2018-09-07 16:27:24 +02:00
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/*
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* Copyright (C) 2018 The Android Open Source Project
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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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*/
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package android.hardware.neuralnetworks@1.2;
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import @1.0::ErrorStatus;
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import @1.1::ExecutionPreference;
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import @1.1::IDevice;
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2018-10-31 16:43:12 +01:00
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import IPreparedModelCallback;
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2018-09-07 16:27:24 +02:00
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/**
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* This interface represents a device driver.
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*/
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interface IDevice extends @1.1::IDevice {
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2018-09-20 20:35:42 +02:00
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/**
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* Get the version string of the driver implementation.
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*
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* The version string must be a unique token among the set of version strings of
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* drivers of a specific device. The token identifies the device driver's
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* implementation. The token must not be confused with the feature level which is solely
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* defined by the interface version. This API is opaque to the Android framework, but the
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* Android framework may use the information for debugging or to pass on to NNAPI applications.
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*
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* Application developers sometimes have specific requirements to ensure good user experiences,
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* and they need more information to make intelligent decisions when the Android framework cannot.
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* For example, combined with the device name and other information, the token can help
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* NNAPI applications filter devices based on their needs:
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* - An application demands a certain level of performance, but a specific version of
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* the driver cannot meet that requirement because of a performance regression.
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* The application can blacklist the driver based on the version provided.
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* - An application has a minimum precision requirement, but certain versions of
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* the driver cannot meet that requirement because of bugs or certain optimizations.
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* The application can filter out versions of these drivers.
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*
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* @return status Error status returned from querying the version string. Must be:
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* - NONE if the query was successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if the query resulted in an
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* unspecified error
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* @return version The version string of the device implementation.
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* Must have nonzero length
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*/
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getVersionString() generates (ErrorStatus status, string version);
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2018-09-20 22:30:31 +02:00
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/**
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* Get the type of a given device.
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*
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* The device type can be used to help application developers to distribute
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* Machine Learning workloads and other workloads such as graphical rendering.
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* E.g., for an app which renders AR scenes based on real time object detection
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* results, the developer could choose an ACCELERATOR type device for ML
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* workloads, and reserve GPU for graphical rendering.
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*
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* @param status Error status returned from querying the device type. Must be:
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* - NONE if the query was successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if the query resulted in an
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* unspecified error
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* @param type The DeviceType of the device. Please note, this is not a
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* bitfield of DeviceTypes. Each device must only be of a
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* single DeviceType.
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*/
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getType() generates (ErrorStatus status, DeviceType type);
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2019-03-16 01:26:32 +01:00
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/**
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* Gets the capabilities of a driver.
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*
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* @return status Error status of the call, must be:
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* - NONE if successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if there is an unspecified error
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* @return capabilities Capabilities of the driver.
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*/
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getCapabilities_1_2() generates (ErrorStatus status, Capabilities capabilities);
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2018-11-20 16:29:01 +01:00
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/**
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* Gets information about extensions supported by the driver implementation.
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*
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* All extension operations and operands must be fully supported for the
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* extension to appear in the list of supported extensions.
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*
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* @return status Error status of the call, must be:
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* - NONE if successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if there is an unspecified error
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* @return extensions A list of supported extensions.
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*/
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getSupportedExtensions()
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generates (ErrorStatus status, vec<Extension> extensions);
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2018-09-07 16:27:24 +02:00
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/**
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* Gets the supported operations in a model.
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*
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* getSupportedOperations indicates which operations of a model are fully
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* supported by the vendor driver. If an operation may not be supported for
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* any reason, getSupportedOperations must return false for that operation.
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*
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* @param model A model whose operations--and their corresponding operands--
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* are to be verified by the driver.
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* @return status Error status of the call, must be:
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* - NONE if successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if there is an unspecified error
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* - INVALID_ARGUMENT if provided model is invalid
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* @return supportedOperations A list of supported operations, where true
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* indicates the operation is supported and false indicates the
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* operation is not supported. The index of "supported" corresponds with
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* the index of the operation it is describing.
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*/
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getSupportedOperations_1_2(Model model)
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generates (ErrorStatus status, vec<bool> supportedOperations);
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2019-01-12 02:41:11 +01:00
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/**
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2019-02-26 01:58:58 +01:00
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* Gets the caching requirements of the driver implementation.
