Closed Morefre closed 1 year ago
Hi @Janspiry ,
In continue to my comment @ErezYosef https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement/issues/14#issuecomment-918394583
I think its better to use the dedicated torchvision functions instead of implement them.
My Suggested implementation:
totensor = torchvision.transforms.ToTensor() hflip = torchvision.transforms.RandomHorizontalFlip() imgs = [totensor(img) for img in img_list] if split == 'train': imgs = torch.stack(imgs, 0) imgs = hflip(imgs) imgs = torch.unbind(imgs, dim=0) ret_img = [img * (min_max[1] - min_max[0]) + min_max[0] for img in imgs]
Originally posted by @ErezYosef in https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement/issues/14#issuecomment-918503497
This code is incorrect because it randomly flips every image independently, as shown in the figures below:
In continue to my comment @ErezYosef https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement/issues/14#issuecomment-918394583
My Suggested implementation:
Originally posted by @ErezYosef in https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement/issues/14#issuecomment-918503497
This code is incorrect because it randomly flips every image independently, as shown in the figures below: