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':
IsInCollision = pendulum.IsInCollision
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
obs_file = None
obc_file = None
dynamics = pendulum.dynamics
jax_dynamics = pendulum.jax_dynamics
enforce_bounds = pendulum.enforce_bounds
cae = cae_identity
mlp = MLP
obs_f = False
#system = standard_cpp_systems.PSOPTPendulum()
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
elif args.env_type == 'cartpole_obs':
IsInCollision = cartpole.IsInCollision
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':
system = _sst_module.PSOPTAcrobot()
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
obs_file = None
obc_file = None
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d
mlp = mlp_acrobot.MLP
obs_f = True
#system = standard_cpp_systems.RectangleObs(obs_list, args.obs_width, 'acrobot')
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
elif args.env_type == 'acrobot_obs_2':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
obs_file = None
obc_file = None
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d_2
mlp = mlp_acrobot.MLP2
obs_f = True
#system = standard_cpp_systems.RectangleObs(obs_list, args.obs_width, 'acrobot')
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
elif args.env_type == 'acrobot_obs_8':
system = _sst_module.PSOPTAcrobot()
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
obs_file = None
obc_file = None
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
mlp = mlp_acrobot.MLP6
cae = CAE_acrobot_voxel_2d_3
obs_f = True
#system = standard_cpp_systems.RectangleObs(obs_list, args.obs_width, 'acrobot')
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
jac_A = jax.jacfwd(jax_dynamics, argnums=0)
jac_B = jax.jacfwd(jax_dynamics, argnums=1)
mpNet0 = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
mpNet1 = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load previously trained model if start epoch > 0
model_path = 'kmpnet_epoch_%d_direction_0.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet0, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet0.cuda()
mpNet0.mlp.cuda()
mpNet0.encoder.cuda()
if args.opt == 'Adagrad':
mpNet0.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet0.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet0.set_opt(torch.optim.SGD,
lr=args.learning_rate,
momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet0, os.path.join(args.model_path, model_path))
# load previously trained model if start epoch > 0
model_path = 'kmpnet_epoch_%d_direction_1.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet1, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet1.cuda()
mpNet1.mlp.cuda()
mpNet1.encoder.cuda()
if args.opt == 'Adagrad':
mpNet1.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet1.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet1.set_opt(torch.optim.SGD,
lr=args.learning_rate,
momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet1, os.path.join(args.model_path, model_path))
_, waypoint_dataset, waypoint_targets, _, _, _, _, _ = data_loader.load_train_dataset(
1, 2, args.data_folder, obs_f, 1, dynamics, enforce_bounds, system,
0.02, 20)
# 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
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
T = 1
# unnormalize function
normalize_func = lambda x: normalize(x, args.world_size)
unnormalize_func = lambda x: unnormalize(x, args.world_size)
# seen
if args.seen_N > 0:
time_file = os.path.join(
args.model_path, 'time_seen_epoch_%d_mlp.p' % (args.start_epoch))
fes_path_, valid_path_ = eval_tasks_mpnet(
mpNet0, mpNet1, args.env_type, seen_test_data, args.model_path,
'seen', normalize_func, unnormalize_func, dynamics, jac_A, jac_B,
enforce_bounds, IsInCollision)
# unseen
if args.unseen_N > 0:
time_file = os.path.join(
args.model_path, 'time_unseen_epoch_%d_mlp.p' % (args.start_epoch))
fes_path_, valid_path_ = eval_tasks_mpnet(
mpNet0, mpNet1, args.env_type, unseen_test_data, args.model_path,
'unseen', normalize_func, unnormalize_func, dynamics, jac_A, jac_B,
enforce_bounds, IsInCollision)
def main(args):
# set seed
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)
np.random.seed(np_seed)
random.seed(py_seed)
# Build the models
# 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':
obs_file = None
obc_file = None
obs_f = True
obs_width = 4.0
step_sz = 0.002
psopt_system = _sst_module.PSOPTCartPole()
cpp_propagator = _sst_module.SystemPropagator()
#system = standard_cpp_systems.RectangleObs(obs, 4., 'cartpole')
dynamics = lambda x, u, t: cpp_propagator.propagate(
psopt_system, x, u, t)
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 100
elif args.env_type == 'cartpole_obs_2':
obs_file = None
obc_file = None
obs_f = True
obs_width = 4.0
step_sz = 0.002
psopt_system = _sst_module.PSOPTCartPole()
cpp_propagator = _sst_module.SystemPropagator()
#system = standard_cpp_systems.RectangleObs(obs, 4., 'cartpole')
dynamics = lambda x, u, t: cpp_propagator.propagate(
psopt_system, x, u, t)
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP2
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 100
elif args.env_type == 'cartpole_obs_3':
obs_file = None
obc_file = None
obs_f = True
obs_width = 4.0
step_sz = 0.002
psopt_system = _sst_module.PSOPTCartPole()
cpp_propagator = _sst_module.SystemPropagator()
#system = standard_cpp_systems.RectangleObs(obs, 4., 'cartpole')
dynamics = lambda x, u, t: cpp_propagator.propagate(
psopt_system, x, u, t)
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP4
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 200
elif args.env_type == 'cartpole_obs_4':
obs_file = None
obc_file = None
obs_f = True
obs_width = 4.0
step_sz = 0.002
psopt_system = _sst_module.PSOPTCartPole()
cpp_propagator = _sst_module.SystemPropagator()
#system = standard_cpp_systems.RectangleObs(obs, 4., 'cartpole')
dynamics = lambda x, u, t: cpp_propagator.propagate(
psopt_system, x, u, t)
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 200
elif args.env_type == 'acrobot_obs':
obs_file = None
obc_file = None
obs_f = True
obs_width = 6.0
#system = standard_cpp_systems.RectangleObs(obs_list, args.obs_width, 'acrobot')
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 4, 1, 0)
mpnet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp, None)
# load net
# load previously trained model if start epoch > 0
model_dir = args.model_dir
if args.loss == 'mse':
if args.multigoal == 0:
model_dir = model_dir + args.env_type + "_lr%f_%s_step_%d/" % (
args.learning_rate, args.opt, args.num_steps)
else:
model_dir = model_dir + args.env_type + "_lr%f_%s_step_%d_multigoal/" % (
args.learning_rate, args.opt, args.num_steps)
else:
if args.multigoal == 0:
model_dir = model_dir + args.env_type + "_lr%f_%s_loss_%s_step_%d/" % (
args.learning_rate, args.opt, args.loss, args.num_steps)
else:
model_dir = model_dir + args.env_type + "_lr%f_%s_loss_%s_step_%d_multigoal/" % (
args.learning_rate, args.opt, args.loss, args.num_steps)
print(model_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_path = 'kmpnet_epoch_%d_direction_%d_step_%d.pkl' % (
args.start_epoch, args.direction, args.num_steps)
torch_seed, np_seed, py_seed = 0, 0, 0
if args.start_epoch > 0:
#load_net_state(mpnet, os.path.join(args.model_path, model_path))
load_net_state(mpnet, os.path.join(model_dir, model_path))
#torch_seed, np_seed, py_seed = load_seed(os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(model_dir, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpnet.cuda()
mpnet.mlp.cuda()
mpnet.encoder.cuda()
if args.opt == 'Adagrad':
mpnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.start_epoch > 0:
#load_opt_state(mpnet, os.path.join(args.model_path, model_path))
load_opt_state(mpnet, os.path.join(model_dir, model_path))
mpnet.eval()
# 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
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
# find path
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_autoscale_on(True)
hl, = ax.plot([], [], 'b')
#hl_real, = ax.plot([], [], 'r')
def update_line(h, ax, new_data):
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 draw_update_line(ax):
ax.relim()
ax.autoscale_view()
fig.canvas.draw()
fig.canvas.flush_events()
# randomly pick up a point in the data, and find similar data in the dataset
# plot the next point
obc, obs, paths, sgs, path_lengths, controls, costs = seen_test_data
for envi in range(2):
for pathi in range(10):
obs_i = obs[envi]
new_obs_i = []
obs_i = obs[envi]
plan_res_path = []
plan_time_path = []
plan_cost_path = []
data_cost_path = []
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
# visualization
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(121)
ax_vel = fig.add_subplot(122)
#ax.set_autoscale_on(True)
ax.set_xlim(-30, 30)
ax.set_ylim(-np.pi, np.pi)
ax_vel.set_xlim(-40, 40)
ax_vel.set_ylim(-2, 2)
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
dx = 1
dtheta = 0.1
feasible_points = []
infeasible_points = []
imin = 0
imax = int(2 * 30. / dx)
jmin = 0
jmax = int(2 * np.pi / dtheta)
for i in range(imin, imax):
for j in range(jmin, jmax):
x = np.array([dx * i - 30, 0., dtheta * j - np.pi, 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[:, 2],
c='yellow')
ax.scatter(infeasible_points[:, 0],
infeasible_points[:, 2],
c='pink')
#for i in range(len(data)):
# update_line(hl, ax, data[i])
draw_update_line(ax)
#state_t = start_state
xs = paths[envi][pathi]
us = controls[envi][pathi]
ts = costs[envi][pathi]
# propagate data
p_start = xs[0]
detail_paths = [p_start]
detail_controls = []
detail_costs = []
state = [p_start]
control = []
cost = []
for k in range(len(us)):
#state_i.append(len(detail_paths)-1)
max_steps = int(ts[k] / step_sz)
accum_cost = 0.
