats, normalize_quats=False)\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n \\n def cuda(self) -> Rotation:\\n \\\"\\\"\\\"\\n Analogous to the cuda() method of torch Tensors\\n\\n Returns:\\n A copy of the Rotation in CUDA memory\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return Rotation(rot_mats=self._rot_mats.cuda(), quats=None)\\n elif(self._quats is not None):\\n return Rotation(\\n rot_mats=None, \\n quats=self._quats.cuda(),\\n normalize_quats=False\\n )\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n def to(self, \\n device: Optional[torch.device], \\n dtype: Optional[torch.dtype]\\n ) -> Rotation:\\n \\\"\\\"\\\"\\n Analogous to the to() method of torch Tensors\\n\\n Args:\\n device:\\n A torch device\\n dtype:\\n A torch dtype\\n Returns:\\n A copy of the Rotation using the new device and dtype\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return Rotation(\\n rot_mats=self._rot_mats.to(device=device, dtype=dtype), \\n quats=None,\\n )\\n elif(self._quats is not None):\\n return Rotation(\\n rot_mats=None, \\n quats=self._quats.to(device=device, dtype=dtype),\\n normalize_quats=False,\\n )\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\\n\\n def detach(self) -> Rotation:\\n \\\"\\\"\\\"\\n Returns a copy of the Rotation whose underlying Tensor has been\\n detached from its torch graph.\\n\\n Returns:\\n A copy of the Rotation whose underlying Tensor has been detached\\n from its torch graph\\n \\\"\\\"\\\"\\n if(self._rot_mats is not None):\\n return Rotation(rot_mats=self._rot_mats.detach(), quats=None)\\n elif(self._quats is not None):\\n return Rotation(\\n rot_mats=None, \\n quats=self._quats.detach(), \\n normalize_quats=False,\\n )\\n else:\\n raise ValueError(\\\"Both rotations are None\\\")\"\n },\n {\n \"identifier\": \"Rigid\",\n \"path\": \"frame2seq/openfold/utils/rigid_utils.py\",\n \"snippet\": \"class Rigid:\\n \\\"\\\"\\\"\\n A class representing a rigid transformation. Little more than a wrapper\\n around two objects: a Rotation object and a [*, 3] translation\\n Designed to behave approximately like a single torch tensor with the \\n shape of the shared batch dimensions of its component parts.\\n \\\"\\\"\\\"\\n def __init__(self, \\n rots: Optional[Rotation],\\n trans: Optional[torch.Tensor],\\n ):\\n \\\"\\\"\\\"\\n Args:\\n rots: A [*, 3, 3] rotation tensor\\n trans: A corresponding [*, 3] translation tensor\\n \\\"\\\"\\\"\\n # (we need device, dtype, etc. from at least one input)\\n\\n batch_dims, dtype, device, requires_grad = None, None, None, None\\n if(trans is not None):\\n batch_dims = trans.shape[:-1]\\n dtype = trans.dtype\\n device = trans.device\\n requires_grad = trans.requires_grad\\n elif(rots is not None):\\n batch_dims = rots.shape\\n dtype = rots.dtype\\n device = rots.device\\n requires_grad = rots.requires_grad\\n else:\\n raise ValueError(\\\"At least one input argument must be specified\\\")\\n\\n if(rots is None):\\n rots = Rotation.identity(\\n batch_dims, dtype, device, requires_grad,\\n )\\n elif(trans is None):\\n trans = identity_trans(\\n batch_dims, dtype, device, requires_grad,\\n )\\n\\n if((rots.shape != trans.shape[:-1]) or\\n (rots.device != trans.device)):\\n raise ValueError(\\\"Rots and trans incompatible\\\")\\n\\n # Force full precision. Happens to the rotations automatically.\\n trans = trans.to(dtype=torch.float32)\\n\\n self._rots = rots\\n self._trans = trans\\n\\n @staticmethod\\n def identity(\\n shape: Tuple[int], \\n dtype: Optional[torch.dtype] = None,\\n device: Optional[torch.device] = None, \\n requires_grad: bool = True,\\n fmt: str = \\\"quat\\\",\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Constructs an identity transformation.\\n\\n Args:\\n shape: \\n The desired shape\\n dtype: \\n The dtype of both internal tensors\\n device: \\n The device of both internal tensors\\n requires_grad: \\n Whether grad should be enabled for the internal tensors\\n Returns:\\n The identity transformation\\n \\\"\\\"\\\"\\n return Rigid(\\n Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt),\\n identity_trans(shape, dtype, device, requires_grad),\\n )\\n\\n def __getitem__(self, \\n index: Any,\\n ) -> Rigid:\\n \\\"\\\"\\\" \\n Indexes the affine transformation with PyTorch-style indices.\\n The index is applied to the shared dimensions of both the rotation\\n and the translation.\\n\\n E.g.::\\n\\n r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None)\\n t = Rigid(r, torch.rand(10, 10, 3))\\n indexed = t[3, 4:6]\\n assert(indexed.shape == (2,))\\n assert(indexed.get_rots().shape == (2,))\\n assert(indexed.get_trans().shape == (2, 3))\\n\\n Args:\\n index: A standard torch tensor index. E.g. 8, (10, None, 3),\\n or (3, slice(0, 1, None))\\n Returns:\\n The indexed tensor \\n \\\"\\\"\\\"\\n if type(index) != tuple:\\n index = (index,)\\n \\n return Rigid(\\n self._rots[index],\\n self._trans[index + (slice(None),)],\\n )\\n\\n def __mul__(self,\\n right: torch.Tensor,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Pointwise left multiplication of the transformation with a tensor.\\n Can be used to e.g. mask the Rigid.\\n\\n Args:\\n right:\\n The tensor multiplicand\\n Returns:\\n The product\\n \\\"\\\"\\\"\\n if not(isinstance(right, torch.Tensor)):\\n raise TypeError(\\\"The other multiplicand must be a Tensor\\\")\\n\\n new_rots = self._rots * right\\n new_trans = self._trans * right[..., None]\\n\\n return Rigid(new_rots, new_trans)\\n\\n def __rmul__(self,\\n left: torch.Tensor,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Reverse pointwise multiplication of the transformation with a \\n tensor.\\n\\n Args:\\n left:\\n The left multiplicand\\n Returns:\\n The product\\n \\\"\\\"\\\"\\n return self.__mul__(left)\\n\\n @property\\n def shape(self) -> torch.Size:\\n \\\"\\\"\\\"\\n Returns the shape of the shared dimensions of the rotation and\\n the translation.\\n \\n Returns:\\n The shape of the transformation\\n \\\"\\\"\\\"\\n s = self._trans.shape[:-1]\\n return s\\n\\n @property\\n def device(self) -> torch.device:\\n \\\"\\\"\\\"\\n Returns the device on which the Rigid's tensors are located.\\n\\n Returns:\\n The device on which the Rigid's tensors are located\\n \\\"\\\"\\\"\\n return self._trans.device\\n\\n def get_rots(self) -> Rotation:\\n \\\"\\\"\\\"\\n Getter for the rotation.\\n\\n Returns:\\n The rotation object\\n \\\"\\\"\\\"\\n return self._rots\\n\\n def get_trans(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Getter for the translation.\\n\\n Returns:\\n The stored translation\\n \\\"\\\"\\\"\\n return self._trans\\n\\n def compose_q_update_vec(self, \\n q_update_vec: torch.Tensor,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Composes the transformation with a quaternion update vector of\\n shape [*, 6], where the final 6 columns represent the x, y, and\\n z values of a quaternion of form (1, x, y, z) followed by a 3D\\n translation.\\n\\n Args:\\n q_vec: The quaternion update vector.\\n Returns:\\n The composed transformation.\\n \\\"\\\"\\\"\\n q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:]\\n new_rots = self._rots.compose_q_update_vec(q_vec)\\n\\n trans_update = self._rots.apply(t_vec)\\n new_translation = self._trans + trans_update\\n\\n return Rigid(new_rots, new_translation)\\n\\n def compose(self,\\n r: Rigid,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Composes the current rigid object with another.\\n\\n Args:\\n r:\\n Another Rigid object\\n Returns:\\n The composition of the two transformations\\n \\\"\\\"\\\"\\n new_rot = self._rots.compose_r(r._rots)\\n new_trans = self._rots.apply(r._trans) + self._trans\\n return Rigid(new_rot, new_trans)\\n\\n def apply(self, \\n pts: torch.Tensor,\\n ) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Applies the transformation to a coordinate tensor.\\n\\n Args:\\n pts: A [*, 3] coordinate tensor.\\n Returns:\\n The transformed points.\\n \\\"\\\"\\\"\\n rotated = self._rots.apply(pts) \\n return rotated + self._trans\\n\\n def invert_apply(self, \\n pts: torch.Tensor\\n ) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Applies the inverse of the transformation to a coordinate tensor.\\n\\n Args:\\n pts: A [*, 3] coordinate tensor\\n Returns:\\n The transformed points.\\n \\\"\\\"\\\"\\n pts = pts - self._trans\\n return self._rots.invert_apply(pts) \\n\\n def invert(self) -> Rigid:\\n \\\"\\\"\\\"\\n Inverts the transformation.\\n\\n Returns:\\n The inverse transformation.\\n \\\"\\\"\\\"\\n rot_inv = self._rots.invert() \\n trn_inv = rot_inv.apply(self._trans)\\n\\n return Rigid(rot_inv, -1 * trn_inv)\\n\\n def map_tensor_fn(self, \\n fn: Callable[torch.Tensor, torch.Tensor]\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Apply a Tensor -> Tensor function to underlying translation and\\n rotation tensors, mapping over the translation/rotation dimensions\\n respectively.\\n\\n Args:\\n fn:\\n A Tensor -> Tensor function to be mapped over the Rigid\\n Returns:\\n The transformed Rigid object\\n \\\"\\\"\\\" \\n new_rots = self._rots.map_tensor_fn(fn) \\n new_trans = torch.stack(\\n list(map(fn, torch.unbind(self._trans, dim=-1))), \\n dim=-1\\n )\\n\\n return Rigid(new_rots, new_trans)\\n\\n def to_tensor_4x4(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Converts a transformation to a homogenous transformation tensor.\\n\\n Returns:\\n A [*, 4, 4] homogenous transformation tensor\\n \\\"\\\"\\\"\\n tensor = self._trans.new_zeros((*self.shape, 4, 4))\\n tensor[..., :3, :3] = self._rots.get_rot_mats()\\n tensor[..., :3, 3] = self._trans\\n tensor[..., 3, 3] = 1\\n return tensor\\n\\n @staticmethod\\n def from_tensor_4x4(\\n t: torch.Tensor\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Constructs a transformation from a homogenous transformation\\n tensor.\\n\\n Args:\\n t: [*, 4, 4] homogenous transformation tensor\\n Returns:\\n T object with shape [*]\\n \\\"\\\"\\\"\\n if(t.shape[-2:] != (4, 4)):\\n raise ValueError(\\\"Incorrectly shaped input tensor\\\")\\n\\n rots = Rotation(rot_mats=t[..., :3, :3], quats=None)\\n trans = t[..., :3, 3]\\n \\n return Rigid(rots, trans)\\n\\n def to_tensor_7(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Converts a transformation to a tensor with 7 final columns, four \\n for the quaternion followed by three for the translation.\\n\\n Returns:\\n A [*, 7] tensor representation of the transformation\\n \\\"\\\"\\\"\\n tensor = self._trans.new_zeros((*self.shape, 7))\\n tensor[..., :4] = self._rots.get_quats()\\n tensor[..., 4:] = self._trans\\n\\n return tensor\\n\\n @staticmethod\\n def from_tensor_7(\\n t: torch.Tensor,\\n normalize_quats: bool = False,\\n ) -> Rigid:\\n if(t.shape[-1] != 7):\\n raise ValueError(\\\"Incorrectly shaped input tensor\\\")\\n\\n quats, trans = t[..., :4], t[..., 4:]\\n\\n rots = Rotation(\\n rot_mats=None, \\n quats=quats, \\n normalize_quats=normalize_quats\\n )\\n\\n return Rigid(rots, trans)\\n\\n @staticmethod\\n def from_3_points(\\n p_neg_x_axis: torch.Tensor, \\n origin: torch.Tensor, \\n p_xy_plane: torch.Tensor, \\n eps: float = 1e-8\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Implements algorithm 21. Constructs transformations from sets of 3 \\n points using the Gram-Schmidt algorithm.\\n\\n Args:\\n p_neg_x_axis: [*, 3] coordinates\\n origin: [*, 3] coordinates used as frame origins\\n p_xy_plane: [*, 3] coordinates\\n eps: Small epsilon value\\n Returns:\\n A transformation object of shape [*]\\n \\\"\\\"\\\"\\n p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1)\\n origin = torch.unbind(origin, dim=-1)\\n p_xy_plane = torch.unbind(p_xy_plane, dim=-1)\\n\\n e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)]\\n e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)]\\n\\n denom = torch.sqrt(sum((c * c for c in e0)) + eps)\\n e0 = [c / denom for c in e0]\\n dot = sum((c1 * c2 for c1, c2 in zip(e0, e1)))\\n e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)]\\n denom = torch.sqrt(sum((c * c for c in e1)) + eps)\\n e1 = [c / denom for c in e1]\\n e2 = [\\n e0[1] * e1[2] - e0[2] * e1[1],\\n e0[2] * e1[0] - e0[0] * e1[2],\\n e0[0] * e1[1] - e0[1] * e1[0],\\n ]\\n\\n rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1)\\n rots = rots.reshape(rots.shape[:-1] + (3, 3))\\n\\n rot_obj = Rotation(rot_mats=rots, quats=None)\\n\\n return Rigid(rot_obj, torch.stack(origin, dim=-1))\\n\\n def unsqueeze(self, \\n dim: int,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Analogous to torch.unsqueeze. The dimension is relative to the\\n shared dimensions of the rotation/translation.\\n \\n Args:\\n dim: A positive or negative dimension index.\\n Returns:\\n The unsqueezed transformation.\\n \\\"\\\"\\\"\\n if dim >= len(self.shape):\\n raise ValueError(\\\"Invalid dimension\\\")\\n rots = self._rots.unsqueeze(dim)\\n trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1)\\n\\n return Rigid(rots, trans)\\n\\n @staticmethod\\n def cat(\\n ts: Sequence[Rigid], \\n dim: int,\\n ) -> Rigid:\\n \\\"\\\"\\\"\\n Concatenates transformations along a new dimension.\\n\\n Args:\\n ts: \\n A list of T objects\\n dim: \\n The dimension along which the transformations should be \\n concatenated\\n Returns:\\n A concatenated transformation object\\n \\\"\\\"\\\"\\n rots = Rotation.cat([t._rots for t in ts], dim) \\n trans = torch.cat(\\n [t._trans for t in ts], dim=dim if dim >= 0 else dim - 1\\n )\\n\\n return Rigid(rots, trans)\\n\\n def apply_rot_fn(self, fn: Callable[Rotation, Rotation]) -> Rigid:\\n \\\"\\\"\\\"\\n Applies a Rotation -> Rotation function to the stored rotation\\n object.\\n\\n Args:\\n fn: A function of type Rotation -> Rotation\\n Returns:\\n A transformation object with a transformed rotation.\\n \\\"\\\"\\\"\\n return Rigid(fn(self._rots), self._trans)\\n\\n def apply_trans_fn(self, fn: Callable[torch.Tensor, torch.Tensor]) -> Rigid:\\n \\\"\\\"\\\"\\n Applies a Tensor -> Tensor function to the stored translation.\\n\\n Args:\\n fn: \\n A function of type Tensor -> Tensor to be applied to the\\n translation\\n Returns:\\n A transformation object with a transformed translation.\\n \\\"\\\"\\\"\\n return Rigid(self._rots, fn(self._trans))\\n\\n def scale_translation(self, trans_scale_factor: float) -> Rigid:\\n \\\"\\\"\\\"\\n Scales the translation by a constant factor.\\n\\n Args:\\n trans_scale_factor:\\n The constant factor\\n Returns:\\n A transformation object with a scaled translation.\\n \\\"\\\"\\\"\\n fn = lambda t: t * trans_scale_factor\\n return self.apply_trans_fn(fn)\\n\\n def stop_rot_gradient(self) -> Rigid:\\n \\\"\\\"\\\"\\n Detaches the underlying rotation object\\n\\n Returns:\\n A transformation object with detached rotations\\n \\\"\\\"\\\"\\n fn = lambda r: r.detach()\\n return self.apply_rot_fn(fn)\\n\\n @staticmethod\\n def make_transform_from_reference(n_xyz, ca_xyz, c_xyz, eps=1e-20):\\n \\\"\\\"\\\"\\n Returns a transformation object from reference coordinates.\\n \\n Note that this method does not take care of symmetries. If you \\n provide the atom positions in the non-standard way, the N atom will \\n end up not at [-0.527250, 1.359329, 0.0] but instead at \\n [-0.527250, -1.359329, 0.0]. You need to take care of such cases in \\n your code.\\n \\n Args:\\n n_xyz: A [*, 3] tensor of nitrogen xyz coordinates.\\n ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates.\\n c_xyz: A [*, 3] tensor of carbon xyz coordinates.\\n Returns:\\n A transformation object. After applying the translation and \\n rotation to the reference backbone, the coordinates will \\n approximately equal to the input coordinates.\\n \\\"\\\"\\\" \\n translation = -1 * ca_xyz\\n n_xyz = n_xyz + translation\\n c_xyz = c_xyz + translation\\n\\n c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)]\\n norm = torch.sqrt(eps + c_x ** 2 + c_y ** 2)\\n sin_c1 = -c_y / norm\\n cos_c1 = c_x / norm\\n zeros = sin_c1.new_zeros(sin_c1.shape)\\n ones = sin_c1.new_ones(sin_c1.shape)\\n\\n c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3))\\n c1_rots[..., 0, 0] = cos_c1\\n c1_rots[..., 0, 1] = -1 * sin_c1\\n c1_rots[..., 1, 0] = sin_c1\\n c1_rots[..., 1, 1] = cos_c1\\n c1_rots[..., 2, 2] = 1\\n\\n norm = torch.sqrt(eps + c_x ** 2 + c_y ** 2 + c_z ** 2)\\n sin_c2 = c_z / norm\\n cos_c2 = torch.sqrt(c_x ** 2 + c_y ** 2) / norm\\n\\n c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))\\n c2_rots[..., 0, 0] = cos_c2\\n c2_rots[..., 0, 2] = sin_c2\\n c2_rots[..., 1, 1] = 1\\n c2_rots[..., 2, 0] = -1 * sin_c2\\n c2_rots[..., 2, 2] = cos_c2\\n\\n c_rots = rot_matmul(c2_rots, c1_rots)\\n n_xyz = rot_vec_mul(c_rots, n_xyz)\\n\\n _, n_y, n_z = [n_xyz[..., i] for i in range(3)]\\n norm = torch.sqrt(eps + n_y ** 2 + n_z ** 2)\\n sin_n = -n_z / norm\\n cos_n = n_y / norm\\n\\n n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3))\\n n_rots[..., 0, 0] = 1\\n n_rots[..., 1, 1] = cos_n\\n n_rots[..., 1, 2] = -1 * sin_n\\n n_rots[..., 2, 1] = sin_n\\n n_rots[..., 2, 2] = cos_n\\n\\n rots = rot_matmul(n_rots, c_rots)\\n\\n rots = rots.transpose(-1, -2)\\n translation = -1 * translation\\n\\n rot_obj = Rotation(rot_mats=rots, quats=None)\\n\\n return Rigid(rot_obj, translation)\\n\\n def cuda(self) -> Rigid:\\n \\\"\\\"\\\"\\n Moves the transformation object to GPU memory\\n \\n Returns:\\n A version of the transformation on GPU\\n \\\"\\\"\\\"\\n return Rigid(self._rots.cuda(), self._trans.cuda())\"\n },\n {\n \"identifier\": \"batched_gather\",\n \"path\": \"frame2seq/openfold/utils/tensor_utils.py\",\n \"snippet\": \"def add(m1, m2, inplace):\\ndef permute_final_dims(tensor: torch.Tensor, inds: List[int]):\\ndef flatten_final_dims(t: torch.Tensor, no_dims: int):\\ndef masked_mean(mask, value, dim, eps=1e-4):\\ndef pts_to_distogram(pts, min_bin=2.3125, max_bin=21.6875, no_bins=64):\\ndef dict_multimap(fn, dicts):\\ndef one_hot(x, v_bins):\\ndef batched_gather(data, inds, dim=0, no_batch_dims=0):\\ndef dict_map(fn, dic, leaf_type):\\ndef tree_map(fn, tree, leaf_type):\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport frame2seq.openfold.np.residue_constants as rc\nfrom typing import Dict\nfrom frame2seq.openfold.np import protein\nfrom frame2seq.openfold.utils.rigid_utils import Rotation, Rigid\nfrom frame2seq.openfold.utils.tensor_utils import (\n batched_gather,\n one_hot,\n tree_map,\n tensor_tree_map,\n)"},"token_num":{"kind":"number","value":11342,"string":"11,342"},"cropped_code":{"kind":"string","value":"\n\ndef build_template_pair_feat(\n batch, \n min_bin, max_bin, no_bins, \n use_unit_vector=False, \n eps=1e-20, inf=1e8\n):\n template_mask = batch[\"template_pseudo_beta_mask\"]\n template_mask_2d = template_mask[..., None] * template_mask[..., None, :]\n\n # Compute distogram (this seems to differ slightly from Alg. 5)\n tpb = batch[\"template_pseudo_beta\"]\n dgram = torch.sum(\n (tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True\n )\n lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2\n upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)\n dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)\n\n to_concat = [dgram, template_mask_2d[..., None]]\n\n aatype_one_hot = nn.functional.one_hot(\n batch[\"template_aatype\"],\n rc.restype_num + 2,\n )\n\n n_res = batch[\"template_aatype\"].shape[-1]\n to_concat.append(\n aatype_one_hot[..., None, :, :].expand(\n *aatype_one_hot.shape[:-2], n_res, -1, -1\n )\n )\n to_concat.append(\n aatype_one_hot[..., None, :].expand(\n *aatype_one_hot.shape[:-2], -1, n_res, -1\n )\n )\n\n n, ca, c = [rc.atom_order[a] for a in [\"N\", \"CA\", \"C\"]]\n rigids = Rigid.make_transform_from_reference(\n n_xyz=batch[\"template_all_atom_positions\"][..., n, :],\n ca_xyz=batch[\"template_all_atom_positions\"][..., ca, :],\n c_xyz=batch[\"template_all_atom_positions\"][..., c, :],\n eps=eps,\n )\n points = rigids.get_trans()[..., None, :, :]\n rigid_vec = rigids[..., None].invert_apply(points)\n\n inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec ** 2, dim=-1))\n\n t_aa_masks = batch[\"template_all_atom_mask\"]\n template_mask = (\n t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]\n )\n template_mask_2d = template_mask[..., None] * template_mask[..., None, :]\n\n inv_distance_scalar = inv_distance_scalar * template_mask_2d\n unit_vector = rigid_vec * inv_distance_scalar[..., None]\n \n if(not use_unit_vector):\n unit_vector = unit_vector * 0.\n \n to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))\n to_concat.append(template_mask_2d[..., None])\n\n act = torch.cat(to_concat, dim=-1)\n act = act * template_mask_2d[..., None]\n\n return act\n\n\ndef build_extra_msa_feat(batch):\n msa_1hot = nn.functional.one_hot(batch[\"extra_msa\"], 23)\n msa_feat = [\n msa_1hot,\n batch[\"extra_has_deletion\"].unsqueeze(-1),\n batch[\"extra_deletion_value\"].unsqueeze(-1),\n ]\n return torch.cat(msa_feat, dim=-1)\n\n\ndef torsion_angles_to_frames(\n r: Rigid,\n alpha: torch.Tensor,\n aatype: torch.Tensor,\n rrgdf: torch.Tensor,\n):\n # [*, N, 8, 4, 4]\n default_4x4 = rrgdf[aatype, ...]\n\n # [*, N, 8] transformations, i.e.\n # One [*, N, 8, 3, 3] rotation matrix and\n # One [*, N, 8, 3] translation matrix\n default_r = r.from_tensor_4x4(default_4x4)\n\n bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))\n bb_rot[..., 1] = 1\n\n # [*, N, 8, 2]\n alpha = torch.cat(\n [bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2\n )\n\n # [*, N, 8, 3, 3]\n # Produces rotation matrices of the form:\n # [\n # [1, 0 , 0 ],\n # [0, a_2,-a_1],\n # [0, a_1, a_2]\n # ]\n # This follows the original code rather than the supplement, which uses\n # different indices.\n\n all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)\n all_rots[..., 0, 0] = 1\n all_rots[..., 1, 1] = alpha[..., 1]\n all_rots[..., 1, 2] = -alpha[..., 0]\n all_rots[..., 2, 1:] = alpha\n\n"},"all_code":{"kind":"string","value":"# Copyright 2021 AlQuraishi Laboratory\n# Copyright 2021 DeepMind Technologies Limited\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\ndef pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):\n is_gly = aatype == rc.restype_order[\"G\"]\n ca_idx = rc.atom_order[\"CA\"]\n cb_idx = rc.atom_order[\"CB\"]\n pseudo_beta = torch.where(\n is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3),\n all_atom_positions[..., ca_idx, :],\n all_atom_positions[..., cb_idx, :],\n )\n\n if all_atom_masks is not None:\n pseudo_beta_mask = torch.where(\n is_gly,\n all_atom_masks[..., ca_idx],\n all_atom_masks[..., cb_idx],\n )\n return pseudo_beta, pseudo_beta_mask\n else:\n return pseudo_beta\n\n\ndef atom14_to_atom37(atom14, batch):\n atom37_data = batched_gather(\n atom14,\n batch[\"residx_atom37_to_atom14\"],\n dim=-2,\n no_batch_dims=len(atom14.shape[:-2]),\n )\n\n atom37_data = atom37_data * batch[\"atom37_atom_exists\"][..., None]\n\n return atom37_data\n\n\ndef build_template_angle_feat(template_feats):\n template_aatype = template_feats[\"template_aatype\"]\n torsion_angles_sin_cos = template_feats[\"template_torsion_angles_sin_cos\"]\n alt_torsion_angles_sin_cos = template_feats[\n \"template_alt_torsion_angles_sin_cos\"\n ]\n torsion_angles_mask = template_feats[\"template_torsion_angles_mask\"]\n template_angle_feat = torch.cat(\n [\n nn.functional.one_hot(template_aatype, 22),\n torsion_angles_sin_cos.reshape(\n *torsion_angles_sin_cos.shape[:-2], 14\n ),\n alt_torsion_angles_sin_cos.reshape(\n *alt_torsion_angles_sin_cos.shape[:-2], 14\n ),\n torsion_angles_mask,\n ],\n dim=-1,\n )\n\n return template_angle_feat\n\n\ndef build_template_pair_feat(\n batch, \n min_bin, max_bin, no_bins, \n use_unit_vector=False, \n eps=1e-20, inf=1e8\n):\n template_mask = batch[\"template_pseudo_beta_mask\"]\n template_mask_2d = template_mask[..., None] * template_mask[..., None, :]\n\n # Compute distogram (this seems to differ slightly from Alg. 5)\n tpb = batch[\"template_pseudo_beta\"]\n dgram = torch.sum(\n (tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True\n )\n lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2\n upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)\n dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)\n\n to_concat = [dgram, template_mask_2d[..., None]]\n\n aatype_one_hot = nn.functional.one_hot(\n batch[\"template_aatype\"],\n rc.restype_num + 2,\n )\n\n n_res = batch[\"template_aatype\"].shape[-1]\n to_concat.append(\n aatype_one_hot[..., None, :, :].expand(\n *aatype_one_hot.shape[:-2], n_res, -1, -1\n )\n )\n to_concat.append(\n aatype_one_hot[..., None, :].expand(\n *aatype_one_hot.shape[:-2], -1, n_res, -1\n )\n )\n\n n, ca, c = [rc.atom_order[a] for a in [\"N\", \"CA\", \"C\"]]\n rigids = Rigid.make_transform_from_reference(\n n_xyz=batch[\"template_all_atom_positions\"][..., n, :],\n ca_xyz=batch[\"template_all_atom_positions\"][..., ca, :],\n c_xyz=batch[\"template_all_atom_positions\"][..., c, :],\n eps=eps,\n )\n points = rigids.get_trans()[..., None, :, :]\n rigid_vec = rigids[..., None].invert_apply(points)\n\n inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec ** 2, dim=-1))\n\n t_aa_masks = batch[\"template_all_atom_mask\"]\n template_mask = (\n t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]\n )\n template_mask_2d = template_mask[..., None] * template_mask[..., None, :]\n\n inv_distance_scalar = inv_distance_scalar * template_mask_2d\n unit_vector = rigid_vec * inv_distance_scalar[..., None]\n \n if(not use_unit_vector):\n unit_vector = unit_vector * 0.\n \n to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))\n to_concat.append(template_mask_2d[..., None])\n\n act = torch.cat(to_concat, dim=-1)\n act = act * template_mask_2d[..., None]\n\n return act\n\n\ndef build_extra_msa_feat(batch):\n msa_1hot = nn.functional.one_hot(batch[\"extra_msa\"], 23)\n msa_feat = [\n msa_1hot,\n batch[\"extra_has_deletion\"].unsqueeze(-1),\n batch[\"extra_deletion_value\"].unsqueeze(-1),\n ]\n return torch.cat(msa_feat, dim=-1)\n\n\ndef torsion_angles_to_frames(\n r: Rigid,\n alpha: torch.Tensor,\n aatype: torch.Tensor,\n rrgdf: torch.Tensor,\n):\n # [*, N, 8, 4, 4]\n default_4x4 = rrgdf[aatype, ...]\n\n # [*, N, 8] transformations, i.e.\n # One [*, N, 8, 3, 3] rotation matrix and\n # One [*, N, 8, 3] translation matrix\n default_r = r.from_tensor_4x4(default_4x4)\n\n bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))\n bb_rot[..., 1] = 1\n\n # [*, N, 8, 2]\n alpha = torch.cat(\n [bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2\n )\n\n # [*, N, 8, 3, 3]\n # Produces rotation matrices of the form:\n # [\n # [1, 0 , 0 ],\n # [0, a_2,-a_1],\n # [0, a_1, a_2]\n # ]\n # This follows the original code rather than the supplement, which uses\n # different indices.\n\n all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)\n all_rots[..., 0, 0] = 1\n all_rots[..., 1, 1] = alpha[..., 1]\n all_rots[..., 1, 2] = -alpha[..., 0]\n all_rots[..., 2, 1:] = alpha\n"},"next_line":{"kind":"string","value":" all_rots = Rigid(Rotation(rot_mats=all_rots), None)"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-25 09:29:36+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4697,"cells":{"repo_name":{"kind":"string","value":"KyanChen/TTP"},"file_path":{"kind":"string","value":"mmpretrain/models/multimodal/clip/clip.py"},"context":{"kind":"list like","value":[{"identifier":"CIFAR100_CATEGORIES","path":"mmpretrain/datasets/categories.py","snippet":"CIFAR100_CATEGORIES = (\n 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',\n 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',\n 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',\n 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',\n 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',\n 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',\n 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain',\n 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree',\n 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy',\n 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket',\n 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail',\n 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper',\n 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train',\n 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf',\n 'woman', 'worm')"},{"identifier":"IMAGENET_SIMPLE_CATEGORIES","path":"mmpretrain/datasets/categories.py","snippet":"IMAGENET_SIMPLE_CATEGORIES = (\n 'tench', 'goldfish', 'great white shark', 'tiger shark',\n 'hammerhead shark', 'electric ray', 'stingray', 'rooster', 'hen',\n 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco',\n 'indigo bunting', 'American robin', 'bulbul', 'jay', 'magpie', 'chickadee',\n 'American dipper', 'kite (bird of prey)', 'bald eagle', 'vulture',\n 'great grey owl', 'fire salamander', 'smooth newt', 'newt',\n 'spotted salamander', 'axolotl', 'American bullfrog', 'tree frog',\n 'tailed frog', 'loggerhead sea turtle', 'leatherback sea turtle',\n 'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'green iguana',\n 'Carolina anole', 'desert grassland whiptail lizard', 'agama',\n 'frilled-necked lizard', 'alligator lizard', 'Gila monster',\n 'European green lizard', 'chameleon', 'Komodo dragon', 'Nile crocodile',\n 'American alligator', 'triceratops', 'worm snake', 'ring-necked snake',\n 'eastern hog-nosed snake', 'smooth green snake', 'kingsnake',\n 'garter snake', 'water snake', 'vine snake', 'night snake',\n 'boa constrictor', 'African rock python', 'Indian cobra', 'green mamba',\n 'sea snake', 'Saharan horned viper', 'eastern diamondback rattlesnake',\n 'sidewinder rattlesnake', 'trilobite', 'harvestman', 'scorpion',\n 'yellow garden spider', 'barn spider', 'European garden spider',\n 'southern black widow', 'tarantula', 'wolf spider', 'tick', 'centipede',\n 'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie grouse', 'peafowl',\n 'quail', 'partridge', 'african grey parrot', 'macaw',\n 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill',\n 'hummingbird', 'jacamar', 'toucan', 'duck', 'red-breasted merganser',\n 'goose', 'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala',\n 'wombat', 'jellyfish', 'sea anemone', 'brain coral', 'flatworm',\n 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton',\n 'chambered nautilus', 'Dungeness crab', 'rock crab', 'fiddler crab',\n 'red king crab', 'American lobster', 'spiny lobster', 'crayfish',\n 'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill',\n 'flamingo', 'little blue heron', 'great egret', 'bittern bird',\n 'crane bird', 'limpkin', 'common gallinule', 'American coot', 'bustard',\n 'ruddy turnstone', 'dunlin', 'common redshank', 'dowitcher',\n 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale',\n 'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese Chin',\n 'Maltese', 'Pekingese', 'Shih Tzu', 'King Charles Spaniel', 'Papillon',\n 'toy terrier', 'Rhodesian Ridgeback', 'Afghan Hound', 'Basset Hound',\n 'Beagle', 'Bloodhound', 'Bluetick Coonhound', 'Black and Tan Coonhound',\n 'Treeing Walker Coonhound', 'English foxhound', 'Redbone Coonhound',\n 'borzoi', 'Irish Wolfhound', 'Italian Greyhound', 'Whippet',\n 'Ibizan Hound', 'Norwegian Elkhound', 'Otterhound', 'Saluki',\n 'Scottish Deerhound', 'Weimaraner', 'Staffordshire Bull Terrier',\n 'American Staffordshire Terrier', 'Bedlington Terrier', 'Border Terrier',\n 'Kerry Blue Terrier', 'Irish Terrier', 'Norfolk Terrier',\n 'Norwich Terrier', 'Yorkshire Terrier', 'Wire Fox Terrier',\n 'Lakeland Terrier', 'Sealyham Terrier', 'Airedale Terrier',\n 'Cairn Terrier', 'Australian Terrier', 'Dandie Dinmont Terrier',\n 'Boston Terrier', 'Miniature Schnauzer', 'Giant Schnauzer',\n 'Standard Schnauzer', 'Scottish Terrier', 'Tibetan Terrier',\n 'Australian Silky Terrier', 'Soft-coated Wheaten Terrier',\n 'West Highland White Terrier', 'Lhasa Apso', 'Flat-Coated Retriever',\n 'Curly-coated Retriever', 'Golden Retriever', 'Labrador Retriever',\n 'Chesapeake Bay Retriever', 'German Shorthaired Pointer', 'Vizsla',\n 'English Setter', 'Irish Setter', 'Gordon Setter', 'Brittany dog',\n 'Clumber Spaniel', 'English Springer Spaniel', 'Welsh Springer Spaniel',\n 'Cocker Spaniel', 'Sussex Spaniel', 'Irish Water Spaniel', 'Kuvasz',\n 'Schipperke', 'Groenendael dog', 'Malinois', 'Briard', 'Australian Kelpie',\n 'Komondor', 'Old English Sheepdog', 'Shetland Sheepdog', 'collie',\n 'Border Collie', 'Bouvier des Flandres dog', 'Rottweiler',\n 'German Shepherd Dog', 'Dobermann', 'Miniature Pinscher',\n 'Greater Swiss Mountain Dog', 'Bernese Mountain Dog',\n 'Appenzeller Sennenhund', 'Entlebucher Sennenhund', 'Boxer', 'Bullmastiff',\n 'Tibetan Mastiff', 'French Bulldog', 'Great Dane', 'St. Bernard', 'husky',\n 'Alaskan Malamute', 'Siberian Husky', 'Dalmatian', 'Affenpinscher',\n 'Basenji', 'pug', 'Leonberger', 'Newfoundland dog', 'Great Pyrenees dog',\n 'Samoyed', 'Pomeranian', 'Chow Chow', 'Keeshond', 'brussels griffon',\n 'Pembroke Welsh Corgi', 'Cardigan Welsh Corgi', 'Toy Poodle',\n 'Miniature Poodle', 'Standard Poodle',\n 'Mexican hairless dog (xoloitzcuintli)', 'grey wolf',\n 'Alaskan tundra wolf', 'red wolf or maned wolf', 'coyote', 'dingo',\n 'dhole', 'African wild dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox',\n 'grey fox', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat',\n 'Egyptian Mau', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar',\n 'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear',\n 'polar bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle',\n 'ladybug', 'ground beetle', 'longhorn beetle', 'leaf beetle',\n 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee', 'ant',\n 'grasshopper', 'cricket insect', 'stick insect', 'cockroach',\n 'praying mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly',\n 'damselfly', 'red admiral butterfly', 'ringlet butterfly',\n 'monarch butterfly', 'small white butterfly', 'sulphur butterfly',\n 'gossamer-winged butterfly', 'starfish', 'sea urchin', 'sea cucumber',\n 'cottontail rabbit', 'hare', 'Angora rabbit', 'hamster', 'porcupine',\n 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'common sorrel horse',\n 'zebra', 'pig', 'wild boar', 'warthog', 'hippopotamus', 'ox',\n 'water buffalo', 'bison', 'ram (adult male sheep)', 'bighorn sheep',\n 'Alpine ibex', 'hartebeest', 'impala (antelope)', 'gazelle',\n 'arabian camel', 'llama', 'weasel', 'mink', 'European polecat',\n 'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo',\n 'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon',\n 'siamang', 'guenon', 'patas monkey', 'baboon', 'macaque', 'langur',\n 'black-and-white colobus', 'proboscis monkey', 'marmoset',\n 'white-headed capuchin', 'howler monkey', 'titi monkey',\n \"Geoffroy's spider monkey\", 'common squirrel monkey', 'ring-tailed lemur',\n 'indri', 'Asian elephant', 'African bush elephant', 'red panda',\n 'giant panda', 'snoek fish', 'eel', 'silver salmon', 'rock beauty fish',\n 'clownfish', 'sturgeon', 'gar fish', 'lionfish', 'pufferfish', 'abacus',\n 'abaya', 'academic gown', 'accordion', 'acoustic guitar',\n 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance',\n 'amphibious vehicle', 'analog clock', 'apiary', 'apron', 'trash can',\n 'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon',\n 'ballpoint pen', 'Band-Aid', 'banjo', 'baluster / handrail', 'barbell',\n 'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'wheelbarrow',\n 'baseball', 'basketball', 'bassinet', 'bassoon', 'swimming cap',\n 'bath towel', 'bathtub', 'station wagon', 'lighthouse', 'beaker',\n 'military hat (bearskin or shako)', 'beer bottle', 'beer glass',\n 'bell tower', 'baby bib', 'tandem bicycle', 'bikini', 'ring binder',\n 'binoculars', 'birdhouse', 'boathouse', 'bobsleigh', 'bolo tie',\n 'poke bonnet', 'bookcase', 'bookstore', 'bottle cap', 'hunting bow',\n 'bow tie', 'brass memorial plaque', 'bra', 'breakwater', 'breastplate',\n 'broom', 'bucket', 'buckle', 'bulletproof vest', 'high-speed train',\n 'butcher shop', 'taxicab', 'cauldron', 'candle', 'cannon', 'canoe',\n 'can opener', 'cardigan', 'car mirror', 'carousel', 'tool kit',\n 'cardboard box / carton', 'car wheel', 'automated teller machine',\n 'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello',\n 'mobile phone', 'chain', 'chain-link fence', 'chain mail', 'chainsaw',\n 'storage chest', 'chiffonier', 'bell or wind chime', 'china cabinet',\n 'Christmas stocking', 'church', 'movie theater', 'cleaver',\n 'cliff dwelling', 'cloak', 'clogs', 'cocktail shaker', 'coffee mug',\n 'coffeemaker', 'spiral or coil', 'combination lock', 'computer keyboard',\n 'candy store', 'container ship', 'convertible', 'corkscrew', 'cornet',\n 'cowboy boot', 'cowboy hat', 'cradle', 'construction crane',\n 'crash helmet', 'crate', 'infant bed', 'Crock Pot', 'croquet ball',\n 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer',\n 'rotary dial telephone', 'diaper', 'digital clock', 'digital watch',\n 'dining table', 'dishcloth', 'dishwasher', 'disc brake', 'dock',\n 'dog sled', 'dome', 'doormat', 'drilling rig', 'drum', 'drumstick',\n 'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar',\n 'electric locomotive', 'entertainment center', 'envelope',\n 'espresso machine', 'face powder', 'feather boa', 'filing cabinet',\n 'fireboat', 'fire truck', 'fire screen', 'flagpole', 'flute',\n 'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen',\n 'four-poster bed', 'freight car', 'French horn', 'frying pan', 'fur coat',\n 'garbage truck', 'gas mask or respirator', 'gas pump', 'goblet', 'go-kart',\n 'golf ball', 'golf cart', 'gondola', 'gong', 'gown', 'grand piano',\n 'greenhouse', 'radiator grille', 'grocery store', 'guillotine',\n 'hair clip', 'hair spray', 'half-track', 'hammer', 'hamper', 'hair dryer',\n 'hand-held computer', 'handkerchief', 'hard disk drive', 'harmonica',\n 'harp', 'combine harvester', 'hatchet', 'holster', 'home theater',\n 'honeycomb', 'hook', 'hoop skirt', 'gymnastic horizontal bar',\n 'horse-drawn vehicle', 'hourglass', 'iPod', 'clothes iron',\n 'carved pumpkin', 'jeans', 'jeep', 'T-shirt', 'jigsaw puzzle', 'rickshaw',\n 'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade',\n 'laptop computer', 'lawn mower', 'lens cap', 'letter opener', 'library',\n 'lifeboat', 'lighter', 'limousine', 'ocean liner', 'lipstick',\n 'slip-on shoe', 'lotion', 'music speaker', 'loupe magnifying glass',\n 'sawmill', 'magnetic compass', 'messenger bag', 'mailbox', 'tights',\n 'one-piece bathing suit', 'manhole cover', 'maraca', 'marimba', 'mask',\n 'matchstick', 'maypole', 'maze', 'measuring cup', 'medicine cabinet',\n 'megalith', 'microphone', 'microwave oven', 'military uniform', 'milk can',\n 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl',\n 'mobile home', 'ford model t', 'modem', 'monastery', 'monitor', 'moped',\n 'mortar and pestle', 'graduation cap', 'mosque', 'mosquito net', 'vespa',\n 'mountain bike', 'tent', 'computer mouse', 'mousetrap', 'moving van',\n 'muzzle', 'metal nail', 'neck brace', 'necklace', 'baby pacifier',\n 'notebook computer', 'obelisk', 'oboe', 'ocarina', 'odometer',\n 'oil filter', 'pipe organ', 'oscilloscope', 'overskirt', 'bullock cart',\n 'oxygen mask', 'product packet / packaging', 'paddle', 'paddle wheel',\n 'padlock', 'paintbrush', 'pajamas', 'palace', 'pan flute', 'paper towel',\n 'parachute', 'parallel bars', 'park bench', 'parking meter',\n 'railroad car', 'patio', 'payphone', 'pedestal', 'pencil case',\n 'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'plectrum',\n 'Pickelhaube', 'picket fence', 'pickup truck', 'pier', 'piggy bank',\n 'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate ship',\n 'drink pitcher', 'block plane', 'planetarium', 'plastic bag', 'plate rack',\n 'farm plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho',\n 'pool table', 'soda bottle', 'plant pot', \"potter's wheel\", 'power drill',\n 'prayer rug', 'printer', 'prison', 'missile', 'projector', 'hockey puck',\n 'punching bag', 'purse', 'quill', 'quilt', 'race car', 'racket',\n 'radiator', 'radio', 'radio telescope', 'rain barrel',\n 'recreational vehicle', 'fishing casting reel', 'reflex camera',\n 'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle',\n 'rocking chair', 'rotisserie', 'eraser', 'rugby ball',\n 'ruler measuring stick', 'sneaker', 'safe', 'safety pin', 'salt shaker',\n 'sandal', 'sarong', 'saxophone', 'scabbard', 'weighing scale',\n 'school bus', 'schooner', 'scoreboard', 'CRT monitor', 'screw',\n 'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe store',\n 'shoji screen / room divider', 'shopping basket', 'shopping cart',\n 'shovel', 'shower cap', 'shower curtain', 'ski', 'balaclava ski mask',\n 'sleeping bag', 'slide rule', 'sliding door', 'slot machine', 'snorkel',\n 'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock',\n 'solar thermal collector', 'sombrero', 'soup bowl', 'keyboard space bar',\n 'space heater', 'space shuttle', 'spatula', 'motorboat', 'spider web',\n 'spindle', 'sports car', 'spotlight', 'stage', 'steam locomotive',\n 'through arch bridge', 'steel drum', 'stethoscope', 'scarf', 'stone wall',\n 'stopwatch', 'stove', 'strainer', 'tram', 'stretcher', 'couch', 'stupa',\n 'submarine', 'suit', 'sundial', 'sunglasses', 'sunglasses', 'sunscreen',\n 'suspension bridge', 'mop', 'sweatshirt', 'swim trunks / shorts', 'swing',\n 'electrical switch', 'syringe', 'table lamp', 'tank', 'tape player',\n 'teapot', 'teddy bear', 'television', 'tennis ball', 'thatched roof',\n 'front curtain', 'thimble', 'threshing machine', 'throne', 'tile roof',\n 'toaster', 'tobacco shop', 'toilet seat', 'torch', 'totem pole',\n 'tow truck', 'toy store', 'tractor', 'semi-trailer truck', 'tray',\n 'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch',\n 'trolleybus', 'trombone', 'hot tub', 'turnstile', 'typewriter keyboard',\n 'umbrella', 'unicycle', 'upright piano', 'vacuum cleaner', 'vase',\n 'vaulted or arched ceiling', 'velvet fabric', 'vending machine',\n 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock',\n 'wallet', 'wardrobe', 'military aircraft', 'sink', 'washing machine',\n 'water bottle', 'water jug', 'water tower', 'whiskey jug', 'whistle',\n 'hair wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle',\n 'airplane wing', 'wok', 'wooden spoon', 'wool', 'split-rail fence',\n 'shipwreck', 'sailboat', 'yurt', 'website', 'comic book', 'crossword',\n 'traffic or street sign', 'traffic light', 'dust jacket', 'menu', 'plate',\n 'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'popsicle',\n 'baguette', 'bagel', 'pretzel', 'cheeseburger', 'hot dog',\n 'mashed potatoes', 'cabbage', 'broccoli', 'cauliflower', 'zucchini',\n 'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber',\n 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith apple',\n 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit',\n 'cherimoya (custard apple)', 'pomegranate', 'hay', 'carbonara',\n 'chocolate syrup', 'dough', 'meatloaf', 'pizza', 'pot pie', 'burrito',\n 'red wine', 'espresso', 'tea cup', 'eggnog', 'mountain', 'bubble', 'cliff',\n 'coral reef', 'geyser', 'lakeshore', 'promontory', 'sandbar', 'beach',\n 'valley', 'volcano', 'baseball player', 'bridegroom', 'scuba diver',\n 'rapeseed', 'daisy', \"yellow lady's slipper\", 'corn', 'acorn', 'rose hip',\n 'horse chestnut seed', 'coral fungus', 'agaric', 'gyromitra',\n 'stinkhorn mushroom', 'earth star fungus', 'hen of the woods mushroom',\n 'bolete', 'corn cob', 'toilet paper')"},{"identifier":"MODELS","path":"mmpretrain/registry.py","snippet":"MODELS = Registry(\n 'model',\n parent=MMENGINE_MODELS,\n locations=['mmpretrain.models'],\n)"},{"identifier":"TOKENIZER","path":"mmpretrain/registry.py","snippet":"TOKENIZER = Registry(\n 'tokenizer',\n locations=['mmpretrain.models'],\n)"},{"identifier":"DataSample","path":"mmpretrain/structures/data_sample.py","snippet":"class DataSample(BaseDataElement):\n \"\"\"A general data structure interface.\n\n It's used as the interface between different components.\n\n The following fields are convention names in MMPretrain, and we will set or\n get these fields in data transforms, models, and metrics if needed. You can\n also set any new fields for your need.\n\n Meta fields:\n img_shape (Tuple): The shape of the corresponding input image.\n ori_shape (Tuple): The original shape of the corresponding image.\n sample_idx (int): The index of the sample in the dataset.\n num_classes (int): The number of all categories.\n\n Data fields:\n gt_label (tensor): The ground truth label.\n gt_score (tensor): The ground truth score.\n pred_label (tensor): The predicted label.\n pred_score (tensor): The predicted score.\n mask (tensor): The mask used in masked image modeling.\n\n Examples:\n >>> import torch\n >>> from mmpretrain.structures import DataSample\n >>>\n >>> img_meta = dict(img_shape=(960, 720), num_classes=5)\n >>> data_sample = DataSample(metainfo=img_meta)\n >>> data_sample.set_gt_label(3)\n >>> print(data_sample)\n <DataSample(\n META INFORMATION\n num_classes: 5\n img_shape: (960, 720)\n DATA FIELDS\n gt_label: tensor([3])\n ) at 0x7ff64c1c1d30>\n >>>\n >>> # For multi-label data\n >>> data_sample = DataSample().set_gt_label([0, 1, 4])\n >>> print(data_sample)\n <DataSample(\n DATA FIELDS\n gt_label: tensor([0, 1, 4])\n ) at 0x7ff5b490e100>\n >>>\n >>> # Set one-hot format score\n >>> data_sample = DataSample().set_pred_score([0.1, 0.1, 0.6, 0.1])\n >>> print(data_sample)\n <DataSample(\n META INFORMATION\n num_classes: 4\n DATA FIELDS\n pred_score: tensor([0.1000, 0.1000, 0.6000, 0.1000])\n ) at 0x7ff5b48ef6a0>\n >>>\n >>> # Set custom field\n >>> data_sample = DataSample()\n >>> data_sample.my_field = [1, 2, 3]\n >>> print(data_sample)\n <DataSample(\n DATA FIELDS\n my_field: [1, 2, 3]\n ) at 0x7f8e9603d3a0>\n >>> print(data_sample.my_field)\n [1, 2, 3]\n \"\"\"\n\n def set_gt_label(self, value: LABEL_TYPE) -> 'DataSample':\n \"\"\"Set ``gt_label``.\"\"\"\n self.set_field(format_label(value), 'gt_label', dtype=torch.Tensor)\n return self\n\n def set_gt_score(self, value: SCORE_TYPE) -> 'DataSample':\n \"\"\"Set ``gt_score``.\"\"\"\n score = format_score(value)\n self.set_field(score, 'gt_score', dtype=torch.Tensor)\n if hasattr(self, 'num_classes'):\n assert len(score) == self.num_classes, \\\n f'The length of score {len(score)} should be '\\\n f'equal to the num_classes {self.num_classes}.'\n else:\n self.set_field(\n name='num_classes', value=len(score), field_type='metainfo')\n return self\n\n def set_pred_label(self, value: LABEL_TYPE) -> 'DataSample':\n \"\"\"Set ``pred_label``.\"\"\"\n self.set_field(format_label(value), 'pred_label', dtype=torch.Tensor)\n return self\n\n def set_pred_score(self, value: SCORE_TYPE):\n \"\"\"Set ``pred_label``.\"\"\"\n score = format_score(value)\n self.set_field(score, 'pred_score', dtype=torch.Tensor)\n if hasattr(self, 'num_classes'):\n assert len(score) == self.num_classes, \\\n f'The length of score {len(score)} should be '\\\n f'equal to the num_classes {self.num_classes}.'\n else:\n self.set_field(\n name='num_classes', value=len(score), field_type='metainfo')\n return self\n\n def set_mask(self, value: Union[torch.Tensor, np.ndarray]):\n if isinstance(value, np.ndarray):\n value = torch.from_numpy(value)\n elif not isinstance(value, torch.Tensor):\n raise TypeError(f'Invalid mask type {type(value)}')\n self.set_field(value, 'mask', dtype=torch.Tensor)\n return self\n\n def __repr__(self) -> str:\n \"\"\"Represent the object.\"\"\"\n\n def dump_items(items, prefix=''):\n return '\\n'.join(f'{prefix}{k}: {v}' for k, v in items)\n\n repr_ = ''\n if len(self._metainfo_fields) > 0:\n repr_ += '\\n\\nMETA INFORMATION\\n'\n repr_ += dump_items(self.metainfo_items(), prefix=' ' * 4)\n if len(self._data_fields) > 0:\n repr_ += '\\n\\nDATA FIELDS\\n'\n repr_ += dump_items(self.items(), prefix=' ' * 4)\n\n repr_ = f'<{self.__class__.__name__}({repr_}\\n\\n) at {hex(id(self))}>'\n return repr_"},{"identifier":"track_on_main_process","path":"mmpretrain/utils/progress.py","snippet":"def track_on_main_process(sequence, description='', total=None):\n if not dist.is_main_process() or disable_progress_bar:\n yield from sequence\n else:\n yield from track(sequence, total=total, description=description)"},{"identifier":"OPENAI_CIFAR100_PROMPT","path":"mmpretrain/models/multimodal/clip/utils.py","snippet":"OPENAI_CIFAR100_PROMPT = [\n lambda c: f'a photo of a {c}.',\n lambda c: f'a blurry photo of a {c}.',\n lambda c: f'a black and white photo of a {c}.',\n lambda c: f'a low contrast photo of a {c}.',\n lambda c: f'a high contrast photo of a {c}.',\n lambda c: f'a bad photo of a {c}.',\n lambda c: f'a good photo of a {c}.',\n lambda c: f'a photo of a small {c}.',\n lambda c: f'a photo of a big {c}.',\n lambda c: f'a photo of the {c}.',\n lambda c: f'a blurry photo of the {c}.',\n lambda c: f'a black and white photo of the {c}.',\n lambda c: f'a low contrast photo of the {c}.',\n lambda c: f'a high contrast photo of the {c}.',\n lambda c: f'a bad photo of the {c}.',\n lambda c: f'a good photo of the {c}.',\n lambda c: f'a photo of the small {c}.',\n lambda c: f'a photo of the big {c}.',\n]"},{"identifier":"OPENAI_IMAGENET_PROMPT","path":"mmpretrain/models/multimodal/clip/utils.py","snippet":"OPENAI_IMAGENET_PROMPT = [\n lambda c: f'a bad photo of a {c}.',\n lambda c: f'a photo of many {c}.',\n lambda c: f'a sculpture of a {c}.',\n lambda c: f'a photo of the hard to see {c}.',\n lambda c: f'a low resolution photo of the {c}.',\n lambda c: f'a rendering of a {c}.',\n lambda c: f'graffiti of a {c}.',\n lambda c: f'a bad photo of the {c}.',\n lambda c: f'a cropped photo of the {c}.',\n lambda c: f'a tattoo of a {c}.',\n lambda c: f'the embroidered {c}.',\n lambda c: f'a photo of a hard to see {c}.',\n lambda c: f'a bright photo of a {c}.',\n lambda c: f'a photo of a clean {c}.',\n lambda c: f'a photo of a dirty {c}.',\n lambda c: f'a dark photo of the {c}.',\n lambda c: f'a drawing of a {c}.',\n lambda c: f'a photo of my {c}.',\n lambda c: f'the plastic {c}.',\n lambda c: f'a photo of the cool {c}.',\n lambda c: f'a close-up photo of a {c}.',\n lambda c: f'a black and white photo of the {c}.',\n lambda c: f'a painting of the {c}.',\n lambda c: f'a painting of a {c}.',\n lambda c: f'a pixelated photo of the {c}.',\n lambda c: f'a sculpture of the {c}.',\n lambda c: f'a bright photo of the {c}.',\n lambda c: f'a cropped photo of a {c}.',\n lambda c: f'a plastic {c}.',\n lambda c: f'a photo of the dirty {c}.',\n lambda c: f'a jpeg corrupted photo of a {c}.',\n lambda c: f'a blurry photo of the {c}.',\n lambda c: f'a photo of the {c}.',\n lambda c: f'a good photo of the {c}.',\n lambda c: f'a rendering of the {c}.',\n lambda c: f'a {c} in a video game.',\n lambda c: f'a photo of one {c}.',\n lambda c: f'a doodle of a {c}.',\n lambda c: f'a close-up photo of the {c}.',\n lambda c: f'a photo of a {c}.',\n lambda c: f'the origami {c}.',\n lambda c: f'the {c} in a video game.',\n lambda c: f'a sketch of a {c}.',\n lambda c: f'a doodle of the {c}.',\n lambda c: f'a origami {c}.',\n lambda c: f'a low resolution photo of a {c}.',\n lambda c: f'the toy {c}.',\n lambda c: f'a rendition of the {c}.',\n lambda c: f'a photo of the clean {c}.',\n lambda c: f'a photo of a large {c}.',\n lambda c: f'a rendition of a {c}.',\n lambda c: f'a photo of a nice {c}.',\n lambda c: f'a photo of a weird {c}.',\n lambda c: f'a blurry photo of a {c}.',\n lambda c: f'a cartoon {c}.',\n lambda c: f'art of a {c}.',\n lambda c: f'a sketch of the {c}.',\n lambda c: f'a embroidered {c}.',\n lambda c: f'a pixelated photo of a {c}.',\n lambda c: f'itap of the {c}.',\n lambda c: f'a jpeg corrupted photo of the {c}.',\n lambda c: f'a good photo of a {c}.',\n lambda c: f'a plushie {c}.',\n lambda c: f'a photo of the nice {c}.',\n lambda c: f'a photo of the small {c}.',\n lambda c: f'a photo of the weird {c}.',\n lambda c: f'the cartoon {c}.',\n lambda c: f'art of the {c}.',\n lambda c: f'a drawing of the {c}.',\n lambda c: f'a photo of the large {c}.',\n lambda c: f'a black and white photo of a {c}.',\n lambda c: f'the plushie {c}.',\n lambda c: f'a dark photo of a {c}.',\n lambda c: f'itap of a {c}.',\n lambda c: f'graffiti of the {c}.',\n lambda c: f'a toy {c}.',\n lambda c: f'itap of my {c}.',\n lambda c: f'a photo of a cool {c}.',\n lambda c: f'a photo of a small {c}.',\n lambda c: f'a tattoo of the {c}.',\n]"},{"identifier":"OPENAI_IMAGENET_PROMPT_SUB","path":"mmpretrain/models/multimodal/clip/utils.py","snippet":"OPENAI_IMAGENET_PROMPT_SUB = [\n lambda c: f'itap of a {c}.',\n lambda c: f'a bad photo of the {c}.',\n lambda c: f'a origami {c}.',\n lambda c: f'a photo of the large {c}.',\n lambda c: f'a {c} in a video game.',\n lambda c: f'art of the {c}.',\n lambda c: f'a photo of the small {c}.',\n]"}],"string":"[\n {\n \"identifier\": \"CIFAR100_CATEGORIES\",\n \"path\": \"mmpretrain/datasets/categories.py\",\n \"snippet\": \"CIFAR100_CATEGORIES = (\\n 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',\\n 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',\\n 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',\\n 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',\\n 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',\\n 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',\\n 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain',\\n 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree',\\n 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy',\\n 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket',\\n 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail',\\n 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper',\\n 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train',\\n 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf',\\n 'woman', 'worm')\"\n },\n {\n \"identifier\": \"IMAGENET_SIMPLE_CATEGORIES\",\n \"path\": \"mmpretrain/datasets/categories.py\",\n \"snippet\": \"IMAGENET_SIMPLE_CATEGORIES = (\\n 'tench', 'goldfish', 'great white shark', 'tiger shark',\\n 'hammerhead shark', 'electric ray', 'stingray', 'rooster', 'hen',\\n 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco',\\n 'indigo bunting', 'American robin', 'bulbul', 'jay', 'magpie', 'chickadee',\\n 'American dipper', 'kite (bird of prey)', 'bald eagle', 'vulture',\\n 'great grey owl', 'fire salamander', 'smooth newt', 'newt',\\n 'spotted salamander', 'axolotl', 'American bullfrog', 'tree frog',\\n 'tailed frog', 'loggerhead sea turtle', 'leatherback sea turtle',\\n 'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'green iguana',\\n 'Carolina anole', 'desert grassland whiptail lizard', 'agama',\\n 'frilled-necked lizard', 'alligator lizard', 'Gila monster',\\n 'European green lizard', 'chameleon', 'Komodo dragon', 'Nile crocodile',\\n 'American alligator', 'triceratops', 'worm snake', 'ring-necked snake',\\n 'eastern hog-nosed snake', 'smooth green snake', 'kingsnake',\\n 'garter snake', 'water snake', 'vine snake', 'night snake',\\n 'boa constrictor', 'African rock python', 'Indian cobra', 'green mamba',\\n 'sea snake', 'Saharan horned viper', 'eastern diamondback rattlesnake',\\n 'sidewinder rattlesnake', 'trilobite', 'harvestman', 'scorpion',\\n 'yellow garden spider', 'barn spider', 'European garden spider',\\n 'southern black widow', 'tarantula', 'wolf spider', 'tick', 'centipede',\\n 'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie grouse', 'peafowl',\\n 'quail', 'partridge', 'african grey parrot', 'macaw',\\n 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill',\\n 'hummingbird', 'jacamar', 'toucan', 'duck', 'red-breasted merganser',\\n 'goose', 'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala',\\n 'wombat', 'jellyfish', 'sea anemone', 'brain coral', 'flatworm',\\n 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton',\\n 'chambered nautilus', 'Dungeness crab', 'rock crab', 'fiddler crab',\\n 'red king crab', 'American lobster', 'spiny lobster', 'crayfish',\\n 'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill',\\n 'flamingo', 'little blue heron', 'great egret', 'bittern bird',\\n 'crane bird', 'limpkin', 'common gallinule', 'American coot', 'bustard',\\n 'ruddy turnstone', 'dunlin', 'common redshank', 'dowitcher',\\n 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale',\\n 'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese Chin',\\n 'Maltese', 'Pekingese', 'Shih Tzu', 'King Charles Spaniel', 'Papillon',\\n 'toy terrier', 'Rhodesian Ridgeback', 'Afghan Hound', 'Basset Hound',\\n 'Beagle', 'Bloodhound', 'Bluetick Coonhound', 'Black and Tan Coonhound',\\n 'Treeing Walker Coonhound', 'English foxhound', 'Redbone Coonhound',\\n 'borzoi', 'Irish Wolfhound', 'Italian Greyhound', 'Whippet',\\n 'Ibizan Hound', 'Norwegian Elkhound', 'Otterhound', 'Saluki',\\n 'Scottish Deerhound', 'Weimaraner', 'Staffordshire Bull Terrier',\\n 'American Staffordshire Terrier', 'Bedlington Terrier', 'Border Terrier',\\n 'Kerry Blue Terrier', 'Irish Terrier', 'Norfolk Terrier',\\n 'Norwich Terrier', 'Yorkshire Terrier', 'Wire Fox Terrier',\\n 'Lakeland Terrier', 'Sealyham Terrier', 'Airedale Terrier',\\n 'Cairn Terrier', 'Australian Terrier', 'Dandie Dinmont Terrier',\\n 'Boston Terrier', 'Miniature Schnauzer', 'Giant Schnauzer',\\n 'Standard Schnauzer', 'Scottish Terrier', 'Tibetan Terrier',\\n 'Australian Silky Terrier', 'Soft-coated Wheaten Terrier',\\n 'West Highland White Terrier', 'Lhasa Apso', 'Flat-Coated Retriever',\\n 'Curly-coated Retriever', 'Golden Retriever', 'Labrador Retriever',\\n 'Chesapeake Bay Retriever', 'German Shorthaired Pointer', 'Vizsla',\\n 'English Setter', 'Irish Setter', 'Gordon Setter', 'Brittany dog',\\n 'Clumber Spaniel', 'English Springer Spaniel', 'Welsh Springer Spaniel',\\n 'Cocker Spaniel', 'Sussex Spaniel', 'Irish Water Spaniel', 'Kuvasz',\\n 'Schipperke', 'Groenendael dog', 'Malinois', 'Briard', 'Australian Kelpie',\\n 'Komondor', 'Old English Sheepdog', 'Shetland Sheepdog', 'collie',\\n 'Border Collie', 'Bouvier des Flandres dog', 'Rottweiler',\\n 'German Shepherd Dog', 'Dobermann', 'Miniature Pinscher',\\n 'Greater Swiss Mountain Dog', 'Bernese Mountain Dog',\\n 'Appenzeller Sennenhund', 'Entlebucher Sennenhund', 'Boxer', 'Bullmastiff',\\n 'Tibetan Mastiff', 'French Bulldog', 'Great Dane', 'St. Bernard', 'husky',\\n 'Alaskan Malamute', 'Siberian Husky', 'Dalmatian', 'Affenpinscher',\\n 'Basenji', 'pug', 'Leonberger', 'Newfoundland dog', 'Great Pyrenees dog',\\n 'Samoyed', 'Pomeranian', 'Chow Chow', 'Keeshond', 'brussels griffon',\\n 'Pembroke Welsh Corgi', 'Cardigan Welsh Corgi', 'Toy Poodle',\\n 'Miniature Poodle', 'Standard Poodle',\\n 'Mexican hairless dog (xoloitzcuintli)', 'grey wolf',\\n 'Alaskan tundra wolf', 'red wolf or maned wolf', 'coyote', 'dingo',\\n 'dhole', 'African wild dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox',\\n 'grey fox', 'tabby cat', 'tiger cat', 'Persian cat', 'Siamese cat',\\n 'Egyptian Mau', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar',\\n 'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear',\\n 'polar bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle',\\n 'ladybug', 'ground beetle', 'longhorn beetle', 'leaf beetle',\\n 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee', 'ant',\\n 'grasshopper', 'cricket insect', 'stick insect', 'cockroach',\\n 'praying mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly',\\n 'damselfly', 'red admiral butterfly', 'ringlet butterfly',\\n 'monarch butterfly', 'small white butterfly', 'sulphur butterfly',\\n 'gossamer-winged butterfly', 'starfish', 'sea urchin', 'sea cucumber',\\n 'cottontail rabbit', 'hare', 'Angora rabbit', 'hamster', 'porcupine',\\n 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'common sorrel horse',\\n 'zebra', 'pig', 'wild boar', 'warthog', 'hippopotamus', 'ox',\\n 'water buffalo', 'bison', 'ram (adult male sheep)', 'bighorn sheep',\\n 'Alpine ibex', 'hartebeest', 'impala (antelope)', 'gazelle',\\n 'arabian camel', 'llama', 'weasel', 'mink', 'European polecat',\\n 'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo',\\n 'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon',\\n 'siamang', 'guenon', 'patas monkey', 'baboon', 'macaque', 'langur',\\n 'black-and-white colobus', 'proboscis monkey', 'marmoset',\\n 'white-headed capuchin', 'howler monkey', 'titi monkey',\\n \\\"Geoffroy's spider monkey\\\", 'common squirrel monkey', 'ring-tailed lemur',\\n 'indri', 'Asian elephant', 'African bush elephant', 'red panda',\\n 'giant panda', 'snoek fish', 'eel', 'silver salmon', 'rock beauty fish',\\n 'clownfish', 'sturgeon', 'gar fish', 'lionfish', 'pufferfish', 'abacus',\\n 'abaya', 'academic gown', 'accordion', 'acoustic guitar',\\n 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance',\\n 'amphibious vehicle', 'analog clock', 'apiary', 'apron', 'trash can',\\n 'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon',\\n 'ballpoint pen', 'Band-Aid', 'banjo', 'baluster / handrail', 'barbell',\\n 'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'wheelbarrow',\\n 'baseball', 'basketball', 'bassinet', 'bassoon', 'swimming cap',\\n 'bath towel', 'bathtub', 'station wagon', 'lighthouse', 'beaker',\\n 'military hat (bearskin or shako)', 'beer bottle', 'beer glass',\\n 'bell tower', 'baby bib', 'tandem bicycle', 'bikini', 'ring binder',\\n 'binoculars', 'birdhouse', 'boathouse', 'bobsleigh', 'bolo tie',\\n 'poke bonnet', 'bookcase', 'bookstore', 'bottle cap', 'hunting bow',\\n 'bow tie', 'brass memorial plaque', 'bra', 'breakwater', 'breastplate',\\n 'broom', 'bucket', 'buckle', 'bulletproof vest', 'high-speed train',\\n 'butcher shop', 'taxicab', 'cauldron', 'candle', 'cannon', 'canoe',\\n 'can opener', 'cardigan', 'car mirror', 'carousel', 'tool kit',\\n 'cardboard box / carton', 'car wheel', 'automated teller machine',\\n 'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello',\\n 'mobile phone', 'chain', 'chain-link fence', 'chain mail', 'chainsaw',\\n 'storage chest', 'chiffonier', 'bell or wind chime', 'china cabinet',\\n 'Christmas stocking', 'church', 'movie theater', 'cleaver',\\n 'cliff dwelling', 'cloak', 'clogs', 'cocktail shaker', 'coffee mug',\\n 'coffeemaker', 'spiral or coil', 'combination lock', 'computer keyboard',\\n 'candy store', 'container ship', 'convertible', 'corkscrew', 'cornet',\\n 'cowboy boot', 'cowboy hat', 'cradle', 'construction crane',\\n 'crash helmet', 'crate', 'infant bed', 'Crock Pot', 'croquet ball',\\n 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer',\\n 'rotary dial telephone', 'diaper', 'digital clock', 'digital watch',\\n 'dining table', 'dishcloth', 'dishwasher', 'disc brake', 'dock',\\n 'dog sled', 'dome', 'doormat', 'drilling rig', 'drum', 'drumstick',\\n 'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar',\\n 'electric locomotive', 'entertainment center', 'envelope',\\n 'espresso machine', 'face powder', 'feather boa', 'filing cabinet',\\n 'fireboat', 'fire truck', 'fire screen', 'flagpole', 'flute',\\n 'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen',\\n 'four-poster bed', 'freight car', 'French horn', 'frying pan', 'fur coat',\\n 'garbage truck', 'gas mask or respirator', 'gas pump', 'goblet', 'go-kart',\\n 'golf ball', 'golf cart', 'gondola', 'gong', 'gown', 'grand piano',\\n 'greenhouse', 'radiator grille', 'grocery store', 'guillotine',\\n 'hair clip', 'hair spray', 'half-track', 'hammer', 'hamper', 'hair dryer',\\n 'hand-held computer', 'handkerchief', 'hard disk drive', 'harmonica',\\n 'harp', 'combine harvester', 'hatchet', 'holster', 'home theater',\\n 'honeycomb', 'hook', 'hoop skirt', 'gymnastic horizontal bar',\\n 'horse-drawn vehicle', 'hourglass', 'iPod', 'clothes iron',\\n 'carved pumpkin', 'jeans', 'jeep', 'T-shirt', 'jigsaw puzzle', 'rickshaw',\\n 'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade',\\n 'laptop computer', 'lawn mower', 'lens cap', 'letter opener', 'library',\\n 'lifeboat', 'lighter', 'limousine', 'ocean liner', 'lipstick',\\n 'slip-on shoe', 'lotion', 'music speaker', 'loupe magnifying glass',\\n 'sawmill', 'magnetic compass', 'messenger bag', 'mailbox', 'tights',\\n 'one-piece bathing suit', 'manhole cover', 'maraca', 'marimba', 'mask',\\n 'matchstick', 'maypole', 'maze', 'measuring cup', 'medicine cabinet',\\n 'megalith', 'microphone', 'microwave oven', 'military uniform', 'milk can',\\n 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl',\\n 'mobile home', 'ford model t', 'modem', 'monastery', 'monitor', 'moped',\\n 'mortar and pestle', 'graduation cap', 'mosque', 'mosquito net', 'vespa',\\n 'mountain bike', 'tent', 'computer mouse', 'mousetrap', 'moving van',\\n 'muzzle', 'metal nail', 'neck brace', 'necklace', 'baby pacifier',\\n 'notebook computer', 'obelisk', 'oboe', 'ocarina', 'odometer',\\n 'oil filter', 'pipe organ', 'oscilloscope', 'overskirt', 'bullock cart',\\n 'oxygen mask', 'product packet / packaging', 'paddle', 'paddle wheel',\\n 'padlock', 'paintbrush', 'pajamas', 'palace', 'pan flute', 'paper towel',\\n 'parachute', 'parallel bars', 'park bench', 'parking meter',\\n 'railroad car', 'patio', 'payphone', 'pedestal', 'pencil case',\\n 'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'plectrum',\\n 'Pickelhaube', 'picket fence', 'pickup truck', 'pier', 'piggy bank',\\n 'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate ship',\\n 'drink pitcher', 'block plane', 'planetarium', 'plastic bag', 'plate rack',\\n 'farm plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho',\\n 'pool table', 'soda bottle', 'plant pot', \\\"potter's wheel\\\", 'power drill',\\n 'prayer rug', 'printer', 'prison', 'missile', 'projector', 'hockey puck',\\n 'punching bag', 'purse', 'quill', 'quilt', 'race car', 'racket',\\n 'radiator', 'radio', 'radio telescope', 'rain barrel',\\n 'recreational vehicle', 'fishing casting reel', 'reflex camera',\\n 'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle',\\n 'rocking chair', 'rotisserie', 'eraser', 'rugby ball',\\n 'ruler measuring stick', 'sneaker', 'safe', 'safety pin', 'salt shaker',\\n 'sandal', 'sarong', 'saxophone', 'scabbard', 'weighing scale',\\n 'school bus', 'schooner', 'scoreboard', 'CRT monitor', 'screw',\\n 'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe store',\\n 'shoji screen / room divider', 'shopping basket', 'shopping cart',\\n 'shovel', 'shower cap', 'shower curtain', 'ski', 'balaclava ski mask',\\n 'sleeping bag', 'slide rule', 'sliding door', 'slot machine', 'snorkel',\\n 'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock',\\n 'solar thermal collector', 'sombrero', 'soup bowl', 'keyboard space bar',\\n 'space heater', 'space shuttle', 'spatula', 'motorboat', 'spider web',\\n 'spindle', 'sports car', 'spotlight', 'stage', 'steam locomotive',\\n 'through arch bridge', 'steel drum', 'stethoscope', 'scarf', 'stone wall',\\n 'stopwatch', 'stove', 'strainer', 'tram', 'stretcher', 'couch', 'stupa',\\n 'submarine', 'suit', 'sundial', 'sunglasses', 'sunglasses', 'sunscreen',\\n 'suspension bridge', 'mop', 'sweatshirt', 'swim trunks / shorts', 'swing',\\n 'electrical switch', 'syringe', 'table lamp', 'tank', 'tape player',\\n 'teapot', 'teddy bear', 'television', 'tennis ball', 'thatched roof',\\n 'front curtain', 'thimble', 'threshing machine', 'throne', 'tile roof',\\n 'toaster', 'tobacco shop', 'toilet seat', 'torch', 'totem pole',\\n 'tow truck', 'toy store', 'tractor', 'semi-trailer truck', 'tray',\\n 'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch',\\n 'trolleybus', 'trombone', 'hot tub', 'turnstile', 'typewriter keyboard',\\n 'umbrella', 'unicycle', 'upright piano', 'vacuum cleaner', 'vase',\\n 'vaulted or arched ceiling', 'velvet fabric', 'vending machine',\\n 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock',\\n 'wallet', 'wardrobe', 'military aircraft', 'sink', 'washing machine',\\n 'water bottle', 'water jug', 'water tower', 'whiskey jug', 'whistle',\\n 'hair wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle',\\n 'airplane wing', 'wok', 'wooden spoon', 'wool', 'split-rail fence',\\n 'shipwreck', 'sailboat', 'yurt', 'website', 'comic book', 'crossword',\\n 'traffic or street sign', 'traffic light', 'dust jacket', 'menu', 'plate',\\n 'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'popsicle',\\n 'baguette', 'bagel', 'pretzel', 'cheeseburger', 'hot dog',\\n 'mashed potatoes', 'cabbage', 'broccoli', 'cauliflower', 'zucchini',\\n 'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber',\\n 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith apple',\\n 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit',\\n 'cherimoya (custard apple)', 'pomegranate', 'hay', 'carbonara',\\n 'chocolate syrup', 'dough', 'meatloaf', 'pizza', 'pot pie', 'burrito',\\n 'red wine', 'espresso', 'tea cup', 'eggnog', 'mountain', 'bubble', 'cliff',\\n 'coral reef', 'geyser', 'lakeshore', 'promontory', 'sandbar', 'beach',\\n 'valley', 'volcano', 'baseball player', 'bridegroom', 'scuba diver',\\n 'rapeseed', 'daisy', \\\"yellow lady's slipper\\\", 'corn', 'acorn', 'rose hip',\\n 'horse chestnut seed', 'coral fungus', 'agaric', 'gyromitra',\\n 'stinkhorn mushroom', 'earth star fungus', 'hen of the woods mushroom',\\n 'bolete', 'corn cob', 'toilet paper')\"\n },\n {\n \"identifier\": \"MODELS\",\n \"path\": \"mmpretrain/registry.py\",\n \"snippet\": \"MODELS = Registry(\\n 'model',\\n parent=MMENGINE_MODELS,\\n locations=['mmpretrain.models'],\\n)\"\n },\n {\n \"identifier\": \"TOKENIZER\",\n \"path\": \"mmpretrain/registry.py\",\n \"snippet\": \"TOKENIZER = Registry(\\n 'tokenizer',\\n locations=['mmpretrain.models'],\\n)\"\n },\n {\n \"identifier\": \"DataSample\",\n \"path\": \"mmpretrain/structures/data_sample.py\",\n \"snippet\": \"class DataSample(BaseDataElement):\\n \\\"\\\"\\\"A general data structure interface.\\n\\n It's used as the interface between different components.\\n\\n The following fields are convention names in MMPretrain, and we will set or\\n get these fields in data transforms, models, and metrics if needed. You can\\n also set any new fields for your need.\\n\\n Meta fields:\\n img_shape (Tuple): The shape of the corresponding input image.\\n ori_shape (Tuple): The original shape of the corresponding image.\\n sample_idx (int): The index of the sample in the dataset.\\n num_classes (int): The number of all categories.\\n\\n Data fields:\\n gt_label (tensor): The ground truth label.\\n gt_score (tensor): The ground truth score.\\n pred_label (tensor): The predicted label.\\n pred_score (tensor): The predicted score.\\n mask (tensor): The mask used in masked image modeling.\\n\\n Examples:\\n >>> import torch\\n >>> from mmpretrain.structures import DataSample\\n >>>\\n >>> img_meta = dict(img_shape=(960, 720), num_classes=5)\\n >>> data_sample = DataSample(metainfo=img_meta)\\n >>> data_sample.set_gt_label(3)\\n >>> print(data_sample)\\n <DataSample(\\n META INFORMATION\\n num_classes: 5\\n img_shape: (960, 720)\\n DATA FIELDS\\n gt_label: tensor([3])\\n ) at 0x7ff64c1c1d30>\\n >>>\\n >>> # For multi-label data\\n >>> data_sample = DataSample().set_gt_label([0, 1, 4])\\n >>> print(data_sample)\\n <DataSample(\\n DATA FIELDS\\n gt_label: tensor([0, 1, 4])\\n ) at 0x7ff5b490e100>\\n >>>\\n >>> # Set one-hot format score\\n >>> data_sample = DataSample().set_pred_score([0.1, 0.1, 0.6, 0.1])\\n >>> print(data_sample)\\n <DataSample(\\n META INFORMATION\\n num_classes: 4\\n DATA FIELDS\\n pred_score: tensor([0.1000, 0.1000, 0.6000, 0.1000])\\n ) at 0x7ff5b48ef6a0>\\n >>>\\n >>> # Set custom field\\n >>> data_sample = DataSample()\\n >>> data_sample.my_field = [1, 2, 3]\\n >>> print(data_sample)\\n <DataSample(\\n DATA FIELDS\\n my_field: [1, 2, 3]\\n ) at 0x7f8e9603d3a0>\\n >>> print(data_sample.my_field)\\n [1, 2, 3]\\n \\\"\\\"\\\"\\n\\n def set_gt_label(self, value: LABEL_TYPE) -> 'DataSample':\\n \\\"\\\"\\\"Set ``gt_label``.\\\"\\\"\\\"\\n self.set_field(format_label(value), 'gt_label', dtype=torch.Tensor)\\n return self\\n\\n def set_gt_score(self, value: SCORE_TYPE) -> 'DataSample':\\n \\\"\\\"\\\"Set ``gt_score``.\\\"\\\"\\\"\\n score = format_score(value)\\n self.set_field(score, 'gt_score', dtype=torch.Tensor)\\n if hasattr(self, 'num_classes'):\\n assert len(score) == self.num_classes, \\\\\\n f'The length of score {len(score)} should be '\\\\\\n f'equal to the num_classes {self.num_classes}.'\\n else:\\n self.set_field(\\n name='num_classes', value=len(score), field_type='metainfo')\\n return self\\n\\n def set_pred_label(self, value: LABEL_TYPE) -> 'DataSample':\\n \\\"\\\"\\\"Set ``pred_label``.\\\"\\\"\\\"\\n self.set_field(format_label(value), 'pred_label', dtype=torch.Tensor)\\n return self\\n\\n def set_pred_score(self, value: SCORE_TYPE):\\n \\\"\\\"\\\"Set ``pred_label``.\\\"\\\"\\\"\\n score = format_score(value)\\n self.set_field(score, 'pred_score', dtype=torch.Tensor)\\n if hasattr(self, 'num_classes'):\\n assert len(score) == self.num_classes, \\\\\\n f'The length of score {len(score)} should be '\\\\\\n f'equal to the num_classes {self.num_classes}.'\\n else:\\n self.set_field(\\n name='num_classes', value=len(score), field_type='metainfo')\\n return self\\n\\n def set_mask(self, value: Union[torch.Tensor, np.ndarray]):\\n if isinstance(value, np.ndarray):\\n value = torch.from_numpy(value)\\n elif not isinstance(value, torch.Tensor):\\n raise TypeError(f'Invalid mask type {type(value)}')\\n self.set_field(value, 'mask', dtype=torch.Tensor)\\n return self\\n\\n def __repr__(self) -> str:\\n \\\"\\\"\\\"Represent the object.\\\"\\\"\\\"\\n\\n def dump_items(items, prefix=''):\\n return '\\\\n'.join(f'{prefix}{k}: {v}' for k, v in items)\\n\\n repr_ = ''\\n if len(self._metainfo_fields) > 0:\\n repr_ += '\\\\n\\\\nMETA INFORMATION\\\\n'\\n repr_ += dump_items(self.metainfo_items(), prefix=' ' * 4)\\n if len(self._data_fields) > 0:\\n repr_ += '\\\\n\\\\nDATA FIELDS\\\\n'\\n repr_ += dump_items(self.items(), prefix=' ' * 4)\\n\\n repr_ = f'<{self.__class__.__name__}({repr_}\\\\n\\\\n) at {hex(id(self))}>'\\n return repr_\"\n },\n {\n \"identifier\": \"track_on_main_process\",\n \"path\": \"mmpretrain/utils/progress.py\",\n \"snippet\": \"def track_on_main_process(sequence, description='', total=None):\\n if not dist.is_main_process() or disable_progress_bar:\\n yield from sequence\\n else:\\n yield from track(sequence, total=total, description=description)\"\n },\n {\n \"identifier\": \"OPENAI_CIFAR100_PROMPT\",\n \"path\": \"mmpretrain/models/multimodal/clip/utils.py\",\n \"snippet\": \"OPENAI_CIFAR100_PROMPT = [\\n lambda c: f'a photo of a {c}.',\\n lambda c: f'a blurry photo of a {c}.',\\n lambda c: f'a black and white photo of a {c}.',\\n lambda c: f'a low contrast photo of a {c}.',\\n lambda c: f'a high contrast photo of a {c}.',\\n lambda c: f'a bad photo of a {c}.',\\n lambda c: f'a good photo of a {c}.',\\n lambda c: f'a photo of a small {c}.',\\n lambda c: f'a photo of a big {c}.',\\n lambda c: f'a photo of the {c}.',\\n lambda c: f'a blurry photo of the {c}.',\\n lambda c: f'a black and white photo of the {c}.',\\n lambda c: f'a low contrast photo of the {c}.',\\n lambda c: f'a high contrast photo of the {c}.',\\n lambda c: f'a bad photo of the {c}.',\\n lambda c: f'a good photo of the {c}.',\\n lambda c: f'a photo of the small {c}.',\\n lambda c: f'a photo of the big {c}.',\\n]\"\n },\n {\n \"identifier\": \"OPENAI_IMAGENET_PROMPT\",\n \"path\": \"mmpretrain/models/multimodal/clip/utils.py\",\n \"snippet\": \"OPENAI_IMAGENET_PROMPT = [\\n lambda c: f'a bad photo of a {c}.',\\n lambda c: f'a photo of many {c}.',\\n lambda c: f'a sculpture of a {c}.',\\n lambda c: f'a photo of the hard to see {c}.',\\n lambda c: f'a low resolution photo of the {c}.',\\n lambda c: f'a rendering of a {c}.',\\n lambda c: f'graffiti of a {c}.',\\n lambda c: f'a bad photo of the {c}.',\\n lambda c: f'a cropped photo of the {c}.',\\n lambda c: f'a tattoo of a {c}.',\\n lambda c: f'the embroidered {c}.',\\n lambda c: f'a photo of a hard to see {c}.',\\n lambda c: f'a bright photo of a {c}.',\\n lambda c: f'a photo of a clean {c}.',\\n lambda c: f'a photo of a dirty {c}.',\\n lambda c: f'a dark photo of the {c}.',\\n lambda c: f'a drawing of a {c}.',\\n lambda c: f'a photo of my {c}.',\\n lambda c: f'the plastic {c}.',\\n lambda c: f'a photo of the cool {c}.',\\n lambda c: f'a close-up photo of a {c}.',\\n lambda c: f'a black and white photo of the {c}.',\\n lambda c: f'a painting of the {c}.',\\n lambda c: f'a painting of a {c}.',\\n lambda c: f'a pixelated photo of the {c}.',\\n lambda c: f'a sculpture of the {c}.',\\n lambda c: f'a bright photo of the {c}.',\\n lambda c: f'a cropped photo of a {c}.',\\n lambda c: f'a plastic {c}.',\\n lambda c: f'a photo of the dirty {c}.',\\n lambda c: f'a jpeg corrupted photo of a {c}.',\\n lambda c: f'a blurry photo of the {c}.',\\n lambda c: f'a photo of the {c}.',\\n lambda c: f'a good photo of the {c}.',\\n lambda c: f'a rendering of the {c}.',\\n lambda c: f'a {c} in a video game.',\\n lambda c: f'a photo of one {c}.',\\n lambda c: f'a doodle of a {c}.',\\n lambda c: f'a close-up photo of the {c}.',\\n lambda c: f'a photo of a {c}.',\\n lambda c: f'the origami {c}.',\\n lambda c: f'the {c} in a video game.',\\n lambda c: f'a sketch of a {c}.',\\n lambda c: f'a doodle of the {c}.',\\n lambda c: f'a origami {c}.',\\n lambda c: f'a low resolution photo of a {c}.',\\n lambda c: f'the toy {c}.',\\n lambda c: f'a rendition of the {c}.',\\n lambda c: f'a photo of the clean {c}.',\\n lambda c: f'a photo of a large {c}.',\\n lambda c: f'a rendition of a {c}.',\\n lambda c: f'a photo of a nice {c}.',\\n lambda c: f'a photo of a weird {c}.',\\n lambda c: f'a blurry photo of a {c}.',\\n lambda c: f'a cartoon {c}.',\\n lambda c: f'art of a {c}.',\\n lambda c: f'a sketch of the {c}.',\\n lambda c: f'a embroidered {c}.',\\n lambda c: f'a pixelated photo of a {c}.',\\n lambda c: f'itap of the {c}.',\\n lambda c: f'a jpeg corrupted photo of the {c}.',\\n lambda c: f'a good photo of a {c}.',\\n lambda c: f'a plushie {c}.',\\n lambda c: f'a photo of the nice {c}.',\\n lambda c: f'a photo of the small {c}.',\\n lambda c: f'a photo of the weird {c}.',\\n lambda c: f'the cartoon {c}.',\\n lambda c: f'art of the {c}.',\\n lambda c: f'a drawing of the {c}.',\\n lambda c: f'a photo of the large {c}.',\\n lambda c: f'a black and white photo of a {c}.',\\n lambda c: f'the plushie {c}.',\\n lambda c: f'a dark photo of a {c}.',\\n lambda c: f'itap of a {c}.',\\n lambda c: f'graffiti of the {c}.',\\n lambda c: f'a toy {c}.',\\n lambda c: f'itap of my {c}.',\\n lambda c: f'a photo of a cool {c}.',\\n lambda c: f'a photo of a small {c}.',\\n lambda c: f'a tattoo of the {c}.',\\n]\"\n },\n {\n \"identifier\": \"OPENAI_IMAGENET_PROMPT_SUB\",\n \"path\": \"mmpretrain/models/multimodal/clip/utils.py\",\n \"snippet\": \"OPENAI_IMAGENET_PROMPT_SUB = [\\n lambda c: f'itap of a {c}.',\\n lambda c: f'a bad photo of the {c}.',\\n lambda c: f'a origami {c}.',\\n lambda c: f'a photo of the large {c}.',\\n lambda c: f'a {c} in a video game.',\\n lambda c: f'art of the {c}.',\\n lambda c: f'a photo of the small {c}.',\\n]\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from abc import abstractmethod\nfrom typing import List, Optional, Tuple, Union\nfrom mmengine.model import BaseModel\nfrom torch import nn\nfrom mmpretrain.datasets.categories import (CIFAR100_CATEGORIES,\n IMAGENET_SIMPLE_CATEGORIES)\nfrom mmpretrain.registry import MODELS, TOKENIZER\nfrom mmpretrain.structures import DataSample\nfrom mmpretrain.utils import track_on_main_process\nfrom .utils import (OPENAI_CIFAR100_PROMPT, OPENAI_IMAGENET_PROMPT,\n OPENAI_IMAGENET_PROMPT_SUB)\nimport numpy as np\nimport torch\nimport torch.nn.functional as F"},"token_num":{"kind":"number","value":10842,"string":"10,842"},"cropped_code":{"kind":"string","value":"\n def initialize_parameters(self) -> None:\n \"\"\"Initialize the parameters.\n\n The pretrained weight will override the initialized parameters by this\n function.\n \"\"\"\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n proj_std = (self.transformer.width**-0.5) * (\n (2 * self.transformer.layers)**-0.5)\n attn_std = self.transformer.width**-0.5\n fc_std = (2 * self.transformer.width)**-0.5\n for block in self.transformer.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n if self.text_projection is not None:\n nn.init.normal_(\n self.text_projection, std=self.transformer.width**-0.5)\n\n def build_attention_mask(self):\n # lazily create causal attention mask,\n # with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(\n self,\n images: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'predict',\n **kwargs,\n ):\n \"\"\"The unified entry for a forward process in both training and test.\n The method accepts the following modes:\n\n - \"predict\": Forward and return a list of data samples contain the\n predict results.\n\n Args:\n images (torch.Tensor): the preprocessed image tensor of shape\n ``(N, C, H, W)``.\n data_samples (List[DataSample], optional): The annotation data\n of every samples. Defaults to None.\n mode (str): Return what kind of value. Defaults to 'predict'.\n \"\"\"\n if mode == 'predict':\n return self.predict(images, data_samples, **kwargs)\n else:\n raise RuntimeError(f'Invalid mode \"{mode}\".')\n\n def extract_image_feat(self, images: torch.Tensor) -> torch.Tensor:\n \"\"\"The function to extract image latent features.\"\"\"\n return self.visual_proj(self.visual(images))[0]\n\n def extract_text_feat(self, texts: torch.Tensor) -> torch.Tensor:\n \"\"\"The function to extract text latent features.\"\"\"\n x = self.token_embedding(texts) # [batch_size, n_ctx, d_model]\n\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)[0]\n\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding\n # (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]),\n texts.argmax(dim=-1)] @ self.text_projection\n\n return x\n\n def extract_feat(\n self, images: torch.Tensor,\n texts: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor]]:\n \"\"\"The function to extract image and text latent features, the input\n image or text can not both be None.\"\"\"\n\n assert images is not None or texts is not None, \\\n 'text and image cannot both be None!'\n if images is None:\n return self.extract_text_feat(texts)\n elif texts is None:\n return self.extract_image_feat(images)\n\n image_features = self.extract_image_feat(images)\n text_features = self.extract_text_feat(texts)\n\n image_features = image_features / image_features.norm(\n dim=-1, keepdim=True)\n text_features = text_features / text_features.norm(\n dim=-1, keepdim=True)\n\n return image_features, text_features\n\n def compute_similarity(self, images, texts):\n \"\"\"Extract images and texts features and compute cosine similarity.\"\"\"\n image_features, text_features = self.extract_feat(\n images=images, texts=texts)\n\n # cosine similarity as logits\n logit_scale = self.logit_scale.exp()\n logits_per_image = logit_scale * image_features @ text_features.t()\n logits_per_text = logits_per_image.t()\n\n # shape (N, N)\n return logits_per_image, logits_per_text\n\n @abstractmethod\n def predict(self,\n images: torch.Tensor,\n"},"all_code":{"kind":"string","value":"# Copyright (c) OpenMMLab. All rights reserved.\n\n\n\nCIFAR100_CATEGORIES = [' '.join(c.split('_')) for c in CIFAR100_CATEGORIES]\nPROTOTYPE_MAP = {\n 'imagenet': IMAGENET_SIMPLE_CATEGORIES,\n 'cifar100': CIFAR100_CATEGORIES,\n}\nPROMPT_MAP = {\n 'openai_imagenet': OPENAI_IMAGENET_PROMPT,\n 'openai_cifar100': OPENAI_CIFAR100_PROMPT,\n 'vanilla': [lambda c: f'a photo of a {c}'],\n 'openai_imagenet_sub': OPENAI_IMAGENET_PROMPT_SUB\n}\n\n\nclass LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16.\"\"\"\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\"\"\"\n orig_type = x.dtype\n ret = super().forward(x.type(torch.float32))\n return ret.type(orig_type)\n\n\nclass CLIP(BaseModel):\n \"\"\"The implementation of `CLIP <https://arxiv.org/abs/2103.00020>`_.\n\n Args:\n vision_backbone (dict): Config dict for vision backbone.\n text_backbone (dict): Config dict for text backbone.\n tokenizer (dict): Config dict for text tokenizer.\n proj_dim (int): Projection dimension for similarity computation.\n text_prototype (str): Text prototype, which can be a key in\n `PROTOTYPE_MAP` or list of text.\n text_prompt (str): The prompt for text prototype.\n Defaults to 'vanilla',which refers to \"a photo of {cls}\".\n context_length (int): The context length to use. Defaults to 77.\n data_preprocessor (Union[dict, nn.Module], optional): The config for\n preprocessing input data. If None or no specified type, it will use\n \"MultiModalDataPreprocessor\" as type.\n See :class:`MultiModalDataPreprocessor` for more details.\n Defaults to None.\n init_cfg (dict, optional): The config to control the initialization.\n Defaults to None.\n \"\"\"\n\n def __init__(self,\n vision_backbone: dict,\n projection: dict,\n text_backbone: dict,\n tokenizer: dict,\n vocab_size: int,\n transformer_width: int,\n proj_dim: int,\n context_length: int = 77,\n data_preprocessor: Optional[dict] = None,\n init_cfg: Optional[dict] = None):\n if data_preprocessor is None:\n data_preprocessor = {}\n data_preprocessor.setdefault('type', 'MultiModalDataPreprocessor')\n data_preprocessor = MODELS.build(data_preprocessor)\n\n super().__init__(\n data_preprocessor=data_preprocessor, init_cfg=init_cfg)\n\n self.context_length = context_length\n\n # build the vision transformer\n self.visual = MODELS.build(vision_backbone)\n\n # build the visual projection\n self.visual_proj = MODELS.build(projection)\n\n # build attn_mask for casual-attn\n text_backbone['attn_mask'] = self.build_attention_mask()\n\n # build the text transformer\n self.transformer = MODELS.build(text_backbone)\n\n self.vocab_size = vocab_size\n self.token_embedding = nn.Embedding(vocab_size, transformer_width)\n self.positional_embedding = nn.Parameter(\n torch.empty(self.context_length, transformer_width))\n self.ln_final = LayerNorm(transformer_width)\n\n self.text_projection = nn.Parameter(\n torch.empty(transformer_width, proj_dim))\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\n self.initialize_parameters()\n\n self.tokenizer = TOKENIZER.build(tokenizer)\n\n self.tokenizer.vocab = self.tokenizer.get_vocab(\n ) # CLIPTokenizer has no attribute named 'vocab', so manually\n\n def initialize_parameters(self) -> None:\n \"\"\"Initialize the parameters.\n\n The pretrained weight will override the initialized parameters by this\n function.\n \"\"\"\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n nn.init.normal_(self.positional_embedding, std=0.01)\n\n proj_std = (self.transformer.width**-0.5) * (\n (2 * self.transformer.layers)**-0.5)\n attn_std = self.transformer.width**-0.5\n fc_std = (2 * self.transformer.width)**-0.5\n for block in self.transformer.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n if self.text_projection is not None:\n nn.init.normal_(\n self.text_projection, std=self.transformer.width**-0.5)\n\n def build_attention_mask(self):\n # lazily create causal attention mask,\n # with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float('-inf'))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(\n self,\n images: torch.Tensor,\n data_samples: Optional[list] = None,\n mode: str = 'predict',\n **kwargs,\n ):\n \"\"\"The unified entry for a forward process in both training and test.\n The method accepts the following modes:\n\n - \"predict\": Forward and return a list of data samples contain the\n predict results.\n\n Args:\n images (torch.Tensor): the preprocessed image tensor of shape\n ``(N, C, H, W)``.\n data_samples (List[DataSample], optional): The annotation data\n of every samples. Defaults to None.\n mode (str): Return what kind of value. Defaults to 'predict'.\n \"\"\"\n if mode == 'predict':\n return self.predict(images, data_samples, **kwargs)\n else:\n raise RuntimeError(f'Invalid mode \"{mode}\".')\n\n def extract_image_feat(self, images: torch.Tensor) -> torch.Tensor:\n \"\"\"The function to extract image latent features.\"\"\"\n return self.visual_proj(self.visual(images))[0]\n\n def extract_text_feat(self, texts: torch.Tensor) -> torch.Tensor:\n \"\"\"The function to extract text latent features.\"\"\"\n x = self.token_embedding(texts) # [batch_size, n_ctx, d_model]\n\n x = x + self.positional_embedding\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)[0]\n\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding\n # (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]),\n texts.argmax(dim=-1)] @ self.text_projection\n\n return x\n\n def extract_feat(\n self, images: torch.Tensor,\n texts: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor]]:\n \"\"\"The function to extract image and text latent features, the input\n image or text can not both be None.\"\"\"\n\n assert images is not None or texts is not None, \\\n 'text and image cannot both be None!'\n if images is None:\n return self.extract_text_feat(texts)\n elif texts is None:\n return self.extract_image_feat(images)\n\n image_features = self.extract_image_feat(images)\n text_features = self.extract_text_feat(texts)\n\n image_features = image_features / image_features.norm(\n dim=-1, keepdim=True)\n text_features = text_features / text_features.norm(\n dim=-1, keepdim=True)\n\n return image_features, text_features\n\n def compute_similarity(self, images, texts):\n \"\"\"Extract images and texts features and compute cosine similarity.\"\"\"\n image_features, text_features = self.extract_feat(\n images=images, texts=texts)\n\n # cosine similarity as logits\n logit_scale = self.logit_scale.exp()\n logits_per_image = logit_scale * image_features @ text_features.t()\n logits_per_text = logits_per_image.t()\n\n # shape (N, N)\n return logits_per_image, logits_per_text\n\n @abstractmethod\n def predict(self,\n images: torch.Tensor,"},"next_line":{"kind":"string","value":" data_samples: DataSample = None) -> DataSample:"},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-12-23 08:36:47+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4698,"cells":{"repo_name":{"kind":"string","value":"see2023/Bert-VITS2-ext"},"file_path":{"kind":"string","value":"train_ms.py"},"context":{"kind":"list like","value":[{"identifier":"config","path":"config.py","snippet":"class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self,\n transcription_path: str,\n cleaned_path: str,\n train_path: str,\n val_path: str,\n config_path: str,\n val_per_lang: int = 5,\n max_val_total: int = 10000,\n clean: bool = True,\n ):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self,\n config_path: str,\n num_processes: int = 2,\n device: str = \"cuda\",\n use_multi_device: bool = False,\n ):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self,\n config_path: str,\n num_processes: int = 2,\n device: str = \"cuda\",\n use_multi_device: bool = False,\n ):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self,\n config_path: str,\n env: Dict[str, any],\n base: Dict[str, any],\n model: str,\n num_workers: int,\n spec_cache: bool,\n keep_ckpts: int,\n ):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self,\n device: str,\n model: str,\n v_model: str,\n config_path: str,\n language_identification_library: str,\n port: int = 7860,\n share: bool = False,\n debug: bool = False,\n ):\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\n def __init__(\n self, models: List[Dict[str, any]], port: int = 5000, device: str = \"cuda\"\n ):\n def from_dict(cls, data: Dict[str, any]):\n def __init__(self, app_key: str, secret_key: str):\n def from_dict(cls, data: Dict[str, any]):\n def __init__(self, config_path: str):"},{"identifier":"TextAudioSpeakerLoader","path":"data_utils.py","snippet":"class TextAudioSpeakerLoader(torch.utils.data.Dataset):\n \"\"\"\n 1) loads audio, speaker_id, text pairs\n 2) normalizes text and converts them to sequences of integers\n 3) computes spectrograms from audio files.\n \"\"\"\n\n def __init__(self, audiopaths_sid_text, hparams):\n self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)\n self.max_wav_value = hparams.max_wav_value\n self.sampling_rate = hparams.sampling_rate\n self.filter_length = hparams.filter_length\n self.hop_length = hparams.hop_length\n self.win_length = hparams.win_length\n self.sampling_rate = hparams.sampling_rate\n self.spk_map = hparams.spk2id\n self.hparams = hparams\n\n self.use_mel_spec_posterior = getattr(\n hparams, \"use_mel_posterior_encoder\", False\n )\n if self.use_mel_spec_posterior:\n self.n_mel_channels = getattr(hparams, \"n_mel_channels\", 80)\n\n self.cleaned_text = getattr(hparams, \"cleaned_text\", False)\n\n self.add_blank = hparams.add_blank\n self.min_text_len = getattr(hparams, \"min_text_len\", 1)\n self.max_text_len = getattr(hparams, \"max_text_len\", 384)\n\n random.seed(1234)\n random.shuffle(self.audiopaths_sid_text)\n self._filter()\n\n def _filter(self):\n \"\"\"\n Filter text & store spec lengths\n \"\"\"\n # Store spectrogram lengths for Bucketing\n # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)\n # spec_length = wav_length // hop_length\n\n audiopaths_sid_text_new = []\n lengths = []\n skipped = 0\n logger.info(\"Init dataset...\")\n for _id, spk, language, text, phones, tone, word2ph in tqdm(\n self.audiopaths_sid_text\n ):\n audiopath = f\"{_id}\"\n if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:\n phones = phones.split(\" \")\n tone = [int(i) for i in tone.split(\" \")]\n word2ph = [int(i) for i in word2ph.split(\" \")]\n audiopaths_sid_text_new.append(\n [audiopath, spk, language, text, phones, tone, word2ph]\n )\n lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))\n else:\n skipped += 1\n logger.info(\n \"skipped: \"\n + str(skipped)\n + \", total: \"\n + str(len(self.audiopaths_sid_text))\n )\n self.audiopaths_sid_text = audiopaths_sid_text_new\n self.lengths = lengths\n\n def get_audio_text_speaker_pair(self, audiopath_sid_text):\n # separate filename, speaker_id and text\n audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text\n\n bert, ja_bert, en_bert, phones, tone, language = self.get_text(\n text, word2ph, phones, tone, language, audiopath\n )\n\n spec, wav = self.get_audio(audiopath)\n sid = torch.LongTensor([int(self.spk_map[sid])])\n\n return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert)\n\n def get_audio(self, filename):\n audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)\n '''\n # from https://github.com/YYuX-1145/Bert-VITS2-Integration-package\n audio, sampling_rate = load_wav_to_torch(filename)\n if sampling_rate != self.sampling_rate:\n raise ValueError(\n \"{} {} SR doesn't match target {} SR\".format(\n filename, sampling_rate, self.sampling_rate\n )\n )\n audio_norm = audio / self.max_wav_value\n audio_norm = audio_norm.unsqueeze(0)\n '''\n spec_filename = filename.replace(\".wav\", \".spec.pt\")\n if self.use_mel_spec_posterior:\n spec_filename = spec_filename.replace(\".spec.pt\", \".mel.pt\")\n try:\n spec = torch.load(spec_filename)\n except:\n if self.use_mel_spec_posterior:\n spec = mel_spectrogram_torch(\n audio_norm,\n self.filter_length,\n self.n_mel_channels,\n self.sampling_rate,\n self.hop_length,\n self.win_length,\n self.hparams.mel_fmin,\n self.hparams.mel_fmax,\n center=False,\n )\n else:\n spec = spectrogram_torch(\n audio_norm,\n self.filter_length,\n self.sampling_rate,\n self.hop_length,\n self.win_length,\n center=False,\n )\n spec = torch.squeeze(spec, 0)\n if config.train_ms_config.spec_cache:\n torch.save(spec, spec_filename)\n return spec, audio_norm\n\n def get_text(self, text, word2ph, phone, tone, language_str, wav_path):\n phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)\n if self.add_blank:\n phone = commons.intersperse(phone, 0)\n tone = commons.intersperse(tone, 0)\n language = commons.intersperse(language, 0)\n for i in range(len(word2ph)):\n word2ph[i] = word2ph[i] * 2\n word2ph[0] += 1\n bert_path = wav_path.replace(\".wav\", \".bert.pt\")\n try:\n bert_ori = torch.load(bert_path)\n assert bert_ori.shape[-1] == len(phone)\n except Exception as e:\n logger.warning(\"Bert load Failed\")\n logger.warning(e)\n\n if language_str == \"ZH\":\n bert = bert_ori\n ja_bert = torch.randn(1024, len(phone))\n en_bert = torch.randn(1024, len(phone))\n elif language_str == \"JP\":\n bert = torch.randn(1024, len(phone))\n ja_bert = bert_ori\n en_bert = torch.randn(1024, len(phone))\n elif language_str == \"EN\":\n bert = torch.randn(1024, len(phone))\n ja_bert = torch.randn(1024, len(phone))\n en_bert = bert_ori\n phone = torch.LongTensor(phone)\n tone = torch.LongTensor(tone)\n language = torch.LongTensor(language)\n return bert, ja_bert, en_bert, phone, tone, language\n\n def get_sid(self, sid):\n sid = torch.LongTensor([int(sid)])\n return sid\n\n def __getitem__(self, index):\n return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])\n\n def __len__(self):\n return len(self.audiopaths_sid_text)"},{"identifier":"TextAudioSpeakerCollate","path":"data_utils.py","snippet":"class TextAudioSpeakerCollate:\n \"\"\"Zero-pads model inputs and targets\"\"\"\n\n def __init__(self, return_ids=False):\n self.return_ids = return_ids\n\n def __call__(self, batch):\n \"\"\"Collate's training batch from normalized text, audio and speaker identities\n PARAMS\n ------\n batch: [text_normalized, spec_normalized, wav_normalized, sid]\n \"\"\"\n # Right zero-pad all one-hot text sequences to max input length\n _, ids_sorted_decreasing = torch.sort(\n torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True\n )\n\n max_text_len = max([len(x[0]) for x in batch])\n max_spec_len = max([x[1].size(1) for x in batch])\n max_wav_len = max([x[2].size(1) for x in batch])\n\n text_lengths = torch.LongTensor(len(batch))\n spec_lengths = torch.LongTensor(len(batch))\n wav_lengths = torch.LongTensor(len(batch))\n sid = torch.LongTensor(len(batch))\n\n text_padded = torch.LongTensor(len(batch), max_text_len)\n tone_padded = torch.LongTensor(len(batch), max_text_len)\n language_padded = torch.LongTensor(len(batch), max_text_len)\n bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\n ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\n en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\n\n spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)\n wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)\n text_padded.zero_()\n tone_padded.zero_()\n language_padded.zero_()\n spec_padded.zero_()\n wav_padded.zero_()\n bert_padded.zero_()\n ja_bert_padded.zero_()\n en_bert_padded.zero_()\n\n for i in range(len(ids_sorted_decreasing)):\n row = batch[ids_sorted_decreasing[i]]\n\n text = row[0]\n text_padded[i, : text.size(0)] = text\n text_lengths[i] = text.size(0)\n\n spec = row[1]\n spec_padded[i, :, : spec.size(1)] = spec\n spec_lengths[i] = spec.size(1)\n\n wav = row[2]\n wav_padded[i, :, : wav.size(1)] = wav\n wav_lengths[i] = wav.size(1)\n\n sid[i] = row[3]\n\n tone = row[4]\n tone_padded[i, : tone.size(0)] = tone\n\n language = row[5]\n language_padded[i, : language.size(0)] = language\n\n bert = row[6]\n bert_padded[i, :, : bert.size(1)] = bert\n\n ja_bert = row[7]\n ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert\n\n en_bert = row[8]\n en_bert_padded[i, :, : en_bert.size(1)] = en_bert\n\n return (\n text_padded,\n text_lengths,\n spec_padded,\n spec_lengths,\n wav_padded,\n wav_lengths,\n sid,\n tone_padded,\n language_padded,\n bert_padded,\n ja_bert_padded,\n en_bert_padded,\n )"},{"identifier":"DistributedBucketSampler","path":"data_utils.py","snippet":"class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\n \"\"\"\n Maintain similar input lengths in a batch.\n Length groups are specified by boundaries.\n Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.\n\n It removes samples which are not included in the boundaries.\n Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.\n \"\"\"\n\n def __init__(\n self,\n dataset,\n batch_size,\n boundaries,\n num_replicas=None,\n rank=None,\n shuffle=True,\n ):\n super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)\n self.lengths = dataset.lengths\n self.batch_size = batch_size\n self.boundaries = boundaries\n\n self.buckets, self.num_samples_per_bucket = self._create_buckets()\n self.total_size = sum(self.num_samples_per_bucket)\n self.num_samples = self.total_size // self.num_replicas\n\n def _create_buckets(self):\n buckets = [[] for _ in range(len(self.boundaries) - 1)]\n for i in range(len(self.lengths)):\n length = self.lengths[i]\n idx_bucket = self._bisect(length)\n if idx_bucket != -1:\n buckets[idx_bucket].append(i)\n\n try:\n for i in range(len(buckets) - 1, 0, -1):\n if len(buckets[i]) == 0:\n buckets.pop(i)\n self.boundaries.pop(i + 1)\n assert all(len(bucket) > 0 for bucket in buckets)\n # When one bucket is not traversed\n except Exception as e:\n print(\"Bucket warning \", e)\n for i in range(len(buckets) - 1, -1, -1):\n if len(buckets[i]) == 0:\n buckets.pop(i)\n self.boundaries.pop(i + 1)\n\n num_samples_per_bucket = []\n for i in range(len(buckets)):\n len_bucket = len(buckets[i])\n total_batch_size = self.num_replicas * self.batch_size\n rem = (\n total_batch_size - (len_bucket % total_batch_size)\n ) % total_batch_size\n num_samples_per_bucket.append(len_bucket + rem)\n return buckets, num_samples_per_bucket\n\n def __iter__(self):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n\n indices = []\n if self.shuffle:\n for bucket in self.buckets:\n indices.append(torch.randperm(len(bucket), generator=g).tolist())\n else:\n for bucket in self.buckets:\n indices.append(list(range(len(bucket))))\n\n batches = []\n for i in range(len(self.buckets)):\n bucket = self.buckets[i]\n len_bucket = len(bucket)\n if len_bucket == 0:\n continue\n ids_bucket = indices[i]\n num_samples_bucket = self.num_samples_per_bucket[i]\n\n # add extra samples to make it evenly divisible\n rem = num_samples_bucket - len_bucket\n ids_bucket = (\n ids_bucket\n + ids_bucket * (rem // len_bucket)\n + ids_bucket[: (rem % len_bucket)]\n )\n\n # subsample\n ids_bucket = ids_bucket[self.rank :: self.num_replicas]\n\n # batching\n for j in range(len(ids_bucket) // self.batch_size):\n batch = [\n bucket[idx]\n for idx in ids_bucket[\n j * self.batch_size : (j + 1) * self.batch_size\n ]\n ]\n batches.append(batch)\n\n if self.shuffle:\n batch_ids = torch.randperm(len(batches), generator=g).tolist()\n batches = [batches[i] for i in batch_ids]\n self.batches = batches\n\n assert len(self.batches) * self.batch_size == self.num_samples\n return iter(self.batches)\n\n def _bisect(self, x, lo=0, hi=None):\n if hi is None:\n hi = len(self.boundaries) - 1\n\n if hi > lo:\n mid = (hi + lo) // 2\n if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:\n return mid\n elif x <= self.boundaries[mid]:\n return self._bisect(x, lo, mid)\n else:\n return self._bisect(x, mid + 1, hi)\n else:\n return -1\n\n def __len__(self):\n return self.num_samples // self.batch_size"},{"identifier":"AudioVisemesLoader","path":"data_utils.py","snippet":"class AudioVisemesLoader(torch.utils.data.Dataset):\n \"\"\"\n loads audio, visemes torch variable pairs from visemes list file .\n file is like: \n ./records/date_time.z.npy|./records/date_time.npy\n \"\"\"\n \n def __init__(self, audio_visemes_list_file, hparams):\n self.audio_visemes_list_items = load_filepaths_and_text(audio_visemes_list_file)\n print('audio_visemes_list_items: ', len(self.audio_visemes_list_items))\n random.seed(1234)\n random.shuffle(self.audio_visemes_list_items)\n self.max_visemes_len = 1210\n self.min_visemes_len = 1190\n self._filter()\n\n\n def _filter(self):\n # check if the file exists, and can parse as torch tensor\n audio_visemes_list_items_new = []\n for audio_file, visemes_file in self.audio_visemes_list_items:\n if os.path.exists(audio_file) and os.path.exists(visemes_file):\n # check using torch.load\n try:\n audio = torch.load(audio_file)\n visemes = np.load(visemes_file)\n if visemes.shape[0] < self.min_visemes_len:\n print('drop this data: --------- visemes.shape[0] < self.min_visemes_len: ', visemes.shape[0], visemes_file)\n continue\n audio_visemes_list_items_new.append([audio_file, visemes_file])\n except Exception as e:\n print('error: ', audio_file, visemes_file)\n print(e)\n self.audio_visemes_list_items = audio_visemes_list_items_new\n print('audio_visemes_list_items after filter: ', len(self.audio_visemes_list_items))\n\n def __getitem__(self, index):\n # read these two torch.tensor\n audio_file, visemes_file = self.audio_visemes_list_items[index]\n audio_z = torch.load(audio_file).squeeze(0).detach()\n # [192, seq_len(1722)]\n\n visemes = np.load(visemes_file)\n visemes = torch.from_numpy(visemes)\n #[seq_len(1194), 61]\n visemes = visemes.transpose(0, 1)\n #[61, seq_len(1194)]\n if visemes.shape[1] > self.max_visemes_len:\n # cut the extra part\n # print('__getitem__ 1 cut visemes from ', visemes.shape[0], ' to ', self.max_visemes_len, 'file: ', visemes_file)\n visemes = visemes[:, :self.max_visemes_len]\n elif visemes.shape[1] < self.max_visemes_len:\n # padding to max_visemes_len with last frame\n # print('__getitem__ 2 padding visemes from ', visemes.shape[0], ' to ', self.max_visemes_len, 'file: ', visemes_file)\n # last_frame = visemes[-1]\n # visemes = np.concatenate([visemes, np.tile(last_frame, (self.max_visemes_len - visemes.shape[0], 1))], axis=0)\n # visemes = torch.from_numpy(visemes)\n pass\n\n visemes_offset = 0.08 # 将visemes延迟n s\n visemes_offset_frames = int(visemes_offset * const_map.ARKIT_FPS)\n visemes = visemes[:, visemes_offset_frames:]\n\n audio_z_offset = 0.0\n audio_z_offset_frames = int(audio_z_offset * const_map.Z_FPS)\n audio_z = audio_z[:, audio_z_offset_frames:]\n\n # 获取二者的时长，将过长的一方多的部分丢弃\n visemes_duration = visemes.shape[1] / const_map.ARKIT_FPS\n audio_z_duration = audio_z.shape[1] / const_map.Z_FPS\n if visemes_duration > audio_z_duration:\n visemes = visemes[:, :int(audio_z_duration * const_map.ARKIT_FPS)]\n elif visemes_duration < audio_z_duration:\n audio_z = audio_z[:, :int(visemes_duration * const_map.Z_FPS)]\n\n\n # print('__getitem__ 3 audio.shape: ', audio.shape, 'visemes.shape: ', visemes.shape,'file: ', visemes_file)\n return audio_z, visemes\n\n def __len__(self):\n return len(self.audio_visemes_list_items)"},{"identifier":"SynthesizerTrn","path":"models.py","snippet":"class SynthesizerTrn(nn.Module):\n \"\"\"\n Synthesizer for Training\n \"\"\"\n\n def __init__(\n self,\n n_vocab,\n spec_channels,\n segment_size,\n inter_channels,\n hidden_channels,\n filter_channels,\n n_heads,\n n_layers,\n kernel_size,\n p_dropout,\n resblock,\n resblock_kernel_sizes,\n resblock_dilation_sizes,\n upsample_rates,\n upsample_initial_channel,\n upsample_kernel_sizes,\n n_speakers=256,\n gin_channels=256,\n use_sdp=True,\n n_flow_layer=4,\n n_layers_trans_flow=4,\n flow_share_parameter=False,\n use_transformer_flow=True,\n **kwargs\n ):\n super().__init__()\n self.n_vocab = n_vocab\n self.spec_channels = spec_channels\n self.inter_channels = inter_channels\n self.hidden_channels = hidden_channels\n self.filter_channels = filter_channels\n self.n_heads = n_heads\n self.n_layers = n_layers\n self.kernel_size = kernel_size\n self.p_dropout = p_dropout\n self.resblock = resblock\n self.resblock_kernel_sizes = resblock_kernel_sizes\n self.resblock_dilation_sizes = resblock_dilation_sizes\n self.upsample_rates = upsample_rates\n self.upsample_initial_channel = upsample_initial_channel\n self.upsample_kernel_sizes = upsample_kernel_sizes\n self.segment_size = segment_size\n self.n_speakers = n_speakers\n self.gin_channels = gin_channels\n self.n_layers_trans_flow = n_layers_trans_flow\n self.use_spk_conditioned_encoder = kwargs.get(\n \"use_spk_conditioned_encoder\", True\n )\n self.use_sdp = use_sdp\n self.use_noise_scaled_mas = kwargs.get(\"use_noise_scaled_mas\", False)\n self.mas_noise_scale_initial = kwargs.get(\"mas_noise_scale_initial\", 0.01)\n self.noise_scale_delta = kwargs.get(\"noise_scale_delta\", 2e-6)\n self.current_mas_noise_scale = self.mas_noise_scale_initial\n if self.use_spk_conditioned_encoder and gin_channels > 0:\n self.enc_gin_channels = gin_channels\n self.enc_p = TextEncoder(\n n_vocab,\n inter_channels,\n hidden_channels,\n filter_channels,\n n_heads,\n n_layers,\n kernel_size,\n p_dropout,\n gin_channels=self.enc_gin_channels,\n )\n self.dec = Generator(\n inter_channels,\n resblock,\n resblock_kernel_sizes,\n resblock_dilation_sizes,\n upsample_rates,\n upsample_initial_channel,\n upsample_kernel_sizes,\n gin_channels=gin_channels,\n )\n self.enc_q = PosteriorEncoder(\n spec_channels,\n inter_channels,\n hidden_channels,\n 5,\n 1,\n 16,\n gin_channels=gin_channels,\n )\n if use_transformer_flow:\n self.flow = TransformerCouplingBlock(\n inter_channels,\n hidden_channels,\n filter_channels,\n n_heads,\n n_layers_trans_flow,\n 5,\n p_dropout,\n n_flow_layer,\n gin_channels=gin_channels,\n share_parameter=flow_share_parameter,\n )\n else:\n self.flow = ResidualCouplingBlock(\n inter_channels,\n hidden_channels,\n 5,\n 1,\n n_flow_layer,\n gin_channels=gin_channels,\n )\n self.sdp = StochasticDurationPredictor(\n hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels\n )\n self.dp = DurationPredictor(\n hidden_channels, 256, 3, 0.5, gin_channels=gin_channels\n )\n\n if n_speakers >= 1:\n self.emb_g = nn.Embedding(n_speakers, gin_channels)\n else:\n self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)\n\n def forward(\n self,\n x,\n x_lengths,\n y,\n y_lengths,\n sid,\n tone,\n language,\n bert,\n ja_bert,\n en_bert,\n ):\n if self.n_speakers > 0:\n g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]\n else:\n g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)\n x, m_p, logs_p, x_mask = self.enc_p(\n x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g\n )\n z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)\n z_p = self.flow(z, y_mask, g=g)\n\n with torch.no_grad():\n # negative cross-entropy\n s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]\n neg_cent1 = torch.sum(\n -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True\n ) # [b, 1, t_s]\n neg_cent2 = torch.matmul(\n -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r\n ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]\n neg_cent3 = torch.matmul(\n z_p.transpose(1, 2), (m_p * s_p_sq_r)\n ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]\n neg_cent4 = torch.sum(\n -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True\n ) # [b, 1, t_s]\n neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4\n if self.use_noise_scaled_mas:\n epsilon = (\n torch.std(neg_cent)\n * torch.randn_like(neg_cent)\n * self.current_mas_noise_scale\n )\n neg_cent = neg_cent + epsilon\n\n attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)\n attn = (\n monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))\n .unsqueeze(1)\n .detach()\n )\n\n w = attn.sum(2)\n\n l_length_sdp = self.sdp(x, x_mask, w, g=g)\n l_length_sdp = l_length_sdp / torch.sum(x_mask)\n\n logw_ = torch.log(w + 1e-6) * x_mask\n logw = self.dp(x, x_mask, g=g)\n logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0)\n l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(\n x_mask\n ) # for averaging\n l_length_sdp += torch.sum((logw_sdp - logw_) ** 2, [1, 2]) / torch.sum(x_mask)\n\n l_length = l_length_dp + l_length_sdp\n\n # expand prior\n m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)\n logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)\n\n z_slice, ids_slice = commons.rand_slice_segments(\n z, y_lengths, self.segment_size\n )\n o = self.dec(z_slice, g=g)\n return (\n o,\n l_length,\n attn,\n ids_slice,\n x_mask,\n y_mask,\n (z, z_p, m_p, logs_p, m_q, logs_q),\n (x, logw, logw_, logw_sdp),\n g,\n )\n\n def infer(\n self,\n x,\n x_lengths,\n sid,\n tone,\n language,\n bert,\n ja_bert,\n en_bert,\n noise_scale=0.667,\n length_scale=1,\n noise_scale_w=0.8,\n max_len=None,\n sdp_ratio=0,\n y=None,\n ):\n # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)\n # g = self.gst(y)\n if self.n_speakers > 0:\n g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]\n else:\n g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)\n x, m_p, logs_p, x_mask = self.enc_p(\n x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g\n )\n logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (\n sdp_ratio\n ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)\n w = torch.exp(logw) * x_mask * length_scale\n w_ceil = torch.ceil(w)\n y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()\n y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(\n x_mask.dtype\n )\n attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)\n attn = commons.generate_path(w_ceil, attn_mask)\n\n m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(\n 1, 2\n ) # [b, t', t], [b, t, d] -> [b, d, t']\n logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(\n 1, 2\n ) # [b, t', t], [b, t, d] -> [b, d, t']\n\n z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale\n z = self.flow(z_p, y_mask, g=g, reverse=True)\n o = self.dec((z * y_mask)[:, :, :max_len], g=g)\n return o, attn, y_mask, (z, z_p, m_p, logs_p)\n\n def get_post_enc_dec(self):\n return self.enc_q, self.dec"},{"identifier":"MultiPeriodDiscriminator","path":"models.py","snippet":"class MultiPeriodDiscriminator(torch.nn.Module):\n def __init__(self, use_spectral_norm=False):\n super(MultiPeriodDiscriminator, self).__init__()\n periods = [2, 3, 5, 7, 11]\n\n discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]\n discs = discs + [\n DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods\n ]\n self.discriminators = nn.ModuleList(discs)\n\n def forward(self, y, y_hat):\n y_d_rs = []\n y_d_gs = []\n fmap_rs = []\n fmap_gs = []\n for i, d in enumerate(self.discriminators):\n y_d_r, fmap_r = d(y)\n y_d_g, fmap_g = d(y_hat)\n y_d_rs.append(y_d_r)\n y_d_gs.append(y_d_g)\n fmap_rs.append(fmap_r)\n fmap_gs.append(fmap_g)\n\n return y_d_rs, y_d_gs, fmap_rs, fmap_gs"},{"identifier":"DurationDiscriminator","path":"models.py","snippet":"class DurationDiscriminator(nn.Module): # vits2\n def __init__(\n self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0\n ):\n super().__init__()\n\n self.in_channels = in_channels\n self.filter_channels = filter_channels\n self.kernel_size = kernel_size\n self.p_dropout = p_dropout\n self.gin_channels = gin_channels\n\n self.drop = nn.Dropout(p_dropout)\n self.conv_1 = nn.Conv1d(\n in_channels, filter_channels, kernel_size, padding=kernel_size // 2\n )\n self.norm_1 = modules.LayerNorm(filter_channels)\n self.conv_2 = nn.Conv1d(\n filter_channels, filter_channels, kernel_size, padding=kernel_size // 2\n )\n self.norm_2 = modules.LayerNorm(filter_channels)\n self.dur_proj = nn.Conv1d(1, filter_channels, 1)\n\n self.LSTM = nn.LSTM(\n 2 * filter_channels, filter_channels, batch_first=True, bidirectional=True\n )\n\n if gin_channels != 0:\n self.cond = nn.Conv1d(gin_channels, in_channels, 1)\n\n self.output_layer = nn.Sequential(\n nn.Linear(2 * filter_channels, 1), nn.Sigmoid()\n )\n\n def forward_probability(self, x, dur):\n dur = self.dur_proj(dur)\n x = torch.cat([x, dur], dim=1)\n x = x.transpose(1, 2)\n x, _ = self.LSTM(x)\n output_prob = self.output_layer(x)\n return output_prob\n\n def forward(self, x, x_mask, dur_r, dur_hat, g=None):\n x = torch.detach(x)\n if g is not None:\n g = torch.detach(g)\n x = x + self.cond(g)\n x = self.conv_1(x * x_mask)\n x = torch.relu(x)\n x = self.norm_1(x)\n x = self.drop(x)\n x = self.conv_2(x * x_mask)\n x = torch.relu(x)\n x = self.norm_2(x)\n x = self.drop(x)\n\n output_probs = []\n for dur in [dur_r, dur_hat]:\n output_prob = self.forward_probability(x, dur)\n output_probs.append(output_prob)\n\n return output_probs"},{"identifier":"WavLMDiscriminator","path":"models.py","snippet":"class WavLMDiscriminator(nn.Module):\n \"\"\"docstring for Discriminator.\"\"\"\n\n def __init__(\n self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False\n ):\n super(WavLMDiscriminator, self).__init__()\n norm_f = weight_norm if use_spectral_norm == False else spectral_norm\n self.pre = norm_f(\n Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)\n )\n\n self.convs = nn.ModuleList(\n [\n norm_f(\n nn.Conv1d(\n initial_channel, initial_channel * 2, kernel_size=5, padding=2\n )\n ),\n norm_f(\n nn.Conv1d(\n initial_channel * 2,\n initial_channel * 4,\n kernel_size=5,\n padding=2,\n )\n ),\n norm_f(\n nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)\n ),\n ]\n )\n\n self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))\n\n def forward(self, x):\n x = self.pre(x)\n\n fmap = []\n for l in self.convs:\n x = l(x)\n x = F.leaky_relu(x, modules.LRELU_SLOPE)\n fmap.append(x)\n x = self.conv_post(x)\n x = torch.flatten(x, 1, -1)\n\n return x"},{"identifier":"VisemesNet","path":"models.py","snippet":"class VisemesNet(nn.Module):\n def active(self, x):\n # active_fun: 0: null, 1: tanh, 2: relu, 3: LeakyReLU\n if self.active_fun == 1:\n return torch.tanh(x)\n elif self.active_fun == 2:\n return torch.relu(x)\n elif self.active_fun == 3:\n return self.leakyReLU(x)\n else:\n return x\n\n def __init__(self, hidden_channels, lstm_bidirectional=True, active_fun = 3, enable_conv=True, \n use_transformer = False, enable_dropout=True):\n super(VisemesNet, self).__init__()\n self.lstm_bidirectional = lstm_bidirectional\n self.lstm_directions = 2 if lstm_bidirectional else 1\n self.use_transformer = use_transformer\n self.enable_dropout = enable_dropout\n if active_fun == 3:\n self.leakyReLU = nn.LeakyReLU(negative_slope=0.01)\n if use_transformer:\n num_heads=8\n num_layers=3\n dim_feedforward=512\n dropout=0.1\n activation=\"relu\"\n self.transformer_encoder_layer = nn.TransformerEncoderLayer(\n d_model=hidden_channels, \n nhead=num_heads,\n dim_feedforward=dim_feedforward,\n dropout=dropout,\n activation=activation,\n batch_first=True\n )\n self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers=num_layers)\n else:\n self.lstm = nn.LSTM(input_size=hidden_channels, hidden_size=128, num_layers=3, batch_first=True, bidirectional=lstm_bidirectional)\n if use_transformer:\n self.fc1 = nn.Linear(hidden_channels, 96)\n else:\n self.fc1 = nn.Linear(128 * self.lstm_directions, 96)\n self.fc2 = nn.Linear(96, 61)\n dropout_rate = 0.5\n if self.enable_dropout:\n self.dropout = nn.Dropout(dropout_rate)\n conv_kernel_pre = 15\n conv_kernel_post = 11\n self.conv1d_pre = nn.Conv1d(in_channels=hidden_channels, out_channels=hidden_channels, kernel_size=conv_kernel_pre, stride=1, padding=conv_kernel_pre//2)\n self.conv1d_post = nn.Conv1d(in_channels=61, out_channels=61, kernel_size=conv_kernel_post, stride=1, padding=conv_kernel_post//2)\n self.enable_conv = enable_conv\n self.active_fun = active_fun\n\n def forward(self, x, y=None):\n # x [batch_size, hidden_channels, seq_len]\n if self.use_transformer:\n return self.forward_transformer(x, y)\n else:\n return self.forward_lstm(x, y)\n\n def forward_transformer(self, x, y=None):\n # x [batch_size, hidden_channels, seq_len]\n if self.enable_conv:\n x = self.conv1d_pre(x)\n # batch_first: True (batch, seq, feature); False (seq, batch, feature).\n x = x.transpose(1, 2)\n\n expressions = self.transformer_encoder(x)\n \n if self.enable_dropout:\n expressions = self.dropout(expressions)\n expressions = self.fc1(expressions)\n # expressions = self.active(expressions)\n if self.enable_dropout:\n expressions = self.dropout(expressions)\n expressions = self.fc2(expressions)\n\n expressions = expressions.transpose(1, 2)\n if self.enable_conv:\n expressions = self.conv1d_post(expressions)\n\n return expressions \n\n def forward_lstm(self, x, y=None):\n # x [batch_size, hidden_channels, seq_len]\n if self.enable_conv:\n x = self.conv1d_pre(x)\n x = x.transpose(1, 2)\n # x [batch_size, seq_len, hidden_channels]\n expressions = None\n expressions, _ = self.lstm(x)\n if self.enable_dropout:\n expressions = self.dropout(expressions)\n expressions = self.fc1(expressions)\n expressions = self.active(expressions)\n if self.enable_dropout:\n expressions = self.dropout(expressions)\n expressions = self.fc2(expressions)\n\n expressions = expressions.transpose(1, 2)\n if self.enable_conv:\n expressions = self.conv1d_post(expressions)\n return expressions\n \n def init_weights(self):\n # 初始化权重\n for m in self.modules():\n if isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight.data)\n if m.bias is not None:\n nn.init.constant_(m.bias.data, 0)\n elif isinstance(m, nn.LSTM):\n for name, param in m.named_parameters():\n if 'weight_ih' in name:\n nn.init.xavier_uniform_(param.data)\n elif 'weight_hh' in name:\n nn.init.orthogonal_(param.data)\n elif 'bias' in name:\n nn.init.constant_(param.data, 0)\n elif isinstance(m, nn.BatchNorm1d):\n nn.init.constant_(m.weight.data, 1)\n nn.init.constant_(m.bias.data, 0)\n elif isinstance(m, nn.Conv1d):\n nn.init.xavier_uniform_(m.weight.data)\n nn.init.constant_(m.bias.data, 0)\n elif isinstance(m, nn.TransformerEncoderLayer):\n for name, param in m.named_parameters():\n if 'weight' in name:\n if param.dim() == 1:\n nn.init.normal_(param.data)\n else:\n nn.init.xavier_uniform_(param.data)\n elif 'bias' in name:\n nn.init.constant_(param.data, 0)\n elif isinstance(m, nn.TransformerEncoder):\n for param in m.parameters():\n if param.dim() > 1:\n nn.init.xavier_uniform_(param.data)\n else:\n nn.init.constant_(param.data, 0)"},{"identifier":"generator_loss","path":"losses.py","snippet":"def generator_loss(disc_outputs):\n loss = 0\n gen_losses = []\n for dg in disc_outputs:\n dg = dg.float()\n l = torch.mean((1 - dg) ** 2)\n gen_losses.append(l)\n loss += l\n\n return loss, gen_losses"},{"identifier":"discriminator_loss","path":"losses.py","snippet":"def discriminator_loss(disc_real_outputs, disc_generated_outputs):\n loss = 0\n r_losses = []\n g_losses = []\n for dr, dg in zip(disc_real_outputs, disc_generated_outputs):\n dr = dr.float()\n dg = dg.float()\n r_loss = torch.mean((1 - dr) ** 2)\n g_loss = torch.mean(dg**2)\n loss += r_loss + g_loss\n r_losses.append(r_loss.item())\n g_losses.append(g_loss.item())\n\n return loss, r_losses, g_losses"},{"identifier":"feature_loss","path":"losses.py","snippet":"def feature_loss(fmap_r, fmap_g):\n loss = 0\n for dr, dg in zip(fmap_r, fmap_g):\n for rl, gl in zip(dr, dg):\n rl = rl.float().detach()\n gl = gl.float()\n loss += torch.mean(torch.abs(rl - gl))\n\n return loss * 2"},{"identifier":"kl_loss","path":"losses.py","snippet":"def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):\n \"\"\"\n z_p, logs_q: [b, h, t_t]\n m_p, logs_p: [b, h, t_t]\n \"\"\"\n z_p = z_p.float()\n logs_q = logs_q.float()\n m_p = m_p.float()\n logs_p = logs_p.float()\n z_mask = z_mask.float()\n\n kl = logs_p - logs_q - 0.5\n kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)\n kl = torch.sum(kl * z_mask)\n l = kl / torch.sum(z_mask)\n return l"},{"identifier":"WavLMLoss","path":"losses.py","snippet":"class WavLMLoss(torch.nn.Module):\n def __init__(self, model, wd, model_sr, slm_sr=16000):\n super(WavLMLoss, self).__init__()\n self.wavlm = AutoModel.from_pretrained(model)\n self.wd = wd\n self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)\n self.wavlm.eval()\n for param in self.wavlm.parameters():\n param.requires_grad = False\n\n def forward(self, wav, y_rec):\n with torch.no_grad():\n wav_16 = self.resample(wav)\n wav_embeddings = self.wavlm(\n input_values=wav_16, output_hidden_states=True\n ).hidden_states\n y_rec_16 = self.resample(y_rec)\n y_rec_embeddings = self.wavlm(\n input_values=y_rec_16.squeeze(), output_hidden_states=True\n ).hidden_states\n\n floss = 0\n for er, eg in zip(wav_embeddings, y_rec_embeddings):\n floss += torch.mean(torch.abs(er - eg))\n\n return floss.mean()\n\n def generator(self, y_rec):\n y_rec_16 = self.resample(y_rec)\n y_rec_embeddings = self.wavlm(\n input_values=y_rec_16, output_hidden_states=True\n ).hidden_states\n y_rec_embeddings = (\n torch.stack(y_rec_embeddings, dim=1)\n .transpose(-1, -2)\n .flatten(start_dim=1, end_dim=2)\n )\n y_df_hat_g = self.wd(y_rec_embeddings)\n loss_gen = torch.mean((1 - y_df_hat_g) ** 2)\n\n return loss_gen\n\n def discriminator(self, wav, y_rec):\n with torch.no_grad():\n wav_16 = self.resample(wav)\n wav_embeddings = self.wavlm(\n input_values=wav_16, output_hidden_states=True\n ).hidden_states\n y_rec_16 = self.resample(y_rec)\n y_rec_embeddings = self.wavlm(\n input_values=y_rec_16, output_hidden_states=True\n ).hidden_states\n\n y_embeddings = (\n torch.stack(wav_embeddings, dim=1)\n .transpose(-1, -2)\n .flatten(start_dim=1, end_dim=2)\n )\n y_rec_embeddings = (\n torch.stack(y_rec_embeddings, dim=1)\n .transpose(-1, -2)\n .flatten(start_dim=1, end_dim=2)\n )\n\n y_d_rs = self.wd(y_embeddings)\n y_d_gs = self.wd(y_rec_embeddings)\n\n y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs\n\n r_loss = torch.mean((1 - y_df_hat_r) ** 2)\n g_loss = torch.mean((y_df_hat_g) ** 2)\n\n loss_disc_f = r_loss + g_loss\n\n return loss_disc_f.mean()\n\n def discriminator_forward(self, wav):\n with torch.no_grad():\n wav_16 = self.resample(wav)\n wav_embeddings = self.wavlm(\n input_values=wav_16, output_hidden_states=True\n ).hidden_states\n y_embeddings = (\n torch.stack(wav_embeddings, dim=1)\n .transpose(-1, -2)\n .flatten(start_dim=1, end_dim=2)\n )\n\n y_d_rs = self.wd(y_embeddings)\n\n return y_d_rs"},{"identifier":"mel_spectrogram_torch","path":"mel_processing.py","snippet":"def mel_spectrogram_torch(\n y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False\n):\n if torch.min(y) < -1.0:\n print(\"min value is \", torch.min(y))\n if torch.max(y) > 1.0:\n print(\"max value is \", torch.max(y))\n\n global mel_basis, hann_window\n dtype_device = str(y.dtype) + \"_\" + str(y.device)\n fmax_dtype_device = str(fmax) + \"_\" + dtype_device\n wnsize_dtype_device = str(win_size) + \"_\" + dtype_device\n if fmax_dtype_device not in mel_basis:\n mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)\n mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(\n dtype=y.dtype, device=y.device\n )\n if wnsize_dtype_device not in hann_window:\n hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(\n dtype=y.dtype, device=y.device\n )\n\n y = torch.nn.functional.pad(\n y.unsqueeze(1),\n (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),\n mode=\"reflect\",\n )\n y = y.squeeze(1)\n\n spec = torch.stft(\n y,\n n_fft,\n hop_length=hop_size,\n win_length=win_size,\n window=hann_window[wnsize_dtype_device],\n center=center,\n pad_mode=\"reflect\",\n normalized=False,\n onesided=True,\n return_complex=False,\n )\n\n spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)\n\n spec = torch.matmul(mel_basis[fmax_dtype_device], spec)\n spec = spectral_normalize_torch(spec)\n\n return spec"},{"identifier":"spec_to_mel_torch","path":"mel_processing.py","snippet":"def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):\n global mel_basis\n dtype_device = str(spec.dtype) + \"_\" + str(spec.device)\n fmax_dtype_device = str(fmax) + \"_\" + dtype_device\n if fmax_dtype_device not in mel_basis:\n mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)\n mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(\n dtype=spec.dtype, device=spec.device\n )\n spec = torch.matmul(mel_basis[fmax_dtype_device], spec)\n spec = spectral_normalize_torch(spec)\n return spec"},{"identifier":"symbols","path":"text/symbols.py","snippet":""}],"string":"[\n {\n \"identifier\": \"config\",\n \"path\": \"config.py\",\n \"snippet\": \"class Resample_config:\\nclass Preprocess_text_config:\\nclass Bert_gen_config:\\nclass Emo_gen_config:\\nclass Train_ms_config:\\nclass Webui_config:\\nclass Server_config:\\nclass Translate_config:\\nclass Config:\\n def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self,\\n transcription_path: str,\\n cleaned_path: str,\\n train_path: str,\\n val_path: str,\\n config_path: str,\\n val_per_lang: int = 5,\\n max_val_total: int = 10000,\\n clean: bool = True,\\n ):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self,\\n config_path: str,\\n num_processes: int = 2,\\n device: str = \\\"cuda\\\",\\n use_multi_device: bool = False,\\n ):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self,\\n config_path: str,\\n num_processes: int = 2,\\n device: str = \\\"cuda\\\",\\n use_multi_device: bool = False,\\n ):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self,\\n config_path: str,\\n env: Dict[str, any],\\n base: Dict[str, any],\\n model: str,\\n num_workers: int,\\n spec_cache: bool,\\n keep_ckpts: int,\\n ):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self,\\n device: str,\\n model: str,\\n v_model: str,\\n config_path: str,\\n language_identification_library: str,\\n port: int = 7860,\\n share: bool = False,\\n debug: bool = False,\\n ):\\n def from_dict(cls, dataset_path: str, data: Dict[str, any]):\\n def __init__(\\n self, models: List[Dict[str, any]], port: int = 5000, device: str = \\\"cuda\\\"\\n ):\\n def from_dict(cls, data: Dict[str, any]):\\n def __init__(self, app_key: str, secret_key: str):\\n def from_dict(cls, data: Dict[str, any]):\\n def __init__(self, config_path: str):\"\n },\n {\n \"identifier\": \"TextAudioSpeakerLoader\",\n \"path\": \"data_utils.py\",\n \"snippet\": \"class TextAudioSpeakerLoader(torch.utils.data.Dataset):\\n \\\"\\\"\\\"\\n 1) loads audio, speaker_id, text pairs\\n 2) normalizes text and converts them to sequences of integers\\n 3) computes spectrograms from audio files.