/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef ANDROID_ML_NN_COMMON_UTILS_H
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#define ANDROID_ML_NN_COMMON_UTILS_H
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#include "HalInterfaces.h"
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#include "NeuralNetworks.h"
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#include "ValidateHal.h"
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#include <android-base/logging.h>
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#include <optional>
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#include <set>
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#include <vector>
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namespace android {
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namespace nn {
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// The number of data types (OperandCode) defined in NeuralNetworks.h.
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const int kNumberOfDataTypes = 14;
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// The number of operation types (OperationCode) defined in NeuralNetworks.h.
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const int kNumberOfOperationTypes = 95;
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// The number of execution preferences defined in NeuralNetworks.h.
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const int kNumberOfPreferences = 3;
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// The number of data types (OperandCode) defined in NeuralNetworksOEM.h.
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const int kNumberOfDataTypesOEM = 2;
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// The number of operation types (OperationCode) defined in NeuralNetworksOEM.h.
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const int kNumberOfOperationTypesOEM = 1;
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// The lowest number assigned to any OEM Code in NeuralNetworksOEM.h.
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const int kOEMCodeBase = 10000;
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/* IMPORTANT: if you change the following list, don't
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* forget to update the corresponding 'tags' table in
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* the initVlogMask() function implemented in Utils.cpp.
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*/
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enum VLogFlags {
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MODEL = 0,
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COMPILATION,
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EXECUTION,
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CPUEXE,
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MANAGER,
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DRIVER
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};
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#define VLOG_IS_ON(TAG) \
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((vLogMask & (1 << (TAG))) != 0)
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#define VLOG(TAG) \
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if (LIKELY(!VLOG_IS_ON(TAG))) \
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; \
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else \
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LOG(INFO)
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extern int vLogMask;
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void initVLogMask();
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#ifdef NN_DEBUGGABLE
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#define SHOW_IF_DEBUG(msg) msg
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#else
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#define SHOW_IF_DEBUG(msg) ""
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#endif
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// DEPRECATED(b/118737105). Use CHECK.
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#define nnAssert(v) CHECK(v)
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#define NN_RETURN_IF_ERROR(expr) \
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do { \
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int _errorCode = (expr); \
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if (_errorCode != ANEURALNETWORKS_NO_ERROR) { \
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return _errorCode; \
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} \
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} while (0)
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// The NN_RET_CHECK family of macros defined below is similar to the CHECK family defined in
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// system/core/base/include/android-base/logging.h
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//
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// The difference is that NN_RET_CHECK macros use LOG(ERROR) instead of LOG(FATAL)
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// and return false instead of aborting.
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// Logs an error and returns false. Append context using << after. For example:
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//
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// NN_RET_CHECK_FAIL() << "Something went wrong";
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//
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// The containing function must return a bool.
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#define NN_RET_CHECK_FAIL() \
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return ::android::nn::FalseyErrorStream() \
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<< "NN_RET_CHECK failed (" << __FILE__ << ":" << __LINE__ << "): "
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// Logs an error and returns false if condition is false. Extra logging can be appended using <<
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// after. For example:
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//
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// NN_RET_CHECK(false) << "Something went wrong";
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//
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// The containing function must return a bool.
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#define NN_RET_CHECK(condition) \
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while (UNLIKELY(!(condition))) NN_RET_CHECK_FAIL() << #condition << " "
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// Helper for NN_CHECK_xx(x, y) macros.
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#define NN_RET_CHECK_OP(LHS, RHS, OP) \
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for (auto _values = ::android::base::MakeEagerEvaluator(LHS, RHS); \
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UNLIKELY(!(_values.lhs OP _values.rhs)); \
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/* empty */) \
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NN_RET_CHECK_FAIL() << #LHS << " " << #OP << " " << #RHS << " (" << #LHS << " = " \
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<< _values.lhs << ", " << #RHS << " = " << _values.rhs << ") "
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// Logs an error and returns false if a condition between x and y does not hold. Extra logging can
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// be appended using << after. For example:
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//
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// NN_RET_CHECK_EQ(a, b) << "Something went wrong";
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//
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// The values must implement the appropriate comparison operator as well as
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// `operator<<(std::ostream&, ...)`.
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// The containing function must return a bool.
