Add rand_augment processing layer (#20716)

* Add rand_augment init

* Update rand_augment init

* Add rand_augment

* Add NotImplementedError

* Add some test cases

* Fix failed test case

* Update rand_augment

* Update rand_augment test

* Fix random_rotation bug

* Add build method to supress warning.

* Add implementation for transform_bboxes

Author: shashaka

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

@@ -152,6 +152,9 @@
     MaxNumBoundingBoxes,
 )
 from keras.src.layers.preprocessing.image_preprocessing.mix_up import MixUp
+from keras.src.layers.preprocessing.image_preprocessing.rand_augment import (
+    RandAugment,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )

keras/api/layers/__init__.py

@@ -152,6 +152,9 @@
     MaxNumBoundingBoxes,
 )
 from keras.src.layers.preprocessing.image_preprocessing.mix_up import MixUp
+from keras.src.layers.preprocessing.image_preprocessing.rand_augment import (
+    RandAugment,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )

keras/src/layers/__init__.py

@@ -96,6 +96,9 @@
     MaxNumBoundingBoxes,
 )
 from keras.src.layers.preprocessing.image_preprocessing.mix_up import MixUp
+from keras.src.layers.preprocessing.image_preprocessing.rand_augment import (
+    RandAugment,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )

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

@@ -0,0 +1,235 @@
+import random
+
+import keras.src.layers as layers
+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
+from keras.src.utils import backend_utils
+
+
+@keras_export("keras.layers.RandAugment")
+class RandAugment(BaseImagePreprocessingLayer):
+    """RandAugment performs the Rand Augment operation on input images.
+
+    This layer can be thought of as an all-in-one image augmentation layer. The
+    policy implemented by this layer has been benchmarked extensively and is
+    effective on a wide variety of datasets.
+
+    References:
+        - [RandAugment](https://arxiv.org/abs/1909.13719)
+
+    Args:
+        value_range: The range of values the input image can take.
+            Default is `(0, 255)`. Typically, this would be `(0, 1)`
+            for normalized images or `(0, 255)` for raw images.
+        num_ops: The number of augmentation operations to apply sequentially
+            to each image. Default is 2.
+        factor: The strength of the augmentation as a normalized value
+            between 0 and 1. Default is 0.5.
+        interpolation: The interpolation method to use for resizing operations.
+            Options include `nearest`, `bilinear`. Default is `bilinear`.
+        seed: Integer. Used to create a random seed.
+
+    """
+
+    _USE_BASE_FACTOR = False
+    _FACTOR_BOUNDS = (0, 1)
+
+    _AUGMENT_LAYERS = [
+        "random_shear",
+        "random_translation",
+        "random_rotation",
+        "random_brightness",
+        "random_color_degeneration",
+        "random_contrast",
+        "random_sharpness",
+        "random_posterization",
+        "solarization",
+        "auto_contrast",
+        "equalization",
+    ]
+
+    def __init__(
+        self,
+        value_range=(0, 255),
+        num_ops=2,
+        factor=0.5,
+        interpolation="bilinear",
+        seed=None,
+        data_format=None,
+        **kwargs,
+    ):
+        super().__init__(data_format=data_format, **kwargs)
+
+        self.value_range = value_range
+        self.num_ops = num_ops
+        self._set_factor(factor)
+        self.interpolation = interpolation
+        self.seed = seed
+        self.generator = SeedGenerator(seed)
+
+        self.random_shear = layers.RandomShear(
+            x_factor=self.factor,
+            y_factor=self.factor,
+            interpolation=interpolation,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_translation = layers.RandomTranslation(
+            height_factor=self.factor,
+            width_factor=self.factor,
+            interpolation=interpolation,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_rotation = layers.RandomRotation(
+            factor=self.factor,
+            interpolation=interpolation,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_brightness = layers.RandomBrightness(
+            factor=self.factor,
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_color_degeneration = layers.RandomColorDegeneration(
+            factor=self.factor,
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_contrast = layers.RandomContrast(
+            factor=self.factor,
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_sharpness = layers.RandomSharpness(
+            factor=self.factor,
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.solarization = layers.Solarization(
+            addition_factor=self.factor,
+            threshold_factor=self.factor,
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.random_posterization = layers.RandomPosterization(
+            factor=max(1, int(8 * self.factor[1])),
+            value_range=self.value_range,
+            seed=self.seed,
+            data_format=data_format,
+            **kwargs,
+        )
+
+        self.auto_contrast = layers.AutoContrast(
+            value_range=self.value_range, data_format=data_format, **kwargs
+        )
+
+        self.equalization = layers.Equalization(
+            value_range=self.value_range, data_format=data_format, **kwargs
+        )
+
+    def build(self, input_shape):
+        for layer_name in self._AUGMENT_LAYERS:
+            augmentation_layer = getattr(self, layer_name)
+            augmentation_layer.build(input_shape)
+
+    def get_random_transformation(self, data, training=True, seed=None):
+        if not training:
+            return None
+
+        if backend_utils.in_tf_graph():
+            self.backend.set_backend("tensorflow")
+
+            for layer_name in self._AUGMENT_LAYERS:
+                augmentation_layer = getattr(self, layer_name)
+                augmentation_layer.backend.set_backend("tensorflow")
+
+        transformation = {}
+        random.shuffle(self._AUGMENT_LAYERS)
+        for layer_name in self._AUGMENT_LAYERS[: self.num_ops]:
+            augmentation_layer = getattr(self, layer_name)
+            transformation[layer_name] = (
+                augmentation_layer.get_random_transformation(
+                    data,
+                    training=training,
+                    seed=self._get_seed_generator(self.backend._backend),
+                )
+            )
+
+        return transformation
+
+    def transform_images(self, images, transformation, training=True):
+        if training:
+            images = self.backend.cast(images, self.compute_dtype)
+
+            for layer_name, transformation_value in transformation.items():
+                augmentation_layer = getattr(self, layer_name)
+                images = augmentation_layer.transform_images(
+                    images, transformation_value
+                )
+
+        images = self.backend.cast(images, self.compute_dtype)
+        return images
+
+    def transform_labels(self, labels, transformation, training=True):
+        return labels
+
+    def transform_bounding_boxes(
+        self,
+        bounding_boxes,
+        transformation,
+        training=True,
+    ):
+        if training:
+            for layer_name, transformation_value in transformation.items():
+                augmentation_layer = getattr(self, layer_name)
+                bounding_boxes = augmentation_layer.transform_bounding_boxes(
+                    bounding_boxes, transformation_value, training=training
+                )
+        return bounding_boxes
+
+    def transform_segmentation_masks(
+        self, segmentation_masks, transformation, training=True
+    ):
+        return self.transform_images(
+            segmentation_masks, transformation, training=training
+        )
+
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
+    def get_config(self):
+        config = {
+            "value_range": self.value_range,
+            "num_ops": self.num_ops,
+            "factor": self.factor,
+            "interpolation": self.interpolation,
+            "seed": self.seed,
+        }
+        base_config = super().get_config()
+        return {**base_config, **config}

