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关于init_proposals #4

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@xsxyh

根据开源代码可知,init_proposals从results_save_total.pkl获取的2D检测结果生成,可以理解为results_save_total.pkl为训练样本经过已经训练好的2D检测器获得的结果。同时在推理时,该部分逻辑也保持一致,训练样本获得的检测结果用于推理是否有作用。如果我对results_save_total.pkl的理解有误,还请告诉我results_save_total.pkl的生成方式和意义。

if True:
initial_proposals = [self.obtain_proposals(
img=rec_img[:, i, ...], img_metas=img_metas[i])
for i in range(rec_img.size(1))] # len_temp 为 len(initial_proposals)

if True:
initial_proposals = [self.obtain_proposals(
img=rec_img[:, i, ...], img_metas=img_metas_temp[i], training_mode=False)
for i in range(rec_img.size(1))] # len_temp 为 len(initial_proposals)

另一方面在推理时,并未用到2D网络,训练也时也是直接获取的results_save_total.pkl,那么该分支是否有训练的必要

bbox_list = [dict() for i in range(len(img_metas))]
bbox_pts = self.simple_test_pts(
img_metas, init_proposals=initial_proposals, **data)
for result_dict, pts_bbox in zip(bbox_list, bbox_pts):
result_dict['pts_bbox'] = pts_bbox
return bbox_list

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