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https://github.com/PaddlePaddle/FastDeploy.git
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279c993483
* Add custom operator for onnxruntime ans fix paddle backend * Polish cmake files and runtime apis * Remove copy libraries * fix some issue * fix bug * fix bug
106 lines
3.7 KiB
C++
106 lines
3.7 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <iostream>
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#include <map>
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#include <string>
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#include <vector>
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#include "fastdeploy/backends/backend.h"
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#include "fastdeploy/backends/tensorrt/common/argsParser.h"
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#include "fastdeploy/backends/tensorrt/common/buffers.h"
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#include "fastdeploy/backends/tensorrt/common/common.h"
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#include "fastdeploy/backends/tensorrt/common/logger.h"
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#include "fastdeploy/backends/tensorrt/common/parserOnnxConfig.h"
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#include "fastdeploy/backends/tensorrt/common/sampleUtils.h"
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#include <cuda_runtime_api.h>
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#include "NvInfer.h"
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namespace fastdeploy {
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using namespace samplesCommon;
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struct TrtValueInfo {
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std::string name;
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std::vector<int> shape;
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nvinfer1::DataType dtype;
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};
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struct TrtBackendOption {
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int gpu_id = 0;
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bool enable_fp16 = false;
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bool enable_int8 = false;
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size_t max_batch_size = 32;
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size_t max_workspace_size = 1 << 30;
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std::map<std::string, std::vector<int32_t>> max_shape;
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std::map<std::string, std::vector<int32_t>> min_shape;
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std::map<std::string, std::vector<int32_t>> opt_shape;
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std::string serialize_file = "";
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};
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std::vector<int> toVec(const nvinfer1::Dims& dim);
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size_t TrtDataTypeSize(const nvinfer1::DataType& dtype);
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FDDataType GetFDDataType(const nvinfer1::DataType& dtype);
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class TrtBackend : public BaseBackend {
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public:
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TrtBackend() : engine_(nullptr), context_(nullptr) {}
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void BuildOption(const TrtBackendOption& option);
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bool InitFromPaddle(const std::string& model_file,
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const std::string& params_file,
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const TrtBackendOption& option = TrtBackendOption(),
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bool verbose = false);
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bool InitFromOnnx(const std::string& model_file,
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const TrtBackendOption& option = TrtBackendOption(),
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bool from_memory_buffer = false);
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bool InitFromTrt(const std::string& trt_engine_file,
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const TrtBackendOption& option = TrtBackendOption());
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bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs);
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int NumInputs() const { return inputs_desc_.size(); }
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int NumOutputs() const { return outputs_desc_.size(); }
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TensorInfo GetInputInfo(int index);
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TensorInfo GetOutputInfo(int index);
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private:
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std::shared_ptr<nvinfer1::ICudaEngine> engine_;
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std::shared_ptr<nvinfer1::IExecutionContext> context_;
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cudaStream_t stream_{};
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std::vector<void*> bindings_;
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std::vector<TrtValueInfo> inputs_desc_;
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std::vector<TrtValueInfo> outputs_desc_;
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std::map<std::string, DeviceBuffer> inputs_buffer_;
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std::map<std::string, DeviceBuffer> outputs_buffer_;
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// Sometimes while the number of outputs > 1
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// the output order of tensorrt may not be same
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// with the original onnx model
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// So this parameter will record to origin outputs
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// order, to help recover the rigt order
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std::map<std::string, int> outputs_order_;
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void GetInputOutputInfo();
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void AllocateBufferInDynamicShape(const std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs);
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bool CreateTrtEngine(const std::string& onnx_model,
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const TrtBackendOption& option);
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};
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} // namespace fastdeploy
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