wehub-resource-sync
110 lines
3.3 KiB
Python
Executable File
110 lines
3.3 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & 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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#
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# SPDX-License-Identifier: Apache-2.0
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import os
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import time
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import torch.utils.data
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from mmcv import Registry, build_from_cfg
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from termcolor import colored
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from torch.utils.data import DataLoader
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from diffusion.data.transforms import get_transform
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from diffusion.utils.logger import get_root_logger
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def custom_collate_fn(batch):
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"""
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custom_collate_fn is used to print the index information when the original collate_fn fails
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"""
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try:
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return torch.utils.data.dataloader.default_collate(batch)
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except Exception as e:
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print(f"Collate error: {e}")
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print(f"Batch info: {[item[3] if len(item) > 3 else 'N/A' for item in batch]}")
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print(f"Batch indices: {[item[4] if len(item) > 4 else 'N/A' for item in batch]}")
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raise
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DATASETS = Registry("datasets")
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DATA_ROOT = "data"
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def set_data_root(data_root):
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global DATA_ROOT
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DATA_ROOT = data_root
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def get_data_path(data_dir):
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if os.path.isabs(data_dir):
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return data_dir
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global DATA_ROOT
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return os.path.join(DATA_ROOT, data_dir)
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def get_data_root_and_path(data_dir):
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if os.path.isabs(data_dir):
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return data_dir
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global DATA_ROOT
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return DATA_ROOT, os.path.join(DATA_ROOT, data_dir)
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def build_dataset(cfg, resolution=224, **kwargs):
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logger = get_root_logger()
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dataset_type = cfg.get("type")
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rank = int(os.environ["RANK"])
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if rank == 0:
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logger.info(f"Constructing dataset {dataset_type}...")
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t = time.time()
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transform = cfg.pop("transform", "default_train")
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transform = get_transform(transform, resolution)
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dataset = build_from_cfg(cfg, DATASETS, default_args=dict(transform=transform, resolution=resolution, **kwargs))
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if rank == 0:
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logger.info(
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f"{colored(f'Dataset {dataset_type} constructed: ', 'green', attrs=['bold'])}"
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f"time: {(time.time() - t):.2f} s, length (use/ori): {len(dataset)}/{dataset.ori_imgs_nums}"
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)
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return dataset
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def build_dataloader(dataset, batch_size=256, num_workers=4, shuffle=True, dataloader_type="video", **kwargs):
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collate_fn = kwargs.pop("collate_fn", custom_collate_fn)
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if "batch_sampler" in kwargs:
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dataloader = DataLoader(
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dataset,
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batch_sampler=kwargs["batch_sampler"],
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num_workers=num_workers,
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pin_memory=True,
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persistent_workers=num_workers > 0,
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collate_fn=collate_fn,
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)
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else:
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dataloader = DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=shuffle,
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num_workers=num_workers,
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pin_memory=True,
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persistent_workers=num_workers > 0,
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collate_fn=collate_fn,
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**kwargs,
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)
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return dataloader
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