yolov5:onnx2caffe

环境：unbuntu18.04
cuda10.1
python3.7.5(anaconda中的)

caffe安装：
1.下载caffe源码
git clone git://github.com/BVLC/caffe.git
安装caffe的依赖：

sudo  apt-get update
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
sudo apt-get install python-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install python-opencv

2.yolov5中有upsample层和transpose层，caffe是没有的，需要手动添加
先下载caffe_plus代码： git clone https://github.com/jnulzl/caffe_plus.git

#将caffe_plus/include/caffe/layers/upsample_layer.hpp caffe_plus/include/caffe/layers/permute_layer.hpp
#复制到caffe/include/caffe/layers/#将caffe_plus/src/caffe/layers/upsample_layer.cpp caffe_plus/src/caffe/layers/upsample_layer.cu caffe_plus/src/caffe/layers/permute_layer.cpp caffe_plus/src/caffe/layers/permute_layer.cu 
#复制到caffe/src/caffe/layers/# 修改caffe.proto文件
vim caffe/src/caffe/proto/caffe.proto
在optional WindowDataParameter window_data_param = 129;（约第423行）后增加代码：
optional PermuteParameter permute_param = 150;
optional UpsampleParameter upsample_param = 151;在末尾增加代码：
message PermuteParameter {// The new orders of the axes of data. Notice it should be with// in the same range as the input data, and it starts from 0.// Do not provide repeated order.repeated uint32 order = 1;
}
message UpsampleParameter {		optional int32 height = 1 [default = 32];optional int32 width = 2 [default = 32];optional int32 height_scale = 3 [default = 2];optional int32 width_scale = 4 [default = 2];enum UpsampleOp {NEAREST = 0;BILINEAR = 1;}optional UpsampleOp mode = 5 [default = BILINEAR];
}

3. 配置Makefike.config
   先：cp Makefike.config.example Makefike.config
   配置内容如下：
   主要注意的是：
   1.cuda版本对应
   2.是否使用opencv（只用来转模型的话可以不要）
   3.编译的python版本，这个一定要和后面使用的python版本一致，我用的python3.7.5，所以编译的时候就要设置为python3.7.5的路径（我的是conda里面的）

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!# cuDNN acceleration switch (uncomment to build with cuDNN).
#USE_CUDNN := 1# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1# uncomment to disable IO dependencies and corresponding data layers
USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 0# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#       You should not set this flag if you will be reading LMDBs with any
#       possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr# 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_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# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 \/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \# $(ANACONDA_HOME)/include/python2.7 \# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python3 python3.7m
PYTHON_INCLUDE := /work/ai_lab/miner/anaconda3/envs/torch/include/python3.7m \/work/ai_lab/miner/anaconda3/envs/torch/lib/python3.7/site-packages/numpy/core/include# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /work/ai_lab/miner/anaconda3/envs/torch/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/local/cuda/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0# enable pretty build (comment to see full commands)
Q ?= @

4.编译caffe:

 make -j8 make pycaffe -j8

5. 下载onnx2caffe的轮子：https://github.com/Wulingtian/yolov5_onnx2caffe（这个版本是针对yolov5改过的，支持upsample和transpose的转换）
6. 转换过程中遇到不支持的类型可以自己修改添加，一般报错都是类型不支持或者pytorch的函数参数与caffe不一致导致，可以自己修改