def local_evaluate(self, agent: Agent, num_episodes: Optional[int] = None):
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(
num_episodes, len(self._env.episodes)
)
)
assert num_episodes > 0, "num_episodes should be greater than 0"
agg_metrics: Dict = defaultdict(float)
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
while not self._env.episode_over:
action = agent.act(observations)
observations = self._env.step(action)
metrics = self._env.get_metrics()
for m, v in metrics.items():
agg_metrics[m] += v
count_episodes += 1
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
return avg_metrics
def evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None) -> Dict[str, float]:
r"""..
:param agent: agent to be evaluated in environment.
:param num_episodes: count of number of episodes for which the
evaluation should be run.
:return: dict containing metrics tracked by environment.
"""
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
agg_metrics: Dict = defaultdict(float)
count_episodes = 0
reward_episodes = 0
step_episodes = 0
success_count = 0
for count_episodes in tqdm(range(num_episodes)):
agent.reset()
observations = self._env.reset()
episode_reward = 0
while not self._env.habitat_env.episode_over:
action = agent.act(observations)
observations, reward, done, info = self._env.step(**action)
logging.debug("Reward: {}".format(reward))
if done:
logging.debug('Episode reward: {}'.format(episode_reward))
episode_reward += reward
step_episodes += 1
metrics = self._env.habitat_env.get_metrics()
for m, v in metrics.items():
agg_metrics[m] += v
reward_episodes += episode_reward
success_count += metrics['spl'] > 0
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
logging.info("Average reward: {} in {} episodes".format(
reward_episodes / count_episodes, count_episodes))
logging.info("Average episode steps: {}".format(step_episodes /
count_episodes))
logging.info('Success rate: {}'.format(success_count / num_episodes))
return avg_metrics
def evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None) -> Dict[str, float]:
r"""..
:param agent: agent to be evaluated in environment.
:param num_episodes: count of number of episodes for which the
evaluation should be run.
:return: dict containing metrics tracked by environment.
"""
self.reset_benchmark()
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
action_history = []
while not self._env.episode_over:
action = agent.act(
observations,
self._env._sim.previous_step_collided,
)
action["action_args"].update(
{"episode": self._env._current_episode})
observations = self._env.step(action)
metrics = self._env.get_metrics()
pprint(metrics)
for m, v in metrics.items():
if m != "distance_to_goal":
self.agg_metrics[m] += v
count_episodes += 1
print(count_episodes)
avg_metrics = {
k: v / count_episodes
for k, v in self.agg_metrics.items()
}
return avg_metrics
def evaluate(
self, agent: Agent, num_episodes: Optional[int] = None
) -> Dict[str, float]:
"""
Args:
agent: agent to be evaluated in environment.
num_episodes: count of number of episodes for which the evaluation
should be run.
Returns:
dict containing metrics tracked by environment.
"""
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(
num_episodes, len(self._env.episodes)
)
)
assert num_episodes > 0, "num_episodes should be greater than 0"
agg_metrics: Dict = defaultdict(float)
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
while not self._env.episode_over:
action = agent.act(observations)
observations = self._env.step(action)
metrics = self._env.get_metrics()
for m, v in metrics.items():
agg_metrics[m] += v
count_episodes += 1
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
return avg_metrics
def evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None) -> Dict[str, float]:
r"""..
:param agent: agent to be evaluated in environment.
:param num_episodes: count of number of episodes for which the
evaluation should be run.
:return: dict containing metrics tracked by environment.
"""
self.reset_benchmark()
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
action_history = []
while not self._env.episode_over:
action = agent.act(
observations,
self._env._current_episode,
)
print("action has been performed")
break
break
#observations = self._env.step(action)
return
def remote_evaluate(
self, agent: Agent, num_episodes: Optional[int] = None
):
