| /* |
| * 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. |
| */ |
| |
| #define LOG_TAG "Utils" |
| |
| #include "NeuralNetworks.h" |
| #include "NeuralNetworksOEM.h" |
| #include "Utils.h" |
| #include "ValidateHal.h" |
| |
| #include <android-base/logging.h> |
| #include <android-base/properties.h> |
| #include <android-base/strings.h> |
| #include <sys/system_properties.h> |
| #include <unordered_map> |
| |
| using ::android::hidl::allocator::V1_0::IAllocator; |
| |
| namespace android { |
| namespace nn { |
| |
| const char kVLogPropKey[] = "debug.nn.vlog"; |
| int vLogMask = ~0; |
| |
| // Split the space separated list of tags from verbose log setting and build the |
| // logging mask from it. note that '1' and 'all' are special cases to enable all |
| // verbose logging. |
| // |
| // NN API verbose logging setting comes from system property debug.nn.vlog. |
| // Example: |
| // setprop debug.nn.vlog 1 : enable all logging tags. |
| // setprop debug.nn.vlog "model compilation" : only enable logging for MODEL and |
| // COMPILATION tags. |
| void initVLogMask() { |
| vLogMask = 0; |
| const std::string vLogSetting = android::base::GetProperty(kVLogPropKey, ""); |
| if (vLogSetting.empty()) { |
| return; |
| } |
| |
| std::unordered_map<std::string, int> vLogFlags = { |
| {"1", -1}, |
| {"all", -1}, |
| {"model", MODEL}, |
| {"compilation", COMPILATION}, |
| {"execution", EXECUTION}, |
| {"cpuexe", CPUEXE}, |
| {"manager", MANAGER}, |
| {"driver", DRIVER}}; |
| |
| std::vector<std::string> elements = android::base::Split(vLogSetting, " ,:"); |
| for (const auto& elem : elements) { |
| const auto& flag = vLogFlags.find(elem); |
| if (flag == vLogFlags.end()) { |
| LOG(ERROR) << "Unknown trace flag: " << elem; |
| continue; |
| } |
| |
| if (flag->second == -1) { |
| // -1 is used for the special values "1" and "all" that enable all |
| // tracing. |
| vLogMask = ~0; |
| return; |
| } else { |
| vLogMask |= 1 << flag->second; |
| } |
| } |
| } |
| |
| namespace { |
| |
| template <typename EntryType, uint32_t entryCount, uint32_t entryCountOEM> |
| EntryType tableLookup(const EntryType (&table)[entryCount], |
| const EntryType (&tableOEM)[entryCountOEM], |
| uint32_t code) { |
| if (code < entryCount) { |
| return table[code]; |
| } else if (code >= kOEMCodeBase && (code - kOEMCodeBase) < entryCountOEM) { |
| return tableOEM[code - kOEMCodeBase]; |
| } else { |
| nnAssert(!"tableLookup: bad code"); |
| return EntryType(); |
| } |
| } |
| |
| }; // anonymous namespace |
| |
| #define COUNT(X) (sizeof(X) / sizeof(X[0])) |
| |
| const char* kTypeNames[kNumberOfDataTypes] = { |
| "FLOAT32", "INT32", "UINT32", |
| "TENSOR_FLOAT32", "TENSOR_INT32", "TENSOR_QUANT8_ASYMM", |
| }; |
| |
| static_assert(COUNT(kTypeNames) == kNumberOfDataTypes, "kTypeNames is incorrect"); |
| |
| const char* kTypeNamesOEM[kNumberOfDataTypesOEM] = { |
| "OEM", "TENSOR_OEM_BYTE", |
| }; |
| |
| static_assert(COUNT(kTypeNamesOEM) == kNumberOfDataTypesOEM, "kTypeNamesOEM is incorrect"); |
| |
| const char* getOperandTypeName(OperandType type) { |
| uint32_t n = static_cast<uint32_t>(type); |
| return tableLookup(kTypeNames, kTypeNamesOEM, n); |
| } |
| |
| // TODO Check if this useful |
| const char* kErrorNames[] = { |
| "NO_ERROR", "OUT_OF_MEMORY", "INCOMPLETE", "NULL", "BAD_DATA", |
| }; |
| |
| const char* kOperationNames[kNumberOfOperationTypes] = { |
| "ADD", |
| "AVERAGE_POOL", |
| "CONCATENATION", |
| "CONV", |
| "DEPTHWISE_CONV", |
| "DEPTH_TO_SPACE", |
| "DEQUANTIZE", |
| "EMBEDDING_LOOKUP", |
| "FLOOR", |
| "FULLY_CONNECTED", |
| "HASHTABLE_LOOKUP", |
| "L2_NORMALIZATION", |
| "L2_POOL", |
| "LOCAL_RESPONSE_NORMALIZATION", |
| "LOGISTIC", |
| "LSH_PROJECTION", |
| "LSTM", |
| "MAX_POOL", |
| "MUL", |
| "RELU", |
| "RELU1", |
| "RELU6", |
| "RESHAPE", |
| "RESIZE_BILINEAR", |
| "RNN", |
| "SOFTMAX", |
| "SPACE_TO_DEPTH", |
| "SVDF", |
| "TANH", |
| "BATCH_TO_SPACE_ND", |
| "DIV", |
| "MEAN", |
| "PAD", |
| "SPACE_TO_BATCH_ND", |
| "SQUEEZE", |
| "STRIDED_SLICE", |
| "SUB", |
| "TRANSPOSE", |
| "ARGMAX", |
| "ARGMIN", |
| "PAD_V2", |
| "BBOX_TRANSFORM", |
| "BIDIRECTIONAL_SEQUENCE_LSTM", |
| "BIDIRECTIONAL_SEQUENCE_RNN", |
| "BOX_WITH_NMS_LIMIT", |
| "CAST", |
| "CHANNEL_SHUFFLE", |
| "DETECTION_OUTPUT", |
| "EMBEDDING_LOOKUP_SPARSE", |
| "EXP", |
| "EXPAND_DIMS", |
| "GATHER", |
| "GENERATE_PROPOSALS", |
| "GREATER", |
| "GREATER_EQUAL", |
| "GROUPED_CONV_2D", |
