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main.cpp
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main.cpp
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/*
Contributors to the tool :
Souradeep Dutta
email : [email protected]
LICENSE : Please see the license file, in the main directory
*/
#include "propagate_intervals.h"
using namespace std;
using namespace std::chrono;
int main(int argc, char ** argv)
{
int run_benchmark_no = -1;
char key[] = "all";
bool run_all = false;
if(argc != 2)
{
cout << "Wrong number of command line arguments : " << endl;
cout << "Please pass the benchmark number to run it. " << endl;
cout << "Exiting... " << endl;
exit(0);
}
else if(strcmp(key, argv[1]) == 0)
{
run_all = true;
}
else
{
sscanf(argv[1], "%d", &run_benchmark_no);
}
vector< vector< datatype > > input_interval(2, vector< datatype >(2,0));
vector< vector< datatype > > input_constraints;
clock_t begin, end;
vector< datatype > output_range(2,0);
// Simple range propagation
// sherlock_parameters.verbosity = true;
// sherlock_parameters.grad_search_point_verbosity = true;
sherlock_parameters.time_verbosity = true;
if((run_benchmark_no == 0) || (run_all))
{
char benchmark_0_name[] = "./network_files/neural_network_information_0" ;
network_handler benchmark_0(benchmark_0_name);
input_interval[0][0] = 0;input_interval[0][1] = 10;
input_interval[1][0] = 0;input_interval[1][1] = 10;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_0.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 0 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 1) || (run_all)) {
// Simple range propagation
char benchmark_1_name[] = "./network_files/neural_network_information_1" ;
sherlock_parameters.MILP_tolerance = 1e-2;
network_handler benchmark_1(benchmark_1_name);
input_interval[0][0] = 0;input_interval[0][1] = 10;
input_interval[1][0] = 0;input_interval[1][1] = 10;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_1.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 1 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 2) || (run_all)) {
// Simple range propagation
char benchmark_2_name[] = "./network_files/neural_network_information_2" ;
sherlock_parameters.gradient_rate = 1e-4;
sherlock_parameters.grad_scaling_factor = 2e1;
sherlock_parameters.MILP_tolerance = 8e-2;
sherlock_parameters.scale_factor_for_M = 1.3;
network_handler benchmark_2(benchmark_2_name);
input_interval[0][0] = 0;input_interval[0][1] = 10;
input_interval[1][0] = 0;input_interval[1][1] = 10;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_2.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 2 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 3) || (run_all)) {
// Simple range propagation
char benchmark_3_name[] = "./network_files/neural_network_information_3" ;
network_handler benchmark_3(benchmark_3_name);
input_interval[0][0] = 0;input_interval[0][1] = 10;
input_interval[1][0] = 0;input_interval[1][1] = 10;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_3.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 3 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 4) || (run_all)) {
// Simple range propagation
char benchmark_4_name[] = "./network_files/neural_network_information_4" ;
network_handler benchmark_4(benchmark_4_name);
input_interval[0][0] = 0;input_interval[0][1] = 10;
input_interval[1][0] = 0;input_interval[1][1] = 10;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_4.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 4 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 5) || (run_all)) {
// Simple range propagation
char benchmark_5_name[] = "./network_files/neural_network_information_5" ;
sherlock_parameters.MILP_tolerance = 2e-2;
network_handler benchmark_5(benchmark_5_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.5);input_interval[0].push_back(0.5);;
input_interval[1].push_back(-0.5);input_interval[1].push_back(0.5);;
input_interval[2].push_back(-0.5);input_interval[2].push_back(0.5);;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_5.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 5 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 6) || (run_all)) {
// Simple range propagation
char benchmark_6_name[] = "./network_files/neural_network_information_6" ;
network_handler benchmark_6(benchmark_6_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.5);input_interval[0].push_back(0.5);;
input_interval[1].push_back(-0.5);input_interval[1].push_back(0.5);;
input_interval[2].push_back(-0.5);input_interval[2].push_back(0.5);;
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_6.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 6 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 7) || (run_all)) {
// Simple range propagation
char benchmark_7_name[] = "./network_files/neural_network_information_7" ;
network_handler benchmark_7(benchmark_7_name);
input_interval.clear();
input_interval.resize(4);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
input_interval[3].push_back(-0.1);input_interval[3].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_7.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 7 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 8) || (run_all)) {
char benchmark_8_name[] = "./network_files/neural_network_information_8" ;
network_handler benchmark_8(benchmark_8_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.5);input_interval[0].push_back(0.5);
input_interval[1].push_back(-0.5);input_interval[1].push_back(0.5);
