nnfw::cker::Conv Class Reference

#include <Conv.h>

Public Member Functions
	Conv ()
void	prepareF32 (const Shape &filter_shape, const float *filter_data, PaddingType padding_type, bool &is_replaced_weights, uint32_t dilationWidthFactor, uint32_t dilationHeightFactor)
void	prepareQ8uPerTensor (const Shape &input_shape, const Shape &kernel_shape, const Shape &output_shape, uint32_t stride_width, uint32_t stride_height, uint32_t dilation_width_factor, uint32_t dilation_height_factor)
void	operator() (const ConvParams &params, const Shape &input_shape, const float *input_data, const Shape &filter_shape, const float *filter_data, const Shape &bias_shape, const float *bias_data, const Shape &output_shape, float *output_data)
void	operator() (const ConvParams &params, const Shape &input_shape, const uint8_t *input_data, const Shape &filter_shape, const uint8_t *filter_data, const Shape &bias_shape, const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data)
void	operator() (const ConvParams &params, const Shape &input_shape, const uint8_t *input_data, const Shape &filter_shape, const uint8_t *filter_data, const int32_t *filter_zero_point, const Shape &bias_shape, const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data)
void	operator() (const ConvParams &params, const Shape &input_shape, const int8_t *input_data, const Shape &filter_shape, const int8_t *filter_data, const Shape &bias_shape, const int32_t *bias_data, const Shape &output_shape, int8_t *output_data)
std::vector< int32_t > &	per_channel_output_multiplier ()
std::vector< int > &	per_channel_output_shift ()

Detailed Description

Definition at line 55 of file Conv.h.

Constructor & Destructor Documentation

Conv()

nnfw::cker::Conv::Conv ( )

inline

Definition at line 58 of file Conv.h.

: _modified_filter_data(), _im2col_shape(4), _need_im2col(false), _prepared(false) {}

Member Function Documentation

operator()() [1/4]

void nnfw::cker::Conv::operator() ( const ConvParams &  params, const Shape &  input_shape, const float *  input_data, const Shape &  filter_shape, const float *  filter_data, const Shape &  bias_shape, const float *  bias_data, const Shape &  output_shape, float *  output_data  )

inline

Definition at line 86 of file Conv.h.

  {
    if (usableMultiThreaded(params.padding_type, params.dilation_width_factor,
                            params.dilation_height_factor))
    {
      bool transposed_in_execution = false;
      if (!_prepared)
      {
        // This means that filter is not constant
        // TODO Apply optimized kernel if multithreaded kernel is slower than optimized kernel by
        // transposing filter data
        transposeFilter(filter_shape, filter_data, transposed_in_execution);
      }
      multithreaded::Conv(params, input_shape, input_data, filter_shape, &_modified_filter_data[0],
                          bias_shape, bias_data, output_shape, output_data);
    }
    else
    {
      // TODO Support optimized kernel
      reference::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
                      bias_data, output_shape, output_data);
    }
  }

References nnfw::cker::multithreaded::Conv(), nnfw::cker::reference::Conv(), nnfw::cker::ConvParams::dilation_height_factor, nnfw::cker::ConvParams::dilation_width_factor, output_shape, and nnfw::cker::ConvParams::padding_type.

operator()() [2/4]

void nnfw::cker::Conv::operator() ( const ConvParams &  params, const Shape &  input_shape, const int8_t *  input_data, const Shape &  filter_shape, const int8_t *  filter_data, const Shape &  bias_shape, const int32_t *  bias_data, const Shape &  output_shape, int8_t *  output_data  )

inline

Definition at line 141 of file Conv.h.

  {
    reference::Conv<int8_t, false>(params, _per_channel_output_multiplier.data(),
                                   _per_channel_output_shift.data(), input_shape, input_data,
                                   filter_shape, filter_data, nullptr /* filter_zero_point */,
                                   bias_shape, bias_data, output_shape, output_data);
  }

References output_shape.

operator()() [3/4]

void nnfw::cker::Conv::operator() ( const ConvParams &  params, const Shape &  input_shape, const uint8_t *  input_data, const Shape &  filter_shape, const uint8_t *  filter_data, const int32_t *  filter_zero_point, const Shape &  bias_shape, const int32_t *  bias_data, const Shape &  output_shape, uint8_t *  output_data  )

inline

Definition at line 130 of file Conv.h.

