def add_new_level(self, level, seed, key, pk3_path):
with self.locks[level]:
num_used_seeds = self.num_seeds_used_in_current_run[level].value
if num_used_seeds < len(self.available_seeds.get(level, [])):
log.warning(
'We should only add new levels to cache if we ran out of pre-generated levels (seeds)'
)
log.warning(
'Num used seeds: %d, available seeds: %d, level: %s, seed %r, key %r',
num_used_seeds,
len(self.available_seeds.get(level, [])),
level,
seed,
key,
)
# some DMLab-30 environments, e.g. language_select_located_object may require different levels even
# for the same seed. This is most likely a bug in DeepMind Lab, because the same seed should generate
# identical environments
path = os.path.join(self.cache_dir, key)
if not os.path.isfile(path):
# copy the cached file DeepMind Lab has written to the cache directory
shutil.copyfile(pk3_path, path)
# add new map to the list of available seeds for this level
# so it can be used next time we run the experiment
lvl_seeds_filename = join(self.cache_dir, level_to_filename(level))
safe_ensure_dir_exists(os.path.dirname(lvl_seeds_filename))
with open(lvl_seeds_filename, 'a') as fobj:
fobj.write(f'{seed} {key}\n')
def _game_init(self, with_locking=True, max_parallel=10):
lock_file = lock = None
if with_locking:
lock_file = doom_lock_file(max_parallel)
lock = FileLock(lock_file)
init_attempt = 0
while True:
init_attempt += 1
try:
if with_locking:
with lock.acquire(timeout=20):
self.game.init()
else:
self.game.init()
break
except Timeout:
if with_locking:
log.debug(
'Another process currently holds the lock %s, attempt: %d',
lock_file,
init_attempt,
)
except Exception as exc:
log.warning(
'VizDoom game.init() threw an exception %r. Terminate process...',
exc)
from seed_rl.envs.env_utils import EnvCriticalError
raise EnvCriticalError()
def __init__(self, level, action_repeat, res_w, res_h, benchmark_mode, renderer, extra_cfg=None):
self._width = res_w
self._height = res_h
self._main_observation = 'DEBUG.CAMERA_INTERLEAVED.PLAYER_VIEW_NO_RETICLE'
self._action_repeat = action_repeat
self._random_state = None
observation_format = [self._main_observation, 'DEBUG.POS.TRANS']
config = {'width': self._width, 'height': self._height}
if extra_cfg is not None:
config.update(extra_cfg)
config = {k: str(v) for k, v in config.items()}
self._dmlab = deepmind_lab.Lab(
level, observation_format, config=config, renderer=renderer, level_cache=level_cache,
)
self._action_set = ACTION_SET
self._action_list = np.array(self._action_set, dtype=np.intc) # DMLAB requires intc type for actions
self._last_observation = None
self._render_scale = 5
self._render_fps = 30
self._last_frame = time.time()
self.action_space = gym.spaces.Discrete(len(self._action_set))
self.observation_space = gym.spaces.Box(low=0, high=255, shape=(self._height, self._width, 3), dtype=np.uint8)
self.benchmark_mode = benchmark_mode
if self.benchmark_mode:
log.warning('DmLab benchmark mode is true! Use this only for testing, not for actual training runs!')
self.seed()
def load_from_checkpoint(cfg):
filename = cfg_file(cfg)
if not os.path.isfile(filename):
raise Exception(
f'Could not load saved parameters for experiment {cfg.experiment}')
with open(filename, 'r') as json_file:
json_params = json.load(json_file)
log.warning('Loading existing experiment configuration from %s',
filename)
loaded_cfg = AttrDict(json_params)
# override the parameters in config file with values passed from command line
for key, value in vars(cfg.cli_args).items():
if loaded_cfg[key] != value:
log.debug(
'Overriding arg %r with value %r passed from command line',
key, value)
loaded_cfg[key] = value
# incorporate extra CLI parameters that were not present in JSON file
for key, value in vars(cfg).items():
if key not in loaded_cfg:
log.debug(
'Adding new argument %r=%r that is not in the saved config file!',
key, value)
loaded_cfg[key] = value
return loaded_cfg
def safe_get(q, timeout=1e6, msg='Queue timeout'):
"""Using queue.get() with timeout is necessary, otherwise KeyboardInterrupt is not handled."""
while True:
try:
return q.get(timeout=timeout)
except Empty:
log.warning(msg)
def close(self):
try:
if self.game is not None:
self.game.close()
except RuntimeError as exc:
log.warning('Runtime error in VizDoom game close(): %r', exc)
if self.viewer is not None:
self.viewer.close()
def is_udp_port_available(port):
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(('', port))
sock.close()
except OSError as exc:
log.warning(f'UDP port {port} cannot be used {str(exc)}')
return False
else:
return True
def maybe_load_from_checkpoint(cfg):
filename = cfg_file(cfg)
if not os.path.isfile(filename):
log.warning(
'Saved parameter configuration for experiment %s not found!',
cfg.experiment)
log.warning('Starting experiment from scratch!')