#print('p_start:')
#print(p_start)
#print('data:')
#print(paths[i][j][k])
# modify it because of small difference between data and actual propagation
p_start = xs[k]
state[-1] = xs[k]
for step in range(1, max_steps + 1):
p_start = dynamics(p_start, us[k], step_sz)
p_start = enforce_bounds(p_start)
detail_paths.append(p_start)
accum_cost += step_sz
if (step % 1 == 0) or (step == max_steps):
state.append(p_start)
#print('control')
#print(controls[i][j])
cost.append(accum_cost)
accum_cost = 0.
#print('p_start:')
#print(p_start)
#print('data:')
#print(paths[i][j][-1])
state[-1] = xs[-1]
#print(len(state))
xs_to_plot = np.array(state)
for i in range(len(xs_to_plot)):
xs_to_plot[i] = wrap_angle(xs_to_plot[i], psopt_system)
ax.scatter(xs_to_plot[:, 0], xs_to_plot[:, 2], c='green')
# draw start and goal
#ax.scatter(start_state[0], goal_state[0], marker='X')
draw_update_line(ax)
ax_vel.scatter(xs_to_plot[:, 1],
xs_to_plot[:, 3],
c='green',
s=0.1)
draw_update_line(ax_vel)
plt.waitforbuttonpress()
# visualize mPNet path
mpnet_paths = []
state = xs[0]
#for k in range(int(len(xs_to_plot)/args.num_steps)):
for k in range(50):
mpnet_paths.append(state)
bi = np.concatenate([state, xs[-1]])
bi = np.array([bi])
bi = torch.from_numpy(bi).type(torch.FloatTensor)
print(bi)
bi = normalize(bi, args.world_size)
bi = to_var(bi)
if obc is None:
bobs = None
else:
bobs = np.array([obc[envi]]).astype(np.float32)
print(bobs.shape)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
bt = mpnet(bi, bobs).cpu()
bt = unnormalize(bt, args.world_size)
bt = bt.detach().numpy()
print(bt.shape)
state = bt[0]
print(mpnet_paths)
xs_to_plot = np.array(mpnet_paths)
print(len(xs_to_plot))
for i in range(len(xs_to_plot)):
xs_to_plot[i] = wrap_angle(xs_to_plot[i], psopt_system)
ax.scatter(xs_to_plot[:, 0], xs_to_plot[:, 2], c='lightgreen')
# draw start and goal
#ax.scatter(start_state[0], goal_state[0], marker='X')
draw_update_line(ax)
ax_vel.scatter(xs_to_plot[:, 1], xs_to_plot[:, 3], c='lightgreen')
draw_update_line(ax_vel)
plt.waitforbuttonpress()
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':
IsInCollision = pendulum.IsInCollision
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
obs_file = None
obc_file = None
cae = cae_identity
mlp = MLP
system = standard_cpp_systems.PSOPTPendulum()
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
max_iter = 100
min_time_steps = 10
max_time_steps = 200
integration_step = 0.002
goal_radius = 0.1
random_seed = 0
sst_delta_near = 0.05
sst_delta_drain = 0.02
vel_idx = [1]
mpNet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load previously trained model if start epoch > 0
model_path = 'kmpnet_epoch_%d.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet.cuda()
mpNet.mlp.cuda()
mpNet.encoder.cuda()
if args.opt == 'Adagrad':
mpNet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet, os.path.join(args.model_path, model_path))
# load train and test data
print('loading...')
if args.seen_N > 0:
seen_test_data = data_loader.load_test_dataset(
N=args.seen_N,
NP=args.seen_NP,
s=args.seen_s,
sp=args.seen_sp,
p_folder=args.path_folder,
obs_f=obs_file,
obc_f=obc_file)
if args.unseen_N > 0:
unseen_test_data = data_loader.load_test_dataset(
N=args.unseen_N,
NP=args.unseen_NP,
s=args.unseen_s,
sp=args.unseen_sp,
p_folder=args.path_folder,
obs_f=obs_file,
obc_f=obc_file)
# test
# testing
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
T = 1
obc, obs, paths, path_lengths = seen_test_data
if obs is not None:
obs = obs.astype(np.float32)
obs = torch.from_numpy(obs)
fes_env = [] # list of list
valid_env = []
time_env = []
time_total = []
normalize_func = lambda x: normalize(x, args.world_size)
unnormalize_func = lambda x: unnormalize(x, args.world_size)
for i in range(len(paths)):
time_path = []
fes_path = [] # 1 for feasible, 0 for not feasible
valid_path = [] # if the feasibility is valid or not
# save paths to different files, indicated by i
# feasible paths for each env
suc_n = 0
for j in range(len(paths[0])):
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_autoscale_on(True)
hl, = ax.plot([], [], 'black')
hl_real, = ax.plot([], [], 'yellow')
time0 = time.time()
time_norm = 0.
fp = 0 # indicator for feasibility
print("step: i=" + str(i) + " j=" + str(j))
p1_ind = 0
p2_ind = 0
p_ind = 0
if path_lengths[i][j] == 0:
# invalid, feasible = 0, and path count = 0
fp = 0
valid_path.append(0)
if path_lengths[i][j] > 0:
fp = 0
valid_path.append(1)
path = [paths[i][j][0], paths[i][j][path_lengths[i][j] - 1]]
start = paths[i][j][0]
end = paths[i][j][path_lengths[i][j] - 1]
#start[1] = 0.
#end[1] = 0.
# plot the entire path
#plt.plot(paths[i][j][:,0], paths[i][j][:,1])
control = []
time_step = []
MAX_NEURAL_REPLAN = 11
if obs is None:
obs_i = None
obc_i = None
else:
obs_i = obs[i]
obc_i = obc[i]
for k in range(path_lengths[i][j]):
update_line(hl, ax, fig, paths[i][j][k])
print('created RRT')
# Run planning and print out solution is some statistics every few iterations.
time0 = time.time()
start = paths[i][j][0]
#end = paths[i][j][path_lengths[i][j]-1]
new_sample = start
print(new_sample)
ax.scatter(new_sample[0], new_sample[1], c='r')
ax.scatter(end[0], end[1], c='g')
for iteration in range(max_iter):
clear_line(hl_real, ax, fig)
#hl_real, = ax.plot([], [], 'yellow')
ip1 = np.concatenate([new_sample, end])
np.expand_dims(ip1, 0)
#ip1=torch.cat((obs,start,goal)).unsqueeze(0)
time0 = time.time()
ip1 = normalize_func(ip1)
ip1 = torch.FloatTensor(ip1)
time_norm += time.time() - time0
ip1 = to_var(ip1)
if obs is not None:
obs = torch.FloatTensor(obs).unsqueeze(0)
obs = to_var(obs)
sample = mpNet(ip1, obs).squeeze(0)
# unnormalize to world size
sample = sample.data.cpu().numpy()
time0 = time.time()
sample = unnormalize_func(sample)
ax.scatter(sample[0], sample[1], c='b')
plt.pause(0.01)
steer, steer_state, steer_control, steer_time_step = plan_general.steerTo(
bvp_solver, start, sample, None, None, step_sz=0.02)
for k in range(len(steer_state)):
update_line(hl_real, ax, fig, steer_state[k])
plt.waitforbuttonpress()
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':
IsInCollision = pendulum.IsInCollision
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
obs_file = None
obc_file = None
dynamics = pendulum.dynamics
jax_dynamics = pendulum.jax_dynamics
enforce_bounds = pendulum.enforce_bounds
cae = cae_identity
mlp = MLP
obs_f = False
#system = standard_cpp_systems.PSOPTPendulum()
#bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
elif args.env_type == 'cartpole_obs':
IsInCollision = cartpole.IsInCollision
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':
IsInCollision = acrobot_obs.IsInCollision
#IsInCollision = lambda x, obs: False
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
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)
xdot = acrobot_obs.dynamics
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d
mlp = mlp_acrobot.MLP
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, 50, 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':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
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)
xdot = acrobot_obs.dynamics
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d_2
mlp = mlp_acrobot.MLP2
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':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
