<reponame>kartik4949/keras-cv<filename>keras_cv/layers/preprocessing/rand_augment.py
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf

from keras_cv import core
from keras_cv.layers import preprocessing as cv_preprocessing
from keras_cv.layers.preprocessing.random_augmentation_pipeline import (
    RandomAugmentationPipeline,
)
from keras_cv.utils import preprocessing as preprocessing_utils


@tf.keras.utils.register_keras_serializable(package="keras_cv")
class RandAugment(RandomAugmentationPipeline):
    """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.

    The policy operates as follows:

    For each augmentation in the range `[0, augmentations_per_image]`,
    the policy selects a random operation from a list of operations.
    It then samples a random number and if that number is less than
    `rate` applies it to the given image.

    References:
        - [RandAugment](https://arxiv.org/abs/1909.13719)

    Args:
        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 setup.
        augmentations_per_image: the number of layers to use in the rand augment policy.
            Defaults to `3`.
        magnitude: magnitude is the mean of the normal distribution used to sample the
            magnitude used for each data augmentation.  magnitude should
            be a float in the range `[0, 1]`.  A magnitude of `0` indicates that the
            augmentations are as weak as possible (not recommended), while a value of
            `1.0` implies use of the strongest possible augmentation.  All magnitudes
            are clipped to the range `[0, 1]` after sampling.  Defaults to `0.5`.
        magnitude_stddev: the standard deviation to use when drawing values
            for the perturbations.  Keep in mind magnitude will still be clipped to the
            range `[0, 1]` after samples are drawn from the normal distribution.
            Defaults to `0.15`.
        rate:  the rate at which to apply each augmentation.  This parameter is applied
            on a per-distortion layer, per image.  Should be in the range `[0, 1]`.
            To reproduce the original RandAugment paper results, set this to `10/11`.
            The original `RandAugment` paper includes an Identity transform.  By setting
            the rate to 10/11 in our implementation, the behavior is identical to
            sampling an Identity augmentation 10/11th of the time.
            Defaults to `1.0`.

    Usage:
    ```python
    (x_test, y_test), _ = tf.keras.datasets.cifar10.load_data()
    rand_augment = keras_cv.layers.RandAugment(
        value_range=(0, 255), augmentations_per_image=3, magnitude=0.5
    )
    x_test = rand_augment(x_test)
    ```
    """

    def __init__(
        self,
        value_range,
        augmentations_per_image=3,
        magnitude=0.5,
        magnitude_stddev=0.15,
        rate=10 / 11,
        seed=None,
        **kwargs,
    ):
        # As an optimization RandAugment makes all internal layers use (0, 255) while
        # and we handle range transformation at the _augment level.
        if magnitude < 0.0 or magnitude > 1:
            raise ValueError(
                f"`magnitude` must be in the range [0, 1], got `magnitude={magnitude}`"
            )
        if magnitude_stddev < 0.0 or magnitude_stddev > 1:
            raise ValueError(
                "`magnitude_stddev` must be in the range [0, 1], got "
                f"`magnitude_stddev={magnitude}`"
            )

        super().__init__(
            layers=RandAugment.get_standard_policy(
                (0, 255), magnitude, magnitude_stddev, seed=seed
            ),
            augmentations_per_image=augmentations_per_image,
            rate=rate,
            **kwargs,
            seed=seed,
        )
        self.magnitude = float(magnitude)
        self.value_range = value_range
        self.seed = seed
        self.magnitude_stddev = float(magnitude_stddev)

    def _augment(self, sample):
        sample["images"] = preprocessing_utils.transform_value_range(
            sample["images"], self.value_range, (0, 255)
        )
        result = super()._augment(sample)
        result["images"] = preprocessing_utils.transform_value_range(
            result["images"], (0, 255), self.value_range
        )
        result["images"]
        return result

    @staticmethod
    def get_standard_policy(value_range, magnitude, magnitude_stddev, seed=None):
        policy = create_rand_augment_policy(magnitude, magnitude_stddev)

        auto_contrast = cv_preprocessing.AutoContrast(
            **policy["auto_contrast"], value_range=value_range, seed=seed
        )
        equalize = cv_preprocessing.Equalization(
            **policy["equalize"], value_range=value_range, seed=seed
        )

        solarize = cv_preprocessing.Solarization(
            **policy["solarize"], value_range=value_range, seed=seed
        )

