def test_env_reset():
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house.yaml')
env = iGibsonEnv(config_file=config_filename, mode='headless')
class DummyTask(object):
def __init__(self):
self.reset_scene_called = False
self.reset_agent_called = False
self.get_task_obs_called = False
def get_task_obs(self, env):
self.get_task_obs_called = True
def reset_scene(self, env):
self.reset_scene_called = True
def reset_agent(self, env):
self.reset_agent_called = True
env.task = DummyTask()
env.reset()
assert env.task.reset_scene_called
assert env.task.reset_agent_called
assert env.task.get_task_obs_called
def test_import_building_viewing():
download_assets()
download_demo_data()
config = parse_config(os.path.join(gibson2.root_path, '../test/test.yaml'))
s = Simulator(mode='headless')
scene = StaticIndoorScene('Rs')
s.import_scene(scene)
assert s.objects == list(range(2))
turtlebot1 = Turtlebot(config)
turtlebot2 = Turtlebot(config)
turtlebot3 = Turtlebot(config)
s.import_robot(turtlebot1)
s.import_robot(turtlebot2)
s.import_robot(turtlebot3)
turtlebot1.set_position([0.5, 0, 0.5])
turtlebot2.set_position([0, 0, 0.5])
turtlebot3.set_position([-0.5, 0, 0.5])
for i in range(10):
s.step()
#turtlebot1.apply_action(np.random.randint(4))
#turtlebot2.apply_action(np.random.randint(4))
#turtlebot3.apply_action(np.random.randint(4))
s.disconnect()
def test_vision_sensor():
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house.yaml')
env = iGibsonEnv(config_file=config_filename, mode='headless')
vision_modalities = ['rgb', 'depth', 'pc', 'normal', 'seg']
vision_sensor = VisionSensor(env, vision_modalities)
vision_obs = vision_sensor.get_obs(env)
assert vision_obs['rgb'].shape == (env.image_height, env.image_width, 3)
assert np.all(0 <= vision_obs['rgb']) and np.all(vision_obs['rgb'] <= 1.0)
assert vision_obs['depth'].shape == (env.image_height, env.image_width, 1)
assert np.all(0 <= vision_obs['depth']) and np.all(
vision_obs['depth'] <= 1.0)
assert vision_obs['pc'].shape == (env.image_height, env.image_width, 3)
assert vision_obs['normal'].shape == (env.image_height, env.image_width, 3)
normal_norm = np.linalg.norm(vision_obs['normal'] * 2 - 1, axis=2)
assert np.sum(np.abs(normal_norm - 1) > 0.1) / \
(env.image_height * env.image_width) < 0.05
assert vision_obs['seg'].shape == (env.image_height, env.image_width, 1)
assert np.all(0 <= vision_obs['seg']) and np.all(vision_obs['seg'] <= 1.0)
def test_import_building_big():
download_assets()
download_demo_data()
s = Simulator(mode='headless')
scene = StaticIndoorScene('Rs')
s.import_scene(scene, texture_scale=1)
assert s.objects == list(range(2))
s.disconnect()
def test_velodyne():
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house.yaml')
env = iGibsonEnv(config_file=config_filename, mode='headless')
velodyne_sensor = VelodyneSensor(env)
velodyne_obs = velodyne_sensor.get_obs(env)
assert (velodyne_obs.shape[1] == 3)
def test_import_stadium():
download_assets()
download_demo_data()
s = Simulator(mode='headless')
scene = StadiumScene()
s.import_scene(scene)
print(s.objects)
assert s.objects == list(range(4))
s.disconnect()
def test_scan_sensor():
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house.yaml')
env = iGibsonEnv(config_file=config_filename, mode='headless')
scan_sensor = ScanSensor(env, ['scan'])
scan_obs = scan_sensor.get_obs(env)['scan']
assert scan_obs.shape == (scan_sensor.n_horizontal_rays,
scan_sensor.n_vertical_beams)
assert np.all(0 <= scan_obs) and np.all(scan_obs <= 1.0)
def test_simulator():
download_assets()
s = Simulator(mode='headless')
scene = StadiumScene()
s.import_scene(scene)
obj = YCBObject('006_mustard_bottle')
for i in range(10):
s.import_object(obj)
