Installation guide

Here we present the steps to install the OpenDenoising benchmark.

Creating a virtual environment

On a directory of your choice, clone the OpenDenoising benchmark repository by using,

$ git clone https://github.com/opendenoising/benchmark

In order to use the present benchmark, we recommend creating a virtual environment for it. On a computer having Python3 installed, a GPU CUDA-compatible, CUDA installed:

• Install virtualenv:

$ sudo apt install virtualenv

• Create a virtual environment anywhere with VENV-NAME the name given to the environment:

$ virtualenv --system-site-packages -p python3 ~/virtualenvironments/VENV_NAME

• Activate the venv:

$ source ~/virtualenvironments/VENV_NAME/bin/activate

If the last command succeeded, the command line should now be preceded by (VENV_NAME). The virtual environment can be exited using:

$ deactivate

Package Requirements

GPU Users

Here is a list of required packages to run OpenDenoising benchmark:

Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
keras2onnx==1.5.0
matplotlib==3.1.0
numpy==1.16.4
onnx==1.5.0
onnx-tf==1.3.0
onnxruntime-gpu==0.5.0
opencv-python==4.1.0.25
pandas==0.24.2
Pillow==6.0.0
PyGithub==1.43.8
scikit-image==0.15.0
scipy==1.3.0
seaborn==0.9.0
six==1.12.0
tensorboard==1.14.0
tensorflow-gpu==1.14.0
tf2onnx==1.5.1
torch==1.2.0
torchvision==0.4.0
tqdm==4.32.2

To install them, you can simply go to the project’s root, and run the following command,

$ pip install -r requirements_gpu.txt

CPU Users

Here is a list of required packages to run OpenDenoising benchmark:

Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
keras2onnx==1.5.0
matplotlib==3.1.0
numpy==1.16.4
onnx==1.5.0
onnx-tf==1.3.0
onnxruntime==0.5.0
opencv-python==4.1.0.25
pandas==0.24.2
Pillow==6.0.0
PyGithub==1.43.8
scikit-image==0.15.0
scipy==1.3.0
seaborn==0.9.0
six==1.12.0
tensorboard==1.14.0
tensorflow==1.14.0
tf2onnx==1.5.1
torch==1.2.0
torchvision==0.4.0
tqdm==4.32.2

To install them, you can simply go to the project’s root, and run the following command,

$ pip install -r requirements_cpu.txt

We recommend you to use a Virtual Environment to run the benchmark.

Note: If you want to run Matlab code in the benchmark, you need to have a Matlab of version at least 2018b, with a valid license. You need to install Matlab’s Python Engine.

[Optional] Matlab dependencies

Our Matlab support covers Matlab Deep Learning Toolbox (training and inference) and Matconvnet (only inference). Here we detail the steps for installing Matlab’s dependencies.

Warning for Matlab users:

If you will use Matlab Deep Learning toolbox with recent GPU cards (such as RTX 2080 ti), you should add the Following lines to your startup script:

warning off parallel:gpu:device:DeviceLibsNeedsRecompiling
try
    gpuArray.eye(2)^2;
catch ME
end
try
    nnet.internal.cnngpu.reluForward(1);
catch ME
end

otherwise, when you run a MatlabModel you can run into errors. For more informations, take a look on this post. You should also add “./OpenDenoising/data/” to Matlab’s Path by using [Set Path](https://fr.mathworks.com/help/matlab/matlab_env/add-remove-or-reorder-folders-on-the-search-path.html).

Adding the Benchmark to matlab’s path

Let “PATH_TO_BENCHMARK” denote the path to the OpenDenoising folder in your computer. To add it to Matlab’s main path, you need to modify the file “pathdef.m”. If you are on Windows, all you have to do is use “set path” tool on Matlab’s main window. However if you are using Linux and you do not have the rights to modify it, you can run the following commands on the terminal,

$ sudo nano /usr/local/MATLAB/R2018b/toolbox/local/pathdef.m

This will open nano on the needed file with the right permissions. You need to write the following line before the default entries,

'PATH_TO_BENCHMARK/data:', ...

Remark: If you are using any third-party software that depends on Matlab (such as BM3D), you also need to include it to the pathdef.

