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AverageMeter

Computes and stores the average and current value.

Attributes:

Name Type Description
val

Results of current batch on current device.
bat

Results of current batch on all devices.
avg

Results of all results on all devices.
sum float

Sum of values.
count float

Number of values.
See Also

[MetricMeter]: Average Meter with metric function built-in. [AverageMeters]: Manage multiple average meters in one object. [MultiTaskAverageMeters]: Manage multiple average meters in one object with multi-task support.

Examples:

Python Console Session
>>> meter = AverageMeter()
>>> meter.update(0.7)
>>> meter.val
0.7
>>> meter.avg
0.7
>>> meter.update(0.9)
>>> meter.val
0.9
>>> meter.avg
0.8
>>> meter.sum
1.6
>>> meter.count
2
>>> meter.reset()
>>> meter.val
0.0
>>> meter.avg
nan
Source code in danling/metrics/average_meter.py
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class AverageMeter:
    r"""
    Computes and stores the average and current value.

    Attributes:
        val: Results of current batch on current device.
        bat: Results of current batch on all devices.
        avg: Results of all results on all devices.
        sum: Sum of values.
        count: Number of values.

    See Also:
        [`MetricMeter`]: Average Meter with metric function built-in.
        [`AverageMeters`]: Manage multiple average meters in one object.
        [`MultiTaskAverageMeters`]: Manage multiple average meters in one object with multi-task support.

    Examples:
        >>> meter = AverageMeter()
        >>> meter.update(0.7)
        >>> meter.val
        0.7
        >>> meter.avg
        0.7
        >>> meter.update(0.9)
        >>> meter.val
        0.9
        >>> meter.avg
        0.8
        >>> meter.sum
        1.6
        >>> meter.count
        2
        >>> meter.reset()
        >>> meter.val
        0.0
        >>> meter.avg
        nan
    """

    v: float = 0
    n: float = 1
    sum: float = 0
    count: float = 0

    def __init__(self) -> None:
        self.reset()

    def reset(self) -> None:
        r"""
        Resets the meter.
        """

        self.v = 0
        self.n = 1
        self.sum = 0
        self.count = 0

    def update(self, value, n: float = 1) -> None:
        r"""
        Updates the average and current value in the meter.

        Args:
            value: Value to be added to the average.
            n: Number of values to be added.
        """

        self.v = value
        self.n = n
        self.sum += value
        self.count += n

    def value(self):
        return self.v / self.n if self.n != 0 else float("nan")

    def batch(self):
        world_size = get_world_size()
        if world_size <= 1:
            return self.value()
        synced_tensor = torch.tensor([self.v * self.n, self.n], dtype=torch.float64).cuda()
        dist.all_reduce(synced_tensor)
        val, count = synced_tensor
        if count == 0:
            return float("nan")
        return (val / count).item()

    def average(self):
        world_size = get_world_size()
        if world_size <= 1:
            return self.sum / self.count if self.count != 0 else float("nan")
        synced_tensor = torch.tensor([self.sum, self.count], dtype=torch.float64).cuda()
        dist.all_reduce(synced_tensor)
        val, count = synced_tensor
        if count == 0:
            return float("nan")
        return (val / count).item()

    @property
    def val(self):
        return self.value()

    @property
    def bat(self):
        return self.batch()

    @property
    def avg(self):
        return self.average()

    def __format__(self, format_spec) -> str:
        return f"{self.val.__format__(format_spec)} ({self.avg.__format__(format_spec)})"

reset

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reset() -> None

Resets the meter.

Source code in danling/metrics/average_meter.py
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def reset(self) -> None:
    r"""
    Resets the meter.
    """

    self.v = 0
    self.n = 1
    self.sum = 0
    self.count = 0

update

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update(value, n: float = 1) -> None

Updates the average and current value in the meter.

Parameters:

Name Type Description Default
value

Value to be added to the average.

 required
n
float

Number of values to be added.

 1
Source code in danling/metrics/average_meter.py
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def update(self, value, n: float = 1) -> None:
    r"""
    Updates the average and current value in the meter.

    Args:
        value: Value to be added to the average.
        n: Number of values to be added.
    """

    self.v = value
    self.n = n
    self.sum += value
    self.count += n

AverageMeters

Bases: MetricsDict

Manages multiple average meters in one object.

See Also

[AverageMeter]: Computes and stores the average and current value. [MultiTaskAverageMeters]: Manage multiple average meters in one object with multi-task support. [MetricMeters]: Manage multiple metric meters in one object.

Examples:

Python Console Session
>>> meters = AverageMeters()
>>> meters.update({"loss": 0.6, "auroc": 0.7, "r2": 0.8})
>>> f"{meters:.4f}"
'loss: 0.6000 (0.6000)\tauroc: 0.7000 (0.7000)\tr2: 0.8000 (0.8000)'
>>> meters['loss'].update(value=0.9, n=1)
>>> f"{meters:.4f}"
'loss: 0.9000 (0.7500)\tauroc: 0.7000 (0.7000)\tr2: 0.8000 (0.8000)'
>>> meters.sum.dict()
{'loss': 1.5, 'auroc': 0.7, 'r2': 0.8}
>>> meters.count.dict()
{'loss': 2, 'auroc': 1, 'r2': 1}
>>> meters.reset()
>>> f"{meters:.4f}"
'loss: 0.0000 (nan)\tauroc: 0.0000 (nan)\tr2: 0.0000 (nan)'
Source code in danling/metrics/average_meter.py
Python
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class AverageMeters(MetricsDict):
    r"""
    Manages multiple average meters in one object.

    See Also:
        [`AverageMeter`]: Computes and stores the average and current value.
        [`MultiTaskAverageMeters`]: Manage multiple average meters in one object with multi-task support.
        [`MetricMeters`]: Manage multiple metric meters in one object.

    Examples:
        >>> meters = AverageMeters()
        >>> meters.update({"loss": 0.6, "auroc": 0.7, "r2": 0.8})
        >>> f"{meters:.4f}"
        'loss: 0.6000 (0.6000)\tauroc: 0.7000 (0.7000)\tr2: 0.8000 (0.8000)'
        >>> meters['loss'].update(value=0.9, n=1)
        >>> f"{meters:.4f}"
        'loss: 0.9000 (0.7500)\tauroc: 0.7000 (0.7000)\tr2: 0.8000 (0.8000)'
        >>> meters.sum.dict()
        {'loss': 1.5, 'auroc': 0.7, 'r2': 0.8}
        >>> meters.count.dict()
        {'loss': 2, 'auroc': 1, 'r2': 1}
        >>> meters.reset()
        >>> f"{meters:.4f}"
        'loss: 0.0000 (nan)\tauroc: 0.0000 (nan)\tr2: 0.0000 (nan)'
    """

    def __init__(self, *args, default_factory: Type[AverageMeter] = AverageMeter, **kwargs) -> None:
        for meter in args:
            if not isinstance(meter, AverageMeter):
                raise ValueError(f"Expected meter to be an instance of AverageMeter, but got {type(meter)}")
        for name, meter in kwargs.items():
            if not isinstance(meter, AverageMeter):
                raise ValueError(f"Expected {name} to be an instance of AverageMeter, but got {type(meter)}")
        super().__init__(*args, default_factory=default_factory, **kwargs)

    @property
    def sum(self) -> FlatDict[str, float]:
        return FlatDict({key: meter.sum for key, meter in self.all_items()})

    @property
    def count(self) -> FlatDict[str, int]:
        return FlatDict({key: meter.count for key, meter in self.all_items()})

    def update(self, *args: Dict, **values: int | float) -> None:  # pylint: disable=W0237
        r"""
        Updates the average and current value in all meters.

