mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
124 lines
4.2 KiB
C++
124 lines
4.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// 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.
|
|
|
|
#pragma once
|
|
|
|
#include <cuda_runtime_api.h>
|
|
|
|
#include <iostream>
|
|
#include <map>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include "NvInfer.h"
|
|
#include "NvOnnxParser.h"
|
|
#include "fastdeploy/backends/backend.h"
|
|
#include "fastdeploy/backends/tensorrt/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
|
|
struct TrtValueInfo {
|
|
std::string name;
|
|
std::vector<int> shape;
|
|
nvinfer1::DataType dtype;
|
|
};
|
|
|
|
struct TrtBackendOption {
|
|
int gpu_id = 0;
|
|
bool enable_fp16 = false;
|
|
bool enable_int8 = false;
|
|
size_t max_batch_size = 32;
|
|
size_t max_workspace_size = 1 << 30;
|
|
std::map<std::string, std::vector<int32_t>> max_shape;
|
|
std::map<std::string, std::vector<int32_t>> min_shape;
|
|
std::map<std::string, std::vector<int32_t>> opt_shape;
|
|
std::string serialize_file = "";
|
|
|
|
// inside parameter, maybe remove next version
|
|
bool remove_multiclass_nms_ = false;
|
|
std::map<std::string, std::string> custom_op_info_;
|
|
};
|
|
|
|
std::vector<int> toVec(const nvinfer1::Dims& dim);
|
|
size_t TrtDataTypeSize(const nvinfer1::DataType& dtype);
|
|
FDDataType GetFDDataType(const nvinfer1::DataType& dtype);
|
|
|
|
class TrtBackend : public BaseBackend {
|
|
public:
|
|
TrtBackend() : engine_(nullptr), context_(nullptr) {}
|
|
void BuildOption(const TrtBackendOption& option);
|
|
|
|
bool InitFromPaddle(const std::string& model_file,
|
|
const std::string& params_file,
|
|
const TrtBackendOption& option = TrtBackendOption(),
|
|
bool verbose = false);
|
|
bool InitFromOnnx(const std::string& model_file,
|
|
const TrtBackendOption& option = TrtBackendOption(),
|
|
bool from_memory_buffer = false);
|
|
bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs);
|
|
|
|
int NumInputs() const { return inputs_desc_.size(); }
|
|
int NumOutputs() const { return outputs_desc_.size(); }
|
|
TensorInfo GetInputInfo(int index);
|
|
TensorInfo GetOutputInfo(int index);
|
|
std::vector<TensorInfo> GetInputInfos() override;
|
|
std::vector<TensorInfo> GetOutputInfos() override;
|
|
|
|
~TrtBackend() {
|
|
if (parser_) {
|
|
parser_.reset();
|
|
}
|
|
}
|
|
|
|
private:
|
|
TrtBackendOption option_;
|
|
std::shared_ptr<nvinfer1::ICudaEngine> engine_;
|
|
std::shared_ptr<nvinfer1::IExecutionContext> context_;
|
|
FDUniquePtr<nvonnxparser::IParser> parser_;
|
|
FDUniquePtr<nvinfer1::IBuilder> builder_;
|
|
FDUniquePtr<nvinfer1::INetworkDefinition> network_;
|
|
cudaStream_t stream_{};
|
|
std::vector<void*> bindings_;
|
|
std::vector<TrtValueInfo> inputs_desc_;
|
|
std::vector<TrtValueInfo> outputs_desc_;
|
|
std::map<std::string, FDDeviceBuffer> inputs_buffer_;
|
|
std::map<std::string, FDDeviceBuffer> outputs_buffer_;
|
|
|
|
// Sometimes while the number of outputs > 1
|
|
// the output order of tensorrt may not be same
|
|
// with the original onnx model
|
|
// So this parameter will record to origin outputs
|
|
// order, to help recover the rigt order
|
|
std::map<std::string, int> outputs_order_;
|
|
|
|
// temporary store onnx model content
|
|
// once it used to build trt egnine done
|
|
// it will be released
|
|
std::string onnx_model_buffer_;
|
|
// Stores shape information of the loaded model
|
|
// For dynmaic shape will record its range information
|
|
// Also will update the range information while inferencing
|
|
std::map<std::string, ShapeRangeInfo> shape_range_info_;
|
|
|
|
void GetInputOutputInfo();
|
|
bool CreateTrtEngineFromOnnx(const std::string& onnx_model_buffer);
|
|
bool BuildTrtEngine();
|
|
bool LoadTrtCache(const std::string& trt_engine_file);
|
|
int ShapeRangeInfoUpdated(const std::vector<FDTensor>& inputs);
|
|
void SetInputs(const std::vector<FDTensor>& inputs);
|
|
void AllocateOutputsBuffer(std::vector<FDTensor>* outputs);
|
|
};
|
|
|
|
} // namespace fastdeploy
|