Fix: correct effective crop_to_aspect_ratio logic and lint issues

keras/src/layers/preprocessing/image_preprocessing/resizing.py

@@ -101,14 +101,27 @@ def __init__(
             self.width_axis = -2
 
     def transform_images(self, images, transformation=None, training=True):
+        # Compute effective crop flag:
+        # only crop if aspect ratios differ and flag is True
+        input_height, input_width = transformation
+        source_aspect_ratio = input_width / input_height
+        target_aspect_ratio = self.width / self.height
+        # Use a small epsilon for floating-point comparison
+        aspect_ratios_match = (
+            abs(source_aspect_ratio - target_aspect_ratio) < 1e-6
+        )
+        effective_crop_to_aspect_ratio = (
+            self.crop_to_aspect_ratio and not aspect_ratios_match
+        )
+
         size = (self.height, self.width)
         resized = self.backend.image.resize(
             images,
             size=size,
             interpolation=self.interpolation,
             antialias=self.antialias,
             data_format=self.data_format,
-            crop_to_aspect_ratio=self.crop_to_aspect_ratio,
+            crop_to_aspect_ratio=effective_crop_to_aspect_ratio,
             pad_to_aspect_ratio=self.pad_to_aspect_ratio,
             fill_mode=self.fill_mode,
             fill_value=self.fill_value,

keras/src/layers/preprocessing/image_preprocessing/resizing_test.py

@@ -124,6 +124,31 @@ def test_crop_to_aspect_ratio(self, data_format):
             ref_out = ref_out.transpose(0, 3, 1, 2)
         self.assertAllClose(ref_out, out)
 
+    @parameterized.parameters([("channels_first",), ("channels_last",)])
+    def test_crop_to_aspect_ratio_no_op_when_aspects_match(self, data_format):
+        # Test that crop_to_aspect_ratio=True behaves identically to False
+        # when source and target aspect ratios match (no cropping should occur).
+        img = np.reshape(np.arange(0, 16), (1, 4, 4, 1)).astype("float32")
+        if data_format == "channels_first":
+            img = img.transpose(0, 3, 1, 2)
+        out_false = layers.Resizing(
+            height=2,
+            width=2,
+            interpolation="nearest",
+            data_format=data_format,
+            crop_to_aspect_ratio=False,
+        )(img)
+        out_true = layers.Resizing(
+            height=2,
+            width=2,
+            interpolation="nearest",
+            data_format=data_format,
+            crop_to_aspect_ratio=True,
+        )(img)
+        # Outputs should be identical when aspect ratios match
+        # (4:4 -> 2:2, both 1:1).
+        self.assertAllClose(out_false, out_true)
+
     @parameterized.parameters([("channels_first",), ("channels_last",)])
     def test_unbatched_image(self, data_format):
         img = np.reshape(np.arange(0, 16), (4, 4, 1)).astype("float32")