mv_deploy

mv_deploy#

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

mv_deploy consists of a model-compiler and necessary header/.cpp files which are required to run inference for a specific NeuralNet model

The “mv_compile” will be built as part of MIVisionX package installer To build and application using mv_compile, the user can use the deployment api from mv_deploy.h. The entire use of the mv_compile and deployment is shown in mv_objdetectsample The sample demonstrates the use of mv_compile utility to do video decoding and inference.

Prerequisites#

  • Ubuntu 18.04/20.04 or CentOS 7/8

  • ROCm supported hardware

    • AMD Radeon GPU or APU required

  • ROCm

  • Build & Install MIVisionX

    • MIVisionX installs model compiler at /opt/rocm/libexec/mivisionx

    • mv_compile installs at /opt/rocm/bin and mvdeploy_api.h installs 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.

  • Usage:

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]

License#

This project is licensed under the MIT License - see the LICENSE.md file for details

Author#

Rajy Rawther - mivisionx.support@amd.com

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