return ANEURALNETWORKS_NO_ERROR;
}
-static bool validOperandIndexes(const hidl_vec<uint32_t> indexes, size_t operandCount)
+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)
{
- for (uint32_t i : indexes)
+ if (inOperandCount > static_cast<uint32_t>(inExpectedTypes.size()) ||
+ outOperandCount > static_cast<uint32_t>(outExpectedInTypes.size()))
{
- if (i >= operandCount)
+ 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) << "Index out of range " << i << "/" << operandCount;
- return false;
+ LOG(ERROR) << "Invalid output tensor type " << toString(operands[outOperandIndexes[i]].type)
+ << " for input " << i << ", expected " << toString(outExpectedInTypes[i]);
+ return ANEURALNETWORKS_BAD_DATA;
}
}
- return true;
+
+ return ANEURALNETWORKS_NO_ERROR;
}
-static bool validOperands(const hidl_vec<Operand> &operands, const hidl_vec<uint8_t> &operandValues,
- size_t poolCount)
+int validateOperation(OperationType opType, uint32_t inputCount, const uint32_t *inputIndexes,
+ uint32_t outputCount, const uint32_t *outputIndexes,
+ const std::vector<Operand> &operands)
{
- for (auto &operand : operands)
+ int opTypeVal = toUnderlying(opType);
+
+ int n = validateOperandList(inputCount, inputIndexes, static_cast<uint32_t>(operands.size()),
+ "ANeuralNetworksModel_addOperation inputs");
+ if (n != ANEURALNETWORKS_NO_ERROR)
{
- if (!validCode(kNumberOfDataTypes, kNumberOfDataTypesOEM, static_cast<uint32_t>(operand.type)))
+ return n;
+ }
+ n = validateOperandList(outputCount, outputIndexes, static_cast<uint32_t>(operands.size()),
+ "ANeuralNetworksModel_addOperation outputs");
+ if (n != ANEURALNETWORKS_NO_ERROR)
+ {
+ return n;
+ }
+
+ auto logInvalidInOutNumber = [opTypeVal, 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[opTypeVal];
+ };
+
+ switch (opType)
+ {
+ case OperationType::ADD:
{
- LOG(ERROR) << "Invalid operand type ";
- return false;
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
}
- /* TODO validate dim with type
- if (!validOperandIndexes(operand.dimensions, mDimensions)) {
- return false;
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
}
- */
- switch (operand.lifetime)
+ case OperationType::FLOOR:
{
- case OperandLifeTime::CONSTANT_COPY:
- if (operand.location.offset + operand.location.length > operandValues.size())
- {
- LOG(ERROR) << "OperandValue location out of range. Starts at " << operand.location.offset
- << ", length " << operand.location.length << ", max " << operandValues.size();
- return false;
- }
- break;
- case OperandLifeTime::TEMPORARY_VARIABLE:
- case OperandLifeTime::MODEL_INPUT:
- case OperandLifeTime::MODEL_OUTPUT:
- case OperandLifeTime::NO_VALUE:
- if (operand.location.offset != 0 || operand.location.length != 0)
- {
- LOG(ERROR) << "Unexpected offset " << operand.location.offset << " or length "
- << operand.location.length << " for runtime location.";
- return false;
- }
- break;
- case OperandLifeTime::CONSTANT_REFERENCE:
- if (operand.location.poolIndex >= poolCount)
- {
- LOG(ERROR) << "Invalid poolIndex " << operand.location.poolIndex << "/" << poolCount;
- return false;
- }
- break;
- // TODO: Validate that we are within the pool.
