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https://github.com/PaddlePaddle/FastDeploy.git
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76df90afc3
* Update all backends load model from buffer * Delete redundant code * Format code style * Format code style * Delete redundant code * Delete redundant code * Add some FDASSERTs * Update load model form memory when cloning engine * Update clone engine code * Update set_model_buffer api parameters with char pointer * Release memory buffer variables after finish init backends * Fix conflict * Fix bug
89 lines
2.9 KiB
C++
89 lines
2.9 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 <memory>
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#include <string>
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#include <vector>
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#include "fastdeploy/core/fd_tensor.h"
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#include "fastdeploy/core/fd_type.h"
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#include "fastdeploy/runtime/runtime_option.h"
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namespace fastdeploy {
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/*! @brief Information of Tensor
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*/
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struct TensorInfo {
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std::string name; ///< Name of tensor
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std::vector<int> shape; ///< Shape of tensor
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FDDataType dtype; ///< Data type of tensor
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friend std::ostream& operator<<(std::ostream& output,
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const TensorInfo& info) {
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output << "TensorInfo(name: " << info.name << ", shape: [";
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for (size_t i = 0; i < info.shape.size(); ++i) {
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if (i == info.shape.size() - 1) {
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output << info.shape[i];
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} else {
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output << info.shape[i] << ", ";
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}
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}
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output << "], dtype: " << Str(info.dtype) << ")";
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return output;
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}
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};
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class BaseBackend {
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public:
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bool initialized_ = false;
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BaseBackend() {}
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virtual ~BaseBackend() = default;
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virtual bool Initialized() const { return initialized_; }
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// Get number of inputs of the model
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virtual int NumInputs() const = 0;
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// Get number of outputs of the model
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virtual int NumOutputs() const = 0;
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// Get information of input tensor
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virtual TensorInfo GetInputInfo(int index) = 0;
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// Get information of output tensor
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virtual TensorInfo GetOutputInfo(int index) = 0;
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// Get information of all the input tensors
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virtual std::vector<TensorInfo> GetInputInfos() = 0;
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// Get information of all the output tensors
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virtual std::vector<TensorInfo> GetOutputInfos() = 0;
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// if copy_to_fd is true, copy memory data to FDTensor
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// else share memory to FDTensor(only Paddle、ORT、TRT、OpenVINO support it)
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virtual bool Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs,
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bool copy_to_fd = true) = 0;
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// Optional: For those backends which can share memory
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// while creating multiple inference engines with same model file
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virtual std::unique_ptr<BaseBackend> Clone(RuntimeOption &runtime_option,
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void *stream = nullptr,
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int device_id = -1) {
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FDERROR << "Clone no support" << std::endl;
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return nullptr;
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}
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};
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} // namespace fastdeploy
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