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Add Fast Image Processor for LayoutLMv2 (#37203)
* add support layoutlmv2 * make style * Apply suggestions from code review Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * add warning and clean up * make style * Update src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
This commit is contained in:
Vinh H. Pham
GitHub
parent
49b9a69a36
commit
e16775d103
@ -310,6 +310,11 @@ print(encoding.keys())
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[[autodoc]] LayoutLMv2ImageProcessor
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- preprocess
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## LayoutLMv2ImageProcessorFast
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[[autodoc]] LayoutLMv2ImageProcessorFast
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- preprocess
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## LayoutLMv2Tokenizer
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[[autodoc]] LayoutLMv2Tokenizer
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@ -102,7 +102,7 @@ else:
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("instructblip", ("BlipImageProcessor", "BlipImageProcessorFast")),
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("instructblipvideo", ("InstructBlipVideoImageProcessor",)),
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("kosmos-2", ("CLIPImageProcessor", "CLIPImageProcessorFast")),
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("layoutlmv2", ("LayoutLMv2ImageProcessor",)),
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("layoutlmv2", ("LayoutLMv2ImageProcessor", "LayoutLMv2ImageProcessorFast")),
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("layoutlmv3", ("LayoutLMv3ImageProcessor",)),
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("levit", ("LevitImageProcessor",)),
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("llama4", ("Llama4ImageProcessor", "Llama4ImageProcessorFast")),
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@ -21,6 +21,7 @@ if TYPE_CHECKING:
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from .configuration_layoutlmv2 import *
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from .feature_extraction_layoutlmv2 import *
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from .image_processing_layoutlmv2 import *
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from .image_processing_layoutlmv2_fast import *
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from .modeling_layoutlmv2 import *
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from .processing_layoutlmv2 import *
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from .tokenization_layoutlmv2 import *
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@ -0,0 +1,164 @@
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# 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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"""Fast Image processor class for LayoutLMv2."""
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from typing import Optional, Union
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from ...image_processing_utils_fast import (
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BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
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BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
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BaseImageProcessorFast,
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BatchFeature,
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DefaultFastImageProcessorKwargs,
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)
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from ...image_transforms import ChannelDimension, group_images_by_shape, reorder_images
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from ...image_utils import ImageInput, PILImageResampling, SizeDict
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from ...processing_utils import Unpack
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from ...utils import (
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TensorType,
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add_start_docstrings,
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is_torch_available,
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is_torchvision_available,
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is_torchvision_v2_available,
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logging,
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requires_backends,
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)
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from .image_processing_layoutlmv2 import apply_tesseract
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logger = logging.get_logger(__name__)
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if is_torch_available():
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import torch
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if is_torchvision_available():
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if is_torchvision_v2_available():
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from torchvision.transforms.v2 import functional as F
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else:
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from torchvision.transforms import functional as F
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class LayoutLMv2FastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
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apply_ocr: Optional[bool]
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ocr_lang: Optional[str]
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tesseract_config: Optional[str]
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@add_start_docstrings(
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"Constructs a fast LayoutLMv2 image processor.",
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BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
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"""
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apply_ocr (`bool`, *optional*, defaults to `True`):
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Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
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the `apply_ocr` parameter in the `preprocess` method.
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ocr_lang (`str`, *optional*):
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The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
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used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
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tesseract_config (`str`, *optional*):
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Any additional custom configuration flags that are forwarded to the `config` parameter when calling
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Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
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`preprocess` method.
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""",
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)
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class LayoutLMv2ImageProcessorFast(BaseImageProcessorFast):
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resample = PILImageResampling.BILINEAR
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size = {"height": 224, "width": 224}
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rescale_factor = None
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do_resize = True
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apply_ocr = True
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ocr_lang = None
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tesseract_config = ""
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valid_kwargs = LayoutLMv2FastImageProcessorKwargs
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def __init__(self, **kwargs: Unpack[LayoutLMv2FastImageProcessorKwargs]):
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super().__init__(**kwargs)
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@add_start_docstrings(
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BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
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"""
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apply_ocr (`bool`, *optional*, defaults to `True`):
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Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
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the `apply_ocr` parameter in the `preprocess` method.