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)
xdot = acrobot_obs.dynamics
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_2
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':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
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)
xdot = acrobot_obs.dynamics
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d_3
mlp = mlp_acrobot.MLP
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':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
obs_file = None
obc_file = None
xdot = acrobot_obs.dynamics
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d_3
mlp = mlp_acrobot.MLP4
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':
IsInCollision = acrobot_obs.IsInCollision
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
obs_file = None
obc_file = None
xdot = acrobot_obs.dynamics
system = _sst_module.PSOPTAcrobot()
cpp_propagator = _sst_module.SystemPropagator()
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
mlp = mlp_acrobot.MLP5
cae = CAE_acrobot_voxel_2d_3
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_8':
IsInCollision = acrobot_obs.IsInCollision
#IsInCollision = lambda x, obs: False
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
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)
xdot = acrobot_obs.dynamics
jax_dynamics = acrobot_obs.jax_dynamics
enforce_bounds = acrobot_obs.enforce_bounds
cae = CAE_acrobot_voxel_2d_3
mlp = mlp_acrobot.MLP6
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
goal_S0 = np.diag([1., 1., 0, 0])
goal_rho0 = 1.0
mpNet0 = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
mpNet1 = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load previously trained model if start epoch > 0
#model_path='kmpnet_epoch_%d_direction_0_step_%d.pkl' %(args.start_epoch, args.num_steps)
model_path = 'kmpnet_epoch_%d_direction_0.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet0, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet0.cuda()
mpNet0.mlp.cuda()
mpNet0.encoder.cuda()
if args.opt == 'Adagrad':
mpNet0.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet0.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet0.set_opt(torch.optim.SGD,
lr=args.learning_rate,
momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet0, os.path.join(args.model_path, model_path))
# load previously trained model if start epoch > 0
#model_path='kmpnet_epoch_%d_direction_1_step_%d.pkl' %(args.start_epoch, args.num_steps)
model_path = 'kmpnet_epoch_%d_direction_1.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet1, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet1.cuda()
mpNet1.mlp.cuda()
mpNet1.encoder.cuda()
if args.opt == 'Adagrad':
mpNet1.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet1.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet1.set_opt(torch.optim.SGD,
lr=args.learning_rate,
momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet1, os.path.join(args.model_path, model_path))
# define informer
circular = system.is_circular_topology()
def informer(env, x0, xG, direction):
x0_x = torch.from_numpy(x0.x).type(torch.FloatTensor)
xG_x = torch.from_numpy(xG.x).type(torch.FloatTensor)
x0_x = normalize_func(x0_x)
xG_x = normalize_func(xG_x)
if torch.cuda.is_available():
x0_x = x0_x.cuda()
xG_x = xG_x.cuda()
if direction == 0:
x = torch.cat([x0_x, xG_x], dim=0)
mpNet = mpNet0
if torch.cuda.is_available():
x = x.cuda()
next_state = mpNet(x.unsqueeze(0), env.unsqueeze(0)).cpu().data
next_state = unnormalize_func(next_state).numpy()[0]
delta_x = next_state - x0.x
# can be either clockwise or counterclockwise, take shorter one
for i in range(len(delta_x)):
if circular[i]:
delta_x[i] = delta_x[i] - np.floor(
delta_x[i] / (2 * np.pi)) * (2 * np.pi)
if delta_x[i] > np.pi:
delta_x[i] = delta_x[i] - 2 * np.pi
# randomly pick either direction
rand_d = np.random.randint(2)
if rand_d < 1 and np.abs(delta_x[i]) >= np.pi * 0.5:
if delta_x[i] > 0.:
delta_x[i] = delta_x[i] - 2 * np.pi
if delta_x[i] <= 0.:
delta_x[i] = delta_x[i] + 2 * np.pi
res = Node(x0.x + delta_x)
cov = np.diag([0.02, 0.02, 0.02, 0.02])
#mean = next_state
#next_state = np.random.multivariate_normal(mean=next_state,cov=cov)
mean = np.zeros(next_state.shape)
rand_x_init = np.random.multivariate_normal(mean=mean,
cov=cov,
size=num_steps)
rand_x_init[0] = rand_x_init[0] * 0.
rand_x_init[-1] = rand_x_init[-1] * 0.
x_init = np.linspace(x0.x, x0.x + delta_x, num_steps) + rand_x_init
## TODO: : change this to general case
u_init_i = np.random.uniform(low=[-4.],
high=[4],
size=(num_steps, 1))
u_init = u_init_i
#u_init_i = control[max_d_i]
cost_i = (num_steps - 1) * step_sz #TOEDIT
#u_init = np.repeat(u_init_i, num_steps, axis=0).reshape(-1,len(u_init_i))
#u_init = u_init + np.random.normal(scale=1., size=u_init.shape)
t_init = np.linspace(0, cost_i, num_steps)
"""
print('init:')
print('x_init:')
print(x_init)
print('u_init:')
print(u_init)
print('t_init:')
print(t_init)
print('xw:')
print(next_state)
"""
else:
x = torch.cat([x0_x, xG_x], dim=0)
mpNet = mpNet1
next_state = mpNet(x.unsqueeze(0), env.unsqueeze(0)).cpu().data
next_state = unnormalize_func(next_state).numpy()[0]
delta_x = next_state - x0.x
# can be either clockwise or counterclockwise, take shorter one
for i in range(len(delta_x)):
if circular[i]:
delta_x[i] = delta_x[i] - np.floor(
delta_x[i] / (2 * np.pi)) * (2 * np.pi)
if delta_x[i] > np.pi:
delta_x[i] = delta_x[i] - 2 * np.pi
# randomly pick either direction
rand_d = np.random.randint(2)
if rand_d < 1 and np.abs(delta_x[i]) >= np.pi * 0.5:
if delta_x[i] > 0.:
delta_x[i] = delta_x[i] - 2 * np.pi
elif delta_x[i] <= 0.:
delta_x[i] = delta_x[i] + 2 * np.pi
#next_state = state[max_d_i] + delta_x
next_state = x0.x + delta_x
res = Node(next_state)
# initial: from max_d_i to max_d_i+1
x_init = np.linspace(next_state, x0.x, num_steps) + rand_x_init
# action: copy over to number of steps
u_init_i = np.random.uniform(low=[-4.],
high=[4],
size=(num_steps, 1))
u_init = u_init_i
cost_i = (num_steps - 1) * step_sz
#u_init = np.repeat(u_init_i, num_steps, axis=0).reshape(-1,len(u_init_i))
#u_init = u_init + np.random.normal(scale=1., size=u_init.shape)
t_init = np.linspace(0, cost_i, num_steps)
return res, x_init, u_init, t_init
def init_informer(env, x0, xG, direction):
if direction == 0:
next_state = xG.x
delta_x = next_state - x0.x
# can be either clockwise or counterclockwise, take shorter one
for i in range(len(delta_x)):
if circular[i]:
delta_x[i] = delta_x[i] - np.floor(
delta_x[i] / (2 * np.pi)) * (2 * np.pi)
if delta_x[i] > np.pi:
delta_x[i] = delta_x[i] - 2 * np.pi
# randomly pick either direction
rand_d = np.random.randint(2)
#print('inside init_informer')
#print('delta_x[%d]: %f' % (i, delta_x[i]))
if rand_d < 1 and np.abs(delta_x[i]) >= np.pi * 0.9:
if delta_x[i] > 0.:
delta_x[i] = delta_x[i] - 2 * np.pi
if delta_x[i] <= 0.:
delta_x[i] = delta_x[i] + 2 * np.pi
res = Node(next_state)
cov = np.diag([0.02, 0.02, 0.02, 0.02])
#mean = next_state
#next_state = np.random.multivariate_normal(mean=next_state,cov=cov)
mean = np.zeros(next_state.shape)
rand_x_init = np.random.multivariate_normal(mean=mean,
cov=cov,
size=num_steps)
rand_x_init[0] = rand_x_init[0] * 0.
rand_x_init[-1] = rand_x_init[-1] * 0.
