Add support for CIFAR10 Dataset in the DCGAN Module

src/pl_bolts/models/gans/dcgan/dcgan_module.py

@@ -14,7 +14,7 @@
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
-    from torchvision.datasets import LSUN, MNIST
+    from torchvision.datasets import LSUN, MNIST, CIFAR10
 else:  # pragma: no cover
     warn_missing_pkg("torchvision")
 
@@ -35,8 +35,7 @@ class DCGAN(LightningModule):
         python dcgan_module.py --gpus 1
 
         # cifar10
-        python dcgan_module.py --gpus 1 --dataset cifar10 --image_channels 3
-
+        python dcgan_module.py --gpus 1 --dataset cifar10
     """
 
     def __init__(
@@ -174,7 +173,7 @@ def cli_main(args=None):
 
     parser = ArgumentParser()
     parser.add_argument("--batch_size", default=64, type=int)
-    parser.add_argument("--dataset", default="mnist", type=str, choices=["lsun", "mnist"])
+    parser.add_argument("--dataset", default="mnist", type=str, choices=["lsun", "mnist", "cifar10"])
     parser.add_argument("--data_dir", default="./", type=str)
     parser.add_argument("--image_size", default=64, type=int)
     parser.add_argument("--num_workers", default=8, type=int)
@@ -202,6 +201,16 @@ def cli_main(args=None):
         )
         dataset = MNIST(root=script_args.data_dir, download=True, transform=transforms)
         image_channels = 1
+    elif script_args.dataset == "cifar10":
+        transforms = transform_lib.Compose(
+            [
+                transform_lib.Resize(script_args.image_size),
+                transform_lib.ToTensor(),
+                transform_lib.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+        dataset = CIFAR10(root=script_args.data_dir, download=True, transform=transforms)
+        image_channels = 3
 
     dataloader = DataLoader(
         dataset, batch_size=script_args.batch_size, shuffle=True, num_workers=script_args.num_workers