BaseRunner

danling.runner.BaseRunner

Base class for all runners.

BaseRunner sets up basic running environment, including seed, deterministic, and logging.

BaseRunner also provides some basic methods, such as, step, state_dict, save_checkpoint, load_checkpoint.

BaseRunner defines all basic attributes and relevant properties such as scores, progress, etc.

ID:

Name	Type	Description
timestamp	str	A time string representing the creation time of run.
name	str	f"{self.config.experiment_name}-{self.config.run_name}".
id	str	f"{self.config.experiment_id:.8}{self.config.run_id:.8}".
uuid	(UUID, property)	uuid5(self.config.run_id, self.id).

Core:

Name	Type	Description
mode	(RunnerMode, property)	Running mode.
config	Config	Running config. See [Config] for details.

Model:

Name	Type	Description
model	Callable
criterion	Callable
optimizer	Any | None
scheduler	Any | None

Data:

Name	Type	Description
datasets	FlatDict	All datasets, should be in the form of {subset: dataset}. Initialised to FlatDict by default.
datasamplers	FlatDict	All datasamplers, should be in the form of {subset: datasampler}. Initialised to FlatDict by default.
dataloaders	FlatDict	All dataloaders, should be in the form of {subset: dataloader}. Initialised to FlatDict by default.
split	str	Current running split.
batch_size	(int, property)	Number of samples per batch in current running split.
batch_size_equivalent	(int, property)	Total batch_size (batch_size * world_size * accum_steps).

datasets, datasamplers, dataloaders should be a dict with the same keys. Their keys should be split (e.g. train, val, test).

Progress:

Name	Type	Description
progress	(float, property)	Running Progress, in range(0, 1).

Results:

Name	Type	Description
results	NestedDict	Results include all metric information of the model. Results should be in the form of {epoch: {subset: {metric: score}}}.
latest_result	(NestedDict, property)	Most recent result, should be in the form of {subset: {metric: score}}.
best_result	(NestedDict, property)	Best result, should be in the form of {subset: {metric: score}}.
scores	(List[float], property)	Score is the core metric that is used to evaluate the performance of the model. Scores should be in the form of {epoch: score}.
latest_score	(float, property)	Most recent score, should be in the form of score.
best_score	(float, property)	Best score, should be in the form of score.
score_split	Optional[str]	The subset to calculate the score. If is None, will use the last set of the result.
score_name	str	The metric name of score. Defaults to "loss".
is_best	(bool, property)	If latest_score == best_score.

A result is a dict with the same split as keys, like dataloaders. A typical result shall look like this:

{
    "train": {
        "loss": 0.1,
        "accuracy": 0.9,
    },
    "val": {
        "loss": 0.2,
        "accuracy": 0.8,
    },
    "test": {
        "loss": 0.3,
        "accuracy": 0.7,
    },
}

scores are dynamically extracted from results by score_split and score_name. They represent the core metric that is used in comparing the performance against different models and settings. For the above results, If score_split = "val", score_name = "accuracy", then scores = 0.9.

IO:

Name	Type	Description
dir	(str, property)	Directory of the run. Defaults to ${self.project_root}/${self.name}-${self.id}/${self.timestamp}).
checkpoint_dir	(str, property)	Directory of checkpoints.
log_path	(str, property)	Path of log file.
checkpoint_dir_name	str	The name of the directory under runner.dir to save checkpoints. Defaults to "checkpoints".

Parallel Training:

Name	Type	Description
world_size	(int, property)	Number of processes.
rank	(int, property)	Process index of all processes.
local_rank	(int, property)	Process index of local processes.
distributed	(bool, property)	If runner is running in distributed mode.
is_main_process	(bool, property)	If current process is the main process of all processes.
is_local_main_process	(bool, property)	If current process is the main process of local processes.

logging:

Name	Type	Description
meters	AverageMeters | MultiTaskAverageMeters	Average meters. Initialised to AverageMeters by default.
metrics	Metrics | MultiTaskMetrics | MetricMeters | None	Metrics for evaluating.
logger	Logger | None
writer	Any | None

See Also

Config: The runeer base that stores runtime information. BaseRunner: The base runner class.

Source code in danling/runner/base_runner.py

class BaseRunner(metaclass=RunnerMeta):  # pylint: disable=too-many-public-methods
    r"""
    Base class for all runners.

    `BaseRunner` sets up basic running environment, including `seed`, `deterministic`, and `logging`.

    `BaseRunner` also provides some basic methods, such as, `step`, `state_dict`, `save_checkpoint`, `load_checkpoint`.

    `BaseRunner` defines all basic attributes and relevant properties such as `scores`, `progress`, etc.

    Attributes: ID:
        timestamp (str): A time string representing the creation time of run.
        name (str): `f"{self.config.experiment_name}-{self.config.run_name}"`.
        id (str): `f"{self.config.experiment_id:.8}{self.config.run_id:.8}"`.
        uuid (UUID, property): `uuid5(self.config.run_id, self.id)`.

    Attributes: Core:
        mode (RunnerMode, property): Running mode.
        config (Config): Running config. See [`Config`] for details.

    Attributes: Model:
        model (Callable):
        criterion (Callable):
        optimizer:
        scheduler:

    Attributes: Data:
        datasets (FlatDict): All datasets, should be in the form of ``{subset: dataset}``.
            Initialised to `FlatDict` by default.
        datasamplers (FlatDict): All datasamplers, should be in the form of ``{subset: datasampler}``.
            Initialised to `FlatDict` by default.
        dataloaders (FlatDict): All dataloaders, should be in the form of ``{subset: dataloader}``.
            Initialised to `FlatDict` by default.
        split (str): Current running split.
        batch_size (int, property): Number of samples per batch in current running split.
        batch_size_equivalent (int, property): Total batch_size (`batch_size * world_size * accum_steps`).

    `datasets`, `datasamplers`, `dataloaders` should be a dict with the same keys.
    Their keys should be `split` (e.g. `train`, `val`, `test`).

    Attributes: Progress:
        progress (float, property): Running Progress, in `range(0, 1)`.

    Attributes: Results:
        results (NestedDict): Results include all metric information of the model.
            Results should be in the form of `{epoch: {subset: {metric: score}}}`.
        latest_result (NestedDict, property): Most recent result, should be in the form of `{subset: {metric: score}}`.
        best_result (NestedDict, property): Best result, should be in the form of `{subset: {metric: score}}`.
        scores (List[float], property): Score is the core metric that is used to evaluate the performance of the model.
            Scores should be in the form of `{epoch: score}`.
        latest_score (float, property): Most recent score, should be in the form of `score`.
        best_score (float, property): Best score, should be in the form of `score`.
        score_split (Optional[str]): The subset to calculate the score.
            If is `None`, will use the last set of the result.
        score_name (str): The metric name of score.
            Defaults to `"loss"`.
        is_best (bool, property): If `latest_score == best_score`.

    A `result` is a dict with the same `split` as keys, like `dataloaders`.
    A typical `result` shall look like this:
    ```python
    {
        "train": {
            "loss": 0.1,
            "accuracy": 0.9,
        },
        "val": {
            "loss": 0.2,
            "accuracy": 0.8,
        },
        "test": {
            "loss": 0.3,
            "accuracy": 0.7,
        },
    }
    ```

    `scores` are dynamically extracted from `results` by `score_split` and `score_name`.
    They represent the core metric that is used in comparing the performance against different models and settings.
    For the above `results`, If `score_split = "val"`, `score_name = "accuracy"`, then `scores = 0.9`.

    Attributes: IO:
        dir (str, property): Directory of the run.
            Defaults to `${self.project_root}/${self.name}-${self.id}/${self.timestamp})`.
        checkpoint_dir (str, property): Directory of checkpoints.
        log_path (str, property):  Path of log file.
        checkpoint_dir_name (str): The name of the directory under `runner.dir` to save checkpoints.
            Defaults to `"checkpoints"`.

    Attributes: Parallel Training:
        world_size (int, property): Number of processes.
        rank (int, property): Process index of all processes.
        local_rank (int, property): Process index of local processes.
        distributed (bool, property): If runner is running in distributed mode.
        is_main_process (bool, property): If current process is the main process of all processes.
        is_local_main_process (bool, property): If current process is the main process of local processes.

