| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| // Contains all the entry points to the C Neural Networks API. |
| // We do basic validation of the operands and then call the class |
| // that implements the functionality. |
| |
| #define LOG_TAG "NeuralNetworks" |
| |
| #include "NeuralNetworks.h" |
| |
| #include "Callbacks.h" |
| #include "CompilationBuilder.h" |
| #include "ExecutionBuilder.h" |
| #include "Manager.h" |
| #include "Memory.h" |
| #include "ModelBuilder.h" |
| #include "NeuralNetworksOEM.h" |
| #include "Tracing.h" |
| #include "Utils.h" |
| |
| #include <cstddef> |
| #include <memory> |
| #include <vector> |
| |
| // Make sure the constants defined in the header files have not changed values. |
| // IMPORTANT: When adding new values, update kNumberOfDataTypes or kNumberOfDataTypesOEM |
| // in Utils.h. |
| static_assert(ANEURALNETWORKS_FLOAT32 == 0, "ANEURALNETWORKS_FLOAT32 has changed"); |
| static_assert(ANEURALNETWORKS_INT32 == 1, "ANEURALNETWORKS_INT32 has changed"); |
| static_assert(ANEURALNETWORKS_UINT32 == 2, "ANEURALNETWORKS_UINT32 has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_FLOAT32 == 3, "ANEURALNETWORKS_TENSOR_FLOAT32 has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_INT32 == 4, "ANEURALNETWORKS_TENSOR_INT32 has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM == 5, |
| "ANEURALNETWORKS_TENSOR_QUANT8_ASYMM has changed"); |
| static_assert(ANEURALNETWORKS_BOOL == 6, "ANEURALNETWORKS_BOOL has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_QUANT16_SYMM == 7, |
| "ANEURALNETWORKS_TENSOR_QUANT16_SYMM has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_FLOAT16 == 8, "ANEURALNETWORKS_TENSOR_FLOAT16 has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_BOOL8 == 9, "ANEURALNETWORKS_TENSOR_BOOL8 has changed"); |
| static_assert(ANEURALNETWORKS_FLOAT16 == 10, "ANEURALNETWORKS_FLOAT16 has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL == 11, |
| "ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL has changed"); |
| static_assert(ANEURALNETWORKS_OEM_SCALAR == 10000, "ANEURALNETWORKS_OEM_SCALAR has changed"); |
| static_assert(ANEURALNETWORKS_TENSOR_OEM_BYTE == 10001, |
| "ANEURALNETWORKS_TENSOR_OEM_BYTE has changed"); |
| |
| // IMPORTANT: When adding new values, update kNumberOfOperationTypes or |
| // kNumberOfOperationTypesOEMin Utils.h. |
| static_assert(ANEURALNETWORKS_ADD == 0, "ANEURALNETWORKS_ADD has changed"); |
| static_assert(ANEURALNETWORKS_AVERAGE_POOL_2D == 1, "ANEURALNETWORKS_AVERAGE_POOL_2D has changed"); |
| static_assert(ANEURALNETWORKS_CONCATENATION == 2, "ANEURALNETWORKS_CONCATENATION has changed"); |
| static_assert(ANEURALNETWORKS_CONV_2D == 3, "ANEURALNETWORKS_CONV_2D has changed"); |
| static_assert(ANEURALNETWORKS_DEPTHWISE_CONV_2D == 4, |
| "ANEURALNETWORKS_DEPTHWISE_CONV_2D has changed"); |
| static_assert(ANEURALNETWORKS_DEPTH_TO_SPACE == 5, "ANEURALNETWORKS_DEPTH_TO_SPACE has changed"); |
| static_assert(ANEURALNETWORKS_DEQUANTIZE == 6, "ANEURALNETWORKS_DEQUANTIZE has changed"); |
| static_assert(ANEURALNETWORKS_EMBEDDING_LOOKUP == 7, |
| "ANEURALNETWORKS_EMBEDDING_LOOKUP has changed"); |
| static_assert(ANEURALNETWORKS_FLOOR == 8, "ANEURALNETWORKS_FLOOR has changed"); |
| static_assert(ANEURALNETWORKS_FULLY_CONNECTED == 9, "ANEURALNETWORKS_FULLY_CONNECTED has changed"); |
| static_assert(ANEURALNETWORKS_HASHTABLE_LOOKUP == 10, |
| "ANEURALNETWORKS_HASHTABLE_LOOKUP has changed"); |
| static_assert(ANEURALNETWORKS_L2_NORMALIZATION == 11, |
| "ANEURALNETWORKS_L2_NORMALIZATION has changed"); |
| static_assert(ANEURALNETWORKS_L2_POOL_2D == 12, "ANEURALNETWORKS_L2_POOL has changed"); |
| static_assert(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION == 13, |
| "ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION has changed"); |
| static_assert(ANEURALNETWORKS_LOGISTIC == 14, "ANEURALNETWORKS_LOGISTIC has changed"); |
| static_assert(ANEURALNETWORKS_LSH_PROJECTION == 15, "ANEURALNETWORKS_LSH_PROJECTION has changed"); |
| static_assert(ANEURALNETWORKS_LSTM == 16, "ANEURALNETWORKS_LSTM has changed"); |
| static_assert(ANEURALNETWORKS_MAX_POOL_2D == 17, "ANEURALNETWORKS_MAX_POOL has changed"); |
| static_assert(ANEURALNETWORKS_MUL == 18, "ANEURALNETWORKS_MUL has changed"); |
| static_assert(ANEURALNETWORKS_RELU == 19, "ANEURALNETWORKS_RELU has changed"); |
| static_assert(ANEURALNETWORKS_RELU1 == 20, "ANEURALNETWORKS_RELU1 has changed"); |
| static_assert(ANEURALNETWORKS_RELU6 == 21, "ANEURALNETWORKS_RELU6 has changed"); |
| static_assert(ANEURALNETWORKS_RESHAPE == 22, "ANEURALNETWORKS_RESHAPE has changed"); |
| static_assert(ANEURALNETWORKS_RESIZE_BILINEAR == 23, "ANEURALNETWORKS_RESIZE_BILINEAR has changed"); |
