def compute_partition(args, embeddings, edg_source, edg_target, diff, xyz=0):
edge_weight = np.ones_like(edg_source).astype('f4')
if args.edge_weight_threshold > 0:
edge_weight[diff > 1] = args.edge_weight_threshold
if args.edge_weight_threshold < 0:
edge_weight = torch.exp(
diff * args.edge_weight_threshold).detach().cpu().numpy() / np.exp(
args.edge_weight_threshold)
ver_value = np.zeros((embeddings.shape[0], 0), dtype='f4')
use_spatial = 0
ver_value = np.hstack((ver_value, embeddings.detach().cpu().numpy()))
if args.spatial_emb > 0:
ver_value = np.hstack(
(ver_value,
args.spatial_emb * xyz)) # * math.sqrt(args.reg_strength)))
#ver_value = xyz * args.spatial_emb
use_spatial = 1 #!!!
pred_components, pred_in_component = libcp.cutpursuit(ver_value, \
edg_source.astype('uint32'), edg_target.astype('uint32'), edge_weight, \
args.reg_strength / (4 * args.k_nn_adj), cutoff=args.CP_cutoff, spatial = use_spatial, weight_decay = 0.7)
#emb2 = libcp.cutpursuit2(ver_value, edg_source.astype('uint32'), edg_target.astype('uint32'), edge_weight, args.reg_strength, cutoff=0, spatial =0)
#emb2 = emb2.reshape(ver_value.shape)
#((ver_value-emb2)**2).sum(0)
#cut = pred_in_component[edg_source]!=pred_in_component[edg_target]
return pred_components, pred_in_component
import sys
sys.path.append("../build/src")
import libcp
import numpy as np
obs = np.array([[10.,20.],[0.,1.],[0.,0. ]], dtype = 'float32')
source = np.array([0, 1], dtype = 'uint32')
target = np.array([1, 2], dtype = 'uint32')
edge_weight = np.array([[1., 1.]], dtype = 'float32')
[comp, in_com] = libcp.cutpursuit(obs, source, target, edge_weight, 1)
# sol = libcp.cutpursuit_reg(obs, source, target, edge_weight, 1)
if os.path.isfile(spg_file):
print(" reading the existing superpoint graph file...")
graph_sp, components, in_component = read_spg(spg_file)
else:
print(" computing the superpoint graph...")
#--- build the spg h5 file --
start = timer()
geof[:,
3] = 2. * geof[:,
3] #increase importance of verticality (heuristic)
graph_nn["edge_weight"] = np.array(
1. / (args.lambda_edge_weight +
graph_nn["distances"] / np.mean(graph_nn["distances"])),
dtype='float32')
print(" minimal partition...")
components, in_component = libcp.cutpursuit(
geof, graph_nn["source"], graph_nn["target"],
graph_nn["edge_weight"], args.reg_strength)
components = np.array(components, dtype='object')
end = timer()
times[1] = times[1] + end - start
print(" computation of the SPG...")
start = timer()
graph_sp = compute_sp_graph(xyz, args.d_se_max, in_component,
components, labels, args.n_labels)
end = timer()
times[2] = times[2] + end - start
write_spg(spg_file, graph_sp, components, in_component)
print("Timer : %5.1f / %5.1f / %5.1f " %
(times[0], times[1], times[2]))
import libcp obs = np.array([[1000.], [0.], [0.]], dtype='float32') source = np.array([[0, 1, 1, 2]], dtype='uint32') target = np.array([[1, 2, 0, 1]], dtype='uint32') edge_weight = np.array([[1., 1., 1., 1.]], dtype='float32') libcp.cutpursuit(obs, source, target, edge_weight, 0.01)