Merge pull request #350 from astro-informatics/tk/cppflow-conan-package

Substitute cppflow submodule with conan package

Author: tkoskela

.github/workflows/cmake.yml

@@ -67,11 +67,6 @@ jobs:
       with:
         submodules: recursive
 
-   # Enable tmate debugging of manually-triggered workflows if the input option was provided
-    - name: Setup tmate session
-      uses: mxschmitt/action-tmate@v3
-      if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
-
     - name: Install Dependencies on Ubunutu
       if: ${{ contains(matrix.os, 'ubuntu') }}
       run: |
@@ -81,12 +76,14 @@ jobs:
     - name: Install Dependencies on MacOS
       if: ${{ contains(matrix.os, 'macos') }}
       run: |
-        # Skip brew update until https://github.com/actions/setup-python/issues/577 is fixed
-        # Seems to be working, alghough the issue is still open
-        brew update
         brew install libtiff open-mpi libyaml ccache
         echo "CMAKE_PREFIX_PATH=/usr/local/opt/libomp" >> $GITHUB_ENV
 
+    # Enable tmate debugging of manually-triggered workflows if the input option was provided
+    - name: Setup tmate session
+      uses: mxschmitt/action-tmate@v3
+      if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
+
     - name: Install Tensorflow API on Ubuntu
       # TODO could this be combined with mac version somehow? if/else?
       if: ${{ contains(matrix.os, 'ubuntu') }}
@@ -132,6 +129,23 @@ jobs:
           ${{ matrix.os }}-${{ matrix.cxx }}
           ${{ matrix.os }}
 
+    - name: Checkout cppflow repo
+      uses: actions/checkout@v3
+      with:
+        repository: UCL/cppflow.git
+        path: cppflow
+        ref: master
+
+    - name: Create cppflow package on gcc
+      if: ${{ contains(matrix.cxx, 'g++-10') }}
+      run: |
+        conan create ./cppflow/ -pr:h=default -pr:b=default -s compiler.libcxx=libstdc++11
+
+    - name: Create cppflow package on apple-clang
+      if: ${{ contains(matrix.cxx, 'clang++') }}
+      run: |
+        conan create ./cppflow/ -pr:h=default -pr:b=default
+
     - name: Conan install on gcc
       if: ${{ contains(matrix.cxx, 'g++-10') }}
       run: conan install ${{github.workspace}} -if ${{github.workspace}}/build -s compiler.libcxx=libstdc++11 --build missing -o mpi=${{matrix.mpi}} -o openmp=${{matrix.omp}} -o cppflow=on -pr:h=default -pr:b=default
@@ -143,7 +157,7 @@ jobs:
     - name: Build
       # Build your program with the given configuration.
       # The Github Actions machines are dual-core so we can build faster using 2 parallel processes
-      run: conan build ${{github.workspace}} -bf ${{github.workspace}}/build 
+      run: conan build ${{github.workspace}} -bf ${{github.workspace}}/build
 
     - name: Test
       working-directory: ${{github.workspace}}/build

.github/workflows/documentation.yml

@@ -12,7 +12,7 @@ on:
         description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
         required: false
         default: false
-    
+
 env:
   CONAN_PRINT_RUN_COMMANDS: 1
   CONAN_REVISIONS_ENABLED: 1
@@ -32,13 +32,13 @@ jobs:
       - name: Setup tmate session
         uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }}
-          
+
       # Note that we are installing doxygen with apt here, and with conan in the Configure step.
       # This is because there is no easy way of extracting the path to the doxygen executable from conan.
       # the apt installed doxygen is used in the Make Docweb step to build the html files,
       # while the conan installed version is used in the build step.
       # The version we install from apt is older than the 1.9.4 we install from conan, but
-      # it is not causing any issues at the moment. 
+      # it is not causing any issues at the moment.
       - name: Install dependencies
         run: |
           sudo apt update
@@ -56,6 +56,16 @@ jobs:
           version: 2.11.0
           os: linux
 
+      - name: Checkout cppflow repo
+        uses: actions/checkout@v3
+        with:
+          repository: UCL/cppflow.git
+          path: cppflow
+          ref: master
+
+      - name: Create cppflow package
+        run: conan create ./cppflow/ -pr:h=default -pr:b=default
+
       - name: Configure
         run: |
           conan install doxygen/1.9.4@#2af713e135f12722e3536808017ba086 --update

.github/workflows/macdebug.yml

@@ -1,6 +1,3 @@
-[submodule "cppflow"]
-	path = cppflow
-	url = [email protected]:UCL/cppflow.git
 [submodule "lexci_models"]
 	path = lexci_models
 	url = [email protected]:astro-informatics/lexci_models.git

