Encode points with backbone and decode into a semantic segmentation map of the same size as input. Args: points (torch.Tensor): Input points of shape [B, N, 3+C]. img_metas (list[dict]): Meta information of each sample. Returns: torch.Ten
(self, points, img_metas)
| 75 | return x |
| 76 | |
| 77 | def encode_decode(self, points, img_metas): |
| 78 | """Encode points with backbone and decode into a semantic segmentation |
| 79 | map of the same size as input. |
| 80 | |
| 81 | Args: |
| 82 | points (torch.Tensor): Input points of shape [B, N, 3+C]. |
| 83 | img_metas (list[dict]): Meta information of each sample. |
| 84 | |
| 85 | Returns: |
| 86 | torch.Tensor: Segmentation logits of shape [B, num_classes, N]. |
| 87 | """ |
| 88 | x = self.extract_feat(points) |
| 89 | out = self._decode_head_forward_test(x, img_metas) |
| 90 | return out |
| 91 | |
| 92 | def _decode_head_forward_train(self, x, img_metas, pts_semantic_mask): |
| 93 | """Run forward function and calculate loss for decode head in |
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