| static_assert(ANEURALNETWORKS_RNN == 24, "ANEURALNETWORKS_RNN has changed"); |
| static_assert(ANEURALNETWORKS_SOFTMAX == 25, "ANEURALNETWORKS_SOFTMAX has changed"); |
| static_assert(ANEURALNETWORKS_SPACE_TO_DEPTH == 26, "ANEURALNETWORKS_SPACE_TO_DEPTH has changed"); |
| static_assert(ANEURALNETWORKS_SVDF == 27, "ANEURALNETWORKS_SVDF has changed"); |
| static_assert(ANEURALNETWORKS_TANH == 28, "ANEURALNETWORKS_TANH has changed"); |
| |
| static_assert(ANEURALNETWORKS_BATCH_TO_SPACE_ND == 29, |
| "ANEURALNETWORKS_BATCH_TO_SPACE_ND has changed"); |
| static_assert(ANEURALNETWORKS_DIV == 30, "ANEURALNETWORKS_DIV has changed"); |
| static_assert(ANEURALNETWORKS_MEAN == 31, "ANEURALNETWORKS_MEAN has changed"); |
| static_assert(ANEURALNETWORKS_PAD == 32, "ANEURALNETWORKS_PAD has changed"); |
| static_assert(ANEURALNETWORKS_SPACE_TO_BATCH_ND == 33, |
| "ANEURALNETWORKS_SPACE_TO_BATCH_ND has changed"); |
| static_assert(ANEURALNETWORKS_SQUEEZE == 34, "ANEURALNETWORKS_SQUEEZE has changed"); |
| static_assert(ANEURALNETWORKS_STRIDED_SLICE == 35, "ANEURALNETWORKS_STRIDED_SLICE has changed"); |
| static_assert(ANEURALNETWORKS_SUB == 36, "ANEURALNETWORKS_TANH has changed"); |
| static_assert(ANEURALNETWORKS_TRANSPOSE == 37, "ANEURALNETWORKS_TRANSPOSE has changed"); |
| |
| static_assert(ANEURALNETWORKS_OEM_OPERATION == 10000, "ANEURALNETWORKS_OEM_OPERATION has changed"); |
| |
| static_assert(ANEURALNETWORKS_FUSED_NONE == 0, "ANEURALNETWORKS_FUSED_NONE has changed"); |
| static_assert(ANEURALNETWORKS_FUSED_RELU == 1, "ANEURALNETWORKS_FUSED_RELU has changed"); |
| static_assert(ANEURALNETWORKS_FUSED_RELU1 == 2, "ANEURALNETWORKS_FUSED_RELU1 has changed"); |
| static_assert(ANEURALNETWORKS_FUSED_RELU6 == 3, "ANEURALNETWORKS_FUSED_RELU6 has changed"); |
| |
| static_assert(ANEURALNETWORKS_PREFER_LOW_POWER == 0, |
| "ANEURALNETWORKS_PREFER_LOW_POWER has changed"); |
| static_assert(ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER == 1, |
| "ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER has changed"); |
| static_assert(ANEURALNETWORKS_PREFER_SUSTAINED_SPEED == 2, |
| "ANEURALNETWORKS_PREFER_SUSTAINED_SPEED has changed"); |
| |
| static_assert(ANEURALNETWORKS_NO_ERROR == 0, "ANEURALNETWORKS_NO_ERROR has changed"); |
| static_assert(ANEURALNETWORKS_OUT_OF_MEMORY == 1, "ANEURALNETWORKS_OUT_OF_MEMORY has changed"); |
| static_assert(ANEURALNETWORKS_INCOMPLETE == 2, "ANEURALNETWORKS_INCOMPLETE has changed"); |
| static_assert(ANEURALNETWORKS_UNEXPECTED_NULL == 3, "ANEURALNETWORKS_UNEXPECTED_NULL has changed"); |
| static_assert(ANEURALNETWORKS_BAD_DATA == 4, "ANEURALNETWORKS_BAD_DATA has changed"); |
| static_assert(ANEURALNETWORKS_OP_FAILED == 5, "ANEURALNETWORKS_OP_FAILED has changed"); |
| static_assert(ANEURALNETWORKS_BAD_STATE == 6, "ANEURALNETWORKS_BAD_STATE has changed"); |
| static_assert(ANEURALNETWORKS_UNMAPPABLE == 7, "ANEURALNETWORKS_UNMAPPABLE has changed"); |
| static_assert(ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE == 8, |
| "ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE has changed"); |
| |
| static_assert(ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES == 128, |
| "ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES has changed"); |
| |
| static_assert(ANEURALNETWORKS_DEVICE_UNKNOWN == 0, "ANEURALNETWORKS_DEVICE_UNKNOWN has changed"); |
| static_assert(ANEURALNETWORKS_DEVICE_OTHER == 1, "ANEURALNETWORKS_DEVICE_OTHER has changed"); |
| static_assert(ANEURALNETWORKS_DEVICE_CPU == 2, "ANEURALNETWORKS_DEVICE_CPU has changed"); |
| static_assert(ANEURALNETWORKS_DEVICE_GPU == 3, "ANEURALNETWORKS_DEVICE_GPU has changed"); |
| static_assert(ANEURALNETWORKS_DEVICE_ACCELERATOR == 4, |
| "ANEURALNETWORKS_DEVICE_ACCELERATOR has changed"); |
| |
| // Make sure that the constants are compatible with the values defined in |
| // hardware/interfaces/neuralnetworks/1.0/types.hal. |
| static_assert(static_cast<int32_t>(OperandType::OEM) == ANEURALNETWORKS_OEM_SCALAR, |
| "OEM != ANEURALNETWORKS_OEM"); |
| static_assert(static_cast<int32_t>(OperandType::FLOAT32) == ANEURALNETWORKS_FLOAT32, |
| "FLOAT32 != ANEURALNETWORKS_FLOAT32"); |
| static_assert(static_cast<int32_t>(OperandType::INT32) == ANEURALNETWORKS_INT32, |
| "INT32 != ANEURALNETWORKS_INT32"); |
| static_assert(static_cast<int32_t>(OperandType::UINT32) == ANEURALNETWORKS_UINT32, |
| "UINT32 != ANEURALNETWORKS_UINT32"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_OEM_BYTE) == ANEURALNETWORKS_TENSOR_OEM_BYTE, |
| "TENSOR_OEM_BYTE != ANEURALNETWORKS_TENSOR_OEM_BYTE"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_FLOAT16) == ANEURALNETWORKS_TENSOR_FLOAT16, |
| "TENSOR_FLOAT16 != ANEURALNETWORKS_TENSOR_FLOAT16"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_FLOAT32) == ANEURALNETWORKS_TENSOR_FLOAT32, |
