# 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, Union
import torch
from monai.metrics import DiceMetric
from monai.utils import exact_version, optional_import, MetricReduction
NotComputableError, _ = optional_import("ignite.exceptions", "0.3.0", exact_version, "NotComputableError")
Metric, _ = optional_import("ignite.metrics", "0.3.0", exact_version, "Metric")
reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.3.0", exact_version, "reinit__is_reduced")
sync_all_reduce, _ = optional_import("ignite.metrics.metric", "0.3.0", exact_version, "sync_all_reduce")
[docs]class MeanDice(Metric):
"""
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,
logit_thresh: float = 0.5,
output_transform: Callable = lambda x: x,
device: Optional[torch.device] = 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.
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 (torch.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,
logit_thresh=logit_thresh,
reduction=MetricReduction.MEAN,
)
self._sum = 0
self._num_examples = 0
[docs] @reinit__is_reduced
def reset(self):
self._sum = 0
self._num_examples = 0
[docs] @reinit__is_reduced
def update(self, output: Sequence[Union[torch.Tensor, dict]]):
if not len(output) == 2:
raise ValueError("MeanDice metric can only support y_pred and y.")
y_pred, y = output
score = self.dice(y_pred, y)
not_nans = 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):
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