Add VQGAN and loss function

1 files changed, 58 insertions, 0 deletions

diff --git a/text_recognizer/criterions/n_layer_discriminator.py b/text_recognizer/criterions/n_layer_discriminator.py
new file mode 100644
index 0000000..e5f8449
--- /dev/null
+++ b/text_recognizer/criterions/n_layer_discriminator.py
@@ -0,0 +1,58 @@
+"""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.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)