From ebafc909853dddbdf5dec12ded96b78ba3afd85a Mon Sep 17 00:00:00 2001 From: IllusionMan1212 Date: Tue, 6 Aug 2024 05:50:12 +0200 Subject: [PATCH] progress --- NeuralNetworks.odin | 2172 +++++++++++++ NeuralNetworksTypes.odin | 6507 ++++++++++++++++++++++++++++++++++++++ doc.odin | 4 + keycodes.odin | 762 +++++ looper.odin | 216 +- multinetwork.odin | 262 ++ native_acitivty.odin | 99 +- native_window.odin | 277 +- native_window_jni.odin | 25 + ndk-build.odin | 41 + window.odin | 191 ++ 11 files changed, 10531 insertions(+), 25 deletions(-) create mode 100644 NeuralNetworks.odin create mode 100644 NeuralNetworksTypes.odin create mode 100644 keycodes.odin create mode 100644 multinetwork.odin create mode 100644 native_window_jni.odin create mode 100644 ndk-build.odin create mode 100644 window.odin diff --git a/NeuralNetworks.odin b/NeuralNetworks.odin new file mode 100644 index 0000000..35732cc --- /dev/null +++ b/NeuralNetworks.odin @@ -0,0 +1,2172 @@ +//+build android +package android + +foreign import android "system:android" + +foreign android { + /** + * Create a {@link ANeuralNetworksMemoryDesc} with no properties. + * + * This only creates the memory descriptor. Its properties should be set with calls to + * {@link ANeuralNetworksMemoryDesc_addInputRole}, + * {@link ANeuralNetworksMemoryDesc_addOutputRole}, and + * {@link ANeuralNetworksMemoryDesc_setDimensions}. + * + * {@link ANeuralNetworksMemoryDesc_finish} must be called once all properties have been set. + * + * {@link ANeuralNetworksMemoryDesc_free} must be called once the memory descriptor + * is no longer needed. + * + * Available since NNAPI feature level 4. + * Available since API feature level 30. + * + * @param desc The {@link ANeuralNetworksMemoryDesc} to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemoryDesc_create :: proc(desc: ^^ANeuralNetworksMemoryDesc) + + /** + * Destroy a memory descriptor. + * + * The memory descriptor need not have been finished by a call to + * {@link ANeuralNetworksMemoryDesc_finish}. + * + * See {@link ANeuralNetworksMemoryDesc} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksMemoryDesc_free(ANeuralNetworksMemoryDesc* desc) __NNAPI_INTRODUCED_IN(30); + + /** + * Specify that a memory object will be playing the role of an input to an execution created from a + * particular compilation. + * + * The compilation and the input index fully specify an input operand. This function + * may be invoked multiple times on the same memory descriptor with different input operands, + * and the same input operand may be specified on multiple memory descriptors. However, + * specifying the same input operand on the same memory descriptor more than once will + * return an error. + * + * The dimensions of the corresponding model operands of all the roles specified by + * {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole} must be compatible with each other. Two + * dimensions are incompatible if both ranks are fully specified but have different values, or if + * there is at least one axis that is fully specified in both but has different values. + * + * At least one of {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole} must be called on a memory descriptor + * before invoking {@link ANeuralNetworksMemoryDesc_finish}. + * + * Attempting to modify a memory descriptor once {@link ANeuralNetworksMemoryDesc_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksMemoryDesc} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor to be modified. + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}, and must outlive the memory + * descriptor. + * @param index The index of the input argument we are referencing from the compilation. It is + * an index into the inputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param frequency A floating-point value within the range (0.0, 1.0]. Describes how likely the + * memory is to be used in the specified role. This is provided as a hint to + * optimize the case when different roles prefer different memory locations or data + * layouts. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemoryDesc_addInputRole(ANeuralNetworksMemoryDesc* desc, + const ANeuralNetworksCompilation* compilation, + uint32_t index, float frequency) + __NNAPI_INTRODUCED_IN(30); + + /** + * Specify that a memory object will be playing the role of an output to an execution created from a + * particular compilation. + * + * The compilation and the output index fully specify an output operand. This function + * may be invoked multiple times on the same memory descriptor with different output operands, + * and the same output operand may be specified on multiple memory descriptors. However, + * specifying the same output operand on the same memory descriptor object more than once will + * return an error. + * + * The dimensions of the corresponding model operands of all the roles specified by + * {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole} must be compatible with each other. Two + * dimensions are incompatible if both ranks are fully specified but have different values, or if + * there is at least one axis that is fully specified in both but has different values. + * + * At least one of {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole} must be called on the memory descriptor + * before invoking {@link ANeuralNetworksMemoryDesc_finish}. + * + * Attempting to modify a memory descriptor once {@link ANeuralNetworksMemoryDesc_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksMemoryDesc} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor to be modified. + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}, and must outlive the memory + * descriptor. + * @param index The index of the output argument we are referencing from the compilation. It is + * an index into the outputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param frequency A floating-point value within the range (0.0, 1.0]. Describes how likely the + * memory is to be used in the specified role. This is provided as a hint to + * optimize the case when multiple roles prefer different memory locations or data + * layouts. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemoryDesc_addOutputRole(ANeuralNetworksMemoryDesc* desc, + const ANeuralNetworksCompilation* compilation, + uint32_t index, float frequency) + __NNAPI_INTRODUCED_IN(30); + + /** + * Set the dimensional information of the memory descriptor. + * + * The specified dimensions must be compatible with the dimensions of the corresponding model + * operands of all the roles specified by {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole}. Two dimensions are incompatible if both ranks + * are fully specified but have different values, or if there is at least one axis that is fully + * specified in both but has different values. + * + * Attempting to modify a memory descriptor once {@link ANeuralNetworksMemoryDesc_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksMemoryDesc} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor to be modified. + * @param rank The number of dimensions. Must be 0 for scalars. + * @param dimensions An array of dimensions. An entry with the value 0 indicates that the + * corresponding axis has an unknown size. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemoryDesc_setDimensions(ANeuralNetworksMemoryDesc* desc, uint32_t rank, + const uint32_t* dimensions) __NNAPI_INTRODUCED_IN(30); + + /** + * Indicate that we have finished modifying a memory descriptor. Required before calling + * {@link ANeuralNetworksMemory_createFromDesc}. + * + * This function must only be called once for a given memory descriptor. + * + * See {@link ANeuralNetworksMemoryDesc} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor to be finished. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemoryDesc_finish(ANeuralNetworksMemoryDesc* desc) __NNAPI_INTRODUCED_IN(30); + + /** + * Creates a memory object from a memory descriptor. + * + * The memory object is created with an uninitialized buffer. A memory object with an uninitialized + * buffer may only be used according to the roles specified by {@link + * ANeuralNetworksMemoryDesc_addOutputRole}, or as the destination memory in {@link + * ANeuralNetworksMemory_copy}. The buffer of a memory object is initialized after the memory object + * is used as an output in a successful execution, or used as the destination memory in a successful + * {@link ANeuralNetworksMemory_copy}. A memory object with an initialized buffer may be used + * according to all roles specified in {@link ANeuralNetworksMemoryDesc}, or as the source or + * destination memory in {@link ANeuralNetworksMemory_copy}. The buffer of a memory object will + * return to the uninitialized state if the memory object is used as an output in a failed + * execution, or used as the destination memory in a failed {@link ANeuralNetworksMemory_copy}. + * + * The dimensions of the memory descriptor are deduced from the dimensions of the corresponding + * model operands of all the roles specified by {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole}, as well as the dimensions set by the call to + * {@link ANeuralNetworksMemoryDesc_setDimensions}, if any. The memory descriptor may have + * unspecified dimensions or rank. In such a case, the same memory object may be used with different + * shapes of outputs in different executions. When the memory is used as an input, the input shape + * must be the same as the output shape from the last execution using this memory object as an + * output, or the last {@link ANeuralNetworksMemory_copy} using this memory object as the + * destination memory. Creating a memory object with unspecified dimensions or rank may fail for + * certain sets of roles. + * + * Using the memory in roles or shapes that are not compatible with the rules specified above will + * return an error. + * + * When calling {@link ANeuralNetworksExecution_setInputFromMemory} or + * {@link ANeuralNetworksExecution_setOutputFromMemory} with the memory object, + * both offset and length must be set to zero and the entire memory region will be + * associated with the specified input or output operand. + * + * Calling {@link ANeuralNetworksModel_setOperandValueFromMemory} with the memory created from this + * function will return an error. + * + * {@link ANeuralNetworksMemory_free} must be called once the memory is no longer needed. + * + * Attempting to create memory from an unfinished memory descriptor will return an error. + * + * The provided {@link ANeuralNetworksMemoryDesc} need not outlive the {@link ANeuralNetworksMemory} + * object. + * + * Available since NNAPI feature level 4. + * + * @param desc The memory descriptor. + * @param memory The memory object to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful; ANEURALNETWORKS_OP_FAILED if the memory is + * created with unspecified dimensions or rank and it is not supported for this set of + * roles. + */ + int ANeuralNetworksMemory_createFromDesc(const ANeuralNetworksMemoryDesc* desc, + ANeuralNetworksMemory** memory) __NNAPI_INTRODUCED_IN(30); + + /** + * Copies data from one memory object to another. + * + * If at most one of the src and dst is created from {@link ANeuralNetworksMemory_createFromDesc}, + * the src and dst must have the same logical size: + * - If the memory is created from {@link ANeuralNetworksMemory_createFromFd}, or if it is created + * from {@link ANeuralNetworksMemory_createFromAHardwareBuffer} with format of + * AHARDWAREBUFFER_FORMAT_BLOB, the logical size equals the size of the memory. + * - If the memory is created from {@link ANeuralNetworksMemory_createFromAHardwareBuffer} with a + * format other than AHARDWAREBUFFER_FORMAT_BLOB, the logical size equals the size when there is + * no padding and the data is tightly packed. This function may fail if the AHardwareBuffer + * cannot be accessed. + * - If the memory is created from {@link ANeuralNetworksMemory_createFromDesc}, the logical size + * equals the size indicated by the {@link OperandCode} multiplied by the number of elements. This + * function will fail if the number of elements is unknown. + * + * If both src and dst are created from {@link ANeuralNetworksMemory_createFromDesc}, they must have + * compatible dimensions. Two dimensions are incompatible if both ranks are fully specified but + * have different values, or if there is at least one axis that is fully specified in both but has + * different values. The dst may have unspecified dimensions or rank. In such a case, the dimensions + * of dst will get updated according to the dimensions of the src. + * + * In both cases, if the src is created from {@link ANeuralNetworksMemory_createFromDesc}, it must + * have been used as an output in a successful execution, or used as the destination memory in a + * successful {@link ANeuralNetworksMemory_copy}. + * + * The src and dst may have different data layout, in which case the data copying is performed + * logically with data layout transformation. + * + * Available since NNAPI feature level 4. + * + * @param src The source memory object. + * @param dst The destination memory object. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksMemory_copy(const ANeuralNetworksMemory* src, const ANeuralNetworksMemory* dst) + __NNAPI_INTRODUCED_IN(30); + + /** + * Get the number of available devices. + * + * @param numDevices Used to return the number of devices. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworks_getDeviceCount(uint32_t* numDevices) __NNAPI_INTRODUCED_IN(29); + + /** + * Get the representation of the specified device. + * + * @param devIndex The index of the specified device. Must be less than the + number of available devices. + * @param device The representation of the specified device. + * The same representation will always be returned for the specified + * device. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworks_getDevice(uint32_t devIndex, ANeuralNetworksDevice** device) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the name of the specified device. + * + * @param device The representation of the specified device. + * @param name The returned name of the specified device. The name will be in UTF-8 + * and will be null-terminated. It will be recognizable as a known device name + * rather than a cryptic string. For devices with feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is + * {@link ANEURALNETWORKS_FEATURE_LEVEL_3} and higher, the format of the name is + * {VENDOR}-{DEVICE}. For devices with feature level + * {@link ANEURALNETWORKS_FEATURE_LEVEL_2} or lower, the format of the name is + * undefined. The name will remain valid for the duration of the application. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksDevice_getName(const ANeuralNetworksDevice* device, const char** name) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the type of a given device. + * + * The device type can be used to help application developers to distribute Machine Learning + * workloads and other workloads such as graphical rendering. + * E.g., for an app which renders AR scenes based on real time object detection results, + * the developer could choose an ACCELERATOR type device for ML workloads, and reserve GPU + * for graphical rendering. + * + * @param device The representation of the specified device. + * @param type The returned {@link DeviceTypeCode} of the specified device. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksDevice_getType(const ANeuralNetworksDevice* device, int32_t* type) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the version of the driver implementation of the specified device. + * + * It’s the responsibility of the driver implementor to insure that this version string + * uniquely distinguishes this implementation from all previous implementations. + * + * This version string must not be confused with the feature level which is solely defined + * by {@link ANeuralNetworksDevice_getFeatureLevel}. There is no implicit ordering of the versions. + * For example, it is not possible to filter all drivers older than a certain version. + * + * Application developers may use this version string to avoid or prefer specific driver + * implementations. For example, an application may want to do so because: + * - A specific version of the driver does not provide the required performance, + * perhaps because of a performance regression. + * - A specific version of the driver has a bug or returns results that don’t match + * the minimum precision requirement for the application. + * + * @param device The representation of the specified device. + * @param version The returned version string of the driver for the specified device. The + * string will be in UTF-8 and will be null-terminated. For devices with feature + * level 28 or lower, "UNKNOWN" will be returned. The version string will remain + * valid for the duration of the application. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksDevice_getVersion(const ANeuralNetworksDevice* device, const char** version) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the NNAPI feature level of the specified NNAPI device. + * + * Each device has a supported feature level, which is the most advanced NNAPI specification + * and features this driver implements. For example, if the driver implements the features + * introduced in {@link ANEURALNETWORKS_FEATURE_LEVEL_2}, but does not implement the features + * introduced after {@link ANEURALNETWORKS_FEATURE_LEVEL_2}, the value would be + * {@link ANEURALNETWORKS_FEATURE_LEVEL_2}. Developers could decide whether or not the specified + * device should be used for a model that has certain feature requirements. + * + * NNAPI device feature level is closely related to NNAPI runtime feature level + * ({@link ANeuralNetworks_getRuntimeFeatureLevel}), which indicates an NNAPI runtime feature + * level (the most advanced NNAPI specification and features that the runtime implements). + * An NNAPI device feature level is always less than or equal to the runtime feature level. + * + * This function produces a {@link FeatureLevelCode} enum value, NOT an Android API level. + * + * @param device The representation of the specified device. + * @param featureLevel {@link FeatureLevelCode} of the most advanced feature this driver implements. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksDevice_getFeatureLevel(const ANeuralNetworksDevice* device, + int64_t* featureLevel) __NNAPI_INTRODUCED_IN(29); + + /** + * Wait until the device is in a live state. + * + * A device may encounter internal errors and temporarily enter a dead state. A + * call that uses a device in such a state will return with the error + * {@link ANEURALNETWORKS_DEAD_OBJECT}. ANeuralNetworksDevice_wait will block until + * the device is in a live state. + * + * @param device The representation of the specified device. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksDevice_wait(const ANeuralNetworksDevice* device) __NNAPI_INTRODUCED_IN(30); + + /** + * Get the supported operations for a specified set of devices. If multiple devices + * are selected, the supported operation list is a union of supported operations of all + * selected devices. + * + * @param model The model to be queried. + * @param devices The set of devices. Must not contain duplicates. + * @param numDevices The number of devices in the set. + * @param supportedOps The boolean array to be filled. True means supported. The size of the + * boolean array must be at least as large as the number of operations + * in the model. The order of elements in the supportedOps array matches + * the order in which the corresponding operations were added to the model. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksModel_getSupportedOperationsForDevices( + const ANeuralNetworksModel* model, const ANeuralNetworksDevice* const* devices, + uint32_t numDevices, bool* supportedOps) __NNAPI_INTRODUCED_IN(29); + + /** + * Create a {@link ANeuralNetworksCompilation} to compile the given model for a specified set + * of devices. If more than one device is specified, the compilation will + * distribute the workload automatically across the devices. The model must be fully + * supported by the specified set of devices. This means that + * ANeuralNetworksModel_getSupportedOperationsForDevices() must have returned true for every + * operation for that model/devices pair. + * + * The user must handle all compilation and execution failures from the + * specified set of devices. This is in contrast to a use of {@link + * ANeuralNetworksCompilation_create}, where the runtime will attempt to recover + * from such failures. + * + * The model passed to this function is termed the "main model" of the + * compilation, to distinguish it from other models referred to by an Operand + * of type {@link ANEURALNETWORKS_MODEL} within this compilation. + * + * @param model The {@link ANeuralNetworksModel} to be compiled. + * @param devices The set of devices. Must not contain duplicates. + * @param numDevices The number of devices in the set. + * @param compilation The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the model is invalid. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksCompilation_createForDevices(ANeuralNetworksModel* model, + const ANeuralNetworksDevice* const* devices, + uint32_t numDevices, + ANeuralNetworksCompilation** compilation) + __NNAPI_INTRODUCED_IN(29); + + /** + * Sets the compilation caching signature and the cache directory. + * + * Provides optional caching information to the runtime for faster repeated + * compilation. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * @param compilation The compilation to be modified. + * @param cacheDir The cache directory for the runtime to store and retrieve caching + * data. It is recommended to use the code cache directory provided + * by the Android runtime. If not using the code cache directory, the + * user should choose a directory local to the application, and is + * responsible for managing the cache entries. + * @param token The token provided by the user to specify a model must be of length + * ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN. The user should ensure that + * the token is unique to a model within the application. The NNAPI + * runtime cannot detect token collisions; a collision will result in a + * failed execution or in a successful execution that produces incorrect + * output values. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksCompilation_setCaching(ANeuralNetworksCompilation* compilation, + const char* cacheDir, const uint8_t* token) + __NNAPI_INTRODUCED_IN(29); + + /** + * Schedule synchronous evaluation of the execution. + * + *

Schedules synchronous evaluation of the execution. Returns once the + * execution has completed and the outputs are ready to be consumed. + *

+ * + * If {@link ANeuralNetworksExecution_setTimeout} was called on this execution, + * and the execution is not able to complete before the timeout duration is + * exceeded, then execution may be aborted, in which case + * ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be returned. If the device has + * a feature level reported by {@link ANeuralNetworksDevice_getFeatureLevel} + * that is lower than 30, then the timeout duration hint will be ignored. + * + * If this execution contains a {@link ANEURALNETWORKS_WHILE} operation, and + * the condition model does not output false within the loop timeout duration, + * then execution will be aborted and ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} + * will be returned. + * + * Before NNAPI feature level 5, this function may only be invoked when the execution is in the + * preparation state. Starting at NNAPI feature level 5, if the user sets the execution to be + * reusable by {@link ANeuralNetworksExecution_setReusable}, this function may also be invoked when + * the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * See {@link ANeuralNetworksExecution_burstCompute} for burst synchronous execution. + * See {@link ANeuralNetworksExecution_startCompute} for regular asynchronous execution. + * See {@link ANeuralNetworksExecution_startComputeWithDependencies} for + * asynchronous execution with dependencies. + * + * Available since NNAPI feature level 3. + * + * @param execution The execution to be scheduled and executed. + * + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + * ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot + * be properly mapped. + */ + int ANeuralNetworksExecution_compute(ANeuralNetworksExecution* execution) __NNAPI_INTRODUCED_IN(29); + + /** + * Get the dimensional information of the specified output operand of the model of the + * latest computation evaluated on {@link ANeuralNetworksExecution}. + * + * This function may only be invoked when the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states. + * + * @param execution The execution to be queried. + * @param index The index of the output argument we are querying. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param rank The rank of the output operand. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE + * if the target output is provided an insufficient buffer at execution time, + * ANEURALNETWORKS_BAD_DATA if the index is invalid. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksExecution_getOutputOperandRank(ANeuralNetworksExecution* execution, + int32_t index, uint32_t* rank) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the dimensional information of the specified output operand of the model of the + * latest computation evaluated on {@link ANeuralNetworksExecution}. The target output operand + * cannot be a scalar. + * + * This function may only be invoked when the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states. + * + * @param execution The execution to be queried. + * @param index The index of the output argument we are querying. It is an index into the lists + * passed to {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param dimensions The dimension array to be filled. The size of the array must be exactly as + * large as the rank of the output operand to be queried in the model. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE + * if the target output is provided an insufficient buffer at execution time, + * ANEURALNETWORKS_BAD_DATA if the index is invalid or if the target is a scalar. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksExecution_getOutputOperandDimensions(ANeuralNetworksExecution* execution, + int32_t index, uint32_t* dimensions) + __NNAPI_INTRODUCED_IN(29); + + /** + * Create a {@link ANeuralNetworksBurst} to apply the given compilation. + * This only creates the burst object. Computation is only performed once + * {@link ANeuralNetworksExecution_burstCompute} is invoked with a valid + * {@link ANeuralNetworksExecution} and {@link ANeuralNetworksBurst}. + * + *

The provided compilation must outlive the burst object.

+ * + * Available since NNAPI feature level 3. + * + * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. + * @param burst The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the compilation is invalid. + */ + int ANeuralNetworksBurst_create(ANeuralNetworksCompilation* compilation, + ANeuralNetworksBurst** burst) __NNAPI_INTRODUCED_IN(29); + + /** + * Destroys the burst object. + * + * Available since NNAPI feature level 3. + * + * @param burst The burst object to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksBurst_free(ANeuralNetworksBurst* burst) __NNAPI_INTRODUCED_IN(29); + + /** + * Schedule synchronous evaluation of the execution on a burst object. + * + *

Schedules synchronous evaluation of the execution. Returns once the + * execution has completed and the outputs are ready to be consumed.

+ * + * If {@link ANeuralNetworksExecution_setTimeout} was called on the execution, + * and the execution is not able to complete before the timeout duration is + * exceeded, then execution may be aborted, in which case + * ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be returned. + * + * If the execution contains a {@link ANEURALNETWORKS_WHILE} operation, and + * the condition model does not output false within the loop timeout duration, + * then execution will be aborted and ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} + * will be returned. If the device has a feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is lower than + * {@link ANEURALNETWORKS_FEATURE_LEVEL_4}, then the timeout duration hint will be ignored. + * + *

There must be at most one {@link ANeuralNetworksExecution} processing at + * any given time for any given burst object. Any + * {@link ANeuralNetworksExecution} launched before the previous has finished + * will result in ANEURALNETWORKS_BAD_STATE.

+ * + * Before NNAPI feature level 5, this function may only be invoked when the execution is in the + * preparation state. Starting at NNAPI feature level 5, if the user sets the execution to be + * reusable by {@link ANeuralNetworksExecution_setReusable}, this function may also be invoked when + * the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * See {@link ANeuralNetworksExecution_compute} for synchronous execution. + * See {@link ANeuralNetworksExecution_startCompute} for regular asynchronous execution. + * See {@link ANeuralNetworksExecution_startComputeWithDependencies} for + * asynchronous execution with dependencies. + * + * Available since NNAPI feature level 3. + * + * @param burst The burst object to execute on. + * @param execution The execution to be scheduled and executed. The execution + * must be created from the same {@link + * ANeuralNetworksCompilation} as the burst object. + * + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + */ + int ANeuralNetworksExecution_burstCompute(ANeuralNetworksExecution* execution, + ANeuralNetworksBurst* burst) __NNAPI_INTRODUCED_IN(29); + + /** + * Creates a shared memory object from an AHardwareBuffer handle. + * + * If the shared memory is backed by an AHardwareBuffer of AHARDWAREBUFFER_FORMAT_BLOB + * format, it can be used the same way as shared memory created from a file handle. See + * {@link ANeuralNetworksMemory} for a description on how to use this shared memory. + * + * If the shared memory is backed by an AHardwareBuffer of a format other than + * AHARDWAREBUFFER_FORMAT_BLOB, it can only be used for model inputs and outputs. + * When calling {@link ANeuralNetworksExecution_setInputFromMemory} or + * {@link ANeuralNetworksExecution_setOutputFromMemory} with the shared memory, both + * offset and length must be set to zero and the entire memory region will be + * associated with the specified input or output operand. There is no guarantee + * that an arbitrary AHardwareBuffer_Format and AHardwareBuffer_UsageFlags combination + * can be used by arbitrary devices. The execution will fail if the selected set of + * devices cannot consume the buffer. + * + * Calling {@link ANeuralNetworksModel_setOperandValueFromMemory} with shared memory + * backed by an AHardwareBuffer of a format other than AHARDWAREBUFFER_FORMAT_BLOB is + * disallowed. + * + * The provided AHardwareBuffer must outlive the ANeuralNetworksMemory object. + * + * Available since NNAPI feature level 3. + * + * @param ahwb The AHardwareBuffer handle. + * @param memory The memory object to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. + * + * @see AHardwareBuffer + */ + #ifdef __ANDROID__ + int ANeuralNetworksMemory_createFromAHardwareBuffer(const AHardwareBuffer* ahwb, + ANeuralNetworksMemory** memory) + __NNAPI_INTRODUCED_IN(29); + #endif // __ANDROID__ + + /** + + * Specifies whether duration of the {@link ANeuralNetworksExecution} is to be + * measured. Evaluation of the execution must not have been scheduled. + * + * By default, duration is not measured. + * + * The {@link ANeuralNetworksExecution} must have been created from an + * {@link ANeuralNetworksCompilation} which in turn was created from + * {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1. + * If the device has a feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is lower than + * {@link ANEURALNETWORKS_FEATURE_LEVEL_3}, then the duration will not be measured. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * Available since NNAPI feature level 3. + * + * @param execution The execution to be modified. + * @param measure 'true' if duration is to be measured, 'false' if not. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksExecution_setMeasureTiming(ANeuralNetworksExecution* execution, bool measure) + __NNAPI_INTRODUCED_IN(29); + + /** + * Get the time spent in the latest computation evaluated on the specified + * {@link ANeuralNetworksExecution}, in nanoseconds. + * + * This function may only be invoked when the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states. + * + * @param execution The execution to be queried. + * @param durationCode The measurement to be queried, specified by {@link DurationCode}. + * @param duration The returned duration. If no measurement was requested by + * {@link ANeuralNetworksExecution_setMeasureTiming}, if the + * device is has a feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is lower + * than {@link ANEURALNETWORKS_FEATURE_LEVEL_3}, or for some other + * reason the duration is not available, UINT64_MAX will be returned. + * A particular device need not support any given measurement. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 3. + */ + int ANeuralNetworksExecution_getDuration(const ANeuralNetworksExecution* execution, + int32_t durationCode, uint64_t* duration) + __NNAPI_INTRODUCED_IN(29); + + /** + * Creates a shared memory object from a file descriptor. + * + * The shared memory is backed by a file descriptor via mmap. + * See {@link ANeuralNetworksMemory} for a description on how to use + * this shared memory. + * + * Available since NNAPI feature level 1. + * + * @param size The requested size in bytes. + * Must not be larger than the file size. + * @param protect The desired memory protection for the mapping. + * It is either PROT_NONE or the bitwise OR of one or + * more of the following flags: PROT_READ, PROT_WRITE. + * @param fd The requested file descriptor. + * The file descriptor has to be mmap-able. The file + * descriptor will be duplicated. + * @param offset The offset to the beginning of the file of the area to map. + * The offset has to be aligned to a page size. + * @param memory The memory object to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. + */ + int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, + ANeuralNetworksMemory** memory) __NNAPI_INTRODUCED_IN(27); + + /** + * Delete a memory object. + * + * Destroys the object used by the run time to keep track of the memory. + * This will free the underlying actual memory if no other code has open + * handles to this memory. + * + * Available since NNAPI feature level 1. + * + * @param memory The memory object to be freed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) __NNAPI_INTRODUCED_IN(27); + + /** + * Create an empty {@link ANeuralNetworksModel}. + * + *

This only creates the object. Computation is performed once + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies} is invoked. + * + * The model should be constructed with calls to + * {@link ANeuralNetworksModel_addOperation} and + * {@link ANeuralNetworksModel_addOperand} + * + *

{@link ANeuralNetworksModel_finish} should be called once the model + * has been fully constructed.

+ * + *

{@link ANeuralNetworksModel_free} should be called once the model + * is no longer needed.

+ * + * Available since NNAPI feature level 1. + * + * @param model The {@link ANeuralNetworksModel} to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_create(ANeuralNetworksModel** model) __NNAPI_INTRODUCED_IN(27); + + /** + * Destroy a model. + * + * The model need not have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param model The model to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksModel_free(ANeuralNetworksModel* model) __NNAPI_INTRODUCED_IN(27); + + /** + * Indicate that we have finished modifying a model. Required before + * calling {@link ANeuralNetworksCompilation_create} and + * {@link ANeuralNetworksCompilation_createForDevices}. + * + * An application must ensure that no other thread uses the model at the same + * time. + * + * This function must only be called once for a given model. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param model The model to be finished. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) __NNAPI_INTRODUCED_IN(27); + + /** + * Add an operand to a model. + * + * The order in which the operands are added is important. The first one added + * to a model will have the index value 0, the second 1, etc. These indexes are + * used as operand identifiers in + * {@link ANeuralNetworksModel_addOperation}, + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}, + * {@link ANeuralNetworksModel_setOperandValue}, + * {@link ANeuralNetworksModel_setOperandValueFromMemory}, + * {@link ANeuralNetworksExecution_setInput}, + * {@link ANeuralNetworksExecution_setInputFromMemory}, + * {@link ANeuralNetworksExecution_setOutput}, and + * {@link ANeuralNetworksExecution_setOutputFromMemory}. + * + *

Every operand must be referenced in exactly one of the following + * ways:

+ *

An operand that is identified as a model input or as a constant + * must not also be identified as a model output with + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.

+ * + * To build a model that can accommodate inputs of various sizes, as + * you may want to do for a CNN, leave unspecified the dimensions that + * will vary at run time. If you do so, fully specify dimensions + * when calling {@link ANeuralNetworksExecution_setInput} or + * {@link ANeuralNetworksExecution_setInputFromMemory}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param model The model to be modified. + * @param type The {@link ANeuralNetworksOperandType} that describes the shape + * of the operand. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call to + * {@link ANeuralNetworksModel_addOperand}. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, + const ANeuralNetworksOperandType* type) + __NNAPI_INTRODUCED_IN(27); + + /** + * Sets an operand to a constant value. + * + * Values of length smaller or equal to + * ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES + * are immediately copied into the model. + * + * For values of length greater than + * ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES, a pointer to + * the buffer is stored within the model. The application must not change the + * content of this region until all executions using this model have + * completed. As the data may be copied during processing, modifying the data + * after this call yields undefined results. The provided buffer must outlive + * this model. + * + * For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory} + * is likely to be more efficient. + * + * To indicate that an optional operand should be considered missing, + * pass nullptr for buffer and 0 for length. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param buffer A pointer to the data to use. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, + const void* buffer, size_t length) + __NNAPI_INTRODUCED_IN(27); + + /** + * Sets an operand's per channel quantization parameters. + * + * Sets parameters required by a tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}. + * This function must be called for every tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} before + * calling {@link ANeuralNetworksModel_finish}. + * + * Available since NNAPI feature level 3. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param channelQuant The per channel quantization parameters for the operand. + * No memory in this struct needs to outlive the call to + * this function. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_setOperandSymmPerChannelQuantParams( + ANeuralNetworksModel* model, int32_t index, + const ANeuralNetworksSymmPerChannelQuantParams* channelQuant) __NNAPI_INTRODUCED_IN(29); + + /** + * Sets an operand to a value stored in a memory object. + * + * The content of the memory is not copied. A reference to that memory is stored + * inside the model. The application must not change the content of the memory + * region until all executions using this model have completed. As the data may + * be copied during processing, modifying the data after this call yields + * undefined results. + * + *

The provided memory must outlive this model.

