fixed linting errors (#10053)

official/vision/beta/projects/panoptic_maskrcnn/modeling/panoptic_maskrcnn_model.py

@@ -119,7 +119,7 @@ class PanopticMaskRCNNModel(maskrcnn_model.MaskRCNNModel):
         **kwargs)
 
     self._config_dict.update({
-        'segmentation_backbone':segmentation_backbone,
+        'segmentation_backbone': segmentation_backbone,
         'segmentation_decoder': segmentation_decoder,
         'segmentation_head': segmentation_head
         })
@@ -129,7 +129,8 @@ class PanopticMaskRCNNModel(maskrcnn_model.MaskRCNNModel):
           '`mask_head` needs to be provided for Panoptic Mask R-CNN.')
     if segmentation_backbone is not None and segmentation_decoder is None:
       raise ValueError(
-          '`segmentation_decoder` needs to be provided for Panoptic Mask R-CNN if `backbone` is not shared.'
+          '`segmentation_decoder` needs to be provided for Panoptic Mask R-CNN'\
+            'if `backbone` is not shared.'
       )
 
     self.segmentation_backbone = segmentation_backbone

official/vision/beta/projects/panoptic_maskrcnn/modeling/panoptic_maskrcnn_model_test.py

@@ -23,7 +23,8 @@ import tensorflow as tf
 
 from tensorflow.python.distribute import combinations
 from tensorflow.python.distribute import strategy_combinations
-from official.vision.beta.projects.panoptic_maskrcnn.modeling import panoptic_maskrcnn_model
+from official.vision.beta.projects.panoptic_maskrcnn.modeling import \
+    panoptic_maskrcnn_model
 from official.vision.beta.modeling.backbones import resnet
 from official.vision.beta.modeling.decoders import fpn
 from official.vision.beta.modeling.decoders import aspp
@@ -316,8 +317,7 @@ class PanopticMaskRCNNModelTest(parameterized.TestCase, tf.test.TestCase):
       self.assertIn('detection_masks', results)
       self.assertIn('segmentation_outputs', results)
       self.assertAllEqual(
-          [2,
-            image_size[0] // (2**level),
+          [2, image_size[0] // (2**level),
            image_size[1] // (2**level), 2],
           results['segmentation_outputs'].numpy().shape)