1 files changed, 0 insertions, 101 deletions
diff --git a/src/text_recognizer/networks/cnn.py b/src/text_recognizer/networks/cnn.py
deleted file mode 100644
index 1807bb9..0000000
--- a/src/text_recognizer/networks/cnn.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""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)