https://github.com/kiritigowda/mivisionx-calibration-tool
MIVisionX Python Calibration Tool using pre-trained ONNX/NNEF/Caffe models to analyze, summarize & calibrate for INT8 on images
https://github.com/kiritigowda/mivisionx-calibration-tool
Last synced: 3 months ago
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MIVisionX Python Calibration Tool using pre-trained ONNX/NNEF/Caffe models to analyze, summarize & calibrate for INT8 on images
- Host: GitHub
- URL: https://github.com/kiritigowda/mivisionx-calibration-tool
- Owner: kiritigowda
- License: mit
- Created: 2019-08-02T00:27:49.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-12T00:13:24.000Z (over 5 years ago)
- Last Synced: 2025-01-14T07:15:13.230Z (4 months ago)
- Language: Python
- Size: 11.4 MB
- Stars: 1
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# MIVisionX Python Calibration Tool
[MIVisionX](https://gpuopen-professionalcompute-libraries.github.io/MIVisionX/) Inference Calibration Tool using pre-trained `ONNX`/`NNEF`/`Caffe` models to analyze, summarize, and calibrate images.
Pre-trained models in [ONNX](https://onnx.ai/), [NNEF](https://www.khronos.org/nnef), & [Caffe](http://caffe.berkeleyvision.org/) 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.
* MIVisionX Calibration Tool - Processing Images
* MIVisionX Calibration Tool - Processing Images Complete
* MIVisionX Calibration Tool - Results
## Calibration Tool Index
* [MIVisionX Model Compiler & Optimizer](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX/tree/master/model_compiler#neural-net-model-compiler--optimizer)
* [Prerequisites](#prerequisites)
* [Docker for MIVisionX Calibration Tool](#use-mivisionx-docker)
* [Usage](#usage)
* [Usage help](#usage-help)
* [Supported Pre-Trained Model Formats](#supported-pre-trained-model-formats)
* [Samples](#samples)
* [Sample-1: Using Pre-Trained ONNX Model](#sample-1---using-pre-trained-onnx-model)
* [Sample-2: Using Pre-Trained Caffe Model](#sample-2---using-pre-trained-caffe-model)
* [Sample-3: Using Pre-Trained NNEF Model](#sample-3---using-pre-trained-nnef-model)
## Prerequisites
* Ubuntu `16.04`/`18.04` or CentOS `7.5`/`7.6`
* [ROCm supported hardware](https://rocm.github.io/ROCmInstall.html#hardware-support)
* AMD Radeon GPU or AMD APU required
* Latest [ROCm](https://github.com/RadeonOpenCompute/ROCm#installing-from-amd-rocm-repositories)
* Build & Install [MIVisionX](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX#linux-1)
### Use MIVisionX Docker
MIVisionX provides developers with [docker images](https://hub.docker.com/u/mivisionx) for [Ubuntu 16.04](https://hub.docker.com/r/mivisionx/ubuntu-16.04), [Ubuntu 18.04](https://hub.docker.com/r/mivisionx/ubuntu-18.04), [CentOS 7.5](https://hub.docker.com/r/mivisionx/centos-7.5), & [CentOS 7.6](https://hub.docker.com/r/mivisionx/centos-7.5). Using docker images developers can quickly prototype and build applications without having to be locked into a single system setup or lose valuable time figuring out the dependencies of the underlying software.