| "HEATMAP_MAX_KEYPOINT", |
| "LESS", |
| "LESS_EQUAL", |
| "LOG", |
| "LOGICAL_AND", |
| "LOGICAL_NOT", |
| "LOGICAL_OR", |
| "LOG_SOFTMAX", |
| "MAXIMUM", |
| "MINIMUM", |
| "NEG", |
| "POW", |
| "PRELU", |
| "PRIOR_BOX", |
| "QUANTIZE", |
| "QUANTIZED_16BIT_LSTM", |
| "RANDOM_MULTINOMIAL", |
| "REDUCE", |
| "ROI_ALIGN", |
| "RSQRT", |
| "SELECT", |
| "SIN", |
| "SLICE", |
| "SPARSE_TO_DENSE", |
| "SPLIT", |
| "SQRT", |
| "TILE", |
| "TOPK_V2", |
| "TRANSPOSE_CONV_2D", |
| "UNIDIRECTIONAL_SEQUENCE_LSTM", |
| "UNIDIRECTIONAL_SEQUENCE_RNN", |
| }; |
| |
| static_assert(COUNT(kOperationNames) == kNumberOfOperationTypes, "kOperationNames is incorrect"); |
| |
| const char* kOperationNamesOEM[kNumberOfOperationTypesOEM] = { |
| "OEM_OPERATION", |
| }; |
| |
| static_assert(COUNT(kOperationNamesOEM) == kNumberOfOperationTypesOEM, |
| "kOperationNamesOEM is incorrect"); |
| |
| const char* getOperationName(OperationType type) { |
| uint32_t n = static_cast<uint32_t>(type); |
| return tableLookup(kOperationNames, kOperationNamesOEM, n); |
| } |
| |
| const uint32_t kSizeOfDataType[]{ |
| 4, // ANEURALNETWORKS_FLOAT32 |
| 4, // ANEURALNETWORKS_INT32 |
| 4, // ANEURALNETWORKS_UINT32 |
| 4, // ANEURALNETWORKS_TENSOR_FLOAT32 |
| 4, // ANEURALNETWORKS_TENSOR_INT32 |
| 1 // ANEURALNETWORKS_TENSOR_SYMMETRICAL_QUANT8 |
| }; |
| |
| static_assert(COUNT(kSizeOfDataType) == kNumberOfDataTypes, "kSizeOfDataType is incorrect"); |
| |
| const bool kScalarDataType[]{ |
| true, // ANEURALNETWORKS_FLOAT32 |
| true, // ANEURALNETWORKS_INT32 |
| true, // ANEURALNETWORKS_UINT32 |
| false, // ANEURALNETWORKS_TENSOR_FLOAT32 |
| false, // ANEURALNETWORKS_TENSOR_INT32 |
| false, // ANEURALNETWORKS_TENSOR_SYMMETRICAL_QUANT8 |
| }; |
| |
| static_assert(COUNT(kScalarDataType) == kNumberOfDataTypes, "kScalarDataType is incorrect"); |
| |
| const uint32_t kSizeOfDataTypeOEM[]{ |
| 0, // ANEURALNETWORKS_OEM |
| 1, // ANEURALNETWORKS_TENSOR_OEM_BYTE |
| }; |
| |
| static_assert(COUNT(kSizeOfDataTypeOEM) == kNumberOfDataTypesOEM, |
| "kSizeOfDataTypeOEM is incorrect"); |
| |
| const bool kScalarDataTypeOEM[]{ |
| true, // ANEURALNETWORKS_OEM |
| false, // ANEURALNETWORKS_TENSOR_OEM_BYTE |
| }; |
| |
| static_assert(COUNT(kScalarDataTypeOEM) == kNumberOfDataTypesOEM, |
| "kScalarDataTypeOEM is incorrect"); |
| |
| uint32_t sizeOfData(OperandType type, const std::vector<uint32_t>& dimensions) { |
| int n = static_cast<int>(type); |
| |
| uint32_t size = tableLookup(kSizeOfDataType, kSizeOfDataTypeOEM, n); |
| |
| if (tableLookup(kScalarDataType, kScalarDataTypeOEM, n) == true) { |
| return size; |
| } |
| |
| for (auto d : dimensions) { |
| size *= d; |
| } |
| return size; |
| } |
| |
| hidl_memory allocateSharedMemory(int64_t size) { |
| static const std::string type = "ashmem"; |
| static sp<IAllocator> allocator = IAllocator::getService(type); |
| |
| hidl_memory memory; |
| |
| // TODO: should we align memory size to nearest page? doesn't seem necessary... |
| allocator->allocate(size, [&](bool success, const hidl_memory& mem) { |
| if (!success) { |
| LOG(ERROR) << "unable to allocate " << size << " bytes of " << type; |
| } else { |
| memory = mem; |
| } |
| }); |
| |
| return memory; |
| } |
| |
| uint32_t alignBytesNeeded(uint32_t index, size_t length) { |
| uint32_t pattern; |
| if (length < 2) { |
| pattern = 0; // No alignment necessary |
| } else if (length < 4) { |
| pattern = 1; // Align on 2-byte boundary |
| } else { |
| pattern = 3; // Align on 4-byte boundary |
| } |
| uint32_t extra = (~(index - 1)) & pattern; |
| return extra; |
| } |
| |
| void logModelToInfo(const V1_0::Model& model) { |
| LOG(INFO) << "V1_0::Model start"; |
| LOG(INFO) << "operands" << toString(model.operands); |
| LOG(INFO) << "operations" << toString(model.operations); |
| LOG(INFO) << "inputIndexes" << toString(model.inputIndexes); |
| LOG(INFO) << "outputIndexes" << toString(model.outputIndexes); |
| LOG(INFO) << "operandValues size" << model.operandValues.size(); |
| LOG(INFO) << "pools" << SHOW_IF_DEBUG(toString(model.pools)); |
| } |
| |
| void logModelToInfo(const V1_1::Model& model) { |
| LOG(INFO) << "V1_1::Model start"; |
| LOG(INFO) << "operands" << toString(model.operands); |
| LOG(INFO) << "operations" << toString(model.operations); |
| LOG(INFO) << "inputIndexes" << toString(model.inputIndexes); |
| LOG(INFO) << "outputIndexes" << toString(model.outputIndexes); |
