def _update(self, **kwargs):
params = kwargs
params["logger"] = params.pop(
"logger",
config_logging(logger=params.get("model_name", self.model_name),
console_log_level="info"))
for key in params:
if key.endswith("_params") and key + "_update" in params:
params[key].update(params[key + "_update"])
# path_override_check
path_check_list = [
"dataset", "root_data_dir", "workspace", "root_model_dir",
"model_dir"
]
_overridden = {}
for path_check in path_check_list:
if kwargs.get(path_check) is None or kwargs[path_check] == getattr(
self, "%s" % path_check):
_overridden[path_check] = False
else:
_overridden[path_check] = True
for param, value in params.items():
setattr(self, "%s" % param, value)
def is_overridden(varname):
return _overridden["%s" % varname]
# set dataset
if is_overridden("dataset") and not is_overridden("root_data_dir"):
kwargs["root_data_dir"] = path_append("$root", "data", "$dataset")
# set workspace
if (is_overridden("workspace") or is_overridden("root_model_dir")
) and not is_overridden("model_dir"):
kwargs["model_dir"] = path_append("$root_model_dir", "$workspace")
# rebuild relevant directory or file path according to the kwargs
_dirs = [
"workspace", "root_data_dir", "data_dir", "root_model_dir",
"model_dir"
]
for _dir in _dirs:
exp = var2exp(kwargs.get(_dir, getattr(self, _dir)),
env_wrap=lambda x: "self.%s" % x)
setattr(self, _dir, eval(exp))
self.validation_result_file = path_append(self.model_dir,
RESULT_JSON,
to_str=True)
self.cfg_path = path_append(self.model_dir, CFG_JSON, to_str=True)
def var2val(self, var):
return eval(var2exp(var, env_wrap=lambda x: "self.%s" % x))
def __init__(self, params_path=None, **kwargs):
"""
Configuration File, including categories:
* directory setting
* optimizer setting
* training parameters
* equipment
* parameters saving setting
* user parameters
Parameters
----------
params_path: str
The path to configuration file which is in json format
kwargs:
Parameters to be reset.
"""
super(Configuration, self).__init__(
logger=config_logging(
logger=self.model_name,
console_log_level=LogLevel.INFO
)
)
params = self.class_var
if params_path:
params.update(self.load_cfg(cfg_path=params_path))
params.update(**kwargs)
for key in params:
if key.endswith("_params") and key + "_update" in params:
params[key].update(params[key + "_update"])
# path_override_check
path_check_list = ["dataset", "root_data_dir", "workspace", "root_model_dir", "model_dir"]
_overridden = {}
for path_check in path_check_list:
if kwargs.get(path_check) is None or kwargs[path_check] == getattr(self, "%s" % path_check):
_overridden[path_check] = False
else:
_overridden[path_check] = True
for param, value in params.items():
setattr(self, "%s" % param, value)
def is_overridden(varname):
return _overridden["%s" % varname]
# set dataset
if is_overridden("dataset") and not is_overridden("root_data_dir"):
kwargs["root_data_dir"] = path_append("$root", "data", "$dataset")
# set workspace
if (is_overridden("workspace") or is_overridden("root_model_dir")) and not is_overridden("model_dir"):
kwargs["model_dir"] = path_append("$root_model_dir", "$workspace")
# rebuild relevant directory or file path according to the kwargs
_dirs = [
"workspace", "root_data_dir", "data_dir", "root_model_dir",
"model_dir"
]
for _dir in _dirs:
exp = var2exp(
kwargs.get(_dir, getattr(self, _dir)),
env_wrap=lambda x: "self.%s" % x
)
setattr(self, _dir, eval(exp))
_vars = [
"ctx"
]
for _var in _vars:
if _var in kwargs:
try:
setattr(self, _var, eval_var(kwargs[_var]))
except TypeError:
pass
self.validation_result_file = path_append(
self.model_dir, "result.json", to_str=True
)
self.cfg_path = path_append(
self.model_dir, "configuration.json", to_str=True
)
def __init__(self, params_json=None, **kwargs):
"""
Configuration File, including categories:
* directory setting
* optimizer setting
* training parameters
* equipment
* parameters saving setting
* user parameters
Parameters
----------
params_json: str
The path to configuration file which is in json format
kwargs:
Parameters to be reset.
"""
super(Configuration, self).__init__(logger=config_logging(
logger=self.model_name, console_log_level=LogLevel.INFO))
params = self.class_var
if params_json:
params.update(self.load_cfg(params_json=params_json))
params.update(**kwargs)
for param, value in params.items():
setattr(self, "%s" % param, value)
# set dataset
if kwargs.get("dataset") and not kwargs.get("root_data_dir"):
kwargs["root_data_dir"] = "$root/data/$dataset"
# set workspace
if (kwargs.get("workspace") or
kwargs.get("root_model_dir")) and not kwargs.get("model_dir"):
kwargs["model_dir"] = "$root_model_dir/$workspace"
# rebuild relevant directory or file path according to the kwargs
_dirs = [
"workspace", "root_data_dir", "data_dir", "root_model_dir",
"model_dir"
]
for _dir in _dirs:
exp = var2exp(kwargs.get(_dir, getattr(self, _dir)),
env_wrap=lambda x: "self.%s" % x)
setattr(self, _dir, eval(exp))
_vars = [
# "ctx"
]
for _var in _vars:
if _var in kwargs:
try:
setattr(self, _var, eval_var(kwargs[_var]))
except TypeError:
pass
self.validation_result_file = path_append(self.model_dir,
"result.json",
to_str=True)
self.cfg_path = path_append(self.model_dir,
"configuration.json",
to_str=True)