    Attributes: logging:
        meters (AverageMeters | MultiTaskAverageMeters): Average meters.
            Initialised to `AverageMeters` by default.
        metrics (Metrics | MultiTaskMetrics | MetricMeters | None): Metrics for evaluating.
        logger:
        writer:

    See Also:
        [`Config`][danling.runner.Config]: The runeer base that stores runtime information.
        [`BaseRunner`][danling.runner.BaseRunner]: The base runner class.
    """

    # DO NOT set default value in class, as they won't be stored in `__dict__`.

    timestamp: str

    _mode: RunnerMode
    _config: Config
    inited: bool = False

    model: Callable | None = None
    criterion: Callable | None = None
    optimizer: Any | None = None
    scheduler: Any | None = None

    datasets: FlatDict
    datasamplers: FlatDict
    dataloaders: FlatDict
    split: str | None = None

    results: NestedDict
    meters: AverageMeters
    metrics: Metrics | MetricMeters | None = None
    logger: logging.Logger | None = None
    writer: Any | None = None

    def __init__(self, config: Config) -> None:
        self.timestamp = get_time_str()
        if "datasets" not in self.__dict__:
            self.datasets = FlatDict()
        if "datasamplers" not in self.__dict__:
            self.datasamplers = FlatDict()
        if "dataloaders" not in self.__dict__:
            self.dataloaders = FlatDict()
        if "results" not in self.__dict__:
            self.results = NestedDict()
        self.meters = AverageMeters()
        self._mode = RunnerMode.train  # type: ignore[assignment]
        # must init config at last to avoid name conflicts
        if not isinstance(config, Config):
            config = Config(config)
        self._config = config
        self.init_distributed()
        self.inited = True

    def __post_init__(self):
        if self.config.seed is not None:
            self.set_seed()
        if self.config.deterministic:
            self.set_deterministic()
        if self.config.log:
            self.init_logging()
        self.init_print()
        if self.config.tensorboard:
            self.init_tensorboard()

    def init_distributed(self) -> None:
        r"""
        Initialise distributed running environment.
        """

    @on_main_process
    def init_logging(self) -> None:
        r"""
        Set up logging.
        """

        os.makedirs(os.path.dirname(self.log_path), exist_ok=True)
        # Why is setting up proper logging so !@?#! ugly?
        logging.config.dictConfig(
            {
                "version": 1,
                "disable_existing_loggers": False,
                "formatters": {
                    "standard": {"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s"},
                },
                "handlers": {
                    "stdout": {
                        "level": "INFO",
                        "formatter": "standard",
                        "class": "logging.StreamHandler",
                        "stream": "ext://sys.stdout",
                    },
                    "logfile": {
                        "level": "DEBUG",
                        "formatter": "standard",
                        "class": "logging.FileHandler",
                        "filename": self.log_path,
                        "mode": "a",
                    },
                },
                "loggers": {
                    "": {
                        "handlers": ["stdout", "logfile"],
                        "level": "DEBUG",
                        "propagate": True,
                    },
                },
            }
        )
        logging.captureWarnings(True)
        self.logger = logging.getLogger("runner")
        self.logger.flush = lambda: [h.flush() for h in self.logger.handlers]  # type: ignore

    def init_print(self, process: int = 0) -> None:
        r"""
        Set up `print`.

        Only print on a specific `process` or when `force = True`.

        Args:
            process: The process to `print` on.

        Notes
        -----
        If `self.config.log = True`, the default `print` function will be override by `logging.info`.
        """

        logger = logging.getLogger("print")
        logger.flush = lambda: [h.flush for h in logger.handlers]  # type: ignore
        import builtins as __builtin__  # pylint: disable=C0415

        builtin_print = __builtin__.print

        @catch
        def print(*args, force=False, end="\n", file=None, flush=False, **kwargs):  # pylint: disable=redefined-builtin
            if self.rank == process or force:
                if self.config.log:
                    logger.info(*args, **kwargs)
                else:
                    builtin_print(*args, end=end, file=file, flush=flush, **kwargs)

        __builtin__.print = print

    @on_main_process
    def init_tensorboard(self, *args, **kwargs) -> None:
        r"""
        Set up Tensoraoard SummaryWriter.
        """
        raise NotImplementedError

    def set_seed(self, seed: int = None, bias: int = None) -> int:  # type: ignore[assignment]
        r"""
        Set up random seed.

        Args:
            seed: Random seed to set.
                Defaults to `self.config.seed` (`config.seed`).

            bias: Make the seed different for each processes.

                This avoids same data augmentation are applied on every processes.

                Defaults to `self.rank`.

                Set to `False` to disable this feature.
        Returns:
            Random seed set.
        """

        seed = seed or self.config.seed  # type: ignore[assignment]
        bias = bias or self.rank
        if bias:
            seed += bias
        if np_random is not None:
            np_random.seed(seed)
        random.seed(seed)
        return seed

    def set_deterministic(self) -> None:
        r"""
        Set up deterministic.
        """

        raise NotImplementedError

    def scale_lr(
        self,
        lr: float,
        lr_scale_factor: float | None = None,
        batch_size_base: int | None = None,
    ) -> float:
        r"""
        Scale learning rate according to [linear scaling rule](https://arxiv.org/abs/1706.02677).
        """

        if lr_scale_factor in self.config:
            lr_scale_factor = self.config.lr_scale_factor

        if lr_scale_factor is None:
            if batch_size_base is None:
                batch_size_base = getattr(self, "batch_size_base", None)
                if batch_size_base is None:
                    raise ValueError("batch_size_base must be specified to auto scale lr")
            lr_scale_factor = self.batch_size_equivalent / batch_size_base
        elif batch_size_base is not None:
            warn(
                "batch_size_base will be ignored if lr_scale_factor is specified", category=RuntimeWarning, stacklevel=2
            )
        lr = lr * lr_scale_factor
        self.config.lr_scale_factor = lr_scale_factor
        return lr

    def update(self, loss, *args, **kwargs) -> None:
        r"""
        Backward loss and step optimizer & scheduler.

        Args:
            loss: loss.
        """

        raise NotImplementedError

    def state_dict(self, cls: Callable = dict) -> Mapping:
        r"""
        Return dict of all attributes for checkpoint.
        """

        return cls(self.config)

    def dict(self, cls: Callable = dict) -> Mapping:
        r"""
        Convert config to Mapping.

        Args:
            cls: Target `clc to convert to.
        """

        return self.config.dict(cls)

    @catch
    def save(self, obj: Any, file: PathStr, main_process_only: bool = True, *args, **kwargs) -> File:
        r"""
        Save any file with supported extensions.

        `Runner.save` internally calls `dl.save`,
        but with additional arguments to allow it save only on the main process.
        Moreover, any error raised by `Runner.save` will be caught and logged.
        """

        if main_process_only and self.is_main_process or not main_process_only:
            return save(obj, file, *args, **kwargs)
        return file

    @staticmethod
    def load(file: PathStr, *args, **kwargs) -> Any:
        r"""
        Load any file with supported extensions.

        `Runner.load` is identical to `dl.load`.
        """

        return load(file, *args, **kwargs)

    @catch
    def json(self, file: File, main_process_only: bool = True, *args, **kwargs) -> None:  # pylint: disable=R1710
        r"""
        Dump Runner config to json file.
        """

        if main_process_only and self.is_main_process or not main_process_only:
            return self.config.json(file, *args, **kwargs)

    @classmethod
    def from_json(cls, file: File, *args, **kwargs) -> BaseRunner:
        r"""
        Construct Runner from json file.