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#define NN_RET_CHECK_EQ(x, y) NN_RET_CHECK_OP(x, y, ==)
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#define NN_RET_CHECK_NE(x, y) NN_RET_CHECK_OP(x, y, !=)
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#define NN_RET_CHECK_LE(x, y) NN_RET_CHECK_OP(x, y, <=)
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#define NN_RET_CHECK_LT(x, y) NN_RET_CHECK_OP(x, y, <)
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#define NN_RET_CHECK_GE(x, y) NN_RET_CHECK_OP(x, y, >=)
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#define NN_RET_CHECK_GT(x, y) NN_RET_CHECK_OP(x, y, >)
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// A wrapper around LOG(ERROR) that can be implicitly converted to bool (always evaluates to false).
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// Used to implement stream logging in NN_RET_CHECK.
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class FalseyErrorStream {
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DISALLOW_COPY_AND_ASSIGN(FalseyErrorStream);
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public:
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FalseyErrorStream() {}
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template <typename T>
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FalseyErrorStream& operator<<(const T& value) {
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mBuffer << value;
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return *this;
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}
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~FalseyErrorStream() { LOG(ERROR) << mBuffer.str(); }
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operator bool() const { return false; }
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private:
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std::ostringstream mBuffer;
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};
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// Return a vector with one entry for each non extension OperandType, set to the
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// specified PerformanceInfo value. The vector will be sorted by OperandType.
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hidl_vec<Capabilities::OperandPerformance> nonExtensionOperandPerformance(PerformanceInfo perf);
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// Update the vector entry corresponding to the specified OperandType with the
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// specified PerformanceInfo value. The vector must already have an entry for
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// that OperandType, and must be sorted by OperandType.
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void update(hidl_vec<Capabilities::OperandPerformance>* operandPerformance, OperandType type,
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PerformanceInfo perf);
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// Look for a vector entry corresponding to the specified OperandType. If
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// found, return the associated PerformanceInfo. If not, return a pessimistic
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// PerformanceInfo (FLT_MAX). The vector must be sorted by OperandType.
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PerformanceInfo lookup(const hidl_vec<Capabilities::OperandPerformance>& operandPerformance,
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OperandType type);
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// Returns true if an operand type is an extension type.
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bool isExtensionOperandType(OperandType type);
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// Returns true if an operation type is an extension type.
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bool isExtensionOperationType(OperationType type);
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// Returns the amount of space needed to store a value of the specified
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// dimensions and type. For a tensor with unspecified rank or at least one
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// unspecified dimension, returns zero.
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//
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// Aborts if the specified type is an extension type.
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//
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// See also TypeManager::getSizeOfData(OperandType, const std::vector<uint32_t>&).
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uint32_t nonExtensionOperandSizeOfData(OperandType type, const std::vector<uint32_t>& dimensions);
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// Returns the amount of space needed to store a value of the dimensions and
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// type of this operand. For a tensor with unspecified rank or at least one
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// unspecified dimension, returns zero.
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//
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// Aborts if the specified type is an extension type.
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//
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// See also TypeManager::getSizeOfData(const Operand&).
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inline uint32_t nonExtensionOperandSizeOfData(const Operand& operand) {
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return nonExtensionOperandSizeOfData(operand.type, operand.dimensions);
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}
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// Returns true if a non-extension operand type is a scalar type.
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//
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// Aborts if the specified type is an extension type.
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//
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// See also TypeManager::isTensorType(OperandType).
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bool nonExtensionOperandTypeIsScalar(int type);
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// Returns the name of the operation type in ASCII.
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std::string getOperationName(OperationType opCode);
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// Returns the name of the operand type in ASCII.
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std::string getOperandTypeName(OperandType type);
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// Whether an operand of tensor type has unspecified dimensions.
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//
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// Undefined behavior if the operand type is a scalar type.
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bool tensorHasUnspecifiedDimensions(int type, const uint32_t* dim, uint32_t dimCount);
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bool tensorHasUnspecifiedDimensions(const Operand& operand);
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bool tensorHasUnspecifiedDimensions(const ANeuralNetworksOperandType* type);
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// Memory is unmapped.
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// Memory is reference counted by hidl_memory instances, and is deallocated
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// once there are no more references.
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hidl_memory allocateSharedMemory(int64_t size);
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// Returns the number of padding bytes needed to align data of the
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// specified length. It aligns object of length:
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// 2, 3 on a 2 byte boundary,
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// 4+ on a 4 byte boundary.
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// We may want to have different alignments for tensors.
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// TODO: This is arbitrary, more a proof of concept. We need
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// to determine what this should be.
