Darknet Yolov3 转换成 caffe格式

需要准备的文件 2个

yolov3.cfg yolov3-voc.weights

export PYTHONPATH=" pwd /distribute/python:$PYTHONPATH"
export LD_LIBRARY_PATH=" pwd /distribute/lib:$LD_LIBRARY_PATH"

darknet2caffe cfg/yolov3.cfg weights/yolov3.weights prototxt/yolov3.prototxt caffemodel/yolov3.caffemodel

python darnet2caffe cfg/yolov4.cfg weights/yolov4.weights prototxt/yolov4.prototxt caffemodel/yolov4.caffemodel

python darnet2caffe cfg/yolov3.cfg weights/yolov3.weights prototxt/yolov3.prototxt caffemodel/yolov3.caffemodel

1544 cd yolov3-tiny/
1546 aria2c -c https://pjreddie.com/media/files/yolov3-tiny.weights
1549 python darknet2caffe.py yolov3-tiny/yolov3-tiny.cfg yolov3-tiny/yolov3-tiny.weights yolov3-tiny/yolov3-tiny.prototxt yolov3-tiny/yolov3-tiny.caffemodel

python darnet2caffe cfg/yolov4.cfg weights/yolov4.weights prototxt/yolov4.prototxt caffemodel/yolov4.caffemodel

python darknet2caffe.py yolov4-fire/yolov4-fire.cfg yolov4-fire/yolov4-fire_best.weights yolov4-fire/yolov4.prototxt yolov4-fire/yolov4.caffemodel

Yolov3转化Caffe框架 darknet2caffe yolov3-tiny & yolov4

git clone https://github.com/ChenYingpeng/darknet2caffe

Requirements
Python2.7
Caffe
Pytorch >= 0.40

git clone https://github.com/BVLC/caffe BVLC

Add Caffe Layers

1. Copy caffe_layers/mish_layer/mish_layer.hpp,caffe_layers/upsample_layer/upsample_layer.hpp into include/caffe/layers/ .
   
2. Copy caffe_layers/mish_layer/mish_layer.cpp mish_layer.cu,caffe_layers/upsample_layer/upsample_layer.cpp upsample_layer.cu into src/caffe/layers/ .
   
3. Copy caffe_layers/pooling_layer/pooling_layer.cpp into src/caffe/layers/ .Note:only work for yolov3-tiny,use with caution.
   
4. Add below code into src/caffe/proto/caffe.proto .
   
   

cp darknet2caffe/caffe_layers/mish_layer/mish_layer.hpp BVLC/include/caffe/layers/
cp darknet2caffe/caffe_layers/upsample_layer/upsample_layer.hpp BVLC/include/caffe/layers/
cp darknet2caffe/caffe_layers/mish_layer/mish_layer.cpp BVLC/src/caffe/layers/
cp darknet2caffe/caffe_layers/mish_layer/mish_layer.cu BVLC/src/caffe/layers/
cp darknet2caffe/caffe_layers/upsample_layer/upsample_layer.cpp BVLC/src/caffe/layers/
cp darknet2caffe/caffe_layers/upsample_layer/upsample_layer.cu BVLC/src/caffe/layers/
cp darknet2caffe/caffe_layers/pooling_layer/pooling_layer.cpp BVLC/src/caffe/layers/

vi src/caffe/proto/caffe.proto
1.加入upsample层并编译Caffe

修改相应的caffe.proto文件，src/caffe/proto/caffe.proto中的LayerParameter的最后一行加入加入：

message LayerParameter {

.....
optional UpsampleParameter upsample_param = 149;

