def test_exponential_shape_scalar_param(self):
expon = Exponential(1.)
self.assertEqual(expon._batch_shape, torch.Size())
self.assertEqual(expon._event_shape, torch.Size())
self.assertEqual(expon.sample().size(), torch.Size((1, )))
self.assertEqual(expon.sample((3, 2)).size(), torch.Size((3, 2)))
self.assertRaises(ValueError, expon.log_prob, self.scalar_sample)
self.assertEqual(
expon.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2)))
self.assertEqual(
expon.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3)))
def jump(self, xi, t):
n = xi.size(0)
# spatial multivariate gaussian
m_gauss = MultivariateNormal(self.mu_jump * torch.ones(n),
self.std_jump * torch.eye(n))
# poisson process, probability of arrival at time t
exp_d = Exponential(self.lambd)
# independent events, mult probabilities
p = torch.exp(m_gauss.log_prob(xi)) * (
1 - torch.exp(exp_d.log_prob(self.last_jump)))
# one sample from bernoulli trial
event = Bernoulli(p).sample([1])
if event:
coord_before = xi
xi = self.jump_event(xi, t) # flatten resulting sampled location
coord_after = xi
# saving jump coordinate info
self.log_jump(t, coord_before, coord_after)
self.last_jump = 0
# if no jump, increase counter for bern trial
else:
self.last_jump += 1
return xi
def test_exponential_shape_tensor_param(self):
expon = Exponential(torch.Tensor([1, 1]))
self.assertEqual(expon._batch_shape, torch.Size((2,)))
self.assertEqual(expon._event_shape, torch.Size(()))
self.assertEqual(expon.sample().size(), torch.Size((2,)))
self.assertEqual(expon.sample((3, 2)).size(), torch.Size((3, 2, 2)))
self.assertEqual(expon.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2)))
self.assertRaises(ValueError, expon.log_prob, self.tensor_sample_2)
def likelihood(self, x, z_obj):
"""Evaluate likelihood of x under model.
Args:
x (torch.Tensor), (n, T, c, w, h) The given sequence of observations.
z_obj (torch.Tensor), (nTO, 4): Samples from z distribution.
Returns:
log_p_xz (torch.Tensor), (nT): Image likelihood.
"""
# reshape x to merge batch and sequences to pseudo batch since spn
# will work on image basis shape (n4, c, w, h)
x_img = x.flatten(end_dim=1)
# 1. Background Likelihood
# reshape to (n4, o, 4) and extract marginalisation information
z_img = z_obj.view(-1, self.c.num_obj, 4)
marginalise_patch, marginalise_bg, overlap_ratios = self.masks_from_z(
z_img)
# flatten, st. shape is (n4, cwh) for both
img_flat = x_img.flatten(start_dim=1)
marg_flat = marginalise_bg.flatten(start_dim=1)
# get likelihood of background under bg_spn, output from (n4, 1) to (n4)
bg_loglik = self.bg_spn.forward(img_flat, marg_flat)[:, 0]
# 2. Patch Likelihoods
# extract patches (n4o, c, w, h) from transformer
patches = self.patches_from_z(x_img, z_obj)
# flatten into (n4o, c w_out h_out)
patches_flat = patches.flatten(start_dim=1)
marginalise_flat = marginalise_patch.flatten(start_dim=1)
# (n4o)
patches_loglik = self.obj_spn.forward(patches_flat,
marginalise_flat)[:, 0]
# scale patch_likelihoods by size of patch to obtain
# well calibrated likelihoods
patches_loglik = patches_loglik * z_obj[:, 0] * z_obj[:, 1]
# shape n4o to n4,o
patches_loglik = patches_loglik.view(-1, self.c.num_obj)
# 3. Add Exponential overlap_penalty
overlap_prior = Exponential(self.c.overlap_beta)
overlap_log_liks = overlap_prior.log_prob(overlap_ratios)
# 4. Assemble final img likelihood E_q(z|x)[log p(x, z)]
# expectation is approximated by a single sample
patches_loglik = patches_loglik.sum(1)
overlap_log_liks = overlap_log_liks.sum(1)
scores = [bg_loglik, patches_loglik, overlap_log_liks]
scores = torch.stack(scores, -1)
# shape (n4)
log_p_xz = scores.sum(-1)
if ((self.step_counter % self.c.print_every == 0)
or (self.step_counter % self.c.plot_every == 0)):
if self.c.debug:
self.prop_dict['bg'] = bg_loglik.mean().detach()
self.prop_dict['patch'] = patches_loglik.mean().detach()
self.prop_dict['overlap'] = overlap_log_liks.mean().detach()
if self.c.debug and self.c.debug_extend_plots:
self.prop_dict['overlap_ratios'] = overlap_ratios.detach()
self.prop_dict['patches'] = patches.detach()
self.prop_dict['marginalise_flat'] = marginalise_flat.detach()
self.prop_dict['patches_loglik'] = patches_loglik.detach()
self.prop_dict['marginalise_bg'] = marginalise_bg.detach()
self.prop_dict['bg_loglik'] = bg_loglik.detach()
return log_p_xz, self.prop_dict