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SOFIE_common.hxx File Reference
#include "TMVA/RTensor.hxx"
#include "ROOT/RSpan.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::DynamicTensorInfo
 
struct  TMVA::Experimental::SOFIE::GNN_Data
 
class  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< Dim > &, const std::vector< Dim > &)
 
bool TMVA::Experimental::SOFIE::UTILITY::AreSameShape (const std::vector< size_t > &, const std::vector< Dim > &)
 
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< Dim > TMVA::Experimental::SOFIE::UTILITY::ComputeStrideFromShape (const std::vector< Dim > &shape)
 
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::string TMVA::Experimental::SOFIE::ConvertDynamicShapeToLength (std::vector< Dim > shape)
 
std::string TMVA::Experimental::SOFIE::ConvertDynamicShapeToString (std::vector< Dim > shape)
 
std::vector< Dim > TMVA::Experimental::SOFIE::ConvertShapeToDim (std::vector< size_t > shape)
 Convert shape from integer format to dynamic one (based on Dim)
 
std::vector< size_t > TMVA::Experimental::SOFIE::ConvertShapeToInt (std::vector< Dim > shape)
 Convert shape based on Dim to integer format.
 
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)
 
template<class T >
std::string TMVA::Experimental::SOFIE::ConvertValuesToString (const std::vector< T > &data)
 
template<class T >
std::string TMVA::Experimental::SOFIE::ConvertValuesToString (size_t n, const T *data)
 
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 >)