ConvOperator2D

public class ConvOperator2D: ComputableOperator

2D Convolution operator.

Input tensors shapes

All tensors in inputTensors should have same shapes

Shape specification

inputTensors: each tensor: [channel, height, width] or [height, width, channel] according to channelPosition
weight: [num_filter，channel, kernelSize[0], kernelSize[1]],
bias: [num_filter],
outputTensors: each tensor: [out_height, out_width, num_filter], i.e., TensorChannelOrder.Last

nil weight

At declaration, weight could be nil. However, if you add this operator through a graph’s operation(_,inputs:,op:) API (i.e. symbolic constructing graph). You must indicate the inputShape attribute so that the graph could estimate the input and output shape information.

Batch process

This operator does not directly support batch data. However, user could use TensorSlice to do the batch processing. Details can be found in Slice tensor and Batch calculation with operators.

Dilation

Currently, calculation with dilation > 1 has not been implemented and supported.

Attributes

operatorLabel
Operator label. Conforms to ComputableOperator
Declaration
Swift

public var operatorLabel: String
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metalKernelFuncLabel
This operator does not operator on GPU. Conforms to ComputableOperator
Declaration
Swift

public var metalKernelFuncLabel:String = "Conv2D"
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computationDelegate
Conforms to ComputableOperator
Declaration
Swift

public var computationDelegate: OperatorCalculationDelegate?
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inputTensors
Conforms to ComputableOperator
Declaration
Swift

public var inputTensors: [Tensor]?
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outputTensors
Conforms to ComputableOperator
Declaration
Swift

public var outputTensors: [Tensor]?
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disableInputOutputCheck
If true, operator will not call inputOutputTensorsCheck() before doing calculation. This is used inside framework to speed up in situation we know it will not be wrong.
Declaration
Swift

public var disableInputOutputCheck: Bool
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trainable
Indicate if this operator would do paramter update.
Declaration
Swift

public var trainable: Bool = true
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mapType
The mapping type of this operator. OneToOne for this operator.
Declaration
Swift

public var mapType: OperatorMappingType
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inPlaceble
conv operator cannot do in-place calculation
Declaration
Swift

public var inPlaceble: Bool = false
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numFilters
The number of fitlers
Declaration
Swift

public var numFilters: Int
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kernelSize
The kernel size. [height, width]
Declaration
Swift

public var kernelSize: [Int]
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stride
Stride. [height, width] Default is [1, 1].
Declaration
Swift

public var stride: [Int]
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dilation
Dilate values. 2D vecotr indicating the dilation value in height and width. Default is [1, 1], i.e. without dilation. TODO: Support dilation
Declaration
Swift

public var dilation: [Int]
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padMode
Padding mode. Default PaddingMode.valid
Declaration
Swift

public var padMode: PaddingMode = PaddingMode.Valid
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channelPosition
Channel position. Default is ImageChannelOrder.First
Declaration
Swift

public var channelPosition: TensorChannelOrder = .First
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weight
The weight tensor.
Declaration
Swift

public var weight: Tensor?
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biasEnabled
If use bias. Default is true.
Declaration
Swift

public var biasEnabled: Bool
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bias
The bias tensor.
Declaration
Swift

public var bias: Tensor?
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inputShape
The input shape Used to indicate the input tensors’ shape. Should not be nil construction from scratch.
Declaration
Swift

public var inputShape: TensorShape?
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calMethod
Calculation method
Declaration
Swift

public var calMethod: ConvMethod
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paddingValue
Padding value when using Same mode
Declaration
Swift

public var paddingValue: Float = 0.0
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Init


                    
                    
                    init(numFilters:kernelSize:stride:padMode:channelPosition:weight:bias:dilation:biasEnabled:computationDelegate:inputTensors:outputTensors:operatorLabel:inputShape:disableInputOutputCheck:calMethod:)

Designated init.

