diff --git a/examples/benchmark.py b/examples/benchmark.py
index 7a0da5bcdbaba827ca3ea00d39ec1b0a1907060c..5edfd6afcca42ddcad143994a840bfd6014ccf4c 100644
--- a/examples/benchmark.py
+++ b/examples/benchmark.py
@@ -7,7 +7,7 @@
import argparse
import habitat
-from habitat.sims.habitat_simulator import SimulatorActions
+from habitat import SimulatorActions
class ForwardOnlyAgent(habitat.Agent):
@@ -15,7 +15,7 @@ class ForwardOnlyAgent(habitat.Agent):
pass
def act(self, observations):
- action = SimulatorActions.MOVE_FORWARD.value
+ action = SimulatorActions.MOVE_FORWARD
return action
diff --git a/examples/new_actions.py b/examples/new_actions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f03b1656b3ce5d9531d4f857b82ba8e901e37e9d
--- /dev/null
+++ b/examples/new_actions.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+r"""
+This is an example of how to add new actions to habitat-api
+
+
+We will use the strafe action outline in the habitat_sim example
+"""
+
+import attr
+import numpy as np
+
+import habitat
+import habitat_sim
+import habitat_sim.utils
+from habitat.sims.habitat_simulator.action_spaces import (
+ HabitatSimV1ActionSpaceConfiguration,
+)
+from habitat_sim.agent.controls import register_move_fn
+
+
+@attr.s(auto_attribs=True, slots=True)
+class NoisyStrafeActuationSpec:
+ move_amount: float
+ # Classic strafing is to move perpendicular (90 deg) to the forward direction
+ strafe_angle: float = 90.0
+ noise_amount: float = 0.05
+
+
+def _strafe_impl(
+ scene_node: habitat_sim.SceneNode,
+ move_amount: float,
+ strafe_angle: float,
+ noise_amount: float,
+):
+ forward_ax = (
+ scene_node.absolute_transformation()[0:3, 0:3] @ habitat_sim.geo.FRONT
+ )
+ strafe_angle = np.deg2rad(strafe_angle)
+ strafe_angle = np.random.uniform(
+ (1 - noise_amount) * strafe_angle, (1 + noise_amount) * strafe_angle
+ )
+
+ rotation = habitat_sim.utils.quat_from_angle_axis(
+ np.deg2rad(strafe_angle), habitat_sim.geo.UP
+ )
+ move_ax = habitat_sim.utils.quat_rotate_vector(rotation, forward_ax)
+
+ move_amount = np.random.uniform(
+ (1 - noise_amount) * move_amount, (1 + noise_amount) * move_amount
+ )
+ scene_node.translate_local(move_ax * move_amount)
+
+
+@register_move_fn(body_action=True)
+class NoisyStrafeLeft(habitat_sim.SceneNodeControl):
+ def __call__(
+ self,
+ scene_node: habitat_sim.SceneNode,
+ actuation_spec: NoisyStrafeActuationSpec,
+ ):
+ print(f"strafing left with noise_amount={actuation_spec.noise_amount}")
+ _strafe_impl(
+ scene_node,
+ actuation_spec.move_amount,
+ actuation_spec.strafe_angle,
+ actuation_spec.noise_amount,
+ )
+
+
+@register_move_fn(body_action=True)
+class NoisyStrafeRight(habitat_sim.SceneNodeControl):
+ def __call__(
+ self,
+ scene_node: habitat_sim.SceneNode,
+ actuation_spec: NoisyStrafeActuationSpec,
+ ):
+ print(
+ f"strafing right with noise_amount={actuation_spec.noise_amount}"
+ )
+ _strafe_impl(
+ scene_node,
+ actuation_spec.move_amount,
+ -actuation_spec.strafe_angle,
+ actuation_spec.noise_amount,
+ )
+
+
+@habitat.registry.register_action_space_configuration
+class NoNoiseStrafe(HabitatSimV1ActionSpaceConfiguration):
+ def get(self):
+ config = super().get()
+
+ config[habitat.SimulatorActions.STRAFE_LEFT] = habitat_sim.ActionSpec(
+ "noisy_strafe_left",
+ NoisyStrafeActuationSpec(0.25, noise_amount=0.0),
+ )
+ config[habitat.SimulatorActions.STRAFE_RIGHT] = habitat_sim.ActionSpec(
+ "noisy_strafe_right",
+ NoisyStrafeActuationSpec(0.25, noise_amount=0.0),
+ )
+
+ return config
+
+
+@habitat.registry.register_action_space_configuration
+class NoiseStrafe(HabitatSimV1ActionSpaceConfiguration):
+ def get(self):
+ config = super().get()
+
+ config[habitat.SimulatorActions.STRAFE_LEFT] = habitat_sim.ActionSpec(
+ "noisy_strafe_left",
+ NoisyStrafeActuationSpec(0.25, noise_amount=0.05),
+ )
+ config[habitat.SimulatorActions.STRAFE_RIGHT] = habitat_sim.ActionSpec(
+ "noisy_strafe_right",
+ NoisyStrafeActuationSpec(0.25, noise_amount=0.05),
+ )
+
+ return config
+
+
+def main():
+ habitat.SimulatorActions.extend_action_space("STRAFE_LEFT")
+ habitat.SimulatorActions.extend_action_space("STRAFE_RIGHT")
+
+ config = habitat.get_config(config_paths="configs/tasks/pointnav.yaml")
+ config.defrost()
+ config.SIMULATOR.ACTION_SPACE_CONFIG = "NoNoiseStrafe"
+ config.freeze()
+
+ env = habitat.Env(config=config)
+ env.reset()
+ env.step(habitat.SimulatorActions.STRAFE_LEFT)
+ env.step(habitat.SimulatorActions.STRAFE_RIGHT)
+ env.close()
+
+ config.defrost()
+ config.SIMULATOR.ACTION_SPACE_CONFIG = "NoiseStrafe"
+ config.freeze()
+
+ env = habitat.Env(config=config)
+ env.reset()
+ env.step(habitat.SimulatorActions.STRAFE_LEFT)
+ env.step(habitat.SimulatorActions.STRAFE_RIGHT)
+ env.close()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/register_new_sensors_and_measures.py b/examples/register_new_sensors_and_measures.py
index a857f18662c2912fd144dc2f992404ca9f0575d1..0d17ed5f2a02840e2552567946a489d56252ea75 100644
--- a/examples/register_new_sensors_and_measures.py