        This function calls `self.from_jsons()` to construct object from json string.
        You may overwrite `from_jsons` in case something is not json serializable.
        """

        with FlatDict.open(file) as fp:
            return cls.from_jsons(fp.read(), *args, **kwargs)

    def jsons(self, *args, **kwargs) -> str:
        r"""
        Dump Runner config to json string.
        """

        return self.config.jsons(*args, **kwargs)

    @classmethod
    def from_jsons(cls, string: str, *args, **kwargs) -> BaseRunner:
        r"""
        Construct Runner from json string.
        """

        return cls(Config.from_jsons(string, *args, **kwargs))

    @catch
    def yaml(self, file: File, main_process_only: bool = True, *args, **kwargs) -> None:  # pylint: disable=R1710
        r"""
        Dump Runner config to yaml file.
        """

        if main_process_only and self.is_main_process or not main_process_only:
            return self.config.yaml(file, *args, **kwargs)

    @classmethod
    def from_yaml(cls, file: File, *args, **kwargs) -> BaseRunner:
        r"""
        Construct Runner from yaml file.

        This function calls `self.from_yamls()` to construct object from yaml string.
        You may overwrite `from_yamls` in case something is not yaml serializable.
        """

        with FlatDict.open(file) as fp:
            return cls.from_yamls(fp.read(), *args, **kwargs)

    def yamls(self, *args, **kwargs) -> str:
        r"""
        Dump Runner config to yaml string.
        """

        return self.config.yamls(*args, **kwargs)

    @classmethod
    def from_yamls(cls, string: str, *args, **kwargs) -> BaseRunner:
        r"""
        Construct Runner from yaml string.
        """

        return cls(Config.from_yamls(string, *args, **kwargs))

    def check_dir(self, action: str = "warn") -> bool:
        r"""
        Check if `self.dir` is not empty.

        Args:
            action (str): The action to perform if `self.dir` is not empty.
            Can be one of ("warn", "raise", "ignore"), default is "warn".
        """

        if action and action not in ("warn", "raise", "ignore"):
            raise ValueError(f"action should be one of warn, raise or ignore, but got {action}")
        if os.listdir(self.dir):
            if action == "warn":
                warn(
                    f"Directory `{self.dir}` is not empty",
                    category=RuntimeWarning,
                    stacklevel=2,
                )
            if action == "raise":
                raise RuntimeError(f"Directory `{self.dir}` is not empty")
            return False
        return True

    @catch
    @on_main_process
    def save_checkpoint(self, epoch: int | None = None) -> None:
        r"""
        Save checkpoint to `self.checkpoint_dir`.

        The checkpoint will be saved to `self.checkpoint_dir/latest.pth`.

        If `self.config.save_interval` is positive and `self.config.epoch + 1` is a multiple of `save_interval`,
        the checkpoint will also be copied to `self.checkpoint_dir/epoch-{self.config.epoch}.pth`.

        If `self.is_best` is `True`, the checkpoint will also be copied to `self.checkpoint_dir/best.pth`.
        """

        epoch = epoch or self.config.epoch
        save_interval = self.config.get("save_interval", -1)
        latest_path = os.path.join(self.checkpoint_dir, "latest.pth")
        self.save(self.state_dict(), latest_path)
        if save_interval > 0 and (epoch + 1) % save_interval == 0:
            save_path = os.path.join(self.checkpoint_dir, f"epoch-{epoch}.pth")
            shutil.copy(latest_path, save_path)
        if self.is_best:
            best_path = os.path.join(self.checkpoint_dir, "best.pth")
            shutil.copy(latest_path, best_path)

    def load_checkpoint(
        self,
        checkpoint: Mapping | bytes | str | os.PathLike | None = None,
        auto_resume: bool | None = None,
        override_config: bool = False,
        *args,
        **kwargs,
    ) -> None:
        """
        Load info from checkpoint.

        Args:
            checkpoint: Checkpoint (or its path) to load.
                Defaults to `self.config.checkpoint`.
            auto_resume: Automatically resume from latest checkpoint if exists.
                Defaults to `False`.
                If is `True` and `checkpoint` is None, will set it to `self.checkpoint_dir/latest.pth`.
            override_config: If True, override runner config with checkpoint config.
                Defaults to `False`.
            *args: Additional arguments to pass to `self.load`.
            **kwargs: Additional keyword arguments to pass to `self.load`.

        Raises:
            FileNotFoundError: If `checkpoint` does not exists.

        See Also:
            [`from_checkpoint`][danling.BaseRunner.from_checkpoint]: Build runner from checkpoint.
            [`load_pretrained`][danling.BaseRunner.load_pretrained]: Load parameters from pretrained checkpoint.
        """

        checkpoint = checkpoint if checkpoint is not None else self.config.get("checkpoint")
        auto_resume = auto_resume if auto_resume is not None else self.config.get("auto_resume", False)

        # TODO: Support loading checkpoints in other format
        if checkpoint is not None:
            if auto_resume:
                warn(
                    "latest checkpoint is preempted by value specified in checkpoint",
                    RuntimeWarning,
                    stacklevel=2,
                )
            if isinstance(checkpoint, (bytes, str, os.PathLike)):
                if not os.path.exists(checkpoint):
                    raise FileNotFoundError(f"checkpoint is set to {checkpoint!r} but does not exist")
                self.config.checkpoint = checkpoint
                ckpt = self.load(checkpoint, *args, **kwargs)
            elif isinstance(checkpoint, Mapping):
                ckpt = checkpoint
            else:
                raise ValueError(f"pretrained is set to {checkpoint!r} but is not a valid checkpoint")
        elif auto_resume:
            checkpoint = os.path.join(self.checkpoint_dir, "latest.pth")
            if os.path.exists(checkpoint):
                self.config.checkpoint = checkpoint
                ckpt = self.load(checkpoint, *args, **kwargs)
            else:
                warn("latest checkpoint does not exits", category=RuntimeWarning, stacklevel=2)
                return
        else:
            raise ValueError("checkpoint is not specified and auto_resume is not set to True")

        # TODO: Wrap state_dict in a dataclass
        self.config.merge(ckpt["runner"], overwrite=override_config)
        if self.model is not None and "model" in ckpt:
            model = self.unwrap(self.model)
            model.load_state_dict(ckpt["model"])
        if self.optimizer is not None and "optimizer" in ckpt:
            self.optimizer.load_state_dict(ckpt["optimizer"])
        if self.scheduler is not None and "scheduler" in ckpt:
            self.scheduler.load_state_dict(ckpt["scheduler"])
        self.config.iter_begin = self.config.iter
        self.config.step_begin = self.config.step
        self.config.epoch_begin = self.config.epoch

    @classmethod
    def from_checkpoint(cls, checkpoint: Mapping | bytes | str | os.PathLike, *args, **kwargs) -> BaseRunner:
        r"""
        Build BaseRunner from checkpoint.

        Args:
            checkpoint: Checkpoint (or its path) to load.
            *args: Additional arguments to pass to `cls.load`.
            **kwargs: Additional keyword arguments to pass to `cls.load`.

        Returns:
            (BaseRunner):
        """

        if isinstance(checkpoint, (bytes, str, os.PathLike)):
            ckpt = cls.load(checkpoint, *args, **kwargs)
        elif isinstance(checkpoint, Mapping):
            ckpt = checkpoint
        else:
            raise ValueError(f"checkpoint is set to {checkpoint} but is not a valid checkpoint")
        runner = cls(**ckpt["runner"])
        runner.load_checkpoint(ckpt, override_config=False)
        return runner

    def load_pretrained(self, checkpoint: Mapping | bytes | str | os.PathLike | None = None, *args, **kwargs) -> None:
        """
        Load parameters from pretrained checkpoint.

        This method only loads the model weights.

        Args:
            checkpoint: Pretrained checkpoint (or its path) to load.
                Defaults to `self.config.pretrained`.
            *args: Additional arguments to pass to `self.load`.
            **kwargs: Additional keyword arguments to pass to `self.load`.

        Raises:
            FileNotFoundError: If `checkpoint` does not exists.

