def get_config(
config_paths: Optional[Union[List[str], str]] = None,
opts: Optional[list] = None,
) -> CN:
r"""Create a unified config with default values overwritten by values from
`config_paths` and overwritten by options from `opts`.
Args:
config_paths: List of config paths or string that contains comma
separated list of config paths.
opts: Config options (keys, values) in a list (e.g., passed from
command line into the config. For example, `opts = ['FOO.BAR',
0.5]`. Argument can be used for parameter sweeping or quick tests.
"""
config = _C.clone()
if config_paths:
if isinstance(config_paths, str):
if CONFIG_FILE_SEPARATOR in config_paths:
config_paths = config_paths.split(CONFIG_FILE_SEPARATOR)
else:
config_paths = [config_paths]
for config_path in config_paths:
config.merge_from_file(config_path)
config.TASK_CONFIG = get_task_config(config.BASE_TASK_CONFIG_PATH)
if opts:
config.CMD_TRAILING_OPTS = opts
config.merge_from_list(opts)
config.freeze()
return config
def get_config(config_paths: Optional[Union[List[str], str]] = None,
opts: Optional[list] = None,
model_dir: Optional[str] = None,
run_type: Optional[str] = None,
overwrite: bool = False) -> CN:
r"""Create a unified config with default values overwritten by values from
`config_paths` and overwritten by options from `opts`.
Args:
config_paths: List of config paths or string that contains comma
separated list of config paths.
opts: Config options (keys, values) in a list (e.g., passed from
command line into the config. For example, `opts = ['FOO.BAR',
0.5]`. Argument can be used for parameter sweeping or quick tests.
model_dir: suffix for output dirs
run_type: either train or eval
overwrite: overwrite model directory
"""
config = merge_from_path(_C.clone(), config_paths)
config.TASK_CONFIG = get_task_config(
config_paths=config.BASE_TASK_CONFIG_PATH)
# config_name = os.path.basename(config_paths).split('.')[0]
if model_dir is not None:
config.MODEL_DIR = model_dir
config.TENSORBOARD_DIR = os.path.join(config.MODEL_DIR, 'tb')
config.CHECKPOINT_FOLDER = os.path.join(config.MODEL_DIR, 'data')
config.VIDEO_DIR = os.path.join(config.MODEL_DIR, 'video_dir')
config.LOG_FILE = os.path.join(config.MODEL_DIR, 'train.log')
config.EVAL_CKPT_PATH_DIR = os.path.join(config.MODEL_DIR, 'data')
if opts:
config.CMD_TRAILING_OPTS = opts
config.merge_from_list(opts)
dirs = [config.VIDEO_DIR, config.TENSORBOARD_DIR, config.CHECKPOINT_FOLDER]
if run_type == 'train':
# check dirs
if any([os.path.exists(d) for d in dirs]):
for d in dirs:
if os.path.exists(d):
logging.warning('{} exists'.format(d))
# if overwrite or input('Output directory already exists! Overwrite the folder? (y/n)') == 'y':
if overwrite:
for d in dirs:
if os.path.exists(d):
shutil.rmtree(d)
config.TASK_CONFIG.defrost()
config.TASK_CONFIG.SIMULATOR.USE_SYNC_VECENV = config.USE_SYNC_VECENV
config.TASK_CONFIG.freeze()
config.freeze()
return config
def get_config(
config_paths: Optional[Union[List[str], str]] = None,
opts: Optional[list] = None,
) -> CN:
r"""Create a unified config with default values overwritten by values from
`config_paths` and overwritten by options from `opts`.
Args:
config_paths: List of config paths or string that contains comma
separated list of config paths.
opts: Config options (keys, values) in a list (e.g., passed from
command line into the config. For example, `opts = ['FOO.BAR',
0.5]`. Argument can be used for parameter sweeping or quick tests.
"""
config = _C.clone()
task_config_opts = []
if config_paths:
if isinstance(config_paths, str):
if CONFIG_FILE_SEPARATOR in config_paths:
config_paths = config_paths.split(CONFIG_FILE_SEPARATOR)
else:
config_paths = [config_paths]
for config_path in config_paths:
# Read config and extract TASK_CONFIG args to add as opts
with open(config_path, "r") as f:
cfg = config.load_cfg(f)
cfg_clone = cfg.clone()
for k in list(cfg.keys()):
if not k.startswith("TASK_CONFIG"):
cfg_clone.pop(k)
else:
cfg.pop(k)
task_config_opts.append(cfg_clone)
config.merge_from_other_cfg(cfg)
config.TASK_CONFIG = get_task_config(config.BASE_TASK_CONFIG_PATH)
for tsk_opts in task_config_opts:
config.merge_from_other_cfg(tsk_opts)
if opts:
config.CMD_TRAILING_OPTS = opts
config.merge_from_list(opts)
config.freeze()
return config
def get_config(
config_paths: Optional[Union[List[str], str]] = None,
opts: Optional[list] = None,
) -> CN:
r"""Create a unified config with default values overwritten by values from
:ref:`config_paths` and overwritten by options from :ref:`opts`.
