Wip1200

.devcontainer/Dockerfile

@@ -1,4 +1,4 @@
-FROM ghcr.io/mrphys/tensorflow-manylinux:1.12.0
+FROM ghcr.io/mrphys/tensorflow-manylinux:1.14.0
 
 # To enable plotting.
 RUN apt-get update && \
@@ -15,6 +15,15 @@ RUN for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip install ipykernel; do
 COPY requirements.txt /tmp/requirements.txt
 RUN for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip install -r /tmp/requirements.txt; done
 
+# For `tf.keras.utils.plot_model`.
+RUN apt-get update && \
+    apt-get install -y graphviz && \
+    for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip install pydot; done
+
+# Reinstall Tensorboard.
+RUN for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip uninstall -y tensorboard tb-nightly; done && \
+    for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip install tensorboard; done
+
 # Create non-root user.
 ARG USERNAME=vscode
 ARG USER_UID=1000

.devcontainer/devcontainer.json

@@ -6,7 +6,8 @@
 	"extensions": [
 		"ms-python.python",
 		"ms-vscode.cpptools",
-		"github.copilot"
+		"github.copilot",
+		"github.vscode-pull-request-github"
 	],
 	// Enable GPUs.
 	"runArgs": [

.github/workflows/build-package.yml

@@ -16,7 +16,7 @@ jobs:
 
     name: Build package
     runs-on: ubuntu-latest
-    
+
     container:
       image: ghcr.io/mrphys/tensorflow-manylinux:1.12.0
 
@@ -56,7 +56,7 @@ jobs:
       - name: Build docs
         run: |
           make docs PY_VERSION=${{ matrix.py_version }}
-      
+
       - name: Upload wheel
         if: startsWith(github.ref, 'refs/tags')
         uses: actions/upload-artifact@v2
@@ -81,12 +81,12 @@ jobs:
 
 
   release:
-    
+
     name: Release
     needs: build
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags')
-    
+
     steps:
 
       - name: Checkout docs branch
@@ -122,7 +122,7 @@ jobs:
         uses: softprops/action-gh-release@v1
         with:
           name: TensorFlow MRI ${{ env.release }}
-          body_path: RELEASE.rst
+          body_path: RELEASE.md
           prerelease: ${{ contains(env.release, 'a') || contains(env.release, 'b') || contains(env.release, 'rc') }}
           fail_on_unmatched_files: true
 

.gitignore

@@ -6,8 +6,9 @@
 __pycache__/
 artifacts/
 build/
+logs/
 third_party/spiral_waveform
 tools/docs/_build
 tools/docs/_templates
 tools/docs/api_docs
-tools/docs/index.rst
+tools/docs/index.md

.vscode/settings.json

@@ -9,5 +9,6 @@
     "python.testing.pytestEnabled": false,
     "python.testing.unittestEnabled": true,
     "python.linting.pylintEnabled": true,
-    "python.linting.enabled": true
+    "python.linting.enabled": true,
+    "notebook.output.textLineLimit": 500
 }

AUTHORS

@@ -0,0 +1,7 @@
+# This file contains a list of individuals and organizations who are authors
+# of this project for copyright purposes. 
+# For a full list of individuals who have contributed to the project, see the
+# CONTRIBUTORS file.
+
+Javier Montalt-Tordera <javier.montalt@outlook.com>
+University College London

CODEOWNERS

@@ -0,0 +1 @@
+* @jmontalt

CONTRIBUTORS

@@ -0,0 +1,6 @@
+# This file contains a list of individuals who have made a contribution to this
+# project. If you are making a contribution, please add yourself to this list
+# using the format:
+# Name <email>
+
+Javier Montalt-Tordera <javier.montalt@outlook.com>

Makefile

@@ -14,10 +14,11 @@ TF_LDFLAGS := $(shell $(PYTHON) -c 'import tensorflow as tf; print(" ".join(tf.s
 CFLAGS := -O3 -march=x86-64 -mtune=generic
 
 CXXFLAGS := $(CFLAGS)
-CXXFLAGS += $(TF_CFLAGS) -fPIC -std=c++14
+CXXFLAGS += $(TF_CFLAGS) -fPIC -std=c++17 -fopenmp
 CXXFLAGS += -I$(ROOT_DIR)
 
 LDFLAGS := $(TF_LDFLAGS)
+LDFLAGS += -lfftw3_omp -lfftw3f_omp -lfftw3 -lfftw3f -lm
 LDFLAGS += -l:libspiral_waveform.a
 