        See Also:
            [`load_checkpoint`][danling.BaseRunner.load_checkpoint]: Load info from checkpoint.
        """

        # TODO: Support loading checkpoints in other format
        checkpoint = checkpoint if checkpoint is not None else self.config.get("pretrained")
        if checkpoint is None:
            raise ValueError("pretrained is not specified")
        if isinstance(checkpoint, (bytes, str, os.PathLike)):
            if not os.path.exists(checkpoint):
                raise FileNotFoundError(f"pretrained is set to {checkpoint!r} but does not exist")
            ckpt = self.load(checkpoint, *args, **kwargs)
        elif isinstance(checkpoint, Mapping):
            ckpt = checkpoint
        else:
            raise ValueError(f"pretrained is set to {checkpoint!r} but is not a valid checkpoint")
        if self.model is not None and "model" in ckpt:
            model = self.unwrap(self.model)
            model.load_state_dict(ckpt["model"])
        else:
            raise ValueError(f"Unable to find model weights in {checkpoint!r}")

    def get_result(self, level: str) -> NestedDict:
        metric = None
        if level == "step":
            result = self.meters.val.clone()
            if self.metrics is not None:
                metric = self.metrics.val
        elif level == "epoch":
            result = self.meters.avg.clone()
            if self.metrics is not None:
                metric = self.metrics.avg
        else:
            raise ValueError(f"level should be one of step or epoch, but got {level}")
        if metric is not None:
            if isinstance(self.metrics, MultiTaskMetrics):
                m = NestedDict()
                for key, value in metric.items():
                    if len(value) == 1:
                        value = next(iter(value.values()))
                    m[key] = value
                metric = m
            if len(metric) == 1:
                metric = next(iter(metric.values()))
            result.update(metric)
        return result

    def append_result(self, result: NestedDict, index: int | None = None) -> None:
        r"""
        Append result to `self.results`.

        Warnings:
            `self.results` is heavily relied upon for computing metrics.

            Failed to use this method may lead to unexpected behavior.
        """

        if index is None:
            index = self.config.epoch
            global __APPEND_RESULT_COUNTER__  # pylint: disable=global-statement
            __APPEND_RESULT_COUNTER__ += 1
            if index == 0 and __APPEND_RESULT_COUNTER__ > 1:
                warn(
                    """
                    Automatically set index to `self.config.epoch`.
                    Please ensure `self.config.epoch` updates before calling `append_result`
                    """,
                    category=RuntimeWarning,
                    stacklevel=2,
                )
        if index in self.results:
            self.results[index].merge(result)
        else:
            self.results[index] = result

    def print_result(self) -> None:
        r"""
        Print latest and best result.
        """

        print(f"latest result: {self.latest_result}")
        print(f"best result: {self.best_result}")

    def step_log(self, split: str, iteration: int, length: int | None = None):
        if length is None:
            length = len(self.dataloaders[split]) - 1
        result = self.get_result("step")
        print(self.format_step_result(result, split, iteration, length))
        if self.mode == "train":
            self.write_result(result, split)
        return result

    def format_step_result(
        self, result: NestedDict, split: str, step: int, length: int, format_spec: str = ".4f"
    ) -> str:
        repr_str = ""
        if split is not None:
            if self.mode == "train":
                repr_str = f"training on {split} "
            elif self.mode == "eval":
                repr_str = f"evaluating on {split} "
            else:
                repr_str = f"running in {self.mode} mode on {split} "
        repr_str += f"[{step}/{length}]\t"
        return repr_str + self.format_result(result, format_spec=format_spec)

    def format_epoch_result(
        self, result: NestedDict, epoch: int | None = None, epoch_end: int | None = None, format_spec: str = ".4f"
    ) -> str:
        epoch = epoch or self.config.epoch
        epoch_end = epoch_end or self.config.epoch_end
        repr_str = f"epoch [{epoch}/{epoch_end - 1}]" if epoch is not None and epoch_end else ""
        return repr_str + self.format_result(result, format_spec=format_spec)

    def format_result(self, result: Mapping, format_spec: str = ".4f") -> str:
        return format_result(result, format_spec=format_spec)

    def write_result(self, result: NestedDict, split: str, step: int | None = None):
        if step is None:
            step = self.step
        for name, score in result.all_items():
            name = name.replace(".", "/")
            if name == "loss" and isinstance(score, AverageMeter):
                score = score.avg
            if isinstance(score, Sequence):
                for i, s in enumerate(score):
                    self.write_score(f"{name}/{i}", s, split, step)
            elif isinstance(score, Mapping):
                for k, s in score.items():
                    self.write_score(f"{name}/{k}", s, split, step)
            else:
                self.write_score(name, score, split, step)

    def write_score(self, name: str, score: float, split: str, step: int):
        if self.writer:
            self.writer.add_scalar(f"{split}/{name}", score, step)

    @catch
    @on_main_process
    def save_result(self) -> None:
        r"""
        Save result to `self.dir`.

        This method will save latest and best result to
        `self.dir/latest.json` and `self.dir/best.json` respectively.
        """

        results_path = os.path.join(self.dir, "results.json")
        self.save(
            {
                "name": self.name,
                "id": self.id,
                "timestamp": self.timestamp,
                "results": self.results,
            },
            results_path,
            indent=4,
        )
        ret = {"name": self.name, "id": self.id, "timestamp": self.timestamp}
        result = self.latest_result
        if isinstance(result, FlatDict):
            result = result.dict()
        # This is slower but ensure id is the first key
        if result is not None:
            ret.update(result)
        latest_path = os.path.join(self.dir, "latest.json")
        self.save(ret, latest_path, indent=4)
        if self.is_best:
            best_path = os.path.join(self.dir, "best.json")
            shutil.copy(latest_path, best_path)

    def unwrap(self, model: Any) -> Any:
        return model

    @cached_property
    def name(self):
        if "name" in self.config:
            return self.config["name"]
        return f"{self.config.experiment_name}-{self.config.run_name}"

    @cached_property
    def id(self):
        return f"{self.config.experiment_id:.8}{self.config.run_id:.8}"

    @cached_property
    def uuid(self) -> UUID:
        r"""
        UUID of the config.
        """

        return uuid5(self.run_uuid, self.id)

    @property
    def mode(self) -> RunnerMode:
        return self._mode

    @mode.setter
    def mode(self, mode: str | RunnerMode) -> None:
        if isinstance(mode, str):
            mode = RunnerMode(mode)
        self._mode = mode

    @property
    def config(self) -> Config:
        return self._config

    @property
    def batch_size(self) -> int:
        r"""
        Batch size.

        Notes:
            If `train` is in `dataloaders`, then `batch_size` is the batch size of `train`.
            Otherwise, `batch_size` is the batch size of the first dataloader.

        Returns:
            (int):
        """

        if self.dataloaders and self.split:
            return self.dataloaders[self.split].batch_size
        batch_size = self.config.get("dataloader.batch_size")
        if batch_size:
            return batch_size
        raise AttributeError("batch_size could not be inferred and is not in config")

    @property
    def batch_size_equivalent(self) -> int:
        r"""
        Actual batch size.

        Returns:
            (int): `batch_size` * `world_size` * `accum_steps`
        """

        return self.batch_size * self.world_size * self.accum_steps

    @cached_property
    def total_epochs(self) -> int:
        if self.config.epoch_end:
            return self.config.epoch_end - self.config.epoch_begin
        raise ValueError("epoch_end is not specified")

    @cached_property
    def total_steps(self) -> int:
        if self.config.step_end:
            return self.config.step_end - self.config.step_begin
        dataset = self.datasets.get("train", next(iter(self.datasets.values())))
        return self.total_epochs * ceil(len(dataset) / self.batch_size / self.world_size)

    @cached_property
    def trainable_steps(self) -> int:
        return ceil(self.total_steps / self.accum_steps)

    @cached_property
    def accum_steps(self) -> int:
        r"""
        Accumulated steps.

        Returns:
            (int):
        """

        return self.config.get("accum_steps", 1)

    @property
    def progress(self) -> float:
        r"""
        Training Progress.

