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| 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: | |
| if other is None: | |
| return 0.5 * torch.sum(torch.pow(self.mean, 2) | |
| + self.var - 1.0 - self.logvar, | |
| dim=[1, 2, 3]) | |
| 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=[1, 2, 3]) | |
| 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" | |
| # Force variances to be Tensors. Broadcasting helps convert scalars to | |
| # Tensors, but it does not work for torch.exp(). | |
| 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) | |
| ) | |