This folder contains an example of how to use TVM to run a PaddleOCR model
on bare metal Cortex(R)-M55 CPU and CMSIS-NN.
This folder contains an example of how to run a PaddleOCR 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
software will already be installed.
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.
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.
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.
If the demo is not run in the ci_cpu Docker container, then you will need the following:
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
-[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)
...
...
@@ -40,19 +42,22 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo
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:
@@ -62,6 +67,12 @@ Type the following command to run the bare metal text recognition application ([
./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
the locations for these can be specified as arguments to run_demo.sh, for example:
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...
@@ -70,13 +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:
- 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 PaddleOCR text recognition model
- 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 outputs.c containing a C array where the output of inference will be stored
- 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.
In this demo, the model we use is an English recognition model based on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md). PP-OCRv3 is the third version of the PP-OCR series model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). This series of models has the following features:
The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features:
- Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details
- Support vertical text recognition, and long text recognition
The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model.
\ No newline at end of file
The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model.