Args:
config_paths: List of config paths or string that contains comma
separated list of config paths.
opts: Config options (keys, values) in a list (e.g., passed from
command line into the config. For example, ``opts = ['FOO.BAR',
0.5]``. Argument can be used for parameter sweeping or quick tests.
"""
config = _C.clone()
if config_paths:
if isinstance(config_paths, str):
if CONFIG_FILE_SEPARATOR in config_paths:
config_paths = config_paths.split(CONFIG_FILE_SEPARATOR)
else:
config_paths = [config_paths]
for config_path in config_paths:
config.merge_from_file(config_path)
if opts:
for k, v in zip(opts[0::2], opts[1::2]):
if k == "BASE_TASK_CONFIG_PATH":
config.BASE_TASK_CONFIG_PATH = v
config.TASK_CONFIG = get_task_config(config.BASE_TASK_CONFIG_PATH)
if opts:
config.CMD_TRAILING_OPTS = config.CMD_TRAILING_OPTS + opts
config.merge_from_list(config.CMD_TRAILING_OPTS)
if config.NUM_PROCESSES != -1:
warnings.warn(
"NUM_PROCESSES is depricated and will be removed in a future version."
" Use NUM_ENVIRONMENTS instead."
" Overwriting NUM_ENVIRONMENTS with NUM_PROCESSES for backwards compatibility."
)
config.NUM_ENVIRONMENTS = config.NUM_PROCESSES
config.freeze()
return config
_C.RL.DDPPO.pretrained = False
# Loads just the visual encoder backbone weights
_C.RL.DDPPO.pretrained_encoder = False
# Whether or not the visual encoder backbone will be trained
_C.RL.DDPPO.train_encoder = True
# Whether or not to reset the critic linear layer
_C.RL.DDPPO.reset_critic = True
# -----------------------------------------------------------------------------
# ORBSLAM2 BASELINE
# -----------------------------------------------------------------------------
_C.ORBSLAM2 = CN()
_C.ORBSLAM2.SLAM_VOCAB_PATH = "project/habitat_baselines/slambased/data/ORBvoc.txt"
_C.ORBSLAM2.SLAM_SETTINGS_PATH = "project/habitat_baselines/slambased/data/mp3d3_small1k.yaml"
_C.ORBSLAM2.MAP_CELL_SIZE = 0.1
_C.ORBSLAM2.MAP_SIZE = 40
_C.ORBSLAM2.CAMERA_HEIGHT = get_task_config(
).SIMULATOR.DEPTH_SENSOR.POSITION[1]
_C.ORBSLAM2.BETA = 100
_C.ORBSLAM2.H_OBSTACLE_MIN = 0.3 * _C.ORBSLAM2.CAMERA_HEIGHT
_C.ORBSLAM2.H_OBSTACLE_MAX = 1.0 * _C.ORBSLAM2.CAMERA_HEIGHT
_C.ORBSLAM2.D_OBSTACLE_MIN = 0.1
_C.ORBSLAM2.D_OBSTACLE_MAX = 4.0
_C.ORBSLAM2.PREPROCESS_MAP = True
_C.ORBSLAM2.MIN_PTS_IN_OBSTACLE = (
get_task_config().SIMULATOR.DEPTH_SENSOR.WIDTH / 2.0)
_C.ORBSLAM2.ANGLE_TH = float(np.deg2rad(15))
_C.ORBSLAM2.DIST_REACHED_TH = 0.15
_C.ORBSLAM2.NEXT_WAYPOINT_TH = 0.5
_C.ORBSLAM2.NUM_ACTIONS = 3
_C.ORBSLAM2.DIST_TO_STOP = 0.05
_C.ORBSLAM2.PLANNER_MAX_STEPS = 500
_C.ORBSLAM2.DEPTH_DENORM = get_task_config().SIMULATOR.DEPTH_SENSOR.MAX_DEPTH
