model_small.py

import torch.nn as nn

class SmallCnn(nn.Module):
    def __init__(self, n_classes):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3)
        )

        self.out = nn.Linear(32, n_classes)


    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)

        # print(x.shape)

        x = x.view(x.size(0), -1)
        logits = self.out(x)
        return logits