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docs/arm_cpu/arm_linux_python_sdk_inference.md
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docs/arm_cpu/arm_linux_python_sdk_inference.md
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# 简介
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本文档以[千分类模型_MobileNetV3](https://ai.baidu.com/easyedge/app/openSource)为例，介绍FastDeploy中的模型SDK， 在**ARM Linux Python** 环境下:（1)图像推理部署步骤； （2）介绍模型推流全流程API，方便开发者了解项目后二次开发。其中ARM Linux C++请参考[ARM Linux C++环境下的推理部署](./ARM-Linux-CPP-SDK-Inference.md)文档。
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**注意**：部分模型（如Tinypose、OCR等）仅支持图像推理，不支持视频推理。
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<!--ts-->
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* [简介](#简介)
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* [环境准备](#环境准备)
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* [1.SDK下载](#1sdk下载)
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* [2.硬件支持](#2硬件支持)
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* [3.python环境](#3python环境)
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* [4.安装依赖](#4安装依赖)
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* [4.1.安装paddlepaddle](#41安装paddlepaddle)
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* [4.2.安装EasyEdge Python Wheel 包](#42安装easyedge-python-wheel-包)
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* [快速开始](#快速开始)
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* [1.文件结构说明](#1文件结构说明)
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* [2.测试Demo](#2测试demo)
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* [2.1预测图像](#21预测图像)
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* [Demo API介绍](#demo-api介绍)
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* [1.基础流程](#1基础流程)
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* [2.初始化](#2初始化)
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* [3.SDK参数配置](#3sdk参数配置)
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* [4.预测图像](#4预测图像)
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* [FAQ](#faq)
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<!--te-->
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# 环境准备
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## 1.SDK下载
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根据开发者模型、部署芯片、操作系统需要，在图像界面[飞桨开源模型](https://ai.baidu.com/easyedge/app/openSource)或[GIthub](https://github.com/PaddlePaddle/FastDeploy)中选择对应的SDK进行下载。
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```shell
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EasyEdge-Linux-x86--[部署芯片]
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├──...
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├──python # Linux Python SDK
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├── # 特定Python版本的EasyEdge Wheel包, 二次开发可使用
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├── BaiduAI_EasyEdge_SDK-1.3.1-cp36-cp36m-linux_aarch64.whl
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├── infer_demo # demo体验完整文件
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│ ├── demo_xxx.py # 包含前后处理的端到端推理demo文件
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│ └── demo_serving.py # 提供http服务的demo文件
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├── tensor_demo # 学习自定义算法前后处理时使用
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│ └── demo_xxx.py
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```
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## 2.硬件支持
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目前支持的ARM架构：aarch64 、armv7hf
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## 3.python环境
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> ARM Linux SDK仅支持Python 3.6
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使用如下命令获取已安装的Python版本号。如果本机的版本不匹配，建议使用[pyenv](https://github.com/pyenv/pyenv)、[anaconda](https://www.anaconda.com/)等Python版本管理工具对SDK所在目录进行配置。
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```shell
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$python3 --version
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```
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接着使用如下命令确认pip的版本是否满足要求，要求pip版本为20.2.2或更高版本。详细的pip安装过程可以参考[官网教程](https://pip.pypa.io/en/stable/installation/)。
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```shell
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$python3 -m pip --version
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```
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## 4.安装依赖
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### 4.1.安装paddlepaddle
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根据具体的部署芯片（CPU/GPU）安装对应的PaddlePaddle的whl包。
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`armv8 CPU平台`可以使用如下命令进行安装：
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```shell
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python3 -m pip install http://aipe-easyedge-public.bj.bcebos.com/easydeploy/paddlelite-2.11-cp36-cp36m-linux_aarch64.whl
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```
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### 4.2.安装EasyEdge Python Wheel 包
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在`python`目录下，安装特定Python版本的EasyEdge Wheel包。`armv8 CPU平台`可以使用如下命令进行安装：
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```shell
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python3 -m pip install -U BaiduAI_EasyEdge_SDK-1.3.1-cp36-cp36m-linux_aarch64.whl
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```
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# 快速开始
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## 1.文件结构说明
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Python SDK文件结构如下：
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```shell
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.EasyEdge-Linux-x86--[部署芯片]
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├── RES # 模型资源文件夹，一套模型适配不同硬件、OS和部署方式
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│ ├── conf.json # Android、iOS系统APP名字需要
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│ ├── label_list.txt # 模型标签文件
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│ ├── model # 模型结构文件
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│ ├── params # 模型参数文件
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│ └── infer_cfg.json # 模型前后处理等配置文件
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├── ReadMe.txt
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├── cpp # C++ SDK 文件结构
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└── python # Python SDK 文件
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├── BaiduAI_EasyEdge_SDK-1.3.1-cp36-cp36m-linux_aarch64.whl #EasyEdge Python Wheel 包
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├── infer_demo
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├── demo_armv8_cpu.py # 图像推理
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├── demo_serving.py # HTTP服务化推理
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└── tensor_demo # 学习自定义算法前后处理时使用
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├── demo_armv8_cpu.py
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```
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## 2.测试Demo
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> 模型资源文件默认已经打包在开发者下载的SDK包中， 默认为`RES`目录。
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### 2.1预测图像