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ocr_lang (`str`, *optional*):
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The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
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used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
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tesseract_config (`str`, *optional*):
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Any additional custom configuration flags that are forwarded to the `config` parameter when calling
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Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
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`preprocess` method.
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""",
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)
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def preprocess(self, images: ImageInput, **kwargs: Unpack[LayoutLMv2FastImageProcessorKwargs]) -> BatchFeature:
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return super().preprocess(images, **kwargs)
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def _preprocess(
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self,
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images: list["torch.Tensor"],
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do_resize: bool,
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size: SizeDict,
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interpolation: Optional["F.InterpolationMode"],
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apply_ocr: bool,
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ocr_lang: Optional[str],
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tesseract_config: Optional[str],
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return_tensors: Optional[Union[str, TensorType]],
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**kwargs,
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) -> BatchFeature:
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# Tesseract OCR to get words + normalized bounding boxes
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if apply_ocr:
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requires_backends(self, "pytesseract")
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words_batch = []
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boxes_batch = []
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for image in images:
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if image.is_cuda:
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logger.warning_once(
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"apply_ocr can only be performed on cpu. Tensors will be transferred to cpu before processing."
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)
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words, boxes = apply_tesseract(
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image.cpu(), ocr_lang, tesseract_config, input_data_format=ChannelDimension.FIRST
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)
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words_batch.append(words)
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boxes_batch.append(boxes)
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# Group images by size for batched resizing
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grouped_images, grouped_images_index = group_images_by_shape(images)
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resized_images_grouped = {}
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for shape, stacked_images in grouped_images.items():
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if do_resize:
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stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
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resized_images_grouped[shape] = stacked_images
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resized_images = reorder_images(resized_images_grouped, grouped_images_index)
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# Group images by size for further processing
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# Needed in case do_resize is False, or resize returns images with different sizes
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grouped_images, grouped_images_index = group_images_by_shape(resized_images)
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processed_images_grouped = {}
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for shape, stacked_images in grouped_images.items():
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# flip color channels from RGB to BGR (as Detectron2 requires this)
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stacked_images = stacked_images.flip(1)
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processed_images_grouped[shape] = stacked_images
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processed_images = reorder_images(processed_images_grouped, grouped_images_index)
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processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
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data = BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
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if apply_ocr:
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data["words"] = words_batch
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data["boxes"] = boxes_batch
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return data
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__all__ = ["LayoutLMv2ImageProcessorFast"]
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@ -12,20 +12,27 @@
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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.testing_utils import require_pytesseract, require_torch
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from transformers.utils import is_pytesseract_available
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import requests
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from transformers.testing_utils import require_pytesseract, require_torch, require_vision
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from transformers.utils import is_pytesseract_available, is_torch_available, is_torchvision_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_pytesseract_available():
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from PIL import Image
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from transformers import LayoutLMv2ImageProcessor
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if is_torchvision_available():
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from transformers import LayoutLMv2ImageProcessorFast
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class LayoutLMv2ImageProcessingTester:
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def __init__(