| LOG(INFO) << "operandValues size" << model.operandValues.size(); |
| LOG(INFO) << "pools" << SHOW_IF_DEBUG(toString(model.pools)); |
| } |
| |
| // Validates the type. The used dimensions can be underspecified. |
| int validateOperandType(const ANeuralNetworksOperandType& type, const char* tag, |
| bool allowPartial) { |
| if (!allowPartial) { |
| for (uint32_t i = 0; i < type.dimensionCount; i++) { |
| if (type.dimensions[i] == 0) { |
| LOG(ERROR) << tag << " OperandType invalid dimensions[" << i |
| << "] = " << type.dimensions[i]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| } |
| if (!validCode(kNumberOfDataTypes, kNumberOfDataTypesOEM, type.type)) { |
| LOG(ERROR) << tag << " OperandType invalid type " << type.type; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (type.type == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| if (type.zeroPoint < 0 || type.zeroPoint > 255) { |
| LOG(ERROR) << tag << " OperandType invalid zeroPoint " << type.zeroPoint; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (type.scale <= 0.f) { |
| LOG(ERROR) << tag << " OperandType invalid scale " << type.scale; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| if (type.type == ANEURALNETWORKS_FLOAT32 || |
| type.type == ANEURALNETWORKS_INT32 || |
| type.type == ANEURALNETWORKS_UINT32 || |
| type.type == ANEURALNETWORKS_OEM_SCALAR) { |
| if (type.dimensionCount != 0 || type.dimensions != nullptr) { |
| LOG(ERROR) << tag << " Invalid dimensions for scalar type"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int validateOperandList(uint32_t count, const uint32_t* list, uint32_t operandCount, |
| const char* tag) { |
| for (uint32_t i = 0; i < count; i++) { |
| if (list[i] >= operandCount) { |
| LOG(ERROR) << tag << " invalid operand index at " << i << " = " << list[i] |
| << ", operandCount " << operandCount; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int validateOperationOperandTypes(const std::vector<Operand>& operands, |
| uint32_t inOperandCount, const uint32_t* inOperandIndexes, |
| const std::vector<OperandType>& inExpectedTypes, |
| uint32_t outOperandCount, const uint32_t* outOperandIndexes, |
| const std::vector<OperandType>& outExpectedInTypes) { |
| if (inOperandCount > static_cast<uint32_t>(inExpectedTypes.size()) || |
| outOperandCount > static_cast<uint32_t>(outExpectedInTypes.size())) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| for (uint32_t i = 0; i < inOperandCount; i++) { |
| if (operands[inOperandIndexes[i]].type != inExpectedTypes[i]) { |
| LOG(ERROR) << "Invalid input tensor type " |
| << toString(operands[inOperandIndexes[i]].type) |
| << " for input " << i << ", expected " << toString(inExpectedTypes[i]); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| for (uint32_t i = 0; i < outOperandCount; i++) { |
| if (operands[outOperandIndexes[i]].type != outExpectedInTypes[i]) { |
| LOG(ERROR) << "Invalid output tensor type " |
| << toString(operands[outOperandIndexes[i]].type) |
| << " for input " << i << ", expected " << toString(outExpectedInTypes[i]); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int validateOperation(ANeuralNetworksOperationType opType, |
| uint32_t inputCount, const uint32_t* inputIndexes, |
| uint32_t outputCount, const uint32_t* outputIndexes, |
| const std::vector<Operand>& operands) { |
| int n = validateOperandList(inputCount, inputIndexes, static_cast<uint32_t>(operands.size()), |
| "ANeuralNetworksModel_addOperation inputs"); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| n = validateOperandList(outputCount, outputIndexes, static_cast<uint32_t>(operands.size()), |
| "ANeuralNetworksModel_addOperation outputs"); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| |
| auto logInvalidInOutNumber = [opType, inputCount, outputCount](int expIn, int expOut) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected " << expIn << ") or output operands (" |
| << outputCount << ", expected " << expOut << ") for operation " |
| << kOperationNames[opType]; |
| }; |
| |
| switch (opType) { |
| case ANEURALNETWORKS_OEM_OPERATION: { |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| case ANEURALNETWORKS_ADD: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_MUL: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_FLOOR: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_DEQUANTIZE: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_DEPTHWISE_CONV_2D: { |
