public static void Main(string[] args)
{
MNIST.Main(args);
AdversarialExampleGeneration.Main(args);
CIFAR10.Main(args);
SequenceToSequence.Main(args);
TextClassification.Main(args);
//ImageTransforms.Main(args);
}
internal static void Main(string[] args)
{
var cwd = Environment.CurrentDirectory;
var dataset = args.Length > 0 ? args[0] : "mnist";
var datasetPath = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), "..", "Downloads", dataset);
var _ = torch.random.manual_seed(1);
//var device = torch.CPU;
var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;
Console.WriteLine($"\n Running AdversarialExampleGeneration on {device.type.ToString()}\n");
Console.WriteLine($"Dataset: {dataset}");
if (device.type == DeviceType.CUDA)
{
_trainBatchSize *= 4;
_testBatchSize *= 4;
_epochs *= 4;
}
var sourceDir = _dataLocation;
var targetDir = Path.Combine(_dataLocation, "test_data");
if (!Directory.Exists(targetDir))
{
Directory.CreateDirectory(targetDir);
Utils.Decompress.DecompressGZipFile(Path.Combine(sourceDir, "train-images-idx3-ubyte.gz"), targetDir);
Utils.Decompress.DecompressGZipFile(Path.Combine(sourceDir, "train-labels-idx1-ubyte.gz"), targetDir);
Utils.Decompress.DecompressGZipFile(Path.Combine(sourceDir, "t10k-images-idx3-ubyte.gz"), targetDir);
Utils.Decompress.DecompressGZipFile(Path.Combine(sourceDir, "t10k-labels-idx1-ubyte.gz"), targetDir);
}
MNIST.Model model = null;
var normImage = torchvision.transforms.Normalize(new double[] { 0.1307 }, new double[] { 0.3081 }, device: (Device)device);
using (var test = new MNISTReader(targetDir, "t10k", _testBatchSize, device: device, transform: normImage)) {
var modelFile = dataset + ".model.bin";
if (!File.Exists(modelFile))
{
// We need the model to be trained first, because we want to start with a trained model.
Console.WriteLine($"\n Running MNIST on {device.type.ToString()} in order to pre-train the model.");
model = new MNIST.Model("model", device);
using (var train = new MNISTReader(targetDir, "train", _trainBatchSize, device: device, shuffle: true, transform: normImage)) {
MNIST.TrainingLoop(dataset, (Device)device, model, train, test);
}
Console.WriteLine("Moving on to the Adversarial model.\n");
}
else
{
model = new MNIST.Model("model", torch.CPU);
model.load(modelFile);
}
model.to((Device)device);
model.eval();
var epsilons = new double[] { 0, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50 };
foreach (var ε in epsilons)
{
var attacked = Test(model, nll_loss(), ε, test, test.Size);
Console.WriteLine($"Epsilon: {ε:F2}, accuracy: {attacked:P2}");
}
}
}
