mirror of
https://github.com/huggingface/transformers.git
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200 lines
8.2 KiB
Python
200 lines
8.2 KiB
Python
# Copyright 2024 HuggingFace Inc.
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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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import requests
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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 PIL import Image
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from transformers import Siglip2ImageProcessor
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if is_torchvision_available():
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from transformers import Siglip2ImageProcessorFast
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class Siglip2ImageProcessingTester:
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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_rescale=True,
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rescale_factor=1 / 255,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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resample=None,
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patch_size=16,
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max_num_patches=256,
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):
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size = size if size is not None else {"height": 18, "width": 18}
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resample = resample if resample is not None else Image.Resampling.BILINEAR
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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_rescale = do_rescale
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self.rescale_factor = rescale_factor
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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.resample = resample
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self.patch_size = patch_size
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self.max_num_patches = max_num_patches
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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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"do_rescale": self.do_rescale,
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"rescale_factor": self.rescale_factor,
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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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"resample": self.resample,
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"patch_size": self.patch_size,
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"max_num_patches": self.max_num_patches,
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}
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def expected_output_image_shape(self, images):
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return self.max_num_patches, self.patch_size * self.patch_size * self.num_channels
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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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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Siglip2
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class Siglip2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Siglip2ImageProcessor if is_vision_available() else None
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fast_image_processing_class = Siglip2ImageProcessorFast 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 = Siglip2ImageProcessingTester(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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# Ignore copy
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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_processing = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "resample"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "patch_size"))
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self.assertTrue(hasattr(image_processing, "max_num_patches"))
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# Ignore copy
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def test_image_processor_from_dict_with_kwargs(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.max_num_patches, 256)
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self.assertEqual(image_processor.patch_size, 16)
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image_processor = self.image_processing_class.from_dict(
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self.image_processor_dict, patch_size=32, max_num_patches=512
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)
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self.assertEqual(image_processor.patch_size, 32)
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self.assertEqual(image_processor.max_num_patches, 512)
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@unittest.skip(reason="not supported")
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# Ignore copy
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def test_call_numpy_4_channels(self):
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pass
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# increase mean tolerance to 1e-3 -> 2e-3
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# Ignore copy
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def test_slow_fast_equivalence(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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dummy_image = Image.open(
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requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
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)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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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.pixel_values, encoding_fast.pixel_values, atol=1e-1, rtol=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(), 2e-3
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)
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# increase mean tolerance to 1e-3 -> 2e-3
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# Ignore copy
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def test_slow_fast_equivalence_batched(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
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self.skipTest(
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reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
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)
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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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.pixel_values, encoding_fast.pixel_values, atol=1e-1, rtol=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(), 2e-3
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)
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