Add pad_to_aspect_ratio flag to ImageConverter

keras_hub/src/layers/preprocessing/image_converter.py

@@ -98,6 +98,7 @@ def __init__(
         scale=None,
         offset=None,
         crop_to_aspect_ratio=True,
+        pad_to_aspect_ratio=False,
         interpolation="bilinear",
         data_format=None,
         **kwargs,
@@ -112,12 +113,19 @@ def __init__(
 
         super().__init__(**kwargs)
 
+        if crop_to_aspect_ratio and pad_to_aspect_ratio:
+            raise ValueError(
+                "Only one of 'crop_to_aspect_ratio' or 'pad_to_aspect_ratio' "
+                "can be True."
+            )
+
         # Create the `Resizing` layer here even if it's not being used. That
         # allows us to make `image_size` a settable property.
         self.resizing = keras.layers.Resizing(
             height=image_size[0] if image_size else None,
             width=image_size[1] if image_size else None,
             crop_to_aspect_ratio=crop_to_aspect_ratio,
+            pad_to_aspect_ratio=pad_to_aspect_ratio,
             interpolation=interpolation,
             data_format=data_format,
             dtype=self.dtype_policy,
@@ -126,6 +134,7 @@ def __init__(
         self.scale = scale
         self.offset = offset
         self.crop_to_aspect_ratio = crop_to_aspect_ratio
+        self.pad_to_aspect_ratio = pad_to_aspect_ratio
         self.interpolation = interpolation
         self.data_format = standardize_data_format(data_format)
 
@@ -182,6 +191,7 @@ def get_config(self):
                 "offset": self.offset,
                 "interpolation": self.interpolation,
                 "crop_to_aspect_ratio": self.crop_to_aspect_ratio,
+                "pad_to_aspect_ratio": self.pad_to_aspect_ratio,
             }
         )
         return config

keras_hub/src/layers/preprocessing/image_converter_test.py

@@ -1,8 +1,10 @@
 import os
 import pathlib
 
+import keras
 import numpy as np
 import pytest
+from absl.testing import parameterized
 from keras import ops
 
 from keras_hub.src.layers.preprocessing.image_converter import ImageConverter
@@ -33,11 +35,21 @@ def test_unbatched(self):
         self.assertAllClose(outputs[:, :, 1], np.ones((4, 4)) * 0.301569)
         self.assertAllClose(outputs[:, :, 2], np.ones((4, 4)) * 0.852353)
 
-    def test_resize_batch(self):
+    @parameterized.parameters(
+        (True, False),
+        (False, True),
+    )
+    @pytest.mark.skipif(
+        keras.config.backend() == "torch",
+        reason="disabled until resize is fixed for torch backend",
+    )  # TODO: remove skip after new release with fix of https://github.com/keras-team/keras/pull/20797
+    def test_resize_batch(self, crop_to_aspect_ratio, pad_to_aspect_ratio):
         converter = ImageConverter(
             image_size=(4, 4),
             scale=(1.0 / 255.0, 0.8 / 255.0, 1.2 / 255.0),
             offset=(0.2, -0.1, 0.25),
+            crop_to_aspect_ratio=crop_to_aspect_ratio,
+            pad_to_aspect_ratio=pad_to_aspect_ratio,
         )
         inputs = np.ones((2, 10, 10, 3)) * 128
         outputs = converter(inputs)
@@ -46,6 +58,15 @@ def test_resize_batch(self):
         self.assertAllClose(outputs[:, :, :, 1], np.ones((2, 4, 4)) * 0.301569)
         self.assertAllClose(outputs[:, :, :, 2], np.ones((2, 4, 4)) * 0.852353)
 
+    def test_pad_and_crop_to_aspect_ratio(self):
+        with self.assertRaisesRegex(ValueError, "Only one of"):
+            _ = ImageConverter(
+                image_size=(4, 4),
+                scale=1 / 255.0,
+                crop_to_aspect_ratio=True,
+                pad_to_aspect_ratio=True,
+            )
+
     def test_config(self):
         converter = ImageConverter(
             image_size=(12, 20),