+ * + * To indicate that an optional operand should be considered missing, + * use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer. + * + * It is disallowed to set an operand value with shared memory backed by an AHardwareBuffer + * of a format other than AHARDWAREBUFFER_FORMAT_BLOB. + * + * It is disallowed to set an operand value with memory created from + * {@link ANeuralNetworksMemory_createFromDesc}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwareBuffer} for information on + * AHardwareBuffer usage. + * + * Available since NNAPI feature level 1. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param memory The memory containing the data. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, + const ANeuralNetworksMemory* memory, + size_t offset, size_t length) + __NNAPI_INTRODUCED_IN(27); + + /** + * Sets an operand to a value that is a reference to another NNAPI model. + * + * The referenced model must already have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * The {@link ANeuralNetworksModel_relaxComputationFloat32toFloat16} setting of + * referenced models is overridden by that setting of the main model of a + * compilation. + * + * The referenced model must outlive the model referring to it. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has + * been called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param value The model to be referenced. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_setOperandValueFromModel(ANeuralNetworksModel* model, int32_t index, + const ANeuralNetworksModel* value) + __NNAPI_INTRODUCED_IN(30); + + /** + * Add an operation to a model. + * + * @param model The model to be modified. + * @param type The {@link ANeuralNetworksOperationType} of the operation. + * @param inputCount The number of entries in the inputs array. + * @param inputs An array of indexes identifying each operand. + * @param outputCount The number of entries in the outputs array. + * @param outputs An array of indexes identifying each operand. + * + * The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, + ANeuralNetworksOperationType type, uint32_t inputCount, + const uint32_t* inputs, uint32_t outputCount, + const uint32_t* outputs) __NNAPI_INTRODUCED_IN(27); + + /** + * Specifies which operands will be the model's inputs and + * outputs. Every model must have at least one input and one output. + * + * An operand cannot be used for both input and output. Doing so will + * return an error. + * + * @param model The model to be modified. + * @param inputCount The number of entries in the inputs array. + * @param inputs An array of indexes identifying the input operands. + * @param outputCount The number of entries in the outputs array. + * @param outputs An array of indexes identifying the output operands. + * + * The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + */ + int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, + const uint32_t* inputs, uint32_t outputCount, + const uint32_t* outputs) + __NNAPI_INTRODUCED_IN(27); + + /** + * Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be + * calculated with range and/or precision as low as that of the IEEE 754 16-bit + * floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * must be calculated using at least the range and precision of the IEEE 754 + * 32-bit floating-point format. + * + * The relaxComputationFloat32toFloat16 setting of the main model of + * a compilation overrides the values of the referenced models. + * + * @param model The model to be modified. + * @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be + * calculated with range and/or precision as low as that of the + * IEEE 754 16-bit floating point format. 'false' indicates + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using + * at least the range and precision of the IEEE 754 32-bit floating + * point format. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * Available since NNAPI feature level 2. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + */ + int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow) + __NNAPI_INTRODUCED_IN(28); + + /** + * Create a {@link ANeuralNetworksCompilation} to compile the given model. + * + * The model passed to this function is termed the "main model" of the + * compilation, to distinguish it from other models referred to by an Operand + * of type {@link ANEURALNETWORKS_MODEL} within this compilation. + * + *

This function only creates the object. Compilation is only performed once + * {@link ANeuralNetworksCompilation_finish} is invoked.

+ * + *

{@link ANeuralNetworksCompilation_finish} should be called once + * all desired properties have been set on the compilation.

+ * + *

{@link ANeuralNetworksModel_free} should be called once the compilation + * is no longer needed.

+ * + *

The provided model must outlive the compilation.

+ * + * The model must already have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param model The {@link ANeuralNetworksModel} to be compiled. + * @param compilation The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the model is invalid. + */ + int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, + ANeuralNetworksCompilation** compilation) + __NNAPI_INTRODUCED_IN(27); + + /** + * Destroy a compilation. + * + * The compilation need not have been finished by a call to + * {@link ANeuralNetworksCompilation_finish}. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param compilation The compilation to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation) + __NNAPI_INTRODUCED_IN(27); + + /** + * Sets the execution preference. + * + *

Provides guidance to the runtime when trade-offs are possible. By default the runtime + * uses PREFER_SINGLE_FAST_ANSWER

+ * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param compilation The compilation to be modified. + * @param preference Either {@link ANEURALNETWORKS_PREFER_LOW_POWER}, + * {@link ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER}, or + * {@link ANEURALNETWORKS_PREFER_SUSTAINED_SPEED}. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, + int32_t preference) __NNAPI_INTRODUCED_IN(27); + + /** + * Indicate that we have finished modifying a compilation. Required before + * calling {@link ANeuralNetworksBurst_create} or + * {@link ANeuralNetworksExecution_create}. + * + * An application must ensure that no other thread uses the compilation at the + * same time. + * + * This function must only be called once for a given compilation. + * + * If {@link ANeuralNetworksCompilation_setTimeout} was called on this + * compilation, and the compilation is not able to be finished before the + * timeout duration is exceeded, then compilation may be aborted, in which case + * ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be returned. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param compilation The compilation to be finished. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) + __NNAPI_INTRODUCED_IN(27); + + /** + * Set the execution priority. + * + * Execution priorities are relative to other executions created by the same + * application (specifically same uid) for the same device. Specifically, + * priorities of executions from one application will not affect executions from + * another application. Similarly, priorities of executions on one device will + * not affect executions on another device. + * + * Higher priority executions may use more compute resources than lower priority + * executions, and may preempt or starve lower priority executions. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since NNAPI feature level 4. + * + * @param compilation The compilation to be modified. + * @param priority The relative priority of the execution compared to other + * executions created by the application. Must be one of + * ANEURALNETWORKS_PRIORITY_*. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ + int ANeuralNetworksCompilation_setPriority(ANeuralNetworksCompilation* compilation, int priority) + __NNAPI_INTRODUCED_IN(30); + + /** + * Set the maximum expected duration for compiling the model. + * + * If the device is not able to complete the compilation within the specified + * duration, the compilation may be aborted. The timeout duration begins at the + * call to {@link ANeuralNetworksCompilation_finish}. + * + * This timeout duration acts as a hint to drivers, and can be used to both free + * up compute resources within the driver and return control back to the + * application quicker than is possible without the hint. It enables drivers + * that are able to estimate how long a compilation will take to abort the + * compilation before it has even started if the driver believes the compilation + * cannot be completed within the timeout duration. Similarly, it enables + * drivers to abort an ongoing compilation if it is taking too long. However, + * this call does not guarantee that the compilation will complete or abort + * within the timeout duration. + * + * By default (i.e., unless ANeuralNetworksCompilation_setTimeout is called), + * the timeout duration for compiling the model is considered infinite. + * + * The {@link ANeuralNetworksCompilation} must have been created with + * {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1, + * otherwise this function will fail with ANEURALNETWORKS_BAD_DATA. If the + * device has a feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is lower than + * {@link ANEURALNETWORKS_FEATURE_LEVEL_4}, then the timeout duration hint will + * be ignored. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * @param compilation The compilation to be modified. + * @param duration The maximum amount of time in nanoseconds that is expected to + * be spent finishing a compilation. If this duration is exceeded, the + * compilation may be aborted. If set to 0, the timeout duration is + * considered infinite. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksCompilation_setTimeout(ANeuralNetworksCompilation* compilation, + uint64_t duration) __NNAPI_INTRODUCED_IN(30); + + /** + * Create a {@link ANeuralNetworksExecution} to apply the given compilation. + * This only creates the object. Computation is only performed once + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies} is invoked. + * + *

The provided compilation must outlive the execution.

+ * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. + * @param execution The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the compilation is invalid. + */ + int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, + ANeuralNetworksExecution** execution) __NNAPI_INTRODUCED_IN(27); + + /** + * Destroy an execution. + * + *

The execution need not have been scheduled by a call to + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies}; but if it has been scheduled, + * then the application must not call {@link ANeuralNetworksExecution_free} + * until the execution has completed (i.e., + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, or + * {@link ANeuralNetworksEvent_wait} has returned). + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) __NNAPI_INTRODUCED_IN(27); + + /** + * Associate a user buffer with an input of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. Once evaluation of the execution has been scheduled, the + * application must not change the content of the buffer until the execution has + * completed. Evaluation of the execution will not change the content of the + * buffer. + * + *

The provided buffer must outlive the execution.

+ * + * If the input is optional, you can indicate that it is omitted by + * passing nullptr for buffer and 0 for length. + * + * Otherwise, if the user has not set the execution to accept padded input buffers by + * calling {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, then the length argument + * must be equal to the raw size of the input (i.e. the size of an element multiplied by the + * number of elements). Passing a length argument with value not equal to the raw size of the input + * will result in ANEURALNETWORKS_BAD_DATA. + * + * Otherwise, if the user has set the execution to accept padded input buffers by calling + * {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, the length argument may be greater + * than the raw size of the input, and the extra bytes at the end of the buffer may be used + * by the driver to access data in chunks, for efficiency. Passing a length argument with value + * less than the raw size of the input will result in ANEURALNETWORKS_BAD_DATA. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * See {@link ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput} and + * {@link ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput} for information on getting + * preferred buffer alignment and padding, to improve performance. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be modified. + * @param index The index of the input argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with + * {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. Unless the input is omitted, this should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setInput}. + * @param buffer The buffer containing the data. + * @param length The size of the data value in bytes plus any end padding. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the input. + */ + int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, const void* buffer, + size_t length) __NNAPI_INTRODUCED_IN(27); + + /** + * Associate a region of a memory object with an input of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. Once evaluation of the execution has been scheduled, the + * application must not change the content of the region until the execution has + * completed. Evaluation of the execution will not change the content of the + * region. + * + *

The provided memory must outlive the execution.

+ * + * If the input is optional, you can indicate that it is omitted by + * using {@link ANeuralNetworksExecution_setInput} instead, passing nullptr for + * buffer and 0 for length. + * + * If the memory is an AHardwareBuffer of a format other than AHARDWAREBUFFER_FORMAT_BLOB created + * from {@link ANeuralNetworksMemory_createFromAHardwareBuffer}, or an opaque memory object created + * from {@link ANeuralNetworksMemory_createFromDesc}, both offset and length must be 0, indicating + * the whole memory is used. + * + * Otherwise, if the user has not set the execution to accept padded input memory objects by + * calling {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, then the length argument + * must be equal to the raw size of the input (i.e. the size of an element multiplied by the + * number of elements). Passing a length argument with value not equal to the raw size of the input + * will result in ANEURALNETWORKS_BAD_DATA. + * + * Otherwise, if the user has set the execution to accept padded input memory objects by calling + * {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, the length argument may be greater + * than the raw size of the input, and the extra bytes at the end of the memory region may be used + * by the driver to access data in chunks, for efficiency. Passing a length argument with value + * less than the raw size of the input will result in ANEURALNETWORKS_BAD_DATA. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwareBuffer} for information on + * AHardwareBuffer usage. + * See {@link ANeuralNetworksMemory_createFromDesc} for information on usage of memory objects + * created from memory descriptors. + * See {@link ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput} and + * {@link ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput} for information on getting + * preferred memory alignment and padding, to improve performance. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be modified. + * @param index The index of the input argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. This should be used to specify the dimensions + * that were left unspecified when the operand was added + * to the model. All other properties of the type must be + * the same as specified in the model. If the type is the + * same as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setInputFromMemory}. + * @param memory The memory containing the data. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The size of the data value in bytes plus any end padding. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the input. + */ + int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length) __NNAPI_INTRODUCED_IN(27); + + /** + * Associate a user buffer with an output of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. Once evaluation of the execution has been scheduled, the + * application must not change the content of the buffer until the execution has + * completed. + * + *

The provided buffer must outlive the execution.

+ * + * If the output is optional, you can indicate that it is omitted by + * passing nullptr for buffer and 0 for length. + * + * Otherwise, if the user has not set the execution to accept padded output buffers by + * calling {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, then the length argument + * must be equal to the raw size of the output (i.e. the size of an element multiplied by the + * number of elements). Passing a length argument with value not equal to the raw size of the output + * will result in ANEURALNETWORKS_BAD_DATA. + * + * Otherwise, if the user has set the execution to accept padded output buffers by calling + * {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, the length argument may be greater + * than the raw size of the output, and the extra bytes at the end of the buffer may be used + * by the driver to access data in chunks, for efficiency. Passing a length argument with value + * less than the raw size of the output will result in ANEURALNETWORKS_BAD_DATA. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * See {@link ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput} and + * {@link ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput} for information on getting + * preferred buffer alignment and padding, to improve performance. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be modified. + * @param index The index of the output argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. Unless the output is omitted, this should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setOutput}. + * Since NNAPI feature level 3, the output operand can have unspecified + * dimensions or rank to be deduced dynamically during the execution. + * However, the user must provide a large enough buffer. The user + * can retrieve the output dimensional information after the execution + * by {@link ANeuralNetworksExecution_getOutputOperandRank} and + * {@link ANeuralNetworksExecution_getOutputOperandDimensions}. + * @param buffer The buffer where the data is to be written. + * @param length The size of the data value in bytes plus any end padding. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the output. + */ + int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, void* buffer, + size_t length) __NNAPI_INTRODUCED_IN(27); + + /** + * Associate a region of a memory object with an output of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. Once evaluation of the execution has been scheduled, the + * application must not change the content of the region until the execution has + * completed. + * + *

The provided memory must outlive the execution.

+ * + * If the output is optional, you can indicate that it is omitted by + * using {@link ANeuralNetworksExecution_setOutput} instead, passing nullptr for + * buffer and 0 for length. + * + * If the memory is an AHardwareBuffer of a format other than AHARDWAREBUFFER_FORMAT_BLOB created + * from {@link ANeuralNetworksMemory_createFromAHardwareBuffer}, or an opaque memory object created + * from {@link ANeuralNetworksMemory_createFromDesc}, both offset and length must be 0, indicating + * the whole memory is used. + * + * Otherwise, if the user has not set the execution to accept padded output memory objects by + * calling {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, then the length argument + * must be equal to the raw size of the output (i.e. the size of an element multiplied by the + * number of elements). Passing a length argument with value not equal to the raw size of the output + * will result in ANEURALNETWORKS_BAD_DATA. + * + * Otherwise, if the user has set the execution to accept padded output memory objects by calling + * {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, the length argument may be greater + * than the raw size of the output, and the extra bytes at the end of the memory region may be used + * by the driver to access data in chunks, for efficiency. Passing a length argument with value + * less than the raw size of the output will result in ANEURALNETWORKS_BAD_DATA. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwareBuffer} for information on + * AHardwareBuffer usage. + * See {@link ANeuralNetworksMemory_createFromDesc} for information on usage of memory objects + * created from memory descriptors. + * See {@link ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput} and + * {@link ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput} for information on getting + * preferred memory alignment and padding, to improve performance. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be modified. + * @param index The index of the output argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the operand. This should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setOutputFromMemory}. + * Since NNAPI feature level 3, the output operand can have unspecified + * dimensions or rank to be deduced dynamically during the execution. + * However, the user must provide a large enough memory. The user + * can retrieve the output dimensional information after the execution + * by {@link ANeuralNetworksExecution_getOutputOperandRank} and + * {@link ANeuralNetworksExecution_getOutputOperandDimensions}. + * @param memory The memory where the data is to be stored. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The size of the data value in bytes plus any end padding. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the output. + */ + int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length) __NNAPI_INTRODUCED_IN(27); + + /** + * Schedule asynchronous evaluation of the execution. + * + *

Schedules asynchronous evaluation of the execution. Once the execution + * has completed and the outputs are ready to be consumed, the returned event + * will be signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that + * event. + *