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2019-01-12 02:41:11 +01:00
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*
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2019-02-26 01:58:58 +01:00
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* There are two types of cache file descriptors provided to the driver: model cache
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* and data cache.
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2019-01-12 02:41:11 +01:00
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*
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2019-02-26 01:58:58 +01:00
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* The data cache is for caching constant data, possibly including preprocessed
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* and transformed tensor buffers. Any modification to the data cache should
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* have no worse effect than generating bad output values at execution time.
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*
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* The model cache is for caching security-sensitive data such as compiled
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* executable machine code in the device's native binary format. A modification
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* to the model cache may affect the driver's execution behavior, and a malicious
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* client could make use of this to execute beyond the granted permission. Thus,
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* the driver must always check whether the model cache is corrupted before
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* preparing the model from cache.
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*
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* getNumberOfCacheFilesNeeded returns how many of each type of cache files the driver
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* implementation needs to cache a single prepared model. Returning 0 for both types
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* indicates compilation caching is not supported by this driver. The driver may
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* still choose not to cache certain compiled models even if it reports that caching
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* is supported.
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*
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* If the device reports that caching is not supported, the user may avoid calling
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* IDevice::prepareModelFromCache or providing cache file descriptors to
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* IDevice::prepareModel_1_2.
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2019-01-12 02:41:11 +01:00
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*
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* @return status Error status of the call, must be:
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* - NONE if successful
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if there is an unspecified error
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2019-02-26 01:58:58 +01:00
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* @return numModelCache An unsigned integer indicating how many files for model cache
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* the driver needs to cache a single prepared model. It must
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* be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
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* @return numDataCache An unsigned integer indicating how many files for data cache
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* the driver needs to cache a single prepared model. It must
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* be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
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2019-01-12 02:41:11 +01:00
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*/
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2019-02-26 01:58:58 +01:00
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getNumberOfCacheFilesNeeded()
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generates (ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache);
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2019-01-12 02:41:11 +01:00
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2018-09-07 16:27:24 +02:00
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/**
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2019-02-26 01:58:58 +01:00
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* Asynchronously creates a prepared model for execution and optionally saves it
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* into cache files.
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2018-09-07 16:27:24 +02:00
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*
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2019-02-26 01:58:58 +01:00
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* prepareModel is used to make any necessary transformations to or alternative
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2018-09-07 16:27:24 +02:00
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* representations to a model for execution, possibly including
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* transformations on the constant data, optimization on the model's graph,
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* or compilation into the device's native binary format. The model itself
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* is not changed.
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*
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2019-02-26 01:58:58 +01:00
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* Optionally, caching information may be provided for the driver to save
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* the prepared model to cache files for faster model compilation time
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* when the same model preparation is requested in the future. There are
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* two types of cache file handles provided to the driver: model cache
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* and data cache. For more information on the two types of cache handles,
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* refer to getNumberOfCacheFilesNeeded.
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*
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* The file descriptors must be opened with read and write permission. A file may
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* have any size, and the corresponding file descriptor may have any offset. The
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* driver must truncate a file to zero size before writing to that file. The file
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* descriptors may be closed by the client once the asynchronous preparation has
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* finished. The driver must dup a file descriptor if it wants to get access to
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* the cache file later.
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*
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2018-09-07 16:27:24 +02:00
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* The model is prepared asynchronously with respect to the caller. The
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2019-02-26 01:58:58 +01:00
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* prepareModel function must verify the inputs to the preparedModel function
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* related to preparing the model (as opposed to saving the prepared model to
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* cache) are correct. If there is an error, prepareModel must immediately invoke
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2018-09-07 16:27:24 +02:00
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* the callback with the appropriate ErrorStatus value and nullptr for the
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2019-02-26 01:58:58 +01:00
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* IPreparedModel, then return with the same ErrorStatus. If the inputs to the
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* prepareModel function that are related to preparing the model are valid and
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* there is no error, prepareModel must launch an asynchronous task
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* to prepare the model in the background, and immediately return from
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* prepareModel with ErrorStatus::NONE. If the asynchronous task fails to launch,
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* prepareModel must immediately invoke the callback with
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* ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return
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* with ErrorStatus::GENERAL_FAILURE.
