PaddlePaddle(5)——简单高效的部署与推理方法（Ⅱ）

转载请注明作者和出处：https://blog.csdn.net/qq_28810395
运行平台： Windows 10
AIstudio官网：https://aistudio.baidu.com/ --飞桨领航团AI达人创造营

一、飞浆模型在地平线开发板部署

1.地平线模型部署流程概述：

2.地平线介绍

二.具体部署流程

1. 模型训练
   飞桨高阶API对基础API进行了封装，模型调用与数据预处理几行代码就可以搞定，真的是灰常方便，建议大家多多使用。
   注意：本次教程使用mobilenetv2进行分类，数据预处理的时候，模型标签是从1-102，但是深度学习标签都是从0开始。
   修改的方法：标签数直接减去1，这样标签数据对齐到0-101，可以直接训练，否则会报错，标签索引超出！

   #只要20行代码，深度学习模型带回家，高阶api，用起来吧
   
   import paddle 
   from paddle.vision.datasets import Flowers
   from paddle.vision.models import MobileNetV2,mobilenet_v2
   from paddle.vision.transforms import ToTensor,Compose, Resize, ColorJitter
   
   print('===============================start train')
   transform = Compose([Resize(size=[228,228]),ToTensor()])#预处理
   train_dataset = Flowers(mode='train',transform=transform)
   
   #标签转换为0-101，不改的话，下面的标签需要改成103
   train_dataset.labels = train_dataset.labels-1  
   #花有102种类别
   #创建模型
   mobilenetv2 = MobileNetV2(num_classes=102)
   # paddle.summary(mobilenetv2,(1,3,228,228)) 测试模型
   
   model = paddle.Model(mobilenetv2)#模型封装
   model.load('./Model/7')#一天只有十个小时，太难了，只能分段训练、、、
   model.prepare(paddle.optimizer.Adam(parameters=model.parameters()),
                 paddle.nn.CrossEntropyLoss(),
                 paddle.metric.Accuracy())
   
   model.fit(train_dataset,
             epochs=200,
             batch_size=64,
             save_dir='Model/cpu',
             save_freq=1,
             verbose=1)
   print('===============================finish train')
   

2. paddle模型转化为onnx模型
   使用百度的paddle2onnx工具进行转化

   !pip insyall paddle2onnx
   
   !pip install onnx
   

   安装好之后，按照官方示例，加载模型，然后使用paddle.onnx.export接口进行转化，现在流行动态图，我们直接使用动态图操作方式，让静态图静静的躺在历史的尘埃里吧！！！
   注意：
   模型输入需要设定batch为1，地平线开发板写死了入口，指定输入为4D,且batch只能为1，因此转化的时候需要设置如下形式：

   input_spec = paddle.static.InputSpec(shape=[1, 3, 224, 224], dtype='float32', name='image')
   
   

   import paddle 
   import paddle2onnx
   import onnx
   from paddle.vision.models import MobileNetV2
   
   print(onnx.__version__,paddle.__version__,paddle2onnx.__version__)
   
   # 实例化模型
   mobilenetv2 = MobileNetV2(num_classes=102)
   
   #封装并加载模型
   model = paddle.Model(mobilenetv2)
   model.load(path='./save_model/0')
   
   #把模型从封装的Model中剥离出来
   net = model.network
   
   # 将模型设置为推理状态
   net.eval()
   
   # 定义输入数据
   input_spec = paddle.static.InputSpec(shape=[1, 3, 224, 224], dtype='float32', name='image')
   
   # ONNX模型导出
   # enable_onnx_checker设置为True，表示使用官方ONNX工具包来check模型的正确性，需要安装ONNX（pip install onnx）
   paddle.onnx.export(net, 'mobilenet_v2', input_spec=[input_spec], opset_version=10, enable_onnx_checker=True)
   

3. onnx模型校验
   成功得到onnx模型之后，我们需要把onnx模型转化为板端部署文件
   地平线提供了天工开物工具链，可以有效的对onnx模型进行验证并转化
   部署第一步，使用工具链对onnx模型进行校验,校验不通过的模型目前无法上板部署，需要调整算子或提交地平线开发人员进行算子支持升级！！
   工具链可在地平线生态社区获取，大家可以自行搜索！
   开发板支持模型列表：
   

   #!/usr/bin/env sh
   # Copyright (c) 2020 Horizon Robotics.All Rights Reserved.
   #
   # The material in this file is confidential and contains trade secrets
   # of Horizon Robotics Inc. This is proprietary information owned by
   # Horizon Robotics Inc. No part of this work may be disclosed,
   # reproduced, copied, transmitted, or used in any way for any purpose,
   # without the express written permission of Horizon Robotics Inc.
   
