cnn_model3.py

import torch.nn as nn
import torch.nn.functional as F


class CNNVariant3(nn.Module):
    def __init__(self):
        super(CNNVariant3, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=7, stride=1, padding=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer5 = nn.Sequential(
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1, stride=1, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.flattened_size = 256 * 8 * 8
        self.fc1 = nn.Linear(self.flattened_size, 512)
        self.fc2 = nn.Linear(512, 4)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = x.view(-1, self.flattened_size)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x