Class ReferenceCPUOps

Reference CPU implementation of IOps

Inheritance
Object
ReferenceCPUOps
ReferenceComputeOps
UnsafeArrayCPUOps
Namespace: Unity.Barracuda
Syntax
public class ReferenceCPUOps : object, IOps

Constructors

ReferenceCPUOps(ITensorAllocator)

Create ReferenceCPUOps

Declaration
public ReferenceCPUOps(ITensorAllocator allocator = null)
Parameters
Type Name Description
ITensorAllocator allocator

allocator

Methods

Abs(Tensor)

Abs

Declaration
public virtual Tensor Abs(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Acos(Tensor)

Acos

Declaration
public virtual Tensor Acos(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Acosh(Tensor)

Acosh

Declaration
public virtual Tensor Acosh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Add(Tensor[])

Add tensors together

Declaration
public virtual Tensor Add(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

ArgMax(Tensor, Int32)

ArgMax

Declaration
public virtual Tensor ArgMax(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ArgMin(Tensor, Int32)

ArgMax

Declaration
public virtual Tensor ArgMin(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Asin(Tensor)

Asin

Declaration
public virtual Tensor Asin(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Asinh(Tensor)

Asinh

Declaration
public virtual Tensor Asinh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Atan(Tensor)

Atan

Declaration
public virtual Tensor Atan(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Atanh(Tensor)

Atanh

Declaration
public virtual Tensor Atanh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

AvgPool2D(Tensor, Int32[], Int32[], Int32[])

2D average pooling

Declaration
public virtual Tensor AvgPool2D(Tensor X, int[] pool, int[] stride, int[] pad)
Parameters
Type Name Description
Tensor X
Int32[] pool

pooling
Int32[] stride

stride
Int32[] pad

padding
Returns
Type Description
Tensor

output Tensor
Implements

Bernoulli(Single)

Bernoulli distribution

Declaration
protected float Bernoulli(float p)
Parameters
Type Name Description
Single p

p
Returns
Type Description
Single

random value

Border2D(Tensor, Int32[], Single)

2D border padding

Declaration
public virtual Tensor Border2D(Tensor X, int[] pad, float value)
Parameters
Type Name Description
Tensor X
Int32[] pad

padding
Single value
Returns
Type Description
Tensor

output Tensor
Implements

Border3D(Tensor, Int32[], Single)

3D border padding

Declaration
public virtual Tensor Border3D(Tensor X, int[] pad, float value)
Parameters
Type Name Description
Tensor X
Int32[] pad

padding
Single value
Returns
Type Description
Tensor

output Tensor
Implements

Ceil(Tensor)

Ceil

Declaration
public virtual Tensor Ceil(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Clip(Tensor, Single, Single)

Clip

Declaration
public virtual Tensor Clip(Tensor X, float min, float max)
Parameters
Type Name Description
Tensor X
Single min

min value
Single max

max value
Returns
Type Description
Tensor

output Tensor
Implements

Concat(Tensor[], Int32)

Concatenate tensors across axis

Declaration
public virtual Tensor Concat(Tensor[] tensors, int axis)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ConstantOfShape(TensorShape, Single)

Creates a constant of shape input

Declaration
public Tensor ConstantOfShape(TensorShape X, float value)
Parameters
Type Name Description
TensorShape X

input shape
Single value

value
Returns
Type Description
Tensor

output Tensor
Implements

Conv2D(Tensor, Tensor, Tensor, Int32[], Int32[], Layer.FusedActivation)

2D convolution

Declaration
public virtual Tensor Conv2D(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor K
Tensor B
Int32[] stride

stride
Int32[] pad

padding
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

Conv2DTrans(Tensor, Tensor, Tensor, Int32[], Int32[], Int32[], Layer.FusedActivation)

Transpose 2D convolution

Declaration
public virtual Tensor Conv2DTrans(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, int[] outputAdjustment, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor K
Tensor B
Int32[] stride

stride
Int32[] pad

padding
Int32[] outputAdjustment

output adjustments
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

Conv3D(Tensor, Tensor, Tensor, Int32[], Int32[], Layer.FusedActivation)

3D convolution

Declaration
public virtual Tensor Conv3D(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor K
Tensor B
Int32[] stride

stride
Int32[] pad

padding
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

Copy(Tensor)

Copy

Declaration
public virtual Tensor Copy(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

CopyAndReshape(Tensor, TensorShape)

