pytorch-tutorial VS BigGAN-PyTorch

I was going through this example of a LSTM language model on github (link).What it does in general is pretty clear to me. But I'm still struggling to understand what calling contiguous() does, which occurs several times in the code.

I tried to move straight to practicing implementing papers and trying to understand other people's codes but failed miserably. I feel like there was too much of a gap between the basic tutorial and being able to implement ideas into code....hence the question: Is there any resource/way to practice pytorch in general? I did find this and this, but I just wanted to hear what others have gone through to become better at PyTorch up to the point they can build stuff from their own ideas

The second link gives you the entire source code for training the model https://github.com/ajbrock/BigGAN-PyTorch/tree/master . Looks like BigGAN.py and BigGANdeep.py are the two files that define the architecture. Can you work with that?

BigGAN (https://github.com/ajbrock/BigGAN-PyTorch) - This is a PyTorch implementation of the BigGAN model for generating high-resolution images. It is trained on a large dataset and can generate a wide range of images, including photographs of animals, objects, and landscapes.

Tangentially, theoretically you can't use BN in the discriminator for WGAN-GP anyway (assuming that you're using the Gulrajani work) because it breaks the sample independence assumptions of the GP. If you have a relatively structured dataset (eg all faces, all cars, all giraffes, etc.) and no class conditioning, look into StyleGAN2-ADA for the best results. If you have a dataset with a lot of variation and a lot of classes try using the BigGAN repo.

BigGan και Deep Dream https://github.com/ajbrock/BigGAN-PyTorch https://github.com/google/deepdream