# coding=utf-8 # 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, is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from transformers import Ovis2ImageProcessor if is_torchvision_available(): from transformers import Ovis2ImageProcessorFast 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): image_processing_class = Ovis2ImageProcessor if is_vision_available() else None fast_image_processing_class = Ovis2ImageProcessorFast if is_torchvision_available() else None 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_processor_list: 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_slow_fast_equivalence_crop_to_patches(self): dummy_image = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)[0] image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True) encoding_slow = image_processor_slow(dummy_image, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, return_tensors="pt") # torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches) self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3 ) def test_slow_fast_equivalence_batched_crop_to_patches(self): # 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) image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True) encoding_slow = image_processor_slow(dummy_images, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, return_tensors="pt") # torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches) self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3 ) def test_crop_to_patches(self): # test slow image processor image_processor = self.image_processor_list[0](**self.image_processor_dict) 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={"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) # test fast image processor (process batch) image_processor = self.image_processor_list[1](**self.image_processor_dict) 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)