}

注意149为新层的ID号，该ID号请根据个人的caffe.proto文件指定即可。

然后再caffe.proto中添加upsample层的参数：

message UpsampleParameter{
optional int32 scale = 1 [default = 1];
}

// NOTE
// Update the next available ID when you add a new LayerParameter field.
// LayerParameter next available layer-specific ID: 148 (last added: swish_param)
// LayerParameter next available layer-specific ID: 149 (last added: upsample_param)
message LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the layer type
@@ -420,6 +420,7 @@ message LayerParameter {
  optional ThresholdParameter threshold_param = 128;
  optional TileParameter tile_param = 138;
  optional WindowDataParameter window_data_param = 129;
  optional UpsampleParameter upsample_param = 148;
// Message that stores parameters used to apply transformation
@@ -1434,3 +1435,7 @@ message PReLUParameter {
  // Whether or not slope parameters are shared across channels.
  optional bool channel_shared = 2 [default = false];
// added by chen for YoloV3
message UpsampleParameter {
  optional int32 scale = 1 [default = 1];
// Message that stores parameters used by MishLayer
message MishParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  optional Engine engine = 2 [default = DEFAULT];

// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
  optional TileParameter tile_param = 138;
422  optional VideoDataParameter video_data_param = 207;
423  optional WindowDataParameter window_data_param = 129;
++optional UpsampleParameter upsample_param = 149; //added by chen for Yolov3, make sure this id 149 not the same as before.
++optional MishParameter mish_param = 150; //added by chen for yolov4,make sure this id 150 not the same as before.
1450 optional bool channel_shared = 2 [default = false];
// added by chen for YoloV3
++message UpsampleParameter{
++  optional int32 scale = 1 [default = 1];
// Message that stores parameters used by MishLayer
++message MishParameter {
++  enum Engine {
++    DEFAULT = 0;
++    CAFFE = 1;
++    CUDNN = 2;
++  }
++  optional Engine engine = 2 [default = DEFAULT];

1. remake caffe
   
   
   

make caffe

转 Yolov3转化Caffe框架详解 - core! - 博客园

1. 安装caffe支持upsample的环境

   git clone https://github.com/twmht/caffe

2. 切换到 upsample 分支 支持yolov3
   git checkout upsample

3. 配置环境caffe编译环境
   cp Makefile.config.example Makefile.config

检测opencv版本

opencv_version 3.4.2

vi Makefile.config

#Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := #-gencode arch=compute_20,code=sm_20 \
                #-gencode arch=compute_20,code=sm_21
                -gencode arch=compute_30,code=sm_30 \
                -gencode arch=compute_35,code=sm_35 \
                -gencode arch=compute_50,code=sm_50 \
                -gencode arch=compute_52,code=sm_52 \
                -gencode arch=compute_60,code=sm_60 \
                -gencode arch=compute_61,code=sm_61 \
                -gencode arch=compute_61,code=compute_61
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# +add 添加以下3行支持hdf5
INCLUDE_DIRS += /usr/include/hdf5/serial/
LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

1. 编译
   make pycaffe ## 生成pycaffe库
   make distribute ## 生成发布的caffe程序
   
2. 导出python使用的caffe库
   
   

export PYTHONPATH=" pwd /distribute/python:$PYTHONPATH"
export LD_LIBRARY_PATH=" pwd /distribute/lib:$LD_LIBRARY_PATH"

转换步骤

First,you should install this repo https://github.com/marvis/pytorch-caffe-darknet-convert ;

Note:this repo need install pytorch and caffe.

Second,you should install upsample_layer into caffe,please check this link https://github.com/BVLC/caffe/pull/6384/commits/4d2400e7ae692b25f034f02ff8e8cd3621725f5c .

Finally,download yolov3.weights and run yolov3_darknet2caffe.py this file.

1) download yolov3.weights 
$ wget https://pjreddie.com/media/files/yolov3.weights
2) run yolov3_darknet2caffe.py this file in this folder (/home/xx/pytorch-caffe-darknet-convert/).
$ python yolov3_darknet2caffe.py yolov3.cfg yolov3.weights yolov3.prototxt yolov3.caffemodel 

1. https://github.com/BVLC/caffe.git
   
   
   
   

pip install torch==0.4.0
Demo
$ darknet2caffe cfg[in] weights[in] prototxt[out] caffemodel[out]

Example

darknet2caffe cfg/yolov3.cfg weights/yolov3.weights prototxt/yolov3.prototxt caffemodel/yolov3.caffemodel

