public L2NormalizationLayerArgument Convert(L2Normalization layer, ConvertContext context)
{
return(new L2NormalizationLayerArgument
{
Channels = (uint)layer.Input.Dimensions[1]
});
}
public void Infer(L2Normalization layer, L2NormalizationLayerArgument argument, InferenceContext context)
{
var inputAlloc = context.MainMemoryMap[layer.Input.Connection.From];
var outputAlloc = context.MainMemoryMap[layer.Output];
argument.Flags = K210LayerFlags.MainMemoryOutput;
argument.MainMemoryInputAddress = inputAlloc.GetAddress();
argument.MainMemoryOutputAddress = outputAlloc.GetAddress();
}
private Layer ConvertNormalize(LayerParameter layerParam)
{
var input = _outputs[layerParam.Bottom[0]];
var param = layerParam.NormalizeParam;
var layer = new L2Normalization(input.Dimensions);
layer.Input.SetConnection(input);
_outputs[layerParam.Top[0]] = layer.Output;
return(layer);
}
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);
}
}
