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# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import importlib
import torch
import torch.distributed as dist
from imaginaire.utils.distributed import master_only_print as print
def _get_train_and_val_dataset_objects(cfg):
r"""Return dataset objects for the training and validation sets.
Args:
cfg (obj): Global configuration file.
Returns:
(dict):
- train_dataset (obj): PyTorch training dataset object.
- val_dataset (obj): PyTorch validation dataset object.
"""
dataset_module = importlib.import_module(cfg.data.type)
train_dataset = dataset_module.Dataset(cfg, is_inference=False)
if hasattr(cfg.data.val, 'type'):
for key in ['type', 'input_types', 'input_image']:
setattr(cfg.data, key, getattr(cfg.data.val, key))
dataset_module = importlib.import_module(cfg.data.type)
val_dataset = dataset_module.Dataset(cfg, is_inference=True)
print('Train dataset length:', len(train_dataset))
print('Val dataset length:', len(val_dataset))
return train_dataset, val_dataset
def _get_data_loader(cfg, dataset, batch_size, not_distributed=False,
shuffle=True, drop_last=True, seed=0):
r"""Return data loader .
Args:
cfg (obj): Global configuration file.
dataset (obj): PyTorch dataset object.
batch_size (int): Batch size.
not_distributed (bool): Do not use distributed samplers.
Return:
(obj): Data loader.
"""
not_distributed = not_distributed or not dist.is_initialized()
if not_distributed:
sampler = None
else:
sampler = torch.utils.data.distributed.DistributedSampler(dataset, seed=seed)
num_workers = getattr(cfg.data, 'num_workers', 8)
persistent_workers = getattr(cfg.data, 'persistent_workers', False)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle and (sampler is None),
sampler=sampler,
pin_memory=True,
num_workers=num_workers,
drop_last=drop_last,
persistent_workers=persistent_workers if num_workers > 0 else False
)
return data_loader
def get_train_and_val_dataloader(cfg, seed=0):
r"""Return dataset objects for the training and validation sets.
Args:
cfg (obj): Global configuration file.
Returns:
(dict):
- train_data_loader (obj): Train data loader.
- val_data_loader (obj): Val data loader.
"""
train_dataset, val_dataset = _get_train_and_val_dataset_objects(cfg)
train_data_loader = _get_data_loader(cfg, train_dataset, cfg.data.train.batch_size, drop_last=True, seed=seed)
not_distributed = getattr(cfg.data, 'val_data_loader_not_distributed', False)
not_distributed = 'video' in cfg.data.type or not_distributed
val_data_loader = _get_data_loader(
cfg, val_dataset, cfg.data.val.batch_size, not_distributed,
shuffle=False, drop_last=getattr(cfg.data.val, 'drop_last', False), seed=seed)
return train_data_loader, val_data_loader
def _get_test_dataset_object(cfg):
r"""Return dataset object for the test set
Args:
cfg (obj): Global configuration file.
Returns:
(obj): PyTorch dataset object.
"""
dataset_module = importlib.import_module(cfg.test_data.type)
test_dataset = dataset_module.Dataset(cfg, is_inference=True, is_test=True)
return test_dataset
def get_test_dataloader(cfg):
r"""Return dataset objects for testing
Args:
cfg (obj): Global configuration file.
Returns:
(obj): Val data loader. It may not contain the ground truth.
"""
test_dataset = _get_test_dataset_object(cfg)
not_distributed = getattr(
cfg.test_data, 'val_data_loader_not_distributed', False)
not_distributed = 'video' in cfg.test_data.type or not_distributed
test_data_loader = _get_data_loader(
cfg, test_dataset, cfg.test_data.test.batch_size, not_distributed,
shuffle=False)
return test_data_loader
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