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VAE.py
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VAE.py
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from __future__ import print_function
import numpy as np
import math
from scipy.misc import logsumexp
import torch
import torch.utils.data
import torch.nn as nn
from torch.nn import Linear
from torch.autograd import Variable
from utils.distributions import log_Bernoulli, log_Normal_diag, log_Normal_standard, log_Logistic_256
from utils.visual_evaluation import plot_histogram
from utils.nn import he_init, GatedDense, NonLinear
from models.Model import Model
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
#=======================================================================================================================
class VAE(Model):
def __init__(self, args):
super(VAE, self).__init__(args)
# encoder: q(z | x)
self.q_z_layers = nn.Sequential(
GatedDense(np.prod(self.args.input_size), 300),
GatedDense(300, 300)
)
self.q_z_mean = Linear(300, self.args.z1_size)
self.q_z_logvar = NonLinear(300, self.args.z1_size, activation=nn.Hardtanh(min_val=-6.,max_val=2.))
# decoder: p(x | z)
self.p_x_layers = nn.Sequential(
GatedDense(self.args.z1_size, 300),
GatedDense(300, 300)
)
if self.args.input_type == 'binary':
self.p_x_mean = NonLinear(300, np.prod(self.args.input_size), activation=nn.Sigmoid())
elif self.args.input_type == 'gray' or self.args.input_type == 'continuous':
self.p_x_mean = NonLinear(300, np.prod(self.args.input_size), activation=nn.Sigmoid())
self.p_x_logvar = NonLinear(300, np.prod(self.args.input_size), activation=nn.Hardtanh(min_val=-4.5,max_val=0))
# weights initialization
for m in self.modules():
if isinstance(m, nn.Linear):
he_init(m)
# add pseudo-inputs if VampPrior
if self.args.prior == 'vampprior':
self.add_pseudoinputs()
# AUXILIARY METHODS
def calculate_loss(self, x, beta=1., average=False):
'''
:param x: input image(s)
:param beta: a hyperparam for warmup
:param average: whether to average loss or not
:return: value of a loss function
'''
# pass through VAE
x_mean, x_logvar, z_q, z_q_mean, z_q_logvar = self.forward(x)
# RE
if self.args.input_type == 'binary':
RE = log_Bernoulli(x, x_mean, dim=1)
elif self.args.input_type == 'gray' or self.args.input_type == 'continuous':
RE = -log_Logistic_256(x, x_mean, x_logvar, dim=1)
else:
raise Exception('Wrong input type!')
# KL
log_p_z = self.log_p_z(z_q)
log_q_z = log_Normal_diag(z_q, z_q_mean, z_q_logvar, dim=1)
KL = -(log_p_z - log_q_z)
loss = - RE + beta * KL
if average:
loss = torch.mean(loss)
RE = torch.mean(RE)
KL = torch.mean(KL)
return loss, RE, KL
def calculate_likelihood(self, X, dir, mode='test', S=5000, MB=100):
# set auxiliary variables for number of training and test sets
N_test = X.size(0)
# init list
likelihood_test = []
if S <= MB:
R = 1
else:
R = S / MB
S = MB
for j in range(N_test):
if j % 100 == 0:
print('{:.2f}%'.format(j / (1. * N_test) * 100))
# Take x*
x_single = X[j].unsqueeze(0)
a = []
for r in range(0, int(R)):
# Repeat it for all training points
x = x_single.expand(S, x_single.size(1))
a_tmp, _, _ = self.calculate_loss(x)
a.append( -a_tmp.cpu().data.numpy() )
# calculate max
a = np.asarray(a)
a = np.reshape(a, (a.shape[0] * a.shape[1], 1))
likelihood_x = logsumexp( a )
likelihood_test.append(likelihood_x - np.log(len(a)))
likelihood_test = np.array(likelihood_test)
plot_histogram(-likelihood_test, dir, mode)
return -np.mean(likelihood_test)
def calculate_lower_bound(self, X_full, MB=100):
# CALCULATE LOWER BOUND:
lower_bound = 0.
RE_all = 0.
KL_all = 0.
I = int(math.ceil(X_full.size(0) / MB))
for i in range(I):
x = X_full[i * MB: (i + 1) * MB].view(-1, np.prod(self.args.input_size))
loss, RE, KL = self.calculate_loss(x,average=True)
RE_all += RE.cpu().data[0]
KL_all += KL.cpu().data[0]
lower_bound += loss.cpu().data[0]
lower_bound /= I
return lower_bound
# ADDITIONAL METHODS
def generate_x(self, N=25):
if self.args.prior == 'standard':
z_sample_rand = Variable( torch.FloatTensor(N, self.args.z1_size).normal_() )
if self.args.cuda:
z_sample_rand = z_sample_rand.cuda()
elif self.args.prior == 'vampprior':
means = self.means(self.idle_input)[0:N]
z_sample_gen_mean, z_sample_gen_logvar = self.q_z(means)
z_sample_rand = self.reparameterize(z_sample_gen_mean, z_sample_gen_logvar)
samples_rand, _ = self.p_x(z_sample_rand)
return samples_rand
def reconstruct_x(self, x):
x_mean, _, _, _, _ = self.forward(x)
return x_mean
# THE MODEL: VARIATIONAL POSTERIOR
def q_z(self, x):
x = self.q_z_layers(x)
z_q_mean = self.q_z_mean(x)
z_q_logvar = self.q_z_logvar(x)
return z_q_mean, z_q_logvar
# THE MODEL: GENERATIVE DISTRIBUTION
def p_x(self, z):
z = self.p_x_layers(z)
x_mean = self.p_x_mean(z)
if self.args.input_type == 'binary':
x_logvar = 0.
else:
x_mean = torch.clamp(x_mean, min=0.+1./512., max=1.-1./512.)
x_logvar = self.p_x_logvar(z)
return x_mean, x_logvar
# the prior
def log_p_z(self, z):
if self.args.prior == 'standard':
log_prior = log_Normal_standard(z, dim=1)
elif self.args.prior == 'vampprior':
# z - MB x M
C = self.args.number_components
# calculate params
X = self.means(self.idle_input)
# calculate params for given data
z_p_mean, z_p_logvar = self.q_z(X) # C x M
# expand z
z_expand = z.unsqueeze(1)
means = z_p_mean.unsqueeze(0)
logvars = z_p_logvar.unsqueeze(0)
a = log_Normal_diag(z_expand, means, logvars, dim=2) - math.log(C) # MB x C
a_max, _ = torch.max(a, 1) # MB x 1
# calculte log-sum-exp
log_prior = a_max + torch.log(torch.sum(torch.exp(a - a_max.unsqueeze(1)), 1)) # MB x 1
else:
raise Exception('Wrong name of the prior!')
return log_prior
# THE MODEL: FORWARD PASS
def forward(self, x):
# z ~ q(z | x)
z_q_mean, z_q_logvar = self.q_z(x)
z_q = self.reparameterize(z_q_mean, z_q_logvar)
# x_mean = p(x|z)
x_mean, x_logvar = self.p_x(z_q)
return x_mean, x_logvar, z_q, z_q_mean, z_q_logvar