source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases.
(mean1, logvar1, mean2, logvar2)
| 63 | |
| 64 | |
| 65 | def normal_kl(mean1, logvar1, mean2, logvar2): |
| 66 | """ |
| 67 | source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 |
| 68 | Compute the KL divergence between two gaussians. |
| 69 | Shapes are automatically broadcasted, so batches can be compared to |
| 70 | scalars, among other use cases. |
| 71 | """ |
| 72 | tensor = None |
| 73 | for obj in (mean1, logvar1, mean2, logvar2): |
| 74 | if isinstance(obj, torch.Tensor): |
| 75 | tensor = obj |
| 76 | break |
| 77 | assert tensor is not None, "at least one argument must be a Tensor" |
| 78 | |
| 79 | # Force variances to be Tensors. Broadcasting helps convert scalars to |
| 80 | # Tensors, but it does not work for torch.exp(). |
| 81 | logvar1, logvar2 = [ |
| 82 | x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) |
| 83 | for x in (logvar1, logvar2) |
| 84 | ] |
| 85 | |
| 86 | return 0.5 * ( |
| 87 | -1.0 |
| 88 | + logvar2 |
| 89 | - logvar1 |
| 90 | + torch.exp(logvar1 - logvar2) |
| 91 | + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) |
| 92 | ) |