Merge branch 'master' into py

docs/source/qs/install.rst

@@ -39,6 +39,15 @@ Alternatively, one can `compile OpenFOAM-7 from source code <https://openfoam.or
 
 **LibCantera** and **PyTorch** can be easily installed via `conda <https://docs.conda.io/en/latest/miniconda.html#linux-installers>`_. If your platform is compatible, run the following command to install the dependencies.
 
+.. code-block:: bash
+
+    conda create -n deepflame python=3.8
+    conda activate deepflame
+    conda install -c conda-forge libcantera-devel 
+    conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
+    conda install pybind11 
+    conda install -c conda-forge easydict
+
 .. Note:: Please go to `the official website of PyTorch <https://pytorch.org/>`_ to check your system compatibility and choose the installation command line that is suitable for your platform.
 
 .. code-block:: bash
@@ -123,8 +132,15 @@ Finally you can build and install DeepFlame:
 
     . install.sh
 
-.. Note:: You may come accross an error regarding shared library ``libmkl_rt.so.2`` when libcantera is installed through cantera channel. If so, go to your conda environment and check the existance of ``libmkl_rt.so.2`` and ``libmkl_rt.so.1``, and then link ``libmkl_rt.so.2`` to ``libmkl_rt.so.1``.
+.. Note:: You may see an error ``fmt`` or ``eigen`` files cannot be found. If so, go to your conda environment and install the packages as follows.
+
+.. code-block:: bash
+
+    conda install fmt 
+    conda install eigen 
 
+.. Note:: You may also come accross an error regarding shared library ``libmkl_rt.so.2`` when libcantera is installed through cantera channel. If so, go to your conda environment and check the existance of ``libmkl_rt.so.2`` and ``libmkl_rt.so.1``, and then link ``libmkl_rt.so.2`` to ``libmkl_rt.so.1``.
+
 .. code-block:: bash
 
     cd ~/miniconda3/envs/deepflame/lib
@@ -144,7 +160,7 @@ DeepFlame also provides users with LibTorch and CVODE (no DNN version) options.
 
     conda create -n df-libtorch python=3.8
     conda activate df-libtorch
-    conda install -c cantera libcantera-devel
+    conda install -c conda-forge libcantera-devel 
 
 Then you can pass your own libtorch path to DeepFlame.
 
@@ -164,7 +180,7 @@ Then you can pass your own libtorch path to DeepFlame.
 
     conda create -n df-notorch python=3.8
     conda activate df-notorch
-    conda install -c cantera libcantera-devel
+    conda install -c conda-forge libcantera-devel 
 
 If the conda env ``df-notorch`` is activated, install DeepFlame by running:
 

src/dfChemistryModel/pytorchFunctions.H

@@ -275,9 +275,9 @@ Foam::scalar Foam::dfChemistryModel<ThermoType>::solve_DNN(const DeltaTType &del
         pybind11::module_ call_torch = pybind11::module_::import("inference"); // import python file
         pybind11::object result = call_torch.attr("inference")(vec0, vec1, vec2); // call python function
         const double* star = result.cast<pybind11::array_t<double>>().data();
-        std::vector<double> outputsVec0(star, star+outputLength[0] * 7); //the float number is sample_length*sample_number
-        std::vector<double> outputsVec1(star+outputLength[0] * 7, star+outputLength[1] * 7);
-        std::vector<double> outputsVec2(star+outputLength[1] * 7, star+outputLength[2] * 7);
+        std::vector<double> outputsVec0(star, star+outputLength[0] * mixture_.nSpecies()); //the float number is sample_length*sample_number
+        std::vector<double> outputsVec1(star+outputLength[0] * mixture_.nSpecies(), star+outputLength[1] * mixture_.nSpecies());
+        std::vector<double> outputsVec2(star+outputLength[1] * mixture_.nSpecies(), star+outputLength[2] * mixture_.nSpecies());
         std::vector<std::vector<double>> results = {outputsVec0, outputsVec1, outputsVec2};
         updateSolutionBuffer(solutionBuffer, results, cellIDBuffer, problemCounter);
         DynamicList<GpuSolution> finalList;
@@ -301,4 +301,4 @@ Foam::scalar Foam::dfChemistryModel<ThermoType>::solve_DNN(const DeltaTType &del
     time_allsolve_ += processingTime.count();
 
     return deltaTMin;
-}
+}

src/dfChemistryModel/torchFunctions.H

@@ -149,9 +149,9 @@ void Foam::dfChemistryModel<ThermoType>::getDNNinputs
 
     if (gpulog_)
     {
-        std::cout<<"inputsDNN0 = "<<inputsDNN0.size()/10 << "\n";
-        std::cout<<"inputsDNN1 = "<<inputsDNN1.size()/10 << "\n";
-        std::cout<<"inputsDNN2 = "<<inputsDNN2.size()/10 << "\n";
+        std::cout<<"inputsDNN0 = "<<inputsDNN0.size()/(mixture_.nSpecies()+3) << "\n";
+        std::cout<<"inputsDNN1 = "<<inputsDNN1.size()/(mixture_.nSpecies()+3) << "\n";
+        std::cout<<"inputsDNN2 = "<<inputsDNN2.size()/(mixture_.nSpecies()+3) << "\n";
     }
 
     return;