def gen_Ball(info):
thread_idx, data_dir, data_names = info['thread_idx'], info['data_dir'], info['data_names']
n_rollout, time_step = info['n_rollout'], info['time_step']
dt, video, image, draw_edge, args, phase = info['dt'], info['video'], info['image'], info['draw_edge'], info['args'], info['phase']
n_ball = info['n_ball']
save_type = info['save_type']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2 ** 32)
attr_dim = args.attr_dim # radius
state_dim = args.state_dim # x, y, xdot, ydot
action_dim = 2 # ddx, ddy
stats = [init_stat(attr_dim), init_stat(state_dim), init_stat(action_dim)]
traj = []
graph = None
engine = BallEngine(dt, state_dim, action_dim=2)
# bar = ProgressBar()
for i in range(n_rollout):
rollout_idx = thread_idx * n_rollout + i
rollout_dir = os.path.join(data_dir, str(rollout_idx))
if save_type==0:
os.system('mkdir -p ' + rollout_dir)
engine.init(n_ball,param_load=args.load_rels) # changed this
n_obj = engine.num_obj
attrs_all = np.zeros((time_step, n_obj, attr_dim))
states_all = np.zeros((time_step, n_obj, state_dim))
actions_all = np.zeros((time_step, n_obj, action_dim))
rel_attrs_all = np.zeros((time_step, engine.param_dim, 2))
act = np.zeros((n_obj, 2))
for j in range(time_step):
state = engine.get_state()
vel_dim = state_dim // 2
pos = state[:, :vel_dim]
vel = state[:, vel_dim:]
if j > 0:
vel = (pos - states_all[j - 1, :, :vel_dim]) / dt
attrs = np.zeros((n_obj, attr_dim))
attrs[:] = engine.radius
attrs_all[j] = attrs
states_all[j, :, :vel_dim] = pos
states_all[j, :, vel_dim:] = vel
rel_attrs_all[j] = engine.param
act += (np.random.rand(n_obj, 2) - 0.5) * 600 - act * 0.1 - state[:, 2:] * 0.1
act = np.clip(act, -1000, 1000)
engine.step(act)
actions_all[j] = act.copy()
datas = [attrs_all, states_all, actions_all, rel_attrs_all]
traj.append(states_all.astype(np.float64))
graph = rel_attrs_all
if save_type == 0:
store_data(data_names, datas, rollout_dir + '.h5') # stores the rollout
engine.render(states_all, actions_all, engine.get_param(), video=video, image=image,
path=rollout_dir, draw_edge=draw_edge, verbose=True)
datas = [datas[i].astype(np.float64) for i in range(len(datas))]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
if save_type == 0:
return stats
else:
return stats, graph, traj
def gen_Cloth(info):
env, env_idx = info['env'], info['env_idx']
thread_idx, data_dir, data_names = info['thread_idx'], info['data_dir'], info['data_names']
n_rollout, time_step = info['n_rollout'], info['time_step']
dt, args, phase = info['dt'], info['args'], info['phase']
vis_width, vis_height = info['vis_width'], info['vis_height']
state_dim = args.state_dim
action_dim = args.action_dim
dt = 1. / 60.
np.random.seed(round(time.time() * 1000 + thread_idx) % 2 ** 32)
stats = [init_stat(state_dim), init_stat(action_dim)]
engine = ClothEngine(dt, state_dim, action_dim)
import pyflex
pyflex.init()
# bar = ProgressBar()
for i in range(n_rollout):
rollout_idx = thread_idx * n_rollout + i
rollout_dir = os.path.join(data_dir, str(rollout_idx))
os.system('mkdir -p ' + rollout_dir)
engine.init(pyflex)
scene_params = engine.scene_params
action = np.zeros(4)
states_all = np.zeros((time_step, engine.n_particles, state_dim))
actions_all = np.zeros((time_step, 1, action_dim))
# drop the cloth down
engine.set_action(action)
engine.step()
for j in range(time_step):
positions = pyflex.get_positions().reshape(-1, 4)[:, :3]
# sample the action
if j % 5 == 0:
ctrl_pts = rand_int(0, 8)
act_lim = 0.05
dx = rand_float(-act_lim, act_lim)
dz = rand_float(-act_lim, act_lim)
dy = 0.05
action = np.array([ctrl_pts, dx, dy, dz])
else:
action[2] = 0.
