wehub-resource-sync
103 lines
3.9 KiB
C++
103 lines
3.9 KiB
C++
//
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// BinaryOpTf.cpp
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// MNNConverter
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//
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// Created by MNN on 2019/01/31.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "TfUtils.hpp"
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#include "tfOpConverter.hpp"
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#include "graph.pb.h"
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DECLARE_OP_CONVERTER(BinartOpTf);
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MNN::OpType BinartOpTf::opType() {
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return MNN::OpType_BinaryOp;
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}
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MNN::OpParameter BinartOpTf::type() {
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return MNN::OpParameter_BinaryOp;
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}
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void BinartOpTf::run(MNN::OpT *dstOp, TmpNode *srcNode) {
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auto parameter = new MNN::BinaryOpT;
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if (srcNode->opType == "Mul" || srcNode->opType == "LogicalAnd") {
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parameter->opType = MNN::BinaryOpOperation_MUL;
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} else if (srcNode->opType == "Sub") {
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parameter->opType = MNN::BinaryOpOperation_SUB;
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} else if (srcNode->opType == "Add" || srcNode->opType == "BiasAdd") {
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parameter->opType = MNN::BinaryOpOperation_ADD;
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} else if (srcNode->opType == "RealDiv") {
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parameter->opType = MNN::BinaryOpOperation_REALDIV;
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} else if (srcNode->opType == "Maximum") {
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parameter->opType = MNN::BinaryOpOperation_MAXIMUM;
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} else if (srcNode->opType == "Minimum") {
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parameter->opType = MNN::BinaryOpOperation_MINIMUM;
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} else if (srcNode->opType == "Less") {
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parameter->opType = MNN::BinaryOpOperation_LESS;
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} else if (srcNode->opType == "LessEqual") {
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parameter->opType = MNN::BinaryOpOperation_LESS_EQUAL;
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} else if (srcNode->opType == "GreaterEqual") {
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parameter->opType = MNN::BinaryOpOperation_GREATER_EQUAL;
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} else if (srcNode->opType == "Greater") {
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parameter->opType = MNN::BinaryOpOperation_GREATER;
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} else if (srcNode->opType == "Equal") {
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parameter->opType = MNN::BinaryOpOperation_EQUAL;
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} else if (srcNode->opType == "FloorDiv") {
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parameter->opType = MNN::BinaryOpOperation_FLOORDIV;
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} else if (srcNode->opType == "FloorMod") {
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parameter->opType = MNN::BinaryOpOperation_FLOORMOD;
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} else if (srcNode->opType == "SquaredDifference") {
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parameter->opType = MNN::BinaryOpOperation_SquaredDifference;
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} else if (srcNode->opType == "Pow") {
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parameter->opType = MNN::BinaryOpOperation_POW;
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} else if (srcNode->opType == "AddV2") {
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parameter->opType = MNN::BinaryOpOperation_ADD;
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} else if (srcNode->opType == "Atan2") {
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parameter->opType = MNN::BinaryOpOperation_ATAN2;
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} else if (srcNode->opType == "LogicalOr") {
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parameter->opType = MNN::BinaryOpOperation_LOGICALOR;
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} else if (srcNode->opType == "NotEqual") {
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parameter->opType = MNN::BinaryOpOperation_NOTEQUAL;
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} else if (srcNode->opType == "TruncateDiv") {
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parameter->opType = MNN::BinaryOpOperation_REALDIV;
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} else if (srcNode->opType == "Mod") {
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parameter->opType = MNN::BinaryOpOperation_MOD;
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} else {
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DLOG(ERROR) << "MNN Converter Not "
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"Supported!!!";
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}
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tensorflow::AttrValue value;
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find_attr_value(srcNode->tfNode, "T", value);
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parameter->T = (MNN::DataType)value.type();
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dstOp->main.value = parameter;
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}
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REGISTER_CONVERTER(BinartOpTf, Mul);
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REGISTER_CONVERTER(BinartOpTf, LogicalAnd);
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REGISTER_CONVERTER(BinartOpTf, Sub);
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REGISTER_CONVERTER(BinartOpTf, Add);
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REGISTER_CONVERTER(BinartOpTf, Maximum);
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REGISTER_CONVERTER(BinartOpTf, RealDiv);
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REGISTER_CONVERTER(BinartOpTf, Minimum);
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REGISTER_CONVERTER(BinartOpTf, Greater);
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REGISTER_CONVERTER(BinartOpTf, Equal);
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REGISTER_CONVERTER(BinartOpTf, BiasAdd);
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REGISTER_CONVERTER(BinartOpTf, Less);
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REGISTER_CONVERTER(BinartOpTf, LessEqual);
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REGISTER_CONVERTER(BinartOpTf, GreaterEqual);
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REGISTER_CONVERTER(BinartOpTf, FloorDiv);
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REGISTER_CONVERTER(BinartOpTf, FloorMod);
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REGISTER_CONVERTER(BinartOpTf, SquaredDifference);
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REGISTER_CONVERTER(BinartOpTf, Pow);
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REGISTER_CONVERTER(BinartOpTf, AddV2);
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REGISTER_CONVERTER(BinartOpTf, Atan2);
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REGISTER_CONVERTER(BinartOpTf, LogicalOr);
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REGISTER_CONVERTER(BinartOpTf, NotEqual);
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REGISTER_CONVERTER(BinartOpTf, TruncateDiv);
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REGISTER_CONVERTER(BinartOpTf, Mod);
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