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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,266 @@ | ||
| // | ||
| // modelCompare.cpp | ||
| // MNN | ||
| // | ||
| // Created by MNN on 2019/01/22. | ||
| // Copyright © 2018, Alibaba Group Holding Limited | ||
| // | ||
|
|
||
| #define MNN_OPEN_TIME_TRACE | ||
|
|
||
| #include <math.h> | ||
| #include <stdlib.h> | ||
| #include <algorithm> | ||
| #include <cstring> | ||
| #include <fstream> | ||
| #include <iostream> | ||
| #include <map> | ||
| #include <sstream> | ||
| #include <string> | ||
| #include <MNN/AutoTime.hpp> | ||
| #include <MNN/Interpreter.hpp> | ||
| #include <MNN/Tensor.hpp> | ||
| #include "core/TensorUtils.hpp" | ||
| #include "rapidjson/document.h" | ||
|
|
||
| template<typename T> | ||
| inline T stringConvert(const char* number) { | ||
| std::istringstream os(number); | ||
| T v; | ||
| os >> v; | ||
| return v; | ||
| } | ||
|
|
||
| using namespace MNN; | ||
|
|
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| static void compareNet(Interpreter* net, Interpreter* net2, MNNForwardType expectType, float tolerance, | ||
| const std::map<std::string, std::shared_ptr<Tensor>>& inputs, const std::string& stopOp, BackendConfig::PrecisionMode precision, int modeNum) { | ||
| std::vector<std::shared_ptr<MNN::Tensor>> correctResult; | ||
| int index; | ||
| MNN::ScheduleConfig expectConfig; | ||
| BackendConfig backendConfig; | ||
| backendConfig.precision = precision; | ||
| expectConfig.type = expectType; | ||
| expectConfig.backendConfig = &backendConfig; | ||
| expectConfig.mode = modeNum; | ||
| auto expectSession = net->createSession(expectConfig); | ||
| auto compareSession = net2->createSession(expectConfig); | ||
|
|
||
| bool allCorrect = true; | ||
|
|
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| MNN::TensorCallBackWithInfo beginCallBack = [&](const std::vector<MNN::Tensor*>& t, const OperatorInfo* op) { | ||
| if (op->name() == stopOp) { | ||
| return false; | ||
| } | ||
| return true; | ||
| }; | ||
| MNN::TensorCallBackWithInfo saveExpect = [&](const std::vector<MNN::Tensor*>& t, const OperatorInfo* op) { | ||
| if (op->name() == stopOp) { | ||
| return false; | ||
| } | ||
| if (op->type() == "Raster") { | ||
| return true; | ||
| } | ||
| for (int i=0; i<t.size(); ++i) { | ||
| auto tensor = t[i]; | ||
| if (tensor->elementSize() <= 0) { | ||
| return true; | ||
| } | ||
| if (tensor->buffer().device == 0 && tensor->buffer().host == nullptr) { | ||
| return true; | ||
| } | ||
| std::shared_ptr<MNN::Tensor> copyTensor(MNN::Tensor::createHostTensorFromDevice(tensor, true)); | ||
| correctResult.emplace_back(copyTensor); | ||
| } | ||
| return true; | ||
| }; | ||
| MNN::TensorCallBackWithInfo compareExpect = [&](const std::vector<MNN::Tensor*>& t, const OperatorInfo* op) { | ||
| if (op->name() == stopOp) { | ||
| return false; | ||
| } | ||
| if (op->type() == "Raster") { | ||
| return true; | ||
| } | ||
| for (int i=0; i<t.size(); ++i) { | ||
| auto tensor = t[i]; | ||
| if (tensor->elementSize() <= 0) { | ||
| return true; | ||
| } | ||
| if (tensor->buffer().device == 0 && tensor->buffer().host == nullptr) { | ||
| return true; | ||
| } | ||
| std::shared_ptr<MNN::Tensor> copyTensor(MNN::Tensor::createHostTensorFromDevice(tensor, true)); | ||
