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neural_test_c++.py
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'''
This is the main file to run gem_end2end network.
It simulates the real scenario of observing a data, puts it inside the memory (or not),
and trains the network using the data
after training at each step, it will output the R matrix described in the paper
https://arxiv.org/abs/1706.08840
and after sevral training steps, it needs to store the parameter in case emergency
happens
To make it work in a real-world scenario, it needs to listen to the observer at anytime,
and call the network to train if a new data is available
(this thus needs to use multi-process)
here for simplicity, we just use single-process to simulate this scenario
'''
from __future__ import print_function
import sys
sys.path.append('deps/sparse_rrt')
sys.path.append('.')
#sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
from sparse_rrt import _sst_module
import sparse_rrt.planners as vis_planners
from sparse_rrt.systems import standard_cpp_systems
from sparse_rrt.visualization import show_image_opencv
#import model.AE.identity as cae_identity
#from model.AE import CAE_acrobot_voxel_2d, CAE_acrobot_voxel_2d_2, CAE_acrobot_voxel_2d_3
#from model import mlp, mlp_acrobot
#from model.mlp import MLP
#from model.mpnet import KMPNet
import numpy as np
import argparse
import os
#import torch
#from gem_eval_original_mpnet import eval_tasks
#from iterative_plan_and_retreat.gem_eval import eval_tasks
#from torch.autograd import Variable
import copy
import os
import gc
import random
#from tools.utility import *
#from plan_utility import pendulum, acrobot_obs
#from sparse_rrt.systems import standard_cpp_systems
#from sparse_rrt import _sst_module
from multiprocessing import Process, Queue
from iterative_plan_and_retreat.data_structure import *
#from iterative_plan_and_retreat.plan_general import propagate
#from plan_utility.data_structure import *
#from plan_utility.plan_general_original_mpnet import propagate
from tools import data_loader
import jax
import time
import matplotlib.pyplot as plt
from plan_utility.line_line_cc import line_line_cc
def IsInCollision(x, obc, obc_width=6.):
STATE_THETA_1, STATE_THETA_2, STATE_V_1, STATE_V_2 = 0, 1, 2, 3
MIN_V_1, MAX_V_1 = -6., 6.
MIN_V_2, MAX_V_2 = -6., 6.
MIN_TORQUE, MAX_TORQUE = -4., 4.
MIN_ANGLE, MAX_ANGLE = -np.pi, np.pi
LENGTH = 20.
m = 1.0
lc = 0.5
lc2 = 0.25
l2 = 1.
I1 = 0.2
I2 = 1.0
l = 1.0
g = 9.81
pole_x0 = 0.
pole_y0 = 0.
pole_x1 = LENGTH * np.cos(x[STATE_THETA_1] - np.pi / 2)
pole_y1 = LENGTH * np.sin(x[STATE_THETA_1] - np.pi / 2)
pole_x2 = pole_x1 + LENGTH * np.cos(x[STATE_THETA_1] + x[STATE_THETA_2] - np.pi / 2)
pole_y2 = pole_y1 + LENGTH * np.sin(x[STATE_THETA_1] + x[STATE_THETA_2] - np.pi / 2)
for i in range(len(obc)):
for j in range(0, 8, 2):
x1 = obc[i][j]
y1 = obc[i][j+1]
x2 = obc[i][(j+2) % 8]
y2 = obc[i][(j+3) % 8]
if line_line_cc(pole_x0, pole_y0, pole_x1, pole_y1, x1, y1, x2, y2):
return True
if line_line_cc(pole_x1, pole_y1, pole_x2, pole_y2, x1, y1, x2, y2):
return True
return False
def main(args):
# set seed
print(args.model_path)
torch_seed = np.random.randint(low=0, high=1000)
np_seed = np.random.randint(low=0, high=1000)
py_seed = np.random.randint(low=0, high=1000)
#torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
# Build the models
#if torch.cuda.is_available():
# torch.cuda.set_device(args.device)
# setup evaluation function and load function
if args.env_type == 'pendulum':
obs_file = None
obc_file = None
obs_f = False
#system = standard_cpp_systems.PSOPTPendulum()
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
elif args.env_type == 'cartpole_obs':
normalize = cartpole.normalize
unnormalize = cartpole.unnormalize
obs_file = None
obc_file = None
dynamics = cartpole.dynamics
jax_dynamics = cartpole.jax_dynamics
enforce_bounds = cartpole.enforce_bounds
cae = CAE_acrobot_voxel_2d
mlp = mlp_acrobot.MLP
obs_f = True
#system = standard_cpp_systems.RectangleObs(obs_list, args.obs_width, 'cartpole')
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