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使用infer_demo文件夹下的demo文件。
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```bash
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python3 demo_x86_cpu.py {模型RES文件夹} {测试图片路径}
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```
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运行效果示例：
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<div align=center><img src="https://user-images.githubusercontent.com/54695910/175854068-28d27c0a-ef83-43ee-9e89-b65eed99b476.jpg" width="300"></div>
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```shell
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2022-06-14 14:40:16 INFO [EasyEdge] [demo_nvidia_gpu.py:38] 140518522509120: Init paddlefluid engine...
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2022-06-14 14:40:20 INFO [EasyEdge] [demo_nvidia_gpu.py:38] 140518522509120: Paddle version: 2.2.2
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{'confidence': 0.9012349843978882, 'index': 8, 'label': 'n01514859 hen'}
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```
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可以看到，运行结果为`index：8，label：hen`，通过imagenet [类别映射表](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)，可以找到对应的类别，即 'hen'，由此说明我们的预测结果正确。
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# Demo API介绍
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本章节主要结合[测试Demo](#2测试Demo)的Demo示例介绍推理API，方便开发者学习后二次开发。
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## 1.基础流程
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> ❗注意，请优先参考SDK中自带demo的使用流程和说明。遇到错误，请优先参考文件中的注释、解释、日志说明。
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`infer_demo/demo_xx_xx.py`
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```python
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# 引入EasyEdge运行库
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import BaiduAI.EasyEdge as edge
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# 创建并初始化一个预测Progam；选择合适的引擎
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pred = edge.Program()
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pred.init(model_dir={RES文件夹路径}, device=edge.Device.CPU, engine=edge.Engine.PADDLE_FLUID) # x86_64 CPU
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# pred.init(model_dir=_model_dir, device=edge.Device.GPU, engine=edge.Engine.PADDLE_FLUID) # x86_64 Nvidia GPU
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# pred.init(model_dir=_model_dir, device=edge.Device.CPU, engine=edge.Engine.PADDLE_LITE) # armv8 CPU
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# 预测图像
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res = pred.infer_image({numpy.ndarray的图片})
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# 关闭结束预测Progam
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pred.close()
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```
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`infer_demo/demo_serving.py`
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```python
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import BaiduAI.EasyEdge as edge
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from BaiduAI.EasyEdge.serving import Serving
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# 创建并初始化Http服务
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server = Serving(model_dir={RES文件夹路径}, license=serial_key)
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# 运行Http服务
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# 请参考同级目录下demo_xx_xx.py里:
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# pred.init(model_dir=xx, device=xx, engine=xx, device_id=xx)
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# 对以下参数device\device_id和engine进行修改
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server.run(host=host, port=port, device=edge.Device.CPU, engine=edge.Engine.PADDLE_FLUID) # x86_64 CPU
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# server.run(host=host, port=port, device=edge.Device.GPU, engine=edge.Engine.PADDLE_FLUID) # x86_64 Nvidia GPU
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# server.run(host=host, port=port, device=edge.Device.CPU, engine=edge.Engine.PADDLE_LITE) # armv8 CPU
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```
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## 2.初始化
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* 接口
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```python
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def init(self,
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model_dir,
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device=Device.CPU,
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engine=Engine.PADDLE_FLUID,
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config_file='conf.json',
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preprocess_file='preprocess_args.json',
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model_file='model',
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params_file='params',
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label_file='label_list.txt',
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infer_cfg_file='infer_cfg.json',
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device_id=0,
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thread_num=1
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):
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"""
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Args:
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model_dir: str
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device: BaiduAI.EasyEdge.Device，比如：Device.CPU
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engine: BaiduAI.EasyEdge.Engine， 比如： Engine.PADDLE_FLUID
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config_file: str
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preprocess_file: str
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model_file: str
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params_file: str
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label_file: str 标签文件
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infer_cfg_file: 包含预处理、后处理信息的文件
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device_id: int 设备ID
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thread_num: int CPU的线程数
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Raises:
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RuntimeError, IOError
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Returns:
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bool: True if success
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"""
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```
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若返回不是True，请查看输出日志排查错误原因。
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## 3.SDK参数配置
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使用 CPU 预测时，可以通过在 init 中设置 thread_num 使用多线程预测。如：
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```python
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pred.init(model_dir=_model_dir, device=edge.Device.CPU, engine=edge.Engine.PADDLE_FLUID, thread_num=4)
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```
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使用 GPU 预测时，可以通过在 init 中设置 device_id 指定需要的GPU device id。如：
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```python
