text: /*
  // Copyright (c) 2019 Intel Corporation
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //      http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  */
  
  ///////////////////////////////////////////////////////////////////////////////////////////////////
  #pragma once
  #include "cldnn.h"
  /// @addtogroup c_api C API
  /// @{
  /// @addtogroup c_topology Network Topology
  /// @{
  /// @addtogroup c_primitives Primitives
  /// @{
  
  #ifdef __cplusplus
  extern "C" {
  #endif
  
  /// @brief Performs forward spatial binary_convolution with weight sharing.
  /// @details Parameters are defined in context of "direct" binary_convolution, but actual algorithm is not implied.
  CLDNN_BEGIN_PRIMITIVE_DESC(binary_convolution)
  /// @brief Defines a shift, relative to (0,0) position of the input buffer, where (0,0) point of the binary_convolution window should start calculations.
  cldnn_tensor input_offset;
  /// @brief Defines shift in input buffer between adjacent calculations of output values.
  cldnn_tensor stride;
  /// @brief Defines gaps in the input - dilation rate k=1 is normal binary_convolution, k=2 means skipping one pixel per input, k=4 means skipping 3 pixels.
  /// As an example in one dimension, a filter w of size 3 would compute over input x the following: w[0]*x[0] + w[1]*x[1] + w[2]*x[2] for dilation of 1.
  /// For dilation 2 the filter would instead compute w[0]*x[0] + w[1]*x[2] + w[2]*x[4].
  cldnn_tensor dilation;
  /// @brief Weights groups count
  uint32_t split;
  /// @brief User-defined output data size of the primitive (w/o padding).
  cldnn_tensor output_size;
  /// @brief Number of feature groups (grouped convolution). If more than 1 then weights/bias count needs to be 1.
  int groups;
  /// @brief Logical value of padding. Can be one of 3 values: 1 - pad bits equal to 1; -1 -> pad bits equal to 0; 0 -> pad is not counted
  float pad_value;
  /// @brief Array of primitive ids containing weights data. Size of array should be equivalent to @p split.
  cldnn_primitive_id_arr weights;
  
  CLDNN_END_PRIMITIVE_DESC(binary_convolution)
  
  CLDNN_DECLARE_PRIMITIVE_TYPE_ID(binary_convolution);
  
  #ifdef __cplusplus
  }
  #endif
  
  /// @}
  /// @}
  /// @}
  
  