| "TENSOR_FLOAT32 != ANEURALNETWORKS_TENSOR_FLOAT32"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_QUANT8_ASYMM) == |
| ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, |
| "TENSOR_QUANT8_ASYMM != ANEURALNETWORKS_TENSOR_QUANT8_ASYMM"); |
| static_assert(static_cast<int32_t>(OperandType::BOOL) == ANEURALNETWORKS_BOOL, |
| "BOOL != ANEURALNETWORKS_BOOL"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_QUANT16_SYMM) == |
| ANEURALNETWORKS_TENSOR_QUANT16_SYMM, |
| "TENSOR_QUANT16_SYMM != ANEURALNETWORKS_TENSOR_QUANT16_SYMM"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_BOOL8) == ANEURALNETWORKS_TENSOR_BOOL8, |
| "TENSOR_BOOL8 != ANEURALNETWORKS_TENSOR_BOOL8"); |
| static_assert(static_cast<int32_t>(OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) == |
| ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, |
| "TENSOR_QUANT8_SYMM_PER_CHANNEL != ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL"); |
| |
| static_assert(static_cast<int32_t>(OperationType::ADD) == ANEURALNETWORKS_ADD, |
| "OperationType::ADD != ANEURALNETWORKS_ADD"); |
| static_assert(static_cast<int32_t>(OperationType::AVERAGE_POOL_2D) == |
| ANEURALNETWORKS_AVERAGE_POOL_2D, |
| "OperationType::AVERAGE_POOL_2D != ANEURALNETWORKS_AVERAGE_POOL_2D"); |
| static_assert(static_cast<int32_t>(OperationType::CONV_2D) == ANEURALNETWORKS_CONV_2D, |
| "OperationType::CONV_2D != ANEURALNETWORKS_CONV_2D"); |
| static_assert(static_cast<int32_t>(OperationType::DEPTHWISE_CONV_2D) == |
| ANEURALNETWORKS_DEPTHWISE_CONV_2D, |
| "OperationType::DEPTHWISE_CONV_2D != ANEURALNETWORKS_DEPTHWISE_CONV_2D"); |
| static_assert(static_cast<int32_t>(OperationType::DEPTH_TO_SPACE) == ANEURALNETWORKS_DEPTH_TO_SPACE, |
| "OperationType::DEPTH_TO_SPACE != ANEURALNETWORKS_DEPTH_TO_SPACE"); |
| static_assert(static_cast<int32_t>(OperationType::DEQUANTIZE) == ANEURALNETWORKS_DEQUANTIZE, |
| "OperationType::DEQUANTIZE != ANEURALNETWORKS_DEQUANTIZE"); |
| static_assert(static_cast<int32_t>(OperationType::EMBEDDING_LOOKUP) == |
| ANEURALNETWORKS_EMBEDDING_LOOKUP, |
| "OperationType::EMBEDDING_LOOKUP != ANEURALNETWORKS_EMBEDDING_LOOKUP"); |
| static_assert(static_cast<int32_t>(OperationType::FLOOR) == ANEURALNETWORKS_FLOOR, |
| "OperationType::FLOOR != ANEURALNETWORKS_FLOOR"); |
| static_assert(static_cast<int32_t>(OperationType::FULLY_CONNECTED) == |
| ANEURALNETWORKS_FULLY_CONNECTED, |
| "OperationType::FULLY_CONNECTED != ANEURALNETWORKS_FULLY_CONNECTED"); |
| static_assert(static_cast<int32_t>(OperationType::HASHTABLE_LOOKUP) == |
| ANEURALNETWORKS_HASHTABLE_LOOKUP, |
| "OperationType::HASHTABLE_LOOKUP != ANEURALNETWORKS_HASHTABLE_LOOKUP"); |
| static_assert(static_cast<int32_t>(OperationType::L2_NORMALIZATION) == |
| ANEURALNETWORKS_L2_NORMALIZATION, |
| "OperationType::L2_NORMALIZATION != ANEURALNETWORKS_L2_NORMALIZATION"); |
| static_assert(static_cast<int32_t>(OperationType::L2_POOL_2D) == ANEURALNETWORKS_L2_POOL_2D, |
| "OperationType::L2_POOL_2D != ANEURALNETWORKS_L2_POOL_2D"); |
| static_assert(static_cast<int32_t>(OperationType::LOCAL_RESPONSE_NORMALIZATION) == |
| ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, |
| "OperationType::LOCAL_RESPONSE_NORMALIZATION != " |
| "ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION"); |
| static_assert(static_cast<int32_t>(OperationType::LOGISTIC) == ANEURALNETWORKS_LOGISTIC, |
| "OperationType::LOGISTIC != ANEURALNETWORKS_LOGISTIC"); |
| static_assert(static_cast<int32_t>(OperationType::LSH_PROJECTION) == ANEURALNETWORKS_LSH_PROJECTION, |
| "OperationType::LSH_PROJECTION != ANEURALNETWORKS_LSH_PROJECTION"); |
| static_assert(static_cast<int32_t>(OperationType::LSTM) == ANEURALNETWORKS_LSTM, |
| "OperationType::LSTM != ANEURALNETWORKS_LSTM"); |
| static_assert(static_cast<int32_t>(OperationType::MAX_POOL_2D) == ANEURALNETWORKS_MAX_POOL_2D, |
| "OperationType::MAX_POOL_2D != ANEURALNETWORKS_MAX_POOL_2D"); |
| static_assert(static_cast<int32_t>(OperationType::MUL) == ANEURALNETWORKS_MUL, |
| "OperationType::MUL != ANEURALNETWORKS_MUL"); |
| static_assert(static_cast<int32_t>(OperationType::RELU) == ANEURALNETWORKS_RELU, |
| "OperationType::RELU != ANEURALNETWORKS_RELU"); |
| static_assert(static_cast<int32_t>(OperationType::RELU1) == ANEURALNETWORKS_RELU1, |
| "OperationType::RELU1 != ANEURALNETWORKS_RELU1"); |
| static_assert(static_cast<int32_t>(OperationType::RELU6) == ANEURALNETWORKS_RELU6, |
| "OperationType::RELU6 != ANEURALNETWORKS_RELU6"); |
| static_assert(static_cast<int32_t>(OperationType::RESHAPE) == ANEURALNETWORKS_RESHAPE, |
| "OperationType::RESHAPE != ANEURALNETWORKS_RESHAPE"); |
| static_assert(static_cast<int32_t>(OperationType::RESIZE_BILINEAR) == |
