Doubt regading prediction with VectorMapNet model

[quangnhat185]

I crop this snippet in dghead.py which is used to perform prediction. It checked and see that batch seems to contain the ground-truth detection (class labels and bounding boxes in batch['det']). Since VectorMapNet supposes to be an end-to-end map learning model, I don't understand why ground truth is require to perform inference here . Did I understand something wrong here?

I want to use your model make prediction with my custom camera data, can you suggest on how to do it?

    @torch.no_grad()
    def inference(self, batch: dict, context: dict, gt_condition=False, **kwargs):
        '''
            num_samples_batch: number of sample per batch (batch size)
        '''
        outs = {}
        bbox_dict = self.det_net(batch['det'], context=context)
        bbox_dict = self.det_net.post_process(bbox_dict)