Copy and reshape Tensor

Declaration
protected virtual Tensor CopyAndReshape(Tensor X, TensorShape shape)
Parameters
Type Name Description
Tensor X

input
TensorShape shape

shape
Returns
Type Description
Tensor

output Tensor

Cos(Tensor)

Cos

Declaration
public virtual Tensor Cos(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Cosh(Tensor)

Cosh

Declaration
public virtual Tensor Cosh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Dense(Tensor, Tensor, Tensor, Layer.FusedActivation)

Dense layer (matrix multiplication) o = x ⨯ w + b

Declaration
public virtual Tensor Dense(Tensor X, Tensor W, Tensor B, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor W
Tensor B
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

Dense3(Tensor, Tensor, Tensor)

rank3 Dense layer (matrix multiplication) o = x ⨯ w + b O: N,,W,C / X: N,,W,C / W:N,,,C / B:N,,,_

Declaration
public virtual Tensor Dense3(Tensor X, Tensor W, Tensor B)
Parameters
Type Name Description
Tensor X
Tensor W
Tensor B
Returns
Type Description
Tensor

output Tensor
Implements

DepthToSpace(Tensor, Int32[], Layer.DepthToSpaceMode)

Depth to space

Declaration
public virtual Tensor DepthToSpace(Tensor X, int[] blocksize, Layer.DepthToSpaceMode mode)
Parameters
Type Name Description
Tensor X
Int32[] blocksize
Layer.DepthToSpaceMode mode

mode
Returns
Type Description
Tensor

output Tensor
Implements

DepthwiseConv2D(Tensor, Tensor, Tensor, Int32[], Int32[], Layer.FusedActivation)

Depthwise 2D convolution

Declaration
public virtual Tensor DepthwiseConv2D(Tensor X, Tensor K, Tensor B, int[] stride, int[] pad, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor K
Tensor B
Int32[] stride

stride
Int32[] pad

padding
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

Div(Tensor[])

Divide tensors o = tensors[0] / tensors[1] / ... / tensors[N-1]

Declaration
public virtual Tensor Div(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

Dropout(Tensor, Single)

Dropout

Declaration
public virtual Tensor Dropout(Tensor X, float alpha)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Returns
Type Description
Tensor

output Tensor
Implements

Elu(Tensor, Single)

ELU

Declaration
public virtual Tensor Elu(Tensor X, float alpha)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Returns
Type Description
Tensor

output Tensor
Implements

Equal(Tensor, Tensor)

Equal

Declaration
public virtual Tensor Equal(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a == b
Implements

Exp(Tensor)

Exponent e^x

Declaration
public virtual Tensor Exp(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Expand(Tensor, TensorShape)

Expand

Declaration
public virtual Tensor Expand(Tensor X, TensorShape newShape)
Parameters
Type Name Description
Tensor X
TensorShape newShape
Returns
Type Description
Tensor

output Tensor
Implements

Flatten(Tensor)

Flatten

Declaration
public virtual Tensor Flatten(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Floor(Tensor)

Floor

Declaration
public virtual Tensor Floor(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Gather(Tensor[], Int32)

Gather

Declaration
public virtual Tensor Gather(Tensor[] tensors, int axis)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Gaussian(Single, Single)

Gaussian distribution

Declaration
protected float Gaussian(float mean, float stdDev)
Parameters
Type Name Description
Single mean

mean
Single stdDev

standard deviation
Returns
Type Description
Single

random value

GetModelExecutionsReporter()

Get model executions reporter

Declaration
public IModelExecutionsReporter GetModelExecutionsReporter()
Returns
Type Description
IModelExecutionsReporter

model executions reporter
Implements

GlobalAvgPool2D(Tensor)

2D global average pooling

Declaration
public virtual Tensor GlobalAvgPool2D(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

GlobalAvgVariancePool2D(Tensor)

2D global average variance pooling

Declaration
public virtual Tensor GlobalAvgVariancePool2D(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

GlobalMaxPool2D(Tensor)

2D global max pooling

Declaration
public virtual Tensor GlobalMaxPool2D(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Greater(Tensor, Tensor)

Greater

Declaration
public virtual Tensor Greater(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a > b
Implements

GreaterEqual(Tensor, Tensor)

Greater or equal

Declaration
public virtual Tensor GreaterEqual(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a >= b
Implements

LeakyRelu(Tensor, Single)

