Shortcuts

Source code for ignite.metrics.metric

import warnings
from abc import ABCMeta, abstractmethod
from collections.abc import Mapping
from functools import wraps
from numbers import Number
from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Tuple, Union

import torch

import ignite.distributed as idist
from ignite.engine import CallableEventWithFilter, Engine, Events

if TYPE_CHECKING:
    from ignite.metrics.metrics_lambda import MetricsLambda

__all__ = ["Metric", "MetricUsage", "EpochWise", "BatchWise", "BatchFiltered"]


[docs]class MetricUsage: """ Base class for all usages of metrics. A usage of metric defines the events when a metric starts to compute, updates and completes. Valid events are from :class:`~ignite.engine.events.Events`. Args: started: event when the metric starts to compute. This event will be associated to :meth:`~ignite.metrics.metric.Metric.started`. completed: event when the metric completes. This event will be associated to :meth:`~ignite.metrics.metric.Metric.completed`. iteration_completed: event when the metric updates. This event will be associated to :meth:`~ignite.metrics.metric.Metric.iteration_completed`. """ def __init__(self, started: Events, completed: Events, iteration_completed: CallableEventWithFilter) -> None: self.__started = started self.__completed = completed self.__iteration_completed = iteration_completed @property def STARTED(self) -> Events: return self.__started @property def COMPLETED(self) -> Events: return self.__completed @property def ITERATION_COMPLETED(self) -> CallableEventWithFilter: return self.__iteration_completed
[docs]class EpochWise(MetricUsage): """ Epoch-wise usage of Metrics. It's the default and most common usage of metrics. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "epoch_wise" def __init__(self) -> None: super(EpochWise, self).__init__( started=Events.EPOCH_STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, )
[docs]class BatchWise(MetricUsage): """ Batch-wise usage of Metrics. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``ITERATION_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on every ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``ITERATION_COMPLETED``. Attributes: usage_name: usage name string """ usage_name: str = "batch_wise" def __init__(self) -> None: super(BatchWise, self).__init__( started=Events.ITERATION_STARTED, completed=Events.ITERATION_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED, )
[docs]class BatchFiltered(MetricUsage): """ Batch filtered usage of Metrics. This usage is similar to epoch-wise but update event is filtered. Metric's methods are triggered on the following engine events: - :meth:`~ignite.metrics.metric.Metric.started` on every ``EPOCH_STARTED`` (See :class:`~ignite.engine.events.Events`). - :meth:`~ignite.metrics.metric.Metric.iteration_completed` on filtered ``ITERATION_COMPLETED``. - :meth:`~ignite.metrics.metric.Metric.completed` on every ``EPOCH_COMPLETED``. Args: args: Positional arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED` kwargs: Keyword arguments to setup :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED` handled by :meth:`~ignite.metrics.metric.Metric.iteration_completed`. """ def __init__(self, *args: Any, **kwargs: Any) -> None: super(BatchFiltered, self).__init__( started=Events.EPOCH_STARTED, completed=Events.EPOCH_COMPLETED, iteration_completed=Events.ITERATION_COMPLETED(*args, **kwargs), )
[docs]class Metric(metaclass=ABCMeta): """ Base class for all Metrics. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Attributes: required_output_keys: dictionary defines required keys to be found in ``engine.state.output`` if the latter is a dictionary. Default, ``("y_pred", "y")``. This is useful with custom metrics that can require other arguments than predictions ``y_pred`` and targets ``y``. See notes below for an example. Note: Let's implement a custom metric that requires ``y_pred``, ``y`` and ``x`` as input for ``update`` function. In the example below we show how to setup standard metric like Accuracy and the custom metric using by an ``evaluator`` created with :meth:`~ignite.engine.create_supervised_evaluator` method. .. code-block:: python # https://discuss.pytorch.org/t/how-access-inputs-in-custom-ignite-metric/91221/5 import torch import torch.nn as nn from