+ * + * ANeuralNetworksEvent_wait must be called to recuperate the resources used + * by the execution. + * + * If {@link ANeuralNetworksExecution_setTimeout} was called on this execution, + * and the execution is not able to complete before the timeout duration is + * exceeded, then execution may be aborted, in which case + * ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be returned through + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksEvent_wait} on the event object. If the device has a + * feature level reported by {@link ANeuralNetworksDevice_getFeatureLevel} that + * is lower than {@link ANEURALNETWORKS_FEATURE_LEVEL_4}, then the timeout + * duration hint will be ignored. + * + * If this execution contains a {@link ANEURALNETWORKS_WHILE} operation, and + * the condition model does not output false within the loop timeout duration, + * then execution will be aborted and ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} + * will be returned through {@link ANeuralNetworksEvent_wait} on the event + * object. + * + * If the device can detect before the execution has started that the execution + * will not complete within the timeout duration, the device may choose to skip + * the execution and instead return ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode}. + * + * Before NNAPI feature level 5, this function may only be invoked when the execution is in the + * preparation state. Starting at NNAPI feature level 5, if the user sets the execution to be + * reusable by {@link ANeuralNetworksExecution_setReusable}, this function may also be invoked when + * the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * See {@link ANeuralNetworksExecution_compute} for synchronous execution. + * See {@link ANeuralNetworksExecution_burstCompute} for burst synchronous execution. + * See {@link ANeuralNetworksExecution_startComputeWithDependencies} for + * asynchronous execution with dependencies. + * + * Available since NNAPI feature level 1. + * + * @param execution The execution to be scheduled and executed. + * @param event The event that will be signaled on completion. event is set to + * NULL if there's an error. + * + * @return ANEURALNETWORKS_NO_ERROR if the evaluation is successfully scheduled. + */ + int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, + ANeuralNetworksEvent** event) __NNAPI_INTRODUCED_IN(27); + + /** + * Set the maximum expected duration of the specified execution. + * + * If the device is not able to complete the execution within the specified + * duration, the execution may be aborted. The timeout duration begins at a + * call to one of: + * - {@link ANeuralNetworksExecution_burstCompute} + * - {@link ANeuralNetworksExecution_compute} + * - {@link ANeuralNetworksExecution_startCompute} + * - {@link ANeuralNetworksExecution_startComputeWithDependencies} + * + * This timeout duration acts as a hint to drivers, and can be used to both free + * up compute resources within the driver and return control back to the + * application quicker than is possible without the hint. It enables drivers + * that are able to estimate how long an execution will take to abort the + * execution before it has even started if the driver believes the execution + * cannot be completed within the timeout duration. Similarly, it enables + * drivers to abort an ongoing execution if it is taking too long. However, this + * call does not guarantee that the execution will complete or abort within the + * timeout duration. + * + * By default (i.e., unless ANeuralNetworksExecution_setTimeout is called), + * the timeout duration for execution is considered infinite. + * + * The {@link ANeuralNetworksExecution} must have been created from an + * {@link ANeuralNetworksCompilation} which in turn was created from + * {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1, + * otherwise this function will fail with ANEURALNETWORKS_BAD_DATA. If the + * device has a feature level reported by + * {@link ANeuralNetworksDevice_getFeatureLevel} that is lower than + * {@link ANEURALNETWORKS_FEATURE_LEVEL_4}, then the timeout duration hint will + * be ignored. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * @param execution The execution to be modified. + * @param duration The maximum amount of time in nanoseconds that is expected to + * be spent executing a model. If this duration is exceeded, the execution + * may be aborted. If set to 0, the timeout duration is considered infinite. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksExecution_setTimeout(ANeuralNetworksExecution* execution, uint64_t duration) + __NNAPI_INTRODUCED_IN(30); + + /** + * Set the maximum duration of WHILE loops in the specified execution. + * + * This is a fuzzy per-loop timeout intended to prevent infinite loops. + * + * If a WHILE loop condition model does not output false within the specified + * duration, the execution will be aborted. + * + * See {@link ANeuralNetworks_getDefaultLoopTimeout} and + * {@link ANeuralNetworks_getMaximumLoopTimeout} for the default + * and maximum timeout values. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * @param execution The execution to be modified. + * @param duration The maximum amount of time in nanoseconds that can be spent + * executing a WHILE loop. If the specified duration value exceeds the value + * produced by {@link ANeuralNetworks_getMaximumLoopTimeout}, it will be + * overridden by that value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_BAD_STATE if execution has started. + * ANEURALNETWORKS_UNEXPECTED_NULL if execution is NULL. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksExecution_setLoopTimeout(ANeuralNetworksExecution* execution, uint64_t duration) + __NNAPI_INTRODUCED_IN(30); + + /** + * Get the default timeout value for WHILE loops. + * + * @return The default timeout value in nanoseconds. + * + * Available since NNAPI feature level 4. + */ + uint64_t ANeuralNetworks_getDefaultLoopTimeout() __NNAPI_INTRODUCED_IN(30); + + /** + * Get the maximum timeout value for WHILE loops. + * + * @return The maximum timeout value in nanoseconds. + * + * Available since NNAPI feature level 4. + */ + uint64_t ANeuralNetworks_getMaximumLoopTimeout() __NNAPI_INTRODUCED_IN(30); + + /** + * Waits until the execution completes. + * + * More than one thread can wait on an event. When the execution completes, + * all threads will be released. + * + * If {@link ANeuralNetworksExecution_setTimeout} was called on the execution + * corresponding to this event, and the execution is not able to complete + * before the duration is exceeded, the execution may be aborted, in which case + * ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be returned here. + * + * If the execution contains a {@link ANEURALNETWORKS_WHILE} operation, and + * the condition model does not output false within the loop timeout duration, + * the execution will be aborted, and ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} + * will be returned here. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param event The event that will be signaled on completion. + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + * ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot + * be properly mapped. + */ + int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) __NNAPI_INTRODUCED_IN(27); + + /** + * Destroys the event. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since NNAPI feature level 1. + * + * @param event The event object to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ + void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) __NNAPI_INTRODUCED_IN(27); + + /** + * Create a {@link ANeuralNetworksEvent} from a sync_fence file descriptor. + * + * The newly created ANeuralNetworksEvent does not take ownership of the provided sync_fence_fd, + * it will instead dup the provided sync_fence_fd and own the duplicate. + * + * @param sync_fence_fd The sync_fence file descriptor. + * @param event The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksEvent_createFromSyncFenceFd(int sync_fence_fd, ANeuralNetworksEvent** event) + __NNAPI_INTRODUCED_IN(30); + + /** + * Get sync_fence file descriptor from the event. + * + * If the ANeuralNetworksEvent is not backed by a sync fence, the sync_fence_fd + * will be set to -1, and ANEURALNETWORKS_BAD_DATA will be returned. + * + * See {@link ANeuralNetworksEvent_createFromSyncFenceFd} and + * {@link ANeuralNetworksExecution_startComputeWithDependencies} to see how to create + * an event backed by a sync fence. + * + * The user takes ownership of the returned fd, and must close the returned file descriptor when + * it is no longer needed. + * + * @param event An event that is backed by a sync fence. + * @param sync_fence_fd The sync_fence file descriptor. The file descriptor will + * be set to -1 if there is an error. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksEvent_getSyncFenceFd(const ANeuralNetworksEvent* event, int* sync_fence_fd) + __NNAPI_INTRODUCED_IN(30); + + /** + * Schedule asynchronous evaluation of the execution with dependencies. + * + * The execution will wait for all the depending events to be signaled before + * starting the evaluation. Once the execution has completed and the outputs + * are ready to be consumed, the returned event will be signaled. Depending on which + * devices are handling the execution, the event could be backed by a sync fence. + * Use {@link ANeuralNetworksEvent_wait} to wait for that event. + * + * ANeuralNetworksEvent_wait must be called to recurperate the resources used + * by the execution. + * + * If parts of the execution are scheduled on devices that do not support fenced execution, + * the function call may wait for such parts to finish before returning. + * + * The function will return an error if any of the events in dependencies is already in a bad + * state. After the execution is scheduled, if any of the events in dependencies does not complete + * normally, the execution will fail, and {@link ANeuralNetworksEvent_wait} on the returned + * event will return an error. + * + * The function will return an error if any of the execution outputs has a tensor operand type + * that is not fully specified. + * + * The function can be passed a timeout duration in nanoseconds. This timeout + * duration acts as a hint to drivers in the same way that the timeout durations + * in {@link ANeuralNetworksCompilation_setTimeout} and {@link + * ANeuralNetworksExecution_setTimeout} act as hints to drivers. The duration + * begins when all waitFor sync fences have been signaled, and can be used + * together with {@link ANeuralNetworksExecution_setTimeout} which specifies the + * maximum timeout duration beginning at the call to + * {@link ANeuralNetworksExecution_startComputeWithDependencies}. + * If the duration is non-zero, the {@link ANeuralNetworksExecution} must have been created + * from an {@link ANeuralNetworksCompilation} which in turn was created from + * {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1, + * otherwise this function will fail with ANEURALNETWORKS_BAD_DATA. If either + * the timeout duration from {@link ANeuralNetworksExecution_setTimeout} or the + * timeout duration passed to this call is exceeded, the execution may be + * aborted, in which case ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} will be + * returned through {@link ANeuralNetworksExecution_startComputeWithDependencies} + * or {@link ANeuralNetworksEvent_wait} on the event object. If the device has a + * feature level reported by {@link ANeuralNetworksDevice_getFeatureLevel} that + * is lower than {@link ANEURALNETWORKS_FEATURE_LEVEL_4}, then the timeout duration + * hints will be ignored. + * + * If this execution contains a {@link ANEURALNETWORKS_WHILE} operation, and + * the condition model does not output false within the loop timeout duration, + * then execution will be aborted and ANEURALNETWORKS_MISSED_DEADLINE_* {@link ResultCode} + * will be returned through {@link ANeuralNetworksEvent_wait} on the event + * object. + * + * Before NNAPI feature level 5, this function may only be invoked when the execution is in the + * preparation state. Starting at NNAPI feature level 5, if the user sets the execution to be + * reusable by {@link ANeuralNetworksExecution_setReusable}, this function may also be invoked when + * the execution is in the completed state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * See {@link ANeuralNetworksExecution_compute} for synchronous execution. + * See {@link ANeuralNetworksExecution_burstCompute} for burst synchronous execution. + * See {@link ANeuralNetworksExecution_startCompute} for regular asynchronous execution. + * + * @param execution The execution to be scheduled and executed. + * @param dependencies A set of depending events. The actual evaluation will not start + * until all the events are signaled. + * @param num_dependencies The number of events in the dependencies set. + * @param duration The maximum amount of time in nanoseconds that is expected to + * be spent executing the model after all dependencies are + * signaled. If set to 0, the timeout duration is considered + * infinite. + * @param event The event that will be signaled on completion. event is set to + * NULL if there's an error. + * + * @return ANEURALNETWORKS_NO_ERROR if the evaluation is successfully scheduled. + * + * Available since NNAPI feature level 4. + */ + int ANeuralNetworksExecution_startComputeWithDependencies( + ANeuralNetworksExecution* execution, const ANeuralNetworksEvent* const* dependencies, + uint32_t num_dependencies, uint64_t duration, ANeuralNetworksEvent** event) + __NNAPI_INTRODUCED_IN(30); + + /** + * Get the NNAPI runtime feature level. + * + * Since API level 31 (NNAPI feature level 5), the NNAPI runtime (libneuralnetworks.so) and its + * API specification can be updated between Android API releases. + * + * On Android devices with API level 31 and newer, for NNAPI runtime feature discovery, + * the NNAPI runtime feature level must be used instead of the Android device API level. + * + * On Android devices with API level 30 and older, the Android API level of the Android + * device must be used for NNAPI runtime feature discovery. Enum values in + * {@link FeatureLevelCode} from feature level 1 to 5 have their corresponding Android + * API levels listed in their documentation, and each such enum value equals the corresponding + * API level. This allows using the Android API level as the feature level. + * This mapping between enum value and Android API level does not exist for feature levels + * after NNAPI feature level 5 and API levels after S (31). + * + * Example usage: + * int device_api_level = android_get_device_api_level(); + * int64_t runtime_feature_level = (device_api_level < __ANDROID_API_S__) ? + * device_api_level : ANeuralNetworks_getRuntimeFeatureLevel(); + * + * Runtime feature level is closely related to NNAPI device feature level + * ({@link ANeuralNetworksDevice_getFeatureLevel}), which indicates an NNAPI device feature level + * (the most advanced NNAPI specification and features that the driver implements). + * This function expresses NNAPI runtime feature level, which indicates the most advanced + * NNAPI specification and features the runtime implements. An NNAPI device feature level is + * always less than or equal to the runtime feature level. + * + * This function returns a {@link FeatureLevelCode} enum value, + * which is the NNAPI specification version that this NNAPI runtime implements. + * It is NOT an Android API level. + * + * Available since NNAPI feature level 5. + */ + int64_t ANeuralNetworks_getRuntimeFeatureLevel() __NNAPI_INTRODUCED_IN(31); + + /** + * Specifies whether the {@link ANeuralNetworksExecution} is able to accept padded input and output + * buffers and memory objects. + * + * By default, the input and output buffers and memory objects of {@link ANeuralNetworksExecution} + * do not allow padding. + * + * Setting the execution to accept padded input and output buffers and memory objects enables the + * length argument of {@link ANeuralNetworksExecution_setInput}, + * {@link ANeuralNetworksExecution_setInputFromMemory}, {@link ANeuralNetworksExecution_setOutput}, + * and {@link ANeuralNetworksExecution_setOutputFromMemory} to be greater than the raw size of the + * operand (i.e. the size of an element multiplied by the number of elements). The extra bytes + * at the end of the buffer or memory region may be used by the driver to access data in chunks, + * for efficiency. + * + * This method must not be called after {@link ANeuralNetworksExecution_setInput}, + * {@link ANeuralNetworksExecution_setInputFromMemory}, {@link ANeuralNetworksExecution_setOutput}, + * or {@link ANeuralNetworksExecution_setOutputFromMemory}. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * @param execution The execution to be modified. + * @param enable 'true' if the execution is to be able to accept padded input and output buffers + * and memory objects, 'false' if not. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if execution is NULL. + * ANEURALNETWORKS_BAD_STATE if {@link ANeuralNetworksExecution_setInput}, + * {@link ANeuralNetworksExecution_setInputFromMemory}, + * {@link ANeuralNetworksExecution_setOutput}, or + * {@link ANeuralNetworksExecution_setOutputFromMemory} has been called on the execution. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksExecution_enableInputAndOutputPadding(ANeuralNetworksExecution* execution, + bool enable) __NNAPI_INTRODUCED_IN(31); + + /** + * Get the preferred buffer and memory alignment of an input to an execution created from a + * particular compilation. + * + * The user may use the returned alignment value to guide the layout of the input buffer or memory + * pool. To achieve the best performance, make sure the address of the buffer passed in + * {@link ANeuralNetworksExecution_setInput}, or the offset value passed in + * {@link ANeuralNetworksExecution_setInputFromMemory}, is a multiple of the perferred alignment + * value of the same input. A driver may choose to allocate a separate buffer and do memory copying + * if the provided buffer or memory does not satisfy the preferred alignment. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}. + * @param index The index of the input argument we are referencing from the compilation. It is + * an index into the inputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param alignment The returned preferred alignment in bytes. It will be a power of 2. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if either compilation or alignment is NULL. + * ANEURALNETWORKS_BAD_STATE if the compilation has not been finished. + * ANEURALNETWORKS_BAD_DATA if the index is out of range. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput( + const ANeuralNetworksCompilation* compilation, uint32_t index, uint32_t* alignment) + __NNAPI_INTRODUCED_IN(31); + + /** + * Get the preferred buffer and memory end padding of an input to an execution created from a + * particular compilation. + * + * The user may use the returned padding value to guide the layout of the input buffer or memory + * pool. To achieve the best performance, make sure the length value passed in + * {@link ANeuralNetworksExecution_setInput} or + * {@link ANeuralNetworksExecution_setInputFromMemory} is greater than or equal to the raw size of + * the input (i.e. the size of an element multiplied by the number of elements) rounding up to + * a multiple of the perferred padding value of the same input. A driver may choose to allocate a + * separate buffer and do memory copying if the provided buffer or memory value does not satisfy + * the preferred padding. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * See {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, + * {@link ANeuralNetworksExecution_setInput}, and + * {@link ANeuralNetworksExecution_setInputFromMemory} for information on passing + * input buffer or memory padding to the driver. + * + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}. + * @param index The index of the input argument we are referencing from the compilation. It is + * an index into the inputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param padding The returned preferred padding in bytes. It will be a power of 2. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if either compilation or padding is NULL. + * ANEURALNETWORKS_BAD_STATE if the compilation has not been finished. + * ANEURALNETWORKS_BAD_DATA if the index is out of range. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput( + const ANeuralNetworksCompilation* compilation, uint32_t index, uint32_t* padding) + __NNAPI_INTRODUCED_IN(31); + + /** + * Get the preferred buffer and memory alignment of an output to an execution created from a + * particular compilation. + * + * The user may use the returned alignment value to guide the layout of the output buffer or memory + * pool. To achieve the best performance, make sure the address of the buffer passed in + * {@link ANeuralNetworksExecution_setOutput}, or the offset value passed in + * {@link ANeuralNetworksExecution_setOutputFromMemory}, is a multiple of the perferred alignment + * value of the same output. A driver may choose to allocate a separate buffer and do memory copying + * if the provided buffer or memory does not satisfy the preferred alignment. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}. + * @param index The index of the output argument we are referencing from the compilation. It is + * an index into the outputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param alignment The returned perferred alignment in bytes. It will be a power of 2. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if either compilation or alignment is NULL. + * ANEURALNETWORKS_BAD_STATE if the compilation has not been finished. + * ANEURALNETWORKS_BAD_DATA if the index is out of range. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput( + const ANeuralNetworksCompilation* compilation, uint32_t index, uint32_t* alignment) + __NNAPI_INTRODUCED_IN(31); + + /** + * Get the preferred memory end padding of an output to an execution created from a particular + * compilation. + * + * The user may use the returned padding value to guide the layout of the output buffer or memory + * pool. To achieve the best performance, make sure the length value passed in + * {@link ANeuralNetworksExecution_setOutput} or + * {@link ANeuralNetworksExecution_setOutputFromMemory} is greater than or equal to the raw size of + * the output (i.e. the size of an element multiplied by the number of elements) rounding up to + * a multiple of the perferred padding value of the same output. A driver may choose to allocate a + * separate buffer and do memory copying if the provided buffer or memory value does not satisfy + * the preferred padding. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * See {@link ANeuralNetworksExecution_enableInputAndOutputPadding}, + * {@link ANeuralNetworksExecution_setOutput}, and + * {@link ANeuralNetworksExecution_setOutputFromMemory} for information on passing + * output buffer or memory padding to the driver. + * + * @param compilation The compilation object. It must already have been finished by calling + * {@link ANeuralNetworksCompilation_finish}. + * @param index The index of the output argument we are referencing from the compilation. It is + * an index into the outputs list passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param padding The returned perferred padding in bytes. It will be a power of 2. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if either compilation or padding is NULL. + * ANEURALNETWORKS_BAD_STATE if the compilation has not been finished. + * ANEURALNETWORKS_BAD_DATA if the index is out of range. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput( + const ANeuralNetworksCompilation* compilation, uint32_t index, uint32_t* padding) + __NNAPI_INTRODUCED_IN(31); + + /** + * Specifies whether the {@link ANeuralNetworksExecution} can be reused for multiple computations. + * + * By default, the {@link ANeuralNetworksExecution} is not reusable. + * + * Setting the execution to be reusable enables multiple computations to be scheduled and evaluated + * on the same execution sequentially, either by means of + * {@link ANeuralNetworksExecution_burstCompute}, {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies}: The application may schedule and + * evaluate a computation again from the completed state of a reusable execution. + * + * This function may only be invoked when the execution is in the preparation state. + * + * See {@link ANeuralNetworksExecution} for information on execution states and multithreaded usage. + * + * @param execution The execution to be modified. + * @param reusable 'true' if the execution is to be reusable, 'false' if not. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * ANEURALNETWORKS_UNEXPECTED_NULL if execution is NULL. + * ANEURALNETWORKS_BAD_STATE if the execution is not in the preparation state. + * + * Available since NNAPI feature level 5. + */ + int ANeuralNetworksExecution_setReusable(ANeuralNetworksExecution* execution, bool reusable) + __NNAPI_INTRODUCED_IN(31); +} diff --git a/NeuralNetworksTypes.odin b/NeuralNetworksTypes.odin new file mode 100644 index 0000000..8a16b68 --- /dev/null +++ b/NeuralNetworksTypes.odin @@ -0,0 +1,6507 @@ +//+build android +package android + +/** + * For {@link ANeuralNetworksModel_setOperandValue}, values with a + * length smaller or equal to this will be immediately copied into + * the model. The size is in bytes. + * + * Available since NNAPI feature level 1. + */ +ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES :: 128 + +/** + * For {@link ANeuralNetworksCompilation_setCaching}, specify the size + * of the cache token required from the application. The size is in bytes. + * + * Available since NNAPI feature level 3. + */ +ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN :: 32 + +/** + * Aliasing to {@link OperationCode}, used in function + * {@link ANeuralNetworksModel_addOperation}. + */ +ANeuralNetworksOperationType :: i32 + +/** + * Operand types. + * + * The type of an operand in a model. + * + * Types prefaced with ANEURALNETWORKS_TENSOR_* must be used for tensor data (i.e., tensors + * with at least one dimension). Types not prefaced by ANEURALNETWORKS_TENSOR_* represent + * scalar values and must have no dimensions. + * + * Although we define many types, most operators accept just a few + * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * and {@link ANEURALNETWORKS_INT32}. + * + * Available since NNAPI feature level 1. + */ +OperandCode :: enum i32 { + /** A 32 bit floating point scalar value. */ + FLOAT32 = 0, + /** A signed 32 bit integer scalar value. */ + INT32 = 1, + /** An unsigned 32 bit integer scalar value. */ + UINT32 = 2, + /** A tensor of 32 bit floating point values. */ + TENSOR_FLOAT32 = 3, + /** A tensor of 32 bit integer values. */ + TENSOR_INT32 = 4, + /** + * A tensor of 8 bit unsigned integers that represent real numbers. + * + * Attached to this tensor are two numbers that can be used to convert the + * 8 bit integer to the real value and vice versa. These two numbers are: + * - scale: a 32 bit floating point value greater than zero. + * - zeroPoint: a 32 bit integer, in range [0, 255]. + * + * The formula is: + * real_value = (integer_value - zeroPoint) * scale. + */ + TENSOR_QUANT8_ASYMM = 5, + /** + * An 8 bit boolean scalar value. + * + * Values of this operand type are either true or false. A zero value + * represents false; any other value represents true. + * + * Available since NNAPI feature level 3. + */ + BOOL = 6, + /** + * A tensor of 16 bit signed integers that represent real numbers. + * + * Attached to this tensor is a number representing real value scale that is + * used to convert the 16 bit number to a real value in the following way: + * realValue = integerValue * scale. + * + * scale is a 32 bit floating point with value greater than zero. + * + * Available since NNAPI feature level 3. + */ + TENSOR_QUANT16_SYMM = 7, + /** + * A tensor of IEEE 754 16 bit floating point values. + * + * Available since NNAPI feature level 3. + */ + TENSOR_FLOAT16 = 8, + /** + * A tensor of 8 bit boolean values. + * + * Values of this operand type are either true or false. A zero value + * represents false; any other value represents true. + * + * Available since NNAPI feature level 3. + */ + TENSOR_BOOL8 = 9, + /** + * An IEEE 754 16 bit floating point scalar value. + * + * Available since NNAPI feature level 3. + */ + FLOAT16 = 10, + /** + * A tensor of 8 bit signed integers that represent real numbers. + * + * This tensor is associated with additional fields that can + * be used to convert the 8 bit signed integer to the real value and vice versa. + * These fields are: + * - channelDim: a 32 bit unsigned integer indicating channel dimension. + * - scales: an array of positive 32 bit floating point values. + * The size of the scales array must be equal to dimensions[channelDim]. + * + * {@link ANeuralNetworksModel_setOperandSymmPerChannelQuantParams} must be used + * to set the parameters for an Operand of this type. + * + * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0). + * + * The formula is: + * realValue[..., C, ...] = + * integerValue[..., C, ...] * scales[C] + * where C is an index in the Channel dimension. + * + * Available since NNAPI feature level 3. + */ + TENSOR_QUANT8_SYMM_PER_CHANNEL = 11, + /** + * A tensor of 16 bit unsigned integers that represent real numbers. + * + * Attached to this tensor are two numbers that can be used to convert the + * 16 bit integer to the real value and vice versa. These two numbers are: + * - scale: a 32 bit floating point value greater than zero. + * - zeroPoint: a 32 bit integer, in range [0, 65535]. + * + * The formula is: + * real_value = (integer_value - zeroPoint) * scale. + * + * Available since NNAPI feature level 3. + */ + TENSOR_QUANT16_ASYMM = 12, + /** + * A tensor of 8 bit signed integers that represent real numbers. + * + * Attached to this tensor is a number representing real value scale that is + * used to convert the 8 bit number to a real value in the following way: + * realValue = integerValue * scale. + * + * scale is a 32 bit floating point with value greater than zero. + * + * Available since NNAPI feature level 3. + */ + TENSOR_QUANT8_SYMM = 13, + /** + * A tensor of 8 bit signed integers that represent real numbers. + * + * Attached to this tensor are two numbers that can be used to convert the + * 8 bit integer to the real value and vice versa. These two numbers are: + * - scale: a 32 bit floating point value greater than zero. + * - zeroPoint: a 32 bit integer, in range [-128, 127]. + * + * The formula is: + * real_value = (integer_value - zeroPoint) * scale. + * + * Available since NNAPI feature level 4. + */ + TENSOR_QUANT8_ASYMM_SIGNED = 14, + /** + * A reference to a model. + * + * {@link ANeuralNetworksModel_setOperandValueFromModel} must be used to set + * the value for an Operand of this type. + * + * Available since NNAPI feature level 4. + */ + MODEL = 15, +} + +/** + * Operation types. + * + * The type of an operation in a model. + * + * Available since NNAPI feature level 1. + */ +OperationCode :: enum { + // Operations below are available since NNAPI feature level 1. + + /** + * Adds two tensors, element-wise. + * + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the sum of both input tensors, optionally + * modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its + * way forward. + * + * Example: + * + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero + * dimension is only compatible with 0 or 1. The size of the output + * dimension is zero if either of corresponding input dimension is zero. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, + * the {@link FuseCode} must be "NONE". + * + * Outputs: + * * 0: The sum, a tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 1. + */ + ADD = 0, + + /** + * Performs a 2-D average pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * sum_{di, dj}( + * input[b, strides[1] * i + di, strides[2] * j + dj, channel] + * ) / sum(1) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + AVERAGE_POOL_2D = 1, + + /** + * Concatenates the input tensors along the given dimension. + * + * The input tensors must have identical {@link OperandCode} and the same + * dimensions except the dimension along the concatenation axis. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * (full support since NNAPI feature level 3, see the input section) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0 ~ n-1: The list of n input tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. + * Before NNAPI feature level 3, all input tensors of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * must have the same scale and zeroPoint as the output tensor. + * Input tensors of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * are allowed to have different scale and zeroPoint. + * Since NNAPI feature level 3, zero-sized tensors are supported. + * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * concatenation axis. + * + * Outputs: + * * 0: The output, a tensor of the same {@link OperandCode} as the input + * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. + * Since NNAPI feature level 3, for a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint values can be different from + * input tensors. Before NNAPI feature level 3 they have to be the same as for the + * input tensors. + * + * Available since NNAPI feature level 1. + */ + CONCATENATION = 2, + + /** + * Performs a 2-D convolution operation. + * + * The CONV_2D op sweeps a 2-D filter that can mix channels together over a + * batch of images, applying the filter to each window of each image of the + * appropriate size. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * sum_{di, dj, k} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, k] * + * filter[channel, di, dj, k] + * ) + bias[channel] + * + * Supported tensor {@link OperandCode} configurations: + * * 32 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * Available since NNAPI feature level 3: + * * 16 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. + * + * * Quantized with symmetric per channel quantization for the filter: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Available since NNAPI feature level 4: + * * Quantized signed (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized signed with filter symmetric per channel quantization + * (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. + * For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) + * must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * * 11: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 12 (dilation factor for height) must be specified as well. + * Available since NNAPI feature level 3. + * * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 11 (dilation factor for width) must be specified as well. + * Available since NNAPI feature level 3. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. + * For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) + * must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same + * type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * * 8: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 9 (dilation factor for height) must be specified as well. + * Available since NNAPI feature level 3. + * * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 8 (dilation factor for width) must be specified as well. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. + * Before NNAPI feature level 3, for output tensor of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition must + * be satisfied: output_scale > input_scale * filter_scale + * + * Available since NNAPI feature level 1. + */ + CONV_2D = 3, + + /** + * Performs a depthwise 2-D convolution operation. + * + * Given an input tensor of shape [batches, height, width, depth_in] and a + * filter tensor of shape [1, filter_height, filter_width, depth_out] + * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV + * applies a different filter to each input channel (expanding from 1 + * channel to channel_multiplier channels for each), then concatenates the + * results together. + * + * The output has depth_out = depth_in * depth_multiplier channels. + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, k * channel_multiplier + q] = + * sum_{di, dj} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, k] * + * filter[1, di, dj, k * channel_multiplier + q] + * ) + bias[k * channel_multiplier + q] + * + * Supported tensor {@link OperandCode} configurations: + * * 32 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * Available since NNAPI feature level 3: + * * 16 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. + * + * * Quantized with symmetric per channel quantization for the filter: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Available since NNAPI feature level 4: + * * Quantized signed (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized signed with filter symmetric per channel quantization + * (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. + * For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) + * must be set to 3. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise + * multiplier. + * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 11: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 13 (dilation factor for height) must be specified as well. + * Available since NNAPI feature level 3. + * * 13: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 12 (dilation factor for width) must be specified as well. + * Available since NNAPI feature level 3. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise + * multiplier. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 8: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 10 (dilation factor for height) must be specified as well. + * Available since NNAPI feature level 3. + * * 10: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 9 (dilation factor for width) must be specified as well. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. Before NNAPI feature level 3, for + * output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input_scale * filter_scale + * + * Available since NNAPI feature level 1. + */ + DEPTHWISE_CONV_2D = 4, + + /** + * Rearranges data from depth into blocks of spatial data. + * + * More specifically, this op outputs a copy of the input tensor where + * values from the depth dimension are moved in spatial blocks to the height + * and width dimensions. The value block_size indicates the input block size + * and how the data is moved. + * + * Chunks of data of size block_size * block_size from depth are rearranged + * into non-overlapping blocks of size block_size x block_size. + * + * The width of the output tensor is input_depth * block_size, whereas the + * height is input_height * block_size. The depth of the input tensor must + * be divisible by block_size * block_size + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. + * block_size must be >=1 and block_size * block_size must be a divisor + * of the input depth. + * * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batch, height*block_size, + * width*block_size, depth/(block_size*block_size)]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + DEPTH_TO_SPACE = 5, + + /** + * Dequantizes the input tensor. + * + * The formula is: + * + * output = (input - zeroPoint) * scale. + * + * Supported input tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported output tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since NNAPI feature level 1. + */ + DEQUANTIZE = 6, + + /** + * Looks up sub-tensors in the input tensor. + * + * This operator takes for input a tensor of values (Values) and + * a one-dimensional tensor of selection indices (Lookups). + * The output tensor is the concatenation of sub-tensors of Values as + * selected by Lookups. + * + * Think of Values as being sliced along its first dimension: + * The entries in Lookups select which slices are concatenated together + * to create the output tensor. + * + * For example, if Values has shape of [40, 200, 300] and + * Lookups has shape of [3], all three values found in Lookups are + * expected to be between 0 and 39. The resulting tensor must + * have shape of [3, 200, 300]. + * + * If a value in Lookups is out of bounds, the operation must fail + * and an error must be reported. + * + * Supported value tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 4) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported value tensor rank: from 2 + * + * Inputs: + * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. + * The values are indices into the first dimension of Values. + * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are + * extracted. + * + * Output: + * * 0: A n-D tensor with the same rank and shape as the Values + * tensor, except for the first dimension which has the same size + * as Lookups' only dimension. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input1. + * + * Available since NNAPI feature level 1. + */ + EMBEDDING_LOOKUP = 7, + + /** + * Computes element-wise floor() on the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and dimensions as + * the input tensor. + * + * Available since NNAPI feature level 1. + */ + FLOOR = 8, + + /** + * Denotes a fully (densely) connected layer, which connects all elements + * in the input tensor with each element in the output tensor. + * + * This layer implements the operation: + * + * outputs = activation(inputs * weights’ + bias) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor of at least rank 2, specifying the input. If rank is + * greater than 2, then it gets flattened to a 2-D Tensor. The + * (flattened) 2-D Tensor is reshaped (if necessary) to + * [batch_size, input_size], where "input_size" corresponds to the + * number of inputs to the layer, matching the second dimension of + * weights, and "batch_size" is calculated by dividing the number of + * elements by "input_size". + * Since NNAPI feature level 3, zero batch_size is supported for this tensor. + * * 1: A 2-D tensor, specifying the weights, of shape + * [num_units, input_size], where "num_units" corresponds to the number + * of output nodes. + * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input + * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, + * with zeroPoint of 0 and bias_scale == input_scale * filter_scale. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: The output tensor, of shape [batch_size, num_units]. Before NNAPI feature level 3, for + * output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following + * condition must be satisfied: output_scale > input_scale * filter_scale. + * + * Available since NNAPI feature level 1. + */ + FULLY_CONNECTED = 9, + + /** + * Looks up sub-tensors in the input tensor using a key-value map. + * + * This operator takes for input a tensor of values (Values), + * a one-dimensional tensor of selection values (Lookups) and + * a one-dimensional tensor that maps these values to Values + * indexes. The output tensor is the concatenation of sub-tensors of + * Values as selected by Lookups via Keys. + * + * Think of Values as being sliced along its outer-most dimension. + * The output is a concatenation of selected slices, with one slice + * for each entry of Lookups. The slice selected is the one at the + * same index as the Maps entry that matches the value in Lookups. + * + * For a hit, the corresponding sub-tensor of Values is included + * in the Output tensor. For a miss, the corresponding sub-tensor in + * Output must have zero values. + * + * For example, if Values has shape of [40, 200, 300], + * Keys should have a shape of [40]. If Lookups tensor has shape + * of [3], three slices are being concatenated, so the resulting tensor + * must have the shape of [3, 200, 300]. If the first entry in Lookups + * has the value 123456, that value must be located in Keys tensor. + * If the sixth entry of Keys contains 123456, the sixth slice of Values + * must be selected. If no entry in Keys has 123456, a slice of zeroes + * must be concatenated. + * + * Supported value tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported value tensor rank: from 2 + * + * Inputs: + * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with + * shape [ k ]. + * * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape + * [ n ]; Keys and Values pair represent a map, i.e., the ith element + * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values + * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in + * ascending order. + * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension + * must be n. + * + * Outputs: + * * 0: Output. A tensor with shape [ k …]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input2. + * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup + * hits (True) or not (False). + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + * + * Available since NNAPI feature level 1. + */ + HASHTABLE_LOOKUP = 10, + + /** + * Applies L2 normalization along the axis dimension. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * input[batch, row, col, channel] / + * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) + * + * By default the axis dimension is the last dimension of the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since NNAPI feature level 3. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be normalized. + * * 1: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, + * specifying the dimension normalization would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} and same shape as input0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 128 and the zeroPoint must be 128. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the scale must be 1.f / 128 and the zeroPoint must be 0. + * + * NOTE: Before NNAPI feature level 4, if the elements along an axis are all zeros, + * the result is undefined. Since NNAPI feature level 4, if the elements along an axis + * are all zeros, the result is logical zero. + * + * Available since NNAPI feature level 1. + */ + L2_NORMALIZATION = 11, + + /** + * Performs an 2-D L2 pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, c] = + * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) / + * sum(1)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * + * Available since NNAPI feature level 1. + */ + L2_POOL_2D = 12, + + /** + * Applies Local Response Normalization along the depth dimension. + * + * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the + * last dimension), and each vector is normalized independently. Within a + * given vector, each component is divided by the weighted, squared sum of + * inputs within depth_radius. + * + * The output is calculated using this formula: + * + * sqr_sum[a, b, c, d] = sum( + * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)) + * output = input / pow((bias + alpha * sqr_sum), beta) + * + * For input tensor with rank less than 4, independently normalizes each + * 1-D slice along specified dimension. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since NNAPI feature level 3. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of + * the normalization window. + * * 2: A scalar, specifying the bias, must not be zero. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias + * value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias + * value must be of {@link ANEURALNETWORKS_FLOAT32}. + * * 3: A scalar, specifying the scale factor, alpha. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the + * alpha value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the + * alpha value must be of {@link ANEURALNETWORKS_FLOAT32}. + * * 4: A scalar, specifying the exponent, beta. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the beta + * value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the beta + * value must be of {@link ANEURALNETWORKS_FLOAT32}. + * * 5: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, + * specifying the dimension normalization would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 1. + */ + LOCAL_RESPONSE_NORMALIZATION = 13, + + /** + * Computes sigmoid activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = 1 / (1 + exp(-input)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 256 and the zeroPoint must be 0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the scale must be 1.f / 256 and the zeroPoint must be -128. + * + * Available since NNAPI feature level 1. + */ + LOGISTIC = 14, + + /** + * Projects an input to a bit vector via locality senstive hashing. + * + * Supported input tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported input tensor rank: from 1 + * + * Inputs: + * * 0: Hash functions. Dim.size == 2, DataType: Float. + * Tensor[0].Dim[0]: Number of hash functions. + * Tensor[0].Dim[1]: Number of projected output bits generated by each + * hash function. + * If the projection type is Sparse: + * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32 + * + * * 1: Input. Dim.size >= 1, no restriction on DataType. + * * 2: Weight. Optional. Dim.size == 1, DataType: Float. + * If not set, each input element is considered to have the same weight + * of 1.0. + * Tensor[1].Dim[0] == Tensor[2].Dim[0] + * * 3: Type: + * Sparse: + * Value LSHProjectionType_SPARSE(=3) (since NNAPI feature level 3). + * Computed bit vector is considered to be sparse. + * Each output element is an int32 made up of multiple bits + * computed from hash functions. + * + * NOTE: To avoid collisions across hash functions, an offset value + * of k * (1 << Tensor[0].Dim[1]) will be added to each signature, + * where k is the index of the hash function. + * + * Value LSHProjectionType_SPARSE_DEPRECATED(=1). + * Legacy behavior that does not include the offset value. + * + * Dense: + * Value LSHProjectionType_DENSE(=2). + * Computed bit vector is considered to be dense. Each output + * element represents a bit and can take the value of either + * 0 or 1. + * + * Outputs: + * * 0: If the projection type is Sparse: + * Output.Dim == { Tensor[0].Dim[0] } + * A tensor of int32 that represents hash signatures. + * + * If the projection type is Dense: + * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } + * A flattened tensor that represents projected bit vectors. + * + * Available since NNAPI feature level 1. + * The offset value for sparse projections was added in NNAPI feature level 3. + */ + LSH_PROJECTION = 15, + + /** + * Performs a single time step in a Long Short-Term Memory (LSTM) layer + * + * The LSTM operation is described by the following equations. + * + * \f{eqnarray*}{ + * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\ + * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\ + * C_t =& clip(f_t \odot C_{t-1} + i_t \odot + * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\ + * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\ + * & & \\ + * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) + * & if\ there\ is\ a\ projection; \\ + * h_t =& & \\ + * & o_t \odot g(C_t) & otherwise. \\ + * \f} + * Where: + * * \f$x_t\f$ is the input, + * * \f$i_t\f$ is the input gate, + * * \f$f_t\f$ is the forget gate, + * * \f$C_t\f$ is the cell state, + * * \f$o_t\f$ is the output, + * * \f$h_t\f$ is the output state, + * * \f$\sigma\f$ is the logistic sigmoid function, + * * \f$g\f$ is the cell input and cell output activation function, usually + * \f$tahn\f$, + * * \f$W_{xi}\f$ is the input-to-input weight matrix, + * * \f$W_{hi}\f$ is the recurrent to input weight matrix, + * * \f$W_{ci}\f$ is the cell-to-input weight matrix, + * * \f$b_i\f$ is the input gate bias, + * * \f$W_{xf}\f$ is the input-to-forget weight matrix, + * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix, + * * \f$W_{cf}\f$ is the cell-to-forget weight matrix, + * * \f$b_f\f$ is the forget gate bias, + * * \f$W_{xc}\f$ is the input-to-cell weight matrix, + * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix, + * * \f$b_c\f$ is the cell bias, + * * \f$W_{xo}\f$ is the input-to-output weight matrix, + * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix, + * * \f$W_{co}\f$ is the cell-to-output weight matrix, + * * \f$b_o\f$ is the output gate bias, + * * \f$W_{proj}\f$ is the projection weight matrix, + * * \f$b_{proj}\f$ is the projection bias, + * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and + * * \f$t_{proj}\f$ is the threshold for clipping the projected output. + * * \f$\odot\f$ is the + * + * Hadamard product that takes two matrices and produces another + * matrix, each element of which is the product of the corresponding + * elements of the input matrices. + * + * Since NNAPI feature level 3 LSTM supports layer normalization. + * In case layer normalization is used, the inputs to internal activation + * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered + * following an approach from section 3.1 from + * https://arxiv.org/pdf/1607.06450.pdf + * + * The operation has the following independently optional inputs: + * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights + * (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all + * have values or neither of them have values (i.e., all set to null). If + * they have values, the peephole optimization is used. + * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights + * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values, + * or none of them have values. If they have no values, coupling of input + * and forget gates (CIFG) is used, in which case the input gate + * (\f$i_t\f$) is calculated using the following equation instead. + * \f{eqnarray*}{ + * i_t = 1 - f_t + * \f} + * In case peephole optimization is used and CIFG is not used + * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the + * cell-to-input weights must have no value. + * * The projection weights (\f$W_{proj}\f$) is required only for the + * recurrent projection layer, and should otherwise have no value. + * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a + * value if the recurrent projection layer exists, and should otherwise + * have no value. + * * (NNAPI feature level 3 or later) The four layer normalization weights either all have + * values or none of them have values. Additionally, if CIFG is used, + * input layer normalization weights tensor is omitted and the other layer + * normalization weights either all have values or none of them have + * values. Layer normalization is used when the values of all the layer + * normalization weights are present. + * + * References: + * + * The default non-peephole non-CIFG implementation is based on: + * http://www.bioinf.jku.at/publications/older/2604.pdf + * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural + * Computation, 9(8):1735-1780, 1997. + * + * The peephole implementation and projection layer is based on: + * https://research.google.com/pubs/archive/43905.pdf + * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory + * recurrent neural network architectures for large scale acoustic + * modeling." INTERSPEECH, 2014. + * (However, the concept of peephole optimization was introduced in work + * prior to this paper.) + * + * The coupling of input and forget gate (CIFG) is based on: + * http://arxiv.org/pdf/1503.04069.pdf + * Greff et al. "LSTM: A Search Space Odyssey" + * + * The layer normalization is based on: + * https://arxiv.org/pdf/1607.06450.pdf + * Jimmy Ba et al. "Layer Normalization" + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * All input and output tensors must be of the same type. + * + * Inputs: + * * 0: The input (\f$x_t\f$). + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 2: The input-to-forget weights (\f$W_{xf}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 3: The input-to-cell weights (\f$W_{xc}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 4: The input-to-output weights (\f$W_{xo}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 12:The input gate bias (\f$b_i\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 13:The forget gate bias (\f$b_f\f$). + * A 1-D tensor of shape [num_units]. + * * 14:The cell bias (\f$b_c\f$). + * A 1-D tensor of shape [num_units]. + * * 15:The output gate bias (\f$b_o\f$). + * A 1-D tensor of shape [num_units]. + * * 16:The projection weights (\f$W_{proj}\f$). Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 17:The projection bias (\f$b_{proj}\f$). Optional. + * A 1-D tensor of shape [output_size]. + * * 18:The output state (in) (\f$h_{t-1}\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 19:The cell state (in) (\f$C_{t-1}\f$). + * A 2-D tensor of shape [batch_size, num_units]. + * * 20:The activation function (\f$g\f$). + * A value indicating the activation function: + * + * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such + * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 + * then clipping is disabled. + * Until NNAPI feature level 3 this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input + * tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this + * scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, + * otherwise if all the input tensors have the type {@link + * ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT16}. + * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the + * projection layer, such that values are bound within + * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + * Until NNAPI feature level 3 this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input + * tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this + * scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, + * otherwise if all the input tensors have the type {@link + * ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT16}. + * Since NNAPI feature level 3 there are additional inputs to this op: + * * 23:The input layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 24:The forget layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 25:The cell layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 26:The output layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The scratch buffer. + * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or + * [batch_size, num_units * 4] without CIFG. + * * 1: The output state (out) (\f$h_t\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 2: The cell state (out) (\f$C_t\f$). + * A 2-D tensor of shape [batch_size, num_units]. + * * 3: The output (\f$o_t\f$). + * A 2-D tensor of shape [batch_size, output_size]. This is effectively + * the same as the current “output state (out)” value. + * + * Available since NNAPI feature level 1. + */ + LSTM = 16, + + /** + * Performs an 2-D max pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * max_{di, dj} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, channel] + * ) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + MAX_POOL_2D = 17, + + /** + * Multiplies two tensors, element-wise. + * + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the product of both input tensors, optionally + * modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the resulting output is the maximum size along each dimension + * of the input operands. It starts with the trailing dimensions, and works + * its way forward. + * + * Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero + * dimension is only compatible with 0 or 1. The size of the output + * dimension is zero if either of corresponding input dimension is zero. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, + * the {@link FuseCode} must be "NONE". + * + * Outputs: + * * 0: The product, a tensor of the same {@link OperandCode} as input0. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the following condition must be satisfied: + * output_scale > input1_scale * input2_scale. + * + * Available since NNAPI feature level 1. + */ + MUL = 18, + + /** + * Computes rectified linear activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = max(0, input) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + RELU = 19, + + /** + * Computes rectified linear 1 activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = min(1.f, max(-1.f, input)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: The output tensor of the same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + RELU1 = 20, + + /** + * Computes rectified linear 6 activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = min(6, max(0, input)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + RELU6 = 21, + + /** + * Reshapes a tensor. + * + * Given tensor, this operation returns a tensor that has the same values as + * tensor, but with a newly specified shape. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 6) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the tensor to be reshaped. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, defining the + * shape of the output tensor. The number of elements implied by shape + * must be the same as the number of elements in the input tensor. + * + * If one component of shape is the special value -1, the size of that + * dimension is computed so that the total size remains constant. In + * particular, a shape of [-1] flattens into 1-D. At most one component + * of shape can be -1. + * + * Outputs: + * * 0: The output tensor, of shape specified by the input shape. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + RESHAPE = 22, + + /** + * Resizes images to given size using the bilinear interpretation. + * + * Resized images must be distorted if their output aspect ratio is not the + * same as input aspect ratio. The corner pixels of output may not be the + * same as corner pixels of input. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Both resizing by shape and resizing by scale are supported. + * + * Inputs (resizing by shape): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * Since NNAPI feature level 3, zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the centers of the 4 corner + * pixels of the input and output tensors are aligned, preserving the + * values at the corner pixels. + * Available since NNAPI feature level 4. + * * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the pixel centers are assumed to + * be at (0.5, 0.5). This is the default behavior of image.resize in + * TF 2.0. If this parameter is True, then align_corners parameter + * must be False. + * Available since NNAPI feature level 4. + * + * Inputs (resizing by scale, since NNAPI feature level 3): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Zero batches is supported for this tensor. + * * 1: A scalar, specifying width_scale, the scaling factor of the width + * dimension from the input tensor to the output tensor. The output + * width is calculated as new_width = floor(width * width_scale). + * The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is + * of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} otherwise. + * * 2: A scalar, specifying height_scale, the scaling factor of the height + * dimension from the input tensor to the output tensor. The output + * height is calculated as new_height = floor(height * height_scale). + * The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is + * of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} otherwise. + * * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the centers of the 4 corner + * pixels of the input and output tensors are aligned, preserving the + * values at the corner pixels. + * Available since NNAPI feature level 4. + * * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the pixel centers are assumed to + * be at (0.5, 0.5). This is the default behavior of image.resize in + * TF 2.0. If this parameter is True, then align_corners parameter + * must be False. + * Available since NNAPI feature level 4. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + RESIZE_BILINEAR = 23, + + /** + * A basic recurrent neural network layer. + * + * This layer implements the operation: + * outputs = state = activation(inputs * input_weights + + * state * recurrent_weights + bias) + * + * Where: + * * “input_weights” is a weight matrix that multiplies the inputs; + * * “recurrent_weights” is a weight matrix that multiplies the current + * “state” which itself is the output from the previous time step + * computation; + * * “bias” is a bias vector (added to each output vector in the batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: weights. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of units. + * * 2: recurrent_weights. + * A 2-D tensor of shape [num_units, num_units], with columns + * corresponding to the weights from each unit. + * * 3: bias. + * A 1-D tensor of shape [num_units]. + * * 4: hidden state (in). + * A 2-D tensor of shape [batch_size, num_units]. + * * 5: fused_activation_function. + * An optional {@link FuseCode} value indicating the + * activation function. If “NONE” is specified then it results in a + * linear activation. + * + * Outputs: + * * 0: hidden state (out). + * A 2-D tensor of shape [batch_size, num_units]. + * + * * 1: output. + * A 2-D tensor of shape [batch_size, num_units]. This is effectively + * the same as the current state value. + * + * Available since NNAPI feature level 1. + */ + RNN = 24, + + /** + * Computes the softmax activation on the input tensor element-wise, per + * batch, by normalizing the input vector so the maximum coefficient is + * zero. + * + * The output is calculated using this formula: + * + * output[batch, i] = + * exp((input[batch, i] - max(input[batch, :])) * beta) / + * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} + * + * For input tensor with rank other than 2, the activation will be applied + * independently on each 1-D slice along specified dimension. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * Tensors with rank other than 2 or 4 are only supported since NNAPI feature level 3. + * + * Inputs: + * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * * 1: A scalar, specifying the positive scaling factor for the exponent, + * beta. If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scalar + * must be of {@link ANEURALNETWORKS_FLOAT32}. + * If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, then the + * scalar must be of {@link ANEURALNETWORKS_FLOAT16}. + * * 2: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, + * specifying the dimension the activation would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 256 and the zeroPoint must be 0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the scale must be 1.f / 256 and the zeroPoint must be -128. + * + * Available since NNAPI feature level 1. + */ + SOFTMAX = 25, + + /** + * Rearranges blocks of spatial data, into depth. + * + * More specifically, this op outputs a copy of the input tensor where + * values from the height and width dimensions are moved to the depth + * dimension. The value block_size indicates the input block size and how + * the data is moved. + * + * Chunks of data of size block_size * block_size from depth are rearranged + * into non-overlapping blocks of size block_size x block_size. + * + * The depth of the output tensor is input_depth * block_size * block_size. + * The input tensor's height and width must be divisible by block_size. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. + * block_size must be >=1 and block_size must be a divisor of both the + * input height and width. + * * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batches, height/block_size, + * width/block_size, depth_in*block_size*block_size]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 1. + */ + SPACE_TO_DEPTH = 26, + + /** + * SVDF op is a kind of stateful layer derived from the notion that a + * densely connected layer that's processing a sequence of input frames can + * be approximated by using a singular value decomposition of each of its + * nodes. The implementation is based on: + * + * https://research.google.com/pubs/archive/43813.pdf + * + * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. + * “Compressing Deep Neural Networks using a Rank-Constrained Topology”. + * INTERSPEECH, 2015. + * + * It processes the incoming input using a 2-stage filtering mechanism: + * * stage 1 performs filtering on the "features" dimension, whose outputs + * get pushed into a memory of fixed-size memory_size. + * * stage 2 performs filtering on the "time" dimension of the memory_size + * memoized outputs of stage 1. + * + * Specifically, for rank 1, this layer implements the operation: + * + * memory = push(conv1d(inputs, weights_feature, feature_dim, + * "ANEURALNETWORKS_PADDING_VALID")); + * outputs = activation(memory * weights_time + bias); + * + * Where: + * * “weights_feature” is a weights matrix that processes the inputs (by + * convolving the input with every “feature filter”), and whose outputs + * get pushed, stacked in order, into the fixed-size “memory” (the oldest + * entry gets dropped); + * * “weights_time” is a weights matrix that processes the “memory” (by a + * batched matrix multiplication on the num_units); + * * “bias” is an optional bias vector (added to each output vector in the + * batch); and + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Each rank adds a dimension to the weights matrices by means of stacking + * the filters. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * All input tensors must be the same type. + * + * Inputs: + * * 0: input. + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: weights_feature. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of units. + * * 2: weights_time. + * A 2-D tensor of shape [num_units, memory_size], where “memory_size” + * corresponds to the fixed-size of the memory. + * * 3: bias. + * An optional 1-D tensor of shape [num_units]. + * * 4: state (in). + * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank]. + * * 5: rank. + * The rank of the SVD approximation. + * * 6: fused_activation_function. + * An optional {@link FuseCode} value indicating the + * activation function. If “NONE” is specified then it results in a + * linear activation. + * + * Outputs: + * * 0: state (out). + * A 2-D tensor of the same {@link OperandCode} as the inputs, with shape + * [batch_size, (memory_size - 1) * num_units * rank]. + * * 1: output. + * A 2-D tensor of the same {@link OperandCode} as the inputs, with shape + * [batch_size, num_units]. + * + * Available since NNAPI feature level 1. + */ + SVDF = 27, + + /** + * Computes hyperbolic tangent of input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = tanh(input) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 128 and the zeroPoint must be 128. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the scale must be 1.f / 128 and the zeroPoint must be 0. + * + * Available since NNAPI feature level 1. + */ + TANH = 28, + + // Operations below are available since NNAPI feature level 2. + + /** + * BatchToSpace for N-dimensional tensors. + * + * This operation reshapes the batch dimension (dimension 0) into M + 1 + * dimensions of shape block_shape + [batch], interleaves these blocks back + * into the grid defined by the spatial dimensions [1, ..., M], to obtain a + * result with the same rank as the input. + * + * This is the reverse of SpaceToBatch. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be reshaped + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block + * sizes for each spatial dimension of the input tensor. All values + * must be >= 1. + * * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 2. + */ + BATCH_TO_SPACE_ND = 29, + + /** + * Element-wise division of two tensors. + * + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the result of dividing the first input tensor + * by the second, optionally modified by an activation function. + * + * For inputs of {@link ANEURALNETWORKS_TENSOR_INT32}, performs + * "floor division" ("//" in Python). For example, + * 5 // 2 = 2 + * -5 // 2 = -3 + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero + * dimension is only compatible with 0 or 1. The size of the output + * dimension is zero if either of corresponding input dimension is zero. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, + * the {@link FuseCode} must be "NONE". + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since NNAPI feature level 2. + */ + DIV = 30, + + /** + * Computes the mean of elements across dimensions of a tensor. + * + * Reduces the input tensor along the given dimensions to reduce. Unless + * keep_dims is true, the rank of the tensor is reduced by 1 for each entry + * in axis. If keep_dims is true, the reduced dimensions are retained with + * length 1. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Must be in the range + * [-rank(input_tensor), rank(input_tensor)). + * + * NOTE: When the operation was introduced, the documentation + * incorrectly stated that if dimensions were empty, the operation + * would reduce across all dimensions. This behavior was never + * implemented. + * + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * + * Available since NNAPI feature level 2. + */ + MEAN = 31, + + /** + * Pads a tensor. + * + * This operation pads a tensor according to the specified paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * (full support since NNAPI feature level 3, see the output section) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be padded. + * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. The shape of the + * tensor must be {rank(input0), 2}. + * padding[i, 0] specifies the number of elements to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of elements to be padded after the + * end of dimension i. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. The + * output tensor has the same rank as input0, and each + * dimension of the output tensor has the same size as the + * corresponding dimension of the input tensor plus the size + * of the padding: + * output0.dimension[i] = + * padding[i, 0] + input0.dimension[i] + padding[i, 1] + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * NOTE: Before NNAPI feature level 3, the pad value for + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. + * Since NNAPI feature level 3, the pad value is always the logical zero. + * + * Available since NNAPI feature level 2. + */ + PAD = 32, + + /** + * SpaceToBatch for N-Dimensional tensors. + * + * This operation divides "spatial" dimensions [1, ..., M] of the input into + * a grid of blocks of shape block_shape, and interleaves these blocks with + * the "batch" dimension (0) such that in the output, the spatial dimensions + * [1, ..., M] correspond to the position within the grid, and the batch + * dimension combines both the position within a spatial block and the + * original batch position. Prior to division into blocks, the spatial + * dimensions of the input are optionally zero padded according to paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * (full support since NNAPI feature level 3, see the output section) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * NCHW is supported since NNAPI feature level 3. + * + * Inputs: + * * 0: An n-D tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block + * sizes for each spatial dimension of the input tensor. All values + * must be >= 1. + * * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. All values must be + * >= 0. The shape of the tensor must be {M, 2}, where M is the number + * of spatial dimensions. + * padding[i, 0] specifies the number of element to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of element to be padded after the + * end of dimension i. + * * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since NNAPI feature level 3. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * NOTE: Before NNAPI feature level 3, the pad value for + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. + * Since NNAPI feature level 3, the pad value is always the logical zero. + * + * Available since NNAPI feature level 2. + */ + SPACE_TO_BATCH_ND = 33, + + /** + * Removes dimensions of size 1 from the shape of a tensor. + * + * Given a tensor input, this operation returns a tensor of the same + * {@link OperandCode} with all dimensions of size 1 removed. If you don't + * want to remove all size 1 dimensions, you can remove specific size 1 + * dimensions by specifying the axes (input1). + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, the tensor to be squeezed. + * * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The + * dimensions to squeeze. If specified only squeezes the dimensions + * listed. Otherwise, squeezes all dimensions. The dimension index + * starts at 0. An error must be reported if squeezing a dimension that + * is not 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. Contains the + * same data as input, but has one or more dimensions of size 1 + * removed. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * If all input dimensions are equal to 1 and are to be squeezed, the + * output shape is [1]. + * + * Available since NNAPI feature level 2. + */ + SQUEEZE = 34, + + /** + * Extracts a strided slice of a tensor. + * + * Roughly speaking, this op extracts a slice of size (end - begin) / stride + * from the given input tensor. Starting at the location specified by begin + * the slice continues by adding stride to the index until all dimensions + * are not less than end. Note that a stride can be negative, which causes a + * reverse slice. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be sliced. + * * 1: begin, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The + * starts of the dimensions of the input tensor to be sliced. The + * length must be of rank(input0). + * * 2: end, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The + * ends of the dimensions of the input tensor to be sliced. The length + * must be of rank(input0). + * * 3: strides, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The + * strides of the dimensions of the input tensor to be sliced. The + * length must be of rank(input0). The entries must be non-zero. + * * 4: begin_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the ith bit + * of begin_mask is set, begin[i] is ignored and the fullest possible + * range in that dimension is used instead. + * * 5: end_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the ith bit of + * end_mask is set, end[i] is ignored and the fullest possible range in + * that dimension is used instead. + * * 6: shrink_axis_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the + * ith bit of shrink_axis_mask is set, the ith dimension specification + * shrinks the dimensionality by 1, taking on the value at index + * begin[i]. In this case, the ith specification must define a + * slice of size 1, e.g. begin[i] = x, end[i] = x + 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0 and rank (n - k), + * where k is the number of bits set in shrink_axis_mask. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * If shrink_axis_mask is true for all input dimensions, the output + * shape is [1]. + * + * Available since NNAPI feature level 2. + */ + STRIDED_SLICE = 35, + + /** + * Element-wise subtraction of two tensors. + * + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the result of subtracting the second input + * tensor from the first one, optionally modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero + * dimension is only compatible with 0 or 1. The size of the output + * dimension is zero if either of corresponding input dimension is zero. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, + * the {@link FuseCode} must be "NONE". + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 2. + */ + SUB = 36, + + /** + * Transposes the input tensor, permuting the dimensions according to the + * perm tensor. + * + * The returned tensor's dimension i corresponds to the input dimension + * perm[i]. If perm is not given, it is set to (n-1...0), where n is the + * rank of the input tensor. Hence by default, this operation performs a + * regular matrix transpose on 2-D input Tensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be transposed. + * Since NNAPI feature level 3, this tensor may be zero-sized. + * * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, + * the permutation of the dimensions of the input tensor. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 2. + */ + TRANSPOSE = 37, + + // Operations below are available since NNAPI feature level 3. + + /** + * Computes the absolute value of a tensor, element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + ABS = 38, + + /** + * Returns the index of the largest element along an axis. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor specifying the input. Must be non-empty. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor. + * If input is 1-dimensional, the output shape is [1]. + * + * Available since NNAPI feature level 3. + */ + // There is no underscore in ARG_MAX to avoid name conflict with + // the macro defined in libc/kernel/uapi/linux/limits.h. + ARGMAX = 39, + + /** + * Returns the index of the smallest element along an axis. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor specifying the input. Must be non-empty. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor. + * If input is 1-dimensional, the output shape is [1]. + * + * Available since NNAPI feature level 3. + */ + ARGMIN = 40, // See ARGMAX for naming discussion. + + /** + * Transform axis-aligned bounding box proposals using bounding box deltas. + * + * Given the positions of bounding box proposals and the corresponding + * bounding box deltas for each class, return the refined bounding box + * regions. The resulting bounding boxes are cliped against the edges of + * the image. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM} + * + * Inputs: + * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the + * bounding box proposals, each line with format [x1, y1, x2, y2]. + * For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, + * the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois + * is supported for this tensor. + * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the + * bounding box delta for each region of interest and each class. The + * bounding box deltas are organized in the following order + * [dx, dy, dw, dh], where dx and dy is the relative correction factor + * for the center position of the bounding box with respect to the width + * and height, dw and dh is the log-scale relative correction factor + * for the width and height. For input0 of type + * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, this tensor should be + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}. Zero num_rois is + * supported for this tensor. + * * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_rois], specifying the batch index of each box. Boxes with + * the same batch index are grouped together. Zero num_rois is + * supported for this tensor. + * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of + * each image in the batch, each line with format + * [image_height, image_width]. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0, with shape + * [num_rois, num_classes * 4], specifying the coordinates of each + * output bounding box for each class, with format [x1, y1, x2, y2]. + * For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the + * scale must be 0.125 and the zero point must be 0. + * + * Available since NNAPI feature level 3. + */ + AXIS_ALIGNED_BBOX_TRANSFORM = 41, + + /** + * A recurrent neural network layer that applies an LSTM cell to a + * sequence of inputs in forward and backward directions. + * + * The op supports cross-linking via an auxiliary input. Regular cell feeds + * one input into the two RNN cells in the following way: + * + * INPUT (INPUT_REVERSED) + * | | + * --------------------- + * | FW_LSTM BW_LSTM | + * --------------------- + * | | + * FW_OUT BW_OUT + * + * An op with cross-linking takes two inputs and feeds them into the RNN + * cells in the following way: + * + * AUX_INPUT (AUX_INPUT_REVERSED) + * | | + * INPUT | (INPUT_R'D.)| + * | | | | + * ----------------------- + * | \ / \ / | + * | FW_LSTM BW_LSTM | + * ----------------------- + * | | + * FW_OUT BW_OUT + * + * The cross-linking mode is enabled iff auxiliary input and auxiliary + * weights are present. While stacking this op on top of itself, this + * allows to connect both forward and backward outputs from previous cell + * to the next cell's input. + * + * Since NNAPI feature level 4 parallel linking mode is supported. The mode is + * enabled if auxiliary input is present but auxiliary weights are omitted. + * In this case, the cell feeds inputs into the RNN in the following way: + * + * INPUT (AUX_INPUT_REVERSED) + * | | + * --------------------- + * | FW_LSTM BW_LSTM | + * --------------------- + * | | + * FW_OUT BW_OUT + * + * While stacking this op on top of itself, this allows to connect both + * forward and backward outputs from previous cell to the next cell's + * corresponding inputs. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 3, either time-major or batch-major. + * + * All input and output tensors must be of the same type. + * + * Inputs: + * * 0: The input. + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, input_size] + * If batch-major: [batch_size, max_time, input_size] + * where "max_time" is the number of timesteps (sequence length), + * "batch_size" corresponds to the batching dimension, and + * "input_size" is the size of the input. + * * 1: The forward input-to-input weights. Optional. + * A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units” + * corresponds to the number of forward cell units. + * * 2: The forward input-to-forget weights. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 3: The forward input-to-cell weights. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 4: The forward input-to-output weights. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 5: The forward recurrent-to-input weights. Optional. + * A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size” + * corresponds to either the number of cell units (i.e., fw_num_units), + * or the second dimension of the “fw_projection_weights”, if defined. + * * 6: The forward recurrent-to-forget weights. + * A 2-D tensor of shape [fw_num_units, fw_output_size]. + * * 7: The forward recurrent-to-cell weights. + * A 2-D tensor of shape [fw_num_units, fw_output_size]. + * * 8: The forward recurrent-to-output weights. + * A 2-D tensor of shape [fw_num_units, fw_output_size]. + * * 9: The forward cell-to-input weights. Optional. + * A 1-D tensor of shape [fw_num_units]. + * * 10: The forward cell-to-forget weights. Optional. + * A 1-D tensor of shape [fw_num_units]. + * * 11: The forward cell-to-output weights. Optional. + * A 1-D tensor of shape [fw_num_units]. + * * 12: The forward input gate bias. Optional. + * A 1-D tensor of shape [fw_num_units]. + * * 13: The forward forget gate bias. + * A 1-D tensor of shape [fw_num_units]. + * * 14: The forward cell gate bias. + * A 1-D tensor of shape [fw_num_units]. + * * 15: The forward output gate bias. + * A 1-D tensor of shape [fw_num_units]. + * * 16: The forward projection weights. Optional. + * A 2-D tensor of shape [fw_output_size, fw_num_units]. + * * 17: The forward projection bias. Optional. + * A 1-D tensor of shape [fw_output_size]. + * * 18: The backward input-to-input weights. Optional. + * A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units” + * corresponds to the number of backward cell units. + * * 19: The backward input-to-forget weights. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 20: The backward input-to-cell weights. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 21: The backward input-to-output weights. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 22: The backward recurrent-to-input weights. Optional. + * A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size” + * corresponds to either the number of cell units (i.e., “bw_num_units”), + * or the second dimension of the “bw_projection_weights”, if defined. + * * 23: The backward recurrent-to-forget weights. + * A 2-D tensor of shape [bw_num_units, bw_output_size]. + * * 24: The backward recurrent-to-cell weights. + * A 2-D tensor of shape [bw_num_units, bw_output_size]. + * * 25: The backward recurrent-to-output weights. + * A 2-D tensor of shape [bw_num_units, bw_output_size]. + * * 26: The backward cell-to-input weights. Optional. + * A 1-D tensor of shape [bw_num_units]. + * * 27: The backward cell-to-forget weights. Optional. + * A 1-D tensor of shape [bw_num_units]. + * * 28: The backward cell-to-output weights. Optional. + * A 1-D tensor of shape [bw_num_units]. + * * 29: The backward input gate bias. Optional. + * A 1-D tensor of shape [bw_num_units]. + * * 30: The backward forget gate bias. + * A 1-D tensor of shape [bw_num_units]. + * * 31: The backward cell gate bias. + * A 1-D tensor of shape [bw_num_units]. + * * 32: The backward output gate bias. + * A 1-D tensor of shape [bw_num_units]. + * * 33: The backward projection weights. Optional. + * A 2-D tensor of shape [bw_output_size, bw_num_units]. + * * 34: The backward projection bias. Optional. + * A 1-D tensor of shape [bw_output_size]. + * * 35: The forward input activation state. + * A 2-D tensor of shape [batch_size, bw_output_size]. + * * 36: The forward input cell state. + * A 2-D tensor of shape [batch_size, bw_num_units]. + * * 37: The backward input activation state. + * A 2-D tensor of shape [batch_size, bw_output_size]. + * * 38: The backward input cell state. + * A 2-D tensor of shape [batch_size, bw_num_units]. + * * 39: The auxiliary input. Optional. + * A 3-D tensor of shape [max_time, batch_size, aux_input_size], + * where “batch_size” corresponds to the batching dimension, and + * “aux_input_size” is the size of the auxiliary input. Optional. See + * the docs above for the usage modes explanation. + * * 40: The forward auxiliary input-to-input weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [fw_num_units, aux_input_size]. + * * 41: The forward auxiliary input-to-forget weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [fw_num_units, aux_input_size]. + * * 42: The forward auxiliary input-to-cell weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [fw_num_units, aux_input_size]. + * * 43: The forward auxiliary input-to-output weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [fw_num_units, aux_input_size]. + * * 44: The backward auxiliary input-to-input weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [bw_num_units, aux_input_size]. + * * 45: The backward auxiliary input-to-forget weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [bw_num_units, aux_input_size]. + * * 46: The backward auxiliary input-to-cell weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [bw_num_units, aux_input_size]. + * * 47: The backward auxiliary input-to-output weights. + * Optional. See the docs above for the usage modes explanation. + * A 2-D tensor of shape [bw_num_units, aux_input_size]. + * * 48: The activation function. + * A value indicating the activation function: + * + * * 49: The clipping threshold for the cell state, such + * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 + * then clipping is disabled. + * If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, + * otherwise if all the input tensors have the type + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be + * of type {@link ANEURALNETWORKS_FLOAT16}. + * * 50: The clipping threshold for the output from the + * projection layer, such that values are bound within + * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + * If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, + * otherwise if all the input tensors have the type + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be + * of type {@link ANEURALNETWORKS_FLOAT16}. + * * 51: merge_outputs + * An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs + * from forward and backward cells should be merged. + * * 52: time_major + * An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format + * of input and output tensors. + * * 53: The forward input layer normalization weights. Optional. + * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 54: The forward forget layer normalization weights. Optional. + * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 55: The forward cell layer normalization weights. Optional. + * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 56: The forward output layer normalization weights. Optional. + * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs + * to activation at output gate. + * * 57: The backward input layer normalization weights. Optional. + * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 58: The backward forget layer normalization weights. Optional. + * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 59: The backward cell layer normalization weights. Optional. + * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 60: The backward output layer normalization weights. Optional. + * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The forward output. + * A 3-D tensor of shape: + * If time-major and not merge_outputs: + * [max_time, batch_size, fw_output_size] + * If time-major and merge_outputs: + * [max_time, batch_size, fw_output_size + bw_output_size] + * If batch-major and not merge_outputs: + * [batch_size, max_time, fw_output_size] + * If batch-major and merge_outputs: + * [batch_size, max_time, fw_output_size + bw_output_size] + * * 1: The backward output. Unused if merge_outputs is true. + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, bw_output_size] + * If batch-major: [batch_size, max_time, bw_output_size] + * * 2: The forward activation state output. + * A 2-D tensor of shape [batch_size, fw_output_size] containing an + * activation state from the last time step in the sequence. This + * output is optional and can be omitted. If this output is present + * then outputs 3-5 must be present as well. + * Available since NNAPI feature level 4. + * * 3: The forward cell state output. + * A tensor of shape [batch_size, fw_cell_size] containing a cell state + * from the last time step in the sequence. This output is optional + * and can be omitted. If this output is present + * then outputs 2, 4, 5 must be present as well. + * Available since NNAPI feature level 4. + * * 4: The backward activation state output. + * A 2-D tensor of shape [batch_size, bw_output_size] containing an + * activation state from the last time step in the sequence. This + * output is optional and can be omitted. If this output is present + * then outputs 2, 3, 5 must be present as well. + * Available since NNAPI feature level 4. + * * 5: The backward cell state output. + * A tensor of shape [batch_size, bw_cell_size] containing a cell state + * from the last time step in the sequence. This output is optional + * and can be omitted. If this output is present + * then outputs 2-4 must be present as well. + * Available since NNAPI feature level 4. + * + * Available since NNAPI feature level 3. + * + * Important: As of NNAPI feature level 3, there is no way to get the output state tensors out + * and NNAPI does not maintain internal states. This operator does not support the usage pattern + * in which multiple cells are chained and state tensors are propagated. + */ + BIDIRECTIONAL_SEQUENCE_LSTM = 42, + + /** + * A recurrent neural network layer that applies a basic RNN cell to a + * sequence of inputs in forward and backward directions. + * + * This Op unrolls the input along the sequence dimension, and implements + * the following operation for each element in the sequence s = + * 1...sequence_length: + * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ + + * fw_state * fw_recurrent_weights’ + fw_bias) + * + * And for each element in sequence t = sequence_length : 1 + * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ + + * bw_state * bw_recurrent_weights’ + bw_bias) + * + * Where: + * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs; + * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the + * current “state” which itself is the output from the previous time step + * computation; + * * “{fw,bw}_bias” is a bias vector (added to each output vector in the + * batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * The op supports cross-linking via an auxiliary input. Regular cell feeds + * one input into the two RNN cells in the following way: + * + * INPUT (INPUT_REVERSED) + * | | + * --------------------- + * | FW_RNN BW_RNN | + * --------------------- + * | | + * FW_OUT BW_OUT + * + * An op with cross-linking takes two inputs and feeds them into the RNN + * cells in the following way: + * + * AUX_INPUT (AUX_INPUT_REVERSED) + * | | + * INPUT | (INPUT_R'D.)| + * | | | | + * ----------------------- + * | \ / \ / | + * | FW_RNN BW_RNN | + * ----------------------- + * | | + * FW_OUT BW_OUT + * + * The cross-linking mode is enabled iff auxiliary input and auxiliary + * weights are present. While stacking this op on top of itself, this + * allows to connect both forward and backward outputs from previous cell + * to the next cell's input. + * + * Since NNAPI feature level 4 parallel linking mode is supported. The mode is + * enabled if auxiliary input is present but auxiliary weights are omitted. + * In this case, the cell feeds inputs into the RNN in the following way: + * + * INPUT (AUX_INPUT_REVERSED) + * | | + * --------------------- + * | FW_RNN BW_RNN | + * --------------------- + * | | + * FW_OUT BW_OUT + * + * While stacking this op on top of itself, this allows to connect both + * forward and backward outputs from previous cell to the next cell's + * corresponding inputs. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to true, then the input has a shape [maxTime, batchSize, + * inputSize], otherwise the input has a shape [batchSize, maxTime, + * inputSize]. + * * 1: fwWeights. + * A 2-D tensor of shape [fwNumUnits, inputSize]. + * * 2: fwRecurrentWeights. + * A 2-D tensor of shape [fwNumUnits, fwNumUnits]. + * * 3: fwBias. + * A 1-D tensor of shape [fwNumUnits]. + * * 4: fwHiddenState. + * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 5: bwWeights. + * A 2-D tensor of shape [bwNumUnits, inputSize]. + * * 6: bwRecurrentWeights. + * A 2-D tensor of shape [bwNumUnits, bwNumUnits]. + * * 7: bwBias. + * A 1-D tensor of shape [bwNumUnits]. + * * 8: bwHiddenState + * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 9: auxInput. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to true, then the input has a shape [maxTime, batchSize, + * auxInputSize], otherwise the input has a shape [batchSize, maxTime, + * auxInputSize]. Can be omitted. See the docs above for the usage + * modes explanation. + * * 10:fwAuxWeights. + * A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted. + * See the docs above for the usage modes explanation. + * * 11:bwAuxWeights. + * A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted. + * See the docs above for the usage modes explanation. + * * 12:fusedActivationFunction. + * A {@link FuseCode} value indicating the activation function. If + * “NONE” is specified then it results in a linear activation. + * * 13:timeMajor + * An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format + * of input and output tensors. + * * 14:mergeOutputs + * An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs + * from forward and backward cells are separate (if set to false) or + * concatenated (if set to true). + * Outputs: + * * 0: fwOutput. + * A 3-D tensor. The first two dimensions of the shape are defined by + * the input 6 (timeMajor) and the third dimension is defined by the + * input 14 (mergeOutputs). If timeMajor is set to true, then the first + * two dimensions are [maxTime, batchSize], otherwise they are set to + * [batchSize, maxTime]. If mergeOutputs is set to true, then the third + * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set + * to fwNumUnits. + * * 1: bwOutput. + * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then + * this tensor is not produced. The shape is defined by the input 6 + * (timeMajor). If it is set to true, then the shape is set to + * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to + * [batchSize, maxTime, bwNumUnits]. + * * 2: The forward hidden state output. + * A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden + * state from the last time step in the sequence. This output is + * optional and can be omitted. If this output is present then output + * 3 must be present as well. + * Available since NNAPI feature level 4. + * * 3: The backward hidden state output. + * A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden + * state from the last time step in the sequence. This output is + * optional and can be omitted. If this output is present then output + * 2 must be present as well. + * Available since NNAPI feature level 4. + * + * Available since NNAPI feature level 3. + * + * Important: As of NNAPI feature level 3, there is no way to get the output state tensors out + * and NNAPI does not maintain internal states. This operator does not support the usage pattern + * in which multiple cells are chained and state tensors are propagated. + */ + BIDIRECTIONAL_SEQUENCE_RNN = 43, + + /** + * Greedily selects a subset of bounding boxes in descending order of score. + * + * This op applies NMS algorithm to each class. In each loop of execution, + * the box with maximum score gets selected and removed from the pending set. + * The scores of the rest of boxes are lowered according to the + * intersection-over-union (IOU) overlapping with the previously selected + * boxes and a specified NMS kernel method. Any boxes with score less + * than a threshold are removed from the pending set. + * + * Three NMS kernels are supported: + * * Hard: score_new = score_old * (1 if IoU < threshold else 0) + * * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU) + * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma) + * + * Axis-aligned bounding boxes are represented by its upper-left corner + * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid + * bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Inputs: + * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score + * of each bounding box proposal. The boxes are grouped by batches in the + * first dimension. Zero num_rois is supported for this tensor. + * * 1: A 2-D Tensor specifying the bounding boxes of shape + * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2]. + * The boxes are grouped by batches in the first dimension. The sequential + * order of the boxes corresponds with input0. For input0 of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should be of + * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and + * scale of 0.125. + * For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, + * with zeroPoint of -128 and scale of 0.125. + * Zero num_rois is supported for this tensor. + * * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_rois], specifying the batch index of each box. Boxes with + * the same batch index are grouped together. + * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, score_threshold. Boxes + * with scores lower than the threshold are filtered before sending + * to the NMS algorithm. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum + * number of selected bounding boxes for each image. Set to a negative + * value for unlimited number of output bounding boxes. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the NMS + * kernel method, options are 0:hard, 1:linear, 2:gaussian. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU + * threshold in hard and linear NMS kernel. This field is ignored if + * gaussian kernel is selected. + * * 7: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the sigma in + * gaussian NMS kernel. This field is ignored if gaussian kernel is + * not selected. + * * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, nms_score_threshold. + * Boxes with scores lower than the threshold are dropped during the + * score updating phase in soft NMS. + * + * Outputs: + * * 0: A 1-D Tensor of the same {@link OperandCode} as input0, with shape + * [num_output_rois], specifying the score of each output box. The boxes + * are grouped by batches, but the sequential order in each batch is not + * guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * or {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the scale and zero point must be the same as input0. + * * 1: A 2-D Tensor of the same {@link OperandCode} as input1, with shape + * [num_output_rois, 4], specifying the coordinates of each + * output bounding box with the same format as input1. The sequential + * order of the boxes corresponds with output0. For type of + * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the scale must be + * 0.125 and the zero point must be 0. + * * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_output_rois], specifying the class of each output box. The + * sequential order of the boxes corresponds with output0. + * * 3: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_output_rois], specifying the batch index of each box. Boxes + * with the same batch index are grouped together. + * + * Available since NNAPI feature level 3. + */ + BOX_WITH_NMS_LIMIT = 44, + + /** + * Casts a tensor to a type. + * + * This operation ignores the scale and zeroPoint of quanized tensors, + * e.g. it treats a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} input + * as a tensor of uint8 values. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * Since NNAPI feature level 4, casting tensors of the following + * {@link OperandCode} to the same {@link OperandCode} is supported: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since NNAPI feature level 3. + */ + CAST = 45, + + /** + * Shuffle the channels of the input tensor. + * + * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE + * divide the channel dimension into num_groups groups, and reorganize the + * channels by grouping channels with the same index in each group. + * + * Along the channel dimension, the output is calculated using this formula: + * + * output_channel[k * num_groups + g] = input_channel[g * group_size + k] + * + * where group_size = num_channels / num_groups + * + * The number of channels must be divisible by num_groups. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be shuffled. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * groups. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the dimension + * channel shuffle would be performed on. Negative index is used to + * specify axis from the end (e.g. -1 for the last axis). Must be in + * the range [-n, n). + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} and same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + CHANNEL_SHUFFLE = 46, + + /** + * Apply postprocessing steps to bounding box detections. + * + * Bounding box detections are generated by applying transformation on a set + * of predefined anchors with the bounding box deltas from bounding box + * regression. A final step of hard NMS is applied to limit the number of + * returned boxes. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying + * the score of each anchor with each class. Class 0 for each + * [batches, num_anchors, 0] is background and will be ignored. + * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with + * the first four values in length_box_encoding specifying the bounding + * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw], + * where dy and dx is the linear-scale relative correction factor for the + * center position of the bounding box with respect to the width and height, + * dh and dw is the log-scale relative correction factor for the width and + * height. All the entries in length_box_encoding beyond the first four + * values are ignored in this operation. + * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each + * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and + * ctr_x are the center position of the box, and h and w are the height + * and the width. + * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling + * factor for dy in bounding box deltas. + * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling + * factor for dx in bounding box deltas. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling + * factor for dh in bounding box deltas. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling + * factor for dw in bounding box deltas. + * * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to use regular + * multi-class NMS algorithm that do NMS separately for each class, + * set to false for a faster algorithm that only do one single NMS + * using the highest class score.. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, max_num_detections, specifying + * the maximum number of boxes for the output. Boxes with the lowest + * scores are discarded to meet the limit. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is + * set to false, specifying the maximum number of classes per detection. + * * 10: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is + * set to true, specifying the maximum number of detections when + * applying NMS algorithm for each single class. + * * 11: A scalar, score_threshold. Boxes with scores lower than the + * threshold are filtered before sending to the NMS algorithm. The + * scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar + * must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * * 13: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to include + * background class in the list of label map for the output, set + * to false to not include the background. When the background + * class is included, it has label 0 and the output classes start + * at 1 in the label map, otherwise, the output classes start at 0. + * + * Outputs: + * * 0: A 2-D tensor of the same {@link OperandCode} as input0, with shape + * [batches, max_num_detections], specifying the score of each output + * detections. + * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the + * coordinates of each output bounding box, with format + * [y1, x1, y2, x2]. + * * 2: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [batches, max_num_detections], specifying the class label for each + * output detection. + * * 3: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape [batches], + * specifying the number of valid output detections for each batch. + * + * Available since NNAPI feature level 3. + */ + DETECTION_POSTPROCESSING = 47, + + /** + * For input tensors x and y, computes x == y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + EQUAL = 48, + + /** + * Computes exponential of x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + EXP = 49, + + /** + * Inserts a dimension of 1 into a tensor's shape. + * + * Given a tensor input, this operation inserts a dimension of 1 at the + * given dimension index of input's shape. The dimension index starts at + * zero; if you specify a negative dimension index, it is counted backward + * from the end. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the dimension + * index to expand. Must be in the range [-(n + 1), (n + 1)). + * + * Outputs: + * * 0: An (n + 1)-D tensor with the same {@link OperandCode} and data as + * input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + EXPAND_DIMS = 50, + + /** + * Gathers values along an axis. + * + * Produces an output tensor with shape + * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] + * where: + * # Vector indices (output is rank(input0)). + * output[a_0, ..., a_n, i, b_0, ..., b_n] = + * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n] + * + * # Higher rank indices (output is rank(input0) + rank(indices) - 1). + * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = + * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor from which to gather values. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis. + * Negative index is used to specify axis from the end + * (e.g. -1 for the last axis). Must be in the range [-n, n). + * * 2: A k-D tensor {@link ANEURALNETWORKS_TENSOR_INT32} of indices. + * The values must be in the bounds of the corresponding dimensions + * of input0. + * + * Outputs: + * * 0: An (n + k - 1)-D tensor with the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + GATHER = 51, + + /** + * Generate aixs-aligned bounding box proposals. + * + * Bounding box proposals are generated by applying transformation on a set + * of predefined anchors with the bounding box deltas from bounding box + * regression. A final step of hard NMS is applied to limit the number of + * returned boxes. + * + * Axis-aligned bounding boxes are represented by its upper-left corner + * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid + * bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Inputs: + * * 0: A 4-D Tensor specifying the score of each anchor at each + * location. With "NHWC" data layout, the tensor shape is + * [batches, height, width, num_anchors]. With "NCHW" data layout, + * the tensor shape is [batches, num_anchors, height, width]. + * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data + * layout, the tensor shape is [batches, height, width, num_anchors * 4]. + * With "NCHW" data layout, the tensor shape is + * [batches, num_anchors * 4, height, width]. The box deltas are encoded + * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale + * relative correction factor for the center position of the bounding box + * with respect to the width and height, dw and dh is the log-scale + * relative correction factor for the width and height. The last + * dimensions is the channel dimension. + * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each + * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor should be of + * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with scale of 0.125. + * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of + * each image in the batch, with format [image_height, image_width]. + * For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this + * tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with + * scale of 0.125. + * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum + * number of boxes before going into the hard NMS algorithm. Boxes + * with the lowest scores are discarded to meet the limit. Set to + * a non-positive value for unlimited number. