wangxiang1230 / SSTAP

Code for our CVPR 2021 Paper "Self-Supervised Learning for Semi-Supervised Temporal Action Proposal".
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请问如何使用i3d特征 #2

Open twinsyssy1018 opened 3 years ago

twinsyssy1018 commented 3 years ago

你好,我看代码的数据处理,把特征都对齐为num_prop*400,然鹅i3d是2048维的特征,不知道应该如何处理。麻烦提供下使用i3d的pipeline ,谢谢

wangxiang1230 commented 3 years ago

num_prop

Thanks for your attention to SSTAP, I3D features are in the format of "Length*Channel", where 100 is the temporal length and 2048 is the channel dimension. Similarly, for TSN features, 100 is also the temporal length and 400 is the channel dimension.

twinsyssy1018 commented 3 years ago

谢谢,我重新训练下模型,但是在数据处理时出现iou的问题呢,我的start,end time 归一化后是0.675 0.85625,在计算 iou_with_anchors时(代码dataset.py的100行:tmp_gt_iou_map = [iou_with_anchors)] ) 出现匹配不上,最后gt_iou_map的size为0,报错了

wangxiang1230 commented 3 years ago

谢谢,我重新训练下模型,但是在数据处理时出现iou的问题呢,我的start,end time 归一化后是0.675 0.85625,在计算 iou_with_anchors时(代码dataset.py的100行:tmp_gt_iou_map = [iou_with_anchors)] ) 出现匹配不上,最后gt_iou_map的size为0,报错了

Sorry, maybe I don't quite understand your question? It should be noted that the difference between TSN and I3D features is only in dimension (400 vs. 2048), you only need to change "feat_dim" (opt.py) from 400 to 2048, and change the path of dataset.

twinsyssy1018 commented 3 years ago

谢谢回复,我的确没有改其他的地方,这个问题我再仔细看看代码吧~~