Class GPUPixelOps

Represents a GPUPixel backend ops.

Inheritance

Object

CPUOps

GPUPixelOps

Inherited Members

CPUOps.GlobalAverageVariancePool(TensorFloat, TensorFloat, Int32)

CPUOps.InstanceNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

CPUOps.AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

CPUOps.Cast(Tensor, DataType)

CPUOps.IsInf(TensorFloat, Boolean, Boolean)

CPUOps.PRelu(TensorFloat, TensorFloat)

CPUOps.Shrink(TensorFloat, Single, Single)

CPUOps.Einsum(String, TensorFloat[])

CPUOps.Transpose(Tensor)

CPUOps.Pow(TensorFloat, TensorInt)

CPUOps.Where(TensorInt, Tensor, Tensor)

CPUOps.Tile(Tensor, Int32[])

CPUOps.ConstantOfShape(TensorShape, Int32)

CPUOps.CompressWithIndices(Tensor, TensorInt, Int32, Int32)

CPUOps.Gather(Tensor, TensorInt, Int32)

CPUOps.GatherElements(Tensor, TensorInt, Int32)

CPUOps.GatherND(Tensor, TensorInt, Int32)

CPUOps.ScatterElements(Tensor, TensorInt, Tensor, Int32, ScatterReductionMode)

CPUOps.ScatterND(TensorFloat, TensorInt, TensorFloat, ScatterReductionMode)

CPUOps.OneHot(TensorInt, Int32, Int32, Int32, Int32)

CPUOps.TopK(TensorFloat, Int32, Int32, Boolean, Boolean)

CPUOps.RoiAlign(TensorFloat, TensorFloat, TensorInt, RoiPoolingMode, Int32, Int32, Int32, Single)

CPUOps.RandomNormal(TensorShape, Single, Single, Nullable<Single>)

CPUOps.RandomUniform(TensorShape, Single, Single, Nullable<Single>)

CPUOps.Bernoulli(TensorFloat, DataType, Nullable<Single>)

CPUOps.MemCopy(Tensor, Tensor, Int32, Int32, Int32)

CPUOps.MemCopyStride(Tensor, Tensor, Int32, Int32, Int32, Int32, Int32, Int32)

CPUOps.SinglePassLSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, TensorFloat, RnnActivation[], Single[], Single[], Boolean, Single, Boolean, Int32, RnnLayout)

CPUOps.PinToDevice(Tensor, Boolean)

CPUOps.Add(TensorInt, TensorInt)

CPUOps.Sub(TensorInt, TensorInt)

CPUOps.Mul(TensorInt, TensorInt)

CPUOps.Div(TensorInt, TensorInt)

CPUOps.Greater(TensorFloat, TensorFloat)

CPUOps.Greater(TensorInt, TensorInt)

CPUOps.GreaterOrEqual(TensorFloat, TensorFloat)

CPUOps.GreaterOrEqual(TensorInt, TensorInt)

CPUOps.Less(TensorFloat, TensorFloat)

CPUOps.LessOrEqual(TensorFloat, TensorFloat)

CPUOps.Equal(TensorFloat, TensorFloat)

CPUOps.Less(TensorInt, TensorInt)

CPUOps.LessOrEqual(TensorInt, TensorInt)

CPUOps.Equal(TensorInt, TensorInt)

CPUOps.Or(TensorInt, TensorInt)

CPUOps.And(TensorInt, TensorInt)

CPUOps.Xor(TensorInt, TensorInt)

CPUOps.Mod(TensorInt, TensorInt)

CPUOps.FMod(TensorInt, TensorInt)

CPUOps.Min(TensorInt[])

CPUOps.Max(TensorInt[])

CPUOps.Sum(TensorInt[])

CPUOps.Abs(TensorInt)

CPUOps.Neg(TensorInt)

CPUOps.Sign(TensorFloat)

CPUOps.Sign(TensorInt)

CPUOps.IsNaN(TensorFloat)

CPUOps.Cast(TensorInt)

