black linting for layoutmodel

Author: lolipopshock

src/layoutparser/models/layoutmodel.py

@@ -8,34 +8,34 @@
 from PIL import Image
 import numpy as np
 
-__all__ = ['Detectron2LayoutModel']
+__all__ = ["Detectron2LayoutModel"]
 
 
 class BaseLayoutModel(ABC):
-
     @abstractmethod
-    def detect(self): pass
+    def detect(self):
+        pass
 
 
 class Detectron2LayoutModel(BaseLayoutModel):
     """Create a Detectron2-based Layout Detection Model
 
     Args:
-        config_path (:obj:`str`): 
-            The path to the configuration file. 
-        model_path (:obj:`str`, None): 
-            The path to the saved weights of the model. 
-            If set, overwrite the weights in the configuration file. 
+        config_path (:obj:`str`):
+            The path to the configuration file.
+        model_path (:obj:`str`, None):
+            The path to the saved weights of the model.
+            If set, overwrite the weights in the configuration file.
             Defaults to `None`.
-        label_map (:obj:`dict`, optional): 
+        label_map (:obj:`dict`, optional):
             The map from the model prediction (ids) to real
-            word labels (strings). 
+            word labels (strings).
             Defaults to `None`.
-        extra_config (:obj:`list`, optional): 
-            Extra configuration passed to the Detectron2 model 
-            configuration. The argument will be used in the `merge_from_list 
+        extra_config (:obj:`list`, optional):
+            Extra configuration passed to the Detectron2 model
+            configuration. The argument will be used in the `merge_from_list
             <https://detectron2.readthedocs.io/modules/config.html
-            #detectron2.config.CfgNode.merge_from_list>`_ function. 
+            #detectron2.config.CfgNode.merge_from_list>`_ function.
             Defaults to `[]`.
 
     Examples::
@@ -45,10 +45,7 @@ class Detectron2LayoutModel(BaseLayoutModel):
 
     """
 
-    def __init__(self, config_path,
-                 model_path=None,
-                 label_map=None,
-                 extra_config=[]):
+    def __init__(self, config_path, model_path=None, label_map=None, extra_config=[]):
 
         cfg = get_cfg()
         config_path = PathManager.get_local_path(config_path)
@@ -57,15 +54,15 @@ def __init__(self, config_path,
 
         if model_path is not None:
             cfg.MODEL.WEIGHTS = model_path
-        cfg.MODEL.DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+        cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
         self.cfg = cfg
 
         self.label_map = label_map
         self._create_model()
 
     def gather_output(self, outputs):
 
-        instance_pred = outputs['instances'].to("cpu")
+        instance_pred = outputs["instances"].to("cpu")
 
         layout = Layout()
         scores = instance_pred.scores.tolist()
@@ -79,9 +76,8 @@ def gather_output(self, outputs):
                 label = self.label_map.get(label, label)
 
             cur_block = TextBlock(
-                Rectangle(x_1, y_1, x_2, y_2),
-                type=label,
-                score=score)
+                Rectangle(x_1, y_1, x_2, y_2), type=label, score=score
+            )
             layout.append(cur_block)
 
         return layout
@@ -101,8 +97,8 @@ def detect(self, image):
 
         # Convert PIL Image Input
         if isinstance(image, Image.Image):
-            if image.mode != 'RGB':
-                image = image.convert('RGB')
+            if image.mode != "RGB":
+                image = image.convert("RGB")
             image = np.array(image)
 
         outputs = self.model(image)