        Args:
            values: Dict of values to be added to the average.
            n: Number of values to be added.

        Raises:
            ValueError: If the value is not an instance of (int, float).
        """  # noqa: E501

        if args:
            if len(args) > 1:
                raise ValueError("Expected only one positional argument, but got multiple.")
            values = args[0].update(values) or args[0] if values else args[0]

        for meter, value in values.items():
            if not isinstance(value, (int, float)):
                raise ValueError(f"Expected values to be int or float, but got {type(value)}")
            self[meter].update(value)

    def set(self, name: str, meter: AverageMeter) -> None:  # pylint: disable=W0237
        if not isinstance(meter, AverageMeter):
            raise ValueError(f"Expected meter to be an instance of AverageMeter, but got {type(meter)}")
        super().set(name, meter)

update

Python
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update(*args: Dict, **values: int | float) -> None

Updates the average and current value in all meters.

Parameters:

Name Type Description Default
values
int | float

Dict of values to be added to the average.

 {}
n

Number of values to be added.

 required

Raises:

Type Description
ValueError

If the value is not an instance of (int, float).
Source code in danling/metrics/average_meter.py
Python
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def update(self, *args: Dict, **values: int | float) -> None:  # pylint: disable=W0237
    r"""
    Updates the average and current value in all meters.

    Args:
        values: Dict of values to be added to the average.
        n: Number of values to be added.

    Raises:
        ValueError: If the value is not an instance of (int, float).
    """  # noqa: E501

    if args:
        if len(args) > 1:
            raise ValueError("Expected only one positional argument, but got multiple.")
        values = args[0].update(values) or args[0] if values else args[0]

    for meter, value in values.items():
        if not isinstance(value, (int, float)):
            raise ValueError(f"Expected values to be int or float, but got {type(value)}")
        self[meter].update(value)

MetricMeter

Bases: AverageMeter

Computes metrics and averages them over time.

Attributes:

Name Type Description
metric Callable

Metric function for computing the value.
ignored_index Optional[int]

Index to be ignored in the computation.
val Optional[int]

Results of current batch on current device.
bat Optional[int]

Results of current batch on all devices.
avg Optional[int]

Results of all results on all devices.
sum Optional[int]

Sum of values.
count Optional[int]

Number of values.
See Also

[AverageMeter]: Average meter for computed values. [MetricMeters]: Manage multiple metric meters in one object.

Examples:

Python Console Session
>>> from danling.metrics.functional import accuracy
>>> meter = MetricMeter(accuracy)
>>> meter.update([0.1, 0.8, 0.6, 0.2], [0, 1, 0, 0])
>>> meter.val
0.75
>>> meter.avg
0.75
>>> meter.update([0.1, 0.7, 0.3, 0.2, 0.8, 0.4], [0, 1, 1, 0, 0, 1])
>>> meter.val
0.5
>>> meter.avg
0.6
>>> meter.sum
6.0
>>> meter.count
10
>>> meter.reset()
>>> meter.val
0.0
>>> meter.avg
nan
Source code in danling/metrics/metric_meter.py
Python
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class MetricMeter(AverageMeter):
    r"""
    Computes metrics and averages them over time.

    Attributes:
        metric: Metric function for computing the value.
        ignored_index: Index to be ignored in the computation.
        val: Results of current batch on current device.
        bat: Results of current batch on all devices.
        avg: Results of all results on all devices.
        sum: Sum of values.
        count: Number of values.

    See Also:
        [`AverageMeter`]: Average meter for computed values.
        [`MetricMeters`]: Manage multiple metric meters in one object.

    Examples:
        >>> from danling.metrics.functional import accuracy
        >>> meter = MetricMeter(accuracy)
        >>> meter.update([0.1, 0.8, 0.6, 0.2], [0, 1, 0, 0])
        >>> meter.val
        0.75
        >>> meter.avg
        0.75
        >>> meter.update([0.1, 0.7, 0.3, 0.2, 0.8, 0.4], [0, 1, 1, 0, 0, 1])
        >>> meter.val
        0.5
        >>> meter.avg
        0.6
        >>> meter.sum
        6.0
        >>> meter.count
        10
        >>> meter.reset()
        >>> meter.val
        0.0
        >>> meter.avg
        nan
    """

    metric: Callable
    preprocess: Callable
    ignored_index: Optional[int] = None

    def __init__(
        self, metric: Callable, preprocess: Callable = default_preprocess, ignored_index: int | None = None
    ) -> None:
        self.metric = metric
        self.preprocess = preprocess
        self.ignored_index = ignored_index
        super().__init__()

    def update(  # type: ignore[override] # pylint: disable=W0237
        self,
        input: Tensor | NestedTensor | Sequence,  # pylint: disable=W0622
        target: Tensor | NestedTensor | Sequence,
    ) -> None:
        r"""
        Updates the average and current value in the meter.

        Args:
            value: Value to be added to the average.
            n: Number of values to be added.
        """
        input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
        n = len(input)
        super().update(self.metric(input, target).item() * n, n=n)

update

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update(input: Tensor | NestedTensor | Sequence, target: Tensor | NestedTensor | Sequence) -> None

Updates the average and current value in the meter.

Parameters:

Name Type Description Default
value

Value to be added to the average.

 required
n

Number of values to be added.

 required
Source code in danling/metrics/metric_meter.py
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def update(  # type: ignore[override] # pylint: disable=W0237
    self,
    input: Tensor | NestedTensor | Sequence,  # pylint: disable=W0622
    target: Tensor | NestedTensor | Sequence,
) -> None:
    r"""
    Updates the average and current value in the meter.

    Args:
        value: Value to be added to the average.
        n: Number of values to be added.
    """
    input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
    n = len(input)
    super().update(self.metric(input, target).item() * n, n=n)

MetricMeters

Bases: AverageMeters

Manages multiple metric meters in one object.