CPUOps.Cast(TensorFloat)

CPUOps.Not(TensorInt)

CPUOps.ReduceMin(TensorInt, Int32[], Boolean)

CPUOps.ReduceMax(TensorInt, Int32[], Boolean)

CPUOps.ReduceSum(TensorInt, Int32[], Boolean)

CPUOps.ReduceSumSquare(TensorInt, Int32[], Boolean)

CPUOps.ReduceProd(TensorInt, Int32[], Boolean)

CPUOps.ReduceL1(TensorInt, Int32[], Boolean)

CPUOps.ArgMax(TensorFloat, Int32, Boolean, Boolean)

CPUOps.ArgMax(TensorInt, Int32, Boolean, Boolean)

CPUOps.ArgMin(TensorFloat, Int32, Boolean, Boolean)

CPUOps.ArgMin(TensorInt, Int32, Boolean, Boolean)

CPUOps.Hardmax(TensorFloat, Int32)

CPUOps.CumSum(TensorFloat, Int32, Boolean, Boolean)

CPUOps.CumSum(TensorInt, Int32, Boolean, Boolean)

CPUOps.Tril(Tensor, Int32)

CPUOps.Triu(Tensor, Int32)

CPUOps.Range(Single, Single, Single)

CPUOps.Range(Int32, Int32, Int32)

CPUOps.PostLayerCleanup()

CPUOps.NewTensor(TensorShape, DataType, AllocScope)

CPUOps.NewTensorFloat(TensorShape, AllocScope)

CPUOps.NewTensorInt(TensorShape, AllocScope)

CPUOps.NewOutputTensorFloat(TensorShape)

CPUOps.NewOutputTensorInt(TensorShape)

CPUOps.NewOutputTensor(TensorShape, DataType)

CPUOps.NewTempTensorFloat(TensorShape)

CPUOps.NewTempTensorInt(TensorShape)

CPUOps.ResetAllocator(Boolean)

CPUOps.Dispose()

CPUOps.Compress(Tensor, TensorInt, Int32)

CPUOps.NonMaxSuppression(TensorFloat, TensorFloat, Int32, Single, Single, CenterPointBox)

CPUOps.LRN(TensorFloat, Single, Single, Single, Int32)

CPUOps.Multinomial(TensorFloat, Int32, Nullable<Single>)

CPUOps.NonZero(TensorFloat)

CPUOps.NonZero(TensorInt)

CPUOps.ScatterND(TensorInt, TensorInt, TensorInt, ScatterReductionMode)

CPUOps.Shape(Tensor, Int32, Int32)

CPUOps.Size(TensorShape)

CPUOps.LSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, RnnDirection, RnnActivation[], Single[], Single[], Boolean, Single, RnnLayout)

Object.ToString()

Object.Equals(Object)

Object.Equals(Object, Object)

Object.ReferenceEquals(Object, Object)

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Namespace: Unity.Sentis

Syntax

public class GPUPixelOps : CPUOps, IOps, IDisposable

Constructors

GPUPixelOps(ITensorAllocator)

Initializes and returns an instance of GPUPixelOps.

Declaration

public GPUPixelOps(ITensorAllocator allocator = null)

Parameters

Type	Name	Description
ITensorAllocator	allocator

Properties

deviceType

Returns the DeviceType for the ops.

Declaration

public override DeviceType deviceType { get; }

Property Value

Type	Description
DeviceType

Overrides

CPUOps.deviceType

Methods

Abs(TensorFloat)

Computes an output tensor by applying the element-wise Abs math function: f(x) = f(x) = |x|.

Declaration

public override TensorFloat Abs(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Abs(TensorFloat)

Acos(TensorFloat)

Computes an output tensor by applying the element-wise Acos trigonometric function: f(x) = acos(x).

Declaration

public override TensorFloat Acos(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Acos(TensorFloat)

Acosh(TensorFloat)

Computes an output tensor by applying the element-wise Acosh trigonometric function: f(x) = acosh(x).

