wehub-resource-sync
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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from dataclasses import dataclass, field
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from typing import Any, Dict, Tuple
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import torch
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import yaml
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@dataclass
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class DataConfig:
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"""
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DataConfig 版本，其中 vae_downsample 是一个三元组。
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"""
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grouped_datasets: Dict[str, Any] = field(default_factory=dict)
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text_cond_dropout_prob: float = 0.1
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vit_cond_dropout_prob: float = 0.4
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vae_cond_dropout_prob: float = 0.1
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# vae_downsample stores temporal, height, and width downsampling factors.
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vae_downsample: Tuple[int, int, int] = (4, 16, 16)
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max_latent_size: int = 64 # by ModelArguments
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vit_patch_size: int = 14 # by ModelArguments
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vit_patch_size_temporal: int = 2 # by ModelArguments
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vit_max_num_patch_per_side: int = 70 # by ModelArguments
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max_num_frames: int = 25 # by ModelArguments
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latent_patch_size: int = None # by ModelArguments
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@classmethod
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def from_yaml(cls, file_path: str) -> 'DataConfig':
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"""从 YAML/JSON 文件创建 DataConfig 实例"""
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with open(file_path, "r") as stream:
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data = yaml.safe_load(stream)
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return cls(grouped_datasets=data)
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class SimpleCustomBatch:
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def __init__(self, batch):
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data = batch[0]
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for key, value in data.items():
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setattr(self, key, value)
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def pin_memory(self):
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for key, value in self.__dict__.items():
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if isinstance(value, torch.Tensor):
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setattr(self, key, value.pin_memory())
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elif isinstance(value, list) and value and all(isinstance(i, torch.Tensor) for i in value):
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setattr(self, key, [i.pin_memory() for i in value])
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return self
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def cuda(self, device):
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for key, value in self.__dict__.items():
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if isinstance(value, torch.Tensor):
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setattr(self, key, value.to(device))
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elif isinstance(value, list) and value and all(isinstance(i, torch.Tensor) for i in value):
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setattr(self, key, [i.to(device) for i in value])
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return self
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def to_dict(self):
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return self.__dict__.copy()
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# Top-level function so it can be pickled.
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def simple_custom_collate(batch):
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return SimpleCustomBatch(batch)
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