MIVisionX Inference Application using pre-trained ONNX/NNEF/Caffe models.
Pre-trained models in ONNX, NNEF, & Caffe formats are supported by MIVisionX. The app first converts the pre-trained models to AMD Neural Net Intermediate Representation (NNIR), once the model has been translated into AMD NNIR (AMD's internal open format), the Optimizer goes through the NNIR and applies various optimizations which would allow the model to be deployed on to target hardware most efficiently. Finally, AMD NNIR is converted into OpenVX C code, which is compiled and wrapped with a python API to run on any targeted hardware.
- Ubuntu
16.04/18.04or CentOS7.5/7.6 - ROCm supported hardware
- AMD Radeon GPU or AMD APU required
- Latest ROCm
- Build & Install MIVisionX
usage: mivisionx_classifier.py [-h]
--model_format MODEL_FORMAT
--model_name MODEL_NAME
--model MODEL
--model_input_dims MODEL_INPUT_DIMS
--model_output_dims MODEL_OUTPUT_DIMS
--label LABEL
[--add ADD]
[--multiply MULTIPLY]
[--video VIDEO]
[--capture CAPTURE]
[--replace REPLACE]
[--verbose VERBOSE]
-h, --help show help message and exit
--model_format pre-trained model format, options:caffe/onnx/nnef [required]
--model_name model name [required]
--model pre_trained model file [required]
--model_input_dims c,h,w - channel,height,width [required]
--model_output_dims c,h,w - channel,height,width [required]
--label labels text file [required]
--add input preprocessing factor [optional - default:0 ]
--multiply input preprocessing factor [optional - default:1 ]
--video video file for classification [optional - default:'']
--capture capture device id [optional - default:0 ]
--replace replace/overwrite model [optional - default:no]
--verbose verbose [optional - default:no]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent