def create_dataloaders(config):
print('Loading Dataset...')
train_dataset = get_training_dataset(config.dataset, config.train_anno, config.train_imgs, config)
print("Loaded {} training images".format(len(train_dataset)))
if config.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=config.ngpus_per_node,
rank=config.rank)
else:
train_sampler = None
train_data_loader = data.DataLoader(
train_dataset, config.batch_size,
num_workers=config.workers,
shuffle=(train_sampler is None),
collate_fn=detection_collate,
pin_memory=True,
sampler=train_sampler
)
test_dataset = get_testing_dataset(config.dataset, config.test_anno, config.test_imgs, config)
print("Loaded {} testing images".format(len(test_dataset)))
if config.distributed:
test_sampler = DistributedSampler(test_dataset, config.rank, config.world_size)
else:
test_sampler = None
test_data_loader = data.DataLoader(
test_dataset, config.batch_size,
num_workers=config.workers,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True,
drop_last=False,
sampler=test_sampler
)
return test_data_loader, train_data_loader
def create_dataloaders(config):
logger.info('Loading Dataset...')
train_dataset = get_training_dataset(config.dataset, config.train_anno,
config.train_imgs, config)
logger.info("Loaded {} training images".format(len(train_dataset)))
if config.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=config.ngpus_per_node,
rank=config.rank)
else:
train_sampler = None
def create_train_data_loader(batch_size):
return data.DataLoader(train_dataset,
batch_size,
num_workers=config.workers,
shuffle=(train_sampler is None),
collate_fn=detection_collate,
pin_memory=True,
sampler=train_sampler)
train_data_loader = create_train_data_loader(config.batch_size)
if config.batch_size_init:
init_data_loader = create_train_data_loader(config.batch_size_init)
else:
init_data_loader = deepcopy(train_data_loader)
if config.distributed:
init_data_loader.num_workers = 0 # PyTorch multiprocessing dataloader issue WA
test_dataset = get_testing_dataset(config.dataset, config.test_anno,
config.test_imgs, config)
logger.info("Loaded {} testing images".format(len(test_dataset)))
if config.distributed:
test_sampler = DistributedSampler(test_dataset, config.rank,
config.world_size)
else:
test_sampler = torch.utils.data.SequentialSampler(test_dataset)
test_data_loader = data.DataLoader(test_dataset,
config.batch_size,
num_workers=config.workers,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True,
drop_last=False,
sampler=test_sampler)
return test_data_loader, train_data_loader, init_data_loader
