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Running
on
Zero
File size: 1,617 Bytes
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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):
# x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
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
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