   set -ex
   cd $(dirname $0) || exit
   model_type="onnx"
   onnx_model="../../../01_common/modelzoo/mapper/classification/mobilenet/mobilenet_v2.onnx"
   output="./mobilenet_checker.log"
   march="bernoulli2"
   
   hb_mapper checker --model-type ${
          model_type} \
                     --model ${
          onnx_model} \
                     --output ${
          output} --march ${
          march}
   

4. onnx模型转化为板端推理文件
   使用地平线的开发工具天公开物，开发套件内包含各种写好的脚本，可以快速实现校验与推理，节省开发者时间！

   #!/bin/bash
   # Copyright (c) 2020 Horizon Robotics.All Rights Reserved.
   #
   # The material in this file is confidential and contains trade secrets
   # of Horizon Robotics Inc. This is proprietary information owned by
   # Horizon Robotics Inc. No part of this work may be disclosed,
   # reproduced, copied, transmitted, or used in any way for any purpose,
   # without the express written permission of Horizon Robotics Inc.
   
   set -e -v
   
   cd $(dirname $0) || exit
   
   config_file="./mobilenet_config.yaml"
   model_type="caffe"
   # build model
   hb_mapper makertbin --config ${
          config_file}  \
                       --model-type  ${
          model_type}
   

   # Copyright (c) 2020 Horizon Robotics.All Rights Reserved.
   #
   # The material in this file is confidential and contains trade secrets
   # of Horizon Robotics Inc. This is proprietary information owned by
   # Horizon Robotics Inc. No part of this work may be disclosed,
   # reproduced, copied, transmitted, or used in any way for any purpose,
   # without the express written permission of Horizon Robotics Inc.
   
   # 模型转化相关的参数
   # model conversion related parameters
   model_parameters:
     # Caffe浮点网络数据模型文件
     # the model file of floating-point Caffe neural network data
     onnx_model: '../../../01_common/modelzoo/mapper/classification/mobilenet/mobilenet_v2.onnx'
     # Caffe网络描述文件
     # the file describes the structure of Caffe neural network
     #prototxt: '../../../01_common/modelzoo/mapper/classification/mobilenet/mobilenet_deploy.prototxt'
     # 适用BPU架构
     # the applicable BPU architecture
     march: "bernoulli2"
     # 指定模型转换过程中是否输出各层的中间结果，如果为True，则输出所有层的中间输出结果，
     # ---------------------------------------------------------------------------------------
     # specifies whether or not to dump the intermediate results of all layers in conversion
     # if set to True, then the intermediate results of all layers shall be dumped
     layer_out_dump: False
     # 用于设置上板模型输出的layout, 支持NHWC和NCHW, 输入None则使用模型默认格式
     # ---------------------------------------------------------------------
     # is used for specifying the layout of model output on dev board
     # both NHWC and NCHW layouts are supported, 
     # if input is None then the default layout of the model will be used
     output_layout: None
     # 日志文件的输出控制参数，
     # debug输出模型转换的详细信息
     # info只输出关键信息
     # warn输出警告和错误级别以上的信息
     # ----------------------------------------------------------------------------------------
     # output control parameter of log file(s),
     # if set to 'debug', then details of model conversion will be dumped
     # if set to 'info', then only important imformation will be dumped
     # if set to 'warn', then information ranked higher than 'warn' and 'error' will be dumped
     log_level: 'debug'
     # 模型转换输出的结果的存放目录
     # the directory in which model conversion results are stored
     working_dir: 'model_output'
     # 模型转换输出的用于上板执行的模型文件的名称前缀
     # model conversion generated name prefix of those model files used for dev board execution
     output_model_file_prefix: 'mobilenetv2_pd'
   
