Add AugMix

examples/layers/preprocessing/aug_mix_demo.py

@@ -0,0 +1,35 @@
+# 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.
+"""aug_mix_demo.py shows how to use the AugMix preprocessing layer.
+
+Operates on the oxford_flowers102 dataset.  In this script the flowers
+are loaded, then are passed through the preprocessing layers.
+Finally, they are shown using matplotlib.
+"""
+
+import demo_utils
+import tensorflow as tf
+
+from keras_cv import layers
+
+
+def main():
+    augmix = layers.AugMix(value_range=[0, 255])
+    ds = demo_utils.load_oxford_dataset()
+    ds = ds.map(augmix, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

keras_cv/layers/__init__.py

@@ -26,6 +26,7 @@
 from tensorflow.keras.layers import Rescaling
 from tensorflow.keras.layers import Resizing
 
+from keras_cv.layers.preprocessing.aug_mix import AugMix
 from keras_cv.layers.preprocessing.auto_contrast import AutoContrast
 from keras_cv.layers.preprocessing.channel_shuffle import ChannelShuffle
 from keras_cv.layers.preprocessing.cut_mix import CutMix

keras_cv/layers/preprocessing/__init__.py

@@ -29,6 +29,7 @@
 from tensorflow.keras.layers import Rescaling
 from tensorflow.keras.layers import Resizing
 
+from keras_cv.layers.preprocessing.aug_mix import AugMix
 from keras_cv.layers.preprocessing.auto_contrast import AutoContrast
 from keras_cv.layers.preprocessing.channel_shuffle import ChannelShuffle
 from keras_cv.layers.preprocessing.cut_mix import CutMix

