Random Resized Crop

keras_cv/layers/__init__.py

@@ -49,6 +49,7 @@
 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

@@ -53,6 +53,7 @@
 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 +1,140 @@
+# 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.layers import BaseImageAugmentationLayer
+from keras_cv.utils import preprocessing
+
+
+@tf.keras.utils.register_keras_serializable(package="keras_cv")
+class RandomResizedCrop(BaseImageAugmentationLayer):
+    """
+    Randomly crops a part of an image and resizes it to provided size.
+
+    Args:
+        target_size: A tuple of two integers used as the target size to crop
+            images to.
+        aspect_ratio_factor: (Optional) A tuple of two floats. Represents the
+            lower and upper bounds for the aspect ratio of the cropped image
+            before resizing it to `target_size`. Defaults to (3./4., 4./3.).
+        area_factor: (Optional) A tuple of two floats, a single float or
+            `keras_cv.FactorSampler`. Represents the lower and upper bounds for
+            the area relative to the original image of the cropped image before
+            resizing it to `target_size`. Defaults to (0.08, 1.0).
+        interpolation: interpolation method used in the `ImageProjectiveTransformV3` op.
+             Supported values are `"nearest"` and `"bilinear"`.
+             Defaults to `"bilinear"`.
+        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,
+                 target_size,
+                 aspect_ratio_factor=(3. / 4., 4. / 3.),
+                 area_factor=(0.08, 1.0),
+                 interpolation="bilinear",
+                 fill_mode="reflect",
+                 fill_value=0.0,
+                 seed=None,
+                 **kwargs):
+        super(RandomResizedCrop, self).__init__(seed=seed, **kwargs)
+
+        self.target_size = target_size
+        self.aspect_ratio_factor = aspect_ratio_factor
+        self.area_factor = preprocessing.parse_factor(area_factor,
+                                                      param_name="area_factor",
+                                                      seed=seed)
+
+        self.interpolation = interpolation
+        self.fill_mode = fill_mode
+        self.fill_value = fill_value
+        self.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.")
+
+    def get_random_transformation(self,
+                                  image=None,
+                                  label=None,
+                                  bounding_box=None):
+        area_factor = self.area_factor()
+        aspect_ratio = tf.random.uniform((),
+                                         minval=self.aspect_ratio_factor[0],
+                                         maxval=self.aspect_ratio_factor[1],
+                                         dtype=tf.float32)
+
+        new_height = tf.clip_by_value(
+            tf.sqrt(area_factor / aspect_ratio), 0.0,
+            1.0)  # to avoid unwanted/unintuitive effects
+        new_width = tf.clip_by_value(tf.sqrt(area_factor * aspect_ratio), 0.0,
+                                     1.0)
+
+        height_offset = tf.random.uniform(
+            (),
+            minval=tf.minimum(0.0, 1.0 - new_height),
+            maxval=tf.maximum(0.0, 1.0 - new_height),
+            dtype=tf.float32,
+        )
+
+        width_offset = tf.random.uniform(
+            (),
+            minval=tf.minimum(0.0, 1.0 - new_width),
+            maxval=tf.maximum(0.0, 1.0 - new_width),
+            dtype=tf.float32,
+        )
+
+        y1 = height_offset
+        y2 = height_offset + new_height
+        x1 = width_offset
+        x2 = width_offset + new_width
+
+        return [[y1, x1, y2, x2]]
+
+    def augment_image(self, image, transformation):
+        image = tf.expand_dims(image, axis=0)
+        boxes = transformation
+
+        # See bit.ly/tf_crop_resize for more details
+        augmented_image = tf.image.crop_and_resize(
+            image,  # image shape: [B, H, W, C]
+            boxes,  # boxes: (1, 4) in this case; represents area
+            # to be cropped from the original image
+            [0],  # box_indices: maps boxes to images along batch axis
+            # [0] since there is only one image
+            self.target_size,  # output size
+        )
+
+        return tf.squeeze(augmented_image, axis=0)
+
+    def get_config(self):
+        config = super().get_config()
+        config.update({
+            "target_size": self.target_size,
+            "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 +1,42 @@
+# 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.layers import preprocessing
+
+
+class RandomResizedCropTest(tf.test.TestCase):
+    def train_augments_image(self):
+        # Checks if original and augmented images are different
+
+        input_image_shape = (4, 300, 300, 3)
+        image = tf.random.uniform(shape=input_image_shape)
+
+        layer = preprocessing.RandomResizedCrop((224, 224))
+        output = layer(image, training=True)
+
+        output_image_resized = tf.image.resize(output, input_image_shape)
+
+        self.assertNotEqual(image, output_image_resized)
+
+    def test_preserves_image(self):
+        # Checks if resultant image stays the same at inference time
+
+        image_shape = (4, 300, 300, 3)
+        image = tf.random.uniform(shape=image_shape)
+
+        layer = preprocessing.RandomResizedCrop((300, 300))
+        output = layer(image, training=False)
+
+        self.assertEqual(image.shape, output.shape)

keras_cv/layers/serialization_test.py

@@ -36,54 +36,96 @@ def config_equals(config1, config2):
 