obj = YCBObject('002_master_chef_can')
for i in range(10):
s.import_object(obj)
for i in range(1000):
s.step()
s.disconnect()
def test_render_rendering_cleaning():
download_assets()
test_dir = os.path.join(gibson2.assets_path, 'test')
for i in range(5):
renderer = MeshRenderer(width=800, height=600)
renderer.load_object(
os.path.join(test_dir,
'mesh/bed1a77d92d64f5cbbaaae4feed64ec1_new.obj'))
renderer.add_instance(0)
renderer.set_camera([0, 0, 1.2], [0, 1, 1.2], [0, 1, 0])
renderer.set_fov(90)
rgb = renderer.render(('rgb'))[0]
assert (np.sum(rgb, axis=(0, 1, 2)) > 0)
GPUtil.showUtilization()
renderer.release()
GPUtil.showUtilization()
def test_render_rendering(record_property):
download_assets()
test_dir = os.path.join(gibson2.assets_path, 'test')
renderer = MeshRenderer(width=800, height=600)
start = time.time()
renderer.load_object(
os.path.join(test_dir,
'mesh/bed1a77d92d64f5cbbaaae4feed64ec1_new.obj'))
elapsed = time.time() - start
renderer.add_instance(0)
renderer.set_camera([0, 0, 1.2], [0, 1, 1.2], [0, 1, 0])
renderer.set_fov(90)
rgb = renderer.render(('rgb'))[0]
record_property("object_loading_time", elapsed)
assert (np.sum(rgb, axis=(0, 1, 2)) > 0)
renderer.release()
def test_env():
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house.yaml')
env = iGibsonEnv(config_file=config_filename, mode='headless')
try:
for j in range(2):
env.reset()
for i in range(300): # 300 steps, 30s world time
s = time()
action = env.action_space.sample()
ts = env.step(action)
print('ts', 1 / (time() - s))
if ts[2]:
print("Episode finished after {} timesteps".format(i + 1))
break
finally:
env.close()
def test_occupancy_grid():
print("Test env")
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house_occupancy_grid.yaml')
nav_env = iGibsonEnv(config_file=config_filename, mode='headless')
nav_env.reset()
nav_env.robots[0].set_position_orientation([0, 0, 0], [0, 0, 0, 1])
nav_env.simulator.step()
action = nav_env.action_space.sample()
ts = nav_env.step(action)
assert np.sum(ts[0]['occupancy_grid'] == 0) > 0
assert np.sum(ts[0]['occupancy_grid'] == 1) > 0
plt.imshow(ts[0]['occupancy_grid'][:, :, 0])
plt.colorbar()
plt.savefig('occupancy_grid.png')
nav_env.clean()
def test_base_planning():
print("Test env")
download_assets()
download_demo_data()
config_filename = os.path.join(gibson2.root_path,
'../test/test_house_occupancy_grid.yaml')
nav_env = iGibsonEnv(config_file=config_filename, mode='headless')
motion_planner = MotionPlanningWrapper(nav_env)
state = nav_env.reset()
nav_env.robots[0].set_position_orientation([0, 0, 0], [0, 0, 0, 1])
nav_env.simulator.step()
plan = None
itr = 0
while plan is None and itr < 10:
plan = motion_planner.plan_base_motion([0.5, 0, 0])
print(plan)
itr += 1
motion_planner.dry_run_base_plan(plan)
assert len(plan) > 0
nav_env.clean()
def __init__(self):
download_assets()
download_demo_data()
from gibson2.core.simulator import Simulator
from gibson2.core.physics.scene import BuildingScene, StadiumScene
from gibson2.core.physics.robot_locomotors import Turtlebot, Husky, Ant, Humanoid, JR2, JR2_Kinova
import yaml
from gibson2.utils.utils import parse_config
import os
import gibson2
from gibson2.utils.assets_utils import download_assets, download_demo_data
download_assets()
download_demo_data()
config = parse_config(os.path.join(gibson2.root_path, '../test/test.yaml'))
def test_import_building():
s = Simulator(mode='headless')
scene = BuildingScene('Rs')
s.import_scene(scene, texture_scale=0.4)
for i in range(15):
s.step()
assert s.objects == list(range(2))
s.disconnect()
def test_import_building_big():
s = Simulator(mode='headless')
scene = BuildingScene('Rs')
s.import_scene(scene, texture_scale=1)
assert s.objects == list(range(2))
s.disconnect()
