ml/models/cnn_pytorch.py

import torch.nn as nn

class CNN(nn.Module):
    def __init__(self, num_classes=6):
        super(CNN, self).__init__()

        self.block1 = self._block(3, 32)
        self.block2 = self._block(32, 64)
        self.block3 = self._block(64, 128)
        self.block4 = self._block(128, 256)

        self.gap = nn.AdaptiveAvgPool2d(1)

        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, num_classes)

        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)

    def _block(self, in_c, out_c):
        return nn.Sequential(
            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_c),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )

    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)

        x = self.gap(x)
        x = x.view(x.size(0), -1)

        x = self.dropout(x)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)

        return x