SOFIE_common.hxx File Reference

#include "TMVA/RTensor.hxx"
#include <stdexcept>
#include <type_traits>
#include <cstdint>
#include <cstring>
#include <string>
#include <vector>
#include <memory>
#include <regex>
#include <sstream>
#include <iostream>

Include dependency graph for SOFIE_common.hxx:

This graph shows which files directly or indirectly include this file:

Classes
struct	TMVA::Experimental::SOFIE::Dim
struct	TMVA::Experimental::SOFIE::GNN_Data
struct	TMVA::Experimental::SOFIE::InitializedTensor
struct	TMVA::Experimental::SOFIE::InputTensorInfo
struct	TMVA::Experimental::SOFIE::TensorInfo

Namespaces
namespace	TMVA
	create variable transformations
namespace	TMVA::Experimental
namespace	TMVA::Experimental::SOFIE
namespace	TMVA::Experimental::SOFIE::BLAS
namespace	TMVA::Experimental::SOFIE::UTILITY

Typedefs
typedef std::int64_t	TMVA::Experimental::SOFIE::int_t

Enumerations
enum class	TMVA::Experimental::SOFIE::ETensorType {   TMVA::Experimental::SOFIE::UNDEFINED = 0 , TMVA::Experimental::SOFIE::FLOAT = 1 , TMVA::Experimental::SOFIE::UNINT8 = 2 , TMVA::Experimental::SOFIE::INT8 = 3 ,   TMVA::Experimental::SOFIE::UINT16 = 4 , TMVA::Experimental::SOFIE::INT16 = 5 , TMVA::Experimental::SOFIE::INT32 = 6 , TMVA::Experimental::SOFIE::INT64 = 7 ,   TMVA::Experimental::SOFIE::STRING = 8 , TMVA::Experimental::SOFIE::BOOL = 9 , TMVA::Experimental::SOFIE::FLOAT16 = 10 , TMVA::Experimental::SOFIE::DOUBLE = 11 ,   TMVA::Experimental::SOFIE::UINT32 = 12 , TMVA::Experimental::SOFIE::UINT64 = 13 , TMVA::Experimental::SOFIE::COMPLEX64 = 14 , TMVA::Experimental::SOFIE::COMPLEX28 = 15 ,   TMVA::Experimental::SOFIE::BFLOAT16 = 16 }

Functions
bool	TMVA::Experimental::SOFIE::UTILITY::AreSameShape (const std::vector< size_t > &, const std::vector< size_t > &)
template<typename T >
T *	TMVA::Experimental::SOFIE::UTILITY::BroadcastConvBias (const T *data, const size_t channel, const std::vector< size_t > &targetShape)
template<typename T >
T *	TMVA::Experimental::SOFIE::UTILITY::BroadcastTensor (const T *data, const std::vector< size_t > &shape, const std::vector< size_t > &targetShape)
std::string	TMVA::Experimental::SOFIE::UTILITY::Clean_name (std::string input_tensor_name)
template<typename Dtype >
void	TMVA::Experimental::SOFIE::UTILITY::col2im (const Dtype *data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype *data_im)
std::vector< size_t >	TMVA::Experimental::SOFIE::UTILITY::ComputeStrideFromShape (const std::vector< size_t > &shape)
	compute stride of a tensor given its shape (assume layout is row-major)
GNN_Data	TMVA::Experimental::SOFIE::Concatenate (GNN_Data &data1, GNN_Data &data2, int axis=0)
template<typename T >
TMVA::Experimental::RTensor< T >	TMVA::Experimental::SOFIE::Concatenate (TMVA::Experimental::RTensor< T > &t1, TMVA::Experimental::RTensor< T > &t2, int axis=0)
std::vector< Dim >	TMVA::Experimental::SOFIE::ConvertShapeToDim (std::vector< size_t > shape)
std::size_t	TMVA::Experimental::SOFIE::ConvertShapeToLength (std::vector< size_t > shape)
std::string	TMVA::Experimental::SOFIE::ConvertShapeToString (std::vector< size_t > shape)
ETensorType	TMVA::Experimental::SOFIE::ConvertStringToType (std::string type)
std::string	TMVA::Experimental::SOFIE::ConvertTypeToString (ETensorType type)
GNN_Data	TMVA::Experimental::SOFIE::Copy (const GNN_Data &data)
template<typename T >
ETensorType	TMVA::Experimental::SOFIE::GetTemplatedType (T)
template<typename T >
void	TMVA::Experimental::SOFIE::UTILITY::Im2col (const T *data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, T *data_col)
	im2col : efficient function to re-arrange input data of convolution to a matrix that can be used by BLAS Use trick to loop on each element of filtered region first and follow input data layout By doing this reads and writes are of consecutive data in memory and one gains in efficiency The resulting matrix will be already transposed and can be used directly in BLAS since output will be a matrix : (channels*kernel_h*kernel_w , output_h*output_w) Example: with an input matrix a1 a2 a3 b1 b2 b3 and a 2x2 kernel (k1,k2,k3,k4) and padding 1 : c1 c2 c3 outpout will be a matrix (4 x 16) the routine will follow output order :
template<typename T >
void	TMVA::Experimental::SOFIE::UTILITY::Im2col_3d (const T *data_im, const int channels, const int depth, const int height, const int width, const int kernel_d, const int kernel_h, const int kernel_w, const int pad_d, const int pad_h, const int pad_w, const int stride_d, const int stride_h, const int stride_w, const int dilation_d, const int dilation_h, const int dilation_w, T *data_col)
	3d implementation
bool	TMVA::Experimental::SOFIE::UTILITY::is_a_ge_zero_and_a_lt_b (int a, int b)
	function to check if a >> 0 and a < MAX using a single comparison / use trick casting to unsigned values so it becomes a single comparison
std::vector< size_t >	TMVA::Experimental::SOFIE::UTILITY::MultidirectionalBroadcastShape (std::vector< std::vector< size_t > >)
void	TMVA::Experimental::SOFIE::BLAS::sgemm_ (const char *transa, const char *transb, const int *m, const int *n, const int *k, const float *alpha, const float *A, const int *lda, const float *B, const int *ldb, const float *beta, float *C, const int *ldc)
template<typename T >
T *	TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast (const T *data, const std::vector< size_t > &shape, const std::vector< size_t > &targetShape)
std::vector< size_t >	TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcastShape (std::vector< size_t >, std::vector< size_t >)