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# -*- coding: utf-8 -*-
# Base CLI to parse Arguments
#
# @ Fabian Hörst, fabian.hoerst@uk-essen.de
# Institute for Artifical Intelligence in Medicine,
# University Medicine Essen
import argparse
import logging
from abc import ABC, abstractmethod
from typing import Tuple, Union
import yaml
from pydantic import BaseModel
class ABCParser(ABC):
"""Blueprint for Argument Parser"""
@abstractmethod
def __init__(self) -> None:
pass
@abstractmethod
def get_config(self) -> Tuple[Union[BaseModel, dict], logging.Logger]:
"""Load configuration and create a logger
Returns:
Tuple[PreProcessingConfig, logging.Logger]: Configuration and Logger
"""
pass
@abstractmethod
def store_config(self) -> None:
"""Store the config file in the logging directory to keep track of the configuration."""
pass
class ExperimentBaseParser:
"""Configuration Parser for Machine Learning Experiments"""
def __init__(self) -> None:
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Start an experiment with given configuration file.",
)
requiredNamed = parser.add_argument_group("required named arguments")
requiredNamed.add_argument(
"--config", type=str, help="Path to a config file", required=True
)
parser.add_argument("--gpu", type=int, help="Cuda-GPU ID")
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument(
"--sweep",
action="store_true",
help="Starting a sweep. For this the configuration file must be structured according to WandB sweeping. "
"Compare https://docs.wandb.ai/guides/sweeps and https://community.wandb.ai/t/nested-sweep-configuration/3369/3 "
"for further information. This parameter cannot be set in the config file!",
)
group.add_argument(
"--agent",
type=str,
help="Add a new agent to the sweep. "
"Please pass the sweep ID as argument in the way entity/project/sweep_id, e.g., user1/test_project/v4hwbijh. "
"The agent configuration can be found in the WandB dashboard for the running sweep in the sweep overview tab "
"under launch agent. Just paste the entity/project/sweep_id given there. The provided config file must be a sweep config file."
"This parameter cannot be set in the config file!",
)
group.add_argument(
"--checkpoint",
type=str,
help="Path to a PyTorch checkpoint file. "
"The file is loaded and continued to train with the provided settings. "
"If this is passed, no sweeps are possible. "
"This parameter cannot be set in the config file!",
)
self.parser = parser
def parse_arguments(self) -> Tuple[Union[BaseModel, dict]]:
"""Parse the arguments from CLI and load yaml config
Returns:
Tuple[Union[BaseModel, dict]]: Parsed arguments
"""
# parse the arguments
opt = self.parser.parse_args() #定义了一个opt变量，用来存储参数
with open(opt.config, "r") as config_file:
yaml_config = yaml.safe_load(config_file)
yaml_config_dict = dict(yaml_config) #将yaml文件转换为字典
opt_dict = vars(opt) #将opt转换为字典
# check for gpu to overwrite with cli argument
if "gpu" in opt_dict: #如果gpu在opt_dict中
if opt_dict["gpu"] is not None:
yaml_config_dict["gpu"] = opt_dict["gpu"] #将opt_dict中的gpu值赋给yaml_config_dict中的gpu
# check if either training, sweep, checkpoint or start agent should be called
# first step: remove such keys from the config file
if "run_sweep" in yaml_config_dict: #如果yaml_config_dict中有run_sweep
yaml_config_dict.pop("run_sweep") #删除yaml_config_dict中的run_sweep
if "agent" in yaml_config_dict:
yaml_config_dict.pop("agent")
if "checkpoint" in yaml_config_dict:
yaml_config_dict.pop("checkpoint")
# select one of the options
if "sweep" in opt_dict and opt_dict["sweep"] is True:
yaml_config_dict["run_sweep"] = True
else:
yaml_config_dict["run_sweep"] = False
if "agent" in opt_dict:
yaml_config_dict["agent"] = opt_dict["agent"]
if "checkpoint" in opt_dict:
if opt_dict["checkpoint"] is not None:
yaml_config_dict["checkpoint"] = opt_dict["checkpoint"]
self.config = yaml_config_dict #将yaml_config_dict赋给self.config
return self.config
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