def _kld_beta_kerman_prior(self, conc1, conc2):
""" Internal function to do a KL-div against the prior.
:param conc1: concentration 1.
:param conc2: concentration 2.
:returns: batch_size tensor of kld against prior.
:rtype: torch.Tensor
"""
prior = PD.Beta(zeros_like(conc1) + 1 / 3, zeros_like(conc2) + 1 / 3)
beta = PD.Beta(conc1, conc2)
return torch.sum(D.kl_divergence(beta, prior), -1)
def _kld_gaussian_N_0_1(mu, logvar):
""" Internal member for kl-div against a N(0, 1) prior
:param mu: mean
:param logvar: log-variance
:returns: batch_size tensor of kld
:rtype: torch.Tensor
"""
standard_normal = D.Normal(zeros_like(mu), ones_like(logvar))
normal = D.Normal(mu, logvar)
return torch.sum(D.kl_divergence(normal, standard_normal), -1)
def z_where_inv(z_where, clip_scale=5.0):
# Take a batch of z_where vectors, and compute their "inverse".
# That is, for each row compute:
# [s,x,y] -> [1/s,-x/s,-y/s]
# These are the parameters required to perform the inverse of the
# spatial transform performed in the generative model.
n = z_where.size(0)
out = torch.cat((LocalizedSpatialTransformerFn.ng_ones(
[1, 1]).type_as(z_where).expand(n, 1), -z_where[:, 1:]), 1)
# Divide all entries by the scale. abs(scale) ensures images arent flipped
scale = torch.max(torch.abs(z_where[:, 0:1]),
zeros_like(z_where[:, 0:1]) + clip_scale)
if torch.sum(scale == 0) > 0:
print("tensor scale of {} dim was 0!!".format(scale.shape))
exit(-1)
nan_check_and_break(scale, "scale")
out = out / scale
# out = out / z_where[:, 0:1]
return out
def _kld_gaussian_N_0_1(mu, logvar):
standard_normal = D.Normal(zeros_like(mu), ones_like(logvar))
normal = D.Normal(mu, logvar)
return torch.sum(D.kl_divergence(normal, standard_normal), -1)
