Examples dcgan (#464)

* mnist added dcgan

* mnist added

Author: surgan12

dcgan/.swp

@@ -14,7 +14,7 @@
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--dataset', required=True, help='cifar10 | lsun | imagenet | folder | lfw | fake')
+parser.add_argument('--dataset', required=True, help='cifar10 | lsun | mnist |imagenet | folder | lfw | fake')
 parser.add_argument('--dataroot', required=True, help='path to dataset')
 parser.add_argument('--workers', type=int, help='number of data loading workers', default=2)
 parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
@@ -60,6 +60,7 @@
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                                ]))
+    nc=3
 elif opt.dataset == 'lsun':
     dataset = dset.LSUN(root=opt.dataroot, classes=['bedroom_train'],
                         transform=transforms.Compose([
@@ -68,16 +69,30 @@
                             transforms.ToTensor(),
                             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                         ]))
+    nc=3
 elif opt.dataset == 'cifar10':
     dataset = dset.CIFAR10(root=opt.dataroot, download=True,
                            transform=transforms.Compose([
                                transforms.Resize(opt.imageSize),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                            ]))
+    nc=3
+
+elif opt.dataset == 'mnist':
+        dataset = dset.MNIST(root=opt.dataroot, download=True,
+                           transform=transforms.Compose([
+                               transforms.Resize(opt.imageSize),
+                               transforms.ToTensor(),
+                               transforms.Normalize((0.5,), (0.5,)),
+                           ]))
+        nc=1
+
 elif opt.dataset == 'fake':
     dataset = dset.FakeData(image_size=(3, opt.imageSize, opt.imageSize),
                             transform=transforms.ToTensor())
+    nc=3
+
 assert dataset
 dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
                                          shuffle=True, num_workers=int(opt.workers))
@@ -87,7 +102,6 @@
 nz = int(opt.nz)
 ngf = int(opt.ngf)
 ndf = int(opt.ndf)
-nc = 3
 
 
 # custom weights initialization called on netG and netD