Split RandomlyResizedCrop into two API surfaces (RandomlyZoomedCrop, …

…RandomCropAndResize) (keras-team#738) * Sync * Added zoom factor to RRC * Used tf.shape * dtype mismatch * Debugging... * Debugging... * Fix example * RRC uses preprocessing.transform now * Minor error * Minor bug * minor issue * minor issue * minor bug * Added unit tests * Fix serialization test * KerasCV simclr api update * Augmenter * Augmenter * serialization test * serialization test * fix failing test * Split RRC API into two layers * Split RRC API into two layers * Format serialization_test * Implemented bounding box support * Add preprocessing * serialization test * serialization test * serialization test * RandomCropAndResize in SimCLR * RandomCropAndResize in SimCLR * Update examples * Update examples * Update examples * Update target_size Co-authored-by: Luke Wood <lukewoodcs@gmail.com>
y-vectorfield · Sep 22, 2022 · 3bcf158 · 3bcf158
1 parent 1490ca9
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diff --git a/...lassification/random_resized_crop_demo.py → ...ng/classification/random_crop_and_zoom.py b/...lassification/random_resized_crop_demo.py → ...ng/classification/random_crop_and_zoom.py
@@ -20,14 +20,22 @@
 
 import demo_utils
 
-from keras_cv.layers.preprocessing import RandomResizedCrop
+from keras_cv.layers import RandomCropAndResize
 
 
 def main():
     many_elephants = demo_utils.load_elephant_tensor(output_size=(300, 300))
-    layer = RandomResizedCrop(
+    layer = RandomCropAndResize(
         target_size=(224, 224),
-        crop_area_factor=(0.08, 1.0),
+        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)

diff --git a/examples/layers/preprocessing/classification/randomly_zoomed_crop.py b/examples/layers/preprocessing/classification/randomly_zoomed_crop.py
@@ -0,0 +1,39 @@
+# 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()
diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py
@@ -56,15 +56,16 @@
     RandomColorDegeneration,
 )
 from keras_cv.layers.preprocessing.random_color_jitter import RandomColorJitter
+from keras_cv.layers.preprocessing.random_crop_and_resize import RandomCropAndResize
 from keras_cv.layers.preprocessing.random_cutout import RandomCutout
 from keras_cv.layers.preprocessing.random_flip import RandomFlip
 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_jpeg_quality import RandomJpegQuality
-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
+from keras_cv.layers.preprocessing.randomly_zoomed_crop import RandomlyZoomedCrop
 from keras_cv.layers.preprocessing.solarization import Solarization
 from keras_cv.layers.regularization.drop_path import DropPath
 from keras_cv.layers.regularization.dropblock_2d import DropBlock2D

diff --git a/keras_cv/layers/preprocessing/__init__.py b/keras_cv/layers/preprocessing/__init__.py
@@ -51,14 +51,15 @@
     RandomColorDegeneration,
 )
 from keras_cv.layers.preprocessing.random_color_jitter import RandomColorJitter
+from keras_cv.layers.preprocessing.random_crop_and_resize import RandomCropAndResize
 from keras_cv.layers.preprocessing.random_cutout import RandomCutout
 from keras_cv.layers.preprocessing.random_flip import RandomFlip
 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_jpeg_quality import RandomJpegQuality
-from keras_cv.layers.preprocessing.random_resized_crop import RandomResizedCrop
 from keras_cv.layers.preprocessing.random_rotation import RandomRotation
 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
+from keras_cv.layers.preprocessing.randomly_zoomed_crop import RandomlyZoomedCrop
 from keras_cv.layers.preprocessing.solarization import Solarization
diff --git a/keras_cv/layers/preprocessing/augmenter_test.py b/keras_cv/layers/preprocessing/augmenter_test.py
@@ -26,7 +26,7 @@ def test_return_shapes(self):
                 preprocessing.Grayscale(
                     output_channels=1,
                 ),
-                preprocessing.RandomResizedCrop(
+                preprocessing.RandomCropAndResize(
                     target_size=(100, 100),
                     crop_area_factor=(1, 1),
                     aspect_ratio_factor=(1, 1),
@@ -46,7 +46,7 @@ def test_in_tf_function(self):
                 preprocessing.Grayscale(
                     output_channels=1,
                 ),
-                preprocessing.RandomResizedCrop(
+                preprocessing.RandomCropAndResize(
                     target_size=(100, 100),
                     crop_area_factor=(1, 1),
                     aspect_ratio_factor=(1, 1),

diff --git a/...yers/preprocessing/random_resized_crop.py → ...s/preprocessing/random_crop_and_resize.py b/...yers/preprocessing/random_resized_crop.py → ...s/preprocessing/random_crop_and_resize.py
@@ -22,7 +22,7 @@
 
 
 @tf.keras.utils.register_keras_serializable(package="keras_cv")
-class RandomResizedCrop(BaseImageAugmentationLayer):
+class RandomCropAndResize(BaseImageAugmentationLayer):
     """Randomly crops a part of an image and resizes it to provided size.
 
