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import argparse
import configparser
from typing import Any, Dict, Optional
import sentencepiece as _sentencepiece
import torch as _torch
import tensorflow as _tensorflow
from pytorch_lightning import Trainer
from ..ml.models import ARCHITECTURE_FACTORY
from .utils import convert_string_to_class
# imports that have to be loaded before lightning to avoid segfaults
_sentencepiece
_tensorflow
_torch
[docs]def parse_arguments_from_config(conf_file: Optional[str] = None) -> argparse.Namespace:
"""Parse arguments from configuration file.
Args:
conf_file: configuration file. Defaults to None, a.k.a. us a default configuration
in ./config/config.ini.
Returns:
the parsed arguments.
"""
parser = argparse.ArgumentParser()
# open config.ini file, either from parser or default file
parser.add_argument(
"--conf_file",
type=str,
help=("config file for the defaults value"),
default="./config/config.ini",
)
# Read config file
args, remaining_argv = parser.parse_known_args()
config = configparser.ConfigParser()
if conf_file:
config.read(conf_file)
else:
config.read(args.conf_file)
# classes that are not model name
general_config_classes = ["general", "trainer", "default"]
# adding a list of all model name into the args
result: Dict[str, Any] = dict()
result["model_list"] = [
i for i in list(config.keys()) if i.lower() not in general_config_classes
]
for key in [*config.keys()]:
# go trough all models parameter, replace the parsed ones from the the config files ones
if key.lower() not in general_config_classes:
model_type = config[key]["type"]
params_from_configfile = dict(config[key])
model = ARCHITECTURE_FACTORY[model_type.lower()]
parser = model.add_model_specific_args(parser, key)
args, _ = parser.parse_known_args()
args_dictionary = vars(args)
params_from_configfile["name"] = key
for i in params_from_configfile:
params_from_configfile[i] = convert_string_to_class(
params_from_configfile[i]
)
params_from_configfile.update(
{
k[: -len(key) - 1]: v
for k, v in args_dictionary.items()
if v is not None and k.endswith("_" + key)
}
)
result[key] = params_from_configfile
elif key.lower() == "trainer" or key.lower() == "general":
params_from_configfile = dict(config[key])
for i in params_from_configfile:
params_from_configfile[i] = convert_string_to_class(
params_from_configfile[i]
)
result.update(params_from_configfile)
# parser Pytorch Trainer arguments
parser = Trainer.add_argparse_args(parser)
# adding basename as the name of the run
parser.add_argument("--basename", type=str)
parser.add_argument("--batch_size", type=int)
parser.add_argument("--num_workers", type=int)
parser.add_argument("--lr", type=float)
parser.add_argument("--validation_split", type=float, default=None)
parser.add_argument("--validation_indices_file", type=str)
args_dictionary = vars(parser.parse_args(remaining_argv))
result.update({k: v for k, v in args_dictionary.items() if v is not None})
result_namespace = argparse.Namespace(**result)
return result_namespace