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cv::Исключение: OpenCV(4.5.0) master_iOS-mac/opencv/modules/dnn/src/dnn.cpp: 3977: ошибка: (-215: ошибка утверждения) !empty() в функции ‘forward’

Это вызывается net.forward():

 CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
cv::dnn::Net net;
net = cv::dnn::readNet("hed_pretrained_bsds.caffemodel" ,"deploy.prototxt");
void process(cv::Matamp; img) {
  cv::Size reso(500,500);
  cv::Mat blob = cv::dnn::blobFromImage(img, 1.0, reso, cv::Scalar(104.00698793, 116.66876762, 122.67891434), false, false);
  net.setInput(blob);
  cv::Mat out = net.forward(); //Runtime ERROR here 
  cv::resize(out.reshape(1, reso.height), out, img.size());
  img = out;
}

 

 class CropLayer : public cv::dnn::Layer
{
public:
    CropLayer(const cv::dnn::LayerParams amp;params) : Layer(params) {}
    
    static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParamsamp; params) {
        return cv::Ptr<cv::dnn::Layer>(new CropLayer(params));
    }
    
    virtual bool getMemoryShapes(const std::vector<std::vector<int> > amp;inputs,
                                 const int requiredOutputs,
                                 std::vector<std::vector<int> > amp;outputs,
                                 std::vector<std::vector<int> > amp;internals) const CV_OVERRIDE {
        CV_UNUSED(requiredOutputs); CV_UNUSED(internals);
        std::vector<int> outShape(4);
        outShape[0] = inputs[0][0];  // batch size
        outShape[1] = inputs[0][1];  // number of channels
        outShape[2] = inputs[1][2];
        outShape[3] = inputs[1][3];
        outputs.assign(1, outShape);
        return false;
    }
    
    virtual void forward(cv::InputArrayOfArrays inputs_arr,
                         cv::OutputArrayOfArrays outputs_arr,
                         cv::OutputArrayOfArrays internals_arr) CV_OVERRIDE {
        std::vector<cv::Mat> inputs, outputs;
        inputs_arr.getMatVector(inputs);
        outputs_arr.getMatVector(outputs);
        cv::Matamp; inp = inputs[0];
        cv::Matamp; out = outputs[0];
        int ystart = (inp.size[2] - out.size[2]) / 2;
        int xstart = (inp.size[3] - out.size[3]) / 2;
        int yend = ystart   out.size[2];
        int xend = xstart   out.size[3];
        const int batchSize = inp.size[0];
        const int numChannels = inp.size[1];
        const int height = out.size[2];
        const int width = out.size[3];
        int sz[] = { (int)batchSize, numChannels, height, width };
        out.create(4, sz, CV_32F);
        for(int i=0; i<batchSize; i  ) {
            for(int j=0; j<numChannels; j  ) {
                cv::Mat plane(inp.size[2], inp.size[3], CV_32F, inp.ptr<float>(i,j));
                cv::Mat crop = plane(cv::Range(ystart,yend), cv::Range(xstart,xend));
                cv::Mat targ(height, width, CV_32F, out.ptr<float>(i,j));
                crop.copyTo(targ);
            }
        }
    }    
};
 

hed_pretrained_bsds.caffemodel: https://firebasestorage.googleapis.com/v0/b/tssst-da0a1.appspot.com/o/hed_pretrained_bsds.caffemodel?alt=media

deploy.prototxt: https://firebasestorage.googleapis.com/v0/b/tssst-da0a1.appspot.com/o/deploy.prototxt?alt=media