# The modules imported below are specific to habitat-challenge remote evaluation.
# These modules are not part of the habitat-api repository.
import evaluation_pb2
import evaluation_pb2_grpc
import evalai_environment_habitat
import grpc
import pickle
import time
time.sleep(60)
def pack_for_grpc(entity):
return pickle.dumps(entity)
def unpack_for_grpc(entity):
return pickle.loads(entity)
def remote_ep_over(stub):
res_env = unpack_for_grpc(
stub.episode_over(evaluation_pb2.Package()).SerializedEntity
)
return res_env["episode_over"]
env_address_port = os.environ.get("EVALENV_ADDPORT", "localhost:8085")
channel = grpc.insecure_channel(env_address_port)
stub = evaluation_pb2_grpc.EnvironmentStub(channel)
base_num_episodes = unpack_for_grpc(
stub.num_episodes(evaluation_pb2.Package()).SerializedEntity
)
num_episodes = base_num_episodes["num_episodes"]
agg_metrics: Dict = defaultdict(float)
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
res_env = unpack_for_grpc(
stub.reset(evaluation_pb2.Package()).SerializedEntity
)
while not remote_ep_over(stub):
obs = res_env["observations"]
action = agent.act(obs)
res_env = unpack_for_grpc(
stub.act_on_environment(
evaluation_pb2.Package(
SerializedEntity=pack_for_grpc(action)
)
).SerializedEntity
)
metrics = unpack_for_grpc(
stub.get_metrics(
evaluation_pb2.Package(
SerializedEntity=pack_for_grpc(action)
)
).SerializedEntity
)
for m, v in metrics["metrics"].items():
agg_metrics[m] += v
count_episodes += 1
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
stub.evalai_update_submission(evaluation_pb2.Package())
return avg_metrics
def local_evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None,
control_period: Optional[float] = 1.0,
frame_rate: Optional[int] = 1):
if num_episodes is None:
num_episodes = len(self._env._env.episodes)
else:
assert num_episodes <= len(self._env._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
agg_metrics: Dict = defaultdict(float)
writer = TensorboardWriter(
'tb_benchmark/', flush_secs=30) # flush_specs from base_trainer.py
count_episodes = 0
print("number of episodes: " + str(num_episodes))
while count_episodes < num_episodes:
print("working on episode " + str(count_episodes))
observations_per_episode = []
agent.reset()
observations_per_action = self._env._env.reset()
# initialize physic-enabled sim env. Do this for every
# episode, since sometimes assets get deallocated
if self._enable_physics:
self._env._env.disable_physics()
self._env._env.enable_physics()
frame_counter = 0
# act until one episode is over
while not self._env._env.episode_over:
action = agent.act(observations_per_action)
observations_per_action = reward_per_action = done_per_action = info_per_action = None
if (self._enable_physics is False):
(observations_per_action, reward_per_action,
done_per_action, info_per_action) = self._env.step(action)
else:
# step with physics. For now we use hard-coded time step of 1/60 secs
# (used in the rigid object tutorial in Habitat Sim)
(observations_per_action, reward_per_action,
done_per_action,
info_per_action) = self._env.step_physics(
action,
time_step=1.0 / 60.0,
control_period=control_period)
# generate an output image for the action. The image includes observations
# and a top-down map showing the agent's state in the environment
# we use frame_rate (num. of frames per action) to reduce computational overhead
if frame_counter % frame_rate == 0:
out_im_per_action = observations_to_image(
observations_per_action, info_per_action)
observations_per_episode.append(out_im_per_action)
frame_counter = frame_counter + 1
# episode ended
# get per-episode metrics. for now we only extract
# distance-to-goal, success, spl
metrics = self._env._env.get_metrics()
per_ep_metrics = {
k: metrics[k]
for k in ['distance_to_goal', 'success', 'spl']
}
# print distance_to_goal, success and spl
for k, v in per_ep_metrics.items():
print(f'{k},{v}')
# calculate aggregated distance_to_goal, success and spl
for m, v in per_ep_metrics.items():
agg_metrics[m] += v
count_episodes += 1
# generate video
generate_video(
video_option=["disk", "tensorboard"],
video_dir='video_benchmark_dir',
images=observations_per_episode,
episode_id=count_episodes - 1,
checkpoint_idx=0,
metrics=per_ep_metrics,
tb_writer=writer,
)
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
return avg_metrics
def evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None) -> Dict[str, float]:
r"""..
:param agent: agent to be evaluated in environment.
:param num_episodes: count of number of episodes for which the
evaluation should be run.
:return: dict containing metrics tracked by environment.
