(file_num, ri_bev_root)
| 16 | |
| 17 | |
| 18 | def read_one_ri_bev_from_seq(file_num, ri_bev_root): |
| 19 | depth_bev_data = np.load(ri_bev_root+file_num+".npy") |
| 20 | depth_bev_data_tensor = torch.from_numpy(depth_bev_data).type(torch.FloatTensor).cuda() |
| 21 | depth_bev_data_tensor = torch.unsqueeze(depth_bev_data_tensor, dim=0) |
| 22 | return depth_bev_data_tensor |
| 23 | |
| 24 | def read_rotated_one_ri_bev_from_seq(file_num, ri_bev_root, divd): |
| 25 | depth_bev_data = np.load(ri_bev_root+file_num+".npy") |