GPU coder Auto code generation for DAGNetwork Fails

Dear cummunity,

I am trying to generate C/C++ code from an DAGNetwork, which is imported from an YOLOv7 ONNX model. When I try to run this model in Simulink Using the Predict block, no errrors occour everything works. I saved the DAGNetwork as *.mat file. Loading and running the model again is working well.

Next i wan to generate C/C++ code from the model via Simulink, I get the error message: ??? Layer hyper-parameters for custom layer 'concatenationLayer' must be numeric scalar, scalar logical, character or string array, or a matrix of type double or single.

Once remove this layer "concatenationLayer", the code generation works perfectly .

I undestand that the concatenationLayer is a custom layer, But I am wondering how can I get ride of this error ?

[Sergio Matiz Romero]

Hi Oualid,

Without having a look at the imported concatenationLayer it is difficult to tell what parameters are not supported code generation types. However, a potential workaround would be to swap this custom concatenationLayer with an equivalent built-in concatenationLayer. You can use the function replaceLayer to do so. You can obtain the layer graph required for the replacement using the function layerGraph. It would be something like:

lgraph = layerGraph(onnxNet);
replaceLayer(lgraph, onnxLayerName, myConcatenationLayer);
newNet = assembleNetwork(lgraph);

After the layer replacement, you can try code generation again

[Oualid]

Thank you for your quick reply.

Please let me provide more details :

The custom Layer recieves 3 Inputs with deffirent shapes and it has one output as shown in the graph bellow, it performs reshaping and concatination. In Matlab as far as i know there is no such layer that do reshaping and concatination at once which means i can not use any predefined layer that support code GEN.

I believe the only way to fix it is to edit the Auto Genrated Layer (Reshape_To_ConcatLayer1211), Presented bellow . I did several modification for this layer and replace it with the original one using (replaceLayer) but still same issue.

I am attaching all the files in this link ( fast_yolo.slx), and the ONNx model.https://drive.google.com/drive/folders/1RIDLkvgTjMushhin6MQ7moUpB1WyBWy2?usp=sharing

classdef Reshape_To_ConcatLayer1211 < nnet.layer.Layer & nnet.layer.Formattable
    % A custom layer auto-generated while importing an ONNX network.
    
    %#codegen
    %#ok<*PROPLC>
    %#ok<*NBRAK>
    %#ok<*INUSL>
    %#ok<*VARARG>
    
    properties (Learnable)
    end
    
    properties
        ONNXParams         % An ONNXParameters object containing parameters used by this layer.
    end
    
    methods
        function this = Reshape_To_ConcatLayer1211(name, onnxParams)
            this.Name = name;
            this.NumInputs = 3;
            this.OutputNames = {'output'};
            this.ONNXParams = onnxParams;
        end
        
        function [output] = predict(this, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566)
            if isdlarray(onnx__Reshape_488)
                onnx__Reshape_488 = stripdims(onnx__Reshape_488);
            end
            if isdlarray(onnx__Reshape_527)
                onnx__Reshape_527 = stripdims(onnx__Reshape_527);
            end
            if isdlarray(onnx__Reshape_566)
                onnx__Reshape_566 = stripdims(onnx__Reshape_566);
            end
            onnx__Reshape_488NumDims = 4;
            onnx__Reshape_527NumDims = 4;
            onnx__Reshape_566NumDims = 4;
            onnxParams = this.ONNXParams;
            [output, outputNumDims] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims, onnxParams, 'Training', false, ...
                'InputDataPermutation', {[4 3 1 2], [4 3 1 2], [4 3 1 2], ['as-is'], ['as-is'], ['as-is']}, ...
                'OutputDataPermutation', {[3 2 1], ['as-is']});
            if any(cellfun(@(A)isempty(A)||~isnumeric(A), {output}))
                fprintf('Runtime error in network. The custom layer ''%s'' output an empty or non-numeric value.\n', 'Reshape_To_ConcatLayer1211');
                error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ConcatLayer1211'));
            end
            output = dlarray(single(output), 'CBT');
            if ~coder.target('MATLAB')
                output = extractdata(output);
            end
        end
        
        function [output] = forward(this, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566)
            if isdlarray(onnx__Reshape_488)
                onnx__Reshape_488 = stripdims(onnx__Reshape_488);
            end
            if isdlarray(onnx__Reshape_527)
                onnx__Reshape_527 = stripdims(onnx__Reshape_527);
            end
            if isdlarray(onnx__Reshape_566)
                onnx__Reshape_566 = stripdims(onnx__Reshape_566);
            end
            onnx__Reshape_488NumDims = 4;
            onnx__Reshape_527NumDims = 4;
            onnx__Reshape_566NumDims = 4;
            onnxParams = this.ONNXParams;
            [output, outputNumDims] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims, onnxParams, 'Training', true, ...
                'InputDataPermutation', {[4 3 1 2], [4 3 1 2], [4 3 1 2], ['as-is'], ['as-is'], ['as-is']}, ...
                'OutputDataPermutation', {[3 2 1], ['as-is']});
            if any(cellfun(@(A)isempty(A)||~isnumeric(A), {output}))
                fprintf('Runtime error in network. The custom layer ''%s'' output an empty or non-numeric value.\n', 'Reshape_To_ConcatLayer1211');
                error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ConcatLayer1211'));
            end
            output = dlarray(single(output), 'CBT');
            if ~coder.target('MATLAB')
                output = extractdata(output);
            end
        end
    end
end

function [output, outputNumDims, state] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims, params, varargin)
%RESHAPE_TO_CONCATFCN Function implementing an imported ONNX network.
%
% THIS FILE WAS AUTO-GENERATED BY importONNXFunction.
% ONNX Operator Set Version: 11
%
% Variable names in this function are taken from the original ONNX file.
%
% [OUTPUT] = Reshape_To_ConcatFcn(ONNX__RESHAPE_488, ONNX__RESHAPE_527, ONNX__RESHAPE_566, PARAMS)
%			- Evaluates the imported ONNX network RESHAPE_TO_CONCATFCN with input(s)
%			ONNX__RESHAPE_488, ONNX__RESHAPE_527, ONNX__RESHAPE_566 and the imported network parameters in PARAMS. Returns
%			network output(s) in OUTPUT.
%
% [OUTPUT, STATE] = Reshape_To_ConcatFcn(ONNX__RESHAPE_488, ONNX__RESHAPE_527, ONNX__RESHAPE_566, PARAMS)
%			- Additionally returns state variables in STATE. When training,
%			use this form and set TRAINING to true.
%
% [__] = Reshape_To_ConcatFcn(ONNX__RESHAPE_488, ONNX__RESHAPE_527, ONNX__RESHAPE_566, PARAMS, 'NAME1', VAL1, 'NAME2', VAL2, ...)
%			- Specifies additional name-value pairs described below:
%
% 'Training'
% 			Boolean indicating whether the network is being evaluated for
%			prediction or training. If TRAINING is true, state variables
%			will be updated.
%
% 'InputDataPermutation'
%			'auto' - Automatically attempt to determine the permutation
%			 between the dimensions of the input data and the dimensions of
%			the ONNX model input. For example, the permutation from HWCN
%			(MATLAB standard) to NCHW (ONNX standard) uses the vector
%			[4 3 1 2]. See the documentation for IMPORTONNXFUNCTION for
%			more information about automatic permutation.
%
%			'none' - Input(s) are passed in the ONNX model format. See 'Inputs'.
%
%			numeric vector - The permutation vector describing the
%			transformation between input data dimensions and the expected
%			ONNX input dimensions.%
%			cell array - If the network has multiple inputs, each cell
%			contains 'auto', 'none', or a numeric vector.
%
% 'OutputDataPermutation'
%			'auto' - Automatically attempt to determine the permutation
%			between the dimensions of the output and a conventional MATLAB
%			dimension ordering. For example, the permutation from NC (ONNX
%			standard) to CN (MATLAB standard) uses the vector [2 1]. See
%			the documentation for IMPORTONNXFUNCTION for more information
%			about automatic permutation.
%
%			'none' - Return output(s) as given by the ONNX model. See 'Outputs'.
%
%			numeric vector - The permutation vector describing the
%			transformation between the ONNX output dimensions and the
%			desired output dimensions.%
%			cell array - If the network has multiple outputs, each cell
%			contains 'auto', 'none' or a numeric vector.
%
% Inputs:
% -------
% ONNX__RESHAPE_488, ONNX__RESHAPE_527, ONNX__RESHAPE_566
%			- Input(s) to the ONNX network.
%			  The input size(s) expected by the ONNX file are:
%				  ONNX__RESHAPE_488:		[Unknown, Unknown, Unknown, Unknown]				Type: FLOAT
%				  ONNX__RESHAPE_527:		[Unknown, Unknown, Unknown, Unknown]				Type: FLOAT
%				  ONNX__RESHAPE_566:		[Unknown, Unknown, Unknown, Unknown]				Type: FLOAT
%			  By default, the function will try to permute the input(s)
%			  into this dimension ordering. If the default is incorrect,
%			  use the 'InputDataPermutation' argument to control the
%			  permutation.
%
%
% PARAMS	- Network parameters returned by 'importONNXFunction'.
%
%
% Outputs:
% --------
% OUTPUT
%			- Output(s) of the ONNX network.
%			  Without permutation, the size(s) of the outputs are:
%				  OUTPUT:		[1, 18900, 85]				Type: FLOAT
%			  By default, the function will try to permute the output(s)
%			  from this dimension ordering into a conventional MATLAB
%			  ordering. If the default is incorrect, use the
%			  'OutputDataPermutation' argument to control the permutation.
%
% STATE		- (Optional) State variables. When TRAINING is true, these will
% 			  have been updated from the original values in PARAMS.State.
%
%
%  See also importONNXFunction

