1 files changed, 0 insertions, 85 deletions
diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
deleted file mode 100644
index 4761486..0000000
--- a/text_recognizer/networks/vqvae/encoder.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""CNN encoder for the VQ-VAE."""
-from typing import List, Tuple
-
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.norm import Normalize
-from text_recognizer.networks.vqvae.residual import Residual
-
-
-class Encoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        hidden_dim: int,
-        channels_multipliers: List[int],
-        dropout_rate: float,
-        activation: str = "mish",
-        use_norm: bool = False,
-        num_residuals: int = 4,
-        residual_channels: int = 32,
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_dim = hidden_dim
-        self.channels_multipliers = tuple(channels_multipliers)
-        self.activation = activation
-        self.dropout_rate = dropout_rate
-        self.use_norm = use_norm
-        self.num_residuals = num_residuals
-        self.residual_channels = residual_channels
-        self.encoder = self._build_compression_block()
-
-    def _build_compression_block(self) -> nn.Sequential:
-        """Builds encoder network."""
-        num_blocks = len(self.channels_multipliers)
-        channels_multipliers = (1,) + self.channels_multipliers
-        activation_fn = activation_function(self.activation)
-
-        encoder = [
-            nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.hidden_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-
-        for i in range(num_blocks):
-            in_channels = self.hidden_dim * channels_multipliers[i]
-            out_channels = self.hidden_dim * channels_multipliers[i + 1]
-            if self.use_norm:
-                encoder += [
-                    Normalize(num_channels=in_channels,),
-                ]
-            encoder += [
-                activation_fn,
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_size=4,
-                    stride=2,
-                    padding=1,
-                ),
-            ]
-
-        for _ in range(self.num_residuals):
-            encoder += [
-                Residual(
-                    in_channels=out_channels,
-                    residual_channels=self.residual_channels,
-                    use_norm=self.use_norm,
-                    activation=self.activation,
-                )
-            ]
-
-        return nn.Sequential(*encoder)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes input into a discrete representation."""
-        return self.encoder(x)