use keras for init variables

keras-team · Nov 6, 2024 · c6b700a · c6b700a
1 parent 581d152
commit c6b700a
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Showing 2 changed files with 14 additions and 21 deletions.
diff --git a/keras_hub/src/models/retinanet/retinanet_image_converter.py b/keras_hub/src/models/retinanet/retinanet_image_converter.py
@@ -1,7 +1,6 @@
 from keras_hub.src.api_export import keras_hub_export
 from keras_hub.src.layers.preprocessing.image_converter import ImageConverter
 from keras_hub.src.models.retinanet.retinanet_backbone import RetinaNetBackbone
-from keras_hub.src.utils.tensor_utils import convert_preprocessing_inputs
 from keras_hub.src.utils.tensor_utils import preprocessing_function
 
 
@@ -30,24 +29,14 @@ def call(self, inputs):
         x = inputs
         # Rescaling Image
         if self.scale is not None:
-            x = x * convert_preprocessing_inputs(
-                self._expand_non_channel_dims(self.scale, x)
-            )
+            x = x * self._expand_non_channel_dims(self.scale, x)
         if self.offset is not None:
-            x = x + convert_preprocessing_inputs(
-                self._expand_non_channel_dims(self.offset, x)
-            )
-
+            x = x + self._expand_non_channel_dims(self.offset, x)
         # By default normalize using imagenet mean and std
         if self.norm_mean:
-            x = x - convert_preprocessing_inputs(
-                self._expand_non_channel_dims(self.norm_mean, x)
-            )
-
+            x = x - self._expand_non_channel_dims(self.norm_mean, x)
         if self.norm_std:
-            x = x / convert_preprocessing_inputs(
-                self._expand_non_channel_dims(self.norm_std, x)
-            )
+            x = x / self._expand_non_channel_dims(self.norm_std, x)
 
         return x
 

diff --git a/keras_hub/src/models/retinanet/retinanet_object_detector_test.py b/keras_hub/src/models/retinanet/retinanet_object_detector_test.py
@@ -1,5 +1,6 @@
-import numpy as np
 import pytest
+from keras import ops
+from keras import random
 
 from keras_hub.src.models.resnet.resnet_backbone import ResNetBackbone
 from keras_hub.src.models.retinanet.anchor_generator import AnchorGenerator
@@ -69,14 +70,17 @@ def setUp(self):
         }
 
         self.input_size = 512
-        self.images = np.random.uniform(
-            low=0, high=255, size=(1, self.input_size, self.input_size, 3)
-        ).astype("float32")
+        self.images = random.uniform(
+            shape=(1, self.input_size, self.input_size, 3),
+            minval=0,
+            maxval=255,
+            dtype="float32",
+        )
         self.labels = {
-            "boxes": np.array(
+            "boxes": ops.convert_to_tensor(
                 [[[20.0, 10.0, 12.0, 11.0], [30.0, 20.0, 40.0, 12.0]]]
             ),
-            "classes": np.array([[0, 2]]),
+            "classes": ops.convert_to_tensor([[0, 2]]),
         }
 
         self.train_data = (self.images, self.labels)