 class SerializationTest(tf.test.TestCase, parameterized.TestCase):
     @parameterized.named_parameters(
-        ("AutoContrast", preprocessing.AutoContrast, {"value_range": (0, 255)}),
-        ("ChannelShuffle", preprocessing.ChannelShuffle, {"seed": 1}),
-        ("CutMix", preprocessing.CutMix, {"seed": 1}),
-        ("Equalization", preprocessing.Equalization, {"value_range": (0, 255)}),
+        ("AutoContrast", preprocessing.AutoContrast, {
+            "value_range": (0, 255)
+        }),
+        ("ChannelShuffle", preprocessing.ChannelShuffle, {
+            "seed": 1
+        }),
+        ("CutMix", preprocessing.CutMix, {
+            "seed": 1
+        }),
+        ("Equalization", preprocessing.Equalization, {
+            "value_range": (0, 255)
+        }),
         ("Grayscale", preprocessing.Grayscale, {}),
-        ("GridMask", preprocessing.GridMask, {"seed": 1}),
-        ("MixUp", preprocessing.MixUp, {"seed": 1}),
+        ("GridMask", preprocessing.GridMask, {
+            "seed": 1
+        }),
+        ("MixUp", preprocessing.MixUp, {
+            "seed": 1
+        }),
         (
             "RandomChannelShift",
             preprocessing.RandomChannelShift,
-            {"value_range": (0, 255), "factor": 0.5},
+            {
+                "value_range": (0, 255),
+                "factor": 0.5
+            },
         ),
         (
             "Posterization",
             preprocessing.Posterization,
-            {"bits": 3, "value_range": (0, 255)},
+            {
+                "bits": 3,
+                "value_range": (0, 255)
+            },
         ),
         (
             "RandomColorDegeneration",
             preprocessing.RandomColorDegeneration,
-            {"factor": 0.5, "seed": 1},
+            {
+                "factor": 0.5,
+                "seed": 1
+            },
         ),
         (
             "RandomCutout",
             preprocessing.RandomCutout,
-            {"height_factor": 0.2, "width_factor": 0.2, "seed": 1},
+            {
+                "height_factor": 0.2,
+                "width_factor": 0.2,
+                "seed": 1
+            },
         ),
         (
             "RandomHue",
             preprocessing.RandomHue,
-            {"factor": 0.5, "value_range": (0, 255), "seed": 1},
+            {
+                "factor": 0.5,
+                "value_range": (0, 255),
+                "seed": 1
+            },
         ),
         (
             "RandomSaturation",
             preprocessing.RandomSaturation,
-            {"factor": 0.5, "seed": 1},
+            {
+                "factor": 0.5,
+                "seed": 1
+            },
         ),
         (
             "RandomSharpness",
             preprocessing.RandomSharpness,
-            {"factor": 0.5, "value_range": (0, 255), "seed": 1},
+            {
+                "factor": 0.5,
+                "value_range": (0, 255),
+                "seed": 1
+            },
         ),
         (
             "RandomShear",
             preprocessing.RandomShear,
-            {"x_factor": 0.3, "x_factor": 0.3, "seed": 1},
+            {
+                "x_factor": 0.3,
+                "x_factor": 0.3,
+                "seed": 1
+            },
         ),
-        ("Solarization", preprocessing.Solarization, {"value_range": (0, 255)}),
+        ("Solarization", preprocessing.Solarization, {
+            "value_range": (0, 255)
+        }),
         (
             "RandAugment",
             preprocessing.RandAugment,
@@ -98,12 +140,20 @@ class SerializationTest(tf.test.TestCase, parameterized.TestCase):
         (
             "RandomAugmentationPipeline",
             preprocessing.RandomAugmentationPipeline,
-            {"layers": [], "augmentations_per_image": 1, "rate": 1.0},
+            {
+                "layers": [],
+                "augmentations_per_image": 1,
+                "rate": 1.0
+            },
         ),
         (
             "RandomChoice",
             preprocessing.RandomChoice,
-            {"layers": [], "seed": 3, "auto_vectorize": False},
+            {
+                "layers": [],
+                "seed": 3,
+                "auto_vectorize": False
+            },
         ),
         (
             "RandomColorJitter",
@@ -117,10 +167,27 @@ class SerializationTest(tf.test.TestCase, parameterized.TestCase):
                 "seed": 1,
             },
         ),
+        (
+            "RandomResizedCrop",
+            preprocessing.RandomResizedCrop,
+            {
+                "target_size": (224, 224),
+                "area_factor": (0.08, 1.0),
+                "aspect_ratio_factor": (3. / 4., 4. / 3.),
+                "interpolation": "bilinear",
+                "fill_mode": "reflect",
+                "fill_value": 0.,
+                "seed": 1,
+            },
+        ),
         (
             "DropBlock2D",
             regularization.DropBlock2D,
-            {"rate": 0.1, "block_size": (7, 7), "seed": 1234},
+            {
+                "rate": 0.1,
+                "block_size": (7, 7),
+                "seed": 1234
+            },
         ),
         (
             "StochasticDepth",