        color = cv_preprocessing.RandomColorDegeneration(**policy["color"], seed=seed)
        contrast = cv_preprocessing.RandomContrast(**policy["contrast"], seed=seed)
        brightness = cv_preprocessing.RandomBrightness(
            **policy["brightness"], value_range=value_range, seed=seed
        )
        shear_x = cv_preprocessing.RandomShear(**policy["shear_x"], seed=seed)
        shear_y = cv_preprocessing.RandomShear(**policy["shear_y"], seed=seed)
        translate_x = cv_preprocessing.RandomTranslation(
            **policy["translate_x"], seed=seed
        )
        translate_y = cv_preprocessing.RandomTranslation(
            **policy["translate_y"], seed=seed
        )
        return [
            auto_contrast,
            equalize,
            solarize,
            color,
            contrast,
            brightness,
            shear_x,
            shear_y,
            translate_x,
            translate_y,
        ]

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "value_range": self.value_range,
                "augmentations_per_image": self.augmentations_per_image,
                "magnitude": self.magnitude,
                "magnitude_stddev": self.magnitude_stddev,
                "rate": self.rate,
                "seed": self.seed,
            }
        )
        # layers is recreated in the constructor
        del config["layers"]
        return config


def auto_contrast_policy(magnitude, magnitude_stddev):
    return {}


def equalize_policy(magnitude, magnitude_stddev):
    return {}


def solarize_policy(magnitude, magnitude_stddev):
    # We cap additions at 110, because if we add more than 110 we will be nearly
    # nullifying the information contained in the image, making the model train on noise
    maximum_addition_value = 110
    addition_factor = core.NormalFactorSampler(
        mean=magnitude * maximum_addition_value,
        stddev=magnitude_stddev * maximum_addition_value,
        min_value=0,
        max_value=maximum_addition_value,
    )
    threshold_factor = core.NormalFactorSampler(
        mean=(255 - (magnitude * 255)),
        stddev=(magnitude_stddev * 255),
        min_value=0,
        max_value=255,
    )
    return {"addition_factor": addition_factor, "threshold_factor": threshold_factor}


def color_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"factor": factor}


def contrast_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomContrast with `factor`?
    # RandomContrast layer errors when factor=0
    factor = max(magnitude, 0.001)
    return {"factor": factor}


def brightness_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomBrightness with `factor`?
    return {"factor": magnitude}


def shear_x_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"x_factor": factor, "y_factor": 0}


def shear_y_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"x_factor": 0, "y_factor": factor}


def translate_x_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomTranslation with `factor`?
    return {"width_factor": magnitude, "height_factor": 0}


def translate_y_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomTranslation with `factor`?
    return {"width_factor": 0, "height_factor": magnitude}


POLICY_PAIRS = {
    "auto_contrast": auto_contrast_policy,
    "equalize": equalize_policy,
    "solarize": solarize_policy,
    "color": color_policy,
    "contrast": contrast_policy,
    "brightness": brightness_policy,
    "shear_x": shear_x_policy,
    "shear_y": shear_y_policy,
    "translate_x": translate_x_policy,
    "translate_y": translate_y_policy,
}


def create_rand_augment_policy(magnitude, magnitude_stddev):
    result = {}
    for name, policy_fn in POLICY_PAIRS.items():
        result[name] = policy_fn(magnitude, magnitude_stddev)
    return result

# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf

from keras_cv import core
from keras_cv.layers import preprocessing as cv_preprocessing
from keras_cv.layers.preprocessing.random_augmentation_pipeline import (
    RandomAugmentationPipeline,
)
from keras_cv.utils import preprocessing as preprocessing_utils


@tf.keras.utils.register_keras_serializable(package="keras_cv")
class RandAugment(RandomAugmentationPipeline):
    """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.

    The policy operates as follows:

    For each augmentation in the range `[0, augmentations_per_image]`,
    the policy selects a random operation from a list of operations.
    It then samples a random number and if that number is less than
    `rate` applies it to the given image.

