Repository: huggingface/transformers
Pull request: Command-a-vision fix

# What does this PR do?
- fix a bug in the image resize code, which affects performance
before fix:
<img width="512" height="512" alt="patch_01" src="https://github.com/user-attachments/assets/baf30355-8f3a-4785-aeef-7864ba8d7e28" />

after fix:
<img width="512" height="512" alt="patch_1" src="https://github.com/user-attachments/assets/9757b38b-8faa-49a1-8b51-7447797a4d9f" />

- add test cases
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Fixes # (issue)


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Changed files:
filename: src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py
patch: @@ -93,8 +93,9 @@ def get_optimal_tiled_canvas(
     patch_size_height, patch_size_width = target_tile_size  # (height == width)
 
     candidate_resolutions = np.array(possible_resolutions) * patch_size_height
-    original_size = np.stack([image_height, image_width])
-    required_scales = candidate_resolutions / original_size
+    # tiles following (width, height) order to align with aspect ratio convention
+    tile_size = np.stack([image_width, image_height])
+    required_scales = candidate_resolutions / tile_size
     required_scale = np.min(required_scales, axis=-1, keepdims=True)  # [n_resolutions, 1]
     if np.all(required_scale < 1):
         # We are forced to downscale, so try to minimize the amount of downscaling
@@ -103,7 +104,7 @@ def get_optimal_tiled_canvas(
         # Pick the resolution that required the least upscaling so that it most closely fits the image
         required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
         best_grid = possible_resolutions[np.argmin(required_scale)]
-    return best_grid
+    return best_grid  # (width, height)
 
 
 @auto_docstring

filename: src/transformers/models/cohere2_vision/modular_cohere2_vision.py
patch: @@ -295,8 +295,9 @@ def get_optimal_tiled_canvas(
     patch_size_height, patch_size_width = target_tile_size  # (height == width)
 
     candidate_resolutions = np.array(possible_resolutions) * patch_size_height
-    original_size = np.stack([image_height, image_width])
-    required_scales = candidate_resolutions / original_size
+    # tiles following (width, height) order to align with aspect ratio convention
+    tile_size = np.stack([image_width, image_height])
+    required_scales = candidate_resolutions / tile_size
     required_scale = np.min(required_scales, axis=-1, keepdims=True)  # [n_resolutions, 1]
     if np.all(required_scale < 1):
         # We are forced to downscale, so try to minimize the amount of downscaling
@@ -305,7 +306,7 @@ def get_optimal_tiled_canvas(
         # Pick the resolution that required the least upscaling so that it most closely fits the image
         required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
         best_grid = possible_resolutions[np.argmin(required_scale)]
-    return best_grid
+    return best_grid  # (width, height)
 
 
 class Cohere2VisionFastImageProcessorKwargs(ImagesKwargs, total=False):

