mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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96 lines
3.5 KiB
C++
Executable File
96 lines
3.5 KiB
C++
Executable File
// 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 <memory>
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#include <string>
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#include <vector>
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#include "fastdeploy/runtime/backends/backend.h"
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#include "fastdeploy/runtime/backends/paddle/option.h"
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#ifdef ENABLE_PADDLE2ONNX
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#include "paddle2onnx/converter.h"
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#endif
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#include "fastdeploy/utils/unique_ptr.h"
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#include "paddle_inference_api.h" // NOLINT
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namespace fastdeploy {
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// convert FD device to paddle place type
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paddle_infer::PlaceType ConvertFDDeviceToPlace(Device device);
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// Share memory buffer with paddle_infer::Tensor from fastdeploy::FDTensor
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void ShareTensorFromFDTensor(paddle_infer::Tensor* tensor, FDTensor& fd_tensor);
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void ShareOutTensorFromFDTensor(paddle_infer::Tensor* tensor,
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FDTensor& fd_tensor);
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// convert paddle_infer::Tensor to fastdeploy::FDTensor
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// if copy_to_fd is true, copy memory data to FDTensor
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/// else share memory to FDTensor
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void PaddleTensorToFDTensor(std::unique_ptr<paddle_infer::Tensor>& tensor,
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FDTensor* fd_tensor, bool copy_to_fd);
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// Convert data type from paddle inference to fastdeploy
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FDDataType PaddleDataTypeToFD(const paddle_infer::DataType& dtype);
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// Convert data type from paddle2onnx::PaddleReader to fastdeploy
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FDDataType ReaderDataTypeToFD(int32_t dtype);
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class PaddleBackend : public BaseBackend {
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public:
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PaddleBackend() {}
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virtual ~PaddleBackend() = default;
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void BuildOption(const PaddleBackendOption& option);
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bool InitFromPaddle(const std::string& model_buffer,
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const std::string& params_buffer,
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const PaddleBackendOption& option = PaddleBackendOption());
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bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs,
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bool copy_to_fd = true) override;
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int NumInputs() const override { return inputs_desc_.size(); }
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int NumOutputs() const override { return outputs_desc_.size(); }
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std::unique_ptr<BaseBackend> Clone(RuntimeOption &runtime_option,
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void* stream = nullptr,
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int device_id = -1) override;
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TensorInfo GetInputInfo(int index) override;
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TensorInfo GetOutputInfo(int index) override;
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std::vector<TensorInfo> GetInputInfos() override;
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std::vector<TensorInfo> GetOutputInfos() override;
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private:
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void
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CollectShapeRun(paddle_infer::Predictor* predictor,
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const std::map<std::string, std::vector<int>>& shape) const;
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void GetDynamicShapeFromOption(
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const PaddleBackendOption& option,
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std::map<std::string, std::vector<int>>* max_shape,
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std::map<std::string, std::vector<int>>* min_shape,
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std::map<std::string, std::vector<int>>* opt_shape) const;
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void SetTRTDynamicShapeToConfig(const PaddleBackendOption& option);
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PaddleBackendOption option_;
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paddle_infer::Config config_;
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std::shared_ptr<paddle_infer::Predictor> predictor_;
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std::vector<TensorInfo> inputs_desc_;
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std::vector<TensorInfo> outputs_desc_;
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};
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} // namespace fastdeploy
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