def build_config(config_path: (str, list), log_dir: str, ckpt_path: str) -> [dict, str]:
"""
Function to initialise log directories,
assert that checkpointed model is the right
type and to parse the configuration for training
:param config_path: list of str, path to config file
:param log_dir: str, path to where training logs to be stored.
:param ckpt_path: str, path where model is stored.
:return: - config: a dictionary saving configuration
- log_dir: the path of directory to save logs
"""
# init log directory
log_dir = build_log_dir(log_dir)
# check checkpoint path
if ckpt_path != "":
if not ckpt_path.endswith(".ckpt"):
raise ValueError("checkpoint path should end with .ckpt")
# load and backup config
config = config_parser.load_configs(config_path)
config_parser.save(config=config, out_dir=log_dir)
return config, log_dir
def build_config(config_path: (str, list), log_dir: str,
ckpt_path: str) -> [dict, str]:
"""
Function to create new directory to log directory to store results.
:param config_path: string or list of strings, path of configuration files
:param log_dir: string, path to store logs.
:param ckpt_path: str, path where model is stored.
:return: - config, configuration dictionary
- log_dir, path of the directory for saving outputs
"""
# check ckpt_path
if not ckpt_path.endswith(".ckpt"):
raise ValueError(
"checkpoint path should end with .ckpt, got {}".format(ckpt_path))
# init log directory
log_dir = build_log_dir(log_dir)
# load config
if config_path == "":
# use default config, which should be provided in the log folder
config = config_parser.load_configs(
"/".join(ckpt_path.split("/")[:-2]) + "/config.yaml")
else:
# use customized config
logging.warning(
"Using customized configuration."
"The code might break if the config of the model doesn't match the saved model."
)
config = config_parser.load_configs(config_path)
return config, log_dir
def build_config(config_path: Union[str, List[str]], log_dir: str,
exp_name: str, ckpt_path: str) -> Tuple[Dict, str, str]:
"""
Function to create new directory to log directory to store results.
:param config_path: path of configuration files.
:param log_dir: path of the log directory.
:param exp_name: experiment name.
:param ckpt_path: path where model is stored.
:return: - config, configuration dictionary.
- exp_name, path of the directory for saving outputs.
"""
# init log directory
log_dir = build_log_dir(log_dir=log_dir, exp_name=exp_name)
# replace the ~ with user home path
ckpt_path = os.path.expanduser(ckpt_path)
# load config
if config_path == "":
# use default config, which should be provided in the log folder
config = config_parser.load_configs(
"/".join(ckpt_path.split("/")[:-2]) + "/config.yaml")
else:
# use customized config
logging.warning(
"Using customized configuration."
"The code might break if the config doesn't match the saved model."
)
config = config_parser.load_configs(config_path)
return config, log_dir, ckpt_path
def build_config(config_path: (str, list), log_root: str, log_dir: str,
ckpt_path: str) -> [dict, str]:
"""
Function to create new directory to log directory to store results.
:param config_path: string or list of strings, path of configuration files
:param log_root: str, root of logs
:param log_dir: string, path to store logs.
:param ckpt_path: str, path where model is stored.
:return: - config, configuration dictionary
- log_dir, path of the directory for saving outputs
"""
# init log directory
log_dir = build_log_dir(log_root=log_root, log_dir=log_dir)
# replace the ~ with user home path
ckpt_path = os.path.expanduser(ckpt_path)
# load config
if config_path == "":
# use default config, which should be provided in the log folder
config = config_parser.load_configs(
"/".join(ckpt_path.split("/")[:-2]) + "/config.yaml")
else:
# use customized config
logging.warning(
"Using customized configuration."
"The code might break if the config doesn't match the saved model."
