Add implementations for random_saturation (#20646)

* Correct bug for MixUp initialization.

* Update format indent

* Add implementations for random_saturation

* change parse_factor method to inner method.

* correct test cases failed.

* correct failed test cases

* Add training argument check condition

* correct source code

* add value_range args description

* update description example

* change _apply_random_saturation method to inline

keras/api/_tf_keras/keras/layers/__init__.py

@@ -173,6 +173,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_rotation import (
     RandomRotation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
+    RandomSaturation,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/api/layers/__init__.py

@@ -173,6 +173,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_rotation import (
     RandomRotation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
+    RandomSaturation,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/src/layers/__init__.py

@@ -117,6 +117,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_rotation import (
     RandomRotation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
+    RandomSaturation,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/src/layers/preprocessing/image_preprocessing/random_saturation.py

@@ -0,0 +1,167 @@
+from keras.src.api_export import keras_export
+from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import (  # noqa: E501
+    BaseImagePreprocessingLayer,
+)
+from keras.src.random import SeedGenerator
+
+
+@keras_export("keras.layers.RandomSaturation")
+class RandomSaturation(BaseImagePreprocessingLayer):
+    """Randomly adjusts the saturation on given images.
+
+    This layer will randomly increase/reduce the saturation for the input RGB
+    images.
+
+    Args:
+        factor: A tuple of two floats or a single float.
+            `factor` controls the extent to which the image saturation
+            is impacted. `factor=0.5` makes this layer perform a no-op
+            operation. `factor=0.0` makes the image fully grayscale.
+            `factor=1.0` makes the image fully saturated. Values should
+            be between `0.0` and `1.0`. If a tuple is used, a `factor`
+            is sampled between the two values for every image augmented.
+            If a single float is used, a value between `0.0` and the passed
+            float is sampled. To ensure the value is always the same,
+            pass a tuple with two identical floats: `(0.5, 0.5)`.
+        value_range: the range of values the incoming images will have.
+            Represented as a two-number tuple written `[low, high]`. This is
+            typically either `[0, 1]` or `[0, 255]` depending on how your
+            preprocessing pipeline is set up.
+        seed: Integer. Used to create a random seed.
+
+    Example:
+    ```python
+    (images, labels), _ = keras.datasets.cifar10.load_data()
+    images = images.astype("float32")
+    random_saturation = keras.layers.RandomSaturation(factor=0.2)
+    augmented_images = random_saturation(images)
+    ```
+    """
+
+    _VALUE_RANGE_VALIDATION_ERROR = (
+        "The `value_range` argument should be a list of two numbers. "
+    )
+
+    def __init__(
+        self,
+        factor,
+        value_range=(0, 255),
+        data_format=None,
+        seed=None,
+        **kwargs,
+    ):
+        super().__init__(data_format=data_format, **kwargs)
+        self._set_factor(factor)
+        self._set_value_range(value_range)
+        self.seed = seed
+        self.generator = SeedGenerator(seed)
+
+    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 get_random_transformation(self, data, training=True, seed=None):
+        if isinstance(data, dict):
+            images = data["images"]
+        else:
+            images = data
+        images_shape = self.backend.shape(images)
+        rank = len(images_shape)
+        if rank == 3:
+            batch_size = 1
+        elif rank == 4:
+            batch_size = images_shape[0]
+        else:
+            raise ValueError(
+                "Expected the input image to be rank 3 or 4. Received: "
+                f"inputs.shape={images_shape}"
+            )
+
+        if seed is None:
+            seed = self._get_seed_generator(self.backend._backend)
+
+        factor = self.backend.random.uniform(
+            (batch_size,),
+            minval=self.factor[0],
+            maxval=self.factor[1],
+            seed=seed,
+        )
+        factor = factor / (1 - factor)
+        return {"factor": factor}
+
+    def transform_images(self, images, transformation=None, training=True):
+        def _apply_random_saturation(images, transformation):
+            adjust_factors = transformation["factor"]
+            adjust_factors = self.backend.cast(
+                adjust_factors, self.compute_dtype
+            )
+            adjust_factors = self.backend.numpy.reshape(
+                adjust_factors, self.backend.shape(adjust_factors) + (1, 1)
+            )
+            images = self.backend.image.rgb_to_hsv(
+                images, data_format=self.data_format
+            )
+            if self.data_format == "channels_first":
+                s_channel = self.backend.numpy.multiply(
+                    images[:, 1, :, :], adjust_factors
+                )
+                s_channel = self.backend.numpy.clip(
+                    s_channel, self.value_range[0], self.value_range[1]
+                )
+                images = self.backend.numpy.stack(
+                    [images[:, 0, :, :], s_channel, images[:, 2, :, :]], axis=1
+                )
+            else:
+                s_channel = self.backend.numpy.multiply(
+                    images[..., 1], adjust_factors
+                )
+                s_channel = self.backend.numpy.clip(
+                    s_channel, self.value_range[0], self.value_range[1]
+                )
+                images = self.backend.numpy.stack(
+                    [images[..., 0], s_channel, images[..., 2]], axis=-1
+                )
+            images = self.backend.image.hsv_to_rgb(
+                images, data_format=self.data_format
+            )
+            return images
+
+        if training:
+            images = _apply_random_saturation(images, transformation)
+        return images
+
+    def transform_labels(self, labels, transformation, training=True):
+        return labels
+
+    def transform_segmentation_masks(
+        self, segmentation_masks, transformation, training=True
+    ):
+        return segmentation_masks
+
+    def transform_bounding_boxes(
+        self, bounding_boxes, transformation, training=True
+    ):
+        return bounding_boxes
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "factor": self.factor,
+                "value_range": self.value_range,
+                "seed": self.seed,
+            }
+        )
+        return config
+
+    def compute_output_shape(self, input_shape):
+        return input_shape

