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Fix Windows support for example code
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allegroai
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c8737dc266
commit
9d7c875d71
@ -15,47 +15,6 @@ from torch.autograd import Variable
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from tensorboardX import SummaryWriter
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from trains import Task
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task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
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writer = SummaryWriter('runs')
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writer.add_text('lstm', 'This is an lstm', 0)
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
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parser.add_argument('--batch-size', type=int, default=64, metavar='N',
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help='input batch size for training (default: 64)')
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parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
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help='input batch size for testing (default: 1000)')
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parser.add_argument('--epochs', type=int, default=2, metavar='N',
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help='number of epochs to train (default: 10)')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.01)')
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parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
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help='SGD momentum (default: 0.5)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--log-interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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args = parser.parse_args()
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args.cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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if args.cuda:
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torch.cuda.manual_seed(args.seed)
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kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
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train_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=False,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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class Net(nn.Module):
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@ -77,53 +36,97 @@ class Net(nn.Module):
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return F.log_softmax(x)
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model = Net()
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if args.cuda:
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model.cuda()
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optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
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def main():
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
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parser.add_argument('--batch-size', type=int, default=64, metavar='N',
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help='input batch size for training (default: 64)')
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parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
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help='input batch size for testing (default: 1000)')
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parser.add_argument('--epochs', type=int, default=2, metavar='N',
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help='number of epochs to train (default: 10)')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.01)')
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parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
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help='SGD momentum (default: 0.5)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--log-interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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args = parser.parse_args()
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args.cuda = not args.no_cuda and torch.cuda.is_available()
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task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
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writer = SummaryWriter('runs')
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writer.add_text('lstm', 'This is an lstm', 0)
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torch.manual_seed(args.seed)
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if args.cuda:
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torch.cuda.manual_seed(args.seed)
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kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
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train_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=False,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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model = Net()
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if args.cuda:
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model.cuda()
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optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
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def train(epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data), Variable(target)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.data.item()))
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niter = epoch*len(train_loader)+batch_idx
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writer.add_scalar('Train/Loss', loss.data.item(), niter)
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def test():
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model.eval()
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test_loss = 0
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correct = 0
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for niter, (data, target) in enumerate(test_loader):
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data, volatile=True), Variable(target)
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output = model(data)
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test_loss += F.nll_loss(output, target, size_average=False).data.item() # sum up batch loss
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pred = output.data.max(1)[1] # get the index of the max log-probability
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pred = pred.eq(target.data).cpu().sum()
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writer.add_scalar('Test/Loss', pred, niter)
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correct += pred
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test_loss /= len(test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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for epoch in range(1, args.epochs + 1):
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train(epoch)
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torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
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test()
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def train(epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data), Variable(target)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.data.item()))
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niter = epoch*len(train_loader)+batch_idx
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writer.add_scalar('Train/Loss', loss.data.item(), niter)
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def test():
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model.eval()
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test_loss = 0
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correct = 0
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for niter, (data, target) in enumerate(test_loader):
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data, volatile=True), Variable(target)
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output = model(data)
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test_loss += F.nll_loss(output, target, size_average=False).data.item() # sum up batch loss
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pred = output.data.max(1)[1] # get the index of the max log-probability
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pred = pred.eq(target.data).cpu().sum()
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writer.add_scalar('Test/Loss', pred, niter)
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correct += pred
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test_loss /= len(test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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for epoch in range(1, args.epochs + 1):
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train(epoch)
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torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
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test()
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if __name__ == "__main__":
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# Hack for supporting Windows OS - https://pytorch.org/docs/stable/notes/windows.html#usage-multiprocessing
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main()
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