namespace ov::op::v1

Overview

namespace v1 {

// namespaces

namespace ov::op::v1::utils;
    namespace ov::op::v1::utils::one_hot;

// structs

template <class T>
struct GetK;

// classes

class Add;
class AvgPool;
class BatchToSpace;
class BinaryConvolution;
class Broadcast;
class ConvertLike;
class Convolution;
class ConvolutionBackpropData;
class DeformableConvolution;
class DeformablePSROIPooling;
class Divide;
class Equal;
class FloorMod;
class Gather;
class GatherTree;
class Greater;
class GreaterEqual;
class GroupConvolution;
class GroupConvolutionBackpropData;
class Less;
class LessEqual;
class LogicalAnd;
class LogicalNot;
class LogicalOr;
class LogicalXor;
class MaxPool;
class Maximum;
class Minimum;
class Mod;
class Multiply;
class NonMaxSuppression;
class NotEqual;
class OneHot;
class Pad;
class Power;
class ReduceLogicalAnd;
class ReduceLogicalOr;
class ReduceMax;
class ReduceMean;
class ReduceMin;
class ReduceProd;
class ReduceSum;
class Reshape;
class Reverse;
class Select;
class Softmax;
class SpaceToBatch;
class Split;
class StridedSlice;
class Subtract;
class TopK;
class Transpose;
class VariadicSplit;

// global functions

template <class T>
void shape_infer(
    const ov::op::v1::BatchToSpace \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const ov::op::v1::Broadcast \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class ConvType>
int64_t calculate_num_spatial(
    const ConvType \* op,
    const PartialShape& input_shape,
    const PartialShape& filters_shape,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class ConvType, class ShapeType>
int64_t calculate_num_spatial(
    const ConvType \* op,
    const ShapeType& input_shape,
    const ShapeType& filters_shape,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class ConvType>
void update_and_validate_attributes(
    ConvType \* op,
    int64_t num_spatial
    );

template <class T>
bool dynamic_check(const int64_t& num_spatial);

bool dynamic_check< PartialShape >(const int64_t& num_spatial);

template <class ConvType, class ShapeType>
bool resolve_auto_pad_for_shape(
    const ConvType \* op,
    CoordinateDiff& pads_begin,
    CoordinateDiff& pads_end,
    const std::vector<ShapeType>& input_shapes,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class DimType>
void divide_ceil(
    const DimType& dividend,
    const typename DimType::value_type& divisor,
    DimType& quotient
    );

template <class DimType>
void divide_floor(
    const DimType& dividend,
    const typename DimType::value_type& divisor,
    DimType& quotient
    );

template <class ConvType, class ShapeType>
void calculate_output_spatial_dims_for_convolution(
    const ConvType \* op,
    const ShapeType& input_shape,
    const ShapeType& filters_shape,
    ShapeType& output_shape,
    const int64_t& num_spatial,
    const Strides& strides,
    const Strides& dilations,
    const CoordinateDiff& pads_begin,
    const CoordinateDiff& pads_end,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class T>
void shape_infer(
    const Convolution \* op,
    const CoordinateDiff& pads_begin,
    const CoordinateDiff& pads_end,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class T>
void shape_infer(
    const GroupConvolution \* op,
    const CoordinateDiff& pads_begin,
    const CoordinateDiff& pads_end,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class ConvType>
int64_t calculate_num_spatial(
    const ConvType \* op,
    const PartialShape& input_shape,
    const PartialShape& filters_shape,
    const PartialShape& output_shapes_shape,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class ConvType, class ShapeType>
int64_t calculate_num_spatial(
    const ConvType \* op,
    const ShapeType& input_shape,
    const ShapeType& filters_shape,
    const ShapeType& output_shapes_shape,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class ConvType>
void update_and_validate_attributes_back_prop(
    ConvType \* op,
    int64_t num_spatial
    );

