#include #include #include #include #include #include #include #include #include namespace migraphx { inline namespace MIGRAPHX_INLINE_NS { namespace onnx { struct parse_convolution : op_parser { std::vector operators() const { return {{"Conv", "convolution"}, {"ConvInteger", "quant_convolution"}}; } instruction_ref parse(const op_desc& opd, const onnx_parser& parser, onnx_parser::node_info info, std::vector args) const { auto op = make_op(opd.op_name); auto values = op.to_value(); auto l0 = args[0]; auto weights = args[1]; auto in_lens = l0->get_shape().lens(); assert(in_lens.size() > 2); auto kdims = in_lens.size() - 2; // ensure pads availabe only when auto_pad is "NOT_SET" check_padding_mode(info, "CONV"); if(contains(info.attributes, "strides")) { values["stride"].clear(); copy(info.attributes["strides"].ints(), std::back_inserter(values["stride"])); check_attr_sizes(kdims, values["stride"].size(), "PARSE_CONV: inconsistent strides"); } if(contains(info.attributes, "dilations")) { values["dilation"].clear(); copy(info.attributes["dilations"].ints(), std::back_inserter(values["dilation"])); check_attr_sizes( kdims, values["dilation"].size(), "PARSE_CONV: inconsistent dilations"); } std::vector padding; if(contains(info.attributes, "pads")) { values["padding"].clear(); copy(info.attributes["pads"].ints(), std::back_inserter(padding)); check_attr_sizes(kdims, padding.size() / 2, "PARSE_CONV: inconsistent paddings"); } if(contains(info.attributes, "auto_pad")) { auto weight_lens = weights->get_shape().lens(); std::vector k_lens(weight_lens.begin() + 2, weight_lens.end()); cal_auto_padding_size(info, values, k_lens, values["dilation"].to_vector(), in_lens, padding); auto auto_pad = info.attributes["auto_pad"].s(); if(auto_pad.find("SAME") != std::string::npos) { values["padding_mode"] = to_value(op::padding_mode_t::same); } } values["padding"] = std::vector(padding.begin(), padding.end()); if(contains(info.attributes, "group")) { values["group"] = parser.parse_value(info.attributes.at("group")).at(); } recalc_conv_attributes(values, kdims); op.from_value(values); auto l1 = info.add_instruction(op, l0, args[1]); return info.add_bias(args, l1, 1); } }; } // namespace onnx } // namespace MIGRAPHX_INLINE_NS } // namespace migraphx