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Author SHA1 Message Date
James Betker
7b60d9e0d8 Fix? cosine loss 2020-09-22 18:18:35 -06:00
James Betker
2e18c4c22d Add CosineEmbeddingLoss to F 2020-09-22 17:10:29 -06:00
James Betker
f40beb5460 Add 'before' and 'after' defs to injections, steps and optimizers 2020-09-22 17:03:22 -06:00
James Betker
419f77ec19 Some new backbones 2020-09-21 12:36:49 -06:00
James Betker
9429544a60 Spinenet: implementation without 4x downsampling right off the bat 2020-09-21 12:36:30 -06:00
James Betker
384e3d54cc Extract images into jpg, have a multiplier & size threshold 2020-09-21 12:36:03 -06:00
James Betker
bde35ced47 Fix recursive detach 2020-09-20 19:08:13 -06:00
James Betker
53a5657850 Fix SSGR 2020-09-20 19:07:15 -06:00
James Betker
17c569ea62 Add geometric loss 2020-09-20 16:24:23 -06:00
James Betker
17dd99b29b Fix bug with discriminator noise addition 
It wasn't using the scale and was applying the noise to the
underlying state variable.
 2020-09-20 12:00:27 -06:00
James Betker
dab8ab8a8f Offer option to configure the size of the normal distribution that the target size is drawn from 2020-09-20 11:59:31 -06:00
James Betker
3138f98fbc Allow discriminator noise to be injected at the loss level, cleans up configs 2020-09-19 21:47:52 -06:00
James Betker
e9a39bfa14 Recursively detach all outputs, even if they are nested in data structures 2020-09-19 21:47:34 -06:00
James Betker
fe82785ba5 Add some new architectures to ssg 2020-09-19 21:47:10 -06:00
James Betker
b83f097082 Get rid of get_debug_values from RRDB, rectify outputs 2020-09-19 21:46:36 -06:00
James Betker
e0bd68efda Add ImageFlowInjector 2020-09-19 10:07:00 -06:00
James Betker
e2a146abc7 Add in experiments hook 2020-09-19 10:05:25 -06:00
James Betker
4f75cf0f02 Revert "Full image dataset operates on lists" 
Going with an entirely new dataset instead..

This reverts commit 36ec32bf11.
 2020-09-18 09:50:43 -06:00
James Betker
36ec32bf11 Full image dataset operates on lists 2020-09-18 09:50:26 -06:00
James Betker
3cb2a9a9d3 New dataset, initial work 2020-09-18 09:49:13 -06:00
James Betker
9a17ade550 Some convenience adjustments to ExtensibleTrainer 2020-09-17 21:05:32 -06:00
James Betker
57fc3f490c Add script for extracting image tiles with reference images 2020-09-17 13:30:51 -06:00
James Betker
9963b37200 Add a new script for loading a discriminator network and using it to filter images 2020-09-17 13:30:32 -06:00
James Betker
f5cd23e2d5 Further patch size adjustments 2020-09-16 16:50:35 -06:00
James Betker
723754c133 Update attention debugger outputting for SSG 2020-09-16 13:09:46 -06:00
James Betker
0b047e5f80 Increase scale of the patch selector random distribution 
This will cause larger slices of an image to appear more frequently,
increasing the difficulty of the generator.
 2020-09-16 08:27:42 -06:00
James Betker
f211575e9d Save models before validation 
Validation often fails with OOM, wasting hours of training time.
Save models first.
 2020-09-16 08:17:17 -06:00
James Betker
0918430572 SSG network 
This branches off of SPSR. It is identical but substantially reduced
in complexity. It's intended to be my long term working arch.
 2020-09-15 20:59:24 -06:00
James Betker
c833bd1eac Misc changes 2020-09-15 20:57:59 -06:00
James Betker
6deab85b9b Add BackboneEncoderNoRef 2020-09-15 16:55:38 -06:00
James Betker
d0321ca5de Don't load amp state dict if amp is disabled 2020-09-14 15:21:42 -06:00
James Betker
94deab2792 Fix error serving gt_fullsize_ref in full_image_dataset 2020-09-14 15:05:44 -06:00
James Betker
ccf8438001 SPSR5 
This is SPSR4, but the multiplexers have access to the output of the transformations
for making their decision.
 2020-09-13 20:10:24 -06:00
James Betker
5b85f891af Only log the name of the first network in the total_loss training set 2020-09-12 16:07:09 -06:00
James Betker
fb595e72a4 Supporting infrastructure in ExtensibleTrainer to train spsr4 
Need to be able to train 2 nets in one step: the backbone will be entirely separate
with its own optimizer (for an extremely low LR).

This functionality was already present, just not implemented correctly.
 2020-09-11 22:57:06 -06:00
James Betker
4e44bca611 SPSR4 
aka - return of the backbone! I'm tired of massively overparameterized generators
with pile-of-shit multiplexers. Let's give this another try..
 2020-09-11 22:55:37 -06:00
James Betker
19896abaea Clean up old SwitchedSpsr arch 
It didn't work anyways, so why not?
 2020-09-11 16:09:28 -06:00
James Betker
4c2ee66fe4 Fix video processor 2020-09-11 13:10:14 -06:00
James Betker
50ca17bb0a Feature mode -> back to LR fea 2020-09-11 13:09:55 -06:00
James Betker
1086f0476b Fix ref branch using fixed filters 2020-09-11 08:58:35 -06:00
James Betker
8c469b8286 Enable memory checkpointing 2020-09-11 08:44:29 -06:00
James Betker
5189b11dac Add combined dataset for training across multiple datasets 2020-09-11 08:44:06 -06:00
James Betker
313424d7b5 Add new referencing discriminator 
Also extend the way losses work so that you can pass
parameters into the discriminator from the config file
 2020-09-10 21:35:29 -06:00
James Betker
9e5aa166de Report the standard deviation of ref branches 
This patch also ups the contribution
 2020-09-10 16:34:41 -06:00
James Betker
668bfbff6d Back to best arch for spsr3 2020-09-10 14:58:14 -06:00
James Betker
992b0a8d98 spsr3 with conjoin stage as part of the switch 2020-09-10 09:11:37 -06:00
James Betker
e0fc5eb50c Temporary commit - noise 2020-09-09 17:12:52 -06:00
James Betker
00da69d450 Temporary commit - ref 2020-09-09 17:09:44 -06:00
James Betker
df59d6c99d More spsr3 mods 
- Most branches get their own noise vector now.
- First attention branch has the intended sole purpose of raw image processing
- Remove norms from joiner block
 2020-09-09 16:46:38 -06:00
James Betker
747ded2bf7 Fixes to the spsr3 
Some lessons learned:
- Biases are fairly important as a relief valve. They dont need to be everywhere, but
  most computationally heavy branches should have a bias.
- GroupNorm in SPSR is not a great idea. Since image gradients are represented
   in this model, normal means and standard deviations are not applicable. (imggrad
   has a high representation of 0).
- Don't fuck with the mainline of any generative model. As much as possible, all
   additions should be done through residual connections. Never pollute the mainline
   with reference data, do that in branches. It basically leaves the mode untrainable.
 2020-09-09 15:28:14 -06:00