for hypo in hypos:
print('%s -> %s' % (hypo.name(), hyper.name()))
hypernyms.append([hypo.index(), hyper.index()])
nodes.insert(0, hypo)
# save hypernym dataset
hypernyms = np.array(hypernyms)
import h5py
f = h5py.File(hypernym_save_path, 'w')
f.create_dataset('hypernyms', data=hypernyms)
f.close()
if __name__ == '__main__':
dataset = 'vrd'
data_config = DatasetConfig(dataset)
if dataset == 'vrd':
from open_relation.dataset.vrd.label_hier.obj_hier import objnet
else:
from open_relation.dataset.vg.label_hier.obj_hier import objnet
label2index = objnet.label2index()
hypernym_save_path = os.path.join(global_config.project_root,
'open_relation', 'label_embedding',
'object', dataset + '_dataset',
'wordnet_with_' + dataset + '.h5')
generate_direct_hypernyms(objnet, hypernym_save_path)
def eval4(label_vecs, label2index, label1, label2):
ind1 = label2index[label1]
ind2= label2index[label2]
vec1 = label_vecs[ind1]
vec2 = label_vecs[ind2]
sub = vec1 - vec2
sub[sub < 0] = 0
sub_square = sub * sub
E = np.sqrt(np.sum(sub_square))
print('P( %s , %s ) = %.2f' % (label1, label2, E))
if __name__ == '__main__':
# label vectors
weight_path = data_config.extra_config[target].config['label_vec_path']
label_vec_file = h5py.File(weight_path, 'r')
label_vecs = np.array(label_vec_file['label_vec'])
# label mapping
label2index = classnet.label2index()
index2label = classnet.index2label()
eval2(label_vecs, classnet)
# eval4(label_vecs, label2index, 'shirt', 'garment.n.01')
# objnet.get_node_by_name('person').show_hyper_paths()