ignite.metrics import Metric, Accuracy from ignite.engine import create_supervised_evaluator class CustomMetric(Metric): required_output_keys = ("y_pred", "y", "x") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def update(self, output): y_pred, y, x = output # ... def reset(self): # ... pass def compute(self): # ... pass model = ... metrics = { "Accuracy": Accuracy(), "CustomMetric": CustomMetric() } evaluator = create_supervised_evaluator( model, metrics=metrics, output_transform=lambda x, y, y_pred: {"x": x, "y": y, "y_pred": y_pred} ) res = evaluator.run(data) .. versionchanged:: 0.4.2 ``required_output_keys`` became public attribute. """ # public class attribute required_output_keys: Optional[Tuple] = ("y_pred", "y") # for backward compatibility _required_output_keys = required_output_keys def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): self._output_transform = output_transform # Check device if distributed is initialized: if idist.get_world_size() > 1: # check if reset and update methods are decorated. Compute may not be decorated if not (hasattr(self.reset, "_decorated") and hasattr(self.update, "_decorated")): warnings.warn( f"{self.__class__.__name__} class does not support distributed setting. " "Computed result is not collected across all computing devices", RuntimeWarning, ) # Some metrics have a large performance regression when run on XLA devices, so for now, we disallow it. if torch.device(device).type == "xla": raise ValueError("Cannot create metric on an XLA device. Use device='cpu' instead.") self._device = torch.device(device) self._is_reduced = False self.reset()
[docs] @abstractmethod def reset(self) -> None: """ Resets the metric to it's initial state. By default, this is called at the start of each epoch. """ pass
[docs] @abstractmethod def update(self, output: Any) -> None: """ Updates the metric's state using the passed batch output. By default, this is called once for each batch. Args: output: the is the output from the engine's process function. """ pass
[docs] @abstractmethod def compute(self) -> Any: """ Computes the metric based on it's accumulated state. By default, this is called at the end of each epoch. Returns: Any: | the actual quantity of interest. However, if a :class:`~collections.abc.Mapping` is returned, it will be (shallow) flattened into `engine.state.metrics` when :func:`~ignite.metrics.metric.Metric.completed` is called. Raises: NotComputableError: raised when the metric cannot be computed. """ pass
[docs] def started(self, engine: Engine) -> None: """Helper method to start data gathering for metric's computation. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. Args: engine: the engine to which the metric must be attached """ self.reset()
[docs] @torch.no_grad() def iteration_completed(self, engine: Engine) -> None: """Helper method to update metric's computation. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. Note: ``engine.state.output`` is used to compute metric values. The majority of implemented metrics accepts the following formats for ``engine.state.output``: ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. ``y_pred`` and ``y`` can be torch tensors or list of tensors/numbers if applicable. Args: engine: the engine to which the metric must be attached .. versionchanged:: 0.4.5 ``y_pred`` and ``y`` can be torch tensors or list of tensors/numbers """ output = self._output_transform(engine.state.output) if isinstance(output, Mapping): if self.required_output_keys is None: raise TypeError( f"Transformed engine output for {self.__class__.__name__} metric should be a tuple/list, " f"but given {type(output)}" ) if not all([k in output for k in self.required_output_keys]): raise ValueError( "When transformed engine's output is a mapping, " f"it should contain {self.required_output_keys} keys, but given {list(output.keys())}" ) output = tuple(output[k] for k in self.required_output_keys) if isinstance(output, Sequence) and all([_is_list_of_tensors_or_numbers(o) for o in output]): if not (len(output) == 2 and len(output[0]) == len(output[1])): raise ValueError( f"Output should have 2 items of the same length, " f"got {len(output)} and {len(output[0])}, {len(output[1])}" ) for o1, o2 in zip(output[0], output[1]): # o1 and o2 are list of tensors or numbers tensor_o1 = _to_batched_tensor(o1) tensor_o2 = _to_batched_tensor(o2, device=tensor_o1.device) self.update((tensor_o1, tensor_o2)) else: self.update(output)
