#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 Flowersfrom paddle.vision.models import MobileNetV2,mobilenet_v2from 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103train_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')

import paddle 
import paddle2onnximport onnxfrom 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)

#!/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}

#!/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 modelhb_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 parametersmodel_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 settinginput_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: 0.017calibration_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 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 parameterscompiler_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

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