- default:
- LOG(ERROR) << "Invalid lifetime";
- return false;
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
}
- }
- return true;
-}
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
-static bool validOperations(const hidl_vec<Operation> &operations, size_t operandCount)
-{
- for (auto &op : operations)
- {
- if (!validCode(kNumberOfOperationTypes, kNumberOfOperationTypesOEM, kNumberOfOperationTypesEx,
- static_cast<uint32_t>(op.type)))
+ 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 OperationType::CONV_2D:
{
- LOG(ERROR) << "Invalid operation type ";
- return false;
+ 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[opTypeVal];
+ 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[opTypeVal];
+ 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);
}
- if (!validOperandIndexes(op.inputs, operandCount) ||
- !validOperandIndexes(op.outputs, operandCount))
+ case OperationType::AVERAGE_POOL_2D:
{
- return false;
+ 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[opTypeVal];
+ 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[opTypeVal];
+ 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);
}
- }
- return true;
-}
+ case OperationType::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[opTypeVal];
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
-// TODO doublecheck
-bool validateModel(const Model &model)
-{
- const size_t operandCount = model.operands.size();
- return (validOperands(model.operands, model.operandValues, model.pools.size()) &&
- validOperations(model.operations, operandCount) &&
- validOperandIndexes(model.inputIndexes, operandCount) &&
- validOperandIndexes(model.outputIndexes, operandCount));
-}
+ 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 OperationType::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[opTypeVal];
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
-bool validRequestArguments(const hidl_vec<RequestArgument> &arguments,
- const hidl_vec<uint32_t> &operandIndexes,
- const hidl_vec<Operand> &operands, size_t poolCount, const char *type)
-{
- const size_t argumentCount = arguments.size();
- if (argumentCount != operandIndexes.size())
- {
- LOG(ERROR) << "Request specifies " << argumentCount << " " << type << "s but the model has "
- << operandIndexes.size();
- return false;
- }
- for (size_t argumentIndex = 0; argumentIndex < argumentCount; argumentIndex++)
- {
- const RequestArgument &argument = arguments[argumentIndex];
- const uint32_t operandIndex = operandIndexes[argumentIndex];
- const Operand &operand = operands[operandIndex];
- if (argument.hasNoValue)
+ 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 OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::RELU6:
{
- if (argument.location.poolIndex != 0 || argument.location.offset != 0 ||
- argument.location.length != 0 || argument.dimensions.size() != 0)
+ 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) << "Request " << type << " " << argumentIndex
- << " has no value yet has details.";
- return false;
+ LOG(ERROR) << "Unsupported input tensor type for operation " << kOperationNames[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
}
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
}
- if (argument.location.poolIndex >= poolCount)
+ case OperationType::TANH:
{
- LOG(ERROR) << "Request " << type << " " << argumentIndex << " has an invalid poolIndex "
- << argument.location.poolIndex << "/" << poolCount;
- return false;
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
}
- // TODO: Validate that we are within the pool.
- uint32_t rank = argument.dimensions.size();
- if (rank > 0)
+ case OperationType::LOGISTIC:
{
- if (rank != operand.dimensions.size())
+ if (inputCount != 1 || outputCount != 1)
{
- LOG(ERROR) << "Request " << type << " " << argumentIndex << " has number of dimensions ("
- << rank << ") different than the model's (" << operand.dimensions.size() << ")";
- return false;
+ logInvalidInOutNumber(1, 1);
+ return ANEURALNETWORKS_BAD_DATA;
}
- for (size_t i = 0; i < rank; i++)
+ auto inputType = operands[inputIndexes[0]].type;
+ std::vector<OperandType> inExpectedTypes;
+ std::vector<OperandType> outExpectedTypes;
+ if (inputType == OperandType::TENSOR_FLOAT32)
{
- if (argument.dimensions[i] != operand.dimensions[i] && operand.dimensions[i] != 0)
- {
- LOG(ERROR) << "Request " << type << " " << argumentIndex << " has dimension " << i
- << " of " << operand.dimensions[i] << " different than the model's "
- << operand.dimensions[i];
- return false;
+ 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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ 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 OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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 OperationType::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 OperationType::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 OperationType::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 OperationType::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 OperationType::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);
+ }
+#if 0 // REF-ANN
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
}
- if (argument.dimensions[i] == 0)
- {
- LOG(ERROR) << "Request " << type << " " << argumentIndex << " has dimension " << i
- << " of zero";
- return false;
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
}
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
+ }
+#endif // REF-ANN
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands, inputCount, inputIndexes, inExpectedTypes,
+ outputCount, outputIndexes, outExpectedTypes);
+ }
+#if 0 // REF-ANN
+ case OperationType::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[opTypeVal];
+ return ANEURALNETWORKS_BAD_DATA;
+ }
+ return validateOperationOperandTypes(operands,
+ inputCount, inputIndexes,
+ inExpectedTypes,
+ outputCount, outputIndexes,
+ outExpectedTypes);
+ }
+#endif // REF-ANN
+ case OperationType::CAST:
+ {
+ // TODO-NNRT : implement validation this operation.