return AttrDict(vars(cfg))
return load_from_checkpoint(cfg)
def dmlab_env_by_name(name):
for spec in DMLAB_ENVS:
if spec.name == name:
return spec
# not a known "named" environment with a predefined spec
log.warning(
'Level %s not found. Interpreting the level name as an unmodified DMLab-30 env name!',
name)
level = name.split('dmlab_')[1]
spec = DmLabSpec(name, level)
return spec
def get_algo_class(algo):
algo_class = Agent
if algo == 'PPO':
from seed_rl.algorithms.ppo.agent_ppo import AgentPPO
algo_class = AgentPPO
elif algo == 'APPO':
from seed_rl.algorithms.appo.appo import APPO
algo_class = APPO
else:
log.warning('Algorithm %s is not supported', algo)
return algo_class
def fetch(self, key, pk3_path):
"""Environment object itself acts as a proxy to the global level cache."""
if not self.env_uses_level_cache:
self.env_uses_level_cache = True
# log.debug('Env %s uses level cache!', self.level_name)
path = join(LEVEL_CACHE_DIR, key)
if os.path.isfile(path):
# copy the cached file to the path expected by DeepMind Lab
shutil.copyfile(path, pk3_path)
return True
else:
log.warning('Cache miss in environment %s key: %s!',
self.level_name, key)
return False
def reset(self):
self._ensure_initialized()
if self.record_to is not None and not self.is_multiplayer:
# does not work in multiplayer (uses different mechanism)
if not os.path.exists(self.record_to):
os.makedirs(self.record_to)
demo_path = self.demo_path(self._num_episodes)
log.warning('Recording episode demo to %s', demo_path)
self.game.new_episode(demo_path)
else:
if self._num_episodes > 0:
# no demo recording (default)
self.game.new_episode()
self.state = self.game.get_state()
img = None
try:
img = self.state.screen_buffer
except AttributeError:
# sometimes Doom does not return screen buffer at all??? Rare bug
pass
if img is None:
log.error(
'Game returned None screen buffer! This is not supposed to happen!'
)
img = self._black_screen()
# Swap current and previous histogram
if self.current_histogram is not None and self.previous_histogram is not None:
swap = self.current_histogram
self.current_histogram = self.previous_histogram
self.previous_histogram = swap
self.current_histogram.fill(0)
self._actions_flattened = None
self._last_episode_info = copy.deepcopy(self._prev_info)
self._prev_info = None
self._num_episodes += 1
return np.transpose(img, (1, 2, 0))
def _load_checkpoint(self, checkpoints_dir):
checkpoints = self._get_checkpoints(checkpoints_dir)
if len(checkpoints) <= 0:
log.warning('No checkpoints found in %s',
experiment_dir(cfg=self.cfg))
return None
else:
latest_checkpoint = checkpoints[-1]
log.warning('Loading state from checkpoint %s...',
latest_checkpoint)
if str(
self.device
) == 'cuda': # the checkpoint will try to load onto the GPU storage unless specified
checkpoint_dict = torch.load(latest_checkpoint)
else:
checkpoint_dict = torch.load(
latest_checkpoint,
map_location=lambda storage, loc: storage)
return checkpoint_dict
def sample(self, proc_idx):
# workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg
signal.signal(signal.SIGINT, signal.SIG_IGN)
timing = Timing()
psutil.Process().nice(10)
num_envs = len(DMLAB30_LEVELS_THAT_USE_LEVEL_CACHE)
assert self.cfg.num_workers % num_envs == 0, f'should have an integer number of workers per env, e.g. {1 * num_envs}, {2 * num_envs}, etc...'
assert self.cfg.num_envs_per_worker == 1, 'use populate_cache with 1 env per worker'
with timing.timeit('env_init'):
env_key = 'env'
env_desired_num_levels = 0
global_env_id = proc_idx * self.cfg.num_envs_per_worker
env_config = AttrDict(worker_index=proc_idx,
vector_index=0,
env_id=global_env_id)
env = create_env(self.cfg.env, cfg=self.cfg, env_config=env_config)
env.seed(global_env_id)
# this is to track the performance for individual DMLab levels
if hasattr(env.unwrapped, 'level_name'):
env_key = env.unwrapped.level_name
env_level = env.unwrapped.level
approx_num_episodes_per_1b_frames = DMLAB30_APPROX_NUM_EPISODES_PER_BILLION_FRAMES[
env_key]
num_billions = DESIRED_TRAINING_LENGTH / int(1e9)
num_workers_for_env = self.cfg.num_workers // num_envs
env_desired_num_levels = int(
(approx_num_episodes_per_1b_frames * num_billions) /
num_workers_for_env)
env_num_levels_generated = len(
dmlab_level_cache.DMLAB_GLOBAL_LEVEL_CACHE[0].
