train.py

import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import argparse
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train_epoch(epoch, args, model, device, data_loader, optimizer):
    model.train()
    for batch_idx, (data, target) in enumerate(data_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(data_loader.dataset),
                100. * batch_idx / len(data_loader), loss.item()))
            if args.dry_run:
                break

def test_epoch(model, device, data_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in data_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(data_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(data_loader.dataset),
        100. * correct / len(data_loader.dataset)))
    
def main():
    # Parser to get command line arguments
    parser = argparse.ArgumentParser(description='MNIST Training Script')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=2, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--no-mps', action='store_true', default=False,
                        help='disables macOS GPU training')
    parser.add_argument('--dry-run', action='store_true', default=False,
                        help='quickly check a single pass')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    parser.add_argument("--resume", action='store_true', default=False, 
                        help="Path to checkpoint to resume from")
    
    args = parser.parse_args()
    use_cuda = torch.cuda.is_available()
    torch.manual_seed(args.seed)
    device = torch.device("cuda" if use_cuda else "cpu")

    # TODO: Load the MNIST dataset for training and testing
    train_kwargs = {'batch_size': args.batch_size}
    test_kwargs = {'batch_size': args.test_batch_size}

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])
    dataset1 = datasets.MNIST('../data', train=True, download=True,
                       transform=transform)
    dataset2 = datasets.MNIST('../data', train=False,
                       transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
    
    model = Net().to(device)

    # TODO: Add a way to load the model checkpoint if 'resume' argument is True
    # TODO: Choose and define the optimizer here
    start_epoch = 1
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    if args.resume:
        if os.path.isfile("model_checkpoint.pt"):
            print("Loading model_checkpoint")
            checkpoint = torch.load("model_checkpoint.pt")
            optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
            model.load_state_dict(checkpoint["model_state_dict"])
            #start_epoch = checkpoint["epoch"] + 1
        else:
            print("No model checkpoint found. Starting from scratch.")
   

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    
    # TODO: Implement the training and testing cycles
    for epoch in range(start_epoch, args.epochs + 1):
        train_epoch(epoch, args, model, device, train_loader, optimizer)
        test_epoch(model, device, test_loader)
        scheduler.step()

        # Hint: Save the model after each epoch
    checkpoint = {
    #"epoch": args.epochs,
    "model_state_dict": model.state_dict(),
    "optimizer_state_dict": optimizer.state_dict(),}
    torch.save(checkpoint, "model_checkpoint.pth")


if __name__ == "__main__":
    main()