+ }
+ case OperationType::GATHER:
+ {
+ // TODO-NNRT : implement validation this operation.
}
+ case OperationType::TOPK_V2:
+ {
+ // TODO-NNRT : implement validation this operation.
+ }
+ case OperationType::TENSORFLOW_MAX:
+ {
+ // TODO-NNRT : implement validation of this operation.
+ }
+ default:
+ return ANEURALNETWORKS_BAD_DATA;
}
- return true;
-}
-
-// TODO doublecheck
-bool validateRequest(const Request &request, const Model &model)
-{
- const size_t poolCount = request.pools.size();
- return (validRequestArguments(request.inputs, model.inputIndexes, model.operands, poolCount,
- "input") &&
- validRequestArguments(request.outputs, model.outputIndexes, model.operands, poolCount,
- "output"));
}
} // namespace rt
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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 "ValidateHal"
+
+#include "ValidateHal.h"
+#include "NeuralNetworks.h"
+#include "Utils.h"
+
+#include "Logging.h"
+
+namespace nnfw
+{
+namespace rt
+{
+
+class MemoryAccessVerifier
+{
+public:
+ MemoryAccessVerifier(const hidl_vec<hidl_memory> &pools)
+ : mPoolCount(pools.size()), mPoolSizes(mPoolCount)
+ {
+ for (size_t i = 0; i < mPoolCount; i++)
+ {
+ mPoolSizes[i] = pools[i].size();
+ }
+ }
+ bool validate(const DataLocation &location)
+ {
+ if (location.poolIndex >= mPoolCount)
+ {
+ LOG(ERROR) << "Invalid poolIndex " << location.poolIndex << "/" << mPoolCount;
+ return false;
+ }
+ const size_t size = mPoolSizes[location.poolIndex];
+ // Do the addition using size_t to avoid potential wrap-around problems.
+ if (static_cast<size_t>(location.offset) + location.length > size)
+ {
+ LOG(ERROR) << "Reference to pool " << location.poolIndex << " with offset " << location.offset
+ << " and length " << location.length << " exceeds pool size of " << size;
+ return false;
+ }
+ return true;
+ }
+
+private:
+ size_t mPoolCount;
+ std::vector<size_t> mPoolSizes;
+};
+
+static bool validateOperands(const hidl_vec<Operand> &operands,
+ const hidl_vec<uint8_t> &operandValues,
+ const hidl_vec<hidl_memory> &pools)
+{
+ uint32_t index = 0;
+ MemoryAccessVerifier poolVerifier(pools);
+ for (auto &operand : operands)
+ {
+ // Validate type and dimensions.
+ switch (operand.type)
+ {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::OEM:
+ {
+ size_t count = operand.dimensions.size();
+ if (count != 0)
+ {
+ LOG(ERROR) << "Operand " << index << ": Scalar data has dimensions of rank " << count;
+ return false;
+ }
+ break;
+ }
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ case OperandType::TENSOR_OEM_BYTE:
+ {
+ if (operand.dimensions.size() == 0)
+ {
+ LOG(ERROR) << "Operand " << index << ": Tensor has dimensions of rank 0";
+ return false;
+ }
+ break;
+ }
+ default:
+ LOG(ERROR) << "Operand " << index << ": Invalid operand type " << toString(operand.type);
+ return false;
+ }
+
+ // TODO Validate the numberOfConsumers.
