代码如下：/*
// Copyright (c) 2018 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
*/
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include "object_detection_demo.h"
#include "detectionoutput.h"
using namespace InferenceEngine;
bool ParseAndCheckCommandLine(int argc, char *argv[]) {
// ---------------------------Parsing and validation of input args--------------------------------------
slog::info << "Parsing input parameters" << slog::endl;
gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
if (FLAGS_h) {
showUsage();
return false;
}
if (FLAGS_ni < 1) {
throw std::logic_error("Parameter -ni should be greater than 0 (default: 1)");
}
if (FLAGS_i.empty()) {
throw std::logic_error("Parameter -i is not set");
}
if (FLAGS_m.empty()) {
throw std::logic_error("Parameter -m is not set");
}
return true;
}
/**
- \brief The entry point for the Inference Engine object_detection demo application
- \file object_detection_demo/main.cpp
-
\example object_detection_demo/main.cpp
/
int main(int argc, char *argv[]) {
try {
/* This demo covers certain topology and cannot be generalized for any object detection one **/
slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << "\n";// ------------------------------ Parsing and validation of input args --------------------------------- if (!ParseAndCheckCommandLine(argc, argv)) { return 0; } /** This vector stores paths to the processed images **/ std::vector<std::string> images; parseImagesArguments(images); if (images.empty()) throw std::logic_error("No suitable images were found"); // ----------------------------------------------------------------------------------------------------- // --------------------------- 1. Load Plugin for inference engine ------------------------------------- slog::info << "Loading plugin" << slog::endl; InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64" , "" }).getPluginByDevice(FLAGS_d); /*If CPU device, load default library with extensions that comes with the product*/ if (FLAGS_d.find("CPU") != std::string::npos) { /** * cpu_extensions library is compiled from "extension" folder containing * custom MKLDNNPlugin layer implementations. These layers are not supported * by mkldnn, but they can be useful for inferencing custom topologies. **/ plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>()); } if (!FLAGS_l.empty()) { // CPU(MKLDNN) extensions are loaded as a shared library and passed as a pointer to base extension IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l); plugin.AddExtension(extension_ptr); slog::info << "CPU Extension loaded: " << FLAGS_l << slog::endl; } if (!FLAGS_c.empty()) { // clDNN Extensions are loaded from an .xml description and OpenCL kernel files plugin.SetConfig({ { PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c } }); slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl; } /** Setting plugin parameter for per layer metrics **/ if (FLAGS_pc) { plugin.SetConfig({ { PluginConfigParams::KEY_PERF_COUNT, PluginConfigParams::YES } }); } /** Printing plugin version **/ printPluginVersion(plugin, std::cout); // ----------------------------------------------------------------------------------------------------- // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------ std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin"; slog::info << "Loading network files:" "\n\t" << FLAGS_m << "\n\t" << binFileName << slog::endl; CNNNetReader networkReader; /** Read network model **/ networkReader.ReadNetwork(FLAGS_m); /** Extract model name and load weigts **/ networkReader.ReadWeights(binFileName); CNNNetwork network = networkReader.getNetwork(); Precision p = network.getPrecision(); // ----------------------------------------------------------------------------------------------------- // --------------------------- 3. Configure input & output --------------------------------------------- // ------------------------------ Adding DetectionOutput ----------------------------------------------- /** * The only meaningful difference between Faster-RCNN and SSD-like topologies is the interpretation * of the output data. Faster-RCNN has 2 output layers which (the same format) are presented inside SSD. * * But SSD has an additional post-processing DetectionOutput layer that simplifies output filtering. * So here we are adding 3 Reshapes and the DetectionOutput to the end of Faster-RCNN so it will return the * same result as SSD and we can easily parse it. */ std::string