Inital commit for refactoring to lightning

5 files changed, 478 insertions, 0 deletions

diff --git a/text_recognizer/networks/vqvae/__init__.py b/text_recognizer/networks/vqvae/__init__.py
new file mode 100644
index 0000000..763953c
--- /dev/null
+++ b/text_recognizer/networks/vqvae/__init__.py
@@ -0,0 +1,5 @@
+"""VQ-VAE module."""
+from .decoder import Decoder
+from .encoder import Encoder
+from .vector_quantizer import VectorQuantizer
+from .vqvae import VQVAE
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
new file mode 100644
index 0000000..8847aba
--- /dev/null
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -0,0 +1,133 @@
+"""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
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
diff --git a/text_recognizer/networks/vqvae/vector_quantizer.py b/text_recognizer/networks/vqvae/vector_quantizer.py
new file mode 100644
index 0000000..f92c7ee
--- /dev/null
+++ b/text_recognizer/networks/vqvae/vector_quantizer.py
@@ -0,0 +1,119 @@
+"""Implementation of a Vector Quantized Variational AutoEncoder.
+
+Reference:
+https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py
+
+"""
+
+from einops import rearrange
+import torch
+from torch import nn
+from torch import Tensor
+from torch.nn import functional as F
+
+
+class VectorQuantizer(nn.Module):
+    """The codebook that contains quantized vectors."""
+
+    def __init__(
+        self, num_embeddings: int, embedding_dim: int, beta: float = 0.25
+    ) -> None:
+        super().__init__()
+        self.K = num_embeddings
+        self.D = embedding_dim
+        self.beta = beta
+
+        self.embedding = nn.Embedding(self.K, self.D)
+
+        # Initialize the codebook.
+        nn.init.uniform_(self.embedding.weight, -1 / self.K, 1 / self.K)
+
+    def discretization_bottleneck(self, latent: Tensor) -> Tensor:
+        """Computes the code nearest to the latent representation.
+
+        First we compute the posterior categorical distribution, and then map
+        the latent representation to the nearest element of the embedding.
+
+        Args:
+            latent (Tensor): The latent representation.
+
+        Shape:
+            - latent :math:`(B x H x W, D)`
+
+        Returns:
+            Tensor: The quantized embedding vector.
+
+        """
+        # Store latent shape.
+        b, h, w, d = latent.shape
+
+        # Flatten the latent representation to 2D.
+        latent = rearrange(latent, "b h w d -> (b h w) d")
+
+        # Compute the L2 distance between the latents and the embeddings.
+        l2_distance = (
+            torch.sum(latent ** 2, dim=1, keepdim=True)
+            + torch.sum(self.embedding.weight ** 2, dim=1)
+            - 2 * latent @ self.embedding.weight.t()
+        )  # [BHW x K]
+
+        # Find the embedding k nearest to each latent.
+        encoding_indices = torch.argmin(l2_distance, dim=1).unsqueeze(1)  # [BHW, 1]
+
+        # Convert to one-hot encodings, aka discrete bottleneck.
+        one_hot_encoding = torch.zeros(
+            encoding_indices.shape[0], self.K, device=latent.device
+        )
+        one_hot_encoding.scatter_(1, encoding_indices, 1)  # [BHW x K]
+
+        # Embedding quantization.
+        quantized_latent = one_hot_encoding @ self.embedding.weight  # [BHW, D]
+        quantized_latent = rearrange(
+            quantized_latent, "(b h w) d -> b h w d", b=b, h=h, w=w
+        )
+
+        return quantized_latent
+
+    def vq_loss(self, latent: Tensor, quantized_latent: Tensor) -> Tensor:
+        """Vector Quantization loss.
+
+        The vector quantization algorithm allows us to create a codebook. The VQ
+        algorithm works by moving the embedding vectors towards the encoder outputs.
+
+        The embedding loss moves the embedding vector towards the encoder outputs. The
+        .detach() works as the stop gradient (sg) described in the paper.
+
+        Because the volume of the embedding space is dimensionless, it can arbitarily
+        grow if the embeddings are not trained as fast as the encoder parameters. To
+        mitigate this, a commitment loss is added in the second term which makes sure
+        that the encoder commits to an embedding and that its output does not grow.
+
+        Args:
+            latent (Tensor): The encoder output.
+            quantized_latent (Tensor): The quantized latent.
+
+        Returns:
+            Tensor: The combinded VQ loss.
+
+        """
+        embedding_loss = F.mse_loss(quantized_latent, latent.detach())
+        commitment_loss = F.mse_loss(quantized_latent.detach(), latent)
+        return embedding_loss + self.beta * commitment_loss
+
+    def forward(self, latent: Tensor) -> Tensor:
+        """Forward pass that returns the quantized vector and the vq loss."""
+        # Rearrange latent representation s.t. the hidden dim is at the end.
+        latent = rearrange(latent, "b d h w -> b h w d")
+
+        # Maps latent to the nearest code in the codebook.
+        quantized_latent = self.discretization_bottleneck(latent)
+
+        loss = self.vq_loss(latent, quantized_latent)
+
+        # Add residue to the quantized latent.
+        quantized_latent = latent + (quantized_latent - latent).detach()
+
+        # Rearrange the quantized shape back to the original shape.
+        quantized_latent = rearrange(quantized_latent, "b h w d -> b d h w")
+
+        return quantized_latent, loss
diff --git a/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py
new file mode 100644
index 0000000..50448b4
--- /dev/null
+++ b/text_recognizer/networks/vqvae/vqvae.py
@@ -0,0 +1,74 @@
+"""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