1 files changed, 0 insertions, 74 deletions
diff --git a/src/text_recognizer/networks/vqvae/vqvae.py b/src/text_recognizer/networks/vqvae/vqvae.py
deleted file mode 100644
index 50448b4..0000000
--- a/src/text_recognizer/networks/vqvae/vqvae.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""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