% Preprocess the input data and arguments:
[onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, params, varargin{:});
% Put all variables into a single struct to implement dynamic scoping:
[Vars, NumDims] = packageVariables(params, {'onnx__Reshape_488', 'onnx__Reshape_527', 'onnx__Reshape_566'}, {onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566}, [onnx__Reshape_488NumDims onnx__Reshape_527NumDims onnx__Reshape_566NumDims]);
% Call the top-level graph function:
[output, outputNumDims, state] = Reshape_To_ConcatGraph1200(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, NumDims.onnx__Reshape_488, NumDims.onnx__Reshape_527, NumDims.onnx__Reshape_566, Vars, NumDims, Training, params.State);
% Postprocess the output data
[output] = postprocessOutput(output, outputDataPerms, anyDlarrayInputs, Training, varargin{:});
end

function [output, outputNumDims1210, state] = Reshape_To_ConcatGraph1200(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims1207, onnx__Reshape_527NumDims1208, onnx__Reshape_566NumDims1209, Vars, NumDims, Training, state)
% Function implementing the graph 'Reshape_To_ConcatGraph1200'
% Update Vars and NumDims from the graph's formal input parameters. Note that state variables are already in Vars.
Vars.onnx__Reshape_488 = onnx__Reshape_488;
NumDims.onnx__Reshape_488 = onnx__Reshape_488NumDims1207;
Vars.onnx__Reshape_527 = onnx__Reshape_527;
NumDims.onnx__Reshape_527 = onnx__Reshape_527NumDims1208;
Vars.onnx__Reshape_566 = onnx__Reshape_566;
NumDims.onnx__Reshape_566 = onnx__Reshape_566NumDims1209;

% Execute the operators:
% Reshape:
[shape, NumDims.onnx__Transpose_500] = prepareReshapeArgs(Vars.onnx__Reshape_488, Vars.onnx__Reshape_613, NumDims.onnx__Reshape_488, 0);
Vars.onnx__Transpose_500 = reshape(Vars.onnx__Reshape_488, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_501] = prepareTransposeArgs(Vars.TransposePerm1201, NumDims.onnx__Transpose_500);
if ~isempty(perm)
    Vars.onnx__Sigmoid_501 = permute(Vars.onnx__Transpose_500, perm);
end

% Sigmoid:
Vars.y = sigmoid(Vars.onnx__Sigmoid_501);
NumDims.y = NumDims.onnx__Sigmoid_501;

% Split:
[Vars.onnx__Mul_503, Vars.onnx__Mul_504, Vars.onnx__Concat_505, NumDims.onnx__Mul_503, NumDims.onnx__Mul_504, NumDims.onnx__Concat_505] = onnxSplit(Vars.y, 4, Vars.SplitSplit1202, 0, NumDims.y);

% Mul:
Vars.onnx__Add_507 = Vars.onnx__Mul_503 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_507 = max(NumDims.onnx__Mul_503, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_509 = Vars.onnx__Add_507 + Vars.onnx__Add_508;
NumDims.onnx__Mul_509 = max(NumDims.onnx__Add_507, NumDims.onnx__Add_508);

% Mul:
Vars.onnx__Concat_511 = Vars.onnx__Mul_509 .* Vars.onnx__Mul_510;
NumDims.onnx__Concat_511 = max(NumDims.onnx__Mul_509, NumDims.onnx__Mul_510);

% Mul:
Vars.onnx__Pow_513 = Vars.onnx__Mul_504 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_513 = max(NumDims.onnx__Mul_504, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_516 = power(Vars.onnx__Pow_513, Vars.onnx__Pow_614);
NumDims.onnx__Mul_516 = max(NumDims.onnx__Pow_513, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_518 = Vars.onnx__Mul_516 .* Vars.onnx__Mul_517;
NumDims.onnx__Concat_518 = max(NumDims.onnx__Mul_516, NumDims.onnx__Mul_517);

% Concat:
[Vars.onnx__Reshape_519, NumDims.onnx__Reshape_519] = onnxConcat(4, {Vars.onnx__Concat_511, Vars.onnx__Concat_518, Vars.onnx__Concat_505}, [NumDims.onnx__Concat_511, NumDims.onnx__Concat_518, NumDims.onnx__Concat_505]);

% Reshape:
[shape, NumDims.onnx__Concat_526] = prepareReshapeArgs(Vars.onnx__Reshape_519, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_519, 0);
Vars.onnx__Concat_526 = reshape(Vars.onnx__Reshape_519, shape{:});

% Reshape:
[shape, NumDims.onnx__Transpose_539] = prepareReshapeArgs(Vars.onnx__Reshape_527, Vars.onnx__Reshape_624, NumDims.onnx__Reshape_527, 0);
Vars.onnx__Transpose_539 = reshape(Vars.onnx__Reshape_527, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_540] = prepareTransposeArgs(Vars.TransposePerm1203, NumDims.onnx__Transpose_539);
if ~isempty(perm)
    Vars.onnx__Sigmoid_540 = permute(Vars.onnx__Transpose_539, perm);
end

% Sigmoid:
Vars.y_3 = sigmoid(Vars.onnx__Sigmoid_540);
NumDims.y_3 = NumDims.onnx__Sigmoid_540;

% Split:
[Vars.onnx__Mul_542, Vars.onnx__Mul_543, Vars.onnx__Concat_544, NumDims.onnx__Mul_542, NumDims.onnx__Mul_543, NumDims.onnx__Concat_544] = onnxSplit(Vars.y_3, 4, Vars.SplitSplit1204, 0, NumDims.y_3);

% Mul:
Vars.onnx__Add_546 = Vars.onnx__Mul_542 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_546 = max(NumDims.onnx__Mul_542, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_548 = Vars.onnx__Add_546 + Vars.onnx__Add_547;
NumDims.onnx__Mul_548 = max(NumDims.onnx__Add_546, NumDims.onnx__Add_547);

% Mul:
Vars.onnx__Concat_550 = Vars.onnx__Mul_548 .* Vars.onnx__Mul_549;
NumDims.onnx__Concat_550 = max(NumDims.onnx__Mul_548, NumDims.onnx__Mul_549);

% Mul:
Vars.onnx__Pow_552 = Vars.onnx__Mul_543 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_552 = max(NumDims.onnx__Mul_543, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_555 = power(Vars.onnx__Pow_552, Vars.onnx__Pow_614);
NumDims.onnx__Mul_555 = max(NumDims.onnx__Pow_552, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_557 = Vars.onnx__Mul_555 .* Vars.onnx__Mul_556;
NumDims.onnx__Concat_557 = max(NumDims.onnx__Mul_555, NumDims.onnx__Mul_556);