   
   # 模型输入相关参数, 若输入多个节点, 则应使用';'进行分隔, 使用默认缺省设置则写None
   # -------------------------------------------------------------------------
   # model input related parameters,
   # please use ";" to seperate when inputting multiple nodes,
   # please use None for default setting
   input_parameters:
     # (可不填) 模型输入的节点名称, 此名称应与模型文件中的名称一致, 否则会报错, 不填则会使用模型文件中的节点名称
     # -------------------------------------------------------------------------------------------------
     # (it's OK to leave blank) node name of model input,
     # it shall be the same as the name of model file, otherwise an error will be reported,
     # the node name of model file will be used when left blank
     #input_name: data
     # 网络实际执行时，输入给网络的数据格式，包括 nv12/rgbp/bgrp/yuv444_128/gray/featuremap,
     # 如果输入的数据为yuv444_128, 模型训练用的是bgrp，则hb_mapper将自动插入YUV到BGRP(NCHW)转化操作
     # ------------------------------------------------------------------------------------------
     # the data formats to be passed into neural network when actually performing neural network
     # whose values includes: nv12/rgbp/bgrp/yuv444_128/gray/featuremap
     # note that hb_mapper will automatically convert YUV into BGRP(NCHW)
     # if the input data is yuv444_128 while BGRP is used in model training
     input_type_rt: 'yuv444_128'
     # 网络训练时输入的数据格式，可选的值为rgbp/bgrp/gray/featuremap/yuv444_128
     # ---------------------------------------------------------------------
     # the data formats in network training
     # available options include: rgbp/bgrp/gray/featuremap/yuv444_128
     input_type_train: 'bgrp'
     # 模型网络的输入大小, 以'x'分隔, 不填则会使用模型文件中的网络输入大小，否则会覆盖模型文件中输入大小
     # input_shape: ''
     # 网络输入的预处理方法，主要有以下几种：
     # no_preprocess 不做任何操作
     # data_mean 减去通道均值mean_value
     # data_scale 对图像像素乘以data_scale系数
     # data_mean_and_scale 减去通道均值后再乘以scale系数
     # -------------------------------------------------------------------------------------------
     # the input size of model network, seperated by 'x'
     # note that the network input size of model file will be used if left blank
     # otherwise it will overwrite the input size of model file
     # input_shape: ''
     # preprocessing methods of network input, consist of the follwing:
     # 'no_preprocess' indicates that no preprocess will be made 
     # 'data_mean' indicates that to minus the channel mean, i.e. mean_value
     # 'data_scale' indicates that image pixels to multiply data_scale ratio
     # 'data_mean_and_scale' indicates that to multiply scale ratio after channel mean is minused
     norm_type: 'data_mean_and_scale'
     # 图像减去的均值, 如果是通道均值，value之间必须用空格分隔
     # --------------------------------------------------------------------------
     # the mean value minused by image
     # note that values must be seperated by space if channel mean value is used
     mean_value: 103.94 116.78 123.68
     # 图像预处理缩放比例，如果是通道缩放比例，value之间必须用空格分隔
     # ---------------------------------------------------------------------------
     # scale value of image preprocess
     # note that values must be seperated by space if channel scale value is used
     scale_value: 0.017
   
   calibration_parameters:
     # 模型量化的参考图像的存放目录，图片格式支持Jpeg、Bmp等格式，输入的图片
     # 应该是使用的典型场景，一般是从测试集中选择20~100张图片，另外输入
     # 的图片要覆盖典型场景，不要是偏僻场景，如过曝光、饱和、模糊、纯黑、纯白等图片
     # 若有多个输入节点, 则应使用';'进行分隔
     # -----------------------------------------------------------------------
     # the directory where reference images of model quantization are stored
     # image formats include JPEG, BMP etc.
     # should be classic application scenarios, usually 20~100 images are picked out from test datasets
     # in addition, note that input images should cover typical scenarios
     # and try to avoid those overexposed, oversaturated, vague, 
     # pure blank or pure white images
     # use ';' to seperate when there are multiple input nodes
     cal_data_dir: './calibration_data_bgrp'
     # 如果输入的图片文件尺寸和模型训练的尺寸不一致时，并且preprocess_on为true，
     # 则将采用默认预处理方法(skimage resize)，
     # 将输入图片缩放或者裁减到指定尺寸，否则，需要用户提前把图片处理为训练时的尺寸
     # ----------------------------------------------------------------------------------
     # In case the size of input image file is different from that of in model training
     # and that preprocess_on is set to True,
     # shall the default preprocess method(skimage resize) be used
     # i.e., to resize or crop input image into specified size
     # otherwise user must keep image size as that of in training in advance
     preprocess_on: False
     # 模型量化的算法类型，支持kl、max，通常采用KL即可满足要求
     # ------------------------------------------------------------
     # types of model quantization algorithms
     # kl and max are supported, usually kl works
     calibration_type: 'kl'
   
