how to implement Feature level Cutmix? about cutmix-pytorch HOT 5 CLOSED

@EmmaW8
In our implementation, we put this cutmix operation into forward function of resnet.py model.
For example, Feature CutMix after layer 1 is,

            ...
            x = self.layer1(x)

            # Feature CutMix
            bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam)
            x[:,:,bbx1:bbx2,bby1:bby2] = x[rand_index,:,bbx1:bbx2,bby1:bby2]

            x = self.layer2(x)
            ...

From our experience, inplace=True option may cause the runtime error, so try with inplace=False.

Thanks!

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Thank you for your reply.
Do you know more about how to implement inplace=False.
Exactly, the runtime error is derived from the gradient calculation for the assignment operation.

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@EmmaW8
In my case, it was worked when I change nn.ReLU(inplace=True) to nn.ReLU(inplace=False).
I hope this would work. :)

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Hello, thanks for your great job!
I have a question about Feature Cutmix: When training with Feature-Level Cutmix, how is the label transformation? Keeping it the same with Image-Level Cutmix or no transformation on the label?

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@zzs1994

I have a question about Feature Cutmix: When training with Feature-Level Cutmix, how is the label transformation? Keeping it the same with Image-Level Cutmix or no transformation on the label?

We do it in the same way of image-level CutMix.

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