"""
Example code of how to initialize weights for a simple CNN network.
Usually this is not needed as default initialization is usually good,
but sometimes it can be useful to initialize weights in a specific way.
This way of doing it should generalize to other network types just make 
sure to specify and change the modules you wish to modify.

Video explanation: https://youtu.be/xWQ-p_o0Uik
Got any questions leave a comment on youtube :)

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-10 Initial coding
*    2022-12-16 Updated with more detailed comments, and checked code still functions as intended.
"""

# Imports
import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.nn.functional as F  # All functions that don't have any parameters


class CNN(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=6,
            kernel_size=3,
            stride=1,
            padding=1,
        )
        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
        self.conv2 = nn.Conv2d(
            in_channels=6,
            out_channels=16,
            kernel_size=3,
            stride=1,
            padding=1,
        )
        self.fc1 = nn.Linear(16 * 7 * 7, num_classes)
        self.initialize_weights()

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc1(x)

        return x

    def initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_uniform_(m.weight)

                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

            elif isinstance(m, nn.Linear):
                nn.init.kaiming_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)


if __name__ == "__main__":
    model = CNN(in_channels=3, num_classes=10)

    for param in model.parameters():
        print(param)
