(d)
| 19 | return -(p*F.log_softmax(ul_y, dim=1)).sum(dim=1).mean(dim=0) |
| 20 | |
| 21 | def _l2_normalize(d): |
| 22 | |
| 23 | d = d.numpy() |
| 24 | d /= (np.sqrt(np.sum(d ** 2, axis=(1, 2, 3))).reshape((-1, 1, 1, 1)) + 1e-16) |
| 25 | return torch.from_numpy(d) |
| 26 | |
| 27 | def kl_div_with_logit(q_logit, p_logit): |
| 28 |