+++ b/examples/register_new_sensors_and_measures.py
@@ -98,11 +98,7 @@ def main():
env = habitat.Env(config=config)
print(env.reset()["agent_position"])
print(env.get_metrics()["episode_info"])
- print(
- env.step(
- habitat.sims.habitat_simulator.SimulatorActions.MOVE_FORWARD.value
- )["agent_position"]
- )
+ print(env.step(habitat.SimulatorActions.MOVE_FORWARD)["agent_position"])
print(env.get_metrics()["episode_info"])
diff --git a/examples/shortest_path_follower_example.py b/examples/shortest_path_follower_example.py
index e78cdd363f6d41a77bdfe6c3afdf3fc7ef15abc0..db781de5ddfb482dc15911752b968b2c5c5fb36b 100644
--- a/examples/shortest_path_follower_example.py
+++ b/examples/shortest_path_follower_example.py
@@ -83,7 +83,7 @@ def shortest_path_example(mode):
best_action = follower.get_next_action(
env.habitat_env.current_episode.goals[0].position
)
- observations, reward, done, info = env.step(best_action.value)
+ observations, reward, done, info = env.step(best_action)
im = observations["rgb"]
top_down_map = draw_top_down_map(
info, observations["heading"], im.shape[0]
diff --git a/habitat/__init__.py b/habitat/__init__.py
index a37e4dc4159f66036a63e90bbc5cb19fecfc1a6d..2b82de2c56124a42e3a8f64c4965d948f6c0ec2f 100644
--- a/habitat/__init__.py
+++ b/habitat/__init__.py
@@ -13,7 +13,13 @@ from habitat.core.embodied_task import EmbodiedTask, Measure, Measurements
from habitat.core.env import Env, RLEnv
from habitat.core.logging import logger
from habitat.core.registry import registry
-from habitat.core.simulator import Sensor, SensorSuite, SensorTypes, Simulator
+from habitat.core.simulator import (
+ Sensor,
+ SensorSuite,
+ SensorTypes,
+ Simulator,
+ SimulatorActions,
+)
from habitat.core.vector_env import ThreadedVectorEnv, VectorEnv
from habitat.datasets import make_dataset
from habitat.version import VERSION as __version__ # noqa
diff --git a/habitat/config/default.py b/habitat/config/default.py
index cd95808cfce74fbd86aecd4b98ee776a62c650e0..8f90919ae65c29ca1fb145f2422edad36d308d91 100644
--- a/habitat/config/default.py
+++ b/habitat/config/default.py
@@ -81,6 +81,7 @@ _C.TASK.COLLISIONS.TYPE = "Collisions"
# -----------------------------------------------------------------------------
_C.SIMULATOR = CN()
_C.SIMULATOR.TYPE = "Sim-v0"
+_C.SIMULATOR.ACTION_SPACE_CONFIG = "v0"
_C.SIMULATOR.FORWARD_STEP_SIZE = 0.25 # in metres
_C.SIMULATOR.SCENE = (
"data/scene_datasets/habitat-test-scenes/" "van-gogh-room.glb"
diff --git a/habitat/core/registry.py b/habitat/core/registry.py
index b77ccb4af5fc62b55051886d14a0ea22dcaf53ca..bf224e53404a6dcd76ca87b00e8551742aa2ac8c 100644
--- a/habitat/core/registry.py
+++ b/habitat/core/registry.py
@@ -26,8 +26,10 @@ Various decorators for registry different kind of classes with unique keys
import collections
from typing import Optional
+from habitat.core.utils import Singleton
-class _Registry:
+
+class Registry(metaclass=Singleton):
mapping = collections.defaultdict(dict)
@classmethod
@@ -160,6 +162,25 @@ class _Registry:
"dataset", to_register, name, assert_type=Dataset
)
+ @classmethod
+ def register_action_space_configuration(
+ cls, to_register=None, *, name: Optional[str] = None
+ ):
+ r"""Register a action space configuration to registry with key 'name'
+
+ Args:
+ name: Key with which the action space will be registered.
+ If None will use the name of the class
+ """
+ from habitat.core.simulator import ActionSpaceConfiguration
+
+ return cls._register_impl(
+ "action_space_config",
+ to_register,
+ name,
+ assert_type=ActionSpaceConfiguration,
+ )
+
@classmethod
def _get_impl(cls, _type, name):
return cls.mapping[_type].get(name, None)
@@ -184,5 +205,9 @@ class _Registry:
def get_dataset(cls, name):
return cls._get_impl("dataset", name)
+ @classmethod
+ def get_action_space_configuration(cls, name):
+ return cls._get_impl("action_space_config", name)
+
-registry = _Registry()
+registry = Registry()
diff --git a/habitat/core/simulator.py b/habitat/core/simulator.py
index b199ba5f30537608c4f81349ddb661cf441c8140..a9b670fd2a5bf74620b9643c48db19c1b0ac8645 100644
--- a/habitat/core/simulator.py
+++ b/habitat/core/simulator.py
@@ -4,14 +4,101 @@
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
+import abc
from collections import OrderedDict
from enum import Enum
from typing import Any, Dict, List, Optional
+import attr
from gym import Space
from gym.spaces.dict_space import Dict as SpaceDict
from habitat.config import Config
+from habitat.core.utils import Singleton
+
+
+@attr.s(auto_attribs=True)
+class ActionSpaceConfiguration(abc.ABC):
+ config: Config
+
+ @abc.abstractmethod
+ def get(self):
+ pass
+
+
+class _DefaultSimulatorActions(Enum):
+ STOP = 0
+ MOVE_FORWARD = 1
+ TURN_LEFT = 2
+ TURN_RIGHT = 3
+ LOOK_UP = 4
+ LOOK_DOWN = 5
+
+
+@attr.s(auto_attribs=True, slots=True)
+class SimulatorActionsSingleton(metaclass=Singleton):
+ r"""Implements an extendable Enum for the mapping of action names
+ to their integer values.