.gitmodules

@@ -29,6 +29,7 @@ Dependencies installation
 - [GCC](https://gcc.gnu.org) v7.3.0 GNU compiler for `C++`.
 - [UCL/GreatCMakeCookOff](https://github.com/UCL/GreatCMakeCookOff) Collection of `CMake` recipes. Downloaded automatically if absent.
 - [OpenMP](http://openmp.org/wp/) v4.8.4 (Trusty) - Optional - Speeds up some of the operations.
+- [Cppflow](https://github.com/UCL/cppflow) - Optional - A warpper for the Tensorflow C API allowing us to read Tensorflow models into SOPT. Needed if you are using a learned prior.
 - [Conan](https://docs.conan.io/en/latest/installation.html) - C++ package manager which installs the following:
   - [Eigen3](http://eigen.tuxfamily.org/index.php?title=Main_Page) v3.2.0 (Trusty) Modern `C++` linear algebra. Downloaded automatically if absent.
   - [spdlog](https://github.com/gabime/spdlog) v* - Optional - Logging library. Downloaded automatically if
@@ -42,45 +43,61 @@ Dependencies installation
 Installing and building SOPT
 ----------------------------
 
-You can build **SOPT** entirely from the source code. Once the mandatory dependencies are present, `git clone` from the [GitHub repository](https://github.com/astro-informatics/sopt):
+You can build **SOPT** entirely from the source code.
 
-``` bash
-git clone https://github.com/astro-informatics/sopt.git
-```
 
-Then, the program can be built with standard `CMake` command:
 
-``` bash
-cd /path/to/code
-mkdir build
-cd build
-conan install .. --build missing
-conan build ..
-```
+1. If you are using a learned prior you must install the Tensorflow C API and `cppflow` package:
+    - Install [TensorFlow C API](https://www.tensorflow.org/install/lang_c)
+    - Clone the UCL fork of cppflow and create a conan package using
 
-To install in directory `INSTALL_FOLDER`, add the following options to the conan build command:
+        ``` bash
+        git clone [email protected]:UCL/cppflow.git
+        conan create ./cppflow/ -pr:h=default -pr:b=default
+        ```
+      Note that conan requires you to specify the host (h) and the build (b) profiles on the command
+      line (`-pr:h=default -pr:b=default`), if you haven't defined them in your conan profile.
 
-``` bash
-conan build .. -bf INSTALL_FOLDER -if .
-```
+1. Once the mandatory dependencies are present, `git clone` from the [GitHub repository](https://github.com/astro-informatics/sopt):
 
-CMake build options should be passed as options to `conan install` using the `-o` flag with a value `on` or `off`. Possible options are
+    ``` bash
+    git clone https://github.com/astro-informatics/sopt.git
+    ```
 
-- tests (default on)
-- benchmarks (default off)
-- examples (default on)
-- logging (default on)
-- openmp (default on)
-- mpi (default on)
-- docs (default off)
-- coverage (default off)
+1. Then, the program can be built using conan:
 
-For example, to build with both MPI and OpenMP off you would use
+    ``` bash
+    cd /path/to/code
+    mkdir build
+    cd build
+    conan install .. --build missing -pr:h=default -pr:b=default
+    conan build ..
+    ```
 
-``` bash
-conan install .. --build missing -o openmp=off -o mpi=off
-conan build ..
-```
+    - To install in directory `INSTALL_FOLDER`, add the following options to the conan build command:
+
+        ``` bash
+        conan build .. -bf INSTALL_FOLDER -if .
+        ```
+
+    - CMake build options should be passed as options to `conan install` using the `-o` flag with a value `on` or `off`. Possible options are
+
+    - tests (default on)
+    - benchmarks (default off)
+    - examples (default on)
+    - logging (default on)
+    - openmp (default on)
+    - mpi (default on)
+    - docs (default off)
+    - coverage (default off)
+	- cppflow (default off)
+
+        For example, to build with both MPI and OpenMP off you would use
+
+        ``` bash
+        conan install .. --build missing -o openmp=off -o mpi=off -pr:h=default -pr:b=default
+        conan build ..
+        ```
 
 Common errors
 -------

README.md

@@ -51,5 +51,6 @@ endif()
 set(SOPT_MPI ${MPI_FOUND})
 
 if(cppflow)
+  find_package(cppflow)
   find_library(TENSORFLOW_LIB tensorflow REQUIRED)
 endif()

cmake_files/dependencies.cmake

@@ -39,6 +39,9 @@ def requirements(self):
         if self.options.logging == 'on':
             self.requires("spdlog/1.9.2")
 
+        if self.options.cppflow == 'on':
+            self.requires("cppflow/2.0.0")
+
     def build_requirements(self):
 
         if self.options.docs == 'on':

conanfile.py

@@ -78,7 +78,7 @@ if (cppflow)
   # https://stackoverflow.com/questions/25676277/cmake-target-include-directories-prints-an-error-when-i-try-to-add-the-source
   # Add /usr/local/include for default location of TensorFlow headers
   target_include_directories(sopt PUBLIC
-    $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/cppflow/include>
+    $<BUILD_INTERFACE:${cppflow_INCLUDE_DIR}>
     $<BUILD_INTERFACE:/usr/local/include>
     $<INSTALL_INTERFACE:cppflow/include/>
     )