elif args.env_type == 'acrobot_obs':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 200, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type == 'acrobot_obs_2':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type == 'acrobot_obs_3':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type == 'acrobot_obs_5':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type == 'acrobot_obs_6':
obs_file = None
obc_file = None
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
obs_width = 6.0
elif args.env_type == 'acrobot_obs_6':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
num_steps = 21
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
goal_S0 = np.diag([1.,1.,0,0])
#goal_S0 = np.identity(4)
goal_rho0 = 1.0
obs_width = 6.0
elif args.env_type == 'acrobot_obs_8':
obs_file = None
obc_file = None
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
obs_f = True
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.02
#num_steps = 21
num_steps = 21#args.num_steps*2
traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 400, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
#traj_opt = lambda x0, x1, step_sz, num_steps, x_init, u_init, t_init:
#def cem_trajopt(x0, x1, step_sz, num_steps, x_init, u_init, t_init):
# u, t = acrobot_obs.trajopt(x0, x1, 500, num_steps, step_sz*1, step_sz*(num_steps-1), x_init, u_init, t_init)
# xs, us, dts, valid = propagate(x0, u, t, dynamics=dynamics, enforce_bounds=enforce_bounds, IsInCollision=lambda x: False, system=system, step_sz=step_sz)
# return xs, us, dts
#traj_opt = cem_trajopt
obs_width = 6.0
goal_S0 = np.diag([1.,1.,0,0])
goal_rho0 = 1.0
if args.env_type == 'pendulum':
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs':
#system = standard_cpp_systems.RectangleObs(obs[i], 4.0, 'cartpole')
step_sz = 0.002
num_steps = 20
goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type in ['acrobot_obs','acrobot_obs_2', 'acrobot_obs_3', 'acrobot_obs_4', 'acrobot_obs_8']:
#system = standard_cpp_systems.RectangleObs(obs[i], 6.0, 'acrobot')
obs_width = 6.0
step_sz = 0.02
num_steps = 21
goal_radius=2.0
random_seed=0
delta_near=0.1
delta_drain=0.05
# load previously trained model if start epoch > 0
#model_path='kmpnet_epoch_%d_direction_0_step_%d.pkl' %(args.start_epoch, args.num_steps)
mlp_path = os.path.join(os.getcwd()+'/c++/','acrobot_obs_MLP_lr0.010000_epoch_2850_step_20.pt')
encoder_path = os.path.join(os.getcwd()+'/c++/','acrobot_obs_encoder_lr0.010000_epoch_2850_step_20.pt')
cost_mlp_path = os.path.join(os.getcwd()+'/c++/','costnet_acrobot_obs_8_MLP_epoch_300_step_20.pt')
cost_encoder_path = os.path.join(os.getcwd()+'/c++/','costnet_acrobot_obs_8_encoder_epoch_300_step_20.pt')
print('mlp_path:')
print(mlp_path)
#####################################################
def plan_one_path(obs_i, obs, obc, start_state, goal_state, goal_inform_state, max_iteration, out_queue):
if args.env_type == 'pendulum':
system = standard_cpp_systems.PSOPTPendulum()
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
step_sz = 0.002
num_steps = 20
traj_opt = lambda x0, x1: bvp_solver.solve(x0, x1, 200, num_steps, 1, 20, step_sz)
elif args.env_type == 'cartpole_obs':
#system = standard_cpp_systems.RectangleObs(obs[i], 4.0, 'cartpole')
system = _sst_module.CartPole()
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
step_sz = 0.002
num_steps = 20
traj_opt = lambda x0, x1, x_init, u_init, t_init: bvp_solver.solve(x0, x1, 200, num_steps, step_sz*1, step_sz*50, x_init, u_init, t_init)
goal_S0 = np.identity(4)
goal_rho0 = 1.0
elif args.env_type in ['acrobot_obs','acrobot_obs_2', 'acrobot_obs_3', 'acrobot_obs_4', 'acrobot_obs_8']:
#system = standard_cpp_systems.RectangleObs(obs[i], 6.0, 'acrobot')
obs_width = 6.0
psopt_system = _sst_module.PSOPTAcrobot()
propagate_system = standard_cpp_systems.RectangleObs(obs, 6., 'acrobot')
distance_computer = propagate_system.distance_computer()
#distance_computer = _sst_module.euclidean_distance(np.array(propagate_system.is_circular_topology()))
step_sz = 0.02
num_steps = 21
goal_radius=2.0
random_seed=0
delta_near=.1
delta_drain=0.05
#print('creating planner...')