| ANEURALNETWORKS_RESIZE_BILINEAR, |
| "OperationType::RESIZE_BILINEAR != ANEURALNETWORKS_RESIZE_BILINEAR"); |
| static_assert(static_cast<int32_t>(OperationType::RNN) == ANEURALNETWORKS_RNN, |
| "OperationType::RNN != ANEURALNETWORKS_RNN"); |
| static_assert(static_cast<int32_t>(OperationType::SOFTMAX) == ANEURALNETWORKS_SOFTMAX, |
| "OperationType::SOFTMAX != ANEURALNETWORKS_SOFTMAX"); |
| static_assert(static_cast<int32_t>(OperationType::SPACE_TO_DEPTH) == ANEURALNETWORKS_SPACE_TO_DEPTH, |
| "OperationType::SPACE_TO_DEPTH != ANEURALNETWORKS_SPACE_TO_DEPTH"); |
| static_assert(static_cast<int32_t>(OperationType::SVDF) == ANEURALNETWORKS_SVDF, |
| "OperationType::SVDF != ANEURALNETWORKS_SVDF"); |
| static_assert(static_cast<int32_t>(OperationType::TANH) == ANEURALNETWORKS_TANH, |
| "OperationType::TANH != ANEURALNETWORKS_TANH"); |
| |
| static_assert(static_cast<int32_t>(OperationType::BATCH_TO_SPACE_ND) == |
| ANEURALNETWORKS_BATCH_TO_SPACE_ND, |
| "OperationType::BATCH_TO_SPACE_ND != ANEURALNETWORKS_BATCH_TO_SPACE_ND"); |
| static_assert(static_cast<int32_t>(OperationType::DIV) == ANEURALNETWORKS_DIV, |
| "OperationType::DIV != ANEURALNETWORKS_DIV"); |
| static_assert(static_cast<int32_t>(OperationType::MEAN) == ANEURALNETWORKS_MEAN, |
| "OperationType::MEAN != ANEURALNETWORKS_MEAN"); |
| static_assert(static_cast<int32_t>(OperationType::PAD) == ANEURALNETWORKS_PAD, |
| "OperationType::PAD != ANEURALNETWORKS_PAD"); |
| static_assert(static_cast<int32_t>(OperationType::SPACE_TO_BATCH_ND) == |
| ANEURALNETWORKS_SPACE_TO_BATCH_ND, |
| "OperationType::SPACE_TO_BATCH_ND != ANEURALNETWORKS_SPACE_TO_BATCH_ND"); |
| static_assert(static_cast<int32_t>(OperationType::SQUEEZE) == ANEURALNETWORKS_SQUEEZE, |
| "OperationType::SQUEEZE != ANEURALNETWORKS_SQUEEZE"); |
| static_assert(static_cast<int32_t>(OperationType::STRIDED_SLICE) == ANEURALNETWORKS_STRIDED_SLICE, |
| "OperationType::STRIDED_SLICE != ANEURALNETWORKS_STRIDED_SLICE"); |
| static_assert(static_cast<int32_t>(OperationType::SUB) == ANEURALNETWORKS_SUB, |
| "OperationType::SUB != ANEURALNETWORKS_SUB"); |
| static_assert(static_cast<int32_t>(OperationType::TRANSPOSE) == ANEURALNETWORKS_TRANSPOSE, |
| "OperationType::TRANSPOSE != ANEURALNETWORKS_TRANSPOSE"); |
| |
| static_assert(static_cast<int32_t>(FusedActivationFunc::NONE) == ANEURALNETWORKS_FUSED_NONE, |
| "FusedActivationFunc::NONE != ANEURALNETWORKS_FUSED_NONE"); |
| static_assert(static_cast<int32_t>(FusedActivationFunc::RELU) == ANEURALNETWORKS_FUSED_RELU, |
| "FusedActivationFunc::RELU != ANEURALNETWORKS_FUSED_RELU"); |
| static_assert(static_cast<int32_t>(FusedActivationFunc::RELU1) == ANEURALNETWORKS_FUSED_RELU1, |
| "FusedActivationFunc::RELU1 != ANEURALNETWORKS_FUSED_RELU1"); |
| static_assert(static_cast<int32_t>(FusedActivationFunc::RELU6) == ANEURALNETWORKS_FUSED_RELU6, |
| "FusedActivationFunc::RELU6 != ANEURALNETWORKS_FUSED_RELU6"); |
| |
| // Make sure that the constants are compatible with the values defined in |
| // hardware/interfaces/neuralnetworks/1.2/types.hal. |
| static_assert(static_cast<int32_t>(DeviceType::OTHER) == ANEURALNETWORKS_DEVICE_OTHER, |
| "DeviceType::OTHER != ANEURALNETWORKS_DEVICE_OTHER"); |
| static_assert(static_cast<int32_t>(DeviceType::CPU) == ANEURALNETWORKS_DEVICE_CPU, |
| "DeviceType::CPU != ANEURALNETWORKS_DEVICE_CPU"); |
| static_assert(static_cast<int32_t>(DeviceType::GPU) == ANEURALNETWORKS_DEVICE_GPU, |
| "DeviceType::GPU != ANEURALNETWORKS_DEVICE_GPU"); |
| static_assert(static_cast<int32_t>(DeviceType::ACCELERATOR) == ANEURALNETWORKS_DEVICE_ACCELERATOR, |
| "DeviceType::ACCELERATOR != ANEURALNETWORKS_DEVICE_ACCELERATOR"); |
| |
| // Asserts for ANeuralNetworksOperandType memory layout |
| static_assert(offsetof(ANeuralNetworksOperandType, type) == 0, |
| "ANeuralNetworksOperandType.type offset != 0"); |
| static_assert(offsetof(ANeuralNetworksOperandType, dimensionCount) == 4, |
| "ANeuralNetworksOperandType.dimensionCount offset != 4"); |
| static_assert(offsetof(ANeuralNetworksOperandType, dimensions) == 8, |
| "ANeuralNetworksOperandType.dimensions offset != 8"); |
| static_assert(offsetof(ANeuralNetworksOperandType, scale) == 8 + sizeof(void*), |
| "ANeuralNetworksOperandType.scale offset != 8 + sizeof(void*)"); |
| static_assert(offsetof(ANeuralNetworksOperandType, zeroPoint) == 12 + sizeof(void*), |
| "ANeuralNetworksOperandType.zeroPoint offset != 12 + sizeof(void*)"); |
| static_assert(sizeof(ANeuralNetworksOperandType) == 16 + sizeof(void*), |
| "ANeuralNetworksOperandType size changed"); |
| static_assert(alignof(ANeuralNetworksOperandType) == alignof(void*), |
| "ANeuralNetworksOperandType alignment changed"); |
| |
