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Hi,

Am trying to deploy a trained Caffe model from DIGITS with OpenCV 3.1.0. I have attempted the sample code on both LeNet and AlexNet models without any problem.

However, for benchmarking purpose, I tried do run SqueezeNet V1.1 on the same sample code but having an error at the getMaxClass() function where the OpenCV reshape function is called.

The full error message:

OpenCV Error: Assertion failed (dims <= 2) in cv::Mat::reshape, file C:\opencv\sources\modules\core\src\matrix.cpp, line 982

Kindly advise on possible solutions.

For your reference, the full code I am running SqueezeNet on is as follows:

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#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;

#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;

/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb)
{
    Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
    Point classNumber;

    minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
    *classId = classNumber.x;
}

std::vector<String> readClassNames(const char *filename = "E:\\GitRepo\\tds-lane\\Release\\labels.txt")
{
    std::vector<String> classNames;

    std::ifstream fp(filename);
    if (!fp.is_open())
    {
        std::cerr << "File with classes labels not found: " << filename << std::endl;
        exit(-1);
    }

    std::string name;
    while (!fp.eof())
    {
        std::getline(fp, name);
        if (name.length())
            classNames.push_back( name.substr(name.find(' ')+1) );
    }

    fp.close();
    return classNames;
}

int main(int argc, char **argv)
{
    cv::dnn::initModule();  //Required if OpenCV is built as static libs

    String modelTxt = "deploy.prototxt";
    String modelBin = "snapshot_iter_37700.caffemodel";
    String imageFile = (argc > 1) ? argv[1] : "Y:\\20160227_191832.511704_2.jpg";

    //! [Read and initialize network]
    Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
    //! [Read and initialize network]

    //! [Check that network was read successfully]
    if (net.empty())
    {
        std::cerr << "Can't load network by using the following files: " << std::endl;
        std::cerr << "prototxt:   " << modelTxt << std::endl;
        std::cerr << "caffemodel: " << modelBin << std::endl;
        std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
        std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
        exit(-1);
    }
    //! [Check that network was read successfully]

    //! [Prepare blob]
    Mat img = imread(imageFile);
    if (img.empty())
    {
        std::cerr << "Can't read image from the file: " << imageFile << std::endl;
        exit(-1);
    }

    //cvtColor(img,img,CV_BGR2GRAY);
    resize(img, img, Size(227, 227));                   //GoogLeNet accepts only 224x224 RGB-images
    dnn::Blob inputBlob = dnn::Blob::fromImages(img);   //Convert Mat to dnn::Blob batch of images
    //! [Prepare blob]

    //! [Set input blob]
    net.setBlob(".data", inputBlob);        //set the network input
    //! [Set input blob]

    //! [Make forward pass]
    net.forward();                          //compute output
    //! [Make forward pass]

    //! [Gather output]
    dnn::Blob prob = net.getBlob("softmax");   //gather output of "prob" layer

    int classId;
    double classProb;
    getMaxClass(prob, &classId, &classProb);//find the best class
    //! [Gather output]

    //! [Print results]
    std::vector<String> classNames = readClassNames();
    std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
    std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
    //! [Print results]

    system("Pause");
    return 0;
} //main