def make_latent_distributions(self, batch, mask, no_proposal=False):
"""
Make latent distributions for the given batch and mask.
No no_proposal is True, return None instead of proposal distribution.
"""
observed = self.make_observed(batch, mask)
if no_proposal:
proposal = None
else:
full_info = torch.cat([batch, mask], 1)
proposal_params = self.proposal_network(full_info)
proposal = normal_parse_params(proposal_params, 1e-3)
prior_params = self.prior_network(torch.cat([observed, mask], 1))
prior = normal_parse_params(prior_params, 1e-3)
return proposal, prior
def make_latent_distributions(self, batch):
"""
Make latent distributions for the given batch and mask.
No no_proposal is True, return None instead of proposal distribution.
"""
prior_params = self.prior_network(batch)
prior = normal_parse_params(prior_params, 1e-3)
return prior
def make_latent_distributions(self, latent_params, no_proposal=False):
"""
Make latent distributions for the given batch and mask.
No no_proposal is True, return None instead of proposal distribution.
"""
# print(latent_params.shape, "=================")
latent = normal_parse_params(latent_params, 1e-3)
return latent
def make_latent_distributions(self,
batch,
mask,
no_proposal=False,
need_observed=True):
"""
Make latent distributions for the given batch and mask.
No no_proposal is True, return None instead of proposal distribution.
"""
if need_observed:
observed = self.make_observed(batch, mask)
else:
observed = batch
# plt.imshow(observed.detach().cpu().numpy()[0,0], cmap='gray')
# plt.show()
if no_proposal:
proposal = None
else:
full_info = torch.cat([batch, mask], 1)
proposal_params = self.proposal_network(full_info)
proposal = normal_parse_params(proposal_params, 1e-3)
prior_params = self.prior_network(torch.cat([observed, mask], 1))
prior = normal_parse_params(prior_params, 1e-3)
return proposal, prior
def sampler(params):
return normal_parse_params(params).mean
def sampler(params, multiple=False):
return normal_parse_params(params, multiple=False).mean