[docs] def completed(self, engine: Engine, name: str) -> None: """Helper method to compute metric's value and put into the engine. It is automatically attached to the `engine` with :meth:`~ignite.metrics.metric.Metric.attach`. If metrics' value is torch tensor, it is explicitly sent to CPU device. Args: engine: the engine to which the metric must be attached name: the name of the metric used as key in dict `engine.state.metrics` .. versionchanged:: 0.4.3 Added dict in metrics results. .. versionchanged:: 0.4.5 metric's value is put on CPU if torch tensor. """ result = self.compute() if isinstance(result, Mapping): if name in result.keys(): raise ValueError(f"Argument name '{name}' is conflicting with mapping keys: {list(result.keys())}") for key, value in result.items(): engine.state.metrics[key] = value engine.state.metrics[name] = result else: if isinstance(result, torch.Tensor): if len(result.size()) == 0: result = result.item() elif "cpu" not in result.device.type: result = result.cpu() engine.state.metrics[name] = result
def _check_usage(self, usage: Union[str, MetricUsage]) -> MetricUsage: if isinstance(usage, str): if usage == EpochWise.usage_name: usage = EpochWise() elif usage == BatchWise.usage_name: usage = BatchWise() else: raise ValueError(f"usage should be 'EpochWise.usage_name' or 'BatchWise.usage_name', get {usage}") if not isinstance(usage, MetricUsage): raise TypeError(f"Unhandled usage type {type(usage)}") return usage
[docs] def attach(self, engine: Engine, name: str, usage: Union[str, MetricUsage] = EpochWise()) -> None: """ Attaches current metric to provided engine. On the end of engine's run, `engine.state.metrics` dictionary will contain computed metric's value under provided name. Args: engine: the engine to which the metric must be attached name: the name of the metric to attach usage: the usage of the metric. Valid string values should be :attr:`ignite.metrics.metric.EpochWise.usage_name` (default) or :attr:`ignite.metrics.metric.BatchWise.usage_name`. Example: .. code-block:: python metric = ... metric.attach(engine, "mymetric") assert "mymetric" in engine.run(data).metrics assert metric.is_attached(engine) Example with usage: .. code-block:: python metric = ... metric.attach(engine, "mymetric", usage=BatchWise.usage_name) assert "mymetric" in engine.run(data).metrics assert metric.is_attached(engine, usage=BatchWise.usage_name) """ usage = self._check_usage(usage) if not engine.has_event_handler(self.started, usage.STARTED): engine.add_event_handler(usage.STARTED, self.started) if not engine.has_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED): engine.add_event_handler(usage.ITERATION_COMPLETED, self.iteration_completed) engine.add_event_handler(usage.COMPLETED, self.completed, name)
[docs] def detach(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> None: """ Detaches current metric from the engine and no metric's computation is done during the run. This method in conjunction with :meth:`~ignite.metrics.metric.Metric.attach` can be useful if several metrics need to be computed with different periods. For example, one metric is computed every training epoch and another metric (e.g. more expensive one) is done every n-th training epoch. Args: engine: the engine from which the metric must be detached usage: the usage of the metric. Valid string values should be 'epoch_wise' (default) or 'batch_wise'. Example: .. code-block:: python metric = ... engine = ... metric.detach(engine) assert "mymetric" not in engine.run(data).metrics assert not metric.is_attached(engine) Example with usage: .. code-block:: python metric = ... engine = ... metric.detach(engine, usage="batch_wise") assert "mymetric" not in engine.run(data).metrics assert not metric.is_attached(engine, usage="batch_wise") """ usage = self._check_usage(usage) if engine.has_event_handler(self.completed, usage.COMPLETED): engine.remove_event_handler(self.completed, usage.COMPLETED) if engine.has_event_handler(self.started, usage.STARTED): engine.remove_event_handler(self.started, usage.STARTED) if engine.has_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED): engine.remove_event_handler(self.iteration_completed, usage.ITERATION_COMPLETED)
