def _setup_actor_critic_agent(self, ppo_cfg: Config) -> None:
r"""
Sets up actor critic and agent for PPO
Args:
ppo_cfg: config node with relevant params
Returns:
None
"""
logger.add_filehandler(ppo_cfg.log_file)
self.actor_critic = Policy(
observation_space=self.envs.observation_spaces[0],
action_space=self.envs.action_spaces[0],
hidden_size=512,
goal_sensor_uuid=self.config.TASK_CONFIG.TASK.GOAL_SENSOR_UUID,
)
self.actor_critic.to(self.device)
self.agent = PPO(
actor_critic=self.actor_critic,
clip_param=ppo_cfg.clip_param,
ppo_epoch=ppo_cfg.ppo_epoch,
num_mini_batch=ppo_cfg.num_mini_batch,
value_loss_coef=ppo_cfg.value_loss_coef,
entropy_coef=ppo_cfg.entropy_coef,
lr=ppo_cfg.lr,
eps=ppo_cfg.eps,
max_grad_norm=ppo_cfg.max_grad_norm,
)
def __init__(self, config: Config):
spaces = {
"pointgoal":
Box(
low=np.finfo(np.float32).min,
high=np.finfo(np.float32).max,
shape=(2, ),
dtype=np.float32,
)
}
if config.INPUT_TYPE in ["depth", "rgbd"]:
spaces["depth"] = Box(
low=0,
high=1,
shape=(config.RESOLUTION, config.RESOLUTION, 1),
dtype=np.float32,
)
if config.INPUT_TYPE in ["rgb", "rgbd"]:
spaces["rgb"] = Box(
low=0,
high=255,
shape=(config.RESOLUTION, config.RESOLUTION, 3),
dtype=np.uint8,
)
observation_spaces = Dict(spaces)
action_spaces = Discrete(4)
self.device = (torch.device("cuda:{}".format(config.PTH_GPU_ID))
if torch.cuda.is_available() else torch.device("cpu"))
self.hidden_size = config.HIDDEN_SIZE
random.seed(config.RANDOM_SEED)
torch.random.manual_seed(config.RANDOM_SEED)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
self.actor_critic = Policy(
observation_space=observation_spaces,
action_space=action_spaces,
hidden_size=self.hidden_size,
)
self.actor_critic.to(self.device)
if config.MODEL_PATH:
ckpt = torch.load(config.MODEL_PATH, map_location=self.device)
# Filter only actor_critic weights
self.actor_critic.load_state_dict({
k.replace("actor_critic.", ""): v
for k, v in ckpt["state_dict"].items() if "actor_critic" in k
})
else:
habitat.logger.error("Model checkpoint wasn't loaded, evaluating "
"a random model.")
self.test_recurrent_hidden_states = None
self.not_done_masks = None
class PPOAgent(Agent):
def __init__(self, config: Config):
self.goal_sensor_uuid = config.GOAL_SENSOR_UUID
spaces = {
self.goal_sensor_uuid:
Box(
low=np.finfo(np.float32).min,
high=np.finfo(np.float32).max,
shape=(2, ),
dtype=np.float32,
)
}
if config.INPUT_TYPE in ["depth", "rgbd"]:
spaces["depth"] = Box(
low=0,
high=1,
shape=(config.RESOLUTION, config.RESOLUTION, 1),
dtype=np.float32,
)
if config.INPUT_TYPE in ["rgb", "rgbd"]:
spaces["rgb"] = Box(
low=0,
high=255,
shape=(config.RESOLUTION, config.RESOLUTION, 3),
dtype=np.uint8,
)
observation_spaces = Dict(spaces)
action_spaces = Discrete(4)
self.device = (torch.device("cuda:{}".format(config.PTH_GPU_ID))
if torch.cuda.is_available() else torch.device("cpu"))
self.hidden_size = config.HIDDEN_SIZE
random.seed(config.RANDOM_SEED)
torch.random.manual_seed(config.RANDOM_SEED)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
self.actor_critic = Policy(
observation_space=observation_spaces,
action_space=action_spaces,
hidden_size=self.hidden_size,
goal_sensor_uuid=self.goal_sensor_uuid,
)
self.actor_critic.to(self.device)
if config.MODEL_PATH:
ckpt = torch.load(config.MODEL_PATH, map_location=self.device)
# Filter only actor_critic weights
self.actor_critic.load_state_dict({
k.replace("actor_critic.", ""): v
for k, v in ckpt["state_dict"].items() if "actor_critic" in k
})
else:
habitat.logger.error("Model checkpoint wasn't loaded, evaluating "
"a random model.")
self.test_recurrent_hidden_states = None
self.not_done_masks = None
def reset(self):
self.test_recurrent_hidden_states = torch.zeros(1,
self.hidden_size,
device=self.device)
self.not_done_masks = torch.zeros(1, 1, device=self.device)
def act(self, observations):
batch = batch_obs([observations])
for sensor in batch:
batch[sensor] = batch[sensor].to(self.device)
with torch.no_grad():
_, actions, _, self.test_recurrent_hidden_states = self.actor_critic.act(
batch,
self.test_recurrent_hidden_states,
self.not_done_masks,
deterministic=False,
)
# Make masks not done till reset (end of episode) will be called
self.not_done_masks = torch.ones(1, 1, device=self.device)
return actions[0][0].item()
class PPOTrainer(BaseRLTrainer):
r"""
Trainer class for PPO algorithm
Paper: https://arxiv.org/abs/1707.06347
"""
supported_tasks = ["Nav-v0"]
def __init__(self, config=None):
super().__init__(config)
self.actor_critic = None
self.agent = None
self.envs = None
self.device = None
self.video_option = []
if config is not None:
logger.info(f"config: {config}")
def _setup_actor_critic_agent(self, ppo_cfg: Config) -> None:
r"""
Sets up actor critic and agent for PPO
Args:
ppo_cfg: config node with relevant params
Returns:
None
"""
logger.add_filehandler(ppo_cfg.log_file)
self.actor_critic = Policy(
observation_space=self.envs.observation_spaces[0],
action_space=self.envs.action_spaces[0],
hidden_size=512,
goal_sensor_uuid=self.config.TASK_CONFIG.TASK.GOAL_SENSOR_UUID,
)
self.actor_critic.to(self.device)
self.agent = PPO(
actor_critic=self.actor_critic,
clip_param=ppo_cfg.clip_param,
ppo_epoch=ppo_cfg.ppo_epoch,
num_mini_batch=ppo_cfg.num_mini_batch,
value_loss_coef=ppo_cfg.value_loss_coef,
entropy_coef=ppo_cfg.entropy_coef,
lr=ppo_cfg.lr,
eps=ppo_cfg.eps,
max_grad_norm=ppo_cfg.max_grad_norm,
)
def save_checkpoint(self, file_name: str) -> None:
r"""
Save checkpoint with specified name
Args:
file_name: file name for checkpoint
Returns:
None
"""
checkpoint = {
"state_dict": self.agent.state_dict(),
"config": self.config,
}
torch.save(
checkpoint,
os.path.join(self.config.TRAINER.RL.PPO.checkpoint_folder,
file_name),
)
def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:
r"""
Load checkpoint of specified path as a dict
Args:
checkpoint_path: path of target checkpoint
*args: additional positional args
**kwargs: additional keyword args
Returns:
dict containing checkpoint info
"""
return torch.load(checkpoint_path, map_location=self.device)
def train(self) -> None:
r"""
Main method for training PPO
Returns:
None
"""
assert (
self.config is not None
), "trainer is not properly initialized, need to specify config file"
self.envs = construct_envs(self.config, NavRLEnv)
ppo_cfg = self.config.TRAINER.RL.PPO
self.device = torch.device("cuda", ppo_cfg.pth_gpu_id)
if not os.path.isdir(ppo_cfg.checkpoint_folder):
os.makedirs(ppo_cfg.checkpoint_folder)
self._setup_actor_critic_agent(ppo_cfg)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
observations = self.envs.reset()
batch = batch_obs(observations)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
rollouts.to(self.device)
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_counts = torch.zeros(self.envs.num_envs, 1)
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_counts = deque(maxlen=ppo_cfg.reward_window_size)
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
with (get_tensorboard_writer(
log_dir=ppo_cfg.tensorboard_dir,
purge_step=count_steps,
flush_secs=30,
)) as writer:
for update in range(ppo_cfg.num_updates):
if ppo_cfg.use_linear_lr_decay:
update_linear_schedule(
self.agent.optimizer,
update,
ppo_cfg.num_updates,
ppo_cfg.lr,
)
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * (
1 - update / ppo_cfg.num_updates)
for step in range(ppo_cfg.num_steps):
t_sample_action = time.time()
# sample actions
with torch.no_grad():
step_observation = {
k: v[step]
for k, v in rollouts.observations.items()
}
(
values,
actions,
actions_log_probs,
recurrent_hidden_states,
) = self.actor_critic.act(
step_observation,
rollouts.recurrent_hidden_states[step],
rollouts.masks[step],
)
pth_time += time.time() - t_sample_action
t_step_env = time.time()
