Method add_convolution_node

src/graph/GraphBuilder.cpp:242–304

}

NodeID GraphBuilder::add_convolution_node(Graph                  &g,
                                          NodeParams              params,
                                          NodeIdxPair             input,
                                          Size2D                  kernel_spatial_extend,
                                          unsigned int            depth,
                                          PadStrideInfo           conv_info,
                                          unsigned int            num_groups,
                                          ConvolutionMethod       method,
                                          FastMathHint            fast_math_hint,
                                          ITensorAccessorUPtr     weights_accessor,
                                          ITensorAccessorUPtr     bias_accessor,
                                          const QuantizationInfo &weights_quant_info,
                                          const QuantizationInfo &out_quant_info)
{
    check_nodeidx_pair(input, g);
    ARM_COMPUTE_ERROR_ON(depth == 0);
    ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0));

    bool has_bias = (bias_accessor != nullptr);

    // Get input tensor descriptor
    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
    const DataLayout       input_data_layout = input_tensor_desc.layout;

    // Create weights node
    TensorDescriptor w_desc = input_tensor_desc;
    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL),
                     get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) / num_groups);
    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::BATCHES), depth);
    if (!weights_quant_info.empty())
    {
        w_desc.quant_info = weights_quant_info;
    }

    NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));

    // Create bias nodes
    NodeID b_nid = EmptyNodeID;
    if (has_bias)
    {
        TensorDescriptor b_desc = input_tensor_desc;
        b_desc.shape            = TensorShape(depth);
        if (is_data_type_quantized_asymmetric(input_tensor_desc.data_type))
        {
            b_desc.data_type = DataType::S32;
        }
        b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
    }

    // Create convolution node and connect
    NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, num_groups, method, fast_math_hint, out_quant_info);
    g.add_connection(input.node_id, input.index, conv_nid, 0);
    g.add_connection(w_nid, 0, conv_nid, 1);
    if (has_bias)
    {
        g.add_connection(b_nid, 0, conv_nid, 2);