fix dtype errors

benchmarks/vectorized_random_translation.py

@@ -222,14 +222,14 @@ def get_random_transformation(self, image=None, **kwargs):
             shape=[batch_size, 1],
             minval=self.height_lower,
             maxval=self.height_upper,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         width_translation = random.uniform(
             shape=[batch_size, 1],
             minval=self.width_lower,
             maxval=self.width_upper,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         return {

benchmarks/vectorized_randomly_zoomed_crop.py

@@ -114,15 +114,15 @@ def get_random_transformation(
             (),
             minval=tf.minimum(0.0, original_height - new_height),
             maxval=tf.maximum(0.0, original_height - new_height),
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
 
         width_offset = random.uniform(
             (),
             minval=tf.minimum(0.0, original_width - new_width),
             maxval=tf.maximum(0.0, original_width - new_width),
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
 

keras_cv/backend/random.py

@@ -16,9 +16,11 @@
 
 if keras_3():
     from keras.random import *  # noqa: F403, F401
+
     # SeedGenerator is imported from `keras.random`
 else:
     from keras_core.random import *  # noqa: F403, F401
+
     class SeedGenerator:
         def __init__(self, seed=None, **kwargs):
             self._current_seed = [seed, 0]
@@ -86,7 +88,8 @@ def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
             seed=make_seed(seed),
             **kwargs,
         )
-
+
+
 def randint(shape, minval=0.0, maxval=1.0, dtype="int32", seed=None):
     kwargs = {}
     if dtype:

keras_cv/layers/preprocessing/grid_mask.py

@@ -186,7 +186,7 @@ def _compute_grid_mask(self, input_shape, ratio):
             shape=(),
             minval=tf.math.minimum(height * 0.5, width * 0.3),
             maxval=tf.math.maximum(height * 0.5, width * 0.3) + 1,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         rectangle_side_len = tf.cast((ratio) * unit_size, tf.float32)
@@ -196,14 +196,14 @@ def _compute_grid_mask(self, input_shape, ratio):
             shape=(),
             minval=0.0,
             maxval=unit_size,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         delta_y = random.uniform(
             shape=(),
             minval=0.0,
             maxval=unit_size,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
 

keras_cv/layers/preprocessing/mosaic.py

@@ -20,19 +20,19 @@
 from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
     BATCHED,
 )
-from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     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,
 )
-from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     LABELS,
 )
-from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     SEGMENTATION_MASKS,
 )
-from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (  # noqa: E501
+from keras_cv.layers.preprocessing.vectorized_base_image_augmentation_layer import (
     VectorizedBaseImageAugmentationLayer,
 )
 from keras_cv.utils import preprocessing as preprocessing_utils
@@ -97,11 +97,10 @@ def __init__(
 
     def get_random_transformation_batch(self, batch_size, **kwargs):
         # pick 3 indices for every batch to create the mosaic output with.
-        permutation_order = random.uniform(
+        permutation_order = random.randint(
             (batch_size, 3),
             minval=0,
             maxval=batch_size,
-            dtype=tf.int32,
             seed=self._seed_generator,
         )
         # concatenate the batches with permutation order to get all 4 images of

keras_cv/layers/preprocessing/random_augmentation_pipeline.py

@@ -103,7 +103,7 @@ def _augment(self, inputs):
                 shape=(),
                 minval=0.0,
                 maxval=1.0,
-                dtype=tf.float32,
+                dtype="float32",
                 seed=self._seed_generator,
             )
             result = tf.cond(

keras_cv/layers/preprocessing/random_choice.py

@@ -88,11 +88,10 @@ def _batch_augment(self, inputs):
             return super()._batch_augment(inputs)
 
     def _augment(self, inputs, *args, **kwargs):
-        selected_op = random.uniform(
+        selected_op = random.randint(
             (),
             minval=0,
             maxval=len(self.layers),
-            dtype=tf.int32,
             seed=self._seed_generator,
         )
         # Warning:

keras_cv/layers/preprocessing/random_crop_and_resize.py

@@ -114,15 +114,15 @@ def get_random_transformation(
             (),
             minval=tf.minimum(0.0, 1.0 - new_height),
             maxval=tf.maximum(0.0, 1.0 - new_height),
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
 
         width_offset = random.uniform(
             (),
             minval=tf.minimum(0.0, 1.0 - new_width),
             maxval=tf.maximum(0.0, 1.0 - new_width),
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
 

keras_cv/layers/preprocessing/random_cutout.py

@@ -132,18 +132,16 @@ def _compute_rectangle_position(self, inputs):
             input_shape[0],
             input_shape[1],
         )
-        center_x = random.uniform(
+        center_x = random.randint(
             [1],
             0,
             image_width,
-            dtype=tf.int32,
             seed=self._seed_generator,
         )
-        center_y = random.uniform(
+        center_y = random.randint(
             [1],
             0,
             image_height,
-            dtype=tf.int32,
             seed=self._seed_generator,
         )
         return center_x, center_y

keras_cv/layers/preprocessing/random_hue.py

@@ -70,7 +70,7 @@ def get_random_transformation_batch(self, batch_size, **kwargs):
             (batch_size,),
             0,
             1,
-            tf.float32,
+            "float32",
             seed=self._seed_generator,
         )
         invert = tf.where(

keras_cv/layers/preprocessing/random_translation.py

@@ -149,14 +149,14 @@ def get_random_transformation_batch(self, batch_size, **kwargs):
             shape=[batch_size, 1],
             minval=self.height_lower,
             maxval=self.height_upper,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         width_translations = random.uniform(
             shape=[batch_size, 1],
             minval=self.width_lower,
             maxval=self.width_upper,
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         return {

keras_cv/layers/regularization/dropblock_2d.py

@@ -220,7 +220,7 @@ def call(self, x, training=None):
 
         random_noise = random.uniform(
             tf.shape(x),
-            dtype=tf.float32,
+            dtype="float32",
             seed=self._seed_generator,
         )
         valid_block = tf.cast(valid_block, dtype=tf.float32)

keras_cv/utils/preprocessing.py

@@ -195,7 +195,7 @@ def random_inversion(seed_generator):
 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, seed=seed_generator
+        (batch_size, 1), 0, 1, dtype="float32", seed=seed_generator
     )
     negate = tf.where(negate > 0.5, -1.0, 1.0)
     return negate