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tests/models/ovis2/test_image_processing_ovis2.py

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+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# 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
+#
+#     http://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 unittest
+
+from transformers.image_utils import SizeDict
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+
+class Ovis2ImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        do_pad=False,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+        do_convert_rgb=True,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 20, "width": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_pad = do_pad
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+            "do_pad": self.do_pad,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class Ovis2ProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Ovis2ImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processor, "do_resize"))
+            self.assertTrue(hasattr(image_processor, "size"))
+            self.assertTrue(hasattr(image_processor, "do_normalize"))
+            self.assertTrue(hasattr(image_processor, "image_mean"))
+            self.assertTrue(hasattr(image_processor, "image_std"))
+            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+
+    def test_backends_equivalence_crop_to_patches(self):
+        """Test equivalence between backends when cropping to patches."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)[0]
+
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict, crop_to_patches=True)
+            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_encoding = encodings[backend_names[0]].pixel_values
+        for backend_name in backend_names[1:]:
+            self.assertTrue(torch.allclose(reference_encoding, encodings[backend_name].pixel_values, atol=1e-1))
+            self.assertLessEqual(
+                torch.mean(torch.abs(reference_encoding - encodings[backend_name].pixel_values)).item(), 1e-3
+            )
+
+    def test_backends_equivalence_batched_crop_to_patches(self):
+        """Test equivalence between backends when cropping to patches (batched)."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        # Prepare image inputs so that we have two groups of images with equal resolution with a group of images with
+        # different resolutions in between
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+        dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+        dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict, crop_to_patches=True)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_encoding = encodings[backend_names[0]].pixel_values
+        for backend_name in backend_names[1:]:
+            self.assertTrue(torch.allclose(reference_encoding, encodings[backend_name].pixel_values, atol=1e-1))
+            self.assertLessEqual(
+                torch.mean(torch.abs(reference_encoding - encodings[backend_name].pixel_values)).item(), 1e-3
+            )
+
+    def test_crop_to_patches(self):
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            if backend_name == "pil":
+                # PIL backend processes single images
+                image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)[0]
+                processed_images, grid = image_processor.crop_image_to_patches(
+                    image,
+                    min_patches=1,
+                    max_patches=6,
+                    patch_size=SizeDict(height=20, width=20),
+                )
+                self.assertEqual(len(processed_images), 5)
+                self.assertEqual(processed_images[0].shape[-2:], (20, 20))
+                self.assertEqual(len(grid), 2)  # (row, col)
+            else:
+                # Torchvision backend processes batches
+                image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)[0]
+                processed_images, grid = image_processor.crop_image_to_patches(
+                    image.unsqueeze(0),
+                    min_patches=1,
+                    max_patches=6,
+                    patch_size=SizeDict(height=20, width=20),
+                )
+                self.assertEqual(len(processed_images[0]), 5)
+                self.assertEqual(processed_images.shape[-2:], (20, 20))
+                self.assertEqual(len(grid[0]), 2)