#### Docker with display option
* Check [docker prerequisites](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX#docker-workflow-sample-on-ubuntu-1604)
* Start docker with display
````
% sudo docker pull mivisionx/ubuntu-16.04:latest
% xhost +local:root
% sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=unix$DISPLAY --privileged --volume $XAUTH:/root/.Xauthority --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-16.04:latest
````
* Test display with MIVisionX sample
````
% export PATH=$PATH:/opt/rocm/mivisionx/bin
% export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/mivisionx/lib
% runvx /opt/rocm/mivisionx/samples/gdf/canny.gdf
````
* Run [Samples](#samples)
## Usage
````
usage: mivisionx_calibration_tool.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
--output_dir OUTPUT_DIR
--image_dir IMAGE_DIR
[--image_val IMAGE_VAL]
[--hierarchy HIERARCHY]
[--add ADD]
[--multiply MULTIPLY]
[--fp16 FP16]
[--replace REPLACE]
[--verbose VERBOSE]
````
### Usage help
```
-h, --help show this 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]
--output_dir output dir to store ADAT results [required]
--image_dir image directory for analysis [required]
--image_val image list with ground truth [optional]
--hierarchy AMD proprietary hierarchical file [optional]
--add input preprocessing factor [optional - default:0]
--multiply input preprocessing factor [optional - default:1]
--fp16 quantize model to FP16 [optional - default:no]
--replace replace/overwrite model [optional - default:no]
--verbose verbose [optional - default:no]
```
## Supported Pre-Trained Model Formats
* Caffe
* NNEF
* ONNX
## Samples
### Sample 1 - Using Pre-Trained ONNX Model
#### Run SqueezeNet on sample images
* **Step 1:** Clone MIVisionX Inference Analyzer Project
````
% cd && mkdir sample-1 && cd sample-1
% git clone https://github.com/kiritigowda/MIVisionX-calibration-tool.git
````
**Note:**
* MIVisionX needs to be pre-installed
* MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
* ONNX model conversion requires ONNX install using `pip install onnx`
* **Step 2:** Download pre-trained SqueezeNet ONNX model from [ONNX Model Zoo](https://github.com/onnx/models#open-neural-network-exchange-onnx-model-zoo) - [SqueezeNet Model](https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz)
````
% wget https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz
% tar -xvf squeezenet.tar.gz
````
**Note:** pre-trained model - `squeezenet/model.onnx`
* **Step 3:** Use the command below to run the inference analyzer
* View inference analyzer usage
```
% cd ~/sample-1/MIVisionX-inference-analyzer/
% python mivisionx_calibration_tool.py -h
```
* Run SqueezeNet Inference Analyzer
```
% python mivisionx_calibration_tool.py --model_format onnx --model_name SqueezeNet --model ~/sample-1/squeezenet/model.onnx --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-1/ --image_dir ./sample/AMD-tinyDataSet --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
```
### Sample 2 - Using Pre-Trained Caffe Model
### Run VGG 16 on sample images
* **Step 1:** Clone MIVisionX Inference Analyzer Project
````
% cd && mkdir sample-2 && cd sample-2
% git clone https://github.com/kiritigowda/MIVisionX-calibration-tool.git
````
**Note:**
* MIVisionX needs to be pre-installed
* MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
* **Step 2:** Download pre-trained VGG 16 caffe model - [VGG_ILSVRC_16_layers.caffemodel](http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel)
````
% wget http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
````
* **Step 3:** Use the command below to run the inference analyzer
* View inference analyzer usage
```
% cd ~/sample-2/MIVisionX-inference-analyzer/
% python mivisionx_calibration_tool.py -h
```
* Run VGGNet-16 Inference Analyzer
```
% python mivisionx_calibration_tool.py --model_format caffe --model_name VggNet-16-Caffe --model ~/sample-2/VGG_ILSVRC_16_layers.caffemodel --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-2/ --image_dir ./sample/AMD-tinyDataSet --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
```
## Sample 3 - Using Pre-Trained NNEF Model
### Run VGG 16 on sample images
* **Step 1:** Clone MIVisionX Inference Analyzer Project
````
% cd && mkdir sample-3 && cd sample-3
% git clone https://github.com/kiritigowda/MIVisionX-calibration-tool.git
````
**Note:**
* MIVisionX needs to be pre-installed
* MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
* NNEF model conversion requires [NNEF python parser](https://github.com/KhronosGroup/NNEF-Tools/tree/master/parser#nnef-parser-project) installed
* **Step 2:** Download pre-trained VGG 16 NNEF model
````
% mkdir ~/sample-3/vgg16
% cd ~/sample-3/vgg16
% wget https://sfo2.digitaloceanspaces.com/nnef-public/vgg16.onnx.nnef.tgz
% tar -xvf vgg16.onnx.nnef.tgz
````
* **Step 3:** Use the command below to run the inference analyzer
* View inference analyzer usage
```
% cd ~/sample-3/MIVisionX-inference-analyzer/
% python mivisionx_calibration_tool.py -h
```
* Run VGGNet-16 Inference Analyzer
```
% python mivisionx_calibration_tool.py --model_format nnef --model_name VggNet-16-NNEF --model ~/sample-3/vgg16/ --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-3/ --image_dir ./sample/AMD-tinyDataSet --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
```