(self, x, t=False)
| 155 | return self.infer_image(*x, t) |
| 156 | |
| 157 | def infer_image(self, x, t=False): |
| 158 | self.model.eval() |
| 159 | x = x.view(1, 3, x.shape[-2], x.shape[-1]) |
| 160 | if t == True: |
| 161 | t1 = time.time() |
| 162 | with torch.no_grad(): |
| 163 | x = self.model(x).view(-1) |
| 164 | if t == True: |
| 165 | t2 = time.time() |
| 166 | t_delta = t2-t1 |
| 167 | return x.detach().cpu().numpy(), t_delta |
| 168 | return x.detach().cpu().numpy() |
| 169 | |
| 170 | def infer_lidar(self, x, t=False): |
| 171 | self.model.eval() |