Declaration

public override TensorFloat Acosh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Acosh(TensorFloat)

Add(TensorFloat, TensorFloat)

Performs an element-wise Add math operation: f(a, b) = a + b.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Add(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Add(TensorFloat, TensorFloat)

Asin(TensorFloat)

Computes an output tensor by applying the element-wise Asin trigonometric function: f(x) = asin(x).

Declaration

public override TensorFloat Asin(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Asin(TensorFloat)

Asinh(TensorFloat)

Computes an output tensor by applying the element-wise Asinh trigonometric function: f(x) = asinh(x).

Declaration

public override TensorFloat Asinh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Asinh(TensorFloat)

Atan(TensorFloat)

Computes an output tensor by applying the element-wise Atan trigonometric function: f(x) = atan(x).

Declaration

public override TensorFloat Atan(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Atan(TensorFloat)

Atanh(TensorFloat)

Computes an output tensor by applying the element-wise Atanh trigonometric function: f(x) = atanh(x).

Declaration

public override TensorFloat Atanh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Atanh(TensorFloat)

AveragePool(TensorFloat, Int32[], Int32[], Int32[])

Calculates an output tensor by pooling the mean values of the input tensor across its spatial dimensions according to the given pool and stride values.

Declaration

public override TensorFloat AveragePool(TensorFloat X, int[] pool, int[] stride, int[] pad)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pool	The size of the kernel along each spatial axis.
Int32[]	stride	The stride along each spatial axis.
Int32[]	pad	The lower and upper padding values for each spatial dimension. For example, [pad_left, pad_right] for 1D, or [pad_top, pad_bottom, pad_left, pad_right] for 2D.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.AveragePool(TensorFloat, Int32[], Int32[], Int32[])

Ceil(TensorFloat)

Computes an output tensor by applying the element-wise Ceil math function: f(x) = ceil(x).

Declaration

public override TensorFloat Ceil(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Ceil(TensorFloat)

Celu(TensorFloat, Single)

Computes an output tensor by applying the element-wise Celu activation function: f(x) = max(0, x) + min(0, alpha * (exp(x / alpha) - 1)).

Declaration

public override TensorFloat Celu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the Celu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Celu(TensorFloat, Single)

Clip(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise Clip math function: f(x) = clamp(x, min, max).

Declaration

public override TensorFloat Clip(TensorFloat X, float min, float max)

Parameters

Type	Name	Description
TensorFloat	X
Single	min	The lower clip value.
Single	max	The upper clip value.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Clip(TensorFloat, Single, Single)

Concat(Tensor[], Int32)

Calculates an output tensor by concatenating the input tensors along a given axis.

Declaration

public override Tensor Concat(Tensor[] tensors, int axis)

Parameters

Type	Name	Description
Tensor[]	tensors	The input tensors.
Int32	axis	The axis along which to concatenate the input tensors.

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Concat(Tensor[], Int32)

ConstantOfShape(TensorShape, Single)

Generates a tensor with a given shape filled with a given value.

Declaration

public override TensorFloat ConstantOfShape(TensorShape X, float value)

Parameters

Type	Name	Description
TensorShape	X	The input tensor shape.
Single	value	The fill value.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ConstantOfShape(TensorShape, Single)

Conv(TensorFloat, TensorFloat, TensorFloat, Int32, Int32[], Int32[], Int32[], FusableActivation)

Applies a convolution filter to an input tensor.

Declaration

public override TensorFloat Conv(TensorFloat X, TensorFloat K, TensorFloat B, int groups, int[] stride, int[] pad, int[] dilation, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	K	The filter tensor.
TensorFloat	B	The optional bias tensor.
Int32	groups	The number of groups that input channels and output channels are divided into.
Int32[]	stride	The optional stride value for each spatial dimension of the filter.
Int32[]	pad	The optional lower and upper padding values for each spatial dimension of the filter.
Int32[]	dilation	The optional dilation value of each spatial dimension of the filter.
FusableActivation	fusedActivation	The fused activation type to apply after the convolution.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Conv(TensorFloat, TensorFloat, TensorFloat, Int32, Int32[], Int32[], Int32[], FusableActivation)

Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)

Applies a transpose convolution filter to an input tensor.

