This folder contains an example of how to run a PP-PicoDet model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware).
Prerequisites
Running environment and prerequisites
-------------
-------------
If the demo is run in the ci_cpu Docker container provided with TVM, then the following
Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script.
software will already be installed.
You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance.
If the demo is not run in the ci_cpu Docker container, then you will need the following:
Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed.
Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following:
- Software required to build and run the demo (These can all be installed by running
- Software required to build and run the demo (These can all be installed by running
-[Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://release/2.5er.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
-[Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://release/2.5er.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
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@@ -37,26 +42,37 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo
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@@ -37,26 +42,37 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo
pip install-r ./requirements.txt
pip install-r ./requirements.txt
```
```
In case2 and case3:
You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP.
For example if you've installed these in ```/opt/arm``` , then you would do the following:
Type the following command to run the bare metal text recognition application ([src/demo_bare_metal.c](./src/demo_bare_metal.c)):
Type the following command to run the bare metal text recognition application ([src/demo_bare_metal.c](./src/demo_bare_metal.c)):
```bash
```bash
./run_demo.sh
./run_demo.sh
```
```
If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables.
```bash
./run_demo.sh --enable_FVP 1
```
If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then
If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then
the locations for these can be specified as arguments to run_demo.sh, for example:
the locations for these can be specified as arguments to run_demo.sh, for example:
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@@ -65,13 +81,14 @@ the locations for these can be specified as arguments to run_demo.sh, for exampl
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@@ -65,13 +81,14 @@ the locations for these can be specified as arguments to run_demo.sh, for exampl
With [run_demo.sh](./run_demo.sh) to run the demo application, it will:
- Download a PP-PicoDet text recognition model
- Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1)
- Download a PP-PicoDet model
- Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN
- Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN
- Create a C header file inputs.c containing the image data as a C array
- Create a C header file inputs.c containing the image data as a C array
- Create a C header file outputs.c containing a C array where the output of inference will be stored
- Create a C header file outputs.c containing a C array where the output of inference will be stored
- Build the demo application
- Build the demo application
- Run the demo application on a Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software
- Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software
- The application will report the text on the image and the corresponding score.
- The application will report the text on the image and the corresponding score.
Using your own image
Using your own image
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@@ -82,9 +99,9 @@ image to be converted into an array of bytes for consumption by the model.
The demo can be modified to use an image of your choice by changing the following line in run_demo.sh
The demo can be modified to use an image of your choice by changing the following line in run_demo.sh
In this demo, the model we used is based on [PP-PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet). Because of the excellent performance, PP-PicoDet are very suitable for deployment on mobile or CPU. And it is released by [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection).
In this demo, the model we used is based on [PP-PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/picodet). Because of the excellent performance, PP-PicoDet are very suitable for deployment on mobile or CPU. And it is released by [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection).
- [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
- [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2)