planner = vis_planners.DeepSMPWrapper(mlp_path, encoder_path, cost_mlp_path, cost_encoder_path,
args.bvp_iter, num_steps, step_sz, propagate_system, 3)
# generate a path by using SST to plan for some maximal iterations
time0 = time.time()
#print('obc:')
#print(obc.shape)
#print(delta_near)
#print(delta_drain)
#print('start_state:')
#print(start_state)
#print('goal_state:')
#print(goal_state)
res_x, res_u, res_t = planner.plan("sst", args.plan_type, propagate_system, psopt_system, obc.flatten(), start_state, goal_inform_state, goal_inform_state, \
goal_radius, max_iteration, distance_computer, \
args.delta_near, args.delta_drain)
#res_x, res_u, res_t = planner.plan("sst", propagate_system, psopt_system, obc.flatten(), start_state, goal_state, goal_inform_state, \
# goal_radius, max_iteration, propagate_system.distance_computer(), \
# delta_near, delta_drain)
plan_time = time.time() - time0
"""
# visualization
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.set_autoscale_on(True)
ax.set_xlim(-np.pi, np.pi)
ax.set_ylim(-np.pi, np.pi)
hl, = ax.plot([], [], 'b')
#hl_real, = ax.plot([], [], 'r')
hl_for, = ax.plot([], [], 'g')
hl_back, = ax.plot([], [], 'r')
hl_for_mpnet, = ax.plot([], [], 'lightgreen')
hl_back_mpnet, = ax.plot([], [], 'salmon')
print(obs)
def update_line(h, ax, new_data):
new_data = wrap_angle(new_data, propagate_system)
h.set_data(np.append(h.get_xdata(), new_data[0]), np.append(h.get_ydata(), new_data[1]))
#h.set_xdata(np.append(h.get_xdata(), new_data[0]))
#h.set_ydata(np.append(h.get_ydata(), new_data[1]))
def remove_last_k(h, ax, k):
h.set_data(h.get_xdata()[:-k], h.get_ydata()[:-k])
def draw_update_line(ax):
#ax.relim()
#ax.autoscale_view()
fig.canvas.draw()
fig.canvas.flush_events()
#plt.show()
def wrap_angle(x, system):
circular = system.is_circular_topology()
res = np.array(x)
for i in range(len(x)):
if circular[i]:
# use our previously saved version
res[i] = x[i] - np.floor(x[i] / (2*np.pi))*(2*np.pi)
if res[i] > np.pi:
res[i] = res[i] - 2*np.pi
return res
dtheta = 0.1
feasible_points = []
infeasible_points = []
imin = 0
imax = int(2*np.pi/dtheta)
for i in range(imin, imax):
for j in range(imin, imax):
x = np.array([dtheta*i-np.pi, dtheta*j-np.pi, 0., 0.])
if IsInCollision(x, obs_i):
infeasible_points.append(x)
else:
feasible_points.append(x)
feasible_points = np.array(feasible_points)
infeasible_points = np.array(infeasible_points)
print('feasible points')
print(feasible_points)
print('infeasible points')
print(infeasible_points)
ax.scatter(feasible_points[:,0], feasible_points[:,1], c='yellow')
ax.scatter(infeasible_points[:,0], infeasible_points[:,1], c='pink')
if len(res_x) != 0:
xs_to_plot = np.array(res_x)
for i in range(len(xs_to_plot)):
xs_to_plot[i] = wrap_angle(xs_to_plot[i], propagate_system)
ax.scatter(xs_to_plot[:,0], xs_to_plot[:,1], c='orange')
print('solution: x')
print(res_x)
print('solution: u')
print(res_u)
print('solution: t')
print(res_t)
# draw start and goal
ax.scatter(start_state[0], goal_state[0], marker='X')
draw_update_line(ax)
plt.waitforbuttonpress()
#im = planner.visualize_nodes(propagate_system)
#sec = input('Let us wait for user input')
#show_image_opencv(im, "planning_tree", wait=True)
"""
print('plan time: %fs' % (plan_time))
if len(res_x) == 0:
print('failed.')
out_queue.put(-1)
else:
print('path succeeded.')
out_queue.put(plan_time)
####################################################################################
# load data
print('loading...')
