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import numpy as np
import datasets
from huggingface_hub import HfApi
api = HfApi()
repo_files = list(api.dataset_info(repo_id="laion/laion2b-en-vit-h-14-embeddings").siblings)
filenames = [x.rfilename for x in repo_files]
img_embs = [x for x in filenames if x.startswith("img_emb/")]
class LAIONEmbeddingsConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(LAIONEmbeddingsConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
class LAIONEmbeddings(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
LAIONEmbeddingsConfig()
]
def _get_features(self) -> datasets.Features:
return datasets.Features({
"embedding": datasets.Sequence(datasets.Value("float32")),
})
def _info(self):
features = self._get_features()
return datasets.DatasetInfo(
features=features,
)
def _split_generators(self, dl_manager):
main_url = "https://huggingface.co/datasets/laion/laion2b-en-vit-h-14-embeddings/resolve/main/"
archive_paths = dl_manager.download([main_url + x for x in img_embs])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"chunks": archive_paths,
"split": "train",
},
),
]
def _generate_examples(self, chunks, split):
for chunk in chunks:
file = np.DataSource().open(chunk)
data = np.load(file.name)
for example in data:
yield "", {
"embedding": example
} |