| if ((inputCount != 11 && inputCount != 8) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected 11 or 8) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 11) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), |
| explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_CONV_2D: { |
| if ((inputCount != 10 && inputCount != 7) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected 10 or 7) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 10) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), |
| explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_AVERAGE_POOL_2D: { |
| if ((inputCount != 10 && inputCount != 7) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected 10 or 7) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 10) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), |
| explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_L2_POOL_2D: { |
| if ((inputCount != 10 && inputCount != 7) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected 10 or 7) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 10) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), |
| explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_MAX_POOL_2D: { |
| if ((inputCount != 10 && inputCount != 7) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected 10 or 7) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 10) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), |
| explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RELU: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RELU1: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RELU6: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_TANH: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_LOGISTIC: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SOFTMAX: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_FULLY_CONNECTED: { |
| if (inputCount != 4 || outputCount != 1) { |
| logInvalidInOutNumber(4, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_CONCATENATION: { |
| if (inputCount < 2 || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" |
| << inputCount << ", expected at least 2) or output operands (" |
| << outputCount << ", expected 1) for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes(inputCount, inputType); |
| std::vector<OperandType> outExpectedTypes = {inputType}; |
| // The last one is the activation function. |
| inExpectedTypes.back() = OperandType::INT32; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_L2_NORMALIZATION: { |
| if (inputCount != 1 || outputCount != 1) { |
| logInvalidInOutNumber(1, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION: { |
| if (inputCount != 5 || outputCount != 1) { |
| logInvalidInOutNumber(5, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::FLOAT32, |
| OperandType::FLOAT32, |
| OperandType::FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RESHAPE: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RESIZE_BILINEAR: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_DEPTH_TO_SPACE: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SPACE_TO_DEPTH: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_EMBEDDING_LOOKUP: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[1]].type; |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_INT32, |
| inputType}; |
| std::vector<OperandType> outExpectedTypes = {inputType}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_HASHTABLE_LOOKUP: { |
| if (inputCount != 3 || outputCount != 2) { |
| logInvalidInOutNumber(3, 2); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[2]].type; |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32, |
| inputType}; |
| std::vector<OperandType> outExpectedTypes = {inputType, |
| OperandType::TENSOR_QUANT8_ASYMM}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_LSH_PROJECTION: { |
| if (inputCount != 4 || outputCount != 1) { |
| logInvalidInOutNumber(4, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[1]].type; |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| inputType, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| std::vector<OperandType> outExpectedTypes = {OperandType::TENSOR_INT32}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_LSTM: { |
| if (inputCount != 23 || outputCount != 4) { |
| logInvalidInOutNumber(23, 4); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::FLOAT32, |
| OperandType::FLOAT32}; |
| std::vector<OperandType> outExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_RNN: { |