% Concat:
[Vars.onnx__Reshape_558, NumDims.onnx__Reshape_558] = onnxConcat(4, {Vars.onnx__Concat_550, Vars.onnx__Concat_557, Vars.onnx__Concat_544}, [NumDims.onnx__Concat_550, NumDims.onnx__Concat_557, NumDims.onnx__Concat_544]);

% Reshape:
[shape, NumDims.onnx__Concat_565] = prepareReshapeArgs(Vars.onnx__Reshape_558, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_558, 0);
Vars.onnx__Concat_565 = reshape(Vars.onnx__Reshape_558, shape{:});

% Reshape:
[shape, NumDims.onnx__Transpose_578] = prepareReshapeArgs(Vars.onnx__Reshape_566, Vars.onnx__Reshape_635, NumDims.onnx__Reshape_566, 0);
Vars.onnx__Transpose_578 = reshape(Vars.onnx__Reshape_566, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_579] = prepareTransposeArgs(Vars.TransposePerm1205, NumDims.onnx__Transpose_578);
if ~isempty(perm)
    Vars.onnx__Sigmoid_579 = permute(Vars.onnx__Transpose_578, perm);
end

% Sigmoid:
Vars.y_7 = sigmoid(Vars.onnx__Sigmoid_579);
NumDims.y_7 = NumDims.onnx__Sigmoid_579;

% Split:
[Vars.onnx__Mul_581, Vars.onnx__Mul_582, Vars.onnx__Concat_583, NumDims.onnx__Mul_581, NumDims.onnx__Mul_582, NumDims.onnx__Concat_583] = onnxSplit(Vars.y_7, 4, Vars.SplitSplit1206, 0, NumDims.y_7);

% Mul:
Vars.onnx__Add_585 = Vars.onnx__Mul_581 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_585 = max(NumDims.onnx__Mul_581, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_587 = Vars.onnx__Add_585 + Vars.onnx__Add_586;
NumDims.onnx__Mul_587 = max(NumDims.onnx__Add_585, NumDims.onnx__Add_586);

% Mul:
Vars.onnx__Concat_589 = Vars.onnx__Mul_587 .* Vars.onnx__Mul_588;
NumDims.onnx__Concat_589 = max(NumDims.onnx__Mul_587, NumDims.onnx__Mul_588);

% Mul:
Vars.onnx__Pow_591 = Vars.onnx__Mul_582 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_591 = max(NumDims.onnx__Mul_582, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_594 = power(Vars.onnx__Pow_591, Vars.onnx__Pow_614);
NumDims.onnx__Mul_594 = max(NumDims.onnx__Pow_591, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_596 = Vars.onnx__Mul_594 .* Vars.onnx__Mul_595;
NumDims.onnx__Concat_596 = max(NumDims.onnx__Mul_594, NumDims.onnx__Mul_595);

% Concat:
[Vars.onnx__Reshape_597, NumDims.onnx__Reshape_597] = onnxConcat(4, {Vars.onnx__Concat_589, Vars.onnx__Concat_596, Vars.onnx__Concat_583}, [NumDims.onnx__Concat_589, NumDims.onnx__Concat_596, NumDims.onnx__Concat_583]);

% Reshape:
[shape, NumDims.onnx__Concat_604] = prepareReshapeArgs(Vars.onnx__Reshape_597, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_597, 0);
Vars.onnx__Concat_604 = reshape(Vars.onnx__Reshape_597, shape{:});

% Concat:
[Vars.output, NumDims.output] = onnxConcat(1, {Vars.onnx__Concat_526, Vars.onnx__Concat_565, Vars.onnx__Concat_604}, [NumDims.onnx__Concat_526, NumDims.onnx__Concat_565, NumDims.onnx__Concat_604]);

% Set graph output arguments from Vars and NumDims:
output = Vars.output;
outputNumDims1210 = NumDims.output;
% Set output state from Vars:
state = updateStruct(state, Vars);
end

function [inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, numDataOutputs, params, varargin)
% Function to validate inputs to Reshape_To_ConcatFcn:
p = inputParser;
isValidArrayInput = @(x)isnumeric(x) || isstring(x);
isValidONNXParameters = @(x)isa(x, 'ONNXParameters');
addRequired(p, 'onnx__Reshape_488', isValidArrayInput);
addRequired(p, 'onnx__Reshape_527', isValidArrayInput);
addRequired(p, 'onnx__Reshape_566', isValidArrayInput);
addRequired(p, 'params', isValidONNXParameters);
addParameter(p, 'InputDataPermutation', 'auto');
addParameter(p, 'OutputDataPermutation', 'auto');
addParameter(p, 'Training', false);
parse(p, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, params, varargin{:});
inputDataPerms = p.Results.InputDataPermutation;
outputDataPerms = p.Results.OutputDataPermutation;
Training = p.Results.Training;
if isnumeric(inputDataPerms)
    inputDataPerms = {inputDataPerms};
end
if isstring(inputDataPerms) && isscalar(inputDataPerms) || ischar(inputDataPerms)
    inputDataPerms = repmat({inputDataPerms},1,3);
end
if isnumeric(outputDataPerms)
    outputDataPerms = {outputDataPerms};
end
if isstring(outputDataPerms) && isscalar(outputDataPerms) || ischar(outputDataPerms)
    outputDataPerms = repmat({outputDataPerms},1,numDataOutputs);
end
end

function [onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, params, varargin)
% Parse input arguments
[inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, 1, params, varargin{:});
anyDlarrayInputs = any(cellfun(@(x)isa(x, 'dlarray'), {onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566}));
% Make the input variables into unlabelled dlarrays:
onnx__Reshape_488 = makeUnlabeledDlarray(onnx__Reshape_488);
onnx__Reshape_527 = makeUnlabeledDlarray(onnx__Reshape_527);
onnx__Reshape_566 = makeUnlabeledDlarray(onnx__Reshape_566);
% Permute inputs if requested:
onnx__Reshape_488 = permuteInputVar(onnx__Reshape_488, inputDataPerms{1}, 4);
onnx__Reshape_527 = permuteInputVar(onnx__Reshape_527, inputDataPerms{2}, 4);
onnx__Reshape_566 = permuteInputVar(onnx__Reshape_566, inputDataPerms{3}, 4);
end

function [output] = postprocessOutput(output, outputDataPerms, anyDlarrayInputs, Training, varargin)
% Set output type:
if ~anyDlarrayInputs && ~Training
    if isdlarray(output)
        output = extractdata(output);
    end
end
% Permute outputs if requested:
output = permuteOutputVar(output, outputDataPerms{1}, 3);
end


%% dlarray functions implementing ONNX operators:

function [Y, numDimsY] = onnxConcat(ONNXAxis, XCell, numDimsXArray)
% Concatentation that treats all empties the same. Necessary because
% dlarray.cat does not allow, for example, cat(1, 1x1, 1x0) because the
% second dimension sizes do not match.
numDimsY = numDimsXArray(1);
XCell(cellfun(@isempty, XCell)) = [];
if isempty(XCell)
    Y = dlarray([]);
else
    if ONNXAxis<0
        ONNXAxis = ONNXAxis + numDimsY;
    end
    DLTAxis = numDimsY - ONNXAxis;
    Y = cat(DLTAxis, XCell{:});
end
end

function varargout = onnxSplit(X, ONNXaxis, splits, numSplits, numDimsX)
% Implements the ONNX Split operator