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uint32_t alignBytesNeeded(uint32_t index, size_t length);
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// Does a detailed LOG(INFO) of the model
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void logModelToInfo(const V1_0::Model& model);
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void logModelToInfo(const V1_1::Model& model);
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void logModelToInfo(const V1_2::Model& model);
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inline std::string toString(uint32_t obj) {
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return std::to_string(obj);
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}
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template <typename Type>
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std::string toString(const std::vector<Type>& range) {
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std::string os = "[";
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for (size_t i = 0; i < range.size(); ++i) {
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os += (i == 0 ? "" : ", ") + toString(range[i]);
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}
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return os += "]";
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}
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inline std::string toString(HalVersion halVersion) {
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switch (halVersion) {
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case HalVersion::UNKNOWN:
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return "UNKNOWN HAL version";
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case HalVersion::V1_0:
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return "HAL version 1.0";
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case HalVersion::V1_1:
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return "HAL version 1.1";
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case HalVersion::V1_2:
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return "HAL version 1.2";
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}
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}
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inline bool validCode(uint32_t codeCount, uint32_t codeCountOEM, uint32_t code) {
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return (code < codeCount) || (code >= kOEMCodeBase && (code - kOEMCodeBase) < codeCountOEM);
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}
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bool validateOperandSymmPerChannelQuantParams(
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const Operand& halOperand, const ANeuralNetworksSymmPerChannelQuantParams& channelQuant,
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const char* tag);
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// Validates an operand type.
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//
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// extensionOperandTypeInfo must be nullptr iff the type is not an extension type.
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//
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// If allowPartial is true, the dimensions may be underspecified.
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int validateOperandType(const ANeuralNetworksOperandType& type,
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const Extension::OperandTypeInformation* const extensionOperandTypeInfo,
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const char* tag, bool allowPartial);
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int validateOperandList(uint32_t count, const uint32_t* list, uint32_t operandCount,
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const char* tag);
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// Returns ANEURALNETWORKS_NO_ERROR if the corresponding operation is defined and can handle the
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// provided operand types in the given HAL version, otherwise returns ANEURALNETWORKS_BAD_DATA.
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int validateOperation(ANeuralNetworksOperationType opType, uint32_t inputCount,
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const uint32_t* inputIndexes, uint32_t outputCount,
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const uint32_t* outputIndexes, const std::vector<Operand>& operands,
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HalVersion halVersion);
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inline size_t getSizeFromInts(int lower, int higher) {
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return (uint32_t)(lower) + ((uint64_t)(uint32_t)(higher) << 32);
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}
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// Convert ANEURALNETWORKS_* result code to ErrorStatus.
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// Not guaranteed to be a 1-to-1 mapping.
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ErrorStatus convertResultCodeToErrorStatus(int resultCode);
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// Convert ErrorStatus to ANEURALNETWORKS_* result code.
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// Not guaranteed to be a 1-to-1 mapping.
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int convertErrorStatusToResultCode(ErrorStatus status);
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// Versioning
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bool compliantWithV1_0(const V1_0::Capabilities& capabilities);
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bool compliantWithV1_0(const V1_1::Capabilities& capabilities);
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bool compliantWithV1_0(const V1_2::Capabilities& capabilities);
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bool compliantWithV1_1(const V1_0::Capabilities& capabilities);
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bool compliantWithV1_1(const V1_1::Capabilities& capabilities);
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bool compliantWithV1_1(const V1_2::Capabilities& capabilities);
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bool compliantWithV1_2(const V1_0::Capabilities& capabilities);
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bool compliantWithV1_2(const V1_1::Capabilities& capabilities);
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bool compliantWithV1_2(const V1_2::Capabilities& capabilities);
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bool compliantWithV1_0(const V1_2::Operand& operand);
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// If noncompliantOperations != nullptr, then
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// precondition: noncompliantOperations->empty()
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// postcondition: *noncompliantOperations consists of the indices of the noncompliant
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// operations; if the compliance check fails for some reason
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// other than a noncompliant operation,
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// *noncompliantOperations consists of the indices of all operations
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bool compliantWithV1_0(const V1_0::Model& model);
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bool compliantWithV1_0(const V1_1::Model& model);
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bool compliantWithV1_0(const V1_2::Model& model,
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std::set<uint32_t>* noncompliantOperations = nullptr);
