config.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import six
import codecs
import os
from ast import literal_eval
from typing import Any, Dict, Optional

import yaml
import paddle

from paddleseg.cvlibs import config_checker as checker
from paddleseg.cvlibs import manager
from paddleseg.utils import logger, utils

_INHERIT_KEY = '_inherited_'
_BASE_KEY = '_base_'


class Config(object):
    """
    Configuration parsing.

    The following hyper-parameters are available in the config file:
        batch_size: The number of samples per gpu.
        iters: The total training steps.
        train_dataset: A training data config including type/data_root/transforms/mode.
            For data type, please refer to paddleseg.datasets.
            For specific transforms, please refer to paddleseg.transforms.transforms.
        val_dataset: A validation data config including type/data_root/transforms/mode.
        optimizer: A optimizer config. Please refer to paddleseg.optimizers.
        loss: A loss config. Multi-loss config is available. The loss type order is 
            consistent with the seg model outputs, where the coef term indicates the 
            weight of corresponding loss. Note that the number of coef must be the 
            same as the number of model outputs, and there could be only one loss type 
            if using the same loss type among the outputs, otherwise the number of
            loss type must be consistent with coef.
        model: A model config including type/backbone and model-dependent arguments.
            For model type, please refer to paddleseg.models.
            For backbone, please refer to paddleseg.models.backbones.

    Args:
        path (str) : The path of config file, supports yaml format only.
        opts (list, optional): Use opts to update the key-value pairs of all options.

    """

    def __init__(
            self,
            path: str,
            learning_rate: Optional[float]=None,
            batch_size: Optional[int]=None,
            iters: Optional[int]=None,
            opts: Optional[list]=None,
            checker: Optional[checker.ConfigChecker]=None, ):
        assert os.path.exists(path), \
            'Config path ({}) does not exist'.format(path)
        assert path.endswith('yml') or path.endswith('yaml'), \
            'Config file ({}) should be yaml format'.format(path)

        self.dic = self._parse_from_yaml(path)
        self.dic = self.update_config_dict(
            self.dic,
            learning_rate=learning_rate,
            batch_size=batch_size,
            iters=iters,
            opts=opts)

        if checker is None:
            checker = self._build_default_checker()
        checker.apply_all_rules(self)

    @property
    def batch_size(self) -> int:
        return self.dic.get('batch_size')

    @property
    def iters(self) -> int:
        return self.dic.get('iters')

    @property
    def to_static_training(self) -> bool:
        return self.dic.get('to_static_training', False)

    @property
    def model_cfg(self) -> Dict:
        return self.dic.get('model', {}).copy()

    @property
    def loss_cfg(self) -> Dict:
        return self.dic.get('loss', {}).copy()

    @property
    def distill_loss_cfg(self) -> Dict:
        return self.dic.get('distill_loss', {}).copy()

    @property
    def lr_scheduler_cfg(self) -> Dict:
        return self.dic.get('lr_scheduler', {}).copy()

    @property
    def optimizer_cfg(self) -> Dict:
        return self.dic.get('optimizer', {}).copy()

    @property
    def train_dataset_cfg(self) -> Dict:
        return self.dic.get('train_dataset', {}).copy()

    @property
    def val_dataset_cfg(self) -> Dict:
        return self.dic.get('val_dataset', {}).copy()

    # TODO merge test_config into val_dataset
    @property
    def test_config(self) -> Dict:
        return self.dic.get('test_config', {}).copy()

    @classmethod
    def update_config_dict(cls, dic: dict, *args, **kwargs) -> dict:
        return update_config_dict(dic, *args, **kwargs)

    @classmethod
    def _parse_from_yaml(cls, path: str, *args, **kwargs) -> dict:
        return parse_from_yaml(path, *args, **kwargs)

    @classmethod
    def _build_default_checker(cls):
        rules = []
        rules.append(checker.DefaultPrimaryRule())
        rules.append(checker.DefaultSyncNumClassesRule())
        rules.append(checker.DefaultSyncImgChannelsRule())
        # Losses
        rules.append(checker.DefaultLossRule('loss'))
        rules.append(checker.DefaultSyncIgnoreIndexRule('loss'))
        # Distillation losses
        rules.append(checker.DefaultLossRule('distill_loss'))
        rules.append(checker.DefaultSyncIgnoreIndexRule('distill_loss'))

        return checker.ConfigChecker(rules, allow_update=True)

    def __str__(self) -> str:
        # Use NoAliasDumper to avoid yml anchor 
        return yaml.dump(self.dic, Dumper=utils.NoAliasDumper)


def parse_from_yaml(path: str):
    """Parse a yaml file and build config"""
    with codecs.open(path, 'r', 'utf-8') as file:
        dic = yaml.load(file, Loader=yaml.FullLoader)

    if _BASE_KEY in dic:
        base_files = dic.pop(_BASE_KEY)
        if isinstance(base_files, str):
            base_files = [base_files]
        for bf in base_files:
            base_path = os.path.join(os.path.dirname(path), bf)
            base_dic = parse_from_yaml(base_path)
            dic = merge_config_dicts(dic, base_dic)

    return dic


def merge_config_dicts(dic, base_dic):
    """Merge dic to base_dic and return base_dic."""
    base_dic = base_dic.copy()
    dic = dic.copy()

    if not dic.get(_INHERIT_KEY, True):
        dic.pop(_INHERIT_KEY)
        return dic

    for key, val in dic.items():
        if isinstance(val, dict) and key in base_dic:
            base_dic[key] = merge_config_dicts(val, base_dic[key])
        else:
            base_dic[key] = val

    return base_dic


def update_config_dict(dic: dict,
                       learning_rate: Optional[float]=None,
                       batch_size: Optional[int]=None,
                       iters: Optional[int]=None,
                       opts: Optional[list]=None):
    """Update config"""
    # TODO: If the items to update are marked as anchors in the yaml file,
    # we should synchronize the references.
    dic = dic.copy()

    if learning_rate:
        dic['lr_scheduler']['learning_rate'] = learning_rate
    if batch_size:
        dic['batch_size'] = batch_size
    if iters:
        dic['iters'] = iters

    if opts is not None:
        for item in opts:
            assert ('=' in item) and (len(item.split('=')) == 2), "--opts params should be key=value," \
                " such as `--opts batch_size=1 test_config.scales=0.75,1.0,1.25`, " \
                "but got ({})".format(opts)

            key, value = item.split('=')
            if isinstance(value, six.string_types):
                try:
                    value = literal_eval(value)
                except ValueError:
                    pass
                except SyntaxError:
                    pass
            key_list = key.split('.')

            tmp_dic = dic
            for subkey in key_list[:-1]:
                assert subkey in tmp_dic, "Can not update {}, because it is not in config.".format(
                    key)
                tmp_dic = tmp_dic[subkey]
            tmp_dic[key_list[-1]] = value

    return dic