keras_cv/layers/preprocessing/aug_mix.py

@@ -0,0 +1,313 @@
+# 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 layers
+from keras_cv.utils import preprocessing
+
+
+@tf.keras.utils.register_keras_serializable(package="keras_cv")
+class AugMix(tf.keras.__internal__.layers.BaseImageAugmentationLayer):
+    """Performs the AugMix data augmentation technique.
+
+    AugMix aims to produce images with variety while preserving the
+    image semantics and local statistics.  During the augmentation process, each image
+    is augmented `num_chains` different ways, each way consisting of `chain_depth`
+    augmentations. Augmentations are sampled from the list: translation, shearing,
+    rotation, posterization, histogram equalization, solarization and auto contrast.
+    The results of each chain are then mixed together with the original
+    image based on random samples from a Dirichlet distribution.
+
+    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.
+        severity: A tuple of two floats, a single float or a `keras_cv.FactorSampler`.
+            A value is sampled from the provided range.  If a float is passed, the
+            range is interpreted as `(0, severity)`. This value represents the
+            level of strength of augmentations and is in the range [0, 1].
+            Defaults to 0.3.
+        num_chains: an integer representing the number of different chains to
+            be mixed. Defaults to 3.
+        chain_depth: an integer or range representing the number of transformations in
+            the chains. Defaults to [1,3].
+        alpha: a float value used as the probability coefficients for the
+            Beta and Dirichlet distributions. Defaults to 1.0.
+        seed: Integer. Used to create a random seed.
+
+    References:
+        [AugMix paper](https://arxiv.org/pdf/1912.02781)
+        [Official Code](https://github.com/google-research/augmix)
+        [Unoffial TF Code](https://github.com/szacho/augmix-tf)
+
+    Sample Usage:
+    ```python
+    (images, labels), _ = tf.keras.datasets.cifar10.load_data()
+    augmix = keras_cv.layers.AugMix([0, 255])
+    augmented_images = augmix(images[:100])
+    ```
+    """
+
+    def __init__(
+        self,
+        value_range,
+        severity=0.3,
+        num_chains=3,
+        chain_depth=[1, 3],
+        alpha=1.0,
+        seed=None,
+        **kwargs,
+    ):
+        super().__init__(seed=seed, **kwargs)
+        self.value_range = value_range
+        self.num_chains = num_chains
+        self.chain_depth = chain_depth
+
+        if isinstance(self.chain_depth, int):
+            self.chain_depth = [self.chain_depth, self.chain_depth]
+
+        self.alpha = alpha
+        self.seed = seed
+        self.auto_vectorize = False
+        self.severity = severity
+        self.severity_factor = preprocessing.parse_factor(
+            self.severity,
+            min_value=0.01,
+            max_value=1.0,
+            param_name="severity",
+            seed=self.seed,
+        )
+
+        # initialize layers
+        self.auto_contrast = layers.AutoContrast(value_range=self.value_range)
+        self.equalize = layers.Equalization(value_range=self.value_range)
+
+    @staticmethod
+    def _sample_from_dirichlet(alpha):
+        gamma_sample = tf.random.gamma(shape=(), alpha=alpha)
+        return gamma_sample / tf.reduce_sum(gamma_sample, axis=-1, keepdims=True)
+
+    @staticmethod
+    def _sample_from_beta(alpha, beta):
+        sample_alpha = tf.random.gamma((), 1.0, beta=alpha)
+        sample_beta = tf.random.gamma((), 1.0, beta=beta)
+        return sample_alpha / (sample_alpha + sample_beta)
+
+    def _sample_depth(self):
+        return self._random_generator.random_uniform(
+            shape=(),
+            minval=self.chain_depth[0],
+            maxval=self.chain_depth[1] + 1,
+            dtype=tf.int32,
+        )
+
+    def _loop_on_depth(self, depth_level, image_aug):
+        op_index = self._random_generator.random_uniform(
+            shape=(), minval=0, maxval=8, dtype=tf.int32
+        )
+        image_aug = self._apply_op(image_aug, op_index)
+        depth_level += 1
+        return depth_level, image_aug
+
+    def _loop_on_width(self, image, chain_mixing_weights, curr_chain, result):
+        image_aug = tf.identity(image)
+        chain_depth = self._sample_depth()
+
+        depth_level = tf.constant([0], dtype=tf.int32)
+        depth_level, image_aug = tf.while_loop(
+            lambda depth_level, image_aug: tf.less(depth_level, chain_depth),
+            self._loop_on_depth,
+            [depth_level, image_aug],
+        )
+        result += tf.gather(chain_mixing_weights, curr_chain) * image_aug
+        curr_chain += 1
+        return image, chain_mixing_weights, curr_chain, result
+
+    def _auto_contrast(self, image):
+        return self.auto_contrast(image)
+
+    def _equalize(self, image):
+        return self.equalize(image)
+
+    def _posterize(self, image):
+        image = preprocessing.transform_value_range(
+            images=image,
+            original_range=self.value_range,
+            target_range=[0, 255],
+        )
+
+        bits = tf.cast(self.severity_factor() * 3, tf.int32)
+        shift = tf.cast(4 - bits + 1, tf.uint8)
+        image = tf.cast(image, tf.uint8)
+        image = tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
+        image = tf.cast(image, self.compute_dtype)
+        return preprocessing.transform_value_range(