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bool compliantWithV1_1(const V1_0::Model& model);
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bool compliantWithV1_1(const V1_1::Model& model);
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bool compliantWithV1_1(const V1_2::Model& model,
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std::set<uint32_t>* noncompliantOperations = nullptr);
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V1_0::Capabilities convertToV1_0(const V1_0::Capabilities& capabilities);
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V1_0::Capabilities convertToV1_0(const V1_1::Capabilities& capabilities);
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V1_0::Capabilities convertToV1_0(const V1_2::Capabilities& capabilities);
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V1_1::Capabilities convertToV1_1(const V1_0::Capabilities& capabilities);
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V1_1::Capabilities convertToV1_1(const V1_1::Capabilities& capabilities);
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V1_1::Capabilities convertToV1_1(const V1_2::Capabilities& capabilities);
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V1_2::Capabilities convertToV1_2(const V1_0::Capabilities& capabilities);
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V1_2::Capabilities convertToV1_2(const V1_1::Capabilities& capabilities);
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V1_2::Capabilities convertToV1_2(const V1_2::Capabilities& capabilities);
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V1_0::Model convertToV1_0(const V1_0::Model& model);
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V1_0::Model convertToV1_0(const V1_1::Model& model);
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V1_0::Model convertToV1_0(const V1_2::Model& model);
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V1_1::Model convertToV1_1(const V1_0::Model& model);
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V1_1::Model convertToV1_1(const V1_1::Model& model);
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V1_1::Model convertToV1_1(const V1_2::Model& model);
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V1_2::Model convertToV1_2(const V1_0::Model& model);
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V1_2::Model convertToV1_2(const V1_1::Model& model);
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V1_2::Model convertToV1_2(const V1_2::Model& model);
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// The IModelSlicer abstract class provides methods to create from an original
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// model a "slice" of that model consisting of the subset of operations that is
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// compliant with a particular HAL version, and a mechanism for mapping
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// operations from the slice back to operations of the original model. The
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// slice is intended to be passed to getSupportedOperations*(), with the mapping
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// used to translate the results of that call from the slice's operations to the
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// original model's operations. The slice has no other purpose (for example, it
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// is not guaranteed to have the same topology as a subgraph of the original
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// model).
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//
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// Note that the original model is not part of the ModelSlicer specification --
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// an instance of a class derived from ModelSlicer is responsible for knowing
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// the original model. getSlice*() methods may be called multiple times on a
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// given instance; the intention is that the instance cache slices internally.
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//
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// The meaning of the return value of the getSlice*() methods is explained by
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// the following example:
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//
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// IModelSlicer* slicer = ...;
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// auto ret = slicer->getSliceV1_0(); // getSliceV1_1() is similar
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// if (ret.has_value()) {
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// const V1_0::Model model = ret->first; // the slice
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// auto mapper = ret->second;
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// // mapper is a functor that takes an operation index in the
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// // slice and returns the corresponding operation index in the
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// // original model. The functor must remain valid for the lifetime
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// // of *slicer.
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// } else {
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// // Could not obtain a slice. For example, perhaps none of the
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// // original model's operations are compliant with V1_0.
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// }
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//
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class IModelSlicer {
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public:
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virtual std::optional<std::pair<V1_0::Model, std::function<uint32_t(uint32_t)>>>
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getSliceV1_0() = 0;
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virtual std::optional<std::pair<V1_1::Model, std::function<uint32_t(uint32_t)>>>
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getSliceV1_1() = 0;
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virtual ~IModelSlicer() = default;
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};
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V1_0::OperationType uncheckedConvertToV1_0(V1_2::OperationType type);
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V1_1::OperationType uncheckedConvertToV1_1(V1_2::OperationType type);
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V1_0::Operand convertToV1_0(const V1_2::Operand& operand);
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V1_2::Operand convertToV1_2(const V1_0::Operand& operand);
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V1_2::Operand convertToV1_2(const V1_2::Operand& operand);
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hidl_vec<V1_2::Operand> convertToV1_2(const hidl_vec<V1_0::Operand>& operands);
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hidl_vec<V1_2::Operand> convertToV1_2(const hidl_vec<V1_2::Operand>& operands);
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#ifdef NN_DEBUGGABLE
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uint32_t getProp(const char* str, uint32_t defaultValue = 0);
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#endif // NN_DEBUGGABLE
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} // namespace nn
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} // namespace android
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#endif // ANDROID_ML_NN_COMMON_UTILS_H
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