wehub-resource-sync
98 lines
3.5 KiB
C++
98 lines
3.5 KiB
C++
//
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// Convolution3D.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 <cstdint>
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#include <vector>
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#include "OpConverter.hpp"
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#include "logkit.h"
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using namespace std;
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class Convolution3DConverter : public OpConverter {
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public:
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Convolution3DConverter() {
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}
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virtual ~Convolution3DConverter() {
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}
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virtual MNN::OpType opType() {
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return MNN::OpType_Convolution3D;
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}
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virtual MNN::OpParameter type() {
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return MNN::OpParameter_Convolution3D;
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}
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virtual void run(MNN::OpT* dstOp, const caffe::LayerParameter& parameters, const caffe::LayerParameter& weight) {
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auto convolution3D = new MNN::Convolution3DT;
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DCHECK(weight.blobs_size() >= 1) << "Convolution3D weight blob ERROR! ==> " << parameters.name();
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dstOp->main.value = convolution3D;
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convolution3D->common = std::unique_ptr<MNN::Convolution3DCommonT>(new MNN::Convolution3DCommonT);
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auto& common = convolution3D->common;
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common->padMode = MNN::PadMode_CAFFE;
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common->relu = common->relu6 = false;
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auto& convProto = parameters.convolution3d_param();
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{ // group must be equal to 1
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const int group = convProto.has_group() ? convProto.group() : 1;
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DCHECK(group == 1) << "Convolution3D not support group convolution";
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}
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{ // kernel_size, kernel_depth
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const int kernel_depth = convProto.kernel_depth();
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const int kernel_size = convProto.kernel_size();
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common->kernels = std::vector<int32_t>({kernel_depth, kernel_size, kernel_size});
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}
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{ // stride, temporal_stride
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const int stride = convProto.stride();
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const int temporal_stride = convProto.temporal_stride();
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common->strides = std::vector<int32_t>({temporal_stride, stride, stride});
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}
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{ // pad, temporal_pad
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const int pad = convProto.pad();
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const int temporal_pad = convProto.temporal_pad();
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common->pads = std::vector<int32_t>({temporal_pad, pad, pad});
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}
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common->dilates = std::vector<int32_t>({1, 1, 1});
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{ // set kernel weight data
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auto& weightBlob = weight.blobs(0);
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DCHECK(weightBlob.shape().dim_size() == 5) << "Conv3D Weight Dimension ERROR!";
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common->outputCount = convProto.num_output();
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DCHECK(weightBlob.has_shape()) << "Caffemodel ERROR!";
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common->inputCount = weightBlob.shape().dim(1);
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int size = 1;
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for (int i = 0; i < weightBlob.shape().dim_size(); ++i) {
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size *= weightBlob.shape().dim(i);
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}
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std::vector<float> weightData;
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weightData.resize(size);
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for (int i = 0; i < size; ++i) {
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weightData[i] = weightBlob.data(i);
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}
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convolution3D->weight = weightData;
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}
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{ // set bias data
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std::vector<float> biasData(convProto.num_output(), 0.0f);
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if (convProto.bias_term() && weight.blobs_size() >= 2) {
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for (int i = 0; i < biasData.size(); ++i) {
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biasData[i] = weight.blobs(1).data(i);
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}
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}
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convolution3D->bias = biasData;
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}
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}
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};
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// https://github.com/facebook/C3D/blob/master/C3D-v1.1/src/caffe/proto/caffe.proto
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static OpConverterRegister<Convolution3DConverter> a("Convolution3D");
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