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Running
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Zero
File size: 1,808 Bytes
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import torch
import numpy as np
from typing import Union, List
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1):
self.feat_dim = feat_dim
self.parameters = parameters
if isinstance(parameters, list):
self.mean = parameters[0]
self.logvar = parameters[1]
else:
self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_dim)
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)
def sample(self):
x = self.mean + self.std * torch.randn_like(self.mean)
return x
def kl(self, other=None, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
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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