 all: lib wheel

README.md

@@ -0,0 +1,135 @@
+<div align="center">
+  <img src="https://raw.githubusercontent.com/mrphys/tensorflow-mri/v0.21.0/tools/assets/tfmri_logo.svg?sanitize=true">
+</div>
+
+[![PyPI](https://badge.fury.io/py/tensorflow-mri.svg)](https://badge.fury.io/py/tensorflow-mri)
+[![Build](https://github.com/mrphys/tensorflow-mri/actions/workflows/build-package.yml/badge.svg)](https://github.com/mrphys/tensorflow-mri/actions/workflows/build-package.yml)
+[![Docs](https://img.shields.io/badge/api-reference-blue.svg)](https://mrphys.github.io/tensorflow-mri/)
+[![DOI](https://zenodo.org/badge/388094708.svg)](https://zenodo.org/badge/latestdoi/388094708)
+
+<!-- start-intro -->
+
+TensorFlow MRI is a library of TensorFlow operators for computational MRI.
+The library has a Python interface and is mostly written in Python. However,
+computations are efficiently performed by the TensorFlow backend (implemented in
+C++/CUDA), which brings together the ease of use and fast prototyping of Python
+with the speed and efficiency of optimized lower-level implementations.
+
+Being an extension of TensorFlow, TensorFlow MRI integrates seamlessly in ML
+applications. No additional interfacing is needed to include a SENSE operator
+within a neural network, or to use a trained prior as part of an iterative
+reconstruction. Therefore, the gap between ML and non-ML components of image
+processing pipelines is eliminated.
+
+Whether an application involves ML or not, TensorFlow MRI operators can take
+full advantage of the TensorFlow framework, with capabilities including
+automatic differentiation, multi-device support (CPUs and GPUs), automatic
+device placement and copying of tensor data, and conversion to fast,
+serializable graphs.
+
+TensorFlow MRI contains operators for:
+
+- Multicoil arrays
+  ([`tfmri.coils`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/coils)):
+  coil combination, coil compression and estimation of coil sensitivity
+  maps.
+- Convex optimization
+  ([`tfmri.convex`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/convex)):
+  convex functions (quadratic, L1, L2, Tikhonov, total variation, etc.) and
+  optimizers (ADMM).
+- Keras initializers
+  ([`tfmri.initializers`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/initializers)):
+  neural network initializers, including support for complex-valued weights.
+- I/O (`tfmri.io`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/io)):
+  additional I/O functions potentially useful when working with MRI data.
+- Keras layers
+  ([`tfmri.layers`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/layers)):
+  layers and building blocks for neural networks, including support for
+  complex-valued weights, inputs and outputs.
+- Linear algebra
+  ([`tfmri.linalg`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg)):
+  linear operators specialized for image processing and MRI.
+- Loss functions
+  ([`tfmri.losses`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/losses)):
+  for classification, segmentation and image restoration.
+- Metrics
+  ([`tfmri.metrics`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/metrics)):
+  for classification, segmentation and image restoration.
+- Image processing
+  ([`tfmri.image`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/image)):
+  filtering, gradients, phantoms, image quality assessment, etc.
+- Image reconstruction
+  ([`tfmri.recon`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/recon)):
+  Cartesian/non-Cartesian, 2D/3D, parallel imaging, compressed sensing.
+- *k*-space sampling
+  ([`tfmri.sampling`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling)):
+  Cartesian masks, non-Cartesian trajectories, sampling density compensation,
+  etc.
+- Signal processing
+  ([`tfmri.signal`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal)):
+  N-dimensional fast Fourier transform (FFT), non-uniform FFT (NUFFT)
+  ([see also `TensorFlow NUFFT`](https://github.com/mrphys/tensorflow-nufft)),
+  discrete wavelet transform (DWT), *k*-space filtering, etc.
+- Unconstrained optimization
+  ([`tfmri.optimize`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/optimize)):
+  gradient descent, L-BFGS.
+- And more, e.g., supporting array manipulation and math tasks.
+
+<!-- end-intro -->
+
+## Installation
+
+<!-- start-install -->
+
+You can install TensorFlow MRI with ``pip``:
+
+```
+pip install tensorflow-mri
+```
+
+Note that only Linux is currently supported.
+
+### TensorFlow Compatibility
+
+Each TensorFlow MRI release is compiled against a specific version of
+TensorFlow. To ensure compatibility, it is recommended to install matching
+versions of TensorFlow and TensorFlow MRI according to the table below.
+
+<!-- start-compatibility-table -->
+
+| TensorFlow MRI Version | TensorFlow Compatibility | Release Date |
+| ---------------------- | ------------------------ | ------------ |
+| v0.22.0                | v2.9.x                   | Jul 24, 2022 |
+| v0.21.0                | v2.9.x                   | Jul 24, 2022 |
+| v0.20.0                | v2.9.x                   | Jun 18, 2022 |
+| v0.19.0                | v2.9.x                   | Jun 1, 2022  |
+| v0.18.0                | v2.8.x                   | May 6, 2022  |
+
+<!-- end-compatibility-table -->
+
+<!-- end-install -->
+
+## Documentation
+
+Visit the [docs](https://mrphys.github.io/tensorflow-mri/) for guides,
+tutorials and the API reference.
+
+## Issues
+
+If you use this package and something does not work as you expected, please
+[file an issue](https://github.com/mrphys/tensorflow-mri/issues/new)
+describing your problem. We're here to help!
+
+## Credits
+
+If you like this software, star the repository! [![Stars](https://img.shields.io/github/stars/mrphys/tensorflow-mri?style=social)](https://github.com/mrphys/tensorflow-mri/stargazers)
+
+If you find this software useful in your research, you can cite TensorFlow MRI
+using its [Zenodo record](https://doi.org/10.5281/zenodo.5151590).
+
+In the above link, scroll down to the "Export" section and select your favorite
+export format to get an up-to-date citation.
+
+## Contributions
+
+Contributions of any kind are welcome! Open an issue or pull request to begin.