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[English](README.md) | 简体中文
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通过TVM在 Arm(R) Cortex(R)-M55 CPU 上运行 PaddleOCR文 本能识别模型
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===============================================================
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此文件夹包含如何使用 TVM 在 Cortex(R)-M55 CPU 上运行 PaddleOCR 模型的示例。
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依赖
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-------------
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本demo运行在TVM提供的docker环境上，在该环境中已经安装好的必须的软件
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在非docker环境中，需要手动安装如下依赖项:
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- 软件可通过[安装脚本](https://github.com/apache/tvm/blob/main/docker/install/ubuntu_install_ethosu_driver_stack.sh)一键安装
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- [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)
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- [cmake 3.19.5](https://github.com/Kitware/CMake/releases/)
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- [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)
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- [Arm(R) Ethos(TM)-U NPU driver stack](https://review.mlplatform.org)
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- [CMSIS](https://github.com/ARM-software/CMSIS_5)
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- python 依赖
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```bash
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pip install -r ./requirements.txt
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```
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- TVM
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- 从源码安装([Install from Source](https://tvm.apache.org/docs/install/from_source.html))
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从源码安装时，需要设置如下字段
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- set(USE_CMSISNN ON)
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- set(USE_MICRO ON)
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- set(USE_LLVM ON)
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- 从TLCPack 安装([TLCPack](https://tlcpack.ai/))
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安装完成后需要更新环境变量，以软件安装地址为`/opt/arm`为例：
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```bash
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export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH
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```
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运行demo
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----------------------------
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使用如下命令可以一键运行demo
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```bash
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./run_demo.sh
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```
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如果 Ethos(TM)-U 平台或 CMSIS 没有安装在 `/opt/arm/ethosu` 中，可通过参数进行设置，例如：
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```bash
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./run_demo.sh --cmsis_path /home/tvm-user/cmsis \
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--ethosu_platform_path /home/tvm-user/ethosu/core_platform
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```
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`./run_demo.sh`脚本会执行如下步骤:
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- 下载 PaddleOCR 文字识别模型
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- 使用tvm将PaddleOCR 文字识别模型编译为 Cortex(R)-M55 CPU 和 CMSIS-NN 后端的可执行文件
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- 创建一个包含输入图像数据的头文件`inputs.c`
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- 创建一个包含输出tensor大小的头文件`outputs.c`
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- 编译可执行程序
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- 运行程序
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- 输出图片上的文字和置信度
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使用自己的图片
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--------------------
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替换 `run_demo.sh ` 中140行处的图片地址即可
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使用自己的模型
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--------------------
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替换 `run_demo.sh ` 中130行处的模型地址即可
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模型描述
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-----------------
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在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3原始文本识别模型的基础上进行适配，最终模型大小为2.7M。
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PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本，该系列模型具有以下特点：
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- 超轻量级OCR系统：检测（3.6M）+方向分类器（1.4M）+识别（12M）=17.0M。
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- 支持80多种多语言识别模型，包括英文、中文、法文、德文、阿拉伯文、韩文、日文等。
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- 支持竖排文本识别，长文本识别。
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