| // Asserts for ANeuralNetworksSymmPerChannelQuantParams memory layout |
| static_assert(offsetof(ANeuralNetworksSymmPerChannelQuantParams, channelDim) == 0, |
| "ANeuralNetworksSymmPerChannelQuantParams.channelDim offset != 4 + sizeof(void*)"); |
| static_assert(offsetof(ANeuralNetworksSymmPerChannelQuantParams, scaleCount) == 4, |
| "ANeuralNetworksSymmPerChannelQuantParams.scaleCount offset != 0"); |
| static_assert(offsetof(ANeuralNetworksSymmPerChannelQuantParams, scales) == 8, |
| "ANeuralNetworksSymmPerChannelQuantParams.scales offset != 4"); |
| static_assert(sizeof(ANeuralNetworksSymmPerChannelQuantParams) == 8 + sizeof(void*), |
| "ANeuralNetworksSymmPerChannelQuantParams size != 8 + sizeof(void*)"); |
| static_assert(alignof(ANeuralNetworksSymmPerChannelQuantParams) == alignof(void*), |
| "ANeuralNetworksOperandType alignment changed"); |
| |
| using android::sp; |
| using namespace android::nn; |
| |
| int ANeuralNetworks_getDeviceCount(uint32_t* numDevices) { |
| if (numDevices == nullptr) { |
| LOG(ERROR) << "ANeuralNetworks_getDeviceCount passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| *numDevices = DeviceManager::get()->getDrivers().size(); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworks_getDevice(uint32_t devIndex, ANeuralNetworksDevice** device) { |
| if (device == nullptr) { |
| LOG(ERROR) << "ANeuralNetworks_getDevice passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| const std::vector<std::shared_ptr<Device>>& devices = DeviceManager::get()->getDrivers(); |
| if (devIndex >= devices.size()) { |
| LOG(ERROR) << "ANeuralNetworks_getDevice passed an invalid device index"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| *device = reinterpret_cast<ANeuralNetworksDevice*>(devices.at(devIndex).get()); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksDevice_getName(const ANeuralNetworksDevice* device, const char** name) { |
| if (device == nullptr || name == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksDevice_getName passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| const Device* d = reinterpret_cast<const Device*>(device); |
| *name = d->getName(); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksDevice_getVersion(const ANeuralNetworksDevice* device, const char** version) { |
| if (device == nullptr || version == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksDevice_getVersion passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| const Device* d = reinterpret_cast<const Device*>(device); |
| *version = d->getVersionString(); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksDevice_getType(const ANeuralNetworksDevice* device, int32_t* type) { |
| if (device == nullptr || type == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksDevice_getType passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| const Device* d = reinterpret_cast<const Device*>(device); |
| int32_t dType = d->getType(); |
| if (dType < 0) { |
| return ANEURALNETWORKS_OP_FAILED; |
| } |
| *type = d->getType(); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksDevice_getFeatureLevel(const ANeuralNetworksDevice* device, |
| int64_t* featureLevel) { |
| if (device == nullptr || featureLevel == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksDevice_getFeatureLevel passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| Device* d = reinterpret_cast<Device*>(const_cast<ANeuralNetworksDevice*>(device)); |
| int64_t dFeatureLevel = d->getFeatureLevel(); |
| if (dFeatureLevel < 0) { |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| *featureLevel = dFeatureLevel; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksModel_getSupportedOperationsForDevices( |
| const ANeuralNetworksModel* model, const ANeuralNetworksDevice* const* devices, |
| uint32_t numDevices, bool* supportedOps) { |
| NNTRACE_RT(NNTRACE_PHASE_COMPILATION, "ANeuralNetworksModel_getSupportedOperationsForDevices"); |
| if (model == nullptr || devices == nullptr || supportedOps == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksModel_getSupportedOperationsForDevices passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| if (numDevices == 0) { |
| LOG(ERROR) << "ANeuralNetworksModel_getSupportedOperationsForDevices passed an empty " |
| "device list"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| const ModelBuilder* m = reinterpret_cast<const ModelBuilder*>(model); |
| if (!m->isFinished() || !m->isValid()) { |
| LOG(ERROR) << "ANeuralNetworksModel_getSupportedOperationsForDevices passed an unfinished " |
| "or invalid Model"; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| |
| Model hidlModel; |
| m->setHidlModel(&hidlModel); |
| const std::vector<uint32_t>& opMap = m->getSortedOperationMapping(); |
