CNN_model.py

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

class BasicCNN(nn.Module):
    def __init__(self, num_classes=39):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 256, 3, padding=1)
        self.bn4 = nn.BatchNorm2d(256)

        self.gap = nn.AdaptiveAvgPool2d((1, 1)) #global average pooling
        self.fc = nn.Linear(256, num_classes) #fc classifier
        self.dropout = nn.Dropout(0.3) #regularise

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2) 
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn3(self.conv3(x)))
        x = F.max_pool2d(x, 2) 
        x = F.relu(self.bn4(self.conv4(x)))
        x = F.max_pool2d(x, 2)
        x = self.gap(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc(x)
        return x