        Returns:
            (float):

        Raises:
            RuntimeError: If no terminal is defined.
        """

        return self.step / self.total_steps

    @property
    def world_size(self) -> int:
        r"""
        Number of processes.
        """

        return 1

    @property
    def rank(self) -> int:
        r"""
        Process index of all processes.
        """

        return 0

    @property
    def local_rank(self) -> int:
        r"""
        Process index of local processes.
        """

        return 0

    @property
    def distributed(self) -> bool:
        r"""
        If runner is running in distributed mode.
        """

        return self.world_size > 1

    @property
    def is_main_process(self) -> bool:
        r"""
        If current process is the main process of all processes.
        """

        return self.rank == 0

    @property
    def is_local_main_process(self) -> bool:
        r"""
        If current process is the main process of local processes.
        """

        return self.local_rank == 0

    @property
    def best_fn(self) -> Callable:
        r"""
        Function to determine the best score from a list of scores.

        By default, the `best_fn` returns `min` if `self.config.score_name` is `loss`,
        otherwise, returns `max`.

        Subclass can override this method to accommodate needs, such as `min`.

        Returns:
            (callable):
        """

        return max if self.config.score_name != "loss" else min

    @property
    def best_index(self) -> int:
        r"""
        Find the best index from all scores.

        Returns:
            (int):
        """

        if not self.scores:
            return 0
        values = list(self.scores.values())
        return self.best_fn(range(len(values)), key=values.__getitem__)

    @property
    def latest_result(self) -> NestedDict | None:
        r"""
        Latest result.
        """

        if not self.results:
            return None
        latest_index = next(reversed(self.results if PY38_PLUS else list(self.results)))  # type: ignore
        ret = self.results[latest_index].clone()
        ret["index"] = latest_index
        return ret

    @property
    def best_result(self) -> NestedDict | None:
        r"""
        Best result.
        """

        if not self.results:
            return None
        best_index = self.best_index
        ret = self.results[best_index].clone()
        ret["index"] = best_index
        return ret

    @property
    def scores(self) -> FlatDict | None:
        r"""
        All scores.

        Scores are extracted from results by `score_split` and `runner.config.score_name`,
        following `[r[score_split][self.config.score_name] for r in self.results]`.

        Scores are considered as the index of the performance of the model.
        It is useful to determine the best model and the best hyper-parameters.

        `score_split` is defined in `self.config.score_split`.
        If it is not set, `DanLing` will use `val` or `validate` if they appear in the `latest_result`.
        If `DanLing` still could not find, it will fall back to the second key in the `latest_result`
        if it contains more that one element, or the first key.

        Note that certain keys are ignored when falling back, they are defined in {IGNORED_SET_NAMES}.
        """

        if not self.results:
            return None
        subsets = [i for i in self.latest_result.keys() if i not in IGNORED_SET_NAMES]  # type: ignore
        score_split = self.config.get("score_split")
        if score_split is None and "val" in subsets:
            score_split = "val"
        if score_split is None and "validate" in subsets:
            score_split = "validate"
        if score_split is None:
            score_split = subsets[1] if len(subsets) > 1 else subsets[0]
        return FlatDict({k: v[score_split][self.config.score_name] for k, v in self.results.items()})

    @property
    def latest_score(self) -> float | None:
        r"""
        Latest score.
        """

        if not self.results:
            return None
        if not PY38_PLUS:
            return next(reversed(list(self.scores.values())))  # type: ignore
        return next(reversed(self.scores.values()))  # type: ignore

    @property
    def best_score(self) -> float | None:
        r"""
        Best score.
        """

        if not self.results:
            return None
        return self.scores[self.best_index]  # type: ignore

    @property
    def is_best(self) -> bool:
        r"""
        If current epoch is the best epoch.
        """

        if not self.results:
            return True
        try:
            return abs(self.latest_score - self.best_score) < 1e-7  # type: ignore
        except TypeError:
            return True

    @property
    @ensure_dir
    def dir(self) -> str:
        r"""
        Directory of the run.
        """

        if "dir" in self.config:
            return self.config.dir
        return os.path.join(self.project_root, f"{self.name}-{self.id}", self.timestamp)

    @cached_property
    def log_path(self) -> str:
        r"""
        Path of log file.
        """

        if "log_path" in self.config:
            return self.config.log_path
        return os.path.join(self.dir, "run.log")

    @property
    @ensure_dir
    def checkpoint_dir(self) -> str:
        r"""
        Directory of checkpoints.
        """

        if "checkpoint_dir" in self.config:
            return self.config.checkpoint_dir
        return os.path.join(self.dir, self.config.checkpoint_dir_name)

    # def __getattribute__(self, name) -> Any:
    #     if name in ("__class__", "__dict__"):
    #         return super().__getattribute__(name)
    #     if name in self.__dict__:
    #         return self.__dict__[name]
    #     if name in dir(self):
    #         return super().__getattribute__(name)
    #     if "config" in self and name in self.config:
    #         return self.config[name]
    #     return super().__getattribute__(name)

    def __getattr__(self, name) -> Any:
        if self.inited:
            if name in self.config:
                return self.config[name]
            if name in dir(self.config):
                return getattr(self.config, name)
        return super().__getattribute__(name)

    def __setattr__(self, name, value) -> None:
        if name in self.__dict__:
            if isinstance(self.__dict__[name], Variable):
                self.__dict__[name].set(value)
            else:
                self.__dict__[name] = value
            return
        if name in dir(self):
            if isinstance(super().__getattribute__(name), Variable):
                super().__getattribute__(name).set(value)
            else:
                object.__setattr__(self, name, value)
            return
        if self.inited:
            if name in self.config:
                if isinstance(self.config[name], Variable):
                    self.config[name].set(value)
                else:
                    self.config[name] = value
                return
            if name in dir(self.config):
                setattr(self.config, name, value)
                return
        object.__setattr__(self, name, value)

    def __contains__(self, name) -> bool:
        return name in dir(self) or ("config" in self.__dict__ and name in dir(self.config))

    def __repr__(self):
        lines = []
        for key, value in self.__dict__.items():
            value_str = repr(value)
            value_str = self._add_indent(value_str)
            lines.append("(" + key + "): " + value_str)

        main_str = self.__class__.__name__ + "("
        if lines:
            main_str += "\n  " + "\n  ".join(lines) + "\n"

        main_str += ")"
        return main_str

    def _add_indent(self, text):
        lines = text.split("\n")
        # don't do anything for single-line stuff
        if len(lines) == 1:
            return text
        first = lines.pop(0)
        # add 2 spaces to each line but the first
        lines = [(2 * " ") + line for line in lines]
        lines = "\n".join(lines)
        lines = first + "\n" + lines
        return lines

uuid cached property

uuid: UUID

UUID of the config.

batch_size property

batch_size: int

Batch size.

Notes

If train is in dataloaders, then batch_size is the batch size of train. Otherwise, batch_size is the batch size of the first dataloader.

Returns:

Type	Description
int

batch_size_equivalent property

batch_size_equivalent: int

Actual batch size.

Returns:

Type	Description
int	batch_size * world_size * accum_steps

accum_steps cached property

accum_steps: int

Accumulated steps.

Returns:

Type	Description
int

progress property

progress: float

Training Progress.

Returns:

Type	Description
float

Raises:

Type	Description
RuntimeError	If no terminal is defined.

world_size property

world_size: int

Number of processes.

rank property

rank: int

Process index of all processes.

local_rank property

local_rank: int

Process index of local processes.

distributed property

distributed: bool

If runner is running in distributed mode.

is_main_process property

is_main_process: bool

If current process is the main process of all processes.

is_local_main_process property

is_local_main_process: bool

If current process is the main process of local processes.

best_fn property

best_fn: Callable

Function to determine the best score from a list of scores.

By default, the best_fn returns min if self.config.score_name is loss, otherwise, returns max.

Subclass can override this method to accommodate needs, such as min.

Returns:

Type	Description
callable

best_index property

best_index: int

Find the best index from all scores.

Returns:

Type	Description
int

latest_result property

latest_result: NestedDict | None

Latest result.

best_result property

best_result: NestedDict | None

Best result.

scores property

scores: FlatDict | None

All scores.

Scores are extracted from results by score_split and runner.config.score_name, following [r[score_split][self.config.score_name] for r in self.results].

Scores are considered as the index of the performance of the model. It is useful to determine the best model and the best hyper-parameters.

score_split is defined in self.config.score_split. If it is not set, DanLing will use val or validate if they appear in the latest_result. If DanLing still could not find, it will fall back to the second key in the latest_result if it contains more that one element, or the first key.

