def test_reconfigure_render(
scene,
sensor_type,
make_cfg_settings,
):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
for sens in all_sensor_types:
make_cfg_settings[sens] = False
make_cfg_settings["scene"] = _test_scenes[-1]
make_cfg_settings[sensor_type] = True
cfg = make_cfg(make_cfg_settings)
with habitat_sim.Simulator(cfg) as sim:
make_cfg_settings["scene"] = scene
sim.reconfigure(make_cfg(make_cfg_settings))
obs, gt = _render_and_load_gt(sim, scene, sensor_type, False)
# Different GPUs and different driver version will produce slightly
# different images; differences on aliased edges might also stem from how a
# particular importer parses transforms
assert np.linalg.norm(
obs[sensor_type].astype(np.float) -
gt.astype(np.float)) < 9.0e-2 * np.linalg.norm(gt.astype(
np.float)), f"Incorrect {sensor_type} output"
sim.close()
def test_smoke_redwood_noise(scene, gpu2gpu, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
make_cfg_settings["depth_sensor"] = True
make_cfg_settings["color_sensor"] = False
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
hsim_cfg = make_cfg(make_cfg_settings)
hsim_cfg.agents[0].sensor_specifications[
0].noise_model = "RedwoodDepthNoiseModel"
for sensor_spec in hsim_cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
with habitat_sim.Simulator(hsim_cfg) as sim:
obs, gt = _render_and_load_gt(sim, scene, "depth_sensor", gpu2gpu)
assert np.linalg.norm(
obs["depth_sensor"].astype(np.float) -
gt.astype(np.float)) > 1.5e-2 * np.linalg.norm(gt.astype(
np.float)), "Incorrect depth_sensor output"
sim.close()
def test_sensors(scene, has_sem, sensor_type, gpu2gpu, sim, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = has_sem
make_cfg_settings["scene"] = scene
cfg = make_cfg(make_cfg_settings)
for sensor_spec in cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
sim.reconfigure(cfg)
obs, gt = _render_and_load_gt(sim, scene, sensor_type, gpu2gpu)
# Different GPUs and different driver version will produce slightly
# different images; differences on aliased edges might also stem from how a
# particular importer parses transforms
assert np.linalg.norm(obs[sensor_type].astype(np.float) -
gt.astype(np.float)) < 5.0e-2 * np.linalg.norm(
gt.astype(
np.float)), f"Incorrect {sensor_type} output"
def test_sensors(
scene,
sensor_type,
gpu2gpu,
frustum_culling,
make_cfg_settings,
):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
for sens in all_sensor_types:
make_cfg_settings[sens] = False
make_cfg_settings[sensor_type] = True
make_cfg_settings["scene"] = scene
make_cfg_settings["frustum_culling"] = frustum_culling
cfg = make_cfg(make_cfg_settings)
for sensor_spec in cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
with habitat_sim.Simulator(cfg) as sim:
obs, gt = _render_and_load_gt(sim, scene, sensor_type, gpu2gpu)
# Different GPUs and different driver version will produce slightly
# different images; differences on aliased edges might also stem from how a
# particular importer parses transforms
assert np.linalg.norm(
obs[sensor_type].astype(np.float) - gt.astype(np.float)
) < 9.0e-2 * np.linalg.norm(
gt.astype(np.float)
), f"Incorrect {sensor_type} output"
def test_semantic_scene(scene, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
cfg = make_cfg(make_cfg_settings)
cfg.agents[0].sensor_specifications = []
sim = habitat_sim.Simulator(cfg)
scene = sim.semantic_scene
for obj in scene.objects:
obj.aabb
obj.aabb.sizes
obj.aabb.center
obj.id
obj.obb.rotation
obj.category.name()
obj.category.index()
for region in scene.regions:
region.id
region.category.name()
region.category.index()
for level in scene.levels:
level.id
def test_initial_hfov(scene, sensor_type, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings["hfov"] = 70
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
assert sim.agents[0]._sensors[sensor_type].hfov == mn.Deg(
70), "HFOV was not properly set"
def test_semantic_scene(scene, sim, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
sim.reconfigure(make_cfg(make_cfg_settings))
scene = sim.semantic_scene
for obj in scene.objects:
obj.aabb
obj.aabb.sizes
obj.aabb.center
obj.id
obj.obb.rotation
obj.category.name()
obj.category.index()
for region in scene.regions:
region.id
region.category.name()
region.category.index()
for level in scene.levels:
level.id
def test_sensors(scene, has_sem, sensor_type, gpu2gpu, sim, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = has_sem
make_cfg_settings["scene"] = scene
cfg = make_cfg(make_cfg_settings)
for sensor_spec in cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
