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Diffstat (limited to 'tests/ExecuteNetwork/ExecuteNetworkParams.cpp')
| -rw-r--r-- | tests/ExecuteNetwork/ExecuteNetworkParams.cpp | 212 |
1 files changed, 212 insertions, 0 deletions
diff --git a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp new file mode 100644 index 0000000000..c298bd614a --- /dev/null +++ b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp @@ -0,0 +1,212 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ExecuteNetworkParams.hpp" + +#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp" +#include <InferenceModel.hpp> +#include <armnn/Logging.hpp> + +#include <fmt/format.h> + +bool IsModelBinary(const std::string& modelFormat) +{ + // Parse model binary flag from the model-format string we got from the command-line + if (modelFormat.find("binary") != std::string::npos) + { + return true; + } + else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos) + { + return false; + } + else + { + throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. " + "Please include 'binary' or 'text'", + modelFormat)); + } +} + +void CheckModelFormat(const std::string& modelFormat) +{ + // Forward to implementation based on the parser type + if (modelFormat.find("armnn") != std::string::npos) + { +#if defined(ARMNN_SERIALIZER) +#else + throw armnn::InvalidArgumentException("Can't run model in armnn format without a " + "built with serialization support."); +#endif + } + else if (modelFormat.find("caffe") != std::string::npos) + { +#if defined(ARMNN_CAFFE_PARSER) +#else + throw armnn::InvalidArgumentException("Can't run model in caffe format without a " + "built with Caffe parser support."); +#endif + } + else if (modelFormat.find("onnx") != std::string::npos) + { +#if defined(ARMNN_ONNX_PARSER) +#else + throw armnn::InvalidArgumentException("Can't run model in onnx format without a " + "built with Onnx parser support."); +#endif + } + else if (modelFormat.find("tensorflow") != std::string::npos) + { +#if defined(ARMNN_TF_PARSER) +#else + throw armnn::InvalidArgumentException("Can't run model in onnx format without a " + "built with Tensorflow parser support."); +#endif + } + else if(modelFormat.find("tflite") != std::string::npos) + { +#if defined(ARMNN_TF_LITE_PARSER) + if (!IsModelBinary(modelFormat)) + { + throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. Only 'binary' format " + "supported for tflite files", + modelFormat)); + } +#else + throw armnn::InvalidArgumentException("Can't run model in tflite format without a " + "built with Tensorflow Lite parser support."); +#endif + } + else + { + throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. " + "Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'", + modelFormat)); + } +} + +void CheckClTuningParameter(const int& tuningLevel, + const std::string& tuningPath, + const std::vector<armnn::BackendId> computeDevices) +{ + if (!tuningPath.empty()) + { + if(tuningLevel == 0) + { + ARMNN_LOG(info) << "Using cl tuning file: " << tuningPath << "\n"; + if(!ValidatePath(tuningPath, true)) + { + throw armnn::InvalidArgumentException("The tuning path is not valid"); + } + } + else if ((1 <= tuningLevel) && (tuningLevel <= 3)) + { + ARMNN_LOG(info) << "Starting execution to generate a cl tuning file: " << tuningPath << "\n" + << "Tuning level in use: " << tuningLevel << "\n"; + } + else if ((0 < tuningLevel) || (tuningLevel > 3)) + { + throw armnn::InvalidArgumentException(fmt::format("The tuning level {} is not valid.", tuningLevel)); + } + + // Ensure that a GpuAcc is enabled. Otherwise no tuning data are used or genereted + // Only warn if it's not enabled + auto it = std::find(computeDevices.begin(), computeDevices.end(), "GpuAcc"); + if (it == computeDevices.end()) + { + ARMNN_LOG(warning) << "To use Cl Tuning the compute device GpuAcc needs to be active."; + } + } + + +} + +void ExecuteNetworkParams::ValidateParams() +{ + // Check compute devices + std::string invalidBackends; + if (!CheckRequestedBackendsAreValid(m_ComputeDevices, armnn::Optional<std::string&>(invalidBackends))) + { + throw armnn::InvalidArgumentException(fmt::format("Some of the requested compute devices are invalid. " + "\nInvalid devices: {} \nAvailable devices are: {}", + invalidBackends, + armnn::BackendRegistryInstance().GetBackendIdsAsString())); + } + + CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices); + + // Check turbo modes + if (m_EnableBf16TurboMode && m_EnableFp16TurboMode) + { + throw armnn::InvalidArgumentException("BFloat16 and Float16 turbo mode cannot be enabled at the same time."); + } + + m_IsModelBinary = IsModelBinary(m_ModelFormat); + + CheckModelFormat(m_ModelFormat); + + // Check input tensor shapes + if ((m_InputTensorShapes.size() != 0) && + (m_InputTensorShapes.size() != m_InputNames.size())) + { + throw armnn::InvalidArgumentException("input-name and input-tensor-shape must " + "have the same amount of elements."); + } + + if (m_InputTensorDataFilePaths.size() != 0) + { + if (!ValidatePaths(m_InputTensorDataFilePaths, true)) + { + throw armnn::InvalidArgumentException("One or more input data file paths are not valid."); + } + + if (m_InputTensorDataFilePaths.size() != m_InputNames.size()) + { + throw armnn::InvalidArgumentException("input-name and input-tensor-data must have " + "the same amount of elements."); + } + } + + if ((m_OutputTensorFiles.size() != 0) && + (m_OutputTensorFiles.size() != m_OutputNames.size())) + { + throw armnn::InvalidArgumentException("output-name and write-outputs-to-file must have the " + "same amount of elements."); + } + + if (m_InputTypes.size() == 0) + { + //Defaults the value of all inputs to "float" + m_InputTypes.assign(m_InputNames.size(), "float"); + } + else if ((m_InputTypes.size() != 0) && + (m_InputTypes.size() != m_InputNames.size())) + { + throw armnn::InvalidArgumentException("input-name and input-type must have the same amount of elements."); + } + + if (m_OutputTypes.size() == 0) + { + //Defaults the value of all outputs to "float" + m_OutputTypes.assign(m_OutputNames.size(), "float"); + } + else if ((m_OutputTypes.size() != 0) && + (m_OutputTypes.size() != m_OutputNames.size())) + { + throw armnn::InvalidArgumentException("output-name and output-type must have the same amount of elements."); + } + + // Check that threshold time is not less than zero + if (m_ThresholdTime < 0) + { + throw armnn::InvalidArgumentException("Threshold time supplied as a command line argument is less than zero."); + } + + // Warn if ExecuteNetwork will generate dummy input data + if (m_GenerateTensorData) + { + ARMNN_LOG(warning) << "No input files provided, input tensors will be filled with 0s."; + } +}
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