def train_agent(agent):
r"""Runs experiment given mode and config
Args:
exp_config: path to config file.
run_type: "train" or "eval.
opts: list of strings of additional config options.
Returns:
None.
"""
config_file = r"/home/userone/workspace/bed1/ppo_custom/ppo_pointnav_example.yaml"
config = get_config(config_file, None)
# config = get_config1(agent)
env = construct_envs(config, get_env_class(config.ENV_NAME))
config.defrost()
config.TASK_CONFIG.DATASET.DATA_PATH = config.DATA_PATH_SET
config.TASK_CONFIG.DATASET.SCENES_DIR = config.SCENE_DIR_SET
config.freeze()
random.seed(config.TASK_CONFIG.SEED)
np.random.seed(config.TASK_CONFIG.SEED)
if agent == "ppo_agent":
trainer = ppo_trainer.PPOTrainer(config)
trainer.train(env)
def _load_real_data(self, config):
real_mem = self.memory_real
config = config.clone()
config.defrost()
config.TASK_CONFIG.DATASET.TYPE = "PepperRobot"
config.ENV_NAME = "PepperPlaybackEnv"
config.NUM_PROCESSES = 1
config.freeze()
real_env = construct_envs(
config, get_env_class(config.ENV_NAME)
)
observations = real_env.reset()
real_mem.insert([observations[0]["rgb"], observations[0]["depth"]])
print(f"Loading {self.real_mem_size} ROBOT observations ...")
for i in range(self.real_mem_size):
outputs = real_env.step([0])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
real_mem.insert([observations[0]["rgb"], observations[0]["depth"]])
print(f"Loaded {self.real_mem_size} observations from ROBOT dataset")
def make_vec_envs(
config,
device,
devices,
seed=100,
task_type="pose",
enable_odometry_noise=None,
odometer_noise_scaling=None,
measure_noise_free_area=None,
):
if task_type == "pose":
config = get_config_pose(config, [])
env_class = PoseRLEnv
else:
config = get_config_exp_nav(config, [])
env_class = ExpNavRLEnv
config.defrost()
config.TASK_CONFIG.SEED = seed
config.TASK_CONFIG.SIMULATOR.SEED = seed
if enable_odometry_noise is not None:
config.TASK_CONFIG.SIMULATOR.ENABLE_ODOMETRY_NOISE = enable_odometry_noise
config.TASK_CONFIG.SIMULATOR.ODOMETER_NOISE_SCALING = odometer_noise_scaling
if measure_noise_free_area is not None:
config.TASK_CONFIG.SIMULATOR.OCCUPANCY_MAPS.MEASURE_NOISE_FREE_AREA = (
measure_noise_free_area)
config.freeze()
envs = construct_envs(config, env_class, devices)
envs = BatchDataWrapper(envs)
envs = TransposeImageWrapper(envs)
envs = RenameKeysWrapper(envs)
envs = DeviceWrapper(envs, device)
return envs
def __init__(self, config=None):
super().__init__(config)
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
self._setup_actor_critic_agent(self.config)
self.rollout = RolloutStorage(self.config.RL.PPO.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
self.config.RL.PPO.hidden_size)
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
self.rollout.to(self.device)
def run_random_agent():
config_file = r"/home/userone/workspace/bed1/ppo_custom/ppo_pointnav_example.yaml"
config = get_config(config_file, None)
# config = get_config1(agent)
env = construct_envs(config, get_env_class(config.ENV_NAME))
config.defrost()
config.TASK_CONFIG.DATASET.DATA_PATH = config.DATA_PATH_SET
config.TASK_CONFIG.DATASET.SCENES_DIR = config.SCENE_DIR_SET
config.freeze()
random.seed(config.TASK_CONFIG.SEED)
np.random.seed(config.TASK_CONFIG.SEED)
random_agent.run(config, env, 3)
def trigger_transfer_learn(agent):
config_file = r"/home/userone/workspace/bed1/ppo_custom/ppo_pointnav_example.yaml"
config = get_config(config_file, None)
# config = get_config1(agent)
env = construct_envs(config, get_env_class(config.ENV_NAME))
config.defrost()
config.TASK_CONFIG.DATASET.DATA_PATH = config.DATA_PATH_SET
config.TASK_CONFIG.DATASET.SCENES_DIR = config.SCENE_DIR_SET
config.freeze()
random.seed(config.TASK_CONFIG.SEED)
np.random.seed(config.TASK_CONFIG.SEED)
if agent == "ppo_agent":
trainer = transfer_ppo.PPOTrainer(config)
trainer.train(env)
def _eval_checkpoint(
self,
checkpoint_path: str,
writer: TensorboardWriter,
checkpoint_index: int = 0,
) -> None:
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
None
"""
# Map location CPU is almost always better than mapping to a CUDA device.
ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")
if self.config.EVAL.USE_CKPT_CONFIG:
config = self._setup_eval_config(ckpt_dict["config"])
else:
config = self.config.clone()
ppo_cfg = config.RL.PPO
config.defrost()
config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
config.freeze()
if len(self.config.VIDEO_OPTION) > 0:
config.defrost()
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, get_env_class(config.ENV_NAME))
self._setup_actor_critic_agent(ppo_cfg)
self.actor_critic.eval()
if self._static_encoder:
self._encoder = self.agent.actor_critic.net.visual_encoder
self.agent.load_state_dict(ckpt_dict["state_dict"])
self.actor_critic = self.agent.actor_critic
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
if self._static_encoder:
batch["visual_features"] = self._encoder(batch)
batch["prev_visual_features"] = torch.zeros_like(
batch["visual_features"])
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers,
self.config.NUM_PROCESSES,
ppo_cfg.hidden_size,
device=self.device,
)
prev_actions = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device,
dtype=torch.long)
not_done_masks = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device)
stats_episodes = dict() # dict of dicts that stores stats per episode
rgb_frames = [[] for _ in range(self.config.NUM_PROCESSES)
] # type: List[List[np.ndarray]]
if len(self.config.VIDEO_OPTION) > 0:
os.makedirs(self.config.VIDEO_DIR, exist_ok=True)
number_of_eval_episodes = self.config.TEST_EPISODE_COUNT
if number_of_eval_episodes == -1:
number_of_eval_episodes = sum(self.envs.number_of_episodes)
else:
total_num_eps = sum(self.envs.number_of_episodes)
if total_num_eps < number_of_eval_episodes:
logger.warn(
f"Config specified {number_of_eval_episodes} eval episodes"
", dataset only has {total_num_eps}.")
logger.warn(f"Evaluating with {total_num_eps} instead.")
number_of_eval_episodes = total_num_eps
pbar = tqdm.tqdm(total=number_of_eval_episodes)
self.actor_critic.eval()
while (len(stats_episodes) < number_of_eval_episodes
and self.envs.num_envs > 0):
current_episodes = self.envs.current_episodes()
with torch.no_grad():
step_batch = batch
(
_,
actions,
_,
test_recurrent_hidden_states,
) = self.actor_critic.act(
batch,
test_recurrent_hidden_states,
prev_actions,
not_done_masks,
deterministic=False,
)
prev_actions.copy_(actions)
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, device=self.device)
if self._static_encoder:
batch["prev_visual_features"] = step_batch["visual_features"]
batch["visual_features"] = self._encoder(batch)
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:
pbar.update()
episode_stats = dict()
episode_stats["reward"] = current_episode_reward[i].item()
episode_stats.update(
self._extract_scalars_from_info(infos[i]))
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 len(self.config.VIDEO_OPTION) > 0:
generate_video(
video_option=self.config.VIDEO_OPTION,
video_dir=self.config.VIDEO_DIR,
images=rgb_frames[i],
episode_id=current_episodes[i].episode_id,
checkpoint_idx=checkpoint_index,
metrics=self._extract_scalars_from_info(infos[i]),
tb_writer=writer,
)
rgb_frames[i] = []
# episode continues
elif len(self.config.VIDEO_OPTION) > 0:
frame = observations_to_image(observations[i], infos[i])
rgb_frames[i].append(frame)
(
self.envs,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
) = self._pause_envs(
envs_to_pause,
self.envs,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
)
num_episodes = len(stats_episodes)
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)
for k, v in aggregated_stats.items():
logger.info(f"Average episode {k}: {v:.4f}")
step_id = checkpoint_index
if "extra_state" in ckpt_dict and "step" in ckpt_dict["extra_state"]:
step_id = ckpt_dict["extra_state"]["step"]
writer.add_scalars(
"eval_reward",
{"average reward": aggregated_stats["reward"]},
step_id,
)
metrics = {k: v for k, v in aggregated_stats.items() if k != "reward"}
if len(metrics) > 0:
writer.add_scalars("eval_metrics", metrics, step_id)
self.envs.close()
def _eval_checkpoint(
self,
checkpoint_path: str,
writer: TensorboardWriter,
checkpoint_index: int = 0,
) -> None:
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
None
"""
ckpt_dict = self.load_checkpoint(checkpoint_path,
map_location=self.device)
config = self._setup_eval_config(ckpt_dict["config"])
ppo_cfg = config.RL.PPO
if len(self.config.VIDEO_OPTION) > 0:
config.defrost()
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(self.config,
get_env_class(self.config.ENV_NAME))
self._setup_actor_critic_agent(ppo_cfg)
self.agent.load_state_dict(ckpt_dict["state_dict"])
self.actor_critic = self.agent.actor_critic
# get name of performance metric, e.g. "spl"
metric_name = self.config.TASK_CONFIG.TASK.MEASUREMENTS[0]
metric_cfg = getattr(self.config.TASK_CONFIG.TASK, metric_name)
measure_type = baseline_registry.get_measure(metric_cfg.TYPE)
assert measure_type is not None, "invalid measurement type {}".format(
metric_cfg.TYPE)
self.metric_uuid = measure_type(None, None)._get_uuid()
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(
self.actor_critic.net.num_recurrent_layers,
self.config.NUM_PROCESSES,
ppo_cfg.hidden_size,
device=self.device,
)
prev_actions = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device,
dtype=torch.long)
not_done_masks = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device)
stats_episodes = dict() # dict of dicts that stores stats per episode
rgb_frames = [
[]
] * self.config.NUM_PROCESSES # type: List[List[np.ndarray]]
if len(self.config.VIDEO_OPTION) > 0:
os.makedirs(self.config.VIDEO_DIR, exist_ok=True)
while (len(stats_episodes) < self.config.TEST_EPISODE_COUNT
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,
prev_actions,
not_done_masks,
deterministic=False,
)
prev_actions.copy_(actions)
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[self.metric_uuid] = infos[i][
self.metric_uuid]
episode_stats["success"] = int(
infos[i][self.metric_uuid] > 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 len(self.config.VIDEO_OPTION) > 0:
generate_video(
video_option=self.config.VIDEO_OPTION,
video_dir=self.config.VIDEO_DIR,
images=rgb_frames[i],
episode_id=current_episodes[i].episode_id,
checkpoint_idx=checkpoint_index,
metric_name=self.metric_uuid,