+
+ This means that new action names can be added, but old action names cannot be
+ removed nor can their mapping be altered. This also ensures that all actions
+ are always contigously mapped in ``[0, len(SimulatorActions) - 1]``
+
+ This accesible as the global singleton SimulatorActions
+ """
+
+ _known_actions: Dict[str, int] = attr.ib(init=False, factory=dict)
+
+ def __attrs_post_init__(self):
+ for action in _DefaultSimulatorActions:
+ self._known_actions[action.name] = action.value
+
+ def extend_action_space(self, name: str) -> int:
+ r"""Extends the action space to accomidate a new action with
+ the name ``name``
+
+ Args
+ name (str): The name of the new action
+
+ Returns
+ int: The number the action is registered on
+
+ Usage::
+
+ from habitat import SimulatorActions
+ SimulatorActions.extend_action_space("MY_ACTION")
+ print(SimulatorActions.MY_ACTION)
+ """
+ assert (
+ name not in self._known_actions
+ ), "Cannot register an action name twice"
+ self._known_actions[name] = len(self._known_actions)
+
+ return self._known_actions[name]
+
+ def has_action(self, name: str) -> bool:
+ r"""Checks to see if action ``name`` is already register
+
+ Args
+ name (str): The name to check
+
+ Returns
+ bool: Whether or not ``name`` already exists
+ """
+
+ return name in self._known_actions
+
+ def __getattr__(self, name):
+ return self._known_actions[name]
+
+ def __getitem__(self, name):
+ return self._known_actions[name]
+
+ def __len__(self):
+ return len(self._known_actions)
+
+
+SimulatorActions = SimulatorActionsSingleton()
class SensorTypes(Enum):
@@ -368,6 +455,13 @@ class Simulator:
raise NotImplementedError
@property
+ def index_stop_action(self):
+ return SimulatorActions.STOP
+
+ @property
+ def index_forward_action(self):
+ return SimulatorActions.MOVE_FORWARD
+
def previous_step_collided(self):
r"""Whether or not the previous step resulted in a collision
diff --git a/habitat/core/utils.py b/habitat/core/utils.py
index b5e338fc31454a3768271a8d88feb3a05663d9d2..e2fa28b7c45078f199811dbe70dc55718f5faaac 100644
--- a/habitat/core/utils.py
+++ b/habitat/core/utils.py
@@ -45,3 +45,14 @@ def tile_images(images: List[np.ndarray]) -> np.ndarray:
def not_none_validator(self, attribute, value):
if value is None:
raise ValueError(f"Argument '{attribute.name}' must be set")
+
+
+class Singleton(type):
+ _instances = {}
+
+ def __call__(cls, *args, **kwargs):
+ if cls not in cls._instances:
+ cls._instances[cls] = super(Singleton, cls).__call__(
+ *args, **kwargs
+ )
+ return cls._instances[cls]
diff --git a/habitat/datasets/utils.py b/habitat/datasets/utils.py
index 67395525c90072e9505a3575e7a238c1d8dd51e3..9684d53712f33d5197b6fd3f0c2372f86c170d38 100644
--- a/habitat/datasets/utils.py
+++ b/habitat/datasets/utils.py
@@ -7,8 +7,7 @@
from typing import List
from habitat.core.logging import logger
-from habitat.core.simulator import ShortestPathPoint
-from habitat.sims.habitat_simulator import SimulatorActions
+from habitat.core.simulator import ShortestPathPoint, SimulatorActions
from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower
from habitat.utils.geometry_utils import quaternion_to_list
@@ -30,17 +29,17 @@ def get_action_shortest_path(
shortest_path = []
action = None
step_count = 0
- while action != SimulatorActions.STOP and step_count < max_episode_steps:
+ while action != sim.index_stop_action and step_count < max_episode_steps:
action = follower.get_next_action(goal_position)
state = sim.get_agent_state()
shortest_path.append(
ShortestPathPoint(
state.position.tolist(),
quaternion_to_list(state.rotation),
- action.value,
+ action,
)
)
- sim.step(action.value)
+ sim.step(action)
step_count += 1
if step_count == max_episode_steps:
logger.warning("Shortest path wasn't found.")
diff --git a/habitat/sims/habitat_simulator/__init__.py b/habitat/sims/habitat_simulator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36c03883f7417808083e5ae3a36ff577df31785d
--- /dev/null
+++ b/habitat/sims/habitat_simulator/__init__.py
@@ -0,0 +1,24 @@
+from habitat.core.registry import registry
+from habitat.core.simulator import Simulator
+
+
+def _try_register_habitat_sim():
+ try:
+ import habitat_sim
+
+ has_habitat_sim = True
+ except ImportError as e:
+ has_habitat_sim = False
+ habitat_sim_import_error = e
+
+ if has_habitat_sim:
+ from habitat.sims.habitat_simulator.habitat_simulator import HabitatSim
+ from habitat.sims.habitat_simulator.action_spaces import (
+ HabitatSimV1ActionSpaceConfiguration,
+ )
+ else:
+
+ @registry.register_simulator(name="Sim-v0")
+ class HabitatSimImportError(Simulator):
+ def __init__(self, *args, **kwargs):
+ raise habitat_sim_import_error
diff --git a/habitat/sims/habitat_simulator/action_spaces.py b/habitat/sims/habitat_simulator/action_spaces.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cee1cbeb297c4f1654fe36a421390d2897124ac
--- /dev/null
+++ b/habitat/sims/habitat_simulator/action_spaces.py
@@ -0,0 +1,55 @@
+from enum import Enum
+
+import attr
+
+import habitat_sim
+from habitat.core.registry import registry
+from habitat.core.simulator import (
+ ActionSpaceConfiguration,
+ Config,
+ SimulatorActions,
+)
+
+
+@registry.register_action_space_configuration(name="v0")
+class HabitatSimV0ActionSpaceConfiguration(ActionSpaceConfiguration):
+ def get(self):
+ return {
+ SimulatorActions.STOP: habitat_sim.ActionSpec("stop"),
+ SimulatorActions.MOVE_FORWARD: habitat_sim.ActionSpec(
+ "move_forward",
+ habitat_sim.ActuationSpec(
+ amount=self.config.FORWARD_STEP_SIZE
+ ),
+ ),
+ SimulatorActions.TURN_LEFT: habitat_sim.ActionSpec(
+ "turn_left",