template <class ConvType, class ShapeType>
bool resolve_auto_pad_for_shape_back_prop(
    const ConvType \* op,
    CoordinateDiff& pads_begin,
    CoordinateDiff& pads_end,
    const std::vector<ShapeType>& input_shapes,
    ShapeType& output_spatial_shape,
    const int64_t& num_non_spatial_data_dims,
    const int64_t& num_non_spatial_filter_dims
    );

template <class T>
void shape_infer(
    const ConvolutionBackpropData \* op,
    const CoordinateDiff& pads_begin,
    const CoordinateDiff& pads_end,
    const T& output_shape_from_input,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class T>
void shape_infer(
    const GroupConvolutionBackpropData \* op,
    const CoordinateDiff& pads_begin,
    const CoordinateDiff& pads_end,
    const T& output_shape_from_input,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class TShape>
std::vector<TShape> shape_infer(
    const GatherTree \* op,
    const std::vector<TShape>& input_shapes
    );

template <class TShape>
void shape_infer(
    const GatherTree \* op,
    const std::vector<TShape>& input_shapes,
    std::vector<TShape>& output_shapes
    );

void resolve_axis(OneHot \* op);

template <class T>
void shape_infer(
    const OneHot \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const Pad \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const Select \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class T>
void shape_infer(
    const ov::op::v1::SpaceToBatch \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <typename T>
void shape_infer(
    const Split \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const StridedSlice \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class TShape>
std::vector<TShape> shape_infer(
    const TopK \* op,
    const std::vector<TShape>& input_shapes,
    const std::map<size_t, HostTensorPtr>& constant_data = {}
    );

template <typename T>
void shape_infer(
    const TopK \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, HostTensorPtr>& constant_data = {}
    );

template <class T>
T calc_output_shape(
    const Transpose \*const op,
    const T& input_shape,
    std::vector<int64_t>& axes_order
    );

template <class T>
void shape_infer(
    const Transpose \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <typename T>
void shape_infer(
    const VariadicSplit \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

} // namespace v1

Detailed Documentation

Global Functions

template <typename T>
void shape_infer(
    const Split \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    )

Shape inference for Split V1 operator.

The split operation cause label lost on splitted dimension even if number of splits is one, because in this case split will be removed by transformation (as NOP) and in fact label will be propagated.

Parameters:

T

Type of shape.

op

Split operator pointer.

input_shapes

Split input shapes.

output_shapes

Split output shapes.

constant_data

Map of constant data.

template <class TShape>
std::vector<TShape> shape_infer(
    const TopK \* op,
    const std::vector<TShape>& input_shapes,
    const std::map<size_t, HostTensorPtr>& constant_data = {}
    )

TopK shape inference.

Parameters:

TShape

Type of shape.

op

Pointer to TopK operator.

input_shapes

Input shapes of TopK.

constant_data

Map of constant data. DEfault empty.

Returns:

Vector of output shapes for

template <typename T>
void shape_infer(
    const TopK \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, HostTensorPtr>& constant_data = {}
    )

TopK shape inference.

Parameters:

TShape

Type of shape.

op

Pointer to TopK operator.

input_shapes

Input shapes of TopK.

output_shapes

Output shapes of TopK

constant_data

Map of constant data. Default empty.

template <class T>
T calc_output_shape(
    const Transpose \*const op,
    const T& input_shape,
    std::vector<int64_t>& axes_order
    )

Calculate transpose output shape.

Parameters:

T

Type of shape

op

Transpose operator pointer.

input_shape

Transpose input shape.

axes_order

Transpose axes order (modified if empty).

Returns:

Output shape

template <class T>
void shape_infer(
    const Transpose \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    )

Do transpose inference on input and output shapes.

Parameters:

T

Type of inference shapes.

op

Transpose operator pointer.

input_shapes

Input shapes of transpose.

output_shapes

Output shapes of transpose which be modified by inference.

constant_data

Map of constant data.