参考文档

不得不知的 MMDetection 学习路线(个人经验版)
轻松掌握 MMDetection 整体构建流程(一)
学习配置文件 MaskRCNN

配置文件的逻辑

整体来说，配置文件包含以下几个部分：

• 数据集和评测器配置
• 模型配置
• 训练和测试的配置
• 优化相关配置
• 钩子配置(TODO)
• 运行相关配置

配置文件最终都是为了适应 Runner 的构建。可以具体查看 runner 的类细节

• mmengine/runner/runner.py
• 执行器
• Runner API

配置参数查找

比如评测器：

val_evaluator = dict(  # 验证过程使用的评测器
    type="CocoMetric",  # 用于评估检测和实例分割的 AR、AP 和 mAP 的 coco 评价指标
    ann_file=data_root + "annotations/instances_val2017.json",  # 标注文件路径
    metric=["bbox", "segm"],  # 需要计算的评价指标，`bbox` 用于检测，`segm` 用于实例分割
    format_only=False,
)
test_evaluator = val_evaluator  # 测试过程使用的评测器

type 参数对应着一个注册器。
mmengine 中 mmengine/registry/root.py 文件可以查看到所有根注册器。

__all__ = [
    'Registry', 'RUNNERS', 'RUNNER_CONSTRUCTORS', 'HOOKS', 'DATASETS',
    'DATA_SAMPLERS', 'TRANSFORMS', 'MODELS', 'WEIGHT_INITIALIZERS',
    'OPTIMIZERS', 'OPTIM_WRAPPER_CONSTRUCTORS', 'TASK_UTILS',
    'PARAM_SCHEDULERS', 'METRICS', 'MODEL_WRAPPERS', 'OPTIM_WRAPPERS', 'LOOPS',
    'VISBACKENDS', 'VISUALIZERS', 'LOG_PROCESSORS', 'EVALUATOR', 'INFERENCERS',
    'DefaultScope', 'traverse_registry_tree', 'count_registered_modules',
    'build_model_from_cfg', 'build_runner_from_cfg', 'build_from_cfg',
    'build_scheduler_from_cfg', 'init_default_scope', 'FUNCTIONS', 'STRATEGIES'
]

配置文件中的 type 都被注册为相应的注册器，这里可以推断对应的 Metric， 而相应的type 一定是一个具体的函数或者类，然后在 mmdet 的核心代码文件夹中查找 metric 有可能存在的文件中，最后在 mmdet/evaluation/metrics/coco_metric.py 找到 CocoMetric. 通过查看 这个类的签名，可以找到相应的配置参数，以及配置参数的说明。

class CocoMetric(BaseMetric):
    """COCO evaluation metric.

    Evaluate AR, AP, and mAP for detection tasks including proposal/box
    detection and instance segmentation. Please refer to
    https://cocodataset.org/#detection-eval for more details.

    Args:
        ann_file (str, optional): Path to the coco format annotation file.
            If not specified, ground truth annotations from the dataset will
            be converted to coco format. Defaults to None.
        metric (str | List[str]): Metrics to be evaluated. Valid metrics
            include 'bbox', 'segm', 'proposal', and 'proposal_fast'.
            Defaults to 'bbox'.
        classwise (bool): Whether to evaluate the metric class-wise.
            Defaults to False.
        proposal_nums (Sequence[int]): Numbers of proposals to be evaluated.
            Defaults to (100, 300, 1000).
        iou_thrs (float | List[float], optional): IoU threshold to compute AP
            and AR. If not specified, IoUs from 0.5 to 0.95 will be used.
            Defaults to None.
        metric_items (List[str], optional): Metric result names to be
            recorded in the evaluation result. Defaults to None.
        format_only (bool): Format the output results without perform
            evaluation. It is useful when you want to format the result
            to a specific format and submit it to the test server.
            Defaults to False.
        outfile_prefix (str, optional): The prefix of json files. It includes
            the file path and the prefix of filename, e.g., "a/b/prefix".
            If not specified, a temp file will be created. Defaults to None.
        file_client_args (dict, optional): Arguments to instantiate the
            corresponding backend in mmdet <= 3.0.0rc6. Defaults to None.
        backend_args (dict, optional): Arguments to instantiate the
            corresponding backend. Defaults to None.
        collect_device (str): Device name used for collecting results from
            different ranks during distributed training. Must be 'cpu' or
            'gpu'. Defaults to 'cpu'.
        prefix (str, optional): The prefix that will be added in the metric
            names to disambiguate homonymous metrics of different evaluators.
            If prefix is not provided in the argument, self.default_prefix
            will be used instead. Defaults to None.
        sort_categories (bool): Whether sort categories in annotations. Only
            used for `Objects365V1Dataset`. Defaults to False.
        use_mp_eval (bool): Whether to use mul-processing evaluation
    """
    default_prefix: Optional[str] = 'coco'

    def __init__(self,
                 ann_file: Optional[str] = None,
                 metric: Union[str, List[str]] = 'bbox',
                 classwise: bool = False,
                 proposal_nums: Sequence[int] = (100, 300, 1000),
                 iou_thrs: Optional[Union[float, Sequence[float]]] = None,
                 metric_items: Optional[Sequence[str]] = None,
                 format_only: bool = False,
                 outfile_prefix: Optional[str] = None,
                 file_client_args: dict = None,
                 backend_args: dict = None,
                 collect_device: str = 'cpu',
                 prefix: Optional[str] = None,
                 sort_categories: bool = False,
                 use_mp_eval: bool = False) -> None:

框架的架构

’