x_init = np.linspace(x0.x, x0.x + delta_x, num_steps) + rand_x_init
## TODO: : change this to general case
u_init_i = np.random.uniform(low=[-4.],
high=[4],
size=(num_steps, 1))
u_init = u_init_i
#u_init_i = control[max_d_i]
#cost_i = 10*step_sz
cost_i = (num_steps - 1) * step_sz
#u_init = np.repeat(u_init_i, num_steps, axis=0).reshape(-1,len(u_init_i))
#u_init = u_init + np.random.normal(scale=1., size=u_init.shape)
t_init = np.linspace(0, cost_i, num_steps)
else:
next_state = xG.x
delta_x = x0.x - next_state
# can be either clockwise or counterclockwise, take shorter one
for i in range(len(delta_x)):
if circular[i]:
delta_x[i] = delta_x[i] - np.floor(
delta_x[i] / (2 * np.pi)) * (2 * np.pi)
if delta_x[i] > np.pi:
delta_x[i] = delta_x[i] - 2 * np.pi
# randomly pick either direction
rand_d = np.random.randint(2)
if rand_d < 1 and np.abs(delta_x[i]) >= np.pi * 0.5:
if delta_x[i] > 0.:
delta_x[i] = delta_x[i] - 2 * np.pi
elif delta_x[i] <= 0.:
delta_x[i] = delta_x[i] + 2 * np.pi
#next_state = state[max_d_i] + delta_x
res = Node(next_state)
# initial: from max_d_i to max_d_i+1
x_init = np.linspace(next_state, next_state + delta_x,
num_steps) + rand_x_init
# action: copy over to number of steps
u_init_i = np.random.uniform(low=[-4.],
high=[4],
size=(num_steps, 1))
u_init = u_init_i
cost_i = (num_steps - 1) * step_sz
#u_init = np.repeat(u_init_i, num_steps, axis=0).reshape(-1,len(u_init_i))
#u_init = u_init + np.random.normal(scale=1., size=u_init.shape)
t_init = np.linspace(0, cost_i, num_steps)
return x_init, u_init, t_init
# 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
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
T = 1
for _ in range(T):
# unnormalize function
normalize_func = lambda x: normalize(x, args.world_size)
unnormalize_func = lambda x: unnormalize(x, args.world_size)
# seen
if args.seen_N > 0:
time_file = os.path.join(
args.model_path,
'time_seen_epoch_%d_mlp.p' % (args.start_epoch))
fes_path_, valid_path_ = eval_tasks(
mpNet0, mpNet1, seen_test_data, args.model_path, time_file,
IsInCollision, normalize_func, unnormalize_func, informer,
init_informer, system, dynamics, xdot, jax_dynamics,
enforce_bounds, traj_opt, step_sz, num_steps)
valid_path = valid_path_.flatten()
fes_path = fes_path_.flatten(
) # notice different environments are involved
seen_test_suc_rate += fes_path.sum() / valid_path.sum()
# unseen
if args.unseen_N > 0:
time_file = os.path.join(
args.model_path,
'time_unseen_epoch_%d_mlp.p' % (args.start_epoch))
fes_path_, valid_path_ = eval_tasks(
mpNet0, mpNet1, unseen_test_data, args.model_path, time_file,
IsInCollision, normalize_func, unnormalize_func, informer,
init_informer, system, dynamics, xdot, jax_dynamics,
enforce_bounds, traj_opt, step_sz, num_steps)
valid_path = valid_path_.flatten()
fes_path = fes_path_.flatten(
) # notice different environments are involved
unseen_test_suc_rate += fes_path.sum() / valid_path.sum()
if args.seen_N > 0:
seen_test_suc_rate = seen_test_suc_rate / T
f = open(
os.path.join(args.model_path,
'seen_accuracy_epoch_%d.txt' % (args.start_epoch)),
'w')
f.write(str(seen_test_suc_rate))
f.close()
if args.unseen_N > 0:
unseen_test_suc_rate = unseen_test_suc_rate / T # Save the models
f = open(
os.path.join(args.model_path,
'unseen_accuracy_epoch_%d.txt' % (args.start_epoch)),
'w')
f.write(str(unseen_test_suc_rate))
f.close()
def main(args):
#global hl
if torch.cuda.is_available():
torch.cuda.set_device(args.device)
# environment setting
cae = cae_identity
mlp = MLP
cpp_propagator = _sst_module.SystemPropagator()
if args.env_type == 'pendulum':
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
system = standard_cpp_systems.PSOPTPendulum()
dynamics = None
enforce_bounds = None
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole':
normalize = cart_pole.normalize
unnormalize = cart_pole.unnormalize
dynamics = cartpole.dynamics
system = _sst_module.CartPole()
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.CartPole()
dynamics = cartpole.dynamics
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'acrobot_obs':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_2':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP2
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_3':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_4':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_5':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_6':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP4
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_7':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP5
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_8':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP6
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
mpnet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load net
# load previously trained model if start epoch > 0
model_dir = args.model_dir
model_dir = model_dir + 'cost_' + args.env_type + "_lr%f_%s_step_%d/" % (
args.learning_rate, args.opt, args.num_steps)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_path = 'cost_kmpnet_epoch_%d_direction_%d_step_%d.pkl' % (
args.start_epoch, args.direction, args.num_steps)
torch_seed, np_seed, py_seed = 0, 0, 0
if args.start_epoch > 0:
#load_net_state(mpnet, os.path.join(args.model_path, model_path))
load_net_state(mpnet, os.path.join(model_dir, model_path))
#torch_seed, np_seed, py_seed = load_seed(os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(model_dir, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpnet.cuda()
mpnet.mlp.cuda()
mpnet.encoder.cuda()
if args.opt == 'Adagrad':
mpnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.start_epoch > 0:
#load_opt_state(mpnet, os.path.join(args.model_path, model_path))
load_opt_state(mpnet, os.path.join(model_dir, model_path))
mpnet.eval()
# load train and test data
print('loading...')
obs, cost_dataset, cost_targets, env_indices, \
_, _, _, _ = data_loader.load_train_dataset_cost(N=args.no_env, NP=args.no_motion_paths,
data_folder=args.path_folder, obs_f=True,
direction=args.direction,
dynamics=dynamics, enforce_bounds=enforce_bounds,
system=system, step_sz=step_sz, num_steps=args.num_steps)
# randomize the dataset before training
data = list(zip(cost_dataset, cost_targets, env_indices))
random.shuffle(data)
dataset, targets, env_indices = list(zip(*data))
dataset = list(dataset)
targets = list(targets)
env_indices = list(env_indices)
dataset = np.array(dataset)
targets = np.array(targets)
env_indices = np.array(env_indices)
val_i = 0
for i in range(0, len(dataset), args.batch_size):
# validation
# calculate the corresponding batch in val_dataset
dataset_i = dataset[i:i + args.batch_size]
targets_i = targets[i:i + args.batch_size]
env_indices_i = env_indices[i:i + args.batch_size]
# record
bi = dataset_i.astype(np.float32)
print('bi shape:')
print(bi.shape)
bt = targets_i
bi = torch.FloatTensor(bi)
bt = torch.FloatTensor(bt)
bi = normalize(bi, args.world_size)
bi = to_var(bi)
bt = to_var(bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices_i].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
print('cost network output: ')
print(mpnet(bi, bobs).cpu().data)
print('target: ')
print(bt.cpu().data)
def main(args):
# load MPNet
#global hl
if torch.cuda.is_available():
torch.cuda.set_device(args.device)
if args.debug:
from sparse_rrt import _sst_module
from plan_utility import cart_pole, cart_pole_obs, pendulum, acrobot_obs
from tools import data_loader
cpp_propagator = _sst_module.SystemPropagator()
if args.env_type == 'pendulum':
if args.debug:
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
system = standard_cpp_systems.PSOPTPendulum()
dynamics = None
enforce_bounds = None
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole':
if args.debug:
normalize = cart_pole.normalize
unnormalize = cart_pole.unnormalize
dynamics = cartpole.dynamics
system = _sst_module.CartPole()
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs':
if args.debug:
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.CartPole()
dynamics = cartpole.dynamics
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'acrobot_obs':
if args.debug:
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(
system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d
elif args.env_type == 'acrobot_obs_8':
if args.debug:
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(
system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
mlp = mlp_acrobot.MLP6
cae = CAE_acrobot_voxel_2d_3
mpnet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load net
# load previously trained model if start epoch > 0
model_dir = args.model_dir
model_dir = model_dir + 'cost_' + args.env_type + "_lr%f_%s_step_%d/" % (
args.learning_rate, args.opt, args.num_steps)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_path = 'cost_kmpnet_epoch_%d_direction_%d_step_%d.pkl' % (
args.start_epoch, args.direction, args.num_steps)
torch_seed, np_seed, py_seed = 0, 0, 0
if args.start_epoch > 0:
#load_net_state(mpnet, os.path.join(args.model_path, model_path))
load_net_state(mpnet, os.path.join(model_dir, model_path))
#torch_seed, np_seed, py_seed = load_seed(os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(model_dir, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpnet.cuda()
mpnet.mlp.cuda()
mpnet.encoder.cuda()
if args.opt == 'Adagrad':
mpnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.start_epoch > 0:
#load_opt_state(mpnet, os.path.join(args.model_path, model_path))
load_opt_state(mpnet, os.path.join(model_dir, model_path))
# load train and test data
print('loading...')
