add doctests for example 1/n

pl_examples/basic_examples/autoencoder.py

@@ -31,6 +31,13 @@
 
 
 class LitAutoEncoder(pl.LightningModule):
+    """
+    >>> LitAutoEncoder()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    LitAutoEncoder(
+      (encoder): ...
+      (decoder): ...
+    )
+    """
 
     def __init__(self):
         super().__init__()

pl_examples/basic_examples/backbone_image_classifier.py

@@ -29,6 +29,13 @@
 
 
 class Backbone(torch.nn.Module):
+    """
+    >>> Backbone()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Backbone(
+      (l1): Linear(...)
+      (l2): Linear(...)
+    )
+    """
     def __init__(self, hidden_dim=128):
         super().__init__()
         self.l1 = torch.nn.Linear(28 * 28, hidden_dim)
@@ -42,6 +49,12 @@ def forward(self, x):
 
 
 class LitClassifier(pl.LightningModule):
+    """
+    >>> LitClassifier(Backbone())  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    LitClassifier(
+      (backbone): ...
+    )
+    """
     def __init__(self, backbone, learning_rate=1e-3):
         super().__init__()
         self.save_hyperparameters()

pl_examples/basic_examples/conv_sequential_example.py

@@ -55,6 +55,12 @@ def forward(self, x):
 
 
 class LitResnet(pl.LightningModule):
+    """
+    >>> LitResnet()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    LitResnet(
+      (sequential_module): Sequential(...)
+    )
+    """
     def __init__(self, lr=0.05, batch_size=32, manual_optimization=False):
         super().__init__()
 

pl_examples/basic_examples/mnist_datamodule.py

@@ -29,6 +29,9 @@
 class MNISTDataModule(LightningDataModule):
     """
     Standard MNIST, train, val, test splits and transforms
+
+    >>> MNISTDataModule()  # doctest: +ELLIPSIS
+    <...mnist_datamodule.MNISTDataModule object at ...>
     """
 
     name = "mnist"

pl_examples/basic_examples/simple_image_classifier.py

@@ -24,6 +24,13 @@
 
 
 class LitClassifier(pl.LightningModule):
+    """
+    >>> LitClassifier()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    LitClassifier(
+      (l1): Linear(...)
+      (l2): Linear(...)
+    )
+    """
     def __init__(self, hidden_dim=128, learning_rate=1e-3):
         super().__init__()
         self.save_hyperparameters()

pl_examples/bug_report_model.py

@@ -28,6 +28,10 @@
 
 
 class RandomDataset(Dataset):
+    """
+    >>> RandomDataset(size=10, length=20)  # doctest: +ELLIPSIS
+    <...bug_report_model.RandomDataset object at ...>
+    """
     def __init__(self, size, length):
         self.len = length
         self.data = torch.randn(length, size)
@@ -40,6 +44,12 @@ def __len__(self):
 
 
 class BoringModel(LightningModule):
+    """
+    >>> BoringModel()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    BoringModel(
+      (layer): Linear(...)
+    )
+    """
 
     def __init__(self):
         """
@@ -113,10 +123,9 @@ def configure_optimizers(self):
 #  parser = ArgumentParser()
 #  args = parser.parse_args(opt)
 
-def run_test():
+def test_run():
 
     class TestModel(BoringModel):
-
         def on_train_epoch_start(self) -> None:
             print('override any method to prove your bug')
 
@@ -140,4 +149,4 @@ def on_train_epoch_start(self) -> None:
 
 if __name__ == '__main__':
     cli_lightning_logo()
-    run_test()
+    test_run()

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -159,20 +159,30 @@ def _unfreeze_and_add_param_group(module: Module,
 class TransferLearningModel(pl.LightningModule):
     """Transfer Learning with pre-trained ResNet50.
 
-    Args:
-        hparams: Model hyperparameters
-        dl_path: Path where the data will be downloaded
+    >>> with TemporaryDirectory(dir='.') as tmp_dir:
+    ...     TransferLearningModel(tmp_dir)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    TransferLearningModel(
+      (feature_extractor): Sequential(...)
+      (fc): Sequential(...)
+    )
     """
-    def __init__(self,
-                 dl_path: Union[str, Path],
-                 backbone: str = 'resnet50',
-                 train_bn: bool = True,
-                 milestones: tuple = (5, 10),
-                 batch_size: int = 8,
-                 lr: float = 1e-2,
-                 lr_scheduler_gamma: float = 1e-1,
-                 num_workers: int = 6, **kwargs) -> None:
-        super().__init__()
+    def __init__(
+            self,
+            dl_path: Union[str, Path],
+            backbone: str = 'resnet50',
+            train_bn: bool = True,
+            milestones: tuple = (5, 10),
+            batch_size: int = 8,
+            lr: float = 1e-2,
+            lr_scheduler_gamma: float = 1e-1,
+            num_workers: int = 6,
+            **kwargs,
+    ) -> None:
+        """
+        Args:
+            dl_path: Path where the data will be downloaded
+        """
+        super().__init__(**kwargs)
         self.dl_path = dl_path
         self.backbone = backbone
         self.train_bn = train_bn