metric_value=infos[i][self.metric_uuid],
tb_writer=writer,
)
rgb_frames[i] = []
# episode continues
elif len(self.config.VIDEO_OPTION) > 0:
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]
prev_actions = prev_actions[state_index]
for k, v in batch.items():
batch[k] = v[state_index]
if len(self.config.VIDEO_OPTION) > 0:
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_metric_mean = aggregated_stats[self.metric_uuid] / num_episodes
episode_success_mean = aggregated_stats["success"] / num_episodes
logger.info(f"Average episode reward: {episode_reward_mean:.6f}")
logger.info(f"Average episode success: {episode_success_mean:.6f}")
logger.info(
f"Average episode {self.metric_uuid}: {episode_metric_mean:.6f}")
writer.add_scalars(
"eval_reward",
{"average reward": episode_reward_mean},
checkpoint_index,
)
writer.add_scalars(
f"eval_{self.metric_uuid}",
{f"average {self.metric_uuid}": episode_metric_mean},
checkpoint_index,
)
writer.add_scalars(
"eval_success",
{"average success": episode_success_mean},
checkpoint_index,
)
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.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())))
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1),
reward=torch.zeros(self.envs.num_envs, 1),
)
window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_rollout_step(rollouts,
current_episode_reward,
running_episode_stats)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
for k, v in running_episode_stats.items():
window_episode_stats[k].append(v.clone())
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
count_steps)
# Check to see if there are any metrics
# that haven't been logged yet
metrics = {
k: v / deltas["count"]
for k, v in deltas.items() if k not in {"reward", "count"}
}
if len(metrics) > 0:
writer.add_scalars("metrics", metrics, count_steps)
losses = [value_loss, action_loss]
writer.add_scalars(
"losses",
{k: l
for l, k in zip(losses, ["value", "policy"])},
count_steps,
)
# log stats
if update > 0 and update % self.config.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))
logger.info("Average window size: {} {}".format(
len(window_episode_stats["count"]),
" ".join("{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items() if k != "count"),
))
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth",
dict(step=count_steps))
count_checkpoints += 1
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.add_new_based_on_cfg()
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.CHECKPOINT_FOLDER)
self._setup_actor_critic_agent(ppo_cfg, train=True)
if self.config.PRETRAINED_CHECKPOINT_PATH:
ckpt_dict = self.load_checkpoint(
self.config.PRETRAINED_CHECKPOINT_PATH, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"], strict=False)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs_augment_aux(observations, self.envs.get_shared_mem())
for sensor in rollouts.observations:
if sensor in batch:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
info_data_keys = ["discovered", "collisions_wall", "collisions_prox"]
log_data_keys = [
"episode_rewards", "episode_go_rewards", "episode_counts",
"current_episode_reward", "current_episode_go_reward"
] + info_data_keys
log_data = dict(
{k: torch.zeros(self.envs.num_envs, 1)
for k in log_data_keys})
info_data = dict({k: log_data[k] for k in info_data_keys})
win_keys = log_data_keys
win_keys.pop(win_keys.index("current_episode_reward"))
win_keys.pop(win_keys.index("current_episode_go_reward"))
windows = dict({
k: deque(maxlen=ppo_cfg.reward_window_size)
for k in log_data.keys()
})
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
train_steps = min(self.config.NUM_UPDATES,
self.config.HARD_NUM_UPDATES)
log_interval = self.config.LOG_INTERVAL
num_updates = self.config.NUM_UPDATES
agent = self.agent
ckpt_interval = self.config.CHECKPOINT_INTERVAL
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(train_steps):
if ppo_cfg.use_linear_clip_decay:
agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, num_updates)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts, log_data["current_episode_reward"],
log_data["current_episode_go_reward"],
log_data["episode_rewards"],
log_data["episode_go_rewards"],
log_data["episode_counts"], info_data)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy,\
aux_loss = self._update_agent(ppo_cfg, rollouts)
# TODO check if LR is init
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
pth_time += delta_pth_time
# ==================================================================================
# -- Log data for window averaging
for k, v in windows.items():
windows[k].append(log_data[k].clone())
value_names = ["value", "policy", "entropy"] + list(
aux_loss.keys())
losses = [value_loss, action_loss, dist_entropy] + list(
aux_loss.values())
stats = zip(list(windows.keys()), list(windows.values()))
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in stats
}
act_ep = deltas["episode_counts"]
counts = max(act_ep, 1.0)
deltas["episode_counts"] *= counts
for k, v in deltas.items():
deltas[k] = v / counts
writer.add_scalar(k, deltas[k], count_steps)
writer.add_scalars("losses",
{k: l
for l, k in zip(losses, value_names)},
count_steps)
# log stats
if update > 0 and update % 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))
if act_ep > 0:
log_txt = f"Average window size {len(windows['episode_counts'])}"
for k, v in deltas.items():
log_txt += f" | {k}: {v:.3f}"
logger.info(log_txt)
logger.info(
f"Aux losses: {list(zip(value_names, losses))}")
else:
logger.info("No episodes finish in current window")
# ==================================================================================
# checkpoint model
if update % ckpt_interval == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
self.device = torch.device("cuda", self.config.TORCH_GPU_ID)
if not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.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
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts,
current_episode_reward,
episode_rewards,
episode_counts,
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy = self._update_agent(
ppo_cfg, rollouts)
pth_time += delta_pth_time
window_episode_reward.append(episode_rewards.clone())
window_episode_counts.append(episode_counts.clone())
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 % self.config.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 % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
def _eval_checkpoint_bruce(
self,
checkpoint_path: str,
checkpoint_index: int = 0,
):
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
actor_critic
batch
not_done_masks
test_recurrent_hidden_states
"""
# Map location CPU is almost always better than mapping to a CUDA device.
ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")
print(ckpt_dict)
if self.config.EVAL.USE_CKPT_CONFIG:
config = self._setup_eval_config(ckpt_dict["config"])
else:
config = self.config.clone()
ppo_cfg = config.RL.PPO
config.defrost()
config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
config.freeze()
if len(self.config.VIDEO_OPTION) > 0:
config.defrost()
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, get_env_class(config.ENV_NAME))
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, device=self.device)
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers,
self.config.NUM_PROCESSES,
ppo_cfg.hidden_size,
device=self.device,
)
prev_actions = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device,
dtype=torch.long)
not_done_masks = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device)
return (self.actor_critic, batch, not_done_masks,
test_recurrent_hidden_states)
def train(self):
# Get environments for training
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.CHECKPOINT_FOLDER)
#logger.info(
# "agent number of parameters: {}".format(
# sum(param.numel() for param in self.agent.parameters())
# )
#)
# Change for the actual value
cfg = self.config.RL.PPO
rollouts = RolloutStorage(
cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
cfg.hidden_size,
)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations)
for sensor in rollouts.observations:
print(batch[sensor].shape)
# Copy the information to the wrapper
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
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
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
'''
def train(self, ckpt_path="", ckpt=-1, start_updates=0) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.local_rank, tcp_store = init_distrib_slurm(
self.config.RL.DDPPO.distrib_backend)
add_signal_handlers()
# Stores the number of workers that have finished their rollout
num_rollouts_done_store = distrib.PrefixStore("rollout_tracker",
tcp_store)
num_rollouts_done_store.set("num_done", "0")
self.world_rank = distrib.get_rank()
self.world_size = distrib.get_world_size()
random.seed(self.config.TASK_CONFIG.SEED + self.world_rank)
np.random.seed(self.config.TASK_CONFIG.SEED + self.world_rank)
self.config.defrost()
self.config.TORCH_GPU_ID = self.local_rank
self.config.SIMULATOR_GPU_ID = self.local_rank
self.config.freeze()
if torch.cuda.is_available():
self.device = torch.device("cuda", self.local_rank)
torch.cuda.set_device(self.device)
else:
self.device = torch.device("cpu")
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
task_cfg = self.config.TASK_CONFIG.TASK
observation_space = self.envs.observation_spaces[0]
aux_cfg = self.config.RL.AUX_TASKS
init_aux_tasks, num_recurrent_memories, aux_task_strings = self._setup_auxiliary_tasks(
aux_cfg, ppo_cfg, task_cfg, observation_space)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
observation_space,
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_memories=num_recurrent_memories)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
self._setup_actor_critic_agent(ppo_cfg, task_cfg, aux_cfg,
init_aux_tasks)
self.agent.init_distributed(find_unused_params=True)
if self.world_rank == 0:
logger.info("agent number of trainable parameters: {}".format(
sum(param.numel() for param in self.agent.parameters()
if param.requires_grad)))
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1),
reward=torch.zeros(self.envs.num_envs, 1), # including bonus
)
window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
prev_time = 0
if ckpt != -1:
logger.info(
f"Resuming runs at checkpoint {ckpt}. Timing statistics are not tracked properly."
