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import os.path as osp
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from mmengine import ConfigDict
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class BaseTestDataset:
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@classmethod
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def setup_class(cls):
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cls.data_prefix = osp.normpath(
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osp.join(osp.dirname(__file__), '../data/'))
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cls.ann_file_prefix = osp.join(cls.data_prefix, 'annotations')
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cls.action_ann_file = osp.join(cls.ann_file_prefix,
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'action_test_anno.json')
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cls.audio_feature_ann_file = osp.join(cls.ann_file_prefix,
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'audio_feature_test_list.txt')
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cls.audio_ann_file = osp.join(cls.ann_file_prefix,
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'audio_test_list.txt')
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cls.frame_ann_file_multi_label = osp.join(
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cls.ann_file_prefix, 'rawframe_test_list_multi_label.txt')
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cls.frame_ann_file_with_offset = osp.join(
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cls.ann_file_prefix, 'rawframe_test_list_with_offset.txt')
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cls.frame_ann_file = osp.join(cls.ann_file_prefix,
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'rawframe_test_list.txt')
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cls.hvu_frame_ann_file = osp.join(cls.ann_file_prefix,
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'hvu_frame_test_anno.json')
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cls.hvu_video_ann_file = osp.join(cls.ann_file_prefix,
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'hvu_video_test_anno.json')
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cls.hvu_video_eval_ann_file = osp.join(
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cls.ann_file_prefix, 'hvu_video_eval_test_anno.json')
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cls.proposal_ann_file = osp.join(cls.ann_file_prefix,
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'proposal_test_list.txt')
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cls.proposal_norm_ann_file = osp.join(cls.ann_file_prefix,
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'proposal_normalized_list.txt')
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cls.rawvideo_test_anno_json = osp.join(cls.ann_file_prefix,
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'rawvideo_test_anno.json')
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cls.rawvideo_test_anno_txt = osp.join(cls.ann_file_prefix,
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'rawvideo_test_anno.txt')
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cls.video_ann_file = osp.join(cls.ann_file_prefix,
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'video_test_list.txt')
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cls.video_ann_file_multi_label = osp.join(
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cls.ann_file_prefix, 'video_test_list_multi_label.txt')
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cls.video_text_ann_file = osp.join(cls.ann_file_prefix,
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'video_text_test_list.json')
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cls.pose_ann_file = osp.join(cls.ann_file_prefix, 'sample.pkl')
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cls.action_pipeline = []
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cls.audio_feature_pipeline = [
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dict(type='LoadAudioFeature'),
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dict(
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type='SampleFrames',
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clip_len=32,
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frame_interval=2,
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num_clips=1),
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dict(type='AudioFeatureSelector')
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]
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cls.audio_pipeline = [
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dict(type='AudioDecodeInit'),
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dict(
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type='SampleFrames',
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clip_len=32,
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frame_interval=2,
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num_clips=1),
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dict(type='AudioDecode')
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]
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cls.frame_pipeline = [
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dict(
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type='SampleFrames',
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clip_len=32,
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frame_interval=2,
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num_clips=1),
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dict(type='RawFrameDecode', io_backend='disk')
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]
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cls.proposal_pipeline = [
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dict(
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type='SampleProposalFrames',
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clip_len=1,
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body_segments=5,
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aug_segments=(2, 2),
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aug_ratio=0.5),
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dict(type='RawFrameDecode', io_backend='disk')
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]
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cls.proposal_test_pipeline = [
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dict(
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type='SampleProposalFrames',
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clip_len=1,
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body_segments=5,
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aug_segments=(2, 2),
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aug_ratio=0.5,
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mode='test'),
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dict(type='RawFrameDecode', io_backend='disk')
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]
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cls.proposal_train_cfg = ConfigDict(
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dict(
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ssn=dict(
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assigner=dict(
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positive_iou_threshold=0.7,
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background_iou_threshold=0.01,
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incomplete_iou_threshold=0.5,
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background_coverage_threshold=0.02,
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incomplete_overlap_threshold=0.01),
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sampler=dict(
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num_per_video=8,
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positive_ratio=1,
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background_ratio=1,
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incomplete_ratio=6,
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add_gt_as_proposals=True),
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loss_weight=dict(
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comp_loss_weight=0.1, reg_loss_weight=0.1),
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debug=False)))
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cls.proposal_test_cfg = ConfigDict(
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dict(
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ssn=dict(
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sampler=dict(test_interval=6, batch_size=16),
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evaluater=dict(
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top_k=2000,
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nms=0.2,
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softmax_before_filter=True,
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cls_top_k=2))))
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cls.proposal_test_cfg_topall = ConfigDict(
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dict(
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ssn=dict(
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sampler=dict(test_interval=6, batch_size=16),
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evaluater=dict(
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top_k=-1,
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nms=0.2,
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softmax_before_filter=True,
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cls_top_k=2))))
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cls.rawvideo_pipeline = []
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cls.video_pipeline = [
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dict(type='OpenCVInit'),
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dict(
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type='SampleFrames',
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clip_len=32,
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frame_interval=2,
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num_clips=1),
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dict(type='OpenCVDecode')
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]
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cls.video_text_pipeline = [
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dict(type='OpenCVInit'),
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dict(
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type='SampleFrames',
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clip_len=32,
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frame_interval=2,
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num_clips=1),
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dict(type='OpenCVDecode'),
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dict(type='CLIPTokenize')
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]
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cls.hvu_categories = [
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'action', 'attribute', 'concept', 'event', 'object', 'scene'
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]
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cls.hvu_category_nums = [739, 117, 291, 69, 1679, 248]
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cls.hvu_categories_for_eval = ['action', 'scene', 'object']
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cls.hvu_category_nums_for_eval = [3, 3, 3]
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cls.filename_tmpl = 'img_{:05d}.jpg'
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