main.py

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
from model import MiniViT

#This is a standard transformation to convert images to PyTorch Tensors

transform = transforms.Compose([transforms.ToTensor()])

# Download and load the CIFAR-10 training dataset
trainset = torchvision.datasets.CIFAR10(root='./data',
                                        train=True,
                                        download=True,
                                        transform=transform)

# Create a DataLoader to handle batching and shuffling
trainloader = torch.utils.data.DataLoader(trainset,
                                          batch_size=4,
                                          shuffle=True)

# --- INSPECT ONE IMAGE ---
# Get one batch of training images

dataiter = iter(trainloader)
images, labels = next(dataiter)

# Select the very first image and its label from the batch
first_image = images[0]
first_label = labels[0]

# Print the shape of the image tensor and its label
print("----Data Inspection---")
print(f"Image shape: {first_image.shape}")
print(f"Label : {first_label.item()}")

model = MiniViT()
# --- TRAINING SETUP ---

# 1. The Loss Function
# CrossEntropyLoss is a standard choice for classification problems.
criterion = nn.CrossEntropyLoss()

# 2. The Optimizer
# Adam is a popular and effective optimizer. We tell it which parameters
# to tune (model.parameters()) and the learning rate (lr).
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# --- THE TRAINING LOOP ---
print("\n--- Starting Training ---")
num_epochs = 20  # Let's train for 5 full cycles through the data

for epoch in range(num_epochs):

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # Get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # --- The 5 Core Steps of Training ---

        # 1. Zero the parameter gradients (important!)
        optimizer.zero_grad()

        # 2. Forward pass: get the model's predictions
        outputs = model(inputs)

        # 3. Calculate the loss (how wrong the model was)
        loss = criterion(outputs, labels)

        # 4. Backward pass: calculate the gradients
        loss.backward()

        # 5. Update the weights: the optimizer tunes the model
        optimizer.step()

        # Print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:  # Print every 2000 mini-batches
            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('--- Finished Training ---')

# --- EVALUATION ---
print("\n--- Starting Evaluation ---")

# First, we need to load the test dataset
testset = torchvision.datasets.CIFAR10(root='./data',
                                       train=False,  # IMPORTANT: use the test set
                                       download=True,
                                       transform=transform)

testloader = torch.utils.data.DataLoader(testset,
                                         batch_size=4,
                                         shuffle=False)  # No need to shuffle for testing

correct = 0
total = 0

# Set the model to evaluation mode (disables dropout, etc.)
model.eval()

# We don't need to calculate gradients for evaluation, which saves memory and computations
with torch.no_grad():
    for data in testloader:
        images, labels = data

        # Get the model's predictions
        outputs = model(images)

        # Find the prediction with the highest score (the predicted class)
        _, predicted = torch.max(outputs.data, 1)

        # Count the total and correct predictions
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f'Accuracy of the network on the 10000 test images: {accuracy:.2f} %')