pl_examples/domain_templates/generative_adversarial_net.py

@@ -37,7 +37,13 @@
 
 
 class Generator(nn.Module):
-    def __init__(self, latent_dim, img_shape):
+    """
+    >>> Generator(img_shape=(1, 8, 8))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Generator(
+      (model): Sequential(...)
+    )
+    """
+    def __init__(self, latent_dim: int = 100, img_shape: tuple = (1, 28, 28)):
         super().__init__()
         self.img_shape = img_shape
 
@@ -64,6 +70,12 @@ def forward(self, z):
 
 
 class Discriminator(nn.Module):
+    """
+    >>> Discriminator(img_shape=(1, 28, 28))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    Discriminator(
+      (model): Sequential(...)
+    )
+    """
     def __init__(self, img_shape):
         super().__init__()
 
@@ -83,6 +95,37 @@ def forward(self, img):
 
 
 class GAN(LightningModule):
+    """
+    >>> GAN(img_shape=(1, 8, 8))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    GAN(
+      (generator): Generator(
+        (model): Sequential(...)
+      )
+      (discriminator): Discriminator(
+        (model): Sequential(...)
+      )
+    )
+    """
+    def __init__(
+            self,
+            img_shape: tuple = (1, 28, 28),
+            lr: float = 0.0002,
+            b1: float = 0.5,
+            b2: float = 0.999,
+            latent_dim: int = 100,
+    ):
+        super().__init__()
+
+        self.save_hyperparameters()
+
+        # networks
+        self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=img_shape)
+        self.discriminator = Discriminator(img_shape=img_shape)
+
+        self.validation_z = torch.randn(8, self.hparams.latent_dim)
+
+        self.example_input_array = torch.zeros(2, self.hparams.latent_dim)
+
     @staticmethod
     def add_argparse_args(parent_parser: ArgumentParser):
         parser = ArgumentParser(parents=[parent_parser], add_help=False)
@@ -96,20 +139,6 @@ def add_argparse_args(parent_parser: ArgumentParser):
 
         return parser
 
-    def __init__(self, hparams: Namespace):
-        super().__init__()
-
-        self.hparams = hparams
-
-        # networks
-        mnist_shape = (1, 28, 28)
-        self.generator = Generator(latent_dim=self.hparams.latent_dim, img_shape=mnist_shape)
-        self.discriminator = Discriminator(img_shape=mnist_shape)
-
-        self.validation_z = torch.randn(8, self.hparams.latent_dim)
-
-        self.example_input_array = torch.zeros(2, self.hparams.latent_dim)
-
     def forward(self, z):
         return self.generator(z)
 
@@ -180,6 +209,10 @@ def on_epoch_end(self):
 
 
 class MNISTDataModule(LightningDataModule):
+    """
+    >>> MNISTDataModule()  # doctest: +ELLIPSIS
+    <...generative_adversarial_net.MNISTDataModule object at ...>
+    """
     def __init__(self, batch_size: int = 64, data_path: str = os.getcwd(), num_workers: int = 4):
         super().__init__()
         self.batch_size = batch_size

pl_examples/domain_templates/imagenet.py

@@ -50,6 +50,12 @@
 
 
 class ImageNetLightningModel(LightningModule):
+    """
+    >>> ImageNetLightningModel(data_path='missing')  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    ImageNetLightningModel(
+      (model): ResNet(...)
+    )
+    """
     # pull out resnet names from torchvision models
     MODEL_NAMES = sorted(
         name for name in models.__dict__
@@ -58,14 +64,14 @@ class ImageNetLightningModel(LightningModule):
 
     def __init__(
             self,
-            arch: str,
-            pretrained: bool,
-            lr: float,
-            momentum: float,
-            weight_decay: int,
             data_path: str,
-            batch_size: int,
-            workers: int,
+            arch: str = 'resnet18',
+            pretrained: bool = False,
+            lr: float = 0.1,
+            momentum: float = 0.9,
+            weight_decay: float = 1e-4,
+            batch_size: int = 4,
+            workers: int = 2,
             **kwargs,
     ):
         super().__init__()