firstLayerName = network.getInputsInfo().begin()->first; int inputWidth = network.getInputsInfo().begin()->second->getTensorDesc().getDims()[3]; int inputHeight = network.getInputsInfo().begin()->second->getTensorDesc().getDims()[2]; DataPtr bbox_pred_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_bbox_name.c_str()); if (bbox_pred_reshapeInPort == nullptr) { throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_bbox_name); } SizeVector bbox_pred_reshapeOutDims = { bbox_pred_reshapeInPort->getTensorDesc().getDims()[0] * bbox_pred_reshapeInPort->getTensorDesc().getDims()[1], 1 }; DataPtr rois_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_proposal_name.c_str()); if (rois_reshapeInPort == nullptr) { throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_proposal_name); } SizeVector rois_reshapeOutDims = { rois_reshapeInPort->getTensorDesc().getDims()[0] * rois_reshapeInPort->getTensorDesc().getDims()[1], 1 }; DataPtr cls_prob_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_prob_name.c_str()); if (cls_prob_reshapeInPort == nullptr) { throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_prob_name); } SizeVector cls_prob_reshapeOutDims = { cls_prob_reshapeInPort->getTensorDesc().getDims()[0] * cls_prob_reshapeInPort->getTensorDesc().getDims()[1], 1 }; /* Detection output */ int normalized = 0; int prior_size = normalized ? 4 : 5; int num_priors = rois_reshapeOutDims[0] / prior_size; // num_classes guessed from the output dims if (bbox_pred_reshapeOutDims[0] % (num_priors * 4) != 0) { throw std::logic_error("Can't guess number of classes. Something's wrong with output layers dims"); } int num_classes = bbox_pred_reshapeOutDims[0] / (num_priors * 4); slog::info << "num_classes guessed: " << num_classes << slog::endl; LayerParams detectionOutParams; detectionOutParams.name = "detection_out"; detectionOutParams.type = "DetectionOutput"; detectionOutParams.precision = p; CNNLayerPtr detectionOutLayer = CNNLayerPtr(new CNNLayer(detectionOutParams)); detectionOutLayer->params["background_label_id"] = "0"; detectionOutLayer->params["code_type"] = "caffe.PriorBoxParameter.CENTER_SIZE"; detectionOutLayer->params["eta"] = "1.0"; detectionOutLayer->params["input_height"] = std::to_string(inputHeight); detectionOutLayer->params["input_width"] = std::to_string(inputWidth); detectionOutLayer->params["keep_top_k"] = "200"; detectionOutLayer->params["nms_threshold"] = "0.3"; detectionOutLayer->params["normalized"] = std::to_string(normalized); detectionOutLayer->params["num_classes"] = std::to_string(num_classes); detectionOutLayer->params["share_location"] = "0"; detectionOutLayer->params["top_k"] = "400"; detectionOutLayer->params["variance_encoded_in_target"] = "1"; detectionOutLayer->params["visualize"] = "False"; detectionOutLayer->insData.push_back(bbox_pred_reshapeInPort); detectionOutLayer->insData.push_back(cls_prob_reshapeInPort); detectionOutLayer->insData.push_back(rois_reshapeInPort); SizeVector detectionOutLayerOutDims = { 7, 200, 1, 1 }; DataPtr detectionOutLayerOutPort = DataPtr(new Data("detection_out", detectionOutLayerOutDims, p, TensorDesc::getLayoutByDims(detectionOutLayerOutDims))); detectionOutLayerOutPort->creatorLayer = detectionOutLayer; detectionOutLayer->outData.push_back(detectionOutLayerOutPort); DetectionOutputPostProcessor detOutPostProcessor(detectionOutLayer.get()); network.addOutput(FLAGS_bbox_name, 0); network.addOutput(FLAGS_prob_name, 0); network.addOutput(FLAGS_proposal_name, 0); // --------------------------- Prepare input blobs ----------------------------------------------------- slog::info << "Preparing input blobs" << slog::endl; /** Taking information about all topology inputs **/ InputsDataMap inputsInfo(network.getInputsInfo()); /** SSD network has one input and one output **/ if (inputsInfo.size() != 1 && inputsInfo.size() != 2) throw std::logic_error("Demo supports topologies only with 1 or 2 inputs"); std::string imageInputName, imInfoInputName; InputInfo::Ptr inputInfo = inputsInfo.begin()->second; SizeVector inputImageDims; /** Stores input image **/ /** Iterating over all input blobs **/ for (auto & item : inputsInfo) { /** Working with first input tensor that stores image **/ if (item.second->getInputData()->getTensorDesc().getDims().size() == 4) { imageInputName = item.first; slog::info << "Batch size is " << std::to_string(networkReader.getNetwork().getBatchSize()) << slog::endl; /** Creating first input blob **/ Precision inputPrecision = Precision::U8; item.second->setPrecision(inputPrecision); } else if (item.second->getInputData()->getTensorDesc().getDims().size() == 2) { imInfoInputName = item.first; Precision inputPrecision = Precision::FP32; item.second->setPrecision(inputPrecision); if ((item.second->getTensorDesc().getDims()[1] != 3 && item.second->getTensorDesc().getDims()[1] != 6) || item.second->getTensorDesc().getDims()[0] != 1) { throw std::logic_error("Invalid input info. Should be 3 or 6 values length"); } } } // ------------------------------ Prepare output blobs ------------------------------------------------- slog::info << "Preparing output blobs" << slog::endl; OutputsDataMap outputsInfo(network.getOutputsInfo()); const int maxProposalCount = detectionOutLayerOutDims[1]; const int objectSize = detectionOutLayerOutDims[0]; /** Set the precision of output data provided by the user, should be called before load of the network to the plugin **/ outputsInfo[FLAGS_bbox_name]->setPrecision(Precision::FP32); outputsInfo[FLAGS_prob_name]->setPrecision(Precision::FP32); outputsInfo[FLAGS_proposal_name]->setPrecision(Precision::FP32); // ----------------------------------------------------------------------------------------------------- // --------------------------- 4. Loading model to the plugin ------------------------------------------ slog::info << "Loading model to the plugin" << slog::endl; ExecutableNetwork executable_network = plugin.LoadNetwork(network, {}); // ----------------------------------------------------------------------------------------------------- // --------------------------- 5. Create infer request ------------------------------------------------- InferRequest infer_request = executable_network.CreateInferRequest(); // ----------------------------------------------------------------------------------------------------- // --------------------------- 6. Prepare input -------------------------------------------------------- /** Collect images data ptrs **/ std::vector<std::shared_ptr<unsigned char>> imagesData, originalImagesData; std::vector<int> imageWidths, imageHeights; for (auto & i : images) { FormatReader::ReaderPtr reader(i.c_str()); if (reader.get() == nullptr) { slog::warn << "Image " + i + " cannot be read!" << slog::endl; continue; } /** Store image data **/ std::shared_ptr<unsigned char> originalData(reader->getData()); std::shared_ptr<unsigned char> data(reader->getData(inputInfo->getTensorDesc().getDims()[3], inputInfo->getTensorDesc().getDims()[2])); if (data.get() != nullptr) { originalImagesData.push_back(originalData); imagesData.push_back(data); imageWidths.push_back(reader->width()); imageHeights.push_back(reader->height()); } } if (imagesData.empty()) throw std::logic_error("Valid input images were not found!"); size_t batchSize = network.getBatchSize(); slog::info << "Batch size is " << std::to_string(batchSize) << slog::endl; if (batchSize != imagesData.size()) { slog::warn << "Number of images " + std::to_string(imagesData.size()) + \ " doesn't match batch size " + std::to_string(batchSize) << slog::endl; slog::warn << std::to_string(std::min(imagesData.size(), batchSize)) + \ " images will be processed" << slog::endl; batchSize = std::min(batchSize, imagesData.size()); } /** Creating input blob **/ Blob::Ptr imageInput = infer_request.GetBlob(imageInputName); /** Filling input tensor with images. First b channel, then g and r channels **/ size_t num_channels = imageInput->getTensorDesc().getDims()[1]; size_t image_size = imageInput->getTensorDesc().getDims()[3] * imageInput->getTensorDesc().getDims()[2]; unsigned char* data = static_cast<unsigned char*>(imageInput->buffer()); /** Iterate over all input images **/ for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++image_id) { /** Iterate over all pixel in image (b,g,r) **/ for (size_t pid = 0; pid < image_size; pid++) { /** Iterate over all channels **/ for (size_t ch = 0; ch < num_channels; ++ch) { /** [images stride + channels stride + pixel id ] all in bytes **/ data[image_id * image_size * num_channels + ch * image_size + pid] = imagesData.at(image_id).get()[pid*num_channels + ch]; } } } if (imInfoInputName != "") { Blob::Ptr input2 = infer_request.GetBlob(imInfoInputName); auto imInfoDim = inputsInfo.find(imInfoInputName)->second->getTensorDesc().getDims()[1]; /** Fill input tensor with values **/ float *p = input2->buffer().as<PrecisionTrait<Precision::FP32>::value_type*>(); for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++image_id) { p[image_id * imInfoDim + 0] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[2]); p[image_id * imInfoDim + 1] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[3]); for (int k = 2; k < imInfoDim; k++) { p[image_id * imInfoDim + k] = 1.0f; // all scale factors are set to 1.0 } } } // ----------------------------------------------------------------------------------------------------- // ---------------------------- 7. Do inference -------------------------------------------------------- slog::info << "Start inference (" << FLAGS_ni << " iterations)" << slog::endl; typedef std::chrono::high_resolution_clock Time; typedef std::chrono::duration<double, std::ratio<1, 1000>> ms; typedef std::chrono::duration<float> fsec; double total = 0.0; /** Start inference & calc performance **/ for (int iter = 0; iter < FLAGS_ni; ++iter) { auto t0 = Time::now(); infer_request.Infer(); auto t1 = Time::now(); fsec fs = t1 - t0; ms d = std::chrono::duration_cast<ms>(fs); total += d.count(); } // ----------------------------------------------------------------------------------------------------- // ---------------------------- 8. Process output ------------------------------------------------------ slog::info << "Processing output blobs" << slog::endl; Blob::Ptr bbox_output_blob = infer_request.GetBlob(FLAGS_bbox_name); Blob::Ptr prob_output_blob = infer_request.GetBlob(FLAGS_prob_name); Blob::Ptr rois_output_blob = infer_request.GetBlob(FLAGS_proposal_name); std::vector<Blob::Ptr> detOutInBlobs = { bbox_output_blob, prob_output_blob, rois_output_blob }; Blob::Ptr output_blob = std::make_shared<TBlob<float>>(Precision::FP32, Layout::NCHW, detectionOutLayerOutDims); output_blob->allocate(); std::vector<Blob::Ptr> detOutOutBlobs = { output_blob }; detOutPostProcessor.execute(detOutInBlobs, detOutOutBlobs, nullptr); const float* detection = static_cast<PrecisionTrait<Precision::FP32>::value_type*>(output_blob->buffer()); std::vector<std::vector<int> > boxes(batchSize); std::vector<std::vector<int> > classes(batchSize); /* Each detection has image_id that denotes processed image */ for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) { float image_id = detection[curProposal * objectSize + 0]; float label = detection[curProposal * objectSize + 1]; float confidence = detection[curProposal * objectSize + 2]; float xmin = detection[curProposal * objectSize + 3] * imageWidths[image_id]; float ymin = detection[curProposal * objectSize + 4] * imageHeights[image_id]; float xmax = detection[curProposal * objectSize + 5] * imageWidths[image_id]; float ymax = detection[curProposal * objectSize + 6] * imageHeights[image_id]; /* MKLDnn and clDNN have little differente in DetectionOutput layer, so we need this check */ if (image_id < 0 || confidence == 0) { continue; } std::cout << "[" << curProposal << "," << label << "] element, prob = " << confidence << " (" << xmin << "," << ymin << ")-(" << xmax << "," << ymax << ")" << " batch id : " << image_id; if (confidence > 0.5) { /** Drawing only objects with >50% probability **/ classes[image_id].push_back(static_cast<int>(label)); boxes[image_id].push_back(static_cast<int>(xmin)); boxes[image_id].push_back(static_cast<int>(ymin)); boxes[image_id].push_back(static_cast<int>(xmax - xmin)); boxes[image_id].push_back(static_cast<int>(ymax - ymin)); std::cout << " WILL BE PRINTED!"; } std::cout << std::endl; } for (size_t batch_id = 0; batch_id < batchSize; ++batch_id) { addRectangles(originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id], boxes[batch_id], classes[batch_id]); const std::string image_path = "out_" + std::to_string(batch_id) + ".bmp"; if (writeOutputBmp(image_path, originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id])) { slog::info << "Image " + image_path + " created!" << slog::endl; } else { throw std::logic_error(std::string("Can't create a file: ") + image_path); } } // ----------------------------------------------------------------------------------------------------- std::cout << std::endl << "total inference time: " << total << std::endl; std::cout << "Average running time of one iteration: " << total / static_cast<double>(FLAGS_ni) << " ms" << std::endl; std::cout << std::endl << "Throughput: " << 1000 * static_cast<double>(FLAGS_ni) * batchSize / total << " FPS" << std::endl; std::cout << std::endl; /** Show performace results **/ if (FLAGS_pc) { printPerformanceCounts(infer_request, std::cout); }}
