def main():
config = parse_config('../configs/turtlebot_demo.yaml')
settings = MeshRendererSettings(enable_shadow=False, msaa=False)
s = Simulator(mode='gui',
image_width=256,
image_height=256,
rendering_settings=settings)
scene = StaticIndoorScene('Rs',
build_graph=True,
pybullet_load_texture=True)
s.import_scene(scene)
turtlebot = Turtlebot(config)
s.import_robot(turtlebot)
for _ in range(10):
obj = YCBObject('003_cracker_box')
s.import_object(obj)
obj.set_position_orientation(np.random.uniform(low=0, high=2, size=3),
[0, 0, 0, 1])
print(s.renderer.instances)
for i in range(10000):
with Profiler('Simulator step'):
turtlebot.apply_action([0.1, 0.1])
s.step()
rgb = s.renderer.render_robot_cameras(modes=('rgb'))
s.disconnect()
def run_demo(self):
config = parse_config(
os.path.join(os.path.dirname(gibson2.__file__),
'../examples/configs/turtlebot_demo.yaml'))
s = Simulator(mode='gui', image_width=700, image_height=700)
scene = StaticIndoorScene('Rs', pybullet_load_texture=True)
s.import_scene(scene)
turtlebot = Turtlebot(config)
s.import_robot(turtlebot)
for i in range(1000):
turtlebot.apply_action([0.1, 0.5])
s.step()
s.disconnect()
class ToyEnv(object):
def __init__(self):
config = parse_config('../../configs/turtlebot_demo.yaml')
hdr_texture = os.path.join(gibson2.ig_dataset_path, 'scenes',
'background', 'probe_02.hdr')
hdr_texture2 = os.path.join(gibson2.ig_dataset_path, 'scenes',
'background', 'probe_03.hdr')
light_modulation_map_filename = os.path.join(gibson2.ig_dataset_path,
'scenes', 'Rs_int',
'layout',
'floor_lighttype_0.png')
background_texture = os.path.join(gibson2.ig_dataset_path, 'scenes',
'background', 'urban_street_01.jpg')
settings = MeshRendererSettings(enable_shadow=False, enable_pbr=False)
self.s = Simulator(mode='headless',
image_width=400,
image_height=400,
rendering_settings=settings)
scene = StaticIndoorScene('Rs')
self.s.import_scene(scene)
#self.s.import_ig_scene(scene)
self.robot = Turtlebot(config)
self.s.import_robot(self.robot)
for _ in range(5):
obj = YCBObject('003_cracker_box')
self.s.import_object(obj)
obj.set_position_orientation(
np.random.uniform(low=0, high=2, size=3), [0, 0, 0, 1])
print(self.s.renderer.instances)
def step(self, a):
self.robot.apply_action(a)
self.s.step()
frame = self.s.renderer.render_robot_cameras(modes=('rgb'))[0]
return frame
def close(self):
self.s.disconnect()
def main():
config = parse_config('../configs/turtlebot_demo.yaml')
settings = MeshRendererSettings()
s = Simulator(mode='gui',
image_width=256,
image_height=256,
rendering_settings=settings)
scene = StaticIndoorScene('Rs',
build_graph=True,
pybullet_load_texture=True)
s.import_scene(scene)
turtlebot = Turtlebot(config)
s.import_robot(turtlebot)
for i in range(10000):
with Profiler('Simulator step'):
turtlebot.apply_action([0.1, -0.1])
s.step()
lidar = s.renderer.get_lidar_all()
print(lidar.shape)
# TODO: visualize lidar scan
s.disconnect()
def benchmark_scene(scene_name, optimized=False, import_robot=True):
config = parse_config(os.path.join(gibson2.root_path, '../test/test.yaml'))
assets_version = get_ig_assets_version()
print('assets_version', assets_version)
scene = InteractiveIndoorScene(scene_name,
texture_randomization=False,
object_randomization=False)
settings = MeshRendererSettings(msaa=False,
enable_shadow=False,
optimized=optimized)
s = Simulator(
mode='headless',
image_width=512,
image_height=512,
device_idx=0,
rendering_settings=settings,
)
s.import_ig_scene(scene)
if import_robot:
turtlebot = Turtlebot(config)
s.import_robot(turtlebot)
s.renderer.use_pbr(use_pbr=True, use_pbr_mapping=True)
fps = []
physics_fps = []
render_fps = []
obj_awake = []
for i in range(2000):
# if i % 100 == 0:
# scene.randomize_texture()
start = time.time()
s.step()
if import_robot:
# apply random actions
turtlebot.apply_action(turtlebot.action_space.sample())
physics_end = time.time()
if import_robot:
_ = s.renderer.render_robot_cameras(modes=('rgb'))
else:
_ = s.renderer.render(modes=('rgb'))
end = time.time()
#print("Elapsed time: ", end - start)
print("Render Frequency: ", 1 / (end - physics_end))
print("Physics Frequency: ", 1 / (physics_end - start))
print("Step Frequency: ", 1 / (end - start))
fps.append(1 / (end - start))
physics_fps.append(1 / (physics_end - start))
render_fps.append(1 / (end - physics_end))
obj_awake.append(s.body_links_awake)
s.disconnect()
plt.figure(figsize=(7, 25))
ax = plt.subplot(6, 1, 1)
plt.hist(render_fps)
ax.set_xlabel('Render fps')
ax.set_title(
'Scene {} version {}\noptimized {} num_obj {}\n import_robot {}'.
