ref_seg_ger.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."""

from glob import glob
import os
import numpy as np
from PIL import Image
from tokenizers import pre_tokenizers
from tokenizers.pre_tokenizers import Digits, Split, Whitespace, Sequence
import datasets
from itertools import chain
import pandas as pd

# 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 = {
    'train': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_train.zip",
    'test': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_test.zip",
}

_LABELS = [
    'publisher', 'source', 'url', 'other', 'author', 'editor', 'lpage',
    'volume', 'year', 'issue', 'title', 'fpage', 'edition'
]

_FEATURES = datasets.Features(
    {
        "id": datasets.Value("string"),
        "tokens": datasets.Sequence(datasets.Value("string")),
        # "attention_mask": datasets.Sequence(datasets.Value("int64")),
        # "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(),
        "labels": datasets.Sequence(datasets.features.ClassLabel(
            names=list(chain.from_iterable([['B-' + x, 'I-' + x] for x in _LABELS])) + ['O']
        )),
        "labels_ref": datasets.Sequence(datasets.features.ClassLabel(
            names=['B-ref', 'I-ref', ]
        ))
        # 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 RefSeg(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    CHUNK_SIZE = 256
    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="full", 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.

    split_tokens = [".", ":", ",", ";", "/", "-", "(", ")"]

    TOKENIZER = Sequence([
                       Whitespace(),
                       Digits(),
                   ] + [Split(x, behavior="isolated") for x in split_tokens])

    #TOKENIZER = Whitespace()

    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
        data_dir = dl_manager.download_and_extract(_URLS)
        # print(data_dir)
        # 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": data_dir['train'],
                    "split": "train",
                },
            ),

            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir['test'],
                    "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)
        # print(split)
        paths = glob(filepath + '/' + split + '/*.csv')
        key = 0
        for f in paths:
            df = pd.read_csv(f, keep_default_na=False)
            input_ids = []
            labels = []
            refs = []
            for i, row in df.iterrows():

                # tokenized_input = row['token'].split(' ')
                tkn = self.TOKENIZER.pre_tokenize_str(row['token'])
                if row['label'] == 'identifier':
                    row['label'] = 'other'
                if not tkn:
                    continue
                tokenized_input, offsets = zip(*tkn)
                tokenized_input = list(tokenized_input)
                for t in range(len(tokenized_input)):
                    if t == 0:
                        refs.append(row['ref'] + '-ref')
                    else:
                        refs.append('I-ref')
                if len(tokenized_input) > 1:
                    if row['tag'] == 'B':
                        if tokenized_input[0] == '':
                            continue
                        input_ids.append(tokenized_input[0])
                        labels.append(row['tag'] + '-' + row['label'])
                        for input_id in tokenized_input[1:]:
                            if input_id == '':
                                continue
                            input_ids.append(input_id)
                            labels.append('I-' + row['label'])
                    elif row['tag'] == 'I':
                        for input_id in tokenized_input:
                            input_ids.append(input_id)
                            labels.append('I-' + row['label'])
                    else:
                        if tokenized_input[0] == '':
                            continue
                        for input_id in tokenized_input:
                            input_ids.append(input_id)
                            labels.append('O')
                elif len(tokenized_input) == 1:
                    if tokenized_input[0] == '':
                        continue
                    input_ids.append(tokenized_input[0])
                    if row['tag'] == 'O':
                        labels.append(row['tag'])
                    else:
                        labels.append(row['tag'] + '-' + row['label'])
                else:
                    continue

            clean_input_ids = []
            clean_labels = []
            clean_refs = []
            for i, input in enumerate(input_ids):
                if input != '':
                    clean_input_ids.append(input)
                    clean_labels.append(labels[i])
                    clean_refs.append(refs[i])
            # n_chunks = int(len(clean_input_ids) / self.CHUNK_SIZE) if len(clean_input_ids) % self.CHUNK_SIZE == 0 \
            #     else int(len(clean_input_ids) / self.CHUNK_SIZE) + 1
            # split_ids = np.array_split(clean_input_ids, n_chunks)
            # split_labels = np.array_split(clean_labels, n_chunks)
            # split_refs = np.array_split(clean_refs, n_chunks)
            # print(clean_input_ids)
            # for chunk_ids, chunk_labels, chunk_refs in zip(clean_input_ids, clean_labels, clean_refs):
                # for chunk_id, index in enumerate(range(0, len(clean_input_ids), self.CHUNK_SIZE)):
                # split_ids = clean_input_ids[index:max(len(clean_input_ids), 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 = clean_labels[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)]
                # split_labels_post = [item for sublist in split_labels for item in sublist]
                # if(len(split_ids) != len(split_labels)):
                #     print(f)
                #     print(len(input_ids), input_ids)
                #     print(len(split_labels), split_labels)
                # for s in split_labels:
                #     if type(s) is not str:
                #         print(f)
                #         print(len(input_ids), input_ids)
                #         print(len(split_labels), split_labels)
                # print(len(split_labels_post), split_labels_post)
                # print(split_labels, len(split_labels))
                # print(split_ids, len(split_ids))

            yield key, {
                "id": f"{os.path.basename(f)}",
                'tokens': clean_input_ids,
                # 'attention_mask': [1] * len(chunk_ids),
                # "bbox": split_bboxes,
                # "RGBs": split_rgbs,
                # "fonts": split_fonts,
                # "image": image,
                # "original_image": original_image,
                "labels": clean_labels,
                "labels_ref": clean_refs
            }
            key += 1