+ // TODO Since we have to validate it, there was no point in including it. For the next
+ // release, consider removing unless we have an additional process in system space
+ // that creates this value. In that case, it would not have to be validated.
+
+ // Validate the scale.
+ switch (operand.type)
+ {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::TENSOR_FLOAT32:
+ if (operand.scale != 0.f)
+ {
+ LOG(ERROR) << "Operand " << index << ": Operand of type "
+ << getOperandTypeName(operand.type) << " with a non-zero scale ("
+ << operand.scale << ")";
+ return false;
+ }
+ break;
+ case OperandType::TENSOR_INT32:
+ // TENSOR_INT32 may be used with or without scale, depending on the operation.
+ if (operand.scale < 0.f)
+ {
+ LOG(ERROR) << "Operand " << index << ": Operand of type "
+ << getOperandTypeName(operand.type) << " with a negative scale";
+ return false;
+ }
+ break;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ if (operand.scale <= 0.f)
+ {
+ LOG(ERROR) << "Operand " << index << ": Operand of type "
+ << getOperandTypeName(operand.type) << " with a non-positive scale";
+ return false;
+ }
+ break;
+ default:
+ // No validation for the OEM types.
+ // TODO We should have had a separate type for TENSOR_INT32 that a scale
+ // and those who don't. Document now and fix in the next release.
+ break;
+ }
+
+ // Validate the zeroPoint.
+ switch (operand.type)
+ {
+ case OperandType::FLOAT32:
+ case OperandType::INT32:
+ case OperandType::UINT32:
+ case OperandType::TENSOR_FLOAT32:
+ case OperandType::TENSOR_INT32:
+ if (operand.zeroPoint != 0)
+ {
+ LOG(ERROR) << "Operand " << index << ": Operand of type "
+ << getOperandTypeName(operand.type) << " with an non-zero zeroPoint "
+ << operand.zeroPoint;
+ return false;
+ }
+ break;
+ case OperandType::TENSOR_QUANT8_ASYMM:
+ if (operand.zeroPoint < 0 || operand.zeroPoint > 255)
+ {
+ LOG(ERROR) << "Operand " << index << ": Operand of type "
+ << getOperandTypeName(operand.type) << " with an invalid zeroPoint "
+ << operand.zeroPoint << ", must be in range [0, 255]";
+ return false;
+ }
+ break;
+ default:
+ // No validation for the OEM types.
+ break;
+ }
+
+ // Validate the lifetime and the location.
+ const DataLocation &location = operand.location;
+ switch (operand.lifetime)
+ {
+ case OperandLifeTime::CONSTANT_COPY:
+ if (location.poolIndex != 0)
+ {
+ LOG(ERROR) << "Operand " << index << ": CONSTANT_COPY with a non-zero poolIndex "
+ << location.poolIndex;
+ return false;
+ }
+ // Do the addition using size_t to avoid potential wrap-around problems.
+ if (static_cast<size_t>(location.offset) + location.length > operandValues.size())
+ {
+ LOG(ERROR) << "Operand " << index << ": OperandValue location out of range. Starts at "
+ << location.offset << ", length " << location.length << ", max "
+ << operandValues.size();
+ return false;
+ }
+ break;
+ case OperandLifeTime::CONSTANT_REFERENCE:
+ if (!poolVerifier.validate(location))
+ {
+ return false;
+ }
+ break;
+ case OperandLifeTime::TEMPORARY_VARIABLE:
+ case OperandLifeTime::MODEL_INPUT:
+ case OperandLifeTime::MODEL_OUTPUT:
+ case OperandLifeTime::NO_VALUE:
+ if (location.poolIndex != 0 || location.offset != 0 || location.length != 0)
+ {
+ LOG(ERROR) << "Operand " << index << ": Unexpected poolIndex " << location.poolIndex
+ << ", offset " << location.offset << ", or length " << location.length
+ << " for operand of lifetime " << toString(operand.lifetime);
+ return false;
+ }
+ break;
+ default:
+ LOG(ERROR) << "Operand " << index << ": Invalid lifetime " << toString(operand.lifetime);
+ return false;
+ }
+
+ // For constants, validate that the length is as expected. The other lifetimes
+ // expect the length to be 0. Don't validate for OEM types.
