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

Question

cv::Exception: OpenCV(4.5.0) master_iOS-mac/opencv/modules/dnn/src/dnn.cpp:3977: error: (-215:Assertion failed) !empty() in function 'forward'
This is triggered by 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::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;
}
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);
}
}
}
};
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