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from typing import Callable, List, Optional, Union
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import mmengine
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from mmengine.fileio import exists
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from mmaction.registry import DATASETS
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from mmaction.utils import ConfigType
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from .base import BaseActionDataset
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@DATASETS.register_module()
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class ActivityNetDataset(BaseActionDataset):
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"""ActivityNet dataset for temporal action localization. The dataset loads
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raw features and apply specified transforms to return a dict containing the
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frame tensors and other information. The ann_file is a json file with
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multiple objects, and each object has a key of the name of a video, and
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value of total frames of the video, total seconds of the video, annotations
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of a video, feature frames (frames covered by features) of the video, fps
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and rfps. Example of a annotation file:
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.. code-block:: JSON
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{
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"v_--1DO2V4K74": {
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"duration_second": 211.53,
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"duration_frame": 6337,
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"annotations": [
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{
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"segment": [
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30.025882995319815,
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205.2318595943838
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],
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"label": "Rock climbing"
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}
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],
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"feature_frame": 6336,
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"fps": 30.0,
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"rfps": 29.9579255898
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},
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"v_--6bJUbfpnQ": {
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"duration_second": 26.75,
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"duration_frame": 647,
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"annotations": [
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{
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"segment": [
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2.578755070202808,
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24.914101404056165
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],
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"label": "Drinking beer"
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}
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],
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"feature_frame": 624,
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"fps": 24.0,
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"rfps": 24.1869158879
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},
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...
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}
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Args:
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ann_file (str): Path to the annotation file.
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pipeline (list[dict | callable]): A sequence of data transforms.
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data_prefix (dict or ConfigDict): Path to a directory where videos are
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held. Defaults to ``dict(video='')``.
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test_mode (bool): Store True when building test or validation dataset.
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Default: False.
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"""
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def __init__(self,
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ann_file: str,
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pipeline: List[Union[dict, Callable]],
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data_prefix: Optional[ConfigType] = dict(video=''),
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test_mode: bool = False,
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**kwargs):
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super().__init__(
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ann_file,
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pipeline=pipeline,
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data_prefix=data_prefix,
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test_mode=test_mode,
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**kwargs)
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def load_data_list(self) -> List[dict]:
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"""Load annotation file to get video information."""
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exists(self.ann_file)
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data_list = []
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anno_database = mmengine.load(self.ann_file)
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for video_name in anno_database:
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video_info = anno_database[video_name]
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feature_path = video_name + '.csv'
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feature_path = '%s/%s' % (self.data_prefix['video'], feature_path)
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video_info['feature_path'] = feature_path
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video_info['video_name'] = video_name
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data_list.append(video_info)
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return data_list
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