if args.debug:
obs, cost_dataset, cost_targets, env_indices, \
_, _, _, _ = data_loader.load_train_dataset_cost(N=args.no_env, NP=args.no_motion_paths,
data_folder=args.path_folder, obs_f=True,
direction=args.direction,
dynamics=dynamics, enforce_bounds=enforce_bounds,
system=system, step_sz=step_sz, num_steps=args.num_steps)
# randomize the dataset before training
data = list(zip(cost_dataset, cost_targets, env_indices))
random.shuffle(data)
dataset, targets, env_indices = list(zip(*data))
dataset = list(dataset)
targets = list(targets)
env_indices = list(env_indices)
dataset = np.array(dataset)
targets = np.array(targets)
env_indices = np.array(env_indices)
# record
bi = dataset.astype(np.float32)
print('bi shape:')
print(bi.shape)
bt = targets
bi = torch.FloatTensor(bi)
bt = torch.FloatTensor(bt)
bi = normalize(bi, args.world_size)
bi = to_var(bi)
bt = to_var(bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
else:
bobs = np.random.rand(1, 1, args.AE_input_size, args.AE_input_size)
bobs = torch.from_numpy(bobs).type(torch.FloatTensor)
bobs = to_var(bobs)
bi = np.random.rand(1, args.total_input_size)
bt = np.random.rand(1, args.output_size)
bi = torch.from_numpy(bi).type(torch.FloatTensor)
bt = torch.from_numpy(bt).type(torch.FloatTensor)
bi = to_var(bi)
bt = to_var(bt)
# set to training model to enable dropout
#mpnet.train()
mpnet.eval()
MLP = mpnet.mlp
encoder = mpnet.encoder
traced_encoder = torch.jit.trace(encoder, (bobs))
encoder_output = encoder(bobs)
mlp_input = torch.cat((encoder_output, bi), 1)
traced_MLP = torch.jit.trace(MLP, (mlp_input))
traced_encoder.save("costnet_%s_encoder_epoch_%d_step_%d.pt" %
(args.env_type, args.start_epoch, args.num_steps))
traced_MLP.save("costnet_%s_MLP_epoch_%d_step_%d.pt" %
(args.env_type, args.start_epoch, args.num_steps))
# test the traced model
serilized_encoder = torch.jit.script(encoder)
serilized_MLP = torch.jit.script(MLP)
serilized_encoder_output = serilized_encoder(bobs)
serilized_MLP_input = torch.cat((serilized_encoder_output, bi), 1)
serilized_MLP_output = serilized_MLP(serilized_MLP_input)
print('encoder output: ', serilized_encoder_output)
print('MLP output: ', serilized_MLP_output)
print('data: ', bt)
def main(args):
#global hl
if torch.cuda.is_available():
torch.cuda.set_device(args.device)
# environment setting
cae = cae_identity
mlp = MLP
cpp_propagator = _sst_module.SystemPropagator()
if args.env_type == 'pendulum':
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
system = standard_cpp_systems.PSOPTPendulum()
dynamics = None
enforce_bounds = None
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole':
normalize = cart_pole.normalize
unnormalize = cart_pole.unnormalize
dynamics = cartpole.dynamics
system = _sst_module.CartPole()
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = [0, 2]
vel_indices = [1, 3]
elif args.env_type == 'cartpole_obs_2':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP2
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = [0, 2]
vel_indices = [1, 3]
elif args.env_type == 'cartpole_obs_3':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP4
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = [0, 2]
vel_indices = [1, 3]
elif args.env_type == 'cartpole_obs_4_small':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 2])
vel_indices = np.array([1, 3])
elif args.env_type == 'cartpole_obs_4_big':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 2])
vel_indices = np.array([1, 3])
elif args.env_type == 'cartpole_obs_4_small_x_theta':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 1])
vel_indices = np.array([2, 3])
elif args.env_type == 'cartpole_obs_4_big_x_theta':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 1])
vel_indices = np.array([2, 3])
elif args.env_type == 'cartpole_obs_4_small_decouple_output':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 2])
vel_indices = np.array([1, 3])
elif args.env_type == 'cartpole_obs_4_big_decouple_output':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3
cae = CAE_cartpole_voxel_2d
# dynamics: None -- without integration to dense trajectory
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
pos_indices = np.array([0, 2])
vel_indices = np.array([1, 3])
elif args.env_type == 'acrobot_obs':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_2':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP2
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_3':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_4':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_5':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_6':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP4
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
pos_indices = [0, 1]
vel_indices = [2, 3]
elif args.env_type == 'acrobot_obs_7':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP5
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_8':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP6
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
# set loss for mpnet
if args.loss == 'mse':
#mpnet.loss_f = nn.MSELoss()
def mse_loss(y1, y2):
l = (y1 - y2) ** 2
l = torch.mean(l, dim=0) # sum alone the batch dimension, now the dimension is the same as input dimension
return l
loss_f_p = mse_loss
loss_f_v = mse_loss
elif args.loss == 'l1_smooth':
#mpnet.loss_f = nn.SmoothL1Loss()
def l1_smooth_loss(y1, y2):
l1 = torch.abs(y1 - y2)
cond = l1 < 1
l = torch.where(cond, 0.5 * l1 ** 2, l1)
l = torch.mean(l, dim=0) # sum alone the batch dimension, now the dimension is the same as input dimension
return l
loss_f_p = l1_smooth_loss
loss_f_v = l1_smooth_loss
elif args.loss == 'mse_decoupled':
def mse_decoupled(y1, y2):
# for angle terms, wrap it to -pi~pi
l_0 = torch.abs(y1[:,0] - y2[:,0]) ** 2
l_1 = torch.abs(y1[:,1] - y2[:,1]) ** 2
l_2 = torch.abs(y1[:,2] - y2[:,2]) # angular dimension
l_3 = torch.abs(y1[:,3] - y2[:,3]) ** 2
cond = (l_2 > 1.0) * (l_2 <= 2.0)
l_2 = torch.where(cond, 2*1.0-l_2, l_2)
l_2 = l_2 ** 2
l_0 = torch.mean(l_0)
l_1 = torch.mean(l_1)
l_2 = torch.mean(l_2)
l_3 = torch.mean(l_3)
return torch.stack([l_0, l_1, l_2, l_3])
loss_f_p = mse_decoupled
loss_f_v = mse_decoupled
elif args.loss == 'l1_smooth_decoupled':
# this only is for cartpole, need to adapt to other systems
#TODO
def l1_smooth_decoupled(y1, y2):
# for angle terms, wrap it to -pi~pi
l_0 = torch.abs(y1[:,0] - y2[:,0])
l_1 = torch.abs(y1[:,1] - y2[:,1]) # angular dimension
cond = (l_1 > 1.0) * (l_1 <= 2.0)
l_1 = torch.where(cond, 2*1.0-l_1, l_1)
# then change to l1_smooth_loss
cond = l_0 < 1
l_0 = torch.where(cond, 0.5 * l_0 ** 2, l_0)
cond = l_1 < 1
l_1 = torch.where(cond, 0.5 * l_1 ** 2, l_1)
l_0 = torch.mean(l_0)
l_1 = torch.mean(l_1)
return torch.stack([l_0, l_1])
def l1_smooth_loss(y1, y2):
l1 = torch.abs(y1 - y2)
cond = l1 < 1
l = torch.where(cond, 0.5 * l1 ** 2, l1)
l = torch.mean(l, dim=0) # sum alone the batch dimension, now the dimension is the same as input dimension
return l
loss_f_p = l1_smooth_decoupled
loss_f_v = l1_smooth_loss
if 'decouple_output' in args.env_type:
print('mpnet using decoupled output')
mpnet_pnet = KMPNet(args.total_input_size, args.AE_input_size, args.mlp_input_size, args.output_size//2,
cae, mlp, loss_f_p)
mpnet_vnet = KMPNet(args.total_input_size, args.AE_input_size, args.mlp_input_size, args.output_size//2,
cae, mlp, loss_f_v)
else:
mpnet_pnet = KMPNet(args.total_input_size//2, args.AE_input_size, args.mlp_input_size, args.output_size//2,
cae, mlp, loss_f_p)
mpnet_vnet = KMPNet(args.total_input_size//2, args.AE_input_size, args.mlp_input_size, args.output_size//2,
cae, mlp, loss_f_v)
# load net
# load previously trained model if start epoch > 0
model_dir = args.model_dir
if args.loss == 'mse':
if args.multigoal == 0:
model_dir = model_dir+args.env_type+"_lr%f_%s_step_%d/" % (args.learning_rate, args.opt, args.num_steps)
else:
model_dir = model_dir+args.env_type+"_lr%f_%s_step_%d_multigoal/" % (args.learning_rate, args.opt, args.num_steps)
else:
if args.multigoal == 0:
model_dir = model_dir+args.env_type+"_lr%f_%s_loss_%s_step_%d/" % (args.learning_rate, args.opt, args.loss, args.num_steps)
else:
model_dir = model_dir+args.env_type+"_lr%f_%s_loss_%s_step_%d_multigoal/" % (args.learning_rate, args.opt, args.loss, args.num_steps)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_pnet_path='kmpnet_pnet_epoch_%d_direction_%d_step_%d.pkl' %(args.start_epoch, args.direction, args.num_steps)
model_vnet_path='kmpnet_vnet_epoch_%d_direction_%d_step_%d.pkl' %(args.start_epoch, args.direction, args.num_steps)
torch_seed, np_seed, py_seed = 0, 0, 0
if args.start_epoch > 0:
#load_net_state(mpnet, os.path.join(args.model_path, model_path))
load_net_state(mpnet_pnet, os.path.join(model_dir, model_pnet_path))
load_net_state(mpnet_vnet, os.path.join(model_dir, model_vnet_path))
#torch_seed, np_seed, py_seed = load_seed(os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(os.path.join(model_dir, model_pnet_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpnet_pnet.cuda()
mpnet_pnet.mlp.cuda()
mpnet_pnet.encoder.cuda()
mpnet_vnet.cuda()
mpnet_vnet.mlp.cuda()
mpnet_vnet.encoder.cuda()
if args.opt == 'Adagrad':
mpnet_pnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet_pnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet_pnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet_pnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.opt == 'Adagrad':
mpnet_vnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet_vnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet_vnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet_vnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.start_epoch > 0:
#load_opt_state(mpnet, os.path.join(args.model_path, model_path))
load_opt_state(mpnet_pnet, os.path.join(model_dir, model_path))
load_opt_state(mpnet_vnet, os.path.join(model_dir, model_path))
# load train and test data
print('loading...')
