Developer Guide and Reference

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

struct dnnl::deconvolution_forward::desc

Overview

Descriptor for a deconvolution forward propagation primitive. More…
#include <dnnl.hpp> struct desc { // fields dnnl_deconvolution_desc_t data; // construction desc( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& bias_desc, const memory::desc& dst_desc, const memory::dims& strides, const memory::dims& padding_l, const memory::dims& padding_r ); desc( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& dst_desc, const memory::dims& strides, const memory::dims& padding_l, const memory::dims& padding_r ); desc( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& bias_desc, const memory::desc& dst_desc, const memory::dims& strides, const memory::dims& dilates, const memory::dims& padding_l, const memory::dims& padding_r ); desc( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& 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 forward propagation primitive.
Construction
desc( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& bias_desc, const memory::desc& dst_desc, const memory::dims& strides, const memory::dims& padding_l, const memory::dims& padding_r )
Constructs a descriptor for a deconvolution forward propagation 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:
aprop_kind
Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm
src_desc
Source memory descriptor.
weights_desc
Weights memory descriptor.
bias_desc
Bias memory descriptor. Passing zero memory descriptor disables the bias term.
dst_desc
Destination memory descriptor.
strides
Vector of strides for 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( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& dst_desc, const memory::dims& strides, const memory::dims& padding_l, const memory::dims& padding_r )
Constructs a descriptor for a deconvolution forward propagation 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:
aprop_kind
Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm
src_desc
Source memory descriptor.
weights_desc
Weights memory descriptor.
dst_desc
Destination memory descriptor.
strides
Vector of strides for 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( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& bias_desc, const memory::desc& 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 forward propagation 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:
aprop_kind
Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm
src_desc
Source memory descriptor.
weights_desc
Weights memory descriptor.
bias_desc
Bias memory descriptor. Passing zero memory descriptor disables the bias term.
dst_desc
Destination memory descriptor.
strides
Vector of strides for 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( prop_kind aprop_kind, algorithm aalgorithm, const memory::desc& src_desc, const memory::desc& weights_desc, const memory::desc& 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 forward propagation 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:
aprop_kind
Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm
src_desc
Source memory descriptor.
weights_desc
Weights memory descriptor.
dst_desc
Destination memory descriptor.
strides
Vector of strides for 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

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