1 files changed, 101 insertions, 0 deletions
diff --git a/text_recognizer/networks/cnn.py b/text_recognizer/networks/cnn.py
new file mode 100644
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+++ b/text_recognizer/networks/cnn.py
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+"""Implementation of a simple backbone cnn network."""
+from typing import Callable, Dict, Optional, Tuple
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn
+
+from text_recognizer.networks.util import activation_function
+
+
+class CNN(nn.Module):
+    """LeNet network for character prediction."""
+
+    def __init__(
+        self,
+        channels: Tuple[int, ...] = (1, 32, 64, 128),
+        kernel_sizes: Tuple[int, ...] = (4, 4, 4),
+        strides: Tuple[int, ...] = (2, 2, 2),
+        max_pool_kernel: int = 2,
+        dropout_rate: float = 0.2,
+        activation: Optional[str] = "relu",
+    ) -> None:
+        """Initialization of the LeNet network.
+
+        Args:
+            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
+            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
+            strides (Tuple[int, ...]): Stride length of the convolutional filter. Defaults to (2, 2, 2).
+            max_pool_kernel (int): 2D max pooling kernel. Defaults to 2.
+            dropout_rate (float): The dropout rate. Defaults to 0.2.
+            activation (Optional[str]): The name of non-linear activation function. Defaults to relu.
+
+        Raises:
+            RuntimeError: if the number of hyperparameters does not match in length.
+
+        """
+        super().__init__()
+
+        if len(channels) - 1 != len(kernel_sizes) and len(kernel_sizes) != len(strides):
+            raise RuntimeError("The number of the hyperparameters does not match.")
+
+        self.cnn = self._build_network(
+            channels, kernel_sizes, strides, max_pool_kernel, dropout_rate, activation,
+        )
+
+    def _build_network(
+        self,
+        channels: Tuple[int, ...],
+        kernel_sizes: Tuple[int, ...],
+        strides: Tuple[int, ...],
+        max_pool_kernel: int,
+        dropout_rate: float,
+        activation: str,
+    ) -> nn.Sequential:
+        # Load activation function.
+        activation_fn = activation_function(activation)
+
+        channels = list(channels)
+        in_channels = channels.pop(0)
+        configuration = zip(channels, kernel_sizes, strides)
+
+        modules = nn.ModuleList([])
+
+        for i, (out_channels, kernel_size, stride) in enumerate(configuration):
+            # Add max pool to reduce output size.
+            if i == len(channels) // 2:
+                modules.append(nn.MaxPool2d(max_pool_kernel))
+            if i == 0:
+                modules.append(
+                    nn.Conv2d(
+                        in_channels, out_channels, kernel_size, stride=stride, padding=1
+                    )
+                )
+            else:
+                modules.append(
+                    nn.Sequential(
+                        activation_fn,
+                        nn.BatchNorm2d(in_channels),
+                        nn.Conv2d(
+                            in_channels,
+                            out_channels,
+                            kernel_size,
+                            stride=stride,
+                            padding=1,
+                        ),
+                    )
+                )
+
+            if dropout_rate:
+                modules.append(nn.Dropout2d(p=dropout_rate))
+
+            in_channels = out_channels
+
+        return nn.Sequential(*modules)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """The feedforward pass."""
+        # If batch dimenstion is missing, it needs to be added.
+        if len(x.shape) < 4:
+            x = x[(None,) * (4 - len(x.shape))]
+        return self.cnn(x)