Inital commit for refactoring to lightning

1 files changed, 0 insertions, 133 deletions

diff --git a/src/text_recognizer/networks/vqvae/decoder.py b/src/text_recognizer/networks/vqvae/decoder.py
deleted file mode 100644
index 8847aba..0000000
--- a/src/text_recognizer/networks/vqvae/decoder.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""CNN decoder for the VQ-VAE."""
-
-from typing import List, Optional, Tuple, Type
-
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.encoder import _ResidualBlock
-
-
-class Decoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        channels: List[int],
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        embedding_dim: int,
-        upsampling: Optional[List[List[int]]] = None,
-        activation: str = "leaky_relu",
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        if dropout_rate:
-            if activation == "selu":
-                dropout = nn.AlphaDropout(p=dropout_rate)
-            else:
-                dropout = nn.Dropout(p=dropout_rate)
-        else:
-            dropout = None
-
-        self.upsampling = upsampling
-
-        self.res_block = nn.ModuleList([])
-        self.upsampling_block = nn.ModuleList([])
-
-        self.embedding_dim = embedding_dim
-        activation = activation_function(activation)
-
-        # Configure encoder.
-        self.decoder = self._build_decoder(
-            channels, kernel_sizes, strides, num_residual_layers, activation, dropout,
-        )
-
-    def _build_decompression_block(
-        self,
-        in_channels: int,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.ModuleList:
-        modules = nn.ModuleList([])
-        configuration = zip(channels, kernel_sizes, strides)
-        for i, (out_channels, kernel_size, stride) in enumerate(configuration):
-            modules.append(
-                nn.Sequential(
-                    nn.ConvTranspose2d(
-                        in_channels,
-                        out_channels,
-                        kernel_size,
-                        stride=stride,
-                        padding=1,
-                    ),
-                    activation,
-                )
-            )
-
-            if i < len(self.upsampling):
-                modules.append(nn.Upsample(size=self.upsampling[i]),)
-
-            if dropout is not None:
-                modules.append(dropout)
-
-            in_channels = out_channels
-
-        modules.extend(
-            nn.Sequential(
-                nn.ConvTranspose2d(
-                    in_channels, 1, kernel_size=kernel_size, stride=stride, padding=1
-                ),
-                nn.Tanh(),
-            )
-        )
-
-        return modules
-
-    def _build_decoder(
-        self,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.Sequential:
-
-        self.res_block.append(
-            nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,)
-        )
-
-        # Bottleneck module.
-        self.res_block.extend(
-            nn.ModuleList(
-                [
-                    _ResidualBlock(channels[0], channels[0], dropout)
-                    for i in range(num_residual_layers)
-                ]
-            )
-        )
-
-        # Decompression module
-        self.upsampling_block.extend(
-            self._build_decompression_block(
-                channels[0], channels[1:], kernel_sizes, strides, activation, dropout
-            )
-        )
-
-        self.res_block = nn.Sequential(*self.res_block)
-        self.upsampling_block = nn.Sequential(*self.upsampling_block)
-
-        return nn.Sequential(self.res_block, self.upsampling_block)
-
-    def forward(self, z_q: Tensor) -> Tensor:
-        """Reconstruct input from given codes."""
-        x_reconstruction = self.decoder(z_q)
-        return x_reconstruction