def inference(self, x, w, relaxed=False, return_energy=False):
self.inference_calls += 1
# extract unary weights
unary_params = self.get_unary_weights(w)
# extract pairwise weights of shape n_edge_types x n_states x n_states
pairwise_params = self.get_pairwise_weights(w)
edges = _make_grid_edges(x, neighborhood=self.neighborhood,
return_lists=True)
n_edges = [len(e) for e in edges]
# replicate pairwise weights for edges of certain type
edge_weights = [np.repeat(pw[np.newaxis, :, :], n, axis=0)
for pw, n in zip(pairwise_params, n_edges)]
edge_weights = np.vstack(edge_weights)
edges = np.vstack(edges)
#if self.inference_method == "qpbo":
#return _inference_qpbo(x, unary_params, pairwise_params,
#self.neighborhood)
#elif self.inference_method == "dai":
#return _inference_dai(x, unary_params, pairwise_params,
#self.neighborhood)
if self.inference_method == "lp":
return _inference_lp(x, unary_params, edge_weights, edges, relaxed,
return_energy=return_energy)
#elif self.inference_method == "ad3":
#return _inference_ad3(x, unary_params, pairwise_params,
#self.neighborhood, relaxed)
else:
raise ValueError("inference_method must be 'qpbo' or 'dai', got %s"
% self.inference_method)
def inference(self, x, w, relaxed=False):
unary_params = self.get_unary_weights(w)
pairwise_params = self.get_pairwise_weights(w)
self.inference_calls += 1
edges = _make_grid_edges(x, neighborhood=self.neighborhood)
if self.inference_method == "qpbo":
return _inference_qpbo(x, unary_params, pairwise_params,
edges)
elif self.inference_method == "dai":
return _inference_dai(x, unary_params, pairwise_params,
edges)
elif self.inference_method == "lp":
return _inference_lp(x, unary_params, pairwise_params,
edges, relaxed)
elif self.inference_method == "ad3":
return _inference_ad3(x, unary_params, pairwise_params,
edges, relaxed)
else:
raise ValueError("inference_method must be 'qpbo' or 'dai', got %s"
% self.inference_method)
