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from omegaconf import OmegaConf |
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from torch.utils.data import DataLoader, default_collate, ConcatDataset |
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from fvcore.common.registry import Registry |
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from .datasets.dataset_wrapper import DATASETWRAPPER_REGISTRY |
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DATASET_REGISTRY = Registry("dataset") |
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DATASET_REGISTRY.__doc__ = """ |
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Registry for datasets, which takes a list of dataset names and returns a dataset object. |
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Currently it performs similar as registering dataset loading functions, but remains in a |
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form of object class for future purposes. |
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""" |
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def get_dataset(cfg, split): |
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assert cfg.data.get(split), f"No valid dataset name in {split}." |
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dataset_list = [] |
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print(split, ': ', ', '.join(cfg.data.get(split))) |
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for dataset_name in cfg.data.get(split): |
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_dataset = DATASET_REGISTRY.get(dataset_name)(cfg, split) |
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assert len(_dataset), f"Dataset '{dataset_name}' is empty!" |
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wrapper = cfg.data_wrapper.get(split, cfg.data_wrapper) if not isinstance(cfg.data_wrapper, str) else cfg.data_wrapper |
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_dataset = DATASETWRAPPER_REGISTRY.get(wrapper)(cfg, _dataset, split=split) |
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if cfg.data.get('use_voxel', None): |
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_dataset = DATASETWRAPPER_REGISTRY.get('VoxelDatasetWrapper')(cfg, _dataset) |
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dataset_list.append(_dataset) |
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print('='*50) |
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print('Dataset\t\t\tSize') |
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total = sum([len(dataset) for dataset in dataset_list]) |
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for dataset_name, dataset in zip(cfg.data.get(split), dataset_list): |
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print(f'{dataset_name:<20} {len(dataset):>6} ({len(dataset) / total * 100:.1f}%)') |
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print(f'Total\t\t\t{total}') |
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print('='*50) |
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if split in ['warmup', 'pretrain', 'train']: |
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dataset_list = ConcatDataset(dataset_list) |
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return dataset_list |
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def build_dataloader(cfg, split='train'): |
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"""_summary_ |
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Unittest: |
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dataloader_train = build_dataloader(default_cfg, split='train') |
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for _item in dataloader_train: |
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print(_item.keys()) |
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Args: |
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cfg (_type_): _description_ |
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split (str, optional): _description_. Defaults to 'train'. |
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Returns: |
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_type_: _description_ |
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""" |
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if split in ['warmup','pretrain']: |
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dataset= get_dataset(cfg, split) |
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collate_fn = getattr(dataset.datasets[0], 'collate_fn', default_collate) |
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return DataLoader(dataset, |
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batch_size=cfg.dataloader.batchsize, |
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num_workers=cfg.dataloader.num_workers, |
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collate_fn = collate_fn, |
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pin_memory=True, |
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persistent_workers = False, |
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shuffle=True, |
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drop_last=True) |
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else: |
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loader_list = [] |
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collate_fn = default_collate |
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if split == 'train': |
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dataset = get_dataset(cfg, split) |
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return DataLoader(dataset, |
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batch_size=cfg.dataloader.get('batchsize_eval', cfg.dataloader.batchsize), |
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num_workers=8, |
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collate_fn = collate_fn, |
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pin_memory=True, |
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persistent_workers = True, |
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drop_last=True, |
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prefetch_factor=4, |
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shuffle=True) |
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else: |
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for dataset in get_dataset(cfg, split): |
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loader_list.append( |
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DataLoader(dataset, |
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batch_size=cfg.dataloader.get('batchsize_eval', cfg.dataloader.batchsize), |
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num_workers=8, |
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collate_fn = collate_fn, |
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pin_memory=True, |
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shuffle=False, |
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prefetch_factor=4, |
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persistent_workers = True)) |
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if len(loader_list) == 1: |
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return loader_list[0] |
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else: |
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return loader_list |
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if __name__ == '__main__': |
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pass |
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