ysharma HF staff commited on
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7fd0092
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  1. distributions.py +92 -0
distributions.py ADDED
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+ import torch
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+ import numpy as np
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+
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+
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+ class AbstractDistribution:
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+ def sample(self):
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+ raise NotImplementedError()
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+
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+ def mode(self):
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+ raise NotImplementedError()
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+
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+
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+ class DiracDistribution(AbstractDistribution):
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+ def __init__(self, value):
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+ self.value = value
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+
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+ def sample(self):
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+ return self.value
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+
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+ def mode(self):
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+ return self.value
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+
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+
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+ class DiagonalGaussianDistribution(object):
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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(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(device=self.parameters.device)
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+ return x
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+
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+ def kl(self, other=None):
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+ if self.deterministic:
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+ return torch.Tensor([0.])
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+ else:
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+ if other is None:
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+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
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+ + self.var - 1.0 - self.logvar,
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+ dim=[1, 2, 3])
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+ else:
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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 - 1.0 - self.logvar + other.logvar,
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+ dim=[1, 2, 3])
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+
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+ def nll(self, sample, dims=[1,2,3]):
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+ if self.deterministic:
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+ return torch.Tensor([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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+
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+
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+ def normal_kl(mean1, logvar1, mean2, logvar2):
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+ """
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+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
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+ Compute the KL divergence between two gaussians.
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+ Shapes are automatically broadcasted, so batches can be compared to
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+ scalars, among other use cases.
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+ """
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+ tensor = None
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+ for obj in (mean1, logvar1, mean2, logvar2):
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+ if isinstance(obj, torch.Tensor):
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+ tensor = obj
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+ break
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+ assert tensor is not None, "at least one argument must be a Tensor"
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+
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+ # Force variances to be Tensors. Broadcasting helps convert scalars to
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+ # Tensors, but it does not work for torch.exp().
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+ logvar1, logvar2 = [
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+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
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+ for x in (logvar1, logvar2)
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+ ]
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+
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+ return 0.5 * (
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+ -1.0
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+ + logvar2
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+ - logvar1
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+ + torch.exp(logvar1 - logvar2)
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+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
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+ )