)
assert ppo_cfg.use_linear_lr_decay is False and ppo_cfg.use_linear_clip_decay is False, "Resuming with decay not supported"
# This is the checkpoint we start saving at
count_checkpoints = ckpt + 1
count_steps = start_updates * ppo_cfg.num_steps * self.config.NUM_PROCESSES
ckpt_dict = self.load_checkpoint(ckpt_path, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"])
if "optim_state" in ckpt_dict:
self.agent.optimizer.load_state_dict(ckpt_dict["optim_state"])
else:
logger.warn("No optimizer state loaded, results may be funky")
if "extra_state" in ckpt_dict and "step" in ckpt_dict[
"extra_state"]:
count_steps = ckpt_dict["extra_state"]["step"]
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
self.agent.load_state_dict(interrupted_state["state_dict"])
self.agent.optimizer.load_state_dict(
interrupted_state["optim_state"])
lr_scheduler.load_state_dict(interrupted_state["lr_sched_state"])
requeue_stats = interrupted_state["requeue_stats"]
env_time = requeue_stats["env_time"]
pth_time = requeue_stats["pth_time"]
count_steps = requeue_stats["count_steps"]
count_checkpoints = requeue_stats["count_checkpoints"]
start_updates = requeue_stats["start_update"]
prev_time = requeue_stats["prev_time"]
with (TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs)
if self.world_rank == 0 else contextlib.suppress()) as writer:
for update in range(start_updates, self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
if EXIT.is_set():
self.envs.close()
if REQUEUE.is_set() and self.world_rank == 0:
requeue_stats = dict(
env_time=env_time,
pth_time=pth_time,
count_steps=count_steps,
count_checkpoints=count_checkpoints,
start_update=update,
prev_time=(time.time() - t_start) + prev_time,
)
save_interrupted_state(
dict(
state_dict=self.agent.state_dict(),
optim_state=self.agent.optimizer.state_dict(),
lr_sched_state=lr_scheduler.state_dict(),
config=self.config,
requeue_stats=requeue_stats,
))
requeue_job()
return
count_steps_delta = 0
self.agent.eval()
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_rollout_step(rollouts,
current_episode_reward,
running_episode_stats)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
# This is where the preemption of workers happens. If a
# worker detects it will be a straggler, it preempts itself!
if (step >=
ppo_cfg.num_steps * self.SHORT_ROLLOUT_THRESHOLD
) and int(num_rollouts_done_store.get("num_done")) > (
self.config.RL.DDPPO.sync_frac * self.world_size):
break
num_rollouts_done_store.add("num_done", 1)
self.agent.train()
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
aux_task_losses,
aux_dist_entropy,
aux_weights,
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
stats_ordering = list(sorted(running_episode_stats.keys()))
stats = torch.stack(
[running_episode_stats[k] for k in stats_ordering],
0).to(self.device)
distrib.all_reduce(stats)
for i, k in enumerate(stats_ordering):
window_episode_stats[k].append(stats[i].clone())
stats = torch.tensor(
[
dist_entropy,
aux_dist_entropy,
] + [value_loss, action_loss] + aux_task_losses +
[count_steps_delta],
device=self.device,
)
distrib.all_reduce(stats)
if aux_weights is not None and len(aux_weights) > 0:
distrib.all_reduce(
torch.tensor(aux_weights, device=self.device))
count_steps += stats[-1].item()
if self.world_rank == 0:
num_rollouts_done_store.set("num_done", "0")
avg_stats = [
stats[i].item() / self.world_size
for i in range(len(stats) - 1)
]
losses = avg_stats[2:]
dist_entropy, aux_dist_entropy = avg_stats[:2]
deltas = {
k: ((v[-1] - v[0]).sum().item()
if len(v) > 1 else v[0].sum().item())
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar(
"reward",
deltas["reward"] / deltas["count"],
count_steps,
)
writer.add_scalar(
"entropy",
dist_entropy,
count_steps,
)
writer.add_scalar("aux_entropy", aux_dist_entropy,
count_steps)
# Check to see if there are any metrics
# that haven't been logged yet
metrics = {
k: v / deltas["count"]
for k, v in deltas.items()
if k not in {"reward", "count"}
}
if len(metrics) > 0:
writer.add_scalars("metrics", metrics, count_steps)
writer.add_scalars(
"losses",
{
k: l
for l, k in zip(losses, ["value", "policy"] +
aux_task_strings)
},
count_steps,
)
writer.add_scalars(
"aux_weights",
{k: l
for l, k in zip(aux_weights, aux_task_strings)},
count_steps,
)
# Log stats
formatted_aux_losses = [
"{:.3g}".format(l) for l in aux_task_losses
]
if update > 0 and update % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tvalue_loss: {:.3g}\t action_loss: {:.3g}\taux_task_loss: {} \t aux_entropy {:.3g}\t"
.format(
update,
value_loss,
action_loss,
formatted_aux_losses,
aux_dist_entropy,
))
logger.info("update: {}\tfps: {:.3f}\t".format(
update,
count_steps /
((time.time() - t_start) + prev_time),
))
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(update, env_time, pth_time,
count_steps))
logger.info("Average window size: {} {}".format(
len(window_episode_stats["count"]),
" ".join(
"{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items() if k != "count"),
))
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"{self.checkpoint_prefix}.{count_checkpoints}.pth",
dict(step=count_steps))
count_checkpoints += 1
self.envs.close()
def train(self, ckpt_path="", ckpt=-1, start_updates=0) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
task_cfg = self.config.TASK_CONFIG.TASK
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
# Initialize auxiliary tasks
observation_space = self.envs.observation_spaces[0]
aux_cfg = self.config.RL.AUX_TASKS
init_aux_tasks, num_recurrent_memories, aux_task_strings = \
self._setup_auxiliary_tasks(aux_cfg, ppo_cfg, task_cfg, observation_space)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
observation_space,
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_memories=num_recurrent_memories)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
self._setup_actor_critic_agent(ppo_cfg, task_cfg, aux_cfg,
init_aux_tasks)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1),
reward=torch.zeros(self.envs.num_envs, 1),
)
window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
if ckpt != -1:
logger.info(
f"Resuming runs at checkpoint {ckpt}. Timing statistics are not tracked properly."
)
assert ppo_cfg.use_linear_lr_decay is False and ppo_cfg.use_linear_clip_decay is False, "Resuming with decay not supported"
# This is the checkpoint we start saving at
count_checkpoints = ckpt + 1
count_steps = start_updates * ppo_cfg.num_steps * self.config.NUM_PROCESSES
ckpt_dict = self.load_checkpoint(ckpt_path, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"])
if "optim_state" in ckpt_dict:
self.agent.optimizer.load_state_dict(ckpt_dict["optim_state"])
else:
logger.warn("No optimizer state loaded, results may be funky")
if "extra_state" in ckpt_dict and "step" in ckpt_dict[
"extra_state"]:
count_steps = ckpt_dict["extra_state"]["step"]
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(start_updates, self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_rollout_step(rollouts,
current_episode_reward,
running_episode_stats)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy, aux_task_losses, aux_dist_entropy, aux_weights = self._update_agent(
ppo_cfg, rollouts)
pth_time += delta_pth_time
for k, v in running_episode_stats.items():
window_episode_stats[k].append(v.clone())
deltas = {
k:
((v[-1] -
v[0]).sum().item() if len(v) > 1 else v[0].sum().item())
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar(
"entropy",
dist_entropy,
count_steps,
)
writer.add_scalar("aux_entropy", aux_dist_entropy, count_steps)
writer.add_scalar("reward", deltas["reward"] / deltas["count"],
count_steps)
# Check to see if there are any metrics
# that haven't been logged yet
metrics = {
k: v / deltas["count"]
for k, v in deltas.items() if k not in {"reward", "count"}
}
if len(metrics) > 0:
writer.add_scalars("metrics", metrics, count_steps)
losses = [value_loss, action_loss] + aux_task_losses
writer.add_scalars(
"losses",
{
k: l
for l, k in zip(losses, ["value", "policy"] +
aux_task_strings)
},
count_steps,
)
writer.add_scalars(
"aux_weights",
{k: l
for l, k in zip(aux_weights, aux_task_strings)},
count_steps,
)
writer.add_scalar(
"success",
deltas["success"] / deltas["count"],
count_steps,
)
# Log stats
if update > 0 and update % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tvalue_loss: {}\t action_loss: {}\taux_task_loss: {} \t aux_entropy {}"
.format(update, value_loss, action_loss,
aux_task_losses, aux_dist_entropy))
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))
logger.info("Average window size: {} {}".format(
len(window_episode_stats["count"]),
" ".join("{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items() if k != "count"),
))
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"{self.checkpoint_prefix}.{count_checkpoints}.pth",
dict(step=count_steps))
count_checkpoints += 1
self.envs.close()
def _eval_checkpoint(
self,
checkpoint_path: str,
writer: TensorboardWriter,
checkpoint_index: int = 0,
) -> None:
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
None
"""
self.add_new_based_on_cfg()