| // init the output array to false for all the operations. |
| std::fill(supportedOps, supportedOps + opMap.size(), false); |
| for (uint32_t i = 0; i < numDevices; i++) { |
| if (devices[i] == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksModel_getSupportedOperationsForDevices passed a nullptr " |
| "as a device"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| for (uint32_t j = i + 1; j < numDevices; j++) { |
| if (devices[i] == devices[j]) { |
| LOG(ERROR) << "ANeuralNetworksModel_getSupportedOperationsForDevices passed " |
| "duplicate devices"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| Device* d = reinterpret_cast<Device*>(const_cast<ANeuralNetworksDevice*>(devices[i])); |
| hidl_vec<bool> supportsByDevice; |
| d->getSupportedOperations(hidlModel, &supportsByDevice); |
| for (uint32_t j = 0; j < supportsByDevice.size(); j++) { |
| uint32_t originalIdx = opMap[j]; |
| supportedOps[originalIdx] |= supportsByDevice[j]; |
| } |
| } |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksCompilation_createForDevices(ANeuralNetworksModel* model, |
| const ANeuralNetworksDevice* const* devices, |
| uint32_t numDevices, |
| ANeuralNetworksCompilation** compilation) { |
| NNTRACE_RT(NNTRACE_PHASE_COMPILATION, "ANeuralNetworksCompilation_createForDevices"); |
| if (model == nullptr || devices == nullptr || compilation == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_createForDevices passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| if (numDevices == 0) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_createForDevices passed an empty device list"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| std::vector<std::shared_ptr<Device>> selectedDevices; |
| for (uint32_t i = 0; i < numDevices; i++) { |
| if (devices[i] == nullptr) { |
| LOG(ERROR) |
| << "ANeuralNetworksCompilation_createForDevices passed a nullptr as a device"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| for (uint32_t j = i + 1; j < numDevices; j++) { |
| if (devices[i] == devices[j]) { |
| LOG(ERROR) |
| << "ANeuralNetworksCompilation_createForDevices passed duplicate devices"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| for (auto& device : DeviceManager::get()->getDrivers()) { |
| if (device.get() == reinterpret_cast<const Device*>(devices[i])) { |
| // Find a match |
| selectedDevices.push_back(device); |
| break; |
| } |
| } |
| } |
| |
| if (selectedDevices.size() != numDevices) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_createForDevices passed an invalid device set"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| CompilationBuilder* c = nullptr; |
| int result = m->createCompilation(&c, selectedDevices); |
| *compilation = reinterpret_cast<ANeuralNetworksCompilation*>(c); |
| return result; |
| } |
| |
| int ANeuralNetworksExecution_compute(ANeuralNetworksExecution* execution) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksExecution_compute"); |
| if (!execution) { |
| LOG(ERROR) << "ANeuralNetworksExecution_compute passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| // TODO validate the rest |
| |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| return r->computeSynchronously(); |
| } |
| |
| int ANeuralNetworksMemory_createFromFd(size_t size, int prot, int fd, size_t offset, |
| ANeuralNetworksMemory** memory) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksMemory_createFromFd"); |
| *memory = nullptr; |
| std::unique_ptr<MemoryFd> m = std::make_unique<MemoryFd>(); |
| if (m == nullptr) { |
| return ANEURALNETWORKS_OUT_OF_MEMORY; |
| } |
| int n = m->set(size, prot, fd, offset); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| *memory = reinterpret_cast<ANeuralNetworksMemory*>(m.release()); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) { |
| NNTRACE_RT(NNTRACE_PHASE_TERMINATION, "ANeuralNetworksMemory_free"); |
| // No validation. Free of nullptr is valid. |
| Memory* m = reinterpret_cast<Memory*>(memory); |
| delete m; |
| } |
| |
| int ANeuralNetworksModel_create(ANeuralNetworksModel** model) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_create"); |
| initVLogMask(); |
| if (!model) { |
| LOG(ERROR) << "ANeuralNetworksModel_create passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = new (std::nothrow) ModelBuilder(); |
| if (m == nullptr) { |
| *model = nullptr; |
| return ANEURALNETWORKS_OUT_OF_MEMORY; |
| } |
| *model = reinterpret_cast<ANeuralNetworksModel*>(m); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| void ANeuralNetworksModel_free(ANeuralNetworksModel* model) { |
| NNTRACE_RT(NNTRACE_PHASE_TERMINATION, "ANeuralNetworksModel_free"); |
| // No validation. Free of nullptr is valid. |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| delete m; |
