67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
# Copyright 2025 ByteDance and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import numpy as np
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters: torch.Tensor, deterministic: bool = 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(
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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=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 = torch.randn(
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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.pow(self.mean, 2) + self.var - 1.0 - self.logvar
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else:
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return 0.5 * (
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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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)
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def nll(self, sample, dims) -> 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 |