Machine-Learning-Collection/ML/Pytorch/CNN_architectures/lenet5_pytorch.py

"""
An implementation of LeNet CNN architecture.

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-05 Initial coding
*    2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""

import torch
import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions


class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
        self.conv1 = nn.Conv2d(
            in_channels=1,
            out_channels=6,
            kernel_size=5,
            stride=1,
            padding=0,
        )
        self.conv2 = nn.Conv2d(
            in_channels=6,
            out_channels=16,
            kernel_size=5,
            stride=1,
            padding=0,
        )
        self.conv3 = nn.Conv2d(
            in_channels=16,
            out_channels=120,
            kernel_size=5,
            stride=1,
            padding=0,
        )
        self.linear1 = nn.Linear(120, 84)
        self.linear2 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(
            self.conv3(x)
        )  # num_examples x 120 x 1 x 1 --> num_examples x 120
        x = x.reshape(x.shape[0], -1)
        x = self.relu(self.linear1(x))
        x = self.linear2(x)
        return x


def test_lenet():
    x = torch.randn(64, 1, 32, 32)
    model = LeNet()
    return model(x)


if __name__ == "__main__":
    out = test_lenet()
    print(out.shape)