outputs = self.envs.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
env_time += time.time() - t_step_env
t_update_stats = time.time()
batch = batch_obs(observations)
rewards = torch.tensor(rewards, dtype=torch.float)
rewards = rewards.unsqueeze(1)
masks = torch.tensor(
[[0.0] if done else [1.0] for done in dones],
dtype=torch.float,
)
current_episode_reward += rewards
episode_rewards += (1 - masks) * current_episode_reward
episode_counts += 1 - masks
current_episode_reward *= masks
rollouts.insert(
batch,
recurrent_hidden_states,
actions,
actions_log_probs,
values,
rewards,
masks,
)
count_steps += self.envs.num_envs
pth_time += time.time() - t_update_stats
window_episode_reward.append(episode_rewards.clone())
window_episode_counts.append(episode_counts.clone())
t_update_model = time.time()
with torch.no_grad():
last_observation = {
k: v[-1]
for k, v in rollouts.observations.items()
}
next_value = self.actor_critic.get_value(
last_observation,
rollouts.recurrent_hidden_states[-1],
rollouts.masks[-1],
).detach()
rollouts.compute_returns(next_value, ppo_cfg.use_gae,
ppo_cfg.gamma, ppo_cfg.tau)
value_loss, action_loss, dist_entropy = self.agent.update(
rollouts)
rollouts.after_update()
pth_time += time.time() - t_update_model
losses = [value_loss, action_loss]
stats = zip(
["count", "reward"],
[window_episode_counts, window_episode_reward],
)
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
count_steps)
writer.add_scalars(
"losses",
{k: l
for l, k in zip(losses, ["value", "policy"])},
count_steps,
)
# log stats
if update > 0 and update % ppo_cfg.log_interval == 0:
logger.info("update: {}\tfps: {:.3f}\t".format(
update, count_steps / (time.time() - t_start)))
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(update, env_time, pth_time,
count_steps))
window_rewards = (window_episode_reward[-1] -
window_episode_reward[0]).sum()
window_counts = (window_episode_counts[-1] -
window_episode_counts[0]).sum()
if window_counts > 0:
logger.info(
"Average window size {} reward: {:3f}".format(
len(window_episode_reward),
(window_rewards / window_counts).item(),
))
else:
logger.info("No episodes finish in current window")
# checkpoint model
if update % ppo_cfg.checkpoint_interval == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
def eval(self) -> None:
r"""
Main method of evaluating PPO
Returns:
None
"""
ppo_cfg = self.config.TRAINER.RL.PPO
self.device = torch.device("cuda", ppo_cfg.pth_gpu_id)
self.video_option = ppo_cfg.video_option.strip().split(",")
if "tensorboard" in self.video_option:
assert (ppo_cfg.tensorboard_dir is not None
), "Must specify a tensorboard directory for video display"
if "disk" in self.video_option:
assert (ppo_cfg.video_dir is not None
), "Must specify a directory for storing videos on disk"
with get_tensorboard_writer(ppo_cfg.tensorboard_dir,
purge_step=0,
flush_secs=30) as writer:
if os.path.isfile(ppo_cfg.eval_ckpt_path_or_dir):
# evaluate singe checkpoint
self._eval_checkpoint(ppo_cfg.eval_ckpt_path_or_dir, writer)
else:
# evaluate multiple checkpoints in order
prev_ckpt_ind = -1
while True:
current_ckpt = None
while current_ckpt is None:
current_ckpt = poll_checkpoint_folder(
ppo_cfg.eval_ckpt_path_or_dir, prev_ckpt_ind)
time.sleep(2) # sleep for 2 secs before polling again
logger.warning(
"=============current_ckpt: {}=============".format(
current_ckpt))
prev_ckpt_ind += 1
self._eval_checkpoint(
checkpoint_path=current_ckpt,
writer=writer,
cur_ckpt_idx=prev_ckpt_ind,
)
def _eval_checkpoint(
self,
checkpoint_path: str,
writer: TensorboardWriter,
cur_ckpt_idx: int = 0,
) -> None:
r"""
Evaluates a single checkpoint
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
cur_ckpt_idx: index of cur checkpoint for logging
Returns:
None
"""
ckpt_dict = self.load_checkpoint(checkpoint_path,
map_location=self.device)
ckpt_config = ckpt_dict["config"]
config = self.config.clone()
ckpt_cmd_opts = ckpt_config.CMD_TRAILING_OPTS
eval_cmd_opts = config.CMD_TRAILING_OPTS
# config merge priority: eval_opts > ckpt_opts > eval_cfg > ckpt_cfg
# first line for old checkpoint compatibility
config.merge_from_other_cfg(ckpt_config)
config.merge_from_other_cfg(self.config)
config.merge_from_list(ckpt_cmd_opts)
config.merge_from_list(eval_cmd_opts)
