parse_config.py

import os
import logging
from pathlib import Path
from functools import reduce, partial
from operator import getitem
from datetime import datetime
from logger import setup_logging
from utils import read_yaml, write_yaml, print_dict
from utils import import_class

class ConfigParser:
    def __init__(self, config, resume=None, modification=None, mode = 'train', run_id=None, dry_run=False):
        """
        class to parse configuration yaml file. Handles hyperparameters for training, initializations of modules, checkpoint saving
        and logging module.
        :param config: Dict containing configurations, hyperparameters for training. contents of `config.yaml` file for example.
        :param resume: String, path to the checkpoint being loaded.
        :param modification: Dict keychain:value, specifying position values to be replaced from config dict.
        :param run_id: Unique Identifier for training processes. Used to save checkpoints and training log. Timestamp is being used as default
        """
        # load config file and apply modification
        self._config = _update_config(config, modification)
        self.resume = resume

        # set save_dir where trained model and log will be saved.
        save_dir = Path(self.config['trainer']['args']['save_dir'])

        exper_name = self.config['name']
        trainer = self.config['trainer']['type']
        deg_type = self.config['data_loader']['args']['deg_type']
        if run_id is None: # use timestamp as default run-id
            run_id = datetime.now().strftime(r'%m%d_%H%M%S')
        self._save_dir = save_dir / deg_type / trainer / exper_name / mode / run_id
        self._log_dir = self._save_dir

        if not dry_run:
            # make directory for saving checkpoints and log.
            exist_ok = run_id == ''
            self.save_dir.mkdir(parents=True, exist_ok=exist_ok)

            # save updated config file to the checkpoint dir
            write_yaml(self.config, self.save_dir / 'config.yaml')

            # configure logging module
            setup_logging(self.log_dir)
            self.log_levels = {
                0: logging.WARNING,
                1: logging.INFO,
                2: logging.DEBUG
            }

    @classmethod
    def from_args(cls, args, options=''):
        """
        Initialize this class from some cli arguments. Used in train, test.
        """
        for opt in options:
            args.add_argument(*opt.flags, default=None, type=opt.type)
        if not isinstance(args, tuple):
            args = args.parse_args()

        if args.device is not None:
            os.environ["CUDA_VISIBLE_DEVICES"] = args.device
        if args.resume is not None:
            resume = Path(args.resume)
            cfg_fname = resume.parent / 'config.yaml'
        else:
            msg_no_cfg = "Configuration file need to be specified. Add '-c config.yaml', for example."
            assert args.config is not None, msg_no_cfg
            resume = None
            cfg_fname = Path(args.config)
        
        config = read_yaml(cfg_fname)
        if args.config and resume:
            # update new config for fine-tuning
            config.update(read_yaml(args.config))

        # parse custom cli options into dictionary
        modification = {opt.target : getattr(args, _get_opt_name(opt.flags)) for opt in options}

        mode = 'train' if not hasattr(args, 'mode') else args.mode
        return cls(config, resume, modification, mode)

    def init_obj(self, name, module, *args, **kwargs):
        """
        Finds a function handle with the name given as 'type' in config, and returns the
        instance initialized with corresponding arguments given.

        `object = config.init_obj('name', module, a, b=1)`
        is equivalent to
        `object = module.name(a, b=1)`
        """
        module_name = self[name]['type']
        module_args = dict(self[name]['args'])
        assert all([k not in module_args for k in kwargs]), 'Overwriting kwargs given in config file is not allowed'
        module_args.update(kwargs)
        return getattr(module, module_name)(*args, **module_args)

    def get_class(self, name, init = True, _class = None):
        """
        Finds a function handle with the name given as 'type' in config, and returns the
        class with the corresponding arguments given.
        
        `Class = config.init_class('name', module, a, b=1)`
        is equivalent to 
        `Class = name.module(a, b=1)`
        This function can import model and loss classes 
        """
        module_name = self[name]['type']
        module_args = dict(self[name]['args'])
        if _class is None:
            Module = '.'.join([name, module_name]) 
        else:
            Module = '.'.join([_class, module_name]) 
        Module = import_class(Module)
        if init:
            Module = Module(**module_args)
        return Module
        
    def init_ftn(self, name, module, *args, **kwargs):
        """
        Finds a function handle with the name given as 'type' in config, and returns the
        function with given arguments fixed with functools.partial.

        `function = config.init_ftn('name', module, a, b=1)`
        is equivalent to
        `function = lambda *args, **kwargs: module.name(a, *args, b=1, **kwargs)`.
        """
        module_name = self[name]['type']
        module_args = dict(self[name]['args'])
        assert all([k not in module_args for k in kwargs]), 'Overwriting kwargs given in config file is not allowed'
        module_args.update(kwargs)
        return partial(getattr(module, module_name), *args, **module_args)

    def __getitem__(self, name):
        """Access items like ordinary dict."""
        return self.config[name]
    
    def __contains__(self, name):
        """Check item exists like ordinary dict."""
        if name in self.config:
            return True
        else:
            return False
    
    def __str__(self):
        return print_dict(self.config)

    def get_logger(self, name, verbosity=2):
        msg_verbosity = 'verbosity option {} is invalid. Valid options are {}.'.format(verbosity, self.log_levels.keys())
        assert verbosity in self.log_levels, msg_verbosity
        logger = logging.getLogger(name)
        logger.setLevel(self.log_levels[verbosity])
        return logger

    # setting read-only attributes
    @property
    def config(self):
        return self._config

    @property
    def save_dir(self):
        return self._save_dir

    @property
    def log_dir(self):
        return self._log_dir

# helper functions to update config dict with custom cli options
def _update_config(config, modification):
    if modification is None:
        return config

    for k, v in modification.items():
        if v is not None:
            _set_by_path(config, k, v)
    return config

def _get_opt_name(flags):
    for flg in flags:
        if flg.startswith('--'):
            return flg.replace('--', '')
    return flags[0].replace('--', '')

def _set_by_path(tree, keys, value):
    """Set a value in a nested object in tree by sequence of keys."""
    keys = keys.split(';')
    _get_by_path(tree, keys[:-1])[keys[-1]] = value

def _get_by_path(tree, keys):
    """Access a nested object in tree by sequence of keys."""
    return reduce(getitem, keys, tree)