# store the rollout information
state = engine.get_state()
states_all[j] = state
tga_path = os.path.join(rollout_dir, '%d.tga' % j)
pyflex.render(capture=True, path=tga_path)
tga = Image.open(tga_path)
tga = np.array(tga)[:, 60:780, :3][:, :, ::-1]
tga = cv2.resize(tga, (vis_width, vis_height), interpolation=cv2.INTER_AREA)
os.system('rm ' + tga_path)
jpg_path = os.path.join(rollout_dir, 'fig_%d.jpg' % j)
cv2.imwrite(jpg_path, tga)
actions_all[j, 0] = action.copy()
engine.set_action(action)
engine.step()
datas = [states_all, actions_all, scene_params]
store_data(data_names, datas, rollout_dir + '.h5')
datas = [datas[j].astype(np.float64) for j in range(len(datas))]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
pyflex.clean()
return stats
def gen_data(self):
# if the data hasn't been generated, generate the data
n_rollout, time_step, dt = self.n_rollout, self.args.time_step, self.args.dt
assert n_rollout % self.args.num_workers == 0
print("Generating data ... n_rollout=%d, time_step=%d" % (n_rollout, time_step))
infos = []
for i in range(self.args.num_workers):
info = {'thread_idx': i,
'data_dir': self.data_dir,
'data_names': self.data_names,
'n_rollout': n_rollout // self.args.num_workers,
'time_step': time_step,
'dt': dt,
'video': self.args.video,
'image': self.args.image,
'draw_edge': self.args.draw_edge,
'phase': self.phase,
'args': self.args,
'vis_height': self.args.height_raw,
'vis_width': self.args.width_raw,
'save_type':self.args.h5}
if self.args.env in ['Ball']:
info['env'] = 'Ball'
info['n_ball'] = self.args.n_ball
elif self.args.env in ['Cloth']:
info['env'] = 'Cloth'
info['env_idx'] = 15
infos.append(info)
cores = self.args.num_workers
pool = mp.Pool(processes=cores)
env = self.args.env
if env in ['Ball']:
data = pool.map(gen_Ball, infos)
elif env in ['Cloth']:
data = pool.map(gen_Cloth, infos)
else:
raise AssertionError("Unknown env")
print("Training data generated, warpping up stats ...")
if self.phase == 'train':
if env in ['Ball']:
self.stat = [init_stat(self.args.attr_dim),
init_stat(self.args.state_dim),
init_stat(self.args.action_dim)]
elif env in ['Cloth']:
self.stat = [init_stat(self.args.state_dim),
init_stat(self.args.action_dim)]
if self.args.h5 != 0 :
data_, graph, trajectories = [core[0] for core in data], [core[1] for core in data], [core[2] for core in data]
else:
data_ = data
for i in range(len(data_)):
for j in range(len(self.stat)):
self.stat[j] = combine_stat(self.stat[j], data_[i][j])
if self.args.h5 !=0 :
store_trajectories(trajectories,self.args.dataf)
store_graph(graph,self.args.dataf)
store_data(self.data_names[:len(self.stat)], self.stat, self.stat_path)
else:
print("Loading stat from %s ..." % self.stat_path)
self.stat = load_data(self.data_names, self.stat_path)
def gen_Swim(info):
thread_idx, data_dir, data_names = info['thread_idx'], info[
'data_dir'], info['data_names']
n_rollout, time_step = info['n_rollout'], info['time_step']
dt, video, args, phase = info['dt'], info['video'], info['args'], info[
'phase']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2**32)
attr_dim = args.attr_dim # actuated, soft, rigid
state_dim = args.state_dim # x, y, xdot, ydot
action_dim = args.action_dim
param_dim = args.param_dim # n_box, k, damping, init_p
act_scale = 500.
act_delta = 250.
