Merge pull request #24 from guanhuaw/feature/pytest

Feature/pytest: use pytest for unit tests

.github/workflows/python-ci.yml

@@ -3,35 +3,50 @@ name: Python-CI
 on:
   push:
     branches:
-      - main
       - master
       - develop
       - feature/*
   pull_request:
     branches:
-      - main
       - master
       - develop
       - feature/*
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.x' # Specify the Python version you need
-
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install -e .
-        pip install ruff pytest
-
-    - name: Lint with Ruff
-      run: |
-        ruff ./mirtorch
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10" # Specify the Python version you need
+          cache: 'pip' # caching pip dependencies
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .
+          pip install ruff pytest
+
+      - name: Lint with Ruff
+        run: |
+          ruff check ./mirtorch
+
+      - name: Test with pytest
+        run: |
+          pytest ./tests/*
+
+      - name: Automated Version Bump
+        if: github.ref == 'refs/heads/master'
+        uses: phips28/gh-action-bump-version@master
+        with:
+          tag-prefix: "v" # or an empty string if you don't want a prefix
+          filename: "pyproject.toml"
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Push changes
+        if: github.ref == 'refs/heads/master'
+        run: git push && git push --tags

.github/workflows/python-publish.yml

@@ -26,14 +26,23 @@ jobs:
       uses: actions/setup-python@v3
       with:
         python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+    - name: Install pypa/build
+      run: >-
+        python3 -m
+        pip install
+        build
+        --user
+    - name: Build a binary wheel and a source tarball
+      run: python3 -m build
+    - name: Store the distribution packages
+      uses: actions/upload-artifact@v3
       with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
+        name: python-package-distributions
+        path: dist/
+    - name: Download all the dists
+      uses: actions/download-artifact@v3
+      with:
+        name: python-package-distributions
+        path: dist/
+    - name: Publish distribution 📦 to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1

.gitignore

@@ -41,3 +41,4 @@ mrt/
 docs/_build
 docs/_static
 docs/_templates
+.ruff_cache

.pre-commit-config.yaml

@@ -14,17 +14,18 @@ repos:
         language_version: python3
         exclude: ^(?:tests|docs|examples)/
 
-  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: 'v0.1.5'
-      hooks:
-      -   id: ruff
-          types_or: [python, pyi, jupyter]
-          args: [ --fix, --exit-non-zero-on-fix ]
-          exclude: ^(?:tests|docs|examples)/
-
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.3.3
+    hooks:
+      # Run the linter.
+      - id: ruff
+        types_or: [python, pyi, jupyter]
+        args: [--fix]
+        exclude: ^(?:tests|docs|examples)/
 
   - repo: https://github.com/codespell-project/codespell
     rev: v2.1.0
     hooks:
       - id: codespell
-        exclude: ^(?:tests|docs|examples)/
+        exclude: ^(?:tests|docs|examples|mirtorch/vendors)/

LICENSE

@@ -49,3 +49,32 @@ Version 3, 29 June 2007
 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
+
+----------------------------- LICENSE for pytorch_wavelets----------------------------
+This licence applies to any parts of this library which are novel in comparison
+to the original DTCWT MATLAB toolbox written by Nick Kingsbury and Cian
+Shaffrey. See the Provenance section of README.rst file for details on any further
+restrictions of use. If you wish to use the DTCWT, you should read that license as well.
+The DWT sections come under this license.
+
+MIT License
+
+Copyright (c) 2020 Fergal Cotter
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -7,7 +7,7 @@ A Py***Torch***-based differentiable ***I***mage ***R***econstruction ***T***ool
 
 The work is inspired by [MIRT](https://github.com/JeffFessler/mirt), a well-acclaimed toolbox for medical imaging reconstruction.
 
-The main objective is to facilitate rapid, data-driven image reconstruction using CPUs and GPUs through fast prototyping and iteration. Researchers can conveniently develop new model-based and learning-based methods (e.g., unrolled neural networks) with abstraction layers. The availability of auto-differentiation enables optimization of imaging protocols and reconstruction parameters using gradient methods.
+The main objective is to facilitate rapid, data-driven medical image reconstruction using CPUs and GPUs, for fast prototyping. Researchers can conveniently develop new model-based and learning-based methods (e.g., unrolled neural networks) with abstraction layers. The availability of auto-differentiation enables optimization of imaging protocols and reconstruction parameters using gradient methods.
 
