def get_transform(args):
if args.transform == 'resize':
rgb_transforms = Compose(
[scale_rgb_to_256_factor,
ToTensor(),
AddNoise((3, 256, 1024))])
gt_transforms = Compose(
[scale_gt_to_256_factor,
ToTensor(), to_longTensor_gt])
elif args.transform == 'crop':
rgb_transforms = Compose(
[center_crop_to_256_factor,
ToTensor(),
AddNoise((3, 256, 1024))])
gt_transforms = Compose(
[center_crop_to_256_factor,
ToTensor(), to_longTensor_gt])
else:
raise ValueError('the value (%s) for --transform is not valid.' %
args.transform)
torchvision_transforms = []
if args.phase == 'train':
grayscale = Grayscale(num_output_channels=3)
grayscale.probability = 0.075
torchvision_transforms.append(grayscale)
colorJitter = ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.2)
colorJitter.probability = 0.1
torchvision_transforms.append(colorJitter)
return rgb_transforms, torchvision_transforms, gt_transforms
elif args.phase == 'test':
return rgb_transforms
def get_transform(args):
if args.transform == 'resize':
rgb_transforms = Compose(
[Resize((128, 512)),
ToTensor(),
AddNoise((3, 128, 512))])
if args.phase == 'train':
gt_transforms = Compose([
Resize((128, 512), interpolation=PIL.Image.NEAREST),
ToTensor(), to_longTensor_gt
])
depth_transforms = Compose(
[Resize((128, 512)),
ToTensor(), to_floatTensor_depth])
else:
gt_transforms = []
depth_transforms = []
elif args.transform == 'crop':
rgb_transforms = Compose(
[center_crop_to_256_factor,
ToTensor(),
AddNoise((3, 128, 512))])
if args.phase == 'train':
gt_transforms = Compose(
[center_crop_to_256_factor,
ToTensor(), to_longTensor_gt])
depth_transforms = Compose(
[center_crop_to_256_factor,
ToTensor(), to_floatTensor_depth])
else:
gt_transforms = []
depth_transforms = []
else:
if args.phase == 'train':
raise ValueError('the value (%s) for --transform is not valid.' %
args.transform)
torchvision_transforms = []
if args.phase == 'train':
grayscale = Grayscale(num_output_channels=3)
grayscale.probability = 0.075
torchvision_transforms.append(grayscale)
colorJitter = ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.2)
colorJitter.probability = 0.1
torchvision_transforms.append(colorJitter)
return rgb_transforms, torchvision_transforms, depth_transforms, gt_transforms
elif args.phase == 'test':
return rgb_transforms
