vista/vwm/modules/distributions/distributions.py

from __future__ import annotations

import numpy as np
import torch


class AbstractDistribution:
    def sample(self):
        raise NotImplementedError

    def mode(self):
        raise NotImplementedError


class DiracDistribution(AbstractDistribution):
    def __init__(self, value):
        self.value = value

    def sample(self):
        return self.value

    def mode(self):
        return self.value


class DiagonalGaussianDistribution:
    def __init__(self, parameters, deterministic=False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)

    def sample(self):
        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
        return x

    def kl(self, other=None):
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            if other is None:
                return 0.5 * torch.sum(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[1, 2, 3]
                )
            else:
                return 0.5 * torch.sum(
                    torch.pow(self.mean - other.mean,
                              2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar,
                    dim=[1, 2, 3]
                )

    def nll(self, sample, dims=[1, 2, 3]):
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            return 0.5 * torch.sum(
                np.log(2.0 * np.pi) + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
                dim=dims
            )

    def mode(self):
        return self.mean