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2018-09-07 16:27:24 +02:00
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*
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* When the asynchronous task has finished preparing the model, it must
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* immediately invoke the callback function provided as an input to
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* prepareModel. If the model was prepared successfully, the callback object
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* must be invoked with an error status of ErrorStatus::NONE and the
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* produced IPreparedModel object. If an error occurred preparing the model,
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* the callback object must be invoked with the appropriate ErrorStatus
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* value and nullptr for the IPreparedModel.
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*
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2019-02-26 01:58:58 +01:00
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* Optionally, the driver may save the prepared model to cache during the
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* asynchronous preparation. Any error that occurs when saving to cache must
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* not affect the status of preparing the model. Even if the input arguments
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* related to the cache may be invalid, or the driver may fail to save to cache,
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* the prepareModel function must finish preparing the model. The driver
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* may choose not to save to cache even if the caching information is
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* provided and valid.
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*
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2018-09-07 16:27:24 +02:00
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* The only information that may be unknown to the model at this stage is
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* the shape of the tensors, which may only be known at execution time. As
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* such, some driver services may return partially prepared models, where
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* the prepared model may only be finished when it is paired with a set of
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* inputs to the model. Note that the same prepared model object may be
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* used with different shapes of inputs on different (possibly concurrent)
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* executions.
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*
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* Multiple threads may call prepareModel on the same model concurrently.
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*
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* @param model The model to be prepared for execution.
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* @param preference Indicates the intended execution behavior of a prepared
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* model.
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2019-02-26 01:58:58 +01:00
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* @param modelCache A vector of handles with each entry holding exactly one
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* cache file descriptor for the security-sensitive cache. The length of
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* the vector must either be 0 indicating that caching information is not provided,
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* or match the numModelCache returned from getNumberOfCacheFilesNeeded. The cache
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* handles will be provided in the same order when retrieving the
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* preparedModel from cache files with prepareModelFromCache.
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* @param dataCache A vector of handles with each entry holding exactly one
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* cache file descriptor for the constants' cache. The length of
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* the vector must either be 0 indicating that caching information is not provided,
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* or match the numDataCache returned from getNumberOfCacheFilesNeeded. The cache
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* handles will be provided in the same order when retrieving the
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* preparedModel from cache files with prepareModelFromCache.
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* @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
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* identifying the prepared model. The same token will be provided when retrieving
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* the prepared model from the cache files with prepareModelFromCache.
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* Tokens should be chosen to have a low rate of collision for a particular
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* application. The driver cannot detect a collision; a collision will result
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* in a failed execution or in a successful execution that produces incorrect
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* output values. If both modelCache and dataCache are empty indicating that
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* caching information is not provided, this token must be ignored.
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2018-09-07 16:27:24 +02:00
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* @param callback A callback object used to return the error status of
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* preparing the model for execution and the prepared model if
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* successful, nullptr otherwise. The callback object's notify function
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* must be called exactly once, even if the model could not be prepared.
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* @return status Error status of launching a task which prepares the model
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* in the background; must be:
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* - NONE if preparation task is successfully launched
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if there is an unspecified error
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2019-02-26 01:58:58 +01:00
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* - INVALID_ARGUMENT if one of the input arguments related to preparing the
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* model is invalid
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2018-09-07 16:27:24 +02:00
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*/
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prepareModel_1_2(Model model, ExecutionPreference preference,
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2019-02-26 01:58:58 +01:00
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vec<handle> modelCache, vec<handle> dataCache,
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uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
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2018-09-07 16:27:24 +02:00
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IPreparedModelCallback callback)
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generates (ErrorStatus status);
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2019-01-12 02:41:11 +01:00
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/**
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* Creates a prepared model from cache files for execution.
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*
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* prepareModelFromCache is used to retrieve a prepared model directly from
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2019-02-26 01:58:58 +01:00
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* cache files to avoid slow model compilation time. There are
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* two types of cache file handles provided to the driver: model cache
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* and data cache. For more information on the two types of cache handles,
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* refer to getNumberOfCacheFilesNeeded.
