/* * The MIT License (MIT) * * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE. */ #include #include #include #include namespace migraphx { inline namespace MIGRAPHX_INLINE_NS { namespace tf { struct parse_batchnorm : op_parser { bool transpose() const { return true; } std::vector operators() const { return {{"FusedBatchNorm"}, {"FusedBatchNormV3"}}; } instruction_ref parse(const op_desc& /*opd*/, const tf_parser& /*parser*/, tf_parser::node_info info, const std::vector& args) const { float epsilon = 1e-5f; float momentum = 0.9f; if(contains(info.attributes, "epsilon")) { epsilon = info.attributes.at("epsilon").f(); } auto op = make_op("batch_norm_inference", {{"epsilon", epsilon}, {"momentum", momentum}}); return info.add_instruction(op, args); } }; } // namespace tf } // namespace MIGRAPHX_INLINE_NS } // namespace migraphx