def _inference_dai(x, unary_params, pairwise_params, edges):
## build graph
n_states = x.shape[-1]
log_unaries = unary_params * x.reshape(-1, n_states)
max_entry = max(np.max(log_unaries), 1)
unaries = np.exp(log_unaries / max_entry)
y = mrf(unaries, edges, np.exp(pairwise_params / max_entry), alg='jt')
y = y.reshape(x.shape[:2])
return y
def inference_dai(unary_potentials, pairwise_potentials, edges):
from daimrf import mrf
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
n_states = unary_potentials.shape[-1]
log_unaries = unary_potentials.reshape(-1, n_states)
max_entry = max(np.max(log_unaries), 1)
unaries = np.exp(log_unaries / max_entry)
y = mrf(unaries, edges.astype(np.int64),
np.exp(pairwise_potentials / max_entry), alg='jt')
y = y.reshape(shape_org)
return y
def inference_dai(unary_potentials,
pairwise_potentials,
edges,
return_energy=False,
alg='jt',
**kwargs):
"""Inference with LibDAI backend.
Parameters
----------
unary_potentials : nd-array
Unary potentials of energy function.
pairwise_potentials : nd-array
Pairwise potentials of energy function.
edges : nd-array
Edges of energy function.
alg : string, (default='jt')
Inference algorithm to use.
Defaults to Junction Tree. THIS WILL BLOW UP for loopy graphs.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from daimrf import mrf
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
n_states = unary_potentials.shape[-1]
log_unaries = unary_potentials.reshape(-1, n_states)
max_entry = max(np.max(log_unaries), 1)
unaries = np.exp(log_unaries / max_entry)
y = mrf(unaries,
edges.astype(np.int64),
np.exp(pairwise_potentials / max_entry),
alg=alg)
y = y.reshape(shape_org)
if return_energy:
return y, compute_energy(unary_potentials, pairwise_potentials, edges,
y)
return y
def inference_dai(unary_potentials, pairwise_potentials, edges):
from daimrf import mrf
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
n_states = unary_potentials.shape[-1]
log_unaries = unary_potentials.reshape(-1, n_states)
max_entry = max(np.max(log_unaries), 1)
unaries = np.exp(log_unaries / max_entry)
y = mrf(unaries,
edges.astype(np.int64),
np.exp(pairwise_potentials / max_entry),
alg='jt')
y = y.reshape(shape_org)
return y
def inference_dai(unary_potentials, pairwise_potentials, edges,
return_energy=False, alg='jt', **kwargs):
"""Inference with LibDAI backend.
Parameters
----------
unary_potentials : nd-array
Unary potentials of energy function.
pairwise_potentials : nd-array
Pairwise potentials of energy function.
edges : nd-array
Edges of energy function.
alg : string, (default='jt')
Inference algorithm to use.
Defaults to Junction Tree. THIS WILL BLOW UP for loopy graphs.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from daimrf import mrf
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
n_states = unary_potentials.shape[-1]
log_unaries = unary_potentials.reshape(-1, n_states)
max_entry = max(np.max(log_unaries), 1)
unaries = np.exp(log_unaries / max_entry)
y = mrf(unaries, edges.astype(np.int64),
np.exp(pairwise_potentials / max_entry), alg=alg)
y = y.reshape(shape_org)
if return_energy:
return y, compute_energy(unary_potentials, pairwise_potentials, edges,
y)
return y