yapf

pl_bolts/models/detection/__init__.py

@@ -1,5 +1,5 @@
 from pl_bolts.models.detection.faster_rcnn import FasterRCNN  # noqa: F401
 
 __all__ = [
-    "FasterRCNN"
+    "FasterRCNN",
 ]

pl_bolts/models/detection/faster_rcnn/faster_rcnn_module.py

@@ -20,9 +20,7 @@ def _evaluate_iou(target, pred):
     from model
     """
     if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-        raise ModuleNotFoundError(
-            'You want to use `torchvision` which is not installed yet.'
-        )
+        raise ModuleNotFoundError('You want to use `torchvision` which is not installed yet.')
 
     if pred["boxes"].shape[0] == 0:
         # no box detected, 0 IOU
@@ -71,9 +69,7 @@ def __init__(
             trainable_backbone_layers: number of trainable resnet layers starting from final block
         """
         if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `torchvision` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `torchvision` which is not installed yet.')
 
         super().__init__()
 

pl_bolts/models/mnist_module.py

@@ -19,9 +19,7 @@ class LitMNIST(LightningModule):
 
     def __init__(self, hidden_dim=128, learning_rate=1e-3, batch_size=32, num_workers=4, data_dir='', **kwargs):
         if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `torchvision` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `torchvision` which is not installed yet.')
 
         super().__init__()
         self.save_hyperparameters()

pl_bolts/models/rl/common/gym_wrappers.py

@@ -30,9 +30,7 @@ class ToTensor(Wrapper):
 
     def __init__(self, env=None):
         if not _GYM_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `gym` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `gym` which is not installed yet.')
 
         super(ToTensor, self).__init__(env)
 
@@ -51,9 +49,7 @@ class FireResetEnv(Wrapper):
 
     def __init__(self, env=None):
         if not _GYM_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `gym` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `gym` which is not installed yet.')
 
         super(FireResetEnv, self).__init__(env)
         assert env.unwrapped.get_action_meanings()[1] == "FIRE"
@@ -80,9 +76,7 @@ class MaxAndSkipEnv(Wrapper):
 
     def __init__(self, env=None, skip=4):
         if not _GYM_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `gym` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `gym` which is not installed yet.')
 
         super(MaxAndSkipEnv, self).__init__(env)
         # most recent raw observations (for max pooling across time steps)
@@ -202,9 +196,7 @@ class DataAugmentation(ObservationWrapper):
 
     def __init__(self, env=None):
         if not _GYM_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
-                'You want to use `gym` which is not installed yet.'
-            )
+            raise ModuleNotFoundError('You want to use `gym` which is not installed yet.')
 
         super().__init__(env)
         self.observation_space = gym.spaces.Box(low=0, high=255, shape=(84, 84, 1), dtype=np.uint8)