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"""TGIF: A New Dataset and Benchmark on Animated GIF Description""" |
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import os |
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import csv |
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import datasets |
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_CITATION = """ |
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@InProceedings{tgif-cvpr2016, |
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author = {Li, Yuncheng and Song, Yale and Cao, Liangliang and Tetreault, Joel and Goldberg, Larry and Jaimes, Alejandro and Luo, Jiebo}, |
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title = "{TGIF: A New Dataset and Benchmark on Animated GIF Description}", |
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booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2016} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. |
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The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. |
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We provide the URLs of animated GIFs in this release. The sentences are collected via crowdsourcing, with a carefully designed |
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annotationinterface that ensures high quality dataset. We provide one sentence per animated GIF for the training and validation splits, |
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and three sentences per GIF for the test split. The dataset shall be used to evaluate animated GIF/video description techniques. |
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""" |
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_URL_BASE = "http://raingo.github.io/TGIF-Release/" |
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_DL_PATH = "data.tar.gz" |
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class TGIFConfig(datasets.BuilderConfig): |
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"""BuilderConfig for TGIF.""" |
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def __init__(self, **kwargs): |
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super(TGIFConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs) |
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class TGIF(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "all" |
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BUILDER_CONFIGS = [ |
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TGIFConfig(name="all", description="All the TGIF dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"video_id": datasets.Value("string"), |
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"caption": datasets.features.Sequence(datasets.Value("string")) |
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} |
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), |
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supervised_keys=None, |
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homepage=_URL_BASE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(_DL_PATH) |
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} |
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train_splits = [ |
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datasets.SplitGenerator( |
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name="train", |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive, |
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"files": dl_manager.iter_archive(_DL_PATH), |
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"split": "train" |
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}, |
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) |
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] |
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dev_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive, |
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"files": dl_manager.iter_archive(_DL_PATH), |
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"split": "dev" |
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}, |
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) |
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] |
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test_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive, |
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"files": dl_manager.iter_archive(_DL_PATH), |
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"split": "test" |
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}, |
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) |
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] |
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return train_splits + dev_splits + test_splits |
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def _generate_examples(self, files, local_extracted_archive, split): |
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"""This function returns the examples.""" |
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dict = {} |
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for path, f in files: |
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if path.endswith(split + ".txt"): |
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print(path) |
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with open(path,'r') as txt_file: |
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for line in txt_file: |
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line = line[0:-1] |
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dict[line] = [] |
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for path, f in files: |
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if path.endswith("tgif-v1.0.tsv"): |
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print(path) |
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with open(path, encoding="utf-8") as tsv_file: |
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tsv_reader = csv.reader(tsv_file, delimiter="\t", quotechar='"' ) |
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for idx, (video_link, text) in enumerate(tsv_reader): |
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try: |
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dict[video_link].append(text) |
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except Exception: |
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pass |
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for idx, video_link in enumerate(dict): |
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yield idx, { |
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"video_id": video_link, |
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"captions": dict[video_link], |
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} |
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