+ habitat_sim.ActuationSpec(amount=self.config.TURN_ANGLE),
+ ),
+ SimulatorActions.TURN_RIGHT: habitat_sim.ActionSpec(
+ "turn_right",
+ habitat_sim.ActuationSpec(amount=self.config.TURN_ANGLE),
+ ),
+ }
+
+
+@registry.register_action_space_configuration(name="v1")
+class HabitatSimV1ActionSpaceConfiguration(
+ HabitatSimV0ActionSpaceConfiguration
+):
+ def get(self):
+ config = super().get()
+ new_config = {
+ SimulatorActions.LOOK_UP: habitat_sim.ActionSpec(
+ "look_up",
+ habitat_sim.ActuationSpec(amount=self.config.TILT_ANGLE),
+ ),
+ SimulatorActions.LOOK_DOWN: habitat_sim.ActionSpec(
+ "look_down",
+ habitat_sim.ActuationSpec(amount=self.config.TILT_ANGLE),
+ ),
+ }
+
+ config.update(new_config)
+
+ return config
diff --git a/habitat/sims/habitat_simulator.py b/habitat/sims/habitat_simulator/habitat_simulator.py
similarity index 91%
rename from habitat/sims/habitat_simulator.py
rename to habitat/sims/habitat_simulator/habitat_simulator.py
index 32da8af5befcfc932b6b0558cb5ef4a040ab3529..b5b896b34cb83c00358b376e41b049bdc1f90f97 100644
--- a/habitat/sims/habitat_simulator.py
+++ b/habitat/sims/habitat_simulator/habitat_simulator.py
@@ -24,6 +24,7 @@ from habitat.core.simulator import (
SensorSuite,
ShortestPathPoint,
Simulator,
+ SimulatorActions,
)
RGBSENSOR_DIMENSION = 3
@@ -42,15 +43,6 @@ def check_sim_obs(obs, sensor):
)
-class SimulatorActions(Enum):
- STOP = 0
- MOVE_FORWARD = 1
- TURN_LEFT = 2
- TURN_RIGHT = 3
- LOOK_UP = 4
- LOOK_DOWN = 5
-
-
@registry.register_sensor
class HabitatSimRGBSensor(RGBSensor):
sim_sensor_type: habitat_sim.SensorType
@@ -205,31 +197,9 @@ class HabitatSim(Simulator):
sensor_specifications.append(sim_sensor_cfg)
agent_config.sensor_specifications = sensor_specifications
- agent_config.action_space = {
- SimulatorActions.STOP.value: habitat_sim.ActionSpec("stop"),
- SimulatorActions.MOVE_FORWARD.value: habitat_sim.ActionSpec(
- "move_forward",
- habitat_sim.ActuationSpec(
- amount=self.config.FORWARD_STEP_SIZE
- ),
- ),
- SimulatorActions.TURN_LEFT.value: habitat_sim.ActionSpec(
- "turn_left",
- habitat_sim.ActuationSpec(amount=self.config.TURN_ANGLE),
- ),
- SimulatorActions.TURN_RIGHT.value: habitat_sim.ActionSpec(
- "turn_right",
- habitat_sim.ActuationSpec(amount=self.config.TURN_ANGLE),
- ),
- SimulatorActions.LOOK_UP.value: habitat_sim.ActionSpec(
- "look_up",
- habitat_sim.ActuationSpec(amount=self.config.TILT_ANGLE),
- ),
- SimulatorActions.LOOK_DOWN.value: habitat_sim.ActionSpec(
- "look_down",
- habitat_sim.ActuationSpec(amount=self.config.TILT_ANGLE),
- ),
- }
+ agent_config.action_space = registry.get_action_space_configuration(
+ self.config.ACTION_SPACE_CONFIG
+ )(self.config).get()
return habitat_sim.Configuration(sim_config, [agent_config])
@@ -256,6 +226,7 @@ class HabitatSim(Simulator):
agent_id,
)
is_updated = True
+
return is_updated
def reset(self):
@@ -273,7 +244,7 @@ class HabitatSim(Simulator):
"STOP action called previously"
)
- if action == SimulatorActions.STOP.value:
+ if action == self.index_stop_action:
self._is_episode_active = False
sim_obs = self._sim.get_sensor_observations()
else:
@@ -398,14 +369,6 @@ class HabitatSim(Simulator):
def close(self):
self._sim.close()
- @property
- def index_stop_action(self):
- return SimulatorActions.STOP.value
-
- @property
- def index_forward_action(self):
- return SimulatorActions.MOVE_FORWARD.value
-
def _get_agent_config(self, agent_id: Optional[int] = None) -> Any:
if agent_id is None:
agent_id = self.config.DEFAULT_AGENT_ID
diff --git a/habitat/sims/registration.py b/habitat/sims/registration.py
index 0f59808d5707c7d2c13706fe2b6d53eac392d7c7..d756906d51fc0d68f3f5d8f083f418cce9f631e9 100644
--- a/habitat/sims/registration.py
+++ b/habitat/sims/registration.py
@@ -7,25 +7,7 @@
from habitat.core.logging import logger
from habitat.core.registry import registry
from habitat.core.simulator import Simulator
-
-
-def _try_register_habitat_sim():
- try:
- import habitat_sim
-
- has_habitat_sim = True
- except ImportError as e:
- has_habitat_sim = False
- habitat_sim_import_error = e
-
- if has_habitat_sim:
- from habitat.sims.habitat_simulator import HabitatSim
- else:
-
- @registry.register_simulator(name="Sim-v0")
- class HabitatSimImportError(Simulator):
- def __init__(self, *args, **kwargs):
- raise habitat_sim_import_error
+from habitat.sims.habitat_simulator import _try_register_habitat_sim
def make_sim(id_sim, **kwargs):
diff --git a/habitat/tasks/nav/shortest_path_follower.py b/habitat/tasks/nav/shortest_path_follower.py
index a358cab5b829423f09fa206b7822f81dfbeaabed..2d7b2f184da88766cbb2c31bb22943e928c77e79 100644
--- a/habitat/tasks/nav/shortest_path_follower.py
+++ b/habitat/tasks/nav/shortest_path_follower.py
@@ -9,7 +9,8 @@ from typing import Union
import numpy as np
import habitat_sim
-from habitat.sims.habitat_simulator import HabitatSim, SimulatorActions
+from habitat.core.simulator import SimulatorActions
+from habitat.sims.habitat_simulator.habitat_simulator import HabitatSim
from habitat.utils.geometry_utils import (
angle_between_quaternions,
quaternion_from_two_vectors,
@@ -58,18 +59,14 @@ class ShortestPathFollower:
)
self._return_one_hot = return_one_hot
- def _get_return_value(
- self, action: SimulatorActions
- ) -> Union[SimulatorActions, np.array]:
+ def _get_return_value(self, action) -> Union[int, np.array]:
if self._return_one_hot:
- return action_to_one_hot(action.value)
+ return action_to_one_hot(action)
else:
return action
- def get_next_action(
- self, goal_pos: np.array
- ) -> Union[SimulatorActions, np.array]:
- r"""Returns the next action along the shortest path.