Declaration

public override TensorFloat Conv2DTrans(TensorFloat X, TensorFloat K, TensorFloat B, int[] stride, int[] pad, int[] outputAdjustment, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	K	The filter tensor.
TensorFloat	B	The optional bias tensor.
Int32[]	stride	The optional stride value for each spatial dimension of the filter.
Int32[]	pad	The optional lower and upper padding values for each spatial dimension of the filter.
Int32[]	outputAdjustment	The output padding value for each spatial dimension in the filter.
FusableActivation	fusedActivation	The fused activation type to apply after the convolution.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)

Copy(Tensor)

Creates a copy of a given input tensor with the same shape and values.

Declaration

public override Tensor Copy(Tensor X)

Parameters

Type	Name	Description
Tensor	X

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Copy(Tensor)

Cos(TensorFloat)

Computes an output tensor by applying the element-wise Cos trigonometric function: f(x) = cos(x).

Declaration

public override TensorFloat Cos(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Cos(TensorFloat)

Cosh(TensorFloat)

Computes an output tensor by applying the element-wise Cosh trigonometric function: f(x) = cosh(x).

Declaration

public override TensorFloat Cosh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Cosh(TensorFloat)

Dense(TensorFloat, TensorFloat, TensorFloat, FusableActivation)

Performs a matrix multiplication operation: f(x, w, b) = X x W + B.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Dense(TensorFloat X, TensorFloat W, TensorFloat B, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	W	The weights tensor.
TensorFloat	B	The bias tensor.
FusableActivation	fusedActivation	The fused activation to apply to the output tensor after the dense operation.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Dense(TensorFloat, TensorFloat, TensorFloat, FusableActivation)

DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

Computes the output tensor by permuting data from depth into blocks of spatial data.

Declaration

public override TensorFloat DepthToSpace(TensorFloat X, int blocksize, DepthToSpaceMode mode)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	blocksize	The size of the blocks to move the depth data into.
DepthToSpaceMode	mode	The ordering of the data in the output tensor as a DepthToSpaceMode.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

Div(TensorFloat, TensorFloat)

Performs an element-wise Div math operation: f(a, b) = a / b.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Div(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Div(TensorFloat, TensorFloat)

Elu(TensorFloat, Single)

Computes an output tensor by applying the element-wise Elu activation function: f(x) = x if x >= 0, otherwise f(x) = alpha * (e^x - 1).

Declaration

public override TensorFloat Elu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Single	alpha	The alpha value to use for the Elu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Elu(TensorFloat, Single)

Erf(TensorFloat)

Computes an output tensor by applying the element-wise Erf activation function: f(x) = erf(x).

Declaration

public override TensorFloat Erf(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Erf(TensorFloat)

Exp(TensorFloat)

Computes an output tensor by applying the element-wise Exp math function: f(x) = exp(x).

Declaration

public override TensorFloat Exp(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Exp(TensorFloat)

Expand(Tensor, TensorShape)

Calculates an output tensor by broadcasting the input tensor into a given shape.

Declaration

public override Tensor Expand(Tensor X, TensorShape newShape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	newShape

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Expand(Tensor, TensorShape)

Floor(TensorFloat)

Computes an output tensor by applying the element-wise Floor math function: f(x) = floor(x).

Declaration

public override TensorFloat Floor(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Floor(TensorFloat)

FMod(TensorFloat, TensorFloat)

Performs an element-wise Mod math operation: f(a, b) = a % b.

The sign of the remainder is the same as the sign of the dividend, as in C#.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat FMod(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.FMod(TensorFloat, TensorFloat)

Gelu(TensorFloat)

Computes an output tensor by applying the element-wise Gelu activation function: f(x) = x / 2 * (1 + erf(x / sqrt(2))).

