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-rw-r--r--src/text_recognizer/networks/cnn.py101
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diff --git a/src/text_recognizer/networks/cnn.py b/src/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)