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 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 pads_begin, pads_end or 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:

if auto_pads != None:
pads_begin[i] = 0
pads_end[i] = 0
Y_i = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1) - pads_begin[i] - pads_end[i] + output_padding[i]

where K_i filter kernel dimension along spatial axis i.

If output_shape is specified, pads_begin and 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):

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

Inputs:

Outputs:

Example

<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>20</dim>
<dim>224</dim>
<dim>224</dim>
</port>
<port id="1">
<dim>20</dim>
<dim>10</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>10</dim>
<dim>447</dim>
<dim>447</dim>
</port>
</output>
</layer>