sim.reconfigure(cfg)
with open(
osp.abspath(
osp.join(
osp.dirname(__file__),
"gt_data",
"{}-state.json".format(osp.basename(
osp.splitext(scene)[0])),
)),
"r",
) as f:
render_state = json.load(f)
state = habitat_sim.AgentState()
state.position = render_state["pos"]
state.rotation = habitat_sim.utils.quat_from_coeffs(
render_state["rot"])
sim.initialize_agent(0, state)
obs = sim.step("move_forward")
assert sensor_type in obs, f"{sensor_type} not in obs"
gt = np.load(
osp.abspath(
osp.join(
osp.dirname(__file__),
"gt_data",
"{}-{}.npy".format(osp.basename(osp.splitext(scene)[0]),
sensor_type),
)))
if gpu2gpu:
import torch
for k, v in obs.items():
if torch.is_tensor(v):
obs[k] = v.cpu().numpy()
# Different GPUs and different driver version will produce slightly different images
assert np.linalg.norm(obs[sensor_type].astype(np.float) -
gt.astype(np.float)) < 1.5e-2 * np.linalg.norm(
gt.astype(
np.float)), f"Incorrect {sensor_type} output"
def test_sensors(
scene,
sensor_type,
gpu2gpu,
frustum_culling,
add_sensor_lazy,
make_cfg_settings,
):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
# We only support adding more RGB Sensors if one is already in a scene
# We can add depth sensors whenever
add_sensor_lazy = add_sensor_lazy and all_base_sensor_types[1] == sensor_type
for sens in all_base_sensor_types:
if add_sensor_lazy:
make_cfg_settings[sens] = (
sens in all_base_sensor_types[:2] and sens != sensor_type
)
else:
make_cfg_settings[sens] = False
make_cfg_settings[sensor_type] = True
make_cfg_settings["scene"] = scene
make_cfg_settings["frustum_culling"] = frustum_culling
cfg = make_cfg(make_cfg_settings)
if add_sensor_lazy:
additional_sensors = cfg.agents[0].sensor_specifications[1:]
cfg.agents[0].sensor_specifications = cfg.agents[0].sensor_specifications[:1]
for sensor_spec in cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
with habitat_sim.Simulator(cfg) as sim:
if add_sensor_lazy:
obs: np.ndarray = sim.reset()
assert len(obs) == 1, "Other sensors were not removed"
for sensor_spec in additional_sensors:
sim.add_sensor(sensor_spec)
if sensor_type in all_exotic_sensor_types:
obs = _render_scene(sim, scene, sensor_type, gpu2gpu)
# Smoke Test.
return
obs, gt = _render_and_load_gt(sim, scene, sensor_type, gpu2gpu)
# Different GPUs and different driver version will produce slightly
# different images; differences on aliased edges might also stem from how a
# particular importer parses transforms
assert np.linalg.norm(
obs[sensor_type].astype(np.float) - gt.astype(np.float)
) < 9.0e-2 * np.linalg.norm(
gt.astype(np.float)
), f"Incorrect {sensor_type} output"
def test_set_custom_light_setup(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
custom_setup_key = "custom_setup_key"
light_setup = sim.get_light_setup(custom_setup_key)
assert len(light_setup) == 0
light_setup.append(LightInfo(position=[1.0, 1.0, 1.0]))
assert sim.get_light_setup() != light_setup
sim.set_light_setup(light_setup, custom_setup_key)
assert sim.get_light_setup(custom_setup_key) == light_setup
def test_smoke_no_sensors(make_cfg_settings):
sims = []
for scene in _test_scenes:
if not osp.exists(scene):
continue
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
cfg = make_cfg(make_cfg_settings)
cfg.agents[0].sensor_specifications = []
sims.append(habitat_sim.Simulator(cfg))
def test_pose_extractors(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
scene_filepath = ""
pose_extractor_names = [
"closest_point_extractor", "panorama_extractor"
]
for name in pose_extractor_names:
extractor = ImageExtractor(scene_filepath,
img_size=(32, 32),
sim=sim,
pose_extractor_name=name)
assert len(extractor) > 1
def test_set_custom_light_setup(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
custom_setup_key = "custom_setup_key"
light_setup = sim.get_light_setup(custom_setup_key)
assert len(light_setup) == 0
# define a point light (w == 1)
light_setup.append(LightInfo(vector=[1.0, 1.0, 1.0, 1.0]))
assert sim.get_light_setup() != light_setup
sim.set_light_setup(light_setup, custom_setup_key)
assert sim.get_light_setup(custom_setup_key) == light_setup
def test_set_default_light_setup(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
light_setup = [LightInfo(position=[1.0, 1.0, 1.0])]
sim.set_light_setup(light_setup)
assert sim.get_light_setup() == light_setup
# ensure modifications to local light setup variable are not reflected in sim
light_setup[0].model = LightPositionModel.CAMERA
assert sim.get_light_setup() != light_setup
sim.set_light_setup(light_setup, DEFAULT_LIGHTING_KEY)