    References:
        - [RandAugment](https://arxiv.org/abs/1909.13719)

    Args:
        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 setup.
        augmentations_per_image: the number of layers to use in the rand augment policy.
            Defaults to `3`.
        magnitude: magnitude is the mean of the normal distribution used to sample the
            magnitude used for each data augmentation.  magnitude should
            be a float in the range `[0, 1]`.  A magnitude of `0` indicates that the
            augmentations are as weak as possible (not recommended), while a value of
            `1.0` implies use of the strongest possible augmentation.  All magnitudes
            are clipped to the range `[0, 1]` after sampling.  Defaults to `0.5`.
        magnitude_stddev: the standard deviation to use when drawing values
            for the perturbations.  Keep in mind magnitude will still be clipped to the
            range `[0, 1]` after samples are drawn from the normal distribution.
            Defaults to `0.15`.
        rate:  the rate at which to apply each augmentation.  This parameter is applied
            on a per-distortion layer, per image.  Should be in the range `[0, 1]`.
            To reproduce the original RandAugment paper results, set this to `10/11`.
            The original `RandAugment` paper includes an Identity transform.  By setting
            the rate to 10/11 in our implementation, the behavior is identical to
            sampling an Identity augmentation 10/11th of the time.
            Defaults to `1.0`.

    Usage:
    ```python
    (x_test, y_test), _ = tf.keras.datasets.cifar10.load_data()
    rand_augment = keras_cv.layers.RandAugment(
        value_range=(0, 255), augmentations_per_image=3, magnitude=0.5
    )
    x_test = rand_augment(x_test)
    ```
    """

    def __init__(
        self,
        value_range,
        augmentations_per_image=3,
        magnitude=0.5,
        magnitude_stddev=0.15,
        rate=10 / 11,
        seed=None,
        **kwargs,
    ):
        # As an optimization RandAugment makes all internal layers use (0, 255) while
        # and we handle range transformation at the _augment level.
        if magnitude < 0.0 or magnitude > 1:
            raise ValueError(
                f"`magnitude` must be in the range [0, 1], got `magnitude={magnitude}`"
            )
        if magnitude_stddev < 0.0 or magnitude_stddev > 1:
            raise ValueError(
                "`magnitude_stddev` must be in the range [0, 1], got "
                f"`magnitude_stddev={magnitude}`"
            )

        super().__init__(
            layers=RandAugment.get_standard_policy(
                (0, 255), magnitude, magnitude_stddev, seed=seed
            ),
            augmentations_per_image=augmentations_per_image,
            rate=rate,
            **kwargs,
            seed=seed,
        )
        self.magnitude = float(magnitude)
        self.value_range = value_range
        self.seed = seed
        self.magnitude_stddev = float(magnitude_stddev)

    def _augment(self, sample):
        sample["images"] = preprocessing_utils.transform_value_range(
            sample["images"], self.value_range, (0, 255)
        )
        result = super()._augment(sample)
        result["images"] = preprocessing_utils.transform_value_range(
            result["images"], (0, 255), self.value_range
        )
        result["images"]
        return result

    @staticmethod
    def get_standard_policy(value_range, magnitude, magnitude_stddev, seed=None):
        policy = create_rand_augment_policy(magnitude, magnitude_stddev)

        auto_contrast = cv_preprocessing.AutoContrast(
            **policy["auto_contrast"], value_range=value_range, seed=seed
        )
        equalize = cv_preprocessing.Equalization(
            **policy["equalize"], value_range=value_range, seed=seed
        )

        solarize = cv_preprocessing.Solarization(
            **policy["solarize"], value_range=value_range, seed=seed
        )

        color = cv_preprocessing.RandomColorDegeneration(**policy["color"], seed=seed)
        contrast = cv_preprocessing.RandomContrast(**policy["contrast"], seed=seed)
        brightness = cv_preprocessing.RandomBrightness(
            **policy["brightness"], value_range=value_range, seed=seed
        )
        shear_x = cv_preprocessing.RandomShear(**policy["shear_x"], seed=seed)
        shear_y = cv_preprocessing.RandomShear(**policy["shear_y"], seed=seed)
        translate_x = cv_preprocessing.RandomTranslation(
            **policy["translate_x"], seed=seed
        )
        translate_y = cv_preprocessing.RandomTranslation(
            **policy["translate_y"], seed=seed
        )
        return [
            auto_contrast,
            equalize,
            solarize,
            color,
            contrast,
            brightness,
            shear_x,
            shear_y,
            translate_x,
            translate_y,
        ]

