mirror of
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109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
from abc import abstractmethod
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from typing import Any, Tuple
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import nn
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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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def sample(self):
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# x = self.mean + self.std * torch.randn(self.mean.shape).to(
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# device=self.parameters.device
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# )
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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):
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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(self, sample, dims=[1, 2, 3]):
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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):
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return self.mean
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class AbstractRegularizer(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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raise NotImplementedError()
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@abstractmethod
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def get_trainable_parameters(self) -> Any:
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raise NotImplementedError()
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class IdentityRegularizer(AbstractRegularizer):
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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return z, dict()
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def get_trainable_parameters(self) -> Any:
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yield from ()
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def measure_perplexity(predicted_indices: torch.Tensor, num_centroids: int) -> Tuple[torch.Tensor, torch.Tensor]:
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# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
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# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
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encodings = F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
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avg_probs = encodings.mean(0)
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perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
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cluster_use = torch.sum(avg_probs > 0)
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return perplexity, cluster_use
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class DiagonalGaussianRegularizer(AbstractRegularizer):
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def __init__(self, sample: bool = True):
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super().__init__()
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self.sample = sample
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def get_trainable_parameters(self) -> Any:
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yield from ()
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def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
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log = dict()
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posterior = DiagonalGaussianDistribution(z)
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if self.sample:
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z = posterior.sample()
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else:
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z = posterior.mode()
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kl_loss = posterior.kl()
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kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
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log["kl_loss"] = kl_loss
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return z, log
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