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'''
Modified from: https://github.com/Linear95/CLUB
'''
import torch
import torch.nn as nn
class CLUB(nn.Module): # CLUB: Mutual Information Contrastive Learning Upper Bound
'''
This class provides the CLUB estimation to I(X,Y)
Method:
mi_est() : provides the estimation with input samples
loglikeli() : provides the log-likelihood of the approximation q(Y|X) with input samples
Arguments:
x_dim, y_dim : the dimensions of samples from X, Y respectively
hidden_size : the dimension of the hidden layer of the approximation network q(Y|X)
x_samples, y_samples : samples from X and Y, having shape [sample_size, x_dim/y_dim]
'''
def __init__(self, x_dim, y_dim, hidden_size):
super(CLUB, self).__init__()
# p_mu outputs mean of q(Y|X)
self.p_mu = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim))
# p_logvar outputs log of variance of q(Y|X)
self.p_logvar = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim),
nn.Tanh())
# self.p_logvar = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
# nn.ReLU(),
# nn.Linear(hidden_size//2, y_dim))
def get_mu_logvar(self, x_samples):
mu = self.p_mu(x_samples)
logvar = self.p_logvar(x_samples)
return mu, logvar
def mi_est(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
# log of conditional probability of positive sample pairs
positive = - (mu - y_samples)**2 /2./logvar.exp()
prediction_1 = mu.unsqueeze(1) # shape [nsample,1,dim]
y_samples_1 = y_samples.unsqueeze(0) # shape [1,nsample,dim]
# log of conditional probability of negative sample pairs
negative = - ((y_samples_1 - prediction_1)**2).mean(dim=1)/2./logvar.exp()
return (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
def loglikeli(self, x_samples, y_samples): # unnormalized loglikelihood
mu, logvar = self.get_mu_logvar(x_samples)
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
class CLUBSample(nn.Module): # Sampled version of the CLUB estimator
def __init__(self, x_dim, y_dim, hidden_size):
super(CLUBSample, self).__init__()
self.p_mu = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim))
self.p_logvar = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim),
nn.Tanh())
def get_mu_logvar(self, x_samples):
mu = self.p_mu(x_samples)
logvar = self.p_logvar(x_samples)
return mu, logvar
def loglikeli(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
def mi_est(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
sample_size = x_samples.shape[0]
#random_index = torch.randint(sample_size, (sample_size,)).long()
random_index = torch.randperm(sample_size).long()
positive = - (mu - y_samples)**2 / logvar.exp()
negative = - (mu - y_samples[random_index])**2 / logvar.exp()
upper_bound = (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
return upper_bound/2.
class CLUBSample_reshape(nn.Module): # Sampled version of the CLUB estimator
def __init__(self, x_dim, y_dim, hidden_size):
super(CLUBSample_reshape, self).__init__()
self.p_mu = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim))
self.p_logvar = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim),
nn.Tanh())
def get_mu_logvar(self, x_samples):
mu = self.p_mu(x_samples)
logvar = self.p_logvar(x_samples)
return mu, logvar
def loglikeli(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
mu = mu.reshape(-1, mu.shape[-1]) # (bs, y_dim) -> (bs, 1, y_dim) -> (bs, T, y_dim) -> (bs*T, y_dim)
logvar = logvar.reshape(-1, logvar.shape[-1])
y_samples = y_samples.reshape(-1, y_samples.shape[-1]) # (bs, T, y_dim) -> (bs*T, y_dim)
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
def mi_est(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
sample_size = mu.shape[0]
random_index = torch.randperm(sample_size).long()
y_shuffle = y_samples[random_index]
mu = mu.reshape(-1, mu.shape[-1]) # (bs, y_dim) -> (bs, 1, y_dim) -> (bs, T, y_dim) -> (bs*T, y_dim)
logvar = logvar.reshape(-1, logvar.shape[-1])
y_samples = y_samples.reshape(-1, y_samples.shape[-1]) # (bs, T, y_dim) -> (bs*T, y_dim)
y_shuffle = y_shuffle.reshape(-1, y_shuffle.shape[-1]) # (bs, T, y_dim) -> (bs*T, y_dim)
positive = - (mu - y_samples)**2 / logvar.exp()
negative = - (mu - y_shuffle)**2 / logvar.exp()
upper_bound = (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
return upper_bound/2.
class CLUBSample_group(nn.Module): # Sampled version of the CLUB estimator
def __init__(self, x_dim, y_dim, hidden_size):
super(CLUBSample_group, self).__init__()
self.p_mu = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim))
self.p_logvar = nn.Sequential(nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim),
nn.Tanh())
def get_mu_logvar(self, x_samples):
mu = self.p_mu(x_samples)
logvar = self.p_logvar(x_samples)
return mu, logvar
def loglikeli(self, x_samples, y_samples): # unnormalized loglikelihood
mu, logvar = self.get_mu_logvar(x_samples) # mu/logvar: (bs, y_dim)
mu = mu.unsqueeze(1).expand(-1, y_samples.shape[1], -1).reshape(-1, mu.shape[-1]) # (bs, y_dim) -> (bs, 1, y_dim) -> (bs, T, y_dim) -> (bs*T, y_dim)
logvar = logvar.unsqueeze(1).expand(-1, y_samples.shape[1], -1).reshape(-1, logvar.shape[-1])
y_samples = y_samples.reshape(-1, y_samples.shape[-1]) # (bs, T, y_dim) -> (bs*T, y_dim)
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0) / 2
def mi_est(self, x_samples, y_samples): # x_samples: (bs, x_dim); y_samples: (bs, T, y_dim)
mu, logvar = self.get_mu_logvar(x_samples)
sample_size = x_samples.shape[0]
#random_index = torch.randint(sample_size, (sample_size,)).long()
random_index = torch.randperm(sample_size).long()
# log of conditional probability of positive sample pairs
mu_exp1 = mu.unsqueeze(1).expand(-1, y_samples.shape[1], -1) # (bs, y_dim) -> (bs, T, y_dim)
# logvar_exp1 = logvar.unqueeze(1).expand(-1, y_samples.shape[1], -1).reshape(-1, logvar.shape[-1])
positive = - ((mu_exp1 - y_samples)**2).mean(dim=1) / logvar.exp() # mean along T
negative = - ((mu_exp1 - y_samples[random_index])**2).mean(dim=1) / logvar.exp() # mean along T
return (positive.sum(dim = -1) - negative.sum(dim = -1)).mean() / 2
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