private Layer ConvertMul(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var inputA = _graph.Tensors(inputs[0]).Value;
var inputB = _graph.Tensors(inputs[1]).Value;
if (inputA.ShapeLength == 0)
{
var layer = new Mul(inputB.GetShapeArray().ToNCHW(), _model.GetScalar <float>(inputA));
_inputs.Add(layer.Input, inputs[1]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
else if (inputB.ShapeLength == 0)
{
var layer = new Mul(inputA.GetShapeArray().ToNCHW(), _model.GetScalar <float>(inputB));
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
else
{
throw new LayerNotSupportedException(op.ToString(), "Only scalar multiply is supported");
}
}
private Layer ConvertFullyConnected(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.FullyConnectedOptions>().Value;
var weights = _graph.Tensors(inputs[1]).Value;
var bias = _graph.Tensors(inputs[2]).Value;
if (input.ShapeLength == 4 && (input.Shape(1) != 1 || input.Shape(2) != 1))
{
var flatten = new TensorflowFlatten(input.GetShapeArray().ToNCHW());
var layer = new FullyConnected(flatten.Output.Dimensions, _model.GetTensor <float>(weights), _model.GetTensor <float>(bias),
options.FusedActivationFunction.ToActivationFunction());
layer.Input.SetConnection(flatten.Output);
_inputs.Add(flatten.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
else
{
var layer = new FullyConnected(input.GetShapeArray().ToNCHW(), _model.GetTensor <float>(weights), _model.GetTensor <float>(bias),
options.FusedActivationFunction.ToActivationFunction());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
}
private Layer ConvertDepthwiseConv2d(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.DepthwiseConv2DOptions>().Value;
var weights = _graph.Tensors(inputs[1]).Value;
var bias = _graph.Tensors(inputs[2]).Value;
var depthMul = options.DepthMultiplier;
if (input.GetShapeArray().ToNCHW()[1] == 1 && depthMul != 1)
{
var layer = new Conv2d(input.GetShapeArray().ToNCHW(), _model.GetTensor <float>(weights).ToOIHW().Transpose(new[] { 1, 0, 2, 3 }), _model.GetTensor <float>(bias),
options.Padding.ToPadding(), options.StrideW, options.StrideH, options.FusedActivationFunction.ToActivationFunction());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
else if (depthMul != 1)
{
throw new LayerNotSupportedException("DEPTHWISE_CONV_2D", "depth_multiplier must be 1");
}
{
var layer = new DepthwiseConv2d(input.GetShapeArray().ToNCHW(), _model.GetTensor <float>(weights).ToOIHW(), _model.GetTensor <float>(bias),
options.Padding.ToPadding(), options.StrideW, options.StrideH, options.FusedActivationFunction.ToActivationFunction());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
}
private Layer ConvertLogistic(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var layer = new Logistic(input.GetShapeArray().ToNCHW());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertLeakyRelu(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.LeakyReluOptions>().Value;
var layer = new LeakyRelu(input.GetShapeArray().ToNCHW(), options.Alpha);
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertReshape(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.ReshapeOptions>().Value;
var layer = new TensorflowReshape(input.GetShapeArray().ToNCHW(), options.GetNewShapeArray().ToNCHW());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertMaximum(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var inputA = _graph.Tensors(inputs[0]).Value;
var inputB = _graph.Tensors(inputs[1]).Value;
var layer = new Maximum(inputA.GetShapeArray().ToNCHW(), inputB.GetShapeArray().ToNCHW());
_inputs.Add(layer.InputA, inputs[0]);
_inputs.Add(layer.InputB, inputs[1]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertMaxPool2d(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.Pool2DOptions>().Value;
var layer = new MaxPool2d(input.GetShapeArray().ToNCHW(), options.Padding.ToPadding(), options.FilterWidth, options.FilterHeight, options.StrideW,
options.StrideH, options.FusedActivationFunction.ToActivationFunction());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertSpaceToBatchNd(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var blockShape = _graph.Tensors(inputs[1]).Value;
var paddings = _graph.Tensors(inputs[2]).Value;
var layer = new SpaceToBatchNd(input.GetShapeArray().ToNCHW(), _model.GetTensor <int>(blockShape), _model.GetTensor <int>(paddings));
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertResizeNearestNeighbor(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var newSize = _model.GetTensor <int>(_graph.Tensors(inputs[1]).Value);
var options = op.BuiltinOptions <tflite.ResizeNearestNeighborOptions>().Value;
var blockShape = _graph.Tensors(inputs[1]).Value;
var layer = new ResizeNearestNeighbor(input.GetShapeArray().ToNCHW(), newSize[1], newSize[0], options.AlignCorners);
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertConv2d(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var options = op.BuiltinOptions <tflite.Conv2DOptions>().Value;
var weights = _graph.Tensors(inputs[1]).Value;
var bias = _graph.Tensors(inputs[2]).Value;
var layer = new Conv2d(input.GetShapeArray().ToNCHW(), _model.GetTensor <float>(weights).ToOIHW(), _model.GetTensor <float>(bias),
options.Padding.ToPadding(), options.StrideW, options.StrideH, options.FusedActivationFunction.ToActivationFunction());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertPad(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var paddings = _graph.Tensors(inputs[1]).Value;
var layer = new Pad(input.GetShapeArray().ToNCHW(), _model.GetTensor <int>(paddings));
if (!layer.Paddings.ToArray().SequenceEqual(new[] { 0, 0, 1, 1, 1, 1, 0, 0 }))
{
throw new LayerNotSupportedException("Pad", "Only paddings of [[0,0],[1,1],[1,1],[0,0]] is supported");
}
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertConcatenation(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var options = op.BuiltinOptions <tflite.ConcatenationOptions>().Value;
if (options.Axis != 3)
{
throw new NotSupportedException("Axis of concatenation must be 3.");
}
var layer = new Concatenation(inputs.Select(x => new ReadOnlyMemory <int>(_graph.Tensors(x).Value.GetShapeArray().ToNCHW())));
for (int i = 0; i < inputs.Length; i++)
{
_inputs.Add(layer.Inputs[i], inputs[i]);
}
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
private Layer ConvertMean(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
var axes = _model.GetTensor <int>(_graph.Tensors(inputs[1]).Value);
if (axes.ToArray().SequenceEqual(new[] { 1, 2 }))
{
var layer = new GlobalAveragePool(input.GetShapeArray().ToNCHW());
_inputs.Add(layer.Input, inputs[0]);
var reshape = new Reshape(layer.Output.Dimensions, new[] { -1, layer.Output.Dimensions[1] });
reshape.Input.SetConnection(layer.Output);
_outputs.Add(op.Outputs(0), layer.Output);
return(reshape);
}
else
{
throw new LayerNotSupportedException(op.ToString(), "Only [1,2] axis mean is supported");
}
}
private Layer ConvertL2Normalization(tflite.Operator op)
{
var inputs = op.GetInputsArray();
var input = _graph.Tensors(inputs[0]).Value;
if (input.ShapeLength == 4 && (input.Shape(1) != 1 || input.Shape(2) != 1))
{
var flatten = new TensorflowFlatten(input.GetShapeArray().ToNCHW());
var layer = new L2Normalization(flatten.Output.Dimensions);
layer.Input.SetConnection(flatten.Output);
_inputs.Add(flatten.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
else
{
var layer = new L2Normalization(input.GetShapeArray().ToNCHW());
_inputs.Add(layer.Input, inputs[0]);
_outputs.Add(op.Outputs(0), layer.Output);
return(layer);
}
}