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[Other] Optimize load model from memory function (#1205)

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Optimize option for runtime
This commit is contained in:
Jason
2023-02-01 15:50:38 +08:00
committed by GitHub
parent 29e93fa2dc
commit b4e322af63
5 changed files with 29 additions and 49 deletions
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4
fastdeploy/runtime/backends/paddle/paddle_backend.cc
View File

@@ -259,8 +259,8 @@ std::unique_ptr<BaseBackend> PaddleBackend::Clone(RuntimeOption& runtime_option,
clone_option.external_stream_ = stream;
if (runtime_option.model_from_memory_) {
FDASSERT(
casted_backend->InitFromPaddle(runtime_option.model_buffer_,
runtime_option.params_buffer_,
casted_backend->InitFromPaddle(runtime_option.model_file,
runtime_option.params_file,
clone_option),
"Clone model from Paddle failed while initialize PaddleBackend.");
} else {

4
fastdeploy/runtime/backends/tensorrt/trt_backend.cc
View File

@@ -731,8 +731,8 @@ std::unique_ptr<BaseBackend> TrtBackend::Clone(RuntimeOption& runtime_option,
clone_option.gpu_id = device_id;
clone_option.external_stream_ = stream;
if (runtime_option.model_from_memory_) {
FDASSERT(casted_backend->InitFromPaddle(runtime_option.model_buffer_,
runtime_option.params_buffer_,
FDASSERT(casted_backend->InitFromPaddle(runtime_option.model_file,
runtime_option.params_file,
clone_option),
"Clone model from Paddle failed while initialize TrtBackend.");
} else {

24
fastdeploy/runtime/runtime.cc
View File

@@ -210,10 +210,10 @@ FDTensor* Runtime::GetOutputTensor(const std::string& name) {
void Runtime::ReleaseModelMemoryBuffer() {
if (option.model_from_memory_) {
option.model_buffer_.clear();
option.model_buffer_.shrink_to_fit();
option.params_buffer_.clear();
option.params_buffer_.shrink_to_fit();
option.model_file.clear();
option.model_file.shrink_to_fit();
option.params_file.clear();
option.params_file.shrink_to_fit();
}
}
@@ -276,8 +276,8 @@ void Runtime::CreatePaddleBackend() {
backend_ = utils::make_unique<PaddleBackend>();
auto casted_backend = dynamic_cast<PaddleBackend*>(backend_.get());
if (pd_option.model_from_memory_) {
FDASSERT(casted_backend->InitFromPaddle(option.model_buffer_,
option.params_buffer_, pd_option),
FDASSERT(casted_backend->InitFromPaddle(option.model_file,
option.params_file, pd_option),
"Load model from Paddle failed while initliazing PaddleBackend.");
ReleaseModelMemoryBuffer();
} else {
@@ -360,7 +360,7 @@ void Runtime::CreateOrtBackend() {
auto casted_backend = dynamic_cast<OrtBackend*>(backend_.get());
if (option.model_format == ModelFormat::ONNX) {
if (option.model_from_memory_) {
FDASSERT(casted_backend->InitFromOnnx(option.model_buffer_, ort_option),
FDASSERT(casted_backend->InitFromOnnx(option.model_file, ort_option),
"Load model from ONNX failed while initliazing OrtBackend.");
ReleaseModelMemoryBuffer();
} else {
@@ -372,8 +372,8 @@ void Runtime::CreateOrtBackend() {
}
} else {
if (option.model_from_memory_) {
FDASSERT(casted_backend->InitFromPaddle(
option.model_buffer_, option.params_buffer_, ort_option),
FDASSERT(casted_backend->InitFromPaddle(option.model_file,
option.params_file, ort_option),
"Load model from Paddle failed while initliazing OrtBackend.");
ReleaseModelMemoryBuffer();
} else {
@@ -424,7 +424,7 @@ void Runtime::CreateTrtBackend() {
auto casted_backend = dynamic_cast<TrtBackend*>(backend_.get());
if (option.model_format == ModelFormat::ONNX) {
if (option.model_from_memory_) {
FDASSERT(casted_backend->InitFromOnnx(option.model_buffer_, trt_option),
FDASSERT(casted_backend->InitFromOnnx(option.model_file, trt_option),
"Load model from ONNX failed while initliazing TrtBackend.");
ReleaseModelMemoryBuffer();
} else {
@@ -436,8 +436,8 @@ void Runtime::CreateTrtBackend() {
}
} else {
if (option.model_from_memory_) {
FDASSERT(casted_backend->InitFromPaddle(
option.model_buffer_, option.params_buffer_, trt_option),
FDASSERT(casted_backend->InitFromPaddle(option.model_file,
option.params_file, trt_option),
"Load model from Paddle failed while initliazing TrtBackend.");
ReleaseModelMemoryBuffer();
} else {

34
fastdeploy/runtime/runtime_option.cc
View File

@@ -21,39 +21,19 @@ namespace fastdeploy {
void RuntimeOption::SetModelPath(const std::string& model_path,
const std::string& params_path,
const ModelFormat& format) {
if (format == ModelFormat::PADDLE) {
model_file = model_path;
params_file = params_path;
model_format = ModelFormat::PADDLE;
} else if (format == ModelFormat::ONNX) {
model_file = model_path;
model_format = ModelFormat::ONNX;
} else if (format == ModelFormat::TORCHSCRIPT) {
model_file = model_path;
model_format = ModelFormat::TORCHSCRIPT;
} else {
FDASSERT(false,
"The model format only can be "
"ModelFormat::PADDLE/ModelFormat::ONNX/ModelFormat::TORCHSCRIPT.");
}
model_file = model_path;
params_file = params_path;
model_format = format;
model_from_memory_ = false;
}
void RuntimeOption::SetModelBuffer(const std::string& model_buffer,
const std::string& params_buffer,
const ModelFormat& format) {
model_file = model_buffer;
params_file = params_buffer;
model_format = format;
model_from_memory_ = true;
if (format == ModelFormat::PADDLE) {
model_buffer_ = model_buffer;
params_buffer_ = params_buffer;
model_format = ModelFormat::PADDLE;
} else if (format == ModelFormat::ONNX) {
model_buffer_ = model_buffer;
model_format = ModelFormat::ONNX;
} else {
FDASSERT(false,
"The model format only can be "
"ModelFormat::PADDLE/ModelFormat::ONNX.");
}
}
void RuntimeOption::UseGpu(int gpu_id) {

12
fastdeploy/runtime/runtime_option.h
View File

@@ -422,14 +422,14 @@ struct FASTDEPLOY_DECL RuntimeOption {
/// Option to configure Paddle Lite backend
LiteBackendOption paddle_lite_option;
std::string model_file = ""; // Path of model file
std::string params_file = ""; // Path of parameters file, can be empty
// If model_from_memory is true, the model_file and params_file is
// binary stream in memory;
// Otherwise, the model_file and params_file means the path of file
std::string model_file = "";
std::string params_file = "";
bool model_from_memory_ = false;
// format of input model
ModelFormat model_format = ModelFormat::PADDLE;
std::string model_buffer_ = "";
std::string params_buffer_ = "";
bool model_from_memory_ = false;
};
} // namespace fastdeploy