ConvolutionBackpropData#

Versioned name: ConvolutionBackpropData-1

Category: Convolution

Short description: Computes the gradients of a Convolution operation with respect to the input. Also known as a Deconvolution or a Transposed Convolution.

Detailed description:

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

ConvolutionBackpropData accepts the same set of attributes as a regular Convolution operation, but they are interpreted in a “backward way”, so they are applied to the output of ConvolutionBackpropData, but not to the input. Refer to a regular Convolution operation for detailed description of each attribute.

If auto_pad is specified, pads_begin and pads_end will be ignored, In this case pads are determined based on the next formulas to correctly align input and output tensors:

total_padding[i] = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1)
                 - output_shape[i] + output_padding[i]
if auto_pads != SAME_UPPER:
    pads_begin[i] = total_padding[i] // 2
    pads_end[i] = total_padding[i] - pads_begin[i]
else:
    pads_end[i] = total_padding[i] // 2
    pads_begin[i] = total_padding[i] - pads_end[i]

Attributes

  • strides

    • Description: strides has the same definition as strides for a regular Convolution but applied in the backward way.

    • Range of values: positive s64 values.

    • Type: s64[]

    • Required: yes

  • pads_begin

    • Description: pads_begin has the same definition as pads_begin for a regular Convolution but applied in the backward way. May be omitted specified, in which case pads are calculated automatically.

    • 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 has the same definition as pads_end for a regular Convolution but applied in the backward way. May be omitted, in which case pads are calculated automatically.

    • 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 has the same definition as dilations for a regular Convolution but applied in the backward way.

    • Range of values: positive s64 values.

    • Type: s64[]

    • Required: yes

  • auto_pad

    • Description: auto_pad has the same definition as auto_pad for a regular Convolution but applied in the backward way.

      • 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 (S means HW for 2D convolution, DHW for 3D convolution)

    • 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 convolution, DHW for 3D convolution)

    • Type: string

    • Default value: XIO

    • Required: no

  • output_shape

    • Description: output_shape denotes the shape of the output tensor.

    • Type: s64[]

    • Required: no

Inputs:

  • 1: output_delta - the gradient tensor with respect to the output. Required.

    • Type: T

  • 2: filter – convolution filter tensor. The format is specified by filter_format attribute. The shape of filter is \((out_channels, in_channels / groups, spatial_shape)\) for OIX format or \((spatial_shape, in_channels / groups, out_channels)\) for XIO format. \(in_channels\) and \(out_channels\) must both be divisible by groups attribute. Required.

    • Type: T

  • 3: output_shape is 1D integer tensor that specifies shape of the output. Optional. If specified, output_shape attribute will be ignored. If not specified, users should define output_shape through attribute. padding amount can be deduced from relation of input and output spatial shapes according to formulas in the description.

    • Type: s32

Outputs:

  • 1: input_delta - the gradient tensor with respect to the input of Convolution.

    • Type: T

Types:

  • T: f32, f16, bf16.

  • 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.