Datasets:
Sub-tasks:
closed-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
import csv | |
import datasets | |
import os | |
import urllib.request | |
_CITATION = """ | |
@InProceedings{tgif-cvpr2016, | |
author = {Li, Yuncheng and Song, Yale and Cao, Liangliang and Tetreault, Joel and Goldberg, Larry and Jaimes, Alejandro and Luo, Jiebo}, | |
title = "{TGIF: A New Dataset and Benchmark on Animated GIF Description}", | |
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
month = {June}, | |
year = {2016} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. | |
The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. | |
We provide the URLs of animated GIFs in this release. The sentences are collected via crowdsourcing, with a carefully designed | |
annotationinterface that ensures high quality dataset. We provide one sentence per animated GIF for the training and validation splits, | |
and three sentences per GIF for the test split. The dataset shall be used to evaluate animated GIF/video description techniques. | |
""" | |
_URL_BASE = "http://raingo.github.io/TGIF-Release/" | |
_DL_URL = "https://github.com/raingo/TGIF-Release/archive/master.zip" | |
class TGIFConfig(datasets.BuilderConfig): | |
"""BuilderConfig for TGIF.""" | |
def __init__(self, **kwargs): | |
super(TGIFConfig, self).__init__( | |
version=datasets.Version("2.1.0", ""), **kwargs) | |
class TGIF(datasets.GeneratorBasedBuilder): | |
DEFAULT_CONFIG_NAME = "all" | |
BUILDER_CONFIGS = [ | |
TGIFConfig(name="all", description="All the TGIF dataset"), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"video_path": datasets.Value("string"), | |
"video_bytes": datasets.Value("large_binary"), | |
"en_global_captions": datasets.features.Sequence(datasets.Value("string")) | |
} | |
), | |
supervised_keys=None, | |
homepage=_URL_BASE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
archive_path = dl_manager.download_and_extract(_DL_URL) | |
archive_data_path = os.path.join( | |
archive_path, "TGIF-Release-master/data/splits/") | |
infos_file = os.path.join( | |
archive_path, "TGIF-Release-master/data/tgif-v1.0.tsv") | |
train_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"split_links_file": os.path.join(archive_data_path, "train.txt"), | |
"infos_file": infos_file | |
}, | |
) | |
] | |
dev_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"split_links_file": os.path.join(archive_data_path, "val.txt"), | |
"infos_file": infos_file | |
}, | |
) | |
] | |
test_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"split_links_file": os.path.join(archive_data_path, "test.txt"), | |
"infos_file": infos_file | |
}, | |
) | |
] | |
return train_splits + dev_splits + test_splits | |
def _generate_examples(self, split_links_file, infos_file): | |
"""This function returns the examples.""" | |
dict = {} | |
with open(split_links_file, encoding="utf-8") as txt_file: | |
for line in txt_file: | |
line = line[0:-1] | |
dict[line] = [] | |
with open(infos_file, encoding="utf-8") as tsv_file: | |
tsv_reader = csv.reader(tsv_file, delimiter="\t", quotechar='"') | |
for idx, (video_link, text) in enumerate(tsv_reader): | |
try: | |
dict[video_link].append(text) | |
except Exception: | |
pass | |
for idx, video_link in enumerate(dict): | |
video_data = urllib.request.urlopen(video_link).read() | |
video_bytes = bytearray(video_data) | |
yield idx, { | |
"video_path": video_link, | |
"video_bytes": video_bytes, | |
"en_global_captions": dict[video_link], | |
} | |