examples/mnist/pytorch_example.py

#  Copyright (c) 2017-2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

###
# Adapted to petastorm dataset using original contents from
# https://github.com/pytorch/examples/mnist/main.py .
###
from __future__ import division, print_function

import argparse

# Must import pyarrow before torch. See: https://github.com/uber/petastorm/blob/master/docs/troubleshoot.rst
import pyarrow  # noqa: F401 pylint: disable=W0611
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms

from examples.mnist import DEFAULT_MNIST_DATA_PATH
from petastorm import make_reader, TransformSpec
from petastorm.pytorch import DataLoader


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    # pylint: disable=arguments-differ
    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


def train(model, device, train_loader, log_interval, optimizer, epoch):
    model.train()
    for batch_idx, row in enumerate(train_loader):
        data, target = row['image'].to(device), row['digit'].to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print('Train Epoch: {} [{}]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), loss.item()))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    count = 0
    with torch.no_grad():
        for row in test_loader:
            data, target = row['image'].to(device), row['digit'].to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()
            count += data.shape[0]

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


def _transform_row(mnist_row):
    # For this example, the images are stored as simpler ndarray (28,28), but the
    # training network expects 3-dim images, hence the additional lambda transform.
    transform = transforms.Compose([
        transforms.Lambda(lambda nd: nd.reshape(28, 28, 1)),
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    # In addition, the petastorm pytorch DataLoader does not distinguish the notion of
    # data or target transform, but that actually gives the user more flexibility
    # to make the desired partial transform, as shown here.
    result_row = {
        'image': transform(mnist_row['image']),
        'digit': mnist_row['digit']
    }

    return result_row


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='Petastorm MNIST Example')
    default_dataset_url = 'file://{}'.format(DEFAULT_MNIST_DATA_PATH)
    parser.add_argument('--dataset-url', type=str,
                        default=default_dataset_url, metavar='S',
                        help='hdfs:// or file:/// URL to the MNIST petastorm dataset '
                             '(default: %s)' % default_dataset_url)
    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('--all-epochs', action='store_true', default=False,
                        help='train all epochs before testing accuracy/loss')
    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', 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')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

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

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

    # Configure loop and Reader epoch for illustrative purposes.
    # Typical training usage would use the `all_epochs` approach.
    #
    if args.all_epochs:
        # Run training across all the epochs before testing for accuracy
        loop_epochs = 1
        reader_epochs = args.epochs
    else:
        # Test training accuracy after each epoch
        loop_epochs = args.epochs
        reader_epochs = 1

    transform = TransformSpec(_transform_row, removed_fields=['idx'])

    # Instantiate each petastorm Reader with a single thread, shuffle enabled, and appropriate epoch setting
    for epoch in range(1, loop_epochs + 1):
        with DataLoader(make_reader('{}/train'.format(args.dataset_url), num_epochs=reader_epochs,
                                    transform_spec=transform),
                        batch_size=args.batch_size) as train_loader:
            train(model, device, train_loader, args.log_interval, optimizer, epoch)
        with DataLoader(make_reader('{}/test'.format(args.dataset_url), num_epochs=reader_epochs,
                                    transform_spec=transform),
                        batch_size=args.test_batch_size) as test_loader:
            test(model, device, test_loader)


if __name__ == '__main__':
    main()