Converting GluonCV* Models

This document provides the instructions and examples on how to use Model Optimizer to convert GluonCV SSD and YOLO-v3 models to IR.

1. Choose the topology available from the GluonCV Moodel 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 the Model Optimizer tool 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 illustrates running the Model Optimizer for the SSD and YOLO-v3 models trained with the NHWC layout and located in the <model_directory>:

• For GluonCV SSD topologies:

  python3 mo_mxnet.py --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data

• For YOLO-v3 topology:
  • To convert the model:

    python3 mo_mxnet.py --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3]

  • To convert the model with replacing the subgraph with RegionYolo layers:

    python3 mo_mxnet.py --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "mo/extensions/front/mxnet/yolo_v3_mobilenet1_voc.json"