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "value_range": self.value_range,
                "augmentations_per_image": self.augmentations_per_image,
                "magnitude": self.magnitude,
                "magnitude_stddev": self.magnitude_stddev,
                "rate": self.rate,
                "seed": self.seed,
            }
        )
        # layers is recreated in the constructor
        del config["layers"]
        return config


def auto_contrast_policy(magnitude, magnitude_stddev):
    return {}


def equalize_policy(magnitude, magnitude_stddev):
    return {}


def solarize_policy(magnitude, magnitude_stddev):
    # We cap additions at 110, because if we add more than 110 we will be nearly
    # nullifying the information contained in the image, making the model train on noise
    maximum_addition_value = 110
    addition_factor = core.NormalFactorSampler(
        mean=magnitude * maximum_addition_value,
        stddev=magnitude_stddev * maximum_addition_value,
        min_value=0,
        max_value=maximum_addition_value,
    )
    threshold_factor = core.NormalFactorSampler(
        mean=(255 - (magnitude * 255)),
        stddev=(magnitude_stddev * 255),
        min_value=0,
        max_value=255,
    )
    return {"addition_factor": addition_factor, "threshold_factor": threshold_factor}


def color_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"factor": factor}


def contrast_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomContrast with `factor`?
    # RandomContrast layer errors when factor=0
    factor = max(magnitude, 0.001)
    return {"factor": factor}


def brightness_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomBrightness with `factor`?
    return {"factor": magnitude}


def shear_x_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"x_factor": factor, "y_factor": 0}


def shear_y_policy(magnitude, magnitude_stddev):
    factor = core.NormalFactorSampler(
        mean=magnitude,
        stddev=magnitude_stddev,
        min_value=0,
        max_value=1,
    )
    return {"x_factor": 0, "y_factor": factor}


def translate_x_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomTranslation with `factor`?
    return {"width_factor": magnitude, "height_factor": 0}


def translate_y_policy(magnitude, magnitude_stddev):
    # TODO(lukewood): should we integrate RandomTranslation with `factor`?
    return {"width_factor": 0, "height_factor": magnitude}


POLICY_PAIRS = {
    "auto_contrast": auto_contrast_policy,
    "equalize": equalize_policy,
    "solarize": solarize_policy,
    "color": color_policy,
    "contrast": contrast_policy,
    "brightness": brightness_policy,
    "shear_x": shear_x_policy,
    "shear_y": shear_y_policy,
    "translate_x": translate_x_policy,
    "translate_y": translate_y_policy,
}


def create_rand_augment_policy(magnitude, magnitude_stddev):
    result = {}
    for name, policy_fn in POLICY_PAIRS.items():
        result[name] = policy_fn(magnitude, magnitude_stddev)
    return result
# Copyright 2022 The KerasCV Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #     https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. 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.      The policy operates as follows:      For each augmentation in the range `[0, augmentations_per_image]`,     the policy selects a random operation from a list of operations.     It then samples a random number and if that number is less than     `rate` applies it to the given image.      References:         - [RandAugment](https://arxiv.org/abs/1909.13719)      Args:         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 setup.         augmentations_per_image: the number of layers to use in the rand augment policy.             Defaults to `3`.         magnitude: magnitude is the mean of the normal distribution used to sample the             magnitude used for each data augmentation.  magnitude should             be a float in the range `[0, 1]`.  A magnitude of `0` indicates that the             augmentations are as weak as possible (not recommended), while a value of             `1.0` implies use of the strongest possible augmentation.  All magnitudes             are clipped to the range `[0, 1]` after sampling.  Defaults to `0.5`.         magnitude_stddev: the standard deviation to use when drawing values             for the perturbations.  Keep in mind magnitude will still be clipped to the             range `[0, 1]` after samples are drawn from the normal distribution.             Defaults to `0.15`.         rate:  the rate at which to apply each augmentation.  This parameter is applied             on a per-distortion layer, per image.  Should be in the range `[0, 1]`.             To reproduce the original RandAugment paper results, set this to `10/11`.             The original `RandAugment` paper includes an Identity transform.  By setting             the rate to 10/11 in our implementation, the behavior is identical to             sampling an Identity augmentation 10/11th of the time.             Defaults to `1.0`.      Usage:     ```python     (x_test, y_test), _ = tf.keras.datasets.cifar10.load_data()     rand_augment = keras_cv.layers.RandAugment(         value_range=(0, 255), augmentations_per_image=3, magnitude=0.5     )     x_test = rand_augment(x_test)     ``` # As an optimization RandAugment makes all internal layers use (0, 255) while # and we handle range transformation at the _augment level. # layers is recreated in the constructor # We cap additions at 110, because if we add more than 110 we will be nearly # nullifying the information contained in the image, making the model train on noise # TODO(lukewood): should we integrate RandomContrast with `factor`? # RandomContrast layer errors when factor=0 # TODO(lukewood): should we integrate RandomBrightness with `factor`? # TODO(lukewood): should we integrate RandomTranslation with `factor`? # TODO(lukewood): should we integrate RandomTranslation with `factor`?