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Working with BYOL in DLAS
BYOL is a technique for pretraining an arbitrary image processing neural network. It is built upon previous self-supervised architectures like SimCLR.
BYOL in DLAS is adapted from an implementation written by lucidrains. It is implemented via two wrappers:
- A Dataset wrapper that augments the LQ and HQ inputs from a typical DLAS dataset. Since differentiable augmentations don't actually matter for BYOL, it makes more sense (to me) to do this on the CPU at the dataset layer, so your GPU can focus on processing gradients.
- A model wrapper that attaches a small MLP to the end of your input network to produce a fixed size latent. This latent is used to produce the BYOL loss which trains the master weights from your network.
Thanks to the excellent implementation from lucidrains, this wrapping process makes training your network on unsupervised datasets extremely easy.
Note: My intent is to adapt BYOL for use on structured models - e.g. models that do not collapse the latent into a flat map. Stay tuned for that..
Training BYOL
In this directory, you will find a sample training config for training BYOL on DIV2K. You will
likely want to insert your own model architecture first. Exchange out spinenet for your
model architecture and change the hidden_layer parameter to a layer from your network
that you want the BYOL model wrapper to hook into.
hint: Your network architecture (including layer names) is printed out when running train.py against your network.
Run the trainer by:
python train.py -opt train_div2k_byol.yml