Attributes:

Name Type Description
ignored_index

Index to be ignored in the computation. Defaults to None.
See Also

[MetricMeter]: Computes metrics and averages them over time. [AverageMeters]: Average meters for computed values.

from danling.metrics.functional import accuracy, auroc, auprc meters = MetricMeters(acc=accuracy, auroc=auroc, auprc=auprc) meters.update([0.1, 0.8, 0.6, 0.2], [0, 1, 0, 0]) meters.sum.dict() {‘acc’: 3.0, ‘auroc’: 4.0, ‘auprc’: 4.0} meters.count.dict() {‘acc’: 4, ‘auroc’: 4, ‘auprc’: 4} meters[‘auroc’].update([0.2, 0.8], [0, 1]) meters.sum.dict() {‘acc’: 3.0, ‘auroc’: 6.0, ‘auprc’: 4.0} meters.count.dict() {‘acc’: 4, ‘auroc’: 6, ‘auprc’: 4} meters.update([[0.1, 0.7, 0.3, 0.2], [0.8, 0.4]], [[0, 0, 1, 0], [0, 0]]) meters.sum.dict() {‘acc’: 6.0, ‘auroc’: 8.4, ‘auprc’: 5.5} meters.count.dict() {‘acc’: 10, ‘auroc’: 12, ‘auprc’: 10} meters[‘auroc’].update([0.4, 0.8, 0.6, 0.2], [0, 1, 1, 0]) meters.avg.dict() {‘acc’: 0.6, ‘auroc’: 0.775, ‘auprc’: 0.55} meters.update(dict(loss=”“)) # doctest: +ELLIPSIS Traceback (most recent call last): TypeError: …update() missing 1 required positional argument: ‘target’

Source code in danling/metrics/metric_meter.py
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class MetricMeters(AverageMeters):
    r"""
    Manages multiple metric meters in one object.

    Attributes:
        ignored_index: Index to be ignored in the computation.
            Defaults to None.

    See Also:
        [`MetricMeter`]: Computes metrics and averages them over time.
        [`AverageMeters`]: Average meters for computed values.

    >>> from danling.metrics.functional import accuracy, auroc, auprc
    >>> meters = MetricMeters(acc=accuracy, auroc=auroc, auprc=auprc)
    >>> meters.update([0.1, 0.8, 0.6, 0.2], [0, 1, 0, 0])
    >>> meters.sum.dict()
    {'acc': 3.0, 'auroc': 4.0, 'auprc': 4.0}
    >>> meters.count.dict()
    {'acc': 4, 'auroc': 4, 'auprc': 4}
    >>> meters['auroc'].update([0.2, 0.8], [0, 1])
    >>> meters.sum.dict()
    {'acc': 3.0, 'auroc': 6.0, 'auprc': 4.0}
    >>> meters.count.dict()
    {'acc': 4, 'auroc': 6, 'auprc': 4}
    >>> meters.update([[0.1, 0.7, 0.3, 0.2], [0.8, 0.4]], [[0, 0, 1, 0], [0, 0]])
    >>> meters.sum.dict()
    {'acc': 6.0, 'auroc': 8.4, 'auprc': 5.5}
    >>> meters.count.dict()
    {'acc': 10, 'auroc': 12, 'auprc': 10}
    >>> meters['auroc'].update([0.4, 0.8, 0.6, 0.2], [0, 1, 1, 0])
    >>> meters.avg.dict()
    {'acc': 0.6, 'auroc': 0.775, 'auprc': 0.55}
    >>> meters.update(dict(loss=""))  # doctest: +ELLIPSIS
    Traceback (most recent call last):
    TypeError: ...update() missing 1 required positional argument: 'target'
    """

    preprocess: Callable
    ignored_index = None

    def __init__(
        self, *args, preprocess: Callable = default_preprocess, ignored_index: int | None = None, **kwargs
    ) -> None:
        self.setattr("preprocess", preprocess)
        self.setattr("ignored_index", ignored_index)
        for meter in args:
            if callable(meter):
                meter = MetricMeter(meter, ignored_index=self.ignored_index)
            if not isinstance(meter, MetricMeter):
                raise ValueError(f"Expected meter to be an instance of MetricMeter, but got {type(meter)}")
        for name, meter in kwargs.items():
            if callable(meter):
                kwargs[name] = meter = MetricMeter(meter, ignored_index=self.ignored_index)
            if not isinstance(meter, MetricMeter):
                raise ValueError(f"Expected {name} to be an instance of MetricMeter, but got {type(meter)}")
        if ignored_index is not None:
            self.setattr("ignored_index", ignored_index)
        super().__init__(*args, default_factory=None, **kwargs)  # type: ignore[arg-type]

    def update(  # type: ignore[override] # pylint: disable=W0221
        self,
        input: Tensor | NestedTensor | Sequence,  # pylint: disable=W0622
        target: Tensor | NestedTensor | Sequence,
    ) -> None:
        r"""
        Updates the average and current value in all meters.

        Args:
            input: Input values to compute the metrics.
            target: Target values to compute the metrics.
        """

        input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
        for meter in self.values():
            meter.update(input, target)

    def set(self, name: str, meter: MetricMeter | Callable) -> None:  # type: ignore[override] # pylint: disable=W0237
        if callable(meter):
            meter = MetricMeter(meter, ignored_index=self.ignored_index)
        if not isinstance(meter, MetricMeter):
            raise ValueError(f"Expected meter to be an instance of MetricMeter, but got {type(meter)}")
        super().set(name, meter)

    def __repr__(self):
        keys = tuple(i for i in self.keys())
        return f"{self.__class__.__name__}{keys}"

update

Python
1

Updates the average and current value in all meters.

Parameters:

Name Type Description Default
input
Tensor | NestedTensor | Sequence

Input values to compute the metrics.

 required
target
Tensor | NestedTensor | Sequence

Target values to compute the metrics.

 required
Source code in danling/metrics/metric_meter.py
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def update(  # type: ignore[override] # pylint: disable=W0221
    self,
    input: Tensor | NestedTensor | Sequence,  # pylint: disable=W0622
    target: Tensor | NestedTensor | Sequence,
) -> None:
    r"""
    Updates the average and current value in all meters.

    Args:
        input: Input values to compute the metrics.
        target: Target values to compute the metrics.
    """

    input, target = self.preprocess(input, target, ignored_index=self.ignored_index)
    for meter in self.values():
        meter.update(input, target)

MultiTaskAverageMeters

Bases: MultiTaskDict

Manages multiple average meters in one object with multi-task support.

See Also

[AverageMeter]: Computes and stores the average and current value. [AverageMeters]: Manage multiple average meters in one object. [MetricMeters]: Manage multiple metric meters in one object.