| } |
| |
| int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_finish"); |
| if (!model) { |
| LOG(ERROR) << "ANeuralNetworksModel_finish passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->finish(); |
| } |
| |
| int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, |
| const ANeuralNetworksOperandType* type) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_addOperand"); |
| if (!model || !type) { |
| LOG(ERROR) << "ANeuralNetworksModel_addOperand passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->addOperand(*type); |
| } |
| |
| int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, |
| const void* buffer, size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_setOperandValue"); |
| if (!model || (!buffer && length != 0)) { |
| LOG(ERROR) << "ANeuralNetworksModel_setOperandValue passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->setOperandValue(index, buffer, length); |
| } |
| |
| int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, |
| const ANeuralNetworksMemory* memory, |
| size_t offset, size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_setOperandValueFromMemory"); |
| if (!model || !memory) { |
| LOG(ERROR) << "ANeuralNetworksModel_setOperandValue passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| const Memory* mem = reinterpret_cast<const Memory*>(memory); |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->setOperandValueFromMemory(index, mem, offset, length); |
| } |
| |
| int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, |
| ANeuralNetworksOperationType type, uint32_t inputCount, |
| const uint32_t* inputs, uint32_t outputCount, |
| const uint32_t* outputs) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_addOperation"); |
| if (!model || !inputs || !outputs) { |
| LOG(ERROR) << "ANeuralNetworksModel_addOperation passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->addOperation(type, inputCount, inputs, outputCount, outputs); |
| } |
| |
| int ANeuralNetworksModel_setOperandSymmPerChannelQuantParams( |
| ANeuralNetworksModel* model, int32_t index, |
| const ANeuralNetworksSymmPerChannelQuantParams* channelQuant) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, |
| "ANeuralNetworksModel_setOperandSymmPerChannelQuantParams"); |
| if (!model || !channelQuant) { |
| LOG(ERROR) << "ANeuralNetworksModel_setOperandSymmPerChannelQuantParams passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->setOperandSymmPerChannelQuantParams(index, *channelQuant); |
| } |
| |
| int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, |
| const uint32_t* inputs, uint32_t outputCount, |
| const uint32_t* outputs) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_identifyInputsAndOutputs"); |
| if (!model || !inputs || !outputs) { |
| LOG(ERROR) << ("ANeuralNetworksModel_identifyInputsAndOutputs passed a nullptr"); |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->identifyInputsAndOutputs(inputCount, inputs, outputCount, outputs); |
| } |
| |
| int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow) { |
| NNTRACE_RT(NNTRACE_PHASE_PREPARATION, "ANeuralNetworksModel_relaxComputationFloat32toFloat16"); |
| if (!model) { |
| LOG(ERROR) << ("ANeuralNetworksModel_relaxComputationFloat32toFloat16 passed a nullptr"); |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| return m->relaxComputationFloat32toFloat16(allow); |
| } |
| |
| int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, |
| ANeuralNetworksCompilation** compilation) { |
| NNTRACE_RT(NNTRACE_PHASE_COMPILATION, "ANeuralNetworksCompilation_create"); |
| if (!model || !compilation) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_create passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model); |
| CompilationBuilder* c = nullptr; |
| int result = m->createCompilation(&c, DeviceManager::get()->getDrivers()); |
| *compilation = reinterpret_cast<ANeuralNetworksCompilation*>(c); |
| return result; |
| } |
| |
| void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation) { |
| NNTRACE_RT(NNTRACE_PHASE_TERMINATION, "ANeuralNetworksCompilation_free"); |
| // No validation. Free of nullptr is valid. |
| // TODO specification says that a compilation-in-flight can be deleted |
| CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation); |
| delete c; |
| } |
| |
| int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, |
| int32_t preference) { |
| NNTRACE_RT(NNTRACE_PHASE_COMPILATION, "ANeuralNetworksCompilation_setPreference"); |
| if (!compilation) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_setPreference passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation); |
| return c->setPreference(preference); |