####

海思模型转换步骤

模型转换的过程中，最好先安装按Anconda，可以创建一个单独的使用环境。

[注]：修改Anconda镜像

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes

修改后，可在home目录下可以发现 .condarc 文件

Dark2caffe[以yolov3转caffe为例]

配置环境（ubuntu16.04）

1. 下载 caffe 源码

2. 安装显卡驱动、cuda和cudnn（以及opencv）

3. 编译caffe源码

注 ：安装了Anconda的话，在base环境下编译，因为编译caffe需要的protoc版本是protobuf-2.6.1，而Anconda默认安装的是3.4.0版本

1. 安装依赖

   sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

   sudo apt-get install --no-install-recommends libboost-all-dev

2. 修改Makefile.config

   首先使用 cp Makefile.config.example Makefile.config 复制caffe给出的Makefile例子。

   1. 若使用cudnn，取消注释 USE_CUDNN := 1

   2. 若使用opencv，取消注释 OPENCV_VERSION := 3 （前提是已经安装好opencv 3系列版本）

   3. 取消注释 WITH_PYTHON_LAYER := 1 ，使得caffe支持python编写的layer

   4. 根据自行安装的cuda版本，删除对应的cuda架构设置

       # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
       # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
       # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
      

      1. 修改INCLUDE_DIRS和LIBRARY_DIRS内的内容
         
         

       # Whatever else you find you need goes here.
       INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /home/q7/Software/Driver/opencv_install/opencv-3.4.2/include
       #在前面取消注释opencv后，在后面增加对应opencv的安装目录
       LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial /home/q7/Software/Driver/opencv_install/opencv-3.4.2/lib
      #在前面取消注释opencv后，在后面增加对应opencv的安装目录
      

3. 在caffe目录下编译caffe： make -j 32 ，并生成caffe.pd.h文件，并拷贝到 caffe/include/caffe/proto 目录下

   protoc src/caffe/proto/caffe.proto --cpp_out=.
     mkdir include/caffe/proto
   mv src/caffe/proto/caffe.pb.h include/caffe/proto
   

   1. 生成支持caffe的python版本： make pycaffe -j 32

   2. 在 /etc/profile 文件的最后，增加如下内容：

      # 2020年 06月 24日 星期三
      # caffe path
      export PYTHONPATH="/home/q7/Project/Hisi/model_convert/caffe/python:$PYTHONPATH"
      

   防止在import caffe过程中，出现 no module named caffe 的错误。

   1. 在caffe目录下测试caffe： make runtest
      
      

   （在Anconda环境下进行）转换darknet模型到caffe

   1. 下载转换工具 darknet2caffe

   2. 环境依赖：Python2.7、Caffe、Pytorch >= 0.40

      1. Pytorch 安装 ：在cuda10版本下 conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch （该命令从Pytorch官网下载安装包，速度较慢；可去掉 -c pytorch ，使得其从自己设置的镜像环境中安装。）
         
         

   3. 在原始caffe中增加caffe层

   注 ：Yolov3中的shortcut层可以用eltwise替代，route层可以用concat替代，而upsample层和Yolo层则需要自己实现，并添加到Caffe中即可。

   1. 拷贝 caffe_layers/mish_layer/mish_layer.hpp,caffe_layers/upsample_layer/upsample_layer.hpp 到 include/caffe/layers/ .

   2. 拷贝 caffe_layers/mish_layer/mish_layer.cpp mish_layer.cu,caffe_layers/upsample_layer/upsample_layer.cpp upsample_layer.cu 到 src/caffe/layers/ .

   3. 拷贝 caffe_layers/pooling_layer/pooling_layer.cpp 到 src/caffe/layers/ .Note:only work for yolov3-tiny,use with caution.

      注 ：执行步骤3的话，在yolov3-tiny转换模型的过程中，就不再需要注释掉yolov3-tiny模型cfg文件中 [maxpool] size=2 stride=1 部分的内容，因为在原有的pooling_layer下，不删除该部分，会出现特征大小不匹配的情况，在覆盖了原有的pooling_layer后，应为处理方式比不同，所以不会出现特征不匹配的情况。

   4. 在 src/caffe/proto/caffe.proto 中增加前面出现++号的部分.

      // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
      message LayerParameter {
      optional TileParameter tile_param = 138;
      optional VideoDataParameter video_data_param = 207;//官网的caffe文件中，暂时没有该配置。可忽略
      optional WindowDataParameter window_data_param = 129;
      ++optional UpsampleParameter upsample_param = 149; //added by chen for Yolov3, make sure this id 149 not the same as before.
      ++optional MishParameter mish_param = 150; //added by chen for yolov4,make sure this id 150 not the same as before.
      // added by chen for YoloV3
      ++message UpsampleParameter{
      ++  optional int32 scale = 1 [default = 1];
      // Message that stores parameters used by MishLayer
      ++message MishParameter {
      ++  enum Engine {
      ++    DEFAULT = 0;
      ++    CAFFE = 1;
      ++    CUDNN = 2;
      ++  }
      ++  optional Engine engine = 2 [default = DEFAULT];
      

   5. 配置完成后重新编译caffe

   6. 在工具目录下（需要先建立一个caffemodel的文件夹，来存储转换后的模型），通过 python cfg/yolov4.cfg weights/yolov4.weights prototxt/yolov4.prototxt caffemodel/yolov4.caffemodel 转换模型；可以修改yolo的版本号，对应不同的转换。

   python darknet2caffe.py cfg/yolov3.cfg weights/yolov3.weights prototxt/yolov3.prototxt caffemodel/yolov3.caffemodel ，yolov3需要增加 darknet2caffe.py

   转换后模型的测试（base环境下）

   1. 配置环境：下载测试工具 caffe-yolov3 ，新建build目录，在目录下编译，整个过程命令如下：
      
      

   git clone https://github.com/ChenYingpeng/caffe-yolov3
   cd caffe-yolov3
   mkdir build
   cd build
   cmake ..
   make -j6
   

将转换darknet模型到caffe步骤中，转换得到的caffemodel和对应的.prototxt文件，分别放在caffemodel（需创建）和prototxt文件夹下。

注 ：可通过修改 src/detector.h 中的 int m_classes = 80; //coco classes 类别信息，来匹配自己训练得到的不同类别数目的模型。

1. 在build目录下测试： ./x86_64/bin/demo ../prototxt/yolov4.prototxt ../caffemodel/yolov4.caffemodel ../images/dog.jpg
   
   

常见错误

1. No module named skimage.io，解决办法： pip install scikit-image

2. ImportError: No module named google.protobuf.internal，解决办法：

   #没有anaconda: 
   pip install protobuf 
   #（编译caffe就需要这个protobuf）
   #有anaconda: 
   conda install protobuf 
   #(最后装了libprotobuf和protobuf 3.5.2)

1. 在caffe-yolov3的build中make时，出现： caffe.pb.h:#error This file was generated by a newer version of protoc which is ，主要是因为build在make过程中使用的protoc版本与编译caffe过程中版本不匹配。
   
   

解决办法：先通过 protoc --version 查看对应的版本，是否与 caffe/include/caffe/proto/caffe.pb.h 中11行和16行的版本要求匹配，不匹配的情况下，先切换到caffe目录下，通过 make clean 清除编译的文件，在对应protoc版本下，通过前文caffe编译过程重新编译。在生成caffe.pd.h文件和拷贝文件的过程中，会覆盖源文件，caffe.pb.h中的protoc版本，会更新为当前环境的版本。之后，在当前环境下重新配置转换后模型的测试环境。

1. 编译 pycaffe时报错：fatal error: numpy/arrayobject.h没有那个文件或目录，解决办法： sudo apt install python-numpy

2. caffe/blob.hpp:9:34: fatal error: caffe/proto/caffe.pb.h: 没有那个文件或目录，解决办法：

   protoc src/caffe/proto/caffe.proto --cpp_out=.
   mkdir include/caffe/proto
   mv src/caffe/proto/caffe.pb.h include/caffe/proto

参考

Appreciate the great work from the following repositories:

• [ caffe-yolov3 ]
  
• [ 海思hi3516dv300开发--yolov3转caffe ]