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@ -73,6 +80,9 @@ class LayoutLMv2ImageProcessingTester:
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@require_pytesseract
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class LayoutLMv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = LayoutLMv2ImageProcessor if is_pytesseract_available() else None
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fast_image_processing_class = (
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LayoutLMv2ImageProcessorFast if (is_torchvision_available() and is_pytesseract_available()) else None
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)
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def setUp(self):
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super().setUp()
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@ -83,46 +93,111 @@ class LayoutLMv2ImageProcessingTest(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, "apply_ocr"))
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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, "apply_ocr"))
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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, {"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, {"height": 18, "width": 18})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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@unittest.skip(reason="Tesseract version is not correct in ci. @Arthur FIXME")
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def test_layoutlmv2_integration_test(self):
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# with apply_OCR = True
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image_processing = LayoutLMv2ImageProcessor()
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from datasets import load_dataset
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ds = load_dataset("hf-internal-testing/fixtures_docvqa", split="test", trust_remote_code=True)
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image = Image.open(ds[0]["file"]).convert("RGB")
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for image_processing_class in self.image_processor_list:
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# with apply_OCR = True
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image_processing = image_processing_class()
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encoding = image_processing(image, return_tensors="pt")
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image = Image.open(ds[0]["file"]).convert("RGB")
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self.assertEqual(encoding.pixel_values.shape, (1, 3, 224, 224))
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self.assertEqual(len(encoding.words), len(encoding.boxes))
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encoding = image_processing(image, return_tensors="pt")
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# fmt: off
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# the words and boxes were obtained with Tesseract 5.3.0
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expected_words = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
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expected_boxes = [[[141, 57, 210, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [695, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
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# fmt: on
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self.assertEqual(encoding.pixel_values.shape, (1, 3, 224, 224))
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self.assertEqual(len(encoding.words), len(encoding.boxes))
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self.assertListEqual(encoding.words, expected_words)
|
||||
self.assertListEqual(encoding.boxes, expected_boxes)
|
||||
# fmt: off
|
||||
# the words and boxes were obtained with Tesseract 5.3.0
|
||||
expected_words = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
|
||||
expected_boxes = [[[141, 57, 210, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [695, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
|
||||
# fmt: on
|
||||
|
||||
# with apply_OCR = False
|
||||
image_processing = LayoutLMv2ImageProcessor(apply_ocr=False)
|
||||
self.assertListEqual(encoding.words, expected_words)
|
||||
self.assertListEqual(encoding.boxes, expected_boxes)
|
||||
|
||||
encoding = image_processing(image, return_tensors="pt")
|
||||
# with apply_OCR = False
|
||||
image_processing = image_processing_class(apply_ocr=False)
|
||||
|
||||
self.assertEqual(encoding.pixel_values.shape, (1, 3, 224, 224))
|
||||
encoding = image_processing(image, return_tensors="pt")
|
||||
|
||||
self.assertEqual(encoding.pixel_values.shape, (1, 3, 224, 224))
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
def test_slow_fast_equivalence(self):
|
||||
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
||||
self.skipTest(reason="Skipping slow/fast equivalence test")
|
||||
|
||||
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
||||
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
||||
|
||||
dummy_image = Image.open(
|
||||
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
|
||||
)
|
||||
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
||||
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
||||
|
||||
encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
|
||||
encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
encoding_slow.pixel_values.float() / 255, encoding_fast.pixel_values.float() / 255, atol=1e-1
|
||||
)
|
||||
)
|
||||
self.assertLessEqual(
|
||||
torch.mean(
|
||||
torch.abs(encoding_slow.pixel_values.float() - encoding_fast.pixel_values.float()) / 255
|
||||
).item(),
|
||||
1e-3,
|
||||
)
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
def test_slow_fast_equivalence_batched(self):
|
||||
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
||||
self.skipTest(reason="Skipping slow/fast equivalence test")
|
||||
|
||||
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
||||
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
||||
|
||||
if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
|
||||
self.skipTest(
|
||||
reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
|
||||
)
|
||||
|
||||
dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
|
||||
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
||||
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
||||
|
||||
encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
|
||||
encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
|
||||
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
encoding_slow.pixel_values.float() / 255, encoding_fast.pixel_values.float() / 255, atol=1e-1
|
||||
)
|
||||
)
|
||||
self.assertLessEqual(
|
||||
torch.mean(
|
||||
torch.abs(encoding_slow.pixel_values.float() - encoding_fast.pixel_values.float()) / 255
|
||||
).item(),
|
||||
1e-3,
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user