| auto expectTensor = correctResult[index++]; | ||
| auto correct = TensorUtils::compareTensors(copyTensor.get(), expectTensor.get(), tolerance, true); | ||
| if (!correct) { | ||
| MNN_PRINT("%s - %d is error\n", op->name().c_str(), i); | ||
| allCorrect = false; | ||
| } | ||
| } | ||
| return allCorrect; | ||
| }; | ||
|
|
||
| for (auto& iter : inputs) { | ||
| Tensor* expectInput = net->getSessionInput(expectSession, iter.first.empty() ? NULL : iter.first.c_str()); | ||
| expectInput->copyFromHostTensor(iter.second.get()); | ||
| Tensor* compareInput = net->getSessionInput(compareSession, iter.first.empty() ? NULL : iter.first.c_str()); | ||
| compareInput->copyFromHostTensor(iter.second.get()); | ||
| } | ||
| correctResult.clear(); | ||
| net->runSessionWithCallBackInfo(expectSession, beginCallBack, saveExpect); | ||
| index = 0; | ||
| net2->runSessionWithCallBackInfo(compareSession, beginCallBack, compareExpect); | ||
| if (allCorrect) { | ||
| MNN_PRINT("Correct ! Run second pass\n"); | ||
| } else { | ||
| return; | ||
| } | ||
| index = 0; | ||
| for (auto& iter : inputs) { | ||
| Tensor* compareInput = net->getSessionInput(compareSession, iter.first.empty() ? NULL : iter.first.c_str()); | ||
| compareInput->copyFromHostTensor(iter.second.get()); | ||
| } | ||
| net2->runSessionWithCallBackInfo(compareSession, beginCallBack, compareExpect); | ||
| if (allCorrect) { | ||
| MNN_PRINT("Correct !\n"); | ||
| } | ||
| } | ||
|
|
||
| int main(int argc, const char* argv[]) { | ||
| if (argc < 3) { | ||
| MNN_PRINT("Usage: ./modelCompare.out origin.mnn origin_quant.mnn [0.05]"); | ||
| } | ||
| // read args | ||
| std::string cmd = argv[0]; | ||
| std::string pwd = "./"; | ||
| auto rslash = cmd.rfind("/"); | ||
| if (rslash != std::string::npos) { | ||
| pwd = cmd.substr(0, rslash + 1); | ||
| } | ||
|
|
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| const char* fileName = argv[1]; | ||
|
|
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| const char* compareFileName = argv[2]; | ||
|
|
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| float tolerance = 0.05f; | ||
| if (argc > 3) { | ||
| tolerance = stringConvert<float>(argv[3]); | ||
| } | ||
| MNN_PRINT("Tolerance Rate: %f\n", tolerance); | ||
|
|
||
| // create net | ||
| MNN_PRINT("Open Model %s, %s\n", fileName, compareFileName); | ||
| std::shared_ptr<MNN::Interpreter> net = | ||
| std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(fileName)); | ||
| net->setSessionMode(Interpreter::Session_Debug); | ||
| std::shared_ptr<MNN::Interpreter> net2 = | ||
| std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(compareFileName)); | ||
| net2->setSessionMode(Interpreter::Session_Debug); | ||
|
|
||
| // create session for get input info | ||
| ScheduleConfig config; | ||
| config.type = MNN_FORWARD_CPU; | ||
| auto session = net->createSession(config); | ||
|
|
||
| std::map<std::string, std::shared_ptr<MNN::Tensor>> inputs; | ||
| std::vector<std::string> inputNames; | ||
| do { | ||
| rapidjson::Document document; | ||
| std::ostringstream jsonNameOs; | ||
| jsonNameOs << pwd << "/input.json"; | ||
| std::ifstream fileNames(jsonNameOs.str().c_str()); | ||
| if (fileNames.fail()) { | ||
| break; | ||
| } | ||
| std::ostringstream output; | ||
| output << fileNames.rdbuf(); | ||
