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55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
import numpy as np
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import torch
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class DiagonalGaussianDistribution:
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def __init__(self, parameters, deterministic=False):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(self.mean).to(
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device=self.parameters.device
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)
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def sample(self):
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x = self.mean + self.std * torch.randn(self.mean.shape).to(
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device=self.parameters.device
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)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.0])
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if other is None:
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return 0.5 * torch.sum(
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torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
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dim=[1, 2, 3],
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)
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=[1, 2, 3],
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)
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def nll(self, sample, dims=None):
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dims = dims if dims is None else [1, 2, 3]
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(
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logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
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dim=dims,
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)
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def mode(self):
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return self.mean
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