Load the same batch with different transformation in Dataloader

I need to load a batch of 16 images and process it (Step 1), and then I need to apply a different transformation to each of these images and then process it (Step 2). I need to iterate Step 2 few times. How to do it in PyTorch?

I think you could apply your workflow as described. Load the images and apply the transformations in a loop for each image using the desired configuration.

Currently I am trying like the following:

def mytransform(self, x):
     for i in x.shape[0]:
          x[i,:,:,:] = self.transform(x[i,:,:,:])
     return x

def train(self):
    for (x,y) in loader:
         xt = self.mytransform(x)

but is there any efficient way to do it?

fixmatch uses strong and weak augmentation on same batch .
it might help you to find a solution.

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