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum + * number of boxes returning from the hard NMS algorithm. Boxes + * with the lowest scores are discarded to meet the limit. Set to + * a non-positive value for unlimited number. + * * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU + * threshold for hard NMS. + * * 9: An {@link ANEURALNETWORKS_FLOAT32} scalar, min_size. Boxes with + * height or width lower than the absolute threshold are filtered out. + * * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and input1. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0, of shape + * [num_output_rois], specifying the score of each output box. + * The boxes are grouped by batches, but the sequential order in + * each batch is not guaranteed. For type of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scale and zero + * point must be the same as input0. + * * 1: A tensor of the same {@link OperandCode} as input3, of shape + * [num_output_rois, 4], specifying the coordinates of each output + * bounding box for each class, with format [x1, y1, x2, y2]. + * The sequential order of the boxes corresponds with output0. + * For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the + * scale must be 0.125 and the zero point must be 0. + * * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_output_rois], specifying the batch index of each box. Boxes + * with the same batch index are grouped together. + * + * Available since NNAPI feature level 3. + */ + GENERATE_PROPOSALS = 52, + + /** + * For input tensors x and y, computes x > y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + ANEURALNETWORKS_GREATER = 53, + /** + * For input tensors x and y, computes x >= y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + GREATER_EQUAL = 54, + + /** + * Performs a grouped 2-D convolution operation. + * + * Given an input tensor of shape [batches, height, width, depth_in] and a + * filter tensor of shape [depth_out, filter_height, filter_width, depth_group] + * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV + * applies a group of different filters to each input channel group, then + * concatenates the results together. + * + * Specifically, the input channels are divided into num_groups groups, each with + * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional + * filters are also divided into num_groups groups, i.e. depth_out is divisible + * by num_groups. GROUPED_CONV applies each group of filters to the corresponding + * input channel group, and the result are concatenated together. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, g * channel_multiplier + q] = + * sum_{di, dj, dk} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, + * g * depth_group + dk] * + * filter[g * channel_multiplier + q, di, dj, dk] + * ) + bias[channel] + * + * where channel_multiplier = depth_out / num_groups + * + * Supported tensor {@link OperandCode} configurations: + * * 16 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. + * + * * 32 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized signed (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized with symmetric per channel quantization for the filter: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * * Quantized signed with filter symmetric per channel quantization + * (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input, where depth_in = num_groups * depth_group. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_group], specifying + * the filter, where depth_out must be divisible by num_groups. For + * tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension (channelDim at + * {@link ANeuralNetworksSymmPerChannelQuantParams}) must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * groups. + * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 11: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input, where depth_in = num_groups * depth_group. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_group], specifying + * the filter, where depth_out must be divisible by num_groups. For + * tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) + * must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * groups. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + GROUPED_CONV_2D = 55, + + /** + * Localize the maximum keypoints from heatmaps. + * + * This operation approximates the accurate maximum keypoint scores and + * indices after bicubic upscaling by using Taylor expansion up to the + * quadratic term. + * + * The bounding box is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A valid bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D Tensor of shape + * [num_boxes, heatmap_size, heatmap_size, num_keypoints], + * specifying the heatmaps, the height and width of heatmaps should + * be the same, and must be greater than or equal to 2. + * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes, + * each with format [x1, y1, x2, y2]. For input0 of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should + * be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint + * of 0 and scale of 0.125. + * For input0 of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor + * should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with + * zeroPoint of -128 and scale of 0.125. + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0, with shape + * [num_boxes, num_keypoints], specifying score of the keypoints. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from input0 scale and zeroPoint. + * * 1: A tensor of the same {@link OperandCode} as input1, with shape + * [num_boxes, num_keypoints, 2], specifying the location of + * the keypoints, the second dimension is organized as + * [keypoint_x, keypoint_y]. + * For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the + * scale must be 0.125 and the zero point must be 0. + * + * Available since NNAPI feature level 3. + */ + HEATMAP_MAX_KEYPOINT = 56, + + /** + * Applies instance normalization to the input tensor. + * + * The values in the output tensor are computed as: + * + * output[b, h, w, c] = + * (input[b, h, w, c] - mean[b, c]) * gamma / + * sqrt(var[b, c] + epsilon) + beta + * + * Where the mean and variance are computed across the spatial dimensions: + * + * mean[b, c] = + * sum_{h, w}(input[b, h, w, c]) / sum(1) + * + * var[b, c] = + * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be normalized. + * * 1: A scalar, specifying gamma, the scale applied to the normalized + * tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if + * input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * * 2: A scalar, specifying beta, the offset applied to the normalized + * tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if + * input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * * 3: A scalar, specifying epsilon, the small value added to variance to + * avoid dividing by zero. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if + * input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} if input0 is of + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * * 4: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} and same shape as input0. + * + * Available since NNAPI feature level 3. + */ + INSTANCE_NORMALIZATION = 57, + + /** + * For input tensors x and y, computes x < y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + LESS = 58, + + /** + * For input tensors x and y, computes x <= y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + LESS_EQUAL = 59, + + /** + * Computes natural logarithm of x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + LOG = 60, + + /** + * Returns the truth value of x AND y element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions + * compatible with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + LOGICAL_AND = 61, + + /** + * Computes the truth value of NOT x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + LOGICAL_NOT = 62, + + /** + * Returns the truth value of x OR y element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions + * compatible with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + LOGICAL_OR = 63, + + /** + * Computes the log softmax activations given logits. + * + * The output is calculated using this formula: + * + * output = logits * beta - log(reduce_sum(exp(logits * beta), axis)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor specifying the input logits. + * * 1: A scalar, specifying the positive scaling factor for the exponent, + * beta. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the beta + * value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the beta + * value must be of {@link ANEURALNETWORKS_FLOAT32}. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: The output tensor of the same {@link OperandCode} and shape as + * input0. + * + * Available since NNAPI feature level 3. + */ + LOG_SOFTMAX = 64, + + /** + * Returns the element-wise maximum of two tensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and compatible dimensions + * with input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + MAXIMUM = 65, + + /** + * Returns the element-wise minimum of two tensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and compatible dimensions + * with input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + MINIMUM = 66, + + /** + * Computes numerical negative value element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + NEG = 67, + + /** + * For input tensors x and y, computes x != y elementwise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. + * + * Available since NNAPI feature level 3. + */ + NOT_EQUAL = 68, + + /** + * Pads a tensor with the given constant value according to the specified + * paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be padded. + * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. The shape of the + * tensor must be {rank(input0), 2}. + * padding[i, 0] specifies the number of elements to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of elements to be padded after + * the end of dimension i. + * * 2: A scalar specifying the value to use for padding input0. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the + * pad value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the + * pad value must be of {@link ANEURALNETWORKS_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the pad value must be of {@link ANEURALNETWORKS_INT32}. The + * scale and zeroPoint are assumed to be the same as in input0. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. The + * output tensor has the same rank as input0, and each + * dimension of the output tensor has the same size as the + * corresponding dimension of the input tensor plus the size + * of the padding: + * output0.dimension[i] = + * padding[i, 0] + input0.dimension[i] + padding[i, 1] + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + PAD_V2 = 69, + + /** + * Computes the power of one value to another. + * + * Given a tensor base and a tensor exponent, this operation computes + * base^exponent elementwise. + * + * This operations supports broadcasting. The size of the output is the + * maximum size along each dimension of the input operands. It starts with + * the trailing dimensions, and works its way forward. + * + * For example: + * base.dimension = {4, 1, 2} + * exponent.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor specifying the base. + * * 1: A tensor specifying the exponent. + * + * Outputs: + * * 0: An output tensor. + * + * Available since NNAPI feature level 3. + */ + POW = 70, + + /** + * Parametric Rectified Linear Unit. + * + * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha + * is a learned array with the same {@link OperandCode} and compatible + * dimensions as input x. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input.dimension = {4, 1, 2} + * alpha.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0, specifying the alpha. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + PRELU = 71, + + /** + * Quantizes the input tensor. + * + * The formula for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} output tensor is: + * + * output = max(0, min(255, round(input / scale) + zeroPoint) + * + * The formula for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} output + * tensor is: + * + * output = max(-128, min(127, round(input / scale) + zeroPoint) + * + * Supported input tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported output tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor, may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0, but with + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or. + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}. + * + * Available since NNAPI feature level 3. + */ + QUANTIZE = 72, + + /** + * A version of quantized LSTM, using 16 bit quantization for internal + * state. + * + * There is no projection layer, so cell state size is equal to the output + * size. + * + * Inputs: + * * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBatches, inputSize] specifying the input to the LSTM + * cell. Tensor is quantized with a fixed quantization range of + * [-1, 127/128] (scale = 1/128, zeroPoint = 128). + * * 1: The input-to-input weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-input part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 2: The input-to-forget weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-forget part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 3: The input-to-cell weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-cell part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 4: The input-to-output weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-output part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 5: The recurrent-to-input weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-input part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 6: The recurrent-to-forget weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-forget + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 7: The recurrent-to-cell weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-cell part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 8: The recurrent-to-output weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-output + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 9: The input gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 10:The forget gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 11:The cell bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 12:The output gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 13: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] specifying the cell state from the + * previous time step of the LSTM cell. It is quantized using a + * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / + * 32768, zeroPoint = 0). + * * 14: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] specifying the output of the LSTM + * cell from previous time-step. Tensor is quantized with a fixed + * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint = + * 128). + * + * + * Outputs: + * * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] which contains a cell state from + * the current time step. Tensor is quantized using a quantization + * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint = + * 0). + * * 1: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] which contains the output value. + * Tensor is quantized with a fixed quantization range of [-1, 127/128] + * (scale = 1/128, zeroPoint = 128). + */ + QUANTIZED_16BIT_LSTM = 73, + + /** + * Draws samples from a multinomial distribution. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 2-D tensor with shape [batches, classes], specifying the + * unnormalized log-probabilities for all classes. + * * 1: A scalar {@link ANEURALNETWORKS_INT32}, specifying the number of + * independent samples to draw for each row slice. + * * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [2], + * specifying seeds used to initialize the random distribution. If both + * provided seeds are 0, both will be randomly generated. + * Outputs: + * * 0: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape + * [batches, samples], containing the drawn samples. + * + * Available since NNAPI feature level 3. + */ + RANDOM_MULTINOMIAL = 74, + + /** + * Reduces a tensor by computing the "logical and" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * + * Available since NNAPI feature level 3. + */ + REDUCE_ALL = 75, + + /** + * Reduces a tensor by computing the "logical or" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * + * Available since NNAPI feature level 3. + */ + REDUCE_ANY = 76, + + /** + * Reduces a tensor by computing the maximum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + REDUCE_MAX = 77, + + /** + * Reduces a tensor by computing the minimum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + REDUCE_MIN = 78, + + /** + * Reduces a tensor by multiplying elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * + * Available since NNAPI feature level 3. + */ + REDUCE_PROD = 79, + + /** + * Reduces a tensor by summing elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * If all dimensions are reduced and keep_dims is false, the output + * shape is [1]. + * + * Available since NNAPI feature level 3. + */ + REDUCE_SUM = 80, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by average pooling sampling points from bilinear interpolation. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * No rounding is applied in this operation. The sampling points are unified + * distributed in the pooling bin and their values are calculated by bilinear + * interpolation. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line with format [x1, y1, x2, y2]. + * For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, + * with zeroPoint of 0 and scale of 0.125. Zero num_rois is + * supported for this tensor. + * * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_rois], specifying the batch index of each box. Boxes with + * the same batch index are grouped together. Zero num_rois is + * supported for this tensor. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * sampling points in height dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_height/out_height). + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * sampling points in width dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_width/out_width). + * * 9: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from the input0 scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + ROI_ALIGN = 81, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by max-pooling. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * Rounding is applied in this operation to ensure integer boundary for + * regions of interest and pooling bins. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line with format [x1, y1, x2, y2]. + * For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, + * with zeroPoint of 0 and scale of 0.125. + * * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape + * [num_rois], specifying the batch index of each box. Boxes with + * the same batch index are grouped together. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + ROI_POOLING = 82, + + /** + * Computes reciprocal of square root of x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 7) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 7) + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + RSQRT = 83, + + /** + * Using a tensor of booleans c and input tensors x and y select values + * elementwise from both input tensors: + * + * O[i] = C[i] ? x[i] : y[i]. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_BOOL8} acting as a + * mask that chooses, based on the value at each element, whether the + * corresponding element in the output should be taken from input1 (if + * true) or input2 (if false). + * * 1: An input tensor of the same shape as input0. + * * 2: An input tensor of the same shape and type as input1. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scales and zeroPoint can be different from input1 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same type and shape as input1 and input2. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + SELECT = 84, + + /** + * Computes sin of x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + SIN = 85, + + /** + * Extracts a slice of specified size from the input tensor starting at a + * specified location. + * + * The starting location is specified as a 1-D tensor containing offsets + * for each dimension. The size is specified as a 1-D tensor containing + * either size of a slice along corresponding dimension or -1. In the latter + * case, all the remaining elements in dimension are included in the slice. + * + * A sum of begin offset and a size of a slice must not exceed size of a + * corresponding dimension. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to take slice from, may be zero-sized. + * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying + * the beginning indices of the slice in each dimension. + * * 2: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying + * the size of the slice in each dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input containing the slice. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * its scale and zeroPoint has to be same as the input0 scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + SLICE = 86, + + /** + * Splits a tensor along a given axis into num_splits subtensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to split. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis along + * which to split. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar indicating the number of + * splits along given axis. Must evenly divide axis size. + * + * Outputs: + * * 0 ~ (num_splits - 1): Resulting subtensors. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + SPLIT = 87, + + /** + * Computes square root of x element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since NNAPI feature level 3. + */ + SQRT = 88, + + /** + * Constructs a tensor by tiling a given tensor. + * + * This operation creates a new tensor by replicating `input` `multiples` + * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]` + * elements, and the values of `input` are replicated `multiples[i]` times + * along the i-th dimension. + * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: multiples, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. + * The length of multiples must be n. + * + * Outputs: + * * 0: A tiled tensor of the same {@link OperandCode} and rank as `input`. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + TILE = 89, + + /** + * Finds values and indices of the k largest entries for the last dimension. + * + * Resulting values in each dimensions are sorted in descending order. If + * two values are equal, the one with larger index appears first. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: k, an {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * top elements to look for along the last dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input, containing the k + * largest elements along each last dimensional slice. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * * 1: An n-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} + * containing the indices of values within the last dimension of input. + * + * Available since NNAPI feature level 3. + */ + TOPK_V2 = 90, + + /** + * Performs the transpose of 2-D convolution operation. + * + * This operation is sometimes called "deconvolution" after Deconvolutional + * Networks, but is actually the transpose (gradient) of + * {@link ANEURALNETWORKS_CONV_2D} rather than an actual deconvolution. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * Supported tensor {@link OperandCode} configurations: + * * 16 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. + * + * * 32 bit floating point: + * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized with symmetric per channel quantization for the filter: + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Available since NNAPI feature level 4: + * * Quantized signed (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized signed with filter symmetric per channel quantization + * (since NNAPI feature level 4): + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. + * * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * Since API level 29, zero batches is supported for this tensor. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. For tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel + * dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the + * same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, + * with zeroPoint of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias must be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * Since API level 29, zero batches is supported for this tensor. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. For tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel + * dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or + * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias should be of the + * same type. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, + * the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, + * with zeroPoint of 0 and bias_scale == input_scale * filter_scale. + * For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, + * the bias must be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 + * and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link ANEURALNETWORKS_TENSOR_INT32} tensor, specifying the output + * tensor shape. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since NNAPI feature level 3. + */ + TRANSPOSE_CONV_2D = 91, + + /** + * A recurrent neural network specified by an LSTM cell. + * + * Performs (fully) dynamic unrolling of input. + * + * This Op unrolls the input along the time dimension, and implements the + * following operation for each element in the sequence + * s = 1...sequence_length: + * outputs[s] = projection(state = activation(LSTMOp(inputs[s]))) + * + * Where LSTMOp is the LSTM op as in {@link ANEURALNETWORKS_LSTM}, + * the "projection" is an optional projection layer from state and output + * and the “activation” is the function passed as the + * “fused_activation_function” argument (if not “NONE”). + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 3, either time-major or batch-major. + * + * All input and output tensors must be of the same type. + * + * Inputs: + * * 0: The input (\f$x_t\f$). + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, input_size] + * If batch-major: [batch_size, max_time, input_size] + * where “max_time” is the number of timesteps (sequence length), + * “batch_size” corresponds to the batching dimension, and + * “input_size” is the size of the input. + * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 2: The input-to-forget weights (\f$W_{xf}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 3: The input-to-cell weights (\f$W_{xc}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 4: The input-to-output weights (\f$W_{xo}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 12:The input gate bias (\f$b_i\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 13:The forget gate bias (\f$b_f\f$). + * A 1-D tensor of shape [num_units]. + * * 14:The cell bias (\f$b_c\f$). + * A 1-D tensor of shape [num_units]. + * * 15:The output gate bias (\f$b_o\f$). + * A 1-D tensor of shape [num_units]. + * * 16:The projection weights (\f$W_{proj}\f$). Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 17:The projection bias (\f$b_{proj}\f$). Optional. + * A 1-D tensor of shape [output_size]. + * * 18:The output state (in) (\f$h_{t-1}\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 19:The cell state (in) (\f$C_{t-1}\f$). + * A 2-D tensor of shape [batch_size, num_units]. + * * 20:The activation function (\f$g\f$). + * A value indicating the activation function: + * + * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such + * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 + * then clipping is disabled. + * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the + * projection layer, such that values are bound within + * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + * * 23:Time-major if true, batch-major if false. + * * 24:The input layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 25:The forget layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 26:The cell layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 27:The output layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The output (\f$o_t\f$). + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * * 1: A tensor of shape [batch_size, output_size] containing a hidden + * state from the last time step in the sequence. This output is + * optional and can be omitted. If this output is present then + * output #2 must be present as well. + * Available since NNAPI feature level 4. + * * 2: A tensor of shape [batch_size, cell_size] containing a cell state + * from the last time step in the sequence. This output is optional + * and can be omitted. + * Available since NNAPI feature level 4. + * + * Available since NNAPI feature level 3. + * + * Important: As of NNAPI feature level 3, there is no way to get the output state tensors out + * and NNAPI does not maintain internal states. This operator does not support the usage pattern + * in which multiple cells are chained and state tensors are propagated. + */ + UNIDIRECTIONAL_SEQUENCE_LSTM = 92, + + /** + * A recurrent neural network layer that applies a basic RNN cell to a + * sequence of inputs. + * + * This layer unrolls the input along the sequence dimension, and implements + * the following operation + * for each element in the sequence s = 1...sequence_length: + * outputs[s] = state = activation(inputs[s] * input_weights’ + state * + * recurrent_weights’ + bias) + * + * Where: + * * “input_weights” is a weight matrix that multiplies the inputs; + * * “recurrent_weights” is a weight matrix that multiplies the current + * “state” which itself is the output from the previous time step + * computation; + * * “bias” is a bias vector (added to each output vector in the batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to 1, then the input has a shape [maxTime, batchSize, + * inputSize], otherwise the input has a shape [batchSize, maxTime, + * inputSize]. + * * 1: weights. + * A 2-D tensor of shape [numUnits, inputSize]. + * * 2: recurrent_weights. + * A 2-D tensor of shape [numUnits, numUnits]. + * * 3: bias. + * A 1-D tensor of shape [numUnits]. + * * 4: hidden state + * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 5: fusedActivationFunction. + * A {@link FuseCode} value indicating the activation function. If + * “NONE” is specified then it results in a linear activation. + * * 6: timeMajor + * An {@link ANEURALNETWORKS_INT32} scalar specifying the shape format + * of input and output tensors. Must be set to either 0 or 1. + * Outputs: + * * 0: output. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to 1, then the output has a shape [maxTime, batchSize, + * numUnits], otherwise the output has a shape [batchSize, maxTime, + * numUnits]. + * * 1: A tensor of shape [batchSize, numUnits] containing hidden state + * from the last time step in the sequence. This output is optional + * and can be omitted. + * Available since NNAPI feature level 4. + * + * Available since NNAPI feature level 3. + * + * Important: As of NNAPI feature level 3, there is no way to get the output state tensors out + * and NNAPI does not maintain internal states. This operator does not support the usage pattern + * in which multiple cells are chained and state tensors are propagated. + */ + UNIDIRECTIONAL_SEQUENCE_RNN = 93, + + /** + * Resizes images to given size using the nearest neighbor interpretation. + * + * Resized images must be distorted if their output aspect ratio is not the + * same as input aspect ratio. The corner pixels of output may not be the + * same as corner pixels of input. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both resizing by shape and resizing by scale are supported. + * + * Inputs (resizing by shape): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Zero batches is supported for this tensor. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 3: An {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the centers of the 4 corner + * pixels of the input and output tensors are aligned, preserving the + * values at the corner pixels. + * Available since NNAPI feature level 4. + * * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the pixel centers are assumed to + * be at (0.5, 0.5). This is the default behavior of image.resize in + * TF 2.0. If this parameter is True, then align_corners parameter + * must be False. + * Available since NNAPI feature level 4. + * + * Inputs (resizing by scale): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Zero batches is supported for this tensor. + * * 1: A scalar, specifying width_scale, the scaling factor of the width + * dimension from the input tensor to the output tensor. The output + * width is calculated as new_width = floor(width * width_scale). + * The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is + * of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} otherwise. + * * 2: A scalar, specifying height_scale, the scaling factor of the height + * dimension from the input tensor to the output tensor. The output + * height is calculated as new_height = floor(height * height_scale). + * The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is + * of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of + * {@link ANEURALNETWORKS_FLOAT32} otherwise. + * * 3: An {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the centers of the 4 corner + * pixels of the input and output tensors are aligned, preserving the + * values at the corner pixels. + * Available since NNAPI feature level 4. + * * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} + * scalar, default to false. If True, the pixel centers are assumed to + * be at (0.5, 0.5). This is the default behavior of image.resize in + * TF 2.0. If this parameter is True, then align_corners parameter + * must be False. + * Available since NNAPI feature level 4. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 3. + */ + RESIZE_NEAREST_NEIGHBOR = 94, + + // Operations below are available since NNAPI feature level 4. + + /** + * Quantized version of {@link ANEURALNETWORKS_LSTM}. + * + * The input and the output use asymmetric quantized types, while the rest + * use symmetric ones. + * + * Inputs: + * * 0: The input to the LSTM cell. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * Shape: [batchSize, inputSize] + * * 1: The input-to-input weights. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, inputSize] + * * 2: The input-to-forget weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, inputSize] + * * 3: The input-to-cell weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, inputSize] + * * 4: The input-to-output weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, inputSize] + * * 5: The recurrent-to-input weights. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, outputSize] + * * 6: The recurrent-to-forget weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, outputSize] + * * 7: The recurrent-to-cell weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, outputSize] + * * 8: The recurrent-to-output weights. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [numUnits, outputSize] + * * 9: The cell-to-input weights (for peephole). Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 10: The cell-to-forget weights (for peephole). Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 11: The cell-to-output weights (for peephole). Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 12: The input gate bias. Quantized with scale being the + * product of input and weights scales and zeroPoint equal to 0. + * Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_INT32} + * Shape: [numUnits] + * * 13: The forget gate bias. Quantized with scale being the + * product of input and weights scales and zeroPoint equal to 0. + * Type: {@link ANEURALNETWORKS_TENSOR_INT32} + * Shape: [numUnits] + * * 14: The cell bias. Quantized with scale being the + * product of input and weights scales and zeroPoint equal to 0. + * Type: {@link ANEURALNETWORKS_TENSOR_INT32} + * Shape: [numUnits] + * * 15: The output gate bias. Quantized with scale being the + * product of input and weights scales and zeroPoint equal to 0. + * Type: {@link ANEURALNETWORKS_TENSOR_INT32} + * Shape: [numUnits] + * * 16: The projection weights. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * Shape: [outputSize, numUnits] + * * 17: The projection bias. Quantized with scale being the + * product of input and weights scales and zeroPoint equal to 0. + * Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_INT32} + * Shape: [outputSize] + * * 18: The output from the previous time step. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * Shape: [batchSize, outputSize] + * * 19: The cell state from the previous time step. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [batchSize, numUnits] + * * 20: The input layer normalization weights. Used to rescale + * normalized inputs to activation at input gate. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 21: The forget layer normalization weights. Used to + * rescale normalized inputs to activation at forget gate. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 22: The cell layer normalization weights. Used to rescale + * normalized inputs to activation at cell gate. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 23: The output layer normalization weights. Used to + * rescale normalized inputs to activation at output gate. Optional. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [numUnits] + * * 24: The cell clip. If provided the cell state is clipped + * by this value prior to the cell output activation. Optional. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 25: The projection clip. If provided and projection is enabled, + * this is used for clipping the projected values. Optional. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 26: The scale of the intermediate result of matmul, + * i.e. input to layer normalization, at input gate. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 27: The scale of the intermediate result of matmul, + * i.e. input to layer normalization, at forget gate. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 28: The scale of the intermediate result of matmul, + * i.e. input to layer normalization, at cell gate. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 29: The scale of the intermediate result of matmul, + * i.e. input to layer normalization, at output gate. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * * 30: The zero point of the hidden state, i.e. input to + * projection. + * Type: {@link ANEURALNETWORKS_INT32}. + * * 31: The scale of the hidden state, i.e. input to + * projection. + * Type: {@link ANEURALNETWORKS_FLOAT32}. + * + * Outputs: + * * 0: The output state (out). + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * Shape: [batchSize, outputSize] + * * 1: The cell state (out). + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * Shape: [batchSize, numUnits] + * * 2: The output. This is effectively the same as the current + * "output state (out)" value. + * Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * Shape: [batchSize, outputSize] + * + * Available since NNAPI feature level 4. + */ + QUANTIZED_LSTM = 95, + + /** + * Executes one of the two referenced models as determined by a boolean + * value. + * + * The inputs and outputs of the two referenced models must agree with the + * signature of this operation. That is, if the operation has (3 + n) inputs + * and m outputs, both models must have n inputs and m outputs with the same + * types, ranks (if specified), dimensions (if specified), scales, + * zeroPoints, and other operand parameters as the corresponding operation + * inputs and outputs. + * + * Inputs: + * * 0: A value of type {@link ANEURALNETWORKS_TENSOR_BOOL8} and shape [1] + * that determines which of the two referenced models to execute. + * The operand must have fully specified dimensions. + * * 1: A {@link ANEURALNETWORKS_MODEL} reference to the model to be + * executed if the condition is true. + * * 2: A {@link ANEURALNETWORKS_MODEL} reference to the model to be + * executed if the condition is false. + * * 3 ~ (n + 2): Inputs to be passed to the model selected for execution. + * + * Outputs: + * * 0 ~ (m - 1): Outputs produced by the selected model. + * + * Available since NNAPI feature level 4. + */ + IF = 96, + + /** + * Executes the body model until the condition model outputs false. + * + * The inputs to this operation are the condition model, the body model, + * and operand values for the first iteration of the loop. The values are + * implicitly split into three groups of input-output, state-only, and + * input-only values, as described below. + * + * The outputs of this operation are the final values of input-output + * operands. + * + * Both the condition and body model receive (m + k + n) inputs. + * * The first m (m >= 1) inputs are input-output operands. For the first + * iteration, these are initialized from the corresponding inputs of the + * WHILE operation. In subsequent iterations, their values come from the + * corresponding outputs of the body model produced during the previous + * iteration. + * * The next k (k >= 0) inputs are state-only operands. They are similar to + * the input-output operands, except that their values are no longer + * available after the loop terminates. + * * The last n (n >= 0) inputs are input-only operands. Their values come + * from the corresponding inputs of the WHILE operation. + * + * The body model produces (m + k) outputs. + * * The first m outputs are input-output operands. They become the outputs + * of the WHILE operation when a termination condition is reached. + * * The last k outputs are state-only operands. Their values are no longer + * available after the loop terminates. + * + * The numbers m, k, and n are inferred by the runtime as follows: + * m = (WHILE operation output count) + * k = (body model output count) - m + * n = (body model input count) - m - k + * + * The pseudo-code below illustrates the flow of a WHILE operation with + * inputs condition, body, initial_input_output, initial_state, input_only + * (m = 1, k = 1, n = 1): + * + * input_output = initial_input_output + * state = initial_state + * while condition(input_output, state, input_only): + * input_output, state = body(input_output, state, input_only) + * return input_output + * + * To prevent infinite loops, there is an implicit execution timeout + * associated with each loop ("loop timeout duration"). See {@link + * ANeuralNetworksExecution_setLoopTimeout}. + * + * Inputs: + * * 0: A {@link ANEURALNETWORKS_MODEL} reference to the condition + * model. The model must have (m + k + n) inputs with + * the same types, ranks (if specified), dimensions (if specified), + * scales, zeroPoints, and other operand parameters as the + * corresponding inputs of the WHILE operation and exactly one output + * of {@link ANEURALNETWORKS_TENSOR_BOOL8} and shape [1]. + * The output operand must have fully specified dimensions. + * * 1: A {@link ANEURALNETWORKS_MODEL} reference to the body model. + * The model must have (m + k + n) inputs and (m + k) outputs with + * the same types, ranks (if specified), dimensions (if specified), + * scales, zeroPoints, and other operand parameters as the + * corresponding inputs and outputs of the WHILE operation. + * * (m inputs): Initial values for input-output operands. + * * (k inputs): Initial values for state-only operands. + * * (n inputs): Values for input-only operands. + * + * Outputs: + * * 0 ~ (m - 1): Outputs produced by the loop. + * + * Available since NNAPI feature level 4. + */ + WHILE = 97, + + /** + * Computes exponential linear activation on the input tensor element-wise. + * + * The output is calculated using the following formula: + * + * ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1)) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor, specifying the input. May be zero-sized. + * * 1: A scalar, specifying the alpha parameter. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, + * the alpha value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * the alpha value must be of {@link ANEURALNETWORKS_FLOAT32}. + * + * Outputs: + * * 0: The output tensor of same shape and type as input0. + * + * Available since NNAPI feature level 4. + */ + ELU = 98, + + /** + * Computes hard-swish activation on the input tensor element-wise. + * + * Hard swish activation is introduced in + * https://arxiv.org/pdf/1905.02244.pdf + * + * The output is calculated using the following formula: + * + * h-swish(x) = x * max(0, min(6, (x + 3))) / 6 + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor, specifying the input. May be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape and type as input0. + * Scale and zero point of this tensor may be different from the input + * tensor's parameters. + * + * Available since NNAPI feature level 4. + */ + HARD_SWISH = 99, + + /** + * Creates a tensor filled with a scalar value. + * + * Supported output tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A 1-D tensor, specifying the desired output tensor shape. + * * 1: A scalar, specifying the value to fill the output tensors with. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, + * the scalar must be of {@link ANEURALNETWORKS_FLOAT16}. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * the scalar must be of {@link ANEURALNETWORKS_FLOAT32}. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, + * the scalar must be of {@link ANEURALNETWORKS_INT32}. + * + * Outputs: + * * 0: The output tensor. + * + * Available since NNAPI feature level 4. + */ + FILL = 100, + + /** + * Returns the rank of a tensor. + * + * The rank of a tensor is the number of dimensions in it. Also known as + * "order", "degree", "ndims". + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} + * * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: The input tensor. + * + * Outputs: + * * 0: A scalar of {@link ANEURALNETWORKS_INT32}, specifying the rank + * of the input tensor. + * + * Available since NNAPI feature level 4. + */ + RANK = 101, + + // Operations below are available since NNAPI feature level 6. + + /** + * Performs multiplication of two tensors in batches. + * + * Multiplies all slices of two input tensors and arranges the individual + * results in a single output tensor of the same batch size. Each pair of + * slices in the same batch have identical {@link OperandCode}. Each + * slice can optionally be adjointed (transpose and conjugate) before + * multiplication. + * + * The two input tensors and the output tensor must be 2-D or higher and + * have the same batch size. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: at least 2 and up to 4 + * + * Inputs: + * * 0: A tensor with 2-D or higher shape [..., r_x, c_x]. + * * 1: A tensor with 2-D or higher shape [..., r_y, c_y]. It has the same + * {@link OperandCode} and batch size as input0. + * * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar adj_x, default + * to false. Set to true to adjoint the slices of input0. + * * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar adj_y, default + * to false. Set to true to adjoint the slices of input1. + * + * Outputs: + * * 0: A tensor with 2-D or higher shape [..., r_o, c_o], where + * r_o = c_x if adj_x else r_x + * c_o = r_y if adj_y else c_y + * + * Available since NNAPI feature level 6. + */ + BATCH_MATMUL = 102, + + /** + * Packs N input tensors (N >= 1) of rank R into one output tensor of rank R+1. + * The tensors are packed along a given axis. + * + * The input tensors must have identical {@link OperandCode} and dimensions. + * + * For example, suppose there are N input tensors of shape (A, B, C). + * If axis is 0, the output tensor will have shape (N, A, B, C). + * If axis is 1, the output tensor will have shape (A, N, B, C). + * + * All dimensions through the axis dimension determine the output tile count; + * the remaining dimensions determine the tile shape. + * + * Return to the example of N input tensors of shape (A, B, C). + * If axis is 0, there are N tiles in the output, each of shape (A, B, C). + * If axis is 1, there are A*N tiles in the output, each of shape (B, C). + * + * The coordinates of a tile within the output tensor are (t[0],...,t[axis]). + * The coordinates of a tile within an input tensor are (t[0],...,t[axis-1]). + * (If axis is 0, an input tensor consists of a single tile.) + * If we index input tensors starting with 0 (rather than by operand number), + * then output_tile[t[0],...,t[axis]] = input_tile[t[axis]][t[0],...,t[axis-1]]. + * That is, all output tile coordinates except for the axis coordinate select + * the corresponding location within some input tensor; and the axis coordinate + * selects the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported input tensor rank: from 1 + * + * Inputs: + * * 0: A scalar of type {@link ANEURALNETWORKS_INT32}, specifying + * the axis along which to pack. The valid range is [0, R+1). + * * 1 ~ N: Input tensors to be packed together. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensors, + * the scales and zeroPoint must be the same for all input tensors, + * and will be the same for the output tensor. + * + * Outputs: + * * 0: The packed tensor. + * + * Available since NNAPI feature level 6. + */ + PACK = 103, + + // Operations below are available since NNAPI feature level 7. + + /** + * Pads a tensor with mirrored values. + * + * This operator specifies one of two padding modes: REFLECT or SYMMETRIC. + * In the case of REFLECT mode, the mirroring excludes the border element + * on the padding side. + * In the case of SYMMETRIC mode, the mirroring includes the border element + * on the padding side. + * + * For example, if the input is the 1-D tensor `[1, 2, 3]` and the padding + * is `[0, 2]` (i.e., pad no elements before the first (and only) dimension, + * and two elements after the first (and only) dimension), then: + * - REFLECT mode produces the output `[1, 2, 3, 2, 1]` + * - SYMMETRIC mode produces the output `[1, 2, 3, 3, 2]` + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be padded. + * * 1: A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. The shape of the + * tensor must be {rank(input0), 2}. + * padding[i, 0] specifies the number of elements to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of elements to be padded after the + * end of dimension i. + * Each padding value must be nonnegative. + * In the case of REFLECT mode, each padding value must be less than the + * corresponding dimension. + * In the case of SYMMETRIC mode, each padding value must be less than or + * equal to the corresponding dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the mode. + * Options are 0:REFLECT and 1:SYMMETRIC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. The + * output tensor has the same rank as input0, and each + * dimension of the output tensor has the same size as the + * corresponding dimension of the input tensor plus the size + * of the padding: + * output0.dimension[i] = + * padding[i, 0] + input0.dimension[i] + padding[i, 1] + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 7. + */ + MIRROR_PAD = 104, + + /** + * Reverses a specified dimension of a tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: up to 8. + * + * Inputs: + * * 0: Input tensor of rank n. + * * 1: Axis tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape [1], + * specifying which dimension of the input tensor is to be reversed. The dimension + * must be in the range [0, n). + * + * Outputs: + * * 0: The reversed tensor of the same shape as the input tensor. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensors, + * the scales and zeroPoint must be the same as input0. + * + * Available since NNAPI feature level 7. + */ + REVERSE = 105, +} + +/** + * Fused activation function types. + * + * Available since NNAPI feature level 1. + */ +FuseCode :: enum { + /** NO fused activation function. */ + NONE = 0, + /** Fused ReLU activation function. */ + RELU = 1, + /** Fused ReLU1 activation function. */ + RELU1 = 2, + /** Fused ReLU6 activation function. */ + RELU6 = 3, +} + +/** + * Implicit padding algorithms. + * + * + * Available since NNAPI feature level 1. + */ +PaddingCode :: enum { + /** + * SAME padding. + * Padding on both ends are the "same": + * padding_to_beginning = total_padding / 2 + * padding_to_end = (total_padding + 1)/2. + * i.e., for even number of padding, padding to both ends are exactly + * the same; for odd number of padding, padding to the ending is bigger + * than the padding to the beginning by 1. + * + * total_padding is a function of input, stride, dilation and filter size. + * It could be computed as follows: + * out_size = (input + stride - 1) / stride + * effective_filter_size = (filter_size - 1) * dilation + 1 + * needed_input = (out_size - 1) * stride + effective_filter_size + * total_padding = max(0, needed_input - input_size) + * The computation is the same for the horizontal and vertical directions. + */ + SAME = 1, + + /** + * VALID padding. + * No padding. When the input size is not evenly divisible by + * the filter size, the input at the end that could not fill + * the whole filter tile will simply be ignored. + */ + VALID = 2, +} + +/** + * Execution preferences. + * + * Available since NNAPI feature level 1. + */ +PreferenceCode :: enum { + /** + * Prefer executing in a way that minimizes battery drain. + * This is desirable for compilations that will be executed often. + */ + PREFER_LOW_POWER = 0, + /** + * Prefer returning a single answer as fast as possible, even if this causes + * more power consumption. + */ + PREFER_FAST_SINGLE_ANSWER = 1, + /** + * Prefer maximizing the throughput of successive frames, for example when + * processing successive frames coming from the camera. + */ + PREFER_SUSTAINED_SPEED = 2, +} + +/** + * Device types. + * + * The type of NNAPI device. + */ +DeviceTypeCode :: enum { + /** The device type cannot be provided. */ + UNKNOWN = 0, + /** The device does not fall into any category below. */ + OTHER = 1, + /** The device runs NNAPI models on single or multi-core CPU. */ + CPU = 2, + /** The device can run NNAPI models and also accelerate graphics APIs such + * as OpenGL ES and Vulkan. */ + GPU = 3, + /** Dedicated accelerator for Machine Learning workloads. */ + ACCELERATOR = 4, +} + +/** + * NNAPI feature levels. + * + * Each update of the NNAPI specification yields a new NNAPI feature level enum value. + * NNAPI feature level corrseponds to an NNAPI specification version that a driver + * and/or the NNAPI runtime can implement. + * + * A feature level up to and including "FEATURE_LEVEL_5" maps directly to + * the Android API level that introduced the corresponding update of the NNAPI + * specification. Feature levels after Android API level 31 have no association with + * API level because the NNAPI specification can be updated between Android API + * releases. Outputs of {@link ANeuralNetworksDevice_getFeatureLevel} and + * {@link ANeuralNetworks_getRuntimeFeatureLevel} must be compared against + * these enum values instead of the Android API level. + */ +FeatureLevelCode :: enum { + /** NNAPI specification available in Android O-MR1, Android NNAPI feature level 1 */ + LEVEL_1 = 27, + /** NNAPI specification available in Android P, Android NNAPI feature level 2 */ + LEVEL_2 = 28, + /** NNAPI specification available in Android Q, Android NNAPI feature level 3 */ + LEVEL_3 = 29, + /** NNAPI specification available in Android R, Android NNAPI feature level 4 */ + LEVEL_4 = 30, + /** + * NNAPI specification available in Android S, Android NNAPI feature level 5. + * After Android S, the NNAPI specification can be updated between Android + * API releases. + */ + LEVEL_5 = 31, + /** Android NNAPI feature level 6 */ + LEVEL_6 = 1000006, + /** Android NNAPI feature level 7 */ + LEVEL_7 = 1000007, + /** Android NNAPI feature level 8 */ + LEVEL_8 = 1000008, +} + +/** + * Result codes. + * + *