+ def get_next_action(self, goal_pos: np.array) -> Union[int, np.array]:
+ """Returns the next action along the shortest path.
"""
if (
np.linalg.norm(goal_pos - self._sim.get_agent_state().position)
@@ -84,13 +81,13 @@ class ShortestPathFollower:
def _step_along_grad(
self, grad_dir: np.quaternion
- ) -> Union[SimulatorActions, np.array]:
+ ) -> Union[int, np.array]:
current_state = self._sim.get_agent_state()
alpha = angle_between_quaternions(grad_dir, current_state.rotation)
if alpha <= np.deg2rad(self._sim.config.TURN_ANGLE) + EPSILON:
return self._get_return_value(SimulatorActions.MOVE_FORWARD)
else:
- sim_action = SimulatorActions.TURN_LEFT.value
+ sim_action = SimulatorActions.TURN_LEFT
self._sim.step(sim_action)
best_turn = (
SimulatorActions.TURN_LEFT
@@ -144,7 +141,7 @@ class ShortestPathFollower:
best_geodesic_delta = -2 * self._max_delta
best_rotation = current_rotation
for _ in range(0, 360, self._sim.config.TURN_ANGLE):
- sim_action = SimulatorActions.MOVE_FORWARD.value
+ sim_action = SimulatorActions.MOVE_FORWARD
self._sim.step(sim_action)
new_delta = current_dist - self._geo_dist(goal_pos)
@@ -168,7 +165,7 @@ class ShortestPathFollower:
reset_sensors=False,
)
- sim_action = SimulatorActions.TURN_LEFT.value
+ sim_action = SimulatorActions.TURN_LEFT
self._sim.step(sim_action)
self._reset_agent_state(current_state)
diff --git a/habitat_baselines/agents/simple_agents.py b/habitat_baselines/agents/simple_agents.py
index 733b82e4c783ea4741da4398c6e9e3267ca2fa54..83568ab7cfb4b617bd25de3192aae0704fb7fe2b 100644
--- a/habitat_baselines/agents/simple_agents.py
+++ b/habitat_baselines/agents/simple_agents.py
@@ -11,13 +11,8 @@ from math import pi
import numpy as np
import habitat
-from habitat.config import Config
+from habitat import SimulatorActions
from habitat.config.default import get_config
-from habitat.sims.habitat_simulator import SimulatorActions
-
-NON_STOP_ACTIONS = [
- v for v in range(len(SimulatorActions)) if v != SimulatorActions.STOP.value
-]
class RandomAgent(habitat.Agent):
@@ -34,18 +29,24 @@ class RandomAgent(habitat.Agent):
def act(self, observations):
if self.is_goal_reached(observations):
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
else:
- action = np.random.choice(NON_STOP_ACTIONS)
+ action = np.random.choice(
+ [
+ SimulatorActions.MOVE_FORWARD,
+ SimulatorActions.TURN_LEFT,
+ SimulatorActions.TURN_RIGHT,
+ ]
+ )
return action
class ForwardOnlyAgent(RandomAgent):
def act(self, observations):
if self.is_goal_reached(observations):
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
else:
- action = SimulatorActions.MOVE_FORWARD.value
+ action = SimulatorActions.MOVE_FORWARD
return action
@@ -56,16 +57,13 @@ class RandomForwardAgent(RandomAgent):
def act(self, observations):
if self.is_goal_reached(observations):
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
else:
if np.random.uniform(0, 1, 1) < self.FORWARD_PROBABILITY:
- action = SimulatorActions.MOVE_FORWARD.value
+ action = SimulatorActions.MOVE_FORWARD
else:
action = np.random.choice(
- [
- SimulatorActions.TURN_LEFT.value,
- SimulatorActions.TURN_RIGHT.value,
- ]
+ [SimulatorActions.TURN_LEFT, SimulatorActions.TURN_RIGHT]
)
return action
@@ -89,20 +87,20 @@ class GoalFollower(RandomAgent):
if angle_to_goal > pi or (
(angle_to_goal < 0) and (angle_to_goal > -pi)
):
- action = SimulatorActions.TURN_RIGHT.value
+ action = SimulatorActions.TURN_RIGHT
else:
- action = SimulatorActions.TURN_LEFT.value
+ action = SimulatorActions.TURN_LEFT
return action
def act(self, observations):
if self.is_goal_reached(observations):
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
else:
angle_to_goal = self.normalize_angle(
np.array(observations[self.goal_sensor_uuid][1])
)
if abs(angle_to_goal) < self.angle_th:
- action = SimulatorActions.MOVE_FORWARD.value
+ action = SimulatorActions.MOVE_FORWARD
else:
action = self.turn_towards_goal(angle_to_goal)
diff --git a/habitat_baselines/agents/slam_agents.py b/habitat_baselines/agents/slam_agents.py
index 846584062e01a202fd8e36c8ef27937e66796f88..9d7b291a517b00667578631066b33bffffc4efb7 100644
--- a/habitat_baselines/agents/slam_agents.py
+++ b/habitat_baselines/agents/slam_agents.py
@@ -19,8 +19,8 @@ import torch.nn.functional as F
import habitat
import orbslam2
+from habitat import SimulatorActions
from habitat.config.default import get_config
-from habitat.sims.habitat_simulator import SimulatorActions
from habitat_baselines.config.default import get_config as cfg_baseline
from habitat_baselines.slambased.mappers import DirectDepthMapper
from habitat_baselines.slambased.monodepth import MonoDepthEstimator
@@ -115,7 +115,7 @@ class RandomAgent(object):
# Act
# Check if we are done
if self.is_goal_reached():
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
else:
action = random.randint(0, self.num_actions - 1)
return action
@@ -132,20 +132,20 @@ class BlindAgent(RandomAgent):
def decide_what_to_do(self):
distance_to_goal = self.obs["pointgoal"][0]
angle_to_goal = norm_ang(np.array(self.obs["pointgoal"][1]))
- command = SimulatorActions.STOP.value
+ command = SimulatorActions.STOP
if distance_to_goal <= self.pos_th:
return command
if abs(angle_to_goal) < self.angle_th:
- command = SimulatorActions.MOVE_FORWARD.value
+ command = SimulatorActions.MOVE_FORWARD
else:
if (angle_to_goal > 0) and (angle_to_goal < pi):
- command = SimulatorActions.TURN_LEFT.value
+ command = SimulatorActions.TURN_LEFT
elif angle_to_goal > pi:
- command = SimulatorActions.TURN_RIGHT.value
+ command = SimulatorActions.TURN_RIGHT
elif (angle_to_goal < 0) and (angle_to_goal > -pi):
- command = SimulatorActions.TURN_RIGHT.value
+ command = SimulatorActions.TURN_RIGHT
else:
- command = SimulatorActions.TURN_LEFT.value
+ command = SimulatorActions.TURN_LEFT
return command
@@ -153,7 +153,7 @@ class BlindAgent(RandomAgent):
self.update_internal_state(habitat_observation)
# Act
if self.is_goal_reached():
- return SimulatorActions.STOP.value
+ return SimulatorActions.STOP
