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