James Betker
6b45b35447
Allow multi_step_lr_scheduler to load a new LR schedule when restoring state
2020-07-31 11:21:11 -06:00
James Betker
e37726f302
Add feature_model for training custom feature nets
2020-07-31 11:20:39 -06:00
James Betker
7629cb0e61
Add FDPL Loss
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New loss type that can replace PSNR loss. Works against the frequency domain
and focuses on frequency features loss during hr->lr conversion.
2020-07-30 20:47:57 -06:00
James Betker
85ee64b8d9
Turn down feadisc intensity
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Honestly - this feature is probably going to be removed soon, so backwards
compatibility is not a huge deal anymore.
2020-07-27 15:28:55 -06:00
James Betker
ebb199e884
Get rid of safety valve (probably being encountered in val)
2020-07-26 22:51:59 -06:00
James Betker
d09ed4e5f7
Misc fixes
2020-07-26 22:44:24 -06:00
James Betker
c54784ae9e
Fix feature disc log item error
2020-07-26 22:25:59 -06:00
James Betker
9a8f227501
Allow separate dataset to pushed in for GAN-only training
2020-07-26 21:44:45 -06:00
James Betker
b06e1784e1
Fix SRG4 & switch disc
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"fix". hehe.
2020-07-25 17:16:54 -06:00
James Betker
e6e91a1d75
Add SRG4
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Back to the idea that maybe what we need is a hybrid
approach between pure switches and RDB.
2020-07-24 20:32:49 -06:00
James Betker
3320ad685f
Fix mega_batch_factor not set for test
2020-07-24 12:26:44 -06:00
James Betker
c50cce2a62
Add an abstract, configurabler weight scheduling class and apply it to the feature weight
2020-07-23 17:03:54 -06:00
James Betker
9ccf771629
Fix feature validation, wrong device
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Only shows up in distributed training for some reason.
2020-07-23 10:16:34 -06:00
James Betker
bba283776c
Enable find_unused_parameters for DistributedDataParallel
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attention_norm has some parameters which are not used to compute grad,
which is causing failures in the distributed case.
2020-07-23 09:08:13 -06:00
James Betker
dbf6147504
Add switched discriminator
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The logic is that the discriminator may be incapable of providing a truly
targeted loss for all image regions since it has to be too generic
(basically the same argument for the switched generator). So add some
switches in! See how it works!
2020-07-22 20:52:59 -06:00
James Betker
106b8da315
Assert that temperature is set properly in eval mode.
2020-07-22 20:50:59 -06:00
James Betker
c74b9ee2e4
Add a way to disable grad on portions of the generator graph to save memory
2020-07-22 11:40:42 -06:00
James Betker
e3adafbeac
Add convert_model.py and a hacky way to add extra layers to a model
2020-07-22 11:39:45 -06:00
James Betker
7f7e17e291
Update feature discriminator further
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Move the feature/disc losses closer and add a feature computation layer.
2020-07-20 20:54:45 -06:00
James Betker
46aa776fbb
Allow feature discriminator unet to only output closest layer to feature output
2020-07-19 19:05:08 -06:00
James Betker
8a9f215653
Huge set of mods to support progressive generator growth
2020-07-18 14:18:48 -06:00
James Betker
47a525241f
Make attention norm optional
2020-07-18 07:24:02 -06:00
James Betker
ad97a6a18a
Progressive SRG first check-in
2020-07-18 07:23:26 -06:00
James Betker
b08b1cad45
Fix feature decay
2020-07-16 23:27:06 -06:00
James Betker
3e7a83896b
Fix pixgan debugging issues
2020-07-16 11:45:19 -06:00
James Betker
a1bff64d1a
More fixes
2020-07-16 10:48:48 -06:00
James Betker
240f254263
More loss fixes
2020-07-16 10:45:50 -06:00
James Betker
6cfa67d831
Fix featuredisc broadcast error
2020-07-16 10:18:30 -06:00
James Betker
8d061a2687
Add u-net discriminator with feature output
2020-07-16 10:10:09 -06:00
James Betker
0c4c388e15
Remove dualoutputsrg
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Good idea, didn't pan out.
2020-07-16 10:09:24 -06:00
James Betker
4bcc409fc7
Fix loadSRG2 typo
2020-07-14 10:20:53 -06:00
James Betker
1e4083a35b
Apply temperature mods to all SRG models
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(Honestly this needs to be base classed at this point)
2020-07-14 10:19:35 -06:00
James Betker
7659bd6818
Fix temperature equation
2020-07-14 10:17:14 -06:00
James Betker
853468ef82
Allow legacy state_dicts in srg2
2020-07-14 10:03:45 -06:00
James Betker
1b1431133b
Add DualOutputSRG
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Also removes the old multi-return mechanism that Generators support.
Also fixes AttentionNorm.
2020-07-14 09:28:24 -06:00
James Betker
a2285ff2ee
Scale anorm by transform count
2020-07-13 08:49:09 -06:00
James Betker
dd0bbd9a7c
Enable AttentionNorm on SRG2
2020-07-13 08:38:17 -06:00
James Betker
4c0f770f2a
Fix inverted temperature curve bug
2020-07-12 11:02:50 -06:00
James Betker
14d23b9d20
Fixes, do fake swaps less often in pixgan discriminator
2020-07-11 21:22:11 -06:00
James Betker
ba6187859a
err5
2020-07-10 23:02:56 -06:00
James Betker
902527dfaa
err4
2020-07-10 23:00:21 -06:00
James Betker
020b3361fa
err3
2020-07-10 22:57:34 -06:00
James Betker
b3a2c21250
err2
2020-07-10 22:52:02 -06:00
James Betker
716433db1f
err1
2020-07-10 22:50:56 -06:00
James Betker
0b7193392f
Implement unet disc
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The latest discriminator architecture was already pretty much a unet. This
one makes that official and uses shared layers. It also upsamples one additional
time and throws out the lowest upsampling result.
The intent is to delete the old vgg pixdisc, but I'll keep it around for a bit since
I'm still trying out a few models with it.
2020-07-10 16:24:42 -06:00
James Betker
812c684f7d
Update pixgan swap algorithm
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- Swap multiple blocks in the image instead of just one. The discriminator was clearly
learning that most blocks have one region that needs to be fixed.
- Relax block size constraints. This was in place to gaurantee that the discriminator
signal was clean. Instead, just downsample the "loss image" with bilinear interpolation.
The result is noisier, but this is actually probably healthy for the discriminator.
2020-07-10 15:56:14 -06:00
James Betker
33ca3832e1
Move ExpansionBlock to arch_util
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Also makes all processing blocks have a conformant signature.
Alters ExpansionBlock to perform a processing conv on the passthrough
before the conjoin operation - this will break backwards compatibilty with SRG2.
2020-07-10 15:53:41 -06:00
James Betker
5e8b52f34c
Misc changes
2020-07-10 09:45:48 -06:00
James Betker
5f2c722a10
SRG2 revival
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Big update to SRG2 architecture to pull in a lot of things that have been learned:
- Use group norm instead of batch norm
- Initialize the weights on the transformations low like is done in RRDB rather than using the scalar. Models live or die by their early stages, and this ones early stage is pretty weak
- Transform multiplexer to use u-net like architecture.
- Just use one set of configuration variables instead of a list - flat networks performed fine in this regard.
2020-07-09 17:34:51 -06:00
James Betker
12da993da8
More fixes...
2020-07-08 22:07:09 -06:00