Xiuan Wan

def _create_label(self, inside_index, anchor, bbox): # label: 1 is positive, 0 is negative, -1 is dont care label = np.empty((len(inside_index),), dtype=np.int32) label.fill(-1) argmax_ious, max_ious, gt_argmax_ious = \ self._calc_ious(anchor, bbox, inside_index)...

Refer to this: # sampler=dict( # train=dict( # type="DistributedGroupSemiBalanceSampler", # if is distributed training，a prefix " DistributedGroup" will be added to "SemiBalanceSampler". # sample_ratio=[1, 4], # by_prob=True, # # at_least_one=True,...