diff options
Diffstat (limited to 'tests/nnapi/include/NeuralNetworksWrapper.h')
| -rw-r--r-- | tests/nnapi/include/NeuralNetworksWrapper.h | 208 |
1 files changed, 37 insertions, 171 deletions
diff --git a/tests/nnapi/include/NeuralNetworksWrapper.h b/tests/nnapi/include/NeuralNetworksWrapper.h index af19008fe..71468e212 100644 --- a/tests/nnapi/include/NeuralNetworksWrapper.h +++ b/tests/nnapi/include/NeuralNetworksWrapper.h @@ -20,17 +20,10 @@ #ifndef __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ #define __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ -// Fix for onert: -// NeuralNetworks.h => NeuralNetworksShim.h -// Additional include NeuralNetworksExShim.h #include "NeuralNetworksShim.h" #include "NeuralNetworksExShim.h" #include <math.h> -// Fix for onert: use boost::optional instead of std::optional -// TODO in onert: introduce and use internal optional library -#include <boost/optional.hpp> -#include <string> #include <vector> namespace nnfw { @@ -44,14 +37,6 @@ enum class Type { TENSOR_FLOAT32 = ANEURALNETWORKS_TENSOR_FLOAT32, TENSOR_INT32 = ANEURALNETWORKS_TENSOR_INT32, TENSOR_QUANT8_ASYMM = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, - BOOL = ANEURALNETWORKS_BOOL, - TENSOR_QUANT16_SYMM = ANEURALNETWORKS_TENSOR_QUANT16_SYMM, - TENSOR_FLOAT16 = ANEURALNETWORKS_TENSOR_FLOAT16, - TENSOR_BOOL8 = ANEURALNETWORKS_TENSOR_BOOL8, - FLOAT16 = ANEURALNETWORKS_FLOAT16, - TENSOR_QUANT8_SYMM_PER_CHANNEL = ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, - TENSOR_QUANT16_ASYMM = ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, - TENSOR_QUANT8_SYMM = ANEURALNETWORKS_TENSOR_QUANT8_SYMM, }; enum class ExecutePreference { @@ -66,102 +51,32 @@ enum class Result { INCOMPLETE = ANEURALNETWORKS_INCOMPLETE, UNEXPECTED_NULL = ANEURALNETWORKS_UNEXPECTED_NULL, BAD_DATA = ANEURALNETWORKS_BAD_DATA, - OP_FAILED = ANEURALNETWORKS_OP_FAILED, - UNMAPPABLE = ANEURALNETWORKS_UNMAPPABLE, - BAD_STATE = ANEURALNETWORKS_BAD_STATE, - OUTPUT_INSUFFICIENT_SIZE = ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE, - UNAVAILABLE_DEVICE = ANEURALNETWORKS_UNAVAILABLE_DEVICE, -}; - -struct SymmPerChannelQuantParams { - ANeuralNetworksSymmPerChannelQuantParams params; - std::vector<float> scales; - - SymmPerChannelQuantParams(std::vector<float> scalesVec, uint32_t channelDim) - : scales(std::move(scalesVec)) { - params = { - .channelDim = channelDim, - .scaleCount = static_cast<uint32_t>(scales.size()), - .scales = scales.size() > 0 ? scales.data() : nullptr, - }; - } - - SymmPerChannelQuantParams(const SymmPerChannelQuantParams& other) - : params(other.params), scales(other.scales) { - params.scales = scales.size() > 0 ? scales.data() : nullptr; - } - - SymmPerChannelQuantParams& operator=(const SymmPerChannelQuantParams& other) { - if (this != &other) { - params = other.params; - scales = other.scales; - params.scales = scales.size() > 0 ? scales.data() : nullptr; - } - return *this; - } }; struct OperandType { ANeuralNetworksOperandType operandType; + // int32_t type; std::vector<uint32_t> dimensions; - // Fix for onert: - // Use boost::optional instead of std::optional - // Default value: std::nullopt -> boost::none - boost::optional<SymmPerChannelQuantParams> channelQuant; - - OperandType(const OperandType& other) - : operandType(other.operandType), - dimensions(other.dimensions), - channelQuant(other.channelQuant) { - operandType.dimensions = dimensions.size() > 0 ? dimensions.data() : nullptr; - } - - OperandType& operator=(const OperandType& other) { - if (this != &other) { - operandType = other.operandType; - dimensions = other.dimensions; - channelQuant = other.channelQuant; - operandType.dimensions = dimensions.size() > 0 ? dimensions.data() : nullptr; - } - return *this; - } - OperandType(Type type, std::vector<uint32_t> d, float scale = 0.0f, int32_t zeroPoint = 0) - : dimensions(std::move(d)), channelQuant(boost::none) { - operandType = { - .type = static_cast<int32_t>(type), - .dimensionCount = static_cast<uint32_t>(dimensions.size()), - .dimensions = dimensions.size() > 0 ? dimensions.data() : nullptr, - .scale = scale, - .zeroPoint = zeroPoint, - }; - } + OperandType(Type type, const std::vector<uint32_t>& d, float scale = 0.0f, + int32_t zeroPoint = 0) + : dimensions(d) { + operandType.type = static_cast<int32_t>(type); + operandType.scale = scale; + operandType.zeroPoint = zeroPoint; - OperandType(Type type, std::vector<uint32_t> data, float scale, int32_t