增加keras-cv模型及训练代码

Keras/keras-cv/examples/layers/preprocessing/classification/channel_shuffle_demo.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.
+"""channel_shuffle_demo.py shows how to use the ChannelShuffle 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():
+    channel_shuffle = layers.ChannelShuffle()
+    ds = demo_utils.load_oxford_dataset()
+    ds = ds.map(channel_shuffle, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/cut_mix_demo.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.
+"""cut_mix_demo.py shows how to use the CutMix 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():
+    cutmix = layers.CutMix()
+    ds = demo_utils.load_oxford_dataset()
+    ds = ds.map(cutmix, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/demo_utils.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.
+"""Utility functions for preprocessing demos."""
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+
+def resize(image, label, img_size=(224, 224), classes=10):
+    image = tf.image.resize(image, img_size)
+    label = tf.one_hot(label, classes)
+    return {"images": image, "labels": label}
+
+
+def load_oxford_dataset(
+    name="oxford_flowers102",
+    batch_size=64,
+    img_size=(224, 224),
+    as_supervised=True,
+):
+    # Load dataset.
+    data, ds_info = tfds.load(name, as_supervised=as_supervised, with_info=True)
+    train_ds = data["train"]
+    classes = ds_info.features["label"].num_classes
+
+    # Get tf dataset.
+    train_ds = train_ds.map(
+        lambda x, y: resize(x, y, img_size=img_size, classes=classes)
+    ).batch(batch_size)
+    return train_ds
+
+
+def visualize_dataset(ds):
+    outputs = next(iter(ds.take(1)))
+    images = outputs["images"]
+    plt.figure(figsize=(8, 8))
+    for i in range(9):
+        plt.subplot(3, 3, i + 1)
+        plt.imshow(images[i].numpy().astype("uint8"))
+        plt.axis("off")
+    plt.show()
+
+
+def gallery_show(images):
+    images = images.astype(int)
+    for i in range(9):
+        image = images[i]
+        plt.subplot(3, 3, i + 1)
+        plt.imshow(image.astype("uint8"))
+        plt.axis("off")
+    plt.show()
+
+
+def load_elephant_tensor(output_size=(300, 300)):
+    elephants = tf.keras.utils.get_file(
+        "african_elephant.jpg", "https://i.imgur.com/Bvro0YD.png"
+    )
+    elephants = tf.keras.utils.load_img(elephants, target_size=output_size)
+    elephants = tf.keras.utils.img_to_array(elephants)
+
+    many_elephants = tf.repeat(tf.expand_dims(elephants, axis=0), 9, axis=0)
+    return many_elephants

