Source code for monai.handlers.iteration_metric

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence, Union

import torch

from monai.handlers.utils import evenly_divisible_all_gather
from monai.metrics import do_metric_reduction
from monai.utils import MetricReduction, exact_version, optional_import

idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
Metric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "Metric")
reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.4", exact_version, "reinit__is_reduced")
    from ignite.engine import Engine
    Engine, _ = optional_import("ignite.engine", "0.4.4", exact_version, "Engine")

[docs]class IterationMetric(Metric): # type: ignore[valid-type, misc] # due to optional_import """ Class for metrics that should be computed on every iteration and compute final results when epoch completed. Similar to the `EpochMetric` in ignite: Args: metric_fn: callable function or class to compute raw metric results after every iteration. expect to return a Tensor with shape (batch, channel, ...) or tuple (Tensor, not_nans). output_transform: transform the ignite.engine.state.output into [y_pred, y] pair. device: device specification in case of distributed computation usage. save_details: whether to save metric computation details per image, for example: mean_dice of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. """ def __init__( self, metric_fn: Callable, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = "cpu", save_details: bool = True, ) -> None: self._is_reduced: bool = False self.metric_fn = metric_fn self.save_details = save_details self._scores: List = [] self._engine: Optional[Engine] = None self._name: Optional[str] = None super().__init__(output_transform, device=device)
[docs] @reinit__is_reduced def reset(self) -> None: self._scores = []
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: """ Args: output: sequence with contents [y_pred, y]. Raises: ValueError: When ``output`` length is not 2. metric_fn can only support y_pred and y. """ if len(output) != 2: raise ValueError(f"output must have length 2, got {len(output)}.") y_pred, y = output def _compute(y_pred, y): score = self.metric_fn(y_pred, y) return score[0] if isinstance(score, (tuple, list)) else score if isinstance(y_pred, (list, tuple)) and isinstance(y, (list, tuple)): # if a list of channel-first data, add batch dim and compute metric, then concat the scores score =[_compute(p_.unsqueeze(0), y_.unsqueeze(0)) for p_, y_ in zip(y_pred, y)], dim=0) else: score = _compute(y_pred, y) self._scores.append(
[docs] def compute(self) -> Any: """ Raises: NotComputableError: When ``compute`` is called before an ``update`` occurs. """ _scores =, dim=0) ws = idist.get_world_size() if ws > 1 and not self._is_reduced: # all gather across all processes _scores = evenly_divisible_all_gather(data=_scores) self._is_reduced = True # save score of every image into engine.state for other components if self.save_details: if self._engine is None or self._name is None: raise RuntimeError("please call the attach() function to connect expected engine first.") self._engine.state.metric_details[self._name] = _scores result: torch.Tensor = torch.zeros(1) if idist.get_rank() == 0: # run compute_fn on zero rank only result = self._reduce(_scores) if ws > 1: # broadcast result to all processes result = idist.broadcast(result, src=0) return result.item() if isinstance(result, torch.Tensor) else result
def _reduce(self, scores) -> Any: return do_metric_reduction(scores, MetricReduction.MEAN)[0]
[docs] def attach(self, engine: Engine, name: str) -> 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. """ super().attach(engine=engine, name=name) # FIXME: record engine for communication, ignite will support it in the future version soon self._engine = engine self._name = name if self.save_details and not hasattr(engine.state, "metric_details"): engine.state.metric_details = {}