Source code for monai.handlers.ignite_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.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence

import torch

from monai.metrics import CumulativeIterationMetric
from monai.utils import 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 IgniteMetric(Metric): # type: ignore[valid-type, misc] # due to optional_import """ Base Metric class based on ignite event handler mechanism. The input `prediction` or `label` data can be a PyTorch Tensor or numpy array with batch dim and channel dim, or a list of PyTorch Tensor or numpy array without batch dim. 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: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`. for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`, output_transform can be `lambda x: (x["pred"], x["label"])`. 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: CumulativeIterationMetric, output_transform: Callable = lambda x: x, 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)
[docs] @reinit__is_reduced def reset(self) -> None: self.metric_fn.reset()
[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 self.metric_fn(y_pred, y)
[docs] def compute(self) -> Any: """ Raises: NotComputableError: When ``compute`` is called before an ``update`` occurs. """ result = self.metric_fn.aggregate() if isinstance(result, (tuple, list)): if len(result) > 1: warnings.warn("metric handler can only record the first value of result list.") result = result[0] 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] = self.metric_fn.get_buffer() return result.item() if isinstance(result, torch.Tensor) else result
[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 = {}