\\n \\\"\\\"\\\"\\n\\n def __init__(self, audiopaths_sid_text, hparams):\\n self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)\\n self.max_wav_value = hparams.max_wav_value\\n self.sampling_rate = hparams.sampling_rate\\n self.filter_length = hparams.filter_length\\n self.hop_length = hparams.hop_length\\n self.win_length = hparams.win_length\\n self.sampling_rate = hparams.sampling_rate\\n self.spk_map = hparams.spk2id\\n self.hparams = hparams\\n\\n self.use_mel_spec_posterior = getattr(\\n hparams, \\\"use_mel_posterior_encoder\\\", False\\n )\\n if self.use_mel_spec_posterior:\\n self.n_mel_channels = getattr(hparams, \\\"n_mel_channels\\\", 80)\\n\\n self.cleaned_text = getattr(hparams, \\\"cleaned_text\\\", False)\\n\\n self.add_blank = hparams.add_blank\\n self.min_text_len = getattr(hparams, \\\"min_text_len\\\", 1)\\n self.max_text_len = getattr(hparams, \\\"max_text_len\\\", 384)\\n\\n random.seed(1234)\\n random.shuffle(self.audiopaths_sid_text)\\n self._filter()\\n\\n def _filter(self):\\n \\\"\\\"\\\"\\n Filter text & store spec lengths\\n \\\"\\\"\\\"\\n # Store spectrogram lengths for Bucketing\\n # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)\\n # spec_length = wav_length // hop_length\\n\\n audiopaths_sid_text_new = []\\n lengths = []\\n skipped = 0\\n logger.info(\\\"Init dataset...\\\")\\n for _id, spk, language, text, phones, tone, word2ph in tqdm(\\n self.audiopaths_sid_text\\n ):\\n audiopath = f\\\"{_id}\\\"\\n if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:\\n phones = phones.split(\\\" \\\")\\n tone = [int(i) for i in tone.split(\\\" \\\")]\\n word2ph = [int(i) for i in word2ph.split(\\\" \\\")]\\n audiopaths_sid_text_new.append(\\n [audiopath, spk, language, text, phones, tone, word2ph]\\n )\\n lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))\\n else:\\n skipped += 1\\n logger.info(\\n \\\"skipped: \\\"\\n + str(skipped)\\n + \\\", total: \\\"\\n + str(len(self.audiopaths_sid_text))\\n )\\n self.audiopaths_sid_text = audiopaths_sid_text_new\\n self.lengths = lengths\\n\\n def get_audio_text_speaker_pair(self, audiopath_sid_text):\\n # separate filename, speaker_id and text\\n audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text\\n\\n bert, ja_bert, en_bert, phones, tone, language = self.get_text(\\n text, word2ph, phones, tone, language, audiopath\\n )\\n\\n spec, wav = self.get_audio(audiopath)\\n sid = torch.LongTensor([int(self.spk_map[sid])])\\n\\n return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert)\\n\\n def get_audio(self, filename):\\n audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)\\n '''\\n # from https://github.com/YYuX-1145/Bert-VITS2-Integration-package\\n audio, sampling_rate = load_wav_to_torch(filename)\\n if sampling_rate != self.sampling_rate:\\n raise ValueError(\\n \\\"{} {} SR doesn't match target {} SR\\\".format(\\n filename, sampling_rate, self.sampling_rate\\n )\\n )\\n audio_norm = audio / self.max_wav_value\\n audio_norm = audio_norm.unsqueeze(0)\\n '''\\n spec_filename = filename.replace(\\\".wav\\\", \\\".spec.pt\\\")\\n if self.use_mel_spec_posterior:\\n spec_filename = spec_filename.replace(\\\".spec.pt\\\", \\\".mel.pt\\\")\\n try:\\n spec = torch.load(spec_filename)\\n except:\\n if self.use_mel_spec_posterior:\\n spec = mel_spectrogram_torch(\\n audio_norm,\\n self.filter_length,\\n self.n_mel_channels,\\n self.sampling_rate,\\n self.hop_length,\\n self.win_length,\\n self.hparams.mel_fmin,\\n self.hparams.mel_fmax,\\n center=False,\\n )\\n else:\\n spec = spectrogram_torch(\\n audio_norm,\\n self.filter_length,\\n self.sampling_rate,\\n self.hop_length,\\n self.win_length,\\n center=False,\\n )\\n spec = torch.squeeze(spec, 0)\\n if config.train_ms_config.spec_cache:\\n torch.save(spec, spec_filename)\\n return spec, audio_norm\\n\\n def get_text(self, text, word2ph, phone, tone, language_str, wav_path):\\n phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)\\n if self.add_blank:\\n phone = commons.intersperse(phone, 0)\\n tone = commons.intersperse(tone, 0)\\n language = commons.intersperse(language, 0)\\n for i in range(len(word2ph)):\\n word2ph[i] = word2ph[i] * 2\\n word2ph[0] += 1\\n bert_path = wav_path.replace(\\\".wav\\\", \\\".bert.pt\\\")\\n try:\\n bert_ori = torch.load(bert_path)\\n assert bert_ori.shape[-1] == len(phone)\\n except Exception as e:\\n logger.warning(\\\"Bert load Failed\\\")\\n logger.warning(e)\\n\\n if language_str == \\\"ZH\\\":\\n bert = bert_ori\\n ja_bert = torch.randn(1024, len(phone))\\n en_bert = torch.randn(1024, len(phone))\\n elif language_str == \\\"JP\\\":\\n bert = torch.randn(1024, len(phone))\\n ja_bert = bert_ori\\n en_bert = torch.randn(1024, len(phone))\\n elif language_str == \\\"EN\\\":\\n bert = torch.randn(1024, len(phone))\\n ja_bert = torch.randn(1024, len(phone))\\n en_bert = bert_ori\\n phone = torch.LongTensor(phone)\\n tone = torch.LongTensor(tone)\\n language = torch.LongTensor(language)\\n return bert, ja_bert, en_bert, phone, tone, language\\n\\n def get_sid(self, sid):\\n sid = torch.LongTensor([int(sid)])\\n return sid\\n\\n def __getitem__(self, index):\\n return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])\\n\\n def __len__(self):\\n return len(self.audiopaths_sid_text)\"\n },\n {\n \"identifier\": \"TextAudioSpeakerCollate\",\n \"path\": \"data_utils.py\",\n \"snippet\": \"class TextAudioSpeakerCollate:\\n \\\"\\\"\\\"Zero-pads model inputs and targets\\\"\\\"\\\"\\n\\n def __init__(self, return_ids=False):\\n self.return_ids = return_ids\\n\\n def __call__(self, batch):\\n \\\"\\\"\\\"Collate's training batch from normalized text, audio and speaker identities\\n PARAMS\\n ------\\n batch: [text_normalized, spec_normalized, wav_normalized, sid]\\n \\\"\\\"\\\"\\n # Right zero-pad all one-hot text sequences to max input length\\n _, ids_sorted_decreasing = torch.sort(\\n torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True\\n )\\n\\n max_text_len = max([len(x[0]) for x in batch])\\n max_spec_len = max([x[1].size(1) for x in batch])\\n max_wav_len = max([x[2].size(1) for x in batch])\\n\\n text_lengths = torch.LongTensor(len(batch))\\n spec_lengths = torch.LongTensor(len(batch))\\n wav_lengths = torch.LongTensor(len(batch))\\n sid = torch.LongTensor(len(batch))\\n\\n text_padded = torch.LongTensor(len(batch), max_text_len)\\n tone_padded = torch.LongTensor(len(batch), max_text_len)\\n language_padded = torch.LongTensor(len(batch), max_text_len)\\n bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\\n ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\\n en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)\\n\\n spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)\\n wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)\\n text_padded.zero_()\\n tone_padded.zero_()\\n language_padded.zero_()\\n spec_padded.zero_()\\n wav_padded.zero_()\\n bert_padded.zero_()\\n ja_bert_padded.zero_()\\n en_bert_padded.zero_()\\n\\n for i in range(len(ids_sorted_decreasing)):\\n row = batch[ids_sorted_decreasing[i]]\\n\\n text = row[0]\\n text_padded[i, : text.size(0)] = text\\n text_lengths[i] = text.size(0)\\n\\n spec = row[1]\\n spec_padded[i, :, : spec.size(1)] = spec\\n spec_lengths[i] = spec.size(1)\\n\\n wav = row[2]\\n wav_padded[i, :, : wav.size(1)] = wav\\n wav_lengths[i] = wav.size(1)\\n\\n sid[i] = row[3]\\n\\n tone = row[4]\\n tone_padded[i, : tone.size(0)] = tone\\n\\n language = row[5]\\n language_padded[i, : language.size(0)] = language\\n\\n bert = row[6]\\n bert_padded[i, :, : bert.size(1)] = bert\\n\\n ja_bert = row[7]\\n ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert\\n\\n en_bert = row[8]\\n en_bert_padded[i, :, : en_bert.size(1)] = en_bert\\n\\n return (\\n text_padded,\\n text_lengths,\\n spec_padded,\\n spec_lengths,\\n wav_padded,\\n wav_lengths,\\n sid,\\n tone_padded,\\n language_padded,\\n bert_padded,\\n ja_bert_padded,\\n en_bert_padded,\\n )\"\n },\n {\n \"identifier\": \"DistributedBucketSampler\",\n \"path\": \"data_utils.py\",\n \"snippet\": \"class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\\n \\\"\\\"\\\"\\n Maintain similar input lengths in a batch.\\n Length groups are specified by boundaries.\\n Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.\\n\\n It removes samples which are not included in the boundaries.\\n Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n dataset,\\n batch_size,\\n boundaries,\\n num_replicas=None,\\n rank=None,\\n shuffle=True,\\n ):\\n super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)\\n self.lengths = dataset.lengths\\n self.batch_size = batch_size\\n self.boundaries = boundaries\\n\\n self.buckets, self.num_samples_per_bucket = self._create_buckets()\\n self.total_size = sum(self.num_samples_per_bucket)\\n self.num_samples = self.total_size // self.num_replicas\\n\\n def _create_buckets(self):\\n buckets = [[] for _ in range(len(self.boundaries) - 1)]\\n for i in range(len(self.lengths)):\\n length = self.lengths[i]\\n idx_bucket = self._bisect(length)\\n if idx_bucket != -1:\\n buckets[idx_bucket].append(i)\\n\\n try:\\n for i in range(len(buckets) - 1, 0, -1):\\n if len(buckets[i]) == 0:\\n buckets.pop(i)\\n self.boundaries.pop(i + 1)\\n assert all(len(bucket) > 0 for bucket in buckets)\\n # When one bucket is not traversed\\n except Exception as e:\\n print(\\\"Bucket warning \\\", e)\\n for i in range(len(buckets) - 1, -1, -1):\\n if len(buckets[i]) == 0:\\n buckets.pop(i)\\n self.boundaries.pop(i + 1)\\n\\n num_samples_per_bucket = []\\n for i in range(len(buckets)):\\n len_bucket = len(buckets[i])\\n total_batch_size = self.num_replicas * self.batch_size\\n rem = (\\n total_batch_size - (len_bucket % total_batch_size)\\n ) % total_batch_size\\n num_samples_per_bucket.append(len_bucket + rem)\\n return buckets, num_samples_per_bucket\\n\\n def __iter__(self):\\n # deterministically shuffle based on epoch\\n g = torch.Generator()\\n g.manual_seed(self.epoch)\\n\\n indices = []\\n if self.shuffle:\\n for bucket in self.buckets:\\n indices.append(torch.randperm(len(bucket), generator=g).tolist())\\n else:\\n for bucket in self.buckets:\\n indices.append(list(range(len(bucket))))\\n\\n batches = []\\n for i in range(len(self.buckets)):\\n bucket = self.buckets[i]\\n len_bucket = len(bucket)\\n if len_bucket == 0:\\n continue\\n ids_bucket = indices[i]\\n num_samples_bucket = self.num_samples_per_bucket[i]\\n\\n # add extra samples to make it evenly divisible\\n rem = num_samples_bucket - len_bucket\\n ids_bucket = (\\n ids_bucket\\n + ids_bucket * (rem // len_bucket)\\n + ids_bucket[: (rem % len_bucket)]\\n )\\n\\n # subsample\\n ids_bucket = ids_bucket[self.rank :: self.num_replicas]\\n\\n # batching\\n for j in range(len(ids_bucket) // self.batch_size):\\n batch = [\\n bucket[idx]\\n for idx in ids_bucket[\\n j * self.batch_size : (j + 1) * self.batch_size\\n ]\\n ]\\n batches.append(batch)\\n\\n if self.shuffle:\\n batch_ids = torch.randperm(len(batches), generator=g).tolist()\\n batches = [batches[i] for i in batch_ids]\\n self.batches = batches\\n\\n assert len(self.batches) * self.batch_size == self.num_samples\\n return iter(self.batches)\\n\\n def _bisect(self, x, lo=0, hi=None):\\n if hi is None:\\n hi = len(self.boundaries) - 1\\n\\n if hi > lo:\\n mid = (hi + lo) // 2\\n if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:\\n return mid\\n elif x <= self.boundaries[mid]:\\n return self._bisect(x, lo, mid)\\n else:\\n return self._bisect(x, mid + 1, hi)\\n else:\\n return -1\\n\\n def __len__(self):\\n return self.num_samples // self.batch_size\"\n },\n {\n \"identifier\": \"AudioVisemesLoader\",\n \"path\": \"data_utils.py\",\n \"snippet\": \"class AudioVisemesLoader(torch.utils.data.Dataset):\\n \\\"\\\"\\\"\\n loads audio, visemes torch variable pairs from visemes list file .\\n file is like: \\n ./records/date_time.z.npy|./records/date_time.npy\\n \\\"\\\"\\\"\\n \\n def __init__(self, audio_visemes_list_file, hparams):\\n self.audio_visemes_list_items = load_filepaths_and_text(audio_visemes_list_file)\\n print('audio_visemes_list_items: ', len(self.audio_visemes_list_items))\\n random.seed(1234)\\n random.shuffle(self.audio_visemes_list_items)\\n self.max_visemes_len = 1210\\n self.min_visemes_len = 1190\\n self._filter()\\n\\n\\n def _filter(self):\\n # check if the file exists, and can parse as torch tensor\\n audio_visemes_list_items_new = []\\n for audio_file, visemes_file in self.audio_visemes_list_items:\\n if os.path.exists(audio_file) and os.path.exists(visemes_file):\\n # check using torch.load\\n try:\\n audio = torch.load(audio_file)\\n visemes = np.load(visemes_file)\\n if visemes.shape[0] < self.min_visemes_len:\\n print('drop this data: --------- visemes.shape[0] < self.min_visemes_len: ', visemes.shape[0], visemes_file)\\n continue\\n audio_visemes_list_items_new.append([audio_file, visemes_file])\\n except Exception as e:\\n print('error: ', audio_file, visemes_file)\\n print(e)\\n self.audio_visemes_list_items = audio_visemes_list_items_new\\n print('audio_visemes_list_items after filter: ', len(self.audio_visemes_list_items))\\n\\n def __getitem__(self, index):\\n # read these two torch.tensor\\n audio_file, visemes_file = self.audio_visemes_list_items[index]\\n audio_z = torch.load(audio_file).squeeze(0).detach()\\n # [192, seq_len(1722)]\\n\\n visemes = np.load(visemes_file)\\n visemes = torch.from_numpy(visemes)\\n #[seq_len(1194), 61]\\n visemes = visemes.transpose(0, 1)\\n #[61, seq_len(1194)]\\n if visemes.shape[1] > self.max_visemes_len:\\n # cut the extra part\\n # print('__getitem__ 1 cut visemes from ', visemes.shape[0], ' to ', self.max_visemes_len, 'file: ', visemes_file)\\n visemes = visemes[:, :self.max_visemes_len]\\n elif visemes.shape[1] < self.max_visemes_len:\\n # padding to max_visemes_len with last frame\\n # print('__getitem__ 2 padding visemes from ', visemes.shape[0], ' to ', self.max_visemes_len, 'file: ', visemes_file)\\n # last_frame = visemes[-1]\\n # visemes = np.concatenate([visemes, np.tile(last_frame, (self.max_visemes_len - visemes.shape[0], 1))], axis=0)\\n # visemes = torch.from_numpy(visemes)\\n pass\\n\\n visemes_offset = 0.08 # 将visemes延迟n s\\n visemes_offset_frames = int(visemes_offset * const_map.ARKIT_FPS)\\n visemes = visemes[:, visemes_offset_frames:]\\n\\n audio_z_offset = 0.0\\n audio_z_offset_frames = int(audio_z_offset * const_map.Z_FPS)\\n audio_z = audio_z[:, audio_z_offset_frames:]\\n\\n # 获取二者的时长，将过长的一方多的部分丢弃\\n visemes_duration = visemes.shape[1] / const_map.ARKIT_FPS\\n audio_z_duration = audio_z.shape[1] / const_map.Z_FPS\\n if visemes_duration > audio_z_duration:\\n visemes = visemes[:, :int(audio_z_duration * const_map.ARKIT_FPS)]\\n elif visemes_duration < audio_z_duration:\\n audio_z = audio_z[:, :int(visemes_duration * const_map.Z_FPS)]\\n\\n\\n # print('__getitem__ 3 audio.shape: ', audio.shape, 'visemes.shape: ', visemes.shape,'file: ', visemes_file)\\n return audio_z, visemes\\n\\n def __len__(self):\\n return len(self.audio_visemes_list_items)\"\n },\n {\n \"identifier\": \"SynthesizerTrn\",\n \"path\": \"models.py\",\n \"snippet\": \"class SynthesizerTrn(nn.Module):\\n \\\"\\\"\\\"\\n Synthesizer for Training\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n n_vocab,\\n spec_channels,\\n segment_size,\\n inter_channels,\\n hidden_channels,\\n filter_channels,\\n n_heads,\\n n_layers,\\n kernel_size,\\n p_dropout,\\n resblock,\\n resblock_kernel_sizes,\\n resblock_dilation_sizes,\\n upsample_rates,\\n upsample_initial_channel,\\n upsample_kernel_sizes,\\n n_speakers=256,\\n gin_channels=256,\\n use_sdp=True,\\n n_flow_layer=4,\\n n_layers_trans_flow=4,\\n flow_share_parameter=False,\\n use_transformer_flow=True,\\n **kwargs\\n ):\\n super().__init__()\\n self.n_vocab = n_vocab\\n self.spec_channels = spec_channels\\n self.inter_channels = inter_channels\\n self.hidden_channels = hidden_channels\\n self.filter_channels = filter_channels\\n self.n_heads = n_heads\\n self.n_layers = n_layers\\n self.kernel_size = kernel_size\\n self.p_dropout = p_dropout\\n self.resblock = resblock\\n self.resblock_kernel_sizes = resblock_kernel_sizes\\n self.resblock_dilation_sizes = resblock_dilation_sizes\\n self.upsample_rates = upsample_rates\\n self.upsample_initial_channel = upsample_initial_channel\\n self.upsample_kernel_sizes = upsample_kernel_sizes\\n self.segment_size = segment_size\\n self.n_speakers = n_speakers\\n self.gin_channels = gin_channels\\n self.n_layers_trans_flow = n_layers_trans_flow\\n self.use_spk_conditioned_encoder = kwargs.get(\\n \\\"use_spk_conditioned_encoder\\\", True\\n )\\n self.use_sdp = use_sdp\\n self.use_noise_scaled_mas = kwargs.get(\\\"use_noise_scaled_mas\\\", False)\\n self.mas_noise_scale_initial = kwargs.get(\\\"mas_noise_scale_initial\\\", 0.01)\\n self.noise_scale_delta = kwargs.get(\\\"noise_scale_delta\\\", 2e-6)\\n self.current_mas_noise_scale = self.mas_noise_scale_initial\\n if self.use_spk_conditioned_encoder and gin_channels > 0:\\n self.enc_gin_channels = gin_channels\\n self.enc_p = TextEncoder(\\n n_vocab,\\n inter_channels,\\n hidden_channels,\\n filter_channels,\\n n_heads,\\n n_layers,\\n kernel_size,\\n p_dropout,\\n gin_channels=self.enc_gin_channels,\\n )\\n self.dec = Generator(\\n inter_channels,\\n resblock,\\n resblock_kernel_sizes,\\n resblock_dilation_sizes,\\n upsample_rates,\\n upsample_initial_channel,\\n upsample_kernel_sizes,\\n gin_channels=gin_channels,\\n )\\n self.enc_q = PosteriorEncoder(\\n spec_channels,\\n inter_channels,\\n hidden_channels,\\n 5,\\n 1,\\n 16,\\n gin_channels=gin_channels,\\n )\\n if use_transformer_flow:\\n self.flow = TransformerCouplingBlock(\\n inter_channels,\\n hidden_channels,\\n filter_channels,\\n n_heads,\\n n_layers_trans_flow,\\n 5,\\n p_dropout,\\n n_flow_layer,\\n gin_channels=gin_channels,\\n share_parameter=flow_share_parameter,\\n )\\n else:\\n self.flow = ResidualCouplingBlock(\\n inter_channels,\\n hidden_channels,\\n 5,\\n 1,\\n n_flow_layer,\\n gin_channels=gin_channels,\\n )\\n self.sdp = StochasticDurationPredictor(\\n hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels\\n )\\n self.dp = DurationPredictor(\\n hidden_channels, 256, 3, 0.5, gin_channels=gin_channels\\n )\\n\\n if n_speakers >= 1:\\n self.emb_g = nn.Embedding(n_speakers, gin_channels)\\n else:\\n self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)\\n\\n def forward(\\n self,\\n x,\\n x_lengths,\\n y,\\n y_lengths,\\n sid,\\n tone,\\n language,\\n bert,\\n ja_bert,\\n en_bert,\\n ):\\n if self.n_speakers > 0:\\n g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]\\n else:\\n g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)\\n x, m_p, logs_p, x_mask = self.enc_p(\\n x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g\\n )\\n z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)\\n z_p = self.flow(z, y_mask, g=g)\\n\\n with torch.no_grad():\\n # negative cross-entropy\\n s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]\\n neg_cent1 = torch.sum(\\n -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True\\n ) # [b, 1, t_s]\\n neg_cent2 = torch.matmul(\\n -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r\\n ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]\\n neg_cent3 = torch.matmul(\\n z_p.transpose(1, 2), (m_p * s_p_sq_r)\\n ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]\\n neg_cent4 = torch.sum(\\n -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True\\n ) # [b, 1, t_s]\\n neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4\\n if self.use_noise_scaled_mas:\\n epsilon = (\\n torch.std(neg_cent)\\n * torch.randn_like(neg_cent)\\n * self.current_mas_noise_scale\\n )\\n neg_cent = neg_cent + epsilon\\n\\n attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)\\n attn = (\\n monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))\\n .unsqueeze(1)\\n .detach()\\n )\\n\\n w = attn.sum(2)\\n\\n l_length_sdp = self.sdp(x, x_mask, w, g=g)\\n l_length_sdp = l_length_sdp / torch.sum(x_mask)\\n\\n logw_ = torch.log(w + 1e-6) * x_mask\\n logw = self.dp(x, x_mask, g=g)\\n logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0)\\n l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(\\n x_mask\\n ) # for averaging\\n l_length_sdp += torch.sum((logw_sdp - logw_) ** 2, [1, 2]) / torch.sum(x_mask)\\n\\n l_length = l_length_dp + l_length_sdp\\n\\n # expand prior\\n m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)\\n logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)\\n\\n z_slice, ids_slice = commons.rand_slice_segments(\\n z, y_lengths, self.segment_size\\n )\\n o = self.dec(z_slice, g=g)\\n return (\\n o,\\n l_length,\\n attn,\\n ids_slice,\\n x_mask,\\n y_mask,\\n (z, z_p, m_p, logs_p, m_q, logs_q),\\n (x, logw, logw_, logw_sdp),\\n g,\\n )\\n\\n def infer(\\n self,\\n x,\\n x_lengths,\\n sid,\\n tone,\\n language,\\n bert,\\n ja_bert,\\n en_bert,\\n noise_scale=0.667,\\n length_scale=1,\\n noise_scale_w=0.8,\\n max_len=None,\\n sdp_ratio=0,\\n y=None,\\n ):\\n # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)\\n # g = self.gst(y)\\n if self.n_speakers > 0:\\n g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]\\n else:\\n g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)\\n x, m_p, logs_p, x_mask = self.enc_p(\\n x, x_lengths, tone, language, bert, ja_bert, en_bert, g=g\\n )\\n logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (\\n sdp_ratio\\n ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)\\n w = torch.exp(logw) * x_mask * length_scale\\n w_ceil = torch.ceil(w)\\n y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()\\n y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(\\n x_mask.dtype\\n )\\n attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)\\n attn = commons.generate_path(w_ceil, attn_mask)\\n\\n m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(\\n 1, 2\\n ) # [b, t', t], [b, t, d] -> [b, d, t']\\n logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(\\n 1, 2\\n ) # [b, t', t], [b, t, d] -> [b, d, t']\\n\\n z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale\\n z = self.flow(z_p, y_mask, g=g, reverse=True)\\n o = self.dec((z * y_mask)[:, :, :max_len], g=g)\\n return o, attn, y_mask, (z, z_p, m_p, logs_p)\\n\\n def get_post_enc_dec(self):\\n return self.enc_q, self.dec\"\n },\n {\n \"identifier\": \"MultiPeriodDiscriminator\",\n \"path\": \"models.py\",\n \"snippet\": \"class MultiPeriodDiscriminator(torch.nn.Module):\\n def __init__(self, use_spectral_norm=False):\\n super(MultiPeriodDiscriminator, self).__init__()\\n periods = [2, 3, 5, 7, 11]\\n\\n discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]\\n discs = discs + [\\n DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods\\n ]\\n self.discriminators = nn.ModuleList(discs)\\n\\n def forward(self, y, y_hat):\\n y_d_rs = []\\n y_d_gs = []\\n fmap_rs = []\\n fmap_gs = []\\n for i, d in enumerate(self.discriminators):\\n y_d_r, fmap_r = d(y)\\n y_d_g, fmap_g = d(y_hat)\\n y_d_rs.append(y_d_r)\\n y_d_gs.append(y_d_g)\\n fmap_rs.append(fmap_r)\\n fmap_gs.append(fmap_g)\\n\\n return y_d_rs, y_d_gs, fmap_rs, fmap_gs\"\n },\n {\n \"identifier\": \"DurationDiscriminator\",\n \"path\": \"models.py\",\n \"snippet\": \"class DurationDiscriminator(nn.Module): # vits2\\n def __init__(\\n self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0\\n ):\\n super().__init__()\\n\\n self.in_channels = in_channels\\n self.filter_channels = filter_channels\\n self.kernel_size = kernel_size\\n self.p_dropout = p_dropout\\n self.gin_channels = gin_channels\\n\\n self.drop = nn.Dropout(p_dropout)\\n self.conv_1 = nn.Conv1d(\\n in_channels, filter_channels, kernel_size, padding=kernel_size // 2\\n )\\n self.norm_1 = modules.LayerNorm(filter_channels)\\n self.conv_2 = nn.Conv1d(\\n filter_channels, filter_channels, kernel_size, padding=kernel_size // 2\\n )\\n self.norm_2 = modules.LayerNorm(filter_channels)\\n self.dur_proj = nn.Conv1d(1, filter_channels, 1)\\n\\n self.LSTM = nn.LSTM(\\n 2 * filter_channels, filter_channels, batch_first=True, bidirectional=True\\n )\\n\\n if gin_channels != 0:\\n self.cond = nn.Conv1d(gin_channels, in_channels, 1)\\n\\n self.output_layer = nn.Sequential(\\n nn.Linear(2 * filter_channels, 1), nn.Sigmoid()\\n )\\n\\n def forward_probability(self, x, dur):\\n dur = self.dur_proj(dur)\\n x = torch.cat([x, dur], dim=1)\\n x = x.transpose(1, 2)\\n x, _ = self.LSTM(x)\\n output_prob = self.output_layer(x)\\n return output_prob\\n\\n def forward(self, x, x_mask, dur_r, dur_hat, g=None):\\n x = torch.detach(x)\\n if g is not None:\\n g = torch.detach(g)\\n x = x + self.cond(g)\\n x = self.conv_1(x * x_mask)\\n x = torch.relu(x)\\n x = self.norm_1(x)\\n x = self.drop(x)\\n x = self.conv_2(x * x_mask)\\n x = torch.relu(x)\\n x = self.norm_2(x)\\n x = self.drop(x)\\n\\n output_probs = []\\n for dur in [dur_r, dur_hat]:\\n output_prob = self.forward_probability(x, dur)\\n output_probs.append(output_prob)\\n\\n return output_probs\"\n },\n {\n \"identifier\": \"WavLMDiscriminator\",\n \"path\": \"models.py\",\n \"snippet\": \"class WavLMDiscriminator(nn.Module):\\n \\\"\\\"\\\"docstring for Discriminator.\\\"\\\"\\\"\\n\\n def __init__(\\n self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False\\n ):\\n super(WavLMDiscriminator, self).__init__()\\n norm_f = weight_norm if use_spectral_norm == False else spectral_norm\\n self.pre = norm_f(\\n Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)\\n )\\n\\n self.convs = nn.ModuleList(\\n [\\n norm_f(\\n nn.Conv1d(\\n initial_channel, initial_channel * 2, kernel_size=5, padding=2\\n )\\n ),\\n norm_f(\\n nn.Conv1d(\\n initial_channel * 2,\\n initial_channel * 4,\\n kernel_size=5,\\n padding=2,\\n )\\n ),\\n norm_f(\\n nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)\\n ),\\n ]\\n )\\n\\n self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))\\n\\n def forward(self, x):\\n x = self.pre(x)\\n\\n fmap = []\\n for l in self.convs:\\n x = l(x)\\n x = F.leaky_relu(x, modules.LRELU_SLOPE)\\n fmap.append(x)\\n x = self.conv_post(x)\\n x = torch.flatten(x, 1, -1)\\n\\n return x\"\n },\n {\n \"identifier\": \"VisemesNet\",\n \"path\": \"models.py\",\n \"snippet\": \"class VisemesNet(nn.Module):\\n def active(self, x):\\n # active_fun: 0: null, 1: tanh, 2: relu, 3: LeakyReLU\\n if self.active_fun == 1:\\n return torch.tanh(x)\\n elif self.active_fun == 2:\\n return torch.relu(x)\\n elif self.active_fun == 3:\\n return self.leakyReLU(x)\\n else:\\n return x\\n\\n def __init__(self, hidden_channels, lstm_bidirectional=True, active_fun = 3, enable_conv=True, \\n use_transformer = False, enable_dropout=True):\\n super(VisemesNet, self).__init__()\\n self.lstm_bidirectional = lstm_bidirectional\\n self.lstm_directions = 2 if lstm_bidirectional else 1\\n self.use_transformer = use_transformer\\n self.enable_dropout = enable_dropout\\n if active_fun == 3:\\n self.leakyReLU = nn.LeakyReLU(negative_slope=0.01)\\n if use_transformer:\\n num_heads=8\\n num_layers=3\\n dim_feedforward=512\\n dropout=0.1\\n activation=\\\"relu\\\"\\n self.transformer_encoder_layer = nn.TransformerEncoderLayer(\\n d_model=hidden_channels, \\n nhead=num_heads,\\n dim_feedforward=dim_feedforward,\\n dropout=dropout,\\n activation=activation,\\n batch_first=True\\n )\\n self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers=num_layers)\\n else:\\n self.lstm = nn.LSTM(input_size=hidden_channels, hidden_size=128, num_layers=3, batch_first=True, bidirectional=lstm_bidirectional)\\n if use_transformer:\\n self.fc1 = nn.Linear(hidden_channels, 96)\\n else:\\n self.fc1 = nn.Linear(128 * self.lstm_directions, 96)\\n self.fc2 = nn.Linear(96, 61)\\n dropout_rate = 0.5\\n if self.enable_dropout:\\n self.dropout = nn.Dropout(dropout_rate)\\n conv_kernel_pre = 15\\n conv_kernel_post = 11\\n self.conv1d_pre = nn.Conv1d(in_channels=hidden_channels, out_channels=hidden_channels, kernel_size=conv_kernel_pre, stride=1, padding=conv_kernel_pre//2)\\n self.conv1d_post = nn.Conv1d(in_channels=61, out_channels=61, kernel_size=conv_kernel_post, stride=1, padding=conv_kernel_post//2)\\n self.enable_conv = enable_conv\\n self.active_fun = active_fun\\n\\n def forward(self, x, y=None):\\n # x [batch_size, hidden_channels, seq_len]\\n if self.use_transformer:\\n return self.forward_transformer(x, y)\\n else:\\n return self.forward_lstm(x, y)\\n\\n def forward_transformer(self, x, y=None):\\n # x [batch_size, hidden_channels, seq_len]\\n if self.enable_conv:\\n x = self.conv1d_pre(x)\\n # batch_first: True (batch, seq, feature); False (seq, batch, feature).\\n x = x.transpose(1, 2)\\n\\n expressions = self.transformer_encoder(x)\\n \\n if self.enable_dropout:\\n expressions = self.dropout(expressions)\\n expressions = self.fc1(expressions)\\n # expressions = self.active(expressions)\\n if self.enable_dropout:\\n expressions = self.dropout(expressions)\\n expressions = self.fc2(expressions)\\n\\n expressions = expressions.transpose(1, 2)\\n if self.enable_conv:\\n expressions = self.conv1d_post(expressions)\\n\\n return expressions \\n\\n def forward_lstm(self, x, y=None):\\n # x [batch_size, hidden_channels, seq_len]\\n if self.enable_conv:\\n x = self.conv1d_pre(x)\\n x = x.transpose(1, 2)\\n # x [batch_size, seq_len, hidden_channels]\\n expressions = None\\n expressions, _ = self.lstm(x)\\n if self.enable_dropout:\\n expressions = self.dropout(expressions)\\n expressions = self.fc1(expressions)\\n expressions = self.active(expressions)\\n if self.enable_dropout:\\n expressions = self.dropout(expressions)\\n expressions = self.fc2(expressions)\\n\\n expressions = expressions.transpose(1, 2)\\n if self.enable_conv:\\n expressions = self.conv1d_post(expressions)\\n return expressions\\n \\n def init_weights(self):\\n # 初始化权重\\n for m in self.modules():\\n if isinstance(m, nn.Linear):\\n nn.init.xavier_uniform_(m.weight.data)\\n if m.bias is not None:\\n nn.init.constant_(m.bias.data, 0)\\n elif isinstance(m, nn.LSTM):\\n for name, param in m.named_parameters():\\n if 'weight_ih' in name:\\n nn.init.xavier_uniform_(param.data)\\n elif 'weight_hh' in name:\\n nn.init.orthogonal_(param.data)\\n elif 'bias' in name:\\n nn.init.constant_(param.data, 0)\\n elif isinstance(m, nn.BatchNorm1d):\\n nn.init.constant_(m.weight.data, 1)\\n nn.init.constant_(m.bias.data, 0)\\n elif isinstance(m, nn.Conv1d):\\n nn.init.xavier_uniform_(m.weight.data)\\n nn.init.constant_(m.bias.data, 0)\\n elif isinstance(m, nn.TransformerEncoderLayer):\\n for name, param in m.named_parameters():\\n if 'weight' in name:\\n if param.dim() == 1:\\n nn.init.normal_(param.data)\\n else:\\n nn.init.xavier_uniform_(param.data)\\n elif 'bias' in name:\\n nn.init.constant_(param.data, 0)\\n elif isinstance(m, nn.TransformerEncoder):\\n for param in m.parameters():\\n if param.dim() > 1:\\n nn.init.xavier_uniform_(param.data)\\n else:\\n nn.init.constant_(param.data, 0)\"\n },\n {\n \"identifier\": \"generator_loss\",\n \"path\": \"losses.py\",\n \"snippet\": \"def generator_loss(disc_outputs):\\n loss = 0\\n gen_losses = []\\n for dg in disc_outputs:\\n dg = dg.float()\\n l = torch.mean((1 - dg) ** 2)\\n gen_losses.append(l)\\n loss += l\\n\\n return loss, gen_losses\"\n },\n {\n \"identifier\": \"discriminator_loss\",\n \"path\": \"losses.py\",\n \"snippet\": \"def discriminator_loss(disc_real_outputs, disc_generated_outputs):\\n loss = 0\\n r_losses = []\\n g_losses = []\\n for dr, dg in zip(disc_real_outputs, disc_generated_outputs):\\n dr = dr.float()\\n dg = dg.float()\\n r_loss = torch.mean((1 - dr) ** 2)\\n g_loss = torch.mean(dg**2)\\n loss += r_loss + g_loss\\n r_losses.append(r_loss.item())\\n g_losses.append(g_loss.item())\\n\\n return loss, r_losses, g_losses\"\n },\n {\n \"identifier\": \"feature_loss\",\n \"path\": \"losses.py\",\n \"snippet\": \"def feature_loss(fmap_r, fmap_g):\\n loss = 0\\n for dr, dg in zip(fmap_r, fmap_g):\\n for rl, gl in zip(dr, dg):\\n rl = rl.float().detach()\\n gl = gl.float()\\n loss += torch.mean(torch.abs(rl - gl))\\n\\n return loss * 2\"\n },\n {\n \"identifier\": \"kl_loss\",\n \"path\": \"losses.py\",\n \"snippet\": \"def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):\\n \\\"\\\"\\\"\\n z_p, logs_q: [b, h, t_t]\\n m_p, logs_p: [b, h, t_t]\\n \\\"\\\"\\\"\\n z_p = z_p.float()\\n logs_q = logs_q.float()\\n m_p = m_p.float()\\n logs_p = logs_p.float()\\n z_mask = z_mask.float()\\n\\n kl = logs_p - logs_q - 0.5\\n kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)\\n kl = torch.sum(kl * z_mask)\\n l = kl / torch.sum(z_mask)\\n return l\"\n },\n {\n \"identifier\": \"WavLMLoss\",\n \"path\": \"losses.py\",\n \"snippet\": \"class WavLMLoss(torch.nn.Module):\\n def __init__(self, model, wd, model_sr, slm_sr=16000):\\n super(WavLMLoss, self).__init__()\\n self.wavlm = AutoModel.from_pretrained(model)\\n self.wd = wd\\n self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)\\n self.wavlm.eval()\\n for param in self.wavlm.parameters():\\n param.requires_grad = False\\n\\n def forward(self, wav, y_rec):\\n with torch.no_grad():\\n wav_16 = self.resample(wav)\\n wav_embeddings = self.wavlm(\\n input_values=wav_16, output_hidden_states=True\\n ).hidden_states\\n y_rec_16 = self.resample(y_rec)\\n y_rec_embeddings = self.wavlm(\\n input_values=y_rec_16.squeeze(), output_hidden_states=True\\n ).hidden_states\\n\\n floss = 0\\n for er, eg in zip(wav_embeddings, y_rec_embeddings):\\n floss += torch.mean(torch.abs(er - eg))\\n\\n return floss.mean()\\n\\n def generator(self, y_rec):\\n y_rec_16 = self.resample(y_rec)\\n y_rec_embeddings = self.wavlm(\\n input_values=y_rec_16, output_hidden_states=True\\n ).hidden_states\\n y_rec_embeddings = (\\n torch.stack(y_rec_embeddings, dim=1)\\n .transpose(-1, -2)\\n .flatten(start_dim=1, end_dim=2)\\n )\\n y_df_hat_g = self.wd(y_rec_embeddings)\\n loss_gen = torch.mean((1 - y_df_hat_g) ** 2)\\n\\n return loss_gen\\n\\n def discriminator(self, wav, y_rec):\\n with torch.no_grad():\\n wav_16 = self.resample(wav)\\n wav_embeddings = self.wavlm(\\n input_values=wav_16, output_hidden_states=True\\n ).hidden_states\\n y_rec_16 = self.resample(y_rec)\\n y_rec_embeddings = self.wavlm(\\n input_values=y_rec_16, output_hidden_states=True\\n ).hidden_states\\n\\n y_embeddings = (\\n torch.stack(wav_embeddings, dim=1)\\n .transpose(-1, -2)\\n .flatten(start_dim=1, end_dim=2)\\n )\\n y_rec_embeddings = (\\n torch.stack(y_rec_embeddings, dim=1)\\n .transpose(-1, -2)\\n .flatten(start_dim=1, end_dim=2)\\n )\\n\\n y_d_rs = self.wd(y_embeddings)\\n y_d_gs = self.wd(y_rec_embeddings)\\n\\n y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs\\n\\n r_loss = torch.mean((1 - y_df_hat_r) ** 2)\\n g_loss = torch.mean((y_df_hat_g) ** 2)\\n\\n loss_disc_f = r_loss + g_loss\\n\\n return loss_disc_f.mean()\\n\\n def discriminator_forward(self, wav):\\n with torch.no_grad():\\n wav_16 = self.resample(wav)\\n wav_embeddings = self.wavlm(\\n input_values=wav_16, output_hidden_states=True\\n ).hidden_states\\n y_embeddings = (\\n torch.stack(wav_embeddings, dim=1)\\n .transpose(-1, -2)\\n .flatten(start_dim=1, end_dim=2)\\n )\\n\\n y_d_rs = self.wd(y_embeddings)\\n\\n return y_d_rs\"\n },\n {\n \"identifier\": \"mel_spectrogram_torch\",\n \"path\": \"mel_processing.py\",\n \"snippet\": \"def mel_spectrogram_torch(\\n y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False\\n):\\n if torch.min(y) < -1.0:\\n print(\\\"min value is \\\", torch.min(y))\\n if torch.max(y) > 1.0:\\n print(\\\"max value is \\\", torch.max(y))\\n\\n global mel_basis, hann_window\\n dtype_device = str(y.dtype) + \\\"_\\\" + str(y.device)\\n fmax_dtype_device = str(fmax) + \\\"_\\\" + dtype_device\\n wnsize_dtype_device = str(win_size) + \\\"_\\\" + dtype_device\\n if fmax_dtype_device not in mel_basis:\\n mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)\\n mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(\\n dtype=y.dtype, device=y.device\\n )\\n if wnsize_dtype_device not in hann_window:\\n hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(\\n dtype=y.dtype, device=y.device\\n )\\n\\n y = torch.nn.functional.pad(\\n y.unsqueeze(1),\\n (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),\\n mode=\\\"reflect\\\",\\n )\\n y = y.squeeze(1)\\n\\n spec = torch.stft(\\n y,\\n n_fft,\\n hop_length=hop_size,\\n win_length=win_size,\\n window=hann_window[wnsize_dtype_device],\\n center=center,\\n pad_mode=\\\"reflect\\\",\\n normalized=False,\\n onesided=True,\\n return_complex=False,\\n )\\n\\n spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)\\n\\n spec = torch.matmul(mel_basis[fmax_dtype_device], spec)\\n spec = spectral_normalize_torch(spec)\\n\\n return spec\"\n },\n {\n \"identifier\": \"spec_to_mel_torch\",\n \"path\": \"mel_processing.py\",\n \"snippet\": \"def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):\\n global mel_basis\\n dtype_device = str(spec.dtype) + \\\"_\\\" + str(spec.device)\\n fmax_dtype_device = str(fmax) + \\\"_\\\" + dtype_device\\n if fmax_dtype_device not in mel_basis:\\n mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)\\n mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(\\n dtype=spec.dtype, device=spec.device\\n )\\n spec = torch.matmul(mel_basis[fmax_dtype_device], spec)\\n spec = spectral_normalize_torch(spec)\\n return spec\"\n },\n {\n \"identifier\": \"symbols\",\n \"path\": \"text/symbols.py\",\n \"snippet\": \"\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import platform\nimport os\nimport torch\nimport torch.distributed as dist\nimport logging\nimport argparse\nimport datetime\nimport gc\nimport commons\nimport utils\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.cuda.amp import autocast, GradScaler\nfrom tqdm import tqdm\nfrom config import config\nfrom data_utils import (\n TextAudioSpeakerLoader,\n TextAudioSpeakerCollate,\n DistributedBucketSampler,\n AudioVisemesLoader,\n)\nfrom models import (\n SynthesizerTrn,\n MultiPeriodDiscriminator,\n DurationDiscriminator,\n WavLMDiscriminator,\n VisemesNet,\n)\nfrom losses import (\n generator_loss,\n discriminator_loss,\n feature_loss,\n kl_loss,\n WavLMLoss,\n)\nfrom mel_processing import mel_spectrogram_torch, spec_to_mel_torch\nfrom text.symbols import symbols"},"token_num":{"kind":"number","value":13247,"string":"13,247"},"cropped_code":{"kind":"string","value":"# flake8: noqa: E402\n\nlogging.getLogger(\"numba\").setLevel(logging.WARNING)\nlogger = logging.getLogger(__name__)\n\ntorch.backends.cuda.matmul.allow_tf32 = True\ntorch.backends.cudnn.allow_tf32 = (\n True # If encontered training problem,please try to disable TF32.\n)\ntorch.set_float32_matmul_precision(\"medium\")\ntorch.backends.cuda.sdp_kernel(\"flash\")\ntorch.backends.cuda.enable_flash_sdp(True)\ntorch.backends.cuda.enable_mem_efficient_sdp(\n True\n) # Not available if torch version is lower than 2.0\nglobal_step = 0\nglobal_visemes_step = 0\n\ndef run_only_visemes(hps):\n # 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数\n global global_visemes_step\n torch.manual_seed(hps.train.seed)\n torch.cuda.set_device(0)\n train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)\n train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,\n batch_size=1, drop_last=True)\n eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)\n eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,\n batch_size=1, pin_memory=True,\n drop_last=False)\n"},"all_code":{"kind":"string","value":"# flake8: noqa: E402\n\nlogging.getLogger(\"numba\").setLevel(logging.WARNING)\nlogger = logging.getLogger(__name__)\n\ntorch.backends.cuda.matmul.allow_tf32 = True\ntorch.backends.cudnn.allow_tf32 = (\n True # If encontered training problem,please try to disable TF32.\n)\ntorch.set_float32_matmul_precision(\"medium\")\ntorch.backends.cuda.sdp_kernel(\"flash\")\ntorch.backends.cuda.enable_flash_sdp(True)\ntorch.backends.cuda.enable_mem_efficient_sdp(\n True\n) # Not available if torch version is lower than 2.0\nglobal_step = 0\nglobal_visemes_step = 0\n\ndef run_only_visemes(hps):\n # 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数\n global global_visemes_step\n torch.manual_seed(hps.train.seed)\n torch.cuda.set_device(0)\n train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)\n train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,\n batch_size=1, drop_last=True)\n eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)\n eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,\n batch_size=1, pin_memory=True,\n drop_last=False)"},"next_line":{"kind":"string","value":" net_v = VisemesNet(hps.model.hidden_channels).cuda()"},"gold_snippet_index":{"kind":"number","value":9,"string":"9"},"created_at":{"kind":"string","value":"2023-12-27 03:09:11+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4699,"cells":{"repo_name":{"kind":"string","value":"chinhsuanwu/ifusion-threestudio"},"file_path":{"kind":"string","value":"extern/ldm_zero123/models/diffusion/ddpm.py"},"context":{"kind":"list like","value":[{"identifier":"AutoencoderKL","path":"extern/ldm_zero123/models/autoencoder.py","snippet":"class AutoencoderKL(pl.LightningModule):\n def __init__(\n self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"image\",\n colorize_nlabels=None,\n monitor=None,\n ):\n super().__init__()\n self.image_key = image_key\n self.encoder = Encoder(**ddconfig)\n self.decoder = Decoder(**ddconfig)\n self.loss = instantiate_from_config(lossconfig)\n assert ddconfig[\"double_z\"]\n self.quant_conv = torch.nn.Conv2d(2 * ddconfig[\"z_channels\"], 2 * embed_dim, 1)\n self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig[\"z_channels\"], 1)\n self.embed_dim = embed_dim\n if colorize_nlabels is not None:\n assert type(colorize_nlabels) == int\n self.register_buffer(\"colorize\", torch.randn(3, colorize_nlabels, 1, 1))\n if monitor is not None:\n self.monitor = monitor\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\n\n def init_from_ckpt(self, path, ignore_keys=list()):\n sd = torch.load(path, map_location=\"cpu\")[\"state_dict\"]\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print(\"Deleting key {} from state_dict.\".format(k))\n del sd[k]\n self.load_state_dict(sd, strict=False)\n print(f\"Restored from {path}\")\n\n def encode(self, x):\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n return posterior\n\n def decode(self, z):\n z = self.post_quant_conv(z)\n dec = self.decoder(z)\n return dec\n\n def forward(self, input, sample_posterior=True):\n posterior = self.encode(input)\n if sample_posterior:\n z = posterior.sample()\n else:\n z = posterior.mode()\n dec = self.decode(z)\n return dec, posterior\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()\n return x\n\n def training_step(self, batch, batch_idx, optimizer_idx):\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n\n if optimizer_idx == 0:\n # train encoder+decoder+logvar\n aeloss, log_dict_ae = self.loss(\n inputs,\n reconstructions,\n posterior,\n optimizer_idx,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"train\",\n )\n self.log(\n \"aeloss\",\n aeloss,\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=True,\n )\n self.log_dict(\n log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False\n )\n return aeloss\n\n if optimizer_idx == 1:\n # train the discriminator\n discloss, log_dict_disc = self.loss(\n inputs,\n reconstructions,\n posterior,\n optimizer_idx,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"train\",\n )\n\n self.log(\n \"discloss\",\n discloss,\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=True,\n )\n self.log_dict(\n log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False\n )\n return discloss\n\n def validation_step(self, batch, batch_idx):\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n aeloss, log_dict_ae = self.loss(\n inputs,\n reconstructions,\n posterior,\n 0,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"val\",\n )\n\n discloss, log_dict_disc = self.loss(\n inputs,\n reconstructions,\n posterior,\n 1,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"val\",\n )\n\n self.log(\"val/rec_loss\", log_dict_ae[\"val/rec_loss\"])\n self.log_dict(log_dict_ae)\n self.log_dict(log_dict_disc)\n return self.log_dict\n\n def configure_optimizers(self):\n lr = self.learning_rate\n opt_ae = torch.optim.Adam(\n list(self.encoder.parameters())\n + list(self.decoder.parameters())\n + list(self.quant_conv.parameters())\n + list(self.post_quant_conv.parameters()),\n lr=lr,\n betas=(0.5, 0.9),\n )\n opt_disc = torch.optim.Adam(\n self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)\n )\n return [opt_ae, opt_disc], []\n\n def get_last_layer(self):\n return self.decoder.conv_out.weight\n\n @torch.no_grad()\n def log_images(self, batch, only_inputs=False, **kwargs):\n log = dict()\n x = self.get_input(batch, self.image_key)\n x = x.to(self.device)\n if not only_inputs:\n xrec, posterior = self(x)\n if x.shape[1] > 3:\n # colorize with random projection\n assert xrec.shape[1] > 3\n x = self.to_rgb(x)\n xrec = self.to_rgb(xrec)\n log[\"samples\"] = self.decode(torch.randn_like(posterior.sample()))\n log[\"reconstructions\"] = xrec\n log[\"inputs\"] = x\n return log\n\n def to_rgb(self, x):\n assert self.image_key == \"segmentation\"\n if not hasattr(self, \"colorize\"):\n self.register_buffer(\"colorize\", torch.randn(3, x.shape[1], 1, 1).to(x))\n x = F.conv2d(x, weight=self.colorize)\n x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0\n return x"},{"identifier":"IdentityFirstStage","path":"extern/ldm_zero123/models/autoencoder.py","snippet":"class IdentityFirstStage(torch.nn.Module):\n def __init__(self, *args, vq_interface=False, **kwargs):\n self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff\n super().__init__()\n\n def encode(self, x, *args, **kwargs):\n return x\n\n def decode(self, x, *args, **kwargs):\n return x\n\n def quantize(self, x, *args, **kwargs):\n if self.vq_interface:\n return x, None, [None, None, None]\n return x\n\n def forward(self, x, *args, **kwargs):\n return x"},{"identifier":"VQModelInterface","path":"extern/ldm_zero123/models/autoencoder.py","snippet":"class VQModelInterface(VQModel):\n def __init__(self, embed_dim, *args, **kwargs):\n super().__init__(embed_dim=embed_dim, *args, **kwargs)\n self.embed_dim = embed_dim\n\n def encode(self, x):\n h = self.encoder(x)\n h = self.quant_conv(h)\n return h\n\n def decode(self, h, force_not_quantize=False):\n # also go through quantization layer\n if not force_not_quantize:\n quant, emb_loss, info = self.quantize(h)\n else:\n quant = h\n quant = self.post_quant_conv(quant)\n dec = self.decoder(quant)\n return dec"},{"identifier":"DDIMSampler","path":"extern/ldm_zero123/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 to(self, device):\n \"\"\"Same as to in torch module\n Don't really underestand why this isn't a module in the first place\"\"\"\n for k, v in self.__dict__.items():\n if isinstance(v, torch.Tensor):\n new_v = getattr(self, k).to(device)\n setattr(self, k, new_v)\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(\n self, ddim_num_steps, ddim_discretize=\"uniform\", ddim_eta=0.0, verbose=True\n ):\n self.ddim_timesteps = make_ddim_timesteps(\n ddim_discr_method=ddim_discretize,\n num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,\n verbose=verbose,\n )\n alphas_cumprod = self.model.alphas_cumprod\n assert (\n alphas_cumprod.shape[0] == self.ddpm_num_timesteps\n ), \"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(\n \"alphas_cumprod_prev\", to_torch(self.model.alphas_cumprod_prev)\n )\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n \"sqrt_alphas_cumprod\", to_torch(np.sqrt(alphas_cumprod.cpu()))\n )\n self.register_buffer(\n \"sqrt_one_minus_alphas_cumprod\",\n to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),\n )\n self.register_buffer(\n \"log_one_minus_alphas_cumprod\", to_torch(np.log(1.0 - alphas_cumprod.cpu()))\n )\n self.register_buffer(\n \"sqrt_recip_alphas_cumprod\", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))\n )\n self.register_buffer(\n \"sqrt_recipm1_alphas_cumprod\",\n to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),\n )\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\n alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,\n verbose=verbose,\n )\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(1.0 - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev)\n / (1 - self.alphas_cumprod)\n * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)\n )\n self.register_buffer(\n \"ddim_sigmas_for_original_num_steps\", sigmas_for_original_sampling_steps\n )\n\n @torch.no_grad()\n def sample(\n self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.0,\n mask=None,\n x0=None,\n temperature=1.0,\n noise_dropout=0.0,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.0,\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 **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):\n ctmp = ctmp[0]\n cbs = ctmp.shape[0]\n if cbs != batch_size:\n print(\n f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\"\n )\n\n else:\n if conditioning.shape[0] != batch_size:\n print(\n f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\"\n )\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n # print(f'Data shape for DDIM sampling is {size}, eta {eta}')\n\n samples, intermediates = self.ddim_sampling(\n conditioning,\n size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask,\n x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n dynamic_threshold=dynamic_threshold,\n )\n return samples, intermediates\n\n @torch.no_grad()\n def ddim_sampling(\n self,\n cond,\n shape,\n x_T=None,\n ddim_use_original_steps=False,\n callback=None,\n timesteps=None,\n quantize_denoised=False,\n mask=None,\n x0=None,\n img_callback=None,\n log_every_t=100,\n temperature=1.0,\n noise_dropout=0.0,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.0,\n unconditional_conditioning=None,\n dynamic_threshold=None,\n t_start=-1,\n ):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = (\n self.ddpm_num_timesteps\n if ddim_use_original_steps\n else self.ddim_timesteps\n )\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = (\n int(\n min(timesteps / self.ddim_timesteps.shape[0], 1)\n * self.ddim_timesteps.shape[0]\n )\n - 1\n )\n timesteps = self.ddim_timesteps[:subset_end]\n\n timesteps = timesteps[:t_start]\n\n intermediates = {\"x_inter\": [img], \"pred_x0\": [img]}\n time_range = (\n reversed(range(0, timesteps))\n if ddim_use_original_steps\n else np.flip(timesteps)\n )\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(iterator):\n index = total_steps - i - 1\n ts = torch.full((b,), step, device=device, dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(\n x0, ts\n ) # TODO: deterministic forward pass?\n img = img_orig * mask + (1.0 - mask) * img\n\n outs = self.p_sample_ddim(\n img,\n cond,\n ts,\n index=index,\n use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised,\n temperature=temperature,\n noise_dropout=noise_dropout,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n dynamic_threshold=dynamic_threshold,\n )\n img, pred_x0 = outs\n if callback:\n img = callback(i, img, pred_x0)\n if img_callback:\n 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(\n self,\n x,\n c,\n t,\n index,\n repeat_noise=False,\n use_original_steps=False,\n quantize_denoised=False,\n temperature=1.0,\n noise_dropout=0.0,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.0,\n unconditional_conditioning=None,\n dynamic_threshold=None,\n ):\n b, *_, device = *x.shape, x.device\n\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:\n e_t = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n if isinstance(c, dict):\n assert isinstance(unconditional_conditioning, dict)\n c_in = dict()\n for k in c:\n if isinstance(c[k], list):\n c_in[k] = [\n torch.cat([unconditional_conditioning[k][i], c[k][i]])\n for i in range(len(c[k]))\n ]\n else:\n c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])\n else:\n c_in = torch.cat([unconditional_conditioning, c])\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\n e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)\n\n if score_corrector is not None:\n assert self.model.parameterization == \"eps\"\n e_t = score_corrector.modify_score(\n self.model, e_t, x, t, c, **corrector_kwargs\n )\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = (\n self.model.alphas_cumprod_prev\n if use_original_steps\n else self.ddim_alphas_prev\n )\n sqrt_one_minus_alphas = (\n self.model.sqrt_one_minus_alphas_cumprod\n if use_original_steps\n else self.ddim_sqrt_one_minus_alphas\n )\n sigmas = (\n self.model.ddim_sigmas_for_original_num_steps\n if use_original_steps\n else self.ddim_sigmas\n )\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(\n (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device\n )\n\n # current prediction for x_0\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n\n print(t, sqrt_one_minus_at, a_t)\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 pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)\n\n # direction pointing to x_t\n dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.0:\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(\n self,\n x0,\n c,\n t_enc,\n use_original_steps=False,\n return_intermediates=None,\n unconditional_guidance_scale=1.0,\n unconditional_conditioning=None,\n ):\n num_reference_steps = (\n self.ddpm_num_timesteps\n if use_original_steps\n else self.ddim_timesteps.shape[0]\n )\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(\n (x0.shape[0],), i, device=self.model.device, dtype=torch.long\n )\n if unconditional_guidance_scale == 1.0:\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(\n torch.cat((x_next, x_next)),\n torch.cat((t, t)),\n torch.cat((unconditional_conditioning, c)),\n ),\n 2,\n )\n noise_pred = e_t_uncond + unconditional_guidance_scale * (\n noise_pred - e_t_uncond\n )\n\n xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next\n weighted_noise_pred = (\n alphas_next[i].sqrt()\n * ((1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt())\n * noise_pred\n )\n x_next = xt_weighted + weighted_noise_pred\n if (\n return_intermediates\n and i % (num_steps // return_intermediates) == 0\n and i < num_steps - 1\n ):\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\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 (\n 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\n @torch.no_grad()\n def decode(\n self,\n x_latent,\n cond,\n t_start,\n unconditional_guidance_scale=1.0,\n unconditional_conditioning=None,\n use_original_steps=False,\n ):\n timesteps = (\n np.arange(self.ddpm_num_timesteps)\n if use_original_steps\n else self.ddim_timesteps\n )\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(\n (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long\n )\n x_dec, _ = self.p_sample_ddim(\n x_dec,\n cond,\n ts,\n index=index,\n use_original_steps=use_original_steps,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n )\n return x_dec"},{"identifier":"CrossAttention","path":"extern/ldm_zero123/modules/attention.py","snippet":"class CrossAttention(nn.Module):\n def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):\n super().__init__()\n inner_dim = dim_head * heads\n context_dim = default(context_dim, query_dim)\n\n self.scale = dim_head**-0.5\n self.heads = heads\n\n self.to_q = nn.Linear(query_dim, inner_dim, bias=False)\n self.to_k = nn.Linear(context_dim, inner_dim, bias=False)\n self.to_v = nn.Linear(context_dim, inner_dim, bias=False)\n\n self.to_out = nn.Sequential(\n nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)\n )\n\n def forward(self, x, context=None, mask=None):\n h = self.heads\n\n q = self.to_q(x)\n context = default(context, x)\n k = self.to_k(context)\n v = self.to_v(context)\n\n q, k, v = map(lambda t: rearrange(t, \"b n (h d) -> (b h) n d\", h=h), (q, k, v))\n\n sim = einsum(\"b i d, b j d -> b i j\", q, k) * self.scale\n\n if exists(mask):\n mask = rearrange(mask, \"b ... -> b (...)\")\n max_neg_value = -torch.finfo(sim.dtype).max\n mask = repeat(mask, \"b j -> (b h) () j\", h=h)\n sim.masked_fill_(~mask, max_neg_value)\n\n # attention, what we cannot get enough of\n attn = sim.softmax(dim=-1)\n\n out = einsum(\"b i j, b j d -> b i d\", attn, v)\n out = rearrange(out, \"(b h) n d -> b n (h d)\", h=h)\n return self.to_out(out)"},{"identifier":"extract_into_tensor","path":"extern/ldm_zero123/modules/diffusionmodules/util.py","snippet":"def extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))"},{"identifier":"make_beta_schedule","path":"extern/ldm_zero123/modules/diffusionmodules/util.py","snippet":"def make_beta_schedule(\n schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3\n):\n if schedule == \"linear\":\n betas = (\n torch.linspace(\n linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64\n )\n ** 2\n )\n\n elif schedule == \"cosine\":\n timesteps = (\n torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s\n )\n alphas = timesteps / (1 + cosine_s) * np.pi / 2\n alphas = torch.cos(alphas).pow(2)\n alphas = alphas / alphas[0]\n betas = 1 - alphas[1:] / alphas[:-1]\n betas = np.clip(betas, a_min=0, a_max=0.999)\n\n elif schedule == \"sqrt_linear\":\n betas = torch.linspace(\n linear_start, linear_end, n_timestep, dtype=torch.float64\n )\n elif schedule == \"sqrt\":\n betas = (\n torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)\n ** 0.5\n )\n else:\n raise ValueError(f\"schedule '{schedule}' unknown.\")\n return betas.numpy()"},{"identifier":"noise_like","path":"extern/ldm_zero123/modules/diffusionmodules/util.py","snippet":"def noise_like(shape, device, repeat=False):\n repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(\n shape[0], *((1,) * (len(shape) - 1))\n )\n noise = lambda: torch.randn(shape, device=device)\n return repeat_noise() if repeat else noise()"},{"identifier":"DiagonalGaussianDistribution","path":"extern/ldm_zero123/modules/distributions/distributions.py","snippet":"class DiagonalGaussianDistribution(object):\n def __init__(self, parameters, deterministic=False):\n self.parameters = parameters\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = torch.exp(0.5 * self.logvar)\n self.var = torch.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = torch.zeros_like(self.mean).to(\n device=self.parameters.device\n )\n\n def sample(self):\n x = self.mean + self.std * torch.randn(self.mean.shape).to(\n device=self.parameters.device\n )\n return x\n\n def kl(self, other=None):\n if self.deterministic:\n return torch.Tensor([0.0])\n else:\n if other is None:\n return 0.5 * torch.sum(\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\n dim=[1, 2, 3],\n )\n else:\n return 0.5 * torch.sum(\n torch.pow(self.mean - other.mean, 2) / other.var\n + self.var / other.var\n - 1.0\n - self.logvar\n + other.logvar,\n dim=[1, 2, 3],\n )\n\n def nll(self, sample, dims=[1, 2, 3]):\n if self.deterministic:\n return torch.Tensor([0.0])\n logtwopi = np.log(2.0 * np.pi)\n return 0.5 * torch.sum(\n logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,\n dim=dims,\n )\n\n def mode(self):\n return self.mean"},{"identifier":"normal_kl","path":"extern/ldm_zero123/modules/distributions/distributions.py","snippet":"def normal_kl(mean1, logvar1, mean2, logvar2):\n \"\"\"\n source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\n Compute the KL divergence between two gaussians.\n Shapes are automatically broadcasted, so batches can be compared to\n scalars, among other use cases.\n \"\"\"\n tensor = None\n for obj in (mean1, logvar1, mean2, logvar2):\n if isinstance(obj, torch.Tensor):\n tensor = obj\n break\n assert tensor is not None, \"at least one argument must be a Tensor\"\n\n # Force variances to be Tensors. Broadcasting helps convert scalars to\n # Tensors, but it does not work for torch.exp().\n logvar1, logvar2 = [\n x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\n for x in (logvar1, logvar2)\n ]\n\n return 0.5 * (\n -1.0\n + logvar2\n - logvar1\n + torch.exp(logvar1 - logvar2)\n + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)\n )"},{"identifier":"LitEma","path":"extern/ldm_zero123/modules/ema.py","snippet":"class LitEma(nn.Module):\n def __init__(self, model, decay=0.9999, use_num_upates=True):\n super().__init__()\n if decay < 0.0 or decay > 1.0:\n raise ValueError(\"Decay must be between 0 and 1\")\n\n self.m_name2s_name = {}\n self.register_buffer(\"decay\", torch.tensor(decay, dtype=torch.float32))\n self.register_buffer(\n \"num_updates\",\n torch.tensor(0, dtype=torch.int)\n if use_num_upates\n else torch.tensor(-1, dtype=torch.int),\n )\n\n for name, p in model.named_parameters():\n if p.requires_grad:\n # remove as '.'-character is not allowed in buffers\n s_name = name.replace(\".\", \"\")\n self.m_name2s_name.update({name: s_name})\n self.register_buffer(s_name, p.clone().detach().data)\n\n self.collected_params = []\n\n def forward(self, model):\n decay = self.decay\n\n if self.num_updates >= 0:\n self.num_updates += 1\n decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))\n\n one_minus_decay = 1.0 - decay\n\n with torch.no_grad():\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n\n for key in m_param:\n if m_param[key].requires_grad:\n sname = self.m_name2s_name[key]\n shadow_params[sname] = shadow_params[sname].type_as(m_param[key])\n shadow_params[sname].sub_(\n one_minus_decay * (shadow_params[sname] - m_param[key])\n )\n else:\n assert not key in self.m_name2s_name\n\n def copy_to(self, model):\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n for key in m_param:\n if m_param[key].requires_grad:\n m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)\n else:\n assert not key in self.m_name2s_name\n\n def store(self, parameters):\n \"\"\"\n Save the current parameters for restoring later.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n temporarily stored.\n \"\"\"\n self.collected_params = [param.clone() for param in parameters]\n\n def restore(self, parameters):\n \"\"\"\n Restore the parameters stored with the `store` method.\n Useful to validate the model with EMA parameters without affecting the\n original optimization process. Store the parameters before the\n `copy_to` method. After validation (or model saving), use this to\n restore the former parameters.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored parameters.\n \"\"\"\n for c_param, param in zip(self.collected_params, parameters):\n param.data.copy_(c_param.data)"},{"identifier":"count_params","path":"extern/ldm_zero123/util.py","snippet":"def count_params(model, verbose=False):\n total_params = sum(p.numel() for p in model.parameters())\n if verbose:\n print(f\"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.\")\n return total_params"},{"identifier":"default","path":"extern/ldm_zero123/util.py","snippet":"def default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d"},{"identifier":"exists","path":"extern/ldm_zero123/util.py","snippet":"def exists(x):\n return x is not None"},{"identifier":"instantiate_from_config","path":"extern/ldm_zero123/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":"isimage","path":"extern/ldm_zero123/util.py","snippet":"def isimage(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)"},{"identifier":"ismap","path":"extern/ldm_zero123/util.py","snippet":"def ismap(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] > 3)"},{"identifier":"log_txt_as_img","path":"extern/ldm_zero123/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(\"data/DejaVuSans.ttf\", size=size)\n nc = int(40 * (wh[0] / 256))\n lines = \"\\n\".join(\n xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)\n )\n\n try:\n draw.text((0, 0), lines, fill=\"black\", font=font)\n except UnicodeEncodeError:\n print(\"Cant encode string for logging. Skipping.\")\n\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\n txts.append(txt)\n txts = np.stack(txts)\n txts = torch.tensor(txts)\n return txts"},{"identifier":"mean_flat","path":"extern/ldm_zero123/util.py","snippet":"def mean_flat(tensor):\n \"\"\"\n https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\n Take the mean over all non-batch dimensions.\n \"\"\"\n return tensor.mean(dim=list(range(1, len(tensor.shape))))"}],"string":"[\n {\n \"identifier\": \"AutoencoderKL\",\n \"path\": \"extern/ldm_zero123/models/autoencoder.py\",\n \"snippet\": \"class AutoencoderKL(pl.LightningModule):\\n def __init__(\\n self,\\n ddconfig,\\n lossconfig,\\n embed_dim,\\n ckpt_path=None,\\n ignore_keys=[],\\n image_key=\\\"image\\\",\\n colorize_nlabels=None,\\n monitor=None,\\n ):\\n super().__init__()\\n self.image_key = image_key\\n self.encoder = Encoder(**ddconfig)\\n self.decoder = Decoder(**ddconfig)\\n self.loss = instantiate_from_config(lossconfig)\\n assert ddconfig[\\\"double_z\\\"]\\n self.quant_conv = torch.nn.Conv2d(2 * ddconfig[\\\"z_channels\\\"], 2 * embed_dim, 1)\\n self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig[\\\"z_channels\\\"], 1)\\n self.embed_dim = embed_dim\\n if colorize_nlabels is not None:\\n assert type(colorize_nlabels) == int\\n self.register_buffer(\\\"colorize\\\", torch.randn(3, colorize_nlabels, 1, 1))\\n if monitor is not None:\\n self.monitor = monitor\\n if ckpt_path is not None:\\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\\n\\n def init_from_ckpt(self, path, ignore_keys=list()):\\n sd = torch.load(path, map_location=\\\"cpu\\\")[\\\"state_dict\\\"]\\n keys = list(sd.keys())\\n for k in keys:\\n for ik in ignore_keys:\\n if k.startswith(ik):\\n print(\\\"Deleting key {} from state_dict.\\\".format(k))\\n del sd[k]\\n self.load_state_dict(sd, strict=False)\\n print(f\\\"Restored from {path}\\\")\\n\\n def encode(self, x):\\n h = self.encoder(x)\\n moments = self.quant_conv(h)\\n posterior = DiagonalGaussianDistribution(moments)\\n return posterior\\n\\n def decode(self, z):\\n z = self.post_quant_conv(z)\\n dec = self.decoder(z)\\n return dec\\n\\n def forward(self, input, sample_posterior=True):\\n posterior = self.encode(input)\\n if sample_posterior:\\n z = posterior.sample()\\n else:\\n z = posterior.mode()\\n dec = self.decode(z)\\n return dec, posterior\\n\\n def get_input(self, batch, k):\\n x = batch[k]\\n if len(x.shape) == 3:\\n x = x[..., None]\\n x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()\\n return x\\n\\n def training_step(self, batch, batch_idx, optimizer_idx):\\n inputs = self.get_input(batch, self.image_key)\\n reconstructions, posterior = self(inputs)\\n\\n if optimizer_idx == 0:\\n # train encoder+decoder+logvar\\n aeloss, log_dict_ae = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n optimizer_idx,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split=\\\"train\\\",\\n )\\n self.log(\\n \\\"aeloss\\\",\\n aeloss,\\n prog_bar=True,\\n logger=True,\\n on_step=True,\\n on_epoch=True,\\n )\\n self.log_dict(\\n log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False\\n )\\n return aeloss\\n\\n if optimizer_idx == 1:\\n # train the discriminator\\n discloss, log_dict_disc = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n optimizer_idx,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split=\\\"train\\\",\\n )\\n\\n self.log(\\n \\\"discloss\\\",\\n discloss,\\n prog_bar=True,\\n logger=True,\\n on_step=True,\\n on_epoch=True,\\n )\\n self.log_dict(\\n log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False\\n )\\n return discloss\\n\\n def validation_step(self, batch, batch_idx):\\n inputs = self.get_input(batch, self.image_key)\\n reconstructions, posterior = self(inputs)\\n aeloss, log_dict_ae = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n 0,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split=\\\"val\\\",\\n )\\n\\n discloss, log_dict_disc = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n 1,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split=\\\"val\\\",\\n )\\n\\n self.log(\\\"val/rec_loss\\\", log_dict_ae[\\\"val/rec_loss\\\"])\\n self.log_dict(log_dict_ae)\\n self.log_dict(log_dict_disc)\\n return self.log_dict\\n\\n def configure_optimizers(self):\\n lr = self.learning_rate\\n opt_ae = torch.optim.Adam(\\n list(self.encoder.parameters())\\n + list(self.decoder.parameters())\\n + list(self.quant_conv.parameters())\\n + list(self.post_quant_conv.parameters()),\\n lr=lr,\\n betas=(0.5, 0.9),\\n )\\n opt_disc = torch.optim.Adam(\\n self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)\\n )\\n return [opt_ae, opt_disc], []\\n\\n def get_last_layer(self):\\n return self.decoder.conv_out.weight\\n\\n @torch.no_grad()\\n def log_images(self, batch, only_inputs=False, **kwargs):\\n log = dict()\\n x = self.get_input(batch, self.image_key)\\n x = x.to(self.device)\\n if not only_inputs:\\n xrec, posterior = self(x)\\n if x.shape[1] > 3:\\n # colorize with random projection\\n assert xrec.shape[1] > 3\\n x = self.to_rgb(x)\\n xrec = self.to_rgb(xrec)\\n log[\\\"samples\\\"] = self.decode(torch.randn_like(posterior.sample()))\\n log[\\\"reconstructions\\\"] = xrec\\n log[\\\"inputs\\\"] = x\\n return log\\n\\n def to_rgb(self, x):\\n assert self.image_key == \\\"segmentation\\\"\\n if not hasattr(self, \\\"colorize\\\"):\\n self.register_buffer(\\\"colorize\\\", torch.randn(3, x.shape[1], 1, 1).to(x))\\n x = F.conv2d(x, weight=self.colorize)\\n x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0\\n return x\"\n },\n {\n \"identifier\": \"IdentityFirstStage\",\n \"path\": \"extern/ldm_zero123/models/autoencoder.py\",\n \"snippet\": \"class IdentityFirstStage(torch.nn.Module):\\n def __init__(self, *args, vq_interface=False, **kwargs):\\n self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff\\n super().__init__()\\n\\n def encode(self, x, *args, **kwargs):\\n return x\\n\\n def decode(self, x, *args, **kwargs):\\n return x\\n\\n def quantize(self, x, *args, **kwargs):\\n if self.vq_interface:\\n return x, None, [None, None, None]\\n return x\\n\\n def forward(self, x, *args, **kwargs):\\n return x\"\n },\n {\n \"identifier\": \"VQModelInterface\",\n \"path\": \"extern/ldm_zero123/models/autoencoder.py\",\n \"snippet\": \"class VQModelInterface(VQModel):\\n def __init__(self, embed_dim, *args, **kwargs):\\n super().__init__(embed_dim=embed_dim, *args, **kwargs)\\n self.embed_dim = embed_dim\\n\\n def encode(self, x):\\n h = self.encoder(x)\\n h = self.quant_conv(h)\\n return h\\n\\n def decode(self, h, force_not_quantize=False):\\n # also go through quantization layer\\n if not force_not_quantize:\\n quant, emb_loss, info = self.quantize(h)\\n else:\\n quant = h\\n quant = self.post_quant_conv(quant)\\n dec = self.decoder(quant)\\n return dec\"\n },\n {\n \"identifier\": \"DDIMSampler\",\n \"path\": \"extern/ldm_zero123/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 to(self, device):\\n \\\"\\\"\\\"Same as to in torch module\\n Don't really underestand why this isn't a module in the first place\\\"\\\"\\\"\\n for k, v in self.__dict__.items():\\n if isinstance(v, torch.Tensor):\\n new_v = getattr(self, k).to(device)\\n setattr(self, k, new_v)\\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(\\n self, ddim_num_steps, ddim_discretize=\\\"uniform\\\", ddim_eta=0.0, verbose=True\\n ):\\n self.ddim_timesteps = make_ddim_timesteps(\\n ddim_discr_method=ddim_discretize,\\n num_ddim_timesteps=ddim_num_steps,\\n num_ddpm_timesteps=self.ddpm_num_timesteps,\\n verbose=verbose,\\n )\\n alphas_cumprod = self.model.alphas_cumprod\\n assert (\\n alphas_cumprod.shape[0] == self.ddpm_num_timesteps\\n ), \\\"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(\\n \\\"alphas_cumprod_prev\\\", to_torch(self.model.alphas_cumprod_prev)\\n )\\n\\n # calculations for diffusion q(x_t | x_{t-1}) and others\\n self.register_buffer(\\n \\\"sqrt_alphas_cumprod\\\", to_torch(np.sqrt(alphas_cumprod.cpu()))\\n )\\n self.register_buffer(\\n \\\"sqrt_one_minus_alphas_cumprod\\\",\\n to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),\\n )\\n self.register_buffer(\\n \\\"log_one_minus_alphas_cumprod\\\", to_torch(np.log(1.0 - alphas_cumprod.cpu()))\\n )\\n self.register_buffer(\\n \\\"sqrt_recip_alphas_cumprod\\\", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))\\n )\\n self.register_buffer(\\n \\\"sqrt_recipm1_alphas_cumprod\\\",\\n to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),\\n )\\n\\n # ddim sampling parameters\\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\\n alphacums=alphas_cumprod.cpu(),\\n ddim_timesteps=self.ddim_timesteps,\\n eta=ddim_eta,\\n verbose=verbose,\\n )\\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(1.0 - ddim_alphas))\\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\\n (1 - self.alphas_cumprod_prev)\\n / (1 - self.alphas_cumprod)\\n * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)\\n )\\n self.register_buffer(\\n \\\"ddim_sigmas_for_original_num_steps\\\", sigmas_for_original_sampling_steps\\n )\\n\\n @torch.no_grad()\\n def sample(\\n self,\\n S,\\n batch_size,\\n shape,\\n conditioning=None,\\n callback=None,\\n normals_sequence=None,\\n img_callback=None,\\n quantize_x0=False,\\n eta=0.0,\\n mask=None,\\n x0=None,\\n temperature=1.0,\\n noise_dropout=0.0,\\n score_corrector=None,\\n corrector_kwargs=None,\\n verbose=True,\\n x_T=None,\\n log_every_t=100,\\n unconditional_guidance_scale=1.0,\\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 **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):\\n ctmp = ctmp[0]\\n cbs = ctmp.shape[0]\\n if cbs != batch_size:\\n print(\\n f\\\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\\\"\\n )\\n\\n else:\\n if conditioning.shape[0] != batch_size:\\n print(\\n f\\\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\\\"\\n )\\n\\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\\n # sampling\\n C, H, W = shape\\n size = (batch_size, C, H, W)\\n # print(f'Data shape for DDIM sampling is {size}, eta {eta}')\\n\\n samples, intermediates = self.ddim_sampling(\\n conditioning,\\n size,\\n callback=callback,\\n img_callback=img_callback,\\n quantize_denoised=quantize_x0,\\n mask=mask,\\n x0=x0,\\n ddim_use_original_steps=False,\\n noise_dropout=noise_dropout,\\n temperature=temperature,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n x_T=x_T,\\n log_every_t=log_every_t,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n dynamic_threshold=dynamic_threshold,\\n )\\n return samples, intermediates\\n\\n @torch.no_grad()\\n def ddim_sampling(\\n self,\\n cond,\\n shape,\\n x_T=None,\\n ddim_use_original_steps=False,\\n callback=None,\\n timesteps=None,\\n quantize_denoised=False,\\n mask=None,\\n x0=None,\\n img_callback=None,\\n log_every_t=100,\\n temperature=1.0,\\n noise_dropout=0.0,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.0,\\n unconditional_conditioning=None,\\n dynamic_threshold=None,\\n t_start=-1,\\n ):\\n device = self.model.betas.device\\n b = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=device)\\n else:\\n img = x_T\\n\\n if timesteps is None:\\n timesteps = (\\n self.ddpm_num_timesteps\\n if ddim_use_original_steps\\n else self.ddim_timesteps\\n )\\n elif timesteps is not None and not ddim_use_original_steps:\\n subset_end = (\\n int(\\n min(timesteps / self.ddim_timesteps.shape[0], 1)\\n * self.ddim_timesteps.shape[0]\\n )\\n - 1\\n )\\n timesteps = self.ddim_timesteps[:subset_end]\\n\\n timesteps = timesteps[:t_start]\\n\\n intermediates = {\\\"x_inter\\\": [img], \\\"pred_x0\\\": [img]}\\n time_range = (\\n reversed(range(0, timesteps))\\n if ddim_use_original_steps\\n else np.flip(timesteps)\\n )\\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(iterator):\\n index = total_steps - i - 1\\n ts = torch.full((b,), step, device=device, dtype=torch.long)\\n\\n if mask is not None:\\n assert x0 is not None\\n img_orig = self.model.q_sample(\\n x0, ts\\n ) # TODO: deterministic forward pass?\\n img = img_orig * mask + (1.0 - mask) * img\\n\\n outs = self.p_sample_ddim(\\n img,\\n cond,\\n ts,\\n index=index,\\n use_original_steps=ddim_use_original_steps,\\n quantize_denoised=quantize_denoised,\\n temperature=temperature,\\n noise_dropout=noise_dropout,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n dynamic_threshold=dynamic_threshold,\\n )\\n img, pred_x0 = outs\\n if callback:\\n img = callback(i, img, pred_x0)\\n if img_callback:\\n 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(\\n self,\\n x,\\n c,\\n t,\\n index,\\n repeat_noise=False,\\n use_original_steps=False,\\n quantize_denoised=False,\\n temperature=1.0,\\n noise_dropout=0.0,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.0,\\n unconditional_conditioning=None,\\n dynamic_threshold=None,\\n ):\\n b, *_, device = *x.shape, x.device\\n\\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:\\n e_t = self.model.apply_model(x, t, c)\\n else:\\n x_in = torch.cat([x] * 2)\\n t_in = torch.cat([t] * 2)\\n if isinstance(c, dict):\\n assert isinstance(unconditional_conditioning, dict)\\n c_in = dict()\\n for k in c:\\n if isinstance(c[k], list):\\n c_in[k] = [\\n torch.cat([unconditional_conditioning[k][i], c[k][i]])\\n for i in range(len(c[k]))\\n ]\\n else:\\n c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])\\n else:\\n c_in = torch.cat([unconditional_conditioning, c])\\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\\n e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)\\n\\n if score_corrector is not None:\\n assert self.model.parameterization == \\\"eps\\\"\\n e_t = score_corrector.modify_score(\\n self.model, e_t, x, t, c, **corrector_kwargs\\n )\\n\\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\\n alphas_prev = (\\n self.model.alphas_cumprod_prev\\n if use_original_steps\\n else self.ddim_alphas_prev\\n )\\n sqrt_one_minus_alphas = (\\n self.model.sqrt_one_minus_alphas_cumprod\\n if use_original_steps\\n else self.ddim_sqrt_one_minus_alphas\\n )\\n sigmas = (\\n self.model.ddim_sigmas_for_original_num_steps\\n if use_original_steps\\n else self.ddim_sigmas\\n )\\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(\\n (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device\\n )\\n\\n # current prediction for x_0\\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\\n\\n print(t, sqrt_one_minus_at, a_t)\\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 pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)\\n\\n # direction pointing to x_t\\n dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t\\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\\n if noise_dropout > 0.0:\\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(\\n self,\\n x0,\\n c,\\n t_enc,\\n use_original_steps=False,\\n return_intermediates=None,\\n unconditional_guidance_scale=1.0,\\n unconditional_conditioning=None,\\n ):\\n num_reference_steps = (\\n self.ddpm_num_timesteps\\n if use_original_steps\\n else self.ddim_timesteps.shape[0]\\n )\\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(\\n (x0.shape[0],), i, device=self.model.device, dtype=torch.long\\n )\\n if unconditional_guidance_scale == 1.0:\\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(\\n torch.cat((x_next, x_next)),\\n torch.cat((t, t)),\\n torch.cat((unconditional_conditioning, c)),\\n ),\\n 2,\\n )\\n noise_pred = e_t_uncond + unconditional_guidance_scale * (\\n noise_pred - e_t_uncond\\n )\\n\\n xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next\\n weighted_noise_pred = (\\n alphas_next[i].sqrt()\\n * ((1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt())\\n * noise_pred\\n )\\n x_next = xt_weighted + weighted_noise_pred\\n if (\\n return_intermediates\\n and i % (num_steps // return_intermediates) == 0\\n and i < num_steps - 1\\n ):\\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\\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 (\\n 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\\n @torch.no_grad()\\n def decode(\\n self,\\n x_latent,\\n cond,\\n t_start,\\n unconditional_guidance_scale=1.0,\\n unconditional_conditioning=None,\\n use_original_steps=False,\\n ):\\n timesteps = (\\n np.arange(self.ddpm_num_timesteps)\\n if use_original_steps\\n else self.ddim_timesteps\\n )\\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(\\n (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long\\n )\\n x_dec, _ = self.p_sample_ddim(\\n x_dec,\\n cond,\\n ts,\\n index=index,\\n use_original_steps=use_original_steps,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n )\\n return x_dec\"\n },\n {\n \"identifier\": \"CrossAttention\",\n \"path\": \"extern/ldm_zero123/modules/attention.py\",\n \"snippet\": \"class CrossAttention(nn.Module):\\n def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):\\n super().__init__()\\n inner_dim = dim_head * heads\\n context_dim = default(context_dim, query_dim)\\n\\n self.scale = dim_head**-0.5\\n self.heads = heads\\n\\n self.to_q = nn.Linear(query_dim, inner_dim, bias=False)\\n self.to_k = nn.Linear(context_dim, inner_dim, bias=False)\\n self.to_v = nn.Linear(context_dim, inner_dim, bias=False)\\n\\n self.to_out = nn.Sequential(\\n nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)\\n )\\n\\n def forward(self, x, context=None, mask=None):\\n h = self.heads\\n\\n q = self.to_q(x)\\n context = default(context, x)\\n k = self.to_k(context)\\n v = self.to_v(context)\\n\\n q, k, v = map(lambda t: rearrange(t, \\\"b n (h d) -> (b h) n d\\\", h=h), (q, k, v))\\n\\n sim = einsum(\\\"b i d, b j d -> b i j\\\", q, k) * self.scale\\n\\n if exists(mask):\\n mask = rearrange(mask, \\\"b ... -> b (...)\\\")\\n max_neg_value = -torch.finfo(sim.dtype).max\\n mask = repeat(mask, \\\"b j -> (b h) () j\\\", h=h)\\n sim.masked_fill_(~mask, max_neg_value)\\n\\n # attention, what we cannot get enough of\\n attn = sim.softmax(dim=-1)\\n\\n out = einsum(\\\"b i j, b j d -> b i d\\\", attn, v)\\n out = rearrange(out, \\\"(b h) n d -> b n (h d)\\\", h=h)\\n return self.to_out(out)\"\n },\n {\n \"identifier\": \"extract_into_tensor\",\n \"path\": \"extern/ldm_zero123/modules/diffusionmodules/util.py\",\n \"snippet\": \"def extract_into_tensor(a, t, x_shape):\\n b, *_ = t.shape\\n out = a.gather(-1, t)\\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\"\n },\n {\n \"identifier\": \"make_beta_schedule\",\n \"path\": \"extern/ldm_zero123/modules/diffusionmodules/util.py\",\n \"snippet\": \"def make_beta_schedule(\\n schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3\\n):\\n if schedule == \\\"linear\\\":\\n betas = (\\n torch.linspace(\\n linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64\\n )\\n ** 2\\n )\\n\\n elif schedule == \\\"cosine\\\":\\n timesteps = (\\n torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s\\n )\\n alphas = timesteps / (1 + cosine_s) * np.pi / 2\\n alphas = torch.cos(alphas).pow(2)\\n alphas = alphas / alphas[0]\\n betas = 1 - alphas[1:] / alphas[:-1]\\n betas = np.clip(betas, a_min=0, a_max=0.999)\\n\\n elif schedule == \\\"sqrt_linear\\\":\\n betas = torch.linspace(\\n linear_start, linear_end, n_timestep, dtype=torch.float64\\n )\\n elif schedule == \\\"sqrt\\\":\\n betas = (\\n torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)\\n ** 0.5\\n )\\n else:\\n raise ValueError(f\\\"schedule '{schedule}' unknown.\\\")\\n return betas.numpy()\"\n },\n {\n \"identifier\": \"noise_like\",\n \"path\": \"extern/ldm_zero123/modules/diffusionmodules/util.py\",\n \"snippet\": \"def noise_like(shape, device, repeat=False):\\n repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(\\n shape[0], *((1,) * (len(shape) - 1))\\n )\\n noise = lambda: torch.randn(shape, device=device)\\n return repeat_noise() if repeat else noise()\"\n },\n {\n \"identifier\": \"DiagonalGaussianDistribution\",\n \"path\": \"extern/ldm_zero123/modules/distributions/distributions.py\",\n \"snippet\": \"class DiagonalGaussianDistribution(object):\\n def __init__(self, parameters, deterministic=False):\\n self.parameters = parameters\\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\\n self.deterministic = deterministic\\n self.std = torch.exp(0.5 * self.logvar)\\n self.var = torch.exp(self.logvar)\\n if self.deterministic:\\n self.var = self.std = torch.zeros_like(self.mean).to(\\n device=self.parameters.device\\n )\\n\\n def sample(self):\\n x = self.mean + self.std * torch.randn(self.mean.shape).to(\\n device=self.parameters.device\\n )\\n return x\\n\\n def kl(self, other=None):\\n if self.deterministic:\\n return torch.Tensor([0.0])\\n else:\\n if other is None:\\n return 0.5 * torch.sum(\\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\\n dim=[1, 2, 3],\\n )\\n else:\\n return 0.5 * torch.sum(\\n torch.pow(self.mean - other.mean, 2) / other.var\\n + self.var / other.var\\n - 1.0\\n - self.logvar\\n + other.logvar,\\n dim=[1, 2, 3],\\n )\\n\\n def nll(self, sample, dims=[1, 2, 3]):\\n if self.deterministic:\\n return torch.Tensor([0.0])\\n logtwopi = np.log(2.0 * np.pi)\\n return 0.5 * torch.sum(\\n logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,\\n dim=dims,\\n )\\n\\n def mode(self):\\n return self.mean\"\n },\n {\n \"identifier\": \"normal_kl\",\n \"path\": \"extern/ldm_zero123/modules/distributions/distributions.py\",\n \"snippet\": \"def normal_kl(mean1, logvar1, mean2, logvar2):\\n \\\"\\\"\\\"\\n source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\\n Compute the KL divergence between two gaussians.\\n Shapes are automatically broadcasted, so batches can be compared to\\n scalars, among other use cases.\\n \\\"\\\"\\\"\\n tensor = None\\n for obj in (mean1, logvar1, mean2, logvar2):\\n if isinstance(obj, torch.Tensor):\\n tensor = obj\\n break\\n assert tensor is not None, \\\"at least one argument must be a Tensor\\\"\\n\\n # Force variances to be Tensors. Broadcasting helps convert scalars to\\n # Tensors, but it does not work for torch.exp().\\n logvar1, logvar2 = [\\n x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\\n for x in (logvar1, logvar2)\\n ]\\n\\n return 0.5 * (\\n -1.0\\n + logvar2\\n - logvar1\\n + torch.exp(logvar1 - logvar2)\\n + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)\\n )\"\n },\n {\n \"identifier\": \"LitEma\",\n \"path\": \"extern/ldm_zero123/modules/ema.py\",\n \"snippet\": \"class LitEma(nn.Module):\\n def __init__(self, model, decay=0.9999, use_num_upates=True):\\n super().__init__()\\n if decay < 0.0 or decay > 1.0:\\n raise ValueError(\\\"Decay must be between 0 and 1\\\")\\n\\n self.m_name2s_name = {}\\n self.register_buffer(\\\"decay\\\", torch.tensor(decay, dtype=torch.float32))\\n self.register_buffer(\\n \\\"num_updates\\\",\\n torch.tensor(0, dtype=torch.int)\\n if use_num_upates\\n else torch.tensor(-1, dtype=torch.int),\\n )\\n\\n for name, p in model.named_parameters():\\n if p.requires_grad:\\n # remove as '.'-character is not allowed in buffers\\n s_name = name.replace(\\\".\\\", \\\"\\\")\\n self.m_name2s_name.update({name: s_name})\\n self.register_buffer(s_name, p.clone().detach().data)\\n\\n self.collected_params = []\\n\\n def forward(self, model):\\n decay = self.decay\\n\\n if self.num_updates >= 0:\\n self.num_updates += 1\\n decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))\\n\\n one_minus_decay = 1.0 - decay\\n\\n with torch.no_grad():\\n m_param = dict(model.named_parameters())\\n shadow_params = dict(self.named_buffers())\\n\\n for key in m_param:\\n if m_param[key].requires_grad:\\n sname = self.m_name2s_name[key]\\n shadow_params[sname] = shadow_params[sname].type_as(m_param[key])\\n shadow_params[sname].sub_(\\n one_minus_decay * (shadow_params[sname] - m_param[key])\\n )\\n else:\\n assert not key in self.m_name2s_name\\n\\n def copy_to(self, model):\\n m_param = dict(model.named_parameters())\\n shadow_params = dict(self.named_buffers())\\n for key in m_param:\\n if m_param[key].requires_grad:\\n m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)\\n else:\\n assert not key in self.m_name2s_name\\n\\n def store(self, parameters):\\n \\\"\\\"\\\"\\n Save the current parameters for restoring later.\\n Args:\\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\\n temporarily stored.\\n \\\"\\\"\\\"\\n self.collected_params = [param.clone() for param in parameters]\\n\\n def restore(self, parameters):\\n \\\"\\\"\\\"\\n Restore the parameters stored with the `store` method.\\n Useful to validate the model with EMA parameters without affecting the\\n original optimization process. Store the parameters before the\\n `copy_to` method. After validation (or model saving), use this to\\n restore the former parameters.\\n Args:\\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\\n updated with the stored parameters.\\n \\\"\\\"\\\"\\n for c_param, param in zip(self.collected_params, parameters):\\n param.data.copy_(c_param.data)\"\n },\n {\n \"identifier\": \"count_params\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def count_params(model, verbose=False):\\n total_params = sum(p.numel() for p in model.parameters())\\n if verbose:\\n print(f\\\"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.\\\")\\n return total_params\"\n },\n {\n \"identifier\": \"default\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def default(val, d):\\n if exists(val):\\n return val\\n return d() if isfunction(d) else d\"\n },\n {\n \"identifier\": \"exists\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def exists(x):\\n return x is not None\"\n },\n {\n \"identifier\": \"instantiate_from_config\",\n \"path\": \"extern/ldm_zero123/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\": \"isimage\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def isimage(x):\\n if not isinstance(x, torch.Tensor):\\n return False\\n return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)\"\n },\n {\n \"identifier\": \"ismap\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def ismap(x):\\n if not isinstance(x, torch.Tensor):\\n return False\\n return (len(x.shape) == 4) and (x.shape[1] > 3)\"\n },\n {\n \"identifier\": \"log_txt_as_img\",\n \"path\": \"extern/ldm_zero123/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(\\\"data/DejaVuSans.ttf\\\", size=size)\\n nc = int(40 * (wh[0] / 256))\\n lines = \\\"\\\\n\\\".join(\\n xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)\\n )\\n\\n try:\\n draw.text((0, 0), lines, fill=\\\"black\\\", font=font)\\n except UnicodeEncodeError:\\n print(\\\"Cant encode string for logging. Skipping.\\\")\\n\\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\\n txts.append(txt)\\n txts = np.stack(txts)\\n txts = torch.tensor(txts)\\n return txts\"\n },\n {\n \"identifier\": \"mean_flat\",\n \"path\": \"extern/ldm_zero123/util.py\",\n \"snippet\": \"def mean_flat(tensor):\\n \\\"\\\"\\\"\\n https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\\n Take the mean over all non-batch dimensions.\\n \\\"\\\"\\\"\\n return tensor.mean(dim=list(range(1, len(tensor.shape))))\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import itertools\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn as nn\nfrom contextlib import contextmanager, nullcontext\nfrom functools import partial\nfrom einops import rearrange, repeat\nfrom omegaconf import ListConfig\nfrom pytorch_lightning.utilities.rank_zero import rank_zero_only\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom torchvision.utils import make_grid\nfrom tqdm import tqdm\nfrom extern.ldm_zero123.models.autoencoder import (\n AutoencoderKL,\n IdentityFirstStage,\n VQModelInterface,\n)\nfrom extern.ldm_zero123.models.diffusion.ddim import DDIMSampler\nfrom extern.ldm_zero123.modules.attention import CrossAttention\nfrom extern.ldm_zero123.modules.diffusionmodules.util import (\n extract_into_tensor,\n make_beta_schedule,\n noise_like,\n)\nfrom extern.ldm_zero123.modules.distributions.distributions import (\n DiagonalGaussianDistribution,\n normal_kl,\n)\nfrom extern.ldm_zero123.modules.ema import LitEma\nfrom extern.ldm_zero123.util import (\n count_params,\n default,\n exists,\n instantiate_from_config,\n isimage,\n ismap,\n log_txt_as_img,\n mean_flat,\n)"},"token_num":{"kind":"number","value":12692,"string":"12,692"},"cropped_code":{"kind":"string","value":" padding=0,\n stride=(stride[0] // df, stride[1] // df),\n )\n fold = torch.nn.Fold(\n output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2\n )\n\n weighting = self.get_weighting(\n kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device\n ).to(x.dtype)\n normalization = fold(weighting).view(\n 1, 1, h // df, w // df\n ) # normalizes the overlap\n weighting = weighting.view(\n (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)\n )\n\n else:\n raise NotImplementedError\n\n return fold, unfold, normalization, weighting\n\n @torch.no_grad()\n def get_input(\n self,\n batch,\n k,\n return_first_stage_outputs=False,\n force_c_encode=False,\n cond_key=None,\n return_original_cond=False,\n bs=None,\n uncond=0.05,\n ):\n x = super().get_input(batch, k)\n T = batch[\"T\"].to(memory_format=torch.contiguous_format).float()\n\n if bs is not None:\n x = x[:bs]\n T = T[:bs].to(self.device)\n\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 cond_key = cond_key or self.cond_stage_key\n xc = super().get_input(batch, cond_key).to(self.device)\n if bs is not None:\n xc = xc[:bs]\n cond = {}\n\n # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.\n random = torch.rand(x.size(0), device=x.device)\n prompt_mask = rearrange(random < 2 * uncond, \"n -> n 1 1\")\n input_mask = 1 - rearrange(\n (random >= uncond).float() * (random < 3 * uncond).float(), \"n -> n 1 1 1\"\n )\n null_prompt = self.get_learned_conditioning([\"\"])\n\n # z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]\n # print('=========== xc shape ===========', xc.shape)\n with torch.enable_grad():\n clip_emb = self.get_learned_conditioning(xc).detach()\n null_prompt = self.get_learned_conditioning([\"\"]).detach()\n cond[\"c_crossattn\"] = [\n self.cc_projection(\n torch.cat(\n [\n torch.where(prompt_mask, null_prompt, clip_emb),\n T[:, None, :],\n ],\n dim=-1,\n )\n )\n ]\n cond[\"c_concat\"] = [\n input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()\n ]\n out = [z, cond]\n if return_first_stage_outputs:\n xrec = self.decode_first_stage(z)\n out.extend([x, xrec])\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 = 1.0 / self.scale_factor * z\n\n if hasattr(self, \"split_input_params\"):\n if self.split_input_params[\"patch_distributed_vq\"]:\n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n uf = self.split_input_params[\"vqf\"]\n bs, nc, h, w = z.shape\n if ks[0] > h or ks[1] > w:\n ks = (min(ks[0], h), min(ks[1], w))\n print(\"reducing Kernel\")\n\n if stride[0] > h or stride[1] > w:\n stride = (min(stride[0], h), min(stride[1], w))\n print(\"reducing stride\")\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(\n z, ks, stride, uf=uf\n )\n\n z = unfold(z) # (bn, nc * prod(**ks), L)\n # 1. Reshape to img shape\n z = z.view(\n (z.shape[0], -1, ks[0], ks[1], z.shape[-1])\n ) # (bn, nc, ks[0], ks[1], L )\n\n # 2. apply model loop over last dim\n"},"all_code":{"kind":"string","value":"\"\"\"\nwild mixture of\nhttps://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py\nhttps://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py\nhttps://github.com/CompVis/taming-transformers\n-- merci\n\"\"\"\n\n\n\n\n__conditioning_keys__ = {\"concat\": \"c_concat\", \"crossattn\": \"c_crossattn\", \"adm\": \"y\"}\n\n\ndef disabled_train(self, mode=True):\n \"\"\"Overwrite model.train with this function to make sure train/eval mode\n does not change anymore.\"\"\"\n return self\n\n\ndef uniform_on_device(r1, r2, shape, device):\n return (r1 - r2) * torch.rand(*shape, device=device) + r2\n\n\nclass DDPM(pl.LightningModule):\n # classic DDPM with Gaussian diffusion, in image space\n def __init__(\n self,\n unet_config,\n timesteps=1000,\n beta_schedule=\"linear\",\n loss_type=\"l2\",\n ckpt_path=None,\n ignore_keys=[],\n load_only_unet=False,\n monitor=\"val/loss\",\n use_ema=True,\n first_stage_key=\"image\",\n image_size=256,\n channels=3,\n log_every_t=100,\n clip_denoised=True,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n given_betas=None,\n original_elbo_weight=0.0,\n v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta\n l_simple_weight=1.0,\n conditioning_key=None,\n parameterization=\"eps\", # all assuming fixed variance schedules\n scheduler_config=None,\n use_positional_encodings=False,\n learn_logvar=False,\n logvar_init=0.0,\n make_it_fit=False,\n ucg_training=None,\n ):\n super().__init__()\n assert parameterization in [\n \"eps\",\n \"x0\",\n ], 'currently only supporting \"eps\" and \"x0\"'\n self.parameterization = parameterization\n print(\n f\"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode\"\n )\n self.cond_stage_model = None\n self.clip_denoised = clip_denoised\n self.log_every_t = log_every_t\n self.first_stage_key = first_stage_key\n self.image_size = image_size # try conv?\n self.channels = channels\n self.use_positional_encodings = use_positional_encodings\n self.model = DiffusionWrapper(unet_config, conditioning_key)\n count_params(self.model, verbose=True)\n self.use_ema = use_ema\n if self.use_ema:\n self.model_ema = LitEma(self.model)\n print(f\"Keeping EMAs of {len(list(self.model_ema.buffers()))}.\")\n\n self.use_scheduler = scheduler_config is not None\n if self.use_scheduler:\n self.scheduler_config = scheduler_config\n\n self.v_posterior = v_posterior\n self.original_elbo_weight = original_elbo_weight\n self.l_simple_weight = l_simple_weight\n\n if monitor is not None:\n self.monitor = monitor\n self.make_it_fit = make_it_fit\n if ckpt_path is not None:\n self.init_from_ckpt(\n ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet\n )\n\n self.register_schedule(\n given_betas=given_betas,\n beta_schedule=beta_schedule,\n timesteps=timesteps,\n linear_start=linear_start,\n linear_end=linear_end,\n cosine_s=cosine_s,\n )\n\n self.loss_type = loss_type\n\n self.learn_logvar = learn_logvar\n self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))\n if self.learn_logvar:\n self.logvar = nn.Parameter(self.logvar, requires_grad=True)\n\n self.ucg_training = ucg_training or dict()\n if self.ucg_training:\n self.ucg_prng = np.random.RandomState()\n\n def register_schedule(\n self,\n given_betas=None,\n beta_schedule=\"linear\",\n timesteps=1000,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n ):\n if exists(given_betas):\n betas = given_betas\n else:\n betas = make_beta_schedule(\n beta_schedule,\n timesteps,\n linear_start=linear_start,\n linear_end=linear_end,\n cosine_s=cosine_s,\n )\n alphas = 1.0 - betas\n alphas_cumprod = np.cumprod(alphas, axis=0)\n alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])\n\n (timesteps,) = betas.shape\n self.num_timesteps = int(timesteps)\n self.linear_start = linear_start\n self.linear_end = linear_end\n assert (\n alphas_cumprod.shape[0] == self.num_timesteps\n ), \"alphas have to be defined for each timestep\"\n\n to_torch = partial(torch.tensor, dtype=torch.float32)\n\n self.register_buffer(\"betas\", to_torch(betas))\n self.register_buffer(\"alphas_cumprod\", to_torch(alphas_cumprod))\n self.register_buffer(\"alphas_cumprod_prev\", to_torch(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)))\n self.register_buffer(\n \"sqrt_one_minus_alphas_cumprod\", to_torch(np.sqrt(1.0 - alphas_cumprod))\n )\n self.register_buffer(\n \"log_one_minus_alphas_cumprod\", to_torch(np.log(1.0 - alphas_cumprod))\n )\n self.register_buffer(\n \"sqrt_recip_alphas_cumprod\", to_torch(np.sqrt(1.0 / alphas_cumprod))\n )\n self.register_buffer(\n \"sqrt_recipm1_alphas_cumprod\", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))\n )\n\n # calculations for posterior q(x_{t-1} | x_t, x_0)\n posterior_variance = (1 - self.v_posterior) * betas * (\n 1.0 - alphas_cumprod_prev\n ) / (1.0 - alphas_cumprod) + self.v_posterior * betas\n # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)\n self.register_buffer(\"posterior_variance\", to_torch(posterior_variance))\n # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain\n self.register_buffer(\n \"posterior_log_variance_clipped\",\n to_torch(np.log(np.maximum(posterior_variance, 1e-20))),\n )\n self.register_buffer(\n \"posterior_mean_coef1\",\n to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),\n )\n self.register_buffer(\n \"posterior_mean_coef2\",\n to_torch(\n (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)\n ),\n )\n\n if self.parameterization == \"eps\":\n lvlb_weights = self.betas**2 / (\n 2\n * self.posterior_variance\n * to_torch(alphas)\n * (1 - self.alphas_cumprod)\n )\n elif self.parameterization == \"x0\":\n lvlb_weights = (\n 0.5\n * np.sqrt(torch.Tensor(alphas_cumprod))\n / (2.0 * 1 - torch.Tensor(alphas_cumprod))\n )\n else:\n raise NotImplementedError(\"mu not supported\")\n # TODO how to choose this term\n lvlb_weights[0] = lvlb_weights[1]\n self.register_buffer(\"lvlb_weights\", lvlb_weights, persistent=False)\n assert not torch.isnan(self.lvlb_weights).all()\n\n @contextmanager\n def ema_scope(self, context=None):\n if self.use_ema:\n self.model_ema.store(self.model.parameters())\n self.model_ema.copy_to(self.model)\n if context is not None:\n print(f\"{context}: Switched to EMA weights\")\n try:\n yield None\n finally:\n if self.use_ema:\n self.model_ema.restore(self.model.parameters())\n if context is not None:\n print(f\"{context}: Restored training weights\")\n\n @torch.no_grad()\n def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):\n sd = torch.load(path, map_location=\"cpu\")\n if \"state_dict\" in list(sd.keys()):\n sd = sd[\"state_dict\"]\n keys = list(sd.keys())\n\n if self.make_it_fit:\n n_params = len(\n [\n name\n for name, _ in itertools.chain(\n self.named_parameters(), self.named_buffers()\n )\n ]\n )\n for name, param in tqdm(\n itertools.chain(self.named_parameters(), self.named_buffers()),\n desc=\"Fitting old weights to new weights\",\n total=n_params,\n ):\n if not name in sd:\n continue\n old_shape = sd[name].shape\n new_shape = param.shape\n assert len(old_shape) == len(new_shape)\n if len(new_shape) > 2:\n # we only modify first two axes\n assert new_shape[2:] == old_shape[2:]\n # assumes first axis corresponds to output dim\n if not new_shape == old_shape:\n new_param = param.clone()\n old_param = sd[name]\n if len(new_shape) == 1:\n for i in range(new_param.shape[0]):\n new_param[i] = old_param[i % old_shape[0]]\n elif len(new_shape) >= 2:\n for i in range(new_param.shape[0]):\n for j in range(new_param.shape[1]):\n new_param[i, j] = old_param[\n i % old_shape[0], j % old_shape[1]\n ]\n\n n_used_old = torch.ones(old_shape[1])\n for j in range(new_param.shape[1]):\n n_used_old[j % old_shape[1]] += 1\n n_used_new = torch.zeros(new_shape[1])\n for j in range(new_param.shape[1]):\n n_used_new[j] = n_used_old[j % old_shape[1]]\n\n n_used_new = n_used_new[None, :]\n while len(n_used_new.shape) < len(new_shape):\n n_used_new = n_used_new.unsqueeze(-1)\n new_param /= n_used_new\n\n sd[name] = new_param\n\n missing, unexpected = (\n self.load_state_dict(sd, strict=False)\n if not only_model\n else self.model.load_state_dict(sd, strict=False)\n )\n print(\n f\"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys\"\n )\n if len(missing) > 0:\n print(f\"Missing Keys: {missing}\")\n if len(unexpected) > 0:\n print(f\"Unexpected Keys: {unexpected}\")\n\n def q_mean_variance(self, x_start, t):\n \"\"\"\n Get the distribution q(x_t | x_0).\n :param x_start: the [N x C x ...] tensor of noiseless inputs.\n :param t: the number of diffusion steps (minus 1). Here, 0 means one step.\n :return: A tuple (mean, variance, log_variance), all of x_start's shape.\n \"\"\"\n mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start\n variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)\n log_variance = extract_into_tensor(\n self.log_one_minus_alphas_cumprod, t, x_start.shape\n )\n return mean, variance, log_variance\n\n def predict_start_from_noise(self, x_t, t, noise):\n return (\n extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t\n - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)\n * noise\n )\n\n def q_posterior(self, x_start, x_t, t):\n posterior_mean = (\n extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start\n + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t\n )\n posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)\n posterior_log_variance_clipped = extract_into_tensor(\n self.posterior_log_variance_clipped, t, x_t.shape\n )\n return posterior_mean, posterior_variance, posterior_log_variance_clipped\n\n def p_mean_variance(self, x, t, clip_denoised: bool):\n model_out = self.model(x, t)\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 if clip_denoised:\n x_recon.clamp_(-1.0, 1.0)\n\n model_mean, posterior_variance, posterior_log_variance = self.q_posterior(\n x_start=x_recon, x_t=x, t=t\n )\n return model_mean, posterior_variance, posterior_log_variance\n\n @torch.no_grad()\n def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):\n b, *_, device = *x.shape, x.device\n model_mean, _, model_log_variance = self.p_mean_variance(\n x=x, t=t, clip_denoised=clip_denoised\n )\n noise = noise_like(x.shape, device, repeat_noise)\n # no noise when t == 0\n nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\n\n @torch.no_grad()\n def p_sample_loop(self, shape, return_intermediates=False):\n device = self.betas.device\n b = shape[0]\n img = torch.randn(shape, device=device)\n intermediates = [img]\n for i in tqdm(\n reversed(range(0, self.num_timesteps)),\n desc=\"Sampling t\",\n total=self.num_timesteps,\n ):\n img = self.p_sample(\n img,\n torch.full((b,), i, device=device, dtype=torch.long),\n clip_denoised=self.clip_denoised,\n )\n if i % self.log_every_t == 0 or i == self.num_timesteps - 1:\n intermediates.append(img)\n if return_intermediates:\n return img, intermediates\n return img\n\n @torch.no_grad()\n def sample(self, batch_size=16, return_intermediates=False):\n image_size = self.image_size\n channels = self.channels\n return self.p_sample_loop(\n (batch_size, channels, image_size, image_size),\n return_intermediates=return_intermediates,\n )\n\n def q_sample(self, x_start, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x_start))\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 get_loss(self, pred, target, mean=True):\n if self.loss_type == \"l1\":\n loss = (target - pred).abs()\n if mean:\n loss = loss.mean()\n elif self.loss_type == \"l2\":\n if mean:\n loss = torch.nn.functional.mse_loss(target, pred)\n else:\n loss = torch.nn.functional.mse_loss(target, pred, reduction=\"none\")\n else:\n raise NotImplementedError(\"unknown loss type '{loss_type}'\")\n\n return loss\n\n def p_losses(self, x_start, 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_out = self.model(x_noisy, t)\n\n loss_dict = {}\n if self.parameterization == \"eps\":\n target = noise\n elif self.parameterization == \"x0\":\n target = x_start\n else:\n raise NotImplementedError(\n f\"Paramterization {self.parameterization} not yet supported\"\n )\n\n loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])\n\n log_prefix = \"train\" if self.training else \"val\"\n\n loss_dict.update({f\"{log_prefix}/loss_simple\": loss.mean()})\n loss_simple = loss.mean() * self.l_simple_weight\n\n loss_vlb = (self.lvlb_weights[t] * loss).mean()\n loss_dict.update({f\"{log_prefix}/loss_vlb\": loss_vlb})\n\n loss = loss_simple + self.original_elbo_weight * loss_vlb\n\n loss_dict.update({f\"{log_prefix}/loss\": loss})\n\n return loss, loss_dict\n\n def forward(self, x, *args, **kwargs):\n # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size\n # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'\n t = torch.randint(\n 0, self.num_timesteps, (x.shape[0],), device=self.device\n ).long()\n return self.p_losses(x, t, *args, **kwargs)\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = rearrange(x, \"b h w c -> b c h w\")\n x = x.to(memory_format=torch.contiguous_format).float()\n return x\n\n def shared_step(self, batch):\n x = self.get_input(batch, self.first_stage_key)\n loss, loss_dict = self(x)\n return loss, loss_dict\n\n def training_step(self, batch, batch_idx):\n for k in self.ucg_training:\n p = self.ucg_training[k][\"p\"]\n val = self.ucg_training[k][\"val\"]\n if val is None:\n val = \"\"\n for i in range(len(batch[k])):\n if self.ucg_prng.choice(2, p=[1 - p, p]):\n batch[k][i] = val\n\n loss, loss_dict = self.shared_step(batch)\n\n self.log_dict(\n loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True\n )\n\n self.log(\n \"global_step\",\n self.global_step,\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=False,\n )\n\n if self.use_scheduler:\n lr = self.optimizers().param_groups[0][\"lr\"]\n self.log(\n \"lr_abs\", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False\n )\n\n return loss\n\n @torch.no_grad()\n def validation_step(self, batch, batch_idx):\n _, loss_dict_no_ema = self.shared_step(batch)\n with self.ema_scope():\n _, loss_dict_ema = self.shared_step(batch)\n loss_dict_ema = {key + \"_ema\": loss_dict_ema[key] for key in loss_dict_ema}\n self.log_dict(\n loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True\n )\n self.log_dict(\n loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True\n )\n\n def on_train_batch_end(self, *args, **kwargs):\n if self.use_ema:\n self.model_ema(self.model)\n\n def _get_rows_from_list(self, samples):\n n_imgs_per_row = len(samples)\n denoise_grid = rearrange(samples, \"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 @torch.no_grad()\n def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):\n log = dict()\n x = self.get_input(batch, self.first_stage_key)\n N = min(x.shape[0], N)\n n_row = min(x.shape[0], n_row)\n x = x.to(self.device)[:N]\n log[\"inputs\"] = x\n\n # get diffusion row\n diffusion_row = list()\n x_start = x[:n_row]\n\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(x_start)\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n diffusion_row.append(x_noisy)\n\n log[\"diffusion_row\"] = self._get_rows_from_list(diffusion_row)\n\n if sample:\n # get denoise row\n with self.ema_scope(\"Plotting\"):\n samples, denoise_row = self.sample(\n batch_size=N, return_intermediates=True\n )\n\n log[\"samples\"] = samples\n log[\"denoise_row\"] = self._get_rows_from_list(denoise_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.learn_logvar:\n params = params + [self.logvar]\n opt = torch.optim.AdamW(params, lr=lr)\n return opt\n\n\nclass LatentDiffusion(DDPM):\n \"\"\"main class\"\"\"\n\n def __init__(\n 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 unet_trainable=True,\n *args,\n **kwargs,\n ):\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__\":\n conditioning_key = None\n ckpt_path = kwargs.pop(\"ckpt_path\", None)\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.unet_trainable = unet_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\n # construct linear projection layer for concatenating image CLIP embedding and RT\n self.cc_projection = nn.Linear(772, 768)\n nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])\n nn.init.zeros_(list(self.cc_projection.parameters())[1])\n self.cc_projection.requires_grad_(True)\n\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\n def make_cond_schedule(\n self,\n ):\n self.cond_ids = torch.full(\n size=(self.num_timesteps,),\n fill_value=self.num_timesteps - 1,\n dtype=torch.long,\n )\n ids = torch.round(\n torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)\n ).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 (\n self.scale_by_std\n and self.current_epoch == 0\n and self.global_step == 0\n and batch_idx == 0\n and not self.restarted_from_ckpt\n ):\n assert (\n self.scale_factor == 1.0\n ), \"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\", 1.0 / z.flatten().std())\n print(f\"setting self.scale_factor to {self.scale_factor}\")\n print(\"### USING STD-RESCALING ###\")\n\n def register_schedule(\n self,\n given_betas=None,\n beta_schedule=\"linear\",\n timesteps=1000,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n ):\n super().register_schedule(\n given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s\n )\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(\n self, samples, desc=\"\", force_no_decoder_quantization=False\n ):\n denoise_row = []\n for zd in tqdm(samples, desc=desc):\n denoise_row.append(\n self.decode_first_stage(\n zd.to(self.device), force_not_quantize=force_no_decoder_quantization\n )\n )\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(\n f\"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented\"\n )\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(\n self.cond_stage_model.encode\n ):\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(\n torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1\n )[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(\n weighting,\n self.split_input_params[\"clip_min_weight\"],\n self.split_input_params[\"clip_max_weight\"],\n )\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(\n L_weighting,\n self.split_input_params[\"clip_min_tie_weight\"],\n self.split_input_params[\"clip_max_tie_weight\"],\n )\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(\n self, x, kernel_size, stride, uf=1, df=1\n ): # 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(\n kernel_size=kernel_size, dilation=1, padding=0, stride=stride\n )\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(\n kernel_size[0], kernel_size[1], Ly, Lx, x.device\n ).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(\n kernel_size=kernel_size, dilation=1, padding=0, stride=stride\n )\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(\n kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),\n dilation=1,\n padding=0,\n stride=(stride[0] * uf, stride[1] * uf),\n )\n fold = torch.nn.Fold(\n output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2\n )\n\n weighting = self.get_weighting(\n kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device\n ).to(x.dtype)\n normalization = fold(weighting).view(\n 1, 1, h * uf, w * uf\n ) # normalizes the overlap\n weighting = weighting.view(\n (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)\n )\n\n elif df > 1 and uf == 1:\n fold_params = dict(\n kernel_size=kernel_size, dilation=1, padding=0, stride=stride\n )\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(\n kernel_size=(kernel_size[0] // df, kernel_size[0] // df),\n dilation=1,\n padding=0,\n stride=(stride[0] // df, stride[1] // df),\n )\n fold = torch.nn.Fold(\n output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2\n )\n\n weighting = self.get_weighting(\n kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device\n ).to(x.dtype)\n normalization = fold(weighting).view(\n 1, 1, h // df, w // df\n ) # normalizes the overlap\n weighting = weighting.view(\n (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)\n )\n\n else:\n raise NotImplementedError\n\n return fold, unfold, normalization, weighting\n\n @torch.no_grad()\n def get_input(\n self,\n batch,\n k,\n return_first_stage_outputs=False,\n force_c_encode=False,\n cond_key=None,\n return_original_cond=False,\n bs=None,\n uncond=0.05,\n ):\n x = super().get_input(batch, k)\n T = batch[\"T\"].to(memory_format=torch.contiguous_format).float()\n\n if bs is not None:\n x = x[:bs]\n T = T[:bs].to(self.device)\n\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 cond_key = cond_key or self.cond_stage_key\n xc = super().get_input(batch, cond_key).to(self.device)\n if bs is not None:\n xc = xc[:bs]\n cond = {}\n\n # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.\n random = torch.rand(x.size(0), device=x.device)\n prompt_mask = rearrange(random < 2 * uncond, \"n -> n 1 1\")\n input_mask = 1 - rearrange(\n (random >= uncond).float() * (random < 3 * uncond).float(), \"n -> n 1 1 1\"\n )\n null_prompt = self.get_learned_conditioning([\"\"])\n\n # z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]\n # print('=========== xc shape ===========', xc.shape)\n with torch.enable_grad():\n clip_emb = self.get_learned_conditioning(xc).detach()\n null_prompt = self.get_learned_conditioning([\"\"]).detach()\n cond[\"c_crossattn\"] = [\n self.cc_projection(\n torch.cat(\n [\n torch.where(prompt_mask, null_prompt, clip_emb),\n T[:, None, :],\n ],\n dim=-1,\n )\n )\n ]\n cond[\"c_concat\"] = [\n input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()\n ]\n out = [z, cond]\n if return_first_stage_outputs:\n xrec = self.decode_first_stage(z)\n out.extend([x, xrec])\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 = 1.0 / self.scale_factor * z\n\n if hasattr(self, \"split_input_params\"):\n if self.split_input_params[\"patch_distributed_vq\"]:\n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n uf = self.split_input_params[\"vqf\"]\n bs, nc, h, w = z.shape\n if ks[0] > h or ks[1] > w:\n ks = (min(ks[0], h), min(ks[1], w))\n print(\"reducing Kernel\")\n\n if stride[0] > h or stride[1] > w:\n stride = (min(stride[0], h), min(stride[1], w))\n print(\"reducing stride\")\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(\n z, ks, stride, uf=uf\n )\n\n z = unfold(z) # (bn, nc * prod(**ks), L)\n # 1. Reshape to img shape\n z = z.view(\n (z.shape[0], -1, ks[0], ks[1], z.shape[-1])\n ) # (bn, nc, ks[0], ks[1], L )\n\n # 2. apply model loop over last dim"},"next_line":{"kind":"string","value":" if isinstance(self.first_stage_model, VQModelInterface):"},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-12-27 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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 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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 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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 |
|---|---|---|---|---|---|---|---|---|---|---|
ielab/llm-rankers | run.py | [
{
"identifier": "SearchResult",
"path": "rankers/rankers.py",
"snippet": "class SearchResult:\n docid: str\n score: float\n text: str"
},
{
"identifier": "PointwiseLlmRanker",
"path": "rankers/pointwise.py",
"snippet": "class PointwiseLlmRanker(LlmRanker):\n\n def __init__(se... | import logging
import ir_datasets
import argparse
import sys
import json
import time
import random
from pyserini.search.lucene import LuceneSearcher
from pyserini.search._base import get_topics
from rankers.rankers import SearchResult
from rankers.pointwise import PointwiseLlmRanker, MonoT5LlmRanker
from rankers.setwise import SetwiseLlmRanker, OpenAiSetwiseLlmRanker
from rankers.pairwise import PairwiseLlmRanker, DuoT5LlmRanker, OpenAiPairwiseLlmRanker
from rankers.listwise import OpenAiListwiseLlmRanker, ListwiseLlmRanker
from tqdm import tqdm | 13,420 | random.seed(929)
logger = logging.getLogger(__name__)
def parse_args(parser, commands):
# Divide argv by commands
split_argv = [[]]
for c in sys.argv[1:]:
if c in commands.choices:
split_argv.append([c])
else:
split_argv[-1].append(c)
# Initialize namespace
args = argparse.Namespace()
for c in commands.choices:
setattr(args, c, None)
# Parse each command
parser.parse_args(split_argv[0], namespace=args) # Without command
for argv in split_argv[1:]: # Commands
n = argparse.Namespace()
setattr(args, argv[0], n)
parser.parse_args(argv, namespace=n)
return args
def write_run_file(path, results, tag):
with open(path, 'w') as f:
for qid, _, ranking in results:
rank = 1
for doc in ranking:
docid = doc.docid
score = doc.score
f.write(f"{qid}\tQ0\t{docid}\t{rank}\t{score}\t{tag}\n")
rank += 1
def main(args):
if args.pointwise:
if 'monot5' in args.run.model_name_or_path:
ranker = MonoT5LlmRanker(model_name_or_path=args.run.model_name_or_path,
tokenizer_name_or_path=args.run.tokenizer_name_or_path,
device=args.run.device,
cache_dir=args.run.cache_dir,
method=args.pointwise.method,
batch_size=args.pointwise.batch_size)
else:
| random.seed(929)
logger = logging.getLogger(__name__)
def parse_args(parser, commands):
# Divide argv by commands
split_argv = [[]]
for c in sys.argv[1:]:
if c in commands.choices:
split_argv.append([c])
else:
split_argv[-1].append(c)
# Initialize namespace
args = argparse.Namespace()
for c in commands.choices:
setattr(args, c, None)
# Parse each command
parser.parse_args(split_argv[0], namespace=args) # Without command
for argv in split_argv[1:]: # Commands
n = argparse.Namespace()
setattr(args, argv[0], n)
parser.parse_args(argv, namespace=n)
return args
def write_run_file(path, results, tag):
with open(path, 'w') as f:
for qid, _, ranking in results:
rank = 1
for doc in ranking:
docid = doc.docid
score = doc.score
f.write(f"{qid}\tQ0\t{docid}\t{rank}\t{score}\t{tag}\n")
rank += 1
def main(args):
if args.pointwise:
if 'monot5' in args.run.model_name_or_path:
ranker = MonoT5LlmRanker(model_name_or_path=args.run.model_name_or_path,
tokenizer_name_or_path=args.run.tokenizer_name_or_path,
device=args.run.device,
cache_dir=args.run.cache_dir,
method=args.pointwise.method,
batch_size=args.pointwise.batch_size)
else: | ranker = PointwiseLlmRanker(model_name_or_path=args.run.model_name_or_path, | 1 | 2023-10-14 01:39:38+00:00 | 16k |
amazon-science/tabsyn | baselines/tabddpm/train.py | [
{
"identifier": "make_dataset",
"path": "utils_train.py",
"snippet": "def make_dataset(\n data_path: str,\n T: src.Transformations,\n task_type,\n change_val: bool,\n concat = True,\n):\n\n # classification\n if task_type == 'binclass' or task_type == 'multiclass':\n X_cat = ... | import os
import sys
import time
import torch
import numpy as np
import pandas as pd
import src
from copy import deepcopy
from utils_train import make_dataset, update_ema
from baselines.tabddpm.models.modules import MLPDiffusion
from baselines.tabddpm.models.gaussian_multinomial_distribution import GaussianMultinomialDiffusion | 11,647 |
def get_model(
model_name,
model_params,
n_num_features,
category_sizes
):
print(model_name)
if model_name == 'mlp':
|
def get_model(
model_name,
model_params,
n_num_features,
category_sizes
):
print(model_name)
if model_name == 'mlp': | model = MLPDiffusion(**model_params) | 2 | 2023-10-10 18:06:31+00:00 | 16k |
ThomasMrY/DisDiff | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import pytorch_lightning as pl
import copy
import os
import pandas as pd
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.diffusionmodules.util import return_wrap | 11,455 | def apply_model(self, x_noisy, t, cond, return_ids=False, sampled_concept= None, sampled_index= None):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, sampled_concept = sampled_concept, sampled_index = sampled_index, **cond)
# if isinstance(x_recon, tuple) and not return_ids:
# return x_recon[0]
# else:
# return x_recon
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
self.ce_loss = nn.CrossEntropyLoss(reduction = "none")
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
self.register_buffer("shift_coef", - to_torch(np.sqrt(alphas)) * (1. - self.alphas_cumprod_prev) / torch.sqrt(1. - self.alphas_cumprod))
self.register_buffer("ddim_coef", -self.sqrt_one_minus_alphas_cumprod)
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
self.load_epoch = sd['epoch']
self.load_step = sd["global_step"]
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
eps_pred = return_wrap(model_out, extract_into_tensor(self.ddim_coef, t, x.shape))
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=eps_pred)
elif self.parameterization == "x0":
x_recon = eps_pred
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
eps_pred = return_wrap(model_out, extract_into_tensor(self.shift_coef, t, x_start.shape))
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(eps_pred, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
pass
# _, loss_dict_no_ema = self.shared_step(batch)
# with self.ema_scope():
# _, loss_dict_ema = self.shared_step(batch)
# loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
# self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
# self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
dis_loss_flag = False,
detach_flag = False,
train_enc_flag = False,
dis_weight = 1.0,
dis_loss_type = "IM",
*args, **kwargs):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
self.dis_loss_flag = dis_loss_flag
self.detach_flag = detach_flag
self.train_enc_flag = train_enc_flag
self.dis_weight = dis_weight
self.dis_loss_type = dis_loss_type
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# def on_train_batch_start(self, batch, batch_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
if hasattr(self.model.diffusion_model,"scale_factor"):
del self.scale_factor
self.register_buffer('scale_factor', self.model.diffusion_model.scale_factor)
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING Pre-Trained STD-RESCALING ###")
else:
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
if self.model.conditioning_key is not None:
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox']:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
else:
xc = super().get_input(batch, cond_key).to(self.device)
else:
xc = x
if not self.cond_stage_trainable or force_c_encode:
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c = self.get_learned_conditioning(xc)
else:
c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
c = c[:bs]
else:
c = None
xc = None
out = [z, c]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False, sampled_concept= None, sampled_index= None):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, sampled_concept = sampled_concept, sampled_index = sampled_index, **cond)
# if isinstance(x_recon, tuple) and not return_ids:
# return x_recon[0]
# else:
# return x_recon
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) | return mean_flat(kl_prior) / np.log(2.0) | 5 | 2023-10-07 09:58:07+00:00 | 16k |
wiio12/LEGO-Prover | lego_prover/prover.py | [
{
"identifier": "IsabelleEnv",
"path": "lego_prover/env/isa_bridge.py",
"snippet": "class IsabelleEnv(gym.Env):\n def __init__(\n self,\n logger=None,\n isabelle_path=\"/Users/wiio/Isabelle2022\",\n working_dir=\"miniF2F\",\n interactive_file=\"miniF2F/interactive.t... | import os
import random
import re
import time
import multiprocessing as mp
import tiktoken
import lego_prover.utils as U
import logging
from lego_prover.env.isa_bridge import IsabelleEnv
from .agents import ActionAgent
from .agents import CurriculumAgent
from .agents import SkillManager
from langchain.schema import HumanMessage | 11,622 |
class Prover:
def __init__(
self,
rank: int = None,
isabelle_path: str = None,
server_port: int = 8000,
model_name: str = "gpt-4",
temperature: int = 0,
action_agent_task_max_retries: int = 4,
curriculum_task_type: str = "simple_curriculum",
curriculum_agent_lock = U.WithEmpty(),
skill_manager_lock = U.WithEmpty(),
chroma_bridge = None,
openai_api_request_timeout: int = 6000,
ckpt_dir: str = "ckpt",
resume: bool = False,
miniF2F_tasks: mp.Queue = None,
):
"""
Initializes a new instance of the Prover class.
