dnn with custom layer: Assertion failed !empty() in function 'forward'

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System information (version)

OpenCV => 4.5.0

Operating System / Platform => iOS 14.1

Compiler => :Xcode 12.1 (12A7403)

I'm getting this error:

libc++abi.dylib: terminating with uncaught exception of type cv::Exception: OpenCV(4.5.0) /Volumes/build-storage/build/master_iOS-mac/opencv/modules/dnn/src/dnn.cpp:3977: error: (-215:Assertion failed) !empty() in function 'forward'

This is triggered by net.forward() - see below please.

using namespace cv;
using namespace std;

class CropLayer : public cv::dnn::Layer

{

public:
    CropLayer(const cv::dnn::LayerParams &params) : Layer(params) {}

static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params) {
    return cv::Ptr<cv::dnn::Layer>(new CropLayer(params));
}

virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
                             const int requiredOutputs,
                             std::vector<std::vector<int> > &outputs,
                             std::vector<std::vector<int> > &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::Mat& inp = inputs[0];
    cv::Mat& 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);
        }
    }
} };

CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
cv::dnn::Net net;
net = cv::dnn::readNet("hed_pretrained_bsds.caffemodel" ,"deploy.prototxt");
void process(cv::Mat& 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;
}

model: https://firebasestorage.googleapis.co... deploy.prototxt: https://firebasestorage.googleapis.co...

Thank you