178 lines
7.5 KiB
Python
178 lines
7.5 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers.image_utils import SizeDict
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from transformers import Ovis2ImageProcessor
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if is_torchvision_available():
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from transformers import Ovis2ImageProcessorFast
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class Ovis2ImageProcessingTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_normalize=True,
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do_pad=False,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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do_convert_rgb=True,
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):
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super().__init__()
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size = size if size is not None else {"height": 20, "width": 20}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_pad = do_pad
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self.do_convert_rgb = do_convert_rgb
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_convert_rgb": self.do_convert_rgb,
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"do_pad": self.do_pad,
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}
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def expected_output_image_shape(self, images):
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return self.num_channels, self.size["height"], self.size["width"]
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class Ovis2ProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Ovis2ImageProcessor if is_vision_available() else None
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fast_image_processing_class = Ovis2ImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = Ovis2ImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processor, "do_resize"))
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self.assertTrue(hasattr(image_processor, "size"))
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self.assertTrue(hasattr(image_processor, "do_normalize"))
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self.assertTrue(hasattr(image_processor, "image_mean"))
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self.assertTrue(hasattr(image_processor, "image_std"))
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self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
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def test_slow_fast_equivalence_crop_to_patches(self):
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dummy_image = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)[0]
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image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True)
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encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
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# torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches)
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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def test_slow_fast_equivalence_batched_crop_to_patches(self):
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# Prepare image inputs so that we have two groups of images with equal resolution with a group of images with
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# different resolutions in between
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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dummy_images += self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict, crop_to_patches=True)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, crop_to_patches=True)
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encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
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# torch.testing.assert_close(encoding_slow.num_patches, encoding_fast.num_patches)
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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def test_crop_to_patches(self):
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# test slow image processor
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image_processor = self.image_processor_list[0](**self.image_processor_dict)
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image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)[0]
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processed_images, grid = image_processor.crop_image_to_patches(
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image,
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min_patches=1,
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max_patches=6,
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patch_size={"height": 20, "width": 20},
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)
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self.assertEqual(len(processed_images), 5)
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self.assertEqual(processed_images[0].shape[:2], (20, 20))
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self.assertEqual(len(grid), 2) # (row, col)
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# test fast image processor (process batch)
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image_processor = self.image_processor_list[1](**self.image_processor_dict)
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image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)[0]
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processed_images, grid = image_processor.crop_image_to_patches(
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image.unsqueeze(0),
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min_patches=1,
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max_patches=6,
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patch_size=SizeDict(height=20, width=20),
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)
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self.assertEqual(len(processed_images[0]), 5)
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self.assertEqual(processed_images.shape[-2:], (20, 20))
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self.assertEqual(len(grid[0]), 2)
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