| 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], |
| } |
|
|
