text: //*****************************************************************************
  // Copyright 2017-2019 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.
  //*****************************************************************************
  
  #include <numeric>
  
  #include "group_conv.hpp"
  
  #include "ngraph/op/concat.hpp"
  #include "ngraph/op/convolution.hpp"
  #include "ngraph/op/slice.hpp"
  #include "ngraph/validation_util.hpp"
  
  using namespace std;
  using namespace ngraph;
  
  const string op::GroupConvolution::type_name{"GroupConvolution"};
  
  op::GroupConvolution::GroupConvolution(const Output<Node>& data_batch,
                                         const Output<Node>& filters,
                                         const Strides& window_movement_strides,
                                         const Strides& window_dilation_strides,
                                         const CoordinateDiff& padding_below,
                                         const CoordinateDiff& padding_above,
                                         const Strides& data_dilation_strides,
                                         const size_t groups,
                                         const PadType& pad_type)
      : FusedOp({data_batch, filters})
      , m_window_movement_strides(window_movement_strides)
      , m_window_dilation_strides(window_dilation_strides)
      , m_padding_below(padding_below)
      , m_padding_above(padding_above)
      , m_data_dilation_strides(data_dilation_strides)
      , m_groups(groups)
      , m_pad_type(pad_type)
  {
      constructor_validate_and_infer_types();
  }
  
  void op::GroupConvolution::pre_validate_and_infer_types()
  {
      auto data_shape = get_input_partial_shape(0);
      auto filters_shape = get_input_partial_shape(1);
      if (data_shape.is_static() && filters_shape.is_static())
      {
          // Data channels
          NODE_VALIDATION_CHECK(this,
                                data_shape.to_shape()[1] % m_groups == 0,
                                "Data channels not a multiple of group size");
          // Output channels
          NODE_VALIDATION_CHECK(this,
                                filters_shape.to_shape()[0] % m_groups == 0,
                                "# Filters not a multiple of group size");
          // Input Filters
          NODE_VALIDATION_CHECK(this,
                                filters_shape.to_shape()[1] * m_groups == data_shape.to_shape()[1],
                                "Incorrect number of channels per filter");
      }
  }
  
  void op::GroupConvolution::post_validate_and_infer_types()
  {
      auto data_shape = get_input_partial_shape(0);
      auto filters_shape = get_input_partial_shape(1);
      if (data_shape.is_static() && filters_shape.is_static())
      {
          if (m_pad_type == PadType::SAME_UPPER || m_pad_type == PadType::SAME_LOWER)
          {
              m_padding_below.clear();
              m_padding_above.clear();
              auto filter_shape = filters_shape.to_shape();
              filter_shape.erase(filter_shape.begin(), filter_shape.begin() + 2); // Remove {O,I}
              infer_auto_padding(data_shape.to_shape(),
                                 filter_shape,
                                 m_window_movement_strides,
                                 m_window_dilation_strides,
                                 m_pad_type,
                                 m_padding_above,
                                 m_padding_below);
          }
      }
  }
  
  Shape op::GroupConvolution::get_weights_dimensions() const
  {
      // reshape weights into 5d tensors that includes groups
      const size_t OC = 0;
      const size_t OC_IN_OUTPUT = 1;
      const size_t IC = 1;
      Shape weights_shape_groups{get_input_shape(1)};
      // adjust output and channel given a number of groups
  
      weights_shape_groups.at(OC) = get_shape().at(OC_IN_OUTPUT) / get_groups();
      weights_shape_groups.at(IC) = get_input_shape(0).at(IC) / get_groups();
      // push_front the number of groups
      weights_shape_groups.insert(weights_shape_groups.begin(), get_groups());
      return weights_shape_groups;
  }
  
  shared_ptr<Node> op::GroupConvolution::copy_with_new_args(const NodeVector& new_args) const
  {
      if (new_args.size() != 2)
      {
          throw ngraph_error("Incorrect number of new arguments");
      }
  
      return make_shared<op::GroupConvolution>(new_args.at(0),
                                               new_args.at(1),
                                               get_window_movement_strides(),
                                               get_window_dilation_strides(),
                                               get_padding_below(),
                                               get_padding_above(),
                                               get_data_dilation_strides(),
                                               get_groups(),
                                               get_pad_type());
  }
  
  NodeVector op::GroupConvolution::decompose_op() const
  {
      auto data = input_value(0);
      auto filters = input_value(1);
      // Split one convolution op to N ops where N is the number of groups
      // and concat results after computation.
      // reference:
      // https://github.com/NervanaSystems/ngraph-mxnet/blob/fdd692/src/ngraph/ngraph_emitter.cc#L822-L856
      std::size_t n_data_channels{data.get_shape().at(1)};
      std::size_t n_filters_channels{filters.get_shape().at(0)};
      std::size_t data_group_size{n_data_channels / m_groups};
      std::size_t filters_group_size{n_filters_channels / m_groups};
      NodeVector convolution_nodes;
  
      // initial bounds for splice
      std::vector<std::size_t> data_lower_bounds(data.get_shape().size());
      std::vector<std::size_t> data_upper_bounds{data.get_shape()};
      std::vector<std::size_t> filters_lower_bounds(filters.get_shape().size());
      std::vector<std::size_t> filters_upper_bounds{filters.get_shape()};
  
      for (std::size_t group{0}; group < m_groups; ++group)
      {
          // slice data
          data_lower_bounds[1] = group * data_group_size;
          data_upper_bounds[1] = (group + 1) * data_group_size;
          auto sliced_data =
              std::make_shared<ngraph::op::Slice>(data, data_lower_bounds, data_upper_bounds);
          // slice filters
          filters_lower_bounds[0] = group * filters_group_size;
          filters_upper_bounds[0] = (group + 1) * filters_group_size;
          auto sliced_filters = std::make_shared<ngraph::op::Slice>(
              filters, filters_lower_bounds, filters_upper_bounds);
  
          convolution_nodes.push_back(
              std::make_shared<ngraph::op::Convolution>(sliced_data,
                                                        sliced_filters,
                                                        m_window_movement_strides,
                                                        m_window_dilation_strides,
                                                        m_padding_below,
                                                        m_padding_above,
                                                        m_data_dilation_strides,
                                                        m_pad_type));
      }
      std::size_t concatenation_axis = 1;
      return {std::make_shared<ngraph::op::Concat>(convolution_nodes, concatenation_axis)};
  }
  
  void op::GroupConvolution::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector& deltas)
  {
      throw ngraph_error("NYI");
  }
  