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