#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);
|
|
#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
|
// read inputnames outputnames
|
vector<string> m_strInputNames, m_strOutputNames;
|
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;
|
}
|
