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import math
import torch
import torch.nn as nn
from ..utils import log_sum_exp
class EncoderBase(nn.Module):
"""docstring for EncoderBase"""
def __init__(self):
super(EncoderBase, self).__init__()
def forward(self, x):
"""
Args:
x: (batch_size, *)
Returns: the tensors required to parameterize a distribution.
E.g. for Gaussian encoder it returns the mean and variance tensors
"""
raise NotImplementedError
def sample(self, input, nsamples):
"""sampling from the encoder
Returns: Tensor1
Tensor1: the tensor latent z with shape [batch, nsamples, nz]
"""
raise NotImplementedError
def encode(self, input, nsamples):
"""perform the encoding and compute the KL term
Returns: Tensor1, Tensor2
Tensor1: the tensor latent z with shape [batch, nsamples, nz]
Tensor2: the tenor of KL for each x with shape [batch]
"""
raise NotImplementedError
def eval_inference_dist(self, x, z, param=None):
"""this function computes log q(z | x)
Args:
z: tensor
different z points that will be evaluated, with
shape [batch, nsamples, nz]
Returns: Tensor1
Tensor1: log q(z|x) with shape [batch, nsamples]
"""
raise NotImplementedError
def calc_mi(self, x):
"""Approximate the mutual information between x and z
I(x, z) = E_xE_{q(z|x)}log(q(z|x)) - E_xE_{q(z|x)}log(q(z))
Returns: Float
"""
raise NotImplementedError |