Shall I apply transforms.Normalize several times when using two model sequentially?

Dear PyTorch fellows,

To give a little bit of context, I’m working with Generative Models (Generative Adversarial Networks- GAN) achieving image to image translation. In my case input and output are images.

To help the model converge, I’m using a semantic segmentation model on the input and the output of the GAN and compute a cross Entropy loss on specific region so that the semantic of the image is kept during the translation.

My question is the following: since my GAN requires a different normalization from my semantic segmentation model:

transform_GAN = transforms.Compose([
                            transforms.Normalize((0.5, 0.5, 0.5),
                                                 (0.5, 0.5, 0.5))

    transform_seg = transforms.Compose(
                 transforms.Normalize((0.485, 0.456, 0.406),\
                                      (0.229, 0.224, 0.225))

Shall I denormalize the output of the first one before applying the transformation of the second ?

Thank you for your help,


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I have not tried 2 different models, but in the similar context, I tried to normalize my images twice sequentially and then unnormalize them exactly in the reverse order but it did not give me the result I was looking for, So I decided to use normalization->unnormalization twice.
But I think you do not need to convert back tensors to PIL images then do some preprocessing again and convert them to tensors. You can do those process without converting to PIL even if have not a class like Resize for tensors, because it would be easy to implement.

Update: thank you Nikronic for your answer, I went with the un-normalization and it leads to the good expected results.

Concerning the resize, I used torch.nn.functional.interpolate

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