wehub-resource-sync
71 lines
1.9 KiB
C++
71 lines
1.9 KiB
C++
//
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// Pooling3DTf.cpp
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// MNNConverter
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//
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// Created by MNN on 2019/09/29.
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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(Pooling3DTf);
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MNN::OpType Pooling3DTf::opType() {
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return MNN::OpType_Pooling3D;
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}
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MNN::OpParameter Pooling3DTf::type() {
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return MNN::OpParameter_Pool3D;
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}
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// input: tensor
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void Pooling3DTf::run(MNN::OpT *dstOp, TmpNode *srcNode) {
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auto pool3d = new MNN::Pool3DT;
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tensorflow::AttrValue value;
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int stride_h = 1;
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int stride_w = 1;
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if (srcNode->opType == "AvgPool3D") {
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pool3d->type = MNN::PoolType_AVEPOOL;
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} else if (srcNode->opType == "MaxPool3D") {
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pool3d->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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std::vector<int32_t> kernels;
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for (int i = 1; i < 4; ++i) {
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kernels.push_back(value.list().i(i));
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}
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pool3d->kernels = kernels;
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}
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if (find_attr_value(srcNode->tfNode, "strides", value)) {
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std::vector<int32_t> strides;
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for (int i = 1; i < 4; ++i) {
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strides.push_back(value.list().i(i));
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}
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pool3d->strides = strides;
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}
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if (find_attr_value(srcNode->tfNode, "padding", value)) {
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if (value.s() == "VALID") {
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pool3d->padType = MNN::PoolPadType_VALID;
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pool3d->pads = std::vector<int32_t>(3, 0);
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} else if (value.s() == "SAME") {
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pool3d->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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dstOp->main.value = pool3d;
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}
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REGISTER_CONVERTER(Pooling3DTf, MaxPool3D);
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REGISTER_CONVERTER(Pooling3DTf, AvgPool3D);
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