zeroPoint, - SymmPerChannelQuantParams&& channelQuant) - : dimensions(std::move(data)), channelQuant(std::move(channelQuant)) { - operandType = { - .type = static_cast<int32_t>(type), - .dimensionCount = static_cast<uint32_t>(dimensions.size()), - .dimensions = dimensions.size() > 0 ? dimensions.data() : nullptr, - .scale = scale, - .zeroPoint = zeroPoint, - }; + operandType.dimensionCount = static_cast<uint32_t>(dimensions.size()); + operandType.dimensions = dimensions.data(); } }; class Memory { - public: +public: Memory(size_t size, int protect, int fd, size_t offset) { mValid = ANeuralNetworksMemory_createFromFd(size, protect, fd, offset, &mMemory) == ANEURALNETWORKS_NO_ERROR; } - Memory(AHardwareBuffer* buffer) { - mValid = ANeuralNetworksMemory_createFromAHardwareBuffer(buffer, &mMemory) == - ANEURALNETWORKS_NO_ERROR; - } - ~Memory() { ANeuralNetworksMemory_free(mMemory); } // Disallow copy semantics to ensure the runtime object can only be freed @@ -176,7 +91,6 @@ class Memory { Memory(Memory&& other) { *this = std::move(other); } Memory& operator=(Memory&& other) { if (this != &other) { - ANeuralNetworksMemory_free(mMemory); mMemory = other.mMemory; mValid = other.mValid; other.mMemory = nullptr; @@ -188,13 +102,13 @@ class Memory { ANeuralNetworksMemory* get() const { return mMemory; } bool isValid() const { return mValid; } - private: +private: ANeuralNetworksMemory* mMemory = nullptr; bool mValid = true; }; class Model { - public: +public: Model() { // TODO handle the value returned by this call ANeuralNetworksModel_create(&mModel); @@ -213,7 +127,6 @@ class Model { Model(Model&& other) { *this = std::move(other); } Model& operator=(Model&& other) { if (this != &other) { - ANeuralNetworksModel_free(mModel); mModel = other.mModel; mNextOperandId = other.mNextOperandId; mValid = other.mValid; @@ -224,30 +137,13 @@ class Model { return *this; } - Result finish() { - if (mValid) { - auto result = static_cast<Result>(ANeuralNetworksModel_finish(mModel)); - if (result != Result::NO_ERROR) { - mValid = false; - } - return result; - } else { - return Result::BAD_STATE; - } - } + Result finish() { return static_cast<Result>(ANeuralNetworksModel_finish(mModel)); } uint32_t addOperand(const OperandType* type) { if (ANeuralNetworksModel_addOperand(mModel, &(type->operandType)) != ANEURALNETWORKS_NO_ERROR) { mValid = false; } - if (type->channelQuant) { - if (ANeuralNetworksModel_setOperandSymmPerChannelQuantParams( - mModel, mNextOperandId, &type->channelQuant.value().params) != - ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } return mNextOperandId++; } @@ -275,7 +171,6 @@ class Model { } } - // Fix for onert: addOperationEx for operation support extension (NeuralNetworksEx.h) void addOperationEx(ANeuralNetworksOperationTypeEx type, const std::vector<uint32_t>& inputs, const std::vector<uint32_t>& outputs) { if (ANeuralNetworksModel_addOperationEx(mModel, type, static_cast<uint32_t>(inputs.size()), @@ -288,34 +183,24 @@ class Model { void identifyInputsAndOutputs(const std::vector<uint32_t>& inputs, const std::vector<uint32_t>& outputs) { if (ANeuralNetworksModel_identifyInputsAndOutputs( - mModel, static_cast<uint32_t>(inputs.size()), inputs.data(), - static_cast<uint32_t>(outputs.size()), - outputs.data()) != ANEURALNETWORKS_NO_ERROR) { + mModel, static_cast<uint32_t>(inputs.size()), inputs.data(), + static_cast<uint32_t>(outputs.size()), + outputs.data()) != ANEURALNETWORKS_NO_ERROR) { mValid = false; } } - - void relaxComputationFloat32toFloat16(bool isRelax) { - if (ANeuralNetworksModel_relaxComputationFloat32toFloat16(mModel, isRelax) == - ANEURALNETWORKS_NO_ERROR) { - mRelaxed = isRelax; - } - } - ANeuralNetworksModel* getHandle() const { return mModel; } bool isValid() const { return mValid; } - bool isRelaxed() const { return mRelaxed; } - protected: +private: ANeuralNetworksModel* mModel = nullptr; // We keep track of the operand ID as a convenience to the caller. uint32_t mNextOperandId = 0; bool mValid = true; - bool mRelaxed = false; }; class Event { - public: +public: Event() {} ~Event() { ANeuralNetworksEvent_free(mEvent); } @@ -331,7 +216,6 @@ class Event { Event(Event&& other) { *this = std::move(other); } Event& operator=(Event&& other) { if (this != &other) { - ANeuralNetworksEvent_free(mEvent); mEvent = other.mEvent; other.mEvent = nullptr; } @@ -346,12 +230,12 @@ class Event { mEvent = newEvent; } - private: +private: ANeuralNetworksEvent* mEvent = nullptr; }; class Compilation { - public: +public: Compilation(const Model* model) { int result = ANeuralNetworksCompilation_create(model->getHandle(), &mCompilation); if (result != 0) { @@ -361,19 +245,12 @@ class Compilation { ~Compilation() { ANeuralNetworksCompilation_free(mCompilation); } - // Disallow copy semantics to ensure the runtime object can only be freed - // once. Copy semantics could be enabled if some sort of reference counting - // or deep-copy system for runtime objects is added later. Compilation(const Compilation&) = delete; Compilation& operator=(const Compilation&) = delete; - // Move semantics to remove access to the runtime object from the wrapper - // object that is being moved. This ensures the runtime object will be - // freed only once. Compilation(Compilation&& other) { *this = std::move(other); } Compilation& operator=(Compilation&& other) { if (this != &other) { - ANeuralNetworksCompilation_free(mCompilation); mCompilation = other.mCompilation; other.mCompilation = nullptr; } @@ -382,27 +259,19 @@ class Compilation { Result setPreference(ExecutePreference preference) { return static_cast<Result>(ANeuralNetworksCompilation_setPreference( - mCompilation, static_cast<int32_t>(preference))); - } - - Result setCaching(const std::string& cacheDir, const std::vector<uint8_t>& token) { - if (token.size() != ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN) { - return Result::BAD_DATA; - } - return static_cast<Result>(ANeuralNetworksCompilation_setCaching( - mCompilation, cacheDir.c_str(), token.data())); + mCompilation, static_cast<int32_t>(preference))); } Result finish() { return static_cast<Result>(ANeuralNetworksCompilation_finish(mCompilation)); } ANeuralNetworksCompilation* getHandle() const { return mCompilation; } - private: +private: ANeuralNetworksCompilation* mCompilation = nullptr; }; class Execution { - public: +public: Execution(const Compilation* compilation) { int result = ANeuralNetworksExecution_create(compilation->getHandle(), &mExecution); if (result != 0) { @@ -424,7 +293,6 @@ class Execution { Execution(Execution&& other) { *this = std::move(other); } Execution& operator=(Execution&& other) { if (this != &other) { - ANeuralNetworksExecution_free(mExecution); mExecution = other.mExecution; other.mExecution = nullptr; } @@ -434,25 +302,25 @@ class Execution { Result setInput(uint32_t index, const void* buffer, size_t length, const ANeuralNetworksOperandType* type = nullptr) { return static_cast<Result>( - ANeuralNetworksExecution_setInput(mExecution, index, type, buffer, length)); + ANeuralNetworksExecution_setInput(mExecution, index, type, buffer, length)); } Result setInputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { return static_cast<Result>(ANeuralNetworksExecution_setInputFromMemory( - mExecution, index, type, memory->get(), offset, length)); + mExecution, index, type, memory->get(), offset, length)); } Result setOutput(uint32_t index, void* buffer, size_t length, const ANeuralNetworksOperandType* type = nullptr) { return static_cast<Result>( - ANeuralNetworksExecution_setOutput(mExecution, index, type, buffer, length)); + ANeuralNetworksExecution_setOutput(mExecution, index, type, buffer, length)); } Result setOutputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { return static_cast<Result>(ANeuralNetworksExecution_setOutputFromMemory( - mExecution, index, type, memory->get(), offset, length)); + mExecution, index, type, memory->get(), offset, length)); } Result startCompute(Event* event) { @@ -462,23 +330,21 @@ class Execution { return result; } - Result compute() { return static_cast<Result>(ANeuralNetworksExecution_compute(mExecution)); } - - Result getOutputOperandDimensions(uint32_t index, std::vector<uint32_t>* dimensions) { - uint32_t rank = 0; - Result result = static_cast<Result>( - ANeuralNetworksExecution_getOutputOperandRank(mExecution, index, &rank)); - dimensions->resize(rank); - if ((result != Result::NO_ERROR && result != Result::OUTPUT_INSUFFICIENT_SIZE) || - rank == 0) { + Result compute() { + ANeuralNetworksEvent* event = nullptr; + Result result = + static_cast<Result>(ANeuralNetworksExecution_startCompute(mExecution, &event)); + if (result != Result::NO_ERROR) { return result; } - result = static_cast<Result>(ANeuralNetworksExecution_getOutputOperandDimensions( - mExecution, index, dimensions->data())); + // TODO how to manage the lifetime of events when multiple waiters is not + // clear. + result = static_cast<Result>(ANeuralNetworksEvent_wait(event)); + ANeuralNetworksEvent_free(event); return result; } - private: +private: ANeuralNetworksExecution* mExecution = nullptr; }; |