def load_results(training_dir):
if not os.path.exists(training_dir):
logger.error('Training directory %s not found', training_dir)
return
manifests = detect_training_manifests(training_dir)
if not manifests:
logger.error('No manifests found in training directory %s',
training_dir)
return
logger.debug('Uploading data from manifest %s', ', '.join(manifests))
# Load up stats + video files
stats_files = []
videos = []
env_infos = []
for manifest in manifests:
with open(manifest) as f:
contents = json.load(f)
# Make these paths absolute again
stats_files.append(
os.path.join(training_dir, contents['stats']))
videos += [(os.path.join(training_dir,
v), os.path.join(training_dir, m))
for v, m in contents['videos']]
env_infos.append(contents['env_info'])
env_info = collapse_env_infos(env_infos, training_dir)
# If only one stats file is present, there is no need to merge and all fields are included
if len(stats_files) == 1:
with open(stats_files[0]) as f:
content = json.load(f)
content.update({
'manifests': manifests,
'env_info': env_info,
'videos': videos
})
return content
else:
data_sources, initial_reset_timestamps, timestamps, episode_lengths, episode_rewards, \
episode_types, initial_reset_timestamp = merge_stats_files(stats_files)
return {
'manifests': manifests,
'env_info': env_info,
'data_sources': data_sources,
'timestamps': timestamps,
'episode_lengths': episode_lengths,
'episode_rewards': episode_rewards,
'episode_types': episode_types,
'initial_reset_timestamps': initial_reset_timestamps,
'initial_reset_timestamp': initial_reset_timestamp,
'videos': videos,
}
def load_results(training_dir):
if not os.path.exists(training_dir):
logger.error('Training directory %s not found', training_dir)
return
manifests = detect_training_manifests(training_dir)
if not manifests:
logger.error('No manifests found in training directory %s',
training_dir)
return
logger.debug('Uploading data from manifest %s', ', '.join(manifests))
# Load up stats + video files
stats_files = []
videos = []
env_infos = []
for manifest in manifests:
with open(manifest) as f:
contents = json.load(f)
# Make these paths absolute again
stats_files.append(
os.path.join(training_dir, contents['stats']))
videos += [(os.path.join(training_dir,
v), os.path.join(training_dir, m))
for v, m in contents['videos']]
env_infos.append(contents['env_info'])
env_info = collapse_env_infos(env_infos, training_dir)
# If several stats files are found, merge lists together and randomly pick single values
all_contents = {}
for file in stats_files:
with open(file) as f:
content = json.load(f)
content.update({
'manifests': manifests,
'env_info': env_info,
'videos': videos
})
if not all_contents:
all_contents.update(content)
else:
for key, value in content.items():
if isinstance(value, list):
all_contents[key].extend(value)
else:
all_contents[key] = value
return all_contents
def _start(self, directory, collect_ep_data_every_callable=None, force=False, resume=False, write_upon_reset=False, uid=None, mode=None):
"""Start monitoring.
Args:
directory (str): A per-training run directory where to record stats.
collect_ep_data_every_callable (Optional[function, False]): function that takes in the index of the episode and outputs a boolean, indicating whether we should record a data on this episode. The default (for collect_ep_data_callable is None) is to take perfect cubes, capped at 1000. False disables data recording.
force (bool): Clear out existing training data from this directory (by deleting every file prefixed with "openaigym.").
resume (bool): Retain the training data already in this directory, which will be merged with our new data
write_upon_reset (bool): Write the manifest file on each reset. (This is currently a JSON file, so writing it is somewhat expensive.)
uid (Optional[str]): A unique id used as part of the suffix for the file. By default, uses os.getpid().
mode (['evaluation', 'training']): Whether this is an evaluation or training episode.
"""
if self.env.spec is None:
logger.warn(
"Trying to monitor an environment which has no 'spec' set. This usually means you did not create it via 'gym.make', and is recommended only for advanced users.")
env_id = '(unknown)'
else:
env_id = self.env.spec.id
if not os.path.exists(directory):
logger.info('Creating monitor directory %s', directory)
if six.PY3:
os.makedirs(directory, exist_ok=True)
else:
os.makedirs(directory)
if collect_ep_data_every_callable is None:
collect_ep_data_every_callable = monitor.capped_cubic_video_schedule
elif collect_ep_data_every_callable == False:
collect_ep_data_every_callable = monitor.disable_videos
elif not callable(collect_ep_data_every_callable):
raise error.Error('You must provide a function, None, or False for collect_ep_data_every_callable'
', not {}: {}'.format(type(collect_ep_data_every_callable), collect_ep_data_every_callable))
self.video_callable = collect_ep_data_every_callable
# Check on whether we need to clear anything
if force:
monitor.clear_monitor_files(directory)
elif not resume:
training_manifests = monitor.detect_training_manifests(directory)
if len(training_manifests) > 0:
raise error.Error('''Trying to write to monitor directory {} with existing monitor files: {}.
You should use a unique directory for each training run, or use 'force=True' to automatically clear previous monitor files.'''.format(
directory, ', '.join(training_manifests[:5])))
self._monitor_id = monitor.monitor_closer.register(self)
self.enabled = True
self.directory = os.path.abspath(directory)
# We use the 'openai-gym' prefix to determine if a file is ours
self.file_prefix = monitor.FILE_PREFIX
self.file_infix = '{}.{}'.format(self._monitor_id, uid if uid else os.getpid())
# Note: 'self.stats_recorder' is used by the parent class... we can not change is name
self.stats_recorder = LearningStatRecorder(directory,
'{}.episode_batch.{}'.format(self.file_prefix,
self.file_infix),
self.learning_monitor_config,
autoreset=self.env_semantics_autoreset,
env_id=env_id)
if not os.path.exists(directory):
os.mkdir(directory)
self.write_upon_reset = write_upon_reset
if mode is not None:
self._set_mode(mode)