Short description: Computes the gradients of a Convolution operation with respect to the input. Also known as a Deconvolution or a Transposed Convolution.
ConvolutionBackpropData takes the input tensor, weights tensor and output shape and computes the output tensor of a given shape. The shape of the output can be specified as an input 1D integer tensor explicitly or determined by other attributes implicitly. If output shape is specified as an explicit input, shape of the output exactly matches the specified size and required amount of padding is computed.
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.
Output shape when specified as an input
output_shape, specifies only spatial dimensions. No batch or channel dimension should be passed along with H, W or other spatial dimensions. If
output_shape is omitted, then
auto_pad are used to determine output spatial shape
[Y_1, Y_2, ..., Y_D] by input spatial shape
[X_1, X_2, ..., X_D] in the following way:
K_i filter kernel dimension along spatial axis
output_shape is specified,
pads_end are ignored, and
auto_pad defines how to distribute padding amount around the tensor. In this case pads are determined based on the next formulas to correctly align input and output tensors (similar to ONNX definition at https://github.com/onnx/onnx/blob/master/docs/Operators.md#convtranspose):
outputtensor. 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– input tensor of rank 3 or greater. Layout is
[N, C_INPUT, X1, ..., XD]. Required.
filter– convolution kernel tensor. Weights have shape
[C_INPUT, C_OUTPUT, K_D, ..., K_1].
C_INPUTis the number of channels in input
datatensor shape, and
C_OUTPUTis the number of channels in the
outputtensor. Spatial size of the kernel
[K_D, ..., K_1]is derived from the shape of this input and aren't specified by any attribute. Required.
output_shapeis 1D integer tensor that specifies spatial shape of the output. Optional. If specified, padding amount is deduced from relation of input and output spatial shapes according to formulas in the description. If not specified, output shape is calculated based on the
pads_endor completely according to
output– output tensor of the same rank as input
datatensor and shape
[N, C_OUTPUT, Y1, ..., YD].