Any NNAPI function can return any result code, including result codes not + * currently documented. Any value other than {@link ANEURALNETWORKS_NO_ERROR} + * indicates a failure of some kind.

+ * + *

Additional information about the nature of a failure can be obtained from + * the device log after enabling NNAPI debugging by setting the debug.nn.vlog + * property to 1, e.g., by calling "adb shell setprop debug.nn.vlog 1".

+ * + * Available since NNAPI feature level 1. + */ +NNResultCode :: enum { + /** + * Operation was successful. + */ + NO_ERROR = 0, + + /** + * Failure caused by not enough available memory. + */ + OUT_OF_MEMORY = 1, + + INCOMPLETE = 2, + + /** + * Failure caused by unexpected null argument. + */ + UNEXPECTED_NULL = 3, + + /** + * Failure caused by invalid function arguments, invalid model definition, + * invalid execution definition or invalid data at execution time. + */ + BAD_DATA = 4, + + /** + * Failure caused by failed model execution. + */ + OP_FAILED = 5, + + /** + * Failure caused by object being in the wrong state. + */ + BAD_STATE = 6, + + /** + * Failure caused by not being able to map a file into memory. + * This may be caused by a file descriptor not being mappable, or an AHardwareBuffer + * not supported by the device. + * Mitigate by reading its content into memory. + */ + UNMAPPABLE = 7, + + /** + * Failure caused by insufficient buffer size provided to a model output. + */ + OUTPUT_INSUFFICIENT_SIZE = 8, + + /** + * Failure caused by a device not being available. + */ + UNAVAILABLE_DEVICE = 9, + + /** + * Failure because a deadline could not be met for a task, but future + * deadlines may still be met for the same task after a short delay. + * + * Available since NNAPI feature level 4. + */ + MISSED_DEADLINE_TRANSIENT = 10, + + /** + * Failure because a deadline could not be met for a task, and future + * deadlines will likely also not be met for the same task even after a + * short delay. + * + * Available since NNAPI feature level 4. + */ + MISSED_DEADLINE_PERSISTENT = 11, + + /** + * Failure because of a resource limitation within the driver, but future + * calls for the same task may still succeed after a short delay. + * + * Available since NNAPI feature level 4. + */ + RESOURCE_EXHAUSTED_TRANSIENT = 12, + + /** + * Failure because of a resource limitation within the driver, and future + * calls for the same task will likely also fail even after a short + * delay. + * + * Available since NNAPI feature level 4. + */ + RESOURCE_EXHAUSTED_PERSISTENT = 13, + + /** + * Failure indicating an object is in a dead state. + * + * Available since NNAPI feature level 4. + */ + DEAD_OBJECT = 14, +} + +/** + * Different duration measurements. + * + * Durations are measured in nanoseconds. + * + * Available since NNAPI feature level 3. + */ +DurationCode :: enum { + // Execution time on hardware (not driver, which runs on host processor). + DURATION_ON_HARDWARE = 0, + // Execution time in driver (including time on hardware). Excludes overhead + // such as that of the runtime itself and the IPC needed for the runtime to + // communicate with the driver. + DURATION_IN_DRIVER = 1, + // Execution time on hardware, after all dependencies have been signaled. + // If no dependencies specified (for example, if the execution was scheduled other + // than with {@link ANeuralNetworksExecution_startComputeWithDependencies}), the + // reported time will be the same as ANEURALNETWORKS_DURATION_ON_HARDWARE. + // Available since NNAPI feature level 4. + FENCED_DURATION_ON_HARDWARE = 2, + // Execution time in driver, after all dependencies have been signaled. Excludes + // overhead such as that of the runtime itself and the IPC needed for the runtime + // to communicate with the driver. + // If no dependencies specified (for example, if the execution was scheduled other + // than with {@link ANeuralNetworksExecution_startComputeWithDependencies}), the + // reported time will be the same as ANEURALNETWORKS_DURATION_IN_DRIVER. + // Available since NNAPI feature level 4. + FENCED_DURATION_IN_DRIVER = 3, +} + +/** + * Relative execution priority. + * + * Available since NNAPI feature level 4. + */ +PriorityCode :: enum { + LOW = 90, + MEDIUM = 100, + HIGH = 110, + DEFAULT = MEDIUM, +} + +/** + * ANeuralNetworksMemory is an opaque type that represents memory. + * + * This type is used to represent shared memory, memory mapped files, + * and similar memories. + * + * By using shared memory, a program can efficiently communicate to the + * runtime and drivers the tensors that define a model. See + * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application + * should typically create one shared memory object that contains every constant tensor + * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be used to + * create shared memory from a file handle. + * {@link ANeuralNetworksMemory_createFromAHardwareBuffer} can be used to + * create shared memory from an AHardwareBuffer handle. + * + * Memory objects can also be used to specify the input and output arguments of + * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory} + * and {@link ANeuralNetworksExecution_setOutputFromMemory}. + * + * When calling {@link ANeuralNetworksModel_setOperandValueFromMemory}, + * {@link ANeuralNetworksExecution_setInputFromMemory} and + * {@link ANeuralNetworksExecution_setOutputFromMemory}, each operand in the shared + * memory object must be aligned on a boundary of a byte size that is a multiple + * of the element type byte size, e.g., a tensor with + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} type must be aligned on 4-byte boundary. + * + * It is the application's responsibility to ensure that there are no uses of + * the memory after calling {@link ANeuralNetworksMemory_free}. This includes + * any model which references this memory because of a call to + * {@link ANeuralNetworksModel_setOperandValueFromMemory}, any compilation + * created using such a model, any execution object or burst object created + * using such a compilation, or any execution which references this memory + * because of a call to {@link ANeuralNetworksExecution_setInputFromMemory} or + * {@link ANeuralNetworksExecution_setOutputFromMemory}. + * + * Available since NNAPI feature level 1. + * + * Starting at NNAPI feature level 4, the application may request creation of device native memory + * from {@link ANeuralNetworksMemoryDesc} to avoid potential memory copying and transformation + * overhead between executions. See also {@link ANeuralNetworksMemoryDesc} and + * {@link ANeuralNetworksMemory_createFromDesc}. + */ +ANeuralNetworksMemory :: struct{} + +/** + * ANeuralNetworksModel is an opaque type that contains a description of the + * mathematical operations that constitute the model. + * + *

Build the model by calling

+ * + * This forms a graph in which each operation and operand is a node, a + * directed edge from an operand to an operation indicates that the + * operand is an input to the operation, and a directed edge from an + * operation to an operand indicates that the operand is an output + * from the operation. This graph must be acyclic. + * + * A model is completed by calling {@link ANeuralNetworksModel_finish}. + * A model is destroyed by calling {@link ANeuralNetworksModel_free}. + * + *

A model cannot be modified once {@link ANeuralNetworksModel_finish} + * has been called on it.

+ * + *

It is the application's responsibility to make sure that only one thread + * modifies a model at a given time. It is however safe for more than one + * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.

+ * + *

It is also the application's responsibility to ensure that there are no + * other uses of the model after calling {@link ANeuralNetworksModel_free}. + * This includes any compilation, execution object or burst object created using + * the model.

+ * + * Available since NNAPI feature level 1. + */ +ANeuralNetworksModel :: struct{} + +/** + * ANeuralNetworksCompilation is an opaque type that can be used to compile + * a machine learning model. + * + *

To use:

+ * + * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. + * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}. + * + *

A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} + * has been called on it.

+ * + *

It is the application's responsibility to make sure that only + * one thread modifies a compilation at a given time. It is however + * safe for more than one thread to use the compilation once + * {@link ANeuralNetworksCompilation_finish} has returned.

+ * + *

It is also the application's responsibility to ensure that there are no other + * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}. + * This includes any execution object or burst object created using the compilation, + * or any memory descriptor with the compilation as part of one of the roles specified by + * {@link ANeuralNetworksMemoryDesc_addInputRole} or + * {@link ANeuralNetworksMemoryDesc_addOutputRole}.

+ * + * Available since NNAPI feature level 1. + */ +ANeuralNetworksCompilation :: struct{} + +/** + * ANeuralNetworksExecution is an opaque type that can be used to apply a machine + * learning model to a set of inputs. + * + *

To use:

+ * + *

An output buffer or memory region must not overlap with any + * other output buffer or memory region, with an input buffer or + * memory region, or with an operand value in a memory object + * ({@link ANeuralNetworksModel_setOperandValueFromMemory}).

+ * + *

An execution is in the preparation state after it is created by + * {@link ANeuralNetworksExecution_create}. An execution may only be modified in the preparation + * state. Scheduling a computation by calling {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, {@link ANeuralNetworksExecution_startCompute}, + * or {@link ANeuralNetworksExecution_startComputeWithDependencies} will change the state of + * the execution object to the computation state. When the computation completes, the state of + * the execution object will change from the computation state to the completed state. + * The computation is completed when {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_burstCompute}, or {@link ANeuralNetworksEvent_wait} + * has returned.

+ * + *

An execution can be applied to a model with + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies} only once. Create new + * executions to do new evaluations of the model.

+ * + *

Starting at NNAPI feature level 5, the application may call + * {@link ANeuralNetworksExecution_setReusable} to set an execution to be reusable for multiple + * computations. The application may schedule and evaluate a computation again from the completed + * state of a reusable execution. The execution cannot be modified between computations.

+ * + *

It is the application's responsibility to make sure that only one thread + * modifies an execution at a given time. It is however safe for more than one + * thread to use {@link ANeuralNetworksEvent_wait} at the same time.

+ * + *

It is also the application's responsibility to ensure that the execution + * either has never been scheduled or has completed (i.e., that + * {@link ANeuralNetworksExecution_burstCompute}, + * {@link ANeuralNetworksExecution_compute}, or + * {@link ANeuralNetworksEvent_wait} has returned) before calling + * {@link ANeuralNetworksExecution_free}.

. + * + *

It is also the application's responsibility to ensure that there are no other + * uses of the execution after calling {@link ANeuralNetworksExecution_free}.

+ * + *

It is the application's responsibility to ensure that there are no concurrent computations + * scheduled and evaluated on the same execution, either by means of + * {@link ANeuralNetworksExecution_compute} or + * {@link ANeuralNetworksExecution_burstCompute} (which are synchronous) + * in different threads, or by means of + * {@link ANeuralNetworksExecution_startCompute} or + * {@link ANeuralNetworksExecution_startComputeWithDependencies} (which are asynchronous). + * It is however safe to schedule and evaluate multiple computations on different executions + * concurrently. (Concurrent uses of {@link ANeuralNetworksExecution_burstCompute} must be on + * different burst objects.) The runtime makes no guarantee on the ordering of + * completion of executions. If it's important to the application, the + * application should enforce the ordering by ensuring that one execution + * completes before the next is scheduled (for example, by scheduling all + * executions synchronously within a single thread, or by scheduling all + * executions asynchronously and using {@link ANeuralNetworksEvent_wait} between + * calls to {@link ANeuralNetworksExecution_startCompute}); or by using + * {@link ANeuralNetworksExecution_startComputeWithDependencies} to make the execution wait for a + * list of events to be signaled before starting the actual evaluation.

+ * + * Available since NNAPI feature level 1. + */ +ANeuralNetworksExecution :: struct{} + +/** + * Parameters for ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL operand. + */ +ANeuralNetworksSymmPerChannelQuantParams :: struct { + /** The index of the channel dimension. */ + channelDim: u32, + /** The size of the scale array. Should be equal to dimension[channelDim] of the Operand. */ + scaleCount: u32, + /** The array of scaling values for each channel. Each value must be greater than zero. */ + scales: [^]f32, +} + +/** + * ANeuralNetworksBurst is an opaque type that can be used to reduce the latency + * of a rapid sequence of executions. It will likely cause overhead if only used + * for a single execution. + * + * ANeuralNetworksBurst serves as a context object for any number of inferences + * using {@link ANeuralNetworksExecution} objects. An ANeuralNetworksBurst + * object and the {@link ANeuralNetworksExecution} objects used with it must all + * have been created from the same {@link ANeuralNetworksCompilation} object. + * + * This object is also used as a hint to drivers, providing insight to the + * lifetime of a rapid sequence of executions. For example, a driver may choose + * to increase the clock frequency of its accelerator for the lifetime of a + * burst object. + * + *

To use:

+ * + * Available since NNAPI feature level 3. + */ +ANeuralNetworksBurst :: struct{} + +/** + * ANeuralNetworksOperandType describes the type of an operand. + * + * This structure is used to describe both scalars and tensors. + * + * A tensor operand type with all dimensions specified is "fully + * specified". Whenever possible (i.e., whenever the dimensions are + * known at model construction time), a tensor operand type should be + * (but is not required to be) fully specified, in order to enable the + * best possible performance. + * + * If a tensor operand's type is not fully specified, the dimensions + * of the operand are deduced from the operand types and values of the + * operation for which that operand is an output or from the corresponding + * {@link ANEURALNETWORKS_IF} or {@link ANEURALNETWORKS_WHILE} operation input + * operand type in the case of referenced model input operands. + * + *

In the following situations, a tensor operand type must be fully + * specified:

+ * + * A tensor operand type of specified rank but some number of + * unspecified dimensions is represented by setting dimensionCount to + * the rank and each unspecified dimension to 0. + * + * Available since NNAPI feature level 1. + * + * Starting at NNAPI feature level 3, a tensor operand type of unspecified rank is + * represented by setting dimensionCount to 0 and dimensions to NULL (just as if + * it were a scalar operand type). + */ +ANeuralNetworksOperandType :: struct { + /** + * The data type, e.g ANEURALNETWORKS_FLOAT32. + */ + type: OperandCode, + + /** + * The number of dimensions (rank). + * + * Must be 0 for scalars. + */ + dimensionCount: u32, + + /** + * The dimensions of the tensor. + * + * Must be nullptr for scalars. + */ + dimensions: [^]u32, + + /** + * The quantization scale. + * + * Must be 0 when not applicable to an operand type. + * + * See {@link OperandCode}. + */ + scale: f32, + + /** + * The quantization zero point. + * + * Must be 0 when not applicable to an operand type. + * + * See {@link OperandCode}. + */ + zeroPoint: i32, +} + +/** + * ANeuralNetworksEvent is an opaque type that represents an event + * that will be signaled once an execution completes. + * + * Available since NNAPI feature level 1. + */ +ANeuralNetworksEvent :: struct{} + +/** + * ANeuralNetworksDevice is an opaque type that represents a device. + * + * This type is used to query basic properties and supported operations of the corresponding + * device, and control which device(s) a model is to be run on. + * + * Available since NNAPI feature level 3. + */ +ANeuralNetworksDevice :: struct{} + +/** + * ANeuralNetworksMemoryDesc is an opaque type that represents a memory descriptor. + * + * A memory descriptor describes the properties of a memory object, and is used by + * {@link ANeuralNetworksMemory_createFromDesc}. + * + * To use: + * - Create a new memory descriptor by calling {@link ANeuralNetworksMemoryDesc_create}. + * - Specify all of the intended input and output roles by calling + * {@link ANeuralNetworksMemoryDesc_addInputRole} and + * {@link ANeuralNetworksMemoryDesc_addOutputRole}. + * - Optionally, specify the memory dimensions by calling + * {@link ANeuralNetworksMemoryDesc_setDimensions}. + * - Complete the memory descriptor with {@link ANeuralNetworksMemoryDesc_finish}. + * - Use the memory descriptor as many times as needed with + * {@link ANeuralNetworksMemory_createFromDesc}. + * - Destroy the memory descriptor with {@link ANeuralNetworksMemoryDesc_free}. + * + * A memory descriptor is completed by calling {@link ANeuralNetworksMemoryDesc_finish}. + * A memory descriptor is destroyed by calling {@link ANeuralNetworksMemoryDesc_free}. + * + * A memory descriptor must not be modified once {@link ANeuralNetworksMemoryDesc_finish} + * has been called on it. + * + * It is the application's responsibility to make sure that only + * one thread modifies a memory descriptor at a given time. It is however + * safe for more than one thread to use the memory descriptor once + * {@link ANeuralNetworksMemoryDesc_finish} has returned. + * + * It is also the application's responsibility to ensure that there are no other + * uses of the memory descriptor after calling {@link ANeuralNetworksMemoryDesc_free}. + * It is however safe to continue using a {@link ANeuralNetworksMemory} object created + * from the memory descriptor. + * + * Available since NNAPI feature level 4. + */ +ANeuralNetworksMemoryDesc :: struct{} + diff --git a/doc.odin b/doc.odin index 7d2bf23..08108fb 100644 --- a/doc.odin +++ b/doc.odin @@ -1,5 +1,9 @@ package android +// +// Based on NDK r26b (26.1.10909125) +// + // Apparently the app still needs to have a manifest, it also MUST use the nativeactivity activity. // Apparently the native code MUST be built as a shared library and then loaded by the JNI(?) // Good ref: https://github.com/xlab/android-go/blob/master/examples/build-android.sh diff --git a/keycodes.odin b/keycodes.odin new file mode 100644 index 0000000..022548c --- /dev/null +++ b/keycodes.odin @@ -0,0 +1,762 @@ +//+build android +package android + +Keycode :: enum { + /** Unknown key code. */ + UNKNOWN = 0, + /** Soft Left key. + * Usually situated below the display on phones and used as a multi-function + * feature key for selecting a software defined function shown on the bottom left + * of the display. */ + SOFT_LEFT = 1, + /** Soft Right key. + * Usually situated below the display on phones and used as a multi-function + * feature key for selecting a software defined function shown on the bottom right + * of the display. */ + SOFT_RIGHT = 2, + /** Home key. + * This key is handled by the framework and is never delivered to applications. */ + HOME = 3, + /** Back key. */ + BACK = 4, + /** Call key. */ + CALL = 5, + /** End Call key. */ + ENDCALL = 6, + /** '0' key. */ + KEY_0 = 7, + /** '1' key. */ + KEY_1 = 8, + /** '2' key. */ + KEY_2 = 9, + /** '3' key. */ + KEY_3 = 10, + /** '4' key. */ + KEY_4 = 11, + /** '5' key. */ + KEY_5 = 12, + /** '6' key. */ + KEY_6 = 13, + /** '7' key. */ + KEY_7 = 14, + /** '8' key. */ + KEY_8 = 15, + /** '9' key. */ + KEY_9 = 16, + /** '*' key. */ + STAR = 17, + /** '#' key. */ + POUND = 18, + /** Directional Pad Up key. + * May also be synthesized from trackball motions. */ + DPAD_UP = 19, + /** Directional Pad Down key. + * May also be synthesized from trackball motions. */ + DPAD_DOWN = 20, + /** Directional Pad Left key. + * May also be synthesized from trackball motions. */ + DPAD_LEFT = 21, + /** Directional Pad Right key. + * May also be synthesized from trackball motions. */ + DPAD_RIGHT = 22, + /** Directional Pad Center key. + * May also be synthesized from trackball motions. */ + DPAD_CENTER = 23, + /** Volume Up key. + * Adjusts the speaker volume up. */ + VOLUME_UP = 24, + /** Volume Down key. + * Adjusts the speaker volume down. */ + VOLUME_DOWN = 25, + /** Power key. */ + POWER = 26, + /** Camera key. + * Used to launch a camera application or take pictures. */ + CAMERA = 27, + /** Clear key. */ + CLEAR = 28, + /** 'A' key. */ + A = 29, + /** 'B' key. */ + B = 30, + /** 'C' key. */ + C = 31, + /** 'D' key. */ + D = 32, + /** 'E' key. */ + E = 33, + /** 'F' key. */ + F = 34, + /** 'G' key. */ + G = 35, + /** 'H' key. */ + H = 36, + /** 'I' key. */ + I = 37, + /** 'J' key. */ + J = 38, + /** 'K' key. */ + K = 39, + /** 'L' key. */ + L = 40, + /** 'M' key. */ + M = 41, + /** 'N' key. */ + N = 42, + /** 'O' key. */ + O = 43, + /** 'P' key. */ + P = 44, + /** 'Q' key. */ + Q = 45, + /** 'R' key. */ + R = 46, + /** 'S' key. */ + S = 47, + /** 'T' key. */ + T = 48, + /** 'U' key. */ + U = 49, + /** 'V' key. */ + V = 50, + /** 'W' key. */ + W = 51, + /** 'X' key. */ + X = 52, + /** 'Y' key. */ + Y = 53, + /** 'Z' key. */ + Z = 54, + /** ',' key. */ + COMMA = 55, + /** '.' key. */ + PERIOD = 56, + /** Left Alt modifier key. */ + ALT_LEFT = 57, + /** Right Alt modifier key. */ + ALT_RIGHT = 58, + /** Left Shift modifier key. */ + SHIFT_LEFT = 59, + /** Right Shift modifier key. */ + SHIFT_RIGHT = 60, + /** Tab key. */ + TAB = 61, + /** Space key. */ + SPACE = 62, + /** Symbol modifier key. + * Used to enter alternate symbols. */ + SYM = 63, + /** Explorer special function key. + * Used to launch a browser application. */ + EXPLORER = 64, + /** Envelope special function key. + * Used to launch a mail application. */ + ENVELOPE = 65, + /** Enter key. */ + ENTER = 66, + /** Backspace key. + * Deletes characters before the insertion point, unlike {@link FORWARD_DEL}. */ + DEL = 67, + /** '`' (backtick) key. */ + GRAVE = 68, + /** '-'. */ + MINUS = 69, + /** '=' key. */ + EQUALS = 70, + /** '[' key. */ + LEFT_BRACKET = 71, + /** ']' key. */ + RIGHT_BRACKET = 72, + /** '\' key. */ + BACKSLASH = 73, + /** ';' key. */ + SEMICOLON = 74, + /** ''' (apostrophe) key. */ + APOSTROPHE = 75, + /** '/' key. */ + SLASH = 76, + /** '@' key. */ + AT = 77, + /** Number modifier key. + * Used to enter numeric symbols. + * This key is not {@link NUM_LOCK}; it is more like {@link AKEYCODE_ALT_LEFT}. */ + NUM = 78, + /** Headset Hook key. + * Used to hang up calls and stop media. */ + HEADSETHOOK = 79, + /** Camera Focus key. + * Used to focus the camera. */ + FOCUS = 80, + /** '+' key. */ + PLUS = 81, + /** Menu key. */ + MENU = 82, + /** Notification key. */ + NOTIFICATION = 83, + /** Search key. */ + SEARCH = 84, + /** Play/Pause media key. */ + MEDIA_PLAY_PAUSE= 85, + /** Stop media key. */ + MEDIA_STOP = 86, + /** Play Next media key. */ + MEDIA_NEXT = 87, + /** Play Previous media key. */ + MEDIA_PREVIOUS = 88, + /** Rewind media key. */ + MEDIA_REWIND = 89, + /** Fast Forward media key. */ + MEDIA_FAST_FORWARD = 90, + /** Mute key. + * Mutes the microphone, unlike {@link VOLUME_MUTE}. */ + MUTE = 91, + /** Page Up key. */ + PAGE_UP = 92, + /** Page Down key. */ + PAGE_DOWN = 93, + /** Picture Symbols modifier key. + * Used to switch symbol sets (Emoji, Kao-moji). */ + PICTSYMBOLS = 94, + /** Switch Charset modifier key. + * Used to switch character sets (Kanji, Katakana). */ + SWITCH_CHARSET = 95, + /** A Button key. + * On a game controller, the A button should be either the button labeled A + * or the first button on the bottom row of controller buttons. */ + BUTTON_A = 96, + /** B Button key. + * On a game controller, the B button should be either the button labeled B + * or the second button on the bottom row of controller buttons. */ + BUTTON_B = 97, + /** C Button key. + * On a game controller, the C button should be either the button labeled C + * or the third button on the bottom row of controller buttons. */ + BUTTON_C = 98, + /** X Button key. + * On a game controller, the X button should be either the button labeled X + * or the first button on the upper row of controller buttons. */ + BUTTON_X = 99, + /** Y Button key. + * On a game controller, the Y button should be either the button labeled Y + * or the second button on the upper row of controller buttons. */ + BUTTON_Y = 100, + /** Z Button key. + * On a game controller, the Z button should be either the button labeled Z + * or the third button on the upper row of controller buttons. */ + BUTTON_Z = 101, + /** L1 Button key. + * On a game controller, the L1 button should be either the button labeled L1 (or L) + * or the top left trigger button. */ + BUTTON_L1 = 102, + /** R1 Button key. + * On a game controller, the R1 button should be either the button labeled R1 (or R) + * or the top right trigger button. */ + BUTTON_R1 = 103, + /** L2 Button key. + * On a game controller, the L2 button should be either the button labeled L2 + * or the bottom left trigger button. */ + BUTTON_L2 = 104, + /** R2 Button key. + * On a game controller, the R2 button should be either the button labeled R2 + * or the bottom right trigger button. */ + BUTTON_R2 = 105, + /** Left Thumb Button key. + * On a game controller, the left thumb button indicates that the left (or only) + * joystick is pressed. */ + BUTTON_THUMBL = 106, + /** Right Thumb Button key. + * On a game controller, the right thumb button indicates that the right + * joystick is pressed. */ + BUTTON_THUMBR = 107, + /** Start Button key. + * On a game controller, the button labeled Start. */ + BUTTON_START = 108, + /** Select Button key. + * On a game controller, the button labeled Select. */ + BUTTON_SELECT = 109, + /** Mode Button key. + * On a game controller, the button labeled Mode. */ + BUTTON_MODE = 110, + /** Escape key. */ + ESCAPE = 111, + /** Forward Delete key. + * Deletes characters ahead of the insertion point, unlike {@link DEL}. */ + FORWARD_DEL = 112, + /** Left Control modifier key. */ + CTRL_LEFT = 113, + /** Right Control modifier key. */ + CTRL_RIGHT = 114, + /** Caps Lock key. */ + CAPS_LOCK = 115, + /** Scroll Lock key. */ + SCROLL_LOCK = 116, + /** Left Meta modifier key. */ + META_LEFT = 117, + /** Right Meta modifier key. */ + META_RIGHT = 118, + /** Function modifier key. */ + FUNCTION = 119, + /** System Request / Print Screen key. */ + SYSRQ = 120, + /** Break / Pause key. */ + BREAK = 121, + /** Home Movement key. + * Used for scrolling or moving the cursor around to the start of a line + * or to the top of a list. */ + MOVE_HOME = 122, + /** End Movement key. + * Used for scrolling or moving the cursor around to the end of a line + * or to the bottom of a list. */ + MOVE_END = 123, + /** Insert key. + * Toggles insert / overwrite edit mode. */ + INSERT = 124, + /** Forward key. + * Navigates forward in the history stack. Complement of {@link BACK}. */ + FORWARD = 125, + /** Play media key. */ + MEDIA_PLAY = 126, + /** Pause media key. */ + MEDIA_PAUSE = 127, + /** Close media key. + * May be used to close a CD tray, for example. */ + MEDIA_CLOSE = 128, + /** Eject media key. + * May be used to eject a CD tray, for example. */ + MEDIA_EJECT = 129, + /** Record media key. */ + MEDIA_RECORD = 130, + /** F1 key. */ + F1 = 131, + /** F2 key. */ + F2 = 132, + /** F3 key. */ + F3 = 133, + /** F4 key. */ + F4 = 134, + /** F5 key. */ + F5 = 135, + /** F6 key. */ + F6 = 136, + /** F7 key. */ + F7 = 137, + /** F8 key. */ + F8 = 138, + /** F9 key. */ + F9 = 139, + /** F10 key. */ + F10 = 140, + /** F11 key. */ + F11 = 141, + /** F12 key. */ + F12 = 142, + /** Num Lock key. + * This is the Num Lock key; it is different from {@link NUM}. + * This key alters the behavior of other keys on the numeric keypad. */ + NUM_LOCK = 143, + /** Numeric keypad '0' key. */ + NUMPAD_0 = 144, + /** Numeric keypad '1' key. */ + NUMPAD_1 = 145, + /** Numeric keypad '2' key. */ + NUMPAD_2 = 146, + /** Numeric keypad '3' key. */ + NUMPAD_3 = 147, + /** Numeric keypad '4' key. */ + NUMPAD_4 = 148, + /** Numeric keypad '5' key. */ + NUMPAD_5 = 149, + /** Numeric keypad '6' key. */ + NUMPAD_6 = 150, + /** Numeric keypad '7' key. */ + NUMPAD_7 = 151, + /** Numeric keypad '8' key. */ + NUMPAD_8 = 152, + /** Numeric keypad '9' key. */ + NUMPAD_9 = 153, + /** Numeric keypad '/' key (for division). */ + NUMPAD_DIVIDE = 154, + /** Numeric keypad '*' key (for multiplication). */ + NUMPAD_MULTIPLY = 155, + /** Numeric keypad '-' key (for subtraction). */ + NUMPAD_SUBTRACT = 156, + /** Numeric keypad '+' key (for addition). */ + NUMPAD_ADD = 157, + /** Numeric keypad '.' key (for decimals or digit grouping). */ + NUMPAD_DOT = 158, + /** Numeric keypad ',' key (for decimals or digit grouping). */ + NUMPAD_COMMA = 159, + /** Numeric keypad Enter key. */ + NUMPAD_ENTER = 160, + /** Numeric keypad '=' key. */ + NUMPAD_EQUALS = 161, + /** Numeric keypad '(' key. */ + NUMPAD_LEFT_PAREN = 162, + /** Numeric keypad ')' key. */ + NUMPAD_RIGHT_PAREN = 163, + /** Volume Mute key. + * Mutes the speaker, unlike {@link MUTE}. + * This key should normally be implemented as a toggle such that the first press + * mutes the speaker and the second press restores the original volume. */ + VOLUME_MUTE = 164, + /** Info key. + * Common on TV remotes to show additional information related to what is + * currently being viewed. */ + INFO = 165, + /** Channel up key. + * On TV remotes, increments the television channel. */ + CHANNEL_UP = 166, + /** Channel down key. + * On TV remotes, decrements the television channel. */ + CHANNEL_DOWN = 167, + /** Zoom in key. */ + ZOOM_IN = 168, + /** Zoom out key. */ + ZOOM_OUT = 169, + /** TV key. + * On TV remotes, switches to viewing live TV. */ + TV = 170, + /** Window key. + * On TV remotes, toggles picture-in-picture mode or other windowing functions. */ + WINDOW = 171, + /** Guide key. + * On TV remotes, shows a programming guide. */ + GUIDE = 172, + /** DVR key. + * On some TV remotes, switches to a DVR mode for recorded shows. */ + DVR = 173, + /** Bookmark key. + * On some TV remotes, bookmarks content or web pages. */ + BOOKMARK = 174, + /** Toggle captions key. + * Switches the mode for closed-captioning text, for example during television shows. */ + CAPTIONS = 175, + /** Settings key. + * Starts the system settings activity. */ + SETTINGS = 176, + /** TV power key. + * On TV remotes, toggles the power on a television screen. */ + TV_POWER = 177, + /** TV input key. + * On TV remotes, switches the input on a television screen. */ + TV_INPUT = 178, + /** Set-top-box power key. + * On TV remotes, toggles the power on an external Set-top-box. */ + STB_POWER = 179, + /** Set-top-box input key. + * On TV remotes, switches the input mode on an external Set-top-box. */ + STB_INPUT = 180, + /** A/V Receiver power key. + * On TV remotes, toggles the power on an external A/V Receiver. */ + AVR_POWER = 181, + /** A/V Receiver input key. + * On TV remotes, switches the input mode on an external A/V Receiver. */ + AVR_INPUT = 182, + /** Red "programmable" key. + * On TV remotes, acts as a contextual/programmable key. */ + PROG_RED = 183, + /** Green "programmable" key. + * On TV remotes, actsas a contextual/programmable key. */ + PROG_GREEN = 184, + /** Yellow "programmable" key. + * On TV remotes, acts as a contextual/programmable key. */ + PROG_YELLOW = 185, + /** Blue "programmable" key. + * On TV remotes, acts as a contextual/programmable key. */ + PROG_BLUE = 186, + /** App switch key. + * Should bring up the application switcher dialog. */ + APP_SWITCH = 187, + /** Generic Game Pad Button #1.*/ + BUTTON_1 = 188, + /** Generic Game Pad Button #2.*/ + BUTTON_2 = 189, + /** Generic Game Pad Button #3.*/ + BUTTON_3 = 190, + /** Generic Game Pad Button #4.*/ + BUTTON_4 = 191, + /** Generic Game Pad Button #5.*/ + BUTTON_5 = 192, + /** Generic Game Pad Button #6.*/ + BUTTON_6 = 193, + /** Generic Game Pad Button #7.*/ + BUTTON_7 = 194, + /** Generic Game Pad Button #8.*/ + BUTTON_8 = 195, + /** Generic Game Pad Button #9.*/ + BUTTON_9 = 196, + /** Generic Game Pad Button #10.*/ + BUTTON_10 = 197, + /** Generic Game Pad Button #11.*/ + BUTTON_11 = 198, + /** Generic Game Pad Button #12.*/ + BUTTON_12 = 199, + /** Generic Game Pad Button #13.*/ + BUTTON_13 = 200, + /** Generic Game Pad Button #14.*/ + BUTTON_14 = 201, + /** Generic Game Pad Button #15.*/ + BUTTON_15 = 202, + /** Generic Game Pad Button #16.*/ + BUTTON_16 = 203, + /** Language Switch key. + * Toggles the current input language such as switching between English and Japanese on + * a QWERTY keyboard. On some devices, the same function may be performed by + * pressing Shift+Spacebar. */ + LANGUAGE_SWITCH = 204, + /** Manner Mode key. + * Toggles silent or vibrate mode on and off to make the device behave more politely + * in certain settings such as on a crowded train. On some devices, the key may only + * operate when long-pressed. */ + MANNER_MODE = 205, + /** 3D Mode key. + * Toggles the display between 2D and 3D mode. */ + 3D_MODE = 206, + /** Contacts special function key. + * Used to launch an address book application. */ + CONTACTS = 207, + /** Calendar special function key. + * Used to launch a calendar application. */ + CALENDAR = 208, + /** Music special function key. + * Used to launch a music player application. */ + MUSIC = 209, + /** Calculator special function key. + * Used to launch a calculator application. */ + CALCULATOR = 210, + /** Japanese full-width / half-width key. */ + ZENKAKU_HANKAKU = 211, + /** Japanese alphanumeric key. */ + EISU = 212, + /** Japanese non-conversion key. */ + MUHENKAN = 213, + /** Japanese conversion key. */ + HENKAN = 214, + /** Japanese katakana / hiragana key. */ + KATAKANA_HIRAGANA = 215, + /** Japanese Yen key. */ + YEN = 216, + /** Japanese Ro key. */ + RO = 217, + /** Japanese kana key. */ + KANA = 218, + /** Assist key. + * Launches the global assist activity. Not delivered to applications. */ + ASSIST = 219, + /** Brightness Down key. + * Adjusts the screen brightness down. */ + BRIGHTNESS_DOWN = 220, + /** Brightness Up key. + * Adjusts the screen brightness up. */ + BRIGHTNESS_UP = 221, + /** Audio Track key. + * Switches the audio tracks. */ + MEDIA_AUDIO_TRACK = 222, + /** Sleep key. + * Puts the device to sleep. Behaves somewhat like {@link POWER} but it + * has no effect if the device is already asleep. */ + SLEEP = 223, + /** Wakeup key. + * Wakes up the device. Behaves somewhat like {@link POWER} but it + * has no effect if the device is already awake. */ + WAKEUP = 224, + /** Pairing key. + * Initiates peripheral pairing mode. Useful for pairing remote control + * devices or game controllers, especially if no other input mode is + * available. */ + PAIRING = 225, + /** Media Top Menu key. + * Goes to the top of media menu. */ + MEDIA_TOP_MENU = 226, + /** '11' key. */ + 11 = 227, + /** '12' key. */ + 12 = 228, + /** Last Channel key. + * Goes to the last viewed channel. */ + LAST_CHANNEL = 229, + /** TV data service key. + * Displays data services like weather, sports. */ + TV_DATA_SERVICE = 230, + /** Voice Assist key. + * Launches the global voice assist activity. Not delivered to applications. */ + VOICE_ASSIST = 231, + /** Radio key. + * Toggles TV service / Radio service. */ + TV_RADIO_SERVICE = 232, + /** Teletext key. + * Displays Teletext service. */ + TV_TELETEXT = 233, + /** Number entry key. + * Initiates to enter multi-digit channel nubmber when each digit key is assigned + * for selecting separate channel. Corresponds to Number Entry Mode (0x1D) of CEC + * User Control Code. */ + TV_NUMBER_ENTRY = 234, + /** Analog Terrestrial key. + * Switches to analog terrestrial broadcast service. */ + TV_TERRESTRIAL_ANALOG = 235, + /** Digital Terrestrial key. + * Switches to digital terrestrial broadcast service. */ + TV_TERRESTRIAL_DIGITAL = 236, + /** Satellite key. + * Switches to digital satellite broadcast service. */ + TV_SATELLITE = 237, + /** BS key. + * Switches to BS digital satellite broadcasting service available in Japan. */ + TV_SATELLITE_BS = 238, + /** CS key. + * Switches to CS digital satellite broadcasting service available in Japan. */ + TV_SATELLITE_CS = 239, + /** BS/CS key. + * Toggles between BS and CS digital satellite services. */ + TV_SATELLITE_SERVICE = 240, + /** Toggle Network key. + * Toggles selecting broacast services. */ + TV_NETWORK = 241, + /** Antenna/Cable key. + * Toggles broadcast input source between antenna and cable. */ + TV_ANTENNA_CABLE = 242, + /** HDMI #1 key. + * Switches to HDMI input #1. */ + TV_INPUT_HDMI_1 = 243, + /** HDMI #2 key. + * Switches to HDMI input #2. */ + TV_INPUT_HDMI_2 = 244, + /** HDMI #3 key. + * Switches to HDMI input #3. */ + TV_INPUT_HDMI_3 = 245, + /** HDMI #4 key. + * Switches to HDMI input #4. */ + TV_INPUT_HDMI_4 = 246, + /** Composite #1 key. + * Switches to composite video input #1. */ + TV_INPUT_COMPOSITE_1 = 247, + /** Composite #2 key. + * Switches to composite video input #2. */ + TV_INPUT_COMPOSITE_2 = 248, + /** Component #1 key. + * Switches to component video input #1. */ + TV_INPUT_COMPONENT_1 = 249, + /** Component #2 key. + * Switches to component video input #2. */ + TV_INPUT_COMPONENT_2 = 250, + /** VGA #1 key. + * Switches to VGA (analog RGB) input #1. */ + TV_INPUT_VGA_1 = 251, + /** Audio description key. + * Toggles audio description off / on. */ + TV_AUDIO_DESCRIPTION = 252, + /** Audio description mixing volume up key. + * Louden audio description volume as compared with normal audio volume. */ + TV_AUDIO_DESCRIPTION_MIX_UP = 253, + /** Audio description mixing volume down key. + * Lessen audio description volume as compared with normal audio volume. */ + TV_AUDIO_DESCRIPTION_MIX_DOWN = 254, + /** Zoom mode key. + * Changes Zoom mode (Normal, Full, Zoom, Wide-zoom, etc.) */ + TV_ZOOM_MODE = 255, + /** Contents menu key. + * Goes to the title list. Corresponds to Contents Menu (0x0B) of CEC User Control + * Code */ + TV_CONTENTS_MENU = 256, + /** Media context menu key. + * Goes to the context menu of media contents. Corresponds to Media Context-sensitive + * Menu (0x11) of CEC User Control Code. */ + TV_MEDIA_CONTEXT_MENU = 257, + /** Timer programming key. + * Goes to the timer recording menu. Corresponds to Timer Programming (0x54) of + * CEC User Control Code. */ + TV_TIMER_PROGRAMMING = 258, + /** Help key. */ + HELP = 259, + NAVIGATE_PREVIOUS = 260, + NAVIGATE_NEXT = 261, + NAVIGATE_IN = 262, + NAVIGATE_OUT = 263, + /** Primary stem key for Wear + * Main power/reset button on watch. */ + STEM_PRIMARY = 264, + /** Generic stem key 1 for Wear */ + STEM_1 = 265, + /** Generic stem key 2 for Wear */ + STEM_2 = 266, + /** Generic stem key 3 for Wear */ + STEM_3 = 267, + /** Directional Pad Up-Left */ + DPAD_UP_LEFT = 268, + /** Directional Pad Down-Left */ + DPAD_DOWN_LEFT = 269, + /** Directional Pad Up-Right */ + DPAD_UP_RIGHT = 270, + /** Directional Pad Down-Right */ + DPAD_DOWN_RIGHT = 271, + /** Skip forward media key */ + MEDIA_SKIP_FORWARD = 272, + /** Skip backward media key */ + MEDIA_SKIP_BACKWARD = 273, + /** Step forward media key. + * Steps media forward one from at a time. */ + MEDIA_STEP_FORWARD = 274, + /** Step backward media key. + * Steps media backward one from at a time. */ + MEDIA_STEP_BACKWARD = 275, + /** Put device to sleep unless a wakelock is held. */ + SOFT_SLEEP = 276, + /** Cut key. */ + CUT = 277, + /** Copy key. */ + COPY = 278, + /** Paste key. */ + PASTE = 279, + /** fingerprint navigation key, up. */ + SYSTEM_NAVIGATION_UP = 280, + /** fingerprint navigation key, down. */ + SYSTEM_NAVIGATION_DOWN = 281, + /** fingerprint navigation key, left. */ + SYSTEM_NAVIGATION_LEFT = 282, + /** fingerprint navigation key, right. */ + SYSTEM_NAVIGATION_RIGHT = 283, + /** all apps */ + ALL_APPS = 284, + /** refresh key */ + REFRESH = 285, + /** Thumbs up key. Apps can use this to let user upvote content. */ + THUMBS_UP = 286, + /** Thumbs down key. Apps can use this to let user downvote content. */ + THUMBS_DOWN = 287, + /** Used to switch current account that is consuming content. + * May be consumed by system to switch current viewer profile. */ + PROFILE_SWITCH = 288, + /** Video Application key #1. */ + VIDEO_APP_1 = 289, + /** Video Application key #2. */ + VIDEO_APP_2 = 290, + /** Video Application key #3. */ + VIDEO_APP_3 = 291, + /** Video Application key #4. */ + VIDEO_APP_4 = 292, + /** Video Application key #5. */ + VIDEO_APP_5 = 293, + /** Video Application key #6. */ + VIDEO_APP_6 = 294, + /** Video Application key #7. */ + VIDEO_APP_7 = 295, + /** Video Application key #8. */ + VIDEO_APP_8 = 296, + /** Featured Application key #1. */ + FEATURED_APP_1 = 297, + /** Featured Application key #2. */ + FEATURED_APP_2 = 298, + /** Featured Application key #3. */ + FEATURED_APP_3 = 299, + /** Featured Application key #4. */ + FEATURED_APP_4 = 300, + /** Demo Application key #1. */ + DEMO_APP_1 = 301, + /** Demo Application key #2. */ + DEMO_APP_2 = 302, + /** Demo Application key #3. */ + DEMO_APP_3 = 303, + /** Demo Application key #4. */ + DEMO_APP_4 = 304, +} diff --git a/looper.odin b/looper.odin index 4c1799b..1b6a70e 100644 --- a/looper.odin +++ b/looper.odin @@ -1,10 +1,96 @@ //+build android package android -import "core:c" - foreign import android "system:android" + +/** Option for for ALooper_prepare(). */ +/** +* This looper will accept calls to ALooper_addFd() that do not +* have a callback (that is provide NULL for the callback). In +* this case the caller of ALooper_pollOnce() or ALooper_pollAll() +* MUST check the return from these functions to discover when +* data is available on such fds and process it. +*/ +ALOOPER_PREPARE_ALLOW_NON_CALLBACKS = 1<<0 + +/** Result from ALooper_pollOnce() and ALooper_pollAll(). */ +ALooperPollResult :: enum i32 { + /** + * The poll was awoken using wake() before the timeout expired + * and no callbacks were executed and no other file descriptors were ready. + */ + WAKE = -1, + + /** + * Result from ALooper_pollOnce() and ALooper_pollAll(): + * One or more callbacks were executed. + */ + CALLBACK = -2, + + /** + * Result from ALooper_pollOnce() and ALooper_pollAll(): + * The timeout expired. + */ + TIMEOUT = -3, + + /** + * Result from ALooper_pollOnce() and ALooper_pollAll(): + * An error occurred. + */ + ERROR = -4, +} + +PollResultOrIdent :: union { + ALooperPollResult, + i32, +} + +/** + * Flags for file descriptor events that a looper can monitor. + * + * These flag bits can be combined to monitor multiple events at once. + */ +ALooperFdFlagsBits :: enum i32 { + /** + * The file descriptor is available for read operations. + */ + INPUT = 0, + + /** + * The file descriptor is available for write operations. + */ + OUTPUT = 1, + + /** + * The file descriptor has encountered an error condition. + * + * The looper always sends notifications about errors; it is not necessary + * to specify this event flag in the requested event set. + */ + ERROR = 2, + + /** + * The file descriptor was hung up. + * For example, indicates that the remote end of a pipe or socket was closed. + * + * The looper always sends notifications about hangups; it is not necessary + * to specify this event flag in the requested event set. + */ + HANGUP = 3, + + /** + * The file descriptor is invalid. + * For example, the file descriptor was closed prematurely. + * + * The looper always sends notifications about invalid file descriptors; it is not necessary + * to specify this event flag in the requested event set. + */ + INVALID = 4, +} + +ALooperFdFlags :: bit_set[ALooperFdFlagsBits; i32] + /** * ALooper * @@ -20,11 +106,135 @@ foreign import android "system:android" */ ALooper :: struct{} +/** + * For callback-based event loops, this is the prototype of the function + * that is called when a file descriptor event occurs. + * It is given the file descriptor it is associated with, + * a bitmask of the poll events that were triggered (typically ALOOPER_EVENT_INPUT), + * and the data pointer that was originally supplied. + * + * Implementations should return 1 to continue receiving callbacks, or 0 + * to have this file descriptor and callback unregistered from the looper. + */ +ALooper_callbackFunc :: #type proc "c" (fd: i32, events: ALooperFdFlags, data: rawptr) -> i32 + foreign android { + /** + * Returns the looper associated with the calling thread, or NULL if + * there is not one. + */ + ALooper_forThread :: proc() -> ^ALooper --- + + /** + * Prepares a looper associated with the calling thread, and returns it. + * If the thread already has a looper, it is returned. Otherwise, a new + * one is created, associated with the thread, and returned. + * + * The opts may be ALOOPER_PREPARE_ALLOW_NON_CALLBACKS or 0. + */ + ALooper_prepare :: proc(opts: i32) -> ^ALooper --- + + /** + * Acquire a reference on the given ALooper object. This prevents the object + * from being deleted until the reference is removed. This is only needed + * to safely hand an ALooper from one thread to another. + */ + ALooper_acquire :: proc(looper: ^ALooper) --- + + /** + * Remove a reference that was previously acquired with ALooper_acquire(). + */ + ALooper_release :: proc(looper: ^ALooper) --- + + /** + * Waits for events to be available, with optional timeout in milliseconds. + * Invokes callbacks for all file descriptors on which an event occurred. + * + * If the timeout is zero, returns immediately without blocking. + * If the timeout is negative, waits indefinitely until an event appears. + * + * Returns ALOOPER_POLL_WAKE if the poll was awoken using wake() before + * the timeout expired and no callbacks were invoked and no other file + * descriptors were ready. + * + * Returns ALOOPER_POLL_CALLBACK if one or more callbacks were invoked. + * + * Returns ALOOPER_POLL_TIMEOUT if there was no data before the given + * timeout expired. + * + * Returns ALOOPER_POLL_ERROR if an error occurred. + * + * Returns a value >= 0 containing an identifier (the same identifier + * `ident` passed to ALooper_addFd()) if its file descriptor has data + * and it has no callback function (requiring the caller here to + * handle it). In this (and only this) case outFd, outEvents and + * outData will contain the poll events and data associated with the + * fd, otherwise they will be set to NULL. + * + * This method does not return until it has finished invoking the appropriate callbacks + * for all file descriptors that were signalled. + */ + ALooper_pollOnce :: proc(timeoutMillis: i32, outFd: ^i32, outEvents: ^i32, outData: ^rawptr) -> PollResultOrIdent --- + /** * Like ALooper_pollOnce(), but performs all pending callbacks until all * data has been consumed or a file descriptor is available with no callback. * This function will never return ALOOPER_POLL_CALLBACK. */ - ALooper_pollAll :: proc(timeoutMillis: c.int, outFd: ^c.int, outEvents: ^c.int, outData: ^rawptr) -> c.int --- + ALooper_pollAll :: proc(timeoutMillis: i32, outFd: ^i32, outEvents: ^i32, outData: ^rawptr) -> PollResultOrIdent --- + + /** + * Wakes the poll asynchronously. + * + * This method can be called on any thread. + * This method returns immediately. + */ + ALooper_wake :: proc(looper: ^ALooper) --- + + /** + * Adds a new file descriptor to be polled by the looper. + * If the same file descriptor was previously added, it is replaced. + * + * "fd" is the file descriptor to be added. + * "ident" is an identifier for this event, which is returned from ALooper_pollOnce(). + * The identifier must be >= 0, or ALOOPER_POLL_CALLBACK if providing a non-NULL callback. + * "events" are the poll events to wake up on. Typically this is ALOOPER_EVENT_INPUT. + * "callback" is the function to call when there is an event on the file descriptor. + * "data" is a private data pointer to supply to the callback. + * + * There are two main uses of this function: + * + * (1) If "callback" is non-NULL, then this function will be called when there is + * data on the file descriptor. It should execute any events it has pending, + * appropriately reading from the file descriptor. The 'ident' is ignored in this case. + * + * (2) If "callback" is NULL, the 'ident' will be returned by ALooper_pollOnce + * when its file descriptor has data available, requiring the caller to take + * care of processing it. + * + * Returns 1 if the file descriptor was added or -1 if an error occurred. + * + * This method can be called on any thread. + * This method may block briefly if it needs to wake the poll. + */ + ALooper_addFd :: proc(looper: ^ALooper, fd: i32, ident: i32, events: ALooperFdFlags, callback: ALooper_callbackFunc, data: rawptr) -> i32 --- + + /** + * Removes a previously added file descriptor from the looper. + * + * When this method returns, it is safe to close the file descriptor since the looper + * will no longer have a reference to it. However, it is possible for the callback to + * already be running or for it to run one last time if the file descriptor was already + * signalled. Calling code is responsible for ensuring that this case is safely handled. + * For example, if the callback takes care of removing itself during its own execution either + * by returning 0 or by calling this method, then it can be guaranteed to not be invoked + * again at any later time unless registered anew. + * + * Returns 1 if the file descriptor was removed, 0 if none was previously registered + * or -1 if an error occurred. + * + * This method can be called on any thread. + * This method may block briefly if it needs to wake the poll. + */ + ALooper_removeFd :: proc(looper: ^ALooper, fd: i32) -> i32 --- } diff --git a/multinetwork.odin b/multinetwork.odin new file mode 100644 index 0000000..6bde84c --- /dev/null +++ b/multinetwork.odin @@ -0,0 +1,262 @@ +//+build android +package android + +foreign import android "system:android" + +/** + * The corresponding C type for android.net.Network#getNetworkHandle() return + * values. The Java signed long value can be safely cast to a net_handle_t: + * + * [C] ((net_handle_t) java_long_network_handle) + * [C++] static_cast(java_long_network_handle) + * + * as appropriate. + */ +net_handle_t :: distinct u64 + +uid_t :: u32 + +/** + * The value NETWORK_UNSPECIFIED indicates no specific network. + * + * For some functions (documented below), a previous binding may be cleared + * by an invocation with NETWORK_UNSPECIFIED. + * + * Depending on the context it may indicate an error. It is