mirror of
https://github.com/openharmony/neural_network_runtime.git
synced 2026-07-01 08:12:02 -04:00
yangyongjie
97 lines
3.5 KiB
C++
97 lines
3.5 KiB
C++
/*
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* Copyright (c) 2022 Huawei Device Co., Ltd.
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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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*/
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#ifndef NEURAL_NETWORK_RUNTIME_NN_TENSOR_H
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#define NEURAL_NETWORK_RUNTIME_NN_TENSOR_H
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#include <string>
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#include <vector>
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#include "interfaces/oem/cpp_api/cpp_type.h"
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#include "interfaces/kits/c/neural_network_runtime.h"
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namespace OHOS {
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namespace NeuralNetworkRuntime {
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using LiteGraphTensorPtr = std::unique_ptr<void, void(*)(void*)>;
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void DestroyLiteGraphTensor(void* tensor);
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class NNTensor {
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public:
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NNTensor() = default;
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~NNTensor();
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NNTensor(NNTensor&& tensor) noexcept;
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NNTensor& operator=(NNTensor&& tensor) noexcept;
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// Copy construction and assignment is not allowed in case of double-free of m_buffer
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NNTensor(const NNTensor& tensor) = delete;
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NNTensor& operator=(const NNTensor& tensor) = delete;
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OH_NN_ReturnCode BuildFromOHNNTensor(const OH_NN_Tensor& nnTensor);
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OH_NN_ReturnCode Build(OH_NN_DataType dataType,
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const std::vector<int32_t>& dimensions,
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const std::vector<QuantParam>& quantParam,
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OH_NN_TensorType type);
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void IdentifyOpParameter();
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void SetName(const std::string& name);
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void SetBuffer(const void* buffer, size_t length);
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OH_NN_ReturnCode SetDimensions(const std::vector<int32_t>& dimensions);
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std::string GetName() const;
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OH_NN_TensorType GetType() const;
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void* GetBuffer() const;
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// Return complete buffer length
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size_t GetBufferLength() const;
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// Return actual data length, since the data can be store in a larger buffer
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size_t GetDataLength() const;
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OH_NN_DataType GetDataType() const;
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uint32_t GetElementCount() const;
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std::vector<int32_t> GetDimensions() const;
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OH_NN_Format GetFormat() const;
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std::vector<QuantParam> GetQuantParam() const;
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LiteGraphTensorPtr ConvertToLiteGraphTensor() const;
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void ConvertToIOTensor(IOTensor& tensor) const;
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bool IsDynamicShape() const;
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bool IsQuantTensor() const;
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bool IsScalar() const;
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bool IsOpParameter() const;
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bool CompareAttribute(const NNTensor& tensor) const;
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private:
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// Used in BuildFromOHNNTensor()
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OH_NN_ReturnCode ParseQuantParams(const OH_NN_QuantParam* quantParams);
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OH_NN_ReturnCode ParseDimensions(const OH_NN_Tensor& nnTensor);
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// Used in Build()
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OH_NN_ReturnCode ParseQuantParams(const std::vector<QuantParam>& quantParams);
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OH_NN_ReturnCode ParseDimensions(const std::vector<int32_t>& dimensions);
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private:
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OH_NN_TensorType m_type {OH_NN_TENSOR};
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OH_NN_DataType m_dataType {OH_NN_FLOAT32};
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OH_NN_Format m_format {OH_NN_FORMAT_NHWC};
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std::string m_name;
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std::vector<int32_t> m_dimensions;
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std::vector<QuantParam> m_quantParams;
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uint32_t m_elementCount {0};
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bool m_isDynamicShape {false};
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bool m_isOpParameter {false};
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void* m_buffer {nullptr};
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size_t m_bufferLength {0};
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size_t m_dataLength {0};
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};
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} // namespace NeuralNetworkRuntime
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} // namespace OHOS
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#endif // NEURAL_NETWORK_RUNTIME_NN_TENSOR_H
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