Args:
rank (int): The rank of the prover process.
isabelle_path (str): The path to the Isabelle directory.
server_port (int): The port number for the server.
model_name (str): The name of the OpenAI model to use.
temperature (int): The temperature for sampling the LLM.
action_agent_task_max_retries (int): The maximum number of retries for an action agent task.
curriculum_task_type (str): The type of curriculum task to use.
curriculum_agent_lock: The lock for the curriculum agent.
skill_manager_lock: The lock for the skill manager.
chroma_bridge: The ChromaBridge object for controlling the keyboard and mouse.
openai_api_request_timeout (int): The timeout for OpenAI API requests.
ckpt_dir (str): The directory for saving checkpoints.
resume (bool): Whether to resume from the checkpoint.
miniF2F_tasks (mp.Queue): The queue for miniF2F tasks.
"""
# init env
self.rank = rank
self.logger = logging.getLogger(f'prover-{rank}')
self.logger.info(f"lego_prover running in rank {rank}")
self.model_name = model_name
self.env = IsabelleEnv(
logger=self.logger,
isabelle_path=isabelle_path,
server_port=server_port
)
self.action_agent_model_name = model_name
self.tokenizer_encoder = tiktoken.encoding_for_model(
self.action_agent_model_name)
self.ckpt_dir = ckpt_dir
self.temperature = temperature
# init agents
self.action_agent = ActionAgent(
logger=self.logger,
model_name=model_name,
temperature=temperature,
request_timeout=openai_api_request_timeout,
ckpt_dir=ckpt_dir,
)
self.action_agent_task_max_retries = action_agent_task_max_retries
self.curriculum_agent = CurriculumAgent(
logger=self.logger,
ckpt_dir=ckpt_dir,
resume=resume,
miniF2F_tasks=miniF2F_tasks,
curriculum_task_type=curriculum_task_type,
curriculum_agent_lock=curriculum_agent_lock,
)
|
class Prover:
def __init__(
self,
rank: int = None,
isabelle_path: str = None,
server_port: int = 8000,
model_name: str = "gpt-4",
temperature: int = 0,
action_agent_task_max_retries: int = 4,
curriculum_task_type: str = "simple_curriculum",
curriculum_agent_lock = U.WithEmpty(),
skill_manager_lock = U.WithEmpty(),
chroma_bridge = None,
openai_api_request_timeout: int = 6000,
ckpt_dir: str = "ckpt",
resume: bool = False,
miniF2F_tasks: mp.Queue = None,
):
"""
Initializes a new instance of the Prover class.
Args:
rank (int): The rank of the prover process.
isabelle_path (str): The path to the Isabelle directory.
server_port (int): The port number for the server.
model_name (str): The name of the OpenAI model to use.
temperature (int): The temperature for sampling the LLM.
action_agent_task_max_retries (int): The maximum number of retries for an action agent task.
curriculum_task_type (str): The type of curriculum task to use.
curriculum_agent_lock: The lock for the curriculum agent.
skill_manager_lock: The lock for the skill manager.
chroma_bridge: The ChromaBridge object for controlling the keyboard and mouse.
openai_api_request_timeout (int): The timeout for OpenAI API requests.
ckpt_dir (str): The directory for saving checkpoints.
resume (bool): Whether to resume from the checkpoint.
miniF2F_tasks (mp.Queue): The queue for miniF2F tasks.
"""
# init env
self.rank = rank
self.logger = logging.getLogger(f'prover-{rank}')
self.logger.info(f"lego_prover running in rank {rank}")
self.model_name = model_name
self.env = IsabelleEnv(
logger=self.logger,
isabelle_path=isabelle_path,
server_port=server_port
)
self.action_agent_model_name = model_name
self.tokenizer_encoder = tiktoken.encoding_for_model(
self.action_agent_model_name)
self.ckpt_dir = ckpt_dir
self.temperature = temperature
# init agents
self.action_agent = ActionAgent(
logger=self.logger,
model_name=model_name,
temperature=temperature,
request_timeout=openai_api_request_timeout,
ckpt_dir=ckpt_dir,
)
self.action_agent_task_max_retries = action_agent_task_max_retries
self.curriculum_agent = CurriculumAgent(
logger=self.logger,
ckpt_dir=ckpt_dir,
resume=resume,
miniF2F_tasks=miniF2F_tasks,
curriculum_task_type=curriculum_task_type,
curriculum_agent_lock=curriculum_agent_lock,
) | self.skill_manager = SkillManager( | 3 | 2023-10-09 04:23:43+00:00 | 16k |
YingqingHe/ScaleCrafter-ptl | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler | 12,845 | @torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def predict_start_from_z_and_v(self, x_t, t, v):
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if reset_ema: assert exists(ckpt_path)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
if reset_ema:
assert self.use_ema
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def predict_start_from_z_and_v(self, x_t, t, v):
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) | noise = noise_like(x.shape, device, repeat_noise) | 15 | 2023-10-11 10:57:55+00:00 | 16k |
bilibini/Lovely_Image_Downloader | dist/py/Python38/site-packages/charset_normalizer/cd.py | [
{
"identifier": "FREQUENCIES",
"path": "dist/py/Python38/site-packages/charset_normalizer/constant.py",
"snippet": "FREQUENCIES: Dict[str, List[str]] = {\n \"English\": [\n \"e\",\n \"a\",\n \"t\",\n \"i\",\n \"o\",\n \"n\",\n \"s\",\n \"r\",\n ... | import importlib
from codecs import IncrementalDecoder
from collections import Counter
from functools import lru_cache
from typing import Counter as TypeCounter, Dict, List, Optional, Tuple
from .constant import (
FREQUENCIES,
KO_NAMES,
LANGUAGE_SUPPORTED_COUNT,
TOO_SMALL_SEQUENCE,
ZH_NAMES,
)
from .md import is_suspiciously_successive_range
from .models import CoherenceMatches
from .utils import (
is_accentuated,
is_latin,
is_multi_byte_encoding,
is_unicode_range_secondary,
unicode_range,
) | 11,265 | if target_have_accents is False and source_have_accents:
continue
character_count: int = len(language_characters)
character_match_count: int = len(
[c for c in language_characters if c in characters]
)
ratio: float = character_match_count / character_count
if ratio >= 0.2:
languages.append((language, ratio))
languages = sorted(languages, key=lambda x: x[1], reverse=True)
return [compatible_language[0] for compatible_language in languages]
def characters_popularity_compare(
language: str, ordered_characters: List[str]
) -> float:
"""
Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
"""
if language not in FREQUENCIES:
raise ValueError("{} not available".format(language))
character_approved_count: int = 0
FREQUENCIES_language_set = set(FREQUENCIES[language])
ordered_characters_count: int = len(ordered_characters)
target_language_characters_count: int = len(FREQUENCIES[language])
large_alphabet: bool = target_language_characters_count > 26
for character, character_rank in zip(
ordered_characters, range(0, ordered_characters_count)
):
if character not in FREQUENCIES_language_set:
continue
character_rank_in_language: int = FREQUENCIES[language].index(character)
expected_projection_ratio: float = (
target_language_characters_count / ordered_characters_count
)
character_rank_projection: int = int(character_rank * expected_projection_ratio)
if (
large_alphabet is False
and abs(character_rank_projection - character_rank_in_language) > 4
):
continue
if (
large_alphabet is True
and abs(character_rank_projection - character_rank_in_language)
< target_language_characters_count / 3
):
character_approved_count += 1
continue
characters_before_source: List[str] = FREQUENCIES[language][
0:character_rank_in_language
]
characters_after_source: List[str] = FREQUENCIES[language][
character_rank_in_language:
]
characters_before: List[str] = ordered_characters[0:character_rank]
characters_after: List[str] = ordered_characters[character_rank:]
before_match_count: int = len(
set(characters_before) & set(characters_before_source)
)
after_match_count: int = len(
set(characters_after) & set(characters_after_source)
)
if len(characters_before_source) == 0 and before_match_count <= 4:
character_approved_count += 1
continue
if len(characters_after_source) == 0 and after_match_count <= 4:
character_approved_count += 1
continue
if (
before_match_count / len(characters_before_source) >= 0.4
or after_match_count / len(characters_after_source) >= 0.4
):
character_approved_count += 1
continue
return character_approved_count / len(ordered_characters)
def alpha_unicode_split(decoded_sequence: str) -> List[str]:
"""
Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
One containing the latin letters and the other hebrew.
"""
layers: Dict[str, str] = {}
for character in decoded_sequence:
if character.isalpha() is False:
continue
character_range: Optional[str] = unicode_range(character)
if character_range is None:
continue
layer_target_range: Optional[str] = None
for discovered_range in layers:
if (
|
def encoding_unicode_range(iana_name: str) -> List[str]:
"""
Return associated unicode ranges in a single byte code page.
"""
if is_multi_byte_encoding(iana_name):
raise IOError("Function not supported on multi-byte code page")
decoder = importlib.import_module(
"encodings.{}".format(iana_name)
).IncrementalDecoder
p: IncrementalDecoder = decoder(errors="ignore")
seen_ranges: Dict[str, int] = {}
character_count: int = 0
for i in range(0x40, 0xFF):
chunk: str = p.decode(bytes([i]))
if chunk:
character_range: Optional[str] = unicode_range(chunk)
if character_range is None:
continue
if is_unicode_range_secondary(character_range) is False:
if character_range not in seen_ranges:
seen_ranges[character_range] = 0
seen_ranges[character_range] += 1
character_count += 1
return sorted(
[
character_range
for character_range in seen_ranges
if seen_ranges[character_range] / character_count >= 0.15
]
)
def unicode_range_languages(primary_range: str) -> List[str]:
"""
Return inferred languages used with a unicode range.
"""
languages: List[str] = []
for language, characters in FREQUENCIES.items():
for character in characters:
if unicode_range(character) == primary_range:
languages.append(language)
break
return languages
@lru_cache()
def encoding_languages(iana_name: str) -> List[str]:
"""
Single-byte encoding language association. Some code page are heavily linked to particular language(s).
This function does the correspondence.
"""
unicode_ranges: List[str] = encoding_unicode_range(iana_name)
primary_range: Optional[str] = None
for specified_range in unicode_ranges:
if "Latin" not in specified_range:
primary_range = specified_range
break
if primary_range is None:
return ["Latin Based"]
return unicode_range_languages(primary_range)
@lru_cache()
def mb_encoding_languages(iana_name: str) -> List[str]:
"""
Multi-byte encoding language association. Some code page are heavily linked to particular language(s).
This function does the correspondence.
"""
if (
iana_name.startswith("shift_")
or iana_name.startswith("iso2022_jp")
or iana_name.startswith("euc_j")
or iana_name == "cp932"
):
return ["Japanese"]
if iana_name.startswith("gb") or iana_name in ZH_NAMES:
return ["Chinese"]
if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES:
return ["Korean"]
return []
@lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT)
def get_target_features(language: str) -> Tuple[bool, bool]:
"""
Determine main aspects from a supported language if it contains accents and if is pure Latin.
"""
target_have_accents: bool = False
target_pure_latin: bool = True
for character in FREQUENCIES[language]:
if not target_have_accents and is_accentuated(character):
target_have_accents = True
if target_pure_latin and is_latin(character) is False:
target_pure_latin = False
return target_have_accents, target_pure_latin
def alphabet_languages(
characters: List[str], ignore_non_latin: bool = False
) -> List[str]:
"""
Return associated languages associated to given characters.
"""
languages: List[Tuple[str, float]] = []
source_have_accents = any(is_accentuated(character) for character in characters)
for language, language_characters in FREQUENCIES.items():
target_have_accents, target_pure_latin = get_target_features(language)
if ignore_non_latin and target_pure_latin is False:
continue
if target_have_accents is False and source_have_accents:
continue
character_count: int = len(language_characters)
character_match_count: int = len(
[c for c in language_characters if c in characters]
)
ratio: float = character_match_count / character_count
if ratio >= 0.2:
languages.append((language, ratio))
languages = sorted(languages, key=lambda x: x[1], reverse=True)
return [compatible_language[0] for compatible_language in languages]
def characters_popularity_compare(
language: str, ordered_characters: List[str]
) -> float:
"""
Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
"""
if language not in FREQUENCIES:
raise ValueError("{} not available".format(language))
character_approved_count: int = 0
FREQUENCIES_language_set = set(FREQUENCIES[language])
ordered_characters_count: int = len(ordered_characters)
target_language_characters_count: int = len(FREQUENCIES[language])
large_alphabet: bool = target_language_characters_count > 26
for character, character_rank in zip(
ordered_characters, range(0, ordered_characters_count)
):
if character not in FREQUENCIES_language_set:
continue
character_rank_in_language: int = FREQUENCIES[language].index(character)
expected_projection_ratio: float = (
target_language_characters_count / ordered_characters_count
)
character_rank_projection: int = int(character_rank * expected_projection_ratio)
if (
large_alphabet is False
and abs(character_rank_projection - character_rank_in_language) > 4
):
continue
if (
large_alphabet is True
and abs(character_rank_projection - character_rank_in_language)
< target_language_characters_count / 3
):
character_approved_count += 1
continue
characters_before_source: List[str] = FREQUENCIES[language][
0:character_rank_in_language
]
characters_after_source: List[str] = FREQUENCIES[language][
character_rank_in_language:
]
characters_before: List[str] = ordered_characters[0:character_rank]
characters_after: List[str] = ordered_characters[character_rank:]
before_match_count: int = len(
set(characters_before) & set(characters_before_source)
)
after_match_count: int = len(
set(characters_after) & set(characters_after_source)
)
if len(characters_before_source) == 0 and before_match_count <= 4:
character_approved_count += 1
continue
if len(characters_after_source) == 0 and after_match_count <= 4:
character_approved_count += 1
continue
if (
before_match_count / len(characters_before_source) >= 0.4
or after_match_count / len(characters_after_source) >= 0.4
):
character_approved_count += 1
continue
return character_approved_count / len(ordered_characters)
def alpha_unicode_split(decoded_sequence: str) -> List[str]:
"""
Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
One containing the latin letters and the other hebrew.
"""
layers: Dict[str, str] = {}
for character in decoded_sequence:
if character.isalpha() is False:
continue
character_range: Optional[str] = unicode_range(character)
if character_range is None:
continue
layer_target_range: Optional[str] = None
for discovered_range in layers:
if ( | is_suspiciously_successive_range(discovered_range, character_range) | 5 | 2023-10-11 09:08:57+00:00 | 16k |
MTgeophysics/mtpy-v2 | mtpy/modeling/modem/residual.py | [
{
"identifier": "Data",
"path": "mtpy/modeling/modem/data.py",
"snippet": "class Data:\n \"\"\"\n Data will read and write .dat files for ModEM and convert a WS data file\n to ModEM format.\n\n ..note: :: the data is interpolated onto the given periods such that all\n stations ... | from pathlib import Path
from .data import Data
from mtpy.modeling.plots import PlotRMS
import numpy as np
import pandas as pd | 14,083 | """
==================
ModEM
==================
residuals class to contain RMS information
revised by JP 2017
revised by AK 2017 to bring across functionality from ak branch
"""
# =============================================================================
# Imports
# =============================================================================
# =============================================================================
| """
==================
ModEM
==================
residuals class to contain RMS information
revised by JP 2017
revised by AK 2017 to bring across functionality from ak branch
"""
# =============================================================================
# Imports
# =============================================================================
# =============================================================================
| class Residual(Data): | 0 | 2023-10-11 22:24:50+00:00 | 16k |
Jacoo-ai/HIC-Yolov5 | detect.py | [
{
"identifier": "attempt_load",
"path": "models/experimental.py",
"snippet": "def attempt_load(weights, map_location=None, inplace=True, fuse=True):\n from models.yolo import Detect, Model\n\n # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a\n model = Ens... | import argparse
import os
import sys
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import onnxruntime
import tensorflow as tf
from pathlib import Path
from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
strip_optimizer, xyxy2xywh
from utils.plots import Annotator, colors
from utils.torch_utils import load_classifier, select_device, time_sync | 11,144 | if len(img.shape) == 3:
img = img[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
if pt:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(img, augment=augment, visualize=visualize)[0]
elif onnx:
if dnn:
net.setInput(img)
pred = torch.tensor(net.forward())
else:
pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
else: # tensorflow model (tflite, pb, saved_model)
imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy
if pb:
pred = frozen_func(x=tf.constant(imn)).numpy()
elif saved_model:
pred = model(imn, training=False).numpy()
elif tflite:
if int8:
scale, zero_point = input_details[0]['quantization']
imn = (imn / scale + zero_point).astype(np.uint8) # de-scale
interpreter.set_tensor(input_details[0]['index'], imn)
interpreter.invoke()
pred = interpreter.get_tensor(output_details[0]['index'])
if int8:
scale, zero_point = output_details[0]['quantization']
pred = (pred.astype(np.float32) - zero_point) * scale # re-scale
pred[..., 0] *= imgsz[1] # x
pred[..., 1] *= imgsz[0] # y
pred[..., 2] *= imgsz[1] # w
pred[..., 3] *= imgsz[0] # h
pred = torch.tensor(pred)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.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 boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# 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 *xyxy, 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
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # 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 bbox to image
c = int(cls) # integer class
# label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
label = None
annotator.box_label(xyxy, label, color=colors(c, True))
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Print time (inference-only)
print(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
print(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 --source path/to/img.jpg --weights yolov5s.pt --img 640
"""
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 / 'yolov5m.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
imgsz=640, # inference size (pixels)
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
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# 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
# Initialize
set_logging()
device = select_device(device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# Load model
w = str(weights[0] if isinstance(weights, list) else weights)
classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']
check_suffix(w, suffixes) # check weights have acceptable suffix
pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes) # backend booleans
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
if pt:
model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
stride = int(model.stride.max()) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
if half:
model.half() # to FP16
if classify: # second-stage classifier
modelc = load_classifier(name='resnet50', n=2) # initialize
modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
elif onnx:
if dnn:
# check_requirements(('opencv-python>=4.5.4',))
net = cv2.dnn.readNetFromONNX(w)
else:
check_requirements(('onnx', 'onnxruntime'))
session = onnxruntime.InferenceSession(w, None)
else: # TensorFlow models
check_requirements(('tensorflow>=2.4.1',))
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
def wrap_frozen_graph(gd, inputs, outputs):
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
tf.nest.map_structure(x.graph.as_graph_element, outputs))
graph_def = tf.Graph().as_graph_def()
graph_def.ParseFromString(open(w, 'rb').read())
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
elif saved_model:
model = tf.keras.models.load_model(w)
elif tflite:
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details() # outputs
int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model
imgsz = check_img_size(imgsz, s=stride) # check image size
# 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
if pt and device.type != 'cpu':
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once
dt, seen = [0.0, 0.0, 0.0], 0
for path, img, im0s, vid_cap in dataset:
t1 = time_sync()
if onnx:
img = img.astype('float32')
else:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img = img / 255.0 # 0 - 255 to 0.0 - 1.0
if len(img.shape) == 3:
img = img[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
if pt:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(img, augment=augment, visualize=visualize)[0]
elif onnx:
if dnn:
net.setInput(img)
pred = torch.tensor(net.forward())
else:
pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
else: # tensorflow model (tflite, pb, saved_model)
imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy
if pb:
pred = frozen_func(x=tf.constant(imn)).numpy()
elif saved_model:
pred = model(imn, training=False).numpy()
elif tflite:
if int8:
scale, zero_point = input_details[0]['quantization']
imn = (imn / scale + zero_point).astype(np.uint8) # de-scale
interpreter.set_tensor(input_details[0]['index'], imn)
interpreter.invoke()
pred = interpreter.get_tensor(output_details[0]['index'])
if int8:
scale, zero_point = output_details[0]['quantization']
pred = (pred.astype(np.float32) - zero_point) * scale # re-scale
pred[..., 0] *= imgsz[1] # x
pred[..., 1] *= imgsz[0] # y
pred[..., 2] *= imgsz[1] # w
pred[..., 3] *= imgsz[0] # h
pred = torch.tensor(pred)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.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 boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# 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 *xyxy, 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
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # 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 bbox to image
c = int(cls) # integer class
# label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
label = None
annotator.box_label(xyxy, label, color=colors(c, True))
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Print time (inference-only)
print(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
print(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 '' | print(f"Results saved to {colorstr('bold', save_dir)}{s}") | 8 | 2023-10-12 08:52:01+00:00 | 16k |
OmicsML/scDiff | scdiff/ext/gears/gears.py | [
{
"identifier": "GEARS_Model",
"path": "scdiff/ext/gears/model.py",
"snippet": "class GEARS_Model(torch.nn.Module):\n \"\"\"\n GEARS model\n\n \"\"\"\n\n def __init__(self, args):\n \"\"\"\n :param args: arguments dictionary\n \"\"\"\n\n super(GEARS_Model, self)._... | from copy import deepcopy
from torch.optim.lr_scheduler import StepLR
from .model import GEARS_Model
from .inference import (
compute_metrics,
deeper_analysis,
evaluate,
non_dropout_analysis,
)
from .utils import (
GeneSimNetwork,
create_cell_graph_dataset_for_prediction,
get_GI_genes_idx,
get_GI_params,
get_mean_control,
get_similarity_network,
loss_fct,
print_sys,
uncertainty_loss_fct,
)
from collections import OrderedDict
from torch_geometric.data import DataLoader
import os
import pickle
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import wandb
import seaborn as sns
import matplotlib.pyplot as plt | 14,147 | path: str
path to save the model
Returns
-------
None
"""
if not os.path.exists(path):
os.mkdir(path)
if self.config is None:
raise ValueError('No model is initialized...')
with open(os.path.join(path, 'config.pkl'), 'wb') as f:
pickle.dump(self.config, f)
torch.save(self.best_model.state_dict(), os.path.join(path, 'model.pt'))
def predict(self, pert_list):
"""
Predict the transcriptome given a list of genes/gene combinations being
perturbed
Parameters
----------
pert_list: list
list of genes/gene combiantions to be perturbed
Returns
-------
results_pred: dict
dictionary of predicted transcriptome
results_logvar: dict
dictionary of uncertainty score
"""
# given a list of single/combo genes, return the transcriptome
# if uncertainty mode is on, also return uncertainty score.
self.ctrl_adata = self.adata[self.adata.obs['condition'] == 'ctrl']
for pert in pert_list:
for i in pert:
if i not in self.pert_list:
raise ValueError(i + " is not in the perturbation graph. "
"Please select from GEARS.pert_list!")
if self.config['uncertainty']:
results_logvar = {}
self.best_model = self.best_model.to(self.device)
self.best_model.eval()
results_pred = {}
results_logvar_sum = {}
for pert in pert_list:
try:
# If prediction is already saved, then skip inference
results_pred['_'.join(pert)] = self.saved_pred['_'.join(pert)]
if self.config['uncertainty']:
results_logvar_sum['_'.join(pert)] = self.saved_logvar_sum['_'.join(pert)]
continue
except:
pass
cg = create_cell_graph_dataset_for_prediction(pert, self.ctrl_adata,
self.pert_list, self.device)
loader = DataLoader(cg, 300, shuffle=False)
batch = next(iter(loader))
batch.to(self.device)
with torch.no_grad():
if self.config['uncertainty']:
p, unc = self.best_model(batch)
results_logvar['_'.join(pert)] = np.mean(unc.detach().cpu().numpy(), axis=0)
results_logvar_sum['_'.join(pert)] = np.exp(-np.mean(results_logvar['_'.join(pert)]))
else:
p = self.best_model(batch)
results_pred['_'.join(pert)] = np.mean(p.detach().cpu().numpy(), axis=0)
self.saved_pred.update(results_pred)
if self.config['uncertainty']:
self.saved_logvar_sum.update(results_logvar_sum)
return results_pred, results_logvar_sum
else:
return results_pred
def GI_predict(self, combo, GI_genes_file='./genes_with_hi_mean.npy'):
"""
Predict the GI scores following perturbation of a given gene combination
Parameters
----------
combo: list
list of genes to be perturbed
GI_genes_file: str
path to the file containing genes with high mean expression
Returns
-------
GI scores for the given combinatorial perturbation based on GEARS
predictions
"""
# if uncertainty mode is on, also return uncertainty score.
try:
# If prediction is already saved, then skip inference
pred = {}
pred[combo[0]] = self.saved_pred[combo[0]]
pred[combo[1]] = self.saved_pred[combo[1]]
pred['_'.join(combo)] = self.saved_pred['_'.join(combo)]
except:
if self.config['uncertainty']:
pred = self.predict([[combo[0]], [combo[1]], combo])[0]
else:
pred = self.predict([[combo[0]], [combo[1]], combo])
|
warnings.filterwarnings("ignore")
class GEARS:
"""
GEARS base model class
"""
def __init__(self, pert_data,
device='cuda',
weight_bias_track=False,
proj_name='GEARS',
exp_name='GEARS'):
"""
Initialize GEARS model
Parameters
----------
pert_data: PertData object
dataloader for perturbation data
device: str
Device to run the model on. Default: 'cuda'
weight_bias_track: bool
Whether to track performance on wandb. Default: False
proj_name: str
Project name for wandb. Default: 'GEARS'
exp_name: str
Experiment name for wandb. Default: 'GEARS'
Returns
-------
None
"""
self.weight_bias_track = weight_bias_track
if self.weight_bias_track:
wandb.init(project=proj_name, name=exp_name)
self.wandb = wandb
else:
self.wandb = None
self.device = device
self.config = None
self.dataloader = pert_data.dataloader
self.adata = pert_data.adata
self.node_map = pert_data.node_map
self.node_map_pert = pert_data.node_map_pert
self.data_path = pert_data.data_path
self.dataset_name = pert_data.dataset_name
self.split = pert_data.split
self.seed = pert_data.seed
self.train_gene_set_size = pert_data.train_gene_set_size
self.set2conditions = pert_data.set2conditions
self.subgroup = pert_data.subgroup
self.gene_list = pert_data.gene_names.values.tolist()
self.pert_list = pert_data.pert_names.tolist()
self.num_genes = len(self.gene_list)
self.num_perts = len(self.pert_list)
self.default_pert_graph = pert_data.default_pert_graph
self.saved_pred = {}
self.saved_logvar_sum = {}
self.ctrl_expression = torch.tensor(
np.mean(self.adata.X[self.adata.obs.condition == 'ctrl'],
axis=0)).reshape(-1, ).to(self.device)
pert_full_id2pert = dict(self.adata.obs[['condition_name', 'condition']].values)
self.dict_filter = {pert_full_id2pert[i]: j for i, j in
self.adata.uns['non_zeros_gene_idx'].items() if
i in pert_full_id2pert}
self.ctrl_adata = self.adata[self.adata.obs['condition'] == 'ctrl']
gene_dict = {g: i for i, g in enumerate(self.gene_list)}
self.pert2gene = {p: gene_dict[pert] for p, pert in
enumerate(self.pert_list) if pert in self.gene_list}
def tunable_parameters(self):
"""
Return the tunable parameters of the model
Returns
-------
dict
Tunable parameters of the model
"""
return {'hidden_size': 'hidden dimension, default 64',
'num_go_gnn_layers': 'number of GNN layers for GO graph, default 1',
'num_gene_gnn_layers': 'number of GNN layers for co-expression gene graph, default 1',
'decoder_hidden_size': 'hidden dimension for gene-specific decoder, default 16',
'num_similar_genes_go_graph': 'number of maximum similar K genes in the GO graph, default 20',
'num_similar_genes_co_express_graph': 'number of maximum similar K genes in the co expression graph, default 20',
'coexpress_threshold': 'pearson correlation threshold when constructing coexpression graph, default 0.4',
'uncertainty': 'whether or not to turn on uncertainty mode, default False',
'uncertainty_reg': 'regularization term to balance uncertainty loss and prediction loss, default 1',
'direction_lambda': 'regularization term to balance direction loss and prediction loss, default 1'
}
def model_initialize(self, hidden_size=64,
num_go_gnn_layers=1,
num_gene_gnn_layers=1,
decoder_hidden_size=16,
num_similar_genes_go_graph=20,
num_similar_genes_co_express_graph=20,
coexpress_threshold=0.4,
uncertainty=False,
uncertainty_reg=1,
direction_lambda=1e-1,
G_go=None,
G_go_weight=None,
G_coexpress=None,
G_coexpress_weight=None,
no_perturb=False,
):
"""
Initialize the model
Parameters
----------
hidden_size: int
hidden dimension, default 64
num_go_gnn_layers: int
number of GNN layers for GO graph, default 1
num_gene_gnn_layers: int
number of GNN layers for co-expression gene graph, default 1
decoder_hidden_size: int
hidden dimension for gene-specific decoder, default 16
num_similar_genes_go_graph: int
number of maximum similar K genes in the GO graph, default 20
num_similar_genes_co_express_graph: int
number of maximum similar K genes in the co expression graph, default 20
coexpress_threshold: float
pearson correlation threshold when constructing coexpression graph, default 0.4
uncertainty: bool
whether or not to turn on uncertainty mode, default False
uncertainty_reg: float
regularization term to balance uncertainty loss and prediction loss, default 1
direction_lambda: float
regularization term to balance direction loss and prediction loss, default 1
G_go: scipy.sparse.csr_matrix
GO graph, default None
G_go_weight: scipy.sparse.csr_matrix
GO graph edge weights, default None
G_coexpress: scipy.sparse.csr_matrix
co-expression graph, default None
G_coexpress_weight: scipy.sparse.csr_matrix
co-expression graph edge weights, default None
no_perturb: bool
predict no perturbation condition, default False
Returns
-------
None
"""
self.config = {'hidden_size': hidden_size,
'num_go_gnn_layers': num_go_gnn_layers,
'num_gene_gnn_layers': num_gene_gnn_layers,
'decoder_hidden_size': decoder_hidden_size,
'num_similar_genes_go_graph': num_similar_genes_go_graph,
'num_similar_genes_co_express_graph': num_similar_genes_co_express_graph,
'coexpress_threshold': coexpress_threshold,
'uncertainty': uncertainty,
'uncertainty_reg': uncertainty_reg,
'direction_lambda': direction_lambda,
'G_go': G_go,
'G_go_weight': G_go_weight,
'G_coexpress': G_coexpress,
'G_coexpress_weight': G_coexpress_weight,
'device': self.device,
'num_genes': self.num_genes,
'num_perts': self.num_perts,
'no_perturb': no_perturb
}
if self.wandb:
self.wandb.config.update(self.config)
if self.config['G_coexpress'] is None:
# calculating co expression similarity graph
edge_list = get_similarity_network(network_type='co-express',
adata=self.adata,
threshold=coexpress_threshold,
k=num_similar_genes_co_express_graph,
data_path=self.data_path,
data_name=self.dataset_name,
split=self.split, seed=self.seed,
train_gene_set_size=self.train_gene_set_size,
set2conditions=self.set2conditions)
sim_network = GeneSimNetwork(edge_list, self.gene_list, node_map=self.node_map)
self.config['G_coexpress'] = sim_network.edge_index
self.config['G_coexpress_weight'] = sim_network.edge_weight
if self.config['G_go'] is None:
# calculating gene ontology similarity graph
edge_list = get_similarity_network(network_type='go',
adata=self.adata,
threshold=coexpress_threshold,
k=num_similar_genes_go_graph,
pert_list=self.pert_list,
data_path=self.data_path,
data_name=self.dataset_name,
split=self.split, seed=self.seed,
train_gene_set_size=self.train_gene_set_size,
set2conditions=self.set2conditions,
default_pert_graph=self.default_pert_graph)
sim_network = GeneSimNetwork(edge_list, self.pert_list, node_map=self.node_map_pert)
self.config['G_go'] = sim_network.edge_index
self.config['G_go_weight'] = sim_network.edge_weight
self.model = GEARS_Model(self.config).to(self.device)
self.best_model = deepcopy(self.model)
def load_pretrained(self, path):
"""
Load pretrained model
Parameters
----------
path: str
path to the pretrained model
Returns
-------
None
"""
with open(os.path.join(path, 'config.pkl'), 'rb') as f:
config = pickle.load(f)
del config['device'], config['num_genes'], config['num_perts']
self.model_initialize(**config)
self.config = config
state_dict = torch.load(os.path.join(path, 'model.pt'), map_location=torch.device('cpu'))
if next(iter(state_dict))[:7] == 'module.':
# the pretrained model is from data-parallel module
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
state_dict = new_state_dict
self.model.load_state_dict(state_dict)
self.model = self.model.to(self.device)
self.best_model = self.model
def save_model(self, path):
"""
Save the model
Parameters
----------
path: str
path to save the model
Returns
-------
None
"""
if not os.path.exists(path):
os.mkdir(path)
if self.config is None:
raise ValueError('No model is initialized...')
with open(os.path.join(path, 'config.pkl'), 'wb') as f:
pickle.dump(self.config, f)
torch.save(self.best_model.state_dict(), os.path.join(path, 'model.pt'))
def predict(self, pert_list):
"""
Predict the transcriptome given a list of genes/gene combinations being
perturbed
Parameters
----------
pert_list: list
list of genes/gene combiantions to be perturbed
Returns
-------
results_pred: dict
dictionary of predicted transcriptome
results_logvar: dict
dictionary of uncertainty score
"""
# given a list of single/combo genes, return the transcriptome
# if uncertainty mode is on, also return uncertainty score.
self.ctrl_adata = self.adata[self.adata.obs['condition'] == 'ctrl']
for pert in pert_list:
for i in pert:
if i not in self.pert_list:
raise ValueError(i + " is not in the perturbation graph. "
"Please select from GEARS.pert_list!")
if self.config['uncertainty']:
results_logvar = {}
self.best_model = self.best_model.to(self.device)
self.best_model.eval()
results_pred = {}
results_logvar_sum = {}
for pert in pert_list:
try:
# If prediction is already saved, then skip inference
results_pred['_'.join(pert)] = self.saved_pred['_'.join(pert)]
if self.config['uncertainty']:
results_logvar_sum['_'.join(pert)] = self.saved_logvar_sum['_'.join(pert)]
continue
except:
pass
cg = create_cell_graph_dataset_for_prediction(pert, self.ctrl_adata,
self.pert_list, self.device)
loader = DataLoader(cg, 300, shuffle=False)
batch = next(iter(loader))
batch.to(self.device)
with torch.no_grad():
if self.config['uncertainty']:
p, unc = self.best_model(batch)
results_logvar['_'.join(pert)] = np.mean(unc.detach().cpu().numpy(), axis=0)
results_logvar_sum['_'.join(pert)] = np.exp(-np.mean(results_logvar['_'.join(pert)]))
else:
p = self.best_model(batch)
results_pred['_'.join(pert)] = np.mean(p.detach().cpu().numpy(), axis=0)
self.saved_pred.update(results_pred)
if self.config['uncertainty']:
self.saved_logvar_sum.update(results_logvar_sum)
return results_pred, results_logvar_sum
else:
return results_pred
def GI_predict(self, combo, GI_genes_file='./genes_with_hi_mean.npy'):
"""
Predict the GI scores following perturbation of a given gene combination
Parameters
----------
combo: list
list of genes to be perturbed
GI_genes_file: str
path to the file containing genes with high mean expression
Returns
-------
GI scores for the given combinatorial perturbation based on GEARS
predictions
"""
# if uncertainty mode is on, also return uncertainty score.
try:
# If prediction is already saved, then skip inference
pred = {}
pred[combo[0]] = self.saved_pred[combo[0]]
pred[combo[1]] = self.saved_pred[combo[1]]
pred['_'.join(combo)] = self.saved_pred['_'.join(combo)]
except:
if self.config['uncertainty']:
pred = self.predict([[combo[0]], [combo[1]], combo])[0]
else:
pred = self.predict([[combo[0]], [combo[1]], combo])
| mean_control = get_mean_control(self.adata).values | 9 | 2023-10-13 14:20:34+00:00 | 16k |
weavel-ai/promptmodel-python | promptmodel/chat_model.py | [
{
"identifier": "DevClient",
"path": "promptmodel/dev_app.py",
"snippet": "class DevClient:\n \"\"\"DevClient main class\"\"\"\n\n def __init__(self):\n self.function_models: List[FunctionModelInterface] = []\n self.chat_models: List[ChatModelInterface] = []\n\n def register(self,... | from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Coroutine, Union
from uuid import uuid4
from litellm import ModelResponse
from promptmodel import DevClient
from promptmodel.llms.llm_proxy import LLMProxy
from promptmodel.utils import logger
from promptmodel.utils.config_utils import (
read_config,
upsert_config,
check_connection_status_decorator,
)
from promptmodel.utils.async_utils import run_async_in_sync
from promptmodel.types.response import LLMStreamResponse, LLMResponse, ChatModelConfig
from promptmodel.types.enums import InstanceType
from promptmodel.types.request import ChatLogRequest
from promptmodel.apis.base import AsyncAPIClient
import sys | 12,995 | message_logs=message_logs,
)
@check_connection_status_decorator
def add_messages(
self,
new_messages: List[Dict[str, Any]],
metadata_list: List[Optional[Dict]] = [],
*args,
**kwargs,
) -> None:
"""Add messages to the chat model.
Args:
new_messages (List[Dict[str, Any]]): list of messages. Each message is a dict with 'role', 'content', and 'function_call'.
"""
# Save messages to Cloud DB
log_uuid_list = [str(uuid4()) for _ in range(len(new_messages))]
run_async_in_sync(
self.llm_proxy._async_chat_log_to_cloud(
session_uuid=str(self.session_uuid),
version_uuid=None,
chat_log_request_list=[
ChatLogRequest(**{"message": message, "uuid": str(uuid4())})
for message in new_messages
],
)
)
self.recent_log_uuid = log_uuid_list[-1]
@check_connection_status_decorator
def run(
self,
functions: Optional[List[Dict[str, Any]]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
stream: Optional[bool] = False,
*args,
**kwargs,
) -> LLMResponse:
"""Run FunctionModel. It does not raise error.
Args:
functions (List[Dict[str, Any]], optional): list of functions to run. Defaults to None.
Returns:
LLMResponse: response from the promptmodel. you can find raw output in response.raw_output or response.api_response['choices'][0]['message']['content'].
Error:
It does not raise error. If error occurs, you can check error in response.error and error_log in response.error_log.
"""
if stream:
def gen():
cache: Optional[LLMStreamResponse] = None
for item in self.llm_proxy.chat_stream(
self.session_uuid, functions, tools
):
yield item
cache: LLMStreamResponse = item
if cache:
self.recent_log_uuid = cache.pm_detail.log_uuid
return gen()
else:
res = self.llm_proxy.chat_run(self.session_uuid, functions, tools)
self.recent_log_uuid = res.pm_detail.log_uuid
return res
# return self.llm_proxy.chat_run(self.session_uuid, functions, self.api_key)
@check_connection_status_decorator
async def arun(
self,
functions: Optional[List[Dict[str, Any]]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
stream: Optional[bool] = False,
*args,
**kwargs,
) -> LLMResponse:
"""Async run FunctionModel. It does not raise error.
Args:
functions (List[Dict[str, Any]], optional): list of functions to run. Defaults to None.
Returns:
LLMResponse: response from the promptmodel. you can find raw output in response.raw_output or response.api_response['choices'][0]['message']['content'].
Error:
It does not raise error. If error occurs, you can check error in response.error and error_log in response.error_log.
"""
if stream:
async def async_gen():
cache: Optional[LLMStreamResponse] = None
async for item in self.llm_proxy.chat_astream(
self.session_uuid, functions, tools
):
yield item
cache: LLMStreamResponse = item
if cache:
self.recent_log_uuid = cache.pm_detail.log_uuid
return async_gen()
else:
res: LLMResponse = await self.llm_proxy.chat_arun(
self.session_uuid, functions
)
self.recent_log_uuid = res.pm_detail.log_uuid
return res
# return await self.llm_proxy.chat_arun(
# self.session_uuid, functions, self.api_key
# )
@check_connection_status_decorator
async def log_score_to_session(
self, score: Optional[Dict[str, Any]] = {}, *args, **kwargs
):
try:
| from __future__ import annotations
class RegisteringMeta(type):
def __call__(cls, *args, **kwargs):
instance: ChatModel = super().__call__(*args, **kwargs)
# Find the global client instance in the current context
client = cls.find_client_instance()
if client is not None:
client.register_chat_model(instance.name)
return instance
@staticmethod
def find_client_instance():
# Get the current frame
frame = sys._getframe(2)
# Get global variables in the current frame
global_vars = frame.f_globals
# Find an instance of Client among global variables
for var_name, var_val in global_vars.items():
if isinstance(var_val, DevClient):
return var_val
return None
class ChatModel(metaclass=RegisteringMeta):
"""
Args:
name (_type_): _description_
version (Optional[ Union[str, int] ], optional): Choose which FunctionModel version to use. Defaults to "deploy". It can be "deploy", "latest", or version number.
api_key (Optional[str], optional): API key for the LLM. Defaults to None. If None, use api_key in .env file.
"""
def __init__(
self,
name,
session_uuid: str = None,
version: Optional[Union[str, int]] = "deploy",
api_key: Optional[str] = None,
):
self.name = name
self.api_key = api_key
self.llm_proxy = LLMProxy(name, version)
self.version = version
self.recent_log_uuid = None
if session_uuid is None:
self.session_uuid = str(uuid4())
instruction, version_details, chat_logs = run_async_in_sync(
LLMProxy.fetch_chat_model(self.name, None, version)
)
config = read_config()
if (
"connection" in config
and "initializing" in config["connection"]
and config["connection"]["initializing"] == True
):
return
elif (
"connection" in config
and "reloading" in config["connection"]
and config["connection"]["reloading"] == True
):
return
else:
run_async_in_sync(
self.llm_proxy._async_make_session_cloud(
self.session_uuid,
version_details["uuid"],
)
)
else:
self.session_uuid = session_uuid
@check_connection_status_decorator
def get_config(
self,
*args,
**kwargs,
) -> ChatModelConfig:
"""Get config for the ChatModel.
It will fetch the published prompt and version config from the Cloud. (It will be saved in cache DB, so there is no extra latency for API call.)
- If you made A/B testing in Web Dashboard, it will fetch the prompt randomly by the A/B testing ratio.
If dev mode is initializing, it will return None
Returns:
ChatModelConfig: config for the ChatModel, which contains prompts and version_detail, message_logs
"""
prompt, version_detail, message_logs = run_async_in_sync(
LLMProxy.fetch_chat_model(self.name, self.session_uuid, self.version)
)
return ChatModelConfig(
system_prompt=prompt,
model=version_detail["model"],
name=self.name,
version_uuid=str(version_detail["uuid"]),
version=version_detail["version"],
message_logs=message_logs,
)
@check_connection_status_decorator
def add_messages(
self,
new_messages: List[Dict[str, Any]],
metadata_list: List[Optional[Dict]] = [],
*args,
**kwargs,
) -> None:
"""Add messages to the chat model.
Args:
new_messages (List[Dict[str, Any]]): list of messages. Each message is a dict with 'role', 'content', and 'function_call'.
"""
# Save messages to Cloud DB
log_uuid_list = [str(uuid4()) for _ in range(len(new_messages))]
run_async_in_sync(
self.llm_proxy._async_chat_log_to_cloud(
session_uuid=str(self.session_uuid),
version_uuid=None,
chat_log_request_list=[
ChatLogRequest(**{"message": message, "uuid": str(uuid4())})
for message in new_messages
],
)
)
self.recent_log_uuid = log_uuid_list[-1]
@check_connection_status_decorator
def run(
self,
functions: Optional[List[Dict[str, Any]]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
stream: Optional[bool] = False,
*args,
**kwargs,
) -> LLMResponse:
"""Run FunctionModel. It does not raise error.
Args:
functions (List[Dict[str, Any]], optional): list of functions to run. Defaults to None.
Returns:
LLMResponse: response from the promptmodel. you can find raw output in response.raw_output or response.api_response['choices'][0]['message']['content'].
Error:
It does not raise error. If error occurs, you can check error in response.error and error_log in response.error_log.
"""
if stream:
def gen():
cache: Optional[LLMStreamResponse] = None
for item in self.llm_proxy.chat_stream(
self.session_uuid, functions, tools
):
yield item
cache: LLMStreamResponse = item
if cache:
self.recent_log_uuid = cache.pm_detail.log_uuid
return gen()
else:
res = self.llm_proxy.chat_run(self.session_uuid, functions, tools)
self.recent_log_uuid = res.pm_detail.log_uuid
return res
# return self.llm_proxy.chat_run(self.session_uuid, functions, self.api_key)
@check_connection_status_decorator
async def arun(
self,
functions: Optional[List[Dict[str, Any]]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
stream: Optional[bool] = False,
*args,
**kwargs,
) -> LLMResponse:
"""Async run FunctionModel. It does not raise error.
Args:
functions (List[Dict[str, Any]], optional): list of functions to run. Defaults to None.
Returns:
LLMResponse: response from the promptmodel. you can find raw output in response.raw_output or response.api_response['choices'][0]['message']['content'].
Error:
It does not raise error. If error occurs, you can check error in response.error and error_log in response.error_log.
"""
if stream:
async def async_gen():
cache: Optional[LLMStreamResponse] = None
async for item in self.llm_proxy.chat_astream(
self.session_uuid, functions, tools
):
yield item
cache: LLMStreamResponse = item
if cache:
self.recent_log_uuid = cache.pm_detail.log_uuid
return async_gen()
else:
res: LLMResponse = await self.llm_proxy.chat_arun(
self.session_uuid, functions
)
self.recent_log_uuid = res.pm_detail.log_uuid
return res
# return await self.llm_proxy.chat_arun(
# self.session_uuid, functions, self.api_key
# )
@check_connection_status_decorator
async def log_score_to_session(
self, score: Optional[Dict[str, Any]] = {}, *args, **kwargs
):
try: | res = await AsyncAPIClient.execute( | 12 | 2023-10-09 03:35:44+00:00 | 16k |
cambridgeltl/ClaPS | algs/genetics.py | [
{
"identifier": "BaseTrainer",
"path": "algs/base_trainer.py",
"snippet": "class BaseTrainer(abc.ABC):\n \"\"\"\n The base trainer class.\n\n Attributes:\n obj_func: the callable function handle for model interfacing.\n logger: an optional logger object.\n bn_calibrator: a ... | import random
import numpy as np
from typing import Any
from .base_trainer import BaseTrainer
from utils.fsc_datasets import PromptedClassificationDataset
from rewards.text_classification_reward import PromptedClassificationReward | 11,416 |
class Genetics:
def __init__(self, crossover_tokenizer, vocab_id):
self.crossover_tokenizer = crossover_tokenizer
self.vocab_id = vocab_id
def mutate(self, x, prob=0.1):
"""
Mutates the input string by replacing tokens with a certain probability.
Args:
x (str): The input string.
prob (float, optional): The probability of replacing each token. Defaults to 0.1.
Returns:
str: The mutated string.
"""
x_list = self.crossover_tokenizer.encode(x)
def pick_another(x_, candidates):
return (
x_
if len(candidates) == 1
else random.choice([v for v in candidates if v != x_])
)
for i, element in enumerate(x_list):
if i == 0 or i == len(x_list) - 1:
continue
if random.random() < prob:
x_list[i] = pick_another(element, self.vocab_id)
out = self.crossover_tokenizer.decode(x_list, skip_special_tokens=True)
return out
def crossover(self, x1, x2):
"""
Performs crossover between two input strings.
Args:
x1 (str): The first input string.
x2 (str): The second input string.
Returns:
str: The crossover result.
"""
def _crossover_helper(v1, v2):
return v1 if random.random() < 0.5 else v2
def _inbalance_helper(v1, v2):
n_tokens = min(len(v1), len(v2))
max_n = max(len(v1), len(v2))
out_token = []
for i in range(n_tokens):
out_token.append(v1[i] if random.random() < 0.5 else v2[i])
for i in range(n_tokens, max_n):
out_token.append(v1[i] if len(v1) > n_tokens else v2[i])
return out_token
x1_tokens = self.crossover_tokenizer.encode(x1)
x2_tokens = self.crossover_tokenizer.encode(x2)
x = _crossover_helper(x1_tokens, x2_tokens)
ret = self.crossover_tokenizer.decode(x, skip_special_tokens=True)
return ret
def random_string(self, length=5):
"""
Generates a random string of a specified length.
Args:
length (int, optional): The length of the random string. Defaults to 5.
Returns:
str: The random string.
"""
choices = self.vocab_id
out = random.choices(choices, k=length)
out = self.crossover_tokenizer.decode(out, skip_special_tokens=True)
return out
def random_extend_pop(self, pop: list, n: int) -> list:
"""
Extends the population with random strings.
Args:
pop (list): The population.
n (int): The number of random strings to generate.
Returns:
list: The extended population.
"""
pop = [p + self.random_string(n) for p in pop]
return pop
class GeneticAlgorithmTrainer(BaseTrainer):
def __init__(
self,
pop_size: int,
mutate_size: int,
crossover_size: int,
epochs: int,
mutate_frac: float,
str_len: int,
stages: int,
n_classes: int,
eval_batch_size: int,
genetics: Genetics,
|
class Genetics:
def __init__(self, crossover_tokenizer, vocab_id):
self.crossover_tokenizer = crossover_tokenizer
self.vocab_id = vocab_id
def mutate(self, x, prob=0.1):
"""
Mutates the input string by replacing tokens with a certain probability.
Args:
x (str): The input string.
prob (float, optional): The probability of replacing each token. Defaults to 0.1.
Returns:
str: The mutated string.
"""
x_list = self.crossover_tokenizer.encode(x)
def pick_another(x_, candidates):
return (
x_
if len(candidates) == 1
else random.choice([v for v in candidates if v != x_])
)
for i, element in enumerate(x_list):
if i == 0 or i == len(x_list) - 1:
continue
if random.random() < prob:
x_list[i] = pick_another(element, self.vocab_id)
out = self.crossover_tokenizer.decode(x_list, skip_special_tokens=True)
return out
def crossover(self, x1, x2):
"""
Performs crossover between two input strings.
Args:
x1 (str): The first input string.
x2 (str): The second input string.
Returns:
str: The crossover result.
"""
def _crossover_helper(v1, v2):
return v1 if random.random() < 0.5 else v2
def _inbalance_helper(v1, v2):
n_tokens = min(len(v1), len(v2))
max_n = max(len(v1), len(v2))
out_token = []
for i in range(n_tokens):
out_token.append(v1[i] if random.random() < 0.5 else v2[i])
for i in range(n_tokens, max_n):
out_token.append(v1[i] if len(v1) > n_tokens else v2[i])
return out_token
x1_tokens = self.crossover_tokenizer.encode(x1)
x2_tokens = self.crossover_tokenizer.encode(x2)
x = _crossover_helper(x1_tokens, x2_tokens)
ret = self.crossover_tokenizer.decode(x, skip_special_tokens=True)
return ret
def random_string(self, length=5):
"""
Generates a random string of a specified length.
Args:
length (int, optional): The length of the random string. Defaults to 5.
Returns:
str: The random string.
"""
choices = self.vocab_id
out = random.choices(choices, k=length)
out = self.crossover_tokenizer.decode(out, skip_special_tokens=True)
return out
def random_extend_pop(self, pop: list, n: int) -> list:
"""
Extends the population with random strings.
Args:
pop (list): The population.
n (int): The number of random strings to generate.
Returns:
list: The extended population.
"""
pop = [p + self.random_string(n) for p in pop]
return pop
class GeneticAlgorithmTrainer(BaseTrainer):
def __init__(
self,
pop_size: int,
mutate_size: int,
crossover_size: int,
epochs: int,
mutate_frac: float,
str_len: int,
stages: int,
n_classes: int,
eval_batch_size: int,
genetics: Genetics, | obj_func: PromptedClassificationReward, | 2 | 2023-10-08 12:39:44+00:00 | 16k |
clessig/atmorep | atmorep/core/atmorep_model.py | [
{
"identifier": "identity",
"path": "atmorep/utils/utils.py",
"snippet": "def identity( func, *args) :\n return func( *args)"
},
{
"identifier": "NetMode",
"path": "atmorep/utils/utils.py",
"snippet": "class NetMode( Enum) :\n indeterminate = 0\n train = 1\n test = 2"
},
{
"i... | import torch
import numpy as np
import code
import atmorep.utils.utils as utils
from atmorep.utils.utils import identity
from atmorep.utils.utils import NetMode
from atmorep.utils.utils import get_model_filename
from atmorep.transformer.transformer_base import prepare_token
from atmorep.transformer.transformer_base import checkpoint_wrapper
from atmorep.datasets.multifield_data_sampler import MultifieldDataSampler
from atmorep.transformer.transformer_encoder import TransformerEncoder
from atmorep.transformer.transformer_decoder import TransformerDecoder
from atmorep.transformer.tail_ensemble import TailEnsemble | 11,801 |
cf = self.cf
self.devices = devices
size_token_info = 6
self.fields_coupling_idx = []
self.fields_index = {}
for ifield, field_info in enumerate(cf.fields) :
self.fields_index[ field_info[0] ] = ifield
# # embedding network for global/auxiliary token infos
# TODO: only for backward compatibility, remove
self.embed_token_info = torch.nn.Linear( cf.size_token_info, cf.size_token_info_net)
torch.nn.init.constant_( self.embed_token_info.weight, 0.0)
self.embeds_token_info = torch.nn.ModuleList()
for ifield, field_info in enumerate( cf.fields) :
self.embeds_token_info.append( torch.nn.Linear( cf.size_token_info, cf.size_token_info_net))
if len(field_info[1]) > 4 and load_pretrained :
# TODO: inconsistent with embeds_token_info -> version that can handle both
# we could imply use the file name: embed_token_info vs embeds_token_info
name = 'AtmoRep' + '_embed_token_info'
mloaded = torch.load( get_model_filename( name, field_info[1][4][0], field_info[1][4][1]))
self.embeds_token_info[-1].load_state_dict( mloaded)
print( 'Loaded embed_token_info from id = {}.'.format( field_info[1][4][0] ) )
else :
# initalization
torch.nn.init.constant_( self.embeds_token_info[-1].weight, 0.0)
self.embeds_token_info[-1].bias.data.fill_(0.0)
# embedding and encoder
self.embeds = torch.nn.ModuleList()
self.encoders = torch.nn.ModuleList()
self.masks = torch.nn.ParameterList()
for field_idx, field_info in enumerate(cf.fields) :
# learnabl class token
if cf.learnable_mask :
mask = torch.nn.Parameter( 0.1 * torch.randn( np.prod( field_info[4]), requires_grad=True))
self.masks.append( mask.to(devices[0]))
else :
self.masks.append( None)
# encoder
self.encoders.append( TransformerEncoder( cf, field_idx, True).create())
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained :
self.load_block( field_info, 'encoder', self.encoders[-1])
self.embeds.append( self.encoders[-1].embed)
# indices of coupled fields for efficient access in forward
self.fields_coupling_idx.append( [field_idx])
for field_coupled in field_info[1][2] :
if 'axial' in cf.encoder_att_type :
self.fields_coupling_idx[field_idx].append( self.fields_index[field_coupled] )
else :
for _ in range(cf.coupling_num_heads_per_field) :
self.fields_coupling_idx[field_idx].append( self.fields_index[field_coupled] )
# decoder
self.decoders = torch.nn.ModuleList()
self.field_pred_idxs = []
for field in cf.fields_prediction :
for ifield, field_info in enumerate(cf.fields) :
if field_info[0] == field[0] :
self.field_pred_idxs.append( ifield)
break
self.decoders.append( TransformerDecoder( cf, field_info ) )
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained :
self.load_block( field_info, 'decoder', self.decoders[-1])
# tail networks
self.tails = torch.nn.ModuleList()
for ifield, field in enumerate(cf.fields_prediction) :
field_idx = self.field_pred_idxs[ifield]
field_info = cf.fields[field_idx]
self.tails.append( TailEnsemble( cf, field_info[1][1], np.prod(field_info[4]) ).create())
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained:
self.load_block( field_info, 'tail', self.tails[-1])
# set devices
for field_idx, field_info in enumerate(cf.fields) :
# find determined device, use default if nothing specified
device = self.devices[0]
if len(field_info[1]) > 3 :
assert field_info[1][3] < 4, 'Only single node model parallelism supported'
assert field_info[1][3] < len(devices), 'Per field device id larger than max devices'
device = self.devices[ field_info[1][3] ]
# set device
if self.masks[field_idx] != None :
self.masks[field_idx].to(device)
self.embeds[field_idx].to(device)
self.encoders[field_idx].to(device)
for field_idx, field in enumerate(cf.fields_prediction) :
field_info = cf.fields[ self.field_pred_idxs[field_idx] ]
device = self.devices[0]
if len(field_info[1]) > 3 :
device = self.devices[ field_info[1][3] ]
self.decoders[field_idx].to(device)
self.tails[field_idx].to(device)
# embed_token_info on device[0] since it is shared by all fields, potentially sub-optimal
self.embed_token_info.to(devices[0]) # TODO: only for backward compatibility, remove
self.embeds_token_info.to(devices[0])
self.checkpoint = identity
if cf.grad_checkpointing :
| ####################################################################################################
#
# Copyright (C) 2022
#
####################################################################################################
#
# project : atmorep
#
# author : atmorep collaboration
#
# description :
#
# license :
#
####################################################################################################
# code.interact(local=locals())
# import horovod.torch as hvd
####################################################################################################
class AtmoRepData( torch.nn.Module) :
def __init__( self, net) :
'''Wrapper class for AtmoRep that handles data loading'''
super( AtmoRepData, self).__init__()
self.data_loader_test = None
self.data_loader_train = None
self.data_loader_iter = None
self.net = net
# ensure that all data loaders have the same seed and hence load the same data
self.rng_seed = net.cf.rng_seed
if not self.rng_seed :
self.rng_seed = int(torch.randint( 100000000, (1,)))
###################################################
def load_data( self, mode : NetMode, batch_size = -1, num_loader_workers = -1) :
'''Load data'''
cf = self.net.cf
if batch_size < 0 :
batch_size = cf.batch_size_max
if num_loader_workers < 0 :
num_loader_workers = cf.num_loader_workers
if mode == NetMode.train :
self.data_loader_train = self._load_data( self.dataset_train, batch_size, num_loader_workers)
elif mode == NetMode.test :
batch_size = cf.batch_size_test
self.data_loader_test = self._load_data( self.dataset_test, batch_size, num_loader_workers)
else :
assert False
###################################################
def _load_data( self, dataset, batch_size, num_loader_workers) :
'''Private implementation for load'''
dataset.load_data( batch_size)
loader_params = { 'batch_size': None, 'batch_sampler': None, 'shuffle': False,
'num_workers': num_loader_workers, 'pin_memory': True}
data_loader = torch.utils.data.DataLoader( dataset, **loader_params, sampler = None)
return data_loader
###################################################
def set_data( self, mode : NetMode, times_pos, batch_size = -1, num_loader_workers = -1) :
cf = self.net.cf
if batch_size < 0 :
batch_size = cf.batch_size_train if mode == NetMode.train else cf.batch_size_test
dataset = self.dataset_train if mode == NetMode.train else self.dataset_test
dataset.set_data( times_pos, batch_size)
self._set_data( dataset, mode, batch_size, num_loader_workers)
###################################################
def set_global( self, mode : NetMode, times, batch_size = -1, num_loader_workers = -1) :
cf = self.net.cf
if batch_size < 0 :
batch_size = cf.batch_size_train if mode == NetMode.train else cf.batch_size_test
dataset = self.dataset_train if mode == NetMode.train else self.dataset_test
dataset.set_global( times, batch_size, cf.token_overlap)
self._set_data( dataset, mode, batch_size, num_loader_workers)
###################################################
def set_location( self, mode : NetMode, pos, years, months, num_t_samples_per_month,
batch_size = -1, num_loader_workers = -1) :
cf = self.net.cf
if batch_size < 0 :
batch_size = cf.batch_size_train if mode == NetMode.train else cf.batch_size_test
dataset = self.dataset_train if mode == NetMode.train else self.dataset_test
dataset.set_location( pos, years, months, num_t_samples_per_month, batch_size)
self._set_data( dataset, mode, batch_size, num_loader_workers)
###################################################
def _set_data( self, dataset, mode : NetMode, batch_size = -1, loader_workers = -1) :
'''Private implementation for set_data, set_global'''
cf = self.net.cf
if loader_workers < 0 :
loader_workers = cf.num_loader_workers
loader_params = { 'batch_size': None, 'batch_sampler': None, 'shuffle': False,
'num_workers': loader_workers, 'pin_memory': True}
if mode == NetMode.train :
self.data_loader_train = torch.utils.data.DataLoader( dataset, **loader_params,
sampler = None)
elif mode == NetMode.test :
self.data_loader_test = torch.utils.data.DataLoader( dataset, **loader_params,
sampler = None)
else :
assert False
###################################################
def normalizer( self, field, vl_idx) :
if isinstance( field, str) :
for fidx, field_info in enumerate(self.cf.fields) :
if field == field_info[0] :
break
assert fidx < len(self.cf.fields), 'invalid field'
normalizer = self.dataset_train.datasets[fidx].normalizer
elif isinstance( field, int) :
normalizer = self.dataset_train.datasets[field][vl_idx].normalizer
else :
assert False, 'invalid argument type (has to be index to cf.fields or field name)'
return normalizer
###################################################
def mode( self, mode : NetMode) :
if mode == NetMode.train :
self.data_loader_iter = iter(self.data_loader_train)
self.net.train()
elif mode == NetMode.test :
self.data_loader_iter = iter(self.data_loader_test)
self.net.eval()
else :
assert False
self.cur_mode = mode
###################################################
def len( self, mode : NetMode) :
if mode == NetMode.train :
return len(self.data_loader_train)
elif mode == NetMode.test :
return len(self.data_loader_test)
else :
assert False
###################################################
def next( self) :
return next(self.data_loader_iter)
###################################################
def forward( self, xin) :
pred = self.net.forward( xin)
return pred
###################################################
def get_attention( self, xin): #, field_idx) :
attn = self.net.get_attention( xin) #, field_idx)
return attn
###################################################
def create( self, pre_batch, devices, create_net = True, pre_batch_targets = None,
load_pretrained=True) :
if create_net :
self.net.create( devices, load_pretrained)
self.pre_batch = pre_batch
self.pre_batch_targets = pre_batch_targets
cf = self.net.cf
self.dataset_train = MultifieldDataSampler( cf.data_dir, cf.years_train, cf.fields,
batch_size = cf.batch_size_start,
num_t_samples = cf.num_t_samples,
num_patches_per_t = cf.num_patches_per_t_train,
num_load = cf.num_files_train,
pre_batch = self.pre_batch,
rng_seed = self.rng_seed,
file_shape = cf.file_shape,
smoothing = cf.data_smoothing,
level_type = cf.level_type,
file_format = cf.file_format,
month = cf.month,
time_sampling = cf.time_sampling,
geo_range = cf.geo_range_sampling,
fields_targets = cf.fields_targets,
pre_batch_targets = self.pre_batch_targets )
self.dataset_test = MultifieldDataSampler( cf.data_dir, cf.years_test, cf.fields,
batch_size = cf.batch_size_test,
num_t_samples = cf.num_t_samples,
num_patches_per_t = cf.num_patches_per_t_test,
num_load = cf.num_files_test,
pre_batch = self.pre_batch,
rng_seed = self.rng_seed,
file_shape = cf.file_shape,
smoothing = cf.data_smoothing,
level_type = cf.level_type,
file_format = cf.file_format,
month = cf.month,
time_sampling = cf.time_sampling,
geo_range = cf.geo_range_sampling,
lat_sampling_weighted = cf.lat_sampling_weighted,
fields_targets = cf.fields_targets,
pre_batch_targets = self.pre_batch_targets )
return self
####################################################################################################
class AtmoRep( torch.nn.Module) :
def __init__(self, cf) :
'''Constructor'''
super( AtmoRep, self).__init__()
self.cf = cf
###################################################
def create( self, devices, load_pretrained=True) :
'''Create network'''
cf = self.cf
self.devices = devices
size_token_info = 6
self.fields_coupling_idx = []
self.fields_index = {}
for ifield, field_info in enumerate(cf.fields) :
self.fields_index[ field_info[0] ] = ifield
# # embedding network for global/auxiliary token infos
# TODO: only for backward compatibility, remove
self.embed_token_info = torch.nn.Linear( cf.size_token_info, cf.size_token_info_net)
torch.nn.init.constant_( self.embed_token_info.weight, 0.0)
self.embeds_token_info = torch.nn.ModuleList()
for ifield, field_info in enumerate( cf.fields) :
self.embeds_token_info.append( torch.nn.Linear( cf.size_token_info, cf.size_token_info_net))
if len(field_info[1]) > 4 and load_pretrained :
# TODO: inconsistent with embeds_token_info -> version that can handle both
# we could imply use the file name: embed_token_info vs embeds_token_info
name = 'AtmoRep' + '_embed_token_info'
mloaded = torch.load( get_model_filename( name, field_info[1][4][0], field_info[1][4][1]))
self.embeds_token_info[-1].load_state_dict( mloaded)
print( 'Loaded embed_token_info from id = {}.'.format( field_info[1][4][0] ) )
else :
# initalization
torch.nn.init.constant_( self.embeds_token_info[-1].weight, 0.0)
self.embeds_token_info[-1].bias.data.fill_(0.0)
# embedding and encoder
self.embeds = torch.nn.ModuleList()
self.encoders = torch.nn.ModuleList()
self.masks = torch.nn.ParameterList()
for field_idx, field_info in enumerate(cf.fields) :
# learnabl class token
if cf.learnable_mask :
mask = torch.nn.Parameter( 0.1 * torch.randn( np.prod( field_info[4]), requires_grad=True))
self.masks.append( mask.to(devices[0]))
else :
self.masks.append( None)
# encoder
self.encoders.append( TransformerEncoder( cf, field_idx, True).create())
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained :
self.load_block( field_info, 'encoder', self.encoders[-1])
self.embeds.append( self.encoders[-1].embed)
# indices of coupled fields for efficient access in forward
self.fields_coupling_idx.append( [field_idx])
for field_coupled in field_info[1][2] :
if 'axial' in cf.encoder_att_type :
self.fields_coupling_idx[field_idx].append( self.fields_index[field_coupled] )
else :
for _ in range(cf.coupling_num_heads_per_field) :
self.fields_coupling_idx[field_idx].append( self.fields_index[field_coupled] )
# decoder
self.decoders = torch.nn.ModuleList()
self.field_pred_idxs = []
for field in cf.fields_prediction :
for ifield, field_info in enumerate(cf.fields) :
if field_info[0] == field[0] :
self.field_pred_idxs.append( ifield)
break
self.decoders.append( TransformerDecoder( cf, field_info ) )
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained :
self.load_block( field_info, 'decoder', self.decoders[-1])
# tail networks
self.tails = torch.nn.ModuleList()
for ifield, field in enumerate(cf.fields_prediction) :
field_idx = self.field_pred_idxs[ifield]
field_info = cf.fields[field_idx]
self.tails.append( TailEnsemble( cf, field_info[1][1], np.prod(field_info[4]) ).create())
# load pre-trained model if specified
if len(field_info[1]) > 4 and load_pretrained:
self.load_block( field_info, 'tail', self.tails[-1])
# set devices
for field_idx, field_info in enumerate(cf.fields) :
# find determined device, use default if nothing specified
device = self.devices[0]
if len(field_info[1]) > 3 :
assert field_info[1][3] < 4, 'Only single node model parallelism supported'
assert field_info[1][3] < len(devices), 'Per field device id larger than max devices'
device = self.devices[ field_info[1][3] ]
# set device
if self.masks[field_idx] != None :
self.masks[field_idx].to(device)
self.embeds[field_idx].to(device)
self.encoders[field_idx].to(device)
for field_idx, field in enumerate(cf.fields_prediction) :
field_info = cf.fields[ self.field_pred_idxs[field_idx] ]
device = self.devices[0]
if len(field_info[1]) > 3 :
device = self.devices[ field_info[1][3] ]
self.decoders[field_idx].to(device)
self.tails[field_idx].to(device)
# embed_token_info on device[0] since it is shared by all fields, potentially sub-optimal
self.embed_token_info.to(devices[0]) # TODO: only for backward compatibility, remove
self.embeds_token_info.to(devices[0])
self.checkpoint = identity
if cf.grad_checkpointing : | self.checkpoint = checkpoint_wrapper | 4 | 2023-10-09 19:42:46+00:00 | 16k |
NKI-AI/ahcore | ahcore/callbacks/wsi_metric_callback.py | [
{
"identifier": "WriteH5Callback",
"path": "ahcore/callbacks/h5_callback.py",
"snippet": "class WriteH5Callback(Callback):\n def __init__(\n self,\n max_queue_size: int,\n max_concurrent_writers: int,\n dump_dir: Path,\n normalization_type: str = str(NormalizationTy... | import itertools
import json
import multiprocessing
import time
import pytorch_lightning as pl
import torch
from collections import namedtuple
from multiprocessing.pool import Pool
from pathlib import Path
from typing import Any, Generator, Optional, cast
from pytorch_lightning import Callback
from ahcore.callbacks import WriteH5Callback
from ahcore.lit_module import AhCoreLightningModule
from ahcore.metrics import WSIMetricFactory
from ahcore.readers import H5FileImageReader, StitchingMode
from ahcore.utils.callbacks import _get_h5_output_filename, _ValidationDataset
from ahcore.utils.data import DataDescription
from ahcore.utils.io import get_logger
from ahcore.utils.manifest import DataManager, ImageMetadata, fetch_image_metadata, get_mask_and_annotations_from_record | 12,999 | assert self._dump_dir
assert self._data_description
assert self._validate_metadata
assert self._data_manager
metrics = []
with multiprocessing.Pool(processes=self._max_processes) as pool:
results_to_filename: dict[list[dict[str, Any]], str] = {}
completed_tasks = 0
# Fill up the initial task pool
for image_metadata in itertools.islice(self._validate_metadata, self._max_processes):
logger.info("Metadata: %s", image_metadata)
# Assemble the task data
# filename", "h5_filename", "metadata", "mask", "annotations"
task_data = prepare_task_data(
image_metadata.filename,
self._dump_dir,
pl_module,
self._data_description,
self._data_manager,
)
# Schedule task
schedule_task(
task_data,
pool,
results_to_filename,
self._class_names,
self._data_description,
self._wsi_metrics,
self._save_per_image,
)
while results_to_filename:
time.sleep(0.1) # Reduce excessive polling
# Check for completed tasks
for result in list(results_to_filename.keys()):
if result.ready():
filename = results_to_filename.pop(result)
try:
metric = result.get()
except Exception as exc:
self._logger.error("%r generated an exception: %s" % (filename, exc))
else:
metrics.append(metric)
self._logger.debug("Metric for %r is %s" % (filename, metric))
completed_tasks += 1
# Schedule a new task if there are more filenames left in the generator
next_metadata = next(self._validate_metadata, None)
while next_metadata:
task_data = prepare_task_data(
next_metadata.filename, # <-- Changed from image_metadata.filename
self._dump_dir,
pl_module,
self._data_description,
self._data_manager,
)
# Schedule task
schedule_task(
task_data,
pool,
results_to_filename,
self._class_names,
self._data_description,
self._wsi_metrics,
self._save_per_image,
)
next_metadata = next(self._validate_metadata, None)
return metrics
def on_validation_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
if not self._dump_dir:
raise ValueError("Dump directory is not set.")
if not self._wsi_metrics:
raise ValueError("WSI metrics are not set.")
assert self._model_name # This should be set in the setup()
# Ensure that all h5 files have been written
self._logger.debug("Computing metrics for %s predictions", len(self._filenames))
computed_metrics = self.compute_metrics(trainer, pl_module)
metrics = self._wsi_metrics.get_average_score(computed_metrics)
results_json_fn = (
self._dump_dir / "outputs" / self._model_name / f"step_{pl_module.global_step}" / "results.json"
)
with open(results_json_fn, "w", encoding="utf-8") as json_file:
json.dump(self._dump_list, json_file, indent=2)
self._wsi_metrics.reset()
# Reset stuff
self._dump_list = []
self._filenames = {}
self._logger.debug("Metrics: %s", metrics)
# TODO: Maybe put this elsewhere?
metrics = {f"validate/{k}": v for k, v in metrics.items()}
pl_module.log_dict(metrics, prog_bar=True)
TaskData = namedtuple("TaskData", ["filename", "h5_filename", "metadata", "mask", "annotations"])
def prepare_task_data(
filename: Path,
dump_dir: Path,
pl_module: pl.LightningModule,
data_description: DataDescription,
data_manager: DataManager,
) -> TaskData:
h5_filename = _get_h5_output_filename(
dump_dir=dump_dir,
input_path=data_description.data_dir / filename,
model_name=str(pl_module.name),
step=pl_module.global_step,
)
image = data_manager.get_image_by_filename(str(filename))
| from __future__ import annotations
logger = get_logger(__name__)
class ComputeWsiMetricsCallback(Callback):
def __init__(self, max_processes: int = 10, save_per_image: bool = True) -> None:
"""
Callback to compute metrics on whole-slide images. This callback is used to compute metrics on whole-slide
images in separate processes.
Parameters
----------
max_processes : int
The maximum number of concurrent processes.
"""
self._data_description: Optional[DataDescription] = None
self._reader = H5FileImageReader
self._max_processes: int = max_processes
self._dump_dir: Optional[Path] = None
self._save_per_image = save_per_image
self._filenames: dict[Path, Path] = {}
self._wsi_metrics: WSIMetricFactory | None = None
self._class_names: dict[int, str] = {}
self._data_manager = None
self._validate_filenames_gen = None
self._model_name: str | None = None
self._validate_metadata_gen: Generator[ImageMetadata, None, None] | None = None
self._dump_list: list[dict[str, str]] = []
self._logger = get_logger(type(self).__name__)
def setup(
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
stage: Optional[str] = None,
) -> None:
if not isinstance(pl_module, AhCoreLightningModule):
# TODO: Make a AhCoreCallback with these features
raise ValueError("AhCoreLightningModule required for WriteTiffCallback.")
self._model_name = pl_module.name
_callback: Optional[WriteH5Callback] = None
for idx, callback in enumerate(trainer.callbacks): # type: ignore
if isinstance(callback, WriteH5Callback):
_callback = cast(WriteH5Callback, trainer.callbacks[idx]) # type: ignore
break
if _callback is None:
raise ValueError(
"WriteH5Callback is not in the trainer's callbacks. "
"This is required before WSI metrics can be computed using this Callback"
)
self._dump_dir = _callback.dump_dir
if pl_module.wsi_metrics is None:
raise ValueError("WSI metrics are not set.")
self._wsi_metrics = pl_module.wsi_metrics
self._data_description = trainer.datamodule.data_description # type: ignore
# For mypy
assert self._data_description
index_map = self._data_description.index_map
assert index_map
if not self._data_description:
raise ValueError("Data description is not set.")
self._class_names = dict([(v, k) for k, v in index_map.items()])
self._class_names[0] = "background"
# Here we can query the database for the validation images
self._data_manager: DataManager = trainer.datamodule.data_manager # type: ignore
def _create_validate_image_metadata_gen(
self,
) -> Generator[ImageMetadata, None, None]:
assert self._data_description
assert self._data_manager
gen = self._data_manager.get_image_metadata_by_split(
manifest_name=self._data_description.manifest_name,
split_version=self._data_description.split_version,
split_category="validate",
)
for image_metadata in gen:
yield image_metadata
@property
def _validate_metadata(self) -> Generator[ImageMetadata, None, None] | None:
return self._validate_metadata_gen
def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
self._validate_metadata_gen = self._create_validate_image_metadata_gen()
def on_validation_batch_end(
self,
trainer: pl.Trainer,
pl_module: pl.LightningModule,
outputs: Any,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
if not self._dump_dir:
raise ValueError("Dump directory is not set.")
filenames = batch["path"] # Filenames are constant across the batch.
if len(set(filenames)) != 1:
raise ValueError(
"All paths in a batch must be the same. "
"Either use batch_size=1 or ahcore.data.samplers.WsiBatchSampler."
)
def compute_metrics(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> list[list[dict[str, dict[str, float]]]]:
assert self._dump_dir
assert self._data_description
assert self._validate_metadata
assert self._data_manager
metrics = []
with multiprocessing.Pool(processes=self._max_processes) as pool:
results_to_filename: dict[list[dict[str, Any]], str] = {}
completed_tasks = 0
# Fill up the initial task pool
for image_metadata in itertools.islice(self._validate_metadata, self._max_processes):
logger.info("Metadata: %s", image_metadata)
# Assemble the task data
# filename", "h5_filename", "metadata", "mask", "annotations"
task_data = prepare_task_data(
image_metadata.filename,
self._dump_dir,
pl_module,
self._data_description,
self._data_manager,
)
# Schedule task
schedule_task(
task_data,
pool,
results_to_filename,
self._class_names,
self._data_description,
self._wsi_metrics,
self._save_per_image,
)
while results_to_filename:
time.sleep(0.1) # Reduce excessive polling
# Check for completed tasks
for result in list(results_to_filename.keys()):
if result.ready():
filename = results_to_filename.pop(result)
try:
metric = result.get()
except Exception as exc:
self._logger.error("%r generated an exception: %s" % (filename, exc))
else:
metrics.append(metric)
self._logger.debug("Metric for %r is %s" % (filename, metric))
completed_tasks += 1
# Schedule a new task if there are more filenames left in the generator
next_metadata = next(self._validate_metadata, None)
while next_metadata:
task_data = prepare_task_data(
next_metadata.filename, # <-- Changed from image_metadata.filename
self._dump_dir,
pl_module,
self._data_description,
self._data_manager,
)
# Schedule task
schedule_task(
task_data,
pool,
results_to_filename,
self._class_names,
self._data_description,
self._wsi_metrics,
self._save_per_image,
)
next_metadata = next(self._validate_metadata, None)
return metrics
def on_validation_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
if not self._dump_dir:
raise ValueError("Dump directory is not set.")
if not self._wsi_metrics:
raise ValueError("WSI metrics are not set.")
assert self._model_name # This should be set in the setup()
# Ensure that all h5 files have been written
self._logger.debug("Computing metrics for %s predictions", len(self._filenames))
computed_metrics = self.compute_metrics(trainer, pl_module)
metrics = self._wsi_metrics.get_average_score(computed_metrics)
results_json_fn = (
self._dump_dir / "outputs" / self._model_name / f"step_{pl_module.global_step}" / "results.json"
)
with open(results_json_fn, "w", encoding="utf-8") as json_file:
json.dump(self._dump_list, json_file, indent=2)
self._wsi_metrics.reset()
# Reset stuff
self._dump_list = []
self._filenames = {}
self._logger.debug("Metrics: %s", metrics)
# TODO: Maybe put this elsewhere?
metrics = {f"validate/{k}": v for k, v in metrics.items()}
pl_module.log_dict(metrics, prog_bar=True)
TaskData = namedtuple("TaskData", ["filename", "h5_filename", "metadata", "mask", "annotations"])
def prepare_task_data(
filename: Path,
dump_dir: Path,
pl_module: pl.LightningModule,
data_description: DataDescription,
data_manager: DataManager,
) -> TaskData:
h5_filename = _get_h5_output_filename(
dump_dir=dump_dir,
input_path=data_description.data_dir / filename,
model_name=str(pl_module.name),
step=pl_module.global_step,
)
image = data_manager.get_image_by_filename(str(filename)) | metadata = fetch_image_metadata(image) | 11 | 2023-10-14 18:04:12+00:00 | 16k |
fury-05/BookRecomendApp | .pythonlibs/lib/python3.10/site-packages/sklearn/linear_model/_omp.py | [
{
"identifier": "MultiOutputMixin",
"path": ".pythonlibs/lib/python3.10/site-packages/sklearn/base.py",
"snippet": "class MultiOutputMixin:\n \"\"\"Mixin to mark estimators that support multioutput.\"\"\"\n\n def _more_tags(self):\n return {\"multioutput\": True}"
},
{
"identifier":... | import warnings
import numpy as np
from math import sqrt
from numbers import Integral, Real
from scipy import linalg
from scipy.linalg.lapack import get_lapack_funcs
from ..base import MultiOutputMixin, RegressorMixin, _fit_context
from ..model_selection import check_cv
from ..utils import as_float_array, check_array
from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
from ..utils.parallel import Parallel, delayed
from ._base import LinearModel, _deprecate_normalize, _pre_fit | 13,460 | overrides `n_nonzero_coefs`.
norms_squared : array-like of shape (n_targets,), default=None
Squared L2 norms of the lines of y. Required if tol is not None.
copy_Gram : bool, default=True
Whether the gram matrix must be copied by the algorithm. A false
value is only helpful if it is already Fortran-ordered, otherwise a
copy is made anyway.
copy_Xy : bool, default=True
Whether the covariance vector Xy must be copied by the algorithm.
If False, it may be overwritten.
return_path : bool, default=False
Whether to return every value of the nonzero coefficients along the
forward path. Useful for cross-validation.
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Returns
-------
coef : ndarray of shape (n_features,) or (n_features, n_targets)
Coefficients of the OMP solution. If `return_path=True`, this contains
the whole coefficient path. In this case its shape is
(n_features, n_features) or (n_features, n_targets, n_features) and
iterating over the last axis yields coefficients in increasing order
of active features.
n_iters : array-like or int
Number of active features across every target. Returned only if
`return_n_iter` is set to True.
See Also
--------
OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP).
orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
lars_path : Compute Least Angle Regression or Lasso path using
LARS algorithm.
sklearn.decomposition.sparse_encode : Generic sparse coding.
Each column of the result is the solution to a Lasso problem.
Notes
-----
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
(https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
"""
Gram = check_array(Gram, order="F", copy=copy_Gram)
Xy = np.asarray(Xy)
if Xy.ndim > 1 and Xy.shape[1] > 1:
# or subsequent target will be affected
copy_Gram = True
if Xy.ndim == 1:
Xy = Xy[:, np.newaxis]
if tol is not None:
norms_squared = [norms_squared]
if copy_Xy or not Xy.flags.writeable:
# Make the copy once instead of many times in _gram_omp itself.
Xy = Xy.copy()
if n_nonzero_coefs is None and tol is None:
n_nonzero_coefs = int(0.1 * len(Gram))
if tol is not None and norms_squared is None:
raise ValueError(
"Gram OMP needs the precomputed norms in order "
"to evaluate the error sum of squares."
)
if tol is not None and tol < 0:
raise ValueError("Epsilon cannot be negative")
if tol is None and n_nonzero_coefs <= 0:
raise ValueError("The number of atoms must be positive")
if tol is None and n_nonzero_coefs > len(Gram):
raise ValueError(
"The number of atoms cannot be more than the number of features"
)
if return_path:
coef = np.zeros((len(Gram), Xy.shape[1], len(Gram)), dtype=Gram.dtype)
else:
coef = np.zeros((len(Gram), Xy.shape[1]), dtype=Gram.dtype)
n_iters = []
for k in range(Xy.shape[1]):
out = _gram_omp(
Gram,
Xy[:, k],
n_nonzero_coefs,
norms_squared[k] if tol is not None else None,
tol,
copy_Gram=copy_Gram,
copy_Xy=False,
return_path=return_path,
)
if return_path:
_, idx, coefs, n_iter = out
coef = coef[:, :, : len(idx)]
for n_active, x in enumerate(coefs.T):
coef[idx[: n_active + 1], k, n_active] = x[: n_active + 1]
else:
x, idx, n_iter = out
coef[idx, k] = x
n_iters.append(n_iter)
if Xy.shape[1] == 1:
n_iters = n_iters[0]
if return_n_iter:
return np.squeeze(coef), n_iters
else:
return np.squeeze(coef)
| """Orthogonal matching pursuit algorithms
"""
# Author: Vlad Niculae
#
# License: BSD 3 clause
premature = (
"Orthogonal matching pursuit ended prematurely due to linear"
" dependence in the dictionary. The requested precision might"
" not have been met."
)
def _cholesky_omp(X, y, n_nonzero_coefs, tol=None, copy_X=True, return_path=False):
"""Orthogonal Matching Pursuit step using the Cholesky decomposition.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Input dictionary. Columns are assumed to have unit norm.
y : ndarray of shape (n_samples,)
Input targets.
n_nonzero_coefs : int
Targeted number of non-zero elements.
tol : float, default=None
Targeted squared error, if not None overrides n_nonzero_coefs.
copy_X : bool, default=True
Whether the design matrix X must be copied by the algorithm. A false
value is only helpful if X is already Fortran-ordered, otherwise a
copy is made anyway.
return_path : bool, default=False
Whether to return every value of the nonzero coefficients along the
forward path. Useful for cross-validation.
Returns
-------
gamma : ndarray of shape (n_nonzero_coefs,)
Non-zero elements of the solution.
idx : ndarray of shape (n_nonzero_coefs,)
Indices of the positions of the elements in gamma within the solution
vector.
coef : ndarray of shape (n_features, n_nonzero_coefs)
The first k values of column k correspond to the coefficient value
for the active features at that step. The lower left triangle contains
garbage. Only returned if ``return_path=True``.
n_active : int
Number of active features at convergence.
"""
if copy_X:
X = X.copy("F")
else: # even if we are allowed to overwrite, still copy it if bad order
X = np.asfortranarray(X)
min_float = np.finfo(X.dtype).eps
nrm2, swap = linalg.get_blas_funcs(("nrm2", "swap"), (X,))
(potrs,) = get_lapack_funcs(("potrs",), (X,))
alpha = np.dot(X.T, y)
residual = y
gamma = np.empty(0)
n_active = 0
indices = np.arange(X.shape[1]) # keeping track of swapping
max_features = X.shape[1] if tol is not None else n_nonzero_coefs
L = np.empty((max_features, max_features), dtype=X.dtype)
if return_path:
coefs = np.empty_like(L)
while True:
lam = np.argmax(np.abs(np.dot(X.T, residual)))
if lam < n_active or alpha[lam] ** 2 < min_float:
# atom already selected or inner product too small
warnings.warn(premature, RuntimeWarning, stacklevel=2)
break
if n_active > 0:
# Updates the Cholesky decomposition of X' X
L[n_active, :n_active] = np.dot(X[:, :n_active].T, X[:, lam])
linalg.solve_triangular(
L[:n_active, :n_active],
L[n_active, :n_active],
trans=0,
lower=1,
overwrite_b=True,
check_finite=False,
)
v = nrm2(L[n_active, :n_active]) ** 2
Lkk = linalg.norm(X[:, lam]) ** 2 - v
if Lkk <= min_float: # selected atoms are dependent
warnings.warn(premature, RuntimeWarning, stacklevel=2)
break
L[n_active, n_active] = sqrt(Lkk)
else:
L[0, 0] = linalg.norm(X[:, lam])
X.T[n_active], X.T[lam] = swap(X.T[n_active], X.T[lam])
alpha[n_active], alpha[lam] = alpha[lam], alpha[n_active]
indices[n_active], indices[lam] = indices[lam], indices[n_active]
n_active += 1
# solves LL'x = X'y as a composition of two triangular systems
gamma, _ = potrs(
L[:n_active, :n_active], alpha[:n_active], lower=True, overwrite_b=False
)
if return_path:
coefs[:n_active, n_active - 1] = gamma
residual = y - np.dot(X[:, :n_active], gamma)
if tol is not None and nrm2(residual) ** 2 <= tol:
break
elif n_active == max_features:
break
if return_path:
return gamma, indices[:n_active], coefs[:, :n_active], n_active
else:
return gamma, indices[:n_active], n_active
def _gram_omp(
Gram,
Xy,
n_nonzero_coefs,
tol_0=None,
tol=None,
copy_Gram=True,
copy_Xy=True,
return_path=False,
):
"""Orthogonal Matching Pursuit step on a precomputed Gram matrix.
This function uses the Cholesky decomposition method.
Parameters
----------
Gram : ndarray of shape (n_features, n_features)
Gram matrix of the input data matrix.
Xy : ndarray of shape (n_features,)
Input targets.
n_nonzero_coefs : int
Targeted number of non-zero elements.
tol_0 : float, default=None
Squared norm of y, required if tol is not None.
tol : float, default=None
Targeted squared error, if not None overrides n_nonzero_coefs.
copy_Gram : bool, default=True
Whether the gram matrix must be copied by the algorithm. A false
value is only helpful if it is already Fortran-ordered, otherwise a
copy is made anyway.
copy_Xy : bool, default=True
Whether the covariance vector Xy must be copied by the algorithm.
If False, it may be overwritten.
return_path : bool, default=False
Whether to return every value of the nonzero coefficients along the
forward path. Useful for cross-validation.
Returns
-------
gamma : ndarray of shape (n_nonzero_coefs,)
Non-zero elements of the solution.
idx : ndarray of shape (n_nonzero_coefs,)
Indices of the positions of the elements in gamma within the solution
vector.
coefs : ndarray of shape (n_features, n_nonzero_coefs)
The first k values of column k correspond to the coefficient value
for the active features at that step. The lower left triangle contains
garbage. Only returned if ``return_path=True``.
n_active : int
Number of active features at convergence.
"""
Gram = Gram.copy("F") if copy_Gram else np.asfortranarray(Gram)
if copy_Xy or not Xy.flags.writeable:
Xy = Xy.copy()
min_float = np.finfo(Gram.dtype).eps
nrm2, swap = linalg.get_blas_funcs(("nrm2", "swap"), (Gram,))
(potrs,) = get_lapack_funcs(("potrs",), (Gram,))
indices = np.arange(len(Gram)) # keeping track of swapping
alpha = Xy
tol_curr = tol_0
delta = 0
gamma = np.empty(0)
n_active = 0
max_features = len(Gram) if tol is not None else n_nonzero_coefs
L = np.empty((max_features, max_features), dtype=Gram.dtype)
L[0, 0] = 1.0
if return_path:
coefs = np.empty_like(L)
while True:
lam = np.argmax(np.abs(alpha))
if lam < n_active or alpha[lam] ** 2 < min_float:
# selected same atom twice, or inner product too small
warnings.warn(premature, RuntimeWarning, stacklevel=3)
break
if n_active > 0:
L[n_active, :n_active] = Gram[lam, :n_active]
linalg.solve_triangular(
L[:n_active, :n_active],
L[n_active, :n_active],
trans=0,
lower=1,
overwrite_b=True,
check_finite=False,
)
v = nrm2(L[n_active, :n_active]) ** 2
Lkk = Gram[lam, lam] - v
if Lkk <= min_float: # selected atoms are dependent
warnings.warn(premature, RuntimeWarning, stacklevel=3)
break
L[n_active, n_active] = sqrt(Lkk)
else:
L[0, 0] = sqrt(Gram[lam, lam])
Gram[n_active], Gram[lam] = swap(Gram[n_active], Gram[lam])
Gram.T[n_active], Gram.T[lam] = swap(Gram.T[n_active], Gram.T[lam])
indices[n_active], indices[lam] = indices[lam], indices[n_active]
Xy[n_active], Xy[lam] = Xy[lam], Xy[n_active]
n_active += 1
# solves LL'x = X'y as a composition of two triangular systems
gamma, _ = potrs(
L[:n_active, :n_active], Xy[:n_active], lower=True, overwrite_b=False
)
if return_path:
coefs[:n_active, n_active - 1] = gamma
beta = np.dot(Gram[:, :n_active], gamma)
alpha = Xy - beta
if tol is not None:
tol_curr += delta
delta = np.inner(gamma, beta[:n_active])
tol_curr -= delta
if abs(tol_curr) <= tol:
break
elif n_active == max_features:
break
if return_path:
return gamma, indices[:n_active], coefs[:, :n_active], n_active
else:
return gamma, indices[:n_active], n_active
@validate_params(
{
"X": ["array-like"],
"y": [np.ndarray],
"n_nonzero_coefs": [Interval(Integral, 1, None, closed="left"), None],
"tol": [Interval(Real, 0, None, closed="left"), None],
"precompute": ["boolean", StrOptions({"auto"})],
"copy_X": ["boolean"],
"return_path": ["boolean"],
"return_n_iter": ["boolean"],
},
prefer_skip_nested_validation=True,
)
def orthogonal_mp(
X,
y,
*,
n_nonzero_coefs=None,
tol=None,
precompute=False,
copy_X=True,
return_path=False,
return_n_iter=False,
):
r"""Orthogonal Matching Pursuit (OMP).
Solves n_targets Orthogonal Matching Pursuit problems.
An instance of the problem has the form:
When parametrized by the number of non-zero coefficients using
`n_nonzero_coefs`:
argmin ||y - X\gamma||^2 subject to ||\gamma||_0 <= n_{nonzero coefs}
When parametrized by error using the parameter `tol`:
argmin ||\gamma||_0 subject to ||y - X\gamma||^2 <= tol
Read more in the :ref:`User Guide <omp>`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data. Columns are assumed to have unit norm.
y : ndarray of shape (n_samples,) or (n_samples, n_targets)
Input targets.
n_nonzero_coefs : int, default=None
Desired number of non-zero entries in the solution. If None (by
default) this value is set to 10% of n_features.
tol : float, default=None
Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs.
precompute : 'auto' or bool, default=False
Whether to perform precomputations. Improves performance when n_targets
or n_samples is very large.
copy_X : bool, default=True
Whether the design matrix X must be copied by the algorithm. A false
value is only helpful if X is already Fortran-ordered, otherwise a
copy is made anyway.
return_path : bool, default=False
Whether to return every value of the nonzero coefficients along the
forward path. Useful for cross-validation.
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Returns
-------
coef : ndarray of shape (n_features,) or (n_features, n_targets)
Coefficients of the OMP solution. If `return_path=True`, this contains
the whole coefficient path. In this case its shape is
(n_features, n_features) or (n_features, n_targets, n_features) and
iterating over the last axis generates coefficients in increasing order
of active features.
n_iters : array-like or int
Number of active features across every target. Returned only if
`return_n_iter` is set to True.
See Also
--------
OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model.
orthogonal_mp_gram : Solve OMP problems using Gram matrix and the product X.T * y.
lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
sklearn.decomposition.sparse_encode : Sparse coding.
Notes
-----
Orthogonal matching pursuit was introduced in S. Mallat, Z. Zhang,
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
(https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
"""
X = check_array(X, order="F", copy=copy_X)
copy_X = False
if y.ndim == 1:
y = y.reshape(-1, 1)
y = check_array(y)
if y.shape[1] > 1: # subsequent targets will be affected
copy_X = True
if n_nonzero_coefs is None and tol is None:
# default for n_nonzero_coefs is 0.1 * n_features
# but at least one.
n_nonzero_coefs = max(int(0.1 * X.shape[1]), 1)
if tol is None and n_nonzero_coefs > X.shape[1]:
raise ValueError(
"The number of atoms cannot be more than the number of features"
)
if precompute == "auto":
precompute = X.shape[0] > X.shape[1]
if precompute:
G = np.dot(X.T, X)
G = np.asfortranarray(G)
Xy = np.dot(X.T, y)
if tol is not None:
norms_squared = np.sum((y**2), axis=0)
else:
norms_squared = None
return orthogonal_mp_gram(
G,
Xy,
n_nonzero_coefs=n_nonzero_coefs,
tol=tol,
norms_squared=norms_squared,
copy_Gram=copy_X,
copy_Xy=False,
return_path=return_path,
)
if return_path:
coef = np.zeros((X.shape[1], y.shape[1], X.shape[1]))
else:
coef = np.zeros((X.shape[1], y.shape[1]))
n_iters = []
for k in range(y.shape[1]):
out = _cholesky_omp(
X, y[:, k], n_nonzero_coefs, tol, copy_X=copy_X, return_path=return_path
)
if return_path:
_, idx, coefs, n_iter = out
coef = coef[:, :, : len(idx)]
for n_active, x in enumerate(coefs.T):
coef[idx[: n_active + 1], k, n_active] = x[: n_active + 1]
else:
x, idx, n_iter = out
coef[idx, k] = x
n_iters.append(n_iter)
if y.shape[1] == 1:
n_iters = n_iters[0]
if return_n_iter:
return np.squeeze(coef), n_iters
else:
return np.squeeze(coef)
def orthogonal_mp_gram(
Gram,
Xy,
*,
n_nonzero_coefs=None,
tol=None,
norms_squared=None,
copy_Gram=True,
copy_Xy=True,
return_path=False,
return_n_iter=False,
):
"""Gram Orthogonal Matching Pursuit (OMP).
Solves n_targets Orthogonal Matching Pursuit problems using only
the Gram matrix X.T * X and the product X.T * y.
Read more in the :ref:`User Guide <omp>`.
Parameters
----------
Gram : ndarray of shape (n_features, n_features)
Gram matrix of the input data: X.T * X.
Xy : ndarray of shape (n_features,) or (n_features, n_targets)
Input targets multiplied by X: X.T * y.
n_nonzero_coefs : int, default=None
Desired number of non-zero entries in the solution. If None (by
default) this value is set to 10% of n_features.
tol : float, default=None
Maximum squared norm of the residual. If not `None`,
overrides `n_nonzero_coefs`.
norms_squared : array-like of shape (n_targets,), default=None
Squared L2 norms of the lines of y. Required if tol is not None.
copy_Gram : bool, default=True
Whether the gram matrix must be copied by the algorithm. A false
value is only helpful if it is already Fortran-ordered, otherwise a
copy is made anyway.
copy_Xy : bool, default=True
Whether the covariance vector Xy must be copied by the algorithm.
If False, it may be overwritten.
return_path : bool, default=False
Whether to return every value of the nonzero coefficients along the
forward path. Useful for cross-validation.
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Returns
-------
coef : ndarray of shape (n_features,) or (n_features, n_targets)
Coefficients of the OMP solution. If `return_path=True`, this contains
the whole coefficient path. In this case its shape is
(n_features, n_features) or (n_features, n_targets, n_features) and
iterating over the last axis yields coefficients in increasing order
of active features.
n_iters : array-like or int
Number of active features across every target. Returned only if
`return_n_iter` is set to True.
See Also
--------
OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP).
orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
lars_path : Compute Least Angle Regression or Lasso path using
LARS algorithm.
sklearn.decomposition.sparse_encode : Generic sparse coding.
Each column of the result is the solution to a Lasso problem.
Notes
-----
Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
(https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
"""
Gram = check_array(Gram, order="F", copy=copy_Gram)
Xy = np.asarray(Xy)
if Xy.ndim > 1 and Xy.shape[1] > 1:
# or subsequent target will be affected
copy_Gram = True
if Xy.ndim == 1:
Xy = Xy[:, np.newaxis]
if tol is not None:
norms_squared = [norms_squared]
if copy_Xy or not Xy.flags.writeable:
# Make the copy once instead of many times in _gram_omp itself.
Xy = Xy.copy()
if n_nonzero_coefs is None and tol is None:
n_nonzero_coefs = int(0.1 * len(Gram))
if tol is not None and norms_squared is None:
raise ValueError(
"Gram OMP needs the precomputed norms in order "
"to evaluate the error sum of squares."
)
if tol is not None and tol < 0:
raise ValueError("Epsilon cannot be negative")
if tol is None and n_nonzero_coefs <= 0:
raise ValueError("The number of atoms must be positive")
if tol is None and n_nonzero_coefs > len(Gram):
raise ValueError(
"The number of atoms cannot be more than the number of features"
)
if return_path:
coef = np.zeros((len(Gram), Xy.shape[1], len(Gram)), dtype=Gram.dtype)
else:
coef = np.zeros((len(Gram), Xy.shape[1]), dtype=Gram.dtype)
n_iters = []
for k in range(Xy.shape[1]):
out = _gram_omp(
Gram,
Xy[:, k],
n_nonzero_coefs,
norms_squared[k] if tol is not None else None,
tol,
copy_Gram=copy_Gram,
copy_Xy=False,
return_path=return_path,
)
if return_path:
_, idx, coefs, n_iter = out
coef = coef[:, :, : len(idx)]
for n_active, x in enumerate(coefs.T):
coef[idx[: n_active + 1], k, n_active] = x[: n_active + 1]
else:
x, idx, n_iter = out
coef[idx, k] = x
n_iters.append(n_iter)
if Xy.shape[1] == 1:
n_iters = n_iters[0]
if return_n_iter:
return np.squeeze(coef), n_iters
else:
return np.squeeze(coef)
| class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel): | 0 | 2023-10-07 13:19:48+00:00 | 16k |
hellloxiaotian/KDNet | test_ccpd.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 json
import os
import numpy as np
import torch
import yaml
from pathlib import Path
from threading import Thread
from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized, TracedModel
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval | 11,939 | for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
with torch.no_grad():
# Run model
t = time_synchronized()
out, train_out = model(img, augment=augment) # inference and training outputs
t0 += time_synchronized() - t
# Compute loss
if compute_loss:
loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
t = time_synchronized()
out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
t1 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(out):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
seen += 1
if len(pred) == 0:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Predictions
predn = pred.clone()
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# W&B logging - Media Panel Plots
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None
# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if plots and batch_i < 3:
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
|
def test(data,
weights=None,
batch_size=32,
imgsz=640,
conf_thres=0.001,
iou_thres=0.6, # for NMS
save_json=False,
single_cls=False,
augment=False,
verbose=False,
model=None,
dataloader=None,
save_dir=Path(''), # for saving images
save_txt=False, # for auto-labelling
save_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True,
wandb_logger=None,
compute_loss=None,
half_precision=True,
trace=False,
is_coco=False,
v5_metric=False):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device = next(model.parameters()).device # get model device
else: # called directly
set_logging()
device = select_device(opt.device, batch_size=batch_size)
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(imgsz, s=gs) # check img_size
if trace:
model = TracedModel(model, device, imgsz)
# Half
half = device.type != 'cpu' and half_precision # half precision only supported on CUDA
if half:
model.half()
# Configure
model.eval()
if isinstance(data, str):
is_coco = data.endswith('coco.yaml')
with open(data) as f:
data = yaml.load(f, Loader=yaml.SafeLoader)
check_dataset(data) # check
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
# Logging
log_imgs = 0
if wandb_logger and wandb_logger.wandb:
log_imgs = min(wandb_logger.log_imgs, 100)
# Dataloader
if not training:
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
prefix=colorstr(f'{task}: '))[0]
if v5_metric:
print("Testing with YOLOv5 AP metric...")
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
with torch.no_grad():
# Run model
t = time_synchronized()
out, train_out = model(img, augment=augment) # inference and training outputs
t0 += time_synchronized() - t
# Compute loss
if compute_loss:
loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
t = time_synchronized()
out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
t1 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(out):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
seen += 1
if len(pred) == 0:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Predictions
predn = pred.clone()
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# W&B logging - Media Panel Plots
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None
# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if plots and batch_i < 3:
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any(): | p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names) | 15 | 2023-10-08 13:05:58+00:00 | 16k |
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 | 13,447 | 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,
| # 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): | 6 | 2023-10-12 02:01:46+00:00 | 16k |
azuline/rose | rose/watcher.py | [
{
"identifier": "update_cache_evict_nonexistent_collages",
"path": "rose/cache.py",
"snippet": "def update_cache_evict_nonexistent_collages(c: Config) -> None:\n logger.debug(\"Evicting cached collages that are not on disk\")\n collage_names: list[str] = []\n for f in os.scandir(c.music_source_... | import asyncio
import contextlib
import logging
import sys
import time
from dataclasses import dataclass
from pathlib import Path
from queue import Empty, Queue
from typing import Literal
from watchdog.events import (
FileSystemEvent,
FileSystemEventHandler,
FileSystemMovedEvent,
)
from watchdog.observers import Observer
from rose.cache import (
update_cache_evict_nonexistent_collages,
update_cache_evict_nonexistent_playlists,
update_cache_evict_nonexistent_releases,
update_cache_for_collages,
update_cache_for_playlists,
update_cache_for_releases,
)
from rose.config import Config | 10,825 | """
The watcher module is architected to decouple the event listener from the event processor.
This is because we must introduce a wait into the event processor, but we do not want to block the
event listener. In order to performantly introduce such a wait, we've made the event processor
async. However, the event listener uses a non-async library, so the event listener is sync.
Why do we need to introduce a wait time into the event processor? Changes to releases occur across
an entire directory, but change events come in one file at a time. If we respond to a file event too
early, we may do silly things like write a new `.rose.{uuid}.toml` file, only to have a pre-existing
one suddenly appear after. We only want to operate on a release once all files have finished
changing.
Let's dive down into the details. The watcher is a process that spawns a background thread and then
runs an event loop. Hierarchically:
Process
Thread -> watchdog/inotify listener that enqueues events
Event Loop -> processes+debounces events asynchronously from the queue
"""
logger = logging.getLogger(__name__)
# Shorten wait times if we are in a test. This way a test runs faster. This is wasteful in
# production though.
WAIT_DIVIDER = 1 if "pytest" not in sys.modules else 10
EventType = Literal["created", "deleted", "modified", "moved"]
EVENT_TYPES: list[EventType] = ["created", "deleted", "modified", "moved"]
@dataclass(frozen=True)
class WatchdogEvent:
type: EventType
collage: str | None = None
playlist: str | None = None
release: Path | None = None
class EventHandler(FileSystemEventHandler): # pragma: no cover
def __init__(self, config: Config, queue: Queue[WatchdogEvent]):
super().__init__()
self.config = config
self.queue = queue
def on_any_event(self, event: FileSystemEvent) -> None:
super().on_any_event(event) # type: ignore
path = event.dest_path if isinstance(event, FileSystemMovedEvent) else event.src_path
logger.debug(f"Notified of {event.event_type} event for {path}")
etype: EventType = event.event_type # type: ignore
if etype not in EVENT_TYPES:
return
# Collage event.
relative_path = path.removeprefix(str(self.config.music_source_dir) + "/")
if relative_path.startswith("!collages/"):
if not relative_path.endswith(".toml"):
return
collage = relative_path.removeprefix("!collages/").removesuffix(".toml")
logger.debug(f"Queueing {etype} event on collage {collage}")
self.queue.put(WatchdogEvent(collage=collage, type=etype))
return
# Playlist event.
if relative_path.startswith("!playlists/"):
if not relative_path.endswith(".toml"):
return
playlist = relative_path.removeprefix("!playlists/").removesuffix(".toml")
logger.debug(f"Queueing {etype} event on playlist {playlist}")
self.queue.put(WatchdogEvent(playlist=playlist, type=etype))
return
# Release event.
with contextlib.suppress(IndexError):
final_path_part = Path(relative_path).parts[0]
if final_path_part == "/":
return
release_dir = self.config.music_source_dir / final_path_part
logger.debug(f"Queueing {etype} event on release {release_dir}")
self.queue.put(WatchdogEvent(release=release_dir, type=etype))
async def handle_event(
c: Config,
e: WatchdogEvent,
wait: float | None = None,
) -> None: # pragma: no cover
if wait:
await asyncio.sleep(wait / WAIT_DIVIDER)
if e.type == "created" or e.type == "modified":
if e.collage:
| """
The watcher module is architected to decouple the event listener from the event processor.
This is because we must introduce a wait into the event processor, but we do not want to block the
event listener. In order to performantly introduce such a wait, we've made the event processor
async. However, the event listener uses a non-async library, so the event listener is sync.
Why do we need to introduce a wait time into the event processor? Changes to releases occur across
an entire directory, but change events come in one file at a time. If we respond to a file event too
early, we may do silly things like write a new `.rose.{uuid}.toml` file, only to have a pre-existing
one suddenly appear after. We only want to operate on a release once all files have finished
changing.
Let's dive down into the details. The watcher is a process that spawns a background thread and then
runs an event loop. Hierarchically:
Process
Thread -> watchdog/inotify listener that enqueues events
Event Loop -> processes+debounces events asynchronously from the queue
"""
logger = logging.getLogger(__name__)
# Shorten wait times if we are in a test. This way a test runs faster. This is wasteful in
# production though.
WAIT_DIVIDER = 1 if "pytest" not in sys.modules else 10
EventType = Literal["created", "deleted", "modified", "moved"]
EVENT_TYPES: list[EventType] = ["created", "deleted", "modified", "moved"]
@dataclass(frozen=True)
class WatchdogEvent:
type: EventType
collage: str | None = None
playlist: str | None = None
release: Path | None = None
class EventHandler(FileSystemEventHandler): # pragma: no cover
def __init__(self, config: Config, queue: Queue[WatchdogEvent]):
super().__init__()
self.config = config
self.queue = queue
def on_any_event(self, event: FileSystemEvent) -> None:
super().on_any_event(event) # type: ignore
path = event.dest_path if isinstance(event, FileSystemMovedEvent) else event.src_path
logger.debug(f"Notified of {event.event_type} event for {path}")
etype: EventType = event.event_type # type: ignore
if etype not in EVENT_TYPES:
return
# Collage event.
relative_path = path.removeprefix(str(self.config.music_source_dir) + "/")
if relative_path.startswith("!collages/"):
if not relative_path.endswith(".toml"):
return
collage = relative_path.removeprefix("!collages/").removesuffix(".toml")
logger.debug(f"Queueing {etype} event on collage {collage}")
self.queue.put(WatchdogEvent(collage=collage, type=etype))
return
# Playlist event.
if relative_path.startswith("!playlists/"):
if not relative_path.endswith(".toml"):
return
playlist = relative_path.removeprefix("!playlists/").removesuffix(".toml")
logger.debug(f"Queueing {etype} event on playlist {playlist}")
self.queue.put(WatchdogEvent(playlist=playlist, type=etype))
return
# Release event.
with contextlib.suppress(IndexError):
final_path_part = Path(relative_path).parts[0]
if final_path_part == "/":
return
release_dir = self.config.music_source_dir / final_path_part
logger.debug(f"Queueing {etype} event on release {release_dir}")
self.queue.put(WatchdogEvent(release=release_dir, type=etype))
async def handle_event(
c: Config,
e: WatchdogEvent,
wait: float | None = None,
) -> None: # pragma: no cover
if wait:
await asyncio.sleep(wait / WAIT_DIVIDER)
if e.type == "created" or e.type == "modified":
if e.collage: | update_cache_for_collages(c, [e.collage]) | 3 | 2023-10-09 14:42:23+00:00 | 16k |
grainseed/monitask | sam/segment_anything/build_sam.py | [
{
"identifier": "Sam",
"path": "sam/segment_anything/modeling/sam.py",
"snippet": "class Sam(nn.Module):\r\n mask_threshold: float = 0.0\r\n image_format: str = \"RGB\"\r\n\r\n def __init__(\r\n self,\r\n image_encoder: ImageEncoderViT,\r\n prompt_encoder: PromptEncoder,\r\... | import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder,MaskDecoderHQ, PromptEncoder, Sam, TwoWayTransformer, TinyViT
| 11,626 | mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.0,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
layer_lr_decay=0.8
),
prompt_encoder=PromptEncoder(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size),
mask_in_chans=16,
),
mask_decoder=MaskDecoderHQ(
num_multimask_outputs=3,
transformer=TwoWayTransformer(
depth=2,
embedding_dim=prompt_embed_dim,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=prompt_embed_dim,
iou_head_depth=3,
iou_head_hidden_dim=256,
vit_dim=160,
),
pixel_mean=[123.675, 116.28, 103.53],
pixel_std=[58.395, 57.12, 57.375],
)
mobile_sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
device = "cuda" if torch.cuda.is_available() else "cpu"
state_dict = torch.load(f, map_location=device)
info = mobile_sam.load_state_dict(state_dict, strict=False)
#print(info)
for n, p in mobile_sam.named_parameters():
if 'hf_token' not in n and 'hf_mlp' not in n and 'compress_vit_feat' not in n and 'embedding_encoder' not in n and 'embedding_maskfeature' not in n:
p.requires_grad = False
return mobile_sam
def build_mobile_sam(checkpoint=None,device="cpu"):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.0,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
layer_lr_decay=0.8
),
prompt_encoder=PromptEncoder(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size),
mask_in_chans=16,
),
mask_decoder=MaskDecoder(
num_multimask_outputs=3,
transformer=TwoWayTransformer(
depth=2,
embedding_dim=prompt_embed_dim,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=prompt_embed_dim,
iou_head_depth=3,
iou_head_hidden_dim=256,
),
pixel_mean=[123.675, 116.28, 103.53],
pixel_std=[58.395, 57.12, 57.375],
)
mobile_sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
state_dict = torch.load(f,map_location=torch.device(device))
mobile_sam.load_state_dict(state_dict,strict=True)
return mobile_sam
sam_model_registry = {
"default": build_sam_vit_h,
"vit_h": build_sam_vit_h,
"vit_l": build_sam_vit_l,
"vit_b": build_sam_vit_b,
"vit_tiny": build_sam_vit_t,
"mobile_sam": build_mobile_sam,
}
def _build_sam(
encoder_embed_dim,
encoder_depth,
encoder_num_heads,
encoder_global_attn_indexes,
checkpoint=None,
device="gpu",
):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
sam = Sam(
| # 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.
def build_sam_vit_h(checkpoint=None,device="cpu"):
return _build_sam(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31],
checkpoint=checkpoint,
)
build_sam = build_sam_vit_h
def build_sam_vit_l(checkpoint=None,device="cpu"):
return _build_sam(
encoder_embed_dim=1024,
encoder_depth=24,
encoder_num_heads=16,
encoder_global_attn_indexes=[5, 11, 17, 23],
checkpoint=checkpoint,
)
def build_sam_vit_b(checkpoint=None,device="cpu"):
return _build_sam(
encoder_embed_dim=768,
encoder_depth=12,
encoder_num_heads=12,
encoder_global_attn_indexes=[2, 5, 8, 11],
checkpoint=checkpoint,
)
def build_sam_vit_t(checkpoint=None,device="cpu"):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.0,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
layer_lr_decay=0.8
),
prompt_encoder=PromptEncoder(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size),
mask_in_chans=16,
),
mask_decoder=MaskDecoderHQ(
num_multimask_outputs=3,
transformer=TwoWayTransformer(
depth=2,
embedding_dim=prompt_embed_dim,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=prompt_embed_dim,
iou_head_depth=3,
iou_head_hidden_dim=256,
vit_dim=160,
),
pixel_mean=[123.675, 116.28, 103.53],
pixel_std=[58.395, 57.12, 57.375],
)
mobile_sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
device = "cuda" if torch.cuda.is_available() else "cpu"
state_dict = torch.load(f, map_location=device)
info = mobile_sam.load_state_dict(state_dict, strict=False)
#print(info)
for n, p in mobile_sam.named_parameters():
if 'hf_token' not in n and 'hf_mlp' not in n and 'compress_vit_feat' not in n and 'embedding_encoder' not in n and 'embedding_maskfeature' not in n:
p.requires_grad = False
return mobile_sam
def build_mobile_sam(checkpoint=None,device="cpu"):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
mobile_sam = Sam(
image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.0,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
layer_lr_decay=0.8
),
prompt_encoder=PromptEncoder(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size),
mask_in_chans=16,
),
mask_decoder=MaskDecoder(
num_multimask_outputs=3,
transformer=TwoWayTransformer(
depth=2,
embedding_dim=prompt_embed_dim,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=prompt_embed_dim,
iou_head_depth=3,
iou_head_hidden_dim=256,
),
pixel_mean=[123.675, 116.28, 103.53],
pixel_std=[58.395, 57.12, 57.375],
)
mobile_sam.eval()
if checkpoint is not None:
with open(checkpoint, "rb") as f:
state_dict = torch.load(f,map_location=torch.device(device))
mobile_sam.load_state_dict(state_dict,strict=True)
return mobile_sam
sam_model_registry = {
"default": build_sam_vit_h,
"vit_h": build_sam_vit_h,
"vit_l": build_sam_vit_l,
"vit_b": build_sam_vit_b,
"vit_tiny": build_sam_vit_t,
"mobile_sam": build_mobile_sam,
}
def _build_sam(
encoder_embed_dim,
encoder_depth,
encoder_num_heads,
encoder_global_attn_indexes,
checkpoint=None,
device="gpu",
):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
sam = Sam(
| image_encoder=ImageEncoderViT(
| 1 | 2023-10-14 13:45:54+00:00 | 16k |
zhaoyizhou1123/mbrcsl | examples/roboverse/run_mbrcsl_mlpdyn_roboverse.py | [
{
"identifier": "EnsembleDynamicsModel",
"path": "offlinerlkit/modules/dynamics_module.py",
"snippet": "class EnsembleDynamicsModel(nn.Module):\n def __init__(\n self,\n obs_dim: int,\n action_dim: int,\n hidden_dims: Union[List[int], Tuple[int]],\n num_ensemble: in... | import numpy as np
import torch
import roboverse
import argparse
import os
import random
import pickle
import datetime
from copy import deepcopy
from typing import Dict, Tuple
from collections import defaultdict
from offlinerlkit.modules import EnsembleDynamicsModel
from offlinerlkit.dynamics import EnsembleDynamics
from offlinerlkit.utils.termination_fns import get_termination_fn
from offlinerlkit.utils.scaler import StandardScaler
from offlinerlkit.utils.roboverse_utils import PickPlaceObsWrapper, DoubleDrawerObsWrapper, get_pickplace_dataset, get_doubledrawer_dataset
from offlinerlkit.utils.logger import Logger, make_log_dirs
from offlinerlkit.policy_trainer import RcslPolicyTrainer, DiffusionPolicyTrainer
from offlinerlkit.utils.none_or_str import none_or_str
from offlinerlkit.policy import SimpleDiffusionPolicy, AutoregressivePolicy | 14,115 |
'''
Recommended hyperparameters:
pickplace, horizon=40, behavior_epoch=30
doubledraweropen, horizon=50, behavior_epoch=40
doubledrawercloseopen, horizon=80, behavior_epoch=40
'''
def get_args():
parser = argparse.ArgumentParser()
# general
parser.add_argument("--algo-name", type=str, default="mbrcsl_mlpdyn")
parser.add_argument("--task", type=str, default="pickplace", help="task name")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=1, help="Dataloader workers, align with cpu number")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--last_eval", action="store_false")
# env config
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--horizon', type=int, default=40, help="max path length for pickplace")
# transformer_autoregressive dynamics
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("--load_dynamics_path", type=none_or_str, default=None)
# Behavior policy (diffusion)
parser.add_argument("--behavior_epoch", type=int, default=30)
parser.add_argument("--num_diffusion_iters", type=int, default=5, help="Number of diffusion steps")
parser.add_argument('--behavior_batch', type=int, default=256)
parser.add_argument('--load_diffusion_path', type=none_or_str, default=None)
parser.add_argument('--task_weight', type=float, default=1.4, help="Weight on task data when training diffusion policy")
parser.add_argument('--sample_ratio', type=float, default=0.8, help="Use (sample_ratio * num_total_data) data to train diffusion policy")
# Rollout
parser.add_argument('--rollout_ckpt_path', type=none_or_str, default=None, help="file dir, used to load/store rollout trajs" )
parser.add_argument('--rollout_epoch', type=int, default=200, help="Max number of epochs to rollout the policy")
parser.add_argument('--num_need_traj', type=int, default=5000, help="Needed valid trajs in rollout")
parser.add_argument("--rollout-batch", type=int, default=200, help="Number of trajs to be sampled at one time")
# RCSL policy (mlp)
parser.add_argument("--rcsl_hidden_dims", type=int, nargs='*', default=[200, 200, 200, 200])
parser.add_argument("--rcsl_lr", type=float, default=1e-3)
parser.add_argument("--rcsl_batch", type=int, default=256)
parser.add_argument("--rcsl_epoch", type=int, default=100)
parser.add_argument("--eval_episodes", type=int, default=100)
parser.add_argument("--holdout_ratio", type=float, default=0.2)
return parser.parse_args()
def rollout_simple(
init_obss: np.ndarray,
dynamics,
|
'''
Recommended hyperparameters:
pickplace, horizon=40, behavior_epoch=30
doubledraweropen, horizon=50, behavior_epoch=40
doubledrawercloseopen, horizon=80, behavior_epoch=40
'''
def get_args():
parser = argparse.ArgumentParser()
# general
parser.add_argument("--algo-name", type=str, default="mbrcsl_mlpdyn")
parser.add_argument("--task", type=str, default="pickplace", help="task name")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=1, help="Dataloader workers, align with cpu number")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--last_eval", action="store_false")
# env config
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--horizon', type=int, default=40, help="max path length for pickplace")
# transformer_autoregressive dynamics
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("--load_dynamics_path", type=none_or_str, default=None)
# Behavior policy (diffusion)
parser.add_argument("--behavior_epoch", type=int, default=30)
parser.add_argument("--num_diffusion_iters", type=int, default=5, help="Number of diffusion steps")
parser.add_argument('--behavior_batch', type=int, default=256)
parser.add_argument('--load_diffusion_path', type=none_or_str, default=None)
parser.add_argument('--task_weight', type=float, default=1.4, help="Weight on task data when training diffusion policy")
parser.add_argument('--sample_ratio', type=float, default=0.8, help="Use (sample_ratio * num_total_data) data to train diffusion policy")
# Rollout
parser.add_argument('--rollout_ckpt_path', type=none_or_str, default=None, help="file dir, used to load/store rollout trajs" )
parser.add_argument('--rollout_epoch', type=int, default=200, help="Max number of epochs to rollout the policy")
parser.add_argument('--num_need_traj', type=int, default=5000, help="Needed valid trajs in rollout")
parser.add_argument("--rollout-batch", type=int, default=200, help="Number of trajs to be sampled at one time")
# RCSL policy (mlp)
parser.add_argument("--rcsl_hidden_dims", type=int, nargs='*', default=[200, 200, 200, 200])
parser.add_argument("--rcsl_lr", type=float, default=1e-3)
parser.add_argument("--rcsl_batch", type=int, default=256)
parser.add_argument("--rcsl_epoch", type=int, default=100)
parser.add_argument("--eval_episodes", type=int, default=100)
parser.add_argument("--holdout_ratio", type=float, default=0.2)
return parser.parse_args()
def rollout_simple(
init_obss: np.ndarray,
dynamics, | rollout_policy: SimpleDiffusionPolicy, | 13 | 2023-10-11 08:36:06+00:00 | 16k |
wilhelmagren/finq | finq/portfolio.py | [
{
"identifier": "Asset",
"path": "finq/asset.py",
"snippet": "class Asset(object):\n \"\"\" \"\"\"\n\n def __init__(\n self,\n data: pd.Series,\n name: str,\n *,\n market: Optional[str] = None,\n index_name: Optional[str] = None,\n price_type: str =... | import logging
import pandas as pd
import numpy as np
import scipy.optimize as scipyopt
import matplotlib.pyplot as plt
from functools import wraps
from tqdm import tqdm
from finq.asset import Asset
from finq.datasets import Dataset
from finq.exceptions import (
FinqError,
InvalidCombinationOfArgumentsError,
InvalidPortfolioWeightsError,
ObjectiveFunctionError,
PortfolioNotYetOptimizedError,
)
from finq.formulas import (
period_returns,
sharpe_ratio,
weighted_returns,
weighted_variance,
)
from typing import (
Any,
Callable,
List,
Dict,
Tuple,
Union,
Optional,
) | 10,853 | return period_returns(self._data, period=period)
def daily_returns_mean(self) -> float:
""" """
return np.mean(period_returns(self._data, period=1), axis=1)
def yearly_returns_mean(self) -> float:
""" """
return np.mean(period_returns(self._data, period=self._n_trading_days), axis=1)
def period_returns_mean(self, period: int) -> float:
""" """
return np.mean(period_returns(self._data, period=period), axis=1)
def daily_covariance(self) -> np.ndarray:
""" """
return np.cov(period_returns(self._data, period=1), rowvar=True)
def yearly_covariance(self) -> np.ndarray:
""" """
return np.cov(
period_returns(self._data, period=self._n_trading_days), rowvar=True
)
def period_covariance(self, period: int) -> np.ndarray:
""" """
return np.cov(period_returns(self._data, period=period), rowvar=True)
def set_objective_function(
self,
function: Callable,
*args: Tuple[Any, ...],
):
""" """
self._objective_function = function
self._objective_function_args = args
def set_objective_constraints(
self,
*constraints,
):
""" """
self._objective_constraints = [{"type": t, "fun": c} for (t, c) in constraints]
def set_objective_bounds(
self,
bounds: Union[Tuple[int, ...], List[Tuple[int, ...]]],
):
""" """
if isinstance(bounds, tuple):
bounds = [bounds for _ in range(self._data.shape[0])]
self._objective_bounds = bounds
def sample_random_portfolios(
self,
n_samples: int,
*,
distribution: Union[str, Callable] = "lognormal",
**kwargs: Dict[str, Any],
):
""" """
if isinstance(distribution, str):
distribution = self._weight_initializations.get(distribution, None)
if distribution is None:
raise ValueError(
"You provided a non valid weight initialization distribution."
