Why transformations go into the dataset and not into the NN itself in Pytorch?
I am new to Pytorch and I am now following the tutorial on transforms. I see that the transformations are configured into the dataset object. I am wondering, however, why aren't they configured within the neural network itself. My naive point of view is that the transformations should be in any case the most external layers of the network, in the same way as the eye comes before the brain to transform light into signals for the brain, and you don't modify the world instead to adapt it to the brain.
So, is there any technical reason for putting the transformations in the dataset instead of the net? Is it a good/bad practice to put the transformations within my neural network instead? Why?

These are some of the reason that can explain why one would do this.

We would like to use the same NN code for training as well as testing / inference. Typically during inference, we don't want to do any transformation and hence one might want to keep it out of the network. However, you may argue that one can just simply use model.training flag to skip the transformation.
Most of the transformations happen on CPU. Doing transformations in dataset allows to easily use multi-processing and prefetching. The dataset code can prefetch the data, transform, and keep it ready to be fed into the NN in a separate thread. If instead, we do it inside the forward function, GPUs will idle during the transformations (as these happen on CPU), likely leading to a longer training time.

