|
import datasets |
|
import json |
|
|
|
import json |
|
import os |
|
|
|
import datasets |
|
|
|
_DESCRIPTION = """\ |
|
Dataset to perform Aesthetic evaluation from Table 2 in ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. |
|
|
|
@misc{xu2023imagereward, |
|
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, |
|
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong}, |
|
year={2023}, |
|
eprint={2304.05977}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
""" |
|
|
|
class ImagerewardEvaluation(datasets.GeneratorBasedBuilder): |
|
VERSION = datasets.Version("1.0.0") |
|
|
|
def _info(self): |
|
features = datasets.Features( |
|
{ |
|
"id": datasets.Value("string"), |
|
"prompt": datasets.Value("string"), |
|
"generations": datasets.Sequence(datasets.Image()), |
|
"ranking": datasets.Sequence(datasets.Value("int32")), |
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
downloaded_files = dl_manager.download_and_extract("./test.json") |
|
images = dl_manager.download_and_extract("./images.zip") |
|
print(downloaded_files, images) |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"metadata_path": downloaded_files, "images_path": os.path.join(images, "test_images")}), |
|
] |
|
|
|
def _generate_examples(self, metadata_path, images_path): |
|
with open(metadata_path, encoding="utf-8") as f: |
|
data = json.load(f) |
|
for item in data: |
|
yield item["id"], { |
|
"id": item["id"], |
|
"prompt": item["prompt"], |
|
"generations": [os.path.join(images_path, x) for x in item["generations"]], |
|
"ranking": item["ranking"], |
|
} |