diff --git a/examples/pytorch_tensorboardX.py b/examples/pytorch_tensorboardX.py
index adc01310..13133b53 100644
--- a/examples/pytorch_tensorboardX.py
+++ b/examples/pytorch_tensorboardX.py
@@ -15,47 +15,6 @@ from torch.autograd import Variable
from tensorboardX import SummaryWriter
from trains import Task
-task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
-
-
-writer = SummaryWriter('runs')
-writer.add_text('lstm', 'This is an lstm', 0)
-# 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=2, 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', 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()
-args.cuda = not args.no_cuda and torch.cuda.is_available()
-
-torch.manual_seed(args.seed)
-if args.cuda:
- torch.cuda.manual_seed(args.seed)
-
-kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
-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, **kwargs)
-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.batch_size, shuffle=True, **kwargs)
class Net(nn.Module):
@@ -77,53 +36,97 @@ class Net(nn.Module):
return F.log_softmax(x)
-model = Net()
-if args.cuda:
- model.cuda()
-optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+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=2, 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', 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()
+ args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+ task = Task.init(project_name='examples', task_name='pytorch with tensorboardX')
+ writer = SummaryWriter('runs')
+ writer.add_text('lstm', 'This is an lstm', 0)
+
+ torch.manual_seed(args.seed)
+ if args.cuda:
+ torch.cuda.manual_seed(args.seed)
+
+ kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
+ 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, **kwargs)
+ 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.batch_size, shuffle=True, **kwargs)
+
+ model = Net()
+ if args.cuda:
+ model.cuda()
+ optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+
+ def train(epoch):
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ if args.cuda:
+ data, target = data.cuda(), target.cuda()
+ data, target = Variable(data), Variable(target)
+ optimizer.zero_grad()
+ output = model(data)
+ loss = F.nll_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.data.item()))
+ niter = epoch*len(train_loader)+batch_idx
+ writer.add_scalar('Train/Loss', loss.data.item(), niter)
+
+ def test():
+ model.eval()
+ test_loss = 0
+ correct = 0
+ for niter, (data, target) in enumerate(test_loader):
+ if args.cuda:
+ data, target = data.cuda(), target.cuda()
+ data, target = Variable(data, volatile=True), Variable(target)
+ output = model(data)
+ test_loss += F.nll_loss(output, target, size_average=False).data.item() # sum up batch loss
+ pred = output.data.max(1)[1] # get the index of the max log-probability
+ pred = pred.eq(target.data).cpu().sum()
+ writer.add_scalar('Test/Loss', pred, niter)
+ correct += pred
+
+ 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)))
+
+ for epoch in range(1, args.epochs + 1):
+ train(epoch)
+ torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
+ test()
-def train(epoch):
- model.train()
- for batch_idx, (data, target) in enumerate(train_loader):
- if args.cuda:
- data, target = data.cuda(), target.cuda()
- data, target = Variable(data), Variable(target)
- optimizer.zero_grad()
- output = model(data)
- loss = F.nll_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.data.item()))
- niter = epoch*len(train_loader)+batch_idx
- writer.add_scalar('Train/Loss', loss.data.item(), niter)
-
-
-def test():
- model.eval()
- test_loss = 0
- correct = 0
- for niter, (data, target) in enumerate(test_loader):
- if args.cuda:
- data, target = data.cuda(), target.cuda()
- data, target = Variable(data, volatile=True), Variable(target)
- output = model(data)
- test_loss += F.nll_loss(output, target, size_average=False).data.item() # sum up batch loss
- pred = output.data.max(1)[1] # get the index of the max log-probability
- pred = pred.eq(target.data).cpu().sum()
- writer.add_scalar('Test/Loss', pred, niter)
- correct += pred
-
- 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)))
-
-
-for epoch in range(1, args.epochs + 1):
- train(epoch)
- torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
-test()
+if __name__ == "__main__":
+ # Hack for supporting Windows OS - https://pytorch.org/docs/stable/notes/windows.html#usage-multiprocessing
+ main()