onnxruntime/samples/python/mnist/pytorch_mnist.py

import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms


# Pytorch model
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, input1):
        out = self.fc1(input1)
        out = self.relu(out)
        out = self.fc2(out)
        return out


def my_loss(x, target, is_train=True):
    if is_train:
        return F.nll_loss(F.log_softmax(x, dim=1), target)
    else:
        return F.nll_loss(F.log_softmax(x, dim=1), target, reduction='sum')

# Helpers
def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if batch_idx == args.train_steps:
            break
        data, target = data.to(device), target.to(device)
        data = data.reshape(data.shape[0], -1)
        optimizer.zero_grad()
        output = model(data)
        loss = my_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(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            data = data.reshape(data.shape[0], -1)
            output = model(data)
            # Stats
            test_loss += my_loss(output, target, False).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--train-steps', type=int, default=-1, metavar='N',
                        help='number of steps to train. Set -1 to run through whole dataset (default: -1)')
    parser.add_argument('--batch-size', type=int, default=20, metavar='N',
                        help='input batch size for training (default: 20)')
    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=1, metavar='N',
                        help='number of epochs to train (default: 1)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    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-path', type=str, default='',
                        help='Path for Saving the current Model')

    # Basic setup
    args = parser.parse_args()
    if not args.no_cuda and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    torch.manual_seed(args.seed)

    # Data loader
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('./data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.batch_size, shuffle=True)

    if args.test_batch_size > 0:
        test_loader = torch.utils.data.DataLoader(
            datasets.MNIST('./data', train=False, transform=transforms.Compose([
                transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])),
            batch_size=args.test_batch_size, shuffle=True)

    # Modeling
    model = NeuralNet(784, 500, 10).to(device)
    optimizer = optim.SGD(model.parameters(), lr=args.lr)

    # Train loop
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        if args.test_batch_size > 0:
            test(model, device, test_loader)

    # Save model
    if args.save_path:
        torch.save(model.state_dict(), os.path.join(args.save_path, "mnist_cnn.pt"))

if __name__ == '__main__':
    main()