+ if (operand.lifetime == OperandLifeTime::CONSTANT_REFERENCE ||
+ operand.lifetime == OperandLifeTime::CONSTANT_COPY)
+ {
+ if (operand.type != OperandType::OEM && operand.type != OperandType::TENSOR_OEM_BYTE)
+ {
+ uint32_t expectedLength = sizeOfData(operand.type, operand.dimensions);
+ if (location.length != expectedLength)
+ {
+ LOG(ERROR) << "Operand " << index << ": For operand " << toString(operand)
+ << " expected a size of " << expectedLength << " but got " << location.length;
+ return false;
+ }
+ }
+ }
+
+ index++;
+ }
+ return true;
+}
+
+static bool validOperationType(OperationType operation)
+{
+ switch (operation)
+ {
+ case OperationType::ADD:
+ case OperationType::AVERAGE_POOL_2D:
+ case OperationType::CONCATENATION:
+ case OperationType::CONV_2D:
+ case OperationType::DEPTHWISE_CONV_2D:
+ case OperationType::DEPTH_TO_SPACE:
+ case OperationType::DEQUANTIZE:
+ case OperationType::EMBEDDING_LOOKUP:
+ case OperationType::FLOOR:
+ case OperationType::FULLY_CONNECTED:
+ case OperationType::HASHTABLE_LOOKUP:
+ case OperationType::L2_NORMALIZATION:
+ case OperationType::L2_POOL_2D:
+ case OperationType::LOCAL_RESPONSE_NORMALIZATION:
+ case OperationType::LOGISTIC:
+ case OperationType::LSH_PROJECTION:
+ case OperationType::LSTM:
+ case OperationType::MAX_POOL_2D:
+ case OperationType::MUL:
+ case OperationType::RELU:
+ case OperationType::RELU1:
+ case OperationType::RELU6:
+ case OperationType::RESHAPE:
+ case OperationType::RESIZE_BILINEAR:
+ case OperationType::RNN:
+ case OperationType::SOFTMAX:
+ case OperationType::SPACE_TO_DEPTH:
+ case OperationType::SVDF:
+ case OperationType::TANH:
+ case OperationType::DIV:
+ case OperationType::STRIDED_SLICE:
+ case OperationType::SUB:
+ case OperationType::OEM_OPERATION:
+ case OperationType::CAST:
+ case OperationType::GATHER:
+ case OperationType::TOPK_V2:
+ case OperationType::TENSORFLOW_MAX:
+ return true;
+ default:
+ return false;
+ }
+}
+
+template <typename VersionedOperation>
+static bool validateOperations(const hidl_vec<VersionedOperation> &operations,
+ const hidl_vec<Operand> &operands)
+{
+ const size_t operandCount = operands.size();
+ // This vector keeps track of whether there's an operation that writes to
+ // each operand. It is used to validate that temporary variables and
+ // model outputs will be written to.
+ std::vector<bool> writtenTo(operandCount, false);
+ for (auto &op : operations)
+ {
+ if (!validOperationType(op.type))
+ {
+ LOG(ERROR) << "Invalid operation type " << toString(op.type);
+ return false;
+ }
+ // TODO Validate the shapes and any known values. This is currently
+ // done in CpuExecutor but should be done here for all drivers.