Examples:

Python Console Session
>>> meters = MultiTaskAverageMeters()
>>> meters.update({"loss": 0.6, "dataset1.cls.auroc": 0.7, "dataset1.reg.r2": 0.8, "dataset2.r2": 0.9})
>>> f"{meters:.4f}"
'loss: 0.6000 (0.6000)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
>>> meters['loss'].update(0.9, n=1)
>>> f"{meters:.4f}"
'loss: 0.9000 (0.7500)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
>>> meters.sum.dict()
{'loss': 1.5, 'dataset1': {'cls': {'auroc': 0.7}, 'reg': {'r2': 0.8}}, 'dataset2': {'r2': 0.9}}
>>> meters.count.dict()
{'loss': 2, 'dataset1': {'cls': {'auroc': 1}, 'reg': {'r2': 1}}, 'dataset2': {'r2': 1}}
>>> meters.reset()
>>> f"{meters:.4f}"
'loss: 0.0000 (nan)\ndataset1.cls.auroc: 0.0000 (nan)\ndataset1.reg.r2: 0.0000 (nan)\ndataset2.r2: 0.0000 (nan)'
>>> meters = MultiTaskAverageMeters(return_average=True)
>>> meters.update({"loss": 0.6, "dataset1.a.auroc": 0.7, "dataset1.b.auroc": 0.8, "dataset2.auroc": 0.9})
>>> f"{meters:.4f}"
'loss: 0.6000 (0.6000)\ndataset1.a.auroc: 0.7000 (0.7000)\ndataset1.b.auroc: 0.8000 (0.8000)\ndataset2.auroc: 0.9000 (0.9000)'
>>> meters.update({"loss": 0.9, "dataset1.a.auroc": 0.8, "dataset1.b.auroc": 0.9, "dataset2.auroc": 1.0})
>>> f"{meters:.4f}"
'loss: 0.9000 (0.7500)\ndataset1.a.auroc: 0.8000 (0.7500)\ndataset1.b.auroc: 0.9000 (0.8500)\ndataset2.auroc: 1.0000 (0.9500)'
Source code in danling/metrics/average_meter.py
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class MultiTaskAverageMeters(MultiTaskDict):
    r"""
    Manages multiple average meters in one object with multi-task support.

    See Also:
        [`AverageMeter`]: Computes and stores the average and current value.
        [`AverageMeters`]: Manage multiple average meters in one object.
        [`MetricMeters`]: Manage multiple metric meters in one object.

    Examples:
        >>> meters = MultiTaskAverageMeters()
        >>> meters.update({"loss": 0.6, "dataset1.cls.auroc": 0.7, "dataset1.reg.r2": 0.8, "dataset2.r2": 0.9})
        >>> f"{meters:.4f}"
        'loss: 0.6000 (0.6000)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
        >>> meters['loss'].update(0.9, n=1)
        >>> f"{meters:.4f}"
        'loss: 0.9000 (0.7500)\ndataset1.cls.auroc: 0.7000 (0.7000)\ndataset1.reg.r2: 0.8000 (0.8000)\ndataset2.r2: 0.9000 (0.9000)'
        >>> meters.sum.dict()
        {'loss': 1.5, 'dataset1': {'cls': {'auroc': 0.7}, 'reg': {'r2': 0.8}}, 'dataset2': {'r2': 0.9}}
        >>> meters.count.dict()
        {'loss': 2, 'dataset1': {'cls': {'auroc': 1}, 'reg': {'r2': 1}}, 'dataset2': {'r2': 1}}
        >>> meters.reset()
        >>> f"{meters:.4f}"
        'loss: 0.0000 (nan)\ndataset1.cls.auroc: 0.0000 (nan)\ndataset1.reg.r2: 0.0000 (nan)\ndataset2.r2: 0.0000 (nan)'
        >>> meters = MultiTaskAverageMeters(return_average=True)
        >>> meters.update({"loss": 0.6, "dataset1.a.auroc": 0.7, "dataset1.b.auroc": 0.8, "dataset2.auroc": 0.9})
        >>> f"{meters:.4f}"
        'loss: 0.6000 (0.6000)\ndataset1.a.auroc: 0.7000 (0.7000)\ndataset1.b.auroc: 0.8000 (0.8000)\ndataset2.auroc: 0.9000 (0.9000)'
        >>> meters.update({"loss": 0.9, "dataset1.a.auroc": 0.8, "dataset1.b.auroc": 0.9, "dataset2.auroc": 1.0})
        >>> f"{meters:.4f}"
        'loss: 0.9000 (0.7500)\ndataset1.a.auroc: 0.8000 (0.7500)\ndataset1.b.auroc: 0.9000 (0.8500)\ndataset2.auroc: 1.0000 (0.9500)'
    """  # noqa: E501

    @property
    def sum(self) -> NestedDict[str, float]:
        return NestedDict({key: meter.sum for key, meter in self.all_items()})

    @property
    def count(self) -> NestedDict[str, int]:
        return NestedDict({key: meter.count for key, meter in self.all_items()})

    def update(self, *args: Dict, **values: float) -> None:  # pylint: disable=W0237
        r"""
        Updates the average and current value in all meters.

        Args:
            values: Dict of values to be added to the average.
            n: Number of values to be added.

        Raises:
            ValueError: If the value is not an instance of (int, float, Mapping).
        """  # noqa: E501

        if args:
            if len(args) > 1:
                raise ValueError("Expected only one positional argument, but got multiple.")
            values = args[0].update(values) or args[0] if values else args[0]

        for meter, value in values.items():
            if not isinstance(value, (int, float, Mapping)):
                raise ValueError(f"Expected values to be int, float, or a Mapping, but got {type(value)}")
            self[meter].update(value)

    # evil hack, as the default_factory must not be set to make `NestedDict` happy
    # this have some side effects, it will break attribute style intermediate nested dict auto creation
    # but everything has a price
    def get(self, name: Any, default=None) -> Any:
        if not name.startswith("_") and not name.endswith("_"):
            return self.setdefault(name, AverageMeter())
        return super().get(name, default)

    def set(self, name: str, meter: AverageMeter | AverageMeters) -> None:  # pylint: disable=W0237
        if not isinstance(meter, (AverageMeter, AverageMeters)):
            raise ValueError(
                f"Expected meter to be an instance of AverageMeter or AverageMeters, but got {type(meter)}"
            )
        super().set(name, meter)

update

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update(*args: Dict, **values: float) -> None

Updates the average and current value in all meters.

Parameters:

Name Type Description Default
values
float

Dict of values to be added to the average.

 {}
n

Number of values to be added.

 required

Raises:

Type Description
ValueError

If the value is not an instance of (int, float, Mapping).
Source code in danling/metrics/average_meter.py
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def update(self, *args: Dict, **values: float) -> None:  # pylint: disable=W0237
    r"""
    Updates the average and current value in all meters.

    Args:
        values: Dict of values to be added to the average.
        n: Number of values to be added.

