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import datasets
import tarfile
from datasets import Features, Value
from datasets.download.download_manager import DownloadManager
from typing import List, NamedTuple, TypedDict, Generator
from PIL import Image
Example = TypedDict('Example', {
'index': int,
'tar': str,
'tar_path': str,
'img': Image.Image,
})
class KeyedExample(NamedTuple):
key: int
example: Example
tar_count = 5
files = [f'./{ix:05d}.tar' for ix in range(tar_count)]
class MyWebdataset(datasets.GeneratorBasedBuilder):
VERSION = '1.0.0'
def _info(self) -> datasets.DatasetInfo:
print(__file__)
return datasets.DatasetInfo(
description="OpenAI guided-diffusion 256px class-conditional unguided samples (50k)",
features=Features({
'index': Value('uint32'),
'tar': Value('string'),
'tar_path': Value('string'),
'img': datasets.Image(),
}),
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": dl_manager.download(files)},
)
]
def _generate_examples(self, filepaths: List[str]) -> Generator[KeyedExample, None, None]:
index = 0
for filepath in filepaths:
with tarfile.open(filepath, "r:") as tar:
for member in tar.getmembers():
with tar.extractfile(member.name) as f:
pil: Image.Image = Image.open(f)
yield KeyedExample(index, Example(
index=index,
tar=filepath,
tar_path=member.path,
img=pil,
))
index += 1 |