| # 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 torch import Tensor | |
| from monai.config.type_definitions import NdarrayOrTensor | |
| def root_sum_of_squares_t(x: Tensor, spatial_dim: int) -> Tensor: | |
| """ | |
| Compute the root sum of squares (rss) of the data (typically done for multi-coil MRI samples) | |
| Args: | |
| x: Input tensor | |
| spatial_dim: dimension along which rss is applied | |
| Returns: | |
| rss of x along spatial_dim | |
| Example: | |
| .. code-block:: python | |
| import numpy as np | |
| x = torch.ones([2,3]) | |
| # the following line prints Tensor([1.41421356, 1.41421356, 1.41421356]) | |
| print(rss(x,spatial_dim=0)) | |
| """ | |
| rss_x: Tensor = (x**2).sum(spatial_dim) ** 0.5 | |
| return rss_x | |
| def root_sum_of_squares(x: NdarrayOrTensor, spatial_dim: int) -> NdarrayOrTensor: | |
| """ | |
| Compute the root sum of squares (rss) of the data (typically done for multi-coil MRI samples) | |
| Args: | |
| x: Input array/tensor | |
| spatial_dim: dimension along which rss is applied | |
| Returns: | |
| rss of x along spatial_dim | |
| Example: | |
| .. code-block:: python | |
| import numpy as np | |
| x = np.ones([2,3]) | |
| # the following line prints array([1.41421356, 1.41421356, 1.41421356]) | |
| print(rss(x,spatial_dim=0)) | |
| """ | |
| rss_x: NdarrayOrTensor = root_sum_of_squares_t(x, spatial_dim) # type: ignore | |
| return rss_x | |