# 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
# 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.
import warnings
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence
import torch
from monai.config import IgniteInfo
from monai.metrics import CumulativeIterationMetric
from monai.utils import min_version, optional_import
idist, _ = optional_import("ignite", IgniteInfo.OPT_IMPORT_VERSION, min_version, "distributed")
Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric")
reinit__is_reduced, _ = optional_import(
"ignite.metrics.metric", IgniteInfo.OPT_IMPORT_VERSION, min_version, "reinit__is_reduced"
)
if TYPE_CHECKING:
from ignite.engine import Engine
else:
Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_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 = {}