Function normalize_attributes

src/normalize_attributes.cpp:188–263

* Assumptions: * Dimensions to pad start from the third dimension (index 2). * Called by compute_shape_op() with the shape of the first input. */

 *  Called by compute_shape_op() with the shape of the first input.
 */
bool normalize_attributes(operation& op, const shape& input_shape)
{
    bool tuned = false;
    auto attrs = op.attributes();
    auto val   = op.to_value();
    if(attrs.contains("normalize_padding"))
    {
        bool use_auto_padding =
            (val.contains("padding_mode") and
             (val.at("padding_mode").to<int>() != migraphx::op::padding_mode_t::default_));
        if(not use_auto_padding)
        {
            auto padding       = val.at(attrs.at("normalize_padding").to<std::string>());
            auto padding_size  = padding.size();
            auto padding_start = 2;
            if(padding_size == 2 * (input_shape.ndim() - padding_start))
                tuned = true;
            else if(padding_size != (input_shape.ndim() - padding_start))
            {
                MIGRAPHX_THROW("normalize_attributes: inconsistent padding vector size ");
            }
            else
            {
                auto result    = tune_pad_attribute(padding);
                val["padding"] = result;
                op.from_value(val);
                tuned = true;
            }
        }
    }
    if(not attrs.contains("normalize_axes"))
    {
        return tuned;
    }

    auto attr_v = attrs.at("normalize_axes").without_key();
    for(const auto& rv : attr_v)
    {
        const auto& key = rv.get_key();
        if(val.contains(key))
        {
            auto message = [&] { return op.name() + ": " + key + ": "; };
            auto vv      = val.at(key).without_key();
            if(vv.is_array())
            {
                std::vector<int64_t> axes;
                if(val.contains("axes"))
                {
                    axes = val.at("axes").without_key().to_vector<int64_t>();
                }
                auto vec    = vv.to_vector<int64_t>();
                auto result = tune_attribute(vec, axes, rv.without_key(), input_shape, message);
                val[key]    = result;
                op.from_value(val);
                val   = op.to_value();
                tuned = true;
            }
            else