70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
from typing import Optional, Tuple
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import numpy as np
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import torch
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from diffusers.utils.torch_utils import randn_tensor
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class DiagonalGaussianDistribution(object):
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def __init__(
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self,
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parameters: torch.Tensor,
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deterministic: bool = False,
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feature_dim: int = 1,
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):
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self.parameters = parameters
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self.feature_dim = feature_dim
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=feature_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(
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self.mean, device=self.parameters.device, dtype=self.parameters.dtype
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)
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def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
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# make sure sample is on the same device as the parameters and has same dtype
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sample = randn_tensor(
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self.mean.shape,
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generator=generator,
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device=self.parameters.device,
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dtype=self.parameters.dtype,
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)
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x = self.mean + self.std * sample
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return x
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def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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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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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
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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(
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self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]
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) -> torch.Tensor:
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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) -> torch.Tensor:
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return self.mean
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