"""
print('[%s] Setting up log directories' % (datetime.datetime.now()))
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
if args.write_dir is not None:
if not os.path.exists(args.write_dir):
os.mkdir(args.write_dir)
os.mkdir(os.path.join(args.write_dir, 'segmentation_last_checkpoint'))
os.mkdir(os.path.join(args.write_dir, 'segmentation_best_checkpoint'))
"""
Load the data
"""
input_shape = (1, args.input_size[0], args.input_size[1])
# load data
print('[%s] Loading data' % (datetime.datetime.now()))
train_xtransform, train_ytransform, test_xtransform, test_ytransform = get_augmenters_2d(
augment_noise=(args.augment_noise == 1))
if args.data == 'epfl':
train = EPFLPixelTrainDataset(input_shape=input_shape,
transform=train_xtransform,
target_transform=train_ytransform,
n_samples=args.n_samples)
test = EPFLPixelTestDataset(input_shape=input_shape,
transform=test_xtransform,
target_transform=test_ytransform)
elif args.data == 'vnc':
train = VNCPixelTrainDataset(input_shape=input_shape,
transform=train_xtransform,
target_transform=train_ytransform,
n_samples=args.n_samples)
test = VNCPixelTestDataset(input_shape=input_shape,
transform=test_xtransform,
print('[%s] Setting up log directories' % (datetime.datetime.now()))
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
if args.write_dir is not None:
if not os.path.exists(args.write_dir):
os.mkdir(args.write_dir)
os.mkdir(os.path.join(args.write_dir, 'tar_segmentation_last'))
os.mkdir(os.path.join(args.write_dir, 'tar_segmentation_best'))
"""
Load the data
"""
input_shape = (1, args.input_size[0], args.input_size[1])
# load source
print('[%s] Loading data' % (datetime.datetime.now()))
# augmenters
src_train_xtransform, src_train_ytransform, src_test_xtransform, src_test_ytransform = get_augmenters_2d(
augment_noise=(args.augment_noise == 1))
tar_train_xtransform, _, tar_test_xtransform, tar_test_ytransform = get_augmenters_2d(
augment_noise=(args.augment_noise == 1))
# load data
src_train = StronglyLabeledVolumeDataset(args.src_data_train,
args.src_labels_train,
input_shape,
transform=src_train_xtransform,
target_transform=src_train_ytransform)
src_test = StronglyLabeledVolumeDataset(args.src_data_test,
args.src_labels_test,
input_shape,
transform=src_test_xtransform,
target_transform=src_test_ytransform)
tar_train = UnlabeledVolumeDataset(args.tar_data_train,
input_shape=input_shape,