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pred.init(model_dir=_model_dir, device=edge.Device.GPU, engine=edge.Engine.PADDLE_FLUID, device_id=0)
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```
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## 4.预测图像
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* 接口
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```python
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def infer_image(self, img,
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threshold=0.3,
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channel_order='HWC',
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color_format='BGR',
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data_type='numpy'):
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"""
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Args:
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img: np.ndarray or bytes
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threshold: float
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only return result with confidence larger than threshold
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channel_order: string
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channel order HWC or CHW
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color_format: string
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color format order RGB or BGR
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data_type: string
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仅在图像分割时有意义。 'numpy' or 'string'
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'numpy': 返回已解析的mask
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'string': 返回未解析的mask游程编码
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Returns:
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list
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"""
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```
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* 返回格式: `[dict1, dict2, ...]`
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| 字段 | 类型 | 取值 | 说明 |
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| ---------- | -------------------- | --------- | ------------------------ |
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| confidence | float | 0~1 | 分类或检测的置信度 |
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| label | string | | 分类或检测的类别 |
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| index | number | | 分类或检测的类别 |
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| x1, y1 | float | 0~1 | 物体检测，矩形的左上角坐标 (相对长宽的比例值) |
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| x2, y2 | float | 0~1 | 物体检测，矩形的右下角坐标（相对长宽的比例值） |
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| mask | string/numpy.ndarray | 图像分割的mask | |
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***关于矩形坐标***
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x1 * 图片宽度 = 检测框的左上角的横坐标
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y1 * 图片高度 = 检测框的左上角的纵坐标
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x2 * 图片宽度 = 检测框的右下角的横坐标
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y2 * 图片高度 = 检测框的右下角的纵坐标
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可以参考 demo 文件中使用 opencv 绘制矩形的逻辑。
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***结果示例***
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i) 图像分类
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```json
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{
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"index": 736,
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"label": "table",
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"confidence": 0.9
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}
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```
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ii) 物体检测
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```json
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{
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"index": 8,
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"label": "cat",
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"confidence": 1.0,
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"x1": 0.21289,
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"y1": 0.12671,
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"x2": 0.91504,
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"y2": 0.91211,
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}
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```
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iii) 图像分割
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```json
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{
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"name": "cat",
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"score": 1.0,
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"location": {
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"left": ...,
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"top": ...,
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"width": ...,
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"height": ...,
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},
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"mask": ...
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}
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```
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mask字段中，data_type为`numpy`时，返回图像掩码的二维数组
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```
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{
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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{0, 0, 0, 1, 1, 1, 0, 0, 0, 0},
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{0, 0, 0, 1, 1, 1, 0, 0, 0, 0},
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{0, 0, 0, 1, 1, 1, 0, 0, 0, 0},
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{0, 0, 0, 1, 1, 1, 0, 0, 0, 0},
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{0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
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}
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其中1代表为目标区域，0代表非目标区域
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```
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data_type为`string`时，mask的游程编码，解析方式可参考 [demo](https://github.com/Baidu-AIP/EasyDL-Segmentation-Demo)
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# FAQ
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1.执行infer_demo文件时，提示your generated code is out of date and must be regenerated with protoc >= 3.19.0
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进入当前项目，首先卸载protobuf
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```shell
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python3 -m pip uninstall protobuf
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```
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安装低版本protobuf
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```shell
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python3 -m pip install protobuf==3.19.0
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```
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Reference in New Issue
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