command = self.decide_what_to_do()
random_action = random.randint(0, self.num_actions - 1)
act_randomly = np.random.uniform(0, 1, 1) < random_prob
@@ -266,10 +266,7 @@ class ORBSLAM2Agent(RandomAgent):
.view(4, 4)
.to(self.device),
)
- if (
- self.action_history[-1]
- == SimulatorActions.MOVE_FORWARD.value
- ):
+ if self.action_history[-1] == SimulatorActions.MOVE_FORWARD:
self.unseen_obstacle = (
previous_step.item() <= 0.001
) # hardcoded threshold for not moving
@@ -333,7 +330,7 @@ class ORBSLAM2Agent(RandomAgent):
)
success = self.is_goal_reached()
if success:
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
self.action_history.append(action)
return action
# Plan action
@@ -485,7 +482,7 @@ class ORBSLAM2Agent(RandomAgent):
return path, planned_waypoints
def planner_prediction_to_command(self, p_next):
- command = SimulatorActions.STOP.value
+ command = SimulatorActions.STOP
p_init = self.pose6D.squeeze()
d_angle_rot_th = self.angle_th
pos_th = self.pos_th
@@ -495,27 +492,27 @@ class ORBSLAM2Agent(RandomAgent):
get_direction(p_init, p_next, ang_th=d_angle_rot_th, pos_th=pos_th)
)
if abs(d_angle) < d_angle_rot_th:
- command = SimulatorActions.MOVE_FORWARD.value
+ command = SimulatorActions.MOVE_FORWARD
else:
if (d_angle > 0) and (d_angle < pi):
- command = SimulatorActions.TURN_LEFT.value
+ command = SimulatorActions.TURN_LEFT
elif d_angle > pi:
- command = SimulatorActions.TURN_RIGHT.value
+ command = SimulatorActions.TURN_RIGHT
elif (d_angle < 0) and (d_angle > -pi):
- command = SimulatorActions.TURN_RIGHT.value
+ command = SimulatorActions.TURN_RIGHT
else:
- command = SimulatorActions.TURN_LEFT.value
+ command = SimulatorActions.TURN_LEFT
return command
def decide_what_to_do(self):
action = None
if self.is_goal_reached():
- action = SimulatorActions.STOP.value
+ action = SimulatorActions.STOP
return action
if self.unseen_obstacle:
- command = SimulatorActions.TURN_RIGHT.value
+ command = SimulatorActions.TURN_RIGHT
return command
- command = SimulatorActions.STOP.value
+ command = SimulatorActions.STOP
command = self.planner_prediction_to_command(self.waypointPose6D)
return command
diff --git a/habitat_baselines/train_ppo.py b/habitat_baselines/train_ppo.py
index 97efa74a62ac5f715445c2b5e021dc00cc1a73c0..c57d79a7fb40ed5ce934f4dde1f650c38a5aff0f 100644
--- a/habitat_baselines/train_ppo.py
+++ b/habitat_baselines/train_ppo.py
@@ -14,10 +14,9 @@ import torch
import habitat
from config.default import get_config as cfg_baseline
-from habitat import logger
+from habitat import SimulatorActions, logger
from habitat.config.default import get_config as cfg_env
from habitat.datasets.registration import make_dataset
-from habitat.sims.habitat_simulator import SimulatorActions
from rl.ppo import PPO, Policy, RolloutStorage
from rl.ppo.utils import batch_obs, ppo_args, update_linear_schedule
@@ -73,7 +72,7 @@ class NavRLEnv(habitat.RLEnv):
def _episode_success(self):
if (
- self._previous_action == SimulatorActions.STOP.value
+ self._previous_action == SimulatorActions.STOP
and self._distance_target() < self._config_env.SUCCESS_DISTANCE
):
return True
diff --git a/notebooks/habitat-api-demo.ipynb b/notebooks/habitat-api-demo.ipynb
index d41b7f1d9fe3c9103be7364dbbfc100e7c322b2f..bd9ed01c9de0970f23ee49e8c362f583a03573f3 100644
--- a/notebooks/habitat-api-demo.ipynb
+++ b/notebooks/habitat-api-demo.ipynb
@@ -39,7 +39,7 @@
"\n",
"config = habitat.get_config(config_paths='../configs/tasks/pointnav_mp3d.yaml')\n",
"config.defrost()\n",
- "config.DATASET.POINTNAVV1.DATA_PATH = '../data/datasets/pointnav/mp3d/v1/val/val.json.gz'\n",
+ "config.DATASET.DATA_PATH = '../data/datasets/pointnav/mp3d/v1/val/val.json.gz'\n",
"config.DATASET.SCENES_DIR = '../data/scene_datasets/'\n",
"config.freeze()\n",
"\n",
@@ -236,7 +236,7 @@
"source": [
"config = habitat.get_config(config_paths='../configs/tasks/pointnav_mp3d.yaml')\n",
"config.defrost()\n",
- "config.DATASET.POINTNAVV1.DATA_PATH = '../data/datasets/pointnav/mp3d/v1/val/val.json.gz'\n",
+ "config.DATASET.DATA_PATH = '../data/datasets/pointnav/mp3d/v1/val/val.json.gz'\n",
"config.DATASET.SCENES_DIR = '../data/scene_datasets/'\n",
"config.SIMULATOR.AGENT_0.SENSORS = ['RGB_SENSOR', 'DEPTH_SENSOR', 'SEMANTIC_SENSOR']\n",
"config.SIMULATOR.SEMANTIC_SENSOR.WIDTH = 256\n",
@@ -283,9 +283,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "notebooks",
+ "display_name": "Python 3",
"language": "python",
- "name": "notebooks"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -297,7 +297,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.3"
}
},
"nbformat": 4,
diff --git a/notebooks/relative_camera_views_transform_and_warping_demo.ipynb b/notebooks/relative_camera_views_transform_and_warping_demo.ipynb
index 57d8e07bb8e6695d357183d51c2886c5790a20b8..769d1bd34a521ffb541274de251d1e5aeaaaeccf 100644
--- a/notebooks/relative_camera_views_transform_and_warping_demo.ipynb
+++ b/notebooks/relative_camera_views_transform_and_warping_demo.ipynb
@@ -46,7 +46,7 @@
"# Set up the environment for testing\n",
"config = habitat.get_config(config_paths=\"../configs/tasks/pointnav_rgbd.yaml\")\n",
"config.defrost()\n",
- "config.DATASET.POINTNAVV1.DATA_PATH = '../data/datasets/pointnav/habitat-test-scenes/v1/val/val.json.gz'\n",
+ "config.DATASET.DATA_PATH = '../data/datasets/pointnav/habitat-test-scenes/v1/val/val.json.gz'\n",
"config.DATASET.SCENES_DIR = '../data/scene_datasets/'\n",
"config.freeze()\n",
"\n",
diff --git a/test/test_demo_notebook.py b/test/test_demo_notebook.py
index ca4e5ad23927aa2a04ce73f0ca6867f031101e56..fd9168df99b3d594b8f7625c2dc093bd123ff4a3 100644
--- a/test/test_demo_notebook.py
+++ b/test/test_demo_notebook.py
@@ -3,6 +3,7 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
+import gc
import pytest
@@ -21,3 +22,8 @@ def test_demo_notebook():
)
else:
pytest.main(["--nbval-lax", "notebooks/habitat-api-demo.ipynb"])
+
+ # NB: Force a gc collect run as it can take a little bit for
+ # the cleanup to happen after the notebook and we get
+ # a double context crash!