    Raises:
        ValueError: If the value is not an instance of (int, float, Mapping).
    """  # noqa: E501

    if args:
        if len(args) > 1:
            raise ValueError("Expected only one positional argument, but got multiple.")
        values = args[0].update(values) or args[0] if values else args[0]

    for meter, value in values.items():
        if not isinstance(value, (int, float, Mapping)):
            raise ValueError(f"Expected values to be int, float, or a Mapping, but got {type(value)}")
        self[meter].update(value)

MultiTaskMetricMeters

Bases: MultiTaskAverageMeters

Examples:

Python Console Session
>>> from danling.metrics.functional import accuracy
>>> metrics = MultiTaskMetricMeters()
>>> metrics.dataset1.cls = MetricMeters(acc=accuracy)
>>> metrics.dataset2 = MetricMeters(acc=accuracy)
>>> metrics
MultiTaskMetricMeters(<class 'danling.metrics.metric_meter.MultiTaskMetricMeters'>,
  ('dataset1'): MultiTaskMetricMeters(<class 'danling.metrics.metric_meter.MultiTaskMetricMeters'>,
    ('cls'): MetricMeters('acc',)
  )
  ('dataset2'): MetricMeters('acc',)
)
>>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset2": ([0.1, 0.4, 0.6, 0.8], [1, 0, 0, 0])})
>>> f"{metrics:.4f}"
'dataset1.cls: acc: 0.5000 (0.5000)\ndataset2: acc: 0.2500 (0.2500)'
>>> metrics.setattr("return_average", True)
>>> metrics.update({"dataset1.cls": [[0.1, 0.4, 0.6, 0.8], [0, 0, 1, 0]], "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 0, 1]}})
>>> f"{metrics:.4f}"
'dataset1.cls: acc: 0.7500 (0.6250)\ndataset2: acc: 0.7500 (0.5000)'
>>> metrics.update(dict(loss=""))
Traceback (most recent call last):
ValueError: Metric loss not found in ...
Source code in danling/metrics/metric_meter.py
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class MultiTaskMetricMeters(MultiTaskAverageMeters):
    r"""
    Examples:
        >>> from danling.metrics.functional import accuracy
        >>> metrics = MultiTaskMetricMeters()
        >>> metrics.dataset1.cls = MetricMeters(acc=accuracy)
        >>> metrics.dataset2 = MetricMeters(acc=accuracy)
        >>> metrics
        MultiTaskMetricMeters(<class 'danling.metrics.metric_meter.MultiTaskMetricMeters'>,
          ('dataset1'): MultiTaskMetricMeters(<class 'danling.metrics.metric_meter.MultiTaskMetricMeters'>,
            ('cls'): MetricMeters('acc',)
          )
          ('dataset2'): MetricMeters('acc',)
        )
        >>> metrics.update({"dataset1.cls": {"input": [0.2, 0.4, 0.5, 0.7], "target": [0, 1, 0, 1]}, "dataset2": ([0.1, 0.4, 0.6, 0.8], [1, 0, 0, 0])})
        >>> f"{metrics:.4f}"
        'dataset1.cls: acc: 0.5000 (0.5000)\ndataset2: acc: 0.2500 (0.2500)'
        >>> metrics.setattr("return_average", True)
        >>> metrics.update({"dataset1.cls": [[0.1, 0.4, 0.6, 0.8], [0, 0, 1, 0]], "dataset2": {"input": [0.2, 0.3, 0.5, 0.7], "target": [0, 0, 0, 1]}})
        >>> f"{metrics:.4f}"
        'dataset1.cls: acc: 0.7500 (0.6250)\ndataset2: acc: 0.7500 (0.5000)'
        >>> metrics.update(dict(loss=""))  # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ValueError: Metric loss not found in ...
    """  # noqa: E501

    def __init__(self, *args, **kwargs):
        super().__init__(*args, default_factory=MultiTaskMetricMeters, **kwargs)

    def update(  # type: ignore[override] # pylint: disable=W0221
        self,
        values: Mapping[str, Tuple[Tensor | NestedTensor | Sequence, Tensor | NestedTensor | Sequence]],
    ) -> None:
        r"""
        Updates the average and current value in all meters.

        Args:
            input: Input values to compute the metrics.
            target: Target values to compute the metrics.
        """

        for metric, value in values.items():
            if metric not in self:
                raise ValueError(f"Metric {metric} not found in {self}")
            if isinstance(self[metric], MultiTaskMetricMeters):
                for met in self[metric].all_values():
                    if isinstance(value, Mapping):
                        met.update(**value)
                    elif isinstance(value, Sequence):
                        met.update(*value)
                    else:
                        raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
            elif isinstance(self[metric], (MetricMeters, MetricMeter)):
                if isinstance(value, Mapping):
                    self[metric].update(**value)
                elif isinstance(value, Sequence):
                    self[metric].update(*value)
                else:
                    raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
            else:
                raise ValueError(
                    f"Expected {metric} to be an instance of MultiTaskMetricMeters, MetricMeters, "
                    f"or MetricMeter, but got {type(self[metric])}"
                )

    # MultiTaskAverageMeters.get is hacked
    def get(self, name: Any, default=None) -> Any:
        return MultiTaskDict.get(self, name, default)

    def set(  # pylint: disable=W0237
        self,
        name: str,
        metric: MetricMeter | MetricMeters | Callable,  # type: ignore[override]
    ) -> None:
        if callable(metric):
            metric = MetricMeter(metric)
        if not isinstance(metric, (MetricMeter, MetricMeters)):
            raise ValueError(
                f"Expected {metric} to be an instance of MetricMeter or MetricMeters, but got {type(metric)}"
            )
        super().set(name, metric)

update

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update(values: Mapping[str, Tuple[Tensor | NestedTensor | Sequence, Tensor | NestedTensor | Sequence]]) -> None

Updates the average and current value in all meters.

Parameters:

Name Type Description Default
input

Input values to compute the metrics.

 required
target

Target values to compute the metrics.

 required
Source code in danling/metrics/metric_meter.py
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def update(  # type: ignore[override] # pylint: disable=W0221
    self,
    values: Mapping[str, Tuple[Tensor | NestedTensor | Sequence, Tensor | NestedTensor | Sequence]],
) -> None:
    r"""
    Updates the average and current value in all meters.

    Args:
        input: Input values to compute the metrics.
        target: Target values to compute the metrics.
    """

    for metric, value in values.items():
        if metric not in self:
            raise ValueError(f"Metric {metric} not found in {self}")
        if isinstance(self[metric], MultiTaskMetricMeters):
            for met in self[metric].all_values():
                if isinstance(value, Mapping):
                    met.update(**value)
                elif isinstance(value, Sequence):
                    met.update(*value)
                else:
                    raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
        elif isinstance(self[metric], (MetricMeters, MetricMeter)):
            if isinstance(value, Mapping):
                self[metric].update(**value)
            elif isinstance(value, Sequence):
                self[metric].update(*value)
            else:
                raise ValueError(f"Expected value to be a Mapping or Sequence, but got {type(value)}")
        else:
            raise ValueError(
                f"Expected {metric} to be an instance of MultiTaskMetricMeters, MetricMeters, "
                f"or MetricMeter, but got {type(self[metric])}"
            )

LRScheduler

Bases: _LRScheduler

General learning rate scheduler.

PyTorch LRScheduler is hard to extend. This class is a wrapper of PyTorch LRScheduler, which provides a more general interface. You only needs to add a new method which calculates a learning rate ratio (range from 0 to 1) with total progress (range from 0 to 1), and everything else will be done automatically.

Moreover, this class has warmup and cooldown built-in. By default, the first 5% and last 20% of training steps will be warmup and cooldown respectively. You can alternate by passing warmup_steps and cooldown_steps, or disable them by setting them to 0.

Parameters:

Name Type Description Default

optimizer

Optimizer

Wrapped optimizer.

 required

total_steps

int

Total number of trainable steps.

 required

final_lr_ratio

Optional[float]

Final learning rate ratio to initial learning rate. Defaults to 1e-3.

