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DRP-AI TVM1 Application Example (RZ/V2H)

This page explains how to use the application provided in this directory, which is the example to run ResNet18 inference on the target board.

Overview

To run the inference with the AI model data compiled by DRP-AI TVM1, inference application is necessary.
This application must use the DRP-AI TVM1 Runtime Library API and must be written in C++.
Here, we explain how to compile and deploy the application example for ResNet 18, which is already compiled in Compile AI models.

File Configuration

File/Directory Details
exe Execution environment required for running the application on the board
toolchain Application compilation toolchain
CMakeLists.txt CMake configuration
tutorial_app.cpp C++ application main source code
*.cpp Other C++ application source code
*.h C++ header file
README.md This file. Instructions to use the application.

Set environment variables

Same as Installation.

How to Build the Application

1. Prepare the environment

Move to the application directory and create build directory.

cd $TVM_ROOT/apps
mkdir build
cd build

Run cmake command.

cmake -DCMAKE_TOOLCHAIN_FILE=./toolchain/runtime.cmake -DV2H=ON ..

2. Build

In the build directory, run the make command.

make -j$(nproc)

After runinng the make command, following file would be generated in the build directory.

  • tutorial_app

How to Run the Application

This section assumes that the user has prepared the Boot Environment on the target board.

1. Copy to the Board

Copy the following files to the rootfs of Boot Environment.

Name Path Details
Runtime Library drp-ai_tvm/obj/build_runtime/${PRODUCT}/libtvm_runtime.so Binary provided under obj directory.
You should use the libtvm_runtime.so in the directory with the corresponding product name.
Model Data drp-ai_tvm/tutorials/resnet* Model compiled in the Compile AI models. DRP-AI Preprocessing Runtime Object files, (preprocess directory) are also included.
Input Data drp-ai_tvm/apps/exe/sample.bmp Windows Bitmap file, which is input data for image classification.
Label List drp-ai_tvm/apps/exe/synset_words_imagenet.txt
drp-ai_tvm/apps/exe/ImageNetLabels.txt		 synset_words_imagenet.txt:Label list for ResNet18 post-processing.
ImageNetLabels.txt:Label list for ResNet50 post-processing when compiling Tensorflow Hub model.
Application drp-ai_tvm/apps/build/tutorial_app Compiled in this page.

The rootfs should look like below.
Note that if you compiled the model in Tensorflow Hub, rename the label list ImageNetLabels.txt to synset_words_imagenet.txt and use it.

/
└── home
    └── root
        └── tvm
            ├── libtvm_runtime.so
            ├── resnet50_v1_onnx
            │   ├── deploy.json
            │   ├── deploy.params
            │   ├── deploy.so
            │   └── preprocess
            │       ├── aimac_desc.bin
            │       ...
            │       └── weight.bin
            ├── sample.bmp
            ├── synset_words_imagenet.txt
            └── tutorial_app

As a working example, a series of commands is described below.

cd $TVM_ROOT/../
mkdir tvm
cp $TVM_ROOT/obj/build_runtime/$PRODUCT/libtvm_runtime.so tvm/
cp $TVM_ROOT/apps/exe/sample.bmp tvm/
cp $TVM_ROOT/apps/exe/ImageNetLabels.txt tvm/
cp $TVM_ROOT/apps/exe/synset_words_imagenet.txt tvm/
cp $TVM_ROOT/apps/build/tutorial_app* tvm/
cp -r $TVM_ROOT/tutorials/resnet50_v1_onnx tvm/
cp -r $TVM_ROOT/tutorials/resnet18_torch/  tvm/
cp -r $TVM_ROOT/tutorials/resnet50_tflite/  tvm/
cp -r $TVM_ROOT/tutorials/resnet50_v1_onnx_cpu/  tvm/

tar cvfz tvm.tar.gz tvm/

2. Run

After boot-up the board, move to the directory you stored the application and run the tutorial_app file.

cd ~
tar xvfz tvm.tar.gz
cd ~/tvm
export LD_LIBRARY_PATH=.
cp -r resnet50_v1_onnx resnet18_onnx
./tutorial_app
#./tutorial_app 5 #run DRP-AI with 315Mhz


rm -r resnet18_onnx
cp -r resnet18_torch resnet18_onnx
./tutorial_app

rm -r resnet18_onnx
cp -r resnet50_v1_onnx_cpu resnet18_onnx
./tutorial_app

rm -r resnet18_onnx
cp synset_words_imagenet.txt synset_words_imagenet.txt.bak
cp ImageNetLabels.txt synset_words_imagenet.txt
cp -r resnet50_tflite resnet18_onnx
./tutorial_app
cp synset_words_imagenet.txt.bak synset_words_imagenet.txt

The application runs the ResNet inference on sample.bmp.

Application Specification

Model Information

ResNet18: ONNX Model Zoo

Pre-processing

The application uses DRP-AI Pre-processing Runtime as pre-processing.
For more details on DRP-AI Pre-processing Runtime, please refer to DRP-AI Pre-processing Runtime Documentation.

Processing details are originally defined in the compile script provided in Compile AI models.
In this example, tutorial_app.cpp changes its parameter to run following preprocessing.
(Bold is changed parameter.)

  • Input data
    • Shape: 640x480x3
    • Format: BGR
    • Order: HWC
    • Type: uint8
  • Output data
    • Shape: 224x224x3
    • Format: RGB
    • Order: CHW
    • Type: float
  • Preprocessing operations:
    • Resize
    • Normalize
      • cof_add =[-123.6875, -116.2500, -103.5000]
      • cof_mul =[0.0171, 0.0175, 0.0174]

Appendix

DRP-AI TVM1 Runtime Library API

Regarding the list of DRP-AI TVM1 Runtime API used in the application, please see MERA Wrapper API References

How to install OpenCV to Linux Package

As a preparation, it is required to setup the Build Environment with Linux Package and DRP-AI Support Package.
Follow the instruction in the DRP-AI Support Package Release Note and before running the bitbake command, carry out the following instructions.

1. Add OpenCV to local.conf

Add the following statement at the end of the build/conf/local.conf file.

IMAGE_INSTALL_append =" opencv "

2. Bitbake

Run the bitbake command as explained in the DRP-AI Support Package.

Footnotes

  1. DRP-AI TVM is powered by EdgeCortix MERA™ Compiler Framework. ↩ ↩2 ↩3 ↩4 ↩5