mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			212 lines
		
	
	
		
			6.2 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			212 lines
		
	
	
		
			6.2 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  testModel.cpp
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| //  MNN
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| //
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| //  Created by MNN on 2019/01/22.
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| //  Copyright © 2018, Alibaba Group Holding Limited
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| //
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| 
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| #define MNN_OPEN_TIME_TRACE
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| 
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| #include <MNN/MNNDefine.h>
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| #include <math.h>
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| #include <stdio.h>
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| #include <stdlib.h>
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| #include <string.h>
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| #include <MNN/AutoTime.hpp>
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| #include <MNN/Interpreter.hpp>
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| #include <MNN/Tensor.hpp>
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| #include <fstream>
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| #include <map>
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| #include <sstream>
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| #include "core/Backend.hpp"
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| #include "core/Macro.h"
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| #include "core/TensorUtils.hpp"
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| 
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| #define NONE "\e[0m"
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| #define RED "\e[0;31m"
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| #define GREEN "\e[0;32m"
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| #define L_GREEN "\e[1;32m"
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| #define BLUE "\e[0;34m"
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| #define L_BLUE "\e[1;34m"
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| #define BOLD "\e[1m"
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| 
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| template<typename T>
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| inline T stringConvert(const char* number) {
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|     std::istringstream os(number);
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|     T v;
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|     os >> v;
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|     return v;
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| }
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| 
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| MNN::Tensor* createTensor(const MNN::Tensor* shape, const char* path) {
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|     std::ifstream stream(path);
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|     if (stream.fail()) {
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|         return NULL;
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|     }
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| 
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|     auto result = new MNN::Tensor(shape, shape->getDimensionType());
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|     auto data   = result->host<float>();
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|     for (int i = 0; i < result->elementSize(); ++i) {
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|         double temp = 0.0f;
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|         stream >> temp;
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|         data[i] = temp;
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|     }
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|     stream.close();
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|     return result;
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| }
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| 
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| int main(int argc, const char* argv[]) {
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| 
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|     // check given & expect
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|     const char* modelPath  = argv[1];
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|     const char* givenName  = argv[2];
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|     const char* expectName = argv[3];
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|     MNN_PRINT("Testing model %s, input: %s, output: %s\n", modelPath, givenName, expectName);
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| 
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|     // create net
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|     auto type = MNN_FORWARD_CPU;
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|     if (argc > 4) {
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|         type = (MNNForwardType)stringConvert<int>(argv[4]);
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|     }
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|     auto tolerance = 0.1f;
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|     if (argc > 5) {
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|         tolerance = stringConvert<float>(argv[5]);
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|     }
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|     MNN::BackendConfig::PrecisionMode precision = MNN::BackendConfig::Precision_High;
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|     if (argc > 6) {
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|         precision = (MNN::BackendConfig::PrecisionMode)stringConvert<int>(argv[6]);
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|     }
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|     std::shared_ptr<MNN::Interpreter> net =
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|     std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(modelPath), [](void* net) {
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|         MNN::Interpreter::destroy((MNN::Interpreter*)net);
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|     });
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| 
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|     // create session
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|     MNN::ScheduleConfig config;
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|     config.type = type;
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|     MNN::BackendConfig backendConfig;
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|     backendConfig.precision = precision;
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|     config.backendConfig = &backendConfig;
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|     auto session         = net->createSession(config);
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|     
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|     // input dims
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|     std::vector<int> inputDims;
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|     if (argc > 7) {
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|         std::string inputShape(argv[7]);
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|         const char* delim = "x";
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|         std::ptrdiff_t p1 = 0, p2;
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|         while (1) {
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|             p2 = inputShape.find(delim, p1);
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|             if (p2 != std::string::npos) {
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|                 inputDims.push_back(atoi(inputShape.substr(p1, p2 - p1).c_str()));
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|                 p1 = p2 + 1;
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|             } else {
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|                 inputDims.push_back(atoi(inputShape.substr(p1).c_str()));
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|                 break;
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|             }
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|         }
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|     }
