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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
from .utils.transforms import *
from .base.batched_sampler import BatchedRandomSampler # noqa: F401
from .co3d import Co3d # noqa: F401
def get_data_loader(dataset, batch_size, num_workers=8, shuffle=True, drop_last=True, pin_mem=True):
import torch
from croco.utils.misc import get_world_size, get_rank
# pytorch dataset
if isinstance(dataset, str):
dataset = eval(dataset)
world_size = get_world_size()
rank = get_rank()
try:
sampler = dataset.make_sampler(batch_size, shuffle=shuffle, world_size=world_size,
rank=rank, drop_last=drop_last)
except (AttributeError, NotImplementedError):
# not avail for this dataset
if torch.distributed.is_initialized():
sampler = torch.utils.data.DistributedSampler(
dataset, num_replicas=world_size, rank=rank, shuffle=shuffle, drop_last=drop_last
)
elif shuffle:
sampler = torch.utils.data.RandomSampler(dataset)
else:
sampler = torch.utils.data.SequentialSampler(dataset)
data_loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
batch_size=batch_size,
num_workers=num_workers,
pin_memory=pin_mem,
drop_last=drop_last,
)
return data_loader
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