This topic demonstrates how to run the Neural Style Transfer sample application, which performs inference of style transfer models.
NOTE: The OpenVINO™ toolkit does not include a pre-trained model to run the Neural Style Transfer sample. A public model from the Zhaw's Neural Style Transfer repository can be used. Read the Converting a Style Transfer Model from MXNet* topic from the Model Optimizer Developer Guide to learn about how to get the trained model and how to convert it to the Inference Engine format (*.xml + *.bin).
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
How It Works
NOTE: By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with
--reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
Running the application with the
-h option yields the following usage message:
python3 style_transfer_sample.py --help
The command yields the following usage message:
usage: style_transfer_sample.py [-h] -m MODEL -i INPUT [INPUT ...]
[-l CPU_EXTENSION] [-d DEVICE]
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-i INPUT [INPUT ...], --input INPUT [INPUT ...]
Required. Path to a folder with images or path to an image files
-l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
Optional. Required for CPU custom layers. Absolute
MKLDNN (CPU)-targeted custom layers. Absolute path to
a shared library with the kernels implementations
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU, GPU, FPGA,
HDDL or MYRIAD is acceptable. Sample will look for a
suitable plugin for device specified. Default value is CPU
-nt NUMBER_TOP, --number_top NUMBER_TOP
Optional. Number of top results
--mean_val_r MEAN_VAL_R, -mean_val_r MEAN_VAL_R
Optional. Mean value of red channel for mean value subtraction in
--mean_val_g MEAN_VAL_G, -mean_val_g MEAN_VAL_G
Optional. Mean value of green channel for mean value subtraction
--mean_val_b MEAN_VAL_B, -mean_val_b MEAN_VAL_B
Optional. Mean value of blue channel for mean value subtraction
Running the application with the empty list of options yields the usage message given above and an error message.
To perform inference of an image using a trained model of NST network on Intel® CPUs, use the following command:
python3 style_transfer_sample.py -i <path_to_image>/cat.bmp -m <path_to_model>/1_decoder_FP32.xml
The application outputs an image (
out1.bmp) or a sequence of images (
out<N>.bmp) which are redrawn in style of the style transfer model used for sample.