[docs] def is_attached(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise()) -> bool: """ Checks if current metric is attached to provided engine. If attached, metric's computed value is written to `engine.state.metrics` dictionary. Args: engine: the engine checked from which the metric should be attached usage: the usage of the metric. Valid string values should be 'epoch_wise' (default) or 'batch_wise'. """ usage = self._check_usage(usage) return engine.has_event_handler(self.completed, usage.COMPLETED)
def __add__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x + y, self, other) def __radd__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x + y, other, self) def __sub__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x - y, self, other) def __rsub__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x - y, other, self) def __mul__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x * y, self, other) def __rmul__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x * y, other, self) def __pow__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x ** y, self, other) def __rpow__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x ** y, other, self) def __mod__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x % y, self, other) def __truediv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__truediv__(y), self, other) def __rtruediv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x.__truediv__(y), other, self) def __floordiv__(self, other: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x, y: x // y, self, other) def __getattr__(self, attr: str) -> Callable: from ignite.metrics.metrics_lambda import MetricsLambda def fn(x: Metric, *args: Any, **kwargs: Any) -> Any: return getattr(x, attr)(*args, **kwargs) def wrapper(*args: Any, **kwargs: Any) -> "MetricsLambda": return MetricsLambda(fn, self, *args, **kwargs) return wrapper def __getitem__(self, index: Any) -> "MetricsLambda": from ignite.metrics.metrics_lambda import MetricsLambda return MetricsLambda(lambda x: x[index], self) def __getstate__(self) -> Dict: return self.__dict__ def __setstate__(self, d: Dict) -> None: self.__dict__.update(d)
[docs]def sync_all_reduce(*attrs: Any) -> Callable: """Helper decorator for distributed configuration to collect instance attribute value across all participating processes and apply the specified reduction operation. See :doc:`metrics` on how to use it. Args: attrs: attribute names of decorated class .. versionchanged:: 0.4.5 - Ability to handle different reduction operations (SUM, MAX, MIN, PRODUCT). """ def wrapper(func: Callable) -> Callable: @wraps(func) def another_wrapper(self: Metric, *args: Any, **kwargs: Any) -> Callable: if not isinstance(self, Metric): raise RuntimeError( "Decorator sync_all_reduce should be used on ignite.metric.Metric class methods only" ) ws = idist.get_world_size() if len(attrs) > 0 and not self._is_reduced: if ws > 1: for attr in attrs: op_kwargs = {} if ":" in attr: attr, op = attr.split(":") valid_ops = ["MIN", "MAX", "SUM", "PRODUCT"] if op not in valid_ops: raise ValueError(f"Reduction operation is not valid (expected : {valid_ops}, got: {op}") op_kwargs["op"] = op t = getattr(self, attr, None) if t is not None: t = idist.all_reduce(t, **op_kwargs) self._is_reduced = True setattr(self, attr, t) else: self._is_reduced = True return func(self, *args, **kwargs) return another_wrapper setattr(wrapper, "_decorated", True) return wrapper
[docs]def reinit__is_reduced(func: Callable) -> Callable: """Helper decorator for distributed configuration. See :doc:`metrics` on how to use it. Args: func: A callable to reinit. """ @wraps(func) def wrapper(self: Metric, *args: Any, **kwargs: Any) -> None: func(self, *args, **kwargs) self._is_reduced = False setattr(wrapper, "_decorated", True) return wrapper
def _is_list_of_tensors_or_numbers(x: Sequence[Union[torch.Tensor, float]]) -> bool: return isinstance(x, Sequence) and all([isinstance(t, (torch.Tensor, Number)) for t in x]) def _to_batched_tensor(x: Union[torch.Tensor, float], device: Optional[torch.device] = None) -> torch.Tensor: if isinstance(x, torch.Tensor): return x.unsqueeze(dim=0) return torch.tensor([x,], device=device)

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 01/28/2022, 4:20:31 PM.

Built with Sphinx using a theme provided by Read the Docs.