     This implementation takes an intuitive approach, where we crop the images to a
@@ -142,6 +142,9 @@ def call(self, inputs, training=True):
     def augment_image(self, image, transformation, **kwargs):
         return self._crop_and_resize(image, transformation)
 
+    def augment_target(self, target, **kwargs):
+        return target
+
     def _resize(self, image, **kwargs):
         outputs = tf.keras.preprocessing.image.smart_resize(
             image, self.target_size, **kwargs
@@ -186,9 +189,6 @@ def _check_class_arguments(
                 f"aspect_ratio_factor={aspect_ratio_factor}"
             )
 
-    def augment_target(self, augment_target, **kwargs):
-        return augment_target
-
     def augment_segmentation_mask(self, segmentation_mask, transformation, **kwargs):
         return self._crop_and_resize(
             segmentation_mask, transformation, method="nearest"

diff --git a/...preprocessing/random_resized_crop_test.py → ...processing/random_crop_and_resize_test.py b/...preprocessing/random_resized_crop_test.py → ...processing/random_crop_and_resize_test.py
@@ -18,7 +18,7 @@
 from keras_cv.layers import preprocessing
 
 
-class RandomResizedCropTest(tf.test.TestCase, parameterized.TestCase):
+class RandomCropAndResizeTest(tf.test.TestCase, parameterized.TestCase):
     height, width = 300, 300
     batch_size = 4
     target_size = (224, 224)
@@ -30,7 +30,7 @@ def test_train_augments_image(self):
         input_image_shape = (self.batch_size, self.height, self.width, 3)
         image = tf.random.uniform(shape=input_image_shape, seed=self.seed)
 
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=self.target_size,
             aspect_ratio_factor=(3 / 4, 4 / 3),
             crop_area_factor=(0.8, 1.0),
@@ -46,7 +46,7 @@ def test_grayscale(self):
         input_image_shape = (self.batch_size, self.height, self.width, 1)
         image = tf.random.uniform(shape=input_image_shape)
 
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=self.target_size,
             aspect_ratio_factor=(3 / 4, 4 / 3),
             crop_area_factor=(0.8, 1.0),
@@ -62,7 +62,7 @@ def test_preserves_image(self):
         image_shape = (self.batch_size, self.height, self.width, 3)
         image = tf.random.uniform(shape=image_shape)
 
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=self.target_size,
             aspect_ratio_factor=(3 / 4, 4 / 3),
             crop_area_factor=(0.8, 1.0),
@@ -84,7 +84,7 @@ def test_target_size_errors(self, target_size):
             ValueError,
             "`target_size` must be tuple of two integers. Received target_size=(.*)",
         ):
-            _ = preprocessing.RandomResizedCrop(
+            _ = preprocessing.RandomCropAndResize(
                 target_size=target_size,
                 aspect_ratio_factor=(3 / 4, 4 / 3),
                 crop_area_factor=(0.8, 1.0),
@@ -101,7 +101,7 @@ def test_aspect_ratio_factor_errors(self, aspect_ratio_factor):
             "`aspect_ratio_factor` must be tuple of two positive floats or "
             "keras_cv.core.FactorSampler instance. Received aspect_ratio_factor=(.*)",
         ):
-            _ = preprocessing.RandomResizedCrop(
+            _ = preprocessing.RandomCropAndResize(
                 target_size=(224, 224),
                 aspect_ratio_factor=aspect_ratio_factor,
                 crop_area_factor=(0.8, 1.0),
@@ -119,7 +119,7 @@ def test_crop_area_factor_errors(self, crop_area_factor):
             "equal to 1 or keras_cv.core.FactorSampler instance. Received "
             "crop_area_factor=(.*)",
         ):
-            _ = preprocessing.RandomResizedCrop(
+            _ = preprocessing.RandomCropAndResize(
                 target_size=(224, 224),
                 aspect_ratio_factor=(3 / 4, 4 / 3),
                 crop_area_factor=crop_area_factor,
@@ -136,7 +136,7 @@ def test_augment_sparse_segmentation_mask(self):
         inputs = {"images": image, "segmentation_masks": mask}
 
         # Crop-only to exactly 1/2 of the size
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=(150, 150),
             aspect_ratio_factor=(1, 1),
             crop_area_factor=(1, 1),
@@ -149,7 +149,7 @@ def test_augment_sparse_segmentation_mask(self):
         self.assertAllClose(output["segmentation_masks"], input_mask_resized)
 
         # Crop to an arbitrary size and make sure we don't do bad interpolation
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=(233, 233),
             aspect_ratio_factor=(3 / 4, 4 / 3),
             crop_area_factor=(0.8, 1.0),
@@ -172,7 +172,7 @@ def test_augment_one_hot_segmentation_mask(self):
         inputs = {"images": image, "segmentation_masks": mask}
 
         # Crop-only to exactly 1/2 of the size
-        layer = preprocessing.RandomResizedCrop(
+        layer = preprocessing.RandomCropAndResize(
             target_size=(150, 150),
             aspect_ratio_factor=(1, 1),
             crop_area_factor=(1, 1),