def get_jigsaw_dataloader(args):
#Only for DA
names, labels = _dataset_info(
join(dirname(__file__), 'txt_lists', args.target + '.txt'))
img_tr = get_train_transformers(args)
train_dataset = Dataset(names,
labels,
args.path_dataset,
img_transformer=img_tr,
beta_scrambled=args.beta_scrambled,
beta_rotated=args.beta_rotated,
beta_odd=args.beta_odd,
rotation=False,
odd=False)
dataset = ConcatDataset([train_dataset])
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True,
drop_last=False)
return loader
def get_val_dataloader(args):
names, labels = _dataset_info(
join(dirname(__file__), 'txt_lists', args.target + '.txt'))
img_tr = get_val_transformer(args)
val_dataset = TestDataset(names,
labels,
args.path_dataset,
img_transformer=img_tr)
dataset = ConcatDataset([val_dataset])
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True,
drop_last=False)
return loader
def get_trainTargetAsSource_dataloader(args):
#used to create dataset of target images ready for jigsaw task ( only in training da!!)
names, labels = _dataset_info(
join(dirname(__file__), 'txt_lists', args.target + '.txt'))
img_transformer = get_train_transformers(args)
train_dataset = Dataset(names,
labels,
args.path_dataset,
img_transformer=img_transformer,
betaJigen=args.betaJigen,
rotation=args.rotation,
oddOneOut=args.oddOneOut)
#val_dataset = TestDataset(names, labels,args.path_dataset, img_transformer=img_tr,betaJigen = args.betaJigen)
dataset = ConcatDataset([train_dataset])
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
return loader