)
portfolios = []
for i in (bar := tqdm(range(n_samples))):
if i % 10:
bar.set_description(
f"Sampling random portfolio {i + 1} from "
f"{distribution.__name__} distribution"
)
portfolio = distribution(**kwargs)
portfolios.append(portfolio / portfolio.sum())
self._random_portfolios = np.transpose(np.concatenate(portfolios, axis=1))
@check_valid_weights
def variance(self) -> float:
""" """
return weighted_variance(
self._weights.T,
self.daily_covariance(),
)
@check_valid_weights
def volatility(self) -> float:
""" """
return np.sqrt(
weighted_variance(
self._weights.T,
self.daily_covariance(),
),
)
@check_valid_weights
def expected_returns(self) -> float:
""" """
return weighted_returns(self._weights.T, self.daily_returns_mean())
@check_valid_weights
| """
MIT License
Copyright (c) 2023 Wilhelm Ågren
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 created: 2023-10-20
Last updated: 2023-11-10
"""
log = logging.getLogger(__name__)
class Portfolio(object):
""" """
# For a full list of `scipy` optimization methods and references, see the link below.
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
_supported_optimization_methods = (
"Nelder-Mead",
"Powell",
"CG",
"BFGS",
"Newton-CG",
"L-BFGS-B",
"TNC",
"COBYLA",
"SLSQP",
"trust-constr",
"dogleg",
"trust-ncg",
"trust-exact",
"trust-krylov",
)
_weight_initializations = {
"lognormal": np.random.lognormal,
"normal": np.random.normal,
"uniform": np.random.uniform,
}
def __init__(
self,
data: Union[Dataset, List[Asset], np.ndarray, pd.DataFrame],
*,
weights: Optional[np.ndarray] = None,
names: Optional[Union[Dict[str, str], List[str]]] = None,
symbols: Optional[Union[Dict[str, str], List[str]]] = None,
confidence_level: float = 0.95,
risk_free_rate: float = 5e-3,
n_trading_days: int = 252,
objective_function: Optional[Callable] = None,
objective_function_args: Tuple[Any, ...] = (),
objective_bounds: Optional[List[Tuple[int, ...]]] = None,
objective_constraints: Optional[Tuple[Dict, ...]] = None,
):
""" """
if isinstance(data, Dataset):
assets = data.as_assets()
data = list(assets.values())
symbols = list(assets.keys())
if not isinstance(data, list):
if names is None and symbols is None and not isinstance(data, pd.DataFrame):
raise InvalidCombinationOfArgumentsError(
"You need to provide the names and ticker symbols of each asset that you "
"want to include in your portfolio if the data you provided is neither a "
"`list` of `Asset` objects or a `pd.DataFrame`. You can also try "
"providing only one of the arguments `names` and `symbols`, but then as "
"a dictionary of the form `key=name` `value=symbol`."
)
if isinstance(data, list):
symbols = [a.name for a in data]
data = np.array([a.data for a in data])
if isinstance(data, pd.DataFrame):
symbols = data.columns
data = data.to_numpy().T
if isinstance(names, dict):
symbols = list(names.values())
names = list(names.keys())
if isinstance(symbols, dict):
names = list(symbols.keys())
symbols = list(symbols.values())
self._data = data
self._weights = weights
self._names = names
self._symbols = symbols
self._confidence_level = confidence_level
self._risk_free_rate = risk_free_rate
self._n_trading_days = n_trading_days
self._random_portfolios = None
self._objective_function = objective_function
self._objective_function_args = objective_function_args
self._objective_bounds = objective_bounds
self._objective_constraints = objective_constraints
def weights_are_normalized(self) -> bool:
""" """
return np.allclose(self._weights.sum(), 1.0, rtol=1e-6)
def initialize_random_weights(
self,
distribution: Union[str, Callable],
*args: Tuple[Any, ...],
**kwargs: Dict[str, Any],
):
""" """
if isinstance(distribution, str):
distribution = self._weight_initializations.get(distribution, None)
if distribution is None:
raise ValueError(
"You provided a non valid weight initialization distribution."
)
weights = distribution(*args, **kwargs)
self._weights = weights / weights.sum()
def check_valid_weights(func) -> Callable:
""" """
@wraps(func)
def _check_valid_weights(self, *args, **kwargs) -> Optional[FinqError]:
""" """
if self._weights is None:
raise PortfolioNotYetOptimizedError(
"Portfolio weights are `None`. Perhaps you have not yet optimized it? "
)
if not self.weights_are_normalized():
raise InvalidPortfolioWeightsError(
"Your portfolio weights are not normalized. Make sure to normalize them "
"(they sum to one) before calculating any analytical quantities. "
)
return func(self, *args, **kwargs)
return _check_valid_weights
def daily_returns(self) -> np.ndarray:
""" """
return period_returns(self._data, period=1)
def yearly_returns(self) -> np.ndarray:
""" """
return period_returns(self._data, period=self._n_trading_days)
def period_returns(self, period: int) -> np.ndarray:
""" """
return period_returns(self._data, period=period)
def daily_returns_mean(self) -> float:
""" """
return np.mean(period_returns(self._data, period=1), axis=1)
def yearly_returns_mean(self) -> float:
""" """
return np.mean(period_returns(self._data, period=self._n_trading_days), axis=1)
def period_returns_mean(self, period: int) -> float:
""" """
return np.mean(period_returns(self._data, period=period), axis=1)
def daily_covariance(self) -> np.ndarray:
""" """
return np.cov(period_returns(self._data, period=1), rowvar=True)
def yearly_covariance(self) -> np.ndarray:
""" """
return np.cov(
period_returns(self._data, period=self._n_trading_days), rowvar=True
)
def period_covariance(self, period: int) -> np.ndarray:
""" """
return np.cov(period_returns(self._data, period=period), rowvar=True)
def set_objective_function(
self,
function: Callable,
*args: Tuple[Any, ...],
):
""" """
self._objective_function = function
self._objective_function_args = args
def set_objective_constraints(
self,
*constraints,
):
""" """
self._objective_constraints = [{"type": t, "fun": c} for (t, c) in constraints]
def set_objective_bounds(
self,
bounds: Union[Tuple[int, ...], List[Tuple[int, ...]]],
):
""" """
if isinstance(bounds, tuple):
bounds = [bounds for _ in range(self._data.shape[0])]
self._objective_bounds = bounds
def sample_random_portfolios(
self,
n_samples: int,
*,
distribution: Union[str, Callable] = "lognormal",
**kwargs: Dict[str, Any],
):
""" """
if isinstance(distribution, str):
distribution = self._weight_initializations.get(distribution, None)
if distribution is None:
raise ValueError(
"You provided a non valid weight initialization distribution."
)
portfolios = []
for i in (bar := tqdm(range(n_samples))):
if i % 10:
bar.set_description(
f"Sampling random portfolio {i + 1} from "
f"{distribution.__name__} distribution"
)
portfolio = distribution(**kwargs)
portfolios.append(portfolio / portfolio.sum())
self._random_portfolios = np.transpose(np.concatenate(portfolios, axis=1))
@check_valid_weights
def variance(self) -> float:
""" """
return weighted_variance(
self._weights.T,
self.daily_covariance(),
)
@check_valid_weights
def volatility(self) -> float:
""" """
return np.sqrt(
weighted_variance(
self._weights.T,
self.daily_covariance(),
),
)
@check_valid_weights
def expected_returns(self) -> float:
""" """
return weighted_returns(self._weights.T, self.daily_returns_mean())
@check_valid_weights | def sharpe_ratio(self) -> float: | 8 | 2023-10-09 19:02:54+00:00 | 16k |
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 | 11,609 |
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:
|
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) | 12 | 2023-10-10 09:40:10+00:00 | 16k |
cpuimage/minSDXLTF | stable_diffusion_xl/stable_diffusion_xl.py | [
{
"identifier": "SimpleTokenizer",
"path": "stable_diffusion_xl/clip_tokenizer.py",
"snippet": "class SimpleTokenizer:\n def __init__(self, bpe_path=None):\n bpe_path = bpe_path or tf.keras.utils.get_file(\n \"bpe_simple_vocab_16e6.txt.gz\",\n \"https://github.com/openai/... | import numpy as np
import tensorflow as tf
from PIL import Image
from scipy.ndimage import correlate1d
from .clip_tokenizer import SimpleTokenizer
from .diffusion_model import DiffusionXLModel
from .image_decoder import ImageDecoder
from .image_encoder import ImageEncoder
from .long_prompt_weighting import get_weighted_text_embeddings
from .scheduler import Scheduler
from .text_encoder_laion import TextEncoderLaion, TextEncoderLaionProj
from .text_encoder_openai import TextEncoderOpenAi | 12,844 | def tokenizer(self):
"""tokenizer returns the tokenizer used for text inputs.
Can be overriden for tasks like textual inversion where the tokenizer
needs to be modified.
"""
if self._tokenizer is None:
self._tokenizer = SimpleTokenizer()
return self._tokenizer
def _get_initial_diffusion_noise(self, batch_size, seed):
if seed is not None:
try:
seed = int(seed)
except:
seed = None
return tf.random.stateless_normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4),
seed=[seed, seed],
)
else:
return tf.random.normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4)
)
def _get_initial_diffusion_latent(self, batch_size, init_latent=None, init_time=None, seed=None,
noise=None):
if noise is None:
noise = self._get_initial_diffusion_noise(batch_size, seed=seed)
if init_latent is None:
latent = noise
else:
latent = self.scheduler.signal_rates[init_time] * np.repeat(init_latent, batch_size, axis=0) + \
self.scheduler.noise_rates[init_time] * noise
return latent
@staticmethod
def _get_pos_ids():
return np.asarray([list(range(MAX_PROMPT_LENGTH))], dtype=np.int32)
class StableDiffusionXL(StableDiffusionXLBase):
"""Keras implementation of Stable Diffusion.
Note that the StableDiffusionXL API, as well as the APIs of the sub-components
of StableDiffusionXL (e.g. ImageEncoder, DiffusionModel) should be considered
unstable at this point. We do not guarantee backwards compatability for
future changes to these APIs.
Stable Diffusion is a powerful image generation model that can be used,
among other things, to generate pictures according to a short text
description (called a "prompt").
Arguments:
img_height: int, height of the images to generate, in pixel. Note that
only multiples of 128 are supported; the value provided will be
rounded to the nearest valid value. Defaults to 1024.
img_width: int, width of the images to generate, in pixel. Note that
only multiples of 128 are supported; the value provided will be
rounded to the nearest valid value. Defaults to 1024.
jit_compile: bool, whether to compile the underlying models to XLA.
This can lead to a significant speedup on some systems. Defaults to
False.
Example:
```python
from stable_diffusion_xl.stable_diffusion_xl import StableDiffusionXL
from PIL import Image
model = StableDiffusionXL(img_height=1024, img_width=1024, jit_compile=True)
img = model.text_to_image(
prompt="A beautiful horse running through a field",
batch_size=1, # How many images to generate at once
num_steps=25, # Number of iterations (controls image quality)
seed=123, # Set this to always get the same image from the same prompt
)
Image.fromarray(img[0]).save("horse.png")
print("saved at horse.png")
```
References:
- [About Stable Diffusion](https://stability.ai/blog/stable-diffusion-announcement)
- [Original implementation](https://github.com/CompVis/stable-diffusion)
""" # noqa: E501
def __init__(
self,
img_height=1024,
img_width=1024,
jit_compile=True,
unet_ckpt=None,
text_encoder_ckpt=None,
text_encoder2_ckpt=None,
vae_ckpt=None,
):
super().__init__(img_height, img_width, jit_compile)
self.unet_ckpt = unet_ckpt
self.text_encoder_ckpt = text_encoder_ckpt
self.text_encoder2_ckpt = text_encoder2_ckpt
self.vae_ckpt = vae_ckpt
@property
def text_encoder_openai(self):
"""text_encoder returns the text encoder with pretrained weights.
Can be overriden for tasks like textual inversion where the text encoder
needs to be modified.
"""
if self._text_encoder_openai is None:
self._text_encoder_openai = TextEncoderOpenAi(MAX_PROMPT_LENGTH, ckpt_path=self.text_encoder_ckpt)
if self.jit_compile:
self._text_encoder_openai.compile(jit_compile=True)
return self._text_encoder_openai
@property
def text_encoder_laion(self):
"""text_encoder returns the text encoder with pretrained weights.
Can be overriden for tasks like textual inversion where the text encoder
needs to be modified.
"""
if self._text_encoder_laion is None:
| # Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
"""Keras implementation of StableDiffusionXL."""
MAX_PROMPT_LENGTH = 77
class StableDiffusionXLBase:
"""Base class for stable diffusion xl model."""
def __init__(self, img_height=1024, img_width=1024, jit_compile=False,
active_lcm=False):
self.img_height = img_height
self.img_width = img_width
# lazy initialize the component models and the tokenizer
self._image_encoder = None
self._text_encoder_laion = None
self._text_encoder_laion_proj = None
self._text_encoder_openai = None
self._diffusion_model = None
self._image_decoder = None
self._tokenizer = None
self.jit_compile = jit_compile
self.active_lcm = active_lcm
self.scheduler = Scheduler(active_lcm=active_lcm)
def text_to_image(
self,
prompt,
negative_prompt=None,
batch_size=1,
num_steps=50,
unconditional_guidance_scale=7.5,
seed=None,
original_size=None,
crops_coords_top_left=(0, 0),
target_size=None,
guidance_rescale=0.7,
callback=None):
encoded_text, add_text_embeds = self.encode_text(prompt)
return self.generate_image(
encoded_text,
add_text_embeds,
negative_prompt=negative_prompt,
batch_size=batch_size,
num_steps=num_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
seed=seed,
original_size=original_size,
crops_coords_top_left=crops_coords_top_left,
target_size=target_size,
guidance_rescale=guidance_rescale,
callback=callback)
def image_to_image(
self,
prompt,
negative_prompt=None,
batch_size=1,
num_steps=50,
unconditional_guidance_scale=7.5,
seed=None,
reference_image=None,
reference_image_strength=0.8,
original_size=None,
crops_coords_top_left=(0, 0),
target_size=None,
guidance_rescale=0.7,
callback=None):
encoded_text, add_text_embeds = self.encode_text(prompt)
return self.generate_image(
encoded_text,
add_text_embeds,
negative_prompt=negative_prompt,
batch_size=batch_size,
num_steps=num_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
seed=seed,
reference_image=reference_image,
reference_image_strength=reference_image_strength,
original_size=original_size,
crops_coords_top_left=crops_coords_top_left,
target_size=target_size,
guidance_rescale=guidance_rescale,
callback=callback)
def inpaint(
self,
prompt,
negative_prompt=None,
batch_size=1,
num_steps=50,
unconditional_guidance_scale=7.5,
seed=None,
reference_image=None,
reference_image_strength=0.8,
inpaint_mask=None,
mask_blur_strength=None,
original_size=None,
crops_coords_top_left=(0, 0),
target_size=None,
guidance_rescale=0.7,
callback=None):
encoded_text, add_text_embeds = self.encode_text(prompt)
return self.generate_image(
encoded_text,
add_text_embeds,
negative_prompt=negative_prompt,
batch_size=batch_size,
num_steps=num_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
seed=seed,
reference_image=reference_image,
reference_image_strength=reference_image_strength,
inpaint_mask=inpaint_mask,
mask_blur_strength=mask_blur_strength,
original_size=original_size,
crops_coords_top_left=crops_coords_top_left,
target_size=target_size,
guidance_rescale=guidance_rescale,
callback=callback)
def encode_text(self, prompt):
"""Encodes a prompt into a latent text encoding.
The encoding produced by this method should be used as the
`encoded_text` parameter of `StableDiffusion.generate_image`. Encoding
text separately from generating an image can be used to arbitrarily
modify the text encoding prior to image generation, e.g. for walking
between two prompts.
Args:
prompt: a string to encode, must be 77 tokens or shorter.
Example:
```python
from keras_cv.models import StableDiffusion
model = StableDiffusionXL(img_height=1024, img_width=1024, jit_compile=True)
encoded_text = model.encode_text("Tacos at dawn")
img = model.generate_image(encoded_text)
```
"""
# Tokenize prompt (i.e. starting context)
context_openai, _ = get_weighted_text_embeddings(self.tokenizer, self.text_encoder_openai, prompt,
model_max_length=MAX_PROMPT_LENGTH,
pad_token_id=49407)
context_laion, add_text_embeds = get_weighted_text_embeddings(self.tokenizer, self.text_encoder_laion, prompt,
model_max_length=MAX_PROMPT_LENGTH,
pad_token_id=0,
text_encoder_pool=self.text_encoder_laion_proj)
return np.concatenate([context_openai, context_laion], axis=-1), add_text_embeds
def gaussian_blur(self, image, radius=3, h_axis=1, v_axis=2):
def build_filter1d(kernel_size):
if kernel_size == 1:
filter1d = [1]
else:
triangle = [[1, 1]]
for i in range(1, kernel_size - 1):
cur_row = [1]
prev_row = triangle[i - 1]
for j in range(len(prev_row) - 1):
cur_row.append(prev_row[j] + prev_row[j + 1])
cur_row.append(1)
triangle.append(cur_row)
filter1d = triangle[-1]
filter1d = np.reshape(filter1d, (kernel_size,))
return filter1d / np.sum(filter1d)
weights = build_filter1d(radius)
# Apply filter horizontally
blurred_image = correlate1d(image, weights, axis=h_axis, output=None, mode="reflect", cval=0.0, origin=0)
# Apply filter vertically
blurred_image = correlate1d(blurred_image, weights, axis=v_axis, output=None, mode="reflect", cval=0.0,
origin=0)
return blurred_image
@staticmethod
def resize(image_array, new_h=None, new_w=None):
h, w, c = image_array.shape
if new_h == h and new_w == w:
return image_array
h_bounds = 0, h - 1
w_bounds = 0, w - 1
y = np.expand_dims(np.linspace(h_bounds[0], h_bounds[1], new_h), axis=-1)
x = np.expand_dims(np.linspace(w_bounds[0], w_bounds[1], new_w), axis=0)
# Calculate the floor and ceiling values of x and y
x_floor = np.floor(x).astype(int)
x_ceil = np.ceil(x).astype(int)
y_floor = np.floor(y).astype(int)
y_ceil = np.ceil(y).astype(int)
# Clip the values to stay within the image bounds
x_floor = np.clip(x_floor, w_bounds[0], w_bounds[1])
x_ceil = np.clip(x_ceil, w_bounds[0], w_bounds[1])
y_floor = np.clip(y_floor, h_bounds[0], h_bounds[1])
y_ceil = np.clip(y_ceil, h_bounds[0], h_bounds[1])
# Calculate the fractional part of x and y
dx = x - x_floor
dy = y - y_floor
# Get the values of the four neighboring pixels
dx = np.expand_dims(dx, axis=-1)
dy = np.expand_dims(dy, axis=-1)
q11 = image_array[y_floor, x_floor, :]
q21 = image_array[y_floor, x_ceil, :]
q12 = image_array[y_ceil, x_floor, :]
q22 = image_array[y_ceil, x_ceil, :]
# Perform bilinear interpolation
top_interp = q11 * (1.0 - dx) + q21 * dx
bottom_interp = q12 * (1.0 - dx) + q22 * dx
interpolated = top_interp * (1.0 - dy) + bottom_interp * dy
return interpolated
def preprocessed_image(self, x):
if type(x) is str:
x = np.array(Image.open(x).convert("RGB"))
else:
x = np.asarray(x)
image_array = self.resize(x, self.img_height, self.img_width)
image_array = np.array(image_array, dtype=np.float32) / 255.0
input_image_array = image_array[None, ..., :3]
input_image_tensor = input_image_array * 2.0 - 1.0
return input_image_array, input_image_tensor
def preprocessed_mask(self, x, blur_radius=5):
if type(x) is str:
x = np.array(Image.open(x).convert("L"))
else:
x = np.asarray(x)
if len(x.shape) == 2:
x = np.expand_dims(x, axis=-1)
mask_array = self.resize(x, self.img_height, self.img_width)
if mask_array.shape[-1] != 1:
mask_array = np.mean(mask_array, axis=-1, keepdims=True)
input_mask_array = np.array(mask_array, dtype=np.float32) / 255.0
if blur_radius is not None:
input_mask_array = self.gaussian_blur(input_mask_array, radius=blur_radius, h_axis=0, v_axis=1)
latent_mask_tensor = self.resize(input_mask_array, self.img_width // 8, self.img_height // 8)
return np.expand_dims(input_mask_array, axis=0), np.expand_dims(latent_mask_tensor, axis=0)
def rescale_noise_cfg(self, noise_cfg, noise_pred_text, guidance_rescale=0.0, epsilon=1e-05):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/abs/2305.08891). See Section 3.4
"""
std_text = np.std(noise_pred_text, axis=tuple(range(1, len(noise_pred_text.shape))), keepdims=True)
std_cfg = np.std(noise_cfg, axis=tuple(range(1, len(noise_cfg.shape))), keepdims=True) + epsilon
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1.0 - guidance_rescale) * noise_cfg
return noise_cfg
def generate_image(
self,
encoded_text,
add_text_embeds,
negative_prompt=None,
batch_size=1,
num_steps=50,
unconditional_guidance_scale=7.5,
diffusion_noise=None,
seed=None,
inpaint_mask=None,
mask_blur_strength=None,
reference_image=None,
reference_image_strength=0.8,
callback=None,
original_size=None,
crops_coords_top_left=(0, 0),
guidance_rescale=0.0,
target_size=None):
"""Generates an image based on encoded text.
The encoding passed to this method should be derived from
`StableDiffusion.encode_text`.
Args:
encoded_text: Tensor of shape (`batch_size`, 77, 768), or a Tensor
of shape (77, 768). When the batch axis is omitted, the same
encoded text will be used to produce every generated image.
batch_size: int, number of images to generate, defaults to 1.
negative_prompt: a string containing information to negatively guide
the image generation (e.g. by removing or altering certain
aspects of the generated image), defaults to None.
num_steps: int, number of diffusion steps (controls image quality),
defaults to 50.
unconditional_guidance_scale: float, controlling how closely the
image should adhere to the prompt. Larger values result in more
closely adhering to the prompt, but will make the image noisier.
Defaults to 7.5.
diffusion_noise: Tensor of shape (`batch_size`, img_height // 8,
img_width // 8, 4), or a Tensor of shape (img_height // 8,
img_width // 8, 4). Optional custom noise to seed the diffusion
process. When the batch axis is omitted, the same noise will be
used to seed diffusion for every generated image.
seed: integer which is used to seed the random generation of
diffusion noise, only to be specified if `diffusion_noise` is
None.
Example:
```python
from stable_diffusion_xl.stable_diffusion_xl import StableDiffusionXL
batch_size = 8
model = StableDiffusionXL(img_height=1024, img_width=1024, jit_compile=True)
e_tacos = model.encode_text("Tacos at dawn")
e_watermelons = model.encode_text("Watermelons at dusk")
e_interpolated = tf.linspace(e_tacos, e_watermelons, batch_size)
images = model.generate_image(e_interpolated, batch_size=batch_size)
```
"""
if diffusion_noise is not None and seed is not None:
raise ValueError(
"`diffusion_noise` and `seed` should not both be passed to "
"`generate_image`. `seed` is only used to generate diffusion "
"noise when it's not already user-specified."
)
context = self._expand_tensor(encoded_text, batch_size)
if negative_prompt is None:
negative_prompt = ""
unconditional_context, unconditional_add_text_embeds = self.encode_text(negative_prompt)
unconditional_context = self._expand_tensor(unconditional_context, batch_size)
if diffusion_noise is not None:
diffusion_noise = np.squeeze(diffusion_noise)
if len(diffusion_noise.shape) == 3:
diffusion_noise = np.repeat(np.expand_dims(diffusion_noise, axis=0), batch_size, axis=0)
# Iterative reverse diffusion stage
self.scheduler.set_timesteps(num_steps)
timesteps = self.scheduler.timesteps[::-1]
init_time = None
init_latent = None
input_image_array = None
input_mask_array = None
latent_mask_tensor = None
if inpaint_mask is not None:
input_mask_array, latent_mask_tensor = self.preprocessed_mask(inpaint_mask, mask_blur_strength)
if input_mask_array is None or latent_mask_tensor is None:
print("wrong inpaint mask:{}".format(inpaint_mask))
if reference_image is not None and (0. < reference_image_strength < 1.):
input_image_array, input_image_tensor = self.preprocessed_image(reference_image)
if input_image_tensor is not None:
num_steps = int(num_steps * reference_image_strength + 0.5)
init_time = timesteps[num_steps]
init_latent = self.image_encoder.predict_on_batch(input_image_tensor)
timesteps = timesteps[:num_steps]
else:
print("wrong reference image:{}".format(reference_image))
latent = self._get_initial_diffusion_latent(batch_size=batch_size,
init_latent=init_latent,
init_time=init_time,
seed=seed,
noise=diffusion_noise)
progbar = tf.keras.utils.Progbar(len(timesteps))
iteration = 0
if original_size is None:
original_size = [self.img_height, self.img_width]
if target_size is None:
target_size = [self.img_height, self.img_width]
add_time_ids = tf.expand_dims(
tf.convert_to_tensor(list(list(original_size) + list(crops_coords_top_left) + list(target_size)),
latent.dtype), axis=0)
for index, timestep in list(enumerate(timesteps))[::-1]:
latent_prev = latent # Set aside the previous latent vector
time_emb = np.repeat(np.reshape(timestep, [1, -1]), batch_size, axis=0)
if unconditional_guidance_scale > 0.0:
unconditional_latent = self.diffusion_model.predict_on_batch(
[latent, time_emb, unconditional_context, add_time_ids, tf.zeros_like(add_text_embeds)])
latent_text = self.diffusion_model.predict_on_batch(
[latent, time_emb, context, add_time_ids, add_text_embeds])
latent = unconditional_latent + unconditional_guidance_scale * (
latent_text - unconditional_latent)
if guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/abs/2305.08891
latent = self.rescale_noise_cfg(latent, latent_text, guidance_rescale=guidance_rescale)
else:
latent = self.diffusion_model.predict_on_batch(
[latent, time_emb, context, add_time_ids, add_text_embeds])
latent = self.scheduler.step(latent, timestep, latent_prev)
if latent_mask_tensor is not None and init_latent is not None:
latent_orgin = self._get_initial_diffusion_latent(batch_size=batch_size,
init_latent=init_latent,
init_time=timestep,
seed=seed,
noise=diffusion_noise)
latent = latent_orgin * (1. - latent_mask_tensor) + latent * latent_mask_tensor
iteration += 1
if callback is not None:
callback(iteration)
progbar.update(iteration)
# Decoding stage
decoded = self.image_decoder.predict_on_batch(latent)
decoded = np.array(((decoded + 1.) * 0.5), dtype=np.float32)
if input_mask_array is not None and input_image_array is not None:
decoded = input_image_array * (1. - input_mask_array) + decoded * input_mask_array
return np.clip(decoded * 255., 0, 255).astype("uint8")
def _expand_tensor(self, text_embedding, batch_size):
"""Extends a tensor by repeating it to fit the shape of the given batch
size."""
text_embedding = np.squeeze(text_embedding)
if len(text_embedding.shape) == 2:
text_embedding = np.repeat(
np.expand_dims(text_embedding, axis=0), batch_size, axis=0
)
return text_embedding
@property
def image_encoder(self):
pass
@property
def text_encoder_openai(self):
pass
@property
def text_encoder_laion(self):
pass
@property
def text_encoder_laion_proj(self):
pass
@property
def diffusion_model(self):
pass
@property
def image_decoder(self):
pass
@property
def tokenizer(self):
"""tokenizer returns the tokenizer used for text inputs.
Can be overriden for tasks like textual inversion where the tokenizer
needs to be modified.
"""
if self._tokenizer is None:
self._tokenizer = SimpleTokenizer()
return self._tokenizer
def _get_initial_diffusion_noise(self, batch_size, seed):
if seed is not None:
try:
seed = int(seed)
except:
seed = None
return tf.random.stateless_normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4),
seed=[seed, seed],
)
else:
return tf.random.normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4)
)
def _get_initial_diffusion_latent(self, batch_size, init_latent=None, init_time=None, seed=None,
noise=None):
if noise is None:
noise = self._get_initial_diffusion_noise(batch_size, seed=seed)
if init_latent is None:
latent = noise
else:
latent = self.scheduler.signal_rates[init_time] * np.repeat(init_latent, batch_size, axis=0) + \
self.scheduler.noise_rates[init_time] * noise
return latent
@staticmethod
def _get_pos_ids():
return np.asarray([list(range(MAX_PROMPT_LENGTH))], dtype=np.int32)
class StableDiffusionXL(StableDiffusionXLBase):
"""Keras implementation of Stable Diffusion.
Note that the StableDiffusionXL API, as well as the APIs of the sub-components
of StableDiffusionXL (e.g. ImageEncoder, DiffusionModel) should be considered
unstable at this point. We do not guarantee backwards compatability for
future changes to these APIs.
Stable Diffusion is a powerful image generation model that can be used,
among other things, to generate pictures according to a short text
description (called a "prompt").
Arguments:
img_height: int, height of the images to generate, in pixel. Note that
only multiples of 128 are supported; the value provided will be
rounded to the nearest valid value. Defaults to 1024.
img_width: int, width of the images to generate, in pixel. Note that
only multiples of 128 are supported; the value provided will be
rounded to the nearest valid value. Defaults to 1024.
jit_compile: bool, whether to compile the underlying models to XLA.
This can lead to a significant speedup on some systems. Defaults to
False.
Example:
```python
from stable_diffusion_xl.stable_diffusion_xl import StableDiffusionXL
from PIL import Image
model = StableDiffusionXL(img_height=1024, img_width=1024, jit_compile=True)
img = model.text_to_image(
prompt="A beautiful horse running through a field",
batch_size=1, # How many images to generate at once
num_steps=25, # Number of iterations (controls image quality)
seed=123, # Set this to always get the same image from the same prompt
)
Image.fromarray(img[0]).save("horse.png")
print("saved at horse.png")
```
References:
- [About Stable Diffusion](https://stability.ai/blog/stable-diffusion-announcement)
- [Original implementation](https://github.com/CompVis/stable-diffusion)
""" # noqa: E501
def __init__(
self,
img_height=1024,
img_width=1024,
jit_compile=True,
unet_ckpt=None,
text_encoder_ckpt=None,
text_encoder2_ckpt=None,
vae_ckpt=None,
):
super().__init__(img_height, img_width, jit_compile)
self.unet_ckpt = unet_ckpt
self.text_encoder_ckpt = text_encoder_ckpt
self.text_encoder2_ckpt = text_encoder2_ckpt
self.vae_ckpt = vae_ckpt
@property
def text_encoder_openai(self):
"""text_encoder returns the text encoder with pretrained weights.
Can be overriden for tasks like textual inversion where the text encoder
needs to be modified.
"""
if self._text_encoder_openai is None:
self._text_encoder_openai = TextEncoderOpenAi(MAX_PROMPT_LENGTH, ckpt_path=self.text_encoder_ckpt)
if self.jit_compile:
self._text_encoder_openai.compile(jit_compile=True)
return self._text_encoder_openai
@property
def text_encoder_laion(self):
"""text_encoder returns the text encoder with pretrained weights.
Can be overriden for tasks like textual inversion where the text encoder
needs to be modified.
"""
if self._text_encoder_laion is None: | self._text_encoder_laion = TextEncoderLaion(MAX_PROMPT_LENGTH, ckpt_path=self.text_encoder2_ckpt) | 6 | 2023-10-14 18:40:16+00:00 | 16k |
spla-tam/SplaTAM | scripts/iphone_demo.py | [
{
"identifier": "relative_transformation",
"path": "datasets/gradslam_datasets/geometryutils.py",
"snippet": "def relative_transformation(\n trans_01: torch.Tensor, trans_02: torch.Tensor, orthogonal_rotations: bool = False\n) -> torch.Tensor:\n r\"\"\"Function that computes the relative homogenou... | import argparse
import os
import shutil
import sys
import time
import json
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import cyclonedds.idl as idl
import cyclonedds.idl.annotations as annotate
import cyclonedds.idl.types as types
from pathlib import Path
from importlib.machinery import SourceFileLoader
from tqdm import tqdm
from datasets.gradslam_datasets.geometryutils import relative_transformation
from utils.common_utils import seed_everything, save_params_ckpt, save_params
from utils.eval_helpers import report_progress
from utils.keyframe_selection import keyframe_selection_overlap
from utils.recon_helpers import setup_camera
from utils.slam_external import build_rotation, prune_gaussians, densify
from scripts.splatam import get_loss, initialize_optimizer, initialize_params, initialize_camera_pose, get_pointcloud, add_new_gaussians
from diff_gaussian_rasterization import GaussianRasterizer as Renderer
from dataclasses import dataclass
from cyclonedds.domain import DomainParticipant, Domain
from cyclonedds.core import Qos, Policy
from cyclonedds.sub import DataReader
from cyclonedds.topic import Topic
from cyclonedds.util import duration | 13,627 | # Initialize Params for first time step
if time_idx == 0:
# Get Initial Point Cloud
mask = (densify_depth > 0) # Mask out invalid depth values
mask = mask.reshape(-1)
init_pt_cld, mean3_sq_dist = get_pointcloud(densify_color, densify_depth, densify_intrinsics, first_frame_w2c,
mask=mask, compute_mean_sq_dist=True,
mean_sq_dist_method=config['mean_sq_dist_method'])
params, variables = initialize_params(init_pt_cld, num_frames, mean3_sq_dist)
variables['scene_radius'] = torch.max(densify_depth)/config['scene_radius_depth_ratio']
# Initialize Mapping & Tracking for current frame
iter_time_idx = time_idx
curr_gt_w2c = gt_w2c_all_frames
curr_data = {'cam': cam, 'im': color, 'depth':depth, 'id': iter_time_idx,
'intrinsics': intrinsics, 'w2c': first_frame_w2c, 'iter_gt_w2c_list': curr_gt_w2c}
tracking_curr_data = curr_data
# Optimization Iterations
num_iters_mapping = config['mapping']['num_iters']
# Initialize the camera pose for the current frame
if time_idx > 0:
params = initialize_camera_pose(params, time_idx, forward_prop=config['tracking']['forward_prop'])
# Tracking
tracking_start_time = time.time()
if time_idx > 0 and not config['tracking']['use_gt_poses']:
# Reset Optimizer & Learning Rates for tracking
optimizer = initialize_optimizer(params, config['tracking']['lrs'], tracking=True)
# Keep Track of Best Candidate Rotation & Translation
candidate_cam_unnorm_rot = params['cam_unnorm_rots'][..., time_idx].detach().clone()
candidate_cam_tran = params['cam_trans'][..., time_idx].detach().clone()
current_min_loss = float(1e20)
# Tracking Optimization
iter = 0
do_continue_slam = False
num_iters_tracking = config['tracking']['num_iters']
progress_bar = tqdm(range(num_iters_tracking), desc=f"Tracking Time Step: {time_idx}")
while True:
iter_start_time = time.time()
# Loss for current frame
loss, variables, losses = get_loss(params, tracking_curr_data, variables, iter_time_idx, config['tracking']['loss_weights'],
config['tracking']['use_sil_for_loss'], config['tracking']['sil_thres'],
config['tracking']['use_l1'], config['tracking']['ignore_outlier_depth_loss'], tracking=True,
visualize_tracking_loss=config['tracking']['visualize_tracking_loss'],
tracking_iteration=iter)
# Backprop
loss.backward()
# Optimizer Update
optimizer.step()
optimizer.zero_grad(set_to_none=True)
with torch.no_grad():
# Save the best candidate rotation & translation
if loss < current_min_loss:
current_min_loss = loss
candidate_cam_unnorm_rot = params['cam_unnorm_rots'][..., time_idx].detach().clone()
candidate_cam_tran = params['cam_trans'][..., time_idx].detach().clone()
# Report Progress
if config['report_iter_progress']:
report_progress(params, tracking_curr_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['tracking']['sil_thres'], tracking=True)
else:
progress_bar.update(1)
# Update the runtime numbers
iter_end_time = time.time()
tracking_iter_time_sum += iter_end_time - iter_start_time
tracking_iter_time_count += 1
# Check if we should stop tracking
iter += 1
if iter == num_iters_tracking:
if losses['depth'] < config['tracking']['depth_loss_thres'] and config['tracking']['use_depth_loss_thres']:
break
elif config['tracking']['use_depth_loss_thres'] and not do_continue_slam:
do_continue_slam = True
progress_bar = tqdm(range(num_iters_tracking), desc=f"Tracking Time Step: {time_idx}")
num_iters_tracking = 2*num_iters_tracking
else:
break
progress_bar.close()
# Copy over the best candidate rotation & translation
with torch.no_grad():
params['cam_unnorm_rots'][..., time_idx] = candidate_cam_unnorm_rot
params['cam_trans'][..., time_idx] = candidate_cam_tran
elif time_idx > 0 and config['tracking']['use_gt_poses']:
with torch.no_grad():
# Get the ground truth pose relative to frame 0
rel_w2c = curr_gt_w2c[-1]
rel_w2c_rot = rel_w2c[:3, :3].unsqueeze(0).detach()
rel_w2c_rot_quat = matrix_to_quaternion(rel_w2c_rot)
rel_w2c_tran = rel_w2c[:3, 3].detach()
# Update the camera parameters
params['cam_unnorm_rots'][..., time_idx] = rel_w2c_rot_quat
params['cam_trans'][..., time_idx] = rel_w2c_tran
# Update the runtime numbers
tracking_end_time = time.time()
tracking_frame_time_sum += tracking_end_time - tracking_start_time
tracking_frame_time_count += 1
if time_idx == 0 or (time_idx+1) % config['report_global_progress_every'] == 0:
try:
# Report Final Tracking Progress
progress_bar = tqdm(range(1), desc=f"Tracking Result Time Step: {time_idx}")
with torch.no_grad():
report_progress(params, tracking_curr_data, 1, progress_bar, iter_time_idx, sil_thres=config['tracking']['sil_thres'], tracking=True)
progress_bar.close()
except:
ckpt_output_dir = save_path.joinpath("checkpoints")
os.makedirs(ckpt_output_dir, exist_ok=True)
save_params_ckpt(params, ckpt_output_dir, time_idx)
print('Failed to evaluate trajectory.')
# Densification & KeyFrame-based Mapping
if time_idx == 0 or (time_idx+1) % config['map_every'] == 0:
# Densification
if config['mapping']['add_new_gaussians'] and time_idx > 0:
densify_curr_data = {'cam': densify_cam, 'im': densify_color, 'depth': densify_depth, 'id': time_idx,
'intrinsics': densify_intrinsics, 'w2c': first_frame_w2c, 'iter_gt_w2c_list': curr_gt_w2c}
# Add new Gaussians to the scene based on the Silhouette
| """
Script to stream RGB-D data from the NeRFCapture iOS App & build a Gaussian Splat on the fly using SplaTAM.
The CycloneDDS parts of this script are adapted from the Instant-NGP Repo:
https://github.com/NVlabs/instant-ngp/blob/master/scripts/nerfcapture2nerf.py
"""
#!/usr/bin/env python3
_BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, _BASE_DIR)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="./configs/iphone/online_demo.py", type=str, help="Path to config file.")
return parser.parse_args()
# DDS
# ==================================================================================================
@dataclass
@annotate.final
@annotate.autoid("sequential")
class SplatCaptureFrame(idl.IdlStruct, typename="SplatCaptureData.SplatCaptureFrame"):
id: types.uint32
annotate.key("id")
timestamp: types.float64
fl_x: types.float32
fl_y: types.float32
cx: types.float32
cy: types.float32
transform_matrix: types.array[types.float32, 16]
width: types.uint32
height: types.uint32
image: types.sequence[types.uint8]
has_depth: bool
depth_width: types.uint32
depth_height: types.uint32
depth_scale: types.float32
depth_image: types.sequence[types.uint8]
dds_config = """<?xml version="1.0" encoding="UTF-8" ?> \
<CycloneDDS xmlns="https://cdds.io/config" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="https://cdds.io/config https://raw.githubusercontent.com/eclipse-cyclonedds/cyclonedds/master/etc/cyclonedds.xsd"> \
<Domain id="any"> \
<Internal> \
<MinimumSocketReceiveBufferSize>10MB</MinimumSocketReceiveBufferSize> \
</Internal> \
<Tracing> \
<Verbosity>config</Verbosity> \
<OutputFile>stdout</OutputFile> \
</Tracing> \
</Domain> \
</CycloneDDS> \
"""
# ==================================================================================================
def dataset_capture_loop(reader: DataReader, save_path: Path, overwrite: bool, n_frames: int, depth_scale: float, config: dict):
rgb_path = save_path.joinpath("rgb")
if rgb_path.exists():
if overwrite:
# Prompt user to confirm deletion
if (input(f"warning! folder '{save_path}' will be deleted/replaced. continue? (Y/n)").lower().strip()+"y")[:1] != "y":
sys.exit(1)
shutil.rmtree(save_path)
else:
print(f"rgb_path {rgb_path} already exists. Please use overwrite=True in config if you want to overwrite.")
sys.exit(1)
print("Waiting for frames...")
# Make directory
images_dir = save_path.joinpath("rgb")
manifest = {
"fl_x": 0.0,
"fl_y": 0.0,
"cx": 0.0,
"cy": 0.0,
"w": 0.0,
"h": 0.0,
"frames": []
}
total_frames = 0 # Total frames received
time_idx = total_frames
num_frames = n_frames # Total frames desired
# Initialize list to keep track of Keyframes
keyframe_list = []
keyframe_time_indices = []
# Init Variables to keep track of ARkit poses and runtimes
gt_w2c_all_frames = []
tracking_iter_time_sum = 0
tracking_iter_time_count = 0
mapping_iter_time_sum = 0
mapping_iter_time_count = 0
tracking_frame_time_sum = 0
tracking_frame_time_count = 0
mapping_frame_time_sum = 0
mapping_frame_time_count = 0
P = torch.tensor(
[
[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]
]
).float()
# Start DDS Loop
while True:
sample = reader.read_next() # Get frame from NeRFCapture
if sample:
print(f"{total_frames + 1}/{n_frames} frames received")
if total_frames == 0:
save_path.mkdir(parents=True, exist_ok=True)
images_dir.mkdir(exist_ok=True)
manifest["w"] = sample.width
manifest["h"] = sample.height
manifest["cx"] = sample.cx
manifest["cy"] = sample.cy
manifest["fl_x"] = sample.fl_x
manifest["fl_y"] = sample.fl_y
manifest["integer_depth_scale"] = float(depth_scale)/65535.0
if sample.has_depth:
depth_dir = save_path.joinpath("depth")
depth_dir.mkdir(exist_ok=True)
# RGB
image = np.asarray(sample.image, dtype=np.uint8).reshape((sample.height, sample.width, 3))
cv2.imwrite(str(images_dir.joinpath(f"{total_frames}.png")), cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
# Depth if avaiable
save_depth = None
if sample.has_depth:
# Save Depth Image
save_depth = np.asarray(sample.depth_image, dtype=np.uint8).view(
dtype=np.float32).reshape((sample.depth_height, sample.depth_width))
save_depth = (save_depth*65535/float(depth_scale)).astype(np.uint16)
save_depth = cv2.resize(save_depth, dsize=(
sample.width, sample.height), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(str(depth_dir.joinpath(f"{total_frames}.png")), save_depth)
# Load Depth Image for SplaTAM
curr_depth = np.asarray(sample.depth_image, dtype=np.uint8).view(
dtype=np.float32).reshape((sample.depth_height, sample.depth_width))
else:
print("No Depth Image Received. Please make sure that the NeRFCapture App \
mentions Depth Supported on the top right corner. Skipping Frame...")
continue
# ARKit Poses for saving dataset
X_WV = np.asarray(sample.transform_matrix,
dtype=np.float32).reshape((4, 4)).T
frame = {
"transform_matrix": X_WV.tolist(),
"file_path": f"rgb/{total_frames}.png",
"fl_x": sample.fl_x,
"fl_y": sample.fl_y,
"cx": sample.cx,
"cy": sample.cy,
"w": sample.width,
"h": sample.height
}
if save_depth is not None:
frame["depth_path"] = f"depth/{total_frames}.png"
manifest["frames"].append(frame)
# Convert ARKit Pose to GradSLAM format
gt_pose = torch.from_numpy(X_WV).float()
gt_pose = P @ gt_pose @ P.T
if time_idx == 0:
first_abs_gt_pose = gt_pose
gt_pose = relative_transformation(first_abs_gt_pose.unsqueeze(0), gt_pose.unsqueeze(0), orthogonal_rotations=False)
gt_w2c = torch.linalg.inv(gt_pose[0])
gt_w2c_all_frames.append(gt_w2c)
# Initialize Tracking & Mapping Resolution Data
color = cv2.resize(image, dsize=(
config['data']['desired_image_width'], config['data']['desired_image_height']), interpolation=cv2.INTER_LINEAR)
depth = cv2.resize(curr_depth, dsize=(
config['data']['desired_image_width'], config['data']['desired_image_height']), interpolation=cv2.INTER_NEAREST)
depth = np.expand_dims(depth, -1)
color = torch.from_numpy(color).cuda().float()
color = color.permute(2, 0, 1) / 255
depth = torch.from_numpy(depth).cuda().float()
depth = depth.permute(2, 0, 1)
if time_idx == 0:
intrinsics = torch.tensor([[sample.fl_x, 0, sample.cx], [0, sample.fl_y, sample.cy], [0, 0, 1]]).cuda().float()
intrinsics = intrinsics / config['data']['downscale_factor']
intrinsics[2, 2] = 1.0
first_frame_w2c = torch.eye(4).cuda().float()
cam = setup_camera(color.shape[2], color.shape[1], intrinsics.cpu().numpy(), first_frame_w2c.cpu().numpy())
# Initialize Densification Resolution Data
densify_color = cv2.resize(image, dsize=(
config['data']['densification_image_width'], config['data']['densification_image_height']), interpolation=cv2.INTER_LINEAR)
densify_depth = cv2.resize(curr_depth, dsize=(
config['data']['densification_image_width'], config['data']['densification_image_height']), interpolation=cv2.INTER_NEAREST)
densify_depth = np.expand_dims(densify_depth, -1)
densify_color = torch.from_numpy(densify_color).cuda().float()
densify_color = densify_color.permute(2, 0, 1) / 255
densify_depth = torch.from_numpy(densify_depth).cuda().float()
densify_depth = densify_depth.permute(2, 0, 1)
if time_idx == 0:
densify_intrinsics = torch.tensor([[sample.fl_x, 0, sample.cx], [0, sample.fl_y, sample.cy], [0, 0, 1]]).cuda().float()
densify_intrinsics = densify_intrinsics / config['data']['densify_downscale_factor']
densify_intrinsics[2, 2] = 1.0
densify_cam = setup_camera(densify_color.shape[2], densify_color.shape[1], densify_intrinsics.cpu().numpy(), first_frame_w2c.cpu().numpy())
# Initialize Params for first time step
if time_idx == 0:
# Get Initial Point Cloud
mask = (densify_depth > 0) # Mask out invalid depth values
mask = mask.reshape(-1)
init_pt_cld, mean3_sq_dist = get_pointcloud(densify_color, densify_depth, densify_intrinsics, first_frame_w2c,
mask=mask, compute_mean_sq_dist=True,
mean_sq_dist_method=config['mean_sq_dist_method'])
params, variables = initialize_params(init_pt_cld, num_frames, mean3_sq_dist)
variables['scene_radius'] = torch.max(densify_depth)/config['scene_radius_depth_ratio']
# Initialize Mapping & Tracking for current frame
iter_time_idx = time_idx
curr_gt_w2c = gt_w2c_all_frames
curr_data = {'cam': cam, 'im': color, 'depth':depth, 'id': iter_time_idx,
'intrinsics': intrinsics, 'w2c': first_frame_w2c, 'iter_gt_w2c_list': curr_gt_w2c}
tracking_curr_data = curr_data
# Optimization Iterations
num_iters_mapping = config['mapping']['num_iters']
# Initialize the camera pose for the current frame
if time_idx > 0:
params = initialize_camera_pose(params, time_idx, forward_prop=config['tracking']['forward_prop'])
# Tracking
tracking_start_time = time.time()
if time_idx > 0 and not config['tracking']['use_gt_poses']:
# Reset Optimizer & Learning Rates for tracking
optimizer = initialize_optimizer(params, config['tracking']['lrs'], tracking=True)
# Keep Track of Best Candidate Rotation & Translation
candidate_cam_unnorm_rot = params['cam_unnorm_rots'][..., time_idx].detach().clone()
candidate_cam_tran = params['cam_trans'][..., time_idx].detach().clone()
current_min_loss = float(1e20)
# Tracking Optimization
iter = 0
do_continue_slam = False
num_iters_tracking = config['tracking']['num_iters']
progress_bar = tqdm(range(num_iters_tracking), desc=f"Tracking Time Step: {time_idx}")
while True:
iter_start_time = time.time()
# Loss for current frame
loss, variables, losses = get_loss(params, tracking_curr_data, variables, iter_time_idx, config['tracking']['loss_weights'],
config['tracking']['use_sil_for_loss'], config['tracking']['sil_thres'],
config['tracking']['use_l1'], config['tracking']['ignore_outlier_depth_loss'], tracking=True,
visualize_tracking_loss=config['tracking']['visualize_tracking_loss'],
tracking_iteration=iter)
# Backprop
loss.backward()
# Optimizer Update
optimizer.step()
optimizer.zero_grad(set_to_none=True)
with torch.no_grad():
# Save the best candidate rotation & translation
if loss < current_min_loss:
current_min_loss = loss
candidate_cam_unnorm_rot = params['cam_unnorm_rots'][..., time_idx].detach().clone()
candidate_cam_tran = params['cam_trans'][..., time_idx].detach().clone()
# Report Progress
if config['report_iter_progress']:
report_progress(params, tracking_curr_data, iter+1, progress_bar, iter_time_idx, sil_thres=config['tracking']['sil_thres'], tracking=True)
else:
progress_bar.update(1)
# Update the runtime numbers
iter_end_time = time.time()
tracking_iter_time_sum += iter_end_time - iter_start_time
tracking_iter_time_count += 1
# Check if we should stop tracking
iter += 1
if iter == num_iters_tracking:
if losses['depth'] < config['tracking']['depth_loss_thres'] and config['tracking']['use_depth_loss_thres']:
break
elif config['tracking']['use_depth_loss_thres'] and not do_continue_slam:
do_continue_slam = True
progress_bar = tqdm(range(num_iters_tracking), desc=f"Tracking Time Step: {time_idx}")
num_iters_tracking = 2*num_iters_tracking
else:
break
progress_bar.close()
# Copy over the best candidate rotation & translation
with torch.no_grad():
params['cam_unnorm_rots'][..., time_idx] = candidate_cam_unnorm_rot
params['cam_trans'][..., time_idx] = candidate_cam_tran
elif time_idx > 0 and config['tracking']['use_gt_poses']:
with torch.no_grad():
# Get the ground truth pose relative to frame 0
rel_w2c = curr_gt_w2c[-1]
rel_w2c_rot = rel_w2c[:3, :3].unsqueeze(0).detach()
rel_w2c_rot_quat = matrix_to_quaternion(rel_w2c_rot)
rel_w2c_tran = rel_w2c[:3, 3].detach()
# Update the camera parameters
params['cam_unnorm_rots'][..., time_idx] = rel_w2c_rot_quat
params['cam_trans'][..., time_idx] = rel_w2c_tran
# Update the runtime numbers
tracking_end_time = time.time()
tracking_frame_time_sum += tracking_end_time - tracking_start_time
tracking_frame_time_count += 1
if time_idx == 0 or (time_idx+1) % config['report_global_progress_every'] == 0:
try:
# Report Final Tracking Progress
progress_bar = tqdm(range(1), desc=f"Tracking Result Time Step: {time_idx}")
with torch.no_grad():
report_progress(params, tracking_curr_data, 1, progress_bar, iter_time_idx, sil_thres=config['tracking']['sil_thres'], tracking=True)
progress_bar.close()
except:
ckpt_output_dir = save_path.joinpath("checkpoints")
os.makedirs(ckpt_output_dir, exist_ok=True)
save_params_ckpt(params, ckpt_output_dir, time_idx)
print('Failed to evaluate trajectory.')
# Densification & KeyFrame-based Mapping
if time_idx == 0 or (time_idx+1) % config['map_every'] == 0:
# Densification
if config['mapping']['add_new_gaussians'] and time_idx > 0:
densify_curr_data = {'cam': densify_cam, 'im': densify_color, 'depth': densify_depth, 'id': time_idx,
'intrinsics': densify_intrinsics, 'w2c': first_frame_w2c, 'iter_gt_w2c_list': curr_gt_w2c}
# Add new Gaussians to the scene based on the Silhouette | params, variables = add_new_gaussians(params, variables, densify_curr_data, | 15 | 2023-11-30 20:26:47+00:00 | 16k |
zhyever/PatchFusion | zoedepth/trainers/zoedepth_custom_trainer.py | [
{
"identifier": "SILogLoss",
"path": "zoedepth/trainers/loss_sample.py",
"snippet": "class SILogLoss(nn.Module):\n \"\"\"SILog loss (pixel-wise)\"\"\"\n def __init__(self, beta=0.15):\n super(SILogLoss, self).__init__()\n self.name = 'SILog'\n self.beta = beta\n\n def forwa... | import os
import torch
import torch.cuda.amp as amp
import torch.nn as nn
import numpy as np
import wandb
import uuid
import torch.distributed as dist
import copy
import torch.optim as optim
import matplotlib.pyplot as plt
from zoedepth.trainers.loss_sample import SILogLoss, DistributionLoss
from zoedepth.trainers.loss import SILogLoss as DenseSILogLoss
from zoedepth.trainers.loss import BudgetConstraint, HistogramMatchingLoss, SSIM, ConsistencyLoss
from zoedepth.utils.config import DATASETS_CONFIG
from zoedepth.utils.misc import compute_metrics
from zoedepth.data.preprocess import get_black_border
from .base_trainer import BaseTrainer, is_rank_zero, colors, flatten
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
from datetime import datetime as dt
from zoedepth.utils.misc import generatemask | 13,062 | # 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
# This file is partly inspired from ZoeDepth (https://github.com/isl-org/ZoeDepth/blob/main/zoedepth/trainers/zoedepth_trainer.py); author: Shariq Farooq Bhat
class Trainer(BaseTrainer):
def __init__(self, config, model, train_loader, test_loader=None, device=None):
self.addf = config.get("addf", False)
self.lazy_epoch = -1
self.boostingdepth = config.get("boostingdepth", False)
super().__init__(config, model, train_loader,
test_loader=test_loader, device=device)
self.device = device
self.silog_loss = SILogLoss(beta=config.get("beta", 0.15))
self.dense_silog_loss = DenseSILogLoss(beta=config.get("beta", 0.15))
print("sigloss's beta is set to {}".format(config.get("beta", 0.15)))
self.scaler = amp.GradScaler(enabled=self.config.use_amp)
self.distribution_loss = DistributionLoss(max_depth=self.config.max_depth)
self.sampled_training = config.get("sampled_training", False)
self.sec_stage = config.get("sec_stage", False)
self.multi_consistency = config.get("multi_consistency", False)
self.use_blur = config.get("use_blur", False)
self.dynamic = config.get("dynamic", False)
if self.dynamic:
self.dynamic_unupdate_rate = config.get("dynamic_unupdate_rate", 0.0)
self.budget_loss = BudgetConstraint(loss_mu=0.0, flops_all=21552.5684, warm_up=True)
self.use_scale_loss = config.get("use_scale_loss", False)
if self.use_scale_loss:
if config.get("scale_type", "ssim"):
self.scale_loss = SSIM(window_size=config.get("window_size", int(11)))
else:
| # 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
# This file is partly inspired from ZoeDepth (https://github.com/isl-org/ZoeDepth/blob/main/zoedepth/trainers/zoedepth_trainer.py); author: Shariq Farooq Bhat
class Trainer(BaseTrainer):
def __init__(self, config, model, train_loader, test_loader=None, device=None):
self.addf = config.get("addf", False)
self.lazy_epoch = -1
self.boostingdepth = config.get("boostingdepth", False)
super().__init__(config, model, train_loader,
test_loader=test_loader, device=device)
self.device = device
self.silog_loss = SILogLoss(beta=config.get("beta", 0.15))
self.dense_silog_loss = DenseSILogLoss(beta=config.get("beta", 0.15))
print("sigloss's beta is set to {}".format(config.get("beta", 0.15)))
self.scaler = amp.GradScaler(enabled=self.config.use_amp)
self.distribution_loss = DistributionLoss(max_depth=self.config.max_depth)
self.sampled_training = config.get("sampled_training", False)
self.sec_stage = config.get("sec_stage", False)
self.multi_consistency = config.get("multi_consistency", False)
self.use_blur = config.get("use_blur", False)
self.dynamic = config.get("dynamic", False)
if self.dynamic:
self.dynamic_unupdate_rate = config.get("dynamic_unupdate_rate", 0.0)
self.budget_loss = BudgetConstraint(loss_mu=0.0, flops_all=21552.5684, warm_up=True)
self.use_scale_loss = config.get("use_scale_loss", False)
if self.use_scale_loss:
if config.get("scale_type", "ssim"):
self.scale_loss = SSIM(window_size=config.get("window_size", int(11)))
else: | self.scale_loss = HistogramMatchingLoss(min_depth=self.config.min_depth, max_depth=self.config.max_depth) | 4 | 2023-12-04 08:43:15+00:00 | 16k |
baaivision/GeoDream | extern/ldm_zero123/models/diffusion/ddpm.py | [
{
"identifier": "AutoencoderKL",
"path": "extern/ldm_zero123/models/autoencoder.py",
"snippet": "class AutoencoderKL(pl.LightningModule):\n def __init__(\n self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"i... | import itertools
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
from contextlib import contextmanager, nullcontext
from functools import partial
from einops import rearrange, repeat
from omegaconf import ListConfig
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm
from extern.ldm_zero123.models.autoencoder import (
AutoencoderKL,
IdentityFirstStage,
VQModelInterface,
)
from extern.ldm_zero123.models.diffusion.ddim import DDIMSampler
from extern.ldm_zero123.modules.attention import CrossAttention
from extern.ldm_zero123.modules.diffusionmodules.util import (
extract_into_tensor,
make_beta_schedule,
noise_like,
)
from extern.ldm_zero123.modules.distributions.distributions import (
DiagonalGaussianDistribution,
normal_kl,
)
from extern.ldm_zero123.modules.ema import LitEma
from extern.ldm_zero123.util import (
count_params,
default,
exists,
instantiate_from_config,
isimage,
ismap,
log_txt_as_img,
mean_flat,
) | 11,975 | key: cond[key][:batch_size]
if not isinstance(cond[key], list)
else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
return self.p_sample_loop(
cond,
shape,
return_intermediates=return_intermediates,
x_T=x_T,
verbose=verbose,
timesteps=timesteps,
quantize_denoised=quantize_denoised,
mask=mask,
x0=x0,
)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(
ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
)
else:
samples, intermediates = self.sample(
cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs
)
return samples, intermediates
@torch.no_grad()
def get_unconditional_conditioning(
self, batch_size, null_label=None, image_size=512
):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [
torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(
self.device
)
]
return cond
@torch.no_grad()
def log_images(
self,
batch,
N=8,
n_row=4,
sample=True,
ddim_steps=200,
ddim_eta=1.0,
return_keys=None,
quantize_denoised=True,
inpaint=True,
plot_denoise_rows=False,
plot_progressive_rows=True,
plot_diffusion_rows=True,
unconditional_guidance_scale=1.0,
unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs,
):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(
batch,
self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N,
)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch[self.cond_stage_key],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif self.cond_stage_key == "class_label":
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch["human_label"],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in [
"eps",
"x0",
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def get_loss(self, pred, target, mean=True):
if self.loss_type == "l1":
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == "l2":
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(
f"Paramterization {self.parameterization} not yet supported"
)
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = "train" if self.training else "val"
loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f"{log_prefix}/loss": loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, "b h w c -> b c h w")
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
loss, loss_dict = self.shared_step(batch)
self.log_dict(
loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
)
self.log(
"global_step",
self.global_step,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=False,
)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]["lr"]
self.log(
"lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(
loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
self.log_dict(
loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(
batch_size=N, return_intermediates=True
)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(
self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs["timesteps"]
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = "concat" if concat_mode else "crossattn"
if cond_stage_config == "__is_unconditional__":
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer("scale_factor", torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if (
self.scale_by_std
and self.current_epoch == 0
and self.global_step == 0
and batch_idx == 0
and not self.restarted_from_ckpt
):
assert (
self.scale_factor == 1.0
), "rather not use custom rescaling and std-rescaling simultaneously"
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer("scale_factor", 1.0 / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != "__is_first_stage__"
assert config != "__is_unconditional__"
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(
self, samples, desc="", force_no_decoder_quantization=False
):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(
self.decode_first_stage(
zd.to(self.device), force_not_quantize=force_no_decoder_quantization
)
)
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(
f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
)
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, "encode") and callable(
self.cond_stage_model.encode
):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(
torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(
weighting,
self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"],
)
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(
L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"],
)
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(
self, x, kernel_size, stride, uf=1, df=1
): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(
kernel_size[0], kernel_size[1], Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1,
padding=0,
stride=(stride[0] * uf, stride[1] * uf),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h * uf, w * uf
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
)
elif df > 1 and uf == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1,
padding=0,
stride=(stride[0] // df, stride[1] // df),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h // df, w // df
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
)
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(
self,
batch,
k,
return_first_stage_outputs=False,
force_c_encode=False,
cond_key=None,
return_original_cond=False,
bs=None,
uncond=0.05,
):
x = super().get_input(batch, k)
T = batch["T"].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange(
(random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1"
)
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [
self.cc_projection(
torch.cat(
[
torch.where(prompt_mask, null_prompt, clip_emb),
T[:, None, :],
],
dim=-1,
)
)
]
cond["c_concat"] = [
input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()
]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
# @torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, "b h w c -> b c h w").contiguous()
z = 1.0 / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
z, ks, stride, uf=uf
)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [
self.first_stage_model.decode(
z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize,
)
for i in range(z.shape[-1])
]
else:
output_list = [
self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])
]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(
z, force_not_quantize=predict_cids or force_not_quantize
)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(
z, force_not_quantize=predict_cids or force_not_quantize
)
else:
return self.first_stage_model.decode(z)
# @torch.no_grad() # wasted two hours to find this bug... why no grad here!
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params["original_image_size"] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
x, ks, stride, df=df
)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
output_list = [
self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])
]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
if self.model.conditioning_key is not None:
assert c is not None
# if self.cond_stage_trainable:
# c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = (
"c_concat" if self.model.conditioning_key == "concat" else "c_crossattn"
)
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(
x_noisy, ks, stride
)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if (
self.cond_stage_key in ["image", "LR_image", "segmentation", "bbox_img"]
and self.model.conditioning_key
): # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert len(c) == 1 # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view(
(c.shape[0], -1, ks[0], ks[1], c.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == "coordinates_bbox":
assert (
"original_image_size" in self.split_input_params
), "BoudingBoxRescaling is missing original_image_size"
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params["original_image_size"]
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [
(
rescale_latent
* stride[0]
* (patch_nr % n_patches_per_row)
/ full_img_w,
rescale_latent
* stride[1]
* (patch_nr // n_patches_per_row)
/ full_img_h,
)
for patch_nr in range(z.shape[-1])
]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [
(
x_tl,
y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h,
)
for x_tl, y_tl in tl_patch_coordinates
]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [
torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(
self.device
)
for bbox in patch_limits
] # list of length l with tensors of shape (1, 2)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), "cond must be dict to be fed into model"
cut_cond = cond["c_crossattn"][0][..., :-2].to(self.device)
adapted_cond = torch.stack(
[torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]
)
adapted_cond = rearrange(adapted_cond, "l b n -> (l b) n")
adapted_cond = self.get_learned_conditioning(adapted_cond)
adapted_cond = rearrange(
adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1]
)
cond_list = [{"c_crossattn": [e]} for e in adapted_cond]
else:
cond_list = [
cond for i in range(z.shape[-1])
] # Todo make this more efficient
# apply model by loop over crops
output_list = [
self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])
]
assert not isinstance(
output_list[0], tuple
) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- pred_xstart
) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(
mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = "train" if self.training else "val"
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f"{prefix}/loss_simple": loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f"{prefix}/loss_gamma": loss.mean()})
loss_dict.update({"logvar": self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f"{prefix}/loss_vlb": loss_vlb})
loss += self.original_elbo_weight * loss_vlb
loss_dict.update({f"{prefix}/loss": loss})
return loss, loss_dict
def p_mean_variance(
self,
x,
c,
t,
clip_denoised: bool,
return_codebook_ids=False,
quantize_denoised=False,
return_x0=False,
score_corrector=None,
corrector_kwargs=None,
):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(
self, model_out, x, t, c, **corrector_kwargs
)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(
self,
x,
c,
t,
clip_denoised=False,
repeat_noise=False,
return_codebook_ids=False,
quantize_denoised=False,
return_x0=False,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(
x=x,
c=c,
t=t,
clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (
0.5 * model_log_variance
).exp() * noise, logits.argmax(dim=1)
if return_x0:
return (
model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
x0,
)
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(
self,
cond,
shape,
verbose=True,
callback=None,
quantize_denoised=False,
img_callback=None,
mask=None,
x0=None,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
batch_size=None,
x_T=None,
start_T=None,
log_every_t=None,
):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {
key: cond[key][:batch_size]
if not isinstance(cond[key], list)
else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = (
tqdm(
reversed(range(0, timesteps)),
desc="Progressive Generation",
total=timesteps,
)
if verbose
else reversed(range(0, timesteps))
)
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != "hybrid"
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(
img,
cond,
ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised,
return_x0=True,
temperature=temperature[i],
noise_dropout=noise_dropout,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(
self,
cond,
shape,
return_intermediates=False,
x_T=None,
verbose=True,
callback=None,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
img_callback=None,
start_T=None,
log_every_t=None,
):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = (
tqdm(reversed(range(0, timesteps)), desc="Sampling t", total=timesteps)
if verbose
else reversed(range(0, timesteps))
)
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != "hybrid"
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(
img,
cond,
ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised,
)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(
self,
cond,
batch_size=16,
return_intermediates=False,
x_T=None,
verbose=True,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
shape=None,
**kwargs,
):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {
key: cond[key][:batch_size]
if not isinstance(cond[key], list)
else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
return self.p_sample_loop(
cond,
shape,
return_intermediates=return_intermediates,
x_T=x_T,
verbose=verbose,
timesteps=timesteps,
quantize_denoised=quantize_denoised,
mask=mask,
x0=x0,
)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(
ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
)
else:
samples, intermediates = self.sample(
cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs
)
return samples, intermediates
@torch.no_grad()
def get_unconditional_conditioning(
self, batch_size, null_label=None, image_size=512
):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [
torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(
self.device
)
]
return cond
@torch.no_grad()
def log_images(
self,
batch,
N=8,
n_row=4,
sample=True,
ddim_steps=200,
ddim_eta=1.0,
return_keys=None,
quantize_denoised=True,
inpaint=True,
plot_denoise_rows=False,
plot_progressive_rows=True,
plot_diffusion_rows=True,
unconditional_guidance_scale=1.0,
unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs,
):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(
batch,
self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N,
)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch[self.cond_stage_key],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif self.cond_stage_key == "class_label":
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch["human_label"],
size=x.shape[2] // 25,
)
log["conditioning"] = xc | elif isimage(xc): | 15 | 2023-12-01 01:59:42+00:00 | 16k |
lucidrains/meshgpt-pytorch | meshgpt_pytorch/trainer.py | [
{
"identifier": "custom_collate",
"path": "meshgpt_pytorch/data.py",
"snippet": "def custom_collate(data, pad_id = -1):\n is_dict = isinstance(first(data), dict)\n\n if is_dict:\n keys = first(data).keys()\n data = [d.values() for d in data]\n\n output = []\n\n for datum in zip... | from pathlib import Path
from functools import partial
from packaging import version
from contextlib import nullcontext, contextmanager
from torch import nn, Tensor
from torch.nn import Module
from torch.utils.data import Dataset, DataLoader
from torch.optim.lr_scheduler import _LRScheduler
from pytorch_custom_utils import (
get_adam_optimizer,
OptimizerWithWarmupSchedule,
add_wandb_tracker_contextmanager
)
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
from beartype import beartype
from beartype.door import is_bearable
from beartype.typing import Optional, Tuple, Type, List
from ema_pytorch import EMA
from meshgpt_pytorch.data import custom_collate
from meshgpt_pytorch.version import __version__
from meshgpt_pytorch.meshgpt_pytorch import (
MeshAutoencoder,
MeshTransformer
)
import torch
import torch.nn.functional as F | 13,006 | optimizer = self.optimizer.state_dict(),
version = __version__,
step = self.step.item(),
config = self.unwrapped_model._config
)
torch.save(pkg, str(path))
def load(self, path):
path = Path(path)
assert path.exists()
pkg = torch.load(str(path))
if version.parse(__version__) != version.parse(pkg['version']):
self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
self.model.load_state_dict(pkg['model'])
self.ema_model.load_state_dict(pkg['ema_model'])
self.optimizer.load_state_dict(pkg['optimizer'])
self.step.copy_(pkg['step'])
def next_data_to_forward_kwargs(self, dl_iter) -> dict:
data = next(dl_iter)
if isinstance(data, tuple):
forward_kwargs = dict(zip(self.data_kwargs, data))
elif isinstance(data, dict):
forward_kwargs = data
maybe_del(forward_kwargs, 'texts', 'text_embeds')
return forward_kwargs
def forward(self):
step = self.step.item()
dl_iter = cycle(self.dataloader)
if self.is_main and self.should_validate:
val_dl_iter = cycle(self.val_dataloader)
while step < self.num_train_steps:
for i in range(self.grad_accum_every):
is_last = i == (self.grad_accum_every - 1)
maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
with self.accelerator.autocast(), maybe_no_sync():
total_loss, (recon_loss, commit_loss) = self.model(
**forward_kwargs,
return_loss_breakdown = True
)
self.accelerator.backward(total_loss / self.grad_accum_every)
self.print(f'recon loss: {recon_loss.item():.3f} | commit loss: {commit_loss.sum().item():.3f}')
self.log(
total_loss = total_loss.item(),
commit_loss = commit_loss.sum().item(),
recon_loss = recon_loss.item()
)
self.optimizer.step()
self.optimizer.zero_grad()
step += 1
self.step.add_(1)
self.wait()
if self.is_main:
self.ema_model.update()
self.wait()
if self.is_main and self.should_validate and divisible_by(step, self.val_every):
total_val_recon_loss = 0.
self.ema_model.eval()
num_val_batches = self.val_num_batches * self.grad_accum_every
for _ in range(num_val_batches):
with self.accelerator.autocast(), torch.no_grad():
forward_kwargs = self.next_data_to_forward_kwargs(val_dl_iter)
val_loss, (val_recon_loss, val_commit_loss) = self.ema_model(
**forward_kwargs,
return_loss_breakdown = True
)
total_val_recon_loss += (val_recon_loss / num_val_batches)
self.print(f'valid recon loss: {total_val_recon_loss:.3f}')
self.log(val_loss = total_val_recon_loss)
self.wait()
if self.is_main and divisible_by(step, self.checkpoint_every):
checkpoint_num = step // self.checkpoint_every
self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{checkpoint_num}.pt')
self.wait()
self.print('training complete')
# mesh transformer trainer
@add_wandb_tracker_contextmanager()
class MeshTransformerTrainer(Module):
@beartype
def __init__(
self,
|
# constants
DEFAULT_DDP_KWARGS = DistributedDataParallelKwargs(
find_unused_parameters = True
)
# helper functions
def exists(v):
return v is not None
def default(v, d):
return v if exists(v) else d
def divisible_by(num, den):
return (num % den) == 0
def cycle(dl):
while True:
for data in dl:
yield data
def maybe_del(d: dict, *keys):
for key in keys:
if key not in d:
continue
del d[key]
# autoencoder trainer
@add_wandb_tracker_contextmanager()
class MeshAutoencoderTrainer(Module):
@beartype
def __init__(
self,
model: MeshAutoencoder,
dataset: Dataset,
num_train_steps: int,
batch_size: int,
grad_accum_every: int,
val_dataset: Optional[Dataset] = None,
val_every: int = 100,
val_num_batches: int = 5,
learning_rate: float = 1e-4,
weight_decay: float = 0.,
max_grad_norm: Optional[float] = None,
ema_kwargs: dict = dict(),
scheduler: Optional[Type[_LRScheduler]] = None,
scheduler_kwargs: dict = dict(),
accelerator_kwargs: dict = dict(),
optimizer_kwargs: dict = dict(),
checkpoint_every = 1000,
checkpoint_folder = './checkpoints',
data_kwargs: Tuple[str, ...] = ['vertices', 'faces', 'face_edges'],
warmup_steps = 1000,
use_wandb_tracking = False
):
super().__init__()
# experiment tracker
self.use_wandb_tracking = use_wandb_tracking
if use_wandb_tracking:
accelerator_kwargs['log_with'] = 'wandb'
if 'kwargs_handlers' not in accelerator_kwargs:
accelerator_kwargs['kwargs_handlers'] = [DEFAULT_DDP_KWARGS]
# accelerator
self.accelerator = Accelerator(**accelerator_kwargs)
self.model = model
if self.is_main:
self.ema_model = EMA(model, **ema_kwargs)
self.optimizer = OptimizerWithWarmupSchedule(
accelerator = self.accelerator,
optimizer = get_adam_optimizer(model.parameters(), lr = learning_rate, wd = weight_decay, **optimizer_kwargs),
scheduler = scheduler,
scheduler_kwargs = scheduler_kwargs,
warmup_steps = warmup_steps,
max_grad_norm = max_grad_norm
)
self.dataloader = DataLoader(
dataset,
batch_size = batch_size,
shuffle = True,
drop_last = True,
collate_fn = partial(custom_collate, pad_id = model.pad_id)
)
self.should_validate = exists(val_dataset)
if self.should_validate:
assert len(val_dataset) > 0, 'your validation dataset is empty'
self.val_every = val_every
self.val_num_batches = val_num_batches
self.val_dataloader = DataLoader(
val_dataset,
batch_size = batch_size,
shuffle = True,
drop_last = True,
collate_fn = partial(custom_collate, pad_id = model.pad_id)
)
if hasattr(dataset, 'data_kwargs') and exists(dataset.data_kwargs):
assert is_bearable(dataset.data_kwargs, List[str])
self.data_kwargs = dataset.data_kwargs
else:
self.data_kwargs = data_kwargs
(
self.model,
self.dataloader
) = self.accelerator.prepare(
self.model,
self.dataloader
)
self.grad_accum_every = grad_accum_every
self.num_train_steps = num_train_steps
self.register_buffer('step', torch.tensor(0))
self.checkpoint_every = checkpoint_every
self.checkpoint_folder = Path(checkpoint_folder)
self.checkpoint_folder.mkdir(exist_ok = True, parents = True)
@property
def ema_tokenizer(self):
return self.ema_model.ema_model
def tokenize(self, *args, **kwargs):
return self.ema_tokenizer.tokenize(*args, **kwargs)
def log(self, **data_kwargs):
self.accelerator.log(data_kwargs, step = self.step.item())
@property
def device(self):
return self.unwrapped_model.device
@property
def is_main(self):
return self.accelerator.is_main_process
@property
def unwrapped_model(self):
return self.accelerator.unwrap_model(self.model)
@property
def is_local_main(self):
return self.accelerator.is_local_main_process
def wait(self):
return self.accelerator.wait_for_everyone()
def print(self, msg):
return self.accelerator.print(msg)
def save(self, path, overwrite = True):
path = Path(path)
assert overwrite or not path.exists()
pkg = dict(
model = self.unwrapped_model.state_dict(),
ema_model = self.ema_model.state_dict(),
optimizer = self.optimizer.state_dict(),
version = __version__,
step = self.step.item(),
config = self.unwrapped_model._config
)
torch.save(pkg, str(path))
def load(self, path):
path = Path(path)
assert path.exists()
pkg = torch.load(str(path))
if version.parse(__version__) != version.parse(pkg['version']):
self.print(f'loading saved mesh autoencoder at version {pkg["version"]}, but current package version is {__version__}')
self.model.load_state_dict(pkg['model'])
self.ema_model.load_state_dict(pkg['ema_model'])
self.optimizer.load_state_dict(pkg['optimizer'])
self.step.copy_(pkg['step'])
def next_data_to_forward_kwargs(self, dl_iter) -> dict:
data = next(dl_iter)
if isinstance(data, tuple):
forward_kwargs = dict(zip(self.data_kwargs, data))
elif isinstance(data, dict):
forward_kwargs = data
maybe_del(forward_kwargs, 'texts', 'text_embeds')
return forward_kwargs
def forward(self):
step = self.step.item()
dl_iter = cycle(self.dataloader)
if self.is_main and self.should_validate:
val_dl_iter = cycle(self.val_dataloader)
while step < self.num_train_steps:
for i in range(self.grad_accum_every):
is_last = i == (self.grad_accum_every - 1)
maybe_no_sync = partial(self.accelerator.no_sync, self.model) if not is_last else nullcontext
forward_kwargs = self.next_data_to_forward_kwargs(dl_iter)
with self.accelerator.autocast(), maybe_no_sync():
total_loss, (recon_loss, commit_loss) = self.model(
**forward_kwargs,
return_loss_breakdown = True
)
self.accelerator.backward(total_loss / self.grad_accum_every)
self.print(f'recon loss: {recon_loss.item():.3f} | commit loss: {commit_loss.sum().item():.3f}')
self.log(
total_loss = total_loss.item(),
commit_loss = commit_loss.sum().item(),
recon_loss = recon_loss.item()
)
self.optimizer.step()
self.optimizer.zero_grad()
step += 1
self.step.add_(1)
self.wait()
if self.is_main:
self.ema_model.update()
self.wait()
if self.is_main and self.should_validate and divisible_by(step, self.val_every):
total_val_recon_loss = 0.
self.ema_model.eval()
num_val_batches = self.val_num_batches * self.grad_accum_every
for _ in range(num_val_batches):
with self.accelerator.autocast(), torch.no_grad():
forward_kwargs = self.next_data_to_forward_kwargs(val_dl_iter)
val_loss, (val_recon_loss, val_commit_loss) = self.ema_model(
**forward_kwargs,
return_loss_breakdown = True
)
total_val_recon_loss += (val_recon_loss / num_val_batches)
self.print(f'valid recon loss: {total_val_recon_loss:.3f}')
self.log(val_loss = total_val_recon_loss)
self.wait()
if self.is_main and divisible_by(step, self.checkpoint_every):
checkpoint_num = step // self.checkpoint_every
self.save(self.checkpoint_folder / f'mesh-autoencoder.ckpt.{checkpoint_num}.pt')
self.wait()
self.print('training complete')
# mesh transformer trainer
@add_wandb_tracker_contextmanager()
class MeshTransformerTrainer(Module):
@beartype
def __init__(
self, | model: MeshTransformer, | 3 | 2023-11-29 14:58:15+00:00 | 16k |
EricGuo5513/momask-codes | train_res_transformer.py | [
{
"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... | import os
import torch
import numpy as np
from torch.utils.data import DataLoader
from os.path import join as pjoin
from models.mask_transformer.transformer import ResidualTransformer
from models.mask_transformer.transformer_trainer import ResidualTransformerTrainer
from models.vq.model import RVQVAE
from options.train_option import TrainT2MOptions
from utils.plot_script import plot_3d_motion
from utils.motion_process import recover_from_ric
from utils.get_opt import get_opt
from utils.fixseed import fixseed
from utils.paramUtil import t2m_kinematic_chain, kit_kinematic_chain
from data.t2m_dataset import Text2MotionDataset
from motion_loaders.dataset_motion_loader import get_dataset_motion_loader
from models.t2m_eval_wrapper import EvaluatorModelWrapper | 13,813 | vq_opt = get_opt(opt_path, opt.device)
vq_model = RVQVAE(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=opt.device)
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
print(f'Loading VQ Model {opt.vq_name}')
vq_model.to(opt.device)
return vq_model, vq_opt
if __name__ == '__main__':
parser = TrainT2MOptions()
opt = parser.parse()
fixseed(opt.seed)
opt.device = torch.device("cpu" if opt.gpu_id == -1 else "cuda:" + str(opt.gpu_id))
torch.autograd.set_detect_anomaly(True)
opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
opt.model_dir = pjoin(opt.save_root, 'model')
# opt.meta_dir = pjoin(opt.save_root, 'meta')
opt.eval_dir = pjoin(opt.save_root, 'animation')
opt.log_dir = pjoin('./log/res/', opt.dataset_name, opt.name)
os.makedirs(opt.model_dir, exist_ok=True)
# os.makedirs(opt.meta_dir, exist_ok=True)
os.makedirs(opt.eval_dir, exist_ok=True)
os.makedirs(opt.log_dir, exist_ok=True)
if opt.dataset_name == 't2m':
opt.data_root = './dataset/HumanML3D'
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
opt.joints_num = 22
opt.max_motion_len = 55
dim_pose = 263
radius = 4
fps = 20
kinematic_chain = t2m_kinematic_chain
dataset_opt_path = './checkpoints/t2m/Comp_v6_KLD005/opt.txt'
elif opt.dataset_name == 'kit': #TODO
opt.data_root = './dataset/KIT-ML'
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
opt.joints_num = 21
radius = 240 * 8
fps = 12.5
dim_pose = 251
opt.max_motion_len = 55
kinematic_chain = kit_kinematic_chain
dataset_opt_path = './checkpoints/kit/Comp_v6_KLD005/opt.txt'
else:
raise KeyError('Dataset Does Not Exist')
opt.text_dir = pjoin(opt.data_root, 'texts')
vq_model, vq_opt = load_vq_model()
clip_version = 'ViT-B/32'
opt.num_tokens = vq_opt.nb_code
opt.num_quantizers = vq_opt.num_quantizers
# if opt.is_v2:
res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
cond_mode='text',
latent_dim=opt.latent_dim,
ff_size=opt.ff_size,
num_layers=opt.n_layers,
num_heads=opt.n_heads,
dropout=opt.dropout,
clip_dim=512,
shared_codebook=vq_opt.shared_codebook,
cond_drop_prob=opt.cond_drop_prob,
# codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
share_weight=opt.share_weight,
clip_version=clip_version,
opt=opt)
# else:
# res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
# cond_mode='text',
# latent_dim=opt.latent_dim,
# ff_size=opt.ff_size,
# num_layers=opt.n_layers,
# num_heads=opt.n_heads,
# dropout=opt.dropout,
# clip_dim=512,
# shared_codebook=vq_opt.shared_codebook,
# cond_drop_prob=opt.cond_drop_prob,
# # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
# clip_version=clip_version,
# opt=opt)
all_params = 0
pc_transformer = sum(param.numel() for param in res_transformer.parameters_wo_clip())
print(res_transformer)
# print("Total parameters of t2m_transformer net: {:.2f}M".format(pc_transformer / 1000_000))
all_params += pc_transformer
print('Total parameters of all models: {:.2f}M'.format(all_params / 1000_000))
mean = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'mean.npy'))
std = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'std.npy'))
train_split_file = pjoin(opt.data_root, 'train.txt')
val_split_file = pjoin(opt.data_root, 'val.txt')
|
def plot_t2m(data, save_dir, captions, m_lengths):
data = train_dataset.inv_transform(data)
# print(ep_curves.shape)
for i, (caption, joint_data) in enumerate(zip(captions, data)):
joint_data = joint_data[:m_lengths[i]]
joint = recover_from_ric(torch.from_numpy(joint_data).float(), opt.joints_num).numpy()
save_path = pjoin(save_dir, '%02d.mp4'%i)
# print(joint.shape)
plot_3d_motion(save_path, kinematic_chain, joint, title=caption, fps=20)
def load_vq_model():
opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
vq_opt = get_opt(opt_path, opt.device)
vq_model = RVQVAE(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=opt.device)
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
print(f'Loading VQ Model {opt.vq_name}')
vq_model.to(opt.device)
return vq_model, vq_opt
if __name__ == '__main__':
parser = TrainT2MOptions()
opt = parser.parse()
fixseed(opt.seed)
opt.device = torch.device("cpu" if opt.gpu_id == -1 else "cuda:" + str(opt.gpu_id))
torch.autograd.set_detect_anomaly(True)
opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
opt.model_dir = pjoin(opt.save_root, 'model')
# opt.meta_dir = pjoin(opt.save_root, 'meta')
opt.eval_dir = pjoin(opt.save_root, 'animation')
opt.log_dir = pjoin('./log/res/', opt.dataset_name, opt.name)
os.makedirs(opt.model_dir, exist_ok=True)
# os.makedirs(opt.meta_dir, exist_ok=True)
os.makedirs(opt.eval_dir, exist_ok=True)
os.makedirs(opt.log_dir, exist_ok=True)
if opt.dataset_name == 't2m':
opt.data_root = './dataset/HumanML3D'
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
opt.joints_num = 22
opt.max_motion_len = 55
dim_pose = 263
radius = 4
fps = 20
kinematic_chain = t2m_kinematic_chain
dataset_opt_path = './checkpoints/t2m/Comp_v6_KLD005/opt.txt'
elif opt.dataset_name == 'kit': #TODO
opt.data_root = './dataset/KIT-ML'
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
opt.joints_num = 21
radius = 240 * 8
fps = 12.5
dim_pose = 251
opt.max_motion_len = 55
kinematic_chain = kit_kinematic_chain
dataset_opt_path = './checkpoints/kit/Comp_v6_KLD005/opt.txt'
else:
raise KeyError('Dataset Does Not Exist')
opt.text_dir = pjoin(opt.data_root, 'texts')
vq_model, vq_opt = load_vq_model()
clip_version = 'ViT-B/32'
opt.num_tokens = vq_opt.nb_code
opt.num_quantizers = vq_opt.num_quantizers
# if opt.is_v2:
res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
cond_mode='text',
latent_dim=opt.latent_dim,
ff_size=opt.ff_size,
num_layers=opt.n_layers,
num_heads=opt.n_heads,
dropout=opt.dropout,
clip_dim=512,
shared_codebook=vq_opt.shared_codebook,
cond_drop_prob=opt.cond_drop_prob,
# codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
share_weight=opt.share_weight,
clip_version=clip_version,
opt=opt)
# else:
# res_transformer = ResidualTransformer(code_dim=vq_opt.code_dim,
# cond_mode='text',
# latent_dim=opt.latent_dim,
# ff_size=opt.ff_size,
# num_layers=opt.n_layers,
# num_heads=opt.n_heads,
# dropout=opt.dropout,
# clip_dim=512,
# shared_codebook=vq_opt.shared_codebook,
# cond_drop_prob=opt.cond_drop_prob,
# # codebook=vq_model.quantizer.codebooks[0] if opt.fix_token_emb else None,
# clip_version=clip_version,
# opt=opt)
all_params = 0
pc_transformer = sum(param.numel() for param in res_transformer.parameters_wo_clip())
print(res_transformer)
# print("Total parameters of t2m_transformer net: {:.2f}M".format(pc_transformer / 1000_000))
all_params += pc_transformer
print('Total parameters of all models: {:.2f}M'.format(all_params / 1000_000))
mean = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'mean.npy'))
std = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'meta', 'std.npy'))
train_split_file = pjoin(opt.data_root, 'train.txt')
val_split_file = pjoin(opt.data_root, 'val.txt')
| train_dataset = Text2MotionDataset(opt, mean, std, train_split_file) | 9 | 2023-11-29 19:21:27+00:00 | 16k |
dvlab-research/LLMGA | llmga/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.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 [... | import math
import numpy as np
import torch
from typing import List, Optional, Tuple, Union
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import deprecate, logging
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput | 12,063 | self.order_list = self.get_order_list(num_inference_steps)
# add an index counter for schedulers that allow duplicated timesteps
self._step_index = None
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
photorealism as well as better image-text alignment, especially when using very large guidance weights."
https://arxiv.org/abs/2205.11487
"""
dtype = sample.dtype
batch_size, channels, *remaining_dims = sample.shape
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
abs_sample = sample.abs() # "a certain percentile absolute pixel value"
s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
s = torch.clamp(
s, min=1, max=self.config.sample_max_value
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s"
sample = sample.reshape(batch_size, channels, *remaining_dims)
sample = sample.to(dtype)
return sample
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(sigma)
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low = log_sigmas[low_idx]
high = log_sigmas[high_idx]
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
def _sigma_to_alpha_sigma_t(self, sigma):
alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
sigma_t = sigma * alpha_t
return alpha_t, sigma_t
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min: float = in_sigmas[-1].item()
sigma_max: float = in_sigmas[0].item()
rho = 7.0 # 7.0 is the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
def convert_model_output(
self,
model_output: torch.FloatTensor,
*args,
sample: torch.FloatTensor = None,
**kwargs,
) -> torch.FloatTensor:
"""
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
integral of the data prediction model.
<Tip>
The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
prediction and data prediction models.
</Tip>
Args:
model_output (`torch.FloatTensor`):
The direct output from the learned diffusion model.
sample (`torch.FloatTensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.FloatTensor`:
The converted model output.
"""
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
raise ValueError("missing `sample` as a required keyward argument")
if timestep is not None:
| # Copyright 2023 TSAIL Team and 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.
# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
def betas_for_alpha_bar(
num_diffusion_timesteps,
max_beta=0.999,
alpha_transform_type="cosine",
):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
Choose from `cosine` or `exp`
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(t):
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(t):
return math.exp(t * -12.0)
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
return torch.tensor(betas, dtype=torch.float32)
class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
"""
`DPMSolverSinglestepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
solver_order (`int`, defaults to 2):
The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
sampling, and `solver_order=3` for unconditional sampling.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
sample_max_value (`float`, defaults to 1.0):
The threshold value for dynamic thresholding. Valid only when `thresholding=True` and
`algorithm_type="dpmsolver++"`.
algorithm_type (`str`, defaults to `dpmsolver++`):
Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The
`dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)
paper, and the `dpmsolver++` type implements the algorithms in the
[DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or
`sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.
solver_type (`str`, defaults to `midpoint`):
Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
lower_order_final (`bool`, defaults to `True`):
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
the sigmas are determined according to a sequence of noise levels {σi}.
lambda_min_clipped (`float`, defaults to `-inf`):
Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the
cosine (`squaredcos_cap_v2`) noise schedule.
variance_type (`str`, *optional*):
Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output
contains the predicted Gaussian variance.
"""
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[np.ndarray] = None,
solver_order: int = 2,
prediction_type: str = "epsilon",
thresholding: bool = False,
dynamic_thresholding_ratio: float = 0.995,
sample_max_value: float = 1.0,
algorithm_type: str = "dpmsolver++",
solver_type: str = "midpoint",
lower_order_final: bool = True,
use_karras_sigmas: Optional[bool] = False,
lambda_min_clipped: float = -float("inf"),
variance_type: Optional[str] = None,
):
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = (
torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# Currently we only support VP-type noise schedule
self.alpha_t = torch.sqrt(self.alphas_cumprod)
self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# settings for DPM-Solver
if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
if algorithm_type == "deis":
self.register_to_config(algorithm_type="dpmsolver++")
else:
raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
self.register_to_config(solver_type="midpoint")
else:
raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
# setable values
self.num_inference_steps = None
timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
self.timesteps = torch.from_numpy(timesteps)
self.model_outputs = [None] * solver_order
self.sample = None
self.order_list = self.get_order_list(num_train_timesteps)
self._step_index = None
def get_order_list(self, num_inference_steps: int) -> List[int]:
"""
Computes the solver order at each time step.
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
"""
steps = num_inference_steps
order = self.config.solver_order
if self.config.lower_order_final:
if order == 3:
if steps % 3 == 0:
orders = [1, 2, 3] * (steps // 3 - 1) + [1, 2] + [1]
elif steps % 3 == 1:
orders = [1, 2, 3] * (steps // 3) + [1]
else:
orders = [1, 2, 3] * (steps // 3) + [1, 2]
elif order == 2:
if steps % 2 == 0:
orders = [1, 2] * (steps // 2)
else:
orders = [1, 2] * (steps // 2) + [1]
elif order == 1:
orders = [1] * steps
else:
if order == 3:
orders = [1, 2, 3] * (steps // 3)
elif order == 2:
orders = [1, 2] * (steps // 2)
elif order == 1:
orders = [1] * steps
return orders
@property
def step_index(self):
"""
The index counter for current timestep. It will increae 1 after each scheduler step.
"""
return self._step_index
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
"""
self.num_inference_steps = num_inference_steps
# Clipping the minimum of all lambda(t) for numerical stability.
# This is critical for cosine (squaredcos_cap_v2) noise schedule.
clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)
timesteps = (
np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1)
.round()[::-1][:-1]
.copy()
.astype(np.int64)
)
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
if self.config.use_karras_sigmas:
log_sigmas = np.log(sigmas)
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
else:
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
self.sigmas = torch.from_numpy(sigmas).to(device=device)
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
self.model_outputs = [None] * self.config.solver_order
self.sample = None
if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0:
logger.warn(
"Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=True`."
)
self.register_to_config(lower_order_final=True)
self.order_list = self.get_order_list(num_inference_steps)
# add an index counter for schedulers that allow duplicated timesteps
self._step_index = None
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
photorealism as well as better image-text alignment, especially when using very large guidance weights."
https://arxiv.org/abs/2205.11487
"""
dtype = sample.dtype
batch_size, channels, *remaining_dims = sample.shape
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
abs_sample = sample.abs() # "a certain percentile absolute pixel value"
s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
s = torch.clamp(
s, min=1, max=self.config.sample_max_value
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s"
sample = sample.reshape(batch_size, channels, *remaining_dims)
sample = sample.to(dtype)
return sample
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(sigma)
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low = log_sigmas[low_idx]
high = log_sigmas[high_idx]
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
def _sigma_to_alpha_sigma_t(self, sigma):
alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
sigma_t = sigma * alpha_t
return alpha_t, sigma_t
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min: float = in_sigmas[-1].item()
sigma_max: float = in_sigmas[0].item()
rho = 7.0 # 7.0 is the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
def convert_model_output(
self,
model_output: torch.FloatTensor,
*args,
sample: torch.FloatTensor = None,
**kwargs,
) -> torch.FloatTensor:
"""
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
integral of the data prediction model.
<Tip>
The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
prediction and data prediction models.
</Tip>
Args:
model_output (`torch.FloatTensor`):
The direct output from the learned diffusion model.
sample (`torch.FloatTensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.FloatTensor`:
The converted model output.
"""
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
raise ValueError("missing `sample` as a required keyward argument")
if timestep is not None: | deprecate( | 3 | 2023-11-27 18:46:55+00:00 | 16k |
sherwinbahmani/4dfy | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio | 13,286 | 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:
raise NotImplementedError
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
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:
raise NotImplementedError
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitVolume): | 4 | 2023-11-29 05:15:56+00:00 | 16k |
rlawjdghek/StableVITON | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
import torchvision.transforms as T
import random
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from torchvision.transforms.functional import resize
from diffusers.models.autoencoder_kl import AutoencoderKLOutput
from diffusers.models.vae import DecoderOutput
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like, zero_module, conv_nd
from ldm.models.diffusion.ddim import DDIMSampler | 13,207 | assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def predict_start_from_z_and_v(self, x_t, t, v):
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
l_cond_simple_weight=1.0,
l_cond_recon_weight=1.0,
**kwargs
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.unet_config = unet_config
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.imagenet_norm = T.Normalize((0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711))
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
self.l_cond_simple_weight = l_cond_simple_weight
self.l_cond_recon_weight = l_cond_recon_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if reset_ema: assert exists(ckpt_path)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
if reset_ema:
assert self.use_ema
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
else:
self.register_buffer('logvar', logvar)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def predict_start_from_z_and_v(self, x_t, t, v):
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None): | noise = default(noise, lambda: torch.randn_like(x_start)) | 2 | 2023-12-02 05:56:58+00:00 | 16k |
AIFSH/NativeSpeaker | src/core.py | [
{
"identifier": "HandleLog",
"path": "src/log_helper.py",
"snippet": "class HandleLog:\n \"\"\"\n 先创建日志记录器（logging.getLogger），然后再设置日志级别（logger.setLevel），\n 接着再创建日志文件，也就是日志保存的地方（logging.FileHandler），然后再设置日志格式（logging.Formatter），\n 最后再将日志处理程序记录到记录器（addHandler）\n \"\"\"\n\n def __init__(s... | import os
import torch
import soundfile as sf
import gc; gc.collect(); torch.cuda.empty_cache(); del cloner
import gc; gc.collect(); torch.cuda.empty_cache(); del diarize_model
import gc; gc.collect(); torch.cuda.empty_cache(); del whisper
from typing import Any
from tqdm import tqdm
from src.log_helper import HandleLog
from moviepy.editor import VideoFileClip,concatenate_videoclips
from pathlib import Path
from pydub import AudioSegment
from src.audio_bgm_split import AudioProcess
from src.voice_clone import VoiceCloner
from src.temp_manager import TempFileManager
from src.translator import Translator
from src.lipsync import LipSync
from src.upscale import Upscale
from src.nfsw import analyse_video
from src.third_part.whisperx import load_model,load_audio,DiarizationPipeline | 12,328 |
seg_len = len(whispher_segments)
cloner = VoiceCloner(self.xt_version_name)
root_path = Path(self.output_file).parent
zimu_txt = os.path.join(root_path,"zimu.txt")
for i, segment in tqdm(enumerate(whispher_segments), desc="voice cloning", total=seg_len):
start = segment['start'] * 1000
end = segment['end'] * 1000
text_list = segment['text_list']
if i == 0:
vocal_cloned_audio += org_vocal[:start]
bgm_audio_extend += bgm_audio[:start]
video_extend_list.append(org_video_clip.subclip(0, start / 1000))
total_cloned_vocal = AudioSegment.silent(0)
if len(text_list) > 0:
for src_text in text_list:
dst_text = self.translotor(src_text,src_lang_code,self.lang_code)
with open(zimu_txt,mode="a",encoding="utf-8",newline="\n") as w:
w.write(src_text)
w.write("\n")
w.write(dst_text)
w.write("\n")
cloned_vocal_path = cloner(text=dst_text,
lang_code=self.lang_code,
speaker_wav=[segment['wav'],speakers_wav[segment['speaker']]])
cloned_vocal = AudioSegment.from_file(cloned_vocal_path)
total_cloned_vocal += cloned_vocal
else:
logger.info(f'no sound there')
total_cloned_vocal = AudioSegment.silent(end - start)
vocal_cloned_audio += total_cloned_vocal
tmp_bgm_audio = bgm_audio[start:end]
bgm_audio_extend += self.bgm_map_vocal(tmp_bgm_audio, total_cloned_vocal)
tmp_video_clip = org_video_clip.subclip(start/1000, end/1000)
tmp_video_clip = self.video_map_vocal(tmp_video_clip, total_cloned_vocal)
video_extend_list.append(tmp_video_clip)
if i < seg_len - 1:
# duration
vocal_cloned_audio += org_vocal[end:whispher_segments[i+1]['start']*1000]
bgm_audio_extend += bgm_audio[end:whispher_segments[i+1]['start']*1000]
video_extend_list.append(org_video_clip.subclip(end/1000, whispher_segments[i+1]['start']))
if i == seg_len - 1:
# duration
vocal_cloned_audio += org_vocal[end:]
bgm_audio_extend += bgm_audio[end:]
video_extend_list.append(org_video_clip.subclip(end/1000))
vocal_cloned_path = os.path.join(root_path,"vocal_cloned.wav")
vocal_cloned_audio.export(vocal_cloned_path, format="wav")
logger.info("vocal_cloned.wav saved in {}, you can check it".format(root_path))
bgm_extend_path = os.path.join(root_path,"bgm_extend.wav")
bgm_audio_extend.export(bgm_extend_path, format="wav")
logger.info("bgm_extend.wav saved in {}, you can check it".format(root_path))
voice_cloned_path = os.path.join(root_path,"voice_cloned.wav")
self.combie_audio(vocal_cloned_audio,bgm_audio_extend,voice_cloned_path)
logger.info("voice_cloned.wav saved in {}, you can check it".format(root_path))
video_extend_path = os.path.join(root_path,"video_extend.mp4")
video_extended = concatenate_videoclips(video_extend_list)
video_extended.write_videofile(video_extend_path,fps=25,audio=False)
logger.info("video_extend.mp4 saved in {}, you can check it".format(root_path))
# delete model if low on GPU resources
logger.critical("[Step 5] Wav2Lip by vocal_cloned.wav and video_extend.mp4")
lipsync = LipSync(self.model_name)
lipsync(video_extend_path,vocal_cloned_path,self.output_file,voice_cloned_path)
logger.critical("[Step 6] Upscale output video last step")
upscaler = Upscale()
upscale_workplace_path = os.path.join(root_path,"upscale_workplace")
upscaler(input_path = self.output_file,output_path=upscale_workplace_path,audio=voice_cloned_path)
self.temp_manager.cleanup()
def combie_audio(self,vocal_audio:AudioSegment,bgm_audio:AudioSegment,file_path):
new_audio = vocal_audio.overlay(bgm_audio)
new_audio.export(file_path, format="wav")
def bgm_map_vocal(self,bgm_audio:AudioSegment,vocal_audio:AudioSegment):
audio_duration = vocal_audio.duration_seconds
ratio = audio_duration / bgm_audio.duration_seconds
print("audio.duration_seconds / bgm.duration_seconds = {}".format(ratio))
tmp_bgm_path = self.temp_manager.create_temp_file(suffix='.wav').name
bgm_audio.export(tmp_bgm_path, format="wav")
bgm_path = self.temp_manager.create_temp_file(suffix='.wav').name
y,sr = sf.read(tmp_bgm_path)
sf.write(bgm_path,y,int(sr*ratio))
bgm_extended = AudioSegment.from_file(bgm_path)
return bgm_extended[:audio_duration * 1000]
def video_map_vocal(self,vido_clip:VideoFileClip,vocal_audio:AudioSegment):
audio_duration = vocal_audio.duration_seconds
video_duration = vido_clip.duration
ratio = video_duration / audio_duration
print("video_duration / audio_duration =ratio:{}".format(ratio))
new_video = vido_clip.fl_time(lambda t: ratio*t,apply_to=['mask', 'audio'])
new_video1 = new_video.set_duration(audio_duration)
new_video2 = new_video1.set_fps(new_video1.fps / video_duration * audio_duration)
return new_video2.subclip(0,audio_duration)
def speech_to_text(self, vocal_file):
| os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
logger = HandleLog()
class Core:
def __init__(self, args) -> None:
cur_path = os.path.dirname(os.path.realpath(__file__)) # current path
self.weights_path = os.path.join(os.path.dirname(cur_path), 'weights') # weights_path to save model
if not os.path.exists(self.weights_path): os.mkdir(self.weights_path) #
self.input_file = args.input_file_path
self.output_file = args.output_file_path
self.lang_code = args.lang_code
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.hf_token = args.hf_token
self.temp_manager = TempFileManager()
self.translotor = Translator()
self.model_name = args.model_name
self.xt_version_name = args.xt_version_name
if analyse_video(args.input_file_path):
raise("sorry! nativespeaker is not for you")
def __call__(self, *args: Any, **kwds: Any) -> Any:
logger.critical("[Step 1] Moviepy split voice and frames from video")
org_voice_path = os.path.join(Path(self.input_file).parent, "org_voice.wav")
org_video_clip = VideoFileClip(self.input_file)
org_video_clip.audio.write_audiofile(org_voice_path,codec='pcm_s16le')
logger.info("save original voice in {}".format(org_voice_path))
logger.critical("[Step 2] H5 Split vocal and bgm from voice")
audio_process = AudioProcess(15)
vocal_file, bgm_file = audio_process.split(org_voice_path)
logger.critical("[Step 3] whisperx from speech to text")
whispher_segments, src_lang_code, speakers_wav = self.speech_to_text(vocal_file)
logger.critical("[Step 4] translate,text to speech,video and voice_cloned aligment")
vocal_cloned_audio = AudioSegment.silent(0)
bgm_audio_extend = AudioSegment.silent(0)
video_extend_list = []
org_vocal = AudioSegment.from_file(vocal_file)
bgm_audio = AudioSegment.from_file(bgm_file)
seg_len = len(whispher_segments)
cloner = VoiceCloner(self.xt_version_name)
root_path = Path(self.output_file).parent
zimu_txt = os.path.join(root_path,"zimu.txt")
for i, segment in tqdm(enumerate(whispher_segments), desc="voice cloning", total=seg_len):
start = segment['start'] * 1000
end = segment['end'] * 1000
text_list = segment['text_list']
if i == 0:
vocal_cloned_audio += org_vocal[:start]
bgm_audio_extend += bgm_audio[:start]
video_extend_list.append(org_video_clip.subclip(0, start / 1000))
total_cloned_vocal = AudioSegment.silent(0)
if len(text_list) > 0:
for src_text in text_list:
dst_text = self.translotor(src_text,src_lang_code,self.lang_code)
with open(zimu_txt,mode="a",encoding="utf-8",newline="\n") as w:
w.write(src_text)
w.write("\n")
w.write(dst_text)
w.write("\n")
cloned_vocal_path = cloner(text=dst_text,
lang_code=self.lang_code,
speaker_wav=[segment['wav'],speakers_wav[segment['speaker']]])
cloned_vocal = AudioSegment.from_file(cloned_vocal_path)
total_cloned_vocal += cloned_vocal
else:
logger.info(f'no sound there')
total_cloned_vocal = AudioSegment.silent(end - start)
vocal_cloned_audio += total_cloned_vocal
tmp_bgm_audio = bgm_audio[start:end]
bgm_audio_extend += self.bgm_map_vocal(tmp_bgm_audio, total_cloned_vocal)
tmp_video_clip = org_video_clip.subclip(start/1000, end/1000)
tmp_video_clip = self.video_map_vocal(tmp_video_clip, total_cloned_vocal)
video_extend_list.append(tmp_video_clip)
if i < seg_len - 1:
# duration
vocal_cloned_audio += org_vocal[end:whispher_segments[i+1]['start']*1000]
bgm_audio_extend += bgm_audio[end:whispher_segments[i+1]['start']*1000]
video_extend_list.append(org_video_clip.subclip(end/1000, whispher_segments[i+1]['start']))
if i == seg_len - 1:
# duration
vocal_cloned_audio += org_vocal[end:]
bgm_audio_extend += bgm_audio[end:]
video_extend_list.append(org_video_clip.subclip(end/1000))
vocal_cloned_path = os.path.join(root_path,"vocal_cloned.wav")
vocal_cloned_audio.export(vocal_cloned_path, format="wav")
logger.info("vocal_cloned.wav saved in {}, you can check it".format(root_path))
bgm_extend_path = os.path.join(root_path,"bgm_extend.wav")
bgm_audio_extend.export(bgm_extend_path, format="wav")
logger.info("bgm_extend.wav saved in {}, you can check it".format(root_path))
voice_cloned_path = os.path.join(root_path,"voice_cloned.wav")
self.combie_audio(vocal_cloned_audio,bgm_audio_extend,voice_cloned_path)
logger.info("voice_cloned.wav saved in {}, you can check it".format(root_path))
video_extend_path = os.path.join(root_path,"video_extend.mp4")
video_extended = concatenate_videoclips(video_extend_list)
video_extended.write_videofile(video_extend_path,fps=25,audio=False)
logger.info("video_extend.mp4 saved in {}, you can check it".format(root_path))
# delete model if low on GPU resources
logger.critical("[Step 5] Wav2Lip by vocal_cloned.wav and video_extend.mp4")
lipsync = LipSync(self.model_name)
lipsync(video_extend_path,vocal_cloned_path,self.output_file,voice_cloned_path)
logger.critical("[Step 6] Upscale output video last step")
upscaler = Upscale()
upscale_workplace_path = os.path.join(root_path,"upscale_workplace")
upscaler(input_path = self.output_file,output_path=upscale_workplace_path,audio=voice_cloned_path)
self.temp_manager.cleanup()
def combie_audio(self,vocal_audio:AudioSegment,bgm_audio:AudioSegment,file_path):
new_audio = vocal_audio.overlay(bgm_audio)
new_audio.export(file_path, format="wav")
def bgm_map_vocal(self,bgm_audio:AudioSegment,vocal_audio:AudioSegment):
audio_duration = vocal_audio.duration_seconds
ratio = audio_duration / bgm_audio.duration_seconds
print("audio.duration_seconds / bgm.duration_seconds = {}".format(ratio))
tmp_bgm_path = self.temp_manager.create_temp_file(suffix='.wav').name
bgm_audio.export(tmp_bgm_path, format="wav")
bgm_path = self.temp_manager.create_temp_file(suffix='.wav').name
y,sr = sf.read(tmp_bgm_path)
sf.write(bgm_path,y,int(sr*ratio))
bgm_extended = AudioSegment.from_file(bgm_path)
return bgm_extended[:audio_duration * 1000]
def video_map_vocal(self,vido_clip:VideoFileClip,vocal_audio:AudioSegment):
audio_duration = vocal_audio.duration_seconds
video_duration = vido_clip.duration
ratio = video_duration / audio_duration
print("video_duration / audio_duration =ratio:{}".format(ratio))
new_video = vido_clip.fl_time(lambda t: ratio*t,apply_to=['mask', 'audio'])
new_video1 = new_video.set_duration(audio_duration)
new_video2 = new_video1.set_fps(new_video1.fps / video_duration * audio_duration)
return new_video2.subclip(0,audio_duration)
def speech_to_text(self, vocal_file): | vocal_audio = load_audio(vocal_file) | 9 | 2023-12-01 12:23:19+00:00 | 16k |
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 | 14,058 | #
# 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):
| #
# 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 | 11 | 2023-11-29 07:10:39+00:00 | 16k |
emdgroup/baybe | examples/Backtesting/impute_mode.py | [
{
"identifier": "Campaign",
"path": "baybe/campaign.py",
"snippet": "class Campaign(SerialMixin):\n \"\"\"Main class for interaction with BayBE.\n\n Campaigns define and record an experimentation process, i.e. the execution of a\n series of measurements and the iterative sequence of events invo... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from baybe import Campaign
from baybe.objective import Objective
from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
from baybe.recommenders import RandomRecommender
from baybe.searchspace import SearchSpace
from baybe.simulation import simulate_scenarios
from baybe.strategies import TwoPhaseStrategy
from baybe.targets import NumericalTarget | 11,199 | ### Example for full simulation loop using a table-based lookup mechanism with incomplete data
# This example shows a simulation for a direct arylation where not all combinations were measured.
# This allows us to access information about previously conducted experiments from .xlsx-files.
# This examples assumes some basic familiarity with using BayBE and the lookup mechanism.
# We refer to [`campaign`](./../Basics/campaign.md) for a more basic example resp.
# to [`full_lookup`](./full_lookup.md) for details on the lookup mechanism.
#### Necessary imports for this example
#### Parameters for a full simulation loop
# For the full simulation, we need to define some additional parameters.
# These are the number of Monte Carlo runs and the number of experiments to be conducted per run.
N_MC_ITERATIONS = 2
N_DOE_ITERATIONS = 5
#### Lookup functionality and data creation
# See [`full_lookup`](./full_lookup.md) for details.
try:
lookup = pd.read_excel("./lookup_withmissing.xlsx")
except FileNotFoundError:
try:
lookup = pd.read_excel("examples/Backtesting/lookup_withmissing.xlsx")
except FileNotFoundError as e:
print(e)
# As usual, we set up some experiment.
# Note that we now need to ensure that the names fit the names in the provided .xlsx file!
dict_solvent = {
"DMAc": r"CC(N(C)C)=O",
"Butyornitrile": r"CCCC#N",
"Butyl Ester": r"CCCCOC(C)=O",
"p-Xylene": r"CC1=CC=C(C)C=C1",
}
dict_base = {
"Potassium acetate": r"O=C([O-])C.[K+]",
"Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]",
"Cesium acetate": r"O=C([O-])C.[Cs+]",
"Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]",
}
dict_ligand = {
"BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)="
"CC=C2OC",
"Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C",
"(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3",
"Tricyclohexylphosphine": r"P(C1CCCCC1)(C2CCCCC2)C3CCCCC3",
"PPh3": r"P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3",
"XPhos": r"CC(C1=C(C2=CC=CC=C2P(C3CCCCC3)C4CCCCC4)C(C(C)C)=CC(C(C)C)=C1)C",
"P(2-furyl)3": r"P(C1=CC=CO1)(C2=CC=CO2)C3=CC=CO3",
"Methyldiphenylphosphine": r"CP(C1=CC=CC=C1)C2=CC=CC=C2",
"1268824-69-6": r"CC(OC1=C(P(C2CCCCC2)C3CCCCC3)C(OC(C)C)=CC=C1)C",
"JackiePhos": r"FC(F)(F)C1=CC(P(C2=C(C3=C(C(C)C)C=C(C(C)C)C=C3C(C)C)C(OC)=CC=C2OC)"
r"C4=CC(C(F)(F)F)=CC(C(F)(F)F)=C4)=CC(C(F)(F)F)=C1",
"SCHEMBL15068049": r"C[C@]1(O2)O[C@](C[C@]2(C)P3C4=CC=CC=C4)(C)O[C@]3(C)C1",
"Me2PPh": r"CP(C)C1=CC=CC=C1",
}
#### Creating the searchspace and the objective
# Here, we create the parameter objects, the searchspace and the objective.
solvent = SubstanceParameter(name="Solvent", data=dict_solvent, encoding="MORDRED")
base = SubstanceParameter(name="Base", data=dict_base, encoding="MORDRED")
ligand = SubstanceParameter(name="Ligand", data=dict_ligand, encoding="MORDRED")
| ### Example for full simulation loop using a table-based lookup mechanism with incomplete data
# This example shows a simulation for a direct arylation where not all combinations were measured.
# This allows us to access information about previously conducted experiments from .xlsx-files.
# This examples assumes some basic familiarity with using BayBE and the lookup mechanism.
# We refer to [`campaign`](./../Basics/campaign.md) for a more basic example resp.
# to [`full_lookup`](./full_lookup.md) for details on the lookup mechanism.
#### Necessary imports for this example
#### Parameters for a full simulation loop
# For the full simulation, we need to define some additional parameters.
# These are the number of Monte Carlo runs and the number of experiments to be conducted per run.
N_MC_ITERATIONS = 2
N_DOE_ITERATIONS = 5
#### Lookup functionality and data creation
# See [`full_lookup`](./full_lookup.md) for details.
try:
lookup = pd.read_excel("./lookup_withmissing.xlsx")
except FileNotFoundError:
try:
lookup = pd.read_excel("examples/Backtesting/lookup_withmissing.xlsx")
except FileNotFoundError as e:
print(e)
# As usual, we set up some experiment.
# Note that we now need to ensure that the names fit the names in the provided .xlsx file!
dict_solvent = {
"DMAc": r"CC(N(C)C)=O",
"Butyornitrile": r"CCCC#N",
"Butyl Ester": r"CCCCOC(C)=O",
"p-Xylene": r"CC1=CC=C(C)C=C1",
}
dict_base = {
"Potassium acetate": r"O=C([O-])C.[K+]",
"Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]",
"Cesium acetate": r"O=C([O-])C.[Cs+]",
"Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]",
}
dict_ligand = {
"BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)="
"CC=C2OC",
"Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C",
"(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3",
"Tricyclohexylphosphine": r"P(C1CCCCC1)(C2CCCCC2)C3CCCCC3",
"PPh3": r"P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3",
"XPhos": r"CC(C1=C(C2=CC=CC=C2P(C3CCCCC3)C4CCCCC4)C(C(C)C)=CC(C(C)C)=C1)C",
"P(2-furyl)3": r"P(C1=CC=CO1)(C2=CC=CO2)C3=CC=CO3",
"Methyldiphenylphosphine": r"CP(C1=CC=CC=C1)C2=CC=CC=C2",
"1268824-69-6": r"CC(OC1=C(P(C2CCCCC2)C3CCCCC3)C(OC(C)C)=CC=C1)C",
"JackiePhos": r"FC(F)(F)C1=CC(P(C2=C(C3=C(C(C)C)C=C(C(C)C)C=C3C(C)C)C(OC)=CC=C2OC)"
r"C4=CC(C(F)(F)F)=CC(C(F)(F)F)=C4)=CC(C(F)(F)F)=C1",
"SCHEMBL15068049": r"C[C@]1(O2)O[C@](C[C@]2(C)P3C4=CC=CC=C4)(C)O[C@]3(C)C1",
"Me2PPh": r"CP(C)C1=CC=CC=C1",
}
#### Creating the searchspace and the objective
# Here, we create the parameter objects, the searchspace and the objective.
solvent = SubstanceParameter(name="Solvent", data=dict_solvent, encoding="MORDRED")
base = SubstanceParameter(name="Base", data=dict_base, encoding="MORDRED")
ligand = SubstanceParameter(name="Ligand", data=dict_ligand, encoding="MORDRED") | temperature = NumericalDiscreteParameter( | 2 | 2023-11-27 17:02:40+00:00 | 16k |
UX-Decoder/LLaVA-Grounding | llava/model/language_model/llava_llama_gd.py | [
{
"identifier": "LlavaMetaModel",
"path": "llava/model/llava_arch.py",
"snippet": "class LlavaMetaModel:\n\n def __init__(self, config):\n super(LlavaMetaModel, self).__init__(config)\n\n if hasattr(config, \"mm_vision_tower\"):\n self.vision_tower = build_vision_tower(config... | from typing import List, Optional, Tuple, Union
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM, LlavaMetaForCausalLM_gd,LlavaMetaForCausalLM_gd_interactive
import torch
import torch.nn as nn
import transformers | 12,155 | # Copyright 2023 Haotian Liu
#
# 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.
IGNORE_INDEX=-100
# @dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
# tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances,tokenizer):
input_ids, labels = tuple([instance[key] for instance in instances]
for key in ("input_ids", "labels"))
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids,
batch_first=True,
padding_value=tokenizer.pad_token_id)
labels = torch.nn.utils.rnn.pad_sequence(labels,
batch_first=True,
padding_value=IGNORE_INDEX)
input_ids = input_ids[:, :tokenizer.model_max_length]
labels = labels[:, :tokenizer.model_max_length]
batch = dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(tokenizer.pad_token_id),
)
if 'image_clip' in instances[0]:
images = [instance['image_clip'] for instance in instances]
if all(x is not None and x.shape == images[0].shape for x in images):
batch['images'] = torch.stack(images)
else:
batch['images'] = images
return batch
class LlavaConfig(LlamaConfig):
model_type = "llava"
| # Copyright 2023 Haotian Liu
#
# 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.
IGNORE_INDEX=-100
# @dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
# tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances,tokenizer):
input_ids, labels = tuple([instance[key] for instance in instances]
for key in ("input_ids", "labels"))
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids,
batch_first=True,
padding_value=tokenizer.pad_token_id)
labels = torch.nn.utils.rnn.pad_sequence(labels,
batch_first=True,
padding_value=IGNORE_INDEX)
input_ids = input_ids[:, :tokenizer.model_max_length]
labels = labels[:, :tokenizer.model_max_length]
batch = dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(tokenizer.pad_token_id),
)
if 'image_clip' in instances[0]:
images = [instance['image_clip'] for instance in instances]
if all(x is not None and x.shape == images[0].shape for x in images):
batch['images'] = torch.stack(images)
else:
batch['images'] = images
return batch
class LlavaConfig(LlamaConfig):
model_type = "llava"
| class LlavaLlamaModel(LlavaMetaModel, LlamaModel): | 0 | 2023-12-04 10:59:21+00:00 | 16k |
daveredrum/SceneTex | models/pipeline/texture_pipeline.py | [
{
"identifier": "TextureMesh",
"path": "models/modules/meshes.py",
"snippet": "class TextureMesh(nn.Module):\n def __init__(self, \n config,\n device\n ): \n \n super().__init__()\n \n self.config = config\n self.device = device\n\n self.num_... | import random
import wandb
import json
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import numpy as np
import pytorch_lightning as pl
import matplotlib.pyplot as plt
import sys
import open_clip
from torch.optim import Adam, AdamW
from torch.optim.lr_scheduler import LinearLR
from omegaconf import OmegaConf
from tqdm import tqdm
from omegaconf import OmegaConf
from PIL import Image
from copy import deepcopy
from pathlib import Path
from pytorch3d.io import (
load_obj,
load_objs_as_meshes
)
from pytorch3d.renderer import TexturesUV
from pytorch3d.ops import interpolate_face_attributes
from models.modules import TextureMesh, Studio, Guidance | 10,858 |
# mat
# customized
sys.path.append("./lib")
class TexturePipeline(nn.Module):
def __init__(self,
config,
stamp,
device
):
super().__init__()
self.config = config
self.stamp = stamp
self.prompt = config.prompt + ", " + config.a_prompt if config.a_prompt else config.prompt
self.n_prompt = config.n_prompt
self.device = device
self.weights_dtype = torch.float16 if self.config.enable_half_precision else torch.float32
print("=> Use precision: {}".format(self.weights_dtype))
pl.seed_everything(self.config.seed)
"""call this after to(device)"""
def configure(self, inference_mode=False):
if not inference_mode:
self.log_name = "_".join(self.config.prompt.split(' '))
self.log_stamp = self.stamp
self.log_dir = os.path.join(self.config.log_dir, self.log_name, self.config.loss_type, self.log_stamp)
# override config
self.config.log_name = self.log_name
self.config.log_stamp = self.log_stamp
self.config.log_dir = self.log_dir
# 3D assets
self._init_mesh()
# studio
self._init_studio()
# instances
self._init_anchors()
if not inference_mode:
# diffusion
self._init_guidance()
# optimization
self._configure_optimizers()
self._init_logger()
if self.config.enable_clip_benchmark:
self.clip, _, self.clip_preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
self.clip_tokenizer = open_clip.get_tokenizer('ViT-B-32')
def _init_studio(self):
self.studio = Studio(self.config, self.device)
def _init_mesh(self):
self.texture_mesh = TextureMesh(self.config, self.device)
def _init_guidance(self):
|
# mat
# customized
sys.path.append("./lib")
class TexturePipeline(nn.Module):
def __init__(self,
config,
stamp,
device
):
super().__init__()
self.config = config
self.stamp = stamp
self.prompt = config.prompt + ", " + config.a_prompt if config.a_prompt else config.prompt
self.n_prompt = config.n_prompt
self.device = device
self.weights_dtype = torch.float16 if self.config.enable_half_precision else torch.float32
print("=> Use precision: {}".format(self.weights_dtype))
pl.seed_everything(self.config.seed)
"""call this after to(device)"""
def configure(self, inference_mode=False):
if not inference_mode:
self.log_name = "_".join(self.config.prompt.split(' '))
self.log_stamp = self.stamp
self.log_dir = os.path.join(self.config.log_dir, self.log_name, self.config.loss_type, self.log_stamp)
# override config
self.config.log_name = self.log_name
self.config.log_stamp = self.log_stamp
self.config.log_dir = self.log_dir
# 3D assets
self._init_mesh()
# studio
self._init_studio()
# instances
self._init_anchors()
if not inference_mode:
# diffusion
self._init_guidance()
# optimization
self._configure_optimizers()
self._init_logger()
if self.config.enable_clip_benchmark:
self.clip, _, self.clip_preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
self.clip_tokenizer = open_clip.get_tokenizer('ViT-B-32')
def _init_studio(self):
self.studio = Studio(self.config, self.device)
def _init_mesh(self):
self.texture_mesh = TextureMesh(self.config, self.device)
def _init_guidance(self): | self.guidance = Guidance(self.config, self.device) | 2 | 2023-11-28 15:38:40+00:00 | 16k |
Vchitect/VBench | vbench/third_party/umt/datasets/build.py | [
{
"identifier": "TubeMaskingGenerator",
"path": "vbench/third_party/umt/datasets/masking_generator.py",
"snippet": "class TubeMaskingGenerator:\n def __init__(self, input_size, mask_ratio):\n self.frames, self.height, self.width = input_size\n self.num_patches_per_frame = self.height * ... | import os
from torchvision import transforms
from .transforms import *
from .masking_generator import TubeMaskingGenerator, RandomMaskingGenerator
from .mae import VideoMAE
from .kinetics import VideoClsDataset
from .kinetics_sparse import VideoClsDataset_sparse
from .ssv2 import SSVideoClsDataset, SSRawFrameClsDataset | 14,384 |
class DataAugmentationForVideoMAE(object):
def __init__(self, args):
self.input_mean = [0.485, 0.456, 0.406] # IMAGENET_DEFAULT_MEAN
self.input_std = [0.229, 0.224, 0.225] # IMAGENET_DEFAULT_STD
normalize = GroupNormalize(self.input_mean, self.input_std)
self.train_augmentation = GroupMultiScaleCrop(args.input_size, [1, .875, .75, .66])
if args.color_jitter > 0:
self.transform = transforms.Compose([
self.train_augmentation,
GroupColorJitter(args.color_jitter),
GroupRandomHorizontalFlip(flip=args.flip),
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
else:
self.transform = transforms.Compose([
self.train_augmentation,
GroupRandomHorizontalFlip(flip=args.flip),
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
if args.mask_type == 'tube':
self.masked_position_generator = TubeMaskingGenerator(
args.window_size, args.mask_ratio
)
elif args.mask_type == 'random':
|
class DataAugmentationForVideoMAE(object):
def __init__(self, args):
self.input_mean = [0.485, 0.456, 0.406] # IMAGENET_DEFAULT_MEAN
self.input_std = [0.229, 0.224, 0.225] # IMAGENET_DEFAULT_STD
normalize = GroupNormalize(self.input_mean, self.input_std)
self.train_augmentation = GroupMultiScaleCrop(args.input_size, [1, .875, .75, .66])
if args.color_jitter > 0:
self.transform = transforms.Compose([
self.train_augmentation,
GroupColorJitter(args.color_jitter),
GroupRandomHorizontalFlip(flip=args.flip),
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
else:
self.transform = transforms.Compose([
self.train_augmentation,
GroupRandomHorizontalFlip(flip=args.flip),
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
if args.mask_type == 'tube':
self.masked_position_generator = TubeMaskingGenerator(
args.window_size, args.mask_ratio
)
elif args.mask_type == 'random': | self.masked_position_generator = RandomMaskingGenerator( | 1 | 2023-11-27 12:41:46+00:00 | 16k |
HyeonHo99/Video-Motion-Customization | showone/models/unet_3d_condition.py | [
{
"identifier": "TransformerTemporalModel",
"path": "showone/models/transformer_temporal.py",
"snippet": "class TransformerTemporalModel(ModelMixin, ConfigMixin):\n \"\"\"\n A Transformer model for video-like data.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16)... | from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import BaseOutput, logging
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
from diffusers.models.embeddings import (
GaussianFourierProjection,
ImageHintTimeEmbedding,
ImageProjection,
ImageTimeEmbedding,
TextImageProjection,
TextImageTimeEmbedding,
TextTimeEmbedding,
TimestepEmbedding,
Timesteps,
)
from diffusers.models.modeling_utils import ModelMixin
from .transformer_temporal import TransformerTemporalModel
from .unet_3d_blocks import (
CrossAttnDownBlock3D,
CrossAttnUpBlock3D,
DownBlock3D,
UNetMidBlock3DCrossAttn,
UNetMidBlock3DSimpleCrossAttn,
UpBlock3D,
get_down_block,
get_up_block,
)
from diffusers.utils import WEIGHTS_NAME
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
import os, json | 13,182 |
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `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.