 Documentation: https://mirtorch.readthedocs.io/en/latest/
 
@@ -16,7 +16,8 @@ Documentation: https://mirtorch.readthedocs.io/en/latest/
 ### Installation
 
 We recommend to [pre-install `PyTorch` first](https://pytorch.org/).
-To install the `MIRTorch` package, after cloning the repo, please try `pip install -e .`(one may modify the package locally with this option.)
+Use `pip install mirtorch` to install.
+To install the `MIRTorch` locally, after cloning the repo, please try `pip install -e .`(one may modify the package locally with this option.)
 
 ------
 
@@ -26,13 +27,13 @@ To install the `MIRTorch` package, after cloning the repo, please try `pip insta
 
 The `LinearMap` class overloads common matrix operations, such as `+, - , *`.
 
-Instances include basic linear operations (like convolution), classical imaging processing, and MRI system matrix (Cartesian and Non-Cartesian, sensitivity- and B0-informed system models). ***NEW!*** MIRTorch recently adds the support for SPPET and CT.
+Instances include basic linear operations (like convolution), classical imaging processing, and MRI system matrix (Cartesian and Non-Cartesian, sensitivity- and B0-informed system models). ***NEW!*** MIRTorch recently adds the support for SPECT and CT.
 
 Since the Jacobian matrix of a linear operator is itself, the toolbox can actively calculate such Jacobians during backpropagation, avoiding the large cache cost required by auto-differentiation.
 
 When defining linear operators, please make sure that all torch tensors are on the same device and compatible. For example, `torch.cfloat` are compatible with `torch.float` but not `torch.double`. Similarly, `torch.chalf` is compatible with `torch.half`.
 When the data is image, there are 2 empirical formats: `[num_batch, num_channel, nx, ny, (nz)]` and `[nx, ny, (nz)]`.
-For some LinearMaps, there is a boolean `batchmode` to control it.
+For some LinearMaps, there is a boolean `batchmode` to control the shape.
 
 #### Proximal operators
 
@@ -81,7 +82,7 @@ This work is inspired by (but not limited to):
 
 * PyLops: https://github.com/PyLops/pylops
 
-If the code is useful to your research, please cite:
+If the code is useful to your research, please consider citing:
 
 ```bibtex
 @article{wang:22:bjork,
@@ -102,6 +103,7 @@ If the code is useful to your research, please cite:
   year={2022}
 }
 ```
+If you use the SPECT code, please consider citing:
 
 ```bibtex
 @ARTICLE{li:23:tet,

mirtorch/alg/fbpd.py

@@ -42,10 +42,10 @@ def __init__(
         h_prox: Prox,
         g_L: float,
         G_norm: float,
-        G: LinearMap = None,
-        tau: float = None,
+        G: LinearMap | None = None,
+        tau: float | None = None,
         max_iter: int = 10,
-        eval_func: Callable = None,
+        eval_func: Callable | None = None,
         p: int = 1,
     ):
         self.max_iter = max_iter
@@ -87,7 +87,7 @@ def run(self, x0: torch.Tensor):
             if self.eval_func is not None:
                 saved.append(self.eval_func(xold))
                 logger.info(
-                    "The cost function at %dth iter in FBPD: %10.3e." % (i, saved[-1])
+                    "The cost function at %dth iter in FBPD: %10.3e.", i, saved[-1]
                 )
         if self.eval_func is not None:
             return xold, saved

mirtorch/alg/fista.py

@@ -63,7 +63,7 @@ def _update_momentum():
             beta = (told - 1) / tnew
             told = tnew
 
-        # initalize parameters
+        # initialize parameters
         xold = x0
         yold = x0
         told = 1.0

mirtorch/linear/__init__.py

@@ -6,6 +6,8 @@
     ConjTranspose,
     BlockDiagonal,
     Kron,
+    Vstack,
+    Hstack,
 )
 from .basics import (
     Diff1d,
@@ -20,7 +22,7 @@
     Patch3D,
     Diffnd,
 )
-from .mri import FFTCn
+from .mri import FFTCn, NuSense, NuSenseGram, Gmri, GmriGram, Sense
 from .wavelets import Wavelet2D
 from .spect import SPECT
 
@@ -46,4 +48,11 @@
     "Patch3D",
     "FFTCn",
     "SPECT",
+    "NuSense",
+    "NuSenseGram",
+    "Gmri",
+    "GmriGram",
+    "Sense",
+    "Vstack",
+    "Hstack",
 ]