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
-
AMD Radeon GPU or APU required
MIVisionX Installation
Note
MIVisionX installs model compiler at /opt/rocm/libexec/mivisionx
mv_compile
is installed at/opt/rocm/bin
andmvdeploy_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