Make lrdvae use quantized mode in eval()

codes/models/gpt_voice/lucidrains_dvae.py

@@ -158,6 +158,9 @@ class DiscreteVAE(nn.Module):
         images = self.decoder(image_embeds)
         return images
 
+    # Note: This module is not meant to be run in forward() except while training. It has special logic which performs
+    # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
+    # more lossy (but useful for determining network performance).
     def forward(
         self,
         img
@@ -166,7 +169,12 @@ class DiscreteVAE(nn.Module):
         logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
         sampled, commitment_loss, codes = self.codebook(logits)
         sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
+
+        if self.training:
             out = self.decoder(sampled)
+        else:
+            # This is non-differentiable, but gives a better idea of how the network is actually performing.
+            out = self.decode(codes)
 
         # reconstruction loss
         recon_loss = self.loss_fn(img, out)
@@ -195,5 +203,6 @@ if __name__ == '__main__':
     #o=v(torch.randn(1,3,256,256))
     #print(o.shape)
     v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=2048, hidden_dim=256)
+    v.eval()
     o=v(torch.randn(1,1,256))
     print(o[-1].shape)