"""
# count = len(self.attn_processors.keys())
# ignore temporal attention
count = len({k: v for k, v in self.attn_processors.items() if "temp_" not in k}.keys()) # Show-1 original line
#count = len(self.attn_processors.keys()) # --> If BoxDiff: use this line
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor") and "temp_" not in name:
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_processor(AttnProcessor())
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop())
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module, value=False):
| # Copyright 2023 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.
# Copyright 2023 The ModelScope 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.
# from diffusers.models.transformer_temporal import TransformerTemporalModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class UNet3DConditionOutput(BaseOutput):
"""
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: torch.FloatTensor
class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
and returns sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the models (such as downloading or saving, etc.)
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, it will skip the normalization and activation layers in post-processing
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
"DownBlock3D",
),
mid_block_type: Optional[str] = "UNetMidBlock3DCrossAttn",
up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: int = 1.0,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
projection_class_embeddings_input_dim: Optional[int] = None,
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads=64,
transfromer_in_opt: bool =False,
):
super().__init__()
self.sample_size = sample_size
self.transformer_in_opt = transfromer_in_opt
if num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
)
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
)
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
)
# input
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
if self.transformer_in_opt:
self.transformer_in = TransformerTemporalModel(
num_attention_heads=8,
attention_head_dim=64,
in_channels=block_out_channels[0],
num_layers=1,
)
# time
if time_embedding_type == "fourier":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
if time_embed_dim % 2 != 0:
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
self.time_proj = GaussianFourierProjection(
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
)
timestep_input_dim = time_embed_dim
elif time_embedding_type == "positional":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
else:
raise ValueError(
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
)
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
post_act_fn=timestep_post_act,
cond_proj_dim=time_cond_proj_dim,
)
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
encoder_hid_dim_type = "text_proj"
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
raise ValueError(
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
)
if encoder_hid_dim_type == "text_proj":
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
elif encoder_hid_dim_type == "text_image_proj":
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
self.encoder_hid_proj = TextImageProjection(
text_embed_dim=encoder_hid_dim,
image_embed_dim=cross_attention_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2
self.encoder_hid_proj = ImageProjection(
image_embed_dim=encoder_hid_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
elif class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
elif class_embed_type == "projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
)
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
# 2. it projects from an arbitrary input dimension.
#
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif class_embed_type == "simple_projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
)
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
else:
self.class_embedding = None
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
self.add_embedding = TextImageTimeEmbedding(
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
)
elif addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif addition_embed_type == "image":
# Kandinsky 2.2
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type == "image_hint":
# Kandinsky 2.2 ControlNet
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=blocks_time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim[i],
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
)
self.down_blocks.append(down_block)
# mid
if mid_block_type == "UNetMidBlock3DCrossAttn":
self.mid_block = UNetMidBlock3DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
)
elif mid_block_type == "UNetMidBlock3DSimpleCrossAttn":
self.mid_block = UNetMidBlock3DSimpleCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
attention_head_dim=attention_head_dim[-1],
resnet_groups=norm_num_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
only_cross_attention=mid_block_only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif mid_block_type is None:
self.mid_block = None
else:
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
reversed_layers_per_block = list(reversed(layers_per_block))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
only_cross_attention = list(reversed(only_cross_attention))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=blocks_time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=reversed_cross_attention_dim[i],
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = get_activation(act_fn)
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@property
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "set_processor"):
processors[f"{name}.processor"] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `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.
"""
# count = len(self.attn_processors.keys())
# ignore temporal attention
count = len({k: v for k, v in self.attn_processors.items() if "temp_" not in k}.keys()) # Show-1 original line
#count = len(self.attn_processors.keys()) # --> If BoxDiff: use this line
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor") and "temp_" not in name:
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_processor(AttnProcessor())
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop())
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module, value=False): | if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): | 6 | 2023-11-29 17:23:45+00:00 | 16k |
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 | 12,095 |
###############################################################################
# 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'],
|
###############################################################################
# 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]), | 8 | 2023-11-27 13:44:01+00:00 | 16k |
zhenzhiwang/intercontrol | sample/global_joint_control.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... | from diffusion.control_diffusion import ControlGaussianDiffusion
from diffusion.respace import SpacedDiffusion
from utils.fixseed import fixseed
from utils.parser_util import edit_control_args
from utils.model_util import load_controlmdm_and_diffusion
from utils import dist_util
from model.cfg_sampler import wrap_model
from data_loaders.get_data import get_dataset_loader
from data_loaders.humanml.scripts.motion_process import recover_from_ric
from data_loaders.humanml_utils import get_control_mask, HML_JOINT_NAMES
from data_loaders.humanml.utils.plot_script import plot_3d_motion
from model.ControlMDM import ControlMDM
import os
import numpy as np
import torch
import data_loaders.humanml.utils.paramUtil as paramUtil
import shutil | 10,812 | # This code is based on https://github.com/openai/guided-diffusion
"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
def main():
args = edit_control_args()
assert args.multi_person == False, 'multi-person is not supported for this script'
fixseed(args.seed)
out_path = args.output_dir
name = os.path.basename(os.path.dirname(args.model_path))
niter = os.path.basename(args.model_path).replace('model', '').replace('.pt', '')
max_frames = 196 if args.dataset in ['kit', 'humanml'] else 60
fps = 12.5 if args.dataset == 'kit' else 20
dist_util.setup_dist(args.device)
if out_path == '':
out_path = os.path.join(os.path.dirname(args.model_path),
'edit_{}_{}_{}_seed{}'.format(name, niter, args.inpainting_mask, args.seed))
if args.text_condition != '':
out_path += '_' + args.text_condition.replace(' ', '_').replace('.', '')
print('Loading dataset...')
assert args.num_samples <= args.batch_size, \
f'Please either increase batch_size({args.batch_size}) or reduce num_samples({args.num_samples})'
# So why do we need this check? In order to protect GPU from a memory overload in the following line.
# If your GPU can handle batch size larger then default, you can specify it through --batch_size flag.
# If it doesn't, and you still want to sample more prompts, run this script with different seeds
# (specify through the --seed flag)
args.batch_size = args.num_samples # Sampling a single batch from the testset, with exactly args.num_samples
data = get_dataset_loader(name=args.dataset,
batch_size=args.batch_size,
num_frames=max_frames,
split='test',
load_mode='train',
size=args.num_samples) # in train mode, you get both text and motion.
# data.fixed_length = n_frames
total_num_samples = args.num_samples * args.num_repetitions
print("Creating model and diffusion...")
| # This code is based on https://github.com/openai/guided-diffusion
"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
def main():
args = edit_control_args()
assert args.multi_person == False, 'multi-person is not supported for this script'
fixseed(args.seed)
out_path = args.output_dir
name = os.path.basename(os.path.dirname(args.model_path))
niter = os.path.basename(args.model_path).replace('model', '').replace('.pt', '')
max_frames = 196 if args.dataset in ['kit', 'humanml'] else 60
fps = 12.5 if args.dataset == 'kit' else 20
dist_util.setup_dist(args.device)
if out_path == '':
out_path = os.path.join(os.path.dirname(args.model_path),
'edit_{}_{}_{}_seed{}'.format(name, niter, args.inpainting_mask, args.seed))
if args.text_condition != '':
out_path += '_' + args.text_condition.replace(' ', '_').replace('.', '')
print('Loading dataset...')
assert args.num_samples <= args.batch_size, \
f'Please either increase batch_size({args.batch_size}) or reduce num_samples({args.num_samples})'
# So why do we need this check? In order to protect GPU from a memory overload in the following line.
# If your GPU can handle batch size larger then default, you can specify it through --batch_size flag.
# If it doesn't, and you still want to sample more prompts, run this script with different seeds
# (specify through the --seed flag)
args.batch_size = args.num_samples # Sampling a single batch from the testset, with exactly args.num_samples
data = get_dataset_loader(name=args.dataset,
batch_size=args.batch_size,
num_frames=max_frames,
split='test',
load_mode='train',
size=args.num_samples) # in train mode, you get both text and motion.
# data.fixed_length = n_frames
total_num_samples = args.num_samples * args.num_repetitions
print("Creating model and diffusion...") | DiffusionClass = ControlGaussianDiffusion if args.filter_noise else SpacedDiffusion | 0 | 2023-11-27 05:28:02+00:00 | 16k |
moonbow721/DPoser | run/completion.py | [
{
"identifier": "DistributedEvalSampler",
"path": "lib/dataset/EvaSampler.py",
"snippet": "class DistributedEvalSampler(Sampler):\n r\"\"\"\n DistributedEvalSampler is different from DistributedSampler.\n It does NOT add extra samples to make it evenly divisible.\n DistributedEvalSampler sho... | import os
import math
import numpy as np
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from absl import app
from absl import flags
from absl.flags import argparse_flags
from ml_collections.config_flags import config_flags
from torch.utils.data import DataLoader
from lib.dataset.EvaSampler import DistributedEvalSampler
from lib.utils.misc import create_mask, linear_interpolation
from tensorboardX import SummaryWriter
from torch.utils.tensorboard import SummaryWriter
from lib.algorithms.advanced.model import ScoreModelFC
from lib.algorithms.advanced import sde_lib, sampling
from lib.algorithms.ema import ExponentialMovingAverage
from lib.algorithms.advanced import utils as mutils
from lib.dataset.AMASS import AMASSDataset, N_POSES, Posenormalizer
from lib.body_model.body_model import BodyModel
from lib.dataset.AMASS import Evaler
| 11,704 | if weighted:
weight = 0.5 * torch.sqrt(1 + SNR)
else:
weight = 0.5
dposer_loss = torch.mean(weight * self.loss_fn(x_0, denoise_data))
return dposer_loss
def get_loss_weights(self):
"""Set loss weights"""
loss_weight = {'data': lambda cst, it: 100 * cst / (1 + it),
'dposer': lambda cst, it: 0.1 * cst * (it + 1)}
return loss_weight
@staticmethod
def backward_step(loss_dict, weight_dict, it):
w_loss = dict()
for k in loss_dict:
w_loss[k] = weight_dict[k](loss_dict[k], it)
tot_loss = list(w_loss.values())
tot_loss = torch.stack(tot_loss).sum()
return tot_loss
def optimize(self, observation, mask, time_strategy='3', lr=0.1,
sample_trun=5.0, sample_time=900, iterations=2, steps_per_iter=100):
total_steps = iterations * steps_per_iter
opti_variable = observation.clone().detach()
opti_variable.requires_grad = True
optimizer = torch.optim.Adam([opti_variable], lr, betas=(0.9, 0.999))
weight_dict = self.get_loss_weights()
loss_dict = dict()
eps = 1e-3
timesteps = torch.linspace(self.sde.T, eps, self.sde.N, device=observation.device)
for it in range(iterations):
for i in range(steps_per_iter):
step = it * steps_per_iter + i
optimizer.zero_grad()
''' ************* DPoser loss *********** '''
if time_strategy == '1':
quan_t = torch.randint(self.sde.N, [1])
elif time_strategy == '2':
quan_t = torch.tensor(sample_time)
elif time_strategy == '3':
quan_t = self.sde.N - math.floor(
torch.tensor(total_steps - step - 1) * (self.sde.N / (sample_trun * total_steps))) - 2
else:
raise NotImplementedError('unsupported time sampling strategy')
t = timesteps[quan_t]
vec_t = torch.ones(self.batch_size, device=observation.device) * t
loss_dict['dposer'] = self.loss(opti_variable, vec_t, quan_t)
loss_dict['data'] = self.data_loss(opti_variable * mask, observation * mask)
''' *********** DPoser loss ************ '''
# Get total loss for backward pass
tot_loss = self.backward_step(loss_dict, weight_dict, it)
tot_loss.backward()
optimizer.step()
opti_variable = observation * mask + opti_variable * (1.0 - mask)
return opti_variable
def inference(rank, args, config):
print(f"Running DDP on rank {rank}.")
setup(rank, args.gpus, args.port)
## Load the pre-trained checkpoint from disk.
device = torch.device("cuda", rank)
POSE_DIM = 3 if config.data.rot_rep == 'axis' else 6
if config.model.type == 'ScoreModelFC':
model = ScoreModelFC(
config,
n_poses=N_POSES,
pose_dim=POSE_DIM,
hidden_dim=config.model.HIDDEN_DIM,
embed_dim=config.model.EMBED_DIM,
n_blocks=config.model.N_BLOCKS,
)
else:
raise NotImplementedError('unsupported model')
model.to(device)
ema = ExponentialMovingAverage(model.parameters(), decay=config.model.ema_rate)
state = dict(optimizer=None, model=model, ema=ema, step=0)
# restore checkpoint
map_location = {'cuda:0': 'cuda:%d' % rank}
checkpoint = torch.load(args.ckpt_path, map_location=map_location)
model.load_state_dict(checkpoint['model_state_dict'])
ema.load_state_dict(checkpoint['ema'])
state['step'] = checkpoint['step']
print(f"=> loaded checkpoint '{args.ckpt_path}' (step {state['step']})")
model.eval()
ema.copy_to(model.parameters())
# Setup SDEs
if config.training.sde.lower() == 'vpsde':
sde = sde_lib.VPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=args.steps)
elif config.training.sde.lower() == 'subvpsde':
sde = sde_lib.subVPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=args.steps)
elif config.training.sde.lower() == 'vesde':
sde = sde_lib.VESDE(sigma_min=config.model.sigma_min, sigma_max=config.model.sigma_max, N=args.steps)
else:
raise NotImplementedError(f"SDE {config.training.sde} unknown.")
# Setup sampling functions
compfn = DPoserComp(model, sde, config.training.continuous, batch_size=args.batch_size)
Normalizer = Posenormalizer(data_path=f'{args.dataset_folder}/{args.version}/train',
normalize=config.data.normalize,
min_max=config.data.min_max, rot_rep=config.data.rot_rep, device=device)
|
try:
except ImportError as e:
try:
except ImportError as e:
print('Tensorboard is not Installed')
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file(
"config", None, "Training configuration.", lock_config=False)
flags.mark_flags_as_required(["config"])
def parse_args(argv):
parser = argparse_flags.ArgumentParser(description='test diffusion model for completion on whole AMASS')
parser.add_argument('--ckpt-path', type=str, default='./pretrained_models/axis-zscore-400k.pth')
parser.add_argument('--dataset-folder', type=str, default='../data/AMASS/amass_processed',
help='the folder includes necessary normalizing parameters')
parser.add_argument('--version', type=str, default='version1', help='dataset version')
parser.add_argument('--bodymodel-path', type=str, default='../body_models/smplx/SMPLX_NEUTRAL.npz',
help='path of SMPLX model')
parser.add_argument('--hypo', type=int, default=1, help='number of hypotheses to sample')
parser.add_argument('--part', type=str, default='left_leg', choices=['left_leg', 'right_leg', 'left_arm',
'right_arm', 'trunk', 'hands',
'legs', 'arms'])
# optional
parser.add_argument('--steps', type=int, default=1000)
parser.add_argument('--sample', type=int, help='sample testset to reduce data for other tasks')
parser.add_argument('--batch_size', type=int, default=100, )
parser.add_argument('--gpus', type=int, help='num gpus to inference parallel')
parser.add_argument('--port', type=str, default='14600', help='master port of machines')
args = parser.parse_args(argv[1:])
return args
def get_dataloader(dataset, num_replicas=1, rank=0, batch_size=10000):
sampler = DistributedEvalSampler(dataset,
num_replicas=num_replicas,
rank=rank,
shuffle=False)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0,
sampler=sampler,
persistent_workers=False,
pin_memory=True,
drop_last=True)
return dataloader
def setup(rank, world_size, port):
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = port
# initialize the process group
dist.init_process_group("gloo", rank=rank, world_size=world_size)
def cleanup():
dist.destroy_process_group()
class DPoserComp(object):
def __init__(self, diffusion_model, sde, continuous, batch_size=1):
self.batch_size = batch_size
self.sde = sde
self.score_fn = mutils.get_score_fn(sde, diffusion_model, train=False, continuous=continuous)
self.rsde = sde.reverse(self.score_fn, False)
# L2 loss
self.loss_fn = nn.MSELoss(reduction='none')
self.data_loss = nn.MSELoss(reduction='mean')
def one_step_denoise(self, x_t, t):
drift, diffusion, alpha, sigma_2, score = self.rsde.sde(x_t, t, guide=True)
x_0_hat = (x_t + sigma_2[:, None] * score) / alpha
SNR = alpha / torch.sqrt(sigma_2)[:, None]
return x_0_hat.detach(), SNR
def multi_step_denoise(self, x_t, t, t_end, N=10):
time_traj = linear_interpolation(t, t_end, N + 1)
x_current = x_t
for i in range(N):
t_current = time_traj[i]
t_before = time_traj[i + 1]
alpha_current, sigma_current = self.sde.return_alpha_sigma(t_current)
alpha_before, sigma_before = self.sde.return_alpha_sigma(t_before)
score = self.score_fn(x_current, t_current, condition=None, mask=None)
score = -score * sigma_current[:, None] # score to noise prediction
x_current = alpha_before / alpha_current * (
x_current - sigma_current[:, None] * score) + sigma_before[
:,
None] * score
alpha, sigma = self.sde.return_alpha_sigma(time_traj[0])
SNR = alpha / sigma[:, None]
return x_current.detach(), SNR
def loss(self, x_0, vec_t, weighted=False, multi_denoise=False):
# x_0: [B, j*6], vec_t: [B],
z = torch.randn_like(x_0)
mean, std = self.sde.marginal_prob(x_0, vec_t)
perturbed_data = mean + std[:, None] * z #
if multi_denoise: # not recommended
denoise_data, SNR = self.multi_step_denoise(perturbed_data, vec_t, t_end=vec_t / (2 * 10), N=10)
else:
denoise_data, SNR = self.one_step_denoise(perturbed_data, vec_t)
if weighted:
weight = 0.5 * torch.sqrt(1 + SNR)
else:
weight = 0.5
dposer_loss = torch.mean(weight * self.loss_fn(x_0, denoise_data))
return dposer_loss
def get_loss_weights(self):
"""Set loss weights"""
loss_weight = {'data': lambda cst, it: 100 * cst / (1 + it),
'dposer': lambda cst, it: 0.1 * cst * (it + 1)}
return loss_weight
@staticmethod
def backward_step(loss_dict, weight_dict, it):
w_loss = dict()
for k in loss_dict:
w_loss[k] = weight_dict[k](loss_dict[k], it)
tot_loss = list(w_loss.values())
tot_loss = torch.stack(tot_loss).sum()
return tot_loss
def optimize(self, observation, mask, time_strategy='3', lr=0.1,
sample_trun=5.0, sample_time=900, iterations=2, steps_per_iter=100):
total_steps = iterations * steps_per_iter
opti_variable = observation.clone().detach()
opti_variable.requires_grad = True
optimizer = torch.optim.Adam([opti_variable], lr, betas=(0.9, 0.999))
weight_dict = self.get_loss_weights()
loss_dict = dict()
eps = 1e-3
timesteps = torch.linspace(self.sde.T, eps, self.sde.N, device=observation.device)
for it in range(iterations):
for i in range(steps_per_iter):
step = it * steps_per_iter + i
optimizer.zero_grad()
''' ************* DPoser loss *********** '''
if time_strategy == '1':
quan_t = torch.randint(self.sde.N, [1])
elif time_strategy == '2':
quan_t = torch.tensor(sample_time)
elif time_strategy == '3':
quan_t = self.sde.N - math.floor(
torch.tensor(total_steps - step - 1) * (self.sde.N / (sample_trun * total_steps))) - 2
else:
raise NotImplementedError('unsupported time sampling strategy')
t = timesteps[quan_t]
vec_t = torch.ones(self.batch_size, device=observation.device) * t
loss_dict['dposer'] = self.loss(opti_variable, vec_t, quan_t)
loss_dict['data'] = self.data_loss(opti_variable * mask, observation * mask)
''' *********** DPoser loss ************ '''
# Get total loss for backward pass
tot_loss = self.backward_step(loss_dict, weight_dict, it)
tot_loss.backward()
optimizer.step()
opti_variable = observation * mask + opti_variable * (1.0 - mask)
return opti_variable
def inference(rank, args, config):
print(f"Running DDP on rank {rank}.")
setup(rank, args.gpus, args.port)
## Load the pre-trained checkpoint from disk.
device = torch.device("cuda", rank)
POSE_DIM = 3 if config.data.rot_rep == 'axis' else 6
if config.model.type == 'ScoreModelFC':
model = ScoreModelFC(
config,
n_poses=N_POSES,
pose_dim=POSE_DIM,
hidden_dim=config.model.HIDDEN_DIM,
embed_dim=config.model.EMBED_DIM,
n_blocks=config.model.N_BLOCKS,
)
else:
raise NotImplementedError('unsupported model')
model.to(device)
ema = ExponentialMovingAverage(model.parameters(), decay=config.model.ema_rate)
state = dict(optimizer=None, model=model, ema=ema, step=0)
# restore checkpoint
map_location = {'cuda:0': 'cuda:%d' % rank}
checkpoint = torch.load(args.ckpt_path, map_location=map_location)
model.load_state_dict(checkpoint['model_state_dict'])
ema.load_state_dict(checkpoint['ema'])
state['step'] = checkpoint['step']
print(f"=> loaded checkpoint '{args.ckpt_path}' (step {state['step']})")
model.eval()
ema.copy_to(model.parameters())
# Setup SDEs
if config.training.sde.lower() == 'vpsde':
sde = sde_lib.VPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=args.steps)
elif config.training.sde.lower() == 'subvpsde':
sde = sde_lib.subVPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=args.steps)
elif config.training.sde.lower() == 'vesde':
sde = sde_lib.VESDE(sigma_min=config.model.sigma_min, sigma_max=config.model.sigma_max, N=args.steps)
else:
raise NotImplementedError(f"SDE {config.training.sde} unknown.")
# Setup sampling functions
compfn = DPoserComp(model, sde, config.training.continuous, batch_size=args.batch_size)
Normalizer = Posenormalizer(data_path=f'{args.dataset_folder}/{args.version}/train',
normalize=config.data.normalize,
min_max=config.data.min_max, rot_rep=config.data.rot_rep, device=device)
| test_dataset = AMASSDataset(root_path=args.dataset_folder,
| 8 | 2023-11-29 15:55:50+00:00 | 16k |
KylinYee/R2-Talker-code | main.py | [
{
"identifier": "NeRFDataset",
"path": "nerf/provider.py",
"snippet": "class NeRFDataset:\n def __init__(self, opt, device, type='train', downscale=1):\n super().__init__()\n \n self.opt = opt\n self.device = device\n self.type = type # train, val, test\n sel... | import torch
import argparse
from nerf.provider import NeRFDataset
from nerf.gui import NeRFGUI
from nerf.utils import *
from nerf.network import NeRFNetwork, R2TalkerNeRF, GeneNeRFNetwork | 13,028 |
parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
# asr
parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
parser.add_argument('--asr_play', action='store_true', help="play out the audio")
parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
# parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
parser.add_argument('--asr_save_feats', action='store_true')
# audio FPS
parser.add_argument('--fps', type=int, default=50)
# sliding window left-middle-right length (unit: 20ms)
parser.add_argument('-l', type=int, default=10)
parser.add_argument('-m', type=int, default=50)
parser.add_argument('-r', type=int, default=10)
opt = parser.parse_args()
if opt.method == 'r2talker':
opt.cond_type = 'idexp'
elif opt.method == 'genefaceDagger':
opt.cond_type = 'idexp'
elif opt.method == 'rad-nerf':
opt.cond_type = 'eo'
if opt.O:
opt.fp16 = True
opt.exp_eye = True
if opt.test:
opt.smooth_path = True
opt.smooth_eye = True
opt.smooth_lips = True
opt.cuda_ray = True
# assert opt.cuda_ray, "Only support CUDA ray mode."
if opt.patch_size > 1:
# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
if opt.finetune_lips:
# do not update density grid in finetune stage
opt.update_extra_interval = 1e9
print(opt)
seed_everything(opt.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if opt.method == 'r2talker':
model = R2TalkerNeRF(opt)
elif opt.method == 'genefaceDagger':
model = GeneNeRFNetwork(opt)
elif opt.method == 'rad-nerf':
model = NeRFNetwork(opt)
# manually load state dict for head
if opt.torso and opt.head_ckpt != '':
model_dict = torch.load(opt.head_ckpt, map_location='cpu')['model']
missing_keys, unexpected_keys = model.load_state_dict(model_dict, strict=False)
if len(missing_keys) > 0:
print(f"[WARN] missing keys: {missing_keys}")
if len(unexpected_keys) > 0:
print(f"[WARN] unexpected keys: {unexpected_keys}")
# freeze these keys
for k, v in model.named_parameters():
if k in model_dict:
# print(f'[INFO] freeze {k}, {v.shape}')
v.requires_grad = False
# print(model)
criterion = torch.nn.MSELoss(reduction='none')
if opt.test:
if opt.gui:
metrics = [] # use no metric in GUI for faster initialization...
else:
# metrics = [PSNRMeter(), LPIPSMeter(device=device)]
metrics = [PSNRMeter(), LPIPSMeter(device=device), LMDMeter(backend='fan')]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
if opt.test_train:
test_set = NeRFDataset(opt, device=device, type='train')
# a manual fix to test on the training dataset
test_set.training = False
test_set.num_rays = -1
test_loader = test_set.dataloader()
else:
test_loader = NeRFDataset(opt, device=device, type='test').dataloader()
# temp fix: for update_extra_states
model.aud_features = test_loader._data.auds
model.eye_areas = test_loader._data.eye_area
if opt.gui:
# we still need test_loader to provide audio features for testing.
|
# torch.autograd.set_detect_anomaly(True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --exp_eye")
parser.add_argument('--test', action='store_true', help="test mode (load model and test dataset)")
parser.add_argument('--test_train', action='store_true', help="test mode (load model and train dataset)")
parser.add_argument('--data_range', type=int, nargs='*', default=[0, -1], help="data range to use")
parser.add_argument('--workspace', type=str, default='workspace')
parser.add_argument('--seed', type=int, default=0)
### training options
parser.add_argument('--iters', type=int, default=200000, help="training iters")
parser.add_argument('--lr', type=float, default=5e-3, help="initial learning rate")
parser.add_argument('--lr_net', type=float, default=5e-4, help="initial learning rate")
parser.add_argument('--ckpt', type=str, default='latest')
parser.add_argument('--num_rays', type=int, default=4096 * 16, help="num rays sampled per image for each training step")
parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
parser.add_argument('--max_steps', type=int, default=16, help="max num steps sampled per ray (only valid when using --cuda_ray)")
parser.add_argument('--num_steps', type=int, default=16, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when NOT using --cuda_ray)")
parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when NOT using --cuda_ray)")
### network backbone options
parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
parser.add_argument('--lambda_amb', type=float, default=0.1, help="lambda for ambient loss")
parser.add_argument('--bg_img', type=str, default='', help="background image")
parser.add_argument('--fbg', action='store_true', help="frame-wise bg")
parser.add_argument('--exp_eye', action='store_true', help="explicitly control the eyes")
parser.add_argument('--fix_eye', type=float, default=-1, help="fixed eye area, negative to disable, set to 0-0.3 for a reasonable eye")
parser.add_argument('--smooth_eye', action='store_true', help="smooth the eye area sequence")
parser.add_argument('--torso_shrink', type=float, default=0.8, help="shrink bg coords to allow more flexibility in deform")
### dataset options
parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
parser.add_argument('--preload', type=int, default=0, help="0 means load data from disk on-the-fly, 1 means preload to CPU, 2 means GPU.")
# (the default value is for the fox dataset)
parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box[-bound, bound]^3, if > 1, will invoke adaptive ray marching.")
parser.add_argument('--scale', type=float, default=4, help="scale camera location into box[-bound, bound]^3")
parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
parser.add_argument('--dt_gamma', type=float, default=1/256, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
parser.add_argument('--min_near', type=float, default=0.05, help="minimum near distance for camera")
parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied (sigma)")
parser.add_argument('--density_thresh_torso', type=float, default=0.01, help="threshold for density grid to be occupied (alpha)")
parser.add_argument('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")
parser.add_argument('--finetune_lips', action='store_true', help="use LPIPS and landmarks to fine tune lips region")
parser.add_argument('--smooth_lips', action='store_true', help="smooth the enc_a in a exponential decay way...")
parser.add_argument('--torso', action='store_true', help="fix head and train torso")
parser.add_argument('--head_ckpt', type=str, default='', help="head model")
### GUI options
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=450, help="GUI width")
parser.add_argument('--H', type=int, default=450, help="GUI height")
parser.add_argument('--radius', type=float, default=3.35, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=21.24, help="default GUI camera fovy")
parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
### else
parser.add_argument('--att', type=int, default=2, help="audio attention mode (0 = turn off, 1 = left-direction, 2 = bi-direction)")
parser.add_argument('--aud', type=str, default='', help="audio source (empty will load the default, else should be a path to a npy file)")
parser.add_argument('--cond_type', type=str, default=None, help="type of driving condition: eo, ds, idexp")
parser.add_argument('--method', type=str, default='r2talker', help="r2talker, genefaceDagger, rad-nerf")
parser.add_argument('--emb', action='store_true', help="use audio class + embedding instead of logits")
parser.add_argument('--ind_dim', type=int, default=4, help="individual code dim, 0 to turn off")
parser.add_argument('--ind_num', type=int, default=10000, help="number of individual codes, should be larger than training dataset size")
parser.add_argument('--ind_dim_torso', type=int, default=8, help="individual code dim, 0 to turn off")
parser.add_argument('--amb_dim', type=int, default=2, help="ambient dimension")
parser.add_argument('--part', action='store_true', help="use partial training data (1/10)")
parser.add_argument('--part2', action='store_true', help="use partial training data (first 15s)")
parser.add_argument('--train_camera', action='store_true', help="optimize camera pose")
parser.add_argument('--smooth_path', action='store_true', help="brute-force smooth camera pose trajectory with a window size")
parser.add_argument('--smooth_path_window', type=int, default=7, help="smoothing window size")
# asr
parser.add_argument('--asr', action='store_true', help="load asr for real-time app")
parser.add_argument('--asr_wav', type=str, default='', help="load the wav and use as input")
parser.add_argument('--asr_play', action='store_true', help="play out the audio")
parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
# parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
parser.add_argument('--asr_save_feats', action='store_true')
# audio FPS
parser.add_argument('--fps', type=int, default=50)
# sliding window left-middle-right length (unit: 20ms)
parser.add_argument('-l', type=int, default=10)
parser.add_argument('-m', type=int, default=50)
parser.add_argument('-r', type=int, default=10)
opt = parser.parse_args()
if opt.method == 'r2talker':
opt.cond_type = 'idexp'
elif opt.method == 'genefaceDagger':
opt.cond_type = 'idexp'
elif opt.method == 'rad-nerf':
opt.cond_type = 'eo'
if opt.O:
opt.fp16 = True
opt.exp_eye = True
if opt.test:
opt.smooth_path = True
opt.smooth_eye = True
opt.smooth_lips = True
opt.cuda_ray = True
# assert opt.cuda_ray, "Only support CUDA ray mode."
if opt.patch_size > 1:
# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
if opt.finetune_lips:
# do not update density grid in finetune stage
opt.update_extra_interval = 1e9
print(opt)
seed_everything(opt.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if opt.method == 'r2talker':
model = R2TalkerNeRF(opt)
elif opt.method == 'genefaceDagger':
model = GeneNeRFNetwork(opt)
elif opt.method == 'rad-nerf':
model = NeRFNetwork(opt)
# manually load state dict for head
if opt.torso and opt.head_ckpt != '':
model_dict = torch.load(opt.head_ckpt, map_location='cpu')['model']
missing_keys, unexpected_keys = model.load_state_dict(model_dict, strict=False)
if len(missing_keys) > 0:
print(f"[WARN] missing keys: {missing_keys}")
if len(unexpected_keys) > 0:
print(f"[WARN] unexpected keys: {unexpected_keys}")
# freeze these keys
for k, v in model.named_parameters():
if k in model_dict:
# print(f'[INFO] freeze {k}, {v.shape}')
v.requires_grad = False
# print(model)
criterion = torch.nn.MSELoss(reduction='none')
if opt.test:
if opt.gui:
metrics = [] # use no metric in GUI for faster initialization...
else:
# metrics = [PSNRMeter(), LPIPSMeter(device=device)]
metrics = [PSNRMeter(), LPIPSMeter(device=device), LMDMeter(backend='fan')]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
if opt.test_train:
test_set = NeRFDataset(opt, device=device, type='train')
# a manual fix to test on the training dataset
test_set.training = False
test_set.num_rays = -1
test_loader = test_set.dataloader()
else:
test_loader = NeRFDataset(opt, device=device, type='test').dataloader()
# temp fix: for update_extra_states
model.aud_features = test_loader._data.auds
model.eye_areas = test_loader._data.eye_area
if opt.gui:
# we still need test_loader to provide audio features for testing. | with NeRFGUI(opt, trainer, test_loader) as gui: | 1 | 2023-12-04 12:51:59+00:00 | 16k |
ubc-vision/vivid123 | vivid123/generation_utils.py | [
{
"identifier": "CLIPCameraProjection",
"path": "vivid123/models/clip_camera_projection.py",
"snippet": "class CLIPCameraProjection(ModelMixin, ConfigMixin):\n \"\"\"\n A Projection layer for CLIP embedding and camera embedding.\n Parameters:\n embedding_dim (`int`, *optional*, defaults ... | import os
import yaml
import re
import torch
import numpy as np
import imageio.v3 as imageio
from typing import List, Any
from yaml.parser import ParserError
from PIL import Image
from diffusers.pipelines import DiffusionPipeline
from diffusers.models import UNet2DConditionModel, AutoencoderKL
from diffusers.schedulers import DPMSolverMultistepScheduler, EulerDiscreteScheduler
from diffusers.pipelines import DiffusionPipeline
from transformers import CLIPVisionModelWithProjection
from .models import CLIPCameraProjection
from .pipelines import ViVid123Pipeline
from .configs import ViVid123BaseSchema | 10,846 | video_linear_end_weight: float = 0.5,
video_start_step_percentage: float = 0.0,
video_end_step_percentage: float = 1.0,
zero123_linear_start_weight: float = 1.0,
zero123_linear_end_weight: float = 1.0,
zero123_start_step_percentage: float = 0.0,
zero123_end_step_percentage: float = 1.0,
):
"""
Prepare the fusion schedule of video diffusion and zero123 at all the denoising steps
Args:
video_linear_start_weight (`float`, *optional*, defaults to 1.0):
The weight of the video diffusion at the start of the video. The weight is linearly increased from
`video_linear_start_weight` to `video_linear_end_weight` during the video diffusion.
video_linear_end_weight (`float`, *optional*, defaults to 0.5):
The weight of the video diffusion at the end of the video. The weight is linearly increased from
`video_linear_start_weight` to `video_linear_end_weight` during the video diffusion.
video_start_step_percentage (`float`, *optional*, defaults to 0.0):
The percentage of the total number of inference steps at which the video diffusion starts. The video
diffusion is linearly increased from `video_linear_start_weight` to `video_linear_end_weight` between
`video_start_step_percentage` and `video_end_step_percentage`.
video_end_step_percentage (`float`, *optional*, defaults to 1.0):
The percentage of the total number of inference steps at which the video diffusion ends. The video
diffusion is linearly increased from `video_linear_start_weight` to `video_linear_end_weight` between
`video_start_step_percentage` and `video_end_step_percentage`.
zero123_linear_start_weight (`float`, *optional*, defaults to 1.0):
The weight of the zero123 diffusion at the start of the video. The weight is linearly increased from
`zero123_linear_start_weight` to `zero123_linear_end_weight` during the zero123 diffusion.
zero123_linear_end_weight (`float`, *optional*, defaults to 1.0):
The weight of the zero123 diffusion at the end of the video. The weight is linearly increased from
`zero123_linear_start_weight` to `zero123_linear_end_weight` during the zero123 diffusion.
zero123_start_step_percentage (`float`, *optional*, defaults to 0.0):
The percentage of the total number of inference steps at which the zero123 diffusion starts. The
zero123 diffusion is linearly increased from `zero123_linear_start_weight` to
`zero123_linear_end_weight` between `zero123_start_step_percentage` and `zero123_end_step_percentage`.
zero123_end_step_percentage (`float`, *optional*, defaults to 1.0):
The percentage of the total number of inference steps at which the zero123 diffusion ends. The
zero123 diffusion is linearly increased from `zero123_linear_start_weight` to
`zero123_linear_end_weight` between `zero123_start_step_percentage` and `zero123_end_step_percentage`.
Return:
A tuple of two tensors,
video_schedule (`torch.Tensor`): The schedule of the video diffusion weighting, with shape `[num_inference_steps]`.
zero123_schedule (`torch.Tensor`): The schedule of the zero123 diffusion weighting, with shape `[num_inference_steps]`.
"""
assert (
video_linear_start_weight >= 0.0 and video_linear_start_weight <= 1.0
), "video_linear_start_weight must be between 0.0 and 1.0"
assert (
video_linear_end_weight >= 0.0 and video_linear_end_weight <= 1.0
), "video_linear_end_weight must be between 0.0 and 1.0"
assert (
video_start_step_percentage >= 0.0 and video_start_step_percentage <= 1.0
), "video_start_step_percentage must be between 0.0 and 1.0"
assert (
video_end_step_percentage >= 0.0 and video_end_step_percentage <= 1.0
), "video_end_step_percentage must be between 0.0 and 1.0"
assert (
zero123_linear_start_weight >= 0.0 and zero123_linear_start_weight <= 1.0
), "zero123_linear_start_weight must be between 0.0 and 1.0"
assert (
zero123_linear_end_weight >= 0.0 and zero123_linear_end_weight <= 1.0
), "zero123_linear_end_weight must be between 0.0 and 1.0"
assert (
zero123_start_step_percentage >= 0.0 and zero123_start_step_percentage <= 1.0
), "zero123_start_step_percentage must be between 0.0 and 1.0"
assert (
zero123_end_step_percentage >= 0.0 and zero123_end_step_percentage <= 1.0
), "zero123_end_step_percentage must be between 0.0 and 1.0"
video_schedule = torch.linspace(
start=video_linear_start_weight,
end=video_linear_end_weight,
steps=int((video_end_step_percentage - video_start_step_percentage) * num_inference_steps),
)
zero123_schedule = torch.linspace(
start=zero123_linear_start_weight,
end=zero123_linear_end_weight,
steps=int((zero123_end_step_percentage - zero123_start_step_percentage) * num_inference_steps),
)
if video_schedule.shape[0] < num_inference_steps:
video_schedule = torch.cat(
[
video_linear_start_weight * torch.ones([video_start_step_percentage * num_inference_steps]),
video_schedule,
video_linear_end_weight
* torch.ones([num_inference_steps - video_end_step_percentage * num_inference_steps]),
]
)
if zero123_schedule.shape[0] < num_inference_steps:
zero123_schedule = torch.cat(
[
zero123_linear_start_weight * torch.ones([zero123_start_step_percentage * num_inference_steps]),
zero123_schedule,
zero123_linear_end_weight
* torch.ones([num_inference_steps - zero123_end_step_percentage * num_inference_steps]),
]
)
return (video_schedule, zero123_schedule)
def save_videos_grid_zeroscope_nplist(video_frames: List[np.ndarray], path: str, n_rows=6, fps=8, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
f = len(video_frames)
h, w, c = video_frames[0].shape
#images = [(image).astype("uint8") for image in video_frames]
os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.imwrite(path, video_frames, fps=fps)
def prepare_pipelines(
ZERO123_MODEL_ID: str = "bennyguo/zero123-xl-diffusers",
VIDEO_MODEL_ID: str = "cerspense/zeroscope_v2_576w",
VIDEO_XL_MODEL_ID: str = "cerspense/zeroscope_v2_XL"
):
zero123_unet = UNet2DConditionModel.from_pretrained(ZERO123_MODEL_ID, subfolder="unet")
|
def prepare_cam_pose_input(
num_frames: int = 25,
delta_elevation_start: float = 0.0,
delta_elevation_end: float = 0.0,
delta_azimuth_start: float = -45.0,
delta_azimuth_end: float = 45.0,
delta_radius_start: float = 0.0,
delta_radius_end: float = 0.0,
):
r"""
The function to prepare the input to the vivid123 pipeline
Args:
delta_elevation_start (`float`, *optional*, defaults to 0.0):
The starting relative elevation angle of the camera, in degree. Relative to the elevation of the reference image.
The camera is facing towards the origin.
delta_elevation_end (`float`, *optional*, defaults to 0.0):
The ending relative elevation angle of the camera, in degree. Relative to the elevation of the reference image.
The camera is facing towards the origin.
delta_azimuth_start (`float`, *optional*, defaults to -45.0):
The starting relative azimuth angle of the camera, in degree. Relative to the elevation of the reference image.
The camera is facing towards the origin.
delta_azimuth_end (`float`, *optional*, defaults to 45.0):
The ending relative azimuth angle of the camera, in degree. Relative to the elevation of the reference image.
The camera is facing towards the origin.
Returns:
"""
cam_elevation = np.radians(np.linspace(delta_elevation_start, delta_elevation_end, num_frames))[..., None]
cam_azimuth = np.radians(np.linspace(delta_azimuth_start, delta_azimuth_end, num_frames))
cam_azimuth_sin_cos = np.stack([np.sin(cam_azimuth), np.cos(cam_azimuth)], axis=-1)
cam_radius = np.linspace(delta_radius_start, delta_radius_end, num_frames)[..., None]
cam_pose_np = np.concatenate([cam_elevation, cam_azimuth_sin_cos, cam_radius], axis=-1)
cam_pose_torch = torch.from_numpy(cam_pose_np)
return cam_pose_torch
# refer to https://stackoverflow.com/a/33507138/6257375
def conver_rgba_to_rgb_white_bg(
image: Image,
H: int = 256,
W: int = 256,
):
input_image = image.convert("RGBA").resize((H, W), Image.BICUBIC)
background = Image.new("RGBA", input_image.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, input_image)
return alpha_composite
def prepare_fusion_schedule_linear(
num_inference_steps: int = 50,
video_linear_start_weight: float = 1.0,
video_linear_end_weight: float = 0.5,
video_start_step_percentage: float = 0.0,
video_end_step_percentage: float = 1.0,
zero123_linear_start_weight: float = 1.0,
zero123_linear_end_weight: float = 1.0,
zero123_start_step_percentage: float = 0.0,
zero123_end_step_percentage: float = 1.0,
):
"""
Prepare the fusion schedule of video diffusion and zero123 at all the denoising steps
Args:
video_linear_start_weight (`float`, *optional*, defaults to 1.0):
The weight of the video diffusion at the start of the video. The weight is linearly increased from
`video_linear_start_weight` to `video_linear_end_weight` during the video diffusion.
video_linear_end_weight (`float`, *optional*, defaults to 0.5):
The weight of the video diffusion at the end of the video. The weight is linearly increased from
`video_linear_start_weight` to `video_linear_end_weight` during the video diffusion.
video_start_step_percentage (`float`, *optional*, defaults to 0.0):
The percentage of the total number of inference steps at which the video diffusion starts. The video
diffusion is linearly increased from `video_linear_start_weight` to `video_linear_end_weight` between
`video_start_step_percentage` and `video_end_step_percentage`.
video_end_step_percentage (`float`, *optional*, defaults to 1.0):
The percentage of the total number of inference steps at which the video diffusion ends. The video
diffusion is linearly increased from `video_linear_start_weight` to `video_linear_end_weight` between
`video_start_step_percentage` and `video_end_step_percentage`.
zero123_linear_start_weight (`float`, *optional*, defaults to 1.0):
The weight of the zero123 diffusion at the start of the video. The weight is linearly increased from
`zero123_linear_start_weight` to `zero123_linear_end_weight` during the zero123 diffusion.
zero123_linear_end_weight (`float`, *optional*, defaults to 1.0):
The weight of the zero123 diffusion at the end of the video. The weight is linearly increased from
`zero123_linear_start_weight` to `zero123_linear_end_weight` during the zero123 diffusion.
zero123_start_step_percentage (`float`, *optional*, defaults to 0.0):
The percentage of the total number of inference steps at which the zero123 diffusion starts. The
zero123 diffusion is linearly increased from `zero123_linear_start_weight` to
`zero123_linear_end_weight` between `zero123_start_step_percentage` and `zero123_end_step_percentage`.
zero123_end_step_percentage (`float`, *optional*, defaults to 1.0):
The percentage of the total number of inference steps at which the zero123 diffusion ends. The
zero123 diffusion is linearly increased from `zero123_linear_start_weight` to
`zero123_linear_end_weight` between `zero123_start_step_percentage` and `zero123_end_step_percentage`.
Return:
A tuple of two tensors,
video_schedule (`torch.Tensor`): The schedule of the video diffusion weighting, with shape `[num_inference_steps]`.
zero123_schedule (`torch.Tensor`): The schedule of the zero123 diffusion weighting, with shape `[num_inference_steps]`.
"""
assert (
video_linear_start_weight >= 0.0 and video_linear_start_weight <= 1.0
), "video_linear_start_weight must be between 0.0 and 1.0"
assert (
video_linear_end_weight >= 0.0 and video_linear_end_weight <= 1.0
), "video_linear_end_weight must be between 0.0 and 1.0"
assert (
video_start_step_percentage >= 0.0 and video_start_step_percentage <= 1.0
), "video_start_step_percentage must be between 0.0 and 1.0"
assert (
video_end_step_percentage >= 0.0 and video_end_step_percentage <= 1.0
), "video_end_step_percentage must be between 0.0 and 1.0"
assert (
zero123_linear_start_weight >= 0.0 and zero123_linear_start_weight <= 1.0
), "zero123_linear_start_weight must be between 0.0 and 1.0"
assert (
zero123_linear_end_weight >= 0.0 and zero123_linear_end_weight <= 1.0
), "zero123_linear_end_weight must be between 0.0 and 1.0"
assert (
zero123_start_step_percentage >= 0.0 and zero123_start_step_percentage <= 1.0
), "zero123_start_step_percentage must be between 0.0 and 1.0"
assert (
zero123_end_step_percentage >= 0.0 and zero123_end_step_percentage <= 1.0
), "zero123_end_step_percentage must be between 0.0 and 1.0"
video_schedule = torch.linspace(
start=video_linear_start_weight,
end=video_linear_end_weight,
steps=int((video_end_step_percentage - video_start_step_percentage) * num_inference_steps),
)
zero123_schedule = torch.linspace(
start=zero123_linear_start_weight,
end=zero123_linear_end_weight,
steps=int((zero123_end_step_percentage - zero123_start_step_percentage) * num_inference_steps),
)
if video_schedule.shape[0] < num_inference_steps:
video_schedule = torch.cat(
[
video_linear_start_weight * torch.ones([video_start_step_percentage * num_inference_steps]),
video_schedule,
video_linear_end_weight
* torch.ones([num_inference_steps - video_end_step_percentage * num_inference_steps]),
]
)
if zero123_schedule.shape[0] < num_inference_steps:
zero123_schedule = torch.cat(
[
zero123_linear_start_weight * torch.ones([zero123_start_step_percentage * num_inference_steps]),
zero123_schedule,
zero123_linear_end_weight
* torch.ones([num_inference_steps - zero123_end_step_percentage * num_inference_steps]),
]
)
return (video_schedule, zero123_schedule)
def save_videos_grid_zeroscope_nplist(video_frames: List[np.ndarray], path: str, n_rows=6, fps=8, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
f = len(video_frames)
h, w, c = video_frames[0].shape
#images = [(image).astype("uint8") for image in video_frames]
os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.imwrite(path, video_frames, fps=fps)
def prepare_pipelines(
ZERO123_MODEL_ID: str = "bennyguo/zero123-xl-diffusers",
VIDEO_MODEL_ID: str = "cerspense/zeroscope_v2_576w",
VIDEO_XL_MODEL_ID: str = "cerspense/zeroscope_v2_XL"
):
zero123_unet = UNet2DConditionModel.from_pretrained(ZERO123_MODEL_ID, subfolder="unet") | zero123_cam_proj = CLIPCameraProjection.from_pretrained(ZERO123_MODEL_ID, subfolder="clip_camera_projection") | 0 | 2023-11-27 22:48:17+00:00 | 16k |
TISUnion/PrimeBackup | prime_backup/debug_entry.py | [
{
"identifier": "logger",
"path": "prime_backup/logger.py",
"snippet": "def __create_logger() -> logging.Logger:\ndef get() -> logging.Logger:"
},
{
"identifier": "CreateBackupAction",
"path": "prime_backup/action/create_backup_action.py",
"snippet": "class CreateBackupAction(CreateBacku... | import time
from pathlib import Path
from prime_backup import logger
from prime_backup.action.create_backup_action import CreateBackupAction
from prime_backup.action.delete_backup_action import DeleteBackupAction
from prime_backup.action.export_backup_action import ExportBackupToDirectoryAction, ExportBackupToTarAction, ExportBackupToZipAction
from prime_backup.action.import_backup_action import ImportBackupAction
from prime_backup.action.list_backup_action import ListBackupAction
from prime_backup.action.migrate_compress_method_action import MigrateCompressMethodAction
from prime_backup.compressors import CompressMethod
from prime_backup.db.access import DbAccess
from prime_backup.types.operator import Operator
from prime_backup.types.tar_format import TarFormat | 13,019 |
def main():
DbAccess.init()
backup_id = 1
logger.get().info('debug entry start')
def create(n: int = 1):
nonlocal backup_id
for i in range(n):
t = time.time()
bka = CreateBackupAction(Operator.player('Steve'), '测试彩色测试')
backup_id = bka.run().id
print('cost', round(time.time() - t, 2), 's')
def create_if_1st():
nonlocal backup_id
t = time.time()
if backup_id == 1:
bka = CreateBackupAction(Operator.player('Steve'), 'test2')
backup_id = bka.run().id
print('cost', round(time.time() - t, 2), 's')
def export(bid=None):
if bid is None:
bid = backup_id
t = time.time()
|
def main():
DbAccess.init()
backup_id = 1
logger.get().info('debug entry start')
def create(n: int = 1):
nonlocal backup_id
for i in range(n):
t = time.time()
bka = CreateBackupAction(Operator.player('Steve'), '测试彩色测试')
backup_id = bka.run().id
print('cost', round(time.time() - t, 2), 's')
def create_if_1st():
nonlocal backup_id
t = time.time()
if backup_id == 1:
bka = CreateBackupAction(Operator.player('Steve'), 'test2')
backup_id = bka.run().id
print('cost', round(time.time() - t, 2), 's')
def export(bid=None):
if bid is None:
bid = backup_id
t = time.time()
| _ = [ExportBackupToDirectoryAction, ExportBackupToTarAction, ExportBackupToZipAction, TarFormat] | 3 | 2023-11-28 19:03:36+00:00 | 16k |
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 | 13,339 | 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),
}
| 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} | 2 | 2023-11-27 07:01:39+00:00 | 16k |
IanYeung/MGLD-VSR | scripts/vsr_val_ddpm_text_T_vqganfin_old.py | [
{
"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 ra... | import argparse, os, sys, glob
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision
import time
import math
import copy
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.archs.arch_util import resize_flow
from scripts.util_flow import forward_backward_consistency_check
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization | 12,910 |
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
|
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps)
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"] | model = instantiate_from_config(config.model) | 0 | 2023-11-30 01:50:29+00:00 | 16k |
Institute4FutureHealth/CHA | tasks/types.py | [
{
"identifier": "ActivityAnalysis",
"path": "tasks/affect/activity_analysis.py",
"snippet": "class ActivityAnalysis(Affect):\n \"\"\"\n **Description:**\n\n This tasks performs average, sum, or trend analysis on the provided raw activity affect data for specific patient.\n \"\"\"\n\n ... | from typing import Dict
from typing import Type
from tasks.affect import ActivityAnalysis
from tasks.affect import ActivityGet
from tasks.affect import SleepAnalysis
from tasks.affect import SleepGet
from tasks.ask_user import AskUser
from tasks.google_translator import GoogleTranslate
from tasks.playwright import Click
from tasks.playwright import CurrentWebPage
from tasks.playwright import ExtractHyperlinks
from tasks.playwright import ExtractText
from tasks.playwright import GetElements
from tasks.playwright import Navigate
from tasks.playwright import NavigateBack
from tasks.read_from_datapipe import ReadDataPipe
from tasks.serpapi import SerpAPI
from tasks.task import BaseTask
from tasks.task_types import TaskType
from tasks.test_file import TestFile | 14,046 |
TASK_TO_CLASS: Dict[TaskType, Type[BaseTask]] = {
TaskType.SERPAPI: SerpAPI,
TaskType.CLICK: Click,
TaskType.GET_CURRENT_PAGE: CurrentWebPage,
TaskType.EXTRACT_HYPERLINKS: ExtractHyperlinks,
TaskType.EXTRACT_TEXT: ExtractText,
TaskType.GET_ELEMENTS: GetElements,
TaskType.NAVIGATE_BACK: NavigateBack,
TaskType.NAVIGATE: Navigate,
TaskType.AFFECT_SLEEP_GET: SleepGet,
TaskType.AFFECT_ACTIVITY_GET: ActivityGet,
TaskType.AFFECT_SLEEP_ANALYSIS: SleepAnalysis,
TaskType.AFFECT_ACTIVITY_ANALYSIS: ActivityAnalysis,
TaskType.GOOGLE_TRANSLATE: GoogleTranslate,
TaskType.ASK_USER: AskUser,
TaskType.TEST_FILE: TestFile,
|
TASK_TO_CLASS: Dict[TaskType, Type[BaseTask]] = {
TaskType.SERPAPI: SerpAPI,
TaskType.CLICK: Click,
TaskType.GET_CURRENT_PAGE: CurrentWebPage,
TaskType.EXTRACT_HYPERLINKS: ExtractHyperlinks,
TaskType.EXTRACT_TEXT: ExtractText,
TaskType.GET_ELEMENTS: GetElements,
TaskType.NAVIGATE_BACK: NavigateBack,
TaskType.NAVIGATE: Navigate,
TaskType.AFFECT_SLEEP_GET: SleepGet,
TaskType.AFFECT_ACTIVITY_GET: ActivityGet,
TaskType.AFFECT_SLEEP_ANALYSIS: SleepAnalysis,
TaskType.AFFECT_ACTIVITY_ANALYSIS: ActivityAnalysis,
TaskType.GOOGLE_TRANSLATE: GoogleTranslate,
TaskType.ASK_USER: AskUser,
TaskType.TEST_FILE: TestFile, | TaskType.READ_FROM_DATAPIPE: ReadDataPipe, | 13 | 2023-12-02 05:10:44+00:00 | 16k |
Czm369/MixPL | projects/Detic_new/detic/centernet_rpn_head.py | [
{
"identifier": "CenterNetUpdateHead",
"path": "mmdet/models/dense_heads/centernet_update_head.py",
"snippet": "class CenterNetUpdateHead(AnchorFreeHead):\n \"\"\"CenterNetUpdateHead is an improved version of CenterNet in CenterNet2.\n Paper link `<https://arxiv.org/abs/2103.07461>`_.\n\n Args:... | import copy
import torch
import torch.nn as nn
from typing import Dict, List, Optional, Sequence, Tuple
from mmcv.cnn import Scale
from mmengine import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.dense_heads import CenterNetUpdateHead
from mmdet.models.utils import unpack_gt_instances
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import bbox2distance
from mmdet.utils import (ConfigType, InstanceList, OptConfigType,
OptInstanceList, reduce_mean)
from .iou_loss import IOULoss | 10,823 | # F.relu(bbox_pred) with bbox_pred.clamp(min=0)
bbox_pred = bbox_pred.clamp(min=0)
return cls_score, bbox_pred # score aligned, box larger
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None
) -> Dict[str, Tensor]:
"""Calculate the loss based on the features extracted by the detection
head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is 4.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
num_imgs = cls_scores[0].size(0)
assert len(cls_scores) == len(bbox_preds)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
all_level_points = self.prior_generator.grid_priors(
featmap_sizes,
dtype=bbox_preds[0].dtype,
device=bbox_preds[0].device)
# 1 flatten outputs
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
# repeat points to align with bbox_preds
flatten_points = torch.cat(
[points.repeat(num_imgs, 1) for points in all_level_points])
assert (torch.isfinite(flatten_bbox_preds).all().item())
# 2 calc reg and cls branch targets
cls_targets, bbox_targets = self.get_targets(all_level_points,
batch_gt_instances)
# 3 pos index for cls branch
featmap_sizes = flatten_points.new_tensor(featmap_sizes)
if self.more_pos:
pos_inds, cls_labels = self.add_cls_pos_inds(
flatten_points, flatten_bbox_preds, featmap_sizes,
batch_gt_instances)
else:
pos_inds = self._get_label_inds(batch_gt_instances,
batch_img_metas, featmap_sizes)
# 4 calc cls loss
if pos_inds is None:
# num_gts=0
num_pos_cls = bbox_preds[0].new_tensor(0, dtype=torch.float)
else:
num_pos_cls = bbox_preds[0].new_tensor(
len(pos_inds), dtype=torch.float)
num_pos_cls = max(reduce_mean(num_pos_cls), 1.0)
cat_agn_cls_targets = cls_targets.max(dim=1)[0] # M
cls_pos_loss, cls_neg_loss = self.loss_cls(
flatten_cls_scores.squeeze(1), cat_agn_cls_targets, pos_inds,
num_pos_cls)
# 5 calc reg loss
pos_bbox_inds = torch.nonzero(
bbox_targets.max(dim=1)[0] >= 0).squeeze(1)
pos_bbox_preds = flatten_bbox_preds[pos_bbox_inds]
pos_bbox_targets = bbox_targets[pos_bbox_inds]
bbox_weight_map = cls_targets.max(dim=1)[0]
bbox_weight_map = bbox_weight_map[pos_bbox_inds]
bbox_weight_map = bbox_weight_map if self.soft_weight_on_reg \
else torch.ones_like(bbox_weight_map)
num_pos_bbox = max(reduce_mean(bbox_weight_map.sum()), 1.0)
if len(pos_bbox_inds) > 0:
bbox_loss = self.loss_bbox(
pos_bbox_preds,
pos_bbox_targets,
bbox_weight_map,
reduction='sum') / num_pos_bbox
else:
bbox_loss = flatten_bbox_preds.sum() * 0
return dict(
loss_bbox=bbox_loss,
loss_cls_pos=cls_pos_loss,
loss_cls_neg=cls_neg_loss)
def loss_and_predict(
self,
x: Tuple[Tensor],
| # Copyright (c) OpenMMLab. All rights reserved.
# from .heatmap_focal_loss import binary_heatmap_focal_loss_jit
INF = 1000000000
RangeType = Sequence[Tuple[int, int]]
@MODELS.register_module()
class CenterNetRPNHead(CenterNetUpdateHead):
"""CenterNetUpdateHead is an improved version of CenterNet in CenterNet2.
Paper link `<https://arxiv.org/abs/2103.07461>`_.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channel in the input feature map.
regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
level points.
hm_min_radius (int): Heatmap target minimum radius of cls branch.
Defaults to 4.
hm_min_overlap (float): Heatmap target minimum overlap of cls branch.
Defaults to 0.8.
more_pos_thresh (float): The filtering threshold when the cls branch
adds more positive samples. Defaults to 0.2.
more_pos_topk (int): The maximum number of additional positive samples
added to each gt. Defaults to 9.
soft_weight_on_reg (bool): Whether to use the soft target of the
cls branch as the soft weight of the bbox branch.
Defaults to False.
loss_cls (:obj:`ConfigDict` or dict): Config of cls loss. Defaults to
dict(type='GaussianFocalLoss', loss_weight=1.0)
loss_bbox (:obj:`ConfigDict` or dict): Config of bbox loss. Defaults to
dict(type='GIoULoss', loss_weight=2.0).
norm_cfg (:obj:`ConfigDict` or dict, optional): dictionary to construct
and config norm layer. Defaults to
``norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)``.
train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
Unused in CenterNet. Reserved for compatibility with
SingleStageDetector.
test_cfg (:obj:`ConfigDict` or dict, optional): Testing config
of CenterNet.
"""
def __init__(self,
num_classes: int,
in_channels: int,
regress_ranges: RangeType = ((0, 80), (64, 160), (128, 320),
(256, 640), (512, INF)),
hm_min_radius: int = 4,
hm_min_overlap: float = 0.8,
more_pos: bool = False,
more_pos_thresh: float = 0.2,
more_pos_topk: int = 9,
soft_weight_on_reg: bool = False,
not_clamp_box: bool = False,
loss_cls: ConfigType = dict(
type='HeatmapFocalLoss',
alpha=0.25,
beta=4.0,
gamma=2.0,
pos_weight=1.0,
neg_weight=1.0,
sigmoid_clamp=1e-4,
ignore_high_fp=-1.0,
loss_weight=1.0,
),
loss_bbox: ConfigType = dict(
type='GIoULoss', loss_weight=2.0),
norm_cfg: OptConfigType = dict(
type='GN', num_groups=32, requires_grad=True),
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
**kwargs) -> None:
super().__init__(
num_classes=num_classes,
in_channels=in_channels,
# loss_bbox=loss_bbox,
loss_cls=loss_cls,
norm_cfg=norm_cfg,
train_cfg=train_cfg,
test_cfg=test_cfg,
**kwargs)
self.soft_weight_on_reg = soft_weight_on_reg
self.hm_min_radius = hm_min_radius
self.more_pos_thresh = more_pos_thresh
self.more_pos_topk = more_pos_topk
self.more_pos = more_pos
self.not_clamp_box = not_clamp_box
self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
self.loss_bbox = IOULoss('giou')
# GaussianFocalLoss must be sigmoid mode
self.use_sigmoid_cls = True
self.cls_out_channels = num_classes
self.regress_ranges = regress_ranges
self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
def _init_layers(self) -> None:
"""Initialize layers of the head."""
self._init_reg_convs()
self._init_predictor()
def forward_single(self, x: Tensor, scale: Scale,
stride: int) -> Tuple[Tensor, Tensor]:
"""Forward features of a single scale level.
Args:
x (Tensor): FPN feature maps of the specified stride.
scale (:obj:`mmcv.cnn.Scale`): Learnable scale module to resize
the bbox prediction.
stride (int): The corresponding stride for feature maps.
Returns:
tuple: scores for each class, bbox predictions of
input feature maps.
"""
for m in self.reg_convs:
x = m(x)
cls_score = self.conv_cls(x)
bbox_pred = self.conv_reg(x)
# scale the bbox_pred of different level
# float to avoid overflow when enabling FP16
bbox_pred = scale(bbox_pred).float()
# bbox_pred needed for gradient computation has been modified
# by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
# F.relu(bbox_pred) with bbox_pred.clamp(min=0)
bbox_pred = bbox_pred.clamp(min=0)
return cls_score, bbox_pred # score aligned, box larger
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None
) -> Dict[str, Tensor]:
"""Calculate the loss based on the features extracted by the detection
head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is 4.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
num_imgs = cls_scores[0].size(0)
assert len(cls_scores) == len(bbox_preds)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
all_level_points = self.prior_generator.grid_priors(
featmap_sizes,
dtype=bbox_preds[0].dtype,
device=bbox_preds[0].device)
# 1 flatten outputs
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
# repeat points to align with bbox_preds
flatten_points = torch.cat(
[points.repeat(num_imgs, 1) for points in all_level_points])
assert (torch.isfinite(flatten_bbox_preds).all().item())
# 2 calc reg and cls branch targets
cls_targets, bbox_targets = self.get_targets(all_level_points,
batch_gt_instances)
# 3 pos index for cls branch
featmap_sizes = flatten_points.new_tensor(featmap_sizes)
if self.more_pos:
pos_inds, cls_labels = self.add_cls_pos_inds(
flatten_points, flatten_bbox_preds, featmap_sizes,
batch_gt_instances)
else:
pos_inds = self._get_label_inds(batch_gt_instances,
batch_img_metas, featmap_sizes)
# 4 calc cls loss
if pos_inds is None:
# num_gts=0
num_pos_cls = bbox_preds[0].new_tensor(0, dtype=torch.float)
else:
num_pos_cls = bbox_preds[0].new_tensor(
len(pos_inds), dtype=torch.float)
num_pos_cls = max(reduce_mean(num_pos_cls), 1.0)
cat_agn_cls_targets = cls_targets.max(dim=1)[0] # M
cls_pos_loss, cls_neg_loss = self.loss_cls(
flatten_cls_scores.squeeze(1), cat_agn_cls_targets, pos_inds,
num_pos_cls)
# 5 calc reg loss
pos_bbox_inds = torch.nonzero(
bbox_targets.max(dim=1)[0] >= 0).squeeze(1)
pos_bbox_preds = flatten_bbox_preds[pos_bbox_inds]
pos_bbox_targets = bbox_targets[pos_bbox_inds]
bbox_weight_map = cls_targets.max(dim=1)[0]
bbox_weight_map = bbox_weight_map[pos_bbox_inds]
bbox_weight_map = bbox_weight_map if self.soft_weight_on_reg \
else torch.ones_like(bbox_weight_map)
num_pos_bbox = max(reduce_mean(bbox_weight_map.sum()), 1.0)
if len(pos_bbox_inds) > 0:
bbox_loss = self.loss_bbox(
pos_bbox_preds,
pos_bbox_targets,
bbox_weight_map,
reduction='sum') / num_pos_bbox
else:
bbox_loss = flatten_bbox_preds.sum() * 0
return dict(
loss_bbox=bbox_loss,
loss_cls_pos=cls_pos_loss,
loss_cls_neg=cls_neg_loss)
def loss_and_predict(
self,
x: Tuple[Tensor], | batch_data_samples: SampleList, | 3 | 2023-11-30 08:58:00+00:00 | 16k |
SEU-ProactiveSecurity-Group/MalPurifier | examples/md_nn_test.py | [
{
"identifier": "Dataset",
"path": "core/defense/dataset.py",
"snippet": "class Dataset(torch.utils.data.Dataset):\n def __init__(self, seed=0, device='cuda', feature_ext_args=None):\n \"\"\"\n 为机器学习模型学习构建数据集。\n \n :param seed: 随机种子\n :param device: 设备类型，'cuda' 或 'c... | import os.path as path
import argparse
import time
import numpy
from core.defense import Dataset
from core.defense import MalwareDetectionDNN
from tools.utils import save_args, get_group_args, to_tensor, dump_pickle, read_pickle | 11,388 | # 使用未来版本特性，确保代码在Python2和Python3中有一致的行为
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# 导入所需的库
# 导入自定义模块
# 初始化argparse对象，用于解析命令行参数
cmd_md = argparse.ArgumentParser(description='arguments for learning malware detector')
# 定义与特征提取相关的命令行参数
feature_argparse = cmd_md.add_argument_group(title='feature')
feature_argparse.add_argument('--proc_number', type=int, default=2,
help='The number of threads for features extraction.') # 特征提取的线程数量
feature_argparse.add_argument('--number_of_smali_files', type=int, default=1000000,
help='The maximum number of smali files to represent each app') # 表示每个应用的smali文件的最大数量
feature_argparse.add_argument('--max_vocab_size', type=int, default=10000,
help='The maximum number of vocabulary size') # 词汇的最大数量
feature_argparse.add_argument('--update', action='store_true',
help='Whether update the existed features.') # 是否更新已存在的特征
# 定义与检测器相关的命令行参数
detector_argparse = cmd_md.add_argument_group(title='detector')
detector_argparse.add_argument('--cuda', action='store_true', default=False,
help='whether use cuda enable gpu or cpu.') # 是否使用CUDA启用GPU
detector_argparse.add_argument('--seed', type=int, default=0,
help='random seed.') # 随机种子
detector_argparse.add_argument('--dense_hidden_units', type=lambda s: [int(u) for u in s.split(',')], default='200,200',
help='delimited list input, e.g., "200,200"') # 密集隐藏单元列表
detector_argparse.add_argument('--dropout', type=float, default=0.6,
help='dropout rate') # dropout率
detector_argparse.add_argument('--alpha_', type=float, default=0.2,
help='slope coefficient of leaky-relu or elu') # leaky-relu或elu的斜率系数
detector_argparse.add_argument('--smooth', action='store_true', default=False,
help='use smooth activation elu (rather than leaky-relu) in the GAT layer.') # 在GAT层使用平滑激活函数elu
detector_argparse.add_argument('--batch_size', type=int, default=128,
help='mini-batch size') # mini-batch大小
detector_argparse.add_argument('--epochs', type=int, default=50,
help='number of epochs to train.') # 训练的epoch数
detector_argparse.add_argument('--lr', type=float, default=0.001,
help='initial learning rate.') # 初始学习率
detector_argparse.add_argument('--weight_decay', type=float, default=0e-4,
help='coefficient of weight decay') # 权重衰减系数
# 定义与数据集相关的命令行参数
dataset_argparse = cmd_md.add_argument_group(title='data_producer')
detector_argparse.add_argument('--cache', action='store_true', default=False,
help='use cache data or not.') # 是否使用缓存数据
# 定义与模式相关的命令行参数
mode_argparse = cmd_md.add_argument_group(title='mode')
mode_argparse.add_argument('--mode', type=str, default='train', choices=['train', 'test'], required=False,
help='learn a model or test it.') # 学习模型或测试模型的模式
mode_argparse.add_argument('--model_name', type=str, default='xxxxxxxx-xxxxxx', required=False,
help='suffix date of a tested model name.') # 测试模型名称的后缀日期
# 定义主函数
def _main():
args = cmd_md.parse_args()
# 根据参数创建数据集
| # 使用未来版本特性，确保代码在Python2和Python3中有一致的行为
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# 导入所需的库
# 导入自定义模块
# 初始化argparse对象，用于解析命令行参数
cmd_md = argparse.ArgumentParser(description='arguments for learning malware detector')
# 定义与特征提取相关的命令行参数
feature_argparse = cmd_md.add_argument_group(title='feature')
feature_argparse.add_argument('--proc_number', type=int, default=2,
help='The number of threads for features extraction.') # 特征提取的线程数量
feature_argparse.add_argument('--number_of_smali_files', type=int, default=1000000,
help='The maximum number of smali files to represent each app') # 表示每个应用的smali文件的最大数量
feature_argparse.add_argument('--max_vocab_size', type=int, default=10000,
help='The maximum number of vocabulary size') # 词汇的最大数量
feature_argparse.add_argument('--update', action='store_true',
help='Whether update the existed features.') # 是否更新已存在的特征
# 定义与检测器相关的命令行参数
detector_argparse = cmd_md.add_argument_group(title='detector')
detector_argparse.add_argument('--cuda', action='store_true', default=False,
help='whether use cuda enable gpu or cpu.') # 是否使用CUDA启用GPU
detector_argparse.add_argument('--seed', type=int, default=0,
help='random seed.') # 随机种子
detector_argparse.add_argument('--dense_hidden_units', type=lambda s: [int(u) for u in s.split(',')], default='200,200',
help='delimited list input, e.g., "200,200"') # 密集隐藏单元列表
detector_argparse.add_argument('--dropout', type=float, default=0.6,
help='dropout rate') # dropout率
detector_argparse.add_argument('--alpha_', type=float, default=0.2,
help='slope coefficient of leaky-relu or elu') # leaky-relu或elu的斜率系数
detector_argparse.add_argument('--smooth', action='store_true', default=False,
help='use smooth activation elu (rather than leaky-relu) in the GAT layer.') # 在GAT层使用平滑激活函数elu
detector_argparse.add_argument('--batch_size', type=int, default=128,
help='mini-batch size') # mini-batch大小
detector_argparse.add_argument('--epochs', type=int, default=50,
help='number of epochs to train.') # 训练的epoch数
detector_argparse.add_argument('--lr', type=float, default=0.001,
help='initial learning rate.') # 初始学习率
detector_argparse.add_argument('--weight_decay', type=float, default=0e-4,
help='coefficient of weight decay') # 权重衰减系数
# 定义与数据集相关的命令行参数
dataset_argparse = cmd_md.add_argument_group(title='data_producer')
detector_argparse.add_argument('--cache', action='store_true', default=False,
help='use cache data or not.') # 是否使用缓存数据
# 定义与模式相关的命令行参数
mode_argparse = cmd_md.add_argument_group(title='mode')
mode_argparse.add_argument('--mode', type=str, default='train', choices=['train', 'test'], required=False,
help='learn a model or test it.') # 学习模型或测试模型的模式
mode_argparse.add_argument('--model_name', type=str, default='xxxxxxxx-xxxxxx', required=False,
help='suffix date of a tested model name.') # 测试模型名称的后缀日期
# 定义主函数
def _main():
args = cmd_md.parse_args()
# 根据参数创建数据集 | dataset = Dataset(feature_ext_args=get_group_args(args, cmd_md, 'feature')) | 0 | 2023-11-27 02:00:23+00:00 | 16k |
Vali-98/XTTS-RVC-UI | rvc.py | [
{
"identifier": "SynthesizerTrnMs256NSFsid",
"path": "infer_pack/models.py",
"snippet": "class SynthesizerTrnMs256NSFsid(nn.Module):\n def __init__(\n self,\n spec_channels,\n segment_size,\n inter_channels,\n hidden_channels,\n filter_channels,\n n_he... | from multiprocessing import cpu_count
from pathlib import Path
from fairseq import checkpoint_utils
from scipy.io import wavfile
from infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
import torch
import librosa
import numpy as np | 11,030 |
class Config:
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
print("16 series/10 series P40 forced single precision")
self.is_half = False
else:
self.gpu_name = None
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
if self.gpu_mem <= 2:
print('Not enough VRAM to load models (Probably)')
self.device = 'cpu'
elif torch.backends.mps.is_available():
print("No supported N-card found, use MPS for inference")
self.device = "mps"
else:
print("No supported N-card found, use CPU for inference")
self.device = "cpu"
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G memory config
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G memory config
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
def load_hubert(device, is_half, model_path):
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path], suffix='', )
hubert = models[0]
hubert = hubert.to(device)
if is_half:
hubert = hubert.half()
else:
hubert = hubert.float()
hubert.eval()
return hubert
def get_vc(device, is_half, config, model_path):
cpt = torch.load(model_path, map_location='cpu')
if "config" not in cpt or "weight" not in cpt:
raise ValueError(f'Incorrect format for {model_path}. Use a voice model trained using RVC v2 instead.')
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
|
class Config:
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
print("16 series/10 series P40 forced single precision")
self.is_half = False
else:
self.gpu_name = None
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
if self.gpu_mem <= 2:
print('Not enough VRAM to load models (Probably)')
self.device = 'cpu'
elif torch.backends.mps.is_available():
print("No supported N-card found, use MPS for inference")
self.device = "mps"
else:
print("No supported N-card found, use CPU for inference")
self.device = "cpu"
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G memory config
x_pad = 3
x_query = 10
x_center = 60
x_max = 65
else:
# 5G memory config
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
def load_hubert(device, is_half, model_path):
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path], suffix='', )
hubert = models[0]
hubert = hubert.to(device)
if is_half:
hubert = hubert.half()
else:
hubert = hubert.float()
hubert.eval()
return hubert
def get_vc(device, is_half, config, model_path):
cpt = torch.load(model_path, map_location='cpu')
if "config" not in cpt or "weight" not in cpt:
raise ValueError(f'Incorrect format for {model_path}. Use a voice model trained using RVC v2 instead.')
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1: | net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) | 0 | 2023-11-30 08:47:28+00:00 | 16k |
ubc-vision/nf-soft-mining | examples/utils.py | [
{
"identifier": "OccGridEstimator",
"path": "nerfacc/estimators/occ_grid.py",
"snippet": "class OccGridEstimator(AbstractEstimator):\n \"\"\"Occupancy grid transmittance estimator for spatial skipping.\n\n References: \"Instant Neural Graphics Primitives.\"\n\n Args:\n roi_aabb: The axis... | import random
import numpy as np
import torch
from typing import Optional, Sequence
from typing import Literal
from typing_extensions import Literal
from datasets.utils import Rays, namedtuple_map
from torch.utils.data._utils.collate import collate, default_collate_fn_map
from nerfacc.estimators.occ_grid import OccGridEstimator
from nerfacc.estimators.prop_net import PropNetEstimator
from nerfacc.grid import ray_aabb_intersect, traverse_grids
from nerfacc.volrend import (
accumulate_along_rays_,
render_weight_from_density,
rendering,
) | 13,479 |
@torch.no_grad()
def render_image_with_occgrid_test(
max_samples: int,
# scene
radiance_field: torch.nn.Module,
estimator: OccGridEstimator,
rays: Rays,
# rendering options
near_plane: float = 0.0,
far_plane: float = 1e10,
render_step_size: float = 1e-3,
render_bkgd: Optional[torch.Tensor] = None,
cone_angle: float = 0.0,
alpha_thre: float = 0.0,
early_stop_eps: float = 1e-4,
# only useful for dnerf
timestamps: Optional[torch.Tensor] = None,
):
"""Render the pixels of an image."""
rays_shape = rays.origins.shape
if len(rays_shape) == 3:
height, width, _ = rays_shape
num_rays = height * width
rays = namedtuple_map(
lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays
)
else:
num_rays, _ = rays_shape
def rgb_sigma_fn(t_starts, t_ends, ray_indices):
t_origins = rays.origins[ray_indices]
t_dirs = rays.viewdirs[ray_indices]
positions = (
t_origins + t_dirs * (t_starts[:, None] + t_ends[:, None]) / 2.0
)
if timestamps is not None:
# dnerf
t = (
timestamps[ray_indices]
if radiance_field.training
else timestamps.expand_as(positions[:, :1])
)
rgbs, sigmas = radiance_field(positions, t, t_dirs)
else:
rgbs, sigmas = radiance_field(positions, t_dirs)
return rgbs, sigmas.squeeze(-1)
device = rays.origins.device
opacity = torch.zeros(num_rays, 1, device=device)
depth = torch.zeros(num_rays, 1, device=device)
rgb = torch.zeros(num_rays, 3, device=device)
ray_mask = torch.ones(num_rays, device=device).bool()
# 1 for synthetic scenes, 4 for real scenes
min_samples = 1 if cone_angle == 0 else 4
iter_samples = total_samples = 0
rays_o = rays.origins
rays_d = rays.viewdirs
near_planes = torch.full_like(rays_o[..., 0], fill_value=near_plane)
far_planes = torch.full_like(rays_o[..., 0], fill_value=far_plane)
t_mins, t_maxs, hits = ray_aabb_intersect(rays_o, rays_d, estimator.aabbs)
n_grids = estimator.binaries.size(0)
if n_grids > 1:
t_sorted, t_indices = torch.sort(torch.cat([t_mins, t_maxs], -1), -1)
else:
t_sorted = torch.cat([t_mins, t_maxs], -1)
t_indices = torch.arange(
0, n_grids * 2, device=t_mins.device, dtype=torch.int64
).expand(num_rays, n_grids * 2)
opc_thre = 1 - early_stop_eps
while iter_samples < max_samples:
n_alive = ray_mask.sum().item()
if n_alive == 0:
break
# the number of samples to add on each ray
n_samples = max(min(num_rays // n_alive, 64), min_samples)
iter_samples += n_samples
# ray marching
(intervals, samples, termination_planes) = traverse_grids(
# rays
rays_o, # [n_rays, 3]
rays_d, # [n_rays, 3]
# grids
estimator.binaries, # [m, resx, resy, resz]
estimator.aabbs, # [m, 6]
# options
near_planes, # [n_rays]
far_planes, # [n_rays]
render_step_size,
cone_angle,
n_samples,
True,
ray_mask,
# pre-compute intersections
t_sorted, # [n_rays, m*2]
t_indices, # [n_rays, m*2]
hits, # [n_rays, m]
)
t_starts = intervals.vals[intervals.is_left]
t_ends = intervals.vals[intervals.is_right]
ray_indices = samples.ray_indices[samples.is_valid]
packed_info = samples.packed_info
# get rgb and sigma from radiance field
rgbs, sigmas = rgb_sigma_fn(t_starts, t_ends, ray_indices)
# volume rendering using native cuda scan
| """
Copyright (c) 2022 Ruilong Li, UC Berkeley.
"""
try:
except ImportError:
NERF_SYNTHETIC_SCENES = [
"chair",
"drums",
"ficus",
"hotdog",
"lego",
"materials",
"mic",
"ship",
]
MIPNERF360_UNBOUNDED_SCENES = [
"garden",
"bicycle",
"bonsai",
"counter",
"kitchen",
"room",
"stump",
]
LLFF_NDC_SCENES = [
"fern",
"flower",
"fortress",
"horns",
"leaves",
"orchids",
"room_llff",
"trex",
]
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def render_image_with_occgrid(
# scene
radiance_field: torch.nn.Module,
estimator: OccGridEstimator,
rays: Rays,
# rendering options
near_plane: float = 0.0,
far_plane: float = 1e10,
render_step_size: float = 1e-3,
render_bkgd: Optional[torch.Tensor] = None,
cone_angle: float = 0.0,
alpha_thre: float = 0.0,
# test options
test_chunk_size: int = 8192,
# only useful for dnerf
timestamps: Optional[torch.Tensor] = None,
):
"""Render the pixels of an image."""
rays_shape = rays.origins.shape
if len(rays_shape) == 3:
height, width, _ = rays_shape
num_rays = height * width
rays = namedtuple_map(
lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays
)
else:
num_rays, _ = rays_shape
def sigma_fn(t_starts, t_ends, ray_indices):
t_origins = chunk_rays.origins[ray_indices]
t_dirs = chunk_rays.viewdirs[ray_indices]
positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
if timestamps is not None:
# dnerf
t = (
timestamps[ray_indices]
if radiance_field.training
else timestamps.expand_as(positions[:, :1])
)
sigmas = radiance_field.query_density(positions, t)
else:
sigmas = radiance_field.query_density(positions)
return sigmas.squeeze(-1)
def rgb_sigma_fn(t_starts, t_ends, ray_indices):
t_origins = chunk_rays.origins[ray_indices]
t_dirs = chunk_rays.viewdirs[ray_indices]
positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
if timestamps is not None:
# dnerf
t = (
timestamps[ray_indices]
if radiance_field.training
else timestamps.expand_as(positions[:, :1])
)
rgbs, sigmas = radiance_field(positions, t, t_dirs)
else:
rgbs, sigmas = radiance_field(positions, t_dirs)
return rgbs, sigmas.squeeze(-1)
results = []
chunk = (
torch.iinfo(torch.int32).max
if radiance_field.training
else test_chunk_size
)
for i in range(0, num_rays, chunk):
chunk_rays = namedtuple_map(lambda r: r[i : i + chunk], rays)
ray_indices, t_starts, t_ends = estimator.sampling(
chunk_rays.origins,
chunk_rays.viewdirs,
sigma_fn=sigma_fn,
near_plane=near_plane,
far_plane=far_plane,
render_step_size=render_step_size,
stratified=radiance_field.training,
cone_angle=cone_angle,
alpha_thre=alpha_thre,
)
rgb, opacity, depth, extras = rendering(
t_starts,
t_ends,
ray_indices,
n_rays=chunk_rays.origins.shape[0],
rgb_sigma_fn=rgb_sigma_fn,
render_bkgd=render_bkgd,
)
chunk_results = [rgb, opacity, depth, len(t_starts)]
results.append(chunk_results)
colors, opacities, depths, n_rendering_samples = [
torch.cat(r, dim=0) if isinstance(r[0], torch.Tensor) else r
for r in zip(*results)
]
return (
colors.view((*rays_shape[:-1], -1)),
opacities.view((*rays_shape[:-1], -1)),
depths.view((*rays_shape[:-1], -1)),
sum(n_rendering_samples),
)
def render_image_with_propnet(
# scene
radiance_field: torch.nn.Module,
proposal_networks: Sequence[torch.nn.Module],
estimator: PropNetEstimator,
rays: Rays,
# rendering options
num_samples: int,
num_samples_per_prop: Sequence[int],
near_plane: Optional[float] = None,
far_plane: Optional[float] = None,
sampling_type: Literal["uniform", "lindisp"] = "lindisp",
opaque_bkgd: bool = True,
render_bkgd: Optional[torch.Tensor] = None,
# train options
proposal_requires_grad: bool = False,
# test options
test_chunk_size: int = 8192,
):
"""Render the pixels of an image."""
rays_shape = rays.origins.shape
if len(rays_shape) == 3:
height, width, _ = rays_shape
num_rays = height * width
rays = namedtuple_map(
lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays
)
else:
num_rays, _ = rays_shape
def prop_sigma_fn(t_starts, t_ends, proposal_network):
t_origins = chunk_rays.origins[..., None, :]
t_dirs = chunk_rays.viewdirs[..., None, :].detach()
positions = t_origins + t_dirs * (t_starts + t_ends)[..., None] / 2.0
sigmas = proposal_network(positions)
if opaque_bkgd:
sigmas[..., -1, :] = torch.inf
return sigmas.squeeze(-1)
def rgb_sigma_fn(t_starts, t_ends, ray_indices):
t_origins = chunk_rays.origins[..., None, :]
t_dirs = chunk_rays.viewdirs[..., None, :].repeat_interleave(
t_starts.shape[-1], dim=-2
)
positions = t_origins + t_dirs * (t_starts + t_ends)[..., None] / 2.0
rgb, sigmas = radiance_field(positions, t_dirs)
if opaque_bkgd:
sigmas[..., -1, :] = torch.inf
return rgb, sigmas.squeeze(-1)
results = []
chunk = (
torch.iinfo(torch.int32).max
if radiance_field.training
else test_chunk_size
)
for i in range(0, num_rays, chunk):
chunk_rays = namedtuple_map(lambda r: r[i : i + chunk], rays)
t_starts, t_ends = estimator.sampling(
prop_sigma_fns=[
lambda *args: prop_sigma_fn(*args, p) for p in proposal_networks
],
prop_samples=num_samples_per_prop,
num_samples=num_samples,
n_rays=chunk_rays.origins.shape[0],
near_plane=near_plane,
far_plane=far_plane,
sampling_type=sampling_type,
stratified=radiance_field.training,
requires_grad=proposal_requires_grad,
)
rgb, opacity, depth, extra = rendering(
t_starts,
t_ends,
ray_indices=None,
n_rays=None,
rgb_sigma_fn=rgb_sigma_fn,
render_bkgd=render_bkgd,
)
ray_indices = torch.arange(0, t_starts.shape[0], device=t_starts.device).repeat_interleave(t_starts.shape[1]).flatten()
unique_indices, inverse = torch.unique(ray_indices, sorted=True, return_inverse=True)
binnums = torch.bincount(inverse)
start_positions = torch.cat([torch.tensor([0], dtype=torch.int, device=ray_indices.device), torch.cumsum(binnums, 0)[:-1]])
ray_a = torch.stack([unique_indices.int(), start_positions.int(), binnums], dim=1)
chunk_results = [rgb, opacity, depth, extra, ray_a]
results.append(chunk_results)
colors, opacities, depths, extras, rays_a = collate(
results,
collate_fn_map={
**default_collate_fn_map,
torch.Tensor: lambda x, **_: torch.cat(x, 0),
},
)
distkwarg = {"ws": extras['weights'].flatten(),
"deltas": (t_ends - t_starts).flatten(),
"ts": t_starts.flatten(),
"rays_a": rays_a}
return (
colors.view((*rays_shape[:-1], -1)),
opacities.view((*rays_shape[:-1], -1)),
depths.view((*rays_shape[:-1], -1)),
extras,
distkwarg
)
@torch.no_grad()
def render_image_with_occgrid_test(
max_samples: int,
# scene
radiance_field: torch.nn.Module,
estimator: OccGridEstimator,
rays: Rays,
# rendering options
near_plane: float = 0.0,
far_plane: float = 1e10,
render_step_size: float = 1e-3,
render_bkgd: Optional[torch.Tensor] = None,
cone_angle: float = 0.0,
alpha_thre: float = 0.0,
early_stop_eps: float = 1e-4,
# only useful for dnerf
timestamps: Optional[torch.Tensor] = None,
):
"""Render the pixels of an image."""
rays_shape = rays.origins.shape
if len(rays_shape) == 3:
height, width, _ = rays_shape
num_rays = height * width
rays = namedtuple_map(
lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays
)
else:
num_rays, _ = rays_shape
def rgb_sigma_fn(t_starts, t_ends, ray_indices):
t_origins = rays.origins[ray_indices]
t_dirs = rays.viewdirs[ray_indices]
positions = (
t_origins + t_dirs * (t_starts[:, None] + t_ends[:, None]) / 2.0
)
if timestamps is not None:
# dnerf
t = (
timestamps[ray_indices]
if radiance_field.training
else timestamps.expand_as(positions[:, :1])
)
rgbs, sigmas = radiance_field(positions, t, t_dirs)
else:
rgbs, sigmas = radiance_field(positions, t_dirs)
return rgbs, sigmas.squeeze(-1)
device = rays.origins.device
opacity = torch.zeros(num_rays, 1, device=device)
depth = torch.zeros(num_rays, 1, device=device)
rgb = torch.zeros(num_rays, 3, device=device)
ray_mask = torch.ones(num_rays, device=device).bool()
# 1 for synthetic scenes, 4 for real scenes
min_samples = 1 if cone_angle == 0 else 4
iter_samples = total_samples = 0
rays_o = rays.origins
rays_d = rays.viewdirs
near_planes = torch.full_like(rays_o[..., 0], fill_value=near_plane)
far_planes = torch.full_like(rays_o[..., 0], fill_value=far_plane)
t_mins, t_maxs, hits = ray_aabb_intersect(rays_o, rays_d, estimator.aabbs)
n_grids = estimator.binaries.size(0)
if n_grids > 1:
t_sorted, t_indices = torch.sort(torch.cat([t_mins, t_maxs], -1), -1)
else:
t_sorted = torch.cat([t_mins, t_maxs], -1)
t_indices = torch.arange(
0, n_grids * 2, device=t_mins.device, dtype=torch.int64
).expand(num_rays, n_grids * 2)
opc_thre = 1 - early_stop_eps
while iter_samples < max_samples:
n_alive = ray_mask.sum().item()
if n_alive == 0:
break
# the number of samples to add on each ray
n_samples = max(min(num_rays // n_alive, 64), min_samples)
iter_samples += n_samples
# ray marching
(intervals, samples, termination_planes) = traverse_grids(
# rays
rays_o, # [n_rays, 3]
rays_d, # [n_rays, 3]
# grids
estimator.binaries, # [m, resx, resy, resz]
estimator.aabbs, # [m, 6]
# options
near_planes, # [n_rays]
far_planes, # [n_rays]
render_step_size,
cone_angle,
n_samples,
True,
ray_mask,
# pre-compute intersections
t_sorted, # [n_rays, m*2]
t_indices, # [n_rays, m*2]
hits, # [n_rays, m]
)
t_starts = intervals.vals[intervals.is_left]
t_ends = intervals.vals[intervals.is_right]
ray_indices = samples.ray_indices[samples.is_valid]
packed_info = samples.packed_info
# get rgb and sigma from radiance field
rgbs, sigmas = rgb_sigma_fn(t_starts, t_ends, ray_indices)
# volume rendering using native cuda scan | weights, _, alphas = render_weight_from_density( | 5 | 2023-11-27 22:12:55+00:00 | 16k |
facebookresearch/SOC-matching | main.py | [
{
"identifier": "get_folder_name",
"path": "SOC_matching/utils.py",
"snippet": "def get_folder_name(cfg):\n folder_name = (\n cfg.method.algorithm\n + \"_\"\n + cfg.method.setting\n + \"_\"\n + str(cfg.method.lmbd)\n + \"_\"\n + str(cfg.method.T)\n ... | import torch
import sys
import logging
import os
import time
import json
import hydra
import traceback
from tqdm.notebook import tqdm
from omegaconf import DictConfig
from SOC_matching.utils import (
get_folder_name,
get_file_name,
control_objective,
save_results,
compute_EMA,
normalization_constant,
)
from SOC_matching.method import (
SOC_Solver,
)
from SOC_matching.experiment_settings.settings import define_variables | 11,786 |
logging.getLogger("lightning.pytorch").setLevel(logging.getLevelName("INFO"))
print(cfg)
print("Found {} CUDA devices.".format(torch.cuda.device_count()))
for i in range(torch.cuda.device_count()):
props = torch.cuda.get_device_properties(i)
print(
"{} \t Memory: {:.2f}GB".format(
props.name, props.total_memory / (1024**3)
)
)
keys = [
"SLURM_NODELIST",
"SLURM_JOB_ID",
"SLURM_NTASKS",
"SLURM_JOB_NAME",
"SLURM_PROCID",
"SLURM_LOCALID",
"SLURM_NODEID",
]
log.info(json.dumps({k: os.environ.get(k, None) for k in keys}, indent=4))
cmd_str = " \\\n".join([f"python {sys.argv[0]}"] + ["\t" + x for x in sys.argv[1:]])
with open("cmd.sh", "w") as fout:
print("#!/bin/bash\n", file=fout)
print(cmd_str, file=fout)
log.info(f"CWD: {os.getcwd()}")
if cfg.method.use_gpu:
cfg.method.device = "cuda:" + str(cfg.method.device_number)
else:
cfg.method.device = "cpu"
torch.manual_seed(cfg.method.seed)
algorithm = cfg.method.algorithm
folder_name = (
cfg.method.algorithm
+ "_"
+ cfg.method.setting
+ "_"
+ str(cfg.method.lmbd)
+ "_"
+ str(cfg.method.T)
+ "_"
+ str(cfg.method.num_steps)
+ "_"
+ str(cfg.method.use_warm_start)
+ "_"
+ str(cfg.method.seed)
+ "_"
+ str(cfg.optim.batch_size)
+ "_"
+ str(cfg.optim.M_lr)
+ "_"
+ str(cfg.optim.nabla_V_lr)
)
ts = torch.linspace(0, cfg.method.T, cfg.method.num_steps + 1).to(cfg.method.device)
folder_name = get_folder_name(cfg)
file_name = get_file_name(folder_name, num_iterations=cfg.method.num_iterations)
EMA_loss = 0
EMA_norm_sqd_diff = 0
EMA_coeff = 0.01
EMA_weight_mean_coeff = 0.002
x0, sigma, optimal_sde, neural_sde, u_warm_start = define_variables(cfg, ts)
if optimal_sde is not None:
ground_truth_control = optimal_sde.u
else:
ground_truth_control = None
state0 = x0.repeat(cfg.optim.batch_size, 1)
########### Compute normalization constant and control L2 error for initial control ############
print(
f"Estimating normalization constant and control L2 error for initial control..."
)
(
normalization_const,
normalization_const_std_error,
norm_sqd_diff_mean,
) = normalization_constant(
neural_sde,
state0,
ts,
cfg,
n_batches_normalization=512,
ground_truth_control=ground_truth_control,
)
print(
f"Normalization_constant (mean and std. error): {normalization_const:5.8E} {normalization_const_std_error:5.8E}"
)
if ground_truth_control is not None:
print(
f"Control L2 error for initial control: {norm_sqd_diff_mean / normalization_const}"
)
########### Compute control loss for optimal control ############
if optimal_sde is not None:
(
optimal_control_objective_mean,
optimal_control_objective_std_error,
) = control_objective(
optimal_sde,
x0,
ts,
cfg.method.lmbd,
cfg.optim.batch_size,
total_n_samples=cfg.method.n_samples_control,
verbose=False,
)
print(
f"Optimal control loss mean: {optimal_control_objective_mean:5.10f}, Optimal control loss std. error: {optimal_control_objective_std_error:5.10f}"
)
| # 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.
log = logging.getLogger(__name__)
@hydra.main(version_base=None, config_path="configs", config_name="soc")
def main(cfg: DictConfig):
logging.getLogger("lightning.pytorch").setLevel(logging.getLevelName("INFO"))
print(cfg)
print("Found {} CUDA devices.".format(torch.cuda.device_count()))
for i in range(torch.cuda.device_count()):
props = torch.cuda.get_device_properties(i)
print(
"{} \t Memory: {:.2f}GB".format(
props.name, props.total_memory / (1024**3)
)
)
keys = [
"SLURM_NODELIST",
"SLURM_JOB_ID",
"SLURM_NTASKS",
"SLURM_JOB_NAME",
"SLURM_PROCID",
"SLURM_LOCALID",
"SLURM_NODEID",
]
log.info(json.dumps({k: os.environ.get(k, None) for k in keys}, indent=4))
cmd_str = " \\\n".join([f"python {sys.argv[0]}"] + ["\t" + x for x in sys.argv[1:]])
with open("cmd.sh", "w") as fout:
print("#!/bin/bash\n", file=fout)
print(cmd_str, file=fout)
log.info(f"CWD: {os.getcwd()}")
if cfg.method.use_gpu:
cfg.method.device = "cuda:" + str(cfg.method.device_number)
else:
cfg.method.device = "cpu"
torch.manual_seed(cfg.method.seed)
algorithm = cfg.method.algorithm
folder_name = (
cfg.method.algorithm
+ "_"
+ cfg.method.setting
+ "_"
+ str(cfg.method.lmbd)
+ "_"
+ str(cfg.method.T)
+ "_"
+ str(cfg.method.num_steps)
+ "_"
+ str(cfg.method.use_warm_start)
+ "_"
+ str(cfg.method.seed)
+ "_"
+ str(cfg.optim.batch_size)
+ "_"
+ str(cfg.optim.M_lr)
+ "_"
+ str(cfg.optim.nabla_V_lr)
)
ts = torch.linspace(0, cfg.method.T, cfg.method.num_steps + 1).to(cfg.method.device)
folder_name = get_folder_name(cfg)
file_name = get_file_name(folder_name, num_iterations=cfg.method.num_iterations)
EMA_loss = 0
EMA_norm_sqd_diff = 0
EMA_coeff = 0.01
EMA_weight_mean_coeff = 0.002
x0, sigma, optimal_sde, neural_sde, u_warm_start = define_variables(cfg, ts)
if optimal_sde is not None:
ground_truth_control = optimal_sde.u
else:
ground_truth_control = None
state0 = x0.repeat(cfg.optim.batch_size, 1)
########### Compute normalization constant and control L2 error for initial control ############
print(
f"Estimating normalization constant and control L2 error for initial control..."
)
(
normalization_const,
normalization_const_std_error,
norm_sqd_diff_mean,
) = normalization_constant(
neural_sde,
state0,
ts,
cfg,
n_batches_normalization=512,
ground_truth_control=ground_truth_control,
)
print(
f"Normalization_constant (mean and std. error): {normalization_const:5.8E} {normalization_const_std_error:5.8E}"
)
if ground_truth_control is not None:
print(
f"Control L2 error for initial control: {norm_sqd_diff_mean / normalization_const}"
)
########### Compute control loss for optimal control ############
if optimal_sde is not None:
(
optimal_control_objective_mean,
optimal_control_objective_std_error,
) = control_objective(
optimal_sde,
x0,
ts,
cfg.method.lmbd,
cfg.optim.batch_size,
total_n_samples=cfg.method.n_samples_control,
verbose=False,
)
print(
f"Optimal control loss mean: {optimal_control_objective_mean:5.10f}, Optimal control loss std. error: {optimal_control_objective_std_error:5.10f}"
)
| soc_solver = SOC_Solver( | 6 | 2023-12-04 20:26:18+00:00 | 16k |
yiwenlu66/learning-qp | src/modules/qp_unrolled_network.py | [
{
"identifier": "QPSolver",
"path": "src/modules/qp_solver.py",
"snippet": "class QPSolver(nn.Module):\n \"\"\"\n Solve QP problem:\n minimize (1/2)x'Px + q'x\n subject to Hx + b >= 0,\n where x in R^n, b in R^m.\n \"\"\"\n def __init__(self, device, n, m,\n P=None, P... | import torch
import numpy as np
import scipy
import functools
import os
from torch import nn
from ..modules.qp_solver import QPSolver
from ..modules.warm_starter import WarmStarter
from ..utils.torch_utils import make_psd, interpolate_state_dicts
from ..utils.mpc_utils import mpc2qp, scenario_robust_mpc, tube_robust_mpc
from ..utils.osqp_utils import osqp_oracle
from ..utils.np_batch_op import np_batch_op
from concurrent.futures import ThreadPoolExecutor | 11,241 | if not self.strict_affine_layer:
self.qb_affine_layer = nn.Linear(input_size, self.n_q_param + self.n_b_param, bias=not self.symmetric)
else:
self.qb_affine_layer = StrictAffineLayer(input_size, self.n_qp, self.m_qp, self.obs_has_half_ref)
if self.n_mlp_output > 0:
self.mlp = mlp_builder(input_size, self.n_mlp_output)
else:
self.mlp = None
# TODO: add preconditioner
self.warm_starter = WarmStarter(device, n_qp, m_qp, fixed_P=shared_PH, fixed_H=shared_PH) if use_warm_starter else None
self.warm_starter_delayed = WarmStarter(device, n_qp, m_qp, fixed_P=shared_PH, fixed_H=shared_PH) if use_warm_starter else None
self.train_warm_starter = train_warm_starter
self.ws_loss_coef = ws_loss_coef
self.ws_update_rate = ws_update_rate
self.ws_loss_shaper = ws_loss_shaper
# P, H are fixed when the model is in test mode, and they are constant across all states (i.e., shared_PH == True)
self.fixed_PH = is_test and shared_PH
# Includes losses generated by the model itself (indepedent of interaction with env), e.g., warm starting & preconditioning
self.autonomous_losses = {}
self.mpc_baseline = mpc_baseline
self.use_osqp_for_mpc = use_osqp_for_mpc
self.imitate_mpc = imitate_mpc
# Whether to consider residual loss during training - this can encourage feasibility of the learned QP problem
self.use_residual_loss = use_residual_loss
# Whether to force the problem to be feasible
self.force_feasible = force_feasible
self.feasible_lambda = feasible_lambda
self.solver = None
self.info = {}
# Reserved for storing the controllers for each simulation instance when robust MPC is enabled
self.robust_controllers = []
# Store info returned by env
self.env_info = {}
# When running batch testing, mask envs already done, to speed up computation (implemented for robust mpc); initialized at inference time since batch size is not known during initialization
self.is_active = None
def initialize_solver(self):
# If the problem is forced to be feasible, the dimension of the solution is increased by 1 (introduce slack variable)
n_qp_actual = self.n_qp + 1 if self.force_feasible else self.n_qp
m_qp_actual = self.m_qp + 1 if self.force_feasible else self.m_qp
# is_warm_starter_trainable is always False, since the warm starter is trained via another inference independent of the solver
# When self.fixed_PH == True, the solver is initialized with fixed P, H matrices; otherwise, P, H are not passed to the solver during initialization time, but computed during the forward pass instead
if not self.fixed_PH:
self.solver = QPSolver(self.device, n_qp_actual, m_qp_actual, warm_starter=self.warm_starter_delayed, is_warm_starter_trainable=False, symmetric_constraint=self.symmetric, buffered=self.force_feasible)
else:
# Should be called after loading state dict
Pinv, H = self.get_PH()
self.solver = QPSolver(self.device, n_qp_actual, m_qp_actual, Pinv=Pinv.squeeze(0), H=H.squeeze(0), warm_starter=self.warm_starter_delayed, is_warm_starter_trainable=False, symmetric_constraint=self.symmetric, buffered=self.force_feasible)
def compute_warm_starter_loss(self, q, b, Pinv, H, solver_Xs):
qd, bd, Pinvd, Hd = map(lambda t: t.detach() if t is not None else None, [q, b, Pinv, H])
X0 = self.warm_starter(qd, bd, Pinvd, Hd)
gt = solver_Xs[:, -1, :].detach()
return self.ws_loss_coef * self.ws_loss_shaper(((gt - X0) ** 2).sum(dim=-1).mean())
def parallel_controller_creation(self, controller_creator, xref_np, bs):
"""
Create robust MPC controlller in parallel
"""
# Helper function for parallel execution
def task_creator(index):
return controller_creator(self.mpc_baseline, xref_np[index, :])
with ThreadPoolExecutor() as executor:
# Executing the tasks in parallel
results = executor.map(task_creator, range(bs))
# Collecting the results
self.robust_controllers.extend(results)
def run_mpc_baseline(self, x, use_osqp_oracle=False):
robust_method = self.mpc_baseline.get("robust_method", None)
x0, xref = self.mpc_baseline["obs_to_state_and_ref"](x)
bs = x.shape[0]
# Conversions between torch and np
t = lambda a: torch.tensor(a, device=x.device, dtype=torch.float)
f = lambda t: t.detach().cpu().numpy()
f_sparse = lambda t: scipy.sparse.csc_matrix(t.cpu().numpy())
if robust_method is None:
# Run vanilla MPC without robustness
eps = 1e-3
n, m, P, q, H, b = mpc2qp(
self.mpc_baseline["n_mpc"],
self.mpc_baseline["m_mpc"],
self.mpc_baseline["N"],
t(self.mpc_baseline["A"]),
t(self.mpc_baseline["B"]),
t(self.mpc_baseline["Q"]),
t(self.mpc_baseline["R"]),
self.mpc_baseline["x_min"] + eps,
self.mpc_baseline["x_max"] - eps,
self.mpc_baseline["u_min"],
self.mpc_baseline["u_max"],
x0,
xref,
normalize=self.mpc_baseline.get("normalize", False),
Qf=self.mpc_baseline.get("terminal_coef", 0.) * t(np.eye(self.mpc_baseline["n_mpc"])) if self.mpc_baseline.get("Qf", None) is None else t(self.mpc_baseline["Qf"]),
)
if not use_osqp_oracle:
solver = QPSolver(x.device, n, m, P=P, H=H)
Xs, primal_sols = solver(q, b, iters=100)
sol = primal_sols[:, -1, :]
else:
|
class StrictAffineLayer(nn.Module):
"""
Layer mapping from obs to (q, b) in the strict affine form.
"""
def __init__(self, input_size, n, m, obs_has_half_ref):
super().__init__()
self.obs_has_half_ref = obs_has_half_ref
self.input_size = input_size
self.q_layer = nn.Linear(self.input_size, n, bias=False)
if not self.obs_has_half_ref:
self.b_layer = nn.Linear(self.input_size // 2, m, bias=True)
else:
self.b_layer = nn.Linear(self.input_size, m, bias=True)
def forward(self, x):
if not self.obs_has_half_ref:
x0 = x[:, :self.input_size // 2]
else:
x0 = x
q = self.q_layer(x)
b = self.b_layer(x0)
return torch.cat([q, b], dim=1)
class QPUnrolledNetwork(nn.Module):
"""
Learn a QP problem from the input using a MLP, then solve the QP using fixed number of unrolled PDHG iterations.
Form of QP:
minimize (1/2)x'Px + q'x
subject to Hx + b >= 0,
where x in R^n, b in R^m.
"""
def __init__(
self, device, input_size, n_qp, m_qp, qp_iter, mlp_builder,
shared_PH=False,
affine_qb=False,
strict_affine_layer=False,
obs_has_half_ref=False,
symmetric=False,
no_b=False,
use_warm_starter=False,
train_warm_starter=False,
ws_loss_coef=1.,
ws_update_rate=0.01,
ws_loss_shaper=lambda x: x ** (1 / 2),
mpc_baseline=None,
use_osqp_for_mpc=False,
imitate_mpc=False,
use_residual_loss=False,
force_feasible=False,
feasible_lambda=10,
is_test=False,
):
"""mlp_builder is a function mapping (input_size, output_size) to a nn.Sequential object.
If shared_PH == True, P and H are parameters indepedent of input, and q and b are functions of input;
Otherwise, (P, H, q, b) are all functions of input.
If affine_qb == True, then q and b are restricted to be affine functions of input.
If strict_affine_layer == True (only effective when affine_qb=True), then:
1. q is linear w.r.t. (x0, xref) (no bias)
2. b is affine w.r.t. x0 (no dependence on xref)
If obs_has_half_ref == True, the policy knows that the observation is in the form (x0, xref), with each taking up half of the dimension of the observation.
If symmetric == True (only effective when affine_qb=True), then:
1. The bias terms are disabled in the modeling of q and b, i.e., q = Wq * x, b = Wb * x.
2. The constraint is assumed to be -1 <= Hx + b <= 1, instead of Hx + b >= 0.
If no_b == True in addition to symmetric == True, then b is skipped altogether, i.e., the constraint is assumed to be -1 <= Hx <= 1.
If mpc_baseline != None and imitate_mpc == False, then the forward function directly returns the solution of the MPC problem, instead of solving the learned QP problem. Can be used for benchmarking MPC.
If mpc_baseline != None and imitate_mpc == True, then the forward function returns the solution of the learned QP problem, but a loss term is computed using the MPC problem. Can be used for supervised imitation learning.
If force_feasible == True, solve the following problem instead of the original QP problem:
minimize_{x,y} (1/2)x'Px + q'x + lambda * y^2
s.t. Hx + b + y * 1 >= 0, y >= 0,
where x in R^n, y in R.
In this case, the solution returned will be of dimension (n + 1).
"""
super().__init__()
self.shared_PH = shared_PH
self.affine_qb = affine_qb
self.strict_affine_layer = strict_affine_layer
self.obs_has_half_ref = obs_has_half_ref
self.device = device
self.input_size = input_size
# QP dimensions: there are the number of variables and constraints WITHOUT considering the slack variable
self.n_qp = n_qp
self.m_qp = m_qp
self.qp_iter = qp_iter
self.symmetric = symmetric
self.no_b = no_b
self.n_P_param = n_qp * (n_qp + 1) // 2
self.n_q_param = n_qp
self.n_H_param = m_qp * n_qp
self.n_b_param = m_qp if not self.no_b else 0
self.n_mlp_output = 0
if not self.shared_PH:
self.n_mlp_output += (self.n_P_param + self.n_H_param)
self.P_params = None
self.H_params = None
else:
self.P_params = nn.Parameter(torch.randn((self.n_P_param,), device=device))
self.H_params = nn.Parameter(torch.randn((self.n_H_param,), device=device))
if not self.affine_qb:
self.n_mlp_output += (self.n_q_param + self.n_b_param)
self.qb_affine_layer = None
else:
if not self.strict_affine_layer:
self.qb_affine_layer = nn.Linear(input_size, self.n_q_param + self.n_b_param, bias=not self.symmetric)
else:
self.qb_affine_layer = StrictAffineLayer(input_size, self.n_qp, self.m_qp, self.obs_has_half_ref)
if self.n_mlp_output > 0:
self.mlp = mlp_builder(input_size, self.n_mlp_output)
else:
self.mlp = None
# TODO: add preconditioner
self.warm_starter = WarmStarter(device, n_qp, m_qp, fixed_P=shared_PH, fixed_H=shared_PH) if use_warm_starter else None
self.warm_starter_delayed = WarmStarter(device, n_qp, m_qp, fixed_P=shared_PH, fixed_H=shared_PH) if use_warm_starter else None
self.train_warm_starter = train_warm_starter
self.ws_loss_coef = ws_loss_coef
self.ws_update_rate = ws_update_rate
self.ws_loss_shaper = ws_loss_shaper
# P, H are fixed when the model is in test mode, and they are constant across all states (i.e., shared_PH == True)
self.fixed_PH = is_test and shared_PH
# Includes losses generated by the model itself (indepedent of interaction with env), e.g., warm starting & preconditioning
self.autonomous_losses = {}
self.mpc_baseline = mpc_baseline
self.use_osqp_for_mpc = use_osqp_for_mpc
self.imitate_mpc = imitate_mpc
# Whether to consider residual loss during training - this can encourage feasibility of the learned QP problem
self.use_residual_loss = use_residual_loss
# Whether to force the problem to be feasible
self.force_feasible = force_feasible
self.feasible_lambda = feasible_lambda
self.solver = None
self.info = {}
# Reserved for storing the controllers for each simulation instance when robust MPC is enabled
self.robust_controllers = []
# Store info returned by env
self.env_info = {}
# When running batch testing, mask envs already done, to speed up computation (implemented for robust mpc); initialized at inference time since batch size is not known during initialization
self.is_active = None
def initialize_solver(self):
# If the problem is forced to be feasible, the dimension of the solution is increased by 1 (introduce slack variable)
n_qp_actual = self.n_qp + 1 if self.force_feasible else self.n_qp
m_qp_actual = self.m_qp + 1 if self.force_feasible else self.m_qp
# is_warm_starter_trainable is always False, since the warm starter is trained via another inference independent of the solver
# When self.fixed_PH == True, the solver is initialized with fixed P, H matrices; otherwise, P, H are not passed to the solver during initialization time, but computed during the forward pass instead
if not self.fixed_PH:
self.solver = QPSolver(self.device, n_qp_actual, m_qp_actual, warm_starter=self.warm_starter_delayed, is_warm_starter_trainable=False, symmetric_constraint=self.symmetric, buffered=self.force_feasible)
else:
# Should be called after loading state dict
Pinv, H = self.get_PH()
self.solver = QPSolver(self.device, n_qp_actual, m_qp_actual, Pinv=Pinv.squeeze(0), H=H.squeeze(0), warm_starter=self.warm_starter_delayed, is_warm_starter_trainable=False, symmetric_constraint=self.symmetric, buffered=self.force_feasible)
def compute_warm_starter_loss(self, q, b, Pinv, H, solver_Xs):
qd, bd, Pinvd, Hd = map(lambda t: t.detach() if t is not None else None, [q, b, Pinv, H])
X0 = self.warm_starter(qd, bd, Pinvd, Hd)
gt = solver_Xs[:, -1, :].detach()
return self.ws_loss_coef * self.ws_loss_shaper(((gt - X0) ** 2).sum(dim=-1).mean())
def parallel_controller_creation(self, controller_creator, xref_np, bs):
"""
Create robust MPC controlller in parallel
"""
# Helper function for parallel execution
def task_creator(index):
return controller_creator(self.mpc_baseline, xref_np[index, :])
with ThreadPoolExecutor() as executor:
# Executing the tasks in parallel
results = executor.map(task_creator, range(bs))
# Collecting the results
self.robust_controllers.extend(results)
def run_mpc_baseline(self, x, use_osqp_oracle=False):
robust_method = self.mpc_baseline.get("robust_method", None)
x0, xref = self.mpc_baseline["obs_to_state_and_ref"](x)
bs = x.shape[0]
# Conversions between torch and np
t = lambda a: torch.tensor(a, device=x.device, dtype=torch.float)
f = lambda t: t.detach().cpu().numpy()
f_sparse = lambda t: scipy.sparse.csc_matrix(t.cpu().numpy())
if robust_method is None:
# Run vanilla MPC without robustness
eps = 1e-3
n, m, P, q, H, b = mpc2qp(
self.mpc_baseline["n_mpc"],
self.mpc_baseline["m_mpc"],
self.mpc_baseline["N"],
t(self.mpc_baseline["A"]),
t(self.mpc_baseline["B"]),
t(self.mpc_baseline["Q"]),
t(self.mpc_baseline["R"]),
self.mpc_baseline["x_min"] + eps,
self.mpc_baseline["x_max"] - eps,
self.mpc_baseline["u_min"],
self.mpc_baseline["u_max"],
x0,
xref,
normalize=self.mpc_baseline.get("normalize", False),
Qf=self.mpc_baseline.get("terminal_coef", 0.) * t(np.eye(self.mpc_baseline["n_mpc"])) if self.mpc_baseline.get("Qf", None) is None else t(self.mpc_baseline["Qf"]),
)
if not use_osqp_oracle:
solver = QPSolver(x.device, n, m, P=P, H=H)
Xs, primal_sols = solver(q, b, iters=100)
sol = primal_sols[:, -1, :]
else: | osqp_oracle_with_iter_count = functools.partial(osqp_oracle, return_iter_count=True) | 7 | 2023-11-28 05:56:22+00:00 | 16k |
Fraunhofer-SCAI/llamol | sample.py | [
{
"identifier": "Transformer",
"path": "model.py",
"snippet": "class Transformer(nn.Module):\n last_loss: Optional[torch.Tensor]\n\n def __init__(self, params: ModelArgs, context_params: ContextArgs):\n super().__init__()\n self.params = params\n self.context_params = context_... | import os
import sys
import time
import pandas as pd
import torch
import numpy as np
import re
import logging
import argparse
import rdkit.rdBase as rkrb
import rdkit.RDLogger as rkl
from contextlib import nullcontext
from tqdm.auto import tqdm
from model import Transformer
from plot_utils import (
check_metrics,
plot_1D_condition,
plot_2D_condition,
plot_3D_condition,
plot_unconditional,
)
from tokenizer import SmilesTokenizer
from typing import Dict, List, Tuple, Union
from rdkit import Chem
from rdkit import DataStructs
from rdkit.Chem.Fingerprints import FingerprintMols | 13,254 | i * gens_per_step : (i + 1) * gens_per_step
]
for c in context_cols.keys()
}
context_dict = {"context": cd, "fragment": None}
if context_smi is not None:
logger.debug(
f"context_smiles: {context_smi}",
)
# NOTE: Remove beginning [CLS] and end token [SEP]
incorporate_selfie = self.tokenizer.encode(context_smi)[
1:-1
]
context_tensor = torch.tensor(
[incorporate_selfie] * gens_per_step,
dtype=torch.long,
device=self.device,
)
context_dict["fragment"] = context_tensor
context_cols = list(context_cols.keys())
else:
context_dict = self.get_context(
context_cols, context_smi, num_examples=gens_per_step
)
# for k in range(num_samples):
y = self.model.generate(
self.tokenizer,
context=context_dict["context"],
fragments=context_dict["fragment"],
start_smiles=start_smiles,
num_gen=gens_per_step,
temperature=temperature,
top_k=top_k,
max_length=max_new_tokens,
device=self.device,
cache_kv=use_kv_cache,
)
new_context = {k: [] for k in context_dict["context"]}
for i, sample in enumerate(y):
# print(sample)
mol = Chem.MolFromSmiles(sample)
if mol is not None:
out_smiles.append(sample)
for k in new_context:
new_context[k].append(
context_dict["context"][k][i].unsqueeze(-1)
)
for k in new_context:
new_context[k] = torch.concat(new_context[k], dim=0)
if context is None:
context = new_context
else:
for k in context:
context[k] = torch.concat(
[context[k], new_context[k]], dim=0
)
pbar.update(1)
logger.info(
f"Number valid generated: {len(out_smiles) / num_samples * 100} %"
)
logger.info("---------------")
if return_context:
return (out_smiles, context)
else:
return out_smiles
@torch.no_grad()
def generate_with_evaluation(
self,
context_cols: Union[List[str], None] = None,
context_smi: Union[str, None] = None,
start_smiles: Union[str, None] = None,
num_samples: int = 50,
max_new_tokens: int = 256,
temperature: float = 1.0,
top_k: Union[int, None] = None,
cmp_context_dict: Union[Dict[str, torch.Tensor], None] = None,
total_gen_steps: int = 1,
use_kv_cache: bool = False,
):
out_smiles, new_context = self.generate(
context_cols=context_cols,
context_smi=context_smi,
start_smiles=start_smiles,
num_samples=num_samples,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
return_context=True,
total_gen_steps=total_gen_steps,
use_kv_cache=use_kv_cache,
)
out_dir = os.path.dirname(self.load_path)
if context_cols is not None:
if len(context_cols) == 1:
plot_1D_condition(
context_cols,
os.path.join(out_dir, "plots"),
new_context,
out_smiles,
temperature,
cmp_context_dict,
context_scaler=None,
)
elif len(context_cols) == 2:
|
# from tqdm.notebook import tqdm
logger = logging.getLogger(__name__)
class Sampler:
def __init__(
self,
load_path: str,
device: str = "cpu",
seed: int = 1337,
dtype: str = "float16",
compile: bool = True,
quantize: bool = False,
) -> None:
self.load_path = load_path
self.device = device
self.dtype = dtype
self.compile = compile
self.quantize = quantize
self.seed = seed
self._init_model()
def _init_model(self):
np.random.seed(self.seed)
torch.cuda.manual_seed(self.seed)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
self.device_type = (
"cuda" if "cuda" in self.device else "cpu"
) # for later use in torch.autocast
ptdtype = {
"float32": torch.float32,
"bfloat16": torch.bfloat16,
"float16": torch.float16,
}[self.dtype]
self.ptdtype = ptdtype
self.ctx = self._autocast()
# init from a model saved in a specific directory
# ckpt_path = os.path.join(out_dir, "ckpt_full_dim=256.pt")
self.model = Transformer.load(self.load_path, device=self.device)
self.model.eval()
if self.quantize:
raise NotImplementedError("Not properly implemented for CPU / GPU")
self.model = torch.ao.quantization.quantize_dynamic(
self.model, # the original model
{torch.nn.Linear}, # a set of layers to dynamically quantize
dtype=torch.qint8,
)
if self.compile:
logger.info("Compiling the model...")
self.model = torch.compile(self.model) # requires PyTorch 2.0 (optional)
self.model = self.model.to(self.device)
# load the tokenizer
self.tokenizer = SmilesTokenizer()
def get_context(
self,
context_col: List[str],
context_smi: str,
num_examples: int = 50,
):
"""
Returns a dictionary in the form of
{
"fragment": torch.tensor,
"context": {
"logp": torch.tensor,
"sascore": torch.tensor,
"mol_weight": torch.tensor
}
}
When context_smi is set to a string, then the "fragment" field is populated.
All of the properties listed in the context_col list is set to the keys and the values are set to a resonable range for each property.
num_examples indicates how many values are sampled for each property.
"""
output_dict = {"context": {}, "fragment": None}
if context_smi is not None:
logger.debug(
f"context_smiles: {context_smi}",
)
# NOTE: Remove beginning [CLS] and end token [SEP]
incorporate_selfie = self.tokenizer.encode(context_smi)[1:-1]
context = torch.tensor(
[incorporate_selfie] * num_examples,
dtype=torch.long,
device=self.device,
)
output_dict["fragment"] = context
if context_col is None:
return output_dict
if "logp" in context_col:
# context = 0.5 * torch.randint(
# -8, 14, (num_examples,), device=self.device, dtype=torch.float
# )
# context = 0.5 * torch.randint(
# -6, 6, (num_examples, 1), device=device, dtype=torch.float
# )
context = torch.tensor(
np.random.choice([-2, 0, 2], (num_examples,)),
device=self.device,
dtype=self.ptdtype,
)
# context = 2.0 * torch.ones(
# (num_examples,1), device=device, dtype=torch.float
# )
# context = -2.0*torch.ones((num_examples,2),device=device,dtype=torch.float)
# context, _ = torch.sort(context, 0)
output_dict["context"]["logp"] = context
if "energy" in context_col:
context = 0.1 * torch.randint(
-15, 15, (num_examples,), device=self.device, dtype=torch.float
)
# context = -2.0*torch.ones((num_examples,2),device=device,dtype=torch.float)
context, _ = torch.sort(context, 0)
output_dict["context"]["energy"] = context
if "sascore" in context_col:
# context = 0.5 * torch.randint(
# 2, 20, (num_examples, ), device=self.device, dtype=torch.float
# )
context = torch.tensor(
np.random.choice([2, 3, 4], (num_examples,)),
device=self.device,
dtype=torch.float,
)
# context = 0.5 * torch.randint(
# 4, 8, (num_examples, 1), device=device, dtype=torch.float
# )
# context = 2.0*torch.ones((num_examples,1),device=device,dtype=torch.float)
# context, _ = torch.sort(context, 0)
output_dict["context"]["sascore"] = context
if "mol_weight" in context_col:
# context = 0.5 * torch.randint(
# 2, 20, (num_examples,), device=self.device, dtype=torch.float
# )
context = torch.tensor(
np.random.choice([2.0, 3.0, 4.0], (num_examples,)),
device=self.device,
dtype=torch.float,
)
# context = 0.5 * torch.randint(
# 2, 20, (num_examples, 1), device=device, dtype=torch.float
# )
# context = 2.5*torch.ones((num_examples,1),device=device,dtype=torch.float)
# context, _ = torch.sort(context, 0)
output_dict["context"]["mol_weight"] = context
return output_dict
def _autocast(self):
if "cuda" in self.device:
if self.dtype == "bfloat16" and torch.cuda.is_bf16_supported():
return torch.cuda.amp.autocast(dtype=torch.bfloat16)
elif self.dtype == "float16":
return torch.cuda.amp.autocast(dtype=torch.float16)
else:
return torch.cuda.amp.autocast(dtype=torch.float32)
else: # cpu
return nullcontext()
@torch.no_grad()
def generate(
self,
context_cols: Union[List[str], None, Dict[str, torch.Tensor]] = None,
context_smi: Union[str, None] = None,
start_smiles: Union[str, None] = None,
num_samples: int = 50,
max_new_tokens: int = 256,
temperature: float = 1.0,
top_k: Union[int, None] = None,
return_context: bool = False,
total_gen_steps: int = 1,
use_kv_cache: bool = False,
) -> Union[List[str], Tuple[List[str], List[float]]]:
"""
Generates a list of SMILES. With the default options it would generate them unconditionally.
Params:
- context_cols : When a list the context is randomly sampled from the get_context method, when given a dictionary the
context values are taken from the dictionary instead.
- context_smi : Further conditioning by the usage of a molecular fragment
. start_smiles : Can be used to start the SMILES with a specific string, the model then generates the next tokens including that start sequence.
- num_samples : Controlls how many SMILES in total will be generated be the model.
- max_new_tokens : Controlls the maximum length of each SMILES (in tokens) that is generated.
- temperature: Controlls the randomness of the model. A temperature = 1.0 means it is the trained distribution. A temperature < 1 is more deterministic and temperature > 1 is more random
- top_k : Clamps the probability distribution to the top k tokens. From these the next token is then sampled from.
- return_context : Whether the context that was given to the model should be returned.
- total_gen_steps : In how many sub steps the generation should be split up to. Useful when generation 10k + SMILES and wanting to chunk these into for example 10 * 1k generations with total_gen_steps = 10.
- use_kv_cache: Runs the generation using kv-caching. It is faster, but takes more memory.
