Error in building caffe model - CPU_only

[Firas Omran]

Hello all,

I am trying to build the model described in this link (https://github.com/leonzfa/iResNet) using make all. However, i am using CPU_only configuration and i am getting the below error. The Make.config file is copied below.

i cannot understand why this error in "data_augmentation_layer" is coming. Please anyone has an idea ?

Thanks 

Error from Terminal:

firas@firas-ThinkPad-L540:~/Documents/MA_Depth_Estimation_Using_Stereo/Disparity_Estimation_Using_Feature_Extraction/CAFFE_ROOT$ make all

examples/siamese examples/cifar10 examples/mnist examples/cpp_classification matlab/+caffe/private python/caffe src/caffe src/caffe/layers src/caffe/solvers src/caffe/test src/caffe/proto src/caffe/util src/gtest tools

CXX src/caffe/layers/data_augmentation_layer.cpp

In file included from ./include/caffe/common.hpp:19:0,

                 from ./include/caffe/blob.hpp:8,

                 from ./include/caffe/layer.hpp:8,

                 from src/caffe/layers/data_augmentation_layer.cpp:9:

src/caffe/layers/data_augmentation_layer.cpp:215:10: error: redefinition of ‘void caffe::DataAugmentationLayer<Dtype>::Backward_gpu(const std::vector<caffe::Blob<Dtype>*>&, const std::vector<bool>&, const std::vector<caffe::Blob<Dtype>*>&)’

 STUB_GPU(DataAugmentationLayer);

          ^

./include/caffe/util/device_alternate.hpp:17:6: note: in definition of macro ‘STUB_GPU’

 void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \

      ^

In file included from src/caffe/layers/data_augmentation_layer.cpp:10:0:

./include/caffe/layers/data_augmentation_layer.hpp:39:18: error: ‘virtual void caffe::DataAugmentationLayer<Dtype>::Backward_gpu(const std::vector<caffe::Blob<Dtype>*>&, const std::vector<bool>&, const std::vector<caffe::Blob<Dtype>*>&)’ previously declared here

     virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,

                  ^

src/caffe/layers/data_augmentation_layer.cpp: In instantiation of ‘void caffe::DataAugmentationLayer<Dtype>::adjust_blobs(std::vector<caffe::Blob<Dtype>*>) [with Dtype = float]’:

src/caffe/layers/data_augmentation_layer.cpp:218:1:   required from here

src/caffe/layers/data_augmentation_layer.cpp:178:130: error: ‘caffe_gpu_memcpy’ was not declared in this scope

              caffe_gpu_memcpy(this->blobs_[2]->count()*sizeof(float), tmp.mutable_cpu_data(), this->blobs_[2]->mutable_cpu_data());

                                                                                                                                  ^

src/caffe/layers/data_augmentation_layer.cpp: In instantiation of ‘void caffe::DataAugmentationLayer<Dtype>::adjust_blobs(std::vector<caffe::Blob<Dtype>*>) [with Dtype = double]’:

src/caffe/layers/data_augmentation_layer.cpp:218:1:   required from here

src/caffe/layers/data_augmentation_layer.cpp:178:130: error: ‘caffe_gpu_memcpy’ was not declared in this scope

make: *** [.build_release/src/caffe/layers/data_augmentation_layer.o] Error 1

Makefile.config

## 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

# 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 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_50,code=compute_50

# 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/local/lib/python2.7/dist-packages/numpy/core/include

# /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 python3.5m

# 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

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# 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 ?= @

[ysy435443]

You can try solution here:

https://github.com/yeahkun/caffe-yolo/issues/19

--
You received this message because you are subscribed to the Google Groups "Caffe Users" group.
To unsubscribe from this group and stop receiving emails from it, send an email to caffe-users...@googlegroups.com.
To post to this group, send email to caffe...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/caffe-users/a809f97d-a43d-458e-907d-eefc93d8ac10%40googlegroups.com.
For more options, visit https://groups.google.com/d/optout.