public static NDArray convert_to_numpy_ndarray(object values)
{
NDArray nd;
switch (values)
{
case NDArray val:
nd = val;
break;
case bool boolVal:
nd = boolVal;
break;
case int intVal:
nd = intVal;
break;
case int[] intVals:
nd = np.array(intVals);
break;
case int[,] intVals:
nd = np.array(intVals);
break;
case long intVal:
nd = intVal;
break;
case long[] intVals:
nd = np.array(intVals);
break;
case long[,] intVals:
nd = np.array(intVals);
break;
case float floatVal:
nd = floatVal;
break;
case float[] floatVals:
nd = floatVals;
break;
case float[,] floatVals:
nd = np.array(floatVals);
break;
case double doubleVal:
nd = doubleVal;
break;
case double[] doubleVals:
nd = np.array(doubleVals);
break;
case double[,] doubleVals:
nd = np.array(doubleVals);
break;
case string strVal:
nd = NDArray.FromString(strVal);
break;
case string[] strVals:
nd = strVals;
break;
case byte[] byteValues:
nd = byteValues;
break;
case byte[,] byteValues:
nd = np.array(byteValues);
break;
default:
throw new NotImplementedException($"convert_to_numpy_ndarray: Support for type {values.GetType()} Not Implemented");
}
return(nd);
}
/// <summary>
/// Create a TensorProto.
/// </summary>
/// <param name="values"></param>
/// <param name="dtype"></param>
/// <param name="shape"></param>
/// <param name="verify_shape"></param>
/// <param name="allow_broadcast"></param>
/// <returns></returns>
public static TensorProto make_tensor_proto(object values, TF_DataType dtype = TF_DataType.DtInvalid, int[] shape = null, bool verify_shape = false, bool allow_broadcast = false)
{
if (allow_broadcast && verify_shape)
{
throw new ValueError("allow_broadcast and verify_shape are not both allowed.");
}
if (values is TensorProto tp)
{
return(tp);
}
if (dtype != TF_DataType.DtInvalid)
{
;
}
bool is_quantized = new TF_DataType[]
{
TF_DataType.TF_QINT8, TF_DataType.TF_QUINT8, TF_DataType.TF_QINT16, TF_DataType.TF_QUINT16,
TF_DataType.TF_QINT32
}.Contains(dtype);
// We first convert value to a numpy array or scalar.
NDArray nparray = null;
var np_dt = dtype.as_numpy_datatype();
if (values is NDArray nd)
{
nparray = nd;
}
else
{
if (values == null)
{
throw new ValueError("None values not supported.");
}
if (np_dt == null)
{
switch (values)
{
case bool boolVal:
nparray = boolVal;
break;
case int intVal:
nparray = intVal;
break;
case int[] intVals:
nparray = np.array(intVals);
break;
case int[,] intVals:
nparray = np.array(intVals);
break;
case long intVal:
nparray = intVal;
break;
case long[] intVals:
nparray = np.array(intVals);
break;
case long[,] intVals:
nparray = np.array(intVals);
break;
case float floatVal:
nparray = floatVal;
break;
case float[] floatVals:
nparray = floatVals;
break;
case float[,] floatVals:
nparray = np.array(floatVals);
break;
case double doubleVal:
nparray = doubleVal;
break;
case double[] doubleVals:
nparray = np.array(doubleVals);
break;
case double[,] doubleVals:
nparray = np.array(doubleVals);
break;
case string strVal:
nparray = strVal;
break;
case string[] strVals:
nparray = strVals;
break;
case byte[] byteValues:
nparray = byteValues;
break;
case byte[,] byteValues:
nparray = np.array(byteValues);
break;
default:
throw new NotImplementedException($"make_tensor_proto: Support for type {values.GetType()} Not Implemented");
}
}
else
{
// convert data type
switch (np_dt.Name)
{
case "Int32":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Convert.ToInt32(values);
}
break;
case "Int64":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Convert.ToInt64(values);
}
break;
case "Single":
if (values.GetType().IsArray)
{
nparray = np.array((float[])values, np_dt);
}
else
{
nparray = Convert.ToSingle(values);
}
break;
case "Double":
if (values.GetType().IsArray)
{
nparray = np.array((double[])values, np_dt);
}
else
{
nparray = Convert.ToDouble(values);
}
break;
case "String":
if (values.GetType().IsArray)
{
nparray = np.array((string[])values, np_dt);
}
else
{
nparray = NDArray.FromString(Convert.ToString(values));
}
break;
case "Boolean":
if (values.GetType().IsArray)
{
nparray = np.array((bool[])values, np_dt);
}
else
{
nparray = Convert.ToBoolean(values);
}
break;
default:
throw new NotImplementedException($"make_tensor_proto: Support for type {np_dt.Name} Not Implemented");
}
}
}
var numpy_dtype = dtypes.as_dtype(nparray.dtype, dtype: dtype);
if (numpy_dtype == TF_DataType.DtInvalid)
{
throw new TypeError($"Unrecognized data type: {nparray.dtype}");
}
// If dtype was specified and is a quantized type, we convert
// numpy_dtype back into the quantized version.
if (is_quantized)
{
numpy_dtype = dtype;
}
bool is_same_size = false;
int shape_size = 0;
// If shape is not given, get the shape from the numpy array.
if (shape == null)
{
shape = nparray.shape;
is_same_size = true;
shape_size = nparray.size;
}
else
{
shape_size = new TensorShape(shape).size;
is_same_size = shape_size == nparray.size;
}
var tensor_proto = new TensorProto
{
Dtype = numpy_dtype.as_datatype_enum(),
TensorShape = tensor_util.as_shape(shape)
};
if (is_same_size && _TENSOR_CONTENT_TYPES.Contains(numpy_dtype) && shape_size > 1)
{
byte[] bytes = nparray.ToByteArray();
tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(bytes.ToArray());
return(tensor_proto);
}
if (numpy_dtype == TF_DataType.TF_STRING && !(values is NDArray))
{
if (values is string str)
{
tensor_proto.StringVal.Add(Google.Protobuf.ByteString.CopyFromUtf8(str));
tensor_proto.TensorShape = tensor_util.as_shape(new int[0]);
}
else if (values is string[] str_values)
{
tensor_proto.StringVal.AddRange(str_values.Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x)));
}
else if (values is byte[] byte_values)
{
tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(byte_values);
}
return(tensor_proto);
}
var proto_values = nparray.ravel();
switch (nparray.dtype.Name)
{
case "Bool":
case "Boolean":
tensor_proto.BoolVal.AddRange(proto_values.Data <bool>());
break;
case "Int32":
tensor_proto.IntVal.AddRange(proto_values.Data <int>());
break;
case "Int64":
tensor_proto.Int64Val.AddRange(proto_values.Data <long>());
break;
case "Single":
tensor_proto.FloatVal.AddRange(proto_values.Data <float>());
break;
case "Double":
tensor_proto.DoubleVal.AddRange(proto_values.Data <double>());
break;
/*case "String":
* tensor_proto.StringVal.AddRange(proto_values.Data<string>().Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x.ToString())));
* break;*/
default:
throw new Exception("make_tensor_proto Not Implemented");
}
return(tensor_proto);
}
public void FromString()
{
var str = NDArray.FromString(hello);
str.Should().BeOfType <char>().And.BeShaped(5).And.BeOfValues('h', 'e', 'l', 'l', 'o');
}
