def binary_loss_array(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes the binary loss without averaging or summing over the batch dimension.
"""
batch_size = x.size(0)
# if not summed over batch_dimension
if len(ldj.size()) > 1:
ldj = ldj.view(ldj.size(0), -1).sum(-1)
# TODO: upgrade to newest pytorch version on master branch, there the nn.BCELoss comes with the option
# reduce, which when set to False, does no sum over batch dimension.
bce = -log_bernoulli(
x.view(batch_size, -1), recon_x.view(batch_size, -1), dim=1)
# ln p(z_k) (not averaged)
log_p_zk = log_normal_standard(z_k, dim=1)
# ln q(z_0) (not averaged)
log_q_z0 = log_normal_diag(z_0, mean=z_mu, log_var=z_var.log(), dim=1)
# ln q(z_0) - ln p(z_k) ]
logs = log_q_z0 - log_p_zk
loss = bce + beta * (logs - ldj)
return loss
def multinomial_loss_function(x_logit,
x,
z_mu,
z_var,
z_0,
z_k,
ldj,
args,
beta=1.):
"""
Computes the cross entropy loss function while summing over batch dimension, not averaged!
:param x_logit: shape: (batch_size, num_classes * num_channels, pixel_width, pixel_height), real valued logits
:param x: shape (batchsize, num_channels, pixel_width, pixel_height), pixel values rescaled between [0, 1].
:param z_mu: mean of z_0
:param z_var: variance of z_0
:param z_0: first stochastic latent variable
:param z_k: last stochastic latent variable
:param ldj: log det jacobian
:param args: global parameter settings
:param beta: beta for kl loss
:return: loss, ce, kl
"""
num_classes = 256
batch_size = x.size(0)
input_size = [3, 28, 28]
# x_logit = x_logit.view(batch_size, num_classes, args.input_size[0], args.input_size[1], args.input_size[2])
x_logit = x_logit.view(batch_size, num_classes, input_size[0],
input_size[1], input_size[2])
# make integer class labels
target = (x * (num_classes - 1)).long()
# - N E_q0 [ ln p(x|z_k) ]
# sums over batch dimension (and feature dimension)
ce = F.cross_entropy(x_logit, target, reduction='sum')
# ln p(z_k) (not averaged)
log_p_zk = log_normal_standard(z_k, dim=1)
# ln q(z_0) (not averaged)
log_q_z0 = log_normal_diag(z_0, mean=z_mu, log_var=z_var.log(), dim=1)
# N E_q0[ ln q(z_0) - ln p(z_k) ]
summed_logs = torch.sum(log_q_z0 - log_p_zk)
# sum over batches
summed_ldj = torch.sum(ldj)
# ldj = N E_q_z0[\sum_k log |det dz_k/dz_k-1| ]
kl = (summed_logs - summed_ldj)
loss = ce + beta * kl
loss = loss / float(batch_size)
ce = ce / float(batch_size)
kl = kl / float(batch_size)
return loss, ce, kl
def binary_loss_function(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes the binary loss function while summing over batch dimension, not averaged!
:param recon_x: shape: (batch_size, num_channels, pixel_width, pixel_height), bernoulli parameters p(x=1)
:param x: shape (batchsize, num_channels, pixel_width, pixel_height), pixel values rescaled between [0, 1].
:param z_mu: mean of z_0
:param z_var: variance of z_0
:param z_0: first stochastic latent variable
:param z_k: last stochastic latent variable
:param ldj: log det jacobian
:param beta: beta for kl loss
:return: loss, ce, kl
"""
reconstruction_function = nn.BCELoss(reduction='sum')
# print('mu: ', z_mu.mean().item(), 'logvar: ', z_var.mean().item())
batch_size = x.size(0)
# - N E_q0 [ ln p(x|z_k) ]
bce = reconstruction_function(recon_x, x)
# ln p(z_k) (not averaged)
log_p_zk = log_normal_standard(z_k, dim=1)
# ln q(z_0) (not averaged)
# print('z0: ', z_0.mean().item(), 'z_k: ', z_k.mean().item())
# log_q_z0 = log_normal_diag(z_0, mean=z_mu, log_var=z_logvar, dim=1)
log_q_z0 = log_normal_diag(z_0, mean=z_mu, log_var=z_var.log(), dim=1)
# N E_q0[ ln q(z_0) - ln p(z_k) ]
summed_logs = torch.sum(log_q_z0 - log_p_zk)
# sum over batches
summed_ldj = torch.sum(ldj)
# ldj = N E_q_z0[\sum_k log |det dz_k/dz_k-1| ]
kl = (summed_logs - summed_ldj)
loss = bce + beta * kl
loss = loss / float(batch_size)
bce = bce / float(batch_size)
kl = kl / float(batch_size)
return loss, bce, kl
def multinomial_loss_array(x_logit,
x,
z_mu,
z_var,
z_0,
z_k,
ldj,
args,
beta=1.):
"""
Computes the discritezed logistic loss without averaging or summing over the batch dimension.
"""
num_classes = 256
batch_size = x.size(0)
x_logit = x_logit.view(batch_size, num_classes, args.input_size[0],
args.input_size[1], args.input_size[2])
# make integer class labels
target = (x * (num_classes - 1)).long()
# - N E_q0 [ ln p(x|z_k) ]
# computes cross entropy over all dimensions separately:
ce = F.cross_entropy(x_logit, target, reduction='none')
# sum over feature dimension
ce = ce.view(batch_size, -1).sum(dim=1)
# ln p(z_k) (not averaged)
log_p_zk = log_normal_standard(z_k.view(batch_size, -1), dim=1)
# ln q(z_0) (not averaged)
log_q_z0 = log_normal_diag(z_0.view(batch_size, -1),
mean=z_mu.view(batch_size, -1),
log_var=z_var.log().view(batch_size, -1),
dim=1)
# ln q(z_0) - ln p(z_k) ]
logs = log_q_z0 - log_p_zk
loss = ce + beta * (logs - ldj)
return loss
args)
else:
raise ValueError('Invalid input type for calculate loss: %s.' %
args.input_type)
return loss
if __name__ == '__main__':
ldj = None
z_0 = z_k = z_mu = torch.ones((256, 64)) * 0.001
z_log_var = torch.ones((256, 64)) * 0.0001
# ln p(z_k) (not averaged)
log_p_zk = log_normal_standard(z_k, dim=1)
# ln q(z_0) (not averaged)
# print('z0: ', z_0.mean().item(), 'z_k: ', z_k.mean().item())
log_q_z0 = log_normal_diag(z_0, mean=z_mu, log_var=z_log_var, dim=1)
# print('log_q_z0', log_q_z0.mean().item(), 'log_p_zk: ', log_p_zk.mean().item())
# N E_q0[ ln q(z_0) - ln p(z_k) ]
difference = log_q_z0 - log_p_zk
# print('mean difference: ', difference.mean().item())
summed_logs = torch.sum(difference)
# print('summed_logs: ', summed_logs.item())
# sum over batches
summed_ldj = torch.sum(ldj) if ldj else 0.
# ldj = N E_q_z0[\sum_k log |det dz_k/dz_k-1| ]
kl = (summed_logs - summed_ldj)
# print('KLD: ', kl.mean().item())