public ONNXTensor Permute(int[] permutations)
{
// transpose both data & shape
var transposedData = Permute(m_Data, permutations);
var transposedShape = ONNXLayout.Permute(m_Shape, permutations);
return(new ONNXTensor(transposedData, transposedShape));
}
public ONNXTensor Reshape(long[] onnxShape)
{
var symbolicShape = ONNXLayout.ConvertSymbolicShapeToBarracuda(onnxShape, "?");
var reshapedData = m_Data.Reshape(symbolicShape);
for (var i = 0; i < onnxShape.Length; ++i)
{
if (onnxShape[i] < 0)
{
onnxShape[i] = reshapedData.shape[i];
}
Debug.Assert(onnxShape[i] == reshapedData.shape[i]);
}
return(new ONNXTensor(reshapedData, onnxShape));
}
public Tensor ToBarracuda(string onnxLayout)
{
Profiler.BeginSample("ONNXTensor.ToBarracuda");
if (onnxLayout == "?")
{
throw new OnnxLayerImportException("Unknown ONNX layout in not supported when converting constant tensor to Barracuda");
}
Debug.Assert(m_Shape.All(v => v > 0));
var permutations = ONNXLayout.AxisPermutationsForMappingONNXLayoutToBarracuda(rank, onnxLayout);
Debug.Assert(rank <= permutations.Length);
var outTensor = Permute(m_Data, permutations);
Profiler.EndSample();
return(outTensor);
}
internal static Tensor Permute(Tensor inTensor, int[] permutations) // TODO: unify Permute() arguments
{
var padPermutationsToBarracudaRank = 4 - permutations.Length;
if (padPermutationsToBarracudaRank > 0)
{
permutations = permutations.Concat(Enumerable.Range(permutations.Length, padPermutationsToBarracudaRank)).ToArray();
}
Debug.Assert(permutations.Length == 4);
// See: https://stackoverflow.com/a/32034565
Profiler.BeginSample("ONNXTensor.Permute");
var outTensor = new Tensor(ONNXLayout.Permute(inTensor.shape.ToArray(), permutations));
Debug.Assert(outTensor.length == inTensor.length);
// {0, 2, 3, 1} => {0, 3, 1, 2}
// {2, 3, 1, 0} => {3, 2, 0, 1}
// => {find_index(0), find_index(1), find_index(2), find_index(3)}
var reversePermute = new int[permutations.Length];
for (var i = 0; i < permutations.Length; ++i)
{
reversePermute[i] = Array.IndexOf(permutations, i);
}
// outTensor strides
var outStrideC = outTensor.channels;
var outStrideWC = outStrideC * outTensor.width;
var outStrideHWC = outStrideWC * outTensor.height;
var outStride = new int[reversePermute.Length];
for (var i = 0; i < reversePermute.Length; ++i)
{
outStride[i] = new[] { 0, outStrideHWC, outStrideWC, outStrideC, 1 }
}
public ONNXTensor(TensorProto onnxTensor)
{
// read shape
var onnxShape = onnxTensor.Dims.ToArray();
if (onnxShape.Any(s => s == 0))
{
// empty tensor, not data
m_Shape = onnxShape;
m_Data = null;
}
else
{
// read data
var shape = ONNXLayout.ConvertShapeToBarracuda(onnxShape, onnxLayout: "?");
float[] data;
if ((onnxTensor.RawData != null) && (!onnxTensor.RawData.IsEmpty))
{
var byteArray = new byte[onnxTensor.RawData.Length];
onnxTensor.RawData.CopyTo(byteArray, 0);
// Double
if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Double)
{
var typedData = new double[shape.length];
Debug.Assert((sizeof(double) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// Float32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Float)
{
data = new float[shape.length];
Debug.Assert((sizeof(float) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, data, 0, byteArray.Length);
}
// Float16
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Float16)
{
var typedData = new UInt16[shape.length];
Debug.Assert((sizeof(UInt16) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => HalfHelper.HalfToSingle(x)).ToArray();
}
// Int8
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int8)
{
var typedData = new sbyte[shape.length];
Debug.Assert((sizeof(sbyte) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// Int16
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int16)
{
var typedData = new short[shape.length];
Debug.Assert((sizeof(short) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// Int32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int32)
{
var typedData = new int[shape.length];
Debug.Assert((sizeof(int) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// Int64
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int64)
{
var typedData = new long[shape.length];
Debug.Assert((sizeof(long) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// UInt8
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint8)
{
var typedData = new byte[shape.length];
Debug.Assert((sizeof(byte) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// UInt16
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint16)
{
var typedData = new ushort[shape.length];
Debug.Assert((sizeof(ushort) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// UInt32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint32)
{
var typedData = new uint[shape.length];
Debug.Assert((sizeof(uint) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// UInt64
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint64)
{
var typedData = new ulong[shape.length];
Debug.Assert((sizeof(ulong) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => (float)x).ToArray();
}
// Bool
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Bool)
{
var typedData = new bool[shape.length];
Debug.Assert((sizeof(bool) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(x => x ? 1.0f : 0.0f).ToArray();
}
else
{
throw new OnnxLayerImportException($"Tensor data type {(TensorProto.Types.DataType)onnxTensor.DataType} is not supported.");
}
}
// Float32
else if ((onnxTensor.FloatData != null) && (onnxTensor.FloatData.Count != 0))
{
Debug.Assert(shape.length == onnxTensor.FloatData.Count);
data = new float[shape.length];
onnxTensor.FloatData.CopyTo(data, 0);
}
// Int32
else if ((onnxTensor.Int32Data != null) && (onnxTensor.Int32Data.Count != 0))
{
Debug.Assert(shape.length == onnxTensor.Int32Data.Count);
data = onnxTensor.Int32Data.Select(x => (float)x).ToArray();
}
// Int64
else if ((onnxTensor.Int64Data != null) && (onnxTensor.Int64Data.Count != 0))
{
Debug.Assert(shape.length == onnxTensor.Int64Data.Count);
data = onnxTensor.Int64Data.Select(x => (float)x).ToArray();
}
else
{
throw new OnnxLayerImportException("Could not read tensor data for constant tensor.");
}
m_Data = new Tensor(shape, new SharedArrayTensorData(data));
m_Shape = onnxShape;
}
}