Keras/keras-cv/examples/layers/preprocessing/classification/fourier_mix_demo.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.
+"""fourier_mix_demo.py shows how to use the FourierMix preprocessing layer.
+Uses 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():
+    fourier_mix = layers.FourierMix(alpha=0.5)
+    ds = demo_utils.load_oxford_dataset()
+    ds = ds.map(fourier_mix, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/grid_mask_demo.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.
+"""gridmask_demo.py shows how to use the GridMask 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
+
+import keras_cv
+from keras_cv.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    gridmask = preprocessing.GridMask(
+        ratio_factor=keras_cv.ConstantFactorSampler(0.3),
+        rotation_factor=0.5,
+        fill_mode="gaussian_noise",
+    )
+    ds = ds.map(gridmask, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/mix_up_demo.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.
+"""mix_up_demo.py shows how to use the MixUp preprocessing layer.
+
+Uses 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    mixup = preprocessing.MixUp(alpha=0.8)
+    ds = ds.map(mixup, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/mosaic_demo.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.
+"""mosaic_demo.py shows how to use the Mosaic 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():
+    mosaic = layers.Mosaic()
+    ds = demo_utils.load_oxford_dataset()
+    ds = ds.map(mosaic, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/rand_augment_demo.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.
+"""rand_augment_demo.py shows how to use the RandAugment preprocessing layer.
+
+Uses 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    rand_augment = preprocessing.RandAugment(
+        value_range=(0, 255), augmentations_per_image=3, magnitude=0.5, rate=0.875
+    )
+    ds = ds.map(rand_augment, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_augmentation_pipeline_demo.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.
+"""rand_augment_demo.py shows how to use the RandAugment preprocessing layer.
+
+Uses 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.layers import preprocessing
+
+
+def create_custom_pipeline():
+    layers = preprocessing.RandAugment.get_standard_policy(
+        value_range=(0, 255), magnitude=0.75, magnitude_stddev=0.3
+    )
+    layers = layers[:4]  # slice out some layers you don't want for whatever reason
+    layers = layers + [preprocessing.GridMask()]
+    return preprocessing.RandomAugmentationPipeline(
+        layers=layers, augmentations_per_image=3
+    )
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    custom_pipeline = create_custom_pipeline()
+    ds = ds.map(custom_pipeline, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_channel_shift_demo.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.
+
+
+"""random_channel_shift_demo.py shows how to use the RandomChannelShift 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    rgbshift = preprocessing.RandomChannelShift(value_range=(0, 255), factor=0.4)
+    ds = ds.map(rgbshift, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_color_degeneration_demo.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.
+"""random_color_degeneration_demo.py shows how to use RandomColorDegeneration.
+
+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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_color_degeneration = preprocessing.RandomColorDegeneration(factor=(0, 1.0))
+    ds = ds.map(random_color_degeneration, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_color_jitter_demo.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.
+
+"""color_jitter_demo.py shows how to use the ColorJitter 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    color_jitter = preprocessing.RandomColorJitter(
+        value_range=(0, 255),
+        brightness_factor=(-0.2, 0.5),
+        contrast_factor=(0.5, 0.9),
+        saturation_factor=(0.5, 0.9),
+        hue_factor=(0.5, 0.9),
+        seed=101,
+    )
+    ds = ds.map(color_jitter, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_crop_and_zoom.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.
+
+"""random_resized_crop_demo.py.py shows how to use the RandomResizedCrop
+preprocessing layer. Operates on an image of elephant. In this script the image
+is loaded, then are passed through the preprocessing layers.
+Finally, they are shown using matplotlib.
+"""
+
+import demo_utils
+
+from keras_cv.layers import RandomCropAndResize
+
+
+def main():
+    many_elephants = demo_utils.load_elephant_tensor(output_size=(300, 300))
+    layer = RandomCropAndResize(
+        target_size=(224, 224),
+        crop_area_factor=(0.8, 1.0),
+        aspect_ratio_factor=(3.0 / 4.0, 4.0 / 3.0),
+    )
+    augmented = layer(many_elephants)
+    demo_utils.gallery_show(augmented.numpy())
+
+    layer = RandomCropAndResize(
+        target_size=(224, 224),
+        crop_area_factor=(0.01, 1.0),
+        aspect_ratio_factor=(3.0 / 4.0, 4.0 / 3.0),
+    )
+    augmented = layer(many_elephants)
+    demo_utils.gallery_show(augmented.numpy())
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_cutout_demo.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.
+"""random_cutout_demo.py shows how to use the RandomCutout 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_cutout = preprocessing.RandomCutout(
+        height_factor=(0.3, 0.9),
+        width_factor=(0.3, 0.9),
+        fill_mode="gaussian_noise",
+    )
+    ds = ds.map(random_cutout, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_gaussian_blur_demo.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.
+"""random_gaussian_blur_demo.py shows how to use the RandomHue 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_gaussian_blur = preprocessing.RandomGaussianBlur(
+        kernel_size=3, factor=(0.0, 3.0)
+    )
+    ds = ds.map(random_gaussian_blur, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_hue_demo.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.
+"""random_hue_demo.py shows how to use the RandomHue 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_hue = preprocessing.RandomHue(factor=(0.0, 1.0), value_range=(0, 255))
+    ds = ds.map(random_hue, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_saturation_demo.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.
+"""random_saturation_demo.py shows how to use the RandomSaturation 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_saturation = preprocessing.RandomSaturation(factor=(0.0, 1.0))
+    ds = ds.map(random_saturation, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/random_shear_demo.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.
+"""random_shear_demo.py shows how to use the RandomShear 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.layers import preprocessing
+
+
+def main():
+    ds = demo_utils.load_oxford_dataset()
+    random_cutout = preprocessing.RandomShear(
+        x_factor=(0, 1),
+        y_factor=0.5,
+    )
+    ds = ds.map(random_cutout, num_parallel_calls=tf.data.AUTOTUNE)
+    demo_utils.visualize_dataset(ds)
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/layers/preprocessing/classification/randomly_zoomed_crop.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 demo_utils
+
+from keras_cv.layers import RandomlyZoomedCrop
+
+
+def main():
+    many_elephants = demo_utils.load_elephant_tensor(output_size=(300, 300))
+    layer = RandomlyZoomedCrop(
+        target_size=(224, 224),
+        zoom_factor=(0.8, 1.2),
+        aspect_ratio_factor=(3.0 / 4.0, 4.0 / 3.0),
+    )
+    augmented = layer(many_elephants)
+    demo_utils.gallery_show(augmented.numpy())
+
+    layer = RandomlyZoomedCrop(
+        target_size=(224, 224),
+        zoom_factor=(0.08, 2.0),
+        aspect_ratio_factor=(3.0 / 4.0, 4.0 / 3.0),
+    )
+    augmented = layer(many_elephants)
+    demo_utils.gallery_show(augmented.numpy())
+
+
+if __name__ == "__main__":
+    main()

Keras/keras-cv/examples/models/generative/stable_diffusion/text_to_image.py

+"""
+Title: Generate an image from a text prompt using StableDiffusion
+Author: fchollet
+Date created: 2022/09/24
+Last modified: 2022/09/24
+Description: Use StableDiffusion to generate an image according to a short text description.
+"""
+
+from PIL import Image
+
+from keras_cv.models import StableDiffusion
+
+model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
+img = model.text_to_image("Photograph of a beautiful horse running through a field")
+Image.fromarray(img[0]).save("horse.png")
+print("Saved at horse.png")