SparseConvTransposeBackpropFilter.h File Reference

#include <tbb/parallel_for.h>
#include <Eigen/Core>
#include <mutex>

Go to the source code of this file.

Namespaces
namespace	open3d
namespace	open3d::ml
namespace	open3d::ml::impl

Macros
#define	FN_PARAMETERS
#define	CALL_TEMPLATE(NORMALIZE)
#define	CALL_TEMPLATE2

Functions
template<class TFeat, class TOut, class TIndex, class TKernelIndex, bool NORMALIZE>
void	open3d::ml::impl::_SparseConvTransposeBackpropFilterCPU (TOut *filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TFeat *out_importance, size_t num_inp, const TFeat *inp_features, const TFeat *inp_neighbors_importance_sum, const int64_t *inp_neighbors_row_splits, const TIndex *neighbors_index, const TKernelIndex *neighbors_kernel_index, const TFeat *neighbors_importance, const int64_t *neighbors_row_splits, const TFeat *out_features_gradient)
template<class TFeat, class TOut, class TIndex, class TKernelIndex>
void	open3d::ml::impl::SparseConvTransposeBackpropFilterCPU (TOut *filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TFeat *out_importance, size_t num_inp, const TFeat *inp_features, const TFeat *inp_neighbors_importance_sum, const int64_t *inp_neighbors_row_splits, const TIndex *neighbors_index, const TKernelIndex *neighbors_kernel_index, const TFeat *neighbors_importance, const int64_t *neighbors_row_splits, const TFeat *out_features_gradient, bool normalize)

Macro Definition Documentation

CALL_TEMPLATE

#define CALL_TEMPLATE

(

NORMALIZE

)

Value:

    if (NORMALIZE == normalize)                                         \
        _SparseConvTransposeBackpropFilterCPU<TFeat, TOut, TIndex,      \
                                              TKernelIndex, NORMALIZE>( \
                FN_PARAMETERS);

CALL_TEMPLATE2

#define CALL_TEMPLATE2

Value:

    CALL_TEMPLATE(true) \
    CALL_TEMPLATE(false)

FN_PARAMETERS

#define FN_PARAMETERS

Value:

    filter_backprop, filter_dims, num_out, out_importance, num_inp,            \
            inp_features, inp_neighbors_importance_sum,                        \
            inp_neighbors_row_splits, neighbors_index, neighbors_kernel_index, \
            neighbors_importance, neighbors_row_splits, out_features_gradient