Declaration

public override TensorFloat Gelu(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Gelu(TensorFloat)

GlobalAveragePool(TensorFloat)

Calculates an output tensor by pooling the mean values of the input tensor across all of its spatial dimensions. The spatial dimensions of the output are size 1.

Declaration

public override TensorFloat GlobalAveragePool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.GlobalAveragePool(TensorFloat)

GlobalMaxPool(TensorFloat)

Calculates an output tensor by pooling the maximum values of the input tensor across all of its spatial dimensions. The spatial dimensions of the output are size 1.

Declaration

public override TensorFloat GlobalMaxPool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.GlobalMaxPool(TensorFloat)

HardSigmoid(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise HardSigmoid activation function: f(x) = clamp(alpha * x + beta, 0, 1).

Declaration

public override TensorFloat HardSigmoid(TensorFloat X, float alpha, float beta)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the HardSigmoid activation function.
Single	beta	The beta value to use for the HardSigmoid activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.HardSigmoid(TensorFloat, Single, Single)

HardSwish(TensorFloat)

Computes an output tensor by applying the element-wise HardSwish activation function: f(x) = x * max(0, min(1, 1/6 * x + 0.5)).

Declaration

public override TensorFloat HardSwish(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.HardSwish(TensorFloat)

LeakyRelu(TensorFloat, Single)

Computes an output tensor by applying the element-wise LeakyRelu activation function: f(x) = x if x >= 0, otherwise f(x) = alpha * x.

Declaration

public override TensorFloat LeakyRelu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the LeakyRelu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.LeakyRelu(TensorFloat, Single)

Log(TensorFloat)

Computes an output tensor by applying the element-wise Log math function: f(x) = log(x).

Declaration

public override TensorFloat Log(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Log(TensorFloat)

LogSoftmax(TensorFloat, Int32)

Computes an output tensor by applying the LogSoftmax activation function along an axis: f(x, axis) = log(Softmax(x, axis)).

Declaration

public override TensorFloat LogSoftmax(TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the LogSoftmax activation function. The default value is -1.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.LogSoftmax(TensorFloat, Int32)

MatMul(TensorFloat, TensorFloat)

Performs a multi-dimensional matrix multiplication operation: f(a, b) = a x b.

Declaration

public override TensorFloat MatMul(TensorFloat X, TensorFloat Y)

Parameters

Type	Name	Description
TensorFloat	X	The first input tensor.
TensorFloat	Y	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.MatMul(TensorFloat, TensorFloat)

MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

Performs a matrix multiplication operation with optional transposes: f(a, b) = a' x b'.

Declaration

public override TensorFloat MatMul2D(TensorFloat X, bool xTranspose, TensorFloat Y, bool yTranspose)

Parameters

Type	Name	Description
TensorFloat	X	The first input tensor.
Boolean	xTranspose	Whether to transpose the first input tensor before performing the matrix multiplication.
TensorFloat	Y
Boolean	yTranspose	Whether to transpose the second input tensor before performing the matrix multiplication.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

Max(TensorFloat[])

Performs an element-wise Max math operation: f(x1, x2 ... xn) = max(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Max(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Max(TensorFloat[])

MaxPool(TensorFloat, Int32[], Int32[], Int32[])

Calculates an output tensor by pooling the maximum values of the input tensor across its spatial dimensions according to the given pool and stride values.

Declaration

public override TensorFloat MaxPool(TensorFloat X, int[] pool, int[] stride, int[] pad)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pool	The size of the kernel along each spatial axis.
Int32[]	stride	The stride along each spatial axis.
Int32[]	pad	The lower and upper padding values for each spatial dimension. For example, [pad_left, pad_right] for 1D, or [pad_top, pad_bottom, pad_left, pad_right] for 2D.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.MaxPool(TensorFloat, Int32[], Int32[], Int32[])

Mean(TensorFloat[])

Performs an element-wise Mean math operation: f(x1, x2 ... xn) = (x1 + x2 ... xn) / n.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Mean(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Mean(TensorFloat[])

