Converting MXNet GluonCV Models¶

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

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.
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 illustrate 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:

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>