all_seeds[env_level]) // num_workers_for_env
log.warning('Worker %d (env %s) generated %d/%d levels!',
proc_idx, env_key, env_num_levels_generated,
env_desired_num_levels)
time.sleep(4)
env.reset()
env_uses_level_cache = env.unwrapped.env_uses_level_cache
self.report_queue.put(dict(proc_idx=proc_idx, finished_reset=True))
self.start_event.wait()
try:
with timing.timeit('work'):
last_report = last_report_frames = total_env_frames = 0
while not self.terminate.value and total_env_frames < self.cfg.sample_env_frames_per_worker:
action = env.action_space.sample()
with timing.add_time(f'{env_key}.step'):
env.step(action)
total_env_frames += 1
with timing.add_time(f'{env_key}.reset'):
env.reset()
env_num_levels_generated += 1
log.debug('Env %s done %d/%d resets', env_key,
env_num_levels_generated,
env_desired_num_levels)
if env_num_levels_generated >= env_desired_num_levels:
log.debug('%s finished %d/%d resets, sleeping...',
env_key, env_num_levels_generated,
env_desired_num_levels)
time.sleep(30) # free up CPU time for other envs
# if env does not use level cache, there is no need to run it
# let other workers proceed
if not env_uses_level_cache:
log.debug('Env %s does not require cache, sleeping...',
env_key)
time.sleep(200)
with timing.add_time('report'):
now = time.time()
if now - last_report > self.report_every_sec:
last_report = now
frames_since_last_report = total_env_frames - last_report_frames
last_report_frames = total_env_frames
self.report_queue.put(
dict(proc_idx=proc_idx,
env_frames=frames_since_last_report))
if get_free_disk_space_mb() < 3 * 1024:
log.error('Not enough disk space! %d',
get_free_disk_space_mb())
time.sleep(200)
except:
log.exception('Unknown exception')
log.error('Unknown exception in worker %d, terminating...',
proc_idx)
self.report_queue.put(dict(proc_idx=proc_idx, crash=True))
time.sleep(proc_idx * 0.1 + 0.1)
log.info('Process %d finished sampling. Timing: %s', proc_idx, timing)
env.close()
def __init__(
self,
task_id,
level,
action_repeat,
res_w,
res_h,
benchmark_mode,
renderer,
dataset_path,
with_instructions,
extended_action_set,
use_level_cache,
gpu_index,
extra_cfg=None,
):
self.width = res_w
self.height = res_h
# self._main_observation = 'DEBUG.CAMERA_INTERLEAVED.PLAYER_VIEW_NO_RETICLE'
self.main_observation = 'RGB_INTERLEAVED'
self.instructions_observation = DMLAB_INSTRUCTIONS
self.with_instructions = with_instructions and not benchmark_mode
self.action_repeat = action_repeat
self.random_state = None
self.task_id = task_id
self.level = level
self.level_name = dmlab_level_to_level_name(self.level)
# the policy index which currently acts in the environment
self.curr_policy_idx = 0
self.curr_cache = dmlab_level_cache.DMLAB_GLOBAL_LEVEL_CACHE[
self.curr_policy_idx]
self.instructions = np.zeros([DMLAB_MAX_INSTRUCTION_LEN],
dtype=np.int32)
observation_format = [self.main_observation]
if self.with_instructions:
observation_format += [self.instructions_observation]
config = {
'width': self.width,
'height': self.height,
'gpuDeviceIndex': str(gpu_index),
'datasetPath': dataset_path,
}
if extra_cfg is not None:
config.update(extra_cfg)
config = {k: str(v) for k, v in config.items()}
self.use_level_cache = use_level_cache
env_level_cache = self if use_level_cache else None
self.env_uses_level_cache = False # will be set to True when this env instance queries the cache
self.last_reset_seed = None
if env_level_cache is not None:
if not isinstance(self.curr_cache,
dmlab_level_cache.DmlabLevelCacheGlobal):
raise Exception(
'DMLab global level cache object is not initialized! Make sure to call'
'dmlab_ensure_global_cache_initialized() in the main thread before you fork any child processes'
'or create any DMLab envs')
self.dmlab = deepmind_lab.Lab(
level,
observation_format,
config=config,
renderer=renderer,
level_cache=env_level_cache,
)
self.action_set = EXTENDED_ACTION_SET if extended_action_set else ACTION_SET
self.action_list = np.array(
self.action_set,
dtype=np.intc) # DMLAB requires intc type for actions
self.last_observation = None
self.render_scale = 5
self.render_fps = 30
self.last_frame = time.time()
self.action_space = gym.spaces.Discrete(len(self.action_set))
self.observation_space = gym.spaces.Dict(
obs=gym.spaces.Box(low=0,
high=255,
shape=(self.height, self.width, 3),
dtype=np.uint8))
if self.with_instructions:
self.observation_space.spaces[
self.instructions_observation] = gym.spaces.Box(
low=0,
high=DMLAB_VOCABULARY_SIZE,
shape=[DMLAB_MAX_INSTRUCTION_LEN],
dtype=np.int32,
)
self.benchmark_mode = benchmark_mode
if self.benchmark_mode:
log.warning(
'DmLab benchmark mode is true! Use this only for testing, not for actual training runs!'
)
self.seed()
