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import datasets |
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from datasets import load_dataset, Dataset, Value, Sequence, Features, DatasetInfo, GeneratorBasedBuilder, Image |
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from pathlib import Path |
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import os |
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import pandas as pd |
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_DESCRIPTION = """\ The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7, |
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000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research |
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and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing. The |
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overarching goal of this dataset is to advance the research in visually situated language understanding towards more |
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hollistic consumption of the visual and textual data. |
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""" |
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_BASE_URL = "https://huggingface.co/datasets/lcolonn/patfig/resolve/main/" |
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_METADATA_URLS = { |
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"annotations_train": "train/annotations_train.parquet", |
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"annotations_test": "test/annotations_test.parquet" |
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} |
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_IMAGES_URLS = { |
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"images_train": "train/train_images.tar.gz", |
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"images_test": "test/test_images.tar.gz", |
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} |
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_URLS = { |
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"train_images": "train/train_images.tar.gz", |
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"test_images": "test/test_images.tar.gz", |
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"annotations_train": "train/annotations_train.parquet", |
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"annotations_test": "test/annotations_test.parquet", |
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} |
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class PatFig(GeneratorBasedBuilder): |
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"""DatasetBuilder for patfig dataset.""" |
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def _info(self): |
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return DatasetInfo( |
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description=_DESCRIPTION, |
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features=Features({ |
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"image": Image(), |
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"image_name": Value("string"), |
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"pub_number": Value("string"), |
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"title": Value("string"), |
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"figs_norm": Sequence(feature=Value("string"), length=-1), |
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"short_description": Sequence(feature=Value("string"), length=-1), |
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"long_description": Sequence(feature=Value("string"), length=-1), |
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"short_description_token_count": Value("int64"), |
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"long_description_token_count": Value("int64"), |
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"draft_class": Value("string"), |
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"cpc_class": Value("string"), |
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"relevant_terms": [{'element_identifier': Value("string"), "terms": Sequence(feature=Value("string"), length=-1)}], |
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"associated_claims": Value("string"), |
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"compound": Value("bool"), |
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"references": Sequence(feature=Value(dtype='string'), length=-1), |
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}), |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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urls_to_download = {key: _BASE_URL + fname for key, fname in _URLS.items()} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"images_dir": downloaded_files["train_images"], "annotations_dir": downloaded_files["annotations_train"]} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"images_dir": f'{downloaded_files["test_images"]}', "annotations_dir": downloaded_files["annotations_test"]} |
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), |
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] |
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def _generate_examples(self, images_dir: str, annotations_dir: str): |
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df = pd.read_csv(annotations_dir) |
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for idx, row in df.iterrows(): |
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image_path = os.path.join(images_dir, row["pub_number"], row["image_name"]) |
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yield idx, { |
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"image": image_path, |
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**row.to_dict(), |
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} |
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