Added parameterized tests to RRC (keras-team#550)

* Added parameterized tests

* Added parameterized tests for aspect_ratio_factor and crop_area_factor

* Formatted

* Formatted

* Minor changes

keras_cv/layers/preprocessing/random_resized_crop.py

@@ -14,6 +14,7 @@
 
 import tensorflow as tf
 
+from keras_cv import core
 from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
     BaseImageAugmentationLayer,
 )
@@ -164,10 +165,30 @@ def _check_class_arguments(
             or isinstance(target_size, int)
         ):
             raise ValueError(
-                "`target_size` must be tuple of two integers."
+                "`target_size` must be tuple of two integers. "
                 f"Received target_size={target_size}"
             )
 
+        if (
+            not isinstance(crop_area_factor, (tuple, list, core.FactorSampler))
+            or isinstance(crop_area_factor, float)
+            or isinstance(crop_area_factor, int)
+        ):
+            raise ValueError(
+                "`crop_area_factor` must be tuple of two positive floats less than or equal to 1 or keras_cv.core.FactorSampler instance. "
+                f"Received crop_area_factor={crop_area_factor}"
+            )
+
+        if (
+            not isinstance(aspect_ratio_factor, (tuple, list, core.FactorSampler))
+            or isinstance(aspect_ratio_factor, float)
+            or isinstance(aspect_ratio_factor, int)
+        ):
+            raise ValueError(
+                "`aspect_ratio_factor` must be tuple of two positive floats or keras_cv.core.FactorSampler instance. "
+                f"Received aspect_ratio_factor={aspect_ratio_factor}"
+            )
+
     def get_config(self):
         config = super().get_config()
         config.update(

keras_cv/layers/preprocessing/random_resized_crop_test.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import tensorflow as tf
+from absl.testing import parameterized
 
 from keras_cv.layers import preprocessing
 
 
-class RandomResizedCropTest(tf.test.TestCase):
+class RandomResizedCropTest(tf.test.TestCase, parameterized.TestCase):
     height, width = 300, 300
     batch_size = 4
     target_size = (224, 224)
@@ -70,3 +71,54 @@ def test_preserves_image(self):
         output = layer(image, training=False)
 
         self.assertAllClose(output, input_resized)
+
+    @parameterized.named_parameters(
+        ("Not tuple or list", dict()),
+        ("Length not equal to 2", [1, 2, 3]),
+        ("Members not int", (2.3, 4.5)),
+        ("Single integer", 5),
+    )
+    def test_target_size_errors(self, target_size):
+        with self.assertRaisesRegex(
+            ValueError,
+            "`target_size` must be tuple of two integers. Received target_size=(.*)",
+        ):
+            _ = preprocessing.RandomResizedCrop(
+                target_size=target_size,
+                aspect_ratio_factor=(3 / 4, 4 / 3),
+                crop_area_factor=(0.8, 1.0),
+            )
+
+    @parameterized.named_parameters(
+        ("Not tuple or list", dict()),
+        ("Single integer", 5),
+        ("Single float", 5.0),
+    )
+    def test_aspect_ratio_factor_errors(self, aspect_ratio_factor):
+        with self.assertRaisesRegex(
+            ValueError,
+            "`aspect_ratio_factor` must be tuple of two positive floats or keras_cv.core.FactorSampler instance. "
+            "Received aspect_ratio_factor=(.*)",
+        ):
+            _ = preprocessing.RandomResizedCrop(
+                target_size=(224, 224),
+                aspect_ratio_factor=aspect_ratio_factor,
+                crop_area_factor=(0.8, 1.0),
+            )
+
+    @parameterized.named_parameters(
+        ("Not tuple or list", dict()),
+        ("Single integer", 5),
+        ("Single float", 5.0),
+    )
+    def test_crop_area_factor_errors(self, crop_area_factor):
+        with self.assertRaisesRegex(
+            ValueError,
+            "`crop_area_factor` must be tuple of two positive floats less than or equal to 1 or keras_cv.core.FactorSampler instance. "
+            "Received crop_area_factor=(.*)",
+        ):
+            _ = preprocessing.RandomResizedCrop(
+                target_size=(224, 224),
+                aspect_ratio_factor=(3 / 4, 4 / 3),
+                crop_area_factor=crop_area_factor,
+            )