"""
with self.ctx:
gens_per_step = num_samples // total_gen_steps
logger.debug(f"Gens per Step: {gens_per_step}")
context = None # {"context": None, "fragment" : None}
out_smiles = []
with tqdm(total=total_gen_steps, desc="Batch") as pbar:
for i in range(total_gen_steps):
if isinstance(context_cols, dict):
# TODO: Test if same length
cd = {
c: context_cols[c][
i * gens_per_step : (i + 1) * gens_per_step
]
for c in context_cols.keys()
}
context_dict = {"context": cd, "fragment": None}
if context_smi is not None:
logger.debug(
f"context_smiles: {context_smi}",
)
# NOTE: Remove beginning [CLS] and end token [SEP]
incorporate_selfie = self.tokenizer.encode(context_smi)[
1:-1
]
context_tensor = torch.tensor(
[incorporate_selfie] * gens_per_step,
dtype=torch.long,
device=self.device,
)
context_dict["fragment"] = context_tensor
context_cols = list(context_cols.keys())
else:
context_dict = self.get_context(
context_cols, context_smi, num_examples=gens_per_step
)
# for k in range(num_samples):
y = self.model.generate(
self.tokenizer,
context=context_dict["context"],
fragments=context_dict["fragment"],
start_smiles=start_smiles,
num_gen=gens_per_step,
temperature=temperature,
top_k=top_k,
max_length=max_new_tokens,
device=self.device,
cache_kv=use_kv_cache,
)
new_context = {k: [] for k in context_dict["context"]}
for i, sample in enumerate(y):
# print(sample)
mol = Chem.MolFromSmiles(sample)
if mol is not None:
out_smiles.append(sample)
for k in new_context:
new_context[k].append(
context_dict["context"][k][i].unsqueeze(-1)
)
for k in new_context:
new_context[k] = torch.concat(new_context[k], dim=0)
if context is None:
context = new_context
else:
for k in context:
context[k] = torch.concat(
[context[k], new_context[k]], dim=0
)
pbar.update(1)
logger.info(
f"Number valid generated: {len(out_smiles) / num_samples * 100} %"
)
logger.info("---------------")
if return_context:
return (out_smiles, context)
else:
return out_smiles
@torch.no_grad()
def generate_with_evaluation(
self,
context_cols: Union[List[str], None] = None,
context_smi: Union[str, None] = None,
start_smiles: Union[str, None] = None,
num_samples: int = 50,
max_new_tokens: int = 256,
temperature: float = 1.0,
top_k: Union[int, None] = None,
cmp_context_dict: Union[Dict[str, torch.Tensor], None] = None,
total_gen_steps: int = 1,
use_kv_cache: bool = False,
):
out_smiles, new_context = self.generate(
context_cols=context_cols,
context_smi=context_smi,
start_smiles=start_smiles,
num_samples=num_samples,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
return_context=True,
total_gen_steps=total_gen_steps,
use_kv_cache=use_kv_cache,
)
out_dir = os.path.dirname(self.load_path)
if context_cols is not None:
if len(context_cols) == 1:
plot_1D_condition(
context_cols,
os.path.join(out_dir, "plots"),
new_context,
out_smiles,
temperature,
cmp_context_dict,
context_scaler=None,
)
elif len(context_cols) == 2: | plot_2D_condition( | 3 | 2023-11-28 09:50:31+00:00 | 16k |
lampmerchant/tashrouter | tashrouter/router/router.py | [
{
"identifier": "RoutingTable",
"path": "tashrouter/router/routing_table.py",
"snippet": "class RoutingTable:\n '''A Router's routing table.'''\n \n STATE_GOOD = 1\n STATE_SUS = 2\n STATE_BAD = 3\n STATE_WORST = 4\n \n def __init__(self, router):\n self._router = router\n self._entry_by_ne... | import logging
from .routing_table import RoutingTable
from .zone_information_table import ZoneInformationTable
from ..datagram import Datagram
from ..service.echo import EchoService
from ..service.name_information import NameInformationService
from ..service.routing_table_aging import RoutingTableAgingService
from ..service.rtmp.responding import RtmpRespondingService
from ..service.rtmp.sending import RtmpSendingService
from ..service.zip.responding import ZipRespondingService
from ..service.zip.sending import ZipSendingService | 11,972 | '''The heart of this whole affair.'''
class Router:
'''A router, a device which sends Datagrams to Ports and runs Services.'''
def __init__(self, short_str, ports):
self._short_str = short_str
self.ports = ports
self._services = (
(EchoService.ECHO_SAS, EchoService()),
| '''The heart of this whole affair.'''
class Router:
'''A router, a device which sends Datagrams to Ports and runs Services.'''
def __init__(self, short_str, ports):
self._short_str = short_str
self.ports = ports
self._services = (
(EchoService.ECHO_SAS, EchoService()), | (NameInformationService.NBP_SAS, NameInformationService()), | 4 | 2023-12-02 15:17:07+00:00 | 16k |
jags111/ComfyUI_Jags_Audiotools | libs/dance_diffusion/dd/inference.py | [
{
"identifier": "SchedulerType",
"path": "libs/diffusion_library/scheduler.py",
"snippet": "class SchedulerType(str, enum.Enum):\n V_DDPM = 'V_DDPM'\n V_SPLICED_DDPM_COSINE = 'V_SPLICED_DDPM_COSINE'\n V_LOG = 'V_LOG'\n V_CRASH = 'V_CRASH'\n \n K_KARRAS = 'K_KARRAS'\n K_EXPONENTIAL =... | import torch
from tqdm.auto import trange
from diffusion.utils import t_to_alpha_sigma
from k_diffusion.external import VDenoiser
from typing import Tuple, Callable
from libs.diffusion_library.scheduler import SchedulerType
from libs.diffusion_library.sampler import SamplerType
from libs.dance_diffusion.base.model import ModelWrapperBase
from libs.dance_diffusion.base.inference import InferenceBase
from libs.util.util import tensor_slerp_2D, PosteriorSampling
from typing import Tuple, Callable
from libs.diffusion_library.scheduler import SchedulerType
from libs.diffusion_library.sampler import SamplerType
from libs.dance_diffusion.base.model import ModelWrapperBase
from libs.dance_diffusion.base.inference import InferenceBase
from libs.util.util import tensor_slerp_2D, PosteriorSampling | 11,160 | callback,
**sampler_args
).float()
def generate_interpolation(
self,
callback: Callable = None,
batch_size: int = None,
# seed: int = None,
interpolation_positions: list[float] = None,
audio_source: torch.Tensor = None,
audio_target: torch.Tensor = None,
expansion_map: list[int] = None,
noise_level: float = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args = None,
sampler: SamplerType = None,
sampler_args = None,
**kwargs
) -> torch.Tensor:
if SamplerType.is_v_sampler(sampler):
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
step_list = step_list[step_list < noise_level]
step_list[-1] += 1e-7 #HACK avoid division by 0 in reverse sampling
model = self.model.model
else:
scheduler_args.update(sigma_max = scheduler_args.get('sigma_max', 1.0) * noise_level)
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
step_list = step_list[:-1] #HACK avoid division by 0 in reverse sampling
model = VDenoiser(self.model.model)
if self.optimize_memory_use and batch_size < 2:
x_0_source = audio_source
x_0_target = audio_target
with self.offload_context(self.model.model):
x_T_source = sampler.sample(
model,
x_0_source,
step_list.flip(0),
callback,
**sampler_args
)
with self.offload_context(self.model.model):
x_T_target = sampler.sample(
model,
x_0_target,
step_list.flip(0),
callback,
**sampler_args
)
x_T = torch.cat([x_T_source, x_T_target], dim=0)
else:
x_0 = torch.cat([audio_source, audio_target], dim=0)
with self.offload_context(self.model.model):
x_T = sampler.sample(
model,
x_0,
step_list.flip(0),
callback,
**sampler_args
)
if SamplerType.is_v_sampler(sampler): #HACK reset schedule after hack
step_list[-1] = 0.0
else:
step_list = torch.cat([step_list, step_list.new_zeros([1])])
x_Int = torch.empty([batch_size, 2, self.model.chunk_size], device=self.device_accelerator)
for pos in range(len(interpolation_positions)):
x_Int[pos] = tensor_slerp_2D(x_T[0], x_T[1], interpolation_positions[pos])
with self.offload_context(self.model.model):
return sampler.sample(
model,
x_Int,
step_list,
callback,
**sampler_args
).float()
def generate_inpainting(
self,
callback: Callable = None,
batch_size: int = None,
seed: int = None,
audio_source: torch.Tensor = None,
expansion_map: list[int] = None,
mask: torch.Tensor = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args = None,
sampler: SamplerType = None,
sampler_args = None,
inpainting_args = None,
**kwargs
) -> torch.Tensor:
self.generator.manual_seed(seed)
method = inpainting_args.get('method')
if(method == 'repaint'):
raise Exception("Repaint currently not supported due to changed requirements")
elif(method == 'posterior_guidance'):
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
if SamplerType.is_v_sampler(sampler):
raise Exception('V-Sampler currently not supported for posterior guidance. Please choose a K-Sampler.')
else:
x_T = audio_source + step_list[0] * torch.randn([batch_size, 2, self.model.chunk_size], generator=self.generator, device=self.device_accelerator)
|
class DDInference(InferenceBase):
def __init__(
self,
device_accelerator: torch.device = None,
device_offload: torch.device = None,
optimize_memory_use: bool = False,
use_autocast: bool = True,
model: ModelWrapperBase = None
):
super().__init__(device_accelerator, device_offload, optimize_memory_use, use_autocast, model)
def generate(
self,
callback: Callable = None,
batch_size: int = None,
seed: int = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args: dict = None,
sampler: SamplerType = None,
sampler_args: dict = None,
**kwargs
):
self.generator.manual_seed(seed)
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)#step_list = step_list[:-1] if sampler in [SamplerType.V_PRK, SamplerType.V_PLMS, SamplerType.V_PIE, SamplerType.V_PLMS2, SamplerType.V_IPLMS] else step_list
if SamplerType.is_v_sampler(sampler):
x_T = torch.randn([batch_size, 2, self.model.chunk_size], generator=self.generator, device=self.device_accelerator)
model = self.model.model
else:
x_T = step_list[0] * torch.randn([batch_size, 2, self.model.chunk_size], generator=self.generator, device=self.device_accelerator)
model = VDenoiser(self.model.model)
with self.offload_context(self.model.model):
return sampler.sample(
model,
x_T,
step_list,
callback,
**sampler_args
).float()
def generate_variation(
self,
callback: Callable = None,
batch_size: int = None,
seed: int = None,
audio_source: torch.Tensor = None,
expansion_map: list[int] = None,
noise_level: float = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args = None,
sampler: SamplerType = None,
sampler_args = None,
**kwargs
) -> torch.Tensor:
self.generator.manual_seed(seed)
audio_source = self.expand(audio_source, expansion_map)
if SamplerType.is_v_sampler(sampler):
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
step_list = step_list[step_list < noise_level]
alpha_T, sigma_T = t_to_alpha_sigma(step_list[0])
x_T = alpha_T * audio_source + sigma_T * torch.randn(audio_source.shape, device=audio_source.device, generator=self.generator)
model = self.model.model
else:
scheduler_args.update(sigma_max = scheduler_args.get('sigma_max', 1.0) * noise_level)
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
x_T = audio_source + step_list[0] * torch.randn(audio_source.shape, device=audio_source.device, generator=self.generator)
model = VDenoiser(self.model.model)
with self.offload_context(self.model.model):
return sampler.sample(
model,
x_T,
step_list,
callback,
**sampler_args
).float()
def generate_interpolation(
self,
callback: Callable = None,
batch_size: int = None,
# seed: int = None,
interpolation_positions: list[float] = None,
audio_source: torch.Tensor = None,
audio_target: torch.Tensor = None,
expansion_map: list[int] = None,
noise_level: float = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args = None,
sampler: SamplerType = None,
sampler_args = None,
**kwargs
) -> torch.Tensor:
if SamplerType.is_v_sampler(sampler):
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
step_list = step_list[step_list < noise_level]
step_list[-1] += 1e-7 #HACK avoid division by 0 in reverse sampling
model = self.model.model
else:
scheduler_args.update(sigma_max = scheduler_args.get('sigma_max', 1.0) * noise_level)
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
step_list = step_list[:-1] #HACK avoid division by 0 in reverse sampling
model = VDenoiser(self.model.model)
if self.optimize_memory_use and batch_size < 2:
x_0_source = audio_source
x_0_target = audio_target
with self.offload_context(self.model.model):
x_T_source = sampler.sample(
model,
x_0_source,
step_list.flip(0),
callback,
**sampler_args
)
with self.offload_context(self.model.model):
x_T_target = sampler.sample(
model,
x_0_target,
step_list.flip(0),
callback,
**sampler_args
)
x_T = torch.cat([x_T_source, x_T_target], dim=0)
else:
x_0 = torch.cat([audio_source, audio_target], dim=0)
with self.offload_context(self.model.model):
x_T = sampler.sample(
model,
x_0,
step_list.flip(0),
callback,
**sampler_args
)
if SamplerType.is_v_sampler(sampler): #HACK reset schedule after hack
step_list[-1] = 0.0
else:
step_list = torch.cat([step_list, step_list.new_zeros([1])])
x_Int = torch.empty([batch_size, 2, self.model.chunk_size], device=self.device_accelerator)
for pos in range(len(interpolation_positions)):
x_Int[pos] = tensor_slerp_2D(x_T[0], x_T[1], interpolation_positions[pos])
with self.offload_context(self.model.model):
return sampler.sample(
model,
x_Int,
step_list,
callback,
**sampler_args
).float()
def generate_inpainting(
self,
callback: Callable = None,
batch_size: int = None,
seed: int = None,
audio_source: torch.Tensor = None,
expansion_map: list[int] = None,
mask: torch.Tensor = None,
steps: int = None,
scheduler: SchedulerType = None,
scheduler_args = None,
sampler: SamplerType = None,
sampler_args = None,
inpainting_args = None,
**kwargs
) -> torch.Tensor:
self.generator.manual_seed(seed)
method = inpainting_args.get('method')
if(method == 'repaint'):
raise Exception("Repaint currently not supported due to changed requirements")
elif(method == 'posterior_guidance'):
step_list = scheduler.get_step_list(steps, self.device_accelerator.type, **scheduler_args)
if SamplerType.is_v_sampler(sampler):
raise Exception('V-Sampler currently not supported for posterior guidance. Please choose a K-Sampler.')
else:
x_T = audio_source + step_list[0] * torch.randn([batch_size, 2, self.model.chunk_size], generator=self.generator, device=self.device_accelerator) | model = PosteriorSampling( | 11 | 2023-11-28 09:09:59+00:00 | 16k |
Matrixeigs/UncertaintyManagementInteroperablePowerTransportationSystems | TestCaseDistributionSystems/uc_mmgs_fully_recourse_problem.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 scipy 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, BUS_I
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_programming_gurobi import mixed_integer_linear_programming as milp
from copy import deepcopy
from TestCaseDistributionSystems.data_format.idx_MG_PV import PBIC_AC2DC, PG, PESS_DC, PBIC_DC2AC, PUG, PESS_CH, \
PMESS, EESS, NX_MG, QBIC, QUG, QG, PPV
from TestCaseDistributionSystems.database_management_pv import DataBaseManagement
from StochasticOptimization.scenario_reduction import ScenarioReduction | 12,828 | self.name = "Unit commitment with tess"
def main(self, power_networks, micro_grids, profile, pv_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,
pv_profile=pv_profile,
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 = {}
| """
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 = "Unit commitment with tess"
def main(self, power_networks, micro_grids, profile, pv_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,
pv_profile=pv_profile,
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() | 15 | 2023-11-27 15:57:53+00:00 | 16k |
andryyy/ehlocomputer | models/listeners.py | [
{
"identifier": "defaults",
"path": "config/defaults.py",
"snippet": "ACCEPT_LANGUAGES = [\"en\", \"de\"]\nMAX_HISTORIC_REVISIONS = 5\nWEBAUTHN_CHALLENGE_TIMEOUT = 30 # seconds\nPROXY_AUTH_TIMEOUT = 300 # seconds\nTABLE_PAGE_SIZE = 10\nTINYDB = {\n \"storage\": RedisLockMiddleware(JSONStorage),\n ... | import json
import os
import re
import uuid
from config import defaults
from config import lego
from config.database import *
from email_validator import validate_email
from pydantic import (
AfterValidator,
BaseModel,
EmailStr,
Field,
FilePath,
HttpUrl,
field_validator,
model_validator,
validator,
)
from pydantic.networks import IPv4Address, IPv6Address
from typing import Annotated, Any, Literal
from . import (
utc_now_as_str,
ensure_list,
to_unique_sorted_str_list,
get_validated_fqdn,
flatten,
) | 12,610 |
class ListenerCreate(BaseModel):
id: Annotated[str, Field(default_factory=lambda: str(uuid.uuid4()))]
name: Annotated[str, Field(min_length=1)]
configuration: dict = {}
historic: list = []
created: Annotated[str, Field(default_factory=utc_now_as_str)]
updated: Annotated[str, Field(default_factory=utc_now_as_str)]
class ListenerLegoConfig(BaseModel):
lego_provider: str
acme_terms_agreed: Literal[True, "true"]
provider_config: dict
acme_server: Annotated[str, AfterValidator(lambda x: str(HttpUrl(x)))]
acme_email: EmailStr
key_type: Literal["EC256", "EC384", "RSA2048", "RSA4096", "RSA8192"] = "RSA2048"
domains: str
@model_validator(mode="before")
@classmethod
def check_lego(self, data: Any) -> Any:
|
class ListenerCreate(BaseModel):
id: Annotated[str, Field(default_factory=lambda: str(uuid.uuid4()))]
name: Annotated[str, Field(min_length=1)]
configuration: dict = {}
historic: list = []
created: Annotated[str, Field(default_factory=utc_now_as_str)]
updated: Annotated[str, Field(default_factory=utc_now_as_str)]
class ListenerLegoConfig(BaseModel):
lego_provider: str
acme_terms_agreed: Literal[True, "true"]
provider_config: dict
acme_server: Annotated[str, AfterValidator(lambda x: str(HttpUrl(x)))]
acme_email: EmailStr
key_type: Literal["EC256", "EC384", "RSA2048", "RSA4096", "RSA8192"] = "RSA2048"
domains: str
@model_validator(mode="before")
@classmethod
def check_lego(self, data: Any) -> Any: | if data.get("lego_provider") not in lego.LEGO_DNS_PROVIDERS.keys(): | 1 | 2023-12-01 08:36:45+00:00 | 16k |
fzmi/ubdd | models/dino/models/dino/dino.py | [
{
"identifier": "box_ops",
"path": "models/dino/util/box_ops.py",
"snippet": "def box_cxcywh_to_xyxy(x):\ndef box_xyxy_to_cxcywh(x):\ndef box_iou(boxes1, boxes2):\ndef generalized_box_iou(boxes1, boxes2):\ndef box_iou_pairwise(boxes1, boxes2):\ndef generalized_box_iou_pairwise(boxes1, boxes2):\ndef mask... | import copy
import math
import torch
import torch.nn.functional as F
from typing import List
from torch import nn
from torchvision.ops.boxes import nms
from models.dino.util import box_ops
from models.dino.util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, interpolate,
is_dist_avail_and_initialized, inverse_sigmoid)
from .backbone import build_backbone
from .matcher import build_matcher
from .segmentation import (DETRsegm, PostProcessPanoptic, PostProcessSegm,
dice_loss)
from .deformable_transformer import build_deformable_transformer
from .utils import sigmoid_focal_loss, MLP
from ..registry import MODULE_BUILD_FUNCS
from .dn_components import prepare_for_cdn,dn_post_process | 11,429 |
l_dict.update(self.get_loss(loss, aux_outputs_known, targets, dn_pos_idx, num_boxes*scalar,
**kwargs))
l_dict = {k + f'_dn_{idx}': v for k, v in l_dict.items()}
losses.update(l_dict)
else:
l_dict = dict()
l_dict['loss_bbox_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_giou_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_ce_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_xy_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_hw_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['cardinality_error_dn'] = torch.as_tensor(0.).to('cuda')
l_dict = {k + f'_{idx}': v for k, v in l_dict.items()}
losses.update(l_dict)
# interm_outputs loss
if 'interm_outputs' in outputs:
interm_outputs = outputs['interm_outputs']
indices = self.matcher(interm_outputs, targets)
if return_indices:
indices_list.append(indices)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, interm_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_interm': v for k, v in l_dict.items()}
losses.update(l_dict)
# enc output loss
if 'enc_outputs' in outputs:
for i, enc_outputs in enumerate(outputs['enc_outputs']):
indices = self.matcher(enc_outputs, targets)
if return_indices:
indices_list.append(indices)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, enc_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_enc_{i}': v for k, v in l_dict.items()}
losses.update(l_dict)
if return_indices:
indices_list.append(indices0_copy)
return losses, indices_list
return losses
def prep_for_dn(self,dn_meta):
output_known_lbs_bboxes = dn_meta['output_known_lbs_bboxes']
num_dn_groups,pad_size=dn_meta['num_dn_group'],dn_meta['pad_size']
assert pad_size % num_dn_groups==0
single_pad=pad_size//num_dn_groups
return output_known_lbs_bboxes,single_pad,num_dn_groups
class PostProcess(nn.Module):
""" This module converts the model's output into the format expected by the coco api"""
def __init__(self, num_select=100, nms_iou_threshold=-1) -> None:
super().__init__()
self.num_select = num_select
self.nms_iou_threshold = nms_iou_threshold
@torch.no_grad()
def forward(self, outputs, target_sizes, not_to_xyxy=False, test=False):
""" Perform the computation
Parameters:
outputs: raw outputs of the model
target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
For evaluation, this must be the original image size (before any data augmentation)
For visualization, this should be the image size after data augment, but before padding
"""
num_select = self.num_select
out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
assert len(out_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), num_select, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
if not_to_xyxy:
boxes = out_bbox
else:
boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
if test:
assert not not_to_xyxy
boxes[:,:,2:] = boxes[:,:,2:] - boxes[:,:,:2]
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
boxes = boxes * scale_fct[:, None, :]
if self.nms_iou_threshold > 0:
item_indices = [nms(b, s, iou_threshold=self.nms_iou_threshold) for b,s in zip(boxes, scores)]
results = [{'scores': s[i], 'labels': l[i], 'boxes': b[i]} for s, l, b, i in zip(scores, labels, boxes, item_indices)]
else:
results = [{'scores': s, 'labels': l, 'boxes': b} for s, l, b in zip(scores, labels, boxes)]
return results
| # ------------------------------------------------------------------------
# DINO
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR model and criterion classes.
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
class DINO(nn.Module):
""" This is the Cross-Attention Detector module that performs object detection """
def __init__(self, backbone, transformer, num_classes, num_queries,
aux_loss=False, iter_update=False,
query_dim=2,
random_refpoints_xy=False,
fix_refpoints_hw=-1,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type='no', # ['no', 'standard']
two_stage_add_query_num=0,
dec_pred_class_embed_share=True,
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
decoder_sa_type = 'sa',
num_patterns = 0,
dn_number = 100,
dn_box_noise_scale = 0.4,
dn_label_noise_ratio = 0.5,
dn_labelbook_size = 100,
):
""" Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_classes: number of object classes
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
fix_refpoints_hw: -1(default): learn w and h for each box seperately
>0 : given fixed number
-2 : learn a shared w and h
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.num_classes = num_classes
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.label_enc = nn.Embedding(dn_labelbook_size + 1, hidden_dim)
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
self.random_refpoints_xy = random_refpoints_xy
self.fix_refpoints_hw = fix_refpoints_hw
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
))
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
))
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == 'no', "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList([
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)])
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_class_embed_share = dec_pred_class_embed_share
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = nn.Linear(hidden_dim, num_classes)
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
# init the two embed layers
prior_prob = 0.01
bias_value = -math.log((1 - prior_prob) / prior_prob)
_class_embed.bias.data = torch.ones(self.num_classes) * bias_value
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)]
if dec_pred_class_embed_share:
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
else:
class_embed_layerlist = [copy.deepcopy(_class_embed) for i in range(transformer.num_decoder_layers)]
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
# two stage
self.two_stage_type = two_stage_type
self.two_stage_add_query_num = two_stage_add_query_num
assert two_stage_type in ['no', 'standard'], "unknown param {} of two_stage_type".format(two_stage_type)
if two_stage_type != 'no':
if two_stage_bbox_embed_share:
assert dec_pred_class_embed_share and dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_class_embed_share and dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
if self.two_stage_add_query_num > 0:
self.init_ref_points(two_stage_add_query_num)
self.decoder_sa_type = decoder_sa_type
assert decoder_sa_type in ['sa', 'ca_label', 'ca_content']
if decoder_sa_type == 'ca_label':
self.label_embedding = nn.Embedding(num_classes, hidden_dim)
for layer in self.transformer.decoder.layers:
layer.label_embedding = self.label_embedding
else:
for layer in self.transformer.decoder.layers:
layer.label_embedding = None
self.label_embedding = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def init_ref_points(self, use_num_queries):
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
if self.random_refpoints_xy:
self.refpoint_embed.weight.data[:, :2].uniform_(0,1)
self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2])
self.refpoint_embed.weight.data[:, :2].requires_grad = False
if self.fix_refpoints_hw > 0:
print("fix_refpoints_hw: {}".format(self.fix_refpoints_hw))
assert self.random_refpoints_xy
self.refpoint_embed.weight.data[:, 2:] = self.fix_refpoints_hw
self.refpoint_embed.weight.data[:, 2:] = inverse_sigmoid(self.refpoint_embed.weight.data[:, 2:])
self.refpoint_embed.weight.data[:, 2:].requires_grad = False
elif int(self.fix_refpoints_hw) == -1:
pass
elif int(self.fix_refpoints_hw) == -2:
print('learn a shared h and w')
assert self.random_refpoints_xy
self.refpoint_embed = nn.Embedding(use_num_queries, 2)
self.refpoint_embed.weight.data[:, :2].uniform_(0,1)
self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2])
self.refpoint_embed.weight.data[:, :2].requires_grad = False
self.hw_embed = nn.Embedding(1, 1)
else:
raise NotImplementedError('Unknown fix_refpoints_hw {}'.format(self.fix_refpoints_hw))
def forward(self, samples: NestedTensor, targets:List=None):
""" The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
It returns a dict with the following elements:
- "pred_logits": the classification logits (including no-object) for all queries.
Shape= [batch_size x num_queries x num_classes]
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
(center_x, center_y, width, height). These values are normalized in [0, 1],
relative to the size of each individual image (disregarding possible padding).
See PostProcess for information on how to retrieve the unnormalized bounding box.
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples)
features, poss = self.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
srcs.append(src)
masks.append(mask)
poss.append(pos_l)
if self.dn_number > 0 or targets is not None:
input_query_label, input_query_bbox, attn_mask, dn_meta =\
prepare_for_cdn(dn_args=(targets, self.dn_number, self.dn_label_noise_ratio, self.dn_box_noise_scale),
training=self.training,num_queries=self.num_queries,num_classes=self.num_classes,
hidden_dim=self.hidden_dim,label_enc=self.label_enc)
else:
assert targets is None
input_query_bbox = input_query_label = attn_mask = dn_meta = None
hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(srcs, masks, input_query_bbox, poss,input_query_label,attn_mask)
# In case num object=0
hs[0] += self.label_enc.weight[0,0]*0.0
# deformable-detr-like anchor update
# reference_before_sigmoid = inverse_sigmoid(reference[:-1]) # n_dec, bs, nq, 4
outputs_coord_list = []
for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(zip(reference[:-1], self.bbox_embed, hs)):
layer_delta_unsig = layer_bbox_embed(layer_hs)
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
layer_outputs_unsig = layer_outputs_unsig.sigmoid()
outputs_coord_list.append(layer_outputs_unsig)
outputs_coord_list = torch.stack(outputs_coord_list)
outputs_class = torch.stack([layer_cls_embed(layer_hs) for
layer_cls_embed, layer_hs in zip(self.class_embed, hs)])
if self.dn_number > 0 and dn_meta is not None:
outputs_class, outputs_coord_list = \
dn_post_process(outputs_class, outputs_coord_list,
dn_meta,self.aux_loss,self._set_aux_loss)
out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord_list[-1]}
if self.aux_loss:
out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
# for encoder output
if hs_enc is not None:
# prepare intermediate outputs
interm_coord = ref_enc[-1]
interm_class = self.transformer.enc_out_class_embed(hs_enc[-1])
out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
# prepare enc outputs
if hs_enc.shape[0] > 1:
enc_outputs_coord = []
enc_outputs_class = []
for layer_id, (layer_box_embed, layer_class_embed, layer_hs_enc, layer_ref_enc) in enumerate(zip(self.enc_bbox_embed, self.enc_class_embed, hs_enc[:-1], ref_enc[:-1])):
layer_enc_delta_unsig = layer_box_embed(layer_hs_enc)
layer_enc_outputs_coord_unsig = layer_enc_delta_unsig + inverse_sigmoid(layer_ref_enc)
layer_enc_outputs_coord = layer_enc_outputs_coord_unsig.sigmoid()
layer_enc_outputs_class = layer_class_embed(layer_hs_enc)
enc_outputs_coord.append(layer_enc_outputs_coord)
enc_outputs_class.append(layer_enc_outputs_class)
out['enc_outputs'] = [
{'pred_logits': a, 'pred_boxes': b} for a, b in zip(enc_outputs_class, enc_outputs_coord)
]
out['dn_meta'] = dn_meta
return out
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [{'pred_logits': a, 'pred_boxes': b}
for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
class SetCriterion(nn.Module):
""" This class computes the loss for Conditional DETR.
The process happens in two steps:
1) we compute hungarian assignment between ground truth boxes and the outputs of the model
2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
"""
def __init__(self, num_classes, matcher, weight_dict, focal_alpha, losses):
""" Create the criterion.
Parameters:
num_classes: number of object categories, omitting the special no-object category
matcher: module able to compute a matching between targets and proposals
weight_dict: dict containing as key the names of the losses and as values their relative weight.
losses: list of all the losses to be applied. See get_loss for list of available losses.
focal_alpha: alpha in Focal Loss
"""
super().__init__()
self.num_classes = num_classes
self.matcher = matcher
self.weight_dict = weight_dict
self.losses = losses
self.focal_alpha = focal_alpha
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
"""Classification loss (Binary focal loss)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
"""
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(indices)
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
dtype=torch.int64, device=src_logits.device)
target_classes[idx] = target_classes_o
target_classes_onehot = torch.zeros([src_logits.shape[0], src_logits.shape[1], src_logits.shape[2]+1],
dtype=src_logits.dtype, layout=src_logits.layout, device=src_logits.device)
target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
target_classes_onehot = target_classes_onehot[:,:,:-1]
loss_ce = sigmoid_focal_loss(src_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) * src_logits.shape[1]
losses = {'loss_ce': loss_ce}
if log:
# TODO this should probably be a separate loss, not hacked in this one here
losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
return losses
@torch.no_grad()
def loss_cardinality(self, outputs, targets, indices, num_boxes):
""" Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
"""
pred_logits = outputs['pred_logits']
device = pred_logits.device
tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
# Count the number of predictions that are NOT "no-object" (which is the last class)
card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
losses = {'cardinality_error': card_err}
return losses
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
assert 'pred_boxes' in outputs
idx = self._get_src_permutation_idx(indices)
src_boxes = outputs['pred_boxes'][idx]
target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0)
loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')
losses = {}
losses['loss_bbox'] = loss_bbox.sum() / num_boxes
loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(
box_ops.box_cxcywh_to_xyxy(src_boxes),
box_ops.box_cxcywh_to_xyxy(target_boxes)))
losses['loss_giou'] = loss_giou.sum() / num_boxes
# calculate the x,y and h,w loss
with torch.no_grad():
losses['loss_xy'] = loss_bbox[..., :2].sum() / num_boxes
losses['loss_hw'] = loss_bbox[..., 2:].sum() / num_boxes
return losses
def loss_masks(self, outputs, targets, indices, num_boxes):
"""Compute the losses related to the masks: the focal loss and the dice loss.
targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
"""
assert "pred_masks" in outputs
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
src_masks = outputs["pred_masks"]
src_masks = src_masks[src_idx]
masks = [t["masks"] for t in targets]
# TODO use valid to mask invalid areas due to padding in loss
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
target_masks = target_masks.to(src_masks)
target_masks = target_masks[tgt_idx]
# upsample predictions to the target size
src_masks = interpolate(src_masks[:, None], size=target_masks.shape[-2:],
mode="bilinear", align_corners=False)
src_masks = src_masks[:, 0].flatten(1)
target_masks = target_masks.flatten(1)
target_masks = target_masks.view(src_masks.shape)
losses = {
"loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes),
"loss_dice": dice_loss(src_masks, target_masks, num_boxes),
}
return losses
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
src_idx = torch.cat([src for (src, _) in indices])
return batch_idx, src_idx
def _get_tgt_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
tgt_idx = torch.cat([tgt for (_, tgt) in indices])
return batch_idx, tgt_idx
def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
loss_map = {
'labels': self.loss_labels,
'cardinality': self.loss_cardinality,
'boxes': self.loss_boxes,
'masks': self.loss_masks,
}
assert loss in loss_map, f'do you really want to compute {loss} loss?'
return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)
def forward(self, outputs, targets, return_indices=False):
""" This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
return_indices: used for vis. if True, the layer0-5 indices will be returned as well.
"""
outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}
device=next(iter(outputs.values())).device
indices = self.matcher(outputs_without_aux, targets)
if return_indices:
indices0_copy = indices
indices_list = []
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_boxes = sum(len(t["labels"]) for t in targets)
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=device)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_boxes)
num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()
# Compute all the requested losses
losses = {}
# prepare for dn loss
dn_meta = outputs['dn_meta']
if self.training and dn_meta and 'output_known_lbs_bboxes' in dn_meta:
output_known_lbs_bboxes,single_pad, scalar = self.prep_for_dn(dn_meta)
dn_pos_idx = []
dn_neg_idx = []
for i in range(len(targets)):
if len(targets[i]['labels']) > 0:
t = torch.range(0, len(targets[i]['labels']) - 1).long().cuda()
t = t.unsqueeze(0).repeat(scalar, 1)
tgt_idx = t.flatten()
output_idx = (torch.tensor(range(scalar)) * single_pad).long().cuda().unsqueeze(1) + t
output_idx = output_idx.flatten()
else:
output_idx = tgt_idx = torch.tensor([]).long().cuda()
dn_pos_idx.append((output_idx, tgt_idx))
dn_neg_idx.append((output_idx + single_pad // 2, tgt_idx))
output_known_lbs_bboxes=dn_meta['output_known_lbs_bboxes']
l_dict = {}
for loss in self.losses:
kwargs = {}
if 'labels' in loss:
kwargs = {'log': False}
l_dict.update(self.get_loss(loss, output_known_lbs_bboxes, targets, dn_pos_idx, num_boxes*scalar,**kwargs))
l_dict = {k + f'_dn': v for k, v in l_dict.items()}
losses.update(l_dict)
else:
l_dict = dict()
l_dict['loss_bbox_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_giou_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_ce_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_xy_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_hw_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['cardinality_error_dn'] = torch.as_tensor(0.).to('cuda')
losses.update(l_dict)
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if 'aux_outputs' in outputs:
for idx, aux_outputs in enumerate(outputs['aux_outputs']):
indices = self.matcher(aux_outputs, targets)
if return_indices:
indices_list.append(indices)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_{idx}': v for k, v in l_dict.items()}
losses.update(l_dict)
if self.training and dn_meta and 'output_known_lbs_bboxes' in dn_meta:
aux_outputs_known = output_known_lbs_bboxes['aux_outputs'][idx]
l_dict={}
for loss in self.losses:
kwargs = {}
if 'labels' in loss:
kwargs = {'log': False}
l_dict.update(self.get_loss(loss, aux_outputs_known, targets, dn_pos_idx, num_boxes*scalar,
**kwargs))
l_dict = {k + f'_dn_{idx}': v for k, v in l_dict.items()}
losses.update(l_dict)
else:
l_dict = dict()
l_dict['loss_bbox_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_giou_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_ce_dn']=torch.as_tensor(0.).to('cuda')
l_dict['loss_xy_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['loss_hw_dn'] = torch.as_tensor(0.).to('cuda')
l_dict['cardinality_error_dn'] = torch.as_tensor(0.).to('cuda')
l_dict = {k + f'_{idx}': v for k, v in l_dict.items()}
losses.update(l_dict)
# interm_outputs loss
if 'interm_outputs' in outputs:
interm_outputs = outputs['interm_outputs']
indices = self.matcher(interm_outputs, targets)
if return_indices:
indices_list.append(indices)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, interm_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_interm': v for k, v in l_dict.items()}
losses.update(l_dict)
# enc output loss
if 'enc_outputs' in outputs:
for i, enc_outputs in enumerate(outputs['enc_outputs']):
indices = self.matcher(enc_outputs, targets)
if return_indices:
indices_list.append(indices)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, enc_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_enc_{i}': v for k, v in l_dict.items()}
losses.update(l_dict)
if return_indices:
indices_list.append(indices0_copy)
return losses, indices_list
return losses
def prep_for_dn(self,dn_meta):
output_known_lbs_bboxes = dn_meta['output_known_lbs_bboxes']
num_dn_groups,pad_size=dn_meta['num_dn_group'],dn_meta['pad_size']
assert pad_size % num_dn_groups==0
single_pad=pad_size//num_dn_groups
return output_known_lbs_bboxes,single_pad,num_dn_groups
class PostProcess(nn.Module):
""" This module converts the model's output into the format expected by the coco api"""
def __init__(self, num_select=100, nms_iou_threshold=-1) -> None:
super().__init__()
self.num_select = num_select
self.nms_iou_threshold = nms_iou_threshold
@torch.no_grad()
def forward(self, outputs, target_sizes, not_to_xyxy=False, test=False):
""" Perform the computation
Parameters:
outputs: raw outputs of the model
target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
For evaluation, this must be the original image size (before any data augmentation)
For visualization, this should be the image size after data augment, but before padding
"""
num_select = self.num_select
out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
assert len(out_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), num_select, dim=1)
scores = topk_values
topk_boxes = topk_indexes // out_logits.shape[2]
labels = topk_indexes % out_logits.shape[2]
if not_to_xyxy:
boxes = out_bbox
else:
boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
if test:
assert not not_to_xyxy
boxes[:,:,2:] = boxes[:,:,2:] - boxes[:,:,:2]
boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
boxes = boxes * scale_fct[:, None, :]
if self.nms_iou_threshold > 0:
item_indices = [nms(b, s, iou_threshold=self.nms_iou_threshold) for b,s in zip(boxes, scores)]
results = [{'scores': s[i], 'labels': l[i], 'boxes': b[i]} for s, l, b, i in zip(scores, labels, boxes, item_indices)]
else:
results = [{'scores': s, 'labels': l, 'boxes': b} for s, l, b in zip(scores, labels, boxes)]
return results
| @MODULE_BUILD_FUNCS.registe_with_name(module_name='dino') | 17 | 2023-12-04 00:27:58+00:00 | 16k |
girgle/DouZero_For_New_HLDDZ | test.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 QGraphicsView, QGraphicsScene, QGraphicsItem, QGraphicsPixmapItem, QInputDialog, QMessageBox
from PyQt5.QtGui import QPixmap, QIcon
from PyQt5.QtCore import QTime, QEventLoop
from MainWindow import Ui_Form
from douzero.env.game import GameEnv
from douzero.evaluation.deep_agent import DeepAgent
from cnocr import CnOcr | 11,932 | '''from GameHelper import GameHelper
import numpy as np
import cv2
from main import MyPyQT_Form as form
from collections import defaultdict
import GameHelper as gh
GeneralBtnPos = (268, 481, 1240, 255)
helper = GameHelper()
helper.ScreenZoomRate = 1.0
img = cv2.imread("chaojijiabei.png")
img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
result = helper.LocateOnScreen("chaojijiabei_btn", img=img, region=GeneralBtnPos)
print(result)'''
# -*- coding: utf-8 -*-
# Created by: Raf
# Modify by: Vincentzyx
ocr = CnOcr(det_model_name='en_PP-OCRv3_det', rec_model_name='en_PP-OCRv3',
cand_alphabet="12345678910JQKA") # 所有参数都使用默认值
| '''from GameHelper import GameHelper
import numpy as np
import cv2
from main import MyPyQT_Form as form
from collections import defaultdict
import GameHelper as gh
GeneralBtnPos = (268, 481, 1240, 255)
helper = GameHelper()
helper.ScreenZoomRate = 1.0
img = cv2.imread("chaojijiabei.png")
img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
result = helper.LocateOnScreen("chaojijiabei_btn", img=img, region=GeneralBtnPos)
print(result)'''
# -*- coding: utf-8 -*-
# Created by: Raf
# Modify by: Vincentzyx
ocr = CnOcr(det_model_name='en_PP-OCRv3_det', rec_model_name='en_PP-OCRv3',
cand_alphabet="12345678910JQKA") # 所有参数都使用默认值
| helper = GameHelper() | 0 | 2023-12-01 04:04:30+00:00 | 16k |
yongzhuo/MacroGPT-Pretrain | macro_gpt/ft_gpt/train.pt.add.py | [
{
"identifier": "CUDA_VISIBLE_DEVICES",
"path": "macro_gpt/ft_gpt/config_macrogpt_1b3_float32.py",
"snippet": "CUDA_VISIBLE_DEVICES = \"0\""
},
{
"identifier": "USE_TORCH",
"path": "macro_gpt/ft_gpt/config_macrogpt_1b3_float32.py",
"snippet": "USE_TORCH = \"1\""
},
{
"identifier"... | import random
import copy
import sys
import os
import bitsandbytes as bnb
import torch.nn as nn
import transformers
import torch
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import CUDA_VISIBLE_DEVICES, USE_TORCH, CPU_NUMS # from config
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from peft import (get_peft_model_state_dict, get_peft_model, LoraConfig)
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.modeling_utils import unwrap_model
from tensorboardX import SummaryWriter
from datasets import load_dataset
from macro_gpt.models.llama.modeling_llama import LlamaForCausalLM as LLMForCausalLM
from macro_gpt.models.llama.tokenization_llama import LlamaTokenizer as LLMTokenizer
from macro_gpt.models.llama.modeling_llama import LlamaConfig as LLMConfig
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import PATH_MODEL_PRETRAIN, DATA_PATH, MODEL_SAVE_DIR, REPO_ID
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import MICRO_BATCH_SIZE, BATCH_SIZE, GRADIENT_ACCUMULATION_STEPS
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import LEARNING_RATE, EPOCHS, SAVE_STEPS, VAL_SET_SIZE, TARGET_MODULES
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import IS_PARALLELIZABLE, MODEL_PARALLEL, USE_CACHE
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import MAX_LENGTH_Q, MAX_LENGTH_A, MAX_LENGTH_QA
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import LORA_DROPOUT, LORA_ALPHA, LORA_R
from macro_gpt.ft_gpt.config_macrogpt_1b3_float32 import PATH_MODEL_CONFIG, PATH_TOKENIZER_PRETRAIN | 13,109 | print_rank_0(out)
return out
def data_collator(batch):
def get_position_ids(seq, bos_token_id):
seq_length = len(seq)
position_ids = torch.arange(seq_length, dtype=torch.long).unsqueeze(0)
return position_ids
def get_masks(seq, special_ids=IDS_ORG):
""" padding-mask """
# mask until ID_SOP
attention_mask = torch.ones((1, len(seq), len(seq)))
attention_mask.tril_()
# ### 如果 padding-right, 也mask掉
# for idx, s in enumerate(seq):
# if s in special_ids:
# attention_mask[..., idx] = 1
attention_mask = (attention_mask < 0.5).bool()
return attention_mask
len_max_batch = [len(batch[i].get("input_ids")) + len(batch[i].get("labels")) + 1
for i in range(len(batch))]
len_max_batch = min(MAX_LENGTH_QA, max(len_max_batch))
batch_attention_mask = []
batch_position_ids = []
batch_input_ids = []
batch_labels = []
for ba in batch:
x, y = ba.get("input_ids"), ba.get("labels")
len_padding = len_max_batch - len(x) - len(y)
if tokenizer.padding_side and tokenizer.padding_side == "left":
labels = [-100] * len_padding + x + y
input_ids = [ID_PAD] * (len_padding) + x + y
else:
labels = x + y + [-100] * len_padding
input_ids = x + y + [ID_PAD] * (len_padding)
tensor_position_ids = get_position_ids(input_ids, bos_token_id=ID_SOP)
tensor_attention_mask = get_masks(input_ids, special_ids=IDS_ORG)
tensor_input_ids = torch.tensor(input_ids, dtype=torch.long)
tensor_labels = torch.tensor(labels, dtype=torch.long)
batch_attention_mask.append(tensor_attention_mask)
batch_position_ids.append(tensor_position_ids)
batch_input_ids.append(tensor_input_ids)
batch_labels.append(tensor_labels)
# print_rank_0(batch_attention_mask)
batch_attention_mask = torch.stack(batch_attention_mask)
batch_position_ids = torch.stack(batch_position_ids)
batch_input_ids = torch.stack(batch_input_ids)
batch_labels = torch.stack(batch_labels)
input_dict = {
# "full_attention_mask": copy.deepcopy(batch_attention_mask),
# "attention_mask": batch_attention_mask,
# "position_ids": batch_position_ids,
"input_ids": batch_input_ids,
"labels": batch_labels,
}
# print_rank_0(input_dict)
return input_dict
def dfs_file(path_dir):
"""
递归获取某个目录下的所有文件(所有层, 包括子目录)
Args:
path_dir[String]:, path of dir, eg. "/home/data"
Returns:
data[List]: data of input, eg. ["2020_01_08.txt"]
"""
path_files = []
for root, dirs, files in os.walk(path_dir): # 分别代表根目录、文件夹、文件
for file in files: # 遍历文件
file_path = os.path.join(root, file) # 获取文件绝对路径
path_files.append(file_path) # 将文件路径添加进列表
files = list(set(path_files))
files.sort() # the same list
return files
def print_rank_0(*args):
""" 只打印 0 号GPU的 """
# if torch.distributed.get_rank() == 0: # 一般用0，当然，可以选任意的rank保存。
# print(*args)
print(*args)
def local_rank_is_0():
""" 判断是哪台机子的 """
# flag = False
# if torch.distributed.get_rank() == 0:
# flag = True
# return flag
return True
# import torch.distributed as dist
# dist.init_process_group(backend='nccl')
# torch.distributed.init_process_group()
tokenizer = LLMTokenizer.from_pretrained(PATH_TOKENIZER_PRETRAIN)
# tokenizer.pad_token = tokenizer.eos_token
# tokenizer.padding_side = "left" # Allow batched inference
tokenizer.padding_side = "right" # Allow batched inference
# ID_gMASK = 64790
# ID_BOS = 64792
# ID_EOS = 64793
# ID_MASK = 64789
# ID_PAD = 2
ID_EOP = 2
ID_SOP = 1
ID_BOS = 1
ID_EOS = 2
ID_PAD = 0
IDS_ORG = [ID_PAD]
# { "<|endoftext|>": 50256,
# "### End": 50257,
# "### Instruction:": 50258,
# "### Response:\n": 50259
# }
model = LLMForCausalLM.from_pretrained(PATH_MODEL_CONFIG)
model.gradient_checkpointing_enable()
model.enable_input_require_grads()
model.is_parallelizable = IS_PARALLELIZABLE
model.model_parallel = MODEL_PARALLEL
| # !/usr/bin/python
# -*- coding: utf-8 -*-
# @time : 2023/3/5 21:04
# @author : Mo
# @function: macro-gpt
path_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
sys.path.append(path_root)
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:3072"
os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES
os.environ["USE_TORCH"] = USE_TORCH
os.environ["OMP_NUM_THREADS"] = CPU_NUMS # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = CPU_NUMS # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = CPU_NUMS # export MKL_NUM_THREADS=1
os.environ["VECLIB_MAXIMUM_THREADS"] = CPU_NUMS # export VECLIB_MAXIMUM_THREADS=1
os.environ["NUMEXPR_NUM_THREADS"] = CPU_NUMS # export NUMEXPR_NUM_THREADS=1
def save_model_state(model, config=None, model_save_dir="./", model_name="adapter_model.bin"):
""" 仅保存 有梯度 的 模型参数(推荐使用) """
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
# save config
if config:
config.save_pretrained(model_save_dir)
# config.to_dict()
# save model
path_model = os.path.join(model_save_dir, model_name)
# grad_params_dict = {k: v.to("cpu") for k, v in model.named_parameters()
# if v.requires_grad == True}
grad_params_dict = {k: v.to("cpu") for k, v in model.named_parameters()}
torch.save(grad_params_dict, path_model)
print_rank_0("******model_save_path is {}******".format(path_model))
def print_rank_0_named_parameters(model, use_print_rank_0_data=False):
""" 打印模型训练参数/数据类型信息 """
trainable_params = 0
all_param = 0
for name, param in model.named_parameters():
if use_print_rank_0_data:
print_rank_0((name, param.data.dtype, param.requires_grad, param.data))
else:
print_rank_0((name, param.data.dtype, param.requires_grad))
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
print_rank_0(f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}")
def prepare_model_for_half_training(model, output_embedding_layer_name="lm_head",
use_gradient_checkpointing=True, layer_norm_names=["layer_norm"]):
r"""
This method wrapps the entire protocol for preparing a model before running a training. This includes:
1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
head to fp32
Args:
model, (`transformers.PreTrainedModel`):
The loaded model from `transformers`
"""
# 不要使用 model.half(), 这样会先截取精度再训练了, 最初data就要保持half
for name, param in model.named_parameters():
# freeze base model's layers
# cast layer norm in fp32 for stability for 8bit models
if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
param.data = param.data.to(torch.float32)
elif output_embedding_layer_name in name: # lm_head也需要是tf.float32(最后一层)
param.data = param.data.to(torch.float32)
else:
param.data = param.data.to(torch.half)
if use_gradient_checkpointing:
# For backward compatibility
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
else:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
# enable gradient checkpointing for memory efficiency
model.gradient_checkpointing_enable()
return model
def generate_prompt(data_point, is_logger=False):
# sorry about the formatting disaster gotta move fast
# text_1 = f"指令：\n{data_point.get('instruction', '')}\n问：\n{data_point.get('input', '')}\n答：\n" \
# if data_point.get('input', '') else f"指令：\n{data_point.get('instruction', '')}\n答：\n"
# text_2 = f"{data_point.get('output', '')}"
text_a = data_point.get("a", "")
prompt_str_1 = text_a
# end with gMASK, <sop>
x = tokenizer.encode(prompt_str_1)
if len(x) > MAX_LENGTH_QA - 2:
x = x[:MAX_LENGTH_QA - 2]
if not x:
x = [ID_PAD, ID_EOS]
if x and x[-1] != ID_EOS:
x += [ID_EOS]
out = {"input_ids": x, "labels": []}
if is_logger:
print_rank_0(prompt_str_1)
print_rank_0(out)
return out
def data_collator(batch):
def get_position_ids(seq, bos_token_id):
seq_length = len(seq)
position_ids = torch.arange(seq_length, dtype=torch.long).unsqueeze(0)
return position_ids
def get_masks(seq, special_ids=IDS_ORG):
""" padding-mask """
# mask until ID_SOP
attention_mask = torch.ones((1, len(seq), len(seq)))
attention_mask.tril_()
# ### 如果 padding-right, 也mask掉
# for idx, s in enumerate(seq):
# if s in special_ids:
# attention_mask[..., idx] = 1
attention_mask = (attention_mask < 0.5).bool()
return attention_mask
len_max_batch = [len(batch[i].get("input_ids")) + len(batch[i].get("labels")) + 1
for i in range(len(batch))]
len_max_batch = min(MAX_LENGTH_QA, max(len_max_batch))
batch_attention_mask = []
batch_position_ids = []
batch_input_ids = []
batch_labels = []
for ba in batch:
x, y = ba.get("input_ids"), ba.get("labels")
len_padding = len_max_batch - len(x) - len(y)
if tokenizer.padding_side and tokenizer.padding_side == "left":
labels = [-100] * len_padding + x + y
input_ids = [ID_PAD] * (len_padding) + x + y
else:
labels = x + y + [-100] * len_padding
input_ids = x + y + [ID_PAD] * (len_padding)
tensor_position_ids = get_position_ids(input_ids, bos_token_id=ID_SOP)
tensor_attention_mask = get_masks(input_ids, special_ids=IDS_ORG)
tensor_input_ids = torch.tensor(input_ids, dtype=torch.long)
tensor_labels = torch.tensor(labels, dtype=torch.long)
batch_attention_mask.append(tensor_attention_mask)
batch_position_ids.append(tensor_position_ids)
batch_input_ids.append(tensor_input_ids)
batch_labels.append(tensor_labels)
# print_rank_0(batch_attention_mask)
batch_attention_mask = torch.stack(batch_attention_mask)
batch_position_ids = torch.stack(batch_position_ids)
batch_input_ids = torch.stack(batch_input_ids)
batch_labels = torch.stack(batch_labels)
input_dict = {
# "full_attention_mask": copy.deepcopy(batch_attention_mask),
# "attention_mask": batch_attention_mask,
# "position_ids": batch_position_ids,
"input_ids": batch_input_ids,
"labels": batch_labels,
}
# print_rank_0(input_dict)
return input_dict
def dfs_file(path_dir):
"""
递归获取某个目录下的所有文件(所有层, 包括子目录)
Args:
path_dir[String]:, path of dir, eg. "/home/data"
Returns:
data[List]: data of input, eg. ["2020_01_08.txt"]
"""
path_files = []
for root, dirs, files in os.walk(path_dir): # 分别代表根目录、文件夹、文件
for file in files: # 遍历文件
file_path = os.path.join(root, file) # 获取文件绝对路径
path_files.append(file_path) # 将文件路径添加进列表
files = list(set(path_files))
files.sort() # the same list
return files
def print_rank_0(*args):
""" 只打印 0 号GPU的 """
# if torch.distributed.get_rank() == 0: # 一般用0，当然，可以选任意的rank保存。
# print(*args)
print(*args)
def local_rank_is_0():
""" 判断是哪台机子的 """
# flag = False
# if torch.distributed.get_rank() == 0:
# flag = True
# return flag
return True
# import torch.distributed as dist
# dist.init_process_group(backend='nccl')
# torch.distributed.init_process_group()
tokenizer = LLMTokenizer.from_pretrained(PATH_TOKENIZER_PRETRAIN)
# tokenizer.pad_token = tokenizer.eos_token
# tokenizer.padding_side = "left" # Allow batched inference
tokenizer.padding_side = "right" # Allow batched inference
# ID_gMASK = 64790
# ID_BOS = 64792
# ID_EOS = 64793
# ID_MASK = 64789
# ID_PAD = 2
ID_EOP = 2
ID_SOP = 1
ID_BOS = 1
ID_EOS = 2
ID_PAD = 0
IDS_ORG = [ID_PAD]
# { "<|endoftext|>": 50256,
# "### End": 50257,
# "### Instruction:": 50258,
# "### Response:\n": 50259
# }
model = LLMForCausalLM.from_pretrained(PATH_MODEL_CONFIG)
model.gradient_checkpointing_enable()
model.enable_input_require_grads()
model.is_parallelizable = IS_PARALLELIZABLE
model.model_parallel = MODEL_PARALLEL | model.config.use_cache = USE_CACHE | 20 | 2023-11-30 12:39:19+00:00 | 16k |
owkin/fedeca | fedeca/scripts/dp_logreg.py | [
{
"identifier": "TorchDPFedAvgAlgo",
"path": "fedeca/algorithms/torch_dp_fed_avg_algo.py",
"snippet": "class TorchDPFedAvgAlgo(TorchFedAvgAlgo):\n \"\"\"To be inherited.\n\n Wraps the necessary operation so a torch model can be trained in the Federated\n Averaging strategy using DP.\n \"\"\"... | import sys
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from itertools import product
from sklearn.metrics import accuracy_score
from substrafl.algorithms.pytorch import TorchNewtonRaphsonAlgo
from substrafl.model_loading import download_algo_state
from substrafl.strategies import FedAvg, NewtonRaphson
from torch.optim import SGD
from fedeca.algorithms.torch_dp_fed_avg_algo import TorchDPFedAvgAlgo
from fedeca.fedeca_core import LogisticRegressionTorch
from fedeca.utils import (
Experiment,
make_accuracy_function,
make_substrafl_torch_dataset_class,
)
from fedeca.utils.survival_utils import CoxData, make_categorical | 11,471 | """Runs the propensity model training part with DP."""
if __name__ == "__main__":
epsilons = [0.1, 1.0, 5.0, 10.0][::-1]
deltas = [10 ** (-i) for i in range(1, 3)]
START_SEED = 42
NDIM = 10
NUM_ROUNDS = 10
NUM_UPDATES = 100
N_REPETITIONS = 5
BACKEND_TYPE = "subprocess"
BATCH_SIZE = 32
na_proportion = 0.0
seeds = np.arange(START_SEED, START_SEED + N_REPETITIONS).tolist()
rng = np.random.default_rng(seeds[0])
# Generating data with strong linear relationship
simu_coxreg = CoxData(
n_samples=300,
ndim=NDIM,
prop_treated=0.5,
propensity="linear",
dtype="float32",
overlap=100.0,
seed=rng,
random_censoring=True,
censoring_factor=0.3,
standardize_features=False,
)
X, T, C, treated, _ = simu_coxreg.generate_data()
# Will make first columns to be categorical
| """Runs the propensity model training part with DP."""
if __name__ == "__main__":
epsilons = [0.1, 1.0, 5.0, 10.0][::-1]
deltas = [10 ** (-i) for i in range(1, 3)]
START_SEED = 42
NDIM = 10
NUM_ROUNDS = 10
NUM_UPDATES = 100
N_REPETITIONS = 5
BACKEND_TYPE = "subprocess"
BATCH_SIZE = 32
na_proportion = 0.0
seeds = np.arange(START_SEED, START_SEED + N_REPETITIONS).tolist()
rng = np.random.default_rng(seeds[0])
# Generating data with strong linear relationship
simu_coxreg = CoxData(
n_samples=300,
ndim=NDIM,
prop_treated=0.5,
propensity="linear",
dtype="float32",
overlap=100.0,
seed=rng,
random_censoring=True,
censoring_factor=0.3,
standardize_features=False,
)
X, T, C, treated, _ = simu_coxreg.generate_data()
# Will make first columns to be categorical | Xcat, Xcont = make_categorical(X, up_to=0) | 6 | 2023-11-27 18:01:37+00:00 | 16k |
aliyun/pai-python-sdk | pai/modelscope/model.py | [
{
"identifier": "ImageLabel",
"path": "pai/api/image.py",
"snippet": "class ImageLabel(object):\n \"\"\"Image Label Class.\"\"\"\n\n # Unofficial Image Label\n UNOFFICIAL_LABEL = \"system.official=false\"\n # Official Image Label\n OFFICIAL_LABEL = \"system.official=true\"\n\n # PAI Im... | import logging
from typing import Any, Dict, List, Optional, Union
from ..api.image import ImageLabel
from ..common.utils import to_semantic_version
from ..model import (
DefaultServiceConfig,
ModelBase,
ResourceConfig,
container_serving_spec,
)
from ..serializers import SerializerBase
from ..session import Session, get_default_session | 11,105 | # 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__)
class ModelScopeModel(ModelBase):
"""A ModelScope ``Model`` that can be deployed in PAI to create a prediction service.
A ModelScopeModel instance includes the model artifact path and information on how to create
prediction service in PAI. By calling the deploy() method, a prediction service is created in
PAI and a :class:`pai.predictor.Predictor` instance is returned that can be used to make
prediction to the service.
Example::
# Initialize a ModelScopeModel.
m: ModelScopeModel = ModelScopeModel(
model_data="oss://bucket-name/path/to/model",
source_dir="./serving/src/",
command="python serving.py",
modelscope_version="latest",
)
# Deploy the model to create an online prediction service.
p: Predictor = m.deploy(
service_name="ms_bert_serving",
instance_type="ecs.gn6i-c4g1.xlarge",
instance_count=1,
options={
"metadata.rpc.keepalive": 5000000,
"features.eas.aliyun.com/extra-ephemeral-storage":"40Gi",
},
)
# Make prediction by sending the data to the online prediction service.
result = p.predict("weather is good")
"""
def __init__(
self,
model_data: Optional[str] = None,
image_uri: Optional[str] = None,
modelscope_version: Optional[str] = None,
command: Optional[str] = None,
source_dir: Optional[str] = None,
git_config: Optional[Dict[str, str]] = 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,
| # 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__)
class ModelScopeModel(ModelBase):
"""A ModelScope ``Model`` that can be deployed in PAI to create a prediction service.
A ModelScopeModel instance includes the model artifact path and information on how to create
prediction service in PAI. By calling the deploy() method, a prediction service is created in
PAI and a :class:`pai.predictor.Predictor` instance is returned that can be used to make
prediction to the service.
Example::
# Initialize a ModelScopeModel.
m: ModelScopeModel = ModelScopeModel(
model_data="oss://bucket-name/path/to/model",
source_dir="./serving/src/",
command="python serving.py",
modelscope_version="latest",
)
# Deploy the model to create an online prediction service.
p: Predictor = m.deploy(
service_name="ms_bert_serving",
instance_type="ecs.gn6i-c4g1.xlarge",
instance_count=1,
options={
"metadata.rpc.keepalive": 5000000,
"features.eas.aliyun.com/extra-ephemeral-storage":"40Gi",
},
)
# Make prediction by sending the data to the online prediction service.
result = p.predict("weather is good")
"""
def __init__(
self,
model_data: Optional[str] = None,
image_uri: Optional[str] = None,
modelscope_version: Optional[str] = None,
command: Optional[str] = None,
source_dir: Optional[str] = None,
git_config: Optional[Dict[str, str]] = 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, | 7 | 2023-12-01 01:40:12+00:00 | 16k |
JunMa11/UHNSeg-Quiz | nnunetv2/inference/predict_from_raw_data.py | [
{
"identifier": "default_num_processes",
"path": "nnunetv2/configuration.py",
"snippet": "ANISO_THRESHOLD = 3 # determines when a sample is considered anisotropic (3 means that the spacing in the low"
},
{
"identifier": "PreprocessAdapterFromNpy",
"path": "nnunetv2/inference/data_iterators.... | import inspect
import multiprocessing
import os
import traceback
import numpy as np
import torch
import nnunetv2
import argparse
import multiprocessing
import argparse
import multiprocessing
from copy import deepcopy
from time import sleep
from typing import Tuple, Union, List, Optional
from acvl_utils.cropping_and_padding.padding import pad_nd_image
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
from batchgenerators.utilities.file_and_folder_operations import load_json, join, isfile, maybe_mkdir_p, isdir, subdirs, \
save_json
from torch import nn
from torch._dynamo import OptimizedModule
from torch.nn.parallel import DistributedDataParallel
from tqdm import tqdm
from nnunetv2.configuration import default_num_processes
from nnunetv2.inference.data_iterators import PreprocessAdapterFromNpy, preprocessing_iterator_fromfiles, \
preprocessing_iterator_fromnpy
from nnunetv2.inference.export_prediction import export_prediction_from_logits, \
convert_predicted_logits_to_segmentation_with_correct_shape
from nnunetv2.inference.sliding_window_prediction import compute_gaussian, \
compute_steps_for_sliding_window
from nnunetv2.utilities.file_path_utilities import get_output_folder, check_workers_alive_and_busy
from nnunetv2.utilities.find_class_by_name import recursive_find_python_class
from nnunetv2.utilities.helpers import empty_cache, dummy_context
from nnunetv2.utilities.json_export import recursive_fix_for_json_export
from nnunetv2.utilities.label_handling.label_handling import determine_num_input_channels
from nnunetv2.utilities.plans_handling.plans_handler import PlansManager, ConfigurationManager
from nnunetv2.utilities.utils import create_lists_from_splitted_dataset_folder
from nnunetv2.paths import nnUNet_results, nnUNet_raw
from nnunetv2.imageio.simpleitk_reader_writer import SimpleITKIO | 11,229 | # mta = SingleThreadedAugmenter(ppa, None)
# else:
# mta = MultiThreadedAugmenter(ppa, None, num_processes, 1, None, pin_memory=pin_memory)
# return mta
def get_data_iterator_from_raw_npy_data(self,
image_or_list_of_images: Union[np.ndarray, List[np.ndarray]],
segs_from_prev_stage_or_list_of_segs_from_prev_stage: Union[None,
np.ndarray,
List[
np.ndarray]],
properties_or_list_of_properties: Union[dict, List[dict]],
truncated_ofname: Union[str, List[str], None],
num_processes: int = 3):
list_of_images = [image_or_list_of_images] if not isinstance(image_or_list_of_images, list) else \
image_or_list_of_images
if isinstance(segs_from_prev_stage_or_list_of_segs_from_prev_stage, np.ndarray):
segs_from_prev_stage_or_list_of_segs_from_prev_stage = [
segs_from_prev_stage_or_list_of_segs_from_prev_stage]
if isinstance(truncated_ofname, str):
truncated_ofname = [truncated_ofname]
if isinstance(properties_or_list_of_properties, dict):
properties_or_list_of_properties = [properties_or_list_of_properties]
num_processes = min(num_processes, len(list_of_images))
pp = preprocessing_iterator_fromnpy(
list_of_images,
segs_from_prev_stage_or_list_of_segs_from_prev_stage,
properties_or_list_of_properties,
truncated_ofname,
self.plans_manager,
self.dataset_json,
self.configuration_manager,
num_processes,
self.device.type == 'cuda',
self.verbose_preprocessing
)
return pp
def predict_from_list_of_npy_arrays(self,
image_or_list_of_images: Union[np.ndarray, List[np.ndarray]],
segs_from_prev_stage_or_list_of_segs_from_prev_stage: Union[None,
np.ndarray,
List[
np.ndarray]],
properties_or_list_of_properties: Union[dict, List[dict]],
truncated_ofname: Union[str, List[str], None],
num_processes: int = 3,
save_probabilities: bool = False,
num_processes_segmentation_export: int = default_num_processes):
iterator = self.get_data_iterator_from_raw_npy_data(image_or_list_of_images,
segs_from_prev_stage_or_list_of_segs_from_prev_stage,
properties_or_list_of_properties,
truncated_ofname,
num_processes)
return self.predict_from_data_iterator(iterator, save_probabilities, num_processes_segmentation_export)
def predict_from_data_iterator(self,
data_iterator,
save_probabilities: bool = False,
num_processes_segmentation_export: int = default_num_processes):
"""
each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properties' keys!
If 'ofile' is None, the result will be returned instead of written to a file
"""
with multiprocessing.get_context("spawn").Pool(num_processes_segmentation_export) as export_pool:
worker_list = [i for i in export_pool._pool]
r = []
for preprocessed in data_iterator:
data = preprocessed['data']
if isinstance(data, str):
delfile = data
data = torch.from_numpy(np.load(data))
os.remove(delfile)
ofile = preprocessed['ofile']
if ofile is not None:
print(f'\nPredicting {os.path.basename(ofile)}:')
else:
print(f'\nPredicting image of shape {data.shape}:')
print(f'perform_everything_on_gpu: {self.perform_everything_on_gpu}')
properties = preprocessed['data_properties']
# let's not get into a runaway situation where the GPU predicts so fast that the disk has to b swamped with
# npy files
proceed = not check_workers_alive_and_busy(export_pool, worker_list, r, allowed_num_queued=2)
while not proceed:
# print('sleeping')
sleep(0.1)
proceed = not check_workers_alive_and_busy(export_pool, worker_list, r, allowed_num_queued=2)
prediction = self.predict_logits_from_preprocessed_data(data).cpu()
if ofile is not None:
# this needs to go into background processes
# export_prediction_from_logits(prediction, properties, configuration_manager, plans_manager,
# dataset_json, ofile, save_probabilities)
print('sending off prediction to background worker for resampling and export')
r.append(
export_pool.starmap_async(
export_prediction_from_logits,
((prediction, properties, self.configuration_manager, self.plans_manager,
self.dataset_json, ofile, save_probabilities),)
)
)
else:
# convert_predicted_logits_to_segmentation_with_correct_shape(prediction, plans_manager,
# configuration_manager, label_manager,
# properties,
# save_probabilities)
print('sending off prediction to background worker for resampling')
r.append(
export_pool.starmap_async(
|
class nnUNetPredictor(object):
def __init__(self,
tile_step_size: float = 0.5,
use_gaussian: bool = True,
use_mirroring: bool = True,
perform_everything_on_gpu: bool = True,
device: torch.device = torch.device('cuda'),
verbose: bool = False,
verbose_preprocessing: bool = False,
allow_tqdm: bool = True):
self.verbose = verbose
self.verbose_preprocessing = verbose_preprocessing
self.allow_tqdm = allow_tqdm
self.plans_manager, self.configuration_manager, self.list_of_parameters, self.network, self.dataset_json, \
self.trainer_name, self.allowed_mirroring_axes, self.label_manager = None, None, None, None, None, None, None, None
self.tile_step_size = tile_step_size
self.use_gaussian = use_gaussian
self.use_mirroring = use_mirroring
if device.type == 'cuda':
# device = torch.device(type='cuda', index=0) # set the desired GPU with CUDA_VISIBLE_DEVICES!
# why would I ever want to do that. Stupid dobby. This kills DDP inference...
pass
if device.type != 'cuda':
print(f'perform_everything_on_gpu=True is only supported for cuda devices! Setting this to False')
perform_everything_on_gpu = False
self.device = device
self.perform_everything_on_gpu = perform_everything_on_gpu
def initialize_from_trained_model_folder(self, model_training_output_dir: str,
use_folds: Union[Tuple[Union[int, str]], None],
checkpoint_name: str = 'checkpoint_final.pth'):
"""
This is used when making predictions with a trained model
"""
if use_folds is None:
use_folds = nnUNetPredictor.auto_detect_available_folds(model_training_output_dir, checkpoint_name)
dataset_json = load_json(join(model_training_output_dir, 'dataset.json'))
plans = load_json(join(model_training_output_dir, 'plans.json'))
plans_manager = PlansManager(plans)
if isinstance(use_folds, str):
use_folds = [use_folds]
parameters = []
for i, f in enumerate(use_folds):
f = int(f) if f != 'all' else f
checkpoint = torch.load(join(model_training_output_dir, f'fold_{f}', checkpoint_name),
map_location=torch.device('cpu'))
if i == 0:
trainer_name = checkpoint['trainer_name']
configuration_name = checkpoint['init_args']['configuration']
inference_allowed_mirroring_axes = checkpoint['inference_allowed_mirroring_axes'] if \
'inference_allowed_mirroring_axes' in checkpoint.keys() else None
parameters.append(checkpoint['network_weights'])
configuration_manager = plans_manager.get_configuration(configuration_name)
# restore network
num_input_channels = determine_num_input_channels(plans_manager, configuration_manager, dataset_json)
trainer_class = recursive_find_python_class(join(nnunetv2.__path__[0], "training", "nnUNetTrainer"),
trainer_name, 'nnunetv2.training.nnUNetTrainer')
network = trainer_class.build_network_architecture(plans_manager, dataset_json, configuration_manager,
num_input_channels, enable_deep_supervision=False)
self.plans_manager = plans_manager
self.configuration_manager = configuration_manager
self.list_of_parameters = parameters
self.network = network
self.dataset_json = dataset_json
self.trainer_name = trainer_name
self.allowed_mirroring_axes = inference_allowed_mirroring_axes
self.label_manager = plans_manager.get_label_manager(dataset_json)
if ('nnUNet_compile' in os.environ.keys()) and (os.environ['nnUNet_compile'].lower() in ('true', '1', 't')) \
and not isinstance(self.network, OptimizedModule):
print('compiling network')
self.network = torch.compile(self.network)
def manual_initialization(self, network: nn.Module, plans_manager: PlansManager,
configuration_manager: ConfigurationManager, parameters: Optional[List[dict]],
dataset_json: dict, trainer_name: str,
inference_allowed_mirroring_axes: Optional[Tuple[int, ...]]):
"""
This is used by the nnUNetTrainer to initialize nnUNetPredictor for the final validation
"""
self.plans_manager = plans_manager
self.configuration_manager = configuration_manager
self.list_of_parameters = parameters
self.network = network
self.dataset_json = dataset_json
self.trainer_name = trainer_name
self.allowed_mirroring_axes = inference_allowed_mirroring_axes
self.label_manager = plans_manager.get_label_manager(dataset_json)
allow_compile = True
allow_compile = allow_compile and ('nnUNet_compile' in os.environ.keys()) and (os.environ['nnUNet_compile'].lower() in ('true', '1', 't'))
allow_compile = allow_compile and not isinstance(self.network, OptimizedModule)
if isinstance(self.network, DistributedDataParallel):
allow_compile = allow_compile and isinstance(self.network.module, OptimizedModule)
if allow_compile:
print('compiling network')
self.network = torch.compile(self.network)
@staticmethod
def auto_detect_available_folds(model_training_output_dir, checkpoint_name):
print('use_folds is None, attempting to auto detect available folds')
fold_folders = subdirs(model_training_output_dir, prefix='fold_', join=False)
fold_folders = [i for i in fold_folders if i != 'fold_all']
fold_folders = [i for i in fold_folders if isfile(join(model_training_output_dir, i, checkpoint_name))]
use_folds = [int(i.split('_')[-1]) for i in fold_folders]
print(f'found the following folds: {use_folds}')
return use_folds
def _manage_input_and_output_lists(self, list_of_lists_or_source_folder: Union[str, List[List[str]]],
output_folder_or_list_of_truncated_output_files: Union[None, str, List[str]],
folder_with_segs_from_prev_stage: str = None,
overwrite: bool = True,
part_id: int = 0,
num_parts: int = 1,
save_probabilities: bool = False):
if isinstance(list_of_lists_or_source_folder, str):
list_of_lists_or_source_folder = create_lists_from_splitted_dataset_folder(list_of_lists_or_source_folder,
self.dataset_json['file_ending'])
print(f'There are {len(list_of_lists_or_source_folder)} cases in the source folder')
list_of_lists_or_source_folder = list_of_lists_or_source_folder[part_id::num_parts]
caseids = [os.path.basename(i[0])[:-(len(self.dataset_json['file_ending']) + 5)] for i in
list_of_lists_or_source_folder]
print(
f'I am process {part_id} out of {num_parts} (max process ID is {num_parts - 1}, we start counting with 0!)')
print(f'There are {len(caseids)} cases that I would like to predict')
if isinstance(output_folder_or_list_of_truncated_output_files, str):
output_filename_truncated = [join(output_folder_or_list_of_truncated_output_files, i) for i in caseids]
else:
output_filename_truncated = output_folder_or_list_of_truncated_output_files
seg_from_prev_stage_files = [join(folder_with_segs_from_prev_stage, i + self.dataset_json['file_ending']) if
folder_with_segs_from_prev_stage is not None else None for i in caseids]
# remove already predicted files form the lists
if not overwrite and output_filename_truncated is not None:
tmp = [isfile(i + self.dataset_json['file_ending']) for i in output_filename_truncated]
if save_probabilities:
tmp2 = [isfile(i + '.npz') for i in output_filename_truncated]
tmp = [i and j for i, j in zip(tmp, tmp2)]
not_existing_indices = [i for i, j in enumerate(tmp) if not j]
output_filename_truncated = [output_filename_truncated[i] for i in not_existing_indices]
list_of_lists_or_source_folder = [list_of_lists_or_source_folder[i] for i in not_existing_indices]
seg_from_prev_stage_files = [seg_from_prev_stage_files[i] for i in not_existing_indices]
print(f'overwrite was set to {overwrite}, so I am only working on cases that haven\'t been predicted yet. '
f'That\'s {len(not_existing_indices)} cases.')
return list_of_lists_or_source_folder, output_filename_truncated, seg_from_prev_stage_files
def predict_from_files(self,
list_of_lists_or_source_folder: Union[str, List[List[str]]],
output_folder_or_list_of_truncated_output_files: Union[str, None, List[str]],
save_probabilities: bool = False,
overwrite: bool = True,
num_processes_preprocessing: int = default_num_processes,
num_processes_segmentation_export: int = default_num_processes,
folder_with_segs_from_prev_stage: str = None,
num_parts: int = 1,
part_id: int = 0):
"""
This is nnU-Net's default function for making predictions. It works best for batch predictions
(predicting many images at once).
"""
if isinstance(output_folder_or_list_of_truncated_output_files, str):
output_folder = output_folder_or_list_of_truncated_output_files
elif isinstance(output_folder_or_list_of_truncated_output_files, list):
output_folder = os.path.dirname(output_folder_or_list_of_truncated_output_files[0])
else:
output_folder = None
########################
# let's store the input arguments so that its clear what was used to generate the prediction
if output_folder is not None:
my_init_kwargs = {}
for k in inspect.signature(self.predict_from_files).parameters.keys():
my_init_kwargs[k] = locals()[k]
my_init_kwargs = deepcopy(
my_init_kwargs) # let's not unintentionally change anything in-place. Take this as a
recursive_fix_for_json_export(my_init_kwargs)
maybe_mkdir_p(output_folder)
save_json(my_init_kwargs, join(output_folder, 'predict_from_raw_data_args.json'))
# we need these two if we want to do things with the predictions like for example apply postprocessing
save_json(self.dataset_json, join(output_folder, 'dataset.json'), sort_keys=False)
save_json(self.plans_manager.plans, join(output_folder, 'plans.json'), sort_keys=False)
#######################
# check if we need a prediction from the previous stage
if self.configuration_manager.previous_stage_name is not None:
assert folder_with_segs_from_prev_stage is not None, \
f'The requested configuration is a cascaded network. It requires the segmentations of the previous ' \
f'stage ({self.configuration_manager.previous_stage_name}) as input. Please provide the folder where' \
f' they are located via folder_with_segs_from_prev_stage'
# sort out input and output filenames
list_of_lists_or_source_folder, output_filename_truncated, seg_from_prev_stage_files = \
self._manage_input_and_output_lists(list_of_lists_or_source_folder,
output_folder_or_list_of_truncated_output_files,
folder_with_segs_from_prev_stage, overwrite, part_id, num_parts,
save_probabilities)
if len(list_of_lists_or_source_folder) == 0:
return
data_iterator = self._internal_get_data_iterator_from_lists_of_filenames(list_of_lists_or_source_folder,
seg_from_prev_stage_files,
output_filename_truncated,
num_processes_preprocessing)
return self.predict_from_data_iterator(data_iterator, save_probabilities, num_processes_segmentation_export)
def _internal_get_data_iterator_from_lists_of_filenames(self,
input_list_of_lists: List[List[str]],
seg_from_prev_stage_files: Union[List[str], None],
output_filenames_truncated: Union[List[str], None],
num_processes: int):
return preprocessing_iterator_fromfiles(input_list_of_lists, seg_from_prev_stage_files,
output_filenames_truncated, self.plans_manager, self.dataset_json,
self.configuration_manager, num_processes, self.device.type == 'cuda',
self.verbose_preprocessing)
# preprocessor = self.configuration_manager.preprocessor_class(verbose=self.verbose_preprocessing)
# # hijack batchgenerators, yo
# # we use the multiprocessing of the batchgenerators dataloader to handle all the background worker stuff. This
# # way we don't have to reinvent the wheel here.
# num_processes = max(1, min(num_processes, len(input_list_of_lists)))
# ppa = PreprocessAdapter(input_list_of_lists, seg_from_prev_stage_files, preprocessor,
# output_filenames_truncated, self.plans_manager, self.dataset_json,
# self.configuration_manager, num_processes)
# if num_processes == 0:
# mta = SingleThreadedAugmenter(ppa, None)
# else:
# mta = MultiThreadedAugmenter(ppa, None, num_processes, 1, None, pin_memory=pin_memory)
# return mta
def get_data_iterator_from_raw_npy_data(self,
image_or_list_of_images: Union[np.ndarray, List[np.ndarray]],
segs_from_prev_stage_or_list_of_segs_from_prev_stage: Union[None,
np.ndarray,
List[
np.ndarray]],
properties_or_list_of_properties: Union[dict, List[dict]],
truncated_ofname: Union[str, List[str], None],
num_processes: int = 3):
list_of_images = [image_or_list_of_images] if not isinstance(image_or_list_of_images, list) else \
image_or_list_of_images
if isinstance(segs_from_prev_stage_or_list_of_segs_from_prev_stage, np.ndarray):
segs_from_prev_stage_or_list_of_segs_from_prev_stage = [
segs_from_prev_stage_or_list_of_segs_from_prev_stage]
if isinstance(truncated_ofname, str):
truncated_ofname = [truncated_ofname]
if isinstance(properties_or_list_of_properties, dict):
properties_or_list_of_properties = [properties_or_list_of_properties]
num_processes = min(num_processes, len(list_of_images))
pp = preprocessing_iterator_fromnpy(
list_of_images,
segs_from_prev_stage_or_list_of_segs_from_prev_stage,
properties_or_list_of_properties,
truncated_ofname,
self.plans_manager,
self.dataset_json,
self.configuration_manager,
num_processes,
self.device.type == 'cuda',
self.verbose_preprocessing
)
return pp
def predict_from_list_of_npy_arrays(self,
image_or_list_of_images: Union[np.ndarray, List[np.ndarray]],
segs_from_prev_stage_or_list_of_segs_from_prev_stage: Union[None,
np.ndarray,
List[
np.ndarray]],
properties_or_list_of_properties: Union[dict, List[dict]],
truncated_ofname: Union[str, List[str], None],
num_processes: int = 3,
save_probabilities: bool = False,
num_processes_segmentation_export: int = default_num_processes):
iterator = self.get_data_iterator_from_raw_npy_data(image_or_list_of_images,
segs_from_prev_stage_or_list_of_segs_from_prev_stage,
properties_or_list_of_properties,
truncated_ofname,
num_processes)
return self.predict_from_data_iterator(iterator, save_probabilities, num_processes_segmentation_export)
def predict_from_data_iterator(self,
data_iterator,
save_probabilities: bool = False,
num_processes_segmentation_export: int = default_num_processes):
"""
each element returned by data_iterator must be a dict with 'data', 'ofile' and 'data_properties' keys!
If 'ofile' is None, the result will be returned instead of written to a file
"""
with multiprocessing.get_context("spawn").Pool(num_processes_segmentation_export) as export_pool:
worker_list = [i for i in export_pool._pool]
r = []
for preprocessed in data_iterator:
data = preprocessed['data']
if isinstance(data, str):
delfile = data
data = torch.from_numpy(np.load(data))
os.remove(delfile)
ofile = preprocessed['ofile']
if ofile is not None:
print(f'\nPredicting {os.path.basename(ofile)}:')
else:
print(f'\nPredicting image of shape {data.shape}:')
print(f'perform_everything_on_gpu: {self.perform_everything_on_gpu}')
properties = preprocessed['data_properties']
# let's not get into a runaway situation where the GPU predicts so fast that the disk has to b swamped with
# npy files
proceed = not check_workers_alive_and_busy(export_pool, worker_list, r, allowed_num_queued=2)
while not proceed:
# print('sleeping')
sleep(0.1)
proceed = not check_workers_alive_and_busy(export_pool, worker_list, r, allowed_num_queued=2)
prediction = self.predict_logits_from_preprocessed_data(data).cpu()
if ofile is not None:
# this needs to go into background processes
# export_prediction_from_logits(prediction, properties, configuration_manager, plans_manager,
# dataset_json, ofile, save_probabilities)
print('sending off prediction to background worker for resampling and export')
r.append(
export_pool.starmap_async(
export_prediction_from_logits,
((prediction, properties, self.configuration_manager, self.plans_manager,
self.dataset_json, ofile, save_probabilities),)
)
)
else:
# convert_predicted_logits_to_segmentation_with_correct_shape(prediction, plans_manager,
# configuration_manager, label_manager,
# properties,
# save_probabilities)
print('sending off prediction to background worker for resampling')
r.append(
export_pool.starmap_async( | convert_predicted_logits_to_segmentation_with_correct_shape, ( | 5 | 2023-12-04 19:43:14+00:00 | 16k |
Zuricho/chroma_pipeline | chroma/layers/structure/protein_graph.py | [
{
"identifier": "Protein",
"path": "chroma/data/protein.py",
"snippet": "class Protein:\n \"\"\"\n Protein: A utility class for managing proteins within the Chroma ecosystem.\n\n The Protein class offers a suite of methods for loading, saving, transforming, and viewing protein structures\n a... | import json
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
from chroma.data.protein import Protein
from chroma.layers import graph
from chroma.layers.basic import FourierFeaturization, PositionalEncoding
from chroma.layers.structure import backbone, geometry, transforms | 12,246 | for i, layer in enumerate(self.edge_layers):
key = json.dumps(self.edge_features[i])
tensor = torch.tensor(param_dictionary[key], dtype=torch.float32)
tensor = tensor.view(1, 1, -1)
self.register_buffer(f"edge_means_{i}", tensor)
return
def _reference_stats(self, reference_pdb):
X, C, _ = Protein.from_PDBID(reference_pdb).to_XCS()
stats_dict = self._feature_stats(X, C)
return stats_dict
def _feature_stats(self, X, C, verbose=False, center=False):
mask_i = chain_map_to_mask(C)
edge_idx, mask_ij = self.graph_builder(X, C)
def _masked_stats(feature, mask, dims, verbose=False):
mask = mask.unsqueeze(-1)
feature = mask * feature
sum_mask = mask.sum()
mean = feature.sum(dims, keepdim=True) / sum_mask
var = torch.sum(mask * (feature - mean) ** 2, dims) / sum_mask
std = torch.sqrt(var)
mean = mean.view(-1)
std = std.view(-1)
if verbose:
frac = (100.0 * std ** 2 / (mean ** 2 + std ** 2)).type(torch.int32)
print(f"Fraction of raw variance: {frac}")
return mean, std
# Collect statistics
stats_dict = {}
# Aggregate node layers
for i, layer in enumerate(self.node_layers):
node_h = layer(X, edge_idx, C)
if center:
node_h = node_h - self.__getattr__(f"node_means_{i}")
mean, std = _masked_stats(node_h, mask_i, dims=[0, 1])
# Store in dictionary
key = json.dumps(self.node_features[i])
stats_dict[key] = mean.tolist()
# Aggregate node layers
for i, layer in enumerate(self.edge_layers):
edge_h = layer(X, edge_idx, C)
if center:
edge_h = edge_h - self.__getattr__(f"edge_means_{i}")
mean, std = _masked_stats(edge_h, mask_ij, dims=[0, 1, 2])
# Store in dictionary
key = json.dumps(self.edge_features[i])
stats_dict[key] = mean.tolist()
# Round to small number of decimal places
stats_dict = {k: [round(f, 3) for f in v] for k, v in stats_dict.items()}
return stats_dict
class ProteinGraph(nn.Module):
"""Build a graph topology given a protein backbone.
Args:
num_neighbors (int): Maximum number of neighbors in the graph.
distance_atom_type (int): Atom type for computing residue-residue
distances for graph construction. Negative values will specify
centroid across atom types. Default is `-1` (centroid).
cutoff (float): Cutoff distance for graph construction. If not None,
mask any edges further than this cutoff. Default is `None`.
mask_interfaces (Boolean): Restrict connections only to within chains,
excluding-between chain interactions. Default is `False`.
criterion (string, optional): Method used for building graph from distances.
Currently supported methods are `{knn, random_log, random_linear}`.
Default is `knn`.
random_alpha (float, optional): Length scale parameter for random graph
generation. Default is 3.
random_temperature (float, optional): Temperature parameter for
random graph sampling. Between 0 and 1 this value will interpolate
between a normal k-NN graph and sampling from the graph generation
process. Default is 1.0.
Inputs:
X (torch.Tensor): Backbone coordinates with shape
`(num_batch, num_residues, 4, 3)`.
C (torch.LongTensor): Chain map with shape
`(num_batch, num_residues)`.
custom_D (torch.Tensor, optional): Optional external distance map, for example
based on other distance metrics, with shape
`(num_batch, num_residues, num_residues)`.
custom_mask_2D (torch.Tensor, optional): Optional mask to apply to distances
before computing dissimilarities with shape
`(num_batch, num_residues, num_residues)`.
Outputs:
edge_idx (torch.LongTensor): Edge indices for neighbors with shape
`(num_batch, num_residues, num_neighbors)`.
mask_ij (torch.Tensor): Edge mask with shape
`(num_batch, num_nodes, num_neighbors)`.
"""
def __init__(
self,
num_neighbors: int = 30,
distance_atom_type: int = -1,
cutoff: Optional[float] = None,
mask_interfaces: bool = False,
criterion: str = "knn",
random_alpha: float = 3.0,
random_temperature: float = 1.0,
random_min_local: float = 20,
deterministic: bool = False,
deterministic_seed: int = 10,
):
super(ProteinGraph, self).__init__()
self.num_neighbors = num_neighbors
self.distance_atom_type = distance_atom_type
self.cutoff = cutoff
self.mask_interfaces = mask_interfaces
| # Copyright Generate Biomedicines, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Layers for building graph representations of protein structure.
This module contains pytorch layers for representing protein structure as a
graph with node and edge features based on geometric information. The graph
features are differentiable with respect to input coordinates and can be used
for building protein scoring functions and optimizing protein geometries
natively in pytorch.
"""
class ProteinFeatureGraph(nn.Module):
"""Graph featurizer for protein chains and complexes.
This module builds graph representations of protein structures that are
differentiable with respect to input coordinates and invariant with respect
to global rotations and translations. It takes as input a batch of
protein backbones (single chains or complexes), constructs a sparse graph
with residues as nodes, and featurizes the backbones in terms of node and
edge feature tensors.
The graph representation has 5 components:
1. Node features `node_h` representing residues in the protein.
2. Edge features `edge_h` representing relationships between residues.
3. Index map `edge_idx` representing graph topology.
4. Node mask `mask_i` that specifies which nodes are present.
5. Edge mask `mask_ij` that specifies which edges are present.
Criteria for constructing the graph currently include k-Nearest Neighbors or
distance-weighted edge sampling.
Node and edge features are specified as tuples to make it simpler to add
additional features and options while retaining backwards compatibility.
Specifically, each node or edge feature type can be added to the list either
in default configuration by a `'feature_name'` keyword, or in modified form
with a `('feature_name', feature_kwargs)` tuple.
Example usage:
graph = ProteinFeatureGraph(
graph_type='knn',
node_features=('dihedrals',),
edge_features=[
'chain_distance',
('dmat_6mer', {'D_function': 'log'})
]
)
node_h, edge_h, edge_idx, mask_i, mask_ij = graph(X, C)
This builds a kNN graph with dihedral angles as node
features and 6mer interatomic distance matrices (process) 6mers, where
the options for post-processing the 6mers are passed as a kwargs dict.
Args:
dim_nodes (int): Hidden dimension of node features.
dim_edges (int): Hidden dimension of edge features.
num_neighbors (int): Maximum degree of the graph.
graph_kwargs (dict): Arguments for graph construction. Default is None.
node_features (list): List of node feature strings and optional args.
Valid feature strings are `{internal_coords}`.
edge_features (list): List of node feature strings and optional args.
Valid feature strings are `{'distances_6mer','distances_chain'}`.
centered (boolean): Flag for enabling feature centering. If `True`,
the features will be will centered by subtracting an empirical mean
that was computed on the reference PDB `centered_pdb`. The statistics
are per-dimension of every node and edge feature. If they have not
previously been computed, the PDB will be downloaded, featurized,
and aggregated into local statistics that are cached in the repo.
centered_pdb (str): PDB code for the reference PDB to compute some
empirical feature statistics from.
Inputs:
X (torch.Tensor): Backbone coordinates with shape
`(num_batch, num_residues, 4, 3)`. The standard atom indices for
for the the third dimension are PDB order (`[N, CA, C, O]`).
C (LongTensor, optional): Chain map with shape
`(num_batch, num_residues)`. The chain map codes positions as `0`
when masked, poitive integers for chain indices, and negative
integers to represent missing residues of the corresponding
positive integers.
custom_D (Tensor, optional): Pre-computed custom distance map
for graph construction `(numb_batch,num_residues,num_residues)`.
If present, this will override the behavior of `graph_type` and used
as the distances for k-nearest neighbor graph construction.
custom_mask_2D (Tensor, optional): Custom 2D mask to apply to `custom_D`
with shape `(numb_batch,num_residues,num_residues)`.
Outputs:
node_h (torch.Tensor): Node features with shape
`(num_batch, num_residues, dim_nodes)`.
edge_h (torch.Tensor): Edge features with shape
`(num_batch, num_residues, num_neighbors, dim_edges)`.
edge_idx (torch.LongTensor): Edge indices for neighbors with shape
`(num_batch, num_residues, num_neighbors)`.
mask_i (torch.Tensor): Node mask with shape `(num_batch, num_residues)`.
mask_ij (torch.Tensor): Edge mask with shape
`(num_batch, num_nodes, num_neighbors)`.
"""
def __init__(
self,
dim_nodes: int,
dim_edges: int,
num_neighbors: int = 30,
graph_kwargs: dict = None,
node_features: tuple = ("internal_coords",),
edge_features: tuple = ("distances_6mer", "distances_chain"),
centered: bool = True,
centered_pdb: str = "2g3n",
):
super(ProteinFeatureGraph, self).__init__()
self.dim_nodes = dim_nodes
self.dim_edges = dim_edges
self.num_neighbors = num_neighbors
graph_kwargs = graph_kwargs if graph_kwargs is not None else {}
self.graph_builder = ProteinGraph(num_neighbors, **graph_kwargs)
self.node_features = node_features
self.edge_features = edge_features
def _init_layer(layer_dict, features):
# Parse option string
custom_args = not isinstance(features, str)
key = features[0] if custom_args else features
kwargs = features[1] if custom_args else {}
return layer_dict[key](**kwargs)
# Node feature compilation
node_dict = {
"internal_coords": NodeInternalCoords,
"cartesian_coords": NodeCartesianCoords,
"radii": NodeRadii,
}
self.node_layers = nn.ModuleList(
[_init_layer(node_dict, option) for option in self.node_features]
)
# Edge feature compilation
edge_dict = {
"distances_6mer": EdgeDistance6mer,
"distances_2mer": EdgeDistance2mer,
"orientations_2mer": EdgeOrientation2mer,
"position_2mer": EdgePositionalEncodings,
"distances_chain": EdgeDistanceChain,
"orientations_chain": EdgeOrientationChain,
"cartesian_coords": EdgeCartesianCoords,
"random_fourier_2mer": EdgeRandomFourierFeatures2mer,
}
self.edge_layers = nn.ModuleList(
[_init_layer(edge_dict, option) for option in self.edge_features]
)
# Load feature centering params as buffers
self.centered = centered
self.centered_pdb = centered_pdb.lower()
if self.centered:
self._load_centering_params(self.centered_pdb)
"""
Storing separate linear transformations for each layer, rather than concat + one
large linear, provides a more even weighting of the different input
features when used with standard weight initialization. It has the
specific effect actually re-weighting the weight variance based on
the number of input features for each feature type. Otherwise, the
relative importance of each feature goes with the number of feature
dimensions.
"""
self.node_linears = nn.ModuleList(
[nn.Linear(l.dim_out, self.dim_nodes) for l in self.node_layers]
)
self.edge_linears = nn.ModuleList(
[nn.Linear(l.dim_out, self.dim_edges) for l in self.edge_layers]
)
return
def forward(
self,
X: torch.Tensor,
C: torch.Tensor,
edge_idx: Optional[torch.LongTensor] = None,
mask_ij: torch.Tensor = None,
custom_D: Optional[torch.Tensor] = None,
custom_mask_2D: Optional[torch.Tensor] = None,
) -> Tuple[
torch.Tensor, torch.Tensor, torch.LongTensor, torch.Tensor, torch.Tensor
]:
mask_i = chain_map_to_mask(C)
if mask_ij is None or edge_idx is None:
edge_idx, mask_ij = self.graph_builder(
X, C, custom_D=custom_D, custom_mask_2D=custom_mask_2D
)
# Aggregate node layers
node_h = None
for i, layer in enumerate(self.node_layers):
node_h_l = layer(X, edge_idx, C)
if self.centered:
node_h_l = node_h_l - self.__getattr__(f"node_means_{i}")
node_h_l = self.node_linears[i](node_h_l)
node_h = node_h_l if node_h is None else node_h + node_h_l
if node_h is None:
node_h = torch.zeros(list(X.shape[:2]) + [self.dim_nodes], device=X.device)
# Aggregate edge layers
edge_h = None
for i, layer in enumerate(self.edge_layers):
edge_h_l = layer(X, edge_idx, C)
if self.centered:
edge_h_l = edge_h_l - self.__getattr__(f"edge_means_{i}")
edge_h_l = self.edge_linears[i](edge_h_l)
edge_h = edge_h_l if edge_h is None else edge_h + edge_h_l
if edge_h is None:
edge_h = torch.zeros(list(X.shape[:2]) + [self.dim_nodes], device=X.device)
# Apply masks
node_h = mask_i.unsqueeze(-1) * node_h
edge_h = mask_ij.unsqueeze(-1) * edge_h
return node_h, edge_h, edge_idx, mask_i, mask_ij
def _load_centering_params(self, reference_pdb: str):
basepath = os.path.dirname(os.path.abspath(__file__)) + "/params/"
if not os.path.exists(basepath):
os.makedirs(basepath)
filename = f"centering_{reference_pdb}.params"
self.centering_file = basepath + filename
key = (
reference_pdb
+ ";"
+ json.dumps(self.node_features)
+ ";"
+ json.dumps(self.edge_features)
)
# Attempt to load saved centering params, otherwise compute and cache
json_line = None
with open(self.centering_file, "a+") as f:
prefix = key + "\t"
f.seek(0)
for line in f:
if line.startswith(prefix):
json_line = line.split(prefix)[1]
break
if json_line is not None:
print("Loaded from cache")
param_dictionary = json.loads(json_line)
else:
print(f"Computing reference stats for {reference_pdb}")
param_dictionary = self._reference_stats(reference_pdb)
json_line = json.dumps(param_dictionary)
f.write(prefix + "\t" + json_line + "\n")
for i, layer in enumerate(self.node_layers):
key = json.dumps(self.node_features[i])
tensor = torch.tensor(param_dictionary[key], dtype=torch.float32)
tensor = tensor.view(1, 1, -1)
self.register_buffer(f"node_means_{i}", tensor)
for i, layer in enumerate(self.edge_layers):
key = json.dumps(self.edge_features[i])
tensor = torch.tensor(param_dictionary[key], dtype=torch.float32)
tensor = tensor.view(1, 1, -1)
self.register_buffer(f"edge_means_{i}", tensor)
return
def _reference_stats(self, reference_pdb):
X, C, _ = Protein.from_PDBID(reference_pdb).to_XCS()
stats_dict = self._feature_stats(X, C)
return stats_dict
def _feature_stats(self, X, C, verbose=False, center=False):
mask_i = chain_map_to_mask(C)
edge_idx, mask_ij = self.graph_builder(X, C)
def _masked_stats(feature, mask, dims, verbose=False):
mask = mask.unsqueeze(-1)
feature = mask * feature
sum_mask = mask.sum()
mean = feature.sum(dims, keepdim=True) / sum_mask
var = torch.sum(mask * (feature - mean) ** 2, dims) / sum_mask
std = torch.sqrt(var)
mean = mean.view(-1)
std = std.view(-1)
if verbose:
frac = (100.0 * std ** 2 / (mean ** 2 + std ** 2)).type(torch.int32)
print(f"Fraction of raw variance: {frac}")
return mean, std
# Collect statistics
stats_dict = {}
# Aggregate node layers
for i, layer in enumerate(self.node_layers):
node_h = layer(X, edge_idx, C)
if center:
node_h = node_h - self.__getattr__(f"node_means_{i}")
mean, std = _masked_stats(node_h, mask_i, dims=[0, 1])
# Store in dictionary
key = json.dumps(self.node_features[i])
stats_dict[key] = mean.tolist()
# Aggregate node layers
for i, layer in enumerate(self.edge_layers):
edge_h = layer(X, edge_idx, C)
if center:
edge_h = edge_h - self.__getattr__(f"edge_means_{i}")
mean, std = _masked_stats(edge_h, mask_ij, dims=[0, 1, 2])
# Store in dictionary
key = json.dumps(self.edge_features[i])
stats_dict[key] = mean.tolist()
# Round to small number of decimal places
stats_dict = {k: [round(f, 3) for f in v] for k, v in stats_dict.items()}
return stats_dict
class ProteinGraph(nn.Module):
"""Build a graph topology given a protein backbone.
Args:
num_neighbors (int): Maximum number of neighbors in the graph.
distance_atom_type (int): Atom type for computing residue-residue
distances for graph construction. Negative values will specify
centroid across atom types. Default is `-1` (centroid).
cutoff (float): Cutoff distance for graph construction. If not None,
mask any edges further than this cutoff. Default is `None`.
mask_interfaces (Boolean): Restrict connections only to within chains,
excluding-between chain interactions. Default is `False`.
criterion (string, optional): Method used for building graph from distances.
Currently supported methods are `{knn, random_log, random_linear}`.
Default is `knn`.
random_alpha (float, optional): Length scale parameter for random graph
generation. Default is 3.
random_temperature (float, optional): Temperature parameter for
random graph sampling. Between 0 and 1 this value will interpolate
between a normal k-NN graph and sampling from the graph generation
process. Default is 1.0.
Inputs:
X (torch.Tensor): Backbone coordinates with shape
`(num_batch, num_residues, 4, 3)`.
C (torch.LongTensor): Chain map with shape
`(num_batch, num_residues)`.
custom_D (torch.Tensor, optional): Optional external distance map, for example
based on other distance metrics, with shape
`(num_batch, num_residues, num_residues)`.
custom_mask_2D (torch.Tensor, optional): Optional mask to apply to distances
before computing dissimilarities with shape
`(num_batch, num_residues, num_residues)`.
Outputs:
edge_idx (torch.LongTensor): Edge indices for neighbors with shape
`(num_batch, num_residues, num_neighbors)`.
mask_ij (torch.Tensor): Edge mask with shape
`(num_batch, num_nodes, num_neighbors)`.
"""
def __init__(
self,
num_neighbors: int = 30,
distance_atom_type: int = -1,
cutoff: Optional[float] = None,
mask_interfaces: bool = False,
criterion: str = "knn",
random_alpha: float = 3.0,
random_temperature: float = 1.0,
random_min_local: float = 20,
deterministic: bool = False,
deterministic_seed: int = 10,
):
super(ProteinGraph, self).__init__()
self.num_neighbors = num_neighbors
self.distance_atom_type = distance_atom_type
self.cutoff = cutoff
self.mask_interfaces = mask_interfaces | self.distances = geometry.Distances() | 5 | 2023-11-28 00:09:40+00:00 | 16k |
BiQiWHU/CMFormer | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "mask2former/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... | from shapely.errors import ShapelyDeprecationWarning
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from mask2former import (
COCOInstanceNewBaselineDatasetMapper,
COCOPanopticNewBaselineDatasetMapper,
InstanceSegEvaluator,
MaskFormerInstanceDatasetMapper,
MaskFormerPanopticDatasetMapper,
MaskFormerSemanticDatasetMapper,
SemanticSegmentorWithTTA,
add_maskformer2_config,
)
import warnings
import copy
import itertools
import logging
import os
import torch
import detectron2.utils.comm as comm | 11,304 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
pass
os.environ['DETECTRON2_DATASETS'] = 'E:/DGtask/datasets'
# MaskFormer
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
pass
os.environ['DETECTRON2_DATASETS'] = 'E:/DGtask/datasets'
# MaskFormer
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance": | mapper = MaskFormerInstanceDatasetMapper(cfg, True) | 3 | 2023-11-29 15:26:53+00:00 | 16k |
PopicLab/insilicoSV | test/test_processing.py | [
{
"identifier": "SV_Simulator",
"path": "insilicosv/simulate.py",
"snippet": "class SV_Simulator:\n def __init__(self, par_file, log_file=None):\n \"\"\"\n par_file: file location to configuration file (.yaml)\n log_file: location to store log file with diagnostic information if ... | from insilicosv.simulate import SV_Simulator
from insilicosv.processing import FormatterIO
from test_simulate import TestObject
from pysam import VariantFile, FastaFile
from collections import defaultdict, Counter
from insilicosv.utils import NestedDict
from insilicosv import utils
from insilicosv import constants
import unittest
import sys
import os | 14,137 | "max_length": [4],
"min_length": [2]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_overlap_simple = {'overlap1': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1HS", "ALR/Alpha"]},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": [2, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap2': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1HS", "ALR/Alpha"]},
"variant_sets": [{"type": "DEL", "number": 4,
"min_length": [1], "max_length": [5],
"num_overlap": [3, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap3': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1", "ALR"]},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": [3, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap4': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": "L1"},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": 2}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap5': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": self.test_overlap_bed_3,
"allow_types": "ALR"},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": 2}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap6': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [2], "max_length": [4],
"num_overlap": [1, 1, 1, 1, 1]},
{"type": "DEL", "number": 5,
"min_length": [6], "max_length": [8],
"num_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap7': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [1],
"num_partial_overlap": [1, 1, 1, 1, 1]},
{"type": "DEL", "number": 5,
"min_length": [2], "max_length": [2],
"num_partial_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap8': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "dDUP", "number": 5,
"min_length": [2, 1], "max_length": [4, 1],
"num_overlap": [1, 1, 1, 1, 1]},
{"type": "dDUP", "number": 5,
"min_length": [6, 1], "max_length": [8, 1],
"num_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap9': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "dDUP", "number": 5,
"min_length": [1, 1], "max_length": [1, 1],
"num_partial_overlap": [1, 1, 1, 1, 1]},
{"type": "dDUP", "number": 5,
"min_length": [1, 1], "max_length": [2, 1],
"num_partial_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf)
}
self.test_objects_alu_mediated = {'alu_med1': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_4},
"variant_sets": [{"type": "DEL", "number": 1,
"min_length": [13], "max_length": [15],
"num_alu_mediated": 1}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
|
class TestProcObject(TestObject):
def __init__(self, ref, par, hap1, hap2, bed, vcf):
self.vcf = vcf
super().__init__(ref, par, hap1, hap2, bed)
def extract_bed_records(self):
# parse bed record into dict for easy comparison
# --> example split bed record: ['chr19', '0', '3', 'chr19', '0', '3', 'DEL', '3', '1/1', 'DEL', '1']
bed_records = []
with open(self.bed) as f:
for line in f:
ln = line.split()
bed_record = {'source_chr': ln[0], 'source_s': ln[1], 'source_e': ln[2],
'target_chr': ln[3], 'target_s': ln[4], 'target_e': ln[5],
'ev_type': ln[6], 'len': ln[7], 'zyg': ln[8], 'parent_type': ln[9],
'sv_id': ln[10]}
bed_records.append(bed_record)
return bed_records
def extract_vcf_records(self):
vcf_records = []
vcf = VariantFile(self.vcf)
for rec in vcf.fetch():
ln = str(rec).split()
# separately parse info field of the form: 'END=45590417;SVTYPE=dDUP;SVLEN=539;TARGET=45581738'
info = {field.split('=')[0]: field.split('=')[1] for field in ln[7].split(';')}
vcf_record = {'CHROM': ln[0], 'POS': ln[1], 'ID': ln[2], 'REF': ln[3], 'ALT': ln[4], 'QUAL': ln[5],
'FILTER': ln[6], 'INFO': info, 'FORMAT': ln[8], 'SAMPLE': ln[9]}
vcf_records.append(vcf_record)
return vcf_records
class TestProcessing(unittest.TestCase):
def setUp(self):
# runs before every test
self.ref_file = "test/inputs/test.fa"
self.par = "test/inputs/par.yaml"
self.hap1 = "test/inputs/test1.fa"
self.hap2 = "test/inputs/test2.fa"
self.bed = "test/inputs/out.bed"
self.vcf = "test/inputs/out.vcf"
self.ins_fasta = "test/inputs/ins_fasta.fa"
self.test_overlap_bed = "test/inputs/example_overlap_events.bed"
self.test_overlap_bed_2 = "test/inputs/example_overlap_events_2.bed"
# test_overlap_bed_3: events with differing chromosome
self.test_overlap_bed_3 = "test/inputs/example_overlap_events_3.bed"
self.test_overlap_bed_4 = "test/inputs/example_overlap_events_4.bed"
self.test_overlap_bed_11 = "test/inputs/example_overlap_events_11.bed"
self.test_objects_simple_events = {'DEL': TestProcObject([self.ref_file, {"chr19": "CTG"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"max_tries": 50, "prioritize_top": True},
"variant_sets": [{"type": "DEL", "number": 1,
"max_length": [3],
"min_length": [3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'DUP': TestProcObject([self.ref_file, {"chr19": "CTG"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"max_tries": 50, "prioritize_top": True},
"variant_sets": [{"type": "DUP", "number": 1,
"max_length": [3],
"min_length": [3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INV': TestProcObject([self.ref_file, {"chr19": "CTG"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"max_tries": 50, "prioritize_top": True},
"variant_sets": [{"type": "INV", "number": 1,
"max_length": [3],
"min_length": [3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INS': TestProcObject([self.ref_file, {"chr19": "C"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"max_tries": 50, "prioritize_top": True},
"variant_sets": [{"type": "INS", "number": 1,
"max_length": [3],
"min_length": [3]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_flanked_inversions = {'dupINVdup': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "dupINVdup", "number": 1,
"max_length": [2, 2, 2],
"min_length": [2, 2, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'delINVdel': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "delINVdel", "number": 1,
"max_length": [2, 2, 2],
"min_length": [2, 2, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'dupINVdel': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "dupINVdel", "number": 1,
"max_length": [2, 2, 2],
"min_length": [2, 2, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'delINVdup': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "delINVdup", "number": 1,
"max_length": [2, 2, 2],
"min_length": [2, 2, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_dispersions = {'dDUP': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "dDUP", "number": 1,
"max_length": [3, 3],
"min_length": [3, 3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INV_dDUP': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "INV_dDUP", "number": 1,
"max_length": [3, 3],
"min_length": [3, 3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'TRA': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "TRA", "number": 1,
"max_length": [3, 3],
"min_length": [3, 3]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_del_inv = {'delINV': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "delINV", "number": 1,
"max_length": [3, 3],
"min_length": [3, 3]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INVdel': TestProcObject([self.ref_file, {"chr19": "ACTGTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "INVdel", "number": 1,
"max_length": [3, 3],
"min_length": [3, 3]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_idel = {'dDUP_iDEL': TestProcObject([self.ref_file, {"chr19": "ACTGTCAG"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "dDUP_iDEL", "number": 1,
"max_length": [3, 3, 2],
"min_length": [3, 3, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INS_iDEL': TestProcObject([self.ref_file, {"chr19": "ACTGTCAG"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "INS_iDEL", "number": 1,
"max_length": [3, 3, 2],
"min_length": [3, 3, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_dup_inv = {'dup_INV': TestProcObject([self.ref_file, {"chr19": "ACTGTCAG"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "dup_INV", "number": 1,
"max_length": [4, 4],
"min_length": [4, 4]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'INV_dup': TestProcObject([self.ref_file, {"chr19": "ACTGTCAG"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "INV_dup", "number": 1,
"max_length": [4, 4],
"min_length": [4, 4]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_INVdup = {'INVdup': TestProcObject([self.ref_file, {"chr19": "ACTG"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"variant_sets": [{"type": "INVdup", "number": 1,
"max_length": [4],
"min_length": [4]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_multievent = {'INVdup': TestProcObject([self.ref_file, {"chr19": "ACTGCTAATGCGTTCACTGCTAATGCGTTC"}],
[self.par,
{"sim_settings": {"reference": self.ref_file,
"max_tries": 200, "prioritize_top": True},
"variant_sets": [{"type": "INVdup", "number": 3,
"max_length": [4],
"min_length": [2]}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
self.test_objects_overlap_simple = {'overlap1': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1HS", "ALR/Alpha"]},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": [2, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap2': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1HS", "ALR/Alpha"]},
"variant_sets": [{"type": "DEL", "number": 4,
"min_length": [1], "max_length": [5],
"num_overlap": [3, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap3': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": ["L1", "ALR"]},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": [3, 2]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap4': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": [self.test_overlap_bed, self.test_overlap_bed_2],
"allow_types": "L1"},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": 2}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap5': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {
"bed": self.test_overlap_bed_3,
"allow_types": "ALR"},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [5],
"num_overlap": 2}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap6': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [2], "max_length": [4],
"num_overlap": [1, 1, 1, 1, 1]},
{"type": "DEL", "number": 5,
"min_length": [6], "max_length": [8],
"num_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap7': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "DEL", "number": 5,
"min_length": [1], "max_length": [1],
"num_partial_overlap": [1, 1, 1, 1, 1]},
{"type": "DEL", "number": 5,
"min_length": [2], "max_length": [2],
"num_partial_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap8': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "dDUP", "number": 5,
"min_length": [2, 1], "max_length": [4, 1],
"num_overlap": [1, 1, 1, 1, 1]},
{"type": "dDUP", "number": 5,
"min_length": [6, 1], "max_length": [8, 1],
"num_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf),
'overlap9': TestProcObject([self.ref_file, {"chr21": "CCTCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTATCCGTCGTACTAAGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_11,
"allow_types": ['Alu', 'L1', 'L2', 'SVA', 'HERVK']},
"variant_sets": [{"type": "dDUP", "number": 5,
"min_length": [1, 1], "max_length": [1, 1],
"num_partial_overlap": [1, 1, 1, 1, 1]},
{"type": "dDUP", "number": 5,
"min_length": [1, 1], "max_length": [2, 1],
"num_partial_overlap": [1, 1, 1, 1, 1]}]}],
self.hap1, self.hap2, self.bed, self.vcf)
}
self.test_objects_alu_mediated = {'alu_med1': TestProcObject([self.ref_file, {"chr21": "CTCCGTCGTACTAAGTCGTACTCCGTCGTACTAAGTCGTA"}],
[self.par, {"sim_settings": {"reference": self.ref_file,
"prioritize_top": True,
"fail_if_placement_issues": True},
"overlap_events": {"bed": self.test_overlap_bed_4},
"variant_sets": [{"type": "DEL", "number": 1,
"min_length": [13], "max_length": [15],
"num_alu_mediated": 1}]}],
self.hap1, self.hap2, self.bed, self.vcf)}
| self.formatter = FormatterIO(self.par) | 1 | 2023-12-01 14:39:20+00:00 | 16k |
BiQiWHU/BWG | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "mask2former/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... | from shapely.errors import ShapelyDeprecationWarning
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from mask2former import (
COCOInstanceNewBaselineDatasetMapper,
COCOPanopticNewBaselineDatasetMapper,
InstanceSegEvaluator,
MaskFormerInstanceDatasetMapper,
MaskFormerPanopticDatasetMapper,
MaskFormerSemanticDatasetMapper,
SemanticSegmentorWithTTA,
add_maskformer2_config,
)
import warnings
import copy
import itertools
import logging
import os
import torch
import detectron2.utils.comm as comm | 12,296 | @classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
mapper = MaskFormerInstanceDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco instance segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco panoptic segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
else:
mapper = None
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_lr_scheduler(cls, cfg, optimizer):
"""
It now calls :func:`detectron2.solver.build_lr_scheduler`.
Overwrite it if you'd like a different scheduler.
"""
return build_lr_scheduler(cfg, optimizer)
@classmethod
def build_optimizer(cls, cfg, model):
weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
defaults = {}
defaults["lr"] = cfg.SOLVER.BASE_LR
defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
torch.nn.SyncBatchNorm,
# NaiveSyncBatchNorm inherits from BatchNorm2d
torch.nn.GroupNorm,
torch.nn.InstanceNorm1d,
torch.nn.InstanceNorm2d,
torch.nn.InstanceNorm3d,
torch.nn.LayerNorm,
torch.nn.LocalResponseNorm,
)
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
for module_name, module in model.named_modules():
for module_param_name, value in module.named_parameters(recurse=False):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
hyperparams = copy.copy(defaults)
if "backbone" in module_name:
hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
if (
"relative_position_bias_table" in module_param_name
or "absolute_pos_embed" in module_param_name
):
print(module_param_name)
hyperparams["weight_decay"] = 0.0
if isinstance(module, norm_module_types):
hyperparams["weight_decay"] = weight_decay_norm
if isinstance(module, torch.nn.Embedding):
hyperparams["weight_decay"] = weight_decay_embed
params.append({"params": [value], **hyperparams})
def maybe_add_full_model_gradient_clipping(optim):
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
optimizer_type = cfg.SOLVER.OPTIMIZER
if optimizer_type == "SGD":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
)
elif optimizer_type == "ADAMW":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer
@classmethod
def test_with_TTA(cls, cfg, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA.
logger.info("Running inference with test-time augmentation ...")
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
pass
os.environ['DETECTRON2_DATASETS'] = 'E:/DGtask/datasets'
# MaskFormer
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
mapper = MaskFormerInstanceDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco instance segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco panoptic segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
else:
mapper = None
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_lr_scheduler(cls, cfg, optimizer):
"""
It now calls :func:`detectron2.solver.build_lr_scheduler`.
Overwrite it if you'd like a different scheduler.
"""
return build_lr_scheduler(cfg, optimizer)
@classmethod
def build_optimizer(cls, cfg, model):
weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
defaults = {}
defaults["lr"] = cfg.SOLVER.BASE_LR
defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
torch.nn.SyncBatchNorm,
# NaiveSyncBatchNorm inherits from BatchNorm2d
torch.nn.GroupNorm,
torch.nn.InstanceNorm1d,
torch.nn.InstanceNorm2d,
torch.nn.InstanceNorm3d,
torch.nn.LayerNorm,
torch.nn.LocalResponseNorm,
)
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
for module_name, module in model.named_modules():
for module_param_name, value in module.named_parameters(recurse=False):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
hyperparams = copy.copy(defaults)
if "backbone" in module_name:
hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
if (
"relative_position_bias_table" in module_param_name
or "absolute_pos_embed" in module_param_name
):
print(module_param_name)
hyperparams["weight_decay"] = 0.0
if isinstance(module, norm_module_types):
hyperparams["weight_decay"] = weight_decay_norm
if isinstance(module, torch.nn.Embedding):
hyperparams["weight_decay"] = weight_decay_embed
params.append({"params": [value], **hyperparams})
def maybe_add_full_model_gradient_clipping(optim):
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
optimizer_type = cfg.SOLVER.OPTIMIZER
if optimizer_type == "SGD":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
)
elif optimizer_type == "ADAMW":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer
@classmethod
def test_with_TTA(cls, cfg, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA.
logger.info("Running inference with test-time augmentation ...") | model = SemanticSegmentorWithTTA(cfg, model) | 6 | 2023-11-29 17:15:46+00:00 | 16k |
opisaac9001/TTS-With-ooba-and-voice | TTS/tts/models/bark.py | [
{
"identifier": "codec_decode",
"path": "TTS/tts/layers/bark/inference_funcs.py",
"snippet": "def codec_decode(fine_tokens, model):\n \"\"\"Turn quantized audio codes into audio array using encodec.\"\"\"\n arr = torch.from_numpy(fine_tokens)[None]\n arr = arr.to(model.device)\n arr = arr.tr... | import os
import numpy as np
from dataclasses import dataclass
from typing import Optional
from coqpit import Coqpit
from encodec import EncodecModel
from transformers import BertTokenizer
from TTS.tts.layers.bark.inference_funcs import (
codec_decode,
generate_coarse,
generate_fine,
generate_text_semantic,
generate_voice,
load_voice,
)
from TTS.tts.layers.bark.load_model import load_model
from TTS.tts.layers.bark.model import GPT
from TTS.tts.layers.bark.model_fine import FineGPT
from TTS.tts.models.base_tts import BaseTTS | 14,198 | model_type="coarse",
)
self.fine_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["fine"], device=self.device, config=self.config, model_type="fine"
)
def train_step(
self,
):
pass
def text_to_semantic(
self,
text: str,
history_prompt: Optional[str] = None,
temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate semantic array from text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy semantic array to be fed into `semantic_to_waveform`
"""
x_semantic = generate_text_semantic(
text,
self,
history_prompt=history_prompt,
temp=temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
return x_semantic
def semantic_to_waveform(
self,
semantic_tokens: np.ndarray,
history_prompt: Optional[str] = None,
temp: float = 0.7,
base=None,
):
"""Generate audio array from semantic input.
Args:
semantic_tokens: semantic token output from `text_to_semantic`
history_prompt: history choice for audio cloning
temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy audio array at sample frequency 24khz
"""
x_coarse_gen = generate_coarse(
semantic_tokens,
self,
history_prompt=history_prompt,
temp=temp,
base=base,
)
x_fine_gen = generate_fine(
x_coarse_gen,
self,
history_prompt=history_prompt,
temp=0.5,
base=base,
)
audio_arr = codec_decode(x_fine_gen, self)
return audio_arr, x_coarse_gen, x_fine_gen
def generate_audio(
self,
text: str,
history_prompt: Optional[str] = None,
text_temp: float = 0.7,
waveform_temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate audio array from input text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy audio array at sample frequency 24khz
"""
x_semantic = self.text_to_semantic(
text,
history_prompt=history_prompt,
temp=text_temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
audio_arr, c, f = self.semantic_to_waveform(
x_semantic, history_prompt=history_prompt, temp=waveform_temp, base=base
)
return audio_arr, [x_semantic, c, f]
def generate_voice(self, audio, speaker_id, voice_dir):
"""Generate a voice from the given audio and text.
Args:
audio (str): Path to the audio file.
speaker_id (str): Speaker name.
voice_dir (str): Path to the directory to save the generate voice.
"""
if voice_dir is not None:
voice_dirs = [voice_dir]
try:
|
@dataclass
class BarkAudioConfig(Coqpit):
sample_rate: int = 24000
output_sample_rate: int = 24000
class Bark(BaseTTS):
def __init__(
self,
config: Coqpit,
tokenizer: BertTokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased"),
) -> None:
super().__init__(config=config, ap=None, tokenizer=None, speaker_manager=None, language_manager=None)
self.config.num_chars = len(tokenizer)
self.tokenizer = tokenizer
self.semantic_model = GPT(config.semantic_config)
self.coarse_model = GPT(config.coarse_config)
self.fine_model = FineGPT(config.fine_config)
self.encodec = EncodecModel.encodec_model_24khz()
self.encodec.set_target_bandwidth(6.0)
@property
def device(self):
return next(self.parameters()).device
def load_bark_models(self):
self.semantic_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["text"], device=self.device, config=self.config, model_type="text"
)
self.coarse_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["coarse"],
device=self.device,
config=self.config,
model_type="coarse",
)
self.fine_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["fine"], device=self.device, config=self.config, model_type="fine"
)
def train_step(
self,
):
pass
def text_to_semantic(
self,
text: str,
history_prompt: Optional[str] = None,
temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate semantic array from text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy semantic array to be fed into `semantic_to_waveform`
"""
x_semantic = generate_text_semantic(
text,
self,
history_prompt=history_prompt,
temp=temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
return x_semantic
def semantic_to_waveform(
self,
semantic_tokens: np.ndarray,
history_prompt: Optional[str] = None,
temp: float = 0.7,
base=None,
):
"""Generate audio array from semantic input.
Args:
semantic_tokens: semantic token output from `text_to_semantic`
history_prompt: history choice for audio cloning
temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy audio array at sample frequency 24khz
"""
x_coarse_gen = generate_coarse(
semantic_tokens,
self,
history_prompt=history_prompt,
temp=temp,
base=base,
)
x_fine_gen = generate_fine(
x_coarse_gen,
self,
history_prompt=history_prompt,
temp=0.5,
base=base,
)
audio_arr = codec_decode(x_fine_gen, self)
return audio_arr, x_coarse_gen, x_fine_gen
def generate_audio(
self,
text: str,
history_prompt: Optional[str] = None,
text_temp: float = 0.7,
waveform_temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate audio array from input text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy audio array at sample frequency 24khz
"""
x_semantic = self.text_to_semantic(
text,
history_prompt=history_prompt,
temp=text_temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
audio_arr, c, f = self.semantic_to_waveform(
x_semantic, history_prompt=history_prompt, temp=waveform_temp, base=base
)
return audio_arr, [x_semantic, c, f]
def generate_voice(self, audio, speaker_id, voice_dir):
"""Generate a voice from the given audio and text.
Args:
audio (str): Path to the audio file.
speaker_id (str): Speaker name.
voice_dir (str): Path to the directory to save the generate voice.
"""
if voice_dir is not None:
voice_dirs = [voice_dir]
try: | _ = load_voice(speaker_id, voice_dirs) | 5 | 2023-11-29 08:15:06+00:00 | 16k |
AILab-CVC/UniRepLKNet | Video/dataset/build.py | [
{
"identifier": "RawFrameClsDataset",
"path": "Video/dataset/datasets.py",
"snippet": "class RawFrameClsDataset(Dataset):\n \"\"\"Load your own raw frame classification dataset.\"\"\"\n\n def __init__(self,\n anno_path,\n data_root,\n mode='train',\n... | import os
from .datasets import RawFrameClsDataset, VideoClsDataset
from .pretrain_datasets import ( # noqa: F401
DataAugmentationForVideoMAEv2, HybridVideoMAE, VideoMAE,
) | 11,089 | # --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
def build_pretraining_dataset(args):
transform = DataAugmentationForVideoMAEv2(args)
dataset = VideoMAE(
root=args.data_root,
setting=args.data_path,
train=True,
test_mode=False,
name_pattern=args.fname_tmpl,
video_ext='mp4',
is_color=True,
modality='rgb',
num_segments=1,
num_crop=1,
new_length=args.num_frames,
new_step=args.sampling_rate,
transform=transform,
temporal_jitter=False,
lazy_init=False,
num_sample=args.num_sample)
print("Data Aug = %s" % str(transform))
return dataset
def build_dataset(is_train, test_mode, args):
if is_train:
mode = 'train'
anno_path = os.path.join(args.data_path, 'train.csv')
elif test_mode:
mode = 'test'
anno_path = os.path.join(args.data_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.data_path, 'val.csv')
if args.data_set == 'Kinetics-400':
if not args.sparse_sample:
| # --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
def build_pretraining_dataset(args):
transform = DataAugmentationForVideoMAEv2(args)
dataset = VideoMAE(
root=args.data_root,
setting=args.data_path,
train=True,
test_mode=False,
name_pattern=args.fname_tmpl,
video_ext='mp4',
is_color=True,
modality='rgb',
num_segments=1,
num_crop=1,
new_length=args.num_frames,
new_step=args.sampling_rate,
transform=transform,
temporal_jitter=False,
lazy_init=False,
num_sample=args.num_sample)
print("Data Aug = %s" % str(transform))
return dataset
def build_dataset(is_train, test_mode, args):
if is_train:
mode = 'train'
anno_path = os.path.join(args.data_path, 'train.csv')
elif test_mode:
mode = 'test'
anno_path = os.path.join(args.data_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.data_path, 'val.csv')
if args.data_set == 'Kinetics-400':
if not args.sparse_sample: | dataset = VideoClsDataset( | 1 | 2023-11-24 07:28:22+00:00 | 16k |
wenquanlu/HandRefiner | handrefiner.py | [
{
"identifier": "handrefiner_root",
"path": "config.py",
"snippet": ""
},
{
"identifier": "create_model",
"path": "cldm/model.py",
"snippet": "def create_model(config_path):\n config = OmegaConf.load(config_path)\n model = instantiate_from_config(config.model).cpu()\n print(f'Lo... | import sys
import os
import argparse
import json
import torch
import numpy as np
import cv2
import numpy as np
import cv2
import config
import cv2
import einops
import numpy as np
import torch
import random
import ast
from config import handrefiner_root
from PIL import Image
from torchvision import transforms
from pytorch_lightning import seed_everything
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from pathlib import Path
from preprocessor.meshgraphormer import MeshGraphormerMediapipe | 10,879 | # input image
parser.add_argument('--input_img', type=str, default="")
# output directory where the rectified images will be saved to
parser.add_argument('--out_dir', type=str, default="")
# file where the mpjpe values will be logged to
parser.add_argument('--log_json', type=str, default="")
# control strength for ControlNet
parser.add_argument('--strength', type=float, default=1.0)
# directory where the depth maps will be saved to. Leaving it empty will disable this function
parser.add_argument('--depth_dir', type=str, default="")
# directory where the masks will be saved to. Leaving it empty will disable this function
parser.add_argument('--mask_dir', type=str, default="")
# whether evaluate the mpjpe error in fixed control strength mode
parser.add_argument('--eval', type=ast.literal_eval, default=False)
# whether use finetuned ControlNet trained on synthetic images as introduced in the paper
parser.add_argument('--finetuned', type=ast.literal_eval, default=True)
# path to the SD + ControlNet weights
parser.add_argument('--weights', type=str, default="")
# batch size
parser.add_argument('--num_samples', type=int, default=1)
# prompt file for multi-image rectification
# see manual.md for file format
parser.add_argument('--prompt_file', type=str, default="")
# prompt for single image rectification
parser.add_argument('--prompt', type=str, default="")
# number of generation iteration for each image to be rectified
# in general, for each input image, n_iter x num_samples number of rectified images will be produced
parser.add_argument('--n_iter', type=int, default=1)
# adaptive control strength as introduced in paper (we tend to use fixed control strength as default)
parser.add_argument('--adaptive_control', type=ast.literal_eval, default=False)
# padding controls the size of masks around the hand
parser.add_argument('--padding_bbox', type=int, default=30)
# set seed
parser.add_argument('--seed', type=int, default=-1)
args = parser.parse_args()
return args
args = parse_args()
if (args.prompt_file != "" and args.prompt != "") or (args.prompt_file == "" and args.prompt == ""):
raise Exception("Please specify one and only one of the --prompt and --prompt_file")
if (args.input_dir != "" and args.input_img != "") or (args.input_dir == "" and args.input_img == ""):
raise Exception("Please specify one and only one of the --input_dir and --input_img")
model = create_model("control_depth_inpaint.yaml").cpu()
if args.finetuned:
model.load_state_dict(load_state_dict(args.weights, location='cuda'), strict=False)
else:
model.load_state_dict(
load_state_dict("models/sd-v1-5-inpainting.ckpt", location="cuda"), strict=False
)
model.load_state_dict(
load_state_dict("models/control_v11f1p_sd15_depth.pth", location="cuda"),
strict=False,
)
model = model.to("cuda")
meshgraphormer = MeshGraphormerMediapipe()
if args.log_json != "":
f_mpjpe = open(args.log_json, 'w')
# prompt needs to be same for all pictures in the same batch
if args.input_img != "":
assert args.prompt_file == "", "prompt file should not be used for single image rectification"
inputs = [args.input_img]
else:
if args.prompt_file != "":
f_prompt = open(args.prompt_file)
inputs = f_prompt.readlines()
else:
inputs = os.listdir(args.input_dir)
for file_info in inputs:
if args.prompt_file != "":
file_info = json.loads(file_info)
file_name = file_info["img"]
prompt = file_info["txt"]
else:
file_name = file_info
prompt = args.prompt
image_file = os.path.join(args.input_dir, file_name)
file_name_raw = Path(file_name).stem
# STEP 3: Load the input image.
image = np.array(Image.open(image_file))
raw_image = image
H, W, C = raw_image.shape
gen_count = 0
for iteration in range(args.n_iter):
depthmap, mask, info = meshgraphormer.get_depth(args.input_dir, file_name, args.padding_bbox)
if args.depth_dir != "":
cv2.imwrite(os.path.join(args.depth_dir, file_name_raw + "_depth.jpg"), depthmap)
if args.mask_dir != "":
cv2.imwrite(os.path.join(args.mask_dir, file_name_raw + "_mask.jpg"), mask)
control = depthmap
|
# STEP 1: Import the necessary modules.
from __future__ import absolute_import, division, print_function
def load():
paths = [handrefiner_root, os.path.join(handrefiner_root, 'MeshGraphormer'), os.path.join(handrefiner_root, 'preprocessor')]
for p in paths:
sys.path.insert(0, p)
load()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
def parse_args():
parser = argparse.ArgumentParser()
# input directory containing images to be rectified
parser.add_argument('--input_dir', type=str, default="")
# input image
parser.add_argument('--input_img', type=str, default="")
# output directory where the rectified images will be saved to
parser.add_argument('--out_dir', type=str, default="")
# file where the mpjpe values will be logged to
parser.add_argument('--log_json', type=str, default="")
# control strength for ControlNet
parser.add_argument('--strength', type=float, default=1.0)
# directory where the depth maps will be saved to. Leaving it empty will disable this function
parser.add_argument('--depth_dir', type=str, default="")
# directory where the masks will be saved to. Leaving it empty will disable this function
parser.add_argument('--mask_dir', type=str, default="")
# whether evaluate the mpjpe error in fixed control strength mode
parser.add_argument('--eval', type=ast.literal_eval, default=False)
# whether use finetuned ControlNet trained on synthetic images as introduced in the paper
parser.add_argument('--finetuned', type=ast.literal_eval, default=True)
# path to the SD + ControlNet weights
parser.add_argument('--weights', type=str, default="")
# batch size
parser.add_argument('--num_samples', type=int, default=1)
# prompt file for multi-image rectification
# see manual.md for file format
parser.add_argument('--prompt_file', type=str, default="")
# prompt for single image rectification
parser.add_argument('--prompt', type=str, default="")
# number of generation iteration for each image to be rectified
# in general, for each input image, n_iter x num_samples number of rectified images will be produced
parser.add_argument('--n_iter', type=int, default=1)
# adaptive control strength as introduced in paper (we tend to use fixed control strength as default)
parser.add_argument('--adaptive_control', type=ast.literal_eval, default=False)
# padding controls the size of masks around the hand
parser.add_argument('--padding_bbox', type=int, default=30)
# set seed
parser.add_argument('--seed', type=int, default=-1)
args = parser.parse_args()
return args
args = parse_args()
if (args.prompt_file != "" and args.prompt != "") or (args.prompt_file == "" and args.prompt == ""):
raise Exception("Please specify one and only one of the --prompt and --prompt_file")
if (args.input_dir != "" and args.input_img != "") or (args.input_dir == "" and args.input_img == ""):
raise Exception("Please specify one and only one of the --input_dir and --input_img")
model = create_model("control_depth_inpaint.yaml").cpu()
if args.finetuned:
model.load_state_dict(load_state_dict(args.weights, location='cuda'), strict=False)
else:
model.load_state_dict(
load_state_dict("models/sd-v1-5-inpainting.ckpt", location="cuda"), strict=False
)
model.load_state_dict(
load_state_dict("models/control_v11f1p_sd15_depth.pth", location="cuda"),
strict=False,
)
model = model.to("cuda")
meshgraphormer = MeshGraphormerMediapipe()
if args.log_json != "":
f_mpjpe = open(args.log_json, 'w')
# prompt needs to be same for all pictures in the same batch
if args.input_img != "":
assert args.prompt_file == "", "prompt file should not be used for single image rectification"
inputs = [args.input_img]
else:
if args.prompt_file != "":
f_prompt = open(args.prompt_file)
inputs = f_prompt.readlines()
else:
inputs = os.listdir(args.input_dir)
for file_info in inputs:
if args.prompt_file != "":
file_info = json.loads(file_info)
file_name = file_info["img"]
prompt = file_info["txt"]
else:
file_name = file_info
prompt = args.prompt
image_file = os.path.join(args.input_dir, file_name)
file_name_raw = Path(file_name).stem
# STEP 3: Load the input image.
image = np.array(Image.open(image_file))
raw_image = image
H, W, C = raw_image.shape
gen_count = 0
for iteration in range(args.n_iter):
depthmap, mask, info = meshgraphormer.get_depth(args.input_dir, file_name, args.padding_bbox)
if args.depth_dir != "":
cv2.imwrite(os.path.join(args.depth_dir, file_name_raw + "_depth.jpg"), depthmap)
if args.mask_dir != "":
cv2.imwrite(os.path.join(args.mask_dir, file_name_raw + "_mask.jpg"), mask)
control = depthmap
| ddim_sampler = DDIMSampler(model) | 3 | 2023-11-24 10:19:23+00:00 | 16k |
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 | 13,813 |
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.