catch (const std::exception& error) {
slog::err << error.what() << slog::endl;
return 1;
}
catch (...) {
slog::err << "Unknown/internal exception happened." << slog::endl;
return 1;
}slog::info << "Execution successful" << slog::endl;
return 0;
}
有如下报错：严重性 代码 说明 项目 文件 行 禁止显示状态
错误 LNK2019 无法解析的外部符号 CreateFormatReader，该符号在函数 "public: cdecl FormatReader::ReaderPtr::ReaderPtr(char const *)" (??0ReaderPtr@FormatReader@@QEAA@PEBD@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误(活动) 无法引用 函数 "InferenceEngine::make_so_pointer(const std::string &name) [其中 T=InferenceEngine::IExtension]" (已声明 所在行数:164，所属文件:"c:\Users\颜俊毅\Desktop\dldt-2018\inference-engine\include\details\ie_so_pointer.hpp") -- 它是已删除的函数 88999 c:\Users\颜俊毅\Documents\Visual Studio 2015\Projects\88999\88999\7521.cpp 102
错误 LNK2019 无法解析的外部符号 __imp_CreateDefaultAllocator，该符号在函数 "protected: virtual class std::shared_ptr const & __cdecl InferenceEngine::TBlob >::getAllocator(void)const " (?getAllocator@?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@MEBAAEBV?$shared_ptr@VIAllocator@InferenceEngine@@@std@@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: cdecl InferenceEngine::BlockingDesc::BlockingDesc(class std::vector > const &,class std::vector > const &)" (imp_??0BlockingDesc@InferenceEngine@@QEAA@AEBV?$vector@_KV?$allocator@_K@std@@@std@@0@Z)，该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: virtual cdecl InferenceEngine::BlockingDesc::~BlockingDesc(void)" (imp_??1BlockingDesc@InferenceEngine@@UEAA@XZ)，该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::Precision const &,class std::vector >,class InferenceEngine::BlockingDesc const &)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBVPrecision@1@V?$vector@_KV?$allocator@_K@std@@@std@@AEBVBlockingDesc@1@@Z)，该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::Precision const &,class std::vector >,enum InferenceEngine::Layout)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBVPrecision@1@V?$vector@_KV?$allocator@_K@std@@@std@@W4Layout@1@@Z)，该符号在函数 "public: cdecl InferenceEngine::Blob::Blob(class InferenceEngine::Precision,enum InferenceEngine::Layout,class std::vector > const &)" (??0Blob@InferenceEngine@@QEAA@VPrecision@1@W4Layout@1@AEBV?$vector@_KV?$allocator@_K@std@@@std@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: virtual cdecl InferenceEngine::TensorDesc::~TensorDesc(void)" (imp_??1TensorDesc@InferenceEngine@@UEAA@XZ)，该符号在函数 "public: cdecl InferenceEngine::Blob::Blob(class InferenceEngine::TensorDesc)" (??0Blob@InferenceEngine@@QEAA@VTensorDesc@1@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: class std::vector > & cdecl InferenceEngine::TensorDesc::getDims(void)" (imp_?getDims@TensorDesc@InferenceEngine@@QEAAAEAV?$vector@_KV?$allocator@_K@std@@@std@@XZ)，该符号在函数 "public: virtual void cdecl InferenceEngine::TBlob >::allocate(void)" (?allocate@?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@UEAAXXZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: class std::vector > const & cdecl InferenceEngine::TensorDesc::getDims(void)const " (imp_?getDims@TensorDesc@InferenceEngine@@QEBAAEBV?$vector@_KV?$allocator@_K@std@@@std@@XZ)，该符号在函数 "public: unsigned int64 __cdecl InferenceEngine::Blob::byteSize(void)const " (?byteSize@Blob@InferenceEngine@@QEBA_KXZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: static enum InferenceEngine::Layout cdecl InferenceEngine::TensorDesc::getLayoutByDims(class std::vector >)" (imp_?getLayoutByDims@TensorDesc@InferenceEngine@@SA?AW4Layout@2@V?$vector@_KV?$allocator@_K@std@@@std@@@Z)，该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::TensorDesc const &)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBV01@@Z)，该符号在函数 "public: cdecl InferenceEngine::TBlob >::TBlob >(class InferenceEngine::TensorDesc const &)" (??0?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@QEAA@AEBVTensorDesc@1@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: cdecl InferenceEngine::Data::Data(class std::basic_string,class std::allocator > const &,class std::vector > const &,class InferenceEngine::Precision,enum InferenceEngine::Layout)" (imp_??0Data@InferenceEngine@@QEAA@AEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@AEBV?$vector@_KV?