# attr, state, action
stats = [init_stat(attr_dim), init_stat(state_dim), init_stat(action_dim)]
engine = SwimEngine(dt, state_dim, action_dim, param_dim)
group_size = args.group_size
sub_dataset_size = n_rollout * args.num_workers // args.n_splits
print('group size', group_size, 'sub_dataset_size', sub_dataset_size)
assert n_rollout % group_size == 0
assert args.n_rollout % args.n_splits == 0
bar = ProgressBar()
for i in bar(range(n_rollout)):
rollout_idx = thread_idx * n_rollout + i
group_idx = rollout_idx // group_size
sub_idx = rollout_idx // sub_dataset_size
num_obj_range = args.num_obj_range if phase in {
'train', 'valid'
} else args.extra_num_obj_range
num_obj = num_obj_range[sub_idx]
rollout_dir = os.path.join(data_dir, str(rollout_idx))
param_file = os.path.join(data_dir, str(group_idx) + '.param')
os.system('mkdir -p ' + rollout_dir)
if rollout_idx % group_size == 0:
init_p = None if not args.regular_data else sample_init_p_flight(
n_box=num_obj, aug=True, train=phase == 'train')
engine.init(param=(num_obj, None, None, init_p))
torch.save(engine.get_param(), param_file)
else:
while not os.path.isfile(param_file):
time.sleep(0.5)
param = torch.load(param_file)
engine.init(param=param)
act_t_param = np.zeros((engine.n_box, 3))
for j in range(time_step):
box_type = engine.init_p[:, 2]
act_t = np.zeros((engine.n_box, action_dim))
for k in range(engine.n_box):
if box_type[k] == 0:
# if this is an actuated box
if j == 0:
act_t_param[k] = np.array([
rand_float(0., 1.),
rand_float(1., 2.5),
rand_float(0, np.pi * 2)
])
if act_t_param[k, 0] < 0.3:
# using smooth action
if j == 0:
act_t[k] = rand_float(-act_delta, act_delta)
else:
lo = max(actions_all[j - 1, k] - act_delta,
-act_scale - 20)
hi = min(actions_all[j - 1, k] + act_delta,
act_scale + 20)
act_t[k] = rand_float(lo, hi)
act_t[k] = np.clip(act_t[k], -act_scale, act_scale)
elif act_t_param[k, 0] < 0.6:
# using random action
act_t[k] = rand_float(-act_scale, act_scale)
else:
# using sin action
act_t[k] = np.sin(j / act_t_param[k, 1] + act_t_param[k, 2]) * \
rand_float(act_scale / 2., act_scale)
engine.set_action(act_t)
states = engine.get_state()
actions = engine.get_action()
pos = states[:, :8].copy()
vec = states[:, 8:].copy()
'''reset velocity'''
if j > 0:
vec = (pos - states_all[j - 1, :, :8]) / dt
if j == 0:
attrs_all = np.zeros((time_step, num_obj, attr_dim))
states_all = np.zeros((time_step, num_obj, state_dim))
actions_all = np.zeros((time_step, num_obj, action_dim))
'''attrs: actuated/soft/rigid'''
assert attr_dim == 3
attrs = np.zeros((num_obj, attr_dim))
for k in range(engine.n_box):
attrs[k, int(engine.init_p[k, 2])] = 1
assert np.sum(attrs[:, 0]) == np.sum(engine.init_p[:, 2] == 0)
assert np.sum(attrs[:, 1]) == np.sum(engine.init_p[:, 2] == 1)
assert np.sum(attrs[:, 2]) == np.sum(engine.init_p[:, 2] == 2)
attrs_all[j] = attrs
states_all[j, :, :8] = pos
states_all[j, :, 8:] = vec
actions_all[j] = actions
data = [attrs, states_all[j], actions_all[j]]
store_data(data_names, data,
os.path.join(rollout_dir,
str(j) + '.h5'))
engine.step()
datas = [
attrs_all.astype(np.float64),
states_all.astype(np.float64),
actions_all.astype(np.float64)
]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
return stats
def gen_data(self):
# if the data hasn't been generated, generate the data
n_rollout, time_step, dt = self.n_rollout, self.args.time_step, self.args.dt
assert n_rollout % self.args.num_workers == 0
print("Generating data ... n_rollout=%d, time_step=%d" %
(n_rollout, time_step))
infos = []
for i in range(self.args.num_workers):
info = {
'thread_idx': i,
'data_dir': self.data_dir,
'data_names': self.data_names,
'n_rollout': n_rollout // self.args.num_workers,
'time_step': time_step,
'dt': dt,
'video': False,
'phase': self.phase,
'args': self.args
}
infos.append(info)
cores = self.args.num_workers
pool = mp.Pool(processes=cores)
env = self.args.env
if env == 'Rope':
data = pool.map(gen_Rope, infos)
elif env == 'Soft':
data = pool.map(gen_Soft, infos)
elif env == 'Swim':
data = pool.map(gen_Swim, infos)
else:
raise AssertionError("Unknown env")
print("Training data generated, warpping up stats ...")