# Map location CPU is almost always better than mapping to a CUDA device.
ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")
# ==========================================================================================
# -- Update config for eval
if self.config.EVAL.USE_CKPT_CONFIG:
config = self._setup_eval_config(ckpt_dict["config"])
else:
config = self.config.clone()
ppo_cfg = config.RL.PPO
# # Mostly for visualization
# config.defrost()
# config.TASK_CONFIG.SIMULATOR.HABITAT_SIM_V0.GPU_GPU = False
# config.freeze()
split = config.TASK_CONFIG.DATASET.SPLIT
config.defrost()
config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP")
config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS")
config.freeze()
# ==========================================================================================
num_procs = self.config.NUM_PROCESSES
device = self.device
cfg = self.config
logger.info(f"env config: {config}")
self.envs = construct_envs(config, get_env_class(self.config.ENV_NAME))
num_envs = self.envs.num_envs
self._setup_actor_critic_agent(ppo_cfg, train=False)
self.agent.load_state_dict(ckpt_dict["state_dict"])
self.actor_critic = self.agent.actor_critic
self.r_policy = self.agent.actor_critic.reachability_policy
aux_models = self.actor_critic.net.aux_models
other_losses = dict({
k: torch.zeros(num_envs, 1, device=device)
for k in aux_models.keys()
})
other_losses_action = dict({
k: torch.zeros(num_envs,
self.envs.action_spaces[0].n,
device=device)
for k in aux_models.keys()
})
num_steps = torch.zeros(num_envs, 1, device=device)
# Config aux models for eval per item in batch
for k, maux in aux_models.items():
maux.set_per_element_loss()
total_loss = 0
if config.EVAL_MODE:
self.agent.eval()
self.r_policy.eval()
# get name of performance metric, e.g. "spl"
metric_name = cfg.TASK_CONFIG.TASK.MEASUREMENTS[0]
metric_cfg = getattr(cfg.TASK_CONFIG.TASK, metric_name)
measure_type = baseline_registry.get_measure(metric_cfg.TYPE)
assert measure_type is not None, "invalid measurement type {}".format(
metric_cfg.TYPE)
self.metric_uuid = measure_type(sim=None, task=None,
config=None)._get_uuid()
observations = self.envs.reset()
batch = batch_obs_augment_aux(observations, self.envs.get_shared_mem())
info_data_keys = ["discovered", "collisions_wall", "collisions_prox"]
log_data_keys = [
"current_episode_reward", "current_episode_go_reward"
] + info_data_keys
log_data = dict({
k: torch.zeros(num_envs, 1, device=device)
for k in log_data_keys
})
info_data = dict({k: log_data[k] for k in info_data_keys})
test_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers,
num_procs,
ppo_cfg.hidden_size,
device=device,
)
prev_actions = torch.zeros(num_procs,
1,
device=device,
dtype=torch.long)
not_done_masks = torch.zeros(num_procs, 1, device=device)
stats_episodes = dict() # dict of dicts that stores stats per episode
stats_episodes_scenes = dict(
) # dict of number of collected stats from
# each scene
max_test_ep_count = cfg.TEST_EPISODE_COUNT
# TODO this should depend on number of scenes :(
# TODO But than envs shouldn't be paused but fast-fwd to next scene
# TODO We consider num envs == num scenes
max_ep_per_env = max_test_ep_count / float(num_envs)
rgb_frames = [[] for _ in range(num_procs)
] # type: List[List[np.ndarray]]
if len(cfg.VIDEO_OPTION) > 0:
os.makedirs(cfg.VIDEO_DIR, exist_ok=True)
video_log_int = cfg.VIDEO_OPTION_INTERVAL
num_frames = 0
plot_pos = -1
prev_true_pos = []
prev_pred_pos = []
while (len(stats_episodes) <= cfg.TEST_EPISODE_COUNT and num_envs > 0):
current_episodes = self.envs.current_episodes()
with torch.no_grad():
prev_hidden = test_recurrent_hidden_states
_, actions, _, test_recurrent_hidden_states, aux_out \
= self.actor_critic.act(
batch,
test_recurrent_hidden_states,
prev_actions,
not_done_masks,
deterministic=False
)
prev_actions.copy_(actions)
if 'action' in batch:
prev_actions = batch['action'].unsqueeze(1).to(
actions.device).long()
for k, v in aux_out.items():
loss = aux_models[k].calc_loss(v, batch, prev_hidden,
prev_actions,
not_done_masks, actions)
total_loss += loss
if other_losses[k] is None:
other_losses[k] = loss
else:
other_losses[k] += loss.unsqueeze(1)
if len(prev_actions) == 1:
other_losses_action[k][0, prev_actions.item()] += \
loss.item()
# ==================================================================================
# - Hacky logs
if plot_pos >= 0:
prev_true_pos.append(batch["gps_compass_start"]
[plot_pos].data[:2].cpu().numpy())
prev_pred_pos.append(aux_out["rel_start_pos_reg"]
[plot_pos].data.cpu().numpy() * 15)
if num_frames % 10 == 0:
xx, yy = [], []
for x, y in prev_true_pos:
xx.append(x)
yy.append(y)
plt.scatter(xx, yy, label="true_pos")
xx, yy = [], []
for x, y in prev_pred_pos:
xx.append(x)
yy.append(y)
plt.scatter(xx, yy, label="pred_pos")
plt.legend()
plt.show()
plt.waitforbuttonpress()
plt.close()
# ==================================================================================
num_steps += 1
outputs = self.envs.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
not_done_masks = torch.tensor(
[[0.0] if done else [1.0] for done in dones],
dtype=torch.float,
device=device,
)
map_values = self._get_mapping(observations, aux_out)
batch = batch_obs_augment_aux(observations,
self.envs.get_shared_mem(),
device=device,
map_values=map_values,
masks=not_done_masks)
valid_map_size = [
float(ifs["top_down_map"]["valid_map"].sum()) for ifs in infos
]
discovered_factor = [
infos[ix]["top_down_map"]["explored_map"].sum() /
valid_map_size[ix] for ix in range(len(infos))
]
seen_factor = [
infos[ix]["top_down_map"]["ful_fog_of_war_mask"].sum() /
valid_map_size[ix] for ix in range(len(infos))
]
rewards = torch.tensor(rewards, dtype=torch.float,
device=device).unsqueeze(1)
log_data["current_episode_reward"] += rewards
# -- Add intrinsic Reward
if self.only_intrinsic_reward:
rewards.zero_()
if self.r_enabled:
ir_rewards = self._add_intrinsic_reward(
batch, actions, rewards, not_done_masks)
log_data["current_episode_go_reward"] += ir_rewards
rewards += ir_rewards
# Log other info from infos dict
for iii, info in enumerate(infos):
for k_info, v_info in info_data.items():
v_info[iii] += info[k_info]
next_episodes = self.envs.current_episodes()
envs_to_pause = []
n_envs = num_envs
for i in range(n_envs):
scene = next_episodes[i].scene_id
if scene not in stats_episodes_scenes:
stats_episodes_scenes[scene] = 0
if stats_episodes_scenes[scene] >= max_ep_per_env:
envs_to_pause.append(i)
# episode ended
if not_done_masks[i].item() == 0:
episode_stats = dict()
episode_stats[self.metric_uuid] = infos[i][
self.metric_uuid]
episode_stats["success"] = int(
infos[i][self.metric_uuid] > 0)
for kk, vv in log_data.items():
episode_stats[kk] = vv[i].item()
vv[i] = 0
episode_stats["map_discovered"] = discovered_factor[i]
episode_stats["map_seen"] = seen_factor[i]
for k, v in other_losses.items():
episode_stats[k] = v[i].item() / num_steps[i].item()
other_losses_action[k][i].fill_(0)
other_losses[k][i] = 0
num_steps[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
print(f"Episode {len(stats_episodes)} stats:",
episode_stats)
stats_episodes_scenes[current_episodes[i].scene_id] += 1
if len(cfg.VIDEO_OPTION
) > 0 and checkpoint_index % video_log_int == 0:
generate_video(
video_option=cfg.VIDEO_OPTION,
video_dir=cfg.VIDEO_DIR,
images=rgb_frames[i],
episode_id=current_episodes[i].episode_id,
checkpoint_idx=checkpoint_index,
metric_name=self.metric_uuid,
metric_value=infos[i][self.metric_uuid],
tb_writer=writer,
)
rgb_frames[i] = []
# episode continues
elif len(cfg.VIDEO_OPTION) > 0:
for k, v in observations[i].items():
if isinstance(v, torch.Tensor):
observations[i][k] = v.cpu().numpy()
frame = observations_to_image(observations[i], infos[i])
rgb_frames[i].append(frame)
# Pop done envs:
if len(envs_to_pause) > 0:
s_index = list(range(num_envs))
for idx in reversed(envs_to_pause):
s_index.pop(idx)
for k, v in other_losses.items():
other_losses[k] = other_losses[k][s_index]
for k, v in log_data.items():
log_data[k] = log_data[k][s_index]
(
self.envs,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
) = self._pause_envs(
envs_to_pause,
self.envs,
test_recurrent_hidden_states,
not_done_masks,
None,
prev_actions,
batch,
rgb_frames,
)
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)
episodes_agg_stats = dict()
for k, v in aggregated_stats.items():
episodes_agg_stats[k] = v / num_episodes
logger.info(f"Average episode {k}: {episodes_agg_stats[k]:.6f}")
for k, v in episodes_agg_stats.items():
writer.add_scalars(f"eval_{k}", {f"{split}_average {k}": v},
checkpoint_index)
print(f"[{checkpoint_index}] average {k}", v)
self.envs.close()
def train(self) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.add_new_based_on_cfg()
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
if not os.path.isdir(self.config.CHECKPOINT_FOLDER):
os.makedirs(self.config.CHECKPOINT_FOLDER)
self._setup_actor_critic_agent(ppo_cfg, train=True)
if self.config.PRETRAINED_CHECKPOINT_PATH:
ckpt_dict = self.load_checkpoint(
self.config.PRETRAINED_CHECKPOINT_PATH, map_location="cpu")
self.agent.load_state_dict(ckpt_dict["state_dict"], strict=False)
logger.info("agent number of parameters: {}".format(
sum(param.numel() for param in self.agent.parameters())))
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.envs.observation_spaces[0],
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers)
rollouts.to(self.device)
observations = self.envs.reset()
batch = batch_obs_augment_aux(observations)
for sensor in rollouts.observations:
if sensor in batch:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
episode_rewards = torch.zeros(self.envs.num_envs, 1)
episode_go_rewards = torch.zeros(self.envs.num_envs,
1) # Grid oracle rewars
episode_counts = torch.zeros(self.envs.num_envs, 1)
current_episode_reward = torch.zeros(self.envs.num_envs, 1)
current_episode_go_reward = torch.zeros(self.envs.num_envs,
1) # Grid oracle rewars
window_episode_reward = deque(maxlen=ppo_cfg.reward_window_size)
window_episode_go_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
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
train_steps = min(self.config.NUM_UPDATES,
self.config.HARD_NUM_UPDATES)
with TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs) as writer:
for update in range(train_steps):
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
for step in range(ppo_cfg.num_steps):
delta_pth_time, delta_env_time, delta_steps = self._collect_rollout_step(
rollouts,
current_episode_reward,
current_episode_go_reward,
episode_rewards,
episode_go_rewards,
episode_counts,
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps += delta_steps
delta_pth_time, value_loss, action_loss, dist_entropy,\
aux_loss = self._update_agent(ppo_cfg, rollouts)
# TODO check if LR is init
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
pth_time += delta_pth_time
window_episode_reward.append(episode_rewards.clone())
window_episode_go_reward.append(episode_go_rewards.clone())
window_episode_counts.append(episode_counts.clone())
value_names = ["value", "policy", "entropy"] + list(
aux_loss.keys())
losses = [value_loss, action_loss, dist_entropy] + list(
aux_loss.values())
stats = zip(
["count", "reward", "reward_go"],
[
window_episode_counts, window_episode_reward,
window_episode_go_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_scalar("reward_go",
deltas["reward_go"] / deltas["count"],
count_steps)
writer.add_scalars(
"losses",
{k: l
for l, k in zip(losses, value_names)},
count_steps,
)
# log stats
if update > 0 and update % self.config.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_go_rewards = (window_episode_go_reward[-1] -
window_episode_go_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} reward_go: {:3f}"
.format(
len(window_episode_reward),
(window_rewards / window_counts).item(),
(window_go_rewards / window_counts).item(),
))
logger.info(
f"Aux losses: {list(zip(value_names, losses))}")
else:
logger.info("No episodes finish in current window")
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
count_checkpoints += 1
self.envs.close()
def train(self, ckpt_path="", ckpt=-1, start_updates=0) -> None:
r"""Main method for training PPO.