obs, waypoint_dataset, waypoint_targets, env_indices, \
_, _, _, _ = data_loader.load_train_dataset(N=args.no_env, NP=args.no_motion_paths,
data_folder=args.path_folder, obs_f=True,
direction=args.direction,
dynamics=dynamics, enforce_bounds=enforce_bounds,
system=system, step_sz=step_sz,
num_steps=args.num_steps, multigoal=args.multigoal)
# randomize the dataset before training
data=list(zip(waypoint_dataset,waypoint_targets,env_indices))
random.shuffle(data)
dataset,targets,env_indices=list(zip(*data))
dataset = list(dataset)
dataset = np.array(dataset)
targets = np.array(targets)
print(np.concatenate([pos_indices, pos_indices+args.total_input_size//2]))
p_dataset = dataset[:, np.concatenate([pos_indices, pos_indices+args.total_input_size//2])]
v_dataset = dataset[:, np.concatenate([vel_indices, vel_indices+args.total_input_size//2])]
if 'decouple_output' in args.env_type:
# only decouple output
print('only decouple output but not input')
p_dataset = dataset
v_dataset = dataset
print(p_dataset.shape)
print(v_dataset.shape)
p_targets = targets[:,pos_indices]
v_targets = targets[:,vel_indices] # this is only for cartpole
# TODO: add string for choosing env
p_targets = list(p_targets)
v_targets = list(v_targets)
#targets = list(targets)
env_indices = list(env_indices)
dataset = np.array(dataset)
#targets = np.array(targets)
env_indices = np.array(env_indices)
# use 5% as validation dataset
val_len = int(len(dataset) * 0.05)
val_p_dataset = p_dataset[-val_len:]
val_v_dataset = v_dataset[-val_len:]
val_p_targets = p_targets[-val_len:]
val_v_targets = v_targets[-val_len:]
val_env_indices = env_indices[-val_len:]
p_dataset = p_dataset[:-val_len]
v_dataset = v_dataset[:-val_len]
p_targets = p_targets[:-val_len]
v_targets = v_targets[:-val_len]
env_indices = env_indices[:-val_len]
# Train the Models
print('training...')
if args.loss == 'mse':
if args.multigoal == 0:
writer_fname = 'pos_vel_%s_%f_%s_direction_%d_step_%d' % (args.env_type, args.learning_rate, args.opt, args.direction, args.num_steps, )
else:
writer_fname = 'pos_vel_%s_%f_%s_direction_%d_step_%d_multigoal' % (args.env_type, args.learning_rate, args.opt, args.direction, args.num_steps, )
else:
if args.multigoal == 0:
writer_fname = 'pos_vel_%s_%f_%s_direction_%d_step_%d_loss_%s' % (args.env_type, args.learning_rate, args.opt, args.direction, args.num_steps, args.loss, )
else:
writer_fname = 'pos_vel_%s_%f_%s_direction_%d_step_%d_loss_%s_multigoal' % (args.env_type, args.learning_rate, args.opt, args.direction, args.num_steps, args.loss, )
writer = SummaryWriter('./runs/'+writer_fname)
record_i = 0
val_record_i = 0
p_loss_avg_i = 0
p_val_loss_avg_i = 0
p_loss_avg = 0.
p_val_loss_avg = 0.
v_loss_avg_i = 0
v_val_loss_avg_i = 0
v_loss_avg = 0.
v_val_loss_avg = 0.
loss_steps = 100 # record every 100 loss
world_size = np.array(args.world_size)
pos_world_size = list(world_size[pos_indices])
vel_world_size = list(world_size[vel_indices])
for epoch in range(args.start_epoch+1,args.num_epochs+1):
print('epoch' + str(epoch))
val_i = 0
for i in range(0,len(p_dataset),args.batch_size):
print('epoch: %d, training... path: %d' % (epoch, i+1))
p_dataset_i = p_dataset[i:i+args.batch_size]
v_dataset_i = v_dataset[i:i+args.batch_size]
p_targets_i = p_targets[i:i+args.batch_size]
v_targets_i = v_targets[i:i+args.batch_size]
env_indices_i = env_indices[i:i+args.batch_size]
# record
p_bi = p_dataset_i.astype(np.float32)
v_bi = v_dataset_i.astype(np.float32)
print('p_bi shape:')
print(p_bi.shape)
print('v_bi shape:')
print(v_bi.shape)
p_bt = p_targets_i
v_bt = v_targets_i
p_bi = torch.FloatTensor(p_bi)
v_bi = torch.FloatTensor(v_bi)
p_bt = torch.FloatTensor(p_bt)
v_bt = torch.FloatTensor(v_bt)
# edit: disable this for investigation of the good weights for training, and for wrapping
if 'decouple_output' in args.env_type:
print('using normalizatino of decoupled output')
# only decouple output but not input
p_bi, v_bi, p_bt, v_bt = normalize(p_bi, args.world_size), normalize(v_bi, args.world_size), normalize(p_bt, pos_world_size), normalize(v_bt, vel_world_size)
else:
p_bi, v_bi, p_bt, v_bt = normalize(p_bi, pos_world_size), normalize(v_bi, vel_world_size), normalize(p_bt, pos_world_size), normalize(v_bt, vel_world_size)
mpnet_pnet.zero_grad()
mpnet_vnet.zero_grad()
p_bi=to_var(p_bi)
v_bi=to_var(v_bi)
p_bt=to_var(p_bt)
v_bt=to_var(v_bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices_i].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
print('-------pnet-------')
print('before training losses:')
print(mpnet_pnet.loss(mpnet_pnet(p_bi, bobs), p_bt))
mpnet_pnet.step(p_bi, bobs, p_bt)
print('after training losses:')
print(mpnet_pnet.loss(mpnet_pnet(p_bi, bobs), p_bt))
p_loss = mpnet_pnet.loss(mpnet_pnet(p_bi, bobs), p_bt)
#update_line(hl, ax, [i//args.batch_size, loss.data.numpy()])
p_loss_avg += p_loss.cpu().data
p_loss_avg_i += 1
print('-------vnet-------')
print('before training losses:')
print(mpnet_vnet.loss(mpnet_vnet(v_bi, bobs), v_bt))
mpnet_vnet.step(v_bi, bobs, v_bt)
print('after training losses:')
print(mpnet_vnet.loss(mpnet_vnet(v_bi, bobs), v_bt))
v_loss = mpnet_vnet.loss(mpnet_vnet(v_bi, bobs), v_bt)
#update_line(hl, ax, [i//args.batch_size, loss.data.numpy()])
v_loss_avg += v_loss.cpu().data
v_loss_avg_i += 1
if p_loss_avg_i >= loss_steps:
p_loss_avg = p_loss_avg / p_loss_avg_i
writer.add_scalar('p_train_loss_0', p_loss_avg[0], record_i)
writer.add_scalar('p_train_loss_1', p_loss_avg[1], record_i)
v_loss_avg = v_loss_avg / v_loss_avg_i
writer.add_scalar('v_train_loss_0', v_loss_avg[0], record_i)
writer.add_scalar('v_train_loss_1', v_loss_avg[1], record_i)
record_i += 1
p_loss_avg = 0.
p_loss_avg_i = 0
v_loss_avg = 0.
v_loss_avg_i = 0
# validation
# calculate the corresponding batch in val_dataset
p_dataset_i = val_p_dataset[val_i:val_i+args.batch_size]
v_dataset_i = val_v_dataset[val_i:val_i+args.batch_size]
p_targets_i = val_p_targets[val_i:val_i+args.batch_size]
v_targets_i = val_v_targets[val_i:val_i+args.batch_size]
env_indices_i = val_env_indices[val_i:val_i+args.batch_size]
val_i = val_i + args.batch_size
if val_i > val_len:
val_i = 0
# record
p_bi = p_dataset_i.astype(np.float32)
v_bi = v_dataset_i.astype(np.float32)
print('p_bi shape:')
print(p_bi.shape)
print('v_bi shape:')
print(v_bi.shape)
p_bt = p_targets_i
v_bt = v_targets_i
p_bi = torch.FloatTensor(p_bi)
v_bi = torch.FloatTensor(v_bi)
p_bt = torch.FloatTensor(p_bt)
v_bt = torch.FloatTensor(v_bt)
if 'decouple_output' in args.env_type:
# only decouple output but not input
p_bi, v_bi, p_bt, v_bt = normalize(p_bi, args.world_size), normalize(v_bi, args.world_size), normalize(p_bt, pos_world_size), normalize(v_bt, vel_world_size)
else:
p_bi, v_bi, p_bt, v_bt = normalize(p_bi, pos_world_size), normalize(v_bi, vel_world_size), normalize(p_bt, pos_world_size), normalize(v_bt, vel_world_size)
p_bi=to_var(p_bi)
v_bi=to_var(v_bi)
p_bt=to_var(p_bt)
v_bt=to_var(v_bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices_i].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
print('-------pnet loss--------')
p_loss = mpnet_pnet.loss(mpnet_pnet(p_bi, bobs), p_bt)
print('validation loss: ' % (p_loss.cpu().data))
p_val_loss_avg += p_loss.cpu().data
p_val_loss_avg_i += 1
print('-------vnet loss--------')
v_loss = mpnet_vnet.loss(mpnet_vnet(v_bi, bobs), v_bt)
print('validation loss: ' % (v_loss.cpu().data))
v_val_loss_avg += v_loss.cpu().data
v_val_loss_avg_i += 1
if p_val_loss_avg_i >= loss_steps:
p_val_loss_avg = p_val_loss_avg / p_val_loss_avg_i
writer.add_scalar('p_val_loss_0', p_val_loss_avg[0], val_record_i)
writer.add_scalar('p_val_loss_1', p_val_loss_avg[1], val_record_i)
v_val_loss_avg = v_val_loss_avg / v_val_loss_avg_i
writer.add_scalar('v_val_loss_0', v_val_loss_avg[0], val_record_i)
writer.add_scalar('v_val_loss_1', v_val_loss_avg[1], val_record_i)
val_record_i += 1
p_val_loss_avg = 0.