| auto outputStr = output.str(); | ||
| document.Parse(outputStr.c_str()); | ||
| if (document.HasParseError()) { | ||
| MNN_ERROR("Invalid json\n"); | ||
| break; | ||
| } | ||
| if (document.HasMember("inputs")) { | ||
| auto inputsInfo = document["inputs"].GetArray(); | ||
| for (auto iter = inputsInfo.begin(); iter !=inputsInfo.end(); iter++) { | ||
| auto obj = iter->GetObject(); | ||
| std::string name = obj["name"].GetString(); | ||
| inputNames.emplace_back(name); | ||
| } | ||
| } | ||
| } while (false); | ||
| if (!inputNames.empty()) { | ||
| MNN_PRINT("Find input.json, use inputs:"); | ||
| for (auto& n : inputNames) { | ||
| MNN_PRINT(" %s, ", n.c_str()); | ||
| } | ||
| MNN_PRINT("\n"); | ||
| for (auto name : inputNames) { | ||
| auto inputTensor = net->getSessionInput(session, name.c_str()); | ||
| std::shared_ptr<MNN::Tensor> givenTensor(new Tensor(inputTensor, inputTensor->getDimensionType())); | ||
| { | ||
| std::ostringstream fileName; | ||
| fileName << pwd << name << ".txt"; | ||
| std::ifstream input(fileName.str().c_str()); | ||
| MNN_ASSERT(!input.fail()); | ||
|
|
||
| int size_w = inputTensor->width(); | ||
| int size_h = inputTensor->height(); | ||
| int bpp = inputTensor->channel(); | ||
| int batch = inputTensor->batch(); | ||
| // auto backend = net->getBackend(session, inputTensor); | ||
| // MNN_ASSERT(!input.fail()); | ||
| MNN_PRINT("Input: %d,%d,%d,%d\n", size_w, size_h, bpp, batch); | ||
| auto inputData = givenTensor->host<float>(); | ||
| auto size = givenTensor->size() / sizeof(float); | ||
| for (int i = 0; i < size; ++i) { | ||
| input >> inputData[i]; | ||
| } | ||
| inputs.insert(std::make_pair(name, givenTensor)); | ||
| } | ||
|
|
||
| } | ||
| } else { | ||
| auto inputTensor = net->getSessionInput(session, NULL); | ||
| std::shared_ptr<MNN::Tensor> givenTensor(new Tensor(inputTensor, inputTensor->getDimensionType())); | ||
| { | ||
| std::ostringstream fileName; | ||
| fileName << pwd << "input_0" | ||
| << ".txt"; | ||
| std::ifstream input(fileName.str().c_str()); | ||
|
|
||
| int size_w = inputTensor->width(); | ||
| int size_h = inputTensor->height(); | ||
| int bpp = inputTensor->channel(); | ||
| int batch = inputTensor->batch(); | ||
| // auto backend = net->getBackend(session, inputTensor); | ||
| // MNN_ASSERT(!input.fail()); | ||
| MNN_PRINT("Input: %d,%d,%d,%d\n", size_w, size_h, bpp, batch); | ||
| auto inputData = givenTensor->host<float>(); | ||
| auto size = givenTensor->size() / sizeof(float); | ||
| for (int i = 0; i < size; ++i) { | ||
| input >> inputData[i]; | ||
| } | ||
| inputs.insert(std::make_pair("", givenTensor)); | ||
| } | ||
| } | ||
| net->releaseSession(session); | ||
| BackendConfig::PrecisionMode precision = BackendConfig::Precision_Normal; | ||
| if (argc > 4) { | ||
| precision = (BackendConfig::PrecisionMode)atoi(argv[4]); | ||
| } | ||
| FUNC_PRINT(precision); | ||
| int modeNum = 1; | ||
| if(argc > 5) { | ||
| modeNum = atoi(argv[5]);//set gpu mode | ||
| } | ||
| FUNC_PRINT(modeNum); | ||
| std::string stopOp = ""; | ||
| if (argc > 6) { | ||
| stopOp = argv[6]; | ||
| } | ||
| FUNC_PRINT_ALL(stopOp.c_str(), s); | ||
| compareNet(net.get(), net2.get(), MNN_FORWARD_CPU, tolerance, inputs, stopOp, precision, modeNum); | ||
|
|
||
| return 0; | ||
| } |