James Betker
8e0e883050
Mods to support labeled datasets & random augs for those datasets
2020-12-15 17:15:56 -07:00
James Betker
26ceca68c0
BYOL with structure!
2020-12-10 15:07:35 -07:00
James Betker
66cbae8731
Add random_dataset for testing
2020-12-09 14:55:05 -07:00
James Betker
97ff25a086
BYOL!
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Man, is there anything ExtensibleTrainer can't train? :)
2020-12-08 13:07:53 -07:00
James Betker
06d1c62c5a
iGPT support!
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Sweeeeet
2020-12-03 15:32:21 -07:00
James Betker
c963e5f2ce
Add ImageFolderDataset
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This one has been a long time coming.. How does torch not have something like this?
2020-12-01 17:45:37 -07:00
James Betker
2d3449d7a5
stylegan2 in ml art school!
2020-11-12 15:42:05 -07:00
James Betker
ff58c6484a
Fixes to unified chunk datasets to support stereoscopic training
2020-10-26 11:12:22 -06:00
James Betker
8e5b6682bf
Add PairedFrameDataset
2020-10-23 20:58:07 -06:00
James Betker
9ead2c0a08
Multiscale training in!
2020-10-17 22:54:12 -06:00
James Betker
24792bdb4f
Codebase cleanup
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Removed a lot of legacy stuff I have no intent on using again.
Plan is to shape this repo into something more extensible (get it? hah!)
2020-10-13 20:56:39 -06:00
James Betker
57814f18cf
More features for multi-frame-dataset
2020-09-28 14:26:15 -06:00
James Betker
254cb1e915
More dataset integration work
2020-09-25 22:19:38 -06:00
James Betker
5189b11dac
Add combined dataset for training across multiple datasets
2020-09-11 08:44:06 -06:00
James Betker
6226b52130
Pin memory in dataloaders by default
2020-09-04 15:30:46 -06:00
James Betker
a65b07607c
Reference network
2020-08-25 11:56:59 -06:00
James Betker
67139602f5
Test modifications
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Allows bifurcating large images put into the test pipeline
This code is fixed and not dynamic. Needs some fixes.
2020-05-19 09:37:58 -06:00
James Betker
585b05e66b
Cap test workers at 10
2020-05-13 09:20:45 -06:00
James Betker
f994466289
Initialize test dataloader with a worker count proportional to the batch size.
2020-05-10 10:49:37 -06:00
James Betker
44b89330c2
Support inference across batches, support inference on cpu, checkpoint
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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
James Betker
3781ea725c
Add Resnet Discriminator with BN
2020-04-29 20:51:57 -06:00
James Betker
e98d92fc77
Allow test to operate on batches
2020-04-23 23:59:09 -06:00
James Betker
12d92dc443
Add GTLQ dataset
2020-04-22 00:40:38 -06:00
James Betker
f4b33b0531
Some random fixes/adjustments
2020-04-22 00:38:53 -06:00
XintaoWang
037933ba66
mmsr
2019-08-23 21:42:47 +08:00
