mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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5be839b322
* add xpu support * fix docs * update code * update doc * update code * update yolov5 * update cmake * add int64_t data support * fix * update download links * add en doc * update code * update xpu options * update doc * update doc * update doc * update lib links * update doc * update code * update lite xpu link * update xpu lib * update doc * update en doc
98 lines
3.1 KiB
C++
Executable File
98 lines
3.1 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/backends/backend.h"
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#include "paddle_api.h" // NOLINT
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namespace fastdeploy {
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struct LiteBackendOption {
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// cpu num threads
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int threads = 1;
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// lite power mode
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// 0: LITE_POWER_HIGH
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// 1: LITE_POWER_LOW
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// 2: LITE_POWER_FULL
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// 3: LITE_POWER_NO_BIND
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// 4: LITE_POWER_RAND_HIGH
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// 5: LITE_POWER_RAND_LOW
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int power_mode = 3;
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// enable fp16
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bool enable_fp16 = false;
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// enable int8
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bool enable_int8 = false;
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// optimized model dir for CxxConfig
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std::string optimized_model_dir = "";
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// TODO(qiuyanjun): support more options for lite backend.
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// Such as fp16, different device target (kARM/kXPU/kNPU/...)
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std::string nnadapter_subgraph_partition_config_path = "";
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bool enable_timvx = false;
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bool enable_xpu = false;
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int device_id = 0;
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int xpu_l3_workspace_size = 0xfffc00;
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bool xpu_locked = false;
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bool xpu_autotune = true;
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std::string xpu_autotune_file = "";
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std::string xpu_precision = "int16";
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bool xpu_adaptive_seqlen = false;
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bool xpu_enable_multi_stream = false;
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};
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// Convert data type from paddle lite to fastdeploy
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FDDataType LiteDataTypeToFD(const paddle::lite_api::PrecisionType& dtype);
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class LiteBackend : public BaseBackend {
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public:
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LiteBackend() {}
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virtual ~LiteBackend() = default;
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void BuildOption(const LiteBackendOption& 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 LiteBackendOption& option = LiteBackendOption());
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bool Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs,
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bool copy_to_fd = true) override; // NOLINT
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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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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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paddle::lite_api::CxxConfig config_;
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std::shared_ptr<paddle::lite_api::PaddlePredictor> predictor_;
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std::vector<TensorInfo> inputs_desc_;
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std::vector<TensorInfo> outputs_desc_;
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std::map<std::string, int> inputs_order_;
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LiteBackendOption option_;
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bool supported_fp16_ = false;
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bool ReadFile(const std::string& filename,
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std::vector<char>* contents,
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const bool binary = true);
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};
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} // namespace fastdeploy
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