private unsafe static void ToStringUInt16()
{
int size = Unsafe.SizeOf <Vector64 <UInt16> >() / sizeof(UInt16);
UInt16[] values = new UInt16[size];
for (int i = 0; i < size; i++)
{
values[i] = TestLibrary.Generator.GetUInt16();
}
Vector64 <UInt16> vector = Vector64.Create(values[0], values[1], values[2], values[3]);
string actual = vector.ToString();
string expected = '<' + string.Join(", ", values.Select(x => x.ToString("G", System.Globalization.CultureInfo.InvariantCulture))) + '>';
bool succeeded = string.Equals(expected, actual, StringComparison.Ordinal);
if (!succeeded)
{
TestLibrary.TestFramework.LogInformation($"Vector64UInt16ToString: Vector64<UInt16>.ToString() returned an unexpected result.");
TestLibrary.TestFramework.LogInformation($"Expected: {expected}");
TestLibrary.TestFramework.LogInformation($"Actual: {actual}");
TestLibrary.TestFramework.LogInformation(string.Empty);
throw new Exception("One or more scenarios did not complete as expected.");
}
}
public void UInt16ArrayTest()
{
var array = new UInt16[] { 0, 1 };
var table = this.fixture.Execute(array);
var ids = table.AsEnumerable().Select(x => x.Field <int>("Id")).ToArray();
Assert.Equal(array.Select(x => (int)x).ToArray(), ids);
}
public void UInt16ArrayTest()
{
var array = new UInt16[] { 0, 1 };
var arrayXmlToCursor = new ArrayXmlToCursor(array);
var table = GetDataTable(arrayXmlToCursor);
var ids = table.AsEnumerable().Select(x => x.Field <int>("Id")).ToArray();
CollectionAssert.AreEqual(array.Select(x => (int)x).ToArray(), ids);
}
public void FillUInt16Array_ShouldProduceDesiredResults_ForBoundedInvocation()
{
// Arrange.
var arrayLength = 180;
var floor = 10000;
var ceiling = 30000;
var array = new UInt16[arrayLength];
using (var randomNumberGenerator = RandomNumberGenerator.Create())
{
// Act.
randomNumberGenerator.FillUInt16Array(array, (UInt16)floor, (UInt16)ceiling);
// Assert.
array.Length.Should().Be(arrayLength);
array.Select(value => Convert.ToDouble(value)).Average().Should().BeGreaterOrEqualTo(15000).And.BeLessOrEqualTo(25000);
}
}
public ONNXTensor(TensorProto onnxTensor)
{
// read shape
var onnxShape = onnxTensor.Dims.ToArray();
var shape = ONNXLayout.ConvertShapeToBarracuda(onnxShape, onnxLayout: "?");
// read data
float[] data;
if ((onnxTensor.RawData != null) && (!onnxTensor.RawData.IsEmpty))
{
var byteArray = new byte[onnxTensor.RawData.Length];
onnxTensor.RawData.CopyTo(byteArray, 0);
// Float32
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();
}
// 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();
}
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;
}
public ONNXTensor(TensorProto onnxTensor)
{
// read shape
var onnxShape = onnxTensor.Dims.Select(v => v <int.MinValue?int.MinValue : v> int.MaxValue ? int.MaxValue : (int)v).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];
Assert.IsTrue((sizeof(double) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => v <int.MinValue?(float)int.MinValue : v> int.MaxValue ? (float)int.MaxValue : (float)v).ToArray();
}
// Float32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Float)
{
data = new float[shape.length];
Assert.IsTrue((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];
Assert.IsTrue((sizeof(UInt16) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => HalfHelper.HalfToSingle(v)).ToArray();
}
// Int8
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int8)
{
var typedData = new sbyte[shape.length];
Assert.IsTrue((sizeof(sbyte) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// Int16
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int16)
{
var typedData = new short[shape.length];
Assert.IsTrue((sizeof(short) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// Int32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int32)
{
var typedData = new int[shape.length];
Assert.IsTrue((sizeof(int) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// Int64
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Int64)
{
var typedData = new long[shape.length];
Assert.IsTrue((sizeof(long) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => v <int.MinValue?(float)int.MinValue : v> int.MaxValue ? (float)int.MaxValue : (float)v).ToArray();
}
// UInt8
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint8)
{
var typedData = new byte[shape.length];
Assert.IsTrue((sizeof(byte) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// UInt16
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint16)
{
var typedData = new ushort[shape.length];
Assert.IsTrue((sizeof(ushort) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// UInt32
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint32)
{
var typedData = new uint[shape.length];
Assert.IsTrue((sizeof(uint) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => (float)v).ToArray();
}
// UInt64
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Uint64)
{
var typedData = new ulong[shape.length];
Assert.IsTrue((sizeof(ulong) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => v > uint.MaxValue ? (float)uint.MaxValue : (float)v).ToArray();
}
// Bool
else if (onnxTensor.DataType == (int)TensorProto.Types.DataType.Bool)
{
var typedData = new bool[shape.length];
Assert.IsTrue((sizeof(bool) * shape.length) == onnxTensor.RawData.Length);
Buffer.BlockCopy(byteArray, 0, typedData, 0, byteArray.Length);
data = typedData.Select(v => v ? 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))
{
Assert.IsTrue(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))
{
Assert.IsTrue(shape.length == onnxTensor.Int32Data.Count);
data = onnxTensor.Int32Data.Select(v => (float)v).ToArray();
}
// Int64
else if ((onnxTensor.Int64Data != null) && (onnxTensor.Int64Data.Count != 0))
{
Assert.IsTrue(shape.length == onnxTensor.Int64Data.Count);
data = onnxTensor.Int64Data.Select(v => v <int.MinValue?(float)int.MinValue : v> int.MaxValue ? (float)int.MaxValue : (float)v).ToArray();
}
else
{
throw new OnnxLayerImportException("Could not read tensor data for constant tensor.");
}
m_Data = new Tensor(shape, new SharedArrayTensorData(data));
m_Shape = onnxShape;
}
}