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# Copyright (c) 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 __future__ import annotations
from collections.abc import Callable
import torch
from monai.metrics.metric import Metric
class MMDMetric(Metric):
"""
Unbiased Maximum Mean Discrepancy (MMD) is a kernel-based method for measuring the similarity between two
distributions. It is a non-negative metric where a smaller value indicates a closer match between the two
distributions.
Gretton, A., et al,, 2012. A kernel two-sample test. The Journal of Machine Learning Research, 13(1), pp.723-773.
Args:
y_mapping: Callable to transform the y tensors before computing the metric. It is usually a Gaussian or Laplace
filter, but it can be any function that takes a tensor as input and returns a tensor as output such as a
feature extractor or an Identity function., e.g. `y_mapping = lambda x: x.square()`.
"""
def __init__(self, y_mapping: Callable | None = None) -> None:
super().__init__()
self.y_mapping = y_mapping
def __call__(self, y: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
return compute_mmd(y, y_pred, self.y_mapping)
def compute_mmd(y: torch.Tensor, y_pred: torch.Tensor, y_mapping: Callable | None) -> torch.Tensor:
"""
Args:
y: 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.
y_pred: second sample (e.g., the reconstructed image). It has similar shape as y.
y_mapping: Callable to transform the y tensors before computing the metric.
"""
if y_pred.shape[0] == 1 or y.shape[0] == 1:
raise ValueError("MMD metric requires at least two samples in y and y_pred.")
if y_mapping is not None:
y = y_mapping(y)
y_pred = y_mapping(y_pred)
if y_pred.shape != y.shape:
raise ValueError(
"y_pred and y shapes dont match after being processed "
f"by their transforms, received y_pred: {y_pred.shape} and y: {y.shape}"
)
for d in range(len(y.shape) - 1, 1, -1):
y = y.squeeze(dim=d)
y_pred = y_pred.squeeze(dim=d)
y = y.view(y.shape[0], -1)
y_pred = y_pred.view(y_pred.shape[0], -1)
y_y = torch.mm(y, y.t())
y_pred_y_pred = torch.mm(y_pred, y_pred.t())
y_pred_y = torch.mm(y_pred, y.t())
m = y.shape[0]
n = y_pred.shape[0]
# Ref. 1 Eq. 3 (found under Lemma 6)
# term 1
c1 = 1 / (m * (m - 1))
a = torch.sum(y_y - torch.diag(torch.diagonal(y_y)))
# term 2
c2 = 1 / (n * (n - 1))
b = torch.sum(y_pred_y_pred - torch.diag(torch.diagonal(y_pred_y_pred)))
# term 3
c3 = 2 / (m * n)
c = torch.sum(y_pred_y)
mmd = c1 * a + c2 * b - c3 * c
return mmd
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