(self, features, mask=None,targets=None, target_queries=None, target_vlp=None, prediction_switch=None, task='seg', extra={})
| 81 | self.processed_features = (mask_features, transformer_encoder_features, multi_scale_features) |
| 82 | |
| 83 | def forward_decoder(self, features, mask=None,targets=None, target_queries=None, target_vlp=None, prediction_switch=None, task='seg', extra={}): |
| 84 | assert self.processed_features is not None, "need to precess features first" |
| 85 | mask_features, transformer_encoder_features, multi_scale_features = self.processed_features |
| 86 | if task == 'teacher': |
| 87 | predictions = self.predictor.forward_teacher(multi_scale_features, mask_features, mask, targets=targets, |
| 88 | target_queries=target_queries, target_vlp=target_vlp, |
| 89 | task=task, extra=extra) |
| 90 | else: |
| 91 | predictions = self.predictor(multi_scale_features, mask_features, mask, targets=targets, |
| 92 | target_queries=target_queries, target_vlp=target_vlp, task=task, extra=extra) |
| 93 | return predictions |
| 94 | |
| 95 | def forward(self, features, mask=None, targets=None, target_queries=None, target_vlp=None, task='seg', extra={}): |
| 96 | return self.layers(features, mask, targets=targets, target_queries=target_queries, target_vlp=target_vlp, task=task, extra=extra) |
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