| } |
| |
| int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) { |
| NNTRACE_RT(NNTRACE_PHASE_COMPILATION, "ANeuralNetworksCompilation_finish"); |
| if (!compilation) { |
| LOG(ERROR) << "ANeuralNetworksCompilation_finish passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation); |
| return c->finish(); |
| } |
| |
| int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, |
| ANeuralNetworksExecution** execution) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksExecution_create"); |
| if (!compilation || !execution) { |
| LOG(ERROR) << "ANeuralNetworksExecution_create passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation); |
| ExecutionBuilder* r = nullptr; |
| int result = c->createExecution(&r); |
| *execution = reinterpret_cast<ANeuralNetworksExecution*>(r); |
| return result; |
| } |
| |
| void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksExecution_free"); |
| // TODO specification says that an execution-in-flight can be deleted |
| // No validation. Free of nullptr is valid. |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| delete r; |
| } |
| |
| int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, const void* buffer, |
| size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_INPUTS_AND_OUTPUTS, "ANeuralNetworksExecution_setInput"); |
| if (!execution || (!buffer && length != 0)) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInput passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| return r->setInput(index, type, buffer, length); |
| } |
| |
| int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, |
| const ANeuralNetworksMemory* memory, size_t offset, |
| size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_INPUTS_AND_OUTPUTS, "ANeuralNetworksExecution_setInputFromMemory"); |
| if (!execution || !memory) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| const Memory* m = reinterpret_cast<const Memory*>(memory); |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| return r->setInputFromMemory(index, type, m, offset, length); |
| } |
| |
| int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, void* buffer, |
| size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_INPUTS_AND_OUTPUTS, "ANeuralNetworksExecution_setOutput"); |
| if (!execution || (!buffer && length != 0)) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutput passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| return r->setOutput(index, type, buffer, length); |
| } |
| |
| int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, |
| const ANeuralNetworksMemory* memory, size_t offset, |
| size_t length) { |
| NNTRACE_RT(NNTRACE_PHASE_INPUTS_AND_OUTPUTS, "ANeuralNetworksExecution_setOutputFromMemory"); |
| if (!execution || !memory) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| const Memory* m = reinterpret_cast<const Memory*>(memory); |
| return r->setOutputFromMemory(index, type, m, offset, length); |
| } |
| |
| int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, |
| ANeuralNetworksEvent** event) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksExecution_startCompute"); |
| if (!execution || !event) { |
| LOG(ERROR) << "ANeuralNetworksExecution_startCompute passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| // TODO validate the rest |
| |
| ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution); |
| |
| // Dynamically allocate an sp to wrap an ExecutionCallback, seen in the NN |
| // API as an abstract event object. The sp<ExecutionCallback> object is |
| // returned when the execution has been successfully launched, otherwise a |
| // nullptr is returned. The sp is used for ref-counting purposes. Without |
| // it, the HIDL service could attempt to communicate with a dead callback |
| // object. |
| std::unique_ptr<sp<ExecutionCallback>> e = std::make_unique<sp<ExecutionCallback>>(); |
| *event = nullptr; |
| |
| int n = r->computeAsynchronously(e.get()); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| *event = reinterpret_cast<ANeuralNetworksEvent*>(e.release()); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksEvent_wait"); |
| if (event == nullptr) { |
| LOG(ERROR) << "ANeuralNetworksEvent_wait passed a nullptr"; |
| return ANEURALNETWORKS_UNEXPECTED_NULL; |
| } |
| |
| sp<ExecutionCallback>* e = reinterpret_cast<sp<ExecutionCallback>*>(event); |
| (*e)->wait(); |
| return convertErrorStatusToResultCode((*e)->getStatus()); |
| } |
| |
| void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "ANeuralNetworksEvent_free"); |
| // No validation. Free of nullptr is valid. |
| if (event) { |
| sp<ExecutionCallback>* e = reinterpret_cast<sp<ExecutionCallback>*>(event); |
| (*e)->wait(); |
| delete e; |
| } |
| } |