Returns:
None
"""
self.envs = construct_envs(
self.config, get_env_class(self.config.ENV_NAME)
)
observation_space = self.envs.observation_spaces[0]
ppo_cfg = self.config.RL.PPO
task_cfg = self.config.TASK_CONFIG.TASK
aux_cfg = self.config.RL.AUX_TASKS
self.device = (
torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available()
else torch.device("cpu")
)
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
self._setup_dqn_agent(ppo_cfg, task_cfg, aux_cfg, [])
self.dataset = RolloutDataset()
self.dataloader = DataLoader(self.dataset, batch_size=16, num_workers=0)
# Use environment to initialize the metadata for training the model
self.envs.close()
if self.config.RESUME_CURIOUS:
weights = torch.load(self.config.RESUME_CURIOUS)['state_dict']
state_dict = self.q_network.state_dict()
weights_new = {}
for k, v in weights.items():
if "model_encoder" in k:
k = k.replace("model_encoder", "visual_resnet").replace("actor_critic.", "")
if k in state_dict:
weights_new[k] = v
state_dict.update(weights_new)
self.q_network.load_state_dict(state_dict)
logger.info(
"agent number of parameters: {}".format(
sum(param.numel() for param in self.q_network.parameters())
)
)
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
if ckpt != -1:
logger.info(
f"Resuming runs at checkpoint {ckpt}. Timing statistics are not tracked properly."
)
assert ppo_cfg.use_linear_lr_decay is False and ppo_cfg.use_linear_clip_decay is False, "Resuming with decay not supported"
# This is the checkpoint we start saving at
count_checkpoints = ckpt + 1
count_steps = start_updates * ppo_cfg.num_steps * self.config.NUM_PROCESSES
ckpt_dict = self.load_checkpoint(ckpt_path, map_location="cpu")
self.q_network.load_state_dict(ckpt_dict["state_dict"])
self.q_network_target.load_state_dict(ckpt_dict["target_state_dict"])
if "optim_state" in ckpt_dict:
self.agent.optimizer.load_state_dict(ckpt_dict["optim_state"])
else:
logger.warn("No optimizer state loaded, results may be funky")
if "extra_state" in ckpt_dict and "step" in ckpt_dict["extra_state"]:
count_steps = ckpt_dict["extra_state"]["step"]
lr_scheduler = LambdaLR(
optimizer=self.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
im_size = 256
with TensorboardWriter(
self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs
) as writer:
update = 0
for i in range(self.config.NUM_EPOCHS):
for im, pointgoal, action, mask, reward in self.dataloader:
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
im, pointgoal, action, mask, reward = collate(im), collate(pointgoal), collate(action), collate(mask), collate(reward)
im = im.to(self.device).float()
pointgoal = pointgoal.to(self.device).float()
mask = mask.to(self.device).float()
reward = reward.to(self.device).float()
action = action.to(self.device).long()
nstep = im.size(1)
hidden_states = None
hidden_states_target = None
# q_vals = []
# q_vals_target = []
step = random.randint(0, nstep-1)
output = self.q_network({'rgb': im[:, step]}, None, None)
mse_loss = torch.pow(output - im[:, step] / 255., 2).mean()
mse_loss.backward()
# for step in range(nstep):
# q_val, hidden_states = self.q_network({'rgb': im[:, step], 'pointgoal_with_gps_compass': pointgoal[:, step]}, hidden_states, mask[:, step])
# q_val_target, hidden_states_target = self.q_network_target({'rgb': im[:, step], 'pointgoal_with_gps_compass': pointgoal[:, step]}, hidden_states_target, mask[:, step])
# q_vals.append(q_val)
# q_vals_target.append(q_val_target)
# q_vals = torch.stack(q_vals, dim=1)
# q_vals_target = torch.stack(q_vals_target, dim=1)
# a_select = torch.argmax(q_vals, dim=-1, keepdim=True)
# target_select = torch.gather(q_vals_target, -1, a_select)
# target = reward + ppo_cfg.gamma * target_select[:, 1:] * mask[:, 1:]
# target = target.detach()
# pred_q = torch.gather(q_vals[:, :-1], -1, action)
# mse_loss = torch.pow(pred_q - target, 2).mean()
# mse_loss.backward()
# grad_norm = torch.nn.utils.clip_grad_norm(self.q_network.parameters(), 80)
self.optimizer.step()
self.optimizer.zero_grad()
writer.add_scalar(
"loss",
mse_loss,
update,
)
# writer.add_scalar(
# "q_val",
# q_vals.max(),
# update,
# )
if update % 10 == 0:
print("Update: {}, loss: {}".format(update, mse_loss))
if update % 100 == 0:
self.sync_model()
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"{self.checkpoint_prefix}.{count_checkpoints}.pth", dict(step=count_steps)
)
count_checkpoints += 1
update = update + 1
def train(self) -> None:
r"""Main method for DD-PPO SLAM.
