def inference(self, x, w, relaxed=False, return_energy=False, invert=False):
"""Inference for x using parameters w.
Finds (approximately)
argmin_y np.dot(w, joint_feature(x, y))
using self.inference_method.
Parameters
----------
x : tuple
Instance of a graph with unary & pairwise potentials.
x=(unaries, edges, pairwise)
unaries are an nd-array of shape (n_nodes, n_states),
edges are an nd-array of shape (n_edges, 2)
pairwise are an nd-array of shape (n_edges, n_states, n_states)
w : ndarray, shape=(size_joint_feature,)
Parameters for the CRF energy function.
relaxed : bool, default=False
We do not support it yet.
return_energy : bool, default=False
Whether to return the energy of the solution (x, y) that was found.
Returns
-------
y_pred : ndarray
By default an integer ndarray of shape=(width, height)
of variable assignments for x is returned.
"""
self._check_size_w(w)
self.inference_calls += 1
unary_potentials = self._get_unary_potentials(x, w)
pairwise_potentials = self._get_pairwise_potentials(x, w)
edges = self._get_edges(x)
if invert:
unary_potentials = -unary_potentials
pairwise_potentials = -pairwise_potentials
if self.inference_method == 'gco':
h = inference_gco(unary_potentials, pairwise_potentials,
edges, n_iter=self.n_iter)
y_ret = Label(h, None, None, True)
elif self.inference_method == 'ad3':
h = inference_ad3(unary_potentials, pairwise_potentials, edges,
relaxed=relaxed, return_energy=False,
n_iterations=self.n_iter)
y_ret = Label(h, None, None, True, relaxed)
elif self.inference_method == 'trw':
from trw import trw
h = trw(-unary_potentials, edges, -pairwise_potentials, max_iter=self.n_iter)
y_ret = Label(h, None, None, True)
return y_ret
def inference(self,
x,
w,
relaxed=False,
return_energy=False,
invert=False):
"""Inference for x using parameters w.
Finds (approximately)
argmin_y np.dot(w, joint_feature(x, y))
using self.inference_method.
Parameters
----------
x : tuple
Instance of a graph with unary & pairwise potentials.
x=(unaries, edges, pairwise)
unaries are an nd-array of shape (n_nodes, n_states),
edges are an nd-array of shape (n_edges, 2)
pairwise are an nd-array of shape (n_edges, n_states, n_states)
w : ndarray, shape=(size_joint_feature,)
Parameters for the CRF energy function.
relaxed : bool, default=False
We do not support it yet.
return_energy : bool, default=False
Whether to return the energy of the solution (x, y) that was found.
Returns
-------
y_pred : ndarray
By default an integer ndarray of shape=(width, height)
of variable assignments for x is returned.
"""
self._check_size_w(w)
self.inference_calls += 1
unary_potentials = self._get_unary_potentials(x, w)
pairwise_potentials = self._get_pairwise_potentials(x, w)
edges = self._get_edges(x)
if invert:
unary_potentials = -unary_potentials
pairwise_potentials = -pairwise_potentials
if self.inference_method == 'gco':
h = inference_gco(unary_potentials,
pairwise_potentials,
edges,
n_iter=self.n_iter)
y_ret = Label(h, None, None, True)
elif self.inference_method == 'ad3':
h = inference_ad3(unary_potentials,
pairwise_potentials,
edges,
relaxed=relaxed,
return_energy=False,
n_iterations=self.n_iter)
y_ret = Label(h, None, None, True, relaxed)
elif self.inference_method == 'trw':
from trw import trw
h = trw(-unary_potentials,
edges,
-pairwise_potentials,
max_iter=self.n_iter)
y_ret = Label(h, None, None, True)
return y_ret
def loss_augmented_inference(self, x, y, w, relaxed=False,
return_energy=False):
self.inference_calls += 1
self._check_size_w(w)
unary_potentials = self._get_unary_potentials(x, w)
pairwise_potentials = self._get_pairwise_potentials(x, w)
edges = self._get_edges(x)
if y.full_labeled:
# loss augment unaries
for label in xrange(self.n_states):
mask = y.full != label
unary_potentials[mask, label] += y.weights[mask]