p_val_loss_avg_i = 0
v_val_loss_avg = 0.
v_val_loss_avg_i = 0
# Save the models
if epoch > 0 and epoch % 50 == 0:
model_pnet_path='kmpnet_pnet_epoch_%d_direction_%d_step_%d.pkl' %(epoch, args.direction, args.num_steps)
model_vnet_path='kmpnet_vnet_epoch_%d_direction_%d_step_%d.pkl' %(epoch, args.direction, args.num_steps)
#save_state(mpnet, torch_seed, np_seed, py_seed, os.path.join(args.model_path,model_path))
save_state(mpnet_pnet, torch_seed, np_seed, py_seed, os.path.join(model_dir,model_pnet_path))
save_state(mpnet_vnet, torch_seed, np_seed, py_seed, os.path.join(model_dir,model_vnet_path))
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
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':
IsInCollision = pendulum.IsInCollision
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
obs_file = None
obc_file = None
cae = cae_identity
mlp = MLP
system = standard_cpp_systems.PSOPTPendulum()
bvp_solver = _sst_module.PSOPTBVPWrapper(system, 2, 1, 0)
max_iter = 200
min_time_steps = 20
max_time_steps = 200
integration_step = 0.002
goal_radius = 0.1
random_seed = 0
sst_delta_near = 0.05
sst_delta_drain = 0.02
vel_idx = [1]
mpNet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp)
# load previously trained model if start epoch > 0
model_path = 'kmpnet_epoch_%d.pkl' % (args.start_epoch)
if args.start_epoch > 0:
load_net_state(mpNet, os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(args.model_path, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpNet.cuda()
mpNet.mlp.cuda()
mpNet.encoder.cuda()
if args.opt == 'Adagrad':
mpNet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpNet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpNet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
if args.start_epoch > 0:
load_opt_state(mpNet, os.path.join(args.model_path, model_path))
# load train and test data
print('loading...')
if args.seen_N > 0:
seen_test_data = data_loader.load_test_dataset(
N=args.seen_N,
NP=args.seen_NP,
s=args.seen_s,
sp=args.seen_sp,
p_folder=args.path_folder,
obs_f=obs_file,
obc_f=obc_file)
if args.unseen_N > 0:
unseen_test_data = data_loader.load_test_dataset(
N=args.unseen_N,
NP=args.unseen_NP,
s=args.unseen_s,
sp=args.unseen_sp,
p_folder=args.path_folder,
obs_f=obs_file,
obc_f=obc_file)
# test
# testing
print('testing...')
seen_test_suc_rate = 0.
unseen_test_suc_rate = 0.
T = 1
obc, obs, paths, path_lengths = seen_test_data
if obs is not None:
obs = obs.astype(np.float32)
obs = torch.from_numpy(obs)
fes_env = [] # list of list
valid_env = []
time_env = []
time_total = []
normalize_func = lambda x: normalize(x, args.world_size)
unnormalize_func = lambda x: unnormalize(x, args.world_size)
low = []
high = []
state_bounds = system.get_state_bounds()
for i in range(len(state_bounds)):
low.append(state_bounds[i][0])
high.append(state_bounds[i][1])
for i in range(len(paths)):
time_path = []
fes_path = [] # 1 for feasible, 0 for not feasible
valid_path = [] # if the feasibility is valid or not
# save paths to different files, indicated by i
# feasible paths for each env
suc_n = 0
sst_suc_n = 0
for j in range(len(paths[0])):
time0 = time.time()
time_norm = 0.
fp = 0 # indicator for feasibility
print("step: i=" + str(i) + " j=" + str(j))
p1_ind = 0
p2_ind = 0
p_ind = 0
if path_lengths[i][j] == 0:
# invalid, feasible = 0, and path count = 0
fp = 0
valid_path.append(0)
if path_lengths[i][j] > 0:
fp = 0
valid_path.append(1)
path = [paths[i][j][0], paths[i][j][path_lengths[i][j] - 1]]
start = paths[i][j][0]
end = paths[i][j][path_lengths[i][j] - 1]
start[1] = 0.
end[1] = 0.
# plot the entire path
#plt.plot(paths[i][j][:,0], paths[i][j][:,1])
"""
planner = SST(
state_bounds=system.get_state_bounds(),
control_bounds=system.get_control_bounds(),
distance=system.distance_computer(),
start_state=start,
goal_state=end,
goal_radius=goal_radius,
random_seed=0,
sst_delta_near=sst_delta_near,
sst_delta_drain=sst_delta_drain
)
"""
planner = RRT(state_bounds=system.get_state_bounds(),
control_bounds=system.get_control_bounds(),
distance=system.distance_computer(),
start_state=start,
goal_state=end,
goal_radius=goal_radius,
random_seed=0)
control = []
time_step = []
MAX_NEURAL_REPLAN = 11
if obs is None:
obs_i = None
obc_i = None
else:
obs_i = obs[i]
obc_i = obc[i]
print('created RRT')
# Run planning and print out solution is some statistics every few iterations.
time0 = time.time()
start = paths[i][j][0]
#end = paths[i][j][path_lengths[i][j]-1]
new_sample = start
sample = start
N_sample = 10
for iteration in range(max_iter // N_sample):
#if iteration % 50 == 0:
# # from time to time use the goal
# sample = end
# #planner.step_with_sample(system, sample, 20, 200, 0.002)
#else:
#planner.step(system, min_time_steps, max_time_steps, integration_step)
#sample = np.random.uniform(low=low, high=high)
for num_sample in range(N_sample):
ip1 = np.concatenate([new_sample, end])
np.expand_dims(ip1, 0)
#ip1=torch.cat((obs,start,goal)).unsqueeze(0)
time0 = time.time()
ip1 = normalize_func(ip1)
ip1 = torch.FloatTensor(ip1)
time_norm += time.time() - time0
ip1 = to_var(ip1)
if obs is not None:
obs = torch.FloatTensor(obs).unsqueeze(0)
obs = to_var(obs)
sample = mpNet(ip1, obs).squeeze(0)
# unnormalize to world size
sample = sample.data.cpu().numpy()
time0 = time.time()
sample = unnormalize_func(sample)
print('sample:')
print(sample)
print('start:')
print(start)
print('goal:')
print(end)
print('accuracy: %f' % (float(suc_n) / (j + 1)))
print('sst accuracy: %f' % (float(sst_suc_n) / (j + 1)))
sample = planner.step_with_sample(system, sample,
min_time_steps,
max_time_steps, 0.002)
#planner.step_bvp(system, 10, 200, 0.002)
im = planner.visualize_nodes(system)
show_image(im, 'nodes', wait=False)
new_sample = planner.step_with_sample(system, end,
min_time_steps,
max_time_steps, 0.002)
solution = planner.get_solution()
if solution is not None:
print('solved.')
suc_n += 1
break
planner = SST(state_bounds=system.get_state_bounds(),
control_bounds=system.get_control_bounds(),
distance=system.distance_computer(),
start_state=start,
goal_state=end,
goal_radius=goal_radius,
random_seed=0,
sst_delta_near=sst_delta_near,
sst_delta_drain=sst_delta_drain)
# Run planning and print out solution is some statistics every few iterations.
time0 = time.time()
start = paths[i][j][0]
#end = paths[i][j][path_lengths[i][j]-1]
new_sample = start
sample = start
N_sample = 10
for iteration in range(max_iter // N_sample):
for k in range(N_sample):
sample = np.random.uniform(low=low, high=high)
planner.step_with_sample(system, sample, min_time_steps,
max_time_steps, integration_step)
im = planner.visualize_tree(system)
show_image(im, 'tree', wait=False)
print('accuracy: %f' % (float(suc_n) / (j + 1)))
print('sst accuracy: %f' % (float(sst_suc_n) / (j + 1)))
planner.step_with_sample(system, end, min_time_steps,
max_time_steps, integration_step)
solution = planner.get_solution()
if solution is not None:
print('solved.')
