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 @@ from typing import Tuple, Union
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_vision_available
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from transformers.utils import is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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@@ -30,6 +30,11 @@ if is_vision_available():
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from transformers import LlavaImageProcessor
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if is_torchvision_available():
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from torchvision.transforms import functional as F
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from transformers import LlavaImageProcessorFast
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class LlavaImageProcessingTester:
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def __init__(
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@@ -50,6 +55,7 @@ class LlavaImageProcessingTester:
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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 {"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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@@ -103,6 +109,7 @@ class LlavaImageProcessingTester:
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Llava
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class LlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = LlavaImageProcessor if is_vision_available() else None
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fast_image_processing_class = LlavaImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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@@ -114,25 +121,27 @@ class LlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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# Ignore copy
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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_pad"))
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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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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_pad"))
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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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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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# Ignore copy
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def test_padding(self):
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@@ -157,45 +166,72 @@ class LlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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result.paste(image, ((height - width) // 2, 0))
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return result
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for i, image_processing_class in enumerate(self.image_processor_list):
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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numpify = i == 0
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torchify = i == 1
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image_inputs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=False, numpify=numpify, torchify=torchify
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)
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# test with images in channel-last and channel-first format
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image)
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padded_image_original = pad_to_square_original(Image.fromarray(image))
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padded_image_original = np.array(padded_image_original)
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# test with images in channel-last and channel-first format (only channel-first for torch)
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image)
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if i == 0:
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padded_image_original = pad_to_square_original(Image.fromarray(image))
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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padded_image = image_processor.pad_to_square(image.transpose(2, 0, 1), input_data_format="channels_first")
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padded_image = padded_image.transpose(1, 2, 0)
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padded_image = image_processor.pad_to_square(
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image.transpose(2, 0, 1), input_data_format="channels_first"
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)
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padded_image = padded_image.transpose(1, 2, 0)
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np.testing.assert_allclose(padded_image, padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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else:
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padded_image_original = pad_to_square_original(F.to_pil_image(image))
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padded_image = padded_image.permute(1, 2, 0)
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np.testing.assert_allclose(padded_image, padded_image_original)
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# test background color
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background_color = (122, 116, 104)
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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padded_image_original = pad_to_square_original(Image.fromarray(image), background_color=background_color)
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padded_image_original = np.array(padded_image_original)
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# test background color
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background_color = (122, 116, 104)
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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if i == 0:
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padded_image_original = pad_to_square_original(
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Image.fromarray(image), background_color=background_color
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)
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else:
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padded_image_original = pad_to_square_original(
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F.to_pil_image(image), background_color=background_color
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)
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padded_image = padded_image.permute(1, 2, 0)
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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background_color = 122
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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padded_image_original = pad_to_square_original(Image.fromarray(image), background_color=background_color)
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padded_image_original = np.array(padded_image_original)
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background_color = 122
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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if i == 0:
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padded_image_original = pad_to_square_original(
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Image.fromarray(image), background_color=background_color
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)
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else:
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padded_image_original = pad_to_square_original(
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F.to_pil_image(image), background_color=background_color
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)
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padded_image = padded_image.permute(1, 2, 0)
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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# background color length should match channel length
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104))
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# background color length should match channel length
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104))
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104, 0, 0))
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104, 0, 0))
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@unittest.skip(reason="LLaVa does not support 4 channel images yet")
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# Ignore copy
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