# Converting an MXNet Style Transfer Model¶

This article provides instructions on how to generate a model for style transfer, using the public MXNet neural style transfer sample.

Step 1 : Download or clone the repository Zhaw’s Neural Style Transfer repository with an MXNet neural style transfer sample.

Step 2 : Prepare the environment required to work with the cloned repository:

Note

Python-tk installation is needed only for Linux. Python for Windows includes it by default.

1. Install packages dependency.

sudo apt-get install python-tk
2. Install Python requirements:

pip3 install --user mxnet
pip3 install --user matplotlib
pip3 install --user scikit-image

Step 3 : Download the pretrained VGG19 model and save it to the root directory of the cloned repository. The sample expects the model vgg19.params file to be in that directory.

Step 4 : Modify source code files of style transfer sample from the cloned repository:

1. Go to the fast_mrf_cnn subdirectory.

cd ./fast_mrf_cnn
2. Open the symbol.py file and modify the decoder_symbol() function. You should see the following code there:

def decoder_symbol():
data = mx.sym.Variable('data')
data = mx.sym.Convolution(data=data, num_filter=256, kernel=(3,3), pad=(1,1), stride=(1, 1), name='deco_conv1')

Replace the code above with the following:

def decoder_symbol_with_vgg(vgg_symbol):
data = mx.sym.Convolution(data=vgg_symbol, num_filter=256, kernel=(3,3), pad=(1,1), stride=(1, 1), name='deco_conv1')
3. Save and close the symbol.py file.

4. Open and edit the make_image.py file. Go to the __init__() function in the Maker class:

decoder = symbol.decoder_symbol()

Modfiy it with the following code:

decoder = symbol.decoder_symbol_with_vgg(vgg_symbol)
5. To join the pretrained weights with the decoder weights, make the following changes: After the code lines for loading the decoder weights:

args = mx.nd.load('%s_decoder_args.nd'%model_prefix)
auxs = mx.nd.load('%s_decoder_auxs.nd'%model_prefix)

arg_dict.update(args)
6. Use arg_dict instead of args as a parameter of the decoder.bind() function. Find the line below:

self.deco_executor = decoder.bind(ctx=mx.gpu(), args=args, aux_states=auxs)

Replace it with the following:

self.deco_executor = decoder.bind(ctx=mx.cpu(), args=arg_dict, aux_states=auxs)
7. Add the following code to the end of the generate() function in the Maker class to save the result model as a .json file:

self.vgg_executor._symbol.save('{}-symbol.json'.format('vgg19'))
self.deco_executor._symbol.save('{}-symbol.json'.format('nst_vgg19'))
8. Save and close the make_image.py file.

Step 5 : Follow the instructions from the README.md file in the fast_mrf_cnn directory of the cloned repository and run the sample with a decoder model. For example, use the following code to run the sample with the pretrained decoder weights from the models folder and output shape:

import make_image
maker = make_image.Maker('models/13', (1024, 768))
maker.generate('output.jpg', '../images/tubingen.jpg')

The models/13 string in the code above is composed of the following substrings:

• models/ path to the folder that contains .nd files with pretrained styles weights.

• 13 prefix pointing to the default decoder for the repository, 13_decoder.

Note

If an error prompts with “No module named cPickle”, try running the script from Step 5 in Python 2. After that return to Python 3 for the remaining steps.

Any style can be selected from collection of pretrained weights. On the Chinese-language page, click the down arrow next to a size in megabytes. Then wait for an overlay box to appear, and click the blue button in it to download. The generate() function generates nst_vgg19-symbol.json and vgg19-symbol.json files for the specified shape. In the code, it is [1024 x 768] for a 4:3 ratio. You can specify another, for example, [224,224] for a square ratio.

Step 6 : Run the Model Optimizer to generate an Intermediate Representation (IR):

1. Create a new directory. For example:

mkdir nst_model
2. Copy the initial and generated model files to the created directory. For example, to copy the pretrained decoder weights from the models folder to the nst_model directory, run the following commands:

cp nst_vgg19-symbol.json nst_model
cp vgg19-symbol.json nst_model
cp ../vgg19.params nst_model/vgg19-0000.params
cp models/13_decoder_args.nd nst_model
cp models/13_decoder_auxs.nd nst_model

Note

Make sure that all the .params and .json files are in the same directory as the .nd files. Otherwise, the conversion process fails.

3. Run the Model Optimizer for Apache MXNet. Use the --nd_prefix_name option to specify the decoder prefix and --input_shape to specify input shapes in [N,C,W,H] order. For example:

mo --input_symbol <path/to/nst_model>/nst_vgg19-symbol.json --framework mxnet --output_dir <path/to/output_dir> --input_shape [1,3,224,224] --nd_prefix_name 13_decoder --pretrained_model <path/to/nst_model>/vgg19-0000.params
4. The IR is generated (.bin, .xml and .mapping files) in the specified output directory, and ready to be consumed by the OpenVINO Runtime.