`fasterRCNNLayers` for simple custom network give error message - matlab

Question

When using fasterRCNNLayers for creating faster rcnn network from custom network, it gives error message for upgrading the network to faster rcnn network. matlab 2022a update 3 is used.
I have traced the error and found the problem is caused by deleting classification layers and adding faster-rcnn layers. What's the restrictions for upgrading network and I can't found related clue from the documents.
My cnn network code is as follows: cup_use_net_h.m
numTrainingFiles = 30;
[imdsTrain,imdsTest] = splitEachLabel(imds,numTrainingFiles,'randomize');
layers = [ ...
imageInputLayer([180 60 1])
convolution2dLayer(5,40)
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(5,10)
reluLayer
maxPooling2dLayer(5,'Stride',5)
fullyConnectedLayer(2,'Name','fc') %% fullyConnectedLayer(10)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'MaxEpochs',100,...
'InitialLearnRate',1e-2, ...
'Verbose',true, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
save("cup_single_mark_net.mat","net");
My fasterRCNNLayers calling program is as follows: cup_faster_rcnn_c.m
% load("cup_single_mark_net.mat");
% trainingDataTable = objectDetectorTrainingData(gTruth);
load("cup_single_mark_net.mat");
singlenet = net;
inputImageSize = [450,400,1];
featureLayer = 'relu_2';
anchorBoxes = [180,60; 150,50];
numClasses = 2;
lgraph = fasterRCNNLayers(inputImageSize,numClasses,anchorBoxes, ...
singlenet,featureLayer)
analyzeNetwork(lgraph);
error message is as follows:
>> cup_faster_rcnn_c
Error using nnet.cnn.LayerGraph>iValidateLayerName
Layer 'fc' does not exist.
Error in nnet.cnn.LayerGraph>iGetDestinationInformation (line 625)
iValidateLayerName( endLayerName, layerNames );
Error in nnet.cnn.LayerGraph/disconnectLayers (line 372)
iGetDestinationInformation(d, this.PrivateDirectedGraph.Nodes.Layers);
Error in vision.internal.cnn.RCNNLayers/insertOrReplaceROIPooling (line 717)
lgraph = lgraph.disconnectLayers(featureExtractionLayer, outLayers{ii});
Error in vision.internal.cnn.RCNNLayers/fastRCNNForNonSequentialNetworks (line 646)
[lgraph, numFiltersLastConvLayer] = insertOrReplaceROIPooling(this, lgraph, featureExtractionLayer);
Error in vision.internal.cnn.RCNNLayers.createFasterRCNN (line 165)
lgraph = fastRCNNForNonSequentialNetworks(obj, numClasses, lgraph, 'faster-rcnn', anchorBoxes, featureExtractionLayer);
Error in fasterRCNNLayers (line 174)
lgraph = vision.internal.cnn.RCNNLayers.createFasterRCNN(...
Error in cup_faster_rcnn_c (line 12)
lgraph = fasterRCNNLayers(inputImageSize,numClasses,anchorBoxes, ...

Answer 1

The feature_layers parameter should be conv_2 not relu_2.