Leaky ReLU

Declaration
public virtual Tensor LeakyRelu(Tensor X, float alpha)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Returns
Type Description
Tensor

output Tensor
Implements

Less(Tensor, Tensor)

Less

Declaration
public virtual Tensor Less(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a < b
Implements

LessEqual(Tensor, Tensor)

Less or equal

Declaration
public virtual Tensor LessEqual(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a < b
Implements

Log(Tensor)

Log

Declaration
public virtual Tensor Log(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

LogicalAnd(Tensor, Tensor)

And

Declaration
public virtual Tensor LogicalAnd(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a && b
Implements

LogicalNot(Tensor)

Not

Declaration
public virtual Tensor LogicalNot(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

Tensor with !x values
Implements

LogicalOr(Tensor, Tensor)

Or

Declaration
public virtual Tensor LogicalOr(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a || b
Implements

LogicalXor(Tensor, Tensor)

Xor

Declaration
public virtual Tensor LogicalXor(Tensor A, Tensor B)
Parameters
Type Name Description
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with true where a xor b
Implements

LogSoftmax(Tensor)

LogSoftmax

Declaration
public virtual Tensor LogSoftmax(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

LRN(Tensor, Single, Single, Single, Int32)

LRN (Local Response Normalization)

Declaration
public virtual Tensor LRN(Tensor X, float alpha, float beta, float bias, int size)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Single beta

beta
Single bias

bias
Int32 size

size
Returns
Type Description
Tensor

output Tensor
Implements

LSTM(Tensor, Tensor[], Tensor[], Tensor[], Tensor[], Tensor, Tensor)

LSTM

Declaration
public virtual Tensor[] LSTM(Tensor X, Tensor[] W, Tensor[] R, Tensor[] Wb, Tensor[] Rb, Tensor hidden, Tensor cell)
Parameters
Type Name Description
Tensor X

The input sequences packed into one 3-D tensor.
Tensor[] W

W parameter weight matrix for input, output, forget, and cell gates - W[iofc]
Tensor[] R

R recurrence weight matrix for input, output, forget, and cell gates - R[iofc]
Tensor[] Wb

W bias vectors for input, output, forget, and cell gates - Wb[iofc]
Tensor[] Rb

R bias vectors for input, output, forget, and cell gates - Rb[iofc]
Tensor hidden

Initial value of the hidden
Tensor cell

Initial value of the cell
Returns
Type Description
Tensor[]

[Y (concatenated intermediate values of the hidden), Y_h (final hidden), Y_c (final cell)]
Implements

MatMul(Tensor, Boolean, Tensor, Boolean)

Simple 2D matrix multiplication O = X ⨯ Y

Declaration
public virtual Tensor MatMul(Tensor X, bool xTranspose, Tensor Y, bool yTranspose)
Parameters
Type Name Description
Tensor X

left Tensor
Boolean xTranspose

X transposed data flag
Tensor Y

right Tensor
Boolean yTranspose

Y transposed data flag
Returns
Type Description
Tensor

output Tensor
Implements

MatMul(Tensor, Int32, Tensor, Int32)

Multidimensional Matrix multiplication o = x ⨯ y

Declaration
public virtual Tensor MatMul(Tensor X, int rankX, Tensor Y, int rankY)
Parameters
Type Name Description
Tensor X
Int32 rankX

rank of x
Tensor Y
Int32 rankY

rank of y
Returns
Type Description
Tensor

output Tensor
Implements

Max(Tensor[])

Max

Declaration
public virtual Tensor Max(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

MaxPool2D(Tensor, Int32[], Int32[], Int32[])

2D max pooling

Declaration
public virtual Tensor MaxPool2D(Tensor X, int[] pool, int[] stride, int[] pad)
Parameters
Type Name Description
Tensor X
Int32[] pool

pooling
Int32[] stride

stride
Int32[] pad

padding
Returns
Type Description
Tensor

output Tensor
Implements

Mean(Tensor[])

Mean

Declaration
public virtual Tensor Mean(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

Min(Tensor[])

Min

Declaration
public virtual Tensor Min(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

Mul(Tensor[])

Multiply tensors together

Declaration
public virtual Tensor Mul(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

Multinomial(Tensor, Int32, Int32)

Multinomial random distribution

Declaration
public virtual Tensor Multinomial(Tensor X, int count, int seed)
Parameters
Type Name Description
Tensor X
Int32 count

count
Int32 seed

seed
Returns
Type Description
Tensor

output Tensor
Implements

Neg(Tensor)

