# 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.
from typing import Callable, Union
from monai.handlers.ignite_metric import IgniteMetric
from monai.metrics import MAEMetric, MSEMetric, PSNRMetric, RMSEMetric
from monai.utils import MetricReduction
[docs]class MeanSquaredError(IgniteMetric):
"""
Computes Mean Squared Error from full size Tensor and collects average over batch, iterations.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
save_details: bool = True,
) -> None:
"""
Args:
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 squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
See also:
:py:class:`monai.metrics.MSEMetric`
"""
metric_fn = MSEMetric(reduction=MetricReduction.MEAN)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
save_details=save_details,
)
[docs]class MeanAbsoluteError(IgniteMetric):
"""
Computes Mean Absolute Error from full size Tensor and collects average over batch, iterations.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
save_details: bool = True,
) -> None:
"""
Args:
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: mean absolute error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
See also:
:py:class:`monai.metrics.MAEMetric`
"""
metric_fn = MAEMetric(reduction=MetricReduction.MEAN)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
save_details=save_details,
)
[docs]class RootMeanSquaredError(IgniteMetric):
"""
Computes Root Mean Squared Error from full size Tensor and collects average over batch, iterations.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
save_details: bool = True,
) -> None:
"""
Args:
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: root mean squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
See also:
:py:class:`monai.metrics.RMSEMetric`
"""
metric_fn = RMSEMetric(reduction=MetricReduction.MEAN)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
save_details=save_details,
)
[docs]class PeakSignalToNoiseRatio(IgniteMetric):
"""
Computes Peak Signal to Noise Ratio from full size Tensor and collects average over batch, iterations.
"""
def __init__(
self,
max_val: Union[int, float],
output_transform: Callable = lambda x: x,
save_details: bool = True,
) -> None:
"""
Args:
max_val: The dynamic range of the images/volumes (i.e., the difference between the
maximum and the minimum allowed values e.g. 255 for a uint8 image).
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: PSNR of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
See also:
:py:class:`monai.metrics.PSNRMetric`
"""
metric_fn = PSNRMetric(max_val=max_val, reduction=MetricReduction.MEAN)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
save_details=save_details,
)