| New file |
| | |
| | | #include "precomp.h" |
| | | |
| | | CTTransformer::CTTransformer(const char* sz_model_dir, int thread_num) |
| | | :env_(ORT_LOGGING_LEVEL_ERROR, ""),session_options{} |
| | | { |
| | | session_options.SetIntraOpNumThreads(thread_num); |
| | | session_options.SetGraphOptimizationLevel(ORT_ENABLE_ALL); |
| | | session_options.DisableCpuMemArena(); |
| | | |
| | | string strModelPath = PathAppend(sz_model_dir, PUNC_MODEL_FILE); |
| | | string strYamlPath = PathAppend(sz_model_dir, PUNC_YAML_FILE); |
| | | |
| | | try{ |
| | | #ifdef _WIN32 |
| | | std::wstring detPath = strToWstr(strModelPath); |
| | | m_session = std::make_unique<Ort::Session>(env_, detPath.c_str(), session_options); |
| | | #else |
| | | m_session = std::make_unique<Ort::Session>(env_, strModelPath.c_str(), session_options); |
| | | #endif |
| | | } |
| | | catch(exception e) |
| | | { |
| | | printf(e.what()); |
| | | } |
| | | // read inputnames outputnamess |
| | | string strName; |
| | | GetInputName(m_session.get(), strName); |
| | | m_strInputNames.push_back(strName.c_str()); |
| | | GetInputName(m_session.get(), strName, 1); |
| | | m_strInputNames.push_back(strName); |
| | | |
| | | GetOutputName(m_session.get(), strName); |
| | | m_strOutputNames.push_back(strName); |
| | | |
| | | for (auto& item : m_strInputNames) |
| | | m_szInputNames.push_back(item.c_str()); |
| | | for (auto& item : m_strOutputNames) |
| | | m_szOutputNames.push_back(item.c_str()); |
| | | |
| | | m_tokenizer.OpenYaml(strYamlPath.c_str()); |
| | | } |
| | | |
| | | CTTransformer::~CTTransformer() |
| | | { |
| | | } |
| | | |
| | | string CTTransformer::AddPunc(const char* sz_input) |
| | | { |
| | | string strResult; |
| | | vector<string> strOut; |
| | | vector<int> InputData; |
| | | m_tokenizer.Tokenize(sz_input, strOut, InputData); |
| | | |
| | | int nTotalBatch = ceil((float)InputData.size() / TOKEN_LEN); |
| | | int nCurBatch = -1; |
| | | int nSentEnd = -1, nLastCommaIndex = -1; |
| | | vector<int64_t> RemainIDs; // |
| | | vector<string> RemainStr; // |
| | | vector<int> NewPunctuation; // |
| | | vector<string> NewString; // |
| | | vector<string> NewSentenceOut; |
| | | vector<int> NewPuncOut; |
| | | int nDiff = 0; |
| | | for (size_t i = 0; i < InputData.size(); i += TOKEN_LEN) |
| | | { |
| | | nDiff = (i + TOKEN_LEN) < InputData.size() ? (0) : (i + TOKEN_LEN - InputData.size()); |
| | | vector<int64_t> InputIDs(InputData.begin() + i, InputData.begin() + i + TOKEN_LEN - nDiff); |
| | | vector<string> InputStr(strOut.begin() + i, strOut.begin() + i + TOKEN_LEN - nDiff); |
| | | InputIDs.insert(InputIDs.begin(), RemainIDs.begin(), RemainIDs.end()); // RemainIDs+InputIDs; |
| | | InputStr.insert(InputStr.begin(), RemainStr.begin(), RemainStr.end()); // RemainStr+InputStr; |
| | | |
| | | auto Punction = Infer(InputIDs); |
| | | nCurBatch = i / TOKEN_LEN; |
| | | if (nCurBatch < nTotalBatch - 1) // not the last minisetence |
| | | { |
| | | nSentEnd = -1; |
| | | nLastCommaIndex = -1; |
| | | for (int nIndex = Punction.size() - 2; nIndex > 0; nIndex--) |
| | | { |
| | | if (m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(PERIOD_INDEX) || m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(QUESTION_INDEX)) |
| | | { |
| | | nSentEnd = nIndex; |
| | | break; |
| | | } |
| | | if (nLastCommaIndex < 0 && m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(COMMA_INDEX)) |
| | | { |
| | | nLastCommaIndex = nIndex; |
| | | } |
| | | } |
| | | if (nSentEnd < 0 && InputStr.size() > CACHE_POP_TRIGGER_LIMIT && nLastCommaIndex > 0) |
| | | { |
| | | nSentEnd = nLastCommaIndex; |
| | | Punction[nSentEnd] = PERIOD_INDEX; |
| | | } |
| | | RemainStr.assign(InputStr.begin() + nSentEnd + 1, InputStr.end()); |
| | | RemainIDs.assign(InputIDs.begin() + nSentEnd + 1, InputIDs.end()); |
