added VICReg losses#12
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zqevans
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May 16, 2023
| # These two losses offer a sort of "repulsion" regularization, across the batch dimension, to keep the | ||
| # space from collapse. Intended to be connected to the latents in the middle of the autoencoder.. | ||
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| def vicreg_var_loss(z, gamma=1, eps=1e-4): |
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These need to made into instantiable "loss modules" like the ones above. Also please add a test for them.
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@zqevans take a look at the new version; not sure I wrote the module class correctly. |
zqevans
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May 17, 2023
| def forward(self, inputs: TensorDict) -> TensorDict: | ||
| z = inputs['latent'] | ||
| # writing with self.... "hooks" to enable extraction of individual losses for logging | ||
| self.var_loss = vicreg_var_loss(inputs, gamma=self.gamma, eps=self.eps) |
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Shouldn't store values in instance variables like this, doesn't make sense semantically.
If you want to return the individual losses, you can do so in the inputs.update() call on line 217.
zqevans
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May 17, 2023
| inputs = accumulate_value( | ||
| inputs, | ||
| { | ||
| "vicreg_loss": self.weight * loss |
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Should be "generator_loss". This line adds the vic_reg loss to the overall accumulated loss while the inputs.update() line below is used to return the individual named losses.
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This adds a few very simple functions that implement VICReg.
If the style of writing these are not to your liking, let me know how I can be more compliant with the style
-- e.g. I didn't find it necessary to create nn.Module Classes for these since they are so simple, but I could so.