sst_suc_n += 1
break
print('accuracy: %f' % (float(suc_n) / (j + 1)))
print('sst accuracy: %f' % (float(sst_suc_n) / (j + 1)))
def main(args):
#global hl
if torch.cuda.is_available():
torch.cuda.set_device(args.device)
# environment setting
multigoal = False
cpp_propagator = _sst_module.SystemPropagator()
if args.env_type == 'pendulum':
normalize = pendulum.normalize
unnormalize = pendulum.unnormalize
system = standard_cpp_systems.PSOPTPendulum()
dynamics = None
enforce_bounds = None
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole':
normalize = cart_pole.normalize
unnormalize = cart_pole.unnormalize
dynamics = cartpole.dynamics
system = _sst_module.CartPole()
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.CartPole()
dynamics = cartpole.dynamics
enforce_bounds = cartpole.enforce_bounds
step_sz = 0.002
num_steps = 20
cae = cae_identity
mlp = MLP
elif args.env_type == 'cartpole_obs_4':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3_no_dropout
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
multigoal = False
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'cartpole_obs_4_multigoal':
normalize = cart_pole_obs.normalize
unnormalize = cart_pole_obs.unnormalize
system = _sst_module.PSOPTCartPole()
mlp = mlp_cartpole.MLP3_no_dropout
cae = CAE_cartpole_voxel_2d
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
#dynamics = None
multigoal = True
enforce_bounds = cart_pole_obs.enforce_bounds
step_sz = 0.002
num_steps = 20
elif args.env_type == 'acrobot_obs':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_2':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP2
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_3':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_2
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_4':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP3
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_5':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_6':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP4
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_7':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP5
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
elif args.env_type == 'acrobot_obs_8':
normalize = acrobot_obs.normalize
unnormalize = acrobot_obs.unnormalize
system = _sst_module.PSOPTAcrobot()
mlp = mlp_acrobot.MLP6
cae = CAE_acrobot_voxel_2d_3
#dynamics = acrobot_obs.dynamics
dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)
enforce_bounds = acrobot_obs.enforce_bounds
step_sz = 0.02
num_steps = 20
# set loss for mpnet
if args.loss == 'mse':
#mpnet.loss_f = nn.MSELoss()
def mse_loss(y1, y2):
l = (y1 - y2)**2
l = torch.mean(
l, dim=0
) # sum alone the batch dimension, now the dimension is the same as input dimension
return l
loss_f = mse_loss
elif args.loss == 'l1_smooth':
#mpnet.loss_f = nn.SmoothL1Loss()
def l1_smooth_loss(y1, y2):
l1 = torch.abs(y1 - y2)
cond = l1 < 1
l = torch.where(cond, 0.5 * l1**2, l1)
l = torch.mean(
l, dim=0
) # sum alone the batch dimension, now the dimension is the same as input dimension
loss_f = l1_smooth_loss
elif args.loss == 'mse_decoupled':
def mse_decoupled(y1, y2):
# for angle terms, wrap it to -pi~pi
l_0 = torch.abs(y1[:, 0] - y2[:, 0])**2
l_1 = torch.abs(y1[:, 1] - y2[:, 1])**2
l_2 = torch.abs(y1[:, 2] - y2[:, 2]) # angular dimension
l_3 = torch.abs(y1[:, 3] - y2[:, 3])**2
cond = (l_2 > 1.0) * (l_2 <= 2.0
) # np.pi after normalization is 1.0
l_2 = torch.where(cond, 2.0 - l_2, l_2)
l_2 = l_2**2
l_0 = torch.mean(l_0)
l_1 = torch.mean(l_1)
l_2 = torch.mean(l_2)
l_3 = torch.mean(l_3)
return torch.stack([l_0, l_1, l_2, l_3])
loss_f = mse_decoupled
mpnet = KMPNet(args.total_input_size, args.AE_input_size,
args.mlp_input_size, args.output_size, cae, mlp, loss_f)
# load net
# load previously trained model if start epoch > 0
model_dir = args.model_dir
model_dir = model_dir + 'cost_' + args.env_type + "_lr%f_%s_step_%d/" % (
args.learning_rate, args.opt, args.num_steps)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_path = 'cost_kmpnet_epoch_%d_direction_%d_step_%d.pkl' % (
args.start_epoch, args.direction, args.num_steps)
torch_seed, np_seed, py_seed = 0, 0, 0
if args.start_epoch > 0:
#load_net_state(mpnet, os.path.join(args.model_path, model_path))
load_net_state(mpnet, os.path.join(model_dir, model_path))
#torch_seed, np_seed, py_seed = load_seed(os.path.join(args.model_path, model_path))
torch_seed, np_seed, py_seed = load_seed(
os.path.join(model_dir, model_path))
# set seed after loading
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
random.seed(py_seed)
if torch.cuda.is_available():
mpnet.cuda()
mpnet.mlp.cuda()
mpnet.encoder.cuda()
if args.opt == 'Adagrad':
mpnet.set_opt(torch.optim.Adagrad, lr=args.learning_rate)
elif args.opt == 'Adam':
mpnet.set_opt(torch.optim.Adam, lr=args.learning_rate)
elif args.opt == 'SGD':
mpnet.set_opt(torch.optim.SGD, lr=args.learning_rate, momentum=0.9)
elif args.opt == 'ASGD':
mpnet.set_opt(torch.optim.ASGD, lr=args.learning_rate)
if args.start_epoch > 0:
#load_opt_state(mpnet, os.path.join(args.model_path, model_path))
load_opt_state(mpnet, os.path.join(model_dir, model_path))
# load train and test data
print('loading...')
obs, cost_dataset, cost_targets, env_indices, \
_, _, _, _ = data_loader.load_train_dataset_cost(N=args.no_env, NP=args.no_motion_paths,
data_folder=args.path_folder, obs_f=True,
direction=args.direction,
dynamics=dynamics, enforce_bounds=enforce_bounds,
system=system, step_sz=step_sz, num_steps=args.num_steps,
multigoal=multigoal)
# randomize the dataset before training
data = list(zip(cost_dataset, cost_targets, env_indices))
random.shuffle(data)
dataset, targets, env_indices = list(zip(*data))
dataset = list(dataset)
targets = list(targets)
env_indices = list(env_indices)
dataset = np.array(dataset)
targets = np.array(targets)
env_indices = np.array(env_indices)
# use 5% as validation dataset
val_len = int(len(dataset) * 0.05)
val_dataset = dataset[-val_len:]
val_targets = targets[-val_len:]
val_env_indices = env_indices[-val_len:]
dataset = dataset[:-val_len]
targets = targets[:-val_len]
env_indices = env_indices[:-val_len]
# Train the Models
print('training...')
writer_fname = 'cost_%s_%f_%s_direction_%d_step_%d' % (
args.env_type, args.learning_rate, args.opt, args.direction,
args.num_steps)
writer = SummaryWriter('./runs/' + writer_fname)
record_i = 0
val_record_i = 0
loss_avg_i = 0
val_loss_avg_i = 0
loss_avg = 0.
val_loss_avg = 0.
loss_steps = 100 # record every 100 loss
for epoch in range(args.start_epoch + 1, args.num_epochs + 1):
print('epoch' + str(epoch))
val_i = 0
for i in range(0, len(dataset), args.batch_size):
print('epoch: %d, training... path: %d' % (epoch, i + 1))
dataset_i = dataset[i:i + args.batch_size]
targets_i = targets[i:i + args.batch_size]
env_indices_i = env_indices[i:i + args.batch_size]
# record
bi = dataset_i.astype(np.float32)
print('bi shape:')
print(bi.shape)
bt = targets_i
bi = torch.FloatTensor(bi)
bt = torch.FloatTensor(bt)
bi = normalize(bi, args.world_size)
mpnet.zero_grad()
bi = to_var(bi)
bt = to_var(bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices_i].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
print('before training losses:')
print(mpnet.loss(mpnet(bi, bobs), bt))
mpnet.step(bi, bobs, bt)
print('after training losses:')
print(mpnet.loss(mpnet(bi, bobs), bt))
loss = mpnet.loss(mpnet(bi, bobs), bt)
#update_line(hl, ax, [i//args.batch_size, loss.data.numpy()])
loss_avg += loss.cpu().data
loss_avg_i += 1
if loss_avg_i >= loss_steps:
loss_avg = loss_avg / loss_avg_i
writer.add_scalar('train_loss', loss_avg, record_i)
record_i += 1
loss_avg = 0.
loss_avg_i = 0
# validation
# calculate the corresponding batch in val_dataset
dataset_i = val_dataset[val_i:val_i + args.batch_size]
targets_i = val_targets[val_i:val_i + args.batch_size]
env_indices_i = val_env_indices[val_i:val_i + args.batch_size]
val_i = val_i + args.batch_size
if val_i > val_len:
val_i = 0
# record
bi = dataset_i.astype(np.float32)
print('bi shape:')
print(bi.shape)
bt = targets_i
bi = torch.FloatTensor(bi)
bt = torch.FloatTensor(bt)
bi = normalize(bi, args.world_size)
bi = to_var(bi)
bt = to_var(bt)
if obs is None:
bobs = None
else:
bobs = obs[env_indices_i].astype(np.float32)
bobs = torch.FloatTensor(bobs)
bobs = to_var(bobs)
loss = mpnet.loss(mpnet(bi, bobs), bt)
print('validation loss: %f' % (loss.cpu().data))
val_loss_avg += loss.cpu().data
val_loss_avg_i += 1
if val_loss_avg_i >= loss_steps:
val_loss_avg = val_loss_avg / val_loss_avg_i
writer.add_scalar('val_loss', val_loss_avg, val_record_i)
val_record_i += 1
val_loss_avg = 0.
val_loss_avg_i = 0
# Save the models
if epoch > 0 and epoch % 50 == 0:
model_path = 'cost_kmpnet_epoch_%d_direction_%d_step_%d.pkl' % (
epoch, args.direction, args.num_steps)
#save_state(mpnet, torch_seed, np_seed, py_seed, os.path.join(args.model_path,model_path))
save_state(mpnet, torch_seed, np_seed, py_seed,
os.path.join(model_dir, model_path))
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
