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パラメータファイルの読み込みを高速化
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@ -256,6 +256,17 @@ Waifu2x::eWaifu2xError Waifu2x::CreateZoomColorImage(const cv::Mat &float_image,
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// 学習したパラメータをファイルから読み込む
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Waifu2x::eWaifu2xError Waifu2x::LoadParameter(boost::shared_ptr<caffe::Net<float>> net, const std::string ¶m_path)
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{
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const std::string caffemodel_path = param_path + ".caffemodel";
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FILE *fp = fopen(caffemodel_path.c_str(), "rb");
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const bool isModelExist = fp != nullptr;
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if (fp) fclose(fp);
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caffe::NetParameter param;
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if (isModelExist && caffe::ReadProtoFromBinaryFile(caffemodel_path, ¶m))
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net->CopyTrainedLayersFrom(param);
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else
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{
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rapidjson::Document d;
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std::vector<char> jsonBuf;
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@ -295,6 +306,9 @@ Waifu2x::eWaifu2xError Waifu2x::LoadParameter(boost::shared_ptr<caffe::Net<float
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try
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{
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std::vector<float> weightList;
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std::vector<float> biasList;
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int count = 0;
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for (auto it = d.Begin(); it != d.End(); ++it)
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{
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@ -334,8 +348,10 @@ Waifu2x::eWaifu2xError Waifu2x::LoadParameter(boost::shared_ptr<caffe::Net<float
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if (!(b1->count() == bias.Size()))
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return eWaifu2xError_FailedConstructModel;
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weightList.resize(0);
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biasList.resize(0);
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size_t weightCount = 0;
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std::vector<float> weightList;
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for (auto it2 = weight.Begin(); it2 != weight.End(); ++it2)
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{
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for (auto it3 = (*it2).Begin(); it3 != (*it2).End(); ++it3)
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@ -350,7 +366,6 @@ Waifu2x::eWaifu2xError Waifu2x::LoadParameter(boost::shared_ptr<caffe::Net<float
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caffe::caffe_copy(b0->count(), weightList.data(), b0Ptr);
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std::vector<float> biasList;
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for (auto it2 = bias.Begin(); it2 != bias.End(); ++it2)
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biasList.push_back((float)it2->GetDouble());
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@ -358,11 +373,16 @@ Waifu2x::eWaifu2xError Waifu2x::LoadParameter(boost::shared_ptr<caffe::Net<float
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count++;
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}
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net->ToProto(¶m);
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caffe::WriteProtoToBinaryFile(param, caffemodel_path);
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}
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catch (...)
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{
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return eWaifu2xError_FailedConstructModel;
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}
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}
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return eWaifu2xError_OK;
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}
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