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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data	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
data_scripts	Some random fixes/adjustments	2020-04-22 00:38:53 -06:00
metrics
models	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
options	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
scripts
temp	Add colab option	2020-05-02 17:47:25 -06:00
utils
requirements.txt
run_scripts.sh
test_Vid4_REDS4_with_GT_DUF.py
test_Vid4_REDS4_with_GT_TOF.py
test_Vid4_REDS4_with_GT.py
test.py	Support inference across batches, support inference on cpu, checkpoint	2020-05-04 08:48:25 -06:00
train.py	Fixup upconv for the next attempt!	2020-05-01 19:56:14 -06:00