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‎README.md

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# Extension package of TVM Deep Learning Complier for Renesas DRP-AI accelerators powered by EdgeCortix MERA™
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[TVM Documentation](https://tvm.apache.org/docs) |
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[TVM Community](https://tvm.apache.org/community) |
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[TVM github](https://github.com/apache/tvm) |
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DRP-AI TVM[^1] is Machine Learning Compiler plugin for [Apache TVM](https://github.com/apache/tvm/) provided by Renesas Electronics Corporation.
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## License
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(C) Copyright EdgeCortix, Inc. 2022
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(C) Copyright Renesas Electronics Corporation 2022
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Contributors Licensed under an Apache-2.0 license.
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## Supported Embedded Platforms
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- Renesas RZ/V2M Evaluation Board Kit
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## Introduction
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### Overview
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This compiler stack is an extension of the DRP-AI Translator to the TVM backend. CPU and DRP-AI can work together for the inference processing of the AI models.
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<img src=./img/tool_stack.png width=350>
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### File Configuration
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| Directory | Details |
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|:---|:---|
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|tutorials |Sample compile script|
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|apps |Sample inference application on the target board|
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|setup | Setup scripts for building a TVM environment |
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|obj |Pre-build runtime binaries|
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|tvm | TVM repository from github |
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|3rd party | 3rd party tools |
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|how-to |Sample to solve specific problems, i.e., How to run validation between x86 and DRP-AI|
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## Installation
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### Requirements
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Required software is listed below.
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- Ubuntu 18.04
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- Python 3.6
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- git
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- [DRP-AI Translator v1.60](#drp-ai-translator)
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- [RZ/V2M Linux Package v1.2.0](#rzv-software)
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- [RZ/V2M DRP-AI Support Package v6.00](#rzv-software)
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##### DRP-AI Translator
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Download the DRP-AI Translator v1.60 from the Software section in [RZ/V2M Software](https://www.renesas.com/rzv2m)　and install it by following the *User's Manual*.
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##### RZ/V Software
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Download the *RZ/V2M Linux Package* and *DRP-AI Support Package* from the Software section in [RZ/V2M Software](https://www.renesas.com/rzv2m) and **build image/SDK** according to the *DRP-AI Support Package Release Note* *1.
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*1 OpenCV library is required to run application example provided in this repository ([Application Example](./apps)).
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To install OpenCV, please see [How to install OpenCV](./apps#how-to-install-opencv-to-linux-package) in Application Example page.
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### Installing DRP-AI TVM[^1]
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Before installing DRP-AI TVM[^1], please install the software listed in [Requirements](#requirements) and build image/SDK with RZ/V2M Linux Package and DRP-AI Support Package.
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#### 1. Clone the respository.
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```sh
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git clone --recursive -b v0.1 <git url.> drp-ai_tvm
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```
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#### 2. Set environment variables.
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Run the following commands to set environment variables.
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Note that environment variables must be set every time when opening the terminal.
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```sh
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export TVM_HOME=<.../drp-ai_tvm>/tvm # Your own path to the cloned repository.
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export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
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export SDK=</opt/poky/2.4.3> # Your own RZ/V2M Linux SDK path.
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export TRANSLATOR=<.../drp-ai_translator/> # Your own DRP-AI Translator path.
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```
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#### 3. Install the minimal pre-requisites.
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```sh
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# Install packagess
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apt update
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DEBIAN_FRONTEND=noninteractive apt install -y software-properties-common
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add-apt-repository ppa:ubuntu-toolchain-r/test
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apt update
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DEBIAN_FRONTEND=noninteractive apt install -y build-essential cmake \
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libomp-dev libgtest-dev libgoogle-glog-dev libtinfo-dev zlib1g-dev libedit-dev \
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libxml2-dev llvm-8-dev g++-9 gcc-9 wget
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apt-get install -y python3-pip
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pip3 install --upgrade pip
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apt-get -y install unzip vim
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pip3 install decorator attrs scipy numpy pytest onnx==1.9.0
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pip3 install torch==1.8.0 torchvision==0.9.0
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```
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If an already recent gcc/g++ compiler is already the default one in the system then the next step is not necessary. If the system default version is **9 or higher** then we are ready. If that is not the case then we can make sure that gcc/g++-9 will be the default compiler by running:
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```sh
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# Update gcc to 9.4
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update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 10
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update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 20
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update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 10
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update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-9 20
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update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
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update-alternatives --set cc /usr/bin/gcc
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update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
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update-alternatives --set c++ /usr/bin/g++
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update-alternatives --set gcc "/usr/bin/gcc-9"
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update-alternatives --set g++ "/usr/bin/g++-9"
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```
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Installing ONNX Runtime Library from precompiled release package.
