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
16.04
/18.04
or CentOS7.5
/7.6
-
AMD Radeon GPU or APU required
Build & Install MIVisionX
MIVisionX installs model compiler at
/opt/rocm/mivisionx
mv_compile installs at
/opt/rocm/mivisionx/bin
and mvdeploy_api.h installs at/opt/rocm/mivisionx/include
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