if self.inference_method == 'gco':
h = inference_gco(unary_potentials, pairwise_potentials, edges,
n_iter=self.n_iter, return_energy=True)
y_ret = Label(h[0], None, y.weights, True)
elif self.inference_method == 'ad3':
h = inference_ad3(unary_potentials, pairwise_potentials, edges,
relaxed=relaxed, return_energy=False,
n_iterations=self.n_iter)
y_ret = Label(h, None, y.weights, True, relaxed)
elif self.inference_method == 'trw':
from trw import trw
h = trw(-unary_potentials, edges, -pairwise_potentials, max_iter=self.n_iter, relaxed=relaxed)
y_ret = Label(h, None, y.weights, True, relaxed)
# count = h[2]
# energy = np.dot(w, self.joint_feature(x, y_ret)) + self.loss(y, y_ret)
#
# if count == 0 and np.abs(energy + h[1]) > 1e-4:
# print 'FULL: energy does not match: %f, %f, difference=%f' % (energy, -h[1],
# energy + h[1])
if return_energy:
return y_ret, h[1]
return y_ret
else:
if self.inference_method != 'gco':
# only gco inference_method supported: we need label costs
raise NotImplementedError
# this is weak labeled example
# use pygco with label costs
label_costs = np.zeros(self.n_states)
c = np.sum(y.weights) / float(self.n_states)
for label in y.weak:
label_costs[label] = c
for label in xrange(0, self.n_states):
if label not in y.weak:
unary_potentials[:, label] += y.weights
h = inference_gco(unary_potentials, pairwise_potentials, edges,
label_costs, n_iter=self.n_iter, return_energy=True)
y_ret = Label(h[0], None, y.weights, False)
# energy = np.dot(w, self.joint_feature(x, y_ret)) + self._kappa(y, y_ret)
# if h[2] == 0 and np.abs(energy + h[1]) > 1e-4:
# print 'energy does not match: %f, %f, difference=%f' % (energy, -h[1], energy + h[1])
return y_ret
def loss_augmented_inference(self,
x,
y,
w,
relaxed=False,
return_energy=False):
self.inference_calls += 1
self._check_size_w(w)
unary_potentials = self._get_unary_potentials(x, w)
pairwise_potentials = self._get_pairwise_potentials(x, w)
edges = self._get_edges(x)
if y.full_labeled:
# loss augment unaries
for label in xrange(self.n_states):
mask = y.full != label
unary_potentials[mask, label] += y.weights[mask]
if self.inference_method == 'gco':
h = inference_gco(unary_potentials,
pairwise_potentials,
edges,
n_iter=self.n_iter,
return_energy=True)
y_ret = Label(h[0], None, y.weights, True)
elif self.inference_method == 'ad3':
h = inference_ad3(unary_potentials,
pairwise_potentials,
edges,
relaxed=relaxed,
return_energy=False,
n_iterations=self.n_iter)
y_ret = Label(h, None, y.weights, True, relaxed)
elif self.inference_method == 'trw':
from trw import trw
h = trw(-unary_potentials,
edges,
-pairwise_potentials,
max_iter=self.n_iter,
relaxed=relaxed)
y_ret = Label(h, None, y.weights, True, relaxed)
# count = h[2]
# energy = np.dot(w, self.joint_feature(x, y_ret)) + self.loss(y, y_ret)
#
# if count == 0 and np.abs(energy + h[1]) > 1e-4:
# print 'FULL: energy does not match: %f, %f, difference=%f' % (energy, -h[1],
# energy + h[1])
if return_energy:
return y_ret, h[1]
return y_ret
else:
if self.inference_method != 'gco':
# only gco inference_method supported: we need label costs
raise NotImplementedError
# this is weak labeled example
# use pygco with label costs
label_costs = np.zeros(self.n_states)
c = np.sum(y.weights) / float(self.n_states)
for label in y.weak:
label_costs[label] = c
for label in xrange(0, self.n_states):
if label not in y.weak:
unary_potentials[:, label] += y.weights
h = inference_gco(unary_potentials,
pairwise_potentials,
edges,
label_costs,
n_iter=self.n_iter,
return_energy=True)
y_ret = Label(h[0], None, y.weights, False)
# energy = np.dot(w, self.joint_feature(x, y_ret)) + self._kappa(y, y_ret)
# if h[2] == 0 and np.abs(energy + h[1]) > 1e-4:
# print 'energy does not match: %f, %f, difference=%f' % (energy, -h[1], energy + h[1])
return y_ret
