200 lines
8.9 KiB
Python
200 lines
8.9 KiB
Python
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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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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"""
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Processor class for LayoutLMv3.
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"""
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import warnings
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from typing import List, Optional, Union
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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from ...utils import TensorType
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class LayoutLMv3Processor(ProcessorMixin):
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r"""
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Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a
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single processor.
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[`LayoutLMv3Processor`] offers all the functionalities you need to prepare data for the model.
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It first uses [`LayoutLMv3ImageProcessor`] to resize and normalize document images, and optionally applies OCR to
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get words and normalized bounding boxes. These are then provided to [`LayoutLMv3Tokenizer`] or
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[`LayoutLMv3TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
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`attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
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into token-level `labels` for token classification tasks (such as FUNSD, CORD).
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Args:
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image_processor (`LayoutLMv3ImageProcessor`, *optional*):
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An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
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tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`, *optional*):
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An instance of [`LayoutLMv3Tokenizer`] or [`LayoutLMv3TokenizerFast`]. The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "LayoutLMv3ImageProcessor"
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tokenizer_class = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
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def __init__(self, image_processor=None, tokenizer=None, **kwargs):
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feature_extractor = None
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if "feature_extractor" in kwargs:
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warnings.warn(
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"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
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" instead.",
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FutureWarning,
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)
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feature_extractor = kwargs.pop("feature_extractor")
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image_processor = image_processor if image_processor is not None else feature_extractor
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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super().__init__(image_processor, tokenizer)
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def __call__(
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self,
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images,
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text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
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boxes: Union[List[List[int]], List[List[List[int]]]] = None,
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word_labels: Optional[Union[List[int], List[List[int]]]] = None,
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add_special_tokens: bool = True,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: Optional[int] = None,
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stride: int = 0,
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pad_to_multiple_of: Optional[int] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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return_tensors: Optional[Union[str, TensorType]] = None,
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**kwargs,
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) -> BatchEncoding:
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"""
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This method first forwards the `images` argument to [`~LayoutLMv3ImageProcessor.__call__`]. In case
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[`LayoutLMv3ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
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bounding boxes along with the additional arguments to [`~LayoutLMv3Tokenizer.__call__`] and returns the output,
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together with resized and normalized `pixel_values`. In case [`LayoutLMv3ImageProcessor`] was initialized with
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`apply_ocr` set to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along
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with the additional arguments to [`~LayoutLMv3Tokenizer.__call__`] and returns the output, together with
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resized and normalized `pixel_values`.
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Please refer to the docstring of the above two methods for more information.
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"""
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# verify input
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if self.image_processor.apply_ocr and (boxes is not None):
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raise ValueError(
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"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
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)
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if self.image_processor.apply_ocr and (word_labels is not None):
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raise ValueError(
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"You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
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)
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# first, apply the image processor
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features = self.image_processor(images=images, return_tensors=return_tensors)
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# second, apply the tokenizer
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if text is not None and self.image_processor.apply_ocr and text_pair is None:
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if isinstance(text, str):
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text = [text] # add batch dimension (as the image processor always adds a batch dimension)
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text_pair = features["words"]
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encoded_inputs = self.tokenizer(
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text=text if text is not None else features["words"],
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text_pair=text_pair if text_pair is not None else None,
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boxes=boxes if boxes is not None else features["boxes"],
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word_labels=word_labels,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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pad_to_multiple_of=pad_to_multiple_of,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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return_tensors=return_tensors,
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**kwargs,
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)
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# add pixel values
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images = features.pop("pixel_values")
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if return_overflowing_tokens is True:
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images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
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encoded_inputs["pixel_values"] = images
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return encoded_inputs
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def get_overflowing_images(self, images, overflow_to_sample_mapping):
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# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
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images_with_overflow = []
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for sample_idx in overflow_to_sample_mapping:
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images_with_overflow.append(images[sample_idx])
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if len(images_with_overflow) != len(overflow_to_sample_mapping):
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raise ValueError(
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"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
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f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
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)
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return images_with_overflow
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
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to the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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return ["input_ids", "bbox", "attention_mask", "pixel_values"]
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@property
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def feature_extractor_class(self):
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warnings.warn(
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"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
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FutureWarning,
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)
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return self.image_processor_class
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@property
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def feature_extractor(self):
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warnings.warn(
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"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
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FutureWarning,
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)
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return self.image_processor
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