% ONNXaxis is origin 0. splits is a vector of the lengths of each segment.
% If numSplits is nonzero, instead split into segments of equal length.
if ONNXaxis<0
    ONNXaxis = ONNXaxis + numDimsX;
end
DLTAxis = numDimsX - ONNXaxis;
if numSplits > 0
    C       = size(X, DLTAxis);
    sz      = floor(C/numSplits);
    splits	= repmat(sz, 1, numSplits);
else
    splits = extractdata(splits);
end
S      = struct;
S.type = '()';
S.subs = repmat({':'}, 1, ndims(X));
splitIndices = [0 cumsum(splits(:)')];
numY = numel(splitIndices)-1;
for i = 1:numY
    from            = splitIndices(i) + 1;
    to              = splitIndices(i+1);
    S.subs{DLTAxis}	= from:to;
    % The first numY outputs are the Y's. The second numY outputs are their
    % numDims. We assume all the outputs of Split have the same numDims as
    % the input.
    varargout{i}        = subsref(X, S);
    varargout{i + numY} = numDimsX;
end
end

function [DLTShape, numDimsY] = prepareReshapeArgs(X, ONNXShape, numDimsX, allowzero)
% Prepares arguments for implementing the ONNX Reshape operator
ONNXShape = flip(extractdata(ONNXShape));            % First flip the shape to make it correspond to the dimensions of X.
% In ONNX, 0 means "unchanged" if allowzero is false, and -1 means "infer". In DLT, there is no
% "unchanged", and [] means "infer".
DLTShape = num2cell(ONNXShape);                      % Make a cell array so we can include [].
% Replace zeros with the actual size if allowzero is true
if any(ONNXShape==0) && allowzero==0
    i0 = find(ONNXShape==0);
    DLTShape(i0) = num2cell(size(X, numDimsX - numel(ONNXShape) + i0));  % right-align the shape vector and dims
end
if any(ONNXShape == -1)
    % Replace -1 with []
    i = ONNXShape == -1;
    DLTShape{i} = [];
end
if numel(DLTShape)==1
    DLTShape = [DLTShape 1];
end
numDimsY = numel(ONNXShape);
end

function [perm, numDimsA] = prepareTransposeArgs(ONNXPerm, numDimsA)
% Prepares arguments for implementing the ONNX Transpose operator
if numDimsA <= 1        % Tensors of numDims 0 or 1 are unchanged by ONNX Transpose.
    perm = [];
else
    if isempty(ONNXPerm)        % Empty ONNXPerm means reverse the dimensions.
        perm = numDimsA:-1:1;
    else
        perm = numDimsA-flip(ONNXPerm);
    end
end
end

%% Utility functions:

function s = appendStructs(varargin)
% s = appendStructs(s1, s2,...). Assign all fields in s1, s2,... into s.
if isempty(varargin)
    s = struct;
else
    s = varargin{1};
    for i = 2:numel(varargin)
        fromstr = varargin{i};
        fs = fieldnames(fromstr);
        for j = 1:numel(fs)
            s.(fs{j}) = fromstr.(fs{j});
        end
    end
end
end

function checkInputSize(inputShape, expectedShape, inputName)

if numel(expectedShape)==0
    % The input is a scalar
    if ~isequal(inputShape, [1 1])
        inputSizeStr = makeSizeString(inputShape);
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, "[1,1]", inputSizeStr));
    end
elseif numel(expectedShape)==1
    % The input is a vector
    if ~shapeIsColumnVector(inputShape) || ~iSizesMatch({inputShape(1)}, expectedShape)
        expectedShape{2} = 1;
        expectedSizeStr = makeSizeString(expectedShape);
        inputSizeStr = makeSizeString(inputShape);
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr));
    end
else
    % The input has 2 dimensions or more
    
    % The input dimensions have been reversed; flip them back to compare to the
    % expected ONNX shape.
    inputShape = fliplr(inputShape);
    
    % If the expected shape has fewer dims than the input shape, error.
    if numel(expectedShape) < numel(inputShape)
        expectedSizeStr = strjoin(["[", strjoin(string(expectedShape), ","), "]"], "");
        error(message('nnet_cnn_onnx:onnx:InputHasGreaterNDims', inputName, expectedSizeStr));
    end
    
    % Prepad the input shape with trailing ones up to the number of elements in
    % expectedShape
    inputShape = num2cell([ones(1, numel(expectedShape) - length(inputShape)) inputShape]);
    
    % Find the number of variable size dimensions in the expected shape
    numVariableInputs = sum(cellfun(@(x) isa(x, 'char') || isa(x, 'string'), expectedShape));
    
    % Find the number of input dimensions that are not in the expected shape
    % and cannot be represented by a variable dimension
    nonMatchingInputDims = setdiff(string(inputShape), string(expectedShape));
    numNonMatchingInputDims  = numel(nonMatchingInputDims) - numVariableInputs;
    
    expectedSizeStr = makeSizeString(expectedShape);
    inputSizeStr = makeSizeString(inputShape);
    if numNonMatchingInputDims == 0 && ~iSizesMatch(inputShape, expectedShape)
        % The actual and expected input dimensions match, but in
        % a different order. The input needs to be permuted.
        error(message('nnet_cnn_onnx:onnx:InputNeedsPermute',inputName, expectedSizeStr, inputSizeStr));
    elseif numNonMatchingInputDims > 0
        % The actual and expected input sizes do not match.
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr));
    end
end
end

function doesMatch = iSizesMatch(inputShape, expectedShape)
% Check whether the input and expected shapes match, in order.
% Size elements match if (1) the elements are equal, or (2) the expected
% size element is a variable (represented by a character vector or string)
doesMatch = true;
for i=1:numel(inputShape)
    if ~(isequal(inputShape{i},expectedShape{i}) || ischar(expectedShape{i}) || isstring(expectedShape{i}))
        doesMatch = false;
        return
    end
end
end

function sizeStr = makeSizeString(shape)
sizeStr = strjoin(["[", strjoin(string(shape), ","), "]"], "");
end

function isVec = shapeIsColumnVector(shape)
if numel(shape) == 2 && shape(2) == 1
    isVec = true;
else
    isVec = false;
end
end
function X = makeUnlabeledDlarray(X)
% Make numeric X into an unlabelled dlarray
if isa(X, 'dlarray')
    X = stripdims(X);
elseif isnumeric(X)
    if isinteger(X)
        % Make ints double so they can combine with anything without
        % reducing precision
        X = double(X);
    end
    X = dlarray(X);
end
end

function [Vars, NumDims] = packageVariables(params, inputNames, inputValues, inputNumDims)
% inputNames, inputValues are cell arrays. inputRanks is a numeric vector.
Vars = appendStructs(params.Learnables, params.Nonlearnables, params.State);
NumDims = params.NumDimensions;
% Add graph inputs
for i = 1:numel(inputNames)
    Vars.(inputNames{i}) = inputValues{i};
    NumDims.(inputNames{i}) = inputNumDims(i);
end
end

function X = permuteInputVar(X, userDataPerm, onnxNDims)
% Returns reverse-ONNX ordering
if onnxNDims == 0
    return;
elseif onnxNDims == 1 && isvector(X)
    X = X(:);
    return;
elseif isnumeric(userDataPerm)
    % Permute into reverse ONNX ordering
    if numel(userDataPerm) ~= onnxNDims
        error(message('nnet_cnn_onnx:onnx:InputPermutationSize', numel(userDataPerm), onnxNDims));
    end
    perm = fliplr(userDataPerm);
elseif isequal(userDataPerm, 'auto') && onnxNDims == 4
    % Permute MATLAB HWCN to reverse onnx (WHCN)
    perm = [2 1 3 4];
elseif isequal(userDataPerm, 'as-is')
    % Do not permute the input
    perm = 1:ndims(X);
else
    % userDataPerm is either 'none' or 'auto' with no default, which means
    % it's already in onnx ordering, so just make it reverse onnx
    perm = max(2,onnxNDims):-1:1;
end
X = permute(X, perm);
end

function Y = permuteOutputVar(Y, userDataPerm, onnxNDims)
switch onnxNDims
    case 0
        perm = [];
    case 1
        if isnumeric(userDataPerm)
            % Use the user's permutation because Y is a column vector which
            % already matches ONNX.
            perm = userDataPerm;
        elseif isequal(userDataPerm, 'auto')
            % Treat the 1D onnx vector as a 2D column and transpose it
            perm = [2 1];
        else
            % userDataPerm is 'none'. Leave Y alone because it already
            % matches onnx.
            perm = [];
        end
    otherwise
        % ndims >= 2
        if isnumeric(userDataPerm)
            % Use the inverse of the user's permutation. This is not just the
            % flip of the permutation vector.
            perm = onnxNDims + 1 - userDataPerm;
        elseif isequal(userDataPerm, 'auto')
            if onnxNDims == 2
                % Permute reverse ONNX CN to DLT CN (do nothing)
                perm = [];
            elseif onnxNDims == 4
                % Permute reverse onnx (WHCN) to MATLAB HWCN
                perm = [2 1 3 4];
            else
                % User wants the output in ONNX ordering, so just reverse it from
                % reverse onnx
                perm = onnxNDims:-1:1;
            end
        elseif isequal(userDataPerm, 'as-is')
            % Do not permute the input
            perm = 1:ndims(Y);
        else
            % userDataPerm is 'none', so just make it reverse onnx
            perm = onnxNDims:-1:1;
        end
end
if ~isempty(perm)
    Y = permute(Y, perm);
end
end

function s = updateStruct(s, t)
% Set all existing fields in s from fields in t, ignoring extra fields in t.
for name = transpose(fieldnames(s))
    s.(name{1}) = t.(name{1});
end
end

