1 files changed, 0 insertions, 161 deletions
diff --git a/text_recognizer/networks/vqvae/pixelcnn.py b/text_recognizer/networks/vqvae/pixelcnn.py
deleted file mode 100644
index b9e6080..0000000
--- a/text_recognizer/networks/vqvae/pixelcnn.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""PixelCNN encoder and decoder.
-
-Same as in VQGAN among other. Hopefully, better reconstructions...
-
-TODO: Add num of residual layers.
-"""
-from typing import Sequence
-
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.vqvae.attention import Attention
-from text_recognizer.networks.vqvae.norm import Normalize
-from text_recognizer.networks.vqvae.residual import Residual
-from text_recognizer.networks.vqvae.resize import Downsample, Upsample
-
-
-class Encoder(nn.Module):
-    """PixelCNN encoder."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        hidden_dim: int,
-        channels_multipliers: Sequence[int],
-        dropout_rate: float,
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.dropout_rate = dropout_rate
-        self.hidden_dim = hidden_dim
-        self.channels_multipliers = tuple(channels_multipliers)
-        self.encoder = self._build_encoder()
-
-    def _build_encoder(self) -> nn.Sequential:
-        """Builds encoder."""
-        encoder = [
-            nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.hidden_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-        num_blocks = len(self.channels_multipliers)
-        in_channels_multipliers = (1,) + self.channels_multipliers
-        for i in range(num_blocks):
-            in_channels = self.hidden_dim * in_channels_multipliers[i]
-            out_channels = self.hidden_dim * self.channels_multipliers[i]
-            encoder += [
-                Residual(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    dropout_rate=self.dropout_rate,
-                    use_norm=True,
-                ),
-            ]
-            if i == num_blocks - 1:
-                encoder.append(Attention(in_channels=out_channels))
-            encoder.append(Downsample())
-
-        for _ in range(2):
-            encoder += [
-                Residual(
-                    in_channels=self.hidden_dim * self.channels_multipliers[-1],
-                    out_channels=self.hidden_dim * self.channels_multipliers[-1],
-                    dropout_rate=self.dropout_rate,
-                    use_norm=True,
-                ),
-                Attention(in_channels=self.hidden_dim * self.channels_multipliers[-1]),
-            ]
-
-        encoder += [
-            Normalize(num_channels=self.hidden_dim * self.channels_multipliers[-1]),
-            nn.Mish(),
-            nn.Conv2d(
-                in_channels=self.hidden_dim * self.channels_multipliers[-1],
-                out_channels=self.hidden_dim * self.channels_multipliers[-1],
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-        return nn.Sequential(*encoder)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Encodes input to a latent representation."""
-        return self.encoder(x)
-
-
-class Decoder(nn.Module):
-    """PixelCNN decoder."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        channels_multipliers: Sequence[int],
-        out_channels: int,
-        dropout_rate: float,
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.out_channels = out_channels
-        self.channels_multipliers = tuple(channels_multipliers)
-        self.dropout_rate = dropout_rate
-        self.decoder = self._build_decoder()
-
-    def _build_decoder(self) -> nn.Sequential:
-        """Builds decoder."""
-        in_channels = self.hidden_dim * self.channels_multipliers[0]
-        decoder = [
-            Residual(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                dropout_rate=self.dropout_rate,
-                use_norm=True,
-            ),
-            Attention(in_channels=in_channels),
-            Residual(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                dropout_rate=self.dropout_rate,
-                use_norm=True,
-            ),
-        ]
-
-        out_channels_multipliers = self.channels_multipliers + (1,)
-        num_blocks = len(self.channels_multipliers)
-
-        for i in range(num_blocks):
-            in_channels = self.hidden_dim * self.channels_multipliers[i]
-            out_channels = self.hidden_dim * out_channels_multipliers[i + 1]
-            decoder.append(
-                Residual(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    dropout_rate=self.dropout_rate,
-                    use_norm=True,
-                )
-            )
-            if i == 0:
-                decoder.append(Attention(in_channels=out_channels))
-            decoder.append(Upsample())
-
-        decoder += [
-            Normalize(num_channels=self.hidden_dim * out_channels_multipliers[-1]),
-            nn.Mish(),
-            nn.Conv2d(
-                in_channels=self.hidden_dim * out_channels_multipliers[-1],
-                out_channels=self.out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-        return nn.Sequential(*decoder)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Decodes latent vector."""
-        return self.decoder(x)