reformat and resolve error

benchmarks/vectorized_mosaic.py

@@ -17,17 +17,20 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
+from tensorflow import keras
+
 from keras_cv import bounding_box
 from keras_cv.layers import Mosaic
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
     IMAGES,
+)
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     LABELS,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 
 class OldMosaic(BaseImageAugmentationLayer):

benchmarks/vectorized_random_flip.py

@@ -28,7 +28,7 @@
 from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
     BOUNDING_BOXES,
 )
-from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     IMAGES,
 )
 
@@ -103,11 +103,11 @@ def get_random_transformation(self, **kwargs):
         flip_vertical = False
         if self.horizontal:
             flip_horizontal = (
-                random.uniform(shape=[]) > 0.5, seed=self._seed_generator
+                random.uniform(shape=[], seed=self._seed_generator) > 0.5
             )
         if self.vertical:
             flip_vertical = (
-                random.uniform(shape=[]) > 0.5, seed=self._seed_generator
+                random.uniform(shape=[], seed=self._seed_generator) > 0.5
             )
         return {
             "flip_horizontal": tf.cast(flip_horizontal, dtype=tf.bool),

benchmarks/vectorized_random_hue.py

@@ -17,12 +17,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
+from tensorflow import keras
+
 from keras_cv.layers import RandomHue
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 
 class OldRandomHue(BaseImageAugmentationLayer):

benchmarks/vectorized_random_rotation.py

@@ -17,14 +17,15 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
+from tensorflow import keras
+
 from keras_cv import bounding_box
 from keras_cv.backend import random
 from keras_cv.layers import RandomRotation
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 H_AXIS = -3
 W_AXIS = -2

benchmarks/vectorized_random_shear.py

@@ -2,16 +2,17 @@
 import warnings
 from unittest.mock import MagicMock
 
-import keras_cv
 import numpy as np
 import tensorflow as tf
+from matplotlib import pyplot as plt
+
+import keras_cv
 from keras_cv import bounding_box
 from keras_cv.layers import RandomShear
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing
-from matplotlib import pyplot as plt
 
 
 # Copied from:

benchmarks/vectorized_random_translation.py

@@ -18,13 +18,14 @@
 import numpy as np
 import tensorflow as tf
 from keras import backend
+from tensorflow import keras
+
 from keras_cv.backend import random
 from keras_cv.layers import RandomTranslation
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 H_AXIS = -3
 W_AXIS = -2

benchmarks/vectorized_random_zoom.py

@@ -18,13 +18,14 @@
 import numpy as np
 import tensorflow as tf
 from keras import backend
+from tensorflow import keras
+
 from keras_cv.backend import random
 from keras_cv.layers import RandomZoom
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 # In order to support both unbatched and batched inputs, the horizontal
 # and vertical axis is reverse indexed

benchmarks/vectorized_randomly_zoomed_crop.py

@@ -16,13 +16,14 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
+from tensorflow import keras
+
 from keras_cv import core
 from keras_cv.layers import RandomlyZoomedCrop
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
-from tensorflow import keras
 
 
 class OldRandomlyZoomedCrop(BaseImageAugmentationLayer):

keras_cv/layers/preprocessing/base_image_augmentation_layer.py

@@ -14,9 +14,11 @@
 
 import keras
 import tensorflow as tf
+
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import keras, scope
+from keras_cv.backend import keras
+from keras_cv.backend import scope
 from keras_cv.backend.config import multi_backend
 from keras_cv.backend.random import SeedGenerator
 from keras_cv.utils import preprocessing

keras_cv/layers/preprocessing/base_image_augmentation_layer_test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import numpy as np
 import tensorflow as tf
+
 from keras_cv import bounding_box
 from keras_cv.backend import random
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (

keras_cv/layers/preprocessing/vectorized_base_image_augmentation_layer.py

@@ -14,9 +14,11 @@
 
 import keras
 import tensorflow as tf
+
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import keras, scope
+from keras_cv.backend import keras
+from keras_cv.backend import scope
 from keras_cv.backend.config import multi_backend
 from keras_cv.backend.random import SeedGenerator
 from keras_cv.utils import preprocessing

keras_cv/utils/preprocessing.py

@@ -13,11 +13,13 @@
 # limitations under the License.
 
 import tensorflow as tf
-from keras_cv import core
-from keras_cv.backend import ops, random
 from tensorflow import keras
 from tensorflow.keras import backend
 
+from keras_cv import core
+from keras_cv.backend import ops
+from keras_cv.backend import random
+
 _TF_INTERPOLATION_METHODS = {
     "bilinear": tf.image.ResizeMethod.BILINEAR,
     "nearest": tf.image.ResizeMethod.NEAREST_NEIGHBOR,
@@ -183,14 +185,19 @@ def random_inversion(seed_generator):
     Returns:
       either -1, or -1.
     """
-    negate = keras.backend.uniform((), 0, 1, dtype=tf.float32) > 0.5
+    negate = (
+        keras.backend.uniform((), 0, 1, dtype=tf.float32, seed=seed_generator)
+        > 0.5
+    )
     negate = tf.cond(negate, lambda: -1.0, lambda: 1.0)
     return negate
 
 
 def batch_random_inversion(seed_generator, batch_size):
     """Same as `random_inversion` but for batched inputs."""
-    negate = random.uniform((batch_size, 1), 0, 1, dtype=tf.float32)
+    negate = random.uniform(
+        (batch_size, 1), 0, 1, dtype=tf.float32, seed=seed_generator
+    )
     negate = tf.where(negate > 0.5, -1.0, 1.0)
     return negate