def get_target_jigsaw_loader(args):
img_transformer, tile_transformer = get_train_transformers(args)
if args.stylized:
name_train, _, labels_train, _ = get_split_dataset_info(
join(dirname(__file__), 'txt_lists', 'Stylized' + args.dataset,
"{}_target".format(args.target),
'%s_train.txt' % args.target), 0)
else:
name_train, _, labels_train, _ = get_split_dataset_info(
join(dirname(__file__), 'txt_lists', 'Vanilla' + args.dataset,
'%s_train.txt' % args.target), 0)
dataset = JigsawDataset(name_train,
labels_train,
patches=False,
img_transformer=img_transformer,
tile_transformer=tile_transformer,
jig_classes=args.jigsaw_n_classes,
bias_whole_image=args.bias_whole_image,
grid_size=args.grid_size)
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
return loader
def get_train_dataloader(args, patches):
dataset_list = args.source
assert isinstance(dataset_list, list)
train_datasets = []
val_datasets = []
img_transformer, tile_transformer = get_train_transformers(args)
limit = args.limit_source
for dname in dataset_list:
name_train, name_val, labels_train, labels_val = get_split_dataset_info(
join(dirname(__file__), 'txt_lists','%s_train.txt' % dname),
args.val_size
)
train_dataset = JigsawDataset(name_train, labels_train, patches=patches,
img_transformer=img_transformer,
tile_transformer=tile_transformer,
jig_classes=args.jigsaw_n_classes,
bias_whole_image=args.bias_whole_image)
if limit:
train_dataset = Subset(train_dataset, limit)
train_datasets.append(train_dataset)
# Validation test => subtracted from train split
val_datasets.append(
JigsawTestDataset(name_val, labels_val, img_transformer=get_val_transformer(args),
patches=patches, jig_classes=args.jigsaw_n_classes))
train_dataset = ConcatDataset(train_datasets)
val_dataset = ConcatDataset(val_datasets)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, pin_memory=True, drop_last=False)
return train_loader, val_loader
def get_train_dataloader(args):
dataset_list = args.source
assert isinstance(dataset_list, list)
datasets = []
val_datasets = []
img_transformer = get_train_transformers(args)
limit = args.limit_source
for dname in dataset_list:
if dname in digits_datasets:
return get_digital_train_dataloader(args, dname)
name_train, name_val, labels_train, labels_val = get_split_dataset_info(
join(dirname(__file__), 'txt_lists', '%s_train.txt' % dname),
args.val_size)
train_dataset = JigsawDataset(name_train,
labels_train,
img_transformer=img_transformer)
if limit:
train_dataset = Subset(train_dataset, limit)
datasets.append(train_dataset)
val_datasets.append(
JigsawDataset(name_val,
labels_val,
img_transformer=get_val_transformer(args)))
dataset = ConcatDataset(datasets)
val_dataset = ConcatDataset(val_datasets)
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True,
drop_last=False)
return loader, val_loader
def get_train_dataloader(args, patches):
dataset_list = args.source
assert isinstance(dataset_list, list)
datasets = []
val_datasets = []
img_transformer, tile_transformer = get_train_transformers(args)
limit = args.limit_source
for dname in dataset_list:
if args.stylized:
name_train, name_val, labels_train, labels_val = get_split_dataset_info(
join(dirname(__file__), 'txt_lists', 'Stylized' + args.dataset,
"{}_target".format(args.target), '%s_train.txt' % dname),
args.val_size)
# print(name_train)
else:
name_train, name_val, labels_train, labels_val = get_split_dataset_info(
join(dirname(__file__), 'txt_lists', 'Vanilla' + args.dataset,
'%s_train.txt' % dname), args.val_size)
train_dataset = JigsawDataset(name_train,
labels_train,
patches=patches,
img_transformer=img_transformer,
tile_transformer=tile_transformer,
jig_classes=args.jigsaw_n_classes,
bias_whole_image=args.bias_whole_image,
grid_size=args.grid_size)
if limit:
train_dataset = Subset(train_dataset, limit)
datasets.append(train_dataset)
if args.jig_only:
val_datasets.append(
JigsawDataset(name_val,
labels_val,
patches=patches,
img_transformer=img_transformer,
tile_transformer=tile_transformer,
jig_classes=args.jigsaw_n_classes,
bias_whole_image=args.bias_whole_image,
grid_size=args.grid_size))
else:
val_datasets.append(
JigsawTestDataset(name_val,
labels_val,
img_transformer=get_val_transformer(args),
patches=patches,
jig_classes=args.jigsaw_n_classes))
#val_datasets.append(JigsawTestDataset(name_val, labels_val, img_transformer=get_val_transformer(args),
# patches=patches, jig_classes=args.jigsaw_n_classes))
dataset = ConcatDataset(datasets)
val_dataset = ConcatDataset(val_datasets)
loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True,
drop_last=False)
return loader, val_loader
