| from typing import Optional |
|
|
| import numpy as np |
| import torch |
|
|
|
|
| class DiagonalGaussianDistribution: |
|
|
| def __init__(self, parameters, deterministic=False): |
| self.parameters = parameters |
| self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) |
| self.logvar = torch.clamp(self.logvar, -30.0, 20.0) |
| self.deterministic = deterministic |
| self.std = torch.exp(0.5 * self.logvar) |
| self.var = torch.exp(self.logvar) |
| if self.deterministic: |
| self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) |
|
|
| def sample(self, rng: Optional[torch.Generator] = None): |
| |
|
|
| r = torch.empty_like(self.mean).normal_(generator=rng) |
| x = self.mean + self.std * r |
|
|
| return x |
|
|
| def kl(self, other=None): |
| if self.deterministic: |
| return torch.Tensor([0.]) |
| else: |
| if other is None: |
|
|
| return 0.5 * torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar |
| else: |
| return 0.5 * (torch.pow(self.mean - other.mean, 2) / other.var + |
| self.var / other.var - 1.0 - self.logvar + other.logvar) |
|
|
| def nll(self, sample, dims=[1, 2, 3]): |
| if self.deterministic: |
| return torch.Tensor([0.]) |
| logtwopi = np.log(2.0 * np.pi) |
| return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, |
| dim=dims) |
|
|
| def mode(self): |
| return self.mean |
|
|
