def main():
t = time()
check_flags()
print(get_model_info_as_str())
data_train = SiameseModelData(FLAGS.dataset_train)
dist_sim_calculator = DistSimCalculator(
FLAGS.dataset_train, FLAGS.ds_metric, FLAGS.ds_algo)
model = create_model(FLAGS.model, data_train.input_dim(),
data_train, dist_sim_calculator)
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver = Saver(sess)
sess.run(tf.global_variables_initializer())
if FLAGS.dataset_val_test == FLAGS.dataset_train:
data_val_test = data_train
else:
# Generalizability test: val test on unseen train and test graphs.
data_val_test = SiameseModelData(FLAGS.dataset_val_test)
eval = Eval(data_val_test, dist_sim_calculator)
try:
train_costs, train_times, val_results_dict = \
train_val_loop(data_train, data_val_test, eval, model, saver, sess)
best_iter, test_results = \
test(data_val_test, eval, model, saver, sess, val_results_dict)
overall_time = convert_long_time_to_str(time() - t)
print(overall_time)
saver.save_overall_time(overall_time)
except:
traceback.print_exc()
else:
return train_costs, train_times, val_results_dict, best_iter, test_results
def _log_model_info(self, logdir, sess):
model_info_table = [["**key**", "**value**"]]
with open(logdir + '/model_info.txt', 'w') as f:
s = get_model_info_as_str(model_info_table)
f.write(s)
model_info_op = \
tf.summary.text(
'model_info', tf.convert_to_tensor(model_info_table))
self.tw.add_summary(sess.run(model_info_op))
def test(data, eval, model, saver, sess, val_results_dict):
best_iter = model.find_load_best_model(sess, saver, val_results_dict)
saver.clean_up_saved_models(best_iter)
gs1, gs2 = eval.get_test_gs_as_tuple(data)
sim_mat, loss_list, time_list = run_pairs_for_val_test(
gs1, gs2, eval, model, saver, sess, 'test')
node_embs_list, graph_embs_mat, emb_time = collect_embeddings(
gs1, gs2, model, saver, sess)
attentions = collect_attentions(gs1, gs2, model, saver, sess)
print('Evaluating...')
results = eval.eval_for_test(sim_mat, loss_list, time_list, node_embs_list,
graph_embs_mat, attentions,
model.get_eval_metrics_for_test(), saver)
if not FLAGS.plot_results:
pretty_print_dict(results)
print('Results generated with {} metrics; collecting embeddings'.format(
len(results)))
print(get_model_info_as_str())
saver.save_test_info(sim_mat, time_list, best_iter, results,
node_embs_list, graph_embs_mat, emb_time, attentions)
return best_iter, results
def main():
t = time()
check_flags()
print(get_model_info_as_str())
data = SiameseModelData()
dist_calculator = DistCalculator(
FLAGS.dataset, FLAGS.dist_metric, FLAGS.dist_algo)
model = create_model(FLAGS.model, data.input_dim(), data, dist_calculator)
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
saver = Saver(sess)
sess.run(tf.global_variables_initializer())
eval = Eval(data, dist_calculator)
train_costs, train_times, val_results_dict = \
train_val_loop(data, eval, model, saver, sess)
best_iter, test_results = \
test(data, eval, model, saver, sess, val_results_dict)
overall_time = convert_long_time_to_str(time() - t)
print(overall_time)
saver.save_overall_time(overall_time)
return train_costs, train_times, val_results_dict, best_iter, test_results
def main():
t = time()
conf_code = extract_config_code()
check_flags()
print(get_model_info_as_str())
data = SiameseModelData(FLAGS.dataset_train)
dist_sim_calculator = DistSimCalculator(FLAGS.dataset_train, FLAGS.ds_metric, FLAGS.ds_algo)
model = create_model(FLAGS.model, data.input_dim(), data, dist_sim_calculator)
os.environ["CUDA_VISIBLE_DEVICES"] = str(FLAGS.gpu)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)
saver = Saver(sess)
sess.run(tf.compat.v1.global_variables_initializer())
train_costs, train_times = train_loop(data, model, saver, sess)
test(data, model, saver, sess)
saver.save_conf_code(conf_code)
overall_time = convert_long_time_to_str(time() - t)
print(overall_time, saver.get_log_dir())
saver.save_overall_time(overall_time)
return train_costs, train_times
def setup_file():
f = open(file_name, 'w')
f.write(get_model_info_as_str())
f.write(','.join(map(str, header)) + '\n')
f.flush()
return f