if self.phase == 'train':
# states [x, y, angle, xdot, ydot, angledot], action [x, xdot]
if env in ['Rope', 'Soft', 'Swim']:
self.stat = [
init_stat(self.args.attr_dim),
init_stat(self.args.state_dim),
init_stat(self.args.action_dim)
]
for i in range(len(data)):
for j in range(len(self.stat)):
self.stat[j] = combine_stat(self.stat[j], data[i][j])
if self.args.gen_stat:
print("Storing stat to %s" % self.stat_path)
store_data(self.data_names, self.stat, self.stat_path)
else:
print("stat will be discarded")
else:
print("Loading stat from %s ..." % self.stat_path)
if env in ['Rope', 'Soft', 'Swim']:
self.stat = load_data(self.data_names, self.stat_path)
def gen_Rope(info):
thread_idx, data_dir, data_names = info['thread_idx'], info[
'data_dir'], info['data_names']
n_rollout, time_step = info['n_rollout'], info['time_step']
dt, video, args, phase = info['dt'], info['video'], info['args'], info[
'phase']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2**32)
attr_dim = args.attr_dim # root, child
state_dim = args.state_dim # x, y, xdot, ydot
action_dim = args.action_dim
param_dim = args.param_dim # n_ball, init_x, k, damping, gravity
act_scale = 2.
ret_scale = 1.
# attr, state, action
stats = [init_stat(attr_dim), init_stat(state_dim), init_stat(action_dim)]
engine = RopeEngine(dt, state_dim, action_dim, param_dim)
group_size = args.group_size
sub_dataset_size = n_rollout * args.num_workers // args.n_splits
print('group size', group_size, 'sub_dataset_size', sub_dataset_size)
assert n_rollout % group_size == 0
assert args.n_rollout % args.n_splits == 0
bar = ProgressBar()
for i in bar(range(n_rollout)):
rollout_idx = thread_idx * n_rollout + i
group_idx = rollout_idx // group_size
sub_idx = rollout_idx // sub_dataset_size
num_obj_range = args.num_obj_range if phase in {
'train', 'valid'
} else args.extra_num_obj_range
num_obj = num_obj_range[sub_idx]
rollout_dir = os.path.join(data_dir, str(rollout_idx))
param_file = os.path.join(data_dir, str(group_idx) + '.param')
os.system('mkdir -p ' + rollout_dir)
if rollout_idx % group_size == 0:
engine.init(param=(num_obj, None, None, None, None))
torch.save(engine.get_param(), param_file)
else:
while not os.path.isfile(param_file):
time.sleep(0.5)
param = torch.load(param_file)
engine.init(param=param)
for j in range(time_step):
states_ctl = engine.get_state()[0]
act_t = np.zeros((engine.num_obj, action_dim))
act_t[0, 0] = (np.random.rand() * 2 -
1.) * act_scale - states_ctl[0] * ret_scale
engine.set_action(action=act_t)
states = engine.get_state()
actions = engine.get_action()
n_obj = engine.num_obj
pos = states[:, :2].copy()
vec = states[:, 2:].copy()
'''reset velocity'''
if j > 0:
vec = (pos - states_all[j - 1, :, :2]) / dt
if j == 0:
attrs_all = np.zeros((time_step, n_obj, attr_dim))
states_all = np.zeros((time_step, n_obj, state_dim))
actions_all = np.zeros((time_step, n_obj, action_dim))
'''attrs: [1, 0] => root; [0, 1] => child'''
assert attr_dim == 2
attrs = np.zeros((n_obj, attr_dim))
# category: the first ball is fixed
attrs[0, 0] = 1
attrs[1:, 1] = 1
assert np.sum(attrs[:, 0]) == 1
assert np.sum(attrs[:, 1]) == engine.num_obj - 1
attrs_all[j] = attrs
states_all[j, :, :2] = pos
states_all[j, :, 2:] = vec
actions_all[j] = actions
data = [attrs, states_all[j], actions_all[j]]
store_data(data_names, data,
os.path.join(rollout_dir,
str(j) + '.h5'))
engine.step()
datas = [
attrs_all.astype(np.float64),
states_all.astype(np.float64),
actions_all.astype(np.float64)
]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
return stats
def gen_data(self):
# if the data hasn't been generated, generate the data
n_rollout, n_particle = self.n_rollout, self.args.n_particle
time_step, dt = self.args.time_step, self.args.dt
print("Generating data ... n_rollout=%d, time_step=%d" %
(n_rollout, time_step))
infos = []
for i in range(self.args.num_workers):
info = {
'thread_idx': i,
'data_dir': self.data_dir,
'data_names': self.data_names,
'n_particle': n_particle,
'n_rollout': n_rollout // self.args.num_workers,
'time_step': time_step,
'dt': dt,
'args': self.args
}
infos.append(info)
cores = self.args.num_workers
pool = mp.Pool(processes=cores)
env = self.args.env
if env == 'Cradle':
data = pool.map(gen_Cradle, infos)
elif env == 'Rope':
data = pool.map(gen_Rope, infos)
elif env == 'Box':
data = pool.map(gen_Box, infos)
else:
raise AssertionError("Unknown env")
print("Training data generated, warpping up stats ...")
