Caffe安装
Caffe框架下载地址：https://github.com/BVLC/caffe
下载完成后解压，进入主目录，执行
代码语言：javascript复制cp Makefile.config.example Makefile.config安装依赖
代码语言：javascript复制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 libatlas-base-dev进入src/caffe/proto文件夹，修改caffe.proto，在最后添加
代码语言：javascript复制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];
}执行命令
代码语言：javascript复制protoc caffe.proto --cpp_out=./回到主目录，修改Makefile.config(该文件的配置跟环境有关系，我这里的配置为NVIDIA 3090, CUDA 12.0, CUDNN为cudnn-linux-x86_64-8.9.6.50_cuda12-archive)
代码语言：javascript复制## 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 := 4
# 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_20,code=sm_20
# CUDA_ARCH := -gencode arch=compute_30,code=sm_30
# CUDA_ARCH := -gencode arch=compute_35,code=sm_35
CUDA_ARCH := -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)/anaconda3/envs/pytorch
PYTHON_INCLUDE := $(ANACONDA_HOME)/include
$(ANACONDA_HOME)/include/python3.9
$(ANACONDA_HOME)/lib/python3.9/site-packages/numpy/core/include
# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python3 python3.9m
# PYTHON_INCLUDE := /usr/include/python3.5m
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := $(ANACONDA_HOME)/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/include/opencv4/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/lib/opencv4/
# 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 ?= @有关Anaconda和Cuda以及Cudnn的设置，请参考乌班图安装Pytorch、Tensorflow Cuda环境
执行
代码语言：javascript复制sudo make all -j16
