train.py

import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import ImageFolder
from torchvision import transforms
from models.cnn import CNNModel
from utils.transforms import get_transforms

def train_model(data_dir='data/intel/seg_train', epochs=10, batch_size=32, save_path='saved_models/cnn_model.pth'):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Load dataset with ImageFolder (expects folder structure: class subfolders)
    full_dataset = ImageFolder(root=data_dir, transform=get_transforms(train=True))
    class_names = full_dataset.classes
    print(f"Classes: {class_names}")

    # Split dataset 80% train, 20% val
    train_size = int(0.8 * len(full_dataset))
    val_size = len(full_dataset) - train_size
    train_ds, val_ds = random_split(full_dataset, [train_size, val_size])

    # Update val transforms (no augmentation)
    val_ds.dataset.transform = get_transforms(train=False)

    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
    val_loader = DataLoader(val_ds, batch_size=batch_size)

    model = CNNModel(num_classes=len(class_names)).to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

    for epoch in range(epochs):
        model.train()
        total_loss = 0
        total_correct = 0

        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            total_loss += loss.item() * images.size(0)
            total_correct += (outputs.argmax(1) == labels).sum().item()

        train_loss = total_loss / len(train_loader.dataset)
        train_acc = total_correct / len(train_loader.dataset)

        # Validation
        model.eval()
        val_loss = 0
        val_correct = 0
        with torch.no_grad():
            for images, labels in val_loader:
                images, labels = images.to(device), labels.to(device)
                outputs = model(images)
                loss = criterion(outputs, labels)
                val_loss += loss.item() * images.size(0)
                val_correct += (outputs.argmax(1) == labels).sum().item()

        val_loss /= len(val_loader.dataset)
        val_acc = val_correct / len(val_loader.dataset)

        print(f"Epoch {epoch+1}/{epochs} — Train loss: {train_loss:.4f}, Train acc: {train_acc:.4f}, Val loss: {val_loss:.4f}, Val acc: {val_acc:.4f}")

    os.makedirs(os.path.dirname(save_path), exist_ok=True)
    torch.save({
        'model_state_dict': model.state_dict(),
        'class_names': class_names
    }, save_path)
    print(f"Model saved to {save_path}")

if __name__ == "__main__":
    train_model()