| if (inputCount != 6 || outputCount != 2) { |
| logInvalidInOutNumber(6, 2); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| std::vector<OperandType> outExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SVDF: { |
| if (inputCount != 7 || outputCount != 2) { |
| logInvalidInOutNumber(7, 2); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::vector<OperandType> inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| std::vector<OperandType> outExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32}; |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_BATCH_TO_SPACE_ND: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SPACE_TO_BATCH_ND: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_PAD: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_PAD_V2: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::TENSOR_INT32, |
| OperandType::FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, OperandType::TENSOR_INT32, |
| OperandType::INT32}; // TODO(b/116699425): Make it UINT8. |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SQUEEZE: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_TRANSPOSE: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_STRIDED_SLICE: { |
| if (inputCount != 7 || outputCount != 1) { |
| logInvalidInOutNumber(7, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_DIV: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SUB: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_MEAN: { |
| if (inputCount != 3 || outputCount != 1) { |
| logInvalidInOutNumber(3, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_ARGMAX: |
| case ANEURALNETWORKS_ARGMIN: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32 || |
| inputType == OperandType::TENSOR_INT32 || |
| inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {inputType, OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_INT32}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_EXPAND_DIMS: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32 || |
| inputType == OperandType::TENSOR_INT32 || |
| inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {inputType, OperandType::INT32}; |
| outExpectedTypes = {inputType}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, |
| inputCount, inputIndexes, |
| inExpectedTypes, |
| outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_SPLIT: { |
| if (inputCount != 3) { |
| LOG(ERROR) << "Invalid number of input operands (" << inputCount << ", expected 3)" |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes = {inputType, OperandType::INT32, |
| OperandType::INT32}; |
| std::vector<OperandType> outExpectedTypes(outputCount, inputType); |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_ROI_ALIGN: { |
| if (inputCount != 5 || outputCount != 1) { |
| logInvalidInOutNumber(5, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_INT32, OperandType::FLOAT32, OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::TENSOR_FLOAT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_GROUPED_CONV_2D: { |
| if ((inputCount != 11 && inputCount != 8) || outputCount != 1) { |
| LOG(ERROR) << "Invalid number of input operands (" << inputCount |
| << ", expected 11 or 8) or output operands (" << outputCount |
| << ", expected 1) for operation " << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::TENSOR_FLOAT32, |
| OperandType::TENSOR_FLOAT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32, |
| OperandType::INT32, OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_QUANT8_ASYMM, |
| OperandType::TENSOR_INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32, |
| OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| if (inputCount == 11) { |
| std::vector<OperandType> explicitScalarTypes(3, OperandType::INT32); |
| inExpectedTypes.insert(inExpectedTypes.end(), explicitScalarTypes.begin(), |
| explicitScalarTypes.end()); |
| } |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| case ANEURALNETWORKS_CHANNEL_SHUFFLE: { |
| if (inputCount != 2 || outputCount != 1) { |
| logInvalidInOutNumber(2, 1); |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| auto inputType = operands[inputIndexes[0]].type; |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| inExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM, OperandType::INT32}; |
| outExpectedTypes = {OperandType::TENSOR_QUANT8_ASYMM}; |
| } else { |
| LOG(ERROR) << "Unsupported input tensor type for operation " |