+ int error = validateOperation(
+ op.type, op.inputs.size(), op.inputs.size() > 0 ? op.inputs.data() : nullptr,
+ op.outputs.size(), op.outputs.size() > 0 ? op.outputs.data() : nullptr, operands);
+ if (error != ANEURALNETWORKS_NO_ERROR)
+ {
+ return false;
+ }
+
+ for (uint32_t i : op.outputs)
+ {
+ const Operand &operand = operands[i];
+ if (operand.lifetime != OperandLifeTime::TEMPORARY_VARIABLE &&
+ operand.lifetime != OperandLifeTime::MODEL_OUTPUT)
+ {
+ LOG(ERROR) << "Writing to an operand with incompatible lifetime "
+ << toString(operand.lifetime);
+ return false;
+ }
+
+ // Check that we only write once to an operand.
+ if (writtenTo[i])
+ {
+ LOG(ERROR) << "Operand " << i << " written a second time";
+ return false;
+ }
+ writtenTo[i] = true;
+ }
+ }
+ for (size_t i = 0; i < operandCount; i++)
+ {
+ if (!writtenTo[i])
+ {
+ const Operand &operand = operands[i];
+ if (operand.lifetime == OperandLifeTime::TEMPORARY_VARIABLE ||
+ operand.lifetime == OperandLifeTime::MODEL_OUTPUT)
+ {
+ LOG(ERROR) << "Operand " << i << " with lifetime " << toString(operand.lifetime)
+ << " is not being written to.";
+ return false;
+ }
+ }
+ }
+ // TODO More whole graph verifications are possible, for example that an
+ // operand is not use as input & output for the same op, and more
+ // generally that it is acyclic.
+ return true;
+}
+
+static bool validatePools(const hidl_vec<hidl_memory> &pools)
+{
+ for (const hidl_memory &memory : pools)
+ {
+ const auto name = memory.name();
+ if (name != "ashmem" && name != "mmap_fd")
+ {
+ LOG(ERROR) << "Unsupported memory type " << name;
+ return false;
+ }
+ if (memory.handle() == nullptr)
+ {
+ LOG(ERROR) << "Memory of type " << name << " is null";
+ return false;
+ }
+ }
+ return true;
+}
+
+static bool validateModelInputOutputs(const hidl_vec<uint32_t> indexes,
+ const hidl_vec<Operand> &operands, OperandLifeTime lifetime)
+{
+ const size_t operandCount = operands.size();
+ for (uint32_t i : indexes)
+ {
+ if (i >= operandCount)
+ {
+ LOG(ERROR) << "Model input or output index out of range: " << i << "/" << operandCount;
+ return false;
+ }
+ const Operand &operand = operands[i];
+ if (operand.lifetime != lifetime)
+ {
+ LOG(ERROR) << "Model input or output has lifetime of " << toString(operand.lifetime)
+ << " instead of the expected " << toString(lifetime);
+ return false;
+ }
+ }
+
+ std::vector<uint32_t> sortedIndexes = indexes;
+ std::sort(sortedIndexes.begin(), sortedIndexes.end());
+ auto adjacentI = std::adjacent_find(sortedIndexes.begin(), sortedIndexes.end());
+ if (adjacentI != sortedIndexes.end())
+ {
+ LOG(ERROR) << "Model input or output occurs multiple times: " << *adjacentI;
+ return false;
+ }
+ return true;
+}
+
+template <typename VersionedModel> static bool validateModelVersioned(const VersionedModel &model)
+{
+ return (
+ validateOperands(model.operands, model.operandValues, model.pools) &&
+ validateOperations(model.operations, model.operands) &&
+ validateModelInputOutputs(model.inputIndexes, model.operands, OperandLifeTime::MODEL_INPUT) &&
+ validateModelInputOutputs(model.outputIndexes, model.operands,
+ OperandLifeTime::MODEL_OUTPUT) &&
+ validatePools(model.pools));
+}
+
+bool validateModel(const Model &model) { return validateModelVersioned(model); }
+
+// Validates the arguments of a request. type is either "input" or "output" and is used
+// for printing error messages. The operandIndexes is the appropriate array of input
+// or output operand indexes that was passed to the ANeuralNetworksModel_identifyInputsAndOutputs.
