MV_deploy reference

MV_deploy reference#

Note

This project has the source code for MIVIsionX model compiler in mv_compile.cpp

The mv_deploy utility consists of a model-compiler and necessary header and .cpp files required to run inference for a specific Neural Net model.

The mv_compile will be built as part of MIVisionX package installer. To build an application using mv_compile, you can use the deployment API from mv_deploy.h. The use of mv_compile and deployment is shown in mv_objdetect sample. The sample demonstrates the use of mv_compile utility to do video decoding and inference.

Prerequisites#

  • Ubuntu 20.04 or 22.04, or CentOS 7 or 8

  • ROCm supported hardware

    • AMD Radeon GPU or APU required

  • ROCm installation

  • MIVisionX Installation

    Note

    MIVisionX installs model compiler at /opt/rocm/libexec/mivisionx mv_compile is installed at /opt/rocm/bin and mvdeploy_api.h is installed at /opt/rocm/include/mivisionx

Usage#

The mv_compile utility generates deployment library, header files, and .cpp files required to run inference for the specified model.

mv_compile
         --model <model_name: name of the trained model with path>          [required]
         --install_folder <install_folder:  the location for compiled model>                [required]
         --input_dims <input_dims: n,c,h,w - batch size, channels, height, width>   [required]
         --backend <backend: name of the backend for compilation>                   [optional - default:OpenVX_Rocm_GPU]
         --fuse_cba <fuse_cba: enable or disable Convolution_bias_activation fuse mode (0/1)> [optional - default: 0]
         --quant_mode <quant_mode: fp32/fp16 - quantization_mode for the model: if enabled the model and weights would be converted [optional -default: fp32]

Examples#

  • Caffe

./mv_compile --model models/model.caffemodel --install_folder install_folder --input_dims 1,3,224,224

  • ONNX

./mv_compile --model models/model.onnx --install_folder install_folder --input_dims 1,3,224,224

  • NNEF

./mv_compile --model models/model.nnef --install_folder install_folder --input_dims 1,3,224,224