(self, sigma)
| 88 | return self.p_[sigma, x] |
| 89 | |
| 90 | def score_norm(self, sigma): |
| 91 | if type(sigma) == torch.Tensor: |
| 92 | sigma = sigma.cpu().numpy() |
| 93 | sigma = np.log(sigma / self.PI) |
| 94 | sigma = (sigma - np.log(self.SIGMA_MIN)) / (np.log(self.SIGMA_MAX) - np.log(self.SIGMA_MIN)) * self.SIGMA_N |
| 95 | sigma = np.round(np.clip(sigma, 0, self.SIGMA_N)).astype(int) |
| 96 | return self.score_norm_[sigma] |
| 97 | |
| 98 | # def score_norm_torch(self, sigma): |
| 99 | # sigma = torch.log(sigma / self.PI) |