|
|
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): |
|
|
|
|
|
print("PATH:", dl_manager.download("lists/chunk_list.json")) |
|
|
thing = json.load(open(dl_manager.download("lists/chunk_list.json"), 'rb')) |
|
|
_CHUNK_LIST = thing |
|
|
|
|
|
|
|
|
_list = [] |
|
|
|
|
|
|
|
|
for chunk in _CHUNK_LIST: |
|
|
_list.append(dl_manager.download(f"data/{chunk}.tar")) |
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
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'] |
|
|
} |