Neg

Declaration
public virtual Tensor Neg(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

NewTensor(Int32, Int32, Int32, Int32, String)

Allocate new Tensor via allocator

Declaration
protected Tensor NewTensor(int b, int h, int w, int ch, string name = "")
Parameters
Type Name Description
Int32 b

batch
Int32 h

height
Int32 w

width
Int32 ch

channels
String name

name
Returns
Type Description
Tensor

new Tensor

NewTensor(Int32, Int32, String)

Allocate new Tensor via allocator

Declaration
protected Tensor NewTensor(int b, int ch, string name = "")
Parameters
Type Name Description
Int32 b

batch
Int32 ch

channels
String name

name
Returns
Type Description
Tensor

new Tensor

NewTensor(TensorShape, String)

Allocate new Tensor via allocator

Declaration
protected Tensor NewTensor(TensorShape s, string name = "")
Parameters
Type Name Description
TensorShape s

shape
String name

name
Returns
Type Description
Tensor

new Tensor

NewTensorLike(Tensor)

Allocate new Tensor similar to specified Tensor t

Declaration
protected Tensor NewTensorLike(Tensor t)
Parameters
Type Name Description
Tensor t

Tensor
Returns
Type Description
Tensor

new Tensor

NewTensorLike(Tensor[])

Allocate new Tensor corresponding to max shape of specified tensors

Declaration
protected Tensor NewTensorLike(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

tensors
Returns
Type Description
Tensor

new Tensor

NonMaxSuppression(Tensor[], Int32, Single, Single, Int32)

Non max suppression tensors[0] - boxes, tensors[1] - scores

Declaration
public Tensor NonMaxSuppression(Tensor[] tensors, int maxOutputBoxesPerClass, float iouThreshold, float scoreThreshold, int centerPointBox)
Parameters
Type Name Description
Tensor[] tensors
Int32 maxOutputBoxesPerClass

max output boxes per class
Single iouThreshold

IOU (Intersection Over Union) threshold
Single scoreThreshold

score threshold
Int32 centerPointBox

center point box
Returns
Type Description
Tensor

output Tensor
Implements

NonZero(Tensor)

Indices for non zero values

Declaration
public Tensor NonZero(Tensor X)
Parameters
Type Name Description
Tensor X

input
Returns
Type Description
Tensor

output Tensor
Implements

Normalization(Tensor, Tensor, Tensor, Int32, Int32, Single, Layer.FusedActivation)

Normalization

Declaration
public virtual Tensor Normalization(Tensor X, Tensor S, Tensor B, int pool, int axis, float epsilon, Layer.FusedActivation fusedActivation)
Parameters
Type Name Description
Tensor X
Tensor S
Tensor B
Int32 pool

pooling
Int32 axis

axis
Single epsilon

threshold
Layer.FusedActivation fusedActivation

fused activation type
Returns
Type Description
Tensor

output Tensor
Implements

OneHot(Tensor, Int32, Single, Single)

One hot

Declaration
public virtual Tensor OneHot(Tensor X, int depth, float onValue, float offValue)
Parameters
Type Name Description
Tensor X
Int32 depth

output depth
Single onValue

on value
Single offValue

off value
Returns
Type Description
Tensor

output Tensor
Implements

Pad2DEdge(Tensor, Int32[])

Edge padding

Declaration
public virtual Tensor Pad2DEdge(Tensor X, int[] pad)
Parameters
Type Name Description
Tensor X
Int32[] pad

padding
Returns
Type Description
Tensor

output Tensor
Implements

Pad2DReflect(Tensor, Int32[])

Reflection padding

Declaration
public virtual Tensor Pad2DReflect(Tensor X, int[] pad)
Parameters
Type Name Description
Tensor X
Int32[] pad

padding
Returns
Type Description
Tensor

output Tensor
Implements

Pad2DSymmetric(Tensor, Int32[])

Symmetric padding

Declaration
public virtual Tensor Pad2DSymmetric(Tensor X, int[] pad)
Parameters
Type Name Description
Tensor X
Int32[] pad

padding
Returns
Type Description
Tensor

output Tensor
Implements

Pow(Tensor, Single)

Power

Declaration
public virtual Tensor Pow(Tensor X, float alpha)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Returns
Type Description
Tensor

output Tensor
Implements

Pow(Tensor[])

Raise tensors to the power o =tensors[0] ^ tensors[1] ^ ... ^ tensors[N-1]

Declaration
public virtual Tensor Pow(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

PRelu(Tensor, Tensor)

PReLU

Declaration
public virtual Tensor PRelu(Tensor X, Tensor S)
Parameters
Type Name Description
Tensor X
Tensor S
Returns
Type Description
Tensor

output Tensor
Implements

Prepare(Tensor)

Prepares tensor for use

Declaration
public virtual Tensor Prepare(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

Tensor
Implements

PrepareNoAlloc(Tensor)

Prepares tensor for use without uploading internal data to device

Declaration
public virtual Tensor PrepareNoAlloc(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

Tensor
Implements

RandomNormal(TensorShape, Single, Single, Int32)

Normal random distribution

Declaration
public virtual Tensor RandomNormal(TensorShape s, float mean, float scale, int seed)
Parameters
Type Name Description
TensorShape s

shape
Single mean

mean
Single scale

scale
Int32 seed

seed
Returns
Type Description
Tensor

output Tensor
Implements

RandomUniform(TensorShape, Single, Single, Int32)

Uniform random distribution

Declaration
public virtual Tensor RandomUniform(TensorShape s, float mean, float scale, int seed)
Parameters
Type Name Description
TensorShape s

shape
Single mean

mean
Single scale

scale
Int32 seed

seed
Returns
Type Description
Tensor

output Tensor
Implements

Reciprocal(Tensor)

Reciprocal (1/x)

Declaration
public virtual Tensor Reciprocal(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

ReduceMax(Tensor, Int32)

Reduce with max

Declaration
public virtual Tensor ReduceMax(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ReduceMean(Tensor, Int32)

Reduce with mean

Declaration
public virtual Tensor ReduceMean(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ReduceMin(Tensor, Int32)

Reduce with min

Declaration
public virtual Tensor ReduceMin(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ReduceProd(Tensor, Int32)

Reduce with product

Declaration
public virtual Tensor ReduceProd(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

ReduceSum(Tensor, Int32)

Reduce with sum

Declaration
public virtual Tensor ReduceSum(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Relu(Tensor)

ReLU

Declaration
public virtual Tensor Relu(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Relu6(Tensor)

ReLU capped to 6

Declaration
public virtual Tensor Relu6(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Resample2D(Tensor, Int32[], Boolean)

Resample 2D

Declaration
public virtual Tensor Resample2D(Tensor X, int[] size, bool bilinear)
Parameters
Type Name Description
Tensor X
Int32[] size

size
Boolean bilinear

bilinear flag
Returns
Type Description
Tensor

output Tensor
Implements

ResetAllocator(Boolean)

Reset internal allocator

Declaration
public virtual void ResetAllocator(bool keepCachedMemory = true)
Parameters
Type Name Description
Boolean keepCachedMemory

keep cached memory flag
Implements

Reshape(Tensor, TensorShape)

Reshape

Declaration
public virtual Tensor Reshape(Tensor X, TensorShape newShape)
Parameters
Type Name Description
Tensor X
TensorShape newShape
Returns
Type Description
Tensor

output Tensor
Implements

Round(Tensor)

Round to nearest integer. In case of halfs, round to nearest even integer

Declaration
public virtual Tensor Round(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

ScaleBias(Tensor, Tensor, Tensor)

Scale bias o = s * x + b, element wise

Declaration
public virtual Tensor ScaleBias(Tensor X, Tensor S, Tensor B)
Parameters
Type Name Description
Tensor X
Tensor S
Tensor B
Returns
Type Description
Tensor

output Tensor
Implements

Selu(Tensor, Single, Single)

SELU

Declaration
public virtual Tensor Selu(Tensor X, float alpha, float gamma)
Parameters
Type Name Description
Tensor X
Single alpha

alpha
Single gamma

gamma
Returns
Type Description
Tensor

output Tensor
Implements

SetModelExecutionsReporter(IModelExecutionsReporter)

Set model executions reporter model executions reporter

Declaration
public void SetModelExecutionsReporter(IModelExecutionsReporter executionsReporter)
Parameters
Type Name Description
IModelExecutionsReporter executionsReporter
Implements

Shape(Tensor, Int32)

Shape of the input

Declaration
public Tensor Shape(Tensor X, int axis = null)
Parameters
Type Name Description
Tensor X

input
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Sigmoid(Tensor)

Sigmoid

Declaration
public virtual Tensor Sigmoid(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Sign(Tensor)

Sign

Declaration
public virtual Tensor Sign(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

