|
|
import pandas as pd |
|
|
from huggingface_hub import hf_hub_url |
|
|
import datasets |
|
|
import os |
|
|
|
|
|
_VERSION = datasets.Version("0.0.2") |
|
|
|
|
|
_DESCRIPTION = "This dataset includes images and conditioning images for XYZ purpose." |
|
|
_HOMEPAGE = "https://www.example.com" |
|
|
_LICENSE = "MIT" |
|
|
_CITATION = """@article{YourDataset2021, |
|
|
title={Your Dataset Title}, |
|
|
author={Your Name}, |
|
|
journal={Your Journal}, |
|
|
year={2021} |
|
|
}""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
_FEATURES = datasets.Features( |
|
|
{ |
|
|
"image": datasets.Image(), |
|
|
"conditioning_image": datasets.Image(), |
|
|
"text": datasets.Value("string"), |
|
|
}, |
|
|
) |
|
|
METADATA_URL = hf_hub_url( |
|
|
"spine-crook/test", |
|
|
filename="train.jsonl", |
|
|
repo_type="dataset", |
|
|
) |
|
|
|
|
|
IMAGES_URL = hf_hub_url( |
|
|
"spine-crook/test", |
|
|
filename="images.zip", |
|
|
repo_type="dataset", |
|
|
) |
|
|
|
|
|
CONDITIONING_IMAGES_URL = hf_hub_url( |
|
|
"spine-crook/test", |
|
|
filename="conditioning_images.zip", |
|
|
repo_type="dataset", |
|
|
) |
|
|
|
|
|
_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION) |
|
|
|
|
|
class Test(datasets.GeneratorBasedBuilder): |
|
|
BUILDER_CONFIGS = [_DEFAULT_CONFIG] |
|
|
DEFAULT_CONFIG_NAME = "default" |
|
|
|
|
|
def _info(self): |
|
|
return datasets.DatasetInfo( |
|
|
description=_DESCRIPTION, |
|
|
features=_FEATURES, |
|
|
supervised_keys=None, |
|
|
homepage=_HOMEPAGE, |
|
|
license=_LICENSE, |
|
|
citation=_CITATION, |
|
|
) |
|
|
|
|
|
def _split_generators(self, dl_manager): |
|
|
metadata_path = dl_manager.download(METADATA_URL) |
|
|
images_dir = dl_manager.download_and_extract(IMAGES_URL) |
|
|
conditioning_images_dir = dl_manager.download_and_extract(CONDITIONING_IMAGES_URL) |
|
|
|
|
|
return [ |
|
|
datasets.SplitGenerator( |
|
|
name=datasets.Split.TRAIN, |
|
|
gen_kwargs={ |
|
|
"metadata_path": metadata_path, |
|
|
"images_dir": images_dir, |
|
|
"conditioning_images_dir": conditioning_images_dir, |
|
|
}, |
|
|
), |
|
|
] |
|
|
|
|
|
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir): |
|
|
metadata = pd.read_json(metadata_path, lines=True) |
|
|
|
|
|
for _, row in metadata.iterrows(): |
|
|
text = row["text"] |
|
|
image_path = os.path.join(images_dir, row["image"]) |
|
|
conditioning_image_path = os.path.join(conditioning_images_dir, row["conditioning_image"]) |
|
|
|
|
|
yield row["image"], { |
|
|
"text": text, |
|
|
"image": image_path, |
|
|
"conditioning_image": conditioning_image_path, |
|
|
} |
|
|
|
