/// <summary>
/// Converts a class vector (integers) to binary class matrix.
/// </summary>
/// <param name="y"></param>
/// <param name="num_classes"></param>
/// <param name="dtype"></param>
/// <returns></returns>
public static NDArray to_categorical(NDArray y, int num_classes = -1, TF_DataType dtype = TF_DataType.TF_FLOAT)
{
var y1 = y.astype(NPTypeCode.Int32).ToArray <int>();
// var input_shape = y.shape[..^1];
var categorical = np.zeros((y.size, num_classes), dtype: dtype.as_numpy_dtype());
// categorical[np.arange(y.size), y] = 1;
for (int i = 0; i < y.size; i++)
{
categorical[i][y1[i]] = 1;
}
return(categorical);
}
/// <summary>
/// Convert given <see cref="Array"/> to <see cref="Tensor"/>.
/// </summary>
/// <param name="array">The array to convert, can be regular, jagged or multi-dim array.</param>
/// <param name="astype">Convert <see cref="Array"/> to given <paramref name="astype"/> before inserting it into a <see cref="Tensor"/>.</param>
/// <exception cref="NotSupportedException"></exception>
public static Tensor ToTensor(Array array, TF_DataType?astype = null)
{
if (array == null)
{
throw new ArgumentNullException(nameof(array));
}
var arrtype = array.ResolveElementType();
var astype_type = astype?.as_numpy_dtype() ?? arrtype;
if (astype_type == arrtype)
{
//no conversion required
if (astype == TF_DataType.TF_STRING)
{
throw new NotSupportedException(); //TODO! when string is fully implemented.
}
if (astype == TF_DataType.TF_INT8)
{
if (array.Rank != 1 || array.GetType().GetElementType()?.IsArray == true) //is multidim or jagged
{
array = Arrays.Flatten(array);
}
return(new Tensor((sbyte[])array));
}
//is multidim or jagged, if so - use NDArrays constructor as it records shape.
if (array.Rank != 1 || array.GetType().GetElementType().IsArray)
{
return(new Tensor(new NDArray(array)));
}
#if _REGEN
#region Compute
switch (arrtype)
{
% foreach supported_dtypes, supported_dtypes_lowercase %
case NPTypeCode.#1: return(new Tensor((#2[])arr));
%
/// <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);
}
// We first convert value to a numpy array or scalar.
NDArray nparray = null;
var np_dt = dtype.as_numpy_dtype();
if (values is NDArray nd)
{
nparray = nd;
}
else
{
if (values == null)
{
throw new ValueError("None values not supported.");
}
nparray = convert_to_numpy_ndarray(values);
if (np_dt != null && np_dt != typeof(string))
{
nparray = nparray.astype(np_dt);
}
}
var numpy_dtype = nparray.dtype.as_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 (quantized_types.Contains(dtype))
{
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);
}
private static EagerTensor convert_to_eager_tensor(object value, Context ctx, TF_DataType dtype = TF_DataType.DtInvalid)
{
ctx.ensure_initialized();
// convert data type
if (dtype != TF_DataType.DtInvalid &&
value.GetType().Name != "NDArray" &&
value.GetType().BaseType.Name != "Array" &&
dtypes.as_base_dtype(dtype) != dtypes.as_dtype(value.GetType()))
{
switch (dtype)
{
case TF_DataType.TF_DOUBLE:
value = Convert.ToDouble(value);
break;
case TF_DataType.TF_FLOAT:
value = Convert.ToSingle(value);
break;
case TF_DataType.TF_INT64:
value = Convert.ToInt64(value);
break;
default:
break;
}
}
else if (dtype != TF_DataType.DtInvalid &&
value is NDArray nd &&
dtypes.as_dtype(nd.dtype) != dtype)
{
value = nd.astype(dtype.as_numpy_dtype());
}
if (dtype == TF_DataType.TF_STRING && value is byte[] bytes)
{
return(new EagerTensor(bytes, ctx.DeviceName, TF_DataType.TF_STRING));
}
switch (value)
{
case EagerTensor val:
return(val);
case NDArray val:
return(new EagerTensor(val, ctx.DeviceName));
case TensorShape val:
return(new EagerTensor(val.dims, ctx.DeviceName));
case string val:
return(new EagerTensor(val, ctx.DeviceName));
case string[] val:
return(new EagerTensor(val, ctx.DeviceName));
case bool val:
return(new EagerTensor(val, ctx.DeviceName));
case byte val:
return(new EagerTensor(val, ctx.DeviceName));
case byte[] val:
return(new EagerTensor(val, ctx.DeviceName));
case byte[,] val:
return(new EagerTensor(val, ctx.DeviceName));
case byte[,,] val:
return(new EagerTensor(val, ctx.DeviceName));
case int val:
return(new EagerTensor(val, ctx.DeviceName));
case int[] val:
return(new EagerTensor(val, ctx.DeviceName));
case int[,] val:
return(new EagerTensor(val, ctx.DeviceName));
case int[,,] val:
return(new EagerTensor(val, ctx.DeviceName));
case long val:
return(new EagerTensor(val, ctx.DeviceName));
case long[] val:
return(new EagerTensor(val, ctx.DeviceName));
case long[,] val:
return(new EagerTensor(val, ctx.DeviceName));
case long[,,] val:
return(new EagerTensor(val, ctx.DeviceName));
case float val:
return(new EagerTensor(val, ctx.DeviceName));
case float[] val:
return(new EagerTensor(val, ctx.DeviceName));
case float[,] val:
return(new EagerTensor(val, ctx.DeviceName));
case float[,,] val:
return(new EagerTensor(val, ctx.DeviceName));
case double val:
return(new EagerTensor(val, ctx.DeviceName));
case double[] val:
return(new EagerTensor(val, ctx.DeviceName));
case double[,] val:
return(new EagerTensor(val, ctx.DeviceName));
case double[,,] val:
return(new EagerTensor(val, ctx.DeviceName));
default:
throw new NotImplementedException($"convert_to_eager_tensor {value.GetType()}");
}
}
/// <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_dtype();
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($"make_tensor_proto: Support for type {values.GetType()} Not Implemented");
}
}
else
{
// convert data type
switch (np_dt.Name)
{
case "Int32":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Converts.ToInt32(values);
}
break;
case "Int64":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Converts.ToInt64(values);
}
break;
case "Single":
if (values.GetType().IsArray)
{
nparray = np.array((float[])values, np_dt);
}
else
{
nparray = Converts.ToSingle(values);
}
break;
case "Double":
if (values.GetType().IsArray)
{
nparray = np.array((double[])values, np_dt);
}
else
{
nparray = Converts.ToDouble(values);
}
break;
case "String":
if (values.GetType().IsArray)
{
nparray = np.array((string[])values, np_dt);
}
else
{
nparray = NDArray.FromString(Converts.ToString(values));
}
break;
case "Boolean":
if (values.GetType().IsArray)
{
nparray = np.array((bool[])values, np_dt);
}
else
{
nparray = Converts.ToBoolean(values);
}
break;
default:
throw new NotImplementedException($"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);
}
