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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__init__.py	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
data_sampler.py
Downsample_dataset.py	Change downsample_dataset to do no image modification	2020-04-28 11:50:04 -06:00
LQ_dataset.py
LQGT_dataset.py	Add doCrop into LQGT	2020-05-02 17:46:30 -06:00
REDS_dataset.py	Change GT_size to target_size	2020-04-22 00:37:41 -06:00
util.py	Print error when image read fails	2020-04-23 23:59:32 -06:00
video_test_dataset.py
Vimeo90K_dataset.py	Change GT_size to target_size	2020-04-22 00:37:41 -06:00