MemSet(Tensor, Int32, Int32, Int32)

Set values of O in indices [offset0, offsetO + length] to value

Declaration

protected override void MemSet(Tensor O, int value, int length = -1, int offsetO = 0)

Parameters

Type	Name	Description
Tensor	O
Int32	value
Int32	length
Int32	offsetO

Overrides

CPUOps.MemSet(Tensor, Int32, Int32, Int32)

Min(TensorFloat[])

Performs an element-wise Min math operation: f(x1, x2 ... xn) = min(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Min(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Min(TensorFloat[])

Mul(TensorFloat, TensorFloat)

Performs an element-wise Mul math operation: f(a, b) = a * b.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Mul(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Mul(TensorFloat, TensorFloat)

Neg(TensorFloat)

Computes an output tensor by applying the element-wise Neg math function: f(x) = -x.

Declaration

public override TensorFloat Neg(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Neg(TensorFloat)

Pad(TensorFloat, Int32[], PadMode, Single)

Calculates the output tensor by adding padding to the input tensor according to the given padding values and mode.

Declaration

public override TensorFloat Pad(TensorFloat X, int[] pad, PadMode padMode, float constant)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pad	The lower and upper padding values for each dimension.
PadMode	padMode	The PadMode to use when padding. The default value is PadMode.Constant.
Single	constant	The constant value to fill with when using PadMode.Constant. The default value is 0.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Pad(TensorFloat, Int32[], PadMode, Single)

Pow(TensorFloat, TensorFloat)

Performs an element-wise Pow math operation: f(a, b) = pow(a, b).

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Pow(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Pow(TensorFloat, TensorFloat)

Reciprocal(TensorFloat)

Computes an output tensor by applying the element-wise Reciprocal math function: f(x) = 1 / x.

Declaration

public override TensorFloat Reciprocal(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Reciprocal(TensorFloat)

ReduceL1(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceL1 operation: f(x1, x2 ... xn) = |x1| + |x2| + ... + |xn|.

Declaration

public override TensorFloat ReduceL1(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceL1(TensorFloat, Int32[], Boolean)

ReduceL2(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceL2 operation: f(x1, x2 ... xn) = sqrt(x1² + x2² + ... + xn²).

Declaration

public override TensorFloat ReduceL2(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceL2(TensorFloat, Int32[], Boolean)

ReduceLogSum(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceLogSum operation: f(x1, x2 ... xn) = log(x1 + x2 + ... + xn).

Declaration

public override TensorFloat ReduceLogSum(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceLogSum(TensorFloat, Int32[], Boolean)

ReduceLogSumExp(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceLogSumExp operation: f(x1, x2 ... xn) = log(e^x1 + e^x2 + ... + e^xn).

Declaration

public override TensorFloat ReduceLogSumExp(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceLogSumExp(TensorFloat, Int32[], Boolean)

ReduceMax(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMax operation: f(x1, x2 ... xn) = max(x1, x2, ... , xn).

Declaration

public override TensorFloat ReduceMax(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceMax(TensorFloat, Int32[], Boolean)

ReduceMean(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMean operation: f(x1, x2 ... xn) = (x1 + x2 + ... + xn) / n.

Declaration

public override TensorFloat ReduceMean(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceMean(TensorFloat, Int32[], Boolean)

ReduceMin(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMin operation: f(x1, x2 ... xn) = min(x1, x2, ... , xn).

Declaration

public override TensorFloat ReduceMin(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceMin(TensorFloat, Int32[], Boolean)

ReduceProd(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceProd operation: f(x1, x2 ... xn) = x1 * x2 * ... * xn.

Declaration

public override TensorFloat ReduceProd(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceProd(TensorFloat, Int32[], Boolean)

ReduceSum(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSum operation: f(x1, x2 ... xn) = x1 + x2 + ... + xn.

