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import math
from abc import abstractmethod
from functools import partial
from typing import Any, Union
import torch
from monai.losses.ssim_loss import SSIMLoss
from monai.metrics.utils import do_metric_reduction
from monai.utils import MetricReduction
from .metric import CumulativeIterationMetric
class RegressionMetric(CumulativeIterationMetric):
"""
Base class for regression metrics.
Input `y_pred` is compared with ground truth `y`.
Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
`y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
Args:
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.
"""
def __init__(
self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False
) -> None:
super().__init__()
self.reduction = reduction
self.get_not_nans = get_not_nans
def aggregate(self, reduction: Union[MetricReduction, str, None] = None): # type: ignore
"""
Args:
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
"""
data = self.get_buffer()
if not isinstance(data, torch.Tensor):
raise ValueError("the data to aggregate must be PyTorch Tensor.")
f, not_nans = do_metric_reduction(data, reduction or self.reduction)
return (f, not_nans) if self.get_not_nans else f
def _check_shape(self, y_pred: torch.Tensor, y: torch.Tensor) -> None:
if y_pred.shape != y.shape:
raise ValueError(f"y_pred and y shapes dont match, received y_pred: [{y_pred.shape}] and y: [{y.shape}]")
# also check if there is atleast one non-batch dimension i.e. num_dims >= 2
if len(y_pred.shape) < 2:
raise ValueError("either channel or spatial dimensions required, found only batch dimension")
@abstractmethod
def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor): # type: ignore
if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):
raise ValueError("y_pred and y must be PyTorch Tensor.")
self._check_shape(y_pred, y)
return self._compute_metric(y_pred, y)
[docs]class MSEMetric(RegressionMetric):
r"""Compute Mean Squared Error between two tensors using function:
.. math::
\operatorname {MSE}\left(Y, \hat{Y}\right) =\frac {1}{n}\sum _{i=1}^{n}\left(y_i-\hat{y_i} \right)^{2}.
More info: https://en.wikipedia.org/wiki/Mean_squared_error
Input `y_pred` is compared with ground truth `y`.
Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
Args:
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
"""
def __init__(
self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False
) -> None:
super().__init__(reduction=reduction, get_not_nans=get_not_nans)
self.sq_func = partial(torch.pow, exponent=2.0)
def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
y_pred = y_pred.float()
y = y.float()
return compute_mean_error_metrics(y_pred, y, func=self.sq_func)
[docs]class MAEMetric(RegressionMetric):
r"""Compute Mean Absolute Error between two tensors using function:
.. math::
\operatorname {MAE}\left(Y, \hat{Y}\right) =\frac {1}{n}\sum _{i=1}^{n}\left|y_i-\hat{y_i}\right|.
More info: https://en.wikipedia.org/wiki/Mean_absolute_error
Input `y_pred` is compared with ground truth `y`.
Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
Args:
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
"""
def __init__(
self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False
) -> None:
super().__init__(reduction=reduction, get_not_nans=get_not_nans)
self.abs_func = torch.abs
def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
y_pred = y_pred.float()
y = y.float()
return compute_mean_error_metrics(y_pred, y, func=self.abs_func)
[docs]class RMSEMetric(RegressionMetric):
r"""Compute Root Mean Squared Error between two tensors using function:
.. math::
\operatorname {RMSE}\left(Y, \hat{Y}\right) ={ \sqrt{ \frac {1}{n}\sum _{i=1}^{n}\left(y_i-\hat{y_i}\right)^2 } } \
= \sqrt {\operatorname{MSE}\left(Y, \hat{Y}\right)}.
More info: https://en.wikipedia.org/wiki/Root-mean-square_deviation
Input `y_pred` is compared with ground truth `y`.
Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
Args:
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
"""
def __init__(
self, reduction: Union[MetricReduction, str] = MetricReduction.MEAN, get_not_nans: bool = False
) -> None:
super().__init__(reduction=reduction, get_not_nans=get_not_nans)
self.sq_func = partial(torch.pow, exponent=2.0)
def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
y_pred = y_pred.float()
y = y.float()
mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)
return torch.sqrt(mse_out)
[docs]class PSNRMetric(RegressionMetric):
r"""Compute Peak Signal To Noise Ratio between two tensors using function:
.. math::
\operatorname{PSNR}\left(Y, \hat{Y}\right) = 20 \cdot \log_{10} \left({\mathit{MAX}}_Y\right) \
-10 \cdot \log_{10}\left(\operatorname{MSE\left(Y, \hat{Y}\right)}\right)
More info: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Help taken from:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/image_ops_impl.py line 4139
Input `y_pred` is compared with ground truth `y`.
Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
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).
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
"""
def __init__(
self,
max_val: Union[int, float],
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
get_not_nans: bool = False,
) -> None:
super().__init__(reduction=reduction, get_not_nans=get_not_nans)
self.max_val = max_val
self.sq_func = partial(torch.pow, exponent=2.0)
def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> Any:
y_pred = y_pred.float()
y = y.float()
mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)
return 20 * math.log10(self.max_val) - 10 * torch.log10(mse_out)
def compute_mean_error_metrics(y_pred: torch.Tensor, y: torch.Tensor, func) -> torch.Tensor:
# reducing in only channel + spatial dimensions (not batch)
# reduction of batch handled inside __call__() using do_metric_reduction() in respective calling class
flt = partial(torch.flatten, start_dim=1)
return torch.mean(flt(func(y - y_pred)), dim=-1, keepdim=True)
[docs]class SSIMMetric(RegressionMetric):
r"""
Build a Pytorch version of the SSIM metric based on the original formula of SSIM
.. math::
\operatorname {SSIM}(x,y) =\frac {(2 \mu_x \mu_y + c_1)(2 \sigma_{xy} + c_2)}{((\mu_x^2 + \
\mu_y^2 + c_1)(\sigma_x^2 + \sigma_y^2 + c_2)}
For more info, visit
https://vicuesoft.com/glossary/term/ssim-ms-ssim/
Modified and adopted from:
https://github.com/facebookresearch/fastMRI/blob/main/banding_removal/fastmri/ssim_loss_mixin.py
SSIM reference paper:
Wang, Zhou, et al. "Image quality assessment: from error visibility to structural
similarity." IEEE transactions on image processing 13.4 (2004): 600-612.
Args:
data_range: dynamic range of the data
win_size: gaussian weighting window size
k1: stability constant used in the luminance denominator
k2: stability constant used in the contrast denominator
spatial_dims: if 2, input shape is expected to be (B,C,W,H). if 3, it is expected to be (B,C,W,H,D)
"""
def __init__(
self, data_range: torch.Tensor, win_size: int = 7, k1: float = 0.01, k2: float = 0.03, spatial_dims: int = 2
):
super().__init__()
self.data_range = data_range
self.win_size = win_size
self.k1, self.k2 = k1, k2
self.spatial_dims = spatial_dims
def _compute_metric(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Args:
x: first sample (e.g., the reference image). Its shape is (B,C,W,H) for 2D data and (B,C,W,H,D) for 3D.
A fastMRI sample should use the 2D format with C being the number of slices.
y: second sample (e.g., the reconstructed image). It has similar shape as x
Returns:
ssim_value
Example:
.. code-block:: python
import torch
x = torch.ones([1,1,10,10])/2 # ground truth
y = torch.ones([1,1,10,10])/2 # prediction
data_range = x.max().unsqueeze(0)
# the following line should print 1.0 (or 0.9999)
print(SSIMMetric(data_range=data_range,spatial_dims=2)._compute_metric(x,y))
"""
ssim_value: torch.Tensor = 1 - SSIMLoss(self.win_size, self.k1, self.k2, self.spatial_dims)(
x, y, self.data_range
)
return ssim_value