54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
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from abc import abstractmethod
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from typing import Any, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ....modules.distributions.distributions import DiagonalGaussianDistribution
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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 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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def measure_perplexity(predicted_indices, num_centroids):
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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 = (
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F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
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)
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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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