Removing legacy code

1 files changed, 0 insertions, 68 deletions

diff --git a/text_recognizer/networks/lenet.py b/text_recognizer/networks/lenet.py
deleted file mode 100644
index 527e1a0..0000000
--- a/text_recognizer/networks/lenet.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""Implementation of the LeNet 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 LeNet(nn.Module):
-    """LeNet network for character prediction."""
-
-    def __init__(
-        self,
-        channels: Tuple[int, ...] = (1, 32, 64),
-        kernel_sizes: Tuple[int, ...] = (3, 3, 2),
-        hidden_size: Tuple[int, ...] = (9216, 128),
-        dropout_rate: float = 0.2,
-        num_classes: int = 10,
-        activation_fn: 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).
-            hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
-                Defaults to (9216, 128).
-            dropout_rate (float): The dropout rate. Defaults to 0.2.
-            num_classes (int): Number of classes. Defaults to 10.
-            activation_fn (Optional[str]): The name of non-linear activation function. Defaults to relu.
-
-        """
-        super().__init__()
-
-        activation_fn = activation_function(activation_fn)
-
-        self.layers = [
-            nn.Conv2d(
-                in_channels=channels[0],
-                out_channels=channels[1],
-                kernel_size=kernel_sizes[0],
-            ),
-            activation_fn,
-            nn.Conv2d(
-                in_channels=channels[1],
-                out_channels=channels[2],
-                kernel_size=kernel_sizes[1],
-            ),
-            activation_fn,
-            nn.MaxPool2d(kernel_sizes[2]),
-            nn.Dropout(p=dropout_rate),
-            Rearrange("b c h w -> b (c h w)"),
-            nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
-            activation_fn,
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(in_features=hidden_size[1], out_features=num_classes),
-        ]
-
-        self.layers = nn.Sequential(*self.layers)
-
-    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.layers(x)