8ab595e427
After doing some thinking and reading on the subject, it occurred to me that I was treating the generator like a discriminator by focusing the network complexity at the feature levels. It makes far more sense to process each conv level equally for the generator, hence the FlatProcessorNet in this commit. This network borrows some of the residual pass-through logic from RRDB which makes the gradient path exceptionally short for pretty much all model parameters and can be trained in O1 optimization mode without overflows again. |
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| .. | ||
| archs | ||
| __init__.py | ||
| base_model.py | ||
| loss.py | ||
| lr_scheduler.py | ||
| networks.py | ||
| SR_model.py | ||
| SRGAN_model.py | ||
| Video_base_model.py | ||