assert sim.get_light_setup() == light_setup
def test_rgba_noise(scene, model_name, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings["depth_sensor"] = False
make_cfg_settings["color_sensor"] = True
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
hsim_cfg = make_cfg(make_cfg_settings)
hsim_cfg.agents[0].sensor_specifications[0].noise_model = model_name
with habitat_sim.Simulator(hsim_cfg) as sim:
obs, gt = _render_and_load_gt(sim, scene, "color_sensor", False)
assert np.linalg.norm(
obs["color_sensor"].astype(float) - gt.astype(float)
) > 1.5e-2 * np.linalg.norm(gt.astype(float)), "Incorrect color_sensor output"
def test_data_extractor_end_to_end(make_cfg_settings):
# Path is relative to simulator.py
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
scene_filepath = ""
extractor = ImageExtractor(scene_filepath,
labels=[0.0],
img_size=(32, 32),
sim=sim)
dataset = MyDataset(extractor)
dataloader = DataLoader(dataset, batch_size=3)
net = TrivialNet()
# Run data through network
for sample_batch in dataloader:
img, _ = sample_batch["rgba"], sample_batch["label"]
img = img.permute(0, 3, 2, 1).float()
net(img)
def test_sensors(scene, has_sem, sensor_type, sim, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["semantic_sensor"] = has_sem
make_cfg_settings["scene"] = scene
sim.reconfigure(make_cfg(make_cfg_settings))
with open(
osp.abspath(
osp.join(
osp.dirname(__file__),
"gt_data",
"{}-state.json".format(osp.basename(
osp.splitext(scene)[0])),
)),
"r",
) as f:
render_state = json.load(f)
state = habitat_sim.AgentState()
state.position = render_state["pos"]
state.rotation = habitat_sim.utils.quat_from_coeffs(
render_state["rot"])
sim.initialize_agent(0, state)
obs = sim.step("move_forward")
assert sensor_type in obs, f"{sensor_type} not in obs"
gt = np.load(
osp.abspath(
osp.join(
osp.dirname(__file__),
"gt_data",
"{}-{}.npy".format(osp.basename(osp.splitext(scene)[0]),
sensor_type),
)))
# Different GPUs and different driver version will produce slightly different images
assert np.linalg.norm(obs[sensor_type].astype(np.float) -
gt.astype(np.float)) < 1.5e-2 * np.linalg.norm(
gt.astype(
np.float)), f"Incorrect {sensor_type} output"
def test_rgb_noise(scene, model_name, sim, make_cfg_settings):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings["depth_sensor"] = False
make_cfg_settings["color_sensor"] = True
make_cfg_settings["semantic_sensor"] = False
make_cfg_settings["scene"] = scene
hsim_cfg = make_cfg(make_cfg_settings)
hsim_cfg.agents[0].sensor_specifications[0].noise_model = model_name
sim.reconfigure(hsim_cfg)
obs, gt = _render_and_load_gt(sim, scene, "color_sensor", False)
assert np.linalg.norm(
obs["color_sensor"].astype(np.float) - gt.astype(np.float)
) > 1.5e-2 * np.linalg.norm(gt.astype(np.float)), "Incorrect color_sensor output"
def test_sensors(
scene,
has_sem,
sensor_type,
gpu2gpu,
frustum_culling,
sim: habitat_sim.Simulator,
make_cfg_settings,
):
if not osp.exists(scene):
pytest.skip("Skipping {}".format(scene))
if not habitat_sim.cuda_enabled and gpu2gpu:
pytest.skip("Skipping GPU->GPU test")
make_cfg_settings = {k: v for k, v in make_cfg_settings.items()}
make_cfg_settings[
"semantic_sensor"] = has_sem and sensor_type == "semantic_sensor"
make_cfg_settings["scene"] = scene
make_cfg_settings["frustum_culling"] = frustum_culling
cfg = make_cfg(make_cfg_settings)
for sensor_spec in cfg.agents[0].sensor_specifications:
sensor_spec.gpu2gpu_transfer = gpu2gpu
# The scene loading may be done differently with/without frustum culling,
# thus we need to force a reload when frustum culling gets swapped
if cfg.sim_cfg.frustum_culling != sim.config.sim_cfg.frustum_culling:
sim.close()
sim.reconfigure(cfg)
obs, gt = _render_and_load_gt(sim, scene, sensor_type, gpu2gpu)
# Different GPUs and different driver version will produce slightly
# different images; differences on aliased edges might also stem from how a
# particular importer parses transforms
assert np.linalg.norm(obs[sensor_type].astype(np.float) -
gt.astype(np.float)) < 9.0e-2 * np.linalg.norm(
gt.astype(
np.float)), f"Incorrect {sensor_type} output"
def sim(make_cfg_settings):
return habitat_sim.Simulator(make_cfg(make_cfg_settings))
def test_topdown_view(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
tdv = TopdownView(sim, height=0.0, meters_per_pixel=0.1)
topdown_view = tdv.topdown_view
assert type(topdown_view) == np.ndarray
def test_get_no_light_setup(make_cfg_settings):
with habitat_sim.Simulator(make_cfg(make_cfg_settings)) as sim:
assert len(sim.get_light_setup(NO_LIGHT_KEY)) == 0