   # 编译器相关参数
   # compiler related parameters
   compiler_parameters:
     # 编译策略，支持bandwidth和latency两种优化模式;
     # bandwidth以优化ddr的访问带宽为目标；
     # latency以优化推理时间为目标
     # ------------------------------------------------------------------------------------------
     # compilation strategy, there are 2 available optimization modes: 'bandwidth' and 'lantency'
     # the 'bandwidth' mode aims to optimize ddr access bandwidth
     # while the 'lantency' mode aims to optimize inference duration
     compile_mode: 'latency'
     # 设置debug为True将打开编译器的debug模式，能够输出性能仿真的相关信息，如帧率、DDR带宽占用等
     # -----------------------------------------------------------------------------------
     # the compiler's debug mode will be enabled by setting to True
     # this will dump performance simulation related information
     # such as: frame rate, DDR bandwidth usage etc.
     debug: False
     # 编译模型指定核数，不指定默认编译单核模型, 若编译双核模型，将下边注释打开即可
     # -------------------------------------------------------------------------------------
     # specifies number of cores to be used in model compilation 
     # as default, single core is used as this value left blank
     # please delete the "# " below to enable dual-core mode when compiling dual-core model
     
     # core_num: 2
     
     # -------------------------------------------------------------------------------------
     # 优化等级可选范围为O0~O3
     # O0不做任何优化, 编译速度最快，优化程度最低,
     # O1-O3随着优化等级提高，预期编译后的模型的执行速度会更快，但是所需编译时间也会变长。
     # 推荐用O2做最快验证
     # ----------------------------------------------------------------------------
     # optimization level ranges between O0~O3
     # O0 indicates that no optimization will be made 
     # the faster the compilation, the lower optimization level will be
     # O1-O3: as optimization levels increase gradually, model execution, after compilation, shall become faster
     # while compilation will be prolonged
     # it is recommended to use O2 for fastest verification
     optimize_level: 'O3'  
   

   完成onnx模型->地平线开发板模型，转化得到的bin文件很小，只有2.4M，小了一个量级以上

5. 部署与测试

   1.构建应用：
   # parallel_process_num的设置，参考您的cpu配置，如果不设置这个环境变量，默认为单进程
   cd samples/04_detection/01_yolov2/runtime_arm
   sh 01_build.sh
   
   2.数据预处理
   # parallel_process_num的设置，参考您的cpu配置，如果不设置这个环境变量，默认为单进程
   export PARALLEL_PROCESS_NUM=${
          parallel_process_num}
   sh 02_preprocess.sh
   处理好的图像将会用于后续的评测eval
   
   3.将构建好的应用传到开发板
   sh 03_scp_to_board.sh ${
          board_ip}
   执行这个命令，会将构建好的应用，通过scp，传输到开发板的 /userdata/samples/mobilenet 目录下。
   
   若要执行单张图片的infer, 则可通过下面的代码操作远程登录开发板进行执行
   ssh root@${
          board_ip}
   cd /userdata/samples/yolov2
   sh dev_board_01_infer.sh
   
   4.执行评测
   sh 04_eval.sh ${
          board_ip}
   该脚本会将图片传输至板上进行运行和评测, 此过程将持续很久.
   
   5.执行性能测试
   sh 05_perf.sh ${
          board_ip}
   
   同时该操作也可从开发板上单独完成, 则可通过下面的代码操作远程登录开发板进行执行
   
   ssh root@${
          board_ip}
   cd /userdata/samples/yolov2
   sh dev_board_03_perf.sh
   
   ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
   最终结果：性能测试，10张图像仅用了34.01ms，速度经得起检验，精度损失极少
   
   I0101 10:41:40.973683  1899 simple_example.cc:172] Whole process statistics:count:10, duration:34.01ms, min:3.342ms, max:3.823ms, average:3.35562ms, fps:294.031/s
   , Infer stage statistics:count:10, duration:33.869ms, min:3.33ms, max:3.794ms, average:3.34312ms, fps:295.255/s
   , Post process stage statistics:count:10, duration:0.125ms, min:0.01ms, max:0.027ms, average:0.011ms, fps:80000/s
   

三、 总结

  多实践尝试，一步一个脚印解决BUG，会越开越熟练。
  详细的做法请查看下面参考信息链接，找原博主问答，这只做笔记记录。
  飞浆高阶API可以快速有效的构建深度学习模型，地平线的天公开物开发板可以快速进行嵌入式开发，而且两方都有丰富的社区生态和专业的解答人员，基于板端的项目多了一个很好的选择。

  地平线部署，背靠背的国产框架+国产AI芯片部署，两相结合，可以不再被卡脖子，相关社区也较活跃，是不错的落地部署选择！

四、参考信息

1. https://aistudio.baidu.com/aistudio/projectdetail/2265112?forkThirdPart=1
   2.https://www.bilibili.com/video/BV1qq4y1X7uZ?p=6