Declaration

Swift

public init(numFilters: Int, kernelSize: [Int],
            stride: [Int] = [1, 1],
            padMode: PaddingMode = .Valid,
            channelPosition: TensorChannelOrder = .First,
            weight: Tensor? = nil,
			bias: Tensor? = nil,
            dilation: [Int] = [1, 1],
			biasEnabled: Bool = true,
            computationDelegate: OperatorCalculationDelegate? = nil,
            inputTensors: [Tensor]? = nil, outputTensors: [Tensor]? = nil,
            operatorLabel: String = "Conv2DOp",
            inputShape: TensorShape? = nil,
            disableInputOutputCheck: Bool = false,
			calMethod: ConvMethod = ConvMethod.Img2Col)

Parameters

`numFilters`
`kernelSize`
`stride`
`padMode`
`channelPosition`
`weight`
`bias`
`dilation`
`computationDelegate`
`inputTensors`
`outputTensors`
`operatorLabel`
`inputShape`
`disableInputOutputCheck`

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Methods


                    
                    
                    outputShape(shapeArray:)

Compute output shape according numFilters, kernelSize, stride and dilation.

Declaration

Swift

public func outputShape(shapeArray shapes: [TensorShape]) -> [TensorShape]?

Parameters


                                shapes

shapes description

Return Value

return value description

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inputOutputTensorsCheck()
Check input and output tensors.
Declaration
Swift

public func inputOutputTensorsCheck() -> (check: Bool, msg: String)
Return Value

return value description

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                    compute(_:)

Compute sync way.

Declaration

Swift

public func compute(_ computationMode: OperatorComputationMode)

Parameters


                                computationMode

mode

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                    computeAsync(_:)

Compute async

Declaration

Swift

public func computeAsync(_ computationMode: OperatorComputationMode)

Parameters


                                computationMode

computationMode

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                    gradCompute(_:)

Declaration

Swift

public func gradCompute(_ computationMode: OperatorComputationMode) -> [String: DataSymbolSupportedDataType]

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                    gradComputAsync(_:)

Declaration

Swift

public func gradComputAsync(_ computationMode: OperatorComputationMode)

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updateParams(grads:LR:)
Undocumented
Declaration
Swift

public func updateParams(grads: [Tensor], LR: Float)
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bindParamSymbols(_:)
Bind according to labels.

-Note: if cannot bind all needed parameters. fatalError will be raised.
Declaration
Swift

public func bindParamSymbols(_ symbols: [GraphSymbol])
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paramSymbols()
Weight as TensorSymbol
Declaration
Swift

public func paramSymbols() -> [GraphSymbol]
Return Value

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cpu()
Cpu calculation
Declaration
Swift

internal func cpu()
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gpu()
GPU calcualtion
Declaration
Swift

internal func gpu()
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                    naive(_:)

Calculate the convolution following naive algorithm

Declaration

Swift

internal func naive(_ mode: OperatorComputationMode)

Parameters


                                mode

mode

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                    naive_gpu(input:output:weightBuffer:biasBuffer:)

Naive gpu calcualtion. No intermediate tensor created

Declaration

Swift

internal func naive_gpu(input: Tensor, output: Tensor, weightBuffer: MTLBufferResource, biasBuffer: MTLBufferResource?)

Parameters

`input`
`output`
`weightBuffer`
`biasBuffer`

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                    naive_cpu(input:output:)

Naive CPU calculation

Declaration

Swift

internal func naive_cpu(input: Tensor, output: Tensor)

Parameters

`input`	input
`output`	output

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                    img2Col(_:)

Use Img2Col to calcualte result.

Convert each input tensor via Img2Col to a 2D tensor A with shape [out_Height x out_Width, channel x kernelSize[0] x kernelSize[1]];
We view weight tensor as a 2D tensor B with shape [num_filter，channel x kernelSize[0] x kernelSize[1]];
Do matrix multiplication AxB with transposeB setting as true.

Declaration

Swift

internal func img2Col(_ mode: OperatorComputationMode)

Parameters


                                mode

computation mode

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cpu_dilation()
Undocumented
Declaration
Swift

internal func cpu_dilation()
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gpu_dilation()
Undocumented
Declaration
Swift

internal func gpu_dilation()
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