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