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| # neural-style-pt Installation | |
| This guide will walk you through multiple ways to setup `neural-style-pt` on Ubuntu and Windows. If you wish to install PyTorch and neural-style-pt on a different operating system like MacOS, installation guides can be found [here](https://pytorch.org). | |
| Note that in order to reduce their size, the pre-packaged binary releases (pip, Conda, etc...) have removed support for some older GPUs, and thus you will have to install from source in order to use these GPUs. | |
| # Ubuntu: | |
| ## With A Package Manager: | |
| The pip and Conda packages ship with CUDA and cuDNN already built in, so after you have installed PyTorch with pip or Conda, you can skip to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt). | |
| ### pip: | |
| The neural-style-pt PyPI page can be found here: https://pypi.org/project/neural-style/ | |
| If you wish to install neural-style-pt as a pip package, then use the following command: | |
| ``` | |
| # in a terminal, run the command | |
| pip install neural-style | |
| ``` | |
| Or: | |
| ``` | |
| # in a terminal, run the command | |
| pip3 install neural-style | |
| ``` | |
| Next download the models with: | |
| ``` | |
| neural-style -download_models | |
| ``` | |
| By default the models are downloaded to your home directory, but you can specify a download location with: | |
| ``` | |
| neural-style -download_models <download_path> | |
| ``` | |
| #### Github and pip: | |
| Following the pip installation instructions | |
| [here](http://pytorch.org), you can install PyTorch with the following commands: | |
| ``` | |
| # in a terminal, run the commands | |
| cd ~/ | |
| pip install torch torchvision | |
| ``` | |
| Or: | |
| ``` | |
| cd ~/ | |
| pip3 install torch torchvision | |
| ``` | |
| Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt. | |
| ### Conda: | |
| Following the Conda installation instructions | |
| [here](http://pytorch.org), you can install PyTorch with the following command: | |
| ``` | |
| conda install pytorch torchvision -c pytorch | |
| ``` | |
| Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt. | |
| ## From Source: | |
| ### (Optional) Step 1: Install CUDA | |
| If you have a [CUDA-capable GPU from NVIDIA](https://developer.nvidia.com/cuda-gpus) then you can | |
| speed up `neural-style-pt` with CUDA. | |
| First download and unpack the local CUDA installer from NVIDIA; note that there are different | |
| installers for each recent version of Ubuntu: | |
| ``` | |
| # For Ubuntu 18.04 | |
| sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb | |
| sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub | |
| ``` | |
| ``` | |
| # For Ubuntu 16.04 | |
| sudo dpkg -i cuda-repo-ubuntu1604-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb | |
| sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub | |
| ``` | |
| Instructions for downloading and installing the latest CUDA version on all supported operating systems, can be found [here](https://developer.nvidia.com/cuda-downloads). | |
| Now update the repository cache and install CUDA. Note that this will also install a graphics driver from NVIDIA. | |
| ``` | |
| sudo apt-get update | |
| sudo apt-get install cuda | |
| ``` | |
| At this point you may need to reboot your machine to load the new graphics driver. | |
| After rebooting, you should be able to see the status of your graphics card(s) by running | |
| the command `nvidia-smi`; it should give output that looks something like this: | |
| ``` | |
| Wed Apr 11 21:54:49 2018 | |
| +-----------------------------------------------------------------------------+ | |
| | NVIDIA-SMI 384.90 Driver Version: 384.90 | | |
| |-------------------------------+----------------------+----------------------+ | |
| | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | |
| | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | |
| |===============================+======================+======================| | |
| | 0 Tesla K80 Off | 00000000:00:1E.0 Off | 0 | | |
| | N/A 62C P0 68W / 149W | 0MiB / 11439MiB | 94% Default | | |
| +-------------------------------+----------------------+----------------------+ | |
| +-----------------------------------------------------------------------------+ | |
| | Processes: GPU Memory | | |
| | GPU PID Type Process name Usage | | |
| |=============================================================================| | |
| | No running processes found | | |
| +-----------------------------------------------------------------------------+ | |
| ``` | |
| ### (Optional) Step 2: Install cuDNN | |
| cuDNN is a library from NVIDIA that efficiently implements many of the operations (like convolutions and pooling) | |
| that are commonly used in deep learning. | |
| After registering as a developer with NVIDIA, you can [download cuDNN here](https://developer.nvidia.com/cudnn). Make sure that you use the approprite version of cuDNN for your version of CUDA. | |
| After dowloading, you can unpack and install cuDNN like this: | |
| ``` | |
| tar -zxvf cudnn-10.1-linux-x64-v7.5.0.56.tgz | |
| sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 | |
| sudo cp cuda/include/cudnn.h /usr/local/cuda/include | |
| ``` | |
