<filename>keras_cv/layers/preprocessing/mix_up.py<gh_stars>0
# 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


@tf.keras.utils.register_keras_serializable(package="keras_cv")
class MixUp(tf.keras.__internal__.layers.BaseImageAugmentationLayer):
    """MixUp implements the MixUp data augmentation technique.

    Args:
        alpha: Float between 0 and 1.  Inverse scale parameter for the gamma
            distribution.  This controls the shape of the distribution from which the
            smoothing values are sampled.  Defaults 0.2, which is a recommended value
            when training an imagenet1k classification model.
        seed: Integer. Used to create a random seed.

    References:
        [MixUp paper](https://arxiv.org/abs/1710.09412).
        [MixUp for Object Detection paper](https://arxiv.org/pdf/1902.04103).

    Sample usage:
    ```python
    (images, labels), _ = tf.keras.datasets.cifar10.load_data()
    mixup = keras_cv.layers.preprocessing.mix_up.MixUp(10)
    augmented_images, updated_labels = mixup({'images': images, 'labels': labels})
    # output == {'images': updated_images, 'labels': updated_labels}
    ```
    """

    def __init__(self, alpha=0.2, seed=None, **kwargs):
        super().__init__(seed=seed, **kwargs)
        self.alpha = alpha
        self.seed = seed

    @staticmethod
    def _sample_from_beta(alpha, beta, shape):
        sample_alpha = tf.random.gamma(shape, 1.0, beta=alpha)
        sample_beta = tf.random.gamma(shape, 1.0, beta=beta)
        return sample_alpha / (sample_alpha + sample_beta)

    def _batch_augment(self, inputs):
        images = inputs.get("images", None)
        labels = inputs.get("labels", None)
        bounding_boxes = inputs.get("bounding_boxes", None)
        if images is None or (labels is None and bounding_boxes is None):
            raise ValueError(
                "MixUp expects inputs in a dictionary with format "
                '{"images": images, "labels": labels}. or'
                '{"images": images, "bounding_boxes": bounding_boxes}'
                f"Got: inputs = {inputs}"
            )
        images, lambda_sample, permutation_order = self._mixup(images)
        if labels is not None:
            labels = self._update_labels(labels, lambda_sample, permutation_order)
            inputs["labels"] = labels
        if bounding_boxes is not None:
            bounding_boxes = self._update_bounding_boxes(
                bounding_boxes, permutation_order
            )
            inputs["bounding_boxes"] = bounding_boxes
        inputs["images"] = images
        return inputs

    def _augment(self, inputs):
        raise ValueError(
            "MixUp received a single image to `call`.  The layer relies on "
            "combining multiple examples, and as such will not behave as "
            "expected.  Please call the layer with 2 or more samples."
        )

    def _mixup(self, images):
        batch_size = tf.shape(images)[0]
        permutation_order = tf.random.shuffle(tf.range(0, batch_size), seed=self.seed)

        lambda_sample = MixUp._sample_from_beta(self.alpha, self.alpha, (batch_size,))
        lambda_sample = tf.reshape(lambda_sample, [-1, 1, 1, 1])

        mixup_images = tf.gather(images, permutation_order)
        images = lambda_sample * images + (1.0 - lambda_sample) * mixup_images

        return images, tf.squeeze(lambda_sample), permutation_order

    def _update_labels(self, labels, lambda_sample, permutation_order):
        labels_for_mixup = tf.gather(labels, permutation_order)

        lambda_sample = tf.reshape(lambda_sample, [-1, 1])
        labels = lambda_sample * labels + (1.0 - lambda_sample) * labels_for_mixup

        return labels

    def _update_bounding_boxes(self, bounding_boxes, permutation_order):
        boxes_for_mixup = tf.gather(bounding_boxes, permutation_order)
        bounding_boxes = tf.concat([bounding_boxes, boxes_for_mixup], axis=1)

        return bounding_boxes

    def get_config(self):
        config = {
            "alpha": self.alpha,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))

<filename>keras_cv/layers/preprocessing/mix_up.py<gh_stars>0
# 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


@tf.keras.utils.register_keras_serializable(package="keras_cv")
class MixUp(tf.keras.__internal__.layers.BaseImageAugmentationLayer):
    """MixUp implements the MixUp data augmentation technique.

