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