# Copyright (c) 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import warnings
from collections.abc import Callable, Sequence
from typing import TYPE_CHECKING, Any, cast
import torch
from torch.nn.modules.loss import _Loss
from monai.config import IgniteInfo
from monai.metrics import CumulativeIterationMetric, LossMetric
from monai.utils import MetricReduction, deprecated, min_version, optional_import
idist, _ = optional_import("ignite", IgniteInfo.OPT_IMPORT_VERSION, min_version, "distributed")
if TYPE_CHECKING:
try:
_, has_ignite = optional_import("ignite")
from ignite.engine import Engine
from ignite.metrics import Metric
from ignite.metrics.metric import reinit__is_reduced
except ImportError:
has_ignite = False
else:
Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric", as_type="base")
reinit__is_reduced, _ = optional_import(
"ignite.metrics.metric", IgniteInfo.OPT_IMPORT_VERSION, min_version, "reinit__is_reduced", as_type="decorator"
)
[docs]
class IgniteMetricHandler(Metric):
"""
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).
loss_fn: A torch _Loss function which is used to generate the LossMetric
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()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
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.
reduction: Argument for the LossMetric, look there for details
get_not_nans: Argument for the LossMetric, look there for details
"""
def __init__(
self,
metric_fn: CumulativeIterationMetric | None = None,
loss_fn: _Loss | None = None,
output_transform: Callable = lambda x: x,
save_details: bool = True,
reduction: MetricReduction | str = MetricReduction.MEAN,
get_not_nans: bool = False,
) -> None:
self._is_reduced: bool = False
self.metric_fn: CumulativeIterationMetric = cast(CumulativeIterationMetric, metric_fn)
self.loss_fn = loss_fn
self.save_details = save_details
self._scores: list = []
self._engine: Engine | None = None
self._name: str | None = None
if self.metric_fn is None and self.loss_fn is None:
raise ValueError("Either metric_fn or loss_fn have to be passed.")
if self.metric_fn is not None and self.loss_fn is not None:
raise ValueError("Either metric_fn or loss_fn have to be passed, but not both.")
if self.loss_fn:
self.metric_fn = LossMetric(loss_fn=self.loss_fn, reduction=reduction, get_not_nans=get_not_nans)
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() # type: ignore
if isinstance(result, torch.Tensor):
result = result.squeeze()
if result.ndim == 0:
result = result.item()
return result
[docs]
def attach(self, engine: Engine, name: str) -> None: # type: ignore[override]
"""
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 = {} # type: ignore
@deprecated(since="1.2", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.")
class IgniteMetric(IgniteMetricHandler):
def __init__(
self,
metric_fn: CumulativeIterationMetric | None = None,
loss_fn: _Loss | None = None,
output_transform: Callable = lambda x: x,
save_details: bool = True,
reduction: MetricReduction | str = MetricReduction.MEAN,
get_not_nans: bool = False,
) -> None:
super().__init__(
metric_fn=metric_fn,
loss_fn=loss_fn,
output_transform=output_transform,
save_details=save_details,
reduction=reduction,
get_not_nans=get_not_nans,
)