| << kOperationNames[opType]; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| return validateOperationOperandTypes(operands, inputCount, inputIndexes, |
| inExpectedTypes, outputCount, outputIndexes, |
| outExpectedTypes); |
| } |
| default: |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| ErrorStatus convertResultCodeToErrorStatus(int resultCode) { |
| switch (resultCode) { |
| case ANEURALNETWORKS_NO_ERROR: |
| return ErrorStatus::NONE; |
| |
| case ANEURALNETWORKS_BAD_DATA: |
| case ANEURALNETWORKS_UNEXPECTED_NULL: |
| return ErrorStatus::INVALID_ARGUMENT; |
| |
| default: |
| LOG(ERROR) << "Unknown result code " << resultCode |
| << " mapped to ErrorStatus::GENERAL_FAILURE"; |
| case ANEURALNETWORKS_BAD_STATE: |
| case ANEURALNETWORKS_INCOMPLETE: |
| case ANEURALNETWORKS_OP_FAILED: |
| case ANEURALNETWORKS_OUT_OF_MEMORY: |
| case ANEURALNETWORKS_UNMAPPABLE: |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| } |
| |
| int convertErrorStatusToResultCode(ErrorStatus status) { |
| switch (status) { |
| case ErrorStatus::NONE: |
| return ANEURALNETWORKS_NO_ERROR; |
| |
| case ErrorStatus::INVALID_ARGUMENT: |
| return ANEURALNETWORKS_BAD_DATA; |
| |
| default: |
| LOG(ERROR) << "Unknown ErrorStatus " << toString(status) |
| << " mapped to ANEURALNETWORKS_OP_FAILED"; |
| case ErrorStatus::DEVICE_UNAVAILABLE: |
| case ErrorStatus::GENERAL_FAILURE: |
| case ErrorStatus::OUTPUT_INSUFFICIENT_SIZE: |
| return ANEURALNETWORKS_OP_FAILED; |
| } |
| } |
| |
| // Versioning |
| |
| bool compliantWithV1_0(V1_0::OperationType) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(V1_1::OperationType operation) { |
| return validOperationType(static_cast<V1_0::OperationType>(operation)); |
| } |
| |
| bool compliantWithV1_1(V1_0::OperationType) { |
| return true; |
| } |
| |
| bool compliantWithV1_1(V1_1::OperationType) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(V1_0::Capabilities) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(const V1_1::Capabilities& capabilities) { |
| return capabilities.relaxedFloat32toFloat16Performance.execTime == |
| capabilities.float32Performance.execTime |
| && |
| capabilities.relaxedFloat32toFloat16Performance.powerUsage == |
| capabilities.float32Performance.powerUsage; |
| } |
| |
| bool compliantWithV1_1(const V1_0::Capabilities&) { |
| return true; |
| } |
| |
| bool compliantWithV1_1(const V1_1::Capabilities&) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(const V1_0::Operation&) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(const V1_1::Operation& operation) { |
| return compliantWithV1_0(operation.type); |
| } |
| |
| bool compliantWithV1_1(const V1_0::Operation&) { |
| return true; |
| } |
| |
| bool compliantWithV1_1(const V1_1::Operation&) { |
| return true; |
| } |
| |
| static bool compliantWithV1_0(const hidl_vec<V1_1::Operation>& operations) { |
| return std::all_of(operations.begin(), operations.end(), |
| [](const V1_1::Operation& operation) { |
| return compliantWithV1_0(operation); |
| }); |
| } |
| |
| bool compliantWithV1_0(const V1_0::Model&) { |
| return true; |
| } |
| |
| bool compliantWithV1_0(const V1_1::Model& model) { |
| // In addition to new enumeration values being introduced in V1_1::Model, a |
| // new flag was introduced to indicate whether or not float32 data can be |
| // calculated using float16 units. This 'relaxComputationFloat32toFloat16' |
| // flag is not relevant in whether a V1_1::Model is compliant with a |
| // V1_0::Model because all 1.0 drivers require strict calculation by default |
| // in the P NN runtime. Even if fp16 calculations are allowed, they can |
| // still be computed by a strict fp32 driver. |
| return compliantWithV1_0(model.operations); |
| } |
| |
| bool compliantWithV1_1(const V1_0::Model&) { |
| return true; |
| } |
| |
| bool compliantWithV1_1(const V1_1::Model&) { |
| return true; |
| } |
| |
| V1_0::OperationType convertToV1_0(V1_0::OperationType type) { |
| return type; |
| } |
| |
| V1_0::OperationType convertToV1_0(V1_1::OperationType type) { |
| if (!compliantWithV1_0(type)) { |
| LOG(ERROR) << "Upcasting non-compliant type " << toString(type) |
| << " from V1_1::OperationType to V1_0::OperationType"; |
| } |
| return static_cast<V1_0::OperationType>(type); |
| } |
| |
| V1_1::OperationType convertToV1_1(V1_0::OperationType type) { |
| return static_cast<V1_1::OperationType>(type); |
| } |
| |
| V1_1::OperationType convertToV1_1(V1_1::OperationType type) { |