 None

final_lr

Optional[float]

Final learning rate.

 None

min_lr

float

Minimal learning rate. Defaults to 1e-9.

 1e-09

strategy

str

Scaling strategy. Defaults to “cosine”.

 'cosine'

warmup_steps

Optional[int]

Number of warmup steps. Defaults to steps // 20.

 None

cooldown_steps

Optional[int]

Number of cooldown steps. Defaults to steps // 5.

 None

last_epoch

int

The index of last epoch. Defaults to -1.

 -1

method

Optional[str]

Method to calculate learning rate given ratio, should be one of “percentile” or “numerical”. Defaults to “percentile” if final_lr_ratio is set, otherwise “numerical”.

 None

Examples:

Python Console Session
>>> from danling.optim import LRScheduler
>>> import torch
>>> from torch import optim
>>> optimizer = optim.SGD([{'params': torch.tensor([0])}], lr=1, momentum=0.9)
>>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='linear')
>>> lrs = []
>>> for epoch in range(5):
...     lrs.append(scheduler.get_lr()[0])
...     scheduler.step()
>>> [round(lr, 10) for lr in lrs]
[0.1, 0.01, 0.001, 0.0001, 1e-09]
>>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='cosine')
>>> lrs = []
>>> for epoch in range(5):
...     lrs.append(scheduler.get_lr()[0])
...     scheduler.step()
>>> [round(lr, 10) for lr in lrs]
[0.3330753446, 0.0187302031, 0.000533897, 3.00232e-05, 1e-09]
>>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='linear', method='numerical')
>>> lrs = []
>>> for epoch in range(5):
...     lrs.append(scheduler.get_lr()[0])
...     scheduler.step()
>>> [round(lr, 2) for lr in lrs]
[0.8, 0.6, 0.4, 0.2, 0.0]
Source code in danling/optim/lr_scheduler/lr_scheduler.py
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class LRScheduler(lr_scheduler._LRScheduler):  # pylint: disable=protected-access
    r"""
    General learning rate scheduler.

    PyTorch LRScheduler is hard to extend.
    This class is a wrapper of PyTorch LRScheduler, which provides a more general interface.
    You only needs to add a new method which calculates a learning rate ratio (range from 0 to 1)
    with total progress (range from 0 to 1), and everything else will be done automatically.

    Moreover, this class has warmup and cooldown built-in.
    By default, the first 5% and last 20% of training steps will be warmup and cooldown respectively.
    You can alternate by passing `warmup_steps` and `cooldown_steps`, or disable them by setting them to 0.

    Args:
        optimizer: Wrapped optimizer.
        total_steps: Total number of trainable steps.
        final_lr_ratio: Final learning rate ratio to initial learning rate.
            Defaults to 1e-3.
        final_lr: Final learning rate.
        min_lr: Minimal learning rate.
            Defaults to 1e-9.
        strategy: Scaling strategy.
            Defaults to "cosine".
        warmup_steps: Number of warmup steps.
            Defaults to `steps // 20`.
        cooldown_steps: Number of cooldown steps.
            Defaults to `steps // 5`.
        last_epoch: The index of last epoch.
            Defaults to -1.
        method: Method to calculate learning rate given ratio, should be one of "percentile" or "numerical".
            Defaults to "percentile" if `final_lr_ratio` is set, otherwise "numerical".

    Examples:
        >>> from danling.optim import LRScheduler
        >>> import torch
        >>> from torch import optim
        >>> optimizer = optim.SGD([{'params': torch.tensor([0])}], lr=1, momentum=0.9)
        >>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='linear')
        >>> lrs = []
        >>> for epoch in range(5):
        ...     lrs.append(scheduler.get_lr()[0])
        ...     scheduler.step()
        >>> [round(lr, 10) for lr in lrs]
        [0.1, 0.01, 0.001, 0.0001, 1e-09]
        >>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='cosine')
        >>> lrs = []
        >>> for epoch in range(5):
        ...     lrs.append(scheduler.get_lr()[0])
        ...     scheduler.step()
        >>> [round(lr, 10) for lr in lrs]
        [0.3330753446, 0.0187302031, 0.000533897, 3.00232e-05, 1e-09]
        >>> scheduler = LRScheduler(optimizer, total_steps=5, final_lr_ratio=1e-5, strategy='linear', method='numerical')
        >>> lrs = []
        >>> for epoch in range(5):
        ...     lrs.append(scheduler.get_lr()[0])
        ...     scheduler.step()
        >>> [round(lr, 2) for lr in lrs]
        [0.8, 0.6, 0.4, 0.2, 0.0]
    """  # noqa: E501

    def __init__(
        self,
        optimizer: Optimizer,
        total_steps: int,
        final_lr_ratio: Optional[float] = None,
        final_lr: Optional[float] = None,
        min_lr: float = 1e-9,
        strategy: str = "cosine",
        warmup_steps: Optional[int] = None,
        cooldown_steps: Optional[int] = None,
        last_epoch: int = -1,
        method: Optional[str] = None,
    ):
        if total_steps <= 0:
            raise ValueError(f"Total steps must be positive, but got {total_steps}")
        if warmup_steps is None:
            warmup_steps = total_steps // 20
        elif warmup_steps > total_steps:
            raise ValueError(f"Warmup steps must be less than total steps, but got {warmup_steps} > {total_steps}")
        elif warmup_steps < 0:
            raise ValueError(f"Warmup steps must be positive, but got {warmup_steps}")
        if cooldown_steps is None:
            cooldown_steps = total_steps // 5
        elif cooldown_steps > total_steps:
            raise ValueError(f"Cooldown steps must be less than total steps, but got {cooldown_steps} > {total_steps}")
        elif cooldown_steps < 0:
            raise ValueError(f"Cooldown steps must be positive, but got {cooldown_steps}")
        if final_lr_ratio is not None:
            if final_lr is not None:
                raise ValueError("Only one of `final_lr_ratio` and `final_lr` should be set, but not both")
            if final_lr_ratio < 0:
                raise ValueError(f"`final_lr_ratio` must be positive, but got {final_lr_ratio}")
            if method is None:
                method = "percentile"
        if final_lr is not None and final_lr < 0:
            raise ValueError(f"`final_lr` must be positive, but got {final_lr}")
        if min_lr < 0:
            raise ValueError(f"`min_lr` must be positive, but got {min_lr}")
        self.strategies = {
            k: v for k, v in self.__class__.__dict__.items() if callable(v) and (not k.startswith("_") or k in "get_lr")
        }
        if strategy not in self.strategies:
            raise ValueError(f"Scaling strategy must be one of {self.strategies.keys()}, but got {strategy}")

        if final_lr_ratio is None and final_lr is None:
            final_lr_ratio = 1e-3
            if method is None:
                method = "percentile"
        if final_lr is not None and min_lr > final_lr:
            min_lr = final_lr
        if method is None:
            method = "numerical"