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2019-01-12 02:41:11 +01:00
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*
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2019-02-26 01:58:58 +01:00
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* The file descriptors must be opened with read and write permission. A file may
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* have any size, and the corresponding file descriptor may have any offset. The
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* driver must truncate a file to zero size before writing to that file. The file
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* descriptors may be closed by the client once the asynchronous preparation has
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* finished. The driver must dup a file descriptor if it wants to get access to
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* the cache file later.
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2019-01-12 02:41:11 +01:00
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*
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* The model is prepared asynchronously with respect to the caller. The
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* prepareModelFromCache function must verify the inputs to the
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* prepareModelFromCache function are correct, and that the security-sensitive
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* cache has not been modified since it was last written by the driver.
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* If there is an error, or if compilation caching is not supported, or if the
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* security-sensitive cache has been modified, prepareModelFromCache must
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* immediately invoke the callback with the appropriate ErrorStatus value and
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* nullptr for the IPreparedModel, then return with the same ErrorStatus. If
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* the inputs to the prepareModelFromCache function are valid, the security-sensitive
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* cache is not modified, and there is no error, prepareModelFromCache must launch an
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* asynchronous task to prepare the model in the background, and immediately return
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* from prepareModelFromCache with ErrorStatus::NONE. If the asynchronous task
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* fails to launch, prepareModelFromCache must immediately invoke the callback
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* with ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then
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* return with ErrorStatus::GENERAL_FAILURE.
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*
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* When the asynchronous task has finished preparing the model, it must
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* immediately invoke the callback function provided as an input to
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* prepareModelFromCache. If the model was prepared successfully, the
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* callback object must be invoked with an error status of ErrorStatus::NONE
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* and the produced IPreparedModel object. If an error occurred preparing
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* the model, the callback object must be invoked with the appropriate
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* ErrorStatus value and nullptr for the IPreparedModel.
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*
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* The only information that may be unknown to the model at this stage is
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* the shape of the tensors, which may only be known at execution time. As
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* such, some driver services may return partially prepared models, where
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* the prepared model may only be finished when it is paired with a set of
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* inputs to the model. Note that the same prepared model object may be
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* used with different shapes of inputs on different (possibly concurrent)
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* executions.
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*
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2019-02-26 01:58:58 +01:00
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* @param modelCache A vector of handles with each entry holding exactly one
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* cache file descriptor for the security-sensitive cache. The length of
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* the vector must match the numModelCache returned from getNumberOfCacheFilesNeeded.
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* The cache handles will be provided in the same order as with prepareModel_1_2.
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* @param dataCache A vector of handles with each entry holding exactly one
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* cache file descriptor for the constants' cache. The length of the vector
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* must match the numDataCache returned from getNumberOfCacheFilesNeeded.
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* The cache handles will be provided in the same order as with prepareModel_1_2.
|
2019-01-12 02:41:11 +01:00
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* @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
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* identifying the prepared model. It is the same token provided when saving
|
2019-02-26 01:58:58 +01:00
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* the cache files with prepareModel_1_2. Tokens should be chosen
|
2019-01-12 02:41:11 +01:00
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* to have a low rate of collision for a particular application. The driver
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* cannot detect a collision; a collision will result in a failed execution
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* or in a successful execution that produces incorrect output values.
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* @param callback A callback object used to return the error status of
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* preparing the model for execution and the prepared model if
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* successful, nullptr otherwise. The callback object's notify function
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* must be called exactly once, even if the model could not be prepared.
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* @return status Error status of launching a task which prepares the model
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* in the background; must be:
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* - NONE if preparation task is successfully launched
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* - DEVICE_UNAVAILABLE if driver is offline or busy
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* - GENERAL_FAILURE if caching is not supported or if there is an
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* unspecified error
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* - INVALID_ARGUMENT if one of the input arguments is invalid
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*/
|
2019-02-26 01:58:58 +01:00
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prepareModelFromCache(vec<handle> modelCache, vec<handle> dataCache,
|
2019-01-12 02:41:11 +01:00
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uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
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IPreparedModelCallback callback)
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generates (ErrorStatus status);
|
2018-09-07 16:27:24 +02:00
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|
};
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