MaximumProgrammer
commented
5 years ago My CmakeFile looks like this
cmake_minimum_required(VERSION 3.0.0) set(CMAKE_VERBOSE_MAKEFILE ON)
project(ssp_blob_classification_caffe VERSION 0.1.0)
include(CTest) enable_testing()
file(GLOB SOURCES .h .cpp )
add_executable(ssp_blob_classification_caffe main.cpp)
find_package(Boost 1.65 REQUIRED COMPONENTS system filesystem regex) include_directories(${Boost_INCLUDE_DIRS})
find_package(OpenCV REQUIRED ) include_directories(${OpenCV_INCLUDE_DIRS} )
find_package(Caffe REQUIRED) include_directories(${Caffe_INCLUDE_DIRS})
set(Caffe_DEFINITIONS " -DUSE_OPENCV -DUSE_LMDB -DUSE_LEVELDB ") set( CMAKE_CXX_FLAGS ${Caffe_DEFINITIONS})
find_package(OpenBLAS REQUIRED) include_directories(${OpenBLAS_INCLUDE_DIRS})
set(CPACK_PROJECT_NAME ${PROJECT_NAME}) set(CPACK_PROJECT_VERSION ${PROJECT_VERSION}) include(CPack)
Caffe_INCLUDE_DIRS - Caffe include directories
Caffe_LIBRARIES - libraries to link against
Caffe_DEFINITIONS - a list of definitions to pass to compiler
target_link_libraries(ssp_blob_classification_caffe ${OpenCV_LIBS} ${Boost_LIBRARIES} ${Caffe_LIBRARIES} ${OpenBLAS_LIB})
and my main.cpp file looks like this
include <caffe/caffe.hpp>
include <opencv2/core/core.hpp>
include <opencv2/highgui/highgui.hpp>
include <opencv2/imgproc/imgproc.hpp>
include
include
include
include
include
include
using namespace caffe; // NOLINT(build/namespaces) using std::string; //using caffe::Net; //using caffe::Blob; //using caffe::Caffe; //using caffe::Datum; //using caffe::Net; //using caffe::shared_ptr; //using caffe::vector; //using caffe::MemoryDataLayer;
/ Pair (label, confidence) representing a prediction. / typedef std::pair<string, float> Prediction;
class Classifier { public: Classifier(const string& model_file, const string& trained_file, const string& mean_file, const string& label_file);
std::vector
private: void SetMean(const string& mean_file);
std::vector
void WrapInputLayer(std::vector
void Preprocess(const cv::Mat& img,
std::vector
private:
sharedptr<Net
Classifier::Classifier(const string& model_file, const string& trained_file, const string& mean_file, const string& label_file) {
ifdef CPU_ONLY
Caffe::set_mode(Caffe::CPU);
else
Caffe::set_mode(Caffe::GPU);
endif
/ Load the network. /
net_.reset(new Net
CHECKEQ(net->num_inputs(), 1) << "Network should have exactly one input."; CHECKEQ(net->num_outputs(), 1) << "Network should have exactly one output.";
Blob
/ Load the binaryproto mean file. / SetMean(mean_file);
/ Load labels. / std::ifstream labels(label_file.c_str()); CHECK(labels) << "Unable to open labels file " << labelfile; string line; while (std::getline(labels, line)) labels.push_back(string(line));
Blob
static bool PairCompare(const std::pair<float, int>& lhs, const std::pair<float, int>& rhs) { return lhs.first > rhs.first; }
/ Return the indices of the top N values of vector v. /
static std::vector
std::vector
/ Return the top N predictions. /
std::vector
N = std::min
return predictions; }
/ Load the mean file in binaryproto format. / void Classifier::SetMean(const string& mean_file) { BlobProto blob_proto; ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
/ Convert from BlobProto to Blob
/ The format of the mean file is planar 32-bit float BGR or grayscale. /
std::vector
/ Merge the separate channels into a single image. / cv::Mat mean; cv::merge(channels, mean);
/* Compute the global mean pixel value and create a mean image
- filled with this value. */ cv::Scalar channelmean = cv::mean(mean); mean = cv::Mat(inputgeometry, mean.type(), channel_mean); }
std::vector
std::vector
Preprocess(img, &input_channels);
net_->Forward();
/ Copy the output layer to a std::vector /
Blob
/* Wrap the input layer of the network in separate cv::Mat objects
- (one per channel). This way we save one memcpy operation and we
- don't need to rely on cudaMemcpy2D. The last preprocessing
- operation will write the separate channels directly to the input
-
layer. / void Classifier::WrapInputLayer(std::vector
* inputlayer = net->input_blobs()[0]; int width = input_layer->width(); int height = input_layer->height(); float input_data = input_layer->mutable_cpu_data(); for (int i = 0; i < input_layer->channels(); ++i) { cv::Mat channel(height, width, CV_32FC1, input_data); input_channels->push_back(channel); input_data += width height; } }
void Classifier::Preprocess(const cv::Mat& img,
std::vector
cv::Mat sample_resized; if (sample.size() != inputgeometry) cv::resize(sample, sample_resized, inputgeometry); else sample_resized = sample;
cv::Mat sample_float; if (numchannels == 3) sample_resized.convertTo(sample_float, CV_32FC3); else sample_resized.convertTo(sample_float, CV_32FC1);
cv::Mat sample_normalized; cv::subtract(samplefloat, mean, sample_normalized);
/* This operation will write the separate BGR planes directly to the
- input layer of the network because it is wrapped by the cv::Mat
-
objects in input_channels. / cv::split(sample_normalized, input_channels);
CHECK(reinterpret_cast<float*>(inputchannels->at(0).data) == net->input_blobs()[0]->cpu_data()) << "Input channels are not wrapping the input layer of the network."; }
int main(int argc, char** argv) { if (argc != 6) { std::cerr << "Usage: " << argv[0] << " deploy.prototxt network.caffemodel" << " mean.binaryproto labels.txt img.jpg" << std::endl; return 1; }
::google::InitGoogleLogging(argv[0]);
string model_file = argv[1]; string trained_file = argv[2]; string mean_file = argv[3]; string label_file = argv[4]; Classifier classifier(model_file, trained_file, mean_file, label_file);
string file = argv[5];