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

@@ -0,0 +1,114 @@
+import numpy as np
+import pytest
+from tensorflow import data as tf_data
+
+from keras.src import backend
+from keras.src import layers
+from keras.src import testing
+
+
+class RandAugmentTest(testing.TestCase):
+    @pytest.mark.requires_trainable_backend
+    def test_layer(self):
+        self.run_layer_test(
+            layers.RandAugment,
+            init_kwargs={
+                "value_range": (0, 255),
+                "num_ops": 2,
+                "factor": 1,
+                "interpolation": "nearest",
+                "seed": 1,
+                "data_format": "channels_last",
+            },
+            input_shape=(8, 3, 4, 3),
+            supports_masking=False,
+            expected_output_shape=(8, 3, 4, 3),
+        )
+
+    def test_rand_augment_inference(self):
+        seed = 3481
+        layer = layers.RandAugment()
+
+        np.random.seed(seed)
+        inputs = np.random.randint(0, 255, size=(224, 224, 3))
+        output = layer(inputs, training=False)
+        self.assertAllClose(inputs, output)
+
+    def test_rand_augment_basic(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.RandAugment(data_format=data_format)
+
+        augmented_image = layer(input_data)
+        self.assertEqual(augmented_image.shape, input_data.shape)
+
+    def test_rand_augment_no_operations(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.RandAugment(num_ops=0, data_format=data_format)
+
+        augmented_image = layer(input_data)
+        self.assertAllClose(
+            backend.convert_to_numpy(augmented_image), input_data
+        )
+
+    def test_random_augment_randomness(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.RandAugment(num_ops=11, data_format=data_format)
+        augmented_image = layer(input_data)
+
+        self.assertNotAllClose(
+            backend.convert_to_numpy(augmented_image), input_data
+        )
+
+    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.RandAugment(data_format=data_format)
+
+        ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
+        for output in ds.take(1):
+            output.numpy()
+
+    def test_rand_augment_tf_data_bounding_boxes(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            image_shape = (1, 10, 8, 3)
+        else:
+            image_shape = (1, 3, 10, 8)
+        input_image = np.random.random(image_shape)
+        bounding_boxes = {
+            "boxes": np.array(
+                [
+                    [
+                        [2, 1, 4, 3],
+                        [6, 4, 8, 6],
+                    ]
+                ]
+            ),
+            "labels": np.array([[1, 2]]),
+        }
+
+        input_data = {"images": input_image, "bounding_boxes": bounding_boxes}
+
+        ds = tf_data.Dataset.from_tensor_slices(input_data)
+        layer = layers.RandAugment(
+            data_format=data_format,
+            seed=42,
+            bounding_box_format="xyxy",
+        )
+        ds.map(layer)

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

@@ -34,7 +34,7 @@ def test_correctness(self):
         seed = 2390
 
         # Always scale up, but randomly between 0 ~ 255
-        layer = layers.RandomBrightness([0, 1.0])
+        layer = layers.RandomBrightness([0.1, 1.0])
         np.random.seed(seed)
         inputs = np.random.randint(0, 255, size=(224, 224, 3))
         output = backend.convert_to_numpy(layer(inputs))
@@ -44,7 +44,7 @@ def test_correctness(self):
         self.assertTrue(np.mean(diff) > 0)
 
         # Always scale down, but randomly between 0 ~ 255
-        layer = layers.RandomBrightness([-1.0, 0.0])
+        layer = layers.RandomBrightness([-1.0, -0.1])
         np.random.seed(seed)
         inputs = np.random.randint(0, 255, size=(224, 224, 3))
         output = backend.convert_to_numpy(layer(inputs))