深度学习之caffe入门一一配置SSD中遇到的问题

用git clone 将代码下载下来之后,需要编译一下,如果之前编译过caffe,直接将之前的makefile.config文件粘贴到caffe-ssd的目录下即可.

我将我的makefile.config文件贴出来,大家可以参考一下.

## 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 := 1
# USE_LEVELDB := 0
USE_LMDB := 1

# 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 lines after *_35 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_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
#BLAS := open
# 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 t
4000
he 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/local/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)/anaconda2
# 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 python3.5m
# 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 := /usr/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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /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

# 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 ?= @

2.如果要尝试训练官方给出的数据集的话,建议从这个百度云http://pan.baidu.com/s/1c1AwrRy,密码:ly70下载,比用wget下的要快好多.这里还要感谢

http://blog.csdn.net/u013738531/article/details/56678247

下载完之后解压放在/caffe-ssd/data下,这个时候要生成标签txt文件和lmdb文件了,一定要注意把/caffe-ssd/data/VOC0712路径下的create_list.sh文件里的路径修改一下,

root_dir=$HOME/下载/caffe/data/VOCdevkit/

这是我的路径,大家对应自己的文件目录修改一下即可.

3.接下来生成lmdb文件.

先将craete_data.sh文件里的data路径改为真实存放VOC数据集的路径.

由于我之前配置过一次caffe,直接在贾扬清大佬的github上clone下来的,不含有ssd,所以再一次配置caffe-ssd的时候,可能环境变量就出问题了,出现了这样:

Traceback (most recent call last):
File "/home/hyhuang/下载/caffe/data/VOC0712/../../scripts/create_annoset.py", line 7, in <module>
from caffe.proto import caffe_pb2
ImportError: No module named caffe.proto
Traceback (most recent call last):
File "/home/hyhuang/下载/caffe/data/VOC0712/../../scripts/create_annoset.py", line 7, in <module>
from caffe.proto import caffe_pb2
ImportError: No module named caffe.proto

还有缺少model_libs的报错,然后我到/caffe-ssd/scripts/create_annoset.py里找了一下,大致跟caffe.proto有关,于是联想可能是caffe和python的接口没有配置好,于是

gedit ~/.bashrc

没有gedit的朋友用sudo apt-get install 安装一下.然后修改弹出的文档最后一行,将那里的路径改为自己的caffe-ssd下的python文件夹,即:

export PYTHONPATH=/home/hyhuang/下载/caffe/python

source ~/.bashrc

再次运行create_data.sh,成功运行,再无报错.