Declaration

public override TensorFloat ReduceSum(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceSum(TensorFloat, Int32[], Boolean)

ReduceSumSquare(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSumSquare operation: f(x1, x2 ... xn) = x1² + x2² + ... + xn².

Declaration

public override TensorFloat ReduceSumSquare(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ReduceSumSquare(TensorFloat, Int32[], Boolean)

Relu(TensorFloat)

Computes an output tensor by applying the element-wise Relu activation function: f(x) = max(0, x).

Declaration

public override TensorFloat Relu(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Relu(TensorFloat)

Relu6(TensorFloat)

Computes an output tensor by applying the element-wise Relu6 activation function: f(x) = clamp(x, 0, 6).

Declaration

public override TensorFloat Relu6(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Relu6(TensorFloat)

Reshape(Tensor, TensorShape)

Calculates an output tensor by copying the data from the input tensor and using a given shape. The data from the input tensor is unchanged.

Declaration

public override Tensor Reshape(Tensor X, TensorShape newShape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	newShape

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Reshape(Tensor, TensorShape)

Resize(TensorFloat, Single[], InterpolationMode, NearestMode, CoordTransformMode)

Calculates an output tensor by resampling the input tensor along the spatial dimensions with given scales.

Declaration

public override TensorFloat Resize(TensorFloat X, float[] scale, InterpolationMode interpolationMode, NearestMode nearestMode = NearestMode.RoundPreferFloor, CoordTransformMode coordTransformMode = CoordTransformMode.HalfPixel)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Single[]	scale	The factor to scale each dimension by.
InterpolationMode	interpolationMode	The InterpolationMode to use for the operation.
NearestMode	nearestMode	The NearestMode to use for the operation when using InterpolationMode.NearestMode. The default is NearestMode.RoundPreferFloor.
CoordTransformMode	coordTransformMode	The CoordTransformMode to use for the operation. The default is CoordTransformMode.HalfPixel.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Resize(TensorFloat, Single[], InterpolationMode, NearestMode, CoordTransformMode)

Round(TensorFloat)

Computes an output tensor by applying the element-wise Round math function: f(x) = round(x).

If the fractional part is equal to 0.5, rounds to the nearest even integer.

Declaration

public override TensorFloat Round(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Round(TensorFloat)

ScaleBias(TensorFloat, TensorFloat, TensorFloat)

Computes the output tensor with an element-wise ScaleBias function: f(x, s, b) = x * s + b.

Declaration

public override TensorFloat ScaleBias(TensorFloat X, TensorFloat S, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	S	The scale tensor.
TensorFloat	B	The bias tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ScaleBias(TensorFloat, TensorFloat, TensorFloat)

Selu(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise Selu activation function: f(x) = gamma * x if x >= 0, otherwise f(x) = (alpha * e^x - alpha).

Declaration

public override TensorFloat Selu(TensorFloat X, float alpha, float gamma)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the Selu activation function.
Single	gamma	The alpha value to use for the Selu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Selu(TensorFloat, Single, Single)

Sigmoid(TensorFloat)

Computes an output tensor by applying the element-wise Sigmoid activation function: f(x) = 1/(1 + e^(-x)).

Declaration

public override TensorFloat Sigmoid(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sigmoid(TensorFloat)

Sin(TensorFloat)

Computes an output tensor by applying the element-wise Sin trigonometric function: f(x) = sin(x).

Declaration

public override TensorFloat Sin(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sin(TensorFloat)

Sinh(TensorFloat)

Computes an output tensor by applying the element-wise Sinh trigonometric function: f(x) = sinh(x).

Declaration

public override TensorFloat Sinh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sinh(TensorFloat)

Slice(Tensor, Int32[], Int32[], Int32[], Int32[])

Calculates an output tensor by slicing the input tensor along given axes with given starts, ends, and steps.

Declaration

public override Tensor Slice(Tensor X, int[] starts, int[] ends, int[] axes, int[] steps)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32[]	starts	The start index along each axis.
Int32[]	ends	The end index along each axis.
Int32[]	axes	The axes along which to slice. If this is null, the layer slices all axes.
Int32[]	steps	The step values for slicing. If this is null, the layer uses step size 1 throughout.