Note that certain keys are ignored when falling back, they are defined in {IGNORED_SET_NAMES}.

latest_score property

latest_score: float | None

Latest score.

best_score property

best_score: float | None

Best score.

is_best property

is_best: bool

If current epoch is the best epoch.

dir property

dir: str

Directory of the run.

log_path cached property

log_path: str

Path of log file.

checkpoint_dir property

checkpoint_dir: str

Directory of checkpoints.

init_distributed

init_distributed() -> None

Initialise distributed running environment.

Source code in danling/runner/base_runner.py

def init_distributed(self) -> None:
    r"""
    Initialise distributed running environment.
    """

init_logging

init_logging() -> None

Set up logging.

Source code in danling/runner/base_runner.py

@on_main_process
def init_logging(self) -> None:
    r"""
    Set up logging.
    """

    os.makedirs(os.path.dirname(self.log_path), exist_ok=True)
    # Why is setting up proper logging so !@?#! ugly?
    logging.config.dictConfig(
        {
            "version": 1,
            "disable_existing_loggers": False,
            "formatters": {
                "standard": {"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s"},
            },
            "handlers": {
                "stdout": {
                    "level": "INFO",
                    "formatter": "standard",
                    "class": "logging.StreamHandler",
                    "stream": "ext://sys.stdout",
                },
                "logfile": {
                    "level": "DEBUG",
                    "formatter": "standard",
                    "class": "logging.FileHandler",
                    "filename": self.log_path,
                    "mode": "a",
                },
            },
            "loggers": {
                "": {
                    "handlers": ["stdout", "logfile"],
                    "level": "DEBUG",
                    "propagate": True,
                },
            },
        }
    )
    logging.captureWarnings(True)
    self.logger = logging.getLogger("runner")
    self.logger.flush = lambda: [h.flush() for h in self.logger.handlers]  # type: ignore

init_print

init_print(process: int = 0) -> None

Set up print.

Only print on a specific process or when force = True.

Parameters:

Name	Type	Description	Default
process	int	The process to print on.	0

Notes

If self.config.log = True, the default print function will be override by logging.info.

Source code in danling/runner/base_runner.py

def init_print(self, process: int = 0) -> None:
    r"""
    Set up `print`.

    Only print on a specific `process` or when `force = True`.

    Args:
        process: The process to `print` on.

    Notes
    -----
    If `self.config.log = True`, the default `print` function will be override by `logging.info`.
    """

    logger = logging.getLogger("print")
    logger.flush = lambda: [h.flush for h in logger.handlers]  # type: ignore
    import builtins as __builtin__  # pylint: disable=C0415

    builtin_print = __builtin__.print

    @catch
    def print(*args, force=False, end="\n", file=None, flush=False, **kwargs):  # pylint: disable=redefined-builtin
        if self.rank == process or force:
            if self.config.log:
                logger.info(*args, **kwargs)
            else:
                builtin_print(*args, end=end, file=file, flush=flush, **kwargs)

    __builtin__.print = print

init_tensorboard

init_tensorboard(*args, **kwargs) -> None

Set up Tensoraoard SummaryWriter.

Source code in danling/runner/base_runner.py

@on_main_process
def init_tensorboard(self, *args, **kwargs) -> None:
    r"""
    Set up Tensoraoard SummaryWriter.
    """
    raise NotImplementedError

set_seed

set_seed(seed: int = None, bias: int = None) -> int

Set up random seed.

Parameters:

Name	Type	Description	Default
seed	int	Random seed to set. Defaults to self.config.seed (config.seed).	None
bias	int	Make the seed different for each processes. This avoids same data augmentation are applied on every processes. Defaults to self.rank. Set to False to disable this feature.	None

Source code in danling/runner/base_runner.py

def set_seed(self, seed: int = None, bias: int = None) -> int:  # type: ignore[assignment]
    r"""
    Set up random seed.

    Args:
        seed: Random seed to set.
            Defaults to `self.config.seed` (`config.seed`).

        bias: Make the seed different for each processes.

            This avoids same data augmentation are applied on every processes.

            Defaults to `self.rank`.

            Set to `False` to disable this feature.
    Returns:
        Random seed set.
    """

    seed = seed or self.config.seed  # type: ignore[assignment]
    bias = bias or self.rank
    if bias:
        seed += bias
    if np_random is not None:
        np_random.seed(seed)
    random.seed(seed)
    return seed

set_deterministic

set_deterministic() -> None

Set up deterministic.

Source code in danling/runner/base_runner.py

def set_deterministic(self) -> None:
    r"""
    Set up deterministic.
    """

    raise NotImplementedError

scale_lr

scale_lr(lr: float, lr_scale_factor: float | None = None, batch_size_base: int | None = None) -> float

Scale learning rate according to linear scaling rule.

Source code in danling/runner/base_runner.py

def scale_lr(
    self,
    lr: float,
    lr_scale_factor: float | None = None,
    batch_size_base: int | None = None,
) -> float:
    r"""
    Scale learning rate according to [linear scaling rule](https://arxiv.org/abs/1706.02677).
    """

    if lr_scale_factor in self.config:
        lr_scale_factor = self.config.lr_scale_factor

    if lr_scale_factor is None:
        if batch_size_base is None:
            batch_size_base = getattr(self, "batch_size_base", None)
            if batch_size_base is None:
                raise ValueError("batch_size_base must be specified to auto scale lr")
        lr_scale_factor = self.batch_size_equivalent / batch_size_base
    elif batch_size_base is not None:
        warn(
            "batch_size_base will be ignored if lr_scale_factor is specified", category=RuntimeWarning, stacklevel=2
        )
    lr = lr * lr_scale_factor
    self.config.lr_scale_factor = lr_scale_factor
    return lr

update

update(loss, *args, **kwargs) -> None

Backward loss and step optimizer & scheduler.

Parameters:

Name	Type	Description	Default
loss		loss.	required

Source code in danling/runner/base_runner.py

def update(self, loss, *args, **kwargs) -> None:
    r"""
    Backward loss and step optimizer & scheduler.

    Args:
        loss: loss.
    """

    raise NotImplementedError

state_dict

state_dict(cls: Callable = dict) -> Mapping

Return dict of all attributes for checkpoint.

Source code in danling/runner/base_runner.py

def state_dict(self, cls: Callable = dict) -> Mapping:
    r"""
    Return dict of all attributes for checkpoint.
    """

    return cls(self.config)

dict

dict(cls: Callable = dict) -> Mapping

Convert config to Mapping.

Parameters:

Name	Type	Description	Default
cls	Callable	Target `clc to convert to.	dict

Source code in danling/runner/base_runner.py

def dict(self, cls: Callable = dict) -> Mapping:
    r"""
    Convert config to Mapping.

    Args:
        cls: Target `clc to convert to.
    """

    return self.config.dict(cls)

save

save(obj: Any, file: PathStr, main_process_only: bool = True, *args, **kwargs) -> File

Save any file with supported extensions.

Runner.save internally calls dl.save, but with additional arguments to allow it save only on the main process. Moreover, any error raised by Runner.save will be caught and logged.

Source code in danling/runner/base_runner.py

@catch
def save(self, obj: Any, file: PathStr, main_process_only: bool = True, *args, **kwargs) -> File:
    r"""
    Save any file with supported extensions.

    `Runner.save` internally calls `dl.save`,
    but with additional arguments to allow it save only on the main process.
    Moreover, any error raised by `Runner.save` will be caught and logged.
    """

    if main_process_only and self.is_main_process or not main_process_only:
        return save(obj, file, *args, **kwargs)
    return file

load staticmethod

load(file: PathStr, *args, **kwargs) -> Any

Load any file with supported extensions.

Runner.load is identical to dl.load.

Source code in danling/runner/base_runner.py

@staticmethod
def load(file: PathStr, *args, **kwargs) -> Any:
    r"""
    Load any file with supported extensions.

