8000 fix avgpool · DiffSharp/DiffSharp@d2e7a33 · GitHub
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Don Syme
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fix avgpool
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‎src/DiffSharp.Core/DiffSharp.AvgPool.fs

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namespace DiffSharp
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[<AutoOpen>]
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module AvgPoolExtensions =
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type Tensor with
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/// <summary>Applies a 1D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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member a.avgpool1d(kernelSize:int, ?stride:int, ?padding:int(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let stride = defaultArg stride kernelSize
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let padding = defaultArg padding 0
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Shape.checkCanAvgpool1d a.dtype a.shape kernelSize stride padding |> ignore
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Tensor.Op
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{ new UnaryOp("avgpool1d") with
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member _.fRaw(a) = a.AvgPool1D(kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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member t.ad_dfda(a, ad, f) = ad.avgpool1d(kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpoolReverse1d(a, kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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}
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a
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member internal a.avgpoolReverse1d(originalInput:Tensor, kernelSize:int, ?stride:int, ?padding:int(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let stride = defaultArg stride kernelSize
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let padding = defaultArg padding 0
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Tensor.Op
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{ new UnaryOp("avgpoolReverse1d") with
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member _.fRaw(a) = a.AvgPoolReverse1D(originalInput.primalRaw, kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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member _.ad_dfda(a, ad, f) = ad.avgpoolReverse1d(originalInput, kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpool1d(kernelSize, stride, padding(* , ceil_mode, count_include_pad *))
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}
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a
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/// <summary>Applies a 1D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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/// <param name="kernelSizes">The sizes of the window to take a max over.</param>
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/// <param name="strides">The strides of the window. Default value is kernelSize.</param>
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/// <param name="paddings">The implicit zero paddings to be added on both sides.</param>
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member a.avgpool2d(?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let kernelSizes, strides, paddings = Shape.resolve2dMaxPoolSizes kernelSize kernelSizes stride strides padding paddings
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Shape.checkCanAvgpool2d a.dtype a.shape kernelSizes strides paddings |> ignore
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Tensor.Op
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{ new UnaryOp("avgpool2d") with
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member _.fRaw(a) = a.AvgPool2D(kernelSizes, strides, paddings(* , ceil_mode, count_include_pad *))
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member _.ad_dfda(a, ad, f) = ad.avgpool2d(kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpoolReverse2d(a, kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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}
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a
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member internal a.avgpoolReverse2d(originalInput:Tensor, ?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let kernelSizes, strides, paddings = Shape.resolve2dMaxPoolSizes kernelSize kernelSizes stride strides padding paddings
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Tensor.Op
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{ new UnaryOp("avgpoolReverse2d") with
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member _.fRaw(a) = a.AvgPoolReverse2D(originalInput.primalRaw, kernelSizes, strides, paddings(* , ceil_mode, count_include_pad *))
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member _.ad_dfda(a, ad, f) = ad.avgpoolReverse2d(originalInput, kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpool2d(kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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}
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a
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/// <summary>Applies a 3D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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/// <param name="kernelSizes">The sizes of the window to take a max over.</param>
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/// <param name="strides">The strides of the window. Default value is kernelSize.</param>
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/// <param name="paddings">The implicit zero paddings to be added on both sides.</param>
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member a.avgpool3d(?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let kernelSizes, strides, paddings = Shape.resolve3dMaxPoolSizes kernelSize kernelSizes stride strides padding paddings
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Shape.checkCanAvgpool3d a.dtype a.shape kernelSizes strides paddings |> ignore
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Tensor.Op
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{ new UnaryOp("avgpool3d") with
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member _.fRaw(a) = a.AvgPool3D(kernelSizes, strides, paddings(* , ceil_mode, count_include_pad *))
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member _.ad_dfda(a, ad, f) = ad.avgpool3d(kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpoolReverse3d(a, kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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}
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a
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member internal a.avgpoolReverse3d(originalInput:Tensor, ?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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let kernelSizes, strides, paddings = Shape.resolve3dMaxPoolSizes kernelSize kernelSizes stride strides padding paddings
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//let ceil_mode = defaultArg ceil_mode false
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//let count_include_pad= defaultArg count_include_pad true
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Tensor.Op
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{ new UnaryOp("avgpoolReverse3d") with
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member _.fRaw(a) = a.AvgPoolReverse3D(originalInput.primalRaw, kernelSizes, strides, paddings(* , ceil_mode, count_include_pad *))
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member _.ad_dfda(a, ad, f) = ad.avgpoolReverse3d(originalInput, kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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member _.fd_dfda(a, f, fd) = fd.avgpool3d(kernelSizes=kernelSizes, strides=strides, paddings=paddings(* , ceil_mode=ceil_mode, count_include_pad=count_include_pad *))
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}
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a
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type dsharp with
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/// <summary>Applies a 1D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="input">The input tensor.</param>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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static member avgpool1d(input: Tensor, kernelSize:int, ?stride:int, ?padding:int(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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input.avgpool2d(kernelSize=kernelSize, ?stride=stride, ?padding=padding(* , ?ceil_mode=ceil_mode, ?count_include_pad=count_include_pad *))
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/// <summary>Applies a 2D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="input">The input tensor.</param>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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/// <param name="kernelSizes">The sizes of the window to take a max over.</param>
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/// <param name="strides">The strides of the window. Default value is kernelSize.</param>
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/// <param name="paddings">The implicit zero paddings to be added on both sides.</param>
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static member avgpool2d(input: Tensor, ?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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input.avgpool2d(?kernelSize=kernelSize, ?stride=stride, ?padding=padding, ?kernelSizes=kernelSizes, ?strides=strides, ?paddings=paddings(* , ?ceil_mode=ceil_mode, ?count_include_pad=count_include_pad *))
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/// <summary>Applies a 2D average pooling over an input signal composed of several input planes, returning the max indices along with the outputs.</summary>
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/// <param name="input">The input tensor.</param>
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/// <param name="kernelSize">The size of the window to take a max over.</param>
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/// <param name="stride">The stride of the window. Default value is kernelSize.</param>
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/// <param name="padding">The implicit zero padding to be added on both sides.</param>
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/// <param name="kernelSizes">The sizes of the window to take a max over.</param>
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/// <param name="strides">The strides of the window. Default value is kernelSize.</param>
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/// <param name="paddings">The implicit zero paddings to be added on both sides.</param>
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static member avgpool3d(input: Tensor, ?kernelSize:int, ?stride:int, ?padding:int, ?kernelSizes:seq<int>, ?strides:seq<int>, ?paddings:seq<int>(* , ?ceil_mode: bool, ?count_include_pad: bool *)) =
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input.avgpool3d(?kernelSize=kernelSize, ?stride=stride, ?padding=padding, ?kernelSizes=kernelSizes, ?strides=strides, ?paddings=paddings(* , ?ceil_mode=ceil_mode, ?count_include_pad=count_include_pad *))
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