if self.phase == 'train' and self.args.gen_stat:
if env in ['Cradle']:
self.stat = [
init_stat(self.args.attr_dim),
init_stat(self.args.state_dim)
]
elif env in ['Rope']:
self.stat = [
init_stat(self.args.attr_dim),
init_stat(self.args.state_dim),
init_stat(self.args.action_dim)
]
elif env in ['Box']:
self.stat = [
init_stat(self.args.state_dim),
init_stat(self.args.action_dim)
]
for i in range(len(data)):
for j in range(len(self.stat)):
self.stat[j] = combine_stat(self.stat[j], data[i][j])
store_data(self.data_names[:len(self.stat)], self.stat,
self.stat_path)
else:
print("Loading stat from %s ..." % self.stat_path)
if env in ['Cradle', 'Rope']:
self.stat = load_data(self.data_names, self.stat_path)
elif env in ['Box']:
self.stat = load_data(self.data_names[:2], self.stat_path)
def gen_Box(info):
thread_idx, data_dir, data_names = info['thread_idx'], info[
'data_dir'], info['data_names']
n_rollout, n_particle, time_step = info['n_rollout'], info[
'n_particle'], info['time_step']
dt, args = info['dt'], info['args']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2**32)
state_dim = args.state_dim # x, y, angle, xdot, ydot, angledot
action_dim = args.action_dim # x, xdot
assert state_dim == 6
assert action_dim == 2
stats = [init_stat(state_dim), init_stat(action_dim)]
engine = BoxEngine(dt, state_dim, action_dim)
states = np.zeros((n_rollout, time_step, n_particle, state_dim))
actions = np.zeros((n_rollout, time_step, 1, action_dim))
viss = np.zeros((n_rollout, time_step, n_particle))
bar = ProgressBar()
for i in bar(range(n_rollout)):
rollout_idx = thread_idx * n_rollout + i
rollout_dir = os.path.join(data_dir, str(rollout_idx))
os.system('mkdir -p ' + rollout_dir)
engine.reset_scene(n_particle)
for j in range(time_step):
engine.set_action(rand_float(-600., 100.))
