Converting MXNet GluonCV Models


Note that OpenVINO support for Apache MXNet is currently being deprecated and will be removed entirely in the future.

This article provides the instructions and examples on how to convert GluonCV SSD and YOLO-v3 models to IR.

  1. Choose the topology available from the GluonCV Model Zoo and export to the MXNet format using the GluonCV API. For example, for the ssd_512_mobilenet1.0 topology:

    from gluoncv import model_zoo, data, utils
    from gluoncv.utils import export_block
    net = model_zoo.get_model('ssd_512_mobilenet1.0_voc', pretrained=True)
    export_block('ssd_512_mobilenet1.0_voc', net, preprocess=True, layout='HWC')

    As a result, you will get an MXNet model representation in ssd_512_mobilenet1.0.params and ssd_512_mobilenet1.0.json files generated in the current directory.

  2. Run model conversion API, specifying the enable_ssd_gluoncv option. Make sure the input_shape parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrate running model conversion for the SSD and YOLO-v3 models trained with the NHWC layout and located in the <model_directory>:

    • For GluonCV SSD topologies:

      mo --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data --output_dir <OUTPUT_MODEL_DIR>
    • For YOLO-v3 topology:

      • To convert the model:

        mo --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --output_dir <OUTPUT_MODEL_DIR>
      • To convert the model with replacing the subgraph with RegionYolo layers:

        mo --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "front/mxnet/   yolo_v3_mobilenet1_voc.  json" --output_dir <OUTPUT_MODEL_DIR>