Repository: pytorch/examples

Issue:
Multiprocessing runtime error freeze_support() in Windows 64 bit 
copied from original issue from below , but looks like it has to be fixed in this examples:

https://github.com/pytorch/pytorch/issues/7485


Discussion:

Resolving patch:
diff --git a/mnist/main.py b/mnist/main.py
--- a/mnist/main.py
+++ b/mnist/main.py
@@ -6,48 +6,6 @@
 import torch.optim as optim
 from torchvision import datasets, transforms
 
-# 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', 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")
-
-
-kwargs = {'num_workers': 1, 'pin_memory': True} if use_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.test_batch_size, shuffle=True, **kwargs)
-
-
 class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()
@@ -66,11 +24,7 @@ def forward(self, x):
         x = self.fc2(x)
         return F.log_softmax(x, dim=1)
 
-model = Net().to(device)
-
-optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-
-def train(epoch):
+def train(args, model, device, train_loader, optimizer, epoch):
     model.train()
     for batch_idx, (data, target) in enumerate(train_loader):
         data, target = data.to(device), target.to(device)
@@ -84,7 +38,7 @@ def train(epoch):
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.item()))
 
-def test():
+def test(args, model, device, test_loader):
     model.eval()
     test_loss = 0
     correct = 0
@@ -101,7 +55,55 @@ def test():
         test_loss, correct, len(test_loader.dataset),
         100. * correct / len(test_loader.dataset)))
 
+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', 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")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_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.test_batch_size, shuffle=True, **kwargs)
+
+
+    model = Net().to(device)
+    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+
+    for epoch in range(1, args.epochs + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test(args, model, device, test_loader)
+
 
-for epoch in range(1, args.epochs + 1):
-    train(epoch)
-    test()
+if __name__ == '__main__':
+    main()
\ No newline at end of file


Test patch: