2 files changed, 207 insertions, 0 deletions
diff --git a/src/text_recognizer/networks/transducer/__init__.py b/src/text_recognizer/networks/transducer/__init__.py
new file mode 100644
index 0000000..fdd6662
--- /dev/null
+++ b/src/text_recognizer/networks/transducer/__init__.py
@@ -0,0 +1,2 @@
+"""Transducer modules."""
+from .tds_conv import TDS2d
diff --git a/src/text_recognizer/networks/transducer/tds_conv.py b/src/text_recognizer/networks/transducer/tds_conv.py
new file mode 100644
index 0000000..018caf2
--- /dev/null
+++ b/src/text_recognizer/networks/transducer/tds_conv.py
@@ -0,0 +1,205 @@
+"""Time-Depth Separable Convolutions.
+
+References:
+    https://arxiv.org/abs/1904.02619
+    https://arxiv.org/pdf/2010.01003.pdf
+
+Code stolen from:
+    https://github.com/facebookresearch/gtn_applications
+
+
+"""
+from typing import List, Tuple
+
+from einops import rearrange
+import gtn
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class TDSBlock2d(nn.Module):
+    """Internal block of a 2D TDSC network."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        img_depth: int,
+        kernel_size: Tuple[int],
+        dropout_rate: float,
+    ) -> None:
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.img_depth = img_depth
+        self.kernel_size = kernel_size
+        self.dropout_rate = dropout_rate
+        self.fc_dim = in_channels * img_depth
+
+        # Network placeholders.
+        self.conv = None
+        self.mlp = None
+        self.instance_norm = None
+
+        self._build_block()
+
+    def _build_block(self) -> None:
+        # Convolutional block.
+        self.conv = nn.Sequential(
+            nn.Conv3d(
+                in_channels=self.in_channels,
+                out_channels=self.in_channels,
+                kernel_size=(1, self.kernel_size[0], self.kernel_size[1]),
+                padding=(0, self.kernel_size[0] // 2, self.kernel_size[1] // 2),
+            ),
+            nn.ReLU(inplace=True),
+            nn.Dropout(self.dropout_rate),
+        )
+
+        # MLP block.
+        self.mlp = nn.Sequential(
+            nn.Linear(self.fc_dim, self.fc_dim),
+            nn.ReLU(inplace=True),
+            nn.Dropout(self.dropout_rate),
+            nn.Linear(self.fc_dim, self.fc_dim),
+            nn.Dropout(self.dropout_rate),
+        )
+
+        # Instance norm.
+        self.instance_norm = nn.ModuleList(
+            [
+                nn.InstanceNorm2d(self.fc_dim, affine=True),
+                nn.InstanceNorm2d(self.fc_dim, affine=True),
+            ]
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward pass.
+
+        Args:
+            x (Tensor): Input tensor.
+
+        Shape:
+            - x: :math: `(B, CD, H, W)`
+
+        Returns:
+            Tensor: Output tensor.
+
+        """
+        B, CD, H, W = x.shape
+        C, D = self.in_channels, self.img_depth
+        residual = x
+        x = rearrange(x, "b (c d) h w -> b c d h w", c=C, d=D)
+        x = self.conv(x)
+        x = rearrange(x, "b c d h w -> b (c d) h w")
+        x += residual
+
+        x = self.instance_norm[0](x)
+
+        x = self.mlp(x.transpose(1, 3)).transpose(1, 3) + x
+        x + self.instance_norm[1](x)
+
+        # Output shape: [B, CD, H, W]
+        return x
+
+
+class TDS2d(nn.Module):
+    """TDS Netowrk.
+
+    Structure is the following:
+        Downsample layer -> TDS2d group -> ... -> Linear output layer
+
+
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        depth: int,
+        tds_groups: Tuple[int],
+        kernel_size: Tuple[int],
+        dropout_rate: float,
+        in_channels: int = 1,
+    ) -> None:
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.depth = depth
+        self.tds_groups = tds_groups
+        self.kernel_size = kernel_size
+        self.dropout_rate = dropout_rate
+
+        self.tds = None
+        self.fc = None
+
+    def _build_network(self) -> None:
+
+        modules = []
+        stride_h = np.prod([grp["stride"][0] for grp in self.tds_groups])
+        if self.input_dim % stride_h:
+            raise RuntimeError(
+                f"Image height not divisible by total stride {stride_h}."
+            )
+
+        for tds_group in self.tds_groups:
+            # Add downsample layer.
+            out_channels = self.depth * tds_group["channels"]
+            modules.extend(
+                [
+                    nn.Conv2d(
+                        in_channels=self.in_channels,
+                        out_channels=out_channels,
+                        kernel_size=self.kernel_size,
+                        padding=(self.kernel_size[0] // 2, self.kernel_size[1] // 2),
+                        stride=tds_group["stride"],
+                    ),
+                    nn.ReLU(inplace=True),
+                    nn.Dropout(self.dropout_rate),
+                    nn.InstanceNorm2d(out_channels, affine=True),
+                ]
+            )
+
+            for _ in range(tds_group["num_blocks"]):
+                modules.append(
+                    TDSBlock2d(
+                        tds_group["channels"],
+                        self.depth,
+                        self.kernel_size,
+                        self.dropout_rate,
+                    )
+                )
+
+            self.in_channels = out_channels
+
+        self.tds = nn.Sequential(*modules)
+        self.fc = nn.Linear(
+            self.in_channels * self.input_dim // stride_h, self.output_dim
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward pass.
+
+        Args:
+            x (Tensor): Input tensor.
+
+        Shape:
+            - x: :math: `(B, H, W)`
+
+        Returns:
+            Tensor: Output tensor.
+
+        """
+        B, H, W = x.shape
+        x = rearrange(
+            x, "b (h1 h2) w -> b h1 h2 w", h1=self.in_channels, h2=H // self.in_channels
+        )
+        x = self.tds(x)
+
+        # x shape: [B, C, H, W]
+        x = rearrange(x, "b c h w -> b w (c h)")
+
+        return self.fc(x)