[Sergio Matiz Romero]

Hi Oualid,

Thank you for sharing additional details on the issue you are facing. Based on the code you shared, the custom layer property ONNXParams in Reshape_To_ConcatLayer1211 is a likely a structure:

[output, outputNumDims, state] = Reshape_To_ConcatGraph1200(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, NumDims.onnx__Reshape_488, NumDims.onnx__Reshape_527, NumDims.onnx__Reshape_566, Vars, NumDims, Training, params.State);

Structure types are unsupported as layer properties for deep learning code generation based on:

Define Custom Deep Learning Layer for Code Generation

Therefore, my recomendation in this case would be to modify the code such that the structure fields are stored as individual properties in the Reshape_To_ConcatLayer constructor. An example of what this would look like is provided below

classdef Reshape_To_ConcatLayer1211 < nnet.layer.Layer & nnet.layer.Formattable   
    properties
        OnnxParam1
        OnnxParam2
        OnnxParam3
    end
    
    methods
        function this = Reshape_To_ConcatLayer1211(name, onnxParams)
            this.Name = name;
            this.NumInputs = 3;
            this.OutputNames = {'output'};

            % Below I use generic names for the different properties and
            % store them individually, avoiding the use of structures

            OnnxParam1 = onnxParams.Param1;
            OnnxParam2 = onnxParams.Param2;
            OnnxParam3 = onnxParams.Param3;
        end

Then you would have to modify the code such that it picks up the individual properties, instead of using the structure ONNXParams.

I hope this helps you solve the issue you are facing

[Oualid]

Hi Sergio,

Thank you for your assistance. I changed the code as you suggested but still have the same issue. Here are the steps I followed :

- 1 Load the ONNX model as DAGnetwork.

net = importONNXNetwork(modelfile,OutputDataFormats="TBC")   

-2 Created my own Graph :

Lgraph = net.layerGraph

-3 Extract the ONNXparameters of the Layer causing the problem ( Reshape_To_ConcatLayer1211 )

Param = Lgraph.Layers(300,1).ONNXParams

4- Create New Layer Named (Reshape_To_ConcatLayer1211) and pass the 5 Parameters one by one : I also changed the Autogenerated Layer to accept 5 params input in the way you suggested .

layer = Reshape_To_ConcatLayer1211('Reshape_To_ConcatFcn',Param.Learnables,Param.Nonlearnables,Param.State,Param.NumDimensions,Param.NetworkFunctionName);

5- Replace The Old layer in Lgraph and create new graph named mygraph :

mygraph= replaceLayer(Lgraph,'Reshape_To_ConcatLayer1211',layer,'ReconnectBy','order')

6-Create My own Network

mynet = assembleNetwork(mygraph)

7- Save it as .mat

8- Load to Simulink and Run sim --->>> everything works perfectly

9- Auto code Gen faills with the exactly same Error .

The modified code is below :

classdef Reshape_To_ConcatLayer1211 < nnet.layer.Layer & nnet.layer.Formattable
    % A custom layer auto-generated while importing an ONNX network.
    
    %#codegen
    %#ok<*PROPLC>
    %#ok<*NBRAK>
    %#ok<*INUSL>
    %#ok<*VARARG>
    
    properties (Learnable)
    end
    
  
    properties
        OnnxParam1
        OnnxParam2
        OnnxParam3
        OnnxParam4
        OnnxParam5
    end
    
    methods
        function this = Reshape_To_ConcatLayer1211(name, onnxParam1,onnxParam2,onnxParam3,onnxParam4,onnxParam5)
            this.Name = name;
            this.NumInputs = 3;
            this.OutputNames = {'output'};
            this.Description = 'output';
            this.Type = 'Relu';
            this.OnnxParam1 = onnxParam1;
            this.OnnxParam2 = onnxParam2;
            this.OnnxParam3 = onnxParam3;
            this.OnnxParam4 = onnxParam4;
            this.OnnxParam5 = onnxParam5;

            

            
        end
        
        function [output] = predict(this, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566)
            if isdlarray(onnx__Reshape_488)
                onnx__Reshape_488 = stripdims(onnx__Reshape_488);
            end
            if isdlarray(onnx__Reshape_527)
                onnx__Reshape_527 = stripdims(onnx__Reshape_527);
            end
            if isdlarray(onnx__Reshape_566)
                onnx__Reshape_566 = stripdims(onnx__Reshape_566);
            end
            onnx__Reshape_488NumDims = 4;
            onnx__Reshape_527NumDims = 4;
            onnx__Reshape_566NumDims = 4;
            Param1=this.OnnxParam1;
            Param2= this.OnnxParam2;
            Param3= this.OnnxParam3;
            Param4= this.OnnxParam4;
            Param5= this.OnnxParam5;

            [output, outputNumDims] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims,Param1,Param2,Param3,Param4,Param5, 'Training', false, ...
                'InputDataPermutation', {[4 3 1 2], [4 3 1 2], [4 3 1 2], ['as-is'], ['as-is'], ['as-is']}, ...
                'OutputDataPermutation', {[3 2 1], ['as-is']});
            if any(cellfun(@(A)isempty(A)||~isnumeric(A), {output}))
                fprintf('Runtime error in network. The custom layer ''%s'' output an empty or non-numeric value.\n', 'Reshape_To_ConcatLayer1211');
                error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ConcatLayer1211'));
            end
            output = dlarray(single(output), 'CBT');
            if ~coder.target('MATLAB')
                output = extractdata(output);
            end
        end
        
        function [output] = forward(this, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566)
            if isdlarray(onnx__Reshape_488)
                onnx__Reshape_488 = stripdims(onnx__Reshape_488);
            end
            if isdlarray(onnx__Reshape_527)
                onnx__Reshape_527 = stripdims(onnx__Reshape_527);
            end
            if isdlarray(onnx__Reshape_566)
                onnx__Reshape_566 = stripdims(onnx__Reshape_566);
            end
            onnx__Reshape_488NumDims = 4;
            onnx__Reshape_527NumDims = 4;
            onnx__Reshape_566NumDims = 4;
            Param1 = this.OnnxParam1;
            Param2= this.OnnxParam2;
            Param3= this.OnnxParam3;
            Param4= this.OnnxParam4;
            Param5= this.OnnxParam5;
            [output, outputNumDims] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims,Param1,Param2,Param3,Param4,Param5, 'Training', true, ...
                'InputDataPermutation', {[4 3 1 2], [4 3 1 2], [4 3 1 2], ['as-is'], ['as-is'], ['as-is']}, ...
                'OutputDataPermutation', {[3 2 1], ['as-is']});
            if any(cellfun(@(A)isempty(A)||~isnumeric(A), {output}))
                fprintf('Runtime error in network. The custom layer ''%s'' output an empty or non-numeric value.\n', 'Reshape_To_ConcatLayer1211');
                error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ConcatLayer1211'));
            end
            output = dlarray(single(output), 'CBT');
            if ~coder.target('MATLAB')
                output = extractdata(output);
            end
        end
    end
end

function [output, outputNumDims, state] = Reshape_To_ConcatFcn(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims, onnx__Reshape_527NumDims, onnx__Reshape_566NumDims, Param1,Param2,Param3,Param4,Param5,varargin)


