Inital commit for refactoring to lightning

1 files changed, 147 insertions, 0 deletions

diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
new file mode 100644
index 0000000..d3adac5
--- /dev/null
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -0,0 +1,147 @@
+"""CNN encoder 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.vector_quantizer import VectorQuantizer
+
+
+class _ResidualBlock(nn.Module):
+    def __init__(
+        self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
+    ) -> None:
+        super().__init__()
+        self.block = [
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
+        ]
+
+        if dropout is not None:
+            self.block.append(dropout)
+
+        self.block = nn.Sequential(*self.block)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply the residual forward pass."""
+        return x + self.block(x)
+
+
+class Encoder(nn.Module):
+    """A CNN encoder network."""
+
+    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,
+        beta: float = 0.25,
+        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.embedding_dim = embedding_dim
+        self.num_embeddings = num_embeddings
+        self.beta = beta
+        activation = activation_function(activation)
+
+        # Configure encoder.
+        self.encoder = self._build_encoder(
+            in_channels,
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            activation,
+            dropout,
+        )
+
+        # Configure Vector Quantizer.
+        self.vector_quantizer = VectorQuantizer(
+            self.num_embeddings, self.embedding_dim, self.beta
+        )
+
+    def _build_compression_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 out_channels, kernel_size, stride in configuration:
+            modules.append(
+                nn.Sequential(
+                    nn.Conv2d(
+                        in_channels, out_channels, kernel_size, stride=stride, padding=1
+                    ),
+                    activation,
+                )
+            )
+
+            if dropout is not None:
+                modules.append(dropout)
+
+            in_channels = out_channels
+
+        return modules
+
+    def _build_encoder(
+        self,
+        in_channels: int,
+        channels: int,
+        kernel_sizes: List[int],
+        strides: List[int],
+        num_residual_layers: int,
+        activation: Type[nn.Module],
+        dropout: Optional[Type[nn.Module]],
+    ) -> nn.Sequential:
+        encoder = nn.ModuleList([])
+
+        # compression module
+        encoder.extend(
+            self._build_compression_block(
+                in_channels, channels, kernel_sizes, strides, activation, dropout
+            )
+        )
+
+        # Bottleneck module.
+        encoder.extend(
+            nn.ModuleList(
+                [
+                    _ResidualBlock(channels[-1], channels[-1], dropout)
+                    for i in range(num_residual_layers)
+                ]
+            )
+        )
+
+        encoder.append(
+            nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
+        )
+
+        return nn.Sequential(*encoder)
+
+    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
+        """Encodes input into a discrete representation."""
+        z_e = self.encoder(x)
+        z_q, vq_loss = self.vector_quantizer(z_e)
+        return z_q, vq_loss