"""
|
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") | 6 | 2023-11-21 20:01:08+00:00 | 16k |
huang-yh/SelfOcc | model/encoder/tpvformer/tpvformer_encoder.py | [
{
"identifier": "BaseEncoder",
"path": "model/encoder/base_encoder.py",
"snippet": "class BaseEncoder(BaseModule):\n \"\"\"Further encode 3D representations.\n image backbone -> neck -> lifter -> encoder -> segmentor\n \"\"\"\n\n def __init__(self, init_cfg=None, **kwargs):\n super().... | from mmseg.registry import MODELS
from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer
from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
from mmengine.model import ModuleList
from torch.nn.init import normal_
from mmengine.logging import MMLogger
from ..base_encoder import BaseEncoder
from ..bevformer.utils import point_sampling
from .utils import get_cross_view_ref_points
from ..bevformer.mappings import GridMeterMapping
from ..bevformer.attention import BEVCrossAttention, BEVDeformableAttention
from .attention import TPVCrossAttention, CrossViewHybridAttention
from .modules import CameraAwareSE
import torch.nn as nn, torch, copy | 11,499 | num_feature_levels=4,
positional_encoding=None,
num_points_cross=[64, 64, 8],
num_points_self=[16, 16, 16],
transformerlayers=None,
num_layers=None,
camera_aware=False,
camera_aware_mid_channels=None,
init_cfg=None):
super().__init__(init_cfg)
# self.bev_inner = bev_inner
# self.bev_outer = bev_outer
# self.range_inner = range_inner
# self.range_outer = range_outer
# assert nonlinear_mode == 'linear_upscale' # TODO
# self.nonlinear_mode = nonlinear_mode
# self.z_inner = z_inner
# self.z_outer = z_outer
# self.z_ranges = z_ranges
self.embed_dims = embed_dims
self.num_feature_levels = num_feature_levels
self.num_cams = num_cams
self.camera_aware = camera_aware
if camera_aware:
if camera_aware_mid_channels is None:
camera_aware_mid_channels = embed_dims
self.camera_se_net = CameraAwareSE(
embed_dims,
camera_aware_mid_channels,
embed_dims)
self.mapping = GridMeterMapping(
# bev_inner,
# bev_outer,
# range_inner,
# range_outer,
# nonlinear_mode,
# z_inner,
# z_outer,
# z_ranges
**mapping_args)
size_h = self.mapping.size_h
size_w = self.mapping.size_w
size_d = self.mapping.size_d
hw_grid = torch.stack(
[torch.arange(size_h, dtype=torch.float).unsqueeze(-1).expand(-1, size_w),
torch.arange(size_w, dtype=torch.float).unsqueeze(0).expand(size_h, -1),
torch.zeros(size_h, size_w)],
dim=-1)
hw_meter = self.mapping.grid2meter(hw_grid)[..., [0, 1]]
zh_grid = torch.stack(
[torch.arange(size_h, dtype=torch.float).unsqueeze(0).expand(size_d, -1),
torch.zeros(size_d, size_h),
torch.arange(size_d, dtype=torch.float).unsqueeze(-1).expand(-1, size_h)],
dim=-1)
zh_meter = self.mapping.grid2meter(zh_grid)[..., [1, 2]]
wz_grid = torch.stack(
[torch.zeros(size_w, size_d),
torch.arange(size_w, dtype=torch.float).unsqueeze(-1).expand(-1, size_d),
torch.arange(size_d, dtype=torch.float).unsqueeze(0).expand(size_w, -1)],
dim=-1)
wz_meter = self.mapping.grid2meter(wz_grid)[..., [0, 2]]
positional_encoding.update({'tpv_meters': [hw_meter, zh_meter, wz_meter]})
self.positional_encoding = build_positional_encoding(positional_encoding)
self.tpv_size = [size_h, size_w, size_d]
# transformer layers
if isinstance(transformerlayers, dict):
transformerlayers = [
copy.deepcopy(transformerlayers) for _ in range(num_layers)]
else:
assert isinstance(transformerlayers, list) and \
len(transformerlayers) == num_layers
self.num_layers = num_layers
self.layers = ModuleList()
for i in range(num_layers):
self.layers.append(build_transformer_layer(transformerlayers[i]))
self.pre_norm = self.layers[0].pre_norm
logger.info('use pre_norm: ' + str(self.pre_norm))
# other learnable embeddings
self.level_embeds = nn.Parameter(
torch.randn(self.num_feature_levels, self.embed_dims))
self.cams_embeds = nn.Parameter(
torch.randn(self.num_cams, self.embed_dims))
# prepare reference points used in image cross-attention and cross-view hybrid-attention
self.num_points_cross = num_points_cross
self.num_points_self = num_points_self
uniform_d = torch.linspace(0, size_d - 1, num_points_cross[2])
hw_3d_grid = torch.cat([
hw_grid[..., [0, 1]].unsqueeze(2).expand(-1, -1, num_points_cross[2], -1),
uniform_d.reshape(1, 1, -1, 1).expand(size_h, size_w, -1, -1)], dim=-1)
ref_3d_hw = self.mapping.grid2meter(hw_3d_grid) # H, W, P0, 3
uniform_w = torch.linspace(0, size_w - 1, num_points_cross[1])
zh_3d_grid = torch.cat([
zh_grid[..., :1].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1),
uniform_w.reshape(1, 1, -1, 1).expand(size_d, size_h, -1, -1),
zh_grid[..., 2:].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1)
], dim=-1)
ref_3d_zh = self.mapping.grid2meter(zh_3d_grid) # Z, H, P1, 3
uniform_h = torch.linspace(0, size_h - 1, num_points_cross[0])
wz_3d_grid = torch.cat([
uniform_h.reshape(1, 1, -1, 1).expand(size_w, size_d, -1, -1),
wz_grid[..., [1, 2]].unsqueeze(2).expand(-1, -1, num_points_cross[0], -1)
], dim=-1)
ref_3d_wz = self.mapping.grid2meter(wz_3d_grid) # W, Z, P2, 3
self.register_buffer('ref_3d_hw', ref_3d_hw.flatten(0, 1).transpose(0, 1), False)
self.register_buffer('ref_3d_zh', ref_3d_zh.flatten(0, 1).transpose(0, 1), False)
self.register_buffer('ref_3d_wz', ref_3d_wz.flatten(0, 1).transpose(0, 1), False)
| logger = MMLogger.get_instance('selfocc')
@MODELS.register_module()
class TPVFormerEncoder(BaseEncoder):
def __init__(
self,
mapping_args: dict,
# bev_inner=128,
# bev_outer=32,
# range_inner=51.2,
# range_outer=51.2,
# nonlinear_mode='linear_upscale',
# z_inner=20,
# z_outer=10,
# z_ranges=[-5.0, 3.0, 11.0],
embed_dims=128,
num_cams=6,
num_feature_levels=4,
positional_encoding=None,
num_points_cross=[64, 64, 8],
num_points_self=[16, 16, 16],
transformerlayers=None,
num_layers=None,
camera_aware=False,
camera_aware_mid_channels=None,
init_cfg=None):
super().__init__(init_cfg)
# self.bev_inner = bev_inner
# self.bev_outer = bev_outer
# self.range_inner = range_inner
# self.range_outer = range_outer
# assert nonlinear_mode == 'linear_upscale' # TODO
# self.nonlinear_mode = nonlinear_mode
# self.z_inner = z_inner
# self.z_outer = z_outer
# self.z_ranges = z_ranges
self.embed_dims = embed_dims
self.num_feature_levels = num_feature_levels
self.num_cams = num_cams
self.camera_aware = camera_aware
if camera_aware:
if camera_aware_mid_channels is None:
camera_aware_mid_channels = embed_dims
self.camera_se_net = CameraAwareSE(
embed_dims,
camera_aware_mid_channels,
embed_dims)
self.mapping = GridMeterMapping(
# bev_inner,
# bev_outer,
# range_inner,
# range_outer,
# nonlinear_mode,
# z_inner,
# z_outer,
# z_ranges
**mapping_args)
size_h = self.mapping.size_h
size_w = self.mapping.size_w
size_d = self.mapping.size_d
hw_grid = torch.stack(
[torch.arange(size_h, dtype=torch.float).unsqueeze(-1).expand(-1, size_w),
torch.arange(size_w, dtype=torch.float).unsqueeze(0).expand(size_h, -1),
torch.zeros(size_h, size_w)],
dim=-1)
hw_meter = self.mapping.grid2meter(hw_grid)[..., [0, 1]]
zh_grid = torch.stack(
[torch.arange(size_h, dtype=torch.float).unsqueeze(0).expand(size_d, -1),
torch.zeros(size_d, size_h),
torch.arange(size_d, dtype=torch.float).unsqueeze(-1).expand(-1, size_h)],
dim=-1)
zh_meter = self.mapping.grid2meter(zh_grid)[..., [1, 2]]
wz_grid = torch.stack(
[torch.zeros(size_w, size_d),
torch.arange(size_w, dtype=torch.float).unsqueeze(-1).expand(-1, size_d),
torch.arange(size_d, dtype=torch.float).unsqueeze(0).expand(size_w, -1)],
dim=-1)
wz_meter = self.mapping.grid2meter(wz_grid)[..., [0, 2]]
positional_encoding.update({'tpv_meters': [hw_meter, zh_meter, wz_meter]})
self.positional_encoding = build_positional_encoding(positional_encoding)
self.tpv_size = [size_h, size_w, size_d]
# transformer layers
if isinstance(transformerlayers, dict):
transformerlayers = [
copy.deepcopy(transformerlayers) for _ in range(num_layers)]
else:
assert isinstance(transformerlayers, list) and \
len(transformerlayers) == num_layers
self.num_layers = num_layers
self.layers = ModuleList()
for i in range(num_layers):
self.layers.append(build_transformer_layer(transformerlayers[i]))
self.pre_norm = self.layers[0].pre_norm
logger.info('use pre_norm: ' + str(self.pre_norm))
# other learnable embeddings
self.level_embeds = nn.Parameter(
torch.randn(self.num_feature_levels, self.embed_dims))
self.cams_embeds = nn.Parameter(
torch.randn(self.num_cams, self.embed_dims))
# prepare reference points used in image cross-attention and cross-view hybrid-attention
self.num_points_cross = num_points_cross
self.num_points_self = num_points_self
uniform_d = torch.linspace(0, size_d - 1, num_points_cross[2])
hw_3d_grid = torch.cat([
hw_grid[..., [0, 1]].unsqueeze(2).expand(-1, -1, num_points_cross[2], -1),
uniform_d.reshape(1, 1, -1, 1).expand(size_h, size_w, -1, -1)], dim=-1)
ref_3d_hw = self.mapping.grid2meter(hw_3d_grid) # H, W, P0, 3
uniform_w = torch.linspace(0, size_w - 1, num_points_cross[1])
zh_3d_grid = torch.cat([
zh_grid[..., :1].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1),
uniform_w.reshape(1, 1, -1, 1).expand(size_d, size_h, -1, -1),
zh_grid[..., 2:].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1)
], dim=-1)
ref_3d_zh = self.mapping.grid2meter(zh_3d_grid) # Z, H, P1, 3
uniform_h = torch.linspace(0, size_h - 1, num_points_cross[0])
wz_3d_grid = torch.cat([
uniform_h.reshape(1, 1, -1, 1).expand(size_w, size_d, -1, -1),
wz_grid[..., [1, 2]].unsqueeze(2).expand(-1, -1, num_points_cross[0], -1)
], dim=-1)
ref_3d_wz = self.mapping.grid2meter(wz_3d_grid) # W, Z, P2, 3
self.register_buffer('ref_3d_hw', ref_3d_hw.flatten(0, 1).transpose(0, 1), False)
self.register_buffer('ref_3d_zh', ref_3d_zh.flatten(0, 1).transpose(0, 1), False)
self.register_buffer('ref_3d_wz', ref_3d_wz.flatten(0, 1).transpose(0, 1), False)
| cross_view_ref_points = get_cross_view_ref_points(size_h, size_w, size_d, num_points_self) | 2 | 2023-11-20 12:49:14+00:00 | 16k |
MobileTeleSystems/CoolGraph | cool_graph/runners.py | [
{
"identifier": "RawDataProcessor",
"path": "cool_graph/data/data_processor.py",
"snippet": "class RawDataProcessor:\n \"\"\"\n Preprocessing datasets.\n\n Args:\n groups_names (Dict[int, str]): Name of groups in nodes.\n group_names_node_features (Dict[str, List[str]]): Name of f... | import os
import pathlib
import hydra
import numpy as np
import optuna
import pandas as pd
import torch
from datetime import datetime
from itertools import product
from pathlib import Path
from typing import Dict, List, Literal, Optional
from hydra import (
compose,
core,
initialize,
initialize_config_dir,
initialize_config_module,
)
from omegaconf import DictConfig, OmegaConf
from optuna.trial import TrialState
from sklearn.model_selection import train_test_split
from torch_geometric.data import Data
from torch_geometric.loader import NeighborLoader, NeighborSampler
from tqdm import tqdm
from cool_graph.data import RawDataProcessor
from cool_graph.data.batch import get_auto_batch_size
from cool_graph.data.loaders import create_loaders
from cool_graph.logging import setup_mlflow_from_config
from cool_graph.parameter_search import (
model_params_to_trial_params,
sample_model_params,
)
from cool_graph.train import Trainer | 12,135 | groups_names=self.groups_names,
mlflow_experiment_name=self.cfg["logging"].get("mlflow_experiment_name"),
n_epochs=self.cfg["training"].get("n_epochs"),
scheduler_params=self.cfg["training"].get("scheduler_params", {}),
scheduler_type=self.cfg["training"].get("scheduler_type"),
target_names=self.target_names,
use_mlflow=self.cfg["logging"].get("use_mlflow", False),
tqdm_disable=False,
target_sizes=self.target_sizes,
**self.cfg["model_params"],
groups_names_num_features=self.groups_names_num_features,
num_edge_features=self.num_edge_features,
metrics=self.metrics,
log_all_metrics=False,
)
result = self.trainer.train()
return result
class HypeRunner(BaseRunner):
"""
Runner for optimization model with Optuna.
https://optuna.readthedocs.io/en/stable/reference/index.html
1st trial - with default config params (hyper_params).
Also, 2nd trial - you can add own trial as argument enqueue_trial in optimazire_run method, and next
trial optuna optimize model params randomly, if set None randomly optimization after 1st default trial.
Args:
data (Data): Loaded dataset.
config (DictConfig): Confif with patams (model_params, logging, training, metrics). Default to None.
config_path (str): Path with config structure (can be loaded with cli get_config). Default to None.
overrides (list): Own params in list. Default to None.
train_size (int): Own train size. Default to None.
test (int): Own test size. Default to None.
seed (int): The desired seed. Default to None.
train_idx (list): List of train indices.
test_idx (list): List of test indices.
Examples
--------
>>> from cool_graph.runners import HypeRunner
>>> from torch_geometric import datasets
>>> # loading amazon dataset
>>> data = datasets.Amazon(root="./data/Amazon", name="Computers").data
>>> runner = HypeRunner(data)
>>> result = runner.run(optimize_run)
Study statistics:
Number of finished trials: 5
Number of complete trials: 5
Best trial:
Value: 0.922
Params:
{'conv_type': 'GraphConv', 'activation': 'leakyrelu', 'lin_prep_len': 1, 'lin_prep_dropout_rate': 0.4,
'lin_prep_weight_norm_flag': True, 'lin_prep_size_common': 512, 'lin_prep_sizes': [256],
'n_hops': 2, 'conv1_aggrs': {'mean': 128, 'max': 64, 'add': 32},
'conv1_dropout_rate': 0.2, 'conv2_aggrs': {'mean': 64, 'max': 32, 'add': 16},
'conv2_dropout_rate': 0.2, 'graph_conv_weight_norm_flag': True}
"""
def __init__(
self,
data: Data,
config: Optional[DictConfig] = None,
config_path: Optional[str] = None,
overrides: Optional[List] = None,
train_size: Optional[int] = None,
test_size: Optional[int] = None,
seed: Optional[int] = None,
train_idx: Optional[List[int]] = None,
test_idx: Optional[List[int]] = None,
):
super().__init__(
data,
config,
config_path,
overrides,
train_size,
test_size,
seed,
train_idx,
test_idx,
)
if config is None:
if config_path is None:
config_path = os.path.join(
os.path.dirname(__file__), "./config/in_memory_data.yaml"
)
config = create_cfg(
config=config_path, overrides=overrides, path_base="cfg"
)
self.study = optuna.study
def optimize_run(
self,
n_trials: int = 100,
storage: Optional[str] = None,
study_name: Optional[str] = None,
enqueue_trial: Optional[List[Dict]] = None,
) -> pd.DataFrame:
if not (hasattr(self, "train_loader") and hasattr(self, "test_loader")):
self.init_loaders()
"""
Method for running objective function in Optuna.
Args:
n_trials (int, optional): The number of trials for each process.
None represents no limit in terms of the number of trials. Defaults to 100.
storage (Optional[str], optional): Database URL. If this argument is set to None,
in-memory storage is used, and the Study will not be persistent. Defaults to None.
study_name (Optional[str], optional): Study name.
If this argument is set to None, a unique name is generated automatically. Defaults to None.
enqueue_trial (Optional[List[Dict]], optional): Enqueue a trial with given parameter values. Defaults to None.
Returns:
trials_dataset (pd.DataFrame): Result dataframe with trial params.
"""
list_with_params = []
def objective(trial) -> float:
|
def create_cfg(config: str, overrides: List[str], path_base: str = "cfg") -> Dict:
assert path_base in ("cfg", "cwd")
core.global_hydra.GlobalHydra.instance().clear()
if os.path.isabs(config):
config_path = pathlib.Path(config).parent
else:
config_path = pathlib.Path(os.getcwd()) / pathlib.Path(config).parent
config_name = pathlib.Path(config).name.replace(".yaml", "")
initialize_config_dir(str(config_path), version_base=None)
cfg = compose(config_name=config_name, overrides=overrides)
return cfg
class ConfigRunner:
r"""Runner for cli mode. Using only in cli.
This class allows to load data + split data per batchs + split data per train/val + training.
See the config full.yaml in ./config for knowing what excactly using as data/logging/model_params/training/metrics.
You can use default params, but also you can change it.
Steps for changing confis:
- make get_config --configs path_where_you_need_configs (default: new path ./configs by itself)
"""
def __init__(self, config: Optional[DictConfig]) -> None:
cfg = OmegaConf.to_container(config, resolve=True)
self.cfg = cfg
self.target_names = cfg["training"]["targets"]
self.groups_names = cfg["data"]["groups_names"]
self.target_weights = cfg["training"]["loss"]["target_weights"]
self.read_edge_attr = cfg["data"].get("read_edge_attr", True)
self.batch_size = cfg["training"]["batch_size"]
self.group_mask_col = cfg["data"]["group_mask_col"]
self.label_mask_col = cfg["data"]["label_mask_col"]
self.label_cols = cfg["data"]["label_cols"]
self.label_index_col = cfg["data"]["label_index_col"]
self.edge_index_cols = cfg["data"]["edge_index_cols"]
self.num_neighbors = cfg["training"]["num_neighbors"]
self.features_edges_names = cfg["data"].get("features_edges")
self.group_names_node_features = cfg["data"]["features"]
self.train_paths = cfg["data"]["train"]
self.val_paths = cfg["data"]["validation"]
self.metrics = cfg["metrics"]
self.chkpt_dir = (
pathlib.Path(cfg["logging"]["checkpoint_dir"]) / str(datetime.now())[:19]
)
os.makedirs(self.chkpt_dir, exist_ok=True)
if self.cfg["logging"].get("use_mlflow", False):
setup_mlflow_from_config(cfg["logging"]["mlflow"])
def init_loaders(self) -> None:
"""
Using RawDataProcessor from cool_graph.data for preprocessing data from disk.
"""
self.train_sampler = RawDataProcessor(
self.groups_names,
self.group_names_node_features,
mon_nodes_path=self.train_paths["nodes_path"],
mon_edges_path=self.train_paths["edges_path"],
mon_labels_path=self.train_paths["labels_path"],
edge_index_cols=self.edge_index_cols,
label_index_col=self.label_index_col,
label_mask_col=self.label_mask_col,
read_edge_attr=self.read_edge_attr,
group_mask_col=self.group_mask_col,
features_edges_names=self.features_edges_names,
label_cols=self.label_cols,
target_names=self.target_names,
)
self.val_sampler = RawDataProcessor(
self.groups_names,
self.group_names_node_features,
mon_nodes_path=self.val_paths["nodes_path"],
mon_edges_path=self.val_paths["edges_path"],
mon_labels_path=self.val_paths["labels_path"],
edge_index_cols=self.edge_index_cols,
label_index_col=self.label_index_col,
label_mask_col=self.label_mask_col,
read_edge_attr=self.read_edge_attr,
group_mask_col=self.group_mask_col,
features_edges_names=self.features_edges_names,
label_cols=self.label_cols,
target_names=self.target_names,
)
def sample_data(
self, seed=0
) -> Dict[Literal["train", "validation"], List[torch.utils.data.DataLoader]]:
"""
Sampling data in batches.
"""
if self.batch_size == "auto":
self._batch_size = get_auto_batch_size(
[len(v) for _, v in self.group_names_node_features.items()],
conv_type=self.cfg["model_params"]["conv_type"],
conv1_aggrs=self.cfg["model_params"]["conv1_aggrs"],
conv2_aggrs=self.cfg["model_params"].get("conv2_aggrs"),
conv3_aggrs=self.cfg["model_params"].get("conv3_aggrs"),
n_hops=self.cfg["model_params"]["n_hops"],
lin_prep_size_common=self.cfg["model_params"]["lin_prep_size_common"],
lin_prep_sizes=self.cfg["model_params"]["lin_prep_sizes"],
edge_attr_repr_sizes=self.cfg["model_params"].get(
"edge_attr_repr_sizes"
),
num_edge_features=len(self.cfg["data"].get("features_edges", [])),
device=self.cfg["training"]["device"],
num_neighbors=self.cfg["training"]["num_neighbors"],
)
else:
self._batch_size = self.batch_size
train_loaders = self.train_sampler.sample_data(
self.num_neighbors, self._batch_size, seed=seed
)
val_loaders = self.val_sampler.sample_data(
self.num_neighbors, self._batch_size, seed=seed
)
return {"train": train_loaders, "validation": val_loaders}
def run(self, seed: int = 0) -> Dict[str, float]:
"""
Train model for train_samples and val_sampler.
Args:
seed (int): seed for training. Default to 0.
Returns:
result (dict): Result of training for each 5 epochs with metrics from config.
"""
if not (hasattr(self, "train_sampler") and hasattr(self, "val_sampler")):
self.init_loaders()
sampled = self.sample_data(seed=seed)
train_loaders = sampled["train"]
val_loaders = sampled["validation"]
self.trainer = Trainer(
train_loaders,
val_loaders,
self.chkpt_dir,
device=self.cfg["training"]["device"],
eval_freq=self.cfg["training"]["eval_freq"],
fill_value=self.cfg["training"]["loss"].get("fill_value"),
initial_lr=self.cfg["training"].get("initial_lr", 0.01),
weight_decay=self.cfg["training"].get("weight_decay", 0.0),
loss_name=self.cfg["training"]["loss"]["name"],
loss_label_smoothing=self.cfg["training"]["loss"].get(
"label_smoothing", False
),
loss_target_weights=self.cfg["training"]["loss"].get("target_weights"),
loss_group_weights=self.cfg["training"]["loss"].get("group_weights"),
groups_names=self.cfg["data"]["groups_names"],
mlflow_experiment_name=self.cfg["logging"].get("mlflow_experiment_name"),
n_epochs=self.cfg["training"].get("n_epochs"),
scheduler_params=self.cfg["training"].get("scheduler_params", {}),
scheduler_type=self.cfg["training"].get("scheduler_type"),
target_names=self.cfg["training"]["targets"],
use_mlflow=self.cfg["logging"].get("use_mlflow", False),
tqdm_disable=False,
**self.cfg["model_params"],
groups_names_num_features={
k: len(v) for k, v in self.group_names_node_features.items()
},
num_edge_features=len(self.cfg["data"].get("features_edges", [])),
metrics=self.metrics,
)
result = self.trainer.train()
return result
class BaseRunner:
def __init__(
self,
data: Data,
config: Optional[DictConfig] = None,
config_path: Optional[str] = None,
overrides: Optional[List] = None,
train_size: Optional[int] = None,
test_size: Optional[int] = None,
seed: Optional[int] = None,
train_idx: Optional[List[int]] = None,
test_idx: Optional[List[int]] = None,
use_edge_attr: bool = False,
**kwargs,
) -> None:
"""
Main class for Basic runner and Runner with Optuna.
Args:
data (Data): A data object describing a homogeneous graph. The data object can hold node-level,
link-level and graph-level attributes. In general, Data tries to mimic the behavior of a regular Python
dictionary. In addition, it provides useful functionality for analyzing graph structures, and provides basic
PyTorch tensor functionalities.
https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html#data-handling-of-graphs
config (DictConfig): Config. Defaults to None.
config_path (str): Path to config. Defaults to None.
overrides (list): Own params. Can ba params from configs and overrides. Defaults to None.
train_size (int): Size for train data. Defaults to None.
test_size (int): Size for test data. Defaults to None.
seed (int): Seed param for training. Defaults to None.
train_idx (list): Indices for train data. Defaults to None.
test_idx (list): Indices for test data. Defaults to None.
use_edge_attr (bool): If attributes exist on edges, it can be used in training. Defaults to False.
"""
if config is None:
if config_path is None:
if use_edge_attr:
config_path = "./config/in_memory_data2.yaml"
else:
config_path = "./config/in_memory_data.yaml"
config_path = os.path.join(os.path.dirname(__file__), config_path)
config = create_cfg(
config=config_path, overrides=overrides, path_base="cfg"
)
cfg = OmegaConf.to_container(config, resolve=True)
self.data = data
self.cfg = cfg
self.test_size = test_size
self.train_size = train_size
self.seed = seed
self.train_idx = train_idx
self.test_idx = test_idx
self.use_edge_attr = use_edge_attr
if use_edge_attr and data.edge_attr is None:
raise BaseException(
"data does not contain edge_attr, please set use_edge_attr=False"
)
self.target_names = cfg["training"]["targets"]
self.target_weights = cfg["training"]["loss"]["target_weights"]
self.batch_size = cfg["training"]["batch_size"]
self.num_neighbors = cfg["training"]["num_neighbors"]
self.metrics = cfg["metrics"]
self.data.group_mask = torch.zeros(len(data.x), dtype=torch.int8)
self.data.label_mask = torch.ones(len(data.x), dtype=torch.bool)
self.groups_names = {0: "x"}
self.groups_names_num_features = {"x": data.x.shape[1]}
if len(data.y.shape) == 2:
self.target_sizes = []
self.target_names = []
self.target_weights = {}
for i in range(data.y.shape[1]):
y_sub = data.y[:, i]
setattr(data, f"y{i}", y_sub)
self.target_sizes.append(len(y_sub.unique()))
self.target_names.append(f"y{i}")
self.target_weights[f"y{i}"] = 1
else:
self.target_names = ["y"]
self.target_sizes = [len(data.y.unique())]
self.target_weights = {"y": 1}
if use_edge_attr:
self.num_edge_features = data.edge_attr.shape[1]
else:
self.num_edge_features = 0
self.chkpt_dir = (
pathlib.Path(cfg["logging"]["checkpoint_dir"]) / str(datetime.now())[:19]
)
for k, v in kwargs.items():
setattr(self, k, v)
if self.cfg["logging"].get("use_mlflow", False):
setup_mlflow_from_config(cfg["logging"]["mlflow"])
def init_loaders(self) -> None:
"""
Sampling data into batches and sampling data with NeighborLoader into list loaders.
"""
if self.batch_size == "auto":
self._batch_size = get_auto_batch_size(
[
self.groups_names_num_features[self.groups_names[i]]
for i in range(len(self.groups_names))
],
conv_type=self.cfg["model_params"]["conv_type"],
conv1_aggrs=self.cfg["model_params"]["conv1_aggrs"],
conv2_aggrs=self.cfg["model_params"].get("conv2_aggrs"),
conv3_aggrs=self.cfg["model_params"].get("conv3_aggrs"),
n_hops=self.cfg["model_params"]["n_hops"],
lin_prep_size_common=self.cfg["model_params"]["lin_prep_size_common"],
lin_prep_sizes=self.cfg["model_params"]["lin_prep_sizes"],
edge_attr_repr_sizes=self.cfg["model_params"].get(
"edge_attr_repr_sizes"
),
num_edge_features=self.num_edge_features,
device=self.cfg["training"]["device"],
num_neighbors=self.num_neighbors,
)
else:
self._batch_size = self.batch_size
if (self.train_idx is None) or (self.test_idx is None):
train_idx, test_idx = train_test_split(
torch.nonzero(self.data.label_mask)[:, 0],
train_size=self.train_size,
test_size=self.test_size,
random_state=self.seed,
shuffle=True,
)
self.train_idx = train_idx
self.test_idx = test_idx
def sample_date_prerpoc(sampled_data: Data) -> Data:
sampled_data.label_mask[sampled_data.batch_size :] = False
for group, name in self.groups_names.items():
x = getattr(sampled_data, name)[sampled_data.group_mask == group]
setattr(sampled_data, name, x)
return sampled_data
loader_train = NeighborLoader(
self.data,
num_neighbors=self.num_neighbors,
batch_size=self._batch_size,
shuffle=True,
input_nodes=self.train_idx,
)
list_loader_train = []
for sampled_data in tqdm(loader_train, desc="Sample data"):
list_loader_train.append(sample_date_prerpoc(sampled_data))
self.train_loader = list_loader_train
loader_test = NeighborLoader(
self.data,
num_neighbors=self.num_neighbors,
batch_size=self._batch_size,
shuffle=True,
input_nodes=self.test_idx,
)
list_loader_test = []
for sampled_data in tqdm(loader_test, desc="Sample data"):
list_loader_test.append(sample_date_prerpoc(sampled_data))
self.test_loader = list_loader_test
class Runner(BaseRunner):
"""
Runner for notebook launch.
Args:
data (Data): A data object describing a homogeneous graph. The data object can hold node-level,
link-level and graph-level attributes. In general, Data tries to mimic the behavior of a regular Python
dictionary. In addition, it provides useful functionality for analyzing graph structures, and provides basic
PyTorch tensor functionalities.
https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html#data-handling-of-graphs
config (DictConfig): Config. Defaults to None.
config_path (str): Path to config. Defaults to None.
overrides (list): Own params. Can ba params from configs and overrides. Defaults to None.
train_size (int): Size for train data. Defaults to None.
test_size (int): Size for test data. Defaults to None.
seed (int): Seed param for training. Defaults to None.
train_idx (int): Indices for train data. Defaults to None.
test_idx (int): Indices for test data. Defaults to None.
use_edge_attr (bool): If attributes exist on edges, it can be used in training. Defaults to False.
Examples
--------
>>> from cool_graph.runners import Runner
>>> from torch_geometric import datasets
>>> # loading amazon dataset
>>> data = datasets.Amazon(root="./data/Amazon", name="Computers").data
>>> runner = Runner(data)
>>> result = runner.run()
>>> result["best_loss"]
{'accuracy': 0.916,
'cross_entropy': 0.286,
'f1_micro': 0.916,
'calc_time': 0.004,
'main_metric': 0.916,
'epoch': 10}
Also you can override params in Runner:
runner = Runner(data, metrics=['accuracy'],
batch_size='auto', train_size=0.7, test_size=0.3,
overrides=['training.n_epochs=1'], config_path=path/to/config)
result = runner.run()
"""
def __init__(
self,
data: Data,
config: Optional[DictConfig] = None,
config_path: Optional[str] = None,
overrides: Optional[List] = None,
train_size: Optional[int] = None,
test_size: Optional[int] = None,
seed: Optional[int] = None,
train_idx: Optional[List[int]] = None,
test_idx: Optional[List[int]] = None,
use_edge_attr: bool = False,
**kwargs,
):
super().__init__(
data,
config,
config_path,
overrides,
train_size,
test_size,
seed,
train_idx,
test_idx,
use_edge_attr,
**kwargs,
)
def run(self) -> Dict[str, float]:
"""
Training model with params in_memory_data/in_memory_data2 config.
See the configs in ./config for knowing what excactly using as logging/model_params/training/metrics.
You can use default params, but also you can change it.
Steps for changing confis:
- make get_config --configs path_where_you_need_configs (default: new path ./configs by itself)
"""
if not (hasattr(self, "train_loader") and hasattr(self, "test_loader")):
self.init_loaders()
self.trainer = Trainer(
self.train_loader,
self.test_loader,
self.chkpt_dir,
device=self.cfg["training"]["device"],
eval_freq=self.cfg["training"]["eval_freq"],
fill_value=self.cfg["training"]["loss"].get("fill_value"),
initial_lr=self.cfg["training"].get("initial_lr", 0.01),
weight_decay=self.cfg["training"].get("weight_decay", 0.0),
loss_name=self.cfg["training"]["loss"]["name"],
loss_label_smoothing=self.cfg["training"]["loss"].get(
"label_smoothing", False
),
loss_target_weights=self.target_weights,
loss_group_weights=self.cfg["training"]["loss"].get("group_weights"),
groups_names=self.groups_names,
mlflow_experiment_name=self.cfg["logging"].get("mlflow_experiment_name"),
n_epochs=self.cfg["training"].get("n_epochs"),
scheduler_params=self.cfg["training"].get("scheduler_params", {}),
scheduler_type=self.cfg["training"].get("scheduler_type"),
target_names=self.target_names,
use_mlflow=self.cfg["logging"].get("use_mlflow", False),
tqdm_disable=False,
target_sizes=self.target_sizes,
**self.cfg["model_params"],
groups_names_num_features=self.groups_names_num_features,
num_edge_features=self.num_edge_features,
metrics=self.metrics,
log_all_metrics=False,
)
result = self.trainer.train()
return result
class HypeRunner(BaseRunner):
"""
Runner for optimization model with Optuna.
https://optuna.readthedocs.io/en/stable/reference/index.html
1st trial - with default config params (hyper_params).
Also, 2nd trial - you can add own trial as argument enqueue_trial in optimazire_run method, and next
trial optuna optimize model params randomly, if set None randomly optimization after 1st default trial.
Args:
data (Data): Loaded dataset.
config (DictConfig): Confif with patams (model_params, logging, training, metrics). Default to None.
config_path (str): Path with config structure (can be loaded with cli get_config). Default to None.
overrides (list): Own params in list. Default to None.
train_size (int): Own train size. Default to None.
test (int): Own test size. Default to None.
seed (int): The desired seed. Default to None.
train_idx (list): List of train indices.
test_idx (list): List of test indices.
Examples
--------
>>> from cool_graph.runners import HypeRunner
>>> from torch_geometric import datasets
>>> # loading amazon dataset
>>> data = datasets.Amazon(root="./data/Amazon", name="Computers").data
>>> runner = HypeRunner(data)
>>> result = runner.run(optimize_run)
Study statistics:
Number of finished trials: 5
Number of complete trials: 5
Best trial:
Value: 0.922
Params:
{'conv_type': 'GraphConv', 'activation': 'leakyrelu', 'lin_prep_len': 1, 'lin_prep_dropout_rate': 0.4,
'lin_prep_weight_norm_flag': True, 'lin_prep_size_common': 512, 'lin_prep_sizes': [256],
'n_hops': 2, 'conv1_aggrs': {'mean': 128, 'max': 64, 'add': 32},
'conv1_dropout_rate': 0.2, 'conv2_aggrs': {'mean': 64, 'max': 32, 'add': 16},
'conv2_dropout_rate': 0.2, 'graph_conv_weight_norm_flag': True}
"""
def __init__(
self,
data: Data,
config: Optional[DictConfig] = None,
config_path: Optional[str] = None,
overrides: Optional[List] = None,
train_size: Optional[int] = None,
test_size: Optional[int] = None,
seed: Optional[int] = None,
train_idx: Optional[List[int]] = None,
test_idx: Optional[List[int]] = None,
):
super().__init__(
data,
config,
config_path,
overrides,
train_size,
test_size,
seed,
train_idx,
test_idx,
)
if config is None:
if config_path is None:
config_path = os.path.join(
os.path.dirname(__file__), "./config/in_memory_data.yaml"
)
config = create_cfg(
config=config_path, overrides=overrides, path_base="cfg"
)
self.study = optuna.study
def optimize_run(
self,
n_trials: int = 100,
storage: Optional[str] = None,
study_name: Optional[str] = None,
enqueue_trial: Optional[List[Dict]] = None,
) -> pd.DataFrame:
if not (hasattr(self, "train_loader") and hasattr(self, "test_loader")):
self.init_loaders()
"""
Method for running objective function in Optuna.
Args:
n_trials (int, optional): The number of trials for each process.
None represents no limit in terms of the number of trials. Defaults to 100.
storage (Optional[str], optional): Database URL. If this argument is set to None,
in-memory storage is used, and the Study will not be persistent. Defaults to None.
study_name (Optional[str], optional): Study name.
If this argument is set to None, a unique name is generated automatically. Defaults to None.
enqueue_trial (Optional[List[Dict]], optional): Enqueue a trial with given parameter values. Defaults to None.
Returns:
trials_dataset (pd.DataFrame): Result dataframe with trial params.
"""
list_with_params = []
def objective(trial) -> float: | self.cfg["model_params"] = sample_model_params( | 5 | 2023-11-22 09:44:16+00:00 | 16k |
HeliosZhao/Animate124 | nerf/network_grid_taichi.py | [
{
"identifier": "trunc_exp",
"path": "activation.py",
"snippet": "class _trunc_exp(Function):\n def forward(ctx, x):\n def backward(ctx, g):\ndef biased_softplus(x, bias=0):"
},
{
"identifier": "NeRFRenderer",
"path": "nerf/renderer.py",
"snippet": "class NeRFRenderer(nn.Module):\n... | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from activation import trunc_exp
from .renderer import NeRFRenderer
from encoding import get_encoder
from .utils import safe_normalize
from tqdm import tqdm | 13,213 |
class MLP(nn.Module):
def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dim_hidden = dim_hidden
self.num_layers = num_layers
net = []
for l in range(num_layers):
net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias))
self.net = nn.ModuleList(net)
def forward(self, x):
for l in range(self.num_layers):
x = self.net[l](x)
if l != self.num_layers - 1:
x = F.relu(x, inplace=True)
return x
def reset_parameters(self):
@torch.no_grad()
def weight_init(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('relu'))
nn.init.zeros_(m.bias)
self.apply(weight_init)
|
class MLP(nn.Module):
def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dim_hidden = dim_hidden
self.num_layers = num_layers
net = []
for l in range(num_layers):
net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias))
self.net = nn.ModuleList(net)
def forward(self, x):
for l in range(self.num_layers):
x = self.net[l](x)
if l != self.num_layers - 1:
x = F.relu(x, inplace=True)
return x
def reset_parameters(self):
@torch.no_grad()
def weight_init(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('relu'))
nn.init.zeros_(m.bias)
self.apply(weight_init)
| class NeRFNetwork(NeRFRenderer): | 1 | 2023-11-23 10:34:08+00:00 | 16k |
alexzhou907/DreamPropeller | extern/ldm_zero123/models/diffusion/ddpm.py | [
{
"identifier": "AutoencoderKL",
"path": "extern/ldm_zero123/models/autoencoder.py",
"snippet": "class AutoencoderKL(pl.LightningModule):\n def __init__(\n self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"i... | import itertools
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
from contextlib import contextmanager, nullcontext
from functools import partial
from einops import rearrange, repeat
from omegaconf import ListConfig
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm
from extern.ldm_zero123.models.autoencoder import (
AutoencoderKL,
IdentityFirstStage,
VQModelInterface,
)
from extern.ldm_zero123.models.diffusion.ddim import DDIMSampler
from extern.ldm_zero123.modules.attention import CrossAttention
from extern.ldm_zero123.modules.diffusionmodules.util import (
extract_into_tensor,
make_beta_schedule,
noise_like,
)
from extern.ldm_zero123.modules.distributions.distributions import (
DiagonalGaussianDistribution,
normal_kl,
)
from extern.ldm_zero123.modules.ema import LitEma
from extern.ldm_zero123.util import (
count_params,
default,
exists,
instantiate_from_config,
isimage,
ismap,
log_txt_as_img,
mean_flat,
) | 11,983 | else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
return self.p_sample_loop(
cond,
shape,
return_intermediates=return_intermediates,
x_T=x_T,
verbose=verbose,
timesteps=timesteps,
quantize_denoised=quantize_denoised,
mask=mask,
x0=x0,
)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(
ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
)
else:
samples, intermediates = self.sample(
cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs
)
return samples, intermediates
@torch.no_grad()
def get_unconditional_conditioning(
self, batch_size, null_label=None, image_size=512
):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [
torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(
self.device
)
]
return cond
@torch.no_grad()
def log_images(
self,
batch,
N=8,
n_row=4,
sample=True,
ddim_steps=200,
ddim_eta=1.0,
return_keys=None,
quantize_denoised=True,
inpaint=True,
plot_denoise_rows=False,
plot_progressive_rows=True,
plot_diffusion_rows=True,
unconditional_guidance_scale=1.0,
unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs,
):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(
batch,
self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N,
)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch[self.cond_stage_key],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif self.cond_stage_key == "class_label":
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch["human_label"],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif isimage(xc):
log["conditioning"] = xc
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in [
"eps",
"x0",
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def get_loss(self, pred, target, mean=True):
if self.loss_type == "l1":
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == "l2":
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(
f"Paramterization {self.parameterization} not yet supported"
)
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = "train" if self.training else "val"
loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f"{log_prefix}/loss": loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, "b h w c -> b c h w")
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
loss, loss_dict = self.shared_step(batch)
self.log_dict(
loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
)
self.log(
"global_step",
self.global_step,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=False,
)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]["lr"]
self.log(
"lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(
loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
self.log_dict(
loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(
batch_size=N, return_intermediates=True
)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(
self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs["timesteps"]
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = "concat" if concat_mode else "crossattn"
if cond_stage_config == "__is_unconditional__":
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer("scale_factor", torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if (
self.scale_by_std
and self.current_epoch == 0
and self.global_step == 0
and batch_idx == 0
and not self.restarted_from_ckpt
):
assert (
self.scale_factor == 1.0
), "rather not use custom rescaling and std-rescaling simultaneously"
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer("scale_factor", 1.0 / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != "__is_first_stage__"
assert config != "__is_unconditional__"
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(
self, samples, desc="", force_no_decoder_quantization=False
):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(
self.decode_first_stage(
zd.to(self.device), force_not_quantize=force_no_decoder_quantization
)
)
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(
f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
)
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, "encode") and callable(
self.cond_stage_model.encode
):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(
torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(
weighting,
self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"],
)
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(
L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"],
)
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(
self, x, kernel_size, stride, uf=1, df=1
): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(
kernel_size[0], kernel_size[1], Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1,
padding=0,
stride=(stride[0] * uf, stride[1] * uf),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h * uf, w * uf
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
)
elif df > 1 and uf == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1,
padding=0,
stride=(stride[0] // df, stride[1] // df),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h // df, w // df
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
)
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(
self,
batch,
k,
return_first_stage_outputs=False,
force_c_encode=False,
cond_key=None,
return_original_cond=False,
bs=None,
uncond=0.05,
):
x = super().get_input(batch, k)
T = batch["T"].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange(
(random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1"
)
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [
self.cc_projection(
torch.cat(
[
torch.where(prompt_mask, null_prompt, clip_emb),
T[:, None, :],
],
dim=-1,
)
)
]
cond["c_concat"] = [
input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()
]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
# @torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, "b h w c -> b c h w").contiguous()
z = 1.0 / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
z, ks, stride, uf=uf
)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [
self.first_stage_model.decode(
z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize,
)
for i in range(z.shape[-1])
]
else:
output_list = [
self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])
]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(
z, force_not_quantize=predict_cids or force_not_quantize
)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(
z, force_not_quantize=predict_cids or force_not_quantize
)
else:
return self.first_stage_model.decode(z)
# @torch.no_grad() # wasted two hours to find this bug... why no grad here!
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params["original_image_size"] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
x, ks, stride, df=df
)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
output_list = [
self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])
]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
def forward(self, x, c, *args, **kwargs):
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
if self.model.conditioning_key is not None:
assert c is not None
# if self.cond_stage_trainable:
# c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = (
"c_concat" if self.model.conditioning_key == "concat" else "c_crossattn"
)
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
fold, unfold, normalization, weighting = self.get_fold_unfold(
x_noisy, ks, stride
)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if (
self.cond_stage_key in ["image", "LR_image", "segmentation", "bbox_img"]
and self.model.conditioning_key
): # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert len(c) == 1 # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view(
(c.shape[0], -1, ks[0], ks[1], c.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == "coordinates_bbox":
assert (
"original_image_size" in self.split_input_params
), "BoudingBoxRescaling is missing original_image_size"
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params["original_image_size"]
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [
(
rescale_latent
* stride[0]
* (patch_nr % n_patches_per_row)
/ full_img_w,
rescale_latent
* stride[1]
* (patch_nr // n_patches_per_row)
/ full_img_h,
)
for patch_nr in range(z.shape[-1])
]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [
(
x_tl,
y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h,
)
for x_tl, y_tl in tl_patch_coordinates
]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [
torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(
self.device
)
for bbox in patch_limits
] # list of length l with tensors of shape (1, 2)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), "cond must be dict to be fed into model"
cut_cond = cond["c_crossattn"][0][..., :-2].to(self.device)
adapted_cond = torch.stack(
[torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]
)
adapted_cond = rearrange(adapted_cond, "l b n -> (l b) n")
adapted_cond = self.get_learned_conditioning(adapted_cond)
adapted_cond = rearrange(
adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1]
)
cond_list = [{"c_crossattn": [e]} for e in adapted_cond]
else:
cond_list = [
cond for i in range(z.shape[-1])
] # Todo make this more efficient
# apply model by loop over crops
output_list = [
self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])
]
assert not isinstance(
output_list[0], tuple
) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- pred_xstart
) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(
mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = "train" if self.training else "val"
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f"{prefix}/loss_simple": loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f"{prefix}/loss_gamma": loss.mean()})
loss_dict.update({"logvar": self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f"{prefix}/loss_vlb": loss_vlb})
loss += self.original_elbo_weight * loss_vlb
loss_dict.update({f"{prefix}/loss": loss})
return loss, loss_dict
def p_mean_variance(
self,
x,
c,
t,
clip_denoised: bool,
return_codebook_ids=False,
quantize_denoised=False,
return_x0=False,
score_corrector=None,
corrector_kwargs=None,
):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(
self, model_out, x, t, c, **corrector_kwargs
)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(
self,
x,
c,
t,
clip_denoised=False,
repeat_noise=False,
return_codebook_ids=False,
quantize_denoised=False,
return_x0=False,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(
x=x,
c=c,
t=t,
clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (
0.5 * model_log_variance
).exp() * noise, logits.argmax(dim=1)
if return_x0:
return (
model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
x0,
)
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(
self,
cond,
shape,
verbose=True,
callback=None,
quantize_denoised=False,
img_callback=None,
mask=None,
x0=None,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
batch_size=None,
x_T=None,
start_T=None,
log_every_t=None,
):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {
key: cond[key][:batch_size]
if not isinstance(cond[key], list)
else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = (
tqdm(
reversed(range(0, timesteps)),
desc="Progressive Generation",
total=timesteps,
)
if verbose
else reversed(range(0, timesteps))
)
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != "hybrid"
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(
img,
cond,
ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised,
return_x0=True,
temperature=temperature[i],
noise_dropout=noise_dropout,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(
self,
cond,
shape,
return_intermediates=False,
x_T=None,
verbose=True,
callback=None,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
img_callback=None,
start_T=None,
log_every_t=None,
):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = (
tqdm(reversed(range(0, timesteps)), desc="Sampling t", total=timesteps)
if verbose
else reversed(range(0, timesteps))
)
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != "hybrid"
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(
img,
cond,
ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised,
)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(
self,
cond,
batch_size=16,
return_intermediates=False,
x_T=None,
verbose=True,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
shape=None,
**kwargs,
):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {
key: cond[key][:batch_size]
if not isinstance(cond[key], list)
else list(map(lambda x: x[:batch_size], cond[key]))
for key in cond
}
else:
cond = (
[c[:batch_size] for c in cond]
if isinstance(cond, list)
else cond[:batch_size]
)
return self.p_sample_loop(
cond,
shape,
return_intermediates=return_intermediates,
x_T=x_T,
verbose=verbose,
timesteps=timesteps,
quantize_denoised=quantize_denoised,
mask=mask,
x0=x0,
)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(
ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
)
else:
samples, intermediates = self.sample(
cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs
)
return samples, intermediates
@torch.no_grad()
def get_unconditional_conditioning(
self, batch_size, null_label=None, image_size=512
):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [
torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(
self.device
)
]
return cond
@torch.no_grad()
def log_images(
self,
batch,
N=8,
n_row=4,
sample=True,
ddim_steps=200,
ddim_eta=1.0,
return_keys=None,
quantize_denoised=True,
inpaint=True,
plot_denoise_rows=False,
plot_progressive_rows=True,
plot_diffusion_rows=True,
unconditional_guidance_scale=1.0,
unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs,
):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(
batch,
self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N,
)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption", "txt"]:
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch[self.cond_stage_key],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif self.cond_stage_key == "class_label":
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch["human_label"],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif isimage(xc):
log["conditioning"] = xc | if ismap(xc): | 16 | 2023-11-27 23:39:49+00:00 | 16k |
CineMingle/CineMingle | Movie_Data_Capture.py | [
{
"identifier": "get_data_from_json",
"path": "scraper.py",
"snippet": "def get_data_from_json(\n file_number: str,\n open_cc: opencc.OpenCC,\n specified_source: str, specified_url: str) -> typing.Optional[dict]:\n \n # iterate through all services and fetch the data 从网站上查询片名解... | import argparse
import json
import os
import random
import re
import sys
import time
import shutil
import typing
import urllib3
import signal
import platform
import config
from datetime import datetime, timedelta
from lxml import etree
from pathlib import Path
from opencc import OpenCC
from scraper import get_data_from_json
from ADC_function import file_modification_days, get_html, parallel_download_files
from number_parser import get_number
from core import core_main, core_main_no_net_op, moveFailedFolder, debug_print | 13,287 | # 100MB的日志文件能缩小到3.7MB。
return filepath
def signal_handler(*args):
"""
A signal handler function for handling operating system signals like Ctrl+C (SIGINT). It defines the behavior of
the application when such signals are received, such as graceful termination.
:param args: Variable argument list, used to handle signal information.
"""
print('[!]Ctrl+C detected, Exit.')
os._exit(9)
def sigdebug_handler(*args):
"""
A signal handler function specifically for toggling debug mode on or off. It alters the debug configuration
based on certain system signals (like window size change in Unix systems).
:param args: Variable argument list, used to handle signal information.
"""
conf = config.getInstance()
conf.set_override(f"debug_mode:switch={int(not conf.debug())}")
print(f"[!]Debug {('oFF', 'On')[int(conf.debug())]}")
# 新增失败文件列表跳过处理，及.nfo修改天数跳过处理，提示跳过视频总数，调试模式(-g)下详细被跳过文件，跳过小广告
def movie_lists(source_folder, regexstr: str) -> typing.List[str]:
"""
Generates a list of movie file paths from the specified source folder. It filters files based on regular
expressions and other criteria, such as file type and size.
:param source_folder: The folder to scan for movie files.
:param regexstr: A regular expression string to filter movie files.
:return: A list of paths to the movie files that match the criteria.
"""
conf = config.getInstance()
main_mode = conf.main_mode()
debug = conf.debug()
nfo_skip_days = conf.nfo_skip_days()
link_mode = conf.link_mode()
file_type = conf.media_type().lower().split(",")
trailerRE = re.compile(r'-trailer\.', re.IGNORECASE)
cliRE = None
if isinstance(regexstr, str) and len(regexstr):
try:
cliRE = re.compile(regexstr, re.IGNORECASE)
except:
pass
failed_list_txt_path = Path(conf.failed_folder()).resolve() / 'failed_list.txt'
failed_set = set()
if (main_mode == 3 or link_mode) and not conf.ignore_failed_list():
try:
flist = failed_list_txt_path.read_text(encoding='utf-8').splitlines()
failed_set = set(flist)
if len(flist) != len(failed_set): # 检查去重并写回，但是不改变failed_list.txt内条目的先后次序，重复的只保留最后的
fset = failed_set.copy()
for i in range(len(flist) - 1, -1, -1):
fset.remove(flist[i]) if flist[i] in fset else flist.pop(i)
failed_list_txt_path.write_text('\n'.join(flist) + '\n', encoding='utf-8')
assert len(fset) == 0 and len(flist) == len(failed_set)
except:
pass
if not Path(source_folder).is_dir():
print('[-]Source folder not found!')
return []
total = []
# source = Path(source_folder).resolve()
source = Path(source_folder)
skip_failed_cnt, skip_nfo_days_cnt = 0, 0
escape_folder_set = set(re.split("[,，]", conf.escape_folder()))
for full_name in source.glob(r'**/*'):
if main_mode != 3 and set(full_name.parent.parts) & escape_folder_set:
continue
if not full_name.suffix.lower() in file_type:
continue
absf = str(full_name)
if absf in failed_set:
skip_failed_cnt += 1
if debug:
print('[!]Skip failed movie:', absf)
continue
is_sym = full_name.is_symlink()
if main_mode != 3 and (is_sym or (full_name.stat().st_nlink > 1 and not conf.scan_hardlink())): # 短路布尔 符号链接不取stat()，因为符号链接可能指向不存在目标
continue # 模式不等于3下跳过软连接和未配置硬链接刮削
# 调试用0字节样本允许通过，去除小于120MB的广告'苍老师强力推荐.mp4'(102.2MB)'黑道总裁.mp4'(98.4MB)'有趣的妹子激情表演.MP4'(95MB)'有趣的臺灣妹妹直播.mp4'(15.1MB)
movie_size = 0 if is_sym else full_name.stat().st_size # 同上 符号链接不取stat()及st_size，直接赋0跳过小视频检测
# if 0 < movie_size < 125829120: # 1024*1024*120=125829120
# continue
if cliRE and not cliRE.search(absf) or trailerRE.search(full_name.name):
continue
if main_mode == 3:
nfo = full_name.with_suffix('.nfo')
if not nfo.is_file():
if debug:
print(f"[!]Metadata {nfo.name} not found for '{absf}'")
elif nfo_skip_days > 0 and file_modification_days(nfo) <= nfo_skip_days:
skip_nfo_days_cnt += 1
if debug:
print(f"[!]Skip movie by it's .nfo which modified within {nfo_skip_days} days: '{absf}'")
continue
total.append(absf)
if skip_failed_cnt:
print(f"[!]Skip {skip_failed_cnt} movies in failed list '{failed_list_txt_path}'.")
if skip_nfo_days_cnt:
print(
f"[!]Skip {skip_nfo_days_cnt} movies in source folder '{source}' who's .nfo modified within {nfo_skip_days} days.")
if nfo_skip_days <= 0 or not link_mode or main_mode == 3:
return total
# 软连接方式，已经成功削刮的也需要从成功目录中检查.nfo更新天数，跳过N天内更新过的
skip_numbers = set()
success_folder = Path(conf.success_folder()).resolve()
for f in success_folder.glob(r'**/*'):
if not re.match(r'\.nfo$', f.suffix, re.IGNORECASE):
continue
if file_modification_days(f) > nfo_skip_days:
continue
|
def check_update(local_version):
"""
Check for updates by comparing the local version of the application with the latest version available on GitHub.
It fetches the latest release information from GitHub and compares the version numbers.
If a new version is available, it prints out the update information.
:param local_version: The current local version of the application.
"""
htmlcode = get_html("https://api.github.com/repos/CineMingle/CineMingle/releases/latest")
data = json.loads(htmlcode)
remote = int(data["tag_name"].replace(".", ""))
local_version = int(local_version.replace(".", ""))
if local_version < remote:
print("[*]" + ("* New update " + str(data["tag_name"]) + " *").center(54))
print("[*]" + "↓ Download ↓".center(54))
print("[*]https://github.com/CineMingle/CineMingle/releases")
print("[*]======================================================")
def argparse_function(ver: str) -> typing.Tuple[str, str, str, str, bool, bool, str, str]:
"""
Parses command-line arguments and returns the parsed values. It sets up the argument parser with various options
for the application and returns the parsed arguments and their values. It also loads configuration from a config file.
:param ver: The version of the application, used for the version argument.
:return: A tuple containing various parsed arguments and flags.
"""
conf = config.getInstance()
parser = argparse.ArgumentParser(epilog=f"Load Config file '{conf.ini_path}'.")
parser.add_argument("file", default='', nargs='?', help="Single Movie file path.")
parser.add_argument("-p", "--path", default='movies', nargs='?', help="Analysis folder path.")
parser.add_argument("-m", "--main-mode", default='', nargs='?',
help="Main mode. 1:Scraping 2:Organizing 3:Scraping in analysis folder")
parser.add_argument("-n", "--number", default='', nargs='?', help="Custom file number of single movie file.")
# parser.add_argument("-C", "--config", default='config.ini', nargs='?', help="The config file Path.")
parser.add_argument("-L", "--link-mode", default='', nargs='?',
help="Create movie file link. 0:moving movie file, do not create link 1:soft link 2:try hard link first")
default_logdir = str(Path.home() / '.mlogs')
parser.add_argument("-o", "--log-dir", dest='logdir', default=default_logdir, nargs='?',
help=f"""Duplicate stdout and stderr to logfiles in logging folder, default on.
default folder for current user: '{default_logdir}'. Change default folder to an empty file,
or use --log-dir= to turn log off.""")
parser.add_argument("-q", "--regex-query", dest='regexstr', default='', nargs='?',
help="python re module regex filepath filtering.")
parser.add_argument("-d", "--nfo-skip-days", dest='days', default='', nargs='?',
help="Override nfo_skip_days value in config.")
parser.add_argument("-c", "--stop-counter", dest='cnt', default='', nargs='?',
help="Override stop_counter value in config.")
parser.add_argument("-R", "--rerun-delay", dest='delaytm', default='', nargs='?',
help="Delay (eg. 1h10m30s or 60 (second)) time and rerun, until all movies proceed. Note: stop_counter value in config or -c must none zero.")
parser.add_argument("-i", "--ignore-failed-list", action="store_true", help="Ignore failed list '{}'".format(
os.path.join(os.path.abspath(conf.failed_folder()), 'failed_list.txt')))
parser.add_argument("-a", "--auto-exit", action="store_true",
help="Auto exit after program complete")
parser.add_argument("-g", "--debug", action="store_true",
help="Turn on debug mode to generate diagnostic log for issue report.")
parser.add_argument("-N", "--no-network-operation", action="store_true",
help="No network query, do not get metadata, for cover cropping purposes, only takes effect when main mode is 3.")
parser.add_argument("-w", "--website", dest='site', default='', nargs='?',
help="Override [priority]website= in config.")
parser.add_argument("-D", "--download-images", dest='dnimg', action="store_true",
help="Override [common]download_only_missing_images=0 force invoke image downloading.")
parser.add_argument("-C", "--config-override", dest='cfgcmd', action='append', nargs=1,
help="Common use config override. Grammar: section:key=value[;[section:]key=value] eg. 'de:s=1' or 'debug_mode:switch=1' override[debug_mode]switch=1 Note:this parameters can be used multiple times")
parser.add_argument("-z", "--zero-operation", dest='zero_op', action="store_true",
help="""Only show job list of files and numbers, and **NO** actual operation
is performed. It may help you correct wrong numbers before real job.""")
parser.add_argument("-v", "--version", action="version", version=ver)
parser.add_argument("-s", "--search", default='', nargs='?', help="Search number")
parser.add_argument("-ss", "--specified-source", default='', nargs='?', help="specified Source.")
parser.add_argument("-su", "--specified-url", default='', nargs='?', help="specified Url.")
args = parser.parse_args()
def set_natural_number_or_none(sk, value):
if isinstance(value, str) and value.isnumeric() and int(value) >= 0:
conf.set_override(f'{sk}={value}')
def set_str_or_none(sk, value):
if isinstance(value, str) and len(value):
conf.set_override(f'{sk}={value}')
def set_bool_or_none(sk, value):
if isinstance(value, bool) and value:
conf.set_override(f'{sk}=1')
set_natural_number_or_none("common:main_mode", args.main_mode)
set_natural_number_or_none("common:link_mode", args.link_mode)
set_str_or_none("common:source_folder", args.path)
set_bool_or_none("common:auto_exit", args.auto_exit)
set_natural_number_or_none("common:nfo_skip_days", args.days)
set_natural_number_or_none("advenced_sleep:stop_counter", args.cnt)
set_bool_or_none("common:ignore_failed_list", args.ignore_failed_list)
set_str_or_none("advenced_sleep:rerun_delay", args.delaytm)
set_str_or_none("priority:website", args.site)
if isinstance(args.dnimg, bool) and args.dnimg:
conf.set_override("common:download_only_missing_images=0")
set_bool_or_none("debug_mode:switch", args.debug)
if isinstance(args.cfgcmd, list):
for cmd in args.cfgcmd:
conf.set_override(cmd[0])
no_net_op = False
if conf.main_mode() == 3:
no_net_op = args.no_network_operation
if no_net_op:
conf.set_override("advenced_sleep:stop_counter=0;advenced_sleep:rerun_delay=0s;face:aways_imagecut=1")
return args.file, args.number, args.logdir, args.regexstr, args.zero_op, no_net_op, args.search, args.specified_source, args.specified_url
class OutLogger(object):
def __init__(self, logfile) -> None:
self.term = sys.stdout
self.log = open(logfile, "w", encoding='utf-8', buffering=1)
self.filepath = logfile
def __del__(self):
self.close()
def __enter__(self):
pass
def __exit__(self, *args):
self.close()
def write(self, msg):
self.term.write(msg)
self.log.write(msg)
def flush(self):
if 'flush' in dir(self.term):
self.term.flush()
if 'flush' in dir(self.log):
self.log.flush()
if 'fileno' in dir(self.log):
os.fsync(self.log.fileno())
def close(self):
if self.term is not None:
sys.stdout = self.term
self.term = None
if self.log is not None:
self.log.close()
self.log = None
class ErrLogger(OutLogger):
def __init__(self, logfile) -> None:
self.term = sys.stderr
self.log = open(logfile, "w", encoding='utf-8', buffering=1)
self.filepath = logfile
def close(self):
if self.term is not None:
sys.stderr = self.term
self.term = None
if self.log is not None:
self.log.close()
self.log = None
def dupe_stdout_to_logfile(logdir: str):
"""
Duplicates the standard output (stdout) and standard error (stderr) to log files. This function creates log files
in the specified directory and redirects stdout and stderr to these files for logging purposes.
:param logdir: The directory where log files will be created and saved.
"""
if not isinstance(logdir, str) or len(logdir) == 0:
return
log_dir = Path(logdir)
if not log_dir.exists():
try:
log_dir.mkdir(parents=True, exist_ok=True)
except:
pass
if not log_dir.is_dir():
return # Tips for disabling logs by change directory to a same name empty regular file
abslog_dir = log_dir.resolve()
log_tmstr = datetime.now().strftime("%Y%m%dT%H%M%S")
logfile = abslog_dir / f'mdc_{log_tmstr}.txt'
errlog = abslog_dir / f'mdc_{log_tmstr}_err.txt'
sys.stdout = OutLogger(logfile)
sys.stderr = ErrLogger(errlog)
def close_logfile(logdir: str):
"""
Closes the log files and restores standard output and error streams. This function is typically called at the end
of the application to ensure that log files are properly closed.
:param logdir: The directory where log files are saved.
"""
if not isinstance(logdir, str) or len(logdir) == 0 or not os.path.isdir(logdir):
return
# 日志关闭前保存日志路径
filepath = None
try:
filepath = sys.stdout.filepath
except:
pass
sys.stdout.close()
sys.stderr.close()
log_dir = Path(logdir).resolve()
if isinstance(filepath, Path):
print(f"Log file '{filepath}' saved.")
assert (filepath.parent.samefile(log_dir))
# 清理空文件
for f in log_dir.glob(r'*_err.txt'):
if f.stat().st_size == 0:
try:
f.unlink(missing_ok=True)
except:
pass
# 合并日志 只检测日志目录内的文本日志，忽略子目录。三天前的日志，按日合并为单个日志，三个月前的日志，
# 按月合并为单个月志，去年及以前的月志，今年4月以后将之按年合并为年志
# 测试步骤：
"""
LOGDIR=/tmp/mlog
mkdir -p $LOGDIR
for f in {2016..2020}{01..12}{01..28};do;echo $f>$LOGDIR/mdc_${f}T235959.txt;done
for f in {01..09}{01..28};do;echo 2021$f>$LOGDIR/mdc_2021${f}T235959.txt;done
for f in {00..23};do;echo 20211001T$f>$LOGDIR/mdc_20211001T${f}5959.txt;done
echo "$(ls -1 $LOGDIR|wc -l) files in $LOGDIR"
# 1932 files in /tmp/mlog
mdc -zgic1 -d0 -m3 -o $LOGDIR
# python3 ./Movie_Data_Capture.py -zgic1 -o $LOGDIR
ls $LOGDIR
# rm -rf $LOGDIR
"""
today = datetime.today()
# 第一步，合并到日。3天前的日志，文件名是同一天的合并为一份日志
for i in range(1):
txts = [f for f in log_dir.glob(r'*.txt') if re.match(r'^mdc_\d{8}T\d{6}$', f.stem, re.A)]
if not txts or not len(txts):
break
e = [f for f in txts if '_err' in f.stem]
txts.sort()
tmstr_3_days_ago = (today.replace(hour=0) - timedelta(days=3)).strftime("%Y%m%dT99")
deadline_day = f'mdc_{tmstr_3_days_ago}'
day_merge = [f for f in txts if f.stem < deadline_day]
if not day_merge or not len(day_merge):
break
cutday = len('T235959.txt') # cut length mdc_20201201|T235959.txt
for f in day_merge:
try:
day_file_name = str(f)[:-cutday] + '.txt' # mdc_20201201.txt
with open(day_file_name, 'a', encoding='utf-8') as m:
m.write(f.read_text(encoding='utf-8'))
f.unlink(missing_ok=True)
except:
pass
# 第二步，合并到月
for i in range(1): # 利用1次循环的break跳到第二步，避免大块if缩进或者使用goto语法
txts = [f for f in log_dir.glob(r'*.txt') if re.match(r'^mdc_\d{8}$', f.stem, re.A)]
if not txts or not len(txts):
break
txts.sort()
tmstr_3_month_ago = (today.replace(day=1) - timedelta(days=3 * 30)).strftime("%Y%m32")
deadline_month = f'mdc_{tmstr_3_month_ago}'
month_merge = [f for f in txts if f.stem < deadline_month]
if not month_merge or not len(month_merge):
break
tomonth = len('01.txt') # cut length mdc_202012|01.txt
for f in month_merge:
try:
month_file_name = str(f)[:-tomonth] + '.txt' # mdc_202012.txt
with open(month_file_name, 'a', encoding='utf-8') as m:
m.write(f.read_text(encoding='utf-8'))
f.unlink(missing_ok=True)
except:
pass
# 第三步，月合并到年
for i in range(1):
if today.month < 4:
break
mons = [f for f in log_dir.glob(r'*.txt') if re.match(r'^mdc_\d{6}$', f.stem, re.A)]
if not mons or not len(mons):
break
mons.sort()
deadline_year = f'mdc_{today.year - 1}13'
year_merge = [f for f in mons if f.stem < deadline_year]
if not year_merge or not len(year_merge):
break
toyear = len('12.txt') # cut length mdc_2020|12.txt
for f in year_merge:
try:
year_file_name = str(f)[:-toyear] + '.txt' # mdc_2020.txt
with open(year_file_name, 'a', encoding='utf-8') as y:
y.write(f.read_text(encoding='utf-8'))
f.unlink(missing_ok=True)
except:
pass
# 第四步，压缩年志 如果有压缩需求，请自行手工压缩，或者使用外部脚本来定时完成。推荐nongnu的lzip，对于
# 这种粒度的文本日志，压缩比是目前最好的。lzip -9的运行参数下，日志压缩比要高于xz -9，而且内存占用更少，
# 多核利用率更高(plzip多线程版本)，解压速度更快。压缩后的大小差不多是未压缩时的2.4%到3.7%左右，
# 100MB的日志文件能缩小到3.7MB。
return filepath
def signal_handler(*args):
"""
A signal handler function for handling operating system signals like Ctrl+C (SIGINT). It defines the behavior of
the application when such signals are received, such as graceful termination.
:param args: Variable argument list, used to handle signal information.
"""
print('[!]Ctrl+C detected, Exit.')
os._exit(9)
def sigdebug_handler(*args):
"""
A signal handler function specifically for toggling debug mode on or off. It alters the debug configuration
based on certain system signals (like window size change in Unix systems).
:param args: Variable argument list, used to handle signal information.
"""
conf = config.getInstance()
conf.set_override(f"debug_mode:switch={int(not conf.debug())}")
print(f"[!]Debug {('oFF', 'On')[int(conf.debug())]}")
# 新增失败文件列表跳过处理，及.nfo修改天数跳过处理，提示跳过视频总数，调试模式(-g)下详细被跳过文件，跳过小广告
def movie_lists(source_folder, regexstr: str) -> typing.List[str]:
"""
Generates a list of movie file paths from the specified source folder. It filters files based on regular
expressions and other criteria, such as file type and size.
:param source_folder: The folder to scan for movie files.
:param regexstr: A regular expression string to filter movie files.
:return: A list of paths to the movie files that match the criteria.
"""
conf = config.getInstance()
main_mode = conf.main_mode()
debug = conf.debug()
nfo_skip_days = conf.nfo_skip_days()
link_mode = conf.link_mode()
file_type = conf.media_type().lower().split(",")
trailerRE = re.compile(r'-trailer\.', re.IGNORECASE)
cliRE = None
if isinstance(regexstr, str) and len(regexstr):
try:
cliRE = re.compile(regexstr, re.IGNORECASE)
except:
pass
failed_list_txt_path = Path(conf.failed_folder()).resolve() / 'failed_list.txt'
failed_set = set()
if (main_mode == 3 or link_mode) and not conf.ignore_failed_list():
try:
flist = failed_list_txt_path.read_text(encoding='utf-8').splitlines()
failed_set = set(flist)
if len(flist) != len(failed_set): # 检查去重并写回，但是不改变failed_list.txt内条目的先后次序，重复的只保留最后的
fset = failed_set.copy()
for i in range(len(flist) - 1, -1, -1):
fset.remove(flist[i]) if flist[i] in fset else flist.pop(i)
failed_list_txt_path.write_text('\n'.join(flist) + '\n', encoding='utf-8')
assert len(fset) == 0 and len(flist) == len(failed_set)
except:
pass
if not Path(source_folder).is_dir():
print('[-]Source folder not found!')
return []
total = []
# source = Path(source_folder).resolve()
source = Path(source_folder)
skip_failed_cnt, skip_nfo_days_cnt = 0, 0
escape_folder_set = set(re.split("[,，]", conf.escape_folder()))
for full_name in source.glob(r'**/*'):
if main_mode != 3 and set(full_name.parent.parts) & escape_folder_set:
continue
if not full_name.suffix.lower() in file_type:
continue
absf = str(full_name)
if absf in failed_set:
skip_failed_cnt += 1
if debug:
print('[!]Skip failed movie:', absf)
continue
is_sym = full_name.is_symlink()
if main_mode != 3 and (is_sym or (full_name.stat().st_nlink > 1 and not conf.scan_hardlink())): # 短路布尔 符号链接不取stat()，因为符号链接可能指向不存在目标
continue # 模式不等于3下跳过软连接和未配置硬链接刮削
# 调试用0字节样本允许通过，去除小于120MB的广告'苍老师强力推荐.mp4'(102.2MB)'黑道总裁.mp4'(98.4MB)'有趣的妹子激情表演.MP4'(95MB)'有趣的臺灣妹妹直播.mp4'(15.1MB)
movie_size = 0 if is_sym else full_name.stat().st_size # 同上 符号链接不取stat()及st_size，直接赋0跳过小视频检测
# if 0 < movie_size < 125829120: # 1024*1024*120=125829120
# continue
if cliRE and not cliRE.search(absf) or trailerRE.search(full_name.name):
continue
if main_mode == 3:
nfo = full_name.with_suffix('.nfo')
if not nfo.is_file():
if debug:
print(f"[!]Metadata {nfo.name} not found for '{absf}'")
elif nfo_skip_days > 0 and file_modification_days(nfo) <= nfo_skip_days:
skip_nfo_days_cnt += 1
if debug:
print(f"[!]Skip movie by it's .nfo which modified within {nfo_skip_days} days: '{absf}'")
continue
total.append(absf)
if skip_failed_cnt:
print(f"[!]Skip {skip_failed_cnt} movies in failed list '{failed_list_txt_path}'.")
if skip_nfo_days_cnt:
print(
f"[!]Skip {skip_nfo_days_cnt} movies in source folder '{source}' who's .nfo modified within {nfo_skip_days} days.")
if nfo_skip_days <= 0 or not link_mode or main_mode == 3:
return total
# 软连接方式，已经成功削刮的也需要从成功目录中检查.nfo更新天数，跳过N天内更新过的
skip_numbers = set()
success_folder = Path(conf.success_folder()).resolve()
for f in success_folder.glob(r'**/*'):
if not re.match(r'\.nfo$', f.suffix, re.IGNORECASE):
continue
if file_modification_days(f) > nfo_skip_days:
continue | number = get_number(False, f.stem) | 4 | 2023-11-25 03:16:13+00:00 | 16k |
abdulhaim/LMRL-Gym | llm_rl_scripts/chess/mc_returns/train_full_games_mc_returns.py | [
{
"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[TextPoli... | 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 transformers.generation import GenerationConfig
from jaxtyping import PyTree
from LLM_RL.environment import Text, text_env_eval, TextTrajectory, TextTrajectoryChain, TokenTrajectoryChain
from LLM_RL.algorithms.mc_returns.data import MCData
from LLM_RL.algorithms.value_rl_base.gpt2.interface import GPT2ValuePolicy
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 LLM_RL.algorithms.mc_returns.gpt2.interface import GPT2MCTrain, GPT2MCInference
from functools import partial
from JaxSeq.logs import pull_logs
from transformers import GPT2TokenizerFast
from llm_rl_scripts.chess.env.env import FenChessHistoryEnv
from JaxSeq.shard_model import copy_sharded_pytree
from LLM_RL.algorithms.mc_returns.base_interface import mc_loss
from LLM_RL.algorithms.mc_returns.train import eval_loss, train_loop
from LLM_RL.algorithms.mc_returns.data import MCData, MCIterableDataset
import tyro
import jax
import jax.numpy as jnp
import os
import optax
import pickle as pkl
import re
import numpy as np
import json | 12,253 |
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 mc_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)
while token_trajectory_chain.next is not None:
yield MCData.from_token_trajectory_chain(token_trajectory_chain, gamma=gamma)
token_trajectory_chain = token_trajectory_chain.next
mc_data = mc_data_generator(train_data_path)
dataset = MCIterableDataset.from_mc_data_iterable(mc_data, tokenizer,
BlockingStrategy(
padding=Padding.RIGHT,
truncation=Truncation.RIGHT,
max_length=max_length,
))
def policy_optim_getter(params: PyTree):
mask = get_weight_decay_mask((
"".join([r"\['ln_[0-9]+'\]", re.escape("['bias']")]),
"".join([r"\['ln_[0-9]+'\]", re.escape("['scale']")]),
re.escape("['ln_f']['bias']"),
re.escape("['ln_f']['scale']"),
"bias",
))(params)
return optax.MultiSteps(
optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
),
every_k_schedule=grad_accum_steps,
)
def value_head_optim_getter(params: PyTree):
mask = get_weight_decay_mask(("bias",))(params)
return optax.MultiSteps(
optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
),
every_k_schedule=grad_accum_steps,
)
model_prng_key = jax.random.PRNGKey(3)
base_train_state, base_model = load_train_state(
model_load_mode=model_load_mode,
model_load_path=convert_path(model_load_path) if model_load_mode != ModelLoadMode.HF else model_load_path,
model_dtype=jnp.float32,
optim_getter=policy_optim_getter,
tokenizer=tokenizer,
mesh=mesh,
prng_key=model_prng_key,
force_pad_embeddings=force_pad_embeddings,
params_dtype=jnp.float32,
)
base_model.config.gradient_checkpointing = gradient_checkpointing
base_model.config.gradient_checkpointing_policy = gradient_checkpointing_policy
pi_beta_params = copy_sharded_pytree(
model=base_model,
pytree=base_train_state.params,
)
q_prng_key = jax.random.PRNGKey(4)
# embed()
q_head_train_state, q_head = load_head_train_state_from_config(
model_config=MLPHeadConfig(
input_dim=base_model.config.n_embd,
hidden_dim=base_model.config.n_embd,
output_dim=base_model.config.vocab_size,
use_bias=True,
layer2_initializer_range=0.0,
layer2_bias_init=0.0,
),
model_dtype=jnp.float32,
optim_getter=value_head_optim_getter,
mesh=mesh,
prng_key=q_prng_key,
pad_to_output_dim=None,
params_dtype=jnp.float32,
)
loop_state = dict()
if should_restore_loop_state and (model_load_mode in {ModelLoadMode.TRAIN_STATE,
ModelLoadMode.TRAIN_STATE_PARAMS,
ModelLoadMode.PARAMS}):
with open(os.path.join(convert_path(model_load_path), 'loop_state.pkl'), 'rb') as f:
loop_state = pkl.load(f)
|
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_give_position_ilql",
n_rounds: int=1,
epochs: int=1,
max_steps: Optional[int]=None,
lr: float=1e-4,
weight_decay: float=0.0,
tau: float=0.95,
cql_weight: float=0.0,
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=150,
log_every: int=256,
eval_every_steps: Optional[int]=100000,
eval_every_epochs: Optional[int]=None,
eval_at_beginning: bool=False,
eval_at_end: bool=True,
save_every_steps: Optional[int]=100000,
save_every_epochs: Optional[int]=None,
save_at_beginning: bool=True,
save_at_end: bool=True,
save_best: bool=False,
max_checkpoints: Optional[int]=None,
save_train_state: bool=True,
save_bf16: bool=True,
policy_max_input_length: int=150,
policy_max_output_length: int=10,
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=False,
):
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 mc_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)
while token_trajectory_chain.next is not None:
yield MCData.from_token_trajectory_chain(token_trajectory_chain, gamma=gamma)
token_trajectory_chain = token_trajectory_chain.next
mc_data = mc_data_generator(train_data_path)
dataset = MCIterableDataset.from_mc_data_iterable(mc_data, tokenizer,
BlockingStrategy(
padding=Padding.RIGHT,
truncation=Truncation.RIGHT,
max_length=max_length,
))
def policy_optim_getter(params: PyTree):
mask = get_weight_decay_mask((
"".join([r"\['ln_[0-9]+'\]", re.escape("['bias']")]),
"".join([r"\['ln_[0-9]+'\]", re.escape("['scale']")]),
re.escape("['ln_f']['bias']"),
re.escape("['ln_f']['scale']"),
"bias",
))(params)
return optax.MultiSteps(
optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
),
every_k_schedule=grad_accum_steps,
)
def value_head_optim_getter(params: PyTree):
mask = get_weight_decay_mask(("bias",))(params)
return optax.MultiSteps(
optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
),
every_k_schedule=grad_accum_steps,
)
model_prng_key = jax.random.PRNGKey(3)
base_train_state, base_model = load_train_state(
model_load_mode=model_load_mode,
model_load_path=convert_path(model_load_path) if model_load_mode != ModelLoadMode.HF else model_load_path,
model_dtype=jnp.float32,
optim_getter=policy_optim_getter,
tokenizer=tokenizer,
mesh=mesh,
prng_key=model_prng_key,
force_pad_embeddings=force_pad_embeddings,
params_dtype=jnp.float32,
)
base_model.config.gradient_checkpointing = gradient_checkpointing
base_model.config.gradient_checkpointing_policy = gradient_checkpointing_policy
pi_beta_params = copy_sharded_pytree(
model=base_model,
pytree=base_train_state.params,
)
q_prng_key = jax.random.PRNGKey(4)
# embed()
q_head_train_state, q_head = load_head_train_state_from_config(
model_config=MLPHeadConfig(
input_dim=base_model.config.n_embd,
hidden_dim=base_model.config.n_embd,
output_dim=base_model.config.vocab_size,
use_bias=True,
layer2_initializer_range=0.0,
layer2_bias_init=0.0,
),
model_dtype=jnp.float32,
optim_getter=value_head_optim_getter,
mesh=mesh,
prng_key=q_prng_key,
pad_to_output_dim=None,
params_dtype=jnp.float32,
)
loop_state = dict()
if should_restore_loop_state and (model_load_mode in {ModelLoadMode.TRAIN_STATE,
ModelLoadMode.TRAIN_STATE_PARAMS,
ModelLoadMode.PARAMS}):
with open(os.path.join(convert_path(model_load_path), 'loop_state.pkl'), 'rb') as f:
loop_state = pkl.load(f)
| loss_fn = partial(mc_loss, cql_weight=cql_weight) | 12 | 2023-11-21 00:16:42+00:00 | 16k |
jzmzhong/Automatic-Prosody-Annotator-with-SSWP-CLAP | src/clap_module/conformer/encoder.py | [
{
"identifier": "ConvolutionModule",
"path": "src/clap_module/conformer/convolution.py",
"snippet": "class ConvolutionModule(nn.Module):\r\n \"\"\"ConvolutionModule in Conformer model.\r\n\r\n Args:\r\n channels (int): The number of channels of conv layers.\r\n kernel_size (int): Ker... | import logging
import torch
import math
from .convolution import ConvolutionModule
from .encoder_layer import EncoderLayer
from .modules import get_activation
from .modules import VGG2L
from .modules import (
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from .embedding import (
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from .modules import LayerNorm
from .multi_layer_conv import (
Conv1dLinear,
MultiLayeredConv1d,
)
from .modules import (
PositionwiseFeedForward,
)
from .modules import repeat
from .sub_sampling import Conv2dSubsampling
from ..feature_fusion import AttentionPool1d, DAF, AFF, iAFF
| 14,271 | # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder definition."""
class Encoder(torch.nn.Module):
"""Conformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, torch.nn.Module]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
normalize_before=True,
concat_after=False,
ffn_layer_type="linear",
ffn_conv_kernel_size=1,
macaron_style=False,
pos_enc_layer_type="abs_pos",
selfattention_layer_type="selfattn",
activation_type="relu",
use_cnn_module=True,
zero_triu=False,
cnn_module_kernel=31,
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
max_seq_len=100,
enable_fusion=False,
fusion_type="",
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self.max_seq_len = max_seq_len
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
| # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder definition."""
class Encoder(torch.nn.Module):
"""Conformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, torch.nn.Module]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
normalize_before=True,
concat_after=False,
ffn_layer_type="linear",
ffn_conv_kernel_size=1,
macaron_style=False,
pos_enc_layer_type="abs_pos",
selfattention_layer_type="selfattn",
activation_type="relu",
use_cnn_module=True,
zero_triu=False,
cnn_module_kernel=31,
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
max_seq_len=100,
enable_fusion=False,
fusion_type="",
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self.max_seq_len = max_seq_len
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
| torch.nn.LayerNorm(attention_dim),
| 11 | 2023-11-25 02:38:32+00:00 | 16k |
facebookresearch/ExPLORe | train_finetuning_pixels.py | [
{
"identifier": "DrQLearner",
"path": "rlpd/agents/drq/drq_learner.py",
"snippet": "class DrQLearner(SACLearner):\n data_augmentation_fn: Callable = struct.field(pytree_node=False)\n\n @classmethod\n def create(\n cls,\n seed: int,\n observation_space: gym.Space,\n a... | import os
import numpy as np
import tqdm
import wandb
import matplotlib.pyplot as plt
import pickle
import roboverse
import types
import jax
import jax.numpy as jnp
from absl import app, flags
from flax.core import FrozenDict
from ml_collections import config_flags
from flax.core import frozen_dict
from flax.training import checkpoints
from rlpd.agents import DrQLearner, PixelRND, PixelRM, PixelBCAgent
from rlpd.data import MemoryEfficientReplayBuffer, ReplayBuffer
from rlpd.evaluation import evaluate
from rlpd.wrappers import wrap_pixels
from rlpd.agents.drq.icvf import PixelICVF
from rlpd import gc_dataset
from gym.wrappers import TimeLimit, FilterObservation, RecordEpisodeStatistics
from rlpd.data import Dataset
from rlpd.data.cog_datasets import COGDataset
from functools import partial | 13,032 | "File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"rm_config",
"configs/pixel_rm_config.py",
"File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"rnd_config",
"configs/pixel_rnd_config.py",
"File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"bc_config",
"configs/pixel_bc_config.py",
"File path to the training hyperparameter configuration",
lock_config=False,
)
flags.DEFINE_string(
"offline_relabel_type",
"gt",
"Whether to use reward from the offline dataset. [gt/pred/min]",
)
flags.DEFINE_boolean("use_rnd_offline", False, "Whether to use rnd offline.")
flags.DEFINE_boolean("use_rnd_online", False, "Whether to use rnd online.")
def combine(one_dict, other_dict):
combined = {}
for k, v in one_dict.items():
if isinstance(v, FrozenDict) or isinstance(v, dict):
if len(v) == 0:
combined[k] = v
else:
combined[k] = combine(v, other_dict[k])
else:
tmp = np.empty(
(v.shape[0] + other_dict[k].shape[0], *v.shape[1:]), dtype=v.dtype
)
tmp[0::2] = v
tmp[1::2] = other_dict[k]
combined[k] = tmp
return FrozenDict(combined)
def add_prefix(prefix, dict):
return {prefix + k: v for k, v in dict.items()}
def main(_):
wandb.init(project=FLAGS.project_name, mode="online")
wandb.config.update(FLAGS)
if FLAGS.save_dir is not None:
log_dir = os.path.join(
FLAGS.save_dir,
f"{FLAGS.env_name}-s{FLAGS.seed}-icvf_{FLAGS.use_icvf}-ours_{FLAGS.use_rnd_offline}",
)
print("logging to", log_dir)
if FLAGS.checkpoint_model:
chkpt_dir = os.path.join(log_dir, "checkpoints")
os.makedirs(chkpt_dir, exist_ok=True)
if FLAGS.checkpoint_buffer:
buffer_dir = os.path.join(log_dir, "buffers")
os.makedirs(buffer_dir, exist_ok=True)
def wrap(env):
return wrap_pixels(
env,
action_repeat=1,
num_stack=1,
camera_id=0,
)
def render(env, *args, **kwargs):
return env.render_obs()
if FLAGS.env_name == "Widow250PickTray-v0":
env_name_alt = "pickplace"
cog_max_path_length = 40
elif FLAGS.env_name == "Widow250DoubleDrawerOpenGraspNeutral-v0":
env_name_alt = "closeddrawer_small"
cog_max_path_length = 50
elif FLAGS.env_name == "Widow250DoubleDrawerCloseOpenGraspNeutral-v0":
env_name_alt = "blockeddrawer1_small"
cog_max_path_length = 80
env = roboverse.make(FLAGS.env_name, transpose_image=False)
env.render = types.MethodType(render, env)
env = FilterObservation(env, ["image"])
env = TimeLimit(env, max_episode_steps=cog_max_path_length) # TODO
env, pixel_keys = wrap(env)
env = RecordEpisodeStatistics(env, deque_size=1)
env.seed(FLAGS.seed)
eval_env = roboverse.make(FLAGS.env_name, transpose_image=False)
eval_env.render = types.MethodType(render, eval_env)
eval_env = FilterObservation(eval_env, ["image"])
eval_env = TimeLimit(eval_env, max_episode_steps=cog_max_path_length) # TODO
eval_env, _ = wrap(eval_env)
eval_env.seed(FLAGS.seed + 42)
dataset_path = os.path.join("data", env_name_alt)
print("Data Path:", dataset_path)
np_rng = np.random.default_rng(FLAGS.seed)
| """
Modified from https://github.com/ikostrikov/rlpd/blob/main/rlpd/train_finetuning_pixels.py
Original lincense information:
MIT License
Copyright (c) 2022 Ilya Kostrikov, Philip J. Ball, Laura Smith
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.
"""
#! /usr/bin/env python
### cog imports ###
### cog imports ###
FLAGS = flags.FLAGS
flags.DEFINE_string("project_name", "explore-cog", "wandb project name.")
flags.DEFINE_string("env_name", "cheetah-run-v0", "Environment name.")
flags.DEFINE_float(
"dataset_subsample_ratio", 0.1, "Ratio of the dataset to subsample (done twice)"
)
flags.DEFINE_bool("use_icvf", False, "Whether to use the icvf encoder")
flags.DEFINE_float("offline_ratio", 0.5, "Offline ratio.")
flags.DEFINE_integer("seed", 42, "Random seed.")
flags.DEFINE_integer("eval_episodes", 100, "Number of episodes used for evaluation.")
flags.DEFINE_integer("log_interval", 1000, "Logging interval.")
flags.DEFINE_integer("eval_interval", 5000, "Eval interval.")
flags.DEFINE_integer("batch_size", 256, "Mini batch size.")
flags.DEFINE_integer("max_steps", 500000, "Number of training steps.")
flags.DEFINE_integer(
"start_training", 5000, "Number of training steps to start training."
)
flags.DEFINE_boolean("tqdm", True, "Use tqdm progress bar.")
flags.DEFINE_string("save_dir", "exp_data_cog", "Directory to save checkpoints.")
flags.DEFINE_bool("checkpoint_model", False, "save model")
flags.DEFINE_bool("checkpoint_buffer", False, "save replay buffer")
flags.DEFINE_integer("utd_ratio", 1, "Update to data ratio.")
flags.DEFINE_float("bc_pretrain_rollin", 0.0, "rollin coeff")
flags.DEFINE_integer(
"bc_pretrain_steps",
10000,
"Pre-train BC policy for a number of steps on pure offline data",
)
config_flags.DEFINE_config_file(
"config",
"configs/rlpd_pixels_config.py",
"File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"rm_config",
"configs/pixel_rm_config.py",
"File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"rnd_config",
"configs/pixel_rnd_config.py",
"File path to the training hyperparameter configuration.",
lock_config=False,
)
config_flags.DEFINE_config_file(
"bc_config",
"configs/pixel_bc_config.py",
"File path to the training hyperparameter configuration",
lock_config=False,
)
flags.DEFINE_string(
"offline_relabel_type",
"gt",
"Whether to use reward from the offline dataset. [gt/pred/min]",
)
flags.DEFINE_boolean("use_rnd_offline", False, "Whether to use rnd offline.")
flags.DEFINE_boolean("use_rnd_online", False, "Whether to use rnd online.")
def combine(one_dict, other_dict):
combined = {}
for k, v in one_dict.items():
if isinstance(v, FrozenDict) or isinstance(v, dict):
if len(v) == 0:
combined[k] = v
else:
combined[k] = combine(v, other_dict[k])
else:
tmp = np.empty(
(v.shape[0] + other_dict[k].shape[0], *v.shape[1:]), dtype=v.dtype
)
tmp[0::2] = v
tmp[1::2] = other_dict[k]
combined[k] = tmp
return FrozenDict(combined)
def add_prefix(prefix, dict):
return {prefix + k: v for k, v in dict.items()}
def main(_):
wandb.init(project=FLAGS.project_name, mode="online")
wandb.config.update(FLAGS)
if FLAGS.save_dir is not None:
log_dir = os.path.join(
FLAGS.save_dir,
f"{FLAGS.env_name}-s{FLAGS.seed}-icvf_{FLAGS.use_icvf}-ours_{FLAGS.use_rnd_offline}",
)
print("logging to", log_dir)
if FLAGS.checkpoint_model:
chkpt_dir = os.path.join(log_dir, "checkpoints")
os.makedirs(chkpt_dir, exist_ok=True)
if FLAGS.checkpoint_buffer:
buffer_dir = os.path.join(log_dir, "buffers")
os.makedirs(buffer_dir, exist_ok=True)
def wrap(env):
return wrap_pixels(
env,
action_repeat=1,
num_stack=1,
camera_id=0,
)
def render(env, *args, **kwargs):
return env.render_obs()
if FLAGS.env_name == "Widow250PickTray-v0":
env_name_alt = "pickplace"
cog_max_path_length = 40
elif FLAGS.env_name == "Widow250DoubleDrawerOpenGraspNeutral-v0":
env_name_alt = "closeddrawer_small"
cog_max_path_length = 50
elif FLAGS.env_name == "Widow250DoubleDrawerCloseOpenGraspNeutral-v0":
env_name_alt = "blockeddrawer1_small"
cog_max_path_length = 80
env = roboverse.make(FLAGS.env_name, transpose_image=False)
env.render = types.MethodType(render, env)
env = FilterObservation(env, ["image"])
env = TimeLimit(env, max_episode_steps=cog_max_path_length) # TODO
env, pixel_keys = wrap(env)
env = RecordEpisodeStatistics(env, deque_size=1)
env.seed(FLAGS.seed)
eval_env = roboverse.make(FLAGS.env_name, transpose_image=False)
eval_env.render = types.MethodType(render, eval_env)
eval_env = FilterObservation(eval_env, ["image"])
eval_env = TimeLimit(eval_env, max_episode_steps=cog_max_path_length) # TODO
eval_env, _ = wrap(eval_env)
eval_env.seed(FLAGS.seed + 42)
dataset_path = os.path.join("data", env_name_alt)
print("Data Path:", dataset_path)
np_rng = np.random.default_rng(FLAGS.seed)
| ds = COGDataset( | 11 | 2023-11-19 21:28:52+00:00 | 16k |
Luo-Z13/pointobb | PointOBB/mmdet/models/roi_heads/PointOBB_head.py | [
{
"identifier": "HEADS",
"path": "PointOBB/mmdet/models/builder.py",
"snippet": "HEADS = MODELS"
},
{
"identifier": "MODELS",
"path": "PointOBB/mmdet/models/builder.py",
"snippet": "MODELS = Registry('models', parent=MMCV_MODELS)"
},
{
"identifier": "build_head",
"path": "Poi... | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
import copy
import numpy as np
import cv2
from mmdet.core import bbox2result, bbox2roi, rbbox2roi, build_assigner, build_sampler, multi_apply
from ..builder import HEADS, MODELS, build_head, build_roi_extractor, build_loss
from .standard_roi_head import StandardRoIHead
from .cascade_roi_head import CascadeRoIHead
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from .test_mixins import BBoxTestMixin, MaskTestMixin
from mmdet.core.bbox import bbox_xyxy_to_cxcywh
from mmdet.core.bbox.transforms import rbbox2result
from mmcv.cnn import Scale, ConvModule
from mmcv.ops import box_iou_rotated
from typing import Any, List, Sequence, Tuple, Union
from torch import Tensor
from mmdet.models.utils.base_bbox_coder import BaseBBoxCoder
from ..detectors.utils import obb2xyxy, regularize_boxes, reduce_mean, obb2poly_np | 14,154 |
RangeType = Sequence[Tuple[int, int]]
INF = 1e8
def meshgrid(x: Tensor,
y: Tensor,
row_major: bool = True) -> Tuple[Tensor, Tensor]:
yy, xx = torch.meshgrid(y, x)
if row_major:
# warning .flatten() would cause error in ONNX exportingF
# have to use reshape here
return xx.reshape(-1), yy.reshape(-1)
else:
return yy.reshape(-1), xx.reshape(-1)
def obb2cxcywh_le90(obboxes):
"""Convert oriented bounding boxes to horizontal bounding boxes.
Args:
obbs (torch.Tensor): [x_ctr,y_ctr,w,h,angle]
Returns:
hbbs (torch.Tensor): [x_lt,y_lt,x_rb,y_rb]
"""
center, w, h, theta = torch.split(obboxes, [2, 1, 1, 1], dim=-1)
Cos, Sin = torch.cos(theta), torch.sin(theta)
x_bias = torch.abs(w / 2 * Cos) + torch.abs(h / 2 * Sin)
y_bias = torch.abs(w / 2 * Sin) + torch.abs(h / 2 * Cos)
bias = torch.cat([x_bias, y_bias], dim=-1)
wh = bias * 2
return torch.cat([center, wh, torch.zeros_like(theta)], dim=-1)
|
RangeType = Sequence[Tuple[int, int]]
INF = 1e8
def meshgrid(x: Tensor,
y: Tensor,
row_major: bool = True) -> Tuple[Tensor, Tensor]:
yy, xx = torch.meshgrid(y, x)
if row_major:
# warning .flatten() would cause error in ONNX exportingF
# have to use reshape here
return xx.reshape(-1), yy.reshape(-1)
else:
return yy.reshape(-1), xx.reshape(-1)
def obb2cxcywh_le90(obboxes):
"""Convert oriented bounding boxes to horizontal bounding boxes.
Args:
obbs (torch.Tensor): [x_ctr,y_ctr,w,h,angle]
Returns:
hbbs (torch.Tensor): [x_lt,y_lt,x_rb,y_rb]
"""
center, w, h, theta = torch.split(obboxes, [2, 1, 1, 1], dim=-1)
Cos, Sin = torch.cos(theta), torch.sin(theta)
x_bias = torch.abs(w / 2 * Cos) + torch.abs(h / 2 * Sin)
y_bias = torch.abs(w / 2 * Sin) + torch.abs(h / 2 * Cos)
bias = torch.cat([x_bias, y_bias], dim=-1)
wh = bias * 2
return torch.cat([center, wh, torch.zeros_like(theta)], dim=-1)
| @HEADS.register_module() | 0 | 2023-11-20 07:50:12+00:00 | 16k |
wangermeng2021/llm-webui | main.py | [
{
"identifier": "login_huggingface",
"path": "src/utils/common.py",
"snippet": "def login_huggingface(token,base_model_name_dropdown):\n if base_model_name_dropdown.lower().find(\"llama\") >= 0:\n if token:\n HUGGINGFACE_HUB_TOKEN = token\n print(\"d1:\",HUGGINGFACE_HUB_T... | import pandas as pd
import math
import numpy as np
import gc
import os,requests
import subprocess,threading
import time
import gradio as gr
import os
import traceback
import numpy as np
import glob
import shutil
import torch
import socket
from src.utils.common import login_huggingface
from src.finetune.huggingface_inference import HuggingfaceInference
from src.finetune.llama_cpp_inference import LlamaCppInference
from src.rag.qa_with_rag import QAWithRAG
from src.utils.common import read_yaml,get_first_row_from_dataset,\
get_runs_model_names_from_dir,get_hg_model_names_from_dir,get_hg_model_names_and_gguf_from_dir,validate_model_path,get_runs_models
from src.utils.chat_prompts import get_model_type,get_chat_history_prompt,get_model_prompt_template
from transformers.training_args import OptimizerNames
from huggingface_hub import hf_hub_download
from src.utils import download_model
from pathlib import Path
from src.finetune.qlora_trainer import QloraTrainer
from src.finetune.qlora_trainer import TRAINING_STATUS
from src.utils.download_huggingface_repo import download_model_wrapper,download_dataset_wrapper | 12,358 | local_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
local_model_root_files = os.listdir(local_model_root_dir)
local_model_list = []
for model_dir in local_model_root_files:
if os.path.isdir(os.path.join(local_model_root_dir, model_dir)):
local_model_list.append(model_dir)
return local_model_list,local_model_root_dir
def get_local_dataset_list():
local_dataset_list = []
local_dataset_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets")
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_infos.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_infos.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_dict.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_dict.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
return local_dataset_list,local_dataset_root_dir
def start_tensorboard_server():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((LOCAL_HOST_IP, 6006))
s.close()
except Exception as e:
tensorboard_cmd = f"tensorboard --logdir {os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')} --reload_multifile True"
tensorboard_proc = subprocess.Popen(tensorboard_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
shell=True, close_fds=True) # bufsize=0, close_fds=True
def init():
global config_dict,transformer_optimizer_list,model_context_window,init_train_file_path,init_val_file_path
global INIT_PREFIX1,INIT_COL1_TEXT,INIT_PREFIX2,INIT_COL2_TEXT,INIT_PREFIX3,INIT_COL3_TEXT,INIT_PREFIX4,INIT_COL4_TEXT,col_names,DATASET_FIRST_ROW
global local_model_list,local_model_root_dir
global base_model_names,base_model_context_window,embedding_model_names,training_base_model_names
global local_dataset_list, local_dataset_root_dir
start_tensorboard_server()
model_zoo_config = read_yaml(os.path.join(os.path.dirname(os.path.abspath(__file__)),"config","model_zoo.yaml"))
transformer_optimizer_list = list(vars(OptimizerNames)["_value2member_map_"].keys())
#get dynamic context window from selected model
model_context_window = [2048,1024,512]
init_train_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets", INIT_DATASET_NAME)
DATASET_FIRST_ROW,split_list = get_first_row_from_dataset(init_train_file_path)
col_names = list(DATASET_FIRST_ROW)
col_names.insert(0,"")
INIT_PREFIX1 = "<s>[INST] "
INIT_PREFIX2 = "here are the inputs "
INIT_PREFIX3 = " [/INST]"
INIT_PREFIX4 = "</s>"
INIT_COL1_TEXT = str(DATASET_FIRST_ROW[col_names[1]])
INIT_COL2_TEXT = str(DATASET_FIRST_ROW[col_names[2]])
INIT_COL3_TEXT = str(DATASET_FIRST_ROW[col_names[3]])
INIT_COL4_TEXT = ""
local_model_list,local_model_root_dir = get_local_model_list()
base_model_names = [model_name for model_name in model_zoo_config["model_list"]]
training_base_model_names = [model_name for model_name in base_model_names if not model_name.endswith(".gguf")]
# base_model_context_window = [model_name[1] for model_name in model_zoo_config["model_list"]]
embedding_model_names = [model_name for model_name in model_zoo_config["embedding_model_list"]]
local_dataset_list, local_dataset_root_dir = get_local_dataset_list()
with gr.Blocks(title="FINETUNE",css="#vertical_center_align_markdown { position:absolute; top:30%;background-color:white;} .white_background {background-color: #ffffff} .none_border {border: none;border-collapse:collapse;}") as demo:
init()
local_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_dataset_root_dir_textbox = gr.Textbox(label="",value=local_dataset_root_dir, visible=False)
local_embedding_model_root_dir_textbox = gr.Textbox(label="", value=os.path.join(os.path.dirname(os.path.abspath(__file__)), "rag", "embedding_models"), visible=False)
local_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_home_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
session_state = gr.State(value={})
# html = gr.HTML("<p align='center';>llm-web-ui</p>",elem_id="header")
with gr.Tab("Home"):
with gr.Row():
# with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## ChatBot", elem_classes="white_background")
with gr.Group():
gr.Markdown("### Chat Model", elem_classes="white_background")
local_home_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
local_home_chat_model_names = get_hg_model_names_and_gguf_from_dir(local_home_chat_model_dir,
runs_model_root_dir)
home_chat_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_home_chat_model_dir})"]
home_chat_model_source_radio = gr.Radio(home_chat_model_source_radio_choices,
label="Chat Model source", show_label=False,
value=home_chat_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_home_chat_model_names_dropdown = gr.Dropdown(base_model_names,
label=f"Chat Model", show_label=False,
allow_custom_value=True,
value=base_model_names[
0] if base_model_names else None,
interactive=True, scale=4, min_width=1)
local_home_chat_model_names_dropdown = gr.Dropdown(local_home_chat_model_names,
label=f"Chat Model", show_label=False,
value=local_home_chat_model_names[
0] if local_home_chat_model_names else None,
interactive=True, scale=4, min_width=1,
visible=False)
download_hub_home_chat_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_home_chat_model_names_btn = gr.Button("Stop", scale=1, visible=False)
refresh_local_home_chat_model_names_btn = gr.Button("Refresh", scale=1, visible=False)
load_home_chat_model_btn = gr.Button("Load Model", scale=1, visible=True)
using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
|
# os.environ['HTTP_PROXY'] = 'http://127.0.0.1:8889'
# os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:8889'
LOCAL_HOST_IP = "0.0.0.0"
TENSORBOARD_URL = "http://" + LOCAL_HOST_IP + ":6006/"
INIT_DATASET_NAME = "test_python_code_instructions_5000_rows"
RAG_DATA_LIST_DROPDOWN = ""
TEXT_SPLITTER_DROPDOWN = ""
CHUNK_SIZE_SLIDER = 0
CHUNK_OVERLAP_SLIDER = -1
SEPARATORS_TEXTBOX = ""
EMBEDDING_MODEL_SOURCE_RADIO = ""
HUB_EMBEDDING_MODEL_NAMES_DROPDOWN = ""
LOCAL_EMBEDDING_MODEL_NAMES_DROPDOWN = ""
CHAT_MODEL_SOURCE_RADIO = ""
HUB_CHAT_MODEL_NAMES_DROPDOWN = ""
LOCAL_CHAT_MODEL_NAMES_DROPDOWN = ""
SEARCH_TOP_K_SLIDER = ""
SEARCH_SCORE_THRESHOLD_SLIDER = ""
training_ret_val = -1
error_msg = ""
current_running_model_name = ""
infer_model = None
stop_generation_status = False
chatbot_history=[]
chatbot_height = 500
rag_chatbot_history=[]
rag_stop_generation_status = False
qa_with_rag = QAWithRAG()
train_param_config = {}
train_param_config["dataset"]={}
train_param_config["model"]={}
train_param_config["training"]={}
model_zoo_config = {}
transformer_optimizer_list = []
model_context_window = 0
init_train_file_path = None
init_val_file_path = None
INIT_PREFIX1 = ""
INIT_PREFIX2 = ""
INIT_PREFIX3 = ""
INIT_PREFIX4 = ""
INIT_COL1_TEXT = ""
INIT_COL2_TEXT = ""
INIT_COL3_TEXT = ""
INIT_COL4_TEXT = ""
col_names = []
DATASET_FIRST_ROW = None
local_model_list = ""
local_model_root_dir = ""
base_model_names = []
training_base_model_names = []
embedding_model_names = []
base_model_context_window = []
local_dataset_list = []
local_dataset_root_dir = ""
def get_local_embedding_model_list():
local_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "rag", "embedding_models")
local_model_root_files = os.listdir(local_model_root_dir)
local_model_list = []
for model_dir in local_model_root_files:
if os.path.isdir(os.path.join(local_model_root_dir, model_dir)):
local_model_list.append(model_dir)
return local_model_list,local_model_root_dir
def get_local_model_list():
local_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
local_model_root_files = os.listdir(local_model_root_dir)
local_model_list = []
for model_dir in local_model_root_files:
if os.path.isdir(os.path.join(local_model_root_dir, model_dir)):
local_model_list.append(model_dir)
return local_model_list,local_model_root_dir
def get_local_dataset_list():
local_dataset_list = []
local_dataset_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets")
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_infos.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_infos.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_dict.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_dict.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
return local_dataset_list,local_dataset_root_dir
def start_tensorboard_server():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((LOCAL_HOST_IP, 6006))
s.close()
except Exception as e:
tensorboard_cmd = f"tensorboard --logdir {os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')} --reload_multifile True"
tensorboard_proc = subprocess.Popen(tensorboard_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
shell=True, close_fds=True) # bufsize=0, close_fds=True
def init():
global config_dict,transformer_optimizer_list,model_context_window,init_train_file_path,init_val_file_path
global INIT_PREFIX1,INIT_COL1_TEXT,INIT_PREFIX2,INIT_COL2_TEXT,INIT_PREFIX3,INIT_COL3_TEXT,INIT_PREFIX4,INIT_COL4_TEXT,col_names,DATASET_FIRST_ROW
global local_model_list,local_model_root_dir
global base_model_names,base_model_context_window,embedding_model_names,training_base_model_names
global local_dataset_list, local_dataset_root_dir
start_tensorboard_server()
model_zoo_config = read_yaml(os.path.join(os.path.dirname(os.path.abspath(__file__)),"config","model_zoo.yaml"))
transformer_optimizer_list = list(vars(OptimizerNames)["_value2member_map_"].keys())
#get dynamic context window from selected model
model_context_window = [2048,1024,512]
init_train_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets", INIT_DATASET_NAME)
DATASET_FIRST_ROW,split_list = get_first_row_from_dataset(init_train_file_path)
col_names = list(DATASET_FIRST_ROW)
col_names.insert(0,"")
INIT_PREFIX1 = "<s>[INST] "
INIT_PREFIX2 = "here are the inputs "
INIT_PREFIX3 = " [/INST]"
INIT_PREFIX4 = "</s>"
INIT_COL1_TEXT = str(DATASET_FIRST_ROW[col_names[1]])
INIT_COL2_TEXT = str(DATASET_FIRST_ROW[col_names[2]])
INIT_COL3_TEXT = str(DATASET_FIRST_ROW[col_names[3]])
INIT_COL4_TEXT = ""
local_model_list,local_model_root_dir = get_local_model_list()
base_model_names = [model_name for model_name in model_zoo_config["model_list"]]
training_base_model_names = [model_name for model_name in base_model_names if not model_name.endswith(".gguf")]
# base_model_context_window = [model_name[1] for model_name in model_zoo_config["model_list"]]
embedding_model_names = [model_name for model_name in model_zoo_config["embedding_model_list"]]
local_dataset_list, local_dataset_root_dir = get_local_dataset_list()
with gr.Blocks(title="FINETUNE",css="#vertical_center_align_markdown { position:absolute; top:30%;background-color:white;} .white_background {background-color: #ffffff} .none_border {border: none;border-collapse:collapse;}") as demo:
init()
local_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_dataset_root_dir_textbox = gr.Textbox(label="",value=local_dataset_root_dir, visible=False)
local_embedding_model_root_dir_textbox = gr.Textbox(label="", value=os.path.join(os.path.dirname(os.path.abspath(__file__)), "rag", "embedding_models"), visible=False)
local_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_home_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
session_state = gr.State(value={})
# html = gr.HTML("<p align='center';>llm-web-ui</p>",elem_id="header")
with gr.Tab("Home"):
with gr.Row():
# with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## ChatBot", elem_classes="white_background")
with gr.Group():
gr.Markdown("### Chat Model", elem_classes="white_background")
local_home_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
local_home_chat_model_names = get_hg_model_names_and_gguf_from_dir(local_home_chat_model_dir,
runs_model_root_dir)
home_chat_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_home_chat_model_dir})"]
home_chat_model_source_radio = gr.Radio(home_chat_model_source_radio_choices,
label="Chat Model source", show_label=False,
value=home_chat_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_home_chat_model_names_dropdown = gr.Dropdown(base_model_names,
label=f"Chat Model", show_label=False,
allow_custom_value=True,
value=base_model_names[
0] if base_model_names else None,
interactive=True, scale=4, min_width=1)
local_home_chat_model_names_dropdown = gr.Dropdown(local_home_chat_model_names,
label=f"Chat Model", show_label=False,
value=local_home_chat_model_names[
0] if local_home_chat_model_names else None,
interactive=True, scale=4, min_width=1,
visible=False)
download_hub_home_chat_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_home_chat_model_names_btn = gr.Button("Stop", scale=1, visible=False)
refresh_local_home_chat_model_names_btn = gr.Button("Refresh", scale=1, visible=False)
load_home_chat_model_btn = gr.Button("Load Model", scale=1, visible=True)
using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
| if validate_model_path(base_model_names[0])[0]: | 9 | 2023-11-25 12:37:21+00:00 | 16k |
danilonumeroso/conar | models/vkc_reasoner.py | [
{
"identifier": "AlgorithmReasoner",
"path": "models/algorithm_reasoner.py",
"snippet": "class AlgorithmReasoner(nn.Module):\n @staticmethod\n def prepare_batch(batch):\n batch = batch.clone()\n for name, tensor in batch.items():\n if not torch.is_tensor(tensor):\n ... | from collections import defaultdict
from pprint import pprint
from torch_geometric.loader import DataLoader
from pytorch_lightning.trainer.supporters import CombinedLoader
from models.algorithm_reasoner import AlgorithmReasoner, LitAlgorithmReasoner
from utils_execution import get_number_of_nodes
from hyperparameters import get_hyperparameters
from datasets._configs import CONFIGS
import torch
import torch_geometric
import torch_geometric.utils as tg_utils
import torch_scatter
import networkx as nx | 13,380 |
class LitVKCReasoner(LitAlgorithmReasoner):
def __init__(self,
hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=True,
transferring=False,
learning_rate=get_hyperparameters()['lr'],
double_process=False,
**algo_reasoner_kwargs):
super().__init__(hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=bias,
transferring=transferring,
learning_rate=learning_rate,
**algo_reasoner_kwargs)
self.algorithm_module = AlgorithmReasoner(
self.dataset.spec,
self.dataset[0],
hidden_dim,
algo_processor,
bias=bias,
transferring=transferring,
timeit=self.timeit,
double_process=double_process,
**algo_reasoner_kwargs)
self.double_process = double_process
self.save_hyperparameters(ignore=['algo_processor'])
def training_step(self, batch, batch_idx):
ret = {'loss': 0, 'losses_dict': defaultdict(list), 'accuracies': defaultdict(list)}
for bb in batch:
ans = super().training_step(bb, batch_idx)
ret['loss'] += ans['loss']
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k].append(v)
ret['loss'] /= len(batch)
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k] = torch.tensor(v).mean()
return ret
def get_VKC_metrics(self, batch, output_logits):
selected_dense = torch_geometric.utils.to_dense_batch(output_logits['output']['selected'], batch=batch.batch)[0]
selected_dense_topk = torch.sort(torch.topk(selected_dense.squeeze(-1), self.dataset.k, dim=-1).indices).values
selected_topk = (selected_dense_topk+batch.ptr[:-1].unsqueeze(-1)).view(-1)
selected_topk_gt = batch.selected.nonzero().squeeze(-1)
selected_batch = batch.batch[selected_topk]
acc_selected_topk = torch_scatter.scatter_mean((selected_topk == selected_topk_gt).float(), selected_batch).mean()
G = tg_utils.to_networkx(batch, to_undirected=True, edge_attrs=['edge_attr'])
mspl = nx.multi_source_dijkstra_path_length(G, sources=selected_topk.tolist(), weight='edge_attr')
mspl = torch.tensor([mspl[i] for i in range(batch.num_nodes)]).to(selected_dense)
farthest = torch_scatter.scatter_max(mspl, batch.batch)[0]
assert (farthest + torch.finfo(torch.float32).eps >= batch.farthest).all()
return {
'acc_topk': acc_selected_topk,
'farthest': farthest.mean(),
'farthest_gt': batch.farthest.mean(),
'farthest_relative_error': ((farthest-batch.farthest)/batch.farthest).mean(),
}
def get_metrics(self,
batch,
all_hint_logits,
output_logits,
all_masks_graph):
accs_dict = super().get_metrics(batch, all_hint_logits, output_logits,
all_masks_graph)
accs_dict.update(**self.get_VKC_metrics(batch,
output_logits))
return accs_dict
def load_dataset(self, split, suffix=''):
split = split+suffix
nns = get_number_of_nodes(self.algorithm, split)
for nn in nns:
self.dataset_kwargs['split'] = split
if (split, nn) not in self._datasets:
self._datasets[(split, nn)] = self.dataset_class(
self.dataset_root,
nn,
|
class LitVKCReasoner(LitAlgorithmReasoner):
def __init__(self,
hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=True,
transferring=False,
learning_rate=get_hyperparameters()['lr'],
double_process=False,
**algo_reasoner_kwargs):
super().__init__(hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=bias,
transferring=transferring,
learning_rate=learning_rate,
**algo_reasoner_kwargs)
self.algorithm_module = AlgorithmReasoner(
self.dataset.spec,
self.dataset[0],
hidden_dim,
algo_processor,
bias=bias,
transferring=transferring,
timeit=self.timeit,
double_process=double_process,
**algo_reasoner_kwargs)
self.double_process = double_process
self.save_hyperparameters(ignore=['algo_processor'])
def training_step(self, batch, batch_idx):
ret = {'loss': 0, 'losses_dict': defaultdict(list), 'accuracies': defaultdict(list)}
for bb in batch:
ans = super().training_step(bb, batch_idx)
ret['loss'] += ans['loss']
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k].append(v)
ret['loss'] /= len(batch)
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k] = torch.tensor(v).mean()
return ret
def get_VKC_metrics(self, batch, output_logits):
selected_dense = torch_geometric.utils.to_dense_batch(output_logits['output']['selected'], batch=batch.batch)[0]
selected_dense_topk = torch.sort(torch.topk(selected_dense.squeeze(-1), self.dataset.k, dim=-1).indices).values
selected_topk = (selected_dense_topk+batch.ptr[:-1].unsqueeze(-1)).view(-1)
selected_topk_gt = batch.selected.nonzero().squeeze(-1)
selected_batch = batch.batch[selected_topk]
acc_selected_topk = torch_scatter.scatter_mean((selected_topk == selected_topk_gt).float(), selected_batch).mean()
G = tg_utils.to_networkx(batch, to_undirected=True, edge_attrs=['edge_attr'])
mspl = nx.multi_source_dijkstra_path_length(G, sources=selected_topk.tolist(), weight='edge_attr')
mspl = torch.tensor([mspl[i] for i in range(batch.num_nodes)]).to(selected_dense)
farthest = torch_scatter.scatter_max(mspl, batch.batch)[0]
assert (farthest + torch.finfo(torch.float32).eps >= batch.farthest).all()
return {
'acc_topk': acc_selected_topk,
'farthest': farthest.mean(),
'farthest_gt': batch.farthest.mean(),
'farthest_relative_error': ((farthest-batch.farthest)/batch.farthest).mean(),
}
def get_metrics(self,
batch,
all_hint_logits,
output_logits,
all_masks_graph):
accs_dict = super().get_metrics(batch, all_hint_logits, output_logits,
all_masks_graph)
accs_dict.update(**self.get_VKC_metrics(batch,
output_logits))
return accs_dict
def load_dataset(self, split, suffix=''):
split = split+suffix
nns = get_number_of_nodes(self.algorithm, split)
for nn in nns:
self.dataset_kwargs['split'] = split
if (split, nn) not in self._datasets:
self._datasets[(split, nn)] = self.dataset_class(
self.dataset_root,
nn, | CONFIGS[self.algorithm][split]['num_samples'], | 4 | 2023-11-20 15:32:43+00:00 | 16k |
harisankar95/pathfinding3D | test/test_path.py | [
{
"identifier": "DiagonalMovement",
"path": "pathfinding3d/core/diagonal_movement.py",
"snippet": "class DiagonalMovement:\n always = 1\n never = 2\n if_at_most_one_obstacle = 3\n only_when_no_obstacle = 4"
},
{
"identifier": "Grid",
"path": "pathfinding3d/core/grid.py",
"sni... | import numpy as np
import pytest
from pathfinding3d.core.diagonal_movement import DiagonalMovement
from pathfinding3d.core.grid import Grid
from pathfinding3d.core.node import GridNode
from pathfinding3d.finder.a_star import AStarFinder
from pathfinding3d.finder.best_first import BestFirst
from pathfinding3d.finder.bi_a_star import BiAStarFinder
from pathfinding3d.finder.breadth_first import BreadthFirstFinder
from pathfinding3d.finder.dijkstra import DijkstraFinder
from pathfinding3d.finder.finder import ExecutionRunsException, ExecutionTimeException
from pathfinding3d.finder.ida_star import IDAStarFinder
from pathfinding3d.finder.msp import MinimumSpanningTree | 11,009 |
finders = [
AStarFinder,
BestFirst,
BiAStarFinder,
DijkstraFinder,
IDAStarFinder,
BreadthFirstFinder,
MinimumSpanningTree,
]
TIME_LIMIT = 10 # give it a 10 second limit.
weighted_finders = [
AStarFinder,
BiAStarFinder,
DijkstraFinder,
MinimumSpanningTree,
]
SIMPLE_MATRIX = np.zeros((5, 5, 5))
SIMPLE_MATRIX[0, 0, 0] = 1
SIMPLE_MATRIX[0, 0, 1] = 1
SIMPLE_MATRIX[0, 0, 2] = 1
SIMPLE_MATRIX[0, 0, 3] = 1
SIMPLE_MATRIX[0, 0, 4] = 1
SIMPLE_MATRIX[1, :, :] = 1
SIMPLE_MATRIX[2, :, :] = 1
SIMPLE_MATRIX[3, :, :] = 1
SIMPLE_MATRIX[4, 0, 0] = 1
SIMPLE_MATRIX[4, 1, 0] = 1
SIMPLE_MATRIX[4, 2, 0] = 1
SIMPLE_MATRIX[4, 3, 0] = 1
SIMPLE_MATRIX[4, 4, 0] = 1
WEIGHTED_SIMPLE_MATRIX = np.copy(SIMPLE_MATRIX)
WEIGHTED_SIMPLE_MATRIX[4, 1, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 2, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 3, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 2, 0] = 99
WEIGHTED_SIMPLE_MATRIX[1, :, :] = 99
WEIGHTED_SIMPLE_MATRIX[2, :, :] = 99
WEIGHTED_SIMPLE_MATRIX[3, :, :] = 99
def test_path():
"""
test if we can find a path
"""
grid = Grid(matrix=SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in finders:
grid.cleanup()
finder = find(time_limit=TIME_LIMIT)
path_, runs = finder.find_path(start, end, grid)
path = []
for node in path_:
if isinstance(node, GridNode):
path.append((node.x, node.y, node.z))
elif isinstance(node, tuple):
path.append((node[0], node[1], node[2]))
print(find.__name__)
print(f"path: {path}")
print(f"length: {len(path)}, runs: {runs}")
assert len(path) == 9
def test_weighted_path():
grid = Grid(matrix=WEIGHTED_SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in weighted_finders:
grid.cleanup()
finder = find(time_limit=TIME_LIMIT)
path_, runs = finder.find_path(start, end, grid)
path = []
for node in path_:
if isinstance(node, GridNode):
path.append((node.x, node.y, node.z))
elif isinstance(node, tuple):
path.append((node[0], node[1], node[2]))
print(find.__name__)
print(f"path: {path}")
print(f"length: {len(path)}, runs: {runs}")
assert len(path) == 11
def test_path_diagonal():
# test diagonal movement
grid = Grid(matrix=SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in finders:
grid.cleanup()
|
finders = [
AStarFinder,
BestFirst,
BiAStarFinder,
DijkstraFinder,
IDAStarFinder,
BreadthFirstFinder,
MinimumSpanningTree,
]
TIME_LIMIT = 10 # give it a 10 second limit.
weighted_finders = [
AStarFinder,
BiAStarFinder,
DijkstraFinder,
MinimumSpanningTree,
]
SIMPLE_MATRIX = np.zeros((5, 5, 5))
SIMPLE_MATRIX[0, 0, 0] = 1
SIMPLE_MATRIX[0, 0, 1] = 1
SIMPLE_MATRIX[0, 0, 2] = 1
SIMPLE_MATRIX[0, 0, 3] = 1
SIMPLE_MATRIX[0, 0, 4] = 1
SIMPLE_MATRIX[1, :, :] = 1
SIMPLE_MATRIX[2, :, :] = 1
SIMPLE_MATRIX[3, :, :] = 1
SIMPLE_MATRIX[4, 0, 0] = 1
SIMPLE_MATRIX[4, 1, 0] = 1
SIMPLE_MATRIX[4, 2, 0] = 1
SIMPLE_MATRIX[4, 3, 0] = 1
SIMPLE_MATRIX[4, 4, 0] = 1
WEIGHTED_SIMPLE_MATRIX = np.copy(SIMPLE_MATRIX)
WEIGHTED_SIMPLE_MATRIX[4, 1, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 2, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 3, 1] = 1
WEIGHTED_SIMPLE_MATRIX[4, 2, 0] = 99
WEIGHTED_SIMPLE_MATRIX[1, :, :] = 99
WEIGHTED_SIMPLE_MATRIX[2, :, :] = 99
WEIGHTED_SIMPLE_MATRIX[3, :, :] = 99
def test_path():
"""
test if we can find a path
"""
grid = Grid(matrix=SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in finders:
grid.cleanup()
finder = find(time_limit=TIME_LIMIT)
path_, runs = finder.find_path(start, end, grid)
path = []
for node in path_:
if isinstance(node, GridNode):
path.append((node.x, node.y, node.z))
elif isinstance(node, tuple):
path.append((node[0], node[1], node[2]))
print(find.__name__)
print(f"path: {path}")
print(f"length: {len(path)}, runs: {runs}")
assert len(path) == 9
def test_weighted_path():
grid = Grid(matrix=WEIGHTED_SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in weighted_finders:
grid.cleanup()
finder = find(time_limit=TIME_LIMIT)
path_, runs = finder.find_path(start, end, grid)
path = []
for node in path_:
if isinstance(node, GridNode):
path.append((node.x, node.y, node.z))
elif isinstance(node, tuple):
path.append((node[0], node[1], node[2]))
print(find.__name__)
print(f"path: {path}")
print(f"length: {len(path)}, runs: {runs}")
assert len(path) == 11
def test_path_diagonal():
# test diagonal movement
grid = Grid(matrix=SIMPLE_MATRIX)
start = grid.node(0, 0, 0)
end = grid.node(4, 4, 0)
for find in finders:
grid.cleanup() | finder = find(diagonal_movement=DiagonalMovement.always, time_limit=TIME_LIMIT) | 0 | 2023-11-21 10:14:12+00:00 | 16k |
yuukawahiroshi/ddb-tools | mixins_ddb.py | [
{
"identifier": "DDIModel",
"path": "utils/ddi_utils.py",
"snippet": "class DDIModel:\n def __init__(self, ddi_bytes: bytes) -> None:\n self.ddi_bytes = ddi_bytes\n self.ddi_data = None\n self.phdc_data = {}\n self.tdb_data = {}\n self.sta_data = {}\n self.ar... | from typing import TypedDict
from utils.ddi_utils import DDIModel, str_to_bytes, str_to_data, stream_reverse_search
import argparse
import io
import re
import os
import os.path
import struct | 11,937 |
ddb_snd_offset = output_stream.tell()
mixins_ddb_stream.seek(snd_offset)
snd_bytes = mixins_ddb_stream.read(snd_len)
hed = snd_bytes[0:4].decode()
if hed != "SND ":
raise Exception("Mixins DDB file is broken")
output_stream.write(snd_bytes)
vqm_meta_list.append({
"idx": vqm_idx,
"epr": epr_list,
"snd_id": snd_id,
"snd": ddb_snd_offset,
"fs": vqm_info["fs"],
"unknown1": vqm_info["unknown1"],
"pitch1": vqm_info["pitch1"],
"pitch2": vqm_info["pitch2"],
"unknown2": vqm_info["unknown2"],
"unknown3": vqm_info["unknown3"],
"dynamics": vqm_info["dynamics"],
})
# Create new DDI
vqm_stream = _create_vqm_stream(vqm_meta_list)
ddi_vqm_bytes = vqm_stream.getvalue()
if "vqm" in src_ddi_model.ddi_data_dict:
ddi_vqm_pos = src_ddi_model.offset_map["vqm"][0]
ddi_vqm_end_pos = src_ddi_model.offset_map["vqm"][1]
else:
ddi_vqm_pos = src_ddi_bytes.find(ddi_footer)
ddi_vqm_end_pos = ddi_vqm_pos
# Bump dbv_len
dbv_len_post = src_ddi_model.offset_map["dbv"][0] + 0x18
src_ddi_stream.seek(dbv_len_post)
src_ddi_dbv_len = int.from_bytes(src_ddi_stream.read(4), byteorder='little')
src_ddi_dbv_len += 1
src_ddi_stream.seek(dbv_len_post)
src_ddi_stream.write(src_ddi_dbv_len.to_bytes(4, byteorder='little'))
src_ddi_bytes = src_ddi_stream.getvalue()
dst_ddi_bytes = byte_replace(src_ddi_bytes, ddi_vqm_pos, ddi_vqm_end_pos - ddi_vqm_pos, ddi_vqm_bytes)
return dst_ddi_bytes
def mixins_sta2vqm(src_ddi_bytes: bytes, output_stream: io.BufferedWriter, mixins_ddi_model: DDIModel, mixins_ddb_stream: io.BufferedReader, sta2vqm_phoneme: str):
mixins_ddi_stream = mixins_ddi_model.ddi_data
print("Reading source DDI...")
src_ddi_model = DDIModel(src_ddi_bytes)
src_ddi_model.read()
src_ddi_stream = src_ddi_model.ddi_data
if "vqm" in src_ddi_model.ddi_data_dict:
print("Source DDI already has vqm stream, continue will replace it and won't remove vqm stream from ddb file.")
print("Continue? (Y/n)", end=" ")
choice = input().strip().lower()
if choice != "y" or choice != "":
return
# Find stationary in mixins
mixins_sta_items = None
for _, sta_items in mixins_ddi_model.sta_data.items():
if sta_items["phoneme"] == sta2vqm_phoneme:
mixins_sta_items = sta_items
break
if mixins_sta_items is None:
raise Exception("Mixins DDI doesn't have stationary entry for phoneme \"%s\"" % sta2vqm_phoneme)
vqm_meta_list: list[VQMMeta] = []
vqm_idx = 0
for sta_idx, sta_item in mixins_sta_items["stap"].items():
output_epr_list = []
# EpR
epr_list = sta_item["epr"]
if len(epr_list) < 100:
print(f"Warning: EpR count is less than 100, EpR count: {len(epr_list)}")
continue
epr_list = epr_list[0:100]
for epr_info in epr_list:
epr_offset = epr_info.split("=")
ddb_epr_offset = output_stream.tell()
epr_offset = int(epr_offset[1], 16)
mixins_ddb_stream.seek(epr_offset)
hed = mixins_ddb_stream.read(4)
if hed != b"FRM2":
raise Exception("Mixins DDB file is broken")
frm_len = int.from_bytes(mixins_ddb_stream.read(4), byteorder='little')
epr_cutoff = epr_offset + frm_len
mixins_ddb_stream.seek(epr_offset)
frm_bytes = mixins_ddb_stream.read(frm_len)
output_stream.write(frm_bytes)
output_epr_list.append(ddb_epr_offset)
# SND
ddi_snd_pos, snd_name = sta_item["snd"].split("=")
snd_offset, snd_id = snd_name.split("_")
ddi_snd_pos = int(ddi_snd_pos, 16)
snd_offset = int(snd_offset, 16)
snd_id = int(snd_id, 16)
| #!/bin/env python3
# I thought what I'd do was, I'd pretend I was one of those deaf-mutes.
from __future__ import annotations
ddi_footer = b'\x05\x00\x00\x00' + "voice".encode()
class SmartFormatter(argparse.HelpFormatter):
def _split_lines(self, text, width):
if text.startswith('R|'):
return text[2:].splitlines()
# this is the RawTextHelpFormatter._split_lines
return argparse.HelpFormatter._split_lines(self, text, width)
class VQMMeta(TypedDict):
idx: str
epr: list[int]
snd_id: int
snd: int
fs: int
unknown1: str
pitch1: float
pitch2: float
unknown2: float
unknown3: float
dynamics: float
def byte_replace(src_bytes: bytes, offset: int, override_len: int, replace_bytes: bytes):
return src_bytes[:offset] + replace_bytes + src_bytes[offset + override_len:]
def parse_args(args=None): # : list[str]
# initialize parser
parser = argparse.ArgumentParser(formatter_class=SmartFormatter)
parser.add_argument('--src_path', required=True,
help='source ddi file path')
parser.add_argument('--mixins_path',
help='the mixins ddi file path. default to be same as src_path')
parser.add_argument('--dst_path',
help='output folder, '
'default to be "./[singer name]/mixins"')
parser.add_argument('--mixins_item',
choices=['vqm', 'sta2vqm'],
default='vqm',
help='R|mixins item, '
'default to be "vqm"\n'
'select from: \n'
' vqm: growl\n'
' sta2vqm: convert stationary entry to growl\n')
parser.add_argument('--sta2vqm_phoneme',
default="Grw",
help='phoneme for sta2vqm, will use this phoneme to generate growl, default to be "Grw"')
# parse args
args = parser.parse_args(args)
src_ddi_path: str = os.path.normpath(args.src_path)
if not os.path.exists(src_ddi_path):
raise Exception("ddi file not exists")
src_path = os.path.dirname(src_ddi_path)
src_singer_name = os.path.splitext(os.path.basename(src_ddi_path))[0]
mixins_ddi_path = args.mixins_path or src_ddi_path
mixins_ddi_path: str = os.path.normpath(mixins_ddi_path)
mixins_path = os.path.dirname(mixins_ddi_path)
mixins_singer_name = os.path.splitext(os.path.basename(mixins_ddi_path))[0]
dst_path: str = args.dst_path
if dst_path is None:
dst_path = os.path.join(src_path, "mixins")
dst_path: str = os.path.normpath(dst_path)
# make dirs
if not os.path.exists(dst_path):
os.makedirs(dst_path)
mixins_item = args.mixins_item
return src_path, src_singer_name, mixins_path, mixins_singer_name, dst_path, mixins_item, args
def _create_vqm_stream(vqm_meta_list: list[VQMMeta]):
# Create VQM struct
vqm_stream = io.BytesIO()
vqm_stream.write(b'\xFF'*8)
vqm_stream.write(b'VQM ')
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write((1).to_bytes(4, byteorder='little'))
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write((1).to_bytes(4, byteorder='little'))
vqm_stream.write(b'\xFF'*8)
vqm_stream.write(b'VQMu')
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write((1).to_bytes(4, byteorder='little'))
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write(len(vqm_meta_list).to_bytes(4, byteorder='little'))
vqm_stream.write(len(vqm_meta_list).to_bytes(4, byteorder='little'))
for vqm_meta in vqm_meta_list:
vqm_stream.write(b'\xFF'*8)
vqm_stream.write(b"VQMp")
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write((0).to_bytes(4, byteorder='little'))
vqm_stream.write((1).to_bytes(4, byteorder='little'))
vqm_stream.write(str_to_bytes(vqm_meta["unknown1"]))
vqm_stream.write(struct.pack("<f", 224.0)) # Unknown
vqm_stream.write(struct.pack("<f", vqm_meta["pitch2"]))
vqm_stream.write(struct.pack("<f", vqm_meta["unknown2"]))
vqm_stream.write(struct.pack("<f", vqm_meta["dynamics"]))
vqm_stream.write(struct.pack("<f", vqm_meta["unknown3"]))
vqm_stream.write((0).to_bytes(4, byteorder='little'))
# EpR
vqm_stream.write(b'\xFF'*4)
vqm_stream.write(len(vqm_meta["epr"]).to_bytes(4, byteorder='little'))
for epr_offset in vqm_meta["epr"]:
vqm_stream.write(epr_offset.to_bytes(8, byteorder='little'))
# SND
vqm_stream.write(vqm_meta["fs"].to_bytes(4, byteorder='little'))
vqm_stream.write(b'\x01\x00')
vqm_stream.write(vqm_meta["snd_id"].to_bytes(4, byteorder='little'))
vqm_stream.write(vqm_meta["snd"].to_bytes(8, byteorder='little'))
vqm_stream.write(b'\xFF'*0x10)
vqm_stream.write(str_to_data(vqm_meta["idx"]))
vqm_stream.write(str_to_data("GROWL"))
vqm_stream.write(str_to_data("vqm"))
return vqm_stream
def mixins_vqm(src_ddi_bytes: bytes, output_stream: io.BufferedWriter, mixins_ddi_model: DDIModel, mixins_ddb_stream: io.BufferedReader):
mixins_ddi_stream = mixins_ddi_model.ddi_data
if "vqm" not in mixins_ddi_model.ddi_data_dict:
raise Exception("Mixins DDI doesn't have vqm stream.")
print("Reading source DDI...")
src_ddi_model = DDIModel(src_ddi_bytes)
src_ddi_model.read()
src_ddi_stream = src_ddi_model.ddi_data
if "vqm" in src_ddi_model.ddi_data_dict:
print("Source DDI already has vqm stream, continue will replace it and won't remove vqm stream from ddb file.")
print("Continue? (Y/n)", end=" ")
choice = input().strip().lower()
if choice != "y" or choice != "":
return
vqm_meta_list: list[VQMMeta] = []
for vqm_idx, vqm_info in mixins_ddi_model.vqm_data.items():
epr_list = []
for epr_info in vqm_info["epr"]:
ddi_epr_pos, epr_offset = epr_info.split("=")
ddb_epr_offset = output_stream.tell()
ddi_epr_pos = int(ddi_epr_pos, 16)
epr_offset = int(epr_offset, 16)
mixins_ddb_stream.seek(epr_offset)
hed = mixins_ddb_stream.read(4).decode()
if hed != "FRM2":
raise Exception("Mixins DDB file is broken")
frm_len = int.from_bytes(mixins_ddb_stream.read(4), byteorder='little')
mixins_ddb_stream.seek(epr_offset)
frm_bytes = mixins_ddb_stream.read(frm_len)
output_stream.write(frm_bytes)
epr_list.append(ddb_epr_offset)
ddi_snd_pos, snd_name = vqm_info["snd"].split("=")
snd_offset, snd_id = snd_name.split("_")
ddi_snd_pos = int(ddi_snd_pos, 16)
snd_offset = int(snd_offset, 16)
snd_id = int(snd_id, 16)
mixins_ddb_stream.seek(snd_offset)
hed = mixins_ddb_stream.read(4).decode()
if hed != "SND ":
raise Exception("Mixins DDB file is broken")
snd_len = int.from_bytes(mixins_ddb_stream.read(4), byteorder='little')
ddb_snd_offset = output_stream.tell()
mixins_ddb_stream.seek(snd_offset)
snd_bytes = mixins_ddb_stream.read(snd_len)
hed = snd_bytes[0:4].decode()
if hed != "SND ":
raise Exception("Mixins DDB file is broken")
output_stream.write(snd_bytes)
vqm_meta_list.append({
"idx": vqm_idx,
"epr": epr_list,
"snd_id": snd_id,
"snd": ddb_snd_offset,
"fs": vqm_info["fs"],
"unknown1": vqm_info["unknown1"],
"pitch1": vqm_info["pitch1"],
"pitch2": vqm_info["pitch2"],
"unknown2": vqm_info["unknown2"],
"unknown3": vqm_info["unknown3"],
"dynamics": vqm_info["dynamics"],
})
# Create new DDI
vqm_stream = _create_vqm_stream(vqm_meta_list)
ddi_vqm_bytes = vqm_stream.getvalue()
if "vqm" in src_ddi_model.ddi_data_dict:
ddi_vqm_pos = src_ddi_model.offset_map["vqm"][0]
ddi_vqm_end_pos = src_ddi_model.offset_map["vqm"][1]
else:
ddi_vqm_pos = src_ddi_bytes.find(ddi_footer)
ddi_vqm_end_pos = ddi_vqm_pos
# Bump dbv_len
dbv_len_post = src_ddi_model.offset_map["dbv"][0] + 0x18
src_ddi_stream.seek(dbv_len_post)
src_ddi_dbv_len = int.from_bytes(src_ddi_stream.read(4), byteorder='little')
src_ddi_dbv_len += 1
src_ddi_stream.seek(dbv_len_post)
src_ddi_stream.write(src_ddi_dbv_len.to_bytes(4, byteorder='little'))
src_ddi_bytes = src_ddi_stream.getvalue()
dst_ddi_bytes = byte_replace(src_ddi_bytes, ddi_vqm_pos, ddi_vqm_end_pos - ddi_vqm_pos, ddi_vqm_bytes)
return dst_ddi_bytes
def mixins_sta2vqm(src_ddi_bytes: bytes, output_stream: io.BufferedWriter, mixins_ddi_model: DDIModel, mixins_ddb_stream: io.BufferedReader, sta2vqm_phoneme: str):
mixins_ddi_stream = mixins_ddi_model.ddi_data
print("Reading source DDI...")
src_ddi_model = DDIModel(src_ddi_bytes)
src_ddi_model.read()
src_ddi_stream = src_ddi_model.ddi_data
if "vqm" in src_ddi_model.ddi_data_dict:
print("Source DDI already has vqm stream, continue will replace it and won't remove vqm stream from ddb file.")
print("Continue? (Y/n)", end=" ")
choice = input().strip().lower()
if choice != "y" or choice != "":
return
# Find stationary in mixins
mixins_sta_items = None
for _, sta_items in mixins_ddi_model.sta_data.items():
if sta_items["phoneme"] == sta2vqm_phoneme:
mixins_sta_items = sta_items
break
if mixins_sta_items is None:
raise Exception("Mixins DDI doesn't have stationary entry for phoneme \"%s\"" % sta2vqm_phoneme)
vqm_meta_list: list[VQMMeta] = []
vqm_idx = 0
for sta_idx, sta_item in mixins_sta_items["stap"].items():
output_epr_list = []
# EpR
epr_list = sta_item["epr"]
if len(epr_list) < 100:
print(f"Warning: EpR count is less than 100, EpR count: {len(epr_list)}")
continue
epr_list = epr_list[0:100]
for epr_info in epr_list:
epr_offset = epr_info.split("=")
ddb_epr_offset = output_stream.tell()
epr_offset = int(epr_offset[1], 16)
mixins_ddb_stream.seek(epr_offset)
hed = mixins_ddb_stream.read(4)
if hed != b"FRM2":
raise Exception("Mixins DDB file is broken")
frm_len = int.from_bytes(mixins_ddb_stream.read(4), byteorder='little')
epr_cutoff = epr_offset + frm_len
mixins_ddb_stream.seek(epr_offset)
frm_bytes = mixins_ddb_stream.read(frm_len)
output_stream.write(frm_bytes)
output_epr_list.append(ddb_epr_offset)
# SND
ddi_snd_pos, snd_name = sta_item["snd"].split("=")
snd_offset, snd_id = snd_name.split("_")
ddi_snd_pos = int(ddi_snd_pos, 16)
snd_offset = int(snd_offset, 16)
snd_id = int(snd_id, 16)
| real_snd_offset = stream_reverse_search(mixins_ddb_stream, b"SND ", snd_offset) | 3 | 2023-11-20 11:37:46+00:00 | 16k |
shercoo/RGDiffSR | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import datetime
import math
import cv2
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import pygame
from collections import OrderedDict
from matplotlib import pyplot as plt
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from torchvision import transforms
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from text_super_resolution.model.VisionLAN.utils import Attention_AR_counter
from text_super_resolution.model.tps_spatial_transformer import TPSSpatialTransformer
from text_super_resolution.model.stn_head import STNHead
from text_super_resolution.model.VisionLAN.VisionLAN import VisionLAN
from utils.render_standard_text import *
from text_super_resolution.loss.semantic_loss import SemanticLoss
from text_super_resolution.utils import ssim_psnr
from pygame import freetype
from utils.metrics import * | 12,312 | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
sem_loss = SemanticLoss()
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
sem_loss = SemanticLoss()
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else: | betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, | 14 | 2023-11-20 06:34:21+00:00 | 16k |
mjavadpur/mj_ONNX_SadTalker | inference_onnx.py | [
{
"identifier": "AnimateFromCoeff",
"path": "src/facerender/animate_onnx.py",
"snippet": "class AnimateFromCoeff():\n\n def __init__(self, sadtalker_path, device):\n\n with open(sadtalker_path['facerender_yaml']) as f:\n config = yaml.safe_load(f)\n\n generator = OcclusionAwa... | from glob import glob
from time import strftime
from argparse import ArgumentParser
from src.facerender.animate_onnx import AnimateFromCoeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
from src.utils.init_path import init_path
from src.utils.preprocess import CropAndExtract
from src.test_audio2coeff import Audio2Coeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
from src.utils.init_path import init_path
from src.face3d.visualize import gen_composed_video
import shutil
import torch
import os, sys, time
import base64 | 13,128 |
# from src.facerender.animate import AnimateFromCoeff
def main(args):
#torch.backends.cudnn.enabled = False
# tts_service = os.getenv("TTS_SERVER")
facerender_batch_size = 10
startInference = time.time()
pic_path = args.source_image
audio_path = args.driven_audio
save_dir = os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S"))
os.makedirs(save_dir, exist_ok=True)
pose_style = args.pose_style
device = args.device
batch_size = args.batch_size
input_yaw_list = args.input_yaw
input_pitch_list = args.input_pitch
input_roll_list = args.input_roll
ref_eyeblink = args.ref_eyeblink
ref_pose = args.ref_pose
current_root_path = os.path.split(sys.argv[0])[0]
sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess)
#init model
preprocess_model = CropAndExtract(sadtalker_paths, device)
audio_to_coeff = Audio2Coeff(sadtalker_paths, device)
animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device)
#crop image and extract 3dmm from image
first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
os.makedirs(first_frame_dir, exist_ok=True)
print('3DMM Extraction for source image')
first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\
source_image_flag=True, pic_size=args.size)
if first_coeff_path is None:
print("Can't get the coeffs of the input")
return
if ref_eyeblink is not None:
ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0]
ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname)
os.makedirs(ref_eyeblink_frame_dir, exist_ok=True)
print('3DMM Extraction for the reference video providing eye blinking')
ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False)
else:
ref_eyeblink_coeff_path=None
if ref_pose is not None:
if ref_pose == ref_eyeblink:
ref_pose_coeff_path = ref_eyeblink_coeff_path
else:
ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0]
ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname)
os.makedirs(ref_pose_frame_dir, exist_ok=True)
print('3DMM Extraction for the reference video providing pose')
ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False)
else:
ref_pose_coeff_path=None
#audio2ceoff
|
# from src.facerender.animate import AnimateFromCoeff
def main(args):
#torch.backends.cudnn.enabled = False
# tts_service = os.getenv("TTS_SERVER")
facerender_batch_size = 10
startInference = time.time()
pic_path = args.source_image
audio_path = args.driven_audio
save_dir = os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S"))
os.makedirs(save_dir, exist_ok=True)
pose_style = args.pose_style
device = args.device
batch_size = args.batch_size
input_yaw_list = args.input_yaw
input_pitch_list = args.input_pitch
input_roll_list = args.input_roll
ref_eyeblink = args.ref_eyeblink
ref_pose = args.ref_pose
current_root_path = os.path.split(sys.argv[0])[0]
sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess)
#init model
preprocess_model = CropAndExtract(sadtalker_paths, device)
audio_to_coeff = Audio2Coeff(sadtalker_paths, device)
animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device)
#crop image and extract 3dmm from image
first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
os.makedirs(first_frame_dir, exist_ok=True)
print('3DMM Extraction for source image')
first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\
source_image_flag=True, pic_size=args.size)
if first_coeff_path is None:
print("Can't get the coeffs of the input")
return
if ref_eyeblink is not None:
ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0]
ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname)
os.makedirs(ref_eyeblink_frame_dir, exist_ok=True)
print('3DMM Extraction for the reference video providing eye blinking')
ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False)
else:
ref_eyeblink_coeff_path=None
if ref_pose is not None:
if ref_pose == ref_eyeblink:
ref_pose_coeff_path = ref_eyeblink_coeff_path
else:
ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0]
ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname)
os.makedirs(ref_pose_frame_dir, exist_ok=True)
print('3DMM Extraction for the reference video providing pose')
ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False)
else:
ref_pose_coeff_path=None
#audio2ceoff | batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still) | 6 | 2023-11-25 06:53:12+00:00 | 16k |
microsoft/Project-BayesDAG | src/causica/preprocessing/data_processor.py | [
{
"identifier": "CausalDataset",
"path": "src/causica/datasets/dataset.py",
"snippet": "class CausalDataset(Dataset):\n \"\"\"\n Class to store the np.ndarray adjacency matrix and samples\n from the intervention distributions as attributes of the Dataset object.\n \"\"\"\n\n def __init__... | import logging
import warnings
import numpy as np
import torch
from typing import Iterable, List, Optional, Tuple, TypeVar, Union
from scipy import sparse
from scipy.sparse import csr_matrix, issparse
from sklearn.exceptions import NotFittedError
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.utils.validation import check_is_fitted
from tqdm import tqdm
from ..datasets.dataset import CausalDataset, Dataset, SparseDataset
from ..datasets.intervention_data import InterventionData
from ..datasets.variables import Variables
from .transforms import IdentityTransform, UnitScaler | 12,549 |
self._txt_unproc_cols, self._txt_proc_cols = [], []
self._num_processed_cols = sum(var.processed_dim for var in self._variables)
def process_data_and_masks(
self,
data: csr_matrix,
data_mask: csr_matrix,
*extra_masks: csr_matrix,
batch_size: int = 1000,
) -> Tuple[csr_matrix, ...]:
"""
Process and validate data, data mask and optionally any number of additional masks. These masks will all be applied
to the data when performing data range validation, in case of e.g. dummy zero data that is masked out by an
additional obs_mask.
