# Copyright 2020 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 typing import Callable, Optional, Sequence
import torch
from monai.metrics import DiceMetric
from monai.utils import MetricReduction, exact_version, optional_import
NotComputableError, _ = optional_import("ignite.exceptions", "0.4.2", exact_version, "NotComputableError")
Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "reinit__is_reduced")
sync_all_reduce, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "sync_all_reduce")
[docs]class MeanDice(Metric): # type: ignore[valid-type, misc] # due to optional_import
"""
Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations.
"""
def __init__(
self,
include_background: bool = True,
to_onehot_y: bool = False,
mutually_exclusive: bool = False,
sigmoid: bool = False,
other_act: Optional[Callable] = None,
logit_thresh: float = 0.5,
output_transform: Callable = lambda x: x,
device: Optional[torch.device] = None,
) -> None:
"""
Args:
include_background: whether to include dice computation on the first channel of the predicted output.
Defaults to True.
to_onehot_y: whether to convert the output prediction into the one-hot format. Defaults to False.
mutually_exclusive: if True, the output prediction will be converted into a binary matrix using
a combination of argmax and to_onehot. Defaults to False.
sigmoid: whether to add sigmoid function to the output prediction before computing Dice.
Defaults to False.
other_act: callable function to replace `sigmoid` as activation layer if needed, Defaults to ``None``.
for example: `other_act = torch.tanh`.
logit_thresh: the threshold value to round value to 0.0 and 1.0. Defaults to None (no thresholding).
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
device: device specification in case of distributed computation usage.
See also:
:py:meth:`monai.metrics.meandice.compute_meandice`
"""
super().__init__(output_transform, device=device)
self.dice = DiceMetric(
include_background=include_background,
to_onehot_y=to_onehot_y,
mutually_exclusive=mutually_exclusive,
sigmoid=sigmoid,
other_act=other_act,
logit_thresh=logit_thresh,
reduction=MetricReduction.MEAN,
)
self._sum = 0.0
self._num_examples = 0
[docs] @reinit__is_reduced
def reset(self) -> None:
self._sum = 0.0
self._num_examples = 0
[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. MeanDice metric 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
score = self.dice(y_pred, y)
assert self.dice.not_nans is not None
not_nans = int(self.dice.not_nans.item())
# add all items in current batch
self._sum += score.item() * not_nans
self._num_examples += not_nans
[docs] @sync_all_reduce("_sum", "_num_examples")
def compute(self) -> float:
"""
Raises:
NotComputableError: When ``compute`` is called before an ``update`` occurs.
"""
if self._num_examples == 0:
raise NotComputableError("MeanDice must have at least one example before it can be computed.")
return self._sum / self._num_examples