Args: x (list[Tensor]): feature maps of multiple scales best_rbboxes (list[list[Tensor]]): best rbboxes of multiple scales of multiple images
(self, x, rbboxes)
| 118 | ]) |
| 119 | |
| 120 | def forward(self, x, rbboxes): |
| 121 | """ |
| 122 | Args: |
| 123 | x (list[Tensor]): |
| 124 | feature maps of multiple scales |
| 125 | best_rbboxes (list[list[Tensor]]): |
| 126 | best rbboxes of multiple scales of multiple images |
| 127 | """ |
| 128 | mlvl_rbboxes = [torch.cat(rbbox) for rbbox in zip(*rbboxes)] |
| 129 | out = [] |
| 130 | for x_scale, rbboxes_scale, ac_scale in zip(x, mlvl_rbboxes, self.ac): |
| 131 | feat_refined_scale = ac_scale(x_scale, rbboxes_scale) |
| 132 | out.append(feat_refined_scale) |
| 133 | return out |
| 134 | |
| 135 | |
| 136 | class FeatureRefineModule(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected