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https://github.com/PaddlePaddle/FastDeploy.git
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ce828ecb38
* 11-02/14:35 * 新增输入数据format错误判断 * 优化推理过程，减少内存分配次数 * 支持多输入rknn模型 * rknn模型输出shape为三维时，输出将被强制对齐为4纬。现在将直接抹除rknn补充的shape，方便部分对输出shape进行判断的模型进行正确的后处理。 * 11-03/17:25 * 支持导出多输入RKNN模型 * 更新各种文档 * ppseg改用Fastdeploy中的模型进行转换 * 11-03/17:25 * 新增开源头 * 11-03/21:48 * 删除无用debug代码，补充注释
104 lines
3.3 KiB
C++
104 lines
3.3 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 "fastdeploy/backends/backend.h"
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#include "fastdeploy/core/fd_tensor.h"
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#include "rknn_api.h" // NOLINT
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#include "fastdeploy/backends/rknpu/rknpu2/rknpu2_config.h"
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#include <cstring>
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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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namespace fastdeploy {
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struct RKNPU2BackendOption {
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rknpu2::CpuName cpu_name = rknpu2::CpuName::RK3588;
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// The specification of NPU core setting.It has the following choices :
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// RKNN_NPU_CORE_AUTO : Referring to automatic mode, meaning that it will
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// select the idle core inside the NPU.
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// RKNN_NPU_CORE_0 : Running on the NPU0 core
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// RKNN_NPU_CORE_1: Runing on the NPU1 core
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// RKNN_NPU_CORE_2: Runing on the NPU2 core
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// RKNN_NPU_CORE_0_1: Running on both NPU0 and NPU1 core simultaneously.
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// RKNN_NPU_CORE_0_1_2: Running on both NPU0, NPU1 and NPU2 simultaneously.
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rknpu2::CoreMask core_mask = rknpu2::CoreMask::RKNN_NPU_CORE_AUTO;
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};
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class RKNPU2Backend : public BaseBackend {
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public:
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RKNPU2Backend() = default;
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virtual ~RKNPU2Backend();
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// RKNN API
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bool LoadModel(void* model);
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bool GetSDKAndDeviceVersion();
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bool SetCoreMask(rknpu2::CoreMask& core_mask) const;
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bool GetModelInputOutputInfos();
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// BaseBackend API
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void BuildOption(const RKNPU2BackendOption& option);
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bool InitFromRKNN(const std::string& model_file,
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const RKNPU2BackendOption& option = RKNPU2BackendOption());
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int NumInputs() const override {
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return static_cast<int>(inputs_desc_.size());
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}
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int NumOutputs() const override {
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return static_cast<int>(outputs_desc_.size());
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}
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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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bool Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs) override;
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private:
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// The object of rknn context.
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rknn_context ctx{};
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// The structure rknn_sdk_version is used to indicate the version
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// information of the RKNN SDK.
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rknn_sdk_version sdk_ver{};
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// The structure rknn_input_output_num represents the number of
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// input and output Tensor
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rknn_input_output_num io_num{};
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std::vector<TensorInfo> inputs_desc_;
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std::vector<TensorInfo> outputs_desc_;
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rknn_tensor_attr* input_attrs_ = nullptr;
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rknn_tensor_attr* output_attrs_ = nullptr;
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rknn_tensor_mem** input_mems_;
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rknn_tensor_mem** output_mems_;
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bool infer_init = false;
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RKNPU2BackendOption option_;
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static void DumpTensorAttr(rknn_tensor_attr& attr);
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static FDDataType RknnTensorTypeToFDDataType(rknn_tensor_type type);
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static rknn_tensor_type FDDataTypeToRknnTensorType(FDDataType type);
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};
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} // namespace fastdeploy
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