Datasets:

galatolo
/

TeTIm-Eval

	import os
	import json
	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	TODO
	"""

	_HOMEPAGE = ""

	_DESCRIPTION = """\
	Text To Image Evaluation (TeTIm-Eval)
	"""

	_URLS = {
	"captioned": "https://huggingface.co/datasets/galatolo/TeTIm-Eval/resolve/main/data/TeTIm-Eval-Mini.zip",
	"uncaptioned": "https://huggingface.co/datasets/galatolo/TeTIm-Eval/resolve/main/data/TeTIm-Eval.zip"
	}

	_CLASSES = [
	"digital_art",
	"sketch_art",
	"traditional_art",
	"baroque_painting",
	"high_renaissance_painting",
	"neoclassical_painting",
	"animal_photo",
	"food_photo",
	"landscape_photo",
	"person_photo"
	]

	_CATEGORIES = [
	"art",
	"painting",
	"photo"
	]

	_MAP_CATEGORY = {
	_CLASSES[0]: _CATEGORIES[0],
	_CLASSES[1]: _CATEGORIES[0],
	_CLASSES[2]: _CATEGORIES[0],

	_CLASSES[3]: _CATEGORIES[1],
	_CLASSES[4]: _CATEGORIES[1],
	_CLASSES[5]: _CATEGORIES[1],

	_CLASSES[6]: _CATEGORIES[2],
	_CLASSES[7]: _CATEGORIES[2],
	_CLASSES[8]: _CATEGORIES[2],
	_CLASSES[9]: _CATEGORIES[2],
	}

	_FOLDERS = {
	"captioned": {
	_CLASSES[0]: "TeTIm-Eval-Mini/sampled_art_digital",
	_CLASSES[1]: "TeTIm-Eval-Mini/sampled_art_sketch",
	_CLASSES[2]: "TeTIm-Eval-Mini/sampled_art_traditional",
	_CLASSES[3]: "TeTIm-Eval-Mini/sampled_painting_baroque",
	_CLASSES[4]: "TeTIm-Eval-Mini/sampled_painting_high-renaissance",
	_CLASSES[5]: "TeTIm-Eval-Mini/sampled_painting_neoclassicism",
	_CLASSES[6]: "TeTIm-Eval-Mini/sampled_photo_animal",
	_CLASSES[7]: "TeTIm-Eval-Mini/sampled_photo_food",
	_CLASSES[8]: "TeTIm-Eval-Mini/sampled_photo_landscape",
	_CLASSES[9]: "TeTIm-Eval-Mini/sampled_photo_person",
	},
	"uncaptioned": {
	_CLASSES[0]: "TeTIm-Eval/sampled_art_digital",
	_CLASSES[1]: "TeTIm-Eval/sampled_art_sketch",
	_CLASSES[2]: "TeTIm-Eval/sampled_art_traditional",
	_CLASSES[3]: "TeTIm-Eval/sampled_painting_baroque",
	_CLASSES[4]: "TeTIm-Eval/sampled_painting_high-renaissance",
	_CLASSES[5]: "TeTIm-Eval/sampled_painting_neoclassicism",
	_CLASSES[6]: "TeTIm-Eval/sampled_photo_animal",
	_CLASSES[7]: "TeTIm-Eval/sampled_photo_food",
	_CLASSES[8]: "TeTIm-Eval/sampled_photo_landscape",
	_CLASSES[9]: "TeTIm-Eval/sampled_photo_person",
	}
	}

	class TeTImConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
	super(TeTImConfig, self).__init__(**kwargs)


	class TeTIm(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	TeTImConfig(
	name="captioned",
	version=datasets.Version("1.0.0", ""),
	description="A random sampling of 300 text-images pairs (30 per category) from the TeTIm dataset, manually annotated by the same person",
	),
	TeTImConfig(
	name="uncaptioned",
	version=datasets.Version("1.0.0", ""),
	description="2500 labelled images (250 per category) from the TeTIm dataset",
	),
	]

	DEFAULT_CONFIG_NAME="captioned"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"image": datasets.Image(),
	"caption": datasets.Value("string"),
	"category": datasets.ClassLabel(num_classes=len(_CATEGORIES), names=_CATEGORIES),
	"class": datasets.ClassLabel(num_classes=len(_CLASSES), names=_CLASSES)
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	target = os.environ.get(f"TETIMEVAL_{self.config.name}", _URLS[self.config.name])
	downloaded_files = dl_manager.download_and_extract(target)

	return [
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"path": downloaded_files}),
	]


	def _generate_examples(self, path):
	id = 0
	for _class, folder in _FOLDERS[self.config.name].items():
	images_folder = os.path.join(path, folder, "images")
	annotations_folder = os.path.join(path, folder, "annotations")

	for image in os.listdir(images_folder):
	image_id = int(image.split(".")[0])
	annotation_file = os.path.join(annotations_folder, f"{image_id}.json")
	with open(annotation_file) as f:
	annotation = json.load(f)

	yield id, {
	"id": id,
	"image": os.path.join(images_folder, image),
	"caption": annotation["caption"],
	"category": _CATEGORIES.index(_MAP_CATEGORY[_class]),
	"class": _CLASSES.index(_class)
	}
	id += 1


	if __name__ == "__main__":
	from datasets import load_dataset
	dataset_config = {
	"LOADING_SCRIPT_FILES": os.path.join(os.getcwd(), "TeTIm-Eval.py"),
	"CONFIG_NAME": "uncaptioned",
	}
	ds = load_dataset(
	dataset_config["LOADING_SCRIPT_FILES"],
	dataset_config["CONFIG_NAME"],
	)
	print(ds)

	for i, e in zip(range(0, 10), ds["test"]):
	print(i, e)