James Betker 44b89330c2 Support inference across batches, support inference on cpu, checkpoint ... This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator.		2020-05-04 08:48:25 -06:00
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archs	Fixup upconv for the next attempt!	2020-05-01 19:56:14 -06:00
__init__.py	Implement downsample GAN	2020-04-24 00:00:46 -06:00
base_model.py	Enable AMP optimizations & write sample train images to folder.	2020-04-21 16:28:06 -06:00
loss.py	mmsr	2019-08-23 21:42:47 +08:00
lr_scheduler.py	mmsr	2019-08-23 21:42:47 +08:00
networks.py	Turn off EVDR (so we dont need the weird convs)	2020-05-02 17:47:14 -06:00
SR_model.py	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
SRGAN_model.py	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
Video_base_model.py	mmsr	2019-08-23 21:42:47 +08:00