ConvolutionBackpropData
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
andpads_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 indata
andoutput
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.