keras/src/layers/preprocessing/image_preprocessing/random_saturation_test.py

@@ -0,0 +1,97 @@
+import numpy as np
+import pytest
+from tensorflow import data as tf_data
+
+import keras
+from keras.src import backend
+from keras.src import layers
+from keras.src import testing
+
+
+class RandomSaturationTest(testing.TestCase):
+    @pytest.mark.requires_trainable_backend
+    def test_layer(self):
+        self.run_layer_test(
+            layers.RandomSaturation,
+            init_kwargs={
+                "factor": 0.75,
+                "seed": 1,
+            },
+            input_shape=(8, 3, 4, 3),
+            supports_masking=False,
+            expected_output_shape=(8, 3, 4, 3),
+        )
+
+    def test_random_saturation_value_range(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)
+
+        layer = layers.RandomSaturation(0.2)
+        adjusted_image = layer(image)
+
+        self.assertTrue(keras.ops.numpy.all(adjusted_image >= 0))
+        self.assertTrue(keras.ops.numpy.all(adjusted_image <= 1))
+
+    def test_random_saturation_no_op(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            inputs = np.random.random((2, 8, 8, 3))
+        else:
+            inputs = np.random.random((2, 3, 8, 8))
+
+        layer = layers.RandomSaturation((0.5, 0.5))
+        output = layer(inputs, training=False)
+        self.assertAllClose(inputs, output, atol=1e-3, rtol=1e-5)
+
+    def test_random_saturation_full_grayscale(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            inputs = np.random.random((2, 8, 8, 3))
+        else:
+            inputs = np.random.random((2, 3, 8, 8))
+        layer = layers.RandomSaturation(factor=(0.0, 0.0))
+        result = layer(inputs)
+
+        if data_format == "channels_last":
+            self.assertAllClose(result[..., 0], result[..., 1])
+            self.assertAllClose(result[..., 1], result[..., 2])
+        else:
+            self.assertAllClose(result[:, 0, :, :], result[:, 1, :, :])
+            self.assertAllClose(result[:, 1, :, :], result[:, 2, :, :])
+
+    def test_random_saturation_full_saturation(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            inputs = np.random.random((2, 8, 8, 3))
+        else:
+            inputs = np.random.random((2, 3, 8, 8))
+        layer = layers.RandomSaturation(factor=(1.0, 1.0))
+        result = layer(inputs)
+
+        hsv = backend.image.rgb_to_hsv(result)
+        s_channel = hsv[..., 1]
+
+        self.assertAllClose(
+            keras.ops.numpy.max(s_channel), layer.value_range[1]
+        )
+
+    def test_random_saturation_randomness(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)[:5]
+
+        layer = layers.RandomSaturation(0.2)
+        adjusted_images = layer(image)
+
+        self.assertNotAllClose(adjusted_images, image)
+
+    def test_tf_data_compatibility(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            input_data = np.random.random((2, 8, 8, 3))
+        else:
+            input_data = np.random.random((2, 3, 8, 8))
+        layer = layers.RandomSaturation(
+            factor=0.5, data_format=data_format, seed=1337
+        )
+
+        ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
+        for output in ds.take(1):
+            output.numpy()