Adds Random Resized Crop

keras_cv/layers/__init__.py

@@ -48,6 +48,7 @@
 from keras_cv.layers.preprocessing.random_cutout import RandomCutout
 from keras_cv.layers.preprocessing.random_gaussian_blur import RandomGaussianBlur
 from keras_cv.layers.preprocessing.random_hue import RandomHue
+from keras_cv.layers.preprocessing.random_resized_crop import RandomResizedCrop
 from keras_cv.layers.preprocessing.random_saturation import RandomSaturation
 from keras_cv.layers.preprocessing.random_sharpness import RandomSharpness
 from keras_cv.layers.preprocessing.random_shear import RandomShear

keras_cv/layers/preprocessing/__init__.py

@@ -52,6 +52,7 @@
 from keras_cv.layers.preprocessing.random_cutout import RandomCutout
 from keras_cv.layers.preprocessing.random_gaussian_blur import RandomGaussianBlur
 from keras_cv.layers.preprocessing.random_hue import RandomHue
+from keras_cv.layers.preprocessing.random_resized_crop import RandomResizedCrop
 from keras_cv.layers.preprocessing.random_saturation import RandomSaturation
 from keras_cv.layers.preprocessing.random_sharpness import RandomSharpness
 from keras_cv.layers.preprocessing.random_shear import RandomShear

keras_cv/layers/preprocessing/random_resized_crop.py

@@ -0,0 +1,162 @@
+# 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 warnings
+
+import tensorflow as tf
+
+from keras_cv.utils import preprocessing
+
+
+@tf.keras.utils.register_keras_serializable(package="keras_cv")
+class RandomResizedCrop(tf.keras.__internal__.layers.BaseImageAugmentationLayer):
+    """Randomly resizes an image then crops to a target size.
+    Args:
+        resize_factor: A tuple of two floats, a single float or `keras_cv.FactorSampler`.
+            `factor` controls the extent to which the image is resized.
+            `factor=0.0` makes this layer perform a no-op operation, while a value of
+            1.0 doubles the size of the image.  If a single float is used, a value
+            between `0.0` and the passed float is sampled.  In order to ensure the value
+            is always the same, please pass a tuple with two identical floats:
+            `(0.5, 0.5)`.
+        crop_size: a tuple of two integers used as the target size to crop images to.
+        interpolation: interpolation method used in the `ImageProjectiveTransformV3` op.
+             Supported values are `"nearest"` and `"bilinear"`.
+             Defaults to `"bilinear"`.
+        aspect_ratio_factor: (Optional) A tuple of two floats, a single float or
+            `keras_cv.FactorSampler`. `factor` used to distort the aspect ratio of the
+            image. `factor=0.0` makes this layer perform a no-op operation, while a
+            value of 1.0 doubles the size of the image.  If a single float is used, a
+            value between `0.0` and the passed float is sampled.  In order to ensure the
+            value is always the same, please pass a tuple with two identical floats:
+            `(0.5, 0.5)`.
+        fill_mode: fill_mode in the `ImageProjectiveTransformV3` op.
+             Supported values are `"reflect"`, `"wrap"`, `"constant"`, and `"nearest"`.
+             Defaults to `"reflect"`.
+        fill_value: fill_value in the `ImageProjectiveTransformV3` op.
+             A `Tensor` of type `float32`. The value to be filled when fill_mode is
+             constant".  Defaults to `0.0`.
+        seed: Integer. Used to create a random seed.
+    """
+
+    def __init__(
+        self,
+        resize_factor,
+        crop_size,
+        aspect_ratio_factor=0.0,
+        interpolation="bilinear",
+        fill_mode="reflect",
+        fill_value=0.0,
+        seed=None,
+        **kwargs,
+    ):
+        super().__init__(seed=seed, **kwargs)
+        self.area_factor = preprocessing.parse_factor(
+            area_factor, param_name="area_factor", seed=seed
+        )
+        self.aspect_ratio_factor = preprocessing.parse_factor(
+            aspect_ratio_factor, param_name="aspect_ratio_factor", seed=seed
+        )
+        if area_factor == 0.0 and aspect_ratio_factor == 0.0:
+            warnings.warn(
+                "RandomResizedCrop received both `area_factor=0.0` and "
+                "`aspect_ratio_factor=0.0`. As a result, the layer will perform no "
+                "augmentation."
+            )
+        self.interpolation = interpolation
+        self.fill_mode = fill_mode
+        self.fill_value = fill_value
+        self.seed = seed
+
+    def get_random_transformation(self, image=None, label=None, bounding_box=None):
+        # random area and aspect ratio
+        random_area = (
+            1.0
+            + preprocessing.random_inversion(self._random_generator)
+            * self.area_factor()
+        )
+        random_aspect_ratio = (
+            1.0
+            + preprocessing.random_inversion(self._random_generator)
+            * self.aspect_ratio_factor()
+        )
+
+        # corresponding height and width (1 = original height/width)
+        new_height = tf.sqrt(random_area / random_aspect_ratio)
+        new_width = tf.sqrt(random_area * random_aspect_ratio)
+
+        # random offsets for the crop, inside or outside the image
+        height_offset = self._random_generator.random_uniform(
+            (),
+            tf.minimum(0.0, 1.0 - new_height),
+            tf.maximum(0.0, 1.0 - new_height),
+            dtype=tf.float32,
+        )
+        width_offset = self._random_generator.random_uniform(
+            (),
+            tf.minimum(0.0, 1.0 - new_width),
+            tf.maximum(0.0, 1.0 - new_width),
+            dtype=tf.float32,
+        )
+
+        return (new_height, new_width, height_offset, width_offset)
+
+    def augment_image(self, image, transformation):
+        height, width, _ = image.shape
+        image = tf.expand_dims(image, axis=0)
+
+        new_height, new_width, height_offset, width_offset = transformation
+
+        transform = RandomResizedCrop._format_transform(
+            [
+                new_width,
+                0.0,
+                width_offset * width,
+                0.0,
+                new_height,
+                height_offset * height,
+                0.0,
+                0.0,
+            ]
+        )
+        image = preprocessing.transform(
+            images=image,
+            transforms=transform,
+            interpolation=self.interpolation,
+            fill_mode=self.fill_mode,
+            fill_value=self.fill_value,
+        )
+
+        return tf.squeeze(image, axis=0)
+
+    def augment_label(self, label, transformation):
+        return label
+
+    @staticmethod
+    def _format_transform(transform):
+        transform = tf.convert_to_tensor(transform, dtype=tf.float32)
+        return transform[tf.newaxis]
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "area_factor": self.area_factor,
+                "aspect_ratio_factor": self.aspect_ratio_factor,
+                "interpolation": self.interpolation,
+                "fill_mode": self.fill_mode,
+                "fill_value": self.fill_value,
+                "seed": self.seed,
+            }
+        )
+        return config

keras_cv/layers/preprocessing/random_resized_crop_test.py

@@ -0,0 +1,29 @@
+# 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
+
+
+class RandomResizedCropTest(tf.test.TestCase):
+    def test_preserves_output_shape(self):
+        image_shape = (4, 300, 300, 3)
+        image = tf.random.uniform(shape=image_shape) * 255.0
+
+        layer = preprocessing.RandomResizeCrop(area_factor=(0.3, 0.8))
+        output = layer(image)
+
+        self.assertEqual(image.shape, output.shape)
+        self.assertNotAllClose(image, output)