input_interval[2].push_back(-0.5);input_interval[2].push_back(0.5);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_8.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 8 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 9) || (run_all)) {
char benchmark_9_name[] = "./network_files/neural_network_information_9" ;
network_handler benchmark_9(benchmark_9_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.5);input_interval[0].push_back(0.5);
input_interval[1].push_back(-0.5);input_interval[1].push_back(0.5);
input_interval[2].push_back(-0.5);input_interval[2].push_back(0.5);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_9.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 9 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 10) || (run_all)) {
char benchmark_10_name[] = "./network_files/neural_network_information_10" ;
sherlock_parameters.gradient_rate = 1e-6;
sherlock_parameters.grad_scaling_factor = 2e1;
sherlock_parameters.grad_switch_count = 1e2;
sherlock_parameters.grad_termination_limit = 1e-6;
sherlock_parameters.MILP_e_tolerance = 1e-15;
sherlock_parameters.scale_factor_for_M = 1.5;
sherlock_parameters.MILP_tolerance = 5e-2;
network_handler benchmark_10(benchmark_10_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_10.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 10 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 11) || (run_all)) {
char benchmark_11_name[] = "./network_files/neural_network_information_11" ;
sherlock_parameters.gradient_rate = 1e-4;
sherlock_parameters.grad_scaling_factor = 5e1;
sherlock_parameters.grad_switch_count = 1e3;
sherlock_parameters.grad_termination_limit = 1e-7;
sherlock_parameters.MILP_tolerance = 5e-1;
sherlock_parameters.MILP_e_tolerance = 1e-10;
sherlock_parameters.scale_factor_for_M = 1;
sherlock_parameters.do_LP_certificate = true;
sherlock_parameters.LP_tolerance_limit = 1e-2;
network_handler benchmark_11(benchmark_11_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_11.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 11 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 12) || (run_all)) {
char benchmark_12_name[] = "./network_files/neural_network_information_12" ;
network_handler benchmark_12(benchmark_12_name);
sherlock_parameters.gradient_rate = 1e-2;
sherlock_parameters.grad_scaling_factor = 5e1;
sherlock_parameters.grad_switch_count = 1e3;
sherlock_parameters.grad_termination_limit = 1e-7;
sherlock_parameters.MILP_tolerance = 5e-1;
sherlock_parameters.MILP_e_tolerance = 1e-10;
sherlock_parameters.scale_factor_for_M = 1;
sherlock_parameters.do_LP_certificate = true;
sherlock_parameters.LP_tolerance_limit = 1e-2;
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_12.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 12 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 13) || (run_all)) {
char benchmark_13_name[] = "./network_files/neural_network_information_13" ;
sherlock_parameters.gradient_rate = 1e-2;
sherlock_parameters.grad_scaling_factor = 5e1;
sherlock_parameters.grad_switch_count = 1e3;
sherlock_parameters.grad_termination_limit = 1e-7;
sherlock_parameters.MILP_tolerance = 5e-1;
sherlock_parameters.MILP_e_tolerance = 1e-10;
sherlock_parameters.scale_factor_for_M = 1;
sherlock_parameters.LP_tolerance_limit = 1e-2;
sherlock_parameters.do_LP_certificate = true;
network_handler benchmark_13(benchmark_13_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_13.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 13 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if ((run_benchmark_no == 14) || (run_all)) {
char benchmark_14_name[] = "./network_files/neural_network_information_14" ;
sherlock_parameters.gradient_rate = 1e-2;
sherlock_parameters.grad_scaling_factor = 5e1;
sherlock_parameters.grad_switch_count = 1e3;
sherlock_parameters.grad_termination_limit = 1e-7;
sherlock_parameters.MILP_tolerance = 5e-1;
sherlock_parameters.MILP_e_tolerance = 1e-10;
sherlock_parameters.scale_factor_for_M = 1;
sherlock_parameters.do_LP_certificate = true;
sherlock_parameters.LP_tolerance_limit = 1e-2;
network_handler benchmark_14(benchmark_14_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_14.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 14 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
if((run_benchmark_no == 15) || (run_all))
{
char benchmark_15_name[] = "./network_files/neural_network_information_15" ;
sherlock_parameters.gradient_rate = 1e-2;
sherlock_parameters.grad_scaling_factor = 5e1;
sherlock_parameters.grad_switch_count = 1e3;
sherlock_parameters.grad_termination_limit = 1e-7;
sherlock_parameters.MILP_tolerance = 5e-1;
sherlock_parameters.MILP_e_tolerance = 1e-10;
sherlock_parameters.scale_factor_for_M = 1;
sherlock_parameters.do_LP_certificate = true;
network_handler benchmark_15(benchmark_15_name);
input_interval.clear();
input_interval.resize(3);
input_interval[0].push_back(-0.1);input_interval[0].push_back(0.1);
input_interval[1].push_back(-0.1);input_interval[1].push_back(0.1);
input_interval[2].push_back(-0.1);input_interval[2].push_back(0.1);
create_constraint_from_interval(input_constraints, input_interval);
begin = clock();
benchmark_15.return_interval_output(input_constraints, output_range, 1);
cout << "output_range = [" << output_range[0] << " , " << output_range[1] << " ]" << endl;
end = clock();
printf("time cost for Sherlock benchmark 15 ------------------ %lf\n", (double)(end - begin) / CLOCKS_PER_SEC);
}
return 0;
}