src/model.py

import torch.nn as nn
import torch

class MyModel(nn.Module):
    def __init__(self, num_classes):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=4, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=1, padding=0)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=1, padding=0)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=4, stride=1, padding=0)
        self.bn4 = nn.BatchNorm2d(128)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=3)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = nn.Linear(6*6*128, 512)
        self.fc2 = nn.Linear(512, num_classes)
        self.flatten = nn.Flatten()
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.relu(self.bn1(self.conv1(x)))
        x = self.pool(x)
        x = self.relu(self.bn2(self.conv2(x)))
        x = self.pool(x)
        x = self.relu(self.bn3(self.conv3(x)))
        x = self.pool2(x)
        x = self.relu(self.bn4(self.conv4(x)))
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

def load_model(model_path, device):
    model = MyModel(num_classes=5)
    model.load_state_dict(torch.load(model_path, map_location=device, weights_only=True))
    model.to(device)
    model.eval()
    return model

def get_gradcam_target_layer(model):
    return model.bn4