    `Runner.load` is identical to `dl.load`.
    """

    return load(file, *args, **kwargs)

json

json(file: File, main_process_only: bool = True, *args, **kwargs) -> None

Dump Runner config to json file.

Source code in danling/runner/base_runner.py

@catch
def json(self, file: File, main_process_only: bool = True, *args, **kwargs) -> None:  # pylint: disable=R1710
    r"""
    Dump Runner config to json file.
    """

    if main_process_only and self.is_main_process or not main_process_only:
        return self.config.json(file, *args, **kwargs)

from_json classmethod

from_json(file: File, *args, **kwargs) -> BaseRunner

Construct Runner from json file.

This function calls self.from_jsons() to construct object from json string. You may overwrite from_jsons in case something is not json serializable.

Source code in danling/runner/base_runner.py

@classmethod
def from_json(cls, file: File, *args, **kwargs) -> BaseRunner:
    r"""
    Construct Runner from json file.

    This function calls `self.from_jsons()` to construct object from json string.
    You may overwrite `from_jsons` in case something is not json serializable.
    """

    with FlatDict.open(file) as fp:
        return cls.from_jsons(fp.read(), *args, **kwargs)

jsons

jsons(*args, **kwargs) -> str

Dump Runner config to json string.

Source code in danling/runner/base_runner.py

def jsons(self, *args, **kwargs) -> str:
    r"""
    Dump Runner config to json string.
    """

    return self.config.jsons(*args, **kwargs)

from_jsons classmethod

from_jsons(string: str, *args, **kwargs) -> BaseRunner

Construct Runner from json string.

Source code in danling/runner/base_runner.py

@classmethod
def from_jsons(cls, string: str, *args, **kwargs) -> BaseRunner:
    r"""
    Construct Runner from json string.
    """

    return cls(Config.from_jsons(string, *args, **kwargs))

yaml

yaml(file: File, main_process_only: bool = True, *args, **kwargs) -> None

Dump Runner config to yaml file.

Source code in danling/runner/base_runner.py

@catch
def yaml(self, file: File, main_process_only: bool = True, *args, **kwargs) -> None:  # pylint: disable=R1710
    r"""
    Dump Runner config to yaml file.
    """

    if main_process_only and self.is_main_process or not main_process_only:
        return self.config.yaml(file, *args, **kwargs)

from_yaml classmethod

from_yaml(file: File, *args, **kwargs) -> BaseRunner

Construct Runner from yaml file.

This function calls self.from_yamls() to construct object from yaml string. You may overwrite from_yamls in case something is not yaml serializable.

Source code in danling/runner/base_runner.py

@classmethod
def from_yaml(cls, file: File, *args, **kwargs) -> BaseRunner:
    r"""
    Construct Runner from yaml file.

    This function calls `self.from_yamls()` to construct object from yaml string.
    You may overwrite `from_yamls` in case something is not yaml serializable.
    """

    with FlatDict.open(file) as fp:
        return cls.from_yamls(fp.read(), *args, **kwargs)

yamls

yamls(*args, **kwargs) -> str

Dump Runner config to yaml string.

Source code in danling/runner/base_runner.py

def yamls(self, *args, **kwargs) -> str:
    r"""
    Dump Runner config to yaml string.
    """

    return self.config.yamls(*args, **kwargs)

from_yamls classmethod

from_yamls(string: str, *args, **kwargs) -> BaseRunner

Construct Runner from yaml string.

Source code in danling/runner/base_runner.py

@classmethod
def from_yamls(cls, string: str, *args, **kwargs) -> BaseRunner:
    r"""
    Construct Runner from yaml string.
    """

    return cls(Config.from_yamls(string, *args, **kwargs))

check_dir

check_dir(action: str = 'warn') -> bool

Check if self.dir is not empty.

Parameters:

Name	Type	Description	Default
action	str	The action to perform if self.dir is not empty.	'warn'

Source code in danling/runner/base_runner.py

def check_dir(self, action: str = "warn") -> bool:
    r"""
    Check if `self.dir` is not empty.

    Args:
        action (str): The action to perform if `self.dir` is not empty.
        Can be one of ("warn", "raise", "ignore"), default is "warn".
    """

    if action and action not in ("warn", "raise", "ignore"):
        raise ValueError(f"action should be one of warn, raise or ignore, but got {action}")
    if os.listdir(self.dir):
        if action == "warn":
            warn(
                f"Directory `{self.dir}` is not empty",
                category=RuntimeWarning,
                stacklevel=2,
            )
        if action == "raise":
            raise RuntimeError(f"Directory `{self.dir}` is not empty")
        return False
    return True

save_checkpoint

save_checkpoint(epoch: int | None = None) -> None

Save checkpoint to self.checkpoint_dir.

The checkpoint will be saved to self.checkpoint_dir/latest.pth.

If self.config.save_interval is positive and self.config.epoch + 1 is a multiple of save_interval, the checkpoint will also be copied to self.checkpoint_dir/epoch-{self.config.epoch}.pth.

If self.is_best is True, the checkpoint will also be copied to self.checkpoint_dir/best.pth.

Source code in danling/runner/base_runner.py

@catch
@on_main_process
def save_checkpoint(self, epoch: int | None = None) -> None:
    r"""
    Save checkpoint to `self.checkpoint_dir`.

    The checkpoint will be saved to `self.checkpoint_dir/latest.pth`.

    If `self.config.save_interval` is positive and `self.config.epoch + 1` is a multiple of `save_interval`,
    the checkpoint will also be copied to `self.checkpoint_dir/epoch-{self.config.epoch}.pth`.

    If `self.is_best` is `True`, the checkpoint will also be copied to `self.checkpoint_dir/best.pth`.
    """

    epoch = epoch or self.config.epoch
    save_interval = self.config.get("save_interval", -1)
    latest_path = os.path.join(self.checkpoint_dir, "latest.pth")
    self.save(self.state_dict(), latest_path)
    if save_interval > 0 and (epoch + 1) % save_interval == 0:
        save_path = os.path.join(self.checkpoint_dir, f"epoch-{epoch}.pth")
        shutil.copy(latest_path, save_path)
    if self.is_best:
        best_path = os.path.join(self.checkpoint_dir, "best.pth")
        shutil.copy(latest_path, best_path)

load_checkpoint

load_checkpoint(checkpoint: Mapping | bytes | str | PathLike | None = None, auto_resume: bool | None = None, override_config: bool = False, *args, **kwargs) -> None

Load info from checkpoint.

Parameters:

Name	Type	Description	Default
checkpoint	Mapping | bytes | str | PathLike | None	Checkpoint (or its path) to load. Defaults to self.config.checkpoint.	None
auto_resume	bool | None	Automatically resume from latest checkpoint if exists. Defaults to False. If is True and checkpoint is None, will set it to self.checkpoint_dir/latest.pth.	None
override_config	bool	If True, override runner config with checkpoint config. Defaults to False.	False
*args		Additional arguments to pass to self.load.	()
**kwargs		Additional keyword arguments to pass to self.load.	{}

Raises:

Type	Description
FileNotFoundError	If checkpoint does not exists.

See Also

from_checkpoint: Build runner from checkpoint. load_pretrained: Load parameters from pretrained checkpoint.

Source code in danling/runner/base_runner.py

def load_checkpoint(
    self,
    checkpoint: Mapping | bytes | str | os.PathLike | None = None,
    auto_resume: bool | None = None,
    override_config: bool = False,
    *args,
    **kwargs,
) -> None:
    """
    Load info from checkpoint.

    Args:
        checkpoint: Checkpoint (or its path) to load.
            Defaults to `self.config.checkpoint`.
        auto_resume: Automatically resume from latest checkpoint if exists.
            Defaults to `False`.
            If is `True` and `checkpoint` is None, will set it to `self.checkpoint_dir/latest.pth`.
        override_config: If True, override runner config with checkpoint config.
            Defaults to `False`.
        *args: Additional arguments to pass to `self.load`.
        **kwargs: Additional keyword arguments to pass to `self.load`.

    Raises:
        FileNotFoundError: If `checkpoint` does not exists.