cpp/sopt/CMakeLists.txt

@@ -4,9 +4,14 @@
 #include <cppflow/cppflow.h>
 #include "cppflow/ops.h"
 #include "cppflow/model.h"
+#include <stdexcept>
 
 namespace sopt {
 namespace cppflowutils {
+
+  // arbitrary constant for imaginary part of image vectors. 
+  const double imaginary_threshold = 1e-10;
+
     cppflow::tensor convert_image_to_tensor(Image<double> const &image, int image_rows, int image_cols){
         // Convert the Sopt::Image of doubles(wrapper for Eigen::Array) to a cppflow::tensor of floats
         // TODO: Make types template parameters
@@ -16,7 +21,7 @@ namespace cppflowutils {
         std::vector<float> input_values(image_rows*image_cols, 1);
         for (int i = 0; i < image.rows(); ++i) {
             for (int j = 0; j < image.cols(); ++j) {
-            input_values[j*image_cols+i] = image(i,j);
+              input_values[j*image_cols+i] = image(i,j);
             }
         }
 
@@ -26,16 +31,58 @@ namespace cppflowutils {
         return input_tensor;
     }
 
+  cppflow::tensor convert_image_to_tensor(Image<std::complex<double>> const &image, int image_rows, int image_cols) {
+    // Convert the Sopt::Image of complex (wrapper for Eigen::Array) to a cppflow::tensor of floats
+    // Only takes the real part for processing, on the assumption that the imaginary part is negligible.
+    // If ratio of imaginary part to real part is greater than the threshold then this will throw an exception.
+    // TODO: Make types template parameters
+    // create a vector of the right shape (model expects extra dimensions on start and end)
+    std::vector<int64_t> input_shape = {1, image_rows, image_cols, 1};
+
+    std::vector<float> input_values(image_rows*image_cols, 1);
+
+    for (int i = 0; i < image.rows(); ++i) {
+        for (int j = 0; j < image.cols(); ++j) {
+          if(std::abs(image(i,j).real()) > cppflowutils::imaginary_threshold)
+          {
+            throw std::runtime_error("Cannot convert to tensorflow format: imaginary component of image is non-negligible.");
+          }
+          input_values[j*image_cols+i] = image(i,j).real();
+        }
+    }
+
+    // create a tensor from vector
+    cppflow::tensor input_tensor(input_values, input_shape);
+    return input_tensor;
+  }
+
   // Convert an image stored in a sopt::Vector<double> to a cppflow::tensor of floats
   cppflow::tensor convert_image_to_tensor(sopt::Vector<double> const &image, int image_rows, int image_cols) {
 
-    std::vector<float> values(&image[0], image.data()+image.size());
-    cppflow::tensor input_tensor(values, {1, image_rows, image_cols, 1});
+    std::vector<float> input_values(&image[0], image.data()+image.size());
+    cppflow::tensor input_tensor(input_values, {1, image_rows, image_cols, 1});
 
     return input_tensor;
 
   }
 
+  // Convert an image stored in a sopt::Vector<double> to a cppflow::tensor of floats
+  cppflow::tensor convert_image_to_tensor(sopt::Vector<std::complex<double>> const &image, int image_rows, int image_cols) {
+
+    std::vector<float> input_values(image_rows);
+    for(int i = 0; i < image_rows; i++)
+    {
+      if(std::abs(image(i).real()) > cppflowutils::imaginary_threshold)
+      {
+        throw std::runtime_error("Cannot conver to tensorflow format: imaginary component of image vector is non negligible.");
+      }
+      input_values[i] = image(i).real();
+    }
+    cppflow::tensor input_tensor(input_values, {1, image_rows, image_cols, 1});
+
+    return input_tensor;
+  }
+
   // Convert model output in a std::vector into a sopt::Image (2D Eigen::Array)
   Eigen::Map<Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic>> convert_tensor_to_image(std::vector<float> model_output, int image_rows, int image_cols){
     // convert tensor of floats to Eigen::Array of doubles

cpp/sopt/cppflow_utils.cc

@@ -5,13 +5,17 @@
 #include "sopt/types.h"
 #include <cppflow/cppflow.h>
 #include "cppflow/ops.h"
+#include <complex>
 
 namespace sopt {
 namespace cppflowutils {
+
 //! Converts a sopt::Image to a cppflow::tensor
 cppflow::tensor convert_image_to_tensor(sopt::Image<double> const &image, int image_rows, int image_cols);
+cppflow::tensor convert_image_to_tensor(sopt::Image<std::complex<double>> const &image, int image_rows, int image_cols);
 //! Converts a sopt::Vector to a cppflow::tensor
 cppflow::tensor convert_image_to_tensor(sopt::Vector<double> const &image, int image_rows, int image_cols);
+cppflow::tensor convert_image_to_tensor(sopt::Vector<std::complex<double>> const &image, int image_rows, int image_cols);
 
 //! Convert a cppflow:tensor to an Eigen::Array
 Eigen::Map<Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic>> convert_tensor_to_image(std::vector<float> model_output, int image_rows, int image_cols);