Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_
(self, img, img_meta, rescale)
| 241 | return seg_logit |
| 242 | |
| 243 | def inference(self, img, img_meta, rescale): |
| 244 | """Inference with slide/whole style. |
| 245 | |
| 246 | Args: |
| 247 | img (Tensor): The input image of shape (N, 3, H, W). |
| 248 | img_meta (dict): Image info dict where each dict has: 'img_shape', |
| 249 | 'scale_factor', 'flip', and may also contain |
| 250 | 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. |
| 251 | For details on the values of these keys see |
| 252 | `mmseg/datasets/pipelines/formatting.py:Collect`. |
| 253 | rescale (bool): Whether rescale back to original shape. |
| 254 | |
| 255 | Returns: |
| 256 | Tensor: The output segmentation map. |
| 257 | """ |
| 258 | |
| 259 | assert self.test_cfg.mode in ['slide', 'whole'] |
| 260 | ori_shape = img_meta[0]['ori_shape'] |
| 261 | assert all(_['ori_shape'] == ori_shape for _ in img_meta) |
| 262 | if self.test_cfg.mode == 'slide': |
| 263 | seg_logit = self.slide_inference(img, img_meta, rescale) |
| 264 | else: |
| 265 | seg_logit = self.whole_inference(img, img_meta, rescale) |
| 266 | output = F.softmax(seg_logit, dim=1) |
| 267 | flip = img_meta[0]['flip'] |
| 268 | if flip: |
| 269 | flip_direction = img_meta[0]['flip_direction'] |
| 270 | assert flip_direction in ['horizontal', 'vertical'] |
| 271 | if flip_direction == 'horizontal': |
| 272 | output = output.flip(dims=(3, )) |
| 273 | elif flip_direction == 'vertical': |
| 274 | output = output.flip(dims=(2, )) |
| 275 | |
| 276 | return output |
| 277 | |
| 278 | def simple_test(self, img, img_meta, rescale=True): |
| 279 | """Simple test with single image.""" |
no test coverage detected