  {
    reference::Conv<uint8_t, true>(params, _per_channel_output_multiplier.data(),
                                   _per_channel_output_shift.data(), input_shape, input_data,
                                   filter_shape, filter_data, filter_zero_point, bias_shape,
                                   bias_data, output_shape, output_data);
  }

References output_shape.

operator()() [4/4]

void nnfw::cker::Conv::operator() ( const ConvParams &  params, const Shape &  input_shape, const uint8_t *  input_data, const Shape &  filter_shape, const uint8_t *  filter_data, const Shape &  bias_shape, const int32_t *  bias_data, const Shape &  output_shape, uint8_t *  output_data  )

inline

Definition at line 112 of file Conv.h.

  {
    if (!_prepared)
    {
      // This means that input or output are dynamic or filter is not constant
      IsRequiredIm2col(input_shape, filter_shape, output_shape, params.stride_width,
                       params.stride_height, params.dilation_width_factor,
                       params.dilation_height_factor);
    }
 
    int im2col_size = _need_im2col ? _im2col_shape.FlatSize() : 1;
    std::vector<uint8_t> im2col_data(im2col_size);
    optimized::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape,
                    bias_data, output_shape, output_data, _im2col_shape, im2col_data.data());
  }

References nnfw::cker::optimized::Conv(), nnfw::cker::ConvParams::dilation_height_factor, nnfw::cker::ConvParams::dilation_width_factor, nnfw::cker::Shape::FlatSize(), output_shape, nnfw::cker::ConvParams::stride_height, and nnfw::cker::ConvParams::stride_width.

per_channel_output_multiplier()

std::vector< int32_t > & nnfw::cker::Conv::per_channel_output_multiplier ( )

inline

Definition at line 150 of file Conv.h.

{ return _per_channel_output_multiplier; }

Referenced by onert::backend::cpu::ops::ConvolutionLayer::prepare().

per_channel_output_shift()

std::vector< int > & nnfw::cker::Conv::per_channel_output_shift ( )

inline

Definition at line 151 of file Conv.h.

{ return _per_channel_output_shift; }

Referenced by onert::backend::cpu::ops::ConvolutionLayer::prepare().

prepareF32()

void nnfw::cker::Conv::prepareF32 ( const Shape &  filter_shape, const float *  filter_data, PaddingType  padding_type, bool &  is_replaced_weights, uint32_t  dilationWidthFactor, uint32_t  dilationHeightFactor  )

inline

Definition at line 60 of file Conv.h.

  {
    if (!_prepared)
    {
      if (usableMultiThreaded(padding_type, dilationWidthFactor, dilationHeightFactor))
      {
        transposeFilter(filter_shape, filter_data, is_replaced_weights);
      }
      _prepared = true;
    }
  }

Referenced by onert::backend::cpu::ops::ConvolutionLayer::prepare().

prepareQ8uPerTensor()

void nnfw::cker::Conv::prepareQ8uPerTensor ( const Shape &  input_shape, const Shape &  kernel_shape, const Shape &  output_shape, uint32_t  stride_width, uint32_t  stride_height, uint32_t  dilation_width_factor, uint32_t  dilation_height_factor  )

inline

Definition at line 74 of file Conv.h.

  {
    if (!_prepared)
    {
      IsRequiredIm2col(input_shape, kernel_shape, output_shape, stride_width, stride_height,
                       dilation_width_factor, dilation_height_factor);
      _prepared = true;
    }
  }

References output_shape.

Referenced by onert::backend::cpu::ops::ConvolutionLayer::prepare().

---

The documentation for this class was generated from the following file:

• compute/cker/include/cker/operation/Conv.h