% Preprocess the input data and arguments:
[onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, varargin{:});
% Put all variables into a single struct to implement dynamic scoping:
[Vars, NumDims] = packageVariables(Param1,Param2,Param3,Param4, {'onnx__Reshape_488', 'onnx__Reshape_527', 'onnx__Reshape_566'}, {onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566}, [onnx__Reshape_488NumDims onnx__Reshape_527NumDims onnx__Reshape_566NumDims]);
% Call the top-level graph function:
[output, outputNumDims, state] = Reshape_To_ConcatGraph1200(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, NumDims.onnx__Reshape_488, NumDims.onnx__Reshape_527, NumDims.onnx__Reshape_566, Vars, NumDims, Training, Param3);
% Postprocess the output data
[output] = postprocessOutput(output, outputDataPerms, anyDlarrayInputs, Training, varargin{:});
end

function [output, outputNumDims1210, state] = Reshape_To_ConcatGraph1200(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, onnx__Reshape_488NumDims1207, onnx__Reshape_527NumDims1208, onnx__Reshape_566NumDims1209, Vars, NumDims, Training, state)
% Function implementing the graph 'Reshape_To_ConcatGraph1200'
% Update Vars and NumDims from the graph's formal input parameters. Note that state variables are already in Vars.
Vars.onnx__Reshape_488 = onnx__Reshape_488;
NumDims.onnx__Reshape_488 = onnx__Reshape_488NumDims1207;
Vars.onnx__Reshape_527 = onnx__Reshape_527;
NumDims.onnx__Reshape_527 = onnx__Reshape_527NumDims1208;
Vars.onnx__Reshape_566 = onnx__Reshape_566;
NumDims.onnx__Reshape_566 = onnx__Reshape_566NumDims1209;

% Execute the operators:
% Reshape:
[shape, NumDims.onnx__Transpose_500] = prepareReshapeArgs(Vars.onnx__Reshape_488, Vars.onnx__Reshape_613, NumDims.onnx__Reshape_488, 0);
Vars.onnx__Transpose_500 = reshape(Vars.onnx__Reshape_488, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_501] = prepareTransposeArgs(Vars.TransposePerm1201, NumDims.onnx__Transpose_500);
if ~isempty(perm)
    Vars.onnx__Sigmoid_501 = permute(Vars.onnx__Transpose_500, perm);
end

% Sigmoid:
Vars.y = sigmoid(Vars.onnx__Sigmoid_501);
NumDims.y = NumDims.onnx__Sigmoid_501;

% Split:
[Vars.onnx__Mul_503, Vars.onnx__Mul_504, Vars.onnx__Concat_505, NumDims.onnx__Mul_503, NumDims.onnx__Mul_504, NumDims.onnx__Concat_505] = onnxSplit(Vars.y, 4, Vars.SplitSplit1202, 0, NumDims.y);

% Mul:
Vars.onnx__Add_507 = Vars.onnx__Mul_503 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_507 = max(NumDims.onnx__Mul_503, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_509 = Vars.onnx__Add_507 + Vars.onnx__Add_508;
NumDims.onnx__Mul_509 = max(NumDims.onnx__Add_507, NumDims.onnx__Add_508);

% Mul:
Vars.onnx__Concat_511 = Vars.onnx__Mul_509 .* Vars.onnx__Mul_510;
NumDims.onnx__Concat_511 = max(NumDims.onnx__Mul_509, NumDims.onnx__Mul_510);

% Mul:
Vars.onnx__Pow_513 = Vars.onnx__Mul_504 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_513 = max(NumDims.onnx__Mul_504, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_516 = power(Vars.onnx__Pow_513, Vars.onnx__Pow_614);
NumDims.onnx__Mul_516 = max(NumDims.onnx__Pow_513, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_518 = Vars.onnx__Mul_516 .* Vars.onnx__Mul_517;
NumDims.onnx__Concat_518 = max(NumDims.onnx__Mul_516, NumDims.onnx__Mul_517);

% Concat:
[Vars.onnx__Reshape_519, NumDims.onnx__Reshape_519] = onnxConcat(4, {Vars.onnx__Concat_511, Vars.onnx__Concat_518, Vars.onnx__Concat_505}, [NumDims.onnx__Concat_511, NumDims.onnx__Concat_518, NumDims.onnx__Concat_505]);

% Reshape:
[shape, NumDims.onnx__Concat_526] = prepareReshapeArgs(Vars.onnx__Reshape_519, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_519, 0);
Vars.onnx__Concat_526 = reshape(Vars.onnx__Reshape_519, shape{:});

% Reshape:
[shape, NumDims.onnx__Transpose_539] = prepareReshapeArgs(Vars.onnx__Reshape_527, Vars.onnx__Reshape_624, NumDims.onnx__Reshape_527, 0);
Vars.onnx__Transpose_539 = reshape(Vars.onnx__Reshape_527, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_540] = prepareTransposeArgs(Vars.TransposePerm1203, NumDims.onnx__Transpose_539);
if ~isempty(perm)
    Vars.onnx__Sigmoid_540 = permute(Vars.onnx__Transpose_539, perm);
end

% Sigmoid:
Vars.y_3 = sigmoid(Vars.onnx__Sigmoid_540);
NumDims.y_3 = NumDims.onnx__Sigmoid_540;

% Split:
[Vars.onnx__Mul_542, Vars.onnx__Mul_543, Vars.onnx__Concat_544, NumDims.onnx__Mul_542, NumDims.onnx__Mul_543, NumDims.onnx__Concat_544] = onnxSplit(Vars.y_3, 4, Vars.SplitSplit1204, 0, NumDims.y_3);

% Mul:
Vars.onnx__Add_546 = Vars.onnx__Mul_542 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_546 = max(NumDims.onnx__Mul_542, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_548 = Vars.onnx__Add_546 + Vars.onnx__Add_547;
NumDims.onnx__Mul_548 = max(NumDims.onnx__Add_546, NumDims.onnx__Add_547);

% Mul:
Vars.onnx__Concat_550 = Vars.onnx__Mul_548 .* Vars.onnx__Mul_549;
NumDims.onnx__Concat_550 = max(NumDims.onnx__Mul_548, NumDims.onnx__Mul_549);

% Mul:
Vars.onnx__Pow_552 = Vars.onnx__Mul_543 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_552 = max(NumDims.onnx__Mul_543, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_555 = power(Vars.onnx__Pow_552, Vars.onnx__Pow_614);
NumDims.onnx__Mul_555 = max(NumDims.onnx__Pow_552, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_557 = Vars.onnx__Mul_555 .* Vars.onnx__Mul_556;
NumDims.onnx__Concat_557 = max(NumDims.onnx__Mul_555, NumDims.onnx__Mul_556);

% Concat:
[Vars.onnx__Reshape_558, NumDims.onnx__Reshape_558] = onnxConcat(4, {Vars.onnx__Concat_550, Vars.onnx__Concat_557, Vars.onnx__Concat_544}, [NumDims.onnx__Concat_550, NumDims.onnx__Concat_557, NumDims.onnx__Concat_544]);

% Reshape:
[shape, NumDims.onnx__Concat_565] = prepareReshapeArgs(Vars.onnx__Reshape_558, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_558, 0);
Vars.onnx__Concat_565 = reshape(Vars.onnx__Reshape_558, shape{:});

% Reshape:
[shape, NumDims.onnx__Transpose_578] = prepareReshapeArgs(Vars.onnx__Reshape_566, Vars.onnx__Reshape_635, NumDims.onnx__Reshape_566, 0);
Vars.onnx__Transpose_578 = reshape(Vars.onnx__Reshape_566, shape{:});

% Transpose:
[perm, NumDims.onnx__Sigmoid_579] = prepareTransposeArgs(Vars.TransposePerm1205, NumDims.onnx__Transpose_578);
if ~isempty(perm)
    Vars.onnx__Sigmoid_579 = permute(Vars.onnx__Transpose_578, perm);
end