    Args:
        alpha: Float between 0 and 1.  Inverse scale parameter for the gamma
            distribution.  This controls the shape of the distribution from which the
            smoothing values are sampled.  Defaults 0.2, which is a recommended value
            when training an imagenet1k classification model.
        seed: Integer. Used to create a random seed.

    References:
        [MixUp paper](https://arxiv.org/abs/1710.09412).
        [MixUp for Object Detection paper](https://arxiv.org/pdf/1902.04103).

    Sample usage:
    ```python
    (images, labels), _ = tf.keras.datasets.cifar10.load_data()
    mixup = keras_cv.layers.preprocessing.mix_up.MixUp(10)
    augmented_images, updated_labels = mixup({'images': images, 'labels': labels})
    # output == {'images': updated_images, 'labels': updated_labels}
    ```
    """

    def __init__(self, alpha=0.2, seed=None, **kwargs):
        super().__init__(seed=seed, **kwargs)
        self.alpha = alpha
        self.seed = seed

    @staticmethod
    def _sample_from_beta(alpha, beta, shape):
        sample_alpha = tf.random.gamma(shape, 1.0, beta=alpha)
        sample_beta = tf.random.gamma(shape, 1.0, beta=beta)
        return sample_alpha / (sample_alpha + sample_beta)

    def _batch_augment(self, inputs):
        images = inputs.get("images", None)
        labels = inputs.get("labels", None)
        bounding_boxes = inputs.get("bounding_boxes", None)
        if images is None or (labels is None and bounding_boxes is None):
            raise ValueError(
                "MixUp expects inputs in a dictionary with format "
                '{"images": images, "labels": labels}. or'
                '{"images": images, "bounding_boxes": bounding_boxes}'
                f"Got: inputs = {inputs}"
            )
        images, lambda_sample, permutation_order = self._mixup(images)
        if labels is not None:
            labels = self._update_labels(labels, lambda_sample, permutation_order)
            inputs["labels"] = labels
        if bounding_boxes is not None:
            bounding_boxes = self._update_bounding_boxes(
                bounding_boxes, permutation_order
            )
            inputs["bounding_boxes"] = bounding_boxes
        inputs["images"] = images
        return inputs

    def _augment(self, inputs):
        raise ValueError(
            "MixUp received a single image to `call`.  The layer relies on "
            "combining multiple examples, and as such will not behave as "
            "expected.  Please call the layer with 2 or more samples."
        )

    def _mixup(self, images):
        batch_size = tf.shape(images)[0]
        permutation_order = tf.random.shuffle(tf.range(0, batch_size), seed=self.seed)

        lambda_sample = MixUp._sample_from_beta(self.alpha, self.alpha, (batch_size,))
        lambda_sample = tf.reshape(lambda_sample, [-1, 1, 1, 1])

        mixup_images = tf.gather(images, permutation_order)
        images = lambda_sample * images + (1.0 - lambda_sample) * mixup_images

        return images, tf.squeeze(lambda_sample), permutation_order

    def _update_labels(self, labels, lambda_sample, permutation_order):
        labels_for_mixup = tf.gather(labels, permutation_order)

        lambda_sample = tf.reshape(lambda_sample, [-1, 1])
        labels = lambda_sample * labels + (1.0 - lambda_sample) * labels_for_mixup

        return labels

    def _update_bounding_boxes(self, bounding_boxes, permutation_order):
        boxes_for_mixup = tf.gather(bounding_boxes, permutation_order)
        bounding_boxes = tf.concat([bounding_boxes, boxes_for_mixup], axis=1)

        return bounding_boxes

    def get_config(self):
        config = {
            "alpha": self.alpha,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))

# 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. MixUp implements the MixUp data augmentation technique.      Args:         alpha: Float between 0 and 1.  Inverse scale parameter for the gamma             distribution.  This controls the shape of the distribution from which the             smoothing values are sampled.  Defaults 0.2, which is a recommended value             when training an imagenet1k classification model.         seed: Integer. Used to create a random seed.      References:         [MixUp paper](https://arxiv.org/abs/1710.09412).         [MixUp for Object Detection paper](https://arxiv.org/pdf/1902.04103).      Sample usage:     ```python     (images, labels), _ = tf.keras.datasets.cifar10.load_data()     mixup = keras_cv.layers.preprocessing.mix_up.MixUp(10)     augmented_images, updated_labels = mixup({'images': images, 'labels': labels})     # output == {'images': updated_images, 'labels': updated_labels}     ```