| Note that the cuDNN backend can only be used for GPU mode. | |
| ### (Optional) Steps 1-3: Install PyTorch with support for AMD GPUs using Radeon Open Compute Stack (ROCm) | |
| It is recommended that if you wish to use PyTorch with an AMD GPU, you install it via the official ROCm dockerfile: | |
| https://rocm.github.io/pytorch.html | |
| - Supported AMD GPUs for the dockerfile are: Vega10 / gfx900 generation discrete graphics cards (Vega56, Vega64, or MI25). | |
| PyTorch does not officially provide support for compilation on the host with AMD GPUs, but [a user guide posted here](https://github.com/ROCmSoftwarePlatform/pytorch/issues/337#issuecomment-467220107) apparently works well. | |
| ROCm utilizes a CUDA porting tool called HIP, which automatically converts CUDA code into HIP code. HIP code can run on both AMD and Nvidia GPUs. | |
| ### Step 3: Install PyTorch | |
| To install PyTorch [from source](https://github.com/pytorch/pytorch#from-source) on Ubuntu (Instructions may be different if you are using a different OS): | |
| ``` | |
| cd ~/ | |
| git clone --recursive https://github.com/pytorch/pytorch | |
| cd pytorch | |
| python setup.py install | |
| cd ~/ | |
| git clone --recursive https://github.com/pytorch/vision | |
| cd vision | |
| python setup.py install | |
| ``` | |
| To check that your torch installation is working, run the command `python` or `python3` to enter the Python interpreter. Then type `import torch` and hit enter. | |
| You can then type `print(torch.version.cuda)` and `print(torch.backends.cudnn.version())` to confirm that you are using the desired versions of CUDA and cuDNN. | |
| To quit just type `exit()` or use Ctrl-D. | |
| Now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt) to install neural-style-pt. | |
| # Windows Installation | |
| If you wish to install PyTorch on Windows From Source or via Conda, you can find instructions on the PyTorch website: https://pytorch.org/ | |
| ### Github and pip | |
| First, you will need to download Python 3 and install it: https://www.python.org/downloads/windows/. I recommend using the executable installer for the latest version of Python 3. | |
| Then using https://pytorch.org/, get the correct pip command, paste it into the Command Prompt (CMD) and hit enter: | |
| ``` | |
| pip3 install torch===1.3.0 torchvision===0.4.1 -f https://download.pytorch.org/whl/torch_stable.html | |
| ``` | |
| After installing PyTorch, download the neural-style-pt Github respository and extract/unzip it to the desired location. | |
| Then copy the file path to your neural-style-pt folder, and paste it into the Command Prompt, with `cd` in front of it and then hit enter. | |
| In the example below, the neural-style-pt folder was placed on the desktop: | |
| ``` | |
| cd C:\Users\<User_Name>\Desktop\neural-style-pt-master | |
| ``` | |
| You can now continue on to [installing neural-style-pt](https://github.com/ProGamerGov/neural-style-pt/blob/master/INSTALL.md#install-neural-style-pt), skipping the `git clone` step. | |
| # Install neural-style-pt | |
| First we clone `neural-style-pt` from GitHub: | |
| ``` | |
| cd ~/ | |
| git clone https://github.com/ProGamerGov/neural-style-pt.git | |
| cd neural-style-pt | |
| ``` | |
| Next we need to download the pretrained neural network models: | |
| ``` | |
| python models/download_models.py | |
| ``` | |
| You should now be able to run `neural-style-pt` in CPU mode like this: | |
| ``` | |
| python neural_style.py -gpu c -print_iter 1 | |
| ``` | |
| If you installed PyTorch with support for CUDA, then should now be able to run `neural-style-pt` in GPU mode like this: | |
| ``` | |
| python neural_style.py -gpu 0 -print_iter 1 | |
| ``` | |
| If you installed PyTorch with support for cuDNN, then you should now be able to run `neural-style-pt` with the `cudnn` backend like this: | |
| ``` | |
| python neural_style.py -gpu 0 -backend cudnn -print_iter 1 | |
| ``` | |
| If everything is working properly you should see output like this: | |
| ``` | |
| Iteration 1 / 1000 | |
| Content 1 loss: 1616196.125 | |
| Style 1 loss: 29890.9980469 | |
| Style 2 loss: 658038.625 | |
| Style 3 loss: 145283.671875 | |
| Style 4 loss: 11347409.0 | |
| Style 5 loss: 563.368896484 | |
| Total loss: 13797382.0 | |
| Iteration 2 / 1000 | |
| Content 1 loss: 1616195.625 | |
| Style 1 loss: 29890.9980469 | |
| Style 2 loss: 658038.625 | |
| Style 3 loss: 145283.671875 | |
| Style 4 loss: 11347409.0 | |
| Style 5 loss: 563.368896484 | |
| Total loss: 13797382.0 | |
| Iteration 3 / 1000 | |
| Content 1 loss: 1579918.25 | |
| Style 1 loss: 29881.3164062 | |
| Style 2 loss: 654351.75 | |
| Style 3 loss: 144214.640625 | |
| Style 4 loss: 11301945.0 | |
| Style 5 loss: 562.733032227 | |
| Total loss: 13711628.0 | |
| Iteration 4 / 1000 | |
| Content 1 loss: 1460443.0 | |
| Style 1 loss: 29849.7226562 | |
| Style 2 loss: 643799.1875 | |
| Style 3 loss: 140405.015625 | |
| Style 4 loss: 10940431.0 | |
| Style 5 loss: 553.507446289 | |
| Total loss: 13217080.0 | |
| Iteration 5 / 1000 | |
| Content 1 loss: 1298983.625 | |
| Style 1 loss: 29734.8964844 | |
| Style 2 loss: 604133.8125 | |
| Style 3 loss: 125455.945312 | |
| Style 4 loss: 8850759.0 | |
| Style 5 loss: 526.118591309 | |
| Total loss: 10912633.0 | |
| ``` | |