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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 json |
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import datasets |
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_CITATION = """ |
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@inproceedings{krishna2017dense, |
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title={Dense-Captioning Events in Videos}, |
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author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos}, |
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booktitle={International Conference on Computer Vision (ICCV)}, |
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year={2017} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions. |
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Each sentence covers an unique segment of the video, describing multiple events that occur. These events |
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may occur over very long or short periods of time and are not limited in any capacity, allowing them to |
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co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in |
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a total of 100k sentences. We find that the number of sentences per video follows a relatively normal |
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distribution. Furthermore, as the video duration increases, the number of sentences also increases. |
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Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more |
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details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials |
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in the paper. |
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""" |
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_URL_BASE = "https://cs.stanford.edu/people/ranjaykrishna/densevid/" |
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class ActivityNetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ActivityNet Captions.""" |
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def __init__(self, **kwargs): |
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super(ActivityNetConfig, self).__init__( |
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version=datasets.Version("2.1.0", ""), **kwargs) |
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class ActivityNet(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "all" |
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BUILDER_CONFIGS = [ |
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ActivityNetConfig( |
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name="all", description="All the ActivityNet Captions 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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"video_path": datasets.Value("string"), |
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"duration": datasets.Value("float32"), |
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"captions_starts": datasets.features.Sequence(datasets.Value("float32")), |
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"captions_ends": datasets.features.Sequence(datasets.Value("float32")), |
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"en_captions": 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_and_extract( |
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_URL_BASE + "captions.zip") |
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train_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"infos_file": os.path.join(archive_path, "train.json") |
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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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"infos_file": os.path.join(archive_path, "val_1.json") |
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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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"infos_file": os.path.join(archive_path, "val_2.json") |
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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, infos_file): |
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"""This function returns the examples.""" |
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with open(infos_file, encoding="utf-8") as json_file: |
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infos = json.load(json_file) |
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for idx, id in enumerate(infos): |
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path = "https://www.youtube.com/watch?v=" + id[2:] |
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starts = [float(timestamp[0]) |
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for timestamp in infos[id]["timestamps"]] |
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ends = [float(timestamp[1]) |
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for timestamp in infos[id]["timestamps"]] |
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captions = [str(caption) for caption in infos[id]["sentences"]] |
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yield idx, { |
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"video_id": id, |
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"video_path": path, |
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"duration": float(infos[id]["duration"]), |
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"captions_starts": starts, |
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"captions_ends": ends, |
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"en_captions": captions, |
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} |
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