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keras-team · Feb 27, 2023 · 3abe7d9 · 3abe7d9
1 parent b62a339
commit 3abe7d9
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Showing 3 changed files with 10 additions and 28 deletions.
diff --git a/benchmarks/vectorized_crop_resize.py b/benchmarks/vectorized_crop_resize.py
@@ -97,9 +97,7 @@ def __init__(
         self.bounding_box_format = bounding_box_format
         self.force_output_dense_images = True
 
-    def get_random_transformation(
-        self, image=None, label=None, bounding_box=None, **kwargs
-    ):
+    def get_random_transformation(self, **kwargs):
         crop_area_factor = self.crop_area_factor()
         aspect_ratio = self.aspect_ratio_factor()
 

diff --git a/keras_cv/layers/preprocessing/random_crop_and_resize.py b/keras_cv/layers/preprocessing/random_crop_and_resize.py
@@ -92,9 +92,7 @@ def __init__(
         self.bounding_box_format = bounding_box_format
         self.force_output_dense_images = True
 
-    def get_random_transformation_batch(
-        self, batch_size, label=None, bounding_box=None, **kwargs
-    ):
+    def get_random_transformation_batch(self, batch_size, **kwargs):
         crop_area_factor = self.crop_area_factor()
         aspect_ratio = self.aspect_ratio_factor()
 

diff --git a/keras_cv/layers/preprocessing/random_crop_and_resize_test.py b/keras_cv/layers/preprocessing/random_crop_and_resize_test.py
@@ -168,9 +168,7 @@ def test_augment_one_hot_segmentation_mask(self):
         mask_shape = (1, self.height, self.width, 1)
         image = tf.random.uniform(shape=input_image_shape, seed=self.seed)
         mask = tf.one_hot(
-            tf.squeeze(
-                np.random.randint(2, size=mask_shape) * (classes - 1), axis=-1
-            ),
+            tf.squeeze(np.random.randint(2, size=mask_shape) * (classes - 1), axis=-1),
             classes,
         )
 
@@ -209,12 +207,8 @@ def test_augment_bounding_box_single(self):
             "boxes": tf.convert_to_tensor([[0, 0, 1, 1]], dtype=tf.float32),
             "classes": tf.convert_to_tensor([0], dtype=tf.float32),
         }
-        output["bounding_boxes"] = bounding_box.to_dense(
-            output["bounding_boxes"]
-        )
-        self.assertAllClose(
-            expected_output["boxes"], output["bounding_boxes"]["boxes"]
-        )
+        output["bounding_boxes"] = bounding_box.to_dense(output["bounding_boxes"])
+        self.assertAllClose(expected_output["boxes"], output["bounding_boxes"]["boxes"])
         self.assertAllClose(
             expected_output["classes"], output["bounding_boxes"]["classes"]
         )
@@ -248,12 +242,8 @@ def test_augment_boxes_batched_input(self):
             ),
             "classes": tf.convert_to_tensor([[0, 0], [0, 0]]),
         }
-        output["bounding_boxes"] = bounding_box.to_dense(
-            output["bounding_boxes"]
-        )
-        self.assertAllClose(
-            expected_output["boxes"], output["bounding_boxes"]["boxes"]
-        )
+        output["bounding_boxes"] = bounding_box.to_dense(output["bounding_boxes"])
+        self.assertAllClose(expected_output["boxes"], output["bounding_boxes"]["boxes"])
         self.assertAllClose(
             expected_output["classes"], output["bounding_boxes"]["classes"]
         )
@@ -283,12 +273,8 @@ def test_augment_boxes_ragged(self):
             "classes": tf.ragged.constant([[0, 0], [0]]),
         }
         expected_output = bounding_box.to_dense(expected_output)
-        output["bounding_boxes"] = bounding_box.to_dense(
-            output["bounding_boxes"]
-        )
-        self.assertAllClose(
-            expected_output["boxes"], output["bounding_boxes"]["boxes"]
-        )
+        output["bounding_boxes"] = bounding_box.to_dense(output["bounding_boxes"])
+        self.assertAllClose(expected_output["boxes"], output["bounding_boxes"]["boxes"])
         self.assertAllClose(
             expected_output["classes"], output["bounding_boxes"]["classes"]
         )
@@ -321,4 +307,4 @@ def random_crop_and_resize_on_batched_image_train(self):
         input_resized = tf.image.resize(image, self.target_size)
         output = layer(image, training=True)
 
-        self.assertNotAllClose(output[0], output[1])
+        self.assertNotAllClose(output[0], output[1])