+static bool validateRequestArguments(const hidl_vec<RequestArgument> &requestArguments,
+ const hidl_vec<uint32_t> &operandIndexes,
+ const hidl_vec<Operand> &operands,
+ const hidl_vec<hidl_memory> &pools, const char *type)
+{
+ MemoryAccessVerifier poolVerifier(pools);
+ // The request should specify as many arguments as were described in the model.
+ const size_t requestArgumentCount = requestArguments.size();
+ if (requestArgumentCount != operandIndexes.size())
+ {
+ LOG(ERROR) << "Request specifies " << requestArgumentCount << " " << type
+ << "s but the model has " << operandIndexes.size();
+ return false;
+ }
+ for (size_t requestArgumentIndex = 0; requestArgumentIndex < requestArgumentCount;
+ requestArgumentIndex++)
+ {
+ const RequestArgument &requestArgument = requestArguments[requestArgumentIndex];
+ const DataLocation &location = requestArgument.location;
+ // Get the operand index for this argument. We extract it from the list
+ // that was provided in the call to ANeuralNetworksModel_identifyInputsAndOutputs.
+ // We assume in this function that the model has been validated already.
+ const uint32_t operandIndex = operandIndexes[requestArgumentIndex];
+ const Operand &operand = operands[operandIndex];
+ if (requestArgument.hasNoValue)
+ {
+ if (location.poolIndex != 0 || location.offset != 0 || location.length != 0 ||
+ requestArgument.dimensions.size() != 0)
+ {
+ LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
+ << " has no value yet has details.";
+ return false;
+ }
+ }
+ else
+ {
+ // Validate the location.
+ if (!poolVerifier.validate(location))
+ {
+ return false;
+ }
+ // If the argument specified a dimension, validate it.
+ uint32_t rank = requestArgument.dimensions.size();
+ if (rank == 0)
+ {
+ // Validate that all the dimensions are specified in the model.
+ for (size_t i = 0; i < operand.dimensions.size(); i++)
+ {
+ if (operand.dimensions[i] == 0)
+ {
+ LOG(ERROR) << "Model has dimension " << i
+ << " set to 0 but the request does specify the dimension.";
+ return false;
+ }
+ }
+ }
+ else
+ {
+ if (rank != operand.dimensions.size())
+ {
+ LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
+ << " has number of dimensions (" << rank << ") different than the model's ("
+ << operand.dimensions.size() << ")";
+ return false;
+ }
+ for (size_t i = 0; i < rank; i++)
+ {
+ if (requestArgument.dimensions[i] != operand.dimensions[i] && operand.dimensions[i] != 0)
+ {
+ LOG(ERROR) << "Request " << type << " " << requestArgumentIndex << " has dimension "
+ << i << " of " << requestArgument.dimensions[i]
+ << " different than the model's " << operand.dimensions[i];
+ return false;
+ }
+ if (requestArgument.dimensions[i] == 0)
+ {
+ LOG(ERROR) << "Request " << type << " " << requestArgumentIndex << " has dimension "
+ << i << " of zero";
+ return false;
+ }
+ }
+ }
+ }
+ }
+ return true;
+}
+
+template <typename VersionedModel>
+static bool validateRequestVersioned(const Request &request, const VersionedModel &model)
+{
+ return (validateRequestArguments(request.inputs, model.inputIndexes, model.operands,
+ request.pools, "input") &&
+ validateRequestArguments(request.outputs, model.outputIndexes, model.operands,
+ request.pools, "output") &&
+ validatePools(request.pools));
+}
+
+bool validateRequest(const Request &request, const Model &model)
+{
+ return validateRequestVersioned(request, model);
+}
+
+} // namespace rt
+} // namespace nnfw
projects / platform / core / ml / nnfw.git / commitdiff