private ONNXTensor BakeNodeIntoConstant(Action <ModelBuilder, ONNXNodeWrapper> opImportAction, ONNXNodeWrapper node)
{
var model = new Model();
var net = new ModelBuilder(model);
// add all inputs as constants
Debug.Assert(node.AreAllInputsConst);
for (var i = 0; i < node.InputCount; ++i)
{
var assumeOnnxLayout = i == 0 ? "NCHW" : "CONST";
var input = node.Inputs[i];
net.Const(input,
constantTensors[input].ToBarracuda(assumeOnnxLayout));
}
// add node that we are going to bake into the constant
opImportAction(net, node);
// bake
var noInputs = new Dictionary <string, Tensor>();
var useCPUforBaking = WorkerFactory.Device.CPU;
var worker = WorkerFactory.CreateWorker(model, useCPUforBaking);
var result = worker.ExecuteAndWaitForCompletion(noInputs);
// convert from Barracuda back into ONNX layout
var onnxData = ONNXTensor.Permute(result, new int[] { 0, 3, 1, 2 }); // NHWC -> NCHW
var onnxShape = onnxData.shape.ToArray().Select(x => (long)x).ToArray();
return(new ONNXTensor(onnxData, onnxShape).SqueezeAll());
}
// Transpose channels first to channels last data in MatMul/GEMM weight tensor
internal static Tensor SwapSpatialDimensionsAndFeaturesInMatMulWeights(Tensor weights, int featureCount)
{
Debug.Assert(featureCount <= weights.flatHeight);
if (featureCount != weights.flatHeight)
{
var shape = weights.shape;
var implicitSpatialDimensionsInWeights = shape.flatHeight / featureCount;
Debug.Assert(shape.flatHeight % featureCount == 0);
// reshape: C__K -> CHWK
weights = weights.Reshape(
new TensorShape(featureCount, implicitSpatialDimensionsInWeights, 1, shape.channels));
// permute: CHWK -> HWCK
weights = ONNXTensor.Permute(weights, new int[] { 1, 0, 2, 3 }); // @TODO: use Permute(, onnxLayout:CHWK)
// reshape: HWCK -> C__K
weights = weights.Reshape(shape);
}
return(weights);
}