    See Also:
        [`from_checkpoint`][danling.BaseRunner.from_checkpoint]: Build runner from checkpoint.
        [`load_pretrained`][danling.BaseRunner.load_pretrained]: Load parameters from pretrained checkpoint.
    """

    checkpoint = checkpoint if checkpoint is not None else self.config.get("checkpoint")
    auto_resume = auto_resume if auto_resume is not None else self.config.get("auto_resume", False)

    # TODO: Support loading checkpoints in other format
    if checkpoint is not None:
        if auto_resume:
            warn(
                "latest checkpoint is preempted by value specified in checkpoint",
                RuntimeWarning,
                stacklevel=2,
            )
        if isinstance(checkpoint, (bytes, str, os.PathLike)):
            if not os.path.exists(checkpoint):
                raise FileNotFoundError(f"checkpoint is set to {checkpoint!r} but does not exist")
            self.config.checkpoint = checkpoint
            ckpt = self.load(checkpoint, *args, **kwargs)
        elif isinstance(checkpoint, Mapping):
            ckpt = checkpoint
        else:
            raise ValueError(f"pretrained is set to {checkpoint!r} but is not a valid checkpoint")
    elif auto_resume:
        checkpoint = os.path.join(self.checkpoint_dir, "latest.pth")
        if os.path.exists(checkpoint):
            self.config.checkpoint = checkpoint
            ckpt = self.load(checkpoint, *args, **kwargs)
        else:
            warn("latest checkpoint does not exits", category=RuntimeWarning, stacklevel=2)
            return
    else:
        raise ValueError("checkpoint is not specified and auto_resume is not set to True")

    # TODO: Wrap state_dict in a dataclass
    self.config.merge(ckpt["runner"], overwrite=override_config)
    if self.model is not None and "model" in ckpt:
        model = self.unwrap(self.model)
        model.load_state_dict(ckpt["model"])
    if self.optimizer is not None and "optimizer" in ckpt:
        self.optimizer.load_state_dict(ckpt["optimizer"])
    if self.scheduler is not None and "scheduler" in ckpt:
        self.scheduler.load_state_dict(ckpt["scheduler"])
    self.config.iter_begin = self.config.iter
    self.config.step_begin = self.config.step
    self.config.epoch_begin = self.config.epoch

from_checkpoint classmethod

from_checkpoint(checkpoint: Mapping | bytes | str | PathLike, *args, **kwargs) -> BaseRunner

Build BaseRunner from checkpoint.

Parameters:

Name	Type	Description	Default
checkpoint	Mapping | bytes | str | PathLike	Checkpoint (or its path) to load.	required
*args		Additional arguments to pass to cls.load.	()
**kwargs		Additional keyword arguments to pass to cls.load.	{}

Returns:

Type	Description
BaseRunner

Source code in danling/runner/base_runner.py

@classmethod
def from_checkpoint(cls, checkpoint: Mapping | bytes | str | os.PathLike, *args, **kwargs) -> BaseRunner:
    r"""
    Build BaseRunner from checkpoint.

    Args:
        checkpoint: Checkpoint (or its path) to load.
        *args: Additional arguments to pass to `cls.load`.
        **kwargs: Additional keyword arguments to pass to `cls.load`.

    Returns:
        (BaseRunner):
    """

    if isinstance(checkpoint, (bytes, str, os.PathLike)):
        ckpt = cls.load(checkpoint, *args, **kwargs)
    elif isinstance(checkpoint, Mapping):
        ckpt = checkpoint
    else:
        raise ValueError(f"checkpoint is set to {checkpoint} but is not a valid checkpoint")
    runner = cls(**ckpt["runner"])
    runner.load_checkpoint(ckpt, override_config=False)
    return runner

load_pretrained

load_pretrained(checkpoint: Mapping | bytes | str | PathLike | None = None, *args, **kwargs) -> None

Load parameters from pretrained checkpoint.

This method only loads the model weights.

Parameters:

Name	Type	Description	Default
checkpoint	Mapping | bytes | str | PathLike | None	Pretrained checkpoint (or its path) to load. Defaults to self.config.pretrained.	None
*args		Additional arguments to pass to self.load.	()
**kwargs		Additional keyword arguments to pass to self.load.	{}

Raises:

Type	Description
FileNotFoundError	If checkpoint does not exists.

See Also

load_checkpoint: Load info from checkpoint.

Source code in danling/runner/base_runner.py

def load_pretrained(self, checkpoint: Mapping | bytes | str | os.PathLike | None = None, *args, **kwargs) -> None:
    """
    Load parameters from pretrained checkpoint.

    This method only loads the model weights.

    Args:
        checkpoint: Pretrained checkpoint (or its path) to load.
            Defaults to `self.config.pretrained`.
        *args: Additional arguments to pass to `self.load`.
        **kwargs: Additional keyword arguments to pass to `self.load`.

    Raises:
        FileNotFoundError: If `checkpoint` does not exists.

    See Also:
        [`load_checkpoint`][danling.BaseRunner.load_checkpoint]: Load info from checkpoint.
    """

    # TODO: Support loading checkpoints in other format
    checkpoint = checkpoint if checkpoint is not None else self.config.get("pretrained")
    if checkpoint is None:
        raise ValueError("pretrained is not specified")
    if isinstance(checkpoint, (bytes, str, os.PathLike)):
        if not os.path.exists(checkpoint):
            raise FileNotFoundError(f"pretrained is set to {checkpoint!r} but does not exist")
        ckpt = self.load(checkpoint, *args, **kwargs)
    elif isinstance(checkpoint, Mapping):
        ckpt = checkpoint
    else:
        raise ValueError(f"pretrained is set to {checkpoint!r} but is not a valid checkpoint")
    if self.model is not None and "model" in ckpt:
        model = self.unwrap(self.model)
        model.load_state_dict(ckpt["model"])
    else:
        raise ValueError(f"Unable to find model weights in {checkpoint!r}")

append_result

append_result(result: NestedDict, index: int | None = None) -> None

Append result to self.results.

Source code in danling/runner/base_runner.py

def append_result(self, result: NestedDict, index: int | None = None) -> None:
    r"""
    Append result to `self.results`.

    Warnings:
        `self.results` is heavily relied upon for computing metrics.

        Failed to use this method may lead to unexpected behavior.
    """

    if index is None:
        index = self.config.epoch
        global __APPEND_RESULT_COUNTER__  # pylint: disable=global-statement
        __APPEND_RESULT_COUNTER__ += 1
        if index == 0 and __APPEND_RESULT_COUNTER__ > 1:
            warn(
                """
                Automatically set index to `self.config.epoch`.
                Please ensure `self.config.epoch` updates before calling `append_result`
                """,
                category=RuntimeWarning,
                stacklevel=2,
            )
    if index in self.results:
        self.results[index].merge(result)
    else:
        self.results[index] = result

print_result

print_result() -> None

Print latest and best result.

Source code in danling/runner/base_runner.py

def print_result(self) -> None:
    r"""
    Print latest and best result.
    """

    print(f"latest result: {self.latest_result}")
    print(f"best result: {self.best_result}")

save_result

save_result() -> None

Save result to self.dir.

This method will save latest and best result to self.dir/latest.json and self.dir/best.json respectively.

Source code in danling/runner/base_runner.py

@catch
@on_main_process
def save_result(self) -> None:
    r"""
    Save result to `self.dir`.

    This method will save latest and best result to
    `self.dir/latest.json` and `self.dir/best.json` respectively.
    """

    results_path = os.path.join(self.dir, "results.json")
    self.save(
        {
            "name": self.name,
            "id": self.id,
            "timestamp": self.timestamp,
            "results": self.results,
        },
        results_path,
        indent=4,
    )
    ret = {"name": self.name, "id": self.id, "timestamp": self.timestamp}
    result = self.latest_result
    if isinstance(result, FlatDict):
        result = result.dict()
    # This is slower but ensure id is the first key
    if result is not None:
        ret.update(result)
    latest_path = os.path.join(self.dir, "latest.json")
    self.save(ret, latest_path, indent=4)
    if self.is_best:
        best_path = os.path.join(self.dir, "best.json")
        shutil.copy(latest_path, best_path)