cnn_model2.py

import torch.nn as nn
import torch.nn.functional as Func


class CNNVariant2(nn.Module):
    def __init__(self):
        super(CNNVariant2, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer3 = 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.layer4 = nn.Sequential(
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, 4)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.global_avg_pool(out)
        out = out.view(out.size(0), -1)
        out = Func.relu(self.fc1(out))
        out = self.fc2(out)
        return out