def __init__(self, prior_info):
prior_type, prior_hyper = prior_info
self.fc1_prior = None
self.fc2_prior = None
self.fc3_prior = None
if prior_type == 'Gamma':
self._prior_gamma(prior_type, prior_hyper)
elif prior_type == 'Weibull':
self._prior_weibull(prior_type, prior_hyper)
elif 'HalfCauchy' in prior_type:
self._prior_halfcauchy(prior_type, prior_hyper)
else:
raise NotImplementedError
self.fc1_prior['direction'] = ('vMF', {
'concentration':
ml_kappa(dim=300, eps=PRIOR_EPSILON)
})
self.fc2_prior['direction'] = ('vMF', {
'concentration':
ml_kappa(dim=100, eps=PRIOR_EPSILON)
})
self.fc3_prior['direction'] = ('vMF', {
'concentration':
ml_kappa(dim=10, eps=PRIOR_EPSILON)
})
def __init__(self, prior_type, n1, n2):
self.fc1_prior = None
self.fc2_prior = None
if prior_type == 'Gamma':
self._prior_gamma()
elif prior_type == 'Weibull':
self._prior_weibull()
elif prior_type == 'HalfCauchy':
self._prior_halfcauchy()
else:
raise NotImplementedError
# self.fc1_prior['direction'] = ('vMF', {'row_softplus_inv_concentration': softplus_inv(ml_kappa(dim=n1, eps=PRIOR_EPSILON)),
# 'col_softplus_inv_concentration': softplus_inv(ml_kappa(dim=n2, eps=PRIOR_EPSILON))})
# self.fc2_prior['direction'] = ('vMF', {'row_softplus_inv_concentration': softplus_inv(ml_kappa(dim=n2, eps=PRIOR_EPSILON)),
# 'col_softplus_inv_concentration': softplus_inv(ml_kappa(dim=1, eps=PRIOR_EPSILON))})
self.fc1_prior['direction'] = ('vMF', {
'row_concentration':
softplus_inv(ml_kappa(dim=n1, eps=PRIOR_EPSILON)),
'col_concentration':
softplus_inv(ml_kappa(dim=n2, eps=PRIOR_EPSILON))
})
self.fc2_prior['direction'] = ('vMF', {
'row_concentration':
softplus_inv(ml_kappa(dim=n2, eps=PRIOR_EPSILON)),
'col_concentration':
softplus_inv(ml_kappa(dim=1, eps=PRIOR_EPSILON))
})
def __init__(self, prior_info):
prior_type, prior_hyper = prior_info
self.conv1_prior = None
self.conv2_prior = None
self.fc1_prior = None
self.fc2_prior = None
self._prior_halfcauchy(prior_type, prior_hyper)
self.conv1_prior['direction'] = ('vMF', {
'row_concentration':
ml_kappa(dim=1 * 25, eps=PRIOR_EPSILON),
'col_concentration':
ml_kappa(dim=20 * 25, eps=PRIOR_EPSILON)
})
self.conv2_prior['direction'] = ('vMF', {
'row_concentration':
ml_kappa(dim=20 * 25, eps=PRIOR_EPSILON),
'col_concentration':
ml_kappa(dim=50 * 25, eps=PRIOR_EPSILON)
})
self.fc1_prior['direction'] = ('vMF', {
'row_concentration':
ml_kappa(dim=800, eps=PRIOR_EPSILON),
'col_concentration':
ml_kappa(dim=500, eps=PRIOR_EPSILON)
})
self.fc2_prior['direction'] = ('vMF', {
'row_concentration':
ml_kappa(dim=500, eps=PRIOR_EPSILON),
'col_concentration':
ml_kappa(dim=10, eps=PRIOR_EPSILON)
})
def __init__(self, batch_shape, event_shape):
self.batch_shape = batch_shape
if isinstance(event_shape, Number):
event_shape = torch.Size([event_shape])
self.event_shape = event_shape
assert len(event_shape) == 1
self.dim = int(event_shape[0])
super(VonMisesFisherReparametrizedSample, self).__init__()
self.loc = Parameter(torch.Tensor(batch_shape + event_shape))
self.softplus_inv_concentration = Parameter(
torch.Tensor(torch.Size([1])))
# Too large kappa slow down rejection sampling, so we set upper bound, which is called in forward pass
self.softplus_inv_concentration_upper_bound = softplus_inv(
ml_kappa(dim=float(event_shape[0]), eps=2e-3))
self.beta_sample = None
self.concentration = None
self.gradient_correction_required = True
self.softplus_inv_concentration_normal_mean = softplus_inv(
ml_kappa(dim=float(event_shape[0]), eps=EPSILON))
self.softplus_inv_concentration_normal_std = 0.001
self.direction_init_method = None
self.rsample = None
self.loc_init_type = 'random'
def reset_parameters(self, hyperparams={}):
if 'vMF' in hyperparams.keys():
if 'direction' in hyperparams['vMF'].keys():
if type(hyperparams['vMF']['direction']) == str:
if hyperparams['vMF']['direction'] == 'kaiming':
self.direction_init_method = torch.nn.init.kaiming_normal_
elif hyperparams['vMF'][
'direction'] == 'kaiming_transpose':
self.direction_init_method = kaiming_transpose
elif hyperparams['vMF']['direction'] == 'orthogonal':
self.direction_init_method = torch.nn.init.orthogonal_
self.direction_init_method(self.loc)
elif type(hyperparams['vMF']['direction']) == torch.Tensor:
self.loc.data.copy_(hyperparams['vMF']['direction'])
self.loc_init_type = 'fixed'
else:
raise NotImplementedError
self.loc.data /= torch.sum(self.loc.data**2,
dim=-1,
keepdim=True)**0.5
if 'softplus_inv_concentration_normal_mean' in hyperparams[
'vMF'].keys():
self.softplus_inv_concentration_normal_mean = hyperparams[
'vMF']['softplus_inv_concentration_normal_mean']
if 'softplus_inv_concentration_normal_mean_via_epsilon' in hyperparams[
'vMF'].keys():
epsilon = hyperparams['vMF'][
'softplus_inv_concentration_normal_mean_via_epsilon']
self.softplus_inv_concentration_normal_mean = softplus_inv(
ml_kappa(dim=float(self.event_shape[0]), eps=epsilon))
if 'softplus_inv_concentration_normal_std' in hyperparams[
'vMF'].keys():
self.softplus_inv_concentration_normal_std = hyperparams[
'vMF']['softplus_inv_concentration_normal_std']
torch.nn.init.normal_(self.softplus_inv_concentration,
self.softplus_inv_concentration_normal_mean,
self.softplus_inv_concentration_normal_std)