Refactor basic GAN tutorial

[jhauret]

Before submitting

• Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
• Did you make sure to update the docs?
• Did you write any new necessary tests?

What does this PR do?

Fixes #306

PR review

Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.

Did you have fun?

I had ! Glad to contribute to the best Pytorch wrapper 🙃

Notes

• The quality of the generated samples remains low, but I don't have the time to tune the architecture more. Also, applying tricks to reach Nash equilibrium like label smoothing could make the example less understandable, so I left it as it is.

• This PR was motivated in the case of some heavy generator and discriminator that we don't want to call multiple times. I removed the repeated calls to the generator (which changes the optimization problem a bit, because the discriminator is trained to detect the same sample that the generator just generated) BUT I did not remove the repeated calls to the discriminator. In fact, trying self.manual_backward(g_loss, retain_graph=True) caused an error when backpropagating on the discriminator loss because the generator weights changed (RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation). If anyone knows how to remove the two calls to the discriminator, I'm curious!

• Logging is fixed and on_validation_epoch_end is now called

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 70%. Comparing base (d85a312) to head (c39a740).

Additional details and impacted files

@@         Coverage Diff         @@
##           main   #357   +/-   ##
===================================
  Coverage    70%    70%           
===================================
  Files         2      2           
  Lines       441    441           
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  Hits        307    307           
  Misses      134    134           

[Borda]

Thank you and @jhauret if you would like to continue with the refactoring any time in the future, all help is welcome! 🐰