expressly + * not used to indicate some notion of the "current default network". + */ +NETWORK_UNSPECIFIED :: net_handle_t(0) + +/** +* Possible values of the flags argument to android_res_nsend and android_res_nquery. +* Values are ORed together. +*/ +ResNsendFlagsBits :: enum u32 { + /** + * Send a single request to a single resolver and fail on timeout or network errors + */ + RESOLV_NO_RETRY = 0, + + /** + * Don't lookup this request in the cache, and don't cache the result of the lookup. + * This flag implies {@link #ANDROID_RESOLV_NO_CACHE_LOOKUP}. + */ + RESOLV_NO_CACHE_STORE = 1, + + /** + * Don't lookup the request in cache. + */ + RESOLV_NO_CACHE_LOOKUP = 2, +} + +ResNsendFlags :: bit_set[ResNsendFlagsBits; u32] + +foreign android { + /** + * All functions below that return an i32 return 0 on success or -1 + * on failure with an appropriate errno value set. + */ + + /** + * Set the network to be used by the given socket file descriptor. + * + * To clear a previous socket binding, invoke with NETWORK_UNSPECIFIED. + * + * This is the equivalent of: [android.net.Network#bindSocket()](https://developer.android.com/reference/android/net/Network.html#bindSocket(java.net.Socket)) + * + * Available since API level 23. + */ + android_setsocknetwork :: proc(network: net_handle_t, fd: i32) -> i32 --- + + + /** + * Binds the current process to |network|. All sockets created in the future + * (and not explicitly bound via android_setsocknetwork()) will be bound to + * |network|. All host name resolutions will be limited to |network| as well. + * Note that if the network identified by |network| ever disconnects, all + * sockets created in this way will cease to work and all host name + * resolutions will fail. This is by design so an application doesn't + * accidentally use sockets it thinks are still bound to a particular network. + * + * To clear a previous process binding, invoke with NETWORK_UNSPECIFIED. + * + * This is the equivalent of: [android.net.ConnectivityManager#bindProcessToNetwork()](https://developer.android.com/reference/android/net/ConnectivityManager.html#bindProcessToNetwork(android.net.Network)) + * + * Available since API level 23. + */ + android_setprocnetwork :: proc(network: net_handle_t) -> i32 --- + + + /** + * Gets the |network| bound to the current process, as per android_setprocnetwork. + * + * This is the equivalent of: [android.net.ConnectivityManager#getBoundNetworkForProcess()](https://developer.android.com/reference/android/net/ConnectivityManager.html#getBoundNetworkForProcess(android.net.Network)) + * Returns 0 on success, or -1 setting errno to EINVAL if a null pointer is + * passed in. + * + * + * Available since API level 31. + */ + android_getprocnetwork :: proc(network: ^net_handle_t) -> i32 --- + + /** + * Binds domain name resolutions performed by this process to |network|. + * android_setprocnetwork takes precedence over this setting. + * + * To clear a previous process binding, invoke with NETWORK_UNSPECIFIED. + * On success 0 is returned. On error -1 is returned, and errno is set. + * + * Available since API level 31. + */ + android_setprocdns :: proc(network: net_handle_t) -> i32 --- + + /** + * Gets the |network| to which domain name resolutions are bound on the + * current process. + * + * Returns 0 on success, or -1 setting errno to EINVAL if a null pointer is + * passed in. + * + * Available since API level 31. + */ + android_getprocdns :: proc(network: ^net_handle_t) -> i32 --- + + + /** + * Perform hostname resolution via the DNS servers associated with |network|. + * + * All arguments (apart from |network|) are used identically as those passed + * to getaddrinfo(3). Return and error values are identical to those of + * getaddrinfo(3), and in particular gai_strerror(3) can be used as expected. + * Similar to getaddrinfo(3): + * - |hints| may be NULL (in which case man page documented defaults apply) + * - either |node| or |service| may be NULL, but not both + * - |res| must not be NULL + * + * This is the equivalent of: [android.net.Network#getAllByName()](https://developer.android.com/reference/android/net/Network.html#getAllByName(java.lang.String)) + * + * Available since API level 23. + */ + android_getaddrinfofornetwork :: proc(network: net_handle_t, node: cstring, service: cstring, hints: ^addrinfo, res: ^^addrinfo) -> i32 --- + + /** + * Look up the {|ns_class|, |ns_type|} Resource Record (RR) associated + * with Domain Name |dname| on the given |network|. + * The typical value for |ns_class| is ns_c_in, while |type| can be any + * record type (for instance, ns_t_aaaa or ns_t_txt). + * |flags| is a additional config to control actual querying behavior, see + * ResNsendFlags for detail. + * + * Returns a file descriptor to watch for read events, or a negative + * POSIX error code (see errno.h) if an immediate error occurs. + * + * Available since API level 29. + */ + android_res_nquery :: proc(network: net_handle_t, dname: cstring, ns_class: i32, ns_type: i32, flags: ResNsendFlags) -> i32 --- + + /** + * Issue the query |msg| on the given |network|. + * |flags| is a additional config to control actual querying behavior, see + * ResNsendFlags for detail. + * + * Returns a file descriptor to watch for read events, or a negative + * POSIX error code (see errno.h) if an immediate error occurs. + * + * Available since API level 29. + */ + android_res_nsend :: proc(network: net_handle_t, msg: [^]byte, msglen: uint, flags: ResNsendFlags) -> i32 --- + + /** + * Read a result for the query associated with the |fd| descriptor. + * Closes |fd| before returning. + * + * Available since 29. + * + * Returns: + * < 0: negative POSIX error code (see errno.h for possible values). |rcode| is not set. + * >= 0: length of |answer|. |rcode| is the resolver return code (e.g., ns_r_nxdomain) + */ + android_res_nresult :: proc(fd: i32, rcode: ^int, answer: [^]byte, anslen: uint) -> i32 --- + + /** + * Attempts to cancel the in-progress query associated with the |nsend_fd| + * descriptor. + * + * Available since API level 29. + */ + android_res_cancel :: proc(nsend_fd: i32) --- + + /* + * Set the socket tag and owning UID for traffic statistics on the specified + * socket. + * + * Subsequent calls always replace any existing parameters. The socket tag and + * uid (if set) are kept when the socket is sent to another process using binder + * IPCs or other mechanisms such as UNIX socket fd passing. Any app can accept + * blame for future traffic performed on a socket originally created by another + * app by calling this method with its own UID (or calling + * android_tag_socket(int sockfd, int tag)). However, only apps holding the + * android.Manifest.permission#UPDATE_DEVICE_STATS permission may assign blame + * to another UIDs. If unset (default) the socket tag is 0, and the uid is the + * socket creator's uid. + * + * Returns 0 on success, or a negative POSIX error code (see errno.h) on + * failure. + * + * Available since API level 33. + */ + android_tag_socket_with_uid :: proc(sockfd: i32, tag: u32, uid: uid_t) -> i32 --- + + /* + * Set the socket tag for traffic statistics on the specified socket. + * + * This function tags the socket with the caller's UID (accepting blame for + * future traffic performed on this socket) even if the socket was originally + * opened by another UID or was previously tagged by another UID. Subsequent + * calls always replace any existing parameters. The socket tag is kept when the + * socket is sent to another process using binder IPCs or other mechanisms such + * as UNIX socket fd passing. The tag is a value defined by the caller and used + * together with uid for data traffic accounting, so that the function callers + * can account different types of data usage for a uid. + * + * Returns 0 on success, or a negative POSIX error code (see errno.h) on + * failure. + * + * Some possible error codes: + * -EBADF Bad socketfd. + * -EPERM No permission. + * -EAFNOSUPPORT Socket family is neither AF_INET nor AF_INET6. + * -EPROTONOSUPPORT Socket protocol is neither IPPROTO_UDP nor IPPROTO_TCP. + * -EMFILE Too many stats entries. + * There are still other error codes that may provided by -errno of + * [getsockopt()](https://man7.org/linux/man-pages/man2/getsockopt.2.html) or by + * BPF maps read/write sys calls, which are set appropriately. + * + * Available since API level 33. + */ + android_tag_socket :: proc(sockfd: i32, tag: u32) -> i32 --- + + /* + * Untag a network socket. + * + * Future traffic on this socket will no longer be associated with any + * previously configured tag and uid. If the socket was created by another UID + * or was previously tagged by another UID, calling this function will clear the + * statistics parameters, and thus the UID blamed for traffic on the socket will + * be the UID that originally created the socket, even if the socket was + * subsequently tagged by a different UID. + * + * Returns 0 on success, or a negative POSIX error code (see errno.h) on + * failure. + * + * One of possible error code: + * -EBADF Bad socketfd. + * Other error codes are either provided by -errno of + * [getsockopt()](https://man7.org/linux/man-pages/man2/getsockopt.2.html) or by + * BPF map element deletion sys call, which are set appropriately. + * + * Available since API level 33. + */ + android_untag_socket :: proc(sockfd: i32) -> i32 --- +} + diff --git a/native_acitivty.odin b/native_acitivty.odin index a5e00f5..f24acfb 100644 --- a/native_acitivty.odin +++ b/native_acitivty.odin @@ -1,7 +1,43 @@ //+build android package android -import "core:c" +foreign import android "system:android" + +/** +* Flags for ANativeActivity_showSoftInput see the Java InputMethodManager +* API for documentation. +*/ +ShowSoftInputFlags :: enum u32 { + /** + * Implicit request to show the input window, not as the result + * of a direct request by the user. + */ + IMPLICIT = 0x0001, + + /** + * The user has forced the input method open (such as by + * long-pressing menu) so it should not be closed until they + * explicitly do so. + */ + FORCED = 0x0002, +} + +/** +* Flags for ANativeActivity_hideSoftInput see the Java InputMethodManager +* API for documentation. +*/ +HideSoftInputFlags :: enum { + /** + * The soft input window should only be hidden if it was not + * explicitly shown by the user. + */ + IMPLICIT_ONLY = 0x0001, + /** + * The soft input window should normally be hidden, unless it was + * originally shown with {@link ANATIVEACTIVITY_SHOW_SOFT_INPUT_FORCED}. + */ + NOT_ALWAYS = 0x0002, +} /** * This structure defines the native side of an android.app.NativeActivity. @@ -54,7 +90,7 @@ ANativeActivity :: struct { /** * The platform's SDK version code. */ - sdkVersion: c.int32_t, + sdkVersion: i32, /** * This is the native instance of the application. It is not used by @@ -105,7 +141,7 @@ ANativeActivityCallbacks :: struct { * saved state will be persisted, so it can not contain any active * entities (pointers to memory, file descriptors, etc). */ - onSaveInstanceState: #type proc "c" (activity: ^ANativeActivity, outSize: ^c.size_t) -> rawptr, + onSaveInstanceState: #type proc "c" (activity: ^ANativeActivity, outSize: ^uint) -> rawptr, /** * NativeActivity has paused. See Java documentation for Activity.onPause() @@ -129,7 +165,7 @@ ANativeActivityCallbacks :: struct { * Focus has changed in this NativeActivity's window. This is often used, * for example, to pause a game when it loses input focus. */ - onWindowFocusChanged: #type proc "c" (activity: ^ANativeActivity, hasFocus: c.int), + onWindowFocusChanged: #type proc "c" (activity: ^ANativeActivity, hasFocus: i32), /** * The drawing window for this native activity has been created. You @@ -194,3 +230,58 @@ ANativeActivityCallbacks :: struct { onLowMemory: #type proc "c" (activity: ^ANativeActivity), } +/** + * This is the function that must be in the native code to instantiate the + * application's native activity. It is called with the activity instance (see + * above); if the code is being instantiated from a previously saved instance, + * the savedState will be non-NULL and point to the saved data. You must make + * any copy of this data you need -- it will be released after you return from + * this function. + */ +ANativeActivity_createFunc :: #type proc "c" (activity: ^ANativeActivity, savedState: rawptr, savedStateSize: uint) + + +foreign android { + /** + * Finish the given activity. Its finish() method will be called, causing it + * to be stopped and destroyed. Note that this method can be called from + * *any* thread it will send a message to the main thread of the process + * where the Java finish call will take place. + */ + ANativeActivity_finish :: proc(activity: ^ANativeActivity) --- + + /** + * Change the window format of the given activity. Calls getWindow().setFormat() + * of the given activity. Note that this method can be called from + * *any* thread it will send a message to the main thread of the process + * where the Java finish call will take place. + */ + // TODO: wtf is format here ?? is there an enum or constants for this proc? + // I think this might be AHardwareBuffer_Format, but annoyingly so, it's u32 while this proc requires i32. + ANativeActivity_setWindowFormat :: proc(activity: ^ANativeActivity, format: i32) --- + + /** + * Change the window flags of the given activity. Calls getWindow().setFlags() + * of the given activity. Note that this method can be called from + * *any* thread it will send a message to the main thread of the process + * where the Java finish call will take place. See window.h for flag constants. + */ + ANativeActivity_setWindowFlags :: proc(activity: ^ANativeActivity, addFlags: WindowFlags, removeFlags: WindowFlags) --- + + /** + * Show the IME while in the given activity. Calls InputMethodManager.showSoftInput() + * for the given activity. Note that this method can be called from + * *any* thread it will send a message to the main thread of the process + * where the Java finish call will take place. + */ + ANativeActivity_showSoftInput :: proc(activity: ^ANativeActivity, flags: ShowSoftInputFlags) --- + + /** + * Hide the IME while in the given activity. Calls InputMethodManager.hideSoftInput() + * for the given activity. Note that this method can be called from + * *any* thread it will send a message to the main thread of the process + * where the Java finish call will take place. + */ + ANativeActivity_hideSoftInput :: proc(activity: ^ANativeActivity, flags: HideSoftInputFlags) --- +} + diff --git a/native_window.odin b/native_window.odin index 68cf3ee..4153a4e 100644 --- a/native_window.odin +++ b/native_window.odin @@ -1,6 +1,74 @@ //+build android package android +/** + * Legacy window pixel format names, kept for backwards compatibility. + * New code and APIs should use AHARDWAREBUFFER_FORMAT_*. + */ +ANativeWindow_LegacyFormat :: enum { + // NOTE: these values must match the values from graphics/common/x.x/types.hal + + /** Red: 8 bits, Green: 8 bits, Blue: 8 bits, Alpha: 8 bits. **/ + RGBA_8888 = AHardwareBuffer_Format.R8G8B8A8_UNORM, + /** Red: 8 bits, Green: 8 bits, Blue: 8 bits, Unused: 8 bits. **/ + RGBX_8888 = AHardwareBuffer_Format.R8G8B8X8_UNORM, + /** Red: 5 bits, Green: 6 bits, Blue: 5 bits. **/ + RGB_565 = AHardwareBuffer_Format.R5G6B5_UNORM, +} + +/** + * Transforms that can be applied to buffers as they are displayed to a window. + * + * Supported transforms are any combination of horizontal mirror, vertical + * mirror, and clockwise 90 degree rotation, in that order. Rotations of 180 + * and 270 degrees are made up of those basic transforms. + */ +ANativeWindowTransform :: enum i32 { + IDENTITY = 0x00, + MIRROR_HORIZONTAL = 0x01, + MIRROR_VERTICAL = 0x02, + ROTATE_90 = 0x04, + + ROTATE_180 = MIRROR_HORIZONTAL | MIRROR_VERTICAL, + ROTATE_270 = ROTATE_180 | ROTATE_90, +} + +/** Compatibility value for ANativeWindow_setFrameRate. */ +ANativeWindow_FrameRateCompatibility :: enum i8 { + /** + * There are no inherent restrictions on the frame rate of this window. When + * the system selects a frame rate other than what the app requested, the + * app will be able to run at the system frame rate without requiring pull + * down. This value should be used when displaying game content, UIs, and + * anything that isn't video. + */ + DEFAULT = 0, + /** + * This window is being used to display content with an inherently fixed + * frame rate, e.g.\ a video that has a specific frame rate. When the system + * selects a frame rate other than what the app requested, the app will need + * to do pull down or use some other technique to adapt to the system's + * frame rate. The user experience is likely to be worse (e.g. more frame + * stuttering) than it would be if the system had chosen the app's requested + * frame rate. This value should be used for video content. + */ + FIXED_SOURCE = 1 +} + +/** Change frame rate strategy value for ANativeWindow_setFrameRate. */ +// Available since API level 31. +ANativeWindow_ChangeFrameRateStrategy :: enum i8 { + /** + * Change the frame rate only if the transition is going to be seamless. + */ + ONLY_IF_SEAMLESS = 0, + /** + * Change the frame rate even if the transition is going to be non-seamless, + * i.e. with visual interruptions for the user. + */ + ALWAYS = 1 +} + /** * Opaque type that provides access to a native window. * @@ -8,28 +76,46 @@ package android */ ANativeWindow :: struct{} +/** + * Struct that represents a windows buffer. + * + * A pointer can be obtained using {@link ANativeWindow_lock()}. + */ +ANativeWindow_Buffer :: struct { + /// The number of pixels that are shown horizontally. + width: i32, + + /// The number of pixels that are shown vertically. + height: i32, + + /// The number of *pixels* that a line in the buffer takes in + /// memory. This may be >= width. + stride: i32, + + /// The format of the buffer. One of AHardwareBuffer_Format. + // TODO: u32/i32 mismatch with the enum. + int32_t format, + + /// The actual bits. + bits: rawptr, + + /// Do not touch. + reserved: [6]u32, +} + foreign import android "system:android" foreign android { /** - * Change the format and size of the window buffers. - * - * The width and height control the number of pixels in the buffers, not the - * dimensions of the window on screen. If these are different than the - * window's physical size, then its buffer will be scaled to match that size - * when compositing it to the screen. The width and height must be either both zero - * or both non-zero. - * - * For all of these parameters, if 0 is supplied then the window's base - * value will come back in force. - * - * \param window pointer to an ANativeWindow object. - * \param width width of the buffers in pixels. - * \param height height of the buffers in pixels. - * \param format one of the AHardwareBuffer_Format constants. - * \return 0 for success, or a negative value on error. - */ - ANativeWindow_setBuffersGeometry :: proc(window: ^ANativeWindow, width: i32, height: i32, format: i32) -> i32 --- + * Acquire a reference on the given {@link ANativeWindow} object. This prevents the object + * from being deleted until the reference is removed. + */ + ANativeWindow_acquire :: proc(window: ^ANativeWindow) --- + + /** + * Remove a reference that was previously acquired with {@link ANativeWindow_acquire()}. + */ + ANativeWindow_release :: proc(window: ^ANativeWindow) --- /** * Return the current width in pixels of the window surface. @@ -44,4 +130,159 @@ foreign android { * \return a negative value on error. */ ANativeWindow_getHeight :: proc(window: ^ANativeWindow) -> i32 --- + + /** + * Return the current pixel format (AHARDWAREBUFFER_FORMAT_*) of the window surface. + * + * \return a negative value on error. + */ + // TODO: should return AHardwarebuffer_Format but i32/u32 mismatch + ANativeWindow_getFormat :: proc(window: ^ANativeWindow) -> i32 --- + + /** + * Change the format and size of the window buffers. + * + * The width and height control the number of pixels in the buffers, not the + * dimensions of the window on screen. If these are different than the + * window's physical size, then its buffer will be scaled to match that size + * when compositing it to the screen. The width and height must be either both zero + * or both non-zero. + * + * For all of these parameters, if 0 is supplied then the window's base + * value will come back in force. + * + * \param window pointer to an ANativeWindow object. + * \param width width of the buffers in pixels. + * \param height height of the buffers in pixels. + * \param format one of the AHardwareBuffer_Format constants. + * \return 0 for success, or a negative value on error. + */ + ANativeWindow_setBuffersGeometry :: proc(window: ^ANativeWindow, width: i32, height: i32, format: i32) -> i32 --- + + /** + * Lock the window's next drawing surface for writing. + * inOutDirtyBounds is used as an in/out parameter, upon entering the + * function, it contains the dirty region, that is, the region the caller + * intends to redraw. When the function returns, inOutDirtyBounds is updated + * with the actual area the caller needs to redraw -- this region is often + * extended by {@link ANativeWindow_lock}. + * + * \return 0 for success, or a negative value on error. + */ + ANativeWindow_lock :: proc(window: ^ANativeWindow, outBuffer: ^ANativeWindow_Buffer, inOutDirtyBounds: ^ARect) -> i32 --- + + /** + * Unlock the window's drawing surface after previously locking it, + * posting the new buffer to the display. + * + * \return 0 for success, or a negative value on error. + */ + ANativeWindow_unlockAndPost :: proc(window: ^ANativeWindow) -> i32 --- + + /** + * Set a transform that will be applied to future buffers posted to the window. + * + * Available since API level 26. + * + * \param window pointer to an ANativeWindow object. + * \param transform combination of {@link ANativeWindowTransform} flags + * \return 0 for success, or -EINVAL if \p transform is invalid + */ + ANativeWindow_setBuffersTransform :: proc(window: ^ANativeWindow, transform: ANativeWindowTransform) -> i32 --- + + /** + * All buffers queued after this call will be associated with the dataSpace + * parameter specified. + * + * dataSpace specifies additional information about the buffer. + * For example, it can be used to convey the color space of the image data in + * the buffer, or it can be used to indicate that the buffers contain depth + * measurement data instead of color images. The default dataSpace is 0, + * ADATASPACE_UNKNOWN, unless it has been overridden by the producer. + * + * Available since API level 28. + * + * \param window pointer to an ANativeWindow object. + * \param dataSpace data space of all buffers queued after this call. + * \return 0 for success, -EINVAL if window is invalid or the dataspace is not + * supported. + */ + ANativeWindow_setBuffersDataSpace :: proc(window: ^ANativeWindow, dataSpace: ADataSpace) -> i32 --- + + /** + * Get the dataspace of the buffers in window. + * + * Available since API level 28. + * + * \return the dataspace of buffers in window, ADATASPACE_UNKNOWN is returned if + * dataspace is unknown, or -EINVAL if window is invalid. + */ + ANativeWindow_getBuffersDataSpace :: proc(window: ^ANativeWindow) -> ADataSpace --- + + /** + * Same as ANativeWindow_setFrameRateWithChangeStrategy(window, frameRate, compatibility, + * ANATIVEWINDOW_CHANGE_FRAME_RATE_ONLY_IF_SEAMLESS). + * + * See ANativeWindow_setFrameRateWithChangeStrategy(). + * + * Available since API level 30. + */ + ANativeWindow_setFrameRate :: proc(window: ^ANativeWindow, frameRate: f32, compatibility: ANativeWindow_FrameRateCompatibility) -> i32 --- + + /** + * Provides a hint to the window that buffers should be preallocated ahead of + * time. Note that the window implementation is not guaranteed to preallocate + * any buffers, for instance if an implementation disallows allocation of new + * buffers, or if there is insufficient memory in the system to preallocate + * additional buffers + * + * Available since API level 30. + */ + ANativeWindow_tryAllocateBuffers :: proc(window: ^ANativeWindow) --- + + /** + * Sets the intended frame rate for this window. + * + * On devices that are capable of running the display at different refresh + * rates, the system may choose a display refresh rate to better match this + * window's frame rate. Usage of this API won't introduce frame rate throttling, + * or affect other aspects of the application's frame production + * pipeline. However, because the system may change the display refresh rate, + * calls to this function may result in changes to Choreographer callback + * timings, and changes to the time interval at which the system releases + * buffers back to the application. + * + * Note that this only has an effect for windows presented on the display. If + * this ANativeWindow is consumed by something other than the system compositor, + * e.g. a media codec, this call has no effect. + * + * You can register for changes in the refresh rate using + * \a AChoreographer_registerRefreshRateCallback. + * + * Available since API level 31. + * + * \param window pointer to an ANativeWindow object. + * + * \param frameRate The intended frame rate of this window, in frames per + * second. 0 is a special value that indicates the app will accept the system's + * choice for the display frame rate, which is the default behavior if this + * function isn't called. The frameRate param does not need to be a + * valid refresh rate for this device's display - e.g., it's fine to pass 30fps + * to a device that can only run the display at 60fps. + * + * \param compatibility The frame rate compatibility of this window. The + * compatibility value may influence the system's choice of display refresh + * rate. See the ANATIVEWINDOW_FRAME_RATE_COMPATIBILITY_* values for more info. + * This parameter is ignored when frameRate is 0. + * + * \param changeFrameRateStrategy Whether display refresh rate transitions caused by this + * window should be seamless. + * A seamless transition is one that doesn't have any visual interruptions, such as a black + * screen for a second or two. See the ANATIVEWINDOW_CHANGE_FRAME_RATE_* values. + * This parameter is ignored when frameRate is 0. + * + * \return 0 for success, -EINVAL if the window, frame rate, or compatibility + * value are invalid. + */ + ANativeWindow_setFrameRateWithChangeStrategy :: proc(window: ^ANativeWindow, frameRate: f32, compatibility: ANativeWindow_FrameRateCompatibility, changeFrameRateStrategy: ANativeWindow_ChangeFrameRateStrategy) -> i32 --- } diff --git a/native_window_jni.odin b/native_window_jni.odin new file mode 100644 index 0000000..70fec9c --- /dev/null +++ b/native_window_jni.odin @@ -0,0 +1,25 @@ +//+build android +package android + +foreign import android "system:android" + + foreign android { + /** + * Return the ANativeWindow associated with a Java Surface object, + * for interacting with it through native code. This acquires a reference + * on the ANativeWindow that is returned be sure to use ANativeWindow_release() + * when done with it so that it doesn't leak. + */ + ANativeWindow_fromSurface :: proc(env: ^JNIEnv, surface: jobject) -> ^ANativeWindow --- + + /** + * Return a Java Surface object derived from the ANativeWindow, for interacting + * with it through Java code. The returned Java object acquires a reference on + * the ANativeWindow maintains it through general Java object's life cycle; + * and will automatically release the reference when the Java object gets garbage + * collected. + * + * Available since API level 26. + */ + ANativeWindow_toSurface :: proc(env: ^JNIEnv, window: ^ANativeWindow) -> jobject --- +} diff --git a/ndk-build.odin b/ndk-build.odin new file mode 100644 index 0000000..e634e59 --- /dev/null +++ b/ndk-build.odin @@ -0,0 +1,41 @@ +//+build android +package android + +/** + * Set to 1 if this is an NDK, unset otherwise. See + * https://android.googlesource.com/platform/bionic/+/master/docs/defines.md. + */ +__ANDROID_NDK__ :: 1 + +/** + * Major version of this NDK. + * + * For example: 16 for r16. + */ +__NDK_MAJOR__ :: 26 + +/** + * Minor version of this NDK. + * + * For example: 0 for r16 and 1 for r16b. + */ +__NDK_MINOR__ :: 1 + +/** + * Set to 0 if this is a release build, or 1 for beta 1, + * 2 for beta 2, and so on. + */ +__NDK_BETA__ :: 0 + +/** + * Build number for this NDK. + * + * For a local development build of the NDK, this is -1. + */ +__NDK_BUILD__ :: 10909125 + +/** + * Set to 1 if this is a canary build, 0 if not. + */ +__NDK_CANARY__ :: 0 + diff --git a/window.odin b/window.odin new file mode 100644 index 0000000..3b3697c --- /dev/null +++ b/window.odin @@ -0,0 +1,191 @@ +//+build android +package android + +/** + * Window flags, as per the Java API at android.view.WindowManager.LayoutParams. + */ +WindowFlagsBits :: enum u32 { + /** + * As long as this window is visible to the user, allow the lock + * screen to activate while the screen is on. This can be used + * independently, or in combination with {@link + * KEEP_SCREEN_ON} and/or {@link + * SHOW_WHEN_LOCKED} + */ + ALLOW_LOCK_WHILE_SCREEN_ON = 0, + /** Everything behind this window will be dimmed. */ + DIM_BEHIND = 1, + /** + * Blur everything behind this window. + * @deprecated Blurring is no longer supported. + */ + BLUR_BEHIND = 2, + /** + * This window won't ever get key input focus, so the + * user can not send key or other button events to it. Those will + * instead go to whatever focusable window is behind it. This flag + * will also enable {@link NOT_TOUCH_MODAL} whether or not that + * is explicitly set. + * + * Setting this flag also implies that the window will not need to + * interact with + * a soft input method, so it will be Z-ordered and positioned + * independently of any active input method (typically this means it + * gets Z-ordered on top of the input method, so it can use the full + * screen for its content and cover the input method if needed. You + * can use {@link ALT_FOCUSABLE_IM} to modify this behavior. + */ + NOT_FOCUSABLE = 3, + /** this window can never receive touch events. */ + NOT_TOUCHABLE = 4, + /** + * Even when this window is focusable (its + * {@link NOT_FOCUSABLE} is not set), allow any pointer events + * outside of the window to be sent to the windows behind it. Otherwise + * it will consume all pointer events itself, regardless of whether they + * are inside of the window. + */ + NOT_TOUCH_MODAL = 5, + /** + * When set, if the device is asleep when the touch + * screen is pressed, you will receive this first touch event. Usually + * the first touch event is consumed by the system since the user can + * not see what they are pressing on. + * + * @deprecated This flag has no effect. + */ + TOUCHABLE_WHEN_WAKING = 6, + /** + * As long as this window is visible to the user, keep + * the device's screen turned on and bright. + */ + KEEP_SCREEN_ON = 7, + /** + * Place the window within the entire screen, ignoring + * decorations around the border (such as the status bar). The + * window must correctly position its contents to take the screen + * decoration into account. + */ + LAYOUT_IN_SCREEN = 8, + /** allow window to extend outside of the screen. */ + LAYOUT_NO_LIMITS = 9, + /** + * Hide all screen decorations (such as the status + * bar) while this window is displayed. This allows the window to + * use the entire display space for itself -- the status bar will + * be hidden when an app window with this flag set is on the top + * layer. A fullscreen window will ignore a value of + * + * SOFT_INPUT_ADJUST_RESIZE; the window will stay + * fullscreen and will not resize. + */ + FULLSCREEN = 10, + /** + * Override {@link FULLSCREEN} and force the + * screen decorations (such as the status bar) to be shown. + */ + FORCE_NOT_FULLSCREEN = 11, + /** + * Turn on dithering when compositing this window to + * the screen. + * @deprecated This flag is no longer used. + */ + DITHER = 12, + /** + * Treat the content of the window as secure, preventing + * it from appearing in screenshots or from being viewed on non-secure + * displays. + */ + SECURE = 13, + /** + * A special mode where the layout parameters are used + * to perform scaling of the surface when it is composited to the + * screen. + */ + SCALED = 14, + /** + * Intended for windows that will often be used when the user is + * holding the screen against their face, it will aggressively + * filter the event stream to prevent unintended presses in this + * situation that may not be desired for a particular window, when + * such an event stream is detected, the application will receive + * a {@link AMOTION_EVENT_ACTION_CANCEL} to indicate this so + * applications can handle this accordingly by taking no action on + * the event until the finger is released. + */ + IGNORE_CHEEK_PRESSES = 15, + /** + * A special option only for use in combination with + * {@link LAYOUT_IN_SCREEN}. When requesting layout in the + * screen your window may appear on top of or behind screen decorations + * such as the status bar. By also including this flag, the window + * manager will report the inset rectangle needed to ensure your + * content is not covered by screen decorations. + */ + LAYOUT_INSET_DECOR = 16, + /** + * Invert the state of {@link NOT_FOCUSABLE} with + * respect to how this window interacts with the current method. + * That is, if FLAG_NOT_FOCUSABLE is set and this flag is set, + * then the window will behave as if it needs to interact with the + * input method and thus be placed behind/away from it; if {@link + * NOT_FOCUSABLE} is not set and this flag is set, + * then the window will behave as if it doesn't need to interact + * with the input method and can be placed to use more space and + * cover the input method. + */ + ALT_FOCUSABLE_IM = 17, + /** + * If you have set {@link NOT_TOUCH_MODAL}, you + * can set this flag to receive a single special MotionEvent with + * the action + * {@link AMOTION_EVENT_ACTION_OUTSIDE} for + * touches that occur outside of your window. Note that you will not + * receive the full down/move/up gesture, only the location of the + * first down as an {@link AMOTION_EVENT_ACTION_OUTSIDE}. + */ + WATCH_OUTSIDE_TOUCH = 18, + /** + * Special flag to let windows be shown when the screen + * is locked. This will let application windows take precedence over + * key guard or any other lock screens. Can be used with + * {@link KEEP_SCREEN_ON} to turn screen on and display windows + * directly before showing the key guard window. Can be used with + * {@link DISMISS_KEYGUARD} to automatically fully dismisss + * non-secure keyguards. This flag only applies to the top-most + * full-screen window. + */ + SHOW_WHEN_LOCKED = 19, + /** + * Ask that the system wallpaper be shown behind + * your window. The window surface must be translucent to be able + * to actually see the wallpaper behind it; this flag just ensures + * that the wallpaper surface will be there if this window actually + * has translucent regions. + */ + SHOW_WALLPAPER = 20, + /** + * When set as a window is being added or made + * visible, once the window has been shown then the system will + * poke the power manager's user activity (as if the user had woken + * up the device) to turn the screen on. + */ + TURN_SCREEN_ON = 21, + /** + * When set the window will cause the keyguard to + * be dismissed, only if it is not a secure lock keyguard. Because such + * a keyguard is not needed for security, it will never re-appear if + * the user navigates to another window (in contrast to + * {@link SHOW_WHEN_LOCKED}, which will only temporarily + * hide both secure and non-secure keyguards but ensure they reappear + * when the user moves to another UI that doesn't hide them). + * If the keyguard is currently active and is secure (requires an + * unlock pattern) than the user will still need to confirm it before + * seeing this window, unless {@link SHOW_WHEN_LOCKED} has + * also been set. + */ + DISMISS_KEYGUARD = 22, +} + +WindowFlags :: bit_set[WindowFlagsBits; u32] +