Returns:
None
"""
#####################################################################
## init distrib and configuration #####################################################################
self.local_rank, tcp_store = init_distrib_slurm(
self.config.RL.DDPPO.distrib_backend
)
# self.local_rank = 1
add_signal_handlers()
# Stores the number of workers that have finished their rollout
num_rollouts_done_store = distrib.PrefixStore(
"rollout_tracker", tcp_store
)
num_rollouts_done_store.set("num_done", "0")
self.world_rank = distrib.get_rank() # server number
self.world_size = distrib.get_world_size()
self.config.defrost()
self.config.TORCH_GPU_ID = self.local_rank # gpu number in one server
self.config.SIMULATOR_GPU_ID = self.local_rank
print("********************* TORCH_GPU_ID: ", self.config.TORCH_GPU_ID)
print("********************* SIMULATOR_GPU_ID: ", self.config.SIMULATOR_GPU_ID)
# Multiply by the number of simulators to make sure they also get unique seeds
self.config.TASK_CONFIG.SEED += (
self.world_rank * self.config.NUM_PROCESSES
)
self.config.freeze()
random.seed(self.config.TASK_CONFIG.SEED)
np.random.seed(self.config.TASK_CONFIG.SEED)
torch.manual_seed(self.config.TASK_CONFIG.SEED)
if torch.cuda.is_available():
self.device = torch.device("cuda", self.local_rank)
torch.cuda.set_device(self.device)
else:
self.device = torch.device("cpu")
#####################################################################
## build distrib NavSLAMRLEnv environment
#####################################################################
print("#############################################################")
print("## build distrib NavSLAMRLEnv environment")
print("#############################################################")
self.envs = construct_envs(
self.config, get_env_class(self.config.ENV_NAME)
)
observations = self.envs.reset()
print("*************************** observations len:", len(observations))
# semantic process
for i in range(len(observations)):
observations[i]["semantic"] = observations[i]["semantic"].astype(np.int32)
se = list(set(observations[i]["semantic"].ravel()))
print(se)
# print("*************************** observations type:", observations)
# print("*************************** observations type:", observations[0]["map_sum"].shape) # 480*480*23
# print("*************************** observations curr_pose:", observations[0]["curr_pose"]) # []
batch = batch_obs(observations, device=self.device)
print("*************************** batch len:", len(batch))
# print("*************************** batch:", batch)
# print("************************************* current_episodes:", (self.envs.current_episodes()))
#####################################################################
## init actor_critic agent
#####################################################################
print("#############################################################")
print("## init actor_critic agent")
print("#############################################################")
self.map_w = observations[0]["map_sum"].shape[0]
self.map_h = observations[0]["map_sum"].shape[1]
# print("map_: ", observations[0]["curr_pose"].shape)
ppo_cfg = self.config.RL.PPO
if (
not os.path.isdir(self.config.CHECKPOINT_FOLDER)
and self.world_rank == 0
):
os.makedirs(self.config.CHECKPOINT_FOLDER)
self._setup_actor_critic_agent(observations, ppo_cfg)
self.agent.init_distributed(find_unused_params=True)
if self.world_rank == 0:
logger.info(
"agent number of trainable parameters: {}".format(
sum(
param.numel()
for param in self.agent.parameters()
if param.requires_grad
)
)
)
#####################################################################
## init Global Rollout Storage
#####################################################################
print("#############################################################")
print("## init Global Rollout Storage")
print("#############################################################")
self.num_each_global_step = self.config.RL.SLAMDDPPO.num_each_global_step
rollouts = GlobalRolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
self.obs_space,
self.g_action_space,
)
rollouts.to(self.device)
print('rollouts type:', type(rollouts))
print('--------------------------')
# for k in rollouts.keys():
# print("rollouts: {0}".format(rollouts.observations))
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
with torch.no_grad():
step_observation = {
k: v[rollouts.step] for k, v in rollouts.observations.items()
}
_, actions, _, = self.actor_critic.act(
step_observation,
rollouts.prev_g_actions[0],
rollouts.masks[0],
)
self.global_goals = [[int(action[0].item() * self.map_w),
int(action[1].item() * self.map_h)]
for action in actions]
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
current_episode_reward = torch.zeros(
self.envs.num_envs, 1, device=self.device
)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1, device=self.device),
reward=torch.zeros(self.envs.num_envs, 1, device=self.device),
)
window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size)
)
print("*************************** current_episode_reward:", current_episode_reward)
print("*************************** running_episode_stats:", running_episode_stats)
# print("*************************** window_episode_stats:", window_episode_stats)
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
start_update = 0
prev_time = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
# interrupted_state = load_interrupted_state("/home/cirlab1/userdir/ybg/projects/habitat-api/data/interrup.pth")
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
self.agent.load_state_dict(interrupted_state["state_dict"])
self.agent.optimizer.load_state_dict(
interrupted_state["optim_state"]
)
lr_scheduler.load_state_dict(interrupted_state["lr_sched_state"])
requeue_stats = interrupted_state["requeue_stats"]
env_time = requeue_stats["env_time"]
pth_time = requeue_stats["pth_time"]
count_steps = requeue_stats["count_steps"]
count_checkpoints = requeue_stats["count_checkpoints"]
start_update = requeue_stats["start_update"]
prev_time = requeue_stats["prev_time"]
deif = {}
with (
TensorboardWriter(
self.config.TENSORBOARD_DIR, flush_secs=self.flush_secs
)
if self.world_rank == 0
else contextlib.suppress()
) as writer:
for update in range(start_update, self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES
)
# print("************************************* current_episodes:", type(self.envs.count_episodes()))
# print(EXIT.is_set())
if EXIT.is_set():
self.envs.close()
if REQUEUE.is_set() and self.world_rank == 0:
requeue_stats = dict(
env_time=env_time,
pth_time=pth_time,
count_steps=count_steps,
count_checkpoints=count_checkpoints,
start_update=update,
prev_time=(time.time() - t_start) + prev_time,
)
save_interrupted_state(
dict(
state_dict=self.agent.state_dict(),
optim_state=self.agent.optimizer.state_dict(),
lr_sched_state=lr_scheduler.state_dict(),
config=self.config,
requeue_stats=requeue_stats,
),
"/home/cirlab1/userdir/ybg/projects/habitat-api/data/interrup.pth"
)
print("********************EXIT*********************")
requeue_job()
return
count_steps_delta = 0
self.agent.eval()
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_global_rollout_step(
rollouts, current_episode_reward, running_episode_stats
)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps_delta += delta_steps
# print("************************************* current_episodes:")
for i in range(len(self.envs.current_episodes())):
# print(" ", self.envs.current_episodes()[i].episode_id," ", self.envs.current_episodes()[i].scene_id," ", self.envs.current_episodes()[i].object_category)
if self.envs.current_episodes()[i].scene_id not in deif:
deif[self.envs.current_episodes()[i].scene_id]=[int(self.envs.current_episodes()[i].episode_id)]
else:
deif[self.envs.current_episodes()[i].scene_id].append(int(self.envs.current_episodes()[i].episode_id))
# This is where the preemption of workers happens. If a
# worker detects it will be a straggler, it preempts itself!
if (
step
>= ppo_cfg.num_steps * self.SHORT_ROLLOUT_THRESHOLD
) and int(num_rollouts_done_store.get("num_done")) > (
self.config.RL.DDPPO.sync_frac * self.world_size
):
break
num_rollouts_done_store.add("num_done", 1)
self.agent.train()
if self._static_encoder:
self._encoder.eval()
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
stats_ordering = list(sorted(running_episode_stats.keys()))
stats = torch.stack(
[running_episode_stats[k] for k in stats_ordering], 0
)
distrib.all_reduce(stats)
for i, k in enumerate(stats_ordering):
window_episode_stats[k].append(stats[i].clone())
stats = torch.tensor(
[value_loss, action_loss, count_steps_delta],
device=self.device,
)
distrib.all_reduce(stats)
count_steps += stats[2].item()
if self.world_rank == 0:
num_rollouts_done_store.set("num_done", "0")
losses = [
stats[0].item() / self.world_size,
stats[1].item() / self.world_size,
]
deltas = {
k: (
(v[-1] - v[0]).sum().item()
if len(v) > 1
else v[0].sum().item()
)
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar(
"reward",
deltas["reward"] / deltas["count"],
count_steps,
)
# Check to see if there are any metrics
# that haven't been logged yet
metrics = {
k: v / deltas["count"]
for k, v in deltas.items()
if k not in {"reward", "count"}
}
if len(metrics) > 0:
writer.add_scalars("metrics", metrics, 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 % self.config.LOG_INTERVAL == 0:
logger.info(
"update: {}\tfps: {:.3f}\t".format(
update,
count_steps
/ ((time.time() - t_start) + prev_time),
)
)
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(
update, env_time, pth_time, count_steps
)
)
logger.info(
"Average window size: {} {}".format(
len(window_episode_stats["count"]),
" ".join(
"{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items()
if k != "count"
),
)
)
# for k in deif:
# deif[k] = list(set(deif[k]))
# deif[k].sort()
# print("deif: k", k, " : ", deif[k])
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"ckpt.{count_checkpoints}.pth",
dict(step=count_steps),
)
print('=' * 20 + 'Save Model' + '=' * 20)
logger.info(
"Save Model : {}".format(count_checkpoints)
)
count_checkpoints += 1
self.envs.close()
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, checkpoint_path):
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
None
"""
self.device = (torch.device("cuda", self.config.TORCH_GPU_ID)
if torch.cuda.is_available() else torch.device("cpu"))
