ConvTransposeBackpropFilters#

Versioned name: ConvTransposeBackpropFilters-1

Category: Convolution

Short description: Computes the gradients of a ConvTranspose operation with respect to the filters.

Detailed description:

ConvTransposeBackpropFilters takes the input tensor, the gradient tensor of output and filter shape (optional) to compute the gradient of filter. The shape of the filter should either be specified as an input 1D integer tensor or be determined by the attribute filter_shape.

Attributes

  • strides

    • Description: strides controls the stride along each spatial axis.

    • Range of values: positive s64 values.

    • Type: s64[]

    • Required: yes

  • pads_begin

    • Description: pads_begin controls the amount of implicit zero padding of each spatial axis.

    • Range of values: Non-negative s64 values.

    • Type: s64[]

    • Required: yes

    • Note: the attribute is ignored when auto_pad attribute is specified.

  • pads_end

    • Description: pads_end controls the amount of implicit zero padding of each spatial axis.

    • Range of values: Non-negative s64 values.

    • Type: s64[]

    • Required: yes

    • Note: the attribute is ignored when auto_pad attribute is specified.

  • dilations

    • Description: dilations controls the spacing between the kernel points.

    • Range of values: positive s64 values.

    • Type: s64[]

    • Required: yes

  • auto_pad

    • Description: auto_pad describes how the padding is calculated.

      • none (not specified): use explicit padding values from pads_begin and pads_end.

      • same_upper (same_lower) the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning).

      • valid - do not use padding.

    • Type: string

    • Default value: none

    • Required: no

    • Note: pads_begin and pads_end attributes are ignored when auto_pad is specified.

  • output_padding

    • Description: output_padding adds additional amount of padding per each spatial axis in the output tensor. It unlocks more elements in the output allowing them to be computed. Elements are added at the higher coordinate indices for the spatial dimensions. Number of elements in output_padding list matches the number of spatial dimensions in data and output tensors.

    • Range of values: Non-negative s64 values.

    • Type: s64[]

    • Default value: all zeros

    • Required: no

  • groups

    • Description: groups denotes the number of groups input channels and output channels are divided into. In_channels and out_channels must both be divisible by groups.

    • Range of values: A positive s64 value.

    • Type: s64

    • Default value: 1

    • Required: no

  • data_format

    • Description: data_format denotes the data format of the input and output data.

    • Range of values: NXC or NCX (X means HW for 2D ConvTranspose, DHW for 3D ConvTranspose)

    • Type: string

    • Default value: NXC

    • Required: no

  • filter_format

    • Description: filter_format denotes the data format of the filter.

    • Range of values: XIO or OIX (X means HW for 2D ConvTranspose, DHW for 3D ConvTranspose)

    • Type: string

    • Default value: XIO

    • Required: no

  • filter_shape

    • Description: filter_shape denotes the shape of the filter.

    • Type: s64[]

    • Default value: None

    • Required: no

Inputs:

  • 1: input_forward - original input tensor of ConvTranspose op. Required.

    • Type: T1

  • 2: output_delta - the gradient tensor with respect to the output of the ConvTranspose. Required.

    • Type: T1

  • 3: filter_shape - 1D integer tensor that specifies shape of the filter. The shape of filter is (out_channels / groups, in_channels, spatial_shape) for OIX format and (spatial_shape, in_channels, out_channels / groups) for XIO format. If specified, filter_shape attribute will be ignored. If not specified, users should pass filter_shape through attribute. In_channels and out_channels must both be divisible by groups. Optional.

    • Type: T2

Outputs:

  • 1: filter_delta - gradient tensor with respect to the filter of the ConvTranspose. The format is specified by filter_format attribute.

    • Type: T1

Types:

  • T1: f32, f16, bf16.

  • T2: s32

  • Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.