1 files changed, 74 insertions, 0 deletions
diff --git a/src/text_recognizer/networks/vqvae/vqvae.py b/src/text_recognizer/networks/vqvae/vqvae.py
new file mode 100644
index 0000000..50448b4
--- /dev/null
+++ b/src/text_recognizer/networks/vqvae/vqvae.py
@@ -0,0 +1,74 @@
+"""The VQ-VAE."""
+
+from typing import List, Optional, Tuple, Type
+
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.vqvae import Decoder, Encoder
+
+
+class VQVAE(nn.Module):
+    """Vector Quantized Variational AutoEncoder."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        channels: List[int],
+        kernel_sizes: List[int],
+        strides: List[int],
+        num_residual_layers: int,
+        embedding_dim: int,
+        num_embeddings: int,
+        upsampling: Optional[List[List[int]]] = None,
+        beta: float = 0.25,
+        activation: str = "leaky_relu",
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+
+        # configure encoder.
+        self.encoder = Encoder(
+            in_channels,
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            embedding_dim,
+            num_embeddings,
+            beta,
+            activation,
+            dropout_rate,
+        )
+
+        # Configure decoder.
+        channels.reverse()
+        kernel_sizes.reverse()
+        strides.reverse()
+        self.decoder = Decoder(
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            embedding_dim,
+            upsampling,
+            activation,
+            dropout_rate,
+        )
+
+    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
+        """Encodes input to a latent code."""
+        return self.encoder(x)
+
+    def decode(self, z_q: Tensor) -> Tensor:
+        """Reconstructs input from latent codes."""
+        return self.decoder(z_q)
+
+    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
+        """Compresses and decompresses input."""
+        if len(x.shape) < 4:
+            x = x[(None,) * (4 - len(x.shape))]
+        z_q, vq_loss = self.encode(x)
+        x_reconstruction = self.decode(z_q)
+        return x_reconstruction, vq_loss