Installing Matlab’s Python engine

Open an terminal, then, go to matlab engine setup folder,

$ cd /usr/local/MATLAB/R2018b/extern/engines/python

Following matlab’s instructions, install the engine on your venv folder,

$ sudo $VENVROOT/bin/python setup.py install --prefix="$VENVROOT/"

Notice that, since we are running the sudo command, the command line will “ignore” all your aliases, so you need to specify the path to your venv python. Equally, the –prefix option specify where matlab will output its files, so that you can run its engine. To test if your installation was succesfull, you can try to execute the following python script:

import matlab.engine
eng = matlab.engine.start_matlab()
x = 4.0
eng.workspace['y'] = x
a = eng.eval('sqrt(y)')
print(a)

Matconvnet installation

Remark: be sure to add Matconvnet to Matlab default path.

Setting up multiple CUDA versions

If you will use [Matconvnet toolbox](http://www.vlfeat.org/matconvnet/), you need to install gcc-6 by running

$ sudo apt install gcc-6 g++-6

before compiling the library on Matlab. Moreover, since the toolbox requires CUDA 9.1 (which is a different version from Tensorflow’s requirement), you need to install multiple CUDA’s on your system (which are independent from each other). Assuming you already have on your system a CUDA version different from 9.1, you need to follow these steps,

• Download CUDA Toolkit 9.1 from NVIDIA’s website, then execute it using the ‘–override’ option, as follows:

$ ./cuda_9.1.85_387.26_linux.run --override

The override option is needed, so that the installer won’t fail because of driver version (if you have a newer version of CUDA, it is likely that you have a more recent driver). Once you run the previous line, the installer will ask you the following questions,

You are attempting to install on an unsupported configuration. Do you wish to continue?
> y
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 387.26?
> n
Install the CUDA 9.1 Toolkit?
> y
Install the CUDA 9.1 Samples?
> y
Enter CUDA Samples Location
> Default location
Enter Toolkit Location
> Default location
Do you want to install a symbolic link at /usr/local/cuda?
> n

By doing this, CUDA 9.1 will be installed on /usr/local/cuda-9.1. The crucial part of having two CUDAs installed, without messing your previous installation, is to not create the symbolic link between cuda-9.1 folder and CUDA folder. Moreover, such choice does not stop you from using CUDA-9.1 in Matconvnet.

• Add the different CUDA paths to LD_LIBRARY_PATH:

$ export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:/usr/local/cuda-9.1/lib64:\$LD_LIBRARY_PATH

At the end of this process, your LD_LIBRARY_PATH should contain the following line as substring:

/usr/local/cuda/lib64:/usr/local/cuda-10.1/lib64:/usr/local/cuda-9.1/lib64

Compiling Matconvnet library

Go to the directory where you extracted matconvnet files, then, after lauching matlab, use the following commands,

cd matlab
CudaPath = "/usr/local/cuda-9.1";
vl_compilenn('EnableGpu', true, 'CudaRoot', CudaPath, 'EnableCudnn', true)

vl_compilenn is a matlab function that will compilate matconvnet library. Here’s what each option means,

EnableGpu: enables GPU usage by matconvnet.
CudaRoot: indicates the path to Cuda's root folder.
EnableCudnn: enables matconvnet to use cudnn acceleration.

obs (27.06.19): For matlab 2018b users, matconvnet compiling script happens to have a bug, which can be easily corrected by replacing line 620 by,

args = horzcat({'-outdir', mex_dir}, ...
flags.base, flags.mexlink, ...
'-R2018a',...
{['LDFLAGS=$LDFLAGS ' strjoin(flags.mexlink_ldflags)]}, ...
{['LDOPTIMFLAGS=$LDOPTIMFLAGS ' strjoin(flags.mexlink_ldoptimflags)]}, ...
{['LINKLIBS=' strjoin(flags.mexlink_linklibs) ' $LINKLIBS']}, ...
objs);

and line 359 to:

flags.mexlink = {'-lmwblas'};

For more informations, consult this github page. After compiling the libary, you should consider adding Matconvnet to Matlab’s path by using Set Path.

Check Driver requirements

• Tensorflow requirements
• Pytorch requirements
• Matlab requirements
• OnnxRuntime requirements

The following table summarizes the driver requirements:

Framework	Cuda Version	Gcc Compiler
Tensorflow 1.14	10.0	7
Matlab 2018b	9.1	6
Pytorch 1.2	10.0
OnnxRuntime 0.5	10.0