$allocator@_K@std@@@3@VPrecision@1@W4Layout@1@@Z)，该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: class InferenceEngine::TensorDesc const & cdecl InferenceEngine::Data::getTensorDesc(void)const " (imp_?getTensorDesc@Data@InferenceEngine@@QEBAAEBVTensorDesc@2@XZ)，该符号在函数 "public: virtual class std::map,class std::allocator >,class std::vector >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::vector > > > > cdecl InferenceEngine::CNNNetwork::getInputShapes(void)" (?getInputShapes@CNNNetwork@InferenceEngine@@UEAA?AV?$map@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V?$vector@_KV?$allocator@_K@std@@@2@U?$less@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V?$vector@_KV?$allocator@_K@std@@@2@@std@@@2@@std@@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: void cdecl InferenceEngine::Data::setPrecision(class InferenceEngine::Precision const &)" (imp_?setPrecision@Data@InferenceEngine@@QEAAXAEBVPrecision@2@@Z)，该符号在函数 "public: void cdecl InferenceEngine::InputInfo::setPrecision(class InferenceEngine::Precision)" (?setPrecision@InputInfo@InferenceEngine@@QEAAXVPrecision@2@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "declspec(dllimport) public: cdecl InferenceEngine::Data::~Data(void)" (imp_??1Data@InferenceEngine@@QEAA@XZ)，该符号在函数 "public: void * __cdecl InferenceEngine::Data::scalar deleting destructor'(unsigned int)" (??_GData@InferenceEngine@@QEAAPEAXI@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1 scalar deleting destructor'(unsigned int)" (??_GCpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAPEAXI@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_findPlugin，该符号在函数 "public: class InferenceEngine::details::SOPointer<class InferenceEngine::IInferencePlugin,class InferenceEngine::details::SharedObjectLoader> __cdecl InferenceEngine::PluginDispatcher::getSuitablePlugin(enum InferenceEngine::TargetDevice)const " (?getSuitablePlugin@PluginDispatcher@InferenceEngine@@QEBA?AV?$SOPointer@VIInferencePlugin@InferenceEngine@@VSharedObjectLoader@details@2@@details@2@W4TargetDevice@2@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_GetInferenceEngineVersion，该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_CreateCNNNetReader，该符号在函数 "public: __cdecl InferenceEngine::CNNNetReader::CNNNetReader(void)" (??0CNNNetReader@InferenceEngine@@QEAA@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::CpuExtensions(void)" (__imp_??0CpuExtensions@Cpu@Extensions@InferenceEngine@@QEAA@XZ)，该符号在函数 "public: __cdecl std::_Ref_count_obj<class InferenceEngine::Extensions::Cpu::CpuExtensions>::_Ref_count_obj<class InferenceEngine::Extensions::Cpu::CpuExtensions><>(void)" (??$?0$$V@?$_Ref_count_obj@VCpuExtensions@Cpu@Extensions@InferenceEngine@@@std@@QEAA@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: virtual __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::~CpuExtensions(void)" (__imp_??1CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA@XZ)，该符号在函数 "public: virtual void * __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::
错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::GetVersion(struct InferenceEngine::Version const * &)const " (?GetVersion@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEBAXAEAPEBUVersion@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::Release(void)" (?Release@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXXZ) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::SetLogCallback(class InferenceEngine::IErrorListener &)" (?SetLogCallback@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXAEAVIErrorListener@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::Unload(void)" (?Unload@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXXZ) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getFactoryFor(class InferenceEngine::ILayerImplFactory * &,class InferenceEngine::CNNLayer const *,struct InferenceEngine::ResponseDesc *)" (?getFactoryFor@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAPEAVILayerImplFactory@4@PEBVCNNLayer@4@PEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getPrimitiveTypes(char * * &,unsigned int &,struct InferenceEngine::ResponseDesc *)" (?getPrimitiveTypes@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAPEAPEADAEAIPEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getShapeInferImpl(class std::shared_ptr &,char const *,struct InferenceEngine::ResponseDesc *)" (?getShapeInferImpl@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAV?$shared_ptr@VIShapeInferImpl@InferenceEngine@@@std@@PEBDPEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK1120 27 个无法解析的外部命令 88999 c:\users\颜俊毅\documents\visual studio 2015\Projects\88999\x64\Debug\88999.exe 1
