update base_augmentation_layer_3d and all subclasses

keras_cv/layers/preprocessing_3d/base_augmentation_layer_3d.py

@@ -16,7 +16,6 @@
 
 from keras_cv.api_export import keras_cv_export
 from keras_cv.backend import config
-from keras_cv.backend.random import SeedGenerator
 
 if config.keras_3():
     base_layer = tf.keras.layers.Layer
@@ -101,15 +100,12 @@ def augment_pointclouds(self, point_clouds, transformation):
     Note that since the randomness is also a common functionality, this layer
     also includes a keras.backend.RandomGenerator, which can be used to
     produce the random numbers. The random number generator is stored in the
-    `self._seed_generator` attribute.
+    `self._random_generator` attribute.
     """
 
     def __init__(self, seed=None, **kwargs):
         # To-do: remove this once th elayer is ported to keras 3
         # https://github.com/keras-team/keras-cv/issues/2136
-        self._seed_generator = SeedGenerator(
-            seed=seed,
-        )
         if config.keras_3():
             raise ValueError(
                 "This layer is not yet compatible with Keras 3."

keras_cv/layers/preprocessing_3d/base_augmentation_layer_3d_test.py

@@ -14,7 +14,6 @@
 import numpy as np
 import tensorflow as tf
 
-from keras_cv.backend import random
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 from keras_cv.tests.test_case import TestCase
 
@@ -28,23 +27,20 @@ def __init__(self, translate_noise=(0.0, 0.0, 0.0), **kwargs):
         self._translate_noise = translate_noise
 
     def get_random_transformation(self, **kwargs):
-        random_x = random.normal(
+        random_x = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._translate_noise[0],
-            seed=self._seed_generator,
         )
-        random_y = random.normal(
+        random_y = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._translate_noise[1],
-            seed=self._seed_generator,
         )
-        random_z = random.normal(
+        random_z = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._translate_noise[2],
-            seed=self._seed_generator,
         )
 
         return {

keras_cv/layers/preprocessing_3d/waymo/frustum_random_dropping_points.py

@@ -6,7 +6,6 @@
 
 from keras_cv import point_cloud
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 
 POINT_CLOUDS = base_augmentation_layer_3d.POINT_CLOUDS
@@ -123,11 +122,10 @@ def get_random_transformation(self, point_clouds, **kwargs):
         frustum_mask = tf.concat(frustum_mask, axis=0)
         # Generate mask along point dimension.
         random_point_mask = (
-            random.uniform(
+            self._random_generator.random_uniform(
                 [1, num_points, 1],
                 minval=0.0,
                 maxval=1,
-                seed=self._seed_generator,
             )
             < self._keep_probability
         )

keras_cv/layers/preprocessing_3d/waymo/global_random_dropping_points.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 
 POINT_CLOUDS = base_augmentation_layer_3d.POINT_CLOUDS
@@ -64,11 +63,10 @@ def get_random_transformation(self, point_clouds, **kwargs):
         num_points = point_clouds.get_shape().as_list()[-2]
         # Generate mask along point dimension.
         random_point_mask = (
-            random.uniform(
+            self._random_generator.random_uniform(
                 [1, num_points, 1],
                 minval=0.0,
                 maxval=1,
-                seed=self._seed_generator,
             )
             < self._keep_probability
         )

keras_cv/layers/preprocessing_3d/waymo/global_random_rotation.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.bounding_box_3d import CENTER_XYZ_DXDYDZ_PHI
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 from keras_cv.point_cloud import coordinate_transform
@@ -85,26 +84,23 @@ def get_config(self):
         }
 
     def get_random_transformation(self, **kwargs):
-        random_rotation_x = random.uniform(
+        random_rotation_x = self._random_generator.random_uniform(
             (),
             minval=-self._max_rotation_angle_x,
             maxval=self._max_rotation_angle_x,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_rotation_y = random.uniform(
+        random_rotation_y = self._random_generator.random_uniform(
             (),
             minval=-self._max_rotation_angle_y,
             maxval=self._max_rotation_angle_y,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_rotation_z = random.uniform(
+        random_rotation_z = self._random_generator.random_uniform(
             (),
             minval=-self._max_rotation_angle_z,
             maxval=self._max_rotation_angle_z,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
         return {
             "pose": tf.stack(

keras_cv/layers/preprocessing_3d/waymo/global_random_scaling.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.bounding_box_3d import CENTER_XYZ_DXDYDZ_PHI
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 
@@ -134,26 +133,23 @@ def get_config(self):
         }
 
     def get_random_transformation(self, **kwargs):
-        random_scaling_x = random.uniform(
+        random_scaling_x = self._random_generator.random_uniform(
             (),
             minval=self._min_x_factor,
             maxval=self._max_x_factor,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_scaling_y = random.uniform(
+        random_scaling_y = self._random_generator.random_uniform(
             (),
             minval=self._min_y_factor,
             maxval=self._max_y_factor,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_scaling_z = random.uniform(
+        random_scaling_z = self._random_generator.random_uniform(
             (),
             minval=self._min_z_factor,
             maxval=self._max_z_factor,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
         if not self._preserve_aspect_ratio:
             return {

keras_cv/layers/preprocessing_3d/waymo/global_random_translation.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.bounding_box_3d import CENTER_XYZ_DXDYDZ_PHI
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 from keras_cv.point_cloud import coordinate_transform
@@ -68,26 +67,23 @@ def get_config(self):
         }
 
     def get_random_transformation(self, **kwargs):
-        random_x_translation = random.normal(
+        random_x_translation = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._x_stddev,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_y_translation = random.normal(
+        random_y_translation = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._y_stddev,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
-        random_z_translation = random.normal(
+        random_z_translation = self._random_generator.random_normal(
             (),
             mean=0.0,
             stddev=self._z_stddev,
             dtype=self.compute_dtype,
-            seed=self._seed_generator,
         )
         return {
             "pose": tf.stack(

keras_cv/layers/preprocessing_3d/waymo/random_copy_paste.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import random
 from keras_cv.bounding_box_3d import CENTER_XYZ_DXDYDZ_PHI
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 from keras_cv.ops import iou_3d
@@ -99,11 +98,10 @@ def get_random_transformation(
         **kwargs
     ):
         del point_clouds
-        num_paste_bounding_boxes = random.uniform(
+        num_paste_bounding_boxes = self._random_generator.random_uniform(
             (),
             minval=self._min_paste_bounding_boxes,
             maxval=self._max_paste_bounding_boxes,
-            seed=self._seed_generator,
         )
         num_paste_bounding_boxes = tf.cast(
             num_paste_bounding_boxes, dtype=tf.int32