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					87 lines
				
				2.3 KiB
			
		
		
			
		
	
	
					87 lines
				
				2.3 KiB
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											6 years ago
										 
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								//==============================================================================
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								//
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								// Copyright (c) 2017-2019 Qualcomm Technologies, Inc.
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								// All Rights Reserved.
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								// Confidential and Proprietary - Qualcomm Technologies, Inc.
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								//
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								//==============================================================================
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								#ifndef _IBUFFER_ATTRIBUTES_HPP
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								#define _IBUFFER_ATTRIBUTES_HPP
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								#include "IUserBuffer.hpp"
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								#include "TensorShape.hpp"
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								#include "ZdlExportDefine.hpp"
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								namespace zdl {
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								    namespace DlSystem {
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								        class UserBufferEncoding;
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								    }
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								}
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								namespace zdl {
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								namespace DlSystem {
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								/**
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								 * @brief IBufferAttributes returns a buffer's dimension and alignment
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								 *        requirements, along with info on its encoding type
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								 */
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								class ZDL_EXPORT IBufferAttributes {
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								public:
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								    /**
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								      * @brief Gets the buffer's element size, in bytes
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								      *
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								      * This can be used to compute the memory size required
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								      * to back this buffer.
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								      *
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								      * @return Element size, in bytes
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								     */
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								    virtual size_t getElementSize() const noexcept = 0;
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								    /**
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								      * @brief Gets the element's encoding type
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								      *
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								      * @return encoding type
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								     */
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								    virtual zdl::DlSystem::UserBufferEncoding::ElementType_t getEncodingType() const noexcept = 0;
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								    /**
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								      * @brief Gets the number of elements in each dimension
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								      *
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								      * @return Dimension size, in terms of number of elements
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								     */
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								    virtual const TensorShape getDims() const noexcept = 0;
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								    /**
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								      * @brief Gets the alignment requirement of each dimension
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								      *
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								      * Alignment per each dimension is expressed as an multiple, for
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								      * example, if one particular dimension can accept multiples of 8,
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								      * the alignment will be 8.
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								      *
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								      * @return Alignment in each dimension, in terms of multiple of
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								      *         number of elements
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								     */
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								    virtual const TensorShape getAlignments() const noexcept = 0;
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								    /**
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								      * @brief Gets the buffer encoding returned from the network responsible
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								      * for generating this buffer. Depending on the encoding type, this will
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								      * be an instance of an encoding type specific derived class.
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								      *
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								      * @return Derived user buffer encoding object.
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								     */
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								    virtual zdl::DlSystem::UserBufferEncoding* getEncoding() const noexcept = 0;
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								    virtual ~IBufferAttributes() {}
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								};
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								/** @} */ /* end_addtogroup c_plus_plus_apis C++ */
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								}
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								}
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								#endif
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