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#
# 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.
# coding: utf-8
from dataclasses import dataclass, field
from typing import Any, Dict, Tuple
import torch
import yaml
@dataclass
class DataConfig:
"""
DataConfig 版本，其中 vae_downsample 是一个三元组。
"""
grouped_datasets: Dict[str, Any] = field(default_factory=dict)
text_cond_dropout_prob: float = 0.1
vit_cond_dropout_prob: float = 0.4
vae_cond_dropout_prob: float = 0.1
# 将 vae_downsample 改为三元组，分别代表 (时间, 高度, 宽度) 的下采样率
vae_downsample: Tuple[int, int, int] = (4, 16, 16)
max_latent_size: int = 64 # by ModelArguments
vit_patch_size: int = 14 # by ModelArguments
vit_patch_size_temporal: int = 2 # by ModelArguments
vit_max_num_patch_per_side: int = 70 # by ModelArguments
max_num_frames: int = 25 # by ModelArguments
latent_patch_size: int = None # by ModelArguments
@classmethod
def from_yaml(cls, file_path: str) -> 'DataConfig':
"""从 YAML/JSON 文件创建 DataConfig 实例"""
with open(file_path, "r") as stream:
data = yaml.safe_load(stream)
return cls(grouped_datasets=data)
class SimpleCustomBatch:
def __init__(self, batch):
data = batch[0]
for key, value in data.items():
setattr(self, key, value)
def pin_memory(self):
for key, value in self.__dict__.items():
if isinstance(value, torch.Tensor):
setattr(self, key, value.pin_memory())
elif isinstance(value, list) and value and all(isinstance(i, torch.Tensor) for i in value):
setattr(self, key, [i.pin_memory() for i in value])
return self
def cuda(self, device):
for key, value in self.__dict__.items():
if isinstance(value, torch.Tensor):
setattr(self, key, value.to(device))
elif isinstance(value, list) and value and all(isinstance(i, torch.Tensor) for i in value):
setattr(self, key, [i.to(device) for i in value])
return self
def to_dict(self):
return self.__dict__.copy()
# 顶层函数（可被 pickle）
def simple_custom_collate(batch):
return SimpleCustomBatch(batch)
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