+            images=image,
+            original_range=[0, 255],
+            target_range=self.value_range,
+        )
+
+    def _rotate(self, image):
+        angle = tf.expand_dims(tf.cast(self.severity_factor() * 30, tf.float32), axis=0)
+        shape = tf.cast(tf.shape(image), tf.float32)
+
+        return preprocessing.transform(
+            tf.expand_dims(image, 0),
+            preprocessing.get_rotation_matrix(angle, shape[0], shape[1]),
+        )[0]
+
+    def _solarize(self, image):
+        threshold = tf.cast(tf.cast(self.severity_factor() * 255, tf.int32), tf.float32)
+
+        image = preprocessing.transform_value_range(
+            image, original_range=self.value_range, target_range=(0, 255)
+        )
+        result = tf.clip_by_value(image, 0, 255)
+        result = tf.where(result < threshold, result, 255 - result)
+        return preprocessing.transform_value_range(
+            result, original_range=(0, 255), target_range=self.value_range
+        )
+
+    def _shear_x(self, image):
+        x = tf.cast(self.severity_factor() * 0.3, tf.float32)
+        x *= preprocessing.random_inversion(self._random_generator)
+        transform_x = layers.RandomShear._format_transform(
+            [1.0, x, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
+        )
+        return preprocessing.transform(
+            images=tf.expand_dims(image, 0), transforms=transform_x
+        )[0]
+
+    def _shear_y(self, image):
+        y = tf.cast(self.severity_factor() * 0.3, tf.float32)
+        y *= preprocessing.random_inversion(self._random_generator)
+        transform_x = layers.RandomShear._format_transform(
+            [1.0, 0.0, 0.0, y, 1.0, 0.0, 0.0, 0.0]
+        )
+        return preprocessing.transform(
+            images=tf.expand_dims(image, 0), transforms=transform_x
+        )[0]
+
+    def _translate_x(self, image):
+        shape = tf.cast(tf.shape(image), tf.float32)
+        x = tf.cast(self.severity_factor() * shape[1] / 3, tf.float32)
+        x = tf.expand_dims(tf.expand_dims(x, axis=0), axis=0)
+        x *= preprocessing.random_inversion(self._random_generator)
+        x = tf.cast(x, tf.int32)
+
+        translations = tf.cast(
+            tf.concat([x, tf.zeros_like(x)], axis=1), dtype=tf.float32
+        )
+        return preprocessing.transform(
+            tf.expand_dims(image, 0), preprocessing.get_translation_matrix(translations)
+        )[0]
+
+    def _translate_y(self, image):
+        shape = tf.cast(tf.shape(image), tf.float32)
+        y = tf.cast(self.severity_factor() * shape[0] / 3, tf.float32)
+        y = tf.expand_dims(tf.expand_dims(y, axis=0), axis=0)
+        y *= preprocessing.random_inversion(self._random_generator)
+        y = tf.cast(y, tf.int32)
+
+        translations = tf.cast(
+            tf.concat([tf.zeros_like(y), y], axis=1), dtype=tf.float32
+        )
+        return preprocessing.transform(
+            tf.expand_dims(image, 0), preprocessing.get_translation_matrix(translations)
+        )[0]
+
+    def _apply_op(self, image, op_index):
+        augmented = image
+        augmented = tf.cond(
+            op_index == tf.constant([0], dtype=tf.int32),
+            lambda: self._auto_contrast(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([1], dtype=tf.int32),
+            lambda: self._equalize(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([2], dtype=tf.int32),
+            lambda: self._posterize(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([3], dtype=tf.int32),
+            lambda: self._rotate(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([4], dtype=tf.int32),
+            lambda: self._solarize(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([5], dtype=tf.int32),
+            lambda: self._shear_x(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([6], dtype=tf.int32),
+            lambda: self._shear_y(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([7], dtype=tf.int32),
+            lambda: self._translate_x(augmented),
+            lambda: augmented,
+        )
+        augmented = tf.cond(
+            op_index == tf.constant([8], dtype=tf.int32),
+            lambda: self._translate_y(augmented),
+            lambda: augmented,
+        )
+        return augmented
+
+    def augment_image(self, image, transformation=None):
+        chain_mixing_weights = AugMix._sample_from_dirichlet(
+            tf.ones([self.num_chains]) * self.alpha
+        )
+        weight_sample = AugMix._sample_from_beta(self.alpha, self.alpha)
+
+        result = tf.zeros_like(image)
+        curr_chain = tf.constant([0], dtype=tf.int32)
+
+        image, chain_mixing_weights, curr_chain, result = tf.while_loop(
+            lambda image, chain_mixing_weights, curr_chain, result: tf.less(
+                curr_chain, self.num_chains
+            ),
+            self._loop_on_width,
+            [image, chain_mixing_weights, curr_chain, result],
+        )
+        result = weight_sample * image + (1 - weight_sample) * result
+        return result
+
+    def augment_label(self, label, transformation=None):
+        return label
+
+    def get_config(self):
+        config = {
+            "value_range": self.value_range,
+            "severity": self.severity,
+            "num_chains": self.num_chains,
+            "chain_depth": self.chain_depth,
+            "alpha": self.alpha,
+            "seed": self.seed,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))