% Sigmoid:
Vars.y_7 = sigmoid(Vars.onnx__Sigmoid_579);
NumDims.y_7 = NumDims.onnx__Sigmoid_579;

% Split:
[Vars.onnx__Mul_581, Vars.onnx__Mul_582, Vars.onnx__Concat_583, NumDims.onnx__Mul_581, NumDims.onnx__Mul_582, NumDims.onnx__Concat_583] = onnxSplit(Vars.y_7, 4, Vars.SplitSplit1206, 0, NumDims.y_7);

% Mul:
Vars.onnx__Add_585 = Vars.onnx__Mul_581 .* Vars.onnx__Pow_614;
NumDims.onnx__Add_585 = max(NumDims.onnx__Mul_581, NumDims.onnx__Pow_614);

% Add:
Vars.onnx__Mul_587 = Vars.onnx__Add_585 + Vars.onnx__Add_586;
NumDims.onnx__Mul_587 = max(NumDims.onnx__Add_585, NumDims.onnx__Add_586);

% Mul:
Vars.onnx__Concat_589 = Vars.onnx__Mul_587 .* Vars.onnx__Mul_588;
NumDims.onnx__Concat_589 = max(NumDims.onnx__Mul_587, NumDims.onnx__Mul_588);

% Mul:
Vars.onnx__Pow_591 = Vars.onnx__Mul_582 .* Vars.onnx__Pow_614;
NumDims.onnx__Pow_591 = max(NumDims.onnx__Mul_582, NumDims.onnx__Pow_614);

% Pow:
Vars.onnx__Mul_594 = power(Vars.onnx__Pow_591, Vars.onnx__Pow_614);
NumDims.onnx__Mul_594 = max(NumDims.onnx__Pow_591, NumDims.onnx__Pow_614);

% Mul:
Vars.onnx__Concat_596 = Vars.onnx__Mul_594 .* Vars.onnx__Mul_595;
NumDims.onnx__Concat_596 = max(NumDims.onnx__Mul_594, NumDims.onnx__Mul_595);

% Concat:
[Vars.onnx__Reshape_597, NumDims.onnx__Reshape_597] = onnxConcat(4, {Vars.onnx__Concat_589, Vars.onnx__Concat_596, Vars.onnx__Concat_583}, [NumDims.onnx__Concat_589, NumDims.onnx__Concat_596, NumDims.onnx__Concat_583]);

% Reshape:
[shape, NumDims.onnx__Concat_604] = prepareReshapeArgs(Vars.onnx__Reshape_597, Vars.onnx__Reshape_618, NumDims.onnx__Reshape_597, 0);
Vars.onnx__Concat_604 = reshape(Vars.onnx__Reshape_597, shape{:});

% Concat:
[Vars.output, NumDims.output] = onnxConcat(1, {Vars.onnx__Concat_526, Vars.onnx__Concat_565, Vars.onnx__Concat_604}, [NumDims.onnx__Concat_526, NumDims.onnx__Concat_565, NumDims.onnx__Concat_604]);

% Set graph output arguments from Vars and NumDims:
output = Vars.output;
outputNumDims1210 = NumDims.output;
% Set output state from Vars:
state = updateStruct(state, Vars);
end

function [inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, numDataOutputs, varargin)
% Function to validate inputs to Reshape_To_ConcatFcn:
p = inputParser;
isValidArrayInput = @(x)isnumeric(x) || isstring(x);
addRequired(p, 'onnx__Reshape_488', isValidArrayInput);
addRequired(p, 'onnx__Reshape_527', isValidArrayInput);
addRequired(p, 'onnx__Reshape_566', isValidArrayInput);

addParameter(p, 'InputDataPermutation', 'auto');
addParameter(p, 'OutputDataPermutation', 'auto');
addParameter(p, 'Training', false);
parse(p, onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, varargin{:});
inputDataPerms = p.Results.InputDataPermutation;
outputDataPerms = p.Results.OutputDataPermutation;
Training = p.Results.Training;
if isnumeric(inputDataPerms)
    inputDataPerms = {inputDataPerms};
end
if isstring(inputDataPerms) && isscalar(inputDataPerms) || ischar(inputDataPerms)
    inputDataPerms = repmat({inputDataPerms},1,3);
end
if isnumeric(outputDataPerms)
    outputDataPerms = {outputDataPerms};
end
if isstring(outputDataPerms) && isscalar(outputDataPerms) || ischar(outputDataPerms)
    outputDataPerms = repmat({outputDataPerms},1,numDataOutputs);
end
end

function [onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, varargin)
% Parse input arguments
[inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566, 1, varargin{:});
anyDlarrayInputs = any(cellfun(@(x)isa(x, 'dlarray'), {onnx__Reshape_488, onnx__Reshape_527, onnx__Reshape_566}));
% Make the input variables into unlabelled dlarrays:
onnx__Reshape_488 = makeUnlabeledDlarray(onnx__Reshape_488);
onnx__Reshape_527 = makeUnlabeledDlarray(onnx__Reshape_527);
onnx__Reshape_566 = makeUnlabeledDlarray(onnx__Reshape_566);
% Permute inputs if requested:
onnx__Reshape_488 = permuteInputVar(onnx__Reshape_488, inputDataPerms{1}, 4);
onnx__Reshape_527 = permuteInputVar(onnx__Reshape_527, inputDataPerms{2}, 4);
onnx__Reshape_566 = permuteInputVar(onnx__Reshape_566, inputDataPerms{3}, 4);
end

function [output] = postprocessOutput(output, outputDataPerms, anyDlarrayInputs, Training, varargin)
% Set output type:
if ~anyDlarrayInputs && ~Training
    if isdlarray(output)
        output = extractdata(output);
    end
end
% Permute outputs if requested:
output = permuteOutputVar(output, outputDataPerms{1}, 3);
end


%% dlarray functions implementing ONNX operators:

function [Y, numDimsY] = onnxConcat(ONNXAxis, XCell, numDimsXArray)
% Concatentation that treats all empties the same. Necessary because
% dlarray.cat does not allow, for example, cat(1, 1x1, 1x0) because the
% second dimension sizes do not match.
numDimsY = numDimsXArray(1);
XCell(cellfun(@isempty, XCell)) = [];
if isempty(XCell)
    Y = dlarray([]);
else
    if ONNXAxis<0
        ONNXAxis = ONNXAxis + numDimsY;
    end
    DLTAxis = numDimsY - ONNXAxis;
    Y = cat(DLTAxis, XCell{:});
end
end

function varargout = onnxSplit(X, ONNXaxis, splits, numSplits, numDimsX)
% Implements the ONNX Split operator

% ONNXaxis is origin 0. splits is a vector of the lengths of each segment.
% If numSplits is nonzero, instead split into segments of equal length.
if ONNXaxis<0
    ONNXaxis = ONNXaxis + numDimsX;
end
DLTAxis = numDimsX - ONNXaxis;
if numSplits > 0
    C       = size(X, DLTAxis);
    sz      = floor(C/numSplits);
    splits	= repmat(sz, 1, numSplits);
else
    splits = extractdata(splits);
end
S      = struct;
S.type = '()';
S.subs = repmat({':'}, 1, ndims(X));
splitIndices = [0 cumsum(splits(:)')];
numY = numel(splitIndices)-1;
for i = 1:numY
    from            = splitIndices(i) + 1;
    to              = splitIndices(i+1);
    S.subs{DLTAxis}	= from:to;
    % The first numY outputs are the Y's. The second numY outputs are their
    % numDims. We assume all the outputs of Split have the same numDims as
    % the input.
    varargout{i}        = subsref(X, S);
    varargout{i + numY} = numDimsX;
end
end

function [DLTShape, numDimsY] = prepareReshapeArgs(X, ONNXShape, numDimsX, allowzero)
% Prepares arguments for implementing the ONNX Reshape operator
ONNXShape = flip(extractdata(ONNXShape));            % First flip the shape to make it correspond to the dimensions of X.
% In ONNX, 0 means "unchanged" if allowzero is false, and -1 means "infer". In DLT, there is no
% "unchanged", and [] means "infer".
DLTShape = num2cell(ONNXShape);                      % Make a cell array so we can include [].
% Replace zeros with the actual size if allowzero is true
if any(ONNXShape==0) && allowzero==0
    i0 = find(ONNXShape==0);
    DLTShape(i0) = num2cell(size(X, numDimsX - numel(ONNXShape) + i0));  % right-align the shape vector and dims
end
if any(ONNXShape == -1)
    % Replace -1 with []
    i = ONNXShape == -1;
    DLTShape{i} = [];
end
if numel(DLTShape)==1
    DLTShape = [DLTShape 1];
end
numDimsY = numel(ONNXShape);
end

function [perm, numDimsA] = prepareTransposeArgs(ONNXPerm, numDimsA)
% Prepares arguments for implementing the ONNX Transpose operator
if numDimsA <= 1        % Tensors of numDims 0 or 1 are unchanged by ONNX Transpose.
    perm = [];
else
    if isempty(ONNXPerm)        % Empty ONNXPerm means reverse the dimensions.
        perm = numDimsA:-1:1;
    else
        perm = numDimsA-flip(ONNXPerm);
    end
end
end

