Params: -- activation: num_samples x dim_feat Returns: -- mu: dim_feat -- sigma: dim_feat x dim_feat
(activations, emb_scale)
| 40 | |
| 41 | |
| 42 | def calculate_activation_statistics(activations, emb_scale): |
| 43 | """ |
| 44 | Params: |
| 45 | -- activation: num_samples x dim_feat |
| 46 | Returns: |
| 47 | -- mu: dim_feat |
| 48 | -- sigma: dim_feat x dim_feat |
| 49 | """ |
| 50 | activations = activations * emb_scale |
| 51 | mu = np.mean(activations, axis=0) |
| 52 | cov = np.cov(activations, rowvar=False) |
| 53 | return mu, cov |
| 54 | |
| 55 | |
| 56 | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): |