        self.final_lr_ratio = final_lr_ratio
        self.final_lr = final_lr
        self.total_steps = total_steps
        self.min_lr = min_lr
        self.strategy = strategy
        self.method = method
        self.warmup_steps = warmup_steps
        self.cooldown_steps = cooldown_steps
        self.cooldown_steps_begin = self.total_steps - self.cooldown_steps
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        step_count = self._step_count
        if step_count > self.total_steps + 1 or step_count < 1:
            warn(
                f"Step count {step_count} is out of range [1, {self.total_steps + 1}]",
                category=RuntimeWarning,
                stacklevel=2,
            )
        return [self._get_lr(lr, step_count) for lr in self.base_lrs]

    def _get_lr(
        self,
        lr: float,
        step_count: Optional[int] = None,
        progress: Optional[float] = None,
        warmup_ratio: Optional[float] = None,
        cooldown_ratio: Optional[float] = None,
        method: Optional[str] = None,
    ) -> float:
        method = method or self.method
        step_count = step_count or self._step_count
        progress = progress or min(max(step_count / self.total_steps, 0.0), 1.0)
        final_lr = self.final_lr if self.final_lr is not None else lr * self.final_lr_ratio  # type: ignore[operator]
        ratio = getattr(self, self.strategy)(progress)
        if method == "percentile":
            lr *= pow(final_lr / lr, ratio)
        elif method == "numerical":
            lr = (1 - ratio) * (lr - final_lr) + final_lr
        else:
            raise ValueError(f"Method must be one of ['percentile', 'numerical'], but got {method}")
        if self.warmup_steps > step_count > 0:
            warmup_ratio = warmup_ratio or step_count / self.warmup_steps
            lr = warmup_ratio * (lr - self.min_lr) + self.min_lr
        elif self.cooldown_steps > 0 and step_count > self.cooldown_steps_begin:
            cooldown_ratio = cooldown_ratio or 1 - (step_count - self.cooldown_steps_begin) / self.cooldown_steps
            lr = cooldown_ratio * (lr - self.min_lr) + self.min_lr
        return max(self.min_lr, lr)

    def linear(self, progress: float) -> float:
        return progress

    def cosine(self, progress: float) -> float:
        return 1 - ((1 + cos(pi * progress)) / 2)

    def constant(self, progress: float) -> float:  # pylint: disable=unused-argument
        return 0.0

    def __repr__(self) -> str:
        return (
            f"{self.__class__.__name__}({self.strategy}, method={self.method}, "
            f"final_lr_ratio={self.final_lr_ratio}, total_steps={self.total_steps}, "
            f"warmup_steps={self.warmup_steps}, cooldown_steps={self.cooldown_steps})"
        )

AccelerateRunner

Bases: TorchRunner, Accelerator

Set up everything for running a job.

AccelerateRunner uses [accelerate][accelerate] as distributed backend to provide seamless distributed training experience.

AccelerateRunner will automatically prepare everything, including model, criterion, optimizer, scheduler, and dataloaders for distribute training, mixed precision, and deepspeed (optional).

In fact, you don’t even need to create dataloaders, just define datasets and AccelerateRunner will create dataloaders for you. AccelerateRunner will inspect the train flag in corresponding dataset to set shuffle and drop_last automatically.

Source code in danling/runner/accelerate_runner.py
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class AccelerateRunner(TorchRunner, Accelerator):  # pylint: disable=too-many-public-methods
    r"""
    Set up everything for running a job.

    `AccelerateRunner` uses [`accelerate`][accelerate] as distributed backend to
    provide seamless distributed training experience.

    `AccelerateRunner` will automatically [`prepare`][accelerate.Accelerator.prepare] everything,
    including `model`, `criterion`, `optimizer`, `scheduler`, and `dataloaders` for distribute training,
    mixed precision, and deepspeed (optional).

    In fact, you don't even need to create `dataloaders`, just define
    `datasets` and `AccelerateRunner` will create `dataloaders` for you.
    `AccelerateRunner` will inspect the `train` flag in corresponding dataset to
    set `shuffle` and `drop_last` automatically.
    """

    _accelerate: FlatDict | None = None

    def __init__(self, config: Config) -> None:
        ac.check()
        TorchRunner.__init__(self, config)
        Accelerator.__init__(self, **self.accelerate)
        if self.distributed:
            object_list = [self.id, self.timestamp]
            dist.broadcast_object_list(object_list)
            self.id, self.timestamp = object_list

    def __post_init__(self) -> None:
        BaseRunner.__post_init__(self)
        self.project_configuration.set_directories(self.dir)
        if self.datasets:
            self.build_dataloaders()
        if self.config.get("log_interval") is None:
            self.config.log_interval = max(ceil(max(len(d) for d in self.dataloaders.values()) / 10), 1)
        self.model, self.criterion, self.optimizer, self.scheduler = self.prepare(
            self.model, self.criterion, self.optimizer, self.scheduler
        )

    def train_step(self, data) -> torch.Tensor:
        with self.autocast(), self.accumulate():
            input = data["input"] if isinstance(data, Mapping) else data[0]
            target = data["target"] if isinstance(data, Mapping) else data[1]
            pred = self.model(**input) if isinstance(input, Mapping) else self.model(input)
            loss = self.criterion(pred, target)
            if self.metrics is not None:
                self.metrics.update(pred.squeeze(-1), target)
            self.advance(loss)
        return loss

    def advance(self, loss) -> None:
        r"""
        Backward loss and step optimizer & scheduler.

        Args:
            zero_grad: Whether to zero the gradients.
        """

        self.backward(loss)
        if self.sync_gradients:
            if self.config.get("max_grad_value") is not None:
                self.clip_grad_value_(self.model.parameters(), self.config.get("max_grad_value"))
            if self.config.get("max_grad_norm") is not None:
                self.clip_grad_norm_(self.model.parameters(), self.config.get("max_grad_norm"))
        self.optimizer.step()
        if self.scheduler is not None:
            self.scheduler.step()
        self.optimizer.zero_grad()
        self.config.step = self.step

    def unwrap(self, model: nn.Module) -> nn.Module:
        return self.unwrap_model(model)

    @property
    def accelerate(self) -> FlatDict:
        if self._accelerate is None:
            self._accelerate = self.get_accelerate_config(self.config)
        return self._accelerate

    @accelerate.setter
    def accelerate(self, config: FlatDict) -> None:
        self._accelerate = config

    @property
    def deepspeed(self) -> dict | None:
        if self.state.deepspeed_plugin is not None:
            return self.state.deepspeed_plugin.deepspeed_config
        return None

    @contextmanager
    def accumulate(self, *models: nn.Module):
        if not models:
            models = (self.model,)
        yield Accelerator.accumulate(self, *models)

    @property
    def device(self) -> torch.device:
        return self.state.device

    @property
    def world_size(self) -> int:
        if "state" in self.__dict__:
            return self.state.num_processes
        return 1

    @property
    def rank(self) -> int:
        if "state" in self.__dict__:
            return self.state.process_index
        return 0

    @property
    def local_rank(self) -> int:
        if "state" in self.__dict__:
            return self.state.local_process_index
        return 0

