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.RandomHorizontalFlip(), transforms.Resize(256), transforms.RandomCrop(256), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) transform_seg = transforms.Compose( [ transforms.ToPILImage(), transforms.Resize((512,512)), transforms.ToTensor(), 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,