| return type; |
| } |
| |
| V1_0::Capabilities convertToV1_0(const V1_0::Capabilities& capabilities) { |
| return capabilities; |
| } |
| |
| V1_0::Capabilities convertToV1_0(const V1_1::Capabilities& capabilities) { |
| if (!compliantWithV1_0(capabilities)) { |
| LOG(ERROR) << "Upcasting non-compliant capabilities " << toString(capabilities) |
| << " from V1_1::Capabilities to V1_0::Capabilities"; |
| } |
| return { .float32Performance = capabilities.float32Performance, |
| .quantized8Performance = capabilities.quantized8Performance }; |
| } |
| |
| V1_1::Capabilities convertToV1_1(const V1_0::Capabilities& capabilities) { |
| return { .float32Performance = capabilities.float32Performance, |
| .quantized8Performance = capabilities.quantized8Performance, |
| .relaxedFloat32toFloat16Performance = capabilities.float32Performance }; |
| } |
| |
| V1_1::Capabilities convertToV1_1(const V1_1::Capabilities& capabilities) { |
| return capabilities; |
| } |
| |
| V1_0::Operation convertToV1_0(const V1_0::Operation& operation) { |
| return operation; |
| } |
| |
| V1_0::Operation convertToV1_0(const V1_1::Operation& operation) { |
| if (!compliantWithV1_0(operation)) { |
| LOG(ERROR) << "Upcasting non-compliant operation " << toString(operation) |
| << " from V1_1::Operation to V1_0::Operation"; |
| } |
| return {.type = convertToV1_0(operation.type), |
| .inputs = operation.inputs, |
| .outputs = operation.outputs}; |
| } |
| |
| V1_1::Operation convertToV1_1(const V1_0::Operation& operation) { |
| return {.type = convertToV1_1(operation.type), |
| .inputs = operation.inputs, |
| .outputs = operation.outputs}; |
| } |
| |
| V1_1::Operation convertToV1_1(const V1_1::Operation& operation) { |
| return operation; |
| } |
| |
| static hidl_vec<V1_0::Operation> convertToV1_0(const hidl_vec<V1_1::Operation>& operations) { |
| hidl_vec<V1_0::Operation> result(operations.size()); |
| std::transform(operations.begin(), operations.end(), result.begin(), |
| [](const V1_1::Operation& operation) { return convertToV1_0(operation); }); |
| return result; |
| } |
| |
| static hidl_vec<V1_1::Operation> convertToV1_1(const hidl_vec<V1_0::Operation>& operations) { |
| hidl_vec<V1_1::Operation> result(operations.size()); |
| std::transform(operations.begin(), operations.end(), result.begin(), |
| [](const V1_0::Operation& operation) { return convertToV1_1(operation); }); |
| return result; |
| } |
| |
| V1_0::Model convertToV1_0(const V1_0::Model& model) { |
| return model; |
| } |
| |
| V1_0::Model convertToV1_0(const V1_1::Model& model) { |
| if (!compliantWithV1_0(model)) { |
| LOG(ERROR) << "Upcasting non-compliant model " << SHOW_IF_DEBUG(toString(model)) |
| << " from V1_1::Model to V1_0::Model"; |
| } |
| return {.operands = model.operands, |
| .operations = convertToV1_0(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools}; |
| } |
| |
| V1_1::Model convertToV1_1(const V1_0::Model& model) { |
| return {.operands = model.operands, |
| .operations = convertToV1_1(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools, |
| .relaxComputationFloat32toFloat16 = false}; |
| } |
| |
| V1_1::Model convertToV1_1(const V1_1::Model& model) { |
| return model; |
| } |
| |
| void logModelToInfo(const V1_2::Model& model) { |
| LOG(INFO) << "V1_2::Model start"; |
| LOG(INFO) << "operands" << toString(model.operands); |
| LOG(INFO) << "operations" << toString(model.operations); |
| LOG(INFO) << "inputIndexes" << toString(model.inputIndexes); |
| LOG(INFO) << "outputIndexes" << toString(model.outputIndexes); |
| LOG(INFO) << "operandValues size" << model.operandValues.size(); |
| LOG(INFO) << "pools" << SHOW_IF_DEBUG(toString(model.pools)); |
| } |
| |
| bool compliantWithV1_0(V1_2::OperationType operation) { |
| return validOperationType(static_cast<V1_0::OperationType>(operation)); |
| } |
| |
| bool compliantWithV1_1(V1_2::OperationType operation) { |
| return validOperationType(static_cast<V1_1::OperationType>(operation)); |
| } |
| |
| bool compliantWithV1_0(const V1_2::Operation& operation) { |
| return compliantWithV1_0(operation.type); |
| } |
| |
| static bool compliantWithV1_0(const hidl_vec<V1_2::Operation>& operations) { |
| return std::all_of(operations.begin(), operations.end(), |
| [](const V1_2::Operation& operation) { |
| return compliantWithV1_0(operation); |
| }); |
| } |
| |
| bool compliantWithV1_0(const V1_2::Model& model) { |
