Modify file structure to separate python and cpp code (#223)

Modify code structure

fastdeploy/backends/tensorrt/trt_backend.h

@@ -0,0 +1,121 @@
+// 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);
+
+  ~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