Intel® oneAPI Deep Neural Network Library Developer Guide and Reference

Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content
Contents

struct dnnl::deconvolution_backward_weights::desc

Overview

Descriptor for a deconvolution weights gradient primitive. More…
#include <dnnl.hpp>

struct desc
{
    // fields

    dnnl_deconvolution_desc_t data;

    // construction

    desc(
        algorithm aalgorithm,
        const memory::desc& src_desc,
        const memory::desc& diff_weights_desc,
        const memory::desc& diff_bias_desc,
        const memory::desc& diff_dst_desc,
        const memory::dims& strides,
        const memory::dims& padding_l,
        const memory::dims& padding_r
        );

    desc(
        algorithm aalgorithm,
        const memory::desc& src_desc,
        const memory::desc& diff_weights_desc,
        const memory::desc& diff_dst_desc,
        const memory::dims& strides,
        const memory::dims& padding_l,
        const memory::dims& padding_r
        );

    desc(
        algorithm aalgorithm,
        const memory::desc& src_desc,
        const memory::desc& diff_weights_desc,
        const memory::desc& diff_bias_desc,
        const memory::desc& diff_dst_desc,
        const memory::dims& strides,
        const memory::dims& dilates,
        const memory::dims& padding_l,
        const memory::dims& padding_r
        );

    desc(
        algorithm aalgorithm,
        const memory::desc& src_desc,
        const memory::desc& diff_weights_desc,
        const memory::desc& diff_dst_desc,
        const memory::dims& strides,
        const memory::dims& dilates,
        const memory::dims& padding_l,
        const memory::dims& padding_r
        );
};

Detailed Documentation

Descriptor for a deconvolution weights gradient primitive.
Construction
desc(
    algorithm aalgorithm,
    const memory::desc& src_desc,
    const memory::desc& diff_weights_desc,
    const memory::desc& diff_bias_desc,
    const memory::desc& diff_dst_desc,
    const memory::dims& strides,
    const memory::dims& padding_l,
    const memory::dims& padding_r
    )
Constructs a descriptor for a deconvolution weights gradient primitive with bias.
All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of
format_tag
.
Arrays
strides
,
padding_l
, and
padding_r
 contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
Parameters:
aalgorithm
Deconvolution algorithm. Possible values are dnnl::algorithm::deconvolution_direct, and dnnl::algorithm::deconvolution_winograd.
src_desc
Source memory descriptor.
diff_weights_desc
Diff weights memory descriptor.
diff_bias_desc
Diff bias memory descriptor. Passing zero memory descriptor disables the bias term.
diff_dst_desc
Diff destination memory descriptor.
strides
Strides for each spatial dimension.
padding_l
Vector of padding values for low indices for each spatial dimension
([[front,] top,] left)
.
padding_r
Vector of padding values for high indices for each spatial dimension
([[back,] bottom,] right)
.
 
desc(
    algorithm aalgorithm,
    const memory::desc& src_desc,
    const memory::desc& diff_weights_desc,
    const memory::desc& diff_dst_desc,
    const memory::dims& strides,
    const memory::dims& padding_l,
    const memory::dims& padding_r
    )
Constructs a descriptor for a deconvolution weights gradient primitive without bias.
All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of
format_tag
.
Arrays
strides
,
padding_l
, and
padding_r
 contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
Parameters:
aalgorithm
Deconvolution algorithm. Possible values are dnnl::algorithm::deconvolution_direct, and dnnl::algorithm::deconvolution_winograd.
src_desc
Source memory descriptor.
diff_weights_desc
Diff weights memory descriptor.
diff_dst_desc
Diff destination memory descriptor.
strides
Strides for each spatial dimension.
padding_l
Vector of padding values for low indices for each spatial dimension
([[front,] top,] left)
.
padding_r
Vector of padding values for high indices for each spatial dimension
([[back,] bottom,] right)
.
 
desc(
    algorithm aalgorithm,
    const memory::desc& src_desc,
    const memory::desc& diff_weights_desc,
    const memory::desc& diff_bias_desc,
    const memory::desc& diff_dst_desc,
    const memory::dims& strides,
    const memory::dims& dilates,
    const memory::dims& padding_l,
    const memory::dims& padding_r
    )
Constructs a descriptor for a dilated deconvolution weights gradient primitive with bias.
All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of
format_tag
.
Arrays
strides
,
dilates
,
padding_l
, and
padding_r
 contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
Parameters:
aalgorithm
Deconvolution algorithm. Possible values are dnnl::algorithm::deconvolution_direct, and dnnl::algorithm::deconvolution_winograd.
src_desc
Source memory descriptor.
diff_weights_desc
Diff weights memory descriptor.
diff_bias_desc
Diff bias memory descriptor. Passing zero memory descriptor disables the bias term.
diff_dst_desc
Diff destination memory descriptor.
strides
Strides for each spatial dimension.
dilates
Dilations for each spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l
Vector of padding values for low indices for each spatial dimension
([[front,] top,] left)
.
padding_r
Vector of padding values for high indices for each spatial dimension
([[back,] bottom,] right)
.
 
desc(
    algorithm aalgorithm,
    const memory::desc& src_desc,
    const memory::desc& diff_weights_desc,
    const memory::desc& diff_dst_desc,
    const memory::dims& strides,
    const memory::dims& dilates,
    const memory::dims& padding_l,
    const memory::dims& padding_r
    )
Constructs a descriptor for a dilated deconvolution weights gradient primitive without bias.
All the memory descriptors may be initialized with the dnnl::memory::format_tag::any value of
format_tag
.
Arrays
strides
,
dilates
,
padding_l
, and
padding_r
 contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.
Parameters:
aalgorithm
Deconvolution algorithm. Possible values are dnnl::algorithm::deconvolution_direct, and dnnl::algorithm::deconvolution_winograd.
src_desc
Source memory descriptor.
diff_weights_desc
Diff weights memory descriptor.
diff_dst_desc
Diff destination memory descriptor.
strides
Strides for each spatial dimension.
dilates
Dilations for each spatial dimension. A zero value means no dilation in the corresponding dimension.
padding_l
Vector of padding values for low indices for each spatial dimension
([[front,] top,] left)
.
padding_r
Vector of padding values for high indices for each spatial dimension
([[back,] bottom,] right)
.

Product and Performance Information

1

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