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;
}
/**
\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 1scalar 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