Remove VQVAE stuff, did not work...

1 files changed, 0 insertions, 59 deletions

diff --git a/text_recognizer/criterion/n_layer_discriminator.py b/text_recognizer/criterion/n_layer_discriminator.py
deleted file mode 100644
index a9f47f9..0000000
--- a/text_recognizer/criterion/n_layer_discriminator.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Pix2pix discriminator loss."""
-from torch import nn, Tensor
-
-from text_recognizer.networks.vqvae.norm import Normalize
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator loss in Pix2Pix."""
-
-    def __init__(
-        self, in_channels: int = 1, num_channels: int = 32, num_layers: int = 3
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.num_channels = num_channels
-        self.num_layers = num_layers
-        self.discriminator = self._build_discriminator()
-
-    def _build_discriminator(self) -> nn.Sequential:
-        """Builds discriminator."""
-        discriminator = [
-            nn.Sigmoid(),
-            nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.num_channels,
-                kernel_size=4,
-                stride=2,
-                padding=1,
-            ),
-            nn.Mish(inplace=True),
-        ]
-        in_channels = self.num_channels
-        for n in range(1, self.num_layers):
-            discriminator += [
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=in_channels * n,
-                    kernel_size=4,
-                    stride=2,
-                    padding=1,
-                ),
-                # Normalize(num_channels=in_channels * n),
-                nn.Mish(inplace=True),
-            ]
-            in_channels *= n
-
-        discriminator += [
-            nn.Conv2d(
-                in_channels=self.num_channels * (self.num_layers - 1),
-                out_channels=1,
-                kernel_size=4,
-                padding=1,
-            )
-        ]
-        return nn.Sequential(*discriminator)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass through discriminator."""
-        return self.discriminator(x)