)
config = config_parser.load_configs(config_path)
return config, log_dir, ckpt_path
def test_build_log_dir():
"""
Test build_log_dir for default directory and custom directory
"""
# use default timestamp based directory
log_dir = build_log_dir(log_dir="")
head, tail = os.path.split(log_dir)
assert head == "logs"
pattern = re.compile("[0-9]{8}-[0-9]{6}")
assert pattern.match(tail)
# use custom directory
log_dir = build_log_dir(log_dir="custom")
head, tail = os.path.split(log_dir)
assert head == "logs"
assert tail == "custom"
def test_build_log_dir(log_dir: str, exp_name: str):
built_log_dir = build_log_dir(log_dir=log_dir, exp_name=exp_name)
head, tail = os.path.split(built_log_dir)
assert head == log_dir
if exp_name == "":
# use default timestamp based directory
pattern = re.compile("[0-9]{8}-[0-9]{6}")
assert pattern.match(tail)
else:
# use custom directory
assert tail == exp_name
def test_build_log_dir(log_root: str, log_dir: str):
"""
Test build_log_dir for default directory and custom directory
"""
built_log_dir = build_log_dir(log_root=log_root, log_dir=log_dir)
head, tail = os.path.split(built_log_dir)
assert head == log_root
if log_dir == "":
# use default timestamp based directory
pattern = re.compile("[0-9]{8}-[0-9]{6}")
assert pattern.match(tail)
else:
# use custom directory
assert tail == log_dir
def build_config(
config_path: (str, list),
log_root: str,
log_dir: str,
ckpt_path: str,
max_epochs: int = -1,
) -> [dict, str]:
"""
Function to initialise log directories,
assert that checkpointed model is the right
type and to parse the configuration for training.
:param config_path: list of str, path to config file
:param log_root: str, root of logs
:param log_dir: str, path to where training logs to be stored.
:param ckpt_path: str, path where model is stored.
:param max_epochs: int, if max_epochs > 0, will use it to overwrite the configuration
:return: - config: a dictionary saving configuration
- log_dir: the path of directory to save logs
"""
# init log directory
log_dir = build_log_dir(log_root=log_root, log_dir=log_dir)
# check checkpoint path
if ckpt_path != "":
if not ckpt_path.endswith(".ckpt"):
raise ValueError(
f"checkpoint path should end with .ckpt, got {ckpt_path}")
# load config
config = config_parser.load_configs(config_path)
# overwrite epochs and save_period if necessary
if max_epochs > 0:
config["train"]["epochs"] = max_epochs
config["train"]["save_period"] = min(max_epochs,
config["train"]["save_period"])
# backup config
config_parser.save(config=config, out_dir=log_dir)
# batch_size in original config corresponds to batch_size per GPU
gpus = tf.config.experimental.list_physical_devices("GPU")
config["train"]["preprocess"]["batch_size"] *= max(len(gpus), 1)
return config, log_dir
def build_config(
config_path: Union[str, List[str]],
log_dir: str,
exp_name: str,
ckpt_path: str,
max_epochs: int = -1,
) -> Tuple[Dict, str, str]:
"""
Function to initialise log directories,
assert that checkpointed model is the right
type and to parse the configuration for training.
:param config_path: list of str, path to config file
:param log_dir: path of the log directory
:param exp_name: name of the experiment
:param ckpt_path: path where model is stored.
:param max_epochs: if max_epochs > 0, use it to overwrite the configuration
:return: - config: a dictionary saving configuration
- exp_name: the path of directory to save logs
"""
# init log directory
log_dir = build_log_dir(log_dir=log_dir, exp_name=exp_name)
# load config
config = config_parser.load_configs(config_path)
# replace the ~ with user home path
ckpt_path = os.path.expanduser(ckpt_path)
# overwrite epochs and save_period if necessary
if max_epochs > 0:
config["train"]["epochs"] = max_epochs
config["train"]["save_period"] = min(max_epochs, config["train"]["save_period"])
# backup config
config_parser.save(config=config, out_dir=log_dir)
# batch_size in original config corresponds to batch_size per GPU
gpus = tf.config.experimental.list_physical_devices("GPU")
config["train"]["preprocess"]["batch_size"] *= max(len(gpus), 1)
return config, log_dir, ckpt_path
