(self, input_data, device=None)
| 271 | return data_output |
| 272 | |
| 273 | def val_func_process(self, input_data, device=None): |
| 274 | input_data = np.ascontiguousarray(input_data[None, :, :, :], dtype=np.float32) |
| 275 | input_data = torch.FloatTensor(input_data).cuda(device) |
| 276 | |
| 277 | with torch.cuda.device(input_data.get_device()): |
| 278 | self.val_func.eval() |
| 279 | self.val_func.to(input_data.get_device()) |
| 280 | with torch.no_grad(): |
| 281 | score = self.val_func(input_data) |
| 282 | score = score[0] |
| 283 | |
| 284 | if self.is_flip: |
| 285 | input_data = input_data.flip(-1) |
| 286 | score_flip = self.val_func(input_data) |
| 287 | score_flip = score_flip[0] |
| 288 | score += score_flip.flip(-1) |
| 289 | # score = torch.exp(score) |
| 290 | # score = score.data |
| 291 | |
| 292 | return score |
| 293 | |
| 294 | def process_image(self, img, crop_size=None): |
| 295 | p_img = img |
no outgoing calls
no test coverage detected