wehub-resource-sync
74 lines
1.8 KiB
C++
74 lines
1.8 KiB
C++
//
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// PoolingTf.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 "graph.pb.h"
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#include "tfOpConverter.hpp"
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DECLARE_OP_CONVERTER(PoolingTf);
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MNN::OpType PoolingTf::opType() {
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return MNN::OpType_Pooling;
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}
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MNN::OpParameter PoolingTf::type() {
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return MNN::OpParameter_Pool;
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}
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// input: tensor
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void PoolingTf::run(MNN::OpT *dstOp, TmpNode *srcNode) {
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auto pool = new MNN::PoolT;
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tensorflow::AttrValue value;
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int kernel_size_h = 1;
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int kernel_size_w = 1;
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int stride_h = 1;
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int stride_w = 1;
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if (srcNode->opType == "AvgPool") {
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pool->type = MNN::PoolType_AVEPOOL;
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} else if (srcNode->opType == "MaxPool") {
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pool->type = MNN::PoolType_MAXPOOL;
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} else {
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DLOG(ERROR) << "Not Support This Pooling Type: " << srcNode->opType;
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}
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if (find_attr_value(srcNode->tfNode, "ksize", value)) {
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kernel_size_h = value.list().i(1);
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kernel_size_w = value.list().i(2);
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}
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pool->kernelX = kernel_size_w;
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pool->kernelY = kernel_size_h;
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if (find_attr_value(srcNode->tfNode, "strides", value)) {
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stride_h = value.list().i(1);
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stride_w = value.list().i(2);
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}
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pool->strideX = stride_w;
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pool->strideY = stride_h;
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if (find_attr_value(srcNode->tfNode, "padding", value)) {
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if (value.s() == "VALID") {
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pool->padType = MNN::PoolPadType_VALID;
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} else if (value.s() == "SAME") {
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pool->padType = MNN::PoolPadType_SAME;
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} else {
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DLOG(ERROR) << "Not Support This Padding Mode";
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}
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}
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pool->padY = 0; // runtime compute this pad
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pool->padX = 0;
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pool->isGlobal = false; // TODO
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dstOp->main.value = pool;
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}
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REGISTER_CONVERTER(PoolingTf, MaxPool);
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REGISTER_CONVERTER(PoolingTf, AvgPool);
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