# Map location CPU is almost always better than mapping to a CUDA device.
ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")
if self.config.EVAL.USE_CKPT_CONFIG:
config = self._setup_eval_config(ckpt_dict["config"])
else:
config = self.config.clone()
ppo_cfg = config.RL.PPO
config.defrost()
config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
config.freeze()
if len(self.config.VIDEO_OPTION) > 0:
config.defrost()
config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP")
config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS")
config.freeze()
self.env = construct_envs(config, get_env_class(config.ENV_NAME))
self._setup_actor_critic_agent(ppo_cfg)
self.agent.load_state_dict(ckpt_dict["state_dict"])
self.actor_critic = self.agent.actor_critic
# get name of performance metric, e.g. "spl"
metric_name = self.config.TASK_CONFIG.TASK.MEASUREMENTS[0]
metric_cfg = getattr(self.config.TASK_CONFIG.TASK, metric_name)
measure_type = baseline_registry.get_measure(metric_cfg.TYPE)
assert measure_type is not None, "invalid measurement type {}".format(
metric_cfg.TYPE)
self.metric_uuid = measure_type(sim=None, task=None,
config=None)._get_uuid()
observations = self.env.reset()
batch = batch_obs(observations, self.device)
current_episode_reward = torch.zeros(self.env.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers,
self.config.NUM_PROCESSES,
ppo_cfg.hidden_size,
device=self.device,
)
prev_actions = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device,
dtype=torch.long)
not_done_masks = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device)
stats_episodes = dict() # dict of dicts that stores stats per episode
rgb_frames = [[] for _ in range(self.config.NUM_PROCESSES)
] # type: List[List[np.ndarray]]
if len(self.config.VIDEO_OPTION) > 0:
os.makedirs(self.config.VIDEO_DIR, exist_ok=True)
self.actor_critic.eval()
while (len(stats_episodes) < self.config.TEST_EPISODE_COUNT
and self.env.num_envs > 0):
current_episodes = self.env.current_episodes()
with torch.no_grad():
(
_,
actions,
_,
test_recurrent_hidden_states,
) = self.actor_critic.act(
batch,
test_recurrent_hidden_states,
prev_actions,
not_done_masks,
deterministic=False,
)
prev_actions.copy_(actions)
outputs = self.env.step([a[0].item() for a in actions])
observations, rewards, dones, infos = [
list(x) for x in zip(*outputs)
]
batch = batch_obs(observations, 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.env.current_episodes()
envs_to_pause = []
n_envs = self.env.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[self.metric_uuid] = infos[i][
self.metric_uuid]
episode_stats["success"] = int(
infos[i][self.metric_uuid] > 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 len(self.config.VIDEO_OPTION) > 0:
generate_video(
video_option=self.config.VIDEO_OPTION,
video_dir=self.config.VIDEO_DIR,
images=rgb_frames[i],
episode_id=current_episodes[i].episode_id,
checkpoint_idx=0,
metric_name=self.metric_uuid,
metric_value=infos[i][self.metric_uuid],
)
rgb_frames[i] = []
# episode continues
elif len(self.config.VIDEO_OPTION) > 0:
frame = observations_to_image(observations[i], infos[i])
rgb_frames[i].append(frame)
(
self.env,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
) = self._pause_envs(
envs_to_pause,
self.env,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
)
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_metric_mean = aggregated_stats[self.metric_uuid] / num_episodes
episode_success_mean = aggregated_stats["success"] / num_episodes
print(f"Average episode reward: {episode_reward_mean:.6f}")
print(f"Average episode success: {episode_success_mean:.6f}")
print(f"Average episode {self.metric_uuid}: {episode_metric_mean:.6f}")
if "extra_state" in ckpt_dict and "step" in ckpt_dict["extra_state"]:
step_id = ckpt_dict["extra_state"]["step"]
print("eval_reward", {"average reward": episode_reward_mean})
print(
f"eval_{self.metric_uuid}",
{f"average {self.metric_uuid}": episode_metric_mean},
)
print("eval_success", {"average success": episode_success_mean})
self.env.close()
def _eval_checkpoint(self,
checkpoint_path: str,
writer: TensorboardWriter,
checkpoint_index: int = 0,
log_diagnostics=[],
output_dir='.',
label='.',
num_eval_runs=1) -> None:
r"""Evaluates a single checkpoint.
Args:
checkpoint_path: path of checkpoint
writer: tensorboard writer object for logging to tensorboard
checkpoint_index: index of cur checkpoint for logging
Returns:
None
"""
if checkpoint_index == -1:
ckpt_file = checkpoint_path.split('/')[-1]
split_info = ckpt_file.split('.')
checkpoint_index = split_info[1]
# Map location CPU is almost always better than mapping to a CUDA device.
ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")
if self.config.EVAL.USE_CKPT_CONFIG:
config = self._setup_eval_config(ckpt_dict["config"])
else:
config = self.config.clone()
ppo_cfg = config.RL.PPO
task_cfg = config.TASK_CONFIG.TASK
config.defrost()
config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
config.freeze()
if len(self.config.VIDEO_OPTION) > 0:
config.defrost()
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, get_env_class(config.ENV_NAME))
# pass in aux config if we're doing attention
aux_cfg = self.config.RL.AUX_TASKS
self._setup_actor_critic_agent(ppo_cfg, task_cfg, aux_cfg)
# Check if we accidentally recorded `visual_resnet` in our checkpoint and drop it (it's redundant with `visual_encoder`)
ckpt_dict['state_dict'] = {
k: v
for k, v in ckpt_dict['state_dict'].items()
if 'visual_resnet' not in k
}
self.agent.load_state_dict(ckpt_dict["state_dict"])
logger.info("agent number of trainable parameters: {}".format(
sum(param.numel() for param in self.agent.parameters()
if param.requires_grad)))
self.actor_critic = self.agent.actor_critic
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
test_recurrent_hidden_states = torch.zeros(
self.actor_critic.net.num_recurrent_layers,
self.config.NUM_PROCESSES,
ppo_cfg.hidden_size,
device=self.device,
)
_, num_recurrent_memories, _ = self._setup_auxiliary_tasks(
aux_cfg, ppo_cfg, task_cfg, is_eval=True)
if self.config.RL.PPO.policy in MULTIPLE_BELIEF_CLASSES:
aux_tasks = self.config.RL.AUX_TASKS.tasks
num_recurrent_memories = len(self.config.RL.AUX_TASKS.tasks)
test_recurrent_hidden_states = test_recurrent_hidden_states.unsqueeze(
2).repeat(1, 1, num_recurrent_memories, 1)
prev_actions = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device,
dtype=torch.long)
not_done_masks = torch.zeros(self.config.NUM_PROCESSES,
1,
device=self.device)
stats_episodes = dict() # dict of dicts that stores stats per episode
rgb_frames = [[] for _ in range(self.config.NUM_PROCESSES)
] # type: List[List[np.ndarray]]
if len(self.config.VIDEO_OPTION) > 0:
os.makedirs(self.config.VIDEO_DIR, exist_ok=True)
number_of_eval_episodes = self.config.TEST_EPISODE_COUNT
if number_of_eval_episodes == -1:
number_of_eval_episodes = sum(self.envs.number_of_episodes)
else:
total_num_eps = sum(self.envs.number_of_episodes)
if total_num_eps < number_of_eval_episodes:
logger.warn(
f"Config specified {number_of_eval_episodes} eval episodes"
", dataset only has {total_num_eps}.")
logger.warn(f"Evaluating with {total_num_eps} instead.")
number_of_eval_episodes = total_num_eps
videos_cap = 2 # number of videos to generate per checkpoint
if len(log_diagnostics) > 0:
videos_cap = 10
# video_indices = random.sample(range(self.config.TEST_EPISODE_COUNT),
# min(videos_cap, self.config.TEST_EPISODE_COUNT))
video_indices = range(10)
print(f"Videos: {video_indices}")
total_stats = []
dones_per_ep = dict()
# Logging more extensive evaluation stats for analysis
if len(log_diagnostics) > 0:
d_stats = {}
for d in log_diagnostics:
d_stats[d] = [
[] for _ in range(self.config.NUM_PROCESSES)
] # stored as nested list envs x timesteps x k (# tasks)
pbar = tqdm.tqdm(total=number_of_eval_episodes * num_eval_runs)
self.agent.eval()
while (len(stats_episodes) < number_of_eval_episodes * num_eval_runs
and self.envs.num_envs > 0):
current_episodes = self.envs.current_episodes()
with torch.no_grad():
weights_output = None
if self.config.RL.PPO.policy in MULTIPLE_BELIEF_CLASSES:
weights_output = torch.empty(self.envs.num_envs,
len(aux_tasks))
(
_,
actions,
_,
test_recurrent_hidden_states,
) = self.actor_critic.act(batch,
test_recurrent_hidden_states,
prev_actions,
not_done_masks,
deterministic=False,
weights_output=weights_output)
prev_actions.copy_(actions)
for i in range(self.envs.num_envs):
if Diagnostics.actions in log_diagnostics:
d_stats[Diagnostics.actions][i].append(
prev_actions[i].item())
if Diagnostics.weights in log_diagnostics:
aux_weights = None if weights_output is None else weights_output[
i]
if aux_weights is not None:
d_stats[Diagnostics.weights][i].append(
aux_weights.half().tolist())
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, device=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):
next_k = (
next_episodes[i].scene_id,
next_episodes[i].episode_id,
)
if dones_per_ep.get(next_k, 0) == num_eval_runs:
envs_to_pause.append(i) # wait for the rest
if not_done_masks[i].item() == 0:
episode_stats = dict()
episode_stats["reward"] = current_episode_reward[i].item()
episode_stats.update(
self._extract_scalars_from_info(infos[i]))
current_episode_reward[i] = 0
# use scene_id + episode_id as unique id for storing stats
k = (
current_episodes[i].scene_id,
current_episodes[i].episode_id,
)
dones_per_ep[k] = dones_per_ep.get(k, 0) + 1
if dones_per_ep.get(k, 0) == 1 and len(
self.config.VIDEO_OPTION) > 0 and len(
stats_episodes) in video_indices:
logger.info(f"Generating video {len(stats_episodes)}")
category = getattr(current_episodes[i],
"object_category", "")
if category != "":
category += "_"
try:
generate_video(
video_option=self.config.VIDEO_OPTION,
video_dir=self.config.VIDEO_DIR,
images=rgb_frames[i],
episode_id=current_episodes[i].episode_id,
checkpoint_idx=checkpoint_index,
metrics=self._extract_scalars_from_info(
infos[i]),
tag=f"{category}{label}",
tb_writer=writer,
)
except Exception as e:
logger.warning(str(e))
rgb_frames[i] = []
stats_episodes[(
current_episodes[i].scene_id,
current_episodes[i].episode_id,
dones_per_ep[k],
)] = episode_stats
if len(log_diagnostics) > 0:
diagnostic_info = dict()
for metric in log_diagnostics:
diagnostic_info[metric] = d_stats[metric][i]
d_stats[metric][i] = []
if Diagnostics.top_down_map in log_diagnostics:
top_down_map = torch.tensor([])
if len(self.config.VIDEO_OPTION) > 0:
top_down_map = infos[i]["top_down_map"]["map"]
top_down_map = maps.colorize_topdown_map(
top_down_map, fog_of_war_mask=None)
diagnostic_info.update(
dict(top_down_map=top_down_map))
total_stats.append(
dict(
stats=episode_stats,
did_stop=bool(prev_actions[i] == 0),
episode_info=attr.asdict(current_episodes[i]),
info=diagnostic_info,
))
pbar.update()
# episode continues
else:
if len(self.config.VIDEO_OPTION) > 0:
aux_weights = None if weights_output is None else weights_output[
i]
frame = observations_to_image(
observations[i], infos[i],
current_episode_reward[i].item(), aux_weights,
aux_tasks)
rgb_frames[i].append(frame)
if Diagnostics.gps in log_diagnostics:
d_stats[Diagnostics.gps][i].append(
observations[i]["gps"].tolist())
if Diagnostics.heading in log_diagnostics:
d_stats[Diagnostics.heading][i].append(
observations[i]["heading"].tolist())
(
self.envs,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
) = self._pause_envs(
envs_to_pause,
self.envs,
test_recurrent_hidden_states,
not_done_masks,
current_episode_reward,
prev_actions,
batch,
rgb_frames,
)
num_episodes = len(stats_episodes)
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)
for k, v in aggregated_stats.items():
logger.info(f"Average episode {k}: {v:.4f}")
step_id = checkpoint_index
if "extra_state" in ckpt_dict and "step" in ckpt_dict["extra_state"]:
step_id = ckpt_dict["extra_state"]["step"]
writer.add_scalars(
"eval_reward",
{"average reward": aggregated_stats["reward"]},
step_id,
)
metrics = {k: v for k, v in aggregated_stats.items() if k != "reward"}
if len(metrics) > 0:
writer.add_scalars("eval_metrics", metrics, step_id)
logger.info("eval_metrics")
logger.info(metrics)
if len(log_diagnostics) > 0:
os.makedirs(output_dir, exist_ok=True)
eval_fn = f"{label}.json"
with open(os.path.join(output_dir, eval_fn), 'w',
encoding='utf-8') as f:
json.dump(total_stats, f, ensure_ascii=False, indent=4)
self.envs.close()
def train(self) -> None:
r"""Main method for DD-PPO.