states[i, j] = engine.get_state()
actions[i, j] = engine.get_action()
viss[i, j] = engine.get_vis(states[i, j])
if j > 0:
states[i, j, :, 3:] = (states[i, j, :, :3] -
states[i, j - 1, :, :3]) / dt
actions[i, j, :, 1] = (actions[i, j, :, 0] -
actions[i, j - 1, :, 0]) / dt
data = [states[i, j], actions[i, j], viss[i, j]]
store_data(data_names, data,
os.path.join(rollout_dir,
str(j) + '.h5'))
engine.step()
datas = [states[i].astype(np.float64), actions[i].astype(np.float64)]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
return stats
def gen_Rope(info):
thread_idx, data_dir, data_names = info['thread_idx'], info[
'data_dir'], info['data_names']
n_rollout, n_particle, time_step = info['n_rollout'], info[
'n_particle'], info['time_step']
dt, args = info['dt'], info['args']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2**32)
attr_dim = args.attr_dim # fixed, moving, radius
state_dim = args.state_dim # x, y, xdot, ydot
action_dim = args.action_dim # xddot, yddot
assert attr_dim == 3
assert state_dim == 4
assert action_dim == 2
act_scale = 15
# attr, state, action
stats = [init_stat(attr_dim), init_stat(state_dim), init_stat(action_dim)]
engine = RopeEngine(dt, state_dim, action_dim)
attrs = np.zeros((n_rollout, time_step, n_particle + 2, attr_dim))
states = np.zeros((n_rollout, time_step, n_particle + 2, state_dim))
actions = np.zeros((n_rollout, time_step, n_particle + 2, action_dim))
bar = ProgressBar()
for i in bar(range(n_rollout)):
rollout_idx = thread_idx * n_rollout + i
rollout_dir = os.path.join(data_dir, str(rollout_idx))
os.system('mkdir -p ' + rollout_dir)
engine.reset_scene(n_particle)
act = np.zeros((n_particle, action_dim))
for j in range(time_step):
f = np.zeros(action_dim)
for k in range(n_particle):
f += (np.random.rand(action_dim) * 2 - 1) * act_scale
act[k] = f
engine.set_action(action=act)
state = engine.get_state()
action = engine.get_action()
states[i, j, :n_particle] = state
states[i, j, n_particle:, :2] = engine.c_positions
actions[i, j, :n_particle] = action
# reset velocity
if j > 0:
states[i, j, :, 2:] = (states[i, j, :, :2] -
states[i, j - 1, :, :2]) / dt
# attrs: [1, 0] => moving; [0, 1] => fixed
n_obj = attrs.shape[2]
attr = np.zeros((n_obj, attr_dim))
attr[0, 1] = 1 # the first ball is fixed
attr[1:n_particle, 0] = 1 # the rest of the balls is free to move
attr[n_particle:, 1] = 1 # the cylinders are fixed
attr[:n_particle, 2] = engine.radius
attr[n_particle:, 2] = engine.c_radius
# assert np.sum(attr[:, 0]) == 14
assert np.sum(attr[:, 1]) == 3
attrs[i, j] = attr
data = [attr, states[i, j], actions[i, j]]
store_data(data_names, data,
os.path.join(rollout_dir,
str(j) + '.h5'))
engine.step()
datas = [
attrs[i].astype(np.float64), states[i].astype(np.float64),
actions[i].astype(np.float64)
]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
return stats
def gen_Cradle(info):
thread_idx, data_dir, data_names = info['thread_idx'], info[
'data_dir'], info['data_names']
n_particle, n_rollout, time_step = info['n_particle'], info[
'n_rollout'], info['time_step']
dt, args = info['dt'], info['args']
np.random.seed(round(time.time() * 1000 + thread_idx) % 2**32)
attr_dim = args.attr_dim # ball, anchor
state_dim = args.state_dim # x, y, xdot, ydot
assert attr_dim == 2
assert state_dim == 4
lim = 300
attr_dim = 2
state_dim = 4
relation_dim = 4
stats = [init_stat(attr_dim), init_stat(state_dim)]
engine = CradleEngine(dt)
n_objects = n_particle * 2 # add the same number of anchor points
attrs = np.zeros((n_rollout, time_step, n_objects, attr_dim))
states = np.zeros((n_rollout, time_step, n_objects, state_dim))
bar = ProgressBar()
for i in bar(range(n_rollout)):
rollout_idx = thread_idx * n_rollout + i
rollout_dir = os.path.join(data_dir, str(rollout_idx))
os.system('mkdir -p ' + rollout_dir)
theta = rand_float(0, 90)
engine.reset_scene(n_particle, theta)
for j in range(time_step):
states[i, j] = engine.get_state()
if j > 0:
states[i, j, :, 2:] = (states[i, j, :, :2] -
states[i, j - 1, :, :2]) / dt
attrs[i, j, :n_particle, 0] = 1 # balls
attrs[i, j, n_particle:, 1] = 1 # anchors
data = [attrs[i, j], states[i, j]]
store_data(data_names, data,
os.path.join(rollout_dir,
str(j) + '.h5'))
engine.step()
datas = [attrs[i].astype(np.float64), states[i].astype(np.float64)]
for j in range(len(stats)):
stat = init_stat(stats[j].shape[0])
stat[:, 0] = np.mean(datas[j], axis=(0, 1))[:]
stat[:, 1] = np.std(datas[j], axis=(0, 1))[:]
stat[:, 2] = datas[j].shape[0]
stats[j] = combine_stat(stats[j], stat)
return stats