Add the example of super_resolution#2885
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Hey @vfdev-5 , thanks for assigning the issue and since I had my mid-terms I could not work on this earlier so I apologise for the delay |
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Thanks for the PR @guptaaryan16
I left few comments.
Can you share execution logs, how much time it takes to run one epoch ?
Were you be able to train a simple SR model and share inference example ?
examples/super_resolution/dataset.py
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| def __getitem__(self, index): | ||
| input = load_img(self.image_filenames[index]) | ||
| target = input.copy() |
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Seems like in this application we are using input image as target, so we can replace BSD data by another built-in torchvision dataset instead of manually code how to download/extract the data
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Yeah I think it will be better (it was implemented this way in pytorch/examples)
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Although it is working well as shown in Colab notebook
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I think this colab notebook will be able to cover this example |
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A lot of things covered in this example were directly present in |
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@guptaaryan16 code in pytorch/examples seems a bit outdated and rather bulky. For ignite, I think it would worth to provide up to date code and with interesting outcome. First question about the results from the colab, how super-resolved image differs from an image upsampled with bicubic mode ? Maybe, it worth to include PSNR for bicubic upsampled image for reference when doing validation stage. |
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Hey @vfdev-5 I have made suggested changes in the colab notebook. Also I was exploring the link https://github.com/Coloquinte/torchSR and I think it will be better to adapt a train script from here rather than |
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Hey @vfdev-5 I have made all suggested changes including adding the torchvision dataset. Please review |
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Thanks for the update @guptaaryan16
I left few comments in the code. I think we can keep this model and the training script but change the training dataset and use additionally CIFAR10 for inference only.
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Hey @vfdev-5 please review |
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Thanks for the updates @guptaaryan16
I left few more comments concerning the code.
Please try to train a model and share training logs and run super_resolve script on a cifar10 image then compare it with the same image upsampled with bicubic interp mode.
examples/super_resolution/main.py
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| dim = self.crop_size // self.scale_factor | ||
| lr_image = resize(lr_image, [dim, dim]) |
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| dim = self.crop_size // self.scale_factor | |
| lr_image = resize(lr_image, [dim, dim]) | |
| size = self.crop_size // self.scale_factor | |
| lr_image = resize(lr_image, [size, size]) |
examples/super_resolution/main.py
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| training_data_loader = DataLoader(dataset=trainset_sr, num_workers=opt.threads, batch_size=opt.batch_size, shuffle=True) | ||
| testing_data_loader = DataLoader( | ||
| dataset=testset_sr, num_workers=opt.threads, batch_size=opt.test_batch_size, shuffle=False |
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| dataset=testset_sr, num_workers=opt.threads, batch_size=opt.test_batch_size, shuffle=False | |
| dataset=testset_sr, num_workers=opt.threads, batch_size=opt.test_batch_size |
| model = model.cuda() | ||
| input = input.cuda() | ||
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| out = model(input) |
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| out = model(input) | |
| model.eval() | |
| with torch.no_grad(): | |
| out = model(input) |
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Hey @vfdev-5 I have updated the colab link with the necessary training logs and updates along with the cifar-10 example |
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LGTM, thanks @guptaaryan16
I approve this PR. Next step would be to integrate it into the CI and use other ignite tools like progress bar to show sort of best practices
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Related to #2878
Description: I have added the example for the super resolution in the
PyTorch/examplesCheck list: