Something Wrong in the "hardloss"?

[cvchanghao]

` hard_loss = 0

    for i in range(real_batch_size):
        confidance_gt = ground_truth_point[i, 0, :, :]
        confidance_gt = confidance_gt.view(1, self.p.grid_y, self.p.grid_x)
        hard_loss =  hard_loss +\
            torch.sum( (1-confidance[i][confidance_gt==1])**2 )/\
            (torch.sum(confidance_gt==1)+1)

        target = confidance[i][confidance_gt==0]
        hard_loss =  hard_loss +\
			torch.sum( ( target[target>0.01] )**2 )/\
			(torch.sum(target>0.01)+1)

        node = hard_sampling.sampling_node(loss = hard_loss.cpu().data, data = data_list[i], previous_node = None, next_node = None)
        self.hard_sampling.insert(node)

you add the hardloss along batches, and insert every single node use the added loss.
Is it right? Maybe should not use the added loss

[cvchanghao]

another question. you update sampling_list every epoch, that means in the first epoch, sampling_list always is None. Is it right?

sampling_list = copy.deepcopy(lane_agent.get_data_list())
lane_agent.sample_reset()