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 main():
check_flags()
data = SiameseModelData()
dist_calculator = DistCalculator(FLAGS.dataset, FLAGS.dist_metric,
FLAGS.dist_algo)
model = create_model(FLAGS.model, data.input_dim())
sess = tf.Session()
saver = Saver(sess)
sess.run(tf.global_variables_initializer())
train_costs, train_times, val_costs, val_times = \
train_val(data, dist_calculator, model, saver, sess)
results = \
test(data, dist_calculator, model, saver, sess)
return train_costs, train_times, val_costs, val_times, 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