(self, features)
| 65 | return features |
| 66 | |
| 67 | def renorm4t2m(self, features): |
| 68 | # renorm to t2m norms for using t2m evaluators |
| 69 | ori_mean = torch.tensor(self.hparams.mean).to(features) |
| 70 | ori_std = torch.tensor(self.hparams.std).to(features) |
| 71 | eval_mean = torch.tensor(self.hparams.mean_eval).to(features) |
| 72 | eval_std = torch.tensor(self.hparams.std_eval).to(features) |
| 73 | features = features * ori_std + ori_mean |
| 74 | features = (features - eval_mean) / eval_std |
| 75 | return features |
| 76 | |
| 77 | def mm_mode(self, mm_on=True): |
| 78 | # random select samples for mm |
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