CNNPredictor_VGG16.h 4.12 KB
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#ifndef CNNPREDICTOR_VGG16
#define CNNPREDICTOR_VGG16

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#include "caffe2/core/common.h"
#include "caffe2/utils/proto_utils.h"
#include "caffe2/core/workspace.h"
#include "caffe2/core/tensor.h"
#include "caffe2/core/init.h"

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// Define USE_GPU for GPU computation. Default is CPU computation.
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//#define USE_GPU

#ifdef USE_GPU
#include "caffe2/core/context_gpu.h"
#endif
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#include <string>
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#include <iostream>
#include <map>

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CAFFE2_DEFINE_string(init_net_CNNPredictor_VGG16, "./model/VGG16/init_net.pb", "The given path to the init protobuffer.");
CAFFE2_DEFINE_string(predict_net_CNNPredictor_VGG16, "./model/VGG16/predict_net.pb", "The given path to the predict protobuffer.");
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using namespace caffe2;
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class CNNPredictor_VGG16{
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    private:
        TensorCPU input;
        Workspace workSpace;
        NetDef initNet, predictNet;
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    public:
        const std::vector<TIndex> input_shapes = {{1,3,224,224}};
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        explicit CNNPredictor_VGG16(){
            init(input_shapes);
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        }

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        ~CNNPredictor_VGG16(){};
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        void init(const std::vector<TIndex> &input_shapes){
            int n = 0;
            char **a[1];
            caffe2::GlobalInit(&n, a);

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            if (!std::ifstream(FLAGS_init_net_CNNPredictor_VGG16).good()) {
                std::cerr << "\nNetwork loading failure, init_net file '" << FLAGS_init_net_CNNPredictor_VGG16 << "' does not exist." << std::endl;
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                exit(1);
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            }

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            if (!std::ifstream(FLAGS_predict_net_CNNPredictor_VGG16).good()) {
                std::cerr << "\nNetwork loading failure, predict_net file '" << FLAGS_predict_net_CNNPredictor_VGG16 << "' does not exist." << std::endl;
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                exit(1);
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            }

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            std::cout << "\nLoading network..." << std::endl;
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            // Read protobuf
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            CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_init_net_CNNPredictor_VGG16, &initNet));
            CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_predict_net_CNNPredictor_VGG16, &predictNet));
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            // Set device type
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            #ifdef USE_GPU
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            predictNet.mutable_device_option()->set_device_type(CUDA);
            initNet.mutable_device_option()->set_device_type(CUDA);
            std::cout << "== GPU mode selected " << " ==" << std::endl;
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            #else
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            predictNet.mutable_device_option()->set_device_type(CPU);
            initNet.mutable_device_option()->set_device_type(CPU);

            for(int i = 0; i < predictNet.op_size(); ++i){
                predictNet.mutable_op(i)->mutable_device_option()->set_device_type(CPU);
            }
            for(int i = 0; i < initNet.op_size(); ++i){
                initNet.mutable_op(i)->mutable_device_option()->set_device_type(CPU);
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            }
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            std::cout << "== CPU mode selected " << " ==" << std::endl;
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            #endif
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            // Load network
            CAFFE_ENFORCE(workSpace.RunNetOnce(initNet));
            CAFFE_ENFORCE(workSpace.CreateNet(predictNet));
            std::cout << "== Network loaded " << " ==" << std::endl;

            input.Resize(input_shapes);
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        }

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        void predict(const std::vector<float> &data, std::vector<float> &predictions){
            //Note: ShareExternalPointer requires a float pointer.
            input.ShareExternalPointer((float *) data.data());

            // Get input blob
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            #ifdef USE_GPU
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            auto dataBlob = workSpace.GetBlob("data")->GetMutable<TensorCUDA>();
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            #else
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            auto dataBlob = workSpace.GetBlob("data")->GetMutable<TensorCPU>();
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            #endif
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            // Copy from input data
            dataBlob->CopyFrom(input);

            // Forward
            workSpace.RunNet(predictNet.name());

            // Get output blob
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            #ifdef USE_GPU
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            auto predictionsBlob = TensorCPU(workSpace.GetBlob("predictions")->Get<TensorCUDA>());
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            #else
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            auto predictionsBlob = workSpace.GetBlob("predictions")->Get<TensorCPU>();
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            #endif
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            predictions.assign(predictionsBlob.data<float>(),predictionsBlob.data<float>() + predictionsBlob.size());

            google::protobuf::ShutdownProtobufLibrary();
        }
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};

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#endif // CNNPREDICTOR_VGG16