mindspore/serving/core/session.cc

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2020-08-18 19:31:57 +08:00
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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 "core/session.h"
#include <grpcpp/grpcpp.h>
#include <string>
#include <map>
#include <vector>
#include <utility>
#include <memory>
#include <chrono>
#include "include/infer_log.h"
#include "serving/ms_service.grpc.pb.h"
#include "core/util/option_parser.h"
#include "core/version_control/version_controller.h"
#include "core/util/file_system_operation.h"
#include "core/serving_tensor.h"
using ms_serving::MSService;
using ms_serving::PredictReply;
using ms_serving::PredictRequest;
namespace mindspore {
namespace serving {
Status Session::CreatDeviceSession(const std::string &device, uint32_t device_id) {
session_ = inference::InferSession::CreateSession(device, device_id);
if (session_ == nullptr) {
MSI_LOG(ERROR) << "Creat Session Failed";
return FAILED;
}
device_type_ = device;
return SUCCESS;
}
Session &Session::Instance() {
static Session instance;
return instance;
}
Status Session::Predict(const PredictRequest &request, PredictReply &reply) {
if (!model_loaded_) {
MSI_LOG(ERROR) << "the model has not loaded";
return FAILED;
}
if (session_ == nullptr) {
MSI_LOG(ERROR) << "the inference session has not be initialized";
return FAILED;
}
std::lock_guard<std::mutex> lock(mutex_);
MSI_LOG(INFO) << "run Predict";
if (request.images_size() > 0) {
ServingImagesRequest serving_images(request);
ServingRequest serving_request(request);
ServingReply serving_reply(reply);
Status ret = session_->ExecuteModel(graph_id_, serving_images, serving_request, serving_reply);
if (ret != SUCCESS) {
MSI_LOG(ERROR) << "execute model with images return failed";
return ret;
}
} else if (request.data_size() > 0) {
ServingRequest serving_request(request);
ServingReply serving_reply(reply);
Status ret = session_->ExecuteModel(graph_id_, serving_request, serving_reply);
if (ret != SUCCESS) {
MSI_LOG(ERROR) << "execute model with datas return failed";
return ret;
}
}
MSI_LOG(INFO) << "run Predict finished";
return SUCCESS;
}
Status Session::Warmup(const MindSporeModelPtr model) {
if (session_ == nullptr) {
MSI_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
return FAILED;
}
std::lock_guard<std::mutex> lock(mutex_);
std::string file_name = model->GetModelPath() + '/' + model->GetModelName();
model_loaded_ = false;
MSI_TIME_STAMP_START(LoadModelFromFile)
auto ret = session_->LoadModelFromFile(file_name, graph_id_);
MSI_TIME_STAMP_END(LoadModelFromFile)
if (ret != SUCCESS) {
MSI_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
return ret;
}
model_loaded_ = true;
MSI_LOG(INFO) << "Session Warmup finished";
return SUCCESS;
}
Status Session::Clear() {
if (session_ != nullptr) {
session_->UnloadModel(graph_id_);
session_->FinalizeEnv();
session_ = nullptr;
}
return SUCCESS;
}
Status Session::GetModelInputsInfo(std::vector<inference::InferTensor> &tensor_list) {
if (!model_loaded_) {
MSI_LOG(ERROR) << "the model has not loaded";
return FAILED;
}
if (session_ == nullptr) {
MSI_LOG(ERROR) << "the inference session has not be initialized";
return FAILED;
}
std::lock_guard<std::mutex> lock(mutex_);
Status ret = session_->GetModelInputsInfo(graph_id_, &tensor_list);
if (ret != SUCCESS) {
MSI_LOG(ERROR) << "get model inputs info failed";
}
return ret;
}
} // namespace serving
} // namespace mindspore