mnist.py

"""
Training hand written digit recognition with MNIST dataset

Copyright (c) 2020 Preferred Networks, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""

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

import pytorch_pfn_extras as ppe
import pytorch_pfn_extras.training.extensions as extensions


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4 * 4 * 50, 500)
        self.fc2 = nn.Linear(500, 10)

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


def train(manager, args, model, device, train_loader):
    while not manager.stop_trigger:
        model.train()
        for _, (data, target) in enumerate(train_loader):
            with manager.run_iteration(step_optimizers=["main"]):
                data, target = data.to(device), target.to(device)
                output = model(data)
                loss = F.nll_loss(output, target)
                ppe.reporting.report({"train/loss": loss.item()})
                loss.backward()


def test(args, model, device, data, target):
    """The extension loops over the iterator in order to
    drive the evaluator progress bar and reporting
    averages
    """
    model.eval()
    data, target = data.to(device), target.to(device)
    output = model(data)
    # Final result will be average of averages of the same size
    test_loss = F.nll_loss(output, target, reduction="mean").item()
    ppe.reporting.report({"val/loss": test_loss})
    pred = output.argmax(dim=1, keepdim=True)

    correct = pred.eq(target.view_as(pred)).sum().item()
    ppe.reporting.report({"val/acc": correct / len(data)})


def main():
    # Training settings
    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
    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=10,
        metavar="N",
        help="number of epochs to train (default: 10)",
    )
    parser.add_argument(
        "--lr",
        type=float,
        default=0.01,
        metavar="LR",
        help="learning rate (default: 0.01)",
    )
    parser.add_argument(
        "--momentum",
        type=float,
        default=0.5,
        metavar="M",
        help="SGD momentum (default: 0.5)",
    )
    parser.add_argument(
        "--no-cuda",
        dest="cuda",
        action="store_false",
        default=True,
        help="disables CUDA training",
    )
    parser.add_argument(
        "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
    )
    parser.add_argument(
        "--save-model",
        action="store_true",
        default=True,
        help="For Saving the current Model",
    )
    parser.add_argument(
        "--snapshot", type=str, default=None, help="path to snapshot file"
    )
    args = parser.parse_args()
    use_cuda = args.cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST(
            "./data_mnist",
            train=True,
            download=True,
            transform=transforms.Compose(
                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
            ),
        ),
        batch_size=args.batch_size,
        shuffle=True,
        **kwargs
    )  # type: ignore[arg-type]
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST(
            "./data_mnist",
            train=False,
            transform=transforms.Compose(
                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
            ),
        ),
        batch_size=args.test_batch_size,
        shuffle=True,
        **kwargs
    )  # type: ignore[arg-type]

    model = Net()
    model.to(device)

    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    # manager.extend(...) also works
    my_extensions = [
        extensions.LogReport(),
        extensions.ProgressBar(),
        extensions.observe_lr(optimizer=optimizer),
        extensions.ParameterStatistics(model, prefix="model"),
        extensions.VariableStatisticsPlot(model),
        extensions.Evaluator(
            test_loader,
            model,
            eval_func=lambda data, target: test(args, model, device, data, target),
            progress_bar=True,
        ),
        extensions.PlotReport(["train/loss", "val/loss"], "epoch", filename="loss.png"),
        extensions.PrintReport(
            [
                "epoch",
                "iteration",
                "train/loss",
                "lr",
                "val/loss",
                "val/acc",
            ]
        ),
        extensions.snapshot(),
    ]
    # Custom stop triggers can be added to the manager and
    # their status accessed through `manager.stop_trigger`
    trigger = None
    # trigger = ppe.training.triggers.EarlyStoppingTrigger(
    #     check_trigger=(1, 'epoch'), monitor='val/loss')
    manager = ppe.training.ExtensionsManager(
        model,
        optimizer,
        args.epochs,
        extensions=my_extensions,
        iters_per_epoch=len(train_loader),
        stop_trigger=trigger,
    )
    # Lets load the snapshot
    if args.snapshot is not None:
        state = torch.load(args.snapshot)
        manager.load_state_dict(state)
    train(manager, args, model, device, train_loader)
    # Test function is called from the evaluator extension
    # to get access to the reporter and other facilities
    # test(args, model, device, test_loader)

    if args.save_model:
        torch.save(model.state_dict(), "mnist_cnn.pt")


if __name__ == "__main__":
    main()