Tensor with 1 if x > 0 -1 if < 0 and 0 if == 0 values
Implements

Sin(Tensor)

Sin

Declaration
public virtual Tensor Sin(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Sinh(Tensor)

Sinh

Declaration
public virtual Tensor Sinh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Softmax(Tensor, Int32)

Softmax

Declaration
public virtual Tensor Softmax(Tensor X, int axis)
Parameters
Type Name Description
Tensor X
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Softplus(Tensor)

Softplus

Declaration
public virtual Tensor Softplus(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

SpaceToDepth(Tensor, Int32[])

Space to depth

Declaration
public virtual Tensor SpaceToDepth(Tensor X, int[] blocksize)
Parameters
Type Name Description
Tensor X
Int32[] blocksize
Returns
Type Description
Tensor

output Tensor
Implements

Sqrt(Tensor)

Sqrt

Declaration
public virtual Tensor Sqrt(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

StridedSlice(Tensor, Int32[], Int32[], Int32[])

Strided slice

Declaration
public virtual Tensor StridedSlice(Tensor X, int[] starts4Dor8D, int[] ends4Dor8D, int[] strides4Dor8D)
Parameters
Type Name Description
Tensor X
Int32[] starts4Dor8D
Int32[] ends4Dor8D
Int32[] strides4Dor8D

stride
Returns
Type Description
Tensor

output Tensor
Implements

Sub(Tensor[])

Subtract tensors o = tensors[0] - tensors[1] - ... - tensors[N-1]

Declaration
public virtual Tensor Sub(Tensor[] tensors)
Parameters
Type Name Description
Tensor[] tensors

input tensors
Returns
Type Description
Tensor

output Tensor
Implements

Swish(Tensor)

Swish

Declaration
public virtual Tensor Swish(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Tan(Tensor)

Tan

Declaration
public virtual Tensor Tan(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Tanh(Tensor)

Tanh

Declaration
public virtual Tensor Tanh(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Tile(Tensor, Int32[])

Tile

Declaration
public virtual Tensor Tile(Tensor X, int[] repeats)
Parameters
Type Name Description
Tensor X
Int32[] repeats

repetition counts
Returns
Type Description
Tensor

output Tensor
Implements

TopKIndices(Tensor, Int32, Int32, Boolean, Boolean)

Top K indices

Declaration
public virtual Tensor TopKIndices(Tensor X, int k, int axis, bool largest, bool sorted)
Parameters
Type Name Description
Tensor X
Int32 k

k
Int32 axis

axis
Boolean largest

largest flag
Boolean sorted

sorted flag
Returns
Type Description
Tensor

output Tensor
Implements

TopKValues(Tensor, Tensor, Int32)

Top K values

Declaration
public virtual Tensor TopKValues(Tensor X, Tensor I, int axis)
Parameters
Type Name Description
Tensor X

input
Tensor I

indices
Int32 axis

axis
Returns
Type Description
Tensor

output Tensor
Implements

Transpose(Tensor)

Transpose matrix

Declaration
public virtual Tensor Transpose(Tensor X)
Parameters
Type Name Description
Tensor X
Returns
Type Description
Tensor

output Tensor
Implements

Transpose(Tensor, Int32[])

Transpose according to permutations

Declaration
public virtual Tensor Transpose(Tensor X, int[] permutations)
Parameters
Type Name Description
Tensor X
Int32[] permutations

new axis order
Returns
Type Description
Tensor

output Tensor
Implements

Upsample2D(Tensor, Int32[], Boolean)

Upsample 2D

Declaration
public virtual Tensor Upsample2D(Tensor X, int[] scale, bool bilinear)
Parameters
Type Name Description
Tensor X
Int32[] scale

scale
Boolean bilinear

bilinear flag
Returns
Type Description
Tensor

output Tensor
Implements

Upsample3D(Tensor, Int32[], Boolean)

Upsample 3D

Declaration
public virtual Tensor Upsample3D(Tensor X, int[] scale, bool trilinear)
Parameters
Type Name Description
Tensor X
Int32[] scale

scale
Boolean trilinear

trilinear flag
Returns
Type Description
Tensor

output Tensor
Implements

Where(Tensor, Tensor, Tensor)

Where

Declaration
public virtual Tensor Where(Tensor C, Tensor A, Tensor B)
Parameters
Type Name Description
Tensor C
Tensor A
Tensor B
Returns
Type Description
Tensor

Tensor with values c ? a : b
Implements