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```sh
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# Install onnx runtime
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wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz -O /tmp/onnxruntime.tar.gz
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tar -xvzf /tmp/onnxruntime.tar.gz -C /tmp/
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mv /tmp/onnxruntime-linux-x64-1.8.1/ /opt/
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```
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Setup DRP-AI TVM[^1] environment.
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```sh
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cd <.../drp-ai_tvm>
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bash setup/make_drp_env.sh
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```
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----
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## Deploy AI models on DRP-AI
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![drawing](./img/deploy_flow.png)
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To deploy the AI model to DRP-AI on the target board, you need to compile the model with DRP-AI TVM[^1] to generate Runtime Model Data (Compile).
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SDK generated from RZ/V Linux Package and DRP-AI Support Package is required to compile the model.
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After compiled the model, you need to copy the file to the target board (Deploy).
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You also need to copy the C++ inference application and DRP-AI TVM[^1] Runtime Library to run the AI model inference.
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Moreover, since DRP-AI TVM[^1] does not support pre/post-processing of AI inference, OpenCV Library is essential.
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Following pages show the example to compile the ResNet18 model and run it on the target board.
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### Compile model with DRP-AI TVM[^1]
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Please see [Tutorial](./tutorials).
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### Run inference on board
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Please see [Application Example](./apps) page.
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### How-to
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Pages above only show the example for ResNet.
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To find more examples, please see [How-to](./how-to) page.
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It includes the sample to solve specific problems, i.e.;
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- how to run application with camera;
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- validation between x86 and DRP-AI, etc.
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----
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For any enquiries, please contact Renesas.
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[^1]: DRP-AI TVM is powered by EdgeCortix MERA™ Compiler Framework.
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# Extension package of TVM Deep Learning Complier for Renesas DRP-AI accelerators powered by EdgeCortix MERA&trade;
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[TVM Documentation](https://tvm.apache.org/docs) |
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[TVM Community](https://tvm.apache.org/community) |
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[TVM github](https://github.com/apache/tvm) |
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DRP-AI TVM[^1] is Machine Learning Compiler plugin for [Apache TVM](https://github.com/apache/tvm/) provided by Renesas Electronics Corporation.
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## License
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(C) Copyright EdgeCortix, Inc. 2022
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(C) Copyright Renesas Electronics Corporation 2022
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Contributors Licensed under an Apache-2.0 license.
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## Supported Embedded Platforms
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- Renesas RZ/V2MA Evaluation Board Kit
17+
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## Introduction
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### Overview
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This compiler stack is an extension of the DRP-AI Translator to the TVM backend. CPU and DRP-AI can work together for the inference processing of the AI models.
21+
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<img src=./img/tool_stack.png width=350>
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### File Configuration
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| Directory | Details |
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|:---|:---|
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|tutorials |Sample compile script|
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|apps |Sample inference application on the target board|
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|setup | Setup scripts for building a TVM environment |
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|obj |Pre-build runtime binaries|
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|tvm | TVM repository from github |
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|3rd party | 3rd party tools |
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|how-to |Sample to solve specific problems, i.e., How to run validation between x86 and DRP-AI|
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## Installation
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### Requirements
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Requirements are listed below.
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- OS : Ubuntu 20.04
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- Python : 3.8
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- Package : git
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- Evaluation Board: RZ/V2MA EVK
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- Related Software Version:
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- [DRP-AI Translator v1.80](#drp-ai-translator)
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- [RZ/V2MA Linux Package v1.0.0](#rzv-software)
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- [RZ/V2MA DRP-AI Support Package v7.20](#rzv-software)
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##### DRP-AI Translator
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Download the DRP-AI Translator v1.80 from the Software section in [DRP-AI](https://www.renesas.com/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai#software) and install it by following the *User's Manual*.
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##### RZ/V Software
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Download the *RZ/V2MA Linux Package* and *DRP-AI Support Package* from [Renesas Web Page](https://www.renesas.com/application/key-technology/artificial-intelligence/ai-accelerator-drp-ai) and **build image/SDK** according to the *DRP-AI Support Package Release Note* *1.
