dev(narugo): add support for vit (7ff62321) · Commits · git-mirror / Imgutils

docs/source/api_doc/preprocess/transformers.rst

+14 −0

Original line number	Diff line number	Diff line
		@@ -57,3 +57,17 @@ create_transforms_from_convnext_processor



		create_vit_transforms
		--------------------------------------------------------------------

		.. autofunction:: create_vit_transforms



		create_transforms_from_vit_processor
		--------------------------------------------------------------------

		.. autofunction:: create_transforms_from_vit_processor

imgutils/preprocess/transformers/init.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -9,3 +9,4 @@ Supported Processors:
		from .base import register_creators_for_transformers, NotProcessorTypeError, create_transforms_from_transformers
		from .clip import create_clip_transforms, create_transforms_from_clip_processor
		from .convnext import create_convnext_transforms, create_transforms_from_convnext_processor
		from .vit import create_vit_transforms, create_transforms_from_vit_processor

imgutils/preprocess/transformers/convnext.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -110,7 +110,7 @@ def create_transforms_from_convnext_processor(processor):
		if isinstance(processor, ConvNextImageProcessor):
		pass
		else:
		raise NotProcessorTypeError(f'Unknown CLIP processor - {processor!r}.')
		raise NotProcessorTypeError(f'Unknown ConvNext processor - {processor!r}.')
		processor: ConvNextImageProcessor

		return create_convnext_transforms(

imgutils/preprocess/transformers/vit.py

0 → 100644

+71 −0

Original line number	Diff line number	Diff line
		from PIL import Image

		from .base import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, _DEFAULT, register_creators_for_transformers, \
		_check_transformers, NotProcessorTypeError
		from ..pillow import PillowRescale, PillowResize, PillowToTensor, PillowNormalize, PillowCompose

		_DEFAULT_SIZE = {"height": 224, "width": 224}


		def create_vit_transforms(
		do_resize: bool = True,
		size=_DEFAULT,
		resample: int = Image.BILINEAR,
		do_rescale: bool = True,
		rescale_factor: float = 1 / 255,
		do_normalize: bool = True,
		image_mean=_DEFAULT,
		image_std=_DEFAULT,
		):
		# Initialize default values
		size = size if size is not _DEFAULT else _DEFAULT_SIZE
		image_mean = image_mean if image_mean is not _DEFAULT else IMAGENET_DEFAULT_MEAN
		image_std = image_std if image_std is not _DEFAULT else IMAGENET_DEFAULT_STD

		transform_list = []

		# Add resize transform if enabled
		if do_resize:
		transform_list.append(
		PillowResize(
		(size["height"], size["width"]),
		interpolation=resample
		)
		)

		# Convert to tensor (always needed)
		transform_list.append(PillowToTensor())

		# Add rescaling if enabled
		# Note: ToTensor already scales to [0,1], so we only need additional scaling if factor != 1/255
		if do_rescale and rescale_factor != 1 / 255:
		transform_list.append(PillowRescale(rescale_factor * 255))

		# Add normalization if enabled0
		if do_normalize:
		transform_list.append(PillowNormalize(mean=image_mean, std=image_std))

		return PillowCompose(transform_list)


		@register_creators_for_transformers()
		def create_transforms_from_vit_processor(processor):
		_check_transformers()
		from transformers import ViTImageProcessor

		if isinstance(processor, ViTImageProcessor):
		pass
		else:
		raise NotProcessorTypeError(f'Unknown ViT processor - {processor!r}.')
		processor: ViTImageProcessor

		return create_vit_transforms(
		do_resize=processor.do_resize,
		size=processor.size,
		resample=processor.resample,
		do_rescale=processor.do_rescale,
		rescale_factor=processor.rescale_factor,
		do_normalize=processor.do_normalize,
		image_mean=processor.image_mean,
		image_std=processor.image_std,
		)

test/preprocess/transformers/test_vit.py

0 → 100644

+53 −0

Original line number	Diff line number	Diff line
		from unittest import skipUnless

		import numpy as np
		import pytest
		from hbutils.testing import tmatrix

		from imgutils.data import load_image
		from imgutils.preprocess.transformers import create_transforms_from_transformers
		from test.testings import get_testfile

		try:
		import transformers
		except (ImportError, ModuleNotFoundError):
		_HAS_TRANSFORMERS = False
		else:
		_HAS_TRANSFORMERS = True


		@pytest.mark.unittest
		class TestPreprocessTransformersViT:
		@skipUnless(_HAS_TRANSFORMERS, 'Transformers required.')
		@pytest.mark.parametrize(*tmatrix({
		'repo_id': [
		"Falconsai/nsfw_image_detection",
		"microsoft/trocr-base-handwritten",
		"microsoft/trocr-base-printed",
		"dima806/facial_emotions_image_detection",
		"rizvandwiki/gender-classification",
		"AdamCodd/vit-base-nsfw-detector",
		"MixTex/ZhEn-Latex-OCR",
		"prithivMLmods/Deep-Fake-Detector-Model",
		],
		'src_image': [
		'png_640.png',
		'png_640_m90.png',
		'nude_girl.png',
		'dori_640.png',
		'nian_640.png',
		]
		}))
		def test_convnext_image_preprocess_align(self, src_image, repo_id):
		from transformers import AutoImageProcessor
		image = load_image(get_testfile(src_image), mode='RGB', force_background='white')
		processor = AutoImageProcessor.from_pretrained(repo_id)

		trans = create_transforms_from_transformers(processor)

		expected_output = processor.preprocess(image)['pixel_values'][0]
		output = trans(image)
		np.testing.assert_array_almost_equal(
		output,
		expected_output,
		)