Skip to content

INSTALL.md

Latest commit

 

History

History
142 lines (101 loc) · 4.96 KB

INSTALL.md

File metadata and controls

142 lines (101 loc) · 4.96 KB

Overview

Neural Artistic Style relies on these dependencies:

The detailed installation steps are explained in the sections below.

Note: This installation in mainly for GNU/Linux distributions.

Neural Artistic Style

  1. Download Neural Artistic Style:

    $ git clone https://github.com/andersbll/neural_artistic_style.git

CUDA

Please refer to the Installation Guides provided by nVidia.

The CUDA toolkit should be installed at /usr/local/cuda/.

cuDNN

  1. Download cuDNN v3.

  2. Extract the tarball file to the CUDA directory:

    $ sudo tar xzf cudnn-7.0-linux-x64-v3.0-prod.tgz -C /usr/local

    The tarball file consists of libcudnn static and shared object libraries, and the library header.

CUDArray

  1. Download CUDArray:

    $ git clone https://github.com/andersbll/cudarray.git

  2. Build CUDArray:

    Before building CUDArray, please make sure Cython>=0.21 has been installed. If not, you can install Cython via Pip:

    $ pip install --user --upgrade cython

    Start to build:

    # Install shared object library
$ make
$ sudo make install  # install into /usr/local/lib by default
$ echo "export LD_LIBRARY_PATH=\"/usr/local/lib:\$LD_LIBRARY_PATH\"" >> $HOME/.bashrc
$ source $HOME/.bashrc

# Install Python modules
$ sudo python setup.py install

    If you get the error messages when executing make, that means you might be using cuDNN v4 instead of v3 (Issue #36):

    src/nnet/cudnn.cpp:206:5: error: cannot convert ‘const float*’ to ‘cudnnConvolutionBwdFilterAlgo_t’ for argument ‘8’ to ‘cudnnStatus_t cudnnConvolutionBackwardFilter(cudnnHandle_t, const void*, cudnnTensorDescriptor_t, const void*, cudnnTensorDescriptor_t, const void*, cudnnConvolutionDescriptor_t, cudnnConvolutionBwdFilterAlgo_t, void*, size_t, const void*, cudnnFilterDescriptor_t, void*)’
     ));
     ^
./include/cudarray/nnet/cudnn.hpp:85:44: note: in definition of macro ‘CUDNN_CHECK’
 #define CUDNN_CHECK(status) { cudnn_check((status), __FILE__, __LINE__); }

compilation terminated due to -Wfatal-errors.
make: *** [src/nnet/cudnn.o] Error 1

DeepPy

  1. Download DeepPy:

    $ git clone https://github.com/andersbll/deeppy.git

  2. Copy the deeppy module direcotry into the Neural Artistic Style directory:

    $ cp -a deeppy/deeppy neural_artistic_style

Pretrained VGG 19 Model

  1. Download the pretrained VGG 19 model:

    $ cd neural_artistic_style
$ wget http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat

The model size is around 510 MB.

Troubleshooting

Out of Memory Issue

If you get the out of memory error messages when executing neural_artistic_style.py (Issue #26):

```
Traceback (most recent call last):
  File "neural_artistic_style.py", line 138, in <module>
    run()
  File "neural_artistic_style.py", line 130, in run
    cost = np.mean(net.update())
  File "neural_artistic_style/style_network.py", line 130, in update
    next_x = layer.fprop(next_x)
  File "neural_artistic_style/deeppy/feedforward/convnet_layers.py", line 71, in fprop
    poolout = self.pool_op.fprop(x)
  File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/nnet/pool.py", line 34, in fprop
    poolout = ca.empty(poolout_shape, dtype=imgs.dtype)
  File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/cudarray.py", line 246, in empty
    return ndarray(shape, dtype=dtype)
  File "/usr/local/lib/python2.7/dist-packages/cudarray-0.1.dev-py2.7-linux-x86_64.egg/cudarray/cudarray.py", line 36, in __init__
    self._data = ArrayData(self.size, dtype, np_data)
  File "cudarray/wrap/array_data.pyx", line 16, in cudarray.wrap.array_data.ArrayData.__init__ (./cudarray/wrap/array_data.cpp:1401)
  File "cudarray/wrap/cudart.pyx", line 12, in cudarray.wrap.cudart.cudaCheck (./cudarray/wrap/cudart.cpp:763)
ValueError: out of memory
```

Here are some solutions:

  1. Use GPU with larger memory.
  2. Use smaller input and style images.
  3. Use CPU instead of GPU. Set CUDARRAY_BACKEND to 'numpy' as workaround.