format(scene_name, assets_version, optimized, scene.get_num_objects(),
import_robot))
ax = plt.subplot(6, 1, 2)
plt.hist(physics_fps)
ax.set_xlabel('Physics fps')
ax = plt.subplot(6, 1, 3)
plt.hist(fps)
ax.set_xlabel('Step fps')
ax = plt.subplot(6, 1, 4)
plt.plot(render_fps)
ax.set_xlabel('Render fps with time')
ax.set_ylabel('fps')
ax = plt.subplot(6, 1, 5)
plt.plot(physics_fps)
ax.set_xlabel('Physics fps with time, converge to {}'.format(
np.mean(physics_fps[-100:])))
ax.set_ylabel('fps')
ax = plt.subplot(6, 1, 6)
plt.plot(obj_awake)
ax.set_xlabel('Num object links awake, converge to {}'.format(
np.mean(obj_awake[-100:])))
plt.savefig('scene_benchmark_{}_o_{}_r_{}.pdf'.format(
scene_name, optimized, import_robot))
def benchmark(render_to_tensor=False, resolution=512):
config = parse_config(os.path.join(gibson2.root_path, '../test/test.yaml'))
s = Simulator(mode='headless',
image_width=resolution,
image_height=resolution,
render_to_tensor=render_to_tensor)
scene = StaticIndoorScene('Rs',
build_graph=True,
pybullet_load_texture=True)
s.import_scene(scene)
turtlebot = Turtlebot(config)
s.import_robot(turtlebot)
n_frame = 500
start = time.time()
for i in range(n_frame):
turtlebot.apply_action([0.1, 0.1])
s.step()
rgb = s.renderer.render_robot_cameras(modes=('rgb'))
physics_render_elapsed = time.time() - start
physics_render_fps = n_frame / physics_render_elapsed
print(
"physics simulation + rendering rgb, resolution {}, render_to_tensor {}: {} fps"
.format(resolution, render_to_tensor, physics_render_fps))
start = time.time()
for i in range(n_frame):
rgb = s.renderer.render_robot_cameras(modes=('rgb'))
render_elapsed = time.time() - start
rgb_fps = n_frame / render_elapsed
print("Rendering rgb, resolution {}, render_to_tensor {}: {} fps".format(
resolution, render_to_tensor, n_frame / render_elapsed))
start = time.time()
for i in range(n_frame):
rgb = s.renderer.render_robot_cameras(modes=('3d'))
render_elapsed = time.time() - start
pc_fps = n_frame / render_elapsed
print("Rendering 3d, resolution {}, render_to_tensor {}: {} fps".format(
resolution, render_to_tensor, n_frame / render_elapsed))
start = time.time()
for i in range(n_frame):
rgb = s.renderer.render_robot_cameras(modes=('normal'))
normal_fps = n_frame / render_elapsed
render_elapsed = time.time() - start
print(
"Rendering normal, resolution {}, render_to_tensor {}: {} fps".format(
resolution, render_to_tensor, n_frame / render_elapsed))
plt.figure()
plt.bar([0, 1, 2, 3], [physics_render_fps, rgb_fps, pc_fps, normal_fps],
color='g')
plt.xticks([0, 1, 2, 3], ['sim+render', 'rgb', '3d', 'normal'])
plt.ylabel('fps')
plt.xlabel('mode')
plt.title('Static Scene Benchmark, resolution {}, to_tensor {}'.format(
resolution, render_to_tensor))
plt.savefig('static_scene_benchmark_res{}_tensor{}.pdf'.format(
resolution, render_to_tensor))
s.disconnect()
