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modeld: retain SNPE and thneed drive model support (#530)
* modeld: Retain pre-20hz drive model support * Method not available anymore on OP * some fixes * Revert "Long planner get accel: new function args (#34288)" * Revert "Fix low-speed allow_throttle behavior in long planner (#33894)" * Revert "long planner: allow throttle reflects usage (#33792)" * Revert "Gate acceleration on model gas press predictions (#33643)" * Reapply "Gate acceleration on model gas press predictions (#33643)" This reverts commit 76b08e37cb8eb94266ad9f6fed80db227e7c3428. * Reapply "long planner: allow throttle reflects usage (#33792)" This reverts commit c75244ca4e9c48084b0205b7c871e1a4e0f4e693. * Reapply "Fix low-speed allow_throttle behavior in long planner (#33894)" This reverts commit b2b7d21b7b685a2785d1beede3d223f0bb954807. * Reapply "Long planner get accel: new function args (#34288)" This reverts commit 74dca2fccf4da59cc8ac62ba9c0ad10ba3fc264b. * don't need * retain snpe * wrong * they're symlinks * remove * put back into VCS * add back * don't include built * Refactor model runner retrieval with caching support Added caching for active model runner type via `ModelRunnerTypeCache` to enhance performance and avoid redundant checks. Introduced a `force_check` flag to bypass the cache when necessary. Updated related code to handle cache clearing during onroad transitions. * Update model runner determination logic with caching fix Enhances `get_active_model_runner` to utilize caching more effectively by ensuring type consistency and updating cache only when necessary. Also updates `is_snpe_model` to pass the `started` state to the runner determination function, improving behavior for dynamic checks. * default to none * enable in next PR * more --------- Co-authored-by: DevTekVE <devtekve@gmail.com>
This commit is contained in:
Jason Wen
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//=============================================================================
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//
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// Copyright (c) 2016 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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#include <initializer_list>
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#include <cstdio>
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#include <memory>
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#include <vector>
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#include "ZdlExportDefine.hpp"
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#ifndef DL_SYSTEM_TENSOR_SHAPE_HPP
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#define DL_SYSTEM_TENSOR_SHAPE_HPP
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namespace DlSystem
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{
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// Forward declaration of tensor shape implementation.
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class TensorShapeImpl;
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}
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namespace zdl
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{
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namespace DlSystem
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{
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/** @addtogroup c_plus_plus_apis C++
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@{ */
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/**
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* @brief .
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*
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* Convenient typedef to represent dimension
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*/
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using Dimension = size_t;
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/**
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* @brief .
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*
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* A class representing the shape of tensor. It is used at the
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* time of creation of tensor.
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*/
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class ZDL_EXPORT TensorShape final
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{
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public:
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/**
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* @brief .
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*
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* Creates a new shape with a list of dims specified in
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* initializer list fashion.
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*
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* @param[in] dims The dimensions are specified in which the last
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* element of the vector represents the fastest varying
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* dimension and the zeroth element represents the slowest
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* varying, etc.
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*
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*/
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TensorShape(std::initializer_list<Dimension> dims);
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/**
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* @brief .
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*
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* Creates a new shape with a list of dims specified in array
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*
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* @param[in] dims The dimensions are specified in which the last
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* element of the vector represents the fastest varying
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* dimension and the zeroth element represents the slowest
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* varying, etc.
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*
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* @param[in] size Size of the array.
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*
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*/
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TensorShape(const Dimension *dims, size_t size);
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/**
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* @brief .
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*
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* Creates a new shape with a vector of dims specified in
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* vector fashion.
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*
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* @param[in] dims The dimensions are specified in which the last
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* element of the vector represents the fastest varying
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* dimension and the zeroth element represents the slowest
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* varying, etc.
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*
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*/
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TensorShape(std::vector<Dimension> dims);
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/**
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* @brief .
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*
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* copy constructor.
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* @param[in] other object to copy.
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*/
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TensorShape(const TensorShape& other);
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/**
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* @brief .
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*
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* assignment operator.
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*/
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TensorShape& operator=(const TensorShape& other);
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/**
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* @brief .
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*
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* Creates a new shape with no dims. It can be extended later
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* by invoking concatenate.
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*/
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TensorShape();
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/**
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* @brief .
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*
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* Concatenates additional dimensions specified in
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* initializer list fashion to the existing dimensions.
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*
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* @param[in] dims The dimensions are specified in which the last
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* element of the vector represents the fastest varying
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* dimension and the zeroth element represents the slowest
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* varying, etc.
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*
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*/
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void concatenate(std::initializer_list<Dimension> dims);
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/**
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* @brief .
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*
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* Concatenates additional dimensions specified in
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* the array to the existing dimensions.
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*
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* @param[in] dims The dimensions are specified in which the last
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* element of the vector represents the fastest varying
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* dimension and the zeroth element represents the slowest
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* varying, etc.
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*
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* @param[in] size Size of the array.
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*
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*/
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void concatenate(const Dimension *dims, size_t size);
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/**
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* @brief .
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*
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* Concatenates an additional dimension to the existing
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* dimensions.
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*
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* @param[in] dim The dimensions are specified in which the last element
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* of the vector represents the fastest varying dimension and the
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* zeroth element represents the slowest varying, etc.
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*
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*/
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void concatenate(const Dimension &dim);
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/**
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* @brief .
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*
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* Retrieves a single dimension, based on its index.
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*
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* @return The value of dimension
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*
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* @throws std::out_of_range if the index is >= the number of
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* dimensions (or rank).
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*/
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Dimension& operator[](size_t index);
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Dimension& operator[](size_t index) const;
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/**
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* @brief .
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*
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* Retrieves the rank i.e. number of dimensions.
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*
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* @return The rank
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*/
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size_t rank() const;
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/**
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* @brief .
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*
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* Retrieves a pointer to the first dimension of shape
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*
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* @return nullptr if no dimension exists; otherwise, points to
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* the first dimension.
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*
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*/
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const Dimension* getDimensions() const;
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~TensorShape();
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private:
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void swap(const TensorShape &other);
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std::unique_ptr<::DlSystem::TensorShapeImpl> m_TensorShapeImpl;
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};
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} // DlSystem namespace
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} // zdl namespace
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/** @} */ /* end_addtogroup c_plus_plus_apis C++ */
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#endif // DL_SYSTEM_TENSOR_SHAPE_HPP
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