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|     for (auto dim : inputDims) {
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|         MNN_PRINT("%d ", dim);
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|     }
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|     MNN_PRINT("\n");
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|     
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|     
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|     auto allInput = net->getSessionInputAll(session);
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|     for (auto& iter : allInput) {
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|         auto inputTensor = iter.second;
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|         
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|         if (!inputDims.empty()) {
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|             MNN_PRINT("===========> Resize Tensor...\n");
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|             net->resizeTensor(inputTensor, inputDims);
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|             net->resizeSession(session);
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|         }
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|         
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|         auto size = inputTensor->size();
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|         if (size <= 0) {
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|             continue;
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|         }
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|         
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|         void* host = inputTensor->map(MNN::Tensor::MAP_TENSOR_WRITE,  inputTensor->getDimensionType());
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|         if(host != nullptr) {
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|             ::memset(host, 0, inputTensor->size());
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|         }
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|         inputTensor->unmap(MNN::Tensor::MAP_TENSOR_WRITE,  inputTensor->getDimensionType(), host);
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|     }
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| 
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|     // write input tensor
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|     auto inputTensor = net->getSessionInput(session, NULL);
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|     std::shared_ptr<MNN::Tensor> givenTensor(createTensor(inputTensor, givenName), [](void* t) {
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|         MNN::Tensor::destroy((MNN::Tensor*)t);
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|     });
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|     if (!givenTensor) {
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| #if defined(_MSC_VER)
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|         printf("Failed to open input file %s.\n", givenName);
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| #else
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|         printf(RED "Failed to open input file %s.\n" NONE, givenName);
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| #endif
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|         return -1;
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|     }
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|     // First time
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|     void* host = inputTensor->map(MNN::Tensor::MAP_TENSOR_WRITE, givenTensor.get()->getDimensionType());
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|     if(host != nullptr) {
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|         ::memcpy(host, givenTensor->host<uint8_t>(), givenTensor->size());
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|     }
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|     inputTensor->unmap(MNN::Tensor::MAP_TENSOR_WRITE,  givenTensor.get()->getDimensionType(), host);
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| 
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|     // infer
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|     net->runSession(session);
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|     // read expect tensor
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|     auto outputTensor = net->getSessionOutput(session, NULL);
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|     std::shared_ptr<MNN::Tensor> expectTensor(createTensor(outputTensor, expectName));
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|     if (!expectTensor.get()) {
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| #if defined(_MSC_VER)
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|         printf("Failed to open expect file %s.\n", expectName);
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| #else
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|         printf(RED "Failed to open expect file %s.\n" NONE, expectName);
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| #endif
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|         return -1;
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|     }
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| 
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|     // compare output with expect
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|     bool correct = MNN::TensorUtils::compareTensors(outputTensor, expectTensor.get(), tolerance, true);
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|     if (!correct) {
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| #if defined(_MSC_VER)
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|         printf("Test Failed %s!\n", modelPath);
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| #else
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|         printf(RED "Test Failed %s!\n" NONE, modelPath);
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| #endif
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|         return -1;
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|     } else {
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|         printf("First run pass\n");
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|     }
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|     // Run Second time
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|     void* host1 = inputTensor->map(MNN::Tensor::MAP_TENSOR_WRITE, givenTensor.get()->getDimensionType());
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|     if(host1 != nullptr) {
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|         ::memcpy(host1, givenTensor->host<uint8_t>(), givenTensor->size());
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|     }
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|     inputTensor->unmap(MNN::Tensor::MAP_TENSOR_WRITE,  givenTensor.get()->getDimensionType(), host1);
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| 
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|     // infer
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|     net->runSession(session);
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|     // read expect tensor
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|     std::shared_ptr<MNN::Tensor> expectTensor2(createTensor(outputTensor, expectName));
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|     correct = MNN::TensorUtils::compareTensors(outputTensor, expectTensor2.get(), tolerance, true);
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| 
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|     if (correct) {
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| #if defined(_MSC_VER)
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|         printf("Test %s Correct!\n", modelPath);
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| #else
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|         printf(GREEN BOLD "Test %s Correct!\n" NONE, modelPath);
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| #endif
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|     } else {
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| #if defined(_MSC_VER)
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|         printf("Test Failed %s!\n", modelPath);
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| #else
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|         printf(RED "Test Failed %s!\n" NONE, modelPath);
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| #endif
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|     }
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|     return 0;
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| }
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