| // See the comment in compliantWithV1_0(const V1_1::Model&). |
| return compliantWithV1_0(model.operations); |
| } |
| |
| bool compliantWithV1_1(const V1_2::Model&) { |
| return true; |
| } |
| |
| V1_0::OperationType convertToV1_0(V1_2::OperationType type) { |
| if (!compliantWithV1_0(type)) { |
| LOG(ERROR) << "Upcasting non-compliant type " << toString(type) |
| << " from V1_2::OperationType to V1_1::OperationType"; |
| } |
| return static_cast<V1_0::OperationType>(type); |
| } |
| |
| V1_1::OperationType convertToV1_1(V1_2::OperationType type) { |
| if (!compliantWithV1_1(type)) { |
| LOG(ERROR) << "Upcasting non-compliant type " << toString(type) |
| << " from V1_2::OperationType to V1_1::OperationType"; |
| } |
| return static_cast<V1_1::OperationType>(type); |
| } |
| |
| V1_2::OperationType convertToV1_2(V1_0::OperationType type) { |
| return static_cast<V1_2::OperationType>(type); |
| } |
| |
| V1_2::OperationType convertToV1_2(V1_1::OperationType type) { |
| return static_cast<V1_2::OperationType>(type); |
| } |
| |
| V1_0::Operation convertToV1_0(const V1_2::Operation& operation) { |
| if (!compliantWithV1_0(operation)) { |
| LOG(ERROR) << "Upcasting non-compliant operation " << toString(operation) |
| << " from V1_1::Operation to V1_0::Operation"; |
| } |
| return {.type = convertToV1_0(operation.type), |
| .inputs = operation.inputs, |
| .outputs = operation.outputs}; |
| } |
| |
| V1_1::Operation convertToV1_1(const V1_2::Operation& operation) { |
| return {.type = convertToV1_1(operation.type), |
| .inputs = operation.inputs, |
| .outputs = operation.outputs}; |
| } |
| |
| template <typename T> |
| V1_2::Operation convertToV1_2(const T& operation) { |
| return {.type = convertToV1_2(operation.type), |
| .inputs = operation.inputs, |
| .outputs = operation.outputs}; |
| } |
| |
| static hidl_vec<V1_0::Operation> convertToV1_0(const hidl_vec<V1_2::Operation>& operations) { |
| hidl_vec<V1_0::Operation> result(operations.size()); |
| std::transform(operations.begin(), operations.end(), result.begin(), |
| [](const V1_2::Operation& operation) { return convertToV1_0(operation); }); |
| return result; |
| } |
| |
| static hidl_vec<V1_1::Operation> convertToV1_1(const hidl_vec<V1_2::Operation>& operations) { |
| hidl_vec<V1_1::Operation> result(operations.size()); |
| std::transform(operations.begin(), operations.end(), result.begin(), |
| [](const V1_2::Operation& operation) { return convertToV1_1(operation); }); |
| return result; |
| } |
| |
| template <typename T> |
| static hidl_vec<V1_2::Operation> convertToV1_2(const hidl_vec<T>& operations) { |
| hidl_vec<V1_2::Operation> result(operations.size()); |
| std::transform(operations.begin(), operations.end(), result.begin(), |
| [](const T& operation) { |
| return convertToV1_2(operation); |
| }); |
| return result; |
| } |
| |
| V1_0::Model convertToV1_0(const V1_2::Model& model) { |
| if (!compliantWithV1_0(model)) { |
| LOG(ERROR) << "Upcasting non-compliant model " << SHOW_IF_DEBUG(toString(model)) |
| << " from V1_1::Model to V1_0::Model"; |
| } |
| return {.operands = model.operands, |
| .operations = convertToV1_0(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools}; |
| } |
| |
| V1_1::Model convertToV1_1(const V1_2::Model& model) { |
| return {.operands = model.operands, |
| .operations = convertToV1_1(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools, |
| .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16}; |
| } |
| |
| V1_2::Model convertToV1_2(const V1_0::Model& model) { |
| return {.operands = model.operands, |
| .operations = convertToV1_2(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools, |
| .relaxComputationFloat32toFloat16 = false}; |
| } |
| |
| V1_2::Model convertToV1_2(const V1_1::Model& model) { |
| return {.operands = model.operands, |
| .operations = convertToV1_2(model.operations), |
| .inputIndexes = model.inputIndexes, |
| .outputIndexes = model.outputIndexes, |
| .operandValues = model.operandValues, |
| .pools = model.pools, |
| .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16}; |
| } |
| |
| #ifdef NN_DEBUGGABLE |
| uint32_t getProp(const char* str, uint32_t defaultValue) { |
| const std::string propStr = android::base::GetProperty(str, ""); |
| if (propStr.size() > 0) { |
| return std::stoi(propStr); |
| } else { |
| return defaultValue; |
| } |
| } |
| #endif // NN_DEBUGGABLE |
| |
| } // namespace nn |
| } // namespace android |
