| | 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
|
| |
|
