def construct_envs(config: Config, env_class: Type[Union[Env,
RLEnv]]) -> VectorEnv:
r"""Create VectorEnv object with specified config and env class type.
To allow better performance, dataset are split into small ones for
each individual env, grouped by scenes.
Args:
config: configs that contain num_processes as well as information
necessary to create individual environments.
env_class: class type of the envs to be created.
Returns:
VectorEnv object created according to specification.
"""
num_processes = config.NUM_PROCESSES
configs = []
env_classes = [env_class for _ in range(num_processes)]
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE,
**{'filter_fn': filter_fn})
scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES
if "*" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
if num_processes > 1:
if len(scenes) == 0:
raise RuntimeError(
"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes"
)
if len(scenes) < num_processes:
raise RuntimeError("reduce the number of processes as there "
"aren't enough number of scenes")
random.shuffle(scenes)
scene_splits = [[] for _ in range(num_processes)]
for idx, scene in enumerate(scenes):
scene_splits[idx % len(scene_splits)].append(scene)
print('Total Process : %d ' % num_processes)
for i, s in enumerate(scene_splits):
print('proc %d :' % i, s)
assert sum(map(len, scene_splits)) == len(scenes)
for i in range(num_processes):
proc_config = config.clone()
proc_config.defrost()
task_config = proc_config.TASK_CONFIG
if len(scenes) > 0:
task_config.DATASET.CONTENT_SCENES = scene_splits[i]
task_config.TASK.CUSTOM_OBJECT_GOAL_SENSOR = habitat.Config()
task_config.TASK.CUSTOM_OBJECT_GOAL_SENSOR.TYPE = 'CustomObjectSensor'
task_config.TASK.CUSTOM_OBJECT_GOAL_SENSOR.GOAL_SPEC = "OBJECT_IMG"
task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (
config.SIMULATOR_GPU_ID)
task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS
proc_config.freeze()
configs.append(proc_config)
envs = habitat.VectorEnv(make_env_fn=make_env_fn,
env_fn_args=tuple(
tuple(
zip(configs, env_classes,
range(num_processes),
[{
'filter_fn': filter_fn
}] * num_processes))))
return envs
def construct_envs(
config: Config,
env_class: Type[Union[Env, RLEnv]],
workers_ignore_signals: bool = False,
) -> VectorEnv:
r"""Create VectorEnv object with specified config and env class type.
To allow better performance, dataset are split into small ones for
each individual env, grouped by scenes.
:param config: configs that contain num_processes as well as information
:param necessary to create individual environments.
:param env_class: class type of the envs to be created.
:param workers_ignore_signals: Passed to :ref:`habitat.VectorEnv`'s constructor
:return: VectorEnv object created according to specification.
"""
num_processes = config.NUM_PROCESSES
configs = []
env_classes = [env_class for _ in range(num_processes)]
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES
if "*" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
if num_processes > 1:
if len(scenes) == 0:
raise RuntimeError(
"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes"
)
if len(scenes) < num_processes:
raise RuntimeError("reduce the number of processes as there "
"aren't enough number of scenes")
random.shuffle(scenes)
scene_splits: List[List[str]] = [[] for _ in range(num_processes)]
for idx, scene in enumerate(scenes):
scene_splits[idx % len(scene_splits)].append(scene)
assert sum(map(len, scene_splits)) == len(scenes)
for i in range(num_processes):
proc_config = config.clone()
proc_config.defrost()
task_config = proc_config.TASK_CONFIG
task_config.SEED = task_config.SEED + i
if len(scenes) > 0:
task_config.DATASET.CONTENT_SCENES = scene_splits[i]
task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (
config.SIMULATOR_GPU_ID)
task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS
proc_config.freeze()
configs.append(proc_config)
envs = habitat.VectorEnv(
make_env_fn=make_env_fn,
env_fn_args=tuple(zip(configs, env_classes)),
workers_ignore_signals=workers_ignore_signals,
)
return envs
def construct_envs(config: Config, env_class: Type[Union[Env,
RLEnv]]) -> VectorEnv:
r"""Create VectorEnv object with specified config and env class type.
To allow better performance, dataset are split into small ones for
each individual env, grouped by scenes.
Args:
config: configs that contain num_processes as well as information
necessary to create individual environments.
env_class: class type of the envs to be created.
Returns:
VectorEnv object created according to specification.
"""
num_processes = config.NUM_PROCESSES
configs = []
env_classes = [env_class for _ in range(num_processes)]
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
if len(scenes) > 0:
random.shuffle(scenes)
if config.MULTIPLY_SCENES:
if len(scenes) < num_processes:
unique_scenes = scenes
scenes = []
while len(scenes) < num_processes:
scenes += unique_scenes
else:
assert len(scenes) >= num_processes, (
"reduce the number of processes as there "
"aren't enough number of scenes")
scene_splits = [[] for _ in range(num_processes)]
for idx, scene in enumerate(scenes):
scene_splits[idx % len(scene_splits)].append(scene)
assert sum(map(len, scene_splits)) == len(scenes)
for i in range(num_processes):
task_config = config.TASK_CONFIG.clone()
task_config.defrost()
if len(scenes) > 0:
task_config.DATASET.CONTENT_SCENES = scene_splits[i]
task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (
config.SIMULATOR_GPU_ID)
task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS
task_config.freeze()
config.defrost()
config.TASK_CONFIG = task_config
config.freeze()
configs.append(config.clone())
envs = habitat.VectorEnv(
make_env_fn=make_env_fn,
env_fn_args=tuple(
tuple(zip(configs, env_classes, range(num_processes)))),
)
return envs
def construct_envs(config: Config, env_class: Type) -> VectorEnv:
r"""
Create VectorEnv object with specified config and env class type.
To allow better performance, dataset are split into small ones for
each individual env, grouped by scenes.
Args:
config: configs that contain num_processes as well as information
necessary to create individual environments.
env_class: class type of the envs to be created.
Returns:
VectorEnv object created according to specification.
"""
trainer_config = config.TRAINER.RL.PPO
rl_env_config = config.TRAINER.RL
task_config = config.TASK_CONFIG # excluding trainer-specific configs
env_configs, rl_env_configs = [], []
env_classes = [env_class for _ in range(trainer_config.num_processes)]
dataset = make_dataset(task_config.DATASET.TYPE)
scenes = dataset.get_scenes_to_load(task_config.DATASET)
if len(scenes) > 0:
random.shuffle(scenes)
assert len(scenes) >= trainer_config.num_processes, (
"reduce the number of processes as there "
"aren't enough number of scenes")
scene_splits = [[] for _ in range(trainer_config.num_processes)]
for idx, scene in enumerate(scenes):
scene_splits[idx % len(scene_splits)].append(scene)
assert sum(map(len, scene_splits)) == len(scenes)
for i in range(trainer_config.num_processes):
env_config = task_config.clone()
env_config.defrost()
if len(scenes) > 0:
env_config.DATASET.CONTENT_SCENES = scene_splits[i]
env_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (
trainer_config.sim_gpu_id)
agent_sensors = trainer_config.sensors.strip().split(",")
env_config.SIMULATOR.AGENT_0.SENSORS = agent_sensors
env_config.freeze()
env_configs.append(env_config)
rl_env_configs.append(rl_env_config)
envs = habitat.VectorEnv(
make_env_fn=make_env_fn,
env_fn_args=tuple(
tuple(
zip(
env_configs,
rl_env_configs,
env_classes,
range(trainer_config.num_processes),
))),
)
return envs