    @cached_property
    def accum_steps(self) -> int:
        return self.gradient_accumulation_steps

    def get_accelerate_config(self, config) -> FlatDict:
        accelerate = FlatDict()
        if "accelerate" in config:
            accelerate.update(config.accelerate)
        if "precision" in config:
            accelerate.mixed_precision = config.precision
        if "dynamo" in config:
            accelerate.dynamo_backend = config.dynamo.upper()
        if "accum_steps" in config:
            accelerate.gradient_accumulation_steps = config.accum_steps
        if "kwargs_handlers" not in accelerate:
            accelerate.kwargs_handlers = []
        # Must NOT set project_dir here as timestamp is not synced yet
        # config.project_dir = self.dir
        if os.getenv("ACCELERATE_USE_DEEPSPEED", "false").lower() == "true":
            deepspeed_config = config.get("deepspeed", os.getenv("ACCELERATE_DEEPSPEED_CONFIG_FILE"))
            accelerate.deepspeed_plugin = DeepSpeedPlugin(hf_ds_config=self.get_deepspeed_config(deepspeed_config))
        return accelerate

    def build_dataloaders(self):
        datasets = {k: d for k, d in self.datasets.items() if k not in self.dataloaders}
        default_kwargs = self.config.setdefault("dataloader", NestedDict())
        dataloader_kwargs = NestedDict({k: default_kwargs.pop(k) for k in self.datasets if k in default_kwargs})
        for k, d in datasets.items():
            dataloader_kwargs.setdefault(k, NestedDict())
            dataloader_kwargs[k].merge(default_kwargs, overwrite=False)
            dataloader_kwargs[k].setdefault("shuffle", getattr(d, "train", True))
            dataloader_kwargs[k].setdefault("drop_last", not getattr(d, "train", True))
            self.dataloaders[k] = utils.data.DataLoader(d, collate_fn=self.collate_fn, **dataloader_kwargs[k])
        default_kwargs.update(dataloader_kwargs)
        for k, d in self.dataloaders.items():
            self.dataloaders[k] = self.prepare(d)

advance

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advance(loss) -> None

Backward loss and step optimizer & scheduler.

Parameters:

Name Type Description Default
zero_grad

Whether to zero the gradients.

 required
Source code in danling/runner/accelerate_runner.py
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def advance(self, loss) -> None:
    r"""
    Backward loss and step optimizer & scheduler.

    Args:
        zero_grad: Whether to zero the gradients.
    """

    self.backward(loss)
    if self.sync_gradients:
        if self.config.get("max_grad_value") is not None:
            self.clip_grad_value_(self.model.parameters(), self.config.get("max_grad_value"))
        if self.config.get("max_grad_norm") is not None:
            self.clip_grad_norm_(self.model.parameters(), self.config.get("max_grad_norm"))
    self.optimizer.step()
    if self.scheduler is not None:
        self.scheduler.step()
    self.optimizer.zero_grad()
    self.config.step = self.step

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:

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{
    "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
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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_step_result(self) -> NestedDict:
        result = self.meters.value()
        if self.metrics is not None:
            return self._merge_result(result, self.metrics.value())
        return result

    def get_epoch_result(self) -> NestedDict:
        result = self.meters.average()
        if self.metrics is not None:
            return self._merge_result(result, self.metrics.average())
        return result

    def _merge_result(self, result, metric_result) -> NestedDict:
        for key, value in metric_result.items():
            if isinstance(value, (Mapping)) and len(value) == 1:
                value = next(iter(value.values()))
            metric_result[key] = value
        result.update(metric_result)
        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_step_result()
        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

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uuid: UUID

UUID of the config.

batch_size property

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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

Python
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batch_size_equivalent: int

Actual batch size.

Returns:

Type Description
int

batch_size * world_size * accum_steps

accum_steps cached property

Python
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accum_steps: int

Accumulated steps.

Returns:

Type Description
int

progress property

Python
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progress: float

Training Progress.

Returns:

Type Description
float

Raises:

Type Description
RuntimeError

If no terminal is defined.

world_size property

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world_size: int

Number of processes.

rank property

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rank: int

Process index of all processes.

local_rank property

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local_rank: int

Process index of local processes.

distributed property

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distributed: bool

If runner is running in distributed mode.

is_main_process property

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is_main_process: bool

If current process is the main process of all processes.

is_local_main_process property

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is_local_main_process: bool

If current process is the main process of local processes.

best_fn property

Python
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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

Python
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best_index: int

Find the best index from all scores.

Returns:

Type Description
int

latest_result property

Python
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latest_result: NestedDict | None

Latest result.

best_result property

Python
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best_result: NestedDict | None

Best result.

scores property

Python
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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

Python
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latest_score: float | None

Latest score.

best_score property

Python
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best_score: float | None

Best score.

is_best property

Python
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is_best: bool

If current epoch is the best epoch.

dir property

Python
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dir: str

Directory of the run.

log_path cached property

Python
1
log_path: str

Path of log file.

checkpoint_dir property

Python
1
checkpoint_dir: str

Directory of checkpoints.

init_distributed

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init_distributed() -> None

Initialise distributed running environment.

Source code in danling/runner/base_runner.py
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def init_distributed(self) -> None:
    r"""
    Initialise distributed running environment.
    """

init_logging

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init_logging() -> None

Set up logging.

Source code in danling/runner/base_runner.py
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@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

Python
1
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
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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

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

Set up Tensoraoard SummaryWriter.

Source code in danling/runner/base_runner.py
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@on_main_process
def init_tensorboard(self, *args, **kwargs) -> None:
    r"""
    Set up Tensoraoard SummaryWriter.
    """
    raise NotImplementedError

set_seed

Python
1
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
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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

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set_deterministic() -> None

Set up deterministic.

Source code in danling/runner/base_runner.py
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def set_deterministic(self) -> None:
    r"""
    Set up deterministic.
    """

    raise NotImplementedError

scale_lr

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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
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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

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

    Args:
        loss: loss.
    """

    raise NotImplementedError

state_dict

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state_dict(cls: Callable = dict) -> Mapping

Return dict of all attributes for checkpoint.

Source code in danling/runner/base_runner.py
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def state_dict(self, cls: Callable = dict) -> Mapping:
    r"""
    Return dict of all attributes for checkpoint.
    """

    return cls(self.config)

dict

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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
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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

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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
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@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

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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
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@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

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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
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@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

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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
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@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

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

Dump Runner config to json string.

Source code in danling/runner/base_runner.py
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def jsons(self, *args, **kwargs) -> str:
    r"""
    Dump Runner config to json string.
    """

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

from_jsons classmethod

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

Construct Runner from json string.

Source code in danling/runner/base_runner.py
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@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

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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
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@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

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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
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@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

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

Dump Runner config to yaml string.

Source code in danling/runner/base_runner.py
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def yamls(self, *args, **kwargs) -> str:
    r"""
    Dump Runner config to yaml string.
    """

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

from_yamls classmethod

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

Construct Runner from yaml string.

Source code in danling/runner/base_runner.py
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@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

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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
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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

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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
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@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

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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
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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

Python
1

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
Python
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@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

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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
Python
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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.