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

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, ColorJitterprint('===============================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')

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 MobileNetV2print(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)

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}

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 -vcd $(dirname $0) || exitconfig_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 dataonnx_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 architecturemarch: "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 dumpedlayer_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 usedoutput_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 dumpedlog_level: 'debug'# 模型转换输出的结果的存放目录# the directory in which model conversion results are storedworking_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 executionoutput_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 traininginput_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_128input_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 minusednorm_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 usedmean_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 usedscale_value: 0.017calibration_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 nodescal_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 advancepreprocess_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 workscalibration_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 durationcompile_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 verificationoptimize_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

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.sh2.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eval3.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.sh4.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

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