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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-31 21:55:10 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-31 21:55:10 +0200
commit3196144ec99e803cef218295ddea592748931c57 (patch)
tree867d38ed08c78b8186fdd9a8abab4257f14d05c7 /text_recognizer/networks/lenet.py
parentd21594211e29c40c135b753e33b248b0737cd76f (diff)
Removing legacy code
Diffstat (limited to 'text_recognizer/networks/lenet.py')
-rw-r--r--text_recognizer/networks/lenet.py68
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)