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### Installing DRP-AI TVM[^1]
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Before installing DRP-AI TVM[^1], please install the software listed in [Requirements](#requirements) and build image/SDK with RZ/V2MA Linux Package and DRP-AI Support Package.
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#### 1. Clone the respository.
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```sh
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git clone --recursive https://github.com/renesas-rz/rzv_drp-ai_tvm.git drp-ai_tvm
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```
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#### 2. Set environment variables.
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Run the following commands to set environment variables.
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Note that environment variables must be set every time when opening the terminal.
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```sh
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export TVM_HOME=<.../drp-ai_tvm>/tvm # Your own path to the cloned repository.
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export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
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export SDK=</opt/poky/3.1.14> # Your own RZ/V2MA Linux SDK path.
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export TRANSLATOR=<.../drp-ai_translator/> # Your own DRP-AI Translator path.
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```
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#### 3. Install the minimal pre-requisites.
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```sh
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# Install packagess
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apt update
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DEBIAN_FRONTEND=noninteractive apt install -y software-properties-common
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add-apt-repository ppa:ubuntu-toolchain-r/test
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apt update
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DEBIAN_FRONTEND=noninteractive apt install -y build-essential cmake \
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libomp-dev libgtest-dev libgoogle-glog-dev libtinfo-dev zlib1g-dev libedit-dev \
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libxml2-dev llvm-8-dev g++-9 gcc-9 wget
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apt-get install -y python3-pip
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pip3 install --upgrade pip
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apt-get -y install unzip vim
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pip3 install decorator attrs scipy numpy pytest onnx==1.9.0
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pip3 install torch==1.8.0 torchvision==0.9.0
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```
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Installing ONNX Runtime Library from precompiled release package.
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```sh
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# Install onnx runtime
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wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz -O /tmp/onnxruntime.tar.gz
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tar -xvzf /tmp/onnxruntime.tar.gz -C /tmp/
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mv /tmp/onnxruntime-linux-x64-1.8.1/ /opt/
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```
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Setup DRP-AI TVM[^1] environment.
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```sh
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cd <.../drp-ai_tvm>
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bash setup/make_drp_env.sh
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```
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----
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## Deploy AI models on DRP-AI
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![drawing](./img/deploy_flow.png)
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To deploy the AI model to DRP-AI on the target board, you need to compile the model with DRP-AI TVM[^1] to generate Runtime Model Data (Compile).
109+
SDK generated from RZ/V Linux Package and DRP-AI Support Package is required to compile the model.
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After compiled the model, you need to copy the file to the target board (Deploy).
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You also need to copy the C++ inference application and DRP-AI TVM[^1] Runtime Library to run the AI model inference.
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Following pages show the example to compile the ResNet18 model and run it on the target board.
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### Compile model with DRP-AI TVM[^1]
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Please see [Tutorial](./tutorials).
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### Run inference on board
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Please see [Application Example](./apps) page.
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### How-to
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Pages above only show the example for ResNet.
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To find more examples, please see [How-to](./how-to) page.
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It includes the sample to solve specific problems, i.e.;
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- how to run application with camera;
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- validation between x86 and DRP-AI, etc.
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### Error List
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If error occurred at compile/runtime operation, please refer [error list](./docs/Error_List.md).
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## Support
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If you have any questions, please contact [Renesas Technical Support](https://www.renesas.com/support).
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----
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For any enquiries, please contact Renesas.
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[^1]: DRP-AI TVM is powered by EdgeCortix MERA™ Compiler Framework.

‎apps/CMakeLists.txt

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include_directories(${TVM_ROOT}/3rdparty/compiler-rt)
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set(TVM_RUNTIME_LIB ${TVM_ROOT}/build_runtime/libtvm_runtime.so)
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set(SRC tutorial_app.cpp MeraDrpRuntimeWrapper.cpp)
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set(SRC tutorial_app.cpp MeraDrpRuntimeWrapper.cpp PreRuntime.cpp)
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set(EXE_NAME tutorial_app)
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add_executable(${EXE_NAME} ${SRC})
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target_link_libraries(${EXE_NAME} ${TVM_RUNTIME_LIB})
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find_package(OpenCV REQUIRED)
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if(OpenCV_FOUND)
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target_include_directories(${EXE_NAME} PUBLIC ${OpenCV_INCLUDE_DIRS})
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target_link_libraries(${EXE_NAME} ${OpenCV_LIBS})
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endif()

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