Returns:
None
"""
self.local_rank, tcp_store = init_distrib_slurm(
self.config.RL.DDPPO.distrib_backend)
add_signal_handlers()
# Stores the number of workers that have finished their rollout
num_rollouts_done_store = distrib.PrefixStore("rollout_tracker",
tcp_store)
num_rollouts_done_store.set("num_done", "0")
self.world_rank = distrib.get_rank()
self.world_size = distrib.get_world_size()
self.config.defrost()
self.config.TORCH_GPU_ID = self.local_rank
self.config.SIMULATOR_GPU_ID = self.local_rank
# Multiply by the number of simulators to make sure they also get unique seeds
self.config.TASK_CONFIG.SEED += (self.world_rank *
self.config.NUM_PROCESSES)
self.config.freeze()
random.seed(self.config.TASK_CONFIG.SEED)
np.random.seed(self.config.TASK_CONFIG.SEED)
torch.manual_seed(self.config.TASK_CONFIG.SEED)
if torch.cuda.is_available():
self.device = torch.device("cuda", self.local_rank)
torch.cuda.set_device(self.device)
else:
self.device = torch.device("cpu")
self.envs = construct_envs(self.config,
get_env_class(self.config.ENV_NAME))
ppo_cfg = self.config.RL.PPO
if (not os.path.isdir(self.config.CHECKPOINT_FOLDER)
and self.world_rank == 0):
os.makedirs(self.config.CHECKPOINT_FOLDER)
self._setup_actor_critic_agent(ppo_cfg)
self.agent.init_distributed(find_unused_params=True)
if self.world_rank == 0:
logger.info("agent number of trainable parameters: {}".format(
sum(param.numel() for param in self.agent.parameters()
if param.requires_grad)))
observations = self.envs.reset()
batch = batch_obs(observations, device=self.device)
obs_space = self.envs.observation_spaces[0]
if self._static_encoder:
self._encoder = self.actor_critic.net.visual_encoder
obs_space = SpaceDict({
"visual_features":
spaces.Box(
low=np.finfo(np.float32).min,
high=np.finfo(np.float32).max,
shape=self._encoder.output_shape,
dtype=np.float32,
),
**obs_space.spaces,
})
with torch.no_grad():
batch["visual_features"] = self._encoder(batch)
rollouts = RolloutStorage(
ppo_cfg.num_steps,
self.envs.num_envs,
obs_space,
self.envs.action_spaces[0],
ppo_cfg.hidden_size,
num_recurrent_layers=self.actor_critic.net.num_recurrent_layers,
)
rollouts.to(self.device)
for sensor in rollouts.observations:
rollouts.observations[sensor][0].copy_(batch[sensor])
# batch and observations may contain shared PyTorch CUDA
# tensors. We must explicitly clear them here otherwise
# they will be kept in memory for the entire duration of training!
batch = None
observations = None
current_episode_reward = torch.zeros(self.envs.num_envs,
1,
device=self.device)
running_episode_stats = dict(
count=torch.zeros(self.envs.num_envs, 1, device=self.device),
reward=torch.zeros(self.envs.num_envs, 1, device=self.device),
)
window_episode_stats = defaultdict(
lambda: deque(maxlen=ppo_cfg.reward_window_size))
t_start = time.time()
env_time = 0
pth_time = 0
count_steps = 0
count_checkpoints = 0
start_update = 0
prev_time = 0
lr_scheduler = LambdaLR(
optimizer=self.agent.optimizer,
lr_lambda=lambda x: linear_decay(x, self.config.NUM_UPDATES),
)
interrupted_state = load_interrupted_state()
if interrupted_state is not None:
self.agent.load_state_dict(interrupted_state["state_dict"])
self.agent.optimizer.load_state_dict(
interrupted_state["optim_state"])
lr_scheduler.load_state_dict(interrupted_state["lr_sched_state"])
requeue_stats = interrupted_state["requeue_stats"]
env_time = requeue_stats["env_time"]
pth_time = requeue_stats["pth_time"]
count_steps = requeue_stats["count_steps"]
count_checkpoints = requeue_stats["count_checkpoints"]
start_update = requeue_stats["start_update"]
prev_time = requeue_stats["prev_time"]
with (TensorboardWriter(self.config.TENSORBOARD_DIR,
flush_secs=self.flush_secs)
if self.world_rank == 0 else contextlib.suppress()) as writer:
for update in range(start_update, self.config.NUM_UPDATES):
if ppo_cfg.use_linear_lr_decay:
lr_scheduler.step()
if ppo_cfg.use_linear_clip_decay:
self.agent.clip_param = ppo_cfg.clip_param * linear_decay(
update, self.config.NUM_UPDATES)
if EXIT.is_set():
self.envs.close()
if REQUEUE.is_set() and self.world_rank == 0:
requeue_stats = dict(
env_time=env_time,
pth_time=pth_time,
count_steps=count_steps,
count_checkpoints=count_checkpoints,
start_update=update,
prev_time=(time.time() - t_start) + prev_time,
)
save_interrupted_state(
dict(
state_dict=self.agent.state_dict(),
optim_state=self.agent.optimizer.state_dict(),
lr_sched_state=lr_scheduler.state_dict(),
config=self.config,
requeue_stats=requeue_stats,
))
requeue_job()
return
count_steps_delta = 0
self.agent.eval()
for step in range(ppo_cfg.num_steps):
(
delta_pth_time,
delta_env_time,
delta_steps,
) = self._collect_rollout_step(rollouts,
current_episode_reward,
running_episode_stats)
pth_time += delta_pth_time
env_time += delta_env_time
count_steps_delta += delta_steps
# This is where the preemption of workers happens. If a
# worker detects it will be a straggler, it preempts itself!
if (step >=
ppo_cfg.num_steps * self.SHORT_ROLLOUT_THRESHOLD
) and int(num_rollouts_done_store.get("num_done")) > (
self.config.RL.DDPPO.sync_frac * self.world_size):
break
num_rollouts_done_store.add("num_done", 1)
self.agent.train()
if self._static_encoder:
self._encoder.eval()
(
delta_pth_time,
value_loss,
action_loss,
dist_entropy,
) = self._update_agent(ppo_cfg, rollouts)
pth_time += delta_pth_time
stats_ordering = list(sorted(running_episode_stats.keys()))
stats = torch.stack(
[running_episode_stats[k] for k in stats_ordering], 0)
distrib.all_reduce(stats)
for i, k in enumerate(stats_ordering):
window_episode_stats[k].append(stats[i].clone())
stats = torch.tensor(
[value_loss, action_loss, count_steps_delta],
device=self.device,
)
distrib.all_reduce(stats)
count_steps += stats[2].item()
if self.world_rank == 0:
num_rollouts_done_store.set("num_done", "0")
losses = [
stats[0].item() / self.world_size,
stats[1].item() / self.world_size,
]
deltas = {
k: ((v[-1] - v[0]).sum().item()
if len(v) > 1 else v[0].sum().item())
for k, v in window_episode_stats.items()
}
deltas["count"] = max(deltas["count"], 1.0)
writer.add_scalar(
"reward",
deltas["reward"] / deltas["count"],
count_steps,
)
# Check to see if there are any metrics
# that haven't been logged yet
metrics = {
k: v / deltas["count"]
for k, v in deltas.items()
if k not in {"reward", "count"}
}
if len(metrics) > 0:
writer.add_scalars("metrics", metrics, 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 % self.config.LOG_INTERVAL == 0:
logger.info("update: {}\tfps: {:.3f}\t".format(
update,
count_steps /
((time.time() - t_start) + prev_time),
))
logger.info(
"update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
"frames: {}".format(update, env_time, pth_time,
count_steps))
logger.info("Average window size: {} {}".format(
len(window_episode_stats["count"]),
" ".join(
"{}: {:.3f}".format(k, v / deltas["count"])
for k, v in deltas.items() if k != "count"),
))
# checkpoint model
if update % self.config.CHECKPOINT_INTERVAL == 0:
self.save_checkpoint(
f"ckpt.{count_checkpoints}.pth",
dict(step=count_steps),
)
count_checkpoints += 1
self.envs.close()
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,
)
