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import datasets
import json
import numpy
import tarfile
import io
_FEATURES = datasets.Features(
{
"id": datasets.Value("string"),
"prompt": datasets.Array3D(shape=(1, 77, 768), dtype="float32"),
"video": datasets.Sequence(feature=datasets.Array3D(shape=(4, 64, 64), dtype="float32")),
"description": datasets.Value("string"),
"videourl": datasets.Value("string"),
"categories": datasets.Value("string"),
"duration": datasets.Value("float"),
"full_metadata": datasets.Value("string"),
}
)
class FunkLoaderStream(datasets.GeneratorBasedBuilder):
"""TempoFunk Dataset"""
def _info(self):
return datasets.DatasetInfo(
description="TempoFunk Dataset",
features=_FEATURES,
homepage="tempofunk.github.io",
citation="""
@misc{TempoFunk2023,
author = {Lopho, Carlos Chavez},
title = {TempoFunk: Extending latent diffusion image models to Video},
url = {tempofunk.github.io},
month = {5},
year = {2023}
}
""",
license="AGPL v3"
)
def _split_generators(self, dl_manager):
# Load the chunk list.
print("PATH:", dl_manager.download("lists/chunk_list.json"))
thing = json.load(open(dl_manager.download("lists/chunk_list.json"), 'rb'))
_CHUNK_LIST = thing
# Create a list to hold the downloaded chunks.
_list = []
# Download each chunk file.
for chunk in _CHUNK_LIST:
_list.append(dl_manager.download(f"data/{chunk}.tar"))
# Return the list of downloaded chunks.
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"chunks": _list,
},
),
]
def _generate_examples(self, chunks):
"""Generate images and labels for splits."""
for chunk in chunks:
tar_data = open(chunk, 'rb')
tar_bytes = tar_data.read()
tar_bytes_io = io.BytesIO(tar_bytes)
response_dict = {}
with tarfile.open(fileobj=tar_bytes_io, mode='r') as tar:
for file_info in tar:
if file_info.isfile():
file_name = file_info.name
#filename format is typ_id.ext
file_type = file_name.split('_')[0]
file_id = file_name.split('_')[1].split('.')[0]
file_ext = file_name.split('_')[1].split('.')[1]
file_contents = tar.extractfile(file_info).read()
if file_id not in response_dict:
response_dict[file_id] = {}
if file_type == 'txt' or file_type == 'vid':
response_dict[file_id][file_type] = numpy.load(io.BytesIO(file_contents))
elif file_type == 'jso':
response_dict[file_id][file_type] = json.loads(file_contents)
for key, value in response_dict.items():
yield key, {
"id": key,
"description": value['jso']['description'],
"prompt": value['txt'],
"video": value['vid'],
"videourl": value['jso']['videourl'],
"categories": value['jso']['categories'],
"duration": value['jso']['duration'],
"full_metadata": value['jso']
} |