Args:
data: Unprocessed data array
data_mask: Data indicating which values in `data` are observed. Can be any dtype provided all values are
either 0 or 1.
extra_masks: Additional masks to be processed, if any. Can be any dtype provided all values are either 0 or
1.
batch_size: Batch size used during data preprocessing for sparse matrices.
Returns:
processed_data: Data with categorical variables expanded to a one-hot encoding, and features normalised.
processed_data_mask: Boolean mask with categorical variables expanded to a one-hot encoding.
processed_extra_masks: Any additional boolean masks with categorical variables expanded to a one-hot
encoding.
"""
if not issparse(data):
(
proc_data,
proc_data_mask,
*proc_extra_masks,
) = self._process_and_check_dense(data, data_mask, *extra_masks)
else:
# Break sparse data into smaller batches and preprocess each as a dense array. Somewhat inefficient but
# allows us to reuse our preprocessing functions and keeps memory usage manageable.
proc_data_list: List[csr_matrix] = []
proc_data_mask_list: List[csr_matrix] = []
proc_extra_masks_lists: Tuple[List[csr_matrix], ...] = tuple([] for mask in extra_masks)
num_rows = data.shape[0]
for start_idx in tqdm(range(0, num_rows, batch_size), desc="Data preprocessing"):
stop_idx = min(start_idx + batch_size, num_rows)
data_batch = data[start_idx:stop_idx].toarray()
data_mask_batch = data_mask[start_idx:stop_idx].toarray()
extra_masks_batch = tuple(mask[start_idx:stop_idx].toarray() for mask in extra_masks)
# TODO: we will currently lose sparsity for rescaled continuous data here, since 0 will be mapped to
# another value. We could multiply by the mask to zero out unobserved data but we need to make sure this
# doesn't have any unintended consequences for cases with more complex masking, e.g. active learning
(
proc_data_batch,
proc_data_mask_batch,
*proc_extra_masks_batch,
) = self._process_and_check_dense(data_batch, data_mask_batch, *extra_masks_batch)
proc_data_list.append(csr_matrix(proc_data_batch))
proc_data_mask_list.append(csr_matrix(proc_data_mask_batch))
for mask_list, mask in zip(proc_extra_masks_lists, proc_extra_masks_batch):
mask_list.append(csr_matrix(mask))
proc_data = sparse.vstack(proc_data_list, format="csr")
proc_data_mask = sparse.vstack(proc_data_mask_list, format="csr")
proc_extra_masks = tuple(
sparse.vstack(proc_mask_list, format="csr") for proc_mask_list in proc_extra_masks_lists
)
return (proc_data, proc_data_mask, *proc_extra_masks)
def _process_and_check_dense(self, data: np.ndarray, data_mask: np.ndarray, *extra_masks: np.ndarray):
"""
Check validity of dense data and masks and process them.
"""
combined_mask = data_mask
for mask in extra_masks:
combined_mask = combined_mask * mask
self.check_data(data, combined_mask)
self.check_mask(data_mask)
for mask in extra_masks:
self.check_mask(mask)
proc_data = self.process_data(data)
proc_data_mask = self.process_mask(data_mask)
proc_extra_masks = tuple(self.process_mask(mask) for mask in extra_masks)
return (proc_data, proc_data_mask, *proc_extra_masks)
def process_intervention_data(
self, intervention_data: Union[InterventionData, Iterable[InterventionData]]
) -> List[InterventionData]:
"""Preprocesses data in the InterventionData format and returns a list of processed InterventionData objects.
Args:
intervention_data (Union[InterventionData, Iterable[InterventionData]]): InterventionData object or list of
InterventionData objects to be processed.
Returns:
List[InterventionData]: List of processed InterventionData objects.
"""
if isinstance(intervention_data, InterventionData):
intervention_data = [intervention_data]
proc_intervention = [
InterventionData(
i.intervention_idxs,
self.process_data_subset_by_group(i.intervention_values, i.intervention_idxs),
self.process_data(i.test_data),
i.conditioning_idxs,
self.process_data_subset_by_group(i.conditioning_values, i.conditioning_idxs),
i.effect_idxs,
self.process_data_subset_by_group(i.intervention_reference, i.intervention_idxs),
self.process_data(i.reference_data) if i.reference_data is not None else None,
)
for i in intervention_data
]
return proc_intervention
def process_dataset(
|
EPSILON = 1e-5
logger = logging.getLogger(__name__)
V = TypeVar("V", np.ndarray, torch.Tensor)
# pylint: disable=protected-access
class DataProcessor:
def __init__(
self,
variables: Variables,
unit_scale_continuous: bool = True,
standardize_data_mean: bool = False,
standardize_data_std: bool = False,
):
"""
Args:
variables (Variables): Information about variables/features used
by this model.
unit_scale_continuous (bool): Scale continuous variables to the range of [0, 1].
standardize_data_mean (bool): Standardize continuous variables to mean=0
standardize_data_std (bool): Standardize continuous variables to std=1
"""
if unit_scale_continuous and (standardize_data_mean or standardize_data_std):
raise ValueError("Cannot unit scale and standardize variables simultanously.")
self._variables = variables
# Call unprocessed columns unproc_cols, processed columns proc_cols
unproc_cols_by_type = self._variables.unprocessed_cols_by_type
proc_cols_by_type = self._variables.processed_cols_by_type
def flatten(lists):
# Flatten proc_cols for continuous and binary unproc_cols, since they will be of form [[1], [2], ...]
return [i for sublist in lists for i in sublist]
if "binary" in unproc_cols_by_type:
self._bin_unproc_cols = unproc_cols_by_type["binary"]
self._bin_proc_cols = flatten(proc_cols_by_type["binary"])
# Save contiguous regions containig binary features to allow for more efficient processing via slicing
self._bin_unproc_regions = self.split_contiguous_sublists(self._bin_unproc_cols)
self._bin_proc_regions = self.split_contiguous_sublists(self._bin_proc_cols)
assert len(self._bin_unproc_regions) == len(self._bin_proc_regions)
else:
self._bin_unproc_cols, self._bin_proc_cols = [], []
if "continuous" in unproc_cols_by_type:
self._cts_unproc_cols = unproc_cols_by_type["continuous"]
self._cts_proc_cols = flatten(proc_cols_by_type["continuous"])
# Save contiguous regions containing continuous features to allow for more efficient processing via slicing
if all(x.overwrite_processed_dim is None for x in self._variables):
self._cts_unproc_regions = self.split_contiguous_sublists(self._cts_unproc_cols)
self._cts_proc_regions = self.split_contiguous_sublists(self._cts_proc_cols)
else:
# For VAEM, we can only take single variable as region
# to allow for processing/reverting mask
self._cts_unproc_regions = [[col_id] for col_id in unproc_cols_by_type["continuous"]]
self._cts_proc_regions = proc_cols_by_type["continuous"]
assert len(self._cts_unproc_regions) == len(self._cts_proc_regions)
if unit_scale_continuous:
self._cts_normalizers = [
UnitScaler(variables[i] for i in unproc_region) for unproc_region in self._cts_unproc_regions
]
elif standardize_data_mean or standardize_data_std:
self._cts_normalizers = [
StandardScaler(with_mean=standardize_data_mean, with_std=standardize_data_std)
for _ in self._cts_unproc_regions
]
else:
self._cts_normalizers = [IdentityTransform()] * len(self._cts_unproc_regions)
else:
self._cts_unproc_cols, self._cts_proc_cols, self._cts_normalizers = [], [], []
if "categorical" in unproc_cols_by_type:
self._cat_unproc_cols = unproc_cols_by_type["categorical"]
self._cat_proc_cols = flatten(proc_cols_by_type["categorical"])
self._cat_proc_cols_grouped = proc_cols_by_type["categorical"]
def get_lower(idx):
return self._variables[idx].lower
def get_upper(idx):
return self._variables[idx].upper
var_categories = [
np.arange(int(get_lower(var_idx)), int(get_upper(var_idx)) + 1) for var_idx in self._cat_unproc_cols
]
self._one_hot_encoder = OneHotEncoder(categories=var_categories, sparse=False, handle_unknown="ignore")
# Fit on dummy data due to an issue in sklearn where the encoder needs to be fitted to data even if the
# categories are specified upon creation.
self._one_hot_encoder.fit(np.array([categories[0] for categories in var_categories]).reshape(1, -1))
else:
self._cat_unproc_cols, self._cat_proc_cols = [], []
self._txt_unproc_cols, self._txt_proc_cols = [], []
self._num_processed_cols = sum(var.processed_dim for var in self._variables)
def process_data_and_masks(
self,
data: csr_matrix,
data_mask: csr_matrix,
*extra_masks: csr_matrix,
batch_size: int = 1000,
) -> Tuple[csr_matrix, ...]:
"""
Process and validate data, data mask and optionally any number of additional masks. These masks will all be applied
to the data when performing data range validation, in case of e.g. dummy zero data that is masked out by an
additional obs_mask.
Args:
data: Unprocessed data array
data_mask: Data indicating which values in `data` are observed. Can be any dtype provided all values are
either 0 or 1.
extra_masks: Additional masks to be processed, if any. Can be any dtype provided all values are either 0 or
1.
batch_size: Batch size used during data preprocessing for sparse matrices.
Returns:
processed_data: Data with categorical variables expanded to a one-hot encoding, and features normalised.
processed_data_mask: Boolean mask with categorical variables expanded to a one-hot encoding.
processed_extra_masks: Any additional boolean masks with categorical variables expanded to a one-hot
encoding.
"""
if not issparse(data):
(
proc_data,
proc_data_mask,
*proc_extra_masks,
) = self._process_and_check_dense(data, data_mask, *extra_masks)
else:
# Break sparse data into smaller batches and preprocess each as a dense array. Somewhat inefficient but
# allows us to reuse our preprocessing functions and keeps memory usage manageable.
proc_data_list: List[csr_matrix] = []
proc_data_mask_list: List[csr_matrix] = []
proc_extra_masks_lists: Tuple[List[csr_matrix], ...] = tuple([] for mask in extra_masks)
num_rows = data.shape[0]
for start_idx in tqdm(range(0, num_rows, batch_size), desc="Data preprocessing"):
stop_idx = min(start_idx + batch_size, num_rows)
data_batch = data[start_idx:stop_idx].toarray()
data_mask_batch = data_mask[start_idx:stop_idx].toarray()
extra_masks_batch = tuple(mask[start_idx:stop_idx].toarray() for mask in extra_masks)
# TODO: we will currently lose sparsity for rescaled continuous data here, since 0 will be mapped to
# another value. We could multiply by the mask to zero out unobserved data but we need to make sure this
# doesn't have any unintended consequences for cases with more complex masking, e.g. active learning
(
proc_data_batch,
proc_data_mask_batch,
*proc_extra_masks_batch,
) = self._process_and_check_dense(data_batch, data_mask_batch, *extra_masks_batch)
proc_data_list.append(csr_matrix(proc_data_batch))
proc_data_mask_list.append(csr_matrix(proc_data_mask_batch))
for mask_list, mask in zip(proc_extra_masks_lists, proc_extra_masks_batch):
mask_list.append(csr_matrix(mask))
proc_data = sparse.vstack(proc_data_list, format="csr")
proc_data_mask = sparse.vstack(proc_data_mask_list, format="csr")
proc_extra_masks = tuple(
sparse.vstack(proc_mask_list, format="csr") for proc_mask_list in proc_extra_masks_lists
)
return (proc_data, proc_data_mask, *proc_extra_masks)
def _process_and_check_dense(self, data: np.ndarray, data_mask: np.ndarray, *extra_masks: np.ndarray):
"""
Check validity of dense data and masks and process them.
"""
combined_mask = data_mask
for mask in extra_masks:
combined_mask = combined_mask * mask
self.check_data(data, combined_mask)
self.check_mask(data_mask)
for mask in extra_masks:
self.check_mask(mask)
proc_data = self.process_data(data)
proc_data_mask = self.process_mask(data_mask)
proc_extra_masks = tuple(self.process_mask(mask) for mask in extra_masks)
return (proc_data, proc_data_mask, *proc_extra_masks)
def process_intervention_data(
self, intervention_data: Union[InterventionData, Iterable[InterventionData]]
) -> List[InterventionData]:
"""Preprocesses data in the InterventionData format and returns a list of processed InterventionData objects.
Args:
intervention_data (Union[InterventionData, Iterable[InterventionData]]): InterventionData object or list of
InterventionData objects to be processed.
Returns:
List[InterventionData]: List of processed InterventionData objects.
"""
if isinstance(intervention_data, InterventionData):
intervention_data = [intervention_data]
proc_intervention = [
InterventionData(
i.intervention_idxs,
self.process_data_subset_by_group(i.intervention_values, i.intervention_idxs),
self.process_data(i.test_data),
i.conditioning_idxs,
self.process_data_subset_by_group(i.conditioning_values, i.conditioning_idxs),
i.effect_idxs,
self.process_data_subset_by_group(i.intervention_reference, i.intervention_idxs),
self.process_data(i.reference_data) if i.reference_data is not None else None,
)
for i in intervention_data
]
return proc_intervention
def process_dataset( | self, dataset: Union[Dataset, CausalDataset, SparseDataset] | 0 | 2023-11-21 12:55:08+00:00 | 16k |
ChenyangGao/python-epub3 | epub3/epub.py | [
{
"identifier": "File",
"path": "epub3/util/file.py",
"snippet": "class File:\n __slots__ = (\"path\", \"fs\", \"open\", \"open_modes\", \"_getattr\")\n ALL_MODES = frozenset(\"rwxab+\")\n\n def __init__(\n self, \n /, \n path=None, \n fs=None, \n open_modes=N... | import errno
import io
import os
import os.path as ospath
import posixpath
from copy import deepcopy
from datetime import datetime
from fnmatch import translate as wildcard_translate
from functools import cached_property, partial
from inspect import getfullargspec, isclass
from io import IOBase, TextIOWrapper
from operator import methodcaller
from os import fsdecode, remove, stat, stat_result, PathLike
from pathlib import PurePosixPath
from posixpath import join as joinpath, normpath
from pprint import pformat
from re import compile as re_compile, escape as re_escape, Pattern
from shutil import copy, copyfileobj
from typing import cast, Any, Callable, Container, Mapping, MutableMapping, Optional
from types import MappingProxyType
from uuid import uuid4
from warnings import warn
from weakref import WeakKeyDictionary, WeakValueDictionary
from urllib.parse import quote, unquote
from zipfile import ZipFile, ZIP_STORED
from .util.file import File, RootFS, TemporaryFS, OPEN_MODES
from .util.helper import guess_media_type, values, items, sup
from .util.proxy import proxy_property, ElementAttribProxy, ElementProxy, NAMESPACES
from .util.remap import remap_links
from .util.stream import PyLinq
from .util.xml import el_add, el_del, el_iterfind, el_set
from .util.undefined import undefined, UndefinedType
from lxml.etree import fromstring, tostring, _Element as Element, _ElementTree as ElementTree # type: ignore
from xml.etree.ElementTree import fromstring, tostring, Element, ElementTree # type: ignore | 13,861 | self._proxy.merge(id_or_attrib)
return self
def update(self, id_or_attrib=None, /, **attrs):
if attrs:
if isinstance(id_or_attrib, Item):
item = id_or_attrib
if item not in self:
raise LookupError(f"no such item: {item!r}")
item.update(attrib=attrs)
elif isinstance(id_or_attrib, str):
id = id_or_attrib
item = super().get(id)
if item is None:
if "href" in attrs:
href = attrs.pop("href")
self.add(href, id=id, attrib=attrs)
else:
raise LookupError(f"no such item: {id!r}")
else:
item.update(attrs)
else:
self._proxy.update(id_or_attrib, **attrs)
elif isinstance(id_or_attrib, Mapping):
self._proxy.update(id_or_attrib)
return self
#################### SubElement Methods ####################
@PyLinq.streamify
def filter(self, /, predicate=None):
if not callable(predicate):
return iter(self.values())
return filter(predicate, self.values())
@PyLinq.streamify
def filter_by_attr(self, predicate=None, attr="media-type", /):
def activate_predicate(predicate):
if predicate is None:
return None
if callable(predicate):
return predicate
elif isinstance(predicate, Pattern):
return predicate.search
elif isinstance(predicate, str):
use_false = False
if predicate.startswith(r"!"):
use_false = True
predicate = predicate[1:]
predicate_startswith = predicate.startswith
if predicate_startswith(r"="):
predicate = predicate[1:].__eq__
elif predicate_startswith(r"~"):
predicate = methodcaller("__contains__", predicate[1:])
elif predicate_startswith(r"^"):
predicate = methodcaller("startswith", predicate[1:])
elif predicate_startswith(r"$"):
predicate = methodcaller("endswith", predicate[1:])
elif predicate_startswith(r";"):
predicate = lambda s, needle=predicate[1:]: needle in s.split()
elif predicate_startswith(r","):
predicate = lambda s, needle=predicate[1:]: needle in s.split(",")
elif predicate_startswith(r"<"):
predicate = re_compile(r"\b"+re_escape(predicate[1:])).search
elif predicate_startswith(r">"):
predicate = re_compile(re_escape(predicate[1:])+r"\b").search
elif predicate_startswith(r"|"):
predicate = re_compile(r"\b"+re_escape(predicate[1:])+r"\b").search
elif predicate_startswith(r"*"):
predicate = re_compile(wildcard_translate(predicate[1:])).fullmatch
elif predicate_startswith(r"/"):
predicate = re_compile(predicate[1:]).search
elif predicate_startswith(r"%"):
predicate = re_compile(predicate[1:]).fullmatch
else:
predicate = predicate.__eq__
if use_false:
predicate = lambda s, _pred=predicate: not _pred(s)
return predicate
elif type(predicate) in (tuple, list):
preds = tuple(pred for p in predicate if (pred:=activate_predicate(p)) is not None)
if not preds:
return None
if type(predicate) is tuple:
return lambda s, _preds=preds: any(p(s) for p in preds)
else:
return lambda s, _preds=preds: all(p(s) for p in preds)
elif isinstance(predicate, Container):
return predicate.__contains__
predicate = activate_predicate(predicate)
if predicate is None:
return filter(lambda item: attr in item, self.values())
return filter(lambda item: attr in item and predicate(item[attr]), self.values())
@PyLinq.streamify
def iter(self, /):
root = self._root
for el in root.iterfind("*"):
if not (el.tag == "item" or el.tag.endswith("}item")):
yield ElementProxy(el)
continue
id = el.attrib.get("id")
href = el.attrib.get("href")
if not href:
if id is None or not super().__contains__(id):
try:
root.remove(el)
warn(f"removed a dangling item element: {el!r}")
except:
pass
else:
item = super().__getitem__(id)
if item._root is not el:
raise RuntimeError(f"different item elements {el!r} and {item._root!r} share the same id {id!r}")
else:
self.pop(id, None)
warn(f"removed an item because of missing href attribute: {item!r}")
continue
href = unquote(href)
if not el.attrib.get("media-type"):
| #!/usr/bin/env python
# coding: utf-8
__author__ = "ChenyangGao <https://chenyanggao.github.io>"
__version__ = (0, 0, 1)
__all__ = ["ePub", "Metadata", "DCTerm", "Meta", "Link", "Manifest", "Item", "Spine", "Itemref"]
try:
except ModuleNotFoundError:
class DCTerm(ElementProxy):
pass
class Meta(ElementProxy):
__protected_keys__ = ("property",)
__optional_keys__ = ("dir", "id", "refines", "scheme", "xml:lang")
class Link(ElementAttribProxy):
__protected_keys__ = ("href", "rel")
__optional_keys__ = ("hreflang", "id", "media-type", "properties", "refines")
class Item(ElementAttribProxy):
__const_keys__ = ("id",)
__protected_keys__ = ("href", "media-type")
__optional_keys__ = ("fallback", "media-overlay", "properties")
__cache_get_state__ = lambda _, manifest: manifest
def __init__(self, root: Element, manifest, /):
super().__init__(root)
self._manifest = manifest
def __eq__(self, other, /):
if type(self) is not type(other):
return NotImplemented
return self._manifest is other._manifest and self._attrib["href"] == other._attrib["href"]
def __fspath__(self, /):
return unquote(self._attrib["href"])
def __hash__(self, /):
return hash((self._root, id(self._manifest)))
def __setitem__(self, key, value, /):
if key == "href":
if value is None:
raise ValueError("can't set href to None")
self.rename(val)
else:
super().__setitem__(key, value)
return self
@property
def filename(self, /):
return PurePosixPath(joinpath(self.home, self))
@property
def home(self, /):
return PurePosixPath(self._manifest._epub._opf_dir)
@property
def name(self, /):
return self.path.name
@property
def path(self, /):
return PurePosixPath(self)
@property
def _parent(self, /):
return posixpath.dirname(unquote(self._attrib["href"]))
@property
def parent(self, /):
return self.path.parent
@property
def parents(self, /):
return self.path.parents
@property
def parts(self, /):
return self.path.parts
@property
def stem(self, /):
return self.path.stem
@property
def suffix(self, /):
return self.path.suffix
@property
def suffixes(self, /):
return self.path.suffixes
def update(self, attrib=None, /, **attrs):
if attrib:
attrib = dict(attrib)
if attrs:
attrib.update(attrs)
else:
attrib = attrs
href = attrib.pop("href", None)
if href:
self.rename(href)
if attrib:
super().update(attrib)
return self
def is_relative_to(self, /, *other):
return self.path.is_relative_to(*other)
def joinpath(self, /, *others):
return PurePosixPath(normpath(joinpath(self._parent, *others)))
__truediv__ = joinpath
def relpath(self, other, /):
return PurePosixPath(posixpath.relpath(other, self._parent))
def relative_to(self, /, *other):
return self.path.relative_to(*other)
def with_name(self, /, name):
return self.path.with_name(str(name))
def with_stem(self, /, stem):
return self.path.with_stem(str(stem))
def with_suffix(self, /, suffix):
return self.path.with_suffix(str(suffix))
def exists(self, /):
return self._manifest.exists(self)
def is_file(self, /):
return self.exists()
def is_dir(self, /):
return False
def is_symlink(self, /):
return False
def glob(self, /, pattern="*", ignore_case=False):
return self._manifest.glob(pattern, self, ignore_case=ignore_case)
def rglob(self, /, pattern="", ignore_case=False):
return self._manifest.rglob(pattern, self, ignore_case=ignore_case)
def iterdir(self, /):
return self._manifest.iterdir(self)
def match(self, /, path_pattern, ignore_case=False):
path_pattern = path_pattern.strip("/")
if not path_pattern:
return False
pattern = joinpath(*posix_glob_translate_iter(path_pattern))
if ignore_case:
pattern = "(?i:%s)" % pattern
return re_compile(pattern).fullmatch(self._attrib["href"]) is not None
def open(
self,
/,
mode="r",
buffering=-1,
encoding=None,
errors=None,
newline=None,
):
return self._manifest.open(
self,
mode=mode,
buffering=buffering,
encoding=encoding,
errors=errors,
newline=newline,
)
def read(self, /, buffering=0):
return self._manifest.read(self, buffering=buffering)
read_bytes = read
def read_text(self, /, encoding=None):
return self._manifest.read_text(self, encoding=encoding)
def remove(self, /):
self._manifest.remove(self)
return self
def rename(self, dest_href, /, repair=False):
return self._manifest.rename(self, dest_href, repair=repair)
def batch_rename(self, mapper, /, predicate=None, repair=False):
return self._manifest.batch_rename(self, mapper, predicate=predicate, repair=repair)
def replace(self, href, /):
self._manifest.replace(self, href)
return self
def stat(self, /) -> Optional[stat_result]:
return self._manifest.stat(self)
def touch(self, /):
self._manifest.touch(self)
return self
unlink = remove
def write(self, /, data):
return self._manifest.write(self, data)
write_bytes = write
def write_text(self, /, text, encoding=None, errors=None, newline=None):
return self._manifest.write_text(self, text, encoding=encoding, errors=errors, newline=newline)
class Itemref(ElementAttribProxy):
__const_keys__ = ("idref",)
__optional_keys__ = ("id", "linear", "properties")
@property
def linear(self, /):
return "no" if self._attrib.get("linear") == "no" else "yes"
@linear.setter
def linear(self, value, /):
self._attrib["linear"] = "no" if value == "no" else "yes"
class Metadata(ElementProxy):
__wrap_class_map__ = {"{*}meta": Meta, "{*}": Link, "dc:*": DCTerm}
def __repr__(self, /):
return f"{super().__repr__()}\n{pformat(self.iter().list())}"
@property
def info(self, /):
return tuple(meta.info for meta in self.iter())
def add(
self,
name: str = "meta",
/,
attrib: Optional[Mapping] = None,
text: Optional[str] = None,
tail: Any = undefined,
**_disregards,
):
return super().add(name, attrib=attrib, text=text)
def dc(
self,
name: str,
text_value: UndefinedType | Optional[str] = undefined,
/,
find_attrib: Optional[Mapping] = None,
attrib: Optional[Mapping] = None,
text: Optional[str] = None,
merge: bool = False,
delete: bool = False,
auto_add: bool = False,
):
if text_value is not undefined:
if find_attrib:
find_attrib = {**find_attrib, "": text_value}
else:
find_attrib = {"": text_value}
return self.setfind(
"dc:%s" % name,
find_attrib=find_attrib,
attrib=attrib,
text=text,
merge=merge,
delete=delete,
auto_add=auto_add,
)
def meta(
self,
preds: str = "",
/,
find_attrib: Optional[Mapping] = None,
attrib: Optional[Mapping] = None,
text: Optional[str] = None,
merge: bool = False,
delete: bool = False,
auto_add: bool = False,
):
return self.setfind(
"{*}meta%s" % preds,
find_attrib=find_attrib,
attrib=attrib,
text=text,
merge=merge,
delete=delete,
auto_add=auto_add,
)
def name_meta(
self,
name,
content: Optional[str] = None,
/,
find_attrib: Optional[Mapping] = None,
attrib: Optional[Mapping] = None,
text: Optional[str] = None,
merge: bool = False,
delete: bool = False,
auto_add: bool = False,
):
if find_attrib:
find_attrib = {**find_attrib, "name": name}
else:
find_attrib = {"name": name}
if content is not None:
find_attrib["content"] = content
return self.meta(
find_attrib=find_attrib,
attrib=attrib,
text=text,
merge=merge,
delete=delete,
auto_add=auto_add,
)
def property_meta(
self,
property,
text_value: UndefinedType | Optional[str] = undefined,
/,
find_attrib: Optional[Mapping] = None,
attrib: Optional[Mapping] = None,
text: Optional[str] = None,
merge: bool = False,
delete: bool = False,
auto_add: bool = False,
):
if find_attrib:
find_attrib = {**find_attrib, "property": property}
else:
find_attrib = {"property": property}
if text_value is not undefined:
find_attrib[""] = text_value
return self.meta(
find_attrib=find_attrib,
attrib=attrib,
text=text,
merge=merge,
delete=delete,
auto_add=auto_add,
)
class ManifestProxy(ElementAttribProxy):
__optional_keys__ = ("id",)
class Manifest(dict[str, Item]):
def __init__(self, /, root: Element, epub):
self._root = root
self._attrib = root.attrib
self._epub = epub
self._proxy = ManifestProxy(root)
self._href_to_id: dict[str, str] = {}
self._href_to_file: dict[str, File] = {}
if len(root):
href_to_id = self._href_to_id
dangling_items = []
for item in root.iterfind("{*}item"):
id = item.attrib.get("id")
href = item.attrib.get("href")
if id is None or not href:
dangling_items.append(item)
continue
id = cast(str, id)
href = cast(str, unquote(href))
super().__setitem__(id, Item(item, self))
href_to_id[href] = id
if dangling_items:
for item in reversed(dangling_items):
root.remove(item)
warn(f"removed a dangling item element: {item!r}")
zfile = epub.__dict__.get("_zfile")
opf_dir = epub._opf_dir
if zfile:
href_to_file = self._href_to_file
for href in href_to_id:
zpath = joinpath(opf_dir, href)
zinfo = zfile.NameToInfo.get(zpath)
if not zinfo or zinfo.is_dir():
warn(f"missing file in original epub: {href!r}")
href_to_file[href] = File(str(uuid4()), self._workfs)
else:
href_to_file[href] = File(zpath, zfile, open_modes="r")
def __init_subclass__(self, /, **kwargs):
raise TypeError("subclassing is not allowed")
def __call__(self, href, /):
if isinstance(href, Item):
if href not in self:
raise LookupError(f"no such item: {href!r}")
return href
if isinstance(href, (bytes, PathLike)):
href = fsdecode(href)
else:
href = str(href)
assert (href := href.strip("/")), "empty href"
try:
id = self._href_to_id[href]
except LookupError as e:
raise FileNotFoundError(errno.ENOENT, f"no such file: {href!r}") from e
return super().__getitem__(id)
def __contains__(self, other, /):
if isinstance(other, Item):
return other._manifest is self and super().__contains__(other["id"])
return super().__contains__(other)
def __delitem__(self, key, /):
pop = self.pop
if isinstance(key, int):
el = self._root[key]
try:
id = el.attrib["id"]
except AttributeError:
try:
self._root.remove(el)
except:
pass
else:
pop(id)
elif isinstance(key, slice):
root = self._root
for el in root[key]:
try:
id = el.attrib["id"]
except AttributeError:
try:
root.remove(el)
except:
pass
else:
pop(id, None)
elif isinstance(key, Item):
if key not in self:
raise LookupError(f"no such item: {key!r}")
pop(key["id"])
elif isinstance(key, str):
pop(key)
else:
raise TypeError("`key` only accepts: `str`, `int`, `slice`, `Item`")
return self
def __getitem__(self, key, /):
def wrap(el):
try:
if el.tag == "item" or el.tag.endswith("}item"):
return Item(el, self)
return ElementProxy(el)
except AttributeError:
return el
if isinstance(key, int):
return wrap(self._root[key])
elif isinstance(key, slice):
return list(map(wrap, self._root[key]))
elif isinstance(key, Item):
if key not in self:
raise LookupError(f"no such item: {key!r}")
return key
elif isinstance(key, str):
return super().__getitem__(key)
else:
raise TypeError("`key` only accepts: `str`, `int`, `slice`, `Item`")
def __setitem__(self, id, value, /):
if id not in self:
raise LookupError(f"no such item: {id!r}")
if isinstance(id, Item):
item = id
else:
item = super().__getitem__(id)
href = unquote(item._attrib["href"])
if isinstance(value, str):
self.rename(href, value)
elif isinstance(value, bytes):
self.write(href, value)
elif isinstance(value, Mapping):
if "open" in value and callable(value["open"]):
self._href_to_file[href] = File(value, open_modes="rb")
else:
item.update(value)
else:
self._href_to_file[href] = File(value, open_modes="rb")
return self
@cached_property
def _workfs(self, /):
if self._epub._maketemp:
return TemporaryFS(self._epub._workroot)
else:
return RootFS(self._epub._workroot)
@cached_property
def href_to_id(self, /):
return MappingProxyType(self._href_to_id)
@cached_property
def href_to_file(self, /):
return MappingProxyType(self._href_to_file)
@property
def home(self, /):
return self._epub._opf_dir
@property
def attrib(self, /):
return self._attrib
@property
def proxy(self, /):
return self._proxy
@property
def info(self, /):
return tuple(item.info for item in self.values())
delete = __delitem__
def clear(self, /):
self._root.clear()
self._href_to_file.clear()
self._href_to_id.clear()
super().clear()
return self
def pop(self, id, /, default=undefined):
if id not in self:
if default is undefined:
raise LookupError(f"no such item: {id!r}")
return default
if isinstance(id, Item):
id = id["id"]
item = super().pop(id)
try:
self._root.remove(item._root)
except:
pass
href = unquote(item._attrib["href"])
self._href_to_id.pop(href, None)
file = self._href_to_file.pop(href, None)
if file is not None and file.check_open_mode("w"):
try:
file.remove()
except:
pass
return item
def popitem(self, /):
id, item = super().popitem()
try:
self._root.remove(item._root)
except:
pass
href = unquote(item._attrib["href"])
self._href_to_id.pop(href, None)
file = self._href_to_file.pop(href, None)
if file is not None and file.check_open_mode("w"):
try:
file.remove()
except:
pass
return id, item
def set(self, id, value, /):
if isinstance(id, Item):
if id not in self:
raise LookupError(f"no such item: {id!r}")
item = id
else:
item = super().get(id)
if item is None:
if isinstance(value, str):
item = self.add(href, id=id)
elif isinstance(value, Mapping) and "href" in value:
if "open" in value and callable(value["open"]):
item = self.add(value["href"], value, id=id)
else:
item = self.add(value["href"], id=id, attrib=value)
else:
raise LookupError(f"no such item: {id!r}")
else:
href = unquote(item._attrib["href"])
if isinstance(value, str):
self.rename(href, value)
elif isinstance(value, bytes):
self.write(href, value)
elif isinstance(value, Mapping):
if "open" in value and callable(value["open"]):
self._href_to_file[href] = File(value, open_modes="rb")
else:
item.update(value)
else:
self._href_to_file[href] = File(value, open_modes="rb")
return item
def setdefault(self, id, value, /):
if isinstance(id, Item):
if id not in self:
raise LookupError(f"no such item: {id!r}")
item = id
else:
item = super().get(id)
if item is None:
if isinstance(value, str):
item = self.add(value, id=id)
elif isinstance(value, Mapping) and "href" in value:
if "open" in value and callable(value["open"]):
item = self.add(value["href"], value, id=id)
else:
item = self.add(value["href"], id=id, attrib=value)
else:
raise LookupError(f"no such item: {id!r}")
else:
if isinstance(value, Mapping) and not ("open" in value and callable(value["open"])):
item.merge(value)
return item
def merge(self, id_or_attrib=None, /, **attrs):
if attrs:
if isinstance(id_or_attrib, Item):
item = id_or_attrib
if item not in self:
raise LookupError(f"no such item: {item!r}")
item.merge(attrib=attrs)
elif isinstance(id_or_attrib, str):
id = id_or_attrib
item = super().get(id)
if item is None:
if "href" in attrs:
href = attrs.pop("href")
self.add(href, id=id, attrib=attrs)
else:
raise LookupError(f"no such item: {id!r}")
else:
item.merge(attrs)
else:
self._proxy.merge(id_or_attrib, **attrs)
elif isinstance(id_or_attrib, Mapping):
self._proxy.merge(id_or_attrib)
return self
def update(self, id_or_attrib=None, /, **attrs):
if attrs:
if isinstance(id_or_attrib, Item):
item = id_or_attrib
if item not in self:
raise LookupError(f"no such item: {item!r}")
item.update(attrib=attrs)
elif isinstance(id_or_attrib, str):
id = id_or_attrib
item = super().get(id)
if item is None:
if "href" in attrs:
href = attrs.pop("href")
self.add(href, id=id, attrib=attrs)
else:
raise LookupError(f"no such item: {id!r}")
else:
item.update(attrs)
else:
self._proxy.update(id_or_attrib, **attrs)
elif isinstance(id_or_attrib, Mapping):
self._proxy.update(id_or_attrib)
return self
#################### SubElement Methods ####################
@PyLinq.streamify
def filter(self, /, predicate=None):
if not callable(predicate):
return iter(self.values())
return filter(predicate, self.values())
@PyLinq.streamify
def filter_by_attr(self, predicate=None, attr="media-type", /):
def activate_predicate(predicate):
if predicate is None:
return None
if callable(predicate):
return predicate
elif isinstance(predicate, Pattern):
return predicate.search
elif isinstance(predicate, str):
use_false = False
if predicate.startswith(r"!"):
use_false = True
predicate = predicate[1:]
predicate_startswith = predicate.startswith
if predicate_startswith(r"="):
predicate = predicate[1:].__eq__
elif predicate_startswith(r"~"):
predicate = methodcaller("__contains__", predicate[1:])
elif predicate_startswith(r"^"):
predicate = methodcaller("startswith", predicate[1:])
elif predicate_startswith(r"$"):
predicate = methodcaller("endswith", predicate[1:])
elif predicate_startswith(r";"):
predicate = lambda s, needle=predicate[1:]: needle in s.split()
elif predicate_startswith(r","):
predicate = lambda s, needle=predicate[1:]: needle in s.split(",")
elif predicate_startswith(r"<"):
predicate = re_compile(r"\b"+re_escape(predicate[1:])).search
elif predicate_startswith(r">"):
predicate = re_compile(re_escape(predicate[1:])+r"\b").search
elif predicate_startswith(r"|"):
predicate = re_compile(r"\b"+re_escape(predicate[1:])+r"\b").search
elif predicate_startswith(r"*"):
predicate = re_compile(wildcard_translate(predicate[1:])).fullmatch
elif predicate_startswith(r"/"):
predicate = re_compile(predicate[1:]).search
elif predicate_startswith(r"%"):
predicate = re_compile(predicate[1:]).fullmatch
else:
predicate = predicate.__eq__
if use_false:
predicate = lambda s, _pred=predicate: not _pred(s)
return predicate
elif type(predicate) in (tuple, list):
preds = tuple(pred for p in predicate if (pred:=activate_predicate(p)) is not None)
if not preds:
return None
if type(predicate) is tuple:
return lambda s, _preds=preds: any(p(s) for p in preds)
else:
return lambda s, _preds=preds: all(p(s) for p in preds)
elif isinstance(predicate, Container):
return predicate.__contains__
predicate = activate_predicate(predicate)
if predicate is None:
return filter(lambda item: attr in item, self.values())
return filter(lambda item: attr in item and predicate(item[attr]), self.values())
@PyLinq.streamify
def iter(self, /):
root = self._root
for el in root.iterfind("*"):
if not (el.tag == "item" or el.tag.endswith("}item")):
yield ElementProxy(el)
continue
id = el.attrib.get("id")
href = el.attrib.get("href")
if not href:
if id is None or not super().__contains__(id):
try:
root.remove(el)
warn(f"removed a dangling item element: {el!r}")
except:
pass
else:
item = super().__getitem__(id)
if item._root is not el:
raise RuntimeError(f"different item elements {el!r} and {item._root!r} share the same id {id!r}")
else:
self.pop(id, None)
warn(f"removed an item because of missing href attribute: {item!r}")
continue
href = unquote(href)
if not el.attrib.get("media-type"): | el.attrib["media-type"] = guess_media_type(href) | 4 | 2023-11-20 14:46:41+00:00 | 16k |
ymp5078/AI-SAM | segment_anything/automatic_mask_generator.py | [
{
"identifier": "Sam",
"path": "segment_anything/modeling/sam.py",
"snippet": "class Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: ImageEncoderViT,\n prompt_encoder: PromptEncoder,\n mask_decode... | import numpy as np
import torch
import cv2 # type: ignore # noqa: F401
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from .modeling import Sam
from .predictor import SamPredictor
from .utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
from pycocotools import mask as mask_utils # type: ignore # noqa: F401 | 11,146 | for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(
points, cropped_im_size, crop_box, orig_size
)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["points"] = uncrop_points(data["points"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(
in_points.shape[0], dtype=torch.int, device=in_points.device
)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"],
self.predictor.model.mask_threshold,
self.stability_score_offset,
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > self.predictor.model.mask_threshold
data["boxes"] = batched_mask_to_box(data["masks"])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(
data["boxes"], crop_box, [0, 0, orig_w, orig_h]
)
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE
| # -*- coding: utf-8 -*-
# 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 SamAutomaticMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
mask_data["segmentations"] = [
coco_encode_rle(rle) for rle in mask_data["rles"]
]
elif self.output_mode == "binary_mask":
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
else:
mask_data["segmentations"] = mask_data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[:2]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(
points, cropped_im_size, crop_box, orig_size
)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["points"] = uncrop_points(data["points"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(
in_points.shape[0], dtype=torch.int, device=in_points.device
)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"],
self.predictor.model.mask_threshold,
self.stability_score_offset,
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > self.predictor.model.mask_threshold
data["boxes"] = batched_mask_to_box(data["masks"])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(
data["boxes"], crop_box, [0, 0, orig_w, orig_h]
)
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE | data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) | 16 | 2023-11-26 23:42:53+00:00 | 16k |
sophiaalthammer/alforrankers | matchmaker/utils/input_pipeline.py | [
{
"identifier": "ConditionalQueryGenerationInferenceReader",
"path": "matchmaker/dataloaders/query_generation_inference_loader.py",
"snippet": "class ConditionalQueryGenerationInferenceReader(DatasetReader):\n \"\"\"\n Read a tsv file containing a passage collection.\n \n Expected format for... | import torch
import numpy
import random
import torch.multiprocessing as mp
from allennlp.data.samplers import BucketBatchSampler, MaxTokensBatchSampler
from allennlp.data.vocabulary import Vocabulary
from allennlp.data.data_loaders import MultiProcessDataLoader
from transformers import T5Tokenizer
from allennlp.data.token_indexers import PretrainedTransformerIndexer
from allennlp.data.tokenizers import PretrainedTransformerTokenizer
from matchmaker.dataloaders.concatenated_reranking_loader import *
from matchmaker.dataloaders.concatenated_training_loader import *
from matchmaker.dataloaders.independent_reranking_loader import *
from matchmaker.dataloaders.independent_training_loader import *
from matchmaker.dataloaders.id_sequence_loader import *
from matchmaker.dataloaders.mlm_masked_sequence_loader import *
from matchmaker.dataloaders.query_generation_inference_loader import ConditionalQueryGenerationInferenceReader
from matchmaker.dataloaders.tas_balanced_training_loader import *
from matchmaker.dataloaders.pseudo_label_training_loader import PseudoLabelDatasetLoader, PseudoLabelTextDatasetLoader
from matchmaker.dataloaders.triple_id_training_loader import TripleIdDatasetLoader
from transformers import AutoTokenizer
from matchmaker.dataloaders.bling_fire_tokenizer import BlingFireTokenizer
from matchmaker.dataloaders.transformer_tokenizer import FastTransformerTokenizer
from matchmaker.modules.bert_embedding_token_embedder import PretrainedBertIndexerNoSpecialTokens
from typing import Dict, Tuple, List | 11,844 | # loader = IrDynamicTripleDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
# qrels_file=run_config["dynamic_qrels_file"], candidate_file=run_config["dynamic_candidate_file"],
# batch_size=int(run_config["batch_size_train"]), queries_per_batch=run_config["dynamic_queries_per_batch"], tokenizer=_tokenizer, token_indexers=_token_indexers,
# max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
# min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
# data_augment=run_config["train_data_augment"], vocab=_vocab)
if run_config["dynamic_sampler_type"] == "tas_balanced":
loader = TASBalancedDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
pairs_with_teacher_scores=run_config["dynamic_pairs_with_teacher_scores"], query_cluster_file=run_config["dynamic_query_cluster_file"],
batch_size=int(run_config["batch_size_train"]), clusters_per_batch=run_config["dynamic_clusters_per_batch"], tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
pair_balancing_strategy=run_config["tas_balanced_pair_strategy"],random_seed =run_config["random_seed"])
elif run_config["dynamic_sampler_type"] == "pseudo_label":
loader = PseudoLabelDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
rankings_with_teacher_scores=run_config["dynamic_rankings_with_teacher_scores"],
selection_type=run_config["pseudo_label_selection_type"],min_pos_score=run_config["pseudo_label_min_pos_score"],
max_diff_to_be_pos=run_config["pseudo_label_max_diff_to_be_pos"],min_diff_to_neg=run_config["pseudo_label_min_diff_to_neg"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "pseudo_labeltext":
loader = PseudoLabelTextDatasetLoader(rankings_with_teacher_scores=run_config["dynamic_rankings_with_teacher_scores"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "triple_ids":
loader = TripleIdDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
triples_with_teacher_scores=run_config["dynamic_triples_with_teacher_scores"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "mlm_pretrain":
loader = MLMDatasetLoader(collection_file=run_config["train_tsv"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"],
random_seed=run_config["random_seed"],
min_doc_length=-1,
mlm_mask_whole_words=True,
mask_probability=run_config["mask_probability"],
mlm_mask_replace_probability=run_config["mlm_mask_replace_probability"],
mlm_mask_random_probability=run_config["mlm_mask_random_probability"],
whole_word_masking=run_config["whole_word_masking"],
random_spans=run_config["random_spans"],
tasb=run_config["tasb"],
tasb_cluster_file=run_config["tasb_cluster_file"],
tasb_weight=run_config["tasb_weight"],
grad_acc=run_config["gradient_accumulation_steps"],
cached_chunk_size=int(run_config["batch_size_train"])/int(run_config["cache_chunk_size"]))
else:
raise ConfigurationError("dynamic sampler type not supported")
return loader
def allennlp_reranking_inference_loader(model_config, run_config, _input_file):
'''
Load examples from a .tsv file in the reranking candidate file format: q_id<tab>d_id<tab>q_text<tab>d_text
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max(run_config["max_doc_length"], run_config["max_query_length"]))
if model_config.get("model_input_type", "") == "concatenated" or model_config["token_embedder_type"] == "bert_cat":
reader = ConcatenatedReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
train_qa_spans=run_config["train_qa_spans"])
else:
reader = IndependentReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config.get("min_doc_length",-1), min_query_length=run_config.get("min_query_length",-1),
query_augment_mask_number=run_config.get("query_augment_mask_number",-1), train_qa_spans=run_config.get("train_qa_spans",False))
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(run_config["batch_size_eval"])*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_sampler=MaxTokensBatchSampler(max_tokens=int(run_config["batch_size_eval"])*run_config["max_doc_length"], sorting_keys=["doc_tokens"], padding_noise=0))
loader.index_with(_vocab)
return loader
def allennlp_query_gen_train_loader(model_config, run_config, _input_file):
'''
Load examples from a .tsv file in the reranking candidate file format: q_id<tab>d_id<tab>q_text<tab>d_text
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max(run_config["max_doc_length"], run_config["max_query_length"]))
reader = IndependentReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config.get("min_doc_length",-1), min_query_length=run_config.get("min_query_length",-1),
query_augment_mask_number=run_config.get("query_augment_mask_number",-1), train_qa_spans=run_config.get("train_qa_spans",False))
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(run_config["batch_size_train"])*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_size=run_config["batch_size_train"])
loader.index_with(_vocab)
return loader
def allennlp_query_gen_inference_loader(model_config, run_config, _input_file,):
'''
Load examples from a .tsv file in the single sequence format: id<tab>text and augment it with conditional query codes
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, run_config["max_doc_length"])
max_length = model_config["max_doc_length"]
batch_size = run_config["collection_batch_size"]
|
#from tokenizers import ByteLevelBPETokenizer,CharBPETokenizer
#from matchmaker.dataloaders.transformer_tokenizer import CustomTransformerTokenizer,CustomTransformerIndexer
mp.set_sharing_strategy("file_system") # VERY MUCH needed for linux !! makes everything faster, but tends to break stuff
def allennlp_single_sequence_loader(model_config, run_config, _input_file, sequence_type, force_exact_batch_size=False):
'''
Load examples from a .tsv file in the single sequence format: id<tab>text
(Using allennlp's v2 multiprocess loader)
'''
if model_config.get("model_input_type", "") == "mlm":
sequence_type == "single_mlm"
if sequence_type == "query":
max_length = run_config.get("overwrite_max_query_length", model_config["max_query_length"])
min_length = model_config.get("min_query_length",-1)
batch_size = run_config["query_batch_size"]
split_document=False
split_document_window_size=-1
if sequence_type == "single_mlm":
max_length = run_config.get("overwrite_max_doc_length", model_config["max_doc_length"])
min_length = model_config.get("min_doc_length", -1)
batch_size = run_config.get("collection_batch_size", run_config["batch_size_train"])
make_multiple_of=run_config.get("make_multiple_of",8)
mask_probability=run_config.get("mask_probability",0.1)
mlm_mask_replace_probability=run_config.get("mlm_mask_replace_probability",0.5)
mlm_mask_random_probability=run_config.get("mlm_mask_random_probability",0.5)
else: # doc
max_length = run_config.get("overwrite_max_doc_length", model_config["max_doc_length"])
min_length = model_config.get("min_doc_length",-1)
batch_size = run_config["collection_batch_size"]
split_document=run_config.get("split_document",False)
split_document_window_size=run_config.get("split_document_window_size",-1)
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max_length)
#if model_config.get("model_input_type", "") == "mlm":
# reader = MLMMaskedSequenceDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
# max_doc_length=max_length, min_doc_length=min_length,
# mask_probability=mask_probability,
# mlm_mask_replace_probability=mlm_mask_replace_probability,
# mlm_mask_random_probability=mlm_mask_random_probability,
# make_multiple_of=make_multiple_of)
reader = IdSequenceDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
split_document=split_document,split_document_window_size=split_document_window_size,
max_seq_length=max_length, min_seq_length=min_length, sequence_type=sequence_type)
if force_exact_batch_size:
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(batch_size)*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_size=int(batch_size))
else:
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(batch_size)*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_sampler=MaxTokensBatchSampler(max_tokens=int(batch_size)*max_length, sorting_keys=["seq_tokens"], padding_noise=0))
loader.index_with(_vocab)
return loader
def allennlp_triple_training_loader(model_config, run_config, _input_file,add_text_to_batch=False):
'''
Load training examples (either in the re-ranking text file format or a dynamic loader)
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max(run_config["max_doc_length"], run_config["max_query_length"]))
if run_config.get("dynamic_sampler", False) == False:
if model_config.get("model_input_type", "") == "concatenated" or model_config["token_embedder_type"] == "bert_cat":
reader = ConcatenatedTrainingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
data_augment=run_config["train_data_augment"], train_pairwise_distillation=run_config["train_pairwise_distillation"],
train_qa_spans=run_config["train_qa_spans"],add_text_to_batch=add_text_to_batch)
else:
reader = IndependentTrainingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
data_augment=run_config["train_data_augment"], train_pairwise_distillation=run_config["train_pairwise_distillation"],
query_augment_mask_number=run_config["query_augment_mask_number"], train_qa_spans=run_config["train_qa_spans"],add_text_to_batch=add_text_to_batch)
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(run_config["batch_size_train"])*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_size=run_config["batch_size_train"])
loader.index_with(_vocab)
else:
#if run_config["dynamic_sampler_type"] == "list":
# loader = IrDynamicTripleDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
# qrels_file=run_config["dynamic_qrels_file"], candidate_file=run_config["dynamic_candidate_file"],
# batch_size=int(run_config["batch_size_train"]), queries_per_batch=run_config["dynamic_queries_per_batch"], tokenizer=_tokenizer, token_indexers=_token_indexers,
# max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
# min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
# data_augment=run_config["train_data_augment"], vocab=_vocab)
if run_config["dynamic_sampler_type"] == "tas_balanced":
loader = TASBalancedDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
pairs_with_teacher_scores=run_config["dynamic_pairs_with_teacher_scores"], query_cluster_file=run_config["dynamic_query_cluster_file"],
batch_size=int(run_config["batch_size_train"]), clusters_per_batch=run_config["dynamic_clusters_per_batch"], tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
pair_balancing_strategy=run_config["tas_balanced_pair_strategy"],random_seed =run_config["random_seed"])
elif run_config["dynamic_sampler_type"] == "pseudo_label":
loader = PseudoLabelDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
rankings_with_teacher_scores=run_config["dynamic_rankings_with_teacher_scores"],
selection_type=run_config["pseudo_label_selection_type"],min_pos_score=run_config["pseudo_label_min_pos_score"],
max_diff_to_be_pos=run_config["pseudo_label_max_diff_to_be_pos"],min_diff_to_neg=run_config["pseudo_label_min_diff_to_neg"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "pseudo_labeltext":
loader = PseudoLabelTextDatasetLoader(rankings_with_teacher_scores=run_config["dynamic_rankings_with_teacher_scores"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "triple_ids":
loader = TripleIdDatasetLoader(query_file=run_config["dynamic_query_file"], collection_file=run_config["dynamic_collection_file"],
triples_with_teacher_scores=run_config["dynamic_triples_with_teacher_scores"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
random_seed =run_config["random_seed"],concatenate_sequences = model_config.get("model_input_type", "") == "concatenated")
elif run_config["dynamic_sampler_type"] == "mlm_pretrain":
loader = MLMDatasetLoader(collection_file=run_config["train_tsv"],
batch_size=int(run_config["batch_size_train"]), tokenizer=_tokenizer,
max_doc_length=run_config["max_doc_length"],
random_seed=run_config["random_seed"],
min_doc_length=-1,
mlm_mask_whole_words=True,
mask_probability=run_config["mask_probability"],
mlm_mask_replace_probability=run_config["mlm_mask_replace_probability"],
mlm_mask_random_probability=run_config["mlm_mask_random_probability"],
whole_word_masking=run_config["whole_word_masking"],
random_spans=run_config["random_spans"],
tasb=run_config["tasb"],
tasb_cluster_file=run_config["tasb_cluster_file"],
tasb_weight=run_config["tasb_weight"],
grad_acc=run_config["gradient_accumulation_steps"],
cached_chunk_size=int(run_config["batch_size_train"])/int(run_config["cache_chunk_size"]))
else:
raise ConfigurationError("dynamic sampler type not supported")
return loader
def allennlp_reranking_inference_loader(model_config, run_config, _input_file):
'''
Load examples from a .tsv file in the reranking candidate file format: q_id<tab>d_id<tab>q_text<tab>d_text
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max(run_config["max_doc_length"], run_config["max_query_length"]))
if model_config.get("model_input_type", "") == "concatenated" or model_config["token_embedder_type"] == "bert_cat":
reader = ConcatenatedReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config["min_doc_length"], min_query_length=run_config["min_query_length"],
train_qa_spans=run_config["train_qa_spans"])
else:
reader = IndependentReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config.get("min_doc_length",-1), min_query_length=run_config.get("min_query_length",-1),
query_augment_mask_number=run_config.get("query_augment_mask_number",-1), train_qa_spans=run_config.get("train_qa_spans",False))
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(run_config["batch_size_eval"])*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_sampler=MaxTokensBatchSampler(max_tokens=int(run_config["batch_size_eval"])*run_config["max_doc_length"], sorting_keys=["doc_tokens"], padding_noise=0))
loader.index_with(_vocab)
return loader
def allennlp_query_gen_train_loader(model_config, run_config, _input_file):
'''
Load examples from a .tsv file in the reranking candidate file format: q_id<tab>d_id<tab>q_text<tab>d_text
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, max(run_config["max_doc_length"], run_config["max_query_length"]))
reader = IndependentReRankingDatasetReader(tokenizer=_tokenizer, token_indexers=_token_indexers,
max_doc_length=run_config["max_doc_length"], max_query_length=run_config["max_query_length"],
min_doc_length=run_config.get("min_doc_length",-1), min_query_length=run_config.get("min_query_length",-1),
query_augment_mask_number=run_config.get("query_augment_mask_number",-1), train_qa_spans=run_config.get("train_qa_spans",False))
loader = MultiProcessDataLoader(reader, data_path=_input_file, num_workers=run_config["dataloader_num_workers"],
max_instances_in_memory=int(run_config["batch_size_train"])*25, quiet=True, start_method="fork" if "fork" in mp.get_all_start_methods() else "spawn",
batch_size=run_config["batch_size_train"])
loader.index_with(_vocab)
return loader
def allennlp_query_gen_inference_loader(model_config, run_config, _input_file,):
'''
Load examples from a .tsv file in the single sequence format: id<tab>text and augment it with conditional query codes
(Using allennlp's v2 multiprocess loader)
'''
_tokenizer, _token_indexers, _vocab = _get_indexer(model_config, run_config["max_doc_length"])
max_length = model_config["max_doc_length"]
batch_size = run_config["collection_batch_size"]
| reader = ConditionalQueryGenerationInferenceReader(tokenizer=_tokenizer, token_indexers=_token_indexers, | 0 | 2023-11-21 10:38:22+00:00 | 16k |
MICLab-Unicamp/medpseg | medpseg/poly_pipeline.py | [
{
"identifier": "PolySeg2DModule",
"path": "medpseg/poly_seg_2d_module.py",
"snippet": "class PolySeg2DModule(pl.LightningModule):\n '''\n Regarding of the name, also works with 3D networks\n '''\n def __init__(self, hparams):\n '''\n Check starter.py for description of all hpa... | import os
import torch
import numpy as np
import cc3d
import SimpleITK as sitk
from medpseg.poly_seg_2d_module import PolySeg2DModule
from medpseg.eval_2d_utils import E2DStackDataset, argon_cpu_count
from torch.nn import functional as F
from tqdm import tqdm
from collections import defaultdict
from operator import itemgetter
from typing import Dict, Optional
from multiprocessing import Queue | 11,678 | label_count.sort(key=itemgetter(1), reverse=True)
label_count.pop(0) # remove largest which should be background
if verbose:
print(f"Label count: {label_count}")
filtered = None
if return_largest > 0:
for i in range(return_largest):
try:
id_max = label_count[i][0]
if filtered is None:
filtered = (labels_out == id_max)
else:
filtered += (labels_out == id_max)
except IndexError:
# We want more components that what is in the image, stop
break
volume = filtered * volume
labels_out = filtered * labels_out
return volume, label_count, labels_out
class PrintInterface():
def __init__(self, tqdm_iter):
self.tqdm_iter = tqdm_iter
self.rot90 = False
def write(self, x):
self.tqdm_iter.put(("write", x))
def progress(self, x):
self.tqdm_iter.put(("iterbar", x))
def image_to_front_end(self, x):
if self.rot90:
x = np.rot90(x, k=2, axes=(0, 1))
self.tqdm_iter.put(("slice", x))
def icon(self):
self.tqdm_iter.put(("icon", ''))
def poly_stack_predict(model: torch.nn.Module, volume: torch.Tensor, batch_size: int, device=torch.device("cuda:0"), info_q: Optional[Queue] = None, uncertainty: Optional[int] = None):
'''
DEVING uncertainty: epistemic uncerainty, predict n times and return the mean and std prediction
'''
e2d_stack_dataloader = E2DStackDataset(volume, extended_2d=1).get_dataloader(batch_size=batch_size, pin_memory=False, num_workers=argon_cpu_count())
outs = defaultdict(list)
np_outs = {}
np_means = {}
np_stds = {}
uncertainty_means = defaultdict(list)
uncertainty_stds = defaultdict(list)
for input_slice in tqdm(e2d_stack_dataloader, desc=f"Slicing with batch size {batch_size}."):
if info_q is not None:
package = input_slice[0].numpy().transpose(1, 2, 0).copy()
# Not necessary anymore with current pre processing: September 2023
# package = (package + 1024) / (600 + 1024)
info_q.image_to_front_end(package)
if uncertainty is None:
out = model(input_slice.to(device), stacking=True)
for key, y_hat in out.items():
outs[key].append(y_hat.cpu())
else:
raise DeprecationWarning("Uncertainty deprecated for now, needs update")
# Save outputs for each branch in buffer
uncertainty_buffer = defaultdict(list)
model.train()
for _ in tqdm(range(uncertainty)): # 8 equivalent to (0, 1, 2) flips
out = model(input_slice.to(device), stacking=True)
for key, y_hat in out.items():
uncertainty_buffer[key].append(y_hat.cpu())
model.eval()
# Collect buffer items into means and STDs for each branch
for key, buffer in uncertainty_buffer.items():
# use stack to keep batch dimension separate from acumulation dimension
# statistics will take that dimension out
buffer = torch.stack(buffer, dim=0)
uncertainty_means[key].append(buffer.mean(dim=0))
uncertainty_stds[key].append(buffer.std(dim=0))
# Certain prediction volumes. Will no run if in uncertain mode.
for key, y_hat in outs.items():
np_outs[key] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
# Compute final volume for uncertainty mean and uncertainty itself (STD)
if uncertainty is not None:
raise DeprecationWarning("Uncertainty deprecated for now, needs update")
for key, y_hat in uncertainty_means.items():
np_means[key] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
for key, y_hat in uncertainty_stds.items():
np_stds[f"{key}_uncertainty"] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
if uncertainty is None:
return np_outs
else:
return np_means, np_stds
class PolySegmentationPipeline():
'''
This pipeline does all targets in a single weight
'''
def __init__(self,
weight="/home/diedre/diedre_phd/phd/models/medseg_25d_a100_long_silver_gold_gdl-epoch=22-step=76176-val_loss_3d=0.25-healthy_dice_3d=0.93-unhealthy_dice_3d=0.71-ggo_dice_3d=0.71-con_dice_3d=0.62-airway_dice_3d=0.90-vessel_dice_3d=0.87.ckpt",
batch_size=1, # increase with high memory gpus
cpu=False,
output_dir=None,
post=False):
self.version = 'silver_gold_gdl'
self.batch_size = batch_size
self.device = torch.device("cpu") if cpu else torch.device("cuda:0")
| '''
Copyright (c) Diedre Carmo, Medical Imaging Computing Lab (MICLab)
https://miclab.fee.unicamp.br/
https://github.com/MICLab-Unicamp/medpseg
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
Independent script
Updated pipeline using a single weight
'''
def get_connected_components(volume, return_largest=2, verbose=False):
'''
volume: input volume
return_largest: how many of the largest labels to return. If 0, nothing is changed in input volume
verbose: prints label_count
returns:
filtered_volume, label_count, labeled_volume
'''
labels_out = cc3d.connected_components(volume.astype(np.int32))
label_count = np.unique(labels_out, return_counts=True)[1]
# Indicate which was the original label and sort by count
label_count = [(label, count) for label, count in enumerate(label_count)]
label_count.sort(key=itemgetter(1), reverse=True)
label_count.pop(0) # remove largest which should be background
if verbose:
print(f"Label count: {label_count}")
filtered = None
if return_largest > 0:
for i in range(return_largest):
try:
id_max = label_count[i][0]
if filtered is None:
filtered = (labels_out == id_max)
else:
filtered += (labels_out == id_max)
except IndexError:
# We want more components that what is in the image, stop
break
volume = filtered * volume
labels_out = filtered * labels_out
return volume, label_count, labels_out
class PrintInterface():
def __init__(self, tqdm_iter):
self.tqdm_iter = tqdm_iter
self.rot90 = False
def write(self, x):
self.tqdm_iter.put(("write", x))
def progress(self, x):
self.tqdm_iter.put(("iterbar", x))
def image_to_front_end(self, x):
if self.rot90:
x = np.rot90(x, k=2, axes=(0, 1))
self.tqdm_iter.put(("slice", x))
def icon(self):
self.tqdm_iter.put(("icon", ''))
def poly_stack_predict(model: torch.nn.Module, volume: torch.Tensor, batch_size: int, device=torch.device("cuda:0"), info_q: Optional[Queue] = None, uncertainty: Optional[int] = None):
'''
DEVING uncertainty: epistemic uncerainty, predict n times and return the mean and std prediction
'''
e2d_stack_dataloader = E2DStackDataset(volume, extended_2d=1).get_dataloader(batch_size=batch_size, pin_memory=False, num_workers=argon_cpu_count())
outs = defaultdict(list)
np_outs = {}
np_means = {}
np_stds = {}
uncertainty_means = defaultdict(list)
uncertainty_stds = defaultdict(list)
for input_slice in tqdm(e2d_stack_dataloader, desc=f"Slicing with batch size {batch_size}."):
if info_q is not None:
package = input_slice[0].numpy().transpose(1, 2, 0).copy()
# Not necessary anymore with current pre processing: September 2023
# package = (package + 1024) / (600 + 1024)
info_q.image_to_front_end(package)
if uncertainty is None:
out = model(input_slice.to(device), stacking=True)
for key, y_hat in out.items():
outs[key].append(y_hat.cpu())
else:
raise DeprecationWarning("Uncertainty deprecated for now, needs update")
# Save outputs for each branch in buffer
uncertainty_buffer = defaultdict(list)
model.train()
for _ in tqdm(range(uncertainty)): # 8 equivalent to (0, 1, 2) flips
out = model(input_slice.to(device), stacking=True)
for key, y_hat in out.items():
uncertainty_buffer[key].append(y_hat.cpu())
model.eval()
# Collect buffer items into means and STDs for each branch
for key, buffer in uncertainty_buffer.items():
# use stack to keep batch dimension separate from acumulation dimension
# statistics will take that dimension out
buffer = torch.stack(buffer, dim=0)
uncertainty_means[key].append(buffer.mean(dim=0))
uncertainty_stds[key].append(buffer.std(dim=0))
# Certain prediction volumes. Will no run if in uncertain mode.
for key, y_hat in outs.items():
np_outs[key] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
# Compute final volume for uncertainty mean and uncertainty itself (STD)
if uncertainty is not None:
raise DeprecationWarning("Uncertainty deprecated for now, needs update")
for key, y_hat in uncertainty_means.items():
np_means[key] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
for key, y_hat in uncertainty_stds.items():
np_stds[f"{key}_uncertainty"] = torch.cat(y_hat).unsqueeze(0).permute(0, 2, 1, 3, 4)
if uncertainty is None:
return np_outs
else:
return np_means, np_stds
class PolySegmentationPipeline():
'''
This pipeline does all targets in a single weight
'''
def __init__(self,
weight="/home/diedre/diedre_phd/phd/models/medseg_25d_a100_long_silver_gold_gdl-epoch=22-step=76176-val_loss_3d=0.25-healthy_dice_3d=0.93-unhealthy_dice_3d=0.71-ggo_dice_3d=0.71-con_dice_3d=0.62-airway_dice_3d=0.90-vessel_dice_3d=0.87.ckpt",
batch_size=1, # increase with high memory gpus
cpu=False,
output_dir=None,
post=False):
self.version = 'silver_gold_gdl'
self.batch_size = batch_size
self.device = torch.device("cpu") if cpu else torch.device("cuda:0") | self.model = PolySeg2DModule.load_from_checkpoint(weight, map_location="cpu").eval() | 0 | 2023-11-21 20:03:33+00:00 | 16k |
DLYuanGod/TinyGPT-V | minigpt4/datasets/builders/image_text_pair_builder.py | [
{
"identifier": "registry",
"path": "minigpt4/common/registry.py",
"snippet": "class Registry:\n def register_builder(cls, name):\n def wrap(builder_cls):\n def register_task(cls, name):\n def wrap(task_cls):\n def register_model(cls, name):\n def wrap(model_cls):\n def ... | import os
import logging
import warnings
from minigpt4.common.registry import registry
from minigpt4.datasets.builders.base_dataset_builder import BaseDatasetBuilder
from minigpt4.datasets.datasets.laion_dataset import LaionDataset
from minigpt4.datasets.datasets.cc_sbu_dataset import CCSBUDataset, CCSBUAlignDataset
from minigpt4.datasets.datasets.text_caps import TextCapDataset
from minigpt4.datasets.datasets.llava_dataset import LlavaDetailDataset, LlavaReasonDataset, LlavaConversationDataset
from minigpt4.datasets.datasets.unnatural_instruction import UnnaturalDataset
from minigpt4.datasets.datasets.multitask_conversation import MultiTaskConversationDataset
from minigpt4.datasets.datasets.flickr import GroundedDetailDataset,CaptionToObjectDataset,PhraseToObjectDataset
from minigpt4.datasets.datasets.vg_dataset import ReferVisualGenomeDataset
from minigpt4.datasets.datasets.coco_dataset import ReferCOCODataset, InvReferCOCODataset
from minigpt4.datasets.datasets.gqa_datasets import GQADataset
from minigpt4.datasets.datasets.aok_vqa_datasets import AOKVQADataset
from minigpt4.datasets.datasets.coco_vqa_datasets import COCOVQADataset
from minigpt4.datasets.datasets.ocrvqa_dataset import OCRVQADataset
from minigpt4.datasets.datasets.coco_caption import COCOCapDataset | 11,462 | self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
@registry.register_builder("llava_conversation")
class LlavaReasonBuilder(BaseDatasetBuilder):
train_dataset_cls = LlavaConversationDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/llava/conversation.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
class AllRefCOCOBuilder(BaseDatasetBuilder):
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
image_path = build_info.image_path
ann_path = build_info.ann_path
datasets = dict()
if not os.path.exists(image_path):
warnings.warn("image path {} does not exist.".format(image_path))
if not os.path.exists(ann_path):
warnings.warn("ann path {} does not exist.".format(ann_path))
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=ann_path,
vis_root=image_path,
dataset=build_info.dataset,
splitBy=build_info.splitBy
)
return datasets
@registry.register_builder("refcoco")
class RefCOCOBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcoco.yaml",
}
@registry.register_builder("refcocop")
class RefCOCOPBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcocop.yaml",
}
@registry.register_builder("refcocog")
class RefCOCOGBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcocog.yaml",
}
@registry.register_builder("invrefcoco")
class RefCOCOBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcoco.yaml",
}
@registry.register_builder("invrefcocop")
class RefCOCOPBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcocop.yaml",
}
@registry.register_builder("invrefcocog")
class RefCOCOGBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcocog.yaml",
}
@registry.register_builder("refvg")
class RefVisualGenomeBuilder(BaseDatasetBuilder):
|
@registry.register_builder("multitask_conversation")
class MultitaskConversationBuilder(BaseDatasetBuilder):
train_dataset_cls = MultiTaskConversationDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/multitask_conversation/default.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
@registry.register_builder("unnatural_instruction")
class UnnaturalInstructionBuilder(BaseDatasetBuilder):
train_dataset_cls = UnnaturalDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/nlp/unnatural_instruction.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
)
return datasets
@registry.register_builder("llava_detail")
class LlavaDetailBuilder(BaseDatasetBuilder):
train_dataset_cls = LlavaDetailDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/llava/detail.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
@registry.register_builder("llava_reason")
class LlavaReasonBuilder(BaseDatasetBuilder):
train_dataset_cls = LlavaReasonDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/llava/reason.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
@registry.register_builder("llava_conversation")
class LlavaReasonBuilder(BaseDatasetBuilder):
train_dataset_cls = LlavaConversationDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/llava/conversation.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
datasets = dict()
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=build_info.ann_path,
vis_root=build_info.image_path,
)
return datasets
class AllRefCOCOBuilder(BaseDatasetBuilder):
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
image_path = build_info.image_path
ann_path = build_info.ann_path
datasets = dict()
if not os.path.exists(image_path):
warnings.warn("image path {} does not exist.".format(image_path))
if not os.path.exists(ann_path):
warnings.warn("ann path {} does not exist.".format(ann_path))
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vis_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_path=ann_path,
vis_root=image_path,
dataset=build_info.dataset,
splitBy=build_info.splitBy
)
return datasets
@registry.register_builder("refcoco")
class RefCOCOBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcoco.yaml",
}
@registry.register_builder("refcocop")
class RefCOCOPBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcocop.yaml",
}
@registry.register_builder("refcocog")
class RefCOCOGBuilder(AllRefCOCOBuilder):
train_dataset_cls = ReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/refcocog.yaml",
}
@registry.register_builder("invrefcoco")
class RefCOCOBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcoco.yaml",
}
@registry.register_builder("invrefcocop")
class RefCOCOPBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcocop.yaml",
}
@registry.register_builder("invrefcocog")
class RefCOCOGBuilder(AllRefCOCOBuilder):
train_dataset_cls = InvReferCOCODataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/coco_bbox/invrefcocog.yaml",
}
@registry.register_builder("refvg")
class RefVisualGenomeBuilder(BaseDatasetBuilder): | train_dataset_cls = ReferVisualGenomeDataset | 14 | 2023-12-28 05:47:18+00:00 | 16k |
jiawei-ren/dreamgaussian4d | diffusers/src/diffusers/pipelines/pipeline_flax_utils.py | [
{
"identifier": "ConfigMixin",
"path": "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 [`~Conf... | import importlib
import inspect
import os
import flax
import numpy as np
import PIL.Image
from typing import Any, Dict, List, Optional, Union
from flax.core.frozen_dict import FrozenDict
from huggingface_hub import create_repo, snapshot_download
from PIL import Image
from tqdm.auto import tqdm
from ..configuration_utils import ConfigMixin
from ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
from ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
from ..utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
BaseOutput,
PushToHubMixin,
http_user_agent,
is_transformers_available,
logging,
)
from transformers import FlaxPreTrainedModel
from diffusers import pipelines
from diffusers import pipelines | 11,032 | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.
if is_transformers_available():
INDEX_FILE = "diffusion_flax_model.bin"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"FlaxModelMixin": ["save_pretrained", "from_pretrained"],
"FlaxSchedulerMixin": ["save_pretrained", "from_pretrained"],
"FlaxDiffusionPipeline": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
"ProcessorMixin": ["save_pretrained", "from_pretrained"],
"ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
def import_flax_or_no_model(module, class_name):
try:
# 1. First make sure that if a Flax object is present, import this one
class_obj = getattr(module, "Flax" + class_name)
except AttributeError:
# 2. If this doesn't work, it's not a model and we don't append "Flax"
class_obj = getattr(module, class_name)
except AttributeError:
raise ValueError(f"Neither Flax{class_name} nor {class_name} exist in {module}")
return class_obj
@flax.struct.dataclass
class FlaxImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
| # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.
if is_transformers_available():
INDEX_FILE = "diffusion_flax_model.bin"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"FlaxModelMixin": ["save_pretrained", "from_pretrained"],
"FlaxSchedulerMixin": ["save_pretrained", "from_pretrained"],
"FlaxDiffusionPipeline": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
"ProcessorMixin": ["save_pretrained", "from_pretrained"],
"ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
def import_flax_or_no_model(module, class_name):
try:
# 1. First make sure that if a Flax object is present, import this one
class_obj = getattr(module, "Flax" + class_name)
except AttributeError:
# 2. If this doesn't work, it's not a model and we don't append "Flax"
class_obj = getattr(module, class_name)
except AttributeError:
raise ValueError(f"Neither Flax{class_name} nor {class_name} exist in {module}")
return class_obj
@flax.struct.dataclass
class FlaxImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
| class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin): | 7 | 2023-12-28 08:17:40+00:00 | 16k |
FoundationVision/UniRef | detectron2/evaluation/coco_evaluation.py | [
{
"identifier": "CfgNode",
"path": "detectron2/config/config.py",
"snippet": "class CfgNode(_CfgNode):\n \"\"\"\n The same as `fvcore.common.config.CfgNode`, but different in:\n\n 1. Use unsafe yaml loading by default.\n Note that this may lead to arbitrary code execution: you must not\n ... | import contextlib
import copy
import io
import itertools
import json
import logging
import numpy as np
import os
import pickle
import pycocotools.mask as mask_util
import torch
import detectron2.utils.comm as comm
from collections import OrderedDict
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from tabulate import tabulate
from detectron2.config import CfgNode
from detectron2.data import MetadataCatalog
from detectron2.data.datasets.coco import convert_to_coco_json
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import create_small_table
from .evaluator import DatasetEvaluator
from detectron2.evaluation.fast_eval_api import COCOeval_opt
from detectron2.evaluation.refcocoeval import RefCOCOeval | 13,051 |
try:
except ImportError:
COCOeval_opt = COCOeval
class COCOEvaluator(DatasetEvaluator):
"""
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
for keypoint detection outputs using COCO's metrics.
See http://cocodataset.org/#detection-eval and
http://cocodataset.org/#keypoints-eval to understand its metrics.
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
the metric cannot be computed (e.g. due to no predictions made).
In addition to COCO, this evaluator is able to support any bounding box detection,
instance segmentation, or keypoint detection dataset.
"""
def __init__(
self,
dataset_name,
tasks=None,
distributed=True,
output_dir=None,
*,
max_dets_per_image=None,
use_fast_impl=True,
kpt_oks_sigmas=(),
allow_cached_coco=True,
force_tasks=None,
refcoco=False
):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in detectron2's standard dataset format
so it can be converted to COCO format automatically.
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm", "keypoints".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will only evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
contains all the results in the format they are produced by the model.
2. "coco_instances_results.json" a json file in COCO's result format.
max_dets_per_image (int): limit on the maximum number of detections per image.
By default in COCO, this limit is to 100, but this can be customized
to be greater, as is needed in evaluation metrics AP fixed and AP pool
(see https://arxiv.org/pdf/2102.01066.pdf)
This doesn't affect keypoint evaluation.
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
Although the results should be very close to the official implementation in COCO
API, it is still recommended to compute results with the official API for use in
papers. The faster implementation also uses more RAM.
kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
See http://cocodataset.org/#keypoints-eval
When empty, it will use the defaults in COCO.
Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
allow_cached_coco (bool): Whether to use cached coco json from previous validation
runs. You should set this to False if you need to use different validation data.
Defaults to True.
"""
self.dataset_name = dataset_name
self._logger = logging.getLogger(__name__)
self._distributed = distributed
self._output_dir = output_dir
self.force_tasks = force_tasks
self.refcoco = refcoco
if use_fast_impl and (COCOeval_opt is COCOeval):
self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
use_fast_impl = False
self._use_fast_impl = use_fast_impl
# COCOeval requires the limit on the number of detections per image (maxDets) to be a list
# with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
# 3rd element (100) is used as the limit on the number of detections per image when
# evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
# we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
if max_dets_per_image is None:
max_dets_per_image = [1, 10, 100]
else:
max_dets_per_image = [1, 10, max_dets_per_image]
self._max_dets_per_image = max_dets_per_image
if tasks is not None and isinstance(tasks, CfgNode):
kpt_oks_sigmas = (
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
)
self._logger.warn(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead."
)
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._cpu_device = torch.device("cpu")
self._metadata = MetadataCatalog.get(dataset_name)
if not hasattr(self._metadata, "json_file"):
if output_dir is None:
raise ValueError(
"output_dir must be provided to COCOEvaluator "
"for datasets not in COCO format."
)
self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
self._metadata.json_file = cache_path
| # Copyright (c) Facebook, Inc. and its affiliates.
try:
except ImportError:
COCOeval_opt = COCOeval
class COCOEvaluator(DatasetEvaluator):
"""
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
for keypoint detection outputs using COCO's metrics.
See http://cocodataset.org/#detection-eval and
http://cocodataset.org/#keypoints-eval to understand its metrics.
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
the metric cannot be computed (e.g. due to no predictions made).
In addition to COCO, this evaluator is able to support any bounding box detection,
instance segmentation, or keypoint detection dataset.
"""
def __init__(
self,
dataset_name,
tasks=None,
distributed=True,
output_dir=None,
*,
max_dets_per_image=None,
use_fast_impl=True,
kpt_oks_sigmas=(),
allow_cached_coco=True,
force_tasks=None,
refcoco=False
):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in detectron2's standard dataset format
so it can be converted to COCO format automatically.
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm", "keypoints".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will only evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
contains all the results in the format they are produced by the model.
2. "coco_instances_results.json" a json file in COCO's result format.
max_dets_per_image (int): limit on the maximum number of detections per image.
By default in COCO, this limit is to 100, but this can be customized
to be greater, as is needed in evaluation metrics AP fixed and AP pool
(see https://arxiv.org/pdf/2102.01066.pdf)
This doesn't affect keypoint evaluation.
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
Although the results should be very close to the official implementation in COCO
API, it is still recommended to compute results with the official API for use in
papers. The faster implementation also uses more RAM.
kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
See http://cocodataset.org/#keypoints-eval
When empty, it will use the defaults in COCO.
Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
allow_cached_coco (bool): Whether to use cached coco json from previous validation
runs. You should set this to False if you need to use different validation data.
Defaults to True.
"""
self.dataset_name = dataset_name
self._logger = logging.getLogger(__name__)
self._distributed = distributed
self._output_dir = output_dir
self.force_tasks = force_tasks
self.refcoco = refcoco
if use_fast_impl and (COCOeval_opt is COCOeval):
self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
use_fast_impl = False
self._use_fast_impl = use_fast_impl
# COCOeval requires the limit on the number of detections per image (maxDets) to be a list
# with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
# 3rd element (100) is used as the limit on the number of detections per image when
# evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
# we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
if max_dets_per_image is None:
max_dets_per_image = [1, 10, 100]
else:
max_dets_per_image = [1, 10, max_dets_per_image]
self._max_dets_per_image = max_dets_per_image
if tasks is not None and isinstance(tasks, CfgNode):
kpt_oks_sigmas = (
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
)
self._logger.warn(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead."
)
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._cpu_device = torch.device("cpu")
self._metadata = MetadataCatalog.get(dataset_name)
if not hasattr(self._metadata, "json_file"):
if output_dir is None:
raise ValueError(
"output_dir must be provided to COCOEvaluator "
"for datasets not in COCO format."
)
self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
self._metadata.json_file = cache_path | convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) | 2 | 2023-12-22 13:31:33+00:00 | 16k |
xhuangcv/humannorm | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF
from tqdm import tqdm
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh | 14,266 |
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
) | self.feature_network = get_mlp( | 8 | 2023-12-23 12:37:48+00:00 | 16k |
Con6924/SPM | evaluate_task.py | [
{
"identifier": "config",
"path": "src/configs/config.py",
"snippet": "PRECISION_TYPES = Literal[\"fp32\", \"fp16\", \"bf16\", \"float32\", \"float16\", \"bfloat16\"]\nclass PretrainedModelConfig(BaseModel):\nclass NetworkConfig(BaseModel):\nclass TrainConfig(BaseModel): \nclass SaveConfig(BaseModel)... | import argparse
import gc
import warnings
import torch
from pathlib import Path
from typing import Literal
from torch.utils.data import DataLoader
from accelerate import PartialState, Accelerator
from src.configs import config
from src.configs.config import RootConfig
from src.configs.generation_config import GenerationConfig
from src.engine import train_util
from src.evaluation import *
from src.models import model_util
from src.models.spm import SPMLayer, SPMNetwork
from src.models.merge_spm import load_state_dict
from src.misc.sld_pipeline import SLDPipeline | 11,603 |
def parse_extra_args(extra_args):
if extra_args is None or extra_args == ['']:
return {}
extra_args_dict = {}
for extra_arg in extra_args:
key, value = extra_arg.split("=")
# convert value to various types
if value.isdigit():
value = int(value)
elif value.replace(".", "", 1).isdigit():
value = float(value)
elif value[0] == "[" and value[-1] == "]":
value = [i.replace('+', ' ') for i in value[1:-1].split(",")]
value = [v.strip() for v in value]
if value[0].isdigit():
value = [int(v) for v in value]
elif value[0].replace(".", "", 1).isdigit():
value = [float(v) for v in value]
extra_args_dict[key] = value
return extra_args_dict
def get_dataloader(args, num_processes=1):
# parse task_args arguments
task_args = parse_extra_args(args.task_args)
task_args["save_folder"] = args.img_save_path
task_args["output_path"] = args.save_path
# parse generation arguments
cfg = parse_extra_args(args.generation_cfg)
cfg = GenerationConfig(**cfg)
dataset_class = None
if args.task == "general":
dataset_class = ClipTemplateDataset
elif args.task == "artwork":
dataset_class = ArtworkDataset
elif args.task == "i2p":
dataset_class = I2PDataset
elif args.task == "coco":
dataset_class = Coco30kGenerationDataset
else:
raise ValueError(f"Unknown task: {args.task}")
dataset = dataset_class(**task_args, base_cfg=cfg)
dataloader = DataLoader(dataset, batch_size=num_processes, num_workers=0, shuffle=False)
return dataloader
def get_evaluator(args):
evaluator_class = None
if args.task == "general":
evaluator_class = ClipEvaluator
elif args.task == "artwork":
evaluator_class = ArtworkEvaluator
elif args.task == "i2p":
evaluator_class = I2PEvaluator
elif args.task == "coco":
evaluator_class = CocoEvaluator
else:
raise ValueError(f"Unknown task: {args.task}")
evaluator = evaluator_class(
save_folder=args.img_save_path, output_path=args.save_path
)
return evaluator
def calculate_matching_score(
prompt_tokens,
prompt_embeds,
erased_prompt_tokens,
erased_prompt_embeds,
matching_metric: MATCHING_METRICS,
special_token_ids: set[int],
weight_dtype: torch.dtype = torch.float32,
):
scores = []
if "allone" in matching_metric:
scores.append(torch.ones(prompt_embeds.shape[0]).to("cpu", dtype=weight_dtype))
if "clipcos" in matching_metric:
clipcos = torch.cosine_similarity(
prompt_embeds.flatten(1, 2), erased_prompt_embeds.flatten(1, 2), dim=-1
).cpu()
scores.append(clipcos)
if "tokenuni" in matching_metric:
prompt_set = set(prompt_tokens[0].tolist()) - special_token_ids
tokenuni = []
for ep in erased_prompt_tokens:
ep_set = set(ep.tolist()) - special_token_ids
tokenuni.append(len(prompt_set.intersection(ep_set)) / len(ep_set))
scores.append(torch.tensor(tokenuni).to("cpu", dtype=weight_dtype))
return torch.max(torch.stack(scores), dim=0)[0]
@torch.no_grad()
def infer_with_spm(
dataloader: DataLoader,
spm_paths: list[str],
matching_metric: MATCHING_METRICS,
facilitate_factor: float = 1.0,
assigned_multipliers: list[float] = None,
finetuned_model_path: str = None,
sld_target_concept: str = None,
base_model: str = "CompVis/stable-diffusion-v1-4",
v2: bool = False,
precision: str = "fp32",
):
spm_model_paths = [
lp / f"{lp.name}_last.safetensors" if lp.is_dir() else lp for lp in spm_paths
]
weight_dtype = config.parse_precision(precision)
if finetuned_model_path is not None and Path(finetuned_model_path).is_dir():
# folder path for the diffuser model
base_model = finetuned_model_path
print(f"Using models from {base_model}")
# load the pretrained SD
|
DIFFUSERS_CACHE_DIR = ".cache/"
UNET_NAME = "unet"
TEXT_ENCODER_NAME = "text_encoder"
MATCHING_METRICS = Literal[
"clipcos",
"clipcos_tokenuni",
"tokenuni",
"allone",
]
distributed_state = PartialState()
accelerator = Accelerator()
def flush():
torch.cuda.empty_cache()
gc.collect()
def parse_extra_args(extra_args):
if extra_args is None or extra_args == ['']:
return {}
extra_args_dict = {}
for extra_arg in extra_args:
key, value = extra_arg.split("=")
# convert value to various types
if value.isdigit():
value = int(value)
elif value.replace(".", "", 1).isdigit():
value = float(value)
elif value[0] == "[" and value[-1] == "]":
value = [i.replace('+', ' ') for i in value[1:-1].split(",")]
value = [v.strip() for v in value]
if value[0].isdigit():
value = [int(v) for v in value]
elif value[0].replace(".", "", 1).isdigit():
value = [float(v) for v in value]
extra_args_dict[key] = value
return extra_args_dict
def get_dataloader(args, num_processes=1):
# parse task_args arguments
task_args = parse_extra_args(args.task_args)
task_args["save_folder"] = args.img_save_path
task_args["output_path"] = args.save_path
# parse generation arguments
cfg = parse_extra_args(args.generation_cfg)
cfg = GenerationConfig(**cfg)
dataset_class = None
if args.task == "general":
dataset_class = ClipTemplateDataset
elif args.task == "artwork":
dataset_class = ArtworkDataset
elif args.task == "i2p":
dataset_class = I2PDataset
elif args.task == "coco":
dataset_class = Coco30kGenerationDataset
else:
raise ValueError(f"Unknown task: {args.task}")
dataset = dataset_class(**task_args, base_cfg=cfg)
dataloader = DataLoader(dataset, batch_size=num_processes, num_workers=0, shuffle=False)
return dataloader
def get_evaluator(args):
evaluator_class = None
if args.task == "general":
evaluator_class = ClipEvaluator
elif args.task == "artwork":
evaluator_class = ArtworkEvaluator
elif args.task == "i2p":
evaluator_class = I2PEvaluator
elif args.task == "coco":
evaluator_class = CocoEvaluator
else:
raise ValueError(f"Unknown task: {args.task}")
evaluator = evaluator_class(
save_folder=args.img_save_path, output_path=args.save_path
)
return evaluator
def calculate_matching_score(
prompt_tokens,
prompt_embeds,
erased_prompt_tokens,
erased_prompt_embeds,
matching_metric: MATCHING_METRICS,
special_token_ids: set[int],
weight_dtype: torch.dtype = torch.float32,
):
scores = []
if "allone" in matching_metric:
scores.append(torch.ones(prompt_embeds.shape[0]).to("cpu", dtype=weight_dtype))
if "clipcos" in matching_metric:
clipcos = torch.cosine_similarity(
prompt_embeds.flatten(1, 2), erased_prompt_embeds.flatten(1, 2), dim=-1
).cpu()
scores.append(clipcos)
if "tokenuni" in matching_metric:
prompt_set = set(prompt_tokens[0].tolist()) - special_token_ids
tokenuni = []
for ep in erased_prompt_tokens:
ep_set = set(ep.tolist()) - special_token_ids
tokenuni.append(len(prompt_set.intersection(ep_set)) / len(ep_set))
scores.append(torch.tensor(tokenuni).to("cpu", dtype=weight_dtype))
return torch.max(torch.stack(scores), dim=0)[0]
@torch.no_grad()
def infer_with_spm(
dataloader: DataLoader,
spm_paths: list[str],
matching_metric: MATCHING_METRICS,
facilitate_factor: float = 1.0,
assigned_multipliers: list[float] = None,
finetuned_model_path: str = None,
sld_target_concept: str = None,
base_model: str = "CompVis/stable-diffusion-v1-4",
v2: bool = False,
precision: str = "fp32",
):
spm_model_paths = [
lp / f"{lp.name}_last.safetensors" if lp.is_dir() else lp for lp in spm_paths
]
weight_dtype = config.parse_precision(precision)
if finetuned_model_path is not None and Path(finetuned_model_path).is_dir():
# folder path for the diffuser model
base_model = finetuned_model_path
print(f"Using models from {base_model}")
# load the pretrained SD | tokenizer, text_encoder, unet, pipe = model_util.load_checkpoint_model( | 4 | 2023-12-26 03:19:16+00:00 | 16k |
dakpinaroglu/Frame2seq | frame2seq/openfold/utils/feats.py | [
{
"identifier": "protein",
"path": "frame2seq/openfold/np/protein.py",
"snippet": "PICO_TO_ANGSTROM = 0.01\nclass Protein:\ndef from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:\ndef from_proteinnet_string(proteinnet_str: str) -> Protein:\ndef get_pdb_headers(prot: Protein, chain... | import math
import numpy as np
import torch
import torch.nn as nn
import frame2seq.openfold.np.residue_constants as rc
from typing import Dict
from frame2seq.openfold.np import protein
from frame2seq.openfold.utils.rigid_utils import Rotation, Rigid
from frame2seq.openfold.utils.tensor_utils import (
batched_gather,
one_hot,
tree_map,
tensor_tree_map,
) | 11,342 |
def build_template_pair_feat(
batch,
min_bin, max_bin, no_bins,
use_unit_vector=False,
eps=1e-20, inf=1e8
):
template_mask = batch["template_pseudo_beta_mask"]
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
# Compute distogram (this seems to differ slightly from Alg. 5)
tpb = batch["template_pseudo_beta"]
dgram = torch.sum(
(tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True
)
lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2
upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)
to_concat = [dgram, template_mask_2d[..., None]]
aatype_one_hot = nn.functional.one_hot(
batch["template_aatype"],
rc.restype_num + 2,
)
n_res = batch["template_aatype"].shape[-1]
to_concat.append(
aatype_one_hot[..., None, :, :].expand(
*aatype_one_hot.shape[:-2], n_res, -1, -1
)
)
to_concat.append(
aatype_one_hot[..., None, :].expand(
*aatype_one_hot.shape[:-2], -1, n_res, -1
)
)
n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
rigids = Rigid.make_transform_from_reference(
n_xyz=batch["template_all_atom_positions"][..., n, :],
ca_xyz=batch["template_all_atom_positions"][..., ca, :],
c_xyz=batch["template_all_atom_positions"][..., c, :],
eps=eps,
)
points = rigids.get_trans()[..., None, :, :]
rigid_vec = rigids[..., None].invert_apply(points)
inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec ** 2, dim=-1))
t_aa_masks = batch["template_all_atom_mask"]
template_mask = (
t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
)
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
inv_distance_scalar = inv_distance_scalar * template_mask_2d
unit_vector = rigid_vec * inv_distance_scalar[..., None]
if(not use_unit_vector):
unit_vector = unit_vector * 0.
to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))
to_concat.append(template_mask_2d[..., None])
act = torch.cat(to_concat, dim=-1)
act = act * template_mask_2d[..., None]
return act
def build_extra_msa_feat(batch):
msa_1hot = nn.functional.one_hot(batch["extra_msa"], 23)
msa_feat = [
msa_1hot,
batch["extra_has_deletion"].unsqueeze(-1),
batch["extra_deletion_value"].unsqueeze(-1),
]
return torch.cat(msa_feat, dim=-1)
def torsion_angles_to_frames(
r: Rigid,
alpha: torch.Tensor,
aatype: torch.Tensor,
rrgdf: torch.Tensor,
):
# [*, N, 8, 4, 4]
default_4x4 = rrgdf[aatype, ...]
# [*, N, 8] transformations, i.e.
# One [*, N, 8, 3, 3] rotation matrix and
# One [*, N, 8, 3] translation matrix
default_r = r.from_tensor_4x4(default_4x4)
bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
bb_rot[..., 1] = 1
# [*, N, 8, 2]
alpha = torch.cat(
[bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2
)
# [*, N, 8, 3, 3]
# Produces rotation matrices of the form:
# [
# [1, 0 , 0 ],
# [0, a_2,-a_1],
# [0, a_1, a_2]
# ]
# This follows the original code rather than the supplement, which uses
# different indices.
all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
all_rots[..., 0, 0] = 1
all_rots[..., 1, 1] = alpha[..., 1]
all_rots[..., 1, 2] = -alpha[..., 0]
all_rots[..., 2, 1:] = alpha
| # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
is_gly = aatype == rc.restype_order["G"]
ca_idx = rc.atom_order["CA"]
cb_idx = rc.atom_order["CB"]
pseudo_beta = torch.where(
is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3),
all_atom_positions[..., ca_idx, :],
all_atom_positions[..., cb_idx, :],
)
if all_atom_masks is not None:
pseudo_beta_mask = torch.where(
is_gly,
all_atom_masks[..., ca_idx],
all_atom_masks[..., cb_idx],
)
return pseudo_beta, pseudo_beta_mask
else:
return pseudo_beta
def atom14_to_atom37(atom14, batch):
atom37_data = batched_gather(
atom14,
batch["residx_atom37_to_atom14"],
dim=-2,
no_batch_dims=len(atom14.shape[:-2]),
)
atom37_data = atom37_data * batch["atom37_atom_exists"][..., None]
return atom37_data
def build_template_angle_feat(template_feats):
template_aatype = template_feats["template_aatype"]
torsion_angles_sin_cos = template_feats["template_torsion_angles_sin_cos"]
alt_torsion_angles_sin_cos = template_feats[
"template_alt_torsion_angles_sin_cos"
]
torsion_angles_mask = template_feats["template_torsion_angles_mask"]
template_angle_feat = torch.cat(
[
nn.functional.one_hot(template_aatype, 22),
torsion_angles_sin_cos.reshape(
*torsion_angles_sin_cos.shape[:-2], 14
),
alt_torsion_angles_sin_cos.reshape(
*alt_torsion_angles_sin_cos.shape[:-2], 14
),
torsion_angles_mask,
],
dim=-1,
)
return template_angle_feat
def build_template_pair_feat(
batch,
min_bin, max_bin, no_bins,
use_unit_vector=False,
eps=1e-20, inf=1e8
):
template_mask = batch["template_pseudo_beta_mask"]
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
# Compute distogram (this seems to differ slightly from Alg. 5)
tpb = batch["template_pseudo_beta"]
dgram = torch.sum(
(tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True
)
lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2
upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)
to_concat = [dgram, template_mask_2d[..., None]]
aatype_one_hot = nn.functional.one_hot(
batch["template_aatype"],
rc.restype_num + 2,
)
n_res = batch["template_aatype"].shape[-1]
to_concat.append(
aatype_one_hot[..., None, :, :].expand(
*aatype_one_hot.shape[:-2], n_res, -1, -1
)
)
to_concat.append(
aatype_one_hot[..., None, :].expand(
*aatype_one_hot.shape[:-2], -1, n_res, -1
)
)
n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
rigids = Rigid.make_transform_from_reference(
n_xyz=batch["template_all_atom_positions"][..., n, :],
ca_xyz=batch["template_all_atom_positions"][..., ca, :],
c_xyz=batch["template_all_atom_positions"][..., c, :],
eps=eps,
)
points = rigids.get_trans()[..., None, :, :]
rigid_vec = rigids[..., None].invert_apply(points)
inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec ** 2, dim=-1))
t_aa_masks = batch["template_all_atom_mask"]
template_mask = (
t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
)
template_mask_2d = template_mask[..., None] * template_mask[..., None, :]
inv_distance_scalar = inv_distance_scalar * template_mask_2d
unit_vector = rigid_vec * inv_distance_scalar[..., None]
if(not use_unit_vector):
unit_vector = unit_vector * 0.
to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))
to_concat.append(template_mask_2d[..., None])
act = torch.cat(to_concat, dim=-1)
act = act * template_mask_2d[..., None]
return act
def build_extra_msa_feat(batch):
msa_1hot = nn.functional.one_hot(batch["extra_msa"], 23)
msa_feat = [
msa_1hot,
batch["extra_has_deletion"].unsqueeze(-1),
batch["extra_deletion_value"].unsqueeze(-1),
]
return torch.cat(msa_feat, dim=-1)
def torsion_angles_to_frames(
r: Rigid,
alpha: torch.Tensor,
aatype: torch.Tensor,
rrgdf: torch.Tensor,
):
# [*, N, 8, 4, 4]
default_4x4 = rrgdf[aatype, ...]
# [*, N, 8] transformations, i.e.
# One [*, N, 8, 3, 3] rotation matrix and
# One [*, N, 8, 3] translation matrix
default_r = r.from_tensor_4x4(default_4x4)
bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
bb_rot[..., 1] = 1
# [*, N, 8, 2]
alpha = torch.cat(
[bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2
)
# [*, N, 8, 3, 3]
# Produces rotation matrices of the form:
# [
# [1, 0 , 0 ],
# [0, a_2,-a_1],
# [0, a_1, a_2]
# ]
# This follows the original code rather than the supplement, which uses
# different indices.
all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
all_rots[..., 0, 0] = 1
all_rots[..., 1, 1] = alpha[..., 1]
all_rots[..., 1, 2] = -alpha[..., 0]
all_rots[..., 2, 1:] = alpha
| all_rots = Rigid(Rotation(rot_mats=all_rots), None) | 1 | 2023-12-25 09:29:36+00:00 | 16k |
KyanChen/TTP | mmpretrain/models/multimodal/clip/clip.py | [
{
"identifier": "CIFAR100_CATEGORIES",
"path": "mmpretrain/datasets/categories.py",
"snippet": "CIFAR100_CATEGORIES = (\n 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',\n 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',\n 'can', 'castle', '... | from abc import abstractmethod
from typing import List, Optional, Tuple, Union
from mmengine.model import BaseModel
from torch import nn
from mmpretrain.datasets.categories import (CIFAR100_CATEGORIES,
IMAGENET_SIMPLE_CATEGORIES)
from mmpretrain.registry import MODELS, TOKENIZER
from mmpretrain.structures import DataSample
from mmpretrain.utils import track_on_main_process
from .utils import (OPENAI_CIFAR100_PROMPT, OPENAI_IMAGENET_PROMPT,
OPENAI_IMAGENET_PROMPT_SUB)
import numpy as np
import torch
import torch.nn.functional as F | 10,842 |
def initialize_parameters(self) -> None:
"""Initialize the parameters.
The pretrained weight will override the initialized parameters by this
function.
"""
nn.init.normal_(self.token_embedding.weight, std=0.02)
nn.init.normal_(self.positional_embedding, std=0.01)
proj_std = (self.transformer.width**-0.5) * (
(2 * self.transformer.layers)**-0.5)
attn_std = self.transformer.width**-0.5
fc_std = (2 * self.transformer.width)**-0.5
for block in self.transformer.resblocks:
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
if self.text_projection is not None:
nn.init.normal_(
self.text_projection, std=self.transformer.width**-0.5)
def build_attention_mask(self):
# lazily create causal attention mask,
# with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(self.context_length, self.context_length)
mask.fill_(float('-inf'))
mask.triu_(1) # zero out the lower diagonal
return mask
def forward(
self,
images: torch.Tensor,
data_samples: Optional[list] = None,
mode: str = 'predict',
**kwargs,
):
"""The unified entry for a forward process in both training and test.
The method accepts the following modes:
- "predict": Forward and return a list of data samples contain the
predict results.
Args:
images (torch.Tensor): the preprocessed image tensor of shape
``(N, C, H, W)``.
data_samples (List[DataSample], optional): The annotation data
of every samples. Defaults to None.
mode (str): Return what kind of value. Defaults to 'predict'.
"""
if mode == 'predict':
return self.predict(images, data_samples, **kwargs)
else:
raise RuntimeError(f'Invalid mode "{mode}".')
def extract_image_feat(self, images: torch.Tensor) -> torch.Tensor:
"""The function to extract image latent features."""
return self.visual_proj(self.visual(images))[0]
def extract_text_feat(self, texts: torch.Tensor) -> torch.Tensor:
"""The function to extract text latent features."""
x = self.token_embedding(texts) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)[0]
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x)
# x.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding
# (eot_token is the highest number in each sequence)
x = x[torch.arange(x.shape[0]),
texts.argmax(dim=-1)] @ self.text_projection
return x
def extract_feat(
self, images: torch.Tensor,
texts: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
"""The function to extract image and text latent features, the input
image or text can not both be None."""
assert images is not None or texts is not None, \
'text and image cannot both be None!'
if images is None:
return self.extract_text_feat(texts)
elif texts is None:
return self.extract_image_feat(images)
image_features = self.extract_image_feat(images)
text_features = self.extract_text_feat(texts)
image_features = image_features / image_features.norm(
dim=-1, keepdim=True)
text_features = text_features / text_features.norm(
dim=-1, keepdim=True)
return image_features, text_features
def compute_similarity(self, images, texts):
"""Extract images and texts features and compute cosine similarity."""
image_features, text_features = self.extract_feat(
images=images, texts=texts)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
# shape (N, N)
return logits_per_image, logits_per_text
@abstractmethod
def predict(self,
images: torch.Tensor,
| # Copyright (c) OpenMMLab. All rights reserved.
CIFAR100_CATEGORIES = [' '.join(c.split('_')) for c in CIFAR100_CATEGORIES]
PROTOTYPE_MAP = {
'imagenet': IMAGENET_SIMPLE_CATEGORIES,
'cifar100': CIFAR100_CATEGORIES,
}
PROMPT_MAP = {
'openai_imagenet': OPENAI_IMAGENET_PROMPT,
'openai_cifar100': OPENAI_CIFAR100_PROMPT,
'vanilla': [lambda c: f'a photo of a {c}'],
'openai_imagenet_sub': OPENAI_IMAGENET_PROMPT_SUB
}
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward function."""
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class CLIP(BaseModel):
"""The implementation of `CLIP <https://arxiv.org/abs/2103.00020>`_.
Args:
vision_backbone (dict): Config dict for vision backbone.
text_backbone (dict): Config dict for text backbone.
tokenizer (dict): Config dict for text tokenizer.
proj_dim (int): Projection dimension for similarity computation.
text_prototype (str): Text prototype, which can be a key in
`PROTOTYPE_MAP` or list of text.
text_prompt (str): The prompt for text prototype.
Defaults to 'vanilla',which refers to "a photo of {cls}".
context_length (int): The context length to use. Defaults to 77.
data_preprocessor (Union[dict, nn.Module], optional): The config for
preprocessing input data. If None or no specified type, it will use
"MultiModalDataPreprocessor" as type.
See :class:`MultiModalDataPreprocessor` for more details.
Defaults to None.
init_cfg (dict, optional): The config to control the initialization.
Defaults to None.
"""
def __init__(self,
vision_backbone: dict,
projection: dict,
text_backbone: dict,
tokenizer: dict,
vocab_size: int,
transformer_width: int,
proj_dim: int,
context_length: int = 77,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[dict] = None):
if data_preprocessor is None:
data_preprocessor = {}
data_preprocessor.setdefault('type', 'MultiModalDataPreprocessor')
data_preprocessor = MODELS.build(data_preprocessor)
super().__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
self.context_length = context_length
# build the vision transformer
self.visual = MODELS.build(vision_backbone)
# build the visual projection
self.visual_proj = MODELS.build(projection)
# build attn_mask for casual-attn
text_backbone['attn_mask'] = self.build_attention_mask()
# build the text transformer
self.transformer = MODELS.build(text_backbone)
self.vocab_size = vocab_size
self.token_embedding = nn.Embedding(vocab_size, transformer_width)
self.positional_embedding = nn.Parameter(
torch.empty(self.context_length, transformer_width))
self.ln_final = LayerNorm(transformer_width)
self.text_projection = nn.Parameter(
torch.empty(transformer_width, proj_dim))
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.initialize_parameters()
self.tokenizer = TOKENIZER.build(tokenizer)
self.tokenizer.vocab = self.tokenizer.get_vocab(
) # CLIPTokenizer has no attribute named 'vocab', so manually
def initialize_parameters(self) -> None:
"""Initialize the parameters.
The pretrained weight will override the initialized parameters by this
function.
"""
nn.init.normal_(self.token_embedding.weight, std=0.02)
nn.init.normal_(self.positional_embedding, std=0.01)
proj_std = (self.transformer.width**-0.5) * (
(2 * self.transformer.layers)**-0.5)
attn_std = self.transformer.width**-0.5
fc_std = (2 * self.transformer.width)**-0.5
for block in self.transformer.resblocks:
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
if self.text_projection is not None:
nn.init.normal_(
self.text_projection, std=self.transformer.width**-0.5)
def build_attention_mask(self):
# lazily create causal attention mask,
# with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(self.context_length, self.context_length)
mask.fill_(float('-inf'))
mask.triu_(1) # zero out the lower diagonal
return mask
def forward(
self,
images: torch.Tensor,
data_samples: Optional[list] = None,
mode: str = 'predict',
**kwargs,
):
"""The unified entry for a forward process in both training and test.
The method accepts the following modes:
- "predict": Forward and return a list of data samples contain the
predict results.
Args:
images (torch.Tensor): the preprocessed image tensor of shape
``(N, C, H, W)``.
data_samples (List[DataSample], optional): The annotation data
of every samples. Defaults to None.
mode (str): Return what kind of value. Defaults to 'predict'.
"""
if mode == 'predict':
return self.predict(images, data_samples, **kwargs)
else:
raise RuntimeError(f'Invalid mode "{mode}".')
def extract_image_feat(self, images: torch.Tensor) -> torch.Tensor:
"""The function to extract image latent features."""
return self.visual_proj(self.visual(images))[0]
def extract_text_feat(self, texts: torch.Tensor) -> torch.Tensor:
"""The function to extract text latent features."""
x = self.token_embedding(texts) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)[0]
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x)
# x.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding
# (eot_token is the highest number in each sequence)
x = x[torch.arange(x.shape[0]),
texts.argmax(dim=-1)] @ self.text_projection
return x
def extract_feat(
self, images: torch.Tensor,
texts: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
"""The function to extract image and text latent features, the input
image or text can not both be None."""
assert images is not None or texts is not None, \
'text and image cannot both be None!'
if images is None:
return self.extract_text_feat(texts)
elif texts is None:
return self.extract_image_feat(images)
image_features = self.extract_image_feat(images)
text_features = self.extract_text_feat(texts)
image_features = image_features / image_features.norm(
dim=-1, keepdim=True)
text_features = text_features / text_features.norm(
dim=-1, keepdim=True)
return image_features, text_features
def compute_similarity(self, images, texts):
"""Extract images and texts features and compute cosine similarity."""
image_features, text_features = self.extract_feat(
images=images, texts=texts)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
# shape (N, N)
return logits_per_image, logits_per_text
@abstractmethod
def predict(self,
images: torch.Tensor, | data_samples: DataSample = None) -> DataSample: | 4 | 2023-12-23 08:36:47+00:00 | 16k |
see2023/Bert-VITS2-ext | train_ms.py | [
{
"identifier": "config",
"path": "config.py",
"snippet": "class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, ... | import platform
import os
import torch
import torch.distributed as dist
import logging
import argparse
import datetime
import gc
import commons
import utils
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
from config import config
from data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler,
AudioVisemesLoader,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
WavLMDiscriminator,
VisemesNet,
)
from losses import (
generator_loss,
discriminator_loss,
feature_loss,
kl_loss,
WavLMLoss,
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 13,247 | # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
global_step = 0
global_visemes_step = 0
def run_only_visemes(hps):
# 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数
global global_visemes_step
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(0)
train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)
train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,
batch_size=1, drop_last=True)
eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)
eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
batch_size=1, pin_memory=True,
drop_last=False)
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
global_step = 0
global_visemes_step = 0
def run_only_visemes(hps):
# 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数
global global_visemes_step
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(0)
train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)
train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,
batch_size=1, drop_last=True)
eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)
eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
batch_size=1, pin_memory=True,
drop_last=False) | net_v = VisemesNet(hps.model.hidden_channels).cuda() | 9 | 2023-12-27 03:09:11+00:00 | 16k |
chinhsuanwu/ifusion-threestudio | extern/ldm_zero123/models/diffusion/ddpm.py | [
{
"identifier": "AutoencoderKL",
"path": "extern/ldm_zero123/models/autoencoder.py",
"snippet": "class AutoencoderKL(pl.LightningModule):\n def __init__(\n self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"i... | import itertools
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
from contextlib import contextmanager, nullcontext
from functools import partial
from einops import rearrange, repeat
from omegaconf import ListConfig
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm
from extern.ldm_zero123.models.autoencoder import (
AutoencoderKL,
IdentityFirstStage,
VQModelInterface,
)
from extern.ldm_zero123.models.diffusion.ddim import DDIMSampler
from extern.ldm_zero123.modules.attention import CrossAttention
from extern.ldm_zero123.modules.diffusionmodules.util import (
extract_into_tensor,
make_beta_schedule,
noise_like,
)
from extern.ldm_zero123.modules.distributions.distributions import (
DiagonalGaussianDistribution,
normal_kl,
)
from extern.ldm_zero123.modules.ema import LitEma
from extern.ldm_zero123.util import (
count_params,
default,
exists,
instantiate_from_config,
isimage,
ismap,
log_txt_as_img,
mean_flat,
) | 12,692 | padding=0,
stride=(stride[0] // df, stride[1] // df),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h // df, w // df
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
)
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(
self,
batch,
k,
return_first_stage_outputs=False,
force_c_encode=False,
cond_key=None,
return_original_cond=False,
bs=None,
uncond=0.05,
):
x = super().get_input(batch, k)
T = batch["T"].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange(
(random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1"
)
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [
self.cc_projection(
torch.cat(
[
torch.where(prompt_mask, null_prompt, clip_emb),
T[:, None, :],
],
dim=-1,
)
)
]
cond["c_concat"] = [
input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()
]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
# @torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, "b h w c -> b c h w").contiguous()
z = 1.0 / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
z, ks, stride, uf=uf
)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
super().__init__()
assert parameterization in [
"eps",
"x0",
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
self.make_it_fit = make_it_fit
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
* noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1.0, 1.0)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
x_start=x_recon, x_t=x, t=t
)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(
x=x, t=t, clip_denoised=clip_denoised
)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(
reversed(range(0, self.num_timesteps)),
desc="Sampling t",
total=self.num_timesteps,
):
img = self.p_sample(
img,
torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised,
)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop(
(batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates,
)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def get_loss(self, pred, target, mean=True):
if self.loss_type == "l1":
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == "l2":
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(
f"Paramterization {self.parameterization} not yet supported"
)
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = "train" if self.training else "val"
loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f"{log_prefix}/loss": loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(
0, self.num_timesteps, (x.shape[0],), device=self.device
).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, "b h w c -> b c h w")
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
loss, loss_dict = self.shared_step(batch)
self.log_dict(
loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
)
self.log(
"global_step",
self.global_step,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=False,
)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]["lr"]
self.log(
"lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(
loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
self.log_dict(
loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(
batch_size=N, return_intermediates=True
)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(
self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs["timesteps"]
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = "concat" if concat_mode else "crossattn"
if cond_stage_config == "__is_unconditional__":
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.unet_trainable = unet_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer("scale_factor", torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if (
self.scale_by_std
and self.current_epoch == 0
and self.global_step == 0
and batch_idx == 0
and not self.restarted_from_ckpt
):
assert (
self.scale_factor == 1.0
), "rather not use custom rescaling and std-rescaling simultaneously"
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer("scale_factor", 1.0 / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != "__is_first_stage__"
assert config != "__is_unconditional__"
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(
self, samples, desc="", force_no_decoder_quantization=False
):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(
self.decode_first_stage(
zd.to(self.device), force_not_quantize=force_no_decoder_quantization
)
)
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")
denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(
f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
)
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, "encode") and callable(
self.cond_stage_model.encode
):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(
torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(
weighting,
self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"],
)
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(
L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"],
)
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(
self, x, kernel_size, stride, uf=1, df=1
): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(
kernel_size[0], kernel_size[1], Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
dilation=1,
padding=0,
stride=(stride[0] * uf, stride[1] * uf),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h * uf, w * uf
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
)
elif df > 1 and uf == 1:
fold_params = dict(
kernel_size=kernel_size, dilation=1, padding=0, stride=stride
)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(
kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
dilation=1,
padding=0,
stride=(stride[0] // df, stride[1] // df),
)
fold = torch.nn.Fold(
output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
)
weighting = self.get_weighting(
kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
).to(x.dtype)
normalization = fold(weighting).view(
1, 1, h // df, w // df
) # normalizes the overlap
weighting = weighting.view(
(1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
)
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(
self,
batch,
k,
return_first_stage_outputs=False,
force_c_encode=False,
cond_key=None,
return_original_cond=False,
bs=None,
uncond=0.05,
):
x = super().get_input(batch, k)
T = batch["T"].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange(
(random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1"
)
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [
self.cc_projection(
torch.cat(
[
torch.where(prompt_mask, null_prompt, clip_emb),
T[:, None, :],
],
dim=-1,
)
)
]
cond["c_concat"] = [
input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()
]
out = [z, cond]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
# @torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, "b h w c -> b c h w").contiguous()
z = 1.0 / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(
z, ks, stride, uf=uf
)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view(
(z.shape[0], -1, ks[0], ks[1], z.shape[-1])
) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim | if isinstance(self.first_stage_model, VQModelInterface): | 2 | 2023-12-27 20:30:33+00:00 | 16k |
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