+ gc.collect()
diff --git a/test/test_habitat_env.py b/test/test_habitat_env.py
index 7e8b08e77808909de07c2969d72d614bec7b41cb..61851e54f63666338e271ee44f90c212cb5a1da7 100644
--- a/test/test_habitat_env.py
+++ b/test/test_habitat_env.py
@@ -12,9 +12,8 @@ import pytest
import habitat
from habitat.config.default import get_config
-from habitat.core.simulator import AgentState
+from habitat.core.simulator import AgentState, SimulatorActions
from habitat.datasets.pointnav.pointnav_dataset import PointNavDatasetV1
-from habitat.sims.habitat_simulator import SimulatorActions
from habitat.tasks.nav.nav_task import NavigationEpisode, NavigationGoal
CFG_TEST = "configs/test/habitat_all_sensors_test.yaml"
@@ -81,9 +80,9 @@ def _vec_env_test_fn(configs, datasets, multiprocessing_start_method):
)
envs.reset()
non_stop_actions = [
- v
- for v in range(len(SimulatorActions))
- if v != SimulatorActions.STOP.value
+ act
+ for act in range(envs.action_spaces[0].n)
+ if act != SimulatorActions.STOP
]
for _ in range(2 * configs[0].ENVIRONMENT.MAX_EPISODE_STEPS):
@@ -135,9 +134,9 @@ def test_threaded_vectorized_env():
envs = habitat.ThreadedVectorEnv(env_fn_args=env_fn_args)
envs.reset()
non_stop_actions = [
- v
- for v in range(len(SimulatorActions))
- if v != SimulatorActions.STOP.value
+ act
+ for act in range(envs.action_spaces[0].n)
+ if act != SimulatorActions.STOP
]
for i in range(2 * configs[0].ENVIRONMENT.MAX_EPISODE_STEPS):
@@ -167,9 +166,9 @@ def test_env():
env.reset()
non_stop_actions = [
- v
- for v in range(len(SimulatorActions))
- if v != SimulatorActions.STOP.value
+ act
+ for act in range(env.action_space.n)
+ if act != SimulatorActions.STOP
]
for _ in range(config.ENVIRONMENT.MAX_EPISODE_STEPS):
act = np.random.choice(non_stop_actions)
@@ -182,7 +181,7 @@ def test_env():
env.reset()
- env.step(SimulatorActions.STOP.value)
+ env.step(SimulatorActions.STOP)
# check for STOP action
assert env.episode_over is True, (
"episode should be over after STOP " "action"
@@ -211,9 +210,9 @@ def test_rl_vectorized_envs():
envs = habitat.VectorEnv(make_env_fn=make_rl_env, env_fn_args=env_fn_args)
envs.reset()
non_stop_actions = [
- v
- for v in range(len(SimulatorActions))
- if v != SimulatorActions.STOP.value
+ act
+ for act in range(envs.action_spaces[0].n)
+ if act != SimulatorActions.STOP
]
for i in range(2 * configs[0].ENVIRONMENT.MAX_EPISODE_STEPS):
@@ -263,9 +262,9 @@ def test_rl_env():
observation = env.reset()
non_stop_actions = [
- v
- for v in range(len(SimulatorActions))
- if v != SimulatorActions.STOP.value
+ act
+ for act in range(env.action_space.n)
+ if act != SimulatorActions.STOP
]
for _ in range(config.ENVIRONMENT.MAX_EPISODE_STEPS):
observation, reward, done, info = env.step(
@@ -276,7 +275,7 @@ def test_rl_env():
assert done is True, "episodes should be over after max_episode_steps"
env.reset()
- observation, reward, done, info = env.step(SimulatorActions.STOP.value)
+ observation, reward, done, info = env.step(SimulatorActions.STOP)
assert done is True, "done should be true after STOP action"
env.close()
@@ -381,10 +380,10 @@ def test_action_space_shortest_path():
unreachable_targets.append(AgentState(position, rotation))
targets = reachable_targets
- shortest_path1 = env.sim.action_space_shortest_path(source, targets)
+ shortest_path1 = env.action_space_shortest_path(source, targets)
assert shortest_path1 != []
targets = unreachable_targets
- shortest_path2 = env.sim.action_space_shortest_path(source, targets)
+ shortest_path2 = env.action_space_shortest_path(source, targets)
assert shortest_path2 == []
env.close()
diff --git a/test/test_habitat_example.py b/test/test_habitat_example.py
index d4884c1223d357a6840f9872a40af01c5a7b4350..493c2412fa038d3ea97553a5d81e64b469bcf7b4 100644
--- a/test/test_habitat_example.py
+++ b/test/test_habitat_example.py
@@ -8,6 +8,7 @@ import pytest
import habitat
from examples import (
+ new_actions,
register_new_sensors_and_measures,
shortest_path_follower_example,
visualization_examples,
@@ -47,3 +48,12 @@ def test_register_new_sensors_and_measures():
pytest.skip("Please download Habitat test data to data folder.")
register_new_sensors_and_measures.main()
+
+
+def test_new_actions():
+ if not PointNavDatasetV1.check_config_paths_exist(
+ config=habitat.get_config().DATASET
+ ):
+ pytest.skip("Please download Habitat test data to data folder.")