ppo_cfg = config.TRAINER.RL.PPO
config.TASK_CONFIG.defrost()
config.TASK_CONFIG.DATASET.SPLIT = "val"
agent_sensors = ppo_cfg.sensors.strip().split(",")
config.TASK_CONFIG.SIMULATOR.AGENT_0.SENSORS = agent_sensors
if self.video_option:
config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP")
config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS")
config.freeze()
logger.info(f"env config: {config}")
self.envs = construct_envs(config, NavRLEnv)
self._setup_actor_critic_agent(ppo_cfg)
self.agent.load_state_dict(ckpt_dict["state_dict"])
self.actor_critic = self.agent.actor_critic
observations = self.envs.reset()
batch = batch_obs(observations)
for sensor in batch:
batch[sensor] = batch[sensor].to(self.device)
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(ppo_cfg.num_processes,
ppo_cfg.hidden_size,
device=self.device)
not_done_masks = torch.zeros(ppo_cfg.num_processes,
1,
device=self.device)
stats_episodes = dict() # dict of dicts that stores stats per episode
rgb_frames = [[]
] * ppo_cfg.num_processes # type: List[List[np.ndarray]]
if self.video_option:
os.makedirs(ppo_cfg.video_dir, exist_ok=True)
while (len(stats_episodes) < ppo_cfg.count_test_episodes
and self.envs.num_envs > 0):
current_episodes = self.envs.current_episodes()
with torch.no_grad():
_, actions, _, test_recurrent_hidden_states = self.actor_critic.act(
batch,
test_recurrent_hidden_states,
not_done_masks,
deterministic=False,
)
outputs = self.envs.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
batch = batch_obs(observations)
for sensor in batch:
batch[sensor] = batch[sensor].to(self.device)
not_done_masks = torch.tensor(
[[0.0] if done else [1.0] for done in dones],
dtype=torch.float,
device=self.device,
)
rewards = torch.tensor(rewards,
dtype=torch.float,
device=self.device).unsqueeze(1)
current_episode_reward += rewards
next_episodes = self.envs.current_episodes()
envs_to_pause = []
n_envs = self.envs.num_envs
for i in range(n_envs):
if (
next_episodes[i].scene_id,
next_episodes[i].episode_id,
) in stats_episodes:
envs_to_pause.append(i)
# episode ended
if not_done_masks[i].item() == 0:
episode_stats = dict()
episode_stats["spl"] = infos[i]["spl"]
episode_stats["success"] = int(infos[i]["spl"] > 0)
episode_stats["reward"] = current_episode_reward[i].item()
current_episode_reward[i] = 0
# use scene_id + episode_id as unique id for storing stats
stats_episodes[(
current_episodes[i].scene_id,
current_episodes[i].episode_id,
)] = episode_stats
if self.video_option:
generate_video(
ppo_cfg,
rgb_frames[i],
current_episodes[i].episode_id,
cur_ckpt_idx,
infos[i]["spl"],
writer,
)
rgb_frames[i] = []
# episode continues
elif self.video_option:
frame = observations_to_image(observations[i], infos[i])
rgb_frames[i].append(frame)
# pausing self.envs with no new episode
if len(envs_to_pause) > 0:
state_index = list(range(self.envs.num_envs))
for idx in reversed(envs_to_pause):
state_index.pop(idx)
self.envs.pause_at(idx)
# indexing along the batch dimensions
test_recurrent_hidden_states = test_recurrent_hidden_states[
state_index]
not_done_masks = not_done_masks[state_index]
current_episode_reward = current_episode_reward[state_index]
for k, v in batch.items():
batch[k] = v[state_index]
if self.video_option:
rgb_frames = [rgb_frames[i] for i in state_index]
aggregated_stats = dict()
for stat_key in next(iter(stats_episodes.values())).keys():
aggregated_stats[stat_key] = sum(
[v[stat_key] for v in stats_episodes.values()])
num_episodes = len(stats_episodes)
episode_reward_mean = aggregated_stats["reward"] / num_episodes
episode_spl_mean = aggregated_stats["spl"] / num_episodes
episode_success_mean = aggregated_stats["success"] / num_episodes
logger.info(
"Average episode reward: {:.6f}".format(episode_reward_mean))
logger.info(
"Average episode success: {:.6f}".format(episode_success_mean))
logger.info("Average episode SPL: {:.6f}".format(episode_spl_mean))
writer.add_scalars(
"eval_reward",
{"average reward": episode_reward_mean},
cur_ckpt_idx,
)
writer.add_scalars("eval_SPL", {"average SPL": episode_spl_mean},
cur_ckpt_idx)
writer.add_scalars(
"eval_success",
{"average success": episode_success_mean},
cur_ckpt_idx,
)