CnD_Classification/train.py

import os
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch import nn, optim
from BN import CatDogClassifier
import time

IMG_SIZE = 128

DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

"""
This function loads all the images from the folder and labels them
"""
def load_images_from_folder(folder, label):
    data = []
    for filename in os.listdir(folder):
        img_path = os.path.join(folder, filename)
        img = cv2.imread(img_path)
        if img is not None:
            img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
            img = img / 255.0
            data.append((img, label))
    return data

if __name__ == "__main__":

    # Loading the dataset
    cat_data = load_images_from_folder("dataset/training_set/cats", label=0)
    dog_data = load_images_from_folder("dataset/training_set/dogs", label=1)

    dataset = cat_data + dog_data
    np.random.shuffle(dataset)

    X = np.array([item[0] for item in dataset], dtype=np.float32)
    Y = np.array([item[1] for item in dataset], dtype=np.int64)

    X = np.transpose(X, (0, 3, 1, 2))

    X_tensor = torch.tensor(X).to(DEVICE)
    Y_tensor = torch.tensor(Y).to(DEVICE)

    dataset = TensorDataset(X_tensor, Y_tensor)
    dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

    model = CatDogClassifier()
    model = model.to(DEVICE)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    num_epochs = 20
    start_time = time.time()

    model.train()
    for epoch in range(num_epochs):
        total_loss = 0
        for images, labels in dataloader:
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()

        print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(dataloader):.4f}")

    print(f"Time taken: {(time.time() - start_time):.2f} seconds")

    torch.save(model, f"bayes_cat_dog_classifier.pth")