docbank.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""DocBank document understanding dataset."""

import os
import datasets

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{li2020docbank,
    title={DocBank: A Benchmark Dataset for Document Layout Analysis},
    author={Minghao Li and Yiheng Xu and Lei Cui and Shaohan Huang and Furu Wei and Zhoujun Li and Ming Zhou},
    year={2020},
    eprint={2006.01038},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
"""

# You can copy an official description
_DESCRIPTION = """\
DocBank is a new large-scale dataset that is constructed using a weak supervision approach. 
It enables models to integrate both the textual and layout information for downstream tasks. 
The current DocBank dataset totally includes 500K document pages, where 400K for training, 50K for validation and 50K for testing.
"""

_HOMEPAGE = "https://doc-analysis.github.io/docbank-page/index.html"

_LICENSE = "Apache-2.0 license"


class DocBank(datasets.GeneratorBasedBuilder):
    """DocBank is a dataset for Visual Document Understanding. 
    It enable models to integrate both textual and layout informtion for downstream tasks."""

    VERSION = datasets.Version("1.1.0")    

    @property
    def manual_download_instructions(self):
        return """\
        Please download the DocBank dataset from https://doc-analysis.github.io/docbank-page/index.html. Uncompress the dataset and use that location in
        --data_dir argument. """
   
    def _info(self):

        features = datasets.Features(
            {
                "image": datasets.Image(),
                "token": datasets.Value("string"),
                "bounding_box": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
                "color": datasets.Sequence(datasets.Sequence(datasets.Value("uint8"))),
                "font": datasets.Value("string"),
                "label": datasets.Value("string"),
            }
        )
        
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        # urls = _URLS[self.config.name]
        # data_dir = dl_manager.download_and_extract(urls)
        
        self.data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join("train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join("dev.jsonl"),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join("test.jsonl"),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        
        print(os.getcwd())
        print(os.path.dirname(os.path.abspath(__file__)))
        
        with open(filepath,'rt') as fp:
            index,basename = eval(fp.readline().strip())
            
            txt_file = self.data_dir+'/DocBank_500K_txt/'+basename+'.txt'
            img_file = self.data_dir+'/DocBank_500K_ori_img/'+basename+'_ori.jpg'
            
            words = []
            bboxes = []
            rgbs = []
            fontnames = []
            structures = []

            with open(txt_file, 'r', encoding='utf8') as fp:
                for line in fp.readlines():
                    tts = line.split('\t')
                    
                    assert len(tts) == 10, f'Incomplete line in file {txt_file}'

                    word = tts[0]
                    bbox = list(map(int, tts[1:5]))
                    rgb = list(map(int, tts[5:8]))
                    fontname = tts[8]
                    structure = tts[9].strip()
                    
                    words.append(word)
                    bboxes.append(bbox)
                    rgbs.append(rgb)
                    fontnames.append(fontname)
                    structures.append(structure)            
               
        yield index, {
            "image": img_file,
            "token": words,
            "bounding_box": bboxes,
            "color": rgbs,
            "font": fontnames,
            "label": structures,
        }