disvae/utils/math.py

import math

from tqdm import trange, tqdm
import torch


def matrix_log_density_gaussian(x, mu, logvar):
    """Calculates log density of a Gaussian for all combination of bacth pairs of
    `x` and `mu`. I.e. return tensor of shape `(batch_size, batch_size, dim)`
    instead of (batch_size, dim) in the usual log density.

    Parameters
    ----------
    x: torch.Tensor
        Value at which to compute the density. Shape: (batch_size, dim).

    mu: torch.Tensor
        Mean. Shape: (batch_size, dim).

    logvar: torch.Tensor
        Log variance. Shape: (batch_size, dim).

    batch_size: int
        number of training images in the batch
    """
    batch_size, dim = x.shape
    x = x.view(batch_size, 1, dim)
    mu = mu.view(1, batch_size, dim)
    logvar = logvar.view(1, batch_size, dim)
    return log_density_gaussian(x, mu, logvar)


def log_density_gaussian(x, mu, logvar):
    """Calculates log density of a Gaussian.

    Parameters
    ----------
    x: torch.Tensor or np.ndarray or float
        Value at which to compute the density.

    mu: torch.Tensor or np.ndarray or float
        Mean.

    logvar: torch.Tensor or np.ndarray or float
        Log variance.
    """
    normalization = - 0.5 * (math.log(2 * math.pi) + logvar)
    inv_var = torch.exp(-logvar)
    log_density = normalization - 0.5 * ((x - mu)**2 * inv_var)
    return log_density


def log_importance_weight_matrix(batch_size, dataset_size):
    """
    Calculates a log importance weight matrix

    Parameters
    ----------
    batch_size: int
        number of training images in the batch

    dataset_size: int
    number of training images in the dataset
    """
    N = dataset_size
    M = batch_size - 1
    strat_weight = (N - M) / (N * M)
    W = torch.Tensor(batch_size, batch_size).fill_(1 / M)
    W.view(-1)[::M + 1] = 1 / N
    W.view(-1)[1::M + 1] = strat_weight
    W[M - 1, 0] = strat_weight
    return W.log()