if args.seen_N > 0:
seen_test_data = data_loader.load_test_dataset(args.seen_N, args.seen_NP,
args.data_folder, obs_f, args.seen_s, args.seen_sp)
if args.unseen_N > 0:
unseen_test_data = data_loader.load_test_dataset(args.unseen_N, args.unseen_NP,
args.data_folder, obs_f, args.unseen_s, args.unseen_sp)
# test
# testing
queue = Queue(1)
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
obc, obs, paths, sgs, path_lengths, controls, costs = seen_test_data
obc = obc.astype(np.float32)
#obc = torch.from_numpy(obc)
#if torch.cuda.is_available():
# obc = obc.cuda()
plan_res = []
plan_times = []
plan_res_all = []
for i in range(len(paths)):
new_obs_i = []
obs_i = obs[i]
plan_res_env = []
plan_time_env = []
for k in range(len(obs_i)):
obs_pt = []
obs_pt.append(obs_i[k][0]-obs_width/2)
obs_pt.append(obs_i[k][1]-obs_width/2)
obs_pt.append(obs_i[k][0]-obs_width/2)
obs_pt.append(obs_i[k][1]+obs_width/2)
obs_pt.append(obs_i[k][0]+obs_width/2)
obs_pt.append(obs_i[k][1]+obs_width/2)
obs_pt.append(obs_i[k][0]+obs_width/2)
obs_pt.append(obs_i[k][1]-obs_width/2)
new_obs_i.append(obs_pt)
obs_i = new_obs_i
#print(obs_i)
for j in range(len(paths[i])):
start_state = sgs[i][j][0]
goal_inform_state = paths[i][j][-1]
goal_state = sgs[i][j][1]
print('environment: %d/%d, path: %d/%d' % (i+1, len(paths), j+1, len(paths[i])))
p = Process(target=plan_one_path, args=(obs_i, obs[i], obc[i], start_state, goal_state, goal_inform_state, 1000, queue))
#plan_one_path(obs_i, obs[i], obc[i], start_state, goal_state, goal_inform_state, 500, queue)
p.start()
p.join()
res = queue.get()
if res == -1:
plan_res_env.append(0)
plan_res_all.append(0)
else:
plan_res_env.append(1)
plan_times.append(res)
plan_res_all.append(1)
print('average accuracy up to now: %f' % (np.array(plan_res_all).flatten().mean()))
print('plan average time: %f' % (np.array(plan_times).mean()))
print('plan time std: %f' % (np.array(plan_times).std()))
plan_res.append(plan_res_env)
print('plan accuracy: %f' % (np.array(plan_res).flatten().mean()))
print('plan average time: %f' % (np.array(plan_times).mean()))
print('plan time std: %f' % (np.array(plan_times).std()))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# for training
parser.add_argument('--model_path', type=str, default='/media/arclabdl1/HD1/YLmiao/results/KMPnet_res/acrobot_obs_lr0.010000_SGD/',help='path for saving trained models')
parser.add_argument('--seen_N', type=int, default=10)
parser.add_argument('--seen_NP', type=int, default=100)
parser.add_argument('--seen_s', type=int, default=0)
parser.add_argument('--seen_sp', type=int, default=800)
parser.add_argument('--unseen_N', type=int, default=0)
parser.add_argument('--unseen_NP', type=int, default=0)
parser.add_argument('--unseen_s', type=int, default=0)
parser.add_argument('--unseen_sp', type=int, default=0)
parser.add_argument('--grad_step', type=int, default=1, help='number of gradient steps in continual learning')
# Model parameters
parser.add_argument('--total_input_size', type=int, default=8, help='dimension of total input')
parser.add_argument('--AE_input_size', type=int, default=32, help='dimension of input to AE')
parser.add_argument('--mlp_input_size', type=int , default=40, help='dimension of the input vector')
parser.add_argument('--output_size', type=int , default=4, help='dimension of the input vector')
parser.add_argument('--learning_rate', type=float, default=0.01)
parser.add_argument('--device', type=int, default=0, help='cuda device')
parser.add_argument('--data_folder', type=str, default='./data/acrobot_obs/')
parser.add_argument('--obs_file', type=str, default='./data/cartpole/obs.pkl')
parser.add_argument('--obc_file', type=str, default='./data/cartpole/obc.pkl')
parser.add_argument('--start_epoch', type=int, default=5000)
parser.add_argument('--env_type', type=str, default='acrobot_obs', help='s2d for simple 2d, c2d for complex 2d')
parser.add_argument('--world_size', nargs='+', type=float, default=[3.141592653589793, 3.141592653589793, 6.0, 6.0], help='boundary of world')
parser.add_argument('--opt', type=str, default='Adagrad')
parser.add_argument('--num_steps', type=int, default=20)
parser.add_argument('--plan_type', type=str, default='tree')
parser.add_argument('--bvp_iter', type=int, default=20)
parser.add_argument('--delta_near', type=float, default=.1)
parser.add_argument('--delta_drain', type=float, default=.05)
args = parser.parse_args()
print(args)
main(args)