"""
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
agg_metrics: Dict = defaultdict(float)
count_episodes = 0
images = []
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
action_history = []
gif_images = []
#if images:
#print("writing file with images")
#for im in images:
# image = Image.fromarray(im[:,:, [2,1,0]])
# gif_images.append(image)
#image = image[:,:, [2,1,0]]
#cv2.imshow("RGB", image)
#cv2.waitKey(0)
#im1 = gif_images[0]
#im1.save("out.gif", save_all=True, append_images=gif_images[1:], duration=1000, loop=0)
gif_images = []
images = []
#print("*"*20 + "Starting new episode" + "*"*20,
# self._env._current_episode.curr_viewpoint.image_id)
#if observations and "heading" in observations:
# print("Episode heading: %s" % observations["heading"])
elapsed_steps = 0
goal_idx = 1
last_goal_idx = len(self._env._current_episode.goals) - 1
images.append(observations["rgb"][:, :, [2, 1, 0]])
observations["images"] = images
print("Target path ",
[str(goal) for goal in self._env._current_episode.goals])
while not self._env.episode_over:
goal_viewpoint = self._env._current_episode.goals[goal_idx]
action = agent.act(
observations,
goal_viewpoint,
)
action["action_args"].update(
{"episode": self._env._current_episode})
if action["action"] == "TELEPORT":
if goal_idx < last_goal_idx:
goal_idx += 1
else:
goal_idx = -1
prev_state = self._env._sim.get_agent_state()
prev_image_id = self._env._current_episode.curr_viewpoint.image_id
prev_heading = observations["heading"]
prev_nav_locations = observations["adjacentViewpoints"]
#print("Taking action %s from %s \n" % (action["action"], self._env._current_episode.curr_viewpoint.image_id))
observations = self._env.step(action)
#pprint(observations["adjacentViewpoints"])
images.append(observations["rgb"][:, :, [2, 1, 0]])
observations["images"] = images
#print("Result of Action in position %s\n" % self._env._current_episode.curr_viewpoint.image_id)
state = self._env._sim.get_agent_state()
image_id = self._env._current_episode.curr_viewpoint.image_id
heading = observations["heading"]
nav_locations = observations["adjacentViewpoints"]
#print("Current position", state.position)
#print("Current rotation", state.rotation)
action_history.append({
"action": action["action"],
"prev_image_id": prev_image_id,
"prev_heading": prev_heading,
"prev_pos": prev_state.position,
"prev_rot": prev_state.rotation,
"prev_nav_locations": prev_nav_locations,
"new_image_id": image_id,
"new_heading": heading,
"new_pos": state.position,
"new_rot": state.rotation,
"nav_locations": nav_locations,
})
#pprint(action_history)
metrics = self._env.get_metrics()
pprint(metrics)
if "navigationError" in metrics and metrics["navigationError"] > 0:
print("Scan %s" % self._env._current_episode.scan)
print("image_id %s" %
str(self._env._current_episode.goals[0].image_id))
for m, v in metrics.items():
if m != "distance_to_goal":
agg_metrics[m] += v
count_episodes += 1
print(count_episodes)
avg_metrics = {k: v / count_episodes for k, v in agg_metrics.items()}
return avg_metrics
def evaluate(self,
agent: Agent,
num_episodes: Optional[int] = None) -> Dict[str, float]:
r"""..
:param agent: agent to be evaluated in environment.
:param num_episodes: count of number of episodes for which the
evaluation should be run.
:return: dict containing metrics tracked by environment.
"""
self.reset_benchmark()
if num_episodes is None:
num_episodes = len(self._env.episodes)
else:
assert num_episodes <= len(self._env.episodes), (
"num_episodes({}) is larger than number of episodes "
"in environment ({})".format(num_episodes,
len(self._env.episodes)))
assert num_episodes > 0, "num_episodes should be greater than 0"
count_episodes = 0
while count_episodes < num_episodes:
agent.reset()
observations = self._env.reset()
action_history = []
#print("*"*20 + "Starting new episode" + "*"*20,
# self._env._current_episode.curr_viewpoint.image_id)
elapsed_steps = 0
while not self._env.episode_over:
action = agent.act(
observations,
elapsed_steps,
self._env._sim.previous_step_collided,
)
action["action_args"].update(
{"episode": self._env._current_episode})
if elapsed_steps == 0 or action["action"] == "TELEPORT":
elapsed_steps += 1
prev_state = self._env._sim.get_agent_state()
prev_image_id = self._env._current_episode.curr_viewpoint.image_id
prev_heading = observations["heading"]
prev_nav_locations = observations["adjacentViewpoints"]
#print("Taking action %s from %s \n" % (action["action"], self._env._current_episode.curr_viewpoint.image_id))
observations = self._env.step(action)
#print("Result of Action in position %s\n" % self._env._current_episode.curr_viewpoint.image_id)
state = self._env._sim.get_agent_state()
image_id = self._env._current_episode.curr_viewpoint.image_id
heading = observations["heading"]
nav_locations = observations["adjacentViewpoints"]
#print("Current position", state.position)
#print("Current rotation", state.rotation)
#print("\n\n")
action_history.append({
"action": action["action"],
"prev_image_id": prev_image_id,
"prev_heading": prev_heading,
"prev_pos": prev_state.position,
"prev_rot": prev_state.rotation,
"prev_nav_locations": prev_nav_locations,
"new_image_id": image_id,
"new_heading": heading,
"new_pos": state.position,
"new_rot": state.rotation,
#"nav_locations": nav_locations,
})
#print("Target path ", [str(goal) for goal in self._env._current_episode.goals])
#pprint(action_history)
metrics = self._env.get_metrics()
if np.isinf(metrics["navigationError"]):
pprint(action_history)
print("Target path ",
[str(goal) for goal in self._env._current_episode.goals])
pprint(metrics)
for m, v in metrics.items():
if m != "distance_to_goal":
self.agg_metrics[m] += v
count_episodes += 1
print(count_episodes)
avg_metrics = {
k: v / count_episodes
for k, v in self.agg_metrics.items()
}
return avg_metrics
