James Betker 8ab595e427 Add FlatProcessorNet ... 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.		2020-04-28 11:49:21 -06:00
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data	Implement downsample GAN	2020-04-24 00:00:46 -06:00
data_scripts	Some random fixes/adjustments	2020-04-22 00:38:53 -06:00
metrics	mmsr	2019-08-23 21:42:47 +08:00
models	Add FlatProcessorNet	2020-04-28 11:49:21 -06:00
options	Enable HighToLowResNet to do a 1:1 transform	2020-04-25 21:36:32 -06:00
scripts	mmsr	2019-08-23 21:42:47 +08:00
utils	mmsr	2019-08-23 21:42:47 +08:00
requirements.txt	Create requirements.txt	2019-11-24 07:48:52 +00:00
run_scripts.sh	mmsr	2019-08-23 21:42:47 +08:00
test_Vid4_REDS4_with_GT_DUF.py	mmsr	2019-08-23 21:42:47 +08:00
test_Vid4_REDS4_with_GT_TOF.py	mmsr	2019-08-23 21:42:47 +08:00
test_Vid4_REDS4_with_GT.py	mmsr	2019-08-23 21:42:47 +08:00
test.py	Config changes for discriminator advantage run	2020-04-25 11:24:28 -06:00
train.py	Config changes for discriminator advantage run	2020-04-25 11:24:28 -06:00