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import json |
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import urllib.parse |
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import datasets |
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_CITATION = """\ |
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@inproceedings{Wu2020not, |
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title={Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision}, |
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author={Wu, Peng and Liu, jing and Shi, Yujia and Sun, Yujia and Shao, Fangtao and Wu, Zhaoyang and Yang, Zhiwei}, |
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booktitle={European Conference on Computer Vision (ECCV)}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Dataset for the paper "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision". \ |
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The dataset is downloaded from the authors' website (https://roc-ng.github.io/XD-Violence/). Hosting this dataset on HuggingFace \ |
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is just to make it easier for my own project to use this dataset. Please cite the original paper if you use this dataset. |
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""" |
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_NAME = "xd-violence" |
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_HOMEPAGE = f"https://huggingface.co/datasets/jherng/{_NAME}" |
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_LICENSE = "MIT" |
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_URL = f"https://huggingface.co/datasets/jherng/{_NAME}/resolve/main/data" |
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class XDViolenceConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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"""BuilderConfig for XD-Violence. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(XDViolenceConfig, self).__init__(**kwargs) |
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class Squad(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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XDViolenceConfig( |
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name="video", |
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description="Video dataset", |
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), |
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XDViolenceConfig( |
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name="rgb", |
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description="RGB visual features of the video dataset", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "video" |
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BUILDER_CONFIG_CLASS = XDViolenceConfig |
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def _info(self): |
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if self.config.name == "rgb": |
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features = datasets.Features( |
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{ |
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"rgb_feats": datasets.Array3D( |
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shape=(None, 10, 2048), |
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dtype="float32", |
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), |
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"binary_target": datasets.ClassLabel( |
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names=["non-violence", "violence"] |
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), |
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"multilabel_targets": datasets.Sequence( |
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datasets.ClassLabel( |
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names=[ |
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"Fighting", |
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"Shooting", |
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"Riot", |
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"Abuse", |
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"Car accident", |
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"Explosion", |
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] |
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) |
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), |
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"frame_annotations": datasets.Sequence( |
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{ |
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"start": datasets.Value("int32"), |
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"end": datasets.Value("int32"), |
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} |
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), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"video_path": datasets.Value("string"), |
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"binary_target": datasets.ClassLabel( |
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names=["non-violence", "violence"] |
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), |
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"multilabel_targets": datasets.Sequence( |
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datasets.ClassLabel( |
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names=[ |
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"Fighting", |
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"Shooting", |
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"Riot", |
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"Abuse", |
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"Car accident", |
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"Explosion", |
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] |
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) |
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), |
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"frame_annotations": datasets.Sequence( |
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{ |
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"start": datasets.Value("int32"), |
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"end": datasets.Value("int32"), |
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} |
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), |
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} |
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) |
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return datasets.DatasetInfo( |
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features=features, |
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description=_DESCRIPTION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_list_fpath = dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train_list.txt")) |
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test_ann_fpath = dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "test_annotations.txt")) |
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print(f"{train_list_fpath=}") |
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print(f"{test_ann_fpath=}") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": train_list_fpath}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": test_ann_fpath}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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pass |
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