+
+ new_actions.main()
diff --git a/test/test_pointnav_dataset.py b/test/test_pointnav_dataset.py
index 4b3ce98bfb07f819615e5cd3931f93dcc931da5c..7a3954c64f03eda8a68d73698736b0c399ba2cd2 100644
--- a/test/test_pointnav_dataset.py
+++ b/test/test_pointnav_dataset.py
@@ -70,7 +70,7 @@ def test_multiple_files_scene_path():
len(scenes) > 0
), "Expected dataset contains separate episode file per scene."
dataset_config.defrost()
- dataset_config.POINTNAVV1.CONTENT_SCENES = scenes[:PARTIAL_LOAD_SCENES]
+ dataset_config.CONTENT_SCENES = scenes[:PARTIAL_LOAD_SCENES]
dataset_config.SCENES_DIR = os.path.join(
os.getcwd(), DEFAULT_SCENE_PATH_PREFIX
)
@@ -98,7 +98,7 @@ def test_multiple_files_pointnav_dataset():
len(scenes) > 0
), "Expected dataset contains separate episode file per scene."
dataset_config.defrost()
- dataset_config.POINTNAVV1.CONTENT_SCENES = scenes[:PARTIAL_LOAD_SCENES]
+ dataset_config.CONTENT_SCENES = scenes[:PARTIAL_LOAD_SCENES]
dataset_config.freeze()
partial_dataset = make_dataset(
id_dataset=dataset_config.TYPE, config=dataset_config
diff --git a/test/test_relative_camera.py b/test/test_relative_camera.py
index fd87dff85d4fa9effa736349c0776fa41fdd77af..0911861d419af66568d4b2e144ec8d4d76454bf8 100644
--- a/test/test_relative_camera.py
+++ b/test/test_relative_camera.py
@@ -3,6 +3,7 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
+import gc
import pytest
@@ -32,3 +33,8 @@ def test_demo_notebook():
"notebooks/relative_camera_views_transform_and_warping_demo.ipynb",
]
)
+
+ # NB: Force a gc collect run as it can take a little bit for
+ # the cleanup to happen after the notebook and we get
+ # a double context crash!
+ gc.collect()
diff --git a/test/test_sensors.py b/test/test_sensors.py
index c99d4e82602c2228f1080512f11aaf49a9760433..5e34e6b5f75a62501fa227e2c150c4e6614fbd43 100644
--- a/test/test_sensors.py
+++ b/test/test_sensors.py
@@ -12,19 +12,9 @@ import pytest
import habitat
from habitat.config.default import get_config
-from habitat.sims.habitat_simulator import SimulatorActions
+from habitat.core.simulator import SimulatorActions
from habitat.tasks.nav.nav_task import NavigationEpisode, NavigationGoal
-NON_STOP_ACTIONS = [
- v for v in range(len(SimulatorActions)) if v != SimulatorActions.STOP.value
-]
-
-MOVEMENT_ACTIONS = [
- SimulatorActions.MOVE_FORWARD.value,
- SimulatorActions.TURN_LEFT.value,
- SimulatorActions.TURN_RIGHT.value,
-]
-
def _random_episode(env, config):
random_location = env._sim.sample_navigable_point()
@@ -121,9 +111,9 @@ def test_collisions():
np.random.seed(123)
actions = [
- SimulatorActions.MOVE_FORWARD.value,
- SimulatorActions.TURN_LEFT.value,
- SimulatorActions.TURN_RIGHT.value,
+ SimulatorActions.MOVE_FORWARD,
+ SimulatorActions.TURN_LEFT,
+ SimulatorActions.TURN_RIGHT,
]
for _ in range(20):
@@ -187,9 +177,14 @@ def test_static_pointgoal_sensor():
)
]
+ non_stop_actions = [
+ act
+ for act in range(env.action_space.n)
+ if act != SimulatorActions.STOP
+ ]
env.reset()
for _ in range(100):
- obs = env.step(np.random.choice(NON_STOP_ACTIONS))
+ obs = env.step(np.random.choice(non_stop_actions))
static_pointgoal = obs["static_pointgoal"]
# check to see if taking non-stop actions will affect static point_goal
assert np.allclose(static_pointgoal, expected_static_pointgoal)
@@ -225,13 +220,18 @@ def test_get_observations_at():
goals=[NavigationGoal(position=goal_position)],
)
]
+ non_stop_actions = [
+ act
+ for act in range(env.action_space.n)
+ if act != SimulatorActions.STOP
+ ]
obs = env.reset()
start_state = env.sim.get_agent_state()
for _ in range(100):
# Note, this test will not currently work for camera change actions
# (look up/down), only for movement actions.
- new_obs = env.step(np.random.choice(MOVEMENT_ACTIONS))
+ new_obs = env.step(np.random.choice(non_stop_actions))
for key, val in new_obs.items():
agent_state = env.sim.get_agent_state()
if not (
diff --git a/test/test_trajectory_sim.py b/test/test_trajectory_sim.py
index 8d2302cbaaf1450ac1e39eaa9f9257a4bdb4edef..285b8e12ee40a4dc32a174cf9197dc1684e0cad2 100644
--- a/test/test_trajectory_sim.py
+++ b/test/test_trajectory_sim.py
@@ -10,9 +10,9 @@ import os
import numpy as np
import pytest
+from habitat import SimulatorActions
from habitat.config.default import get_config
from habitat.sims import make_sim
-from habitat.sims.habitat_simulator import SimulatorActions
def init_sim():
@@ -34,7 +34,7 @@ def test_sim_trajectory():
)
for i, action in enumerate(test_trajectory["actions"]):
- action = SimulatorActions[action].value
+ action = SimulatorActions[action]
if i > 0: # ignore first step as habitat-sim doesn't update
# agent until then
state = sim.get_agent_state()