std::cout << "---------- Prediction for " << file << " ----------" << std::endl;
cv::Mat img = cv::imread(file, -1);
CHECK(!img.empty()) << "Unable to decode image " << file;
std::vector
/ Print the top N predictions. / for (size_t i = 0; i < predictions.size(); ++i) { Prediction p = predictions[i]; std::cout << std::fixed << std::setprecision(4) << p.second << " - \"" << p.first << "\"" << std::endl; } } // USE_OPENCV
drnikolaev
I am trying to compiling the simple mnist aka classification file but it don't know how to handle this kind of error
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:48:14: error: ‘caffe::Net’ is not a template sharedptr<Net > net ;
^~~
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp: In constructor ‘Classifier::Classifier(const string&, const string&, const string&, const string&)’:
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classificationcaffe/main.cpp:66:18: error: ‘caffe::Net’ is not a template
net.reset(new Net(model_file, TEST));
^~~
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:72:3: error: ‘caffe::Blob’ is not a template
Blob inputlayer = net->input_blobs()[0];
^~~~
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:88:3: error: ‘caffe::Blob’ is not a template
Blob mean_blob;
^~~~
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:132:15: error: no matching function for call to ‘caffe::Blob::Blob()’
Blob mean_blob;
^ Dtype caffe::Blob::mutable_cpu_data()
Dtype mutable_cpu_data() { // Keeping PyCaffe intact
^ Classifier::Predict(const cv::Mat&)’:
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:158:3: error: ‘caffe::Blob’ is not a template
Blob inputlayer = net->input_blobs()[0];
^~~~
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:172:3: error: ‘caffe::Blob’ is not a template
Blob const Dtype caffe::Blob::cpu_data() const
const Dtype cpu_data() const {
^)’:
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:184:3: error: ‘caffe::Blob’ is not a template
Blob Dtype caffe::Blob::mutable_cpu_data()
Dtype mutable_cpu_data() { // Keeping PyCaffe intact
^)’:
/home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:232:45: error: no matching function for call to ‘caffe::Blob::cpudata()’
== net->input_blobs()[0]->cpu_data())
^
In file included from /home/mgruber/git/nvcaffe/include/caffe/caffe.hpp:7:0,
from /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:1:
/home/mgruber/git/nvcaffe/include/caffe/blob.hpp:320:16: note: candidate: template const Dtype caffe::Blob::cpu_data() const
const Dtype* cpu_data() const {
^
~~~~ In file included from /home/mgruber/git/nvcaffe/include/caffe/caffe.hpp:7:0, from /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:1: /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:54:12: note: candidate: caffe::Blob::Blob(caffe::Type) explicit Blob(Type dtype) ^~~~ /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:54:12: note: candidate expects 1 argument, 0 provided /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:48:3: note: candidate: caffe::Blob::Blob(caffe::Type, caffe::Type) Blob(Type data_type, Type diff_type) ^~~~ /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:48:3: note: candidate expects 2 arguments, 0 provided /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:139:44: error: no matching function for call to ‘caffe::Blob::mutable_cpu_data()’ float data = mean_blob.mutable_cpu_data(); ^ In file included from /home/mgruber/git/nvcaffe/include/caffe/caffe.hpp:7:0, from /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:1: /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:338:10: note: candidate: template~~~/home/mgruber/git/nvcaffe/include/caffe/blob.hpp:338:10: note: template argument deduction/substitution failed: /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:139:44: note: couldn't deduce template parameter ‘Dtype’ float data = mean_blob.mutable_cpu_data(); ^ /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp: In member function ‘std::vector~~~ /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:320:16: note: template argument deduction/substitution failed: /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:173:47: note: couldn't deduce template parameter ‘Dtype’ const float begin = output_layer->cpu_data(); ^ /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp: In member function ‘void Classifier::WrapInputLayer(std::vector~~~/home/mgruber/git/nvcaffe/include/caffe/blob.hpp:338:10: note: template argument deduction/substitution failed: /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:188:53: note: couldn't deduce template parameter ‘Dtype’ float input_data = input_layer->mutable_cpu_data(); ^ In file included from /home/mgruber/git/nvcaffe/include/caffe/common.hpp:5:0, from /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:11, from /home/mgruber/git/nvcaffe/include/caffe/caffe.hpp:7, from /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:1: /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp: In member function ‘void Classifier::Preprocess(const cv::Mat&, std::vector~~~ /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:320:16: note: template argument deduction/substitution failed: In file included from /home/mgruber/git/nvcaffe/include/caffe/common.hpp:5:0, from /home/mgruber/git/nvcaffe/include/caffe/blob.hpp:11, from /home/mgruber/git/nvcaffe/include/caffe/caffe.hpp:7, from /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classification_caffe/main.cpp:1: /home/mgruber/Filestorage/software_development/project_ssp/ssp_blob_classificationcaffe/main.cpp:232:45: note: couldn't deduce template parameter ‘Dtype’ == net->input_blobs()[0]->cpu_data())