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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""

import csv
import os
from glob import glob

import numpy as np
from PIL import Image
from transformers import LayoutXLMTokenizerFast
import datasets

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "sample": "http://hyperion.bbirke.de/data/docbank/sample_resized.zip",
    "data": {
        'train': 'http://hyperion.bbirke.de/data/geocite/train.zip',
        'test': 'http://hyperion.bbirke.de/data/geocite/test.zip',
    },
}

_FEATURES = datasets.Features(
    {
        "id": datasets.Value("string"),
        "words": datasets.Sequence(datasets.Value("string")),
        "bbox": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
        # "RGBs": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
        # "fonts": datasets.Sequence(datasets.Value("string")),
        #"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
        "original_image": datasets.features.Image(),
        "dataset": datasets.Value("string"),
        #"labels": datasets.Sequence(feature=datasets.Value(dtype='int64'))
        "labels": datasets.Sequence(datasets.features.ClassLabel(
            names=['abstract', 'author', 'caption', 'equation', 'figure', 'footer', 'paragraph',
                   'reference', 'section', 'table', 'title']
            # names=['abstract', 'author', 'caption', 'date', 'equation', 'figure', 'footer', 'list', 'paragraph',
            #        'reference', 'section', 'table', 'title']
        ))
        # These are the features of your dataset like images, labels ...
    }
)


def load_image(image_path, size=None):
    image = Image.open(image_path).convert("RGB")
    w, h = image.size
    if size is not None:
        # resize image
        image = image.resize((size, size))
        image = np.asarray(image)
        image = image[:, :, ::-1]  # flip color channels from RGB to BGR
        image = image.transpose(2, 0, 1)  # move channels to first dimension
    return image, (w, h)


# def normalize_bbox(bbox, size):
#     return [
#         int(1000 * int(bbox[0]) / size[0]),
#         int(1000 * int(bbox[1]) / size[1]),
#         int(1000 * int(bbox[2]) / size[0]),
#         int(1000 * int(bbox[3]) / size[1]),
#     ]
#
#
# def simplify_bbox(bbox):
#     return [
#         min(bbox[0::2]),
#         min(bbox[1::2]),
#         max(bbox[2::2]),
#         max(bbox[3::2]),
#     ]
#
#
# def merge_bbox(bbox_list):
#     x0, y0, x1, y1 = list(zip(*bbox_list))
#     return [min(x0), min(y0), max(x1), max(y1)]


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class Docbank(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    CHUNK_SIZE = 512
    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="sample", version=VERSION,
                               description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="data", version=VERSION,
                               description="This part of my dataset covers a second domain"),
    ]

    # DEFAULT_CONFIG_NAME = "small"  # It's not mandatory to have a default configuration. Just use one if it make sense.
    TOKENIZER = LayoutXLMTokenizerFast.from_pretrained("microsoft/layoutxlm-base", only_label_first_subword=False)
    LABELS = ['abstract', 'author', 'caption', 'date', 'equation', 'figure', 'footer', 'list', 'paragraph', 'reference', 'section', 'table', 'title']
    ID2LABEL = {k: v for k, v in enumerate(LABELS)}
    LABEL2ID = {v: k for k, v in enumerate(LABELS)}

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

        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)
        #print(data_dir)
        train_txts = glob(data_dir['train'] + '/train/txt/*.csv')
        #print(train_txts[0])
        train_data = [(txt, data_dir['train'] + '/train/img/' + os.path.basename(txt)[:-4] + '.jpg') for txt in train_txts]
        test_txts = glob(data_dir['test'] + '/test/txt/*.csv')
        test_data = [(txt, data_dir['test'] + '/test/img/' + os.path.basename(txt)[:-4] + '.jpg') for txt in test_txts]
        # with open(os.path.join(data_dir, "train.csv")) as f:
        #     files_train = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']),
        #                     'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in
        #                    csv.DictReader(f, skipinitialspace=True)]
        # with open(os.path.join(data_dir, "test.csv")) as f:
        #     files_test = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']),
        #                    'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in
        #                   csv.DictReader(f, skipinitialspace=True)]
        # with open(os.path.join(data_dir, "validation.csv")) as f:
        #     files_validation = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']),
        #                          'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in
        #                         csv.DictReader(f, skipinitialspace=True)]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": train_data,
                    "split": "train",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": files_validation,
            #         "split": "validation",
            #     },
            # ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": test_data,
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        # print(filepath)
        key = 0
        for f_fp_txt, f_fp_img in filepath:
            dataset = f_fp_txt.split(".")[-2].split("_")[-1]
            #print(f_fp_txt)
            f_id = key
            #f_fp_txt = f['filepath_txt']
            #f_fp_img = f['filepath_img']
            tokens = []
            bboxes = []
            # rgbs = []
            # fonts = []
            labels = []

            #image, size = load_image(f_fp_img, size=224)
            original_image, _ = load_image(f_fp_img)


            try:
                with open(f_fp_txt, encoding='utf-8') as csvfile:
                    reader = csv.DictReader(csvfile, delimiter=',')
                    for row in reader:
                        #print(row)
                        # normalized_bbox = normalize_bbox(row[1:5], size)
                        normalized_bbox = [int(row['x0']), int(row['y0']), int(row['x1']), int(row['y1'])]
                        tokens.append(row['token'])
                        bboxes.append(normalized_bbox)
                        #print(f'Before: {row[9]}')
                        label = row['label']
                        if (label == "list") or (label == "date"):
                            label = "paragraph"
                        labels.append(label)
                        #print(f'After: {row[9]}')
                        # tokenized_input = self.TOKENIZER(
                        #     row[0],
                        #     add_special_tokens=False,
                        #     return_offsets_mapping=False,
                        #     return_attention_mask=False,
                        #     max_length=512, truncation=True
                        # )
                        #
                        # for tkn in tokenized_input['input_ids']:
                        #     tokens.append(tkn)
                        #     bboxes.append(normalized_bbox)
                        #     # rgbs.append(row[5:8])
                        #     # fonts.append(row[8])
                        #     labels.append(row[9])

            except:
                continue

            #print('Processing...')
            # processed = self.TOKENIZER(
            #     tokens,
            #     boxes=bboxes,
            #     word_labels=labels,
            #     add_special_tokens=False,
            #     return_offsets_mapping=False,
            #     return_attention_mask=False,
            # )
            #print(processed)

            # for chunk_id, index in enumerate(range(0, len(tokens), self.CHUNK_SIZE)):
            #     split_tokens = tokens[index:index + self.CHUNK_SIZE]
            #     split_bboxes = bboxes[index:index + self.CHUNK_SIZE]
            #     # split_rgbs = rgbs[index:index + self.CHUNK_SIZE]
            #     # split_fonts = fonts[index:index + self.CHUNK_SIZE]
            #     split_labels = labels[index:index + self.CHUNK_SIZE]

                #tokenized = self.TOKENIZER(processed['words'], boxes=processed['boxes'])

            yield key, {
                "id": f"file_{f_id}",
                'words': tokens,
                "bbox": bboxes,
                # "RGBs": split_rgbs,
                # "fonts": split_fonts,
                #"image": image,
                "original_image": original_image,
                "dataset": dataset,
                "labels": labels
            }
            key += 1