Fix CI

keras-team · Oct 2, 2024 · dfe8af7 · dfe8af7
1 parent 1071028
commit dfe8af7
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Showing 3 changed files with 23 additions and 27 deletions.
diff --git a/keras/api/_tf_keras/keras/layers/__init__.py b/keras/api/_tf_keras/keras/layers/__init__.py
@@ -160,9 +160,6 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_rotation import (
     RandomRotation,
 )
-from keras.src.layers.preprocessing.image_preprocessing.random_shear import (
-    RandomShear,
-)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

diff --git a/keras/api/layers/__init__.py b/keras/api/layers/__init__.py
@@ -160,9 +160,6 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_rotation import (
     RandomRotation,
 )
-from keras.src.layers.preprocessing.image_preprocessing.random_shear import (
-    RandomShear,
-)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

diff --git a/keras/src/layers/preprocessing/image_preprocessing/random_rotation.py b/keras/src/layers/preprocessing/image_preprocessing/random_rotation.py
@@ -66,12 +66,16 @@ class RandomRotation(BaseImagePreprocessingLayer):
         seed: Integer. Used to create a random seed.
         fill_value: a float represents the value to be filled outside
             the boundaries when `fill_mode="constant"`.
+        data_format: string, either `"channels_last"` or `"channels_first"`.
+            The ordering of the dimensions in the inputs. `"channels_last"`
+            corresponds to inputs with shape `(batch, height, width, channels)`
+            while `"channels_first"` corresponds to inputs with shape
+            `(batch, channels, height, width)`. It defaults to the
+            `image_data_format` value found in your Keras config file at
+            `~/.keras/keras.json`. If you never set it, then it will be
+            `"channels_last"`.
     """
 
-    _VALUE_RANGE_VALIDATION_ERROR = (
-        "The `value_range` argument should be a list of two numbers. "
-    )
-
     _SUPPORTED_FILL_MODE = ("reflect", "wrap", "constant", "nearest")
     _SUPPORTED_INTERPOLATION = ("nearest", "bilinear")
 
@@ -82,14 +86,12 @@ def __init__(
         interpolation="bilinear",
         seed=None,
         fill_value=0.0,
-        value_range=(0, 255),
         data_format=None,
         **kwargs,
     ):
         super().__init__(factor=factor, data_format=data_format, **kwargs)
         self.seed = seed
         self.generator = SeedGenerator(seed)
-        self._set_value_range(value_range)
         self.fill_mode = fill_mode
         self.interpolation = interpolation
         self.fill_value = fill_value
@@ -106,19 +108,6 @@ def __init__(
                 f"{self._SUPPORTED_INTERPOLATION}."
             )
 
-    def _set_value_range(self, value_range):
-        if not isinstance(value_range, (tuple, list)):
-            raise ValueError(
-                self.value_range_VALIDATION_ERROR
-                + f"Received: value_range={value_range}"
-            )
-        if len(value_range) != 2:
-            raise ValueError(
-                self.value_range_VALIDATION_ERROR
-                + f"Received: value_range={value_range}"
-            )
-        self.value_range = sorted(value_range)
-
     def transform_images(self, images, transformation, training=True):
         images = self.backend.cast(images, self.compute_dtype)
         if training:
@@ -138,7 +127,21 @@ def transform_labels(self, labels, transformation, training=True):
     def transform_bounding_boxes(
         self, bounding_boxes, transformation, training=True
     ):
-        raise NotImplementedError
+        boxes = bounding_boxes["boxes"]
+        shape = self.backend.shape(boxes)
+        ones = self.backend.ones((shape[0], shape[1], 1, 1))
+        homogeneous_boxes = self.backend.concatenate([boxes, ones], axis=2)
+        transformed_boxes = self.backend.matmul(
+            transformation["rotation_matrix"], homogeneous_boxes
+        )
+        # Convert back to xyxy format
+        transformed_boxes = (
+            transformed_boxes[:, :, :2, :] / transformed_boxes[:, :, 2:3, :]
+        )
+        transformed_boxes = self.backend.reshape(
+            transformed_boxes, (shape[0], shape[1], 4)
+        )
+        return {"boxes": transformed_boxes, "labels": bounding_boxes["labels"]}
 
     def transform_segmentation_masks(
         self, segmentation_masks, transformation, training=True
@@ -233,7 +236,6 @@ def compute_output_shape(self, input_shape):
     def get_config(self):
         config = {
             "factor": self.factor,
-            "value_range": self.value_range,
             "data_format": self.data_format,
             "fill_mode": self.fill_mode,
             "fill_value": self.fill_value,