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Slice(Tensor, Int32[], Int32[], Int32[], Int32[])

Softmax(TensorFloat, Int32)

Computes an output tensor by applying the Softmax activation function along an axis: f(x, axis) = exp(X) / ReduceSum(exp(X), axis).

Declaration

public override TensorFloat Softmax(TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the Softmax activation function. The default value is -1.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Softmax(TensorFloat, Int32)

Softplus(TensorFloat)

Computes an output tensor by applying the element-wise Softplus activation function: f(x) = ln(e^x + 1).

Declaration

public override TensorFloat Softplus(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Softplus(TensorFloat)

Softsign(TensorFloat)

Computes an output tensor by applying the element-wise Softsign activation function: f(x) = x/(|x| + 1).

Declaration

public override TensorFloat Softsign(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Softsign(TensorFloat)

SpaceToDepth(TensorFloat, Int32)

Computes the output tensor by permuting data from blocks of spatial data into depth.

Declaration

public override TensorFloat SpaceToDepth(TensorFloat X, int blocksize)

Parameters

Type	Name	Description
TensorFloat	X
Int32	blocksize	The size of the blocks to move the depth data into.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.SpaceToDepth(TensorFloat, Int32)

Split(Tensor, Int32, Int32, Int32)

Calculates an output tensor by splitting the input tensor along a given axis between start and end.

Declaration

public override Tensor Split(Tensor X, int axis, int start, int end)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32	axis	The axis along which to split the input tensor.
Int32	start	The inclusive start value for the split.
Int32	end	The exclusive end value for the split.

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Split(Tensor, Int32, Int32, Int32)

Sqrt(TensorFloat)

Computes an output tensor by applying the element-wise Sqrt math function: f(x) = sqrt(x).

Declaration

public override TensorFloat Sqrt(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sqrt(TensorFloat)

Square(TensorFloat)

Computes an output tensor by applying the element-wise Square math function: f(x) = x * x.

Declaration

public override TensorFloat Square(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Square(TensorFloat)

Sub(TensorFloat, TensorFloat)

Performs an element-wise Sub math operation: f(a, b) = a - b.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Sub(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sub(TensorFloat, TensorFloat)

Sum(TensorFloat[])

Performs an element-wise Sum math operation: f(x1, x2 ... xn) = x1 + x2 ... xn.

This supports numpy-style broadcasting of input tensors.

Declaration

public override TensorFloat Sum(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Sum(TensorFloat[])

Swish(TensorFloat)

Computes an output tensor by applying the element-wise Swish activation function: f(x) = sigmoid(x) * x = x / (1 + e^{-x}).

Declaration

public override TensorFloat Swish(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Swish(TensorFloat)

Tan(TensorFloat)

Computes an output tensor by applying the element-wise Tan trigonometric function: f(x) = tan(x).

Declaration

public override TensorFloat Tan(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Tan(TensorFloat)

Tanh(TensorFloat)

Computes an output tensor by applying the element-wise Tanh activation function: f(x) = tanh(x).

Declaration

public override TensorFloat Tanh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.Tanh(TensorFloat)

ThresholdedRelu(TensorFloat, Single)

Computes an output tensor by applying the element-wise ThresholdedRelu activation function: f(x) = x if x > alpha, otherwise f(x) = 0.

Declaration

public override TensorFloat ThresholdedRelu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the ThresholdedRelu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Overrides

CPUOps.ThresholdedRelu(TensorFloat, Single)

Transpose(Tensor, Int32[])

Calculates an output tensor by permuting the axes and data of the input tensor according to the given permutations.

Declaration

public override Tensor Transpose(Tensor X, int[] permutations)

Parameters

Type	Name	Description
Tensor	X
Int32[]	permutations	The axes to sample the output tensor from in the input tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Overrides

CPUOps.Transpose(Tensor, Int32[])