Refactoring of ImageProcessorFast (#35069)
* add init and base image processing functions * add add_fast_image_processor to transformers-cli * add working fast image processor clip * add fast image processor to doc, working tests * remove "to be implemented" SigLip * fix unprotected import * fix unprotected vision import * update ViTImageProcessorFast * increase threshold slow fast ewuivalence * add fast img blip * add fast class in tests with cli * improve cli * add fast image processor convnext * add LlavaPatchingMixin and fast image processor for llava_next and llava_onevision * add device kwarg to ImagesKwargs for fast processing on cuda * cleanup * fix unprotected import * group images by sizes and add batch processing * Add batch equivalence tests, skip when center_crop is used * cleanup * update init and cli * fix-copies * refactor convnext, cleanup base * fix * remove patching mixins, add piped torchvision transforms for ViT * fix unbatched processing * fix f strings * protect imports * change llava onevision to class transforms (test) * fix convnext * improve formatting (following Pavel review) * fix handling device arg * improve cli * fix * fix inits * Add distinction between preprocess and _preprocess, and support for arbitrary kwargs through valid_extra_kwargs * uniformize qwen2_vl fast * fix docstrings * add add fast image processor llava * remove min_pixels max_pixels from accepted size * nit * nit * refactor fast image processors docstrings * cleanup and remove fast class transforms * update add fast image processor transformers cli * cleanup docstring * uniformize pixtral fast and make _process_image explicit * fix prepare image structure llava next/onevision * Use typed kwargs instead of explicit args * nit fix import Unpack * clearly separate pops and gets in base preprocess. Use explicit typed kwargs * make qwen2_vl preprocess arguments hashable
This commit is contained in:
Yoni Gozlan
@@ -20,7 +20,7 @@ import numpy as np
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from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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from transformers.models.llava_next.image_processing_llava_next import select_best_resolution
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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_vision_available
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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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@@ -33,6 +33,9 @@ if is_vision_available():
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from transformers import LlavaNextImageProcessor
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if is_torchvision_available():
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from transformers import LlavaNextImageProcessorFast
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class LlavaNextImageProcessingTester:
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def __init__(
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@@ -52,6 +55,7 @@ class LlavaNextImageProcessingTester:
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image_std=OPENAI_CLIP_STD,
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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 {"shortest_edge": 20}
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crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
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self.parent = parent
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@@ -102,6 +106,7 @@ class LlavaNextImageProcessingTester:
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@require_vision
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class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = LlavaNextImageProcessor if is_vision_available() else None
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fast_image_processing_class = LlavaNextImageProcessorFast if is_torchvision_available() else None
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->LlavaNext
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def setUp(self):
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@@ -114,26 +119,28 @@ class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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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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image_processing = self.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, "size"))
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self.assertTrue(hasattr(image_processing, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "center_crop"))
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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, "do_convert_rgb"))
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self.assertTrue(hasattr(image_processing, "image_grid_pinpoints"))
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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, "size"))
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self.assertTrue(hasattr(image_processing, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "center_crop"))
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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, "do_convert_rgb"))
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self.assertTrue(hasattr(image_processing, "image_grid_pinpoints"))
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.test_image_processor_from_dict_with_kwargs
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"shortest_edge": 20})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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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.size, {"shortest_edge": 20})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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def test_select_best_resolution(self):
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possible_resolutions = [[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]
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@@ -143,59 +150,62 @@ class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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self.assertEqual(best_resolution, (672, 336))
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def test_call_pil(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_pytorch(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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@unittest.skip(
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reason="LlavaNextImageProcessor doesn't treat 4 channel PIL and numpy consistently yet"
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@@ -204,19 +214,20 @@ class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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pass
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def test_nested_input(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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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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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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# Test batched as a list of images
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched as a list of images
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched as a nested list of images, where each sublist is one batch
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image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
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encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
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# Test batched as a nested list of images, where each sublist is one batch
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image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
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encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 1445, 3, 18, 18)
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self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
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# Image processor should return same pixel values, independently of ipnut format
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self.assertTrue((encoded_images_nested == encoded_images).all())
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# Image processor should return same pixel values, independently of ipnut format
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self.assertTrue((encoded_images_nested == encoded_images).all())
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