| import torch
|
| import numpy as np
|
|
|
|
|
| class AbstractDistribution:
|
| def sample(self):
|
| raise NotImplementedError()
|
|
|
| def mode(self):
|
| raise NotImplementedError()
|
|
|
|
|
| class DiracDistribution(AbstractDistribution):
|
| def __init__(self, value):
|
| self.value = value
|
|
|
| def sample(self):
|
| return self.value
|
|
|
| def mode(self):
|
| return self.value
|
|
|
|
|
| class DiagonalGaussianDistribution(object):
|
| 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):
|
| x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
|
| return x
|
|
|
| def kl(self, other=None):
|
| if self.deterministic:
|
| return torch.Tensor([0.])
|
| else:
|
| sum_dim = list(range(1,len(self.mean.shape)))
|
| if other is None:
|
|
|
| return 0.5 * torch.sum(torch.pow(self.mean, 2)
|
| + self.var - 1.0 - self.logvar,
|
| dim=sum_dim)
|
| else:
|
| return 0.5 * torch.sum(
|
| torch.pow(self.mean - other.mean, 2) / other.var
|
| + self.var / other.var - 1.0 - self.logvar + other.logvar,
|
| dim=sum_dim)
|
|
|
| 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
|
|
|
|
|
| def normal_kl(mean1, logvar1, mean2, logvar2):
|
| """
|
| source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
|
| Compute the KL divergence between two gaussians.
|
| Shapes are automatically broadcasted, so batches can be compared to
|
| scalars, among other use cases.
|
| """
|
| tensor = None
|
| for obj in (mean1, logvar1, mean2, logvar2):
|
| if isinstance(obj, torch.Tensor):
|
| tensor = obj
|
| break
|
| assert tensor is not None, "at least one argument must be a Tensor"
|
|
|
|
|
|
|
| logvar1, logvar2 = [
|
| x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
|
| for x in (logvar1, logvar2)
|
| ]
|
|
|
| return 0.5 * (
|
| -1.0
|
| + logvar2
|
| - logvar1
|
| + torch.exp(logvar1 - logvar2)
|
| + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
|
| )
|
|
|