filename: tests/models/cohere2_vision/test_image_processing_cohere2_vision.py
patch: @@ -190,3 +190,128 @@ def test_call_numpy_4_channels(self):
                 image_std=(1.0, 1.0, 1.0, 1.0),
             ).pixel_values
             self.assertEqual(tuple(encoded_images.shape), (70, 4, 30, 30))
+
+    def test_crop_to_patches_aspect_ratio(self):
+        """Test that row/column ordering is correct when cropping non-square images to patches.
+
+        This test verifies that patches can be stitched back to reconstruct the original image,
+        which validates that the row/column ordering in get_optimal_tiled_canvas is correct.
+        If row/column are swapped, the image would be resized to wrong dimensions and patches
+        would not match the original content.
+        """
+        for image_processing_class in self.image_processor_list:
+            patch_size = 64
+            image_processor = image_processing_class(
+                do_resize=True,
+                size={"height": patch_size, "width": patch_size},
+                do_normalize=False,  # Disable normalization to preserve pixel values
+                do_rescale=False,  # Disable rescaling to preserve pixel values
+                crop_to_patches=True,
+                min_patches=1,
+                max_patches=6,  # Allow up to 6 patches to test asymmetric grids like 2x3
+            )
+
+            # Create a 2:3 aspect ratio image (2 rows x 3 columns of patches)
+            # This asymmetric grid will fail if rows/columns are swapped
+            num_rows, num_cols = 2, 3
+            image_height = patch_size * num_rows  # 128
+            image_width = patch_size * num_cols  # 192
+
+            # Create image with unique color for each patch position
+            test_image = Image.new("RGB", (image_width, image_height))
+            for row in range(num_rows):
+                for col in range(num_cols):
+                    patch_idx = row * num_cols + col  # 0-5
+                    color = (patch_idx * 40 + 20, 0, 0)  # Unique red values: 20, 60, 100, 140, 180, 220
+                    for y in range(patch_size):
+                        for x in range(patch_size):
+                            test_image.putpixel(
+                                (col * patch_size + x, row * patch_size + y),
+                                color,
+                            )
+
+            # Process image
+            result = image_processor(test_image, return_tensors="pt")
+            patches = result.pixel_values
+            num_patches_result = result.num_patches
+
+            # Should produce 7 patches (6 grid patches + 1 thumbnail)
+            self.assertEqual(num_patches_result.tolist(), [7])
+            self.assertEqual(tuple(patches.shape), (7, 3, patch_size, patch_size))
+
+            # Verify each patch has the correct color (excluding thumbnail which is last)
+            # Patches should be ordered row by row: (0,0), (0,1), (0,2), (1,0), (1,1), (1,2)
+            for patch_idx in range(6):
+                expected_red = patch_idx * 40 + 20
+                actual_red = patches[patch_idx, 0, 0, 0].item()  # Red channel, top-left pixel
+                self.assertEqual(
+                    actual_red,
+                    expected_red,
+                    f"Patch {patch_idx} has wrong color. Expected red={expected_red}, got {actual_red}. "
+                    f"This indicates row/column ordering is incorrect.",
+                )
+
+            # Stitch patches back and verify against original
+            stitched = torch.zeros(3, image_height, image_width)
+            for patch_idx in range(6):
+                row = patch_idx // num_cols
+                col = patch_idx % num_cols
+                stitched[
+                    :,
+                    row * patch_size : (row + 1) * patch_size,
+                    col * patch_size : (col + 1) * patch_size,
+                ] = patches[patch_idx]
+
+            original_tensor = torch.tensor(np.array(test_image)).permute(2, 0, 1).float()
+            self.assertTrue(
+                torch.allclose(stitched, original_tensor),
+                "Patches do not stitch back to original image - row/column ordering may be wrong",
+            )
+
+    def test_get_number_of_image_patches_aspect_ratio(self):
+        """Test that get_number_of_image_patches returns correct count for non-square images.
+
+        This directly tests the row/column unpacking fix by verifying patch counts match
+        the expected grid layout. If rows/columns are swapped, the wrong grid would be
+        chosen for asymmetric images.
+        """
+        for image_processing_class in self.image_processor_list:
+            patch_size = 64
+            image_processor = image_processing_class(
+                size={"height": patch_size, "width": patch_size},
+                crop_to_patches=True,
+                min_patches=1,
+                max_patches=12,
+            )
+
+            # Test 1: Tall image (4 rows x 1 column) should give 5 patches (4 + thumbnail)
+            tall_patches = image_processor.get_number_of_image_patches(
+                height=patch_size * 4,  # 256
+                width=patch_size,  # 64
+                images_kwargs={},
+            )
+            self.assertEqual(tall_patches, 5, "Tall image (4:1) should produce 5 patches")
+
+            # Test 2: Wide image (1 row x 4 columns) should give 5 patches (4 + thumbnail)
+            wide_patches = image_processor.get_number_of_image_patches(
+                height=patch_size,  # 64
+                width=patch_size * 4,  # 256
+                images_kwargs={},
+            )
+            self.assertEqual(wide_patches, 5, "Wide image (1:4) should produce 5 patches")
+
+            # Test 3: Asymmetric image (2 rows x 3 columns) should give 7 patches
+            asym_patches = image_processor.get_number_of_image_patches(
+                height=patch_size * 2,  # 128
+                width=patch_size * 3,  # 192
+                images_kwargs={"max_patches": 6},
+            )
+            self.assertEqual(asym_patches, 7, "Asymmetric image (2:3) should produce 7 patches")
+
+            # Test 4: Opposite asymmetric (3 rows x 2 columns) should also give 7 patches
+            asym_patches2 = image_processor.get_number_of_image_patches(
+                height=patch_size * 3,  # 192
+                width=patch_size * 2,  # 128
+                images_kwargs={"max_patches": 6},
+            )
+            self.assertEqual(asym_patches2, 7, "Asymmetric image (3:2) should produce 7 patches")