| | | InputStr.assign(InputStr.begin(), InputStr.begin() + nSentEnd + 1); // minit_sentence |
| | | Punction.assign(Punction.begin(), Punction.begin() + nSentEnd + 1); |
| | | } |
| | | |
| | | NewPunctuation.insert(NewPunctuation.end(), Punction.begin(), Punction.end()); |
| | | vector<string> WordWithPunc; |
| | | for (int i = 0; i < InputStr.size(); i++) |
| | | { |
| | | if (i > 0 && !(InputStr[i][0] & 0x80) && (i + 1) <InputStr.size() && !(InputStr[i+1][0] & 0x80))// �м��Ӣ�ģ� |
| | | { |
| | | InputStr[i] = InputStr[i]+ " "; |
| | | } |
| | | WordWithPunc.push_back(InputStr[i]); |
| | | |
| | | if (Punction[i] != NOTPUNC_INDEX) // �»��� |
| | | { |
| | | WordWithPunc.push_back(m_tokenizer.Id2Punc(Punction[i])); |
| | | } |
| | | } |
| | | |
| | | NewString.insert(NewString.end(), WordWithPunc.begin(), WordWithPunc.end()); // new_mini_sentence += "".join(words_with_punc) |
| | | NewSentenceOut = NewString; |
| | | NewPuncOut = NewPunctuation; |
| | | // last mini sentence |
| | | if(nCurBatch == nTotalBatch - 1) |
| | | { |
| | | if (NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(COMMA_INDEX) || NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(DUN_INDEX)) |
| | | { |
| | | NewSentenceOut.assign(NewString.begin(), NewString.end() - 1); |
| | | NewSentenceOut.push_back(m_tokenizer.Id2Punc(PERIOD_INDEX)); |
| | | NewPuncOut.assign(NewPunctuation.begin(), NewPunctuation.end() - 1); |
| | | NewPuncOut.push_back(PERIOD_INDEX); |
| | | } |
| | | else if (NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(PERIOD_INDEX) && NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(QUESTION_INDEX)) |
| | | { |
| | | NewSentenceOut = NewString; |
| | | NewSentenceOut.push_back(m_tokenizer.Id2Punc(PERIOD_INDEX)); |
| | | NewPuncOut = NewPunctuation; |
| | | NewPuncOut.push_back(PERIOD_INDEX); |
| | | } |
| | | } |
| | | } |
| | | for (auto& item : NewSentenceOut) |
| | | strResult += item; |
| | | return strResult; |
| | | } |
| | | |
| | | vector<int> CTTransformer::Infer(vector<int64_t> input_data) |
| | | { |
| | | Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault); |
| | | vector<int> punction; |
| | | std::array<int64_t, 2> input_shape_{ 1, (int64_t)input_data.size()}; |
| | | Ort::Value onnx_input = Ort::Value::CreateTensor<int64_t>(m_memoryInfo, |
| | | input_data.data(), |
| | | input_data.size(), |
| | | input_shape_.data(), |
| | | input_shape_.size()); |
| | | |
| | | std::array<int32_t,1> text_lengths{ (int32_t)input_data.size() }; |
| | | std::array<int64_t,1> text_lengths_dim{ 1 }; |
| | | Ort::Value onnx_text_lengths = Ort::Value::CreateTensor( |
| | | m_memoryInfo, |
| | | text_lengths.data(), |
| | | text_lengths.size() * sizeof(int32_t), |
| | | text_lengths_dim.data(), |
| | | text_lengths_dim.size(), ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32); |
| | | std::vector<Ort::Value> input_onnx; |
| | | input_onnx.emplace_back(std::move(onnx_input)); |
| | | input_onnx.emplace_back(std::move(onnx_text_lengths)); |
| | | |
| | | try { |
| | | auto outputTensor = m_session->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), m_szInputNames.size(), m_szOutputNames.data(), m_szOutputNames.size()); |
| | | std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape(); |
| | | |
| | | int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>()); |
| | | float * floatData = outputTensor[0].GetTensorMutableData<float>(); |
| | | |
| | | for (int i = 0; i < outputCount; i += CANDIDATE_NUM) |
| | | { |
| | | int index = Argmax(floatData + i, floatData + i + CANDIDATE_NUM-1); |
| | | punction.push_back(index); |
| | | } |
| | | } |
| | | catch (std::exception const &e) |
| | | { |
| | | printf(e.what()); |
| | | } |
| | | return punction; |
| | | } |
| | | |
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