%% Utility functions:

function s = appendStructs(varargin)
% s = appendStructs(s1, s2,...). Assign all fields in s1, s2,... into s.
if isempty(varargin)
    s = struct;
else
    s = varargin{1};
    for i = 2:numel(varargin)
        fromstr = varargin{i};
        fs = fieldnames(fromstr);
        for j = 1:numel(fs)
            s.(fs{j}) = fromstr.(fs{j});
        end
    end
end
end

function checkInputSize(inputShape, expectedShape, inputName)

if numel(expectedShape)==0
    % The input is a scalar
    if ~isequal(inputShape, [1 1])
        inputSizeStr = makeSizeString(inputShape);
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, "[1,1]", inputSizeStr));
    end
elseif numel(expectedShape)==1
    % The input is a vector
    if ~shapeIsColumnVector(inputShape) || ~iSizesMatch({inputShape(1)}, expectedShape)
        expectedShape{2} = 1;
        expectedSizeStr = makeSizeString(expectedShape);
        inputSizeStr = makeSizeString(inputShape);
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr));
    end
else
    % The input has 2 dimensions or more
    
    % The input dimensions have been reversed; flip them back to compare to the
    % expected ONNX shape.
    inputShape = fliplr(inputShape);
    
    % If the expected shape has fewer dims than the input shape, error.
    if numel(expectedShape) < numel(inputShape)
        expectedSizeStr = strjoin(["[", strjoin(string(expectedShape), ","), "]"], "");
        error(message('nnet_cnn_onnx:onnx:InputHasGreaterNDims', inputName, expectedSizeStr));
    end
    
    % Prepad the input shape with trailing ones up to the number of elements in
    % expectedShape
    inputShape = num2cell([ones(1, numel(expectedShape) - length(inputShape)) inputShape]);
    
    % Find the number of variable size dimensions in the expected shape
    numVariableInputs = sum(cellfun(@(x) isa(x, 'char') || isa(x, 'string'), expectedShape));
    
    % Find the number of input dimensions that are not in the expected shape
    % and cannot be represented by a variable dimension
    nonMatchingInputDims = setdiff(string(inputShape), string(expectedShape));
    numNonMatchingInputDims  = numel(nonMatchingInputDims) - numVariableInputs;
    
    expectedSizeStr = makeSizeString(expectedShape);
    inputSizeStr = makeSizeString(inputShape);
    if numNonMatchingInputDims == 0 && ~iSizesMatch(inputShape, expectedShape)
        % The actual and expected input dimensions match, but in
        % a different order. The input needs to be permuted.
        error(message('nnet_cnn_onnx:onnx:InputNeedsPermute',inputName, expectedSizeStr, inputSizeStr));
    elseif numNonMatchingInputDims > 0
        % The actual and expected input sizes do not match.
        error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr));
    end
end
end

function doesMatch = iSizesMatch(inputShape, expectedShape)
% Check whether the input and expected shapes match, in order.
% Size elements match if (1) the elements are equal, or (2) the expected
% size element is a variable (represented by a character vector or string)
doesMatch = true;
for i=1:numel(inputShape)
    if ~(isequal(inputShape{i},expectedShape{i}) || ischar(expectedShape{i}) || isstring(expectedShape{i}))
        doesMatch = false;
        return
    end
end
end

function sizeStr = makeSizeString(shape)
sizeStr = strjoin(["[", strjoin(string(shape), ","), "]"], "");
end

function isVec = shapeIsColumnVector(shape)
if numel(shape) == 2 && shape(2) == 1
    isVec = true;
else
    isVec = false;
end
end
function X = makeUnlabeledDlarray(X)
% Make numeric X into an unlabelled dlarray
if isa(X, 'dlarray')
    X = stripdims(X);
elseif isnumeric(X)
    if isinteger(X)
        % Make ints double so they can combine with anything without
        % reducing precision
        X = double(X);
    end
    X = dlarray(X);
end
end

function [Vars, NumDims] = packageVariables(Param1,Param2,Param3,Param4, inputNames, inputValues, inputNumDims)
% inputNames, inputValues are cell arrays. inputRanks is a numeric vector.
Vars = appendStructs(Param1, Param2,Param3);
NumDims = Param4;
% Add graph inputs
for i = 1:numel(inputNames)
    Vars.(inputNames{i}) = inputValues{i};
    NumDims.(inputNames{i}) = inputNumDims(i);
end
end

function X = permuteInputVar(X, userDataPerm, onnxNDims)
% Returns reverse-ONNX ordering
if onnxNDims == 0
    return;
elseif onnxNDims == 1 && isvector(X)
    X = X(:);
    return;
elseif isnumeric(userDataPerm)
    % Permute into reverse ONNX ordering
    if numel(userDataPerm) ~= onnxNDims
        error(message('nnet_cnn_onnx:onnx:InputPermutationSize', numel(userDataPerm), onnxNDims));
    end
    perm = fliplr(userDataPerm);
elseif isequal(userDataPerm, 'auto') && onnxNDims == 4
    % Permute MATLAB HWCN to reverse onnx (WHCN)
    perm = [2 1 3 4];
elseif isequal(userDataPerm, 'as-is')
    % Do not permute the input
    perm = 1:ndims(X);
else
    % userDataPerm is either 'none' or 'auto' with no default, which means
    % it's already in onnx ordering, so just make it reverse onnx
    perm = max(2,onnxNDims):-1:1;
end
X = permute(X, perm);
end

function Y = permuteOutputVar(Y, userDataPerm, onnxNDims)
switch onnxNDims
    case 0
        perm = [];
    case 1
        if isnumeric(userDataPerm)
            % Use the user's permutation because Y is a column vector which
            % already matches ONNX.
            perm = userDataPerm;
        elseif isequal(userDataPerm, 'auto')
            % Treat the 1D onnx vector as a 2D column and transpose it
            perm = [2 1];
        else
            % userDataPerm is 'none'. Leave Y alone because it already
            % matches onnx.
            perm = [];
        end
    otherwise
        % ndims >= 2
        if isnumeric(userDataPerm)
            % Use the inverse of the user's permutation. This is not just the
            % flip of the permutation vector.
            perm = onnxNDims + 1 - userDataPerm;
        elseif isequal(userDataPerm, 'auto')
            if onnxNDims == 2
                % Permute reverse ONNX CN to DLT CN (do nothing)
                perm = [];
            elseif onnxNDims == 4
                % Permute reverse onnx (WHCN) to MATLAB HWCN
                perm = [2 1 3 4];
            else
                % User wants the output in ONNX ordering, so just reverse it from
                % reverse onnx
                perm = onnxNDims:-1:1;
            end
        elseif isequal(userDataPerm, 'as-is')
            % Do not permute the input
            perm = 1:ndims(Y);
        else
            % userDataPerm is 'none', so just make it reverse onnx
            perm = onnxNDims:-1:1;
        end
end
if ~isempty(perm)
    Y = permute(Y, perm);
end
end

function s = updateStruct(s, t)
% Set all existing fields in s from fields in t, ignoring extra fields in t.
for name = transpose(fieldnames(s))
    s.(name{1}) = t.(name{1});
end
end